UNIVERSITY OF CALIFORNIA
Los Angeles
New Roles of DNA Methylation in the Epigenome and
Developmental Overgrowth Syndrome
A dissertation submitted in partial satisfaction of the
requirements for the degree Doctor of Philosophy
in Molecular Biology
by
Andrew King
2017
© Copyright by
Andrew King
2017 ABSTRACT OF THE DISSERTATION
New Roles of DNA Methylation in the Epigenome and
Developmental Overgrowth Syndrome
by
Andrew King
Doctor of Philosophy in Molecular Biology
University of California, Los Angeles, 2017
Professor Michael F. Carey, Chair
DNA methylation is an epigenetic mark classically described to repress gene expression in a long and stable manner. DNA methylation plays important roles throughout development as well as throughout life where it maintains many tissue-specific genes in the silent state. DNA methylation is also altered in many diseases. Despite recent interest and innovation in measuring genome-wide DNA methylation, its function in the genome and its role in disease is not well understood. In this body of work, I aim to understand DNA methylation’s role in the cell through perturbation of DNA methylation in two separate contexts: normal mouse embryonic stem cells
(mESCs) and a mESC-derived neural stem cell (NSC) disease model.
In the first part of my dissertation (Chapter 2), global perturbation of DNA methylation in mESCs are used to address DNA methylation’s relationship with other members of the epigenome, namely histone modifications. Using a series of mESCs with DNA
ii methyltransferases knocked-out and subsequently rescued, I dissect the causal effects of DNA
methylation. By measuring how histone modifications and gene expression change with respect
to global DNA methylation, I address whether DNA methylation is capable of regulating histone
modifications and gene expression in mESCs. I find that genome-wide DNA demethylation
alters occupancy of histone modifications in multiple genomic contexts. Most interestingly,
global remethylation of the genome reverses changes in histone modification occupancy,
indicating causal regulation by DNA methylation.
In the second part of my dissertation (Chapter 3), highly specific perturbations in DNA
methylation are made in mESCs and mESC-derived NSCs to understand what role DNA
methylation may play in disease pathogenesis. Mutations recently described in the gene
DNMT3A cause an overgrowth syndrome associated with intellectual disability named Tatton-
Brown Rahman Syndrome (TBRS). Through genome editing, I establish a cellular model for
TBRS where the endogenous Dnmt3a gene contains knock-in of individual missense mutations
homologous to those found in TBRS patients. Differentiation of TBRS mESCs into NSCs along
with measurements of global DNA methylation, gene expression, and histone modifications
reveal downstream consequences of several of these mutations. I find that TBRS-associated
DNMT3A mutations largely result in loss of methylation at specific DNMT3A targets. Of
particular interest are a group of imprinted genes with known roles in growth and intellectual
function. Furthermore, specific DNA methylation changes are also found to associate with
changes in histone modification in NSCs.
iii The dissertation of Andrew King is approved.
Guoping Fan
Jason Ernst
Harley Kornblum
William Lowry
Michael F. Carey, Committee Chair
University of California, Los Angeles
2017
iv
I dedicate this dissertation to my family, without whom I could not have achieved.
v Table of Contents
List of Figures ...... viii
Acknowledgements ...... x
VITA ...... xii
Chapter 1 : Introduction to DNA Methylation ...... 1
Historical Context ...... 3
Epigenetic Landscapes ...... 6
DNA Methylation Mechanisms – Lessons from Biochemistry ...... 13
Biological Functions ...... 18
DNA Methylation in Disease ...... 21
Relation to this Dissertation ...... 26
References ...... 28
Chapter 2 : Reversible Regulation of Promoter and Enhancer Histone Landscape by DNA Methylation in Mouse Embryonic Stem Cells ...... 40
Introduction ...... 41
Dnmt Reconstitution in Demethylated mESCs Restores Global Cytosine Methylation and Causes Various Changes in Histone Modifications ...... 43
DNA Methylation is Required for Maintenance and Reestablishment of Promoter H3K27me3 ...... 45
Impact of DNA Methylation on Promoter H3K27me3 and Gene Expression Differs Between Bivalent and Silent Promoters ...... 47
DNA Methylation Maintains and Reestablishes Silent or Primed States at Enhancer Elements ...... 48
De Novo Enhancers Are Tissue-Specific and Contain Methylation-Sensitive Transcription Factors ...... 51
Regulation of H3K27me3 Depends on 5-Methylcytosine and DNMT Catalytic Activity52
vi DNA Methylation Regulation of H3K27me3 is Mediated by PRC2 Targeting ...... 53
DNMT3 Isoform Differences in Histone Modification Regulation ...... 54
Discussion ...... 56
Experimental Procedures ...... 60
Figures...... 68
References ...... 83
Chapter 3 : Molecular and genomic defects of mutations in DNMT3A-associated Overgrowth Syndrome with Intellectual Disability ...... 90
Introduction ...... 91
Establishing a Cellular Model to Study Overgrowth-Associated DNMT3A Mutations .. 92
DNMT3A Mutants Differ in Functionality in mESC and mNSCs ...... 93
DNA Methylation Alterations Occur at Sites of de novo Methylation in NSCs ...... 95
Epigenetic Signatures of OG-DMRs and Antagonism of H3K27me3 ...... 97
Altered Gene Expression in Overgrowth Mutant NSCs ...... 99
Structural Interpretation of DNMT3A Mutations ...... 101
Discussion ...... 103
Experimental Procedures ...... 106
Figures...... 111
References ...... 123
Chapter 4: Discussion and Concluding Remarks...... 126
First principles of epigenetics ...... 127
Understanding epigenetic alterations in disease context ...... 130
References ...... 133
vii List of Figures
Figure 2.1. Alteration of DNA Methylation Causes Selective Genome-wide Changes in Histone
Modifications ...... 68
Figure 2.2. DNA Methylation Causally Regulates the Maintenance and Establishment of
Promoter H3K27me3 ...... 69
Figure 2.3. DNA Methylation Regulates Histone Modification Status and Gene Activity at
Enhancers ...... 72
Figure 2.4. DNA Methylation-Dependent Loci Are Tissue-Specific Promoters and Enhancers . 73
Figure 2.5. Regulation of H3K27me3 Depends on DNMT Catalytic Activity ...... 74
Figure 2.6. DNA Methylation Directly Regulates Loci Gaining H3K27me3 via PRC2 Occupancy
...... 75
Figure 2.7. DNA Methylation Regulation of Histone Modifications Across Promoter and
Enhancer Contexts ...... 76
Figure 2.8. Overview of RRBS and ChIP-seq Data ...... 77
Figure 2.9. Histone Modification at Promoters Change with Global Methylation ...... 78
Figure 2.10. Comparison of H3K27ac and H3K27me3 loss and rescue in DKO and TKO mESCs
...... 79
Figure 2.11. Enriched de novo Transcription Factor Motifs in DNA Methylation-Sensitive
Enhancers ...... 80
Figure 2.12. Isoform-specific effects at promoters and enhancers ...... 82
Figure 3.1. DNMT3A mutants differ in functionality in genome methylation ...... 112
Figure 3.2. DNA methylation changes occur at sites of de novo methylation in NSCs ...... 113
viii Figure 3.3. Epigenetic signatures of OG-DMRs and antagonism with H3K27me3 ...... 116
Figure 3.4. Gene expression changes in mutant NSCs ...... 117
Figure 3.5. Structural analysis of Dnmt3a mutants ...... 118
Figure 3.6. Cellular characterization of cell lines ...... 120
Figure 3.7. WGBS DNA methylation metrics ...... 121
Figure 3.8. DNA Methylation and H3K27me3 of Nr2f2 in developing mouse embryos ...... 122
ix Acknowledgements
I would like to acknowledge my advisor Guoping Fan for providing me not only the
opportunity to work in his laboratory, but also a broad range of activities including grant writing,
manuscript review, lab management, and collaboration. I am thankful for the projects that I find
exceptionally fascinating and the diversity of work that is performed in the lab.
I am thankful for the comments and direction provided by my committee members:
Professors Michael Carey, Jason Ernst, Harley Kornblum, and Bill Lowry. I would also like to
acknowledge members of the Fan laboratory. I am especially grateful to Dr. Kevin Huang, Dr.
Kumi Sakurai, and Dr. Ganlu Hu for helping me get started and for making the lab a warm and
welcoming place. Also included are the talented undergraduates, Stella Joh, Kai Chang, Harrison
Wu, and Katherine Sheu, whom I’ve had the pleasure of working with.
Members of the Broad Stem Cell Research Center Sequencing Core including Suhua
Feng and Shawn Cokus have been invaluable in generating sequencing data. Also, members of
the Center of Excellence for Stem Cell Genomics (CESCG) at the Salk Institute for Biological
Studies, including Joe Nery, Cesar Barragan, Manoj Hariharan, whom I relied on heavily.
Lastly, I will never forget the mentorship from Professors Sylvie Garneau-Tsodikova and
Ivan Maillard who have been my role models as scientists from my first steps into biology. I am
also grateful to Professor Gopalan Srinivasan who generously provided my first research
experience. It was in the environment cultitvated by these mentors that nurtured by passion and
excitement for research.
Chapter 2 of this dissertation is adapted from a publication in Cell Reports: King, A.D.,
Huang, K., Rubbi, L., Liu, S., Wang, C.Y., Wang, Y., Pellegrini, M., Fan, G. (2016). Reversible
Regulation of Promoter and Enhancer Histone Landscape by DNA Methylation in Mouse
x Embryonic Stem Cells. Cell Reports 17, 289-302 doi: 10.1016/j.celrep.2016.08.083. It was originally published as an open access article under the Creative Commons CC BY license. This work was co-authored with Kevin Huang, Liudmilla Rubbi, and Shuo Liu. Kevin Huang helped conceive the original project and provided guidance and discussion throughout. Liudmilla Rubbi prepared RRBS libraries used in the analysis of DNA methylation. Shuo Liu performed mass spectrometry measurements for analysis of global DNA methylation.
Chapter 3 is unpublished work performed by Andrew King. I would like to acknowledge
Yang Yu for helping perform mass spectrometry measurements of global DNA methylation and
Katherine Sheu helped generate several cell lines.
This work would not have been possible without financial support from the National
Institute of Dental and Craniofacial Research (R21DE022928 and R01DE025474) and from the
California Institute for Regenerative Medicine (CIRM) Center for Excellence for Stem Cell
Genomics (GC1R-06673-A). I am grateful for training support from the Eli and Edythe Broad
Stem Cell Research Center (BSCRC), Rose Hills Foundation, and UCLA Molecular Biology
Institute Philip Whitcome fellowship.
xi VITA
2010 B.S.E. in Chemical Engineering, minor in Biochemistry
magna cum laude
University of Michigan
PUBLICATIONS
King, A.D., Huang, K., Rubbi, L., Liu, S., Wang, C.Y., Wang, Y., Pellegrini, M., Fan, G. (2016). Reversible Regulation of Promoter and Enhancer Histone Landscape by DNA Methylation in Mouse Embryonic Stem Cells. Cell Reports 17, 289-302. doi:10.1016/j.celrep.2016.08.083
Chen, Y.A., King, A.D., Wu, C.Y., Liao, W.H., Peng, C.C., Tung, Y.C. (2011). Generation of oxygen gradients in microfluidic devices for cell culture using spatially confined chemical reactions. Lab Chip 11, 3626-3633. doi: 10.1039/c1lc20325h
Sheoran, A., King, A., Velasco, A., Pero, J.M., Garneau-Tsodikova, S. (2008). Characterization of TioF, a tryptophan 2,3-dioxygenase involved in 3-hydroxyquinaldic acid formation during thiocoraline biosynthesis. Molecular Biosystems 4(6), 622-628. doi: 10.1039/b801391h
xii
Chapter 1 :
Introduction to DNA Methylation
1 One of the most fascinating questions in biology pertains to how multicellular organisms generate such an enormous diversity of functional cell types and how these numerous cell types remember their developmental history. Anyone who has tried to organize a large group of people has experienced the challenge of coordination. The challenge faced by cells in an animal is enormous from this perspective, where cells in a developing embryo are one among billions and whose time-frames operate at a scale orders of magnitude smaller than that of the whole organism. Yet this feat of coordination is accomplished by every developing embryo.
One of the key processes allowing this to occur is epigenetic regulation. Epigenetics is currently defined as the heritable transfer of information across both cells and organisms without changing the underlying DNA sequence. This system forms the basis of cellular memory and confers the ability for cells to be both rigid and plastic in their behaviors. It is these properties that contribute to ideas of fate determination, differentiation, restriction, and potential that are key to understanding how cells normally navigate development and what the consequences are in processes of disease.
In this introduction, I will describe the rich history in the best characterized epigenetic modification – DNA methylation. How our current understanding of DNA methylation reflects a post-revisionist view of DNA methylation where the currents of evidence had led scientists to overestimate the functionality of DNA methylation in the 1980s. This was soon followed by a period of revision, where the function of DNA methylation was moderated. In the post-genomic era, detailed study of DNA methylation has hinted at vital roles that are still not fully understood.
This dissertation aims to contribute to furthering our understanding about DNA methylation’s role in gene regulation, development, and disease.
2 Historical Context
Foundations: 5-methylcytosine, the prototypical epigenetic regulator
Observations of phenomena, such as the ability for imaginal discs in Drosophila
melanogaster to retain their determined state after more than 70 transplants, equivalent to one thousand replication cycles, motivated questions relating to stability of cellular phenotypes and the mechanisms behind these properties (Gehring, 1976). DNA methylation was first described
in animals in the mid-twentieth century with measurement of 5-methylcytosine from several sources (Hotchkiss, 1948; Wyatt, 1951). Different tissues showed tissue-specific methylation patterns and changes across different development stages (Kappler, 1971; Monk et al., 1987;
Vanyushin et al., 1970).
DNA methyltransferases were first postulated by Werner Arber to serve as the
“modification” enzymes in the bacterial restriction and modification (R-M) system that yielded the discovery of restriction endonucleases (Arber, 1965). In the bacterial R-M system, methylation of host bacterial DNA protected it from host-expressed methylation-sensitive restriction enzymes. However, bacteriophage DNA, which would be unmethylated, was subject to digestion. The first bacterial DNA methylase was shortly isolated from H. influenzae in
Nobel-winning work by Hamilton Smith and colleagues (Roy and Smith, 1973).
Two landmark articles speculating on how epigenetic mechanisms may function are
widely cited as a starting point to the formalization of epigenetics and how DNA methylation
may operate as an epigenetic mark (Holliday and Pugh, 1975; Riggs, 1975). Central to the
hypothesis is the ability for ordered control of transcription to occur through methylation of
DNA without change to the sequence. This hypothesis, which has since been demonstrated
experimentally is the basis of how we define epigenetics today. Taking note from bacterial DNA
3 methylases, the model proposed, there would need to exist two enzymes – one that initially
writes the DNA methylation mark and a second that takes the hemimethylated DNA as substrate and copies the information to the palindromic site while not having activity on nonmethylated
DNA, a maintenance enzyme. Methylation patterns could then be written and propagated via this two-part system. This model provided a mechanism for inheritance, not relying on DNA base changes, well before evidence of existence of these enzymes existed.
Classic Roles for DNA Methylation in Gene Regulation – Overview of the 1980s
The classic role of DNA methylation stemmed from early observations on the variability
of DNA methylation at several different genes across different tissues and the correlation of
actively expressed genes with hypomethylation and silenced genes with hypermethylation. The
1980s were punctuated by mapping of methylation patterns using newly described methylation
sensitive restriction enzymes discovered by Adrian Bird (Bird and Southern, 1978) and
functional tests on how DNA methylation regulated gene activity, which is reviewed extensively
during the period (Bird, 1984; Doerfler, 1983; Razin and Cedar, 1991; Razin and Riggs, 1980).
Additionally, on the mechanistic level, methylation near that 5’ end of genes, which was being
actively studied for transcriptional initiation, implicated a role for DNA methylation in the
blocking of transcription. From the experiments in this decade, several paradigms for how DNA
methylation is understood were established.
Paradigms of DNA methylation regulation
The early study of DNA methylation is mostly characterized by measurement of
methylation as a modification of DNA and correlations in its level with activity of genes in
4 different tissues. Many correlations existed where DNA methylation was absent when genes were on and present when genes were silenced, leading to the growing notion that DNA methylation regulated gene expression. Here I describe a number of key experiments that established DNA methylation as a driver of regulation, moving beyond the correlations previously observed.
Some of the earliest in vivo evidence was shown by Rudolf Jaenisch’s lab, where they observed de novo methylation and subsequent silencing of transduced viruses at the stage of pre- implantation embryos, but not in post-implantation embryos (Jähner et al., 1982). This strongly supported the causal nature of DNA methylation in repression of genes. Separately, gene transfer experiments where unmethylated versus in vitro methylated DNA, transduced into cells, showed different activity at the well-studied globin gene (Busslinger et al., 1983).
Following the discovery of the demethylation activity of 5-azacytidine by Peter Jones and colleagues and its ability to alter differentiation states (Jones and Taylor, 1980), several 5- azacytidine demethylation studies showed cell state changes, including those involved in the discovery of master regulator transcription factor MyoD, argued strongly for DNA methylation as a key regulator of differentiation state (Davis et al., 1987). Other experiments also proved that
DNA methylation can not only silence genes, but are also stably maintained for generations of cells (Wigler et al., 1981).
The most dramatic example of DNA methylation repression is where DNA methylation partakes in silencing the entire X chromosome during X-chromosome inactivation in female cells
(Monk, 1986). It is evident that silenced X chromosomes are stably repressed for the life of the organism. The notion that DNA methylation actively silenced the inactive X chromosome was
5 supported through experiments showing 5-azacytidine reactivating genes located on the inactive
X chromosome (Mohandas et al., 1981)
The foundation of how DNA methylation and to some extent epigenetics as a whole
operates, was established in the 1980s with the seminal studies demonstrating functions of DNA
methylation. These studies strongly provided evidence for the idea that DNA methylation silences genes. Extrapolation from a limited number of studies set forth an idea that DNA methylation may be a key regulator throughout development. It is these very experiments that
were the basis of my undergraduate and medical instruction on DNA methylation. However, the
extent to which these foundational experiments prove generalized causality is limited. In part,
this is due to the limited contexts studied, both genomic contexts as well as in different tissues of
the body. Future studies of genomic contexts would be better understood in the post-genomic
era. However we are still understanding the different biological contexts to this day.
Epigenetic Landscapes
DNA methylation occurs in CpG dinucleotide context, which is simply consecutive
cytosine and guanosine nucleotides. This context is unique in that it is rotationally symmetric
and exists as a palindrome, where a methylated cytosine on one strand (5’-CG-3’) is matched on
the complementary strand (3’-GC-5’). Mammalian genomes are depleted of CpG dinucleotides
as a result of spontaneous deamination of methyl-cytosine to thymine (Bird, 1980; Coulondre et
al., 1978; Josse et al., 1961). Due to this phenomenon, only 0.9% of the human genome consists
of CpG dinucleotide, compared to 6.25% as would be expected by random chance or 4% given a
G+C fraction of approximately 40%. Despite their rarity, 60-90% of CpGs are found methylated
throughout the vertebrate genomes.
6
Rudimentary maps in the pre-genomic era
CpG islands are one of the first genomic features relating to DNA methylation that were mapped and provided an indication of DNA methylation’s function in the genome. The landmark study by Adrian Bird and colleagues identified CpG islands using methylation sensitive restriction enzymes HpaII and HhaI in the mouse genome (Bird et al., 1985). Bird et al. observed that most vertebrate genomes were methylated and poorly cleaved by methylation-sensitive restriction enzymes. However, a consistent small fraction accounting for <1% of DNA contained the majority of HpaII cut sites across tissues and were shown to map to islands of unmethylated
DNA near genes.
