Three-dimensional organisation of the Runx1 locus

Three-dimensional organisation of the Runx1

locus

Amarni Thomas

A thesis submitted for the degree of

Doctor of Philosophy

Department of Pathology

University of Otago

Dunedin, New Zealand

April 2021

ii

Abstract

The proper regulation of gene expression is crucial for cell-fate and lineage

determination of hematopoietic stem cells (HSCs). Dysregulated expression of genes that

drive haematopoietic differentiation can cause blood disorders, including leukaemia.

Connecting hematopoietic genes with their regulatory elements is central to correct gene

regulation, and is orchestrated by chromatin modifiers, including the genome organising

protein complex, cohesin.

The developmental transcription factor RUNX1/AML1 is a well-known

leukaemia-associated gene. Runx1 is an important regulator of haematopoiesis in

vertebrates; it is crucial for early myeloid differentiation, and plays a vital role in adult

blood development. Genetic disruptions to the RUNX1 gene are frequently associated

with Acute Myeloid Leukaemia (AML).

Despite the well-established role of RUNX1 in haematopoiesis and disease

contribution, the cis-regulatory mechanisms that modulate RUNX1 require further

elucidation. Gene regulatory elements, such as enhancers, located in non-coding DNA

are likely to be important for Runx1 transcription.

My PhD studies provided insight into mouse Runx1 enhancer functions and

highlighted the features of conserved human enhancers. I also used circular chromosome

conformation capture sequencing (4C-seq) to identify DNA interactions with the

previously identified +24 RUNX1 enhancer, which is essential for HSC expression of

RUNX1. Using cohesin-deficient K562 leukaemia cells, my work shows that cohesin is

required for the +24 enhancer (also known as eR1) to anchor an insulated neighbourhood

that shields RUNX1 from outside influences. Striking results from this work show that

the +24 Runx1 enhancer is a significant cohesin-dependent organiser of Chromosome 21

during megakaryocyte differentiation.

iii

In summary, my PhD studies identified a bouquet of enhancer-anchored

connections that regulate RUNX1 transcription in leukaemia cells at steady state and

during differentiation, and explain why cohesin loss dysregulates the expression of this

important leukaemogenic gene.

iv

Acknowledgements

First and foremost, I would like to thank my supervisor, Prof. Julia Horsfield for

her encouragement and guidance throughout the course of my PhD, as well as her

valuable career advice and support. Many thanks to the Chromosome Structure &

Development Lab Group for friendship, collegiality and making lab work enjoyable:

Alice, Amy, Anastasia, Bridget, Bryony, Caitlin, Chi, Jisha, Judith, Lisa, Megan,

Michael, Nabila, Nima, Noel, Sarada, Stephanie, Trent, Tanushree, Ute. Thanks to all the

wonderful writing group friends for the engaging discussions and enjoyable days

together.

I would also like to thank those who have helped with this PhD project: Dr.

William Schierding for his advice and bioinformatics training. Dr. Aaron Stevens for

guidance on G-quadruplex characterisation. Ute Zellhuber-McMillan for helping me

breed and characterise the F1 zebrafish lines. Noel Jhinku for making sure all the

zebrafish are well cared for. Dr. Robert Day for running my samples on the MiSeq. To

Dr. Judith Marsman for guidance with the 4C experiment. Dr. Jisha Antony for providing

counsel with lab experiments.

To my committee members, Dr. Jisha Antony, Dr. Judith Marsman, Prof. Julia

Horsfield, Prof. Justin O’Sullivan, Prof. Ian Morison, and my convenor Prof. Stephen

Robertson, for supporting me during my studies and providing exciting discussions.

Thanks to Prof. Motomi Osato for providing some of the Runx1 enhancer DNA essential

to this project, and for hosting me along with Prof. Hitoshi Takizawa at International

Research Centre for Medical Sciences Research, Kumamoto University, Japan. Thank

you to the Liggins Institute, University of Auckland, Prof. Justin O’Sullivan and Dr.

William Schierding for providing a space for me to learn bioinformatics and analsye my

data.

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I gratefully acknowledge the funding I have received during the course of my

PhD. Cancer Research Trust New Zealand for awarding me a scholarship, University of

Otago Pūtea Tautoko - Student Fund, and Health Research South - Dunedin School of

Medicine for additional fees and stipend support. Marsden Fund (administered by the

Royal Society of New Zealand), Health Research Council of New Zealand and

Leukaemia and Blood Cancer New Zealand for supporting this project. International

Research Centre for Medical Sciences Research, Kumamoto University, Japan for

awarding me an internship to further develop molecular techniques. Lorne Genome

Conference, and Leukaemia and Blood Cancer New Zealand for providing travel

funding.

Thank you to the various workplaces who have made my PhD journey a unique

and fulfilling experience; Pathology Department, Research And Enterprise, Te Huka

Mātauraka, Disability Information And Support, Genetics Teaching Programme And

Genetics Otago.

Finally, the biggest thanks to my Mum, Dad, my brother Blake and my friends

for their enthusiasm, support and for brightening every day.

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Table of Contents

Abstract .......................................................................................................................... ii

Acknowledgements ........................................................................................................ iv

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

List of Figures ............................................................................................................ xvii

List of Tables ................................................................................................................ xxi

List of symbols and abbreviations............................................................................ xxiv

Nomenclature .......................................................................................................... xxviii

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

1.1 Haematopoiesis .................................................................................................. 1

1.1.1 Primitive and definitive haematopoiesis .................................................... 1

1.1.2 Transcription factors involved in haematopoiesis ...................................... 5

1.2 Haematological disorders .................................................................................. 6

1.2.1 Familial platelet disorder ............................................................................ 7

1.2.2 Acute Myeloid Leukaemia ......................................................................... 7

1.2.3 Leukaemogenesis ....................................................................................... 8

1.2.4 AML patient survival and treatment......................................................... 10

1.2.5 Cohesin mutation contributes to AML ..................................................... 12

1.3 RUNX1 is a leukaemia gene............................................................................. 16

1.3.1 Role of RUNX1 in normal embryonic and adult developmental processes..

.................................................................................................................. 18

1.3.2 Regulation of Runx1 ................................................................................. 18

1.3.3 Role of RUNX1 in AML progression ....................................................... 21

1.4 Three-dimensional genome structure............................................................... 24

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1.4.1 Topologically associated domains (TADs) .............................................. 25

1.5 Regulators of gene transcription ...................................................................... 27

1.5.1 Cohesin acts with other regulators to determine gene expression ............ 28

1.5.2 Cohesin and Runx1 ................................................................................... 29

1.5.3 Regulatory elements ................................................................................. 32

1.5.3.1 Enhancers .............................................................................................. 32

1.5.3.2 Silencers and insulators ........................................................................ 34

1.5.4 Techniques to determine three-dimensional conformation ...................... 35

1.5.5 Genome structure and disease .................................................................. 36

1.5.6 Regulatory regions of Runx1 .................................................................... 37

1.6 Zebrafish as a model organism ........................................................................ 39

1.6.1 Haematopoiesis in zebrafish ..................................................................... 42

1.7 Overall hypothesis and aims of this PhD ......................................................... 43

2 Chapter 2: Methods ................................................................................................ 46

2.1 Ethics consent and protocol approval .............................................................. 46

2.1.1 Zebrafish ................................................................................................... 46

2.1.2 Escherichia coli (E. coli) .......................................................................... 46

2.1.3 Cell culture ............................................................................................... 46

2.2 Materials .......................................................................................................... 46

2.3 Molecular biology techniques .......................................................................... 46

2.3.1 Plasmids and reporter constructs .............................................................. 46

2.3.2 Nanodrop .................................................................................................. 47

2.3.3 Agarose gel electrophoresis ...................................................................... 48

2.3.4 Primer design ............................................................................................ 48

2.3.5 Sequence confirmation and alignment ..................................................... 48

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2.3.6 Measuring Runx1 +24 enhancer G-quadruplexes..................................... 49

2.3.6.1 Runx1 +24 enhancer G-quadruplexes Oligo design ............................. 49

2.3.6.2 CD Spectrophotometer protocol ........................................................... 49

2.3.7 Site-Directed Mutagenesis of Runx1 +24 enhancer ................................. 50

2.3.7.1 Runx1 +24 enhancer Site-Directed Mutagenesis primer design ........... 50

2.3.7.2 Mutant Strand Synthesis Reaction (Thermal Cycling) ......................... 50

2.3.7.3 DnpI Digestion of the Mutant Strand Amplification Products ............. 51

2.3.8 Transformation of plasmids in competent cells........................................ 51

2.3.9 Plasmid purification preparation .............................................................. 52

2.3.10 Tol2 transposase mRNA synthesis ........................................................... 52

2.4 In vivo Zebrafish approaches ........................................................................... 53

2.4.1 Zebrafish strains ....................................................................................... 53

2.4.2 Maintenance ............................................................................................. 53

2.4.3 Feeding ..................................................................................................... 53

2.4.4 Breeding and embryo collection ............................................................... 54

2.4.5 Growing embryos ..................................................................................... 54

2.5 Zebrafish microinjection .................................................................................. 54

2.5.1 Needle pulling and calibration.................................................................. 54

2.5.2 Microinjection .......................................................................................... 56

2.5.3 Microscopy ............................................................................................... 56

2.6 Transgenic zebrafish analysis .......................................................................... 56

2.6.1 Zebrafish enhancer assay .......................................................................... 57

2.7 In vitro Cell culture and maintenance of cell lines .......................................... 57

2.7.1 Cell lines ................................................................................................... 57

2.7.2 Reviving cells from liquid nitrogen stocks ............................................... 58

ix

2.7.3 Splitting cells for new passage ................................................................. 59

2.7.3.1 Adherent cells (PNX, BHK/MKL, NIH/3T3) ...................................... 59

2.7.3.2 Suspension cells (K562, HPC7)............................................................ 59

2.7.3.3 K562 STAG2 R614-/- mutant lines ...................................................... 59

2.7.4 Freezing cells for liquid nitrogen storage ................................................. 60

2.8 Expression analysis .......................................................................................... 61

2.8.1 Total RNA purification............................................................................. 61

2.8.2 cDNA synthesis ........................................................................................ 61

2.8.3 Quantitative reverse-transcriptase PCR (qRT-PCR) ................................ 61

2.8.4 pGL4.23 Transfection............................................................................... 63

2.8.5 pGL4.23 Transfection analysis ................................................................. 63

2.8.6 Statistical analysis of luciferase assays .................................................... 64

2.9 In silico approaches for Enhancer analysis ...................................................... 64

2.9.1 Epigenetic analyses using ENCODE data ................................................ 64

2.9.2 Comparative analysis of cancer genomic datasets ................................... 66

2.9.3 Single nucleotide polymorphism (SNP) analysis ..................................... 67

2.10 Circular Chromatin Conformation Capture (4C) ............................................. 67

2.10.1 4C Design ................................................................................................. 68

2.10.2 Cross-linking of K562 cells ...................................................................... 68

2.10.3 Isolation of nuclei ..................................................................................... 69

2.10.4 First Digestion .......................................................................................... 69

2.10.5 First restriction enzyme inactivation ........................................................ 70

2.10.6 Ligation..................................................................................................... 70

2.10.7 Reverse cross-linking and DNA purification ........................................... 71

2.10.8 Second digestion ....................................................................................... 71

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2.10.9 Removal of second restriction enzyme..................................................... 72

2.10.10 Second Ligation .................................................................................... 72

2.10.11 DNA purification with linear polyacrylamide (LPA) ........................... 73

2.10.12 4C Polymerase Chain Reaction ............................................................ 73

2.11 4C-seq Library preparation .............................................................................. 74

2.11.1 PCR purification ....................................................................................... 74

2.11.2 Adaptor ligation ........................................................................................ 74

2.11.3 Library purification .................................................................................. 76

2.11.4 Equal mixing of baits................................................................................ 77

2.11.4.1 MiSeq library .................................................................................... 77

2.11.4.2 HiSeq Library .................................................................................... 77

2.11.5 Next generation sequencing ..................................................................... 78

2.12 Processing of sequenced reads ......................................................................... 78

2.12.1 Generating read counts and data quality reports ...................................... 78

2.12.2 Demultiplexing of libraries based on indexed adaptors ........................... 78

2.12.3 Trimming of adaptors ............................................................................... 78

2.12.4 Demultiplexing of baits based on 4C primers .......................................... 78

2.12.4.1 HiSeq ................................................................................................. 79

2.12.4.2 MiSeq ................................................................................................ 79

2.13 Mapping of sequenced reads............................................................................ 80

2.13.1 Generating restriction enzyme digestion of Human Genome (hg19) ....... 80

2.13.2 Generate Bowtie2 reference files of RE digested genome. ...................... 81

2.13.3 Mapping of reads ...................................................................................... 81

2.13.4 Creating a counts file from mapped data .................................................. 81

2.13.5 Removing self-ligated and undigested reads ............................................ 82

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2.13.6 Removal of background noise .................................................................. 82

2.14 Analysing 4C interactions ................................................................................ 82

2.14.1 Identification of statistically significant interactions per condition ......... 83

2.14.2 Generation of ggplots ............................................................................... 84

2.14.3 Differential interactions using DESeq2 .................................................... 84

2.14.4 Overlap of significant interactions with genes ......................................... 84

2.14.5 Overlap of significant interactions with protein binding sites and enhancer

characteristics .......................................................................................................... 84

3 Chapter 3: Functional analysis of Runx1 enhancers ........................................... 85

3.1 Runx1 enhancers .............................................................................................. 85

3.1.1 Previously identified enhancers of Runx1 ................................................ 85

3.1.2 Previously identified long-range interactions with Runx1 promoters ...... 92

3.1.3 Functional enhancer characterisation assays ............................................ 97

3.2 Hypothesis ....................................................................................................... 98

3.3 Aims ................................................................................................................. 98

3.4 Characteristics of previously identified regulatory regions ............................. 99

3.4.1 Epigenetic and conservation analyses using ENCODE data .................... 99

Single nucleotide polymorphism (SNP) analysis .................................................. 106

3.4.2 Comparative analysis of cancer genomic datasets ................................. 111

3.5 Zebrafish enhancer reporter assays ................................................................ 114

3.5.1 Zebrafish Enhancer assay F0: ................................................................. 116

3.5.2 Zebrafish Enhancer assay F1: ................................................................. 120

3.6 Genomic characteristics of 4C identified functional novel enhancers .......... 121

3.6.1 Conservation and epigenetic analysis ..................................................... 122

3.6.2 Single nucleotide polymorphism (SNP) analysis ................................... 122

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3.7 Chapter summary ........................................................................................... 124

4 Chapter 4: Three-dimensional (3D) connections of Runx1 +24 ....................... 127

4.1 Introduction .................................................................................................... 127

4.1.1 Secondary structure of DNA .................................................................. 129

4.1.1.1 Secondary structure of Runx1 +24 ..................................................... 129

4.2 Hypothesis ..................................................................................................... 132

4.3 Aims ............................................................................................................... 132

4.4 Enhancer characteristics of RUNX1 +24 ....................................................... 133

4.4.1 Epigenetic analysis ................................................................................. 133

4.4.2 Single nucleotide polymorphism (SNP) analysis ................................... 133


4.5 Characterising Runx1 +24 G-quadruplex structure ....................................... 134

4.5.1 CD spectroscopy ..................................................................................... 134

4.5.2 Runx1 +24 G-quadruplex Luciferase reporter assay .............................. 136

4.6 Circular Chromatin Conformation Capture (4C) of Human RUNX1 +24 ..... 140

4.6.1 Quality check of 4C data ........................................................................ 141

4.6.2 Detection of significant interactions ....................................................... 143

4.7 Identification and Genomic Characteristics of Human RUNX1 +24 3D

Connections .............................................................................................................. 145

4.7.1 Visualisation of significant cis interactions ............................................ 145

4.7.2 Overview of significant near-bait interactions ....................................... 148

4.7.3 Significant interactions with known RUNX1 regulators ........................ 151

4.7.4 Significant interactions with other genes ............................................... 152

4.7.5 Overall significant RUNX1 +24 cis and near-bait interactions .............. 154

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4.7.6 Trans interactions ................................................................................... 157

4.8 Chapter summary ........................................................................................... 159

4.8.1 RUNX1 +24 characteristics..................................................................... 159

4.8.2 RUNX1 +24 interactions ......................................................................... 161

5 Chapter 5: Cohesin insulates 3D connections of RUNX1 +24 during

megakaryocyte differentiation .................................................................................. 164

5.1 Introduction .................................................................................................... 164

5.2 Hypothesis ..................................................................................................... 166

5.3 Aims ............................................................................................................... 166

5.4 RUNX1 +24 significant interactions during normal megakaryocyte

differentiation............................................................................................................ 167

5.4.1 Visualisation of significant interactions during normal megakaryocyte

differentiation. ....................................................................................................... 167

5.4.2 Overview of significant changes in interactions during normal

megakaryocyte differentiation. ............................................................................. 168

5.4.3 Visualisation of significant near-bait interactions during normal


5.4.4 Overview of significant changes in near-bait interactions during normal



5.4.6 Overall significant RUNX1 +24 and near-bait interactions in K562 cells

during megakaryocyte differentiation ................................................................... 177

5.5 The effect of STAG2 deficiency on RUNX1 +24 significant interactions during

differentiation of K562 cells ..................................................................................... 180

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5.5.1 Visualisation of significant interactions during cohesin impaired

“differentiation” .................................................................................................... 180

5.5.2 The effect of STAG2 deficiency on interactions during differentiation of

K562 cells ............................................................................................................. 181

5.5.3 The effect of STAG2 deficiency on near-bait interactions during

differentiation of K562 cells ................................................................................. 190

5.5.4 Significant changes in near-bait interactions caused by STAG2 deficiency

................................................................................................................ 191


5.5.6 Overall changes in and near-bait interactions mediated by RUNX1 +24

interactions during differentiation of K562 cells, WT versus STAG2-deficient .. 197

5.6 Chapter summary ........................................................................................... 200

5.6.1 RUNX1 +24 enhancer connections during K562 differentiation to

megakaryocytes ..................................................................................................... 200

5.6.1.1 Distant connections ............................................................................. 200

5.6.1.2 Connections within the RUNX1 TAD ................................................. 201

5.6.2 RUNX1 +24 enhancer connections during PMA treatment in STAG2

mutants 201

5.6.2.1 Baseline conditions: ............................................................................ 201

5.6.2.2 Mid-differentiation.............................................................................. 202

5.6.2.3 Post-differentiation ............................................................................. 202

6 Chapter 6: Discussions and Conclusions ............................................................ 204

6.1 Regulators of Runx1....................................................................................... 205

6.1.1 Mouse Runx1 enhancers ......................................................................... 205

6.1.2 Conserved human RUNX1 regulators ..................................................... 206

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6.1.3 G-quadruplex .......................................................................................... 208

6.1.4 Regulatory RNAs ................................................................................... 209

6.1.4.1 Enhancer RNA (eRNA) ...................................................................... 209

6.1.4.2 Long non-coding RNAs ...................................................................... 210

6.1.4.3 G-Quadruplex RNAs .......................................................................... 210

6.1.5 Could mutational load in regulatory elements also contribute to disease

progression? .......................................................................................................... 211

6.2 Organisational role of the RUNX1 +24 enhancer .......................................... 217

6.3 RUNX1 +24 enhancer connections during megakaryocyte differentiation ... 218

6.4 Cohesin/STAG2 dependency of RUNX1 +24 enhancer connections in K562

cells ....................................................................................................................... 221

6.4.1 K562 Limitations .................................................................................... 224

6.5 Future directions ............................................................................................ 224

6.5.1 RUNX1 +24 G-quadruplex ..................................................................... 225

6.5.2 Functional analysis of RUNX1 regulatory regions ................................. 225

6.5.3 RUNX1 +24 connections with genes ...................................................... 226

6.5.4 Roles of non-coding RNA in RUNX1 regulation ................................... 227

6.5.5 Further questions proposed by this research .......................................... 228

6.5.6 Clinical significance ............................................................................... 229

6.6 Overall conclusions........................................................................................ 229

References ................................................................................................................... 231

Appendix I: Materials ................................................................................................ 260

Chemical reagents and consumables used in this project ..................................... 260

Solutions and buffers ................................................................................................ 262

Appendix II: Plasmid maps ....................................................................................... 264

xvi

Zebrafish enhancer detector vector (ZED) ............................................................ 264

pCS-TP .................................................................................................................. 265

pGL4.23-GW ........................................................................................................ 266

pRL-TK ................................................................................................................. 267

pUC19 ................................................................................................................... 268

Appendix III: Primer list ........................................................................................... 269

Appendix IV: Prediction of G-quadruplexes ........................................................... 271

Appendix V: Sequencing report ................................................................................ 273

Appendix VI: Publication .......................................................................................... 275

Appendix VII: RUNX1 regulatory region Hg38 coordinates ................................. 287

Appendix VIII: 4C data processing .......................................................................... 288

RUNX1 +24 .......................................................................................................... 289

Appendix IX: WT 12 DMSO interaction peaks....................................................... 290

WT 12 DMSO cis significant interactions ............................................................ 290

WT 12 DMSO near-bait significant interactions .................................................. 292

Appendix X: Trans interaction peaks....................................................................... 297

Appendix XI: List of significant changes (cis) ......................................................... 301

WT cis significant changes during differentiation ................................................ 301

WT and STAG2 cis comparisons .......................................................................... 302

Appendix XII: List of significant changes (near-bait) ............................................ 305

WT near-bait significant changes during differentiation ...................................... 305

WT and STAG2 near-bait comparisons ................................................................ 307

Appendix XIII: List of gene connections .................................................................. 321

Appendix XIV: List of significant changes STAG2 12 DMSO compared STAG2 72

PMA ............................................................................................................................. 327

xvii

List of Figures

Figure 1.1: Schematic of haematopoiesis cell generation locations during human

development. .................................................................................................................... 3

Figure 1.2: Vertebrate primitive and definitive haematopoiesis cell development

pathways. .......................................................................................................................... 5

Figure 1.3: Structure of cohesin complex. ...................................................................... 13

Figure 1.4: The Runx protein. ........................................................................................ 17

Figure 1.5: Comparative genomic organisation of the Runx1 locus in human, mouse and

zebrafish. ........................................................................................................................ 20

Figure 1.6: RUNX1 gene common isoforms and leukaemic fusion isoforms. .............. 22

Figure 1.7: RUNX1 TAD Hi-C contact map of RUNX1 region in K562 cells. .............. 27

Figure 1.8: Cohesin mediated cis-regulatory DNA elements. ........................................ 34

Figure 1.9: Identified Runx1 regulatory regions............................................................. 39

Figure 1.10: Diagram of normal Danio rerio (zebrafish) development. ......................... 41

Figure 1.11: Schematic of haematopoiesis cell generation locations during zebrafish

development. .................................................................................................................. 43

Figure 2.1: Calibration slide with close up of 1 mm scale, each division is 0.01 mm. .. 55

Figure 2.2: Haemocytometer slide used for counting cells, each shaded grid is 1 mm2. 60

Figure 2.3: Quantitative PCR primer locations at human RUNX1. ................................ 62

Figure 2.4: Schematic outline of 4C process. ................................................................. 68

Figure 3.1: Schematic overview of the mouse Runx1 locus (chromosome 16:93,220,074-

92,589,466). .................................................................................................................... 96

Figure 3.2: Schematic overview of human RUNX1 locus (chromosome 21: 36,148,773-

36,872,777). .................................................................................................................... 97

Figure 3.3: Proportions of cancerous primary site mutations of regulatory regions. ... 113

xviii

Figure 3.4: Schematic representation of ZED assay vector. ........................................ 115

Figure 3.5: Putative mouse enhancers are active in hematopoietic regions in zebrafish.

...................................................................................................................................... 118

Figure 3.6: . Quantitative summary of GFP expression patterns generated by putative

mouse enhancers in zebrafish. ...................................................................................... 119

Figure 4.1: Hi-C contact map of the RUNX1 region in K562 cells. ............................. 128

Figure 4.2: Schematic representation of mouse +24 individual G-quadruplex. ........... 130

Figure 4.3: Predicted representation of Runx1 +24 mCRE G-quadruplex and mutated +24

mCRE. .......................................................................................................................... 134

Figure 4.4: CD spectroscopy of wild-type and mutant +24 G4s. ................................. 135

Figure 4.5: Schematic representation of the predicted Runx1+24 mCRE G-quadruplex in

parallel conformation and when base subsitutions are made at the +24 mCRE. ......... 136

Figure 4.6: Diagram of Luciferase reporter construct pGL4.23-GW. .......................... 137

Figure 4.7: Schematic representation of pGL4 Runx1+24 mCRE G-quadruplex in parallel

conformation and mutated +24 mCREs. ...................................................................... 138

Figure 4.8: Luciferase activity in response to the various +24 mCREs in the NIH/3T3

cell line. ........................................................................................................................ 139

Figure 4.9: RUNX1 +24 Mapped read count. ............................................................... 141

Figure 4.10: RUNX1 +24 4C quality evaluation. ......................................................... 143

Figure 4.11: RUNX1 +24 cis 4C profile (chr21) in K562 cells (WT 12 DMSO)......... 146

Figure 4.12: RUNX1 +24 significant cis interactions. .................................................. 147

Figure 4.13: Profile of RUNX1 +24 significant cis interactions. .................................. 148

Figure 4.14: RUNX1 +24 near-bait 4C profile. ............................................................ 149

Figure 4.15: RUNX1 +24 significant near-bait interactions (chr21:34999764-37572609).

...................................................................................................................................... 151

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Figure 4.16: RUNX1 +24 chromatin hub. ..................................................................... 155

Figure 5.1: Megakaryocyte differentiation with PMA stimulation RUNX1 +24 WT cis

4C profiles. ................................................................................................................... 168

Figure 5.2: Differentiation-induced changes to the RUNX1 +24 WT cis interactions. 170

Figure 5.3: Differentiation-induced changes in the number of RUNX1 +24 significant cis

interactions with super-enhancers in WT K562 cells. .................................................. 171

Figure 5.4: Megakaryocyte differentiation with PMA stimulation RUNX1 +24 WT near-

bait 4C profile. .............................................................................................................. 172

Figure 5.5: Differentiation-induced changes to the RUNX1 +24 WT near-bait

interactions. .................................................................................................................. 174

Figure 5.6: Changes to the RUNX1 +24-anchored chromatin hub during megakaryocyte

differentiation of K562 cells. ........................................................................................ 179

Figure 5.7: Time course of PMA stimulation of all RUNX1 +24 cis 4C profiles. ....... 181

Figure 5.8: Significant changes to the RUNX1 +24 cis baseline (12 DMSO) interactions

due to cohesin deficiency. ............................................................................................ 183

Figure 5.9: Features of RUNX1 +24 significant cis interactions in WT baseline and

STAG2 baseline cells (12 DMSO). .............................................................................. 184

Figure 5.10: Significant changes to the RUNX1 +24 12h PMA treatment cis interactions


Figure 5.11: Significant changes to the RUNX1 +24 72h PMA treatment cis interactions


Figure 5.12: Profile of RUNX1 +24 significant cis interactions with super-enhancers

during megakaryocyte differentiation with PMA stimulation in WT and STAG2 mutant

cells. .............................................................................................................................. 189

Figure 5.13: Time course PMA stimulation of all RUNX1 +24 near-bait 4C profiles. 191

xx

Figure 5.14: Significant changes to the RUNX1 +24 baseline (12 DMSO) of near-bait

interactions due to cohesin deficiency. ......................................................................... 193

Figure 5.15: Significant changes to the RUNX1 +24 near-bait interactions after 12h PMA

treatment due to cohesin deficiency. ............................................................................ 194

Figure 5.16: Significant changes to the RUNX1 +24 near-bait interactions after 72h PMA

treatment due to cohesin deficiency. ............................................................................ 196

Figure 5.17: The effect of cohesin deficiency on the RUNX1 +24 enhancer-anchored

chromatin hub in baseline (12 DMSO) conditions in K562 cells. ............................... 198


chromatin hub in mid-differentiation (12 PMA) conditions in K562 cells. ................. 199


chromatin hub in post-differentiation (72 PMA) conditions in K562 cells.................. 200

Figure 6.1:Schematic overview of human RUNX1 locus (hg19: chromosome 21:

36,148,773-36,872,777). .............................................................................................. 207

Appendix VIII Figure 1: RUNX1 +24 demultiplexed read count................................. 289

Appendix VIII Figure 2: RUNX1 +24 Mapped read count. ......................................... 289

Appendix VIII Figure 3: RUNX1 +24 4C quality evaluation. ...................................... 290

Appendix X Figure 1: RUNX1 +24 trans chromosome 1 reads. .................................. 297

Appendix X Figure 2: RUNX1 +24 chr 1 trans interactions. ....................................... 298



xxi

List of Tables

Table 2.1: Plasmids used in this study............................................................................ 47

Table 2.2: Runx1 +24 enhancer G-quadruplexes Oligo ................................................. 49

Table 2.3: Mutant strand synthesis reaction ................................................................... 51

Table 2.4: Cell lines used in this study ........................................................................... 58

Table 2.5: qRT-PCR reaction mix .................................................................................. 62

Table 2.6: Transfection components per well ................................................................ 63

Table 2.7: Analysed cell lines ........................................................................................ 66

Table 2.8: Analysed ENCODE annotations ................................................................... 66

Table 2.9: 4C-PCR reagents .......................................................................................... 74

Table 2.10: Optimised PCR conditions for 4C-PCR primers........................................ 74

Table 2.11: 4C-Seq Library and sequencing adapters ................................................... 76

Table 2.12: Primer sequences and lengths used for demultiplexing of 4C baits ........... 80

Table 2.13: Length of sequenced reads after trimming ................................................. 81

Table 3.1: Runx1 +24 regulatory region ........................................................................ 86

Table 3.2: Identified mouse Runx1 regulatory regions with available classifications ... 88

Table 3.3: Identified human RUNX1 regulatory regions with available classifications . 91

Table 3.4: Identified long range interactions with putative mouse Runx1 regulatory

regions. ........................................................................................................................... 94

Table 3.5: Analysed ENCODE annotations ................................................................. 101

Table 3.6: Conservation and annotations of previously identified Runx1 regulators ... 101

Table 3.7: Cell types with regulatory region RNA expression .................................... 105

Table 3.8: SNP analysis of previously identified Runx1 regulators ............................. 107

Table 3.9: Spread of 237 Runx1 regulatory region mutations that were found in ICGC

dataset represented in a range of primary tissue sites. .................................................. 112

xxii

Table 3.10: Conservation and annotations of novel Runx1 regulators ......................... 122

Table 3.11: SNP analysis of novel m-371 enhancer ..................................................... 123

Table 4.1: Conservation and annotations of RUNX1 +24 ............................................ 133

Table 4.2: Type and name of genes that interact with RUNX1 +24 ............................. 153

Table 4.3: Name of genes that interact with Runx1 +24 in HPC7s .............................. 154

Table 4.4: Annotations of identified putative regulatory region connections .............. 157

Table 4.5: Annotations of identified trans interactions ................................................ 158

Table 5.1: Annotations of identified putative regulatory region connections .............. 176

Table 5.2: Number of genes connected to RUNX1 +24 in WT cells ............................ 177

Table 5.3: Number of genes connected to RUNX1 +24 in WT cells ............................ 197

Table 6.1: Summary of conserved RUNX1 regulatory region analysis ........................ 215

Appendix III Table.1: Details of the primers used in this project ............................... 269

Appendix VII Table 1: Hg38 Genome coordinates for regulatory regions ................. 287

Appendix VIII Table 1: Read counts before and after primer demultiplexing ........... 288

Appendix IX Table 1: WT 12 DMSO cis interactions ................................................. 290

Appendix IX Table 2: WT 12 DMSO near-bait interactions ....................................... 292

Appendix XI Table 1: Differentially decreased cis interactions in WT 12 h PMA cells

compared to WT 12 h DMSO K562 cells .................................................................... 301

Appendix XI Table 2: Differentially decreased cis interactions in WT 72 h PMA cells

compared to WT 12 h DMSO K562 cells .................................................................... 301

Appendix XI Table 3: Differentially decreased cis interactions in STAG2 12h DMSO

cells compared to WT 12h DMSO K562 cells ............................................................. 302

Appendix XI Table 4: Differentially decreased cis interactions in STAG2 12 h PMA cells

compared to WT 12 h PMA K562 cells ....................................................................... 303

xxiii

Appendix XI Table 5: Differentially decreased cis interactions in STAG2 72 h PMA cells

compared to WT 72 h PMA K562 cells ....................................................................... 303

Appendix XII Table 1: Differentially connected near-bait interactions in WT 12 h PMA

cells compared to WT 12 h DMSO K562 cells ............................................................ 305

Appendix XII Table 2: Differentially connected near-bait interactions in WT 72 h PMA

cells compared to WT 12 h DMSO K562 cells ............................................................ 305

Appendix XII Table 3: Differentially connected near-bait interactions in STAG2 12 h

DMSO cells compared to WT 12 h DMSO K562 cells ............................................... 307


PMA cells compared to WT 12 h PMA K562 cells ..................................................... 316


PMA cells compared to WT 72 h PMA K562 cells ..................................................... 316

Appendix XIII Table 1: List of gene connecting to RUNX1 +24 connections in each

condition ....................................................................................................................... 321

Appendix XIV Table 1: Differential cis interactions in STAG2 72 h PMA cells compared

to STAG2 12 h DMSO K562 cells ............................................................................... 327

Appendix XIV Table 2: Differential near-bait interactions in STAG2 72 h PMA cells

compared to STAG2 12 h DMSO K562 cells .............................................................. 336

xxiv

List of symbols and abbreviations

°C Degrees Celsius 3C Chromosome Conformation Capture 3D Three-dimensional 4C Circularised Chromosome Conformation Capture 4C-seq 4C Sequencing 5C Chromosome Conformation Capture Carbon Copy AGM Aorta-Gonad Mesonephros AML Acute Myeloid Leukaemia

AML1 Acute Myeloid Leukemia 1 Protein

ANOVA One-Way Analysis of Variance

ATAC-seq Assay for Transposase-Accessible Chromatin Using Sequencing

ATP Adenosine Triphosphate

BET Bromodomain And Extra-Terminal Motif

BLAT Blast-Like Alignment Tool

bp Base Pair

BS Binding Sites

CAGE Cap-Analysis Gene Expression

CD Circular Dichroism

cDNA Complementary DNA

ChIA-PET Chromatin Interaction Analysis Using Paired-End Sequencing

ChIP Chromatin Immunoprecipitation

ChIP-seq Chromatin Immunoprecipitation and Sequencing

chr Chromosome

ChromHMM Chromatin State Predictions

CML Chronic Myelogenous Leukaemia

CMML Chronic Myelomonocytic Leukaemia

CRE cis-Regulatory Element

CTCF CCCTC Binding Factor

xxv

DA Dorsal Aorta

DMEM Dulbecco’s Modified Eagle’s Medium

DMSO Dimethyl Sulfoxide

DNA Deoxyribonucleic Acid

DNase I HS Dnase I Hypersensitive Sites

DNase-seq Dnase I Hypersensitive Sites Sequencing

dpf Days Post Fertilisation

DS-AMKL Down’s Syndrome Acute Megakaryoblastic Leukaemia

dsDNA Double-Stranded DNA ENCODE Encyclopedia of DNA Elements eQTL Expression Quantitative Trait Locus E. coli Escherichia coli eRNA Enhancer RNA FACs Fluorescence-Activated Cell Sorting FBS Fetal Bovine Serum FISH Fluorescent In Situ Hybridization FPD Familial Platelet Disorder G4 G-Quadruplex GFP Green Fluorescent Protein GTEx Genotype-Tissue Expression GWAS Genome-Wide Association Studies h hours H3K27ac Histone H3 Lysine 27 Acetylation H3K27me3 Histone H3 Lysine 27 Trimethylation H3K36me1 Histone H3 Lysine 36 Monomethylation H3K4me1 Histone H3 Lysine 4 Monomethylation H3K4me2 Histone H3 Lysine 4 Dimethylation H3K4me3 Histone H3 Lysine 4 Trimethylation H3K9ac Histone H3 Lysine 9 Acetylation HE Hemogenic Endothelium Hi-C Genome-Wide Chromatin Conformation Capture HL-60 Human Myelocytic Leukaemia Cell Line HPC7 Mouse Haematopoietic Progenitor Cells hpf Hours Post Fertilisation HS Hypersensitivity Sites HSCs Haematopoietic Stem Cells HSPC Hematopoietic Stem/Progenitor Cells IAC Intra -Arterial Clusters ICGC International Cancer Genome Consortium ICM Inner Cell Mass IMDM Isocove’s Modified Dulbecco’s Media INS Insulator Assay Vector inv Inversion of Chromosome

xxvi

K562 Chronic Myelogeneous Leukaemia Cells kb Kilobase L Litre LB Luria Broth LD Linkage Disequilibrium lncRNA Long Non-Coding RNA LPA Linear Polyacrylamide m Mouse MAF Minor Allele Frequency mb Megabase MDS Myelodysplastic Syndrome µg Microgram mg Milligram µL Microlitre mL Millilitre mm Micrometre mM Millimolar MPN Myeloproliferative Neoplasms mRNA Messenger Ribonucleic Acid µS Microsiemens MTG Monothiolglycerol ng Nanogram NIH/3T3 Mouse Embryonic Fibroblast Cells nl Nanolitre Registered Trademark Unregistered Trademark P Passage P1 Promoter 1 P2 Promoter 2 P3 Promoter 3 PBS Phosphate Buffered Saline pc Post Conception pCarA Cardiac Actin Promoter PCR Polymerase Chain Reaction pf Post Fertilisation PMA Phorbol 12- Myristate 13-Acetate PRC2 Polycomb Repressive Complex 2 PRO-seq Precision Run-On Sequencing PS Penicillin Streptomycin QC Quality Control qRT-PCR Quantitative Reverse-Transcriptase PCR R3C RNA‐Guided Chromatin Conformation Capture RACE Rapid Amplification Of cDNA Ends RE Restriction Enzyme RFP Red Fluorescent Protein RLU Relative Luciferase Units RNA Ribonucleic Acid RNA Pol II RNA Polymerase II RNA-seq Rpm Revolutions Per Minute

xxvii

Runx Runt-Related Runx1 Runt-Related Transcription Factor 1 Runx2 Runt-Related Transcription Factor 2 Runx3 Runt-Related Transcription Factor 3 S.E.M Standard Error of Mean SAM Sequence Alignment/Map sc Single Cell SCF Stem Cell Factor SDS Sodium Dodecyl Sulfate SELEX Systematic Evolution of Ligands By Exponential Enrichment SINE Short Interspersed Element siRNA Small Interfering RNA SNP Single Nucleotide Polymorphism t Translocation of Chromosome TAD Topologically Associated Domain TCGA The Cancer Genome Atlas TFBS Transcription Factor Binding Sites TFs Transcription Factors TL Tübingen long fin TSS Transcriptional Site Start UCSC University Of California, Santa Cruz UV Ultraviolet V Volt v/v Volume Per Volume WT Wild Type ZED Zebrafish Enhancer Detector

xxviii

Nomenclature

Specific gene and protein nomenclature applied to each species. All the gene

names are italicised, while protein names are not.

Species Gene Protein Zebrafish runx1 runx1 Mouse Runx1 Runx1 Human RUNX1 RUNX1 Multiple species Runx1 Runx1

xxix

Names of previously identified Runx1 enhancers used throughout the thesis

Name used in thesis Also known as +24 +23/ +24/ eR1/ RE1 m-371 -371 m-368 -368 m-354 -354 m-328 -328 m-327 -327 m-322 -322 m-321 -321 m-303 -303 m-101 -101 m-59 -59 m-58 -58 m-48 -48 m-43 -43 m-42 -42 m+1 +1 m+3 +3 m+110 +110 m+181 +181 m+204 +204 -139 -139 / E1 -188 -188/ E4 -250 -250/ E6 +62 +62 intron 5.2 Intron 5.2/RE2

1

1 Chapter 1: Introduction

1.1 Haematopoiesis

Haematopoiesis is the formation, development and differentiation of all the blood

cellular components, and is fundamentally conserved throughout vertebrate evolution

(Davidson et al., 2004). Haematopoietic stem cells (HSCs) are the source of all blood cells that

are required throughout the lifetime of an organism. HSCs are capable of self-renewal and

differentiation into progenitor cells, which generate specialised haematopoietic cell lineages

(Dobrzycki et al., 2020). Haematopoiesis begins in the early stages of embryo development

(primitive) and carries on into adulthood (definitive) to maintain a steady supply of

haematopoietic cells over the lifespan of an organism (Orkin et al., 2008).

Correct spatiotemporal transcription of haematopoietic genes is essential in the

initiation of embryonic haematopoiesis, as well as in the maintenance of haematopoiesis in

adults (Dobrzycki et al., 2020). Combinations of regulatory factors (transcription factors,

enhancers, mediators, cohesins, insulators, silencers) help coordinate differentiation,

proliferation, ensure survival of haematopoietic cells, and maintain correct levels of

haematopoietic gene expression throughout life (Peng et al., 2018). Misexpression of crucial

haematopoietic genes can lead to failure to produce HSCs, or haematopoietic disorders that can

lead to leukaemia (Wilson et al., 2011). Therefore, understanding the drivers of normal

haematopoiesis can provide opportunities for intervention when haematopoiesis is

dysregulated.

1.1.1 Primitive and definitive haematopoiesis

In vertebrate embryos, haematopoiesis occurs in two sequential waves: “primitive” and

“definitive” (Orkin et al., 2008). Each wave occurs in anatomically distinct locations that vary

throughout development (Figure 1.1). The initial wave, known as primitive haematopoiesis,

2

specifies endothelial to haematopoietic precursors and takes place in the mammalian yolk sac

(Figure 1.1A) (Davidson et al., 2004; Dzierzak et al., 1998; Ivanovs et al., 2017; Julien et al.,

2016). The primary function of this wave is the production of red blood cells (erythrocytes),

which enable tissue oxygenation as the embryo undergoes rapid growth (Orkin et al., 2008).

The primitive wave is transient and rapidly superseded by the adult-type definitive

haematopoiesis. Definitive haematopoiesis produces multilineage self-renewing HSCs and

allows for the life-long production of all blood cells (Ciau-Uitz et al., 2014) (Figure 1.2). HSC

specification begins in cells from an endothelial precursor, termed the hemogenic endothelium

(HE) (Figure 1.2) (Bonkhofer et al., 2019; Gritz et al., 2016). Expression of the haematopoietic

gene Runx1 defines the distinct HE population (Gritz et al., 2016; Kalev-Zylinska et al., 2002).

These cells appear from HE in the aorta-gonad-mesonephros (AGM) region, then subsequently

colonise the fetal liver (Figure 1.1B) (Bonkhofer et al., 2019; Gritz et al., 2016). Finally, the

permanent location for HSCs to seed is in the bone marrow (Figure 1.1C) (Julien et al., 2016;

Martin et al., 2011). In mammals, initial production of B- and T- lymphocytes occurs in bone

marrow, and then cells migrate to other sites to complete development (Germain, 2002; Loder

et al., 1999) (Figure 1.1C).

3

Figure 1.1: Schematic of haematopoiesis cell generation locations during human development.

A) Primitive haematopoiesis occurs in the yolk sac blood island (blue) 16-17 days post conception (pc). B) Definitive haematopoiesis begins first in the Aorta-gonad-mesonephros (purple) 4 weeks pc, then in the foetal liver (orange) 5 weeks pc. C) Bone marrow (pink) is the lifelong source of adult definitive haematopoiesis from 12 weeks pc. The thymus (yellow) is the site of T lymphocyte development, whereas the spleen (brown) develops B lymphocytes. Figure is from Thomas (2015).

HSCs are required throughout an organism’s life to replenish precursors of multiple

lineages, as mature haematopoietic cells are predominantly short lived (Orkin et al., 2008).

HSCs must self-renew to maintain their own population, as well as produce differentiated blood

cells. In adult humans (Homo sapiens) and mice (Mus musculus), a limited number of HSCs

reside in the bone marrow and commence the cascade of cell differentiation (Albacker et al.,

2009). HSCs are at the root of progenitor cells which gradually produce mature haematopoietic

cells; this haematopoietic model is described as a multi-step process with cells becoming more

lineage-specific at each step (Figure 1.2). In this model, self-renewing HSCs, at the top give

rise to common progenitors/stem cells for the myeloid and lymphoid lineages. Following this

first step in lineage-restriction, myeloid stem cells give rise to proerythroblasts,

4

megakaryoblasts, myeloblasts and monoblasts; lymphoid stem cells, on the other hand, give

rise to B- and T- lymphocytes and large granular lymphocytes (Orkin et al., 2008).

Proerythroblasts differentiate into erythrocytes, these cells contain haemoglobin and

transporting oxygen from the lungs to different sites of the body. Erythrocytes make up 40-

45% of blood volume (Popel, 1989).

White blood cells or leukocytes are generated from both myeloid and lymphoid cell

types and contribute about ~1% of blood volume. Leukocytes are crucial in protection against

infection as they form the major part of the innate immune system (Lay et al., 1968; Trinchieri,

1989). Megakaryoblasts produce megakaryocytes and platelets; during thrombopoiesis,

megakaryocytes break into fragments to make platelets. Platelets are fundamental to blood

coagulation and wound healing (Opneja et al., 2019). Myeloblasts generate granulocytes;

basophils, neutrophils and eosinophils. Neutrophils are transient immediate-responders to

general infection, while basophils and eosinophils are predominantly connected to allergies

and inflammation (Ginhoux et al., 2014; van Furth et al., 1972). Monoblasts differentiate into

circulating monocytes and tissue-resident macrophages (Ginhoux et al., 2014; van Furth et al.,

1972).

Lymphoid derived T-lymphocytes and B-lymphocytes are crucial to adaptive

immunity. Large granular lymphocytes are innate cytotoxic cells that target cells that are virally

infected or cancerous; they target these compromised cells through the occurrence of

immunoglobulin or major histocompatibility complex class 1 receptors (Lay et al., 1968;

Trinchieri, 1989).

5

Figure 1.2: Vertebrate primitive and definitive haematopoiesis cell development pathways.

Primitive haematopoiesis is the initial wave of blood production which is transient and rapidly replaced by the adult-type definitive haematopoiesis (Highlighted in grey). Definitive haematopoiesis is the life-long production of all blood cells. Haematopoietic stem cells reside in the bone marrow in adult mammals and yield blood precursors devoted to unilineage differentiation and production of mature blood cells. HSCs are also capable of self-renewal.

1.1.2 Transcription factors involved in haematopoiesis

It is well established that a number of transcription factors are required to regulate the

fate determination of haematopoietic stem cell populations. These transcription factors include

6

(but are not limited to) Tal1, Runx1, Gata1, Gata2, Lmo2, Spi1, Notch and c-Myb in most

vertebrates (Okuda et al., 2001).

Primitive haematopoiesis is principally regulated by two factors; Gata1 (erythroid) and

Spi1 (myeloid) (Cantor et al., 2002). Mesodermal haematopoietic cell-fate specification is

facilitated by the basic-helix-loop-helix factors (Tal1 and Lmo2) in both haematopoietic waves

(Kim et al., 2007).

Erg and Gata2 are required for HSC survival and self-renewal (Ezoe et al., 2002;

Taoudi et al., 2011). Maintenance of HSCs is also co-ordinated through Notch, Wnt and Hox

signalling pathway factors. Runx1 is crucial for making HSCs in the AGM and subsequently

in definitive haematopoiesis Runx1 protein is required for correct megakaryocyte and

lymphocyte production (Ichikawa et al., 2004; North et al., 1999; North et al., 2004).

Dysregulation of normal haematopoiesis leads to a diverse range of diseases collectively called

haematological malignancies.

1.2 Haematological disorders

Misexpression of transcription factors can lead to failure to produce HSCs, or

haematopoietic disorders that can lead to leukaemia (Dobrzycki et al., 2020). There are many

different types of haematological disorders; including chromic myeloid leukaemia (CML),

acute myeloid leukaemia, familial platelet disorder, B- or T- cell lymphoblastic leukaemia.

This thesis will focus on myeloid dysplasias (acute myeloid leukaemia and familial platelet

disorder) as RUNX1 has a central role in disease progression.

Disruption of Runx1 leads to the complete loss of definitive haematopoiesis in all

lineages (Kalev-Zylinska et al., 2002; Okuda et al., 1996; Wang et al., 1996). Somatic

mutations in the RUNX1 gene are one of the most frequent mutations identified in the patients

with myeloid malignancies (Harada et al., 2005; Harada et al., 2009; Hayashi et al., 2017).

RUNX1 (runt-related transcription factor 1) is also known as AML1 (acute myeloid leukaemia

7

1 protein), as it was initially cloned from a chromosomal breakpoint normally associated with

acute myeloid leukaemia (Meyers et al., 1993; Miyoshi et al., 1991; Okuda et al., 2001).

RUNX1 mutations can result in familial platelet disorder, an inheritable blood disease, which

predisposes individuals to acute myeloid leukaemia (Ito et al., 2015).

1.2.1 Familial platelet disorder

Familial platelet disorder (FPD) is characterised by inherited platelet defects caused by

germline mutations in RUNX1 (Luddy et al., 1978; Song et al., 1999). Clinically, patients

present with mild to moderate thrombocytopenia, platelet dysfunction, bleeding, and an

increased predisposition of developing acute myeloid leukaemia (around a 35% of patients)

(Owen et al., 2008). Many RUNX1 variants are reported only in individual families; more than

70 families with inherited RUNX1 mutations have been identified (Sood et al., 2017). FPD can

almost exclusively be attributed to RUNX1 mutation or downregulation. RUNX1 mutations in

FPD generate null, hypomorphic, or dominant-negative alleles (Churpek et al., 2015; Krutein

et al., 2021; Latger-Cannard et al., 2016; Matheny et al., 2007; Song et al., 1999).

1.2.2 Acute Myeloid Leukaemia

Leukaemia is a common name given to cancers that develop in the bone marrow and

blood cells. Leukaemia occurs due to the dysregulation of a normal cell, which then enables

one cell type to proliferate in excess. Leukaemia is normally classified according to the original

cell lineage in which the cancer occurs. i.e., myeloid or lymphoid (Figure 1.2). The leukaemia

is further distinguished into two types: acute leukaemia, which progresses at a fast pace and

chronic leukaemia, which develops over a longer time period (Teittinen et al., 2012).

Therefore, Acute myeloid leukaemia (AML) is a fast developing leukaemia, which

occurs in the myeloid cell lineage. Whilst found predominantly in individuals over 65, 15-20%

of AML cases occur in childhood. (O'Donnell et al., 2012).

8

Chromosomal abnormalities such as t(9;21), t(9;11), t(8;21), inv(16), t(15;17) are

clinically distinctive in AMLs and indicate a genetic basis to leukemogenesis. However, major

chromosome abnormalities only describe 45% of AML cases (Betz et al., 2010). Unlike other

adult cancers, AML genomes have fewer somatic mutations, with a total of 23 genes

significantly mutated (CGARN, 2013). Molecular classification of AMLs can offer prognostic

information (Betz et al., 2010). Somatic driver mutations in genes have been identified in ~

80% of patients across various myeloid malignancies subtypes (Shallis et al., 2018). Within

the myeloid malignancies, a median of three somatic mutations per patient was observed

(Shallis et al., 2018). Several studies have detected at least one driver gene mutation in AML

patients (86%) (Bullinger et al., 2010; CGARN, 2013; Hou et al., 2020a; Papaemmanuil et al.,

2016; Yu et al., 2020). RUNX1 and other mutations (TP53, EZH2, ETV6, ASXL1, SRSF2)

predict poor overall survival (Yu et al., 2020). The prognostic significance of these mutations

appears to be maintained irrespective of whether these mutations are early or late events in

disease progression (Yu et al., 2020).Understanding the mutational profile of malignant cells,

helps define which treatments will be successful and the likelihood of relapse or achieving

remission (Assi et al., 2019; Conway O’Brien et al., 2014; Papaemmanuil et al., 2016; Shallis

et al., 2018; Tyner et al., 2018).

1.2.3 Leukaemogenesis

Gilliland et al., 2002 suggested a conventional model of leukaemogenesis, in which

they proposed that two different types of genetic mutations were necessary for malignant

transformation of the myeloid precursor (Gilliland et al., 2002; Jordan, 2002). Class I mutations

are understood to lead to unrestrained cellular proliferation and evasion of apoptosis, whereas

Class II mutations are associated with preventing differentiation. It is therefore suggested that

alone, Class I mutations lead to myeloproliferative diseases, however Class II mutations lead

to the development of myelodysplastic syndromes (Conway O’Brien et al., 2014).

9

Mutations in epigenetic modifier genes including DNA methylation and histone

modification have been found in a significant proportion of AML (Jaiswal et al., 2019; Shih et

al., 2012); these mutations do not fit into Class I or II. It is also been proposed that mutations

have to occur at a particular time in cell development and in a certain order to progress to

leukaemia (Murati et al., 2012). This evidence indicates that Gilliland’s conventional model is

an oversimplification of a more complex process. Each novel mutation and its potential roles

need to be thoroughly evaluated so that updated models of AML development can be defined.

As healthy individuals age, the haematopoietic cells become burdened with mutations

typically seen in AML patients (Young et al., 2016; Zink et al., 2017). Abnormal and excessive

growth of myeloid cells (myeloid neoplasms) develop due to a gradual gain of somatic

mutations in haematopoietic cells (Kim et al., 2016). Expansion of these myeloid neoplasms

may lead to a pre-leukaemic state. Pre-leukaemic clones are dynamic and can have several

founder mutations (Kim et al., 2016); even after treatment, these mutations can persist through

to complete remission and then lead to relapse with additional secondary mutations (Shlush et

al., 2014).

Founder haematopoietic mutations are largely characterised by mutations in genes

related to epigenetic control of the genome; these mutations are frequently observed in pre-

leukaemic clones (Desai et al., 2018). Chromatin and RNA splicing-associated factor

mutations can arise later, or alongside epigenetic gene mutations. Additionally, mutations in

transcription factors, and genes encoding signalling proteins occur as late events leading to

AML transformation (Papaemmanuil et al., 2016). Desai et al., (2018) studied 188 patients and

revealed mutations in epigenetic genes and splicing factors were evident up to 9.6 years before

AML onset; however, mutations in transcription factors were found to be late events tied to the

onset of AML (Desai et al., 2018). Therefore, AML is characterised by combinatorial

mutations that are attained in a hierarchical manner.

10

1.2.4 AML patient survival and treatment

The genetic and epigenetic profile of malignant cells influences the threat of relapse

and the likelihood of achieving remission (Conway O’Brien et al., 2014). Despite the high

variability in AML subtypes, the outcome for most is unfavourable. Patients aged 60 and under

achieve a remission with chemotherapy in approximately 70-80% of cases, whereas for those

over 60 it is 50% chance of remission (Bower et al., 2016; Burnett et al., 2011; Sweeney et al.,

2019). Despite achieving remission, without ongoing treatment most patients subsequently

relapse, typically within 6 months, and relapse is coupled with poor prognosis (Goyal et al.,

2016; Sweeney et al., 2019)

A small number of therapies targeted to mutational events have been developed for

patients with AML, although the current standard of care remains largely unchanged over the

past 30–40 years (Tyner et al., 2018). Rigorous chemotherapy is the conventional treatment

for AML and depending on the cancer’s genetic profile, bone marrow transplantation may also

be used (Ferrara et al., 2008). The combination of cytotoxic chemotherapy drugs used varies

depending on the patient’s age and karyotype (Ferrara et al., 2013). Chemotherapy drugs are

designed to target proliferating cells; however, other cells rapidly divide including those in

bone marrow, gastrointestinal mucosa, hair follicles and gonads, which results in adverse side

effects (Corrie, 2008).

Advanced age in most patients affects their ability to survive the side effects of

chemotherapy (Ferrara et al., 2008). In addition to improved antibiotic, antifungal, and antiviral

drugs to reduce infections, developments to improve supportive care include high quality

platelet transfusions and elimination of post transfusion hepatitis (Ferrara et al., 2013).

Increased understanding of the different disease mechanisms may detect potential therapeutic

targets and refine treatment strategies, and consequently improve survival rates.

11

Some targeted therapies for leukaemias exist, such as imatinib mesylate (Gleevec),

which is a very efficient treatments of Philadelphia chromosome positive chronic myeloid

leukemia (Kantarjian et al., 2002a; Kantarjian et al., 2002b). AML treatment has expanded to

include inhibitors for gene mutation-targeted therapies. For example, patients with FLT3-ITD–

mutated AML who were treated with FLT3-inhibitor therapy at diagnosis have demonstrated

significant survival benefit (Daver et al., 2015; DiNardo et al., 2016; Röllig et al., 2015; Stein,

2015; Stone et al., 2015).

AML with mutated transcription factor RUNX1, is considered a biologically distinct

subgroup due to its distinctive clinico-pathologic features and inferior prognosis (Gaidzik et

al., 2016). Currently no targeted or specific therapeutic options exist for RUNX1-mutated

patients, or patients with other high-risk genetic lesions such as chromatin-spliceosome

(including cohesin subunits) mutations, TP53 mutations, or complex cytogenetics (DiNardo et

al., 2016).

Papammanuil et al., (2016) found that 4% of AML patients sequenced contained no

driver mutations (Papaemmanuil et al., 2016). The molecular events that cause the

development and pathology of this leukaemia have so far not been completely defined.

Difficulties arise for clinicians when providing prognoses to AML patients without well-known

mutations. Changes to the known leukaemia genes by mutation in non-coding regulatory

regions, which could alter gene regulation; rather than mutations in protein-coding sequence,

may explain why not all instances of leukaemia harbour mutations in known genes. The cell-

fate of haematopoietic cells from a common progenitor relies upon the differential expression

of key haematopoietic genes. Mutations in these key genes, such as RUNX1, or changes to gene

regulation, could consequently alter normal blood development, and lead to the development

of leukaemia.

12

1.2.5 Cohesin mutation contributes to AML

Cohesin is a chromosome-associated multiprotein ring complex made up of four major

subunits: two structural maintenance subunits, Smc1a and Smc3, which form a closed ring

together with Rad21 and Stag1 or Stag2 (Figure 1.3) (Costantino et al., 2020; Feeney et al.,

2010; Gruber et al., 2003; Nasmyth et al., 2009).This multi-subunit protein complex was

originally characterised for its crucial role in sister chromatid cohesion, as it ensures accurate

sister chromatid segregation during mitosis (Downes et al., 1991; Michaelis et al., 1997).

Cohesin also plays a role in DNA double strand break repair, chromatin loop extrusion and

regulation of gene expression. Complete loss of cohesin function stops mitosis and thus results

in cell death. Reductions in cohesin functions alters the development of multiple organ systems

in humans, indicating that the correct expression of developmental genes are sensitive to

cohesin dosage (Cuartero et al., 2018b; Deardorff et al., 2020).

Somatic mutations to cohesin subunits are typically heterozygous, mutually exclusive,

and result in loss of function (17% frameshift, 70% missense)(CGARN, 2013). Mutations in

RAD21, SMC3, and STAG1 subunits are always heterozygous, whereas mutations in the X

chromosome-located genes SMC1A and STAG2 can result in complete loss of function due to

male hemizygosity, or wild-type silencing during X-inactivation in females (Kon et al., 2013;

Thota et al., 2014; Tsai et al., 2017). STAG2 and STAG1 have redundant roles in cell division,

therefore complete loss of STAG2 is tolerated due to partial compensation by STAG1 (Tsai et

al., 2017) Nevertheless, STAG1 and STAG2 have non-redundant roles in facilitating three-

dimensional (3D) genome organisation (Kojic et al., 2018). Combined loss of both STAG2 and

STAG1 is lethal to cells (Benedetti et al., 2017; Van Der Lelij et al., 2017).

Cohesin proteins are mutated, have reduced function, or are overexpressed in multiple

cancer types including brain, breast, prostate, bladder, pancreatic, colorectal, Ewing sarcoma,

and myeloid leukaemia (Balbás-Martínez et al., 2013; Brennan et al., 2013; Brohl et al., 2014;

13

Evers et al., 2014; Guo et al., 2013; Jones et al., 2012; Rhodes et al., 2011; Solomon et al.,

2013; Stephens et al., 2012; Taylor et al., 2014; Welch et al., 2012). STAG2 is one of 12 genes

that is mutated at significant frequencies in at least four different tumour types (Lawrence et

al., 2014).

Figure 1.3: Structure of cohesin complex.

Smc1 (blue), Smc3 (purple), Rad21 (pink) and Stag1 or Stag2 (yellow) form a tripartite ring. Figure is from Thomas (2015).

Multiple studies observed cohesin mutations in myeloid malignancies, providing

evidence of a role for cohesin in pathogenesis (Au et al., 2016; CGARN, 2013; Kon et al.,

2013; Leeke et al., 2014; Lindsley et al., 2015; Papaemmanuil et al., 2016; Rocquain et al.,

2010; Ruffalo et al., 2015; Thol et al., 2014; Wang et al., 2016; Welch et al., 2012; Welch et

al., 2016). Cohesin insufficiency has been shown to impair differentiation and alter overall

chromatin accessibility, as well as the transcription of RUNX1 (Mazumdar et al., 2015;

Mullenders et al., 2015; Viny et al., 2015).

Cohesin mutant AML is rarely aneuploid, with over half of AML patients exhibiting a

normal karyotype, demonstrating that mitotic defects are not likely to be involved in

14

leukemogenesis (Kon et al., 2013). AML patients with cohesin mutations do not typically have

a higher observed presence of chromosomal translocations (Fisher et al., 2017a; Heimbruch et

al., 2021). Except RAD21 mutations, which have been reported to co-occur with t(8:21) in

AML patients (Tsai et al., 2017; Wilde et al., 2019),

Deep sequencing established that mutations in subunits or regulators of the cohesin

complex occur in 20% of AML samples (CGARN, 2013; Fisher et al., 2017a; Heimbruch et

al., 2021; Leeke et al., 2014; Losada, 2014). Somatic cohesin defects are found in 12% of

myeloid malignancy patients and a further 15% of patients had low cohesin expression (Thota

et al., 2014). Previous research has highlighted that cohesin mutations occur early in

leukaemogenesis and mainly arise in pre-leukaemic cells (CGARN, 2013; Corces et al., 2017;

Kon et al., 2013; Thol et al., 2014; Welch et al., 2012).

