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Functional genomics of the VNN1 gene
Luke Aris Diepeveen
BSc, Hons (Genetics)
School of Chemistry and Biochemistry
University of Western Australia
Supervisors:
W/Prof Lawrence Abraham
A/Prof Daniela Ulgiati
This thesis is presented for the degree of Doctor of Philosophy at the University of
Western Australia
July 2013
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Declaration I declare that this thesis describes my own research and work not previously submitted
at any other university.
Luke Aris Diepeveen
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Abstract
Chromatin accessibility is considered an excellent measure of the functionality of
genetic variants (Mikkelson et al., 2007; Birney et al., 2010; Pang et al., 2013). It is
common to use reporter gene and gel shift assays for determining the functionality of
genetic variants, however, both approaches are limited by scalability and are lacking in
chromatin context. The present study uses a novel next-gen sequencing-based approach
(‘CHA-seq’) which translates Chromatin Accessibility by Real-Time PCR (CHART-
PCR) into a robust method in order to assess allele-specific differences in chromatin
arrangement across an extended genomic region. CHA-seq is a means of simultaneously
sequencing genetic variants, mapping nucleosomes and nuclease hypersensitivity sites,
and assessing SNP functionality.
Genetic variants within the VNN1 gene have been associated with cardiovascular
disease (Jacobo-Albavera et al., 2012; Fava et al., 2010; Zhu and Cooper, 2007), and as
such, VNN1 represents an ideal gene model for functional assessment. Göring et al.
(2007) identified two candidate functional SNPs, namely -137 (rs4897612) and -587
(rs2050153), in the VNN1 gene promoter that have been experimentally validated using
chromatin arrangement assays as part of this current research. Genotyped cell lines
derived from the San Antonio Family Heart Study (SAFHS) (Göring et al., 2007),
carrying different -137 and -587 alleles, were characterised for allele-dependent
differences in chromatin arrangement as an indicator of function. The CHART-PCR and
CHA-seq results showed allele-specific chromatin accessibility at the -137 and the -587
variants. Further CHA-seq analysis across an extended genomic interval revealed
several candidate functional genetic variants, including the rs2267951 SNP in intron 2
of VNN1. Experimental validation using EMSA showed allele-specific DNA-complexes
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at the rs2267951 locus, therefore supporting the CHA-seq results. Of the genetic
variants selected for functional assessment, both CHA-seq and other experimental
validation assays have given an equivalent measure of function in VNN1.
Bioinformatic analysis of the VNN1 promoter showed the presence of a CpG island, of
which one CpG site was disrupted by the -587A allele. Bisulfite sequencing showed
allele-specific DNA methylation patterns at the -720 CpG site which were dependent on
the allele at the -587 locus. Therefore, this current study provides evidence that allele-
specific chromatin differences can be seen at sites remote from the actual genetic variation.
The development of enhanced genome-wide in vivo approaches such as CHA-seq
allows for a more cost and time-effective assessment of whether a variant does indeed
regulate chromatin arrangement and gene expression, yielding insights into the long
range allele-specific mechanisms of action.
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List of Publications Curran, J., Jowett, J., Abraham, L., Diepeveen, L.A., Elliott, K., Dyer, T., Kerr- Bayles, L., Johnson, M., Comuzzie, A., Moses, E.K, et al. (2009). "Genetic variation in PARL influences mitochondrial content." Human Genetics 127(2): 183–190.
Kaskow, B.J., Diepeveen, L.A., Proffitt, J.M., Rea, A.J., Ulgiati, D., Blangero, J., Moses, E.K., Abraham, L.J. (2013). “Molecular prioritisation strategies to identify functional genetic variants in the cardiovascular disease-associated expression QTL Vanin-1.” European Journal of Human Genetics doi:10.1038/ejhg.2013.208
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Table of Contents Declaration ........................................................................................................................ 2 Abstract ............................................................................................................................. 3 List of Publications ........................................................................................................... 5 Table of Contents .............................................................................................................. 6 List of Figures and Tables ................................................................................................. 9 Abbreviations .................................................................................................................. 11 Acknowledgements ......................................................................................................... 13 Chapter 1: Literature Review ...................................................................................... 15
1.1 Human genetic variation ....................................................................................... 15 1.2 Classes of human genetic variation ....................................................................... 18 1.3 Complex diseases and genetic association studies ................................................ 21
1.3.1 Candidate gene approach ............................................................................... 24 1.3.2 Genome-wide association studies (GWAS) ................................................... 27
1.4 Experimental validation of candidate genetic variation ........................................ 31 1.4.1 Reporter gene assays ...................................................................................... 34 1.4.2 Electrophoretic Mobility Shift Assay............................................................. 36 1.4.3 Chromatin Immunoprecipitation .................................................................... 38 1.4.4 CHART-PCR ................................................................................................. 40 1.4.5 Bisulfite sequencing ....................................................................................... 43 1.4.6 Genome-wide functional assays ..................................................................... 47 1.4.7 Bioinformatic analyses ................................................................................... 49
1.5 VNN1 – A gene model for novel experimental validation assays and cardiovascular disease ................................................................................................. 51
1.5.1 VNN1 – The novel cardiovascular risk-gene .................................................. 52 1.5.2 Gene models in cardiovascular disease – case studies ................................... 53
1.6 Research justification and PhD aims..................................................................... 57 Chapter 2: General methods and materials................................................................ 61
2.1 General reagents, biochemicals, buffers and solutions ......................................... 61 2.2 Bacterial Methods ................................................................................................. 62
2.2.1 Bacterial culture medium ............................................................................... 62 2.2.2 Bacterial culturing conditions ........................................................................ 63 2.2.3 TOPO cloning in bacteria............................................................................... 64 2.2.4 Transformation of Bacteria and blue-white colony selection ........................ 64
2.3 Eukaryotic Methods .............................................................................................. 65 2.3.1 Eukaryotic cells and culturing ........................................................................ 65 2.3.2 Eukaryotic cell counting with Trypan Blue staining ..................................... 65 2.3.3 Cyropreservation storage and revival of eukaryotic cell lines ....................... 66
2.4 Oligonucleotides ................................................................................................... 66 2.5 Isolation and purification of plasmid and eukaryotic DNA .................................. 69
2.5.1 Mini-prep purification of plasmid DNA ........................................................ 69 2.5.2 Isolation and purification of DNA from eukaryotic cells .............................. 69
2.6 Analysis of nucleic acids ....................................................................................... 70 2.6.1 Nano-drop analysis of nucleic acids .............................................................. 70 2.6.2 Gel Electrophoresis ........................................................................................ 70
2.7 Polymerase Chain Reaction (PCR) ....................................................................... 71 2.8 Sanger DNA Sequencing ...................................................................................... 71
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2.9 Experimental validation methods for SNP functionality ...................................... 72 2.9.1 Chromatin Accessibility by Real-Time PCR (CHART-PCR) ....................... 72
2.9.1.1 Nuclei isolation ....................................................................................... 72 2.9.1.2 Nuclease digestion of chromatin ............................................................. 72 2.9.1.3 Collection of DNA fragments ................................................................. 73 2.9.1.4 qPCR and analysis .................................................................................. 73
2.9.2 Electrophoretic Mobility Shift Assay (EMSA) .............................................. 74 2.9.2.1 Nuclear protein extraction ....................................................................... 74 2.9.2.2 Binding reaction preparation ................................................................... 75 2.9.2.3 Gel electrophoresis and detection ........................................................... 76
2.9.3 Bisulfite Sequencing ...................................................................................... 77 2.9.3.1 Bisulfite treatment ................................................................................... 78 2.9.3.2 PCR amplification ................................................................................... 78 2.9.3.3 Cloning and transformation..................................................................... 78 2.9.3.4 DNA sequencing and analysis ................................................................ 79
2.10 Targeted-CHA-seq (T-CHA-seq)........................................................................ 79 2.10.1 T-CHA-seq library preparation .................................................................... 80
2.10.1.1 Isolation of nuclei from cell lines.......................................................... 80 2.10.1.2 Digesting DNA with chromatin accessibility agent .............................. 81 2.10.1.3 NEBNext DNA Sample Prep kit processing ......................................... 81
2.10.2 PCR amplification ........................................................................................ 82 2.10.3 TOPO cloning and transformation ............................................................... 84 2.10.4 Sanger DNA sequencing .............................................................................. 84 2.10.5 Analysis and bioinformatics ......................................................................... 84
2.11 Extended CHA-seq (CHA-seq) ........................................................................... 85 2.11.1 Library preparation....................................................................................... 86 2.11.2 Bead selection .............................................................................................. 86 2.11.3 Cluster generation and deep sequencing ...................................................... 86 2.11.4 Analysis with IGV 2.1.................................................................................. 86
2.12 Statistical Analyses ............................................................................................. 87 Chapter 3: In vivo assessment of VNN1 promoter SNPs using two chromatin-
sensitive nucleases in CHART-PCR ............................................................................ 88 3.1 Introduction ........................................................................................................... 88 3.2 Methods ................................................................................................................. 91 3.3 Results ................................................................................................................... 93
3.3.1 Assessment of relative chromatin accessibility within the VNN1 gene promoter using DNase I .......................................................................................... 93
3.3.1.1 Chromatin accessibility of VNN1 SNPs relative to SPA-2 and β-actin control loci using DNase I................................................................................... 93 3.3.1.2 Assessment of allele-specific chromatin accessibility at VNN1 SNPs using DNase I ...................................................................................................... 95
3.3.2 Assessment of relative chromatin accessibility within the VNN1 gene promoter using MNase ............................................................................................ 97
3.3.2.1 Chromatin accessibility of VNN1 SNPs relative to SPA-2 and β-actin control loci using MNase .................................................................................... 97 3.3.2.2 Assessment of allele-specific chromatin accessibility at VNN1 SNPs using MNase ....................................................................................................... 99
3.4 Discussion ........................................................................................................... 102 Chapter 4: CHA-seq - A novel functional variant screen ....................................... 106
4.1 Introduction ......................................................................................................... 106
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4.2 CHA-seq method development ........................................................................... 110 4.2.1 Traditional chromatin arrangement assays – issues ..................................... 111 4.2.2 Targeted CHA-seq (T-CHA-seq) development ........................................... 113
4.2.2.1 T-CHA-seq library preparation – DNase I digestion optimisation ....... 113 4.2.2.2 T-CHA-seq library preparation – adapter-ligation optimisation ........... 115 4.2.2.3 PCR amplification - optimisation.......................................................... 117 4.2.2.4 T-CHA-seq methodology following PCR amplification ...................... 121
4.2.3 Extended CHA-seq method development .................................................... 121 4.2.3.1 Library preparation – MNase digestion optimization ........................... 122 4.2.3.2 CHA-seq methodology post-library preparation ................................... 124
4.3 Results ................................................................................................................... 124 4.3.1 Targeted CHA-seq (T-CHA-seq) ................................................................. 124 4.3.2 CHA-seq ....................................................................................................... 129
4.3.2.1 Allele-specific nuclease activity at the -587 SNP ................................. 134 4.3.2.2 Nucleosome mapping within the -587 VNN1 promoter region ............. 136
4.3.3 CHA-seq for the VNN2 and VNN3 genes ..................................................... 138 4.4 Discussion ........................................................................................................... 140
Chapter 5 – DNA methylation analysis of the -587 promoter region in the VNN1
gene ............................................................................................................................... 144 5.1 Introduction ......................................................................................................... 144 5.2 Materials and Methods ........................................................................................ 145 5.3 Results ................................................................................................................. 146
5.3.1 Predicted CpG sites in the -587 promoter region of the VNN1 gene ........... 146 5.3.2 Bisulfite sequencing of the -587 promoter region in the VNN1 gene and BISMA analysis .................................................................................................... 148 5.3.3 Bioinformatic analysis of the -720 CpG site in the VNN1 gene promoter ... 150
5.4 Discussion ........................................................................................................... 152 Chapter 6 – Experimental validation of candidate functional SNP using EMSA 155
6.1 Introduction ......................................................................................................... 155 6.2 Methods ............................................................................................................... 155 6.3 PARL Results ...................................................................................................... 158
6.3.1 EMSA and bioinformatic analysis of the -191 SNP in the PARL gene promoter ................................................................................................................ 158
6.4 VNN1 Results ...................................................................................................... 160 6.4.1 Bioinformatic analysis of the rs2267951 VNN1 SNP .................................. 160 6.4.2 EMSA analysis of the rs2267951 VNN1 SNP ............................................. 162
6.5 Discussion ........................................................................................................... 164 Chapter 7 – General discussion and future research directions ............................. 166
7.1 CHA-seq contribution to functional research ..................................................... 166 7.2 Contribution to VNN1 research ........................................................................... 168 7.3 Future research directions ................................................................................... 170
References ..................................................................................................................... 173 Appendix – Supplementary Tables and Figures ........................................................... 204
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List of Figures and Tables Table 1.1: Characteristics of traditional experimental validation assays for the assessment of candidate functional genetic variants ....................................................... 33 Figure 1.1 Principles of CHART-PCR............................................................................ 41 Figure 1.2 Principles of methylation analysis using bisulfite genomic sequencing ....... 44 Table 2.1: Primer design guidelines ................................................................................ 68 Table 2.2: PCR primers used in T-CHA-seq .................................................................. 83 Table 3.1: CHART-PCR primers .................................................................................... 92 Figure 3.1: Chromatin accessibility differences at the -137 and -587 VNN1 SNPs relative to SPA-2 and β-actin control regions ................................................................. 94 Figure 3.2: Chromatin accessibility differences at the -137 and -587 VNN1 SNPs ........ 96 Figure 3.3: Chromatin accessibility differences at the -137 and -587 VNN1 SNPs relative to SPA-2 and β-actin control regions ................................................................. 98 Figure 3.4: Chromatin accessibility differences at the -137 VNN1 SNP ...................... 100 Figure 3.5: Chromatin accessibility differences at the -587 VNN1 SNP ...................... 101 Figure 4.1 Principles of CHA-seq and T-CHA-seq. ..................................................... 109 Figure 4.2: Chromatin accessibility differences between replicates at the β-actin promoter using CHART-PCR ....................................................................................... 112 Figure 4.3: Electrophoretic patterns of DNase 1-digested chromatin ........................... 114 Figure 4.4: Adapter ligation to DNase I-digested chromatin ........................................ 116 Figure 4.5: T-CHA-seq primer positions in the -137 VNN1 promoter region .............. 118 Figure 4.6: T-CHA-seq ‘forward’ PCR amplification of DNase I library fragments ... 119 Figure 4.7: T-CHA-seq ‘reverse’ PCR amplification of DNase I library fragments .... 120 Figure 4.8: Electrophoretic patterns of MNase-digested chromatin ............................. 123 Figure 4.9: Assessment of the chromatin arrangement across the VNN1 promoter using T-CHA-seq .................................................................................................................... 126 Figure 4.10: Assessment of the chromatin arrangement across the VNN1 promoter using T-CHA-seq .................................................................................................................... 128 Table 4.1: Heterozygous VNN1 SNPs captured in CHA-seq. ....................................... 131 Figure 4.11: Distribution of heterozygous SNPs captured in CHA-seq across the VNN1 gene relative to allele skew ........................................................................................... 133 Figure 4.12: Assessment of the allele-specific chromatin accessibility across the -587 VNN1 promoter region using CHA-seq ........................................................................ 135 Figure 4.13: Assessment of the chromatin arrangement across the -587 VNN1 promoter region using CHA-seq ................................................................................................... 137 Table 4.2: Heterozygous VNN2 and VNN3 SNPs captured in CHA-seq ...................... 139 Figure 5.1: BISMA prediction of VNN1 CpG island length ......................................... 147 Figure 5.2: Methylation of the -587 SNP ...................................................................... 149 Figure 5.3: Bioinformatic analysis of -720 CpG site in the VNN1 gene promoter ....... 151 Table 6.1: EMSA oligonucleotides for PARL and VNN1 SNPs ................................... 157 Figure 6.1: EMSA for the -191 PARL SNP .................................................................. 159 Figure 6.2: Bioinformatic analysis of the rs2267951 SNP in the VNN1 gene .............. 161 Figure 6.3: EMSA for the rs2267951 VNN1 SNP relative to the -191 PARL SNP ...... 163
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Table A.1: Description of general biochemicals and reagents ...................................... 204 Table A.2: Distributers for laboratory equipment used in research .............................. 207 Table A.3: Distributers for general laboratory disposables used in this research ......... 208 Table A.4: DNase I cutting at the -137 and -587 VNN1 gene promoter SNPs, with SPA-
2 and β-actin control loci in SAFHS cell 0054 ............................................................. 209 Table A.5: DNase I cutting at the -137 and -587 VNN1 gene promoter SNPs in homozygous SAFHS cell lines ..................................................................................... 210 Table A.6: MNase cutting at the -137 and -587 VNN1 gene promoter SNPs, with SPA-2 and β-actin control loci in heterozygous Ramos cell line ............................................. 211 Table A.7: MNase cutting at the -137 VNN1 gene promoter SNP in different SAFHS cell lines ........................................................................................................................ 212 Table A.8: MNase cutting at the -587 VNN1 gene promoter SNP in different SAFHS cell lines ........................................................................................................................ 213
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Abbreviations Abbreviation Description
º Degrees % Percentage ANOVA Analysis of Variance ATCC American Type Culture Collection BISMA Bisulfite Sequencing DNA Methylation Analysis bp Base pair BP Blood pressure BSA Bovine Serum Albumin C Celsius CD Crohn’s disease C/EBP CCAAT/enhancer binding protein CHART-PCR Chromatin accessibility by Real-Time PCR CHA-seq Chromatin accessibility by sequencing CHD Coronary heart disease ChIP Chromatin Immunoprecipitation cm Centimeters cM Centi-morgan CO2 Carbon dioxide CR Complement Receptor CVCD Common Variant, Common Disease CVD Cardiovascular disease ddH2O Double deionised water DMSO Dimethyl sulphoxide DNA Deoxyribonucleic acid DNase I Deoxyribonuclease I ds Double-stranded DTT Dithiothreitol EBV Epstein-Barr Virus E.coli Escherichia coli
EDTA Ethylenediaminetetraacetic acid EGTA Ethylene glycol tetraacetic acid EMSA Electrophoretic Mobility Shift Assay eQTL Expression Quantitative Trait Loci FBG Fasting blood glucose FBS Fetal Bovine Serum FH Familial hypercholesterolemia g Gram GWAS Genome-wide Association Study HCl Hydrochloric acid HDL-C High-density lipoprotein cholesterol HEPES Hydroxyethyl piperazineethanesulfonic acid Hg Mercury HGP Human Genome Project HLA Human leukocyte antigen IGV Integrative Genomics Viewer indel Insertion or deletion kb Kilobase pairs
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L Litre(s) LB Luria-Bertani LD Linkage disequilibrium LDL Low-density lipoprotein m Milli (1 x 10-3) M Molar Mb Megabase pairs min Minute(s) MNase Micrococcal nuclease mRNA Messenger RNA NaOH Sodium Hydroxide nm Nanometers oligo Oligonucleotide PARL Presenilin Associated Rhomboid-Like PBS Phosphate Buffered Saline PCR Polymerase Chain Reaction PMSF Phenylmethylsulphonylfluoride Poly dI:dC Poly (deoxyinosinic-deoxycytidylic) acid PSI pounds per square inch qPCR Quantitative real-time PCR QTL Quantitative Trait Loci RA Rheumatoid arthritis RNA Ribonucleic acid rpm Revolutions per minute RVCD Rare Variant, Common Disease SAFHS San Antonio Family Heart Study SD Standard deviation SDS Sodium dodecyl sulphate seq Sequencing SNP Single Nucleotide Polymorphism Sp Specificity protein SPA-2 Surfactant protein 2 T1D Type 1 diabetes TAE Tris-acetate-EDTA T-CHA-seq Targeted chromatin accessibility by sequencing TE Tris-EDTA TF Transcription factor TNF Tumour Necrosis Factor Tris 2-amino-2-hydroxymethyl-1,3-propanediol U International enzyme units µ Micro (1 x 10-6) UV Ultraviolet V Volts VNN Vanin v/v Volume per volume w/w Weight per weight w/v Weight per volume X Times
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Acknowledgements
First and foremost I would like to thank my supervisors W/Prof Lawrie Abraham and
A/Prof Daniela Ulgiati. It has been an honour to be one of your PhD students. You each
have taught me, both consciously and un-consciously, how to become a good scientist. I
appreciate all the contributions of time, ideas, and funding to make my PhD experience
productive and enriching.
Importantly, I wish to thank the University of Western Australia, the School of
Chemistry and Biochemistry and the Scholarships department for all of the funding,
education and opportunities I have been given. I must also recognise the amazing
opportunity I had to travel to the Texas Biomedical Research Institute. I am fortunate
for the time I was able to spend there, for the use of the deep sequencing facilities, and
for the cell-lines that were provided for this project. I must sincerely thank all concerned
for this opportunity, especially my supervisor Lawrie.
I must express my thanks the members of the Abraham/Ulgiati group have contributed
immensely to my personal and professional time at the University of Western Australia.
The group has been a source of friendships as well as good advice and collaboration. I
am grateful to all members including Des, Hui Min, Mahdad, Mark, Tim, Nick,
Michelle, Freya and others.
My time at the University of Western Australia was made enjoyable in large part due to
the many friends and groups that became a part of my life. I am appreciative of the
support from members of the School of Chemistry and Biochemistry including Amber,
Yat-zu, and Steve, Paul, Adam and Nehal. Thank you.
In particular, I would like to personally address the contributions of certain individuals.
Firstly Belinda, you have been a constant source of joy and inspiration. I am thankful
for all of your kind words and guidance. You are a true friend.
Alex, you have been both a friend and a role model. I could not have asked for a better
role model who is inspirational, supportive, and thoughtful.
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Rhonda, your determination and passion was a source of inspiration for me. When times
were tough, you always managed to help me through.
Josh and Robert, you are both such amazing people. I know you both will achieve so
much in science, and life.
George, I have always enjoyed discussing our joint love of tennis and the West Coast
Eagles. More importantly, I am forever grateful for the opportunity you have given me
to work in your laboratory during the final months of writing my thesis. I also must
thank the other members of your group, Adam, Sarah, Robyn, Connie, Ken, Anne,
Aurelian, Megan, Ros, Karen and Bernard for making my time in the Yeoh laboratory
so fruitful and enriching.
Liz, you are an inspiration to me as both a scientist and person. You are both kind and
caring, and I am so grateful for your guidance. You have given me so much, and I am
truly grateful.
I would have never achieved what I have in life without the love and support of my
family. Mum, Dad, Lara, Pippa, you are my inspiration. Thank you for everything and
for always being there for me.
To my dearest wife, Becky, you are my world. I dedicate this PhD to you, because
without you I have nothing. I was so lucky to have found you during the course of this
PhD and I cannot wait for our future together.
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Chapter 1: Literature Review
1.1 Human genetic variation
The last decade has seen significant effort committed to cataloguing human genetic
variants and their association with phenotypic differences or disease (Frazer et al., 2009;
Bodmer and Bonilla, 2008; Mackay et al., 2009). Since the completion of the Human
Genome project (HGP) in 2001, most common variants (minor allele frequency > 1%)
have been identified and assessed in genetic studies for statistical association with
complex traits and diseases (Collins et al., 2003; Bulik-Sullivan et al., 2012; Kathiresan
and Srivastava, 2012). Although genetic studies in humans have identified potential
disease-risks and novel biological insights, limited progress has been made to identify
the heritable component(s) of any complex human trait (Frazer et al., 2009).
Characterising and cataloguing genetic variation will assist in understanding the
functional mechanism behind which associated variants lead to certain phenotypic traits
or disease. Dissection of such mechanisms will enable the prioritization of candidate
genetic variants for further investigation (Stower, 2013).
One of the first large-scale studies to characterise human genetic variation was the
‘International HapMap Project’ (The International HapMap Consortium, 2003; The
International HapMap Consortium, 2004). This study aimed to characterize the
influence of human genetic variation on phenotype with a focus on understanding the
range of patterns and frequencies of genetic variants (Frazer et al., 2009). The major
objective of the HapMap project was to genotype at least one common variant every
five kilobases across the actively transcribed regions of the human genome (The
International HapMap Consortium, 2007). Between the HapMap project launch in 2002
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and the phase II publication of results in 2007, more than 3.1 million single nucleotide
polymorphisms (SNPs) were catalogued in 270 individuals from four geographically
distinct populations (The International HapMap Consortium, 2007). More recently,
HapMap phase III results provide genotype data on approximately 1.5 million SNPs
from 1397 individuals in 11 populations (Pemberton et al., 2010). With the aid of
newer, high-throughput DNA sequencing technologies, over 10 million SNPs have now
been catalogued and are publically available online through the HapMap project
database (http://hapmap.ncbi.nlm.nih.gov/).
Ancestral and de novo mutations, in addition to the admixture generated by DNA
recombination, are the primary sources of genetic variation in a given population
(Jenkins et al., 2012; Peter et al., 2012). In humans, the contribution of de novo
mutation and recombination has been estimated on average to be 60 novel
mutations/variants per generation (Conrad et al., 2011), with an average rate of 0.5 ×
10−9 mutations base-pair−1 year−1 over the past million years (Scally and Durbin, 2012).
This estimate, however, suggests a mutation rate roughly half that of previously derived
measurements from fossil calibration and suggests a higher-than-previously-thought
contribution of DNA recombination to evolution (Scally and Durbin, 2012). Human de
novo mutation is not only an important substrate for selection and evolution (Lynch,
2010), but also to fitness through the generation of deleterious alleles as demonstrated
by Kong et al. (2012). Kong and co-workers determined that a father’s age at
conception was associated with an increase (two additional mutations per year) in de
novo mutation rate of SNPs, and subsequently increased disease-risk of schizophrenia
and autism in an Icelandic population.
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Typically, DNA recombination occurs within 1-2 kb genomic ‘hotspots’. More than
30,000 hotspots have been identified in humans, suggesting that they are a ubiquitous
feature of the human genome (Myers et al., 2008; Myers et al., 2006). For instance,
known recombination hotspots have been reported within the human Prdm9 gene
(Baudat et al., 2010), the TAP2 gene (Jeffreys et al., 2000), the RNF212 gene (Kong et
al., 2002) and within the chromosome region 11p15.5 (Sandovici et al., 2006). The
average rate of recombination per generation in humans is 1.13 cM/Mb, however rates
vary significantly (0.1-3 cM/Mb) across the genome (Auton and McVean, 2012).
Variation in DNA recombination rate can be attributed to factors such as age and sex
(Hussin et al., 2011), genetic variation (Fledel-Alon et al., 2011), and chromosome size
(Farré et al., 2012). Wang et al. (2012) compared DNA recombination and de novo
mutation rates and found a recombination rate of approximately 23 events per cell,
whereas the aneuploidy rate ranged from 2-10% in sperm.
Gene expression is the mechanism by which genetic information is used in the synthesis
of a functional product such as RNAs and protein (Cambien and Tiret, 2007). The
functional impact of most genetic variants is hypothesised to be neutral (i.e. no
observable phenotypic impact), with many variants achieving high frequency in human
populations by chance (Powell, 2012; Cambien and Tiret, 2007). However, subsets of
genetic variants occurring within regulatory or coding regions can influence gene
expression levels or alter the amino acid sequence of proteins. This can result in
observable differences in phenotype (Cambien and Tiret, 2007). For example, Love-
Gregory et al. (2011) identified four metabolic syndrome risk-SNPs (rs1761667,
rs3211909, rs3211913, rs3211938) located within the CD36 gene which resulted in
reduced CD36 protein expression. These SNPs were also found to be associated with
levels of free fatty acids and high-density lipoprotein (HDL). More recently, Keenan et
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al. (2012) reported that a coding SNP (rs4844609) in the CR1 gene, which correlated
with a Ser1610Thr amino acid change, was associated with episodic memory decline in
Alzheimer’s disease. Keenan and co-workers suggested that the rs4844609 SNP alters
the conformation of the functional domain of the CR1 protein, leading to the
development of Alzheimer’s symptoms. Given that ‘functional’ genetic variants can
interrupt or alter cellular mechanisms, understanding the role of genetic variation is
fundamental to disease studies in determining how a particular disease or phenotype
arises (Girirajan et al., 2011).
1.2 Classes of human genetic variation
Human genetic variants are termed as either common or rare based on the frequency of
the minor allele in a population, and is divided into two classes based on nucleotide
composition; single nucleotide variants and structural variants (Eichler et al., 2007;
Redon et al., 2006; Altshuler et al., 2010). Structural variations primarily consist of
insertions and deletions (‘indels’); in addition to copy number variations and
retrotransposons (Alkan et al., 2011; Muers, 2010; Mills et al., 2011). Single variants
account for approximately 90% of all genetic variation and occur on average every 100-
300 base pairs (Thomas et al., 2011).
The ‘1000 Genomes Project’ was established to catalogue all classes of human genetic
variation (Altshuler et al., 2010; Abecasis et al., 2012). This project consisted of
sequencing 1092 genomes using a low-coverage (2-6 fold) whole-genome
combinatorial approach. This approach consisted of targeted (50-100 fold) exonic and
dense SNP genotype data in conjunction with bioinformatic methods for selecting high
resolution variant calls. The 1000 genome project generated a haplotype map of 38
million SNPs, 1.4 million short insertions and deletions and more than 14,000 large
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deletions (Altshuler et al., 2010; Abecasis et al., 2012). Ultimately, this project has
created the most detailed public catalogue of human genetic variation and is publically
available online (http://www.1000genomes.org/).
Common genetic variants in human populations are the focus of most current research
studies that target phenotypic traits (Marian and Belmont, 2011). Genetic variants
associated with a measurable allele-specific influence on the function of a gene product,
gene expression, or phenotype are considered to be functional (Wang et al., 2011;
Mottagui-Tabar et al., 2005; Knight, 2005; Jian et al., 2011). Functional genetic
variants that contribute to phenotypic differences amongst individuals have been
associated with cell function (Bonetti et al., 2012; Johnson et al., 2012; Collins et al.,
1997; Syvanen et al., 1999) and disease (Bartoszewski et al., 2010; Zhang et al., 2011;
Pratt and Dzau, 1999; Wang and Moult, 2001). Ultimately, the position of genetic
variants within a gene directly influences the potential functional impact on the gene
(Guo and Jamison, 2005). For example, SNPs occurring within regulatory regions,
especially near the transcription start site, can influence gene expression levels and
promote alternative splicing (Pickrell et al., 2010). Interestingly, genetic variants
located within regulatory regions that are distal to a particular gene, such as enhancer
elements, can also influence gene expression (Chavan et al., 2010; Steidl et al., 2007).
In contrast, SNPs within coding regions can alter the structure and activity of the
encoded functional products which may include both RNAs and proteins (Nackley et
al., 2006; Hoffmeyer et al., 2000). For instance, SNPs within exons of protein-coding
genes can alter the subsequent protein structure and activity as shown by Schaeffeler et
al. (2006) in the TPMT gene and Diogo et al. (2013) in the IL2RA, IL2RB and CD2
genes.
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In contrast to single nucleotide variants, structural variants can have a more significant
influence on phenotype, often resulting in clinical manifestations such as Down and
Turner syndromes (Feuk and Carson, 2006; Lee and Scherer, 2010), and Angelman and
Prader–Willi syndromes (Sanders et al., 2011). The molecular consequences of
structural variants are locus-dependent, with variants occurring within coding regions
resulting in gene fusions or truncations. Alternatively, variants within regulatory regions
can lead to alterations in gene expression levels (Weischenfeldt et al., 2013). For
example, overlapping 1.5-3Mb deletions within the chromosome 22q11 band
containing genes such as TBX1, COMT, and CRKL, can lead to DiGeorge syndrome and
velocardio facial syndrome due to haploinsufficiency of a number of encoded proteins
(Maynard et al., 2002; Weischenfeldt et al., 2013).
In response to research evidence suggesting that most candidate functional variants are
located within regulatory rather than coding regions, a public research consortium
designated as the ‘ENCODE’ project was launched in 2003 to improve the functional
annotation of genetic variation in the human genome (Birney et al., 2007; Rosenbloom
et al., 2012). The objective of the ENCODE research was to identify and catalogue all
functional elements and variants in human and other mammalian genomes (Birney et
al., 2007; Rosenbloom et al., 2012). As of January 2012, 27 biochemical assays
(including RNA-seq, ChIP-seq, and DNA methylation assays) had been submitted. This
data covered 200 distinct human cell types and is available for public use
(http://genome.ucsc.edu). While the ENCODE project has generated significant
amounts of novel information, a detailed understanding of the default function and
interactions of regulatory variants in the phenotype of different cells and tissues remains
incomplete (Rosenbloom et al., 2012).
