A Dissertation Entitled Mapping and CRISPR/Cas9 Gene Editing For

A Dissertation

entitled

Mapping and CRISPR/Cas9 Gene Editing for Identifying Novel Genomic Factors

Influencing Blood Pressure

Harshal Waghulde

Submitted to the Graduate Faculty as partial fulfillment of the requirements for the

Doctor of Philosophy Degree in Biomedical Sciences

______Bina Joe, PhD, Committee Chair

______Guillermo Vazquez, PhD, Committee Member

______Kathryn Eisenmann, PhD, Committee Member

______Jennifer Hill, PhD, Committee Member

______Jiang Tian, PhD, Committee Member

______Amanda Bryant-Friedrich, PhD, Dean College of Graduate Studies

The University of Toledo

August 2016

This document is copyrighted material. Under copyright law, no parts of this document may be reproduced without the expressed permission of the author. An Abstract of

Mapping and CRISPR/Cas9 Gene Editing for Identifying Novel Genomic Factors Influencing Blood Pressure

Harshal Waghulde

Submitted to the Graduate Faculty as partial fulfillment of the requirements for the Doctor of Philosophy Degree in Biomedical Sciences Degree in Doctor of Philosophy Degree in Biomedical Sciences

The University of Toledo

August 2016

Hypertension is a complex polygenic trait and a significant risk factor for cardiovascular and metabolic diseases. Rodent models serve as tools to identify causal genes for complex traits. This dissertation is comprised of two projects. Project 1 utilizes substitution mapping as an approach to locate blood pressure quantitative trait loci (BP

QTLs) on rat chromosome 5 (RNO5) and project 2 utilizes Clustered Regularly

Interspaced Short Palindromic Repeats (CRISPR)/CRISPR Associated proteins 9 (Cas9) genetic engineering as an approach to explore the physiological function of G-protein coupled estrogen receptor (Gper1) in a rat model of hypertension.

Previously, using linkage analysis and substitution mapping, two closely-linked interactive blood pressure quantitative trait loci (QTLs), BP QTL1 and BP QTL2, have been defined within 117894038bp-131853815bp region (RGSC 3.4 version) on rat chromosome 5 (RNO5). This was done by using a series of congenic strains consisting of genomic segments of the Dahl salt-sensitive (S) rat substituted with that of the normotensive Lewis (LEW) rat. Through the construction and characterization of a panel of S.LEW bicongenic strains and corresponding S.LEW monocongenic strains, definitive iii evidence of epistasis (genetic interaction) between BP QTL 1 (7.77Mb) and BP QTL 2

(4.18Mb) has been documented. In order to further map these interacting QTLs, we constructed a new panel of 7 bicongenic strains and monitored their blood pressure by radiotelemetry. The data obtained from these new strains further resolved BP QTL1 from

7.77Mb to 2.93Mb. It was also evident that the QTL2 is not a single QTL, but consists of at least 3 QTLs (2.26Mb, 1.31Mb and 175kb) with contrasting effects on blood pressure.

In the second project, we utilized CRISPR/Cas9 genetic engineering approach to study the physiological role of G-protein coupled estrogen receptor (Gper1) in the Dahl-salt sensitive (S) rat. A link between gut microbiota and blood pressure (BP) regulation was previously demonstrated in our laboratory. Gut microbiotal transplantation from Dahl-salt resistant (R) rats into genetically hypertensive Dahl-salt sensitive (S) rats caused an elevation in BP, which was associated with an increase in plasma acetate. Acetate is a short chain fatty acid, which is a known ligand for two of the G-protein coupled receptors, Gpr41 and Olfr78. Deletion of either Gpr41 or Olfr78 is reported to affect BP.

Because S and R rats do not have allelic variations of Gpr41 and Olfr78, the observed increased plasma acetate being associated with elevated blood pressure cannot be attributed to these two receptors alone. This led us to hypothesize that yet unknown receptors of acetate exist on the rat genome to regulate BP. To test this hypothesis, we focused on a more recently discovered G-protein coupled estrogen receptor (Gper1) which belongs to the same class of orphan receptors as Gpr41. To completely disrupt

Gper1 in S rats, we employed clustered regularly interspaced short palindromic repeats/CRISPR associated protein 9 (CRISPR/Cas9) approach with two gRNAs each targeting one end of the rat Gper1 gene. The resultant Gper1-/- rats had significantly

iv lower BP and increased vasorelaxation to acetylcholine compared to wild type S rats.

Further, to examine whether the presence or absence of Gper1 influence vascular response to short chain fatty acids (acetate, propionate and butyrate), wire myograph studies were conducted using small mesenteric arteries (SMAs). While a rapid contraction effect of acetate and butyrate in phenylephrine pre-contracted arteries were similar, the sustained relaxation following rapid contraction was significantly decreased in vessels from Gper1-/- rats. Because gut microbiota is the source of short chain fatty acids, we conducted microbiotal transplantation studies, data from which demonstrated that the observed BP lowering effect of Gper1-/- was abolished. Collectively, the results point to Gper1 as a novel short chain fatty acid receptor.

This dissertation is dedicated to my beloved parents who have been with me at every step of the way, through good and the bad times, for their unconditional love, guidance, and support, and for raising me to be the person that I am today. I also dedicate my work to my wife for instilling in me the confidence that I am capable of doing anything I put my mind to.

Acknowledgements

I am grateful to my mentor Dr. Bina Joe for accepting me as a student in her laboratory and giving me exciting projects to work on. It is worth-mentioning that her endless motivation, valuable guidance and meticulousness about every detail helped me develop a genuine interest in the subject and persistently encouraged me to do top notch research.

Apart from research, I also learnt many other things from her that would be really helpful to me in the future. I would also like to thank my ex-colleague and best friend Dr. Resmi

Pillai for helping me understand the difficult genomic concepts quickly and also for her unwavering support inside as well as outside the lab.

I am deeply grateful to my advisory committee members Dr. Vazquez, Dr. Eisenmann,

Dr. Hill and Dr. Tian for their valuable suggestions for my research. I further extend my utmost gratitude to our collaborators for their help in the generation of knock out rats. I also thank my past and present lab mates and the Department of Physiology and

Pharmacology for all their help and support in this course of my scientific journey.

My vote of thanks will be incomplete without mentioning my parents and my brother who have always been a source of encouragement and inspiration for me, as well as my in-laws for their support and understanding. Last but not the least I would like to thank my wife, Priya, for her undying love and support. She is always by my side and helped me find the right direction in every task I took in my hand.

Table of Contents

Abstract ...... iii

Acknowledgements ...... vi

Table of Contents ...... vii

List of Tables ...... xi

List of Figures ...... xii

List of Abbreviations ...... xv

1 Introduction…...... 1

1.1 Complex traits and genetics ...... 1

1.2 Missing heritability ...... 2

1.2.1 Epistasis ...... 4

1.2.2 Yet undiscovered variants ...... 4

1.3 Hypertension as a complex polygenic trait ...... 6

1.3.1 Why study the genetics of hypertension? ...... 7

1.3.2 The rat as a physiological model of hypertension ...... 7

1.4 Genetic methods for analysis of inherited hypertension ...... 9

1.4.1 Genetic linkage analysis ...... 9

1.4.1.1 Genetic linkage analysis using rat models ...... 9

1.4.2 Substitution mapping using congenic strains ...... 11

1.4.3 Genome-wide association studies (GWAS)...... 14 vii

1.4.4 Genome editing as a tool to study hypertension ...... 15

1.4.4.1 CRISP/Cas9 ...... 17

1.5 The emerging role of Gper1 in human physiology and diseases ...... 20

1.6 Link of G-protein coupled receptors (GPCRs) and gut microbiota ...... 21

1.7 Goals for the dissertation ...... 25

2 Materials and Methods ...... 26

2.1 Animals…...... 26

2.2 Animal diets ...... 26

2.3 Genotyping and DNA sequencing ...... 27

2.4 Construction of congenic strains ...... 27

2.5 Generation of a CRISPR/Cas9 mediated Gper1-/- rat ...... 28

2.5.1 Designing of guide RNAs (gRNAs) ...... 28

2.5.2 Transgenesis and genotyping of founders...... 28

2.6 Assessment of Gper1 expression in heart homogenates ...... 30

2.7 Blood pressure measurement by Radiotelemetry ...... 30

2.8 Renal function assessment through urinary protein excretion (UPE) ...... 31

2.9 Vascular reactivity by wire myograph method ...... 31

2.10 Microbiotal transplantation study ...... 32

2.11 Genomic DNA isolation, 16S rRNA gene sequencing, and analysis of

microbiotal composition ...... 33

2.11.1 PCR Amplification of 16S rRNA gene ...... 34

2.11.2 Library Purification, Verification and Sequencing ...... 34

2.11.3 Quality Filtering and OTU Picking...... 35

viii

2.11.4 Alpha Diversity Analysis ...... 35

2.11.5 Beta Diversity Analysis ...... 36

2.11.6 Taxonomic Comparisons ...... 36

2.11.7 Enriched Taxa (Kruskal-Wallis) ...... 36

2.12 SCFA induced ex-vivo relaxation in rat mesenteric arteries ...... 36

2.13 Statistical analyses ...... 37`

3 Fine mapping of epistatic genetic determinants of blood pressure on rat

chromosome 5 (RNO5) ...... 38

3.1 Introduction ...... 38

3.2 Results…...... 41

3.2.1 A new panel of bicongenic strains...... 41

3.2.2 Blood pressure of congenic strains by radiotelemetry...... 41

3.2.3 Sequence variants within the newly localized QTLs...... 53

3.3 Discussion...... 54

4 Effects of the gut microbiota on host blood pressure is modulated by G-protein

coupled estrogen receptor (Gper1) ...... 57

4.1 Introduction ...... 57

4.2 Results…...... 59

4.2.1 CRISPR/Cas9 mediated generation of Gper1-/- rats using two

gRNAs...... 59

4.2.2 Morphometric characteristics...... 66

4.2.3 Attenuation of hypertension in Gper1-/- rats...... 71

4.2.4 Superior vascular function of Gper1-/- rats...... 71

4.2.5 Vascular responses of 17β-estradiol and aldosterone on mesenteric

vessels...... 79

4.2.6 Reversible BP lowering effect in Gper1-/- rats after transplantation of

S rat gut microbiota...... 82

4.2.7 Microbial sequencing in fecal samples of S and Gper1-/- rats...... 84

4.2.8 Alpha diversity analysis in S and Gper1-/- fecal samples...... 86

4.2.9 Beta diversity analysis of S and Gper1-/- fecal samples...... 87

4.2.10 Taxonomic comparisons of 16S rRNA gene sequence between S

and Gper1-/- rat’s fecal samples at day 4...... 90

4.2.11 Taxonomic comparisons of 16S rRNA gene sequence between S

and Gper1-/- rat’s fecal samples at day 28...... 92

4.2.12 Enriched Taxa (Kruskal-Wallis) within S and Gper1-/- fecal

samples...... 93

4.2.13 Vascular responses of short chain fatty acids on rat small

mesenteric vessels...... 96

4.3 Discussion…...... 99

5 Summary……...... 104

References ...... 107

A Microsatellite markers developed during the construction of bicongenic sub

strains of S.LEW(5)X6BX9X5 ...... 123

B Publications and presentations ...... 126

List of Tables

1.1 Candidate BP controlling loci prioritized by high resolution substitution mapping

in SS/Jr rat ...... 13

3.1 Physical sizes of the QTL intervals and number of variants ...... 53

3.2 List of SNP and INDELs within protein-coding genes in QTL1 ...... 54

4.1 Off Target Analysis – rGper1 5’ ...... 60

4.2 Off Target Analysis – rGper1 3’ ...... 62

4.3 Body weights and tissue weights of wild-type hypertensive S rats and Gper1-/-

rats…...... 69

4.4 Total number of sequences per sample after quality filtering and OTU picking

…...... 85

4.5 List of significantly enriched (Kruskal-Wallis p<0.01) taxa within S and Gper1-/-

cohorts at day 4 …...... 95

4.5 List of significantly enriched (Kruskal-Wallis p<0.01) taxa within S and Gper1-/-

cohorts at day 28 …...... 96

A.1 Microsatellite markers developed during the construction of bicongenic substrains

of S.LEW(5)X6BX9X5 ...... 123

List of Figures

1-1 Feasibility of identifying genetic variants by risk allele frequency and strength of

genetic effect (odds ratio)...... 3

1-2 The construction of recombinant inbred animals which are genetic mosaic of the

two founding strains, consomic and congenic strains...... 12

1-3 General mechanism of CRISPR/Cas9 induced modification in genome...... 18

2-1 Protocol for transgenesis ...... 29

2-2 Protocol for microbiotal transplantation study ...... 33

3-1 Schematic representation of the monocongenic and bicongenic strains ...... 40

3-2 Schematic representation of the new iteration of bicongenic strains ...... 44

3-3 (a-g) Blood pressure of the bicongenic strains by radiotelemetry ...... 46-52

4-1 Single nucleotide polymorphism (SNP) check for 5’ end gRNAs ...... 64

4-2 Single nucleotide polymorphism (SNP) check for 3’ end gRNAs ...... 64

4-3 Mismatch detection assay ...... 65

4-4 RNA validation via deletion PCR ...... 66

4-5 Screening animals for CRISPR/Cas9 mediated deletion of Gper1 ...... 67

4-6 Morphometric characteristics of S and Gper1-/- rats ...... 70

4-7 Attenuation of blood pressure of Gper1-/- female rats ...... 72

4-8 Attenuation of blood pressure of Gper1-/- male rats ...... 73

xii

4-9 Urinary protein excretion (UPE) of Gper1-/- rats was comparable to that of wild-

type hypertensive rats...... 74

4-10 Gper1-/- female rats demonstrated superior vascular function compared with wild-

type hypertensive rats ...... 75

4-11 Gper1-/- male rats demonstrated superior vascular function compared with wild-

type hypertensive rats...... 77

4-12 Gper1-/- female rats demonstrated a trend for decrease in vasorelaxant effect of

aldosterone in endothelium intact mesenteric arteries compared with wild-type

hypertensive rats...... 80

4-13 Gper1-/- male rats demonstrated a significant decrease in vasorelaxant effect of

aldosterone in endothelium intact mesenteric arteries compared with wild-type

hypertensive rats...... 81

4-14 The blood pressure protecting effect in Gper1-/- female rats was reversible with

transplantation of cecal content from wild-type hypertensive rats...... 82

4-15 The blood pressure protecting effect in Gper1-/- male rats was reversible with

transplantation of cecal content from wild-type hypertensive rats...... 83

4-16 Summary graph showing the effect of S rat gut microbiota on the blood pressure

regulatory effect of Gper1 ...... 84

4-17 Alpha diversity rarefaction curves reveal differences in observed species richness

between S and Gper1-/- fecal samples...... 87

4-18 Principal Coordinates Analysis (PCoA) plots at day 4 and day 28 ...... 88

4-19 Principal Coordinates Analysis (PCoA) plots at day 4 ...... 89

4-20 Principal Coordinates Analysis (PCoA) plots at day 28 ...... 90

xiii

4-21 Relative abundance plots to display differences in general microbial community

structure between fecal samples collected from rats at day 4 ...... 91

4-22 Relative abundance plots to display differences in general microbial community

structure between fecal samples collected from rats at day 28 ...... 93

4-23 Decreased relaxation of rat small mesenteric arteries (SMAs) to 5mM sodium

acetate and sodium butyrate in Gper1-/- rats compared with S rats ...... 98

xiv

List of Abbreviations

ACh ...... Acetylcholine ANOSIM ...... Analysis of similarity

BP ...... Blood Pressure bp...... Base pairs

CSS ...... Cumulative Sum Scaling CCRC ...... Cumulative concentration response curve cM ...... centimorgan CRISPR/Cas9 ...... Clustered Regularly Interspaced Short Palindromic Repeats/CRISPR Associated proteins 9 DBP ...... Diastolic Blood Pressure DSB ...... Double stranded break

ES cells...... Embryonic stem cells

FBW ...... Final Body Weight FFA ...... Free fatty acids

Gb ...... Giga base pairs GLP-1 ...... Glucagon-like peptide 1 Gper1 ...... G-protein coupled estrogen receptor 1 GPR30 ...... G-protein coupled receptor 30 Gpr41 ...... G-protein coupled receptor 41 Gpr43 ...... G-protein coupled receptor 43 GWAS ...... Genome Wide Association Studies gRNA ...... guide RNA

HR ...... Heart Rate HDR ...... homology-directed repairs

INDELS ...... INsertions and DELetions IUPHAR ...... International Union of Basic and Clinical Pharmacology kb...... kilo bases

LCFA ...... Long-chain fatty acids LEW ...... Lewis rat strain LOD ...... Logarithm Of Odds

NHEJ ...... Non-homologous end joiningMCFA Medium-chain fatty acids mm Hg ...... milliliter of mercury mRNA ...... messenger RNA Mb ...... Mega bases MAP ...... Mean Arterial Pressure MNS ...... Milan Normotensive Strain

OTU ...... Operational taxonomic unit

PAM ...... Protospacer adjacent motif PCoA ...... Principal coordinates analyses PCR ...... Polymerase chain reaction PE ...... Phenylephrine PP ...... Pulse Pressure PSS ...... Physiological salt solution

QTL ...... Quantitative trait locus

RNO5 ...... Rat chromosome 5 RT-PCR...... Reverse transcriptase-polymerase chain reaction

S ...... Dahl salt-sensitive S rat SNP ...... Single-Nucleotide Polymorphisms SBP ...... Systolic Blood Pressure SCFA...... Short-chain fatty acids SHR ...... Spontaneously Hypertensive Rats SHRSP ...... Spontaneously Hypertensive Rats Stroke-Prone SMAs ...... Small mesenteric arteries

TALEN ...... Transcription activator-like effector nucleases

UPE ...... Urinary Protein Estimation

WKY ...... Wistar Kyoto Rats

ZFN ...... Zinc finger nuclease

xvi

Chapter 1

Introduction

1.1 Complex traits and genetics

Investigation of genetic basis of human diseases (traits) is key to understand the mechanism behind phenotypic variation and design better treatment strategies.

Identification of genes and their interaction networks can unravel new targets for therapeutic intervention. Etiologically, most human genetic traits can be classified into two categories as monogenic and polygenic. Monogenic traits (such as cystic fibrosis) are those which are under the influence of variation within a single gene that can be a point mutation (single nucleotide change), insertion or deletion of the DNA sequence that encodes proteins or variation in non-coding DNA resulting in gene splicing. This results in alteration of three-dimensional structure of the protein thereby causing phenotypic variation. However complex polygenic traits (such as blood pressure) are influenced by multiple genes and their interaction with environmental factors such as diet, exercise or others. The genetic spectrum of complex traits is broad and highly complicated whereby it is difficult to pinpoint a particular gene from among the group of genes influencing that

particular polygenic trait. There are a number of factors responsible for this complexity including but not limited to gene-gene interaction, genetic heterogeneity and low penetrance [1].

While monogenic traits follow Mendelian principles of inheritance, genetic dissection of complex traits have been frustrating because they do not follow any predictable pattern of inheritance. However, this distinction is sometimes overly simplistic as some of the

Mendelian traits can exhibit allelic heterogeneity [2, 3].

1.2 Missing heritability

The phenotypic diversity that exist within and between species is the most ubiquitous characteristic of life. Unraveling the heritable basis of this phenotypic variation is daunting albeit a fundamental task of significant importance in biomedical research [2].

Genome wide association studies (GWAS) to date have investigated the genetic basis of common human polygenic traits, identifying thousands of loci and key biological pathways [4-11]. However, the genetic variants identified so far appear to explain only a very small proportion of estimated heritability (20- 50%) [12-14]. The majority of the heritability remains unexplained for most of the complex traits. For example, at least 40 loci associated with human height have been reported with an estimated heritability of

80%. However, they explain only 5% heritability despite studies of tens of thousands of subjects [8, 14]. Failure to explain this large proportion of heritability is referred as

‘missing heritability’. Many explanations have been suggested for this missing heritability. These include: much larger numbers of variants with smaller effects yet to be detected; low power to detect gene-gene interactions or epistasis; an inadequate 2

accounting for shared environment among relatives; and yet undisclosed variants or rarer variants possibly with larger effects but poorly identified with available technologies

[14].

Figure 1-1 explains the role of rare variants towards unexplained heritability. The contribution of variants of low minor allele frequency (MAF) or of rare variants towards missing heritability have been greatly appreciated. The MAF are roughly defined as

0.5%

0.5%, unless effect sizes are very large (as in monogenic conditions), detection of associations becomes unlikely [14].

Figure 1-1. Feasibility of identifying genetic variants by risk allele frequency and strength of genetic effect (odds ratio). Most emphasis and interest lies in identifying

associations with characteristics shown within diagonal dotted lines (Figure adapted with permission from [14]).

1.2.1 Epistasis

Originally defined by Bateson [15], epistasis refers to the phenomenon in which the phenotypic effect of an allele of one gene is concealed by the alleles of another gene [16-

19]. Complex traits such as blood pressure are under the influence of a number of loci on the genome. The effect of alleles at different loci can be additive or their combined effect can be higher or lower than the sum of individual alleles. The interacting partners can be located on two different chromosomes or closely linked on the same chromosome [20,

21]. The first proof of epistasis in blood pressure regulation was provided by Rapp et al. in 1998 using a double congenic strain on rat chromosome 2 and 10 [21]. In this study they found that the combined effect of two genetic loci was less than the addition of both.

Later Garrett and Rapp reported that epistasis also exists in closely linked loci on the same chromosome (rat chromosome 5) in 2002 for which a definitive evidence was provided using monocongenic and bicongenic strains by Pillai et al. in 2013 [20, 22].

Epistasis has been extensively studied in model organisms, agricultural species and humans [18, 19]. Given that epistasis is pervasive in model organisms, it is also possible to be contributing immensely to the genetic architecture of human complex traits [23].

1.2.2 Yet undiscovered variants

Although traits associate with variants that occur more frequently in the protein-coding regions, majority (80%) of the variants fall outside of coding regions [14]. This means

that one should include both protein-coding and non-coding regions in search of variants associated with traits. Non-coding RNAs were previously referred as “Junk DNA” but since the early 2000’s more and more non-coding RNAs are being functionally annotated in genome studies. A non-coding RNA or ncRNA by definition is a functional RNA molecule but does not encode a protein. According to genome-wide studies, the human genome is transcribed into many thousands of regulatory non-protein-coding RNAs including microRNAs (miRNAs), long non-coding RNAs (lncRNAs), small interfering

RNAs and PIWI-interacting RNAs. It is evident now that these RNAs play crucial role in regulation of gene expression.

