A Dissertation
entitled
Studies on the Holobiont and Blood Pressure Regulation
by
Sarah Galla
Submitted to the Graduate Faculty as partial fulfillment of the requirements for the
Doctor of Philosophy Degree in
Biomedical Sciences
______Bina Joe, Ph.D., Committee Chair
______Matam Vijay-Kumar, Ph.D., Committee Member
______Deepak Malhotra, MD, Ph.D., Committee Member
______Guillermo Vazquez, Ph.D., Committee Member
______Edwin Sanchez, Ph.D., Committee Member
______Cyndee Gruden, Ph.D., Dean College of Graduate Studies
The University of Toledo
May 2019
Copyright 2019, Sarah Lynn Galla
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
Studies on the Holobiont and Blood Pressure Regulation
by
Sarah Galla
Submitted to the Graduate Faculty as partial fulfillment of the requirements for the Doctor of Philosophy Degree in Biomedical Sciences
The University of Toledo
May 2019
Hypertension, or elevated blood pressure (BP), is very prevalent in the United
States with over a third of the population being affected, and is a risk factor for cardiovascular disease, kidney disease, and stroke. BP is a complex, polygenic trait that is regulated by the genome, as well as the environment. Recent studies have found that in addition to the genome, microbiota are also known to regulate BP. Microbiota are composed of microorganisms that live within the body in a symbiotic relationship. The term ‘holobiont’ is used to refer to all of the symbiotic relationships within the body as a unit. In this study, we aimed to determine the role that the host genome, the microbiome, and the interactions between them have in BP regulation. To do this, we performed multiple studies. First, we studied the role of a single gene, the G-protein coupled estrogen receptor (Gper1) on BP regulation by using a previously developed Gper1-/- rat model on the Dahl Salt Sensitive (S) rat background. To understand the function of
Gper1, we performed a quantitative proteomic study on endothelial cells, and found 150 differentially expressed proteins. Immune cell migration was one of the most differentially expressed pathways, suggesting a role of Gper1 in immune cell migration.
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To study the role of the microbiome, we gave two hypertensive rat strains, the S rat and Spontaneously Hypertensive Rat (SHR), one of three different antibiotics: minocycline, neomycin, or vancomycin. We found that in the S rat, regardless of the type, antibiotics increased BP. However, in the SHR, two of the antibiotics caused a lowering of BP, while one had no effect. These BP changes were accompanied by changes in bacterial abundance and diversity. Moreover, the S rats had a heightened inflammatory state in their colons, while the SHR did not. This suggested a role of the host immune-microbiome interaction in BP regulation.
In addition to adult hypertension, there is an increasing prevalence of pediatric hypertension that has not been well studied. Therefore, we aimed to study if the same mechanisms by which microbiota alter BP in adults were still evident in adolescents. We hypothesized that altering the microbiota during adolescence would affect the development of hypertension. We found that S rats given amoxicillin, the most common pediatric antibiotic, had a reduced BP compared to controls. This reduced BP was associated with microbiotal changes, most notably, a drastic decrease in the
Firmicutes/Bacteroidetes ratio, the most common marker of gut dysbiosis. This reduced dysbiosis caused a reduction in both colon and kidney inflammation, that persisted weeks after the cessation of amoxicillin, and caused the noted reduction in BP.
Microbiota initially begin to develop at birth and are heavily influenced by maternal factors. Therefore, we hypothesized that maternal antibiotic use during pregnancy and lactation would alter BP. We performed this experiment and found that male S rats whose mother was given amoxicillin had a reduced BP. However, female
iv offspring did not have this BP effect. The male offspring had reduced F/B ratios, while the females did not, suggesting a potential link to the sex-specific BP effect.
In conclusion, through the use of experiments to study both the host genome and the microbiome, we have found that the genome and microbiome, as well as the interactions between the two, regulate blood pressure. These studies highlight the importance of personalized medicine in the treatment of hypertension, as well as in the prescription of antimicrobials, specifically antibiotics, at any age.
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This dissertation is dedicated to my loving husband. Thank you for your continuous understanding and encouragement. You have been by my side every step of the way, and
I am incredibly grateful. Also, to my parents, thank you for always supporting me and believing in me even when I did not fully believe in myself.
Acknowledgements
I am very grateful to my mentor, Dr. Joe, for accepting me into her lab and patiently forming me into the scientist I am today. She allowed me to study more clinically-oriented projects that she knew I would have more interest in as an MD/PhD student. She spent her time teaching me not only valuable lessons in research, but also lessons that are applicable to every area of life. Her constant belief in me has inspired me to further my research career, and I know that she will always be there for me to offer guidance.
I would also like to thank my committee members, Dr. Vijay-Kumar, Dr.
Malhotra, Dr. Vazquez, and Dr. Sanchez for your support and advice during my graduate school journey. I am also very thankful to our collaborators who have provided me with valuable insight and assistance. Additionally, I would like to thank the Department of
Physiology and Pharmacology for all of the help and support I have received.
Finally, I would be remiss without thanking my lab mates. To Saroj Chakraborty,
Blair Mell, Dr. Xi Cheng, Dr. Ji-Youn Yeo, Dr. Youjie Zhang and Dr. Cam McCarthy, thank you for all of your help and support in the past few years. I would not have been able to accomplish this work without your time, knowledge, and guidance. Thank you for patiently explaining difficult concepts to me and for providing me with great feedback.
Everything you have done is greatly appreciated.
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Table of Contents
Abstract ...... iii
Acknowledgements ...... v
Table of Contents ...... vi
List of Tables ...... xi
List of Figures ...... xii
List of Abbreviations ...... xv
1 Microbiotal-Host Interactions and Hypertension...... 1
1.1 Abstract ...... 1
1.2 Introduction ...... 2
1.3 Gut microbiota, the host genome and hypertension...... 5
1.4 Gut microbiotal studies using genetic models of hypertension ...... 8
1.5 Beyond Gut microbiota, other organ-specific microbiota links to hypertension
...... 13
1.6 The host as a ‘Holobiont’ ...... 14
1.7 The translational value of understanding the host-microbial relationships
within a ‘holobiont’...... 16
1.8 Probiotics and Hypertension ...... 18
1.9 Conclusion ...... 19
1.10 Summary 21 vi
1.11 References ...... 22
2 Host Genomics and Blood Pressure Regulation ...... 41
2.1 Introduction ...... 41
2.2 Methods ...... 43
2.2.1 Animals and diet ...... 43
2.2.2 Endothelial cell culture ...... 44
2.2.3 Protein Identification and relative quantitation by TMT labeling and
LC-Tandem MS ...... 45
2.2.4 Data analysis ...... 47
2.2.5 Immunoblot analysis ...... 48
2.2.6 RNA isolation and Quantitative PCR analysis ...... 48
2.2.7 Permeability Assay ...... 49
2.2.8 Transendothelial Migration assay ...... 49
2.2.9 Immunohistochemistry ...... 50
2.2.10 Cell culture with acetate...... 51
2.2.11 Statistical analysis ...... 51
2.3 Results ...... 51
2.3.1 Altered cellular proteome in Gper1-/- rats ...... 51
2.3.2 Multiple endothelial cell proteomic networks affected by deletion of
Gper1 ...... 64
2.3.3 Confirmation of proteomic data ...... 68
2.3.4 Permeability and monocyte migration are not changed in vitro ...... 69
2.3.5 Macrophage infiltration of small resistance arteries is unchanged ...69
vii
2.3.6 Determination of phenotype as salt-sensitive ...... 70
2.3.7 Acetate supplementation in cell culture reverses some RNA
expression ...... 71
2.4 Discussion ...... 72
3 Disparate Effects of Antibiotics on Hypertension ...... 76
3.1 Abstract ...... 76
3.2 Introduction ...... 77
3.3 Methods ...... 79
3.3.1 Animals and diet ...... 79
3.3.2 Blood pressure measurements by radiotelemetry ...... 80
3.3.3 Antibiotic administration ...... 80
3.3.4 Collection of fecal content ...... 80
3.3.5 Genomic DNA Isolation, 16S rRNA Gene Sequencing, and Analysis
of Microbiotal Composition ...... 80
3.3.6 Short Chain Fatty Acid analysis ...... 82
3.3.7 Statistical analysis ...... 83
3.4 Results ...... 83
3.4.1 Blood Pressure and Heart Rate ...... 83
3.4.2 Gut microbiotal diversity ...... 87
3.4.3 Taxonomic Comparisons ...... 89
3.4.4 Short Chain Fatty Acids ...... 91
3.5 Discussion ...... 93
3.6 Acknowledgements ...... 97
viii
3.7 References ...... 99
4 The Developing Gut Microbiome and Hypertension ...... 106
4.1 Introduction ...... 106
2.2 Methods ...... 108
4.2.1 Animals and diet ...... 108
4.2.2 Blood pressure measurements by radiotelemetry ...... 108
4.2.3 Antibiotic administration ...... 109
4.2.4 Breeding and antibiotic administration ...... 110
4.2.5 Collection of fecal content ...... 110
4.2.6 Food intake measurement ...... 110
4.2.7 Genomic DNA Isolation, 16S rRNA Gene Sequencing and Analysis
of Microbiotal Composition ...... 110
4.2.8 Gavage of Lactobacillus murinus ...... 111
4.2.9 Statistical analysis ...... 111
4.3 Results ...... 111
4.3.1 Blood Pressure is Reduced with Amoxicillin Administration in
Young Rats...... 111
4.3.2 Amoxicillin Increased Body Weight in Young Rats ...... 113
4.3.3 Amoxicillin Treatment Alters the Gut Microbiota in Young Rats .114
4.3.4 Amoxicillin Treatment during Pregnancy and Lactation Alters
Maternal Gut Microbiota ...... 117
4.3.5 Amoxicillin Treatment during Gestation Reduces Blood Pressure in
Male Offspring, but not Females ...... 118
ix
4.3.6 Amoxicillin Treatment during Gestation Alters the Gut Microbiota
of Male and Female Rats ...... 120
4.3.7 Amoxicillin Treatment Decreases Inflammation in the Gut while
Neomycin Increases Inflammation ...... 123
4.3.8 Oral gavage with Lactobacillus murinus did not alter BP ...... 125
4.4 Discussion ...... 127
5 Discussion ...... 132
5.1 The holobiont ...... 132
5.2 The role of the genome ...... 133
5.3 The role of the microbiome...... 135
5.3.1 Altering the microbiota of hypertensive rats ...... 135
5.3.2 Altering the microbiota during adolescence ...... 137
5.3.3 Altering the developing microbiota ...... 138
5.4 Conclusion ...... 139
References ...... 141
x
List of Tables
2.1 Significantly altered proteins from Gper1-/- and Gper1+/+ endothelial cells ...... 54
2.2 Ingenuity Pathway Analysis predicted altered pathways...... 66
xi
List of Figures
1-1 Relative number of publications on microbiota ...... 4
1-2 Current and future prospects for microbiota and hypertension research ...... 16
2-1 Quantitative proteomic study design ...... 47
2-2 Heatmap of differentially expressed proteins ...... 53
2-3 Ingenuity Pathway Analysis predicted functions ...... 64
2-4 Top predicted altered pathways ...... 67
2-5 Cd99, Fibrillin-1, and Vcam-1 expression level confirmations ...... 68
2-6 Endothelial cell permeability and migration ...... 69
2-7 Macrophage infiltration in resistance arteries ...... 70
2-8 Low-salt systolic blood pressure ...... 71
2-9 RNA expression levels are reversed with acetate ...... 72
3-1 Systolic Blood Pressure in S rats and SHR with antibiotics ...... 84
3-2 Diastolic BP, mean arterial pressure, and heart rate ...... 86
3-3 Microbiotal diversity is reduced in S rats and SHR ...... 88
3-4 Phyla changes with antibiotic administration ...... 91
3-5 Short chain fatty acid levels in S rats and SHR ...... 92
4-1 Experimental Design ...... 109
4-2 Blood pressure of adolescent rats on amoxicillin ...... 112
4-3 Diurnal rhythm of amoxicillin treated rats...... 113 xii
4-4 Body weight and food intake ...... 114
4-5 Unweighted beta-diversity with antibiotic treatment ...... 115
4-6 Alpha-diversity is different with antibiotic treatment ...... 116
4-7 Phyla are significantly changed with amoxicillin ...... 116
4-8 Maternal alpha-diversity is reduced with amoxicillin treatment ...... 117
4-9 SBP, DBP, MAP, HR of offspring of amoxicillin-treated rats ...... 118
4-10 Male offspring of amoxicillin-treated rats have reduced SBP ...... 119
4-11 Alpha-diversity of male offspring ...... 120
4-12 Alpha-diversity of female offspring...... 122
4-13 Phyla of male and female offspring on low-salt diet ...... 122
4-14 Phyla of male and female offspring on high-salt diet ...... 123
4-15 Rorγ(t) levels in antibiotic-treated rats ...... 124
4-16 Nlrp3 inflammasome markers in antibiotic-treated rats ...... 125
4-17 Lactobacillus levels in antibiotic-treated rats ...... 126
4-18 SBP of rats treated with Lactobacillus ...... 126
xiii
List of Abbreviations
ACTH ...... Adrenocorticotropic hormone ANOVA ...... Analysis of variance
BCA ...... Bicinchoninic assay BP ...... Blood pressure BW ...... Body weight
C ...... Celcius CD68 ...... Cluster of differentiation 68 CD99 ...... Cluster of differentiation 99 CDNA ...... Complementary deoxyribonucleic acid CRISPR/CAS9 ...... Clustered regularly interspaced short palindromic repeats/CRISPR- associated protein 9
DA ...... Dalton DBP ...... Diastolic blood pressure DMEM ...... Dulbecco’s Modified Eagle Media DNA ...... Deoxyribonucleic acid DNTP ...... Deoxyribonucleotide triphosphate DTT ...... Dithiothreitol
F/B...... Firmicutes/Bacteroidetes FBS ...... Fetal bovine serum FN-1 ...... Fibronectin-1
G ...... Grams GAPDH ...... Glyceraldehyde 3-phosphate dehydrogenase GPCR ...... G-protein coupled receptor GPER1 ...... G-protein coupled estrogen receptor
HR ...... Heart Rate
IGA ...... Immunoglobulin A IHC ...... Immunohistochemistry IL-15 ...... Interleukin 15 IL-18 ...... Interleukin 18 xiv
IL-1β ...... Interleukin 1β IPA ...... Ingenuity Pathway Analysis
KDa ...... KiloDalton KG ...... Kilogram
LBRC ...... Lateral border recycling complex LC-MS ...... Liquid Chromatography/ Mass Spectrometry
MAP ...... Mean Arterial Pressure MCP-1 ...... Monocyte chemoattractant protein 1 MG ...... miligram MIN ...... Minute MLK ...... Myosin light chain kinase MM ...... milliMolar
NIH ...... National Institutes of Health NLRP3 ...... Nucleotide-binding domain, leucine-rich-containing family, pyrin domain-containing-3
OLFR78 ...... Olfactory receptor 78
PAK...... p21-activated kinase PBS ...... Phosphate Buffered Saline PCOA ...... Principle coordinates analysis PCR ...... Polymerase chain reaction PGK-1 ...... Phosphoglycerate kinase 1
QIIME ...... Quantitative Insights into Microbial Ecology QPCR ...... Quantitative polymerase chain reaction
R RATS ...... Dahl Salt-Resistant rats RHOA ...... Ras homolog gene family, member A RIPA ...... Radioimmunoprecipitation assay RNA ...... Ribonucleic acid RORγ(T) ...... Retinoic acid-related orphan receptor gammaT RPM ...... Revolutions per minute RRNA ...... ribosomal ribonucleic acid
S RAT ...... Dahl Salt-Sensitive Rat SBP ...... Systolic blood pressure SCD1 ...... Stearoyl-coenzyme A desaturase 1 SCFA...... Short chain fatty acid SGK-1 ...... Serum and glucocorticoid-regulated kinase 1 SHR ...... Spontaneously hypertensive rat SHRSP ...... Spontaneously hypertensive stroke-prone rat xv
TE ...... Tris and ethylenediaminetetraacetic acid TEAB ...... Triethylammonium bicarbonate TH17 ...... T-helper 17 TLR5 ...... Toll-like receptor 5 TMT ...... Tandem Mass Tag
VCAM-1 ...... Vascular cell adhesion molecule 1
WKY ...... Wistar Kyoto WT ...... Wild-type
μL ...... Microliter μM ...... Micromolar
xvi
Chapter 1
Microbiotal-Host Interactions and Hypertension
Galla, S., et al., Microbiotal-Host Interactions and Hypertension. Physiology (Bethesda),
2017. 32(3): p. 224-233.
1.1 Abstract
Hypertension, or elevated blood pressure (BP), has been extensively researched over decades and clearly demonstrated to be caused due to a combination of host genetic and environmental factors. While much research remains to be conducted to pin-point the precise genetic elements on the host genome that control BP, new lines of evidence are emerging to indicate that besides the host genome, the genomes of all indigenous commensal microorganisms, collectively referred to as the microbial metagenome or microbiome, is an important, but largely understudied, determinant of BP. Unlike the rigid host genome, the microbiome or the ‘second genome’ can be altered by diet or microbiotal transplantation in the host. This possibility is attractive from the perspective of exploiting the microbiotal composition for clinical management of inherited
1
hypertension. Thus, focusing on the limited current literature supporting a role for the microbiome in BP regulation, this review highlights the need to further explore the role of the co-existence of host and the microbiota as an organized biological unit called the
‘holobiont’ in the context of BP regulation.
1.2 Introduction
Evolution of complex life forms, such as humans, is often thought of as occurring due to the contributions of ‘Nature’ and ‘Nurture’, interpreted in biology as genes and environment. In the post-genomic era, armed with advanced technologies to query our genomes we are learning more and more about the dividing line between nature and nurture as a fallacy. This is because we are now able to appreciate that there is a
‘plasticity’ from both ends. On one hand, the ‘plasticity’ of the genome to accommodate changes in response to the environment in the form of epigenetic changes and on the other hand, the ‘plasticity’ of environmental factors to include genomes other than that of the host, i.e., the microbiomes. Microbes have colonized and co-evolved with plants and animals to the extent that they are perhaps constantly remodeling inside their hosts to suit their survival, regardless of the fact that we, their hosts, consider them our environmental factors. Another way of looking at this symbiotic relationship between the host and microbes living within the host is to look at this relationship as an ecosystem. Coined by
Lynn Margulis in 1991, the term ‘holobiont’ describes how macro-species, such as mammals, live in symbiosis with other micro-species, whereby all individuals that participate in a particular symbiosis are ‘bionts’ and the entire organism that is comprised of these bionts is a ‘holobiont’ (70). 2
In the human body there are about 10:1 numbers of microbial to human cells, and about 130 times the numbers of microbial genes (4). Although these numbers are staggering, it is only in recent years that attention is beginning to be given to explore ways by which microbes contribute to governing host physiology and the transition to pathophysiology. The reason for this surge in recent interest can be attested to rapid technological advancements in low cost sequencing, whereby sequence variability in the
16S RNA genes of microbes, collectively called ‘microbiota’, are exploited to determine their identities (18, 85). To attest to this point, note that a survey of articles published in
PubMed using the search term ‘Microbiota’ shows a surge in publications since the turn of the 21st Century, which marks the beginning of the genome sequencing era (Figure 1-
1). Furthermore, of these articles, there are very few that focus on microbiota and hypertension (Figure 1-1, Note: Less than half of the number of the articles retrieved for microbiota and hypertension are original articles. The remainder are review articles on the topic of microbiota and hypertension). The term microbiota in the literature largely refers to bacteria, but should encompass all microscopic organisms including viruses, bacteriophages, fungi and protozoans. The genomes of microbiota are collectively referred to as the microbiome and similar newer terms are emerging to describe the collective genomes of viruses, fungi and protozoa living in the holobiont as virome, mycobiome and protozoan genome, respectively. To date, there are no reports of viromes, mycobiomes or protozoan genomes described for their associations with blood pressure
(BP) regulation. For the purpose of this review, the term microbiota will be used for
3
describing bacteria investigated for their links to the regulation of BP.