21
1.3 Complex diseases and genetic association studies
Complex diseases, as opposed to ‘simple’ single-gene Mendelian disorders, are
typically caused by combinations of genetic variants and/or mutations that
combinatorially result in dysregulation of components of a cellular system or pathway
(Cho et al., 2012). Complex diseases such as cancer, cardiovascular disease and
diabetes are associated with the effects of multiple genes in combination with
environmental and lifestyle factors (Erler and Linding, 2012; Kathiresan and Srivastava,
2012; Kodama et al., 2012). Consequently, most complex diseases are heterogeneous
given the unique genetic variation that exists between individuals and the vast array of
external environmental factors (Cho et al., 2012).
Fundamental to medical research is the identification of functional genetic variants and
the characterisation of the mechanisms through which they influence the phenotype of
complex diseases (Stranger et al., 2011). Thus far, genetic variation has been associated
with the disease phenotypes in terms of predisposition for disease initiation, progression
and severity (Duff, 2006; Knowles and Drumm, 2012). Two common hypotheses, the
‘common variant-common disease’ (CVCD) and the ‘rare-variant-common disease’
(RVCD) are used to describe the genetic contribution of individual susceptibility to
complex diseases (Schork et al., 2009). The CVCD hypothesis suggests that a collection
of common alleles (alleles with a population frequency of ≥0.05) increase the risk of
common disease onset and progression (Marian and Belmont, 2011). This hypothesis
suggests that common genetic variants confer a modest cumulative and interactive
effect in complex disease phenotype such as diabetes or cardiovascular disease (Liu and
Song, 2010). The RVCD hypothesis, on the other hand, suggests that variants with
lower-frequency (alleles with a population frequency of <0.005) and higher-penetrance
significantly contribute to disease phenotype (Bodmer and Bonilla 2008). For example,
22
a number of rare genetic variants have been demonstrated to contribute to diseases such
as hypertrophic cardiomyopathy (Marian, 2010) susceptibility and thus allow enhanced
detection of the diseases.
Coding variants were the initial targets of complex trait research (Bodmer and Bonilla,
2008; Majewski and Pastinen, 2011; Bostein and Risch, 2003), with casual rare variants
associated with common complex disorders often through amino acid substitution
(Ladouceur et al., 2012; Cohen et al., 2004). There is increasing evidence supporting the
RVCD hypothesis, with rare coding variants associated with common human disorders
including breast cancer (Schork et al., 2009), type I diabetes (Nejentsev et al., 2009),
hypertension (Ji et al., 2008), and cholesterol disorders associated with sterol absorption
and plasma LDL-cholesterol levels (Cohen et al., 2004; Cohen et al., 2006). For
example, Goldgar et al. (2011) identified a number of truncating variants, and a rare
SNP (‘c.7271T>G’) in the ATM gene that were associated with a significantly increased
risk of breast cancer. The penetrance of these variants was similar to that conferred by
mutations in the known breast cancer susceptibility gene BRCA2. While a focus of
complex disease research has been to identify susceptibility variants, recent studies have
identified rare genetic variants that have a ‘protective’ role in complex traits (Dai et al.,
2012). For instance, multiple rare variants have been shown to act protectively against
type I diabetes (Nejentsev et al., 2009) and psoriasis (Li et al., 2010).
The CVCD hypothesis proposes that common diseases are typically correlated with
multiple susceptibility alleles, with early supporting evidence from the ApoE ε4 allele
in Alzheimer's disease (Saunders et al. 1993; Genin et al., 2011) and a Factor V allele
(‘1691C�A’) in deep-vein thrombosis (Bertina et al. 1994; Knight, 2005). More
recently, common genetic variants have been identified in Crohn's disease (CD)
23
(Yamazaki et al., 2005; Mathew, 2008) and type 1 diabetes (Todd et al., 2007). In 2005,
Yamazakiet et al. identified a common intronic SNP (‘14,340T→C’) in the TNFSF15
gene, which encodes the tumour necrosis super-family member 15, which contributes to
CD susceptibility. A subsequent study by Duerr et al. (2006) screened a North-
American sample and reported strong association of multiple common SNPs
(rs2066843 and rs2076756) within a known CD susceptibility gene, CARD15. Common
variants associated with gene expression levels have also been linked to common
complex diseases such as breast cancer (Zhang et al., 2011). For instance, Zhang et al.
(2011) identified a common SNP (rs1044129) located in a micro-RNA binding site
(miR-367) in the 3’ untranslated region (3’UTR) of the RYR3 gene that was associated
with a higher risk of breast cancer. Zhang et al. (2011) reported that miR-367 had a
higher binding affinity (resulting in a decrease in gene expression) in the presence of the
major allele (A) compared to the minor allele (G). Genotypes in which the G allele was
present were associated with a higher risk for breast cancer development, calcification
and survival of the patient (Zhang et al., 2011).
In order to identify the genetic component of human diseases, early ‘family studies’
were performed to trace the distribution of traits from parental generations to progeny
and involved the determination of mathematical segregation ratios (Borecki and
Province, 2008). Family studies have been successful in the identification of genes
associated with monogenic diseases including Duchenne muscular dystrophy (Jones et
al., 2012) and Huntington disease (Anderson et al., 2004). However, this approach has
had limited success with complex traits and diseases. Complex diseases do not exhibit
patterns of Mendelian inheritance. Consequently, genetic association approaches are
used to identify genetic risk-variants in favour of family studies (Cambien and Tiret,
2007). Primarily through advancements in genotyping technologies, the focus of gene
24
mapping in humans has shifted towards the use of genetic association studies (Stranger
et al., 2011).
Genetic association studies incorporate large sets of SNPs and other markers located in
known candidate regions or genes to statistically determine possible causal variants that
show greater prevalence in case vs control samples (Doris, 2011). Genetic association is
dependent upon the existence of linkage disequilibrium (LD) between genetic variants
in close physical proximity within the genome Thus, the identification of possible
casual variants is possible due to the presence of LD with detected variants (Stranger et
al., 2011). Thus far, over 800 genotype/SNP-phenotype associations have been
identified from 600 published association studies covering 150 complex human diseases
(Bakir-Gungor and Sezerman, 2011). While association studies have provided great
insight into potential disease pathways, this success must be put into perspective by
considering inherent limitations (Hirschhorn et al., 2009; McCarthy et al., 2008). For
example, a review by Jakobsdottir et al. (2009) demonstrates that while genetic
associations are valuable for establishing research hypotheses, strong genetic
association does not guarantee the identification of causal genetic risk variation between
cases and controls. Ultimately, a more detailed background of the nature of the complex
disease and possible genomic regions of susceptibility and/or biochemical pathways is
required to further refine lists of potential disease-risk variants (Jostins and Barrett,
2011; Pociot et al., 2010).
1.3.1 Candidate gene approach
In order to identify and map novel genetic variation underlying human diseases or traits
in association studies, researchers utilise either candidate gene or genome-wide
approaches (Altshuler et al., 2008). Candidate gene studies target genetic variation
25
within specific genes of interest, with candidates generally selected based on prior
knowledge of the biological or functional effect of a gene in a particular phenotype or
disease (Zhu and Zhao, 2007). The key assumption in candidate gene approaches is that
a gene functionally associated with a disease-causing gene is likely to contribute to the
same or similar disease state (Ideker and Sharan, 2008; Goh et al. 2007).
Based on functional data in mice, Kurreeman et al. (2007) targeted complement
component 5 (C5) and TNF receptor-associated factor 1 (TRAF1) as candidate genes for
rheumatoid arthritis (RA). Kurreeman and co-workers identified the rs10818488 SNP to
be associated with a 6.1% increased risk of RA. The A allele of this SNP also found to
be correlated with increased RA disease progression. Candidate gene approaches have
also been used by Muise et al. (2012) to identify a novel missense variant (‘c.113
G→A’) in the neutrophil cytosolic factor 2 (NCF2) gene that increases the susceptibility
to and severity of inflammatory bowel disease. Muise and co-workers determined that
the c.113 G→A variant correlated with increased disease progression through reduced
binding of the NCF2 gene product, p67-phox, to the NADPH oxidase complex gene,
RAC2.
Typically, candidate gene studies have high statistical power, but the selection of
candidate genes is intrinsically subjective and often arbitrary (Moreau and Tranchevent,
2012). As such, traditional candidate gene studies are limited by a dependence on prior
knowledge of the biochemical, functional or physiological impact of possible
candidates, which is often incomplete or unknown (Zhu and Zhao, 2007). However,
recently computational strategies have been developed that integrate DNA sequence
data, gene expression information, functional characterisation and research literature to
26
allow improved candidate gene selection, as reviewed by Moreau and Tranchevent
(2012).
A review by Zhu and Zhao (2007) outlines three common approaches for improved
candidate gene selection in genetic association studies. The first approach, ‘position-
dependent’, integrates genome sequence information and candidate gene analyses to
identify candidate genes primarily based on the physical linkage within a chromosomal
locus previously associated with the trait of interest (Zhu and Zhao, 2007). The second
approach, outlined by Zhu and Zhao (2007), is ‘comparative genomics’. This approach
enables the identification and characterisation of putative candidates using a cross-
species approach in human-disease models. Thirdly, the ‘function-dependent’ strategy
incorporates analyses of expression of a gene associated with a particular trait at
different phenotypic stages, including signalling and regulatory pathways combined
with genome-wide transcriptional profiles, to prioritise candidate genes (Zhu and Zhao,
2007). Ultimately, combining these selection strategies together with the use of
bioinformatic tools can substantially reduces the number of candidate genes for further
analysis as reported by Moreau and Tranchevent (2012). For example, Rajab et al.
(2010) used a prioritisation tool, GeneDistiller, in combination with different candidate
gene approaches, to prioritize 74 genes from a 2 megabase region on chromosome 17
that was associated with cardiac arrythmias. Rajab and co-workers used this integrated
selection strategy to identify a mutation in the leading candidate gene PTRF, which
became the focus of subsequent research (Rajab et al., 2010).
Although the candidate gene approach is an effective and economical method for direct
gene discovery, the number of causative genetic faults that have been experimentally
validated is limited, thereby restricting the number candidate genes (Zhu and Zhao,
27
2007). Furthermore, the candidate gene approach has been criticized due to the non-
replication of results and the inability to include all potential causative genes and
polymorphisms, suggesting a large number of false-positive reports (Tabor et al., 2002;
Morgan et al., 2007; Pasche and Yi, 2011). Ultimately, the capabilities of traditional
candidate gene approaches are limited by the dependence on existing knowledge of the
underlying biology of a particular disease or phenotype (Zhu and Zhao, 2007). Given
that detailed characterisation of genes or variants underpinning most complex diseases
remains unknown, integrated selection strategies may represent the most effective
candidate gene approach in future research (Moreau and Tranchevent, 2012; Zhu and
Zhao, 2007).
1.3.2 Genome-wide association studies (GWAS)
With advances in high-throughput DNA sequencing technologies, genetic association
studies have extended trait mapping to a genome-wide scale, allowing the correlation of
allele frequencies of thousands of genetic variants with phenotypic variation in a
population (Stranger et al., 2011). Already with these advances, genome-wide
association studies (GWAS) have yielded evidence for the involvement of genetic
variants in complex human diseases including autism (Wang et al., 2009), asthma
(Sleiman et al., 2010), type 1-diabetes (Todd et al., 2007; Hakonarson et al., 2007), and
cancer (Tomlinson et al., 2012; Tao et al., 2012). GWA studies targeting human cancers
alone have yielded over 200 common low-penetrance susceptibility variants (Freedman
et al., 2011), including rs16892766 (Tomlinson et al., 2008) and rs3802842 (Tenesa et
al. 2008) in colorectal cancer, and rs1465618 and rs12621278 in prostate cancer (Eeles
et al., 2009).
28
One of the fundamental advantages of GWAS is that it does not require prior
knowledge of trait etiology and genomic structure, as opposed to the candidate gene
approach (Stranger et al., 2011). Consequently, the GWAS approach can identify causal
genes or variants that have not previously been associated with disease expression and
enable the characterisation of non-coding regions and pleiotropic genes in an unbiased
way (Gottesman et al., 2012). Essential to the GWAS approach is the necessity to
distinguish between the direct effects of functional variants themselves and indirect
associations due to LD in large sets of candidate variants generated in each study
(McCarthy et al., 2008; Teng et al., 2012). As such, the utilisation of bioinformatic and
computational approaches in GWAS are critical for the prioritisation of variants most
likely to contribute to the observed association and assists the selection of candidates for
subsequent experimental validation (Wang et al., 2011; Teng et al., 2012).
A number of bioinformatic tools such as TRAP (Thomas-Chollier et al., 2011),
BCRANK (Ameur et al., 2009), is-rSNP (Macintyre et al., 2010) and AASsites (Faber
et al., 2011), have been designed to prioritise candidate functional SNPs located within
regulatory regions such as transcription factor binding sites and exonic splice sites.
Furthermore, a number of systems for SNP prioritisation, as proposed by Lee and
Shatkay (2009) and Nica et al. (2010), combine multiple independent prediction tools to
enhance candidate SNP selection. For example, Nica et al. (2010) developed a novel
SNP prioritisation approach that accounts for local LD structure and integrates
quantitative trait loci (QTLs) and GWAS data to determine the specific association
indicators that arise from cis-acting QTLs. Nica et al. (2010) used their SNP
prioritisation approach to identify 35 candidate disease-associated SNPs from GWAS
and QTL datasets, of which the leading candidate disease-risk SNP, a non-synonymous
SNP (rs3764261) in the MT1H gene, was highly associated with high density
29
lipoprotein cholesterol (HDL-C). Previously, the MT1H gene has been associated with
inflammatory bowel diseases (Goyette et al., 2008).
In the last decade, GWAS have identified hundreds of genetic variants associated with
complex diseases such as systemic lupus erythematosus, multiple sclerosis, Crohn's
disease, rheumatoid arthritis and type 1 diabetes (T1D) (Hu and Daly, 2012; Hindorff et
al., 2009). In one such study, Shan et al. (2012) identified five out of nine previously
identified GWAS candidate loci to be significantly associated with breast cancer in a
Tunisian population. The five loci included the rs1219648 and rs2981582 SNPs from
the FGFR2 gene, rs8051542 in the TNRC9 gene, rs889312 of the MAP3K1 gene and the
rs13281615 SNP located on chromosome 8 (8q24). Shan and co-workers identified
homozygous rs2981582 genotypes to be strongly associated with lymph node negative
breast cancer, with the minor allele also linked to an increased risk of ER+ tumor
development and metastasis (Shan et al., 2012). GWA studies have also identified
candidate disease-risk SNPs in known T1D-susceptibility genes, such as HLA, INS,
PTPN22, IFIH1 and CTLA4 (Reddy et al., 2011). Moreover, genetic determinants
within the HLA class II genes are considered to account for almost 50% of T1D-risk
(Grant and Hakonarson, 2009).
GWAS have identified more than 2,000 complex trait-SNP associations, with numbers
increasing rapidly (Casto and Feldman, 2011). Regardless of the success as a screening
approach for identifying genetic variations involved in complex traits, GWAS research
based on SNP analysis are unable to identify causal genetic variants without
experimental validation (Li, M et al., 2011). Apart from the significant cost and
resources required of GWAS, a major criticism has been that the majority of the
candidate SNPs identified in such studies are only associated with minor increases in
30
disease susceptibility and therefore possess limited predictive value (Manolio, 2010). In
fact, the median odds ratio per candidate risk-SNP has been calculated as 1.33, which is
considered small given that it does not account for the significant proportion of the
heritable variation (Hindorff et al., 2009). Furthermore, without proper research design
it is possible for a number of variables to potentially confound GWAS results, including
sex, age and population stratification (Novembre et al., 2008). Confounding factors
from GWAS research design can also arise from lack of well-defined case and control
groups, controls for multiple testing and inadequate sample size (Pearson and Manolio,
2008). The resolution of GWAS is also the source of some debate, with typical screens
only able to identify partial chromosomal regions of QTLs at the mega-base level
through DNA markers which usually specify many candidate genes (Zhu and Zhao,
2007).
The results of GWAS can be considered as a source of preliminary genetic information
that serves as the basis for further analysis. Such further analysis is required to
accumulate evidence for subsequent prioritization and identification of strong candidate
variation for experimental validation (Cantor et al., 2010). Ultimately, the integration of
traditional and fine mapping data, cross-species resources, literature, bioinformatic
resources, and high-throughput sequence-based and gene expression data in GWA
studies will improve the validity of candidate variant lists (McCarthy et al., 2008; Li
and Patra, 2010). Although GWAS is an effective screening approach, experimental
validation is still required to determine actual functional variants within a prioritised
candidate gene or GWAS hits and reveal the functional mechanisms underlying disease
traits (Stranger et al., 2011)
31
1.4 Experimental validation of candidate genetic variation
Functional genomics, through experimental validation methods, attempts to elucidate
the molecular basis of biological functions by characterising the relative transcriptional
or functional potential of a genomic region (Werner, 2010). Reporter gene assays and
the Electrophoretic Mobility Shift Assay (EMSA) are benchmark tests for
experimentally validating candidate functional genetic variation within regulatory
regions as shown by Pang et al. (2013), Low et al. (2012), Knight (2005), Meckler et al.
(2013) and Lattka et al. (2010). Reporter gene assays measure transcript abundance and,
in turn, allow the elucidation of allele- or haplotype-specific effects on gene expression
levels (Cheng and Inglese, 2012; Liu et al., 2009; Seldon et al., 1986; Alam and Cook,
1990). Comparatively, EMSA can discriminate differences in binding of proteins such
as transcription factors at a particular locus, which may be caused by allelic differences
in the DNA (Albers et al., 2012; Hellman and Fried, 2007; Garner and Revzin, 1981).
Alternative experimental validation methods characterise genetic variants by assaying
for different mechanistic roles for functionality including chromatin arrangement
(Chromatin accessibility by Real-Time PCR) (Cruikshank et al., 2012; Rao et al., 2001)
and allele-specific FAIRE (Formaldehyde-Assisted Isolation of Regulatory Elements)
(Smith et al., 2012; Giresi et al., 2007) and chromatin conformation capture (Wang, S et
al., 2013), DNA methylation (bisulfite sequencing) (Shoemaker et al., 2010; Kagami et
al., 2010; Frommer et al., 1992), and in vivo DNA-protein interactions (Chromatin
Immunoprecipitation) (Johnson et al. 2007; Robertson et al., 2007; O’Neill and Turner,
1995). The results of experimental validation methods can also be complemented
through the use of bioinformatic analysis (Johnson, 2009; Schaefer et al., 2012) and
high-throughput DNA sequencing (Kingsley, 2011).
32
The development of post-association validation methods is required for characterizing
the function of trait- or disease-associated genetic variants which may influence disease
susceptibility (Gibson, 2012; Conde et al., 2013). Consequently, experimental
validation methodologies that integrate various biological data sets with genome-wide
and candidate gene results will help elucidate the mechanistic role of associated SNPs,
thus enabling the identification of causal disease variants for the development of
therapeutic interventions (Yanase et al., 2006; Kazani et al., 2010; Crews et al., 2012).
Several methodologies for experimental validation of genetic variant functionality are
currently available and assay for different mechanistic roles that lead to variant
functionality (Table 1.1). Combinations of various experimental validation methods
have been used to characterise the functional role of genetic variants in a range of
complex human diseases including schizophrenia (Li, M et al., 2011), breast cancer
(Wang et al., 2010), and type 2 diabetes (Pang et al., 2013).
33
Table 1.1: Characteristics of traditional experimental validation assays for the
assessment of candidate functional genetic variants
A summary of characteristics associated with current functional assays for assessing genetic variants, including: the functional mechanism assayed for (“Mechanism”), the type of assay (“Approach”), the issues and limitations associated with each assay (“Issues/Limitations”) and literature examples citing the use of the assay (“References”). Functional assay Mechanism Approach Issues/Limitations Reference(s)
Reporter gene assay
Relative transcript abundance
in vitro • Locus-specific
• Associated with minor fold changes in relative expression
• Confounding factors such as transfection efficiency and construct-concentration effects
Cheng and Inglese, (2012)
Liu et al. (2009)
Seldon et al. (1986)
Alam and Cook (1990)
Karimi et al. (2009)
EMSA DNA-protein binding interactions
in vitro • Locus-specific
• Target DNA sequence must be known prior to testing (oligo design)
• Inability to measure the co-operation of genomic sites that are essential for binding
Smith et al. (2010)
Carey et al. (2009)
Garner and Revzin (1981)
Knight (2005)
ChIP DNA-protein binding interactions
in vivo • Protein-specific
• Cumbersome (long)
• Requirement for significant numbers of cells
Wright et al. (2010)
O’Neill and Turner (1995)
Collas (2010)
CHART-PCR Chromatin accessibility
in vivo • Locus-specific
• Influenced by other SNPs and regulatory elements within the target region
• Target locus sequence complexity influences optimal amplification
• Methodological issues related to optimal nuclease digestion
Cruickshank et al. (2012)
Rao et al. (2001)
Cruickshank et al. (2009)
Bisulfite sequencing
Methylation patterns in vivo • Locus-specific
• Requirement for significant numbers of cells/chromatin due to harsh bisulfite treatment
• Target DNA sequence must be known prior to testing (oligo design)
Zhang, Y et al. (2009)
Shoemakers et al. (2010)
Frommer et al. (1992)
Grigg and Clark (1994)
Allele-specific FAIRE
Allelic effects on chromatin accessibility and regulatory potential
in vivo • Expensive
• Relies on the existence of a ChIP-grade antibody to recognise each DNA-bound transcription factor
Smith et al. (2012)
Giresi et al. (2007)
Chromatin conformation capture
Haplotype effects on chromatin conformation
in vivo • Detection of a product does not always mean a specific interaction has occurred between two regions
• Requirement of a great number of cells
Wang, S et al. (2013)
34
Published genetic association studies can identify candidate functional genetic variants
within gene promoters. Further studies have identified allele-specific transcription factor
binding patterns that lead to altered gene expression. This represents one possible
mechanism for genetic variants contributing to human disease (Butter et al., 2012). For
example, Li, M et al. (2011) identified a schizophrenia risk SNP (rs3755557) in the
GSK3β gene promoter that alters transcription factor binding affinities, resulting in a
higher promoter activity when the risk allele (A) is present as determined by reporter
gene and gel shift assays in multiple cell lines. More recently, McManus et al. (2012)
found that a hypertension risk-SNP (-1651T/C) in the CYP11B2 gene promoter
influenced binding of the transcriptional repressor APEX1, with the -1651T allele
associated with increased transcriptional activity in comparison to the -1651C allele,
where the repressor binds with decreased affinity.
1.4.1 Reporter gene assays
Reporter gene assays use allelic regulatory regions, such as gene promoters, to drive the
expression of a reporter gene (Karimi et al., 2009). Reporter gene expression can be
assayed so as to detect relative differences in transcript abundance as a result of
particular promoter variants in a cell line following transfection (Cheng and Inglese,
2012; Liu et al., 2009; Seldon et al., 1986; Alam and Cook, 1990). Allelic reporter
constructs that significantly differ in relative transcript abundance relative to controls
are considered to contain functional genetic variants (Karimi et al., 2009).
The reporter gene assay was originally designed to define the major transcriptional
elements that have an effect on basal and/or inducible gene expression, but has been
refined for various applications (Karimi et al., 2009). Further adjustments to the initial
35
methodology (Seldon et al., 1986; Alam and Cook, 1990) have been made, including
alternate methods of transfection and transformation, and the use of inducible
promoters. For example, Karimi et al. (2009) reports the use of a stable transfection
method that utilises FLP-recombinase to functionally assess the G-308A polymorphism
in the TNF gene promoter. Furthermore, the integration of a droplet-based micro-fluidic
system has been used to perform a quantitative reporter gene assay, and has increased
the resolution to the single cell level, allowing accurate measurement of the expression-
response profile for a nuclear receptor agonist; hormone 20E (Baret et al., 2010).
Reporter gene assays have been used to characterise the role of disease-risk variants in a
variety of human diseases, including cardiovascular diseases (Huang et al., 2007), acute
lung injury (Marzec et al., 2007), and viral infections such as hepatitis C (He et al.,
2009). For instance, Huang et al. (2007) identified a functional SNP (-764G/C) in the
IFN-γ promoter region that conferred a two- to three-fold higher promoter activity in the
presence of the G allele relative to C using reporter gene assays. Huang and co-workers
suggested that this SNP is functionally important in determining viral clearance and
treatment response in hepatitis C virus-infected patients. Alternatively, Marzec et al.
(2007) also used reporter gene assays to identify a promoter SNP (-617C/A) in the
NRF2 gene that is associated with reduced luciferase activity and is suggested to be
associated with acute lung injury. In cardiovascular disease research, He et al. (2009)
observed a 14-45% reduction in luciferase reporter expression in the presence of the
+190C coronary heart disease risk-allele (rs1043618) in the HSPA1A gene, relative to
the +190G allele in cell culture.
Although reporter gene assays are considered benchmark experimental tests for
function, typical reporter gene approaches are associated with inherent limitations that
36
confound functional assessment (Karimi et al., 2009). For instance, Karimi et al. (2009)
reviewed factors associated with transient reporter gene assays that confound the
assessment of allelic differences by testing a SNP (-308G/A) in the TNF gene promoter
identified variables that influenced allele-specific TNF transcript abundance included:
the method of transfection, host cell growth history, the amount (and quality) of DNA
and control vector used in transfection for normalisation purposes. An early study by
Smith and Hager (1997) also reported that aberrant transcriptional activity in reporter
gene assays can be influenced by the lack of appropriate chromatin arrangement.
Furthermore, in vitro gene expression, as measured by reporter gene assays, does not
necessarily correlate with in vivo gene expression (Makar et al., 2001). Makar et al.
(2001) determined that an intronic silencing region (CRS) in the human CR2 gene did
not silence transcription using in vitro reporter gene assays, but was functional upon in
vivo testing. Makar and co-workers found that the CRS region was functional only in in
vivo assays where the construct was stably integrated into cells to provide the
appropriate chromatin context.
1.4.2 Electrophoretic Mobility Shift Assay
Electrophoretic Mobility Shift Assay (EMSA) is an in vitro gel separation technique
based on electrophoretic migration of DNA-protein complexes (Hellman and Fried,
2007; Garner and Revzin, 1981). EMSA can differentiate between differences in
binding of proteins to regions of DNA which differ by as little as a single nucleotide
(SNPs). Furthermore, EMSA can be used to determine whether more than one protein
can bind to the region, as demonstrated in the KRT1 (Tao et al., 2006), ADRB2 (Naka et
al., 2012) and TNFSF4 gene promoters (Ria et al., 2011). Although variations in EMSA
methodology have been described in the literature, typically functional genetic variants
are identified by comparing migratory differences of a control binding reaction (with no
37
protein added) relative to allele-specific (or, gene-specific) DNA-protein reactions
(Hellman and Fried, 2007).
EMSA has been used to characterise the role of functional variants in human disease
studies including variants associated with increased risk of cardiovascular disease
(Göring et al., 2007; Musunuru et al., 2010). For example, Göring et al. (2007) used
EMSA to identify an additional DNA-protein binding complex in the presence of the -
137T allele within the promoter region of the cardiovascular risk-gene VNN1 that was
not present with the -137G allele. Bioinformatic analysis using the online software P-
Match (Chekmenev et al., 2005) supported the EMSA results and predicted binding of
the transcription factor (TF) Sp1 in the presence of the -137T allele (Göring et al.,
2007). Another study by Curran et al. (2009) used EMSA to identify allele-specific
binding of proteins at the -191T/C SNP in the promoter of the type 2 diabetes risk-gene
PARL. Bioinformatic analysis using P-Match (Chekmenev et al., 2005) supported the
EMSA findings and revealed that the -191T allele is embedded in a consensus Sp1
binding site whereas the -191C SNP ablated the core sequence. More recently,
Musunuru et al. (2010) used EMSA to identify an allele-specific CCAAT/enhancer
binding protein (C/EBP) transcription factor binding site, localized around the
rs12740374 SNP, which influences the hepatic expression of the SORT1 gene.
Supershift EMSA extends the capabilities of traditional EMSA methodology by using
antibodies specific to target proteins to confirm the identity of DNA-binding proteins
(Carey et al., 2009; Leong et al., 2003). For instance, Neumann et al. (2000) used
supershift EMSA to confirm the identity of 2 major DNA-protein binding complexes
observed in the κB gene within mature dendritic cells. Neumann and co-workers
identified p65/RelA-containing heterodimers (complex 1) and RelB-containing
38
heterodimers (complex 2) binding to oligonucleotides specific for the κB gene in mature
dendritic cell nuclear extract, indicating Rel-directed regulation of the gene encoding
the κB transcription factor. More recently, Smith et al. (2010) identified a novel
candidate functional SNP (rs327) in the LPL gene using supershift EMSA, which
confirmed the in vitro binding of the FOXA2 transcription factor to the T allele-
containing locus.
A recent review by Holden and Tacon (2011) cites a number of confounding factors in
EMSA, such as nuclear extract purity and the requirement of post-translational
modifications that influence in vitro DNA-protein interactions. Furthermore, Dekker
and Haisma (2009) have identified that histone acetyltransferase activation may be
required for the binding of particular proteins to genomic DNA, thereby limiting the
potential pool of candidate regulatory proteins assessed. A study by Hager et al. (2009)
describes a further limitation associated with EMSA whereby structural changes in
chromatin, which facilitate DNA-protein binding, are not taken into account in EMSA.
1.4.3 Chromatin Immunoprecipitation
Chromatin profiling represents an effective means of identifying functional disease-risk
genetic variants and for the detection of regulatory activity associated with chromatin
context (Ernst et al., 2011; Coetzee et al., 2010). The annotation of chromatin provides
a reflection of the transcriptional potential of a genomic region or of a genetic variant,
as chromatin plays a central role in mediating regulatory signals and controlling access
of transcription factors and other elements to the DNA (Ernst et al., 2011). The
characterisation of regulatory regions, in terms of histone modifications and
transcription factor-occupied regions, can be assessed through the direct (DNA-protein
binding) interaction of selected transcription factors with candidate genetic variants or
39
haplotypes (Freedman et al., 2011; Hon et al., 2009). For example, chromatin
immunoprecipitation (ChIP) is an in vivo experimental technique consisting of the
selective immunoprecipitation of a protein of interest from a chromatin preparation to
determine the associated DNA sequences (Collas, 2010; O’Neill and Turner, 1995).
Since initial publications in 1995 (O’Neill and Turner, 1995), the ChIP methodology
has been refined in order to target genetic variants for functional analysis, through the
comparison of chromatin derived from cell lines containing genetic variants of interest
(Wright et al., 2010). For example, Wright et al. (2010) used ChIP to identify allele-
specific enrichment of the TCF4 and beta-catenin proteins to a 300 bp region centred
around the rs6983267 SNP in familial colorectal cancer (CRC) cell lines. Wright and
co-workers found the ChIP DNA samples analysed using the TCF4 and beta-catenin
antibodies showed enrichment with the cancer-associated rs6983267G allele in contrast
to the T allele. ChIP has also been used by Menendez et al. (2006) to identify allele-
specific enrichment of the p53 transcription factor in the FLT-1 promoter encompassing
a SNP (‘C519T’). Menendez and co-workers identified p53 enrichment in the presence
of the T allele and determined an association between the observed allele-specific p53
interaction and angiogenesis-associated diseases, including cancer (Menendez et al.,
2006).
Despite proving useful in disease research, ChIP is associated with a number of inherent
limitations, relating to low signal strength (relative to controls) and resolution, that may
lead to inconclusive results (Collas, 2010; Carey et al., 2009). Carey and co-workers
also report that the use of ChIP cannot solely demonstrate the functionality of a protein
at a genomic region of interest (Carey et al., 2009). Experimentally, targeted ChIP
approaches can also be cumbersome and require significant numbers of cells (Collas,
40
2010), especially when targeting SNPs rather than the traditional target of haplotypes
(i.e. haploChIP') (Ameur et al., 2009; Hultman et al., 2010). Adding to the complexity,
the functional assessment of SNPs using ChIP requires the assessment of multiple
regions around and including a SNP in order to validate the influence of specific SNPs
(Charles, 2005).
1.4.4 CHART-PCR
Chromatin accessibility by Real-Time PCR (CHART-PCR) is an in vivo assay (Figure
1.1) that is able to map chromatin accessibility across a gene and regulatory sequences
(Rao et al., 2001; Cruickshank et al., 2012). CHART-PCR assigns a relative value of
accessibility of DNA binding proteins to a specific region of DNA and is an indication
of the chromatin arrangement and transcriptional activity at that site (Rao et al., 2001;
Hager et al., 1993; Poke et al., 2012). CHART-PCR is based on the concept that
localized ‘open’ chromatin conformation confers greater sensitivity to nuclease
digestion. The degree of sensitivity (or ‘chromatin accessibility’) can be amplified and
measured by quantitative real-time PCR (qPCR) (Mummidi et al., 2007).