Mutations in cohesin subunits occur in other myeloid malignancies, including

myelodysplastic syndrome (MDS), Down Syndrome acute megakaryoblastic leukaemia (DS-

AMKL), myeloproliferative neoplasms (MPN), chronic myelogenous leukaemia (CML), and

chronic myelomonocytic leukaemia (CMML) (Kon et al., 2013; Thota et al., 2014; Yoshida et

al., 2013). Cohesin mutations are recurrent in myeloid malignancies but not in lymphoid

malignancies, consistant with evidence showing cohesin-deficient cells have myeloid-skewed

differentiation(Chen et al., 2019; Cuartero et al., 2018b; Viny et al., 2019). Cohesin mutated

myeloid leukaemia cells have reduced levels of chromatin-bound cohesin components,

implying a loss of function mechanism (Kon et al., 2013). Cohesin mutations found in patients

are mutually exclusive, illustrating that a mutation in just one member of the complex is

sufficient to decrease cohesin activity (Kon et al., 2013; Thota et al., 2014). Patients with

cohesin mutations have a lower 5 year survival rate of 20% when compared to cohesin-WT

AML patients (48%) (Heimbruch et al., 2021). Cohesin mutations co-occur with other

leukaemogenic mutations, indicating cooperation between pathways (Welch et al., 2012).

15

Cohesin mutations have been shown to impair the differentiation of HSPCs in humans

and mice (Mazumdar et al., 2015; Mullenders et al., 2015; Viny et al., 2019; Viny et al., 2015).

Stag2 deletion in HSPCs had altered hematopoietic function, increased self-renewal, and

impaired differentiation. (Viny et al., 2019). Typically increased inflammatory signals in

HSPCs promotes myeloid differentiation. Chen et al., (2019) demonstrated that HSPCs with

impaired cohesin function had reduced inflammatory gene expression and increased resistance

to the differentiation inducing inflammation stimuli (Chen et al., 2019). STAG2 is frequently

mutated in myeloid malignancies and shows co-mutation patterns with other drivers, including

RUNX1. Combined loss of Runx1 and Stag2 leads to myeloid-skewed expansion of HSPC and

MDS in mice (Ochi et al., 2020). Concurrent loss of Stag1 and Stag2 in HSPCs is lethal to

cells. Alone, Stag2 loss decreases the chromatin accessibility and differentiation gene

transcription (Viny et al., 2019). Cohesin subunits have dose-dependent consequences which

alter chromatin structure and HSC function in mice (Viny et al., 2015). Complete loss of Smc3

induced bone marrow aplasia and mitotic defects, whereas Smc3 haploinsufficiency reduced

transcriptional capacity (Viny et al., 2015). Haploinsufficiency of Smc3 increased self-renewal

of cells and when combined with Flt3-ITD mutation could induce acute leukaemia in mice

(Viny et al., 2015). Chin et al., (2020) highlighted that cohesin mutations are synthetic lethal

with Wnt pathway stimulation, and suggested that cohesin mutant cells could progress to

oncogenesis by enhancing their Wnt signaling (Chin et al., 2020). Depletion of Rad21

enhanced self renewal in HSPCs by derepression of Polycomb Repressive Complex 2 (PRC2)

target genes (Fisher et al., 2017b). Fisher et al., (2017) showcased that cohesin function is

crucial for proper PRC2 complex mediated gene silencing, and dysregulation of this epigenetic

mechanism may underly leukemogenesis in AML patients with cohesin mutations (Fisher et

al., 2017b).

16

Together these studies provide evidence that cohesin mutations are a causal event in

acute myeloid leukaemogenesis. Overall cohesin mutations are implicated as a putative genetic

precursor for development of AML, but how the disease develops from these mutations has

not been resolved. One possibility is that impairment of cohesin’s regulatory role leads to

dysregulation of other important leukaemogenic genes.

1.3 RUNX1 is a leukaemia gene

RUNX1 belongs to the family of Runx transcription factors (TFs), which are essential

for lineage-specific gene expression in major developmental pathways.

Runx genes encode transcription factor proteins defined by the highly-conserved Runt

DNA-binding domain (Figure 1.4). The conserved Runt domain is a fundamental functional

component of Runx proteins (Rennert et al., 2003). The Runt domain is required for DNA

binding and interacting with the conserved non-DNA-binding partner CBFβ (Figure 1.4). Runx

family members all have important roles during lineage-specifying steps in both embryonic

and adult developmental processes; they appear to be involved in cell quiescence, proliferation,

lineage commitment and cell-fate determination in mammals and invertebrates (Coffman,

2003, 2009; Groner et al., 2017).

17

Figure 1.4: The Runx protein.

The Runt domain (dark blue) of the Runx protein (blue) directs binding to the Runx DNA-motif (grey box) near the promoter of target genes (black right angled arrow). It also binds with core-binding factor-β (CBF-β) protein (purple). The Runx proteins either activate or repress gene transcription through interactions with other transcription factors (TFs)(Orange) and co-activators (arrows), or co-repressors (blocked line).

Runx1 is essential for definitive haematopoiesis (Levanon et al., 2004; Li et al., 2002).

Runx2 is crucial for osteoblast development and proper bone formation (Komori et al., 1997).

Runx3 is needed for the development of dorsal root ganglia proprioceptive neurons and proper

control of gastric epithelium growth (Levanon et al., 2002; Li et al., 2002). Because each Runx

protein has a particular biological function, mutations in Runx genes are often the cause for

specific diseases. RUNX1 mutations can cause FPD (Ito et al., 2015). Point mutations in the

runt domain of RUNX2 are reported in cleidocranial dysplasia, in which patients exhibit

skeletal deformities of the collarbone, teeth and cranium (Ito et al., 2015; Otto et al., 2002).

Point mutations of RUNX3 are associated with stomach and bladder cancers (Ito et al., 2015).

Each gene contains P1 (distal promoter) and P2 (proximal promoter) that generate

alternative Runx isoforms (Levanon et al., 2004; Rennert et al., 2003). A dual promoter

structure is a characteristic feature of vertebrate Runx genes, as invertebrates only have P2

(Rennert et al., 2003). Since all vertebrate Runx genes have this conserved dual promoter

structure, it is likely to be crucial for correct regulation (Rennert et al., 2003).

18

1.3.1 Role of RUNX1 in normal embryonic and adult developmental

processes

Runx1 expression is crucial in early myeloid differentiation, as well as playing an

essential role in adult blood development (Okuda et al., 2001). Runx1-/- mice fail to generate

haematopoietic cell clusters from the vessel wall and consequentially cannot generate

haematopoietic stem cells (HSCs), leading to mid-gestation lethality (Okuda et al., 1996;

Yokomizo et al., 2001). The Runx1 protein initiates transcription of genes that help control the

development of blood cells. In particular, it plays an important role in the development of

pluripotent haematopoietic stem cells that have the potential to develop into all types of mature

blood cells. (Ichikawa et al., 2004; North et al., 1999; North et al., 2004).

As well as haematopoiesis, RUNX1 plays a role in development of dorsal root ganglia,

cranial sensory neurons, myofibroblasts and mesenchymal stem cells, regulation of hair follicle

stem cells, mammary stem cells, gastric stem cells, neural crest stem cells, and some possible

roles in intestinal stem cells and oral epithelial stem cells (Groner et al., 2017; Inoue et al.,

2008; Kim et al., 2014; Kramer et al., 2006; Theriault et al., 2004).

1.3.2 Regulation of Runx1

The dual promoter structure of the Runx genes allows for many different isoforms of

the gene to be produced (Figure 1.5) (Levanon et al., 2004). The regulation machinery of these

genes and spatiotemporal regulation of Runx are poorly understood. It is possible that the

spatial-temporal regulation is driven by different 3D arrangements of the chromosome in

different cell types.

In mammals, the dual promoter structure of Runx1 allows for the transcription of at

least three diverse isoforms (Levanon et al., 2004). Transcription from P2 produces Runx1a

and Runx1b, whereas transcription from P1 generates Runx1c (Figure 1.5) (Bee et al., 2009b).

P1 and P2 expression alone is not consistent with Runx1 tissue specificity, suggesting

19

additional enhancer elements are required for correct initiation of runx1 (Marsman et al., 2014).

The 5’UTR of Runx1c is conserved between mammals and zebrafish, and may be important

for the regulation of Runx1 expression (Marsman et al., 2014). Lam et al., 2009 showed the

first exon of P1 transcript was not needed for definitive or primitive haematopoiesis in

zebrafish (Lam et al., 2009).

Expression of Runx1-P1 and-P2 isoforms is tightly controlled during haematopoiesis.

In early mouse haematopoiesis, before the generation of HSCs, expression of the P2 isoforms

are prominent (Bee et al., 2010; Pozner et al., 2007). Runx1-P1 is expressed shortly after P2,

its expression is co-ordinated with the generation of HSCs (Bee et al., 2009b; Bee et al., 2010).

The main isoform in the mouse fetal liver is P1 driven, the liver is the main site of definitive

HSPC development (Bee et al., 2009b).

Horsfield et al., 2007 showed that homozygote rad21 cohesin mutants lack cell type

specific expression of runx1 and runx3, highlighting that cohesin is necessary for correct runx

expression. Cohesin may provide an additional mechanism by which Runx expression can be

coordinated with cell cycle, allowing cell proliferation and differentiation (Horsfield et al.,

2007).

20

Figure 1.5: Comparative genomic organisation of the Runx1 locus in human, mouse and zebrafish.

The exons (grey) are numbered according to Marsman et al., (2014). Untranslated regions are shown as white boxes. Distances between promoters (black right-angled arrows) is indicated in kilobases (kb). Figure is from Thomas (2015).

21

1.3.3 Role of RUNX1 in AML progression

The developmental transcription factor RUNX1/AML1 has been identified as a classic

leukaemia gene (Okuda et al., 2001). Germline mutations to the Runt binding domain result in

FPD with patients having a predisposition to acute myelogenous leukaemia (Ito et al., 2015).

Disruptions such as truncations and translocations to RUNX1 are frequently associated with

AML (Betz et al., 2010; Tighe et al., 1993).

RUNX1 is important for the differentiation of cells of all haematopoietic lineages, and

there are two categories of genetic changes: translocations and somatic mutations (Bellissimo

et al., 2017). In AML, RUNX1 mutations that occur due to chromosomal translocations have a

favourable prognosis, compared to somatic mutations which are associated with a poorer

prognosis (Byrd et al., 2002; Grimwade et al., 1998; Schlenk et al., 2003).

RUNX1 is altered by chromosomal translocations; these disruptions can generate

oncogenic fusion proteins like RUNX1-ETO t(8;21) and ETV6-RUNX1 t(12;21) (Figure 1.6).

10-15% of all AML cases are characterised by the most common translocation t(8;21)

(Reikvam et al., 2011; Tighe et al., 1993). This translocation creates a chimeric gene of the 5'-

region of the RUNX1 gene fused with the 3'-region of the ETO gene (RUNX1-ETO) (Erickson

et al., 1992; Miyoshi et al., 1993). RUNX1-ETO protein is predicted to bind to several target

genes and dysregulate haematopoiesis which results in AML (Ajore et al., 2010; Gardini et al.,

2008). The breakpoints in characterised RUNX1 translocations are often downstream of the

Runt homology domain (De Braekeleer et al., 2011). ETV6-RUNX1 translocation is the most

frequent gene fusion (~25%) in paediatric acute lymphoblastic leukemia (ALL)(Sun et al.,

2017). It is important to note that RUNX1 changes can lead to AML as well as to ALL (Sood

et al., 2017). This is not often the case for other genes involved in haematological malignancies.

Translocations can also create truncated RUNX1 protein that interfere with the wildtype protein

https://en.wikipedia.org/wiki/RUNX1

22

product (Cheng et al., 2018). Chromosomal translocations in RUNX1 often result in

haematopoietic malignancies, though the molecular mechanisms are not often described.

Figure 1.6: RUNX1 gene common isoforms and leukaemic fusion isoforms.

The exons are depicted as grey and blue, the runt binding domain is blue. Untranslated regions are shown as white boxes. Distances between the two promoters P1 and P2 (black right angled arrows) is indicated in kilobases (kb). Straight arrows indicate common chromosomal breakpoints. Isoforms and common leukaemic fusion isoforms are depicted.

Cheng et al., 2018 characterised a translocation that disrupts a regulatory element

between RUNX1 P1 and P2. This regulatory element was shown to “normally” silence RUNX1

P2 (Cheng et al., 2018). They proposed that the translocation increased P2 transcription and

may activate P1 expression via a positive feedback loop (Cheng et al., 2018). RUNX1a isoform

is transcribed from P2 and its overexpression has been previously noted in AML (Liu et al.,

2009). RUNX1a overexpression has also been observed in patients with myelodysplastic

23

syndromes and myeloproliferative neoplasms; interestingly, levels of expression increased as

the disease progressed (Sakurai et al., 2017).

Studies have identified breakpoints in similar regions between the distal promoters in

cases of paediatric AML, B-lineage acute lymphoblastic leukaemia, and FPD (Buijs et al.,

2012; Cheng et al., 2018; Gandemer et al., 2007). The occurrence of DNA double strand breaks

which underpin translocations have been linked to 3D genome organisation (Cuartero et al.,

2018a).

Mutations are significantly associated with increasing age and higher platelet counts.

3% of paediatric and 15% of adult de novo AML patients have somatic RUNX1 point mutations

(Sood et al., 2017). Further mutations are usually required on top of RUNX1 alteration for the

progression to leukaemia (Asou, 2003). Molecular mechanisms for haematopoietic

malignancies include disruption of intron 1, which deregulates promoters and causes RUNX1

overexpression (Gandemer et al., 2007). Aberrant regulation of RUNX1 promoters was seen in

the human myeloid leukaemia (HL-60) cell line, implying that promoter dysregulation may

lead to leukaemogenesis (Marsman et al., 2014).

These previous studies indicate that progression to AML can occur by disruption of

RUNX1 regulation. This disruption can be a translocation which alters the genome structure or

somatic mutations which alter the RUNX1 transcription. Cheng et al., 2018 showcased that

dysregulation of regulatory elements can produce aberrant RUNX1 and progress

leukaemogenesis.

The 3D genome influences the regulation of genes at the transcriptional level

(Holwerda et al., 2012; Lieberman-Aiden et al., 2009). For instance, 3D folding of chromatin

provides a mechanism for distal enhancers to interact with target promoters (Deng et al., 2010).

In cases where no mutation is found in the coding sequence, instead there may be mutations in

regulatory sequences, such as enhancers (Ng et al., 2010).

24

1.4 Three-dimensional genome structure

Though the genome is often represented as linear in order to view the annotations of its

features, the genome is in reality arranged in three-dimensions. The eukaryotic genome is

tightly packaged to sit within the spatial limits of a cell nucleus; yet it must permit movement,

remain flexible and allow for access of transcription and regulatory factors to the DNA

sequence (Deng et al., 2010).

Genome organisation relies on two mechanisms: compartmentalisation and chromatin

loop formation. (Nuebler et al., 2018). Nuebler et al., (2018) previously showed that active

process of loop extrusion counteracts compartmental segregation, which indicates that these

two mechanisms are mutually exclusive (Nuebler et al., 2018). Genome-wide chromatin

conformation capture (Hi-C) is a technique used to systematically interrogate interaction

frequencies between genomic regions. The development of Hi-C technology has demonstrated

the presence of spatially insulated genomic regions: topologically associated domains (TADs),

organised by chromatin loop extrusion (Dixon et al., 2012). Hi-C also revealed that each

genome territory separates into two chromosome compartments (compartmentalisation),

euchromatic (active) and heterochromatic (inactive) (Lieberman-Aiden et al., 2009).

Chromatin loop extrusion model uses the cohesin complex and CCCTC binding factor

(CTCF) to form stable DNA loops, which act as functional barriers between active and inactive

chromatin. Loop extrusion by cohesin and CTCF is important for the formation of TADs, and

cohesin and CTCF are enriched at TAD boundaries (Dixon et al., 2012; Rao et al., 2014). Loss

of CTCF relaxes the global domain structures, which results in increased contacts between

domains; loss of cohesin mostly affects interactions within domains (Li et al., 2013b; Zuin et

al., 2014). These observations have led to the chromatin loop extrusion model to describe how

TADs are formed: in this model, cohesin begins to extrude a chromatin loop until it encounters

an obstruction associated with the DNA (CTCF). Think of the DNA as a thread being pulled

25

through a needle’s eye (cohesin) to make a loop. The CTCF only functions as a barrier when

it is bound to the DNA in a specific orientation (Rao et al., 2014); directionality of CTCF sites

is important for loop formation, as sites that anchor loops are convergent (Merkenschlager et

al., 2016).

Compartmentalisation depicts the process in which proteins arrange themselves into

condensates and act as membrane-less organelles that cluster specific molecules (Hyman et al.,

2014; Shin et al., 2018). In these condensates there are often co-localisation of similar

transcription factors or polycomb proteins, suggesting that like attracts like. Hence why there

is euchromatic and heterochromatic compartments (Stadhouders et al., 2019).

1.4.1 Topologically associated domains (TADs)

TADs are characterised by an elevated number of chromatin interactions within a

genomic locality, with fewer interactions to outside regions (Lieberman-Aiden et al., 2009;

Rao et al., 2014). Genes positioned within the same TAD often have the same histone

modifications and are co-regulated (Rao et al., 2014). TADs are present in a wide range

different metazoan phyla, suggesting that they are ancient features of the genome (Harmston

et al., 2017). TADs have been under intense selective pressure, and act on the same functional

subset of genes, at least back to the common ancestor of chordates and arthropods (Harmston

et al., 2017).

Harmston et al., 2017 propose that chromatin structure is mainly defined by the need

to regulate important developmental genes. TADs often contain the regulatory elements

(enhancers, silencers and insulators) required to regulate the genes within that TAD. TADs

containing clusters of regulatory regions are enriched with conserved CTCF sites when

compared to TADs without clusters of regulatory regions (Harmston et al., 2017). This

suggests that these regions are isolated from neighbouring domains. Gómez-Marín et al., 2015

highlighted that diverging CTCF sites are a signature of TAD borders that are maintained

26

between species, suggesting that specific 3D organisation is highly conserved and under

intense selection pressure (Gómez-Marín et al., 2015).

Disruption of TAD boundaries allows erroneous contacts with regulatory elements of

neighbouring domains, which leads to misexpression of genes, including the activation of

oncogenes driving cancer progression (Kloetgen et al., 2019; Pope et al., 2014; Taub et al.,

1982; Zhan et al., 2017)(Bohlander, 2000; Look, 1997; Rawat et al., 2004).

Taub et al., (1982) described t(8;14) translocations in Burkitt's lymphoma which alter

gene expression through enhancer hijacking (Taub et al., 1982). Rewat et al., (2004) showed

that in the t(12;13) translocation, overexpression of CDX2 gene is leukaemogenic rather than

the ETV6-CDX2 fusion(Rawat et al., 2004). Chromosomal translocations like RUNX1-ETO

disrupt the well-established and conserved RUNX1 TAD (Figure 1.7).

27

Figure 1.7: RUNX1 TAD Hi-C contact map of RUNX1 region in K562 cells.

Hi-C data generated by Rao et al., (2014) was visualised in 3D genome browser at a 25-kb resolution (Wang et al., 2018). Every square is coloured according to the frequency of two regions interacting. The alternating yellow and blue bars are predicted TADs. RUNX1 is annotated according to its transcription direction (reverse strand). RUNX1 sits within its own isolated TAD.

1.5 Regulators of gene transcription

Most genes display individual characteristics that determine the site, level, and timing

of expression throughout development and the cell cycle (Kadauke et al., 2009). Accurate

spatiotemporal and quantitative gene expression is crucial for normal development and, in

many cases, is achieved by the interaction of promoters with cis-regulatory elements. In

addition, the chromatin must be ‘open’ for external factors to facilitate regulatory functions of

the DNA (Gilbert et al., 2004).

Genomic regions have been shown to be highly conserved across a diverse set of

vertebrate species (Abbasi et al., 2007). While some conserved regions correspond to coding

genes and non-coding RNAs, the remainder are unlikely to produce a functional transcript

(Rebolledo-Jaramillo et al., 2014). Non-coding DNA can play crucial roles in the regulation of

28

gene expression, as some conserved non-coding regions can contain transcriptional regulatory

modules, such as enhancers, insulators or silencers (Deng et al., 2010).

Highly conserved developmental genes are surrounded by clusters of conserved

regulatory elements. Expression patterns driven by regulatory regions often correlate with the

function of the closest genes; however, the overlap can be inconsistent with regulatory elements

controlling expression in genes further afield (Marsman et al., 2014; Smemo et al., 2014;

Spieler et al., 2014). Long-range chromatin interactions between promoters and regulatory

elements can be mediated by scaffolding proteins and transcription factors to regulate gene

expression (Phillips-Cremins et al., 2013).

The syntenic organisation of clusters of regulatory regions supports the idea that there

are combinations of regulatory elements controlling the complex expression patterns of

developmental genes. Synteny is required to retain the regulatory regions in cis with the genes

under long range interaction (Ritter et al., 2010).

1.5.1 Cohesin acts with other regulators to determine gene expression

Cohesin controls a large number of genes, as evidenced by altered expression profiles

when the multi-subunit protein is knocked down in mouse embryonic stem cells and drosophila

neuronal cells (Kagey et al., 2010; Schaaf et al., 2009). One of the potential mechanisms by

which cohesin regulates cell-type specific gene expression is by facilitating long-range

interactions through the formation of DNA loops. These chromatin loops could connect

regulatory elements and gene promoters over large distances (Strom et al., 2012).

Depending on the nature of the regulatory elements, formation of chromatin loops may

either facilitate or inhibit transcription from the interacting promoter. Cohesin and CTCF act

together to form regions that isolate enhancers from interacting with inappropriate gene

promoters (Canela et al., 2017; Phillips-Cremins et al., 2013); this can lead to cell-type and

gene-type specific transcriptional programmes. Simonis et al., 2006 showed that the mouse β-

29

globin locus is active in the fetal liver and inactive in the fetal brain due to CTCF and cohesin

mediated regulation. CTCF binding motifs sit within the β-globin locus and CTCF binding

varies depending on the cell type; different CTCF binding patterns change the 3D structure of

the β-globin locus, consequentially altering regulation in a cell-specific manner (Phillips et al.,

2009; Simonis et al., 2006). In human cells, both CTCF and cohesin were shown to be

necessary for long-range loop formations that are cell-type specific (Hou et al., 2010).

Disease-causing mutations are also found in cellular machinery that maintain 3D

structure, such as cohesin and CTCF. Hnisz et al., (2016) reported leukaemia cell genomes

with recurrent microdeletions that modified CTCF boundary sites. These mutations were

sufficient to activate proto-oncogenes (Hnisz et al., 2016). Schierding et al., (2020) highlighted

that several genes that are coregulated with cohesin loci are themselves intolerant to loss-of-

function mutations. These results emphasised the significance of correct regulation of cohesin

genes and indicated new pathways of cohesin related disease progression (Schierding et al.,

2020). Mutations in cohesin could inhibit cohesin binding to regulatory elements, thus varying

interactions with promoters and consequent gene expression. Alternatively, mutations in

regulatory elements to which cohesin binds could affect the binding of regulatory proteins and

modify transcription of the regulatory element’s gene target. Katainen et al., (2015) reported

recurrent mutations to CTCF and cohesin binding sites in cancer samples (Katainen et al.,

2015).

1.5.2 Cohesin and Runx1

Tissue-specific regulation of runx1 transcription in zebrafish is reliant on cohesin

(Horsfield et al., 2007). Horsfield et al., (2007) observed that a null mutation in the rad21

subunit of cohesin prevented runx1 expression in the haematopoietic mesoderm, but not in

Rohon-Beard neurons in the developing zebrafish embryo (Horsfield et al., 2007). This study

30

presented the first evidence that the transcriptional role of cohesin is tissue-specific in

haematopoietic precursors.

Subsequently, it was established that zebrafish runx1 is directly bound by cohesin and

CTCF at the promoters and contains an intronic regulatory element between runx1 promoters

(Marsman et al., 2014). Marsman et al., (2014) showed that depletion of cohesin in zebrafish

decreased the total amount of runx1 transcript produced overall, but the relative expression of

P1-regulated runx1 increased. Cohesin depletion in human myelocytic leukaemia (HL-60) cell

line enhanced RUNX1 transcription, implying that cohesin’s transcriptional role could be

conserved in humans (Marsman et al., 2014).

Down’s syndrome patients with acute megakaryoblastic leukaemia (DS-AMKL)

contain three copies of the RUNX1 gene (owing to trisomy 21), as well as a high frequency of

cohesin mutation (53% ) (Yoshida et al., 2013). As normal Runx1 expression requires cohesin,

the elevated rate of cohesin mutations in DS-AMKL patients indicates the strong possibility

that cohesin mutation is actively contributing to leukaemia progression (Leeke et al., 2014).

AML patients with mutations in genes regulating RNA splicing, chromatin (cohesin)

or in the RUNX1 gene have poor prognoses, with the various mutations contributing

independently and additively to the disease (Papaemmanuil et al., 2016). RUNX1 and cohesin

mutations co-occur or are enriched in 27-52% of AML samples (Heimbruch et al., 2021).

Mazumdar et al., (2015) showed that cohesin mutant cell lines with impaired differentiation

had increased chromatin accessibility and binding of ERG, GATA2 and RUNX1 (Mazumdar

et al., 2015).

Recently, Antony et al., (2020) showed that mutation of the STAG2 subunit of cohesin

in K562 cells disrupted RUNX1 expression during megakaryocyte differentation (Antony et

al., 2020). In the STAG2 mutant, RUNX1 showed increased chromatin accessibility. This

research also established that in wildtype K562 cells, RUNX1-P1 had a gradual increase in

31

transcription during phorbol 12-myristate 13-acetate (PMA)-stimulated megakaryocyte

differentiation. However, STAG2 mutants cells showed an aberrant spike in transcription of

RUNX1-P2 6-12h post-PMA stimulation (Antony et al., 2020). After 48h stimulation the

aberrant RUNX1 transcription observed in STAG2 mutant cells had returned to baseline. This

implies that the initial response to PMA stimulation in cohesin mutant cells (that have increased

chromatin accessibility at RUNX1), leads to unrestrained transcription (Antony et al., 2020).

Antony et al., (2020) decreased BRD4 binding and suppressed enhancer-driven

transcription by using a bromodomain and extra-terminal motif (BET) inhibitor protein called

JQ1 (Antony et al., 2020). BRD4 is a bromodomain-containing protein that interacts with

active enhancers (Bhagwat et al., 2016). When wildtype cells were JQ1-treated concurrently

with PMA, there was reduced RUNX1-P2 and ERG expression in parental cells, and

dramatically dampened aberrant expression peaks in STAG2 mutant cells. RUNX1-P1

expression is essential for megakaryocyte development and its transcription was completely

blocked by JQ1 in both parental and STAG2 mutant cells. These results suggest that STAG2

depletion deconstrains the chromatin surrounding RUNX1, which produces aberrant enhancer-

amplified transcription in response to differentiation signal. This study also highlighted that

the correct expression of RUNX1 is enhancer-driven.

Ochi et al., (2020) observed reduced enhancer–promoter loops in Runx1 and Stag2

double knockout mouse models. These double knockouts lead to myeloid-skewed expansion

of hematopoietic stem/progenitor cells (HSPC) and myelodysplastic syndromes (MDS) in

mice. The reduced loops were associated with genes important for regulation of HSPCs; these

same genes were downregulated in cohesin-mutated leukaemia samples (Ochi et al., 2020). In

the absence of Stag2 in HSPCs, Stag1 can still maintain TAD boundaries, however Stag1

cannot retain intra-TAD interactions, at differentiation genes (Viny et al., 2019).

32

The occurrence of RUNX1 and cohesin dysregulation in AML subtypes and in vitro

models provides further evidence that these two factors are likely to play important roles in

normal and abnormal haematopoiesis. Additionally, these studies support previous work that

suggests cohesin mediates interactions between cis-regulatory elements and the promoters of

Runx1 (Antony et al., 2020; Marsman, 2016; Marsman et al., 2014; Ochi et al., 2020).

1.5.3 Regulatory elements

Crucial to the development of multicellular organisms is the ability of cells to acquire

new cell-fates. Cell-fate decisions are driven by environmental cues that activate signal

transduction in the nucleus – the environmental cues initiate transcription factors that define

the specific gene-expression program for each cell type. Transcription factors take effect by

binding to defined DNA motifs within gene regulatory elements to control the rates of

transcription (Griffiths et al., 1999). There are two main types of protein regulated transcription

elements: enhancers and insulators (Figure 1.8).

1.5.3.1 Enhancers

Enhancers are regions characteristically found in non-coding DNA regions that

positively regulate promoters to stimulate transcription by recruiting specific transcription

factors, chromatin remodelling proteins, and RNA polymerase II (Marsman et al., 2012).

Enhancers are generally identified by distinct histone modifications including; histone H3

lysine 4 monomethylation (H3K4me1), histone H3 lysine 4 dimethylation (H3K4me2) histone

H3 lysine 4 trimethylation (H3K4me3), histone H3 lysine 9 acetylation (H3K9ac), histone H3

lysine 36 monomethylation (H3K36me1) and histone H3 lysine 27 acetylation (H3K27ac)

(Ong et al., 2012; Zentner et al., 2011). These histone marks determine which regions of DNA

are accessible for transcription factor binding (Gates et al., 2017; Lawrence et al., 2016). The

pattern of histone marks suggests whether a region is active, inactive, or poised, and

accordingly whether the enhancer status correlates with gene activity. Active transcription is

33

associated with histone marks such as H3K4me3 and H3K9ac at active promoters, H3K4me1

and H3K4me2 at active enhancers, and H3K27ac at both active promoters and enhancers.

Heterochromatic regions are zones of gene inactivation and are associated with H3K27me3

(Heintzman et al., 2007; Kolovos et al., 2012; Pokholok et al., 2005). Active enhancer

sequences are characterised by bound RNA polymerase II (RNA Pol II) and can produce

enhancer RNAs (eRNA) that contribute to gene regulation (Peng et al., 2018). These regions

are often identifiable by transcription factor binding, acetyltransferase p300, cohesin, and/or

CTCF (Zentner et al., 2011). In addition, active enhancers usually lack nucleosomes and are

sensitive to DNase I digestion (Kolovos et al., 2012). Enhancers regulate spatiotemporal gene

expression, and therefore direct developmental programmes (Ong et al., 2012).

The location or orientation of an enhancer is independent of its ability to interact with

a promoter. Enhancers have been traditionally identified based on their ability to drive

transcription in reporter gene assays (Peng et al., 2018). Enhancers can also be predicted by

computational methods, including: computational analysis of conserved non-coding sequence

and transcription factor binding motifs (Pennacchio et al., 2006; Visel et al., 2008; Woolfe et

al., 2004); analysis of chromatin immunoprecipitation and sequencing (ChIP-seq) to locate

binding of transcription factors, mediators, and histone modifications at the regions of interest

(Murakawa et al., 2016; Visel et al., 2009); chromatin accessibility assays to identify areas of

open chromatin (DNase-seq, ATAC-seq) (Buenrostro et al., 2013; Thurman et al., 2012);

detection of enhancer RNAs such as cap-analysis gene expression (CAGE) (Andersson et al.,

2014); revealing promoter-enhancer interactions using chromosome conformation capture (3C,

R3C, 4C, 5C, Hi-C, Promoter capture) (Dekker et al., 2002; Dostie et al., 2006; Lieberman-

Aiden et al., 2009; Simonis et al., 2006); and chromatin interaction analysis using paired-end

sequencing (ChIA-PET) (Fullwood et al., 2009).

34

1.5.3.2 Silencers and insulators

Conversely, suppression of gene expression during differentiation and cell cycle

progression is facilitated by silencers and insulators. Silencers prevent gene expression by

producing non-coding transcripts that recruit polycomb repressor complexes to target genes

(Boyer et al., 2006).

Insulators block the interaction between enhancers and promoters by confining regions

within repressed or active chromatin boundaries; insulators establish chromatin domains

(Kolovos et al., 2012; Levine, 2010; Splinter et al., 2006; West et al., 2002). Mutation of

insulators changes the pattern of gene expression and can lead to developmental defects

(Feinberg, 2007).

Figure 1.8: Cohesin mediated cis-regulatory DNA elements.

A) An enhancer (green) is recruited to activate gene expression through a physical interaction with the gene promoter (black arrow). Transcription factors (blue) are often present for binding. B) An insulator (orange) excludes the enhancer from interacting with the promoter, which prevents expression. CTCF (red) can demarcate domains of active and inactive chromatin by binding with cohesin (multi-coloured ring) and forming long-range loop formations. Figure is from Thomas (2015).

The three-dimensional genome conformation is crucial for efficient gene regulation.

CTCF and cohesin help mediate enhancer-promoter, insulator-insulator and insulator-promoter

interactions (Raab et al., 2010).

35

1.5.4 Techniques to determine three-dimensional conformation

A significant portion of our understanding of nuclear organisation has been discovered

through microscopic study of the nucleus, using techniques like fluorescent in situ

hybridization (FISH)(Dekker et al., 2015). However, as the resolution of these techniques is

low, it is difficult to reliably distinguish between a looped or linear conformation of two regions

(Williamson et al., 2014).

The majority of recent discoveries of sequence-specific chromatin structure have been

made using chromatin capture techniques which map physical genome interactions (Sanyal et

al., 2011; Splinter et al., 2011). 3C, 4C, 5C, and Hi-C involve chemical cross-linking of the

genomes capturing regions that are in close proximity, fragmenting chromatin (by digestion

with restriction enzymes), religating cross-linked regions, and identifying the interacting

regions by DNA sequencing (Hakim et al., 2012; Sanyal et al., 2011; Splinter et al., 2011).

Most of these methods allow the unbiased and genome-wide mapping of interactions without

prior knowledge of partners, thus permitting the discovery of new interactions.

Chromosome conformation capture (3C) investigates previously selected regions and

determines if there is an interaction between them (one versus one)(Dekker et al., 2002).

Circular chromosome conformation capture (4C), is utilised to investigate all interactions with

a chosen region, or ‘bait’, and can be detected through the use of next-generation sequencing

(one versus many)(Simonis et al., 2006; Zhao et al., 2006). 4C is typically used to identify all

genomic loci that interact with a particular gene or regulatory element. Chromosome

conformation capture carbon copy (5C) detects all interactions within a genomic region (many

versus many)(Dostie et al., 2006). Hi-C is determined from a promoter perspective and is used

to analyse all interactions within a genome (Lieberman-Aiden et al., 2009). Hi-C allows for

high throughput analysis and genome interaction maps to be created, which is valuable for

36

determining genomic structure. However, detection of specific local interactions is limited by

its resolution, detection of local interactions is better achieved using 4C.

1.5.5 Genome structure and disease

Recent research shows that disrupting the boundaries of TADs severely alters the

spatial organisation of a locus, which allows ectopic enhancer-promoter interactions and leads

to aberrant expression of target genes (Gómez-Marín et al., 2015; Lupiáñez et al., 2015).

Gómez-Marín et al., 2015 demonstrated that vertebrate Six genes share an evolutionarily-

conserved 3D chromatin architecture. When the conserved intergenic region containing the

TAD border between six3 and six2 was removed, there was a dramatic change to the gene

expression profile. This research demonstrates that evolutionarily-conserved TAD borders are

crucial to prevent competition between promoters for enhancers in opposing regulatory

landscapes (Acemel et al., 2017). This concept has been challenged by Williamson et al.,

(2019) who demonstrated that perturbations to the sonic hedgehog (Shh) TAD structure had

little or no detectable effect on Shh expression patterns or levels of Shh expression during

development (Williamson et al., 2019).

Multiple studies have shown the 3D context of the genome is a key determinant in the

development and progression of cancer. The probability of 3D contact between two loci

influences the extent of somatic copy number alterations in cancer cells (Engreitz et al., 2012;

Fudenberg et al., 2011; Zhang et al., 2012). Physical proximity is a major player of genomic

rearrangements which result in translocations (Chiarle et al., 2011; Klein et al., 2011).

Mutations to cohesin and CTCF have been shown to dysregulate 3D organisation of the

genome and perturb normal development (Anania et al., 2020; Hnisz et al., 2016).

Hypermethylation of specific CTCF sequences resulted in a loss of CTCF at a domain

boundary, which caused aberrant expression of an oncogene (Flavahan et al., 2016).

37

Northcott et al., (2014) and Weischenfeldt et al., (2017) describe somatic variants that

disrupt genome organisation, and result in enhancer jacking in cancer cells (Northcott et al.,

2014; Weischenfeldt et al., 2017).

Disease-associated variants are found in regulatory elements and in genes with equal

frequency, making these regions highly relevant to cancer (Ernst et al., 2011). More than 95%

of genome-wide association studies (GWAS) single nucleotide polymorphisms (SNPs) are

located in intergenic regions, of which over 75% are associated with open chromatin (DNase I

HS sites) implying a strong association to regulatory elements (Maurano et al., 2012). A variant

in a regulatory region may cause differential regulation of a cancer gene, and could be

functionally equivalent to a mutation in the coding sequence of the cancer gene itself. Recent

studies have shown alterations to regulatory regions reported in cancers (Zhang et al., 2020).

It is therefore possible that mutations in enhancers or insulators may change their function, and

subsequently could lead to altered regulation of key blood development gene(s) and the onset

of leukaemia (Stranger et al., 2007).

Understanding the connection between the 3D organisation of the genome and its

underlying biological function will allow a better interpretation of human pathogenesis.

1.5.6 Regulatory regions of Runx1

Both the specific haematopoietic and non-haematopoietic expression of Runx1 appear

to rely on enhancer elements, as the Runx1 promoters by themselves in vivo are unable to drive

any Runx1-specific activity (Bee et al., 2009a). Antony et al., (2020) highlighted the enhancer

regulated activity in aberrant megakaryocytic differentiation (Antony et al., 2020).

Understanding Runx1 enhancers and how they contribute to Runx1 organisation is imperative

to understanding the regulatory landscape of Runx1.

Numerous groups recognised an intronic enhancer of Runx1 specific to haematopoietic

cells. This mouse regulatory region is variously termed +23/ +24/eR1/RE1 (Bee et al., 2009a;

38

Koh et al., 2015; Markova et al., 2011; Ng et al., 2010; Nottingham et al., 2007). Significantly,

this enhancer was shown to be active in haemogenic endothelial cells and haematopoietic cell

clusters in the AGM equivalent region in both mice and zebrafish (Markova et al., 2011; Ng et

al., 2010; Schütte et al., 2016). Bee et al., (2009) showed the enhancer worked with both

promoters to drive HSC-specific gene expression (Bee et al., 2009a). Interestingly, this well-

known enhancer was also described as a silencer in HEK293 cells (Markova et al., 2011).

Other putative regulatory elements have been identified upstream of Runx1 and

between the P1 and P2 promoters in humans and mice (Figure 1.9)(Cheng et al., 2018; Gunnell

et al., 2016; Markova et al., 2011; Marsman, 2016; Marsman et al., 2017; Ng et al., 2010;

Schütte et al., 2016; Wilson et al., 2016).

Schütte et al., (2016) and Ng et al., (2010) identified 12 possible regulators of Runx1

in mice, with some regions functionally validated (Ng et al., 2010; Schütte et al., 2016). My

previous masters project investigated the functions of the putative regulatory regions described

by Ng et al., (2010) (Thomas, 2015). Nine further putative regulators were identified because

they physically interacted with Runx1 P1, P2 or +24 enhancer in mouse hematopoietic

progenitor cells (Marsman, 2016) (Figure 1.9A).

In humans, the interactions between RUNX1 promoters and two regulatory regions were

identified using chromosome conformation capture for two regions (Cheng et al., 2018;

Markova et al., 2011). Gunnell et al., (2016), characterised the functions of three potential

RUNX1 regulatory elements (Gunnell et al., 2016) (Figure 1.9B).

39

Figure 1.9: Identified Runx1 regulatory regions.

Regulatory elements are annotated with orange circles, grey boxes annotate exons. The blue boxes show the exons encoding the Runt binding domain. The two Runx1 promoters are represented by black right angled arrows A) Identified Human RUNX1 regulatory elements B) Identified mouse Runx1 regulatory regions.

However, not all of these elements have been tested for regulatory functionality, nor

examined for Runx1 promoter-enhancer interactions using chromosome conformation capture.

Interaction between regulatory elements and promoters of the Runx1 gene cannot be predicted,

as less than 50% of enhancers contact the nearest gene promoter. Previous research has used

zebrafish to characterise the functional roles of regulatory elements in vivo and demonstrated

the value of this animal model for studies of non-coding DNA (Bessa et al., 2009; Marsman et

al., 2014).

The spatiotemporal regulation of Runx1 is beginning to be understood; it is possible

that mutations in regulatory regions may explain AML progression in patients who have no

known protein-coding mutations, and perhaps in patients who have cohesin mutations.

1.6 Zebrafish as a model organism

The small tropical zebrafish (Danio rerio) is an important model organism for a variety

of studies. As developmental genes are highly conserved amongst vertebrates, development of

zebrafish can be compared to that of the mice and humans (Bopp et al., 2006; Kari et al., 2007;

Lieschke et al., 2007). Advantages of using zebrafish as an animal model for research include

the zebrafish’s rapid development and ability to lay hundreds of eggs in each mating, allowing

40

for large sample sizes. Additionally, their small size means they are simple and cost-efficient

to maintain and breed. In contrast to mammals, zebrafish eggs are externally fertilised and the

resulting embryos develop outside of the mother. The embryos are transparent, which allows

development to be easily visualised through a microscope (Teittinen et al., 2012).

Zebrafish develop rapidly, with fish reaching sexual maturity by 3-4 months post-

fertilisation. All major organs are formed by 5 days post-fertilisation (dpf) (Haffter et al., 1996)

(Figure 1.10). The zygote remains at the one-cell stage for approximately 40 minutes, allowing

for injections of reporter constructs, which can reveal gene expression in the developing

embryo. At ten hours post-fertilisation (hpf), somites and organs begin to form (Albacker et

al., 2009). The zebrafish begin to hatch from their clear chorions at 48 hpf, and they are

swimming freely by three dpf (Bopp et al., 2006).

41

Figure 1.10: Diagram of normal Danio rerio (zebrafish) development.

A) 1-cell stage embryo approximately 20 minutes post fertilisation (pf). B) 2-cell stage embryo approximately 40 minutes pf. C) Zebrafish embryo in clear chorion approximately 36 hours pf. D) Zebrafish embryo hatched from chorion approximately 48 hours pf. E) Zebrafish embryo 3-5 days pf. F) Zebrafish reach adulthood at approximately 3-4 months pf. Figure is from Thomas (2015).

Although the fish genome is immensely rearranged compared to the human genome,

several areas of local synteny and larger chromosomal regions are preserved (Martin et al.,

2011). The entire zebrafish genome has been sequenced and 70% of human genes have at least

one obvious zebrafish orthologue (Howe et al., 2013).

42

1.6.1 Haematopoiesis in zebrafish

The combination of visualisation, manipulation, and large-scale capabilities of

zebrafish allows for novel genome-wide studies. Zebrafish are particularly informative for the

analysis of haematopoietic development as blood circulation begins around 23-26 hpf within

the transparent embryos (Albacker et al., 2009). Comparable to other vertebrates,

haematopoiesis in zebrafish occurs in primitive and definitive waves (Paik et al., 2010;

Rasighaemi et al., 2015). The zebrafish primitive wave of haematopoiesis arises within the

intermediate cell mass (ICM) and generates primitive erythrocytes and limited myeloid

populations (Figure 1.11A). The ICM is functionally analogous to the embryonic yolk sac in

mammals (Davidson et al., 2004). These blood cells are seen in circulation until about four

dpf. The first definitive HSC markers are seen at 26 hpf, in the ventral wall of the dorsal aorta

(Figure 1.11B) (Kulkeaw et al., 2012; Orkin et al., 2008). The ventral wall of the dorsal aorta

of zebrafish is the equivalent of the AGM in mammals (Martin et al., 2011). HSCs emerge by

endothelial-to-haematopoietic transition in the dorsal aorta in zebrafish (Julien et al., 2016).

Finally, in zebrafish the permanent niche of HSCs is the kidney marrow (Figure 1.11C),

however in mammals, the permanent location for HSCs to seed is in the bone marrow

(Albacker et al., 2009).

43

Figure 1.11: Schematic of haematopoiesis cell generation locations during zebrafish development.

A) Primitive haematopoiesis occurs in the intermediate cell mass (blue) 12 hours post fertilisation (pf). B) Definitive haematopoiesis begins first in the ventral wall of the dorsal aorta (purple) 26 hours pf, then in the caudal haematopoietic tissue (orange) 2 days pf and finally in the kidney marrow (pink) 4 days pf. C) Kidney marrow (pink) is the life-long source of adult definitive haematopoiesis. Figure is from Thomas (2015).

1.7 Overall hypothesis and aims of this PhD

Runx1 has differing isoforms which are crucial to distinctive developmental and cell

cycle processes. Runx1 is essential for definitive haematopoiesis and dysregulations of RUNX1

are frequently associated with AML progression. Despite the well-established role of RUNX1

in haematopoiesis and disease contribution, there is limited understanding of the regulatory

mechanisms that drive cell-specific expression.

Previous identification of the +24 Runx1 regulatory element as an enhancer in

haematopoietic cells in zebrafish and mice, as well as dysregulation of a RUNX1 silencer

element in a leukaemia patient has highlighted the importance of regulatory elements in driving

correct RUNX1 expression. RUNX1-P1 expression is essential for megakaryocyte development

44

and is driven by enhancers. Investigating 3D structure of RUNX1 and functions of possible

regulatory elements may reveal important regulatory connections and provide rationale for

disease progressions.

Cohesin is crucial in correct genome organisation and gene regulation, and enhancer-

promoter interactions. Cohesin mutations are frequently seen in myeloid malignancies and in

several solid tumour types. The occurrence of RUNX1 and cohesin dysregulation in AML

subtypes and in vitro models suggests these two factors are likely to play important roles in

normal and abnormal haematopoiesis.

The overall aim of my study is to explore the potential of regulatory regions and cohesin

to modulate the transcription of Runx1.

I hypothesise that regions that interact with Runx1 promoters and Runx1 +24 enhancer

may have enhancer capabilities, and some may be conserved in humans. Furthermore, I predict

RUNX1 +24 is an important regulator of RUNX1 expression during megakaryocyte

differentiation, and coupled with reduced cohesin the correct genomic organisation cannot be

maintained.

To examine this hypothesis, first, I analysed the putative Runx1 regions

bioinformatically, to establish genomic characteristics and conservation, in order to determine

enhancer potential. I subsequently tested regions identified by 4C for functional ability and

cell-type specific regulation using zebrafish reporter assays (Chapter 3).

Second, I investigated RUNX1 +24 structural and genomic characteristics. I then

identified the significant 3D connections of RUNX1 +24 using chromosome conformation

capture (Chapter 4).

Third, to elucidate important connections of RUNX1 +24 during normal megakaryocyte

differentiation, I used chromosome conformation capture of megakaryocyte differentiation

time points and compared them to the baseline RUNX1 +24 interactions. To understand how

45

cohesin and 3D chromosome organisation influences RUNX1 transcription and impacts disease

progression, I compared the wild type differentiation interaction profiles to STAG2 mutant

cells at the same time points of PMA stimulation (Chapter 5).

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2 Chapter 2: Methods

2.1 Ethics consent and protocol approval

2.1.1 Zebrafish

The University of Otago Animal Ethics Committee approved all zebrafish work (AEC

#95/14). University of Otago Animal Welfare Office Animal Ethics Training was undertaken

and any manipulations involved larvae no older than 30 days. Relevant work with transgenic

Zebrafish was approved by IBSC/EPA (GMC100225, GMD101727).

2.1.2 Escherichia coli (E. coli)

All E.coli work was undertaken under the approval codes GMC100216, GMC100219,

GMD101715, GMD101717.

2.1.3 Cell culture

All work with human cell lines was carried out under approval codes GMC100228,

GMD101730. Animal cell line work was performed under approval codes GMC100227,

GMD101729.

2.2 Materials

All materials, solutions and buffers used in this study are listed in Appendix I.

2.3 Molecular biology techniques

The mouse regulatory elements were previously identified in Ng et al., (2010) and in

Judith Marsman’s PhD (Marsman, 2016).

2.3.1 Plasmids and reporter constructs

Plasmids used in this project are listed in Table 2.1.

To determine the regulatory potential of selected mouse regulatory regions in vivo,

Zebrafish enhancer detector vector (ZED) and pGL4.23-GW reporter constructs were used.

47

mouse regulatory elements were previously cloned into ZED and pGL4.23-GW(Marsman,

2016; Thomas, 2015).

Maps of all plasmids are shown in Appendix II.

Table 2.1: Plasmids used in this study

Plasmid Relevant characteristics

Antibiotic selection

Source

Zebrafish enhancer reporter plasmids Zebrafish enhancer detector vector (ZED)

RFP expression across somites as internal control. Cell-specific GFP expression when an enhancer is cloned into vector.

Ampicillin José Bessa lab, Institute for Molecular and Cell Biology, Portugal (Bessa et al., 2009)

Zebrafish mRNA plasmid pCS-TP Tol2 mRNA plasmid

for integration of enhancer DNA into zebrafish

Ampicillin (Kawakami, 2005)

Cell Culture enhancer reporter plasmid

pGL4.23-GW Luciferase assay reporter construct

Ampicillin Jorge Ferrer lab, Imperial Centre for Translational and Experimental Medicine, Imperial College London (Pasquali et al., 2014)

Cell culture Luciferase control plasmid

pRL-TK Renilla control plasmid for luciferase assay

Ampicillin Promega

Control plasmid

pUC19 Control plasmid for transformation and transfection

Ampicillin Addgene

2.3.2 Nanodrop

Quantity and quality of plasmid DNA, genomic DNA or RNA was measured by

spectrophotometry, using a Nanodrop ND-1000, version 3.81 Spectrophotometer. 1 µL of

48

DNA or RNA was added to the well to measure sample purity and concentration by the

absorbance spectrum of UV-visible light.

2.3.3 Agarose gel electrophoresis

To determine if the production of PCR, mRNA or a restriction digest was successful,

products were run on an agarose gel. Unless otherwise stated 2 µL of PCR, mRNA or restriction

digest product was mixed with 8 µL of 1x loading dye and loaded onto a 60mL 1% agarose

gel, with 1x SYBR Green I (ThermoFisher) added before the gel set. A 1 kb plus DNA ladder

(Invitrogen) was used as a marker of band sizes. Gels were run at 100 Volts for 1 hour, then

visualised and photographed using a BioRad UV light transilluminator.

2.3.4 Primer design

To determine the best primers, the online program Primer3 was employed (Koressaar

et al., 2007; Untergasser et al., 2012). Primers were selected based on optimised efficiency (i.e.

Primer GC%, 18-13 bp length) and ordered from Integrated DNA Technologies.

2.3.5 Sequence confirmation and alignment

All sequence confirmations were carried out by Genetic Analysis Services (Department

of Anatomy, University of Otago) according to their protocol using a capillary ABI 3730xl

DNA Analyser. 200 ng of plasmid DNA was supplied for sequencing. Primer list is provided

in Appendix III.

To align sequences to their appropriate reference genome the sequences BLAST-like

alignment tool (BLAT) was applied (Kent, 2002). Sequences were viewed using 4Peaks

(Griekspoor et al., 2005). Ambiguous base calls were examined by eye and manually corrected

if necessary.

49

2.3.6 Measuring Runx1 +24 enhancer G-quadruplexes

To test the if Runx1 +24 enhancer contains G-quadruplex structures Circular dichroism

(CD) photospectrometer was used.

2.3.6.1 Runx1 +24 enhancer G-quadruplexes Oligo design

The G-quadruplex oligos were designed directly from the mouse Runx1 +24 predicted

G-quadruplexes (Appendix IV). A negative control Runx1 +24 oligo was also designed. The

negative oligo should not be able to function as a G-quadruplex.

Oligos (100 nM) were commercially synthesised (Integrated DNA Technologies,

Singapore), and reconstituted in nuclease-free H2O.

Table 2.2: Runx1 +24 enhancer G-quadruplexes Oligo

Oligo Sequence Runx1 +24 G-quadruplexes Oligo GGTGGGGGTGGG Runx1 +24 negative Oligo GGTGTGTGTGGG

2.3.6.2 CD Spectrophotometer protocol

To determine if the predicted G-quadruplexes Runx1 +24 enhancer are present in vitro,

CD spectrometer readings were used to measure ellipticity.

4 µM of each oligonucleotide was heated to 95 °C for 10 minutes then left to cool

(slowly in the heating block) to room temperature (22 °C) overnight for CD analyses the

following day. CD experiments were carried out in different buffers: 100 mM KCl pH 7

(positive control buffers) 100 mM NaCl, and 100 mM LiCl (negative control buffer). The

desired buffer pH was adjusted by relative quantities of Na2H2PO4 /Na2HPO4.H20 and

determined using a pH meter.

CD measurements were performed on a CD Spectrophotometer (Department of

Biochemistry, University of Otago) with a 1 mm path length quartz curvette (approx. 8000

µL). CD spectra were collected across 340 nm to 220 nm in 1 nm increments (continuous

50

scanning) and the reported spectra corresponded to the average of at least two scans. An

appropriate buffer blank correction was used for all spectra. The scanning speed of 200

nm/minute was used for preliminary investigations, with a response of 1 second and a band

width of 1 nm.

CD melting studies were performed to measure if there was a change in ellipticity as a

function of temperature across all wavelengths. Temperature was increased from 25 °C to 95

°C at a rate of 0.25°C min-1.

2.3.7 Site-Directed Mutagenesis of Runx1 +24 enhancer

To test whether the G-quadruplexes were functionally important for Runx1 +24

enhancer capability, pGL4.23-Runx1+24 G-quadruplex sites were mutated. Quikchange II

Site-Directed Mutagenesis Kit (Agilent Technology) was used to mutagenise the Runx1+24

enhancer within the pGL4.23-Runx1+24.

2.3.7.1 Runx1 +24 enhancer Site-Directed Mutagenesis primer design

Primers were designed using the online program QuikChange Primer Design

(https://www.agilent.com/store/primerDesignProgram.jsp) to ensure the desired mutation

(deletion or insertion) were in the middle of the primer with ~10-15 bases of correct sequence

on both sides and had a minimum GC content of 40%. The list of primers is available in

Appendix III.

2.3.7.2 Mutant Strand Synthesis Reaction (Thermal Cycling)

To generate point mutations in pGL4.23-Runx1+24, the mutant strand synthesis

reaction was PCR amplified. PCR amplification was performed in an Applied Biosystem

Thermal Cycler 2.08 as follows: 95 °C for 30 seconds; 12 cycles of 95 °C for 30 seconds, 55

°C for 1 minute, 68 °C for 4 minutes, finally 72 °C for 2 minutes. Following temperature

cycling, the reaction was cooled on ice for 2 minutes.

https://www.agilent.com/store/primerDesignProgram.jsp

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Table 2.3: Mutant strand synthesis reaction

Ligation components Amount per 50µL (reaction volume)

PfuUltra HF DNA polymerase reaction buffer (10x) 5 µL pGL4.23-Runx1+24 50 ng Oligonucleotide primer 2 125 ng Oligonucleotide primer 2 125n g dNTP mix 1 µL nuclease-free H2O to 50 µL PfuUltra HF DNA polymerase (2.5U/µL) 1 µL

2.3.7.3 DnpI Digestion of the Mutant Strand Amplification Products

1 µL DpnI (10U/µL) was added to the amplification reactions and incubated at 37 °C for 1

hour to digest any nonmutated supercoiled template dsDNA. Plasmids were then transformed

into competent cells (Section 2.3.8).

2.3.8 Transformation of plasmids in competent cells

All bacterial work was carried out using standard aseptic techniques.

Competent Escherichia coli (E. coli) STABLE3 (One Shot) and XL1 Blue (One Shot)

cells were used for transformation procedures. STABLE3 and XL1 Blue competent cells were

prepared in house.

pUC19 was used as a positive control for all transformations. All plasmid

transformations were carried out with a LacZ recombination positive control and MilliQ H2O

negative recombination control.

1 µL plasmid DNA was added to a vial of E. coli competent cells and mixed gently and

incubated on ice for 15 minutes. Samples were transferred to a water bath at 42°C for 30

seconds before placing on ice. 250 µL prewarmed Luria Broth (LB) was added and cells were

incubated at 37 °C for 1 hour with shaking (200 rpm).

After transformation, cells were spread on agar plates containing appropriate antibiotic,

Ampicillin (Sigma-Aldrich). The plates for the LacZ transformation control contained 50 µL

of 20 mg/mL X-gal (Sigma-Aldrich). 50 µL of competent cell mixture was spread on the first

52

half of the prewarmed plate, before cells were pelleted by centrifugation. Most of the

supernatant was discarded, and all remaining cells were resuspended in the remaining

supernatant and spread on the other half of the selective plate. Plates were incubated at 37 °C

overnight.

Recombination efficiency was identified by the blue/white colony ratio on the LacZ

plate. 2-4 individual colonies from successful transformations were picked with a wire loop.

Bacteria were then incubated at 37 °C overnight with shaking (200 rpm) in starter culture (4

mL LB containing 100 µg/mL of the appropriate antibiotic).

To prepare a large amount of plasmid, 1 mL of starter culture was added to 99 mL of

LB containing 100 µg/mL of the appropriate antibiotic, this was incubated at 37 °C overnight

with shaking (200 rpm).

To establish glycerol stocks of each successful transformation, 750 µL of the starter

culture was combined with 250 µL of glycerol (VMR Prolabo) and stored at -80°C.

2.3.9 Plasmid purification preparation

After glycerol stocks were made, the remaining bacterial culture was centrifuged at

13,300 g for 1 minute and the supernatant was discarded. Plasmids were prepared using the

Qiagen plasmid Miniprep kit for initial restriction enzyme digestion or sequencing

confirmation. Preparation of plasmids for experiments used the Macherey-Nagel maxi prep kit;

both were executed according to manufacturer’s manual. Plasmid DNA was eluted in 40 µL or

500 µL of elution buffer (5 mM Tris-HCl pH 8.5) respectively and stored at -20 °C.

2.3.10 Tol2 transposase mRNA synthesis

Tol2 sites permit transposon-mediated integration into the zebrafish genome with high

efficiency in the presence of Tol2 transposase mRNA (Kawakami, 2005). In order to

successfully integrate ZED reporter construct into zebrafish host genome, Tol2 mRNA is

53

required (Bessa et al., 2009). Ambion mMESSAGE mMACHINE® kit was used for in vitro

synthesis of large amounts of capped mRNA. To confirm successful mRNA synthesis the

product was run on a 1% agarose gel according to section 2.3.3.

2.4 In vivo Zebrafish approaches

Feeding, maintenance and breeding were performed according to the Otago Zebrafish

Facility Standard Operating Procedures.

2.4.1 Zebrafish strains

The strains of Zebrafish used in this project were wild-type (WT) AB/PS, and (WT)

WIK. AB/PS is an in-house line, previously created by outcrossing an AB fish with a pet-shop

fish. Transgenic lines created in this project were outcrossed onto Tübingen long fin (TL) fish

for easy differentiation between the transgenic and WT lines.

2.4.2 Maintenance

Zebrafish (Danio rerio) were housed in the Otago Zebrafish Facility (OZF), Pathology

Department, University of Otago, Dunedin. Each tank was kept at a temperature of 24-30 °C,

pH of 6.5-8.5 and a conductivity of 200-1000 µS. Water in the facility was purified using

ZebTEC system (Techniplast) of biological filters and UV light. Stocking density was

maintained at or below the maximum number of fish per 3.5 L tank; 15-20 adults or 20-30

younger fish (5-40 days old).

2.4.3 Feeding

Zebrafish were fed three times a day; two dry feedings and one live feeding. The dry

feed (ZM Ltd) was given morning and evening. The live feed was given to adult Zebrafish in

the afternoon and consisted of artemia (brine shrimp). Younger fish were given rotifers, as they

are smaller than artemia.

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2.4.4 Breeding and embryo collection

Wild type Zebrafish breeding pairs were set up in specialised breeding tanks the

afternoon prior to embryo collection. The tanks contain a false bottom and a removable barrier

to separate male and females. The false bottom allows the eggs to fall through once they are

laid to prevent the parents consuming them. The natural internal breeding cue is light. The OZF

has automated lighting; from 10 pm until 8 am it is dark. Removal of barriers allows the

zebrafish to mate. To regulate breeding, barriers are removed from the tank just prior to

collection.

Embryos are collected through a fine sieve 10 minutes after laying, washed twice with

system water and transferred to a petri dish with E3 embryo medium. E3 medium promotes

growth and survival. Methylene blue (Sigma-Aldrich) is also added as it acts as an anti-fungal

agent, as zebrafish eggs are prone to fungal infection.

2.4.5 Growing embryos

Embryos are grown in a petri dish in E3 medium at 28.5 °C for normal development or

22.5 °C for slower development. The embryos hatch naturally at 2-3 days post fertilisation or

the chorion can be removed earlier using #55 Dumont Forceps. When the chorions are

discarded the media is changed to E3 without methylene blue.

2.5 Zebrafish microinjection

2.5.1 Needle pulling and calibration

A 1.0 mm OD borosilicate glass capillary (Harvard Apparatus) was pulled to form 2

needles using pulling programme 6 (P= 500, Heat= 600, Pull= 50, Vel = 75, Time= 120) on a

Sutter p-97 Flaming/Brown glass micropipette puller.