Figure 1-1: Relative number of publications on microbiota versus publications on microbiota in the field of hypertension. Data were collected using the search feature from PubMed on 1/4/2017 using the search terms 'microbiota' or 'microbiota AND hypertension'. Each bar represents the total number of research articles plus review articles published per year. The distal gut is the major site for microbiota colonization in our bodies and has therefore received the most attention for research. Commensal gut microbiota perform a variety of functions that are important to the host. Perhaps one of the most obvious functions of gut microbiota is to help the host to digest food and generate energy (47).
For example, the microbiota in the colon ferment plant derived complex carbohydrates
(dietary fiber), as well as other carbohydrates that resist host digestive enzymes (83), and generate short chain fatty acids (SCFAs), which provide additional calories to the host
4
(67, 102). These SCFAs are then transported throughout the body and exert epigenetic and physiological effects (87, 88). Microbiota are also important for the metabolism of host lipids (45). Additionally, gut bacteria are an important source for biotin, vitamins K
(47), B12 (62), and essential amino acids (60). Further, the gut microbiota are also important for strengthening the barrier function of the intestinal epithelium by inducing the expression of small proline-rich protein 2A, which is important for desmosome maintenance (66). Symbiotic microbiota also act as a first line of defense against invading microbes. The microbiota compete with invading microbes for the same environment and resources, thus inhibiting the growth of invading enteropathogens by a process known as colonization resistance/competitive exclusion (50). There is also evidence that the gut microbiota influences the host immune system (i) by affecting the expression of immunoglobulins (IgA) in Peyer’s patches (25, 68), and (ii) by influencing differentiation of regulatory T cells through SCFAs (109). Newer functions of gut microbiota are being discovered due to the observations that they contribute importantly to the pathophysiology of a variety of disorders such as obesity, colitis, inflammatory diseases, metabolic syndrome, liver disease and kidney disease (3, 7, 8, 11-17, 19, 20, 29,
31, 33-37, 46, 55, 57, 59, 64, 65, 69, 72, 75-77, 80, 86, 94, 96, 97, 100, 103, 107, 108,
112-114, 116, 119, 120, 122, 125, 128, 131, 133).
1.3 Gut microbiota, the host genome and hypertension
A study reported using Toll-like receptor 5 (Tlr5) knockout mice was among the first to describe the relationship between the host genome, gut microbiota and BP regulation. Tlr5 is a receptor for bacterial flagellin and deletion of Tlr5 in mice led to 5
alterations in their gut microbiotal composition (119) resulting in the development of spontaneous colitis and metabolic syndrome. An interesting point to note is that the widely held notion that the relative abundance ratio of Firmicutes to Bacteroidetes (F/B ratio) being indicative of microbiotal dysbiosis, or microbial imbalance, was not the case with Tlr5-/- mice (119). The gut bacterial communities of Tlr5-/- and WT mice had similar relative abundances of Firmicutes and Bacteroidetes. Instead, consistent differences were noted in a total of 116 bacterial phylotypes from various phyla which were either enriched or reduced in Tlr5-/- mice relative to WT mice. Importantly, the Tlr5-/- mice presented with elevated BP as one of the features in addition to other hallmark features of metabolic syndrome. To determine whether the changes in the gut microbiota were a cause or consequence of the metabolic syndrome in Tlr5-/- mice, cecal microbiota from
Tlr5-/- mice were transplanted into WT germ-free mice. The transplanted microbiota from Tlr5-/- mice conferred many phenotypes to the WT germ-free hosts, including hyperphagia, obesity, hyperglycemia, insulin resistance, colomegaly, and elevated colonic proinflammatory cytokines, but unfortunately, BP was not examined in this study
(119). In terms of defining the underlying molecular mechanism, the authors of this primary finding speculated that the absence of Tlr5 produced alterations in the gut microbiota that induce low-grade inflammatory signaling, which in-turn could cross- desensitize metabolic (insulin) receptor signaling, leading to hyperphagia and associated metabolic syndrome (119). Taken together, although these studies suggest that the changes in the gut microbiota observed in the Tlr5-/- mice are likely to be a contributing factor in the development of metabolic syndrome in the mice, it remains to be formally tested for whether this is indeed the case with BP. 6
Since this study was reported, other groups have obtained evidence for additional, but functionally different, receptors on the host genome that also interact with gut microbiota to regulate BP. These are the G-protein coupled receptors and Olfactory receptors. The commonality for these two classes of receptors is that several family members of these two groups of proteins are receptors for SCFAs. The most common
SCFAs synthesized by microbiota are butyrate, propionate, and acetate. The idea of
SCFAs having physiological effects has been around for a while. In 1991, Kristev et al. found that when butyrate and other SCFAs were added to smooth muscle, there was increased prostaglandin synthesis and smooth muscle contraction and hypertension. They concluded that SCFAs can adversely regulate BP and contribute to hypertension (56).
Precisely how SCFAs influenced BP mechanistically was unknown at that time.
The notion that SCFAs bind to specific receptors to regulate BP is relatively nascent. One of the first receptors implicated in SCFA control of BP is an olfactory receptor found in the kidney, Olfr78 (87-89). Olfr78 was localized to the major branches of the renal artery and the juxtaglomerular afferent arteriole, an important site for renin secretion, as well as some smooth muscle cells in the heart, diaphragm, skeletal muscle and skin (88). Olfr78 localized to the juxtaglomerular apparatus has been implicated in renin release (89). It was found that this receptor can use SCFAs as a ligand, particularly propionate (88). Propionate was found to induce a hypotensive response acutely, theoretically through its binding to Olfr78. However, when Olfr78 was knocked out, the hypotensive response was still present, and even increased. Therefore, Pluznick et al. concluded that the Olfr78 receptor acts to increase BP through release of renin, and there must be other receptors that propionate binds to in order to lower BP (87). Two of these 7
receptors were subsequently identified as G-protein coupled receptors, Gpr41 and Gpr43, and have been localized to small resistance vessels (88). When propionate binds to these receptors, they induce vasodilation of the vessels, thus lowering BP (87). Gpr41 knockout mice had elevated systolic BP, that was not salt-sensitive (79). However, high levels of propionate were also found to activate Olfr78 induced renin release, thereby raising BP
(87).
Hydrogen sulfide is another gut microbiotal metabolite (105) that plays a role in the regulation of BP. It has been found that hydrogen sulfide released in the colon helps lower BP (111) mostly through vasodilation. The mechanism behind this vasodilation is still not clear. In addition to hydrogen sulfide, gut bacteria also release indole as a metabolite (23), which may have cardiovascular and renal effects (6, 132).
1.4 Gut microbiotal studies using genetic models of hypertension
Beyond the above mentioned single gene effects, evidence for the host-microbiotal cross- talk emanated from studies reported with two of the widely used genetic models of hypertension, the Dahl Salt-sensitive (S) rat and the Spontaneously hypertensive rat
(SHR). The genetic origins of these two hypertensive strains are distinct and are thoroughly described in the review by Rapp (92). The S rats originated from Sprague
Dawley rats, whereas the SHR originated from Wistar rats. The normotensive counterparts of S and SHR are the Dahl Salt-resistant (R) rats and the Wistar Kyoto
(WKY) rats, respectively. Mell et al. reported that fecal microbiota of hypertensive S rats and normotensive R rats had different microbiotal compositions (71). In particular, the S rats had higher levels of the phylum Bacteroidetes (71). Yang et al. studied SHR and 8
WKY rats and reported that there was a difference in their microbiotal composition (129).
They found an increased Firmicutes/Bacteroidetes (F/B) ratio in the SHR, which has previously been implicated in metabolic disease (30, 53, 123), as well as decreased diversity of the microbiota (129). SHR were also found to have less acetate and butyrate fermenting bacteria, such as Coprococcus and Pseudobutyrivibrio. It was also found that there were more lactate-producing bacteria in SHR, such as Streptococcus and
Turicibacter (129). In another study, it was found that feeding high fructose and salt diets to Wistar rats resulted in higher body weights, insulin resistance, and BP, and resulted in elevated levels of acetate (130). Since acetate is one of the SCFAs produced by gut microbiota, it was concluded that the high fructose and salt caused a disturbance in the gut microbiota (130). Perry et al. found that increased acetate can activate the parasympathetic nervous system and lead to obesity through hyperphagia, increased ghrelin, and increased insulin secretion (84). These findings suggest that the gut microbiota might be a target for the treatment of obesity (84).
Different approaches were used to further examine the effects of resident microbiota of S rats and SHR on hypertension. In the S rat, Mell et al. performed a cecal transplant by oral gavage and found that the S rats that received cecal contents from R rats had higher
BP compared to the BP of S rats that received cecal content from S rats (71). Increased circulating acetate and heptanoate were associated with the observed increase in BP of S rats given cecal content from R rats (71). A similar cecal transplant study was reported by Durgan et al., by oral gavage of cecal content from either normotensive or hypertensive obstructive sleep apnea rats to normotensive rats (24). They found that rats that received the cecal contents of the obstructive sleep apnea hypertensive rats 9
demonstrated an increase in BP, while those receiving cecal contents from normotensive rats did not. They concluded that hypertension is transferable by gut microbiota (24). In another study, Adnan et al. performed a gut microbiota transplant between spontaneously hypertensive stroke prone (SHRSP) rats and WKY rats. They found that the WKY rats which received gut microbiota from SHRSP rats had an increased systolic BP. While these results are compelling, the BP data were collected by tail cuff method only (1), an indirect method of recording BP, as opposed to telemetry, which can result in a lower sensitivity. In the SHR however, cecal transplantation studies are not reported. Instead, treatment using the antibiotic minocycline is reported to be sufficient to eliminate microbiotal dysbiosis and lower BP (129). This, however, was not the case with S rats, wherein treatment with an antibiotic per se, did not alter BP (71). There are potentially two reasons for this observed dichotomy. First, S rats are not reported to have dysbiosis as measured by the typical F/B ratio. Second, the antibiotic used in the study with S rats was vancomycin, which, unlike minocycline, does not cross the blood-brain barrier. In any case, the evidence from both strains is definitive to point to alterations in gut microbiotal composition that are linked to hypertension. Further studies will be required to clarify whether the genomes of S rats and SHR are permissive to specifically different microbiota to reside in them and thereby influence the extent of their BP.
Another interesting aspect pertaining to S rats and SHR is that these two models, despite being hypertensive, are divergent in many aspects. For example, the BP of the S rat is highly sensitive to dietary salt whereas SHR is not. Secondly, the S rat is susceptible to renal disease, which the SHR is not. Major gaps in knowledge exist on how dietary salt impacts microbiotal composition and the extent to which microbiota influence 10
hypertension independent of influencing renal disease. It is also worth noting that differences in phenotypes such as insulin-resistance and abnormalities in carbohydrate and lipid metabolism and others, co-exist along with differences in BP between the hypertensive rats (S and SHR) and their normotensive controls, the R and WKY rats, respectively (91, 93, 95, 104). Therefore, any observations of changes in microbiota between these strains cannot be interpreted as directly related to BP alone. Knowledge gained from these host-microbiotal associations are intriguing to ask the even more important question of whether there are alleles that were fixed during the selection process that cause a differential microbiotal inhabitation in the guts of selectively bred hypertensive rats compared to their relative normotensive ‘control’ strains. In other words, ‘Is there a genetic basis for the host genome-microbiotal associations to be passed on as an inherited feature from one generation to the next?’ While the differences observed in microbiotal populations between inbred strains is appreciative, the experimental design of comparing inbred strains is insufficient to detect and pinpoint a genetic basis for the observed host-microbiotal associations. This is because these strains differ by millions of genomic variants throughout their genomes, inheritance of only some, but not all, of which could be related to host-microbiotal interactions.
Differences in microbiota could not have influenced the genome of the host because the genome of the host is inherited from its parents even before it developed its gut where microbiota reside. The use of the unidirectional nature of this genetic argument depends completely on being able to demonstrate Mendelian segregation of discrete genotypes and associated microbiota in segregating populations for BP and associating the microbiotal populations with BP differences. Therefore, it follows by logic that genetic 11
linkage studies to track Mendelian inheritance must be employed to define and understand the inherited nature of these observations of host-microbiotal correlations in inbred genetic models of hypertension. This, for now, is a completely unexplored area of research. If quantitative trait loci (QTLs) that causally influence gut microbiotal compositions are detected, our goal to pin-point host genomic factors causally influencing microbiotal compositions can be furthered by constructing and comparing congenic strains or genetically modified strains with appropriate control strains that are identical in genomic background throughout the genome except for the QTL region.
Questions on the mechanisms by which gut microbiota impact hypertension are drawing focused attention to the view that the gut along with its microbiota serve as a central node interacting with multiple other organs, whereby the connections are referred to as ‘axes’ interacting with the node. Dominating current thinking about such ‘axes’ for hypertension and microbiota is the ‘gut-brain-bone marrow axis’ proposed by Santisteban et al., who suggest that the dysfunction of the gut-brain-bone marrow axis could be associated with hypertension (101). Data in support for this proposed axis as a hypothesis is largely drawn from reports on phenotypes other than hypertension, whereby, studies testing this
‘triad’ hypothesis for hypertension per se are anticipated. Similarly, driven by the influences of the gut microbiota on specific renal G-protein coupled and Olfactory receptors, another emerging school of thought is the ‘gut-renal axis’. Evidence in support of the gut-renal axis can be found in the literature pertaining to independent reports of gut microbiota impacting renal disease (5, 39, 40, 48, 49, 51, 58, 61, 63, 71, 73, 80, 99, 110,
117, 118, 126, 127). Another axis that remains to be explored is the gut-liver axis. Given that hypertension is a hallmark of metabolic syndrome, there is an intriguing article in the 12
literature that prompts research in the gut-liver axis for hypertension (106). Tlr5-/- mice, which present with elevated BP, also display elevated hepatic neutral lipids (cholesterol esters and triglycerides) enriched with oleate and increased liver stearoyl CoA desaturase
(SCD1) expression, both of which were dependent on the gut microbiotal composition
(106). Deletion of hepatic SCD1 not only prevented hepatic neutral lipids but also resulted in amelioration of metabolic syndrome in Tlr5-/- mice, thus demonstrating a key role of the gut microbiota-liver axis in the pathogenesis of metabolic diseases. Because
Tlr5-/- mice have elevated BP compared to the wild-type control, it is plausible that the gut-liver axis is also important for BP regulation (119).
1.5 Beyond Gut microbiota, other organ-specific microbiota links to hypertension
While the idea of experimentally approaching the link between hypertension and gut microbiota is emerging, it may be limiting because the microbiota in our bodies are not limited to the gut. Oral and dermal microbiota are pertinent to consider as modulators of salt-sensitive hypertension not only because of their exposed surfaces to bacteria, but also because the oral cavity is the primary site in contact with diet (including salt) and the skin is an excretory organ for salt via sweat. To date, there are no studies reported on dermal microbiota and their role in hypertension. However, although there are no direct studies on the oral microbiome, there are reports suggesting a link. For example, a direct relationship is reported between the levels of subgingival periodontal bacteria and the prevalence of hypertension. An increase in the number of oral microbiota has also been reported in hypertensive patients taking antihypertensive medications (22, 81).
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Additionally, an increase in certain oral microbiotal species such as Anaeroglobus has also been identified in symptomatic atherosclerosis (26). Besides, the oral cavity has a central role in regulating nitric oxide (NO) production from dietary nitrates and nitrites, which are mainly present in green leafy vegetables (41). Reduction from nitrate to nitrite by oral commensal bacteria is an obligatory step for further NO generation. This central role of the oral microbiota in regulating a vasodilator such as NO presents an intriguing possibility to study the role of oral microbiota in hypertension. In fact, interruption of this pathway through the use of antibacterial mouthwash was paralleled by a small elevation of systolic BP (10, 41). Clearly further studies are warranted for both the oral and dermal microbiota.
1.6 The host as a ‘Holobiont’
Experimental considerations of such organ-specific microbiotal relationships with the host (gut, oral, or dermal) to control BP may still be limiting to understand the full spectrum of the host-microbiotal symbiosis. This is because such experiments are pairwise evaluations of limited defined relationships of the microbiota with the whole host. The co-existence of the host and all the microorganisms living within it is defined by evolution, ecology and the sum effect of the genomes of the microbiota and the host.
The concept that the host has evolutionarily never been autonomous, but is an organized biological unit called the ‘holobiont’ (70) composed of millions of individual microorganisms further expands the limitation of pairwise comparisons of microbes in any particular organ. This allows for thinking in broader terms for host-microbiotal interactions to shape physiology. Thus, our current research strategy of exploring gut microbiota in hypertension or other similar organ-specific microbiota in focused ‘axes’ 14
such as the gut-brain, gut-liver or gut-kidney, etc. may still lack the ability to capture the full extent to which microbiota may influence hypertension. For example, Vikram et al. found that the gut microbiome remotely controls vascular microRNA-204 to regulate endothelial vasorelaxation (121). The questions of whether other microorganisms besides bacteria, such as viruses, fungi and protozoa that also exist as part of the holobiont, influence BP regulation remain currently unknown (Figure 1-2).
Despite these limitations, studies are emerging that capture host genomic associations with the microbiome that are not limited to organ-specific experimental designs. For example, recent genome-wide association studies are reporting, in an unbiased manner, the precise points on the host genome which are associated with the abundant presence of certain microbiota (9, 115, 124). It is interesting to note that in these human studies, many loci showing significant association with microbiome traits were found in close proximity to loci having effects on complex disease risks (9, 115, 124). Understanding host–microbiome interactions in this context may further illuminate historic and evolutionary events affecting the emergence and distribution of disease predisposition in different populations. This field of research remains vastly unexplored, but interesting clues are emerging for this to be promising at least in BP regulation. For example, the inheritance of BP is known to be linked to genes in the steroid biosynthetic pathway (32), and interestingly, the study by Wang et al. found that associations with several individual microbiotal taxa and expression QTL analyses converged on genomic loci involved in sterol biosynthesis, implying that genetic variation in sterol biosynthesis may shape the microbiotal composition (124).
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Figure 1-2: Current status and future prospects for research on microbiota as a factor contributing to blood pressure homeostasis. The current literature in the field, which is limited to the contributions of the gut-renal and gut-brain- bone marrow axes are shown in green. Significant knowledge gaps in the field are represented by the various question marks, linked to the contemplation of the total factors from the host and microbiota, represented by the holobiont.