41
Figure 1.1 Principles of CHART-PCR. CHART-PCR consists of firstly the isolation of intact nuclei. Following isolation and washing, nuclei are incubated with or without nuclease (such as micrococcal nuclease or deoxyribonuclease 1) giving “undigested” and “digested” samples. Accessibility reactions are subsequently terminated and genomic DNA recovered and prepared for analysis by quantitative real-time PCR in order to quantify target regions. Comparing the quantity of targeted genomic sequences in digested relative to undigested samples provides a measure of chromatin accessibility and expressed as a percentage of nuclease cutting across the region specified by the primers. Figure derived from www.epigentek.com
42
Mikkelson et al. (2007) and Birney et al. (2010) found that chromatin state can be read
in an allele-specific manner using SNPs, which can influence the localised chromatin
context and accessibility within genes. Moreover, Pang et al. (2013) reported that allele-
specific chromatin accessibility differences can influence gene expression, phenotype,
and functionality. CHART-PCR has been used to characterise functional chromatin
accessibility in genes including CYP19 (Fürbass et al., 2008), IL-2 (Rao et al., 2001),
and E-selectin (Edelstein et al., 2005). More recently, CHART-PCR has been refined in
order to differentiate between regions of DNA which differ by as little as a single SNP,
as shown in the CR2 gene (Cruickshank et al., 2012). Cruickshank and co-workers used
CHART-PCR to identify a systemic lupus erythematosus (SLE) risk-SNP (rs3813946)
in the 5′ untranslated region (UTR) of the CR2 gene that resulted in allele-specific
MNase digestion and transcription factor binding within the SNP-containing region.
These data confirmed the functional effects of rs3813946 on CR2 transcription
(Cruickshank et al., 2012), which had been previously found a decrease in
transcriptional activity of CR2 in the presence of the C allele relative to the T allele (Wu
et al., 2007).
While chromatin accessibility plays an essential role in transcriptional regulation,
CHART-PCR as an assay has limited potential due to locus-specific effects, with
confounding variability resulting from other SNPs and regulatory elements within the
target region (Cruickshank et al., 2009). CHART-PCR can also be considered as a low
throughput assay, with inconsistencies arising when comparing multiple cell lines due
to fluctuations in the degree of nuclease digestion (Karimi, 2007). Other sources of
potential variation can be attributed to quantitative real-time PCR (qPCR)
inconsistencies from: improper primer design (in low complexity regions), DNA hetero-
43
duplex formation, mis-priming and primer dimerisation (Freeman et al., 1999; Bustin et
al., 2005; and Git et al., 2010).
1.4.5 Bisulfite sequencing
DNA methylation is an epigenetic mechanism that involves the addition of a methyl
group to particular cytosine residues in the DNA. This process can drastically affect
transcriptional regulation during development, with dysregulation of this mechanism
associated with diseases such as type 2 diabetes (Volkmar et al., 2012) and cancer
(Deng et al., 2009; Suzuki et al., 2012). Typically, DNA methylation is associated with
reduced gene expression either directly, through reducing the accessibility of
transcription factors, or indirectly, by enhancing the formation of heterochromatin (Caro
et al., 2012; Watt and Molloy, 1988; Boyes et al., 1991). The process is reversible by
de-methylation of methylated residues (Holliday et al., 1991). Bisulfite sequencing
enables the identification of the methylation status of cystosines in targeted genomic
regions and is dependent on the action of bisulfite (Figure 1.2). Bisulfite preferentially
deaminates unmethylated cytosines, which are converted to uracil upon desulfonation,
while methylated cytosines are unaffected due to the presence of the methyl
modification (Benoukraf et al., 2013; Frommer et al., 1992). The amplification and
subsequent sequencing of target regions from bisulfite-treated DNA permits the
characterisation of the methylation status at single-nucleotide resolution (Deng et al.,
2009).
44
Figure 1.2 Principles of methylation analysis using bisulfite genomic sequencing. After treatment with sodium bisulfite, unmethylated cytosine residues are converted to uracil whereas 5-methylcytosine (5mC) remains unaffected. After PCR amplification, uracil residues are converted to thymine. DNA methylation status can be determined by direct PCR sequencing or cloning sequencing. Figure and legend obtained form Li and Tollefsbol (2011).
45
Bisulfite sequencing is particularly useful for the characterisation of CpG islands, as
increased numbers of CG dinucleotides in associated primers increase the specificity of
targeted amplification when performing PCR as they allow a tighter interaction with the
target region (Rauch et al., 2005). CpG islands are found within, or in close proximity
to, more than 70% of mammalian gene promoters (Hodges et al., 2009; Saxonov et al.,
2006). They can influence the chromatin conformation and subsequently affect the
transcriptional potential of a gene, depending on the methylation status of CG
dinucleotides within the island (Bird, 2002; Goll and Bestor, 2005). Robertson (2005)
reviewed the effects of irregular DNA methylation and aberrant regulation of CpG
island methylation status and noted associations with human diseases, including cancers
(van Rijnsoever et al., 2002; Dong et al., 2005) and cardiovascular disease (Dong et al.,
2002). Genetic variants located in CpG sites can greatly influence DNA methylation
and therefore affect chromatin conformation and accessibility. As such, allele-specific
differences in chromatin accessibility can often be detected and indicate SNP
functionality (Mikkelsen et al., 2007; Shoemakers et al., 2010). For example, Zhang, Y
et al. (2009) used bisulfite sequencing to assess DNA methylation of CpG islands on
chromosome 21 in leukocytes and identified significant allele-specific methylation due
to candidate functional SNPs in three chromosomal regions, including the promoter of
the CBR1 gene (dbSNPs rs41563015 and rs25678). Zhang and co-workers found that
DNA methylation varied by as much as 85% between alleles and was associated with
mono-allelic gene silencing of the methylated allele (Zhang, Y et al., 2009).
A review by Tycko (2010) describes two mechanistic models describing the role of
genetic variants situated within CpG islands. The first model suggests that variants can
either create or delete CpG sites, which in turn influence the propensity of adjacent non-
polymorphic CpG sites to become cytosine-methylated (Tycko, 2010). The alternative
46
model describes that a portion of CpG-associated genetic variants could modulate the
binding of transcription factors in either a positive or negative manner (Tycko, 2010).
For example, Boumber et al. (2008) found a 12 bp indel polymorphism in a Sp1 GTF-
binding site in a CpG island within the RIL gene promoter that influences the propensity
of the gene to become methylated in leukaemias.
Zhang and Jeltsch (2010) review sources of potential variability associated with the
bisulfite sequencing approach, including: clonal DNA amplification, incomplete
bisulfite conversion, non-CpG methylation and insufficient sequence depth. Recent
advances in high-throughput sequencing technologies, however, may overcome the lack
of sequencing depth required to generate conclusive evidence and also allows the
assessment of multiple genomic regions (Statham et al., 2012). For instance,
Shoemakers et al. (2010) used high-throughput bisulfite sequencing to identify
heterozygous SNPs in CpG sites (‘CpG-SNPs’) that disrupt DNA methylation.
Shoemakers and co-workers identified that allele-specific methylation (ASM) was
present on 23-37% of heterozygous SNPs in cell culture, with a significant proportion
of ASM (38-88%) influenced by SNPs disrupting potential CpG sites. In a related study,
Gertz et al. (2011) also used high-throughput bisulfite sequencing to identify allele-
specific DNA methylation on the X-chromosome, and found allele-specific methylation
at SNPs across six individuals of a three-generation family. Gertz and co-workers found
that 80% of the variation in DNA methylation was dependent on sequence. Historically,
technology has limited genome-wide scale approaches in epigenomics, but the
emergence of high-throughput sequencing technologies will allow the determination of
the global allele-specific methylation status (Callinan and Feinberg, 2006).
47
1.4.6 Genome-wide functional assays
The last decade has seen significant advances over the traditional Sanger et al. (1977)
chain-termination DNA sequencing approach, with current high-throughput (‘deep’ or
‘next-gen’) technologies capable of re-sequencing the human genome in approximately
a week using a single instrument (Ståhl and Lundeberg, 2012; Metzker, 2010; Shendure
and Ji 2008). Since the completion of the HGP, research has focussed on identifying the
functions of particular DNA sequences at a genome-wide level. Combining new high-
throughput sequencing with other techniques provides a more complete picture of how
the genome and various regulatory mechanisms result in particular biological functions
(Wu et al., 2006). Recent genome-wide functional assays that utilise high-throughput
sequencing technologies have enabled the direct evaluation of the role of genetic
variants in gene expression and function (Pastinen, 2010).
Deep sequencing technologies have enabled functional assays such as ChIP, to be
extended to a genome-wide scale (Collas, 2010; Barski et al., 2007; Statham et al.,
2012). A review by Collas (2010) provides a detailed evaluation of the existing
integrated approaches that combine deep sequencing and ChIP methodologies (‘ChIP-
seq’) for the profiling of histone modifications, histone variants, and transcription factor
occupancy of the DNA. For example, ChIP-seq was used initially by Barski et al.
(2007) to characterise histone modifications associated with H2A.Z, RNA polymerase
II, and the insulator binding protein CTCF in the human genome. More recently,
Statham et al. (2012) developed an enhanced ChIP-seq methodology for directly
determining the methylation status of histone-modified DNA through the combination
of high-throughput bisulfite sequencing and ChIP (‘BisChIP-seq’). Statham and co-
workers used BisChIP-seq to identify allele-specific methylation associated with
H3K27me3 histones in normal and prostate cancer cells (Statham et al., 2012).
48
Allele-specific gene expression has been widely studied, with an estimated 20% of
human polymorphic sites showing 1.5-fold expression differences (Zhang, K et al.,
2009), and 30% of human transcripts showing a 1.2-fold difference between alleles (Ge
et al., 2009). Thus far, the annotation of human genetic variation associated with gene
expression has centred on expression quantitative trait loci (eQTL) mapping. However,
next-gen sequencing technologies have enabled the development of genome-wide
functional assays targeting allele-specific gene expression (or ‘cis-regulatory
variation’), DNA–protein interactions and chromatin configuration (Pastinen, 2010;
Cookson et al., 2009). For example, early studies characterizing allelic-specific gene
expression utilised deep sequencing to target the human transcriptome (‘RNA-seq’), as
shown by Montgomery et al. (2010) and Pickrell et al. (2010). RNA-seq is dependent
upon the correlation between sequence reads associated with polymorphic sites and
RNA abundance, which enables the detection of genetic variants that show potentially
skewed expression (Pastinen, 2010). For instance, Pickrell et al. (2010) used RNA-seq
to identify 138 SNPs that were associated with eQTLs, including one SNP (rs7639979)
showing allele-specific expression (GG>GA>AA) in the TSP50 gene in Nigerian
lymphoblastoid cell lines.
While genome-wide functional assays have overcome some limitations related to
traditional assays, such as locus-specificity, large-scale approaches involving
comprehensive genome coverage are associated with both false positives and false
negatives (Mardis and Wilson, 2009; Grunenfelder and Winzler, 2002; Huttlin et al.,
2007). Furthermore, the quality and resolution of datasets from seemingly related
studies can vary considerably (von Mering et al., 2002). In order to resolve the effects
of confounding factors, the results from such genome-wide functional experiments must
49
be validated by performing further experiments (Martin and Drubin, 2003; Mattocks et
al., 2010).
A current focus in functional genomics is the elucidation of causal genetic variants that
are essential for cis-regulatory control of gene expression, with association studies
implicating a functional role of these genetic variants in complex traits and disease
(Pastinen, 2010). Given that the genes and variants associated with complex traits can
be diverse, the integration of multiple whole-genome approaches including GWAS and
genome-wide functional assays such as ChIP-seq and RNA-seq, are essential for
characterising the genetic basis of complex traits (Hawkins et al., 2010; Cowie et al.,
2006). This integrated approach enables the functional characterisation of genetic
variants with single nucleotide resolution, and overcomes limitations associated with
benchmark tests such as reporter gene and gel shifts assays (Pastinen, 2010; Karimi et
al., 2009). Genome-wide approaches generate large numbers of experimental data
points, with difficulties arising when attempting to discern functionally relevant results
from large-scale datasets (Salmon-Divon et al., 2010). As such, bioinformatic tools
represent an ideal approach for the prioritisation of candidate genetic variants from
genome-wide datasets for further investigation (Kann, 2010; Sun et al., 2009; Salmon-
Divon et al., 2010).
1.4.7 Bioinformatic analyses
Bioinformatic approaches are important for the prediction, prioritisation and verification
of functional genetic variants in the human genome, as reviewed by Huang et al. (2009)
and Fernald et al. (2011). Many bioinformatic approaches exist for the prioritisation of
functional genetic variation by identifying transcription factor binding motifs that
contain potential functional variants. Such tools include JASPAR (Sandelin et al.,
50
2004); P-Match (Chekmenev et al., 2005); TRANSFAC (Wingender et al., 1996; Matys
et al., 2006); and AliBaba2 (Grabe, 2002) and often utilise various methods to improve
the validity of results. For example, P-Match integrates known transcription factor
binding sites from the TRANSFAC database (Wingender et al., 1996) with pattern
matching and weight matrix algorithms to increase recognition accuracy and reduce
error rate (Chekmenev et al., 2005). P-Match has been used to predict transcription
factor binding sites in fish (Sun et al., 2006), mice (Jiang et al., 2008) and humans
(Göring et al., 2007). Similarly, AliBaba2 utilises the TRANSFAC database
(Wingender et al., 1996) to identify transcription factor binding sites by generating
predictive matrices based on input DNA sequences (Grabe, 2002), as demonstrated in
the MTA1 gene (Reddy et al., 2009) and the SVEP1 gene (Glait-Santar and Benayahu,
2011). AliBaba2 has been used to predict transcription factor binding sites in different
organisms including insects (Ursic-Bedoya et al., 2008), fish (Yeh and Klesius, 2008)
and humans (Moosa et al., 2011).
The structural influence of genetic variants at the protein level can be predicted using
bioinformatic tools as reported by Lin et al. (2005), Cuff et al. (1998) and Faraggi et al.
(2012). For example, YASPIN is an online database that utilises 7-state local structure
combined with Markov chain models to predict protein secondary structures (Lin et al.,
2005). YASPIN has been used by Pool et al. (2012) and Fallahi-Sichani et al. (2011) to
determine protein secondary structures. Other tools that are being developed to better
predict secondary and tertiary structures include SPINE X (Faraggi et al., 2012) and
FPGA accelerator (Xia et al., 2011).
With recent advancements in high-throughput sequencing technologies, new challenges
have arisen that require further bioinformatic power. Such challenges include complex
51
multiple sequence alignments from large datasets and the non-reproducibility present
amongst various current tools, as reviewed by Aniba et al. (2010). However, these
limitations are, in part, overcome by the integration of multiple bioinformatic tools.
Such a high-throughput approach is likely to increase the likelihood of identifying
casual genetic variants in human traits and disease (Huang et al., 2009). For example,
Lee and Shatkay (2008) developed a functional SNP (F-SNP) application that
incorporates 16 different bioinformatic tools to predict the splicing, transcriptional,
translational and post-translation effects of SNPs. Multiple bioinformatic tools, via the
F-SNP database, have been used to predict functional SNPs in the FOXE1 gene (Landa
et al., 2009), and genes encoding for proline metabolic enzymes (Hu et al., 2008). Xu
and Taylor (2009) use an equivalent approach called ‘SNPinfo’ for candidate SNP
prioritisation that integrates published GWAS results with LD data and in silico
predictions of functional characteristics. The SNPinfo database has been used in studies
to predict functional SNPs in the LIN28B gene (Permuth-Wey et al., 2011) and the
TNFSF8 gene (Wei et al., 2011). Typically, most genetic variants identified in GWAS
have minor effects on disease susceptibility. Thus, the integration of bioinformatic
approaches and functional data is essential for the prioritisation and identification of
candidate functional genetic variants (Moore et al. 2010).
1.5 VNN1 – A gene model for novel experimental validation assays and cardiovascular disease
The VNN1 gene has been associated with a number of human diseases (Kaskow et al.,
2012) including inflammation (Martin et al., 2004; Berruyer et al., 2006; Jansen et al.,
2009), malaria (Min-Oo et al., 2007), type 1 diabetes (Roisin-Bouffay et al., 2008) and
cardiovascular disease (Göring et al., 2007; Zhu and Cooper, 2007). Initial
cardiovascular studies concerning the VNN1 gene targeted transcript levels as an
52
endophenotype to identify the VNN1 gene as a quantitative trait locus associated with
high density lipoprotein–cholesterol levels and cardiovascular phenotypes (Göring et
al., 2007). More recently, genetic variants within the VNN1 gene have been found to be
associated with cardiovascular disease as shown by Jacobo-Albavera et al. (2012), Fava
et al. (2010) and Zhu and Cooper (2007). Furthermore, statistical and functional genetic
assays have experimentally assessed a number of functional genetic variants within the
VNN1 gene including the ‘-708G/A’, ‘-667G/A’, ‘-612C/A’, ‘-587G/A’, ‘-137T/G’
SNPs in the gene promoter (Göring et al., 2007) and the exonic rs2294757 SNP (Kerkel
et al., 2008). Reviews exemplify VNN1 as a novel cardiovascular risk-gene and an ideal
gene model for further functional analysis given the experimentally validated
associations of VNN1 genetic variation with cardiovascular phenotypes (Göring et al.,
2007; Kerkel et al., 2008).
1.5.1 VNN1 – The novel cardiovascular risk-gene
In humans, the VNN1 gene is located on the antisense strand of chromosome 6 (Entrez
Gene cytogenetic band: 6q23-24) within a cluster of three genes (VNN1, VNN2, and
VNN3) which occupy a 80 kb region and comprise the Vanin gene family (Martin et
al., 2001; Galland et al., 1998). The VNN1 gene is 33,198 bp in length and consists of
seven exons (Martin et al. 2001; http://www.genecards.org/cgi-
bin/carddisp.pl?gene=VNN1). The dbSNP database includes over 40 million validated
SNPs in the most recent human genome build (dbSNP build 135), with over seven-
hundred SNPs (779 as of July 2012) located within the VNN1 gene (Sherry et al., 2001;
http://www.ncbi.nlm.nih.gov/projects/SNP/). Orthologs for the VNN1 gene have been
identified in 15 species including mice (Martin et al., 2001) and rats (Fugmann et al.,
2011). VNN1 mRNA expression has been reported in numerous human cells and tissues
including leukocytes, kidney, liver, and the intestine (Galland et al., 1998; Jansen et al.
53
2009). A genome-wide profiling study by Yanai et al. (2005) identified VNN1
expression levels to be highest within whole blood and liver.
The VNN1 gene encodes for a 513 amino acid protein, namely Vanin 1, which was first
reported to have pantetheinase activity by Aurrand-Lions et al. (1996). Aurrand-Lions
et al. (1996) identified similar pantetheinase activity in Vanin family members VNN2
and VNN3, who also found that Vanin 1 played a role in regulating late adhesion steps
of thymus homing under physiological (non-inflammatory) conditions. Aurrands-Lion
et al. (1996) also reported the Vanin 1 protein to have a sub-cellular location on the cell
membrane, linked to a lycosylphosphatidylinositol (GPI)-anchor. In a subsequent study,
Marras et al. (1999) defined Vanin 1 pantetheinase activity to involve the hydrolysis of
one carbo-amide linkage in d-pantetheine to form d-pantothenate (vitamin
B5/pantothenic acid) and cysteamine (2-aminoethanethiol). A recent review by Kaskow
et al. (2012) reported that Vanin 1-directed hydrolysis of pantetheine impacts on
cellular processes including fatty acid metabolism and oxidative stress response.
1.5.2 Gene models in cardiovascular disease – case studies
Cardiovascular diseases (CVDs) represent the leading cause of death worldwide (Moran
et al., 2012), and affected more than 80 million people in the United States in 2012
alone (Kathiresan and Srivastava, 2012). To elucidate the genetic risk factors of CVD,
early research focused on the APOE gene, which encodes the lipid-transport protein
apolipoprotein E (Rall and Mahley, 1992; Visvikis-Siest and Marteau, 2006; Willer et
al., 2008). The APOE gene is polymorphic with two non-synonymous SNPs (rs429358
and rs7412) which correspond to three haplotypes (ε2, ε3, ε4) with varying population
frequencies (Bekris et al., 2008). The polymorphisms alter the amino acid sequence at
positions 112 and 158 and result in three apolipoprotein E isoforms which differ in
54
structure and function (Kuhlmann et al., 2010; McIntosh et al., 2012). A report by
Cambien and Tiret (2007) determined that ε4 carriers (allele frequency >20% in the
population) had a 40% higher risk of CHD than ε3 homozygotes, and shows the link
between genetic variation and susceptibility risk for cardiovascular disease. More
recently, the APOE gene has also been associated with Alzheimer’s disease (Genin et
al., 2011).
At the molecular level, combinations of genetic variants are assumed to dysregulate
particular cellular pathways and contribute to human diseases such as CVD (Kim et al.,
2011). Although few causal genetic risk factors have been associated with complex
CVDs, the genetic mechanisms underlying monogenic cardiovascular traits have been
more extensively characterised (Nabel, 2003; Glazier et al., 2002; Timpson et al.,
2012). For example, low-density lipoprotein (LDL) is a risk factor for monogenic forms
of CVD through the inactivation of hepatic LDL receptors, which modulates LDL levels
in plasma (Timpson et al., 2012). Familial hypercholesterolemia (FH) is a monogenic
disorder associated with mutations within the LDLR gene, leading to a deficit in LDL
receptors and subsequently elevated plasma cholesterol levels (Nabel, 2003; Kassim et
al., 2010). Published estimates indicate in excess of 600 mutations and variants in the
LDLR gene have been identified in patients with FH, with heritability of variants
estimated to exceed 50% (Goldstein et al., 2001; Cambien and Tiret, 2007; Browning
and Browning, 2013).
Hypertension is a complex cardiovascular condition associated with elevated blood
pressure in arteries and confers an increased susceptibility to heart and renal failure,
stroke, and myocardial infarction (Messerli et al., 2007). Many genetic risk factors
associated with increased blood pressure have been identified (Ehret et al., 2011; Lifton
55
et al., 2001). However, the genetic basis of primary hypertension remains poorly
understood; in part due to confounding environmental and lifestyle factors such as
dietary salt consumption (He and MacGregor, 2009), physical activity (Dickinson et al.,
2006), and obesity (Haslam and James, 2005) which influence the CVD phenotype
(Taylor et al., 2009). Thus far, hypertension research has focused on the identification
of genetic risk variants in the nuclear genome; however, a recent study by Liu, C et al.
(2012) identified a number of SNPs within the mitochondrial genome that contribute to
increased susceptibility to hypertension. Liu and co-workers identified a non-
synonymous mitochondrial SNP 5913G>A (Asp>Asn) in the cytochrome C oxidase
subunit 1 of respiratory complex IV that was found to have a significant association
with blood pressure (BP) and fasting blood glucose (FBG) levels (Liu, C et al., 2012).
On average, carriers of the rare 5913A allele were at greater risk of developing
hypertension, with 7mm Hg higher systolic BP than observed at baseline levels and
17mg/dL higher mean FBG (Liu, C et al., 2012). Lui and co-workers identified a
second mitochondrial SNP, 3316G>A (Ala>Thr) in the NADH dehydrogenase subunit 1
of complex I that was also highly associated with BP and FBG levels (Liu, C et al.,
2012). The two individuals with the rare 3316A allele had 17 and 25mg/dL higher FBG
than observed at baseline levels (Liu, C et al., 2012). Estimates of the contribution of
genetic variants to hypertension range from 30 to 50% (Tanira and Balushi, 2005),
which reflects the importance of identifying and characterising the role of functional
genetic variants in human diseases.
To elucidate the role of VNN1 in CVD, Göring et al. (2007) used genome-wide
transcriptional profiles (of lymphocytes) and bioinformatic tools to identify genetic
variants in the VNN1 gene promoter that were highly associated with transcript
abundance and high-density lipoprotein cholesterol (HDL-C) concentrations. Göring
56
and co-workers reported five SNPs (-708G/A, -667G/A, -612C/A, -587G/A, -137T/G)
in the VNN1 gene promoter that were identified to be putatively functional based on the
genetic association with transcript abundance and HLD-C concentration (Göring et al.,
2007). The -137T/G SNP had the highest association with HDL-C concentrations and
was functionally assessed using EMSA, which indicated an allele-specific binding
profile of DNA-protein complexes (Göring et al., 2007). Bioinformatic analysis of the -
137T/G SNP using P-Match identified allele-specific stimulating protein 1 (Sp1)
binding in the presence of the -137T allele (Göring et al., 2007). The transcription factor
Sp1 has previously been associated with high gene expression and contains a zinc finger
protein motif which directly binds to DNA and allows the TF to enhance transcriptional
activity (Jiang et al., 2005). More recently, Jacobo-Albavera et al. (2012) also found
VNN1 gene expression and the -137T/G SNP to be associated with HDL-C levels in
Mexican children.
To determine the role of DNA methylation in VNN1 gene expression, Kerkel et al.
(2008) used a methylation-sensitive restriction endonuclease (HpaII) to assess
heterozygous VNN1 genetic variants their effect on gene expression. Kerkel and co-
workers identified the A allele of an exonic SNP, rs2294757, to have preferential
upregulation of gene expression, indicating allele-specific methylation and the presence
of functional SNPs in the VNN1 gene (Kerkel et al., 2008). Subsequently, Fava et al.
(2010) found an association between the rs2294757 SNP and cardiovascular disease
symptoms such as hypertension.
The identification of causal genetic risk factors represents the primary focus of
cardiovascular research, with genetic variants in the VNN1 gene representing novel
targets for analysis based on published studies (Zhu and Cooper, 2007; Fava et al.,
57
2010; Jacobo-Albavera et al., 2012; Göring et al., 2007; Kerkel et al., 2008). Clearly,
the VNN1 gene is involved with cardiovascular phenotypes, however, the specific roles
of these genetic variants in disease is not obvious (Kaskow et al., 2012). While
predictive bioinformatic tools and genetic association studies have been used to identify
candidate functional variants in the VNN1 gene (Zhu and Cooper, 2007; Fava et al.,
2010), further experimental research is required to elucidate the role of VNN1 genetic
variants in CVD in order to complement functional studies, such as those done by
Göring et al. (2007) and Kerkel et al. (2008).
1.6 Research justification and PhD aims
Recent advances in bioinformatics and functional genomics have allowed greater
precision in predicting the function of human genetic variants in complex traits and
disease (Collas, 2010; Statham et al., 2012). However, a review of the literature
indicates there is a still a clear requirement for robust, rapid and reproducible
experimental validation methods for assessing candidate functional genetic variants,
such as those within the VNN1 gene. The VNN1 gene is clearly involved in
inflammation, but specific roles in other pathways are unclear, with dysregulation of the
VNN1 gene also resulting in the manifestation of other diseases (Kaskow et al., 2012).
Vanin 1 pantetheinase activity is critical in maintenance of normal cellular processes.
Abnormal gene activity, and consequent dysregulation of such processes, can lead to
disease and indicates the importance of characterising the effect of genetic variants on
gene expression (Kaskow et al., 2012). In fact, recently published studies show that
VNN1 genetic variants have a role in cardiovascular disease (Jacobo-Albavera et al.,
2012; Zhu and Cooper, 2007; Fava et al., 2010; Göring et al., 2007; Kerkel et al.,
2008), which promotes VNN1 as the ideal gene model for undertaking functional
analyses using novel experimental validation approaches. Since there is limited
58
information regarding the functional mechanism by which genetic variants influence
human disease, this thesis will significantly contribute to the understanding of the role
of functional genetic variants in the cardiovascular risk-gene VNN1.
The current study will include the development of a novel in vivo assay for the
identification and characterisation of functional genetic variants within the VNN1 gene,
which overcomes limitations associated with functional assays available at the
commencement of this study (2009). This novel assay, namely ‘CHA-seq’, is based on
the measurement of chromatin accessibility across regions containing genetic variants to
account for patterns of transcription factor binding, nucleosomal/histone positioning and
DNA methylation, which are directly associated with functional genetic variation. The
VNN1 gene was used as a model for the development of CHA-seq, with the aim of
extending this approach to a genome-wide scale using novel high-throughput
sequencing technologies.
The aims of this PhD research were to:
1. Use existing chromatin arrangement methods (i.e. CHART-PCR) to assess the
functionality of candidate genetic variants in the VNN1 gene promoter.
2. Develop a novel experimental validation assay (‘CHA-seq’) to evaluate the
functionality of candidate genetic variants in the VNN1 gene promoter.
3. Experimentally validate candidate functional genetic variants in the VNN1
gene promoter derived from the CHA-seq approach.
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To address the first aim of this study, CHART-PCR was used to characterise chromatin
accessibility in the VNN1 gene promoter using cultured lymphocytic cell lines derived
from the San Antonio Family Heart Study (SAFHS) (Göring et al., 2007), in addition to
the Ramos lymphocytic cell line obtained from the American Type Culture Collection
(ATCC). Cell lines were selected for functional analysis based on the particular
genotypes present at the -137T/G and -587G/A SNP loci in the VNN1 gene promoter.
The -137T/G and -587G/A VNN1 SNPs were selected for further functional analysis
based on preliminary findings in our laboratory and the results of published studies such
as Göring et al. (2007). The -137T/G and -587G/A SNPs were assessed using two
chromatin-sensitive nucleases (micrococcal nuclease and deoxyribonuclease 1) in
CHART-PCR to determine the presence or absence of allele-specific chromatin
accessibility, which is indicative of functionality.
To address the second aim, chromatin derived from the SAFHS cell lines with different
allelic combinations were used to develop the novel ‘CHA-seq’ approach to
functionally characterise genetic variants in the VNN1 gene. The CHA-seq approach
incorporated chromatin-sensitive nucleases and high-throughput DNA sequencing in
order to simultaneously identify and functionally assess candidate SNPs based on
chromatin accessibility. CHA-seq was used to validate the findings of CHART-PCR of
the candidate functional -137T/G and -587G/A SNPs and to screen for other novel
candidate functional SNPs in the VNN1 gene.
To address the third research aim, novel candidate functional VNN1 SNPs identified
from CHA-seq were selected for further functional assessment in order to characterise
the role of the candidate genetic variants in the VNN1 gene. A combined bioinformatic
and experimental validation approach was used to confirm the CHA-seq results which
60
incorporated bioinformatic or in silico predictive methods such as P-Match and
AliBaba, in addition to ‘traditional’ experimental methods such as EMSA and bisulfite
sequencing.
61
Chapter 2: General methods and materials
2.1 General reagents, biochemicals, buffers and solutions
Biochemical reagents used in the current study were of the highest grade available and
purchased from primarily Sigma Chemical Company (Sigma, Australia), Roche
Diagnostics Corporation (Roche Diagnostics Australia Pty Ltd, Australia), Invitrogen
Corporation, Australia Corporation (Invitrogen Corporation, Australia), QIAGEN
(QIAGEN, Australia) and BD Biosciences (Becton, Dickinson and Co, USA). All
disposable plastic (polypropylene and polystyrene) lab-ware was purchased sterile from
Sarstedt (SARSTEDT AG and Co, Germany), Nalgene (Nalge Nunc International,
USA) or Corning Life Sciences (Corning Incorporated, USA), with a detailed list of lab-
ware shown in Table A.3 (Appendix – Supplementary Tables and Figures).
Biochemicals and buffers were prepared in accord with the methods described by
Sambrook and Russell (2001) using sterile non-pyrogenic (Baxter Internation Inc.,
USA), double deionised Millipore-filtered water (ddH2O), Dulbecco’s phosphate
buffered saline (Invitrogen Corporation, Australia) or molecular biology-grade organic
solvents as required. Solutions were prepared in sterile plastic tubes or glass vessels that
had been previously washed several times with ddH2O and autoclaved (30min at 121˚C,
15 PSI). Solutions were sterilised, depending on the contents, by autoclaving (30min at
121˚C, 15 PSI) or using a 0.22µm Acrodisc (Pall Corp, Australia) syringe filter units.
Commonly used 1X Tris-EDTA (TE) and 50X Tris-Acetate-EDTA (TAE) buffers were
made up routinely for general laboratory use. To make a 100 mL solution of TE Buffer,
1 mL of 1 M Tris-HCl (pH 8.0) and 0.2 mL EDTA (0.5 M) was added together in a
glass beaker and made up to volume with double distilled water. pH was adjusted to 8.0
62
using 1 M HCl and 1 M NaOH. TAE buffer was prepared as a 50X stock solution by
dissolving 242 g Tris Base in sterile double distilled water, adding 57.1 mL Glacial
Acetic Acid, and 100 mL of 0.5M EDTA (pH 8.0) solution, and maded up to 1 L with
water. This stock solution was diluted 1/50 with water to make a 1X working solution.