Injection solutions were well mixed and 2 µL loaded with a microloader pipette into

the needle. The needle was placed in the needle holder of an air-pressure MPPI3 system. 10

55

µL of Halocarbon oil 27 (Sigma-Aldrich) was pipetted onto a stage micrometre calibration

slide. Pressing the injection pedal released a droplet of injected solution, which floated on the

oil. The drop was measured to ensure it was 1 nl (Figure 2.1). Clean forceps were used to trim

needle tip and pulse duration was adjusted to change drop size when necessary. The pulse

duration of the pressure injector was set between 4.5 and 6.5 to minimise needle breakage and

embryo damage.

Figure 2.1: Calibration slide with close up of 1 mm scale, each division is 0.01 mm.

The grey circle represents the size of a 1 nl droplet

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2.5.2 Microinjection

Injections were achieved by lining embryos up on a ridged plate and, under

magnification of a Leica M80 microscope, guiding the needle tip through the chorion to pierce

the yolk sac.

ZED injections were optimised and the following concentrations were injected 30ng/µL

ZED vector DNA, 120 ng/µL Tol2 transposase mRNA and Ultrapure DNase – RNase free

distilled H20.

The vector solutions were directly injected into the yolk sac at the one cell stage, to

ensure the dividing cell could uptake and integrate the plasmid into the host genome. For each

injected group, uninjected controls of the same batch were kept to observe normal development

and death rates.

2.5.3 Microscopy

In order to screen embryos for fluorescence, embryos were anaesthetised in 0.5ng/µL

tricaine and placed on a microscope slide. GFP3 and dsRED2 filters were applied to view

fluorescence.

If fluorescence was observed, samples were removed from E3 tricaine solution and

embryos were mounted in 3% methylcellulose (Sigma-Aldrich). The dechorionated embryos

were orientated with anterior on the left and dorsal at the top. Embryos were first imaged with

bright field, and then filters were employed to photograph the fluorescence.

Images were taken using a Leica M205 fluorescence microscope on the Leica

Application software.

2.6 Transgenic zebrafish analysis

To find out the function of the putative enhancers identified by Dr. Judith Marsman

ZED zebrafish reporter assays were used. DNA sequences of the putative enhancers were

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previously cloned into ZED reporter constructs, then injected into the first cell of zebrafish

embryos. The enhancer test vectors contain Tol2 transposon sites which allow the reporter gene

constructs to be integrated into the gDNA. Transient expression of assays can be analysed in

F0; however, the F1 generation contain a germ line integration of the constructs, to avoid the

problem of a mosaic expression pattern seen in F0.

2.6.1 Zebrafish enhancer assay

ZED F0 embryos were dechorionated then imaged at 20-24 hours post fertilisation (hpf)

or 40 hpf using a Leica M205FA stereomicroscope and were imaged with a DFC490 camera

and LAS software (Leica Microsystems), images were processed using Adobe Photoshop.

ZED F1 embryos were also viewed at 20-24 hpf or 40 hpf 72 hpf and embryos with

were examined for GFP.

2.7 In vitro Cell culture and maintenance of cell lines

2.7.1 Cell lines

Cell lines were stored in a Thermo Scientific liquid nitrogen dewar. All cells were

maintained at 37 °C in a humidified environment with 5% CO2 incubator

HPC7 cell line was cultured in Isocove’s Modified Dulbecco’s Media (IMDM)

containing 10% fetal bovine serum (FBS) (Gibco, Life Technology) and 10% stem cell factor

(SCF) conditioned media, 1% Penicillin Streptomycin (P/S), 1.5x 10-4 M monothiolglycerol

(MTG).

BHK/MKL was cultured in Dulbecco’s Modified Eagle’s Medium (DMEM) containing

10% FBS. These cells contain a mouse SCF expression vector and the media collected from

these cell lines was used to produce SCF used for HPC7 media.

PNX and NIH/3T3 were cultured in DMEM containing 10% FBS.

K562 and K562 cell line variations were cultured in IMDM containing 10% FBS.

Differentiation to megakaryocytes was induced using 30 nM of phorbol 12- myristate 13-

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acetate (Kuvardina et al., 2015)(PMA, Sigma-Aldrich). Control treatment of K562 cell line

variations used Dimethyl Sulfoxide (DMSO)(Sigma-Aldrich), which was prepared in the same

way as PMA.

Table 2.4: Cell lines used in this study

Cell line Media Remarks References K562 (Chronic Myelogeneous Leukaemia cells)

IMDM + 10% fetal bovine serum (FBS).

Sourced originally from ATCC (p10)

(Lozzio et al., 1975)

K562 STAG2 R614* -/-

IMDM + 10% FBS

Generated by Dr. Jisha Antony, a Postdoc in the Horsfield lab (p20)

(Antony et al., 2020)

PNX (Phoenix cells)

DMEM + 10% FBS

Sourced originally from Braithwaite lab (Pathology department, University of Otago, Dunedin, New Zealand)

(Swift et al., 1999)

HPC7 (mouse haematopoietic progenitor cells)

IMDM +10% SCF + 10% FBS + 1% P/S, 1.5x 10-4 M MTG

Sourced originally from Kathy Knezevic (Lowy Cancer Research centre, Sydney, Australia)

(Kolterud et al., 1998)

BHK/MKL (Baby Hamster Kidney cells)

DMEM + 10% FBS

Sourced originally from Kathy Knezevic (Lowy Cancer Research centre, Sydney, Australia)

(Yusta et al., 1999)

NIH/3T3 (Mouse embryonic fibroblast cells)

DMEM + 10% FBS

Sourced originally from Braithwaite lab (p15) (Pathology department, University of Otago, Dunedin, New Zealand)

(Todaro et al., 1963)

2.7.2 Reviving cells from liquid nitrogen stocks

Cell vials were removed from liquid nitrogen stocks and transferred on ice to 37 °C

water bath. Vials were rapidly thawed and the cell suspension added to 10 mL of pre-warmed

media in 15 mL falcon tube. Cells were pelleted by centrifugation (250 g for 5 minutes) and

supernatant was aspirated. Cells were resuspended in 10 mL of fresh media and plated in a

medium culture flask.

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2.7.3 Splitting cells for new passage

2.7.3.1 Adherent cells (PNX, BHK/MKL, NIH/3T3)

The cells were allowed to form a monolayer and reach 70-90% confluence before

splitting for a new passage. The media was carefully aspirated and adherent cells were washed

with phosphate buffered saline (PBS). 0.005% trypsin (Life Technologies) was added and flask

was then incubated at 37 °C for 3-5 minutes. Media was added to flask to deactivate trypsin.

Cells were gently pipetted up and down to create a single cell suspension. Cells were spun at

250 g for 5 minutes and media was removed. The pellet was resuspended in fresh media to

create single cell suspension. 1 mL of cell suspension was added to a new culture flask with 10

mL fresh media to make a new passage. 1 mL of suspension was collected for cell counting

(Figure 2.2). Number of cells/mL was determined using a haemocytometer slide on a

microscope stage or Luna automatic cell counter.

2.7.3.2 Suspension cells (K562, HPC7)

Cells were observed every day for signs of growth. 1 mL of cell suspension was counted

each day to determine growth patterns and when the cell density reaches 0.7-0.8x106 cells/mL

the culture was split to approximately 0.4x106 cells/mL with fresh media. Number of cells/mL

was determined using a haemocytometer slide on a microscope stage. Cells were spun at 250

g for 5 minutes and media was removed and the pellet was resuspended in fresh media.

2.7.3.3 K562 STAG2 R614-/- mutant lines

Cells were semi-adherent so were grown in suspension cell flasks and treated as

suspension cells (Section 2.7.3.2).

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Figure 2.2: Haemocytometer slide used for counting cells, each shaded grid is 1 mm2..

The black circles represent cells in the grid. The number of cells in each shaded grid is counted. The average is determined by dividing the total by 5. The number of cells/mL is calculated by grid averaged dilution factor (2)x 10 000

2.7.4 Freezing cells for liquid nitrogen storage

Single cell suspension (2.7.3) cells were centrifuged for 5 minutes at 250 g and

supernatant removed. The pellet was gently suspended in equal parts media and cryoprotectant

(20% DMSO in FBS), 500 µl of each per intended vial of frozen cells except for K562 and

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K562 cell line variations which were suspended in media and 5% DMSO 1 mL per intended

vial. The cell suspension was aliquoted into each cryovial and then transferred to Nalgene Mr

Frosty. Mr Frosty was stored at -80 °C overnight then transferred to the liquid nitrogen dewar.

2.8 Expression analysis

2.8.1 Total RNA purification

To examine gene expression in cell lines, RNA was extracted from K562 and K562

STAG2 R614* mutant cell lines. Cells (5x105) were collected by centrifugation. The extraction

procedure was carried out in accordance with the RNA purification method described in

Macherey-Nagel user manual. Concentration and purity of RNA was determined by Nanodrop

technique described in section 2.3.2. RNA was eluted in 40 µL Ultrapure DNase – RNase

free distilled H2O (Life Technology) and stored at -80 °C until required.

2.8.2 cDNA synthesis

Complementary DNA (cDNA) was synthesised from the RNA extracted in section

2.8.1 using 4 µL of qScript cDNA SuperMix (Quantabio), 500 ng of total RNA and RNase-

free H2O in a 20 µL reaction. Reverse transcription was performed in an Applied Biosystem

Thermal Cycler 2.08, at 25°C for 5 minutes, 42 °C for 30 minutes and finally 85 °C for 5

minutes. cDNA products were stored at -20 °C until required.

2.8.3 Quantitative reverse-transcriptase PCR (qRT-PCR)

qRT-PCR is a technique that quantitatively measures RNA following cDNA

conversion. This is achieved through real-time PCR. qRT-PCR was used to measure

endogenous ERG, KLF1, P1-/P2- generated, and total RUNX1 expression in K567 and K562

STAG2 R614* mutant lines cell lines.

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Figure 2.3: Quantitative PCR primer locations at human RUNX1.

Schematic of human RUNX1 gene and transcripts are shown with exons in boxes and the 5’ and 3’ untranslated regions in white. P1 and P2 promoters are indicated with a black hooked arrow. Primers used for analysis of P1, P2 and total RUNX1 transcript expression by quantitative PCR are indicated with black arrowheads.

Relative gene expression was normalised to reference genes H-CYCLOPHILIN and

GAPDH. Dr. Jisha Antony previously validated each primer for 80-100% efficiency, with an

external standard curve, generated by serial dilutions of the cDNA. A list of primers is available

in Appendix III.

qRT-PCR mix was made for each primer set, in each cell line (Table 2.5).

Table 2.5: qRT-PCR reaction mix

Reagent Amount per 20µL (reaction volume) cDNA (500 ng) 1 µL 3µM Forward primer 1 µL 3µM Reverse primer 1 µL SYBR Green (2x) 10 µL Ultrapure DNase – RNase free distilled H20

7 µL

qRT-PCR was performed using LightCycler® 480 Instrument II (Roche Life Science)

using the following programme: 95 °C for 30 seconds and 40 cycles of 95 °C for 5 seconds, 60

°C for 30 seconds.

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Analysis was carried out using qBase software, which utilised the ∆∆CT method. Graphs were

created using Prism 7 (GraphPad).

2.8.4 pGL4.23 Transfection

To determine the ability of the Runx1 +24 G-quadruplex to act as an enhancer in mouse

cells lines, NIH/3T3 cells were transfected with the pGL4.23 +24, pGL4.23 +24 g->t ,

pGL4.23 +24 t->a. Cultures were plated in a 96-well plate at 8x 103 cells per well, and cells

were 30-50% confluent at time of transfection. Cells were transfected using Lipofectamine

3000 (Invitrogen) according to the manufacturer’s instructions. Plates were incubated at 37 °C

for 48 hours. A Renilla luciferase reporter construct was co-transfected with the mouse

regulatory elements pGL4 reporter constructs. Firefly and Renilla luciferases have distinct

evolutionary origins and dissimilar enzyme structures and substrate requirements. Renilla acts

as a transfection control.

Table 2.6: Transfection components per well

Component per well Amount per well Adherent cells 8x103 cells

Lipofectamine 3000 dilution

Opti-MEM Media (Gibco, Life Technology)

5 µL

Lipofectamine 3000 0.2 µL Master Mix

Opti-MEM Media 5 µL Plasmid DNA 50 ng P3000 Reagent 0.2 µL Renilla 20 ng

2.8.5 pGL4.23 Transfection analysis

If pGL4.23-GW contains an enhancer there should be an increase in expression of

luciferase, which can be determined with a luminometer. Dual-Glo Luciferase Assay System

(Invitrogen) was used according to manufacturer’s instructions to measure luminescence.

Dual-Glo Reagent was added in equal parts to the volume of culture medium and mixed well.

Firefly luminescence was measured by Victor X3 plate reader after 10 minutes. Dual-Glo

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Stop & Glo Reagent, equal to the original culture medium, was added to each well and mixed.

After 10 minutes Renilla luminescence was measured by Victor X3 plate reader.

2.8.6 Statistical analysis of luciferase assays

All analyses were performed in Prism 6. Significance of the differences observed was

assessed using ANOVA.

2.9 In silico approaches for Enhancer analysis

Genomic sequences surrounding the Runx1 locus for human (Homo sapiens) and mouse

(Mus musculus) were obtained from publicly available genome assemblies [human

(GRCh38/hg38 and GRCh37/hg19) assembly, and mouse (mm9 and mm10) assembly] on the

UCSC genome browser (Kent et al., 2002; Waterston et al., 2002). All genome alignments

were performed in accordance with previous Ng et al., 2010 alignments (Ng et al., 2010).

2.9.1 Epigenetic analyses using ENCODE data

ENCODE is the Encyclopaedia of DNA elements, a publicly available database of

functional protein, RNA and DNA elements in the human and mouse genomes, including

epigenetic marks and regulatory factors. Numerous other genomes are available for analysis

(Rosenbloom et al., 2013), but were not used in this study. To identify any patterns in

functionally validated regulatory elements as enhancers, various haematopoietic cell lines were

selected for analysis (Table 2.7). The ENCODE datasets were used to detect the presence of

various histone modifications, DNase I hypersensitivity sites, transcription factors and cohesin

binding sites (Table 2.8) (Barski et al., 2007; Raney et al., 2014; Rosenbloom et al., 2013;

Wilson et al., 2016). Analysis of ENCODE annotations was carried out using data submitted

by Stanford, Yale, University of Washington (UW) and Ludwig Institute for Cancer Research

(LICR).

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In order to visualise the combined ENCODE cell line data, all sequences were

annotated using Geneious, program version R8.0.4 (Biomatters) (Kearse et al., 2012).

To determine the enhancer locations in different genome assemblies UCSC ‘liftOver’

tool was utilised to convert coordinates within a species (Rosenbloom et al., 2013).

To determine the human genome co-ordinates for functionally validated Runx1

enhancers, the identified enhancer sequences were searched using UCSC BLAT tool and NCBI

BLAST tool (Altschul et al., 1990; Kent, 2002). More genomic features of the conserved

enhancer regions were deduced by analysing the human coordinates using FANTOM 5

(Andersson et al., 2014; Lizio et al., 2015) and UCSC Genome Browser (Table 2.8).

ENCODE also has Chromatin State predictions (ChromHMM) for K562 cells (Kellis

et al., 2017). ChromHMM annotates the data based on epigenetic information. The predicted

chromatin states are active promoter, promoter flanking, inactive promoter, candidate strong

enhancer, candidate weak enhancer, distal CTCF/candidate insulator, transcription associated,

low activity proximal to active states, polycomb repressed, and heterochromatin /repetitive

/copy number variation. Transcription associated refers to transcriptional transition or

elongation (Ernst et al., 2010; Ernst et al., 2011).

Further regulatory markers for K562 cells were uploaded into UCSC genome browser

including; super-enhancers (Khan et al., 2016), silencers (Pang et al., 2020), cohesin mediated

chromatin accessibility (Antony et al., 2020).

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Table 2.7: Analysed cell lines

Cell line Origin K562 Human Chronic Myelogeneous

Leukaemia cells Table 2.8: Analysed ENCODE annotations

Regulatory association ENCODE annotation Open chromatin DNase I hypersensitivity (HS) Transcription initiation RNA Pol II (phosphorylated at serine

5) Cohesin (subunit) RAD21, SMC3 CTCF binding sites CTCF Histone acetyltransferase that is recruited to transcriptionally active enhancers (an increase in the expression of target genes)

p300

H3K4me1/2 demethylase (mediates transcriptional repression)

LSD1

Super-enhancer previously annotated by Khan et al., (2016)

K562 Super-enhancers

Silencer previously annotated by Peng et al., (2020)

Silencers

Cohesin mediated chromatin accessibility previously annotated by Antony et al., (2020)

Chromatin sites that show increased accessibility in the absence of cohesin

Chromatin State ChromHMM Enhancer Enhancer-associated

histone methylation or acetylation marks

H3K27ac, H3K4me1, H3K4me2

Active promoters H3K27ac, H3K9ac, H3K4me3

Repressed promoters H3K9me3, H3K27me3 Haematopoiesis transcription factors

Erythroid development GATA1 Haematopoietic SPI1 Blood/ Stem progenitors TAL1,GATA2

2.9.2 Comparative analysis of cancer genomic datasets

To assess whether any AML patients had mutations in the conserved regulatory

elements regions, publicly available cancer genomic datasets were explored. Online tools were

used to categorise publicly available patient information, into different tumour types for

analysis. Sequence data at the RUNX1 locus was downloaded from all available AML cases.

The large-scale cancer genomics datasets were retrieved from International Cancer

Genome Consortium (ICGC) Data Portal [human Feb. 2009 (GRCh37/ hg19) assembly]

67

(Hudson et al., 2014; Zhang et al., 2011), The c-BioPortal for Cancer Genomics [human Feb.

2009 (GRCh37/ hg19) assembly] (Cerami et al., 2012; Gao et al., 2013) and The Cancer

Genome Atlas (TCGA) Data Portal [human Mar. 2006 (NCBI36/ hg18) assembly](CGARN,

2013).

2.9.3 Single nucleotide polymorphism (SNP) analysis

To deduce if there were any reported SNPs in each region GWAS catalog

(GRCh38/hg38) (Buniello et al., 2019), HaploReg v4.1 (GRCh37/hg19)(Ward et al., 2016)

were used. GWAS catalog shows SNPs chromosome locations and highlights any publications

that report on the function of that SNP. HaploReg v4.1 displays the genome characteristics and

transcription factor binding data for each SNP and predicts regulatory motif changes.

To establish if any SNPs had a minor allele frequency (MAF) of >1% Genotype-Tissue

Expression (GTEx) portal v8 (GRCh38/hg38) (https://gtexportal.org/home/) was used.

gnomAD browser (Karczewski et al., 2020) v2 1.1 (GRCh37/hg19) and v3 (GRCh38/hg38)

reviewed SNPs with MAF >1% for any ClinVar associations and/ or publications.

To identify which genes a SNP physically interacts with, the CoDeS3D pipeline

(Fadason et al., 2017) was used to interrogate Hi-C chromatin interaction libraries. This

pipeline identifies interactions across the genome with the SNPs, and then identifies SNPs

where the genetic variant correlates with the expression level of the spatially associated partner

gene [the SNP is an expression quantitative trait locus (eQTL)].

2.10 Circular Chromatin Conformation Capture (4C)

Circular chromatin conformation capture (4C) was used to elucidate the all interactions

with a chosen region or ‘bait’ (Figure 2.4). 4C sequencing libraries were prepared from 4C

material, each containing PCR products amplified by bait-specific primer pairs. Chromatin

connections with the bait were detected through the use of next-generation sequencing and data

processing.

https://gtexportal.org/home/

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Figure 2.4: Schematic outline of 4C process.

Chromatin in close contact is cross-linked using formaldehyde (1), then digested with the 1st Restriction Enzyme (RE) (2). Chromatin is ligated to fuse the RE cut ends of DNA fragments (3). Chromatin is reverse cross-linked using heat (4), then digested by a 2nd RE (5). DNA fragments are then ligated to form small circles, then regions of interest are PCR amplified using bait specific primers (6). Regions of interest are then deep-sequenced (7).

2.10.1 4C Design

To determine the best primers for the chosen 4C bait regions (P1-RUNX1, P2-RUNX1,

RUNX1+24, ERG+85), the sequences for each bait were searched for possible restriction

enzyme digest sites. An appropriate primary restriction enzyme would leave a bait fragment of

>500 bp for 4-bp cutters or >1500 for 6-bp cutters. An appropriate secondary enzyme would

chop the bait to be ideally >250 bp long. Once appropriate enzymes were selected, the online

program Primer3 was employed (Koressaar et al., 2007; Untergasser et al., 2012). All primers

were aligned to the genome using BLAST and BLAT. Primers were selected based on

optimised efficiency (i.e. Primer GC%, 18-13 bp length, shortest length to the restriction

enzyme site) and ordered from Integrated DNA Technologies. Primers are listed in Appendix

III.

2.10.2 Cross-linking of K562 cells

37% fomaldehyde was heated up to 65 °C for 15 minutes whilst vortexing every five

minutes then left to cool to room temperature. Two million cells in 20 mL petri dish were cross-

69

linked in 2% formaldehyde by addition of 1.1 mL of 37% formaldehyde to media, and mixed

into media with gentle swirling. Cells were then incubated with gentle rocking in a hybe oven

at 21 °C for 10 minutes. To stop cross-linking activity, 1 mL of 2.5 M glycine was added to

media with gentle swirling and then incubated for 5 minutes on ice with occasional swirling.

Cells were scraped off the dish and into a 50 mL falcon tube. Cells were spun at 250 g for 8

minutes at 4 °C. The supernatant was decanted, cells were washed (not resuspended) with 10

mL ice-cold 1x PBS and centrifuged at 250 g for 8 minutes at 4 °C. The supernatant was

decanted, cells were resuspended with 10 mL ice-cold 1x PBS and centrifuged at 250 g for 8

minutes at 4 °C. The supernatant was removed with a 10 mL pipette and the remaining volume

was removed with a p200 pipette. Pellets were stored at -80 °C.

2.10.3 Isolation of nuclei

Frozen pellets were resuspended in 10 mL lysis buffer (Appendix I) (1 mL first) and

incubated on ice for 30 minutes. Cells were Dounce homogenised 10 times up and down, then

centrifuged at 600 g for 5 minutes at 4 °C. The supernatant was removed and the pellet was

resuspended in 1 mL of ice-cold PBS and transferred to 1.5 mL Eppendorf tube. Cells were

centrifuged at 600 g for 5 minutes at 4 °C. The supernatant was decanted and the remaining

volume removed using a p200 pipette.

2.10.4 First Digestion

The pellets from 1.11.3 contain 2 million cells. Pellets of the same samples were

combined for a total of 4 million cells. Four million nuclei were resuspended in 76 µL

Ultrapure DNase – RNase free distilled H20 and 4 µL 10% sodium dodecyl sulfate (SDS)

and incubated at 60 °C for 10 minutes. SDS was neutralised by addition of 26.72 µL 15%

Triton X-100 and 245.28 µL Ultrapure DNase – RNase free distilled H20, then incubated at

37 °C for 20 minutes. Any aggregate was broken up every 5 minutes. 16 µL of sample was

taken out as undigested control and stored at -20 °C. 80 µL restriction buffer and 14 µL DpnII

70

restriction enzyme (50,000U/mL) (for 4 million cells; increased proportionally for more cells)

were added and the total volume was made up to 800 µL with Ultrapure DNase – RNase free

distilled H20. Samples were digested with tubes placed horizontally at 37 °C for 4 hours without

shaking. Every 10 minutes for the first hour and every 30 minutes after that samples were

gently mixed by inverting the tube. Lids were sealed with parafilm to prevent evaporation.

After digestion, 32 µL of sample was taken out as the digested control and stored at -20°C.

To analyse digestion success, undigested and digested controls were treated with

Proteinase K (Invitrogen). The undigested sample had 2.5 µL of 10 mg/mL Proteinase K and

70 µL of 10 mM Tris-HCl pH 7.5 added. The digested sample had 5 µL of 10 mg/mL Proteinase

K and 58 µL of 10 mM Tris-HCl pH 7.5 added, and both samples were incubated at 65 °C for

1 hour. 20 µl of each sample was run on 0.6% agarose gel for 1 hour at 100 Volts.

2.10.5 First restriction enzyme inactivation

The first restriction enzyme was heat inactivated at 65 °C for 20 minutes

2.10.6 Ligation

Two 15 mL Falcon tubes were prepared for ligation; one for ligated sample and one for

non-ligated sample. Both the samples had 2336 µL of Ultrapure DNase – RNase free distilled

H20 800 µL ligase buffer, 40 µL adenosine triphosphate (ATP) and 784 µL of sample added.

For the ligated sample, 40 µL Invitrogen T4 DNA ligase (500 U at 1 U/µL). For the non-ligated

sample 40 µL of Ultrapure DNase – RNase free distilled H20 was added. Samples were

incubated at 16 °C overnight while shaking vertically at 30 rpm. After incubation at 16 °C,

samples were incubated at room temperature for 30 minutes. After ligation, 90 µL of sample

was taken out as ligated and non-ligated controls and stored at -20 °C.

To check ligation efficiency, ligated and non-ligated controls were treated with 5 µL

of 10mg/mL Proteinase K at 65 °C for 1 hour. 40 µL of ligated and non-ligated controls were

run alongside 20 µL of digested control on 0.6% agarose gel for 1 hour at 100 Volts.

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2.10.7 Reverse cross-linking and DNA purification

To reverse crosslink samples, 75 µg (7.5 µL of 10 mg/mL) of Proteinase K was added

to ligated and non-ligated samples and incubated overnight at 65 °C. For samples that were

treated with PMA for 72 hours, 150 µg of Proteinase K was added and samples were incubated

overnight at 65 °C.

RNA was degraded by addition of 75 µg (7.5 µL of 10 mg/mL) RNaseA to samples

and incubation at 37 °C for 45 minutes.

DNA was purified by phenol/chloroform extraction. An equal volume of

Phenol:Chloroform:Isoamyl Alcohol 25:24:1 (Sigma-Aldrich) was added to the samples.

Samples were vortexed for 30 seconds and spun at 4000 g (maximum speed) for 15 minutes at

room temperature. The aqueous phase was transferred to 15 mL Falcon tubes.

DNA was ethanol precipitated by adding 3 M NaOAC pH 5.6 to a concentration of 240

mM and 2.5 volumes absolute ethanol (Scharlab). Samples were vortexed and incubated at -80

°C for 1.5 hours or until frozen solid. Samples were centrifuged at 4000 g for 1 hour at 4 °C.

The supernatant was discarded and 5 mL of 70% ethanol was added. Samples were spun at

4000 g for 15 minutes at room temperature. The supernatant was discarded and tubes were re-

spun to collect any residual solution. Residual supernatant was removed using a p200 pipette.

Pellets were air-dried for around 10 minutes at room temperature and resuspended in 400 µL

of 10 mM Tris-HCl pH 7.5.

To check DNA purification of the undigested and digested sample, 60 µL of the sample

was taken out, 20 µL of which was run on 0.6% agarose gel for 1 hour at 100 Volts.

2.10.8 Second digestion

Samples were transferred to 1.5 mL tubes for digestion with CviQI. 40 µL of 10x

Buffer, 340 μL of sample and 80 U (8 μL) of 10000 U/mL CviQI and 12 µL of Ultrapure

DNase – RNase free distilled H20 (for a total of 400 µL), were digested overnight at 25 °C

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while shaking with tubes placed horizontally at 30 rpm. Lids were sealed with parafilm to

prevent evaporation. After digestion, 10 µL of each sample was taken out as digested control

and combined with 100 µL 10 mM Tris-HCl pH 7.5 and stored at -20 °C.

To analyse digestion success, 20 μL of digested samples were run on a 0.6% agarose

gel for 1 hour at 100 Volts alongside purified ligated samples (Section 2.10.6).

2.10.9 Removal of second restriction enzyme

As CviQI cannot be heat inactivated, phenol:chloroform extraction was used to remove

restriction enzyme. Total volume was made up to 400 µL with Ultrapure DNase – RNase

free distilled H20, then an equal volume of phenol:chloroform was added to samples. Samples

were vortexed for 30 seconds, then centrifuged at 15000 rpm (max speed) for 10 minutes at

room temperature. The aqueous phase was transferred to 2 mL tube.

DNA was ethanol precipitated by adding 3 M NaOAC pH 5.6 to a concentration of 240

mM and 2.5 volumes absolute ethanol. Samples were vortexed and incubated at -80 °C for 1.5

hours or until frozen solid. Samples were centrifuged at 15000 rpm for 20 minutes at 4 °C.

Supernatant was removed and 1 mL of 70% ethanol was added. Samples were spun at 15000

rpm for 5 minutes at room temperature. The supernatant was removed and tubes were re-spun

to collect any residual solution. Residual supernatant was removed using a p200 pipette. Pellets

were air-dried for around 5-10 minutes at room temperature and resuspended in 400 µL of 10

mM Tris-HCl pH 7.5.

To check DNA purification, 10 µL of the sample was taken out, of which 2.5 µL was

run on 0.6% agarose gel for 1 hour at 100 Volts with the digested sample.

2.10.10 Second Ligation

Samples were transferred to 50 mL falcon tubes for ligation. To each sample, 1120 µL

ligation buffer, 56 µL of ATP and 40 µL ligase was added and the total volume was made up

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to 5600 µL with Ultrapure DNase – RNase free distilled H20. Samples were incubated at

16°C overnight at 30rpm.

To check ligation, 80 µL of sample was taken out. 20 µL was run on 0.6% agarose gel

for 1 hour at 100 Volts with the purified digested sample.

2.10.11 DNA purification with linear polyacrylamide (LPA)

DNA was purified by adding 22.4 µL 20µg/mL linear polyacrylamide (LPA), NaOAC

to a final concentration of 240 mM, and 2 volumes of Ethanol to the samples. Samples were

incubated at -80 °C for 1.5 hours. Samples were then centrifuged at 4000 rpm (max speed) for

1 hour at 4 °C. The supernatant was removed and 5 mL 70% ethanol was added. Samples were

centrifuged at 4000 rpm for 15 minutes at room temperature. The supernatant was removed

and tubes were re-spun to collect any residual solution. Residual supernatant was removed

using a p200 pipette. Pellets were air-dried for around 5-10 minutes at room temperature and

resuspended in 160 µL of 10 mM Tris-HCl pH 7.5.

The concentration of samples was measured using the Qubit® 3.0 Fluorometer (Life

Technologies) and Qubit® double-stranded DNA (dsDNA) High Sensitivity Assay kit (Life

Technologies) according to the manufacturer’s instructions. Samples were stored at -20 °C.

2.10.12 4C Polymerase Chain Reaction

A total of 344 ng of DNA per bait was amplified by Polymerase Chain Reaction (PCR)

using Q5® Hot Start High-Fidelity 2x Master mix (New England Biolabs). For the final 4C-

PCR reactions a no-template control was used to control for DNA contamination. PCR reaction

conditions are listed below. Optimised amplification conditions, per primer set, are listed in

Table 2.10. Primer sequences are listed in Appendix III. Amplified products were run on 1.5%

agarose gel for 45 minutes.

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Table 2.9: 4C-PCR reagents

Reagent Amount per 25µL (reaction volume) Q5® High-Fidelity 2x Master Mix 12.5 µL 10 µM Forward primer 1.25 µL 10 µM Reverse primer 1.25 µL Template DNA 86 ng nuclease-free H2O to 25 µL

The PCR protocol was optimised by varying the temperature of annealing phase (63.2

°C-67.8 °C). Amplification was performed in an Applied Biosystem Thermal Cycler 2.08 as

follows: 98°C for 30 seconds; 30 cycles of 98 °C for 10 seconds, 63.2 °C-67.8 °C for 15

seconds, 72 °C for 2 minutes, finally 72 °C for 2 minutes, 4 °C for ∞.

Table 2.10: Optimised PCR conditions for 4C-PCR primers

Primer set Annealing temperature (°C) RUNX1 P1 64.7 °C RUNX1 P2 67.8 °C RUNX1 +24 63.2 °C ERG +85 64.2 °C

2.11 4C-seq Library preparation

2.11.1 PCR purification

PCR products were cleaned up using the QIAquick PCR purification kit (Qiagen)

according to the manufacturer’s instructions. For each 4C-PCR, one column was used (the

maximum binding capacity of a column is 10 µg). DNA from column was eluted in a total of

30 µL elution buffer (5 mM Tris-HCl pH 8.5).

The concentration of samples was measured using the Qubit® 3.0 Fluorometer (Life

Technologies) and Qubit® double-stranded DNA (dsDNA) High Sensitivity Assay kit (Life

Technologies) according to the manufacturer’s instructions and samples were stored at -20 °C.

2.11.2 Adaptor ligation

Adapter ligation was performed by Dr. Jisha Antony (Horsfield lab, University of Otago).

75

Libraries were prepared using TruSeq® DNA PCR-Free preparation kit (Illumina)

according to the manufacturer’s instructions for Low Sample preparation. Based on the lowest

DNA concentration, 29 ng from each bait were pooled according to sample and the total

volume was made up to 50 µL with Ultrapure DNase – RNase free distilled H20.

Purified amplicon concentration was measured by Qubit® Fluorometer using dsDNA

High Sensitivity assay kit and average size of PCR products was measured on a 2100

Bioanalyser (Agilent Technologies) using a High Sensitivity DNA Kit (Agilent Technologies)

according to the manufacturer’s instructions. Bioanalyser data was analysed using 2100 Expert

software (Agilent Technologies).

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Table 2.11: 4C-Seq Library and sequencing adapters

Library TruSeq® i7index

I7 Index sequence

TruSeq® i5 index

I5 Index sequence

WT_12_DMSO_1 UDI0001 CCGCGGTT UDI0001 AGCGCTAG

WT_12_DMSO_2 UDI0002 TTATAACC UDI0002 GATATCGA

WT_12_DMSO_3 UDI0003 GGACTTGG UDI0003 CGCAGACG

WT_12_DMSO_no_ligase UDI0004 AAGTCCAA UDI0004 TATGAGTA

WT_12_PMA_1 UDI0005 ATCCACTG UDI0005 AGGTGCGT

WT_12_PMA_2 UDI0006 GCTTGTCA UDI0006 GAACATAC

WT_12_PMA_3 UDI0007 CAAGCTAG UDI0007 ACATAGCG

WT_72_PMA_1 UDI0024 ATGAGGCC UDI0024 GTTAATTG

WT_72_PMA_2 UDI0009 AGTTCAGG UDI0009 CCAACAGA

WT_72_PMA_3 UDI0010 GACCTGAA UDI0010 TTGGTGAG

WT_72_PMA_no_ligase UDI0012 CTCTCGTC UDI0012 TATAACCT

ST37_12_DMSO_1 UDI0021 GGTACCTT UDI0021 AAGACGTC

ST37_12_DMSO_2 UDI0022 AACGTTCC UDI0022 GGAGTACT

ST37_12_DMSO_3 UDI0015 GGCTTAAG UDI0015 TCGTGACC

ST37_12_DMSO_no_ligase UDI0016 AATCCGGA UDI0016 CTACAGTT

ST37_12_PMA_1 UDI0017 TAATACAG UDI0017 ATATTCAC

ST37_12_PMA_2 UDI0018 CGGCGTGA UDI0018 GCGCCTGT

ST37_12_PMA_3 UDI0019 ATGTAAGT UDI0019 ACTCTATG

ST37_72_PMA_1 UDI0020 GCACGGAC UDI0020 GTCTCGCA

ST37_72_PMA_2 UDI0013 CCAAGTCT UDI0013 AAGGATGA

ST37_72_PMA_3 UDI0014 TTGGACTC UDI0014 GGAAGCAG

ST37_72_PMA_no _ligase UDI0023 GCAGAATT UDI0023 ACCGGCCA

2.11.3 Library purification

Library purification was performed by Dr. Jisha Antony (Horsfield lab, University of Otago).

Libraries were purified by removing fragments above 1 kb using Agencourt®

AMPure® XP beads (Beckman Coulter). The total volume was made up to 50 µL with

Ultrapure DNase – RNase free distilled H20. Beads were equilibrated at RT for at least 30

77

minutes and vortexed briefly before use. 0.5x of magnetic beads was added per sample and

mixed at 1800 rpm at room temperature on the thermomixer for 1 minute and then incubated

at room temperature for further 5 minutes. Samples were spun at 300 g for 1 minute at room

temperature. Beads were separated using a magnet until solution was clear (around 5 minutes)

and supernatant was removed. Beads were washed twice with 200 µL of 70-80% ethanol

(freshly made). Tubes were air dried at room temperature for 5 minutes. The magnet was

removed and 20 µL of TruSeq® DNA PCR-Free Resuspension buffer was added and mixed

until homogeneous. Solutions were incubated for 5 minutes at room temperature. The magnet

was applied until solution was clear (1-2 minutes). Eluates were transferred to DNA low-bind

0.2 mL tubes (Eppendorf).

2.11.4 Equal mixing of baits

Purified amplicon concentration was measured by Qubit® Fluorometer using dsDNA

High Sensitivity assay kit and average size of PCR products was measured on a 2100

Bioanalyser (Agilent Technologies) using a High Sensitivity DNA Kit (Agilent Technologies)

according to the manufacturer’s instructions. Bioanalyser data was analysed using 2100 Expert

software (Agilent Technologies).

2.11.4.1 MiSeq library

Samples were pooled by Dr. Jisha Antony (Horsfield lab, University of Otago) based

on average fragment size: 1 µL for libraries with average size of <1.5 kb and 2 µL for libraries

with average size of >1.5 kb. After pooling, samples were vacuum dried to reduce volume to

10 µL. 1 µL was run on the Bioanalyser.

2.11.4.2 HiSeq Library

Baits were mixed equimolarly based on concentration and average fragment size bait

mixed and adjusted based on the percentage of demultiplexed 4C baits obtained from an initial

MiSeq run.

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2.11.5 Next generation sequencing

Final libraries were sequenced as 100 bp single-reads on an IlluminaTM HiSeq 2500 by

Otago Genomics Facility (OGF). Because the 4C-seq libraries have low diversity, they were

combined with 15% PhiX control (v/v). A trial library (PCR comparison) was sequenced as

151 bp reads on an IlluminaTM MiSeq by Dr. Rob Day (Cancer Genetics, Department of

Biochemistry, University of Otago).

2.12 Processing of sequenced reads

All code used and generated for this project can be found in Github:

(https://github.com/thoam810/4C_analysis).

2.12.1 Generating read counts and data quality reports

Quality and number of initial reads and demultiplexed reads were checked using:

https://github.com/thoam810/4C_analysis/blob/master/FASTQC_analysis_bash. Quality of

reads was assessed using FastQC software (Andrews, 2010), for each bait within the libraries

before and after demultiplexing.

2.12.2 Demultiplexing of libraries based on indexed adaptors

Libraries were demultiplexed based on 8 base index barcodes (see Table 2.11). For the

HiSeq run, libraries were demultiplexed by OGF (see Appendix V for report); for MiSeq runs,

libraries were demultiplexed automatically after the run.

2.12.3 Trimming of adaptors

Adaptor sequences were removed from bait demultiplexed reads by Sequencing

providers (HiSeq - OGF) and (MiSeq - Dr. Rob Day).

2.12.4 Demultiplexing of baits based on 4C primers

Baits were demultiplexed by Dr. William Schierding (University of Auckland).

https://github.com/thoam810/4C_analysis

https://github.com/thoam810/4C_analysis/blob/master/FASTQC_analysis_bash

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2.12.4.1 HiSeq

Samples were sequenced using HiSeq2500 100 bp single end reads on two lanes. To

join reads for the different lanes samples files were concatenated

(https://github.com/thoam810/4C_analysis/blob/master/Concatenate_seq_bash). Baits were

demultiplexed based on 4C primers up to and including the restriction site, as listed in Table

2.12 (https://github.com/thoam810/4C_analysis/blob/master/demux_4C_hiseq.sl). Only reads

that had the forward and reverse primer and bait sequences in the correct orientation were

selected. No mismatches were allowed. Primer sequences were not removed from the reads.

2.12.4.2 MiSeq

Sequences used for demultiplexing of baits are 4C primers up to and including the

restriction site (no mismatches were allowed) as listed in Table 2.12.

https://github.com/thoam810/4C_analysis/blob/master/Concatenate_seq_bash

https://github.com/thoam810/4C_analysis/blob/master/demux_4C_hiseq.sl

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Table 2.12: Primer sequences and lengths used for demultiplexing of 4C baits

Primer Sequence Length (bp) RUNX1 P1 F AACCAAGTCTGTCAGTCTCCTGTAC 25 RUNX1 P1 R GCTAACAAGCAGAGGAAGCAATATTTGTATAGAAT

AACAGAAAAAAAAGATC 52

RUNX1 P2 F GCCGGCTCCTGGAATTGGCCCGCGCGCCCCCGCCGCCGCGCCGCGCGCTACTGTAC

56

RUNX1 P2 R AGGATGTGCGTGCGTGTGTAACCCGAGCCGCCCGATC

37

RUNX1 +24 F ACGTGTCCACATTTTTCAGAAGTTGAATGGTTTTGTGTAATGGTAC

46

RUNX1 +24 R CAGTTACTTCGCCCATTGCTTTTTCTAAAGGATC 34 ERG +85 F CTCGCTGCTTAGGTGTAGTCCTTAGGAATGAAAATC

TGAACGTAGTAC 48

ERG +85 R TGGGGGAGACTGACAAATAGATC 23

2.13 Mapping of sequenced reads

Mapping of sequenced reads was carried out on Linex Server according to 4Cker

(Raviram et al., 2016).

2.13.1 Generating restriction enzyme digestion of Human Genome (hg19)

4C-Seq reads are usually mapped to a reduced genome which consists of unique

sequence fragments adjacent to the primary restriction enzyme sites in the genome. Mapping

to a reduced genome helps to remove inauthentic ligation events that do not result from

crosslinking.

The 4Cker script created a custom reduced genome using oligoMatch (Kent et al.,

2002) and fastaFromBed (Bedtools).

The fragment length should be longer than the length of the sequenced read (100 bp)

minus the size of adapter barcode (8 bp) and primer including the restriction enzyme (RE)

sequence (various bp see Table 2.11 and 2.12). As the longest sequence read was 69 bp

therefore, 70 bp fragment length for digestion was chosen.

In order to map reads to regions of Homo sapiens hg19 reference genome, the Human

genome file (Broad Institute) was digested into 70 bp fragments using an edited 4Cker script

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and primary RE DpnII file (4Cker).

(https://github.com/thoam810/4C_analysis/blob/master/RE_digest_genome).

Table 2.13: Length of sequenced reads after trimming

Primer Sequenced read length after trimming (bp) RUNX1 P1 F 67 RUNX1 P1 R 40 RUNX1 P2 F 36 RUNX1 P2 R 55 RUNX1 +24 F 46 RUNX1 +24 R 58 ERG +85 F 44 ERG +85 R 69

2.13.2 Generate Bowtie2 reference files of RE digested genome.

The DpnII reduced genome was used to map 4C-Seq single-end reads using Bowtie2

(Langmead et al., 2012)

Bowtie2 requires specific index files which were created using the reduced genome

files: https://github.com/thoam810/4C_analysis/blob/master/bowtie2_build.

2.13.3 Mapping of reads

The 4C-seq single-end reads were aligned to the Bowtie2 DpnII reduced genome using:

https://github.com/thoam810/4C_analysis/blob/master/Alignment_bowtie. This generates

Sequence Alignment/Map (SAM) files of aligned and unaligned reads.

The sequenced reads were trimmed from the 5’ end based on the adapter sequence

length and bait primer sequence including the RE length. The 3’ end was trimmed by 5bp based

on the demultiplexed quality reports.

2.13.4 Creating a counts file from mapped data

The SAM output from bowtie2 reads (Section 2.13.2) were used to create a counts file

(.bedGraph) with number of reads per RE fragment using:

https://github.com/thoam810/4C_analysis/blob/master/mapped_data_counts_bedGraph. This

https://github.com/thoam810/4C_analysis/blob/master/RE_digest_genome

https://github.com/thoam810/4C_analysis/blob/master/bowtie2_build

https://github.com/thoam810/4C_analysis/blob/master/Alignment_bowtie

https://github.com/thoam810/4C_analysis/blob/master/mapped_data_counts_bedGraph

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generates a tabulated bedGraph file with 4 columns: Chromosome number, location start of

fragment, location end of fragment, count.

2.13.5 Removing self-ligated and undigested reads

The self-ligated and undigested fragments were removed from the count files before

analysing 4C interactions using:

https://github.com/thoam810/4C_analysis/blob/master/remove_self_ligated_undigested.

To remove the self-ligated and undigested fragments for each bait, the coordinates for

the primer sequences (not including RE sequence) were found in the human reference

genome. Following this the sequence before and after the primer sequence were found. The

fragments that match the coordinates for the fragments before, after and including the primer

were removed.

2.13.6 Removal of background noise

Fragments that had reads in controls are considered background noise and these reads

were removed from the statistically significant interactions using:

https://github.com/thoam810/4C_analysis/blob/master/remove_controls.

Reads at fragments 10 kb up- and downstream of the bait were removed using:

https://github.com/thoam810/4C_analysis/blob/master/remove_bait_10kb.

2.14 Analysing 4C interactions

4C interaction analyses were done in R on a Linux server.

The interactions of a bait across the genome will differ depending on proximity to bait,

with closer interactions (near-bait) occurring at a higher frequency, followed by cis interaction

then trans interactions having the least amount of interaction with the bait region. 4Cker

provides different analyses for near-bait, cis and trans interactions.

4Cker interaction analysis checks if each samples passes quality control (QC) criteria.

To pass 4Cker QC, samples must have more than 1 million reads, more than 40% of reads on

https://github.com/thoam810/4C_analysis/blob/master/remove_self_ligated_undigested

https://github.com/thoam810/4C_analysis/blob/master/remove_controls

https://github.com/thoam810/4C_analysis/blob/master/remove_bait_10kb

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the cis chromosome and more than 40% coverage in the 200 kb region near the bait. Krijger et

al., 2020 state that 50,000-100,000 reads are sufficient to generate 4C profiles that correlate to

the 4C profile of 1 million reads (Krijger et al., 2020). For this 4C, samples with more than

400,000 reads with more that 40% reads in cis and >30% reads in near-bait would be allowed

in analysis.

When checking RUNX1 P1, RUNX1 P2 and ERG +85 baits they did not pass the quality

control criteria, so these reads were not processed further.

2.14.1 Identification of statistically significant interactions per condition

To determine significant interactions, 4Cker reduces bias of PCR artifacts as fragments

with counts greater than the 75th quantile within a given window by trimming to this value.

As 4C signal is generally higher around the bait region and decreases in far-cis and

trans, different adaptive window sizes were used for read normalisation and creating smooth

spline in each data set.

Adaptive windows were used to obtain an analogous number of observed fragments for

each window. Window size is determined by the amount of signal detected in the region. This

results in small windows near the bait and regions where there is high coverage and larger

windows further away from the bait where there is low coverage.

The smooth spline (with a smoothing parameter of 0.75) is fitted to the window sizes

separately for each analysis. Significant interactions are determined by what read counts in

domains are called by all replicates, and are greater than the 90th quantile of the adaptive

window.

The value of 'k' determines the size of the adaptive windows. The following k values

were optimised based on comparison of raw reads to the normalised reads and analysed reads.

For the analysis near the bait k = 3, entire bait chromosome analysis k = 3 and trans analysis

used k = 15.

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Significant 4C interactions for each condition were determined using;

https://github.com/thoam810/4C_analysis/blob/master/Analysing_4C_interactions.

2.14.2 Generation of ggplots

To plot the 4C profiles for all the conditions ggplot was utilised

(https://github.com/thoam810/4C_analysis/blob/master/4C_ggplots).

2.14.3 Differential interactions using DESeq2

Differential analysis was carried out between two conditions on the windows that are

interacting with the bait in at least 2 conditions. 4Cker has optimised this analysis for near-bait

and cis interactions. Significantly different domains were determined with a p-value of 0.05

using (https://github.com/thoam810/4C_analysis/blob/master/DESeq_diff_analysis).

2.14.4 Overlap of significant interactions with genes

Significant interactions were overlapped with UCSC K562 reference gene list using

(https://github.com/thoam810/4C_analysis/blob/master/map_gene_list).

2.14.5 Overlap of significant interactions with protein binding sites and

enhancer characteristics

See section 2.9.1

https://github.com/thoam810/4C_analysis/blob/master/Analysing_4C_interactions

https://github.com/thoam810/4C_analysis/blob/master/4C_ggplots

https://github.com/thoam810/4C_analysis/blob/master/DESeq_diff_analysis

https://github.com/thoam810/4C_analysis/blob/master/map_gene_list

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3 Chapter 3: Functional analysis of Runx1 enhancers

Some of the results from this chapter contributed to Marsman, J., Thomas, A., Osato,

M., O'Sullivan, J. M., & Horsfield, J. A. (2017). A DNA contact map for the mouse Runx1

gene identifies novel haematopoietic enhancers. Scientific Reports, 7, 13347 (Appendix VI).

3.1 Runx1 enhancers

The precise spatiotemporal expression of Runx1 requires the activity of cis-regulatory

elements, the majority of which have not been defined. In AML and other myeloid

malignancies where no mutation is found in the coding sequence, there may instead be

mutations in regulatory sequences that affect gene function (Cornish et al., 2019).

Understanding the regulatory landscape of RUNX1 will further increase understanding

of haematopoiesis as well as identify potential new regions for driver mutations in myeloid

malignancies. Recent studies have identified recurrent mutations in regulatory elements of

genes associated with oncogenesis, such in as the TAL1, ETV1, and PAX5 enhancers

(Mansour et al., 2014; Orlando et al., 2018; Puente et al., 2015), highlighting the importance

for determining functionality of non-coding elements.

Regulatory regions can be highly tissue-specific, are often spread over across the

chromatin, and only a small proportion of distal enhancers target the nearest transcript (Javierre

et al., 2016; Mifsud et al., 2015).

3.1.1 Previously identified enhancers of Runx1

Cis-regulatory elements (CREs), such as enhancers are able to control the expression

of genes by long-range chromatin interactions (Marsman et al., 2012).

Several research groups identified an important intronic Runx1 enhancer in mice:

Runx1 +23/+24/eR1/RE1 mouse conserved non-coding element (Bee et al., 2009a; Ng et al.,

2010; Nottingham et al., 2007) (Table 3.1). The naming of the element refers to the kilobases

86

(kb) downstream of each group’s reference point. The inconsistency of enhancer naming

between groups is because Ng et al., (2010) uses the transcriptional start site (TSS), +1 as the

reference point, whilst the other groups refer to this enhancer being 23.5 kb downstream from

the ATG of exon 1. The term eR1 proposed by Koh et al., (2015) refers to enhancer of Runx1

(Koh et al., 2015), whereas Tang et al., (2019) named it regulatory element 1 (RE1) (Tang et

al., 2018). Throughout this thesis, this previously described intronic enhancer will be referred

to as +24.

The +24 enhancer is responsible for the activation of Runx1 expression in

hematopoietic stem/progenitor cells (HSPCs) (Ng et al., 2010; Nottingham et al., 2007). Most

enhancers evolve rapidly and are rarely conserved at the DNA sequence level among species

due to positive evolutionary selection (Villar et al., 2015). Runx1 +24 is highly conserved in

eukaryotes, indicating an essential role in gene regulation. The discovery of +24 regulatory

element as a haemogenic endothelial cell-specific enhancer in mouse and zebrafish embryos

provided an insight into a mechanism for the targeted transcriptional regulation of Runx1

(Markova et al., 2011; Ng et al., 2010; Schütte et al., 2016).

Table 3.1: Runx1 +24 regulatory region

Regulatory region location (kb)

Name in this thesis

Identification methods Functional validation

Mouse and Human Runx1 relative to P1 +23/ +24/ eR1/RE1

+24 In silico conservation (interspecies DNA sequence homology) and DNase I hypersensitive sites (Markova et al., 2011; Ng et al., 2010; Nottingham et al., 2007).

Haematopoietic enhancer activity during mouse and zebrafish development. Luciferase enhancer activity in 416B, K562 and Jurkat haematopoietic cell lines. Thought to be a silencer in HEK293 cells (Markova et al., 2011; Marsman et al., 2017; Ng et al., 2010; Schütte et al., 2016)

This chapter will focus on the other candidate regulatory regions for Runx1, while

Chapter 4 provides a detailed analysis of the Runx1 +24 enhancer.

87

In this thesis enhancers are labelled according to sequence origin, and location relative

to Runx1 transcriptional start site (TSS): + means downstream from the Runx1 TSS at the P1

promoter, whereas – refers to upstream e.g. +62 is 62 kilobases (kb) downstream, from Runx1

TSS. If the sequence for the Runx1 regulatory element was discovered in mice, (m) is used

before the location label, human is unlabelled e.g. +62 sequence information is from humans

and m+110 is from mice.

In mice, Schütte et al., (2016) and Ng et al., (2010) used a combination of techniques

to identify 12 possible regulators of Runx1 with some functional identification (Table 3.2). My

previous masters project investigated the functions of the putative mouse regulatory regions

described by Ng et al., (2010).

Markova et al., (2011), Gunnell et al., (2010), Wang et al., (2014), Cheng et al., (2018),

and Nie et al., (2019) characterised the functions of potential human RUNX1 regulatory

elements. Some of these discoveries included identifying long-range interactions with RUNX1

promoters (Table 3.3).

Enhancers were predicted by several different methods including; computational

analysis of non-coding sequence for inter-species conservation, predicted transcription factor

binding motifs; analysis of chromatin immunoprecipitation sequencing (ChIP-seq) to locate

binding of transcription factors, mediators, and histone modifications at the regions of interest;

revealing promoter-enhancer interactions using chromosome conformation capture (4C, Hi-C)

and chromatin interaction analysis using paired-end sequencing (ChIA-PET).

In this Chapter I describe the conservation and features of previously identified and

novel Runx1 CREs, and identify the functional enhancer capability of regions previously

identified by 4C. This information will provide a deeper understanding of the regulation of

Runx1 transcription, with implications for causality and treatment of leukaemias with RUNX1

dysregulation.

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Table 3.2: Identified mouse Runx1 regulatory regions with available classifications


Name in this thesis Identification methods Functional validation

-368 m-368 In silico Conservation (interspecies DNA sequence homology) and transcription factor binding motif analysis of sequences (Ng et al., 2010)

Potential enhancer activity within hematopoietic cells/tissues in 20-24 hpf zebrafish embryos (Thomas, 2015)

-328 m-328 ChIP-seq analysis of haematopoietic TFs (ERG, FLI1, GATA2, GFI1B, LYL1, MEIS1, SPI1, RUNX1 and TAL1) and H3K27ac (Schütte et al., 2016).

No enhancer activity observed in mice (Schütte et al., 2016).


Identified, but not tested for enhancer activity (Schütte et al., 2016).

-101 m-101 In silico Conservation (interspecies DNA sequence homology) and transcription factor binding motif analysis of sequences (Ng et al., 2010)

Potential enhancer activity within hematopoietic cells in 20-24 hpf zebrafish embryos (Thomas, 2015)

-59 m-59 ChIP-seq analysis of haematopoietic TFs (ERG, FLI1, GATA2, GFI1B, LYL1, MEIS1, SPI1, RUNX1 and TAL1) and H3K27ac (Schütte et al., 2016)..

Haematopoietic enhancer activity in E11.5 transgenic mice and luciferase enhancer activity in 416B cells (Schütte et al., 2016).


No enhancer activity observed in mice (Schütte et al., 2016).

89




No enhancer activity in observed mice (Schütte et al., 2016).

+1 m+1 ChIP-seq analysis of haematopoietic TFs (ERG, FLI1, GATA2, GFI1B, LYL1, MEIS1, SPI1, RUNX1 and TAL1) and H3K27ac (Schütte et al., 2016).

Identified, but not tested for enhancer activity (Schütte et al., 2016).

+3 m+3 ChIP-seq analysis of haematopoietic TFs (ERG, FLI1, GATA2, GFI1B, LYL1, MEIS1, SPI1, RUNX1 and TAL1) and H3K27ac (Schütte et al., 2016)..


+110 m+110 In silico Conservation (interspecies DNA sequence homology) and transcription factor binding motif analysis of sequences (Ng et al., 2010)

Haematopoietic enhancer activity in E11.5 transgenic mice, potential enhancer activity within hematopoietic cells in 20-24 hpf zebrafish embryos and luciferase enhancer activity in 416B and Hela cell lines (Schütte et al., 2016; Thomas, 2015).

+181 m+181 ChIP-seq analysis of haematopoietic TFs (ERG, FLI1, GATA2, GFI1B, LYL1, MEIS1, SPI1, RUNX1 and TAL1) and H3K27ac (Schütte et al., 2016).

No enhancer activity in observed mice (Schütte et al., 2016).

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+204 m+204 ChIP-seq analysis of haematopoietic TFs (ERG, FLI1, GATA2, GFI1B, LYL1, MEIS1, SPI1, RUNX1 and TAL1) and H3K27ac (Schütte et al., 2016)..


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Table 3.3: Identified human RUNX1 regulatory regions with available classifications



-139 / E1 -139 ChIP-seq analysis of EBNA2 (Gunnell et al., 2016).

Luciferase enhancer activity in GM12878 and EBV positive Burkitt’s Lymphoma cell lines (Gunnell et al., 2016).

-188/ E4 -188 ChIP-seq analysis of EBNA2 (Gunnell et al., 2016).

Alone drives a 4.3 fold increase in luciferase enhancer activity in GM12878 and EBV positive Burkitt’s Lymphoma cell lines. When combined with -139 and -250 it drives a five-fold increase in enhancer activity (Gunnell et al., 2016).

-250/ E6 -250 ChIP-seq analysis of EBNA2 (Gunnell et al., 2016).

Luciferase enhancer activity in GM12878 and EBV positive Burkitt’s Lymphoma cell lines (Gunnell et al., 2016).

+62 +62 ChIA-PET (RNA polymerase II) and DNase I hypersensitive sites and conserved binding sites for the SNAG repressors GFI1/GFI1B and SNAI1 (Cheng et al., 2018).

Silencer activity in K562, OCl-AML3, U937 cell lines (Cheng et al., 2018).

Intron 5.2/RE2 intron 5.2 In silico Conservation (interspecies DNA sequence homology) and DNase I hypersensitive sites (Markova et al., 2011).

No enhancer or silencer activity observed in K562, Jurkat and HEK293 cell lines (Markova et al., 2011).

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3.1.2 Previously identified long-range interactions with Runx1

promoters

Interaction between predicted CREs and promoters of the Runx1 gene cannot be

assumed, as fewer than 50% of enhancers contact the nearest gene promoter (Li et al., 2012).

In order to determine the long-range interaction between Runx1 promoters and its CREs, Dr.

Judith Marsman performed circular chromosome conformation capture (4C) (Marsman, 2016)

in the mouse haematopoietic progenitor cell line, HPC7. 4C detects interactions with chosen

regions or “baits” through use of next-generation sequencing. HPC7 cells resemble early

haematopoietic progenitors (Kolterud et al., 1998) and were chosen because Runx1 expression

is crucial for the development of definitive haematopoietic stem- and progenitor cells (Cai et

al., 2011; Growney et al., 2005; Ichikawa et al., 2004; Okuda et al., 1996; Wang et al., 1996).

Quantitative PCR (qPCR) analysis showed that in HPC7 cells, the Runx1 P1 isoform is highly

expressed, whereas the Runx1 P2 isoform is not (Marsman, 2016).

Dr. Marsman’s selected baits for Runx1 4C, situated at the Runx1 P1 and Runx1 P2

promoters and at the Runx1 +24 enhancer. For each of these regions, one bait was located at

the core region, and another bait was located at a nearby (1.5-2 kb away) cohesin/CTCF binding

site.

Cohesin/CTCF binding sites upstream of Runx1 P1 and downstream of the Runx1 +24

enhancer are present in CH12, MEL and HPC7 cells, whereas cohesin/CTCF binding sites

upstream of Runx1 P2 are not present in HPC7 cells (Wilson et al., 2016). 4C analysis with

baits at the Runx1 -P1 and -P2 promoters allows for comparison of interactions between the

active Runx1 -P1 promoter and inactive -P2 promoter.

Runx1 P1 interacted with seven regions, as well as with the Runx1 +24 enhancer (Table

3.4). Each regulatory element recruits multiple blood transcription factors in HPC7 cells,

suggesting they function as haematopoietic-specific enhancers. They are named according to

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their distance from the Runx1 P1 transcriptional start site: -371, -354, -327, -321, -303, -58, -

48, and +110.

Of these putative enhancers, two had been previously described; conserved Runx1 m-

368, m+110 (Ng et al., 2010; Thomas, 2015).

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Table 3.4: Identified long range interactions with putative mouse Runx1 regulatory regions.


Name in this thesis Identification methods Interactions identified Functional validation

-371 m-371 3D interaction with Runx1 Promoters described by 4C and transcription factor binding motif analysis (Marsman, 2016).

Interacts with +24 and P1 in HPC7 cells identified by 4C (Marsman, 2016).

Not investigated.

-368 m-368 Conservation and transcription factor binding motif analysis (Ng et al., 2010)

Interacts with +24 and P1 in HPC7 identified by 4C (Marsman, 2016). Interacts with P1 in HPC-7 cells identified by Capture Hi-C (Wilson et al., 2016).

Potential enhancer activity within hematopoietic cells/tissue in 20-24 hpf zebrafish embryos (Thomas, 2015)


Interacts with +24 and P1 in HPC7 cells identified by 4C (Marsman, 2016). Interacts with P1 in HPC-7 cells identified by Capture Hi-C (Wilson et al., 2016).

Not investigated.



Not investigated.



Not investigated.