1.7 The translational value of understanding the host-microbial relationships within a ‘holobiont’
16
If normal physiology is attributed to symbiotic relationships between the host and the microbes in a holobiont, disruptions of such symbiotic relationships are to be viewed as contributing to pathophysiology. Disruptions are possible either through mutations on the host genome or through alterations in the microbial genomes. Because host mutations are not easily remedied, the microbes causing the disruptions of symbiotic relationships with the host are relatively easier targets for alleviating pathologies. A time-tested way to do this is to use antibiotic or probiotic therapy. Honour (42) draws attention to this topic as being as old as 30 years, wherein experimental increase of BP in Sprague-Dawley rats through the action of corticosterone or ACTH was preventable by the administration of vancomycin or neomycin. Honour et al. also note that oral neomycin decreased the development of high BP in the SHR (43). Clinically, hypertension in patients with a rare disorder of synthesis of cortisol due to a genetic defect of steroid cytochrome 17- hydroxylase is attributed to an excess of deoxycorticosterone, which causes renal sodium retention and therefore hypertension. The urinary steroid metabolites of these patients were analyzed and found to have a high proportion of 21-deoxycorticosterone steroids and the micro-organism Eubacterium lentum was found to be capable of catalyzing 21- dehydroxylation of corticosteroids (27, 28). However, formal antibiotic administration studies in this case are not reported. These steroids may inhibit dehydrogenase activity to decrease protection of the kidney mineralocorticoid receptors, leading to the renal sodium retention (74). Previously, Honour et al. found 11-oxygenated 21-deoxysteroids in the urine of patients with 17α-hydroxylase deficiency syndrome and concluded that it was caused by microbial 21-dehydroxylation (44). In recent years, evidence is accumulating to suggest an overall variation in gut microbiota from hypertensive and normotensive 17
subjects. Kim et al. analyzed fecal samples from hypertensive and normotensive patients and found that there was less diversity and abundance of microbiota in hypertensive patients (54). Yang et al. also report such a variation between hypertensive and normotensive subjects, albeit using a small cohort (129). Trials are currently underway to further test the possibility of the antibiotic minocycline as an agent to lower BP of resistant hypertensives (129).
1.8 Probiotics and Hypertension
Several studies report beneficial effects of the use of probiotics on hypertension.
Hata et al. found that when sour milk with Lactobacillus helveticus and Saccharomyces cerevisiae were given to hypertensive patients, both the systolic and diastolic BPs were lower (38). In an another study Agerholm-Larsen et al. found that Enterococcus faecium and Streptococcus thermophiles, when added to yogurt administered to participants over an eight week period decreased their systolic BP (2). Kawase et al. reported that
Lactobacillus casei and S. thermophilus in fermented milk in healthy males caused a decrease in systolic BP, also over an 8 week period (52). Naruszewicz et al. discovered that in heavy smokers, Lactobacillus plantarum added to rose-hip drink significantly reduced systolic BP. They concluded that probiotics could be used as a preventative measure against cardiovascular disease in those that are at risk (78).
Overall, the older and more recent data in humans and rat models provide a fundamental rationale to further explore possibilities for treating hypertension by altering microbiota with antibiotic, probiotic or other dietary factors, as well as through fecal transplantations. Fecal transplantations are already being used to successfully treat
18
relapsing Clostridium difficile infections (21, 82, 90, 98). Whether or not fecal transplantation would be successful in treating hypertension needs to be studied further.
1.9 Conclusion
The role of microbiota in hypertension is a relatively new field of study. To date, there are limited publications, most of which are focused on the gut microbiota.
Nevertheless, the evidence is strong for further expanding our questions on the identities of the microbiota and the mechanisms by which they operate to exert BP regulation.
While there is great potential for using microbiota in treatments for hypertension and other diseases, there are some obvious limitations that are thwarting full exploration of this possibility. The work that is currently reported focuses on correlative observations, whereby key questions on cause-effect relationships remain to be tested with appropriate experimental designs. There is a perceived ‘rush’ to answer questions on mechanistic aspects even before fundamental studies are conducted to move the field from mere correlative studies to cause-effect relationships. A concerted, systems biology approach exploring microbiome-metagenomics-metabolomics is lacking to delineate how the microbiome could causally impact hypertension.
While technology may no longer be a limiting factor for querying microbiota or their metabolites, there are other perceivable technical difficulties to study the gut microbiota such as the inability to culture all the gut bacteria in the laboratory, whereby, the bacteria cultured and studied in the laboratory may not be entirely representative of what is present in the gut. This technical challenge is, however, not limited to the field of hypertension research. Overall, a broader emerging perspective of the host as being a part 19
of an ecosystem called the ‘holobiont’ is needed to expand our current understanding of bi-directional relationships between the macro (host) and micro-species to impact BP and contemplate appropriate clinical management strategies for hypertension.
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1.10 Summary
Gut microbiota have been implicated in a number of diseases including obesity, colitis, inflammatory diseases, metabolic syndrome, liver disease and kidney disease metabolic diseases. While hypertension is a common disorder in America, with 1/3 of the population being affected, it has been studied less than the other diseases with regard to the impact of gut microbiota. There is definitive evidence in the literature pointing to a strong evidence for a link between gut microbiota and blood pressure. This review surveys the existing literature linking microbiota to hypertension, and explores the idea that host-microbiotal interactions are an important contributor to the etiology of hypertension. The review identifies gaps in knowledge including the important lack of our current understanding of whether alterations in gut microbiota observed to be associated with elevated blood pressure cause hypertension or result as a consequence of hypertension. In any case, this field of research is nascent and exciting due to the overarching possibility that manipulating microbiota is clinically feasible and could be contemplated as a treatment for hypertensive subjects.
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Chapter 2
Host Genomics and Blood Pressure Regulation
2.1 Introduction
G-protein coupled receptors are increasingly being recognized with newer roles in blood pressure (BP) regulation (Jose, Soares-da-Silva et al. 2010, Gonzalez-Hernandez,
Marichal-Cancino et al. 2016, Natarajan, Hori et al. 2016, Garcia, Gilani et al. 2017,
Mayor, Cruces-Sande et al. 2017). The G-protein coupled estrogen receptor (Gper1) is a relatively recently discovered G-protein coupled receptor which belongs to the rhodopsin-like receptor super family (Owman, Blay et al. 1996, Takada, Kato et al. 1997,
Bodhankar and Offner 2011). Originally categorized as an orphan receptor (Zimmerman,
Budish et al. 2016), Gper1 is now recognized as a receptor for two ligands, estrogen
(Haas, Bhattacharya et al. 2009, Martensson, Salehi et al. 2009, Jessup, Lindsey et al.
2010, Gencel, Benjamin et al. 2012) and aldosterone (Gros, Ding et al. 2011, Briet and
Schiffrin 2013, Gros, Ding et al. 2013). Due to the feature of Gper1 being an estrogen receptor, the context for research on Gper1 has been extensively focused on cancer.
Several studies have shown that disturbances in Gper1 expression are associated with development of breast, endometrial and prostate cancer (Lam, Ouyang et al. 2014,
41
Wegner, Wanger et al. 2014, Jacenik, Cygankiewicz et al. 2016), and roles of Gper1 in the nervous system are also emerging (Srivastava and Evans 2013). Identification of aldosterone as a second ligand prompted studies of Gper1 in BP regulation.
Recent evidence suggests that the receptor can elicit vasodilator effects and alter
BP (Evanson, Goldsmith et al. 2018). However, much of the research done has shown that this receptor can either raise or lower BP, depending on the model. Given these conflicting reports, it is likely that the involvement of Gper1 in the regulation of cardiovascular disease may be contextually dependent on the genomic background. We examined the effect of deletion of Gper1 on the blood pressure of the Dahl Salt-Sensitive
(S) rat model, whose genome is highly permissive for the development of hypertension.
The genome of the S rat was edited using the CRISPR/Cas9 technology. Our studies showed that both male and female Gper1-/- rats maintained on a high sodium (2% NaCl) diet exhibited a significant decrease in both systolic and diastolic BP compared to
Gper1+/+ hypertensive rats (Waghulde, Cheng et al. 2018). This was paralleled by a marked improvement in the endothelium-dependent vasorelaxation of resistance arteries in Gper1-/- rats compared to controls.
Another important observation of this study was that Gper1-/- rats had significantly different gut microbiota than Gper1+/+ rats (Waghulde, Cheng et al. 2018).
In order to determine if the altered gut microbiota were contributing to the BP lowering effect, a cecal transplant was performed. When Gper1-/- rats were given the cecal contents of Gper1+/+ rats by oral gavage, the BP protective effect was abolished. In order to determine how the microbiota are contributing to BP regulation, we analyzed the serum short chain fatty acids (SCFAs). We found that the Gper1-/- rats with the cecal contents of 42
Gper1+/+ rats had elevated levels of acetate. This was significant because we also determined that Gper1-/- small mesenteric arteries relaxed to a lesser degree when exposed to acetate than Gper1+/+ small mesenteric arteries. Therefore, this suggested that the altered gut microbiota through their metabolites may be responsible for the BP protective effect of the Gper1 deletion.
G-protein coupled receptors (GPCRs) have been implicated in immune functions, specifically with migration of immune cells (Zabel, Agace et al. 1999, Cabral-Marques,
Marques et al. 2018). The role of GPCRs in migration may explain the role of GPCRs in both cancer, as well as cardiovascular disease, as immune cell migration is crucial to the development of both. Recently, studies have reported that Gper1 may also be involved in immune cell function, specifically in monocytes/macrophages (Pelekanou, Kampa et al.
2016). Altering migration of immune cells would explain the improved endothelial cell function noted in our animals. Therefore, we hypothesized that by deleting Gper1, immune cell migration is altered, which led to improved vasorelaxation. To test this hypothesis, an unbiased mass-spectrometry based quantitative proteomic approach was used to determine the alterations in the endothelial cell-specific proteome. We used the list of differentially expressed proteins to identify predicted pathways that are altered. By using this list, we were able to identify a possible mechanistic link between Gper1 and immune cell migration.
2.2 Methods
2.2.1 Animals and diet
43
All animal procedures and protocols used in this report were approved by the University of Toledo 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 College of Medicine and Life
Sciences. Rats were bred and maintained on a low salt diet (0.3% NaCl; Harlan Teklad diet 7034, Madison, WI). The Harlan Teklad diet (TD94217) was used for experiments involving a high salt regimen (2% NaCl).
2.2.2 Endothelial cell culture
Thoracic aortas from Gper1-/- rats and S rats on a low salt diet were dissected. The fat was cleaned off the aortas with crude dissection. The aorta was cut open, placed intimal side down in 5 ml of 0.2% collagenase in serum-free DMEM, and incubated for 30 minutes. The endothelial cells were then removed with a cell scraper and washed with 5 ml of 10% FBS in DMEM. The cells were collected and added to a 15 ml falcon tube.
The tube was spun at 2000rpm for 10 minutes. The supernatant was removed and the pellet was rinsed with PBS. The tube was spun at 2000 rpm for 5 minutes. The supernatant was removed, the pellet was rinsed with PBS, and the tube was again spun at
2000 rpm for 5 minutes. The supernatant was removed and the pellet was resuspended in culture media. 1ml was plated in endothelial cell culture media and grown in at 37°C and
5% CO2.
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2.2.3 Protein Identification and relative quantitation by TMT labeling and LC-
Tandem MS
The cultured endothelial cells were lysed and the proteins were isolated as described
(Erickson, Jedrychowski et al. 2015) and shown in Figure 2-1. Briefly, the cultured endothelial cells (from the Gper1-/- and S rats (n=5)) were washed with PBS and scraped off the plate with a cell scraper. The cells were centrifuged at 2000 rpm for 10 minutes at
4ºC. The supernatant was removed, 1ml of PBS was added, the pellet was resuspended, and it was transferred to a 1.5 ml tube. The tube was centrifuged at 2000 rpm for 5 minutes at 4ºC. The supernatant was removed completely. The size of the pellet was estimated and twice that volume of RIPA buffer with proteinase was added. The tube was vortexed, then placed on ice. The tube was vortexed every five minutes for an hour. After the hour, the tube was centrifuged at 4ºC, 13000rpm for 15 minutes. The supernatant was added to new, clean tubes.
TMT labeling and offline fractionation: The Tandem Mass Tag (TMT) labeling was performed using TMT-10plex isobaric reagents according to the manufacturer’s protocol with minor modifications (ThermoFisher Scientific). Briefly, 100 μg of protein from each sample was reduced with DTT (5 mM) at 45 °C for 1 h followed by alkylation with 2- chloroacetamide (15 mM) at room temperature for 30 min. Proteins were precipitated by adding 6 volumes of ice cold acetone and incubating overnight at -20 °C. Precipitated proteins were pelleted by centrifuging at 8000g for 10 min at 4 °C and supernatant was discarded. The pellet was resuspended in 100 μl of 100 mM TEAB and digested overnight at 37 °C by adding 2.5 mg of sequencing grade, modified porcine trypsin
(Promega, V5113). TMT reagents were reconstituted in 41 ml of anhydrous acetonitrile 45
and digested peptides were transferred to the TMT reagent vial and incubated at room temperature for 1 h. The Gper1-/- samples were labeled with TMT channels 126, 127N,
128N, 129N, and 130N, while the WT hypertensive samples were labeled with TMT channels 127C, 129C, 130C, 128C, and 131 (figure 4a). The reaction was quenched by adding 8 ml of 5% hydroxylamine and incubating it for further 15 min. All samples were combined and dried. Prior to MS analysis, 100 μg of the peptides were fractionated (10 fractions) using high pH reverse phase fractionation kit following the manufacturer’s protocol (Pierce, Cat #84868). Fractions were dried and reconstituted in 12 ul of loading buffer (0.1% formic acid and 2% acetonitrile).
Liquid Chromatography-mass spectrometry analysis (LC-Multinotch MS3): In order to obtain more accurate quantitation, recently developed multinotch-MS3 method was employed (McAlister GC et al). Orbitrap Fusion (Thermo Fisher Scientific) and RSLC
Ultimate 3000 nano-UPLC (Dionex) were used to acquire the data. Two μl of each fraction was resolved on a nano-capillary reverse phase column (Acclaim PepMap C18, 2 micron, 75 μm i.d. x 50 cm, ThermoScientific) using a 0.1% formic/acetonitrile gradient at 300 nl/min (2-22% acetonitrile in 150 min; 22-32% acetonitrile in 40 min; 20 min wash at 90% followed by 50 min re-equilibration) and directly sprayed on to Orbitrap
Fusion using EasySpray source (ThermoFisherScientific). Mass spectrometer was set to collect one MS1 scan (Orbitrap; 120K resolution; AGC target 2x105; max IT 100 ms) followed by data-dependent, “Top Speed” (3 seconds) MS2 scans (collision induced dissociation; ion trap; NCE ~35%; AGC 5x103; max IT 100 ms). For multinotch-MS3, top 10 precursors from each MS2 were fragmented by HCD followed by Orbitrap
46
analysis (NCE ~55%; 60K resolution; AGC 5x104; max IT 120 ms, 100-500 m/z scan range).
Figure 2-1. Schematic of quantitative proteomic study design and heatmap of significantly upregulated and downregulated protein expression in Gper1-/- rats compared to Gper1+/+ rats. This is a schematic representing the experimental design.
2.2.4 Data analysis
Proteome Discoverer (v2.1; Thermo Fisher) was used for data analysis. MS2 spectra were searched against TrEMBL Rattus protein database (release 2016-04-13; 27785 sequences) using the following search parameters: MS1 and MS2 tolerance were set to 10 ppm and 0.6 Da, respectively; carbamidomethylation of cysteines (57.02146 Da) and
TMT labeling of lysine and N-termini of peptides (229.16293 Da) were considered static 47
modifications; oxidation of methionine (15.9949 Da) and deamidation of asparagine and glutamine (0.98401 Da) were considered variable. Identified proteins and peptides were filtered to retain only those that passed ≤1% FDR threshold. Quantitation was performed using high-quality MS3 spectra (Average signal-to-noise ratio of 10, <30% isolation interference, and data was normalized against total peptide).
2.2.5 Immunoblot analysis
Proteins were isolated from cultured endothelial cells as mentioned above. Protein concentrations were calculated using a BCA assay. Protein (75 ng) from each sample, as well as 10µl of ladder (Bio-Rad, Precision Plus Protein Standards) was loaded into the gel. The membrane was blotted for Gapdh as a control and Cd99 from CellSignaling.
Chemiluminescence was used for detection. Quantification was performed with ImageJ
1.50i software.
2.2.6 RNA isolation and Quantitative PCR analysis
RNA was isolated from cultured endothelial cells using TRIzol reagent (Invitrogen). 1µg of RNA was used to obtain cDNA by reverse transcription with SuperScript III kit
(Invitrogen). Levels of Vcam-1 (Forward primer: CTCCTCTCGGGAAATGCCAC, reverse primer: CCACCTGAGATCCAGGGGAGA) and Fibrillin-1 (Forward primer:
GCTGGACCGAAGTGGTGGAA, reverse primer: AGCGAGTATCGACACAGCCC) expression were analyzed by quantitative PCR (Applied Biosystems) and expression levels relative to Pgk-1 (Forward primer: GCTTTCTAACAAGCTGACTTTGG, reverse
48
primer: CGTTATCTGGTTGTTCTTCATAGG) were calculated by the 2-ΔΔCт method
(Livak and Schmittgen 2001).
2.2.7 Permeability Assay: An in vitro permeability assay was performed as described in
(Monaghan-Benson and Wittchen 2011). Briefly, endothelial cells were cultured from the thoracic aorta, as described above. 500,000 cells were plated onto the transwell (Costar,
0.4μm polycarbonate membrane, polystyrene). Cells were allowed to form a monolayer.
After three days, media was removed and new media with either 4kDa or 70kDa FITC-
Dextran (Molecular Probes), or normal media was added to the top of the transwell. At various timepoints, media was collected from the bottom well and the fluorescence was measured using a plate reader (FluroStar Optima).
2.2.8 Transendothelial Migration assay: Gelatin solution (Sigma, 2% in H2O) was warmed to 37ºC. Fifty microliters of the gelatin was added to the top well of each transwell in the transwell plate (Costar, 5.0μm polycarbonate membrane, polystyrene).
After incubation for 30 minutes, the gelatin was aspirated and the membrane was rinsed with PBS. Cultured endothelial cells suspended in culture media were counted with a hemacytometer. About 200,000 cells in 100μl of culture media were plated onto the gelatin treated membrane, while 500μl of culture media was added to the bottom well.
The cells were allowed to grow to confluence for two days. On the second day, the media was aspirated. MCP-1 (2ng/μl) was added to culture media, and 500μl was added to the bottom well. Monocytes (100 μl) cultured from the bone marrow of rat femurs (as described in (Tano, Smedlund et al. 2011) were added to the top well. Gper1-/- monocytes 49
were added to the Gper1-/- endothelial cell coated plates, while Gper1+/+ monocytes were added to the Gper1+/+ endothelial cell coated plates. The plates were placed in the incubator for 3 hours. After three hours, the media in the top and bottom wells was aspirated. The transwell was covered in trypan blue (0.4%), and then the membrane was excised. The membrane was placed on a microscope slide and a cover slip was placed on top of it. The membrane was examined by microscopy and the migrated monocytes were counted.
2.2.9 Immunohistochemistry
Small resistance arteries were dissected from Gper1+/+ and Gper1-/- rats and cleaned thoroughly. The arteries were paraformaldehyde-fixed, paraffin- embedded, and sectioned at the University of Toledo. Immunohistochemistry was performed as described in (Szasz, Wenceslau et al. 2016). Briefly, sections were dewaxed with
Histochoice Clearing Agent (Vector Laboratories). They were then washed with isopropanol and epitope unmasking was performed by incubation in Deco Antigen
Retrieval Solution (5 min, 95°C). Endogenous peroxidase activity was blocked (0.3%
H2O2 in PBS) for 30 min and sections were blocked for nonspecific binding by incubating for 30 min with competing serum (1.5% in PBS). In a humidified chamber, sections were incubated overnight at 4°C with competing serum (negative control) or with anti-Cd68 antibody (Mouse anti-rat CD68, Bio-Rad). Sections were washed three times in PBS, incubated for 30 min with biotinylated secondary antibody, washed three times in PBS and incubated for 30 min with Vectastain ABC Elite Reagent. Sections were then exposed to diaminobenzidine/H2O2, and reactions were stopped with PBS.