Where necessary, reagents were weighed using a model 310 electronic balance (MG
Scientific, USA) and heated and/or stirred with a Thermolyne Cimarec 2 magnetic
stirrer (Thermo Fisher Scientific Inc, USA). The pH of solutions was measured using a
PerpHecT pH/temperature meter (Orion Research, USA).
General biochemicals and reagents utilised in the current study were sourced from
various distributers listed in detail in Table A.1 (Appendix – Supplementary Tables and
Figures), with a detailed list of the corresponding equipment used in this research
shown in Table A.2 (Appendix – Supplementary Tables and Figures).
2.2 Bacterial Methods
2.2.1 Bacterial culture medium
Microbiological reagents were obtained from Difco Laboratories (Becton, Dickinson
and Company, USA), BDH Laboratories (Merck, Germany) and Sigma Chemical
Company (Sigma, Australia). Bacterial cell culture media and ampicillin antibiotics
were prepared in accord with the methods described by Sambrook and Russell (2001).
All media and solutions were prepared to the prescribed volumes with double-deionised
water and balanced to pH 7 with 1 M HCl and 1 M NaOH. To facilitate positive
bacterial selection, the antibiotic ampicillin is added to bacterial culture media prior to
experimentation at a concentration of 100 µg/µL. Aliquots of ampicillin were stored at -
20oC for no more than 1 week.
63
1 L of LB (Luria-Bertaini) broth was prepared by dissolving 10 g Bacto Tryptone, 5 g
Yeast Extract and 10 g NaCl into sterile double distilled water and made up to 1 L with
water. pH was adjusted to 8.0 using 1 M HCl and 1 M NaOH, followed by autoclaving
at 121°C for 30 minutes. LB broth was supplemented with 100 mg/mL ampicillin
immediately prior to use. Luria-Bertani (LB) agar was prepared using the same
methodology as LB broth, with the addition of 15 g of Bacto Agar. Following
autoclaving, LB agar was cooled to ~60°C and supplemented with 100 mg/mL
ampicillin, and poured (~25 mL) into perti dishes and left to cool at room temperature
until set. Once the agar was set, plates were sealed in parafilm and stored at 4oC until
required, however, LB agar dishes were stored for no longer than two weeks. Prior to
use, plates were warmed in a 37oC incubator for 30 minutes and supplemented with 20
µL X-gal (Promega, USA) for subsequent selection of white recombinant colonies using
traditional blue-white colony screening (Section 2.2.4).
2.2.2 Bacterial culturing conditions
All recombinant cloning and bacterial procedures were carried out using the One Shot
MAX efficiency DH5α-T1R strain of Escherichia Coli (Life Technologies, Australia),
stored at -80oC, with the following genotype: F- φ80lacZ∆M15 ∆(lacZYA-argF) U169
deoR recA1 endA1 hsdR17 (rk-, mk+) phoA supE44 λ- thi-1 gyrA96 relA1 tonA
Bacterial cells were grown at 37oC with 5% CO2 in a CO2 Water Jacketed Incubator
(Forma Scientific, USA).
64
2.2.3 TOPO cloning in bacteria
The TOPO TA Cloning kit (Invitrogen Corporation, Australia) was used in this project
to incorporate Taq-polymerase-amplified PCR products into the pCR2.1-TOPO plasmid
vector and conducted according to the manufacturer’s instructions, with the following
modifications to the reaction set-up. In a 1.5 mL micro-centrifuge tube, 1 µL of pCR2.1
TOPO vector was added to 1 µL of ‘salt solution’ and 1-4 µL of fresh PCR product.
Reaction volume was made up to 6 µL with sterile double distilled water and mixed
gently by pipette. TOPO Reaction was left to incubate for five minutes at room
temperature, followed immediately by transformation (Section 2.2.4) or stored for later
use at -20°C. TOPO reactions were subsequently transformed into competent E.Coli
cells (refer to section 2.2.4) or stored at -80°C for use at a later date.
2.2.4 Transformation of Bacteria and blue-white colony selection
Bacterial transformations were performed using One Shot MAX Efficiency DH5α-T1R
Competent Cells (Invitrogen Corporation, Australia). In brief, 2 µL of pCR2.1-TOPO
plasmid vector containing sequence of interest was added to one 50 µL vial of
competent cells and stored on ice for 30 minutes. Cells were then heat shocked at 42°C
for 45 seconds, followed by two minutes on ice, and subsequently the addition of 500
µL of pre-warmed (37°C) LB broth (See Section 2.2.1). The transformation reaction
was then incubated in 37°C water bath for one hour, and then transferred onto LB agar
plates (See Section 2.2.1) and grown overnight at 37°C with 5% CO2. Blue-white
colony selection was used in this current study for the selection of recombinant bacterial
colonies and performed using the method as described by Sambrook and Russell (2001).
65
2.3 Eukaryotic Methods
2.3.1 Eukaryotic cells and culturing
All mammalian cell lines were cultured at 37°C with 5% CO2 in a humidified incubator
(Forma Scientific, USA). Human haematopoietic suspension cell lines including Ramos
(RA 1) were obtained from the American Tissue Culture Collection (ATCC, USA), and
Epstein Bar Virus (EBV) transformed lymphoblastoid cell lines were obtained from the
SAFHS cohort (Göring et al., 2007). SAFHS cell lines included 0054, 0194, 0306, 0525,
0029, 0188. Cells were propagated in RPMI 1640 cell culture media (Invitrogen
Corporation, Australia) supplemented with 10% (v/v) heat inactivated foetal bovine
serum (FBS; Invitrogen Corporation, Australia), antibiotics (100 U/mL penicillin and
100 µg/mL streptomycin, Invitrogen Corporation, Australia) and 4 mM L-glutamine
(Invitrogen Corporation, Australia). FBS was heat inactivated at 56˚C for three hours,
dispensed into 50 mL aliquots and stored at -20˚C until used. Suspension cell cultures
were grown in filter-top tissue culture flasks (Nalge Nunc International, USA) and
maintained between 0.5-1.5 x 106 viable cells/mL by subculturing every 2 days.
2.3.2 Eukaryotic cell counting with Trypan Blue staining
To calculate cell density, a 10 µL aliquot of cells were transferred to a 1.5 mL
Eppendorf tube and mixed with 20 µL of Trypan Blue Stain (Sigma, Australia). Stained
cells were loaded into a Neubauer Counting Chamber (ProSciTech, Australia) and
counted at 20x magnification under a Wild M40 light microscope (Wild Heerbrugg,
Gais, Switzerland). Cells appearing white in colour and uniformly spherical were
considered viable and were counted. Cells appearing dark blue in colour were deemed
non-viable and were not considered. Cell density was calculated by counting the
number viable cells from the four quadrants of the Counting Chamber and then dividing
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by the number of quadrants considered (4), and then multiplying by the dilution factor
(3) to give cell density in terms of x 104 cells/mL.
2.3.3 Cyropreservation storage and revival of eukaryotic cell lines
All mammalian cell lines were maintained in storage by cryopreservation. For long-
term storage, cells were grown in complete media for approximately five days post-
revival from cryopreservation storage (or until cells are in mid-late log growth phase)
and were harvested through centrifugation at 1000 rpm (Jouan C312 swinging rotor
centrifuge) for five minutes at room temperature in 50 mL Falcon tubes (Sarstedt,
Germany). Following the removal of the supernatant, pellet cells were resuspended in
complete media to a density of 2-3 x 106 cells/mL. 700 µL Aliquots of resuspended
cells were added to 200 µL FBS and 100 µL Dimethyl sulfoxide (DMSO) and stored in
1.5 mL cryovials (Nalge Nunc International, USA) at -80°C for 1-2 days before being
transferred to liquid nitrogen for long term storage.
To revive the cells from cryopreservation, vials were initially thawed at 37°C and then
transferred to 9 mL complete-media in a 15 mL Falcon tube, and harvested by
centrifugation at 1000 rpm (Jouan C312 swinging rotor centrifuge) for five minutes at
room temperature. Following the removal of the supernatant, pelleted cells were
resuspended in 2-5 mL of fresh complete growth media and transferred to a sterile 25
cm2 filter-top culture flask (Stratagene, USA) and grown at 37°C with 5% CO2.
2.4 Oligonucleotides
All oligonucleotide primers were obtained from Sigma-Aldrich Pty Ltd, Castle Hill
NSW, Australia (Sigma, Australia) purified by desalting for use in Polymerase Chain
Reactions (PCR) (Section 2.8) and DNA sequencing (Section 3.9), whereas
67
oligonucleotides used for EMSA were obtained cleaned by High-performance liquid
chromatography. Oligonucleotides, supplied desiccated, were resuspended in an
appropriate volume of sterile water to a final concentration of 100 µM, vortexed, and
centrifuged. Aliquots of 10 µM were prepared and stored at -20oC until required.
Oligonucleotides for use in PCR or qPCR were designed in accord with the commonly
used guidelines outlined in Table 2.1. Oligonucleotides required for use in the
assessment of SNPs in EMSA (Section 2.10.2) were designed to span fifteen
nucleotides either side (both 5’ and 3’) of a SNP (total length of 31 bp including SNP)
with the forward (5’ to 3’) oligonucleotide of each pair possessing a 5’ biotin
modification. To simulate double stranded DNA (dsDNA), 10 µL of 100 µM forward
and reverse oligos in TE buffer (See Section 2.1) were added together into the same 1.5
mL Eppendorf tube and denatured at 95˚C for ten minutes. The tube was left to cool for
two hours at room temperature to facilitate ligation of the single stranded oligos to form
a double stranded nucleic acid complex.
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Table 2.1: Primer design guidelines
Shown are the primer design parameters, derived from those specified by Sigma-Aldrich (http://www.sigmaaldrich.com/etc/medialib/docs/Sigma-Aldrich/General_Information/1/oligo_architect_glossary.Par.0001.File.tmp/oligo_architect_glossary.pdf), that were followed in this current research.
Characteristic Parameters
Length 20-25 bp
Secondary structure None to weak
Repetitive sequences None preferably, but no more than 3 identical nucleotides in a row.
GC cap (3’ G or C nucleotide, or ideally GC di-nucleotide)
Present
GC content 40-60%
Predicted primer dimer (using Sigma Genosys online primer design software)
None
Melting temperature ~65˚C, with a difference of less than 1˚C between forward and reverse primers
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2.5 Isolation and purification of plasmid and eukaryotic DNA
2.5.1 Mini-prep purification of plasmid DNA
Plasmids carrying DNA sequence of interest were isolated and purified using the
QIAGEN Spin Mini-prep kit (QIAGEN, Australia) in accord with the manufacturer’s
instructions. Prior to purification, transformed bacteria were spread onto LB agar plates
(See Section 2.2.1) supplemented with 100 µg/mL ampicillin and incubated at 37˚C
overnight. The following day individual bacterial colonies were picked and inoculated
in 5 mL of LB broth (See Section 2.2.1) containing ampicillin (100 µg/mL) and
incubated overnight at 37˚C with vigorous shaking (200-250 rpm in the Jouan C312
swinging rotor centrifuge). Bacterial cells were then harvested by centrifugation at 3000
rpm (Jouan C312 swinging rotor centrifuge) for ten minutes. DNA was subsequently
isolated from the cell pellet according to protocol supplied with the QIAGEN Plasmid
Purification kits. The purified DNA was resuspended in 100 µL endotoxin-free Buffer
TE (QIAGEN, Australia) and stored at -20˚C.
2.5.2 Isolation and purification of DNA from eukaryotic cells
Eukaryotic cell lines carrying genomic DNA of interest was isolated and purified using
the QIAamp DNA blood mini kit (QIAGEN, Australia) in accord with the
manufacturer’s instructions. Prior to purification, cell lines of interest in culture media
were harvested by centrifugation at 1000 rpm (Jouan C312 swinging rotor centrifuge)
for five minutes. Following the removal of the supernatant, genomic DNA was
extracted and purified using the QIAamp DNA blood mini kit (QIAGEN, Australia)
according to the manufacturer’s protocol outlined in the QIAamp DNA Mini and Blood
Mini Handbook that was supplied with the kit. The purified DNA was resuspended in
150 µL endotoxin-free Buffer TE (QIAGEN, Australia) and stored at -20˚C.
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2.6 Analysis of nucleic acids
2.6.1 Nano-drop analysis of nucleic acids
To determine the concentration and relative purity of nucleic acid samples, 1 µL
aliquots were loaded onto a Nanodrop ND-100 spectrophotometer (NanoDrop, USA)
for analysis. Absorbance is measured at two wavelengths: 260 nm for nucleic acids, and
280 nm for peptide bonds. The 260 nm measurement was used to calculate the
concentration of the nucleic acid sample. The relative purity of the sample was
determined from the A260/280 ratio. Samples relatively free from protein contamination
return an A260/280 ratio between 1.6 and 2.0.
2.6.2 Gel Electrophoresis
1-2% (w/v) agarose (Promega Corporation, USA) gels containing 0.5 µg/mL ethidium
bromide (Sigma, Australia) were prepared using 1x TAE Buffer (See Section 2.1). DNA
samples were adjusted to approximately 18 µL with ddH2O and combined with 2 µL of
Gel Loading Dye Blue (6X) (New England Biolabs, USA) immediately prior to loading
the gel. DNA samples were run alongside maker standards such as the NEB PCR
Marker (sizes: 50 bp, 150 bp, 300 bp, 500 bp, 766 bp) and the NEB 1 kb DNA ladder
(sizes: 500 bp, 1000 bp, 1500 bp, 2000 bp, 3000 bp, 4000 bp, 5-10 kb bands
indistinguishable) from New England Biolabs (New England Biolabs, USA).
Electrophoresis was performed using a BIO-RAD Electrophoresis tank (BIO-RAD,
USA) filled with TAE buffer to cover the gel and with a constant voltage of 90V for 30-
60 minutes. Following gel electrophoresis, separated DNA fragments were visualised
by fluorescence of ethidium bromide using the Spectroline UV trans-illuminator
(Spectronics, USA) and images captured using the FinePix S1 digital camera (Fujifilm
Corporation, Japan).
71
2.7 Polymerase Chain Reaction (PCR)
In general, conventional PCR amplifications were carried out using the PTC-
Thermocycler (MJ Research Inc) and Platinum Taq polymerase (Invitrogen
Corporation, Australia) according to the manufacturer’s instructions. Briefly, PCR was
performed in sterile 0.2 mL Eppendorf tubes with 50 µL reactions including 5 µL DNA
template (~50-100 ng), 5 µL 10x PCR reaction buffer, 2 µL MgCl2 (50 mM), 2 µL
dNTPs (dATP, dGTP, dCTP, and dTTP at 10 mM each, Roche Diagnostics) 2 µL
primer mix (10 µM each forward and reverse sequence specific primers), 0.5 µL
Platinum Taq polymerase and 33.5 µL sterile water. Typical thermo-cycling conditions
were as follows: initial denaturation of 95˚C for 5 minutes, followed by 10-40 cycles of
95˚C denaturation for 30 seconds, 58-68˚C (5°C less than primer melting temperature)
annealing for 30 seconds and 72˚C extension for 1 minute; followed by extension at
72˚C for 5 minutes; finally cooled to 4˚C until required.
2.8 Sanger DNA Sequencing
Samples to be sequenced by traditional Sanger-based chemistry
(http://www.agrf.org.au/index.php?id=72#Purified DNA Process) were sent to
Australian Genome Research Facility (AGRF) on Murray St, Perth, Western Australia.
For AGRF sequencing of purified plasmid DNA, 1 µg of DNA was adjusted to a
volume of 12 µL in sterile water and was added to 1 µL of 10 mM of the appropriate
primer in a 1.5 mL Eppendorf tube. Samples were then sent to AGRF for sequencing.
For those sequences integrated into the pCR2.1 TOPO vector, M13F primer (5´-
GTAAAACGACGGCCAG-3´) was used.
72
2.9 Experimental validation methods for SNP functionality
2.9.1 Chromatin Accessibility by Real-Time PCR (CHART-PCR)
The CHART-PCR protocol was originally developed by Frances Shannon and co-
worker at the Australian National University (Rao et al., 2001). In brief, CHART-PCR
consists of firstly the isolation of intact nuclei. Following isolation and washing, nuclei
were incubated with or without nuclease (micrococcal nuclease or deoxyribonuclease 1)
giving “undigested” and “digested” samples. Accessibility reactions were subsequently
terminated and genomic DNA recovered and prepared for analysis by quantitative real-
time PCR in order to quantify target regions. Comparing the quantity of targeted
genomic sequences in digested relative to undigested samples provides a measure of
chromatin accessibility and expressed as a percentage of nuclease cutting across the
region specified by the primers.
2.9.1.1 Nuclei isolation
Approximately 1x107 cultured cells were harvested by centrifugation in 50 mL Falcon
tubes at 1000 rpm (Jouan C312 swinging rotor centrifuge) for five minutes at 4°C. The
cell pellet was washed once with ice-cold PBS before re-suspension in 1 mL ice-cold
Igepal Lysis buffer (10 mM Tris.HCl [pH 7.4], 10 mM NaCl, 3 mM MgCl2, 0.5%
Igepal CA-630, 0.15 mM spermine and 0.5 mM spermidine) and five minutes
incubation on ice. Nuclei were collected by centrifugation at 3000 rpm (Jouan C312
swinging rotor centrifuge) (4°C) for five minutes.
2.9.1.2 Nuclease digestion of chromatin
Isolated nuclei were washed with 1 mL of Digestion buffer without CaCl2 (10 mM
Tris.HCl [pH 7.4], 15 mM NaCl, 60 mM KCl, 0.15 mM spermine and 0.5 mM
spermidine) and centrifuged at 3000 rpm (Jouan C312 swinging rotor centrifuge) (4°C)
for five minutes. Washed nuclei were then resuspended in 350 µL of Digestion buffer
73
with CaCl2 (prepared by the addition of 25 µL of 1 M CaCl2 and 150 µL of 1 M MgCl2
to 25 mL of Digestion buffer without CaCl2). This cell suspension was divided into six
50 µL aliquots in sterile, labelled 1.5 mL Eppendorf tubes with each containing
approximately 1.25 x 106 nuclei. Triplicate samples were used for both MNase/DNase I
accessibility reactions (“digested”) and for controls incubated without nuclease
(“undigested”). 50 µL of pre-warmed (five minutes at 37°C) Digestion buffer (with
CaCl2) without nuclease was added to “undigested” samples, while 50 µL of pre-
warmed (five minutes at 37°C) Digestion buffer (with CaCl2 and MgCl2) supplemented
with 20 U/mL MNase (New England Biolabs, USA) or DNase I (Invitrogen
Corporation, Australia) was added to the “digested” samples. All samples were then
incubated in a 37°C water bath for ten minutes. To stop digestion, 30 µL stop solution
(3.3% SDS (w/v), 66 mM EDTA [pH 8.1], 6.6 mM EGTA [pH 8.1]) was added
followed by the addition of 20 µL of proteinase K (New England Biolabs, USA).
2.9.1.3 Collection of DNA fragments
Genomic DNA was isolated using the QIAamp DNA blood mini kit (QIAGEN,
Australia) using reagents and centrifuges at room temperature according to the
manufacturer’s instructions. DNA was recovered in 150 µL 1x TE buffer (See Section
2.1). Eluates were analysed using the Nanodrop ND-100 spectrophotometer (NanoDrop,
USA) routinely to ensure the intergrity of individual assays (Section 2.6.1).
2.9.1.4 qPCR and analysis
Quantitative real time PCR (qPCR) was performed using the SensiMix SYBR No-ROX
Kit (Bioline Pty Ltd, Australia) prepared in a 96-well plate (Bio-Rad Laboratories, USA)
using 5 µL DNA (approximately 100 ng), 2 µL of primer mix (forward and reverse
specific primers at 10 mM each), 10 µL of 2X SensiMix SYBR - No-ROX, and made up
74
to a final reaction volume of 20 µL with sterile ddH2O. Thermal cycling conditions for the
CFX96 Real-Time Detection System (Biorad Laboratories Inc., USA) were as follows:
95°C for 10 minutes; followed by 40 cycles of 95°C for 15 seconds, 64°C for 15 seconds
and 72°C for 15 seconds. Percentage chromatin accessibility was calculated as a ratio of
relative abundance of digested to undigested amplicon, with the measurement calculated
using the CFX Manager software (Biorad Laboratories Inc., USA). Statistics were
performed using GraphPad Prism v4 (GraphPad Software, USA).
2.9.2 Electrophoretic Mobility Shift Assay (EMSA)
The majority of EMSA protocols are based on those initially described by Garner and
Revzin (1981). In short, the EMSA approach consists of firstly the isolation of nuclear
protein extract from a cultured cell line of interest. Following isolation and washing,
allelic double-stranded oligonucleotides specifying a target genomic region were
incubated with or without nuclear protein extract. Binding reactions were terminated
and prepared for gel electrophoresis, and subsequently quantified using the
‘Chemilluminescent nucleic acid detection module’ (Thermo-Scientific, Australia) to
identify the presence of allele-specific DNA-protein binding complexes indicative of
functionality. For the functional assessment of genetic variants using EMSA, migratory
differences of allelic samples incubated in nuclear extract are compared to relative to a
control binding reaction absent of protein relative to determine the presence or absence
of allele-specific DNA-protein complexes.
2.9.2.1 Nuclear protein extraction
Nuclei were collected from 5x107 cells from culture cell lines by firstly harvesting by
centrifugation at 1000 rpm (Jouan C312 swinging rotor centrifuge). Following the
removal of supernatant, the cell pellet was resuspended in 5 mL ice-cold PBS. Cells
were collected by centrifugation again, re-suspended in 1 mL of ice-cold PBS and
75
transferred to a clean 1.5 mL Eppendorf tube. Cells were centrifuged down at 14000
rpm (Jouan C312 swinging rotor centrifuge), 4C for one minute, with the subsequent
supernatant discarded. The cell pellet was resuspended in 200 µL ice cold Buffer A (10
mM HEPES [pH 7.9], 1.5 mM MgCl2, 10 mM KCl, 0.5 mM DTT, supplemented prior
to use with 2 µL of 0.5 mM PMSF and 1 µL of 1 µg/mL pepstatin for every 1 mL used)
using a 28 gauge needle through which cells pass 6-8 times on ice. The cell suspension
was then collected by centrifugation at 14000 rpm (Jouan C312 swinging rotor
centrifuge), 4˚C, for ten seconds, with the resulting supernatant discarded. The crude
nuclei were extracted by re-suspending the cell pellet in 30 µL ice cold Buffer C (20
mM HEPES [pH 7.9], 1.5 mM MgCl2, 420 mM KCl, 0.5 mM DTT, 0.2 mM EDTA [pH
8.0], 25% [v/v] Glycerol, supplemented with 2 µL of 0.5 mM PMSF, 1 µL of 1 µg/mL
pepstatin and 1 µL of 1ug/mL leupeptin prior to use for every 1 mL used), followed by
mixing and incubation on ice for 20 minutes. An equal volume of Buffer D (20 mM
HEPES [pH 7.9], 0.5 mM DTT, 0.2 mM EDTA [pH 8.0], 20% [v/v] Glycerol,
supplemented with 2 µL of 0.5 mM PMSF, 1 µL of 1 µg/mL pepstatin and 1 µL of 1
µg/mL leupeptin prior to use for every 1 mL used) was added to the nuclei and mixed.
Finally, the nuclei were collected by centrifugation at 14000 rpm (Jouan C312 swinging
rotor centrifuge), 4˚C, for ten minutes. The purified nuclei were subsequently divided
into 20 µL aliquots, snap frozen in liquid nitrogen and stored at -80˚C until used.
2.9.2.2 Binding reaction preparation
To prepare the DNA retardation gel (Invitrogen Corporation, Australia) for
electrophoresis, the gel wells were washed with 0.5X running buffer (diluted from 5X
stock of 54 g trimza base, 27.5 g boric acid, added to 20 mL 0.5 M EDTA, and made-up
to 1 L with sterile water) with a Pasteur pipette to remove bubbles and contaminants.
The gel was assembled into the electrophoresis tank (Invitrogen Corporation, Australia),
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and filled with 0.5X running buffer. The gel was pre-run at 100V for 30 minutes.
During the incubation the DNA binding reaction was prepared in 1.5 mL Eppendorf
tubes on ice with nuclear extract (4 µL of 20% ficoll, 2 µL of 200 mM HEPES [pH 7.9],
1 µL of 20 mM EDTA [pH 8.0], 2 µL of 10 mM DTT, 1 µL of Poly dI:dC, 6 µL of
sterile water and 1.5 µg of nuclear extract) and without (4 µL of 20% ficoll, 2 µL of 200
mM HEPES [pH 7.9], 1 µL of 1 M KCl, 1 µL of 20 mM EDTA [pH 8.0], 2 µL of 10
mM DTT, 1 µL of Poly dI:dC and 6.5 µL of sterile water) followed by 10 minutes
incubation on ice. Following incubation, labeled sequence-specific oligonucleotides
were added to the binding reactions (2.5 µL in the nuclear protein reaction and 8 µL in
the reaction without extract) and left to incubate on ice for 30 minutes.
2.9.2.3 Gel electrophoresis and detection
Samples were mixed with 2 µL of bromophenol blue dye (Merck, Australia) and loaded
quickly onto the pre-run retardation gel, with an additional lane of dye to visually
monitor the migration progress of the samples. The gel was run for one hour at 100V.
Following the electrophoresis, the gel tank was reassembled with the addition of a nylon
membrane (Invitrogen Corporation, Australia) placed in direct contact with the DNA
retardation gel, and run at 30V for 30 minutes to facilitate the transfer of the DNA-
protein complexes formed in the binding reaction. The membrane was then removed
from the gel tank, covered with a sheet of plastic wrap, and exposed on a UV trans-
illuminator for ten minutes. The detection of immobilized nucleic acids was carried out
using the ‘Chemilluminescent nucleic acid detection module’ (Thermo-Scientific,
Australia) according to the manufacturer’s protocol. The membrane was then exposed
onto chemiluminescence film (GE Healthcare Limited, Australia) by firmly pressing the
membrane onto a sheet of film in a film cassette (Amersham, UK) for up to five
minutes, followed by the submersion of the exposed film in developer solution (diluted
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1:5 with sterile water prior to use) (Kodak, Australia) until bands became visible. The
film was then immediately placed into fixer solution (diluted 1:5 with sterile water prior
to use) (Kodak, Australia) to terminate the reaction. Excess fixer solution was removed
by rinsing the film in water. All exposure steps took place in a dark room with minimal
illumination. Genetic variants associated with allele-specific DNA-protein complexes in
EMSA were considered to be functional.
2.9.3 Bisulfite Sequencing
The majority of current bisulfite sequencing methodologies are based on those initially
described by Frommer et al. (1992). In brief, bisulfite sequencing consists of firstly the
bisulfite treatment of genomic DNA isolated from a cultured cell line of interest using
the EpiTect Bisulfite Kit (QIAGEN, Australia). Following isolation and washing,
genomic DNA was enriched for a genomic region of interest using sequence-specific
primers in PCR. Bisulfite-enriched target regions were subsequently cloned into TOPO
vectors using the TOPO TA Cloning kit (Invitrogen Corporation, Australia), and
transformed into competent E.coli. Following blue-white colony selection, positive
recombinant colonies were picked and grown overnight at 37˚C in LB broth
supplemented with ampicillin and subsequently purified using mini-prep spin columns
(QIAGEN, Australia). Finally, purified DNA was sequenced and analysed using
bioinformatic tools (BISMA) to determine the presence of allele-specific DNA
methylation patterns. Genetic variants that were associated with allele-specific DNA
methylation following bisulfite sequencing were considered to be functional.
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2.9.3.1 Bisulfite treatment
Genomic DNA was isolated from seven cultured human leukocyte cell lines from the
SAFHS cohort carrying different -587 genotypes (two -587G/G homozygotes, three -
587G/A heterozygotes and two -587A/A homozygotes) and purified using DNA Blood
mini columns (QIAGEN, Australia). Isolated DNA was treated with bisulfite to convert
unmethylated cytosine bases into uracils, and subsequently purified using the EpiTect
Bisulfite Kit (QIAGEN, Australia).
2.9.3.2 PCR amplification
The converted DNA was enriched for the VNN1 gene promoter by PCR using primers
(5’-CAGTGAGCCTGTACCCCTGG-3’ and 5’-TATTCTCCCCATGTGTGTGCC-3’)
flanking the predicted CpG island (between -758 to -496 upstream of the VNN1
transcriptional start site) and PCR amplified using 40 cycles with the protocol outlined
in Section 2.7.
2.9.3.3 Cloning and transformation
The amplification products were cloned (Section 2.2.3) into the pCR2.1-TOPO vector
(Invitrogen Corporation, Australia) and transformed (Section 2.2.4) into competent
E.coli (Invitrogen Corporation, Australia) for subsequent Sanger-based DNA
sequencing (Australian Genome Research Facility, Perth, Western Australia). The
transformation mix was plated onto LB-agar plates (Section 2.2.1) supplemented with
amplicillin and X-gal for blue-white colony selection (Section 2.2.4). White
recombinant colonies were picked the following day and grown overnight at 37˚C in LB
broth (Section 2.2.1) supplemented with ampicillin and subsequently purified (Section
2.5.1) using mini-prep spin columns (QIAGEN, Australia).
79
2.9.3.4 DNA sequencing and analysis
Isolated clones were then sent for Sanger-based sequencing (Australian Genome
Research Facility, Perth, Western Australia) using the endogenous M13F primer (5´-
GTAAAACGACGGCCAG-3´). Sequences were analysed using BISMA
(http://biochem.jacobs-university.de/BDPC/BISMA/manual_unique.php) to identify the
methylation states of predicted CG di-nucleotides in CpG islands. Allele-specific
differences in methylation patterns, localised near a particular SNP, suggest the
potential for differential gene expression and functionality. Further bioinformatic
verification was performed using AliBaba 2.1 (http://www.gene-
regulation.com/pub/programs/alibaba2/index.html).
2.10 Targeted-CHA-seq (T-CHA-seq) In the present study, two novel in vivo approaches (‘targeted’ CHA-seq [T-CHA-seq] and
CHA-seq) were developed in order to characterise allele-specific differences in chromatin
arrangement within the VNN1 gene. Firstly in T-CHA-seq, a DNase I-digested genomic
library was prepared from a -137T/G heterozygous cell line from the SAFHS cohort and
prepared using the NEBNext DNA Sample Prep kit (New England Biolabs, USA).
Following library preparation DNA fragments were enriched for the -137 promoter
region of the VNN1 gene using two rounds of sequence-specific primers in PCR. VNN1-
enriched fragments were subsequently cloned into TOPO vectors using the TOPO TA
Cloning kit (Invitrogen Corporation, Australia), and transformed into competent E.coli.
Following blue-white colony selection, positive recombinant colonies were picked and
grown overnight at 37˚C in LB broth supplemented with ampicillin and subsequently
purified using mini-prep spin columns (QIAGEN, Australia). This T-CHA-seq library
was subsequently sequenced using traditional Sanger sequencing in order to map nuclease
digestion sites around the -137 SNP. Finally, purified DNA was sequenced and analysed
using bioinformatic tools (BLAST) to align allelic sequence reads to the -137 promoter
80
region and determine the presence of an allele-specific bias (“allele-skew”) in sequence
reads as a direct reflection of localised chromatin accessibility and functionality. To
facilitate a direct comparison between different alleles at a given SNP, SAFHS cell lines
containing heterozygous SNPs were used and loci correlating with high allele-skews
were considered to be functional genetic variants.
2.10.1 T-CHA-seq library preparation
2.10.1.1 Isolation of nuclei from cell lines
Nuclei from 1 x 108 cells (~100 mL) of a eukaryotic cell line were harvested by
centrifugation at 1000 rpm (Jouan C312 swinging rotor centrifuge) for five minutes at
4OC (using two 50 mL Falcon tubes). Following the removal of supernatant, the cell
pellet was washed in 50 mL ice-cold PBS and collected again by centrifugation. The
pellet was subsequently resuspended in 4.5 mL Igepal Lysis buffer (10 mM Tris.HCl
[pH 7.4], 10 mM NaCl, 3 mM MgCl2, 0.5% Igepal CA-630, 0.15 mM spermine and 0.5
mM spermidine), and incubated on ice for five minutes while swirling occasionally.