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Name in this thesis Identification methods Interactions identified Functional validation

Interacts with P1 in HPC-7 cells identified by Capture Hi-C (Wilson et al., 2016).



Not investigated.



Not investigated.



Not investigated.

+110 m+110 Conservation and transcription factor binding motif analysis (Ng et al., 2010)

Interacts with P1 in HPC7 cells identified by 4C (Marsman, 2016).

Haematopoietic enhancer activity in E11.5 transgenic mice, potential enhancer activity in hematopoietic sites in 20-24 hpf zebrafish embryos and luciferase enhancer activity in 416B and Hela cell lines (Schütte et al., 2016; Thomas, 2015).

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4C data identified new cis elements bound by TFs, that could have regulatory functions.

The Runx1 +24 enhancer interacted with all of the putative elements, except m+110. Runx1 P1

connected to all the identified regions excluding m-303. Conversely, the Runx1 P2 promoter

showed fewer interactions with enhancer clusters, but did interact with the putative regulatory

sites m-303, m-48 and m+24.

Figure 3.1: Schematic overview of the mouse Runx1 locus (chromosome 16:93,220,074-92,589,466).

Previously identified regulatory regions are annotated with orange circles, novel regions detected by 4C (Marsman, 2016) are represented by yellow circles, grey boxes annotate exons. The blue boxes show the exons encoding the Runt binding domain. The two Runx1 promoters are represented by black right angled arrows.

In humans, the interactions between RUNX1 promoters and regulatory regions were

identified using 3C for two regions, +62 and intron 5.2. +62 interacts with RUNX1 -P2 in K562

and OCl-AML3 cell lines (Cheng et al., 2018). Intron 5.2 Interacts with RUNX1 -P1 and P2 in

K562, Jurkat and HEK293 cell lines (Markova et al., 2011).

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Figure 3.2: Schematic overview of human RUNX1 locus (chromosome 21: 36,148,773-36,872,777).

Each human RUNX1 regulatory region is annotated with orange circles, grey boxes annotate exons. The blue boxes show the exons encoding the Runt binding domain. The two RUNX1 promoters are represented by black right angled arrows.

3.1.3 Functional enhancer characterisation assays

In order to determine the enhancer function of these identified regions, enhancer assays

can be used. Luciferase assays show the ability of a regulatory element to drive expression of

the firefly luciferase reporter gene Luc2 in cell lines. If the luciferase vector contains an

enhancer, there should be an increase in expression of luciferase, which can be determined with

a luminometer.

Zebrafish reporter assays can identify cell-type specific gene expression driven by

putative enhancers. If the zebrafish vector contains an enhancer there should be expression of

green fluorescent protein (GFP) in the cells that can respond to that enhancer. This is

determined by viewing zebrafish embryos during development under a fluorescent microscope.

The zebrafish enhancer assay vectors contain integration sites so that the reporter gene

constructs can be incorporated into the host genome. The Horsfield lab currently utilises three

reporter assays; ISceI-pBSII SK+ which integrates using I-Sce I sites, and Tol2Gata2 and ZED

which use Tol2 sites. Transient expression can be analysed in F0, however the F1 generation

contain a germ line integration of the constructs, to avoid the problem of a mosaic expression

pattern seen in F0.

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By using zebrafish enhancer assays I can define whether these putative regulatory

elements influence any cell- and/or stage-specific Runx1 haematopoietic expression. It is

possible that mutations or defects in the regulation of in such elements may explain AML

progression in patients who have no known protein-coding mutations or perhaps patients who

have cohesin mutations. It is also necessary to determine whether the identified mouse

enhancers are conserved in humans.

3.2 Hypothesis

I hypothesise that the regions identified by Runx1 4C have enhancer capabilities and

some of them may be conserved in humans. As most enhancers rapidly evolve and are rarely

conserved at a sequence level between species, any regions that are conserved may have an

essential roles in RUNX1 gene regulation. Understanding the regulatory mechanisms that drive

cell-specific expression of RUNX1 in haematopoiesis will have implications for causality and

treatment of leukaemias with RUNX1 dysregulation.

3.3 Aims

1) Elucidate conservation and genomic characteristics of previously identified and

novel Runx1 cis-regulatory elements using bioinformatic tools.

2) Identify the enhancer ability of putative cis-regulatory elements previously

identified by 4C, using zebrafish reporter assays.

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3.4 Characteristics of previously identified regulatory regions

In order to understand the overall contributions of regulatory regions to Runx1

haematopoietic expression and their potential to be dysregulated in myeloid malignancies, in

silico analyses were carried out. Bioinformatic tools can be used to determine conservation of

a regulatory regions, which cells these regions are active in and what transcription factors bind

to them.

3.4.1 Epigenetic and conservation analyses using ENCODE data

ENCODE project data from RUNX1-dependent Human Chronic Myelogenous

Leukaemia cells (K562) were analysed at each conserved locus (Barski et al., 2007; Raney et

al., 2014; Rosenbloom et al., 2013; Wilson et al., 2016) using the UCSC Genome Browser.

Elements included in the search for regulatory features were transcription factor

binding sites (TFBS), open chromatin (DNase I hypersensitivity), histone modifications, CTCF

binding sites, RNA polymerase II (Pol II), cohesin (RAD21 and SMC3 subunits) binding sites

(Table 3.5).

ENCODE also has Chromatin State predictions (ChromHMM) for K562 cells (Kellis

et al., 2017). ChromHMM annotates the data based on epigenetic information. The predicted

chromatin states are active promoter, promoter flanking, inactive promoter, candidate strong

enhancer, candidate weak enhancer, distal CTCF/candidate insulator, transcription associated,

polycomb repressed and heterochromatin/repetitive/copy number variation. Transcription

associated refers to transcriptional transition or elongation (Ernst et al., 2010; Ernst et al.,

2011).

Further gene regulatory markers for K562 cells were uploaded into UCSC genome

browser including; super-enhancers from dbSUPER (Khan et al., 2016), silencers annotated

by Pang et al., (2020) (Pang et al., 2020), cohesin-mediated chromatin accessibility (Antony

et al., 2020).

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Conservation analysis identifies whether these regions could be important for human

haematopoiesis. The UCSC BLAT tool (Kent, 2002) and NCBI BLAST tool (Altschul et al.,

1990) were used to determine the human co-ordinates of mouse (m) Runx1 -368, -327, -321, -

58, -43, -42, +3, +1, +110, +181, +204. Mouse Runx1 m-368, m-368, m-327, m-58, m-43,

m+3, m+110 and m+204 were all found to be conserved in humans (Table 3.6). Runx1 m+204

sequence sits within the previously described RUNX1 intron 5.2.

When gathering sequence information about these regulatory regions I noted that three

regions identified by Dr. Judith Marsman corresponded to the regions described by Schütte et

al., (2016); Marsman’s m-327 is Schütte’s m-328, Marsman’s m-321 is Schütte’s m-322 and

Marsman’s m-58 is Schütte’s m-59. These will be referred to from now as m-327, m-321 and

m-58.

To convert coordinates within a species UCSC ‘liftOver’ tool was utilised (Rosenbloom

et al., 2013). Appendix VII contains the Hg38 locations of Runx1 regulatory regions.

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Table 3.5: Analysed ENCODE annotations

Regulatory association ENCODE annotation Open chromatin DNase I hypersensitivity (HS) Transcription initiation RNA Pol II (phosphorylated at serine

5) Cohesin (subunit) RAD21, SMC3 CTCF binding sites CTCF Histone acetyltransferase that is recruited to transcriptionally active enhancers (an increase in the expression of target genes)

p300

H3K4me1/2 demethylase (mediates transcriptional repression)

LSD1

Super-enhancer previously annotated by Khan et al., (2016)

K562 Super-enhancers

Silencer previously annotated by Pang et al., (2020)

Silencers

Cohesin mediated chromatin accessibility previously annotated by Antony et al., (2020)

Chromatin sites that show increased accessibility in the absence of cohesin (STAG2)

Chromatin State ChromHMM Enhancer Enhancer-associated

histone methylation or acetylation marks

H3K27ac, H3K4me1, H3K4me2

Active promoters H3K27ac, H3K9ac, H3K4me3

Repressed promoters H3K9me3, H3K27me3 Haematopoiesis transcription factors

Erythroid development GATA1 Haematopoietic SPI1 Blood/ Stem progenitors TAL1, GATA2

Table 3.6: Conservation and annotations of previously identified Runx1 regulators

Regulatory region location relative to P1 (kb)

Genome coordinates (Hg19)

ENCODE annotation ChromHMM annotation

Cohesin-mediated chromatin accessibility

m-368 chr21:36849117-36849289

RNA Pol II, p300, LSD1, H3K27ac, H3K4me1, H3K4me3, H3K9ac, H3K9me3, H3K27me3

Strong Enhancer

No change in accessibility in K562 STAG2 mutants

m-327 chr21:36800068-36801396

p300, H3K4me1, H3K9ac, H3K9me3, H3K27me3

Repressed No change in accessibility in K562 STAG2 mutants

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m-321 Not conserved between humans and mice

N/A N/A N/A

-250 chr21:36669712-36670621

RNA Pol II, p300, LSD1, H3K9me3, H3K27me3

Repressed No change in accessibility in K562 STAG2 mutants

-188 chr21:36608329-36608806

p300, H3K4me3, H3K9me3, H3K27me3, SPI1

Transcription Associated


-139 chr21:36561619-36562555

p300, H3K4me1, H3K4me3, H3K9ac, H3K9me3, SPI1

Weak enhancer, Transcription Associated


m-101 chr21:36542872-36543055

RNA Pol II, p300, LSD1, H3K27ac, H3K4me1, H3K4me3, H3K9me3,



m-58 chr21:36478706-36478906

DNase I HS, RNA Pol II, CTCF, p300, LSD1, H3K27ac, H3K4me1, H3K4me3, H3K9ac, H3K9me3, GATA1, TAL1, GATA2

Strong Enhancer


m-43 chr21:36464084-36464260

RNA Pol II, CTCF, p300, LSD1, H3K4me3, H3K9ac, H3K9me3

Weak Enhancer



N/A N/A N/A

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m+1 Not conserved between humans and mice

N/A N/A N/A

m+3 chr21:36418472-36418744

RNA Pol II, CTCF, p300, LSD1, H3K4me1, H3K4me3, H3K9ac, H3K9me3



+62 chr21:36358933-36359953

DNase I HS, RNA Pol II, CTCF, p300, LSD1, H3K4me1, H3K4me3, H3K9ac, H3K9me3, H3K27me3

Weak Enhancer


m+110 chr21:36280710-36281200

DNase I HS, RNA Pol II, RAD21, p300, LSD1, H3K27ac, H3K4me1, H3K4me3, H3K9ac, H3K9me3, H3K27me3 GATA1, TAL1, GATA2

Strong Enhancer

Increased accessibility in K562 STAG2 mutants

m+181 Not conserved between humans and mice

N/A N/A N/A

Intron 5.2 containing m+204

chr21:36179311-36181581

DNase I HS, RNA Pol II, CTCF, p300, LSD1, H3K27ac, H3K4me1H3K9ac, H3K9me3, H3K27me3



m+204 Referred to from now as Intron 5.2 this thesis

chr21:36179925-36180456

DNase I HS, RNA Pol II, CTCF, p300, LSD1, H3K27ac, H3K4me1H3K9ac, H3K9me3, H3K27me3



Based on the ChromHMM data it appears that the in RUNX1-dependent K562 line m-

368, m-58, m+110 are all operating as strong enhancers, whilst -139, m-43, and +62 are

functioning as weak enhancers. Remarkably, +62 a known silencer in K562, also has enhancer-

associated histone methylation marks. Intron 5.2, m-101 and m+3 show the same regulatory

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associations as the enhancers, however, they are labelled as transcription associated.

ChromHMM also reported -139 and -188 as transcription associated. Transcription associated

annotation could be due to their close proximity with exons when compared to the regions

annotated as enhancers, or it may indicate that these regions are producing RNA, such as

enhancer RNA (eRNA). Intron 5.2 and m+110 had increased accessibility in cohesin mutant

K562 cells. Antony et al., (2020) found that sites of increased accessibility were enriched for

motifs of enhancer-regulating bZIP or AP-1 factors (Antony et al., 2020). These regulatory

regions did not overlap with any known K562 super-enhancers or silencers. Interestingly, m-368

and m-327 are both within LOC100506403 an uncharacterised non-coding RNA and may

therefore play a role in its regulation as well.

m-368, -250, m-101, m-58, m-43, m+3, +62, m+110 and intron 5.2 were all bound by

LSD1 a mediator of transcriptional repression usually seen in silencers. SPI1 a haematopoietic

transcription factor was only bound to -139 and -188. Blood stem progenitor transcription

factor TAL1 and GATA2, as well as, GATA1 a marker of erythroid development were found

to bind m+110 and m-58.

These annotations provide a good overview of the likeliness of a region to be regulatory.

However, the analysis is limited to the types of cell lines that have been used for ChiP-seq by

previous groups, as well as which transcription factors were chosen. This may explain why no

consistent expression patterns could be observed in the available K562 data.

To further determine enhancer expression, FANTOM 5 data was considered. Rather

than assessing enhancers via chromatin accessibility and transcription factor binding,

FANTOM 5 detects enhancer RNAs using cap-analysis gene expression (CAGE). The

FANTOM 5 dataset is a compilation of many cell types and RNA transcription across the

genome. It can predict enhancer activity status based on whether short segments of RNA are

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transcribed from the regulatory region. By understanding which cell types enhancers are

transcribed in, a pattern of enhancer arrangement may become apparent.

The FANTOM 5 data showed that not all regulatory regions produced enhancer RNA

(eRNA) in the cells available types for analysis. Regions m-368, -188, m-58, m-101 did not

have any eRNA expression. Although eRNA is typically used for enhancer identification, not

all active enhancers produce detectable eRNA (Mikhaylichenko et al., 2018; Sartorelli et al.,

2020). It remains unclear whether these enhancers produce low levels of eRNAs that are not

easily detectable with current methods.

All other regions expressed RNA in haematopoietic cells, however there was no

consistent expression patterns in other tissues (Table 3.7).

Table 3.7: Cell types with regulatory region RNA expression


Cell type expressing regulatory region RNA

m-368 Not expressed

m-327 Skin, Liver, Brain, Lung, Haematopoiesis, Stomach, Oesophagus, Colon, Gall Bladder, Mesenchymal

-250 Prostate, Brain, Haematopoiesis, Colon, Gall Bladder, Mesenchymal

-188 Not expressed

-139 Liver, Breast, Brain, Lung, Haematopoiesis, Oesophagus,

m-101 Not expressed

m-58 Not expressed

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Cell type expressing regulatory region RNA

m-43 Brain, Lung, Haematopoiesis,

m+3 Haematopoiesis, Mesenchymal, Kidney

+62 Skin, Prostate, Liver, Breast, Brain, Lung, Haematopoiesis, Ovary, Oesophagus, Colon, Mesenchymal, Kidney

m+110 Prostate, Haematopoiesis

Intron 5.2

Skin, Prostate, Brain, Lung, Haematopoiesis, Stomach, Colon, Mesenchymal, Kidney

Single nucleotide polymorphism (SNP) analysis

Ernst. et al., (2011) highlighted that disease variants, such as single nucleotide polymorphisms

(SNPs) in genome-wide association studies (GWAS) frequently overlapped with enhancer

elements relevant to the disease cell type (Ernst et al., 2011). To determine if there were any

SNPs in each regulatory region, GWAS catalog (Buniello et al., 2019) and HaploReg v4.1

(Ward et al., 2016) were used. SNPs were also analysed to ensure they had a minor allele

frequency (MAF) of >1 % using the Genotype-Tissue Expression (GTEx) portal v8

(https://gtexportal.org/home/). gnomAD browser v2 1.1 and v3 (Koch, 2020) were used to

review SNPs with MAF >1 % for any ClinVar associations and/or publications. Some of the

SNPs identified in this research (Table 3.8) were linked to SNP variants with r2 values of >=

0.8 and r2 >= 1. As this was a preliminary investigation these linked SNPs were not investigated

further.

Multiple SNPs were identified in all of the regulatory regions except m-101. m-368,

m+110 and intron 5.2 contained SNPs that have functional effects in haematopoietic cells

(Table 3.8). Therefore, mutations in these regulatory regions have the potential to alter TF

https://gtexportal.org/home/

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binding sites and affect the regulatory activity of that region of DNA. Some of the other SNPs

may be functional, but have not yet been investigated.

Table 3.8: SNP analysis of previously identified Runx1 regulators

Regulatory region

Number of SNPs (MAF>=1%)

SNP name Predicted changes SNP function

m-368 3 rs116951441

not predicted to change any motifs

Not investigated

rs16993221 A to T change of rs16993221 alters 2 regulatory motifs BATF and IRF which work together in immune response.

Related to white blood cell count a crucial marker which contributes to chronic inflammation (p value: 2E-8)(Kong et al., 2013).

rs909143 The A to G change in rs909143 is predicted to affect 3 regulatory motifs

Has an eQTL with oesophagus and is related to gene expression of Kynurenine 3-monooxygenase gene in peripheral blood monocytes (p value: 1.84E-06)(Zeller et al., 2010).

m-327 2 rs4817723 not predicted to change any motifs.

Not investigated

rs12106380 T to G change of rs12106380 alters 6 regulatory motifs including GATA binding sites.

Not investigated

-250 3 rs57911917 not predicted to change any motifs

Not investigated

rs2834885 C to A change of rs2834885 alters 1 regulatory motif, PEBP.

Not investigated

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Regulatory region



rs61607093 T to C change of rs61607093 alters 20 binding motifs including GATA3, Nanog, SPI1, Pax5 and p300

Not investigated

-188 1 rs35526434 C to T change alters 12 regulatory motifs including SPI1, PAX5 and p300.

Not investigated

-139 3 rs189789980

T to G change alters 3 regulatory motifs.

Not investigated

rs140039393

TTA to T change alters 4 binding motifs including Sox5.

Not investigated

rs2834825 G to A change alters 19 motifs including Pou2f2.

Not investigated

m-101 0 Not applicable

Not applicable Not applicable

m-58 13 rs2834768 G to A change of rs2834768 alters 13 regulatory motifs including MYC, TAL1 and multiple GATA sites.

Not investigated

rs2834769 C to T alters 3 binding motifs.

Not investigated

rs13049322 not predicted to change any motifs

Not investigated

rs73374626 G to A change alters 5 motifs including MYC

Not investigated

rs9984842 C to T is predicted to alter 12 motifs including KLF4, KLF7 and SP1.

Not investigated

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Regulatory region



rs113221662

C to T has 7 predicted motif binding alterations including GATA3 and SP1.

Not investigated

rs66840558 2 altered motifs. Not investigated

rs144641305

contains a change from AAG to A and is predicted to affect 10 binding sites including p300, PAX5, NANOG

Not investigated


Not investigated


Not investigated

rs2834770 A to G change alters CEBPβ site and Hdx (brain related).

Not investigated

rs7283199 C to T change which predicts Sox17 (which modulates WNT3A and represses RUNX1 expression) and HDAC2 motif changes

Has eQTLs reported with skin and thyroid

rs41360844 G to C shows 2 predicted regulatory motif changes.

Not investigated

m-43 2 rs73902837 C to G predicted to change 2 regulatory regions

Not investigated

rs2834756 T to C change alters 4 regulatory motifs

Not investigated

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Regulatory region



m+3 1 rs9978978 T to C change to change 1 regulatory motif

Not investigated

+62 2 rs933131 G to A change alters 4 regulatory motifs including Pax5 and Pax8.

Not investigated

rs2834716. not predicted to alter any regulatory motifs

Not investigated

m+110 2 rs73900579 T to C change alters 2 regulatory motifs CEBPα and Pax4.

Has an eQTL with brain related to red cell distribution width (Kichaev et al., 2019).

rs201708857

A to AG change is predicted to alter 20 regulatory motifs including CEBPA, SP1 and Pou2f2.

Not investigated

Intron 5.2

3 rs2268276 (G major - A minor) alters 2 regulatory motifs.

These two SNPs (rs2268276 and Rs2249650) were found to be associated with acute myeloid leukaemia susceptibility. The different SNPs are in LD and change the ability of the enhancer. A (rs2249650) G (rs2268276) bases and AA have more enhancer capability that GA and GG. GA has the most reduced enhancer capability. The SNPs also affect SPI1 binding capability; AG has strong SPI1 binding, GA has weak SPI1 binding, whereas AA and GG have medium SPI1 binding capability (Xu et al., 2016)

Rs2249650 (G major- A minor) alters 4 binding motifs including CEBPA and Pouf2f.

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Regulatory region



rs8130772 A to G alters 5 binding motifs including SP1 and FLI1.

Not investigated

To determine a potential regulatory relationship, Dr. William Schierding and I

identified which genes a SNP physically interacts with and whether that SNP-gene interaction

is supported by an allele-specific change in gene expression (CoDeS3D pipeline (Fadason et

al., 2018)). First, CoDeS3D interrogates 70 Hi-C chromatin interaction libraries (from a diverse

set of human cell lines) to identify interactions across the genome with the SNPs. Next, each

SNP is tested for an allele-specific association to the expression level of the spatially associated

partner gene (the SNP is an expression quantitative trait locus (eQTL).

Despite finding many regulatory spatial interactions between RUNX1 regulatory region

SNPs and RUNX1, none of them were supported by a significant eQTL interaction (FDR<

0.05). One reason for this lack of significance is that the CoDeS3D pipeline uses the GTEx

portal for eQTL outputs, which only can test 54 tissues sampled from healthy older individuals.

GTEx also only measures whole blood, rather than individual cell types, so cell type- or

disease-specific effects may not be detected.


To assess whether any AML patients had mutations in the conserved regulatory regions,

publicly available cancer genomic datasets were explored. Online tools were used to categorise

publicly available patient information into different tumour types for analysis.

The large-scale cancer genomics datasets were retrieved from International Cancer

Genome Consortium (ICGC) Data Portal (Hudson et al., 2014; Zhang et al., 2011), the c-

BioPortal for Cancer Genomics (Cerami et al., 2012; Gao et al., 2013) and the Cancer Genome

Atlas (TCGA) Data Portal (CGARN, 2013).

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International Cancer Genome Consortium (ICGC) was the only database that allowed

for analysis of non-coding regions. RUNX1 regulatory regions (as defined in Table 3.6) were

analysed for mutations in cancer datasets, with 237 mutations found (Table 3.9). The number

of mutations observed in the different RUNX1 regulatory regions in haematopoietic- related

cancers was relatively low (6.75%). The majority of mutations in these regions occurred in

skin-related cancers (39.24%) (Table 3.9). However it is worth noting that even normal skin

has a high mutational load relative to other tissues (García-Nieto et al., 2019; Martincorena et

al., 2015).

Table 3.9: Spread of 237 Runx1 regulatory region mutations that were found in ICGC dataset represented

in a range of primary tissue sites.

Primary site of mutation Representation of found mutations (%) Skin 39.24% Liver 10.97% Brain 8.44% Oesophagus 8.02% Haematopoietic 6.75% Prostate 4.22% Breast 3.80% Pancreas 3.80% Lung 3.38% Ovary 2.53% Gall Bladder 2.53% Mesenchymal 1.69% Head and Neck 1.69% Stomach 1.27% Kidney 0.84% Colorectal 0.42% Uterus 0.42%

The regulatory regions showed different frequencies of mutations in this dataset (Figure

3.3). m-58 had the highest frequency of mutations with 81 incidences, followed by intron 5.2

(41) and then m-327 (27). m-58 also showed the highest incidence of mutation in

haematopoietic cancers (4). Each enhancer had a relatively diverse profile of primary mutations

sites (Figure 3.3)

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Figure 3.3: Proportions of cancerous primary site mutations of regulatory regions.

Each of the regulatory regions displayed diverse cancerous primary site profiles. Primary site profiles are colour coded. Key is at the bottom of the figure. Total number of mutations excluding skin in each regulatory region shown at top of each bar. As normal skin has a high mutational load relative to other tissues it was not included in this diagram.

Some of the regulatory region SNPs (from Section 3.4.2) were reported in patients: an

oesophageal adenocarcinoma patient had the m-327 T>G rs12106380 SNP, another

oesophageal adenocarcinoma sample had the same G to A change reported in m-58 rs8129889

SNP. Three patients had -250 SNP rs2834885, two with acute myeloid leukaemia and one with

oesophageal adenocarcinoma. Four patients with malignant lymphoma had m-58 mutations

chr21:g.36480715C>A (rs199811665), chr21:g.36480761A>C and chr21:g.36481470C>T

(rs1440069314). None of these SNPs were reported in the GTEx portal. Two SNPs in +62 were

found in patients with acute myeloid leukaemia rs933131 and rs2834716. Interestingly, the

patient with rs2834716 also had the intron 5.2 SNPs (rs2268276 and rs2249650) which have

been previously associated with acute myeloid leukaemia susceptibility (Xu et al., 2016).

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Intron 5.2 SNP rs2268276 was also found in a patient with lung cancer. Three other patients

also reported intron 5.2 SNP rs2249650, two with lung cancer, one with paediatric brain

tumour.

The genomic data in the ICGC Data portal contains multiple mutations per patient. The

analyses of these regions shows that some of the reported SNPs are found in AML patients,

however, it is unknown whether many of these SNP have a functional impact in these patients.

As was done for the analysis of intron 5.2 SNPs (Xu et al., 2016), there is a need to analyse

each of these SNPs for functional capability. Interestingly, the majority of cancers reported

with mutations in RUNX1 regulatory regions were not blood-specific, instead reflecting the

roles of RUNX1 in other tissues (Groner et al., 2017). The tissues highlighted as expressing

regulatory regions in FANTOM 5 data (Table 3.7) were the same primary sites of mutation in

ICGC data (Table 3.9, Figure 3.3).

3.5 Zebrafish enhancer reporter assays

To understand the tissue-specific activity of Runx1 regions that directly interact with Runx1 -

P1, -P2 and +24 (Marsman, 2016), zebrafish reporter assays were used.

DNA sequences of the putative enhancers were amplified from mouse gDNA then

cloned into zebrafish enhancer detector assay reporter constructs (Figure 3.4), and injected into

one-cell zebrafish embryos.

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Figure 3.4: Schematic representation of ZED assay vector.

This vector contains two cassettes, the first cassette consists of cardiac actin promoter (blue) upstream of Red Fluorescent Protein (red). The second cassette contains the gateway cassette (yellow), which is immediately downstream of the gata2 promoter (blue), downstream of the green fluorescent protein (green). The second cassette is protected by insulator sequence (purple). Purple dotted line indicates the insulated sequence, which prevents positional effects from surrounding DNA, following integration. Tol2 sites (orange) allow for integration of vector into zebrafish genome. The vector contains two excision cassettes, one targeted by Flip-recombinase (FRT sites) and the other by Cre-recombinase (LoxP sites). Figure from Thomas (2015).

The zebrafish enhancer detector assay (ZED) is designed to have two different

cassettes; one to test enhancer activity of the regulatory element in a cell type specific manner

and a second cassette which functions as a an internal control for assessing injection efficiency

and a transgenic marker. If the regulatory region acts as an enhancer it will cooperate with the

gata2 promoter to drive cell-specific GFP expression. The enhancer detection cassette is

bordered by two insulator sequences which prevent interaction between the two cassettes. The

two insulator sequences also prevent position effects from the surrounding DNA following

integration, eliminating false positives. LoxP and FRT sites allow for deletion of either cassette

using Flipase or Cre recombinase. The internal control cassette utilises the cardiac actin

promoter (pCarA) to drive, Red Fluorescent Protein (RFP) expression in the somites, which

serves as a marker for successful transgene integration. The second cassette designed to test

the cell-type specific functional ability of a putative enhancer contains; a gateway entry site for

the putative enhancer, a gata2 minimal promoter and the enhanced GFP reporter gene.

Tol2 sites permit transposon-mediated integration into the zebrafish genome with high

efficiency in the presence of Tol2 transposase mRNA (Kawakami, 2005). Germline integration

into the host genome allows for expression of the ZED cassette in subsequent generations. If

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vector is successfully integrated, non-mosaic expression patterns are seen. Unsuccessful or

partial integration can lead to inconsistent and mosaic expression.

3.5.1 Zebrafish Enhancer assay F0:

All putative mouse Runx1 enhancer regions identified by Dr. Marsman’s 4C (ZED

m+110, m-48, m-58, m-303, m-321, m-327, m-354, m-368 and m-371) and ZED +24 were

tested for enhancer activity in a zebrafish enhancer assay (Figure 3.4).

Runx1 +24 was used as a positive control and empty ZED assay vector was utilised as

a negative control. (Figure 3.5 A). Runx1 +24 drove GFP expression specifically in the

intermediate cell mass (ICM) and posterior blood island, which are sites of hematopoietic

progenitor cell production, at 20-24 hours post-fertilisation (hpf) (de Jong et al., 2005).

Eight of the nine putative enhancer regions ZED m+110, m-48, m-58, m-303, m-321,

m-354, m-368 and m-371, showed similar expression patterns to +24 (Figure 3.5 B). These 8

regions functioned as hematopoietic-specific enhancers in zebrafish, indicating that their

enhancer activity is conserved.

ZED m-303 also showed expression in the keratinocytes at 20-24 hours post

fertilisation (hpf) and 48 hpf as well as haematopoietic activity at 20-24 hpf (Figure 3.5 C).

ZED empty and ZED m-327 had no observed enhancer activity at these timepoints (Figure 3.5

A,B).

Further analysis of these Runx1 enhancers highlighted that each region had individual

expression patterns (Figure 3.6). Of the enhancer patterns +24, and m-368 had similar patterns

with the highest expression in the dorsal aorta (DA), followed by expression in the intermediate

cell mass (ICM). ICM is the site of the primitive wave of haematopoiesis and it generates

primitive erythrocytes and limited myeloid populations. The DA is a main axial vessel, which

carries blood away from the heart, towards the tail. The first wave of definitive haematopoiesis

arises in the ventral wall of the DA.

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The m-371, m-354, -m-321, m-58 and m-48 enhancers drove the highest expression in

the ICM. m+110 produced even expression in the DA and ICM as well as in the tail bud of the

zebrafish. m-303 produced the highest expression keratinocytes in the tail lining, followed by

DA expression. High DA expression suggests m-371, m-368, m-354 and m+110 have the

potential to act as blood cell-specific enhancers, and may be involved in the initial wave of

definitive haematopoiesis.

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Figure 3.5: Putative mouse enhancers are active in hematopoietic regions in zebrafish.

Whole mount representative lateral images of zebrafish embryos (20-24 hpf [A, B] and ~48 hpf [C]) that were injected with enhancer-GFP plasmids at the one-cell stage; left hand panels are merges with bright field, right hand panels are GFP fluorescence. (A) negative control ZED plasmid shows zero (image) or non-specific fluorescence, while positive control (+24) shows GFP expression in hematopoietic cells of the posterior blood island (red arrows) and intermediate cell mass (yellow arrows). (B) enhancer activity of m+110, m-48, m-58, m-303, m-321, m-327, m-354, m-368, m-371. (C) The m-303 enhancer also expressed GFP in keratinocytes (white arrow). The numbers in the right hand panels represent the number exhibiting the representative phenotype out of the total number of fluorescent embryos analysed. Figure originally used in Marsman et al., (2017) (Marsman et al., 2017) This image is used with permission under Creative Commons CC BY license.

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Figure 3.6: . Quantitative summary of GFP expression patterns generated by putative mouse enhancers in

zebrafish.

For each enhancer construct (m+110, m- 48, m-58, m-303, m-321, m-327, m-354, m-368, -m371) and the negative (ZED) and positive (+24) controls, GFP expression data in different tissues is shown. Diagrams represent pooled expression from at least 5 photographed embryos per putative enhancer (20-24 hpf), displayed on illustrations of a 24 hpf zebrafish embryo. Categories of expression zones are colour-coded: key is at the bottom of the Figure. Bar graphs display the percentage of the total number of GFP-expressing embryos that show expression in each tissue category for each putative enhancer. The total number of expressing embryos analysed per construct is displayed in the top right corner of each graph. Figure originally used in Marsman et al., (2017) (Marsman et al., 2017) This image is used with permission under Creative Commons CC BY license.

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The expression of enhancer assays in injected generation of zebrafish (F0) is mosaic

(not expressed in every cell), however this still provides information about tissue specific

activities for the identified enhancers. F1 fish can be grown and used for further analysis.

3.5.2 Zebrafish Enhancer assay F1:

Although strong enhancers were detected using transient GFP expression in zebrafish

reporter assays, it is more informative to analyse the F1 fluorescence, as each embryo should

have complete integration of the construct in all cells (Bessa et al., 2009). Cell-specific GFP

expression through development would reveal more precisely tissue-specific spatiotemporal

activity of the enhancer.

The injected generation, F0, which presented complete fluorescence in the somites of

embryos were selected to produce transgenic lines. When the F0 were sexually mature they

were out-crossed with wild type fish in order to determine the individual integration success of

the assay into the gametes. F1 fish can be grown and used for further analysis.

Ute Zellhuber-McMillan and I bred all the fish lines to try establish stable F1 lines.

Together we bred 11 different fish enhancer lines, from which over 140 fish were paired for

breeding and 116 fish produced embryos. The fish produced 15,314 embryos which were

screened for green fluorescence (20-24 hpf) and red fluorescence (48 hpf), however no

embryos showed any control somite RFP or enhancer GFP.

Lack of fluorescence does not appear to be a Tol2 mRNA issue, as others lines in the

lab have made successful F1 lines from the same Tol2 mRNA (Ketharnathan et al., 2018). It is

unlikely that the lack of fluorescence is due to injection technique as I clearly observed

fluorescence in F0, and have completed successful double injections for other projects during

my PhD (Meier et al., 2018). This could be a blood enhancer specific issue, as none of the

different plasmids used have produced successfully integrated F1 lines (I previously have used

ZED, SCE, Tol2Gata2). Personal communication with the Zon lab indicated that other

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researchers have been unsuccessful in making haematopoietic specific enhancer F1 lines using

assays that contained the zebrafish gata2 promoter. It has been proposed that due to the delicate

nature of haematopoiesis, if the plasmid DNA is integrated into the zebrafish genome, it is

actively repressed to maintain the correct haematopoiesis. As the plasmid insert is repressed

there is no RFP or GFP expressed in F1 fish.

3.6 Genomic characteristics of 4C identified functional novel

enhancers

To understand if there was a rationale to the cell type patterns observed in the zebrafish

enhancer assays (Figure 3.6), Dr. Judith Marsman analysed the Runx1 4C identified regulatory

regions (Table 3.4) (Runx1 m+24, m+110, m-48, m-58, m-303, m-321, m-327, m-354, m-368

and m-371) in the available mouse haematopoietic progenitor cells (HPC7) ENCODE data. No

consistent patterns could be observed. Runx1 -327 had similar transcription factor binding to

other observed enhancers but no observed enhancer activity in zebrafish embryos. Interestingly

the Runx1 -368 does not bind any transcription factors, shows no sign of open chromatin

(DNase I HS site) and therefore does not appear to have a regulatory function in developing

haematopoietic cells.

Of the Runx1 4C identified regulatory regions, m-371, m-354, m-303 and m-48 were

novel. As noted in section 3.1.1 m-368, m-327, m-321, m-58 and m+110 were identified by

other groups (Ng et al., 2010; Schütte et al., 2016).

In order to further understand the novel Runx1 enhancers identified by Judith Marsman

(m-371, m-354, m-303 and m-48), I carried out the same in silico analysis used to define the

previously identified Runx1 regions (Section 3.4).

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3.6.1 Conservation and epigenetic analysis

The only novel Runx1 enhancer that is conserved in humans is m-371 (Table 3.10).

This region shows transcription factor binding sites and chromatin modifications similar to that

found at m-58 and m+110. ChromHMM annotates this region as a strong enhancer like m-368,

m-58 and m+110. The m-371 enhancer region sits within the same uncharacterised non-coding

RNA LOC100506403 as m-368 and m-327.

m-354, m-303 and m-48 are mouse specific novel Runx1 enhancers.

Table 3.10: Conservation and annotations of novel Runx1 regulators




Cohesin mediated chromatin accessibility

m-371 chr21:36855111-36856085

DNase I HS, RNA Pol II, CTCF, p300, LSD1, H3K27ac, H3K4me1, H3K4me3, H3K9ac, H3K9me3, H3K27me3, GATA1, TAL1, GATA2

Strong Enhancer

Increased accessibility in K562 cohesin mutants


N/A N/A N/A


N/A N/A N/A


N/A N/A N/A


The FANTOM 5 data showed that m-371 RNA was actively expressed in skin and

prostate cells. SNP analysis of m-371 showed that one of the two SNPs in this region is related

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to Kynurenine 3-monooxygenase (KMO) expression in peripheral blood monocytes (Table

3.11) identical to m-368 SNP rs909143. KMO is typically researched in a neurological context,

as high KMO expression leads to neurodegeneration (Breton et al., 2000).

The 3D spatial interactions found by CoDeS3D pipeline between RUNX1 m-371 SNPs

and RUNX1, were not supported by any significant eQTL interactions.

Table 3.11: SNP analysis of novel m-371 enhancer

Regulatory region



m-371 2 rs2834944 The T to C change alters 4 regulatory motifs.

Not investigated

rs2834945 The T to C change is predicted to affect 11 regulatory motifs including GATA2 and PAX3 and PAX5.

Has an eQTL with oesophagus and is related to Gene expression of KMO in peripheral blood monocytes (p value: 1.36E-06)(Zeller et al., 2010).


The majority of m-371 mutations (n=23) presented with skin as the primary site

(40.90%), followed by prostate, liver and breast primary sites (13.63% each). m-371 also

presented in oesophagus (9.09%), pancreas (4.54%) and ovary (4.54%).

The FANTOM5 expression of m-371 in skin and prostate cells overlaps with the

primary tissues that contain m-371 mutations. This suggests that m-371 is an enhancer in skin

and prostate cells, as it is producing eRNA. Mutations in this enhancer, in skin and prostate

tissues may alter normal cell development.

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3.7 Chapter summary

This work identified conservation of 9 out of the 16 mouse Runx1 regulatory regions

from mouse to human (m-371, m-368, m-327, m-101, m-58, m-43, m+3, m+110, m+204 ). m-

354, m-321, m-303, m-48, m-42, m+1 and m+181 are not conserved in human.

In K562 cells ChromHMM annotates m-368, m-58, m+110 -139, m-43, and +62 as

enhancers. Intron 5.2, m-101 and m+3 show the same regulatory associations as the enhancers,

however, they are instead labelled as transcription associated. This could be due to their close

proximity with exons when compared to the regions annotated as enhancers. ChromHMM also

reported -139 and -188 as transcription associated. m-371, m-368 and m-327 all sit within

LOC100506403, an uncharacterised non-coding RNA.

m-368, -250, m-101, m-58, m-43, m+3, +62, m+110 and intron 5.2 were all bound by

LSD1, a mediator of transcriptional repression usually seen in silencers. SPI1, a haematopoietic

transcription factor was only bound to -139 and -188. Blood stem progenitor transcription

factors TAL1 and GATA2, as well as GATA1, a marker of erythroid development, were found

to bind m-371, m+110 and m-58.

Cheng et al., (2018) characterised +62 as a silencer of RUNX1 P2 in K562, OCI-AML3

and U937 cells (Cheng et al., 2018). However in K562 cells +62 has ENCODE and

ChromHMM annotations associated with enhancer activity.

Intron 5.2, m+110 and m-371 regions were annotated as differentially opened

chromatin in the ATAC-seq of the cohesin mutant (Antony et al., 2020). This suggests these

enhancers’ interactions could be dependent on cohesin. In this chapter, I observed a

coincidence of cohesin subunit Rad21 binding in the absence of CTCF with +110 enhancer,

and CTCF binding with m-371, m-58, m-43, m+3, +62 and intron 5.2. This suggests that CTCF

and cohesin could independently mediate a subset of enhancer-promoter looping in

combination with transcription factors.

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Nine of the 13 conserved RUNX1 regulatory regions produced eRNA in human

haematopoietic cells. SNP analysis identified SNPs in each of the conserved regulatory regions

except m-101. Of the 35 SNPs 5 had previously reported functional effects in haematopoietic

cells. The available AML genomic data is relatively limited because the majority of analyses

were done with exome sequencing data rather than whole genome sequencing data.

I functionally characterised novel Runx1 enhancers m-371, m-354, m-303 and m-48

using zebrafish enhancer assays. I also characterised previously identified m+110, -58, -321, -

327, -368 showing that all of these have enhancer capability except m-327. Despite the lack of

activity seen in embryos expressing m-327, this region spatially connects to the active promoter

Runx1 P1 and +24 in HPC7 cells so it may have another regulatory role in this hub. My analysis

showed enhancer activity in haematopoietic tissues of developing zebrafish embryos,

highlighting that they appear to be important in both primitive and definitive waves of

haematopoiesis.

After I functionally analysed the mouse enhancers using zebrafish reporter assays, Zhu

et al., (2020) further characterised m-371 using single cell (sc) ATAC-seq and scRNA-seq

from developing mice. m-371 is only accessible in pre- haemogenic endothelial (HE) cells and

in the intra -arterial clusters (IAC) in mice. This suggested it could initiate Runx1 P1 in pre-

HEs and contribute to expression of P1 in IACs (Zhu et al., 2020).

Of the enhancers functionally identified, all except +110 interacted with Runx1 +24

(Marsman, 2016), suggesting that +24 could be anchoring an enhancer hub. These CREs are

conserved between mice and humans suggesting they are fundamental for the correct regulation

of RUNX1.

This chapter has provided an insight into mouse Runx1 enhancer functions and

highlighted the features of conserved human enhancers. As shown in Dr. Judith Marsman’s 4C

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analysis +24 is a central hub that binds most active Runx1 enhancers in HPC7s. +24 may also

be the organiser of correct enhancer-promoter interactions for Runx1 regulation.

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4 Chapter 4: Three-dimensional (3D) connections of

Runx1 +24

4.1 Introduction

The three-dimensional (3D) organisation of the genome is non-random (Holwerda et

al., 2012; Lieberman-Aiden et al., 2009). The genome is organised into topologically

associated domains (TADs), formed by an elevated number of chromatin interactions within a

genomic locality, with fewer interactions to outside regions (Lieberman-Aiden et al., 2009;

Rao et al., 2014). Genes located within the same TAD often have the same histone

modifications and are co-regulated (Rao et al., 2014). The borders of domains are enriched for

cohesin and CTCF binding sites, housekeeping genes, short interspersed element (SINE)

retrotransposons and transfer RNAs (Dixon 2012). Loss of CTCF relaxes the global domain

structures, which increases contacts between domains, whereas loss of cohesin mostly affects

interactions within domains (Li et al., 2013b; Zuin et al., 2014). 3D connections of the genome

are predominantly detected by Hi-C and Promoter Capture sequencing, allowing for high

throughput analysis and subsequently the creation of genome interaction maps (example shown

in Figure 4.1).

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Figure 4.1: Hi-C contact map of the RUNX1 region in K562 cells.

Hi-C data generated by Rao et al., (2014) from chromosome 21 was visualised in 3D genome browser (http://3dgenome.fsm.northwestern.edu/view.php) at a 25-kb resolution (Wang et al., 2018). Every square is coloured according to the frequency of two regions interacting. The alternating yellow and blue bars are predicted TADs. The dark red bars are DNase I hypersensitive sites (DHS) in the same cell type. Locations of genes are annotated according to their transcription direction, forward strand (black) and reverse strand (blue).

In Chapter 3 I described the characterisation and function of Runx1 regulatory regions.

The majority of functional haematopoietic-specific Runx1 enhancers interacted with +24 in

mouse hematopoietic progenitor (HPC7) cells (Marsman, 2016). Runx1 +24, a well

characterised enhancer, interacts with promoters (P1 and P2) of Runx1 (Markova et al., 2011;

Marsman, 2016). I hypothesise that Runx1 +24 may nucleate an enhancer hub, thereby

controlling Runx1 connections and contributing to the overall Runx1 organisation and

expression.

Previous work from Dr. Antony described RUNX1 transcription in normal and cohesin

deficient megakaryocytic differentiation (Antony et al., 2020). Runx1 +24 is crucial to

hemogenic endothelial cells in mouse and zebrafish (Markova et al., 2011; Marsman et al.,

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2017; Ng et al., 2010; Schütte et al., 2016). Interestingly this well-known enhancer was also

described as a silencer in the non-haematopoietic cell line, HEK293 (Markova et al., 2011).

Understanding where in the genome Runx1 +24 connects to and how it contributes to Runx1

organisation is imperative to understanding the transcriptional regulatory landscape of Runx1.

4.1.1 Secondary structure of DNA

G-quadruplexes are secondary structures that are often present in telomeres and also

gene promoters; however, their function remains elusive. Previous studies on G-quadruplexes

indicate possible roles for them in controlling gene expression (Du et al., 2007; Eddy et al.,

2008; Zhao et al., 2007).

4.1.1.1 Secondary structure of Runx1 +24

In my previous Master’s work, a possible G-quadruplex (G4) structure was identified

in the Runx1 +24 mouse cis-regulatory element (CRE). Despite multiple attempts and different

primers, the mCRE was never sequenced the full way through using Sanger sequencing. When

compared back to the mouse (mm9) +24 mCRE sequence sent by Professor Motomi Osato, it

was noted that the sequences stopped in the same place every time. The online tool QGRS

mapper was used to predict if there was a G-quadruplex structure (Kikin et al., 2006).

Two predicted quadruplex structures sit either side of the +24 region that could not be

sequenced. One was in the sense direction and the other was in the antisense direction.

Sequences can be found in Appendix IV. Each predicted structure has the potential to form a

four-stranded structure stabilised by G-quartets (Figure 4.2). G-quadruplex structures derive

stability from hydrogen bonding between guanines and stacking of G-quartets (Maizels et al.,

2013). Due to time constraints I have not investigated to see if this structure is predicted in the

human sequence.

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Figure 4.2: Schematic representation of mouse +24 individual G-quadruplex.

Four guanines form a G-quartet, a planar ring around a central channel (represented by each grey rectangle). G-quadruplex structures derive stability from hydrogen bonding between guanines and stacking of G-quartets .

In the work described in this Chapter, I sought to understand how Runx1 +24 influences

the 3D structure and connectivity of the Runx1 locus. To understand how the formation of the

G-quadruplexes influences transcription, I measured the predicted mouse Runx1 G-quadruplex

3D structure integrity using a Circular Dichroism (CD) photospectrometer. I then determined

whether G-quadruplex structure impacts on enhancer capability by using luciferase assays in

mouse fibroblast (NIH/3T3) cells.

I used circular chromatin conformation capture (4C) to identify DNA-DNA interactions

with the RUNX1 +24 enhancer region. 4C is an unbiased approach that detects genome-wide

interactions with a chosen region or “bait” by using next generation sequencing of captured

fragments (van de Werken et al., 2012a). Identification of RUNX1 +24-anchored interactions

has potential to further elucidate the role of this enhancer by measuring its genomic

connectivity. 4C was performed in the K562 (chronic myelogenous leukaemia) cell line. In

K562, RUNX1 P1 expression was previously shown to have a key role in megakaryocyte

lineage determination (Draper et al., 2017). K562 cells can be induced to differentiate to the

megakaryocytic lineage (Kuvardina et al., 2015; Pencovich et al., 2011).

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Majority of 3D chromatin mapping has been done in steady state cells. Cohesin

depletion has been shown to alter, and in some cases result in the complete collapse of TADs

(Waldman, 2020). Cuartero et al., (2018) and Antony et al., (2020) have recently shown upon

differentiation stimulation, the gene expression changes are more distinct in cohesin depleted

cells (Antony et al., 2020; Cuartero et al., 2018b). To determine the influence of cohesin and

enhancer specific connections in lineage specific regulation, the chromatin architecture should

be investigated before, during and after the differentiation process.

To understand the role of cohesin in enhancer connections and leukemic gene

regulation, K562 cells with CRISPR-CAS9 engineered STAG2 mutations were used to

determine the connectivity of RUNX1 +24. K562 cells were induced to differentiate to the

megakaryocytic lineage, using phorbol 12-myristate 13-acetate (PMA), so the role of RUNX1

+24 could be further understood. K562 lines with patient-specific STAG2 R614* mutation (-/-

) were previously generated by Dr. Antony.

Antony et al., (2020) highlighted the altered phenotype of K562 cells with STAG2

mutation using RNA seq (Antony et al., 2020). Using ATAC-seq Dr. Antony also noted altered

chromatin accessibility in K562 cells with STAG2 mutation. The RNA seq and ATAC seq

revealed that the gene expression changes observed are associated with altered chromatin

accessibility at regulatory regions and super-enhancers in STAG2 mutants (Antony et al.,

2020).

K562 cells are labelled as wild-type (WT) cells with the treatment condition (PMA or

DMSO) and timeframe also included in the nomenclature. STAG2 refers to the STAG2-mutant

K562 cells that were CRISPR edited to contain R614* mutation in the STAG2 subunit (Antony

et al., 2020). The baseline treatment condition was collected after 12 hours (h) of DMSO

treatment (12 DMSO). Cells were collected after 12 h of PMA treatment (12 PMA) and 72 h

of PMA treatment (72 PMA). 12 h of PMA treatment was chosen based on the aberrant RUNX1

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and ERG expression spikes seen in STAG2 mutants at this timepoint (Antony et al., 2020) and

72 h is the time point when the aberrant expression spikes of RUNX1 seen in STAG2 mutants

return to non-PMA stimulated levels (Antony et al., 2020).

I chose RUNX1 +24 as a ‘bait’ to capture chromatin interactions because it is a strong

and well-characterised enhancer of RUNX1 transcription and therefore is likely to make

important regulatory contacts during haematopoietic cell differentiation. Identifying the +24

contacts in leukaemia cells may enhance our understanding of haematopoiesis and the

development of leukaemia.

4.2 Hypothesis

I hypothesise that RUNX1 +24 makes 3D genomic contacts that are important in the

regulation of RUNX1 expression.

4.3 Aims

1) Identify enhancer characteristics of RUNX1 +24 using bioinformatic tools.

2) Characterise a possible G-quadruplex structure in the mouse Runx1 +24 enhancer.

3) Identify the 3D connections of human RUNX1 +24 using 4C in K562 cells.

4) Determine genomic characteristics of significant chromatin interactions using

bioinformatic tools.

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4.4 Enhancer characteristics of RUNX1 +24

RUNX1 +24 was analysed in the same manner as the other Runx1 regulatory regions

(Section 3.5).

4.4.1 Epigenetic analysis

The +24 region shows similar haematopoietic transcription factor binding sites and

chromatin modifications to m-371 m-58 and m+110. However, +24 has additional cohesin

(SMC3) and SPI1 binding. ChromHMM annotates this region as a strong enhancer, similar to

m-371, m-368, m-58 and m+110. Interestingly, +24 showed no change in chromatin

accessibility in K562 cohesin-mutant cells relative to wild type K562.

Table 4.1: Conservation and annotations of RUNX1 +24

Enhancer location relative to P1 (kb)




+24 chr21:36399106-36399322

DNase I HS, RNA Pol II, SMC3, CTCF, p300, LSD1, H3K27ac, H3K4me1, H3K4me3, H3K9ac, H3K9me3, GATA1, SPI1, TAL1, GATA2

Strong Enhancer No change in accessibility in K562 cohesin mutants


FANTOM5 data showed that +24 eRNA is actively expressed in haematopoietic cells.

No SNPs with a minor allele frequency (MAF) of >1% were detected in this region.

Consequently, 3D spatial interactions were not detected by the CoDeS3D pipeline due to the

lack of SNPs.


The International Cancer Genome Consortium (ICGC) reports 5 mutations in this

region from 5 donors. Primary sites of +24 mutations presented evenly with ovary, prostate,

breast, lung and haematopoietic cells (20%) in ICGC database. There is only one

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haematopoietic mutation chr21:g.36399191A>T reported in a malignant lymphoma. The

FANTOM5 expression of +24 in haematopoietic cells overlaps with the primary ICGC tissues

that contained +24 mutations. The low number of mutations and lack of SNPs may suggest this

area is important for haematopoesis and a mutation may prevent normal development.

4.5 Characterising Runx1 +24 G-quadruplex structure

4.5.1 CD spectroscopy

G-quadruplexes (G4s) are generally classified according to strand orientation, loop

location and the number of strands involved in formation. To verify if the G4s predicted can

form in vitro, preliminary CD spectrometer readings were taken.

The predicted G4 structure GGTGGGGGTGGG, within the Runx1 +24 sequence is

made up of two G-quartets (Appendix IV) . Mutated G4 GGTGTGTGTGGG was generated as

a negative control with two Ts so that only one quartet can form, one quartet should be

insufficient for this structure to function as a G-quadruplex (Figure 4.3).

Figure 4.3: Predicted representation of Runx1 +24 mCRE G-quadruplex and mutated +24 mCRE.

A) Predicted Runx1 +24 mCRE G-quadruplex structure with two G-quartets (Grey rectangles). B) Predicted mutated Runx1 +24 mCRE structure. Blue Ts show the mutated base pairs so only one quartet can form.

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CD experiments are carried out in different buffers: 100 mM KCl pH 7 (positive control

buffers); 100 mM NaCl, and 100 mM LiCl (negative control buffer). It is expected that G4s

will form in the KCl buffer because the buffer provides the K+ cation required to stabilise the

quartet. Both NaCl and LiCl donate fewer stable cations so the G4 should be less prominent;

at 95 °C, NaCl and LiCl should not support formation of the G4 structure.

Preliminary scans showed that at 25 °C the predicted +24 mCRE G-quadruplex was

forming a parallel stranded G4 formation (Figure 4.4A-C and Figure 4.5A). The mutated G-

quadruplex was unable to form any secondary structure because the DNA is in a parallel

formation (Figure 4.5B). The increase of temperature to 95 °C significantly decreased

structural stability in all buffers except for KCl (Figure 4.4D-F).

Figure 4.4: CD spectroscopy of wild-type and mutant +24 G4s.

Molar ellipticity (deg.cm2.dmol-1) is on the vertical axis and wavelength (nm) is on the horizontal axis. The blue line represents the CD spectra for the wild-type sequence and the orange line represents CD spectra for the mutant sequence. CD spectra of +24 G4 and mutant at A) 25 °C in the presence of 100 mM KCl. B) 25 °C in the presence of 100 mM NaCl. C) 25 °C in the presence of 100 mM LiCl. D) 95 °C in the presence of 100 mM KCl. E) 95 °C in the presence of 100 mM NaCl. F) 95 °C in the presence of 100 mM LiCl.

These preliminary scans indicate that a parallel G4 forms in vitro for the native

sequence. Based on the CD Spectra, the +24 G4 presents a positive peak at 260 nm and a

negative peak at 240 nm. This specific spectroscopic signature is representative of a parallel

G4, which suggests the strands are orientated in the same direction (Gattuso et al., 2016).

Figure 4.5 shows a schematic representation of the normal and mutated G4.

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Figure 4.5: Schematic representation of the predicted Runx1+24 mCRE G-quadruplex in parallel

conformation and when base subsitutions are made at the +24 mCRE.

A) Predicted Runx1 +24 mCRE G-quadruplex structure in parallel formation with two G-quartets, based on CD spectrometer results (grey rectangles). Red arrows indicate 5’-3’ DNA arrangement B) Predicted mutated Runx1 +24 mCRE structure. Blue Ts show the mutated base pairs, preventing the formation of parallel quartets.

4.5.2 Runx1 +24 G-quadruplex Luciferase reporter assay

In order to determine whether an intact G-quadruplex is required for the functional

enhancer capability of the Runx1 +24 enhancer, luciferase reporter assays with mutated and

normal G-quadruplexes were used. The relative ability of constructs to drive expression of the

firefly luciferase reporter gene Luc2 (Phoinus pyralis) was determined. Luc2 was previously

cloned into the eukaryotic reporter gene vector pGL4.23-GW (Figure 4.6) (Pasquali et al.,

2014). A functional enhancer cloned into the Gateway site of pGL4.23-GW will increase

expression of Luc2 when transfected into a responsive cell line, which can be determined with

a luminometer.

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Figure 4.6: Diagram of Luciferase reporter construct pGL4.23-GW.

This 4283 bp vector contains Gateway cassette (yellow) immediately upstream of the minimal promoter (blue). The minimal promoter (minP), a TATA-box promoter element, drives low basal expression, which allows for sensitive responses to promoterless regions of interest. The minP sits immediately upstream of the Luciferase reporter gene Luc2 (green) (Paguio et al., 2005). Figure from Thomas et al., (2015).

Runx1 +24 and modified +24 were cloned into the pGL4.23-GW (pGL4) vector (Figure

4.7) . The empty pGL4 plasmid was included to monitor background luciferase activity of the

reporter construct and acted as a negative control.

Runx1 +24 pGL4 has previously shown enhancer activity (Thomas, 2015). If the G-

quadruplex influences enhancer activity, we would expect the G to T mutation (g->t) in Runx1

+24 pGL4 to alter luciferase activity (Figure 4.7B). This g->t mutation is the same as the G4

mutant analysed in the CD analysis, so we know the G4 is unable to form. A control mutant,

Runx1 +24 pGL4 t->a (Figure 4.7 C) that alters base pairs in the loop region without affecting

the overall G4 structure was also generated.

I generated Runx1 +24 pGL4 g->t and t->a G4 mutants using site-directed mutagenesis.

This allowed for targeted base pairs to be altered efficiently. Mutations were confirmed by

Sanger sequencing.

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Figure 4.7: Schematic representation of pGL4 Runx1+24 mCRE G-quadruplex in parallel conformation

and mutated +24 mCREs.

A) Predicted Runx1 +24 mCRE G-quadruplex structure in parallel formation with two G-quartets (grey rectangles). B) Mutated g->t Runx1 +24 mCRE structure. Blue Ts show the mutated base pairs and no quartets form in parallel formation. C) Mutated t->a Runx1 +24 mCRE structure. Blue As show the mutated base pairs and two quartets still form in parallel formation.

A Renilla luciferase reporter construct was co-transfected with the Runx1 +24 or +24

modified pGL4 reporter constructs. Firefly and Renilla luciferases have distinct evolutionary

origins, dissimilar enzyme structures and substrate requirements. These features allow these

two distinct luciferases to be used as dual reporters when co-transfected in a single well (Sherf

et al., 1996). Renilla was used as an internal control, to which the measurement of the pGL4

construct variants could be normalised.

The luminescence emitted was measured 48 h after transfection. To calculate the

relative luciferase units (RLU) of each reporter construct, the pGL4 luciferase value was

normalised to Renilla value to account for variations in transfection efficiency associated with

inter-sample variation. Results were expressed as Luciferase/Renilla ratios with vectors

carrying putative enhancers relative to the ratio of empty pGL4.23 vector (control).

In my previous Master’s project, NIH/3T3 (mouse fibroblast) cells showed a significant

upregulation of Runx1 +24 which indicates that +24 operates as an enhancer in this cell line.

NIH/3T3 cells express Runx1 P1, Runx1 P2 and total Runx1 at similar levels to the reference

genes (Thomas, 2015).

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NIH/3T3 cell lines were co-transfected with the normal and mutated +24 pGL4

constructs (Figure 4.7) and Renilla, then assayed for their relative luciferase activity. Six

individual replicates were executed for each construct, and three biological repeats were

performed (Figure 4.8).

Figure 4.8: Luciferase activity in response to the various +24 mCREs in the NIH/3T3 cell line.

The data show enhancer activity of normal and mutated +24 pGL4 constructs compared to empty pGL4.23-GW plasmid in NIH/3T3 cells, p value <0.05 (*) <0.001(****). Mutated G-quadruplex g->t had significantly less enhancer activity than mutated t->a +24. However, there was no significant difference in enhancer expression when the unaltered Runx1 +24 was compared to the mutated +24s enhancer expression. The data were analysed by ANOVA using GraphPad Prism 6 and are represented as mean +/- standard error of mean (S.E.M) of at least three experiments (n=3). pGL4 luciferase value was normalised to Renilla value and expressed as relative luciferase units (RLU).

In the NIH/3T3 cell line, significant enhancement of luciferase activity was caused by

the Runx1 enhancer +24 mCRE (p <0.001) (Figure 4.8). The mutated Runx1 +24 G-

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quadruplexes showed similar enhancement profiles to the normal Runx1+24 (p <0.001). The

non G4 forming mutant (g->t) showed significantly reduced expression when compared to the

control G4 mutant (t->a) (p <0.05). There was no significant difference between the unaltered

Runx1 +24 and the mutated Runx1 +24. Runx1 +24 g->t exhibited a minor decrease in enhancer

expression, whereas Runx1 +24 t->a showed a slight increase in enhancer expression when

compared to the unaltered Runx1 +24. This suggests that alterations to the loop regions of the

G4 could alter enhancer capability. However, the altered enhancement shown in the mutants is

modest, and was not statistically significant when compared to the normal Runx1 +24 G4.

G-quadruplexes are also capable of producing their own RNA, which have functional

roles (Beltran et al., 2019; Wang et al., 2017). This was not investigated in this research project.

4.6 Circular Chromatin Conformation Capture (4C) of

Human RUNX1 +24

Circular chromatin conformation capture (4C) was used to elucidate the connectivity

of the RUNX1 +24 enhancer region. The 4C sequencing libraries were prepared from 4C

material, each containing PCR products amplified by bait-specific primer pairs. The processing

of the sequencing data is available in Appendix VIII.

The guideline for the amount of mapped reads required for successful calling of 4C

data is 1 million, but identification of interactions can be done with less than 300,000 reads

(van de Werken et al., 2012a). A recent study has shown that 50,000–100,000 cis-mapped 4C

reads are sufficient to generate reproducible 4C profiles (Krijger et al., 2020). The number of

mapped reads for RUNX1 +24 bait in K562 and STAG2 mutant cells, in each library is shown

in Figure 4.9.