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Sections were counterstained with hematoxylin, dried, and mounted, and photographs were taken.
2.2.10 Cell culture with acetate
Endothelial cells were cultured from the rats, as mentioned above. After two cell passes, the cells were plated on new plates. Once they reached confluence, new media was added. The media was either standard, or supplemented with 5mM acetate. The cells were then collected after either 4 hours or 24 hours and their RNA was isolated, as described above.
2.2.11 Statistical analysis
All statistical analyses of blood pressure studies were conducted using GraphPad Prism 5
(version 5.02). Data was analyzed by independent sample Student’s t-test. The data is presented as the mean ± standard error (Mean ± SEM). A p-value of <0.05 was used as a threshold for statistical significance.
2.3 Results
2.3.1 Altered cellular proteome in Gper1-/- rats
Once we determined there was improved vascular function in an endothelium-dependent manner, we sought out the alterations in the cellular proteome of endothelial cells to begin to understand the mechanism. We found 48 significantly upregulated proteins and
51
82 significantly downregulated proteins in Gper1-/- thoracic aorta endothelial cells compared to Gper1+/+ thoracic aorta endothelial cells (Figure 2-2, Table 2.1). Cd99, or cluster of differentiation 99, was the most significantly upregulated protein in the Gper1-/- endothelial cells with a 7.98 fold increase noted by the quantitative proteomic approach
(Table 2.1).
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Figure 2-2. Heatmap of differentially expressed proteins between Gper1-/- endothelial cells and Gper1+/+ endothelial cells. Gper1-/- endothelial cells are shown on the left and Gper1+/+ endothelial cells are on the right. A red color indicates higher expression while a blue color indicates a lower expression, with a deeper hue indicating greater differences. The proteins are listed in the table below.
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Table 2.1: Significantly altered proteins from Gper1-/- and Gper1+/+ rat cells. Abundance Ratio: (Sample) / t-test; p- Description Gene ID (Control) value
Cd99 protein [OS=Rattus norvegicus] Cd99 7.98 0.004038139
Ephrin B1 [OS=Rattus norvegicus] Efnb1 5.516 0.01805719
Cadherin 13 [OS=Rattus norvegicus] Cdh13 3.623 0.018441485 fibrillin 1, isoform CRA_a [OS=Rattus norvegicus] Fbn1 2.63 0.000672749
Elastin microfibril interfacer 1 (Predicted), isoform CRA_b [OS=Rattus norvegicus] Emilin1 1.898 0.023335037
Protein RGD1562932 [OS=Rattus norvegicus] RGD1562932 1.855 0.016635855
Protein Klhl32 [OS=Rattus norvegicus] Klhl32 1.725 0.002985966
Protein Medag [OS=Rattus norvegicus] Medag 1.68 0.034044133 alpha-2-macroglobulin [OS=Rattus norvegicus] A2m 1.62 7.94426E-06
Versican core protein [OS=Rattus norvegicus] Vcan 1.606 0.007557456 fibronectin [OS=Rattus norvegicus] Fn1 1.576 0.00572803
Kin of IRRE-like protein 1 [OS=Rattus norvegicus] Kirrel 1.556 0.001146798
Protein Hspg2 [OS=Rattus norvegicus] AABR07073181.1 1.526 0.012804137
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Abundance t-test; p- Description Gene ID Ratio: value
Protein Aplf [OS=Rattus norvegicus] Aplf 1.496 0.006822628
Vascular cell adhesion protein 1 [OS=Rattus norvegicus] Vcam1 1.474 0.018066411
Transferrin receptor protein 1 [OS=Rattus norvegicus] Tfrc 1.472 0.002009698
Protein Armc8 [OS=Rattus norvegicus] Armc8 1.454 0.04381557
P2X purinoceptor [OS=Rattus norvegicus] P2rx5 1.421 0.01105928
Protein Lrp1 [OS=Rattus norvegicus] Lrp1 1.402 0.006629342 rCG35178, isoform CRA_b [OS=Rattus norvegicus] Ten1 1.4 0.016031453
Protein Vasn [OS=Rattus norvegicus] Vasn 1.367 0.007009383
Protein Reck [OS=Rattus norvegicus] Reck 1.365 0.014229302
Receptor-type tyrosine-protein phosphatase [OS=Rattus norvegicus] Ptpra 1.352 0.012235513
Protein Porcn [OS=Rattus norvegicus] Porcn 1.262 0.01278859
Protein Mcat [OS=Rattus norvegicus] Mcat 1.248 0.02036237
Inter-alpha-trypsin inhibitor heavy chain H3 [OS=Rattus norvegicus] Itih3 1.247 0.008188798
55
Abundance Ratio: (Sample) / t-test; p- Description Gene ID (Control) value laminin, alpha 5, isoform CRA_a [OS=Rattus norvegicus] Lama5 1.227 0.037579093
Protein Dag1 [OS=Rattus norvegicus] Dag1 1.224 0.013944996 insulin-like growth factor binding protein 7, isoform CRA_b [OS=Rattus norvegicus] Igfbp7 1.203 0.042639835
Arf-GAP domain and FG repeat- containing protein 1 [OS=Rattus norvegicus] Agfg1 1.176 0.000552427
Protein Clptm1l [OS=Rattus norvegicus] Clptm1l 1.175 0.016709017
G protein beta subunit-like, isoform CRA_a [OS=Rattus norvegicus] Mlst8 1.171 0.020698756
Transcriptional coactivator YAP1 [OS=Rattus norvegicus] Yap1 1.161 0.004321318
MAP2K4delta [OS=Rattus norvegicus] Map2k4 1.16 0.007124801
Ras-related protein Rab-11B [OS=Rattus norvegicus] Rab11b 1.158 0.019706348
NDUFA7 protein [OS=Rattus norvegicus] Ndufa7 1.154 0.020188356 poly [ADP-ribose] polymerase [OS=Rattus norvegicus] Parp3 1.149 0.026096515
Protein Dcp1a [OS=Rattus norvegicus] Dcp1a 1.146 0.049339007
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Abundance Ratio: (Sample) / t-test; p- Description Gene ID (Control) value beta II spectrin-short isoform [OS=Rattus norvegicus] Sptbn1 1.109 0.028072471 guanidinoacetate N- methyltransferase [OS=Rattus norvegicus] Gamt 1.105 0.033307
Heterogeneous nuclear ribonucleoprotein M [OS=Rattus norvegicus] Hnrnpm 1.095 0.007857172
Heterogeneous nuclear ribonucleoprotein U-like 1 (Predicted) [OS=Rattus norvegicus] Hnrnpul1 1.094 0.003331023
Heterogeneous nuclear ribonucleoprotein A1 [OS=Rattus norvegicus] Hnrnpa1 1.091 0.043338204
Heterochromatin protein 1-binding protein 3 [OS=Rattus norvegicus] Hp1bp3 1.087 0.022666049
BPY2 interacting protein 1 (Predicted), isoform CRA_b [OS=Rattus norvegicus] Map1s 1.081 0.033832948
Protein Wipi1 [OS=Rattus norvegicus] Wipi1 1.068 0.037625966 high mobility group box 1 [OS=Rattus norvegicus] Hmgb1; Hmg1l1 1.064 0.020977941 ap-3 complex subunit mu-1 [OS=Rattus norvegicus] Ap3m1 0.939 0.01724035 bleomycin hydrolase [OS=Rattus norvegicus] Blmh 0.939 0.045918883
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Abundance Ratio: (Sample) / t-test; p- Description Gene ID (Control) value
Protein Eprs [OS=Rattus norvegicus] Eprs 0.938 0.036781611
Importin subunit alpha [OS=Rattus norvegicus] Kpna3 0.938 0.037313103
F-actin-capping protein subunit beta [OS=Rattus norvegicus] Capzb 0.937 0.029562491 alpha-1,4 glucan phosphorylase [OS=Rattus norvegicus] Pygb 0.936 0.049374437
Protein Vps4b [OS=Rattus norvegicus] Vps4b 0.935 0.033963694 phosphoribosylglycinamide formyltransferase, isoform CRA_a [OS=Rattus norvegicus] Gart 0.934 0.02204342
Importin 4 (Predicted), isoform CRA_b [OS=Rattus norvegicus] Ipo4 0.933 0.008368317
Myc box-dependent-interacting protein 1 [OS=Rattus norvegicus] Bin1 0.933 0.038590792
DEAH (Asp-Glu-Ala-His) box polypeptide 9 (Predicted) [OS=Rattus norvegicus] Dhx9 0.93 0.009826041
Protein Arhgap10 [OS=Rattus norvegicus] Arhgap10 0.925 0.021960579
60S ribosomal protein L18 [OS=Rattus norvegicus] Rpl18 0.922 0.006410875 eukaryotic translation initiation factor 5A [OS=Rattus norvegicus] Eif5a2 0.917 0.005996774
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Abundance Ratio: (Sample) / t-test; p- Description Gene ID (Control) value
RuvB-like helicase [OS=Rattus norvegicus] RGD1564855 0.917 0.042655185
Exocyst complex component 4 [OS=Rattus norvegicus] Exoc4 0.916 0.036698385
Protein Abr [OS=Rattus norvegicus] Abr 0.904 0.037502248
Protein Mms19 [OS=Rattus norvegicus] Mms19l; Mms19 0.903 0.039795271
Protein Raly [OS=Rattus norvegicus] Raly 0.902 0.01303391
15 kDa selenoprotein [OS=Rattus norvegicus] Sep15 0.902 0.027176519
Inositol 1,4,5-trisphosphate receptor type 3 [OS=Rattus norvegicus] Itpr3 0.895 0.022006467
Protein Sec61a2 [OS=Rattus norvegicus] Sec61a2 0.894 0.018527326 glia maturation factor beta [OS=Rattus norvegicus] Gmfb 0.892 0.023080824
Nucleosome assembly protein 1-like 1 [OS=Rattus norvegicus] Nap1l1 0.887 0.025160205
Protein Uggt2 [OS=Rattus norvegicus] Uggt2 0.884 0.049798987 dihydrofolate reductase [OS=Rattus norvegicus] Dhfr 0.882 0.038757609
Protein Tmx3 [OS=Rattus norvegicus] Tmx3 0.876 0.032222297
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Abundance Ratio: (Sample) / t-test; p- Description Gene ID (Control) value
Protein Snw1 [OS=Rattus norvegicus] Snw1 0.867 0.040039005
Protein Sec23a [OS=Rattus norvegicus] Sec23a 0.867 0.043730313
Protein kinase B beta [OS=Rattus norvegicus] Akt2 0.866 0.020518242
Protein Cisd3 [OS=Rattus norvegicus] Cisd3 0.855 0.030789245
Farnesyltransferase, CAAX box, alpha [OS=Rattus norvegicus] Fnta 0.844 0.007705086
Telomerase protein component 1 [OS=Rattus norvegicus] Tep1 0.844 0.020776268
Protein Vps18 [OS=Rattus norvegicus] Vps18 0.836 0.016565033
YLP motif-containing protein 1 [OS=Rattus norvegicus] Ylpm1 0.834 0.046380862
Protein Ascc2 [OS=Rattus norvegicus] Ascc2 0.833 0.004146378
Ubiquitin-conjugating enzyme E2 D3 [OS=Rattus norvegicus] Ube2d3 0.832 0.003166456 protein NDRG3 [OS=Rattus norvegicus] Ndrg3 0.83 0.002777278
Gephyrin isoform [OS=Rattus norvegicus] Gphn 0.83 0.031783975
Protein Utp20 [OS=Rattus norvegicus] Utp20 0.83 0.04163864
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Abundance Ratio: (Sample) / t-test; p- Description Gene ID (Control) value
Hebp1 protein [OS=Rattus norvegicus] Hebp1 0.828 0.012809272
Protein Pex16 [OS=Rattus norvegicus] Pex16 0.823 0.027075048
Protein Pdcd11 [OS=Rattus norvegicus] Pdcd11 0.814 0.002694826
Cb1-727 [OS=Rattus norvegicus] Rpa1 0.814 0.004054818
Glutathione peroxidase [OS=Rattus norvegicus] Gpx7 0.814 0.010121008
Lysosome-associated membrane glycoprotein 1 [OS=Rattus norvegicus] Lamp1 0.809 0.038738543
LRRGT00070 [OS=Rattus norvegicus] Thoc1 0.804 0.047547173
Protein RGD1307929 [OS=Rattus norvegicus] RGD1307929 0.798 0.025406165 guanine nucleotide binding protein, alpha 11 [OS=Rattus norvegicus] Gna11 0.793 0.039637711
Protein Mrpl12 [OS=Rattus norvegicus] Mrpl12 0.791 0.014248187 serine/threonine kinase 3 (STE20 homolog, yeast) [OS=Rattus norvegicus] Stk3 0.782 0.048922781
CB1 cannabinoid receptor- interacting protein 1 [OS=Rattus norvegicus] Cnrip1 0.781 0.038175724
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Abundance Ratio: (Sample) / t-test; p- Description Gene ID (Control) value
Protein Lrrc69 [OS=Rattus norvegicus] Lrrc69 0.779 0.018662338
Nadk protein [OS=Rattus norvegicus] Nadk 0.777 0.026459774
Protein LOC100910143 [OS=Rattus Pigg; norvegicus] LOC100910143 0.777 0.046652864
Protein Samd4b [OS=Rattus norvegicus] Samd4b 0.775 0.022541553
Nuclear factor 1 [OS=Rattus norvegicus] Nfia 0.775 0.036849809
Multiple inositol polyphosphate phosphatase 1 [OS=Rattus norvegicus] Minpp1 0.754 0.014656716
Protein Ext2 [OS=Rattus norvegicus] Ext2 0.75 0.007538375
Cytoplasmic dynein 2 heavy chain 1 [OS=Rattus norvegicus] Dync2h1 0.746 0.030769763
Thiopurine S-methyltransferase [OS=Rattus norvegicus] Tpmt 0.743 0.017758192 vacuolar protein sorting 52 (yeast), isoform CRA_b [OS=Rattus norvegicus] Vps52 0.739 0.004703347
Amyloid beta A4 precursor protein- binding family B member 1 [OS=Rattus norvegicus] Apbb1 0.735 0.017302833
Protein Irf2bp2 [OS=Rattus Irf2bp2; norvegicus] LOC679357 0.73 0.001194435
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Abundance Ratio: (Sample) / t-test; p- Description Gene ID (Control) value
2-hydroxyphytanoyl-CoA lyase [OS=Rattus norvegicus] Hacl1 0.714 0.044109057
Protein Phip [OS=Rattus norvegicus] Phip 0.705 0.0107056
Protein Mrpl28 [OS=Rattus norvegicus] Mrpl28 0.701 0.024202461
Protein Rrp1b [OS=Rattus norvegicus] Rrp1b 0.695 0.010674677
Protein Nop10 [OS=Rattus norvegicus] Nop10 0.693 0.040453999
Amylo-1, 6-glucosidase, 4-alpha- glucanotransferase (Glycogen debranching enzyme, glycogen storage disease type III) (Predicted), isoform CRA_a [OS=Rattus norvegicus] Agl 0.687 0.027654372
60S ribosomal protein L14 [OS=Rattus norvegicus] Rpl14 0.679 0.005157527 nitric oxide synthase-interacting protein [OS=Rattus norvegicus] Nosip 0.67 0.000120468
Protein LOC100361838 [OS=Rattus norvegicus] Snrpel1 0.661 0.005701145
Protein Sphk2 [OS=Rattus norvegicus] Sphk2 0.656 0.048776463
Protein Zbed5 [OS=Rattus norvegicus] Zbed5 0.608 0.039551966
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Abundance Ratio: (Sample) / t-test; p- Description Gene ID (Control) value
Protein Cyp4f37 [OS=Rattus norvegicus] Cyp4f37 0.596 0.003690575
Nudt16l1 protein [OS=Rattus norvegicus] Nudt16l1 0.561 0.0062018
Protein Gtf3c4 [OS=Rattus norvegicus] Gtf3c4 0.543 0.018575625
rCG46567, isoform CRA_a [OS=Rattus norvegicus] Rgs16 0.472 0.005187181
ATP-binding cassette, sub-family G (WHITE), member 4 [OS=Rattus norvegicus] Abcg4 0.423 0.001448714
2.3.2 Multiple endothelial cell proteomic networks affected by deletion of Gper1
The list of altered proteins in the endothelial cells of Gper1-/- and Gper1+/+ rats were analyzed using Ingenuity Pathway Analysis (Figure 2-3). Among the suggested altered pathways, many were involved in cell-to-cell signaling and interactions such as tight junction signaling. Additionally, there were pathways involved in migration of monocytes and macrophages (Table 2.2) and movement of lymphocytes. Pathways for lymphocyte migration were also indicated to be increased in the Gper1-/- endothelial cells, by at least 6 different highly upregulated proteins, including ephrin b1, fibronectin
1, and the myosin light chain kinase. The multiple pathways implicated in
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Figure 2-3. Ingenuity Pathway Analysis predicted altered functions in endothelial cells. The list of significantly upregulated proteins from Gper1-/- and Gper1+/+ was analyzed through Ingenuity Pathway Analysis (IPA). The predicted pathways were sorted by functions and the top 10 pathways are shown here. transendothelial migration that are predicted to be upregulated in Gper1-/- rats suggests an important role of this pathway in the cardioprotective effects of this model. In addition to cellular migration, there were other pathways predicted to be altered (Figure 2-4) including inhibition of matrix metalloproteases, RhoA signaling, glycine cleavage, acute phase response signaling, lymphotoxin β receptor signaling, hepatic fibrosis/hepatic stellate cell activation, ephrin B signaling, Il-15 signaling, regulation of actin-based motility by Rho, assembly of RNA Polymerase III complex, actin cytoskeleton signaling, and PAK signaling.
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Table 2.2: Ingenuity Pathway Analysis predicted altered pathways and the protein expression changes. This table shows predicted altered pathways that are involved in migration. The pathways, along with the altered associated proteins and their fold changes are listed.
Prediction (based
Genes in on measurement Expr Fold
ID dataset direction) Change Findings
Movement of
Lymphocytes
Ppia PPIA Increased 1.45 Increases (1)
Vcam1 VCAM1 Increased 1.845 Increases (12)
Lrp1 LRP1 Increased 1.8 Increases (1)
Mylk MYLK Increased 1.652 Increases (3)
Efnb1 EFNB1 Increased 6.192 Increases (1)
Fn1 FN1 Increased 1.995 Increases (16)
Ptpra PTPRA Affected 1.662 Affects (1)
Migration of Macrophages
Vcan VCAN Increased 2.062 Increases (1)
Fn1 FN1 Affected 1.995 Affects (2)
Lrp1 LRP1 Decreased 1.8 Decreases (4)
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Mylk MYLK Increased 1.652 Increases (2)
Transendothelial Migration of Monocytes
Fn1 FN1 Increased 1.995 Increases (1)
Vcam1 VCAM1 Increased 1.845 Increases (1)
Transmigration of Phagocytes
Fn1 FN1 Increased 1.995 Increases (1)
Vcam1 VCAM1 Increased 1.845 Increases (2)
Mylk MYLK Increased 1.652 Increases (1)
Figure 2-4: Top predicted altered pathways using Ingenuity Pathway Analysis.
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2.3.3 Confirmation of proteomic data
In order to confirm the list of altered proteins, we performed Immunoblots for the most differentiated proteins. As seen in Figure 2-5ab, Cd99 is upregulated in Gper1-/- rats compared to Gper1+/+ rats. Unfortunately, other proteins were not able to be confirmed with Immunoblots due to the lack of quality rat antibodies. However, using quantitative
PCR, we were able to confirm the upregulation of Fibrillin-1 and Vcam-1 (Figure 2-5cd).