Cells were transferred to six 1.5 mL Eppendorf tubes and centrifuged at 3000 rpm
(Jouan C312 swinging rotor centrifuge) for five minutes at 4ºC. Resulting supernatant
was then removed and the remaining crude nuclei were washed with 1 mL Digestion
buffer without CaCl2 (10 mM Tris.HCl [pH 7.4], 15 mM NaCl, 60 mM KCl, 0.15 mM
spermine and 0.5 mM spermidine), and centrifuged at 3000 rpm (Jouan C312 swinging
rotor centrifuge) for five minutes at 4OC. The nuclei were then resuspended in 300 µL
warmed Digestion buffer with CaCl2 (prepared by the addition of 25 µL of 1 M CaCl2
and 150 µL of 1 M MgCl2 to 25 mL of Digestion buffer without CaCl2).
81
2.10.1.2 Digesting DNA with chromatin accessibility agent
The nuclei from all six 1.5 mL Eppendorf tubes were transferred to a 50 mL Falcon tube
(~1.8 mL) and warmed at 37°C for two minutes. Following the incubation, 1.8 mL
DNase I (T-CHA-seq) (2 µL of 251 U/µL DNase I [Invitrogen Corporation, Australia]
was diluted into 51 µL Digestion buffer with CaCl2 [Section 2.9.1.2], with 36 µL of this
dilution added to 1.8 mL of Digestion buffer with CaCl2 to generate a 1X working
solution) dilution was added to the nuclei and digested for six minutes at 37°C. A 2 mL
aliquot of buffer AL (QiaAmp DNA blood mini kit, QIAGEN, Australia) was then
added to the digested nuclei and mixed by inversion. An aliquot of 2.2 mL PBS and 200
µL Qiagen Protease (QiaAmp DNA blood mini kit, QIAGEN, Australia) was combined
with digested nuclei. The samples were then transferred to four 1.5 mL Eppendorf tubes
and incubated at 55ºC overnight to terminate digestion. Digested nuclei were diluted
with 7 mL sterile water and 10 mL buffer AL, and were subsequently mixed by
inversion. Samples were purified to isolate digested DNA fragments using the QiaAmp
DNA blood mini kit (QIAGEN, Australia) according to the manufacturer’s protocol.
2.10.1.3 NEBNext DNA Sample Prep kit processing
A T-CHA-seq library of compatible DNA fragments was prepared for deep sequencing
using the NEBNext DNA Sample Prep kit (New England Biolabs, USA) in accord with
the manufacturer’s instructions provided with the kit. The NEBNext DNA Sample Prep
kit (New England Biolabs, USA) consists of several workflow steps including end-
repair and adapter sequence annealing and adapter-specific amplification using PCR.
Size selection was required to generate a library with an average size of 330 bp for
subsequent deep sequencing using the Illumina/Solexa Genome Analyser II (Illumina,
USA). Consequently, the prepared T-CHA-seq library was sized using Agarose Gel
82
Electrophoresis (Section 2.6.2) by relative comparison to a control (100 bp DNA ladder,
New England Biolabs, USA) on a 2% agarose gel (and run for 30 min at 90V). Gel
pieces containing library fragments of sizes between 150-400 bp (mono- and di-
nucleosomal sizes) were cut out while visualising with a dark reader - blue light
transilluminator (Clare Chemical Research, USA). Library fragments were purified
using the QIAquick Gel Extraction kit (QIAGEN, Australia).
2.10.2 PCR amplification
Two rounds of PCR amplification (Section 2.7) using nested sets of sequence-specific
primers (Table 2.2) were used to increase target yield of the -137 VNN1 locus from both
the forward (5’ to 3’) and reverse (3’ to 5’) direction using the protocol outlined in
Section 2.7, with 20 cycles for each round. In each round of amplification, one of the
target specific primers was paired with an adapter-specific primer to ensure only library
fragments are amplified. After each round of amplification, products were purified
using the QIAquick PCR purification Kit (QIAGEN, Australia) to ensure no primer
sequence were present in subsequent rounds of PCR. Following two rounds of
amplification for each of the ‘forward’ and ‘reverse’ libraries consisting of enriched -
137 VNN1 sequence, libraries were combined to ensure the entire -137 promoter region
was mapped for DNase I cut sites.
83
Table 2.2: PCR primers used in T-CHA-seq. Two rounds of PCR amplification were performed using nested sets of sequence-specific primers of the sequence shown (“DNA sequence”), in order to increase target yield of the -137 VNN1 locus from both the forward (5’ to 3’) and reverse (3’ to 5’) direction. In each round of amplification, one corresponding VNN1-specific primer was paired with an adapter-specific primer to ensure only library fragments are amplified. After each round of amplification, products are purified using the QIAquick PCR purification Kit (QIAGEN, Australia) to ensure no primer sequence is present in subsequent rounds of PCR.
Primer
orientation
Round of
nested PCR
DNA sequence (5’ to 3’)
Novel forward primers
First round 5’-GCAGCCCTAGCGAACTAATG-3’
Second round
5’-TCTTGAGTTAATCTTCACAAATTACTGC-3’
Novel reverse primers
First round 5’-GTCCAGAACTTAGTACCTTAAACTACGGAAG 3’
Second round
5’-CGCTATGATTTCATAGCATTGTTACATAAC 3’
Adapter-specific primer
First and second round
5’-ACTCTTTCCCTACACGACGC-3’
84
2.10.3 TOPO cloning and transformation
The PCR-enriched amplification products were cloned (Section 2.2.3) into the pCR2.1-
TOPO vector (Invitrogen Corporation, Australia) and transformed (Section 3.2.4) into
One-Shot chemically competent E.coli (Invitrogen Corporation, Australia). The
transformation mix was plated onto LB-agar plates (Section 2.2.1) supplemented with
amplicillin and X-gal for blue-white colony selection (Section 2.2.4). Positive white
recombinant colonies were picked the following day and grown overnight at 37˚C in LB
broth (Section 2.2.1) supplemented with ampicillin and subsequently purified (Section
2.5.1) using mini-prep spin columns (QIAGEN, Australia).
2.10.4 Sanger DNA sequencing
Prepared T-CHA-seq library fragments were sequenced by traditional Sanger-based
chemistry (http://www.agrf.org.au/index.php?id=72#Purified DNA Process) at the
Australian Genome Research Facility (AGRF) on Murray St, Perth, Western Australia
using 1 µg of DNA in 12 µL and 1 µL of 10 mM M13F primer (5´-
GTAAAACGACGGCCAG-3´).
2.10.5 Analysis and bioinformatics
In order to map DNase I digestion sites around the -137 SNP and determine the presence or
absence of an allele-specific bias in read number (or ‘allele-skew’), T-CHA-seq
sequence reads were mapped to the -137 promoter region using bioinformatic alignment
tools such as BLAST, which are available online at the National Center for
Biotechnology Information (NCBI) website (http://blast.ncbi.nlm.nih.gov/Blast.cgi).
Significant allele-skews were considered to be functional.
85
2.11 Extended CHA-seq (CHA-seq) A MNase-digested genomic library was prepared from a -587G/A heterozygous cell line
from the SAFHS cohort and subjected to high-throughput Illumina sequencing in order to
map allelic-nuclease digestion sites across the entire VNN1 gene. This CHA-seq approach
was developed as a refinement in methodology over the targeted T-CHA-seq approach,
in order to screen larger genomic regions for functional genetic variants. As in T-CHA-
seq, significant allele-skews were taken as a direct reflection of allele-specific nuclease
accessibility and functionality at heterozygous loci.
In summary, CHA-seq consisted of firstly the isolation of intact chromatin from a -
587G/A heterozygous cell line from the SAFHS of interest, followed by the nuclease
digestion with micrococcal nuclease and preparation of a DNA fragment library
compatible for next-gen sequencing using the NEBNext DNA Sample Prep kit (New
England Biolabs, USA). Following library preparation DNA fragments were enriched
for the Vanin gene family using sequence-specific biotinylated probes specific for the
SureSelect DNA Target Enrichment system. Bead-enriched fragments were
subsequently processed for cluster generation on the Illumina cluster station (Agilent,
USA) and sequenced using Illumina next-gen sequencing technology on the Illumina
Genome Analyzer II platform (Agilent, USA) in accord with the manufacturer’s
instructions. Finally, sequence reads were analysed using the Integrated Genome
Viewer (IGV) 2.1 (Broad Institute) software and aligned to the hg19 reference
sequence. Raw sequence data was used to interpret chromatin accessibility by
comparing numbers of allelic sequence reads at heterozygous SNPs. Genetic variants
with significant allele-specific bias in sequence read number (‘allele-skew”) were
considered to be functional.
86
2.11.1 Library preparation
CHA-seq library preparation was equivalent to that of T-CHA-seq, as described in
Section 2.10.1, with the only modification being the substitution of MNase for DNase I
in order to map nucleosome positions, rather than mapping transcription factor binding
sites.
2.11.2 Bead selection
In order to increase sequencing coverage of the Vanin gene family, a bead selection step
was included in the CHA-seq methodology. Following preparation of the MNase-
digested library using NEBNext DNA Sample Prep kit (New England Biolabs, USA),
target sequences containing the entire Vanin gene family were supplied to Agilent to
synthesise pools of biotinylated probes specific for the SureSelect DNA Target
Enrichment system. Libraries were purified using a bead-based selection protocol with
the SureSelect Target Enrichment System - Illumina Single-End Sequencing Platform
Library Prep Version 1.2 (Agilent, USA) according to the manufacturer’s instructions.
2.11.3 Cluster generation and deep sequencing
Prepared libraries were processed for cluster generation on the Illumina cluster station
(Agilent, USA) and subsequently sequenced using Illumina next-gen sequencing
technology on the Illumina Genome Analyzer II platform (Agilent, USA) in accord with
the manufacturer’s instructions.
2.11.4 Analysis with IGV 2.1
DNA sequence reads were then analysed using the Integrated Genome Viewer (IGV)
2.1 (Broad Institute) software and aligned to the hg19 reference sequence. The IGV
software allowed the visualisation of base sequence, read lengths and polymorphisms.
87
Raw sequence data was used to interpret chromatin accessibility by comparing numbers
of allelic sequence reads at heterozygous SNPs. Genetic variants with significant allele-
skews were considered to be functional.
2.12 Statistical Analyses
Statistical analyses (t-test, ANOVA, distribution test) using Graphpad Prism 5 software
(http://www.graphpad.com/prism/prism.htm) were used to determine statistical
significance in mean chromatin accessibilities between sample sets for each target locus
in CHART-PCR. For this research, CHART-PCR data was obtained from a minimum
of three independent experiments performed in triplicate. Statistical significance (*) as
shown was determined using student’s unpaired t-test using a confidence interval of
95% (P<0.05).
Bioinformatic analysis was performed by submitting a sequence of interest to publicly
available online databases which match to large collections of documented regulators,
enabling the prediction of transcription factor binding sites occurring in the submitted
sequence. Based on the predictions from multiple databases, a model can be developed
as to the role the submitted sequence has in a particular function in a biochemical
pathway. For example, EMSA (section 2.10.2) was used to validate allele-specific
DNA-protein binding differences predicted from online databases such as AliBaba2.1
(Grabe, 2002), and P-Match (Chekmenev et al., 2005).
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Chapter 3: In vivo assessment of VNN1 promoter SNPs using two chromatin-sensitive nucleases in CHART-PCR
3.1 Introduction
Chromatin arrangement is central to the regulation of cellular signals, controlling access
of transcription factors to the DNA, and consequently, gene expression (Ernst et al.,
2011). Furthermore, genetic variants may influence chromatin arrangement and
accessibility within genes (Mikkelson et al., 2007; Birney et al., 2010), potentially
affecting gene expression and phenotype (Pang et al., 2013). CHART-PCR was initially
designed to characterise the chromatin arrangement and in vivo transcription factor
binding differences within the IL-2 proximal gene promoter (Rao et al., 2001). Since
2001, the CHART-PCR methodology has been refined to enable cell-type and allele-
specific comparisons of chromatin accessibility associated with genetic variants, as
demonstrated for the CR2 gene (Cruikshank et al., 2012).
In CHART-PCR, chromatin accessibility can be assessed using nucleases which digest
DNA depending on the chromatin arrangement, including restriction enzymes Dra I and
HinfI (Rao et al., 2001; Bulanenkova et al., 2011). A key assumption of CHART-PCR
is that the degree of chromatin accessibility in a particular genomic region is directly
related to the level of physical protection to nuclease digestion conferred by the
occupancy of histones and bound proteins (Bell et al., 2011; An et al., 1998; Grayling et
al., 1997). Micrococcal Nuclease (MNase) and Deoxyribonuclease I (DNase I) are
commonly used nucleases in chromatin studies. They differ in terms of recognition sites
and the mode of nucleolytic digestion (Xiao et al., 2007). Complementary structural
chromatin information can be derived from the results of nuclease digestion including
89
the positions of nucleosomes and hypersensitive sites (Sajan et al., 2012; Mannherz et
al., 1995; Smith et al., 1983; Heins et al., 1967).
DNase I is an endonuclease which preferentially cleaves DNA at phosphodiester bonds
adjacent to a pyrimidine nucleotide, producing 5'-phosphate-terminated
oligonucleotides and 3’-hydroxylated ends (Liu, Z et al., 2012). DNase I footprinting is
an approach commonly used to map the chromatin arrangement of gene promoter
regions, potential transcription factor binding sites and areas of hypersensitivity within
the genome (Boyle et al., 2011; Drouin et al., 1997; Decker et al., 2009). The
integration of a deep sequencing approach has extended DNase I hypersensitive site and
chromatin mapping to a genome-wide scale (‘DNase-seq’) (John et al., 2011; Thurman
et al., 2012).
MNase (or S7 nuclease) is an endo-exonuclease, typically isolated from Staphylococcus
aureus, that preferentially digests naked DNA and linker DNA between adjacent
nucleosomes, thereby enriching for nucleosome-associated DNA which is partially or
completely protected from MNase cutting (Chung et al., 2010; Axel, 1975; Rizzo et al.,
2012). The occupancy and position of nucleosomes can therefore be assayed using
MNase (Rizzo et al., 2012; Li, Z et al., 2011; Yigit et al., 2013). Typically, the
quantification of MNase-digested DNA fragments has been performed using indirect-
end-label analysis (Kent and Mellor, 1995; Wu and Winston, 1997), or by qPCR as in
CHART-PCR (Cruikshank et al., 2008; Cruikshank et al., 2012). More recently, MNase
nucleosome mapping has shifted away from the quantification of reduced cutting
frequency, to a genome-wide approach that targets the abundance of recovered
nucleosome-sized DNA fragments by microarrays (Lantermann et al., 2010) or high-
90
throughput sequencing technologies (‘MNase-seq’) (Wei et al., 2012; Gaffney et al.
2012, Li et al., 2012).
To interpret the level of nuclease digestion in CHART-PCR, control genomic regions
are fundamental to establishing benchmark chromatin accessibility in order to compare
samples between different cell lines (Rao et al., 2001; Cruikshank et al., 2012). For
example, Surfactant protein 2 (SPA-2) and Beta-actin (β-actin) have been used in
CHART-PCR as chromatin accessibility controls to characterise the chromatin state of
the human CR2 gene with MNase in a number of immortalised human cell lines (U937,
REH, Ramos and Raji) (Cruikshank et al., 2008). Cruikshank et al. (2008) determined
an SPA-2 regulatory region to be representative of transcriptionally silent
heterochromatin, as this region has not been found to be expressed in hematopoietic
cells (Li et al., 1998). Alternatively, β-actin has been demonstrated to have
constitutively high gene expression in most eukaryotic non-muscle cells and therefore is
commonly used as an internal control for the quantification of mRNA levels (Zhang et
al., 2005; Danilition et al., 1991). For example, Cruikshank et al. (2012) used sequence
near the transcriptional start site of the β-actin gene as a control in order to characterise
the chromatin accessibility of a functional SNP (rs3813946) in the CR2 gene using
CHART-PCR (Cruikshank et al., 2012). Consequently, SPA-2 and β-actin were used as
control genomic regions in the current study to interpret the relative level of chromatin
accessibility in CHART-PCR and to facilitate comparisons between multiple cell lines.
In this chapter, the relative level of chromatin accessibility was elucidated at candidate
functional SNPs in the VNN1 gene model. The degree of chromatin accessibility in
DNA heterozygous for both -137 (rs4897612) and -587 (rs2050153) promoter variants,
which are 450bp apart, was assessed by comparing digestion patterns to SPA-2 and β-
91
actin controls. Furthermore, both nucleases were used to assess the VNN1 promoter for
allele-specific chromatin accessibility by examining the level of DNase I
hypersensitivity sites and the degree of nucleosome protection from MNase in cell lines
homozygous for both the -137 and -587 SNPs.
3.2 Methods
A detailed description of the CHART-PCR methodology is shown in Section 2.9.1
(Chapter 2), with a brief summary included below.
Nuclei were isolated and digested with 20 U/mL nuclease using approximately 1 x 107
genotyped Ramos (ATCC, USA) and SAFHS cohort cells. Nuclease-digested DNA
fragments were purified using the QIAamp DNA blood mini kit (QIAGEN, Australia),
followed by quantitative real time PCR using approximately 100 ng DNA, 0.5µM of
primers (See Table 3.1) and SensiMix SYBR No Rox (Bioline Pty Ltd, Australia) in a
final reaction volume of 20 µL. Thermal cycling conditions for the CFX96 Real-Time
Detection System (Biorad Laboratories Inc., USA) were as follows: 95°C for 10 min;
followed by 40 cycles of 95°C for 15 seconds, 64°C for 15 seconds and 72°C for 15
seconds. Percentage chromatin accessibility was determined as a ratio of relative
abundance of digested to undigested amplicon, calculated using the CFX Manager
software (Biorad Laboratories Inc., USA). Statistics were performed using GraphPad
Prism v4 (GraphPad Software, USA).
92
Table 3.1: CHART-PCR primers
Sequence-specific forward and reverse (FWD and REV) primers were used for quantitative real-time PCR analysis of the -587 and -137 VNN1 SNPs and the SPA-2 and β-actin promoter sequences. Specific sequences are shown (“Primer sequence”) and were derived from published reports of promoter sequences regulating SPA-2 (Cruikshank et al., 2008; Li et al., 1998) and β-actin (Cruikshank et al., 2008; Danilition et al., 1991) or designed based on the sequence surrounding the VNN1 SNPs. Forward (FWD) and reverse (REV) primers are indicated by suffix. Pairs used in qPCR assays are grouped together and share the same prefix along with the predicted amplicon size (“Expected amplicon size”).
Primer designation Primer sequence (5’ to 3’) Expected
amplicon size (bp)
-587 CHART-PCR FWD GAGAATCACATGAACCCGAGAGG 129
-587 CHART-PCR REV GTGAGGACACCACTCTAATCTTTAACACAG
-137 CHART-PCR FWD CCTAGCGAA CTAATGTACATAGAGTTCTTGAG 104
-137 CHART-PCR REV AACGCTATGATTTCATAGCATTGTTACATAAC
SPA-2 CHART-PCR FWD
CTAAGTATTCCTCCAGCCTGAGTGTTC 145
SPA-2 CHART-PCR REV
GGTGGACAACAGCATTTATAGCATG
β-actin CHART-PCR FWD
CTGCACTGTGCGGCGAAGCCGGTG 177
β-actin CHART-PCR REV
CGGACGCGGTCTCGGCGGTGGTG
93
3.3 Results
3.3.1 Assessment of relative chromatin accessibility within the VNN1 gene promoter using DNase I
3.3.1.1 Chromatin accessibility of VNN1 SNPs relative to SPA-2 and β-actin control loci using DNase I
To determine the relative level of DNase I digestion within the -137 and -587 VNN1
promoter regions, the CHART-PCR methodology was applied on DNase I-digested
chromatin from heterozygous -137T/G and -587G/A cell lines and compared to control
sequences near the transcriptional start site of SPA-2 and β-actin. The results showed
that the -137 SNP had an equivalent DNase I cutting (23.0%) to that of β-actin (20.5%),
indicating high chromatin accessibility (Figure 3.1). In contrast, the level of DNase I
cutting observed at the -587 locus was 17.1%, implying a moderate-to-high accessibility
by comparison to controls (β-actin = 20.5% and SPA-2 = 7.5%). These results provide
evidence that the chromatin states of the -137 and -587 promoter regions are
transcriptionally active, at a level equivalent to that of the β-actin gene promoter.
94
-137
T/G
-587
G/A
SPA-2
B-A
ctin
0
5
10
15
20
25
30
35
*P=0.0048
*P=0.005
*P=0.024
*P=0.010
Genotype
Ch
rom
ati
n A
cc
es
sib
ilit
y
(% c
utt
ing
)
Figure 3.1: Chromatin accessibility differences at the -137 and -587 VNN1 SNPs
relative to SPA-2 and β-actin control regions. Chromatin accessibility at each VNN1 SNP, expressed as a ratio of undigested to DNase I-digested chromatin (% cutting). The data were obtained from the Ramos cell line which is heterozygous at each SNP (genotype) and assayed in triplicate from independent cultures (n=3). DNase I cutting values for each replicate are shown in full in Table A.4 (Appendix – Supplementary Tables and Figures). Significant difference between genotypes (P<0.05) was determined by an unpaired Student’s t-test and indicated by an asterisk (*). Error bars represent standard error of the mean across replicates of each sample.
95
3.3.1.2 Assessment of allele-specific chromatin accessibility at VNN1 SNPs using DNase I
To evaluate allele-specific chromatin accessibility, DNase I was used to characterise
chromatin derived from cell lines, homozygous at the -137 (-137T/T and -137G/G) and
-587 (-587G/G and -587A/A) loci. The CHART-PCR results showed significant
(P<0.05) allele-specific DNase I digestion at both loci (Figure 3.2). The -137T/T
homozygote (30.4%) had a 1.2-fold higher degree of DNase I cutting than the -137G/G
homozygote (25.5%). Similarly, the -587A/A homozygote (22.6%) possessed a 1.2-fold
higher degree of DNase I cutting than -587G/G (18.2%). On average, 1.3-fold higher
frequency of DNase I cutting was observed in the -137 homozygotes compared to -587
homozygotes (Figure 3.2), which supports the results from chromatin comparison in
heterozygous cell lines (Figure 3.1), which demonstrated the -137 locus has a higher
level of chromatin accessibility and transcriptional potential
96
-137
T/T
-137
G/G
-587
G/G
-587
A/A
0
5
10
15
20
25
30
35 *P=0.003
*P=0.001
*P=0.004
*P=0.003
*P=0.047
Genotype
Ch
rom
ati
n A
cc
es
sib
ilit
y
(% c
utt
ing
)
Figure 3.2: Chromatin accessibility differences at the -137 and -587 VNN1 SNPs. Chromatin accessibility at each VNN1 SNP, expressed as a ratio of undigested to DNase I-digested chromatin (% cutting). The data were obtained from cultured human cell lines derived from the SAFHS cohort for each genotype and assayed in triplicate from independent cultures (n=3). DNase I cutting values for each replicate are shown in full in Table A.5 (Appendix – Supplementary Tables and Figures). Significant difference between genotypes (P<0.05) was determined by an unpaired Student’s t-test and indicated by an asterisk (*). Error bars represent standard error of the mean across replicates of each sample.
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3.3.2 Assessment of relative chromatin accessibility within the VNN1 gene promoter using MNase
3.3.2.1 Chromatin accessibility of VNN1 SNPs relative to SPA-2 and β-actin control loci using MNase
MNase was used to evaluate the relative level of chromatin accessibility in the VNN1
promoter region surrounding the heterozygous -137T/G and -587G/A loci by relative
comparison to SPA-2 and β-actin controls as per the CHART-PCR method. A mean
MNase cutting frequency of 30.8% was observed at the -137 locus, 25.9% at -587,
20.4% at SPA-2 and 29.9% for the β-actin locus (Figure 3.3). These results are
suggestive that the -587 SNP had low-to-moderate chromatin accessibility, whilst the -
137 SNP had moderate-high chromatin accessibility, similar to that of the β-actin
control. The observed levels of chromatin accessibility at this locus using MNase
support the data obtained using DNase I (Figure 3.1). Results obtained using MNase
and DNase I (Figure 3.3) provide evidence that the -137 and -587 promoter reigons are
transcriptionally active regions with moderate-high chromatin accessibility
98
-137
T/G
-587
G/A
SPA-2
B-A
ctin
0
5
10
15
20
25
30
35
*P=0.027
*P=0.008
*P=0.027
*P=0.015
*P=0.007
Genotype
Ch
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ati
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cc
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sib
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y
(% c
utt
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)
Figure 3.3: Chromatin accessibility differences at the -137 and -587 VNN1 SNPs
relative to SPA-2 and β-actin control regions. MNase-treated cell lines of the genotypes shown were used to calculate chromatin accessibility expressed as a ratio of undigested to MNase digested DNA (% cutting). The data were obtained from the Ramos cell line and assayed in triplicate from independent cultures (n=3). MNase cutting values for each replicate are shown in full in Table A.6 (Appendix – Supplementary Tables and Figures). Significance (P<0.05) was determined between genotypes by an unpaired Student’s t-test and shown by an asterix (*). Error bars show the standard error of the mean between replicates of each sample.
99
3.3.2.2 Assessment of allele-specific chromatin accessibility at VNN1 SNPs using MNase
To differentiate allele-specific chromatin accessibility differences at SNPs within the
VNN1 gene promoter, the chromatin accessibility of DNA from cells homozygous and
heterozygous for the -137 and -587 SNPs were compared using CHART-PCR (MNase
digestion). The results showed a MNase cutting percentage in -137G/G homozygotes of
13.6% (cell line 0306) and 13.6% (cell line 0525), which was significantly lower than
that of the -137T/T homozygotes with 24.8% (cell line 0188) and 29.1% (cell line 0194)
(Figure 3.4A). The -137T/G heterozygotes had a MNase cutting level intermediate to
that of the -137 homozygotes with 18.8% (cell line 0029) and 16.6% (cell line 0054)
(Figure 3.4A). The average nuclease cutting for each genotype were 13.6% for the -
137G/G homozygotes, 26.9% for the -137T/T homozygotes, and 17.7% for the
heterozygotes (Figure 3.4B). The -137T/T homozygote had a 2-fold higher nuclease
cutting level than that of -137G/G, indicating the presence of allele-specific chromatin
accessibility at the -137 locus.
At the -587 locus, the -587A/A homozygotes had a MNase cutting level of 26.6% (cell
line 0306) and 27.6% (cell line 0525), which was higher than that of the -587G/G
homozygotes with 7.2% (cell line 0188) and 13.9% (cell line 0194) (Figure 3.5A). The
degree of MNase cutting in -587G/A heterozygotes was intermediate compared to the -
587 homozygotes with 15.0% (cell line 0029) and 16.3% (cell line 0054) (Figure 3.5A).
The average MNase cutting of the genotypes were 10.6% for the -587G homozygotes,
27.1% for the -587A homozygotes, and 15.6% for the heterozygotes (Figure 3.5B). The
-587A homozygote had a 2.6-fold higher degree of MNase cutting than that of -587G/G,
indicating the presence of allele-specific chromatin accessibility at the -587 locus.
100
A
0306
(G/G
)
0525
(G/G
)
0029
(T/G
)
0054
(T/G
)
0188
(T/T
)
0194
(T/T
)
0
5
10
15
20
25
30
35
SAFHS cohort ID (-137 genotype)
Ch
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sib
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(% c
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)
B
-137
G/G
-137
T/G
-137
T/T
0
5
10
15
20
25
30
35
*P=0.0179
*P<0.0001
*P<0.0003
Genotype
Ch
rom
ati
n A
ccessib
ilit
y(%
cu
ttin
g)
Figure 3.4: Chromatin accessibility differences at the -137 VNN1 SNP. MNase-
treated cell lines of the genotypes shown were used to calculate chromatin accessibility expressed as a ratio of undigested to MNase-digested DNA (% cutting). Figure 3.4A shows the data obtained from cultured cell lines derived from the SAFHS cohort for each genotype and assayed in quadruplicate or greater from independent cultures (n≥4). MNase cutting values for each replicate are shown in full in Table A.7 (Appendix – Supplementary Tables and Figures). Averages of the genotypes are shown in Figure 3.4B. Significance (P<0.05) between means was established by One-way ANOVA, with Turkey's Multiple Comparison Test used to determine significance between cell lines (genotypes) as shown in Figure 3.4B as an asterix (*). Error bars show the standard error of the mean between replicates of each sample.
101
A
0306
(A/A
)
0525
(A/A
)
0029
(G/A
)
0054
(G/A
)
0188
(G/G
)
0194
(G/G
)
0
5
10
15
20
25
30
35
SAFHS cohort ID (-587 genotype)
Ch
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sib
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(% c
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)
B
-587
A/A
-587
G/A
-587
G/G
0
5
10
15
20
25
30
35*P<0.0001
*P<0.0001
*P=0.0048
Genotype
Ch
rom
ati
n A
cc
es
sib
ilit
y
(% c
utt
ing
)
Figure 3.5: Chromatin accessibility differences at the -587 VNN1 SNP. MNase-treated cell lines of the genotypes shown were used to calculate chromatin accessibility expressed as a ratio of undigested to MNase digested DNA (% cutting). Figure 3.5A shows the data were obtained from cultured human cell lines derived from the SAFHS cohort for each genotype and assayed in quadruplicate or greater from independent cultures (n≥4). MNase cutting values for each replicate are shown in full in Table A.8 (Appendix – Supplementary Tables and Figures). Averages of the genotypes are shown in Figure 3.5B. Significance (P<0.05) between means was established by One-way ANOVA, with Turkey's Multiple Comparison Test used to determine significance between cell lines (genotypes) as shown in Figure 3.4B as an asterix (*). Error bars show the standard error of the mean between replicates of each sample.
102
3.4 Discussion
CHART-PCR results in the present study are suggestive that the -137 and -587 SNPs
have an allele-specific effect on chromatin accessibility in VNN1, which supports the
findings of Göring et al. (2007) which identified these SNPs to be candidate functional
genetic variants. The overall level of nucleosome protection within the VNN1 gene
promoter localised to the -137 (30.8% for -137T/G) and -587 (25.9% for -587G/A) loci,
was found to be low relative to control genomic regions (20.4% for SPA-2 and 29.9%
for β-actin). This observation supports research that indicates that the VNN1 gene is
highly expressed within leukocytes (Galland et al., 1998; Jansen et al. 2009; Yanai et
al., 2005). Furthermore, the level of DNA-binding protein occupancy at the -137 and -
587 VNN1 SNPs, as a measure of DNase I hypersensitivity using CHART-PCR, was
shown to be approximately equivalent (23.0% for -137T/G, and 17.1% for -587G/A) to
that of the transcriptionally-active β-actin locus (20.5%). Consequently, chromatin
within the VNN1 promoter is likely to confer accessibility to the binding of transcription
factors and other potential regulators. In fact, allele-specific DNA-protein binding
complexes have been experimentally demonstrated at the -137 VNN1 SNP using
EMSA, with Sp1 being the likely candidate in the presence of the T allele (Göring et al.,
2007). Sp1 has been associated with high gene expression, and contains a zinc finger
protein motif which directly binds DNA and enhances transcription (Jiang et al., 2005).
Since high chromatin accessibility is observed in the presence of the -137T allele,
increased VNN1 expression due to greater Sp1 accessibility would be expected.
Alternatively, the 2-fold difference in the level of nucleosome protection in the presence
of the -137 alleles may also be sufficient to inhibit the binding of the Sp1 transcription
factor reported by Göring et al. (2007). Consequently, the allele-specific effects in
chromatin arrangement at the -137 and -587 variants are likely to influence VNN1 gene
expression.
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At the -587 VNN1 SNP, the CHART-PCR results showed allele-specific chromatin
accessibility with DNase I (1.2-fold higher at -587A/A) and MNase (2.6-fold higher at -
587A/A), demonstrating that this genetic variant has a functional role at the chromatin
level. While Göring et al. (2007) identified the -587 SNP to be a functional candidate
based on statistical approaches, no allele-specific DNA-protein binding complexes were
found with EMSA (Göring et al., 2007). Therefore, both findings indicate the functional
mechanism at the -587 SNP is not due to allele-specific chromatin accessibility
contributing to differential transcription factor binding, as seen at the -137 SNP. In
Chapter 5 of the present study, the VNN1 gene promoter was assessed for allele-specific
DNA methylation associated with the -587 SNP using bisulfite sequencing.
