Epigenetic modifications in essential hypertension
Ms. Ingrid A Wise
B.BiomedSci, H.Biomed Sci (Molecular Biology)
A thesis submitted for the degree of Doctor of Philosophy at
Federation University Australia in April, 2018
School of Biomedical Sciences
Abstract
Background: Hypertension (HTN) is a complex, multifactorial, quantitative trait under polygenic control that affects more than one billion people globally. Despite advances in our understanding of the pathophysiology of HTN and the implementation of more effective treatment and prevention strategies, HTN remains one of the world’s great public health problems. The accepted inference from genome-wide association studies (GWAS) is that the genetic code lays the foundation for transcriptomic changes and in turn physiological change. On the other side of the coin, environmental factors (smoking, diet, chemical exposure) can in turn affect DNA itself in genes relevant to blood pressure (BP). Variation in epigenetic forms of modification may thus explain additional phenotypic variation in BP and provide new clues to the physiological processes influencing its regulation. DNA methylation is one of these epigenetic mechanisms responsible for changes to gene expression, activated by interaction with environmental triggers. DNA methylation is a reversible epigenetic modifier of specific dinucleotide sites called CpGs, which consists of a transfer of a methyl group derived from S-adenosyl-L-methionine to position five of a cytosine ring, forming 5mC. Pathophysiologically, the kidney is known as the key organ of BP regulation and one of the most important contributors to HTN. According to the hypothesis put forward by Guyton, over 40 years ago, the control of BP in the steady-state and longer-term is critically dependent on renal mechanisms. In fact, almost all monogenic forms of HTN are driven by rare mutations in genes involved in salt handling in the distal nephron. It is therefore crucial to understand kidney DNA methylation changes that may drive gene expression in kidney and lead to HTN.
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Hypothesis: The central hypothesis underpinning this PhD thesis is that alterations in kidney specific DNA methylation plays a fundamental role in modulating gene expression changes involved in the regulation of BP and pathophysiology of EH.
Aims: This PhD thesis focuses on characterising the role of DNA methylation in the hypertensive kidney using array and RNA-sequencing methods. Three major aims are addressed:
• Aim 1: To characterise blood and kidney global DNA methylation dynamics and its
functional role in the hypertensive population (Chapter 3).
• Aim 2: To determine the role of genome-wide, loci specific DNA methylation in the
hypertensive human kidney (Chapter 4).
• Aim 3: To understand the relationship between DNA methylation and differential
expression of genes associated with BP and HTN in the human kidney (Chapter 5).
Results: In Aim 1 global DNA methylation changes were characterised in peripheral blood leukocyte and kidney DNA of the hypertensive (HT) population using he ELISA method. We found no association between HTN diagnosis and global methylation percentage in either peripheral blood leukocytes or kidney DNA. However, a negative correlation was found between global methylation and diastolic blood pressure (DBP), yet this relationship was not evident after adjustment for the effect of antihypertensive medication. Furthermore, we investigated the sensitivity of ELISA-based global methylation detection by calculating the percentage of global methylation in kidney using array based methods; the results were similar, demonstrating no
ii | Page association between HTN diagnosis and median kidney methylation β-value or M-value. Similar findings were evident for systolic blood pressure (SBP). Interestingly, for β-values, a linear relationship between median global methylation percentage and DBP was established but this finding was not transferable for M-values.
In Aim 2 we examined a total of 94 human kidneys to investigate loci specific methylation patterns of HTN on the renal genome. Our methylation analysis identified one HTN-associated CpG site, three SBP-associated CpG sites and 19 DBP-associated CpG sites; including four CpG sites previously identified in large genome-wide, peripheral blood studies of HTN. We discover novel differential methylation in CpGs not previously associated with BP in the human kidney; among these are sites involved in pathways regulating transport of glucose, organic acids and amine compounds; circadian rhythm; neural development; transport of monocarboxylates; peptidase activity; transcriptional regulation; insulin signaling; cell cycle progression; metabolism; G- coupled protein receptor (GCPR) signalling; nuclear receptors in lipid metabolism and toxicity and RNA processing. We also conduct a replication analysis of BP-associated, methylated CpG sites in the human kidney and pre-existing GWAS findings in blood. Four kidney loci overlap with blood-based GWAS CpG sites, suggesting that loci specific methylation studies may have cross- tissue applicability.
In Aim 3 we examined 180 human kidneys to uncover genetic signatures of HTN in the renal transcriptome. The transcriptome analysis of 180 kidneys revealed 14 HTN-associated genes, 1 gene associated with DBP and 6 genes associated with DBP; including those involved in smooth muscle response to BP fluctuation and BP response to salt intake in salt sensitive humans.
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Furthermore, as DNA methylation is a known regulator of gene expression we investigated whether differential DNA methylation in proximity to HTN-associated renal genes correlated to their renal expression. We identify that DNA methylation and renal gene expression are tightly linked in the regulation of pathways that modulate vascular function, cellular communication, inflammation and sodium regulation. In particular, we identify a close relationship between renal
DNA methylation and expression FAM50B, PC and report a strong connection with this relationship to chronic BP elevation.
Conclusion: This PhD Thesis provides novel evidence for implication of renal DNA methylation in HTN pathogenesis and a tightly linked relationship between DNA methylation and expression of genes associated with HTN and BP. Together, this PhD Thesis provides new insights into the role of renal DNA methylation in BP modulation. This Thesis provides a new framework for the epigenetic regulation of transcription in the human kidney and points towards DNA methylation as a key modulator of renal BP-associated gene expression.
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Declaration
This thesis is composed of my original work, and contains no material previously published or written by another person except where due reference has been made in the text. I have clearly stated the contribution by others to jointly-authored works that I have included in my thesis.
I have clearly stated the contribution of others to my thesis as a whole, including statistical assistance, survey design, data analysis, significant technical procedures, professional editorial advice, and any other original research work used or reported in my thesis. The content of my thesis is the result of work I have carried out since the commencement of my research higher degree candidature and does not include a substantial part of work that has been submitted to qualify for the award of any other degree or diploma in any university or other tertiary institution. I have clearly stated which parts of my thesis, if any, have been submitted to qualify for another award.
I acknowledge that an electronic copy of my thesis must be lodged with the University Library and, subject to the policy and procedures of Federation University Australia, the thesis be made available for research and study in accordance with the Copyright Act 1968 unless a period of embargo has been approved by the Dean of the Graduate School.
I acknowledge that copyright of all material contained in my thesis resides with the copyright holder(s) of that material. Where appropriate I have obtained copyright permission from the copyright holder to reproduce material in this thesis.
Signed: Ms Ingrid A Wise
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Publications during candidature
Publications arising from this thesis
1. Wise IA, & Charchar FJ. (2016). Epigenetic modifications in essential hypertension.
International Journal of Molecular Sciences, March 25; 17(4): 451. doi:
10.3390/ijms17040451.
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Presentations arising from this thesis
1. Wise I. A. & Charchar, F. J. (2015). Epigenetic modification in essential hypertension.
Federation University HDR Research Conference, (Ballarat, Australia, 2015).
2. Wise I. A, Tomaszewski, M. & Charchar, F. J. (2016). Decreased kidney global
methylation leads to increased blood pressure. Federation University HDR Research
Conference, (Ballarat, Australia, 2016).
3. Wise I. A, Prestes, P., Tomaszewski, M. & Charchar, F. J. (2016). Decreased kidney global
methylation leads to increased blood pressure. 26th Scientific Meeting of the International
Society of Hypertension (ISH), (Seoul, Korea, 2016).
4. Wise I. A, Tomaszewski, M. & Charchar, F. J. (2016). The involvement of kidney DNA
methylation in blood pressure regulation. 2016 Australasian Genomic Technologies
Association (AGTA), (Auckland, New Zealand, 2016).
5. Wise I. A, Prestes, P., Tomaszewski, M. & Charchar, F. J. (2016). The involvement of
kidney DNA methylation in blood pressure regulation. 2016 Joint Annual Scientific
Meeting (HBPRCA), (Hobart, Australia, 2016).
6. Wise I. A, Prestes, P., Tomaszewski, M. & Charchar, F. J. (2017). The role of kidney DNA
methylation in hypertensive gene expression. Federation University HDR Research
Conference, (Ballarat, Australia, 2017).
7. Wise I. A, Prestes, P., Tomaszewski, M. & Charchar, F. J. (2017). The involvement of
kidney DNA methylation in blood pressure regulation. International Society of
Hypertension, New Investigator Symposium (ISH), (Singapore, 2017).
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8. Wise I. A, Prestes, P., Tomaszewski, M. & Charchar, F. J. (2017). The involvement of
kidney DNA methylation in blood pressure regulation. 13th Asia-Pacific Congress of
Hypertension (APCH), (Singapore, 2017).
9. Wise I. A, Tomaszewski, M., Prestes, P., Akbarov, A. & Charchar, F. J. (2017). Specificity
of DNA methylation in the hypertensive kidney. 2017 Annual Scientific Meeting of the
High Blood Pressure Research Council Australia (HBPRCA), (Melbourne, Australia,
2017).
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Contributions by others to the thesis
All of the work presented in this Thesis is my own work except the following:
1. Sample collection for this project was conducted with assistance of Professor Maciej
Tomaszewski (TRANSLATE collaborator).
2. Methylation array sequencing was performed with the assistance of the Australian Genome
Research Facility, (Perth, Australia).
3. RNA-sequencing was performed with the assistance of the Australian Genome Research
Facility, (Perth, Australia) and samples were prepared with the assistance of Dr Priscilla Prestes
(research fellow Federation University Australia).
4. Computational analysis scripts for methylation array and differential gene expression were
developed with assistance from Dr Artur Akbarov (research fellow at University of
Manchester).
5. Bioinformatics analysis (including primary and secondary analysis of RNA-seq datasets to
generate counted-read matrix as well as tertiary analysis, such as MDS plots, heat maps, GSEA
analyses, genome features, and datasets integration) were generated by under the supervision
of Dr Artur Akbarov and Professor Maciej Tomaszewski (University of Manchester). Statement of parts of the thesis submitted to qualify for the award of another degree
None.
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Acknowledgements
A PhD is an enduring and daring adventure. It requires more dedication and focus than anything I have contemplated or encountered. It is the end of one’s education path but the beginning of one’s research career. It takes dedication, persistence and honesty to conquer the “PhD Mountain” and I am quite sure it should come with a government health warning.
I feel very privileged for the opportunity to pursue my PhD at Federation University under my primary supervisor, Professor Fadi Charchar, and two co-supervisors, Associate Professor Mark
Myers and Dr Scott Nankervis. Three equally decent and respectable men as well as great scientists, who have given me invaluable guidance in scientific training and have taught me important lessons for life. To Fadi, thank you for taking me on board three years ago, it changed my life and lit a fire for my passion for research. You have taught me the importance of collaboration, patience, critical thinking and remaining grounded. Perhaps the most important lesson you gave me is to always have great passion in what you do, and accept that not everything must be done alone. Thank you Fadi for being such a great mentor. To Mark, you first introduced me to the idea of joining the research team, a process that will undoubtedly change my life forever;
I cannot thank you enough. To Scott, thank you for being a voice of sanity, reason and support throughout this monumental process. You reassurance that I know what I am doing during my outbursts of imposter syndrome have been invaluable. I also thank you for giving me so many opportunities for growth during my time at Federation University, and trusting me to take on new tasks, perhaps at the peril of undergraduate students. Thank you to the three of you for creating such a positive, helpful and encouraging environment over the 11 years that I have been at
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Federation University. I would also like to express my gratitude to Professor Maciej Tomaszewski for offering a warm welcome to me to work in your lab at Manchester University. Thanks for creating such a dynamic and friendly scientific environment. To Dr Artur Arkbarov, thank you for all for your patient guidance during the steep learning track to the bioinformatics world. I could not achieve so much without your help. To Dr James Eales, Dr Priscilla Prestes and the rest of the team, thank you for your friendly help in scientific questions and participation in life when I was quite sure I had no idea what I was doing.
The journey of obtaining a PhD in science would be difficult without the important insightful discussions and technical support from others. To Dr Jack Harvey and Mrs Fahima Ahmady, thank you for helping me throughout various difficulties I met during the course of my PhD. To members of postgraduate lab: Dr Scott Booth, Dr Alex Nield, Dr Usha Kalluri, Mr Nathan Devries, and the rest of the team, collaborators at the Fiona Elsey Cancer Research Centre, and all members, thank you for offering me your great support in science and day to day company. This lab is great because of you all; your unique contributions to science and research make for a wild, but enjoyable ride.
Most importantly I would like to thank my dear friend and colleague Michelle Maier, thank you for being there over the last 7 years; I couldn’t have done it without you. Our weekly phone calls were priceless and crucial to the success of my PhD; our friendship and sharing of the ups and downs of scientific life has built many precious memories. Of course, this PhD would not be possible without generous funding support. Thanks to Federation University Australia and
University of Manchester for who have funding this project over the last 3 years. Also, a big great thanks to all of the personnel in the School of Applied and Biomedical Science administrative
xi | Page sector. Thank you for covering administration, facility service, financial and IT support to make my life easier.
To my mum Ruth, thank you for leading the way down the academic path. You’re an inspirational woman and the best role model a daughter could ask for. To my brother Morgan, who has been a pillar of support and also a great source of procrastination, thanks for being you. Don’t ever change. To my father Garry and brother Lachlan, thank you for sharing the ups and downs that this process has bought on. To my partner Nick, thank you for being there, for your support, and for always believing in me even when I lost faith. I am quite sure that to this day, you still have no idea what I do. Lastly, to my son Henry, I hope that one day I can inspire you like mum inspired me. Thanks for being so understanding even when I bore you to death.
Each and every one of you has had an irreplaceable role to play in achieving this PhD. I am forever grateful.
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Keywords
Essential hypertension; epigenetics, DNA methylation; kidney; RNA-seq; methylation array; hypertension; blood pressure; gene expression
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Australian and New Zealand Standard Research Classifications (ANZSRC)
ANZSRC code: 060404, Epigenetics (incl. Genome Methylation and Epigenomics), 40% ANZSRC code: 060405, Genetics, 30% ANZSRC code: 060102, Bioinformatics, 30%
Fields of Research (FoR) Classification
FoR code: 0601, Biochemistry and Cell Biology, 30% FoR code: 0604, Genetics, 70%
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To mum
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Summary of table of contents
Abstract ...... i Declaration ...... v Publications during candidature ...... vi Contributions by others to the thesis ...... ix Statement of parts of the thesis submitted to qualify for the award of another degree ...... ix Acknowledgements ...... x Keywords ...... xiii List of Figures…………………………………………………………………………………...xxi List of Tables ...... xxiii Abbreviations and Definitions ...... xxv Chapter 1. A review of the literature ...... 1 Chapter 2. General methods ...... 35 Chapter 3. Global leukocyte and kidney DNA methylation in essential hypertension ...... 49 Chapter 4. Genome-wide loci specific DNA methylation in essential hypertension ...... 72 Chapter 5. Differential gene expression in essential hypertension ...... 111 Chapter 6. Discussion ...... 155 Chapter 7. List of references………….…………………………………………………...170
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Table of contents
Abstract ...... i Declaration ...... v Publications during candidature ...... vi Contributions by others to the thesis ...... ix Statement of parts of the thesis submitted to qualify for the award of another degree ...... ix Acknowledgements ...... x Keywords ...... xiii Australian and New Zealand Standard ...... xiv Fields of Research (FoR) Classification ...... xiv List of Figures ...... xxi List of Tables...... xxiii Abbreviations and Definitions ...... xxv Chapter 1. A review of the literature ...... 1 1.0 Abstract ...... 2 1.1 Blood pressure ...... 3 1.2 Blood pressure regulation ...... 3 1.3 Hypertension ...... 5 1.4 Essential hypertension ...... 6 1.5 Etiological factors in essential hypertension ...... 8 1.6 The kidney and essential hypertension ...... 10 1.7 Epigenetics ...... 10 1.7.1 Non-coding RNAs and hypertension ...... 13 1.7.2 Histone modification and hypertension ...... 18 1.7.3 DNA Methylation and hypertension ...... 23 1.8 Conclusions ...... 31 1.9 Hypothesis and Aims ...... 32 Chapter 2. General methods ...... 35 2.1 Participants ...... 36 2.2 Sample collection ...... 37 2.2.1 Effects of coexistant cancer on the renal transcriptome ...... 37 2.3 Global kidney and leukocyte DNA methylation detection ...... 38 2.4 Genome-wide, loci specific DNA methylation analysis...... 39
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2.4.1 Detection of differentially methylated CpG sites ...... 39 2.4.2 Methylation data pre-processing ...... 40 2.4.2.1 Methylation data preprocessing computational analysis ...... 40 2.4.3 Methylation data normalisation ...... 41 2.4.3.1 Methylation data normalisation computational analysis ...... 41 2.4.4 Methylation data statistical analysis ...... 44 2.5 RNA-sequencing analysis ...... 45 2.5.1 Identification of hypertension-associated renal signatures ...... 45 2.5.2 RNA-sequencing pre-processing ...... 45 2.5.2.1 RNA-sequencing preprocessing computational analysis ...... 46 2.5.3 RNA-sequencing data normalisation ...... 46 2.5.3.1 RNA-sequencing preprocessing computational analysis ...... 46 Chapter 3. Global leukocyte and kidney DNA methylation in essential hypertension ...... 49 3.0 Abstract ...... 50 3.1 Introduction ...... 51 3.2 Methods...... 54 3.2.1 Participants and sample collection ...... 54 3.2.2 Global kidney and leukocyte DNA methylation detection ...... 54 3.2.3 Global methylation - statistical analysis ...... 56 3.2.4 Genome-wide, loci specific DNA methylation analysis – array based calculation of global methylation ...... 56 3.2.4.1 Array based calculation of global methylation – statistical analysis ...... 57 3.3 Results ...... 57 3.3.1 ELISA-based global DNA methylation 5mC percentage in peripheral blood leukocytes ...... 57 3.3.2 ELISA-based global DNA methylation 5mC percentage in the kidney ...... 61 3.3.3 Methylation array-based global DNA methylation calculation in the kidney ...... 65 3.3.4 Comparative of peripheral blood and kidney DNA methylation 5mC percentage . 66 3.4 Discussion ...... 68 3.4.1 Global methylation and hypertension diagnosis ...... 68 3.4.2 Global methylation and blood pressure ...... 69 3.4.3 Array-based calculation of global methylation ...... 69 3.4.4 Peripheral blood methylation as a surrogate for methylation in kidney ...... 70 3.5 Conclusion and future direction ...... 71 Chapter 4. Genome-wide loci specific DNA methylation in essential hypertension ...... 72
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4.0 Abstract ...... 73 4.1 Introduction ...... 74 4.2 Methods...... 78 4.2.1 Participants and sample collection ...... 78 4.2.2 Genome-wide, loci specific DNA methylation analysis ...... 79 4.2.2.1 Detection of differentially methylated CpG sites ...... 79 4.2.2.2 Methylation data pre-processing ...... 79 4.2.2.2.1 Methylation data preprocessing computational analysis ...... 80 4.2.2.3 Methylation data normalisation ...... 80 4.2.2.3.1 Methylation data normalisation computational analysis ...... 81 4.2.2.4 Methylation data statistical analysis ...... 81 4.3 Results ...... 82 4.3.1 Genome-wide methylation analysis in the hypertensive kidney – CpG sites ...... 82 4.3.2 Genome-wide methylation analysis in the hypertensive kidney – CpG islands ..... 82 4.3.3 Replication analysis of blood pressure-associated methylated CpG sites in the kidney and in blood ...... 104 4.4 Discussion ...... 106 4.4.1 Genome-wide methylation analysis in the hypertensive kidney – CpG sites ...... 107 4.4.2 Genome-wide methylation analysis in the hypertensive kidney – CpG islands ... 107 4.4.3 Replication analysis of blood pressure-associated methylated CpG sites in the kidney and in blood ...... 109 4.5 Conclusion and future direction ...... 110 Chapter 5. Differential gene expression in essential hypertension ...... 111 5.0 Abstract ...... 112 5.1 Introduction ...... 113 5.2 RNA-Sequencing methods ...... 114 5.2.1 Participants and sample collection ...... 114 5.2.2 Identification of hypertension-associated renal signatures ...... 115 5.2.3 RNA-sequencing pre-processing ...... 116 5.2.3.1 RNA-sequencing preprocessing computational analysis ...... 116 5.2.4.1 RNA-sequencing preprocessing computational analysis ...... 117 5.3 Results ...... 117 5.3.1 Differential gene expression in the hypertensive kidney ...... 117 5.3.2 Correlation analysis analysis of blood pressure-associated kidney genes and blood pressure GWAS ...... 133
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5.3.3 DNA methylation of hypertension-associated genes and their expression in the kidney ...... 134 5.3.5 Reverse analysis of BP-associated methylated CpG sites and known GWAS BP gene loci ...... 137 5.3.6 Differentially methylated and expressed renal genes that are identified in blood- based BP GWAS ...... 137 5.4 Discussion ...... 149 5.4.1 Hypertension-associated genes in the human kidney ...... 149 5.4.2 Correlation of BP-associated kidney genes and BP GWAS ...... 150 5.4.3 The role of DNA methylation in hypertension-associated renal gene expression 151 5.4.4 Reverse analysis of BP-associated methylated renal CpG sites and known GWAS BP loci ...... 152 5.4.5 Differentially methylated and expressed renal genes that are identified in blood- based HTN GWAS ...... 153 5.4.5 Effects of coexistant cancer on the renal transcriptome ...... 154 5.5 Conclusion and future direction ...... 154 Chapter 6. Discussion...... 155 6.1 Overview ...... 156 6.2 Global methylation and essential hypertension – relationships and limitations ...... 159 6.3 Pathways affected by CpG methylation and differential expression in the hypertensive kidney ...... 160 6.3.1 Pathways involved with hypertension-associated differential CpG loci methylation ...... 160 6.3.1.1 Renal methylation and the autonomic nervous system control of BP ...... 160 6.3.1.2 Renal methylation and vasculature control of BP ...... 162 6.3.1.3 Renal methylation and kidney-based mechanisms of BP control ...... 162 6.3.2 Pathways involved in differential gene expression in the hypertensive kidney ... 164 6.3.2.1 Renal gene expression and vasculature control of BP ...... 164 6.3.2.2 Renal gene expression and kidney-based mechanisms of BP control ...... 165 6.4 DNA methylation is associated with altered expression of renal genes ...... 166 6.4.1 Alterations in renal DNA methylation increase expression of Pyruvate Carboxylase (PC) ...... 166 6.4.2 Alterations in renal DNA methylation decrease expression of Family With Sequence Similarity 50 Member B (FAM50B) ...... 167 6.5 Double PHD finger (DPF3) is a candidate of methylation inheritance ...... 168 6.6 Summary and future directions ...... 168 Chapter 7. List of references ...... 170 xx | Page
List of Figures
Figure 1.1 Influences on phenotype manifestation 7
Figure 1.2 Impact of epigenetic modifications 12
Figure 1.3 MicroRNA biogenesis 14
Figure 1.4 Histone modification 19
Figure 1.5 Epigenetic DNA methylation 25
Figure 1.6 Study design flow chart for PhD project 34
Figure 2.1 Inter-array normalization using the Dasen method 42
Principal components plot demonstrating normalization of log Figure 2.2 43 transformed β-values to M-values
Figure 2.3 Principal components plot demonstrating mismatched gender labels 44
Figure 2.4 Principal components plot of TRANSLATE normalisation process 48
Global methylation percentage - normotensive and hypertensive Figure 3.1 60 peripheral blood leukocytes
Global methylation percentage – unadjusted blood pressure: peripheral Figure 3.2 60 blood leukocytes
Global methylation percentage – adjusted blood pressure: peripheral Figure 3.3 61 blood leukocytes
Figure 3.4 Global methylation percentage - normotensive and hypertensive kidney 64
Figure 3.5 Global methylation percentage – unadjusted blood pressure: kidney 64
Figure 3.6 Global methylation percentage – adjusted blood pressure: kidney 65
Figure 3.7 Global methylation percentage - leukocyte and kidney 68
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A- Differentially expressed genes associated with hypertension diagnosis in the hypertensive kidney Figure 5.1 120 B- Volcano plot of differentially expressed genes associated with hypertension diagnosis in the hypertensive kidney
A- Differentially expressed genes associated with systolic blood pressure in the hypertensive kidney Figure 5.2 121 B- Volcano plot of differentially expressed genes associated with systolic blood pressure in the hypertensive kidney
A- Differentially expressed genes associated with diastolic blood pressure in the hypertensive kidney Figure 5.3 122 B- Volcano plot of differentially expressed genes associated with diastolic blood pressure in the hypertensive kidney
Methylation of FAM50B and PC shows an association with renal Figure 5.4 134 expression
Illustration of the relationship of methylation at various individual CpG Figure 5.5 135 sites with hypertension incidence in the TRANSLATE population
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List of Tables Table 1.1 Summary of key non-coding RNA findings that influence hypertension 13
Table 1.2 Summary of histone modification findings in relation to hypertension 20
Summary of methylation findings in relation to hypertension and Table 1.3 29 cardiovascular pathology
Table 3.1 Summary of global methylation findings 53
Table 3.2 Antibody solution preparation for 5mC ELISA protocol 55
Table 3.3 Leukocyte sample numeric variables – global methylation 58
Table 3.4 Leukocyte sample categorical variables – global methylation 59
Table 3.5 Kidney methylation sample numeric variables – global methylation 62
Table 3.6 Kidney methylation sample categorical variables – global methylation 63
Matching leukocyte and kidney samples used for tissue correlation Table 3.7 67 analyses – numeric variables of interest
Matching leukocyte and kidney samples used for tissue correlation Table 3.8 67 analyses – categorical variables of interest
Summary of genome-wide methylation findings in relation to Table 4.1 77 hypertension
Kidney methylation sample numeric variables – loci specific, genome- Table 4.2 83 wide methylation
Kidney methylation sample categorical variables – loci specific, Table 4.3 84 genome-wide methylation
23 CpG sites associated with essential hypertension and co-existent Table 4.4 85 phenotypes
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Full names and biological characterisation of genes associated with the Table 4.5 23 differentially methylated CpG sites in hypertension and blood 87 pressure
Differentially methylated renal CpG islands associated with Table 4.6 hypertension and its associated phenotypes 96
Individual CpG sites located within differentially methylation islands Table 4.7 associated with hypertension and blood pressure 97
Peripheral blood replicated, blood pressure-associated, differentially Table 4.8 105 methylated CpGs in the human kidney
Table 5.1 Numeric variables for RNA-sequencing analysis in the human kidney 118
Table 5.2 Categorical variable for RNA-sequencing analysis in the human kidney 119
Differentially expressed renal genes associated with hypertension and Table 5.3 123 its associated phenotypes
Full names and biological characterisation of genes differentially Table 5.4 124 expressed in the hypertensive kidney
Correlation analysis of blood pressure-associated kidney genes and Table 5.5 133 blood pressure GWAS
Differentially methylated and differentially expressed genes in the Table 5.6 136 hypertensive kidney
Reverse analysis of GWAS BP loci and loci associated with Table 5.7 138 differentially methylated CpGs in the human kidney
Characterisation of GWAS loci that overlap with regions containing Table 5.8 142 differentially methylated CpGs in the kidney
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Abbreviations and Definitions 3’UTR 3 prime untranslated region
450K Illumina Infinium HumanMethylation 450K BeadChip
5’ UTR 5 prime untranslated region
5hmC 5-hydroxymethylcytosine
5mC 5-methylcytosine
ABCG4 ATP Binding Cassette Subfamily G Member 4
AC007099.1 LOC101927884 gene (RNA Gene)
ACE Angiotensin converting enzyme
Ace1 Angiotensin converting enzyme receptor 1
ACSM3 Acyl-CoA Synthetase Medium Chain Family Member 3
ADD1 Adducin 1
ADD3 Adducin 3
Adj Adjusted
AdoHcy S-Adenosylhomocysteine
AMP Adenosine monophosphate
AGTR1 Angiotensin II Receptor Type 1
AMP Adenosine monophosphate
ANKRD11 Ankyrin Repeat Domain 11
ARHGAP24 Rho GTPase Activating Protein 24
AT2 Angiotensin II receptor
Atgr1a Angiotensin 1a receptor
ATP1A1 ATPase Na+/K+ Transporting Subunit Alpha 1
β2AR β2 adrenergic receptor
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BBX HMG-Box Containing
BCL6B B-Cell CLL/Lymphoma 6B
BMI Body mass index
BOLA3 BolA Family Member 3 bp Base pairs
BP Blood pressure
C9orf95 Nicotinamide Riboside Kinase 1
C15orf23 Kinetochore-Localized Astrin-Binding Protein
C19orf70 Chromosome 19 Open Reading Frame 70
CAD Coronary artery disease cAMP Cyclic adenosine monophosphate
CDC34 Cell Division Cycle 34 cDNA Complementary DNA
CDK6 Cyclin Dependent Kinase 6
CFTR Cystic Fibrosis Transmembrane Conductance Regulator
Chr Chromosome
CIRBP-AS1 CIRBP Antisense RNA 1
CKD Chronic kidney disease
CLDN5 Claudin 5
COG3 Component Of Oligomeric Golgi Complex 3
CpG Cytosine-guanine dinucleotide
CPT1A Carnitine Palmitoyltransferase 1A
CVD Cardiovascular disease
CXXC5 CXXC Finger Protein 5
CYP17A1 Cytochrome P450 17A1
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DCP2 Decapping MRNA 2
DBP Diastolic blood pressure
DDT D-dopachrome decarboxylase
DNA Deoxyribonucleic acid
DNAJC17 DnaJ Heat Shock Protein Family (Hsp40) Member C17
DNMT DNA methyltransferases
DOT 1 Disruptor of telomeric silencing-1
DOT1L DOT1-like histone H3K79 methyltransferase
DPF3 Double PHD Fingers 3
DPT Dermatopontin
ECE-1c Endothelin-converting enzyme-1c
EH Essential hypertension
ENaC Renal epithelium sodium channels eNOS Endothelial nitric oxide synthase
EWAS Epigenome-wide association study
FABP3 Fatty Acid Binding Protein 3
FAM50B Family With Sequence Similarity 50 Member B
FDR False discovery rate
FRG1 FSHD Region Gene 1
FUBP1 Far Upstream Element Binding Protein 1
FUZ Fuzzy Planar Cell Polarity Protein
GCK Glucokinase
GRSF G-Rich RNA Sequence Binding Factor 1
GWAS Genome-wide association study
H3Ac Histone H3 acetylation
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H3K27me3 Trimethylation of lysine 27 on the histone H3 protein subunit
H3K4me3 Trimethylation of lysine 4 on the histone H3 protein subunit
H3K79 Histone H3 lysine 79
HCCA2 YY1 associated protein 1
Hg19 Human Genome version 19
HPO Human phenotype ontology database
HSD11B2 Hydroxysteroid dehydrogenase 11 β2 enzyme
HTN Hypertension
IGFBP3 Insulin-like growth factor-binding protein 3
ILF2 Interleukin enhancer-binding factor 2
INAFM2 InaF Motif Containing 2
KIAA0664 Clustered Mitochondria Homolog
KCNK3 Potassium channel subfamily K member 3
LINC01554 Long Intergenic Non-Protein Coding RNA 1554 lncRNA Long non-coding RNA
LRP LDL Receptor Related Protein 1
MFSD3 Major Facilitator Superfamily Domain Containing 3 mmHg Millimeters of mercury mmol/L Millimole per liter mRNA Messenger RNA miRNA Micro-RNA
MLP Maternal low protein
N_shore North shore of CpG island
NADPH Nicotinamide adenine dinucleotide phosphate
NCC Sodium chloride co-transporter
xxviii | Page ncRNA Non-coding RNA
NDUSF8 NADH: Ubiquinone Oxidoreductase Core Subunit S8
NET The norepinephrine transporter
NEUROG1 Neurogenin 1
NKCC1 Na+-K+-2Cl- cotransporter 1
NPPA Natriuretic peptide precursor A
NPR2 Natriuretic peptide receptor B
NTRK2 Neurotrophic Receptor Tyrosine Kinase 2 oligo-dT Primer is single-stranded sequence of deoxythymine
OSR1 Protein odd-skipped-related 1 p22phox Human neutrophil cytochrome b light chain
PAD Peripheral artery disease
PBL Peripheral blood leukocyte
PBMC Peripheral blood mononuclear cell
PC Protein coding
PC gene Pyruvate Carboxylase
PCA Principle components analysis
PLCH2 PLC-eta family of the phosphoinositide-specific phospholipase C
PPP2R5A Protein Phosphatase 2 Regulatory Subunit B'Alpha
PCNXL3 Pecanex Homolog 3
PDE3A Phosphodiesterase 3A
PEER Probabilistic estimation of expression residuals
Phenodata Phenotypic data
PHTF1 Putative Homeodomain Transcription Factor 1
PIGF Phosphatidylinositol Glycan Anchor Biosynthesis Class F
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PIP4K2A Phosphatidylinositol-5-Phosphate 4-Kinase Type 2 Alpha piRNAs PIWI-interacting RNAs
PITPNA Phosphatidylinositol Transfer Protein Alpha
PKNOX2 PBX/Knotted 1 Homeobox 2
PNMT Phenyl-ethanolamine N-methyltransferase poly-A Polyadenylation
PRCP Prolylcarbopeptidase
PRDM6 PR/SET Domain 6
PSMB9 Proteasome Subunit Beta 9
RAAS Renin-angiotensin-aldosterone system
REEP3 Receptor Accessory Protein 3
Refseq Referenced sequenced
RINF CXXC Finger Protein 5; Retinoid-Inducible Nuclear Factor
RIOK3 RIO Kinase 3
RNA Ribonucleic acid
RNA-seq RNA sequencing
S_shore South shore of CpG island sACE Somatic angiotensin converting enzyme
SBP Systolic blood pressure
SCG2 Secretogranin II
SD Sprague-Dawley
SHR Spontaneously hypertensive rat
Sic2a2 Glucose transporter 2, glucokinase siRNA Small interfering RNA
SIRT7 NAD-dependent deacetylase sirtuin 7
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SLC1A5 Solute carrier family 1 member 5
SLC12A7 Solute carrier family 12 member 7
SLC14A2 Solute carrier family 14 member 2
SLC16A1-AS1 SLC16A1 Antisense RNA 1
SLC17A7 Solute carrier family 17 member 7
SLC22A7 Solute carrier family 22 member 7
SLC26A10 Solute carrier family 26 member 10
SLC45A4 Solute carrier family 45 member 4
SMARCAL1 SWI/SNF Related, Matrix Associated, Actin Dependent Regulator Of Chromatin, Subfamily A Like 1
SMG1P3 Nonsense Mediated MRNA Decay Associated PI3K Related Kinase Pseudogene 3
SNP Single nucleotide polymorphism
SNS Sympathetic nervous system
SPTB Spectrin Beta, Erythrocytic
SULF1 Sulfatase 1
SYCE1 Synaptonemal Complex Central Element Protein 1
TAF1B TATA-Box Binding Protein Associated Factor, RNA Polymerase I Subunit B
TALDO1 Transaldolase 1
TBX2 T-box transcription factor 2
TET ten-eleven translocation
TET1 Tet Methylcytosine Dioxygenase 1
TET3 Tet Methylcytosine Dioxygenase 3 tHcy Total homocysteine
THE Tetrahydocortisone
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THF Tetrahydrocortisol
TM2D1 TM2 Domain Containing 1
TPM Transcripts per million
TRIM41 Tripartite Motif Containing 41
TSIX XIST antisense transcript
TSS Transcription start site
TSS200 Within 200 base pairs of the transcription start site
TSS1500 Within 1500 base pairs of the transcription start site
TTBK1 Tau Tubulin Kinase 1
TXNRD1 Thioredoxin Reductase 1
UCSC University of California, Santa Cruz – genome browser
µmol/L Micromole per liter
USFP1 Ubiquitin-Fold Modifier 1 VARS Valyl-TRNA Synthetase
WNK With-no-lysine
WNK4 With-no-lysine4
WKY Wistar Kyoto rat
XIST X-inactive specific transcript
YWHAQ Tyrosine 3-Monooxygenase/Tryptophan 5-Monooxygenase Activation Protein Theta
ZFYVE19 Zinc Finger FYVE-Type Containing 19
ZMIZ1 Zinc Finger MIZ-Type Containing 1
ZMPSTE24 Zinc Metallopeptidase STE24
ZNF224 Zinc Finger Protein 224
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ZNF526 Zinc Finger Protein 526
ZNF540 Zinc Finger Protein 540
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Chapter 1. A review of the literature
“A mind once stretched by a new idea never regains its old dimensions” ~Oliver Wendell Holmes Sr
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Chapter 1. A review of the literature
1.0 Abstract
Essential hypertension (EH) is a complex, polygenic condition with no single causative agent.