While this shows only the transcripts are upregulated, it suggests that the protein levels would likewise be changed. Therefore, we have confirmed the proteomic data of these three proteins.
Figure 2-5: Cd99, Fibrillin-1, and Vcam-1 are significantly different in endothelial cells from Gper1-/- rats compared to the endothelial cells from Gper1+/+ rats. The membrane was blotted with rabbit anti-rat Cd99 antibody and rabbit anti-rat Gapdh as a control (A). The image was quantified using ImageJ (B). Figure 2-5 C shows the qPCR results of Vcam-1 and Fibrillin-1 (D) expression.
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2.3.4 Permeability and monocyte migration are not changed in vitro
Cd99 was the most upregulated protein, with a predicted 8-fold increase in Gper1-/- endothelial cells. As Cd99 functions in both permeability and immune cell migration, we next tested both of these functions using in vitro assays. The permeability of endothelial cells were not different (Figure 2-6a), and the migration of monocytes across an endothelial cell layer was also not changed (Figure 2-6b).
Figure 2-6: No significant changes in endothelial cell permeability or migrated monocytes in vitro. Figure 2-6a shows the fluorescence intensity at different time points after the permeability assay. There are no significant changes. Figure 2-6b shows no changes in migrated monocytes across an endothelial cell layer in vitro.
2.3.5 Macrophage infiltration of small resistance arteries is unchanged
In order to determine if the predicted altered macrophage migration is altering infiltration of arteries, we performed an immunohistochemistry analysis for Cd68, a macrophage cell marker, of small resistance arteries. We found that between Gper1-/- and Gper1+/+ arteries there was no difference in macrophage infiltration (Figure 2-7).
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Figure 2-7: No change in macrophage infiltration in Gper1-/- and Gper1+/+ small resistance arteries. Immunohistochemistry images showing no significant difference in Cd68. A) Gper1+/+ arteries are shown on the left while B) Gper1- /- arteries are on the right. C) Optical density of Cd68 signaling after Fiji analysis.
2.3.6 Determination of phenotype as salt-sensitive
To determine whether the BP phenotype observed in Gper1-/- rats was specifically salt dependent, the BP study was repeated in rats maintained on a low salt diet. The results can be seen in Figure 2-8. The SBP was not changed between Gper1-/- rats and Gper1+/+ rats maintained on a low salt diet. This confirms that the phenotype observed in salt sensitive.
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Systolic BP 180 Gper1+/+ 170 Gper1-/-
160
150
SBP (mmHg) SBP 140
130 7 8 9 10 11 12 Weeks of age
Figure 2-8: No change in SBP between Gper1-/- and Gper1+/+ rats on a low-salt diet. This figure shows the systolic BP of Gper1-/- and Gper1+/+ rats maintained on a low-salt diet. This data confirms that the BP lowering phenotype is salt-sensitive. N=8/group
2.3.7 Acetate supplementation in cell culture reverses some RNA expression
To determine if circulating acetate levels that were altered with the cecal transplant could influence this mechanism, we cultured endothelial cells either in the presence of acetate, or in control media. After either 4 or 24 hours with the media, we isolated the RNA from these cells. We then used qPCR to analyze expression levels of the genes that had the greatest differences in the proteomic data we collected. As shown in Figure 2-9, Cd99 and Vcam-1 expression, which was elevated in the proteomic data, had reduced expression when exposed to acetate. This shows that the pathways that were altered and protective in Gper1-/- rats were reversed with acetate exposure. This explains the absence of lowered BP in the Gper1-/- rats given Gper1+/+ cecal contents, as they had higher levels of acetate after the cecal transplant.
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Figure 2-9: Endothelial cells exposed to acetate had reduced expression of Cd99 and Vcam-1. This figure shows the expression of Cd99 (A) and Vcam-1 (B) after 24 hours of acetate exposure of Gper1-/- and Gper1+/+ endothelial cells. N=5/group
2.4 Discussion
The role of G-protein coupled receptors (GPCR) in cardiovascular diseases, including hypertension, and cancer has been confirmed. A specific GPCR that has been implicated in BP regulation is the G-protein coupled estrogen receptor (Gper1). Through the use of CRISPR/Cas9 technology, we have previously created a global Gper1-/- on the
Dahl Salt-Sensitive rat background. These Gper1-/- rats have reduced BP compared to the wild-type Gper1+/+ rats. Additionally, we have found that these rats have improved endothelium-dependent vascular reactivity. While these changes were noted, the mechanism by which these rats had improved BP control was still unknown.
In order to dissect the mechanism by which Gper1 deletion improves vascular reactivity in an endothelium-dependent manner, we performed an unbiased quantitative proteomic analysis of endothelial cells in both Gper1-/- and Gper1+/+ rats (Figure 2-1).
This analysis led to the discovery of 130 significantly differentially expressed proteins
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(Figure 2-2). Among these proteomic alterations the top-scoring protein for upregulation was an 8-fold increase in Cd99 in the Gper1-/- endothelial cells. Cd99, a transmembrane protein located on endothelial cells and leukocytes, is known to be involved in transendothelial migration (Petri and Bixel 2006), as well as permeability. Through its action in the lateral border recycling compartment, which aids in the last step of diapedesis, it promotes both paracellular and transcellular migration (Nourshargh,
Hordijk et al. 2010). The mechanisms by which it increases permeability are unknown; however, there are reports that it inhibits the ß1-integrin affinity for the extracellular matrix (Lee, Lee et al. 2012). Interestingly, there are reports of G-protein coupled receptors (GPCRs) signaling to regulate permeability of endothelial cells. These reports indicate that through calcium signaling, the GPCRs regulate myosin light chain kinase
(Mlk) and Ras homolog family member A to open junctions in endothelial cells and promote permeability(Muller 2016). These calcium stores also act to activate the LBRC, leading to an increase in transendothelial migration (Huang, Manning et al. 1993).
However, the permeability of endothelial cells was not altered in these animals (Figure 2-
6a).
To further explore possible mechanisms, the proteomic data collected was analyzed using Ingenuity Pathway Analysis and a list of potentially altered pathways was collected. The top pathway categories are shown in Figure 2-3. The largest functional pathway altered is cellular movement. Among the predicted altered pathways are altered macrophage migration and infection pathways, as well as altered tight junction signaling, and attachment of endothelial cells, which support the findings in this study. In addition to these pathways, it is also reported there may be altered glycine cleavage, Ras homolog 73
gene family, member A signaling, and formation of reactive oxygen species, among others. Previous reports of genetic deletion of Gper1 in mice have shown improved cardiovascular health by reduced superoxide formation (Meyer, Fredette et al. 2016), which this pathway analysis supports. Macrophage activation involves production of reactive oxygen species such as superoxide radicals whereby, this pathway could be contributing to the improved BP regulation and vascular function that is observed.
The evidence collected suggests that Gper1 has a role in transendothelial migration. Transendothelial migration is a crucial pathway for the health of arteries. In cases of inflammation or infections, leukocytes and lymphocytes travel through the endothelial layer of arteries to clear the inflammation and infections. In situations of acute inflammation, macrophages are crucial to the resolution of the inflammation, and the continued functionality of the blood vessels. Moreover, Gper1 has known roles in cancer where migration is a critical component. We believe Gper1 has a role in immune cell migration and that deletion of Gper1 caused an increase in compensatory mechanisms to normalize migration. This is supported by the suggested increased transendothelial migration in the proteomic data, but the lack of physiological changes in macrophage infiltration in small resistance arteries of Gper1-/- and Gper1+/+ rats (Figure
2-7) and a lack of in vitro changes in monocyte migration (Figure 2-6b). These results suggest a role of Gper1 in increased transendothelial migration, which has implications not only in cardiovascular disease and hypertension, but in cancer as well.
Another important finding of this study was the role of the gut microbiota in regulating the discovered pathways. We found that two of the most upregulated proteins,
Cd99 and Vcam-1, which are involved in cellular migration, were down regulated with 74
acetate exposure. This was an important finding as in our previously published study, a cecal transplant from wild-type rats into Gper1-/- rats abolished the BP protective effect
(Waghulde, Cheng et al. 2018). When the serum SCFAs were analyzed, we found an increase in acetate levels in the Gper1-/- rats with wild-type cecal contents. Therefore, when taken together these data suggest that the cecal transplant caused dysbiosis in the gut, leading to increased acetate. This increased circulating acetate acted on endothelial cells to reverse the protective migratory pathways, leading to an increased BP.
This study shows a novel, endothelial cell-specific, proteomic profile that reveals a role of Gper1 in migration, which has implications not only in cardiovascular disease and hypertension, but in cancer as well. It also shows the role of the gut microbiota and their circulating metabolites in regulating the migratory pathway.
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Chapter 3
Disparate Effects of Antibiotics on Hypertension
Galla, S., et al., Disparate Effects of Antibiotics on Hypertension. Physiol Genomics,
2018.
3.1 Abstract
Gut microbiota are associated with a variety of complex polygenic diseases. The usage of broad-spectrum antibiotics by patients affected by such diseases is an important environmental factor to consider, because antibiotics, which are widely prescribed to curb pathological bacterial infections, also indiscriminately eliminate gut commensal microbiota. However, the extent to which antibiotics reshape gut microbiota and per se contribute to these complex diseases is understudied. Because genetics play an important role in predisposing individuals to these modern diseases, we hypothesize that the extent to which antibiotics influence complex diseases depends on the host genome and metagenome. The current study tests this hypothesis in the context of hypertension, which is a serious risk factor for cardiovascular diseases. A 3 x 2 factorial design was used to test the blood pressure (BP) and microbiotal effects of three different antibiotics,
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neomycin, minocycline, and vancomycin, on two well-known, pre-clinical, genetic models of hypertension, the Dahl Salt-Sensitive (S) rat and the Spontaneously
Hypertensive Rat (SHR), both of which develop hypertension, but for different genetic reasons. Regardless of the class, oral administration of antibiotics increased systolic blood pressure of the S rat, while minocycline and vancomycin, but not neomycin, lowered systolic blood pressure in the SHR. These disparate BP effects were accompanied by significant alterations in gut microbiota. Our study highlights the need to consider an individualized approach for the usage of antibiotics among hypertensives, as their BP could be affected differentially based on their individual genetic and microbiotal communities.
3.2 Introduction
The discovery and use of antibiotics played a dominant role in protecting humans from infectious diseases, which were a leading cause of death in the 19th and 20th centuries.
As a result, human life expectancy has significantly climbed over the centuries (46).
However, this rise in human lifespan is accompanied by a surge in diseases of the modern industrialized society such as hypertension, diabetes, colitis, several neurological disorders and cancer. Recent studies indicate that there are strong associations between gut microbiotal communities and each one of these modern illnesses that plaque humanity (1-4, 8, 10, 12-16, 18, 20, 24-26, 28-30, 34, 35, 37-39, 41, 43, 45). Because antibiotics not only eliminate pathogenic bacteria, but also get rid of beneficial
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commensal bacteria, especially in the gut, this raises the question of safety in the usage of antibiotics by patients with such modern ailments.
Host genetics are an important factor contributing to complex polygenic diseases, which are the same modern illnesses mentioned above as also being impacted by microbiota.
Together, the host genome and the collective genomes of the microbiota that reside within the host, represent a holobiont, wherein the variation of the host genome along with the alterations presented by its metagenome, act in concert to determine an individualized level of response to environmental factors influencing host health. Viewed from the context of usage of antibiotics as an environmental factor, this leads to the possibility that individual responses to antibiotics may vary depending on the host and its microbiota.
In this study, we test this possibility by using two distinct, widely used, genetically well- defined preclinical models of hypertension to simulate two individual populations with disparate genetic predisposition to the development of hypertension. The Dahl Salt-
Sensitive (S) rat is a genetic model of hypertension, which mimics features of essential hypertension with increased sensitivity to dietary salt and renal disease as especially noted in African American populations (7). Gut transplantation studies in this model have highlighted the contributions of microbiota in the regulation of hypertension (24).
Compared to the S rat, the second preclinical model, the Spontaneously Hypertensive Rat
(SHR), is relatively salt-insensitive and presents with hypertension in the absence of renal complications. The genetic factors driving hypertension in these two models are identified to be different, whereby, we examined the null hypothesis that regardless of
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these variations on the host genomes, three different classes of antibiotics represented by neomycin (aminoglycoside), minocycline (tetracycline) and vancomycin (glycopeptide) would impart a similar direction of change on their blood pressure (BP). Our results disproved this null hypothesis. We found that in the S rat, antibiotic administration causes an elevation in BP, while in the SHR administration of the same antibiotic causes either a reduction in BP or no change. These disparate effects were correlated by distinct alterations in gut microbiotal compositions. Based on these observations, we propose that antibiotic usage could have individualized effects on hypertensive patients, which is determined by their own unique genetic and microbiota.
3.3 Methods
3.3.1 Animals and diet
All animals were from our colony maintained at the University of Toledo College of
Medicine and Life Sciences. All animal procedures and protocols used were approved by the University of Toledo 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 College of Medicine and
Life Sciences. The Spontaneously Hypertensive Rat (SHR) strain was originally obtained from Harlan Sprague Dawley (Indianapolis, IN). Rats were bred and maintained on a low salt diet (0.3% NaCl; Harlan Teklad diet TD 7034, Madison, WI). The Harlan Teklad diet
(TD94217) was used for experiments involving a high salt regimen (2% NaCl).
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3.3.2 Blood pressure measurements by radiotelemetry
All rats were weaned at 30 days of age and maintained on a low-salt diet (0.3% NaCl,
Harlan Teklad) until 6 weeks of age. At this time, S rats were switched to a high-salt diet
(2% NaCl) for an additional period of 38 days, while SHR were maintained on a low salt diet. After 38 days on a high-salt diet regimen for S rats (low-salt diet for SHR), rats were surgically implanted with radiotelemetry transmitters (HDS10) (Data Science
International, St Paul, MN) as previously described by our laboratory (23). Rats were individually housed and allowed to recover from surgery for 3 days before recording blood pressure. All rats were the same age at the time of surgery.
3.3.3 Antibiotic administration
After the 3 day recovery period from the radiotelemetry surgery, the systolic blood pressures (SBP) were taken, and the rats were grouped. They received normal drinking water or water supplemented with neomycin (0.5g/L, Gibco), vancomycin (50mg/kg/day
(Hospira)), or minocycline (50mg/kg/day (Novaplus)). The water bottles were replaced once a week.
3.3.4 Collection of fecal content
Prior to sacrifice, fecal contents were collected from the rats. The fecal content of each animal was snap-frozen on dry ice and was stored at −80°C to be used at a later time.
3.3.5 Genomic DNA Isolation, 16S rRNA Gene Sequencing, and Analysis of
Microbiotal Composition 80
Fecal DNA was extracted from one fecal pellet (approximately 0.2g) using
QIAamp®PowerFecal®DNA kit (Qiagen). We followed Illumina User Guide: 16S
Metagenomic Sequencing Library Preparation-Preparing 16S Ribosomal RNA Gene
Amplicons for the Illumina MiSeq System (Part # 15044223 Rev. B). The 16S rRNA gene V3-V4 region was amplified by PCR using Illumina sequencing primers: 5'
TCGTCGGCAGCGTCAGATGTGTATAAGAGACAGCCTACGGGNGGCWGCAG and 5'
GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAGGGACTACHVGGGTWTCTA
AT. For index PCR, Nextera XT index kit (FC-131-1002) was used to attach dual indices. For 25uL reaction mixture, each reaction was prepared using X1 reaction buffer
(Invitrogen, Thermo Fisher Scientific, Waltham, MA, USA), 0.5 U Taq polymerase
(Invitrogen), 200 nM of each primer, 2mM MgCl2, 0.2 mM dNTPs and 2uL of 5ng/uL
DNA (1st PCR) or purified PCR product (2nd PCR). The thermocycling was performed in a BioRad ThermoCycler and the cycling conditions were as follows: initial denaturation at 95C for 5 min, followed by 25 cycles of 95 C for 30s, 58C for 30s, 72C for 30s, and a final extension at 72C for 5min. Each PCR amplicon was purified in two rounds using AMPure XP beads (Beckman Coulter Inc.m Brea, CA). Concentration of purified index PCR products was measured using the Qubit dsDNA HS Assay kit with
QubitR 3.0 fluorometer (LifeTechnologies, Carlsbad, CA, USA). The 4nM each amplicon was pooled equally and the pooled amplicon size was checked on a 2100
Bioanalyzer (Agilent). Following Illumina User Guide Illumina MiSeq System, 10pM denatured and diluted library was mixed with 10pM PhiX control spike-in to be 10%
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PhiX in the final volume. Then, it was loaded on Illumina MiSeq Reagent Kit v3 with
2X300 cycles.
Raw 16S sequencing data was processed and analyzed using a bioinformatics pipeline of multiple software including USEARCH (11) and Quantitative Insights Into Microbial
Ecology (QIIME) software package (version 1.9.1) (5). Raw paired-end reads were merged to create consensus sequences and then quality filtered using USEARCH (11)
(version 9). Chimeric sequences were identified and filtered using Quantitative Insights
Into microbial Ecology (QIIME) (5) combined with the USEARCH (version 6) algorithm. Open reference operational taxonomic units (OTUs) were subsequently picked using QIIME combined with the USEARCH (version 6) algorithm, and taxonomy assignment was performed using Greengenes (9) as reference database at the 97% similarity threshold. Alpha-diversity (PD_whole_tree) and beta-diversity (unweighted
UniFrac metrics) were calculated using QIIME package.
3.3.6 Short Chain Fatty Acid analysis
25 µL of plasma was extracted with acetonitrile spiked with internal standards, and the supernatant was then mixed with 200 mM 3-nitrophenylhydrazine and 120 mM N-(3- dimethylaminopropyl)-N1-ethylcarbodiimide in a 2:1:1 (v/v/v) ratio. The samples were derivatized at 40°C for 30 minutes and then injected into an Agilent 6410 triple quadrupole mass spectrometer equipped with an electrospray ionization (22) source in negative-ion mode coupled to an Agilent 1290 infinity HPLC system with an Acquity
UPLC BEH C18 column (2.1x100 mm, 1.7 µm; Waters, Milford MA). Solvent A was formic acid (0.01%, v/v) in water, and solvent B was formic acid (0.01%, v/v) in 82
acetonitrile. Quantitation was performed by calibration to internal standards and standard curves on the Mass Hunter quantitative suite version B.06.00 (Santa Clara). All levels are expressed in µM (17, 24, 40).
3.3.7 Statistical analysis
All statistical analysis was performed using a one-way ANOVA on GraphPad Prism 5.02, followed by either a Dunnett’s post-hoc test for significance, or a Tukey test for comparison, as noted in the text. A p-value of <0.05 was considered to be significant.