Unlike the rest of the genome, CpG islands displayed CpG content close to the expected value based on random chance. These islands were found to be GC-rich, have high CpG content, and remained unmethylated during evolution. CpG islands were more formally defined after measurement of CpG islands near genes in several different vertebrate genomes. The classic definition is a region greater than 200 base pairs in length with >50% G+C content and >0.6 observed/expected ratio of CpG (Gardiner-Garden and Frommer, 1987). In their original description, Bird et al. estimated around 30,000 CpG islands in the haploid genome. Sequencing of the human and mouse genomes, eventually showed these estimates to be very close, with approximately 27,000 CpG islands in human and 15,500 in mouse (Mouse Genome Sequencing
Consortium et al., 2002)
Soon after their discovery, CpG islands were found to be associated with housekeeping genes and lacking at tissue-specific genes (Bird, 1986). In a review, Bird writes,
7
Given that there are usually rather few CpGs near tissue specific genes … one would not
expect to find CpG or methylation built into the activation mechanism of genes of this type.
This conclusion is heretical given recent interest in DNA methylation as a mechanism of
controlling tissue specific genes (Bird, 1986)
In observing the first glimpse of methylation’s landscape through analysis of CpG islands, the relationship between DNA methylation, genomic context, and biological function were starting to be clarified. Analysis of CpG content in the fashion of identifying CpG islands has resulted in classification of promoters into low and high CpG content promoters with distinguishing biological functions and frequency of methylation (Saxonov et al., 2006). While
CpG islands have been shown to be constitutively unmethylated, recent studies have shown that the adjacent 2-kb, termed CpG island shores, show more variability in methylation between tissues as well as between normal and cancer (Irizarry et al., 2009)
Epigenetic landscapes in the genomic era
Genome-wide DNA methylation maps are important both for understanding generalities of DNA methylation in the genome as well as finding where differences occur between cells.
The techniques to measure DNA methylation have improved dramatically, particularly over the last two decades. The field first driven by innovations in array technology followed by sequencing technology has moved from a reliance on methylation sensitive restriction enzymes, to methylation-specific antibodies, and finally to the current gold-standard of whole-genome bisulfite sequencing (WGBS). With the improvement in the ability to create high-resolution and
8 genome-wide maps, the understanding in DNA methylation’s role in biology has become more
apparent.
The first genome-wide maps utilized immunoprecipitation of methylation-specific
antibodies and were in the flowering plant Arabidopsis, due to its relatively small genome (135
Mbp) and well characterized DNA methylation system with several genetic knockouts of the
methylation machinery available. Unexpectedly, these maps showed heavy methylation genome-
wide, particularly in gene bodies, which correlated with expression level (Zhang et al., 2006;
Zilberman et al., 2007). Additionally, promoters that were methylated tended to exhibit tissue-
specific gene expression. These patterns of global DNA methylation were shortly confirmed
using the higher resolution whole-genome bisulfite sequencing (WGBS), which involves
bisulfite conversion of fragmented genomic DNA followed by next-generation sequencing
(Cokus et al., 2008; Lister et al., 2008). Previously, methylation of CpG islands on the inactive X
(Xi) chromosome suggested that DNA methylation would be higher across Xi than the active X
chromosome (Xa). Surprisingly, early genome-wide experiments in human cells showed that the
Xa, in fact, had twice as much methylation than Xi, where extra methylation was concentrated within gene bodies (Hellman and Chess, 2007; Weber et al., 2005). Gene body methylation has since been studied for its mechanism of establishment and functional role (Baubec et al., 2015;
Neri et al., 2017).
The study of methylation maps in mammalian genomes paralleled studies in Arabidopsis.
However, at around 3 billion base-pairs, human and mouse genomes faced more challenges in scaling up. Several early studies tested new technologies to measure DNA methylation genome- wide. These approaches were often constrained to limited regions of the genome, most often due to reliance on methylation-sensitive restriction sites or focused DNA probe sets. From these
9 studies, it was shown that methylation could distinguish between different cell types based on the
methylation state of CpGs across the genome (Bibikova et al., 2006). Additionally, by sampling
many additional sites, CpG islands were also found to be methylated in a tissue-specific manner
in normal tissues. For example, using methylation array CGH showed, Ching et al. found several promoter-associated differentially methylated CpG islands between astrocytes and PBLs (Ching et al., 2005). Separately, using protein domains with specific affinity for unmethylated CpG sites in combination with an array containing CpG islands, Illingworth et al. found that around 6-8% of CpG islands were methylated in somatic tissues and found 1,657 differentially methylated
CpG islands in one or more tissues studied (Illingworth et al., 2008). Furthermore, the methylation status of CpG islands generally associated with gene expression, where methylated
CpG islands tended to show low expression. From early genome-wide maps in mammals, CpG islands were shown to not always stay unmethylated, rather methylation occured in a tissue- specific manner.
As the use of whole-genome bisulfite sequencing becomes more wide-spread, the full complement of methylation levels at the majority of CpG and non-CpG sites can be easily measured. One goal of these studies was to identify individual regulatory elements and how these regulatory elements are differentially modified by DNA methylation. In one of the earliest large-scale bisulfite sequencing studies in human, measuring DNA methylation on chromosomes
6, 20, and 22 from 12 different human tissues revealed an underrepresentation of tissue-specific differentially methylated regions in CpG islands with some located up to 100 kb away from annotated genes (Eckhardt et al., 2006). Consistent with this, recent systematic studies in both mouse and human across dozens of tissues reveal that the most dynamic methylation sites across tissues are distal to promoters. On average, nearly 75% of tissue-specific DMRs were found near
10 distal regulatory elements rather than near promoters (Hon et al., 2013; Ziller et al., 2013). These
systematic studies identified hundreds of thousands of tissue DMRs, which is closer to the order
of magnitude of distal regulatory elements, such as enhancers, than promoter-proximal regions.
Relationship between methylation and histone landscape
In the same period that DNA methylation landscapes were being mapped, great efforts
were made in parallel on mapping the landscape of chromatin features through large
collaborative projects like the ENCODE and Roadmap Epigenomics projects. Numerous
genome-wide maps of histone modifications allowed a better understanding of the epigenomic
landscape (Barski et al., 2007; Hawkins et al., 2010; Heintzman et al., 2007; Mikkelsen et al.,
2007). Examples include the identification of bivalent promoters, which are marked by both
active H3K4me3 and repressive H3K27me3 histone modifications, found to mark developmental
genes (Bernstein et al., 2006). Other combinations of histone modifications also predicted
enhancer elements across the genome (Creyghton et al., 2010; Heintzman et al., 2007, 2009;
Rada-Iglesias et al., 2011).
Cross-referencing newly generated DNA methylation maps with histone modification
maps identified where DNA methylation may function in the epigenetic architecture and
revealed associations between DNA methylation and the histone landscape. Work from our
laboratory and others studying the methylome of embryonic stem cells found that DNA
methylation at promoters varied based on chromatin context (Fouse et al., 2008; Meissner et al.,
2008; Weber et al., 2007). Specifically, methylated promoters were predominately found in a
minority of low/intermediate CpG-content promoters lacking histone modifications, while promoters with higher CpG contents were mostly unmethylated and marked with active histone
11 modifications. Low CpG content promoters were enriched for lineage specific genes, whereas
developmental genes were generally associated with higher CpG content and bivalent histone modifications. Measurement of histone modification and DNA methylation during stem cell differentiation showed correlation between the loss of histone modifications and DNA hypermethylation (Meissner et al., 2008).
In the first WGBS study in mouse embryonic stem cells, the authors identified low- methylated regions (<30%) that were enriched in K4me1 and p300, resembling distal regulatory regions (Stadler et al., 2011). The authors also tested how these low methylated regions are formed by manipulating CTCF binding sites on reporter transgenes and found that introduction
of binding sites led to local reduction in DNA methylation, arguing that regulatory factor binding
is able to regulate methylation levels. Together, mapping of multiple layers of the epigenome have revealed a complex landscape, where genes have different modes of epigenetic regulation during different stages of development and different genomic contexts.
However, relating between epigenetic modification and gene expression is ultimately limited by association. Mapping studies have allowed a high-resolution view across different tissues, allowing association between variable sites and regions of functional importance. Newer studies now are utilizing traditional genetic systems measured using genome-wide maps to understand how genetic perturbations affect the epigenetic landscape.
12 DNA Methylation Mechanisms – Lessons from Biochemistry
Cloning of DNMTs
During the inception of epigenetics as a field, DNA methylation was functionally
recognized as a regulatory mark, however the players that wrote the mark onto the genome in mammals were only hypothesized. It was not until the end of the 1980s that the first mammalian
DNA methyltransferase (DNMT) was identified and cloned by Tim Bestor with the late Vernon
Ingram (Bestor et al., 1988). This first methyltransferase, now known as DNMT1, was shown to have preference for hemimethylated DNA, or 5-methylcytosine present on only one strand, which is typically found after DNA replication as the newly synthesized strand has not been methylated. Thus, the newly cloned DNMT enzyme fulfilled the postulated function of the maintenance methyltransferase.
An observation about the sequence of DNMT1 was that it showed striking similarity in
its C-terminal domain to bacterial methyltransferases, including several key conserved motifs.
Comparative analysis between 45 methyltransferases showed high levels of conservation of 10 amino acid motifs throughout the methyltransferase domain (Kumar et al., 1994). Utilizing this sequence similarity between mammalian and bacterial methyltransferases, Masaki Okano and En
Li identified and cloned mammalian Dnmt3a and Dnmt3b by searching human EST clones for these conserved sequences (Okano et al., 1998). Unlike DNMT1 these new enzymes showed activity on unmethylated DNA, and thus fulfilled the role of de novo DNA methyltransferase enzymes.
While identification of the mammalian methylation machinery confirmed previous notions about how epigenetic information could be propagated between generations of cells, it remained difficult to understand how patterns of methylation arose. Mammalian
13 methyltransferases were found to have little sequence specificity aside from increased activity towards CpG dinucleotides, in contrast to bacterial methyltransferases that had sequence specificity like their restriction enzyme counterparts. The identification of a few general acting methyltransferases with little sequence specificity necessitates regulation through interaction with other proteins.
Biochemical insights into DNMT function
Both de novo and maintenance DNMTs share a similar domain structure, with a large N- terminal regulatory domains and C-terminal catalytic methyltransferase domain. Mammalian
DNMT3A and DNMT3B share similar domain arrangements with a N-terminal variable region,
PWWP domain, ADD domain, followed by a C-terminal catalytic domain.
The PWWP domain is named after the Pro-Trp-Trp-Pro motif found in many nuclear proteins. In DNMT3A, PWWP has been shown to interact with chromatin and is necessary for
DNMT to localize to chromatin targets (Chen et al., 2004; Ge et al., 2004; Qiu et al., 2002). The
ADD domain is a unique zinc finger motif named after ATRX, DNMT3, and DNMT3L proteins in which it was identified (Aapola et al., 2000). The ADD domain consists of a C2C2 GATA- like finger and an imperfect PHD finger, ending with a long C-terminal alpha helix that contacts the GATA finger forming a self-contained domain (Argentaro et al., 2007). The ADD domain of
DNMT3A/3L has been shown to interact with the H3 histone tail, specifically sensing the presence of H3K4 and H3K9 methylation states (Li et al., 2011; Otani et al., 2009; Zhang et al.,
2010). DNMT1 meanwhile is organized into a N-terminal PCNA binding domain, nuclear localization signal, replication foci targeting (RFT) domain, a CXXC domain, two bromo- adjacent homology (BAH) domains, and a C-terminal catalytic domain (Leonhardt et al., 1992) .
14 Recently, structural and biochemical experiments have provided evidence for novel
regulatory principles of DNMT (Jeltsch and Jurkowska, 2016). Most recently, structures of
DNMT3A have shown the existence of intrinsic autoinhibitory mechanisms that limit activity of
the enzymes outside of proper contexts. Autoinhibition in DNMT3A is mediated through
interactions between the ADD domain and the C-terminal catalytic domain, which normally block the catalytic sites from DNA access in the absence of activating conditions (Guo et al.,
2014b). When the ADD domain interacts with histone H3 tails, specifically tails unmethylated at the H3K4 position, DNMT3A undergoes a large conformational change moving the ADD domain away from the catalytic site allowing access and methylation of DNA. Autoinhibition has also been demonstrated for DNMT1, where the RFT domain blocks the catalytic site while the CXXC domain positions unmethylated DNA away from the catalytic site inhibiting DNMT1 in the absence of methylation and coactivators (Song et al., 2011; Takeshita et al., 2011).
Lastly, biochemical studies have shown that de novo methyltransferases do not operate as monomers, but rather interact with one another in different combinations. The most well studied complex is a heterotetramer consisting of a central DNMT3A dimer and two DNMT3L molecules on the periphery (3L-3A-3A-3L) (Jia et al., 2007). DNMT3L is an inactive homolog of DNMT3A consisting of an ADD domain and a catalytically inactive methyltransferase
domain. The interaction of DNMT3L with DNMT3A increases the catalytic activity of
DNMT3A (Gowher et al., 2005; Suetake et al., 2004). Both homotypic interactions with
DNMT3A and heterotypic interactions with DNMT3L occur through the C-terminal catalytic
domain of DNMT3A and have been well characterized. DNMT3A has also been shown to
mainly interact with DNMT3B in ESCs (Li et al., 2007).
15 DNA Methylation interactions with chromatin
Activity of de novo DNMTs were shown to differ between naked DNA and chromatin templates (Zhang et al., 2010). Furthermore, methylation of H3K4 on chromatin templates in vitro further reduced methyltransferase activity indicating responsiveness to chromatin context on the activity of DNMTs. An increasing number of interactions have been described between
DNMT and chromatin, indicating a high level of regulation of when and where DNA methylation can occur. I will focus on specific examples of how de novo DNMTs have interacted with the different modifications of histone tails. As mentioned earlier, the conserved domains in the N-terminal regulatory regions of DNMT3A/3B confer the ability of de novo DNMTs to interact with specific histone modifications. The PWWP domain of DNMT3A was shown to specifically interact with H3K36me3, where binding increased DNMT3A activity (Dhayalan et al., 2010). Most recently, studies of genome-wide localization of DNMT3B have found that the
PWWP domain targets DNMT3B to gene bodies through interactions with H3K36me3 histone modification (Baubec et al., 2015; Morselli et al., 2015).
Another key interaction of the DNMT3A/3L ADD domain is with H3K4, as previously described (Otani et al., 2009). The interaction of DNMT3A with unmethylated H3K4 is involved in interruption of intrinsic autoinhibition. The ADD domain of both DNMT3A and DNMT3L show high binding affinity for unmethylated H3K4 and low affinities for methylated forms. This is thought to prevent DNMT from aberrantly methylating active promoters. DNMT3A enzymes engineered with mutations in the ADD domain could bind and methylate more in H3K4me3 marked regions with generalized repression of genes (Noh et al., 2015).
Fewer studies have investigated the interaction between DNMT and H3K27 histone modifications. One highly cited study showed EZH2, the writer for H3K27me3, recruits
16 DNMT3A/3B in cancer cells via the ADD domain (Viré et al., 2006). For some time H2K27me3 and DNA methylation were thought to be mutually exclusive, but improved techniques showed overlapping regions in the genome (Brinkman et al., 2012). Most recently, DNA methylation was shown to inhibit writing of H3K27me3 (Jermann et al., 2014).
Lastly, DNA methylation also has a close relationship with methylation of H3K9 and cooperate in closed chromatin structure and gene repression. DNA methylation was first shown to require H3K9 histone methyltransferases in Arabidopsis thaliana, indicating that H3K9me can direct DNA methylation (Jackson et al., 2002). In support of this idea, interaction between
DNMTs, SUV39H1, and HP1 were shown in vitro (Fuks et al., 2003; Smallwood et al., 2007).
Interactions among DNMTs, H3K9 histone methyltransferases, and HP1 form a positive feedback loop allowing establishment of heterochromatin in different environments across the genome (Du et al., 2015; Epsztejn-Litman et al., 2008). The relationship between DNA methylation and H3K9 methylation is complicated by the fact there are five known H3K9 histone methyltransferases in mammals. Knockout of different H3K9 histone methyltransferases resulted in specific loss of DNA methylation at different sites. For example, Suv39h1/2 is required for de novo DNA methylation at pericentric heterochromatin (Lehnertz et al., 2003), while knockout of G9a resulted in hypomethylation of a few dozen specific loci (Ikegami et al.,
2007). However, the converse is not always true, where genome-wide demethylation in mESCs resulted in minimal changes in H3K9 (Karimi et al., 2011; Tsumura et al., 2006). However, in human cancer cell lines, DNMT knockout does reveal global reduction in K9me2/3, suggesting the relationship may differ based on cellular context.
17 Biological Functions
Genetics - Revisiting function through experimental models
The function of DNA methylation in the cellular context was studied soon after cloning
of the respective Dnmt genes. Knockout of Dnmt1 in mouse embryonic stem cells and derivative
mouse lines revealed an essential role for DNA methylation in early development (Lei et al.,
1996). In this study, homozygous mutations causing genomic hypomethylation led to arrest as
early as the 8-somite stage. Heterozygous knockouts on the other hand showed no abnormalities.
Interestingly, homozygous knockout ESCs were viable. However, upon differentiation to
embryoid bodies, knockout cells were unable to differentiate compared to normal cells.
Conditional Dnmt1 deletion in fibroblasts also resulted in cell death indicating a differential requirement or distinct roles for DNA methylation in embryonic development and differentiated somatic cells (Jackson-Grusby et al., 2001). Further study of Dnmt1 knockouts showed chromosomal instability, reactivation of transposable and viral elements, and initiation of tumorigenesis (Holm et al., 2005; Kim et al., 2004).
Studies of de novo DNA methyltransferases showed similar requirements for DNA methylation during development (Okano et al., 1999). Homozygous Dnmt3b knockout mice resembled Dnmt1 knockouts showing an embryonic lethal phenotype. Mutant embryos
developed normally before E9.5, but showed neural tube defects and other abnormalities later in
development. In contrast, Dnmt3a knockout mice developed to term, but became runted and died
several weeks after birth. Double mutant embryos (Dnmt3a-/-, Dnmt3b-/-) showed abnormal
morphology as early as E8.5 and died before E11.5. Finally, in the most extreme case, where all
active methyltransferases were knocked out, triple-knockout (TKO) ESCs were shown to
18 maintain pluripotency with no changes in H3K9 and H3K4 localization in cells (Tsumura et al.,
2006).
At the same time as the mouse studies of DNMT were conducted, human cell line models
were used to study the role of DNMTs in human, principally in the study of human cancers. In
the lab of Stephen Baylin, Bert Vogelstein, Kenneth Kinzler, and others, hypomethylated human
colorectal carcinoma cell line (HCT116) showed demethylation of repeat sequence, loss of
imprinting, growth suppression, and chromosomal instability (Egger et al., 2006; Karpf and
Matsui, 2005; Rhee et al., 2002). The most complete demethylation of human cells, utilizing a full loss-of-function allele in DNMT1 alone, led to “mitotic catastrophe”, which was characterized by severe mitotic defects and cell death, consistent with results from Dnmt1 knockout in mouse fibroblasts (Chen et al., 2007).
Roles during development
DNA methylation is highly dynamic during the earliest stages of development where
rapid and global changes in DNA methylation occur prior to implantation of the blastocyst into the uterine wall (Guo et al., 2014a; Santos et al., 2002; Smith et al., 2014). Specifically, global demethylation occurs during the preimplantation, followed by global remethylation. During demethylation, parental imprints are reset. After the initial landscape of DNA methylation is established for pluripotency around the post-implantation step of early embryogenesis, DNA methylation is both added de novo to silence programs no longer needed in more differentiated cells as well removed concomitant with activation of specific developmental programs
(Bogdanović et al., 2016; Li et al., 2007).