In addition to the rarer variants and epistasis as factors contributing to missing heritability, epigenetic factors have been evolved as causal and not consequential as a result of BP [24]. Epigenetics is a phenomenon of gene-environment interaction including methylation of DNA and modification of histones. Owing to the fact that epigenetics does not alter the underlying sequence of a gene, it is apparent that the approaches to detect the genomic variants are highly likely to miss epigenetic factors.

Studies directed towards detection of such epigenetic factors for hypertension are progressing. These include the associations of epigenetic events involving expression of renin-angiotensin genes, nephron development and blood vessel remodeling [25-28]. But in order to investigate the role of epigenetics in the context of missing heritability, it is necessary to design studies that are aimed at differentiating between the causal and consequential factors associated with hypertension [24].

Added further to complications are the SNPs within gene deserts as potentially contributing to diseases by regulating gene function from a distance [29]. This is because 5

of the twists and turns that pack 3 m of DNA into a roughly spherical nucleus. Within this spherical nucleus, chromosome contact other chromosomes to form intricate 3D DNA network. Therefore, two loci that are physically at a distance from each other, come spatially together to regulate genetic functions such as regulation, reading repair and replication. Methods are evolving to study the intra-cellular DNA organization, e.g., chromosomal conformation capture (3C). These studies have confirmed that loci on separate chromosomes or the same chromosome but far away from each other on a linear scale can interact in space to regulate multiple gene expressions [30, 31].

1.3 Hypertension as a complex polygenic trait

Hypertension is a major public health concern, and constitutes a prime risk factor for severely devastating conditions such as coronary artery diseases, stroke incidences, end stage renal disease and peripheral vascular diseases [17, 32-35]. Despite major advances in our understanding of its pathophysiology and availability of efficient treatment strategies, hypertension remains a leading cause of morbidity and associated mortality

[35]. In the United States, about 77.9 million (1 out of every 3) adults have high blood pressure [36]. According to the Centers for Disease Control and Prevention (CDC) report,

69% of people who have a first heart attack, 77% of people who have a first stroke, and

74% of people with chronic heart failure have high blood pressure. Recent projections show that by 2030, the prevalence of hypertension will increase 7.2% from the 2013 estimates [36].

1.3.1 Why study genetics of hypertension?

On the basis of studies in families, twins and adopted children, a significant proportion of phenotypic variation of BP has been determined to be inheritable [17, 32]. Estimates for the heritability of systolic and diastolic blood pressure (BP) generally range from 31% to

68% [9, 24, 37-39]. This implies that blood pressure has significant genetic components and we have certain genetic elements on our genome that predispose some of us to develop high blood pressure [37]. Although several monogenic forms of hypertension

(which develops as a result defects in a single gene) have been clearly identified, essential hypertension is now well understood as being a complex polygenic trait with complexities such as gene-gene and gene-environment interactions [32]. Non- genetic/environmental factors influencing development of high BP include stress, diet and physical exercise. Because of all these confounding factors genetic dissection of essential hypertension in humans has remained a daunting task. This challenging task is however important to be undertaken in order to gain deeper insight into understanding pathophysiology of essential hypertension and also to improve clinical management strategies for maintenance of normal physiological BP [40]. Some of these factors can be largely minimized or controlled using rodent models of hypertension which serve as a tool to understand the genomic contributors to blood pressure regulation.

1.3.2 The rat as a physiological model of hypertension

Like in humans, blood pressure is a well-recognized "Quantitative trait" in rats.

Quantitative traits are those that show continuous variation from low to high values [17].

The loci that control quantitative traits are called quantitative trait loci (QTLs). The term

QTL describes broad chromosomal regions that may contain one or more loci controlling respective quantitative trait. The principle strategy for the discovery of genes involved in the development of hypertension in the rat has been the identification of QTLs [41]. This is important, especially to understand the molecular mechanism behind BP regulation.

Given the complex nature of BP regulation, which is confounded by variability of factors that cannot be controlled in humans, animal models were generated to study the genetics of hypertension under permissible controlled environment [17]. Several inbred rat strains of hypertensive rats have thereby become available to study the genetic elements controlling blood pressure. These include the Dahl salt-sensitive (S) rat, the spontaneously hypertensive rats (SHR), the Milan hypertensive strain (MHS), the stroke- prone SHR (SHRSP), genetically hypertensive rat (GH), the Sabra DOCA-salt sensitive rat (SBH), the Lyon hypertensive rat (LH), the fawn-hooded hypertensive rat (FHH), the inherited stress induced arterial hypertension rat (ISIAH) and the Munich Wistar Fromter rat (MWF) [17, 42, 43].

The hypertensive model used in this study is Dahl Salt-sensitive (S) rat which was developed by selectively breeding rats for sensitivity to high salt diet (8% NaCl). The salt sensitive rats develop hypertension despite being maintained on a low-salt (0.3% NaCl) diet. The extent of their hypertension is severe and often fatal with a high salt diet, ranging from 2-8% NaCl [17, 44, 45]. The response to salt is inherited and polygenic

[46]. Therefore, this rat strain serves as an interesting model for interaction of salt as an environmental factor with genotype [46].

1.4 Genetic methods for analysis of inherited hypertension

1.4.1 Genetic linkage analysis

The linkage studies were the early genome wide studies to identify genetic loci associated with human essential hypertension. The idea of genetic linkage studies is based on the identification of a genetic marker which is inherited and thereby co-segregate with the extent of blood pressure of an individual [47]. If the marker and the disease gene are in close proximity to each other, they will not undergo the meiotic recombination event and so are considered to be linked with each other. In this way the affected gene can be mapped by measuring recombination with respect to series of markers on the whole genome. Numerous genetic linkage studies have been conducted in humans of different races such as Nigerian, Chinese and Caucasian populations to localize the loci linked with hypertension on different chromosomes [48-50]. Since 1995, a more comprehensive linkage studies have been reported including the one conducted by the Family Blood

Pressure Program (FBPP), which was published in 2011. This study included, a total of

13,044 individuals (4.226 African American, 2.154 Asian, 4,229 Caucasian and 2,435

Mexican American) and identified five different BP QTLs on human chromosome

6p22.3, 8q23.1, 20q13.12, 21q21.1 and 21q31.3 [51].

1.4.1.1 Genetic linkage analysis using rat models

Similar to linkage analysis in humans, genomic regions that are inherited in rats can be tracked on chromosomes. The advantage of ‘tracking’ or finding linkage of inherited genome segments in rats is that rat populations can be raised on specifically controlled

environmental regimens. [40]. This involves mapping the location of BP causative genes on the rat genome with progressively improved resolution. The regions are tracked by following meiotic recombination events on chromosomes. The results provide information regarding the location of inherited alleles on a chromosome, the magnitude of BP effect and the mode of inheritance of each causative locus [40]. The first full genome scans for BP QTLs were done by Hilbert et al. and Jacob et al. using microsatellite markers on F2 population of rats generated from hypertensive SHRSP rats and normotensive Wistar Kyoto (WKY) rats. Since then many genetic linkage analyses have been performed using various inbred hypertensive rat strains [52, 53]. These studies indicate that BP QTLs are widely distributed throughout the rat genome. Generally genetic linkage analysis makes use of DNA markers to locate the chromosomal regions of QTLs at centimorgan (cM) level [17, 54]. DNA markers/microsatellite markers/short tandem repeats (STR) are short repeats of DNA sequence (mono-, di-, tri, or tertranucleotide repeated DNA sequence) scattered throughout the non-coding DNA in eukaryotes. These repeats are numerous and highly polymorphic with regard to length and are easily genotyped with polymerase chain reaction (PCR) [17]. These microsatellite markers are inherited as simple Mendelian traits and are readily located on genetic linkage maps. The next step is then to determine which genetic markers cosegregate with

BP in order to find approximate location of QTL [17]. The caveat of this approach is it is merely a statistical estimate of the QTL location. Nevertheless, by determining the maximum likelihood score referred to as LOD (logarithm of odds) score [which is "log of the ratio of likelihood of there being a QTL present vs. the likelihood of no QTL being present at particular map position"] [17] at many selected points in an interval between 10

markers, a LOD plot is generated. The X axis of a LOD plot is the location on a given chromosome and the Y axis is the LOD score. The peak of the LOD plot gives most likely position of QTL and height of the peak gives measure of statistical significance

[17, 55]. As a rough approximation LOD score of 1.9 is considered as suggestive significance however LOD>3.0 is considered to be significant [56].

1.4.2 Substitution mapping using congenic strains

Linkage analysis results in the identification of broad chromosomal regions in the range of 10-30cM, which corresponds to 10-30Mb of DNA. Taking into consideration the 2.75

Gb size of the rat genome, this region contains approximately 100-300 genes. This resolution is too low to identify the actual BP regulating gene. Thus congenic or consomic strains are constructed to perform initial low resolution studies to confirm and define BP QTLs. This method is called as “substitution mapping”. This is then followed by construction of substrains to enable genetic dissection and fine mapping in order to resolve the QTL interval to identify or prioritize a candidate BP locus. A congenic strain is developed by substituting a segment of chromosome prioritized by linkage analysis with a significant or suggestive LOD score (in case of consomic, the entire chromosome) from one inbred strain (donor strain) to another (recipient strain)

(Figure 1-2). This is achieved by breeding donor (low BP strain) and recipient strain

(high BP strain) to obtain F1 generation and then breeding the F1 to recipient. The offsprings thus obtained in the first back cross are genotyped using tail biopsy DNA for markers across a putative QTL on a particular chromosome.

Figure 1-2. The construction of recombinant inbred animals which are genetic mosaic of the two founding strains, consomic and congenic strains. The figure is adapted and modified with permission from Delles et al. [41].

The crossing of donor and recipient chromosomes involves meiosis and thus recombinant offsprings are obtained. The animals with specific donor chromosomal segment are selected on the basis of genotyping and such heterozygous offsprings are backcrossed again to the recipient strain to obtain the desired recombinants. This process is repeated

8-10 times. At each backcross, half of the unlinked heterozygous loci outside the congenic region become homozygous for recipient allele thus rendering the genetic background enriched for alleles of the recipient strain except for the desired introgressed segment, which will harbor alleles of the donor strain. At the end of 8-10 back crosses

>99% of donor genomic background is replaced by the recipient genome. The next step is to selectively breed rats to fix the donor chromosomal segment in the homozygous state on the background of the recipient strain, producing the desired congenic strain. Once the 12

BP QTL is confirmed in the congenic strain initially generated, new congenic strains with a smaller segment of donor chromosome are constructed to fine map the QTL to a shorter segment containing fewer number of genes. The congenic strains are denoted as

“Recipient strain. Donor strain” For example, S.LEW refers to the introgression of Lewis

(LEW) genomic segment on the background of S rat. In this dissertation, LEW normotensive strain is the specific rat donor strain and the genetically hypertensive S strain is the recipient strain.

For more than 30 years of research, our lab has been successful not only in generating the inbred Dahl Salt-sensitive (S) rat, but also identified more than 16 BP QTLs on the rat genome. Our lab has also developed several novel congenic strains and achieved an unparalleled resolution for mapping BP QTLs, some to the order of <42.5kb. The genes prioritized as BP QTLs from our investigations are summarized in Table 1.1 [24].

Table 1.1 Candidate BP controlling loci prioritized by high resolution substitution mapping in SS/Jr rat [Table adapted from Joe and Shapiro [24] and modified with updates] Rat Locus Gene Affected Molecular Genomic Mapped Location Homologous Genetic Prioritized by Symbol Mechanism Size of on the Rat Human Association High-Resolution the Genome (Rat Genomic to Human Substitution Mapped Chromosome Segment Cardiovascular Mapping of Location Number: From (Human Disease the S/Jr Rat Base Pairs to Chromosome Base Pairs) Number: From Base Pairs to Base Pairs) 11β-Hydroxylase Cyp11b1 Steroid biogenesis 177 kb 7: 112,800,232– 8: 143,720,961– Yes [57] 112 978 080 143,928,382 A disintegrin-like Adamts16 Unknown 804.6 kb 1: 30,876,304– 5: 4,859,244– Yes [58] metalloproteinase 31,680,901 5,888,257 with (∗17:2,374,083– thrombospondin 3,178,680) motifs, 16 Rififylin Rffl/Carp- Endocytic recycling 42.5 kb 10: 71,028,112– 17: 33,342,355– Yes [59] 2 in cardiomyocytes 71,070,581 33,397,897 and proximal tubules Nuclear receptor Nr2f2 Transcriptional 7.4 Mb† 1: 132,162,132– 15: 92,108,680– Yes [60] subfamily networks with other 139,471,537 99,502,957 2, group F, transcription factors family 2 Secreted Spp2 unknown 787.9 kb 9:95,117,755- 2:234050679- No [61] phosphoprotein-2 95,905,612 234077134 13

All mapped locations on the human genome were obtained by blast searching the human genome assembly with rat genome sequences at http://www.ensembl.org. ∗This region is erroneously mapped to rat chromosome 17 in multiple online rat genome databases. The correct location on rat chromosome 1 is obtained from the Celera rat genome assembly. †This is not considered very high-resolution mapping but is mentioned here because of the parallel observation that Nr2f2 is a very highly prioritized gene in a human GWAS.

1.4.3 Genome-wide association studies (GWAS)

Since the human genome sequence was deciphered, genome-wide association studies

(GWAS) have been extremely successful tool to identify numerous single nucleotide polymorphisms (SNPs) associated with common human disease [2, 14, 62, 63]. In

GWAS, details about the status of allelic variants and their association with a particular disease are collected through whole genome sequencing. For instance, by comparing the genome of hypertensive and normotensive individuals, one can find thousands of SNPs occurring frequently in hypertensive individuals than normotensive individuals and recognize an association of those SNPs with the development of hypertension.

In 2007, the first GWAS for hypertension was carried out by Wellcome Trust Case

Control Consortium using 14, 000 individuals [64] but could not detect any significantly associated loci with hypertension. In 2009, a larger GWAS including 34,433 individuals of European ancestry from the Global Blood Pressure Genetics consortium was conducted to test 2.5 million SNPs [65]. This study identified eight loci associated with systolic blood pressure (SBP) and diastolic blood pressure (DBP). Another GWAS including 200,000 individuals of European decent identified 16 novel loci associated with the regulation of blood pressure [66]. Similarly, several other GWAS has been reported in literature proving evidence regarding locations of loci associated with hypertension.

The detail information about all the GWAS can be obtained from the online catalog of all 14

GWAS maintained by NIH (https://www.genome.gov/26525384/catalog-of-published- genomewide-association-studies/).

Although GWAS has been a successful strategy to prioritize candidate variants, the contribution of GWAS was greatly debated for two reasons. The first one is, that it does not take into account the heritability aspect because it involves a large group of unrelated individuals. Another drawback is that GWAS does not differentiate between causal factors and associated factors. This means that the GWAS is just a screening step and prioritized candidates must be validated to substantiate the association identified by

GWAS. One approach to do so is to utilize appropriate genome editing technology to generate model organism mutants of candidate genes with associated SNPs and characterizing the mutant strain.

1.4.4 Genome editing as a tool to study hypertension

In recent years, a series of programmable genome editing technologies have emerged which enable an efficient and targeted modification of genomes of eukaryotes [67]. These technologies are based on the use of endonuclease catalytic domains tethered to DNA binding domains, which together induce double-stranded breaks (DSB) at the target genomic loci [68]. These double stranded breaks are then restored by the error-prone non homologous end joining (NHEJ) process that introduces small insertions or deletions at the cut site thereby generating knock-out animals [68, 69]. The targeted knock-ins can also be created via homology-directed repairs (HDR) using a co-injection of a plasmid that contains two flanked homology arms together with the nuclease [69].

So far, 4 major classes of customizable DNA binding proteins have been engineered: meganucleases the source of which is microbial mobile genetic elements, zinc-finger nucleases (ZFNs) based on eukaryotic transcription factors, transcription activator-like effector nucleases (TALENs) derived from the bacteria Xanthomonas, and the recent small RNA molecule guided DNA nuclease cas9 known as Clustered Regularly

Interspaced Short Palindromic Repeats (CRISPRs)/CRISPR Associated proteins 9 (Cas9)

[67, 68, 70].

The mechanisms of DNA editing for meganucleases, ZFNs and TALENs is based on recognition of specific DNA sequence by protein-DNA interaction. All the three methods have two components, the nuclease component and the DNA binding domain. The meganucleases integrate its nuclease and DNA binding domains, however, ZFNs and

TALENs have individual molecules targeting three and one nucleotide of the target DNA respectively. Of the above three methods, meganucleases have not been extensively adopted for genome editing technology because of the lack of correspondence between the target DNA sequence specificity and the meganucleases protein residues[67]. ZFNs and TALENs have been widely used as genomic tool in various species including rats, mice, zebra fish, nematodes and fruit flies but these technologies are limited by their variable efficiency, high cost, and cumbersome assembly [71]. Now, CRISPR/Cas9 system has stormed the genome editing technology because of its number of advantages over these other techniques.

1.4.4.1 Clustered Regularly Interspaced Short Palindromic Repeats

(CRISPR)/CRISPR Associated proteins 9 (Cas9)

CRISPR/Cas9 genomic editing technology is an exploitation of a bacterial adaptive immunity against bacteriophages. This technology utilizes RNA-guided nucleases to cleave foreign genetic material. CRISPRs is basically a family of DNA repeats found in most archeal (90%) and bacterial (40%) genomes [72]. There are three types of CRISPR systems (CRISPR I-III) identified across a wide range of bacteria and archea. Each of these systems comprises a cluster of CRISPR-associated (Cas) genes, noncoding RNAs and a distinctive array of repetitive elements (direct repeats) [69, 72]. The repeats are interspaced by protospacers, short variable sequences derived from exogenous DNA targets. The repeats and protospacers together constitute CRISPR RNA array [68, 73].

The protospacer is always associated with a protospacer associated motif within the DNA target which is specific for the CRISPR system.

Of the three CRISPR systems, type II system has been well characterized, consisting of cas9 (nuclease), the CRISPR RNA (crRNA) array that encodes the guide RNA and an additional trans-activating CRISPR RNA (tracrRNA) that is required for the maturation and the processing of crRNA[68, 69, 72, 73]. The crRNA and trancRNA together called as guide RNA (gRNA). The gRNA then forms a chimera with cas9. Each crRNA unit is comprised of 20 nucleotides guide sequence that direct the cas9 to 20 base pairs of target

DNA sequence. In the bacterial CRISPR system derived from Streptococcus pyogenes, in order to induce DSB, the target DNA must immediately precede the 5’-NGG protospacer adjacent motif (PAM) sequence (Figure 1-3) [68].

Figure 1-3. General mechanism of CRISPR/Cas9 induced modification in genome. The guide RNA (gRNA) is a complex of two small RNA molecules called CRISPR RNA (crRNA) and trans-activating CRISPR RNA (tracrRNA). The gRNA is microinjected in a rat embryo at single cell stage along with mRNA encoding Cas9 nuclease protein. The Cas9 protein remains inactive in the absence of gRNA. The gRNA binds to Cas9 protein and induces conformational change in the protein. This conformational change converts the inactive Cas9 to its active form. The activated Cas9 then stochastically search for target DNA with its PAM sequence. When Cas9 finds a potential target sequence it induces double stranded cut on the target DNA. Figure adapted and modified with permission from [70].

The CRISPR/Cas9 technology offers several advantages over ZFNs and TALENs:

1. Simple design- The ribonucleotide formation, and not the protein/DNA recognition,

being a key for target specificity, gRNA can be designed to target nearly any

sequence on the genome [74].

2. Efficiency and cost- The system is highly efficient because of the fact that the

modifications can be introduced by microinjecting the gRNA and mRNA encoding

cas9 proteins directly into the fertilized eggs. This eliminates the conventional

laborious process of creating a vector, transfecting and selecting embryonic stem

cells (ES), growing and aggregation of preimplantation embryos with the ES cells,

transplantation to pseudo pregnant females and waiting for pups to be born. In

contrast with the conventional method which takes 1-2 years, CRISPR system takes

only 1-2 months to generate homozygous and heterozygous mutants. Also the

system is efficient to create specific mutations [74].

3. Multiplexing: With the use of CRISPR multiple genes can be mutated at a time.

Shah et al have successfully used CRISPR system and multiplexed pool injections to

examine 48 loci and identified two novel genes involved in electric synapse

formation in zebra fish [74-76]. In another report by Wang et al., CRISPR system

have been used to simultaneously disrupt five genes in embryonic stem cells with

high efficiency. They also demonstrated that co-injection of cas9 and single gRNA

targeting two different genes into zygotes generated the mice with biallelic

mutations in both the genes with 80% efficiency [77]. The ability to create animals

with multiple mutations not only save the cost of making mutants of individual

genes and then studying them but also provide an excellent opportunity to

understand the genetic epistasis phenomenon in animals models of complex diseases

[74, 77].