WT 12 PMA replicate 2 and WT 72 PMA replicate 2 had very low mapped reads, (1172

and 58721 respectively), however the rest of the libraries each had over 370,000 reads. This is

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sufficient for successful interaction identification. Controls were cells that went through the 4C

process without the ligation steps.

Figure 4.9: RUNX1 +24 Mapped read count.

The number of mapped reads (millions) is shown for the individual conditions in replicate 1, 2, 3 or control.

4.6.1 Quality check of 4C data

Mapped reads were assigned to restriction fragments resulting in a read count per

fragment. To determine whether my Runx1 +24 4C data is of good quality and comparable to

other published 4C studies, I assessed the distribution of reads across fragments and correlation

of read counts between replicates.

The quality is measured by the proportion of reads on the same chromosome as the bait

(cis) and the proportion of reads around the bait viewpoint (near-bait:1 megabase either side of

the bait) (van de Werken et al., 2012a). The majority of the reads should be in close proximity

to the bait, captured and represented in the read count (van de Werken et al., 2012a). A high

number of reads on different chromosomes (trans) would indicate that the data originated from

random ligation events and therefore the data is of low quality. The general guideline of good

quality 4C data should have approximately 40% of reads in cis and at least 40% coverage in

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the near-bait (Raviram et al., 2016; van de Werken et al., 2012a). For each condition, the

coverage of reads in cis and near-bait was calculated for all 3 replicates (Figure 4.10). The

majority of conditions had over 40% reads in cis and over 40% of fragments covered in the

near-bait. The STAG2 72 PMA replicate 1 had more than 40% coverage of fragments in near-

bait, but percentage of reads in cis was lower than 40% (31%). A low proportion of reads in

cis can indicate fixing conditions of the chromatin are not optimal, as more trans interactions

are detected, or that the bait forms a large number of trans interactions for structural or

regulatory purposes. Dr. Marsman previously allowed the +24 bait to be analysed in HPC7

cells with lower than 40% coverage in cis. The same parameter was extended to this analysis.

WT 12 PMA replicate 2 and WT 72 PMA replicate 2 had less than 40% reads in the near-bait

(14% and 39%). This factor combined with their low read count meant they were excluded

from further analysis.

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Figure 4.10: RUNX1 +24 4C quality evaluation.

The coverage of fragments that had more than one read present within 0.1 megabases (Mb) on either side of the bait was plotted against the percentage of reads in cis over total. 4C data of WT 12 DMSO (light blue), WT 12 PMA (blue), WT 72 PMA (dark blue), STAG2 12 DMSO (yellow), STAG2 12 PMA (orange), STAG2 72 PMA (red) is plotted. Three points, corresponding to replicate 1, 2 and 3 are presented for each condition. Grey dotted lines indicate cut-off point for good quality data.

All samples that passed quality control were analysed using 4Cker, a bioinformatics

pipeline developed to analyse 4C and determine significant interactions (Raviram et al., 2016).

4.6.2 Detection of significant interactions

The interactions of a bait across the genome will differ depending on proximity to bait,

with closer interactions (near-bait) occurring at a higher frequency, followed by cis interaction

then trans interactions having the least amount of interaction with the bait region. 4Cker

provides different analyses for near-bait, cis and trans interactions.

Near-bait analysis was carried out for 1 Mb either side of the +24 bait (within the

RUNX1 domain). Cis analysis is carried out across chromosome 21, but excluded the near-bait

region. Trans analysis is carried out across all chromosomes except 21.

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Statistically significant interactions were detected using the 4Cker package (Raviram

et al., 2016) using an adaptive window size of 3 fragments. Because the 4C signal is generally

higher around the bait region and decreases in far-cis and trans, different adaptive window

sizes were used for read normalisation and creating smooth spline in each data set. Adaptive

windows were used to obtain an analogous number of observed fragments for each window.

Window size is determined by the amount of signal detected in the region.

Fragments in close proximity to 4C baits have a higher read count compared to

fragments further away and when examined with the cis 4C analysis near-bait interactions

would be statistically significantly enriched. For this reason, cis interactions and near-bait

interactions were analysed separately, with near-bait analysis requiring a higher p-value to be

determined statistically significant by the 4Cker programme than the p-values of cis

interactions (Raviram et al., 2016). Significant interactions were visualised in the UCSC

browser.

To reduce errors due to mis-priming, fragments that had one or more reads in the control

samples were removed from the matching cell type and condition. Additionally, fragments

located 10 kb either side of RUNX1 +24 were removed. Fragments within 10 kb of the bait are

not informative because bait-proximal fragments have a high read count regardless of whether

there is a true interaction present or not. 4Cker combined the processed read counts per

fragment by normalising to the total number of reads in each replicate (Raviram et al., 2016).

Differential analysis between conditions was carried out between two conditions on the

windows that are interacting with the bait in at least 2 conditions. 4Cker has optimised this

analysis for near-bait and cis interactions. Significantly different domains were determined

with a p-value of 0.05.

This Chapter will interpret the RUNX1 +24 connections in normal K562 cells (WT 12

DMSO). Analysis of the other conditions is described in further detail in Chapter 5.

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4.7 Identification and Genomic Characteristics of Human

RUNX1 +24 3D Connections

4.7.1 Visualisation of significant cis interactions

The mean normalised read count of all three WT 12 h DMSO replicates was plotted

across chromosome 21 locations using ggplot (Wickham et al., 2007) to generate the cis profile

of RUNX1 +24 connections in baseline K562 cells (Figure 4.11). Reads had a high coverage

around the +24 (5 megabases (Mb) surrounding the bait). Figure 4.11 reveals that RUNX1 +24

makes DNA-DNA contacts across the entire chromosome 21. The large normalised read peak

that shows an interaction around 45,000,000 (*) with PDE9A and BC033260.

Phosphodiesterase 9A (PDE9A) mRNA is detected throughout the body, but predominately in

the brain, whereas BC033260 is an uncharacterised long non-coding RNA (Harms et al., 2019;

Patel et al., 2018).

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Figure 4.11: RUNX1 +24 cis 4C profile (chr21) in K562 cells (WT 12 DMSO).

Ggplot of the cis 4C profile, normalised read count of all WT 12 DMSO replicates is on the vertical axis and position on chromosome 21 is on the horizontal axis. * shows the large normalised read peak around 45,000,000 with PDE9A and BC033260.

WT 12 DMSO showed 115 significant cis interactions across the long arm of

chromosome 21. Once the overlap of the near-bait region is removed, there are 31 significant

cis interactions (Appendix IX) (Figure 4.12). These interactions extended across the known

TADs and ranging from chr21:16193679 to chr21:45164775. These interactions were further

categorised based on their chromatin characteristics using K562 ChromHMM and dbSUPER

enhancer annotations.

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Figure 4.12: RUNX1 +24 significant cis interactions.

Chromosome 21 with RUNX1 +24 interactions represented by black arches. Each interaction is colour-coded according to its ChromHMM profile: key is at the bottom left of the figure. Super-enhancers were determined from dbSuper. Chromosome is aligned to the K562 Hi-C contact map. Yellow and blue bands show the predicted K562 TADs.

The majority of the interactions were with ChromHMM annotated regulatory elements

(enhancers and insulators) (Figure 4.13). Some regions that interact with RUNX1 +24 exhibit

multiple ChromHMM annotations, for example, location chr21:16193679-16290033

contained both insulator and strong enhancer regions. The region was also identified as

containing super-enhancers as determined by dbSUPER (Khan et al., 2016). The main

proportion of RUNX1 +24 interactions were to enhancer regions (58%), followed by

interactions to active promoters and insulators (both 32%). It also connects with some super-

enhancers so may be facilitating or restricting their 3D connections. Interestingly the +24

enhancer also interacted with regions of polycomb repressed chromatin (29%). This indicates

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that +24 is not just acting as an enhancer hub (as previously described in Section 3.7); it may

also be holding regions of chromosome 21 to ensure that the chromatin is correctly organised

into inactive and active domains.

Figure 4.13: Profile of RUNX1 +24 significant cis interactions.

Percentage of interactions is on the vertical axis. Horizontal axis shows the ChromHMM annotations. Individual interactions can have multiple annotations.

4.7.2 Overview of significant near-bait interactions

Near-bait analysis considers the interaction 1 Mb either side of the bait (Raviram et al.,

2016). The mean normalised read count of all three WT 12 h DMSO replicates was plotted

across chromosome 21 near-bait locations using ggplot (Wickham et al., 2007) (Figure 4.14).

Domains are formed by high proportions of chromatin interactions in a genomic region, and

less to outside regions. Domain boundaries can be predicted by a decrease in read count

between two neighbouring regions. The TAD domain boundaries have already been reported

for K562 cells and were determined by Hi-C (Rao et al., 2014). In most human cell lines,

RUNX1 P1 and P2 promoters form TAD domain boundaries (Marsman, 2016).

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When visualising the near-bait normalised read counts, the majority of reads are located

within 5 Mb surrounding the bait (Figure 4.14). Figure 4.14 indicates the near-bait genomic

interactions RUNX1 +24 is involved in.

Figure 4.14: RUNX1 +24 near-bait 4C profile.

Ggplot of the near-bait 4C profile, normalised read count is on the vertical axis and position on chromosome 21 is on the horizontal axis. Rao et al., (2014) K562 domain boundaries are shown in grey dotted lines. Domain locations in K562 cells were obtained from Rao et al., (2014) (Rao et al., 2014). Black arrows represent the RUNX1 promoters. RUNX1 is transcribed telomere to centromere implied by the direction of the black arrows of the RUNX1 promoters. * shows the large normalised read peak at human equivalent region of RUNX1 m -368.

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When the K562 TAD domain boundaries are overlapped with the normalised WT 12

DMSO +24 reads we see RUNX1 +24 connections are primarily contained within the TAD

domain. However the interactions are not constrained by the upstream boundary, as there is a

similar read count between two neighbouring regions.

Interestingly the large normalised read peak (*) between 36,500,000 and 37,000,000

reflects an interaction with human equivalent region of RUNX1 m -368.

WT 12 DMSO showed 143 significant near-bait interactions (Appendix IX) (Figure

4.16). The majority of these interactions were upstream of RUNX1 P1. Similar to the cis

connections, most interactions connect to regions within genes or in intronic regions, implying

RUNX1 +24 connects with regulatory regions.

Genes within domains are potentially co-expressed and co-regulated with RUNX1. Dr.

Marsman defined the genes at the domain boundaries of K562 cells as CBR1, CLIC6, KCNE2

and SMIM11 (Marsman, 2016). These genes were determined to be involved in cellular

homeostasis and likely to have uniform expression in most cell types. Dixon et al., (2012)

hypothesised that genes with consistent transcription are frequently present at domain

boundaries and their high expression may contribute to boundary formation (Dixon et al.,

2012).

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Figure 4.15: RUNX1 +24 significant near-bait interactions (chr21:34999764-37572609).

Chromosome 21 with positional zoom shown by the black box. RUNX1 is transcribed telomere to centromere implied by the direction of the black arrows of the RUNX1 promoters. In near-bait black box RUNX1 +24 interactions represented by black arches. RUNX1 +24 is represented by the yellow line. RUNX1 promoters are represented by blue lines and interactions to these promoters are shown in blue. RUNX1 regulatory regions are represented by orange lines and interactions to these regions are shown in orange. Chromosome is aligned to the UCSC K562 gene list and annotations. Dotted grey lines show Rao et al., (2014) K562 domain boundaries.

4.7.3 Significant interactions with known RUNX1 regulators

As demonstrated in the previous chapter RUNX1 has multiple different regulatory

regions that lie in the intronic and intergenic regions of RUNX1. I investigated whether RUNX1

+24 interacted with any of the characterised RUNX1 regulatory regions in K562 cells. Based

on the steady increase of RUNX1 P1 transcription after PMA stimulation (megakaryocyte

differentiation) in K562s (Antony et al., 2020), it was predicted that the RUNX1 +24 would

connect to multiple enhancers, as well as connecting with P1. Curiously the only RUNX1

promoter that +24 connected to in WT 12 DMSO was P2. RUNX1 +24 interacts with some of

the characterized human RUNX1 regulators examined in the previous chapter; m-368, m-101,

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m-58, +62, m+110. RUNX1 +24 also interacted with the gene encoding the lncRNA RUNXOR.

Of note, there were a plethora of identified RUNX1 regulatory regions that were not connected

to RUNX1 +24, which is different to the Runx1 +24 connections seen in HPC7 cells. This

further supports the notion that different RUNX1 enhancers are used depending on cell type. It

is important to note that as this 4C only captures interactions from the +24 viewpoint. Other

enhancers may play important roles in RUNX1 regulation and connect directly with the RUNX1

promoters, without a RUNX1+24 interaction.

4.7.4 Significant interactions with other genes

To investigate which genes may be co-regulated with RUNX1, interactions occurring

with other genes were identified. Across Chromosome 21 RUNX1 +24 interacted with 63 genes

(Table 4.2). Gene interaction analysis identifies a connection with an overlap in the same

genomic region as a gene and does not necessarily reflect an interaction with a promoter or

exon of a gene. As seen in the ChromHMM analysis, the majority of interactions were with

regulatory regions (enhancers and insulators) across the chromosome (Figure 4.13).

The majority of genes that +24 interacts with are protein-coding genes (43%). However,

the other 57% of connections are to non-coding RNAs. A large proportion of these RNAs are

uncharacterised.

Non-coding RNAs have been shown to be involved in many biological processes,

including regulating transcription, cell-fate programming and maintaining genome 3D

organisation (Flynn et al., 2014; Geisler et al., 2013; Holoch et al., 2015; Li et al., 2019;

Nozawa et al., 2019; Sun et al., 2018). In particular, long non-coding RNAs have been shown

to activate or suppress transcriptional activity by interacting with chromatin modulating

proteins (Fan et al., 2015; Han et al., 2014; Jain et al., 2016; Prensner et al., 2013; Scarola et

al., 2015). In WT 12 DMSO, the connections to these RNAs may be important in maintaining

the chromatin states, to ensure correct gene regulation and overall maintenance of this cell type.

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Table 4.2: Type and name of genes that interact with RUNX1 +24

Type Name

Protein-coding genes AP000322.53, BACE2, BRWD1, CBR1, CLIC6, DSCR3, DSCR4, ERG, GRIK1, HLCS, HMGN1, IFNAR2, IL10RB, LCA5L, PDE9A, PDXK, PKNOX1, PRDM15, PSMG1, RCAN1, RIPK4, SCAF4, SETD4, SMIM11A, SMIM11B, USP25, WRB

Pseudogene GAPDHP16, METTL21AP1, POLR2CP1, RAD23BLP, RBMX2P1, RBPMSLP, RNF6P1, RPL34P3, TIMM9P2

Antisense RNA AL773572.7, AP000688.11, AP001412.1, AP001626.2, AP001630.5, BRWD1-AS1, BRWD1-AS2, GRIK1-AS1, IL10RB-AS1, PLAC4

Long intergenic non-coding RNAs (LincRNA)

AF127936.3, AF127936.5, AF127936.7, AF015720.3, AP000318.2, AP000255.6, LINC00159, LINC01671, LINC01426, LOC100506403

Micro RNA (miRNA) MIR3197, MIR5692B

Miscellaneous RNA (miscRNA)

Y_RNA

Sense intronic RNA AP000688.14

Sense overlapping RNA BRWD1-IT1, RUNXOR

Small nuclear RNA (snRNA)

RNU6-992P

When comparing the expression of the gene list with Antony et al., (2020)’s RNA seq

expression analysis of K562s, there were no consistent patterns (expressed or not expressed)

of the connected genes in WT. Notably this supports the theory that the RUNX1 +24 is helping

to organise different domains across the chromosome into active and inactive domains.

Gene ontology analysis of genes that interact with RUNX1 +24 was not appropriate as

these genes were not consistently on or off. However, there was an exciting connection to ERG

Promoter 3. ERG is crucial to haematopoietic cell specific gene regulation and is also inactive

in WT 12 DMSO. ERG is a transcription factor involved in the differentiation and maturation

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of megakaryocyte cells. ERG dysregulation is associated with several haematopoietic tumour

types including AML (Reddy et al., 1987; Shimizu et al., 1993). Dr. Marsman also found that

Runx1 +24 interacted with ERG in HPC7 cells (Marsman, 2016) (Table 4.3).

Altered +24 activity or connections may therefore contribute to haematopoietic

dysregulation.

The connections between Runx1 +24 and genes; Brwd1, Clic6, Dscr3, Erg, Hlcs,

Hmgn1 are seen in both HPC7 and K562 WT 12 DMSO cells (Table 4.3).Unlike in HPC7

cells +24 does not interact with TIAM1 in WT 12 DMSO cells (Table 4.3). TIAM1 is involved

in cell adhesion and migration and is dysregulated in haematopoietic cancers (Habets et al.,

1995; Ives et al., 1998).

Table 4.3: Name of genes that interact with Runx1 +24 in HPC7s

Gene name

Interactions observed with both HPC7 Runx1 +24 4C and K562 RUNX1 +24 4C

Brwd1, Clic6, Dscr3, Erg, Hlcs, Hmgn1

Interactions only observed in HPC7 Runx1 +24 4C

Atp5o, B3galt5, Cbr3, Chaf1b, Cldn14, Cryzl1, Dopey2, GM10785, Itsn1, Kcnj6, Kcnj15, Morc3, Mrps6, Slc5a3, Son, Tiam1, TMEM50b, Ttc3, 1600002D24Rik

4.7.5 Overall significant RUNX1 +24 cis and near-bait interactions

The cis connections showed significant interactions with ERG P3 and super-enhancer

regulatory regions between SAMSN1 and NRIP1 genes (Figure 4.17). The WT 12 D RUNX1

+24 connects with the known enhancers m-368, m-101, m-58, m+62, m+110. It also interacts

with known RUNX1 regulators long non-coding RNA RUNXOR and RUNX1 P2. Within the

near-bait connections, RUNX1 +24 connected with RUNX1 Exon 3. RUNX1 +24 also

connected to three previously undescribed regions +89, -96 and a regulatory region near

SLC5A3 Exon 2 and a MRPS6 intron (Figure 4.16).

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Figure 4.16: RUNX1 +24 chromatin hub.

Yellow boxes represent UCSC annotated gene promoters and/or exons. Blue boxes show described RUNX1 regulators. Orange boxes annotate novel putative regulators.

The novel connections were further analysed using the same in silico approach (Chapter

3) to describe other regulatory regions. These annotations are described in Table 4.4.

ERG P3 is described by ChromHMM as an insulator and transcription-associated

region. Transcription-associated refers to transcriptional transition or elongation (Ernst et al.,

2010; Ernst et al., 2011). This region has both cohesin and CTCF binding as well as

haematopoietic transcription factor binding (SPI1, TAL1, GATA2). This region shows no

change in chromatin accessibility in cohesin mutant K562 cells.

The novel RUNX1 regulatory regions -96 and +89 are both annotated as weak enhancers

with transcription association. Intriguingly neither site is shown as open as there are no DNase

HS sites. +89 binds RNA Pol II, whereas -96 does not. These regions show no change in

chromatin accessibility in K562 cohesin mutant cells.

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The novel regulatory region near SLC5A3 Exon 2 and a MRPS6 intron was annotated

by ChromHMM as containing both strong and weak enhancer regions. These regions showed

comparable transcription factor binding sites and chromatin modifications to known RUNX1

enhancers m-371, m-58 and m+110. This region also had increased accessibility in the cohesin

mutant K562 cells.

The cis connection and super-enhancer regulatory region between SAMSN1 and NRIP1

was annotated as containing strong enhancers, weak enhancers and insulators. This region has

both cohesin and CTCF binding as well as haematopoietic transcription factor binding of SPI1

and TAL1. This region showed decreased chromatin accessibility in cohesin mutant K562

cells. This region also contained uncharacterised super-enhancers (Hnisz et al., 2013; Khan et

al., 2016).

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Table 4.4: Annotations of identified putative regulatory region connections

Feature/ Regulatory region location relative to RUNX1 P1 (kb)


ENCODE annotation in K562 cells

ChromHMM annotation in K562 cells


ERG P3 chr21:39852633-39906157

DNase I HS, RNA Pol II, RAD21, SMC3, CTCF, p300, LSD1, H3K4me1, H3K27me3, SPI1, TAL1, GATA2

Insulator, Transcription Associated

No change in accessibility in K562 cohesin mutants

-96 chr21:36517085-36518628

P300, LSD1, H3K27ac, H3K4me1, H3K4me3, H3K9me3, TAL1

Weak Enhancer, Transcription Associated


+89 chr21:36332447-36333601

RNA Pol II, CTCF, p300, LSD1, H3K4me1, H3K4me3, H3K9ac, H3K9me3

Weak Enhancer, Transcription Associated


SLC5A3 Exon 2 MRPS6 intron

chr21:35474191-35488395

DNase I HS, RNA Pol II, CTCF, p300, LSD1, H3K27ac, H3K4me1, H3K4me3, H3K9ac, H3K9me3, GATA1, TAL1, GATA2

Strong Enhancer, Weak Enhancer,

Increased accessibility in K562 cohesin mutants

region between SAMSN1 and NRIP1 (cis interaction)

chr21:16193679-16290033

DNase I HS, RAD21, SMC3 CTCF, p300, H3K27ac, H3K4me1, H3K4me3, H3K9ac, H3K9me3, SPI1, TAL1,

Strong enhancer, Weak enhancer, Insulator

Decreased accessibility in K562 cohesin mutants

4.7.6 Trans interactions

The trans interactions of RUNX1 +24 could not be significantly investigated using

4Cker. The 4Cker analysis mapped the trans interactions to broad areas across various

chromosomes (Appendix X). This may be because when the software annotates regions as

significant, it does so based on the average of reads across the inputted window. As there were

low read numbers across most chromosomes, the software combined the reads across the

chromosome. This meant there were large (10,000 bp+) “interactions” called which spanned

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across the different chromosomes. This gave no clear overview of sequencing read peaks hence

trans interactions were unable to be interpreted.

This 4C was designed to understand the cis and near-bait with the use of a 4 bp cutter.

Raviram et al., (2016) highlight in their experiment that 6 bp cutters are optimal for detecting

trans interactions (Raviram et al., 2016). The genome is cut more frequently with 4 bp cutters

and this increases resolution by over tenfold compared to 6 bp cutters (Raviram et al., 2016;

Van De Werken et al., 2012b).

To understand the trans interactions that could be detected, I looked at the raw read

peaks across the genome after control reads were removed (Appendix X). Based on peaks that

contained reads in 2 or more replicates I detected three trans interactions (Table 4.5). All peaks

were in intergenic regions and contained minimal ENCODE annotations.

Table 4.5: Annotations of identified trans interactions

Closest annotated Feature/gene





Region between KCNA2 and KCNA10

chr1:111,112,479-111,112,575

p300, H3K27me3 Polycomb repressed


NLGN1 intergenic region

chr3:173,646,070-173,646,151

No binding Heterochromatin No change in accessibility in K562 cohesin mutants

Region between DDX58 and ACO1

chr9:32,452,626-32,452,711

p300 Transcription Associated


HPC7 cells had one trans interaction between RUNX1 +24 and the overlapping

mitochondrial genes Gu332589 and Ak018753, however this was not seen in K562 cells.

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RUNX1 is often translocated in AML and is usually with Chromosome 3 (EVI1), 8

(ETO), 16 (CBFB) and 12 (ETV6) (Sood et al., 2017). The RUNX1 +24 trans interactions do

not overlap with these frequent RUNX1 translocation points. The trans interaction on

chromosome 3 is over 4,782,000 bp away from EVI1.

It should be noted however that the K562 cells are unlikely to have a completely normal

karyotype and already carry BCR-ABL translocation. The BCR gene is normally on

chromosome number 22, ABL gene is normally on chromosome number 9 (McGahon et al.,

1997). Because the trans interactions were not able to be subjected to the same stringency as

cis and near-bait interactions, analysis of these interactions was not pursued further.

4.8 Chapter summary

4.8.1 RUNX1 +24 characteristics

RUNX1 +24 showed strong enhancer characteristics and active binding of

haematopoietic transcription factors. The FANTOM5 data showed that there is active

production of the +24 enhancer RNA (eRNA) in haematopoietic cell types.

Several studies have reported functional roles of eRNAs that foster enhancer-promoter

looping formation, promote transcription factor and co-regulator binding and support target

gene transcription (Bose et al., 2017; Hou et al., 2020b; Lai et al., 2013; Lam et al., 2013; Li

et al., 2013a; Melo et al., 2013; Panigrahi et al., 2018; Rahnamoun et al., 2018; Sigova et al.,

2015; Tsai et al., 2018). Enhancer RNA can also assist pause-release of RNA Pol II which

facilitates transcription elongation (Schaukowitch et al., 2014).

There are no SNPs with a MAF of >1% reported in RUNX1 +24 region. This could be

an underestimation, due to the limitations in identifying significant SNPs in haematopoietic

cells reported in the previous chapter (Section 3.4.2). The available AML genomic data

reported a low number (5) of mutations.

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CRISPR/Cas9 editing of RUNX1 +24 (by targeting the whole intron) showed reduced

RUNX1 expression in OCI-AML5 cells (Mill et al., 2019). The majority of edited cells were

eradicated via apoptosis, while the viable edited cells had significantly decreased RUNX1

expression and inhibited growth (Mill et al., 2019). This research shows the importance of the

P1-P2 intron, which contains multiple RUNX1 regulatory elements.

The CD analysis showed that the G-quadruplexes observed in the mouse +24 element

are capable of forming parallel G-quadruplexes. G-quadruplex RNA is capable of targeting and

removing Polycomb repressive complex 2 from genes and allowing RNA-mediated regulation

(Beltran et al., 2019; Wang et al., 2017).

Luciferase assays showed that the mutations of G-quadruplex or mouse Runx1 +24 had

no significant effect on enhancer function. Given the overall organisational role of RUNX1 +24

seen in the 4C and possibility of G-quadruplex RNA, this assay is restricted in its ability to

assess the functional nature of this G-quadruplex, because it doesn’t take non-

transcriptional/structural roles into account. Therefore, luciferase assays may not capture the

changes in capability when the +24 G-quadruplex is mutated. Furthermore, the G-quadruplex

analysis was specific to the mouse sequence, so it may not recapitulate the function of the

human sequence. If the human sequence contains a G-quadruplex, CD analysis should be

repeated to determine the type of G-quadruplex, or its functional capacity.

It would be interesting to further investigate the possible functional roles of RUNX1

+24 eRNA and +24 G-quadruplex RNA. This could be done using precision run-on sequencing

(PRO-seq) and rapid amplification of cDNA ends (RACE). Pro-seq can define the position,

amount, and orientation of transcriptionally active RNA polymerases in the genome to a single-

nucleotide resolution (Hou et al., 2020b). RACE can map eRNAs by identifying the 5′ and 3′

ends of RNA and RT-qPCR can determine the expression levels of these eRNAs (Hou et al.,

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2020b). The eRNAs could also be knocked down using small interfering RNA (siRNA) or

mutated using CRISPR/Cas9 (Hou et al., 2020b).

If the G-quadruplex was capable of forming RNA it would be fascinating to investigate

its targets or ability to bind to Runx1 protein. Previously Fukunaga et al., (2020) showed the

ability of a G-quadruplex RNA to bind to the ETO and dissociate the leukemic RUNX1-ETO

fusion protein from DNA (Fukunaga et al., 2020). This suggests that some G-quadruplex RNA

may serve as potential therapeutic agents for leukaemia.

4.8.2 RUNX1 +24 interactions

In K562 cells, RUNX1 +24 interactions occurred across the whole of Chromosome 21.

Previously, Hi-C and promoter capture-C showed conservative interactions of promoters

within TADs. Unexpectedly, the +24 enhancer interacted across the whole chromosome and

was not limited to the reported domain structure.

The majority of RUNX1 +24 interactions were with regulatory regions, however it is

connected to both active and inactive chromatin. This unexpected finding suggests that RUNX1

+24 is playing a role in the organisation of chromosome 21 in K562s, rather than a traditional

enhancer role.

The RUNX1 +24 connections were predominantly with non-coding RNAs, whose

expression profiles cannot be determined by the existing RNA-seq data. Antony et al., (2020)

RNA-seq data was unstranded and poly A+ enriched. Often non-coding RNA overlaps with

genes, if this applies to these non-coding RNAs, they would not get annotated by this data set.

The connections from RUNX1 +24 to non-coding RNAs, imply that RUNX1 +24 may be

providing connections across the chromatin to ensure correct transcription and repression for

maintenance of K562 cells. As non-coding RNAs are important for correct enhancer-promoter

looping, 3D genome organisation, and cell-fate processes, the +24 may be safeguarding the

chromatin structure and gene expression.

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In HPC7 cells Runx1 +24 connected to the active P1 and inactive P2 Runx1 promoters

and connected to 7 Runx1 regulatory regions (m-371, m-368, m-354, m-327, m-321, m-303,

m-58, m-48). RUNX1 +24 in K562 cells connected to only one RUNX1 promoter (P2) which

is active as well as a small number of identified RUNX1 regulatory regions (m-368, m-101, m-

58, +62, and m+110). This reinforces the notion of different enhancer recruitment in order to

produce different cell types and generation of different RUNX1 isoforms.

There were some novel interactions identified; RUNXOR, ERG P3 and putative

regulatory regions -96, +89, SLC5A3 Exon 2 MRPS6 intron and a super-enhancer region

between SAMSN1 and NRIP1. These putative regulatory regions could be investigated further

for functional roles in K562 cells by using enhancer and insulator assays, including analysing

the combinatory roles of the regions.

RUNX1 +24 could be important for overall organisation and 3D structure of

chromosome 21. RUNX1 +24 interacts with coding genes, non-coding RNA, super-enhancers

and regulatory elements, and may be maintaining these connections to prevent transcription,

rather than the expected enhancer role. Further investigation to define the RNA-DNA

interactions proposed by this research could determine the importance of RUNX1+24 eRNAs,

possible G-quadruplex RNA and non-coding RNA connections seen in K562s. This could be

carried out by utilising previously generated RNA-DNA interactomes for K562s cells such as

Red-C from Gavrilov et al., (2020) (Gavrilov et al., 2020).

Some other questions arising from this research include: are there other well established

enhancers that connect across the chromosome? If so, are they significant for genome

organisation? Are there equivalents of RUNX1 +24 on different chromosomes? These could be

further discovered by using enhancers as baits for 4C experiments or establishing an equivalent

of Capture-C for enhancers.

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This chapter describes my work determining how RUNX1 +24 interacts with other

DNA regions in steady-state K562 cells. However, enhancers become active when cells are

subjected to various stimuli, such as in differentiation. Cohesin insufficiency has been shown

to impair differentiation, alter overall chromatin accessibility, as well as the transcription of

RUNX1 (Mazumdar et al., 2015; Mullenders et al., 2015; Viny et al., 2015). RUNX1 P1 is

crucial for megakaryocyte differentiation and it is predicted that enhancers play key roles in

inducing the correct RUNX1 promoters. The following chapter describes experiments in which

the K562 cells were induced to differentiate to the megakaryocytic lineage, so the role of

RUNX1 +24 could be further understood. It also details how cohesin insufficiency affects the

RUNX1 +24 connections.

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5 Chapter 5: Cohesin insulates 3D connections of RUNX1

+24 during megakaryocyte differentiation

5.1 Introduction

The previous chapter established the connectivity of the RUNX1 +24 enhancer and

suggests that this region connects to multiple DNA regions spanning chromosome 21 in K562

cells. RUNX1 P1 is crucial for megakaryocyte differentiation and it is predicted that enhancers

play key roles in selecting and inducing the correct RUNX1 promoters. Dr. Antony discovered

that enhancer activity is altered during megakaryocytic differentiation in cohesin-mutant cells

(Antony et al., 2020). Cohesin insufficiency impairs HSC differentiation, alters overall

chromatin accessibility, as well as the transcription of RUNX1 (Mazumdar et al., 2015;

Mullenders et al., 2015; Viny et al., 2015)

Overall, cohesin mutations are implicated as a putative genetic pathway for

development of AML, but how the disease arises from these mutations has not been

determined. Cohesin mutations co-occur with other mutations in leukaemogenic genes

indicating cooperation between pathways (Welch et al., 2012). One possibility is that

impairment of cohesin’s regulatory role leads to dysregulation of other important

leukaemogenic genes. The gene encoding cohesin subunit STAG2 is the most frequently

mutated of the cohesin genes (Van Der Lelij et al., 2017). STAG2 mutation can lead to complete

loss of STAG2 function due to mosaic inactivation of the X-linked wild type allele. Several

studies have shown STAG2 preferentially occupies enhancers and promoters, and promotes

cell-type-specific transcription, a function that is not compensated by cohesin-STAG1 (Arruda

et al., 2020; Cuadrado et al., 2019; De Koninck et al., 2020; Kojic et al., 2018; Viny et al.,

2019).

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K562 cells with a patient-specific STAG2 R614* null mutation were previously

generated by Dr. Antony. Wild type (WT) and STAG2 mutant R614* K562 cells were

previously subjected to RNA-seq, revealing genome-wide gene expression changes. Using

ATAC-seq, Dr. Antony also noted altered chromatin accessibility in K562 cells with STAG2

mutation. The RNA-seq and ATAC-seq revealed that the gene expression changes observed

are associated with altered chromatin accessibility at regulatory regions and super-enhancers

in STAG2 mutants (Antony et al., 2020).

RUNX1 and ERG are important for HSC differentiation and both have intergenic

regions that are distinguished as super-enhancers in CD34+ cells (Khan et al., 2016). In Dr.

Antony’s dataset, ERG was the top differentially upregulated super-enhancer-associated gene

in the STAG2-/- mutant, while RUNX1 was one the genes that showed differential chromatin

accessibility.

K562 cells can be induced to differentiate towards the megakaryocyte lineage using

phorbol 12-myristate 13-acetate (PMA). Antony et al., (2020) used quantitative PCR (qPCR)

to measure specific gene expression changes over 72 hours (h) of PMA stimulation. WT cells

undergoing normal megakaryocyte differentiation had a gradual increase in transcription of

RUNX1 from its P1 promoter: RUNX1-P1 and ERG. However, STAG2-deficient cells showed

an aberrant spike in transcription of RUNX1 from its P2 promoter: RUNX1-P2 and ERG 6-12h

post-PMA stimulation (Antony et al., 2020)..

Dr. Antony showed that by 48 h post-stimulation, aberrant RUNX1 and ERG

transcription had returned to baseline in STAG2-deficient cells. This implies that PMA

stimulation of STAG2-deficient cells, which at baseline have increased chromatin accessibility

at RUNX1 and ERG, leads to transient unrestrained transcription (Antony et al., 2020).

Antony et al., (2020) used a bromodomain and extra-terminal motif (BET) inhibitor

protein called JQ1, to decrease BRD4 binding and suppress enhancer-driven transcription

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(Antony et al., 2020). BRD4 is a bromodomain-containing protein that interacts with active

enhancers (Bhagwat et al., 2016). When K562 cells were treated with JQ1 concurrently with

PMA, there was reduced RUNX1-P2 and ERG expression in WT cells, and dramatically

dampened aberrant expression peaks in STAG2-deficient cells (Antony et al., 2020). . RUNX1-

P1 transcription was completely blocked by JQ1 in both WT and STAG2-deficient cells

(Antony et al., 2020). These results suggest that STAG2 depletion de-constrains the chromatin

surrounding RUNX1 and ERG, which produces aberrant enhancer-amplified transcription in

response to differentiation signal.

This Chapter describes the results of my research on: 1) the role of RUNX1 +24 in

megakaryocyte differentiation and 2) how cohesin insufficiency affects spatial connections

initiated by the RUNX1 +24 enhancer. For the first objective, K562 cells were induced with

PMA to differentiate towards the megakaryocytic lineage, so that the spatial connections

initiated by RUNX1 +24 during the differentiation process could be determined. For the second

objective, K562 cells with the STAG2 R614* null mutation were differentiated in parallel to

understand how cohesin deficiency affects +24 enhancer connections.

This experiment highlighted how the connectivity changes upon cohesin depletion and

may provide a mechanism for how cohesin mutations can lead to haematopoietic dysregulated

RUNX1 expression.

5.2 Hypothesis

I hypothesise that RUNX1+24 connections will alter during megakaryocyte

differentiation and without STAG2 these connections will not be maintained.

5.3 Aims

3) Elucidate connections of RUNX1 +24 during normal megakaryocyte differentiation.

4) Determine the change in significant chromatin interactions in STAG2-deficient

cells after PMA stimulation.

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5.4 RUNX1 +24 significant interactions during normal

megakaryocyte differentiation.

The conditions chosen for this 4C were based on the gene expression analyses shown

by Antony et al., (2020). Differentiation to megakaryocytes was induced using PMA

(Kuvardina et al., 2015). Control treatment of K562 (WT) and STAG2-deficient cell lines used

Dimethyl Sulfoxide (DMSO), as the solvent in place of PMA. 12 h was chosen based on

abnormal transcription observed at this time in STAG2-deficient mutants. An end timepoint of

72 h was chosen because by this time, WT cells displayed differentiation markers and gene

transcription profiles consistent with megakaryocytes (Antony et al., 2020).

4Cker differential analysis was carried out on the windows that interact with the

RUNX1 +24 bait in at least 2 conditions, using DESeq2 (Love et al., 2014; Raviram et al.,

2016). This analysis only works for near-bait and cis interactions. A plot of significant

interactions was generated with a p-value of 0.05, however, to tease out true differences in

interactions, a more stringent cut-off for significance utilised an adjusted p value of 0.01.

Hereafter all p values refer to each interaction’s adjusted p value.

Significantly altered interactions and their log fold changes were visualised in the

UCSC browser.

5.4.1 Visualisation of significant interactions during normal


The normalised read count for all WT conditions (WT 12 DMSO (control), WT 12

PMA, WT 72 PMA) was plotted across chromosome 21 using ggplot (Wickham et al., 2007)

to generate the cis profile of Runx1 +24 connections in unedited K562 cells (Figure 5.1). Reads

had a high coverage around the +24 bait. The profile remains relatively similar during

megakaryocyte differentiation; however, the number of normalised reads was reduced post-

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stimulation. The large normalised read peak that shows an interaction around 45,000,000 (*)

with PDE9A and BC033260 in WT 12 DMSO dramatically decreased during differentiation.

Figure 5.1: Megakaryocyte differentiation with PMA stimulation RUNX1 +24 WT cis 4C profiles.

ggplots of the cis 4C profiles for WT DMSO 1, WT PMA 12 h and WT PMA 72 h. Normalised read count is on the vertical axis and position on chromosome 21 is on the horizontal axis. * large peak at PDE9A and BC033260 in WT 12 DMSO.

5.4.2 Overview of significant changes in interactions during normal


Significantly altered interactions and their log fold changes were visualised in the

UCSC browser. Synonymous with WT 12 DMSO; the WT 12 PMA and WT 72 PMA profiles

showed that significant cis interactions spanned across the long arm of chromosome 21 (Figure

5.2). A list of all the statistically significant changes between conditions is available in

Appendix XI.

Over the differentiation time course RUNX1 +24 loses connections with a number of

regions across the chromatin. The significant cis differences between WT 12 DMSO and WT

12 PMA was loss of connection to ERG Promoter 3 (P3) with a Log2 fold change of -7.84 (p

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value =0.0097) and regulatory region between SAMSN1 and NRIP1 (Log2 fold change of -

8.01, p value =0.0049) (Figure 5.2).

ERG is repressed in WT 12 DMSO control K562 cells. However, during differentiation

ERG expression slowly increases (Antony et al., 2020). ERG has three known promoters, and

can positively regulate its own expression (Thoms et al., 2011). The data suggest that the

RUNX1 +24 connection in WT 12 DMSO control K562 cells might constrain ERG expression,

and upon PMA treatment, ERG’s P3 is released to facilitate the expression of ERG.

When comparing the WT 12 DMSO and WT 72 PMA there are 12 lost interactions

(Log2 fold change ranges from -5.78 to -8.6, p values range from 0.0017 to 0.0081), including

loss of connections to ERG P3 and the regulatory region between SAMSN1 and NRIP1 (see

Appendix XI). Overall, megakaryocyte differentiation of K562 cells causes the RUNX1+24

enhancer to lose cis-interactions along chromosome 21.

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Figure 5.2: Differentiation-induced changes to the RUNX1 +24 WT cis interactions.

Cis 4C profiles for WT DMSO 1, WT PMA 12 h and WT PMA 72 h. Chromosome 21 with WT DMSO 12 h RUNX1 +24 interactions represented by grey arches. Green arches show gained interactions compared to baseline (WT DMSO 12 h). Blue arches underneath the chromosome sketches highlight lost interactions compared to baseline. Each interaction is colour-coded according to its ChromHMM profile: key is at the bottom left of the figure. Super-enhancers were determined from dbSuper.

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The ChromHMM annotations do not necessarily reflect the nature of these elements

during differentiation. The annotations were determined in steady-state K562 cells and

represent the baseline (WT 12 DMSO); therefore, the chromatin composition could be altered

after PMA stimulation.

The super-enhancers were identified regardless of the chromatin status as they were

from the collection of cells in dbSUPER (Khan et al., 2016). In WT 12 DMSO 16% (n= 5/31)

of the cis connections were with super-enhancers. In Chapter 4 I noted that RUNX1 +24

interactions with super-enhancers may be facilitating or restricting their 3D connections. There

is a decrease in super-enhancer cis-connections during differentiation (10.3%) (n= 3/29) (WT

12 PMA), the proportion of interactions with super-enhancers increases to 13.6% (n= 3/22)

after megakaryocyte differentiation at WT 72 PMA (Figure 5.3).

Figure 5.3: Differentiation-induced changes in the number of RUNX1 +24 significant cis interactions with

super-enhancers in WT K562 cells.

Percentage of interactions is on the vertical axis. Horizontal axis shows the treatment conditions WT DMSO (D) 12h, WT PMA (P) 12h and WT PMA 72h

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5.4.3 Visualisation of significant near-bait interactions during normal


The normalised read counts of all three time points in WT cells were plotted across

chromosome 21 near-bait locations using ggplot (Wickham et al., 2007) (Figure 5.4). The

majority of reads are located within specific regions surrounding RUNX1 +24 in WT 12

DMSO. Resembling the cis WT profiles, the number of normalised reads decreases as the cells

differentiate. Interestingly, the large normalised read peak which highlighted an interaction

with RUNX1 m -368 (*) and the surrounding area decreases in comparison to the baseline WT

12 DMSO connections (Figure 5.4). Overall, megakaryocyte differentiation of K562 cells

causes the RUNX1+24 enhancer to lose interactions within the RUNX1 TAD.

Figure 5.4: Megakaryocyte differentiation with PMA stimulation RUNX1 +24 WT near-bait 4C profile.

ggplot of the near-bait 4C profiles for WT DMSO 12 h, WT PMA 12 h and WT PMA 72 h, normalised read count is on the vertical axis and position on chromosome 21 is on the horizontal axis. The asterisk shows the large normalised read peak at human equivalent region of RUNX1 m -368.

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5.4.4 Overview of significant changes in near-bait interactions during

normal megakaryocyte differentiation.

Similar to the cis results RUNX1 +24 progressively lost connections during

megakaryocyte differentiation. However, unlike cis interactions WT 12 PMA and WT 72 PMA

near-bait also showed some significantly increased connections (Figure 5.5). The statistically

significant changes between conditions is available in Appendix XII.

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Figure 5.5: Differentiation-induced changes to the RUNX1 +24 WT near-bait interactions.

Near-bait 4C profiles for WT DMSO 12 h, WT PMA 12 h and WT PMA 72 h. Chromosome 21 with WT DMSO 12 h RUNX1 +24 interactions represented by grey arches. Green arches show gained interactions compared to baseline (WT DMSO 12 h). Blue arches underneath the diagrams highlight lost interactions compared to baseline. RUNX1 +24 is represented by the yellow line. RUNX1 promoters are represented by blue lines and interactions to these promoters are shown in blue. RUNX1 regulatory regions are represented by orange lines and interactions to these regions are shown in orange. Near-bait region is aligned to the UCSC K562 gene list and annotations. Dotted grey lines show Rao et al., (2014) K562 domain boundaries.

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The previous chapter demonstrated the multiple near-bait connections surrounding

RUNX1 +24. The near-bait interactions observed during megakaryocyte differentiation reflect

the overall trend of a decrease in connections to RUNX1 +24. When assessing the WT 12

DMSO and WT 12 PMA there were 3 lost connections; -96, +89 and the SLC5A3 Exon 2

MRPS6 intron region (Log2 fold changes of -7.81,-7.62 and -8.94 and p values of 0.0034,

0.0015, 0.0014 respectively), connections that were previously described in Chapter 4.

Despite the overall loss of connections, after 12 h of PMA treatment there were some

significantly increased interactions with RUNX1 +24 (Log2 fold change ranges from 6.77 to

8.25, p values range from 5.3E-7 to 0.0020) see Appendix XII. These include some possible

regulatory regions +14, +54, +68, +142 between RUNX1 P1 and P2 (Table 5.1). RUNX1-IT1,

RUNX1 promoter 1, RUNX1 exon 1 and 2 are also recruited to the RUNX1 +24 hub (Figure

5.6). RUNX1-IT1 is a long non coding RNA which has been previously correlated with RUNX1.

(Liu et al., 2020). Expression of the RUNX1-IT1 enhanced RUNX1 transcription in pancreatic

cancer cells (Liu et al., 2020). Connections with m-368, m-58, RUNXOR, m+3, +62, m+11,

RUNX1 P2 and exon 3 remain stable with RUNX1 +24 during megakaryocyte differentiation.

The RUNX1 protein is capable regulating its own gene transcription (Martinez et al.,

2016). RUNX1 P1-driven expression is important for megakaryocyte differentiation (Draper et

al., 2017). During megakaryocyte differentiation RUNX1 P1-driven expression of RUNX1

gradually increases (Antony et al., 2020). In baseline K562 cells (WT 12 DMSO), RUNX1 P1

is not connected to RUNX1 +24 and the RUNX1 P1 driven expression is repressed (Chapter 4).

However, after PMA simulation RUNX1 P1 is recruited to RUNX1 +24 along with other

possible enhancers (Figure 5.6). In contrast, RUNX1 P2 transcript is stably expressed

throughout differentiation (Antony et al., 2020). Consistent with this, the connection between

RUNX1 P2 to RUNX1 +24 remains unchanged throughout differentiation.

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After differentiating to megakaryocytes (WT 72 PMA) more near-bait connections are

lost (Log2 fold change ranges from -3.93 to -12.36, p values range from 1.4E-35 to 0.0095).

Contacts with some of the of the possible regulatory regions that were significant at 12 h after

PMA treatment +14, +54, +68, +142 are lost in differentiated cells. Interactions with RUNXOR,

RUNX1-IT1, RUNX1 P1, RUNX1 exon 1 and 2 are also lost. The only connection gained at 72

h of differentiation was with m-371 (Log2 fold changes of 5.95, p value of 0.0069).

Chromatin status at the near-bait regions with significantly increased connections was

analysed using ENCODE data as for Chapter 3 and 4 (Table 5.1).

Table 5.1: Annotations of identified putative regulatory region connections





+14 chr21:36406427-36407599

RNA Pol II, CTCF, p300, LSD1, H3K27ac, H3K4me1, H3K4me3, H3K9me3, H3K27me3

Strong enhancer, Weak enhancer,

+54 chr21:36366550-36367612

DNase I HS, RNA Pol II, p300, LSD1, H3K27ac, H3K4me1, H3K4me3, H3K9ac, H3K9me3, H3K27me3

Strong enhancer, Weak enhancer,

+68 chr21:36352605-36353690

RNA Pol II, p300, LSD1, H3K4me1, H3K4me3, H3K9ac, H3K9me3

Weak enhancer, weak transcription

+142 chr21:36278644-36280049

DNase I HS, CTCF, p300, LSD1, H3K27ac, H3K4me1, H3K4me3, H3K9ac, H3K9me3

Strong enhancer


Connections with genes were identified using the same methods as Section 4.7.4 and

are listed in Appendix XIII. The number of gene connections changes during differentiation

(Table 5.2). Interestingly the number of connected genes increased from 64 in the baseline to

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154 (12 PMA) and then decreased to 140 (72 PMA). This is opposite to the trend of connections

which decreases overall. These connections need further investigation to determine gene type,

establish their expression level, as well as ascertain if there are any patterns in the type of gene.

However, as the gene interaction reflects an overlap with the region of a gene rather than a

promoter or exon of a gene, it may not be appropriate to assume the gene’s expression is

controlled by RUNX1 +24.

Table 5.2: Number of genes connected to RUNX1 +24 in WT cells

WT 12 DMSO WT 12 PMA WT 72 PMA

Number of genes connected to RUNX1 +24

64 154 140

5.4.6 Overall significant RUNX1 +24 and near-bait interactions in K562

cells during megakaryocyte differentiation

During PMA treatment there is an overall loss of connections to RUNX1 +24, however

as the cells are differentiating there are some significant changes in the near-bait. RUNX1 +24

maintains connections with the known enhancers m-368, m-58, m+3, +62, m+110 during

differentiation (Figure 5.6). It also sustains connections with RUNX1 P2 and RUNX1 exon 3.

However, the novel connections determined in Chapter 4 to -96, +89, SLC5A3 Exon 2 MRPS6

intron and super-enhancer regulatory regions between SAMSN1 and NRIP1 are lost during

differentiation. The connections to known RUNX1 regulator long non-coding RNA RUNXOR,

and ERG P3 are also lost during differentiation (Figure 5.6).

During differentiation (12 h post PMA treatment) The +24 enhancer makes some novel

connections to putative regulatory elements +14, +54, +68, +142 (Figure 5.6, Table 5.1)

RUNX1 +24 also gains connections with the RUNX1 promoter 1, exon 1 and 2.

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After differentiation RUNX1 +24 loses the novel connections gained at 12 h of PMA

treatment (+14, +54, +68, +142), and with the long non-coding RNA RUNX1-IT1, RUNX1 P1,

exons 1 and 2. At 72 h once differentiation has occurred, there is a significant increase in the

interaction between RUNX1 +24 and m-371. m-371 is an important haematopoietic enhancer

in developing zebrafish and mice.

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Figure 5.6: Changes to the RUNX1 +24-anchored chromatin hub during megakaryocyte differentiation of

K562 cells.

Yellow boxes represent UCSC annotated gene promoters and/or exons. Blue boxes show described RUNX1 regulators. Orange boxes annotate novel putative RUNX1 regulators.

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5.5 The effect of STAG2 deficiency on RUNX1 +24 significant

interactions during differentiation of K562 cells

The STAG2 mutants 4C data was prepared in the same way as WT (STAG2 12 DMSO,

STAG2 12 PMA, STAG2 72 PMA). STAG2 mutants were compared to WT equivalent

condition and timepoint.

5.5.1 Visualisation of significant interactions during cohesin impaired

“differentiation”

The normalised read count of all WT conditions (WT 12 DMSO, WT 12 PMA, WT 72

PMA) and STAG2 mutant conditions (STAG2 12 DMSO, STAG2 12 PMA, STAG2 72 PMA)

were plotted across chromosome 21 using ggplot (Wickham et al., 2007) to generate the cis

profile of Runx1 +24 connections in baseline K562 cells and cohesin mutants (Figure 5.7). The

STAG2 mutant cells have a dramatic increase in connections across chromosome 21. Reads

had a high coverage around the +24 bait.

A comparison of the +24-mediated connections in WT 12 DMSO versus STAG2 12

DMSO show a similarly high number of reads around the RUNX1 +24 bait. However, the read

coverage across chromosome 21 is higher in STAG2 12 DMSO cells.

During PMA-induced differentiation, WT K562 cells lose connections overall. In

contrast, STAG2 mutants generally retained, or even gained, connections following stimulation

with PMA. After 12 h of PMA stimulation the pattern of connections in STAG2 mutants

approximates the wild type, but with a higher read and more distant connections. After 72 h of

PMA stimulation there was a dramatic increase in connections across chromosome 21 in

STAG2 72 PMA compared to WT 72 PMA.

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Figure 5.7: Time course of PMA stimulation of all RUNX1 +24 cis 4C profiles.

ggplots of the cis 4C profiles for WT DMSO 1, WT PMA 12 h, WT PMA 72 h, STAG2 DMSO 12 h, STAG2 PMA 12 h and STAG2 PMA 72 h. Normalised read count is on the vertical axis and position on chromosome 21 is on the horizontal axis

5.5.2 The effect of STAG2 deficiency on interactions during

differentiation of K562 cells

Similar to the baseline WT cells, the baseline STAG2 mutant cells (STAG2 12 DMSO)

had significant cis interactions spanning the long arm of chromosome 21 (Figure 5.8). A list of

all the statistically significant changes between conditions is available in Appendix XI.

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When comparing the WT 12 DMSO and STAG2 12 DMSO, there are 4 interactions

that are not present in the cohesin mutant baseline (Log2 fold change ranges from -6.66 to 5.15,

p values range from 0.0003 to 0.0078) including the super-enhancer regulatory region between

SAMSN1 and NRIP1 (Log2 fold change of -5.64, p value of 0.0047) (Appendix XI). However,

there are 20 regions interacting with RUNX1 +24 in STAG2 12 DMSO (Log2 fold change

ranges from 3.85 to 6.04, p values range from 0.0002 to 0.0097) that are not present in WT 12

DMSO (Figure 5.8). Overall, STAG2 deficiency causes the RUNX1+24 enhancer to make more

connections than in the WT cells.

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Figure 5.8: Significant changes to the RUNX1 +24 cis baseline (12 DMSO) interactions due to cohesin

deficiency.

Cis 4C profiles for K562 STAG2 mutant DMSO 12h cells on Chromosome 21 with K562 WT DMSO 12h RUNX1 +24 interactions represented by grey arches. Green arches show a gained interaction compared to WT. Blue arches underneath the diagrams highlight lost interactions in STAG2 mutant cells compared to WT. Each interaction is colour-coded according to its ChromHMM profile: key is at the bottom left of the figure. Super-enhancers were determined from dbSuper.

These interactions were further categorised based on the K562 chromatin

characteristics using ChromHMM and dbSUPER annotations. Some regions that interact with

RUNX1 +24 exhibit multiple ChromHMM annotations (Figure 5.9).

Compared to WT cells, STAG2 mutant cells RUNX1 +24 have slightly fewer

interactions with enhancer regions (WT = 58%, STAG2 = 50%), active promoters (WT = 32%,

STAG2 = 28%), heterochromatin (WT = 10%, STAG2 = 7%), and polycomb repressed (WT

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= 29%, STAG2 = 28%) regions. Interactions between RUNX1+24 and insulators increased

(WT = 32%, STAG2 = 48%). Poised promoters (WT = 16%, STAG2 = 22%) and super-

enhancers (WT = 16%, STAG2 = 24%) also had an increased number of connections with

RUNX1+24.

Figure 5.9: Features of RUNX1 +24 significant cis interactions in WT baseline and STAG2 baseline cells

(12 DMSO).

Percentage of interactions is on the vertical axis. Horizontal axis shows the ChromHMM annotations. Individual interactions can have multiple annotations.

During megakaryocyte differentiation of WT K562 cells (12 h post PMA stimulation),

the RUNX1 +24 enhancer lost 2 cis connections to ERG Promoter 3 (P3) and the regulatory

region between SAMSN1 and NRIP1. STAG2 12 PMA also lost the connection to ERG P3.

The connection to regulatory region between SAMSN1 and NRIP1 was not present in STAG2

12 DMSO, and this loss was maintained in STAG2 12 PMA (Figure 5.10). Most connections

observed in WT cells were conserved in the cohesin STAG2 mutant. However, one additional

cis connection was observed in STAG2 12 PMA (Log2 fold change of 8.83, p value of 0.0023)

(Appendix XI).

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The overall similarity between RUNX1 +24 enhancer-mediated connections formed in

WT 12 PMA and STAG2 12 PMA was somewhat surprising based on the presence of

numerous new connections seen in the STAG2 baseline cells (Figure 5.8).

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Figure 5.10: Significant changes to the RUNX1 +24 12h PMA treatment cis interactions due to cohesin

deficiency.

Cis 4C profiles for STAG2 mutant PMA 12h Chromosome 21 with time matched K562 WT PMA 12h RUNX1 +24 interactions represented by grey arches (WT 12 PMA). Green arches show a gained interaction compared to WT. Blue arches underneath the diagrams highlight lost interactions in STAG2 mutant cells compared to WT baseline. Each interaction is colour-coded according to its ChromHMM profile: key is at the bottom left of the figure. Super-enhancers were determined from dbSuper.

After 72 h of PMA treatment, the WT cells significantly lost connections across the

chromosome. In contrast, 72 h of PMA stimulation in STAG2 mutant cells led to the RUNX1

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+24 enhancer dramatically gaining cis connections when compared to the WT treatment

condition (Figure 5.11).

There are 35 significantly increased cis connections in STAG2 72 h PMA cells (Log2

fold change ranges from 4.75 to 10.09, p values range from 3.5E-5 to 0.0094) see Appendix

XI. This correlates with the normalised read profile seen in Figure 5.7.

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Figure 5.11: Significant changes to the RUNX1 +24 72h PMA treatment cis interactions due to cohesin

deficiency.

Cis 4C profiles for STAG2 mutant PMA 72h Chromosome 21 with time matched K562 WT PMA 72h RUNX1 +24 interactions represented by grey arches (WT 72 PMA). Green arches show an interaction that has been significantly gained compared to time matched WT condition. WT blue arches underneath the diagram show lost interactions compared to WT baseline. The STAG2 blue arch underneath the diagram shows an interaction that has been lost when compared to WT 72 PMA. Each interaction is colour-coded according to its ChromHMM profile: key is at the bottom left of the figure. Super-enhancers were determined from dbSuper.

All interactions observed across PMA treatment and cell line were further categorised

using super-enhancer annotations derived from dbSUPER (Khan et al., 2016). dbSUPER

annotations extend across multiple different cell-types. Compared to WT cells, in STAG2

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mutant cells the RUNX1 +24 enhancer forms more interactions with super-enhancer regions

(Figure 5.12).

At the WT baseline (WT 12 DMSO) 16% (n= 5/31) of the connections RUNX1 +24

makes are with super-enhancers; this is increased in STAG2-deficient cells (STAG2 12

DMSO) to 24% (n= 11/46). During PMA-induced differentiation (12 h) the RUNX1+24

enhancer forms similar interactions in both WT and STAG2 mutant cells; 10% of the

connections are with super-enhancers (n= 3/29 and n= 3/30 respectively). 72 h after PMA

treatment, 14% (n= 3/22) of the connections to RUNX1 +24 are with super-enhancers in WT

cells. In contrast, in STAG2 mutant cells the percentage of interactions between RUNX1 +24

and super-enhancers is 22% (n= 12/55)(Figure 5.12).

Figure 5.12: Profile of RUNX1 +24 significant cis interactions with super-enhancers during megakaryocyte

differentiation with PMA stimulation in WT and STAG2 mutant cells.

Percentage of interactions is on the vertical axis. Horizontal axis shows the K562 cells treatment conditions WT DMSO (D) 12h, WT PMA (P) 12h and WT PMA 72h in green and STAG2 mutant STAG2 DMSO (D) 12h, STAG2 PMA (P) 12h and STAG2 PMA 72h in blue.

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5.5.3 The effect of STAG2 deficiency on near-bait interactions during

differentiation of K562 cells

The normalised read count of all WT conditions (WT 12 DMSO, WT 12 PMA, WT 72

PMA) and STAG2 mutant conditions (STAG2 12 DMSO, STAG2 12 PMA, STAG2 72 PMA)

were plotted across chromosome 21 near-bait locations using ggplot (Wickham et al., 2007)

(Figure 5.13). During differentiation of WT cells, the majority of reads are located within

specific regions surrounding the RUNX1 +24 enhancer, and read count decreases as the cells

differentiate. However, RUNX1 +24-mediated interactions in STAG2 mutant cells give rise to

distinctly different interaction profiles.

Compared with WT cells, STAG2 12 DMSO lacks the peak around RUNX1 +24 and

has a higher level of background reads compared to WT 12 DMSO. During differentiation,

after 12 h of PMA treatment the RUNX1 +24-mediated interactions in STAG2 12 PMA

resembles the pattern of normalised reads seen in the WT 12 PMA. After 72 h there is a

dramatic increase in connections across the near-bait region in STAG2 72 PMA compared to

WT 72 PMA. The normalised read count profiles reflect the gain of connections in STAG2

mutants seen in the cis profiles.

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Figure 5.13: Time course PMA stimulation of all RUNX1 +24 near-bait 4C profiles.

ggplot of the near-bait 4C profiles for WT DMSO 1, WT PMA 12 h, WT PMA 72 h, STAG2 DMSO 12 h, STAG2 PMA 12 h and STAG2 PMA 72 h. Normalised read count is on the vertical axis and position on chromosome 21 is on the horizontal axis.

5.5.4 Significant changes in near-bait interactions caused by

STAG2 deficiency

When comparing the WT 12 DMSO and STAG2 12 DMSO there are 176 interactions

present in WT cells that are not present in the cohesin mutant baseline (Log2 fold change ranges

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from -10.69 to -2.62, p values range from 2.9E-17 to 0.0087). The statistically significant

changes between conditions is available in Appendix XII. These are within the 5 Mb region

surrounding the bait (Figure 5.14) The connections that decreased include RUNXOR, m+3,

+62, m+110, RUNX1 exon 3 and RUNX1 P2. However, in the STAG2 12 DMSO there are 83

regions interacting with RUNX1 +24 that are not present in WT 12 DMSO (Log2 fold change

ranges from 3.31 to 7.40, p values range from 4.4E-6 to 0.0096).

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Figure 5.14: Significant changes to the RUNX1 +24 baseline (12 DMSO) of near-bait interactions due to

cohesin deficiency.