Two control genomic regions, namely SPA-2 and β-actin, were assessed using CHART-
PCR to demonstrate the relative level of chromatin accessibility to DNase I and MNase
within the VNN1 gene promoter. SPA-2 and β-actin were selected to represent
transcriptionally-silent heterochromatin and transcriptionally-active euchromatin
controls respectively (Li et al., 1998; Zhang et al., 2005; Danilition et al., 1991;
Cruikshank et al., 2012). Importantly, the chromatin accessibilities of the two control
regions were found to be significantly different using both DNase I and MNase, which
matched the findings of Cruikshank et al (2008). For instance, the present study
reported 1.5-fold higher MNase digestion at the β-actin locus relative to SPA-2 in
Ramos cells, with Cruikshank et al (2008) reporting a similar fold difference (2.0) using
an equivalent methodology in Ramos cells. Therefore, both SPA-2 and β-actin are
effective as control genomic regions for chromatin accessibility within VNN1.
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While the mode of nucleolytic digestion and the recognition sites differed for DNase I
and MNase, both nucleases were able to identify allele-specific chromatin accessibility
at the -137 and -587 VNN1 SNPs. Functionality, in terms of allele-specific chromatin
accessibility, was assigned to SNPs that showed a significant difference (P<0.05)
between alleles as implemented by Cruikshank et al. (2012) at the rs3813946 SNP in
the CR2 gene. The approach used in the present study, extended upon that of
Cruikshank et al. (2012) to include DNase I, which gave equivalent results to that of
MNase. While the digestion level of MNase and DNase I differed acrossed cell lines,
both nucleases corresponded with equivalent fold differences when comparing the
heterozygous -137T/G and -587G/A (1.3-fold with DNase I and 1.2-fold with MNase)
in Ramos cells. Therefore, the use both DNase I and MNase was adequate for the
identification of functional genetic variants based on chromatin accessibility and
potentially could be applied to other nucleases and genetic variants.
The selection of both DNase I and MNase for use in chromatin arrangement studies
provided a greater level of confidence in assigning function to genetic variants. Taken
together, the results of CHART-PCR using DNase I and MNase indicate
complementary structural chromatin information regarding the VNN1 gene promoter,
including the relative positions of nucleosomes and hypersensitive sites.
Although CHART-PCR studies have enabled the in vivo characterisation of chromatin
arrangement and transcriptional potential in multiple genes and organisms (Fürbass et
al., 2008; Su et al., 2004; Goriely et al., 2004; Pompei et al., 2007), variability can
arise when targeting the alleles of individual genetic variants. Given the requirement for
the comparison of chromatin between different homozygotic cell lines, chromatin
accessibility values derived from CHART-PCR remain a relative measures approach
105
(Cruikshank et al., 2008), rather than an ‘absolute’ figure that accounts for all biological
variation when comparing multiple cell lines. For example, cell line-specific MNase
digestion was observed at both the -137 and -587 VNN1 SNPs in the present study.
MNase digestion levels at the -137T/G locus in the Ramos cell line was 30.8%, as
compared to 18.8% (‘0029’) and 16.6% (‘0054) in the SAFHS cell lines. In this case,
the characteristics of the cell line such as genotype and culture conditions also need to
be considered when interpreting CHART-PCR measurements (Baral et al., 2012).
Whole-genome comparisons of heterozygotic cell lines overcome biological variation
associated with assessment of multiple cell lines by simultaneously assessing both
alleles at a given genetic variant in the context of a genome (Pastinen, 2010).
Consequently, the development of novel genome-wide approaches for targeting
chromatin accessibility at genetic variants may overcome these inherent limitations
associated with current assays such as CHART-PCR.
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Chapter 4: CHA-seq - A novel functional variant screen
4.1 Introduction
Genetic variants correlating with allele-specific effects on gene expression or phenotype
are considered to be functional (Wang et al., 2011; Jian et al., 2011). Functional genetic
variants have been associated with cell function (Bonetti et al., 2012; Johnson et al.,
2012) and disease (Bartoszewski et al., 2010; Zhang et al., 2011). Functional genetic
variation can be attributed to allelic sequence changes in regulatory elements, which
may alter gene expression, often without an obvious effect on phenotype (Kleinjan and
van Heyningen, 2005; Pastinen et al., 2004). For example, Pastinen et al. (2004)
surveyed allelic imbalances in gene expression in heterozygous human cells, and
reported that 23 of the 126 genes tested showed a bias in gene expression toward one
allele. This allelic imbalance approach involved quantitative genotyping of
heterozygous individuals for genetic variants within RNA transcripts and compared
observed allele ratios to assumed equimolar (50:50) allele ratios. Pastinen et al. (2004)
determined an average observed allele ratio of 37:63 as the cutoff for determining the
presence of a significant allelic imbalance. As such, an allelic imbalance of greater than
37:63 is a likely indicator of functional genetic variants.
Traditionally, reporter gene and gel shift assays have been considered sufficient to
assess the in vitro functionality of genetic variants; however there are many variables
that may confound results (Karimi et al., 2009; Holden and Tacon, 2011). Furthermore,
these ‘gold standard’ approaches have inherent limitations including locus-specificity
and the lack of endogenous chromatin context (Hager et al., 2009). While more recent
in vivo approaches such as CHART-PCR can be used to functionally interrogate
107
endogenous chromatin contexts, the applicability of using existing locus-specific assays
to interpret genetic variants in a genome-wide context remains time and cost-inefficient
(Barski et al., 2007).
With advances in DNA sequencing technologies, genome-wide approaches have
become efficient alternatives for identifying functional genetic variants (Hunt et al.,
2008; Satake et al., 2009; Collas, 2010; Statham et al., 2012). The use of sequencing to
characterise chromatin arrangement, as opposed to quantitative real-time PCR in the
CHART-PCR approach, not only allows scalability but also eliminates PCR-based
issues, including primer design, hetero-duplex formation, mis-priming and primer
dimerisation (Freeman et al., 1999; Bustin et al., 2005; and Git et al., 2010). The
current study demonstrates a novel sequencing-based approach for assessing
functionality of non-coding sequence variation, by developing strategies that assess
function in an in vivo context.
The development of strategies that assess differences in chromatin arrangement as a
measure of functional difference in an in vivo context represents an ideal approach to
greatly increase time and cost-efficiency and to assign function to a particular genetic
variant within an extended genomic region. The current study translates traditional
chromatin arrangement assays such as the CHART-PCR method into a novel, robust
approach (CHA-seq) in order to assess allele-specific differences in chromatin arrangement
across an extended genomic region, based on next generation sequencing. The CHA-seq
approach represents a means of simultaneously sequencing genetic variants, mapping
nucleosomes and nuclease hypersensitivity sites, and assessing SNP functionality. The
development of enhanced genome-wide in vivo approaches allows for a more time and
108
cost-effective assessment of whether a variant does indeed regulate gene expression and
yields insights into the long range allele-specific mechanisms of action.
In this study, two novel in vivo approaches (‘targeted CHA-seq’ [T-CHA-seq] and
‘extended CHA-seq’ [CHA-seq]) were developed in order to characterise allele-specific
differences in chromatin arrangement within the VNN1 gene (Figure 4.1). For the
development of T-CHA-seq, a DNase I-digested genomic library was prepared from a -
137T/G heterozygous cell line from the SAFHS cohort and amplified for the -137 SNP
using two rounds of ‘nested’ PCR. This T-CHA-seq library was subsequently sequenced
using traditional Sanger sequencing in order to map nuclease digestion sites around the -137
SNP. For CHA-seq, a MNase-digested genomic library was prepared from a -587G/A
heterozygous cell line from the SAFHS cohort, and subjected to high-throughput Illumina
sequencing in order to map allelic-nuclease digestion sites across the entire VNN1, VNN2
and VNN3 genes. Subsequently, library sequence reads for both approaches were
compared to establish the presence of an allele-specific bias in read number (or ‘allele-
skew’) as a direct reflection of allele-specific nuclease digestion and functionality. To
facilitate a direct comparison between different alleles at a given SNP, SAFHS cell lines
containing heterozygous SNPs were used in the two novel approaches and loci
correlating with high allele-skews were considered to be functional genetic variants.
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Figure 4.1 Principles of CHA-seq and T-CHA-seq. A: CHA-seq consists of firstly the preparation of a MNase-digested genomic library using the NEBNext DNA Sample Prep kit (New England Biolabs) on a -587G/A heterozygous cell line from the SAFHS cohort. After bead selection to enrich for genes of interest, high-throughput Illumina sequencing is performed and reads are mapped to the human genome using the Integrated Genome Viewer (IGV)(Broad Institute) in order to map allelic-nuclease digestion sites across the
VNN1, VNN2 and VNN3 genes. B: T-CHA-seq consists of firstly the preparation of a DNase I-digested genomic library using the NEBNext DNA Sample Prep kit (New England Biolabs) on a -137T/G heterozygous cell line from the SAFHS cohort. After amplification of the -137 SNP using two rounds of ‘nested’ PCR, the library is cloned in competent E.coli and subsequently sequenced using Sanger sequencing in order to map nuclease digestion sites around the -137 SNP.
CHA-seq T-CHA-seq
A. B.
110
4.2 CHA-seq method development
The two novel CHA-seq methods required optimisation at multiple key steps in library
preparation (nuclease digestion and adapter-ligation) and PCR amplification
(optimisation of the PCR conditions in T-CHA-seq) as shown in Sections 4.2.2 (T-
CHA-seq) and 4.2.3 (CHA-seq), with the optimised CHA-seq methodologies described
in detail in Chapter 2 (Section 2.10 for T-CHA-seq and Section 2.11 for CHA-seq).
In order to determine the presence or absence of an allele-specific bias in chromatin
accessibility via CHA-seq, a proportion is calculated based on the sequence reads of
MNase or DNase-digested fragments containing each allele at a given heterozygous
SNP locus relative to the total number of reads. This allelic proportion relative to a
random distribution (50%), or ‘allele-skew’, is a direct reflection of chromatin
arrangement and nuclease-sensitivity. For example, if 60% of sequence reads contained
the A allele at a given heterozygous SNP locus (and consequently 40% the alternate
allele), then the allele-skew (from a random distribution) is 10%. A two-tailed Student’s
t-test was then applied to determine statistical significance (P<0.05). Those SNPs that
recorded a CHA-seq allele ratio of greater than 37:63, or 13% allele skew, were
identified as candidate functional variants based on the allele bias cutoff set by Pastinen
et al. (2004).
T-CHA-seq is a site-specific and more cost-effective approach for functional assessment
of genetic variants, in comparison to CHA-seq, as it does not require deep sequencing.
The T-CHA-seq method is of particular use when candidate SNPs or preliminary data is
available regarding functionality. Alternatively, CHA-seq is a large-scale functional
screen providing the DNA base sequence, novel SNP identification, chromatin
arrangement assessment, nucleosomal positioning information, and the ability to predict
111
areas of nuclease hypersensitivity (and consequently transcription factor binding sites).
A limitation of CHA-seq, when using Illumina sequencing technology, is the inherent
error rate of 0.99% for comparisons of sequence reads (Illumina Inc, 2009). Therefore,
when interpreting CHA-seq results, this error rate was considered when determining the
viability of further functional assays for candidate SNPs.
4.2.1 Traditional chromatin arrangement assays – issues
Although the traditional CHART-PCR approach has been sufficient for characterising
the chromatin arrangement at specific genomic loci as evident in the literature
(Cruikshank et al., 2012; Rao et al., 2001), this approach requires extensive
optimisation to obtain consistent results. Of particular importance to the reproducibility
of CHART-PCR, is the normalisation to chromatin accessibility controls. Variation in
the chromatin accessibility of control loci such as β-actin (Figure 4.2), illustrates that
there are inconsistencies arising from the chromatin preparation which can significantly
influence chromatin accessibility values when comparing replicates and multiple cell
lines. In order to overcome these issues, extensive optimisation was required in order to
target VNN1 SNPs for the establishment of consistent chromatin accessibility
measurements using CHART-PCR (Chapter 3). Essential to this optimisation was the
establishment of an optimal nuclease concentration to reliably discriminate chromatin
accessibility. For example, the β-actin locus shown in Figure 4.2 had a higher MNase
concentration (4 enzyme units) and hence higher nuclease digestion to that used in the
optimised CHART-PCR methodology, shown in Figure 3.3 (1 enzyme unit). Partial
nuclease digestion proved better at discriminating chromatin accessibility and was used
in CHART-PCR (Chapter 3) and CHA-seq in the current study.
112
0029
(1)
0029
(2)
0029
(3)
0054
(1)
0054
(2)
0054
(3)
0331
(1)
0331
(2)
0331
(3)
0
80
90
100
SAFHS chohort ID (replicate number)
Ch
rom
ati
n A
cc
es
sib
ilit
y
(% c
utt
ing
)
Figure 4.2: Chromatin accessibility differences between replicates at the β-actin
promoter using CHART-PCR. MNase-treated cell lines from the SAFHS cohort (‘0029’, ‘0054’ and ‘0331’) were used to calculate chromatin accessibility expressed as a ratio of undigested to MNase-digested DNA (% cutting) at sequence near the β-actin
promoter in CHART-PCR. The data were obtained from SAFHS cell lines shown and assayed in triplicate (n=3). No significance (P<0.05) was observed between samples as determined by one-way ANOVA (P value = 0.98).
113
4.2.2 Targeted CHA-seq (T-CHA-seq) development
To demonstrate the utility of CHA-seq as a functional assay, a targeted CHA-seq (T-
CHA-seq) approach was developed to verify the results of CHART-PCR (Chapter 3)
which compared allele-specific chromatin accessibility at the -137 VNN1 SNP. T-CHA-
seq was applied to SAFHS cell line ‘0331’, which was heterozygous at the -137T/G
locus, and assessed for chromatin accessibility using DNase I. In this case, DNase I was
selected as the chromatin-sensitive nuclease based on its ability to discriminate genomic
regions that are under protein occupancy, given that the -137 locus is likely to be
regulated by Sp1 (Göring et al., 2007).
4.2.2.1 T-CHA-seq library preparation – DNase I digestion optimisation
Chromatin was isolated from a SAFHS cell line (‘0331’) which was heterozygous for
multiple VNN1 SNPs (including -137 and -587) and treated with DNase I. Various
reaction conditions and enzyme concentrations were trialled. In order to determine
optimal digestion, prepared chromatin was exposed to different DNase I concentrations
(1 U, 2 U, 3 U, 4 U and 6 U) and incubation lengths at 37˚C (2, 3, 4, 5 and 6 minutes) as
shown in Figure 4.3. The 6 minute incubation using 6 U DNase I (lane 7) was selected
based on the presence of a higher proportion of fragments located within the mono- and
di-nucleosomal size range (~100-400 bp) which was optimal for subsequent library
preparation steps using the NEBNext DNA Sample Prep kit (New England Biolabs,
USA). Additional experimentation was conducted to assess longer nuclease incubation
periods which showed that digestion was complete after six minutes. Optimal DNase I
digestion (6 minutes incubation using 6 U) was used for all subsequent preparations.
114
Figure 4.3: Electrophoretic patterns of DNase 1-digested chromatin. Electrophoresis was performed using a 1% agarose gel stained with ethidium bromide and run for 45 minutes at 90V. The NEB PCR Marker was loaded in lane 1 along with the NEB 100 bp DNA ladder in lane 14, undigested chromatin derived from the SAFHS cohort in lane 2 (3 µg chromatin) and lane 8 (1 µg chromatin, note faint band due to loading spillage). 3 µg of chromatin derived from the SAFHS cohort was incubated in 6 U of DNase I with different incubation times and loaded in lane 3 (2 minutes), lane 4 (3 minutes), lane 5 (4 minutes), lane 6 (5 minutes) and lane 7 (6 minutes). 1 µg of SAFHS chromatin incubated for 6 minutes incubated in different DNase I concentrations was loaded in lane 9 (1 U), lane 10 (2 U), lane 11 (3 U), lane 12 (4 U) and lane 13 (6 U). Optimal digestion of the SAFHS chromatin was established using 6 minute incubation with 6 U DNase I.
115
4.2.2.2 T-CHA-seq library preparation – adapter-ligation optimisation
DNase 1-digested fragments within the 100-400 bp range were selected following
agarose gel electrophoresis, cut out using a scalpel, and purified using the QIAquick Gel
Extraction kit (QIAGEN, Australia). The fragments were subsequently prepared
according to the protocol outlined in the NEBNext DNA Sample Prep kit (New England
Biolabs, USA). It was of particular importance to ensure the optimal size of DNA
fragments following the ligation of adapters using agarose gel electrophoresis. As
shown in Figure 4.4, the highest proportion of fragments were approximately 300 bp
(di-nucleosomal) as estimated by comparison to the NEB PCR marker (New England
Biolabs, USA). An additional 50 bp was also observed, which matches the size of the
adapters used in the library preparation and was subsequently removed using the
QIAquick PCR purification kit (QIAGEN, Australia).
116
Figure 4.4: Adapter ligation to DNase I-digested chromatin. Electrophoresis was performed using a 1% agarose gel stained with ethidium bromide and run for 45 minutes at 90V. The NEB PCR Marker was loaded in lane 1 along with DNase I-digested fragments that had been prepared using the NEBNext DNA Sample Prep kit (New England Biolabs, USA) following the ligation of adapter sequence in lane 2.
50bp
150bp
300bp
500bp
766bp
1 2
117
4.2.2.3 PCR amplification - optimisation
In order to target specific genomic regions, namely the -137 VNN1 SNP, PCR
amplification (Section 2.7) was used to enrich prepared library fragments following
preparation using the NEBNext DNA Sample Prep kit (New England Biolabs, USA).
Two rounds of PCR amplification using ‘nested’ sets (primer sets positioned internally
to the previous set) of VNN1-specific primers were designed to increase the yield of
fragments containing the -137 locus from the forward (first round primer: 5’-
GCAGCCCTAGCGAACTAATG-3’ and second round primer: 5’-
TCTTGAGTTAATCTTCACAAATTACTGC-3’) and reverse (first round primer: 5’-
GTCCAGAACTTAGTACCTTAAACTACGGAAG 3’ and second round primer: 5’-
CGCTATGATTTCATAGCATTGTTACATAAC 3’) direction (Figure 4.5). In each
round of ‘nested’ PCR amplification, a VNN1-specific primer was paired with an
adapter-specific primer (5’-ACTCTTTCCCTACACGACGC-3’) to ensure only library
fragments were amplified. The results following PCR amplification showed the
presence of a ‘smear’ which represented a range of different sized fragments. The size
of the first and second round of ‘forward’ amplicons were within a range of
approximately 100-350 bp, indicating the presence of different VNN1-enriched
fragments (Figure 4.6), with the reverse library resulting in equivalent sized amplicons
(Figure 4.7). The forward and reverse -137T/G amplicon libraries were subsequently
pooled and sequenced using Sanger-based chemistry (Section 4.3.1).
118
5’-CTATGGGTCAAGGAGAGAGGCCTGGAACACATCTTTCCTTCACCGAAA
TCAGGAGGAACCAACTCTGCTGACACCTTCATCTGGGACTCCCACCCTCCAG
AACTGCAAAGCAATAAATTTTTTATTTTTTACACCACCCAGTTTATTGTATTT
TGTTAGGCAGCCCTAGCGAACTAATGTACATAGAGTTCTTGAGTTAATCTTCA
CAAATTACTGCAATAAGGKAGGGTCTTTTGTTATGTAACAATGCTATGAAAT
CATAGCGTTTTCTTAATTAACTTCCGTAGTTTAAGGTACTAAGTTCTGGACACC
ACGTGTCTTCTTTCTATAAATACCAGGACATGCTCTGTTTTTCAGCACT-3’
Figure 4.5: T-CHA-seq primer positions in the -137 VNN1 promoter region. Two rounds of PCR amplification were performed using nested sets of sequence-specific primers in order to increase target yield of the -137 VNN1 locus from both the forward (5’ to 3’) and reverse (3’ to 5’) direction. Shown are the consensus sequences of the first (italics) and second-round ‘forward’ (black boxes) and ‘reverse’ (grey boxes) T-CHA-seq PCR primers within the VNN1 promoter region relative to the -137 SNP (K). In each round of amplification, one corresponding VNN1-specific primer was paired with an adapter-specific primer to ensure only library fragments were amplified.
119
I-digested library
1 2 3 4 5 6
Figure 4.6: T-CHA-seq ‘forward’ PCR amplification of DNase I library fragments. Electrophoresis was performed using a 1% agarose gel stained with ethidium bromide and run for 45 minutes at 90V. The NEB PCR Marker (sizes: 50 bp, 150 bp, 300 bp, 500 bp, 766 bp) was loaded in lane 1 and 6 along with the ‘forward’ (5’ to 3’) PCR amplification products of the T-CHA-seq library in lane 2 (first round amplicons) and lane 4 (second round amplicons). Controls (no template) were run in lane 3 (first round) and 5 (second round). *Note: A 1% agarose gel was selected to confirm whether amplification was successful rather than to establish the size range which was determined in Figure 4.4.
50bp
150bp
300bp
500bp
766bp
120
1 2 3 4 5
Figure 4.7: T-CHA-seq ‘reverse’ PCR amplification of DNase I library fragments. Electrophoresis was performed using a 1% agarose gel stained with ethidium bromide and run for 45 minutes at 90V. The NEB PCR Marker (sizes: 50 bp, 150 bp, 300 bp, 500 bp, 766 bp) was loaded in lane 1 along with the ‘reverse’ (3’ to 5’) PCR amplification products of the T-CHA-seq library in lane 2 (first round amplicons) and lane 4 (second round amplicons). Controls (no template) were run in lane 3 (first round) and 5 (second round). *Note: A 1% agarose gel was selected to confirm whether amplification was successful rather than to establish the size range which was determined in Figure 4.4.
50bp
150bp
300bp
500bp
766bp
121
4.2.2.4 T-CHA-seq methodology following PCR amplification
Following PCR enrichment of the DNase I-digested libraries for the -137 VNN1 SNP,
TOPO TA cloning (Invitrogen Corporation, Australia) and bacterial transformation
steps using One Shot MAX Efficiency DH5α-T1R Competent Cells (Invitrogen
Corporation, Australia) were performed (See Sections 2.2.3 and 2.2.4) in order to isolate
and amplify single library fragments within plasmid vectors, which were subsequently
purified and sequenced using Sanger-based chemistry. Following blue-white colony
selection (See Section 2.2.4), positive bacterial clones carrying enriched library
fragments were purified using the QIAGEN Spin Mini-prep kit (QIAGEN, Australia)
(See Section 2.5.1), and subsequently sequenced using Sanger-based chemistry at the
Australian Genome Research Facility (AGRF) (See Section 2.8). The DNA sequence
reads were downloaded (http://www.agrf.org.au/) and then processed using
bioinformatic tools (BLAST). Results are shown in Figures 4.10 and 4.11.
4.2.3 Extended CHA-seq method development
The CHA-seq approach was modified from the targeted T-CHA-seq approach in order
to screen larger genomic regions for functional genetic variants (Section 2.11). CHA-
seq required a bead selection step (as opposed to locus-specific primers in T-CHA-seq)
and deep-sequencing in order to extend the target assay region. CHA-seq was applied to
a SAFHS cell line (‘0331’) which was heterozygous for multiple VNN1 SNPs
(including -137 and -587) and assessed for chromatin accessibility using MNase. The
use of MNase generated detailed information regarding nucleosome positions and
relative levels of nucleosome protection across the VNN1 gene that extends upon the
CHART-PCR results (Chapter 3).
122
4.2.3.1 Library preparation – MNase digestion optimization
Chromatin was isolated from a SAFHS cell line (‘0331’) and treated with MNase under
various reaction conditions to determine optimal nuclease digestion to generate a higher
distribution of fragments within the mono- and di-nucleosomal size range (Figure 4.8).
Prepared chromatin was exposed to different MNase concentrations (0.5, 1, and 2
enzyme units) and incubation times (5, 10 and 15 minutes) (Figure 4.8). As shown in
Figure 4.8, a ten minute incubation using one enzyme unit of MNase was selected as
optimal and thus utilised for subsequent library preparation using the NEBNext DNA
Sample Prep kit (New England Biolabs, USA).
123
1 2 3 4 5 6 7
Figure 4.8: Electrophoretic patterns of MNase-digested chromatin. Electrophoresis was performed using a 1% agarose gel stained with ethidium bromide and run for 45 minutes at 90V. The NEB 1 kb DNA ladder (5-10 kb bands indistinguishable) was loaded in lane 1 along with micrococcal nuclease-digested chromatin isolated from a SAFHS cohort cell line (‘0331’) using different incubation lengths and enzyme concentrations in lanes 2 (10 minute digestion, 0.5 U MNase), 3 (10 minute digestion, 1 U MNase), 4 (10 minute digestion, 2 U MNase), 5 (5 minute digestion, 1 U MNase), 6 (10 minute digestion, 1 U MNase) and 7 (15 minute digestion, 1 U MNase).
500bp
1000bp
1500bp
2000bp
3000bp 4000bp
124
4.2.3.2 CHA-seq methodology post-library preparation
Following preparation of the MNase-digested library using the NEBNext DNA Sample
Prep kit (New England Biolabs, USA), target sequences containing the entire Vanin
gene family were supplied to Agilent to synthesise pools of biotinylated probes specific
for the SureSelect DNA Target Enrichment system. Libraries were purified using a
bead-based selection protocol with the SureSelect Target Enrichment System - Illumina
Single-End Sequencing Platform Library Prep Version 1.2 (Agilent, USA) in
accordance with the manufacturer’s instructions.
Prepared libraries were processed for cluster generation on the Illumina cluster station
(Agilent, USA) and subsequently sequenced using Illumina next-gen sequencing
technology on the Illumina Genome Analyzer II platform (Agilent, USA). DNA
sequence reads were analysed using the IGV 2.1 software and aligned to the hg19
reference sequence. Raw sequence data was used to interpret chromatin accessibility by
comparing numbers of allelic sequence reads at heterozygous SNPs (Table 4.1 and 4.3).
The optimised protocol for CHA-seq is described in detail in Chapter 2 (Section 2.11).
4.3 Results
4.3.1 Targeted CHA-seq (T-CHA-seq)
To assess allele-specific chromatin accessibility, a DNase I-treated genomic library was
prepared from a -137G/A heterozygous cell line from the SAFHS cohort, enriched for the -
137 locus using two rounds of PCR amplification, and subjected to Sanger sequencing to
map the nuclease digestion sites. The results showed the presence of an area of high
nuclease accessibility directly adjacent (5’ upstream) to the -137 SNP, suggesting the
influence of histone or bound protein protection localised to the -137 locus (Figure 4.9).
Comparison of the number of sequenced library fragments ending at a particular nucleotide
(and hence mapping the sites of digestion by the nuclease) for each -137 variant, indicated
125
an allele-specific bias (allele skew) in the digestion pattern (Figure 4.9). Only 11.4% of the
total number of fragments carried -137G, while the majority carried the T allele (89.6%).
This is consistent with the results of the CHART-PCR (Chapter 3) that showed the -137G
allele to be correlated with greater chromatin condensation than the -137T allele.
126
Figure 4.9: Assessment of the chromatin arrangement across the VNN1 promoter
using T-CHA-seq. Shown is the total coverage across the VNN1 promoter sequence, the number of reads (vertical axis) carrying the -137 variant (K), and the position of the 3’ terminal nucleotide located in the region derived from T-CHA-seq results. Sequence reads containing the -137T allele are shown as black columns and those with the -137G allele in grey. Sites of digestion by DNase I are plotted across (horizontal axis) the -137 VNN1 promoter region, with the number of sequenced fragments ending at each nucleotide indicated by the vertical axis for each -137 allele (T or G). The areas of highest sequence reads (15) are 15, 16, and 17 base-pairs upstream of the -137 SNP and contain the T allele. The number of reads containing the -137T allele (132) were higher than that of the -137G allele (17) indicating an allelic bias towards the T allele.
127
To further delineate exact positions of DNase I hypersensitive sites within the -137
promoter region, the total number of T-CHA-seq sequence reads were mapped to the -
137 VNN1 promoter region and included those reads terminating prior to the -137 locus
(Figure 4.10). There appears to be two regions of higher DNase I accessibility (≥13
reads) adjacent to the -137 SNP (1-5 bp 3’ downstream, and 11-15 bp 5’ upstream of the
SNP) (Figure 4.12).
128
Analysis of hypersensitivity about the -137 VNN1 gene promoter SNP using sequenced
library fragment ends
Figure 4.10: Assessment of the chromatin arrangement across the VNN1 promoter
using T-CHA-seq. Shown is the total sequence read coverage across the VNN1 gene promoter (horizontal axis) with the position of the 3’ terminal nucleotide located in the -137 (K) promoter region derived from T-CHA-seq results. Numbers of sequence reads are shown (vertical axis) as black columns, with the areas of highest sequence reads (16) occurring at positions -136 and -135.
129
4.3.2 CHA-seq
To map nuclease digestion sites across the entire Vanin gene family, including the VNN1
gene, a MNase-digested genomic library was prepared from a -587 G/A heterozygous cell
line from the SAFHS cohort and subjected to deep sequencing. Using a prepared library of
DNA fragments covering the VNN1 gene, 41 heterozygous SNPs were captured
following sequencing and analysed using the Integrated Genome Viewer (IGV) 2.1
software (Broad Institute) to determine the location and allele skew for each VNN1 SNP
(Table 4.1).
To assign rs numbers, the chromosome position (based on the hg19 human reference
genome) of captured CHA-seq SNPs were matched to the dbSNP database
(http://www.ncbi.nlm.nih.gov/snp) and verified by comparing CHA-seq reads to the
SNP sequence in dbSNP. At the time of analysis (29-6-13), there was 1042 SNPs in
dbSNP that matched to the VNN1 gene. A total of 12 heterozygous SNPs captured in
CHA-seq corresponded with rs numbers. The remaining SNPs may be either novel
genetic variants within the SAFHS cohort, or base substitutions introduced as part of
Illumina sequencing error. For example, the SNP at chromosome position 133,035,606
in the VNN1 promoter had not been assigned an rs number at the time of
experimentation (2010), but has since been designated rs45513194. Therefore, the novel
SNPs identified in the current study may be verified in future studies. To reduce the
likelihood of error introduced by the Illumina sequencing, only those captured SNPs
with rs numbers were considered for further functional analysis.
A summary of the distribution of captured heterozygous SNPs across the VNN1 gene
indicates a number of densely populated genomic regions carrying captured
heterozygous SNPs (Figure 4.11). Of interest is the presence of five intronic SNPs that
130
are co-located between chromosomal positions 133,023,083 to 133,023,087 which
clearly show an active region that affects the conformation of VNN1 chromatin (Figure
4.11). Of the 41 captured SNPs in CHA-seq, 37 were found to be significantly different
from random distribution (50:50) between alleles (P<0.005) (Table 4.1). Noticeably, the
-587 SNP (88% A and 12% G) and the rs2267951 SNP (85% T, and 15% C) have an
allele-specific influence on VNN1 chromatin, with a further 21 SNPs that are also likely
to contribute (greater than 13% + 1% Illumina sequencing error rate) based on the allele
ratio cutoff set by Pastinen et al. (2004).
131
Table 4.1: Heterozygous VNN1 SNPs captured in CHA-seq. Shown is a summary of the Integrated Genome Viewer (IGV) software (Broad Institute) output from CHA-seq that detected 41 heterozygous SNPs in the VNN1 gene by comparison to the hg19 human reference sequence. Shown for each captured VNN1 SNP are the genomic positions relative to the transcriptional start site (TSS), position within the VNN1 gene (promoter/5’upstream, intronic or exonic), alleles, total CHA-seq sequence reads carrying the SNP, rs SNP numbers (N/A = no dbSNP match), allele frequencies (allele 1 and 2), and allele skew as a direct reflection of chromatin accessibility and functionality (percentage). Allele-skews that were statistically significant using the critical t-value (2.97% at P=0.005, df=41, 2-tailed) are bolded as shown. The range of nucleosomal protection at genetic variants in the VNN1 gene was between 1-38%. The average allele skew across all captured VNN1 SNPs was 13.6%, with the rs2050153 (-587) variant having the highest recorded allele-skew (38%).