In chapter 4 of this dissertation, we have utilized a similar strategy of multiplexing gRNAs to target a single exon gene called as G-protein coupled estrogen receptor

(Gper1) in rats. The reasons for choosing Gper1 are as follows:

1.5 The emerging role of Gper1 in human physiology and diseases

Gper1 is a relatively newly discovered G-protein coupled receptor which belongs to the rhodopsin-like receptor super family. It is widely distributed in various tissues including heart, blood vessels, breast, ovaries, uterus, testis, bone marrow, lungs, smooth and skeletal muscles and central nervous system [78-83]. Since no ligand was known for this receptor, it was previously known as orphan receptor, GPR30. In 2000, GPR30 was shown to be activated by estrogen in breast cancer cell line [84]. Since then the receptor was renamed as Gper or Gper1 by the International Union of Basic and Clinical

Pharmacology (IUPHAR) in 2007 [85]. Gper1 is localized on cell membrane and endoplasmic reticulum and known to mediate rapid non-genomic signaling of estrogen

[79, 86-88]. However some investigators have published controversial reports about the nature and signaling capabilities of this receptor [89-92]. Several studies have shown that disturbances with GPER expression are associated with development of cancer such as breast, endometrial and prostate cancer [82, 93, 94]. The role of this receptor in the nervous system is also emerging [95]. Growing evidence supports the role of Gper1 in the regulation of obesity and glucose tolerance, both of which are hallmarks of metabolic syndrome [86, 88]. Some reports have shown that activation of Gper1 mediate anti- inflammatory effect in rodent models of ischemia reperfusion injury and multiple sclerosis [96-99]. There are some reports which link Gper1 with blood pressure 20

regulation via vascular smooth muscle cell Ca2+ handling [86, 100]. In female mice, lack of functional Gper1 resulted in elevated blood pressure after 6 months of age and was found to be associated with reduced circumference and an increased media to lumen ratio of resistance arteries [88, 100]. Also it has been reported that chronic intravenous infusion of Gper-1 agonist, G-1 lowers mean arterial blood pressure in normotensive male and hypertensive and ovariectomized female mRen2-Lew rats [101]. G-1 at 1-

10µM relaxes agonist induced force in different vessel preparations like rat mesenteric artery and rat carotid artery and aorta, pointing out that Gper1 regulates vascular contractility [86, 101, 102]. It has also recently been discovered that aldosterone plays an important role in regulation of vascular contractility via activation of Gper1. Aldosterone, through Gper1 activation, stimulates extracellular signal-regulated kinase (ERK) pathway and thereby causes proapoptotic, antiproliferative as well as vasodilatory effects [103-

105]. Gper1 maps on to chromosome 7p22, a region implicated in arterial hypertension in humans, suggesting a role of Gper1 in the regulation of blood pressure [86]. Also, in a recent survey on normal healthy adults it has been shown that impaired GPER function might be associated with increased blood pressure and risk of hypertension [106].

1.6 Linkage of G-protein coupled receptors (GPCRs) and gut microbiota

G-protein coupled receptors (GPCRs) constitute the most diverse and one of the largest group of gene families [107, 108]. The superfamily include at least 800 seven transmembrane receptors that are involved in variety of physiological and pathological functions [109]. They form a major class of drug targets and approximately 36% of the pharmaceuticals in modern medicine target human GPCRs. However, for more than 140 21

receptors, which structurally belong to this superfamily, no known targets have been discovered. These receptors are referred as ‘orphan receptors’. The sequence similarity of some orphan receptors with the well-known receptors allow researchers to construct hypotheses regarding the chemical nature of their targets and their role in physiological pathways. Recent studies have begun to deorphanize these GPCRs and evaluate their functions. It is evident from numerous studies that these orphan receptors play an important role in physiology [110]. Examples in this category include Gpr40, Gpr41,

Gpr42 and Gpr43 [107, 111]. Gpr41 and Gpr43 were found to share 98% overall amino acid identity and 100% identity in the transmembrane domain [107, 111]. The deorphanization of Gpr40, Gpr41 and Gpr43 in 2003 also provided evidence to support that they respond to free fatty acids (FFAs).

Depending on the number of carbon atoms in the structure, FFAs are divided into three categories: short-chain fatty acids (SCFA), consisting of 1-6 carbon atoms, medium-chain fatty acids (MCFA), consisting of 7-12 carbon atoms and long-chain fatty acids (LCFA), having more than 12 carbon atoms. Gpr40 is activated by LCFAs and believed to play role in glucose-stimulated insulin secretion [112-114]. Gper120 and Gpr84 are activated by both MCFA and LCFA and activation of Gpr120 has been associated with secretion of glucagon-like peptide-1 (GLP-1), repression of macrophage-induced inflammation and increased insulin sensitivity [115, 116]. Gpr41 and Gpr43 are reported to be specifically activated by SCFAs especially acetate, propionate and butyrate [107, 114, 117-119].

SCFAs are produced in high concentration in cecum and large intestine as a result of fermentation of carbohydrates by resident bacteria (gut microbiota) [107, 111, 120].

Recently, a role of microbiota in the pathophysiology of various diseases have been 22

greatly appreciated. These include diabetes [121, 122], immune function associated diseases [122], bowel disorders [123, 124], liver diseases [125] and metabolic syndrome

[126]. Gut microbiota, which is made up of trillions of microbes is mainly composed of 4 phyla: 1) Firmicutes, 2) Bacterodetes, 3) Actionobacteria, and 4) Proteobacteria.

Maintenance of whole body homeostasis and intestinal immunity depends on delicate balance in the gut microbiota [127]. Gut dysbiosis (imbalance in gut microbiota) leads to devastating pathophysiological consequences. As a measure of characterization of healthy microbiota, the ratio of Firmicutes to Bacteriodetes, known as F/B ratio is studied and used as biomarker for pathological conditions [127-129].

Acetate, propionate and butyrate are the main SCFAs produced by gut microbiota in molar proportions of 60:20:18, respectively. These SCFAs are then absorbed in portal circulation and believed to act as vasodilators [130]. A growing body of evidence suggests that gut microbiota, through production of SCFAs as metabolic byproducts, play an important role in host physiology. SCFAs have been reported to modulate adiposity and inflammation via signaling through Gpr41 and Gpr43 [107, 119, 131]. Pluznick et al. demonstrated that Gpr41 and two other SCFAs, Gpr43 and Olfr78 are expressed in kidney and blood vessels [132]. In this study they reported that intravenous dose of propionate in wild-type mice caused robust decrease in blood pressure which was absent in Gpr41-/- mice. They also found that this effect was opposed by Olfr78. They concluded that blood pressure is modulated by interaction of SCFA receptors. Additionally, in our recent study, we have demonstrated the link between gut microbiota and regulation of blood pressure in Dahl S rats [133]. When we transplanted a single bolus of Dahl salt- resistant (R) rat’s cecal content to the S rats, we found exacerbation of blood pressure of 23

the S rats. This was associated with elevated level plasma acetate and heptanoate in S rats transplanted with R rat gut microbiota. Another recent report showed altered gut microbiota in SHR and WKY rats and claimed the association of gut dysbiosis with high blood pressure [127]. All these reports point towards a significant link of gut microbiota with the physiology of multiple organ systems. Evidence also specifically indicate that certain G-protein coupled receptors are activated by SCFAs in the gut microbiota to influence and regulate blood pressure.

1.7 Goals for the dissertation

There are two goals for this dissertation:

Goal 1. To map BP QTLs on rat chromosome 5 with improved resolution using the traditional substitution mapping approach.

The aims to achieve this goal are i) To develop newer iterations of congenic substrains using previously constructed congenic strains harboring BP QTLs, as a parental strain. ii) To monitor the blood pressure of all congenic substrains. iii) To analyze the genotype-phenotype correlation of the congenic substrains and BP and locate the BP QTLs.

Goal 2. To study the link between BP, Gper1, and gut microbiota using Dahl salt- sensitive rat as the hypertensive model.

The aims to achieve this goal are i) To develop Gper1 knockout rat model using CRISPR/Cas9 genetic engineering technique on the genomic background of S rat. ii) To study the blood pressure and vascular reactivity of the homozygous mutants and compare with the control hypertensive rat (S rat). iii) To study the effect of S rat gut microbiota on the blood pressure of Gper1 knockout rats.

Chapter 2

Materials and methods

2.1 Animals

All animal procedures and protocols used in this report were approved by the University of Toledo, Health Science Campus, Institutional Animal Care and Use Committee.

Experiments were conducted in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals. The inbred Dahl salt-sensitive (SS/Jr or S) rat strain was from the animal colony maintained at The University of Toledo, Health

Science Campus. The Lewis rats (LEW/NCrlBR or LEW rats) were originally obtained from Charles River Laboratories (Wilmington, MA) and maintained in The University of

Toledo Department of Laboratory Animal Research.

2.2 Animal Diet

The rats were bred and maintained on a low salt diet (0.3% NaCl) from Harlan Teklad diet 7034 (Madison, WI). The Harlan Teklad diet (TD94217) was used for the rats that were used for experiments on high salt (2% NaCl).

2.3 Genotyping and DNA sequencing

The rat tail biopsies of the congenic rats were digested in lysis buffer overnight and genomic DNA was extracted using the Promega Wizard SV-96, genomic DNA purification system. The microsatellite markers and SNP markers were identified from the DNA sequence in the region of interest and appropriate primers were designed to using Ensembl website (www. ensembl.org). Following amplification using PCR, products were resolved by gel electrophoresis and polymorphic microsatellite or SNP markers of S and LEW were identified and subsequently used for genotyping the congenic strains [22, 134]. In the case of SNPs, the PCR products were sequenced using standard read sequencing services (http://www.operon.com/).

2.4 Construction of congenic strains

The bicongenic strains used in this study were derived from the parental bicongenic strain

S.LEW(5)X6BX9X5 developed in our laboratory [22]. The parental strain was bred with

S strain to generate an F1 population which were then intercrossed to generate an F2 population. F2 rats that had recombination on RNO5 between appropriate markers

(between D5Mco39 and D5Mco 41 for QTL1 and between D5Mco42 and D5Mco47 for

QTL2) were selected and backcrossed to the parental S strain to fix the recombination to homozygosity. To determine the extent of the introgressed LEW alleles containing segments retained within each bicongenic strain, additional markers were used.

2.5 Generation of a CRISPR/Cas9 mediated Gper1-/- rat

2.5.1 Designing of guide RNAs (gRNAs)

A Number of gRNAs were designed to target 5’ and 3’ end of rat Gper1 (NM_133573.1) by our collaborator at the Genome Engineering Center, Washington University, St. Louis,

MO (http://geic.wustl.edu/). The gRNAs were subjected to bioinformatics analysis to detect potential off-target sites on the rat genome. The gRNAs rGper1 5'.g32a for 5 end’ and rGper1 3'.g34a for 3’ end, which were with minimum number of predicted off-target sites were chosen for the transgenesis.

2.5.2 Transgenesis and genotyping of founders

Transgenesis experiment was conducted at the University of Michigan Transgenic, Core

Facility (http://www.med.umich.edu/tamc/). A mixture of rGper1 5'.g32a RNA(2.5 ng/µL), rGper1 3'.g34a RNR(2.5 ng/µL) and a validated Cas9 mRNA(5ng/µL) was injected into one cell SS/Jr (S) rat embryo (Figure 2-1). The embryos were then implanted into ten pseudo-pregnant Sprague-Dawley female rats. A total of 125 pups were born. At 14 days of age, the pups were tagged and tail clipped. Tail DNA was extracted and PCR amplified using primers designed to hybridize to the 5’ and 3’ ends of

Gper1. To identify the 5’ deletion status of Gper1, forward primer

(GGAGAGGAAGAGAGCGATCA) and reverse primer

(TTTTTCCCTTCCCATGTCAC) were used. To identify 3’deletion status of Gper1, forward primer (ATGACCTCACAGCCTTCTGG) and reverse primer

(TGCCTGAATCCCCTCATCTA) were used. However, to identify complete deletion 28

mutants, forward primer of 3’end (ATGACCTCACAGCCTTCTGG) and reverse primer of 5’ (TGCCTGAATCCCCTCATCTA) end were used. To confirm complete deletion status of Gper1 from the genome of homozygous founders, the PCR products of the homozygous founders were dispatched for sequencing to MWG operon sequencing service (http://www.operon.com/). The sequencing data were analyzed using Sequencher

4.10.1.

Stud male Donor female

Fertilized ova

Designed and validated Oviduct transfer gRNA for Microinjection Gper-1

Pseudopregnant female Vasectomized male

Figure 2-1. Protocol for transgenesis. The fertilized ova were collected from pseudo pregnant females and microinjected with the mixture of gRNAs targeting 3’ and 5’ end of Gper1 along with Cas9 mRNA. The pups born were genotyped using appropriate primers to confirm the founders.

2.6 Assessment of Gper1 expression in heart homogenates

To ensure that Gper1 homozygous gene deletion resulted in loss of Gper1 mRNA in homozygous rats, mRNA from the heart tissue of Gper1 homozygous mutants and wild- type S rats was extracted using TRIzol Reagent (Life Technologies), and cDNA was obtained by reverse transcription with SuperScript III (Invitrogen) using an Oligo dT primer. The cDNA samples were PCR amplified using Gper1 exon–specific primers

(sense 5′ CAGCAATATGTGATCGCTCTCT 3′ and antisense 5′

AAGCTGATGTTCACCACCAA 3′).

2.7 Blood pressure measurements by radiotelemetry

All wild-type hypertensive S rats and congenic rats or Gper1-/- rats were concomitantly bred, housed and studied to minimize environmental effects. All the rats were weaned at

30 days of age and fed with 0.3% NaCl low-salt diet (Harlan Teklad) till 6 weeks of age.

At the age of 6 weeks, the rats were fed with 2% NaCl high-salt containing diet for an additional 24 days. After the high-salt diet regimen, the Gper1 rats or congenic rats and wild-type hypertensive rats were surgically implanted with radiotelemetry transmitters

(C40 for males and C10 for females) (Data Science International, St Paul, MN) such that the body of the transmitter was placed into the left flank and the probe was inserted into the femoral arteries and advanced into the lower abdominal aortae. Rats were individually housed and allowed to recover from surgery for 3 days before recording blood pressure.

2.8 Renal function assessment through urinary protein excretion (UPE)

After BP measurements, wild-type hypertensive rats and Gper1-/- were housed individually in metabolic cages and urine samples were collected over a period of 24 hours. To determine protein concentrations of the urine samples, the pyrogallol-based

QuanTtest Red Total Protein Assay from Quantimetrix (Redondo Beach, CA) was used.

A VERSAmax microplate reader from Molecular Devices (Sunnyvale, CA) was used to determine absorbance at 600 nm. Protein concentrations were determined by reading against the absorbance of the QuanTtest human protein standards (25–200 mg dl-1). UPE data are presented as milligrams of protein over a 24 h period (mg of protein per 24 h).

2.9 Vascular reactivity by the wire myograph method

To record vascular reactivity, the wild-type hypertensive and Gper1-/- rats were euthanized with CO2 inhalation method. The mesentery was immediately excised and placed in ice-cold physiological salt solution (PSS) consisting of mmoles l-1 of 118.5

NaCl, 4.7 KCl, 2.5 CaCl2, 1.2 MgSO4, 1.2 KH2PO4, 25.0 NaHCO3 and 5.5 D-glucose aerated with 95% O2–5% CO2. Using a dissecting microscope, the mesenteric arteries were gently cleaned of adipose and connective tissue and cut into segments of approximately 2mm in length. The segments were then mounted in a Mulvany– Halpern style small vessel wire myograph chamber (Model 620M, Danish Myotechnology,

Aarhus, Denmark) filled with 6ml PSS, maintained at 370C, and continuously aerated with 95% O2, 5% CO2 to measure vascular reactivity [135-138]. After 45 min of equilibration period, a standardized computer-assisted normalization procedure was

performed to set the pretension of the arteries using LabChart8 software (AD

Instruments, Colorado Springs, CO). This involves setting a circumference of the arteries equivalent to 90% of that which they would have at a transmural pressure of 100mmHg.

Segments were washed with PSS and left to equilibrate for another 30min. Vessel contractility was then assessed with the high- K+ (120 mmol l-1) solution. After washing the segment three to four times with PSS, cumulative concentration-response curve to phenylephrine (0.1–100 mM) was obtained. After another 30 min wash, the segments were precontracted with submaximal dose of phenylephrine until the plateau was achieved. Dose-dependent relaxation responses to ACh (0.001–10 mM) and sodium nitroprusside (1 nM– 10 mM) were assessed by constructing cumulative concentration- response curve. Relaxation in response to ACh and sodium nitroprusside were expressed as a percentage of the level of pre-constriction induced by submaximal dose of phenylephrine.

2.10 Microbiotal transplantation study

In a separate experiment, pups were weaned after 4 weeks of age and implanted with C-

10 radiotransmitters (Data Sciences International, St. Paul, MN) (Day 0) to record their blood pressure (Figure 2-2). After the animals recovered from surgery (3 days), a baseline systolic blood pressure reading was recorded for an hour (Day 4). On day 4, the resident microbiota was depleted using the protocol described previously [133, 139].

Briefly, the animals were given an antibiotic combination of 50 mg/kg/day of

Vancomycin (Hospira) and 50 mg/kg/day of Meropenem (Novaplus) in their drinking water along with 50 mg/kg/day of the gastric suppressant Omeprazole (SIGMA) by oral 32

gavage for 3 days. On day 4, the cecal content of 6 rats raised and maintained along with the experimental rats was pooled and transplanted to experimental S rats and Gper1-/- rats by oral gavage. Post-gavage, blood pressure of the rats was recorded for 1h twice a week for 5 weeks.

Resident microbiota

-/- 4 weeks old S rat Gper1 rat

Day 0 Radiotelemetry

50mg/kg/day Antibiotics Day 1-3 + 50mg/kg/day omeprazole Day 4

S rat Gper1-/- rat

S rats Pooled cecal content

Recording of blood pressure twice a week

Figure 2-2. Protocol for microbiotal transplantation study.

2.11 Genomic DNA isolation, 16S rRNA gene sequencing, and analysis of microbiotal composition

The fecal pellets of wild-type S rats (n=6-7) and Gper1-/- (n=7-8) rats were collected at day 4 (before microbiotal transplantation) and day 28 (after microbiotal transplantation) and dispatched for microbial community analysis to Wright Labs, LLC, PA. 33

2.11.1 PCR Amplification of 16S rRNA gene

Illumina iTag Polymerase Chain Reactions (PCR) were performed at a total volume of 25

μL for each sample and contained final concentrations of 1X PCR buffer, 0.8 mM dNTP's, 0.625 U Taq, 0.2 μM 515F forward primer, 0.2 μM Illumina 806R reverse barcoded primer and ~10 ng of template DNA per reaction. PCR was carried out on a MJ

Research PTC-200 thermocycler (Bio-Rad, Hercules, CA) using the following cycling conditions: 98 °C for 3 min; 35 cycles of 98 °C for 1 min, 55 °C for 40 s, and 72 °C for 1 min; 72 °C for 10 min; and kept at 4 °C. PCR products were visualized on a 1%

CYBRsafe E-gel [140].

2.11.2 Library Purification, Verification and Sequencing

Pooled PCR products were gel purified using the Qiagen Gel Purification Kit (Qiagen,

Frederick, MD). Clean PCR products were quantified using the Qubit 2.0 Fluorometer

(Life Technologies, Carlsbad, CA), and samples were combined in equimolar amounts.

Prior to submission for sequencing, libraries were quality checked using the 2100

Bioanalyzer DNA 1000 chip (Agilent Technologies, Santa Clara, CA). Pooled libraries were stored at -20 ˚C until they were shipped on dry ice to the California State University

[141]Library pools were size verified using the Fragment Analyzer CE (Advanced

Analytical Technologies Inc., Ames IA) and quantified using the Qubit High Sensitivity dsDNA kit (Life Technologies, Carlsbad, CA). After dilution to a final concentration of 1 nM and a 10% spike of PhiX V3 library (Illumina, San Diego CA), pools were denatured for 5 minutes in an equal volume of 0.1 N NaOH then further diluted to 12 pM in

Illumina’s HT1 buffer. The denatured and PhiX-spiked 12 pM pool was loaded on an

Illumina MiSeq V2 300 cycle kit cassette with 16S rRNA library sequencing primers and set for 150 base, paired-end reads.

2.11.3 Quality Filtering and OTU Picking

Sequences were trimmed at a length of 150 bp and quality filtered at an expected error of less than 0.5% using USEARCH v7 [142]. After quality filtering, reads were analyzed using the QIIME 1.9.0 software package [140, 143], chimeric sequences were identified using USEARCH61 [144]. A total of 3,433,793 sequences were obtained after quality filtering and chimera checking. Open reference operational taxonomic units (OTUs) were picked using the USEARCH61 algorithm [144], and taxonomy assignment was performed using the Greengenes 16S rRNA gene database (13-5 release, 97%) [145].

Assigned taxonomy were organized into a BIOM formatted OTU (operational taxonomic unit) table, which was summarized within Qiime 1.9.0. (Note: an OTU table contains each collected sample and the abundance of each unique bacterial taxon identified within its respective sample).

2.11.4 Alpha Diversity Analysis

Alpha diversity rarefaction curves were generated within the QIIME 1.9.0 sequence analysis package using an unverified OTU table. Multiple rarefactions were conducted on sequences across all samples from minimum depth of 0 sequences, to a maximum depth of 25,000 sequences, with a step size of 2,500 sequences/sample for 20 iterations. Alpha

rarefactions were then collated and plotted using observed species richness metrics.

Samples with less than 25,000 sequences were not included in alpha diversity analyses.

2.11.5 Beta Diversity Analysis

Principal coordinates analyses (PCoA) plots and ANOSIM tests for significance were generated from a weighted unifrac distance matrix made within Qiime 1.9.0. Relative abundance plots were produced from a Cumulative Sum Scaling (CSS) normalized OTU table [141].

2.11.6 Taxonomic Comparisons

Assigned taxonomy was organized into a BIOM formatted OTU table, which was summarized within Qiime 1.9.0. Bar plots were generated from summarized taxonomy outputs, in which the 9 most abundant OTUs were displayed at their highest identified taxonomic ranking, with all remaining taxa grouped in an “other” category.

2.11.7 Enriched Taxa (Kruskal-Wallis)

Kruskal-Wallis tests for significance were calculated within Qiime 1.9.0 using a CSS normalized OTU table to identify enriched taxa (p<0.01) within Gper1-/- and control cohorts at day 4 and day 28.

2.12 SCFA induced ex-vivo relaxation in rat mesenteric arteries

Vasorelaxation effect of short chain fatty acids (Acetate, propionate and butyrate) was studied on rat small mesenteric arteries of S and Gper1-/- with modification of the 36

protocol described previously [120]. Female wild-type hypertensive (n=6) and Gper1-/- rats (n=6) were euthanized with CO2 inhalation method and the arteries were prepared as given in section 2.9. The arteries were pre-constricted using a concentration of phenylephrine approximately 80% of the maximum (10μM). After 3 min of continuous contraction, the Krebs buffer was replaced with another Krebs buffer containing phenylephrine (PE) and either acetate, propionate or butyrate (5mM). The artery was allowed to relax for next 10 minutes. A 5-minute resting period was allowed between applications of drugs. The tension readings were recorded every minute and then normalized to express the fraction of values recorded at 3 min (just before change of solution).

2.13 Statistical analysis

All statistical analyses of BP studies were done using GraphPad Prism 5 (version 5.02).

Data were analyzed by independent sample t-test. Data are presented as the mean±standard error (Mean±SEM). A p value of <0.05 was used as a threshold for statistical significance.