3.4 Results
3.4.1 Blood Pressure and Heart Rate
As seen in Figure 3-1a, the S rats treated with neomycin, minocycline, or vancomycin had elevated systolic blood pressure (SBP) compared to controls over the three-week period. Importantly, the diurnal rhythms of the S rats given any of the three antibiotics were unchanged, as shown by the last 24 hour BP recording in Figure 3-1b. Moreover, when comparing the SBP of the S rats in the light and dark phases by a Dunnett’s post- hoc test for significance, BP of animals receiving minocycline and neomycin were significantly increased compared to controls in both the light and dark phase. Animals treated with vancomycin were, however, not significantly different in either phase, despite the strong trend for an increased SBP (Figure 3-1c). When comparing the BP effect in S rats and SHR by a Tukey test, there are significant differences for all three antibiotics in both the light and dark phases, indicating that the null hypothesis is indeed proven wrong. The diastolic BP (DBP) in the dark phase of S rats given minocycline was 83
a) d) S rat Systolic BP over time SHR Systolic BP over time 260 * 185 * Control Control Neomycin 180 Neomycin Minocycline 240 Minocycline Vancomycin 175 Vancomycin
170 220 165
200 Systolic BP (mmHg) Systolic Systolic BP (mmHg) Systolic 0 1 2 3 0 1 2 3 Weeks Weeks
b) S rat Systolic BP e) SHR Systolic BP 260 Control Control Neomycin 180 Neomycin 240 Minocycline Minocycline Vancomycin Vancomycin 170 220
200 160 Systolic BP (mmHg) Systolic Systolic BP (mmHg) Systolic 0 5 10 15 20 25 0 5 10 15 20 25 Hours Hours c) f) S rat Systolic BP Effect SHR Systolic BP Effect
40 * * Neomycin 5 * Neomycin Minocycline 30 * Minocycline Vancomycin 0 Vancomycin 20
10 -5
0 SBP Effect (mmHg) SBPEffect -10 SBP Effect (mmHg) SBPEffect Light Dark Light Dark
Figure 3-1: S rats on antibiotics have an increased BP, while SHR on antibiotics do not. Figure 1a shows the systolic blood pressure (SBP) of S rats (n=5) over three weeks. Antibiotics were administered at week zero. The mean and SEMs are shown. There was no significance at timepoints 0 and 1 week, while minocycline was significant at weeks 2 and 3. Figure 1b shows the 24 hour recording from the last time point of Figure 1a. The light phase is shown with a yellow background, while the dark phase has a grey background. Each data point represents a 4 hour moving average. Figure 1c shows the SBP effect of each antibiotic on S rats. Means were calculated by subtracting the average SBP of the control rats from the average of each rat in the antibiotic groups for either the light or dark phase. Neomycin and minocycline were significantly (p<0.05) higher than the control animals as calculated by ANOVA, followed by Dunnett’s post-hoc test. Figure 1d shows the systolic blood pressure (SBP) of SHR (n=6) over three weeks. Antibiotics were administered at week zero. The mean and SEMs are shown. Figure 1e shows the 24 hour recording from the last time point of Figure 1d. The light phase is shown with a yellow background, while the dark phase has a grey background. Each data point represents a 4 hour moving average. Figure 1f shows the SBP effect of each antibiotic on SHR. Means were calculated by subtracting the average SBP of the control rats from the average of each rat in the antibiotic groups for either the light or dark phase. 84
increased compared to the S rats without antibiotics, however, the other two antibiotics did not change the dark phase DBP, and there were no changes in the light phase DBP
(Figure 3-2a). The mean arterial pressures (MAP) in the dark phase were significantly elevated in S rats given minocycline compared to controls, but were not changed in the rats given vancomycin or neomycin, or any of the rats in the light phase (Figure 3-2b).
The heart rates (HR) were unchanged in any of the antibiotic treatment groups (Figure 3-
2c). In contrast, as seen in Figure 3-1d, the SHR given minocycline or vancomycin had reduced SBP compared to controls, while neomycin did not alter the SBP. Again, the diurnal rhythms were not altered with antibiotic treatment (Figure 3-1e). In SHR, none of the antibiotics caused a significant change in SBP in the light and dark phases, however minocycline and vancomycin had a strong trend towards decreasing SBP in both phases
(Figure 3-1f). In the SHR, the DBP, MAP, and HR were not changed (Figures 3-2d-f).
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a) d)
S rat Diastolic BP SHR Diastolic BP 200 120 Control Control Neomycin Neomycin 115 180 Minocycline Minocycline Vancomycin Vancomycin 110
160 105
Diastolic BP (mmHg) BP Diastolic 140 100
0 5 10 15 20 25 BP(mmHg) Diastolic 0 5 10 15 20 25 Hours Hours b) e) S rat Mean Arterial Pressure SHR Mean Arterial Pressure 220 140 Control Control Neomycin 135 Neomycin 200 Minocycline Minocycline Vancomycin 130 Vancomycin
125 180
120
MAP (mmHg) MAP MAP (mmHg) MAP
160 115 0 5 10 15 20 25 0 5 10 15 20 25 Hours Hours c) f) S rat Heart Rate SHR Heart Rate 500 Control Control Neomycin 360 Neomycin Minocycline Minocycline 450 340 Vancomycin Vancomycin 320 400
300
HR(beats/min) HR (beats/min) HR 280 350 0 5 10 15 20 25 0 5 10 15 20 25 Hours Hours
Figure 3-2: Diastolic BP, mean arterial pressure and heart rate. Figure 3-2a shows the diastolic BP (DBP) recordings over the last 24 hours in the three week period of the same S rats as above. Each data point represents a 4 hour moving average. The light phase is shown with a yellow background, while the dark phase has a grey background. Rats treated with minocycline had increased DBP in the dark phase only. Figure 3-2b is the mean arterial pressure (MAP) of each of these S rats over the same 24 hour period. Only minocycline showed significance and only in the dark phase. Figure 3-2c is the heart rate (HR) of S rats over the 24 hour period at the end of the three week antibiotic treatment, and none of the rats showed significance. The mean and SEMs are shown. Figure 3-2d shows the diastolic BP of SHR over 24 hours from the end of the three week period. Figure 3-2e is the mean arterial pressure (MAP) of each of these SHR over the same 24 hour period. Figure 3-2f shows the HR of these SHR in the same 24 hour period. SHR had no significant differences.
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3.4.2 Gut microbiotal diversity
In order to determine the effect that gut microbiotal diversity has on BP with antibiotic treatment, both the alpha- and beta-diversities were analyzed. First, when comparing the alpha-diversity of controls, there is an increased diversity noted in the SHR compared to the S rat (Figures 3-3a,b). Second, each of the antibiotics has an effect on diversity in both strains. As seen in Figure 3-3a, the S rats treated with neomycin had significantly reduced alpha-diversity (p=0.002, p-values for the last data points only) when compared to the control group. However, the S rats treated with minocycline did not have a significant difference in alpha-diversity. The S rats treated with vancomycin did have a significant reduction in alpha-diversity (p=0.002, p-values for the last data points only).
In contrast, all three antibiotic treatment groups had significantly reduced alpha- diversities compared to controls in the SHR (Figure 3-3b) with p-values of 0.001, 0.028 and 0.002 (p-values for the last data points only), respectively for the neomycin, minocycline and vancomycin treatment groups. Despite slight differences in significance levels, all three antibiotics caused a similar pattern of reducing diversity, regardless of the strain. The unweighted beta-diversities in the S rats are plotted in the principal coordinate analysis (PCoA) plots shown in Figure 3-3c. The S rats treated with any antibiotic had distinct bacterial communities. The unweighted beta-diversities in the SHR are plotted in
Figure 3-3d. As shown, the SHR again showed significant changes in the bacterial communities.
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Figure 3-3: Microbiotal diversity is reduced in S rats and SHR with antibiotic treatment. Figure 3-3a shows the alpha-diversity (PD_whole_tree) of the same S rats, both control and with antibiotics, whose BP are recorded in Figure 1. Both neomycin and vancomycin had significantly reduced diversity (p=0.002, p-values for the last data points only) compared to controls, while minocycline was not significant. Figure 3-3b is the alpha-diversity of SHR. All three antibiotics had a significantly reduced diversity than controls in SHR (neomycin p=0.001, minocycline p=0.028, and vancomycin p=0.002). This analysis was conducted on fecal samples collected at the 3 week time point from Figure 1. The red lines indicate controls, the blue line indicates rats with minocycline, the orange line is rats with neomycin, and the green line is rats with vancomycin. Figure 3-3c is a principle coordinate analysis (PCoA) plot of the unweighted beta-diversity in S rats. The S rats treated with any antibiotic had a different unweighted beta-diversity. Figure 3-3d is a PCoA plot of the unweighted beta-diversity in SHR. As shown, the SHR had significant changes in beta-diversity. Again, red indicates controls, the blue indicates rats with minocycline, the orange is rats with neomycin, and the green is rats with vancomycin.
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3.4.3 Taxonomic Comparisons
Phyla: In order to determine the specific differences in the gut microbiota, each group was analyzed and plotted in figure 3-4. Importantly, S rats without any treatment (Figure
3-4a) have different bacterial communities compared to SHR (Figure 3-4e). While
Bacteroidetes levels are similar between the two strains, there is a greater amount of
Firmicutes in SHR than S rats. Additionally, S rats have greater amounts of
Proteobacteria compared to SHR. These changes suggest that the genome selects for gut microbiota. However, these two strains of hypertensive rats were developed in the past in two different environments (Japan for the SHR and the US for S rats, at the time they were originally selected), so it is possible that the differences in gut microbiota were originally due to the varying environments. In addition, the S rats in this study were maintained on a high-salt diet, while the SHR were not. Salt is a known regulator of gut microbiota (44), and therefore, the possibility of salt having a role in the bacterial differences of the S rat and SHR should not be ignored. Antibiotics altered the phyla of both S rats and SHR. In the S rats treated with neomycin, there was an increase in the phyla Bacteroidetes, Cyanobacteria, Fusobacteria, and Verrucomicrobia, accompanied with a decrease in the phyla Actinobacteria, Deferribacteres, Firmicutes, Proteobacteria,
TM7, and Tenericutes (Figures 3-4a,b). In the minocycline treated S rats, there was an increase in Firmicutes, Proteobacteria, and Verrucomicrobia, while there was a decrease in Actinobacteria, Bacteroidetes, Cyanobacteria, Deferribacteres, TM7, and Tenericutes
(Figures 3-4a,c). The vancomycin treated S rats had an increase in the phyla
Bacteroidetes, Cyanobacteria, Elusimicrobia, Fusobacteria, Proteobacteria, and
Verrucomicrobia, and a decrease in the phyla Actinobacteria, Deferribacteres, Firmicutes, 89
TM7, and Tenericutes (Figures 3-4a,d). In SHR treated with neomycin, there was an increase in the phyla Bacteroidetes, Cyanobacteria, Elusimicrobia, and Verrucomicrobia, while there was a decrease in the phyla Firmicutes, Proteobacteria, TM7, and Tenericutes
(Figures 3-4e,f). In the minocycline treated SHR, there was an increase in the phyla
Actinobacteria, Cyanobacteria, Deferribacteres, and Firmicutes, while there was a decrease in Bacteroidetes, Proteobacteria, TM7, and Tenericutes (Figures 3-4e,g). In the vancomycin treated SHR, there was an increase in the phyla Bacteroidetes,
Cyanobacteria, Proteobacteria, and Verrucomicrobia, and a decrease in Firmicutes, TM7, and Tenericutes (Figures 3-4e,h).
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Figure 3-4: Phyla changes with antibiotic administration. The bacteria from fecal samples collected at three weeks after antibiotic administration (n=6/group) were identified using 16S sequencing. The phyla abundances are shown in Figure 3-4. Figures 3-4a-d show the S rats, while Figures 3-4e-h show the SHR.
3.4.4 Short Chain Fatty Acids
In the S rats, there were no significant changes in any of the SCFA levels measured
(Figure 3-5a). However, in SHR, proprionate levels were decreased with vancomycin and 91
neomycin treatment (p<0.05). Isovalerate was also reduced, but only with vancomycin treatment (Figure 3-5b).
a) Short Chain Fatty Acids S rat 450 400 Control 350
M) Neomycin
300 10 Minocycline 8 Vancomycin 6 4
Concentration ( Concentration 2 0
Acetate Butyrate Valerate Hexanoate Octanoate ProprionateIsobutyrate Isovalerate Heptanoate
b)
Short Chain Fatty Acids SHR 450 Control 400
M) Neomycin
350 15 Minocycline Vancomycin 10
5 Concentration ( Concentration * * 0 *
Acetate Butyrate Valerate Octanoate PropionateIsobutyrate Isovalerate HexanoateHeptanoate
Figure 3-5: Short chain fatty acids are altered in SHR with antibiotics. The levels of SCFAs in plasma at three weeks after antibiotic administration (n=6/group) were calculated and are shown in Figure 3-5. Figure 3-5a shows S rat SCFA levels, while Figure 3-5b shows SCFA levels in SHR. SCFA levels are shown in μM. (* p<0.05)
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3.5 Discussion
This study aimed to determine if antibiotic administration affects BP, and we have found that this does indeed happen. However, surprisingly, each antibiotic does not alter
BP in the same manner: the genetic background and gut microbiome of the rat determined how the BP was altered with antibiotics. One of the strains used in this study is the S rat, which is a salt-sensitive model of hypertension, and is reported to represent features of the African American hypertensive patient population (7). For this study, we also used SHR, which are spontaneously hypertensive and do not exhibit a salt-sensitive hypertensive phenotype. In addition to the different types of hypertension these rats develop, there are also differences in other disease pathways. For example, S rats typically develop renal disease, while SHR are more prone to stroke. We found that in S rats the SBP are significantly elevated in both the light and dark cycle when they are treated with neomycin or minocycline. While the vancomycin did not cause a significant increase in SBP in either the light or dark phase, there is an evident trend for an increase in BP, with the average increase being 20-30mmHg. This increase is substantial, and despite the lack of statistical significance, it is important to note for hypertensive patients.
This increase in SBP was accompanied by an increase in DBP and MAP in rats treated with minocycline. The HR of these rats was not affected by any antibiotic.
In contrast, the light and dark phase SBP of SHR are not increased when the rats are treated with any of the three oral antibiotics. Minocycline and vancomycin caused a trend for a reduced SBP, with vancomycin having the largest effect. Again, while these changes were not significantly different, even a trend for reduction is important to note for hypertensive patients. The DBP and the MAP were not significant for any of the 93
antibiotics tested. Additionally, the HR was not altered with any treatment. The discrepancy in these BP effects between S rats and SHR emphasize the importance of studying the host genome-gut microbiome interactions because the divergent effects may be due to either the genome, the microbiome, or the interaction between the two.
Moreover, reports in the literature have shown that minocycline can lower BP, however, this was found in Sprague Dawley rats that had angiontensin-II induced hypertension
(45). This is a different strain of rats than what was used in this study, indicating again that the host genome has an effect on BP response to antibiotics. Additionally, minocycline is known to cross the blood-brain barrier, providing evidence that there may be confounding neurological variables acting in concert with the gut microbiota to prompt the disparate BP effects of this antibiotic.
Minocycline caused an increase in both the SBP and DBP of S rats, however, the other two antibiotics only caused an increase in SBP of S rats. While we do not know the exact mechanism of this primarily systolic hypertension, it is possible that metabolites from the gut microbiota are contributing to arterial stiffening, and therefore the evident systolic hypertension. SCFAs are known metabolites of gut microbiota that contribute to systolic hypertension (27). However, in this study, there were no changes in SCFA levels in S rats with any antibiotic treatment. The only significant changes noted were in SHR treated with vancomycin and neomycin. Propionate levels were decreased with these two antibiotics, and isovalerate was reduced with vancomycin only (Figure 3-5). However, in
SHR there were no significant changes in BP. Therefore, there may be other bacterial metabolites that are affecting the BP changes noted.
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Despite the discrepancies in BP effects of the antibiotics, the change in diversity in the gut microbiota is similar dependent on which antibiotic was administered. This is expected, as each antibiotic is most effective against a certain population of bacteria.
Neomycin, an aminoglycoside, is most efficacious against gram-negative aerobes or facultative anaerobes, such as Proteobacteria (19), which was indeed the case in this study (Figure 3-4). Minocycline, a tetracycline, is active against both gram-positive and gram-negative bacteria (6). Vancomycin, a glycopeptide antibiotic, inhibits cell wall synthesis, and is therefore only active against gram-positive bacteria (33). This was seen in our study with a reduction in Firmicutes, which are gram-positive bacteria (Figure 3-
4). What is surprising is that similar alterations in these bacteria affect the host BP in different ways. This again points to the importance of assessing the host genome-gut microbiome interaction, rather than confining the assessment of such contributions to the microbiome alone.
Antibiotics are being progressively studied for their role in drug resistance.
Therefore, more care is being taken when prescribing antibiotics to patients. While this is very important, perhaps a more important reason to avoid the prolific use of antibiotics is because they disturb gut microbiotal homeostasis and thereby contribute to pathophysiological consequences, depending on the patient genome and metagenome.
There is already evidence for a role of antibiotics in human BP control. One case study showed that vancomycin treatment for an infection caused hypotension in a hypertensive patient, despite temporary discontinuation of her anti-hypertensive medications (31).
The Firmicutes/Bacteroidetes (F/B) ratio is often considered to be an indicator of gut dysbiosis and disease: a lower ratio is correlated with health, while a higher ratio is 95
correlated with dysbiosis and disease. This ratio has been reported to correlate with the
BP of the SHR and its normotensive counterpart, the Wistar Kyoto rat (WKY) (45).
However, previously we have shown that the F/B ratio is not a good indicator of disease in the S rats, as the R rat has a higher F/B ratio than the S rat does, despite the reduced
BP (24). In this study, we further confirmed that the F/B ratio does not correlate with elevated BP, in either the S rat or the SHR (Figures 3-4a-h). An increased F/B ratio was found in both S rats and SHR treated with minocycline, despite the opposite effects of minocycline on BP. Moreover, neomycin and vancomycin both caused a decreased F/B ratio, again despite the opposite BP effects observed in the two rat strains. These data suggest that the F/B ratio can be altered by antibiotics, but does not have any correlation with BP.
In addition to the F/B ratio, there are reports that bacterial diversity is correlated with BP (45). Yang et al reported that administration of minocycline increased bacterial diversity in the SHR compared to WKY. However, the BP lowering effect as a result of minocycline treatment in this study was not reported using the SHR, but with
Angiotensin II infused Sprague Dawley (SD) rats (45). Our study indicates that in both in the S rat and SHR, bacterial diversity is reduced with any antibiotic (Figure 3-3), and therefore, not correlated with alterations in blood pressure.
Specific bacteria have also been found to be regulators of health and disease (8,
15, 21, 32, 44). Some of the beneficial bacteria noted in the literature are Akkermansia and Lactobacillus. Akkermansia has been found to be beneficial for metabolic health, including protection from obesity and insulin resistance (8), and Lactobacillus has been
96
found to prevent salt-induced hypertension (44). However, in this study, Akkermansia and Lactobacillus were not found to correlate with BP.
In addition to Lactobacillus, we have previously reported a negative correlation between S rat BP and Veillonellaceae (24). In this study, we saw differential changes of
Veillonellaceae levels in the S rats, however, these changes were not associated with the
BP changes in the S rat or SHR.
Sulfate-reducing bacteria, by increasing hydrogen sulfide levels, have also been implicated in BP regulation (36, 41, 42), and are therefore associated with host health.
One of the sulfate-reducing bacteria is Desulfovibrio. In this study, S rats treated with any antibiotic had a reduction in Desulfovibrio ,which could be one mechanism by which the
BP of S rats is increased with antibiotic treatment. However, in SHR, Desulfovibrio levels are not associated with BP changes. Therefore, even though the sulfate-reducing bacteria might play a role in the S rat BP changes with antibiotics, they do not play a role in the SHR.
In conclusion, by using two strains of rats in this study, the S rat and SHR, we have shown that the host genome plays an important role in how BP will be affected differentially by antibiotic treatment. This highlights the importance of further studies to determine the mechanism behind these differing effects. Our study has important translational implications, and serves as a basis for exploring similar disparities in human hypertensive patients.