Chr 6
position
Genomic location Alleles Total
reads
rs SNP
number
Allele
1 %
Allele
2 %
Allele
skew
133,035,897 promoter/5'upstream T-C 2817 rs7760367 56 44 6
133,035,856 promoter/5'upstream C-T 3250 rs13204527 53 46 3.5
133,035,776 promoter/5'upstream A-G 5253 rs2050153
(-587)
88 12 38
133,035,606 promoter/5'upstream G-C 4955 rs45513194 61 39 11
133,035,099 exon 1 A-G 1432 rs2294757 65 34 15.5
133,034,654 intron 1 A-T 1069 N/A 55 45 5
133,034,653 intron 1 G-A 1070 N/A 54 46 4
133,034,652 intron 1 G-C 1070 N/A 54 45 4.5
133,034,650 intron 1 T-C 1070 N/A 54 46 4
133,034,647 intron 1 A-T 1070 N/A 55 45 5
133,034,549 intron 1 C-T 1099 N/A 56 44 5.5
133,034,550 intron 1 C-T 1099 N/A 55 45 6
133,034,553 intron 1 C-T 1099 N/A 56 44 6
133,030,761 intron 2 G-A 758 rs148984574 56 44 6
133,023,821 intron 2 C-T 1439 rs113832566 66 34 16
133,023,820 intron 2 A-G 1439 N/A 66 34 16
133,023,174 intron 2 A-C 1175 N/A 61 39 11
133,023,167 intron 2 A-C 1175 N/A 61 39 11
133,023,087 intron 2 G-C 1951 N/A 71 26 22.5
133,023,086 intron 2 A-T 1951 N/A 73 26 23.5
133,023,085 intron 2 G-A 1951 N/A 73 27 23
133,023,084 intron 2 A-G 1951 N/A 74 26 24
133,023,083 intron 2 C-A 1913 rs78040505 73 27 23
133,020,445 intron 2 C-T 1806 N/A 66 34 16
133,020,383 intron 2 A-T 1913 N/A 68 32 18
133,020,380 intron 2 T-A 1913 N/A 68 32 18
133,020,379 intron 2 T-G 1913 N/A 68 32 18
133,020,376 intron 2 G-T 1912 rs2267949 68 32 18
133,019,859 intron 2 T-C 1522 rs2267951 85 15 35
133,019,482 intron 2 T-C 1041 N/A 51 49 1
133,013,475 exon 5 T-G 2044 N/A 73 27 23
133,013,474 exon 5 T-C 2043 N/A 73 27 23
132
133,013,374 exon 5 A-C 1524 N/A 69 31 19
Chr 6
position
Genomic location Alleles Total
reads
rs SNP
number
Allele
1 %
Allele
2 %
Allele
skew
133,013,369 exon 5 A-C 1520 N/A 69 31 19
133,012,903 intron 5 A-G 846 N/A 51 49 1
133,012,888 intron 5 G-A 846 N/A 51 49 1
133,012,885 intron 5 A-T 846 N/A 51 49 1
133,012,858 intron 5 A-T 1254 N/A 65 35 15
133,010,071 intron 5 T-C 854 N/A 72 28 22
133,005,239 intron 6 C-T 1656 rs45525739 70 30 20
133,004,992 intron 6 A-T 3015 rs9389025 65 35 15
Average 63.5 36.2 13.6
133
Figure 4.11: Distribution of heterozygous SNPs captured in CHA-seq across the
VNN1 gene relative to allele skew. Shown is a graphical summary of the Integrated Genome Viewer (IGV) software (Broad Institute) output from CHA-seq that detected 41 heterozygous SNPs in the VNN1 gene by comparison to the hg19 human reference sequence. Shown are the positions of captured VNN1 SNPs relative to the transcriptional start site (x-axis) and the associated allele-skew as a percentage (y-axis). Exonic (black rectangles), intronic (black bar between exons) and promoter (points with negative x-axis values) regions are shown in the VNN1 gene. The highest recorded allele-skews were observed at the -587 SNP (38%) in the promoter region, and at the rs2267951 SNP (35%) within intron 2 (red arrows).
134
4.3.2.1 Allele-specific nuclease activity at the -587 SNP
To interpret allele-specific areas of MNase sensitivity within the -587 VNN1 promoter
region derived from CHA-seq, the number of sequenced library fragments ending at a
particular nucleotide (and hence mapping the sites of digestion by the nuclease) for each -
587 variant were compared (Figure 4.12). An allele-specific bias in the digestion pattern
was shown for the -587 locus. In fact, only 12% of the total number of fragments carried -
587G, with the majority carrying the A allele (88%).
In the presence of the -587A allele, there were two areas (20-30 bp upstream from the -
587 locus and 70-80 bp downstream) that correlated with a high number of terminating
sequenced reads (Figure 4.12). Therefore, it is likely the -587A locus is located with an
active chromatin region. These findings are consistent with the CHART-PCR results
which showed that the presence of the -587G allele correlates with greater chromatin
condensation than when the A allele is observed.
135
Figure 4.12: Assessment of the allele-specific chromatin accessibility across the -
587 VNN1 promoter region using CHA-seq. Shown is a graphical summary of the Integrated Genome Viewer (IGV) software (Broad Institute) output from CHA-seq that detected 41 heterozygous SNPs in the VNN1 gene by comparison to the hg19 human reference sequence. The CHA-seq results shown represent the total sequence coverage (ticks every 10 base-pairs) across the -587 (red) promoter region (horizontal axis) in the VNN1 gene along with the positions and numbers of the 3’ terminal nucleotide following micrococcal nuclease digestion (vertical axis). Sequence reads containing the -587A allele are shown above the horizontal axis and reads containing the -587G allele are shown below the horizontal axis. The numbers shown on these columns represent the number of sequenced fragments whose 3’ nucleotide terminates at that position. Of
note were two areas (indicated by black arrows) containing the -587A allele that were densely populated and possessed the highest number of terminating sequenced reads, suggesting the presence of active chromatin regions.
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4.3.2.2 Nucleosome mapping within the -587 VNN1 promoter region
To map nucleosome positions within the -587 VNN1 promoter region (and determine
the relative level of nucleosome protection), sequenced CHA-seq library fragments
containing the -587 locus (G or A) were analysed using the Internal Genome Viewer
(IGV) 2.1 software from the Broad Institute (Figure 4.13). The region surrounding the -
587 SNP displayed a periodicity in the number of MNase cut sites that was consistent
with the positioning of the protective nucleosomes in the region (Figure 4.13). The
‘novel’ SNP (newly identified at the time of this research, but since has been designated
rs45513194) shows partial protection and is positioned at the edge of a potential
nucleosome, whereas the -587 SNP (rs2050153) is located in an area of high sequence
read frequency, indicating the presence of little or no influence from nucleosomes. The
remaining two SNPs (rs7760367 and rs3204527) within the -587 VNN1 promoter region
are likely to be non-functional SNPs due to nucleosome protection.
137
Figure 4.13: Assessment of the chromatin arrangement across the -587 VNN1
promoter region using CHA-seq. Shown is an adapted summary of the Integrated Genome Viewer (IGV) software (Broad Institute) output from CHA-seq showing the -587 (rs2050153) promoter region in the VNN1 gene correlated with the total sequence coverage across the region. Sequence reads (grey bars) corresponding to particular loci are shown along the horizontal axis (genomic position), with read number on the vertical axis. Also shown are the putative positions of the nucleosomes (dashed ovals) based on sequence coverage, with the -587 locus predicted to lie within a region with no nucleosome protection. The labelled red arrows indicate the positions of genetic variants located within the -587 promoter region, with the percentage of reads carrying each allele proportionally represented as red and blue bars underneath each arrow. It is of note is that the -587 SNP is predicted to occur within an area with little to no nucleosome protection.
VNN1 gene promoter (chromosome position)
CH
A-s
eq r
ead
s (n
um
ber
)
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4.3.3 CHA-seq for the VNN2 and VNN3 genes
The VNN2 and VNN3 genes were subjected to CHA-seq using a prepared MNase-
digested library isolated from a -587G/A heterozygous cell line (SAFHS cohort) in order
to map nuclease digestion sites and illustrate the scalability of the CHA-seq approach.
As shown in Table 4.2, 24 heterozygous SNPs were captured across both genes and
analysed using IGV to determine the location and allele skew of SNPs. All of the 24
VNN2 and VNN3 SNPs captured in CHA-seq were significantly skewed (P<0.005), of
which 11 were in the VNN2 gene and 13 located in the VNN3 gene. Of the 11 VNN2
SNPs, 4 were located in the promoter region and the remaining 7 positioned within
intronic regions. In the VNN3 gene, 3 were located in the promoter and 10 located
within intronic sequences. The SNP located within intron 3 of the VNN3 gene
(chromosome 6 position 133,048,287) had the highest recorded allele skew (28%) and
is a likely candidate for further functional assessment (Table 4.2).
There were 24 SNPs in the dbSNP database (http://www.ncbi.nlm.nih.gov/snp) that
matched to the VNN2 gene and 389 SNPs to the VNN3 gene at the time of analysis (29-
6-13). In CHA-seq, one captured heterozygous SNP at chromosome position
133,048,214 in VNN3 matched to the rs9493417 in dbSNP.
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Table 4.2: Heterozygous VNN2 and VNN3 SNPs captured in CHA-seq. Shown is a summary of the Integrated Genome Viewer (IGV) software (Broad Institute) output from CHA-seq which detected 11 heterozygous SNPs in the VNN2 gene and 13 in the VNN3 gene by comparison to the hg19 human reference sequence. Shown for each captured VNN1 SNP are the genomic positions (chromosome position), position within the VNN2 and VNN3 genes (promoter/5’upstream, intronic or exonic), alleles, total CHA-seq sequence reads carrying the SNP, rs SNP numbers (N/A = no dbSNP match), allele frequencies (allele 1 and 2), and allele skew as a direct reflection of chromatin accessibility and functionality (percentage). Allele skews that were statistically significant as determined by their critical t-value (3.09% at P=0.005, df=24, 2-tailed) are bolded. The range of nucleosomal protection at genetic variants in the VNN2 and VNN3 genes was between 5-28%. The average allele skew across all captured VNN2 and VNN3 SNPs was 14.6%, with the VNN3 variant at chromosome position 133,048,287 possessing the highest recorded allele skew (28%) (red).
SNP location
(chromosome
position) Gene/genomic location Alleles
Total
reads
rs SNP
number
Allele
1 %
Allele 2
%
Allele
skew
133,085,576 VNN2 promoter/5'upstream A-T 1445 N/A 39 61 11
133,085,564 VNN2 promoter/5'upstream C-T 1448 N/A 61 39 11
133,085,553 VNN2 promoter/5'upstream C-T 1451 N/A 61 39 11
133,085,552 VNN2 promoter/5'upstream A-T 1451 N/A 60 40 10
133,079,482 VNN2 intron 1 C-T 803 N/A 33 67 17
133,076,897 VNN2 intron 4 A-G 711 N/A 29 71 21
133,073,728 VNN2 intron 4 A-C 1382 N/A 63 37 13
133,073,726 VNN2 intron 4 A-T 1385 N/A 37 63 13
133,073,721 VNN2 intron 4 C-T 1384 N/A 63 36 13
133,072,720 VNN2 intron 4 C-T 1518 N/A 61 39 11
133,070,042 VNN2 intron 6 A-G 1033 N/A 45 55 5
133,056,346 VNN3 promoter/5’upstream G-T 1227 N/A 31 69 19
133,056,344 VNN3 promoter/5’upstream A-T 1227 N/A 69 31 19
133,056,340 VNN3 promoter/5’upstream A-T 1227 N/A 31 69 19
133,053,371 VNN3 intron 2 C-T 859 N/A 66 34 16
133,053,369 VNN3 intron 2 A-G 859 N/A 34 66 16
133,053,367 VNN3 intron 2 C-T 859 N/A 34 66 16
133,048,325 VNN3 intron 3 A-G 1669 N/A 37 63 13
133,048,324 VNN3 intron 3 A-G 1669 N/A 62 38 12
133,048,321 VNN3 intron 3 G-T 1669 N/A 62 38 12
133,048,316 VNN3 intron 3 A-T 2268 N/A 72 28 22
133,048,287 VNN3 intron 3 C-T 2875 N/A 78 22 28
133,048,214 VNN3 intron 3 A-G 1689 rs9493417 37 63 13
133,045, 554 VNN3 intron 5 A-C 634 N/A 40 60 10
Average 14.6
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4.4 Discussion
Genetic variants that alter chromatin accessibility and transcription factor binding
comprise a central regulatory mechanism by which sequence variation leads to changes
in gene expression in humans (Degner et al., 2012). A key result from this present study
is the utility of a novel sequencing approach (CHA-seq) to characterise functional
genetic variants on a larger scale and more time and cost-effectively than existing
chromatin arrangement assays (CHART-PCR).
T-CHA-seq was applied to the -137 promoter region in VNN1, which showed a
significant allele-bias in sequence reads (38.5% for the -137T allele). Taken together,
CHART-PCR and T-CHA-seq results are suggestive that the -137 variant confers allele-
specific chromatin accessibility differences in the VNN1 gene promoter. Göring et al.
(2007) identified allele-specific binding at the -137 variant using EMSA, supporting the
findings of both T-CHA-seq and CHART-PCR approaches from the present study.
Furthermore, the mapping of nuclease cut sites identified that the -137 locus is present
within a region of high DNase I hypersensitivity. Consequently, this result and the
findings of Göring et al. (2007) support the presence of bound proteins at the -137 locus
that confer allele-specific nuclease protection.
In order to assess allele-specific differences in chromatin arrangement across an extended
genomic region, the CHART-PCR assay was translated into a genome-wide format (CHA-
seq). CHA-seq involved the use of a prepared library of DNA fragments covering the
VNN1 gene and which captured 41 heterozygous SNPs. Of these, 37 showed a
significant allele skew (P<0.005), including the SNP captured at position 133,035,099
(rs2294757) which has been reported to have an allele-specific influence in DNA
methylation (Kerkel et al., 2008). Previously, Pastinen et al. (2004) determined an
average observed allele ratio of 37:63 as the cutoff for determining the presence of a
141
significant allelic imbalance. Taking this allele skew as a baseline for functional
candidates and incorporating the 1% Illumina sequencing error rate (i.e. 14%), CHA-seq
has identified 23 novel candidate SNPs that have an allele-specific role in VNN1
chromatin organisation. Of the 23 candidate functional SNPs, 17 occur in intronic
regions, five in exonic regions and one in the promoter (-587). The majority (17/23) of
candidate SNPs, however, are localised to the promoter and intronic regions of the
VNN1 gene, and are suggestive of a region under significant transcriptional regulation
through allele-specific effects on chromatin (Tsukada et al., 2006). Interestingly, the
rs2267951 SNP in intron 2 recorded the second highest allele skew in CHA-seq and was
selected for further in vitro characterisation by EMSA in order to verify SNP
functionality and the veracity of the CHA-seq results (Chapter 6). The remaining
genetic variants with significant allele skews represent ideal candidates for further
functional validation.
Comparison of the number of sequenced library fragments ending at a particular nucleotide
(and hence mapping the sites of digestion by the nuclease) for each -587 variant indicated
an allele-specific bias in nuclease digestion, with the majority of sequence reads terminating
within the promoter when carrying the -587A allele (88%). Furthermore, the -587
promoter region displayed a periodicity in the number of MNase cut sites that correlated
with nucleosome positions within the region. The nucleosome periodicity suggests that the -
587A locus is located within an active euchromatic region, which therefore indicates
greater transcriptional potential. Furthermore, the level of predicted protection of SNPs
within the -587 promoter region was inversely correlated to the allele skew at these
SNPs. Consequently, this may indicate that candidate functional variants with
significant allele-specific chromatin accessibility or CHA-seq allele-skews, such as the
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-587 variant, have reduced nucleosome protection against MNase digestion due to
histone proteins.
To assess the scalability of the CHA-seq approach, this method was applied to the
VNN2 and VNN3 genes. The results of CHA-seq showed the presence of two candidate
functional SNPs within the VNN2 gene and eight in the VNN3 gene, based on a
comparison to the 14% baseline allele skew. All candidate SNPs in the VNN2 and VNN3
genes were present within non-coding regions, suggesting that the role of these genetic
variants is likely to be associated with chromatin structure and/or transcriptional
regulation. Further experimental validation would assist in the elucidation of the
functional mechanisms associated with these VNN2 and VNN3 candidate variants.
It was expected that CHA-seq would capture all heterozygous SNPs across the entire
VNN1 sequence, including the -137 SNP, using a prepared MNase-digested library from
the SAFHS cohort (note that T-CHA-seq used DNase I). However, a periodicity was
observed in densely populated and under-populated regions with respect to SNP-
containing sequence reads over extended genomic regions. The cause of this unexpected
periodicity is unclear and may be due to inconsistent capture of MNase-digested library
fragments during bead selection (see Section 4.2). Consequently, further optimisation of
the CHA-seq methodology is required to ensure capture of all genetic variants in other
CHA-seq libraries. Since the completion of the current study, a CHA-seq library using
DNase I was prepared using an optimised methodology developed as part of this
research, however, the -137 locus was again not captured. This research area is subject
to further experimentation. An additional limitation of CHA-seq, is the inherent
Illumina sequencing error rate (1%), which may introduce false polymorphisms with
DNA sequence reads.
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In the present study, CHA-seq has identified multiple candidate functional genetic
variants across the VNN1, VNN2 and VNN3 genes. The CHA-seq results are suggestive
that this approach may represent a time- and cost-effective alternative for identifying
and characterising functional genetic variants. These candidate functional variants in the
Vanin gene family will provide targets for verification and future research. This
potential application however, requires knowledge of the heritable component of allele-
specific chromatin accessibility. Consequently, CHA-seq would represent an ideal
approach for rapidly determining chromatin accessibility patterns within familial
groups.
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Chapter 5 – DNA methylation analysis of the -587 promoter region in the VNN1 gene
5.1 Introduction
DNA methylation plays a crucial role in transcriptional regulation and is typically
associated with gene silencing either directly, by reducing the accessibility of
transcription factors to the DNA, or indirectly, by enhancing the formation of
heterochromatin (Caro et al., 2012; Watt and Molloy, 1988; Boyes et al., 1991). These
changes are reversible upon de-methylation (Holliday et al., 1991). Bisulfite sequencing
is the standard approach for characterising DNA methylation patterns and is dependent
upon the treatment of genomic DNA with bisulfite prior to sequencing, converting
unmethylated cytosines to uracils and leaving methylated cytosines in their original
state (Rohde et al., 2010; Frommer et al., 1992). Bisulfite sequencing is particularly
useful for the characterisation of genetic variants located within CpG islands, which can
influence chromatin and gene expression depending upon the methylation state of
associated CpG sites (Bird, 2002; Goll and Bestor, 2005); as seen in the CBR1 gene
(Zhang, Y et al., 2009) and the RIL gene (Boumber et al., 2008).
Göring et al. (2007) identified the -587 VNN1 variant to be putatively functional based
on the genetic association with transcript abundance and HLD-C concentration in the
SAFHS cohort. Functional analysis identified no allele-specific DNA-protein
complexes at the -587 locus using EMSA, indicating that differential transcription
factor regulation was not the functional mechanism at this locus (Göring et al., 2007).
Taken together, the CHART-PCR (Chapter 3) and CHA-seq (Chapter 4) results
indicated that the -587 VNN1 variant was in a region of condensed chromatin, hence it
was important to determine whether the methylation status of the -587 locus was
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directly responsible for the difference. Previously, Kerkel et al. (2008) reported
differential CpG methylation in the VNN1 gene using three haematopoietic cell lines,
and identified a functional exonic SNP (rs2294757) that was associated with
preferential gene expression through allele-specific methylation in the VNN1 gene. In
this current study, bisulfite sequencing was performed on genotyped lymphoblastoid cell
lines from the SAHFS population to assess the DNA methylation status of the -587
promoter region in the VNN1 gene.
5.2 Materials and Methods
A detailed description of the bisulfite sequencing methodology is shown in Section
2.9.3 (Chapter 2), with a brief summary included below.
Genomic DNA was isolated from seven cultured human leukocyte cell lines from the
SAFHS cohort carrying different -587 genotypes (two -587G/G homozygotes, three -
587G/A heterozygotes and two -587A/A homozygotes) and purified using DNA Blood
mini columns (QIAGEN, Australia). Isolated DNA was treated with bisulfite to convert
unmethylated cytosines bases into uracils, and subsequently purified using EpiTect spin
columns (QIAGEN, Australia). The converted DNA was enriched for the VNN1 gene
promoter by PCR using primers (5’-CAGTGAGCCTGTACCCCTGG-3’ and 5’-
TATTCTCCCCATGTGTGTGCC-3’) flanking the predicted CpG island (between -758
to -496 upstream of the VNN1 transcriptional start site). The amplification products
were cloned into the pCR2.1-TOPO vector (Life Technologies Co., USA) for
sequencing (Australian Genome Research Facility, Perth, Western Australia).
Sequences were analysed using the online analysis program BISMA (Bisulfite
Sequencing DNA Methylation Analysis) (http://biochem.jacobs-
university.de/BDPC/BISMA/) using the default analysis parameters to identify the
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methylation state of predicted CG di-nucleotides in CpG islands. Further bioinformatic
verification was performed using AliBaba 2.1 (http://www.gene-
regulation.com/pub/programs/alibaba2/index.html) in order to determine whether
transcription factor binding sites occured within CpG sites.
5.3 Results
5.3.1 Predicted CpG sites in the -587 promoter region of the VNN1 gene
To investigate DNA methylation in the VNN1 gene promoter, bioinformatic analysis
was performed in the region surrounding the -587 SNP, indicating the presence of a
CpG island. A 2 kb region upstream of the transcriptional start site of the VNN1 gene
was analysed using BISMA, which identified the presence of a CpG island composed of
10 CG di-nucleotides (Figure 5.1). Interestingly, the -587G allele was imbedded within
a CG di-nucleotide. However, the -587A allele ablates the CG-dinucleotide and reduces
the length of the CpG island (Figure 5.1). In fact, the predicted CpG island length was
264 bp in the presence of the -587G allele, but only 204 bp when the A allele was
present (Figure 5.1).
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Figure 5.1: BISMA prediction of VNN1 CpG island length. Shown are bioinformatic predictions from Bisulfite Sequencing DNA Methylation Analysis (BISMA) (http://biochem.jacobs-university.de/BDPC/BISMA/) which interrogated the VNN1 gene promoter for potential CpG islands in the presence of different -587 alleles (R= G or A, in red). BISMA predicted the presence of 10 methylated CG di-nucleotides (grey boxes) in the VNN1 gene promoter. Black dots illustrate the predicted CpG island length of 264 bp when the -587G allele is present, with grey dots correlating with a CpG island length of 204 bp when the -587A allele is present. Of interest is that the presence of the -587A allele disrupts the formation of a CpG site which occurs in the presence of the -587G allele and therefore correlates with an allele-specific difference in the length (60 bp) of the CpG island.
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5.3.2 Bisulfite sequencing of the -587 promoter region in the VNN1 gene and BISMA analysis
Seven genotyped cell lines from the SAFHS cohort, differing in their -587 genotype,
were assessed using bisulfite sequencing to determine the DNA methylation status of
the predicted VNN1 promoter CpG island (Figure 5.2). As shown in Figure 5.2, the -
587G site in both homozygotes and heterozygotes shows near complete cytosine
methylation (on the opposite strand). In fact, all CpG di-nucleotides displayed heavy
methylation, except at the -720 CpG site. For this site, the methylation pattern
correlated with the genotype at the -587 locus. In haplotypes carrying the -587G allele,
the -720 CpG site was generally un-methylated (38 out of 48 sequences). Conversely,
when the -587A allele was present, the -720 CpG site was almost exclusively cytosine
methylated (41 out of 42 sequences). This pattern was present even in the heterozygous
lines, indicating that the allele-specific pattern is not cell line specific but rather is
determined by genotype. The biological consequence of this is uncertain, but is likely
responsible for the -587 allelic differences apparent in chromatin arrangement. The
observed variability in unmethylated CpG sites (apart from the -720 site) may be
attributed to factors such as: clonal DNA amplification, incomplete bisulfite conversion
or non-CpG methylation (Zhang and Jeltsch, 2010).
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Figure 5.2: Methylation of the -587 SNP. Bisulfite sequencing analysis of genotyped lymphoblastoid lines from the SAFHS population. Cell line designations and -587 genotype are shown on the left. Black boxes indicate DNA methylation, whereas white boxes indicate no methylation. “A” represents the alternate allele at -587 which is not a substrate for CpG methylation. The grey shading indicates heterozygous cell lines. Near complete methylation (41 out of 42 sequences) was seen at the -587G allele. Additionally, the methylation status at -587 appears to affect methylation at -720, a non-variant site.
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5.3.3 Bioinformatic analysis of the -720 CpG site in the VNN1 gene promoter
The bisulfite sequencing results and bioinformatic analysis showed an association
between the methylation status of the -720 CpG site and the allele present at the -587
locus (Figure 5.2). To determine a potential functional mechanism of allele-specific
DNA methylation at the -720 CpG site, bioinformatic analysis was conducted using
AliBaba 2.1 (http://www.gene-regulation.com/pub/programs/alibaba2/index.html).
Analysis revealed a CCAAT/enhancer-binding protein beta (C/EBPβ) transcription
factor binding site at the -720 locus (Figure 5.3). Furthermore, the GATA-binding factor
1 (GATA-1) transcription factor was predicted to bind downstream of the -720 locus
(Figure 5.3).
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Figure 5.3: Bioinformatic analysis of -720 CpG site in the VNN1 gene promoter. Shown is an adapted output from the online bioinformatic tool AliBaba 2.1 (Grabe, 2002), which interrogated the -720 promoter region in the VNN1 gene (red) for potential transcription factor binding sites. A CCAAT/enhancer-binding protein beta (C/EBPbeta) binding site (AliBaba score: 20) encompasses the -720 CpG site as shown, while a GATA-binding factor 1 (GATA -1) binding site (AliBaba score: 29) is predicted further downstream (3’ direction).
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5.4 Discussion
Chromatin arrangement is dependent upon DNA sequence and methylation status
(Statham et al., 2012). A genetic variant may alter chromatin arrangement over a large
genomic region whereby alternate alleles affect chromatin arrangement and hence
differentially regulate gene expression. In the current study, the chromatin architecture
was assessed in the -587 promoter region of VNN1. The CHART-PCR and CHA-seq
results showed that the -587G variant resides within a region of condensed chromatin.
Conversely, when the A allele is present at the -587 locus, chromatin is less condensed.
Furthermore, methylation at the -587G allele was supported through BISMA analysis.
Additionally, the length of the CpG island surrounding the -587G allele is greater than
that surrounding the -587A allele. The results of the current study suggest that the
methylation status of the VNN1 gene promoter at the -587 SNP directly affect chromatin
structure and the access of the transcriptional machinery to this gene. Thus, the allele
present at the -587 SNP is likely to be functional and influence gene expression.
Interestingly, DNA methylation at a site 133 bp upstream (-720) of the -587 SNP
appears to be inversely correlated with the -587 genotype, suggesting that allele-
dependant chromatin changes can be seen at sites remote from the actual genetic
variation. Typically, the -587G allele was methylated whereas the -720 CpG site was
unmethylated. In contrast, the -587A allele was not methylated and was seemingly
associated with a methylated -720 CpG site. Kerkel et al. (2008) reported a similar
pattern of differential CpG methylation at the VNN1 -720 site in three haematopoietic
cell lines derived from the perifieral blood of non-specified adults. Using methylation-
sensitive SNP analysis (MSNP), VNN1 was identified as a gene regulated by allele-
specific DNA methylation (Kerkel et al., 2008). However, in contrast to the current
study, Kerkel et al. (2008) suggest that the genotype at -587 does not correlate with -
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720 methylation status which was a novel finding in the current study. The absence of a
correlation in the Kerkel et al. (2008) study may be attributed to the difference in the
selection of experimental validation method, population-specific differences, or
unknown -137 genotype. In fact, the CHART-PCR and CHA-seq results are suggestive
that the -137 variant contributes to the differences in chromatin seen in the VNN1
promoter. However, given the almost complete linkage disequilibrium that exists
between the -587 and -137 loci in the SAFHS population (Göring et al., 2007), it is
unclear which genetic variant has a functional role in affecting VNN1 chromatin
arrangement. A possible solution may be to re-analyse the results of the Kerkel et al.
(2008) study by accounting for the -137 genotype, or to functionally assess a cell line
from the SAFHS cohort carrying -137 and -587 loci that are not in linkage
disequilibrium.
Besides the -720 CpG site, the bisulfite sequencing data showed overall methylation of
the VNN1 CpG island regardless of -587 genotype. Given the linkage disequilibrium
between -587 and -137 in the SAFHS population (Göring et al., 2007), it is possible that
the effects seen at the -720 site are due to allele-specific events at the -137 site. A
feasible mechanism is that allele-specific Sp1 transcription factor binding leads to
allele-specific changes to chromatin arrangement of an extended promoter region
upstream of the VNN1 gene, and consequently, differential gene expression.
The bisulfite sequencing and bioinformatic analysis showed that allele-specific changes
to chromatin arrangement of an extended VNN1 promoter region may correlate with
differential C/EBPβ binding at the -720 CpG site. C/EBPβ is a transcriptional activator
of genes involved in immune and inflammatory responses (Akira et al., 1990).
Previously, C/EBPβ binding has been experimentally demonstrated to be influenced by
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DNA methylation in mammalian cell lines (Ikeda et al., 2008). Given that VNN1 is a
key regulator of responses to inflammatory stimuli (Min-Oo et al., 2007), C/EBPβ
represents a candidate activator of the VNN1 gene under the influence of allele-specific
DNA methylation. Experimental verification using ChIP-seq or EMSA could be
conducted at a later date to confirm the C/EBPβ interaction in the VNN1 promoter.
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Chapter 6 – Experimental validation of candidate functional SNP using EMSA
6.1 Introduction
A number of candidate functional genetic variants were identified in VNN1 using CHA-
seq (Chapter 4), including the rs2267951 SNP which recorded the second-highest allele-
skew (35%), which could influence VNN1 expression. Previously, Göring et al. (2007)
used genome-wide transcriptional profiles and bioinformatic analyses to identify cis-
regulatory genetic variants in VNN1. However, there have been no reports that the
rs2267951 variant is a functional candidate. In the current study, EMSA was used to
experimentally validate the functionality of the rs2267951 SNP and to substantiate the
veracity of the CHA-seq approach.
In order to provide a positive control for allele-specific nuclear factor binding in EMSA,
a candidate functional SNP (‘-191T/C’) in the Presenilin Associated Rhomboid-Like
(PARL, formally PSARL) gene promoter was selected based on strong genetic
association with mitochondrial content levels (Curran et al., 2009). Following
functional analysis of the -191T/C SNP, EMSA and bioinformatic analysis was applied
to experimentally validate the CHA-seq results for the rs2267951 VNN1 SNP.
6.2 Methods
A detailed description of the EMSA methodology is shown in Section 2.9.2 (Chapter 2).
Of note, complementary 5' biotinylated oligonucleotides representing both allelic forms
of the promoter variants were obtained commercially from Sigma-Aldrich Pty Ltd
(Sigma, Australia). The sequences of the EMSA oligonucleotides are shown in Table
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6.1. Nuclear extract preparation, binding reactions and gel analysis and detection were
performed as described previously (Section 2.9.2).
For EMSA assessment at the -191T/C SNP, nuclear extract was isolated from the Jurkat
E6-1 cell line (ATCC #TIB-152) (Curran et al., 2009), whereas nuclear extract was
isolated from the ‘0331’ SAFHS cell line for the rs2267951 SNP. The 0331 cell line
was selected as it was previously used for CHA-seq experiments (Chapter 4).
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Table 6.1: EMSA oligonucleotides for PARL and VNN1 SNPs
Electrophoretic Mobility Shift Assays (EMSA) were performed using sets of sequence-specific oligonucleotides obtained from Sigma-Aldrich Pty Ltd (Sigma, Australia) of the sequence shown (“DNA SEQUENCE/MODIFICATION”). Oligonucleotides were designed to target the -191 PARL SNP (rows 1-4) and the rs2267951 VNN1 SNP (rows 5-8). Two pairs of allelic oligonucleotides were required for each DNA-protein binding reaction targeting a particular SNP. Each pair consisted of two complementary base sequences (forward from 5’ to 3’ and reverse from 3’ to 5’) that differ only by the SNP allele (as shown in bold). The forward oligonucleotide from each complementary pair also possessed a 5’ biotin modification to facilitate visualisation of DNA-protein binding complexes. Forward (FWD) and reverse (REV) primers are indicated by their respective suffix. Pairs used in EMSA assays are grouped together and share the same prefix. OLIGONUCLEOTIDE
DESIGNATION
DNA Sequence/Modification (5’-3’)
-191 EMSA (RARE ALLELE) FWD
5’ Biotin – GAGCCAGCTGAGCAGCGGGAGGGGAAGCGGT 3’
-191 EMSA (RARE ALLELE) REV
5’ ACCGCTTCCCCTCCCGCTGCTCAGCTGGCTC 3’
-191 EMSA (COMMON ALLELE) FWD
5’ Biotin – GAGCCAGCTGAGCAGTGGGAGGGGAAGCGGT 3’
-191 EMSA (COMMON ALLELE) REV
5’ ACCGCTTCCCCTCCCACTGCTCAGCTGGCTC 3’
RS2267951 EMSA (T
ALLELE) FWD
5’ – Biotin – CTGAGATTGGATTTTGTGGGGTGAGGTGAGT- 3’
RS2267951 EMSA (T
ALLELE) REV
5’- ACTCACCTCACCCCACAAAATCCAATCTCAG -3’
RS2267951 EMSA (C
ALLELE) FWD
5’ – Biotin – CTGAGATTGGATTTCGTGGGGTGAGGTGAGT- 3’
RS2267951 EMSA (C
ALLELE) REV
5’ – ACTCACCTCACCCCACGAAATCCAATCTCAG -3’
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6.3 PARL Results
6.3.1 EMSA and bioinformatic analysis of the -191 SNP in the PARL gene promoter
To provide a positive control for allele-specific nuclear factor binding, EMSA was
performed by comparing sequences that differed only at the -191T/C nucleotide. The
results shown in Figure 6.1 suggests that the -191 SNP did affect binding of nuclear
factors, with a major complex preferentially binding to the -191T allele in nuclear
extract conditions (lane 4). This complex is absent in the -191C sample (lane 3). In
order identify transcription factor binding sites within the PARL gene promoter,
bioinformatic analysis using P-Match was performed, and revealed that the -191T allele
is embedded in a Sp1 binding site (TRANSFAC 6.0 Core similarity = 0.957, matrix
similarity = 0.967). In contrast, the -191C SNP disrupts the core sequence. These results
suggest that the -191 SNP contributes to allele-specific differences in PARL expression,
possibly via Sp1-mediated regulation.