Chapter 3

Fine mapping of epistatic genetic determinants of blood pressure on rat chromosome 5 (RNO5)

3.1 Introduction

Previously we demonstrated that epistatic interaction exists between two closely linked blood pressure quantitative trail loci (BP QTLs) to regulate blood pressure on rat chromosome 5 (RNO5) [22]. A unique panel of monocongenic and bicongenic strains was constructed to capture the two genomic segments interacting with each other on rat

RNO5. Both the QTLs, QTL1 and QTL2 were identified on the bicongenic strain

S.LEW(5)X6BX9X5 and they were mapped to 7.77Mb and 4.18Mb respectively (Figure

3-1). The two QTLs were located between markers D5Mco39 and D5Mco41 for QTL1 and D5Mco42 and D5Mco47 for QTL2. This study provided a definitive evidence of the presence of two closely linked epistatically interacting BP QTLs on RNO5. The current work described in this chapter was conducted to further fine map the interacting QTLs to shorter genomic segments. To do so, a new iteration of bicongenic substrains was constructed using S.LEW(5)X6BX9X5 [described in reference [22]; Figure 3-1] as a parental strain. Additional markers were designed and used for genotyping of the newly constructed substrains, to determine the extent of introgressed LEW genomic segments

on the new bicongenic strains (Appendix A). The blood pressure of each bicongenic sub strain was measured and compared with the concomitantly raised Dahlsalt-sensitive (S) hypertensive rats on a high salt, 2% NaCl containing, diet regimen. The strategy to map the BP QTLs was to keep one introgressed region constant and then map the other one with respect to BP data. The results of this fine mapping study which not only improved the localization of the 2 BP QTLs, but also led to the discovery of 2 additional closely linked BP QTLs are presented.

Physiol Genomics 45: 729–736, 2013

Figure 3-1. Schematic representation of the monocongenic and bicongenic strains. The physical map of rat chromosome 5 (RNO5) along with the markers (microsatellite as well as SNP) and their locations according to the Ensembl database (http://www.ensembl.org, RGSC 3.4) are shown on the left. The vertical bars alongside of the physical map represent schematics of congenic strains. Genomic segments represented in black are introgressed LEW segments. The regions of recombination are represented by white segments flanking each of the monocongenic strains. Long white colored segments connecting the 2 regions of recombination in between black colored segments within a bicongenic strain were genotyped and confirmed as being comprised 40

of Dahl salt sensitive (S) genotype. The blood pressure (BP) effect of each strain compared with S (n=15–20 per group) is shown as a black bar below the schematic of the congenic strain. BP data were analyzed by a 1-way ANOVA for each group of congenic strains. The inferred locations of the BP QTL are indicated as orange bars on the right side [not significant (ns)]. Figure adapted with permission from [22]

3.2 Results

3.2.1 A new panel of bicongenic strains

A further iteration of 7 new bicongenic substrains was constructed from the strain

S.LEW(5)X6BX9X5 [labelled as S.LEW(5) strain 1 in Figure 3-2], which was backcrossed with S. The resultant F1 population was intercrossed to derive an F2 population. Rats with recombinants within the LEW introgressed segments were selected and bred to homozygosity to generate the new bicongenic strains depicted in Figure 3-1.

The new congenic strains had varying sizes of introgressed LEW genomic segments on both the QTLs. All the bicongenic strains were phenotyped for blood pressure by radiotelemetry along with concomitantly raised S rats. All BP measurements were on rats fed with 2% NaCl diet.

3.2.2 Blood pressure of congenic strains by radiotelemetry

Figure 3-2 shows the new iteration of congenic substrains developed from parental bicongenic strain S.LEW(5)X6BX9X5 [labelled as S.LEW(5) strain 1]. Four of the congenic substrains, S.LEW(5) strain 2, S.LEW(5) strain 3, S.LEW(5) strain 4, and

S.LEW(5) strain 8, had significantly lower SBP than that of concomitantly raised hypertensive S rats (Figure 3-3a, 3-3b, 3-3c, 3-3g). However the remaining three

congenic substrains S.LEW(5) strain 5, S.LEW(5) strain 6, and S.LEW(5) strain 7, had significantly higher SBP than that of S rat (Figure 3-3d, 3-3e, 3-3f).

Let us consider S.LEW(5) strain 2 and S.LEW(5) strain 3 in Figure 3-2. These 2 strains contain LEW alleles within the previously mapped QTL2 similar to that of S.LEW(5) strain 1. The only noticeable differences between S.LEW(5) strain 2 and S.LEW(5) strain

3 are within the LEW introgressed segments of the proximal QTL, previous BP QTL1.

Both strains S.LEW(5) strain 2 and S.LEW(5) strain 3 retain a BP lowering effect.

Because the LEW introgressed segment of S.LEW(5) strain 3 is shorter than that of

S.LEW(5) strain 2, the proximal BP QTL can be mapped to the LEW introgressed segment of S.LEW(5) strain 3, from 7.77Mb (D5Mco41-D5Mco58) to 2.93Mb

[RNO5(129.92)- D5Mco58]. Now, let us consider S.LEW(5) strain 4. This strain has proximal congenic segment representing BP QTL1 similar to that of S.LEW(5) strain 1.

However, the distal segment with LEW alleles within the previously mapped BP QTL 2 in S.LEW(5) strain 4 is shorter spanning from marker D5Mco47 to RNO5-

120.15Mb(~2.26Mb) compared to S.LEW(5) strain 1. Because S.LEW(5) strain 4 retained a BP lowering effect, LEW alleles within the distal shorter congenic segment are sufficient for imparting the BP lowering effect of QTL 2 (Figure 3-3c). This further allowed us to map the originally identified BP QTL2 from 4.18Mb to 2.26Mb.

Phenotypic evaluation of the next 3 strains, S.LEW(5) strain 5- S.LEW(5) strain 7 did not demonstrate a BP lowering effect, but had significantly elevated BP compared to S. This was indicative of further complexity in the BP regulation imparted by LEW alleles in this region. Let us consider S.LEW(5) strain 5. This strain again has proximal congenic segment representing BP QTL1 similar to that of S.LEW(5) strain 4. However distal 42

segment with LEW alleles was longer than that of S.LEW(5) strain 4 because of the additional 1.31Mb introgressed LEW genomic segment between markers RNO5

120.15Mb and RNO5(121.46). Taking into consideration the presence of both the QTLs in this strain, the BP lowering effect should have been retained in this strain. However we observed an overwhelming, significant elevation in blood pressure of S.LEW(5) strain 5 as compared to S (Figure 3-3d). This means that the 1.31Mb region harbors genetic elements contributing towards increasing the blood pressure. Therefore, this segment was labelled as BP QTL3 and shown in orange color at the far right side of the schematic

(Figure 3-2).

Now, let us consider S.LEW(5) strain 6 and S.LEW(5) strain 7. These are two more strains that have distal LEW introgressed segments similar to S.LEW(5) strain 5. Also the proximal segments containing LEW alleles are within the previously defined QTL1.

Therefore phenotyping of S.LEW(5) strain 6 and S.LEW(5) strain 7 confirms the presence of an BP increasing QTL3 in that region (Figure 3-3e, f). However, there is one potential caveat to that interpretation, that congenic limits of S.LEW(5) strain 6 and

S.LEW(5) strain 7 for BP QTL 1 are not similar to S.LEW(5) strain 5.

Finally, let us consider S.LEW(5) strain 8. The proximal introgressed LEW segment in

S.LEW(5) strain 8 similar to that of S.LEW(5) strain 7. However, S.LEW(5) strain 8 contains additional LEW alleles within the 175kb segment spanning from markers

RNO5-121.46 to RNO5-121.63. Because of this the blood pressure effect in S.LEW(5) strain 8 was reversed (Figure 3-3g). This allowed us to identify new BP lowering QTL within this segment. Therefore, this 175kb segment was labelled as BP QTL 4.

Figure 3-2. Schematic representation of the new iteration of bicongenic strains. Physical map of rat chromosome 5 (RNO5) along with the microsatellite and SNP markers and their locations according to the Ensembl database (www.ensembl.com, RGSC 3.4) are shown in the left. The vertical bars alongside of the physical map represent schematics of the bicongenic strains. Long white colored segments connecting the 2 regions of recombination in between the black colored segments within the bicongenic strains were genotyped and confirmed as being comprised of Dahl salt- sensitive (S) genotype. The blood pressure effect is shown as black bars at the bottom of the congenic strains as measured by radio telemetry method. BP data were analyzed by independent student’s t test. Previously identified BP QTLs are indicated as orange bars at the far left side of the schematics along with their sizes. Newly identified BP QTLs are indicated as green (BP decreasing) and orange (BP increasing) bars at the far right side of the schematics. [Note: The parental bicongenic strain S.LEW(5)X6BX9X5 is represented as S.LEW(5) strain 1]

Figure 3-3a. Radiotelemetry measures of systolic blood pressure (SBP), diastolic blood pressure (DBP), pulse pressure (PP), mean arterial pressure (MAP) and heart rate of S and congenic strain S.LEW(5) strain 2 (n=12/group). The data plotted is obtained by telemetry recording once every 5 min continuously and averaged for 4 hour intervals and is represented as Mean ± SEM. SBP, DBP, and MAP are significantly higher in strain S.LEW(5) strain 2 as compared to S rat at all time points (p<0.01). For heart rate *p<0.05. 46

Figure 3-3b. Radiotelemetry measures of systolic blood pressure (SBP), diastolic blood pressure (DBP), pulse pressure (PP), mean arterial pressure (MAP) and heart rate of S and congenic strain S.LEW(5) strain 3 (n=11/group). The data plotted is obtained by telemetry recording once every 5 min continuously and averaged for 4 hour intervals and is represented as Mean ± SEM. SBP, DBP, and MAP are significantly higher in strain S.LEW(5) strain 3 as compared to S rat at all time points (p<0.01). 47

Figure 3-3c. Radiotelemetry measures of systolic blood pressure (SBP), diastolic blood pressure (DBP), pulse pressure (PP), mean arterial pressure (MAP) and heart rate of S and congenic strain S.LEW(5) strain 4 (n=6/group). The data plotted is obtained by telemetry recording once every 5 min continuously and averaged for 4 hour intervals and is represented as Mean ± SEM. SBP, DBP, and MAP are significantly lower in strain S.LEW(5) strain 4 as compared to S rat at all time points (p<0.01).

Figure 3-3d. Radiotelemetry measures of systolic blood pressure (SBP), diastolic blood pressure (DBP), pulse pressure (PP), mean arterial pressure (MAP) and heart rate of S (n=8) and bicongenic strain S.LEW(5) strain 5 (n=8). The data plotted is obtained by telemetry recording once every 5 min continuously and averaged for 4 hour intervals and is represented as Mean ± SEM. SBP, DBP, PP and MAP are significantly higher in strain S.LEW(5) strain 5 as compared to S rat at all time points (p<0.01).

Figure 3-3e. Radiotelemetry measures of systolic blood pressure (SBP), diastolic blood pressure (DBP), pulse pressure (PP), mean arterial pressure (MAP) and heart rate of S (n=6) and congenic strain S.LEW(5) strain 6 (n=8). The data plotted is obtained by telemetry recording once every 5 min continuously and averaged for 4 hour intervals and is represented as Mean ± SEM.

Figure 3-3f. Radiotelemetry measures of systolic blood pressure (SBP), diastolic blood pressure (DBP), pulse pressure (PP), mean arterial pressure (MAP) and heart rate of S and congenic strain S.LEW(5) strain 7 (n=12/group). The data plotted is obtained by telemetry recording once every 5 min continuously and averaged for 4 hour intervals and is represented as Mean ± SEM. SBP, DBP, and MAP are significantly higher in strain S.LEW(5) strain 7 as compared to S rat at all time points (p<0.01). For heart rate *p<0.05, **p<0.01.

Figure 3-3g. Radiotelemetry measures of systolic blood pressure (SBP), diastolic blood pressure (DBP), pulse pressure (PP), mean arterial pressure (MAP) and heart rate of S and congenic strain S.LEW(5) strain 8 (n=12/group). Rats were surgically implanted with large C40 radiotelemetry transmitters, allowed to recover for a week and BP was recorded over a period of 3 days. The data plotted is obtained by telemetry recording once every 5 min continuously and averaged for 4 hour intervals and is represented as Mean ± SEM. SBP, DBP, and MAP are significantly higher in strain S.LEW(5) strain 8 as compared to S rat at all time points (p<0.01). For heart rate *p<0.05, **p<0.01. 52

3.2.3 Sequence variants within the newly localized QTLs

Using BLAST search tool, the locations of the identified QTLs were queried. The genomic sizes of these QTLs as per RGSC 3.4 and RGSC 6.0 are given in Table 3.1.

Table 3.1 Physical sizes of the QTL intervals and number of variants

QTL Location Size of the RGSC 6.0 Size of the Number Number RGSC 3.4 QTL, base QTL, base of of SNPs pairs pairs INDELs

QTL1 128923057- 2,930,758 127422203- 2,745,209 2169 4078 131853815 130167412

QTL2 117894038- 2,259,569 116151282- 2,049,525 234 14 120153607 118200807

QTL3 120153607- 1,308,593 118200807- 1,426,669 109 111 121462200 119627476

QTL4 121462200- 174,858 119627476- 183,308 212 560 121637058 119810784

As per RGSC 3.4, the QTL 1 is located within 2.93Mb in size and contains 40 annotations, QTL2 is located within 2.26Mb and contains 20 annotations, QTL3 is located within 1.31Mb and contains 12 annotations and QTL 4 is located within 175kb and contains only 1 annotation (Rat Genome Database, rgd.mcw.edu). Genomic DNA sequencing of the QTL regions 1, 2, 3 and 4 identified a number of INDEL and SNP variants between S and LEW (Table 3.1). Majority of the variants within all the QTLs

were outside protein coding region. However, there are few variants in the protein coding region in QTL1 (Table 3.2).

Table 3.2 List of SNP and INDELs within protein-coding genes in QTL1

Gene name Variation type Position (RGSC 3.4) SNP/INDEL Zyg11a Nonsynonymous coding 129,290,749 Zyg11b Nonsynonymous coding 129367632 F1LTV2_RAT Nonsynonymous coding 130543135 F1M5A1_RAT Nonsynonymous coding 130615414 D3ZTC2_RAT Nonsynonymous coding 130812948 SNPs D4AB12_RAT Nonsynonymous coding 131086881 Slc1a7 Splice site, Intronic 129114018 Scp2 Splice site, Intronic 129128047 Zyg11a Splice site, Intronic 129293730 Faf1 Splice site, Intronic 131357775 Zcchc11 Frameshift coding 129852071 Osbpl9 Splice site, Intronic 130447499 INDELs F1M5A1_RAT Splice site, Intronic 130597518 Elavl4 Splice site, Intronic 131679103

3.3 Discussion

The cardinal points of the study are i) epistatic interactions occur more frequently than one can expect and ii) epistasis can be viewed as a major factor contributing towards missing heritability with respect to complex polygenic traits such as hypertension. There is reasonable amount of literature supporting the pervasive nature of epistasis. Brem et al. in 2005, demonstrated the strong genetic interactions between QTLs for levels of gene transcripts in the yeast Saccharomyces cerevisiae [146]. Khan et al. [147] and Chou et al

[148] in 2011, demonstrated evident epistasis among collection of five mutations that increase growth rate in bacteria. Shao et al. in 2008, with the help of a panel of chromosome substitution strains, demonstrated a strong epistasis for blood, bone and 54

metabolic traits in mice [149]. Epistasis has been implicated in regulation of many complex traits such as cancer, neurological disorders, essential hypertension and other cardiovascular disorders [18, 22, 149-157].

The BP QTLs on rat chromosome 5 correspond to the human chromosome 1p31.3 region

(www.ensebl.org). A number of studies have investigated genetic markers associated with hypertension in human chromosome 1p arm [158-160]. However, none of the studies have documented epistasis regulating blood pressure in this region to date.

The present study provides clear evidence for a high level of epistasis within a single short genomic segment. Other than blood pressure, potentially interacting loci on the same chromosome have also been identified for phenotypes such as insulin-dependent diabetes mellitus in non-obese diabetic mouse [161, 162] and polycystic kidney disease in mouse [163]. Thus the study described in this chapter, using a mammalian model organism, warrants investigation into the possibility of epistasis to regulate blood pressure between closely linked loci on the same chromosome in humans. There appear to be very few reports in human studies that associate the epistatic interaction regulating blood pressure. These include a study by Sookoian et al. which have suggested the potential interaction of serotonin transporter and CLOCK gene variation to regulate metabolic syndrome susceptibility in rotating shift workers. Other report is by Lanzani at al. which have showed the statistical association of epistatic interaction between adducing family genes ADD1 and ADD3 with variation in blood pressure.

The goal of this study was to fine map the previously identified two interacting BP QTLs on RNO5 to shorter genomic segments in order to resolve them further. Because we have already provided a definitive evidence for the presence of epistatic interaction between 55

these QTLs, we focused on studying only bicongenic strains for the blood pressure effect in this study [22]. The newly identified 4 QTLs on RNO5 have reduced number of annotations compared with the previously identified 2 QTLs with QTL4 only has one annotation. Taking into account the fact that majority of the variants in the identified

QTLs are within protein non-coding region, it is logical to think that epistatic interactions between protein coding and/or non-coding genetic elements such as microRNA or

LncRNAs or chromosome conformations are highly possible factors to regulate blood pressure in these rats.

The inferences made in this study were based on the differential segments of all the strains phenotyped. In order to get a definitive evidence for the presence of the identified

QTLs in this study, more strains encompassing only the identified QTL are required which are not available this time.

In summary, although there are still several annotations in the identified QTLs to make any conclusion about the interacting partners causing BP effect, we could at least achieve a milestone of identifying additional QTL with contrasting effects on RNO5. This study is a classic example of why identifying genes regulating complex traits are difficult to identify. This is because large number of tightly linked QTLs with contrasting effects exist within the genome.

Chapter 4 Effects of the gut microbiota on host blood pressure is modulated by G-protein coupled estrogen receptor (Gper1)

4.1 Introduction

As mentioned earlier in chapter 1 section 1.5, it has been recently discovered that G- protein coupled estrogen receptor (Gper1 or Gpr30) is the third estrogen receptor isotype localized on cell membrane and endoplasmic reticulum [79, 86-88]. However, increasing evidence suggests that estrogen is not the sole ligand for Gper1, but that Gper1 is activated more potently by aldosterone than estrogen [103, 164]. However, there are a few reports which claim that Gper1 is still an orphan receptor and is not activated by estrogen [90, 91].

Gper1 has been implicated in blood pressure regulation in some mice and rat models.

Because blood pressure is a polygenic trait (regulated by many genes on the genome), these limited studies conducted using mice or rats that are without a genetic background permissive for the development of hypertension, are recognizable as a potential limitation for our ability to fully explore and understand the function of Gper1 in the context of hypertension. The goal of this study was to expand our knowledge on the potential role of

Gper1 in cardiovascular and renal physiology and to evaluate the gender differences (if any) that may persist in the highly hypertension permissive background.

In this study we describe the creation of a novel Gper1 gene deletion model that is built on the genome of the genetically hypertensive Dahl Salt-sensitive (S) rat using a

CRISPR/Cas9 method with the modification of multiplexing gRNA in order to ensure complete knock out of this single exon gene. The characterization of this model demonstrates that Gper1 is an important, genomic context dependent, regulator of blood pressure. Furthermore, vascular reactivity studies point out that BP regulatory effect of

Gper1 is potentially through vasculature. This study is innovative because the design of the study allows for expanding our knowledge on the potential role of Gper1 in cardiovascular and renal physiology and evaluating the gender differences that may persist in the highly hypertension permissive background.

Furthermore, because Gper1 belongs to the class of orphan G-protein coupled receptors some of which have been recently deciphered as being receptors for short chain fatty acids, we sought to study the function of Gper1 as a SCFA receptor. As a rich source of short chain fatty acids, we transplanted the gut microbiota (in the form of cecal content) of S rat in the Gper1-/- rats and followed the physiological effects as a result of this manipulation. Data obtained from blood pressure monitoring and the vascular phenotyping are presented.

4.2 Results

4.2.1 CRISPR/Cas9 mediated generation of Gper1-/- rats using two gRNAs

Rat Gper1 is a single exon gene with 1128 base pair located on chromosome 12 (RNO12) and comprised of 375 amino acids. The gRNA rGper1.g30 and rGper1.g32 showed minimum number of short_0 off-target sites and thus were selected for targeting 5’ end of

Gper1. Similarly, the gRNAs rGper1.g2 and rGper1.g34 showed minimum number of short_0 off-target sites and thus were selected for targeting 3’ end of Gper1 (Table 4.1 and 4.2). Furthermore, no common SNPs were detected in the recommended gRNA as analyzed by UCSC genome browser (Figure 4-1 and 4-2). Also, mismatch detection assay using rat C6 glioma cells showed that the recommended gRNAs were able to cut the target site to smaller fragments (Figure 4-3). The mismatch detection assay also showed that the gRNAs rGper1.g34 (gRNA1) and rGper1.g32 (gRNA2) had better non- homologous end joining (NHEJ) frequency and hence were used for transgenesis. RNA validation performed via deletion PCR using left primer for 5’ end and right primer for 3’ end showed that the deletion PCR product obtained was 544bps vs. wild type PCR product, 1484bps (Figure 4-4). This confirms that Gper1 has been completely deleted from these cells.