Despite advances in our understanding of the pathophysiology of EH, HTN remains one of the world’s leading public health problems. Furthermore, there is increasing evidence that epigenetic modifications are as important as genetic predisposition in the development of EH. Indeed, a complex and interactive genetic and environmental system exists to determine an individual’s risk of EH. Epigenetics refers to all heritable changes to the regulation of gene expression as well as chromatin remodelling, without involvement of nucleotide sequence changes. Epigenetic modification is recognized as an essential process in biology, but is now being investigated for its role in the development of specific pathologic conditions, including EH. Epigenetic research will provide insights into the pathogenesis of BP regulation that cannot be explained by classic
Mendelian inheritance. This review concentrates on epigenetic modifications to DNA structure, including the influence of non-coding RNAs (ncRNA) on HTN development.
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1.1 Blood pressure
BP refers to the force per unit area that is exerted on a vessel by the contained blood, which is expressed in millimeters of mercury (mmHg). Unless otherwise stated BP refers to the systemic arterial BP in the largest arteries near to the heart. Pressure gradients – the differences in BP within the vascular system – provide the driving force that ensures blood circulates from areas of high pressure to those of low. Systemically, any fluid driven by a pump through a circuit of closed channels operates under pressure, where the fluid nearest to the pump experiences the greatest exerted pressure [1]. Fundamentally the pumping actions of the heart generates blood flow, and pressure is generated when the flow is opposed by resistance. Therefore, systemic BP is highest in the aorta and declines throughout the pathway, where it reaches 2-6 mmHg in the right atrium [1].
The largest drop in pressure exists in the arterioles, which offer the most resistance to BP.
Regardless of the strength of the pressure gradient, blood will continue to flow until it circulates in its entirety, back to the heart [1].
1.2 Blood pressure regulation
BP is primarily regulated by the autonomic nervous system, vasculature or the kidney. The autonomic nervous system is a collection of afferent and efferent neurons that link the central nervous system with visceral effectors [2]. The two arms of the autonomic nervous system, the sympathetic and the parasympathetic, consist of parallel and differentially regulated pathways that are made up of cholinergic neurons (preganglionic neurons). These peripheral ganglia and associated networks contain motor neurons that control smooth muscle and other visceral targets.
The sympathetic ganglia control cardiovascular targets [2]. Although BP fluctuates substantially with various activities, the 24-hour control of BP is tightly regulated. Abnormalities to the control
3 | Page of BP can emerge from the autonomic nervous system, in baroreceptors, chemoreceptors, renal afferents or the central circuitry [2]. The neural control of BP operates via parasympathetic neurons that innervate the heart and three main classes of sympathetic efferent neurons (baroreceptive, thermosensitive and glucosensitive cardiovascular) which innervate blood vessels in the heart, kidneys and the adrenal medulla. It is this large group of efferent neurons that has a dominant role in the regulation of long and short term BP control, where the activity of these efferent neurons at rest is the biggest determinant of long-term BP control. Resting activity is determined by a core network of neurons that reside in the rostral ventrolateral medulla, the spinal cord, the hypothalamus and the nucleus of the solitary tract [2].
The endothelium is an integral component of the vascular wall and is a major regulator of BP [3].
Vasodilator factors such as nitric oxide, prostacyclin and endothelium-derived hyperpolarising factors are released by the endothelium which drive vascular control of BP [3]. Decreases in endothelial produced vasodilators reduce vascular relaxation and as a consequence increases vascular resistance and BP. Vasoconstrictors such as thromboxane, angiotensin II and endothelin are also produced by the endothelium. Of these, one of the most potent vasoconstrictors is endothelin, where studies identify endothelin receptor subtypes and isoforms in the kidney and blood vessels affect sodium balance and vascular function [4-6].
The kidney plays a central role in the regulation of arterial BP, where renal control of extracellular volume and renal perfusion pressure are closely involved in maintaining the arterial circulation and BP [7]. Renal artery perfusion pressure is a primary regulator of sodium excretion, a process labelled natriuresis, and directly influences activity of various vasoactive systems such as the
4 | Page renin-angiotensin-aldosterone system (RAAS) [7]. According to theories by Guyton and colleagues, many factors affect renal control of body fluid volume, and thereby arterial pressure.
These include nervous effects acting on the kidney, hormonal factors affecting the kidney and the pathology of the kidney. Regardless of the underlying cause, all long term arterial BP regulation must involve the system that regulates body fluid volume in some way. Therefore, long term arterial pressure regulation must involve a balance between intake and output of water and salt and almost always involves the kidneys [8]. One of the primary systems that regulate fluid balance and BP is the RAAS. When renal blood flow is reduced, the kidneys convert prorenin to renin which is secreted directly into circulation [9]. The circulating plasma renin then facilitates the conversion of angiotensinogen (released by the liver) to angiotensin I, which is further converted to angiotensin II by angiotensin converting enzyme (ACE) found in the lungs. Angiotensin II is one of the primary vasoconstriction peptides, which results in the narrowing of blood vessels and ultimately an increase in BP [9]. Presence of angiotensin II also prompts the release of aldosterone from the adrenal glands where it initiates increased blood uptake of sodium and water in the renal tubules. To maintain electrolyte homeostasis, potassium is excreted during this process and the volume of extracellular fluid increases, further increasing BP. One of the most studied causes to abnormally high or chronically high BP is dysregulation to the RAAS [10]. To date, the RAAS is one of the most targeted systems for pharmaceutical intervention of high BP, HTN and renal failure.
1.3 Hypertension
HTN affects more than one billion people globally and is a major risk factor for stroke, chronic kidney disease, and myocardial infarction [11]. Despite advances in our understanding of the
5 | Page pathophysiology of HTN and the implementation of more effective treatment and prevention strategies, HTN remains one of the world’s great public health problems [12]. The public health impact created by HTN is due to its contribution to cardiovascular disease (CVD) events, such as heart attack, congestive heart failure, peripheral vascular disease, and stroke [13]. Furthermore, there is increasing evidence that epigenetic modifications are as important as genetic predisposition in the development of HTN. Indeed, a complex and interactive genetic and environmental system exists to determine an individual’s risk of HTN (Figure 1.1).
1.4 Essential hypertension
EH, also referred to as primary or idiopathic, is defined as high BP in which a secondary cause, such as renal disease, renal failure, aldosteronism, other causes of secondary HTN or Mendelian
(monogenic) forms are not present [14]. Human EH is a complex, multifactorial, quantitative trait under polygenic control [15] and is estimated to be responsible for a larger proportion of the global disease burden and premature death than any other disease risk factor [16]. EH accounts for more than 90% of all HTN cases; where the heterogeneous disorder may have multiple causal factors that lead to chronically elevated BP [14]. In industrialised countries, an individual’s risk of developing EH (BP≥ 140/90 mmHg) during their lifetime exceeds 90% [17].
BP is a highly variable quantitative trait, with a strong positive and continuous correlation to the risk of CVD, renal disease, and mortality, even in the normotensive (NT) range [18]. EH is diagnosed in adults when the average of two or more SBP readings, on multiple subsequent occasions consistently reach ≥140 mmHg, or when the average of two or more DBP readings, on multiple subsequent occasions consistently reach ≥ 90 mmHg [19]. In the aging population (> 50
6 | Page years), SBP of more than 140 mmHg is identified as a more prominent CVD risk factor than DBP; notwithstanding, the risk of CVD begins at 115/75 mmHg, and doubles with each increment of
20/10 mmHg [19].
Figure 1.1 Influences on phenotype manifestation. Development of polygenic conditions such as EH depend on a complex but interactive genetic and environmental system.
The single best prevention of HTN-associated cardiovascular complications is the reduction of BP.
All antihypertensive medications lower BP (by definition), however differences between medications exist with respect to reduction of target-organ disease and prevention of major cardiovascular events (stroke, myocardial infarction, renal failure). To ensure the best BP control and reduce HTN-associated risk, most patients receive two or more drug types and concomitant
7 | Page statin treatment [17]. Despite the availability of medications and our understanding of the disease pathophysiology, HTN and its risk factors remain uncontrolled in most patients.
1.5 Etiological factors in essential hypertension
Although there is no single identifiable causative agent recognised for EH; a number of factors increase BP, including genetics [20] obesity [21], insulin resistance [22], high alcohol intake [23], high salt intake [24], aging [25], sedentary lifestyle [26], stress [27], low dietary potassium [28], and low dietary calcium [29]. Further, many of these factors are additive, such as obesity and alcohol intake [14]. There are many gene loci that have been identified as contributors to EH [20,
30]. These were discovered via a plethora of studies that compare allele frequencies of polymorphisms in candidate genes between the HT and NT populations and linkage studies that involve pedigrees affected by rare monogenic forms of HTN [31]. This research uncovered large advances in the genetic understanding of EH and in identification of 13 single gene mutations that are responsible for various forms of familial HTN [31, 32]. More recently, genome-wide scans have become the standard tool for analysis of complex human traits, as it allows for detection of novel susceptibility genes that candidate approaches inevitably miss [31]. The genetic influence on BP is often expressed in terms of heritability, i.e. the fraction of the total interindividual variance attributable to genes. This index requires comparison of BP between pairs of subjects showing different familial connections and therefore differing degrees of genetic similarly; as such studies involving extended families, twins and adoptees were conducted in an attempt to isolate environmental influences [31]. Many pitfalls surround studies of heritability, but the range reported in the literature is 30-60% for BP [33]; with high likelihood of shared responsibility of several causal genes.
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The genetic component of EH is tightly linked with environmental influences, such as salt intake, lack of exercise and weight gain, to produce the disease phenotype [34]. Numerous HTN- associated genetic variants have been uncovered by GWAS [35, 36] which has resulted in publication of genes annotated to HTN in the human phenotype ontology database (HPO) [37].
The discovery of genetic variation responsible for elevated BP is important to allow better treatment and management of HTN. Whereas, recognition of additional environmental contributors will allow for nonpharmacological prevention and education. More importantly, there are interactions between genetic and environmental factors, that influence specific phenotypes such as sympathetic nerve activity, RAAS, and endothelial factors [38]. These intermediary phenotypes determine vascular resistance, cardiac output and, as a consequence, BP. Recognition of genetic variants that contribute to EH is complicated by the multifactorial nature of the disease.
Cardiac output and peripheral resistance, the two core phenotypes that determine BP, are regulated by intermediary phenotypes, for example the autonomic nervous system, hormones (vasopressor/ vasodepressor), cardiovascular system structure, body fluid volume and renal function [39]; thereby highlighting the number of genes that could participate in the development of HTN. A major challenge of HTN research is to identify the underlying mechanisms unencumbered by compounding secondary effects. Furthermore, a major hinderance to the advancement of genetic research in HTN lies in the fact that numerous interacting QTLs can influence the sample phenotype and that multiple QTLs are involved in the overall BP status [40]. Added to the polygenic complexity are inherent limitations, such as phenotyping variability, genetic heterogeneity, sampling bias and the availability of effector tissues, and the disparity in model assumption and environmental complications. Moreover, conclusions made on human EH are
9 | Page based on correlative results as no experimental manipulations are permissible, making cause-effect relationships difficult to determine in human studies [40].
1.6 The kidney and essential hypertension
Disruptions to renal function is closely associated with HTN; where kidney disease provokes HTN and, on the other hand, HTN aggravates renal dysfunction [41]. Furthermore, almost all of the monogenic forms of HTN affect sites in the kidney that handle salt excretion and transport [7].
The kidney makes a major hemodynamic contribution to EH and has long been implicated in the pathogenesis of the disease. The notion that EH could be due to alterations to renal genetics stems from the results of renal transplantation in spontaneously hypertensive rats (SHR) [42, 43]. It was discovered that a renal graft from a HT rat raises the BP of the NT recipient, while grafting a NT donor kidney into a HT recipient caused a fall in BP. Hence Dahl’s famous pronouncement “blood pressure travels with the kidney”. This suggests that some renal abnormalities predispose to HTN.
More recently, it was shown that nephron numbers are reduced in established EH [44]. In those with identified renal dysfunction, the kidney raises BP via several mechanisms. For example, the pressure-natriuresis relationship is shifted to the right, that is, sodium excretion requires high renal perfusion pressures [41]. Further, inappropriate activation of the RAAS, increased sympathetic tone (independent of glomerular filtration rate), impaired endothelial vasodilation and alteration in pulse pressure profile all contribute to the known renal involvement in EH pathogenesis [41].
1.7 Epigenetics
Epigenetics refers to all heritable changes to the regulation of gene activity, without changes to the
DNA sequence itself [45]. In other words, “epigenetics” refers to analysis of the human genome
10 | Page from the perspective of chromatin and chromosomes, as well as to the quintessential issue of the
DNA sequence. Epigenetic modifications can be provoked by a variety of factors, including environmental influences during foetal and childhood development, chemical exposure, aging, dietary habits, and the use of recreational drugs and some prescription medications [46].
The launch of international initiatives, the Human Epigenome Project [47], which first released data in 2003, and the International Human Epigenome Consortium [48] formed in 2010, attest to the rapid expansion of interest in epigenetics. Epigenetic inheritance is an essential mechanism that enables the stable propagation of gene activity states from one generation of cells to the next
[49]; it is helping to resolve puzzling aspects of heredity, while explaining how the same genome can give rise to apparently contrasting phenotypic traits [50]. Chromatin consists of nucleosomes:
DNA strands wrapped around histones. Several distinct biochemical processes can epigenetically modify both DNA and histones (Figure 1.2) [46]. These include RNA-based mechanisms mediated by small non-coding microRNAs [51] (Figure 1.2, Figure 1.3) post-translational histone modifications (Figure 1.2, Figure 1.4), and DNA methylation (Figure 1.2, Figure 1.5). Epigenetic modification is recognised as essential in biological processes and in recent years there has been studies investigating its role in the development of specific pathologic conditions. Research into epigenetic mechanisms will provide insights into the pathogenesis of BP regulation that cannot be explained by classic Mendelian inheritance.
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Figure 1.2 Impact of epigenetic modifications. DNA wrapped around nucleosomes are made up of four pairs of histone proteins. Histone proteins are prone to epigenetic modification, primarily acetylation and methylation but also phosphorylation, sumoylation, and biotinylation. These modifications change the formation of chromatin to either an open (active) or closed (inactive) state, thereby altering their transcriptional activity. DNA methylation changes the structure of DNA itself, allowing active transcription or silencing of genes. The outcome of DNA methylation is dependent on the location of the methylated site. miRNAs primarily target the 3’ UTR of mRNA (although 5’ targeting is possible). This negatively regulates the quantity of the encoded protein produced via degradation of mRNA molecules or by post-transcriptional regulation of mRNA stability.
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1.7.1 Non-coding RNAs and hypertension
Non-coding RNAs (ncRNA) are implicated in several epigenetic processes, notably small RNAs that can influence histone modifications and cytosine methylation which are connected with gene expression regulation [52]. NcRNA modification can produce effects analogous to classical DNA or histone epigenetic mechanisms described earlier, or can induce HTN via separate, distinct processes. Essentially, epigenetics allow the same genome to show alternative phenotypes based on different epigenetic influences. Some of the most complicated and researched epigenetic phenomena including X-chromosome inactivation, parental imprinting and paramutation have an
RNA contribution, either directly or indirectly [52]. While most ncRNAs have no identified function yet, it is conceivable that all are participants in epigenetic mechanisms which are yet to be clearly defined. NcRNAs have a strong influence on the “central dogma” of biology. The past view that all DNA was transcribed into RNA and then translated into protein has now been updated to include the fundamental role of ncRNAs on the regulation protein levels. Recently, several small and mid-sized ncRNAs have been described and are known to have a role in the regulation of transcription, post-transcription and translation. Small ncRNAs include PIWI-interacting RNAs
(piRNAs), transcription initiation RNAs, and microRNAs (miRNAs) [53, 54]. Mid-size ncRNAs include small nucleolar RNAs, promoter upstream transcripts, transcription-start-site associated
RNAs (TSS-associated), and promoter-associated small RNAs. Long ncRNAs (lncRNAs) are most commonly associated with a reduction in transcription but may also have a role in the regulation of miRNA levels [54]. Here we deliver a summary of some key miRNAs involved in
HTN as well as a review of lncRNAs.
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Figure 1.3 MicroRNA biogenesis. MiRNA biogenesis involves down-regulation of gene transcription via target mRNA degradation or by mRNA translation blockage. RISC: RNA-induced silencing complex.
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Table 1.1 Summary of key non-coding RNA findings that influence hypertension
Reference Tissue Type/ Sample Size Findings
Goyal, et al. Rat: Tissues: brain; 20 maternal mmu-miR-27a and mmu-miR-27b [55] low protein pups, 17 control regulate ACE1 and was upregulated (3.3- pups and 8.8-fold respectively) in MLP rat; mmu-mir-330 regulates angiotensin II type 2 receptor (AT2) and was downregulated 3.5-fold in MLP rat.
Sethupathy, Human: Fibroblasts from Has-miR-155 binds to 3’UTR of AGR1 et al. [56] monozygotic twin; n = 2 mRNA “A” allele causing a reduction in AGTR1 mRNA, reducing the pressor effect in response to angiotensin II.
Cheng, et Human: Endothelial cells from Has-miR-155 up-regulated in al. [57] pre-eclamptic placentas preeclampsia placentas, indicating involvement in regulation of AGTR1.
Marques, et Human: Tissue: kidney; Sample Has-miR-181a & has-miR-663 is able to al. [58] 1: 42 mixed sex, Polish bind 3’UTR of renin mRNA, found to be individuals of mixed HTN under-expressed in EH. These miRNA status; Sample 2: 22 male only, able to regulate renin mRNA directly, mixed HTN status. All samples explaining overexpression of renin in EH untreated for HTN kidney.
Wang, et al. Mouse: Tissue: Mesenteric siRNA targeting p22phox mRNA [59] arterioles; 16 male C57Bl/5 demonstrated inhibition of contractile mice; 16 sham mouse control response from angiotensin II, consequently lowering BP. Cabili, et al. Human: Tissue: 24 various and lincRNAs may promote the transcription [60] cells lines; 24 human samples of their neighbouring coding genes, including those implicated in EH and BP regulation. Annilo, et Human and Mouse: various Seven BP candidate genes ADD3, NPPA, al. [61] tissues and cell lines; n = not ATP1A1, NPR2, CYP17A1,ACSM3 and disclosed SLC14A2 were connected with cis- lncRNA transcripts.
MLP, maternal low protein; EH, essential hypertension; mRNA, messenger RNA; miRNA, micro RNA; lincRNA, long non-coding RNA; HTN, hypertension; siRNA, silencing RNA; BP, blood pressure
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MiRNAs are the most commonly studied small ncRNA; currently there is no research available investigating the involvement of the other types of small and mid-sized ncRNAs in EH. There are more than 1800 miRNAs in the genome, each approximately 22 nucleotides in length [62]. miRNAs down-regulate protein-coding genes by binding to target sequences in the 3’ (5’ targeting is uncommon but possible) untranslated region of a target mRNA, resulting in mRNA degradation or repression of mRNA translation [63] (Figure 1.3). Evidence suggests a single miRNA can regulate numerous mRNAs, so it is plausible that miRNAs are causative agents for complex deletions [63]. miRNAs are increasingly implicated as regulators of cardiovascular system [64], including modulation of arterial pressure [65] (Table 1.1).
In regards to the RAAS-regulated genes, it appears that miRNAs may also have a role modulating
ACE mRNA transcription [66]. MiRNA has-miR-155 was found by Sethupathy et al [56] to target the polymorphic sequence located in the 3’UTR of AGTR1 mRNA. This site has a Mendelian C
(minor) and an A (major) allele. The minor-C allele is associated with EH [56]. Has-miR-155 binds most effectively to the A allele, leading to a reduction in AGTR1 mRNA in EH individuals who inherit this allele. Individuals with the C allele did not have reduced AGTR1 mRNA and exhibited a greater pressor effect in response to angiotensin II [56]. Has-miR-155 is also found to be up-regulated in preeclampsia placentas; it regulates AGTR1 expression in umbilical vein endothelial cells [57]. A recent GWAS documented expression of mRNAs and miRNAs in kidneys from untreated EH individuals, which were removed as a consequence of renal cancer [58]. Two miRNAs (has-miR-181a and has-miR-663) with the ability to bind to the 3’UTR of renin mRNA were found to be under-expressed in EH. These miRNAs were able to regulate the expression of a reporter gene and renin-mRNA itself, which explains over-expression of renin mRNA seen in EH
16 | Page kidney [58]. Disruptor of telemetric silencing – 1 (DOT1) causes hypermethylation of H3 [67], small interfering RNAs (siRNA) have been shown to have an opposite effect by silencing the expression of the DOT1 Gene [68]. Furthermore, siRNAs have been observed to have a role in modulating the RAAS system [68]. Angiotensin II was shown to induce a contractile response in smooth muscle cells extracted from mice overexpressing p22phox, a subunit of NADPH oxidase responsible for activating NADPH oxidase and generating superoxide anions in atherosclerotic plaques in human blood vessels [69]. siRNA targeting p22phox inhibits the contractile response from angiotensin II, lowering BP [59].
Large intergenic non-coding RNAs (lincRNAs) have been identified as both a modulator of health development [70-72] and disease states [73], and may regulate the expression of neighboring genes and distant loci [74]. LincRNAs are more tissue specific than protein-coding genes [60], suggesting they have selective functions in different tissues. Cabili et al [60] described the annotation and tissue-specific expression of all lincRNAs. Their gene ontology analyses were based on lincRNA expression in different tissues [60]. The protein-coding genes located close to lincRNA loci included genes previously associated with EH and BP regulation, for example, those encoding corticotropin-releasing hormone [60], suggesting many lincRNAs have an enhancer-like function that promotes transcription of their neighboring coding genes [75].
Long non-coding RNAs (lncRNAs), a heterogeneous group of non-coding transcripts longer than
200 nucleotides, regulate their targets by influencing epigenetic control, mRNA processing, translation or chromatin structure. LncRNAs have been implicated in several biological processes, including transcriptional regulation by epigenetic mechanisms [74]. An exemplary representation
17 | Page is X-chromosome inactivation present in the female genome of mammals which involves the inactivation of one copy of a female sexual chromosome by DNA methylation. This process is coordinated by the X-inactive specific transcript (XIST) and the XIST antisense transcript (TSIX) ncRNAs, very long ncRNAs whose expression can be regulated by epigenetic mechanisms such as DNA methylation [52]. LncRNAs have a potential to influence CVD, stroke, BP and HTN.
Seven BP candidate genes ADD3, NPPA, ATP1A1, NPR2, CYP17A1, ACSM3, and SLC14A2 were connected with cis-lncRNA transcripts [61]. Of these, the lncRNA NPPA-AS was selected for further investigation which revealed NPPA-AS has a demonstrated influence on the splicing of the natriuretic peptide precursor A (NPPA) gene (present in cardiac hypertrophy and heart failure) and, therefore, has potential CVD involvement [61].
1.7.2 Histone modification and hypertension
DNA is packaged into the dynamic protein structure of chromatin, whose basic unit is the nucleosome. A nucleosome comprises of two copies each of the histone proteins H2A, H2B, H3, and H4 [76]. Post-translational modifications regulate gene expression by controlling the dynamics of chromatin. Modifications occurring at residues in histone tails include acetylation and methylation (Figure 1.4) as well as phosphorylation, sumoylation, and biotinylation [76].
Epigenetic histone modification occurs when the N-terminal tail is subjected to a variety of post- translational modifications [46]. Up to 60 possible modifications can occur. Specifically, the nucleotide lysine can be modified by methylation, acetylation, ubiquitylation, or sumoylation, whereas arginine can only be modified by methylation, and serine and threonine are modified only by phosphorylation [46]. While each histone modification pattern has a unique impact on the corresponding nucleosome, generally, the outcomes are similar: histone acetylation promotes gene
18 | Page transcription while deacetylation represses transcription; histone lysine methylation in position 79 inhibits while histone arginine methylation activates gene transcription; histone lysine hypermethylation or mono-methylation at position 9 can have opposing effects—respectively silencing or activating the target gene [77] (Table 1.2).
Figure 1.4 Histone modification. Post-translational changes in the form of histone modifications influence gene expression by controlling chromatin dynamics. Methylation is commonly associated with gene silencing and can directly interfere with the binding of transcription factors. Histone tail acetylation can increase the access of transcription factors to DNA by transforming condensed chromatin into a more relaxed structure.
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Table 1.2 Summary of histone modification findings in relation to hypertension
Reference Tissue Type/ Sample Size Findings
Han et al. Rat: Tissue: Aorta, renal artery; Up-regulated histone modifier H3K27me3 [78] SHR and WKY in renal artery of SHR correlated with HTN improvement after resveratrol intake.
Fish, et al. Human: Umbilical vein Endothelial cell nucleosomes [79] endothelial cells corresponding to eNOS enriched in various histones relevant to eNOS expression.
Lee et al. Rat: Tissue: adrenal gland, aorta, Higher expression of Ace1 mRNA & [80] heart, kidney, liver, and lung. protein in SHR. Ace1 promoter enriched SHR and WKY with H3Ac and H3K4me3 in SHR.
Cho, et al. Rat: Tissue: Mesenteric artery, Nkcc1 up-regulated in SD rat. Acetylated [21] aorta; SD and Sham rat. histone H3 up-regulated, trimethylated histone H3 down-regulated.
Duart, et Human: Peripheral blood; First DOT1L strongly associated with increased al. [22 sample: 206 mixed sex, NT; BP in Caucasians. Possibly via mediation of Second sample: 730 mixed sex, hypermethylation of H3. HTN and NT.