19 Early evidence of DNA methylation’s involvement in neural development was during the neurogenic to gliogenic switch, where changes in DNA methylation of Gfap coincided with the period (Takizawa et al., 2001; Teter et al., 1994, 1996). As a result of Dnmt knockout mice being embryonic lethal or dying shortly after birth, the study of DNA methylation in the nervous system relied on conditional knockouts, work largely done by Guoping Fan and Rudolf Jaenisch.
The first in vivo of hypomethylation in the entire CNS precursors compartment via Nestin-Cre- meditated Dnmt1 knockout resulted in neonatal death due to disruption in breathing control centers in the brain (Fan et al., 2001). Specifically, in severely hypomethylated cells, neural cells died immediately after birth, while in mosaic animals, most cells were eliminated postnatally.
Hypomethylation additionally resulted in precocious differentiation to glial cells in early embryos due to JAK-STAT pathway activation (Fan et al., 2005).
Studying methylation’s role in more specific regions on the brain, Emx1-Cre-driven
Dnmt1 knockout mice showed defects in cortical lamination as well as progressive degeneration in early postnatal stages (Golshani et al., 2005). Follow-up studies on surviving hypomethylated postnatal neuron showed defects, including dendritic arborization and excitability (Hutnick et al.,
2009). Hypomethylation in postmitotic neurons led to learning deficits in CamK2a-Cre-mediated
Dnmt knockout mice (Feng et al., 2010).
Separate from global loss of methylation using Dnmt1, Dnmt3a knockout in Nes-Cre mice resulted in loss of motor neurons in hypoglossal nucleus and defects in neuromuscular junctions of the diaphragm (Nguyen et al., 2007). Furthermore, cultured neural stem cells from
Dnmt3a knockout mice showed precocious differentiation with increased glial differentiation
(Wu et al., 2010, 2012). Mapping of DNA methylation profiles of the developing and postnatal brain showed increased non-CpG methylation after birth, particularly in the neuronal lineage
20 compared to glial. The increases in mCH coincided with periods of synaptogenesis and synaptic
pruning, indicating potentially specific roles for DNA methylation in postnatal brain
development and maturation. Interestingly, DNMT3A binding is enriched at sites of mCH in
neurons (Lister et al., 2013). DNMT3A has also been studied in other adult stem cells including
hematopoietc stem cells (HSCs), where DNMT3A was found to be essential for maintenance of
multipotency in HSCs (Challen et al., 2012).
One interesting concept that has recently arisen is of different modes of methylation
pattern maintenance (Shipony et al., 2014). By contrasting DNA methylation dynamics and noise
in human embryonic stem cells and somatic fibroblast cell lines, distinct modes of maintenance
were observed between the two states. Embryonic stem cells utilize a “dynamic equilibrium” model involving high levels of demethylation and de novo methylation. Meanwhile, fibroblast
cells utilize the more traditional static or “clonal maintenance” model, where methylation
patterns are highly reliant on DNMT1 maintenance methylation. These different modes
dominating different stages of development may explain some differences observed between
ESCs and somatic cell lines.
DNA Methylation in Disease
DNA methylation, like the broader epigenetic apparatus, has been identified to play roles
in nearly every tissue studied. Likewise, alterations in DNA methylation are also increasingly
recognized in the mechanism of more and more diseases. Here I will briefly describe three classic and more relevant disease states where methylation changes are either widespread or play
key mechanistic roles.
21 Cancer
As interest in DNA methylation as a gene regulatory mechanism increased in the 1980s,
investigations of how DNA methylation differed between normal and cancerous tissues were
conducted. Among these early studies, Andrew Feinberg, Bert Vogelstein, and others observed
that genes would progressively become hypomethylated, where the degree of hypomethylation
correlated to disease stage (Feinberg and Vogelstein, 1983; Gama-Sosa et al., 1983). Around the
same time, serendipitous measurement of DNA methylation around the calcitonin CpG island
promoter showed aberrant hypermethylation in cancer compared to normal counterparts (Baylin
et al., 1986). Measurement of additional loci together with identification of examples where bona fide tumor suppressors were silenced due to methylation led to the broader application of the
hypermethylation concept (Baylin et al., 1998; Jones, 1996). Furthermore, a distinct group of cancers showed higher levels of CpG island methylation, termed the CpG island methylator
phenotype (CIMP), first in colorectal carcinomas (Issa, 2004; Toyota et al., 1999). Together, the
hypo- and hypermethylation phenotypes have been observed in many different cancers. Today,
the epigenetic alterations in human cancers are often described as a combination of global
hypomethylation with regional hypermethylation where hypermethylation is able to methylation
and silence tumor suppressor genes, contributing to the progression of disease (Esteller, 2008).
Recently specific mutations in the epigenetic machinery have been identified in a large
proportion of hematological malignancies. Specifically, mutations of DNMT3A at the hotspot
residue R882 are found in approximately one fifth to a quarter of acute myeloid leukemia (AML)
cases (Ley et al., 2010). This mutation was recently shown to act in a dominant-negative fashion,
acting through disruption of DNMT3A dimerization (Russler-Germain et al., 2014). While
22 current research has improved our understanding of how the mutations alter DNMT3A function,
how the methylation changes precisely lead to malignancy are still unclear.
Imprinting disorders
Among the human disorders rooted in epigenetics, one of the most well-known classes
are disorders resulting from misexpression of imprinted genes. Imprinted genes are a class of
genes that are differentially expressed depending on the parent-of-origin. Early evidence of imprinting was supported by nonequivalence of parental genomes, shown by the necessity of both paternal and maternal genomes for development. Formal studies of imprinting utilizing random insertion of transgenes in mice and observation of parent-of-origin effects identified methylation as a main mechanism for imprinting (Reik et al., 1987; Sapienza et al., 1987; Swain et al., 1987). Furthermore, the pattern of expression correlated with the methylation state.
As a class, imprinting disorders often have phenotypes involving growth and neurocognition (Constância et al., 2004). One of the oldest and best described imprinting disorders is Beckwith-Wiedemann syndrome (BWS), described first in 1964 by Drs. Beckwith and Wiedemann. BWS is characterized by pathognomonic features including macroglossia, midline abdominal wall defects, and somatic overgrowth. It was not until 1989 that the locus was linked to 11p15 and a year later hypothesized to be caused by an imprinted gene due to the observed parental patterns of deletion and duplication (Brown et al., 1990; Ping et al., 1989). In the following years, further study of the Igf2 imprinting locus culminated in generation of animal models recapitulating BWS features (Sun et al., 1997; Zhang et al., 1997).
The number of imprinted genes has steadily grown across the last few decades with the overall number on the order of a hundred imprinted genes. New genome-wide technologies have
23 enabled systematic identification of novel predicted imprinting regions and have even identified
tissue-specific imprints. Many of these genes are clustered together and controlled locally via
imprinting control regions. Associated with many imprinted loci are developmental disorders
that exhibit reciprocal phenotypes. For example, Beckwith-Wiedemann syndrome and Silver-
Russell syndrome display overgrowth and growth restriction respectively, both due to alterations
at chr 11p15. Similarly, more recently described Kagami-Ogata syndrome and Temple syndrome
exhibit increased and decreased birth weight respectively, where both are due to alterations at chr
14q32 (Ogata and Kagami, 2016; Temple et al., 2007).
Overgrowth Syndromes
Related to Beckwith Wiedemann syndrome is another class of developmental disorders
exhibiting somatic overgrowth resulting from mutation of the epigenetic machinery.
Haploinsufficiency of NSD1, a H3K36-specific histone methyltransferase (HMTase), was found
to cause Sotos syndrome, a syndrome characterized by overgrowth, macrocephaly, brain
anomalies and seizure, and mental retardation (Kurotaki et al., 2002). Later, autosomal dominant
mutations in EZH2, a H3K27-specific HMTase was found to cause Weaver syndrome, characterized by tall stature (>2.5 SD) and head circumference, facial features including a broad forehead and hypertelorism (abnormally distant eyes), and mild intellectual disability (IQ < 70)
(Gibson et al., 2012; Tatton-Brown et al., 2011). Furthermore, additional mutations in SETD2
(H3K36 HMTase) for Sotos overgrowth syndrome and EED (H3K27 HMTase) for Weaver syndrome were identified, indicating that the epigenetic marks rather than protein-specific interactions cause the phenotype (Cohen et al., 2015; Luscan et al., 2014). Most recently,
DNMT3A was identified in a cohort of overgrowth syndrome patients without a known etiology
24 (Tatton-Brown et al., 2014). The newly named Tatton-Brown-Rahman Syndrome (TBRS) presents with increased height and head circumference, mild to moderate intellectual disability in all cases, and variable penetrance of other findings (e.g. cardiovascular malformations, scoliosis).
With the recent discovery and only 13 patients described in literature, our knowledge of
DNMT3A overgrowth syndrome is limited.
Distinct from constitutional overgrowth syndromes, regional mosaic overgrowth syndromes are also well known, with Proteus/Wiedemann syndrome popularized by the Elephant
Man in popular culture. It is hypothesized that these syndromes are caused by mutations that would be lethal in the non-mosaic state. Proteus syndrome results from mosaic mutations in
AKT1, causing disproportionate and asymmetric localized overgrowth in skin, connective tissue, and the brain (Lindhurst et al., 2011). The PI3K-AKT signaling axis is also downstream of constitutional Beckwith-Wiedemann Syndrome, where IGF2 signaling through IGF1R, activates the PI3K-AKT or Ras pathway (Sun et al., 1997; Weksberg et al., 2010; Zhang et al., 1997).
Together, overgrowth syndromes predominantly influence components of the PI3K-AKT and
Ras pathways or epigenetic machinery.
An interesting observation that hints at the molecular underpinnings of overgrowth syndromes is the shared presence in many malignancies. NSD1, the causal gene in Sotos syndrome, is also found fused to NUP98 in recurrent t(5;11)(q35;p15.5) translocations in childhood AML and epigenetically silenced in neuroblastoma and gliomas (Berdasco et al.,
2009; Jaju et al., 2001). Similarly, mutations and upregulation of EZH2, the causal gene in
Weaver syndrome, are found in a number of hematopoietic malignancies as well as other solid tumors (Ernst et al., 2010; Varambally et al., 2002).
25 Relation to this Dissertation
As presented in the introduction, early studies of DNA methylation's silencing
functionality prematurely attributed an instructive role for DNA methylation in gene regulation.
Observation that CpG islands were generally hypomethylated provided early indication that
DNA methylation may not have as direct a role as previously imagined. Together with evidence
showing silencing of endogenous loci precedes methylation, the hypothesis of DNA methylation
as a driver of gene regulation fell by the wayside. Recent observations in more comprehensive
genome-wide maps have renewed interest in DNA methylation as tissue-specific differentially
methylated regions are found colocalized with features characteristic of distal regulatory
elements, such as enhancers.
Epigenetic mechanisms are general tools used in cells to remember and respond to its
environment. However, this system interfaces with many different tissue-specific cues and instructions forming tissue-specific outcomes. Our understanding of the basic principles of how
DNA methylation are relatively well established, but new understanding is focused on the interactions with a number of other regulators. With this in mind, to understand the role of DNA
methylation in development and disease, it is necessary to understand not only the first principles
on which DNA methylation operates, but also how and where it operates in specific contexts. In this dissertation, I attempt to address these two major aspects important to understanding DNA methylation.
Foremost is understanding the first principles of how DNA methylation functions among the epigenomic community and its role in gene regulation. One fundamental question relating to this is whether DNA methylation acts as a driver or a follower in the epigenetic and gene regulatory hierarchy. Chapter two delves into asking a fundamental question about the order of
26 events. The debate preceding the work described in this chapter was over DNA methylation’s place in the regulatory hierarchy. The dramatic iPSC reprogramming experiments by Yamanaka
and others, reminiscent of 5-azacytidine transdifferentiation of 3T3 and 10T½ experiments in the
1980s, provided strong evidence that transcription factors alone can control a cell’s state.
Mapping of Polycomb-repressive complex and its associated histone modifications also revealed histone modifications as the major repressive mechanism of developmental genes. Furthermore, highly cited work in cancer cells provided a mechanistic argument that DNA methylation was recruited by histone modifications.
The second question is what role does DNA methylation have in the proper biological setting, particularly in a recently discovered disease that results from mutations of DNMT3A that have unknown functional consequences. Also, to what extent can classic methylation function explain new diseases with mutations in the DNA methylation machinery and what can new paradigms coming out from this past decade teach us? Chapter three thus takes a global omics view of a cellular model of a human overgrowth syndrome asking how alterations in the methylation machinery effect the epigenome.
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39
Chapter 2 :
Reversible Regulation of Promoter and Enhancer Histone Landscape by
DNA Methylation in Mouse Embryonic Stem Cells
40 Introduction
Recent studies have revealed that global DNA methylation is dramatically altered during
pre- and post-implantation development (Guo et al., 2014a; Smith et al., 2014), primordial germ cell reprogramming (Gkountela et al., 2015; Guo et al., 2015; Tang et al., 2015), as well as stem
cell differentiation (Xie et al., 2013), and cellular reprogramming (Lister et al., 2011). A major
challenge in the field has been to understand how drastic changes in methylomes contribute to altered transcriptional programs associated with cell fate commitment and differentiation. The prevailing hypothesis posits that DNA methylation is a crucial silencer of pluripotency and
tissue-specific genes via promoter hypermethylation. However, gene promoters account for a
tiny fraction of the genome, and increasing evidence repudiates the obligate role for promoter
methylation in gene silencing (Bogdanovic et al., 2011; Hammoud et al., 2014; Noh et al., 2015).
For instance, pluripotency genes can be silenced during differentiation in the absence of
promoter methylation (Sinkkonen et al., 2008). Therefore, it is becoming increasingly clear that
DNA methylation works in conjunction with other factors to properly regulate gene expression.
DNA methylation and histone modifications are two mediators of epigenetic regulation.
These two marks cooperate at many times during development, including silencing of
pluripotency genes, genomic imprinting, and X chromosome inactivation (Cedar and Bergman,
2009). Currently, two models describe the relationship between DNA methylation and histone
modifications. A number of studies support the idea that DNA methylation is targeted and
patterned by histone modifications in the “follower” model, where DNA methylation acts
downstream in the regulatory hierarchy. For example, de novo DNA methyltransferases (DNMT)
are shown to recognize unmethylated histone H3 (H3K4me0) at promoters specifying methylation patterns at promoters (Guo et al., 2014b; Ooi et al., 2007; Otani et al., 2009; Zhang
41 et al., 2010). Similarly, H3K36me3 has recently been shown to target DNMT3B to gene bodies
contributing to genic methylation (Baubec et al., 2015; Morselli et al., 2015). Alternatively,
mounting evidence argues for an instructive role for DNA methylation that regulates histone
modification patterns, acting higher in the hierarchy. Under certain circumstances, DNA methylation has been found to be antagonistic to H3K27me3 at promoters. At these sites, methylated DNA is found to exclude binding of PRC2 components to their targets, providing a mechanistic basis for mutual exclusion (Bartke et al., 2010; Jermann et al., 2014). In addition,
DNMT3A enzyme was found to facilitate neurogenic gene expression through the exclusion of
Polycomb protein binding in gene bodies (Wu et al., 2010). In short, the complex relationship between DNA methylation and histone modifications remains to be illustrated, possibly in a cell- type and/or genomic region-specific manner.
To understand how DNA methylation may coordinate with histone modifications to regulate genome-wide gene expression, we and others have leveraged hypomethylated mouse embryonic stem cells (mESCs), which are viable despite complete loss of genomic DNA methylation. We have previously found that mESCs null of DNA methylation show upregulation of genes primarily associated with bivalent (H3K4me3/H3K27me3 positive) or unmarked
(H3K4me3/H3K27me3 double negative) gene promoters in wild-type cells (Fouse et al., 2008).
In contrast, minimal changes in H3K9me3 occupancy were observed in hypomethylated mESCs, leading to the idea that DNA methylation and H3K9me3 act non-redundantly (Karimi et al.,
2011). Meanwhile, H3K27me3 is dramatically redistributed in response to DNA hypomethylation (Brinkman et al., 2012; Cooper et al., 2014; Reddington et al., 2013). Despite these findings, it is still inconclusive whether the changes of histone modifications observed in
DNA methylation null mESCs are directly or indirectly caused by hypomethylation.
42 In this current study, we set out to understand how DNA methylation shapes the histone landscape and transcriptome in mESCs. To study this, we employed a mESC model allowing sequential establishment of fully methylated, globally demethylated, and various intermediate
levels of hypomethylation followed by subsequent measurement of histone modifications and
RNA transcriptome across all states. Thus, our experimental setup is designed to determine
whether DNA methylation acts upstream or downstream with respect to select histone
modifications. We show that DNA methylation regulates occupancy of both H3K27me3 and
H3K27ac at gene promoters and tissue-specific enhancer elements. Furthermore, changes in
H3K27me3 and H3K27ac histone modifications are reversed upon DNMT reconstitution, dependent on DNMT catalytic activity, indicating the ability of DNA methylation to outcompete established chromatin states.
RESULTS
Dnmt Reconstitution in Demethylated mESCs Restores Global Cytosine Methylation
and Causes Various Changes in Histone Modifications
To dissect causal relationships between DNA methylation and histone modifications we
simultaneously knocked-out all three DNA methyltransferases, Dnmt1, Dnmt3a, and Dnmt3b via
Cre-lox recombination to generate triple Dnmt knockout (TKO) mouse ES cells that are
completely devoid of DNA methylation after several cell passages (Figure 2.1A). By contrast,
double knockout of the de novo DNA methyltransferases Dnmt3a and Dnmt3b (DKO) leads to
slower global demethylation (presumably due to the robust Dnmt1 maintenance enzyme) and
reaches 90% loss of global methylation by 30 passages (Jackson et al., 2004) (Figure 2.1B). In
these two demethylated mESC systems, we then reconstituted Dnmt3a1, Dnmt3a2, Dnmt3b1
43 isoforms individually (Figure 2.1A and Figure 2.8A). Reconstitution of the de novo DNMTs in
TKO mESCs resulted in increased global methylation to approximately half of WT levels
(Figure 2.1B). In DKO mESCs, reconstitution of Dnmt3a1, Dnmt3a2, or Dnmt3b1 led to a greater increase of methylation to >70% WT levels, indicating a significant contribution from
Dnmt1. Profiling the DNA methylomes of TKO and DKO reconstitution cell lines using reduced representation bisulfite sequencing (RRBS) showed global levels of cytosine methylation consistent with mass spectrometry results (Figure 2.1B and Figure 2.8B). Together, these cell lines enable the study of relationships between varying global methylation levels and histone occupancy.
Mapping average methylation across all genes, we find similar methylation distribution patterns, but different amplitudes proportional to global methylation levels (Figure 2.8C).
Inspection of individual CpG sites revealed that the amplitude differences between cell lines are explained by cytosines being partially methylated rather than a skewed distribution of fully methylated and unmethylated CpGs (Figure 2.8B). Together, our data suggests that Dnmt3a and
Dnmt3b isoforms are all capable of shaping the overall DNA methylome patterning.
Using ChIP-seq, we profiled genomic occupancy of H3K4me3, associated with active
promoters, H3K27me3 associated with repressive chromatin, H3K27ac, associated with active
promoters and enhancers (Creyghton et al., 2010), and H3K4me1, associated with regulatory
regions including enhancers (Barski et al., 2007; Heintzman et al., 2007). Comparison across
wild-type (WT), TKO, DKO, and Dnmt reconstitution cell lines reveals selective global changes
in specific histone modifications. Genome-wide correlation between all histone modifications
and cell lines revealed that correlation generally exists among each histone modification. For
44 example, H3K4me3 across samples are highly correlated (Pearson correlation, r > 0.96) (Figure
2.1C), indicating that H3K4me3 is largely unaffected by global hypomethylation.