Table 4.1 Off Target Analysis – rGper1 5’

Name gRNA sequence long_0 long_1 long_2 short_0 rGper1.g1 CTTGCCCCGTCTGCCTCCACNGG 1 1 1 29 rGper1.g10 GCATGCCTGGCTCACTTCCCNGG 1 1 2 30 rGper1.g11 CATGCCTGGCTCACTTCCCANGG 1 1 2 38 rGper1.g12 GGGAATCACCTTTGTGAAGANGG 1 1 1 31 rGper1.g13 GCAAGTCCTGAAAGCTTCTANGG 1 1 1 17 rGper1.g14 CAAGTCCTGAAAGCTTCTACNGG 1 1 1 18 rGper1.g15 GGCTTCCCGTAGAAGCTTTCNGG 1 1 2 15 rGper1.g16 AGCTTTCAGGACTTGCTGAANGG 1 1 3 18 rGper1.g17 GCTTTCAGGACTTGCTGAAANGG 1 1 3 28 rGper1.g18 TGACAGCTCCATCTTCACAANGG 1 1 1 15 rGper1.g19 TCTTCACAAAGGTGATTCCCNGG 1 1 3 17 rGper1.g2 TGCCTCCACAGGTCCTCAGANGG 1 1 2 48 rGper1.g20 CTTCACAAAGGTGATTCCCTNGG 1 1 3 15 rGper1.g21 GATTCCCTGGGAAGTGAGCCNGG 1 1 2 48 rGper1.g22 AAGTGAGCCAGGCATGCGTGNGG 1 1 2 64 rGper1.g23 GAGCCAGGCATGCGTGAGGCNGG 1 1 2 7 rGper1.g24 TGAGGCAGGAAGAATCGTCCNGG 1 1 2 1 rGper1.g25 GAGGCAGGAAGAATCGTCCTNGG 1 1 2 4 rGper1.g26 GTTTGTCTCAGAAAACCAGANGG 1 1 2 111 rGper1.g27 TTTGTCTCAGAAAACCAGAANGG 1 1 8 125 rGper1.g28 AGAAAACCAGAAGGGCAGACNGG 1 1 5 58 rGper1.g29 GAAAACCAGAAGGGCAGACANGG 1 1 7 66 rGper1.g3 CAGGTCCTCAGAAGGTAAGCNGG 1 1 4 24 rGper1.g30 AGAAGGGCAGACAGGGCGTCNGG 1 1 1 7

rGper1.g31 GAAGGGCAGACAGGGCGTCTNGG 1 1 3 3 rGper1.g32 GGCAGACAGGGCGTCTGGGCNGG 1 1 1 8 rGper1.g33 CAGGGCGTCTGGGCTGGTGCNGG 1 1 1 47 rGper1.g34 AGGGCGTCTGGGCTGGTGCCNGG 1 1 1 23 rGper1.g35 CGTCTGGGCTGGTGCCGGGCNGG 1 1 2 13 rGper1.g36 TGCTGCCTGCTTACCTTCTGNGG 1 2 5 13 rGper1.g37 GCTTACCTTCTGAGGACCTGNGG 1 1 3 78 rGper1.g38 TACCTTCTGAGGACCTGTGGNGG 1 1 4 34 rGper1.g39 TGAGGACCTGTGGAGGCAGANGG 1 1 3 116 rGper1.g4 TCAGAAGGTAAGCAGGCAGCNGG 1 1 13 37 rGper1.g40 GAGGACCTGTGGAGGCAGACNGG 1 1 4 55 rGper1.g41 AGGACCTGTGGAGGCAGACGNGG 1 1 3 19 rGper1.g5 AGGCAGCAGGCGTTCCTGCCNGG 1 1 4 9 rGper1.g6 AGACGCCCTGTCTGCCCTTCNGG 1 1 1 45 rGper1.g7 TCTGGTTTTCTGAGACAAACNGG 1 1 3 31 rGper1.g8 TCTGAGACAAACAGGCTCCCNGG 1 1 5 44 rGper1.g9 CTTCCTGCCTCACGCATGCCNGG 1 1 3 16

Table 4.2 Off Target Analysis – rGper1 3’

Name gRNA sequence long_0 long_1 long_2 short_0 rGper1 3'.g2 CCTCCTAGAGGAAAACGGACNGG 1 1 1 8 rGper1 3'.g34 GGATGGCAGTATCCATGAGCNGG 1 1 1 10 rGper1 3'.g1 AGGTACCTCCTAGAGGAAAANGG 1 1 3 41 rGper1 3'.g10 ACACTCCTAGCACAGGTGGTNGG 1 1 1 25 rGper1 3'.g11 TGTCATACTCTAAACCCCAGNGG 1 1 1 14 rGper1 3'.g12 TACTCTAAACCCCAGTGGCTNGG 1 1 3 32 rGper1 3'.g13 ACTCTAAACCCCAGTGGCTANGG 1 1 2 27 rGper1 3'.g14 CTCTAAACCCCAGTGGCTAGNGG 1 1 2 45 rGper1 3'.g15 TCTAAACCCCAGTGGCTAGGNGG 1 1 3 18 rGper1 3'.g16 GGCTAGGGGGAAATGTCACANGG 1 1 3 58 rGper1 3'.g17 AGGGGGAAATGTCACATGGCNGG 1 1 1 30 rGper1 3'.g18 GGGGGAAATGTCACATGGCTNGG 1 1 1 32 rGper1 3'.g19 TCACATGGCTGGGTCACCTCNGG 1 1 2 33 rGper1 3'.g20 CACATGGCTGGGTCACCTCTNGG 1 1 2 27 rGper1 3'.g21 ACATGGCTGGGTCACCTCTGNGG 1 1 2 29 rGper1 3'.g22 TGGGTCACCTCTGGGGCTGCNGG 1 1 2 78 rGper1 3'.g23 GGGTCACCTCTGGGGCTGCTNGG 1 1 3 50 rGper1 3'.g24 CTTCCTGACTGCCCAGCTCANGG 1 1 4 51 rGper1 3'.g25 GATACTGCCATCCAGATTCANGG 1 1 3 26 rGper1 3'.g26 GCCATCCAGATTCAAGGCAGNGG 1 1 2 37 rGper1 3'.g27 CCATCCAGATTCAAGGCAGTNGG 1 1 1 21 rGper1 3'.g28 ACATTGACCTCTGCCCTCAANGG 1 1 5 41 rGper1 3'.g29 CATTGACCTCTGCCCTCAAANGG 1 1 4 26 rGper1 3'.g3 CTCCTAGAGGAAAACGGACANGG 1 1 1 14

rGper1 3'.g30 GTGGTGCCCTTTGAGGGCAGNGG 1 2 5 60 rGper1 3'.g31 GGGCAGAGGTCAATGTCATGNGG 1 1 3 16 rGper1 3'.g32 GTGGCCCACTGCCTTGAATCNGG 1 1 3 24 rGper1 3'.g33 CCCACTGCCTTGAATCTGGANGG 1 1 2 11 rGper1 3'.g35 GATGGCAGTATCCATGAGCTNGG 1 1 4 19 rGper1 3'.g36 TATCCATGAGCTGGGCAGTCNGG 1 1 1 34 rGper1 3'.g37 AAGAATCCCAGCAGCCCCAGNGG 1 1 2 87 rGper1 3'.g38 TGACATTTCCCCCTAGCCACNGG 1 1 4 5 rGper1 3'.g39 GACATTTCCCCCTAGCCACTNGG 1 1 2 15 rGper1 3'.g4 TCCTAGAGGAAAACGGACAGNGG 1 1 3 14 rGper1 3'.g40 ACATTTCCCCCTAGCCACTGNGG 1 1 2 26 rGper1 3'.g41 CTGGGGTTTAGAGTATGACANGG 1 1 2 13 rGper1 3'.g42 GCTCGCCCACCACCTGTGCTNGG 1 1 2 21 rGper1 3'.g43 TGCTAGGAGTGTGCAGCTCCNGG 1 1 1 42 rGper1 3'.g44 GCTAGGAGTGTGCAGCTCCTNGG 1 1 1 39

Figure 4-1. Single nucleotide polymorphism (SNP) check for 5’ end gRNAs. Snap shot of analyzed sequence encompassing the gRNA sites of 5’ end of Gper1 for common (>1%) SNPs using the UCSC genome browser. The red box shows there are no common SNPs present in the recommended gRNAs targeting 5’ end of Gper1.

Figure 4-2. Single nucleotide polymorphism (SNP) check for 3’ end gRNAs. Snap shot of analyzed sequence encompassing the gRNA sites of 3’ end of Gper1 for common

(>1%) SNPs using the UCSC genome browser. The red box shows there are no common SNPs present in the recommended gRNAs targeting 3’ end of Gper1.

Figure 4-3. Mismatch detection assay. Representative gel pictures of the mismatch detection assay for activity in rat G6 glioma cells for a) 5’ end gRNA rGper.g32 and b) for 3’ end gRNA rGper.g34. The original uncut product size is 426 for g32 and 596 for g34. The cut products are shown with blue arrows and the sizes are given near the band. The non-homologous end joining frequencies (NHEJ) for g32 and g34 are 25% and 20% respectively. [Image courtesy of Dr. Shondra Miller, Washington University, MO].

- DNA RNA

600 Deletion 500 400 product 300

200

100

Figure 4-4. RNA validation via deletion PCR. Representative gel picture showing deletion product at ~544 bps. [Image courtesy of Dr. Shondra Miller, Washington University, MO].

One hundred and twenty-five pups were born out of 10 microinjected pseudo-pregnant rats. Among these, 5 pups were homozygous founders, 21 pups were heterozygous founders and 12 pups had partial deletion of Gper1 either at 5’ or 3’ end of the gene.

Only homozygous founders were used for phenotyping studies. The homozygous founders had complete deletion of Gper1 (Figure 4-5a), which was further confirmed with DNA sequencing (Figure 4-5b). Furthermore, the heart tissues of homozygous founders did not express mRNA for Gper1 as demonstrated by RT-PCR (Figure 4-5c).

All the founders were viable, reproduced similar to wild-type S rats.

4.2.2 Morphometric characteristics

The Gper1-/- females as well as males showed significantly higher body weights than that of age matched concomitantly raised wild-type hypertensive rat. However, there was no

difference in kidney weight to body weight and heart weight to body weight ratio between these strains in both the sexes (Table 4.3). Also, the nasal-anal length (NAL) of

Gper1-/- rats measured at the age of 6 weeks was significantly higher than that of wild- type rats in both females as well as males (Figure 4-6).

Figure 4-5. Screening animals for CRISPR/Cas9 mediated deletion of Gper1. a) The schematic of position of gRNAs cutting sites and the primers used for genotyping and representative agarose gel picture of PCR amplified tail DNA samples from pups born post-microinjection of CRISPR/Cas9 mediated deletion of Gper1. Primer A+B and C+D encompass 3’ and 5’ ends of Gper1 however Primer A+D encompasses entire gene. The first lane after DNA ladder in all three gels is wild-type S rat DNA, Animal # 1 through 10 are homozygous founders which show no band in 3’ and 5’ primer ends and shorter DNA band (~750 bps) in Primer A+D compared with S band (2333 bps). b) Representative sequencing results from the PCR products of homozygous founders shown in panel A detected 1530 bps deletion in the DNA of Gper1 mutant rats. c) RT- PCR analysis of mRNA expression of Gper1 in the heart homogenate using 18S RNA as a housekeeping gene (RT- reverse transcriptase).

Table 4.3 Body weights and tissue weights of wild-type hypertensive S rats and

Gper1-/- rats

Heart wt/body Kidney wt/body Strain Gender Body weight(g) wt(mg/g) wt(mg/g)

Wild-type Females (n=6) 227.83±4.59 4.98±0.23 5.13±0.26 hypertensive (S) rat Males (n=12) 344.25±4.06 4.70±0.07 5.71±0.1

Females (n=6) 244.08±3.73** 4.87±0.2 5.22±0.90 Gper1-/- rat Males (n=12) 357.75±4.83* 4.65±0.14 6.12±0.21

Values are expressed as Mean ±standard error. *p<0.05, **p<0.01

Figure 4-6. Morphometric characteristics of S and Gper1-/- rats. a) Nasal-anal length (NAL) in females and b) NAL in males c) representative rat pictures showing longer body length of Gper1-/- rat as compared to wild-type rat.

4.2.3 Attenuation of hypertension in Gper1-/- rats

After 24 days of high salt diet (2%NaCl), the Gper1-/- rats and the wild-type hypertensive rats maintained normal diurnal rhythms of systolic and diastolic BP as measured by radiotelemetry. However, throughout the observation period, both systolic and diastolic

BP of the Gper1-/- rats were consistently lower than that of the wild-type hypertensive rats. Similarly, pulse pressure and mean arterial pressure were also consistently lower in

Gper1-/- rats as compared to wild-type hypertensive rats. The BP lowering effect was observed in females as well as males (Fig. 4-7, 4-8).

We also monitored the urinary protein excretion (UPE) as a measure of renal function in the Gper1-/- rats, but we did not observe any significant difference as compared to wild- type hypertensive rats in males as well as females (Fig. 4-9).

4.2.4 Superior vascular function of Gper1-/- rats

Because Gper1 is expressed in blood vessels [85, 86, 102], we further tested the vascular reactivity of secondary and tertiary order mesenteric artery to vasorelaxants like acetylcholine (ACh) and sodium nitroprusside. The endothelium-dependent vasorelaxation responses of the mesenteric arteries from Gper1-/- as measured using cumulative concentration response curve of ACh rats in both the sexes were significantly higher than that of the wild-type rats (Figure 4-10, 4-11). However, the endothelium independent relaxation of sodium nitroprusside was not altered with complete deletion of

Gper1.

Figure 4-7. Attenuation of blood pressure of Gper1-/- female rats. Radiotelemetry measurement of a) systolic blood pressure (SBP), b) diastolic blood pressure (DBP), c) pulse pressure and (PP) d) mean arterial pressure (MAP) of wild-type hypertensive rats (n= 8) and Gper1-/- rats (n=7). Rats were monitored for BP, 3 days after recovery from surgical implantation of radiotelemetry transmitters. Data plotted are the 4 hours moving average of recordings obtained every 5 min continuously for 24h. Levels of statistical significance for all data were analyzed by independent student’s t-test. Blood pressure of Gper1-/- female rats was significantly lower than that of wild-type hypertensive rats.

Figure 4-8. Attenuation of blood pressure of Gper1-/- male rats. Radiotelemetry measurement of a) systolic blood pressure (SBP), b) diastolic blood pressure (DBP), c) pulse pressure (PP) and d) mean arterial pressure (MAP) of wild-type hypertensive rats (n= 10) and Gper1-/-rats (n=12). Rats were monitored for BP, 3 days after recovery from surgical implantation of radiotelemetry transmitters. Data plotted are the 4 hours moving average of recordings obtained every 5 min continuously for 24h. Levels of statistical significance for all data were analyzed by independent student’s t-test. Blood pressure of Gper1-/-male rats was significantly lower than that of wild-type hypertensive rats.

Figure 4-9. Urinary protein excretion (UPE) of Gper1-/- rats was comparable to that of wild-type Hypertensive rats. Total 24h urine protein was assessed in a) females and b) males Gper1-/- and wild-type hypertensive rats as described in methods section. The urinary protein excretion data are presented as milligrams of proteins per 24h. The UPE of Gper1-/- rats was comparable to that of wild-type hypertensive rats.

Figure 4-10. Gper1-/- female rats demonstrated superior vascular function compared with wild-type hypertensive rats. Third order mesenteric arteries from female wild-type

hypertensive rats and Gper1-/- rats were dissected and mounted on wire myograph chamber as described in Methods section. (a) Cumulative concentration response curve (CCRC) to phenylephrine (1nM-10µM) of mesenteric arteries was recorded from wild- type hypertensive (n=12) and Gper1-/- rats (n=12). (b) Endothelium-dependent relaxation to acetylcholine (ACh) was assessed by adding increasing concentrations of ACh to the vessel preparation. CCRC to ACh (1nM-10µM) of mesenteric arteries was recorded from wild-type hypertensive (n=9) and Gper1-/- rats (n=11). Relaxation of ACh was expressed as a percentage of level of pre-contraction induced by submaximal dose of phenylephrine. (c) Endothelium-independent relaxation to sodium nitroprusside was assessed by adding increasing concentrations of sodium nitroprusside to the vessel preparation. CCRC to sodium nitroprusside (1nM-10µM) of mesenteric arteries was recorded from wild-type hypertensive (n=11) and Gper1(-/-) rats (n=12). Relaxation of sodium nitroprusside was expressed as a percentage of level of pre-contraction induced by submaximal dose of phenylephrine. The bar graphs are the maximum response recordings of respective vasoconstrictor and vasorelaxants. The CCRC data were analyzed by two-way ANOVA followed by Bonferroni correction for multiple comparisons. All other data were analyzed by independent student’s t-test, *p<0.05, **p<0.01. (WT- Wild-type hypertensive rat).

Figure 4-11. Gper1-/- male rats demonstrated superior vascular function compared with wild-type hypertensive rats. Third order mesenteric arteries from male wild-type hypertensive rats and Gper1-/- rats were dissected and mounted on wire myograph chamber as described in Methods section. (a) Cumulative concentration response curve (CCRC) to phenylephrine (1nM-10µM) of mesenteric arteries was recorded from wild- type hypertensive (n=12) and Gper1-/- rats (n=14). (b) Endothelium-dependent relaxation to acetylcholine (ACh) was assessed by adding increasing concentrations of ACh to the vessel preparation. CCRC to ACh (1nM-10µM) of mesenteric arteries was recorded from wild-type hypertensive (n=11) and Gper1-/- rats (n=12). Relaxation of ACh was expressed as a percentage of level of pre-contraction induced by submaximal dose of phenylephrine. (c) Endothelium-independent relaxation to sodium nitroprusside was assessed by adding increasing concentrations of sodium nitroprusside to the vessel preparation. CCRC to sodium nitroprusside (1nM-10µM) of mesenteric arteries was recorded from wild-type hypertensive (n=10) and Gper1-/- rats (n=12). Relaxation of sodium nitroprusside was expressed as a percentage of level of pre-contraction induced by submaximal dose of phenylephrine. The bar graphs are the maximum response recordings of respective vasoconstrictor and vasorelaxants. The CCRC data were analyzed by two-way ANOVA followed by Bonferroni correction for multiple comparisons. All other data were analyzed by independent student’s t-test, *p<0.05. (WT- Wild-type hypertensive rat).

4.2.5 Vascular responses of 17β-estradiol and aldosterone on mesenteric vessels

The Gper1 has been reported to mediate the rapid vascular effects of estrogen and aldosterone in normotensive animal models [103, 104, 165, 166]. To validate these findings and determine the specificity of the vascular functions of estrogen and aldosterone in our model we sought to assess the effect of these steroid hormones on the vascular reactivity of endothelium intact secondary and tertiary mesenteric arteries of the wild-type hypertensive and Gper1-/- rats. We did not find any difference in the vascular reactivity of estrogen on the phenylephrine-mediated constriction between wild-type hypertensive and Gper1-/- rats. However, the vasodilatory effect of aldosterone on the phenylephrine-mediated constriction was significantly reduced in the Gper1-/- rats mesenteric arteries as compared to wild-type rats indicating the more specificity of Gper1 for aldosterone in the salt sensitive animal model (Figure 4-12, 4-13). The decrease in vasodilatory effect of aldosterone was more pronounced in males knock out rats than in females.

Figure 4-12. Gper1-/- female rats demonstrated a trend for decrease in vasorelaxant effect of aldosterone in endothelium intact mesenteric arteries compared with wild- type hypertensive rats. Second and third order mesenteric arteries of from female wild- type hypertensive rats and Gper1-/- rats were dissected and mounted on wire myograph chamber similar to vascular reactivity study. Cumulative concentration response curve (CCRC) to (a) 17β-estradiol (1nM-10µM) and (b) Aldosterone (1nM-10µM) of mesenteric arteries was recorded from wild-type hypertensive (n=8) and Gper1-/- rats (n=9). The bar graphs to the right of each CCRC are the maximum response recordings of respective steroid. The CCRC data were analyzed by two-way ANOVA followed by Bonferroni correction for multiple comparisons. All other data were analyzed by independent student’s t-test, *p<0.05. (WT- Wild-type hypertensive rat).

Figure 4-13. Gper1-/- male rats demonstrated a significant decrease in vasorelaxant effect of aldosterone in endothelium intact mesenteric arteries compared with wild- type hypertensive rats. Second and third order mesenteric arteries of from female wild- type hypertensive rats and Gper1-/- rats were dissected and mounted on wire myograph chamber similar to vascular reactivity study. Cumulative concentration response curve (CCRC) to (a) 17β-estradiol (1nM-10µM) and (b) Aldosterone (1nM-10µM) of mesenteric arteries was recorded from wild-type hypertensive (n=9) and Gper1-/- rats (n=9). The bar graphs to the right of each CCRC are the maximum response recordings of respective steroid. The CCRC data were analyzed by two-way ANOVA followed by Bonferroni correction for multiple comparisons. All other data were analyzed by independent student’s t-test, *p<0.05. (WT- Wild-type hypertensive rat).

4.2.6 Reversible BP lowering effect in Gper1-/- rats after transplantation of S rat gut microbiota

With the introduction of cecal content of wild-type hypertensive S rats in Gper1-/- rats, the blood pressure protecting effect in the Gper1-/- rats was reversed and we observed further elevation of blood pressure in Gper1-/- rats as compared to S rats in females as well as males. (Figure 4-14, 4-15 and 4-16).

Figure 4-14. The blood pressure protecting effect in Gper1-/- female rats was reversible with transplantation of cecal content from wild-type hypertensive rats. Radio telemetry measurement of a) systolic blood pressure (SBP), b) diastolic blood pressure (DBP), c) pulse pressure (PP) and d) mean arterial pressure (MAP) of wild-type hypertensive rats (n= 8) and Gper1-/- rats (n=7), 21 days after transplantation of cecal

content of wild-type hypertensive rats. Levels of statistical significance for all data were analyzed by independent student’s t-test. *p<0.05.

Figure 4-15. The blood pressure protecting effect in Gper1-/- male rats was reversible with transplantation of cecal content from wild-type hypertensive rats. Radio telemetry measurement of a) systolic blood pressure (SBP), b) diastolic blood pressure (DBP), c) pulse pressure (PP) and d) mean arterial pressure (MAP) of wild-type hypertensive rats (n= 8) and Gper1-/- rats (n=7), 21 days after transplantation of cecal content of wild-type hypertensive rats. Levels of statistical significance for all data were analyzed by independent student’s t-test. *p<0.05.

Figure 4-16 shows the summary of blood pressure effect before and after transplantation of S rat gut microbiota in Wild-type hypertensive S rat and Gper1-/- rats in females as well as males.

Figure 4-16. Summary graph showing the effect of gut microbiota on the blood pressure regulatory effect of Gper1. Without transplantation of cecal content from S rats, Gper1-/- had significant blood pressure lowering effect in females (p<0.001) as well as males (p<0.001). With the transplantation of cecal content of S rats, the blood pressure protective effect in Gper1-/- was abolished and further elevation of BP was observed.

4.2.7 Microbial sequencing in fecal samples of S and Gper1-/- rats

To determine the microbial composition of S and Gper1-/- rats, fecal samples were sequenced for 16S rRNA gene. A total of 3,433,793 sequences were obtained after quality filtering and chimera picking (Table 4.4).