3.6 Acknowledgements 97
This work was supported by Institutional funding from the University of Toledo College of Medicine to the University of Toledo Microbiome Consortium. AVM acknowledges support (K08HL130944) from the National Heart, Lung, and Blood Institute. MVK acknowledges RO1 grant funding (CA219144) from the NIH.
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3.7 References
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Chapter 4
The Developing Gut Microbiome and Hypertension
4.1 Introduction
Numerous recent studies have found links between gut microbiota and hypertension
(Mell, Jala et al. 2015, Yang, Santisteban et al. 2015, Galla, Chakraborty et al. 2018).
However, most of the reported associations exist between older individuals. The link between the developing gut microbiome and pediatric hypertension has not been as thoroughly studied. One way to alter the gut microbiome is through oral antibiotics.
Antibiotics are the most prescribed class of drugs worldwide. Antibiotics are prescribed to people of all ages, starting at birth, and are even prescribed throughout pregnancy.
While the direct effects of antibiotics have been well studied, the effects of antibiotics through altering the microbiota have not been as thoroughly studied. Changes in the microbiota have well been established to influence various disease states, including cancer, metabolic diseases, immunological diseases, neurological diseases, and others
(Sartor 2008, Cani, Possemiers et al. 2009, Sekirov, Russell et al. 2010, Vijay-Kumar,
Aitken et al. 2010, Pluznick, Protzko et al. 2013, Festi, Schiumerini et al. 2014, Pluznick
2014, Blaut 2015, Gkolfakis, Dimitriadis et al. 2015, Goto, Kurashima et al. 2015, Jose
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and Raj 2015, Lankelma, Nieuwdorp et al. 2015, Mell, Jala et al. 2015, Miele, Giorgio et al. 2015, Pekkala, Munukka et al. 2015, Singh, Chassaing et al. 2015, Yang, Santisteban et al. 2015, Boulange, Neves et al. 2016, Dao, Everard et al. 2016, Durgan, Ganesh et al.
2016, Gerard 2016, Nagao-Kitamoto, Kitamoto et al. 2016, Perry, Peng et al. 2016,
Singh, Kumar et al. 2016, Tomasova, Dobrowolski et al. 2016, Adnan, Nelson et al.
2017, Galla, Chakraborty et al. 2017, Galla, Chakraborty et al. 2018). The microbiota is known to influence BP through various metabolites, such as short chain fatty acids
(SCFAs). SCFAs can bind to receptors on vasculature to elicit a vasodilatory or vasoconstrictory response, which will then cause changes in BP (Pluznick 2014,
Natarajan, Hori et al. 2016). Additionally, microbiota are known to alter inflammation in the gut, as well as systemically, and this systemic inflammation is associated with BP.
One example of this is that higher levels of T-helper 17 (Th17) cells are correlated with higher BP (Wilck, Matus et al. 2017). Moreover, activation of the nucleotide-binding domain, leucine-rich-containing family, pyrin domain-containing-3 (Nlrp3) inflammasome is associated with hypertension (Chakraborty, Galla et al. 2018).
In order to further study the role of the developing gut microbiota in hypertension, we used the Dahl Salt-Sensitive (S) rats. These rats are a well-established model of salt- sensitive, essential hypertension. They have also been used to show the first link between gut microbiota and hypertension (Mell et al. 2018). Previously, we have shown that administration of neomycin, minocycline, or vancomycin to S rats caused an increase in blood pressure (Galla et al. 2018). Therefore, we hypothesized that altering the developing microbiota in S rats through antibiotic administration early in life, or during gestation, would cause an increase in BP. However, the opposite is actually true. In 107
young rats, amoxicillin administration caused a reduction in BP that persisted after the cessation of the antibiotic. Moreover, when given to pregnant rats, the male offspring exhibited reduced BP, while female offspring had no change in BP. These BP changes were associated with significant changes in the microbiotal community, and were also associated with decreased inflammation in both the colon and the kidney.
4.2 Methods
4.2.1 Animals and diet
All animal procedures and protocols used were approved by the University of Toledo
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 College of Medicine and Life
Sciences. Rats were maintained on a low-salt diet (0.3% NaCl; Harlan Teklad diet TD
7034, Madison, WI). The Harlan Teklad diet (TD94217) was used for experiments involving a high-salt regimen (2% NaCl).
4.2.2 Blood pressure measurements by radiotelemetry
All rats were weaned at 30 days of age. In the first study, a week after weaning S rats were surgically implanted with radiotelemetry transmitters (C10) (Data Science
International, St Paul, MN) as previously described by our laboratory (Mell, Jala et al.
2015). After surgery, they were switched to a high-salt diet (2% NaCl) (Figure 4-1a). In
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the second study, rats underwent the surgical implantation of radiotelemetry transmitters
(C10) a week after weaning but were maintained on a low-salt diet (0.3% NaCl). After 19 days the rats were then switched to the high-salt diet (Figure 4-1b). Rats were individually housed and allowed to recover from surgery for 3 days before recording blood pressure. All rats were the same age at the time of surgery.
dams
Figure 4-1: Experimental design. Figure 4-1a shows the timeline for the adolescent rats given amoxicillin, while figure 4-1b shows the timeline for the rats given amoxicillin during gestation and lactation.
4.2.3 Antibiotic administration
In the first study, after the 3 day recovery period from the radiotelemetry surgery, the systolic blood pressures (SBP) were taken, and the rats were grouped. They received either normal drinking water or water supplemented with amoxicillin (50mg/kg/day,
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Sigma). After four weeks, antibiotic administration was stopped and BP monitoring was continued.
4.2.4 Breeding and Antibiotic administration
S rats were bred at 8 weeks of age. Two weeks after pairing, the males and females were separated and the females were either maintained on normal drinking water, or they were given water supplemented with amoxicillin (50mg/kg/day, Sigma). The amoxicillin was continued until the pups were weaned. After weaning, all of the rats were maintained on normal drinking water.
4.2.5 Collection of fecal content
At various time points, including weaning, day of diet switching, and at sacrifice, fecal contents were collected from the rats. The feces were collected directly from the animals.
The fecal content of each animal was snap-frozen on dry ice and was stored at −80°C to be used at a later time.
4.2.6 Food Intake Measurement
Single-housed rats were each given 100 grams of food. After 24 hours, the food from each cage was weighed. The difference was calculated.
4.2.7 Genomic DNA Isolation, 16S rRNA Gene Sequencing, and Analysis of
Microbiotal Composition
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Fecal DNA was extracted from one fecal pellet (approximately 0.2g) as described in
(Galla, Chakraborty et al. 2018). PCR library preparation, 16S rRNA gene sequencing and analysis were performed as previously described (Galla, Chakraborty et al. 2018).
4.2.8 Gavage of Lactobacillus murinus
S rats were aged to 42 days. At this time, they were started on a high-salt (2% NaCl) diet
(Harlan Teklad diet (TD94217)). This same day, we began the oral gavage of
Lactobacillus murinus (ATCC 35020). The rats were gavaged five days a week with L. murinus in 1mL PBS. The control group was gavaged five days a week with 1 mL PBS.
The BP of the rats was measured using radiotelemtry, as described above. The gavage was continued for six weeks.
4.2.9 Statistical analysis
All statistical analysis was performed using student’s t-test on GraphPad Prism 5.02A p- value of <0.05 was considered to be significant.
4.3 Results
4.3.1 Blood Pressure is Reduced with Amoxicillin Administration in Young Rats
The rats given amoxicillin had significantly reduced systolic blood pressure (SBP), diastolic blood pressure (DBP), and mean arterial pressure (MAP), but did not have any changes in heart rate (HR) as seen in Figure 4-2, compared to controls. The change in
SBP was evident after three weeks of amoxicillin, however, the DBP and MAP were significantly different beginning after 2.5 weeks of amoxicillin. Interestingly, the 111
cessation of antibiotics did not cause a normalization of BP, but instead caused a more significant change in BP. Importantly, antibiotic administration did not alter the diurnal rhythms of the BP in these rats, as seen in Figure 4-3.
Figure 4-2: Blood pressure of adolescent rats on amoxicillin is decreased. Figure 4-2a shows the SBP, 4-2b shows the DBP, 4-2c shows the MAP, and 4-2d shows the HR. The SBP, DBP, and Map are all decreased with amoxicillin but the HR is unchanged. The antibiotics were removed after 24 days. n=12/group, * p<0.05, **p<0.01
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Figure 4-3: The diurnal rhythm of the rat SBP was not altered with amoxicillin. The control animals are shown in red, while the amoxicillin-treated animals are shown in blue. The amoxicillin group had significantly lower SBP at almost every time point but did not lose the diurnal rhythm.
4.3.2 Amoxicillin Increased Body Weight in Young Rats
Despite the fact that amoxicillin reduced BP in S rats, it caused an increased body weight
(BW) in the rats. As seen in Figure 4-4a, the BW began to trend as increased after two weeks on amoxicillin. This was then significant on day 18 (Figure 4-4b), but after stopping antibiotics, the BW normalized to the same level as the control animals. A 24- hour food intake study was performed on day 22. Interestingly, the amoxicillin-treated animals did not have a higher food intake, and although not significant, the control animals had a tendency for a higher food intake, despite weighing less (Figure 4-4c).
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Figure 4-4: Rats treated with amoxicillin had an increased body weight, but not an increase in food intake. The body weight of the rats treated with amoxicillin is shown in blue, while the control animals are in red. Figure 4-4a shows the difference in body weight from the control, while 4-4b shows the actual weight of the animals. Figure 4-4c shows the result of a 24 hour food intake study. N=12/group, *p<0.05
4.3.3 Amoxicillin Treatment Alters the Gut Microbiota in Young Rats
In the young, treated rats, fecal samples were collected at three different time points: one week after starting amoxicillin, the last day of amoxicillin treatment, and at sacrifice.
These time points are denoted 1, 2, and 3, respectively. At time point 1, or one week after starting amoxicillin, there were no changes in BP. However, as seen in Figure 4-5, there
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Figure 4-5: Unweighted beta-diversity is different with antibiotic treatment. Three time points were collected for comparison: after 7 days on amoxicillin (1), the last day of amoxicillin (2), and at sacrifice (3). The three amoxicillin- treated time points are labeled A1, A2, and A3, respectively, while the control groups are labeled C1, C2, and C3, respectively. N=6/group were already significant differences in the gut microbiotal community and beta-diversity.
At time point 2, the BP was significantly different and the differences in microbiota between the control and antibiotic treated groups remained. After stopping amoxicillin, the microbiota of the antibiotic group was no longer clustered with the other antibiotic time points but began to revert back to the control groups. Interestingly, despite these changes in beta-diversity, the BP remained significantly different at this time point.
In addition to the beta-diversity, the alpha-diversity is also different between the antibiotic treated groups and the control groups. As seen in Figure 4-6, the first two time points are almost identical in the antibiotic treatment groups, while the third time point shows the bacterial diversity lying between the control groups and the first two antibiotic
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group time points.
Figure 4-6: Alpha-diversity is different with antibiotic treatment. As discussed in figure 4-5, each group was measured at three different time points denoted 1, 2, and 3. These same time points were measured for the alpha-diversity and the results are shown in figure 4-6.
Figure 4-7: The phyla are significantly changed with amoxicillin treatment. Figure 4-7 shows the phyla changes from the second time point, or the last day of amoxicillin-treatment. There is a large reduction in Firmicutes, accompanied by a large increase in Bacteroidetes.
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The specific phyla that were changed with amoxicillin treatment can be seen in Figure 4-
7. The fecal samples of control and antibiotic rats at time point 2 (the last day of antibiotic treatment) are shown. As can be seen, there is a large reduction in Firmicutes with an accompanying increase in percentage of Bacteroidetes.
4.3.4 Amoxicillin Treatment during Pregnancy and Lactation Alters Maternal Gut
Microbiota
Pregnant and nursing rats were given amoxicillin treatment. Before examining the offspring, we first confirmed that the maternal microbiota was altered. As seen in figure
4-8, the alpha-diversity of the maternal gut microbiota was significantly reduced with amoxicillin treatment. This is consistent with what we noted in the previous study, whereby amoxicillin treatment reduced the alpha-diversity (Figure 4-6).
Dams-Control
Dams-Antibiotics
Figure 4-8: Maternal alpha-diversity is reduced with amoxicillin treatment. Consistent with what was found in the previous study, maternal alpha-diversity is reduced in the amoxicillin treatment group (red) compared to the control group (blue). N=3/group
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4.3.5 Amoxicillin Treatment during Gestation Reduces Blood Pressure in Male
Offspring, but not Females
In order to determine if altering the gut microbiota of rats during its original formation would cause changes in BP, amoxicillin was administered to pregnant S rats. The BP of the offspring was measured. Figure 4-9 shows the SBP, DBP, MAP, and HR of the male
Figure 4-9: Male offspring of amoxicillin-treated rats had reduced BP but females did not. Shown here are the SBP (Figure 4-9a), DBP (Figure 4-9b), MAP (Figure 4-9c) and HR (Figure 4-9d) of male and female offspring of rats who received amoxicillin. N=6-8/group and female control and treated rats. These rats were originally maintained on a low-salt diet after weaning (day 0). After 19 days, they were switched to a high-salt diet. As shown, there was no change in SBP, DBP, MAP, or HR prior to the high-salt diet.
However, after switching to a high-salt diet, there were significant changes in SBP, DBP, and MAP at day 29, and HR at day 23 in male rats only. In female rats, there was no
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significant change in SBP, DBP, MAP, or HR at any time point. Whether this change is related to the high-salt diet or to the aging of the rats is unknown. As shown in Figure 4-
10a, there was no change in the diurnal rhythm of the male or female rats. When analyzing the SBP in day/night cycles, there was a significant difference in both day and night of male rats (Figure 4-10b), but female rats did not have any significant changes in
SBP at this time, but there were trends for a reduced BP (Figure 4-10c,d). Therefore, amoxicillin treatment of the mother causes a sex-specific lowering of BP in offspring.
Figure 4-10: Male offspring of amoxicillin-treated rats have a reduced SBP, but female offspring do not. Figure 4-10a shows a 24 hour SBP recording for male offspring. Figure 4-10b shows the day and night average SBP of these animals. Figure 4-10c shows a 24 hour SBP recording of female offspring while Figure 4-10d shows the day and night average SBP of these animals. N=6-8/group, ***p<0.001
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4.3.6 Amoxicillin Treatment during Gestation Alters the Gut Microbiota of Male and Female Rats
To determine if amoxicillin treatment during gestation altered the gut microbiota of offspring, we analyzed fecal samples collected from male and female offspring. These samples were collected at two time points: prior to starting the high-salt diet and at sacrifice. Consistent with the previous findings, we found that amoxicillin treatment reduced the alpha-diversity in male rats on both the low-salt and high-salt diet (Figure 4-
11). Interestingly, female rats on a low-salt diet had no difference in alpha-
Figure 4-11: Male offspring of amoxicillin-treated rats have a reduced alpha- diversity. Figure 4-11 shows the alpha-diversity of male offspring on low salt (time 1) and high salt (time 2). N=6/group diversity compared to controls (Figure 4-12). When started on the high-salt diet, however, the alpha-diversity was lower in the antibiotic group than the control group.
This difference between males and females could account for the difference in BP effect noted. Moreover, when comparing the male and female phyla at the first time point (on a low-salt diet), we found that the male rats had a reduced Firmicutes/Bacteroidetes ratio
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compared to controls, while female rats did not (Figure 4-13). We also noted that the males had a reduction in Proteobacteria, while the same was not found in females (Figure
4-13). This indicates that the gut microbiota of male rats was more susceptible to the maternal amoxicillin than the female rats were. This increased susceptibility was correlated with a reduced F/B ratio, and attenuation in the development of hypertension.
When these rats were given a high-salt diet, both male and female rats exhibited an increased alpha-diversity (Figures 4-11, 4-12). Moreover, both male and female controls had an increase in Firmicutes and a reduction in Bacteroidetes in response to a high-salt diet (Figure 4-14), therefore, an increased F/B ratio. This indicates that a high-salt diet caused dysbiosis in untreated males and females. This dysbiosis was correlated with an increased BP in both male and female rats (Figure 4-9, day 20 compared to day 12). In both male and female offspring of amoxicillin treated rats, there was an even greater increase in Firmicutes and reduction in Bacteroidetes on a high-salt diet (Figure 4-14).
This suggests that the microbiotal response to salt was similar between the two groups, and that the salt may not be responsible for the sex-specific phenotype noted.
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Figure 4-12: Female offspring of amoxicillin-treated rats do not have a reduced alpha-diversity. Figure 4-12 shows the alpha-diversity of female offspring on a low-salt diet (time 1) and a high-salt diet (time 2). N=6/group
Figure 4-13: Male and female offspring of amoxicillin-treated rats have different microbiotal phyla compared to controls on a low-salt diet. Figure 4-13 shows the phyla of female offspring on a low-salt diet (top) and the phyla of male offspring on a low-salt diet (bottom). The blue color corresponds to Firmicutes while the green color corresponds to Bacteroidetes. N=6/group
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Figure 4-14: Male and female offspring of amoxicillin-treated rats have different microbiotal phyla compared to controls on a high-salt diet. Figure 4-14 shows the phyla of female offspring on a high-salt diet (top) and the phyla of male offspring on a high-salt diet (bottom). The blue color corresponds to Firmicutes while the green color corresponds to Bacteroidetes. N=6/group
4.3.7 Amoxicillin Treatment Decreases Inflammation in the Gut while Neomycin
Increases Inflammation
In order to determine if antibiotic treatment causes changes in gut inflammation, different inflammatory markers were measured in the gut. In young animals treated with amoxicillin, there was a reduction in Rorγ(t) (Figure 4-15), a marker for T helper 17
(Th17) cells. This reduction of Th17 cells is associated with a reduction in BP, an
123
association that has previously been reported. In a separate study, we administered neomycin to aged rats (Galla, Chakraborty et al. 2018). In that study, we found an
Ror(t) 4 *
3
2
1
0 *
Neomycin Amoxicillin Fold Change Relative to Control Relative Change Fold
Figure 4-15: Rats treated with neomycin had increased levels of Rorγ(t), while rats treated with amoxicillin had reduced levels of Rorγ(t). N=6 for neomycin and n=8 for amoxicillin. *p<0.05 increase in Rorγ(t) (Figure 4-15), which was associated with an increased BP. Therefore, neomycin administration causes changes in the gut microbiota that lead to increased levels of Th17 and increased BP, while administration of amoxicillin causes changes in the gut microbiota that lead to decreased levels of Th17 and reduced BP.
Additionally, when neomycin was given to hypertensive rats, there was an increase in the expression of the Nlrp3 inflammasome, as well as an increase in its function, as seen by the increased Il-1β and Il-18 in the colon (Figure 4-16a). However, in young rats treated with amoxicillin, there was a reduction in Nlrp3 inflammasome expression in the kidney, and a reduction of Il-1β in the colon (Figure 4-16b). Therefore, neomycin administration
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increased the pro-hypertensive activity of the Nlrp3 inflammasome, while amoxicillin treatment decreased its activity.
a) b) Nlrp3 inflammasome markers Amoxicillin-treated Nlrp3 3 * Nlrp3 * *p<0.05 1.0 n=6 *p<0.05 0.8 n=8 2 * 0.6
1 0.4 Nlrp3 Il-18 Il-1 * 0.2
0 0.0 Fold Change Relative to Control Relative Change Fold to Control Relative Change Fold
Figure 4-16: Neomycin treatment increases expression and activity of the Nlrp3 inflammasome, while amoxicillin decreases its expression. Figure 4-16a shows the expression of inflammatory markers in the colons of neomycin treated animals, while figure 4-16b shows inflammatory markers in the kidneys of amoxicillin treated animals. N=6-8/group, *p<0.05
4.3.8 Oral gavage with Lactobacillus murinus did not alter BP
There are reported links between Th17 cells and Lactobacillus. We found that in the young rats given amoxicillin, there was a reduction in Rorγ(t) levels, and a decrease in
Lactobacillus (Figure 4-17). In the older rats that were given neomycin, minocycline, and vancomycin, there was an increase in Rorγ(t) levels, and an increase in Lactobacillus
(Figure 4-17). Because we saw this consistent correlation, we gave S rats multiple oral gavages of Lactobacillus murinus to determine if this could increase their BP. We found, however, that there were no significant changes in SBP with L. murinus (Figure 4-18).