Among H3K27ac datasets, inter-sample correlation is also high (r ~ 0.93), however there may be few site-specific differences resulting from hypomethylation. By contrast, H3K27me3 appears to be most sensitive to varying levels of global DNA methylation. WT have 0.63 and
0.65 Pearson correlation with TKO and DKO respectively, >0.7 correlation with reconstitution in
TKO, and >0.8 correlation with reconstitution in DKO (Figure 2.1C, lower left corner).
Correlations are also seen between histone modifications, for example between H3K4me1 and
H3K27ac (r ~ 0.40) and between H3K27me3 and H3K4me3 in WT (r ~ 0.18), suggesting co- localization in a significant portion of the genome. Epigenetic profiling across mESC lines of different global methylation states is shown at several genomic loci, illustrating histone modification changes sensitive to DNA methylation (Figure 2.1D). Overall, this indicates a broad role for DNA methylation in influencing histone modifications, with different histone modifications showing different relationships.
DNA Methylation is Required for Maintenance and Reestablishment of Promoter
H3K27me3
As we observed the greatest global variation in H3K27me3 occupancy in response to
DNA hypomethylation, we aimed to further investigate the relationship between DNA
methylation and H3K27me3 at promoters. We first organized promoters by chromatin
environment, categorizing promoters into H3K4me3+, bivalent, H3K27me3+, and silent devoid of both H3K4me3 and H3K27me3 histone modifications (Figure 2.2A). Consistent with previous studies, we find that H3K27me3 is reduced at nearly all bivalent promoters
45 (3,231/5,362 = 60.3%) in demethylated mESCs (Brinkman et al., 2012; Cooper et al., 2014)
(Figure 2.2A and 2.2B). The loss of H3K27me3 at bivalent promoters is also associated with a
small gain in H3K27ac and loss of H3K4me1 at a subgroup of promoters (Figure 2.9A-C). In contrast, for a subgroup of silent promoters (H3K4me3 and H3K27me3 negative) (705/9,068 =
7.8%), we find increased H3K27me3 occupancy in the demethylated state. For example,
promoters of Cdkn2a and Pdgfb are bivalently marked and upon demethylation, entirely lose
H3K27me3 (Figure 2.2C). In the case of Cdkn2a, the promoter is seen to gain H3K27ac. In
contrast, Trpv1, which is devoid of H3K4me3 and H3K27me3, gains H3K27me3 upon
demethylation (Figure 2.2C). Interestingly, this increase of H3K27me3 extends to adjacent
regions including the gene body. Together, these data indicate that DNA methylation is
necessary for the maintenance of normal promoter H3K27me3 patterns in a context-specific
manner.
We next asked whether DNA methylation can reestablish normal H3K27me3 patterns by
reconstituting Dnmt in demethylated mESCs. Expression of Dnmt3a1, Dnmt3a2, and Dnmt3b1
in TKO and DKO mESCs restore variable levels of H3K27me3 occupancy around promoters
(Figure 2.2A and 2.2D). At bivalent promoters, Dnmt3a1 and Dnmt3a2 appear to have a slightly stronger impact in restoring H3K27me3 compared to Dnmt3b1. Nonetheless, neither of the three enzymes alone could fully recapitulate WT patterns when reconstituted in TKO mESCs (Figure
2.9D). By contrast, reconstitution of Dnmt3a1, Dnmt3a2, and Dnmt3b1 in DKO mESCs all show greater H3K27me3 rescue, particularly Dnmt3a1, which shows H3K27me3 patterns/levels phenocopying WT (Figure 2.2A and 2.2D). Meanwhile, at silent promoters, where demethylation leads to increased levels of H3K27me3 occupancy, we also observe differences in rescue between TKO and DKO reconstitution cell lines. Whereas TKO Dnmt reconstitution
46 appears to have weak rescue of H3K27me3 at silent promoter, all Dnmt isoforms in DKO are able to restore H3K27me3 down to near WT levels (Figures 2.2D and 2.9D). These data show the ability of reconstituted DNMT to reestablish WT histone modification patterns, indicating
DNMT’s ability to outcompete established chromatin states (i.e. the demethylated epigenome) at
gene promoters.
Impact of DNA Methylation on Promoter H3K27me3 and Gene Expression Differs
Between Bivalent and Silent Promoters
To determine the role of DNA methylation in regulation of histone modifications at
promoters, we quantified methylation in different categories of promoters (Figure 2.2E, top).
Bivalent promoters are hypomethylated with an average methylation fraction of 0.17, while
silent promoters are hypermethylated with an average of 0.88 (Figure 2.2E). As CpG islands are
frequently found at gene promoters and are hypomethylated, we measured the CpG content of
promoters in each category, represented as CpG observed-to-expected ratio (CpG O/E). We find bivalent promoters are CpG-rich (O/E > 0.6), consistent with the association of bivalent promoters with CpG islands (Bernstein et al., 2006). Silent promoters that gain H3K27me3 are characterized by low CpG content (O/E < 0.4), consistent with low CpG-content promoters
(LCPs) being predominately methylated in mESCs (Fouse et al., 2008; Weber et al., 2007).
Analysis of DNA methylation in KO and reconstitution cell lines show loss and recovery of WT methylation levels and patterns (Figure 2.2E, bottom). Bivalent promoters, such as Cdkn2a and
Pdgfb, showed a characteristic hypomethylated CpG island surrounding the TSS, while silent promoters, such as Trpv1, were heavily methylated (Figure 2.2C and 2.2E).
47 Comparing H3K27me3 occupancy at the TSS with global 5mC between cell lines reveals
strong associations between DNA methylation and promoter H3K27me3 (Figure 2.2F).
H3K27me3 positively correlates with global DNA methylation at bivalent promoters (Pearson
correlation, r = 0.84) whereas H3K27me3 is anticorrelated with global DNA methylation at
silent promoters (r = 0.95) (Figure 2.2F). These data suggest a predictive ability of global DNA
methylation levels on H3K27me3 occupancy in two promoter contexts.
To test whether alterations in histone modification resulting from demethylation changed gene expression, we profiled the transcriptome from all cell lines. Genes with bivalent promoters
generally had low expression (RPKM < 10), but significantly increased (mean RPKM increase
from 3.9 to 4.8) upon demethylation (Figure 2.2G). Alternatively, silent promoters were not
expressed (RPKM < 1) and on average remained silenced despite demethylation. In the case of
bivalent promoter gene Cdkn2a, we saw gene expression increase over 2-fold in DKO with
resuppression upon remethylation in Dnmt reconstitution mESCs.
DNA Methylation Maintains and Reestablishes Silent or Primed States at Enhancer
Elements
Differential DNA methylation is prevalent at tissue-specific enhancers (Andersson et al.,
2014; Elliott et al., 2015; Hon et al., 2013; Stadler et al., 2011; Thurman et al., 2012; Ziller et al.,
2013), suggesting a regulatory role for DNA methylation at enhancers elements. We therefore extended our analysis to enhancers, analyzing H3K27ac/me3 and H3K4me1 modifications
(Heintzman et al., 2007). We predicted that genomic hypomethylation would lead to activation of silent enhancers either as appearance of active enhancers (marked with both H3K27ac and
48 H3K4me1) or appearance of poised enhancers (marked with H3K27me3 and H3K4me1) in TKO
(Figure 2.3A) (Creyghton et al., 2010; Rada-Iglesias et al., 2011; Zentner et al., 2011).
To identify putative methylation-dependent enhancer sites, we identified differentially
enriched peaks between demethylated DKO/TKO and their respective methylated wild-type
mESCs. Differential peaks were overlapped with H3K4me1 and those within 2 kb of known TSS
were excluded to avoid identification of gene promoters. Identifying differential peaks common
to both DKO and TKO yielded a number of changes in the chromatin state of candidate
enhancers upon genomic hypomethylation (Figure 2.3A). For example, we observed 138 (1.3%)
and 551 (5.2%) peaks gaining and losing H3K27ac respectively. Similarly, we found 1,041
(46.5%) peaks gained and 162 (7.2%) peaks lost H3K27me3 in response to global demethylation. We noted that candidate enhancers that become active or poised in Dnmt KO mESCs predominately arise de novo from previously primed enhancer loci in WT mESCs
(Figure 2.3B). A few new active or poised enhancers derive from the other indicating that methylation does not regulate transitions between active and poised status.
We then sought to determine whether or not remethylation would be able to return the enhancers to their original state. If chromatin state took precedence over DNA methylation, we would expect histone modification status of reconstituted cell lines to resemble the demethylated state. On the other hand, if DNA methylation is capable of acting upstream in the hierarchy, the chromatin state would be reversible. We find that for both H3K27ac and H3K27me3, reestablishing DNA methylation returns putative enhancers to the wild-type state, exemplified by
de novo intergenic active enhancer upstream of Foxd3 as previously characterized in TKO
mESCs (Domcke et al., 2015), and an intragenic poised enhancer in Tmem104 (Figure 2.3C).
The histone modification changes characterized in demethylated DKO and TKO mESCs were
49 uniformly reversible across all enhancers identified (Figures 2.3D and 2.10). Similar to rescue at promoters, Dnmt reconstitution in DKO more efficiently reestablishes wild-type patterns of enhancers compared to in TKO (Figure 2.3D).
Enhancers that tend to lose H3K27ac or H3K27me3 are relatively hypomethylated in WT mESCs, whereas enhancers that gain H3K27ac or H3K27me3 are relatively hypermethylated
(Figure 2.3E). This is consistent with antagonism between modifications of the H3K27 residue with DNA methylation (Bartke et al., 2010; Jermann et al., 2014). Measuring CpG content, all enhancers sensitive to DNA methylation consistently show low CpG O/E (O/E < 0.4) (Figure
2.3E). In contrast with CpG-rich bivalent promoters that lose H3K27me3, poised enhancers losing H3K27me3 are low in CpG content.
Lastly, to test whether chromatin state at enhancers alters gene expression, we measured gene expression at both genes nearest to each enhancer (data not shown) as well as a subset of promoters with previously confirmed Hi-C interactions with DNA methylation-sensitive enhancers we identified (Shen et al., 2012). Expression changes at genes associated with methylation-sensitive enhancers varied in their response to demethylation where each category included genes up- and down-regulated (Figure 2.3F). Median fold change within enhancer categories indicated nearly equal up- and downregulation, however several genes exhibit expression patterns indicative of regulation by enhancer methylation. In the case of Elmo1, gene expression increased greater than 2-fold in association with a de novo active enhancer within the first intron (Figure 2.3G). Reconstitution of Dnmt also returned both expression of Elmo1 as well as the intragenic enhancer to wild-type levels. Together, this shows that there are hundreds of enhancers in mESCs where DNA methylation is the primary factor in determining the modification status of chromatin.
50
De Novo Enhancers Are Tissue-Specific and Contain Methylation-Sensitive
Transcription Factors
To characterize DNA methylation-sensitive enhancers, we cross-referenced these
enhancers with previously identified tissue-specific enhancers to assess their identity (Shen et al.,
2012) (Figure 2.4A). Of the active enhancers that significantly gain or lose H3K27ac, 33.3%
(46/138) and 40.8% (225/551) overlapped with tissue-specific enhancers respectively. In both categories, mESC-specific enhancers were found, with nearly two-thirds of active enhancers
losing H3K27ac predictably consisting of mESC-specific enhancers. De novo active enhancers
that gained H3K27ac showed overlap with several testes, E14.5 liver, placenta, and kidney-
specific enhancers with the remainder divided between 14 other tissues (Figure 2.4A). Both
enhancers that gain and lose H3K27me3 also overlapped with tissue-specific enhancers showing
33.2% (346/1,041) and 23.5% (38/162) overlap respectively. Interestingly, enhancers with de
novo poised enhancers overlap with enhancers from a variety of different tissue types.
Meanwhile, enhancers losing H3K27me3 are predominately specific to testes (76%), with a
smaller contribution from other tissue types.
To determine how DNA methylation regulates enhancers we assessed DNA methylation-
sensitive enhancers for enrichment of transcription factor motifs. We focused on enhancers
gaining H3K27ac or H3K27me3, representing de novo active or poised enhancers, as sites losing
these marks are already active/poised and are predictably enriched in pluripotency factors
(Figure 2.11). We find that de novo active enhancers (gain H3K27ac in Dnmt KO) are strongly
enriched in motifs for NRF1 (16%) and Nkx family transcription factors (87%) (Figure 2.4B).
Interestingly, NRF1 was recently described to be a DNA methylation-dependent transcription
51 factor where methylation of CpG within the binding motif affected binding to DNA (Domcke et
al., 2015). In our data, we identified several instances of de novo methylation-dependent active
enhancers with underlying methylated NRF1 motifs (Figure 2.4C). For example, at a testes- specific enhancer upstream of Zfp92, we find a DNA methylation-dependent peak with adjacent
Nkx2.5 and NRF1 motifs. NRF1 at this enhancer is methylated at an intermediate level (0.29-
0.57) in mESCs and is specifically demethylated (<0.05) in adult germline stem cells (AGSCs) of the testis (Hammoud et al., 2014). Similarly, a placenta-specific enhancer is found intragenically in Ears2 where a local NRF1 motif is methylated (0.83) in mESCs. The motif for
ZBTB33 (also known as Kaiso), a transcriptional regulator described to bind methylated CGCG motif in vitro, was also found at 6 sites (4.4%).
At de novo poised enhancers (gain H3K27me3 in Dnmt KO) we find enrichment of several motifs containing the canonical E-box sequence CACGTG as well as motifs for pluripotency factor Esrrb (Figure 2.4B). Notably, Myc and Max were also identified as putative methylation-dependent transcription factors (Domcke et al., 2015). Together, these data show
that DNA methylation can act upstream of modified H3K27, likely by means of modulating
binding of DNA methylation-dependent transcription factors.
Regulation of H3K27me3 Depends on 5-Methylcytosine and DNMT Catalytic
Activity
In understanding the relationship between DNA methylation and histone modifications, it
is important to distinguish between effects directly conferred by 5-methylcytosine (5mC),
DNMT-protein interactions, or other indirect effects. To test whether the methylcytosine moiety
of DNA itself or DNMT protein is responsible for reestablishing H3K27me3 at gene promoters,
52 we reconstituted a catalytically null mutant of Dnmt3b1 (Dnmt3b1MUT) that contains missense
mutations in the essential PC motif in DKO mESCs. Promoter H3K27me3 in the reconstitution with catalytic mutant resembled DKO at both bivalent and silent promoters. This demonstrates
that the ability of DNMT to reestablish H3K27me3 patterns in TKO and DKO mESCs is
dependent on DNMT catalytic activity and 5mC (Figures 2.5A and 2.5B). Similarly, poised
enhancer elements gaining/losing H3K27me3 also show inability of Dnmt3b1MUT to rescue
H3K27me3 redistribution compared to wild-type Dnmt3b (Figure 2.5C). For example,
Dnmt3b1MUT showed lack of regulation of H3K27me3 at promoters of bivalent Cdkn2a and silent Trpv1, as well as intragenic de novo poised enhancer in Tmem104 and intergenic poised enhancer lost upstream of Foxp4 (Figure 2.5D). This is consistent with previous study of
Dnmt3a1 catalytic mutants in neural stem cells, which show an inability to reverse
PRC2/H3K27me3 occupancy (Wu et al., 2010).
DNA Methylation Regulation of H3K27me3 is Mediated by PRC2 Targeting
We have shown that both promoters and enhancers prone to gaining H3K27me3 are
heavily methylated in WT, consistent with a direct antagonism between DNA methylation and
H3K27me3. To test whether this direct relationship extends to the respective catalytic enzymes, we analyzed published genome binding data of DNMT3A2, DNMT3B1, and SUZ12 (Baubec et al., 2015; Cooper et al., 2014). Similar to measurements of DNA methylation profiles at promoters and enhancers, we find that DNMT3A2 and DNMT3B1 are preferentially bound to silent promoters and de novo poised enhancers that both gain H3K27me3 in TKO (Figures 2.6A and 2.6B). Likewise, SUZ12 binding mirrors H3K27me3 occupancy, where SUZ12 binding increases at silent promoters in TKO (Cooper et al., 2014). In a similar manner, SUZ12 increases
53 strongly at de novo poised enhancers (Figures 2.6A and 2.6B). The strong presence of DNMT
and 5mC at silent promoters and de novo poised enhancers that gain SUZ12 and H3K27me3
upon demethylation suggests direct antagonism between DNA methylation and PRC2 complex
as previously described in NPCs (Wu et al., 2010). In contrast, bivalent promoters are
unmethylated and lack DNMT binding, implying an indirect mechanism for regulating
H3K27me3 and PRC2.
DNMT3 Isoform Differences in Histone Modification Regulation
For the two catalytically active de novo methyltransferases, multiple isoforms have been
characterized. These isoforms are expressed in a tissue and developmentally-regulated manner.
In mESCs, Dnmt3a2 and Dnmt3b1 are the major isoforms, while Dnmt3a1 is expressed at a lower level and increases in expression throughout development (Chen et al., 2002; Feng et al.,
2005). Methylation patterns are determined through differential expression of Dnmt3a and
Dnmt3b isoforms (Chen et al., 2003), but are redundant as certain targets such as Oct4 and
Nanog are still able to be methylated in single knockouts (Li et al., 2007). We therefore asked whether different Dnmt3 isoforms have unique or shared targets across the genome. Segmenting the genome into 500 bp windows, we found that the vast majority of methylated windows (26M bases) can be methylated by all three isoforms (90%). Interestingly, we do identify a tiny fraction
of the genome (in total about 200 kb or <1%) that appear either Dnmt3a1-specific, Dnmt3a2- specific, or Dnmt3b1-specific. Therefore, the three Dnmt3 isoforms have highly redundant genomic targets.
We next evaluated the effect on histone modification by different Dnmt isoforms. One challenge in measuring these differences is the inherent lack of quantitative ability of
54 conventional ChIP-seq, precluding accurate and quantitative comparison between isoforms. We therefore compared histone modifications between Dnmt reconstitution lines, and performed comparison with reference to background to account for systematic differences between ChIP- seq libraries.
Evaluation of H3K27me3 at gene promoters showed that all DNMT isoforms studied were able to regulate H3K27me3 and reestablish WT patterns to some extent indicating redundancy in their function. This is also evident in the ability of different isoforms to reestablish
similar global methylation levels (Figure 2.1B). However, of the three enzymes, Dnmt3b1
reconstitution showed the least recapitulation towards WT despite similar ability to remethylate the genome. Measuring the ratio of H3K27me3 occupancy between Dnmt3 isoforms at bivalent promoters, we find that Dnmt3a1 and Dnmt3a2 restore H3K27me3 to higher levels than
Dnmt3b1 (Figures 2.12A-B). Similarly, at silent promoters, both Dnmt3a2 and Dnmt3a1
resuppress H3K27me3 to a greater extent than Dnmt3b1 (Figures 22D and 2.12A-B). At
enhancers, we found Dnmt3a1 and Dnmt3a2 show greater recovery of H3K27me3 at poised
enhancers losing H3K27me3 and greater resuppression of H3K27me3 at de novo poised
enhancers than Dnmt3b1 (Figures 2.12C-D). Likewise, comparison of H3K27ac changes
between reconstitution lines showed that both Dnmt3a2 and Dnmt3a1 restored WT patterns more than Dnmt3b1 (Figure 2.12E). Overall, these comparisons indicate a potential bias between
Dnmt3a and Dnmt3b in the regulation of histone modifications at promoters and enhancer elements.
55 Discussion
In the current study, we combined sequential genetic alteration of DNA methylation
levels with systematic and comprehensive mapping of histone modifications to assess how DNA
methylation influences the epigenomic and transcriptomic landscape. Our experiments show the
active histone modification H3K4me3 acts independently of DNA methylation, while DNA
methylation acts upstream of H3K27me3, H3K27ac, and H3K4me1, which have regulatory
consequences at gene promoters and enhancers. Another key finding in our study was the
reversibility of the histone landscape in response to newly established methylcytosine marks.