Table 4.4 Total number of sequences per sample after quality filtering and OTU picking

Sequences Per Rat ID Strain Time point Sample S8863 Day 4 76009 S8865 Day 4 70033 S8856 Day 4 12747 S8866 S Day 4 237974 S8855 Day 4 134085 S8835 Day 4 133043 S8870 Day 4 131580 38968 Day 4 234908 38942 Day 4 200013 38948 Day 4 186959 38966 Day 4 177799 Gper1-/- 38969 Day 4 165312 38947 Day 4 123860 38970 Day 4 112163 38941 Day 4 106770 S8870 Day 28 135845 S8856 Day 28 113528 S8835 Day 28 73383 S S8866 Day 28 73207 S8863 Day 28 53506 S8855 Day 28 48370 38970 Day 28 263296 38968 Day 28 257042 38941 Day 28 119063 38942 Gper1-/- Day 28 82563 38948 Day 28 44521 38969 Day 28 41755 38947 Day 28 24459 Total sequences 3433793

High quality sequence data was obtained for all fecal samples, which were incorporated into bioinformatics pipeline. Overall, sequencing depth of quality-filtered data ranged from 12,747 to 263,296 sequences per sample. All the samples were able to be incorporated into a cumulative sum scaling (CSS) normalized OTU table, as sequencing depth exceeded 1000 sequences for each sample [167].

4.2.8 Alpha diversity analysis of S and Gper1-/- fecal samples

Alpha diversity analyses revealed distinct differences in bacterial species richness between cohorts on two different time points, as a range of 125 to 825 unique bacterial

Operational Taxonomic Units (OTUs >97%) were observed within each fecal sample

(Figure 4-17). Species richness comparisons display that the number of unique bacterial species identified within the rat microbiome at day 28 were higher in comparison to day 4 in S as well as Gper1-/- rats, but there was no difference in species richness between S and Gper1-/- groups on both time points.

Figure 4-17. Alpha Diversity rarefaction curves reveal differences in observed species richness between S and Gper1-/- fecal samples. Alpha diversity rarefaction curves for the 4 rat cohorts (Gper1-/- day 4, Gper1-/- day 28, S day 4 and S day 28). The day 28 cohorts display higher species richness in comparison to day 4 rats.

4.2.9 Beta diversity analysis of S and Gper1-/- fecal samples

Beta diversity analyses revealed distinct microbial community structures within the day 4

(left on PC1 axis) and day 28 (right on PC1 axis) cohorts (Figure 4-18). The ANOSIM test for significance confirms significant clustering between the four cohorts (P= 0.03), indicating significant phylogenetic differences between the fecal samples. To further observe differences in beta diversity between Gper1-/- and S rats within each time-point, separate PCoA plots were generated for day 4 (Figure 4-19) and day 28 (figure 4-20) rats.

Figure 4-18. Principal Coordinates Analysis (PCoA) plots at day 4 and day 28. PCoA plots were used to visualize differences in weighted Unifrac distances of fecal samples from the S rat day 4 cohort (highlighted in green), Gper1-/- rat day 4 cohort (highlighted in blue), S rat day 28 (highlighted in orange) and Gper1-/- rat day 28 (highlighted in red) samples. Points clustered more closely together are more similar in terms of phylogenetic distance, whereas points that are distant from each other are phylogenetically distinct. Fecal samples cluster significantly based on their specific grouping (day 4/28, Gper1-/- y/n, ANOSIM p = .03).

Figure 4-19. Principal Coordinates Analysis (PCoA) plots at Day 4. PCoA plots were used to visualize differences in weighted Unifrac distances of Gper1-/- and control rats after at the 4 day sampling point. Significantly distinct clustering can be observed between the two cohorts, yielding an ANOSIM P-value of .004, indicating that microbial community structure is significantly different between the two cohorts (Gper_KO is same as Gper1-/-).

Figure 4-20. Principal Coordinates Analysis (PCoA) plot at day 28. PCoA plots were used to visualize differences in weighted Unifrac distances of Gper1-/- and control rats after at the day 28 sampling point. Clustering between the two cohorts does not appear as distinct in comparison to the day 4 cohort, yielding an ANOSIM P-value of .056, indicating that microbial community structure is not significantly different between the two cohorts (Gper_KO is same as Gper1-/-).

4.2.10 Taxonomic comparisons of 16S rRNA gene sequence between S and Gper1-/- rat’s fecal samples at day 4

Taxonomic assignments of 16S rRNA gene sequences revealed general microbial community composition within the fecal samples (Figure 4-21). Distinct differences between the Gper1-/- and cohort at day 4 were observed. The Parabacteroides presented the greatest difference between the two cohorts, as the average relative abundance dropped from 10.1% within the Gper1-/- cohort, to 2.7% within the S cohort, a 7.4% decrease. Bacteroidales S24-7 and unclassified enterobacteriaceae also appear to be enriched within the Gper1-/- cohort, with 2.1% and 1.7% increases in relative abundance in comparison to the S cohort, respectively. The S cohort yielded a greater abundance of 90

unclassified clostridiales taxa (a 7.1% increase) in comparison to the Gper1-/- cohort.

These data provide insight as to which taxa are driving the shifts in microbial community composition between these two sample groupings.

Figure 4-21. Relative abundance plots to display differences in general microbial community structure between fecal samples collected from rats at day 4. The figure displays the 9 most abundant OTUs observed across all fecal samples (at day 4), all remaining taxa were grouped in the “other” category. Certain prevalent OTUs appear to be enriched within either the control (shown at the bottom of the y axis) or the Gper1-/- (top of the y-axis) cohorts. While the unclassified enterobacteraceae and parabacteroidetes appear to have a much higher relative abundance within the Gper-/- cohort, the unclassified clostridiales and anaeroplasma are higher in abundance within the control cohort (Gper_KO is same as Gper1-/-).

4.2.11 Taxonomic comparisons of 16S rRNA gene sequence between S and Gper1-/- rat’s fecal samples at day 28

Taxonomic assignments of 16S rRNA gene sequences revealed fewer differences in microbial community makeup between day 28 S and day 28 Gper1-/- rats when compared to day 4 rats (Figure 4-22). Of all 9 most prevalent taxa, only the ruminococcus appear to be enriched within the Gper1-/- cohort, presenting a 1.2% increase in abundance when compared to the control cohort. All remaining prevalent taxa appear to yield similar relative abundances between the two sample groupings.

These data confirm the findings indicated by PCoA and phylum level comparisons, in that there are distinct microbial community differences between the two cohorts at day 4, but that differences in overall community structure appear to decrease in the day 28 samples. To unearth differences in less prevalent taxa between the two cohorts, a

Kruskal-Wallis test was utilized to identify enriched taxa within the each of the 4 sample cohorts.

Figure 4-22. Relative abundance plots to display differences in general microbial community structure between fecal samples collected from rats at day 28. The figure displays the 9 most abundant OTUs observed across all fecal samples (at day 28), all remaining taxa were grouped in the “other” category. Interestingly, fewer differences can be observed across the prevalent taxa within the day 28 samples. Only one prevalent OTU, the Ruminococcus yielded a difference in relative abundance greater than 1% between the two cohorts (Gper_KO is same as Gper1-/-).

4.2.12 Enriched Taxa (Kruskal-Wallis) within S and Gper1-/- fecal samples

At day 4, a total of 31 significantly enriched taxa (Kruskal-Wallis p<0.01) were identified within both cohorts. 20 OTUs were enriched within the S cohort, whereas 11 were enriched within the Gper1-/- cohort (Table 4.5). Interestingly, 11 of the 31 enriched taxa within the S cohort match to an unclassified clostridiales OTU, whereas only 1 of the 20 enriched taxa within the Gper1-/- cohort fall match an unclassified clostridiales OTU.

Within the Gper1-/-, two distinct taxa matching to the pseudomonas genus are found to be significantly enriched, in addition to an enterococcus taxon.

At day 28, a total of 11 significantly enriched taxa (Kruskal-Wallis p<.01) were identified within both cohorts. 7 were enriched within the control cohort, whereas 4 OTUs were enriched within the Gper1-/- cohort (Table 4.6). At this time point, there is a clear decline in the number of enriched clostridiales OTUs within the control cohort when compared to the day 4 cohort.

Table 4.5 List of significantly enriched (Kruskal-Wallis p<0.01) taxa within S and Gper1-/- cohorts at day 4 [Enriched taxa are highlighted in yellow]

Test- P S Mean Gper1-/- Mean Taxonomy Statistic 10.5755 0.0011 8.8089 2.7814 Turicibacter 10.4143 0.0013 8.4053 0.9894 Unclassified Clostridiaceae 9.8182 0.0017 5.2423 0.0000 Unclassified Ruminococcaceae 9.8182 0.0017 5.7552 0.0000 Unclassified Clostridiales 9.8182 0.0017 3.4745 0.0000 Unassigned 9.8182 0.0017 3.7100 0.0000 Unclassified Clostridiales 9.8182 0.0017 4.8516 0.0000 Unclassified Clostridiales 9.0698 0.0026 9.4684 3.5148 Unclassified Clostridiaceae 7.0975 0.0077 11.8375 2.2636 Unclassified Clostridiales 7.5949 0.0059 3.4487 0.0000 Unclassified Clostridiales 7.5949 0.0059 3.1787 0.0000 Fusobacterium 7.5949 0.0059 2.2705 0.0000 Unclassified Clostridiales 7.5949 0.0059 5.2509 0.0000 Unclassified Clostridiales 7.5949 0.0059 2.1034 0.0000 Turicibacter 7.5949 0.0059 5.0913 0.0000 Unclassified Clostridiales 7.5949 0.0059 2.8869 0.0000 Unclassified Clostridiales 7.5949 0.0059 2.5104 0.0000 Unclassified Clostridiales 7.5949 0.0059 4.5862 0.0000 Pediococcus 7.5949 0.0059 2.7412 0.0000 Lactobacillus 7.5949 0.0059 2.9673 0.0000 Unclassified Clostridiales 9.4577 0.0021 0.0000 7.8505 Unclassified Clostridiales 8.5714 0.0034 0.2950 3.0331 Pseudomonas 7.8720 0.0050 0.2950 2.4386 Ruminococcus 7.7143 0.0055 3.0792 7.4373 Enterococcus 7.5571 0.0060 0.7106 3.9680 Pseudomonas 7.5170 0.0061 0.0000 2.4070 Unclassified Clostridiales 7.5170 0.0061 0.0000 2.5027 Variovorax paradoxus 7.5170 0.0061 0.0000 2.1481 Bacteroides 7.5170 0.0061 0.0000 2.9893 Unclassified Aeromonadaceae 7.5170 0.0061 0.0000 2.4207 Acidovorax 7.5170 0.0061 0.0000 1.8631 Unclassified Methylophilaceae

Table 4.6 List of significantly enriched (Kruskal-Wallis p<0.01) taxa within S and Gper1-/- cohorts at day 28 [Enriched taxa are highlighted in yellow]

Test- P S Gper1 KO Taxonomy Statistic Mean Mean 8.6379 0.0033 2.9135 0.3905 Unclassified Erysipelotrichaceae 8.1633 0.0043 5.8712 4.0593 Coprococcus 8.1250 0.0044 2.5461 0.0000 Ruminococcus 8.1250 0.0044 2.9612 0.0000 Unclassified Lachnospiraceae 7.5755 0.0059 4.4882 0.7926 Unclassified Clostridiales 7.4492 0.0063 4.6066 1.3908 Bacteroidales S24-7 6.9703 0.0083 1.6798 0.0611 Unclassified Lachnospiraceae 6.9149 0.0085 0.1132 2.2120 Oscillospira 9.2542 0.0023 0.3697 4.2947 Unclassified Clostridiales 8.2540 0.0041 0.7563 3.2374 Unclassified Lachnospiraceae 7.8145 0.0052 0.0000 1.5996 Unclassified Clostridiales

4.2.13 Vascular responses of short chain fatty acids on rat small mesenteric vessels

As shown in Figure 4-23, acetate, propionate and butyrate at 5mM concentration caused an initial rapid, transient contraction more than the contraction elicited by PE, followed by sustained, slower relaxation lasting up to 10 minutes. This is then followed by reassertion of progressive contractile response of PE. Acetate induced relaxation was significantly different between wild-type hypertensive rats and Gper1-/- rats at the time points 9 min, 10min and 11min. As a measure of amplitude of SCFAs induced relaxation, we calculated the average of maximum relaxation occurred at these time points and compared within the groups. The acetate induced relaxation was significantly decreased in Gper1-/- rats mesenteric arteries as compared to control rats mesenteric arteries (55±5

% Vs. 43±4%, n=33-36 SMAs/group, p=0.01). Butyrate induced relaxation was also

significantly different between wild-type hypertensive rats and Gper1-/- rats at the time points 13 min, 14 min and 15 min. Also the average maximum relaxation induced by butyrate at these time points were significantly decreased after deletion of Gper1 in the mutant rats mesenteric arteries (61±5 % Vs. 47±4%, n=34-36 SMAs/group, p<0.001).

Propionate induced relaxation was not significantly different between wild-type hypertensive and Gper1-/- rats at any time point (64±4 % Vs. 61±4%, n=34-36

SMAs/group, p=0.98).

Figure 4-23. Decreased relaxation of rat small mesenteric arteries (SMAs) to 5mM sodium acetate and sodium butyrate in Gper1-/- rats compared with S rats. Mean phenylephrine (10 μM) contraction amplitude measured are 1-minute time intervals and normalized to amplitude at 3 minutes. The solution was changed at 3 min to that containing short chain fatty acids. The relaxation plot for a) acetate, b) propionate and c) butyrate is shown and compared between wild-type hypertensive and Gper-/- rat SMAs.

4.3 Discussion

Complex traits such as blood pressure are regulated by many genes on the genome.

Mammalian genomes are composite mixtures of both susceptible and resistant alleles for the phenotypic expressivity of any given trait. For example, a mammalian genome consists of nucleotides at certain locations that confer susceptibility to the development of high blood pressure as well as nucleotides at certain other locations that confer resistance to the development of high blood pressure. It is the net effect of these various alleles and the extent of their permissive nature that, in combination with environmental factors determines the final extent of blood pressure of the mammal. Thus, even if the effect of a certain gene to lower blood pressure is profound, such an effect can be masked if the organism has multiple other powerful genomic elements that confer resistance to the development of high blood pressure. Therefore, studying the effects of individual gene effects in the context of a genome that is not permissive to the development of hypertension may represent a missed opportunity for understanding the phenotypic effect of the individual gene in question. This concept can be exemplified with a very good example of a gene, Rififylin (Rffl) which was previously prioritized as a candidate gene regulating blood pressure in our laboratory [59]. Using a congenic strain of hypertensive

Dahl S rat introgressed with genomic segment from normotensive Lewis rat, a blood pressure quantitative locus was fine mapped to <42.5 kb on rat chromosome 10. This critical region contains 171 variants and only one protein-coding candidate gene, called

Rififylin (Rffl). The congenic strain containing Lewis allele at this QTL region showed elevated blood pressure, developed cardiac hypertrophy and had a shorter QT interval.

This was associated with elevated mRNA and protein expression of Rffl. One of the possibilities for this overexpression is that the genetic elements in trans on the remainder of the S rat genome that interact with LEW alleles within the 42.5 kb QTL region to regulate the expression of Rffl. Other similar examples are available in the literature wherein QTLs are detected using parental strains with similar phenotypes such as between two hypertensive strains (S and SHR) or between two normotensive strains

(WKY and BN strains) [168-172]. This lends support to the view that a genetically permissive context enhances the chances of detecting effects of BP susceptibility conferring alleles. Through this research, we not only got an opportunity to explore for the functionality of Gper1 in the context of hypertension, but also the study represents demonstrable success in implementing the use of two gRNAs as a viable means to knockout a single exonic gene such as Gper1.

In the current study, for the first time, we have demonstrated that the physiological behavior of a given gene is dependent on the genetic background on which it is studied.

Another aspect of this study is to unravel the mechanism behind rapid vascular effects of

Gper1 using genetically hypertensive animal model. The rapid non-genomic effects of the vasoactive steroids such as estrogen and aldosterone have been greatly appreciated for more than half century for their role in cardiovascular homeostasis. Gper1 a is newly characterized receptor, which has been implicated in mediating the rapid vascular effects of estrogen and aldosterone by many scientists. Previously estrogen was considered as the only ligand for Gper1 to mediate the vascular effects, but it has been demonstrated that aldosterone is 1000 times more potent than estrogen for activating Gper1[103, 164].

It has been reported that in endothelium-intact aortic rings, aldosterone mediated 100

vasodilatory effects on phenylephrine-stimulated constriction via classical both MR and

Gper1. We have observed the reduction in this vasodilatory effect of aldosterone in the mesenteric resistance arteries after compete deletion of Gper1.

The blood pressure effects we obtained as a result of global deletion of Gper1 in rat is opposite to what is reported in the mouse study [88]. There are two possible explanations for this discrepancy: 1) Because the genetic background of mice was highly resistant to developing hypertension compared to the Dahl salt-sensitive rat we used in this study; or

2) The difference in gut microbiota composition between the two species. There are a number of reports in the literature which describe the role of gut microbiota in the metabolic syndrome. Hypertension being the hallmark of metabolic syndrome is recently being studied as influenced by the composition of host gut microbiota.

To address the microbiota question we monitored the microbial composition in four weeks old S rats and Gper1-/- rats. Surprisingly, despite being similar genetic background, the gut microbial composition between these strains was very different as shown by

PCoA plots and the taxonomic comparison data on day 4 (Figure 4-18 and Figure 4-20).

This indicates that the deletion of Gper1 in the S rats significantly altered the microbiota in the mutants as compared to S rats.

To study the gene effect independent of the gut microbial effect, we normalized the gut microbiota of Gper1-/- by transplanting the cecal content of S rats into the Gper1-/- rats and then monitored the blood pressure again. The results showed that with complete reversal of gut microbiota, the blood pressure lowering effect of Gper1-/- was abolished.

This suggested that the microbiota has significant role in the regulation of blood pressure by Gper1. It is possible that the strong influence of gut microbiome concealed the effect 101

of Gper1-/-.Normalization of gut microbiota in both the groups revealed the true gene effect which is consistent with the literature [88].

Furthermore, microbiotal screening before and after cecal content transplantation (Day 4 and Day 28 respectively) revealed a variety of alterations in the overall microbiotal composition of S and Gper1-/- rats. The pretransplantation microbial profile shows significantly high levels of clostridiales under the phylum Firmicates in the S rats as compared to Ger1-/- rats. However, posttransplantation, at day 28 there is no clear difference in clostridiales between S and the Gper1-/-. Recent study by Yang et al. demonstrated an increased Firmicutes/Bacteriodetes ratio in the SHR (hypertensive strain) as compared to WKY rats (normotensive strain) [127]. They found similar gut dysbiosis pattern in Angiotensin II infusion rat model and in a small cohort of hypertensive patients. Significantly high levels of Firmicutes which is suggestive of higher F/B ratio in S rats could explain the higher blood pressure in these rat as compared to Gper1-/- rats before transplantation. Because both S and Gper-/- have same genomic background, the difference in microbial composition could be attributed to deletion of

Gper1 gene.

To explore the potential mechanisms by which different microbial compositions regulate

BP in S and Gper1-/- rats, we next focused on short chain fatty acids. As discussed earlier in section 1.6, SCFAs, mainly acetate, propionate and butyrate are produced in the cecum and large intestine by gut microbiota from where they are absorbed in the portal circulation and act as vasodilators [120, 130]. To date, Gpr41 and Gpr43 are two receptors which have been proved to be short chain fatty acid receptors [107, 132]. Gpr30 or Gper1 is another orphan receptor but the physiological role of Gper1 as a short chain 102

fatty acid receptor is not yet studied. In the present study we made an attempt to study

Gper1 as a novel short chain fatty acid receptor in Gper1 knock-out rat model. This was achieved by adding the main short chain fatty acids, acetate, propionate and butyrate in the small mesenteric arteries preparation of S and Gper1-/- rats. It has been demonstrated previously that short chain fatty acids act as vasorelaxants in in vitro studies [120]. Of the three SCFAs studied, acetate and butyrate induced relaxation in phenylephrine precontracted arteries was reduced in the mesenteric artery preparation of Gper1-/- rats indicating that Gper1 is required for these short chain fatty acids to cause relaxation in vessels. Thus the data presented in this study point towards Gper1 being a previously unexplored, potentially novel ligand for SCFAs. However more detailed studies including ligand binding assays are required in order to have definitive evidence of SCFAs-Gper1 interaction [107].

103

Chapter 5

Summary

Blood pressure is a complex polygenic trait which is controlled by genetic and non- genetic factors. Because hypertension is a major public health concern and also coupled with complications in identifying genetic determinants of hypertension in human studies alone, researchers have selectively bred rats for high blood pressure to provide animal model. Previously work with these animal models was restricted to studying physiological control of blood pressure. But with the advancement of genetic techniques and other resources for rat, researchers are now able to investigate the underlying genetic causes of hypertension.

Genetic linkage analysis is the most effective tool of identifying regions of chromosome harboring genetic elements responsible for particular trait (e.g. blood pressure). Genetic dissection of polygenic hypertension has remained daunting task because of many factors one of which is epistasis and is discussed in this dissertation. Epistasis which refers to the gene-gene interaction is pervasive on the genome. Mapping between genotype and phenotype is greatly obscured if there is epistasic interaction between underlying genetic elements. In current dissertation, we have identified four closely linked epistatically interacting regions on rat chromosome 5 which were previously reported to have only 104

two. The results are indicative of presence of more epistatically interacting elements than one can predict. Similar genetic interactions underlying complex traits have been reported in the literature including odor sensing behavior in Drosophila melanogaster, the skeletal architecture in mice and large-scale studies of yeast. This research adds data points in the literature that epistasis is one of the major contributor towards missing heritability of complex traits such as blood pressure in humans.

Apart from the host genome, a gut microbiome residing in the intestines of the host play significant role in the physiology of host. The symbiotic relationship between a commensal bacteria and vertebrate immune system has been reported. Several studies over past 30 years suggested the role of gut microbiota in development of hypertension in rats and humans. We explored the role of gut microbiota in genetic model of hypertension with respect to Gper1 which is known for its role as estrogen receptor.