Mu, et al. Mouse: Tissue: Kidney; WNK4 down-regulation caused increased [23] norepinephrine infused-C57 H3 & H4 acetylation, leading to BL/6j, Adrb1 knockout and overexpression of NCC and therefore Adrb2 knockout mice. promoting HTN onset. eNOS, endothelial nitric oxide synthase; SD, Sprague–Dawley rat; DOT1L, DOT1-like histone H3K79 methyltransferase; NCC, sodium chloride co-transporter; WNK4, with-no-lysine kinase 4. HTN, hypertension; SHR: spontaneously hypertensive rat, WKY: Wistar Kyoto rat; Ace1; angiotensin converting enzyme receptor 1; BP; blood pressure; NKCC1: Na+-K+-2Cl_ cotransporter 1; ACE: angiotensin converting enzyme
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Histone modification affecting arterial pressure levels has been documented in a variety of human and animal tissues, including vascular smooth muscle. Vascular oxidative stress can contribute to endothelial dysfunction—a hallmark of HTN—and the development of HTN. A study by Bhatt et al [81] documented the beneficial effects of resveratrol on endothelial function in the SHR. A later study of the mechanisms behind this effect was conducted by Han et al [78]. Epigenetic modifications, in the form of up-regulated H3K27me3 expression was observed in the renal artery of a salt-sensitive HTN model of the Wistar rat [78], possibly correlating with the improvement observed in HTN status but this was not clear. Endothelial nitric oxide synthase (eNOS) is primarily responsible for the production of nitric oxide in the vascular endothelium, and plays a key role in the regulation of vascular tone [78]. eNOS activity is thought to be down-regulated in
CVD. Fish et al [79] found that eNOS expression appears to be controlled by cell-specific histone modifications. It was also demonstrated that the nucleosomes of endothelial cells corresponding to the eNOS core promoter were enriched in lysine 9 of histone H3 and lysine 12 acetylation of histone H4, and of di- and tri-methylation of lysine 4 of histone H3. Fish et al [79] observed that all of these epigenetic histone modifications were significantly important for the expression of eNOS [79].
The study by Riviere et al [82] noting the modulation of sACE by CpG methylation also documented the involvement of histone deacetylases in the same process. Later findings of Lee et al [80] further validated these observations. Riviere et al [82] also found that tissues from SHRs exhibit higher expression levels of Ace1 mRNA and protein than those of WKY controls. They found the Ace1 promoter regions of the SHR tissues were more enriched with H3Ac and
H3K4me3, and concluded that Ace1 is locally up-regulated in SHR tissues via histone code modifications [80].
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In line with observations by Lee et al [83], Cho et al [84] found that levels of the membrane transporter Nkcc1 mRNA and protein in the aortas of Sprague–Dawley (SD) rats were significantly increased after administration of an angiotensin II infusion. Cho et al [84] found that acetylated histone H3 (an activating histone), was significantly increased together with greatly decreased trimethylated histone H3 (a deactivating histone) [84], suggesting that both histone modification and/or DNA demethylation have a role in the epigenetic up-regulation of Nkcc1 during HTN development.
DOT 1 (mentioned earlier) is a methyl-transferase, which enhances methylation of lysine 79 residue of histone H3 (H3K79) [46]. DOT1-mediated hypermethylation of H3 disrupts silencing of genes associated with maintaining telomere length during DNA repair, located in the telomeric regions of chromosomes [85]. This disruption correlates with a decrease in connective tissue growth factor transcription, increased intracellular cAMP and alterations in the adaption of blood vessels to stressors-associated with HTN [85]. In Caucasians, DOT1-like histone H3K79 methyltransferase (DOT1L) is strongly associated with increased BP response to hydrochlorothiazide [67].
Activation of the renal sympathetic nervous system has long been thought to play a crucial role in the development of salt-reactive HTN [86]. Over activity of renal sympathetic pathways can promote the activation of several mechanisms which lead to increased sodium retention, including stimulation of renin release, reduced renal blood flow, and increased sodium reabsorption in the loop of Henle [87, 88]. Serine-threonine kinases have a key role in renal tubular sodium reabsorption; specifically, the with-no-lysine (WNK) family of serine-threonine kinases [89]. In
NT mice, a low-sodium diet decreases expression of renal WNK4, whereas a high sodium diet
22 | Page increases its expression [90, 91]. Renal sympathetic activity can also promote sodium reabsorption via activation of the β2 adrenergic receptor (β2AR) leading to cyclic AMP (cAMP) production, and increased activity of renal epithelium sodium channels (ENaC) [92]. WNK4 signalling targets
ENaC, and may also mediate the prohypertensive effects of renal sympathetic over activity. In addition to the classical cAMP pathway, cAMP modulates gene transcription via inhibition histone deacetylase-8 activity leading to increased histone acetylation [93, 94]. Mu et al [95] found that salt loading triggers β-adrenergic-mediated down-regulation of renal WNK4, increases salt retention, and consequently leads to the development of salt sensitive HTN. WNK4 was recently found to be negatively regulated by the glucocorticoid receptor [65]. Mu et al [95] also noted that the underlying mechanism down-regulating renal WNK4 is cAMP dependent modulation of histone acetylation, which consequently increased histone H3 and H4 acetylation. Acetylation of
H3 and H4 seems to favour binding of the glucocorticoid receptor to a WNK4 promoter region that contains a negative glucocorticoid-responsive element [95]. Mu et al found WNK4 expression was inhibited via these pathways leading to overexpression of the sodium chloride co-transporter
(NCC) and the onset of HTN [95].
1.7.3 DNA Methylation and hypertension
Epigenetic DNA modification (methylation) occurs when a methyl group derived from S- adenosyl-L-methionine is bound to position 5 of the cytosine ring, forming 5mC [96] (Figure 1.5).
DNA methylation is tightly regulated by DNA methyltransferases (DNMTs), specifically
DNMT1, DNMT3A and DNMT3B [97]. Methylation of the newly synthesized DNA strand during replication is orchestrated by DNMT1 [97]. This ensures transfer of specific methylation patterns, known as marks, during cell division [97]. Alternatively, DNMT3A and DNMT3B modulate de
23 | Page novo DNA methylation [97]. Collectively, these DNMTs are responsible for the maintenance and regulation of methylation and are essential for normal development [98, 99]. Methylation occurs at various locations throughout the genome, at sites called CpGs. Most commonly, methylation resulting in a functional change in gene expression occurs in locations called CpG islands [46].
CpG islands are short sequences of DNA with a high linear frequency of 5’-CpG-3’ sequences.
The “p” denotes the phosphoesteric bond linking the cytosine and the guanine nucleotides [77].
CpG islands are often located in promoter regions and approximately 40% of genes contain CpG islands in the end 5’ region (promoter, untranslated region, and exon1). The rest of the genome
(intergenic and intronic regions) is considered CpG poor [96]. In healthy somatic cells, up to 90% of CpG dinucleotide sites are methylated, except for those located in the promoter region, which appear to be somewhat protected from such modification [96]. Non-CpG island DNA methylation has also been reported to influence protein-DNA interactions, gene expression and chromatin structure, and stability [100]. Functionally, DNA methylation suppresses gene transcription, so
DNA hypermethylation typically results in gene silencing [46].
Different degrees of DNA methylation have been correlated with variable onset, timing, and severity of EH (Table 1.3). Global genomic DNA methylation can be quantified by measuring the amount of 5mC present in a DNA sample. Smolarek et al [101] found a correlation between decreased levels of 5mC in peripheral blood with an increase in EH grade severity. The concentration of 5mC was calculated as a ratio of spot intensities and a significant deficit in 5mC was observed in healthy controls, relative to levels found in the two severity grades of EH. The mean level of 5mC was 1.80 ± 0.69 in healthy controls and 1.14 ± 0.48 in all HT subjects
(specifically, 1.29 ± 0.50 grade 1, 0.99 ± 0.42 grade 2), indicating that global DNA methylation
24 | Page levels decrease as the severity of EH increases. However in this study, the percentage of 5mC was expressed as a coefficient “R” value, and the method for calculating “R” was not disclosed.
Figure 1.5 Epigenetic DNA methylation. DNA methylation involves the binding of a methyl group to the 5’ carbon of cytosine ring. This primarily occurs at CpG islands and results in inhibition of gene transcription, particularly if it occurs in the gene promoter region. It may also promote transcription if it is located at gene exons sites. DNA methylation is an essential biological process which is linked with several epigenetic phenomena including X- chromosome inactivation, genomic imprinting and repetitive element repression.
The most robust data on the involvement of methylation in BP regulation comes from the study by Kato et al [102]. Kato and colleagues performed a genome-wide association and replication 25 | Page study and identified genetic variants at 12 new loci that correlated with BP modulation in 320,251 individuals of East Asian, European, and South Asian ancestry [102]. An investigation of the relationship between the sentinel BP single nucleotide polymorphisms (SNPs) with local DNA methylation in 1904 South Asians (peripheral blood; Illumina Human Methylation 450 Bead Chip array (Erasmus Medical Centre, Rotterdam, The Netherlands)) revealed a two-fold enrichment between sequence variation and DNA methylation. Twenty-eight of the 35 sentinel SNPs were associated with one or more methylation markers [102], suggesting that DNA methylation may lie on the regulatory pathway linking sequence variation BP. Genes associated with the 12 newly identified SNP loci include those involved with vascular smooth muscle (IGFBP3, KCNK3,
PDE3A, PRDM6) and renal function (ARHGAP24, OSR1, SLC22A7, TBX2) [102]. Investigation of cross-tissue patterns of DNA methylation at the leading CpG sites associated with sentinel SNPs showed that DNA methylation in the blood was closely correlated with methylation patterns of various tissues (liver, muscle, subcutaneous and visceral fat).
A more recent, large scale study by Richard et al [103] discovered that heritable methylation sites, independent of known genetic variants, explain an additional 1.4% and 2.0% of the inter-individual variation in SBP and DBP respectively. Although these studies provide tantalising evidence for the role of methylation all were conducted in the easily accessible leukocyte DNA. Since DNA methylation patterns are highly variable across different tissues [36], utilisation of peripheral blood for non-blood based diseases and phenotypes has not been validated and further research is needed to investigate this correlation with a HT affector organ, such as the kidney.
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Initial DNA methylation research centered on correlating EH with the global level of 5mC [101], but more recent studies have focused on the methylation of specific DNA sequences. The hydroxysteroid dehydrogenase 11 β2 enzyme (HSD11B2) converts cortisol to cortisone, which is found in circulatory concentrations up to 1000 times higher than aldosterone (the primary mineralocorticoid hormone and sodium (Na) transport modulator of the RAAS [46]. Cortisol and aldosterone bind mineralocorticoid receptors with equal affinity, but due to the difference in concentration of the hormones, cortisol has a larger role in sodium reabsorption by the kidneys and, in consequence, plays a larger role in fluid-related arterial pressure [46]. HSD11B2-mediated degradation of cortisol to cortisone is disrupted when the promoter region of the HSD11B2 gene is hypermethylated [104, 105]. The resulting imbalance in the active metabolites of cortisol and cortisone, tetrahydrocortisol (THF), and tetrahydocortisone (THE), respectively, promotes the onset of HTN [104, 105], in accord with the findings by Friso et al. [106]. Friso et al. [106] also found that HSD11B2 promoter methylation was associated with EH, with parallel disruptions to the THF/THE ratio.
Due to the well-known involvement of the RAAS on arterial pressure regulation, any changes in the activation status of RAAS-regulated genes have a pronounced effect on an individual’s potential to develop HTN. This effect has been extensively tested in animal models of HTN
[66].Hypomethylation of the promoter regions of the angiotensin II type 1b receptor gene in the adrenal glands of the maternal low protein (MLP) rat promoted HTN through an exaggerated response to salt [66]. The same effect was seen in a second study, where maternal protein deficiency during pregnancy induced hypomethylation of the promoter regions of RAAS- responsive genes, such as angiotensin converting enzyme (ACE), causing predisposition to HTN
27 | Page in offspring as well as cognitive deficits [55]. The human PRCP gene encodes a lysosomal prolylcarboxypeptidase protein, which is implicated in cleavage of c-terminal amino acids linked to proline in peptides such as angiotensin II and III [92]. Methylation profiling conducted on young
African males revealed that the PRCP gene was hypomethylated in EH subjects [107]. Similarly, when expression patterns of the angiotensin 1a receptor (Atgr1a) were analysed in both SHR and its NT control, the WKY, expression of Atgr1a was significantly increased by week 20 in the SHR.
Bisulfite sequencing revealed that the Atgr1a promoter from endothelial cells in the aorta and mesenteric arteries of the SHR rats became progressively hypomethylated with age compared to their WKY counterparts [108], suggesting heightened Atgr1a expression in the SHR was related to the hypomethylation of the Atgr1a promoter and may have a role in the maintenance of high
BP. Somatic ACE (sACE) converts angiotensin I to the active form, angiotensin II and is, therefore, a key regulator of BP [82]. Bisulfite sequencing conducted on cultured endothelial cells and WKYs revealed that hypermethylation was associated with transcriptional repression of sACE, indicating possible epigenetic involvement of sACE modulation in HT [82].
Membrane transporters such as Na+-K+-2Cl- cotransporter 1 (NKCC1), participate directly with fluid and electrolyte loss and therefore arterial pressure regulation [83]. NKCC1, expressed by the
Sic2a2 gene, mediates the transport of sodium, potassium and chloride into and out of the cells
[46]. Changes in ion fluxes have been implicated in EH, particularly an augmented, passive influx
Na+, K+, Rb+, and Cl- in HT vascular smooth muscle cells [83]. Hypomethylation of the Sic2a2 gene promoter in the SHR aorta and heart, result in increased expression of NKCC1 and is positively correlated with HTN [83]. Methyl CpG binding protein 2 methylates the norepinephrine transporter gene, silencing its expression [46]. Hypermethylation of the norepinephrine transporter
28 | Page gene, which transports norepinephrine and dopamine from the synaptic gap back to the pre- synaptic neuron, has been shown to lead to increased transport responsiveness, resulting in EH
[109]. Furthermore, phenyl-ethanolamine N-methyltransferase (PNMT), which can act similarly to methyl CpG binding protein 2, has been shown to exacerbate the decrease in norepinephrine uptake, thereby enhancing the local and systemic catecholaminergic effect [109].
Table 1.3 Summary of methylation findings in relation to hypertension and cardiovascular pathology Reference Tissue Type/ Sample Size Findings
Smolarek, Human: Peripheral blood; 60 Mean 5mC amount in DNA significantly et al. [101] with EH; 30 controls decreased as HTN severity increased.
Kato, et al. Blood, muscle, liver, fat; Discovered to be correlated with BP [20] 320,251 individuals of East modulation. Two-fold enrichment discovered Asian, European and South between DNA methylation and sentinel BP Asian ancestry SNPs, providing evidence for DNA methylation role in BP regulation.
Richard et Human: Peripheral blood, The replicated methylation sites are heritable al. [103] 17,010 individuals of and independent of known BP genetic variants, European, African explaining an additional 1.4% and 2.0% of the American, and Hispanic interindividual variation in SBP and DBP, ancestry respectively.
Friso, et Human: Peripheral blood; 25 HSD11B2 gene promoter methylation al. [21] with EH; 32 with prednisone associated with EH onset via disruption to therapy THF/THE ratio.
Goyal, et Rat: Tissues: brain; 20 MLP Hypomethylation of RAAS system genes such al. [22 pups, 17 control pups as ACE resulting in HTN in offspring.
Wang, et Human: Peripheral blood; 8 PRCP gene hypomethylated in EH, linked to al. [23] EH; 8 control disruption in cleavage of angiotensin II and III
Pei, et al. Tissue: aorta; 6 SHR; 6 Atgr1a gene progressively hypomethylated as [24] WKY control SHR age progressed. Indicating increased expression of Atgr1a in aging SHR
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Riviere, et Rat: Cultured endothelial Hypermethylation associated with al. [25] cells from WKY transcriptional repression of sACE, indicating a role for epigenetics in sACE modulation during HTN. Lee, et al. Rat: Tissues: aorta, heart; Hypomethylation of Sic2a2 gene lead to [26] SHR and WKY increased expression of NKCC1 which was positively correlated with HTN.
Kim et al. Human: PBL; 286; 129 Elevated PBL DNA methylation is positively [110] male, 157 female associated to CVD prevalence/ predisposing conditions.
Lund et al. Human: monocyte cell line Altered DNA methylation present in aorta and [111] Mice: PBMC; PBL; aorta PBMC of apolipoprotein E mutant mice. Atherogenic lipoproteins promote global hypermethylation in human monocyte cell line.
Bellizzi et Human: 37; PBL Loss of global DNA methylation levels and al. [112] frailty occurs in aging.
Castro et Human: plasma; 17 vascular DNA methylation status significantly correlated al [113] disease, 15 controls with plasma tHcy and AdoHcy.
Stenvinkel Human: 191; 37 CKD stage Global DNA hypermethylation is associated et al. [114] 3 & 4; 98 CKD stage 5; 20 with inflammation and increased mortality in haemodialysis patients; 36 CKD healthy controls
Zhao et al. Human: PBL; 84 Global hypermethylation associated with [115] monozygotic twins insulin resistance.
EH: essential hypertension; MLP: maternal low protein; HTN: Hypertension; SHR: spontaneously hypertensive rat, WKY: Wistar Kyoto rat; 5mC: 5-methyl-cytosine; ACE: angiotensin converting enzyme; RAAS: renin-angiotensin-aldosterone system; sACE: somatic ACE; SNPs: single nucleotide polymorphisms; NKCC1: Na+-K+-2Cl_ cotransporter 1; BP: blood pressure
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1.8 Conclusions
We are entering a new era of understanding how the genome interacts with the environment to affect disease pathogenesis. There is now emerging evidence that epigenetic, as well as genetic, factors are key players in regulating and maintaining BP, and strong evidence for a complex interaction of genetic and environmental factors that influence the risk of HTN in each individual
(Figure 1.2). Many epigenetic studies are, however, limited by the fact that only blood is studied rather than the effector tissues. The utility of blood methylation status in epigenetic research is yet to be determined. Furthermore, the polygenic complexity of HTN and the limited knowledge on some epigenetic modifiers makes it more challenging to decipher the exact mechanisms involved.
Further studies of the epigenetic modulators in humans will be needed to elucidate the exact mechanisms involved and to determine more effective therapeutic applications.
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1.9 Hypothesis and Aims
What we know The critical concept that can be derived from the current literature review is that we are entering a new era of understanding how the genome interacts with the environment to affect disease pathogenesis. There is now emerging evidence that epigenetic, as well as genetic, factors are key players in regulating and maintaining BP, and strong evidence for a complex interaction of genetic and environmental factors that influence the risk of HTN in each individual.
The vast majority of epigenetic studies are limited through the use of peripheral blood for non- blood based pathologies, rather than the effector tissues. The applicability of blood as a surrogate tissue for the methylation status in other tissues is yet to be determined. Furthermore, the polygenic complexity of HTN and the limited knowledge on the role of tissue specific DNA methylation makes it more challenging to decipher the exact mechanisms involved.
What we do not know As one of the most important and stable epigenetic marks, DNA methylation has emerged as a powerful regulator of developmental processes but its role in EH remains elusive. Moreover, a broader conceptual framework for understanding how DNA methylation influences BP in a key affector organ of HTN is lacking.
Hypothesis The central hypothesis underpinning this PhD thesis is that alterations in kidney specific DNA methylation plays a fundamental role in modulating gene expression changes involved the regulation of BP and the pathogenesis of EH.
Aims: To test the hypothesis, this PhD thesis focuses on characterizing the role of DNA methylation in the HT kidney using array and RNA-sequencing methods (Figure 1.6):
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Aim 1: To characterize global DNA methylation dynamics and its functional role in the hypertensive population (Chapter 3).
Aim 2: To determine the role of genome-wide, loci specific DNA methylation in the hypertensive human kidney and investigate associations between our renal findings and previous studies conducted in blood (Chapter 4).
Aim 3: To understand the relationship between DNA methylation and differential expression of genes associated to BP and HTN in the human kidney and investigate existing overlap between our kidney findings and previous blood based GWAS (Chapter 5).
To address the abovementioned aims, a number of classic molecular biology techniques, as well as contemporary next-generation sequencing approaches have been employed. These molecular techniques, as well as the human population they are applied to, are described in detail in the following General Methods chapter (Chapter 2).
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Figure 1.6 Study design flow chart for PhD project. This PhD thesis investigates the role of kidney specific DNA methylation in the modulation of gene expression in genes involved in HTN. Furthermore, we investigate the overlap between HTN- associated kidney methylation and expression and large GWAS datasets available from human blood.
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Chapter 2. General methods
“Do not go where the path may lead, go instead where there is no path and leave a trail”
~ Ralph Waldo Emerson
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Chapter 2. General methods
2.1 Participants
All studies have been approved by the University of Manchester Human Bioethical Committee.
All TRANScriptome of renaL humAn TissuE (TRANSLATE) study individuals gave written informed consent for participation. The studies adhered to the Declaration of Helsinki.
All analyses conducted in this PhD utilised peripheral blood and kidney samples collected from participants of the TRANSLATE study. TRANSLATE is a unique resource of 265, white
European patients who underwent elective unilateral nephrectomy due to sporadic non-invasive renal cancer in one of three nephrology-urology centres: Silesian Renal Tissue Bank [116, 117],
TRANSLATE P (recruitment conducted in Western Poland) and TRANSLATE Z (recruitment conducted in Southern Poland) [118]. The study recruited adults (aged >18 years) with no personal history of nephropathy.
All participants of the TRANSLATE study underwent detailed clinical phenotyping, which consisted of documentation of clinical history (via coded questionnaire), basic anthropometry as well as triplicate measures of BP by mercury sphygmomanometer. HTN diagnosis was assigned to individuals who had elevated BP greater than >140/90 mmHg on at least two separate occasions, and/or were taking antihypertensive medications [117].
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2.2 Sample collection
Renal samples, consisting of approximately 1 cm3 of kidney tissue (both medulla and cortex), were collected from the contralateral healthy (unaffected by cancer) pole of the kidney immediately post nephrectomy. This method was previously employed in other studies as a means of securing
“control” tissue for gene expressional analyses [119]. Histological sections were also taken at this time to confirm cancer and tissue margins. These samples were immediately transferred to either
RNA later (Ambion, Austin, Tex) or dry tubes and preserved at -70°C before renal DNA and RNA extractions [116-118].
Kidney DNA was extracted from 94 homogenised renal tissue samples using Qiagen purification kits for total DNA (Qiagen). Leukocyte DNA was extracted from 75 peripheral blood using
QIAamp DNA mini blood extractions kit (Qiagen). Kidney RNA was extracted from 180 using the RNeasy kit (Qiagen). The various sample sizes were chosen based on the plate sequencing size
(ELISA anlaysis), the 450K methylation chip size (methylation analysis) and the availability of the appropriate renal samples with matching clinical phenodata to reduce confounders (all samples).
2.2.1 Effects of coexistant cancer on the renal transcriptome
The potential effect of coexistent cancer on the transcriptome of the TRANSLATE “healthy” renal tissue was comprehensively discussed by Marques et al [120], where the global expression profiles of three different cohorts was compared. The three groups contained either healthy tissue from nephrectomy due to cancer, noncancerous kidney tissue, or clear cell renal carcinoma tissue. These sections were analysed using hierarchical clustering analysis and RNA-sequencing. Results from
37 | Page this study showed tight clustering of global expression patterns between healthy tissue from nephrectomy and subjects who did not have cancer. Highlighting that it was unlikely that the transcriptome program in cells within neoplastically unaffected apparently healthy renal tissue were largely influenced by the presence of cancer in other parts of the kidney [120].
2.3 Global kidney and leukocyte DNA methylation detection
In Chapter 3 the global methylation analysis utilised 75 human peripheral blood leukocyte samples
(Chapter 3: Table 3.3 and Table 3.4) and 94 human kidney samples (Chapter 3: Table 3.5 and 3.6) from the TRANSLATE study, respectively. Of the 75 peripheral blood leukocyte samples, 28 were a subset from the same participants that provided renal tissue. Kidney and leukocyte DNA (100ng) underwent global DNA methylation analysis using the 5mC ELISA kit (Zymo Research, USA).
Where 100ng of DNA from each sample (at 10ng/µL) was added to a PCR tube before adding
90µL of 5mC coating buffer to make a final volume of 100µL. The DNA was then denatured at
98°C for five minutes, transferred immediately to ice for ten minutes, samples were transferred to
5mC ELISA 96 well plate, covered, and then incubated at 37ºC for one hour. During the blocking stage, each well was washed three times with 200µL of 5mC ELISA buffer, before a final addition of 200µL of 5mC ELISA buffer was added and the plate was incubated at 37°C for 30 minutes.
During the antibody addition stage, the buffer was discarded from the wells and 100µL of prepared antibody mix (Chapter 3: Table 3.2) was added to each well before incubation at 37ºC for one hour. During the colour development stage, the buffer was discarded from the wells, and the wells were washed in triplicate with 200 µL of 5mC buffer. HRP developer (100µL) was added to each well and allowed to develop for 60 minutes at room temperature. All samples were measured in duplicate and calibrated against 0%-100% methylated controls with a standard curve. Absorbance
38 | Page was measured at 450 nm using the Multiskan FC Microplate Photometer (Thermo Scientific,
Australia) and average global DNA methylation was calculated using the formula generated from the standard curve. Global methylation in the kidney was further investigated using the data generated from the Infinium HumanMethylation 450K BeadChip (Illumina) (method 2.4) by the calculating the mean of all CpG site beta-values between the HT and NT samples.
2.4 Genome-wide, loci specific DNA methylation analysis
2.4.1 Detection of differentially methylated CpG sites
In Chapter 4 the genome-wide, loci specific DNA methylation analysis was conducted using 94 human kidney samples (Chapter 4: Table 4.6 and 4.7) from the TRANSLATE study. The subset of TRANSLATE human renal DNA to be utilised in this section was chosen to ensure a balanced, yet sufficient, representation of hypertensive patients, both medicated and non-medicated.
Furthermore, age and gender were balanced between the normotensive and hypertensive populations as much as possible with no histological signs of nephrosclerosis. Genome-wide kidney DNA methylation was investigated using the Infinium HumanMethylation 450K BeadChip
(Illumina) with assistance from the Australian Genome Research Facility (Perth, Australia). Where
1000ng of kidney DNA underwent quality analysis using resolution on a 0.8% agarose gen and
Nanodrop assessment. From this, 750ng of high quality kidney DNA underwent bisulphite conversion with the use of the Zymo EZ DNA Methylation Kit (Zymo Research, USA). Bisuphite converted kidney DNA (4uL, at 50ng/mL) was then hybridised with the Infinium
HumanMethylation450 BeadChip (Illumina). The 450K BeadChip uses two types of Illumina chemistry to provide a comprehensive DNA methylation status of over 485,000 CpG sites spanning 99.9% of Refseq genes (with an average of 17 CpG sites per gene region distributed
39 | Page across the promoter, 5’ untranslated region, first exon, gene body and 3’ untranslated region).
Apart from a vast majority of CpG islands (covered in 96%), the array provides coverage for island shores and the regions flanking them. The arrays were then processed through Illumina iScan to deliver rapid, high resolution imaging of the methylation arrays. DNA methylation is represented as a β-value (0-1) which corresponds to the percentage of methylation at a given CpG site (0%-
100%, respectively). Data was imported into and analysed using the bioinformatics software,
Partek (Genome Suite, version 6.6, Singapore) as well as the statistical computing software, R
(RStudio, GNU General Public License).
2.4.2 Methylation data pre-processing
For every CpG site interrogated via the 450K array, the intensity of the scanned signal is used to calculate a methylated and non-methylated measurement. The raw IDAT files hold this intensity information which was extracted from the Illumina iScan. These files also contain a detection P- value for every probe, which indicated how well the signal intensity was interpreted (small P values correlating with good probe performance).
2.4.2.1 Methylation data preprocessing computational analysis
No probes with a high detection P-value (P = ≥0.5) were detected. Probes located on sex chromosomes were removed as the data set contains both males and females, as well as cross reactive probes which target repetitive sequences or co-hybridize to alternative sequences that are highly homologous to the intended targets and therefore risk generating spurious signals [121].
These cross-reactive probes were removed to reduce the risk of invalid conclusion and lack of validation in downstream analysis. Similarly, sites that overlapped with SNPs located within a
40 | Page probe or within ten base pairs of a probe were excluded, a total of 418,581 probes remained for analysis.
2.4.3 Methylation data normalisation
The Illumina 450K array contains two probe types. Type I probes originate from the 450K predecessor, the 27K array and contain two probes in the same colour channel to measure methylated and unmethylated intensities [122]. Type II probes were designed differently, and use one probe and two different colour channels to make the same measurement [122]. The two different probe types generate different signals. Therefore, to avoid probe type specific false positives, identify technical variation, and increase the chances of detecting DNA methylation changes caused by HTN, raw β-values underwent intra-array normalisation.
2.4.3.1 Methylation data normalisation computational analysis
Raw β-values underwent intra-array normalisation using the Dasen method (Figure 2.1) implemented through the Bioconductor wateRmelon package (version 3.6) [123]. The Dasen method also performs between-array normalisation, where the background difference between type I and type II probes for both methylated and unmethylated signal intensities are adjusted prior to quantile normalisation so that chips ran at different time points are comparable The β-value is then generated from the adjusted signal intensities using the equation 1.
Equation 1: β = methylated probe intensity / methylated probe intensity + unmethylated probe intensity.
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Figure 2.1 Inter-array normalisation using the Dasen method. To avoid probe type specific false positives, identify technical variation, and increase the changes of detecting DNA methylation changes caused by HTN, raw β-values underwent intra-array normalisation using the Dasen method implemented through the Bioconductor wateRmelon package (version 3.6).
All β-values were log-transformed into M-values (Figure 2.2) using the logit function (log2 (β/ (1-
β))). M-values are a more valid alternative for detecting DNA methylation changes, as they eliminate the heteroscedasticity of higher-and lower-end β-value [124]. The M-value is calculated as the log2 ratio of the intensity of the methylated probe versus the unmethylated probe (see
42 | Page equation 2). It should also be noted that logit transformation further reduced type I/ type II probe bias [124]. Two samples were excluded from further analyses due to identification mislabeling
(Figure 2.3)
Equation 2:
*+,(./,123456,7)9: !" = %&'( ) = *+,(./,;<123456,7)9:
Figure 2.2 Principal components plot demonstrating normalisation of log transformed β- values to M-values. All β-values were log-transformed into M-values using the logit function (log2 (β/ (1-β))). M-values are a more valid alternative for detecting DNA methylation changes, as they eliminate the heteroscedasticity of higher-and lower-end β-value [124]. The M-value is calculated as the log2 ratio of the intensity of the methylated probe versus the unmethylated probe. Logit transformation further reduced type I/ type II probe bias [124].
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2.4.4 Methylation data statistical analysis
Differentially methylated, individual, CpG sites and islands in the HT and NT kidney were determined using regression (RStudio, GNU General Public License), with adjustment made for the antecedent covariates age, BMI, smoker status and gender. Two samples [PKPOZ2, PKPOZ3] were excluded from the analysis due to mismatched labelling (Figure 2.3).
Figure 2.3 Principal components plot demonstrating mismatched gender labels. Kidney DNA methylation samples clustered by gender to display mismatched labelling on samples PKPOZ2 and PKPOZ3. These two samples were removed from both the global (Chapter 3) and the loci specific methylation analysis (Chapter 4).