Near-bait 4C profiles for STAG2 mutant DMSO 12h Chromosome 21 with time matched K562 WT DMSO 12h RUNX1 +24 interactions represented by grey arches. Green arches show a gained interaction compared to time matched WT condition. Blue arches underneath the diagram represent lost interactions when compared to WT DMSO 12h. RUNX1 +24 is represented by the yellow line. RUNX1 promoters are represented by blue lines and interactions to these promoters are shown in blue. RUNX1 regulatory regions are represented by orange lines and interactions to these regions are shown in orange. Near-bait region is aligned to the UCSC K562 gene list and annotations. Dotted grey lines show Rao et al., (2014) K562 domain boundaries.

During megakaryocyte differentiation (12h post PMA stimulation) the WT cells

RUNX1 +24 significantly lost connections to -96, +89 and the SLC5A3 Exon 2 MRPS6 intron

region. STAG2 12 PMA maintains the connection with -96 and the SLC5A3 Exon 2 MRPS6

intron region (Log2 fold changes of 8.07 and 8.888, p values of 0.0053 and 0.0003

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respectively). STAG2 12 PMA has one decreased connection when compared to WT 12 PMA

(Log2 fold change of -7.10, p value of 0.0033) see Appendix XII. In contrast to the DMSO

baseline, the cohesin mutant STAG2 12 PMA showed very similar connections to WT (Figure

5.15).

Figure 5.15: Significant changes to the RUNX1 +24 near-bait interactions after 12h PMA treatment due to

cohesin deficiency.

Near-bait 4C profiles for STAG2 mutant PMA 12h Chromosome 21 with time matched K562 WT PMA 12h RUNX1 +24 interactions represented by grey arches. WT green arches show gained interactions compared to WT DMSO 12h. The STAG2 green arch shows an interaction that has been significantly gained compared to time matched WT condition. WT blue arches beneath the diagram highlight lost interactions compared to WT baseline. The STAG2 blue arch beneath the diagram shows a lost interaction compared to WT 12 PMA. RUNX1 +24 is represented by the yellow line. RUNX1 promoters are represented by blue lines and interactions to these promoters are shown in blue. RUNX1 regulatory regions are represented by orange lines and interactions to these regions are shown in orange. Near-bait region is aligned to the UCSC K562 gene list and annotations. Dotted grey lines show Rao et al., (2014) K562 domain boundaries.

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WT 72 PMA RUNX1 +24 had decreased interactions with +14, +54, +68, +142 regions,

RUNXOR, RUNX1 P1, as well as RUNX1 exon 1 and 2 and an increased connection with m-

371. STAG2 72 PMA also lost connections with +14, +54, +68, +142 regions, RUNX1 P1, and

RUNX1 exon 1, exon 2. However, RUNX1 +24 STAG2 72 PMA maintained connections with

RUNXOR and RUNX1-IT1 (Log2 fold changes of 8.28 and 4.15, p values of 2.0E-5 and 0.0096

respectively). RUNX1 +24 had a decreased connection with RUNX1 exon 3, m+3 and m-371

when compared with its WT counterpart (Log2 fold changes ranges from -6.05 to -7.37, p

values range from 3.6E-6 to 0.0003) There was a dramatic increase in the number of

connections to RUNX1 +24 however these regions require further investigation (Figure 5.16).

The connections are different between the STAG2 12 DMSO condition and STAG2 72

PMA as shown in Appendix XIV. These were not analysed further than looking at the adjusted

p value and Log2 fold change. Although these conditions have different profiles they do show

a similar trend; When compared to the equivalent WT condition, there is a decrease in the near-

bait connections 5 Mb either side of the bait and a dramatic increase in connections to RUNX1

+24 from regions 5 Mb to 10 Mb away from the bait.

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Figure 5.16: Significant changes to the RUNX1 +24 near-bait interactions after 72h PMA treatment due to

cohesin deficiency.

Near-bait 4C profiles for STAG2 mutant PMA 72 h Chromosome 21 with time matched K562 WT PMA 72h RUNX1 +24 interactions represented by grey arches. WT green arches show gained interactions compared to WT DMSO 12 h. STAG2 green arches show gained interactions compared to time matched WT condition. WT blue arches beneath the diagram highlight lost interactions compared to WT baseline. STAG2 blue arches beneath the diagram highlight lost interactions compared to WT 72 PMA. RUNX1 +24 is represented by the yellow line. RUNX1 promoters are represented by blue lines and interactions to these promoters are shown in blue. RUNX1 regulatory regions are represented by orange lines and interactions to these regions are shown in orange. Near-bait region is aligned to the UCSC K562 gene list and annotations. Dotted grey lines show Rao et al., (2014) K562 domain boundaries.


Connections with genes were identified using the same methods as Section 5.4.5 and

the resulting output is listed in Appendix XIII. The number of gene connections in STAG2

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mutants is different to WT connections. STAG2 has almost double the number of gene

connections (121) at baseline compared to WT (64). The number of associated genes decreased

as the cells were treated with PMA (Table 5.3). Interestingly, despite the similar connection

profiles of +24 in WT and STAG2 mutant cells after 12 h of PMA treatment, the number of

gene connections varies between mutant and wild type, with fewer genes connected in the

STAG2 mutant cells. Surprisingly, even though the overall numbers of connections increased

in STAG2 mutants during differentiation compared with WT, the number of connections that

were associated with genes decreased. This may be because the gain in connections is to

intronic and intergenic regions, and/or there are multiple connections within the same gene. It

may also reflect the inability of STAG2 mutant cells to differentiate, as genes involved in

differentiation are not being recruited. These results are inconclusive without further

investigation as mentioned in Section 5.4.5.

Table 5.3: Number of genes connected to RUNX1 +24 in WT cells

WT 12 DMSO

WT 12 PMA

WT 72 PMA

STAG2 12 DMSO

STAG2 12 PMA

STAG2 72 PMA

Number of genes connected to RUNX1 +24

64 154 140 121 37 47

5.5.6 Overall changes in and near-bait interactions mediated by RUNX1

+24 interactions during differentiation of K562 cells, WT versus

STAG2-deficient

After 72 h PMA treatment, WT K562 cells have features consistent with

megakaryocyte identity. During megakaryocyte differentiation in WT K562 cells, there is an

overall loss of connections to the RUNX1 +24 enhancer, but a gain in connected genes. In

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contrast, the RUNX1 +24 enhancer gains extra connections in STAG2 mutants, but loses

connections to other genes.

Figure 5.17 compares the baseline (12 DMSO) WT and STAG2 RUNX1 +24

connections with regions that may be important for megakaryocyte differentiation. WT 12

DMSO maintains connections with m-368, -96, m-58, m+3, m+62, +89, m+110, SLC5A3 Exon

2 MRPS6 intron, RUNXOR, RUNX1 P2, RUNX1 exon 3, ERG P3 and super-enhancer

regulatory regions between SAMSN1 and NRIP1. STAG2 12 DMSO maintains connections

with m-368, -96, m-58, +89, SLC5A3 Exon 2 MRPS6 intron, RUNX1 P2 and ERG P3.

However, it loses connections with m+3, m+62, m+110, RUNXOR, RUNX1 exon 3 and super-

enhancer regulatory regions between SAMSN1 and NRIP1.

Figure 5.17: The effect of cohesin deficiency on the RUNX1 +24 enhancer-anchored chromatin hub in

baseline (12 DMSO) conditions in K562 cells.


In WT K562 cells, during differentiation (12 h PMA) the RUNX1 +24 enhancer gains

connections to +14, +54, +68, +142, RUNX1-IT1, RUNX1 promoter 1, exon 1 and 2. RUNX1

+24 maintains connections seen in WT 12 DMSO with m-368, m-58, m+3, m+62, m+110,

RUNXOR, RUNX1 P2, RUNX1 exon 3. In WT K562 cells, RUNX1 +24 decreased interactions

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with -96, +89, SLC5A3 Exon 2 MRPS6 intron region, ERG P3 and super-enhancer regulatory

regions between SAMSN1 and NRIP1. The RUNX1 +24 enhancer in STAG2-deficient cells (12

h PMA) has a similar interaction landscape as WT 12 PMA, the obvious differences being that

STAG2 12 PMA RUNX1 +24 further maintains the interaction with -96 and SLC5A3 Exon 2

MRPS6 intron region, which are lost in the WT (Figure 5.18).

Figure 5.18: The effect of cohesin deficiency on the RUNX1 +24 enhancer-anchored chromatin hub in mid-

differentiation (12 PMA) conditions in K562 cells.


In WT K562 cells, after differentiation (72 h PMA) the RUNX1 +24 enhancer interacts

with m-371, m-368, m-58, m+3, +62, m+110, RUNX1 P2 and exon 3. The RUNX1 +24

enhancer loses connections with +14, +54, +68, +142, RUNX1-IT1, RUNXOR , RUNX1

promoter 1, exon1 and 2. In STAG2-deficient cells (72 h PMA) RUNX1 +24 interacts with m-

368, m-58, +62, m+110, RUNX1 P2, RUNX1-IT1, RUNXOR. The RUNX1 +24 enhancer loses

connections with m-371, m+3, and RUNX1 exon 3 when compared to the WT (Figure 5.19)

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Figure 5.19: The effect of cohesin deficiency on the RUNX1 +24 enhancer-anchored chromatin hub in post-

differentiation (72 PMA) conditions in K562 cells.


Figure 5.17, 5.18 and 5.19 give an overview of the changes to the WT identified

connections mediated by the RUNX1 +24 enhancer. However, these summaries do not illustrate

the gained connections in STAG2 mutant cells. The regions that have increased interactions

with the RUNX1 +24 enhancer in STAG2 mutant cells require further analysis to determine

their functional relevance.

5.6 Chapter summary

5.6.1 RUNX1 +24 enhancer connections during K562 differentiation to

megakaryocytes

5.6.1.1 Distant connections

The RUNX1 +24 enhancer makes connections spanning chromosome 21 during

megakaryocyte differentiation. As differentiation occurs, there is a steady decrease of

interactions across the chromosome, including loss of the connection between RUNX1 +24 and

ERG P3. Because ERG expression slowly increases during differentiation, it is possible that

the interaction between the RUNX1 +24 enhancer and ERG P3 inhibits transcription from this

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promoter in baseline conditions. During the onset of differentiation, the loss of ERG P3

connection with RUNX1 +24 may in turn allow ERG P3 initiate expression of ERG.

5.6.1.2 Connections within the RUNX1 TAD

During megakaryocyte differentiation, the RUNX1 +24 enhancer displays a dynamic

interaction landscape in WT K562 cells. Upon PMA treatment, RUNX1+24 connects with

possible regulatory elements -96, +14, +54, +68, +89, +142 and known regulator m-371.

Interactions between RUNX1 +24 and +14, +54, +68 and +142 were only observed during

differentiation (12h PMA). All of these regions were annotated in by ChromHMM as enhancers

in steady-state K562 cells. RUNX1 +24 maintains contact with a cluster of regulatory regions

m-368, m-58, m+3, +62 and m+110. Previously m-368, m-58, m+3 and m+110 had only been

described as enhancers in mice but were shown to be conserved in humans in Chapter 3.

Upon stimulation with PMA, K562 cells start to transcribe RUNX1 from the P1

promoter. P1-driven transcription of RUNX1 is essential for megakaryocyte differentiation.

Consistent with the idea that the interaction between RUNX1 +24 and RUNX1 P1 stimulates

P1-driven transcription, the connection between RUNX1 +24 and RUNX1 P1 increases during

differentiation (12h PMA). In contrast, RUNX1 P2 transcript is minimally expressed

throughout differentiation. The connection between RUNX1 P2 to RUNX1 +24 remains

unchanged throughout differentiation. After differentiation there is an increase in the

connection between RUNX1 +24 and m-371.

5.6.2 RUNX1 +24 enhancer connections during PMA treatment in

STAG2 mutants

5.6.2.1 Baseline conditions:

In contrast to WT K562 cells, in STAG2-deficient cells the RUNX1 +24 enhancer

makes many more connections across chromosome 21. Increased connections corresponded

with regions that are annotated as insulators and poised promoters. Despite the overall increase

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in interactions, some connections that are present in WT K562 cells are lost. RUNX1 +24 loses

connections with m+3, m+62, m+110, RUNXOR, RUNX1 exon 3 and super-enhancer

regulatory regions between SAMSN1 and NRIP1.

5.6.2.2 Mid-differentiation

In STAG2-deficient K562 cells, transcription of RUNX1 from the P1 promoter fails to

be activated by PMA stimulation, and instead, there is ectopic expression from the P2

promoter. From this finding (and from the baseline connectivity) the expectation was that after

12 h of PMA treatment the RUNX1 +24 enhancer would have a vastly different interaction

profile in STAG2 mutants compared to WT. Surprisingly, the RUNX1 +24 enhancer forms

similar connections after 12 h PMA treatment in both WT and STAG2-deficient cells. The

RUNX1 +24 enhancer maintains the interaction with -96 and SLC5A3 Exon 2 MRPS6 intron

region, which are lost in the WT, in STAG2 mutants. Although ERG P3 is released upon PMA

stimulation in STAG2 mutant cells, just as it is in WT, expression of ERG is inappropriately

sensitised in STAG2 mutant cells. Further investigation is required to determine how ERG

becomes ectopically activated.

5.6.2.3 Post-differentiation

STAG2 mutant cells retained stem cell-like properties and failed to activate

megakaryocyte markers (Antony et al., 2020). After 72 h PMA treatment of STAG2 mutant

cells, interactions mediated by the RUNX1 +24 enhancer no longer resembles the WT profile.

The RUNX1 +24 enhancer mediates a vastly increased number of connections, and the profiles

of normalised read counts are chaotic when compared to the profiles of the WT RUNX1 +24

connections (Figure 5.7 and 5.14). After 72 h PMA treatment of STAG2 mutant cells, the

RUNX1 +24 enhancer maintains connections with RUNX1-IT1, RUNXOR, which are lost in

WT K562. The RUNX1 +24 enhancer lost interaction with m-371, m+3, and RUNX1 exon 3 in

STAG2 mutant cells; connections that are maintained in WT K562 (Figure 5.19).

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The RUNX1 +24 enhancer contacted more super-enhancer regions in STAG2 mutant

cells compared to WT K562.

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6 Chapter 6: Discussions and Conclusions

Runx1 is a transcription factor that is crucial for definitive haematopoiesis (Okuda et

al., 1996; Wang et al., 1996). RUNX1 has a significant role in the development to myeloid

malignancies, especially in Acute Myeloid Leukaemia (AML) (Ichikawa et al., 2013; Okuda

et al., 1996). runx1 expression in haematopoietic progenitor cells in zebrafish is reliant on

cohesin (Horsfield et al., 2007). Cohesin mutations have been recurrently identified in AML

and other myeloid malignancies (Leeke et al., 2014). Because karyotypically normal cells are

found with cohesin mutations, the mechanism by which cohesin mutation leads to leukaemia

is thought to be by dysregulation of target genes rather than mitotic defects (Thol et al., 2014).

Cohesin insufficiency impairs differentiation, alters overall chromatin accessibility, as well as

the transcription of RUNX1 (Mazumdar et al., 2015; Mullenders et al., 2015; Viny et al., 2015).

Cohesin regulates Runx1 expression, and RUNX1 plays a central role in the development of

myeloid malignancies, therefore cohesin-dependent RUNX1 expression may represent an

underlying mechanism that leads to myeloid malignancies (Leeke et al., 2014). The precise

spatiotemporal expression of Runx1 requires cis-regulatory elements, the majority of which

have not been defined.

In my PhD project, I set out to investigate the regulatory potential of Runx1 4C-

identified regions, and determine conservation of mouse Runx1 regulators in humans. This

work has provided an insight into mouse Runx1 enhancer functions and highlighted the features

of conserved human enhancers. Runx1 +24 was identified as an important intronic Runx1

enhancer in mice, and is crucial for the activation of Runx1 expression in hematopoietic

stem/progenitor cells (HSPCs) (Ng et al., 2010; Nottingham et al., 2007). Runx1 +24 is highly

conserved in eukaryotes, indicating an essential role in gene regulation. Runx1 +24 binds most

active Runx1 enhancers in mice HPCs and may anchor a regulatory hub that organises the

correct enhancer-promoter interactions for Runx1 regulation. Suppression of enhancers by BET

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inhibitor proteins alters the expression of RUNX1 in haematopoietic cells (Antony et al., 2020).

I hypothesised that the RUNX1 +24 enhancer anchors an enhancer hub in human cells and is

important for correct RUNX1 expression, which may be disrupted in AML.

Chromatin conformation capture techniques were used in this project to understand the

role of RUNX1 +24 enhancer in 3D chromatin connections, and as a hub for functional

regulatory regions. The RUNX1 +24 enhancer connected across chromosome 21, defying

known TADs. It interacted with coding genes, non-coding RNA loci, super-enhancers and

regulatory elements, and may be maintaining these connections to prevent transcription, rather

than have a traditional enhancer role. The data suggests that the RUNX1 +24 enhancer could

be important for overall organisation and 3D structure of chromosome 21.

The results from my project highlighted the dynamic changes in the RUNX1 +24 hub

connections with RUNX1 and ERG, genes central in correct megakaryocyte development,

during differentiation of the chronic myeloid leukaemia cell line K562. Chromatin interaction

data identified putative RUNX1 regulators and demonstrated how cohesin insufficiency affects

the RUNX1 +24 enhancer connections. In cohesin-deficient cells, the number of RUNX1 +24

connections dramatically increased across chromosome 21. This suggests that cohesin STAG2

mutation leads to a de-constrained chromatin environment. Loss of insulation of chromatin

domains in STAG2 mutants enables aberrant enhancer-amplified transcription and may

contribute to the pathogenesis of AML.

6.1 Regulators of Runx1

6.1.1 Mouse Runx1 enhancers

This thesis describes the functional characterisation of novel Runx1 enhancers m-371,

m-354, m-303 and m-48, as well as the previously identified m+110, m-58, m-321, m-327, m-

368. The data show that all these elements have enhancer capability except m-327. Despite the

lack of activity seen in zebrafish embryos expressing m-327, this putative regulatory region

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spatially connects to the active (P1) promoter of Runx1 and the Runx1 +24 enhancer in mouse

HPC7 cells, so it may have another regulatory role in this hub. All enhancers except m+110

interacted with the Runx1 +24 enhancer (Marsman, 2016), suggesting that the Runx1 +24

enhancer could be anchoring an enhancer hub in mouse HPC7 cells.

My analysis of these enhancers in haematopoietic tissues of developing zebrafish

embryos shows that they appear to be active during the primitive wave of haematopoiesis. A

complete summary of the spatiotemporal activity of the mouse Runx1 regulatory regions in

zebrafish is difficult to interpret, because zebrafish embryos were not continuously observed

throughout development. Some of the putative enhancer regions may drive cell-specific gene

expression at developmental periods not observed.

Recent research has demonstrated the potential of zebrafish models to assess the

function of human and mouse cis-regulatory elements, regardless of their sequence

conservation in zebrafish (Bhatia et al., 2013; Bhatia et al., 2014; Chahal et al., 2019;

Ketharnathan et al., 2018; Rainger et al., 2014; Ravi et al., 2013; Yuan et al., 2018). The

transcription factors (TFs) required by cis-regulatory elements for their function are still

present and utilised in the appropriate cell and tissue types (Bessa et al., 2009; Mann et al.,

2019). Enhancer assays highlight the value of the zebrafish model as a way to determine

functionality of regulatory sequences from other species.

6.1.2 Conserved human RUNX1 regulators

I found that 9 out of the 16 mouse Runx1 regulatory regions are conserved in human

(based on sequence analysis and ENCODE annotations), increasing the number of ‘known’

putative human RUNX1 regulatory regions from 6 to 14 (Figure 6.1). Conservation between

mice and humans suggests that these regulatory regions are fundamental for the correct

regulation of RUNX1. m-354, m-321, m-303, m-48, m-42, m+1 and m+181 were not conserved

in human.

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Figure 6.1:Schematic overview of human RUNX1 locus (hg19: chromosome 21: 36,148,773-36,872,777).

Black text annotates the previously identified human RUNX1 regulatory regions. Blue text highlights the conserved mouse regulatory regions. Each regulatory region is annotated with orange circles, grey boxes annotate exons. The blue boxes show the exons encoding the Runt binding domain. The two RUNX1 promoters are represented by black right angled arrows.

Bioinformatic analysis of RUNX1 regulatory regions showed that not all of them appear

to be active in K562 cells (Table 6.1). ChromHMM annotates m-327 and -250 as repressed,

and m-368, m-58, m+110 -139, m-43, +24, +62 as active enhancers. Intron 5.2, m-101 and

m+3 show chromatin features of enhancers; however, they are labelled as transcription-

associated alongside -139 and -188. Transcription-associated annotation may be due to their

close proximity with exons when compared to the regions annotated as enhancers or it may

indicate that these regions are producing RNA, for instance enhancer RNA (eRNA).

The RUNX1 +24 enhancer has strong enhancer characteristics and recruits

haematopoietic transcription factors SP1, TAL1, GATA1 and GATA2. SPI also located to -

139 and -188. Haematopoietic transcription factors TAL1, GATA1 and GATA2 bind to m-

371, m-58 and m+110. These regions showed transcription factor binding sites and chromatin

modifications similar to that found at the RUNX1 +24 enhancer.

The RUNX1 +24 enhancer also recruits LSD1, a mediator of transcriptional repression

usually seen in silencers. Interestingly, nine other regulatory regions (m-371, m-368, -250, m-

101, m-58, m+3, +62, m+110 and intron 5.2) had LSD1 binding in K562 cells. Cheng et al.,

(2018) treated haematopoietic cells (chronic myelogenous leukaemia (K562), acute myeloid

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leukaemia (OCI-AML3) and histiocytic lymphoma (U937)) with a LSD1 inhibitor and

observed upregulation of RUNX1, which implicates the role of LSD1 in repression of RUNX1

(Cheng et al., 2018). Kerenyi et al., (2013) previously showed Lsd1 is a crucial epigenetic

regulator of haematopoiesis in mice, because it represses key hematopoietic stem cell genes

including Runx1 (Kerenyi et al., 2013).

Cheng et al., (2018) characterised +62 as a silencer of RUNX1 P2 by measuring the

repressive effect of various deletion/mutant +62 luciferase constructs on transcription from the

P2 promoter. This functional assay was carried out in K562, OCI-AML3 and U937 cells

(Cheng et al., 2018). However, in K562 cells +62 has ENCODE and ChromHMM annotations

associated with enhancer activity. These regions with enhancer and silencer annotations may

have multiple roles depending on combinations of connections being made with the region

(promoters and other regulatory regions) and the cell type.

ATAC-seq (Antony et al., 2020) showed that intron 5.2, m+110 and m-371 regions

were in differentially open chromatin regions in STAG2 mutant K562 cells. This suggests that

cohesin influences the accessibility of these enhancers. I observed a coincidence of cohesin

subunit Rad21 binding in the absence of CTCF with m+110 enhancer, and CTCF binding with

m-371, m-58, m-43, m+3, +62 and intron 5.2. This suggested that CTCF and cohesin could

independently mediate a subset of enhancer-promoter looping in combination with

transcription factors.

6.1.3 G-quadruplex

The Circular Dichroism (CD) analysis showed that the predicted G-quadruplexes

observed in the mouse +24 element are capable of forming parallel G-quadruplexes in vitro.

The G-quadruplex analysis was specific to the mouse sequence so it may not reflect the human

sequence, the type of G-quadruplex, or its functional capacity. Luciferase assays showed that

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the mutations of G-quadruplex in mouse Runx1 +24 had no significant effect on enhancer

function when tested in isolation in mouse fibroblast (NIH/3T3) cells.

6.1.4 Regulatory RNAs

Non-coding RNAs are involved in many biological processes, including transcription

regulation, cell-fate programming and genome 3D organisation (Beltran et al., 2019; Flynn et

al., 2014; Geisler et al., 2013; Holoch et al., 2015; Li et al., 2019; Nozawa et al., 2019; Sun et

al., 2018; Wang et al., 2017). Types of non-coding RNA include enhancer RNA (eRNA), Long

non-coding RNAs (LncRNAs) as well as G-quadruplex RNA.

6.1.4.1 Enhancer RNA (eRNA)

eRNA has been shown to be involved in functional roles including; assisting enhancer-

promoter looping formation, supporting pause-release of RNA Pol II which facilitates

transcription elongation, promoting transcription factor and co-regulator binding and aiding

target gene transcription (Bose et al., 2017; Hou et al., 2020b; Lai et al., 2013; Lam et al.,

2013; Li et al., 2013a; Melo et al., 2013; Panigrahi et al., 2018; Rahnamoun et al., 2018;

Schaukowitch et al., 2014; Sigova et al., 2015; Tsai et al., 2018). FANTOM5 data showed ten

of the 14 RUNX1 regulatory regions (m-371, m-327, -250, -139, m-43, m+3, +24, +62, m+110

and intron 5.2) produced eRNA in haematopoietic cells. Although eRNA is typically used for

enhancer identification, not all active enhancers produce detectable eRNA (Mikhaylichenko et

al., 2018; Sartorelli et al., 2020). It remains unclear whether these enhancers produce low levels

of eRNAs that are not easily detectable with current methods. ChromHMM labelled -188, -

139, m-101, m+3 and intron 5.2 as transcription-associated. This may be attributed to enhancer

RNA transcription, as m-139, m+3 and intron 5.2 all produce eRNAs. It remains to be

investigated whether the transcription association observed in m-188 and m-101 may also due

to enhancer RNA production.

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6.1.4.2 Long non-coding RNAs

Long non-coding RNAs (LncRNAs) have been shown to activate or suppress

transcriptional activity by interacting with chromatin modulating proteins (Fan et al., 2015;

Han et al., 2014; Jain et al., 2016; Prensner et al., 2013; Scarola et al., 2015). RUNX1-IT1 and

RUNXOR LncRNAs have been shown to regulate RUNX1. Relatively little is known about

RUNX1-IT1, a long non-coding RNA expressed from in between m+3 and RUNX1 +24.

However, Liu et al., (2020) found RUNX1-IT1 enhanced the transcription of the RUNX1 gene

in pancreatic cancer cells (Liu et al., 2020).

Previous studies show RUNXOR, a long non-coding RNAs 3.8kb upstream of RUNX1

has the ability to regulate the RUNX1 P1 promoter (Nie et al., 2019; Wang et al., 2014).

RUNXOR activates P1 and causes a shift to P1 RUNX1c isoform expression. The binding of

RUNXOR lncRNA elicits DNA demethylation and induces active histone modification markers

in the P1 CpG island. Therefore RUNXOR could be essential for the P1/P2 switch during

normal haematopoiesis and potential tumorigenesis (Nie et al., 2019).

m-371, m-368 and m-327 all sit within an uncharacterised non-coding RNA

LOC100506403. Like RUNX1-IT1 and RUNXOR, this long non-coding RNA could also play

a role in regulating RUNX1, with these regions helping to determine its expression. My 4C data

showed that in baseline conditions (12 DMSO), wild type K562 cells had a dramatic peak of

normalised reads that indicates a connection to m-368. Which may indicate an essential role of

LOC100506403 in WT 12 DMSO cells.

6.1.4.3 G-Quadruplex RNAs

G-quadruplex RNA is capable of targeting and removing the Polycomb repressive

complex 2 from genes and allowing RNA-mediated regulation (Beltran et al., 2019; Wang et

al., 2017). Because part of the mouse Runx1 +24 enhancer is capable of forming a G-

quadruplex, it is possible that the Runx1 +24 enhancer RNA may also form a G-quadruplex.

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Previously Fukunaga et al., (2020) showed that G-quadruplex RNA binds the ETO protein and

dissociates the leukemic RUNX1-ETO fusion protein from DNA (Fukunaga et al., 2020). This

suggests that some G-quadruplex RNAs may have potential as therapeutic agents for

leukaemia.

6.1.5 Could mutational load in regulatory elements also contribute to

disease progression?

Single nucleotide polymorphisms (SNPs) are present in each of the human RUNX1 conserved

regulatory regions, except +24 and m-101. m-371, m-368, m-58, m+110 and intron 5.2 SNPs

were reported in haematopoietic, brain, thyroid, skin and oesophagus cells. SNP frequency

could be an underestimate because of the scarcity of whole genome sequence in individual

haematopoietic cell types, AML, and other myeloid malignancy samples; this limits the ability

to identify significant SNPs and mutations in haematopoietic cells. The GTEx portal annotates

SNPs with MAF >1%, as well as eQTL outputs. The GTEx portal contains 54 tissues sampled

from healthy older individuals, and also only measures whole blood, rather than individual cell

types, so cell type- or disease-specific effects may not be detected.

There were no SNPS with MAF of >1% present in the RUNX1 +24 enhancer. This

suggests that there is strong selection pressure to prevent mutation of the RUNX1 +24 enhancer.

Consistent with this, functional studies suggest that the region containing RUNX1 +24 enhancer

is functionally important in haematopoietic cells. To determine the importance of the RUNX1

+24 enhancer for RUNX1 expression and function, Mill et al., (2019) CRISPR/Cas9 edited the

whole intron between RUNX1 P1 and P2 in OCI-AML5 cells (Mill et al., 2019). The majority

of the edited cells were eradicated via apoptosis, and the viable edited cells had significantly

decreased RUNX1 expression and slower growth (Mill et al., 2019). It is not clear whether the

observed phenotypes correspond uniquely to deletion of the RUNX1 +24 enhancer since the

entire P1-P2 intron was removed. Rather, this research shows the P1-P2 intronic region, which

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contains multiple RUNX1 regulatory elements, is important for cell survival and RUNX1

expression.

Of the 35 SNPs identified in the conserved RUNX1 regulatory regions, 6 had previously

reported functional effects in haematopoietic cells. m-371 contains rs2834945, a T to C change

that is predicted to affect 11 regulatory motifs including GATA2. rs2834945 is related to

expression of Kynurenine 3-monooxygenase (KMO) in peripheral blood monocytes (Zeller et

al., 2010). KMO encodes a mitochondrion outer membrane protein that catalyses the

hydroxylation of L-tryptophan metabolite, L-kynurenine, to form L-3-hydroxykynurenine

(Alberati-Giani et al., 1997). KMO is typically investigated in a neurological context, as high

KMO expression leads to neurodegeneration (Breton et al., 2000).

m-368 harbours two functional SNPs: rs16993221 and rs909143. rs16993221 is related

to white blood cell count; it is a crucial marker that contributes to chronic inflammation (Kong

et al., 2013). The A to T change in rs16993221 alters two regulatory motifs, BATF and IRF,

which work together in immune response. The A to G change in rs909143 is also related to

expression of KMO in peripheral blood monocytes (Zeller et al., 2010) and is predicted to

affect two regulatory motifs (NR3C1, POU2F2).

Within m+110 sits rs73900579, a T to C change that alters two regulatory motifs and

is related to red cell distribution width (Kichaev et al., 2019).

Intron 5.2 contains two functional SNPS, rs2249650 and rs2268276; both are associated

with AML susceptibility. The different SNPs are in linkage disequilibrium (LD) and change

the ability of intron 5.2 to act as an enhancer. A (rs2249650) G (rs2268276) bases and AA have

more enhancer capability that GA and GG. GA has the most reduced enhancer capability. The

SNPs also affect SPI1 binding capability; AG has strong SPI1 binding, GA has weak SPI1

binding, whereas AA and GG have medium SPI1 binding capability (Xu et al., 2016).

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Although not all SNPs found in this project have been functionally analysed, some of

the regulatory region SNPs were reported in patients with acute myeloid leukaemia. Two AML

patients had -250 SNP rs2834885, and two SNPs in +62 (rs933131 and rs2834716) were also

found in patients with AML. Interestingly, the patient with +62 rs2834716 also had the intron

5.2 SNPs (rs2268276 and rs2249650) which were previously associated with AML

susceptibility (Xu et al., 2016).

Five patients with malignant lymphoma reported mutations within regulatory regions

m-58 and +24 that are not reported as SNPs with a MAF of >1%. Four patients had m-58

mutations chr21:g.36480715C>A (rs199811665), chr21:g.36480761A>C and

chr21:g.36481470C>T (rs1440069314). One case of malignant myeloma reported a mutation

within +24, chr21:g.36399191A>T.

The available AML genomic data is relatively limited because the majority of analyses

were performed with exome sequencing data, rather than whole genome sequencing data. Lack

of whole genome data and individual SNP significance could lead to an underestimate of the

functional contribution of enhancer-region mutations to myeloproliferative disorders.

Xu et al., (2016)’s analysis of intron 5.2 SNPs showing that SNPs are functionally

relevant to leukaemia suggests a need to analyse each of the SNPs within regulatory regions

for functional capability. Determining and categorising SNPs may help us understand each

patient’s disease progression, individual responses to drugs, or susceptibility to relapse. Some

patients had SNPs with previously described functional effects on haematopoietic cells.

However, the SNP data for myeloproliferative disorders lacks significance because the

majority of SNP eQTL information comes from analysis of whole blood, rather than each

specific cell type.

There is good evidence suggesting that RUNX1 regulatory regions intron 5.2 and +62

are directly involved in disease progression, so further investigation of these regulatory regions

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is necessary. Intron 5.2 contains SNPs that are associated with AML susceptibility, and in

addition, this regulatory region is the site of translocation in t(8;21) found in AML patients in

which RUNX and ETO genes recombine. Schnake et al., (2019) identified a long non-coding

RNA in intron 5.2, at a site of these frequent chromosomal translocations. This long non-coding

RNA results in a more relaxed chromatin organisation at intron 5.2 (at the location of the

breakpoint). Consequently, it is possible that this feature acts as a determinant of a higher

probability of double stranded breakages in myeloid cells, resulting in translocations (Schnake

et al., 2019).

The discovery of RUNX1 silencer +62 resulted from characterisation of a novel

t(5;21)(q13;q22) translocation involving RUNX1 that was acquired during the progression of

myelodysplastic syndrome to AML in a paediatric patient (Cheng et al., 2018). This

translocation did not generate RUNX1 fusion, but rather it aberrantly upregulated the P2

isoform of RUNX1. The authors state that the translocation facilitated upregulation of RUNX1

P2 because the silencer at +62 was removed.

Several groups have described chromatin rearrangements that lead to “enhancer

hijacking”, whereby enhancers from somewhere else in the genome are rearranged next to

genes leading to overexpression (Bohlander, 2000; Gröschel et al., 2014; Look, 1997;

Northcott et al., 2014; Peifer et al., 2015; Taub et al., 1982; Zhang et al., 2020). Taub et al.,

(1982) described t(8;14) translocations in Burkitt's lymphoma which alter gene expression

through enhancer hijacking (Taub et al., 1982). Rewat et al., (2004) showed that in the t(12;13)

translocation, overexpression of CDX2 gene is leukaemogenic rather than the ETV6-CDX2

fusion(Rawat et al., 2004).

The majority of translocations occur between RUNX1 P1 and P2; while the disruption

caused to enhancers has not yet been carefully categorised, these may be examples of enhancer

hijacking.

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Table 6.1: Summary of conserved RUNX1 regulatory region analysis

Regulatory region


FANTOM5 eRNA

Functional enhancer

Cohesin binding K562

CTCF binding K562

Accessibility of chromatin in STAG2 mutant K562

SNPs Investigated Functional SNPs

Mutations seen in haematopoietic cells in cancer patients

m-371 Strong Enhancer ✓ ✓ ✗ ✓ Increased 2 1 ✗

m-368 Strong Enhancer ✗ ✓ ✗ ✗ No change 3 2 ✓

m-327 Repressed ✓ ✗ ✗ ✗ No change 2 0 ✗

-250 Repressed ✓ ✓ ✗ ✗ No change 3 0 ✓

-188 Transcription Associated

✗ ✓ ✗ ✗ No change 1 0 ✗

-139 Weak enhancer, Transcription Associated

✓ ✓ ✗ ✗ No change 3 0 ✗

m-101 Transcription Associated

✗ No available data

✗ ✗ No change 0 0 ✗

m-58 Strong Enhancer ✗ ✓ ✗ ✓ No change 13 0 ✓

m-43 Weak Enhancer ✓ No available data

✗ ✓ No change 2 0 ✗

m+3 Transcription Associated

✓ No available data

✗ ✓ No change 1 0 ✗

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Regulatory region


FANTOM5 eRNA

Functional enhancer

Cohesin binding K562

CTCF binding K562

Accessibility of chromatin in STAG2 mutant K562

SNPs Investigated Functional SNPs

Mutations seen in haematopoietic cells in cancer patients

+24 Strong enhancer ✓ ✓ ✓ ✓ No change 0 0 ✓

+62 Weak Enhancer ✓ No available data

✗ ✓ No change 2 0 ✓

m+110 Strong Enhancer ✓ ✓ ✓ ✗ Increased 2 1 ✓

Intron 5.2 containing m+204


✓ ✓ ✗ ✓ Increased 3 2 ✓

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6.2 Organisational role of the RUNX1 +24 enhancer

The mouse Runx1 +24 enhancer binds most other active Runx1 enhancers in murine

HPCs, and enhancer suppression in K562 results in altered RUNX1 expression. Therefore, I

hypothesised that the RUNX1 +24 enhancer organises a chromatin hub in human cells that is

crucial for correct RUNX1 expression. Circular chromosome conformation capture (4C) is a

technique that can be used to determine 3D chromatin interactions from a given viewpoint, or

‘bait’ (van de Werken et al., 2012a). I used the RUNX1 +24 enhancer as ‘bait’ in a 4C

experiment to determine the interacting DNA partners of this enhancer in K562 cells.

The majority of RUNX1 +24 interactions were with other regulatory regions that

include both active and inactive chromatin. The connections between the RUNX1 +24 enhancer

and other genes were predominantly with loci encoding non-coding RNAs.

Within the RUNX1 TAD, the RUNX1 +24 enhancer connects with other RUNX1

regulatory regions (m-368, m-101, m-58, and m+110) that had previously only been described

in mice, but have been shown in this thesis to be conserved in humans. RUNX1 +24 connected

to only one RUNX1 promoter (P2) and the previously identified +62 enhancer. RUNX1 +24

does not connect to all RUNX1 regulatory regions, consistent with the idea that different

enhancer recruitment occurs in diverse cell types to generate different RUNX1 isoforms. There

were some novel interactions identified; RUNXOR, ERG P3 and putative regulatory regions -

96, +89, SLC5A3 Exon 2 MRPS6 intron and a super-enhancer region between SAMSN1 and

NRIP1.

My 4C analysis shows that in K562 cells, the RUNX1 +24 enhancer makes multiple

connections within the RUNX1 TAD (‘near-bait’) and further afield on chromosome 21 (‘cis’).

The chromatin conformation methods, Hi-C and promoter Capture-C, show that gene

promoters usually form interactions within their own TADs. Due to the high resolution of 4C

interactions, it is apparent that the RUNX1 +24 enhancer was not constrained to within-TAD

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interactions, but also formed connections across the whole of chromosome 21. These

interactions outside of TAD also altered with differentiation, so they are unlikely to be artifacts

of the 4C technique. This surprising finding suggests that the RUNX1 +24 enhancer possibly

organises a chromatin hub in K562 cells that involves much of chromosome 21. This implies

that the RUNX1 +24 enhancer forms a chromatin hub that ensures correct regulation of

transcription of several chromosome 21-linked loci in K562 cells.

6.3 RUNX1 +24 enhancer connections during megakaryocyte

differentiation

My 4C analysis in K562 cells shows that the RUNX1 +24 enhancer makes multiple

connections across chromosome 21. During megakaryocyte differentiation, transcription

factors are induced to initiate gene expression consistent with megakaryocyte development. Do

these transcription factors alter connections made by the RUNX1 +24 enhancer? To determine

differences in connections, I used the RUNX1 +24 enhancer as ‘bait’ in a 4C experiment in

K562 cells undergoing PMA-induced differentiation, sampled at mid-point (12 h) and end-

point (72 h) stages. I found that as differentiation occurs, there is a steady decrease in

interactions mediated by the RUNX1 +24 enhancer across chromosome 21, including a

decrease in strength of connection between RUNX1 +24 and ERG P3.

ERG expression slowly increases during megakaryocyte differentiation. ERG P3 is

constrained in the baseline K562 cells (12 DMSO) and shows insulator annotations. Following

PMA treatment, ERG P3 has a weakened connection with RUNX1 +24. This may be to allow

ERG P3 to activate and facilitate the expression of ERG. In steady-state K562 cells, the RUNX1

+24 enhancer could be safeguarding chromatin structure, including the insulation of regions to

prevent transcription e.g., at ERG P3 or at loci encoding non-coding RNA. Once the cell has

begun the differentiation process, RUNX1 +24 “releases” these regions to allow for their

domain specific regulation and/or expression as the cells differentiate to megakaryocytes.

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The dynamic interaction landscape of the RUNX1 +24 enhancer during differentiation

of K562 cells highlights the need to further understand the roles of regulatory regions in

different cell types. During PMA treatment, the RUNX1 +24 enhancer forms new connections

with possible regulatory elements +14, +54, +68, +142 and known regulator m-371.

Interactions between the RUNX1 +24 enhancer and +14, +54, +68 and +142 and RUNX1-IT1

were only observed during differentiation (12 h PMA). These interactions were not observed

in steady-state K562 cells. Therefore, these regulatory regions may be important for RUNX1

regulation during megakaryocyte differentiation. All of these regions were annotated by

ChromHMM as enhancers in steady-state K562 cells, but this chromatin signature may not

reflect the nature of these elements during differentiation. The RUNX1 +24 enhancer maintains

existing contacts with regulatory regions m-368, m-58, m+3, +62 and m+110 throughout

differentiation.

The RUNX1 P2 transcript is minimally expressed throughout differentiation. The

connection between RUNX1 P2 to the RUNX1 +24 enhancer remains unchanged throughout

differentiation. Previously, +62 was shown to be a silencer of RUNX1 P2. RUNX1 +24 could

act to mediate the silencer interaction between P2 and +62. In contrast, interaction between the

RUNX1 +24 enhancer and RUNX1 P1 is increased during differentiation (12h PMA).

Transcription of RUNX1 from its P1 promoter is crucial for megakaryocyte differentiation

(Draper et al., 2017). Consistent with this, K562 cells had a gradual increase in transcription

of the P1 isoform of RUNX1 during PMA-induced differentiation (Antony et al., 2020).

In the presence of enhancer suppressor JQ1, RUNX1 P1 expression is suppressed,

suggesting that P1 expression in these cells is enhancer-driven (Antony et al., 2020). Since the

RUNX1 +24 enhancer recruits P1 during differentiation, its enhancer activity could be the target

of JQ1-mediated suppression of RUNX1 transcription.

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Connection between RUNX1 +24, RUNX1 P1, and known RUNX1 regulatory RNAs

RUNXOR and RUNX1 -IT1 all decrease after differentiation. I speculate that it is likely,

following activation of RUNX1 P1 transcript during differentiation, that the RUNX1 +24

enhancer releases connections to regions that are crucial for the RUNX1 P1 switch because

they are no longer required.

After differentiation (72 h PMA), there is an increase in frequency of the connection

between RUNX1 +24 and m-371. This region was shown to act as a primitive haematopoietic-

specific enhancer in developing zebrafish (Chapter 3) and activates RUNX1 P1 in developing

mice (Zhu et al., 2020). The increase in connection of the RUNX1 +24 enhancer with m-371

in differentiated K562 megakaryocyte cells may indicate a role for m-371 in megakaryocyte

identity. m-371 also sits within LOC100506403 – this long non-coding RNA could also play a

similar role to RUNX1-IT1 and RUNXOR in regulating RUNX1, with the connection to m-371

helping to determine its expression.

Although the RUNX1 promoters dynamically interact with the RUNX1 +24 enhancer,

the 4C analysis is unable to determine what other regulatory elements the promoters connect

to. This is because the 4C analysis used only the bait within the RUNX1 +24 enhancer as the

viewpoint for 3D interactions. The combination of regulators used to control transcription of

RUNX1 appears to be very dynamic, but also suggest cell type dependency. For example,

despite previous evidence of intron 5.2 and the -250, -188 and -139 cluster acting as RUNX1

regulators, these regions did not interact with the RUNX1 +24 enhancer upon megakaryocyte

differentiation. They could instead interact directly with the promoter(s) or act in another cell

type.

Vukadin et al., (2020) describe SON protein as a negative regulator of RUNX1 and

inhibitor of megakaryocyte differentiation in chronic myeloid leukaemia cells. They showed

that SON obstructs megakaryocytic differentiation by directly binding to RUNX1 P2 and two

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enhancer regions (the RUNX1 +24 enhancer and a “novel” +139 enhancer) (Vukadin et al.,

2020). +139 overlaps with m+110, suggesting that they represent the same human RUNX1

enhancer. This research supports the idea that the maintained connection of +m110/+139 with

the RUNX1 +24 enhancer during megakaryocyte differentiation is crucial for correct RUNX1

expression.

6.4 Cohesin/STAG2 dependency of RUNX1 +24 enhancer

connections in K562 cells

The cohesin complex mediates tissue-specific enhancer-promoter and enhancer-

enhancer communication (Canela et al., 2017; Phillips-Cremins et al., 2013; Strom et al.,

2012). Furthermore, evidence from our group shows convincingly that cohesin is required for

the normal transcription of Runx1 in developmental contexts (Antony et al., 2020; Horsfield et

al., 2007; Marsman et al., 2014). However, the exact mechanism of how cohesin regulates

Runx1 transcription is still unknown. It is possible that cohesin may influence the connections

between the Runx1 gene and its regulatory elements, and regulate Runx1 transcription in this

manner. To uncover potential mechanisms, I sought to determine how cohesin STAG2 mutation

affects connections formed by the RUNX1 +24 enhancer in steady-state K562 cells, and those

responding to a signal to undergo megakaryocyte differentiation.

In steady-state K562 cells, STAG2 mutation dramatically increased the number of

connections formed by the RUNX1 +24 enhancer in comparison to unedited wild type cells.

The new connections formed by the RUNX1 +24 enhancer in STAG2-deficient cells included

regions annotated by ChromHMM and dbSUPER as insulators, poised promoters and super-

enhancers; however, it is possible that these regions do not have the same chromatin features

in STAG2 mutant cells as they do in wild type. STAG2 mutant K562 cells have differentially

altered chromatin accessibility, including increased accessibility at super-enhancers and

decreased accessibility at CTCF sites (Antony et al., 2020).

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ChromHMM annotates a region as an insulator based on a high frequency of CTCF

protein binding sites, CTCF motifs, and/or a higher frequency of H3K4me1 (Ernst et al., 2010).

CTCF sites are frequently found in TAD boundary regions or within super-enhancers (Shin,

2019). The STAG2 paralogue, STAG1, preferentially locates to CTCF sites and TAD

boundaries (Arruda et al., 2020), while STAG2 is normally found at enhancers, promoters, and

Polycomb-repressed domains (Arruda et al., 2020). Absence of STAG2 is tolerated because

STAG1 can substitute for many of STAG2’s functions in the cohesin complex (Benedetti et

al., 2017; Van Der Lelij et al., 2017). Increase in interactions of the RUNX1 +24 enhancer with

insulators and super-enhancers upon STAG2-deficiency raises the possibility that STAG1-

cohesin mediates new contacts at CTCF sites.

Chromatin organisation relies on two mechanisms: compartmentalisation and

chromatin loop extrusion, which are often considered to be counteracting each other (Nuebler

et al., 2018). In the chromatin loop extrusion model, cohesin and CTCF form stable loops that

act as functional barriers between active and inactive chromatin. Loop extrusion by cohesin is

crucial for the formation of TADs, and correct transcription relies on the insulated

neighbourhoods of TADs, including the constraint of enhancers to their gene targets (Dixon et

al., 2012; Nuebler et al., 2018; Rao et al., 2014). Compartmentalisation describes the process

in which proteins (including chromatin proteins) self-organise into phase-separated

condensates and act as membrane-less organelles that cluster specific molecules. Co-

localisation of transcription factors or polycomb group proteins are frequently observed,

suggesting like attracts like (Hyman et al., 2014; Shin et al., 2018; Stadhouders et al., 2019).

These membrane-less organelles have been suggested to carry out crucial roles in a range of

cellular processes (Boeynaems et al., 2018).

I hypothesise that increased interactions in STAG2-deficient K562 cells reflect a

scenario where compromised loop extrusion may not properly balance compartmentalisation.

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Without STAG2, cohesin’s functions in domain organisation are compromised. As a result,

TAD boundaries may be destabilised, allowing the RUNX1 +24 enhancer to connect more

broadly across chromosome 21. Upon STAG2 depletion, compartmentalisation is the dominant

mechanism used to organise chromatin. This may explain the increase in connections from the

RUNX1 +24 enhancer to super-enhancers.

Super-enhancers are described as regions that specify cell identity, are densely bound

by mediators, chromatin factors and lineage-regulating master TFs, such as GATA1 and TAL1

in K562 cells (Hnisz et al., 2013; Huang et al., 2018; Khan et al., 2018; Parker et al., 2013;

Wang et al., 2019; Whyte et al., 2013). Super-enhancers contain a significantly higher

frequency of chromatin interactions compared to regular enhancers (Huang et al., 2018; Wang

et al., 2019). These characteristics indicate that RUNX1 +24 is acting as a super-enhancer in

K562 cells.

Wang et al., (2019) highlighted super-enhancers capability to attract the formation of

phase-separated condensates, and capacity to generate 3D genome structures that specifically

initiate the super-enhancer’s target genes (Wang et al., 2019). Sebari et al., (2018) provided

evidence that transcriptional coactivators such as BRD4 are components of the phase-separated

condensate, and that these condensates co-localise with super-enhancers (Sabari et al., 2018).

BRD4 recruitment required for transcription is known to occur independent of enhancer-

promoter connections (Crump et al., 2021). Together, these studies provide an explanation for

the surprisingly, similar connections forming, during differentiation (12 PMA) in STAG2

mutant cells and wild type.

The transcription factors that respond to PMA induction may be able to mediate correct

RUNX1 +24 connections regardless of cohesin deficiency. Transcription-specific 3D structure

could reflect phase-separated condensates formation , which are used to specifically initiate the

super-enhancer’s target genes. However, when transcription induction stops (72 PMA),

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connections go back to being abnormal. This also reflects the observations of Antony et al.,

(2020); who found that the use of BET inhibitor, JQ1 to reduce BRD4 binding and dampens

super-enhancer driven transcription, prevented the aberrant RUNX1 and ERG signal-induced

transcription in STAG2 mutant cells (Antony et al., 2020).

6.4.1 K562 Limitations

This thesis used K562 cells to determine RUNX1 +24 connectivity. K562 are a chronic

myelogenous leukaemic (CML) cell line which expresses the BCR-ABL fusion gene

(McGahon et al., 1997). Previous research has highlighted that cohesin mutations occur early

in leukaemogenesis and mainly arise in pre-leukaemic cells (CGARN, 2013; Corces et al.,

2017; Kon et al., 2013; Thol et al., 2014; Welch et al., 2012). The CRISPR/Cas9 edited STAG2

subunit is an additional mutation, which sequentially occurred after leukaemogenesis, as such,

changes observed may be due to compounding effect of existing K562 mutational profile.

Cohesin mutations are more frequently observed in AML samples than CML, so interactions

may not reflect AML samples.

Observing RUNX1 +24 connections in other cell lines would be needed to see which of

the extensive connections are replicated. However, the K562 and STAG2 mutant K562 cell

lines were effective for understanding dynamic 3D connections during differentiation, and

highlighting possible cis-regulatory regions.

6.5 Future directions

The hypothesis of my study was that Runx1 expression is regulated by regulatory

elements that connect with super-enhancer Runx1 +24, and that these connections are

dependent on cohesin. Although my results did identify some functional enhancers and their

conservation in human genome, it would be worthwhile investigating the other functional roles

of these regions, their combinatory effects, and which other cell types utilise these regions.

Some putative regulatory regions had dynamic interactions with the RUNX1 +24 enhancer

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depending on the stage of differentiation and presence of STAG2-cohesin. However, these

regions have no functional data so require further analysis.

6.5.1 RUNX1 +24 G-quadruplex

The Runx1 +24 G-quadruplex analysis was specific to the mouse sequence so it may

not reflect the human sequence, the type of G-quadruplex, or its functional capacity. The CD

analysis should be repeated with the human RUNX1 +24 G-quadruplexes.

G-quadruplexes can be selectively targeted by small binding ligands that can disrupt

translation (Bugaut et al., 2012). This has initiated a new approach in RNA-directed drug

design. If the G-quadruplex is present and functional in the human +24 region, further research

is required to validate it as a drug target. If there is dysregulation of the RUNX1 +24 enhancer

region, targeting of the G-quadruplexes may prove effective.

6.5.2 Functional analysis of RUNX1 regulatory regions

I only tested the functional enhancer capability of mouse Runx1 regulatory regions

identified in HPC7 cells by 4C. However, it would be worthwhile to further characterise the

enhancer capabilities of previously identified RUNX1 regulators; -250, -188, -139, m-101, m-

43, +62, and 4C identified regions; -96, +14, +54, +68, +89, +142, SLC5A3 Exon 2 MRPS6

intron and super-enhancer region between SAMSN1 and NRIP1.

When determining the enhancer potential of regulatory regions, it would be useful to

investigate other developmental time-points in more detail to analyse the different waves of

haematopoiesis. Primitive haematopoiesis occurs in zebrafish 12 hpf; definitive

haematopoiesis begins first in the ventral wall of the dorsal aorta from 36 hpf, then in the caudal

haematopoietic tissue 2 dpf, and finally in the kidney marrow 4 dpf. Viewing embryos during

these important time-points may provide further insight into Runx1 regulation. A semi-

automated imaging technique could be used to efficiently analyse more time-points (Romano

et al., 2014).

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To determine the cell type-specific enhancer properties an alternative cell-specific

enhancer reporter without a gata2 promoter could be used, so that the plasmids can be

successfully integrated into F1 lines. As the enhancer reporter plasmids contain fluorescent

markers, fluorescence-activated cell sorting (FACs) can be used to find which zebrafish

haematopoietic cells contain GFP expression driven by the putative enhancers. This method

can be used to determine which haematopoietic cells have transcriptional machinery that

recognizes the enhancers based upon the fluorescent characteristics of each cell.

Expression patterns of these enhancers show no distinct patterns of collaboration, or

independence of roles between the enhancers. Enhancers could be working in combinations or

may be functionally redundant. It would be interesting to analyse enhancer function further

with combinations of candidates and/or CRISPR/Cas9 edited candidates in reporter assays.

Interestingly regulatory regions (m-368, -250, m-101, -96, m-58, m+3, +14, +54, +62,

+68, +89 m+110, +142, intron 5.2 and SLC5A3 Exon 2 MRPS6 intron) showed histone

demethylase LSD1 binding in K562 cells indicating they have potential to be epigenetically

regulated (repressed or activated). I have tested, their enhancer potential but It would be useful

to functionally test all regions for silencer activity to provide further clarity about the

regulatory capability of these regions. Silencer activity could be determined by measuring the

repressive effect of various deletion/mutant regulatory region luciferase constructs on RUNX1

promoters. Silencer activity could also be measured by using an insulator assay vector (INS)

in zebrafish; this vector expresses GFP across somites as internal control, and there is GFP

expression variance when an insulator is cloned into vector (Bessa et al., 2009; Marsman et

al., 2014).

6.5.3 RUNX1 +24 connections with genes

Although RUNX1 P1 and P2 promoters dynamically interact with the RUNX1 +24

enhancer, the 4C analysis could not determine what other DNA regions the P1 and P2

https://en.wikipedia.org/wiki/Fluorescent

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promoters independently connect to during megakaryocyte differentiation. Another 4C that

uses RUNX1 P1 and P2 as baits throughout differentiation would determine what connections

these promoters make independently of the RUNX1 +24 enhancer.

Future RNA-seq to determine gene expression levels during megakaryocyte

differentiation would be informative and subsequent integration with the current 4C data would

be useful on determining whether the RUNX1 +24 enhancer connections to genes are the

scaffolds for transcription.

The connection and subsequent release of silenced ERG P3 may be crucial for correct

ERG transcription regulation. The role of the P3 promoter in ERG expression and isoform-

specific transcription could be analysed further to understand the dynamic role of the RUNX1

+24 enhancer in ERG regulation.

6.5.4 Roles of non-coding RNA in RUNX1 regulation

It is possible that non-coding RNA elements including long non-coding RNA, eRNA,

and G-quadruplex RNA could influence RUNX1 regulation. However, more research is

required to determine the individual functional contributions of these RNAs in RUNX1

expression.

Chapter 3 described evidence that eRNA is expressed at most RUNX1 regulatory

regions. It would be informative to analyse the functional capability of these eRNAs, their

interactions, and their influence on RUNX1 regulation. It would be especially exciting to further

investigate the possible functional roles of the RUNX1 +24 enhancer eRNA and possible +24

G-quadruplex RNA. This could be done using precision run-on sequencing (PRO-seq) and

rapid amplification of cDNA ends (RACE). Pro-seq can define the position, amount, and

orientation of transcriptionally active RNA polymerases in the genome to a single-nucleotide

resolution (Hou et al., 2020b). RACE can map eRNAs by identifying the 5′ and 3′ ends of

RNA, and RT-qPCR can determine the expression levels of these eRNAs (Hou et al., 2020b).

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eRNA targets could be analysed using RNA‐guided Chromatin Conformation Capture (R3C)

as demonstrated by Wang et al., (2014). Functionality could be analysed by eRNA knock down

using small interfering RNA (siRNA) or mutated using CRISPR/Cas9 (Hou et al., 2020b).

If the G-quadruplex is capable of forming RNA, it would be fascinating to investigate

its targets or ability to bind to RUNX1 protein. This could be carried out by isolating sequences

that bind to given targets with high affinity and specificity, using systematic evolution of

ligands by exponential enrichment (SELEX) (Ellington et al., 1990; Tuerk et al., 1990).

The results of this research also indicate a possible role for uncharacterised non-coding

RNA LOC10050640 in RUNX1 regulation. Regulatory regions m-371, m-368 and m-327 all

sit within LOC10050640 and may be involved in the regulation of this long non-coding RNA.

The role of RUNX1–IT1 in haematopoietic cells is currently unknown. More broadly, research

is also needed to determine the roles of other uncharacterised non-coding RNAs that the

RUNX1 +24 enhancer also makes connections with. Further investigation to define the RNA-

DNA interactions proposed by this research could determine the importance of the RUNX1 +24

enhancer eRNAs, possible G-quadruplex RNA and non-coding RNA connections seen in K562

cells. This could be carried out by utilising previously generated RNA-DNA interactomes for

K562 cells such as Red-C from Gavrilov et al., (2020) (Gavrilov et al., 2020).

6.5.5 Further questions proposed by this research

Several questions from this research remain unanswered, for example: are there other

strong enhancers that, like the RUNX1 +24 enhancer, connect across an entire chromosome? If

so, are they significant for genome organisation? Are there equivalents of the RUNX1 +24

enhancer on different chromosomes? These could be further discovered by using enhancers as

baits for 4C experiments, or by establishing an equivalent of Capture-C for enhancers.

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6.5.6 Clinical significance

Tumour sequencing studies are helpful, but to have medical benefits to the patient, the

mechanisms of disease must be understood beyond a statistical correlation. Analysis of

available SNP and cancer mutation databases identified SNPs and mutations in RUNX1-

associated regulatory regions. It would be informative to carry out the same analysis on the

putative regulatory regions identified by the 4C.

Other than +62 and intron 5.2, the relevance of mutations to these regions to the

progression of AML is unknown. My study sets the scene for functional analyses to precisely

determine how RUNX1 is regulated, including CRISPR/Cas9-mediated interference with

enhancer activity. The results of my research also provide a rationale for screening patients

with mutations in enhancer regions. By analysing deep sequencing of AML patient samples

that have no identifiable mutations in the RUNX1 coding region or in other leukaemia genes,

mutations in regulatory regions that lead to dysregulated RUNX1 expression may be

discovered. Sequencing analyses that identify enhancer mutations could not only explain a

patient’s AML progression, they could provide future therapeutic targets.

6.6 Overall conclusions

The precise spatiotemporal expression of Runx1 requires cis-regulatory elements, the

majority of which have not been previously defined. In this PhD thesis, I describe research that

provides insight into mouse Runx1 enhancer functions, and highlights the features of conserved

human enhancers. My study identifies the regulatory regions that interact with the RUNX1 +24

enhancer and that are potentially crucial for megakaryocyte differentiation.

One of the more exciting findings to emerge from this study is that the RUNX1 +24

enhancer forms DNA contacts across chromosome 21, defying the boundaries of known TADs.

The RUNX1 +24 enhancer interacts with coding genes, non-coding RNA, super-enhancers and

regulatory elements, and may also maintain connections that prevent transcription as well as

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enhance it. The findings suggest that the RUNX1 +24 enhancer could be important for overall

organisation and 3D structure of chromosome 21.

The results of my investigation show that cohesin STAG2-depletion de-constrains the

chromatin, allowing increased connections to the RUNX1 +24 enhancer hub and increased

super-enhancer interactions. This may result in aberrant enhancer-amplified transcription in

response to differentiation signals.

Overall, my study identified novel mechanisms for RUNX1 regulation, demonstrated

the role of the RUNX1 +24 enhancer in megakaryocyte differentiation, and the effect of cohesin

depletion on genome connectivity during differentiation.

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Appendix I: Materials

All solutions were made using sterile milli-Q water unless otherwise stated. The pH

adjustments were made using 1 M Hydrochloric acid (HCl) (BDH Prolabo) or Sodium

Hydroxide (NaOH) (VMR Prolabo). All primers were synthesized by ‘Integrated DNA

technologies’ [(IDT), Custom Science, NZ] unless otherwise specified.