Using a CRISPR/Cas9 technique, we deleted Gper1 in Dahl salt-sensitive (S) rats and studies the effect of this deletion on blood pressure. We found the blood pressure lowering effect in the mutants as compared to S rat. Analysis of gut microbial composition showed that despite being similar genetic background they had completely different microbiota. This results provide evidence that deletion of a gene plays important role in the microbial composition in the gut. When we normalized the gut microbiota in the mutants with that of S rat, we found that the blood pressure effect was reversed. This is second evidence of the involvement of gut microbiota in the regulation of blood pressure. To date there are two genes, Olfr78 and Gpr41, reported for gut microbial links to hypertension. Since there are no variants in the rat genes for Olfr78 and Gpr41, it is fair to conclude that Gper1 is mediating the blood pressure effect through gut microbiota. 105

Further, in vitro studies provided third evidence that since the vasorelaxant effect of

SCFAs (acetate and butyrate) is significantly reduced at the peak time point in Gper1-/-

Gper1 could be the potentially novel receptor for SCFAs. However further specific studied are required to support this data.

106

References

1. Yazbek, S.N., et al., Deep congenic analysis identifies many strong, context- dependent QTLs, one of which, Slc35b4, regulates obesity and glucose homeostasis. Genome Res, 2011. 21(7): p. 1065-73.

2. Fu, W., T.D. O'Connor, and J.M. Akey, Genetic architecture of quantitative traits and complex diseases. Curr Opin Genet Dev, 2013. 23(6): p. 678-83.

3. Iyengar, S.K. and R.C. Elston, The genetic basis of complex traits: rare variants or "common gene, common disease"? Methods Mol Biol, 2007. 376: p. 71-84.

4. Hirschhorn, J.N., Genomewide association studies--illuminating biologic pathways. N Engl J Med, 2009. 360(17): p. 1699-701.

5. Lander, E.S., Initial impact of the sequencing of the human genome. Nature, 2011. 470(7333): p. 187-97.

6. Manolio, T.A., L.D. Brooks, and F.S. Collins, A HapMap harvest of insights into the genetics of common disease. J Clin Invest, 2008. 118(5): p. 1590-605.

7. Barrett, J.C., et al., Genome-wide association defines more than 30 distinct susceptibility loci for Crohn's disease. Nat Genet, 2008. 40(8): p. 955-62.

8. Visscher, P.M., Sizing up human height variation. Nat Genet, 2008. 40(5): p. 489- 90.

9. Ehret, G.B., Genome-wide association studies: contribution of genomics to understanding blood pressure and essential hypertension. Curr Hypertens Rep, 2010. 12(1): p. 17-25.

107

10. Ehret, G.B., et al., Replication of the Wellcome Trust genome-wide association study of essential hypertension: the Family Blood Pressure Program. Eur J Hum Genet, 2008. 16(12): p. 1507-11.

11. Levy, D., et al., Genome-wide association study of blood pressure and hypertension. Nat Genet, 2009. 41(6): p. 677-87.

12. Zuk, O., et al., The mystery of missing heritability: Genetic interactions create phantom heritability. Proc Natl Acad Sci U S A, 2012. 109(4): p. 1193-8.

13. Eichler, E.E., et al., Missing heritability and strategies for finding the underlying causes of complex disease. Nat Rev Genet, 2010. 11(6): p. 446-50.

14. Manolio, T.A., et al., Finding the missing heritability of complex diseases. Nature, 2009. 461(7265): p. 747-53.

15. Bateson, W., Facts Limiting the Theory of Heredity. Science, 1907. 26(672): p. 649-60.

16. Deng, A.Y., Genetic mechanisms of polygenic hypertension: fundamental insights from experimental models. J Hypertens, 2015. 33(4): p. 669-80; discussion 680.

17. Rapp, J.P., Genetic analysis of inherited hypertension in the rat. Physiol Rev, 2000. 80(1): p. 135-72.

18. Rapp, J.P., Theoretical model for gene-gene, gene-environment, and gene-sex interactions based on congenic-strain analysis of blood pressure in Dahl salt- sensitive rats. Physiol Genomics, 2013. 45(16): p. 737-50.

19. Rapp, J.P. and B. Joe, Do epistatic modules exist in the genetic control of blood pressure in Dahl rats? A critical perspective. Physiol Genomics, 2013. 45(24): p. 1193-5.

20. Garrett, M.R. and J.P. Rapp, Two closely linked interactive blood pressure QTL on rat chromosome 5 defined using congenic Dahl rats. Physiol Genomics, 2002. 8(2): p. 81-6.

108

21. Rapp, J.P., M.R. Garrett, and A.Y. Deng, Construction of a double congenic strain to prove an epistatic interaction on blood pressure between rat chromosomes 2 and 10. J Clin Invest, 1998. 101(8): p. 1591-5.

22. Pillai, R., et al., Isolation and high-throughput sequencing of two closely linked epistatic hypertension susceptibility loci with a panel of bicongenic strains. Physiol Genomics, 2013. 45(16): p. 729-36.

23. Mackay, T.F., Epistasis and quantitative traits: using model organisms to study gene-gene interactions. Nat Rev Genet, 2014. 15(1): p. 22-33.

24. Joe, B. and J.I. Shapiro, Molecular mechanisms of experimental salt-sensitive hypertension. J Am Heart Assoc, 2012. 1(3): p. e002121.

25. Millis, R.M., Epigenetics, essential hypertension and renin-angiotensin system upregulation in the offspring of water-deprived pregnant rats. Hypertens Res, 2009. 32(12): p. 1051-2.

26. Millis, R.M., Epigenetics and hypertension. Curr Hypertens Rep, 2011. 13(1): p. 21-8.

27. Shirodkar, A.V. and P.A. Marsden, Epigenetics in cardiovascular disease. Curr Opin Cardiol, 2011. 26(3): p. 209-15.

28. Zhang, D., et al., Epigenetics and the control of epithelial sodium channel expression in collecting duct. Kidney Int, 2009. 75(3): p. 260-7.

29. Schierding, W., W.S. Cutfield, and J.M. O'Sullivan, The missing story behind Genome Wide Association Studies: single nucleotide polymorphisms in gene deserts have a story to tell. Front Genet, 2014. 5: p. 39.

30. Ling, J.Q., et al., CTCF mediates interchromosomal colocalization between Igf2/H19 and Wsb1/Nf1. Science, 2006. 312(5771): p. 269-72.

31. Lomvardas, S., et al., Interchromosomal interactions and olfactory receptor choice. Cell, 2006. 126(2): p. 403-13.

109

32. Hamet, P., et al., Hypertension: genes and environment. J Hypertens, 1998. 16(4): p. 397-418.

33. Deng, A.Y., Genetic basis of polygenic hypertension. Hum Mol Genet, 2007. 16 Spec No. 2: p. R195-202.

34. Messerli, F.H., B. Williams, and E. Ritz, Essential hypertension. Lancet, 2007. 370(9587): p. 591-603.

35. Carretero, O.A. and S. Oparil, Essential hypertension. Part I: definition and etiology. Circulation, 2000. 101(3): p. 329-35.

36. Go, A.S., et al., Heart disease and stroke statistics--2013 update: a report from the American Heart Association. Circulation, 2013. 127(1): p. e6-e245.

37. Basson, J., J. Simino, and D.C. Rao, Between candidate genes and whole genomes: time for alternative approaches in blood pressure genetics. Curr Hypertens Rep, 2012. 14(1): p. 46-61.

38. Levy, D., et al., Evidence for a gene influencing blood pressure on chromosome 17. Genome scan linkage results for longitudinal blood pressure phenotypes in subjects from the framingham heart study. Hypertension, 2000. 36(4): p. 477-83.

39. Doris, P.A., The genetics of blood pressure and hypertension: the role of rare variation. Cardiovasc Ther, 2011. 29(1): p. 37-45.

40. Joe, B., et al., Substitution mapping of a blood pressure quantitative trait locus to a 2.73 Mb region on rat chromosome 1. J Hypertens, 2003. 21(11): p. 2077-84.

41. Delles, C., et al., Genetics of hypertension: from experimental animals to humans. Biochim Biophys Acta, 2010. 1802(12): p. 1299-308.

42. Joe, B. and M.R. Garrett, Substitution Mapping, in Cardiovascular Genomics, M.K. Rai, et al., Editors. 2005, Humana Press: Totowa, NJ. p. 41-58.

110

43. Joe, B. and M.R. Garrett, Genetic analysis of inherited hypertension in the rat, in Genetics of Hypertension, A. Dominiczak and J.M. Connell, Editors. 2007, Elsevier. p. 177-200.

44. Pinto, Y.M., M. Paul, and D. Ganten, Lessons from rat models of hypertension: from Goldblatt to genetic engineering. Cardiovasc Res, 1998. 39(1): p. 77-88.

45. Rapp, J.P. and H. Dene, Development and characteristics of inbred strains of Dahl salt-sensitive and salt-resistant rats. Hypertension, 1985. 7(3 Pt 1): p. 340-9.

46. Somova, L.I., M.L. Channa, and M.S. Khan, An experimental rat model of salt- sensitive hypertension; biochemical and morphological parameters and sympathetic nervous system. J S Afr Vet Assoc, 1999. 70(1): p. 14-7.

47. Cheng, X. and B. Joe, Genetics of Hypertension. Colloquium Series on Integrated Systems Physiology: From Molecule to Function, 2015. 7(3): p. 1-56.

48. Adeyemo, A., et al., Genetic effects on blood pressure localized to chromosomes 6 and 7. J Hypertens, 2005. 23(7): p. 1367-73.

49. Guo, Y., et al., A genome-wide linkage and association scan reveals novel loci for hypertension and blood pressure traits. PLoS One, 2012. 7(2): p. e31489.

50. Koivukoski, L., et al., Meta-analysis of genome-wide scans for hypertension and blood pressure in Caucasians shows evidence of susceptibility regions on chromosomes 2 and 3. Hum Mol Genet, 2004. 13(19): p. 2325-32.

51. Simino, J., et al., Five blood pressure loci identified by an updated genome-wide linkage scan: meta-analysis of the Family Blood Pressure Program. Am J Hypertens, 2011. 24(3): p. 347-54.

52. Hilbert, P., et al., Chromosomal mapping of two genetic loci associated with blood-pressure regulation in hereditary hypertensive rats. Nature, 1991. 353(6344): p. 521-9.

53. Jacob, H.J., et al., Genetic mapping of a gene causing hypertension in the stroke- prone spontaneously hypertensive rat. Cell, 1991. 67(1): p. 213-24.

111

54. Zhu, M. and S. Zhao, Candidate gene identification approach: progress and challenges. Int J Biol Sci, 2007. 3(7): p. 420-7.

55. Rapp, J.P. and B. Joe, Use of contiguous congenic strains in analyzing compound QTLs. Physiol Genomics, 2012. 44(2): p. 117-20.

56. Deng, A.Y., et al., Genetic mapping of two new blood pressure quantitative trait loci in the rat by genotyping endothelin system genes. J Clin Invest, 1994. 93(6): p. 2701-9.

57. Freel, E.M., et al., Phenotypic consequences of variation across the aldosterone synthase and 11-beta hydroxylase locus in a hypertensive cohort: data from the MRC BRIGHT Study. Clin Endocrinol (Oxf), 2007. 67(6): p. 832-8.

58. Joe, B., et al., Positional identification of variants of Adamts16 linked to inherited hypertension. Hum Mol Genet, 2009. 18(15): p. 2825-38.

59. Gopalakrishnan, K., et al., Augmented rififylin is a risk factor linked to aberrant cardiomyocyte function, short-QT interval and hypertension. Hypertension, 2011. 57(4): p. 764-71.

60. Joe, B., et al., Transcriptional profiling with a blood pressure QTL interval- specific oligonucleotide array. Physiol Genomics, 2005. 23(3): p. 318-26.

61. Nie, Y., et al., High-resolution mapping of a novel rat blood pressure locus on chromosome 9 to a region containing the Spp2 gene and colocalization of a QTL for bone mass. Physiol Genomics, 2016. 48(6): p. 409-19.

62. Bentley, D.R., Decoding the human genome sequence. Hum Mol Genet, 2000. 9(16): p. 2353-8.

63. Lander, E.S., et al., Initial sequencing and analysis of the human genome. Nature, 2001. 409(6822): p. 860-921.

64. Wellcome Trust Case Control, C., Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls. Nature, 2007. 447(7145): p. 661-78.

112

65. Newton-Cheh, C., et al., Genome-wide association study identifies eight loci associated with blood pressure. Nat Genet, 2009. 41(6): p. 666-76.

66. International Consortium for Blood Pressure Genome-Wide Association, S., et al., Genetic variants in novel pathways influence blood pressure and cardiovascular disease risk. Nature, 2011. 478(7367): p. 103-9.

67. Hsu, P.D., E.S. Lander, and F. Zhang, Development and applications of CRISPR- Cas9 for genome engineering. Cell, 2014. 157(6): p. 1262-78.

68. Ran, F.A., et al., Genome engineering using the CRISPR-Cas9 system. Nature Protocols, 2013. 8(11): p. 2281-2308.

69. Yoshimi, K., et al., Allele-specific genome editing and correction of disease- associated phenotypes in rats using the CRISPR-Cas platform. Nat Commun, 2014. 5: p. 4240.

70. Pennisi, E., The CRISPR craze. Science, 2013. 341(6148): p. 833-6.

71. Moreno-Mateos, M.A., et al., CRISPRscan: designing highly efficient sgRNAs for CRISPR-Cas9 targeting in vivo. Nat Methods, 2015. 12(10): p. 982-8.

72. Horvath, P. and R. Barrangou, CRISPR/Cas, the immune system of bacteria and archaea. Science, 2010. 327(5962): p. 167-70.

73. Jinek, M., et al., A programmable dual-RNA-guided DNA endonuclease in adaptive bacterial immunity. Science, 2012. 337(6096): p. 816-21.

74. Young, S.A., R.J. Aitken, and M. Ikawa, Advantages of using the CRISPR/Cas9 system of genome editing to investigate male reproductive mechanisms using mouse models. Asian J Androl, 2015. 17(4): p. 623-7.

75. Shah, A.N., et al., Rapid reverse genetic screening using CRISPR in zebrafish. Nat Methods, 2015. 12(6): p. 535-40.

76. Shah, A.N., et al., Rapid Reverse Genetic Screening Using CRISPR in Zebrafish. Zebrafish, 2016. 13(2): p. 152-3. 113

77. Wang, H., et al., One-step generation of mice carrying mutations in multiple genes by CRISPR/Cas-mediated genome engineering. Cell, 2013. 153(4): p. 910- 8.

78. Brailoiu, E., et al., Distribution and characterization of estrogen receptor G protein-coupled receptor 30 in the rat central nervous system. J Endocrinol, 2007. 193(2): p. 311-21.

79. Gencel, V.B., et al., Vascular effects of phytoestrogens and alternative menopausal hormone therapy in cardiovascular disease. Mini Rev Med Chem, 2012. 12(2): p. 149-74.

80. Meyer, M.R., et al., Deletion of G protein-coupled estrogen receptor increases endothelial vasoconstriction. Hypertension, 2012. 59(2): p. 507-12.

81. Meyer, M.R., E.R. Prossnitz, and M. Barton, The G protein-coupled estrogen receptor GPER/GPR30 as a regulator of cardiovascular function. Vascul Pharmacol, 2011. 55(1-3): p. 17-25.

82. Jacenik, D., A.I. Cygankiewicz, and W.M. Krajewska, The G protein-coupled estrogen receptor as a modulator of neoplastic transformation. Mol Cell Endocrinol, 2016. 429: p. 10-8.

83. Olde, B. and L.M. Leeb-Lundberg, GPR30/GPER1: searching for a role in estrogen physiology. Trends Endocrinol Metab, 2009. 20(8): p. 409-16.

84. Filardo, E.J., et al., Estrogen-induced activation of Erk-1 and Erk-2 requires the G protein-coupled receptor homolog, GPR30, and occurs via trans-activation of the epidermal growth factor receptor through release of HB-EGF. Mol Endocrinol, 2000. 14(10): p. 1649-60.

85. Novella, S., et al., Vascular Aging in Women: is Estrogen the Fountain of Youth? Front Physiol, 2012. 3: p. 165.

86. Haas, E., et al., Regulatory role of G protein-coupled estrogen receptor for vascular function and obesity. Circ Res, 2009. 104(3): p. 288-91.

114

87. Jessup, J.A., et al., Attenuation of salt-induced cardiac remodeling and diastolic dysfunction by the GPER agonist G-1 in female mRen2.Lewis rats. PLoS One, 2010. 5(11): p. e15433.

88. Martensson, U.E., et al., Deletion of the G protein-coupled receptor 30 impairs glucose tolerance, reduces bone growth, increases blood pressure, and eliminates estradiol-stimulated insulin release in female mice. Endocrinology, 2009. 150(2): p. 687-98.

89. Levin, E.R., G protein-coupled receptor 30: estrogen receptor or collaborator? Endocrinology, 2009. 150(4): p. 1563-5.

90. Otto, C., et al., GPR30 does not mediate estrogenic responses in reproductive organs in mice. Biol Reprod, 2009. 80(1): p. 34-41.

91. Otto, C., et al., G protein-coupled receptor 30 localizes to the endoplasmic reticulum and is not activated by estradiol. Endocrinology, 2008. 149(10): p. 4846-56.

92. Razandi, M., et al., Proximal events in signaling by plasma membrane estrogen receptors. J Biol Chem, 2003. 278(4): p. 2701-12.

93. Lam, H.M., et al., Targeting GPR30 with G-1: a new therapeutic target for castration-resistant prostate cancer. Endocr Relat Cancer, 2014. 21(6): p. 903-14.

94. Wegner, M.S., et al., Ceramide synthases CerS4 and CerS5 are upregulated by 17beta-estradiol and GPER1 via AP-1 in human breast cancer cells. Biochem Pharmacol, 2014. 92(4): p. 577-589.

95. Srivastava, D.P. and P.D. Evans, G-protein oestrogen receptor 1: trials and tribulations of a membrane oestrogen receptor. J Neuroendocrinol, 2013. 25(11): p. 1219-30.

96. Blasko, E., et al., Beneficial role of the GPR30 agonist G-1 in an animal model of multiple sclerosis. J Neuroimmunol, 2009. 214(1-2): p. 67-77.

97. Weil, B.R., et al., Signaling via GPR30 protects the myocardium from ischemia/reperfusion injury. Surgery, 2010. 148(2): p. 436-43. 115

98. Yates, M.A., et al., GPR30, but not estrogen receptor-alpha, is crucial in the treatment of experimental autoimmune encephalomyelitis by oral ethinyl estradiol. BMC Immunol, 2010. 11: p. 20.

99. Zhang, B., et al., Estradiol and G1 reduce infarct size and improve immunosuppression after experimental stroke. J Immunol, 2010. 184(8): p. 4087- 94.

100. Holm, A., et al., The G protein-coupled estrogen receptor 1 (GPER1/GPR30) agonist G-1 regulates vascular smooth muscle cell Ca(2)(+) handling. J Vasc Res, 2013. 50(5): p. 421-9.

101. Lindsey, S.H., et al., Chronic treatment with the G protein-coupled receptor 30 agonist G-1 decreases blood pressure in ovariectomized mRen2.Lewis rats. Endocrinology, 2009. 150(8): p. 3753-8.

102. Broughton, B.R., A.A. Miller, and C.G. Sobey, Endothelium-dependent relaxation by G protein-coupled receptor 30 agonists in rat carotid arteries. Am J Physiol Heart Circ Physiol, 2010. 298(3): p. H1055-61.

103. Gros, R., et al., Aldosterone mediates its rapid effects in vascular endothelial cells through GPER activation. Am J Physiol Cell Physiol, 2013. 304(6): p. C532-40.

104. Gros, R., et al., GPR30 expression is required for the mineralocorticoid receptor- independent rapid vascular effects of aldosterone. Hypertension, 2011. 57(3): p. 442-51.

105. Briet, M. and E.L. Schiffrin, Vascular actions of aldosterone. J Vasc Res, 2013. 50(2): p. 89-99.

106. Feldman, R.D., et al., A common hypofunctional genetic variant of GPER is associated with increased blood pressure in women. Br J Clin Pharmacol, 2014.

107. Le Poul, E., et al., Functional characterization of human receptors for short chain fatty acids and their role in polymorphonuclear cell activation. J Biol Chem, 2003. 278(28): p. 25481-9.

116

108. Bockaert, J. and J.P. Pin, Molecular tinkering of G protein-coupled receptors: an evolutionary success. EMBO J, 1999. 18(7): p. 1723-9.

109. Tang, X.L., et al., Orphan G protein-coupled receptors (GPCRs): biological functions and potential drug targets. Acta Pharmacol Sin, 2012. 33(3): p. 363-71.

110. Pluznick, J.L., Extra sensory perception: the role of Gpr receptors in the kidney. Curr Opin Nephrol Hypertens, 2014. 23(5): p. 507-12.

111. Ulven, T., Short-chain free fatty acid receptors FFA2/GPR43 and FFA3/GPR41 as new potential therapeutic targets. Front Endocrinol (Lausanne), 2012. 3: p. 111.

112. Halder, S., S. Kumar, and R. Sharma, The therapeutic potential of GPR120: a patent review. Expert Opin Ther Pat, 2013. 23(12): p. 1581-90.

113. Mancini, A.D. and V. Poitout, The fatty acid receptor FFA1/GPR40 a decade later: how much do we know? Trends Endocrinol Metab, 2013. 24(8): p. 398-407.

114. Oh, D.Y. and W.S. Lagakos, The role of G-protein-coupled receptors in mediating the effect of fatty acids on inflammation and insulin sensitivity. Curr Opin Clin Nutr Metab Care, 2011. 14(4): p. 322-7.

115. Hirasawa, A., et al., Free fatty acids regulate gut incretin glucagon-like peptide-1 secretion through GPR120. Nat Med, 2005. 11(1): p. 90-4.

116. Oh, D.Y., et al., GPR120 is an omega-3 fatty acid receptor mediating potent anti- inflammatory and insulin-sensitizing effects. Cell, 2010. 142(5): p. 687-98.

117. Bellahcene, M., et al., Male mice that lack the G-protein-coupled receptor GPR41 have low energy expenditure and increased body fat content. Br J Nutr, 2013. 109(10): p. 1755-64.

118. Brown, A.J., et al., The Orphan G protein-coupled receptors GPR41 and GPR43 are activated by propionate and other short chain carboxylic acids. J Biol Chem, 2003. 278(13): p. 11312-9.