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2.5 RNA-sequencing analysis
2.5.1 Identification of hypertension-associated renal signatures
In Chapter 5, RNA was extracted from the kidney tissue of 180 participants of the TRANSLATE study (Chapter 5: Table 5.1 and 5.2) and preserved in RNAlater (Ambion, Austin, Texas). A total of 1µg of kidney RNA was extracted from all kidneys and subjected to Illumina Tru-Seq RNA
Sample Preparation protocol. Briefly, oligo-dT attached magnetic beads are used to purify the poly-A containing mRNA molecules. Following purification, the mRNA is fragmented using divalent cations under elevated temperature. The cleaved RNA fragments are then copied into first strand cDNA using reverse transcriptase and random primers and second strand cDNA using DNA
Polymerase I and RNase H. The cDNA fragments the undergo end repair processing, have the addition of a single ‘A’ base, and then ligation of the adapters. The products are then purified and enriched with PCR to create the final cDNA library. Kidney samples were then sequenced with
75-bp paired end reads on an Illumina HiSeq 4000. All generated raw reads were stored in FASTQ format. The base call and read quality were evaluated using FASTQC. The input library complexity was assessed using RNA-SeQC. The pre-processing of reads for adapter trimming was conducted by Trimmomatic. The reads were then pseudo-aligned to the GRCh38 Ensembl transcriptome reference (Ensembl release 83); and expression was quantified in transcripts per million (TPM) at a transcript level using Kallisto. Transcript expression values were then summed to give gene-level quantification.
2.5.2 RNA-sequencing pre-processing
The generated raw reads from RNA-sequencing were stored in FASTQ format, where the base call and read quality were evaluated using FASTQC. Input library complexity was assessed using
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RNA-SeQC and pre-processing of reads for adapter trimming was conducted using Trimmomatic.
The reads were then pseudo-aligned to the GRCh38 Ensembl transcriptome reference (Ensembl release 83; and expression was quantified in transcripts per million (TPM) at a transcript level using Kallisto; a RNA-quantification program that is two orders of magnitude faster than previous approaches and achieves similar accuracy [125].
2.5.2.1 RNA-sequencing preprocessing computational analysis
Kallisto removes unnecessary computational technicality from RNA-sequencing analysis by pseudo-aligning reads to a reference, producing a list of transcripts that are compatible with each read while avoiding alignment of single bases. Transcript expression values were then summed to give gene-level quantification. Outlier samples were removed based on a statistic described in
Wright et al [126] or based on pairwise correlation between samples, where samples were excluded if their median correlation was below 0.75. Samples without matching genotype data were also excluded. Only genes located on autosomal chromosomes were considered for downstream analyses. A threshold of genes expression was set at TPM>0.1 in at least 50% of samples and minimal read counts ≥6. A gene was also removed if its interquartile range equaled zero. Only genes that passed all of the above RNA-seq quality control filers were selected for downstream analyses.
2.5.3 RNA-sequencing data normalisation
2.5.3.1 RNA-sequencing preprocessing computational analysis
Prior to any statistical analyses, gene-level TPM values were normalised (Figure 2.4 A-C). First, log2 of TPM values were normalised across samples using robust quantile normalisation with an
46 | Page offset of one. The quantiles normalised gene expression values were transformed using rank-based inverse normal transformation. Outlying observations were set to missing using residuals from robust linear regression and were then imputed using linear regression. To account for hidden variation in RNA-seq data due to technical processing (such as batch effects or sample processing in pre-sequencing stage), we used probabilistic estimation of expression residuals (PEER) method
[127] and estimated 30 hidden factors for TRANSLATE Study. The numbers of hidden factors were chosen based on sample sizes of each dataset as recommended in GTEx eQTL analysis [128].
The estimated hidden factors were used as covariates in all downstream regression analyses where appropriate.
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Figure 2.4 Principal components plot of TRANSLATE normalisation process. A) PCA plot of TRANSLATE sample RNA- sequencing raw TPM reads demonstrates clustering by batch using the first two principal components. B) PCA plot of TRABSLATE sample RNA-sequencing quantile normalised data showing clustering by batch using the first two principal components. Batch 3 was removed from the analysis due to small sizing, and sample ID KID 18 was removed due to probable sample contamination. C) PCA plot of TRANSLATE sample residuals demonstrates that PEER has removed the batch effects and clustering is no longer visible by batch number; plotted using the first two principal components.
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Chapter 3. Global leukocyte and kidney DNA methylation in essential hypertension
“Somewhere something incredible is waiting to be known”
~ Carl Sagan
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Chapter 3. Global leukocyte and kidney DNA methylation in essential hypertension
3.0 Abstract
It is evident that a combination of lifestyle factors and genetic predisposition contribute to the development of EH. However, relatively little is known about the mechanisms by which global
DNA methylation influences BP. The purpose of this section of my PhD is to investigate the relationship between EH and global methylation levels in leukocyte and kidney DNA. We found no relationship between HTN diagnosis and global methylation percentage in either peripheral blood leukocyte or kidney DNA. However, a negative correlation was found between global methylation percentage in the kidney and DBP. This relationship was not evident after adjustment for the use of antihypertensive medication. Our array-based calculation of global methylation found no association between HTN diagnosis/ BP values, and median kidney methylation beta- value or m-value. This conflicting information highlights the need for a more sensitive 5mC detection method than the ELISA assay. Finally, to validate peripheral blood as a surrogate tissue for methylation findings in the kidney, we investigated the correlation between 5mC percentage in both leukocyte and kidney DNA. No correlation was evident, suggesting that blood may not provide a reliable alternative to HTN related human tissue that is more difficult to obtain, such as the kidney.
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3.1 Introduction
Global methylation refers to the measure of genome-wide cytosine methylation, expressed as a percentage [129]. However this term has been used interchangeably, and perhaps inappropriately, to describe measurement of cytosine methylation of repetitive elements or of multiple gene regions
[129]. The role of DNA methylation in cancer has been extensively discussed [130-141]. The key findings state broadly that tumour development is associated with increased DNA methylation at
CpG islands, loss-of-imprinting, and epigenetic remodelling of repeat elements, particularly loss of DNA methylation at satellite DNA [142, 143]. Studies reporting the association between global
DNA methylation and the etiology of complex, non-cancerous diseases are more prevalent in the last decade (Table 3.1). Interestingly, recent data shows an association between global DNA methylation and the severity of CVD [110], atherosclerotic lesions [111, 144], frailty in the elderly
[112], plasma homocysteine levels [113], chronic kidney disease [114], insulin resistance [115], coronary artery disease [145] and HTN [146].
Studies reporting on global methylation in the HT population are scarce, highlighting the need for further research in this area. However, the small portion of information available, suggests that global methylation percentage in humans decreases as the severity of HTN increases [101].
Furthermore, global DNA methylation in the kidney has not been investigated. Implication of the kidney in HTN can be dated back almost 200 years [147, 148], but it wasn’t until 1972 that Guyton and Colleagues developed a mature hypothesis suggesting that the kidney governs the level of BP by regulating extracellular fluid volume, which Guyton called pressure-natriuresis [8]. The hypothesis by Guyton and Colleagues further stated that any modification of the pressure- natriuresis response can result in chronic elevation of intra-arterial pressure via an equilibrium shift between salt and water [8]. Cross transplantation studies conducted in the 1970s further
51 | Page highlighted the key role of intrinsic functions of the kidney in the pathogenesis of HTN [42, 149,
150]. Studies by Dahl et al [149, 150] discovered that transplantation of a from a Dahl HT (salt sensitive) kidney into a NT (salt resistant) rat induced HTN in the recipient. Contrastingly, reciprocal transplantation of a kidney from a NT rat into a Dahl HT rat abolishes HTN. These findings were also investigated in the SHR and the Milan HT rat with comparable findings [42,
151]. This was followed by a human study in 1982 [152], where six patients with EH received kidney transplants from NT donors. At a 4.5 year follow-up, all patients were NT and had evidence of reversal of HT damage to the heart and retinal vessels [152]. It is predicted that these effects are a consequence of epigenetic modifications.
Although the design of GWAS and epigenome-wide association studies appear to be similar, the dynamic nature of epigenomics presents challenges for successful study design that are not encountered in GWAS. Among these challenges is the selection and collection of the most appropriate tissue to be investigated, since epigenetic landscapes are tissue-specific and genomic context dependent [153]. For epigenome-wide association studies, availability of human target tissue is problematic. Therefore, surrogate tissues are often used, and peripheral blood is the most common. However, the utilisation of peripheral blood for the majority of epigenome research presents a question to the applicability of blood for non-blood based diseases and phenotypes. In this chapter we hypothesised that blood is a quality surrogate tissue for methylation patterns in the kidney. Therefore, the aim of this section of my PhD is to determine if global DNA methylation in the kidney is correlated to HTN diagnosis. Further, can peripheral blood leukocytes provide a surrogate for cross-tissue patterns of DNA methylation in the HT kidney? Lastly, the existing
52 | Page methods for measurement of 5mC are unreliable, therefore we aim to investigate the use of array- based methods for the calculation of global 5mC percentage.
Table 3.1 Summary of global methylation findings
Reference Tissue Type/ Sample Size Findings
Kim et al. Human: PBL; 286; 129 male, Elevated PBL DNA methylation is positively [110] 157 female associated to CVD prevalence/ predisposing conditions.
Lund et al. Human: monocyte cell line Altered DNA methylation present in aorta and [111] Mice: PBMC; PBL; aorta PBMC of apolipoprotein E mutant mice. Atherogenic lipoproteins promote global hypermethylation in human monocyte cell line.
Bellizzi et Human: 37; PBLs Loss of global DNA methylation levels and al. [112] frailty occurs in aging.
Castro et Human: plasma; 17 vascular DNA methylation status significantly correlated al. [113] disease, 15 controls with plasma tHcy and AdoHcy.
Stenvinkel Human: 191; 37 CKD stage 3 & Global DNA hypermethylation is associated with et al. [114] 4; 98 CKD stage 5; 20 inflammation and increased mortality in CKD haemodialysis patients; 36 healthy controls
Zhao et al. Human: PBL; 84 monozygotic Global hypermethylation associated with insulin [115] twins resistance.
Sharma et Human PBL; 287; 137 CAD Global DNA methylation significantly increased al. [145] patients, 150 controls in CAD patients.
Smolarek Human: Peripheral blood; 60 Mean 5mC amount in DNA significantly et al. [101] with EH; 30 controls decreased as HTN severity increased.
PBL, peripheral blood leukocytes; PBMC, peripheral blood monocytes; AdoHcy, S- adenosylhomocysteine; tHcy, total homocysteine; HTN, hypertension; CAD, coronary artery disease; CKD, chronic kidney disease
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3.2 Methods
3.2.1 Participants and sample collection
For recruitment details of TRANSLATE study participants refer to Chapter 2. Renal samples, consisting of approximately 1 cm3 of kidney tissue, were collected from the healthy (unaffected by cancer) pole of the kidney immediately post nephrectomy. These samples were immediately transferred to either RNA later (Ambion, Austin, Tex) or dry tubes and preserved at -70°C before renal DNA and RNA extractions [116-118].
Kidney DNA was extracted from 96 homogenised renal tissue samples using Qiagen purification kits for total DNA (Qiagen). Leukocyte DNA was extracted from peripheral blood using QIAamp
DNA mini blood extractions kit (Qiagen). Histological processing of the kidney tissue was also conducted at this time.
3.2.2 Global kidney and leukocyte DNA methylation detection
For the global methylation analyses we utilised 75 human peripheral blood leukocyte samples
(Table 3.3 and Table 3.4) and 94 human kidney samples (Table 3.5 and 3.6) from the
TRANSLATE study, respectively. Of the 75 peripheral blood leukocyte samples, 28 were a subset from the same participants that provided renal tissue. Kidney and leukocyte DNA (100ng) underwent global DNA methylation analysis using the 5mC ELISA kit (Zymo Research, USA).
Where 100ng of DNA from each sample (at 10ng/µL) was added to a PCR tube before adding
90µL of 5mC coating buffer to make a final volume of 100µL. The DNA was then denatured at
98°C for five minutes, transferred immediately to ice for ten minutes, samples were transferred to
5mC ELISA 96 well plate, covered, and then incubated at 37ºC for one hour. During the blocking
54 | Page stage, each well was washed three times with 200µL of 5mC ELISA buffer, before a final addition of 200µL of 5mC ELISA buffer was added and the plate was incubated at 37°C for 30 minutes.
During the antibody addition stage, the buffer was discarded from the wells and 100µL of prepared antibody mix (Table 3.2) was added to each well before incubation at 37ºC for one hour. During the colour development stage, the buffer was discarded from the wells, and the wells were washed in triplicate with 200µLof 5mC buffer. HRP developer (100µL) was added to each well and allowed to develop for 60 minutes at room temperature.
Table 3.2 Antibody solution preparation for 5mC ELISA protocol
Dilution Volume (µl) Example (18 wells)
5mC ELISA Buffer N/A (# wells + 2) 100 2,000 µl Anti-5-Methylcytosine 1:2,000 Buffer Vol. / 2,000 1 µl Secondary Antibody 1:1,000 Buffer Vol. / 1,000 2 µl Antibody solution preparation for 5mC ELISA protocol. Prepared antibody mix for detection of 5mC in peripheral blood and kidney samples.
All samples were measured in duplicate and calibrated against 0%-100% methylated controls
(provided pre-made with the Zymo ELISA kit) with a standard curve. Absorbance was measured at 450 nm using the Multiskan FC Microplate Photometer (Thermo Scientific, Australia) and average global DNA methylation was calculated using the formula generated from the standard curve.
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Global methylation in the kidney was further investigated using the data generated from the
Infinium HumanMethylation 450K BeadChip (Illumina) (Chapter 2: method 2.4) by the calculating the mean of all CpG site beta-values between the HT and NT samples.
3.2.3 Global methylation - statistical analysis
An analysis of variance (ANOVA) was used to determine the relationship between global methylation of the kidney/ leukocyte DNA and HTN (IBM SPSS for Windows: version 21), with adjustment made for the antecedent covariates gender, age, and BMI. Five samples [PKPOZ3,
PKPOZ2, PKPOZ67, PKPOZ71, and PKPOZ82] were excluded from the kidney analysis due to miss labelling of samples or miscalculated global 5mC. Similarly, four samples [KID6, PKZAB68,
PKZAB11, and KID46] were excluded from the leukocyte DNA analysis due to missing phenodata. Further, a Pearson’s correlation analysis was used to determine any relationship between global methylation of the kidney/ leukocyte DNA and BP (IBM SPSS for Windows: version 21). A Pearson’s correlation analysis was also utilised to determine if peripheral blood leukocytes provide a surrogate for cross-tissue patterns of methylation in the kidney (IBM SPSS for Windows: version 21).
3.2.4 Genome-wide, loci specific DNA methylation analysis – array based calculation of global methylation
Genome-wide kidney DNA methylation was investigated using the Infinium HumanMethylation
450K BeadChip (Illumina) according to the manufacturer’s guidelines with assistance from the
Australian Genome Research Facility (Perth, Australia). For a description of this methodology see
Chapter 2 methods.
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3.2.4.1 Array based calculation of global methylation – statistical analysis
To further validate the global kidney DNA findings, the median beta-value and m-value for all samples included in the 450K methylation array were calculated using R software. Linear regression was used to investigate the relationship between median global beta-value/ m-value and
HTN diagnosis. This data was also adjusted for the effects from age, BMI, smoker status and gender. Two samples [PKPOZ2, PKPOZ3] (Chapter 2: Figure 2.3) were excluded from the analysis due to mismatched labelling.
3.3 Results
3.3.1 ELISA-based global DNA methylation 5mC percentage in peripheral blood leukocytes
A total of 75 peripheral blood leukocyte samples from TRANSLATE study were included in the global methylation analysis. The clinical characteristics of the leukocyte sample participants are outlined in Table 3.3 and Table 3.4. We used ELISA-based chemistry (Zymo Research, Integrated
Sciences, NSW) to investigate the relationship between EH and mean global 5mC percentage in peripheral blood leukocytes. After adjusting for the ELISA plate, HT leukocyte 5mC percentage was not significantly different to the mean 5mC percentage in the NT leukocyte controls (P=.383)
(Figure 3.1). Further, no correlation was evident between leukocyte global DNA methylation percentage and unadjusted SBP (r=0.06, P=.825) and unadjusted DBP (r=0.10, P=.478) (Figure
3.2), or adjusted SBP (r=0.07, P=.724) and adjusted DBP (r=0.14, P=.279) (Figure 3.3).
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Table 3.3 Leukocyte sample numeric variables – global methylation
Variables Mean ± SD Median (IQR) N
Age (years) 63.2 ± 12.8 62.5 (58.0-70.8) 75
Body mass index (BMI) 27.9 ± 5.1 27.7 (25.3-30.4) 75
SBP (mmHg) 133.8 ± 15.0 133.3 (126.7-141.7) 75
DBP (mmHg) 78.2 ± 10.0 78.3 (73.3-83.3) 75
Waist circumference (cm) 95.7 ± 16.0 96.0 (88.0-104.0) 73 {2}
Adjusted SBP (mmHg) 143.0 ± 20.8 143.3 (133.3-154.1) 75
Adjusted DBP (mmHg) 74.3 ± 11.7 83.3 (78.3-90.0) 75
Creatinine µmol/L 82.2 ± 21.9 76.9 (67.4-89.3) 75
Serum sodium mmol/L 140.6 ± 3.0 141.0 (139.3-142.3) 73 {2}
Serum potassium mmol/L 4.5 ± 0.6 4.5 (4.2-4.7) 72 {3}
Glucose mmol/L 6.0 ± 0.9 5.6 (5.1-6.0) 59 {16}
Four samples excluded due to missing phenodata: KID 46, KID 6, PKZAB 11, PKZAB 68; {x}, excluded/ unknown
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Table 3.4 Leukocyte sample categorical variables – global methylation Variable N
Hypertension % Yes 52 (70%)
Sex (M) 44 (59%)
Diabetes (Y) 14 (19%)
Smoker status 72 {2}
Current smoker 3 (4%)
Ex-smoker 34 (47%)
Non-smoker 35 (49%)
Chronic Kidney Disease (Y) 2 (3%)
Coronary Artery Disease (Y) 15 (20%)
Peripheral Artery Disease (Y) 4 (5%) {2]
Antihypertension medication (Y) 45 (61%)
Lipid lowering treatment (Y) 17 (24%) {2}
Alcohol consumption 72 {2}
Drinker 42 (58%)
Excessive drinker 0 (0%)
Teetotal 30 (42%)
Paternal history of Hypertension (Y) 7 (10%) {2}
Maternal history of Hypertension (Y) 18 (25%) {2}
( x %), percentage of the total analysed population; { x }, excluded/ unknown. Four samples excluded due to missing phenodata: KID 46, KID 6, PKZAB 11, PKZAB 68
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Figure 3.1 Global methylation percentage - normotensive and hypertensive peripheral blood leukocytes. HT leukocyte 5mC percentage is not significantly different to the NT leukocyte control P=.383.
Figure 3.2 Global methylation percentage – unadjusted blood pressure: peripheral blood leukocytes. Global methylation percentage showed no correlation to unadjusted (for medication effects) SBP (r=0.06, P=.825) or unadjusted DBP (r=0.10, P=.478).
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Figure 3.3 Global methylation percentage – adjusted blood pressure: peripheral blood leukocytes. Global methylation percentage showed no correlation to adjusted SBP (r=0.07, P=.724) or adjusted DBP (r=0.14, P=.279). BP is adjusted (+15mmHg systolic, +10mmHg diastolic) to account for the effects of antihypertensive medication.
3.3.2 ELISA-based global DNA methylation 5mC percentage in the kidney
Ninety-four renal samples from the TRANSLATE study were included in the ELISA-based global methylation analysis in the human kidney. The clinical characteristics of the renal sample participants are outlined in Table 3.5 and Table 3.6. After adjustment, the HT kidney 5mC percentage was not significantly different to the mean 5mC percentage in the NT kidney controls
(P=.245) (Figure 3.4). However, a negative correlation was found in the kidney for unadjusted
DBP (r=-0.23, P=.037), but not unadjusted SBP (r=-0.11, P=.374) (Figure 3.5). The significant
61 | Page negative correlation for was not preserved when BP values were adjusted for the effect of antihypertensive medication (+15 mmHg systolic, +10 mmHg diastolic) for both SBP (r=-0.08,
P=.265) and DBP (r=-0.17, P=.068) (Figure 3.6).
Table 3.5 Kidney methylation sample numeric variables – global methylation Variables Mean ± SD Median (IQR) N
Age (years) 63.2 ± 13.8 63.0 (56.0-69.8) 94
Body mass index (BMI) 28.4 ± 5.9 28.1 (24.9-30.8) 94
Systolic blood pressure (mmHg) 140.5 ± 20.3 141.0 (130.0-150.3) 93 {1}
Diastolic blood pressure (mmHg) 86.1 ± 12.0 86.7 (80.0-92.0) 93 {1}
Waist circumference (cm) 97.24 ± 13.5 96.0 (91.50-105.0) 92 {2}
Adjusted systolic blood pressure (mmHg) 149.6 ± 26.7 150.3 (135.0-161.7) 93 {1}
Adjusted diastolic blood pressure (mmHg) 92.1 ± 18.4 91.0 (83.3-101.7) 93 {1}
Creatinine µmol/L 89.4 ± 31.4 86.3 (71.1-102.5) 94
Serum sodium mmol/L 139.8 ± 3.7 140.0 (138.0-141.7) 92 {2}
Serum potassium mmol/L 4.4 ± 0.7 4.3 (4.0-4.7) 91 {3}
Glucose mmol/L 6.0 ± 0.8 5.8 (5.5-6.3) 82 {12}
Two samples excluded due to mismatched phenodata: PKPOZ 2, PKPOZ 3; {x}, excluded/ unknown
Table 3.6 Kidney methylation sample categorical variables – global methylation
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Variable N
Hypertension % Yes 69 (73%)
Sex (M) 64 (68%)
Diabetes (Y) 17 (18%)
Smoker status 93 {1}
Current smoker 12 (13%)
Ex-smoker 42 (45%)
Non-smoker 39 (41%)
Chronic Kidney Disease (Y) 1 (1%)
Coronary Artery Disease (Y) 18 (19%)
Peripheral Artery Disease (Y) 5 (5%) {2}
Antihypertension medication (Y) 56 (60%)
Lipid lowering treatment (Y) 13 (14%) {2}
Alcohol consumption 92 {2}
Drinker 64 (68%)
Excessive drinker 0 (0%)
Teetotal 28 (30%)
Paternal history of Hypertension (Y) 15 (16%) {1}
Maternal history of Hypertension (Y) 24 (26%) {1}
( x %), percentage of the total analysed population; { x }, excluded/ unknown Two samples excluded due to mismatched phenodata: PKPOZ 2, PKPOZ 3
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Figure 3.4 Global methylation percentage - normotensive and hypertensive kidney. After adjustment for the effects of antecedent covariates (age, BMI, gender), the HT kidney 5mC percentage was not significantly different to the mean 5mC percentage in the NT kidney controls (P=.245).
Figure 3.5 Global methylation percentage – unadjusted blood pressure: kidney. A negative correlation trend is evident for unadjusted (for medication effects) SBP (r=-0.11, P=.374) however significance level was not reached. Unadjusted DBP showed a significant negative correlation (r=-0.23, P=.037).
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Figure 3.6 Global methylation percentage – adjusted blood pressure: kidney. Significance level was not reached for the negative correlation trend for 5mC percentage and SBP (r=-0.08, P=.265) and DBP (r=-0.17, P=.068). These BP values are adjusted for the effects of antihypertensive medication (+15mmHg systolic, +10mmHg diastolic).
3.3.3 Methylation array-based global DNA methylation calculation in the kidney
Inconsistent findings from previous studies of global methylation has highlighted the need for a more a reliable 5mC measurement method. To date, calculation of global methylation has not been obtained using array based methylation data. Therefore, to validate the ELISA-based kidney DNA methylation 5mC percentage findings, the median beta-value and M-value for all kidney samples was calculated from data obtained from the Infinium HumanMethylation450 array. No association between HTN diagnosis and median kidney methylation beta-value (P=.850) or M-value (P=.510) was evident. Similar findings were evident for SBP beta-value (P=.074) and M-value (P=.798), adjusted SBP beta-value (P=.199) and M-value (P=.778). Interestingly, for beta-values, a linear
65 | Page relationship between median global methylation percentage and DBP was established (P=.002), however this finding was not transferable for M-values (P=.459). These results were consistent when adjusted BP values were used; adjusted-SBP (beta-value P=.218, M-value P=.703), DBP values (beta-value P=.023, M-value P=.573). It should be noted that all array-based analyses were adjusted for antecedent covariates, BMI, age, smoker status and gender.
3.3.4 Comparative of peripheral blood and kidney DNA methylation 5mC percentage
Patterns of DNA methylation are highly variable across tissue types. To date, majority of studies of methylation in HTN has been conducted on tissues other than the kidney. To assess the applicability of peripheral blood methylation findings to those of the kidney, we investigated the correlation between global DNA methylation 5mC percentages of the two tissue types, where both blood and kidney DNA was available for the same participant (Table 3.7, Table 3.8). No significant correlation was evident between leukocyte and kidney global DNA methylation 5mC percentage
(r=0.11, P=.244) (Figure 3.7).
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Table 3.7 Matching leukocyte and kidney samples used for tissue correlation analyses – numeric variables of interest
Variables Mean ± SD Median (IQR) N
Age (years) 63.9 ± 11.5 63.5 (58.5-73.0) 28
Body mass index (BMI) 28.2 ± 4.2 28.1 (25.5-30.2) 28
Systolic blood pressure (mmHg) 130.4 ± 25.2 132.3 (125.0-142.3) 28
Diastolic blood pressure (mmHg) 80.5 ± 7.9 80.0 (76.0-86.7) 28
Adjusted systolic blood pressure (mmHg) 143.9 ± 10.3 141.9 (135.0-153.1) 28
Adjusted diastolic blood pressure (mmHg) 86.2 ± 7.6 86.7 (80.0-90.2) 28
Table 3.8 Matching leukocyte and kidney samples used for tissue correlation analyses - categorical variables of interest Variable N
Hypertension % Yes 22 (79%)
Sex (M) 20 (71%)
Diabetes (Y) 10 (36%)
Antihypertension medication (Y) 17 (61%)
( x %), percentage of the total analysed population
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Figure 3.7 Global methylation percentage - leukocyte and kidney. No correlation is evident for between leukocyte and kidney 5mC percentage (r=0.11, P=.244). Suggesting that blood may not be a good surrogate tissue for kidney based methylation analyses findings.
3.4 Discussion
3.4.1 Global methylation and hypertension diagnosis
It is evident that a combination of lifestyle factors and genetic predisposition contribute to the development of EH. The purpose of this section of my PhD is to investigate the relationship between global methylation as a surrogate of epigenetic changes in leukocyte and kidney DNA and EH. Unlike previous research findings which report a correlation between global DNA methylation and disease, our findings suggest no relationship between HTN diagnosis and global methylation percentage in both peripheral blood leukocytes and kidney DNA. However, as HTN
68 | Page is tightly linked with both age [154] and BMI [155], it is likely that associations were lost when we adjusted for these covariates. Where the relationship between HT and increasing age can exist without weight gain [154] and overweight and obesity can infer an increased risk of EH in children as young as 6-8 years old [155]. The difficulty with investigating global DNA methylation percentage in EH is discovering correlations that are not due to the effects of age and BMI.
3.4.2 Global methylation and blood pressure
Unlike the multidimensional nature of EH, the relationship between global DNA methylation and
BP, as a singular phenotypical measurement, is more extensively investigated. We found a negative correlation between increasing global methylation percentage in the kidney and DBP reading, however this correlation was not preserved when DBP values were adjusted for the effect of anti-hypertensive medication. This is similar to findings by Smokarek [101] who concluded that the global methylation percentage decreased as BP increases. However, there is conflicting evidence about global DNA methylation and BP. A study by Kim et al [110] found that elevated, not decreased, DNA methylation was positively correlated to CVD and its complications. This conflicting information highlights the need for a more sensitive 5mC detection method.
3.4.3 Array-based calculation of global methylation
In this study, we investigate the sensitivity of 5mC detection using the ELISA method by comparing the ELSA results with those calculated using an array-based method. In most cases,
ELISA manufacturer’s claim that the ELISA kit possesses a discriminating power of 1:1000 when comparing methylated to unmethylated DNA. However, only large changes in DNA methylation
(~1.5-2 fold) can be resolved by this method due to the high level of inter- and intra-assay
69 | Page variability [156]. ELISA based assays are typically prone to high variability (based on quality of the monoclonal antibody and effectivity of wash steps); thus, they are only suitable for the rough estimation of DNA methylation. However, they are quick and easy to perform and provide a cheap method for identification of large changes in global DNA methylation. Futhermore, the Zymo 5mC
ELISA kit is unable to discriminate between 5mC and 5-hydroxymethylcytosine (5hmC) detection. This is of significant consequence as functionally, the production of 5hmC seems to promote gene expression during active demethylation [157]. Our array-based calculation of global methylation found no association between HTN diagnosis and median kidney methylation beta- value or m-value. Similar findings were evident for SBP and adjusted SBP. A correlation was found for DBP, however when the more stringent, m-value was utilised, this relationship was not evident.
3.4.4 Peripheral blood methylation as a surrogate for methylation in kidney
Epigenome-wide studies of HTN focus on modification markers in peripheral blood, since in most cases, the target tissue is not accessible from a significant number of live individuals. However,
DNA methylation can show tissue-specific patterns, which highlights the need for validation of the suitability of peripheral blood findings as an investigative tool of DNA methylation in the HT population. We compare the blood findings to the kidney as an essential link between the kidney and BP control has long been established. Previous cross-transplantation studies have supported a key role for intrinsic functions of the kidney in the pathogenesis of HTN [149, 150]. In this study, we investigate kidney and leukocyte global methylation patterns in HT participants, where both tissue types were available, to determine if methylation patterns in blood closely correlate to methylation patterns in the kidney. Our results show no significant correlation between global
70 | Page methylation patterns in the kidney and peripheral blood, suggesting that blood may not be a good template for measuring global DNA modification mechanisms in the kidney. These findings are in contrast to those of Kato et al [102] who found that blood methylation patterns closely relates to those of liver and adipose; however the findings by Kato refer to loci specific methylation, not global methylation analysis. Our findings can partly be explained by findings by Reinius et al
[158], who demonstrated that global CpG methylation between various whole blood cell lines differed by up to 22%, indicating that whole blood methylation results may be unintelligible. This is of particular importance to our study as the homogenised kidney samples contained a variety of different cell types, and therefore the overall methylation percentage may be unreliable.
3.5 Conclusion and future direction
Our findings demonstrate that in the kidney, but not peripheral blood leukocyte, global DNA methylation is correlated to DBP. However no relationship is evident for HTN diagnosis, regardless of the tissue type. Furthermore, peripheral blood and kidney global DNA methylation patterns are not correlated in both the HT and NT samples, indicating that blood may not be a good surrogate tissue for kidney global methylation studies. Furthermore, interpretation of methylation profiles where multiple cell types are investigated should be done so with great caution, and differences resulting from varying proportions of cell types should be considered. Collectively, these findings highlight the importance of further research on the involvement of loci specific kidney DNA methylation in EH. In the following chapter, the involvement of genome-wide, loci specific DNA methylation in HTN are examined to further understand the mechanisms driving chronic BP elevation in humans.