Thus, DNA methylation is situated within a hierarchy of epigenetic regulation where it is
required for regulation of some histone modifications.
Measurement of histone modifications showed different responses to genome-wide
demethylation. For example, H3K4me3 did not change in response to global hypomethylation.
This is in contrast to somatic cells, where hypomethylation results in appearance of new
H3K4me3 peaks, albeit far fewer than H3K27me3 (Reddington et al., 2013). This indicates that
DNMT acts downstream of H3K4me3 in ESCs. The downstream position of DNA methylation is
consistent with mechanistic studies of de novo methylation showing exclusion of DNMT binding
to sites marked with H3K4me3 (Noh et al., 2015; Ooi et al., 2007; Otani et al., 2009; Zhang et
al., 2010). H3K4me1 on the other hand showed subtle changes at promoters in Dnmt KO
indicating sensitivity to DNA methylation. Binding affinity of H3K4me1 to DNMT3A-ADD and
DNMT3L was over an order of magnitude greater than H3K4me3, indicating methylation states of H3K4 differ in their relationship with DNA methylation (Noh et al., 2015; Ooi et al., 2007).
H3K4me3 therefore belongs to a list of histone modifications including H3K9me3 and
H3K36me3 that dictate DNMT localization in ESCs.
56 One model for the relationship between H3K27me3 and DNA methylation is that EZH2 targets DNMT for de novo methylation. The preferential de novo methylation at Polycomb targets in many cancers lends credence to this notion (Gal-Yam et al., 2008). EZH2 and
DNMT1/3A/3B were shown to physically interact, where EZH2 is required for DNMT binding at EZH2 targets in HeLa cells (Viré et al., 2006). Based on this model, DNMTs would not influence H3K27me3 as PRC2 acts upstream of DNMT. However, in contrast to this model, we find that loss of DNA methylation affects the maintenance of H3K27me3 patterns, which are then able to be reestablished with DNMT reconstitution, indicating that DNA methylation functionally acts upstream of PRC2 activity. DNA methylation acting upstream of histone modifications is not unprecedented, where DNA methylation has been shown to target H3K9 methyltransferase activity in fibroblasts (Fuks et al., 2003). Recently, DNA methylation was found to recruit SETDB1 and consequently H3K9me3 to key developmental genes in preadipocytes (Matsumura et al., 2015). However, in ESCs, DNA methylation is not required for
H3K9me3 deposition (Karimi et al., 2011). These differences could be due to intrinsic differences between embryonic and somatic cells or particular cell states.
We found the relationship between DNA methylation and H3K27me3 is dependent on genomic context. The negative correlation at silent promoter is consistent with the antagonism between DNA methylation and H3K27me3 (Bartke et al., 2010; Fouse et al., 2008; Mikkelsen et al., 2007). However, the positive correlation at bivalent promoters is unexplained. Bivalent promoters are unmethylated precluding a direct influence of methylcytosine on PRC2 at these sites. The leading thought is that antagonism between DNA methylation and PRC2 constrains
H3K27me3 within CpG islands, which is lost in TKO cells (Brinkman et al., 2012; Reddington et al., 2013). In this case, in cells lacking DNA methylation, H3K27me3 is aberrantly deposited
57 to regions normally restricted by DNA methylation, resulting in depletion of H3K27me3 at sites
such as bivalent promoters. In other words, DNA demethylated regions act as a sink for PcG activity. Alternatively, PRC2 has been shown to bind as a default state to CpG-rich DNA, while being obstructed by active gene expression (Jermann et al., 2014; Riising et al., 2014). DNA methylation could therefore influence gene expression of bivalent genes and consequently regulate H3K27me3. The order of events between PRC2 binding and gene activity are not yet known, preventing evaluation of such a mechanism. It remains of great interest mechanistically how DNA methylation regulates H3K27me3 at bivalent promoters.
The relationship between DNA methylation and chromatin state at enhancers has been largely been based on association with little indication of order of events (Andersson et al., 2014;
Elliott et al., 2015; Hon et al., 2013; Ziller et al., 2013). Our data showing de novo gain of
H3K27me3 and H3K27ac at enhancers upon demethylation demonstrates DNA methylation’s regulation of chromatin state. This is in agreement with previous work showing loss of H3K27ac resulting from hypermethylation by Tet2 knockout (Hon et al., 2014; Lu et al., 2014). Notably, our findings showed reversibility of H3K27me3 and H3K27ac changes at both promoters and enhancers. This indicates that DNA methylation can establish silent chromatin states, even over preexisting active states.
At de novo active enhancers, DNA methylation most likely regulates H3K27ac at active enhancers through modulation of transcription factor binding. Recent results showed DNA
methylation competes with binding of transcription factor NRF1, where changes in NRF1
binding were reversible between 2i and Serum conditions (Domcke et al., 2015). We identified
the NRF1 binding motif enriched at methylation-sensitive active enhancers, indicating regulation
of “settler” transcription factor binding as a target of regulation by DNA methylation. We also
58 identified motifs for Kaiso, a factor that binds methylated CpG in vitro and recruits deacetylation
complex NCoR to repress gene expression (Yoon et al., 2003). The huge diversity in transcription factors and their individual relationships with DNA methylation represent many
potential mechanisms underlying methylation-dependent changes in histone modifications.
Several models describing the relationship between DNA methylation and transcription factors
have been previously reviewed (Blattler and Farnham, 2013).
Similarly, poised enhancers showed reversible regulation by DNA methylation. Studies
supporting the idea that the three-dimensional architecture of the genome is structured into dense
polycomb interfaces (so-called polycomb bodies) have shown evidence of higher-order
interactions among H3K27me3 enriched sites (Joshi et al., 2015; Li et al., 2015; Wani et al.,
2016). It remains to be seen whether DNA methylation can control higher-order interactions via
regulation of histone modifications and other structural proteins.
Another question in the field that lacks understanding is what differences exist between
Dnmt isoforms. Early evidence suggested strong redundancy between isoforms in ESCs as single
gene knockouts of either Dnmt3a or Dnmt3b showed minimal changes in DNA methylation
(Chen et al., 2003; Okano et al., 1999). Knockdown of Dnmt isoforms revealed hypermethylation of highly-transcribed gene bodies upon Dnmt3b knockdown whereas Dnmt3a knockdown resulted in global hypomethylation (Tiedemann et al., 2014). DNMT3B was also found to selectively localize to gene bodies of highly transcribed genes through H3K36me3, while DNMT3A showed decreased methylation of highly transcribed genes (Baubec et al.,
2015). In our results, we find a dominant role for Dnmt3a isoforms over Dnmt3b in reestablishing H3K27me3 patterns at both promoters and enhancers. This provides some clues
59 that differential expression of Dnmt isoforms may play a role in regulating chromatin state and
gene expression, but much still remains to be studied.
In sum, this research extends the role of DNA methylation and provides a comprehensive
resource benefitting the understanding of how alterations in DNA methylation sculpt the rest of
the epigenome. Our results present a model where DNA methylation plays an active role in
establishing chromatin states in addition to its classic roles in promoter silencing. Thus, DNA
methylation is important yet underappreciated in regulation of chromatin across the mammalian
genome.
Experimental Procedures
ES Cell Line Generation
Mice containing flanking loxP sites in exons 4 and 5 of Dnmt12loxP (Jackson-Grusby et
al., 2001), exons 19 of Dnmt3a2loxP (Kaneda et al., 2004), and exons 16-19 of Dnmt3b2loxP loci
(Dodge et al., 2005; Lin et al., 2006), were crossed and bred to establish a line of triple floxed
(TFX) mice. mESCs were derived from TFX mouse embryos. All three DNA
methyltransferases, Dnmt1, Dnmt3a, and Dnmt3b, were simultaneously knocked-out via Cre/lox recombination to generate triple knockout (TKO) mESCs. Previous Dnmt triple knockouts either utilized knockout of Dnmt3a/3b in combination with knockdown of Dnmt1 or sequential knockout of de novo Dnmt3a/3b then Dnmt1 (Meissner et al., 2005; Tsumura et al., 2006).
Double knockout (DKO) mESC lines with Dnmt3a and Dnmt3b knock-out are as previously described (Okano et al., 1999). For reconstitution, Dnmt isoforms were cloned into pCAG-IRES-
Blast vector at the EcoRI cloning site as previously described (Chen et al., 2003). Briefly, constructs were transfected by electroporation with Gene Pulser II (Bio-Rad) and selected with 5
60 ug/mL Blasticidin. Individual clones were picked and expanded for a minimal of five passages to
generate stable reconstitution cell lines. In total, the following cell lines were generated for this
study: TKO, TKO+Dnmt3a1, TKO+Dnmt3a2, TKO+Dnmt3b1, DKO+Dnmt3a1,
DKO+Dnmt3a2, DKO+Dnmt3b1, and DKO+Dnmt3b1MUT.
ES Cell Culture
Mouse embryonic stem cells (mESCs) were grown on irradiated mouse embryonic
fibroblasts (MEF) and maintained in DMEM (Thermo) supplemented with 15% fetal bovine
serum, 1 mM GlutaMAX (Life Technologies), 1X Non-essential amino acids (NEAA), 1X
Penicillin/Streptomycin (Life Technologies), mLIF, and 0.001% β-mercaptoethanol (Sigma).
Chromatin Immunoprecipitation
ChIP was performed using protocol as described previously (Wang et al., 2005), with minor modification. Briefly, mESCs were pre-plated three times to deplete MEF feeders before
collection. Briefly, 2x107 cells were fixed with 1% formaldehyde for 8 min after which
chromatin was isolated and sonicated to 150-300 bp using Bioruptor (Diagenode). 5x106 cells worth of sonicated chromatin was precleared with Protein A/G Dynabeads (Life Technologies) for 1 hour then incubated in antibody at 4°C overnight. Antibodies for immunoprecipitation were all ChIP grade and validated previously in ENCODE datasets
(http://compbio.med.harvard.edu/antibodies/about) (Egelhofer et al., 2011). 2 ug of anti-
H3K4me3 antibody (Abcam ab8580), 5 ug of anti-H3K27me3 antibody (Active Motif 39155), 5 ug of anti-H3K27ac antibody (Active Motif 39133), and 5 ug of anti-H3K4me1 antibody
(Abcam ab8895) were used for ChIP. Chromatin was then immunoprecipitated with Protein A/G
61 Dynabeads for > 2 hours. ChIP was washed successively by two rounds of low-salt buffer, two rounds of high-salt buffer, two rounds of LiCl buffer, and finally two rounds of TE buffer. ChIP
DNA was eluted in TE, reverse crosslinked overnight at 65°C, and purified by incubation with
RNase A, then proteinase K, and finally phenol-chloroform extraction and ethanol precipitation.
ChIP-seq and Analysis
ChIP DNA was quantified using Qubit Fluorometer (Life Technologies) and approximately 10 ng were used for libraries construction using Kapa Library Preparation Kit for
Illumina sequencing (Kapa Biosystems) following manufacturer’s recommendations. ChIP-seq libraries were quantified using Qubit Fluorometer and Bioanalyzer High Sensitivity DNA
Analysis Kit (Agilent). Libraries were pooled and sequenced using the Illumina HiSeq 2000 machine as either 50-bp or 100-bp single-end sequencing reads. Sequenced reads were trimmed and mapped to mouse genome (mm9) using bowtie 1.1.0 allowing up to two mismatches and only unique alignments using the following options: “–v2 –m1 --best” (Langmead et al., 2009) .
Mapped reads were processed using Homer tool suite v4.7 where reads were extended by fragment length and normalized to 1x107 tags per sample (Heinz et al., 2010). Peak calling was done using Homer’s findPeaks function using input to filter peaks. Parameters for H3K4me3 included “-F 8”; H3K27me3 included “-size 1000 -minDist 3000 -F 4 -tagThreshold 32”;
H3K27ac included “-F 4”; H3K4me1 included “-size 1000 -minDist 1000 -nfr”. Peaks were further filtered using hard cut-offs for average reads/bp to retain only the most robust peaks.
Global correlation analysis was performed by first dividing the mouse genome into non- overlapping 1-kb windows using the BEDTools suite (Quinlan and Hall, 2010) and measuring coverage of ChIP-seq tags, which were extended by the approximate fragment length of 200 bp.
62 A matrix containing tag counts in 1 kb bins for each histone modification in each cell type was
then arranged in R, where empty bins were eliminated and counts were normalized across
samples. Matrix of pair-wise Pearson correlations were then calculated and used to generate
correlation heatmap.
Promoter categories were identified using overlap of H3K4me3 and H3K27me3.
Candidate active and poised enhancers were identified by first identifying differential peaks for both H3K27ac and H3K27me3 in J1-DKO and TFX-TKO pairs. Differential peaks identified in
both were then overlapped with H3K4me1 peaks and excluded from promoter regions. Promoter
and enhancer analysis was performed by identifying genomic regions and quantifying
normalized tags between samples within genomic regions in bed format. Scatterplot and boxplot
analyses used normalized tag counts summed over 2 kb from the center of genomic features of
interest. Heatmaps were generated from Homer -ghist output and visualized using Java
TreeView (Saldanha, 2004).
Reduced Representation Bisulfite Sequencing
Genomic DNA was purified from mESCs using standard phenol-chloroform-isoamyl alcohol extraction followed by ethanol precipitation. RRBS libraries were generated starting
from 400-800 ng of genomic DNA as previously described by Meissner et al. with minor
modifications. Briefly, MspI- generated fragments were end-repaired and adenylated before
being ligated with Illumina TruSeq adapters. DNA purifications after each enzymatic reaction as
well as size-selection of adapter-ligated fragments ranging from 200-350 bp were carried out
using AMPure XP beads (Beckman Coulter). Bisulfite conversion was performed with EpiTect
kit (Qiagen). In order to optimize the efficiency, bisulfite conversion, carried out as described by
63 the manufacturer, was performed twice. Bisulfite-converted libraries were amplified using
MyTaq Mix (Bioline) with the following program: 98°C for 2min, (98°C for 15sec, 60°C for
30sec, 72°C for 30sec) 12 cycles, 72°C for 5 min, 4°C indefinitely. Libraries were sequenced using Illumina HiSeq 2000 machine as 100-bp single-end sequencing reads.
DNA Methylation Analysis
Sequenced reads were trimmed and mapped to mouse genome (mm9) using Trim Galore and Bismark. Methylation state of individual CpGs were determined using Bismark Methylation
Extractor and used to generate bedGraph tracks. Cytosines that were covered by at least 5 reads were kept for downstream analysis. Cytosine coverage maps were generated using Bismark’s coverage2cytosine function and converted to tag format using Homer’s makeTagDirectory function with options –format bismark –mCcontext CG –precision 2. Definition of global 5mC levels was calculated by summation of average methylation per site and dividing by total cytosines. Analysis of methylation at specific genomic features was performed using Homer’s annotatePeaks function with –ratio option. Methylation at bivalent and silent promoters was calculated by averaging fractional CpG methylation across 2 kb surrounding all bivalent and silent promoter TSS. Metaplot and heatmap analysis were similarly generated from –hist and – ghist outputs. Whole genome bisulfite sequencing data were re-analyzed from the literature.
RNA-seq and Analysis
Total RNA was purified using RNeasy per manufacturer’s instructions (Qiagen). PolyA- tailed RNA was purified from total RNA and libraries were constructed using the TruSeq RNA
Library Prep Kit v2.0 (Illumina) following manufacturer’s recommendations. RNA-seq libraries
64 were quantified using Qubit Fluorometry and Bioanalyzer High Sensitivity DNA Analysis Kit.
Libraries were pooled and sequenced using the Illumina HiSeq 2000 machine as 50-bp or 100-bp
single-end sequencing reads. Reads with mapped to the mouse genome (mm9) with STAR aligner (Dobin et al., 2013).
Gene Ontology Analysis
Genes of which promoters were identified to have histone modification changes were compiled and analyzed using DAVID (Huang et al., 2007). Gene ontology of candidate enhancers was performed by formatting enhancer regions and random nonpromoter H3K4me1 background regions in BED genomic intervals format. These genomic intervals were then analyzed with GREAT (McLean et al., 2010).
Published Datasets
ChIP-seq datasets for DNMT3A2 and DNMT3B1 in mESCs were obtained from
GSE57413 (Baubec et al., 2015), SUZ12 in WT and Dnmt TKO mESCs from ERP005575
(Cooper et al., 2014), and H3K27me3 in WT and Dnmt TKO mESCs from GSE28254
(Brinkman et al., 2012) and ERP005575 (Cooper et al., 2014). Whole-genome bisulfite sequencing data for WT mESC was obtained from GSE30206 (Stadler et al., 2011), GSE48519
(Hon et al., 2014), and for DKO mESCs from GSE47894 (Leung et al., 2014).
Mass Spectrometry
One microgram of genomic DNA was digested by nuclease P1 (0.5 U), alkaline phosphatase (0.05 U), phosphodiesterase 1 (0.0005 U) and 2 (0.00025 U), following previously
65 published procedures (Liu et al., 2013). For liquid chromatography-tandem mass spectrometry- based quantification of 5-methyl-2’-deoxycytidine (5-mdC) and 2’-deoxyguanosine (dG), 600 fmol of [1’,2’,3’,4’,5’-13C5]-5-mdC and 16.2 pmol of [15N5]-dG were added to 200 ng of digested DNA. Enzymes were then removed by chloroform extraction, and the aqueous layer was subjected to LC-MS/MS analysis on an LTQ linear ion-trap mass spectrometer (Thermo
Fisher Scientific, San Jose, CA) coupled with an Agilent 1200 capillary HPLC (Agilent
Technologies, Santa Clara, CA). A 0.5 × 250 mm Zorbax SB-C18 column (5 μm in particle size,
Agilent Technologies, Santa Clara, CA) was eluted at a flow rate of 8.0 μL/min. A solution of 2 mM sodium bicarbonate (pH 7.0, solution A) and methanol (solution B) were used as mobile phases, and solvent composition was linearly changed from 0-20% B in 5 min and subsequently to 70% B in 25 min. The instrument was operated in positive-ion ESI-MSn mode to monitor the transitions of m/z 242126 for 5-mdC, m/z 247126 for [1’,2’,3’,4’,5’-13C5]-5-mdC, m/z
268152134 for dG, and m/z 273157139 for [15N5]-dG. Electrospray ionization conditions were as follows: electrospray voltage, 5 kV; capillary temperature, 275°C; capillary voltage, 9 V; tube lens voltage, 40 V; sheath gas flow rate, 15 arbitrary units. The calibration curves for the quantifications of 5-mdC and dG were generated by spiking isotope-labeled standards (600 fmol of [1’,2’,3’,4’,5’-D5]-5-mdC or 16.2 pmol of [15N5]-dG) with different amounts of the corresponding unlabeled analytes. The level of 5-mdC was expressed as a percentage of dG.
Accession Numbers
66 ChIP-seq and RNA-seq are available in the Gene Expression Omnibus (GEO) under accession number GSE77004.
67 Figures
Figure 2.1. Alteration of DNA Methylation Causes Selective Genome-wide Changes in Histone Modifications (A) Schematic of experimental design. Wild-type, TKO, DKO, and Dnmt Reconstitution cell lines each are assayed for DNA methylation by RRBS, histone modifications by ChIP-seq, and gene expression using RNA-seq. (B) Left: Mass spectrometry measurement of global 5mC levels between WT (n=6), TKO (n=7), TKO+Dnmt3a1 (m=5), TKO+Dnmt3a2 (n=6), TKO+Dnmt3b1 (n=4), and DKO and reconstitution cell lines (n=2). Data represented as mean ± SEM. Right: Global 5mC levels measured by RRBS. (C) Global Pearson correlation analysis between H3K27me3, H3K4me1, H3K4me3, and H3K27ac histone modifications across mESC lines. Correlation were calculated based on 1-kb genome-wide bins. (D) Genome browser tracks of RRBS and histone modification ChIP-seq between WT, KO, and Dnmt reconstitution mESC lines. Regions of interest exhibiting histone modification changes are boxed.