Chemical reagents and consumables used in this project

Chemical (Abbreviation) Manufacturer Agar Bacteriological Oxoid Ltd, UK Ampicillin Sigma-Aldrich, USA Adenosine Triphosphate (ATP) Sigma-Aldrich, USA Agencourt® AMPure® XP beads Beckman Coulter Bacto Tryptone Bectone Dickinson and Co, USA Bgl II restriction enzyme mix New England Biolabs, UK Calcium Chloride VMR Prolabo, UK Chloramphenicol Sigma-Aldrich, USA CviQI restriction enzyme New England Biolabs, UK CviQI reaction 10x Buffer New England Biolabs, UK D-(+)-Glucose Sigma-Aldrich, USA Dimethyl sulfoxide (DMSO) Sigma-Aldrich, USA DNase Roche Applied Science,

Germany DMEM Gibco, Life Technology, USA DMSO Sigma-Aldrich, USA DpnI restriction enzyme New England Biolabs, UK DpnI reaction 10x buffer New England Biolabs, UK DpnII restriction enzyme New England Biolabs, UK DpnII reaction 10x buffer New England Biolabs, UK Dual-Glo® Luciferase Assay System Invitrogen, USA Eco RI restriction enzyme New England Biolabs, UK Eco RI reaction 10x buffer New England Biolabs, UK Ethanol Scharlab, Spain Ethyl 3-aminobenzoate methanesulfonate salt Sigma-Aldrich, USA Ethylene diamine tetracetic acid (EDTA) Fisher Scientific, USA Fetal bovine serum (FBS), NZ origin Gibco, Life Technology, USA Formaldehyde Sigma-Aldrich, USA Gateway LR Clonase II enzyme mix Invitrogen, USA Glacial acetic acid Analar Normapur, NZ Glycerol VMR Prolabo, UK Glycine Sigma-Aldrich, USA Halocarbon oil 27 Sigma-Aldrich, USA Hydrochloric acid BDH Prolabo, UK IMDM Gibco, Life Technology, USA T4 DNA ligase Invitrogen, USA

261

Chemical (Abbreviation) Manufacturer Ligase Buffer Invitrogen, USA Linear Polyacrylamide (LPA) Invitrogen, USA Lipofectamine 3000 Invitrogen, USA Lithium Chloride Sigma-Aldrich, USA Magnesium Sulfate Sigma-Aldrich, USA Meganuclease I-Sce I enzyme mix Roche Applied Science,

Germany Methylcellulose Sigma-Aldrich, USA Methylene Blue Sigma-Aldrich, USA Na2HPO4.H20 Merck, Germany Na2H2PO4 Merck, Germany OPTI-MEM reduced serum media Gibco, Life Technology, USA Phosphate buffer saline (PBS) solution Gibco, Life Technology, USA pCR8/GW/TOPO TA Cloning kit Life Technology, USA Penicillin Streptomycin Gibco, Life Technology, USA PfuUltra HF DNA polymerase Agilent Technologies, USA PfuUltra HF DNA polymerase reaction 10x buffer

Agilent Technologies, USA

Phenol:Chloroform:Isoamyl Alcohol 25:24:1 Sigma-Aldrich, USA Platinum Taq Polymerase Life Technology, USA PMA Sigma-Aldrich, USA Potassium Chloride BDH Prolabo, UK Proteinase K Invitrogen, USA P3000 Life Technology, USA qScript cDNA SuperMix Quanta BioSciences, USA Q5® Hot Start High-Fidelity 2x Master mix New England Biolabs RNaseA ThermoFisher, USA Sodium Bicarbonate Sigma-Aldrich, USA Sodium Chloride VMR Prolabo, UK Sodium Dodecyl Sulfate Sigma-Aldrich, USA Sodium Hydroxide VMR Prolabo, UK Spectinomycin Sigma-Aldrich, USA SYBR ® Green ThermoFisher, USA

Triton X-100 Sigma-Aldrich, USA

Trypan blue stain Sigma-Aldrich, USA

Trypsin-EDTA (0.5%), No phenol red Gibco, Life Technology, USA

Ultrapure Agarose Life Technology, USA

Ultrapure DNase – RNase free distilled water Life Technology, USA

X-gal Sigma-Aldrich, USA

Yeast extract Oxoid Ltd, UK

1 kb Plus DNA ladder Invitrogen, USA

1 M Tris (pH 8) J.T Baker, USA

262

Chemical (Abbreviation) Manufacturer 10x Transcription Buffer Roche Applied Science,

Germany 10 mM dNTPs Invitrogen, USA

Solutions and buffers

Luria-Broth (LB) Medium 10 g Bacto Tryptone (Becton Dickinson and Co), 5 g Yeast extract (Oxoid Ltd), 10 g Sodium Chloride (NaCl) (VMR Prolabo), make up to 1 litre with milli Q water. Sterilise by autoclaving.

LB agar Pre- autoclave LB medium with 1.5 % Agar Bacteriologial (Oxoid Ltd). Sterilise by autoclaving.

70% Ethanol Absolute ethanol dissolved in ddH2O at a final concentration of 70%

1x Tris-Acetate-EDTA (TAE) buffer 1 M TRIS (pH 8) (J.T Baker), Glacial acetic acid and 0.5 M (Analar Normapur) Ethylene diamine tetracetic acid (EDTA) (Fisher Scientific) (pH 8) added to milli Q H2O at a final concentration of 40 mM, 20 mM and 1 mM, respectively

1 % Agarose Gel 1 % Ultrapure Agarose (Life Technology) in 1 x TAE. For a 60 ml gel

1 x E3 (5 mM Sodium Chloride (NaCl) (VMR Prolabo), 0.17 mM Potassium Chloride (KCl) (BDH Prolabo), 0.33 mM CaCl2 (VMR Prolabo), 0.33 mM Magnesium Sulfate (MgSO4) (Sigma-Aldrich), 10 % (v/v) Methylene Blue (Sigma-Aldrich))

Tricaine 400 mg Tricaine powder (Ethyl 3-aminobenzoate methanesulfonate salt) (Sigma-Aldrich), 97.9 ml ddH2O, ~2.1 ml 1 M Tris pH 9. Adjust to pH 7. Store in freezer.

3 % Methylcellulose 3 % Methylcellulose (Sigma-Aldrich) in 1 x E3 solution.

Cyroprotectant Solution 20 % DMSO in FBS. Lysis buffer (10mL) 100 µL of 1 M Tris-HCl (pH 8.0), 20 µL

of 5 M NaCl, 200 µL of 20 % of NP-40, 1 tablet of protease inhibitors (Roche Diagnostics)

3 M NaOAC pH 5.6 246.1g sodium acetate (NaOAC), make up to 1 litre with milli Q water. Adjust to pH 5.6. Filter sterilise the solution.

263

2.5 M glycine 75 g glycine ), make up to 1 litre with milli Q water. Filter sterilise the solution.

264

Appendix II: Plasmid maps

Plasmid maps of constructs used in this study are presented below. Map of Zebrafish

enhancer detector vector (ZED) was obtained from Bessa et al., 2009 (Bessa et al., 2009).

Map of PCS-TP was obtained from Kawakami Lab website

(https://ztrap.nig.ac.jp/map2.html). pGL4.23-GW was obtained from Addgene

(https://www.addgene.org/60323/). pRL-TK map is from the Promega: pRL Renilla

Luciferase Control Reporter Vectors website:

(https://worldwide.promega.com/products/luciferase-assays/genetic-reporter-vectors-and-

cell-lines/prl-renilla-luciferase-control-reporter-vectors/?catNum=E2231). Map of pUC19

was obtained from Addgene (https://www.addgene.org/browse/sequence/74677/).

Zebrafish enhancer detector vector (ZED)

https://ztrap.nig.ac.jp/map2.html

https://www.addgene.org/60323/

https://worldwide.promega.com/products/luciferase-assays/genetic-reporter-vectors-and-cell-lines/prl-renilla-luciferase-control-reporter-vectors/?catNum=E2231

https://worldwide.promega.com/products/luciferase-assays/genetic-reporter-vectors-and-cell-lines/prl-renilla-luciferase-control-reporter-vectors/?catNum=E2231

https://www.addgene.org/browse/sequence/74677/

265

pCS-TP

266

pGL4.23-GW

267

pRL-TK

268

pUC19

269

Appendix III: Primer list

Appendix III Table.1: Details of the primers used in this project

Primer name Direction Sequence ZED insert confirmation mCNE1 forward AAAGGGAACAAAAGCTGGAG mCNE2 reverse AGTCGCTTCTCTTCGGTTGA pGL4.23-GW insert confirmation RVprimer3 forward CTAGCAAAATAGGCTGTCCC Runx1 +24 Site-Directed Mutagenesis Runx1 +24 g->t

forward CGTGTCAGGAAGGGTGTGTGTGGAACTTACACCTC

reverse GAGGTGTAAGTTCCACACACACCCTTCCTGACACG Runx1 +24 t->a

forward AGGTGTAAGTTCCTCCCCCTCCCTTCCTGACAC

reverse GTGTCAGGAAGGGAGGGGGAGGAACTTACACCT qRT-PCR primers Human GAPDH forward TGCACCACCAACTGCTTAGC reverse GGCATGGACTGTGGTCATGAG Human H-CYCLOPHILIN

forward ACGGCGAGCCCTTGG

reverse TTTCTGCTGTCTTTGGGACCT Human RUNX1 P1

forward TTTTCAGGAGGAAGCGATGG

reverse TGGCATCGTGGACGTCTCTA Human RUNX1 P2

forward TTGTGATGCGTATCCCCGTA

reverse TCATCATTGCCAGCCATCAC Human RUNX1 total

forward AGACCCTGCCCATCGCTTTC

reverse GGTTCTTCATGGCTGCGGTA Human ERG forward CGCAGAGTTATCGTGCCAGCAGAT reverse CCATATTCTTTCACCGCCCACTCC Human GATA1 forward TTGCCACATCCCCAAGGCGG reverse GGGGGAGGGGCTCTGAGGTC Human KLF1 forward TGACTTCCTCAAGTGGTGGC reverse GGTGAGGAGGAGATCCAGGT 4C bait primers Human RUNX1 P1

forward AACCAAGTCTGTCAGTCTCCTG

reverse GCTAACAAGCAGAGGAAGCAA Human RUNX1 +24

forward CACGTGTCCACATTTTTCAGA

reverse CAGTTACTTCGCCCATTGCT Human RUNX1 P2

forward GCCGGCTCCTGGAATTGG

reverse AGGATGTGCGTGCGTGTGTA

270

Primer name Direction Sequence Human ERG +85

forward CTCGCTGCTTAGGTGTAGTCC

reverse TGGGGGAGACTGACAAATAGA

271

Appendix IV: Prediction of G-quadruplexes

During my master’s work the sequence of +24 Runx1 mouse regulatory region was

obtained using forward (AAAGGGAACAAAAGCTGGAG) and reverse primers

(AGTCGCTTCTCTTCGGTTGA). Despite multiple attempts and different primers, the region

was never sequenced the full way through. When compared back to the mm9 +24 regulatory

element sequence sent by Dr. Motomi Osato, it was noted that the sequences stopped in the

same place every time. The online tool QGRS mapper was used to predict if there was a G-

quadruplex structure (Kikin et al., 2006). The Osato +24 mouse regulatory region sequence is

shown below. The area that could not be sequenced is underlined, the probable G-quadruplex

is highlighted in yellow. The most likely G-quadruplex is GGTGGGGGTGG, reverse

complement CCACCCCCACC. C rich residues could also be forming i-motifs, However these

are less likely to be biologically relevant as they form depending on the cell’s pH.

Forward sequence

TCAGCTCTGCACTGCACTAAGGAGTTTTTTATGGAAAGCAATGGTCCCAGT

GGCTGAAGGGTCCAGCAAGTGGCTGGTAGAGCCAGCAAGGAGCGATGGAGGGA

TGGTGTGAGGAGGAGACAGGAAGGGAGGCGGTGACACAGGCCTTCACAGGGCC

GCTGCAGCTAGGAACTGCTGCAAAAGCAAGCTGCCCACGTTATCAGTGGCGCGC

AGGCCTGCGGTTTTCTCGCTCTTGCAACCCGGCTTCAACTGCCGGTTTATTTTTCG

ACAAACAGGATGCCTCCATCTGAGGCTGTCAGCATCCCAGGCTCTTTGAGAAGA

AAAGAGGTAGTGAGGGCCCCACCCTAGAGCCCAGCCGGGAGGGGTGGGTGAGA

GGTCCTGTTTCTAGATGCTTCCCAGAGAAGTGAGAACCTAGCAGGTGCAGTGGCC

AGGGTTCAGGAGCGTGTCAGGAAGGGTGGGGGTGGAACTTACACCTCCCACCCC

CACCAGACTTAAATGGACTCCCCACCTCTGCATGGGTCCCAAGGCT

Reverse sequence

272

AGCCTTGGGACCCATGCAGAGGTGGGGAGTCCATTTAAGTCTGGTGGGGG

TGGGAGGTGTAAGTTCCACCCCCACCCTTCCTGACACGCTCCTGAACCCTGGCCA

CTGCACCTGCTAGGTTCTCACTTCTCTGGGAAGCATCTAGAAACAGGACCTCTCA

CCCACCCCTCCCGGCTGGGCTCTAGGGTGGGGCCCTCACTACCTCTTTTCTTCTCA

AAGAGCCTGGGATGCTGACAGCCTCAGATGGAGGCATCCTGTTTGTCGAAAAAT

AAACCGGCAGTTGAAGCCGGGTTGCAAGAGCGAGAAAACCGCAGGCCTGCGCG

CCACTGATAACGTGGGCAGCTTGCTTTTGCAGCAGTTCCTAGCTGCAGCGGCCCT

GTGAAGGCCTGTGTCACCGCCTCCCTTCCTGTCTCCTCCTCACACCATCCCTCCAT

CGCTCCTTGCTGGCTCTACCAGCCACTTGCTGGACCCTTCAGCCACTGGGACCAT

TGCTTTCCATAAAAAACTCCTTAGTGCAGTGCAGAGCTGA

When the overlapping sequence data was examined, a small chunk of sequence is

unable to be read (underlined area). Two predicted quadruplex structures sit either side of the

region that could not be sequenced.

AGCCTTGGGACCCATGCAGAGGTGGGGAGTCCATTTAAGTCTGGTGGGGG

TGGGAGGTGTAAGTTCCACCCCCACCCTTCCTGACACGCTCCTGAACCCTGGCCA

CTGCACCTGCTAGGTTCTCACTTCTCTGGGAAGCATCTAGAAACAGGACCTCTCA

CCCACCCCTCCCGGCTGGGCTCTAGGGTGGGGCCCTCACTACCTCTTTTCTTCTCA

AAGAGCCTGGGATGCTGACAGCCTCAGATGGAGGCATCCTGTTTGTCGAAAAAT

AAACCGGCAGTTGAAGCCGGGTTGCAAGAGCGAGAAAACCGCAGGCCTGCGCG

CCACTGATAACGTGGGCAGCTTGCTTTTGCAGCAGTTCCTAGCTGCAGCGGCCCT

GTGAAGGCCTGTGTCACCGCCTCCCTTCCTGTCTCCTCCTCACACCATCCCTCCAT

CGCTCCTTGCTGGCTCTACCAGCCACTTGCTGGACCCTTCAGCCACTGGGACCAT

TGCTTTCCATAAAAAACTCCTTAGTGCAGTGCAGAGCTG

273

Appendix V: Sequencing report

Libraries were demultiplexed by Otago Genomics Facility (OGF). A summary of the

sequencing for this project is below. The sequencing metrics are within Illumina's performance

specifications for low diversity libraries.

275

Appendix VI: Publication

Some of the results from Chapter 3 contributed to Marsman, J., Thomas, A., Osato, M.,

O'Sullivan, J. M., & Horsfield, J. A. (2017). A DNA contact map for the mouse Runx1 gene

identifies novel haematopoietic enhancers. Scientific Reports, 7, 13347

1SCIeNTIfIC REPORTs | 7: 13347 | DOI:10.1038/s41598-017-13748-8

www.nature.com/scientificreports

A DNA Contact Map for the Mouse Runx1 Gene Identifies Novel Haematopoietic EnhancersJudith Marsman1, Amarni Thomas1, Motomi Osato2,3, Justin M. O’Sullivan 4,5 & Julia A. Horsfield 1,5

The transcription factor Runx1 is essential for definitive haematopoiesis, and the RUNX1 gene is frequently translocated or mutated in leukaemia. Runx1 is transcribed from two promoters, P1 and P2, to give rise to different protein isoforms. Although the expression of Runx1 must be tightly regulated for normal blood development, the mechanisms that regulate Runx1 isoform expression during haematopoiesis remain poorly understood. Gene regulatory elements located in non-coding DNA are likely to be important for Runx1 transcription. Here we use circular chromosome conformation capture sequencing to identify DNA interactions with the P1 and P2 promoters of Runx1, and the previously identified +24 enhancer, in the mouse multipotent haematopoietic progenitor cell line HPC-7. The active promoter, P1, interacts with nine non-coding regions that are occupied by transcription factors within a 1 Mb topologically associated domain. Eight of nine regions function as blood-specific enhancers in zebrafish, of which two were previously shown to harbour blood-specific enhancer activity in mice. Interestingly, the +24 enhancer interacted with multiple distant regions on chromosome 16, suggesting it may regulate the expression of additional genes. The Runx1 DNA contact map identifies connections with multiple novel and known haematopoietic enhancers that are likely to be involved in regulating Runx1 expression in haematopoietic progenitor cells.

Runx1 is a key regulator of haematopoietic development. Deletion of Runx1 in mouse embryos is lethal in embry-onic stage (E) 12.5 due to the complete absence of definitive blood cell progenitors accompanied by extensive haemorrhaging1,2. Runx1 is crucial for haematopoietic stem cell (HSC) emergence and maintenance during development3, since conditional ablation of Runx1 in adult mice results in HSC exhaustion4. In acute myeloid leukaemia (AML) and myelodysplastic syndrome, RUNX1 function is frequently altered through mutations or translocations5, resulting in dysregulation of its target genes. While mutations directly affecting the RUNX1 pro-tein are common in leukaemia, mutations in regulatory elements that affect RUNX1 expression remain enigmatic. As yet unidentified mutations in regulatory elements, such as enhancers, could alter Runx1 expression, resulting in abnormal haematopoiesis.

Runx1 is transcribed from two promoters, P1 and P2, to give rise to different protein isoforms6. Expression of these isoforms is tightly controlled during haematopoiesis. At the onset of mouse haematopoiesis (E7.5), preced-ing the generation of HSCs, expression of the P2 isoform(s) is predominant7,8. P1 is expressed soon after P2, and its expression is synchronised with the generation of HSCs7,9. P1 expression is predominant in the mouse fetal liver, the main site of definitive haematopoietic stem/progenitor cell (HSPC) development from E12.5 onward9.

Regulatory elements, such as enhancers, can control the expression of genes via long-range chromatin interactions10. One previously identified Runx1 enhancer is located 24 kb downstream of the P1 transcriptional start site11,12. The +24 enhancer (also known as the +2313, or the +23.512 enhancer) is active in HSCs that express Runx1 dur-ing mouse embryogenesis11–13. The human equivalent of the +24 enhancer (+32 kb downstream of P1) directly contacts the promoters of RUNX1 in leukaemia cell lines14. In addition to the +24 enhancer, putative regulatory

1Department of Pathology, Dunedin School of Medicine, University of Otago, Dunedin, New Zealand. 2Cancer Science Institute, National University of Singapore, Singapore, 117599, Singapore. 3International Research Center for Medical Sciences, Kumamoto University, Kumamoto, Japan. 4Liggins Institute, The University of Auckland, Private Bag, 92019, Auckland, New Zealand. 5Maurice Wilkins Centre for Molecular Biodiscovery, The University of Auckland, Private Bag, 92019, Auckland, New Zealand. Correspondence and requests for materials should be addressed to J.A.H. (email: [email protected])

Received: 17 March 2017

Accepted: 2 October 2017

Published: xx xx xxxx

OPEN

http://orcid.org/0000-0003-2927-450X

http://orcid.org/0000-0002-9536-7790

mailto:[email protected]

www.nature.com/scientificreports/


elements for RUNX1 have been identified upstream of RUNX1-P1 and between P1 and P2; however, whether they directly contact the RUNX1 promoters has not been investigated15,16.

Here we used circular chromosome conformation capture sequencing (4C-seq) to identify regulatory ele-ments that interact with an active Runx1 P1 promoter, versus an inactive P2 promoter. While Hi-C provides genome-wide contact profiles, and Capture Hi-C enriches for interactions with preselected genomic features (usually promoters), 4C-seq can generate very high resolution contact profiles from ‘baits’ of particular interest17. Therefore, 4C-seq can yield richer information about a selected genomic region than Hi-C or Capture Hi-C. HPC-7 is a well characterised mouse HSPC line with genomic annotations, including transcription factor (TF) binding, histone modifications and chromatin accessibility18–20. 4C-seq in HPC-7 cells identified nine haemato-poietic enhancers that interact with the P1 promoter and +24 enhancer, and that are occupied by haematopoietic TFs. Four of these were previously identified, three of which were functionally tested and two out of the three showed activity during mouse haematopoiesis15. Here we assessed the activity of all nine enhancers in zebrafish, and show that eight are active in zebrafish haematopoiesis. Further, the +24 enhancer was highly interactive both within a topologically associated domain (TAD) harbouring Runx1, as well as with loci outside the TAD. Collectively, our results point to the formation of a local ‘active chromatin hub’ controlling Runx1 expression in haematopoietic cells.

ResultsWe first confirmed that P1 is actively expressed in HPC-7 cells, while P2 is silent (Supplementary Fig. S1). 4C-seq in HPC-7 cells using the P1 and P2 promoters and the +24 enhancer as ‘baits’ identified genomic interactions at Runx1 (Fig. 1a). Bait locations were designed taking into account cohesin and CTCF binding sites near both promoters and the +24 enhancer. 4C baits were designed to regions of interest (P1, +24, P2), allowing for com-parison of interactions between the active P1 promoter and inactive P2 promoter, with secondary baits located at nearby cohesin/CTCF (cc) binding sites (P1cc, +24cc, P2cc) (Fig. 1a).

4C-seq in HPC-7 cells confirms the presence of a 1.1 Mb domain harbouring Runx1. For each bait, two replicate 4C-seq libraries and one control library were sequenced. Reads were predominantly located within a 1.1 Mb region surrounding the Runx1 gene (Fig. 1b,c), representing a TAD harbouring Runx1. From visual inspection of the contact profile (Fig. 1b,c), domain boundaries appear to be present at the Cbr1/Setd4 genes upstream of Runx1, and Clic6 downstream (Fig. 1b). A comparison with existing Hi-C data from mouse embryonic stem cells, mouse CH12 (erythroleukaemia) cells, human GM12878 (lymphoblastoid), human K562 (myeloid leukaemia) and human IMR90 (foetal lung) cells revealed that TAD boundaries are conserved, and that our 4C data is consistent with existing Hi-C data (Supplementary Fig. S2). Most P1 and +24 interac-tions take place upstream of the Runx1 gene (Fig. 1b,c). In contrast, there are fewer upstream interactions from P2, while downstream interactions are retained (Fig. 1b,c). There are no other coding genes within the Runx1 domain; however, three inactive non-coding genes19, Mir802 and the long non-coding RNAs 1810053B23Rik and 1700029J03Rik, are located near the upstream border of the TAD.

Interactions from ‘cc’ baits (cohesin/CTCF binding sites) were similar to their nearby corresponding baits at P1, +24 and P2 (Fig. 1b). We investigated whether significant interactions for the ‘cc’ baits have more overlap with other cohesin or CTCF binding sites than for the non ‘cc’ baits, but did not find any difference (data not shown). The ‘cc’ baits may be too close to the corresponding P1, P2 and +24 baits to resolve unique interactions, although they are separated by at least one restriction fragment.

Chromatin interactions anchored by the Runx1 P1 and P2 promoters and +24 enhancer. Most of the significant interactions for P1, P2 and the +24 enhancer occur within the 1.1 Mb domain (Fig. 1b). There are ~40–50 significant interactions for P1cc/P1 baits, ~60–75 for +24/+24cc baits, and ~30 for P2/P2cc baits (Fig. 1d). Strikingly, the +24 enhancer in particular forms many significant interactions outside the Runx1 domain (Fig. 1b,d). Many of these long-range interactions are with other genes or gene promoters on mouse chro-mosome 16 (Fig. 2). Expression levels of these genes in human tissues according to Genotype-Tissue Expression (GTEx, https://www.gtexportal.org/home/)21 did not reveal obvious tissue-specific expression patterns. However, among the genes contacted by +24 are Erg, a haematopoietic TF, and Tiam1 (T-cell lymphoma invasion and metastasis 1), involved in cell adhesion and cell migration. These distant connections suggest that +24 may also regulate other haematopoietic genes in adjacent domains.

Identification of haematopoietic enhancers. We hypothesised that DNA connections formed from the active P1 promoter and the +24 enhancer may correspond to haematopoietic enhancers that regulate Runx1 expression. To identify putative enhancers, we aligned significantly interacting sites with the occupancy of thirteen TFs involved in haematopoietic progenitor cell production; enhancer histone modifications and DNase I hyper-sensitivity sites18–20; and conserved non-coding elements (CNE), which were identified based on comparative genomics alignment and retroviral integration site mapping to indicate potential DNaseI hypersensitive sites11. We note that the +24 enhancer binds all thirteen haematopoietic progenitor TFs in HPC-7 cells (Fig. 3).

We selected putative enhancers based on the binding of at least six TFs involved in haematopoietic progenitor cell development (Fig. 3), and the presence of a significant interaction directly at, or within 2 kb, of the TF binding cluster. Based on these criteria, we found eight other potential enhancers within the Runx1 domain that form con-nections to the baits. These enhancers were named according to their distance from the P1 transcriptional start site: −371, −354, −327, −321, −303, −58, −48, and +110 (Fig. 3b). An additional interacting region at −368, a CNE, was located adjacent to a cluster of haematopoietic TFs (Fig. 3b). The +24 and +110 are also CNEs11. The putative enhancers either form distinct blocks upstream of Runx1-P1 (from −371 to −303 and −48 to −58), or fall between the P1 and P2 promoters (+24 and +110). Four out of nine of the identified putative enhancers

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Figure 1. Runx1 interaction profile reflects the activity of the P1 and P2 promoters and the +24 enhancer. (a) Location of 4C baits at the mouse Runx1 gene (mm10). Baits are P1cc, P1, +24, +24cc, P2cc and P2; ‘cc’: cohesin and CTCF binding site. ChIP-seq data of Rad21 (blue) and CTCF (pink) in MEL and CH12 cells were obtained from ENCODE51 and in HPC-7 cells from18,19. Exons are in grey and the +24 enhancer is in green. (b) 4C-seq profile (read per million normalised running mean of nine successive DpnII digestion fragments) for one replicate per bait with significant interactions (the top fifth percentile of interactions with a FDR of <0.01) that overlap in both replicates shown in a ~2.5 Mb region surrounding the Runx1 gene. The highest significant interactions (see Methods) are in red and other significant interactions in orange. Red dotted lines indicate the location of estimated domain boundaries. (c) Read distribution for both replicates of each bait. Line graphs represent the cumulative percentage of reads on chromosome 16 (y-axis) versus the distance from the bait (x-axis), shown in genomic coordinates on chromosome 16 (genome version mm10). The 0-value of each line indicates the location of each bait. (d) The number of significant interactions that overlap in both replicates inside (light grey) and outside (dark grey) the domain for each bait.



(−327, −321, −58 and +110) were identified previously based on the binding of at least three blood TFs and the presence of a H3K27ac peak (denoting active chromatin) from a subset of the same HPC-7 datasets that we have used here15.

Eight out of nine putative enhancers (−371, −368, −354, −327, −321, −58, −48, and +110) form long-range interactions with the P1 promoter, the +24 enhancer, or both (Fig. 3b and Supplementary Table S1). The excep-tion was −303, which does not interact with P1, but instead with the +24 and the P2 promoter. The +24 enhancer interacts promiscuously within the whole domain. Filtering at maximum stringency (see Methods) showed that the +24 enhancer connects to all putative enhancers and both Runx1 promoters (Fig. 3b). In contrast, the inac-tive P2 promoter connects only to −303 and +24 (Fig. 3b and Supplementary Table S1). A specific interaction between +110 and P2 could not be resolved owing to a contiguous block of significant interactions throughout the short region between +110 and P2. A model of Runx1 interactions based on the 4C-seq results is shown in Fig. 4.

Long range chromatin interactions at haematopoietic enhancers. Long-range chromatin inter-actions can be mediated by cohesin and CTCF22, and cohesin is involved in transcription regulation at active genes23. We found that four of the nine enhancer loci (in addition to the +24 enhancer) coincide with Rad21 (cohesin) binding in the absence of CTCF (Fig. 3b and Supplementary Table S1). This is consistent with the idea that cohesin (but not necessarily CTCF) mediates local DNA-DNA interactions within TADs22. All Rad21 binding sites interacted with at least one ‘cc’ bait, therefore cohesin could mediate at least a subset of enhancer-promoter communication events in HPC-7 cells.

We compared the 4C interactions identified with recently published Capture Hi-C data in HPC-7 cells18 (Supplementary Fig. S3). Capture Hi-C data was only available for interactions anchored at P1, and has a lower coverage and resolution than our 4C-seq study (an average of ~18,000 reads per promoter for Capture Hi-C with a 6-cutter, and over 1 million reads per bait for 4C-seq with a 4-cutter). The Capture Hi-C study in HPC-7 cells identified 15 P1-interacting regions that were reproduced in our study (Supplementary Fig. S3). All of these are upstream of Runx1-P1, and most are within the −368 to −303 enhancer cluster (Supplementary Fig. S3). Therefore, our study provides additional Runx1-anchored interactions that were not previously described as con-nected to Runx1 promoters (enhancers −371, −48, −58 and +110).

Figure 2. Interactions of the Runx1 locus with other genes. Significant interactions that overlap with gene bodies or gene promoters (starting from 2 kb upstream of the gene transcriptional start site) are shown for the P1cc/P1 (a), +24/ +24cc (b) and P2cc/P2 baits (c) (assembly mm10).

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In vivo characterization of haematopoietic enhancers. Enhancer regions interacting with Runx1 recruit haematopoietic TFs in HPC-7 cells, therefore we determined if these regions act as enhancers in vivo. Each of the putative enhancers was tested for the ability to drive tissue-specific GFP expression in zebrafish embryos. Eight out of nine drove GFP expression specifically in the intermediate cell mass and posterior blood island, which are sites of haematopoietic progenitor cell production at 20–24 hours post-fertilisation (hpf)24 (Fig. 5a,b and Supplementary Fig. S4). Two of these (−58 and +110) were previously shown to be active during mouse haematopoiesis15. The −303 enhancer also expressed GFP in keratinocytes, particularly after 24 hpf (Figs 5c, S4). Interestingly, −303 was the only enhancer identified that interacted with the P2 promoter of Runx1, rather than P1. Despite the occupancy of multiple haematopoietic TFs in HPC-7 cells, and the presence of similar TF binding motifs compared to the other enhancers (Fig. 3 and Supplementary Tables 1 and 2, we did not observe enhancer activity for −327, consistent with a previous study in mice15.

In summary, we have assigned in vivo function to multiple putative enhancers upstream of Runx1 that were previously identified in silico18. These regions not only drive haematopoietic expression, but also physically con-nect with the Runx1-P1 promoter, lending confidence to the concept that they are bona fide regulators of Runx1 transcription.

Figure 3. The active P1 promoter and +24 enhancer interact with clusters of haematopoietic transcription factor binding sites. Significant interactions within the domain for each bait are shown together with the reference genes (mm9), CNEs11, TF binding sites (green), Rad21 and CTCF binding sites (black), H3K27ac, H3K4me3 and DNaseI hypersensitivity sites in HPC-7 cells18–20. A 3 Mb region (a) and two zoomed-in views (b) are shown. Locations of TF binding clusters containing at least six TF binding sites at the same location, and a significant interaction directly at or within 2 kb of each cluster, are indicated by arrows and named according to their distance from the P1 transcriptional start site. See also Supplementary Fig. S3.

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Discussion4C analysis in HPC-7 cells generated a high-resolution connectivity map of the genomic region harbouring Runx1, and confirmed previously identified upstream connections from P1 to putative regulatory elements18. Runx1 appears to be contained within a ~1 Mb chromatin domain, consistent with Hi-C analyses in other cell types25,26. We observed multiple connections both up- and downstream from all 4C-seq baits (Runx1-P1, Runx1-P2 and +24 enhancer). Significantly, there were many more upstream connections anchored by the active elements (Runx1-P1 and +24).

Surprisingly, the +24 enhancer forms many up- and downstream connections outside of the Runx1 TAD. These comprise up to one-third of all connections formed and include other haematopoietic genes, such as Tiam1 downstream, and Erg upstream. Erg and Tiam1 dysregulation is associated with several tumor types, including AML and B- and T-cell lymphomas27,28. Strikingly, these genes are located over 2 Mb away from Runx1. These findings raise the interesting possibility that the +24 enhancer acts as a scaffold to recruit multiple promoters, enhancers and TFs over long distances in cis. Connections to +24 identified by our 4C-seq may point to the identity of some of these genes.

Our 4C-seq data identified multiple chromatin connections that intersect with some of the strongest indicators of upstream enhancers characterised by the binding of clusters of TFs and epigenetic modifications18,20. Eight out of nine of these DNA regions are able to drive haematopoietic expression in zebrafish, strongly suggesting an in vivo function. Although previous studies had identified transcriptional activity for a subset of these enhancers15, or an interaction with the P1 promoter for an overlapping subset18, no single prior study has shown that enhanc-ers both interact with Runx1 promoters (and +24), and have transcriptional activity. Table 1 provides an overview of findings from these previous studies together with additional evidence from our study that connects spatial proximity with function. Two enhancers, termed −59 and +110, were previously shown to drive LacZ expression in mice15; these are equivalent to the −58 and +110 enhancers identified here. Our data show in addition that these regions are in contact with P1 and the +24 enhancer (Table 1). While the −327 enhancer has similar char-acteristics as the other enhancers identified here, neither a previous study in mice15 nor our study identified any enhancer activity for it (Table 1). The −368, −354, −327, −321 and −303 enhancers were found to interact with P1 in a Capture Hi-C study in HPC-7 cells (Table 1). Our study confirms the presence of these interactions, and in addition, shows that these enhancers also interact with either the +24 enhancer or P2 (Table 1).

The −303 enhancer interacts with P2 rather than P1 and, in addition to being active in haematopoietic sites, it drives expression in keratinocytes, indicating that it could act in a complex that keeps P2 silent; and/or that it regulates expression of Runx1 in other tissues. Interestingly, Runx1 is actively expressed in mouse keratinocytes where it is important for hair follicle development29. Neuronal cells also express Runx130, and there may be addi-tional regulatory elements that control Runx1 expression in a manner distinct from haematopoietic expression. In support of this idea, we previously determined that cohesin and CTCF influence runx1 expression in haemato-poietic, but not neuronal cells, in zebrafish31,32.

Cohesin and CTCF organise chromatin structure and when present in combination, they appear to negatively correlate with the HSPC TFs in HPC-7 cells18. However, we observed a coincidence of cohesin subunit Rad21 bind-ing in the absence of CTCF with four out of nine identified enhancers, as well as the +24 enhancer. This suggests that CTCF-independent cohesin mediates a subset of enhancer-promoter looping in combination with TFs. This interpretation is consistent with previously identified CTCF-independent functions for cohesin in genome organi-zation and transcription33. Importantly, cohesin mutations are prevalent in AML and other myeloid malignancies34, and are categorised together with RUNX1 and spliceosome mutations in a genetic category that confers poor prognosis in AML5. Cohesin mutations led to increased chromatin accessibility of Runx1 as measured by ATAC-seq.35, raising the possibility that spatiotemporal regulation of Runx1 is cohesin-dependent in mouse and human, as was previously observed in zebrafish31.

Figure 4. Model of chromatin interactions based on Runx1 4C-seq. 4C-seq baits are shown in red and identified interacting enhancers in blue. P1 is active, and P2 is inactive. P1 interacts with −371, −368, −354, −327, −321, −58, −48, +24 and +110; +24 interacts with −371, −368, −354, −327, −321, −303, −58, −48, +110 and P2; and P2 interacts with −303 and +24.



4C-seq in HPC-7 cells has provided new high resolution connectivity data that sheds light on the genomic organization of Runx1, an important haematopoietic transcription factor. These data confirm and extend pre-vious analyses (Table 1), and furthermore, provide insight into the function of enhancers that have potential to

Figure 5. Putative mouse enhancers are active in haematopoietic regions in zebrafish. Whole mount representative lateral images of zebrafish embryos (20–24 hpf [a,b] and ~48 hpf [c]) that were injected with enhancer-GFP plasmids at the one-cell stage; left hand panels are merges with bright field, right hand panels are GFP fluorescence. (a) Negative control ZED plasmid shows zero (image) or non-specific fluorescence, while the positive control (+24) shows GFP expression in haematopoietic cells of the posterior blood island (red arrows) and intermediate cell mass (yellow arrows), which represent primitive blood cells. (b) Enhancer activity of +110, −48, −58, −303, −321, −327, −354, −368, −371. GFP expression was observed in primitive haematopoietic cells of the posterior blood island (red arrows) and intermediate cell mass (yellow arrows). (c) The −303 enhancer also expressed GFP in keratinocytes (white arrows in b,c). The numbers in the right hand panels represent the number exhibiting the representative phenotype out of the total number of fluorescent embryos analysed. See also Supplementary Fig. S4.

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regulate Runx1 expression. The data presented here set the scene for functional analyses to precisely determine how Runx1 is regulated, including CRISPR/Cas9-mediated interference with enhancer activity. They also provide a rationale for screening patients with myeloproliferative disorders for mutations in enhancer regions.

MethodsCell culture. HPC-7 cells were maintained at a density of 1–10 × 105 cells/mL in Iscove’s modified Dulbecco’s media (Gibco®) supplemented with 3.024 g/L sodium bicarbonate, 10% fetal bovine serum (Moregate, New Zealand), 10% stem cell factor conditioned media and 0.15 mM monothiolglycerol (Sigma-Aldrich) as previously described36. SCF-conditioned media was obtained from culturing BHK-MKL cells maintained in Dulbecco’s mod-ified Eagle media (Sigma-Aldrich) supplemented with 3.5 g/L glucose, 3.7 g/L Sodium Bicarbonate and 10% FBS.

4C-seq library preparation. 4C library preparation was performed as previously described37 with modi-fications. Three libraries were generated, two replicates (passage 8 and passage 10 cells) and one control (a 1:1 mix of both replicates for which the first ligation step was omitted). Cells were cross-linked in 2% formaldehyde, 5% FBS and 1x PBS for 10 minutes at room temperature while rotating. Formaldehyde was quenched with a final concentration of 125 mM glycine for 5 minutes on ice while inverting several times. Cell pellets were washed twice with ice-cold 1x PBS.

Nuclei were harvested by lysing the cell pellets in ice-cold lysis buffer (10 mM Tris pH 8.0, 10 mM NaCl, 0.2% NP-40, protease inhibitors) for 10 minutes on ice. Nuclei were then resuspended in 1.2x DpnII restriction buffer (New England Biolabs) and 0.3% SDS and incubated for 1 hour at 37 °C while shaking. Triton X-100 was then added to a final concentration of 1.8% and the reaction was left at 37 °C while shaking for another hour. Chromatin was digested with 800 U of DpnII overnight at 37 °C while shaking. DpnII was inactivated by adding SDS to a final concentration of 1.3% and incubating at 65 °C for 20 minutes. Nuclei were diluted into a volume of 7 mL containing 1.01x T4 DNA ligase buffer (Life Technologies) and Triton-X100 at a final concentration of 1% and incubated at 37 °C for 1 hour. Ligations were carried out with 100 U of T4 ligase (Life Technologies) for 4.5 hours at 16 °C and 30 minutes at room temperature while shaking. For control libraries, ligase was omitted. Samples were proteinase K treated and reverse-crosslinked overnight at 65 °C. Samples were then treated with RNase A at 37 °C for 30 minutes. DNA was purified by phenol/chloroform extraction and ethanol precipitated.

A second digestion was performed with 25 U of BfaI (New England Biolabs) for P1 and P2 baits or MseI (New England Biolabs) for +24 baits in restriction buffer overnight at 37 °C while shaking. Restriction was inactivated by adding SDS to a final concentration of 1.3% and incubating at 65 °C for 20 minutes. A second ligation was performed in the same way as the first ligation, except that ligations were incubated overnight. DNA was purified with two phenol/chloroform extractions and one chloroform extraction followed by ethanol precipitation. DNA

Enhancer location relative to P1 (kb)

Interactions identified by other studies Previous functional validation Findings from our study

−371 — NoneInteracts with +24 and P1. Enhancer activity in hematopoietic sites in 20–24 hpf zebrafish embryos.

−368 Interacts with P1 in HPC-7 cells (Capture Hi-C by Wilson et al.)18 None

Interacts with +24 and P1. Enhancer activity in hematopoietic sites in 20–24 hpf zebrafish embryos.



−327 Interacts with P1 in HPC-7 cells (Capture Hi-C by Wilson et al.)18 No enhancer activity in mice (Schutte et al.)15 Interacts with +24 and P1. No enhancer

activity in zebrafish.

−321 Interacts with P1 in HPC-7 cells (Capture Hi-C by Wilson et al.)18

Identified, but not tested for enhancer activity (Schutte et al.)15



Interacts with +24 and P2. Enhancer activity in hematopoietic sites in 20–24 hpf zebrafish embryos and in keratinocytes from 20 hpf.

−58 —Haematopoietic enhancer activity in E11.5 transgenic mice and luciferase enhancer activity in 416B cells (Schutte et al.)15


−48 — NoneInteracts with +24 and P1. Enhancer activity in hematopoietic sites in 20–24 hpf zebrafish embryos.

+24Interacts with P1 and P2 in human leukaemia cell lines (3 C by Markova et al.)14

Haematopoietic enhancer activity in mouse and zebrafish development and luciferase enhancer activity in 416B cells (Nottingham et al., 2009, Ng et al., 2010, Schutte et al.)11,12,15

Interacts with P1 and P2. Enhancer activity in hematopoietic sites in 20–24 hpf zebrafish embryos.

+110 —Haematopoietic enhancer activity in E11.5 transgenic mice and luciferase enhancer activity in 416B cells (Schutte et al.)15

Interacts with P1. Enhancer activity in hematopoietic sites in 20–24 hpf zebrafish embryos.

Table 1. Previous findings on enhancers and new findings from this study.



concentrations were measured using a Qubit® 3.0 Fluorometer (Life Technologies) and Qubit® double-stranded DNA (dsDNA) High Sensitivity Assay kit (Life Technologies).

For each bait, a total of 1 μg of DNA was amplified by PCR using Q5® High-Fidelity DNA Polymerase (New England Biolabs). Bait primer sequences are listed in Supplementary Table S3. PCR products were purified using the QIAquick PCR Purification kit (QIAGEN). DNA concentrations were measured using a Qubit® 3.0 Fluorometer and average fragment size by the 2100 Bioanalyser (Agilent Technologies) using a High Sensitivity DNA Kit (Agilent Technologies). Amplicons from the six different baits were mixed equally based on the concen-tration, average fragment size and ratio of demultiplexed 4C baits obtained from an initial MiSeq run. Libraries were prepared with Prep2SeqTM DNA Library Prep Kit from IlluminaTM (Affymetrix) and TruSeq® adaptors (Illumina). Libraries were mixed equimolarly and sequenced as 125 bp paired-end reads on two IlluminaTM HiSeq 2500 lanes by New Zealand Genomics Limited.

4C-seq data analysis. 4C-seq data was analysed in command-line and the R statistical environment38, and visualised using the University of California, Santa Cruz (UCSC) genome browser (http://genome.ucsc.edu/) with mouse assemblies mm9 or mm1039,40, or with the R package ggplot241. Baits were demultiplexed based on bait primer sequences up to and including the digestion site using a custom awk script, allowing 0 mismatches. Only read pairs that had the forward and reverse bait sequences in the correct orientation were selected. Adapter sequences, bait sequences up to but excluding the digestion site, and bases with a Phred quality score under 20 were then trimmed from the reads, using the fastq-multx, fastq-mcf and cleanadaptors v1.24 tools42,43. Quality of reads was assessed using FastQC (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/)44.

Reads with a minimum length of 30 bp were mapped to the mm10 reference genome using Bowtie145, allow-ing 0 mismatches. Mapped reads were assigned to DpnII digestion fragments using fourSig46. The following reads were removed from the files: 1) self-ligated reads, 2) uncut reads (fragments adjacent to baits), and 3) reads at fragments that have at least 1 read in the control (non-ligated) library. The running mean was calculated from the sum of read counts from nine successive fragments, which was obtained using fourSig46, and was read per million normalised.

Significant interaction calling was performed using the R package fourSig with the following settings: window size of 3, 1000 iterations, fdr of 0.01, fdr.prob of 0.05 (which selects the top fifth percentile of interactions with a FDR of <0.01), and only included mappable fragments46. Significant interactions were called for two regions: 1) the whole of chr16, and 2) from chr16:92,250,000-93,635,000 (within the domain). Significant interactions in both replicates were overlapped using the bedIntersect tool from UCSC47.

In the fourSig package, significant interactions can be categorised into three categories: 1) interactions that are significant after the reads from the fragment with the highest read count is removed, 2) interactions that are significant when the fragment with the highest read count is averaged to the read counts of the neighbouring frag-ments, and 3) interactions that are significant only when all fragment read counts are included46. For this study, only category 1 and 2 interactions that overlap between both replicates were included, as they are more likely to represent true interactions (because they span multiple fragments), and were previously shown to be more reproducible between replicates than single-fragment interactions46. Furthermore, we distinguished category 1 interactions that overlap between both replicates from other category 1 and 2 interactions by colouring them red and orange, respectively, to visualise the most significant interactions. For conversion from assembly mm10 to mm9, the liftOver tool from UCSC was used (http://genome.ucsc.edu/)47. Gene annotations used in Figures are UCSC reference genes.

Zebrafish enhancer assay. Runx1 regulatory regions were amplified from HPC-7 gDNA or from I-SceI-zhsp70 plasmid containing the −368, +24 and +110 sequences11 (primer sequences are in Supplementary Table S3), and cloned into the zebrafish enhancer detection vector48. Plasmid purifications were performed with the NucleoSpin® Plasmid or NucleoBond® Xtra Midi prep kits (Machery-Nagel). Primers amplified the TF binding peak +/− 200 bp, except for −321 which (due to a repetitive region) did not have a 200-bp extension on the 3′ end. The −368, +24 and +110 fragments are 471, 529 and 579 bp, respectively11. A mixture of 30 pg vector DNA and 120 pg Tol2 transposase mRNA49 was injected into 1-cell zebrafish embryos. Embryos were imaged at 20–24 or ~48 hpf using a Leica M205FA stereomicroscope with a DFC490 camera and LAS software (Leica Microsystems), images were processed using Adobe Photoshop. Zebrafish were maintained as described previously50 and zebrafish handling and procedures were carried out in accordance with the Otago Zebrafish Facility Standard Operating Procedures. The University of Otago Animal Ethics Committee approved all zebraf-ish research under approval AEC 48/11.

Identification of conserved non-coding elements. Mouse conserved non-coding elements (mCNEs) were identified as described previously11.

ChIP-seq, Capture Hi-C and DNase I hypersensitivity data. Occupancy of the transcription factors Erg, Fli1, Scl, Runx1, Gata2, E2A, Ldb1, Lyl1, Lmo2, Gfi1b, Meis1, Myb, phospho-Stat1, Pu.1, Stat3, Eto2, Cebp-α, Cebp-β, Elf1, Nfe2, p53, cMyc, Egr1, E2f4, cFos, Mac and Jun; Rad21 and CTCF; H3K27ac and H3K4me3; DNase I hypersensitivity sites; and Capture Hi-C data in HPC-7 cells was obtained from previously published data18–20. Rad21, Smc3 and CTCF chromatin immunoprecipitation sequencing (ChIP-seq) data in MEL and CH12 cells were obtained from ENCODE51.

Availability of data. The 4C-seq dataset is accessible through GEO Series accession number GSE86994 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc = GSE86994).

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1 0SCIeNTIfIC REPORTs | 7: 13347 | DOI:10.1038/s41598-017-13748-8

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AcknowledgementsHPC-7 and BHK/MKL cells were kindly provided by Kathy Knezevic and John Pimanda. TruSeq® adaptors were kindly provided by Ian Morison. The authors would like to thank Anita Dunbier and Sofie Van Huffel for help with development of 4C protocols, and Noel Jhinku for expert management of the Otago Zebrafish Facility. This research was funded by the Royal Society of NZ Marsden Fund [grant number 11-UOO-027 to JAH] and the Health Research Council of NZ [grant number 15/229 to JAH].

Author ContributionsJ.M., A.T., M.O., J.M.O., J.A.H., designed experiments; J.M., A.T., performed experiments; J.M., A.T., M.O., J.M.O., J.A.H. analysed data; A.T. produced the graphic for Fig. S4; J.M. and J.A.H. wrote the paper with input from the other authors.

Additional InformationSupplementary information accompanies this paper at https://doi.org/10.1038/s41598-017-13748-8.Competing Interests: The authors declare that they have no competing interests.Publisher's note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or

format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Cre-ative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not per-mitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/. © The Author(s) 2017

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287

Appendix VII: RUNX1 regulatory region Hg38

coordinates

Appendix VII Table 1: Hg38 Genome coordinates for regulatory regions



m-371 chr21:35482813-35483787 m-368 chr21:35476819-35476991 m-327 chr21:35428582-35428982 m-321 Not conserved between humans and mice -250 chr21:35297414-35298323 -188 chr21:35236032-35236509 -139 chr21:35189322-35190258 m-101 chr21:35170398-35170666 m-58 chr21:35106408-35106609 m-43 chr21:35091787-35091963 m+24 chr21:35026809-35027025 m-42 Not conserved between humans and mice m+1 Not conserved between humans and mice m+3 chr21:35046175-35046447 +62 chr21:34986636-34987656 m+110 chr21:34908536-34908897 m+181 Not conserved between humans and mice Intron 5.2 containing m+204 chr21:34807014-34809284

288

Appendix VIII: 4C data processing

The 4C sequencing libraries were prepared from 4C material, each containing PCR

products amplified by bait specific primer pairs. The read counts were determined from the

original sequencing library counts given by Otago Genomics Facility (Appendix VIII Table 1).

The baits were demultiplexed by Dr. William Schierding (University of Auckland). The

number of reads that demultiplexed to baits within each library was also determined (Appendix

VIII Table 1).

Appendix VIII Table 1: Read counts before and after primer demultiplexing

Library Original Read Count

Demultiplexed Read Count

4533_16 K562 WT DMSO 1 11,747,923 3,973,768 4533_17 K562 WT DMSO 2 6,124,022 3,218,192 4533_18 K562 WT DMSO 3 4,231,900 2,511,463 4533_19 Control K562 WT DMSO 2,854,280 1,395,817 4533_20 K562 WT 12h PMA 1 4,568,245 3,389,303 4533_21 K562 WT 12h PMA 2 6,851,198 1,672,071 4533_22 K562 WT 12h PMA 3 4,223,579 1,999,157 4533_23 K562 WT 72h PMA 1 6,349,712 4,105,424 4533_24 K562 WT 72h PMA 2 2,934,889 2,094,222 4533_25 K562 WT 72h PMA 3 4,357,508 2,707,894 4533_26 Control K562 WT 72 PMA 3,087,335 1,844,770 4533_27 K562 -/- DMSO 1 3,948,820 2,712,143 4533_28 K562 -/- DMSO 2 4,173,877 2,528,795 4533_29 K562 -/- DMSO 3 7,181,545 3,739,103 4533_30 Control K562 -/- DMSO 31,255,638 92,067 4533_31 K562 -/- 12h PMA 1 7,077,762 3,115,038 4533_32 K562 -/- 12h PMA 2 8,657,941 3,624,719 4533_33 K562 -/- 12h PMA 3 3,914,040 2,136,044 4533_34 K562 -/- 72h PMA 1 6,648,572 3,795,374 4533_35 K562 -/- 72h PMA 2 6,463,080 3,398,746 4533_36 K562 -/- 72h PMA 3 23,085,885 3,480,265 4533_37 Control K562 -/- 72 PMA 4,041,648 2,265,435

After demultiplexing of primers, the reverse sequenced reads were mapped to a reduced

genome based on the primary RE used in the first digestion (Dpn II). The majority of RUNX1

+24 reads aligned to the reduced genome. Appendix VIII Figure 1 and 2 have very similar read

289

numbers. The quality evaluation compares the read distributions to determine if a replicate is

high enough quality to be included in the 4C interaction analysis (Appendix VIII Figure 3).

RUNX1 +24

Appendix VIII Figure 1: RUNX1 +24 demultiplexed read count.

The number of demultiplexed reads (millions) is shown for the individual conditions in replicate 1, 2, 3 or

control.

Appendix VIII Figure 2: RUNX1 +24 Mapped read count.

The number of mapped reads (millions) is shown for the individual conditions in replicate 1, 2, 3 or control.

290

Appendix VIII Figure 3: RUNX1 +24 4C quality evaluation.

The coverage of fragments that had more than one read present within 0.1 mb on either side of the bait was plotted against the percentage of reads in cis over total. 4C data of WT 12 DMSO (light blue), WT 12 PMA (blue), WT 72 PMA (dark blue), STAG2 12 DMSO (yellow), STAG2 12 PMA (orange), STAG2 72 PMA (red) is plotted. Three points, corresponding to replicate 1, 2 and 3 are presented for each condition. Grey dotted lines indicate cut-off point for good quality data.

Appendix IX: WT 12 DMSO interaction peaks

Table of all the statistically significant interactions with RUNX1 +24 in WT 12 DMSO

cells

WT 12 DMSO cis significant interactions

Appendix IX Table 1: WT 12 DMSO cis interactions

Chromosome location (Chr21)

Start End 16102849 16290033

17054388 17167830

18444178 18546244

22870115 23124047

291


Start End 24350542 24638310

27615547 27687280

28569260 28782031

31067134 31287024

33094489 33125735

33479993 33507790

34626170 34659645

34,861,779 34,886,331

35064167 35082568

37435929 37444466

37450343 37453283

37471800 37474965

38,064,789 38,088,359

38205868 38217764

38326452 38338678

38630433 38645058

39302387 39354604

39457439 39483268

39837197 39941694

40549637 40807167

41078121 41274056

42531449 42597547

43171342 43223493

43,820,942 43,905,318

44021469 44133209

44350268 44425222

45126172 45164937

292

WT 12 DMSO near-bait significant interactions

Appendix IX Table 2: WT 12 DMSO near-bait interactions


Start End 35474191 35488395

36120292 36122672

36123862 36126228

36125047 36127409

36156784 36159142

36157963 36160322

36243806 36245595

36253381 36255044

36264639 36266171

36265409 36266933

36271428 36272893

36277225 36278641

36277936 36279346

36279349 36280749

36280051 36281446

36280751 36282141

36281449 36282833

36282836 36284211

36288291 36289632

36290969 36292295

36293617 36294930

36294275 36295584

36305857 36307115

36313951 36315177

36314565 36315788

36316400 36317616

36317009 36318223

36317617 36318829

293


Start End 36324230 36325417

36326010 36327189

36328367 36329537

36328953 36330121

36329538 36330704

36331867 36333024

36332447 36333601

36333602 36334752

36335327 36336470

36335900 36337041

36337042 36338179

36337611 36338746

36338180 36339312

36338747 36339877

36339878 36341004

36340442 36341565

36341005 36342126

36341566 36342686

36343803 36344915

36346578 36347680

36347130 36348230

36347681 36348780

36348231 36349329

36348781 36349876

36353148 36354232

36353690 36354773

36357470 36358544

36358545 36359617

36361223 36362291

36361757 36362824

36363358 36364423

294


Start End 36367613 36368674

36368143 36369205

36370796 36371857

36371327 36372388

36375572 36376634

36376103 36377166

36377697 36378762

36379295 36380362

36379828 36380896

36380361 36381430

36380895 36381965

36382500 36383573

36383036 36384111

36384110 36385187

36386806 36387890

36387347 36388434

36409375 36410564

36409969 36411162

36412365 36413572

36412969 36414179

36414790 36416010

36415400 36416624

36416012 36417240

36416626 36417857

36417242 36418476

36417859 36419097

36418478 36419719

36425395 36426671

36426033 36427312

36426673 36427955

36427314 36428599

295


Start End 36427956 36429244

36428600 36429891

36429246 36430539

36432493 36433799

36433146 36434455

36433800 36435112

36441738 36443074

36443075 36444413

36447775 36449123

36449124 36450475

36449799 36451151

36455222 36456582

36455902 36457263

36464790 36466165

36465477 36466854

36468234 36469615

36468924 36470307

36469616 36471000

36470308 36471694

36471001 36472388

36474478 36475874

36475176 36476573

36479385 36480795

36482213 36483633

36482923 36484346

36483634 36485060

36484347 36485775

36485061 36486492

36489374 36490822

36499642 36501131

36501880 36503377

296


Start End 36507905 36509423

36508664 36510185

36517085 36518628

36547820 36549434

36548627 36550243

36549435 36551053

36551054 36552676

36569979 36571652

36575859 36577549

36598256 36600012

36599134 36600892

36613306 36615093

36689136 36690961

36690048 36691870

36692776 36694589

36699984 36701773

36700879 36702664

36707985 36709750

36716767 36718514

36769828 36771700

36770764 36772642

36845802 36848109

297

Appendix X: Trans interaction peaks

The trans interactions of RUNX1 +24 could not be significantly investigated using 4C-

ker. The 4Cker analysis showed bands that spanned across large areas of the various

chromosomes the trans interactions that could be detected, I looked at the raw read peaks across

the genome (Appendix IX Figure 1). Based on peaks that contained reads in 2 or more

replicates I detected three trans interactions (Appendix IX Figure 2-4). All peaks were in

intergenic regions and contained minimal ENCODE annotations.

Appendix X Figure 1: RUNX1 +24 trans chromosome 1 reads.

UCSC genome browser snapshot of normalised read count showing chromosome (chr) 1 reads from all three replicates.

298

Appendix X Figure 2: RUNX1 +24 chr 1 trans interactions.

UCSC genome browser snapshot of normalised read count showing reads from all three replicates highlighting the possible chr 1 trans interaction

299


UCSC genome browser snapshot of normalised read count showing reads from all three replicates highlighting the possible chr 3 trans interaction

300


UCSC genome browser snapshot of normalised read count showing reads from all three replicates highlighting the possible chr 9 trans interaction.