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Chapter 4. Genome-wide loci specific DNA methylation in essential hypertension
“Science is a beautiful gift to humanity, we should not distort it”
~ A. P. J Abdul Kalam
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Chapter 4. Genome-wide loci specific DNA methylation in essential hypertension 4.0 Abstract
Gene-specific DNA methylation involving several biological pathways related to the pathogenesis of HTN has been discovered. However these studies relied on either, a small number of samples or used only peripheral blood tissue. This section of my PhD contributes to the knowledge of the polygenic form of HTN by highlighting the mechanism responsible for altered gene expression in the kidney and provides several new insights into HTN-related DNA methylation changes in the human kidney. Firstly, we identify individual CpG sites where differential methylation is associated with either HTN diagnosis, or SBP and DBP. We discover novel differential methylation in CpGs not previously associated to BP in the human kidney, among these are sites involved in pathways regulating transport of glucose, organic acids and amine compounds; circadian rhythm; neural development; transport of monocarboxylates; peptidase activity; transcriptional regulation; insulin signalling; cell cycle progression; metabolism; GCPR signalling; nuclear receptors in lipid metabolism and toxicity; and RNA processing. Second, we identify differentially methylated CpG islands that are associated with either HTN diagnosis or well as SBP and DBP. Among these are differentially methylated islands located in the NTRK2,
SLC12A7 and SLC17A7 gene regions, all which have been previously implicated in BP-related studies. In finality, we conduct a correlation analysis of BP-associated, methylated CpG sites in the kidney and pre-existing GWAS findings of BP-associated loci in blood. We found four loci that overlap in the human kidney and in blood methylation studies, including CXXC5, CDK6,
PPP2R5A and ANKRD11; highlighting the cross-tissue applicability of our findings.
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4.1 Introduction
In Chapter 3, global DNA methylation was found to be a possible modulator of DBP in the kidney.
However, a direct relationship between global DNA methylation and HTN was not evident.
Further, the methods for detecting global methylation require more refining. In this section of my thesis, I investigate the role of epigenome-wide, loci specific DNA methylation in the kidney, as a potential driver of BP change in the HT population. Studies identifying multiple genetic loci
(Table 4.1), each with a small contribution to the common polygenic form of HTN, have been uncovered by GWAS in very large number of subjects [102, 103]. The accepted inference from
GWAS is that the genetic code lays the foundation for transcriptomic changes and in turn physiological changes. On the other side of the coin, environmental factors (smoking, diet, chemical exposure) [159] can in turn affect DNA itself [34] in genes relevant to BP. Variation in epigenetic forms of modification may thus explain additional phenotypic variation in BP and provide new clues to the physiological processes influencing its regulation. DNA methylation is one of these epigenetic mechanisms responsible for changes to gene expression, activated by interaction with environmental triggers [102, 160].
The most robust study on the involvement of loci specific DNA methylation in BP regulation comes from a 2017 GWAS [102]. This study identified genetic variants at 12 new loci that correlated to BP modulation. An investigation of the relationship between the sentinel BP single nucleotide polymorphisms (SNPs) with local DNA methylation in 1,904 South Asians (peripheral blood; Illumina Human Methylation 450 Bead Chip array (Erasmus Medical Centre, Rotterdam,
The Netherlands) revealed a two-fold enrichment between sequence variation and DNA methylation. Twenty-eight of the 35 sentinel SNPs were associated with one or more methylation
74 | Page markers [102], suggesting that DNA methylation may lie on the regulatory pathway linking sequence variation and BP. Genes associated with the 12 newly identified SNP loci include those involved with vascular smooth muscle (IGFBP3, KCNK3, PDE3A, PRDM6) and renal function
(ARHGAP24, OSR1, SLC22A7, TBX2) [102].
A more recent, large scale GWAS by Richard et al [103] discovered that heritable methylation sites, independent of known genetic variants, explain an additional 1.4% and 2.0% of the inter- individual variation in SBP and DBP respectively. Although these studies provide tantalising evidence for the role of methylation all were conducted in the easily accessible leukocyte DNA.
Since DNA methylation patterns are highly variable across different tissues [36], utilisation of peripheral blood for non-blood based diseases and phenotypes has not been validated.
Pathophysiologically, the kidney is known as the key organ of BP regulation and one of the most important contributors to HTN. According to the hypothesis put forward by Guyton, over 40 years ago [161, 162], the control of BP in the steady-state and longer-term is critically dependent on renal mechanisms. In fact, almost all monogenic forms of HTN are driven by rare mutations in genes involved in salt handling in the distal nephron. It is therefore crucial to understand kidney
DNA methylation changes that may drive gene expression in kidney and lead to HTN.
Furthermore, as DNA methylation patterns display tissue specificity, it is important to characterise
DNA methylation changes in the kidney itself. Recent genome-wide approaches have discovered that some CpG islands have distinct methylation patterns in various tissues, with the most dramatic changes evident in germ cells and somatic tissues [163-165]. Few studies have addressed this in human somatic tissues, and to date no studies have investigated kidney DNA methylation patterns
75 | Page associated with HTN. In this chapter we hypothesised that genome-wide, locus specific DNA methylation is the fundamental link driving chronic BP elevation in the human kidney. Therefore, here we conducted a genome-wide analysis of DNA methylation changes in the human kidney to identify epigenetic signatures of HTN using the largest collection of apparently healthy human renal tissue.
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Table 4.1 Summary of genome-wide methylation findings in relation to hypertension Reference Tissue Type/ Sample Size Findings
Kato, et Blood, muscle, liver, fat; Discovered to be correlated with BP modulation. al. [20] 320,251 individuals of East Two-fold enrichment discovered between DNA Asian, European and South methylation and sentinel BP SNPs, providing Asian ancestry evidence for DNA methylation role in BP regulation.
Richard et Human: Peripheral blood, The replicated methylation sites are heritable and al. [103] 17,010 individuals of independent of known BP genetic variants, European, African American, explaining an additional 1.4% and 2.0% of the and Hispanic ancestry interindividual variation in SBP and DBP, respectively.
Friso, et Human: Peripheral blood; 25 HSD11B2 gene promoter methylation associated al. [21] with EH; 32 with prednisone with EH onset via disruption to THF/The ratio. therapy Goyal, et Rat: Tissues: brain; 20 MLP Hypomethylation of RAAS system genes such as al. [22 pups, 17 control pups ACE resulting in HTN in offspring.
Wang, et Human: Peripheral blood; 8 PRCP gene hypomethylated in EH, linked to al. [23] EH; 8 control disruption in cleavage of angiotensin II and III
Pei, et al. Tissue: aorta; 6 SHR; 6 WKY Atgr1a gene progressively hypomethylated as [24] control SHR age progressed. Indicating increased expression of Atgr1a in aging SHR.
Riviere, et Rat: Cultured endothelial cells Hypermethylation associated with transcriptional al. [25] from WKY repression of sACE, indicating a role for epigenetics in sACE modulation during HTN.
Lee, et al. Rat: Tissues: aorta, heart; SHR Hypomethylation of Sic2a2 gene lead to [26] and WKY increased expression of NKCC1 which was positively correlated with HTN.
EH: essential hypertension; MLP: maternal low protein; HTN: Hypertension; SHR: spontaneously hypertensive rat, WKY: Wistar Kyoto rat; 5mC: 5-methyl-cytosine; ACE: angiotensin converting enzyme; RAAS: renin-angiotensin-aldosterone system; sACE: somatic ACE; SNPs: single nucleotide polymorphisms; NKCC1: Na+-K+-2Cl_ cotransporter 1; BP: blood pressure.
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4.2 Methods
4.2.1 Participants and sample collection
The genome-wide, loci specific methylation analysis utilised 94 human kidney samples (Table 4.2 and Table 4.3) from the TRANSLATE study. All participants of the TRANSLATE study underwent detailed clinical phenotyping which consisted of documentation of clinical history (via coded questionnaire), basic anthropometry as well as triplicate measures of BP by mercury sphygmomanometer. HTN diagnosis was assigned to individuals who had elevated BP greater than
>140/90 mmHg on at least two separate occasions, and/or were taking antihypertensive medications [117]. The subset of TRANSLATE human renal DNA to be utilised in this section was chosen to ensure a balanced, yet sufficient, representation of hypertensive patients, both medicated and non-medicated. Furthermore, age and gender were balanced between the normotensive and hypertensive populations as much as possible with no histological signs of nephrosclerosis.
Renal samples, consisting of approximately 1 cm3 of kidney tissue, were collected from the healthy
(unaffected by cancer) pole of the kidney immediately post nephrectomy. Kidney DNA was extracted from 96 homogenised renal tissue samples using Qiagen purification kits for total DNA
(Qiagen). Leukocyte DNA was extracted from peripheral blood using QIAamp DNA mini blood extractions kit (Qiagen).
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4.2.2 Genome-wide, loci specific DNA methylation analysis
4.2.2.1 Detection of differentially methylated CpG sites
Genome-wide kidney DNA methylation was investigated using the Infinium HumanMethylation
450K BeadChip (Illumina) with assistance from the Australian Genome Research Facility (Perth,
Australia). Where 1000ng of kidney DNA underwent quality analysis using resolution on a 0.8% agarose gel and Nanodrop assessment. From this, 750ng of high quality kidney DNA underwent bisulphite conversion with the use of the Zymo EZ DNA Methylation Kit (Zymo Research, USA).
Bisuphite converted kidney DNA (4uL, at 50ng/mL) was then hybridised with the Infinium
HumanMethylation450 BeadChip (Illumina). The 450K BeadChip uses two types of Illumina chemistry to provide a comprehensive DNA methylation status of over 485,000 CpG sites spanning 99.9% of Refseq genes (with an average of 17 CpG sites per gene region distributed across the promoter, 5’ untranslated region, first exon, gene body and 3’ untranslated region).
Apart from a vast majority of CpG islands (covered in 96%), the array provides coverage for island shores and the regions flanking them. The arrays were then processed through Illumina iScan to deliver rapid, high resolution imaging of the methylation arrays. DNA methylation is represented as a β-value (0-1) which corresponds to the percentage of methylation at a given CpG site (0%-
100%, respectively).
4.2.2.2 Methylation data pre-processing
For every CpG site interrogated via the 450K array, the intensity of the scanned signal is used to calculate a methylated and non-methylated measurement. The raw IDAT files hold this intensity information which was extracted from the Illumina iScan. These files also contain a detection P-
79 | Page value for every probe, which indicated how well the signal intensity was interpreted (small P values correlating with good probe performance).
4.2.2.2.1 Methylation data preprocessing computational analysis
No probes with a high detection P-value (P = ≥0.5) were detected. Probes located on sex chromosomes were removed as the data set contains both males and females, as well as cross reactive probes which target repetitive sequences or co-hybridize to alternative sequences that are highly homologous to the intended targets and therefore risk generating spurious signals [121].
These cross-reactive probes were removed to reduce the risk of invalid conclusion and lack of validation in downstream analysis. Similarly, sites that overlapped with SNPs located within a probe or within ten base pairs of a probe were excluded, a total of 418,581 CPG probes remained for analysis.
4.2.2.3 Methylation data normalisation
The Illumina 450K array contains two probe types. Type I probes originate from the 450K predecessor, the 27K array and contain two probes in the same colour channel to measure methylated and unmethylated intensities [122]. Type II probes were designed differently, and use one probe and two different colour channels to make the same measurement [122]. The two different probe types generate different signals. Therefore, to avoid probe type specific false positives, identify technical variation, and increase the chances of detecting DNA methylation changes caused by HTN, raw β-values underwent intra-array normalisation.
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4.2.2.3.1 Methylation data normalisation computational analysis
Raw β-values underwent intra-array normalisation using the Dasen method implemented through the Bioconductor wateRmelon package (version 3.6) [123]. The Dasen method also performs between-array normalisation, where the background difference between type I and type II probes for both methylated and unmethylated signal intensities are adjusted prior to quantile normalisation so that chips ran at different time points are comparable The β-value is then generated from the adjusted signal intensities using the equation 1.
Equation 1:
β = methylated probe intensity / methylated probe intensity + unmethylated probe intensity.
All β-values were log-transformed into M-values using the logit function (log2(β/(1-β))). M-values are a more valid alternative for detecting DNA methylation changes, as they eliminate the heteroscedasticity of higher-and lower-end β-value [124]. The M-value is calculated as the log2 ratio of the intensity of the methylated probe versus the unmethylated probe (see equation 2). It should also be noted that logit transformation further reduced type I/ type II probe bias [124].
Equation 2:
*+,(./,123456,7)9: !" = %&'( ) = *+,(./,;<123456,7)9:
4.2.2.4 Methylation data statistical analysis
Differentially methylated individual CpG sites and islands in the HT and NT kidney were determined using regression (RStudio, GNU General Public License), with adjustment made for the antecedent covariates age, BMI, smoker status and gender. Two samples [PKPOZ2, PKPOZ3] were excluded from the analysis due to mismatched labelling (Chapter 2, Figure 2.1).
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4.3 Results
4.3.1 Genome-wide methylation analysis in the hypertensive kidney – CpG sites
We used 94 renal samples from the TRANSLATE study (Table 4.2 and Table 4.3) to investigate the association of EH diagnosis and its co-existent phenotypic traits with DNA methylation at
418,581 CpGs interrogated with the Illumina HumanMethylation450 BeadChip. Methylation levels at one CpG site (SLC17A7) was associated with HTN diagnosis at an adjusted P-value of
<.05; three CpG sites (FUBP1, SLC16A4 and PAPLN) were associated with SBP (adjusted P-value
<.05); 19 CpGs (FUBP1, ZNF526, BBX, PKNOX2, SCG2, PCNXL3, C15orf23, PSMB9, TRIM41,
TALDO1, REEP3, FRG1, VDR, VARS, SPTB, PHTF1, RIOK3, ZFYVE19, DCP2) were associated with DBP (adjusted P-value <.02). Associations of these 23 CpGs (Table 4.4) were adjusted for known confounders to HTN and high BP, being body mass index, smoking status, age and gender.
Full names and biological characterisation of these genes are available in Table 4.5.
4.3.2 Genome-wide methylation analysis in the hypertensive kidney – CpG islands
We also investigated the association of differentially methylated CpG islands with HTN diagnosis and its co-existent phenotypic traits (Table 4.6). Two CpG islands were associated with HTN diagnosis, located in the HCCA2 (P = 1.44E-05) and SLC17A7 (P = 1.46E-05) gene. One differentially methylated CpG island was associated with SBP, located in ILF2 (adjusted P<.03).
Lastly, 11 CpG islands were associated with DBP (adjusted P<.01), covering the ZNF224,
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ZMPSTE24, PIGF/ CRIPT, BCL6B, FUZ, SLC12A7, KIAA0664, NTRK2, SLC45A4 and COG3 genes. Individual CpG sites located within islands are outlined in Table 4.7.
Table 4.2 Kidney methylation sample numeric variables – loci specific, genome-wide methylation
Variables Mean ± SD Median (IQR) N
Age (years) 63.2 ± 13.8 63.0 (56.0-69.8) 94 Body mass index (BMI) 28.4 ± 5.9 28.1 (24.9-30.8) 94 Systolic blood pressure (mmHg) 140.5 ± 20.3 141.0 (130.0-150.3) 93 {1} Diastolic blood pressure (mmHg) 86.1 ± 12.0 86.7 (80.0-92.0) 93 {1} Waist circumference (cm) 97.24 ± 13.5 96.0 (91.50-105.0) 92 {2} Adjusted systolic blood pressure (mmHg) 149.6 ± 26.7 150.3 (135.0-161.7) 93 {1} Adjusted diastolic blood pressure (mmHg) 92.1 ± 18.4 91.0 (83.3-101.7) 93 {1} Creatinine µmol/L 89.4 ± 31.4 86.3 (71.1-102.5) 94 Serum sodium mmol/L 139.8 ± 3.7 140.0 (138.0-141.7) 92 {2} Serum potassium mmol/L 4.4 ± 0.7 4.3 (4.0-4.7) 91 {3} Glucose mmol/L 6.0 ± 0.8 5.8 (5.5-6.3) 82 {12} Two samples excluded due to mismatched phenodata: PKPOZ 2, PKPOZ 3; {x}, excluded/ unknown
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Table 4.3 Kidney methylation sample categorical variables - loci specific, genome-wide methylation Variable N
Hypertension % Yes 69 (73%)
Sex (M) 64 (68%)
Diabetes (Y) 17 (18%)
Smoker status 93 {1}
Current smoker 12 (13%)
Ex-smoker 42 (45%)
Non-smoker 39 (41%)
Chronic Kidney Disease (Y) 1 (1%)
Coronary Artery Disease (Y) 18 (19%)
Peripheral Artery Disease (Y) 5 (5%) {2}
Antihypertension medication (Y) 56 (60%)
Lipid lowering treatment (Y) 13 (14%) {2}
Alcohol consumption 92 {2}
Drinker 64 (68%)
Excessive drinker 0 (0%)
Teetotal 28 (30%)
Paternal history of Hypertension (Y) 15 (16%) {1}
Maternal history of Hypertension (Y) 24 (26%) {1}
( x %), percentage of the total analysed population; { x }, excluded/ unknown Two samples excluded due to mismatched phenodata: PKPOZ 2, PKPOZ 3
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Table 4.4 23 CpG sites associated with essential hypertension and BP
Location Location relative to Adj P- CpG probe relative to Fold P-Value Associated Chr Position Gene CpG β Value name gene change phenotype island cg23409374 19 49934742 SLC17A7 Body Island 4.812 0.700 <.05 8.24E-08 HTN cg08228724 1 78444087 FUBP1 Body N-Shore 6.098 1.008 <.05 7.21E-08 SBP cg17749509 1 110934279 SLC16A4 TSS1500 Open Sea 5.265 0.992 <.05 1.68E-07 SBP cg10300255 14 73706367 PAPLN 5’UTR Island 4.661 1.007 <.05 3.11E-07 SBP cg08228724 1 78444087 FUBP1 Body N_Shore 5.776 1.014 <.02 1.69E-07 DBP cg23324123 19 42730293 ZNF526 Body Island 5.559 0.991 <.02 2.10E-07 DBP cg15208284 3 107242730 BBX 5'UTR Island 5.205 1.009 <.02 3.02E-07 DBP cg22956116 11 125034643 PKNOX2 5'UTR N_Shore 5.051 1.010 <.02 3.53E-07 DBP cg20101489 2 224467074 SCG2 5'UTR OpenSea 5.045 1.015 <.02 3.55E-07 DBP cg07707380 11 65396146 PCNXL3 Body OpenSea 4.965 0.993 <.02 3.86E-07 DBP cg01301233 15 40675452 C15orf23 Body Island 4.865 1.009 <.02 4.27E-07 DBP TSS1500; cg16501436 6 32821039 PSMB9; TAP1 Island 4.862 1.008 <.02 4.28E-07 DBP 1stExon cg13228972 5 180651008 TRIM41 1stExon Island 4.739 1.008 <.02 4.85E-07 DBP cg09351035 11 748060 TALDO1 Body S_Shore 4.729 1.009 <.02 4.91E-07 DBP cg01109287 10 65281299 REEP3 5'UTR Island 4.666 1.010 <.02 5.23E-07 DBP cg01543582 4 190862539 FRG1 Body S_Shore 4.629 1.009 <.02 5.43E-07 DBP cg23654431 12 48298967 VDR TSS200 Island 4.384 1.009 <.02 6.98E-07 DBP
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cg21368481 6 31747210 VARS Body OpenSea 4.291 0.992 <.02 7.68E-07 DBP cg26210003 14 65290110 SPTB TSS1500 OpenSea 4.260 1.007 <.02 7.92E-07 DBP cg00419759 1 114301964 PHTF1 TSS200 Island 4.260 1.008 <.02 7.93E-07 DBP cg00306421 18 21033656 RIOK3 Body Island 4.249 1.009 <.02 8.01E-07 DBP ZFYVE19; 5'UTR; cg15059511 15 41099719 Island 4.248 1.009 <.02 8.02E-07 DBP DNAJC17 TSS200 cg18179833 5 112312951 DCP2 Body Island 4.247 1.009 <.02 8.03E-07 DBP Position is Hg19. β – coefficient from linear regression models. Abbreviations: Chr, chromosome; CpG, cytosine-phosphate-guanine; TSS1500, 1500 base pair to the transcription start site; 5’UTR, 5-prime untranslated region; body, body of gene; HTN, hypertension diagnosis; SBP, systolic blood pressure; DBP, diastolic blood pressure; Island, located within a CpG island; OpenSea, not associated with any CpG islands (lone CpG site); N_Shore, north shore of CpG island (<2kbp from island); S_Shore, south shore of CpG island (<2kbp from island)
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Table 4.5 Full names and biological characterisation of genes associated with the 23 differentially methylated CpG sites in hypertension BP
Kidney Cellular Gene Gene predominant Molecular function Biological Primary role Chr Gene description component symbol biotype expression (GO) process (GO) (OMIM) (GO) (HPA) L-glutamate transmembrane transporter activity, integral neural retina inorganic phosphate component of development, transmembrane plasma Solute Carrier transport, ion protein transporter activity, membrane, Glutamate SLC17A7 19 Family 17 No transport, sodium coding extracellularly glutamate- membrane, transport Member 7 ion transport, gated chloride channel synaptic phosphate ion activity, symporter vesicle, cell transport activity, sodium: junction inorganic phosphate symporter activity transcription, DNA-templated, regulation of DNA binding, single- transcription, stranded DNA binding, Far Upstream DNA-templated, biology and Protein DNA binding Nucleus, FUBP1 1 Element Binding No transcription by pathology of coding transcription factor nucleoplasm Protein 1 RNA polymerase oligodendrocytes activity, RNA binding, II, positive protein binding regulation of gene expression
monocarboxylic acid integral Solute Carrier monocarboxylic catalyzes the Protein transmembrane component of SLC16A4 1 Family 16 Yes acid transport, transport of coding transporter activity, plasma Member 4 transport monocarboxylates symporter activity membrane
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Kidney Cellular Gene Gene predominant Molecular function Biological Primary role Chr Gene description component symbol biotype expression (GO) process (GO) (OMIM) (GO) (HPA) nonenzymatic proteolysis, papilin influences serine-type negative cell Papilin, endopeptidase inhibitor regulation of rearrangements Protein Proteoglycan Like activity, peptidase peptidase activity, extracellular and modulates PAPLN 14 No coding Sulfated activity, zinc ion binding, negative region metalloproteinase Glycoprotein peptidase inhibitor regulation of s during activity endopeptidase development, activity turnover, and repair
FUBP1 1 As described above
Zinc Finger Protein DNA binding, metal ion transcription, transcriptional ZNF526 19 Protein 526, No nucleus coding binding DNA-templated regulation KIAA1951 transcription, DNA-templated, necessary for cell nucleus, Protein BBX, HMG-Box regulation of cycle progression BBX 3 No DNA binding nucleoplasm, coding Containing transcription, from G1 to S cytosol DNA-templated, phase bone development
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Kidney Cellular Gene Gene predominant Molecular function Biological Primary role Chr Gene description component symbol biotype expression (GO) process (GO) (OMIM) (GO) (HPA)
RNA polymerase II regulation of nucleus, regulatory region transcription, nucleoplasm, sequence-specific sequence-specific DNA DNA-templated, cytoplasm, Protein PBX/Knotted 1 DNA binding and PKNOX2 11 No binding, DNA binding, regulation of actin coding Homeobox 2 actin monomer actin monomer binding, transcription by cytoskeleton, binding sequence-specific DNA RNA polymerase microtubule binding, actin filament II cytoskeleton binding
MAPK cascade, extracellular angiogenesis, region, negative extracellular involved in regulation of space, chemotaxis of cytokine activity, protein endothelial cell endoplasmic monocytes and Protein SCG2 2 Secretogranin II No binding, chemoattractant proliferation, reticulum endothelial cells coding activity positive lumen, and in regulation regulation of secretory of endothelial cell endothelial cell granule, proliferation proliferation, dense core inflammatory granule response
integral Protein Pecanex Homolog PCNXL3 11 No - - component of - coding 3 membrane
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Kidney Cellular Gene Gene predominant Molecular function Biological Primary role Chr Gene description component symbol biotype expression (GO) process (GO) (OMIM) (GO) (HPA)
mitotic sister chromosome, chromatid kinetochore- centromeric Kinetochore segregation, cell microtubule region, Protein Localized protein cycle, spindle interaction for C15orf23 15 No kinetochore, coding Astrin/SPAG5 binding organization, faithful condensed Binding Protein chromosome chromosome chromosome segregation, cell segregation kinetochore, division spindle pole,
proteasome complex,
nucleus, tightly linked nucleoplasm, cluster of endopeptidase activity, MAPK cascade, cytoplasm, interferon- Proteasome threonine-type protein cytosol; inducible genes Subunit Beta 9; endopeptidase activity, polyubiquitinatio within the MHC protein binding, n, stimulatory C- mitochondrio PSMB9; with an essential Protein Transporter 1, peptidase activity, type lectin n, 6 No role in antigen TAP1 coding ATP Binding hydrolase activity; receptor signaling endoplasmic processing; Cassette pathway, immune reticulum,
Subfamily B ATP binding, peptide system processes; endoplasmic translocates Member transporter ATPase reticulum peptides from the activity adaptive immune membrane, cytosol to MHC response microtubule class I molecules organizing in the ER center, membrane
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Kidney Cellular Gene Gene predominant Molecular function Biological Primary role Chr Gene description component symbol biotype expression (GO) process (GO) (OMIM) (GO) (HPA) protein binding, zinc ion intracellular, binding, transferase nucleus, Protein Tripartite Motif TRIM41 5 No activity, identical protein - nucleolus, - coding Containing 41 binding, metal ion cytoplasm, binding nuclear body
carbohydrate metabolic
sedoheptulose-7- process, xylulose nucleus, phosphate:D- biosynthetic nucleoplasm, glyceraldehyde-3- process, fructose cytoplasm, key enzyme of the Protein phosphate 6-phosphate TALDO1 11 Transaldolase 1 No cytosol, pentose phosphate coding glyceronetransferase metabolic intracellular pathway activity, protein binding, process, pentose- membrane- transferase activity, phosphate shunt, bounded carbohydrate binding, pentose- organelle monosaccharide binding phosphate shunt, non-oxidative branch endoplasmic microtubule- reticulum nuclear envelope binding protein membrane, Receptor organization, cell required to ensure Protein microtubule, REEP3 10 Accessory Protein No - cycle, mitotic proper cell coding membrane, 3 nuclear envelope division and integral reassembly, cell nuclear envelope component of division reassembly membrane
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Kidney Gene Cellular Gene predominant Molecular function Biological Primary role Chr Gene description component symbol biotype expression (GO) process (GO) (OMIM) (GO) (HPA)
mRNA splicing, nucleus, over-expression via spliceosome, spliceosomal leads to abnormal RNA binding, actin rRNA processing, Protein FSHD Region complex, alternative FRG1 4 No binding, protein binding, mRNA coding Gene 1 nucleolus, splicing of actin filament binding processing, cytoplasm, specific pre- muscle organ Cajal body mRNAs development, RNA splicing
negative
regulation of nucleus, transcription by nucleoplasm, intracellular DNA binding, RNA polymerase cytoplasm, hormone receptor contributes to DNA II, cell receptor that specifically Protein Vitamin D binding transcription morphogenesis, VDR 12 No complex, binds coding Receptor factor activity, steroid skeletal system RNA 1,25(OH)2D3 and hormone receptor development, polymerase II mediates its activity, nuclear receptor transcription, transcription effects activity, protein binding DNA-templated, factor regulation of complex transcription, DNA-templated
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Kidney Gene Cellular Gene predominant Molecular function Biological Primary role Chr Gene description component symbol biotype expression (GO) process (GO) (OMIM) (GO) (HPA)
Translation, tRNA aminoacylation for protein aminoacyl-tRNA editing translation, valyl- neurodevelopmen activity, aminoacyl- tRNA cytoplasm, tal disorder with Protein Valyl-TRNA tRNA ligase activity, aminoacylation, VARS 6 No mitochondrio microcephaly, coding Synthetase valine-tRNA ligase regulation of n, cytosol seizures, and activity, protein binding, translational cortical atrophy ATP binding fidelity, aminoacyl-tRNA metabolism involved in translational fidelity
MAPK cascade, actin binding, Ras ER to Golgi cytoplasm, anemia, neonatal guanyl-nucleotide vesicle-mediated Golgi hemolytic, fatal Protein Spectrin Beta, exchange factor activity, transport, apparatus, and near-fatal, SPTB 14 No coding Erythrocytic structural constituent of cytoskeleton cytosol, Elliptocytosis-3, cytoskeleton, protein organization, cytoskeleton, Spherocytosis, binding, ankyrin binding axon guidance, cell cortex type 2 actin filament capping
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Kidney Gene Cellular Gene predominant Molecular function Biological Primary role Chr Gene description component symbol biotype expression (GO) process (GO) (OMIM) (GO) (HPA) nucleus, endoplasmic transcription, Putative reticulum, transcriptional DNA binding, DNA DNA-templated, Protein Homeodomain cis-Golgi regulator that PHTF1 1 No binding transcription regulation of coding Transcription network, control a variety factor activity transcription, Factor 1 integral of cell fates DNA-templated component of membrane immune system RIO Kinase 3 process, rRNA protein serine/threonine processing, cytoplasm, SudD (Suppressor kinase activity, protein protein cytosol, Protein RIOK3 18 Of BimD6, No binding, ATP binding, phosphorylation, preribosome, - coding Aspergillus kinase activity, chromosome small subunit Nidulans) transferase activity segregation, precursor Homolog phosphorylation
cell cycle, abscission, cytoplasm, Zinc Finger protein binding, lipid negative centrosome, Members of the FYVE-Type binding, regulation of microtubule HSP40 family, Containing 19; phosphatidylinositol-3- cytokinesis, cell organizing ZFYVE19; such as Protein phosphate binding, metal division; center, 15 No DNAJC17, are DNAJC17 coding DnaJ Heat Shock ion binding; cytoskeleton, involved in Protein Family negative midbody; transcriptional (Hsp40) Member nucleic acid binding, regulation of regulation C17 RNA binding transcription by nucleus, RNA polymerase cytoplasm II,
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Kidney Gene Cellular Gene predominant Molecular function Biological Primary role Chr Gene description component symbol biotype expression (GO) process (GO) (OMIM) (GO) (HPA) nuclear- transcribed mRNA catabolic process, nonsense- mediated decay key component of deadenylation- RNA binding, 5-3 an mRNA- dependent exoribonuclease activity, P-body, decapping decapping of protein binding, nucleus, complex required Protein Decapping nuclear- DCP2 5 No hydrolase activity, nucleoplasm, for removal of the coding MRNA 2 transcribed exoribonuclease activity, cytoplasm, 5-prime cap from mRNA, mRNA producing 5- cytosol mRNA prior to its catabolic process, phosphomonoesters degradation from negative the 5’UTR regulation of telomere maintenance via telomerase, regulation of mRNA stability Position is Hg19. Abbreviations: Chr, chromosome; CpG, cytosine-phosphate-guanine; 5’UTR, 5-prime untranslated region; HTN, hypertension diagnosis; SBP, systolic blood pressure; DBP, diastolic blood pressure; mRNA, messenger; DNA, deoxyribonucleic acid; rRNA, ribosomal RNA; ER, endoplasmic reticulum; tRNA, transfer RNA; HPA, human protein atlas; GO, gene ontology; MHC, major histocompatibility complex
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Table 4.6 Differentially methylated renal CpG islands associated with hypertension and BP
CpG island Chr UCSC gene Contains β Fold Adjusted P-Value Associated name CpGs in change P-Value phenotype Enhancer chr11:1715347-1715607 11 HCCA2 TRUE 2.912 0.800 <.20 1.44E-05 HTN chr19:49934316-49934868 19 SLC17A7 - 2.905 1.062 <.20 1.46E-05 HTN chr2:121492857-121495234 2 - - 2.605 1.002 <.03 1.07E-06 SBP chr19: 44598374-44598854 19 ZNF224 - 7.638 1.006 <.01 1.08E-08 DBP chr1:40723796-40724010 1 ZMPSTE24 - 3.356 1.006 <.01 8.42E-07 DBP chr2:46843650-46844332 2 PIGF/ CRIPT - 2.737 1.004 <.01 1.59E-06 DBP chr17:6925400-6927527 17 BCL6B TRUE 2.371 0.997 <.01 2.30E-06 DBP chr19:50316211-50316469 19 FUZ - 2.239 1.003 <.01 2.41E-06 DBP chr6:111279644-111280210 6 GTF3C6 - 2.293 1.000 <.01 2.50E-06 DBP chr5:1085140-1085847 5 SLC12A7 - 2.096 0.994 <.01 3.06E-06 DBP chr17:2600975-2601980 17 KIAA0664 - 1.824 0.997 <.01 4.04E-06 DBP chr9:87283178-87285704 9 NTRK2 - 1.814 1.004 <.01 4.08E-06 DBP chr8:142227937-142229160 8 SLC45A4 - 1.798 0.997 <.01 4.15E-06 DBP chr13:46038953-46039681 13 COG3 - 1.756 1.003 <.01 4.33E-06 DBP Position is Hg19. β – coefficient from linear regression models. Abbreviations: Chr, chromosome; CpG, cytosine-phosphateguanine; HTN, hypertension diagnosis; SBP, systolic blood pressure; DBP, diastolic blood pressure; S_Shore, south shore of CpG island (<2kbp from island); N_north shore of CpG island (<2kbp from island); island, located within island; S_shelf, located <2kbp from island south shore; N_shelf, located <2kbp from island north shore.