68
Figure 2.2. DNA Methylation Causally Regulates the Maintenance and Establishment of Promoter H3K27me3 (A) Heatmap of H3K4me3 (red) and H3K27me3 (blue) at ± 5 kb region centered around all Refseq TSS ranked by H3K4me3. Promoter categories are shown on left. Representative genes from bivalent and silent promoter categories are shown on the right. (B) Comparison of H3K27me3 between TKO vs WT at bivalent (top) and silent (bottom) promoters. Grey dots include all promoters and colored dots are statistically different between TKO and WT (p < 0.0001). Data are represented as H3K27me3 read coverage in log-
69 transformed tags per 10 million (TPM) reads within 2 kb surrounding TSS of individual promoters. (C) Genome browser tracks showing gene model, CpG islands, RRBS DNA methylation, H3K27me3/H3K27ac, and H3K4me1 (from top to bottom) at different loci. Promoter regions of Cdkn2a and Pdgfb (bivalent) and Trpv1 (silent) are shown boxed. (D) H3K27me3 occupancy in WT, demethylated DKO, and remethylated Dnmt reconstitution lines. Promoter metaplot and boxplot quantification of H3K27me3 occupancy between cell lines for bivalent (left) and silent (right) promoters. Metaplot represented as normalized H3K27me3 reads (in tags per 10 million; TPM) measured in 100 bp bins across ± 5 kb centered at TSS. Boxplot data similarly represented as H3K27me3 TPM within 2 kb surrounding TSS. (E) Comparison of fractional CpG methylation and observed-to-expected CpG ratio between bivalent and silent promoters in 2 kb surrounding TSS (top). Promoter metaplot showing average CpG methylation at ± 5 kb region centered around TSS (bottom). (F) Promoter H3K27me3 correlates with global DNA methylation (measured by mass spec) at bivalent and silent promoters. Data represented as normalized H3K27me3 in TPM within 5 kb of promoters and global 5mC (as in Figure 1B) are shown for each cell line. H3K27me3 TPM represents an average across all bivalent and silent promoters respectively. Note y-axis scale is different between bivalent and silent promoters. (G) Reads per kilobase million reads (RPKM) expression values for individual genes (left) and all bivalent and silent promoters (right). Bivalent promoters are expressed at a higher level in DKO relative to WT (P = 4.75x10-7, Kolmogorov-Smirnov test).
70
71 Figure 2.3. DNA Methylation Regulates Histone Modification Status and Gene Activity at Enhancers (A) Altered histone modification occupancy at putative enhancer sites. Metaplot indicates H3K27ac and H3K27me3 read coverage from DKO (solid) and TKO (dashed), normalized by depth in 100 bp bins within ± 5 kb around non-promoter H3K4me1 peaks. (B) Schematic showing changes in candidate enhancer status between primed (H3K4me1+), active (H3K4me1+/H3K27ac+), and poised (H3K4me1+/H3K27me3+) states. Numbers in circles indicate number of sites in WT mESC. Arrows represent changes occurring with demethylation in DKO/TKO. (C) Genome browser visualization of histone modification changes at candidate de novo active (chr4:99,210,711-99,257,661; 80 kb upstream of Foxd3) and poised (chr11:115,049,693- 115,089,693; Tmem104 intragenic) enhancer sites. (D) Heatmap analysis of histone modification changes to Dnmt DKO/TKO and reconstitution. Heatmap shows H3K4me1, H3K27ac, H3K27me3, and 5mCpG ± 5 kb genomic region centered on H3K4me1 separated by candidate enhancer categories: H3K27ac Gain (n=138), H3K27ac Loss (n=551), H3K27me3 Gain (n=1,041), H3K27me3 Loss (n=162). (E) Comparison of fractional CpG methylation (top) and observed-to-expected CpG ratio (bottom) in 2 kb surrounding enhancer center between categories of DNA methylation-sensitive enhancers. (F) Distribution of log2 fold-change (DKO/WT) in gene expression (in RPKM) between enhancer categories. (G) Representative example of gene expression changes at Elmo1 (chr13:20,175,630- 20,231,247) showing increase in H3K27ac at intronic enhancer (boxed and labeled with E) and expression in DKO and reversion with Dnmt reconstitution.
72
Figure 2.4. DNA Methylation-Dependent Loci Are Tissue-Specific Promoters and Enhancers (A) Distribution of candidate enhancers among previously identified tissue-specific enhancers (Shen et al., 2012). Tissues with candidate enhancer overlap >4% are labeled, all remaining tissues are grouped in “Other” category. (B) DNA sequence motifs enriched in de novo DNA methylation-dependent active and poised enhancer categories. (C) Examples of tissue-specific enhancers overlapping differentially methylated regions and methylation-dependent transcription factor motifs enriched in our analysis. Genome browser tracks show enhancers upstream of Zfp92 (chrX:70,622,988-70,673,292) and intragenic to Ears2 (chr7:129,179,862-129,202,881). Whole genome-bisulfite sequencing tracks are shown for mESC, adult germline stem cell (AGSC) from testis, and placenta (Stadler et al., 2011; Hammoud et al., 2014; Hon et al., 2013).
73
Figure 2.5. Regulation of H3K27me3 Depends on DNMT Catalytic Activity (A) Heatmap of H3K4me3 (red) and H3K27me3 (blue) at ± 5 kb region centered around all Refseq TSS ranked by H3K4me3. H3K27me3 compared between DKO, DKO+Dnmt3b1 (wild- type), and DKO+Dnmt3b1MUT (catalytic mutant). (B) Comparison of H3K27me3 occupancy between wild-type Dnmt3b1 and catalytic mutant reconstitution at bivalent and silent promoters. Average profile of H3K27me3 across ± 5 kb centered on TSS and quantification between cell lines shown for bivalent (top) and silent promoters (bottom). (C) Comparison between reconstitution of wild-type Dnmt3b1 and Dnmt3b1MUT at enhancers. Metaplot showing average profiles of H3K27me3 occupancy centered on de novo poised enhancers (top) and poised enhancers losing H3K27me3 (bottom). (D) Genome browser tracks showing examples of H3K27me3 changes at promoters of Cdkn2a and Trpv1 as well as de novo poised enhancer in Tmem104 and poised enhancer upstream of Foxp4 (chr17:48,106,091-48,116,091).
74
Figure 2.6. DNA Methylation Directly Regulates Loci Gaining H3K27me3 via PRC2 Occupancy (A) Heatmap comparison of 5mC, DNMT3A2/B1, H3K27me3, and SUZ12 between different genomic contexts. Data is shown within +/- 5 kb centered on TSS or center of enhancer and quantified as averaged depth-normalized tags in 100 bp bins. (B) Metaplot profiles showing average occupancy of DNMT3A2/3B1 and SUZ12 across silent promoters (top) and de novo poised enhancers (bottom) (Baubec et al., 2015; Cooper et al., 2014).
75
Figure 2.7. DNA Methylation Regulation of Histone Modifications Across Promoter and Enhancer Contexts Schematic of DNA methylation shaping of the epigenome. At promoters (left) hypomethylation results in loss or gain of H3K27me3, but not H3K4me3. Loss/gain of H3K27me3 is associated with H3K4me3 occupancy, where bivalent promoters marked by both H3K4me3 and H3K27me3 lose H3K27me3 with hypomethylation, while silent promoters absent of histone modifications gain H3K27me3 in the demethylated state. At enhancers (right) methylation represses enhancers, which become poised (gain H3K27me3) or active (gain H3K27ac) upon global demethylation. Reconstitution of DNMT in all contexts reestablishes WT patterns.
76
Figure 2.8. Overview of RRBS and ChIP-seq Data (A) RT-PCR quantification of Dnmt1, Dnmt3a, and Dnmt3b in WT, TKO, DKO, and respective reconstitution cell lines. (B) Histogram plot of all CpG sites measured by RRBS that are covered by at least 5 reads. X- axis indicates the methylation level of individual CpG sites defined by the number of methylated reads divided by the total number of methylated (C) and unmethylated (T) reads (C/[T+C]). (C) Metaplot showing average methylation profiles across all genes; DKO shown as solid lines and TKO shown as dashed lines. (D) ChIP-seq reproducibility. Comparison of H3K27me3 WT and TKO ChIP-seq data with published datasets. Top row compares between biological replicates of WT (r=0.895), WT from ‘B’ Brinkman et al. (r=0.858), and WT from ‘C’ Cooper et al. (r=0.761) respectively from left to right. Bottom row compares between biological replicates of TKO (r=0.872), TKO from ‘B’ Brinkman et al. (r=0.835), and TKO from ‘C’ Cooper et al. (r=0.836) respectively from left to right.
77
Figure 2.9. Histone Modification at Promoters Change with Global Methylation (A) Metaplot profiles showing average profiles of H3K4me3, H3K4me1, and H3K27ac centered on TSS of bivalent promoters. (B) H3K27me3 loss at bivalent promoters coincides with a general gain in H3K27ac. Scatter plot showing ratio (TKO/WT) of H3K27ac versus H3K27me3. (C) H3K27me3 loss at bivalent promoters coincides with a general loss in H3K4me1. Scatter plot showing ratio (TKO/WT) of H3K4me1 versus H3K27me3. (D) H3K27me3 occupancy in wild-type, demethylated TKO, and remethylated Dnmt reconstitution lines. Promoter metaplot and boxplot quantification of H3K27me3 occupancy between cell lines for bivalent and silent promoters. Metaplot represented as normalized H3K27me3 reads (in tags per 10 million; TPM) measured in 100 bp bins across ± 5 kb centered at TSS. Boxplot data similarly represented as H3K27me3 TPM within 2 kb surrounding TSS
78
Figure 2.10. Comparison of H3K27ac and H3K27me3 loss and rescue in DKO and TKO mESCs (A) Metaplot profiles of H3K27ac occupancy, measured in tags per 10 million reads, across de novo active enhancers (Gain H3K27ac in KO) and active enhancers that lose H3K27ac. (B) Metaplot profiles of H3K27me3 occupancy, measured in tags per 10 million reads, across de novo poised enhancers (Gain H3K27me3 in KO) and poised enhancers that lose H3K27me3.
79
Figure 2.11. Enriched de novo Transcription Factor Motifs in DNA Methylation-Sensitive Enhancers (A-D) Top enriched de novo motifs from Homer for (A) de novo active enhancers; (B) de novo poised enhancers; (C) active enhancers losing H3K27ac; (D) poised enhancers losing H3K27me3 categories.
80
81
Figure 2.12. Isoform-specific effects at promoters and enhancers (A-D) Histogram of H3K27me3 normalized read count at promoters/enhancers as a ratio of Dnmt3a/Dnmt3b1. Blue line represents histogram of H3K27me3 ratio at promoter/enhancer categories, while grey line in all plots are H3K27me3 ratio measured at all promoters, representing systematic differences in ChIP-seq signal. P-value shown for two sample t-test. (A) Comparison of Dnmt3a2 and Dnmt3b1 at promoters. Background ratio measured at all promoters. (B) Comparison of Dnmt3a1 and Dnmt3b1 at promoters. (C) Comparison of Dnmt3a2 and Dnmt3b1 at enhancers. (D) Comparison of Dnmt3a1 and Dnmt3b1 at enhancers. (E) Histogram of H3K27ac normalized read counts at enhancers as a ratio of Dnmt3a/Dnmt3b1.
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89
Chapter 3 :
Molecular and genomic defects of mutations in DNMT3A-associated
Overgrowth Syndrome with Intellectual Disability
90 Introduction
Mutations in epigenetic machinery have revealed a class of genetic overgrowth syndromes with a unique etiology in a class of epigenetic regulators (Tatton-Brown et al., 2017).
Mutations in DNMT3A were recently identified in one group of distinct Overgrowth Syndrome with Intellectual disability patients, named Tatton-Brown Rahman Syndrome (TBRS) (Tatton-
Brown et al., 2014). Variants in DNMT3A are also found in autism spectrum patients suggesting a broader role for mutation on intellectual function (Sanders et al., 2015). Previously most well characterized DNMT3A mutations occur in the methyltransferase domain or are involved in
dimerization as in missense mutation at the R882 residue (Ley et al., 2010; Russler-Germain et
al., 2014). Disease-associated variants found in regulatory domains have not been well studied.
Given their newfound prevalence in an epigenetic-related overgrowth and intellectual disability disorder, it is important to understand their function.
DNA methyltransferase activity differs between naked DNA and chromatin, indicating
DNMT activity responds to epigenomic context (Zhang et al., 2010). Recently, mechanisms of intrinsic auto-inhibition in DNMT3A by regulatory domains have been described providing a means to understand mutations in these regions (Guo et al., 2014). The effect of these mutations
within the cellular environment are unknown and will be important in understanding how these
mutations relate to problems found in overgrowth syndrome patients.
DNA methylation patterns are cell-type specific in human and mouse (Hon et al., 2013;
Ziller et al., 2013). De novo DNMTs such as DNMT3A are not intrinsically sequence specific,
therefore the domain structure of DNMT3A contributes to genomic specificity. Recent work has
shown that alterations in DNMT3A’s regulatory domains can dramatically alter the specificity
and function of DNMT3A (Noh et al., 2015).
91 We therefore address the question of what effect mutations in DNMT3A associated with
TBRS have in a cellular context. We utilize genome editing and neural differentiation to study the molecular effects of mutant DNMT3A. Here we study overgrowth associated mutations found in all three DNMT3A conserved domains. Mutations in DNMT3A showed variation in functional defects. We identified a consistent set of 341 overgrowth-associated differentially methylated regions (OG-DMRs) in NSCs that were found near CpG islands and developmental genes. Surprisingly, OG-DMRs were enriched for a number of imprinted genes associated with growth and intellectual function phenotypes in humans. OG-DMRs were also associated with changes in histone modification H3K27me3. Relating the mutations found in different domains, we find consistency between structural understanding and functional effects of methylation in the genome.
RESULTS
Establishing a Cellular Model to Study Overgrowth-Associated DNMT3A
Mutations
In the original description of TBRS, 13 mutations were found to be spread across the
entire DNMT3A enzyme (Tatton-Brown et al., 2014). We chose four of the originally described
mutations to investigate further, with at least one mutation in each of DNMT3A’s conserved
domains (Figure 3.1A). I310N is found in the PWWP domain, G532S and M548K are both in
the ADD domain, and P904L is in the methyltransferase domain. P904L, however, is not near the conserved catalytic site nor does it overlap with known dimerization surfaces. Each of these human mutations are found in highly conserved regions of both the human and mouse protein.
For each mutation, the homologous residue in the mouse genome was identified and used. To
92 introduce these mutations we utilized CRISPR-Cas9 to introduce the missense mutations in the
endogenous Dnmt3a locus and exploiting ssODN-associated homology directed repair (Figure
3.1B). Individual ESC colonies were screened, subcloned, and confirmed by Sanger sequencing
(Figure 3.1C). CRISPR modification of the alleles was efficient with clones being
predominately indels and indels mixed with knock-in. We were unable to obtain heterozygous
knock-in therefore all experiments were performed in homozygous knock-in lines, herein
referred to as mutant lines. Homozygous mESC lines for I306N (IN), G528S (GS), M544K
(MK), and P900L (PL) were obtained.
Wild-type and mutant mESC were then differentiated through Pax6+/RC2+ cortical neural progenitors and subsequently expanded in neural stem cell (NSC) conditions to stable and homogenous neural stem cells (Conti et al., 2005; Gaspard et al., 2009) (Figure 3.1D). NSCs expressed Nestin and were proliferative (Figure 3.6). No overt differences were observed upon differentiation.
DNMT3A Mutants Differ in Functionality in mESC and mNSCs
We first assessed global methylation in mutant mESC and NSCs by measurement of 5- methylcytosine via mass spectrometry (Figure 3.1E). Mutant mESCs were observed to differ in global methylation where IN were hypomethylated in both mESCs and NSCs, similar to KO cells. Meanwhile, GS and PL mutant lines, showed no difference in mESCs. GS NSCs showed some variability and were similar to wild-type J1 NSCs. PL NSCs showed moderately diminished methylation, but not to the extent of IN NSCs.
Next, in order to better understand the effect of mutations in the cellular context, we utilized an ectopic expression system in stably transfected cell lines (Figures 3.1F and 3.1G). In
93 ESCs, both de novo methyltransferases Dnmt3a and Dnmt3b are expressed and have been shown
to be functionally redundant where both are able to methylate targets in the absence of the other
(King et al., 2016). Furthermore, while Dnmt3a is known to operate as dimers, Dnmt3a and
Dnmt3b also interact in ESC (Li et al., 2007). We therefore reconstituted wild-type and mutant
DNMT3A in DKO (Figure 3.1H, left). We have previously shown that reconstitution of wild- type Dnmt in DKO allows robust measurement of de novo methylation activity as the presence of Dnmt1 can maintain de novo methylated sites rather than being passively diluted (King et al.,
2016). Our DKO lines were also late passage allowing the genome to fully demethylate. As expected, DKO mESCs were dramatically hypomethylated. Reconstitution of wild-type
DNMT3A consistently remethylated to an average of 2.4% global 5mdC, around 80% of normal
J1 mESCs. Reconstitution of IN mutant DNMT3A surprisingly only resulted in 0.9% 5mdC, equivalent to 30% of normal. In contrast, both GS and PL mutant DNMT3A remethylated the genome to similar levels as wild-type DNMT3A at 2.5% and 2.0% (83% and 67% of normal) in
GS and PL respectively.
Given that IN mutant DNMT3A shown minimal activity on its own, we next ectopic expressed the three mutants in wild-type ESC grown in two different culture conditions. We cultured mESCs in both ground-state conditions, which normally are hypomethylated with lower expression of endogenous Dnmt3a and Dnmt3b, as well as conventional conditions that show high methylation. In both ground-state and conventional cultures, all three mutant DNMT3A were able to increase methylation (Figure 3.1H, right). These experiments in ESCs are summarized and show that GS and PL mutants show the ability to methylate a demethylated genome in the absence of endogenous Dnmt3a or Dnmt3b, while IN shows minimal activity
94 (Figure 3.1I). However, in the presence of endogenous de novo Dnmts, all three mutants studied
were showed the ability to increase global methylation.
DNA Methylation Alterations Occur at Sites of de novo Methylation in NSCs
We next aimed to understand where the methylation changes were occurring in the
genome. We therefore collected and analyzed whole-genome bisulfite sequencing (WGBS) data in duplicate with 8-12X coverage per replicate over an average of 25 million CpG sites across all
samples. Replicates were consistent, with all replicates having Pearson correlation >0.8 at CpG
sites with >10X coverage fitting with ENCODE quality standards (Figure 3.7). Global analysis
of CpG methylation showed increased methylation in neural stem cells compared to embryonic stem cells where methylated CpG sites increased from 62% in ESCs to 75% in NSCs (Figure
3.2A). Nearly all overgrowth mutants showed reduced proportions of methylation CpGs with KO at 67%, IN at 65%, MK at 70%, and PL at 70%. Meanwhile, 75% of CpG sites in GS NSCs were methylation, similar to wild-type J1 NSCs.
Dnmt3a has been reported to be involved in methylation in the Non-CpG context, so we also measured methylation in CHG and CHH contexts between mutants. Methylation in Non-
CpG sites account for approximately 23% of all methylation in mESC and lowered to 18% in
NSCs. Non-CpG methylation ranged from 17.3% to 19.8% in mutants (Figure 3.7).