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Figure 6.1: EMSA for the -191 PARL SNP. Shown are the results of the ESMA that compares the gel-migratory patterns of -191T/C specific oligos in the absence of nuclear extract (lane 1 and 2), and in the presence of Jurkat lymphocyte nuclear extract (lanes 3 and 4) and analysed on a 6% non-denaturing polyacrylamide gel. A difference in banding patterns is observed between lanes 3 and 4 which differ in -191 alleles indicate the presence of an additional complex (indicated by arrowhead) in the presence of T allele (lane 4) under nuclear extract conditions. In contrast, this band was not seen in the presence of the C allele (lane 3). No complex was observed without the presence of nuclear extract (lanes 1 and 2).
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6.4 VNN1 Results
6.4.1 Bioinformatic analysis of the rs2267951 VNN1 SNP
To determine whether the rs2267951 SNP is embedded within a transcription factor
binding site, bioinformatic analysis was conducted using AliBaba 2.1 (TRANSFAC 7.0)
(http://www.gene-regulation.com/pub/programs/alibaba2/index.html) which predicted
C/EBPα and (yeast-specific) MIG1 binding sites specifically at the T allele (Figure 6.2).
Conversely, no transcription factor binding sites were predicted at the rs2267951 locus
in the presence of the C allele. However, Sp1 was predicted to bind downstream (3’) of
both alleles (Figure 6.2).
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A
B
Figure 6.2: Bioinformatic analysis of the rs2267951 SNP in the VNN1 gene. Shown is the output from the online bioinformatic tool AliBaba 2.1 (Grabe, 2002), which interrogated the VNN1 gene region (red text) surrounding the rs2267951 SNP (labelled) for potential transcription factor binding sites. As shown in Figure 6.2A, three transcription factor binding sites were predicted within VNN1 in the presence of the T allele of the rs2267951, including CCAAT/enhancer-binding protein alpha (C/EBPalp)(AliBaba score: 23), MIG1 (AliBaba score: 27) and Specificity Protein 1 (Sp1) (AliBaba score: 34). As shown in Figure 6.2B is the AliBaba 2.1 output which identified a Sp1 binding site (AliBaba score: 34) in the VNN1 gene region (red text) in which the C allele of the rs2267951 is embedded. Note that the T allele introduces two transcription factor binding sites (C/EBPalpa, MIG1) which were not observed in the presence of the C allele.
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6.4.2 EMSA analysis of the rs2267951 VNN1 SNP
To determine whether the rs2267951 SNP was associated with allele-specific DNA-
protein binding in vitro, as observed in the positive control (-191 PARL SNP), EMSA
was undertaken on sequences that differed by only the single rs2267951 nucleotide. The
results shown in Figure 6.3B indicated that the rs2267951 SNP does affect binding of
nuclear factors, with a major complex preferentially binding to the T allele in nuclear
extract derived from the SAFHS cohort (lane 4). This complex was absent in the
presence of the C allele (lane 3). A similar EMSA result was observed at the -191T/C
nucleotide (Figure 6.3A).
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A B
Figure 6.3: EMSA for the rs2267951 VNN1 SNP relative to the -191 PARL SNP. Shown are the results of the ESMA that compares the gel-migratory patterns of rs2267951-specific oligonucleotides (Figure 6.3B on right) in the absence of nuclear extract (lane 1 and 2) and in the presence of SAFHS (cell line ‘0331’) nuclear extract (lane 3 and 4) relative to the positive control at the -191 PARL SNP (Figure 6.3A on left). Figure 6.3A shows a difference in banding patterns between lanes 3 and 4 which differ in -191 alleles. An additional complex (indicated by arrowhead) is seen the presence of T allele (lane 4) under nuclear extract conditions in contrast to the C allele (lane 3), where no band is present. No complex was observed without the presence of nuclear extract (lane 1 and 2). Figure 6.3B shows a difference in banding patterns between lanes 3 and 4 which differ in rs2267951 alleles. An additional complex (indicated by arrowhead) is seen in the presence of T allele (lane 4) under nuclear extract conditions, in contrast to the C allele (lane 3), where this band is not present. No complex was observed without nuclear extract (lane 1 and 2).
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6.5 Discussion In the current study, two genetic variants (-191T/C PARL SNP and rs2267951 VNN1
SNP) were validated for functionality at the molecular level using EMSA and
bioinformatic analysis. The purpose of these analyses was to verify the veracity of the
CHA-seq results for the rs2267951 SNP in the VNN1 gene.
EMSA results and bioinformatic analyses where suggestive that the presence of the
common -191T SNP introduces a Sp1 transcription factor binding site into the PARL
promoter. This binding site creates a transcription domain that could regulate PARL
expression when the T allele is present. This is verified by the statistical analysis which
indentifies an association of the -191T/C SNP with mitochondrial content levels (Curran
et al., 2009). Taken together, the -191T/C SNP represents a functional genetic variant
and has been used in this current study as a positive control for allele-specific nuclear
factor binding.
For VNN1, the EMSA results show the presence of an additional DNA-protein complex
in the presence of the T allele at the rs2267951 locus. This result confirms that the
rs2267951 variant would be functional in CHA-seq. Bioinformatic analysis suggested a
CCAAT/enhancer-binding protein alpha (C/EBPα) transcription factor binding site in
the VNN1 gene in the presence of the T allele. C/EBPα is a transcription factor that is
encoded by the CEBPA gene in humans (Dufour et al., 2009; Szpirer et al., 1992; Cao
et al., 1991). The C/EBPα protein contains the basic Leucine Zipper Domain (bZIP
domain), which permits the binding to target promoters and enhancers (Dufour et al.,
2009; Ellenberger, 1994). For example, C/EBPα has been associated with body weight
homeostasis as a transcription enhancer (Miller et al., 1996). A potential consequence of
allele-specific C/EBPα regulation would be increased VNN1 expression in the presence
of the T allele. Given the association between body weight and cardiovascular disease
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(Lavie et al., 2009; Berrington de Gonzalez et al., 2010), the rs2267951 SNP may
confer allele-specific functional differences in cardiovascular phenotypes.
The results of EMSA provide further experimental evidence of function for the
rs2267951 variant, which is supported by the bioinformatic analysis. This analysis
revealed an allele-specific C/EBPα binding site in the presence of the T allele in VNN1.
Therefore, the EMSA and bioinformatic results are suggestive that CHA-seq is a
reasonable measure of function for genetic variants.
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Chapter 7 – General discussion and future research directions
7.1 CHA-seq contribution to functional research Reporter gene and gel shift assays have been used successfully over the last two decades to
determine the functionality of genetic variants. However, both approaches possess
limitations that affect their utility. For example, these assays lack scalability and do not
provide the proper chromatin context and hence are not always representative of function in
an in vivo context. In order to overcome limitations associated with current functional
assays, two novel in vivo assays (T-CHA-seq and CHA-seq) were developed as part of
the current study to provide a reasonable alternative for functional assessment of genetic
variants. The CHA-seq approach was developed in order to quantitate differences in
chromatin arrangement as a measure of functional differences at genetic variants by
extending CHART-PCR into a genome-wide format using next-gen DNA sequencing.
The CHART-PCR method has been refined to enable cell-type and allele-specific
comparisons of chromatin accessibility associated with genetic variants (Cruikshank et
al., 2012), however, the assay is not efficiently scalable in the current format.
Traditionally, CHART-PCR consists of the targeted amplification of specific genomic
regions of interest. However, this process requires significant time and resources to
characterise entire genes or genomes and thus is limited by its ability to analyse short
target sequences. To overcome this limitation, next-gen sequencing was incorporated as
part of the CHA-seq approach in order to provide a cost- and time-efficient alternative
for the quantification of chromatin accessibility at genetic variants across extended
genomic regions. The CHA-seq approach has been used successfully to analyse the
Vanin gene family in the current study. Other CHA-seq features of this approach
include the ability to: generate a high-resolution DNA sequence map specifying the
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alleles of genetic variants within a region or genome of interest, identify novel SNPs,
map nucleosome positions relative to genetic variants, and to functionally assess genetic
variants through interpretation of allele-specific biases in sequence read numbers
(‘allele skew’).
Chromatin accessibility is considered a reliable measure of the functionality of genetic
variants (Mikkelson et al., 2007; Birney et al., 2010; Pang et al., 2013) and has been
used extensively in the current research to identify and characterise candidate functional
genetic variants in the VNN1 gene. Traditionally, chromatin arrangement, as measured
by CHART-PCR, is calculated as a ratio of relative abundance of digested to undigested
amplicon and expressed as a percentage of cutting within a genomic region. In contrast,
chromatin arrangement, as measured by the CHA-seq approach, is calculated as an
allele-specific bias in sequence read numbers at genetic variants. At the -137 and -587
VNN1 SNPs, both CHA-seq and CHART-PCR results showed matching allele-specific
chromatin arrangement, which is indicative that DNA sequencing is a viable alternative
for the quantification of functional genetic variants.
The selection of chromatin-sensitive nuclease is an important feature in the
interpretation of CHA-seq results, since nucleases may differ in recognition sites and
mode of nucleolytic digestion. While MNase has been used to map nucleosome
occupancies and positions (Rizzo et al., 2012), DNase I is commonly used to map gene
promoter regions for chromatin arrangement, potential transcription factor binding sites
and areas of hypersensitivity (Boyle et al., 2011; Drouin et al., 1997). Consequently, the
selection of nuclease for the CHA-seq approach was a means for studying specific
functional properties of genetic variants. For example, DNase I was specifically selected
in T-CHA-seq to assess chromatin accessibility associated with the allele-specific Sp1
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binding at the -137 locus (Göring et al., 2007). As such, the use of multiple nucleases
can provide complementary information regarding the chromatin structure in a
particular region. This allows a more conclusive interpretation of the specific functional
mechanism of genetic variants.
7.2 Contribution to VNN1 research A significant output of the current study was the characterisation of three experimentally
verified functional genetic variants, namely rs4897612 (-137), rs2050153 (-587) and
rs2267951 in the VNN1 gene. Taken together, the results from CHART-PCR (Chapter 3)
and CHA-seq (Chapter 4) showed that the -137 and -587 SNPs within the VNN1 promoter,
previously predicted to be functional through extensive statistical interrogation of transcript
levels from the SAFHS cohort (Göring et al., 2007), were actually functional due to allele-
specific chromatin accessibility. Previously, Göring et al. (2007) used EMSA to show the
presence of allele-specific DNA-protein complexes in the presence of the -137T allele that
are consistent with the ability to preferentially bind the activating transcription factor Sp1
and the allele-specific chromatin accessibility observed in CHART-PCR (Chapter 3). In
contrast, Göring et al. (2007) reported that the -587 alleles displayed no differences in
transcription factor binding using EMSA. However, the application of the CHART-PCR
and CHA-seq methods showed significant allelic differences in chromatin accessibility
surrounding the -587 SNP and consequently indicated the functional potential at this variant
in addition to the -137 SNP. As the -587G allele is contained within a CG di-nucleotide and
hence is a potential site of cytosine methylation, BISMA analysis of the region surrounding
the -587 SNP revealed allele-specific differences in the length of the CpG island predicted
in the VNN1 promoter. Assessment of the methylation status of the region surrounding the -
587 SNP, using bisulfite sequencing, showed an allele-specific methylation pattern at a
single CpG site (-720 locus) located upstream of the -587 SNP. This indicated that allele-
specific chromatin differences can be seen at sites remote from the causal genetic variant(s).
169
Similar results have been previously reported in a study by Kerkel et al. (2008) in which
three haematopoietic cell samples showed a similar pattern of differential CpG methylation
at the -720 site to that in the SAFHS cell lines used in the current study. However, in
contrast to the results of the current research, the results of Kerkel et al. (2008) suggest that
the -587 genotype does not correlate with the -720 methylation status. Given the strong
linkage disequilibrium between the -587 and -137 SNPs in the SAFHS cohort, as reported
by Göring et al. (2007), it is possible that the effects seen at the -720 site are due to allele-
specific events at the -137 locus. Potentially, the allele-specific DNA methylation may be
caused by allele-specific Sp1 transcription factor binding, resulting in allele-specific
chromatin arrangement of an extended promoter region upstream of the VNN1 gene. Such
an occurrence could lead to differential gene expression. Allele-specific changes to
chromatin arrangement within an extended genomic region have been demonstrated
previously by Shoemakers et al. (2010) and Zhang, Y et al. (2009).
Given the nature of the functional effects of the -137 and -587 variants, it is possible that
the effects of these variants are independent. However, given the linkage disequilibrium
observed between them, it is more likely that the two functional variants are part of a
haplotype that regulates genotype-specific VNN1 expression. This interaction may be
complicated by the presence of additional functional genetic variants (CHA-seq candidates
in Chapter 4) located in regions distinct from the VNN1 gene promoter. For instance, the
CHA-seq results were suggestive that the rs2267951 VNN1 SNP to be functional based on
a significant allele-skew, which was subsequently verified using EMSA and
bioinformatic analysis supporting the presence of allele-specific C/EBPα binding at this
site. Potentially, functional haplotypes containing multiple functional genetic variants
may exist in genes other than VNN1.
170
Given that VNN1 appears to play a significant role in determining levels of ‘protective’
HDL-cholesterol (Göring et al., 2007; Jacobo-Albavera et al., 2012), functional genetic
variants associated with high levels of VNN1 gene expression, such as the -137 and -587
SNPs, may lead to reduced HDL-C levels which is known to contribute to an increased
risk of CVD.
7.3 Future research directions
The elucidation of functional genetic variants that alter human phenotypes, such as
disease susceptibility, is essential to characterising the relationship between genotype
and phenotype (Stranger et al., 2011; Cooper and Shendure, 2011). Although
comprehensive catalogues of human genetic variation exist, the identification of causal
functional variants in particular phenotypes or disease has proven difficult (Cooper and
Shendure, 2011). Perhaps the most comprehensive catalogue of functional genetic
variants is the ENCODE database which was developed in order to improve the
annotation of functional genetic variation in the human genome (Birney et al., 2007;
Rosenbloom et al., 2012). While ENCODE predictions are useful in facilitating
hypothesis generation and improving the prioritisation of variants, they are insufficient
for verifying the biological function and interactions of regulatory variants in the
phenotype of different cells and tissues when not supported by other data (Rosenbloom
et al., 2012). For example, a candidate functional variant may be predicted by the
ENCODE database to disrupt a consensus transcription factor binding motif, however,
these predictions are limited without experimental validation to verify allele-specific
binding and the associated biological consequence. Consequently, functional assays,
such as CHA-seq and T-CHA-seq that incorporate prioritisation and experimental
validation may represent a more time and cost-efficient alternative to greatly improve
the rate of discovery of functional genetic variants in phenotypes of interest.
171
The results of the current study are suggestive that CHA-seq is a time and cost-effective
in vivo approach for the identification of functional variation in extended genomic
regions. A key recommendation for the application of CHA-seq to cell lines possessing
genetic variants of interest is to incorporate one or more chromatin-sensitive nucleases
in the CHA-seq approach, followed by the application of T-CHA-seq to interrogate the
effects of individual genetic variants. The incorporation of additional experimental
validation tests, such as EMSA and bisulfite sequencing, will enhance the positive
identification of functional variants by providing further supporting evidence. Further
CHA-seq experiments will ultimately involve the generation of whole genome maps
across different populations. The application of CHA-seq to populations comprised of
familial groups would enable confirmation that observed allele-specific patterns are
heritable. The identification of heritable functional variants, in contrast to cell or
individual-specific functional variants, would further establish the veracity of the CHA-
seq approach to identify true functional variants.
Previous Vanin research used transcript levels as an endophenotype to identify VNN1 as
a quantitative trait locus associated with high density lipoprotein–cholesterol levels and
cardiovascular phenotypes (Göring et al., 2007). More recently, genetic variants within
VNN1 have been found to be associated with cardiovascular disease (Jacobo-Albavera
et al., 2012; Fava et al., 2010; Zhu and Cooper, 2007). However, limited experimental
validation has been performed to determine specific functional variants in VNN1
(Göring et al., 2007; Kerkel et al., 2008). This current research, has contributed to the
number of experimentally validated functional genetic variants in VNN1 through the
application of the novel CHA-seq assay.
172
The research described in this thesis was able to establish that allele-specific chromatin
differences can be seen at sites distinct from the actual genetic variant, and suggested
the presence of co-operative functional genetic variants within the VNN1 gene. As
functional variants may operate in a cell or tissue-dependent manner (Dimas et al.,
2009), the findings of the current study would require further interrogation using
different cell types, to elucidate whether the mechanisms of action associated with
functional variants are ubiquitous or specific to certain cells.
Of interest in the current study was the presence of a periodicity in densely populated
and under-populated regions with respect to SNP-containing CHA-seq reads over
extended genomic regions. It is not known whether this periodicity is a direct reflection
of large-scale chromatin arrangement across the VNN1 gene or a consequence of library
preparation. In either case, this observation requires further testing in order to optimise
CHA-seq for entire genomes.
The importance of characterising genetic variation is becoming apparent due to the
increasing number of functional variants associated with a wide array of diseases
(Stranger et al., 2011). Consequently, the knowledge of an individual’s SNP genotype
may provide a basis for assessing susceptibility to disease and the subsequent selection
of optimal treatments (Shastry, 2007). Alternatively, these functional genetic variants
may facilitate the development of customised genetic therapies. Functional VNN1
variants identified in the present study may represent potential targets for therapeutic
intervention, or represent genetic markers for cardiovascular disease susceptibility.
Ultimately, the long term goal in this field of research is to be able to predict disease
susceptibility based on genotype and knowledge of which genetic variants are
functional (Cooper and Shendure, 2011).
173
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Appendix – Supplementary Tables and Figures
Table A.1: Description of general biochemicals and reagents
As shown are the biochemical reagents (“Biochemical/reagent”) that were used in the current study. Biochemicals were of the highest grade available and purchased from a variety of distributers (“Distributer/Supplier”).
Biochemical/reagent Distributer/Supplier
1X Dulbecco’s Phosphate Buffered Saline (PBS)
Life Technologies Australia Pty Ltd, Mulgrave, VIC, Australia (Life Technologies, Australia)
100 base pair DNA Ladder (500 µg/mL) New England Biolabs, Ipswich, MA, USA (New England Biolabs, USA)
1 kilobase DNA Ladder (500 µg/mL) New England Biolabs, Ipswich, MA, USA (New England Biolabs, USA)
Agarose, analytical grade Promega Corporation, Madison WI, USA (Promega, USA)
Ampicillin sodium salt Sigma-Aldrich Pty Ltd, Castle Hill, NSW Australia (Sigma, Australia)
Bacto-tryptone Difco Laboratories, Detroit MI, USA (Difco, USA)
Bacto-yeast extract Difco Laboratories, Detroit MI, USA (Difco, USA)
Boric acid Sigma-Aldrich Pty Ltd, Castle Hill, NSW Australia (Sigma, Australia)
Bovine Serum Albumin (BSA) New England Biolab, Ipswich, MA, USA (New England Biolabs, USA)
Bromophenol blue Merck Pty Limited, Kilsyth, Victoria, Austraila (Merck, Australia)
DH5α-T1R Chemically Competent Escherichia Coli
Life Technologies Australia Pty Ltd, Mulgrave, VIC, Australia (Life Technologies, Australia)
Difco Agar Difco Laboratories, Detroit, MI, USA (Difco, USA)
Dimethyl sulfoxide (DMSO) Merck Pty. Ltd, Kilsyth VIC, Australia (Merch, Australia)
Deoxyribonuclease I (DNase I) Invitrogen Corporation, Mount Waverley, VIC, Australia (Invitrogen Corporation, Australia)
Dithiothreitol (DTT) Sigma-Aldrich Pty Ltd, Castle Hill, NSW Australia (Sigma, Australia)
Ethylenediaminetetraacetic acid (EDTA) Sigma-Aldrich Pty Ltd, Castle Hill, NSW Australia (Sigma, Australia)
Ethylene glycol tetraacetic acid (EGTA) Sigma-Aldrich Pty Ltd, Castle Hill, NSW Australia (Sigma, Australia)
Ethanol, absolute APS Ajax Finechem, Auburn NSW, Australia (APS Ajax Finechem, Australia)
205
Biochemical/reagent Distributer/Supplier
Ethidium Bromide Sigma-Aldrich Pty Ltd, Castle Hill, NSW Australia (Sigma, Australia)
Fetal Bovine Serum (FBS) Life Technologies, Gaitherburg MD, USA (Life Technologies, USA)
Ficoll GE Healthcare Technologies, Sydney, Australia (GE Healthcare Limited, Australia)
Gel Loading Dye Blue (6X) New England Biolabs, Ipswich, MA, USA (New England Biolabs, USA)
Glycerol Merck Pty Limited, Kilsyth, Victoria, Austraila (Merck, Australia)
HEPES MP Biomedicals, LLC, Ohio, USA (MP Biomedicals, USA)
Hi-Di Formamide Applied Biosystems Pty Ltd, Scoresby Victoria, Australia (Applied Biosystems, Australia)
Hydrogen Chloride School of Biochemistry Chemical Store
Isopropanol (propan-2-ol) Merck Pty. Limited, Kilsyth VIC, Australia (Merch, Australia)
Liquid Nitrogen School of Biochemistry Chemical Store, Crawley, WA, Australia
Leupeptin Sigma-Aldrich Pty Ltd, Castle Hill, NSW Australia (Sigma, Australia)
Magnesium Chloride Sigma-Aldrich Pty Ltd, Castle Hill, NSW Australia (Sigma, Australia)
Micrococcal Nuclease (MNase) New England Biolab, Ipswich, MA, USA (New England Biolabs, USA)
Penicillin-Streptomycin 10 000 Units/mL Penicillin 10 000 µg/mL Streptomycin
Life Technologies Australia Pty Ltd, Mulgrave, VIC, Australia (Life Technologies, Australia)
Pepstatin Sigma-Aldrich Pty Ltd, Castle Hill, NSW Australia (Sigma, Australia)
Phosphate Buffered Saline (PBS) Life Technologies, Gaitherburg MD, USA (Life Technologies, USA)
Phenylmethylsulfonyl fluoride (PMSF) Sigma-Aldrich Pty Ltd, Castle Hill, NSW Australia (Sigma, Australia)
Poly-deoxy-inosinic-deoxy-cytidylic (Poly[d(I-C)])
Roche Diagnostics Corporation, Indianapolis IN, USA (Roche Diagnostics, USA)
Potassium Chloride CHEM-SUPPLY, Gillman, SA, Australia
Proteinase K New England Biolab, Ipswich, MA, USA (New England Biolabs, USA)
RPMI-1640 cell culture medium Life Technologies, Gaitherburg MD, USA (Life Technologies, USA)
Sodium Acetate Laboratory Supply, Osborn Park, WA, Australia (Laboratory Supply, Australia)
Sodium Chloride Merck Pty. Limited, Kilsyth VIC, Australia (Merch, Australia)
Sodium dodecyl sulphate (SDS) Sigma-Aldrich Pty Ltd, Castle Hill, NSW
206
Australia (Sigma, Australia)
Biochemical/reagent Distributer/Supplier
Sodium Hydroxide School of Biochemistry Chemical Store, Crawley, WA, Australia
Trimza base Sigma-Aldrich Pty Ltd, Castle Hill, NSW Australia (Sigma, Australia)
Trypan Blue Stain Sigma-Aldrich Pty Ltd, Castle Hill, NSW Australia (Sigma, Australia)
Water for Irrigation Baxter Healthcare Pty Ltd, Old Toongabbie, NSW, Australia
X-Gal (5-bromo-4-chloro-3-indolyl-beta-D-galacto-pyranoside)
Promega Corporation, Madison WI, USA (Promega, USA)
207
Table A.2: Distributers for laboratory equipment used in research
As shown is the equipment (“Equipment”) that was used in the current study. Equipment was of the highest standard available and was previously purchased from a variety of distributers (“Distributer/Supplier”).
Equipment Distributer/Supplier
Beckman J2-MC Centrifuge Beckman Coulter Inc., Fullerton CA, USA (Beckman Coulter, USA)
Bioline Shaking Incubator Edwards Instrument Company, Narellan NSW, Australia (Edwards Instrument Company, Australia)
CFX96 Real-Time PCR Detection System Bio-Rad Laboratories, CA, USA (Bio-Rad Laboratories, USA)
C02 Water Jacketed Incubator Forma Scientific Inc., Marietta OH, USA (Forma Scientific, USA)
Dark reader - blue light transilluminator Clare Chemical Research, CO, USA (Clare Chemical Research, USA)
Electrophoresis Tank BIO-RAD Laboratories, Hercules, CA, USA (BIO-RAD, USA)
Eppendorf Centrifuge 5415C Eppendorf AG, Hamberg, Germany (Eppendorf, Germany)
Eppendorf Refridgerative Microcentrifuge 5417R
Eppendorf AG, Hamberg, Germany (Eppendorf, Germany)
Hypercassette Amersham International plc, Buckinghamshire, England, UK (Amersham, UK)
Jouan C312 swinging rotor centrifuge Jouan, St-Herblain, France (Jouan, France)
Nanodrop ND-100 NanoDrop Technologies, DE, USA (NanoDrop, USA)
Neubauer Counting Chamber ProSciTech, Kirwan, QLD, Australia
PTC-100 Programmable Thermal Controller
MJ Research Inc., San Francisco, CA, USA (MJ Research, USA)
Spectroline UV Transilluminator Spectronics Corporation, Westbury, NY, USA (Spectronics, USA)
Wild M40 light microscope Wild Heerbrugg, Gais, Switzerland
XCell SureLock Electrophoresis Cell Invitrogen Corporation, Mount Waverley, VIC, Australia (Invitrogen Corporation, Australia)
208
Table A.3: Distributers for general laboratory disposables used in this research
As shown are the general laboratory disposables (“Disposables”) that were used in the current study. Disposables were of the highest standard available and were purchased from a variety of distributers (“Distributer/Supplier”).
Disposables Distributer/Supplier
1.5mL Cryovials Nalge Nunc International, NY, USA (Nalge Nuc International, USA)
96-well plates Bio-Rad Laboratories, CA, USA (Bio-Rad Laboratories, USA)
15ml and 50mL Falcon Tubes Sarstedt Inc., Nürnberg, Germany (Sarstedt, Germany)
0.2mL and 1.5ml Eppendorf micro-centrifuge tubes
Eppendorf AG, Hamberg, Germany (Eppendorf, Germany)
25 cm2 and 75 cm2 filter-top culture flask Stratagene, La Jolla, CA, USA
Petri dishes Sarstedt Inc., Nürnberg, Germany (Sarstedt, Germany)
Pasteur pipettes Sarstedt Inc., Nürnberg, Germany (Sarstedt, Germany)
209
Table A.4: DNase I cutting at the -137 and -587 VNN1 gene promoter SNPs, with
SPA-2 and β-actin control loci in SAFHS cell 0054
DNase I-treated cultured cell line of the genotypes (“Genotype”) shown were used to calculate chromatin accessibility expressed as a ratio of undigested to DNase I-digested (DNase I cutting [%]). The data were obtained from the Ramos cell line and assayed in triplicate (n=3). The average DNase I cutting values for each replicate are shown in addition to the overall average (“Average”) of each genotype.
Genotype -137T/G -587G/A SPA-2 β-actin
DNase I cutting
(%)
19.995 15.991 3.754 17.761
24.990 15.310 7.218 20.387
23.940 19.949 11.591 23.417
Average 23.0 17.1 7.5 20.5
210
Table A.5: DNase I cutting at the -137 and -587 VNN1 gene promoter SNPs in
homozygous SAFHS cell lines
DNase I-treated cell lines of the genotypes (“Genotype”) shown were used to calculate chromatin accessibility expressed as a ratio of undigested to DNase I-digested (DNase I cutting [%]). The data were obtained from cultured human cell lines derived from the SAFHS cohort for each genotype and assayed in triplicate (n=3). The average DNase I cutting values for each replicate are shown in addition to the overall average (“Average”) of each genotype.
Genotype 137TT 137GG 587GG 587AA
DNase I cutting (%) 31.815 25.357 20.203 24.558
29.767 26.024 17.761 22.562
29.604 24.974 16.576 20.650
Average 30.4 25.5 18.2 22.6
211
Table A.6: MNase cutting at the -137 and -587 VNN1 gene promoter SNPs, with
SPA-2 and β-actin control loci in heterozygous Ramos cell line
MNase-treated cell lines of the genotypes (“Genotype”) shown were used to calculate chromatin accessibility expressed as a ratio of undigested to MNase-digested (MNase cutting [%]). As shown are the data obtained from cultured cell lines derived from the
SAFHS cohort for each genotype and assayed in triplicate (n=3). The average MNase cutting values for each replicate are shown in addition to the overall average (“Average”) of each genotype.
Genotype -137T/G -587G/A SPA-2 β-actin
MNase cutting (%) 28.327 25.346 17.166 28.105
30.898 26.273 21.606 30.466
33.118 26.193 22.299 31.191
Average 30.8 25.9 20.4 29.9
212
Table A.7: MNase cutting at the -137 VNN1 gene promoter SNP in different
SAFHS cell lines
MNase-treated cell lines of the genotypes (“Genotype”) shown were used to calculate chromatin accessibility expressed as a ratio of undigested to MNase-digested (MNase cutting [%]). As shown are the data obtained from cultured cell lines (“Cell line designation") derived from the SAFHS cohort for each genotype and assayed in
quadruplicate or greater (n≥4) based on availability of frozen stocks. The average MNase cutting values for each replicate are shown in addition to the overall average (“Average”) of each genotype.
Cell line
designation 0306 0525 0029 0054 0188 0194
Genotype -137G/G -137G/G -137T/G -137T/G -137T/T -137T/T
MNase
cutting (%)
11.747 12.340 21.510 12.653 22.957 22.727
12.433 21.123 19.013 13.713 21.399 38.598
10.698 11.564 19.319 16.756 29.553 19.720
12.974 10.188 25.238 23.097 26.713 28.998
19.938 12.554 16.266 24.455 29.462
16.548 23.647 35.055
13.903
Average 13.6 13.6 18.8 16.6 24.8 29.1
213
Table A.8: MNase cutting at the -587 VNN1 gene promoter SNP in different
SAFHS cell lines
MNase-treated cell lines of the genotypes (“Genotype”) shown were used to calculate chromatin accessibility expressed as a ratio of undigested to MNase-digested (MNase cutting [%]). As shown are the data obtained from cultured cell lines (“Cell line designation") derived from the SAFHS cohort for each genotype and assayed in
quadruplicate or greater (n≥4) based on availability of frozen stocks. The average MNase cutting values for each replicate are shown in addition to the overall average (“Average”) of each genotype.
Cell line
designation 0306 0525 0029 0054 0188 0194
Genotype -587A/A -587A/A -587G/A -587G/A -587G/G -587G/G
MNase
cutting (%)
32.842 33.869 9.057 12.544 7.509 16.278
18.842 31.503 18.037 16.039 9.276 12.497
19.361 27.52 17.154 20.028 2.247 16.353
38.251 20.535 15.615 16.427 7.442 10.318
29.265 24.748 13.712 9.32
21.325 15.773
15.645
Average 26.6 27.6 15.0 16.3 7.2 13.9