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Table 4.7 Individual CpG sites located within differentially methylated islands – associated with hypertension and blood pressure
Location of CpG CpG island identification CpG site UCSC gene symbol UCSC gene name Chr Enhancer relative to island cg01467630 HCCA2 N_Shore TRUE cg09004134 HCCA2 N_Shore cg11271329 HCCA2 YY1 Associated Island TRUE cg16914552 HCCA2 Protein 1/ N_Shore TRUE cg19749898 HCCA2 Hepatocellular N_Shelf chr11:1715347-1715607 11 cg22755008 HCCA2 Cancinoma- Island TRUE cg00490097 HCCA2; KRTAP5-6 Associated Protein S_Shelf cg05096964 HCCA2 2 N_Shore TRUE cg09750643 HCCA2; KRTAP5-6 S_Shelf cg26745757 HCCA2 Island TRUE
cg02288564 SLC17A7 Island Solute Carrier cg23409374 SLC17A7 Island Chr19:49934316-49934868 Family 17 Member 19 cg03282345 SLC17A7 Island 7 cg18150383 SLC17A7 N_Shore
cg04882233 Island cg05366156 Island cg07154861 Island cg08572016 Island cg12620942 Island cg13467765 Island chr2:121492857-121495234 2 cg14598373 Island cg24740509 Island cg25432992 Island cg02381820 Island cg03575764 Island cg09417299 Island
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CpG island identification CpG site UCSC gene symbol UCSC gene name Chr Location of CpG Enhancer relative to island cg09925014 Island cg13010161 N_Shelf cg14123232 Island chr2:121492857-121495234 cg18210026 2 Island cg20219891 S_Shore cg25371590 S_Shore cg26373051 N_Shore
cg10646633 ZNF224 Island cg23041193 ZNF224 Island cg25952543 ZNF224 N_Shore cg26489638 ZNF224 Island cg26649251 ZNF224 Island cg01508027 ZNF224 S_Shelf cg03238595 ZNF224 Island Zinc Finger Protein chr19:44598374-44598854 cg03486540 ZNF224 19 Island 224 cg03600886 ZNF224 Island cg12477902 ZNF224 S_Shore cg18244544 ZNF224 N_Shore cg18728325 ZNF224 Island cg22012835 ZNF224 N_Shore cg23986727 ZNF224 N_Shore cg24206978 ZNF224 Island
cg03982820 ZMPSTE24 Island chr1:40723796-40724010 cg09795680 ZMPSTE24 Zinc Island cg16044925 ZMPSTE24 Metalopeptidase 1 Island cg16418374 ZMPSTE24 STE24 Island cg25275331 ZMPSTE24 Island
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CpG island identification CpG site UCSC gene symbol UCSC gene name Chr Location of CpG Enhancer relative to island chr1:40723796-40724010 cg24615751 ZMPSTE24 1 Island
cg13172254 PIGF; CRIPT Island cg18454701 CRIPT; PIGF Island Phosphatidylinosito cg24868028 PIGF; CRIPT S_Shore l Glycan Anchor cg25653703 PIGF; CRIPT Island Biosynthesis Class cg00441231 PIGF; CRIPT Island F/ CXXC Repeat cg01091258 PIGF; CRIPT S_Shore Containing chr2:46843650-46844332 cg03971247 PIGF 2 N_Shelf Interactor Of PDZ3 cg05265562 CRIPT S_Shelf Domain cg06654134 CRIPT; PIGF S_Shore B Cell cg08813236 CRIPT; PIGF Island CLL/Lymphoma cg17456231 PIGF; CRIPT N_Shore 6B cg21493583 PIGF; CRIPT N_Shore cg25246012 CRIPT; PIGF S_Shore
cg03738253 BCL6B Island cg06700146 BCL6B Island cg10137837 BCL6B Island cg21040575 MIR497; MIR195 N_Shelf cg23925513 N_Shore cg24720583 BCL6B B Cell Island chr17:6925400-6927527 cg00641210 BCL6B CLL/Lymphoma Island cg02329670 MIR497; MIR195 6B N_Shelf cg03693706 BCL6B S_Shore cg04430582 MIR195; MIR497 N_Shelf cg05560728 BCL6B N_Shore TRUE cg07073814 BCL6B S_Shelf
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Location of CpG CpG island identification CpG site UCSC gene symbol UCSC gene name Chr Enhancer relative to island cg07139087 BCL6B Island cg08786079 BCL6B S_Shelf cg10805819 N_Shore cg11263011 MIR195; MIR497 B Cell N_Shelf chr17:6925400-6927527 cg11353035 BCL6B CLL/Lymphoma Island cg14392652 BCL6B 6B Island cg15165676 BCL6B Island cg22975671 BCL6B Island
cg11398523 FUZ Island cg19763319 FUZ Island cg21712019 FUZ Island cg22708738 FUZ Island cg23348158 FUZ S_Shore Fuzzy Planar Cell cg01139412 FUZ S_Shore chr19:50316211-50316469 Polarity Protein 19 cg02784848 FUZ S_Shore
cg07452097 FUZ S_Shore cg08288330 FUZ N_Shore cg10166205 FUZ S_Shore cg10971790 FUZ Island cg23307385 FUZ S_Shore
cg00531924 GTF3C6 Island cg00858015 GTF3C6 Island cg02930763 General N_Shelf chr6:111279644-111280210 cg04140496 GTF3C6 Transcription N_Shore 6 cg12072757 GTF3C6 Factor IIIC Subunit Island cg18659022 GTF3C6 6 Island cg18873593 GTF3C6 Island cg00894869 GTF3C6 S_Shore
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CpG island identification CpG site UCSC gene symbol UCSC gene name Chr Location of CpG Enhancer relative to island cg02359132 GTF3C6 N_Shore cg05449607 GTF3C6 N_Shore cg06234342 GTF3C6 General N_Shore chr6:111279644-111280210 cg06351970 GTF3C6 Transcription Island 6 cg14801546 GTF3C6 Factor IIIC Subunit S_Shelf cg18710383 GTF3C6 6 Island cg25531478 GTF3C6 N_Shore cg26219797 GTF3C6 Island
cg04380229 SLC12A7 Solute Carrier N_Shore chr5:1085140-1085847 Family 12 Member 5 cg12199905 SLC12A7 7 Island
cg08409553 KIAA0664 Island cg10479243 KIAA0664 N_Shore cg11990813 KIAA0664 S_Shore cg12035564 KIAA0664 N_Shore cg20306425 KIAA0664 N_Shore Clustered cg01132515 KIAA0664 Island Mitochondria chr17:2600975-2601980 cg05389310 KIAA0664 17 N_Shore Homolog cg12994911 KIAA0664 Island
cg13051028 KIAA0664 Island cg16575427 KIAA0664 S_Shore cg20201957 KIAA0664 S_Shore cg20243898 KIAA0664 S_Shore cg21733020 KIAA0664 N_Shore
cg13723118 NTRK2 Neurotrophic Island chr9:87283178-87285704 cg13965062 NTRK2 Receptor Tyrosine 9 Island cg01009697 NTRK2 Kinase 2 Island
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CpG island identification CpG site UCSC gene symbol UCSC gene name Chr Location of CpG Enhancer relative to island cg01292475 NTRK2 Island cg03628748 NTRK2 Island cg08470639 NTRK2 Island Neurotrophic cg09539438 NTRK2 Island chr9:87283178-87285704 Receptor Tyrosine 9 cg09926027 NTRK2 Island Kinase 2 cg13504245 NTRK2 N_Shore cg22402007 NTRK2 N_Shore
cg10555010 SLC45A4 N_Shore cg16341495 SLC45A4 Island cg18190534 SLC45A4 N_Shelf cg02607124 SLC45A4 Solute Carrier N_Shore cg04685302 SLC45A4 Family 45 Member Island chr8:142227937-142229160 8 cg06588735 SLC45A4 4 N_Shore cg13560841 SLC45A4 Island cg20080616 SLC45A4 N_Shore cg24855956 SLC45A4 N_Shelf cg26441120 SLC45A4 N_Shore
cg00603717 COG3 Island cg05597554 COG3 Island cg24452930 COG3 Island cg02070564 COG3 Island cg07439474 COG3 Island Component Of cg07530947 COG3 S_Shelf chr13:46038953-46039681 Oligomeric Golgi 13 cg09002231 N_Shelf Complex 3 cg10486909 COG3 N_Shore cg15201599 COG3 Island cg18379624 COG3 N_Shore cg18580551 COG3 S_Shore cg27523951 COG3 N_Shore
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Table 4.7 Individuals CpG sites located within differentially methylated renal CpG islands associated with hypertension and blood pressure. Abbreviations: S_Shore, south shore of CpG island (<2kbp from island); N_north shore of CpG island (<2kbp from island); island, located within island; S_shelf, located <2kbp from island south shore; N_shelf, located <2kbp from island north shore.
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4.3.3 Replication analysis of blood pressure-associated methylated CpG sites in the kidney and in blood
We used data obtained from the kidney methylation array with matching BP methylation data obtained from a meta-analysis conducted in the blood of 17,010 individuals of mixed ancestry [103]. Of the 126
CpG sites found by Richard et al [103], 4 overlap with loci that are differentially methylated in the kidney and associated with BP (Table 4.8). These include CXXC Finger Protein 5 (CXXC5), involved in chromatin regulation and acetylation; Cyclin Dependent Kinase 6 (CDK6), important for cell cycle progression; Protein Phosphatase 2 Regulatory Subunit B'Alpha (PPP2R5A), responsible for negative regulation of cell growth and division and have been implicated in dilated cardiomyopathy; and
Ankyrin Repeat Domain 11 (ANKRD11), a histone modification regulator.
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Table 4.8 Peripheral blood replicated, blood pressure-associated, differentially methylated CpGs in the human kidney. UCSC β β CpG Adj P- CpG P- Adj P- gene Chr Position (CpG P-value Chr Position (CpG number value number value value symbol site) site) TRANSLATE kidney samples Relative blood samples
cg22885332 139034002 CXXC5 cg00906476 5 139046200 -0.414 4.91E-05 <.20 5 -0.414 4.91E-05 <.20 cg04104695 139058749
cg06688763 922382017 CDK6 cg02689293 7 92464971 1.493 1.36E-05 <.05 7 1.493 1.36E-05 <.05 cg15261712 92238248
PPP2R5A cg05346855 1 212460169 3.023 2.81E-06 <.05 cg12417775 1 212463238 3.023 2.81E-06 <.05
ANKRD11 cg08130612 16 89350233 0.069 5.94E-05 <.05 cg05288253 16 89552259 0.069 5.94E-05 <.05
Position is Hg19. β – coefficient from linear regression models. Abbreviations: Chr, chromosome; CpG, cytosine-phosphate-guanine; FDR, false discovery rate.
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4.4 Discussion
In Chapter 3 we investigate the association of global methylation with chronic BP elevation. Chapter
4 focuses on the relationship between genome-wide, loci specific DNA methylation and chronic BP elevation. This section of my PhD contributes to the knowledge of the polygenic form of HTN by highlighting a potential mechanism responsible for altered gene expression in the kidney and provides several new insights into HTN-related DNA methylation changes in the human kidney.
Gene-specific DNA methylation involving several biological pathways related to the pathogenesis of
HTN has been discovered. These include altered methylation of the RAAS genes, AT1b [166, 167],
AT1aR [108], ACE [55, 82, 168], ECE-1c [169] and ADD1 [170]; renal sodium retention system
(RSRS) genes, HSD11B2 [106, 171, 172], NKCC1 [83, 84]; and sympathetic nervous system (SNS) genes, NET [109] and FABP3 [173]. Altered DNA methylation of the glucokinase gene, GCK, is also significantly associated with increased risk of onset of EH [174]. Genome-wide methylation analysis investigates methylation in all promoter and non-promoter CpGs. CpG sites associated with the SULF1,
PRCP, NEUROG1, PITPNA, SLC26A10, CDC34, C9orf95, YWHAQ, SIRT7 and CLDN5 genes were discovered in a genome-wide investigation of peripheral blood leukocytes in eight young, HT, African males and eight age matched controls [107]. Similarly, CpGs associated with the FZD7M, LRP,
TTBK1, USFP1, SYCE1, NDUSF8, ZNF540 and ABCG4 genes were discovered in a methylation microarray of six HT and four controls [175]. A recent study investigating methylation of BP loci, conducted in 4,513 European individuals identified four (TAF1B-YWHAQ, ZMIZ1, CPT1A, SLC1A5)
CpG loci with a relationship to BP [103]. However, these studies relied on either a small number of samples or used only peripheral blood tissue.
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4.4.1 Genome-wide methylation analysis in the hypertensive kidney – CpG sites
Firstly, we identify CpG sites where differential methylation is strongly associated with HTN diagnosis as well as SBP and DBP. A large portion of these CpG sites have not been identified in BP related methylation investigations before, and many of them appear to lie in regions responsible for transcriptional regulation. Our analysis was focused primarily on the identification of the genome-wide
HTN-associated differential DNA methylation in the kidney. Our findings highlight key changes to
CpG sites located in pathways involving transport of glucose, organic acids and amine compounds; circadian rhythm; neural development; transport of monocarboxylates; peptidase activity; transcriptional regulation; insulin signalling; cell cycle progression; metabolism; GCPR signalling; nuclear receptors in lipid metabolism and toxicity; and RNA processing.
4.4.2 Genome-wide methylation analysis in the hypertensive kidney – CpG islands
Second, we identify differentially methylated CpG islands that are associated with HTN diagnosis as well as SBP and DBP. Of particular interest is the methylation of the CpG island located within the
NTRK2 gene region. NTRK2 is a member of the neurotrophic tyrosine receptor kinase (NTRK) family with bias expression in the kidney, brain, gall bladder and adipose. Upon neurotrophin binding, NTRK2 phosphorylates itself and members of the MAPK pathway, leading to cellular differentiation [176].
However, a study by Thameem et al [176] identified SNP variations within the NTRK2 gene exhibited associations with renal function. After adjustment for gender, age, diabetes, BP and BP medication,
NTRK2 variants were identified as a possible regulator of eGFR.
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Identification of genes responsible for cation-coupled Cl- transport occurred in the early 1990s, in the rectal gland of the shark Squalus acanthias [177] and the urinary bladder of the winter flounder
Pseudopleuronectes americanus [178]. These fish expressed high activities of Na-K-2Cl and Na-Cl transporters respectively, which ultimately lead to their identification. Subsequently, the electroneutral cation-chloride-coupled cotransporter gene family (SLC12) was identified. This nine-member gene family encompasses two major branches, one includes two bumetanide-sensitive Na+-K+-2Cl- cotransporters and the thiazide sensitive Na+-Cl- cotransporter. In this branch, two genes (SLC12A1 and SLC12A3) exhibit kidney-specific expression. The second branch contains four genes (SLC12A4-
7) which encode electroneutral K+-Cl- cotransporters [179]. These four genes are co-expressed in majority of tissues and have roles related to cell volume regulation, transepithelial salt transport, hearing and function of the peripheral nervous system [179]. Differential methylation related to DBP was found in the CpG island associated with the SLC12A7 gene in renal tissue. Suggesting that methylation at this island has a significant role to play in the regulation of DBP.
Numerous aspect of human physiology, such as temperature regulation, hormonal control, autonomic tone and metabolism are controlled by circadian rhythms, which are regulated by the suprachiasmatic nucleus [180]. Imbalance in the circadian system contributes to the development of disease, such as obesity, metabolic syndrome diabetes and CVD [181]. For CVD and HTN it is known that BP is influenced by circadian rhythms independent of sleep/wake and fasted/feeding cycles [182].
Futhermore, recent studies emphasise a role for peripheral clock genes in cellular processes that regulate BP. In the kidney PER1 is associated with regulation of renal epithelial sodium channels [183], while other clock genes have also been implicated in vascular endothelial function (Per2) [184] and thrombogenesis (BMAL1, CLOCK, and NPAS2) [185]. Solute Carrier Family 17 Member (SLC17A7)
108 | Page encodes a protein that exhibits extracellular glutamate-gated chloride channel activity (VGLUT1).
Differential methylation of the CpG island associated with the SLC17A7 gene region displayed associations with HTN diagnosis. This suggests a role for methylation in the human kidney in the regulation of circadian clock genes, potentially implicating methylation in the regulation of the suprachiasmatic nucleus.
4.4.3 Replication analysis of blood pressure-associated methylated CpG sites in the kidney and in blood
Four (located in CXXC5, CDK6, PPP2R5A and ANKRD11) of our HTN-related changes in loci-specific methylation were in line with blood-based methylation findings in previous studies, which highlights the cross-tissue applicability of our findings. At the molecular level the protein coded by CXXC5, also known as RINF, contains a zinc finger domain that is identical to that of TET1, TET3 and CXXC4; these are epigenetic modulators suggested to be involved in the erasure of DNA-methylation marks, as
TET proteins are known to oxidise 5mC to 5hmC [186]. Expression of CDK6 is known to be upregulated in the spontaneously HT rat [187], similar to these findings CpG loci cg02689293 located in the 5’UTR if CDK6 was hypermethylated and overexpressed in the human kidney. PP2R5A has a role in the regulation of the Cystic Fibrosis Transmembrane Conductance Regulator (CFTR) and is known to be involved in the pathophysiology of dilated cardiomyopathy type 1D. Lastly, ANKRD11 is a chromatin regulator which modulates histone acetylation. Although the actions of the ANKRD11 gene are not yet understood, differential methylation in this gene region has been associated with renal fibrosis and chronic kidney disease [188].
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4.5 Conclusion and future direction
Our findings identify kidney DNA methylation as a mechanism driving chronic BP elevation in humans. It has been demonstrated in Chapter 4 that DNA methylation in the kidney has a role in the regulation of all major pathways regulating the key modulators of BP (epithelial function, neurotransmitter uptake, and electrolyte balance). We recognise the limited size of our kidney methylation dataset. The power limitation is the most likely reason why our study uncovered limited associations between BP and HTN after stringent correction for multiple testing. Despite these constraints, our analysis is the first to identify differential kidney DNA methylation as a potential mechanism driving chronic BP elevation. As the first study of its’ kind, it is important to investigate the role of differential DNA methylation on the renal transcriptome.
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Chapter 5. Differential gene expression in essential hypertension
“Dreams are often the most profound when they seem the most crazy”
~Sigmund Freud
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Chapter 5. Differential gene expression in essential hypertension
5.0 Abstract
Elevated BP is a major risk factor for CVD with a significant genetic contribution. [12]. It is important to understand the epigenetic contribution to the manifestation of HTN as family studies indicate the BP level is determined in an individual by complex interactions between life course exposures and their genetic background To date, over 900 genetic loci have been identified which have an association with
BP variance [20]. Further, as the kidney is a key modulator of BP and recognised as the primary organ for BP control, it is crucial to investigate signatures of HTN in an effector organ. In this chapter we hypothesised that DNA methylation induces lasting effects on renal HTN-related gene expression.
Therefore, in this section of my PhD thesis we investigate kidney-specific, genome-wide transcriptional changes that are linked to HTN and the role DNA methylation has in the regulation of HTN-related genes. We identify that DNA methylation and renal gene expression are tightly linked in the regulation of pathways that modulate vascular function, cellular communication, inflammation and sodium regulation. In particular we have identified a close relationship between renal DNA methylation and expression of FAM50B and PC and report a strong connection with this relationship to chronic BP elevation.
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5.1 Introduction
In chapter 4, genome-wide loci specific DNA methylation was identified as a key epigenetic modulator in the HT kidney. Further, HTN is linked to differential methylation at specific CpGs that are located in sites that govern chronic elevation in BP. However, the effect of differential DNA methylation as a driver of transcriptional change in the HT kidney requires more defining. DNA methylation is known for its ability to induce long-lasting transcriptional repression, and elevated BP is a major risk factor for CVD with a significant genetic contribution. Family studies indicate the BP level is determined in an individual by complex interactions between life course exposures and their genetic background [12].
HTN, as most human diseases, arises from a complex interaction between environmental and genetic factors. For BP, family and twin based studies have estimated the heritability range of elevated SBP to
48% to 60% and elevated DBP to 34% to 67% [189-191]. Until 2011, genetic loci identified in BP
GWAS accounted for less than 2.5% of the phenotypic variance [192], with more recent BP GWAS modestly increasing this estimate to approximately 3.5% [193-195]. In GWAS for HTN related phenotypes, interactions have successfully been identified for alcohol consumption [196], BMI [197], smoking status [198], education [199], and sodium intake [200], all which are considered modifiable through lifestyle alteration. These findings highlight the importance of investigation of genome- environment interaction, as it may contribute to the knowledge gap that cannot be explained by heritability.
Numerous GWAS have identified robust associations between genome loci and BP. The most comprehensive study, conducted in over one million individuals, identifies 901 gene loci that are associated with BP [20]. This study more than triple the number of loci for BP traits and contribute significantly to the missing heritability influencing BP level in the European population with extension
113 | Page to other ancestries. However these findings explain only a small proportion of the estimated heritability of this complex phenotype. The marginal effects of variants modified by environmental factors may be missed by traditional marker-based analyses, therefore additional genetic factors may be detected through analysis of gene-environment interactions. Further, as the kidney is a key modulator of BP and recognised as the primary organ for BP control, it is crucial to investigate signatures of HTN in an effector organ. In this chapter we hypothesised that DNA methylation induces lasting effects on renal
HTN-related gene expression. Therefore, in this section of my PhD thesis we investigate kidney- specific, genome-wide transcriptional changes that are linked to HTN and the role DNA methylation has in the modulation of these genes.
5.2 RNA-Sequencing methods
5.2.1 Participants and sample collection
The genome-wide expression analysis utilised 180 human kidney samples (Table 5.1 and Table 5.2) from the TRANSLATE study. All participants of the TRANSLATE study underwent detailed clinical phenotyping which consisted of documentation of clinical history (via coded questionnaire), basic anthropometry as well as triplicate measures of BP by mercury sphygmomanometer. HTN diagnosis was assigned to individuals who had elevated BP greater than >140/90 mmHg on at least two separate occasions, and/or were taking antihypertensive medications [117].
Renal samples, consisting of approximately 1 cm3 of kidney tissue, were collected from the healthy
(unaffected by cancer) pole of the kidney immediately post nephrectomy. Kidney DNA was extracted from 96 homogenised renal tissue samples using Qiagen purification kits for total DNA (Qiagen).
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Leukocyte DNA was extracted from peripheral blood using QIAamp DNA mini blood extractions kit
(Qiagen).
5.2.2 Identification of hypertension-associated renal signatures
RNA was extracted from the kidney tissue of 180 participants of the TRANSLATE study and preserved in RNAlater (Ambion, Austin, Texas). A total of 1µg of kidney RNA was extracted from all kidneys and subjected to Illumina Tru-Seq RNA Sample Preparation protocol. Briefly, oligo-dT attached magnetic beads are used to purify the poly-A containing mRNA molecules. Following purification, the mRNA is fragmented using divalent cations under elevated temperature. The cleaved RNA fragments are then copied into first strand cDNA using reverse transcriptase and random primers and second strand cDNA using DNA Polymerase I and RNase H. The cDNA fragments the undergo end repair processing, have the addition of a single ‘A’ base, and then ligation of the adapters. The products are then purified and enriched with PCR to create the final cDNA library. Kidney samples were then sequenced with 75-bp paired end reads on an Illumina HiSeq 4000. All generated raw reads were stored in FASTQ format. The base call and read quality were evaluated using FASTQC. The input library complexity was assessed using RNA-SeQC. The pre-processing of reads for adapter trimming was conducted by Trimmomatic. The reads were then pseudo-aligned to the GRCh38 Ensembl transcriptome reference (Ensembl release 83); and expression was quantified in transcripts per million
(TPM) at a transcript level using Kallisto. Transcript expression values were then summed to give gene- level quantification.
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5.2.3 RNA-sequencing pre-processing
The generated raw reads from RNA-sequencing were stored in FASTQ format, where the base call and read quality were evaluated using FASTQC. Input library complexity was assessed using RNA-SeQC and pre-processing of reads for adapter trimming was conducted using Trimmomatic. The reads were then pseudo-aligned to the GRCh38 Ensembl transcriptome reference (Ensembl release 83; and expression was quantified in transcripts per million (TPM) at a transcript level using Kallisto; a RNA- quantification program that is two orders of magnitude faster than previous approaches and achieves similar accuracy [125].
5.2.3.1 RNA-sequencing preprocessing computational analysis
Kallisto removes unnecessary computational technicality from RNA-sequencing analysis by pseudo- aligning reads to a reference, producing a list of transcripts that are compatible with each read while avoiding alignment of single bases. Transcript expression values were then summed to give gene-level quantification. Outlier samples were removed based on a statistic described in Wright et al [126] or based on pairwise correlation between samples, where samples were excluded if their median correlation was below 0.75. Samples without matching genotype data were also excluded. Only genes located on autosomal chromosomes were considered for downstream analyses. A threshold of genes expression was set at TPM>0.1 in at least 50% of samples and minimal read counts ≥6. A gene was also removed if its interquartile range equaled zero. Only genes that passed all of the above RNA-seq quality control filers were selected for downstream analyses.
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5.2.4 RNA-sequencing data normalisation
5.2.4.1 RNA-sequencing preprocessing computational analysis
Prior to any statistical analyses, gene-level TPM values were normalised (Figure 2.4 A-C). First, log2 of TPM values were normalised across samples using robust quantile normalisation with an offset of one. The quantiles normalised gene expression values were transformed using rank-based inverse normal transformation. Outlying observations were set to missing using residuals from robust linear regression and were then imputed using linear regression. To account for hidden variation in RNA-seq data due to technical processing (such as batch effects or sample processing in pre-sequencing stage), we used probabilistic estimation of expression residuals (PEER) method [127] and estimated 30 hidden factors for TRANSLATE Study. The numbers of hidden factors were chosen based on sample sizes of each dataset as recommended in GTEx eQTL analysis [128]. The estimated hidden factors were used as covariates in all downstream regression analyses where appropriate.
5.3 Results
5.3.1 Differential gene expression in the hypertensive kidney
We used 180 samples from the TRANSLATE study (Table 5.1 and Table 5.2) to investigate renal signature of HTN using RNA-seq. Expression of fourteen genes was associated with HTN diagnosis
(P<.0001, FDR<.25) (Figure 5.1 a/b, Table 5.3). Eleven of them are protein-coding genes and three are classified as antisense RNA genes. Further, one antisense-RNA gene (Figure 5.2 a/b, Table 5.3) was associated with SBP (P=1.71E-06, FDR<.03), and remained so when adjusted for the effects of antihypertension medication. Lastly, six genes were associated with DBP (P<.0001) (Figure 5.3 a/b,
Table 5.3), including four protein coding, a long non-coding RNA and one transcribed unprocessed pseudogene. The individual biological characteristics of the 21 differentially expressed genes are
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Table 5.1 Numeric variables for RNA-sequencing analysis in the human kidney
Variables Mean ± SD Median (IQR) N
Age (years) 61.3 ± 10.7 62 (55.0-68.3) 180
Body mass index (BMI) 27.8 ± 5.3 26.9 (24.3-30.5) 180
Systolic blood pressure (mmHg) 138.1 ± 14.0 138 (127.0-148.3) 165 {15}
Diastolic blood pressure (mmHg) 83.3 ± 8.7 83.3 (78.3-90.0) 165 {15}
Waist circumference (cm) 95.4 ± 15.9 96.0 (85.0-104.0) 145 {35}
Adjusted systolic blood pressure (mmHg) 147.3 ± 17.6 148.3 (133.3-160.3) 165 {15}
Adjusted diastolic blood pressure (mmHg) 89.5 ± 10.6 90.0 (81.7-98.3) 165 {15}
Creatinine µmol/L 87.0 ± 25.6 83.7 (70.0-97.6) 180
Serum sodium mmol/L 140.1 ± 2.9 140.0 (138.8-142.0) 132 {48}
Serum potassium mmol/L 4.4 ± 0.4 4.4 (4.1-4.7) 131 {49}
Glucose mmol/L 6.0 ± 1.7 5.7 (5.3-6.3) 143 {37}
{ x }, excluded/ unknown
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Table 5.2 Categorical variable for RNA-sequencing analysis in the human kidney Variable N
Hypertension % Yes 126 (70%)
Sex (M) 99 (55%)
Diabetes (Y) 32 (18%)
Smoker status 161 {19}
Current smoker 19 (12%)
Ex-smoker 71 (44%)
Non-smoker 71 (44%)
Chronic Kidney Disease (Y) 1 (0.6%)
Coronary Artery Disease (Y) 32 (18%)
Peripheral Artery Disease (Y) 9 (6%) {35}
Antihypertension medication (Y) 108 (60%)
Lipid lowering treatment (Y) 29 (20%) {35}
Alcohol consumption 157 {23}
Drinker 96 (61%)
Excessive drinker 1 (0.6%
Teetotal 60 (38%)
Paternal history of Hypertension (Y) 19 (14%) {41}
Maternal history of Hypertension (Y) 45 (32%) {38}
( x %), percentage of the total analysed population; { x }, excluded/ unknown
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Figure 5.1 A) Differentially expressed genes associated with hypertension diagnosis in the hypertensive kidney. Manhattan plot of genes differentially expressed genes associated with HTN diagnosis in the HT kidney. Of the fourteen genes (FDR<.25, P<.0001), 11 of them were protein- coding genes and three were classified as antisense RNA genes. B) Volcano plot of differentially expressed genes associated with hypertension diagnosis in the hypertensive kidney. Of the 14 genes discovered via differential gene expression analysis (FDR<.25, P<.0001), seven genes are overexpressed in the HT kidney and seven genes are underexpressed.