Pair-wise comparison between each mutant NSC and wild-type J1 identified methylation changes consistent with changes observed at the global level. Specifically, in KO, IN, MK, and
PL NSC lines, significant DMRs were predominately lower in mutant lines compared to wild-
type J1-derived NSCs (Figure 3.2C, red dots). In contrast, GS NSCs showed many more regions
with increased methylation compared to J1 NSCs. Differentially methylated regions identified
95 for each pair were approximately 1kb and highly similar between mutations showing reduced methylation (Figure 3.2C, bottom).
As to not exclude any sites changing in methylation, we took the union of all differentially methylated regions identified by pair-wise comparison yielding a total of 508 sites.
The majority of OG-DMRs (341/508 = 67%) showed consistently lower methylation in comparison to J1 NSCs. We identify these 341 sites as overgrowth-associated differentially methylated regions (OG-DMRs) (Figure 3.2D). The remainder of sites were consistently methylated/unmethylated across all NSCs with a few individual lines showing differences, possibly due to variability in differentiation and culture in individual lines or batches.
Interestingly, we observe that OG-DMRs tend to be hypomethylated in mESCs indicating the loss of de novo methylation during neural differentiation (Figures 3.2C and 3.2D). Consistent with this, OG-DMRs were enriched in ontology terms relating to embryo organ development, pattern specification, and regionalization. Promoter associated transcription factors ontologies were also enriched in OG-DMRs (Figure 3.2E).
Of all OG-DMRs 113 (33%) were within 2-kb and 49 (14%) within 500 bp of a TSS.
Similarly, 116 (34%) overlapped with a CpG island and an additional 96 were within 4-kb of a
CpG island, commonly referred to as CpG island shores and shelves (Figure 3.2F). DNA methylation has been shown to most variable at CpG island shores rather than within CpG islands themselves (Irizarry et al., 2009; Ziller et al., 2013). As CpG content can distinguish
between CpG islands and neighboring shores, we measured CpG content of OG-DMRs represented as observed-to-expected CpG ratio. We find that most DMRs have CpG contents
lower than the CpG content normally found within CpG islands (>0.6), rather the CpG is (Figure
3.2G).
96
Epigenetic Signatures of OG-DMRs and Antagonism of H3K27me3
Having found that two-thirds of OG-DMRs are found near CpG island shores/shelves and
one-third are distal to promoters and CpG islands, we next asked whether these sites correspond
to cis-regulatory elements that may be responsible in gene regulating during development.
Towards this question, we profiled H3K4me3, H3K4me1, H3K27ac, and H3K27me3 in wild-
type J1 NSCs to assess the epigenetic signatures of OG-DMRs (Figure 3.3A).
Separating between promoter and CpG island-associated OG-DMRs and those distal, we
find OG-DMRs in a number of different chromatin contexts. Across both categories of OG-
DMRs, we see many sites that are found near H3K4me1 without H3K4me3, H3K27ac, or
H3K27me3 present with 45% (106/235) and 40% (42/106) of representation between
promoter/CGI-associated and non-promoter/CGI OG-DMRs respectively (Figure 3.3B). These
regions have been referred to as primed enhancer or permissive regulatory regions, which are
capable of activity upon activation. Promoter/CGI-associated OG-DMRs were more abundant in
regions marked by H3K27me3 where nearly 20% (46/235) are co-occupied by H3K4me1 and
H3K27me3 (poised enhancers) with a further 5% (12/235) solely marked with H3K27me3. In
contrast, 6% (6/106) are found in either poised enhancer or polycomb-repressed contexts in non-
promoter OG-DMRs. Four non-promoter DMRs showed overlap with H3K4me3, which is
unexpected. Visual inspection revealed that one was approximately 5 kb from Sox2, which is
found in a broad H3K4me3 domain nearly 10 kb wide. These sites are located distal to proximal
promoters, but may still be involved in regulation. Lastly, non-promoter/non-CGI OG-DMRs tended to more associated with larger hypermethylated regions. While we identify these sites as
other, these could be equivalent to quiescent regions that are heavily methylated in larger
97 genomic studies (Figure 3.3B). While 19% (44/235) of promoter/CGI OG-DMRs are in the
other category, nearly half (46%; 49/106) of non-promoter DMRs were found to not be
significantly marked with the histone modifications we measured.
Previously, Dnmt3a has been shown to regulate neural genes through antagonism of
PRC2 and H3K27me3 in postnatal NSCs and ESC-derived NPCs (Wu et al., 2010). We therefore
sought to answer whether DNA methylation changes at OG-DMRs were associated with changes
in H3K27me3. Profiling H3K27me3 in all mutant NSC lines, we find that a number of sites
show increase in H3K27me3 (Figure 3.3C). While nonpromoter OG-DMRs show an
appreciable increase in H3K27me3 at several sites and on average across all sites, the higher
levels of H3K27me3 at more noticeable at promoter/CGI-associated DMRs (Figure 3.3D).
Despite the size of DMRs limited to an average of approximately 680 bp, we observed broad
changes in H3K27me3 spanning multiple kilobases. Taking the Nr2f2 locus as an example, we
see dramatically higher H3K27me3 occupancy most evident in KO and IN NSCs in comparison
to wild-type J1 NSCs (Figure 3.3E). The OG-DMR near Nr2f2 is located 3 kb downstream of
the 3’UTR of Nr2f2 and is bordered by several CpG islands. The differences in H3K27me3 vary
with nearly all mutants showing increases on the left of the OG-DMR, however only KO and IN
show increases on the right, overlapping the Nr2f2 gene. One interesting observation was that
across all OG-DMRs, nearly all mutant NSC lines showed increased H3K27me3 despite minor
reductions in DNA methylation at OG-DMRs in GS mutant NSCs. In the case of Nr2f2, we see
that all mutants including GS mutants show reduced DNA methylation.
We had previously shown that OG-DMRs are associated with many ontologies relating to developmental biological processes. We wondered whether these de novo methylation events that are disrupted in overgrowth syndrome, may relate to fate restriction. We therefore utilized a
98 comprehensive set of tissue-specific differentially methylated regions (tsDMRs) that were
identified across 17 adult mouse tissues spanning all three germ layers to assess whether OG-
DMRs were associated with methylation events important in tissue-specificity (Hon et al., 2013).
We found that half of the OG-DMRs in our study overlapped with a tsDMR with 49% and 56% in promoter/CGI-associated OG-DMRS and non-promoter respectively. Overlap occurred broadly across all tissues (Figure 3.3F). For example, Nr2f2 OG-DMR overlapped with bone marrow, spleen, thymus, and olfactory bulb tsDMRs. Visual inspection of methylation revealed methylation over this region with hypomethylation specific to blood-related tissues and olfactory bulb.
Referencing DNA methylation measurements from developing mouse embryos by
ENCODE, the Nr2f2 OG-DMR is progressively methylated during forebrain development between E10.5 and E15.5 (Figure 3.8A). Meanwhile, in heart and intestinal tissues, the OG-
DMR is hypermethylated at all stages. Furthermore, comparison with H3K27me3 at similar stages during mouse embryonic development, showed an associated decrease in H3K27me3 matching the progressive increase in methylation in both forebrain and hindbrain (Figure 3.8B).
Together, this indicates that OG-DMRs show substantial tissue specificity and dynamics during
development, suggesting a role in specifying cell fate.
Altered Gene Expression in Overgrowth Mutant NSCs
After identifying both DNA methylation and histone modification alterations in Dnmt3a
mutant NSC lines, we asked whether any gene expression changes could be observed in mutant
NSCs. Comparison between mutant and wild-type J1 NSCs resulted in 87 differentially
expressed genes (DEGs) shared between KO, IN, GS, and PL NSCs (Figure 3.4A). The majority
99 of DEGs were higher in mutant NSCs, where 82% (80/87) genes showed higher gene expression.
Expression patterns of DEGs clustered into three general patterns. In the first group, DEGs are
normally expressed in ESCs and are silenced in wild-type NSCs. However, in nearly all mutant
lines, we observe a moderate derepression. The second group of DEGs are expressed in the
opposite pattern where expression is low in ESCs and increases in wild-type NSCs. In mutant
NSCs, however, the genes are further upregulated. In the last group, DEGs are heterogeneously
expressed in ESCs and downregulated in wild-type J1 NSCs. Nearly all mutant NSCs showed
derepression. Looking at enrichment within curated gene sets, we found the latter two groups
shared similar functional terms, including focal adhesion signaling and osteoblast genes (Figure
3.4B). The first group was enriched in placental genes as well as fibroblast proliferation and
regulation of ERK signaling.
To find out how many DEGs may be regulated directly by changes in DNA methylation,
we overlapped our set of 87 DEGs with 304 unique genes closest to each OG-DMR and found
minimal overlap. Only five genes were differentially expressed and were near OG-DMRs
(Figure 3.4C). Included in these five genes are Col5a2, where the associated OG-DMR is found overlapping the TSS with a 20-50% reduction in DNA methylation between KO, IN, and PL
lines suggesting direct regulation. The remainder of genes have DMRs that are not located near
the TSS.
We last asked whether changes in DNA methylation can lead to derepression without statistically significant gene expression changes, which may be the case if genes of different fates are unmethylated, but not activated due to cellular context. We therefore looked at promoter/CGI-associated OG-DMRs only and compared changes in methylation with changes in expression for each gene. On average, mutant NSCs showed a 30-40% lower methylation at
100 promoter-associated OG-DMRs. Similarly, expression of associated genes increased on average
between 1.4 and 1.6-fold (Figure 3.4D). Looking at individual genes, we see that the majority of
hypomethylated OG-DMRs show minor increase with a handful of genes being upregulated at
higher levels. However, while these genes show larger fold-changes, they are expressed at low
absolute levels.
Together, this indicates that while a number of epigenetic changes are occurring as a
result of overgrowth-associated Dnmt3a mutations, the link between DMRs and expression
changes does not immediately result in expression changes through direct action of DNA
methylation at gene promoters.
Structural Interpretation of DNMT3A Mutations
To interpret changes in methylation, we mapped the mutation to crystal structures and
structural models of DNMT3A. DNMT3A has been shown to form a heterotetrameric complex consisting of catalytically inactive coactivator DNMT3L and DNMT3A (Jia et al., 2007).
Visualizing the location of DNMT3A mutants on a complete structural model including PWWP,
ADD, C-terminal MTase domain, and nucleosomes, it is apparent these mutations are spatially
distal to both the active site and the interfaces surfaces (Figure 3.5A) (Rondelet et al., 2016). We
therefore aimed to study the structural context of each mutant to corroborate our functional
findings in NSCs.
I310N is located in the second beta strand of the canonical beta-barrel core found in
PWWP domains (Figure 3.5B). The PWWP domain was found to be essential for chromatin targeting as mutation of individual residues within the domain completely abolish targeting, as is found in human ICF syndrome. The beta-barrel constitutes a hydrophobic core, which is
101 important for the stability of the individual domain. Mutation of conserved hydrophobic residues
in the PWWP domain of DNMT3B abrogated targeting, which was hypothesized to affect the
structure of the hydrophobic core (Ge et al., 2004). We predict the I310 mutation to asparagine
would disrupt the hydrophobic core due to polarity.
The activity of Dnmt3a differs between naked DNA and chromatinized DNA due to
repression of catalytic activity by the N-terminal portion of DNMT3A (Zhang et al., 2010).
Crystal structures of DNMT3A revealed that large conformational changes occur due to movement of the N-terminal ADD domain and its interaction with either histone H3 tail or self- interactions with the C-terminal methyltransferase domain (Figure 3.5C, left) (Guo et al., 2014).
In the active state, the ADD domain interacts with histone H3 tail and is positioned away from the active site. In the inactive state, however, the ADD domain interacts intimately with the methyltransferase domain, particularly between an ADD domain loop containing several acidic residues and a pocket of the methyltransferase domain formed by basic residues (Figure 3.5C, right). Mutation of the acidic residues in the loop to alanine showed reduced autoinhibition (Guo et al., 2014). In silico mutation of G532S showed all three rotamers of serine in conflict within the confinements of the ADD-MTase interface. Interestingly, D529N has recently been identified in Sotos-like cohort of overgrowth patients (Tlemsani et al., 2016). We predict G532S mutation to interfere with the autoinhibitory mechanism, due to destabilization of the ADD- methyltransferase domain interface in the inactive state.
Lastly, P904 normally exists in the terminal most helix of the methyltransferase domain.
The terminal helix in the catalytic domain normally abuts the ADD domain in the active form when H3 is bound (Guo et al., 2014). This terminal helix normally exists with a 30 degree kink that is highly likely due to P904. Consistent with most characterized kinked helices, proline is
102 found at the +2 position relative to the kink (Wilman et al., 2014). We predicted that P904L
mutation will result in removal of the kink potentially leading to straightening of the terminal
helix. In silico mutation of P904L results in all three rotamers clashing with steric conflict due to
the bulky side-chain of leucine (Figure 3.5E). Furthermore, modeling the straightened helix, we find it is compatible with the remainder of the methyltransferase domain, suggesting a straightened helix could exist with functional methyltransferase activity, but with altered stability of the active state.
Discussion
In this work, we aimed to understand the effect that regulatory domain mutations had on
the ability of DNMT3A to methylate in a cellular context. We particularily looked at DNMT3A
mutations associated with TBRS and studied these mutations in mESCs and mNSCs. One
question of interest is whether DNMT3A mutations in TBRS act as a gain- or loss-of-function.
We found that DNMT3A containing overgrowth-associated G532S and P904L mutations were
capable of methylation activity in mESCs, in contrast to AML-associated R882 residue
mutations that show minimal activity (Kim et al., 2013). On the other hand, we found that
DNMT3A with overgrowth-associated I310N mutation was incapable of methylation in
demethylated mESCs, but showed methylation activity when ectopically expressed in wild-type
mESCs. Together, these data indicate that mutations in the regulatory domains can differ based
on the location of the mutation, specifically that the PWWP domain is important for intrinsic
activity of DNMT3A as opposed to other regulatory regions, such the ADD domain and non-
catalytic and non-multimerization regions of the MTase domain.
103 Utilizing genome-wide base-pair resolution DNA methylation maps of knock-in mutant
NSCs, we also identified differentially methylated regions. In Dnmt3a knock-out, I306N,
M544K, and P900L NSC lines, regions of differential methylation showed almost exclusively hypomethylation. In contrast, G528S NSCs showed both hyper- and hypomethylated regions.
Previous study of Dnmt3a knock-out HSCs had also showed both hyper- and hypomethylation at specific genomic regions, indicating a more complex role for Dnmt3a and specificity for the region of mutation (Challen et al., 2012).
Interestingly, a set of consistently hypomethylated regions shared across mutant NSCs, which we termed overgrowth-associated differentially methylated regions (OG-DMRs), were enriched for a number of imprinted genes associated with growth and intellectual function phenotypes in humans. Included in this list were Meg3, Peg3, Peg1 (Mest), and Asb4. The
Dlk1/Meg3 locus is known for the reciprocal diseases Kagami-Ogata Syndrome and Temple
syndrome, which result in increased and decreased birth weight respectively (Ogata and Kagami,
2016; Temple et al., 2007). It is an appealing idea for mutations in epigenetic machinery, such as
DNMT3A, to act through imprinted genes as these genes are epigenetically regulated and
coordinate both somatic growth as well as intellectual function.
OG-DMRs were also associated with changes in histone modification H3K27me3 where
hypomethylation was associated with increased H3K27me3. This observation is consistent with
previous work showing the ability of DNA methylation to regulate histone modification occupancy as well as experiments showing antagonism between DNA methylation and
H3K27me3 at neural genes in postnatal NSCs (King et al., 2016; Wu et al., 2010). If H3K27me3 modifications do indeed change as a direct consequence of DNMT3A mutation-associated hypomethylation, this could be another means of gene expression alteration.
104 Lastly, we observed differences in methylation depending on the location of the
overgrowth-assocated mutations. We analyzed the location of these mutations in three-
dimensional structural models of DNMT3A. Relating the mutations to their location within
known domains, we find that our functional DNA methylation data matches the predicted
functional effect based on the structural location of each mutation. In our hDNMT3A
reconstitution experiments in DKO mESCs, we found I310N to behave differently from G532S
and P904L. This difference is explained by previous work showing that the PWWP domain is necessary for targeting Dnmt3a to chromatin (Ge et al., 2004). In knock-in mNSCs, we found
G528S to differ from I306N, M544K, and P900L. This difference is explained by structural
features central to the auto-inhibitory mechanism intrinsic to DNMT3A and the fact that G532 in
hDNMT3A (G528 in mouse) plays an essential and unique role, differing from other regulatory
residues (Guo et al., 2014).
105 Experimental Procedures
Mouse Embryonic Stem Cell Culture
Feeder-dependent mouse embryonic stem cells (mESCs) were grown on irradiated mouse
embryonic fibroblasts (MEF) and maintained in DMEM (Thermo) supplemented with 15% fetal
bovine serum (GE HyClone), 1 mM GlutaMAX (Life Technologies), 1X Non-essential amino
acids (NEAA), 1X Penicillin/Streptomycin (Life Technologies), 1000 U/mL LIF (ESGRO;
Millipore), and 0.001% β-mercaptoethanol (Sigma). Ground state feeder-free mESCs were
cultured on gelatinized culture plates in mESC medium with the addition of 1 uM CHIR99021
(Stemgent 04-0004) and 3 uM PD0325901 (Stemgent 04-0006).
Mouse Neural differentiation
ESC neural induction was performed as previously described with minor modification (Gaspard
et al., 2009). Briefly, mESCs were adapted to feeder-free conditions and seeded at low densities
(5,000-10,000 cells/cm2) on gelatinized culture plates. Cells were grown as a monolayer in
defined default medium (DDM), a DMEM-F12-based medium supplemented with N-2 (Thermo
Fisher), for 10 days with addition of 1 uM cyclopamine (Calbiochem) starting on day 2. Neural progenitors were replated on culture dishes coated with 33.3 ug/mL poly-D-lysine (Corning) and
3.33 ug/mL laminin (Corning) on day 10 post-induction. Progenitors were expanded in neural stem cell expansion medium as previously described (Conti et al., 2005). Briefly, cells were grown in DMEM-F12-based medium supplemented with N-2 (Thermo Fisher), 10 ng/mL hEGF
(Peprotech), and 10 ng/mL bFGF (Sino Biological). Cells were replated for minimally two passages to maintain a more steady-state and homogenous culture, which are referred to as
106 neural stem cells. Neural stem cells were collected for analysis generally four passages into expansion.
Generation of Knock-in mouse embryonic stem cell lines sgRNAs were designed using the Broad Institute sgRNA design tool, prioritizing distance from site of mutation and on-target efficacy scores (Doench et al., 2016). Sequences were synthesized as complementary oligonucleotides (IDT) and cloned into vector pX459 (Addgene 48139) containing specific sgRNA, Cas9, and puromycin selection marker (Ran et al., 2013).
Corresponding single-stranded oligo donors (ssODNs) were designed to match target-strand and included silent mutations disabling PAM sites, RFLP sites for genotyping, and overgrowth disease mutations (Richardson et al., 2016). Transfection was carried out using Lipofectamine
3000 per manufacturer’s instruction, where 4 ug each of sgRNA-Cas9 plasmid DNA and ssODN were transfected into approximately 50,000 cells in 6-well format. Transient selection using 2 ug/mL Puromycin with 1 uM Scr7 was started 24 hours after transfection for a total of 48 hours.
ESCs were allowed to recover for an additional 24-48 hours. mESCs were then transferred to new well at clonal density, individual colonies were expanded then manually picked into 96-well plates for subsequent genotyping and expansion.