301

Appendix XI: List of significant changes (cis)

Lists of all the statistically significant cis changes between conditions

WT cis significant changes during differentiation

Appendix XI Table 1: Differentially decreased cis interactions in WT 12 h PMA cells compared to WT 12 h DMSO K562 cells

Chromosome location (Chr21) log2FoldChange Adjusted p value

Start End

16193679 16290033 -8.0131141 0.00497879

39852633 39906157 -7.8384879 0.00978024

Appendix XI Table 2: Differentially decreased cis interactions in WT 72 h PMA cells compared to WT 12 h DMSO K562 cells


Start End

22876545 23117188 -7.4296378 0.00362255

24372136 24630993 -5.7785282 0.00383509

28570440 28782508 -7.895935 0.00817325

31223207 31350841 -8.8622479 0.00193125

34626170 34659645 -6.4021928 0.00554615

34861883 34886153 -6.2869926 0.00584545

38065170 38088150 -7.50213 0.00211092

39302647 39354164 -7.8347453 0.00178676

43171342 43273433 -6.5989095 0.00645605

43821554 43904818 -8.3977348 0.00050987

302

WT and STAG2 cis comparisons

Appendix XI Table 3: Differentially decreased cis interactions in STAG2 12h DMSO cells compared to WT 12h DMSO K562 cells


Start End

15789630 15905266 4.93016724 0.00656758

16193679 16290033 -5.6375836 0.00465524

16337170 16447658 4.39988775 0.00144947

26749583 26912507 6.04072918 0.00276931

30766929 30848918 6.02534263 0.00029361

30975222 31069884 3.84968539 0.00943295

34232784 34274588 5.95696286 0.00150594

34738802 34767851 3.99116934 0.00827214

34965396 34986172 4.06506145 0.0074603

35064266 35082455 -5.5186585 0.00780053

38065170 38088150 -5.1459938 0.00711804

38099808 38123104 3.88469303 0.00595205

38111456 38134838 5.55176215 0.00821735

38790145 38825524 4.39693366 0.00587422

39225824 39276819 4.59711214 0.00883934

39251322 39302562 4.68523129 0.00971659

39379992 39431602 5.13275693 0.0034974

39561223 39613465 5.48301289 0.00504078

40092657 40145586 5.52330128 0.00367745

40383991 40437879 4.6125032 0.00692396

41082235 41192923 -6.659979 0.00036723

41137579 41253995 -6.518779 0.00213839

42327921 42467118 4.93843987 0.00575986

44546192 44629845 4.3041222 0.00671446

44588018 44672916 6.00732878 0.00022353

303

Appendix XI Table 4: Differentially decreased cis interactions in STAG2 12 h PMA cells compared to WT 12 h PMA K562 cells


Start End

36516987 36518381 8.83443992 00.0023261 Appendix XI Table 5: Differentially decreased cis interactions in STAG2 72 h PMA cells compared to WT 72 h PMA K562 cells


Start End

17658804 17929142 8.77960408 3.54E-05

17795376 18062908 5.99772569 0.00353314

18653456 18833421 5.86735877 0.00232339

22762987 22996867 6.37260654 0.00782695

22876545 23117188 10.0927403 1.21E-05

26749583 26912507 8.04883755 0.00207552

27308690 27428828 7.25476493 0.00509219

27367527 27490129 7.74087843 0.00509219

27426968 27553290 8.28178011 0.00214456

28570440 28782508 8.32963928 0.00275066

28997265 29212346 6.8981528 0.00358326

29427589 29629288 6.36801489 0.00379672

30646160 30726999 6.42810586 0.00190139

31826262 31999444 4.97135124 0.00939023

32513939 32620098 5.65073304 0.00866759

33094489 33156981 4.74896145 0.00866759

34169753 34211768 7.7060914 0.00015066

34943785 34965232 7.39162623 0.0010998

34954679 34975784 6.62312295 0.00399488

37516547 37524058 5.84397019 0.00095948

37520302 37527914 5.92987926 0.00232339

37535840 37543879 8.92597012 0.00015066

37720265 37734202 8.49654733 0.00315786

304


Start End

38755847 38789797 5.92670113 0.00296832

39431609 39483268 5.62911118 0.00855271

40198325 40251084 5.08928081 0.00207552

41812481 41960561 6.81246138 0.00351648

43821554 43904818 7.13610776 0.00388488

44313418 44387118 6.5161103 0.00156909

44927619 45009217 6.82470303 0.00866759

46067813 46187451 6.81361389 0.00263408

46127632 46249867 7.68983358 0.00052257

47058514 47214415 6.88001354 0.00124792

47136465 47289912 5.62989411 0.00233245

47432325 47569238 6.40367744 0.00142847

305

Appendix XII: List of significant changes (near-bait)

Lists of all the statistically significant near-bait changes between conditions

WT near-bait significant changes during differentiation

Appendix XII Table 1: Differentially connected near-bait interactions in WT 12 h PMA cells compared to WT 12 h DMSO K562 cells


Start End

35474191 35488395 -8.9377799 0.00013656

36278644 36280049 6.80655514 0.00194086

36332447 36333601 -7.6163228 0.00154466

36352605 36353690 8.07237223 0.00044141

36366550 36367612 6.76625148 0.00046604

36406427 36407599 8.25342937 5.34E-07

36410566 36411762 7.97117671 1.17E-05

36420971 36422856 7.47613931 0.00204346

36517085 36518628 -7.8123099 0.00337409

Appendix XII Table 2: Differentially connected near-bait interactions in WT 72 h PMA cells compared to WT 12 h DMSO K562 cells


Start End

36278644 36280049 -8.4916179 0.00011101

36324230 36325417 -9.7177426 8.72E-05

36326010 36327189 -9.9312191 1.98E-06

36349329 36350423 -5.0850069 0.00025395

36351516 36352604 -6.4430378 0.00243444

36352061 36353147 -6.4430378 0.00243444

36352605 36353690 -9.8028067 2.51E-05

36353690 36354773 -10.007951 8.07E-06

36357470 36358544 -11.333388 2.69E-13

306


Start End

36366550 36367612 -8.4053431 2.59E-05

36375572 36376634 -11.252854 2.29E-13

36377697 36378762 -10.717736 6.58E-10

36379295 36380362 -12.360236 5.70E-15

36379828 36380896 -4.8535823 2.15E-05

36380361 36381430 -3.9254633 0.00055942

36380895 36381965 -4.0813855 0.00262689

36383573 36384648 -3.5709246 0.00168646

36384110 36385187 -12.034618 6.48E-15

36384648 36385726 -10.183231 3.33E-06

36387347 36388434 -5.9110333 8.45E-07

36390068 36391163 -6.9307593 1.69E-11

36390614 36391712 -3.8287182 0.00154049

36391711 36392813 -13.697569 9.54E-21

36392261 36393366 -13.781015 9.54E-21

36393365 36394474 -10.689796 1.86E-12

36393918 36395030 -11.706035 8.92E-16

36394473 36395587 -11.160013 7.62E-14

36395028 36396145 -8.4361378 1.20E-05

36395586 36396705 -12.292107 8.92E-16

36396144 36397266 -12.317332 3.42E-16

36397827 36398958 -17.523158 1.40E-35

36399523 36400657 -12.06068 1.38E-18

36400090 36401227 -11.753278 5.96E-16

36400658 36401798 -3.7399723 0.00119426

36401228 36402371 -12.495422 8.92E-16

36401799 36402945 -13.775696 1.32E-20

36402372 36403521 -8.2326817 6.06E-12

36403523 36404678 -4.7434083 5.04E-06

36404100 36405259 -3.3574145 0.00265467

36405261 36406426 -5.5279129 2.50E-06

307


Start End

36405843 36407012 -11.666767 7.09E-14

36406427 36407599 -9.8620529 3.89E-08

36407013 36408189 -9.4757263 8.22E-07

36410566 36411762 -9.6137529 7.05E-07

36412365 36413572 -10.271704 1.98E-06

36412969 36414179 -5.4552266 0.00954996

36420971 36422225 -9.2193533 0.00014574

36421598 36422856 -9.2193533 0.00014574

36427314 36428599 -10.593799 3.32E-09

36465477 36466854 -9.4216804 8.20E-06

36479385 36480795 -4.1398039 0.00622221

36508664 36510185 -8.5117676 0.00151198 36598256 36600012 -5.3745211 0.00237202

37108487 37112215 5.94585879 0.0068571 WT and STAG2 near-bait comparisons

Appendix XII Table 3: Differentially connected near-bait interactions in STAG2 12 h DMSO cells compared to WT 12 h DMSO K562 cells


Start End

35442448 35454754 5.14667346 0.00604543

35488228 35503117 7.20660582 6.37E-05

35510591 35526316 5.13712908 0.00958697

35600268 35616837 5.63045291 0.00190404

35608619 35625055 5.39764041 0.00313695

35686013 35699118 5.05439121 0.00336181

35692775 35705461 6.00241513 0.00036696

35699325 35711598 5.92593227 0.00511254

35734347 35744470 4.69026964 0.00330434

35739543 35749398 4.87776795 0.00739212

308


Start End

35903813 35913177 6.16813376 0.0003288

35922480 35931468 5.15510497 0.00202299

35979513 35986245 4.15306452 0.00272369

36120292 36122672 -5.0846932 0.00165723

36123862 36126228 -6.3081783 0.00204766

36125047 36127409 -6.3378073 0.00217929

36126230 36128589 5.37656029 0.00055046

36127411 36129767 5.44025738 0.00532178

36156784 36159142 -5.9130815 0.00347325

36157963 36160322 -4.9202346 0.00236422

36215926 36218078 5.72381578 0.00143193

36238267 36240133 5.10258129 0.00313695

36243806 36245595 -7.2643134 2.92E-05

36253381 36255044 -5.5051643 0.00051481

36261516 36263082 5.54363525 0.00201202

36264639 36266171 -4.6884026 0.00198188

36265409 36266933 -4.6884026 0.00198188

36266175 36267691 7.33095815 2.50E-05

36271428 36272893 -6.5493485 0.00012837

36277225 36278641 -8.5036482 6.15E-07

36277936 36279346 -8.5019255 7.48E-09

36279349 36280749 -6.1583448 0.00133455

36280051 36281446 -7.2873161 5.50E-06

36280751 36282141 -8.5801776 1.12E-09

36281449 36282833 -8.9300293 1.04E-10

36282836 36284211 -5.7549271 0.00062342

36288291 36289632 -6.526985 8.70E-05

36290969 36292295 -4.543678 0.00052385

36292958 36294274 5.09197601 0.00514143

36293617 36294930 -6.0870697 8.83E-06

36294275 36295584 -6.0372376 0.00018546

309


Start End

36305857 36307115 -7.33992 5.14E-05

36313951 36315177 -7.1939107 1.38E-05

36314565 36315788 -8.4854587 3.55E-10

36316400 36317616 -6.0665073 0.00091451

36317009 36318223 -7.5011631 7.10E-06

36317617 36318829 -6.6905219 0.00020166

36324230 36325417 -6.4434308 0.00219053

36326010 36327189 -6.7090075 0.00020387

36328367 36329537 -6.1060016 0.00101977

36328953 36330121 -6.8940376 0.00025188

36329538 36330704 -5.8264151 0.00098554 36331867 36333024 -6.3782038 0.00173938

36333602 36334752 -7.5083462 3.30E-05

36335327 36336470 -7.6558644 5.77E-06

36335900 36337041 -7.9329818 1.75E-09

36337042 36338179 -6.6785373 0.00186816

36337611 36338746 -5.3144725 0.00281319

36338180 36339312 -4.119617 0.00606764

36338747 36339877 -5.0564483 0.00087849

36339878 36341004 -5.4894373 0.00023521

36340442 36341565 -5.1490454 0.00109411

36341005 36342126 -5.5925002 0.00243223

36341566 36342686 -5.4010668 0.00219053

36343803 36344915 -6.9091289 0.00063124

36346578 36347680 -5.7559427 2.67E-05

36347130 36348230 -5.6191847 0.00011139

36347681 36348780 -5.9721228 7.62E-05

36348231 36349329 -6.7553451 5.15E-06

36348781 36349876 -8.3451442 5.28E-08

36349329 36350423 -7.2373471 1.21E-08

36353148 36354232 -7.6190587 2.98E-08

310


Start End

36353690 36354773 -6.7603357 0.00052385

36357470 36358544 -8.2201229 5.59E-10

36358545 36359617 -5.0586849 0.00053423

36361223 36362291 -6.3245385 0.00018379

36361757 36362824 -7.7342382 5.85E-07

36363358 36364423 -4.2037648 0.00842511

36367613 36368674 -5.3934605 0.00532178

36368143 36369205 -7.6240911 1.65E-07

36368674 36369735 -7.8172476 1.57E-08

36369205 36370266 -6.0627492 0.0004902

36370796 36371857 -7.1648961 7.03E-07

36371327 36372388 -7.1668825 6.26E-07

36375572 36376634 -8.1453593 5.00E-10

36376103 36377166 -9.3578651 5.39E-14

36376634 36377698 -8.6138615 3.31E-10

36377166 36378230 -7.2504001 8.36E-07

36377697 36378762 -7.5580953 4.60E-07

36378230 36379295 -8.4133492 1.87E-09

36378762 36379828 -9.1784314 2.31E-12

36379295 36380362 -9.2348118 1.02E-11

36379828 36380896 -9.6763744 6.47E-14

36380361 36381430 -2.6218818 0.00652073

36380895 36381965 -3.3237615 0.00243029

36381430 36382501 -5.9783881 0.00249267

36382500 36383573 -5.7246547 0.00453991

36383036 36384111 -8.4451345 2.92E-11

36383573 36384648 -9.2083949 6.67E-14

36384110 36385187 -8.9215142 1.54E-11

36384648 36385726 -6.9420433 0.00025483

36386806 36387890 -8.5415748 3.55E-10

36387347 36388434 -9.0233909 1.55E-11

311


Start End

36387890 36388978 -8.8873084 1.85E-11

36388433 36389523 -7.9683448 1.95E-08

36388977 36390069 -6.9327362 2.49E-06

36389522 36390615 -8.2426883 3.30E-09

36390068 36391163 -9.9377838 7.34E-15

36390614 36391712 -9.6751239 3.60E-14

36391162 36392262 -9.0775585 9.77E-12

36391711 36392813 -10.61135 2.95E-17

36392261 36393366 -10.689834 2.95E-17

36393365 36394474 -7.5904273 4.51E-09

36393918 36395030 -8.6248651 2.98E-12

36394473 36395587 -8.0668095 2.29E-10

36395028 36396145 -5.2569823 0.0015807

36395586 36396705 -9.1849292 2.02E-12

36396144 36397266 -9.2193345 6.15E-13

36396704 36397829 -9.0894818 8.09E-14

36397265 36398393 -8.967531 6.11E-12

36400090 36401227 -8.675502 1.80E-12

36400658 36401798 -9.2692792 7.93E-14

36401228 36402371 -9.378327 2.02E-12

36401799 36402945 -5.038744 7.61E-08

36402372 36403521 -3.5759767 0.00187333

36403523 36404678 -9.3643406 4.72E-14

36404100 36405259 -10.122268 6.08E-17

36404680 36405841 -9.1409659 2.10E-11

36405261 36406426 -8.2309386 4.38E-10

36405843 36407012 -8.5509922 1.38E-10

36407013 36408189 3.99025471 0.00241178

36408190 36409373 -7.4028371 1.83E-08

36409375 36410564 -5.8642315 0.00036696

36409969 36411162 -6.3031778 5.49E-05

312


Start End

36412365 36413572 -7.0352108 0.00016797

36412969 36414179 -7.7426722 1.46E-05

36414790 36416010 -4.6024336 0.00552198

36415400 36416624 -7.9606583 3.87E-08

36416012 36417240 -8.2026183 5.28E-08

36416626 36417857 -6.4008285 1.11E-06

36417242 36418476 -7.2248679 3.94E-07

36417859 36419097 -8.6052284 3.53E-10

36418478 36419719 -9.0721955 2.10E-11

36425395 36426671 -6.1071262 0.00045891

36426033 36427312 -3.2468646 0.00427006

36426673 36427955 -5.5419205 1.03E-07

36427314 36428599 -7.4220256 1.58E-06

36427956 36429244 -7.2037424 6.20E-07

36428600 36429891 -7.6476059 5.36E-09

36429246 36430539 -6.7768232 2.50E-05

36432493 36433799 -5.4958363 0.00874461

36433146 36434455 -6.6494338 0.00223817

36433800 36435112 -6.8541375 0.00085553

36441738 36443074 -5.8566927 0.00652073

36443075 36444413 -5.9206517 0.00249267

36446428 36447774 7.39577926 2.96E-05

36447775 36449123 -6.6516338 4.41E-06

36449124 36450475 -5.6525322 0.00666041

36449799 36451151 -5.6819926 0.00326723

36455222 36456582 -5.9937473 0.00190552

36455902 36457263 -4.5299829 0.00652073

36464790 36466165 -6.4190745 0.00010023

36465477 36466854 -6.1987382 0.00070554

36468234 36469615 -5.8512261 0.00088366

36468924 36470307 -6.8216637 1.71E-06

313


Start End

36469616 36471000 -6.8430583 7.69E-07

36470308 36471694 -7.453335 1.17E-07

36471001 36472388 -7.582741 2.40E-06

36471694 36473083 -6.7916012 0.0001583

36474478 36475874 -9.0531798 5.21E-13

36475176 36476573 -7.7620541 1.05E-06

36476574 36477976 4.5685569 0.0094656

36482213 36483633 -6.0956116 0.00219053

36482923 36484346 -6.1011021 0.00196216

36483634 36485060 -7.5770118 3.59E-05

36484347 36485775 -8.8197034 1.55E-11

36485061 36486492 -4.1300057 0.00056245

36489374 36490822 -4.6474085 0.00292553

36499642 36501131 -5.0309972 0.00810328

36501880 36503377 -5.8853815 0.00109411

36507905 36509423 -5.2677341 0.00433536

36510186 36511710 4.20340063 0.00745852

36547820 36549434 -5.0367016 0.00268999

36548627 36550243 -6.1675094 3.01E-05

36549435 36551053 -5.6311423 0.00690644

36551054 36552676 -6.0681017 0.0046105

36569979 36571652 -5.8495268 0.00299595

36575859 36577549 -8.1808458 8.61E-10

36586087 36587810 5.45802724 0.00225476

36599134 36600892 -7.70849 0.00071704

36613306 36615093 -6.6973562 1.05E-06

36639644 36641497 4.98883571 0.00388645

36643358 36645221 6.65546515 0.00076291

36644289 36646155 4.44734443 0.00452901

36675275 36677141 6.26116299 0.00199485

36678070 36679930 4.12581365 0.00954415

314


Start End

36681781 36683630 5.27931924 0.00840389

36689136 36690961 -6.0643028 0.0065149

36690048 36691870 -6.2983822 0.00056685

36692776 36694589 -6.5063716 0.00100762

36699984 36701773 -5.0619114 0.00491749

36700879 36702664 -5.3418691 0.00359391

36707985 36709750 -5.6312265 0.00214273

36716767 36718514 -6.0836102 0.00242154

36720257 36721999 4.76772143 0.00383356

36760629 36762445 6.69185046 6.37E-05

36769828 36771700 -5.4890296 0.00539627

36770764 36772642 -5.0261707 0.00831542

36780318 36782271 5.18649867 0.00453307

36817180 36819432 5.41118856 0.00788438

36820565 36822832 4.62864398 0.0055187

36830832 36833128 3.91908454 0.00316182

36833129 36835428 6.36933438 0.00084076

36834279 36836579 7.26272323 8.89E-05

36838884 36841188 4.91703132 0.00840389

36845802 36848109 -5.5795055 0.00055954

36861965 36864276 5.4450722 0.00057203

36863120 36865432 5.94395026 0.00075854

36880462 36882777 5.11807537 0.00104606

36895580 36897926 3.31146263 0.00889445

36896753 36899103 4.04485602 0.00281761

36905029 36907416 4.68161925 0.00606764

36915906 36918366 4.73789359 0.0032947

36917136 36919606 4.96756688 0.00784981

36958414 36961325 4.74411509 0.00074215

36959870 36962799 5.84583263 0.00204766

36981249 36984462 4.96011295 0.00056245

315


Start End

36982856 36986093 5.03870752 0.00056245

36991078 36994449 4.9324713 0.00491749

37008653 37012362 4.85873578 0.00382578

37028134 37032277 6.1739032 0.00104737

37030206 37034396 5.79386359 0.00225476

37032301 37036541 6.74526785 0.00074215

37034421 37038710 5.82107512 0.00222416

37036565 37040905 5.76476449 0.00064035

37038735 37043124 5.90300992 0.00011139

37043150 37047640 4.45955355 0.00235656

37052283 37056975 5.01655284 0.00666041

37095061 37100368 5.23676744 0.00434717

37103038 37108370 5.31556159 0.00195862

37105704 37111038 5.79920985 0.00054327

37159176 37163807 4.10587535 0.00840389

37170498 37174877 7.33275151 4.41E-06

37172687 37177018 5.49103364 0.00612315

37174853 37179137 5.24234056 0.00313695

37181212 37185366 5.85259436 0.00105398

37195362 37199276 4.48212577 0.00190552

37222017 37225778 5.30380422 0.00634469

37237127 37240932 6.31057776 0.00142198

37256273 37260121 6.2641119 0.00062633

37258197 37262045 6.06703571 0.00217929

37260121 37263969 6.10036762 0.00109411

37317455 37321316 5.98073692 0.00022105

37319385 37323257 5.83117255 0.00190552

37325210 37329115 6.6395281 5.82E-05

37356988 37360994 5.84564246 0.00074215

37358991 37362993 5.94765868 0.00165723

37382680 37386536 5.32308698 0.00195583

316


Start End

37384608 37388447 5.56009357 0.00199325 Appendix XII Table 4: Differentially connected near-bait interactions in STAG2 12 h PMA cells compared to WT 12 h PMA K562 cells


Start End

35474191 35488395 8.88363712 0.00039423

36329538 36330704 -7.104548 0.00325246

36517085 36518628 8.07291617 0.00534208 Appendix XII Table 5: Differentially connected near-bait interactions in STAG2 72 h PMA cells compared to WT 72 h PMA K562 cells

Chromosome location (Chr21) log2FoldChange Adjusted p value Start

Start End

35616930 35633181 6.39080745 0.00248597

35820274 35829140 5.5848525 0.00097742

35824675 35833604 5.60497619 0.002314

35861078 35870515 5.67484009 0.00573638

35865777 35875254 6.38202372 0.00294739

36037368 36041588 5.93157339 0.00653349

36085729 36088522 5.19853978 2.60E-05

36087139 36089905 3.82433892 0.00644468

36095322 36097948 5.91682845 0.00492169

36103113 36105633 5.99928094 0.00569621

36126230 36128589 5.74903532 0.00151667

36212646 36214832 6.07280288 0.00314296

36230511 36232484 4.39631305 0.00082114

36231504 36233463 5.13758583 0.0092545

36245601 36247365 4.08172618 0.00950164

36246489 36248241 4.13413137 0.00769454

317


Start End

36247371 36249111 3.68908087 0.00950164

36255049 36256690 4.35292358 0.00644468

36255874 36257506 4.11377844 0.00961065

36264639 36266171 -6.0507818 0.00031558

36265409 36266933 -6.0507818 0.00031558

36277936 36279346 -6.3021965 0.00037603

36278644 36280049 11.6729562 1.33E-09

36280751 36282141 3.78712369 0.0070179

36281449 36282833 3.48142505 0.00992013

36290302 36291632 2.88412113 0.00779809

36293617 36294930 4.04452087 0.0011826

36294275 36295584 4.6285964 0.00333127

36296890 36298187 -5.8193993 0.0048044

36321841 36323037 3.32492625 0.00601396

36326010 36327189 9.70851274 6.56E-06

36329538 36330704 -5.3848816 0.00813909

36353690 36354773 9.46576918 4.07E-05

36357470 36358544 10.7800466 6.46E-11

36358008 36359081 -6.1100997 0.0006696

36358545 36359617 -5.509932 0.00471123

36359081 36360153 4.41661147 0.00242101

36359617 36360688 3.72348938 0.00011918

36368143 36369205 -5.3552475 0.00242101

36375572 36376634 10.5041155 7.68E-11

36378230 36379295 -5.9803434 0.00038571

36378762 36379828 -6.2605281 4.97E-05

36379828 36380896 -4.7731068 0.00242101

36380895 36381965 3.59887114 0.00569621

36384110 36385187 10.3846842 1.70E-10

36386806 36387890 -5.2050704 0.00189496

36387890 36388978 -7.26707 3.12E-06

318


Start End

36388433 36389523 -7.0197525 2.31E-05

36389522 36390615 3.71566016 0.00693617

36390614 36391712 -5.8030608 0.00010992

36391162 36392262 -5.3553286 0.00091074

36396704 36397829 -6.1154249 1.69E-05 36397265 36398393 -5.9315806 0.00011918

36400658 36401798 -5.4946701 0.00018284

36401799 36402945 9.67500252 8.95E-10

36403523 36404678 -4.5884313 0.002314

36404100 36405259 -6.7406599 1.60E-06

36405261 36406426 4.97333288 4.59E-05

36408190 36409373 -6.8682933 6.56E-06

36409375 36410564 4.14902314 0.00961065

36412365 36413572 9.90821104 8.81E-06

36412969 36414179 5.19516829 0.006363

36415400 36416624 -5.3966426 0.002314

36417242 36418476 3.55214792 0.00964533

36418478 36419719 -7.3674023 3.66E-06

36427314 36428599 8.28077489 2.02E-05

36438413 36439740 6.19923091 0.00355447

36439076 36440405 5.34938019 0.00781592

36447101 36448448 3.42138535 0.00964533

36447775 36449123 3.57663208 0.00973886

36462046 36463416 5.37629124 0.00091074

36466854 36468233 4.03332205 0.00678425

36467544 36468924 4.03332205 0.00678425

36468924 36470307 -5.051629 0.00303796

36470308 36471694 3.53892576 0.00955617

36473780 36475175 4.22259507 0.00195782

36474478 36475874 3.02786348 0.00781592

36476574 36477976 7.73871874 0.00086778

319


Start End

36483634 36485060 -5.6762586 0.00949249

36525620 36527182 -5.0371467 0.0098543

36548627 36550243 3.83613999 0.00674913

36564157 36565814 -5.8356448 0.00350161

36564985 36566644 -5.6011714 0.002314

36574173 36575858 3.91851685 0.0048044

36576704 36578397 4.09421663 0.00569621

36581802 36583511 4.57197733 0.00781592

36583512 36585226 -5.8699908 0.00294739

36598256 36600012 6.96899254 1.69E-05

36599134 36600892 8.45991798 0.0005704

36613306 36615093 4.45512151 0.00042992

36624088 36625898 4.88082738 0.00813909

36624993 36626806 5.09223372 0.00189172

36626807 36628624 3.59833853 0.00779809

36627716 36629535 3.92259217 0.0098543

36638718 36640569 5.23900589 0.00950164

36639644 36641497 5.30117871 0.00781592

36654596 36656478 6.9292274 0.0008546

36728951 36730687 4.62761545 0.00314296

36729819 36731555 4.37422906 0.00775817

36735029 36736767 9.11645074 0.0001077

36739379 36741122 4.39342681 0.00346245

36756124 36757918 5.238339 0.00779809

36777412 36779341 3.42447573 0.00798702

36786243 36788249 3.67964767 0.00678425

36787246 36789261 3.86273218 0.00632354

36810491 36812704 5.61215239 0.002314

36845802 36848109 5.854747 0.0001077

36846955 36849263 5.16856943 3.66E-06

36848109 36850417 3.85732002 0.00035811

320


Start End

36853880 36856189 -5.2477179 0.00973886

36885096 36887417 5.06153769 0.00964533

36895580 36897926 4.68289553 0.00156111

36896753 36899103 4.72151154 0.00314296

36911032 36913456 4.01547479 0.00709354

36933626 36936251 4.86453523 0.00272994

36934938 36937577 5.12703258 0.00248597

37056999 37061790 5.78297835 0.00747781

37059395 37064233 6.08828716 0.00242101

37061814 37066698 5.31314356 0.00813909

37084541 37089766 6.46491345 0.002314

37087154 37092404 6.25238487 0.00091074

37113699 37119014 5.88738966 0.00303796

37116357 37121658 5.63425786 0.00967932

37170498 37174877 4.73204438 0.00882689

37187383 37191422 4.94111543 0.00781592

37199264 37203129 7.52992906 0.00015582

37205031 37208841 4.95470397 0.00758695

37212613 37216380 5.08057016 6.35E-05

37214497 37218259 5.81850888 0.00082114

37246671 37250505 9.29052153 1.69E-05

37263969 37267814 6.15339897 0.00314296

37283130 37286944 5.57851522 0.00272994

37302154 37305960 5.87814043 0.00346245

37305962 37309776 3.96493041 0.00604611

37307869 37311688 4.63176966 0.00011918

37309778 37313604 4.43627766 0.00426435

37313608 37317450 5.33377368 0.00398626

37315529 37319381 5.18200797 0.00781592

37346971 37350974 7.54451589 0.00069152

37348973 37352979 6.87174577 0.00729703

321


Start End

37374884 37378805 5.53180004 0.00346245

Appendix XIII: List of gene connections

To investigate which genes may be co-regulated with RUNX1, interactions occurring

with other genes were identified. Gene interaction analysis identifies a connection with an

overlap in the same genomic region as a gene, and does not necessarily reflect an interaction

with a promoter or exon of a gene.

Appendix XIII Table 1: List of gene connecting to RUNX1 +24 connections in each condition

WT 12 D WT 12 P WT 72 P STAG2 12 D

STAG2 12 P STAG2 72 P

AF127936.3 AF015262.2 AF064858.10

AF064858.10

AF240627.2 AF064858.6

AF127936.5 AF015720.3 AF064858.11

AF127577.11

AP000255.6 AP000253.1

AF127936.7 AF064858.6 AF064858.6 AIRE AP000688.14

AP000254.8

AL773572.7 AF127577.12

AF064858.8 AP000255.6 BRWD1 AP000962.1

AP000255.6 AF127577.13

AF064860.5 AP000320.6 BRWD1-AS1

AP001628.6

AP000688.11

AF129075.5 AF064860.7 AP000563.2 BRWD1-AS2

AP001628.7

AP000688.14

AL773572.7 AF124730.4 AP000569.9 CBR3-AS1 AP001630.5

AP001412.1 AP000223.42

AF127936.3 AP000688.11

CLIC6 BX322557.10

AP001626.2 AP000233.4 AF127936.5 AP000688.14

DOPEY2 C21orf62-AS1

AP001630.5 AP000282.3 AF129075.5 AP000688.15

HLCS CLIC6

BACE2 AP000320.6 AF246928.1 AP000705.6 HMGN1 DSCAM BRWD1 AP000474.1 AGPAT3 AP000705.7 IL10RB DSCR4 BRWD1-AS1

AP000563.2 AJ006998.2 AP000705.8 ITSN1 ERVH48-1

BRWD1-AS2

AP000688.11

AL109761.5 AP000959.2 KCNE2 H2AFZP1

BRWD1-IT1 AP000688.14

AP000255.6 AP001136.2 LINC00160 HMGN1

322



CBR1 AP000688.29

AP000318.2 AP001412.1 LINC01426 KCNE1

CLIC6 AP000695.4 AP000320.6 AP001628.6 LINC01436 KCNE1B DSCR3 AP000696.2 AP000322.5

3 AP003900.6 MIR548X LINC00160

DSCR4 AP000705.6 AP000477.2 ATP5J MIR548XHG

LINC00205

GAPDHP16 AP000705.7 AP000477.3 BAGE2 MORC3 LINC00315 GRIK1 AP000705.8 AP000569.9 bP-

2171C21.3 PAXBP1 LINC00334

GRIK1-AS1 AP001042.1 AP000688.14

bP-2171C21.4

PAXBP1-AS1

LINC01426

HLCS AP001043.1 AP000688.15

bP-2189O9.2

SCAF4 LINC01549

HMGN1 AP001412.1 AP000688.29

BRWD1 SETD4 LINC01668

IFNAR2 AP001610.5 AP000695.6 C21orf62-AS1

SYNJ1 MIR5692B

IL10RB AP001619.2 AP001042.1 CBR3-AS1 Y_RNA MIR99A IL10RB-AS1

AP001619.3 AP001437.1 CLIC6 LOC100506403

MIR99AHG

LCA5L AP003900.6 AP001469.5 CRYZL1 RUNXOR MIRLET7C LINC00159 ATP5J AP001469.7 CYCSP41 AF015720.3 NDUFV3 LINC01671 BACE2 AP001469.9 DNMT3L AP000322.5

3 PDE9A

METTL21AP1

BACH1 AP001625.4 DOPEY2 CH507-396I9.3

POFUT2

MIR3197 BAGE2 AP001625.5 DSCR3 CMP21-97G8.2

RNU6-113P

MIR5692B BRWD1 AP001625.6 DSCR4 KCNE1 SCAF4 PDE9A BTF3L4P1 ATP5J DSCR8 KCNE1B SETD4 PDXK BTF3P6 BACH1 DYRK1A RCAN1 SNORA81 PKNOX1 BX322557.1

0 BRWD1 ERG RUNX1-IT1 SOD1

PLAC4 C2CD2 BRWD1-IT1 EZH2P1

TRAPPC10 POLR2CP1 CBR1 BTF3L4P1 FAM207A

WDR4

PRDM15 CCT8 C21orf91 FAM3B

Y_RNA PSMG1 CHAF1B C21orf91-

OT1 FDX1P2

LOC100506403

RAD23BLP CLDN14 C2CD2 GABPA

RUNXOR RBMX2P1 CLDN17 CBR1 GART

AF015720.3

RBPMSLP CLIC6 CBR3-AS1 GRIK1

AP000322.53

RIPK4 CXADR CCT8 HLCS

CH507-396I9.3

323



RNF6P1 CYCSP41 CH507-396I9.3

HUNK

CMP21-97G8.2

RNU6-992P DOPEY2 CLDN14 IL10RB

RUNX1-IT1 RUNX1 DSCAM CLIC6 ITGB2

SCAF4 DSCAM-IT1 CMP21-97G8.1

ITGB2-AS1

SETD4 DSCR3 CXADR ITSN1

TIMM9P2 DYRK1A DSCAM JAM2

USP25 ERG DYRK1A KCNE2

WRB EVA1C ERG KRTAP10-10

Y_RNA EZH2P1 ETS2 KRTAP10-11

AF015720.3 FAM3B EVA1C KRTAP10-6

AP000318.2 FDX1P2 EXOSC3P1 KRTAP10-7

AP000322.53

GABPA FDX1P2 KRTAP10-8

LINC01426 GART GABPA KRTAP10-9

RCAN1 GRIK1 GAPDHP14 LCA5L

RPL34P3 HLCS GAPDHP16 LINC00114

SMIM11A HLCS-IT1 GRIK1 LINC00160

SMIM11B HUNK GRIK1-AS1 LINC00649

LOC100506403

IMMTP1 HLCS LINC01424

RUNXOR ITSN1 HMGN1 LINC01547

JAM2 HUNK LINC01684

KCNE2 IGSF5 LL21NC02-1C16.1

KCNJ6 ITSN1 LL21NC02-

1C16.2

KCNJ6-AS1 JAM2 MAPK6PS2

KRTAP10-12

KCNE1 MIR3648-2

KRTAP10-13P

KCNE2 MIR3687-2

KRTAP12-1 KCNJ15 MIR6508

KRTAP12-2 KRTAP10-1 MX2

KRTAP12-3 KRTAP10-

10 NCAM2

KRTAP12-4 KRTAP10-

11 NDUFV3

LINC00160 KRTAP10-2 NRIP1

LINC00205 KRTAP10-3 PFKL

LINC00310 KRTAP10-4 PIGP

324



LINC00315 KRTAP10-5 PRDM15

LINC00334 KRTAP10-6 PSMG1

LINC00649 KRTAP10-7 PTTG1IP

LINC01426 KRTAP10-8 RCAN1

LINC01436 KRTAP10-9 RIPK4

LINC01684 LINC00160 RNF6P1

LINC01700 LINC00189 RNU4-45P

LLPHP2 LINC00310 RP1-

100J12.1

LTN1 LINC00649 RP11-

313P22.1

MAP3K7CL LINC01426 RP11-

589M2.1

MIR155HG LINC01436 RPL3P1

MIR3197 LSS RRP1B

MORC3 LTN1 RSPH1

MRPL39 MAP3K7CL RWDD2B

MRPS6 MCM3AP SAMSN1

MX1 MCM3AP-AS1

SCAF4

MX2 MEMO1P1 SETD4

MYL6P1 METTL21AP1

SH3BGR

NRIP1 MRPS6 SMIM11A

OLIG2 OLIG2 SMIM11B

PIGP PAXBP1 SNRPGP13

PLAC4 PAXBP1-AS1

SON

POFUT2 PIGP SUMO3

PRDM15 POLR2CP1 TIAM1

PSMG1 RCAN1 TPTE

RN7SL163P RIMKLBP1 TSPEAR

RNA5SP489 RN7SL163P TTC3

RNGTTP1 RNF6P1 UBASH3A

RNU4-45P RP1-

100J12.1 UBE2G2

RNU6-1149P

RP11-313P22.1

USP16

RNU6-696P RPL12P9 VN1R7P

RNU6-992P RPL23P2 WDR4

RP1-100J12.1

RPL34P3 WRB

325



RP1-225L15.1

RPL39P40 LOC100506403

RP11-313P22.1

RSPH1 AF015720.3

RP11-589M2.1

RWDD2B AP000318.2

RPL23P2 SAMSN1 KCNE1

RPL34P3 SCAF4 LINC00310

RPL39P40 SETD4 LINC01426

RPSAP64 SLC37A1 MIR802

RWDD2B SMIM11A MRPS6

SAMSN1 SMIM11B RPS20P1

SAMSN1-AS1

SYNJ1 RPS5P2

SETD4 TIMM9P2 SLC5A3

SMIM11A TSPEAR

SMIM11B TTC3

SNORA3 U3

SNORA62 USP16

SNORA80A WRB

SRSF9P1 Y_RNA

TIAM1 ZBTB21

TMPRSS2 ZNF295-

AS1

TPTE LOC100506

403

TSPEAR AF015720.3

TTC3 C21orf140

UBASH3A CH507-

396I9.7

UMODL1 CMP21-

97G8.2

UMODL1-AS1

KCNE1B

URB1 MIR802

URB1-AS1 RPS20P1

USP16 RUNX1

VN1R7P SLC5A3

Y_RNA SNORA11

LOC100506403

RUNXOR

AP000318.2

326



AP000322.53

C21orf140

CH507-396I9.3

CH507-396I9.7

CMP21-97G8.1

CMP21-97G8.2

KCNE1

RCAN1

RPS5P2

SLC5A3

RUNX1-IT1

327

Appendix XIV: List of significant changes STAG2 12

DMSO compared STAG2 72 PMA

Appendix XIV Table 1: Differential cis interactions in STAG2 72 h PMA cells compared to STAG2 12 h DMSO K562 cells


Start End

16193679 16290033 5.86391632 0.00342781

16287686 16392414 -4.8616436 0.00409359

16337170 16447658 -6.2817559 0.00012778

22535850 22765112 -5.3708546 0.00321359

22876545 23117188 5.43274265 0.00202892

23369288 23626457 -5.6528021 0.00375885

23878355 24124540 5.05342004 0.00364158

24761799 25024643 6.15905653 0.00200644

32389417 32510405 4.90346805 0.00474697

32513939 32620098 4.80628151 0.00831305

33218357 33277752 -4.894004 0.00644409

33999313 34042381 4.99233029 0.0072539

34253734 34295441 -4.5073628 0.00819366

34643232 34676058 -5.6689901 0.00199124

35143611 35160271 -5.7105858 0.00205897

35152010 35168532 -5.8140152 0.00203572

35160337 35176727 -5.8371813 0.00382979

35232484 35247888 -5.8832819 0.00186227

35240230 35255545 -5.9020399 0.00219482

35300676 35315394 -6.3841202 0.00031727

35308065 35322722 -7.0977311 0.00049094

35436900 35450845 -5.4020436 0.00279414

35443887 35457803 -5.7110624 0.00808936

35478603 35492364 -6.1583276 0.00397153

35485500 35499228 -7.3015989 0.00041831

328


Start End

35506094 35519714 -5.6341086 0.00993049

35784920 35794816 4.30548294 0.00509029

35823560 35832710 4.42665524 0.00753341

35832755 35841723 5.20170137 0.00913565

35859255 35867690 8.36421118 6.47E-06

35880124 35888128 -5.3281192 0.00847348

35903755 35911258 -5.2503345 0.00664818

36021891 36026806 -6.535467 0.0020892

36052207 36056497 6.36072775 0.00183374

36074896 36078750 6.26158833 0.00049999

36078768 36082551 6.79553391 0.00253267

36097095 36100555 -5.3974861 0.00744211

36103984 36107328 4.6414395 0.00806999

36105670 36108987 4.80922853 0.00076676

36107342 36110631 4.92457287 0.00813439

36109000 36112262 3.82601073 0.00829254

36112275 36115485 5.15725287 0.00445428

36113893 36117077 6.39617859 0.00122301

36115497 36118656 6.28087387 0.00227957

36117089 36120223 5.47469903 0.0007658

36118668 36121778 5.74373025 0.00023888

36120235 36123321 4.29131238 0.00397123

36124863 36127878 7.69970523 0.00031408

36157164 36159733 5.31664623 0.00466991

36178077 36180394 -6.4238107 0.00442351

36182697 36184962 6.33079209 0.0010642

36183836 36186089 4.35662179 0.00424197

36194887 36197021 6.2781405 0.00149473

36200196 36202277 4.9283002 0.00932299

36213393 36215346 8.73987292 5.24E-06

36216323 36218250 -6.0057821 0.00371672

329


Start End

36243603 36245303 7.23413186 0.00012512

36250339 36251990 6.41840825 0.00232423

36253634 36255261 4.29466569 0.00520545

36255264 36256880 6.54826795 3.05E-06

36256075 36257685 6.7786123 1.28E-06

36257688 36259287 -5.7516519 0.0022048

36266370 36267913 -5.4044968 0.00522296

36267144 36268682 -5.5749242 0.00318493

36269452 36270976 -6.1952272 0.00042122

36270979 36272494 5.24954146 8.02E-05

36271738 36273249 5.05029119 0.00466991

36278479 36279951 9.11879907 4.07E-08

36279217 36280685 9.10568924 1.27E-08

36280687 36282148 9.96475177 2.53E-11

36281420 36282876 10.0030672 2.11E-11

36282150 36283603 7.2543056 5.17E-05

36282878 36284327 7.267999 2.71E-05

36287208 36288634 5.4507541 0.0009367

36287923 36289346 5.48484313 0.00103209

36289348 36290764 -5.3562441 0.00474829

36290765 36292175 6.16887781 7.97E-07

36291472 36292878 8.49998497 9.96E-10

36293581 36294976 9.48666016 1.69E-11

36294280 36295673 9.42356641 4.13E-09

36305263 36306608 7.78796024 0.00024295

36309954 36311281 7.00839478 7.71E-05

36311282 36312604 3.874062 0.00903138

36311944 36313264 9.62390448 2.18E-10

36313924 36315237 4.9712562 0.00731705

36316548 36317852 6.15266037 0.00170019

36317201 36318502 9.11561781 1.50E-09

330


Start End

36317853 36319152 9.31437377 2.64E-07

36321741 36323028 3.48271698 0.00296189

36322386 36323671 5.0790562 0.0032143

36324953 36326231 4.94922112 0.00085693

36331937 36333195 6.78039204 0.0016612

36333196 36334450 5.39157606 0.00498829

36335078 36336327 9.16501077 1.40E-07

36335703 36336952 8.22164842 4.77E-09

36336952 36338198 8.75800874 1.10E-05

36337576 36338819 8.33783655 6.32E-09

36338198 36339441 3.92381229 0.00396733

36338820 36340061 5.05953517 0.00189014

36340062 36341300 6.38840711 6.78E-06

36340682 36341919 6.09958357 0.00069069

36341301 36342536 5.71437787 0.00202855

36343771 36345001 7.75787409 0.00011951

36346844 36348068 7.40707966 1.11E-07

36347456 36348680 6.98617274 8.03E-07

36348069 36349291 8.01430446 1.16E-07

36348680 36349902 7.63758264 3.05E-06

36349292 36350512 -4.1354402 0.00625147

36352947 36354161 9.36897263 1.97E-10

36353555 36354768 6.77679685 0.0008522

36357190 36358398 8.10083758 6.32E-09

36357795 36359002 -5.1152283 0.0021679

36359002 36360208 6.20858216 9.77E-07

36360811 36362014 5.65592073 0.00093715

36361413 36362615 7.08985193 3.05E-06

36363217 36364417 5.6998418 0.00062424

36368612 36369807 7.96066213 7.07E-08

36369210 36370405 7.79227784 5.41E-06

331


Start End

36370405 36371599 6.98964564 2.43E-05

36371003 36372196 8.48952557 4.77E-09

36372196 36373389 -4.7126163 0.00681217

36375178 36376369 7.97514 1.05E-08

36375774 36376965 8.46543677 4.33E-10

36376369 36377560 6.87164047 3.99E-06

36376965 36378155 6.49272082 1.46E-05

36377560 36378750 6.51577197 0.00051865

36379940 36381129 7.76836353 1.83E-09

36382913 36384101 8.19486754 1.83E-09

36383507 36384695 8.70214754 7.00E-11

36384101 36385289 7.88858675 3.34E-08

36388853 36390041 8.66212113 5.90E-09

36389447 36390636 8.76037028 9.96E-10

36400743 36401934 7.95025266 4.13E-09

36401339 36402530 5.74612908 0.00069069

36403126 36404318 -6.0357879 0.00025132

36404915 36406108 8.17048154 8.37E-09

36405511 36406704 8.2239719 4.77E-09

36407899 36409094 7.28607373 8.75E-06

36409692 36410889 8.8601203 2.25E-08

36410290 36411487 9.3885889 8.84E-08

36412086 36413284 7.17619303 0.00012778

36412685 36413884 8.01748321 9.23E-06

36414484 36415685 5.86499505 0.0007024

36416286 36417488 6.88736529 2.17E-05

36416887 36418089 10.1733878 5.63E-13

36417488 36418691 9.59154074 1.79E-10

36425940 36427152 9.46629636 2.11E-11

36426546 36427760 6.31447739 2.88E-08

36427153 36428367 3.93077012 0.00822862

332


Start End

36427760 36428975 6.59679415 6.53E-05

36428367 36429583 7.65349876 1.40E-07

36428975 36430192 7.26378089 1.10E-06

36438757 36439987 6.91199385 0.00065793

36439372 36440603 6.14925001 0.00924672

36444930 36446170 4.71329953 0.00943458

36447413 36448657 9.30726002 7.08E-10

36448035 36449280 7.82083118 3.06E-05

36454913 36456170 5.79710857 0.00306027

36455542 36456800 6.12998107 0.00253267

36460592 36461860 -6.8333284 0.00091342

36469524 36470810 7.30008641 1.92E-07

36470167 36471454 8.5845074 5.10E-09

36470810 36472099 8.68900968 2.08E-07

36474685 36475982 9.99750841 5.63E-13

36475334 36476631 6.07165637 0.00049999

36477933 36479237 6.63647668 0.0002694

36484482 36485800 8.23693073 5.18E-06

36485141 36486460 4.67621314 0.00031408

36493775 36495114 -5.8752384 0.00266934

36507315 36508686 5.44741229 0.00466335

36508000 36509373 7.91544296 2.06E-05

36525409 36526824 -5.4466584 0.00614928

36526116 36527533 -5.4517141 0.00176164

36527534 36528954 6.18594195 0.00418337

36547749 36549219 6.62979028 0.00219296

36548484 36549956 9.16765578 9.96E-10

36552172 36553653 4.78145992 0.00044474

36562621 36564128 6.40969238 0.00313258

36564884 36566396 -4.6857336 0.00779839

36567155 36568672 9.22163454 1.40E-07

333


Start End

36567914 36569433 9.05790068 8.03E-07

36568673 36570195 7.16735245 0.0015729

36571722 36573251 -4.8063868 0.00855029

36574785 36576321 3.51398395 0.00253267

36575553 36577091 8.58721717 1.94E-08

36576322 36577862 9.07635381 9.96E-10

36577092 36578634 4.08026932 0.00253267

36580956 36582508 4.44860024 0.00118968

36581732 36583285 5.43783997 0.00051611

36584065 36585624 5.19288271 0.00393797

36594271 36595854 6.15128015 0.00160141

36598238 36599831 5.36914918 0.00046146

36599035 36600629 9.99934587 1.32E-06

36609474 36611093 -4.3698555 0.00462971

36612719 36614346 5.11568005 0.00397123

36613532 36615161 9.58710989 1.69E-11

36620074 36621719 5.03147673 0.00847348

36625022 36626677 5.05492652 0.00065793

36625849 36627507 4.03410268 0.00869538

36629171 36630836 -7.003066 0.00091276

36630003 36631671 -6.9765212 0.0007058

36631672 36633343 -5.2309112 0.00349047

36632508 36634181 -5.0187545 0.0068581

36635020 36636700 3.92357996 0.00346738

36635860 36637541 4.70853391 0.00589323

36644310 36646011 -5.1397395 0.00534331

36678230 36680011 -6.1696126 0.00065238

36680012 36681798 -5.4258578 0.00152795

36680905 36682693 -5.8357132 0.00152115

36692611 36694428 8.50167259 2.29E-05

36699911 36701747 5.08698912 0.00891689

334


Start End

36700829 36702667 5.57415448 0.00397999

36701748 36703588 5.27879921 0.00574901

36708215 36710072 7.88117736 1.56E-05

36711005 36712870 7.86184739 0.00085693

36721336 36723230 9.67340601 1.30E-06

36722283 36724180 7.52574543 0.00011951

36737632 36739576 5.64719404 0.0033521

36740553 36742506 8.81583236 1.11E-07

36755374 36757378 5.10121704 0.00235956

36760401 36762422 -6.4717096 0.0009367

36761411 36763436 -6.7162771 6.46E-06

36769564 36771619 6.72228717 0.00399923

36785192 36787309 -5.2462769 0.00187615

36793723 36795876 -6.4362647 0.00016942

36803497 36805693 6.06936737 0.00025205

36809012 36811232 9.17706758 4.08E-06

36810122 36812348 8.86807383 5.75E-06

36819097 36821364 -5.9229886 0.00024525

36829396 36831713 -6.6250797 0.00085578

36847072 36849476 3.19423968 0.00602858

36853113 36855549 5.81392578 0.0073898

36862945 36865433 -6.4243639 0.00175456

36880669 36883253 -5.789861 0.00360003

36915511 36918300 -5.4894569 0.00108972

36938338 36941266 6.49251367 0.00022981

36947191 36950174 6.6451659 0.00209437

36951679 36954690 8.74416556 2.80E-07

36957728 36960776 -4.267691 0.00693789

36962314 36965390 5.81521445 0.00010327

36963852 36966937 6.6754402 3.30E-05

36973177 36976319 5.71456785 0.00410491

335


Start End

36990714 36993959 -5.8539048 0.00393797

36992337 36995591 -5.6809478 0.00545778

37008816 37012162 -5.3731915 0.00642581

37036116 37039601 -7.0225175 0.00051611

37106625 37110350 -6.2762985 0.00160141

37157395 37161183 5.53000647 0.00410395

37187773 37191580 5.65172598 0.00093715

37212576 37216405 4.03696126 0.0009367

37245306 37249188 6.70840535 0.00035383

37247247 37251133 6.7562262 0.00031408

37376917 37381656 6.39608048 0.00034011

37430352 37435904 -6.2005704 0.00083817

37456313 37462375 -6.1134865 0.00932299

37539859 37548012 -5.5442088 0.00334075

37592907 37602638 6.7819362 0.0009367

37634048 37645088 -4.2859502 0.00785999

37674649 37687034 -5.2094426 0.00642581

37865474 37884227 7.43317842 0.00011951

38182100 38205843 6.36973826 0.00315036

38426343 38451967 -6.075009 0.00175456

38439155 38464954 -4.9213404 0.00473672

38601369 38630247 -5.4469672 0.00837855

38790145 38825524 -4.5339137 0.00832257

38844384 38882175 -4.3926606 0.0076947

39379992 39431602 -6.0661361 0.00202892

39959632 40013021 7.35958173 0.00038437

40383991 40437879 -5.9831322 0.00186227

40521163 40578110 -6.1239067 0.00160141

40549637 40607654 -7.4662199 3.51E-05

41082235 41192923 5.30969732 0.00720145

41960980 42110232 5.70041547 0.00511626

336


Start End

42327921 42467118 -5.3236343 0.0072539

44313418 44387118 7.77763181 3.70E-06

44588018 44672916 -4.6105679 0.00815263

45203237 45280637 5.66002205 0.00410491

Appendix XIV Table 2: Differential near-bait interactions in STAG2 72 h PMA cells compared to STAG2 12 h DMSO K562 cells


Start End

35436685 35448601 -6.0857456 0.00059104

35442448 35454754 -6.2731354 0.00225731

35448407 35461102 -5.3307194 0.00758347

35460911 35474374 -5.4099975 0.00396781

35474191 35488395 -6.6574472 0.00021416

35481117 35495673 -6.5801675 0.00070013

35488228 35503117 -7.5036046 0.00015947

35510591 35526316 -5.794641 0.00682029

35758987 35768060 -5.2165839 0.00436163

35820274 35829140 3.92392103 0.00762037

35903813 35913177 -5.522952 0.00277216

35917872 35926974 -5.7170437 0.00574955

35922480 35931468 -6.113011 0.00110525

36018477 36023451 -5.7857828 0.00392621

36021017 36025886 -6.7927842 0.00064853

36039518 36043657 -6.1093502 0.00451839

36053474 36057119 5.74975949 0.00063965

36067476 36070695 8.09423417 3.10E-05

36073869 36076922 5.75049924 0.00332039

36078448 36081394 6.09584481 0.00765344

36097958 36100545 -5.8280405 0.00249966

337


Start End

36106893 36109371 8.88047564 6.43E-07

36108138 36110604 8.41167605 7.90E-06

36111836 36114270 5.79605247 0.0008147

36113058 36115482 4.50555696 0.00858253

36114274 36116689 5.70466809 0.00362316

36117896 36120289 4.91532515 0.00054761

36119096 36121482 5.09296712 0.0005213

36120292 36122672 8.16806929 9.78E-08

36121485 36123860 3.52660994 0.00623036

36123862 36126228 6.83826906 0.00081453

36125047 36127409 7.17074017 0.00050799

36157963 36160322 4.70290259 0.00407907

36178044 36180403 -6.5938745 0.00186164

36182762 36185114 5.77498867 0.00250278

36183939 36186289 3.78948919 0.0093649

36209319 36211537 6.65970543 0.00332039

36212646 36214832 5.17999525 0.00343304

36213745 36215920 4.60367943 0.00623036

36215926 36218078 -6.2287521 0.00162394

36243806 36245595 6.03604449 0.00077209

36253381 36255044 6.4051837 4.04E-05

36255049 36256690 5.40118095 5.45E-05

36255874 36257506 6.13577976 4.78E-06

36257511 36259122 -6.071433 0.0005773

36261516 36263082 -5.5745924 0.00440467

36266175 36267691 -5.693073 0.00186164

36266936 36268445 -5.8929383 0.00092378

36269199 36270685 -4.7459043 0.00396781

36271428 36272893 7.39629365 1.32E-05

36278644 36280049 8.47626445 2.67E-07

36279349 36280749 5.88273561 0.00229365

338


Start End

36280751 36282141 9.32271791 9.28E-11

36281449 36282833 9.35755075 5.57E-11

36282143 36283523 6.57986117 0.00012543

36282836 36284211 6.59308126 7.17E-05

36288291 36289632 8.41409446 2.34E-07

36290969 36292295 4.93437188 0.00015032

36293617 36294930 8.87598018 5.57E-11

36294275 36295584 8.73704516 2.01E-08

36296239 36297539 -4.9893355 0.00789699

36296890 36298187 -4.8207939 0.00789699

36314565 36315788 7.49896603 7.50E-08

36316400 36317616 5.93787377 0.00130598

36317009 36318223 7.64886122 5.92E-06

36317617 36318829 8.6586326 7.12E-07

36321841 36323037 2.85880135 0.00530882

36322440 36323634 4.47423164 0.00624517

36326010 36327189 6.48630118 0.00040072

36331867 36333024 6.10489364 0.00279432

36333602 36334752 5.22177302 0.00589405

36335327 36336470 8.61424774 3.23E-07

36335900 36337041 8.53667475 1.96E-10

36337042 36338179 8.08807553 0.00012543

36337611 36338746 5.3259128 0.00297428

36339878 36341004 6.67090109 5.25E-06

36340442 36341565 4.41760746 0.00624517

36341005 36342126 5.29524101 0.00458437

36341566 36342686 5.04193019 0.00472812

36343803 36344915 6.01427329 0.00340964

36346578 36347680 6.91611197 3.18E-07

36347130 36348230 6.51125992 5.99E-06

36348231 36349329 7.41012673 6.09E-07

339


Start End

36348781 36349876 7.07821381 8.52E-06

36353148 36354232 8.71656398 3.22E-10

36353690 36354773 6.2181543 0.00160582

36357470 36358544 7.66678156 1.93E-08

36358008 36359081 -5.798246 0.00022941

36359081 36360153 5.45257467 2.37E-05

36361223 36362291 5.91568037 0.00057599

36361757 36362824 5.96298918 0.00024763

36368674 36369735 6.1916237 2.00E-05

36369205 36370266 7.16297674 2.76E-05

36370796 36371857 7.9559838 4.25E-08

36371327 36372388 7.95799941 3.91E-08

36372388 36373449 -5.0616276 0.00231887

36375572 36376634 7.39662037 4.22E-08

36376103 36377166 7.9155326 4.23E-10

36376634 36377698 7.20737095 3.50E-07

36377166 36378230 6.00247042 9.26E-05

36381430 36382501 6.25125031 0.00167117

36383036 36384111 7.58060482 7.62E-09

36383573 36384648 8.19239228 9.17E-11

36384110 36385187 7.27158077 1.01E-07

36388977 36390069 8.04050851 4.58E-08

36389522 36390615 8.18851773 9.50E-09

36402372 36403521 -3.3526677 0.00615

36402947 36404099 -6.4052802 5.56E-05

36404680 36405841 7.07708111 6.41E-07

36405261 36406426 7.67635857 1.65E-08

36407013 36408189 -6.0414894 0.00012123

36409375 36410564 8.16295853 2.63E-07

36409969 36411162 8.58969704 1.89E-08

36412365 36413572 6.67171768 0.00045194

340


Start End

36412969 36414179 7.48261385 3.87E-05

36414790 36416010 5.19805826 0.00169526

36416012 36417240 6.47370689 4.25E-05

36416626 36417857 8.32922355 1.96E-10

36417242 36418476 8.93449959 3.22E-10

36417859 36419097 8.70673298 4.23E-10

36425395 36426671 6.48656042 0.0001889

36426033 36427312 3.57574031 0.00163044

36426673 36427955 5.26153509 7.12E-07

36427314 36428599 5.10900159 0.00187829

36427956 36429244 7.04581518 1.63E-06

36428600 36429891 7.15848213 1.01E-07

36429892 36431189 -5.9821829 0.00141003

36438413 36439740 5.99517147 0.00297428

36439076 36440405 6.23445205 0.00130598

36446428 36447774 -5.7569719 0.00197578

36447101 36448448 4.89204718 1.31E-05

36447775 36449123 8.79337306 7.24E-10

36455222 36456582 5.4545644 0.00504532

36460677 36462045 -7.0332119 0.0002511

36469616 36471000 6.74924793 1.63E-06

36470308 36471694 7.98815314 2.01E-08

36471001 36472388 8.00746203 7.12E-07

36474478 36475874 9.60736861 1.17E-13

36475176 36476573 5.68780928 0.0007157

36481504 36482922 -5.8163104 0.00162394

36484347 36485775 8.59528198 1.52E-10

36485061 36486492 3.8841857 0.00132208

36493739 36495204 -6.1089012 0.00074837

36507148 36508663 5.26009344 0.00630545

36507905 36509423 7.34675317 4.36E-05

341


Start End

36513242 36514775 -5.3173362 0.00395153

36516314 36517855 -5.5017113 0.00467387

36520951 36522503 -5.0549633 0.00674828

36525620 36527182 -5.9381324 0.00050799

36529533 36531103 -5.2801418 0.00162394

36537423 36539010 -5.510789 0.00049537

36538217 36539806 -5.2311225 0.00605158

36547820 36549434 6.90041921 2.56E-05

36548627 36550243 8.56415831 3.08E-09

36549435 36551053 8.4001037 3.10E-05

36551865 36553489 4.07906958 0.00112378

36564157 36565814 -5.3641166 0.00220175

36564985 36566644 -5.5645765 0.00050915

36566645 36568309 8.87079792 3.00E-06

36567477 36569143 8.44738811 3.57E-06

36571653 36573330 -5.0946442 0.00277375

36575015 36576703 2.89255552 0.00485284

36575859 36577549 9.23549274 3.18E-11

36576704 36578397 3.36271086 0.00769729

36580949 36582655 3.66835445 0.00352294

36581802 36583511 4.64640089 0.00160582

36598256 36600012 4.60675253 0.00126352

36599134 36600892 9.27003353 3.57E-05

36609742 36611522 -5.0781173 0.00030496

36612414 36614199 -6.4072496 0.00079017

36613306 36615093 9.02751731 5.57E-11

36615989 36617781 -5.1702582 0.00498024

36617782 36619578 5.98150753 0.00140614

36624993 36626806 4.22364429 0.00234029

36628625 36630448 -7.2845038 0.00034806

36629536 36631361 -7.3250371 0.00023556

342


Start End

36631362 36633192 -5.386783 0.00028369

36632277 36634110 -5.2554689 0.00229365

36635950 36637793 3.72955132 0.00432525

36644289 36646155 -5.4874679 0.00201106

36645222 36647089 -5.7016239 0.00455515

36663073 36664956 -6.1681355 0.00220175

36678070 36679930 -5.9626629 0.00093922

36679928 36681783 -6.3506823 6.52E-05

36680855 36682707 -6.4303147 0.0002511

36681781 36683630 -5.8688247 0.00575549

36692776 36694589 7.82142793 5.81E-05

36693683 36695492 -4.2375019 0.0092254

36700879 36702664 4.90280398 0.00861558

36710630 36712389 7.35556765 0.00067607

36711509 36713267 7.26403527 0.00171581

36721128 36722869 9.0055117 2.52E-06

36734160 36735898 -4.5910204 0.00809055

36749016 36750781 -6.8938244 5.59E-05

36755229 36757019 4.44296764 0.00348182

36756124 36757918 8.00306112 1.79E-05

36760629 36762445 -6.8698039 0.00022941

36761537 36763358 -7.1788108 2.34E-07

36764276 36766112 5.80709303 0.00407907

36769828 36771700 6.11400191 0.00186684

36770764 36772642 5.66022215 0.00272098

36774541 36776448 4.68783611 0.00156114

36785244 36787241 -5.7724043 0.00021416

36793363 36795435 -6.8419092 3.79E-06

36794399 36796481 -6.1091525 0.00200109

36801784 36803931 5.77163822 0.00433589

36803935 36806100 5.34462276 0.00182957

343


Start End

36809388 36811594 8.14513954 6.21E-05

36810491 36812704 4.52197815 0.00407907

36811597 36813817 -4.3276266 0.00634438

36829685 36831979 -7.1834139 5.23E-05

36838884 36841188 -5.5325229 0.00652333

36845802 36848109 10.2038466 5.57E-11

36846955 36849263 2.65570785 0.00758347

36863120 36865432 -6.7298954 0.00057648

36867743 36870055 5.59197015 0.00506037

36876991 36879304 -6.1757115 0.00017592

36880462 36882777 -6.1215596 0.00056329

36902651 36905026 -6.0752014 0.00498024

36912244 36914677 -6.6823463 0.00103827

36915906 36918366 -5.6577655 0.00187829

36947079 36949855 5.96104556 0.00452362

36948467 36951259 6.78019674 0.0004733

36958414 36961325 -5.2806253 0.00051959

36962808 36965773 5.01921642 0.00058573

36964290 36967274 5.59505943 0.00045611

36973386 36976488 7.34270572 6.52E-05

36991078 36994449 -6.363725 0.00112452

36992763 36996164 -5.6514451 0.0034993

37008653 37012362 -5.7851037 0.0020801

37043150 37047640 -6.4945969 0.00013199

37045395 37049936 -4.1547449 0.00943078

37097715 37103034 -4.5778842 0.00451839

37105704 37111038 -4.6751958 0.00650289

37134665 37139771 6.31946179 0.00049537

37187383 37191422 4.83341234 0.00257763

37212613 37216380 3.51274477 0.00163945

37229550 37233329 6.17766054 0.00200687

344


Start End

37246671 37250505 6.06968467 0.00092378

37256273 37260121 -6.2127862 0.00179831

37360992 37364988 4.59646079 0.00856585

37376844 37380751 5.86772378 0.00112378

37380743 37384617 -5.7182386 0.00630545

Three-dimensional organisation of the Runx1 locus

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Transcript of Three-dimensional organisation of the Runx1 locus