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Figure 5.2 A) Differentially expressed genes associated with systolic blood pressure in the hypertensive kidney. Manhattan plot of genes differentially expressed genes associated with SBP in the HT kidney. One gene, AC007099.1 (FDR<.02, P<1.7E-06), is an antisense gene located on chromosome 2. B) Volcano plot of differentially expressed genes associated with systolic blood pressure in the hypertensive kidney. Antisense gene, AC007099.1, is underexpressed (FDR<.02, P<1.7E-06) and is associated with SBP in the HT kidney.
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Figure 5.3 A) Differentially expressed genes associated with diastolic blood pressure in the hypertensive kidney. Manhattan plot of genes differentially expressed genes associated with DBP in the HT kidney. Of the six genes (FDR<.998, P<.001), four of them were protein-coding genes, one is classified as long non-coding and one is a pseudogene. B) Volcano plot of differentially expressed genes associated with diastolic blood pressure in the hypertensive kidney. Of the 6 genes discovered via differential gene expression analysis (FDR<.998, P<.001), 5 genes are overexpressed in the HT kidney and associated with DBP and one (PIP4K2A) is underexpressed.
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Table 5.3 Differentially expressed renal genes associated with hypertension and blood pressure
St. FDR P-Value Gene Associated ENSMBL ID Chr Gene start Gene end Gene β error biotype phenotype ENSG00000162604 1 62146718 62191095 TM2D1 0.504 0.108 <.01 8.06E-06 PC HTN ENSG00000267493 19 1267813 1270240 CIRBP-AS1 -0.657 0.144 <.01 1.08E-05 AS HTN ENSG00000259330 15 40617417 40618916 INAFM2 -0.300 0.070 <.15 3.58E-05 AS HTN ENSG00000173599 11 66615704 66725847 PC 0.196 0.047 <.20 6.32E-05 PC HTN ENSG00000149527 1 2357419 2436969 PLCH2 0.317 0.077 <.20 6.42E-05 PC HTN ENSG00000138375 2 217277137 217347776 SMARCAL1 -0.261 0.066 <.25 <.0001 PC HTN ENSG00000226419 1 113499037 113589677 SLC16A1-AS1 -0.266 0.068 <.25 <.0001 AS HTN ENSG00000174917 19 5678432 5680907 C19orf70 0.186 0.048 <.25 <.0001 PC HTN ENSG00000099977 22 24317830 24322660 DDT 0.252 0.065 <.25 <.0001 PC HTN ENSG00000163170 2 74362525 74375121 BOLA3 0.232 0.061 <.25 <.0001 PC HTN ENSG00000205683 14 73086004 73360824 DPF3 0.301 0.079 <.25 <.0001 PC HTN ENSG00000198431 12 104609557 104744083 TXNRD1 -0.245 0.065 <.25 <.0001 PC HTN ENSG00000132463 4 71681499 71705662 GRSF1 -0.305 0.081 <.25 <.0001 PC HTN ENSG00000145945 6 3849620 3851551 FAM50B -0.236 0.063 <.25 <.0001 PC HTN ENSG00000150867 2 75751194 75769832 AC007099.1 -0.013 0.002 <.02 <1.7E-06 AS SBP ENSG00000150867 10 22823778 230033484 PIP4K2A -0.019 0.005 0.998 9.52E-05 PC DBP ENSG00000143196 1 168664697 168698502 DPT 0.029 0.008 0.998 <.001 PC DBP ENSG00000176387 16 67464555 67471456 HSD11B2 0.019 0.005 0.998 <.001 PC DBP ENSG00000236882 5 95187936 95195837 LINC01554 0.026 0.007 0.998 <.001 lincRNA DBP ENSG00000167700 8 145734457 145736596 MFSD3 0.009 0.002 0.998 <.001 PC DBP ENSG00000180747 16 21458004 21531765 SMG1P3 0.012 0.003 0.998 <.001 Pseudo DBP Position is Hg19. β – coefficient from linear regression models. Abbreviations: Chr, chromosome; PC, protein coding; lincRNA, long non-coding RNA; pseudo, pseudogene; AS, antisense; HTN, hypertension; SBP, systolic blood pressure; DBP, diastolic blood pressure. 123 | Page
Table 5.4 Full names and biological characterisation of genes differentially expressed in the hypertensive kidney Kidney Cellular Gene Gene Gene predominant Molecular function Biological process Primary role Chr component symbol biotype description expression (GO) (GO) (OMIM) (GO) (HPA) May participate apoptotic process, G- integral G-protein coupled in amyloid-beta- TM2 protein coupled component of protein receptor activity, induced TM2D1 1 domain N receptor signaling plasma coding beta-amyloid apoptosis via its containing 1 pathway, apoptotic membrane, binding interaction with signaling pathway membrane beta-APP42
affiliated with CIRBP- CIRBP 19 antisense - - - - the non-coding AS1 Antisense RNA class RNA 1
membrane, affiliated with InaF motif INAFM2 15 antisense - - - integral the lncRNA containing 2 component of class membrane pyruvate metabolic process, nucleotide binding, gluconeogenesis, lipid catalytic activity, metabolic process, biotin carboxylase biotin metabolic activity, pyruvate cytoplasm, ligase activity protein Pyruvate process, metabolic PC 11 N carboxylase activity, mitochondrion, and biotin coding carboxylase process, negative protein binding, ATP mitochondrial carboxylase regulation of gene binding, biotin matrix, cytosol activity expression, viral RNA binding, ligase genome packaging, activity positive regulation by host of viral release
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Kidney Cellular Gene Gene Gene predominant Molecular function Biological process Primary role Chr component symbol biotype description expression (GO) (GO) (OMIM) (GO) (HPA)
phosphatidylinositol phospholipase C lipid metabolic activity, process, signal this phospholipase C transduction, phospholipase activity, signal biological_process, activity is very intracellular, transducer activity, lipid catabolic sensitive to cytoplasm, protein Phospholipa calcium ion binding, process, intracellular calcium. May be PLCH2 1 N plasma coding se C eta 2 phosphoric diester signal transduction, important for membrane, hydrolase activity, inositol phosphate formation and membrane hydrolase activity, metabolic process, maintenance of metal ion binding, phosphatidylinositol the neuronal calcium-dependent metabolic process, network in the phospholipase C phosphatidylinositol- postnatal brain activity mediated signaling
SWI/SNF related, DNA strand nucleus, matrix renaturation, DNA nucleoplasm, May play an associated, nucleotide binding, metabolic process, DNA important role in actin SMARCAL protein helicase activity, DNA repair, replication DNA damage 2 dependent N 1 coding protein binding, ATP regulation of factor A response by regulator of binding transcription from complex, site acting at stalled chromatin, RNA polymerase II of double- replication forks subfamily a promoter strand break like 1
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Kidney Cellular Gene Gene Gene predominant Molecular function Biological process Primary role Chr component symbol biotype description expression (GO) (GO) (OMIM) (GO) (HPA)
affiliated with SLC16A1- SLC16A1 1 antisense - - - - the non-coding AS1 Antisense RNA class RNA 1
nucleoplasm, mitochondrion, plays crucial mitochondrial roles in the inner maintenance of Chromsome membrane, crista junctions, protein 19 open membrane, inner membrane C19orf70 19 N - cristae formation coding reading integral architecture, and frame 70 component of formation of membrane, contact sites to mitochondrial the outer crista junction, membrane MICOS complex negative regulation of dopachrome macrophage isomerase activity, chemotaxis, positive Tautomerization cytokine receptor regulation of tumor extracellular of D- binding, lyase necrosis factor D- space, dopachrome protein activity, D- production, melanin DDT 22 dopachrome N cytoplasm, with coding dopachrome biosynthetic process, tautomerase extracellular decarboxylation decarboxylase positive regulation of exosome to give 5,6- activity, inflammatory dihydroxyindole phenylpyruvate response, positive tautomerase activity regulation of ERK1 and ERK2 cascade 126 | Page
Kidney Cellular Gene Gene Gene predominant Molecular function Biological process Primary role Chr component symbol biotype description expression (GO) (GO) (OMIM) (GO) (HPA)
Acts as a mitochondrial iron-sulfur (Fe- S) cluster BolA protein assembly factor BOLA3 2 family N - - mitochondrion coding that facilitates member 3 (Fe-S) cluster insertion into a subset of mitochondrial proteins
acts as a tissue- specific anchor between histone transcription, DNA- acetylations and templated, regulation methylations Double protein nucleic acid binding, of transcription, DNA- nucleus, and chromatin DPF3 14 PHD N coding zinc ion binding, templated, nervous nucleoplasm, remodeling. It fingers 3 metal ion binding system developments, nBAF complex thereby covalent chromatin probably plays modification an essential role in heart and skeletal muscle development
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Kidney Cellular Gene Gene Gene predominant Molecular function Biological process Primary role Chr component symbol biotype description expression (GO) (GO) (OMIM) (GO) (HPA)
response to oxygen thioredoxin-disulfide radical, mesoderm reductase activity formation, selenium protein binding compound metabolic electron carrier process, signal activity, protein transduction, disulfide gastrulation, cell oxidoreductase proliferation, fibrillar center activity, nucleobase-containing cell, nucleus, oxidoreductase oxidoreductase small molecule nucleoplasm, activity and protein Thioredoxin activity, interconversion, cytoplasm, TXNRD1 12 N flavin adenine coding reductase 1 oxidoreductase regulation of lipid mitochondrion, dinucleotide activity, acting on a metabolic process, cytosol, binding sulfur group of electron transport extracellular donors, NAD(P) as chain, cellular exosome acceptor, flavin response to oxidative adenine dinucleotide stress, cell redox binding, homeostasis, methylselenol oxidation-reduction reductase activity, process, methylseleninic acid, cellular oxidant eductase activity detoxification
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Kidney Cellular Gene Gene Gene predominant Molecular function Biological process Primary role Chr component symbol biotype description expression (GO) (GO) (OMIM) (GO) (HPA)
regulator of
post- mRNA transcriptional polyadenylation, RNA cytoplasm, mitochondrial processing, mRNA G-rich RNA mitochondrion, gene expression, protein nucleic acid binding, processing, tRNA GRSF1 4 sequence N ribonucleoprot required for coding RNA binding, processing, binding ein granule, assembly of the mRNA binding anterior/posterior factor 1 mitochondrial mitochondrial pattern specification, nucleoid ribosome and morphogenesis of for recruitment embryonic epithelium of mRNA and lncRNA
Family with nucleus, protein sequence nucleoplasm, Plays a role in FAM50B 6 N protein binding - coding similarity intercellular circadian clock 50 member bridge B
AC007099 affiliated with 2 antisense N/A - - - - .1 the non-coding RNA class
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Kidney Cellular Gene Gene Gene predominant Molecular function Biological process Primary role Chr component symbol biotype description expression (GO) (GO) (OMIM) (GO) (HPA) phospholipid metabolic process, nucleotide binding, phosphatidylinositol ATP binding, kinase biosynthetic process, activity, regulation of phosphatidylinositol autophagy, regulation phosphate kinase of nucleus, phosphatidylino activity, 1- Phosphatidy phosphatidylinositol nucleoplasm, sitol phosphate phosphatidylinositol- linositol-5- 3-kinase signaling, cytoplasm, kinase activity Protein 4-phosphate 5-kinase PIP4K2A 10 Phosphate No phosphorylation, autophagosom and 1- coding activity, 4-Kinase megakaryocyte e, cytosol, phosphatidylino phosphatidylinositol- Type 2 development, plasma sitol-5- 5-phosphate 4-kinase Alpha phosphatidylinositol membrane, phosphate 4- activity, ansferase metabolic process, membrane kinase activity activity, osphatidylinositol, phosphatidylinositol- phosphorylation, 3-phosphate 4-kinase positive regulation of activity autophagosome assembly
extracellular Seems to region, cell adhesion, negative mediate Protein Dermatopon proteinaceous DPT 1 No - regulation of cell adhesion by cell coding tin extracellular proliferation, collagen surface integrin matrix, fibril organization binding extracellular space
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Kidney Cellular Gene Gene Gene predominant Molecular function Biological process Primary role Chr component symbol biotype description expression (GO) (GO) (OMIM) (GO) (HPA)
response to hypoxia
regulation of blood cytoplasm, volume by renal endoplasmic aldosterone, reticulum, glucocorticoid 11-beta- endoplasmic biosynthetic process, hydroxysteroid reticulum female pregnancy, Hydroxyster dehydrogenase membrane, Protein Enhanced in glucocorticoid oxidoreductase HSD11B2 16 oid 11-Beta [NAD(P)] activity, membrane, coding kidney and metabolic process, activity and Dehydrogen steroid binding, integral intestine response to food, steroid binding ase 2 oxidoreductase component of response to insulin, activity, NAD membrane, response to drug, binding intracellular response to steroid membrane- hormone, response to bounded glucocorticoid, organelle oxidation-reduction process Long Intergenic affiliated with LINC0155 5 lincRNA Non-Protein - - - - the non-coding 4 Coding RNA class RNA 1554
Major integral Facilitator transport, proton component of solute:proton Protein Superfamily solute:proton transport, MFSD3 8 No plasma symporter coding Domain symporter activity transmembrane membrane, activity Containing transport membrane 3 131 | Page
Kidney Cellular Gene Gene Gene predominant Molecular function Biological process Primary role Chr component symbol biotype description expression (GO) (GO) (OMIM) (GO) (HPA) SMG1P3, Nonsense Mediated MRNA Transcribed Decay SMG1P3 16 unprocessed Associated - - - - pseudogene pseudogene PI3K Related Kinase Pseudogene 3 Position is Hg19. Abbreviations: Chr, chromosome; CpG, cytosine-phosphate-guanine; 5’UTR, 5-prime untranslated region; HTN, hypertension diagnosis; SBP, systolic blood pressure; DBP, diastolic blood pressure; mRNA, messenger; DNA, deoxyribonucleic acid; rRNA, ribosomal RNA; ER, endoplasmic reticulum; tRNA, transfer RNA; HPA, human protein atlas; GO, gene ontology; MHC, major histocompatibility complex; nBAF, neuron specific chromatin remodelling complex
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5.3.2 Correlation analysis of blood pressure-associated kidney genes and blood pressure GWAS
We used kidney expression data to compare against known BP-associated genes from the most recent and comprehensive HTN GWAS [20]. We found an overlap for two differentially expressed kidney genes and the BP-associated GWAS genes in blood (Table 5.5). DPF3, is located on chromosome 14 and is a transcription regulator that binds acetylated histones and is a component of the BAF chromatin remodelling complex. In humans, DPF3 is significantly upregulated in the hypertrophic hearts of individuals with aortic stenosis and cardiomyopathy [201]. Interestingly, we also discovered differential renal gene expression of the HSD11B2 gene. Mutations to HSD11B2 have been identified as causative for a monogenic form of HTN; in the form of apparent mineralocorticoid excess [202].
Table 5.5 Correlation analysis of blood pressure-associated kidney genes and blood pressure GWAS St. FDR P- Gene Associated ENSMBL ID Chr Position Gene β error Value biotype phenotype
ENSG00000162604 14 73279420 DPF3 0.301 0.079 <.25 <.0001 PC HTN
ENSG00000176387 16 67464555 HSD11B2 0.019 0.005 .998 <.001 PC DBP
Position is Hg19. β – coefficient from linear regression models. Abbreviations: Chr, chromosome; PC, protein coding; HTN, hypertension
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5.3.3 DNA methylation of hypertension-associated genes and their expression in the kidney
Methylation of cytosine residues in DNA plays a role in gene regulation, affecting DNA interactions with chromatin proteins and specific transcription factors [11]. Therefore, we examined whether differences in CpG methylation of kidney DNA in proximity to HTN-associated genes correlate with their renal expression. This analysis was conducted in 68 TRANSLATE samples with both array- derived renal DNA methylation data and kidney transcriptome data available. We examined the extent of DNA methylation in all CpG sites mapping to a cis window of 250kb. After correction for multiple testing, methylation of two genes (FAM50B, PC) (Figure 5.4) showed an association with its renal expression. The strongest association was identified for the cg23344241 probe mapping upstream of the PC gene (P<.0001, FDR<.35) (Table 5.6). Together, these data demonstrate an association between hyper/hypo methylation upstream of two of the HTN-associated renal genes and its expression in the kidney (Figure 5.5A/ B).
Figure 5.4 Methylation of FAM50B and PC shows an association with renal expression. Venn diagram of genes differentially methylated and differentially expressed in the HT kidney. Methylation of two genes, PC and FAM50B, showed an association with its renal expression. Where bold indicates increased expression; *, CpG site is located in within an island.
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Figure 5.5 Illustration of the relationship of methylation at various individual CpG sites with hypertension incidence in the TRANSLATE population. A) Decreased methylation at cg23344241; cg12032489; cg08971859; cg03094226 was identified as associated with HTN diagnosis. Increased expression of PC was associated with decreased methylation at the previously mentioned sites; which suggests that DNA methylation influences HTN incidence through the expression of PC. B) Methylation at cg07898446; cg17739279; cg13289019 was identified as associated with HTN diagnosis. Decreased expression of FAM50B was associated with methylation at the previously mentioned sites; which suggests that DNA methylation influences HTN incidence through the reduced expression of FAM50B.
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Table 5.6 Differentially methylated and differentially expressed genes in the hypertensive kidney
Location
of CpG Enhancer Gene Gene UCSC ENSMBL ID Chr CpG probe relative β P-value FDR region symbol location to island ENSG00000173599 PC 11 cg23344241 66615704 OpenSea TSS200 1.30 <.0001 <.35
ENSG00000173599 PC 11 cg12032489 66615704 N_Shore Body 1.30 <.0001 <.35
ENSG00000173599 FAM50B 6 cg07898446 3849620 Island TSS1500 -2.19 <.0001 <.35
ENSG00000145945 FAM50B 6 cg17739279 3849620 N_Shore TSS1500 -1.49 <.0001 <.35
ENSG00000145945 PC 11 cg08971859 66615704 OpenSea TSS200 1.33 <.0001 <.35
ENSG00000173599 PC 11 cg03094226 66615704 OpenSea Body 1.03 <.0001 <.35 TRUE
ENSG00000173599 FAM50B 6 cg13289019 3849620 Island TSS1500 -3.15 <.0001 <.35
Position is Hg19. β – coefficient from linear regression models. Abbreviations: Chr, chromosome; CpG, cytosine-phosphateguanine; TSS1500, 1500 base pair to the transcription start site; body, body of gene; N_shore, north shore to CpG island.
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5.3.5 Reverse analysis of BP-associated methylated CpG sites and known GWAS BP gene loci
We investigated the role of kidney DNA methylation in known GWAS BP loci. Therefore we conducted a reverse analysis of genes that are known to influence BP (GWAS), with differentially methylated CpG sites in the human kidney We used data obtained from the kidney methylation array together with the most comprehensive BP loci GWAS conducted using the blood of over one million individuals [20]. The BP-associated GWAS by Evangelou et al [20] identifies over 535 novel and over
901 total gene loci that are associated with BP modulation. Of the BP-associated sites identified in the
GWAS, two overlap with loci that are differentially methylated in the kidney and associated with SBP and 28 overlap with loci that are differentially methylated in the kidney and associated with DBP (Table
5.7). A summary of the biological characterisation of the genes is available in Table 5.8.
5.3.6 Differentially methylated and expressed renal genes that are identified in blood-based BP GWAS
We used a less stringent kidney methylation analysis to compare against known BP-associated genes in the largest BP GWAS conducted in over one million individuals [20]. This analysis revealed one loci, DPF3 (Table 5.9), that is both differentially methylated in the kidney (P=.019), differentially expressed in the kidney (P=<.001), and has been identified as BP associated loci in BP GWAS
(P=6.58E-17).
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Table 5.7 Reverse analysis of GWAS BP loci and loci associated with differentially methylated CpGs in the human kidney UCSC β CpG UCSC gene gene Chr Position (CpG P-value FDR Chr Position β P-value number symbol symbol site) TRANSLATE kidney samples Relative blood GWAS samples
KIAA1755 cg01202124 20 36888790 -0.388 5.58E-05 <.20 1KIAA1755 20 36849007 0.116 3.37E-11
CMIP cg09678340 16 81506476 0.226 2.95E-05 <.20 2CMIP 16 81574197 -0.241 2.0E-09
KDM4B cg02659867 19 5033098 0.837 2.67E-05 <.05 1KDM4B 19 5066330 -0.234 9.01E-22
RARRES2 cg14442992 7 150039342 0.263 4.86E-05 <.05 1RARRES2 7 150050111 0.167 2.72E-10
PKD2L1 cg25155149 10 102048137 -0.266 8.42E-05 <.05 1PKD2L1 10 102075479 0.172 1.81E-17
PIAS1 cg06790275 15 68346669 -0.284 8.58E-05 <.05 1PIAS1 15 68454523 -0.095 3.77E-10
OSBPL7 cg00265783 17 45891043 0.947 2.39E-05 <.05 1OSBPL7 17 45888374 -0.4132 4.60E-31
COL15A1 cg13865810 9 101706936 2.737 3.77E-06 <.05 1COL15A1 9 101739709 0.178 1.29E-23
FBRSL1 cg21441868 12 133125416 0.653 3.24E-05 <.05 1FBRSL1 12 133086888 0.315 2.70E-10
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UCSC β CpG UCSC gene gene Chr Position (CpG P-value FDR Chr Position β P-value number symbol symbol site)
TRANSLATE kidney samples Relative blood GWAS samples
FOXF1 cg20368988 16 86542943 2.040 7.73E-06 <.05 1FOXF1 16 86437811 0.192 5.40E-10
MCPH1 cg03755955 8 6424269 -0.158 7.53E-05 <.05 1MCPH1 8 6379932 0.132 5.90E-14
ZFP36L1 cg15558971 14 69260307 -0.423 9.93E-05 <.05 1ZFP36L1 14 69260028 0.142 4.40E-11
SPRY2 cg22417789 13 80915465 1.342 1.59E-05 <.05 1SPRY2 13 80707408 -0.138 5.20E-11
DPYSL2 cg00926238 8 26436304 0.447 4.01E-05 <.05 1DPYSL2 8 26445194 0.231 1.80E-10
ATP10A cg00288824 15 26108663 1.919 8.76E-06 <.05 1ATP10A 15 26105602 -0.110 9.50E-10
HDAC4 cg15713391 2 240033280 1.486 1.37E-05 <.05 1HDAC4 2 239864732 -0.124 3.60E-09
BCAT1 cg22814146 12 25055518 0.427 4.10E-05 <.05 1BCAT1 12 24770878 -0.197 6.00E-12
KLHL29 cg13874979 2 23929590 0.334 4.51E-05 <.05 1KLHL29 2 23950200 -0.152 1.40E-11
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UCSC β CpG UCSC gene gene Chr Position (CpG P-value FDR Chr Position β P-value number symbol symbol site)
TRANSLATE kidney samples Relative blood GWAS samples
WNT3A cg00779928 1 228225068 0.429 4.09E-05 <.05 2WNT3A 1 228191075 0.235 3.7E-09
FOSL2 cg06911519 2 28615662 1.149 1.94E-05 <.05 2FOSL2 2 28635740 0.365 2.2E-07
GATA2 cg14784847 3 128209252 -0.127 7.29E-05 <.05 2GATA2 3 128201889 0.861 2.6E-09
CPEB4 cg06697488 5 173314932 0.687 3.13E-05 <.05 2CPEB4 5 173377636 -0.232 1.6E-07
RSPO3 3RSPO3 Alias cg03983331 6 127441605 2.407 5.30E-06 <.05 Alias 6 127115454 0.850 5.9E-05 TBSD2 TBSD2
CDK6 cg02689293 7 92464971 1.493 1.36E-05 <.05 4CDK6 7 92264410 −0.268 3.82E-08
SLC20A2 cg03612649 8 42292332 3.221 2.30E-06 <.05 2SLC20A2 8 42324765 -0.253 1.3E-07
EHBP1L1 cg02532488 11 65343341 0.027 6.21E-05 <.05 5EHBP1L1 11 65353906 0.909 9.9E-09
MYH6 cg14541311 14 23877130 1.148 1.94E-05 <.05 2MYH6 14 23865885 0.261 2.9E-07
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UCSC β CpG UCSC gene gene Chr Position (CpG P-value FDR Chr Position β P-value number symbol symbol site)
TRANSLATE kidney samples Relative blood GWAS samples
AMH cg00299047 19 2252069 0.704 3.07E-05 <.05 2AMH 19 2232221 N/A 9.4E-06
JAG1 cg20319775 20 10653482 1.336 1.60E-05 <.05 2JAG1 20 10969030 -0.432 3.9E-19
SLC24A3 cg27382790 20 19193057 1.008 2.24E-05 <.05 2SLC24A3 20 19465907 0.326 1.2E-09
Position is Hg19. β – coefficient from linear regression models. Abbreviations: Chr, chromosome; CpG, cytosine-phosphate-guanine; FDR, false discovery rate’ N/A, beta-coefficient not available; 1 GWAS data obtained from Evangelou et al (2017) [20]; 2 GWAS data obtained from Warren et al (2017) [30]; 3 GWAS data obtained from Franceschini et al (2013) [203]; 4 GWAS data obtained from Tragante et al (2014) [204]; 5 GWAS data obtained from Simino et al (2014) [205].
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Table 5.8 Characterisation of GWAS loci that overlap with regions containing differentially methylated CpGs in the kidney
UCSC UCSC gene Enhancer location Gene Associated UCSC Gene CpG number Chr Position symbol region relative to biotype phenotype name gene Uncharacterized Protein KIAA1755 cg01202124 20 36888790 TRUE Body SBP Protein coding KIAA1755
Protein CMIP cg09678340 16 81506476 TRUE Body SBP C-Maf Inducing coding Protein
Protein KDM4B cg02659867 19 5033098 Body DBP Lysine coding Demethylase 4B
Protein Retinoic Acid RARRES2 cg14442992 7 150039342 TSS1500 DBP coding Receptor Responder 2
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UCSC UCSC gene Enhancer location Gene Associated UCSC Gene CpG number Chr Position symbol region relative to biotype phenotype name gene
Polycystin 2 Like Protein 1, Transient PKD2L1 cg25155149 10 102048137 3'UTR DBP coding Receptor Potential Cation Channel
Protein Protein Inhibitor PIAS1 cg06790275 15 68346669 1stExon DBP coding Of Activated STAT 1
Protein Oxysterol OSBPL7 cg00265783 17 45891043 Body DBP coding Binding Protein Like 7
Protein Collagen Type COL15A1 cg13865810 9 101706936 Body DBP coding XV Alpha 1 Chain
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UCSC UCSC gene Enhancer location Gene Associated UCSC Gene CpG number Chr Position symbol region relative to biotype phenotype name gene Protein FBRSL1 cg21441868 12 133125416 Body DBP Fibrosin Like 1 coding
Protein FOXF1 cg20368988 16 86542943 TSS1500 DBP Forkhead Box F1 coding
Protein MCPH1 cg03755955 8 6424269 Body DBP Microcephalin 1 coding
Protein ZFP36 Ring ZFP36L1 cg15558971 14 69260307 TSS1500 DBP coding Finger Protein Like 1
Protein Sprouty RTK SPRY2 cg22417789 13 80915465 TSS1500 DBP coding Signaling Antagonist 2
Protein DPYSL2 cg00926238 8 26436304 Body DBP Dihydropyrimidin coding ase Like 2
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UCSC UCSC gene Enhancer location Gene Associated UCSC Gene CpG number Chr Position symbol region relative to biotype phenotype name gene
ATPase Protein Phospholipid ATP10A cg00288824 15 26108663 TSS1500 DBP coding Transporting 10A (Putative)
Protein Histone HDAC4 cg15713391 2 240033280 Body DBP coding Deacetylase 4
Protein Branched Chain BCAT1 cg22814146 12 25055518 TRUE Body DBP coding Amino Acid Transaminase 1
Protein Kelch Like KLHL29 cg13874979 2 23929590 TRUE 3'UTR DBP coding Family Member 29
Protein Wnt Family WNT3A cg00779928 1 228225068 Body DBP coding Member 3A
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UCSC UCSC gene Enhancer location Gene Associated UCSC Gene CpG number Chr Position symbol region relative to biotype phenotype name gene
Protein FOS Like 2, AP-1 FOSL2 cg06911519 2 28615662 TSS200 DBP coding Transcription Factor Subunit
Protein GATA2 cg14784847 3 128209252 5'UTR DBP GATA Binding coding Protein 2
Cytoplasmic Protein CPEB4 cg06697488 5 173314932 TSS1500 DBP Polyadenylation coding Element Binding Protein 4 R-Spondin 3 Alias RSPO3 Protein Thrombospondin cg03983331 6 127441605 Body DBP Alias TBSD2 coding Type-1 Domain- Containing Protein 2
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UCSC UCSC gene Enhancer location Gene Associated UCSC Gene CpG number Chr Position symbol region relative to biotype phenotype name gene
Protein CDK6 cg02689293 7 92464971 5'UTR DBP Cyclin Dependent coding Kinase 6
Protein Solute Carrier SLC20A2 cg03612649 8 42292332 Body DBP coding Family 20 Member 2
Protein EH Domain EHBP1L1 cg02532488 11 65343341 TSS200 DBP coding Binding Protein 1 Like 1
Protein Myosin Heavy MYH6 cg14541311 14 23877130 5'UTR DBP coding Chain 6
Protein Anti-Mullerian AMH cg00299047 19 2252069 3'UTR DBP coding Hormone
Protein JAG1 cg20319775 20 10653482 Body DBP Jagged 1 coding
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UCSC UCSC gene Enhancer location Gene Associated UCSC Gene CpG number Chr Position symbol region relative to biotype phenotype name gene Solute Carrier Protein SLC24A3 cg27382790 20 19193057 TSS1500 DBP Family 24 coding Member 3 Position is Hg19. Individual CpG site-associated gene loci characterisation. Abbreviations: Chr, chromosome; CpG, cytosine- phosphateguanine; TSS1500, 1500 base pair to the transcription start site; 5’UTR, 5-prime untranslated region; body, body of gene; HTN, hypertension diagnosis; SBP, systolic blood pressure; DBP, diastolic blood pressure.