Generation of ectopic expression mouse embryonic stem cell lines
Site-directed mutagenesis for each mutation was carried out on pcDNA3/Myc-DNMT3A
(Addgene 35521). EcoRI/MfeI digested Myc-DNMT3A fragments were inserted into EcoRI cloning site of pCAG-IRES-Blast vector as previously described (Chen et al., 2003; King et al.,
2016). Note the EcoRI site is not conserved. Briefly, constructs were linearized with MluI and
107 transfected by electroporation with Gene Pulser II using 250V and 500 uF in PBS (Bio-Rad) into
J1 and DKO mESCs and selected with 5 ug/mL Blasticidin until stable resistant clones grew out.
DKO mESC lines with Dnmt3a and Dnmt3b knock-out are as previously described (Okano et al.,
1999). Cells were maintained in 5 ug/mL blasticidin during routine growth.
Quantification of DNA Methylation by Mass Spectrometry
Measurement of 5-mdC and dG were conducted as previously described (Yu et al., 2016).
Genomic DNA was extracted using phenol-chloroform-isoamyl alcohol extraction followed by
ethanol precipitation. One microgram of cellular DNA was enzymatically digested into
nucleoside mixtures. Enzymes in the digestion mixture were removed by chloroform extraction
and the resulting aqueous layer subjected directly to LC-MS analysis for quantification of 5-mdC and dG. The amounts of 5-mdC and dG (in moles) in the nucleoside mixtures were calculated from area ratios of peaks found in selected-ion chromatograms (SICs) for the analytes over their
corresponding stable isotope-labeled standards, the amounts of the labeled standards added (in
moles), and the calibration curves. The final levels of 5-mdC, in terms of percentages of dG,
were calculated by comparing the moles of 5-mdC relative to the moles of dG.
Whole Genome Bisulfite Sequencing (WGBS)
Genomic DNA was purified from mESCs and NSCs using standard phenol-chloroform-isoamyl alcohol extraction followed by ethanol precipitation. Genomic DNA was spiked with 0.5%
lambda DNA, fragmented to 250-350 bp using Bioruptor (Diagenode), and concentrated by
ethanol precipitation. 500 ng of fragmented DNA was used for bisulfite conversion using EZ
DNA Methylation-Direct Kit (Zymo). Libraries were constructed using Accel-NGS Methyl-Seq
108 DNA Library Kit (Swift) according to manufacturer’s recommendations. WGBS libraries were
quantified using Qubit Fluorometer and Agilent 2200 TapeStation (Agilent). Libraries were
pooled and sequenced using the Illumina HiSeq 4000 as 150-bp paired-end sequencing reads.
Chromatin immunoprecipitation and ChIP-seq
ChIP was performed using protocol as described previously (King et al., 2016). Briefly, 2x107
neural stem cells were fixed with 1% formaldehyde for 10 min after which chromatin was
isolated and sonicated to 150-300 bp using Bioruptor (Diagenode). 5*106 cells worth of
sonicated chromatin was precleared with Protein A/G Dynabeads (Life Technologies) for 1 hour
then incubated in antibody at 4°C overnight. Antibodies for immunoprecipitation were all ChIP
grade and validated previously in ENCODE datasets
(http://compbio.med.harvard.edu/antibodies/about) (Egelhofer et al., 2011). 2 ug of anti-
H3K4me3 antibody (Abcam ab8580), 5 ug of anti-H3K4me1 antibody (Abcam ab8895), 5 ug of
anti-H3K27me3 antibody (Active Motif 39155), and 5 ug of anti-H3K27ac antibody (Active
Motif 39133) were used for ChIP. Chromatin was then immunoprecipitated with Protein A/G
Dynabeads for > 2 hours. ChIP was washed successively by two rounds of low-salt buffer, two
rounds of high-salt buffer, two rounds of LiCl buffer, and finally two rounds of TE buffer. ChIP
DNA was eluted in TE, reverse crosslinked overnight at 65°C, and purified by incubation with
RNase A, then proteinase K, and finally phenol-chloroform extraction and ethanol precipitation.
ChIP DNA was quantified using Qubit Fluorometer (Life Technologies) and
approximately 10 ng were used for libraries construction using Kapa Library Preparation Kit for
Illumina sequencing (Kapa Biosystems) following manufacturer’s recommendations. ChIP-seq
libraries were quantified using Qubit Fluorometer and Agilent 2200 TapeStation (Agilent).
109 Libraries were pooled and sequenced using the Illumina HiSeq 4000 machine as 50-bp single- end sequencing reads.
RNA-seq
As previously described (King et al., 2016). Total RNA was purified using RNeasy per manufacturer’s instructions (Qiagen). PolyA-tailed RNA was purified from total RNA and libraries were constructed using the TruSeq RNA Library Prep Kit v2.0 (Illumina) following manufacturer’s recommendations. RNA-seq libraries were quantified using Qubit Fluorometry and Bioanalyzer High Sensitivity DNA Analysis Kit. Libraries were pooled and sequenced using the Illumina HiSeq 2500 machine as 100-bp single-end sequencing reads.
110 Figures
111 Figure 3.1. DNMT3A mutants differ in functionality in genome methylation A: Schematic showing domain structure of DNMT3A and amino acid sequence homology across species. PWWP domain is shown in orange, ADD domain shown in green, and catalytic methyltransferase domain shown in yellow. B: Schematic of Dnmt3a gene structure with exons shown as blocks. Sequence used for CRISPR-Cas9 genome editing is shown for IN, GS, and PL mutations in inset. PAM sequence is highlighted in green, overgrowth substitution is shown in red, and RFLP genotyping site is italicized and underlined. C: Example of genotyping result from several P904L clones. Top: RFLP analysis using silent HhaI knock-in. Bottom: Sanger sequencing results from individual clones. D: Neural differentiation scheme with representative immunofluorescent images of embryonic stem cells (left), neural progenitor intermediates (middle), and neural stem cells (right). E: Mass spectrometry measurements of global methylation in wild-type J1 versus knock-in ESC and NSCs. F: Schematic of CAG-based expression vector. G: Table indicating de novo Dnmt complements in different cell lines studied. H: Mass spectrometry measurements of global methylation in DKO reconstitution mESCs and ecotopic expression in J1 mESCs. I: Summary of mass spectrometry measurements of global methylation across ectopic expression cell lines.
112
Figure 3.2. DNA methylation changes occur at sites of de novo methylation in NSCs A: Global methylation distribution between unmethylated (<20%), partially methylated (20- 70%), and methylated (>70%) in CpG context. B: Global correlation of promoter methylation between ESC and NSCs. C: Differentially methylated regions in each mutant NSC lines. Above: volcano plots showing p- value and absolute methylation changes. Methylation change are shown as difference in average
113 methylation between mutant and wild-type NSCs. Negative values indicate lower methylation and positive values indicate high methylation in mutants compared to wild-type J1 NSC. Below: Metaplot profiles showing average spatial differences in DNA methylation. D: Heatmap of methylation values across the union of differentially methylated regions (n=508). Regions shown as rows, were organized by k-means clustering. Clusters that show consistent changes are indicated by red vertical line, referred to as overgrowth-associated differentially methylated regions (OG-DMRs). E: Ontology enrichment of OG-DMRs. F: Overlap between OG-DMRs, CpG-islands/shores/shelves, and promoters (within 2kb). G: CpG content comparison between OG-DMRs, CpG islands, CpG island shores, and background genome.
114
115
Figure 3.3. Epigenetic signatures of OG-DMRs and antagonism with H3K27me3 A: Heatmap of H3K4me3, H3K4me1, H3K27ac histone modifications, 5mCpG, and locations of CpG islands. B: Classification of promoter/CGI-associated OG-DMR and non-promoter/non-CGI OG-DMR into different chromatin contexts based on histone modification combinations in NSC C: Heatmap of H3K27me3 between wild-type J1 and mutant NSCs. D: Metaplot profiles of DNA methylation (top) and H3K72me3 (bottom) across all mutant NSC lines. E: Genome browser view around the Nr2f2 locus. Top: smoothed DNA methylation profiles across all NSCs. Colors are as shown previously. Middle: H3K27me3 signal across the genomic region with gene models and CpG islands (green). Bottom: CpG content plot with CpG islands highlighted in red. F: Distribution of OG-DMRs between tissue-specific DMRs.
116
Figure 3.4. Gene expression changes in mutant NSCs A: Heatmap of differentially expressed genes (n=87) between wild-type and mutant neural stem cells. Values are shown as log2(RPKM) centered by subtraction of the row-wise mean with reds higher and blues lower than the mean. Rows were clustered by k-means clustering. B: Functional term enrichment within DEG clusters. C: Overlap between promoter/CGI associated OG-DMR and differentially expressed genes D: Left: Average changes in DNA methylation and fold-change expression between lines. Right: Scatter plot comparison between log-scale fold-change in expression and absolute change in DNA methylation. Each point represents one promoter region in one NSC.
117
Figure 3.5. Structural analysis of Dnmt3a mutants A: Mapping of overgrowth associated mutants on a structural model of DNMT3A (PWWP- ADD-C-terminal domain)-DNMT3L heterotetramer (Rondelet et al., 2016). B: Crystal structure of PWWP domain with key features highlighted (Wu et al., 2011). IN mutation is marked in red. Hydrophobic residues of the beta-barrel are marked in black.
118 C: Left: Different conformations of ADD domain (green) in active and inactive state (Xu et al. 2014). The molecule of SAH shown in stick representation to indicate location of active site. In the inactive state, the ADD domain contacts the methyltransferase domain (yellow) hindering access to the active site. Right: Key residues involved in the ADD-MTase interface in the inactive state. D: Left: Location of the terminal helix (orange) within the methyltransferase domain (yellow). Right: Pro900 causes a 30 degree kink the in the terminal helix. E: All three rotamers of P904L show steric clashing with adjacent structures.
119
Figure 3.6. Cellular characterization of cell lines A: Expression of Nestin across knock-in neural stem cell lines as measured by flow cytometry. B: Cell cycle analysis of neural stem cell lines where % cells in S-phase (red), G1 (green), and G2 (grey) are shown (n=2-3). Error bars represent standard deviation. C: Comparison of growth curves between wild-type and mutant neural stem cells. D: Evaluation of ectopic expression and reconstitution of DNMT in mESCs. Top: RT-PCR quantification of exogenous DNMT expression across cell lines. Bottom: Immunofluorescent staining of DNMT3A reconstitution in DKO mESCs.
120
Figure 3.7. WGBS DNA methylation metrics A: Comparison of WGBS samples between biological replicates. Pearson correlation is shown for CpG sites with >10X coverage. B: Base pair resolution methylation infromation across WGBS samples. Top: Extent of methylation with percent of individual CpG sites that are methylation (>70%), partially methylated (20-70%), or unmethylated (<20%). Middle: Distribution of 5mC between CpG and non-CpG contexts. Bottom: Distribution of 5mC between CHH and CHG contexts C: Genome browser view near Meg3 imprinted locus. Smoothed DNA methylation profile shown for each cell lines with colors as shown previously. Gene model, ChromHMM, and CpG content are shown below. Two known DMRs, intergenic DMR (Ig-DMR) and Meg3-DMR, involved in gene regulation are shown.
121
Figure 3.8. DNA Methylation and H3K27me3 of Nr2f2 in developing mouse embryos A: WGBS tracks at the Nr2f2 locus across developmental stages E10.5-P0 in Forebrain, Hindbrain, Gut, and Heart tissues (data from ENCODE Consortium). B: H3K27me3 tracks at Nr2f2 locus across developmental stages similar to panel A.
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Chapter 4:
Discussion and Concluding Remarks
126 DNA methylation is one of the most well studied and understood epigenetic regulators in
the genome. However, as we appreciate new complexities, such as genomic and biological
contexts, our fundamental understanding of DNA methylation is continually being refined. My
work presented in this dissertation looks at DNA methylation in these new contexts. Here I
discuss how my findings relate to the broader field and introduce some interesting frontiers in the field of DNA methylation.
First principles of epigenetics
Interaction with transcription factors
Histone modifications have been an invaluable marker used to identify different environments across the genome due to strong associations between specific modifications and functional activity, such as binding of activating transcription factors. In Chapter 2, I observed that distal regions that became activated upon global demethylation, as indicated by gain of
H3K27ac, were reversibly silenced upon remethylation of the genome (King et al., 2016). The
reversible changes in H3K27ac likely indicate the reversible binding of an activating
transcription factor. This was previously shown to be true, specifically for the transcription
NRF1, where NRF1 bound to its motif in the absence of DNA methylation and was associated
with a gain in H3K27ac (Domcke et al., 2015). The NRF1 motif was also identified by motif
enrichment in my work described in Chapter 2 (King et al., 2016). Of note, reversibility was
shown in the study by Domcke et al., albeit by switching culture conditions (2i versus conventional serum), which may introduce other confounding variables. Nonetheless, these two studies together suggest that DNA methylation has an important role in regulating a subset of transcription factors.
127 Several recent studies have further investigated the relationship between DNA
methylation and transcription factors (Kribelbauer et al., 2017; Yin et al., 2017). Utilizing
massive protein binding assays to DNA templates, the authors identified that around 60% of
transcription factors studied could bind methylated sequence and whose binding was influenced
either positively or negatively. More than half of these transcription factors were “MethylPlus”,
which exhibited enhanced binding when the motif was methylated. Among these MethylPlus
transcription factors was OCT4 (POU5F1), which showed greater occupancy in hypermethylated
cells. Based on my work along with a number of recent studies, I believe it is likely that DNA
methylation plays a much more important role in regulating transcription factor binding than previously thought. It will be interesting to cross-reference future transcription factor binding data with histone modification changes in methylation altered mESCs.
Interface of epigenetic machinery with environmental cues
The epigenetic apparatus is an interface between the highly variable and dynamic
environment, changing day-to-day, and the contrastingly static landscape of the genome,
changing slowly across lifetimes. Epigenetics, thus, allows for different interpretations of the
same genome based on the environment. DNA methylation has been shown to change rapidly in
response to environmental changes. One of the most striking examples of this comes from a
study measuring methylation changes in rats undergoing fear conditioning. In this set of
experiments, odor fear conditioning in rats resulted in DNA methylation alterations at olfactory
genes in sperm within a mere 10 days (Dias and Ressler, 2014). Another example is of stimulus-
induced methylation changes in the mouse brain, where changes in DNA methylation can be
observed as soon as 4 hours after stimulation (Guo et al., 2011).
128 New mechanisms shed light on how environmental changes could alter DNA methylation
in short timespans. Recent work demonstrated that phosphorylation of DNMT3A by CK2 could regulate localization of Dnmt3a to repeats in heterochromatin (Deplus et al., 2014). Post- translational modifications of the de novo DNMTs have not received much attention. Future work studying how post-translational regulation of DNA methylation may yield exciting lessons on how certain regions of the genome can respond exquisitely quickly to environmental stimuli.
Cause versus Consequence
One of the most important questions relating to DNA methylation remains whether DNA
methylation is a cause or consequence of particular changes in the genome. Evidence exists
supporting both arguments. My work in Chapter 2 has provided some support that DNA
methylation is capable of serving an instructive or causal role at promoters and enhancers.
However this is limited to ESCs and the generality must still be investigated.
This question is most pertinent to DNA methylation’s activity at specific genomic
features. For example, what does DNA methylation do at CpG island shores? Could DNA
methylation at CpG island shores be regulating the activity of these promoters? In Chapter 2, I
found that global DNA methylation regulates H3K27me3 at bivalent promoters, which are
highly associated with CpG island promoters. In Chapter 3, I also find that DNA methylation
changes at specific OG-DMRs also are associated with H3K27me3 changes near CpG islands.
Interestingly, DNMT3A has been shown to localize adjacent to CpG islands in neural stem cells
(Wu et al., 2010). However, little is known about whether this localization is a cause or a
consequence. Future studies will require specific targeting of methylation changes and
measurement of whether DNMTs can cause local changes in chromatin structure.
129
Emerging concepts
One of the ground-breaking studies of DNA methylation in the last few years has been a
novel study of methylation dynamics in different cell states (Shipony et al., 2014). Shipony et al.
used a combination of bisulfite sequencing and unique molecular identifiers (UMI), which allowed measurement of methylation status of individual DNA molecules, to explain the observation that DNA methylation in ESC and testis is highly homogenous compared to all other tissues (Landan et al., 2012). They found that ESCs utilize a turnover-based equilibrium, which reinforces the methylation state. In contrast, somatic cells use clonal maintenance, leading to the emergence of epipolymorphisms or epimutations.
As a consequence of these findings and previous work, Amos Tanay’s group has provided a mechanism of how alterations in methylation patterns may occur in cancers (Landan et al., 2012). In the same study, the authors investigated regions that are hypomethylated in cancer cell lines and showed that regions with higher noise in ESCs and clonal maintenance in fibroblasts have a predisposition for hypermethylation in cancer cell lines. Cancer cell lines, similar to fibroblasts and T cells, showed more clonal methylation memory, which shows that these cells can accumulate and clonally propagate epimutations. Thus, in addition to genetic mutations, the genome can accumulate epimutations. The extent that epimutations play a role in disease, and how these methylation alterations function are of great interest.
Understanding epigenetic alterations in disease context
Similar to the increasing complexity and challenge moving from single gene Mendelian
disorders to complex traits encoded by multiple genes, epigenetic-focused diseases also fall on
130 the same spectrum. Single locus epigenetic diseases such as imprinting disorders have been
studied and well understood. The mechanism of the Beckwith-Wiedemann locus on chr11 has been finely mapped with the identity and function of most genomic elements understood.
However, many epigenetically-related disease are not consequences of individual loci or have not yet had their main actors identified. For example, in cancers, global methylation changes occur in conjunction with genetic alterations. In some cases, genetic alterations are associated with epigenetic changes, such as the mutator phenotype often resulting from hypermethylation of
DNA repair genes associated with the methylator phenotype. The multitude of possible effects makes studying these epigenetic diseases particularly difficult.
In the case of overgrowth syndromes, we are currently trying to answer some of the most
elementary questions about this new class genetic diseases rooted in mutations of the epigenetic
machinery. It is unclear what effect the mutations have on the epigenetic machinery and whether
the changes effect multiple processes or converge on a single pathway. In Chapter 3, I answer
some of these questions in the first step towards understanding one example of this class of
disorders. Mutations in almost all cases are loss of function. Although there are several hundred
loci exhibiting changes in methylation and several dozen genes differentially expressed, very few
genes show correlation between methylation and expression owing to few promoters under direct
regulation by DNA methylation. This is consistent with the one publication to date measuring
global methylation and gene expression in peripheral blood leukocytes from DNMT3A
overgrowth patients, where the authors found no trends between expression and methylation
(Spencer et al., 2017). However, methylation changes are enriched in imprinted genes with
known function in growth and intellectual function. This provides early evidence that mutations
131 in epigenetic machinery may converge on individual mechanisms rather than multiple disparate gene programs.
Many technical challenges exist for studying epigenetic changes in overgrowth syndromes. Most studies utilizing genomic techniques have been aimed at investigating differences between cell types. In Chapter 3, I aim to identify specific changes arising from single point mutations in DNMT3A. Not only are the downstream effects of DNMT not known, but the effect sizes are often far smaller than differences related to cell types. Furthermore, point mutations may alter other properties, such as dynamics/noise. Another challenge that will require further attention is how truly generalizable are the fundamental principles of how DNA methylation operates. As noted previously, the emerging concept that cells in different developmental stages maintain DNA methylation patterns in different ways demonstrates that caution is needed moving forward.
In conclusion, this dissertation takes two key steps into furthering the field’s understanding about DNA methylation. The first is to better understand the fundamental principles by which DNA methylation acts in the epigenome. In this work, I’ve shown that DNA methylation is capable of playing an instructive role, causing remodeling of the histone modification landscape in response to global DNA methylation changes in mouse embryonic stem cells. The second step is to understand the role of DNA methylation in a biological context.
Overgrowth-associated mutations in Dnmt3a were shown to result in loss-of-function at a number of sites, including known imprinted loci.
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