117

119. Samuel, B.S., et al., Effects of the gut microbiota on host adiposity are modulated by the short-chain fatty-acid binding G protein-coupled receptor, Gpr41. Proc Natl Acad Sci U S A, 2008. 105(43): p. 16767-72.

120. Knock, G., et al., Propionate-induced relaxation in rat mesenteric arteries: a role for endothelium-derived hyperpolarising factor. J Physiol, 2002. 538(Pt 3): p. 879-90.

121. Davis-Richardson, A.G. and E.W. Triplett, A model for the role of gut bacteria in the development of autoimmunity for type 1 diabetes. Diabetologia, 2015. 58(7): p. 1386-93.

122. Panzer, A.R. and S.V. Lynch, Influence and effect of the human microbiome in allergy and asthma. Curr Opin Rheumatol, 2015. 27(4): p. 373-80.

123. Devkota, S. and E.B. Chang, Interactions between Diet, Bile Acid Metabolism, Gut Microbiota, and Inflammatory Bowel Diseases. Dig Dis, 2015. 33(3): p. 351- 6.

124. Goto, Y., Y. Kurashima, and H. Kiyono, The gut microbiota and inflammatory bowel disease. Curr Opin Rheumatol, 2015. 27(4): p. 388-96.

125. van Best, N., P.L. Jansen, and S.S. Rensen, The gut microbiota of nonalcoholic fatty liver disease: current methods and their interpretation. Hepatol Int, 2015. 9(3): p. 406-15.

126. Honour, J.W., Historical perspective: gut dysbiosis and hypertension. Physiol Genomics, 2015. 47(10): p. 443-6.

127. Yang, T., et al., Gut dysbiosis is linked to hypertension. Hypertension, 2015. 65(6): p. 1331-40.

128. Mariat, D., et al., The Firmicutes/Bacteroidetes ratio of the human microbiota changes with age. BMC Microbiol, 2009. 9: p. 123.

129. Sanz, Y. and A. Moya-Perez, Microbiota, inflammation and obesity. Adv Exp Med Biol, 2014. 817: p. 291-317.

118

130. Mortensen, F.V., et al., Short chain fatty acids relax isolated resistance arteries from the human ileum by a mechanism dependent on anion-exchange. Pharmacol Toxicol, 1994. 75(3-4): p. 181-5.

131. Maslowski, K.M., et al., Regulation of inflammatory responses by gut microbiota and chemoattractant receptor GPR43. Nature, 2009. 461(7268): p. 1282-6.

132. Pluznick, J.L., et al., Olfactory receptor responding to gut microbiota-derived signals plays a role in renin secretion and blood pressure regulation. Proc Natl Acad Sci U S A, 2013. 110(11): p. 4410-5.

133. Mell, B., et al., Evidence for a link between gut microbiota and hypertension in the Dahl rat. Physiol Genomics, 2015. 47(6): p. 187-97.

134. Saad, Y., et al., Congenic interval mapping of RNO10 reveals a complex cluster of closely-linked genetic determinants of blood pressure. Hypertension, 2007. 50(5): p. 891-8.

135. Cheng, Z.J., et al., Endothelial dysfunction and salt-sensitive hypertension in spontaneously diabetic Goto-Kakizaki rats. Hypertension, 2001. 37(2 Pt 2): p. 433-9.

136. Cosentino, F., et al., Nitric-oxide-mediated relaxations in salt-induced hypertension: effect of chronic beta1 -selective receptor blockade. J Hypertens, 2002. 20(3): p. 421-8.

137. Martinez-Revelles, S., et al., Endothelial dysfunction in rat mesenteric resistance artery after transient middle cerebral artery occlusion. J Pharmacol Exp Ther, 2008. 325(2): p. 363-9.

138. Pacurari, M., et al., Endothelial cell transfusion ameliorates endothelial dysfunction in 5/6 nephrectomized rats. Am J Physiol Heart Circ Physiol, 2013. 305(8): p. H1256-64.

139. Manichanh, C., et al., Reshaping the gut microbiome with bacterial transplantation and antibiotic intake. Genome Res, 2010. 20(10): p. 1411-9.

119

140. Caporaso, J.G., et al., QIIME allows analysis of high-throughput community sequencing data. Nat Methods, 2010. 7(5): p. 335-6.

141. Paulson, J.N., et al., Differential abundance analysis for microbial marker-gene surveys. Nat Methods, 2013. 10(12): p. 1200-2.

142. Edgar, R.C., UPARSE: highly accurate OTU sequences from microbial amplicon reads. Nat Methods, 2013. 10(10): p. 996-8.

143. Caporaso, J.G., et al., Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc Natl Acad Sci U S A, 2011. 108 Suppl 1: p. 4516- 22.

144. Edgar, R.C., Search and clustering orders of magnitude faster than BLAST. Bioinformatics, 2010. 26(19): p. 2460-1.

145. DeSantis, T.Z., et al., Greengenes, a chimera-checked 16S rRNA gene database and workbench compatible with ARB. Appl Environ Microbiol, 2006. 72(7): p. 5069-72.

146. Brem, R.B., et al., Genetic interactions between polymorphisms that affect gene expression in yeast. Nature, 2005. 436(7051): p. 701-3.

147. Khan, A.I., et al., Negative epistasis between beneficial mutations in an evolving bacterial population. Science, 2011. 332(6034): p. 1193-6.

148. Chou, H.H., et al., Diminishing returns epistasis among beneficial mutations decelerates adaptation. Science, 2011. 332(6034): p. 1190-2.

149. Shao, H., et al., Genetic architecture of complex traits: large phenotypic effects and pervasive epistasis. Proc Natl Acad Sci U S A, 2008. 105(50): p. 19910-4.

150. Bell, J.T., et al., Two-dimensional genome-scan identifies novel epistatic loci for essential hypertension. Hum Mol Genet, 2006. 15(8): p. 1365-74.

151. Wang, X., et al., Widespread genetic epistasis among cancer genes. Nat Commun, 2014. 5: p. 4828. 120

152. Ritchie, M.D., et al., Multifactor-dimensionality reduction reveals high-order interactions among estrogen-metabolism genes in sporadic breast cancer. Am J Hum Genet, 2001. 69(1): p. 138-47.

153. Arnegard, M.E., et al., Genetics of ecological divergence during speciation. Nature, 2014. 511(7509): p. 307-11.

154. Gong, L.I. and J.D. Bloom, Epistatically interacting substitutions are enriched during adaptive protein evolution. PLoS Genet, 2014. 10(5): p. e1004328.

155. Lareau, C.A. and B.A. McKinney, Network theory for data-driven epistasis networks. Methods Mol Biol, 2015. 1253: p. 285-300.

156. Ritchie, M.D., Finding the epistasis needles in the genome-wide haystack. Methods Mol Biol, 2015. 1253: p. 19-33.

157. Li, B.E. and P. Ernst, Two decades of leukemia oncoprotein epistasis: the MLL1 paradigm for epigenetic deregulation in leukemia. Exp Hematol, 2014. 42(12): p. 995-1012.

158. Glenn, C.L., et al., Linkage and association of tumor necrosis factor receptor 2 locus with hypertension, hypercholesterolemia and plasma shed receptor. Hum Mol Genet, 2000. 9(13): p. 1943-9.

159. Pan, W.H., et al., Linkage analysis with candidate genes: the Taiwan young-onset hypertension genetic study. Hum Genet, 2000. 107(3): p. 210-5.

160. Rice, T., et al., Genome-wide linkage analysis of systolic and diastolic blood pressure: the Quebec Family Study. Circulation, 2000. 102(16): p. 1956-63.

161. Lyons, P.A., et al., Mapping by genetic interaction: high-resolution congenic mapping of the type 1 diabetes loci Idd10 and Idd18 in the NOD mouse. Diabetes, 2001. 50(11): p. 2633-7.

162. Rogner, U.C., et al., Three loci on mouse chromosome 6 influence onset and final incidence of type I diabetes in NOD.C3H congenic strains. Genomics, 2001. 74(2): p. 163-71.

121

163. Iakoubova, O., et al., Genetic analysis of modifying loci on mouse chromosome 1 that affect disease severity in a model of recessive PKD. Physiol Genomics, 1999. 1(2): p. 101-5.

164. Feldman, R.D. and R. Gros, Unraveling the mechanisms underlying the rapid vascular effects of steroids: sorting out the receptors and the pathways. Br J Pharmacol, 2011. 163(6): p. 1163-9.

165. Liu, S.L., et al., Aldosterone regulates vascular reactivity: short-term effects mediated by phosphatidylinositol 3-kinase-dependent nitric oxide synthase activation. Circulation, 2003. 108(19): p. 2400-6.

166. Revankar, C.M., et al., A transmembrane intracellular estrogen receptor mediates rapid cell signaling. Science, 2005. 307(5715): p. 1625-30.

167. Costea, P.I., et al., A fair comparison. Nat Methods, 2014. 11(4): p. 359.

168. Kumarasamy, S., et al., Refined mapping of blood pressure quantitative trait loci using congenic strains developed from two genetically hypertensive rat models. Hypertens Res, 2011. 34(12): p. 1263-70.

169. Toland, E.J., et al., Blood pressure and proteinuria effects of multiple quantitative trait loci on rat chromosome 9 that differentiate the spontaneously hypertensive rat from the Dahl salt-sensitive rat. J Hypertens, 2008. 26(11): p. 2134-41.

170. Garrett, M.R., H. Dene, and J.P. Rapp, Time-course genetic analysis of albuminuria in Dahl salt-sensitive rats on low-salt diet. J Am Soc Nephrol, 2003. 14(5): p. 1175-87.

171. Llamas, B., et al., Genetic determinants of systolic and pulse pressure in an intercross between normotensive inbred rats. Hypertension, 2006. 48(5): p. 921-6.

172. Garrett, M.R., B. Joe, and S. Yerga-Woolwine, Genetic linkage of urinary albumin excretion in Dahl salt-sensitive rats: influence of dietary salt and confirmation using congenic strains. Physiol Genomics, 2006. 25(1): p. 39-49.

122

Appendix A

Microsatellite markers developed during the construction of bicongenic sub strains of S.LEW(5)X6BX9X5

Marker Position (RGSC Primers v3.4) RNO5(130.32)-F 130327985 CCCTTTAGTAAATTAGGCATTGTAAA RNO5(130.32)-R TGCTAATTCAAGACCAGCCTA RNO5(130.25)-F 130250230 GGATCAAGATGTAGTCAGATCAAGA RNO5(130.25)-R CTGTGTCCCCTACCAGAAGC RNO5(130.24)-F 130248896 GATGGGTCAGTGGGTAATGG RNO5(130.24)-R CCCTTCAGGATCTGGTTTCA RNO5(130.23)-F 130230946 CAGCCAGGAAAGGTCATTTT RNO5(130.23)-R AGATGGCTGAGCAGGTAAGG RNO5(130.14)-F 130143919 GTGCCAGGCGTTTATATGGT RNO5(130.14)-R ATGTTAGGCAGCCCACAACT RNO5(130.13)-F 130137025 AAGTTCACTCACCAGCCAGTC RNO5(130.13)-R TGTGTGTGTGTACATTGATGTGTG RNO5(130.09)-F 130094109 TGGATTCACATACTCTCATGTGC RNO5(130.09)-R TGAATCAACATGAACTCACGA RNO5(130.02)-F 130023232 GGATAAGTATCCCAAAAGAAATAAGAA RNO5(130.02)-R AGCCAGTCTTGGCTGGATAG RNO5(129.94)-F 129948412 TGCCCTAGATAGGTCCATCC RNO5(129.94)-R GACCACCTGGAATTTGATCC RNO5(129.58)-F 129588118 GCTGGGTAGCCAAAGATGAA RNO5(129.58)-R CGACAAGGTAAACTTTATTCTCCA RNO5(129.54)-F 129546954 CCACCATGTACAGTGCAACA RNO5(129.54)-R TGGCAGAGGAAAGTGGATCT RNO5(129.51)-F 129511655 CCCCCAAGTCTGACAATCTG RNO5(129.51)-R CTACAAAGTTGGGGGAAAGC RNO5(129.44)-F 129447821 TGGGATAGGTACAGTCTTTGTTCA 123

RNO5(129.44)-R GGGCAGCATCACTAGAGTCC RNO5(129.39)-F 129391485 TCCATCCTTCCAGGTAGTAAACA RNO5(129.39)-R ATTTGAGCTGGGCCTGAAT RNO5(129.37)-F 129377708 TTTTCAAGTATGTCTGTATGTCTGTCT RNO5(129.37)-R CGCAAGCTTCAATATACTTTCTTTTT RNO5(129.31)-F 129319827 TGCAGGCAAAATATCAATGC RNO5(129.31)-R CAGTGTTTCAGCTTGGATGG RNO5(129.28)-F 129285642 AGCTCCCAGCCATCCTAACT RNO5(129.28)-R CCTGCAGAACCATTGAGAAAG RNO5(128.92)-F 128923057 TTTCCCAAATGCTCAGAAGC RNO5(128.92)-R AAGTCTTCCAGGGAGCCAAC RNO5(128.87)-F 128877779 ACCAGACAGTGCGAATCTGA RNO5(128.87)-R CACCCAAGGGGTTCATTTAG RNO5(126.39)-F 126397583 CAGGGGCAAAGCTATAGAGG RNO5(126.39)-R CCAGGTTGAGCAAGAGTTCC RNO5(126.36)-F 126368264 CCAAATGTGATCATCTCGTAGC RNO5(126.36)-R AGTAGAGCATGCCTAACACAGAA RNO5(126.30)-F 126304823 ATGACAAGTTGCCCTGCTCT RNO5(126.30)-R ACCCACAACTGGCCAAGG RNO5(125.23)-F 125236041 TGTTTTCCTTCCTGCCTCTT RNO5(125.23)-R GGGAGAAGAACCAAAGCTTAAA RNO5(121.65)-F 121651296 ACCCACTTCCTTCATCCAGA RNO5(121.65)-R TTTGAGGTGTTTCCCACAGC RNO5(121.63)-F 121637058 GCTTTGGAGGATTCTGTTGC RNO5(121.63)-R GAGGGAAGTGTGTGCTAGCTG RNO5(121.58)-2F 121580511 CACATGTTTTTCTGAGTCAGCTT RNO5(121.58)-2R CAGTAGCTAGGAAATGGAACCAG RNO5(121.58)-F 121580389 TCCACTCCATTTCTGGGAGA RNO5(121.58)-R TGCACAAAACAGAGAGACAGAGA RNO5(121.52)-F 121521749 TCATCTTGGTGGAAAGTTCTG RNO5(121.52)-R CGCTCGATGGTCGATATAAA RNO5(121.46)-F 121462200 TTCCTCGTTTTGCAACTTGT RNO5(121.46)-R GGGGACTAATTCAACAGCAAA RNO5(121.45)-F 121459053 TCTCTGTGGAGGAAAAGATGA RNO5(121.45)-R CCGCTACACAGGCAAACAT RNO5(121.44)-F 121443268 CAGAAGGCTGAAGGTGTGTG RNO5(121.44)-R TGATCCTGGAAGCTTTTCATTT RNO5(121.43)-F 121437864 ACTTCTGTTCCCACCCCTTC

124

RNO5(121.43)-R TTAGTCCTGCATCCCTCAGC RNO5(121.41)-F 121418087 CAGCCCAGCAATGTTCTTTA RNO5(121.41)-R TGAACCTTAAGCCTGGCACT RNO5(121.39)-F 121392155 TTGAGTTCAAATCTAGCCTTGGT RNO5(121.39)-R TTTCTCCGCCTTTTCATCTG RNO5(119.44)-F 119449693 AAGTGGTGAATAAAGCTACAGGAGA RNO5(119.44)-R AGGTTGATCATCCCTCACCTT

125

Appendix B

Publications and Presentations

Selected Peer-reviewed Publications

1. Waghulde H, Cheng X, Mell B, Miller S, Filipiak W, Saunders T, Joe B. Effects of

the gut microbiota on host blood pressure is modulated by G-protein coupled

estrogen receptor (Gper1). (Manuscript in preparation)

2. Waghulde H, Pillai R, Cheng X, Mell B, Joe B. Modeling precision medicine

through deep congenic sequencing. (Manuscript in preparation)

3. Nie Y, Kumarasamy S, Waghulde H, Cheng X, Mell B, Czernik PJ, Lecka-Czernik

B, Joe B. High resolution mapping of a novel rat blood pressure locus on

chromosome 9 to a region containing the Spp2 gene and co-localization of a QTL

for bone mass. Physiol Genomics. 2016 Jun;48(6):409-19

4. Xi Cheng, Harshal Waghulde, Blair Mell, Kathryn Smedlund, Guillermo

Vazquez, Bina Joe. Pleiotropic Effect of a High Resolution Mapped Blood Pressure

QTL on Tumorigenesis. PLoS One. 2016 Apr 13;11(4)

5. Mell B, Jala VR, Mathew AV, Byun J, Waghulde H, Zhang Y, Haribabu B, Vijay-

Kumar M, Pennathur S, Joe B. Evidence for a link between gut microbiota and

hypertension in the Dahl rat. Physiol Genomics. 2015 Jun;47(6):187-97. 126

6. Kumarasamy S, Waghulde H, Gopalakrishnan K, Mell B, Morgan E, Joe B.

Mutation within the hinge region of the transcription factor Nr2f2 attenuates salt-

sensitive hypertension. Nat Commun. 2015 Feb 17;6:6252.

7. Mell B, Abdul-Majeed S, Kumarasamy S, Waghulde H, Pillai R, Nie Y, Joe B.

Multiple blood pressure loci with opposing blood pressure effects on rat

chromosome 1 in a homologous region linked to hypertension on human

chromosome 15. Hypertens Res. 2015 Jan;38(1):61-7.

8. Pillai R, Waghulde H, Nie Y, Gopalakrishnan K, Kumarasamy S, Farms P, Garrett

MR, Atanur SS, Maratou K, Aitman TJ, Joe B. Isolation and high-throughput

sequencing of two closely linked epistatic hypertension susceptibility loci with a

panel of bicongenic strains. Physiol Genomics. 2013 Aug 15;45(16):729-36.

9. Waghulde, H., Kamble, S., Patankar, P., Jaiswal, B., Pattanayak, S., Bhagat, C.,

Mohan, M. Antioxidant activity, phenol and flavonoid contents of seeds of Punica

granatum (Punicaceae) and Solanum torvum (Solanaceae). Pharmacologyonline 1:

193-202 (2011).

10. M Mohan, H Waghulde, S Kasture. Effect of Pomegranate juice in Angiotensin II

induced hypertension in Diabetic Wistar rats. Phytother Res. 2010 Jun; 24 Suppl

2:S196- 203.

11. Harshal Waghulde, Mahalaxmi Mohan, Sanjay Kasture, R Balaraman. Punica

granatum attenuates Angiotensin II induced hypertension in Wistar rats.”

International Journal of PharmTech Research. Jan-Mar 2010. Vol.2, No.1, pp 60-

67.

127

Presentations

1. Harshal Waghulde, Xi Cheng, Blair Mell, Shondra Miller, Wanda Filipiak,

Thomas Saunders, Bina Joe. G-protein coupled estrogen receptor (Gper1): A

potentially novel receptor for acetate, a gut microbiotal metabolite. 43th Annual

Pharmacology Colloquium 2016, Ann Arbor (Poster presentation)

2. Harshal Waghulde, Xi Cheng, Blair Mell, Shondra Miller, Wanda Filipiak,

Thomas Saunders, Bina Joe. Development of a Novel Gper1 Knock out Rat Model

Using a Modified CRISPR/Cas9 Technology. CRISPR/Cas Revolution 2015, Cold

Spring Harbor (Poster presentation)

3. Harshal Waghulde, Shondra Miller, Wanda Filipaik, Blair Mell, Thomas

Saunders, Bina Joe. Development of a Novel Gper1 Knock out Rat Model Using a

Modified CRISPR/Cas9 Technology. 42th Annual Pharmacology Colloquium

2015, Toledo (Oral presentation)

4. Harshal Waghulde, Shondra Miller, Wanda Filipaik, Blair Mell, Thomas

Saunders, Bina Joe. Development of a Novel Gper1 Knock out Rat Model Using a

Modified CRISPR/Cas9 Technology. Experimental Biology 2015, Boston (Oral

presentation)

5. Harshal Waghulde, Shondra Miller, Wanda Filipaik, Blair Mell, Thomas

Saunders, Bina Joe. Development of a Novel Gper1 Knock out Rat Model Using a

Modified CRISPR/Cas9 Technology. Council on Hypertension Scientific Sessions

2015 (Poster presentation)

6. Harshal Waghulde, Resmi Pillai, Ying Nie, Bina Joe. Genetic Determinants of

Obesity and Hypertension. Graduate Research Forum 2014 (Oral presentation) 128

7. H. Waghulde, R. Pillai, Y. Nie, K. Gopalakrishnan, S. Kumarasamy, P. Farms, M.

Garrett, S. Atanur, T. Aitman, B. Joe. Epistasis accounts for ‘Missing Heritability’

of two closely-linked blood pressure loci. Experimental Biology 2013, Boston

(Oral presentation*).

8. H. Waghulde, R. Pillai, B. Joe. Identification of Obesity-Related Epistatic Genetic

Determinants on Rat Chromosome 5 Using a Panel of Bicongenic Strains.

Graduate Research Forum 2013 (Poster presentation)

9. H. Waghulde, R. Pillai, B. Joe. Identification of Obesity-Related Epistatic Genetic

Determinants on Rat Chromosome 5 Using a Panel of Bicongenic Strains. 40th

Annual Pharmacology Colloquium 2013, Detroit (Poster presentation)

10. H. Waghulde, R Pillai, Y. Nie, B. Joe. Genetic determinants of obesity and

hypertension on rat chromosome 5. Cold Spring Harbor Laboratory meeting 2013

(Oral presentation).

11. H. Waghulde, M. Mohan. Punica granatum attenuates Angiotensin II induced

hypertension in Wistar rats. International Conference on Integrative and

Personalized Medicine and 42nd Annual Conference of Indian Pharmacological

Society (IPS) 2009 (Poster presentation)

12. H. Waghulde, S. Gosde, M. Mohan. Effect of Myricetin on blood pressure and

metabolic alteration in fructose hypertensive rat” at International Conference on

Translational Pharmacology and 41st Annual Conference of Indian Pharmacology

Society (IPS) 2008 (Poster Presentation).

*Selected as ‘one of four’ trainee highlight finalist of Physiological Genomics Group of

American Physiological Society. 129