Table 5.9: Differentially methylated kidney-associated CpGs that are identified in blood-based HTN GWAS Differentially Differentially UCSC gene CpG β (CpG site) expressed expressed Chr Position P-value name number (P-value) (P-value) TRANSLATE GWAS DPF3 cg26057784 14 73362214 -3.256 .019 <.001 6.58E-171
Position is Hg19. β – coefficient from linear regression models. Abbreviations: Chr, chromosome; CpG, cytosine-phosphate-guanine; FDR, false discovery rate’ N/A, beta-coefficient not available; 1 GWAS data obtained from Evangelou et al (2017) [20]
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5.4 Discussion
Despite the identification of numerous genes associated with BP modulation in blood, signatures of
HTN and BP have not been identified in the human kidney. This section of my PhD thesis offers a step forward in understanding the genetic architecture of EH and its’ associated phenotypical traits in the human kidney. The 21 loci, 19 of which are novel, identified here open the vista of entirely new biology and highlight gene regions in tissue not previously investigated in BP studies. The clinical relevance of these findings are timely, as the global prevalence of individuals with SBP over the recommended threshold (110-115mmHg) now exceeds 3.5 billion; above which risk of cardiovascular complications increases in a continuous fashion [176].
5.4.1 Hypertension-associated genes in the human kidney
Our study provides new insight into HTN-related gene expression changes within the human kidney.
Of these, some of the top locus for HTN diagnosis, SBP and DBP has been selected for discussion.
TM2D1 encodes beta-amyloid peptide-binding protein which has been established as a causative factor in neuron death and cognitive impairment [206]. Vascular beta-amyloid deposition is a common pathology of aging and is directly toxic to smooth muscle cells, often resulting in vascular rupture and impairment of the vascular response to changes in systemic BP [206]. To this end, the identified increase in renal expression with HTN most likely represents one of the mechanisms responsible for increased BP, perhaps via increased beta-amyloid deposition in renal vasculature. AC007099.1 is an antisense long non-coding RNA (lcRNA) associated with systolic BP. While very little is known about this lcRNA it has been previously identified as a potential biomarker in colorectal cancer [207] and is now identified as a HTN-associated change to renal gene expression. Finally, HSD11B2 has long been associated to HTN and BP modulation. Expression of HSD11B2 is enhanced in the kidney and
149 | Page intestine, mutations of which cause a rare monogenic juvenile HT syndrome (apparent mineralocorticoid excess or AME) [208]. Altered expression of this gene is expected in our study, as variants of the HSD11B2 gene contribute to enhanced salt-induced BP response in humans and the kidney is a key organ regulating salt balance.
5.4.2 Correlation of BP-associated kidney genes and BP GWAS
We used kidney expression data to compare against known BP-associated genes from the most recent and comprehensive HTN GWAS [20]. We found an overlap for two differentially expressed kidney genes and the BP-associated GWAS genes in blood (Table 5.5). Of particular interest is the differentially expressed renal gene DPF3, which overlaps BP-associated GWAS in peripheral blood and HSD11B2 which is known causative agent of a monogenic form of HTN [202]. DPF3, is located on chromosome 14 and is a transcription regulator that binds acetylated histones and is a component of the BAF chromatin remodelling complex. In humans, DPF3 is significantly upregulated in the hypertrophic hearts of individuals with aortic stenosis and cardiomyopathy [201]. HSD11B2 catalyses the glucocorticoid cortisol to the inactive metabolite cortisone in aldosterone-selective epithelial tissues, such as the kidney [202]. Mutations to this gene cause apparent mineralocorticoid excess syndrome and is recognised as a monogenic cause of HTN as discussed above. “There are possible limitations to this analysis, where the cause of so few overlapping loci may be due to tissue specific genetic expression between blood and kidney. Furthermore, the volume of samples sequenced is significantly less in the TRANSLATE cohort, and may be a hinderance to statistical power. However, given the unique nature of the human renal samples it is encouraging to see some overlap in a limited sample size.
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5.4.3 The role of DNA methylation in hypertension-associated renal gene expression
To identify gene transcription that functionally mediates the relationship between methylation of cytosine residues and BP we examined whether differences in CpG methylation of kidney DNA in proximity to HTN-associated genes correlate with their renal expression. Methylation of two genes
(FAM50B, PC) showed an association with its renal expression, demonstrating an association between hyper/hypo methylation upstream of two of the HTN-associated renal genes and its expression in the kidney. Interestingly, FAM50B, which plays a role in the circadian clock, is adjacent to a differentially methylated region (DMR). Methylation levels of FAM50B is similar in multiple tissues and is unmethylated in sperm, suggesting that it is likely to be a primary maternal DMR [209]. Further, methylation in CpG sites located in FAM50B are positively associated with telomere length [210] and has been recognised as belonging to a set of 353 ‘age-predictor’ CpGs [211]. As a known risk factor for HTN, it is reassuring to see an epigenetic signature of chronological age. PC, among other functions, is involved in the synthesis of the neurotransmitter glutamate, a well-established endogenously produced excitatory agonist [212]. With respect to cardiovascular control, glutamate has been known to have an effect on BP since 1954, with intravenous glutamate inducing a pressor response in dogs and rabbits [213-215]. It is likely that the overexpression of PC seen in our study is contributing to upregulation of glutamate activity and thereby an increase in BP. The association between methylation and gene expression in the kidney is similar to findings conducted in peripheral blood leukocytes, where a functional and causal relationship connecting methylation and expression at
TSPAN2 was identified, and TSPAN2 was recognised as a modulator of BP [103].
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5.4.4 Reverse analysis of BP-associated methylated renal CpG sites and known GWAS BP loci
We investigated the role of DNA methylation in known GWAS BP loci and to identify the cross-tissue applicability of blood and kidney GWAS studies we conducted a reverse analysis of genes that are known to influence BP (GWAS) in blood, with differentially methylated CpG sites in the human kidney
We used data obtained from the kidney methylation array together with the most comprehensive BP loci GWAS conducted using the blood of over one million individuals [20]. We identified 30 differentially methylated kidney CpGs that are associated with known BP-associated genes in blood based GWAS [20]. Of the two loci associated with SBP, KIAA1755 has been identified as an independent low-frequency non-synonymous variant in heart rate regulation [216]. The other, CMIP, is associated with high-density lipoprotein cholesterol levels in individuals of European ancestry [217].
Of the 28 loci associated with DBP, many have been previously implicated in HTN and BP in recent studies. RSPO3 is associated with the regulation of genes involved in Wnt signalling. Wnt signalling pathways and genetic variation to its signalling peptides have recently been associated with metabolic syndrome, HTN and diabetes [218]. WNT3 is also differentially methylated in the HT kidney. Studies investigating down-regulation of Wnt3 report impaired nitric oxide production [219] and on the other side of the coin SHRs treated with Wnt3a displayed a depressor response [219].
Similarly, Notch signalling plays a fundamental role in vascular development [220]. This pathway is activated by cell surface receptors that respond to cell attached ligands. Among these is Jagged-1
(JAG1), a protein that allows cells to signal each other. The JAG1 gene responsible for the production of this protein is differentially methylated in the HT renal genome. Pathway analysis revealed that
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HDAC4, a gene encoding a histone deacetylase previously recognised as overexpressed in the SHR
[221], is tightly linked with the activity from JAG1. It is possible that the proinflammatory effect of
HDAC4 is further exacerbating JAG1 dysregulation in HTN vascular development.
The correlation between vascular calcification and HTN is well established in human studies [222].
There are rare genetic disorders that lead to exaggerated or early onset vascular calcification [223]; among the research in the area is identification of mutations to the SLC20A2 gene, which encodes the type III sodium-dependent phosphate transporter 2. Dysfunction of this gene leads to impaired phosphate transport and as a consequence altered phosphate homeostasis. This results in vascular and pericapillary calcification of the basal ganglia [224]. SLC20A2 is among the gene loci differentially methylated in the HTN kidney; research linking basal ganglia calcification and renal dysfunction is scarce, however studies of hemodialysis patients [225] and those with end-stage diabetic renal failure
[226-228] highlight the relationship between impaired function of renal SLC20A2 and vascular calcification. Our findings suggest a relationship between differential methylation of SLC20A2 and
HTN possibly via disruptions to phosphate transport mechanisms.
5.4.5 Differentially methylated and expressed renal genes that are identified in blood-based HTN GWAS
In humans, DPF3 is significantly upregulated in the hypertrophic hearts of individuals with aortic stenosis and cardiomyopathy [201]. DPF3 is particularly noteworthy as it is both differentially methylated and expressed in the kidney and overlaps with blood-based BP GWAS [20] indicating that the methylation status of DPF3 may be inherited.
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5.4.5 Effects of coexistant cancer on the renal transcriptome
The potential effect of coexistent cancer on the transcriptome of the TRANSLATE “healthy” renal tissue was comprehensively discussed by Marques et al [120], where the global expression profiles of three different cohorts was compared. The three groups contained either healthy tissue from nephrectomy due to cancer, noncancerous kidney tissue, or clear cell renal carcinoma tissue. These sections were analysed using hierarchical clustering analysis and RNA-sequencing. Results from this study showed tight clustering of global expression patterns between healthy tissue from nephrectomy and subjects who did not have cancer. Highlighting that it was unlikely that the transcriptome program in cells within neoplastically unaffected apparently healthy renal tissue were largely influenced by the presence of cancer in other parts of the kidney [120].
5.5 Conclusion and future direction
Our findings identify DNA methylation as a key driver of HTN-associated (causative or as a consequence of) transcriptional change in the human kidney. Further we report strong connections between HTN-associated gene expression in the kidney and findings from large scale GWAS previously conducted in peripheral blood. It has been demonstrated in Chapter 5 that DNA methylation and renal gene expression are tightly linked in the regulation of pathways that modulate vascular function, cellular communication, inflammation and sodium regulation. In particular we have identified a close relationship between renal DNA methylation and expression of FAM50B and PC and report a strong connection with this relationship to chronic BP elevation. Our findings suggest that further research on the connection between DNA methylation and expression of BP-associated loci is likely to yield additional insights into the complexity of HTN pathobiology.
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Chapter 6. Discussion
“The wise person reads both books and life itself” ~ Lin Yutang
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Chapter 6. Discussion
6.1 Overview
EH is a disease of alteration to the regulation of mechanisms that control arterial pressure. These include neural (primarily, adrenergic function) and catecholamine factors; the renopressor system
(RAAS); renal excretory function including the propensity to maintain normal balance of sodium water and additional electrolytes; and other factors controlling hormonal, electrolytic and fluid balance [229].
In addition, other physiologic systems reduce arterial pressure, including histamine, kallikrein-kinin and prostaglandin systems [229]. Of these, the human kidney is recognised as a key organ of BP regulation and one of the largest contributors to the development of EH. GWAS have traditionally focused on DNA modulation and expression of genes that regulate BP and ultimately lead to hypertension, in peripheral blood samples [20, 30, 103]. Therefore, there is interest in defining the epigenetic and genetic mechanisms that regulate BP and drive further dysregulation in the human kidney. The role of epigenetics in EH has gained considerable attention as one of the factors bridging the gap between inherited and lifestyle risk factors, however this hypothesis has never been formally tested in renal tissue. Moreover the role of renal DNA methylation in gene expression remains largely unstudied and poorly understood.
In this Thesis, 3 major questions have been asked and addressed:
1. What is the role of global kidney DNA methylation in EH and is there a correlation between
kidney and blood global DNA methylation percentage?
2. Does renal loci specific, genome-wide DNA methylation have a role in EH diagnosis as well
as elevated BP?
3. What is the relationship between renal DNA methylation and renal gene expression in EH?
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The major findings of this Thesis are:
1. Global methylation in the kidney and in peripheral blood leukocytes does not have an
association to EH diagnosis, but kidney global methylation does have an association with
DBP (Chapter 3).
2. Global methylation findings between peripheral blood findings and kidney DNA are not
correlated (Figure 3.7, 3.8).
3. Loci specific DNA methylation is an essential mechanism involved in the development of EH
(Chapter 4).
I. Several arterial pressure modulating pathways contain differentially methylated CpGs,
such as: circadian clock; neural development; transport of monocarboxylates; peptidase
activity; insulin signalling; transcriptional regulation; cell cycle progression;
metabolism and RNA processing (Table 4.4, 4.5).
II. Differentially methylated CpG islands are also found in pathways that modulate BP
such as: MAPK leading to cellular differentiation, and possible regulation of eGFR; cell
volume regulation, salt transport and peripheral nervous system function; circadian
clock (Table 4.6, 4.7).
III. Differentially methylated CpG sites identified in renal DNA replicate the findings of
blood based genome-wide methylation studies. These include roles such as: oxidation
of 5mC to 5hmC; members of cell cycle progression, which are also implicated in the
SHR model; genes previously implicated in dilated cardiomyopathy; regulators of
histone acetylation (Table 4.8).
4. Expression of 21 renal genes (19 novel) are associated with EH and BP and there is a significant
overlap between kidney RNA-seq findings and blood-based GWAS (Chapter 5).
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I. Differential expression of HT renal genes involves loci that are associated with smooth
muscle cell function; glucocorticoid conversion; salt balance (Table 5.3, 5.4, Figure
5.1, 5.2, 5.3).
II. Differentially expressed loci in the kidney replicate the findings of blood-based GWAS.
These include roles such as: transcription regulation; cardiac hypertrophy;
glucocorticoid conversion; salt balance (Table 5.5).
5. DNA methylation is a primary epigenetic driver of HTN-associated renal gene expression
(Chapter 5).
I. Methylation of two genes, which regulate circadian clock and neurotransmitter uptake,
is associated with its over/ underexpression. Where hypermethylation causes
underexpression and hypomethylation caused overexpression.
II. A reverse analysis of renal CpG sites and loci previously identified blood-based BP
GWAS revealed methylation pattern similarities between the two tissues types. These
include roles such as: heart rate regulation; cholesterol production; Wnt signalling;
Notch pathway vascular development; inflammation; phosphate transport mechanisms.
III. Analysis of DPF3 in BP GWAS GWAS [20] indicates that the methylation status of
DPF3 may be inherited.
This PhD Thesis provides new insight in to the role of DNA methylation on the development of HTN and modulation of BP. This Thesis identifies novel relationships between DNA methylation and transcription in the human kidney that influence genes implicated in HTN; suggesting a significant role for renal epigenetic modification in the altered function of various genes that regulate atrial pressure.
In addition, this Thesis uncovers previously unidentified kidney expressed genes that contribute to, and that are affected by, HTN. Collectively, this Thesis supports the hypothesis that HTN development and
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BP regulation are governed by renal gene expression changes influenced by renal DNA methylation.
The following sections will discuss the importance of these findings and their broad implications for our understanding of the role of the kidney in HTN pathophysiology.
6.2 Global methylation and essential hypertension – relationships and limitations
The research outcomes from this PhD Thesis demonstrate that global methylation in the kidney and in peripheral blood leukocytes are not associated with HTN diagnosis. However, it was identified that kidney global methylation has an association with DBP but this relationship was not conserved after the values were adjusted for the use of antihypertensive medication. Furthermore, for global methylation kidney and blood 5mC percentage is not correlated, which is indicative that blood may not be a good surrogate for global methylation studies of non-blood-based diseases.
Many studies debate the reliability of global methylation measurement techniques. High performance liquid chromatography – ultraviolet (HPLC-UV) is considered the “gold-standard” assay for quantifying the amount of 5mC present in a hydrolysed DNA sample. However this method is limited by the utilisation of specialized equipment required to conduct the analyses and requirement of large quantities of DNA (~10µL) [230] Liquid chromatography coupled with mass spectrometry (LC-
MS/MS) is highly sensitive and offers an alternative to HPLC-UV and requires smaller hydrolysed
DNA quantities (50-100ng) [230]. LC-MS/MS is designed to detect levels of methylation ranging from
0.05% to 10% and has been validated to detect differences corresponding to ~5% in global methylation percentage [230]. However, expertise and equipment required for LC-MS/MS is not common and for this reason, it is not a routine method used for global methylation analysis. The third alternative for
159 | Page global methylation detection is the enzyme-linked immunosorbent assay (ELISA) method (used in this
PhD). There are several commercially available ELISA kits which enable fast and simple detection of
5mC. The major limitation of ELISA based detection is the likelihood of inter and intra-assay variability, and therefore can only provide estimates of DNA methylation. Furthermore, due to variability ELISA detection can only visualise large changes in DNA methylation (~1.5-2 fold). The
Zymo 5mC assay is unable to discriminate between 5mc and 5hmC detection. This is of significant consequence as functionally, the production of 5hmC seems to promote gene expression during active demethylation [157].
6.3 Pathways affected by CpG methylation and differential expression in the hypertensive kidney
There are several biological mechanisms that ensure BP is tightly regulated. These include the autonomic nervous system, the vasculature itself, and the kidney. This PhD Thesis highlights differential DNA methylation and gene expression in key loci that correspond to each of these key biological regulators of BP.
6.3.1 Pathways involved with hypertension-associated differential CpG loci methylation
6.3.1.1 Renal methylation and the autonomic nervous system control of BP
The neural control of BP operates via parasympathetic neurons that innervate the heart and three main classes of sympathetic efferent neurons (baroreceptive, thermosensitive and glucosensitive cardiovascular) which innervate blood vessels in the heart, kidneys and the adrenal medulla.
Methylation of renal SLC17A7 was revealed via pathway analysis to have a major role in the Glutamate
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Neurotransmitter Release Cycle. In the kidney glutamate contributes to ammonia secretion and regulation of acid-base balance [231]. Glutamate is also the principal excitatory neurotransmitter in the brain [231]. Excessive activation of glutamate receptors in the CNS induce an excitotoxicity response, which is associated with various forms of neurodegeneration [232]. Conversely hypo-activity of glutamate receptors is associated with sever cognitive disturbance, including schizophrenia [233]. A study by Westhoff et al [234] report a robust correlation between mean atrial pressure and glutamate concentration, here we report that methylation in SLC17A7 provides a potential link between BP and neurotransmitter status.
The secretogranin family of proteins, such as the one expressed by secretogranin II (SCG2) has a major role in the biology of catecholaminergic systems, such as autonomic neurons and their physiologic targets [235, 236]. Proteolytic cleavage of granins generates an array of bioactive peptides which, in aggregate, have physiological effects on cardiovascular, metabolic and endocrine systems via modulation of the sympathetic nervous system [235]. Studies indicate that granins have a role in the pathogenesis of HTN via autonomic control of BP. In particular, SCG2 derived secretoneurin peptide stimulates migration and proliferation of vascular smooth muscle cells [237], providing a direct link to vascular development and remodeling. Renal methylation of SCG2 is associated with DBP, implicating
DNA methylation in SCG2 mediated vascular function. SNP analyses conducted by Wen et al [236] displayed significant association of SCG2 –associated SNPs with elevated BP and HTN diagnosis.
DNA methylation induced variation to SCG2 expression alter the angiogenic and vasculogenic activites of secretonurin and may influence susceptibility to HTN via actions of the cardiovascular system.
Similar to the actions of insulin and angiotensin II, secretoneurin activates phosphatidylinositol/ Akt pathways [238], which regulates BP via nitric oxide production [239].
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6.3.1.2 Renal methylation and vasculature control of BP
The endothelium is an integral component of the vascular wall and is a major regulator of BP [3], acting as a door that either blocks or allows access to the inflamed organ. CD95 (FAS/ apoptosis antigen 1) belongs to the tumor necrosis factor receptor superfamily [240]. Recent data reveals that CD95 promotes anti-apoptotic signals, promotes inflammation and contributes to carcinogenesis [240]. CD95 is overexpressed at the surface of endothelial cells covering blood vessels of patients affected by inflammatory disorders [240] and can simulate production of nitric oxide thereby controlling BP [241].
DCP2 is a component of mRNA decay machinery acting as an mRNA decapping enzyme. Renal differential methylation of DCP2 is identified in pathway analysis to be implicated in expression of
CD95-associated endothelial dysfunction, offering a complex biological role for DCP2 in the context of endothelial survival and function. It is likely that methylation of DCP2 is operating via interactions with tristetraprolin, which can induce reduced nitric oxide-mediated vasorelaxation [242].
6.3.1.3 Renal methylation and kidney-based mechanisms of BP control
The transaldolase enzyme is involved in the reversible section of the pentose phosphate pathway.
Transaldolase deficiency (OMIM 606003), like HTN, is a multisystem disorder, characterised by abnormal polyol concentrations in body fluids, hepatosplenomegaly, hepatic fibrosis, coagulopathy, thrombocytopenia and dysmorphic features [243]. Differential methylation of the TALDO1 gene, which codes for transaldolase, is associated with DBP in the human kidney. A study by Loeffen et al
[243] found that transaldolase deficiency was linked to renal low molecular weight, proteinuria and hypercalciuria. In addition renal tubular dysfunction was present and chronic renal failure had developed in aging patients [243]. It is therefore likely that differential methylation in TALDO1 is influencing renal function directly.
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The RAAS is a key player of BP control and regulation of electrolyte and body fluid homeostasis [244].
It has been demonstrated that the JAK-STAT pathway mediates angiotensin II-triggered gene transcription, in turn acting as an amplifying system that contributes to further intrarenal RAAS activation [244]. There is mounting evidence that JAK is implicated in the development of RAAS- associated diseases and, in consequence, JAK is now acknowledge as a key target for clinical and biochemical studies associated with the RAAS system. Additionally, the interactions between JAK and angiotensin II are important in the context of HTN. The RAAS and inflammatory cytokines synergise to raise BP [245], with angiotensin II systemically and locally increasing levels of interleukin 6 and interferon γ [245]; leading to activation of the JAK-signal transducers and activators of transcription
(STAT) pathway. The activated JAK-STAT pathway is a potent stimulus to protein expression of angiotensinogen [244]. Pathway analysis reveals that differential methylation of TALDO1 is associated with JAK-STAT signalling after interleukin 12, potentially contributing to the local synthesis of angiotensin II and the progression of HTN.
VDR is differentially methylated in the kidney and the main activity of vitamin D is mediated by the vitamin D receptor (VDR). Inadequate vitamin D status is linked to many non-skeletal chronic diseases including CVD and HTN. Vitamin D can downregulate RAAS, and cellular treatment with active vitamin D has been demonstrated to directly reduce promotor activity on the renin gene [246].
Futhermore, in the VDR knockout mouse, renin expression and plasma angiotensin II concentration are dramatically increased and result in increased BP [246]. In human studies, individuals with insufficient vitamin D have higher plasma concentration of renin [247], angiotensin II [247, 248] and renal plasma flow response to infused angiotensin II was blunted [248]. The mechanisms by which vitamin D influences renin gene transcription were found by Yuan et al [249], who identified that
163 | Page calcitriol is not involved in the vitamin D response element pathway but rather bind to the cyclic adenosine monophosphate (c-AMP) response element. c-AMP is a primary intracellular signal in renin expression, in this manner, the c-AMP response element in the promotor region of renin is bound to
CREB and triggers gene transcription [249].
6.3.2 Pathways involved in differential gene expression in the hypertensive kidney
6.3.2.1 Renal gene expression and vasculature control of BP
Redox signalling is implicated in the pathogenesis and progression of HTN via different physiological and pathological processes in the vasculature. Dysregulation of vasomotor function and structural remodelling of blood vessels contribute to HTN as high BP is partially determined by peripheral vascular resistance [250]. In this Thesis differential expression of TXNRD1 is associated with HTN.
TXNRD1 codes for a protein called Thioredoxin Reductase 1, which pays a key role in redox homeostasis [250]. Reactive oxygen species increase vascular tone by influencing the regulatory role of the endothelium and directly effecting the contractility of vascular smooth muscle cells [250]. It is likely that altered expression of TXNRD1 is contributing to HTN status via aberrant redox signalling pathways leading to excessive production of reactive oxygen species, and thereby altered vascular function.
Phosphoinositides are acidic phospholipids of cell membranes which are found primarily in the cytoplasmic leaflet and serve as membrane recognition sites as well as act as membrane delimited second messengers modulating the activities of some membrane proteins [251]. Renal differential expression of PIP4K2A was recognised in the kidney to be involved in receptor tyrosine kinase
164 | Page signalling. Receptor tyrosine kinases contribute to vascular remodelling via close interaction with many downstream targets, where over activation of receptor tyrosine kinases is responsible for several pathological conditions including CVD [252]. Receptor tyrosine kinases are key players in vascular biology, and are activated by ligand-dependent and independent mechanisms such as trans-activation of G protein coupled receptors, mineralocorticoid receptors and reactive oxygen species.
6.3.2.2 Renal gene expression and kidney-based mechanisms of BP control
The renal HSD11B2 gene encodes the beta-hydroxysteroid dehydrogenase type 2 enzyme, which inactivates 11- hydroxysteroid in the kidney. This activity protects the nonselective mineralocorticoid receptor from occupying glucocorticoids. It is via this mechanism that loss of function to the HSD11B2 gene results in overstimulation of the mineralocorticoid receptor to cause salt-sensitive HTN [253].
Differential expression of HSD11B2 is identified in this thesis and associated with DBP. This is consistent with essential HTN studies by Williams et al [253] and monogenic studies of HSD11B2
[254].
Mitochondria are the primary cellular energy producers, and are present in abundance, in the heart, kidney and brain. As well as their role in the maintenance of cellular respiration, these membrane bound organelles also modulate functions of cellular proliferation, apoptosis, generation of reactive oxygen species and cellular calcium homeostasis [255]. Therefore, any damage to the structure or function of the mitochondria will compromise the overall function of the cell. Futhermore, mitochondrial abnormalities and dysfunction have been identified in experimental models of HTN [255]. One of the renal genes responsible for mitochondrial cristae formation is C19orf70, which was differentially expressed in the HT population. This is in line with several studies of experimental HTN which report
165 | Page renal mitochondrial injury [256-258]. Here we demonstrate that altered expression of C19orf70 is a key signature of mitochondrial function in the HT kidney.
6.4 DNA methylation is associated with altered expression of renal genes
6.4.1 Alterations in renal DNA methylation increase expression of Pyruvate Carboxylase (PC)
Disruption to pyruvate metabolism may arrive from mutations to many of the genes coding for enzymes that regulate it [259]. PC encodes pyruvate carboxylase which uses carbon dioxide and adenosine triphosphate to produce oxaloacetate and adenosine diphosphate [260]. The result of disruptions to this process are highly variable but may include lactic acidosis, developmental delay and elevate proline and alanine [260]. The oxaloacetate produced by PC is a critical intermediate in metabolism, which links carbohydrate metabolism, lipid, amino acid and nucleotide metabolism [261]. Pyruvate utility also includes provision of carbon to several major biosynthetic pathways that intersect the citric acid cycle. Together oxaloacetate and citrate support the major biosynthetic pathways of gluconeogenesis and lipogenesis [259]. Expression of PC is ubiquitous in the human body, with higher expression in the liver, kidney adipose and heart [259]. The brain is reliant upon glucose and pyruvate metabolism to generate cellular energy. Therefore, perturbations to glucose and pyruvate metabolism have striking neurological consequences [259]. Here we report disruptions to the expression of PC in the renal transcriptome are associated with HTN. This finding is supported by previous works by demonstrating that disruptions to the citric acid cycle has been implicated in resistance forms of HTN, where increased levels of oxaloacetate was associated with resistant HTN cases [262]. Furthermore, PC, among other functions, is involved in the synthesis of the neurotransmitter glutamate, a well-
166 | Page established endogenously produced excitatory agonist [212]. With respect to cardiovascular control, glutamate has been known to have an effect on BP since 1954, with intravenous glutamate inducing a pressor response in dogs and rabbits [213-215]. It is likely that the overexpression of PC seen in our study is contributing to upregulation of glutamate activity and thereby an increase in BP.
6.4.2 Alterations in renal DNA methylation decrease expression of Family With Sequence Similarity 50 Member B (FAM50B)
Current management strategies for HTN gives small consideration to the biological rhythms inherit to the disease process. However, studies indicate that the absence of nocturnal BP “dipping” and an increased morning surge of BP increases is associated with overall CVD risk [263]. A study by Cuspidi et al [264] demonstrated that chronic absence of nocturnal BP dipping is associated with increased left ventricular mass, a thicker interventricular septum and increased diameter of the left atrium. There is also growing evidence linking the early morning rise in BP to cardiovascular risk [265]. FAM50B is a key regulatory gene of the circadian clock, which may have corresponding implications for the RAAS.
RAAS displays peak plasma concentrations of renin activity, ACE and aldosterone just before morning wake times [266]. It is likely that altered RAAS activation is an end product of the circadian variations under regulation by FAM50B. Interestingly FAM50B, is adjacent to a differentially methylated region
(DMR). Methylation levels of FAM50B is similar in multiple tissues and is unmethylated in sperm, suggesting that it is likely to be a primary maternal DMR [209]. Further, methylation in CpG sites located in FAM50B are positively associated with telomere length [210] and has been recognised as belonging to a set of 353 ‘age-predictor’ CpGs [211]. As a known risk factor for HTN, it is not surprising to see an epigenetic signature of chronological age.
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6.5 Double PHD finger (DPF3) is a candidate of methylation inheritance
Analysis of DPF3 in our renal methylation and expression findings coupled with data from BP GWAS
[20] indicates that the methylation status of DPF3 may be inherited. In humans, DPF3 is significantly upregulated in the hypertrophic hearts of individuals with aortic stenosis and cardiomyopathy [201].
During development Dpf3 is expressed in the heart and somites of mice, chicken and zebrafish [267], where knockdown studies in zebrafish resulted in reduced cardiac contractibility. DPF3 is associated with the BAF chromatin remodeling complex and binds methylated and acetylated lysine residues of histone 3 and 4 [267]. Our analysis of DPF3 adds a complex layer to the BAF complex and indicates that DPF3 has a role to play in the recruitment of chromatin remodelling complexed to acetylated histones.
6.6 Summary and future directions
The kidney has a key regulatory role during the onset of HTN and periods of sustained elevated BP.
Futhermore, epigenetic modification to the kidney in the form of DNA methylation is a complex and interactive link to expression of genes that modulate BP. Lessons from previous methylation studies suggest a role for DNA methylation in the multisystem and polygenic nature of HTN. Locus specific targeting of the epigenome is critical to enhance our understanding of modifiers such as DNA methylation on BP; however, for effective analysis a deeper understanding the interaction between
HTN-associated systems is required. In addition, a more comprehensive understanding of the relationship between the renal epigenome and transcription changes during HTN onset is necessary and could shape the future of therapeutic advancement. Some of the renal genes mentioned in this thesis, where the RNA or protein products are clinically measurable (DPF3 and FAM50B) may become
168 | Page attractive targets for the development of new diagnostics, for example to detect early renal signatures of HTN onset. Further studies on the altered expression of these genes in context to HTN onset and severity would be beneficial, particularly in a larger cohort with blood and renal samples available with the possibility of extending investigations onto sorted cell subsets. Indeed, pharmacological and lifestyle therapies informed by genomics are already available for patients with HTN. However, due to the multifaceted nature of the disease and the development of resistant forms of HTN, more targeted interventions are needed. Further omics-based investigation of the kidney could help facilitate the advancement of the current treatment strategies for HTN, from management of its modifiable risk factors into individualised preventative protection strategies.
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Chapter 7. List of references
“Learning is a kind of natural food for the mind” ~ Cicero
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Chapter 7. List of references
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