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Lactobacillus 15
10
5 Lactobacillus 0
-5 Change in Change -10 Amoxicillin Neomycin Minocycline Vancomycin
Figure 4-17: Neomycin, minocycline, and vancomycin increase Lactobacillus levels, while amoxicillin reduces it. Figure 4-17 shows the changes in the level of Lactobacillus in the gut microbiota compared to the control.
High Salt SBP 200 High Salt (Control) High Salt (Lactobacillus)
180 n=7/group
160
140 0 10 20 30 Hours Systolic blood pressureblood Systolic (mmHg)
Figure 4-18: Rats given Lactobacillus by daily gavage had no change in BP compared to controls. SBP is shown here over the course of 28 hours.
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4.4 Discussion
Recent studies have highlighted the role of the gut microbiota in blood pressure regulation. However, the vast majority of these studies have found the association between microbiota and BP in older, already hypertensive individuals. Few studies have examined the role of the developing microbiota in BP regulation. We previously found that administration of neomycin, minocycline, or vancomycin in hypertensive S rats caused an increase in BP. We aimed to study the role of the developing microbiota in BP regulation through antibiotic administration during adolescence or during gestation. For this study, we chose to use amoxicillin, as this is the most commonly prescribed pediatric antibiotic.
We found that in rats given amoxicillin immediately after weaning there was a decrease in BP. Interestingly, this decreased BP persisted even after the cessation of antibiotics. In order to determine the role of the microbiota in this change, we collected fecal samples at three time points and analyzed them for bacterial composition. The first time point was 7 days after amoxicillin was started. At this time point, there was no change in BP (Figure 4-2), but there were significant changes in bacterial diversity
(Figures 4-5 and 4-6). The second time point selected was the last day of amoxicillin treatment. At this time point, the BP was significantly lower (Figure 4-2), and there were still significant changes in bacterial diversity (Figures 4-5 and 4-6). Importantly, the bacterial communities in the amoxicillin treated rats at these two time points were almost identical. This shows that amoxicillin changed the bacteria prior to the change in BP, and that continued amoxicillin did not further change the bacteria much. After this time point, the amoxicillin was discontinued and the rats were given normal drinking water. Prior to 127
sacrifice, the fecal samples were again collected and analyzed. At this point, BP remained significantly lower (Figure 4-2), but the bacterial community began to shift back towards the control microbiota (Figures 4-5 and 4-6). This shows that even though the microbiota began to normalize, the BP change remained fixed. Therefore, temporarily altering the developing microbiota of children may have lasting effects on BP.
Interestingly, in the young animals given amoxicillin, there was a transient increase in body weight that was not accompanied by an increase in food intake. Current studies have shown that altering the microbiota through the use of antibiotics may inhibit thermogenesis and browning of white adipose tissue in mice (Li, Li et al. 2019). This could explain the increased body weight noted in the amoxicillin-treated animals and is something that should be examined in future studies.
Next, instead of altering the developing microbiota after weaning, we altered it during gestation and early life. In the offspring of rats given amoxicillin during pregnancy, we again found a decrease in BP. However, this reduced BP was only found after giving the rats a high-salt diet. Moreover, this BP effect was only seen in males. We first measured the BP of the offspring after weaning while maintained on a low-salt diet.
On day 19 these rats were switched to a high-salt diet. Prior to the switch the BP of males and females, control and treated, were all tightly clustered with no differences. After they were given a high-salt diet, the BP began to differentiate. Males whose mother was given amoxicillin during pregnancy had a lower BP compared to those with no treatment.
Amoxicillin treatment altered the alpha-diversity of maternal gut microbiota
(Figure 4-8). It caused a reduction in species numbers, which was consistent with the previous study. Interestingly, male offspring also exhibited this reduction in alpha- 128
diversity (Figure 4-11), but female offspring did not (Figure 4-12). The male offspring had a reduced F/B ratio compared to controls before starting on a high-salt diet, while the female offspring did not, but instead had a slightly increased F/B ratio (Figure 4-13). This indicates the males were more susceptible to gut microbiotal changes from the mothers, and this susceptibility helped to correct microbiotal dysbiosis and protect them from the development of hypertension.
In the first amoxicillin study, we found a persistent BP effect even after microbiotal changes began to reverse. In the second study, we found that the BP effects were only salt-sensitive. Combined, this led us to believe that changing the microbiota created permanent changes within the host that led to a salt-sensitive reduction in BP.
There are a few studies that have been done to determine the mechanism linking gut microbiota and hypertension, specifically salt-sensitive hypertension. Wilk et al. found that a high-salt diet increases Th17 cells and BP, and that Lactobacillus, a gut commensal, can reduce this effect (Wilck, Matus et al. 2017). The colon of these animals was analyzed for Th17 cells through RNA levels of cell specific markers, such as Rorγ(t).
We found that in amoxicillin treated rats there was a reduction in this marker (Figure 4-
15). In a previous study, we found that neomycin increased BP of hypertensive, aged S rats (Galla et al. 2018). In these rats, there was an increase in Rorγ(t) (Figure 4-15).
Once we identified Rorγ(t) levels were decreased in the colons of rats treated with amoxicillin and increased in rats treated with neomycin, we examined their microbiota content for differences in Lactobacillus. In the neomycin treated rats there was an increase in Lactobacillus, while in the amoxicillin treated rats there was a decrease in
Lactobacillus (Figure 4-17). This suggests that Lactobacillus levels are positively 129
correlated with BP. We next tested whether Lactobacillus was able to change BP when fed to S rats. However, there were no changes in BP with daily gavages of Lactobacillus murinus (Figure 4-18). Therefore, we conclude that while Lactobacillus is correlated with
BP changes, it is not able to change BP per se.
In addition to Th17 cells, there is also evidence of the Nlrp3 inflammasome activity being associated with an increase in salt-sensitive hypertension (Chakraborty,
Galla et al. 2018). We tested the levels of Nlrp3, as well as its by-products, Il-1β and Il-
18, in the colons of rats treated with neomycin. We found an increase of all three markers in these animals (Figure 4-16a). As seen in Figure 3-1a, there was an increase in the BP of these animals. Therefore, with neomycin, Nlrp3 inflammasome activity increases which contributes to the increased BP. With amoxicillin treatment, we found no change in Nlrp3 expression or activity in the colon. However, we found a reduction in Nlrp3 expression in the kidney (Figure 4-16b). As amoxicillin lowered BP (Figure 4-2), this evidence supports that Nlrp3 inflammasome activity may contribute to the BP effect noted.
In conclusion, we have found that altering the microbiota of rats during adolescence causes lasting effects on BP, even after the cessation of antibiotics. This lasting BP effect is due to a reduction in inflammation in both the kidney and the colon.
This is in direct contrast to a previous study in which we found that administration of neomycin to aged rats causes an increase in BP and an increase in inflammation in the colon. Moreover, we found that disrupting the microbiota during its development by administering antibiotics to pregnant mothers causes sex-specific BP effects. These
130
studies showed the ability of the microbiota to work through host mechanisms, such as inflammation, to cause lasting effects on host physiology.
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Chapter 5
Discussion
5.1 The holobiont
Termed by Lynn Margulis, the ‘holobiont’ is a term used to describe macro- species and micro-species living together in a symbiotic state (Margulis and Fester 1991).
It is readily applicable to human beings. In humans, there is the influence of the host on physiological processes, specifically the genes and environment. The food that is consumed, as well as lifestyle, interacts with the genome to exert various effects.
However, recently, there has been evidence of micro-species also having strong effects.
These micro-species include bacteria, viruses, and fungi that live on skin and throughout the body and have a crucial role in normal bodily functions. For example, the microbiota is essential for digestion of fiber (McBurney 1991, May, Mackie et al. 1994, Fava,
Lovegrove et al. 2006). While microbiota have a role in normal functions, they have also been implicated in numerous diseases, including cardiovascular and metabolic diseases
(Cani, Possemiers et al. 2009, Sekirov, Russell et al. 2010, Vijay-Kumar, Aitken et al.
2010, Pluznick, Protzko et al. 2013, Festi, Schiumerini et al. 2014, Pluznick 2014, Blaut
2015, Jose and Raj 2015, Lankelma, Nieuwdorp et al. 2015, Mell, Jala et al. 2015, Miele,
132
Giorgio et al. 2015, Qi, Aranda et al. 2015, Singh, Chassaing et al. 2015, Yang,
Santisteban et al. 2015, Boulange, Neves et al. 2016, Dao, Everard et al. 2016, Durgan,
Ganesh et al. 2016, Gerard 2016, Natarajan, Hori et al. 2016, Perry, Peng et al. 2016,
Tomasova, Dobrowolski et al. 2016, Adnan, Nelson et al. 2017, Galla, Chakraborty et al.
2017, Li, Zhao et al. 2017, Tang, Kitai et al. 2017, Chakraborty, Galla et al. 2018, Galla,
Chakraborty et al. 2018, Sircana, De Michieli et al. 2018, Waghulde, Cheng et al. 2018).
Cardiovascular diseases are the leading cause of death in the United States, and hypertension is an important risk factor for cardiovascular disease. Therefore, we aimed to further understand the role of the holobiont in blood pressure (BP) regulation. The studies described in the previous chapters have shown how both the genome and microbiome influence BP, and more importantly, how they influence each other in order to exert BP changes.
5.2 The role of the genome
As mentioned above, the holobiome is composed of the host genome, as well as the microbiome. We aimed to study both components of the holobiome and their contributions to BP regulation. First, we examined the role of the genome. The first project performed involved studying one known BP regulatory protein, the G protein coupled estrogen receptor, or Gper1. To study this protein, we created a global gene deletion using CRISPR/Cas9 technology. This deletion was created on the Dahl Salt-
Sensitive, or S, rat background, due to it being thoroughly studied in the context of salt- sensitive hypertension.
As seen in chapter 2, the deletion of Gper1 caused a reduction in BP. This BP lowering effect was only observed in a salt sensitive manner (Figure 2-8). The improved 133
BP was accompanied by improved vascular function. Interestingly, the deletion of a single gene, Gper1, not only created a change in BP, but it also changed the microbiome of the rats (Waghulde, Cheng et al. 2018). This finding suggests that the genome of an individual heavily influences the composition of the microbiome, and that even a single gene change can create changes.
To further study the role of the genome in both BP and microbiota regulation, we performed a study in both S rats, as well as Spontaneously Hypertensive Rats (SHR). We used the two divergent strains to represent the two populations of human hypertensive patients: the salt-sensitive and salt-resistant groups. We altered the microbiome by administering three different broad-spectrum antibiotics and measured the BP for any changes. We found that BP changed with antibiotic administration in a genome- dependent manner. In the S rat, all three antibiotics caused an increase in BP Figure 3-1a, but in the SHR, two antibiotics lowered BP, while one had no effect Figure 3-1d. We next examined the microbiotal composition of these two strains and found that in age matched control rats, the microbiome was significantly different between the two strains.
This again suggests the role of the genome in selecting for the microbiome.
There are numerous reports showing quantitatively trait loci (QTLs), or genomic regions that have an influence on BP (Deng and Rapp 1994, Cicila, Dukhanina et al.
1997, Deng, Dene et al. 1997, Deng and Rapp 1997, Garrett, Dene et al. 1998, Rapp,
Garrett et al. 1998, Cicila, Choi et al. 1999, Dukhanina, Sverdlov et al. 1999, Saad,
Garrett et al. 1999, Garrett, Saad et al. 2000, Rapp 2000, Joe, Letwin et al. 2005, Saad,
Toland et al. 2008, Pillai, Waghulde et al. 2013, Mell, Abdul-Majeed et al. 2015, Cheng,
Waghulde et al. 2016, Nie, Kumarasamy et al. 2016). These QTLS directly show the role 134
of the genome in BP regulation. However, there are far fewer studies that examine the role of the genome in regulating the microbiome. In these two studies, we were able to show how the genome has a direct influence on the microbiotal composition, and how these changes have a role in the regulation of BP.
5.3 The role of the microbiome
As mentioned above, the evidence that the genome alters BP is overwhelming.
We have also now shown that the genome selects for the microbiome and regulates it.
The next thing we aimed to study is how the microbiota, acting within the holobiont, regulate BP. In order to do this, we took a few different approaches. Primarily, we altered the microbiota through the use of antibiotics and monitored BP changes. There were three questions that we aimed to answer. First, will altering the microbiota of already hypertensive rats affect their BP? Second, will altering the microbiota of pre-hypertensive rats prevent the development of hypertension? Lastly, will altering the microbiota of the developing rat affect their BP?
5.3.1 Altering the microbiota of hypertensive rats
To answer the first question, we administered three different antibiotics from varied classes to both S rats, as well as SHR, as mentioned above. By analyzing the microbiota of both strains, we found that all three antibiotics altered the microbiotal composition Figure 3-3. Moreover, these alterations were consistent independent of the host genome. We did find, however, that the BP effects were dependent on the host genome. In order to dissect these discrepancies we analyzed the short chain fatty acids
(SCFAs), bacterial metabolites. We found that altering the microbiota only changed levels of two SCFAs in SHR, but did not change any in the S rats (Figure 3-5). 135
In order to understand how the microbiota are influencing BP without many changes in SCFAs, we next examined the immune status of the gut in these animals.
There are numerous reports that inflammation is directly correlated with BP. Specifically,
T-helper 17 (Th17) cells (Wilck, Matus et al. 2017) and the Nlrp3 inflammasome
(Chakraborty, Galla et al. 2018). We tested markers for both of these pathways in the colon and the kidneys of these rats. In SHR, we found no significant changes for any of these markers, but there was a trend for a decrease in Sgk-1 in the kidneys of rats treated with minocycline and vancomycin. However, in S rats, there was an increase in Sgk-1 in the colons of animals treated with minocycline. There was also an increase in Rorγ(t) of colons in animals treated with neomycin and a trend in those treated with vancomycin
(Figure 4-15). Both of these markers are indicative of Th17 cells differentiation. These results suggest that in animals with greater Th17 cells, there is an increase in BP, specifically the S rats. In the SHR, the inverse may be true. Minocycline and vancomycin caused a reduction in BP, which was accompanied with a trend for reduction in kidney inflammation. Neomycin had no change in inflammatory markers in the SHR, and also did not change BP in the SHR. In S rats treated with neomycin, there was an increase in the Nlrp3-inflammasome expression, as well as Il-18 and Il-1β, products of the inflammasome (Figure 4-16a). This increased activation is associated with an increase in
BP, which is consistent with literature (Chakraborty, Galla et al. 2018). These findings suggest that the microbiota act by altering pathways in the holobiont to alter BP.
The other study that we performed with the Gper1 gene deletion rats also helped to answer this question. In these rats, the microbiota was significantly altered compared to the wild-type rats. A cecal transplant was performed between the Gper1-/- and Gper1+/+ 136
rats in order to determine if the microbiota changes were responsible for the observed BP change. We found that this transplant was able to reverse the BP lowering effect of the gene deletion. Moreover, this change was associated with altered levels of SCFA, specifically acetate (Waghulde, Cheng et al. 2018). This suggested that acetate may be responsible for the BP changes noted. To test this hypothesis, arteries from these rats were tested in the presence of acetate, which showed differences between the wild-type and deletion arteries. We further tested this hypothesis by incubating endothelial cells isolated from Gper1-/- and Gper1+/+ rats with acetate and measuring protein and mRNA expression. We found that the acetate reversed some of the protein differences found in the original Gper1-/- and Gper1+/+ rats (Figure 2-9). This led us to conclude that the microbiota are responsible for the BP changes noted by altering levels of circulating acetate. Again, these findings suggest that the microbiota do not act alone, but rather by altering other pathways in the holobiont to alter BP.
5.3.2 Altering microbiota during adolescence
Next, to determine if altering microbiota of pre-hypertensive rats will affect the development of hypertension, we again disrupted the microbiota of S rats through the use of antibiotics. However, instead of allowing the rats to develop hypertension prior to treatment, we administered amoxicillin to rats immediately after weaning. We found that amoxicillin treatment attenuated the development of hypertension (Figure 4-2). We next analyzed the microbiota and found that amoxicillin significantly changed the microbiotal composition. After the change in BP was noted, we discontinued amoxicillin but found that the BP change continued to be significant. After discontinuing amoxicillin, the
137
microbiota began to revert back to the control microbiota. However, even after two and a half weeks the microbiota were not completely reverted back to control levels.
While the changes in microbiota were transient, the BP effects were long-lasting.
Therefore, we examined the inflammatory status of the colons in these animals, as we did in the previous study. We found that the animals treated with amoxicillin had a reduction in Rorγ(t), suggesting a reduction in Th17 cells, which was again associated with a reduction in BP (Figure 4-15). These animals also had a reduction in kidney Nlrp3 expression (Figure 4-16b). These findings show that transiently altering the microbiota can create changes in inflammatory status and BP that last after the microbiota once again normalize.
5.3.3 Altering the developing microbiota
In order to determine if altering the developing microbiota will cause changes in
BP, we again used antibiotics. However, instead of giving them to the rats themselves, they were administered to pregnant S rats. The antibiotic treatment was continued for the mothers until the time of weaning. Therefore, the offspring were exposed to antibiotics indirectly during gestation and while breast-feeding.
We found that amoxicillin treatment during gestation protected male rats from developing hypertension when exposed to salt (Figure 4-9). This finding was interesting for two reasons. First, the BP of female rats was no different, showing that this BP protection is occurring in a sex dependent manner. Second, the BP protection only occurred once the rats were exposed to a high-salt diet. While on a low-salt diet, the BP of treatment and control groups were no different (Figure 4-9). The microbiota were then analyzed prior to starting the high-salt diet and the last day of the high-salt diet. The male 138
offspring had a reduced alpha-diversity compared to controls on both the low-salt and high-salt diet (Figure 4-11), however, the female offspring had no change in alpha- diversity while on a low-salt diet, and a reduced alpha-diversity on a high-salt diet
(Figure 4-12). The reduced alpha-diversity in male rats on a low-salt diet was accompanied by a reduced F/B ratio (Figure 4-13), indicating a correction of gut dysbiosis. However, when exposed to a high-salt diet, the offspring of the amoxicillin- treated rats had an increased F/B ratio compared to controls (Figure 4-14). In female offspring, there was an increased F/B ratio in the low-salt time point (figure 4-13), and an even greater increase in the F/B ratio after switching to high-salt (Figure 4-14). This suggests that in both males and females, a high-salt diet creates dysbiosis, but it is not responsible for the sex-specific BP phenotype noted.
5.4 Conclusion
All of the data described in the previous chapters highlight the important and significant roles of the genome and microbiome, or holobiome, in BP regulation. We have shown that the genome selects for the microbiome, and that together they have the ability to both raise and lower BP. As hypertension is a critical risk factor for cardiovascular disease, the leading cause of death in the United States, this is a very important study. A person’s genome is fixed and unable to be altered after birth.
Therefore, genes are not ideal, potential therapeutic targets for hypertension. However, now we have shown the microbiome has a significant role in BP regulation. The microbiome is something that can be altered through the use of a simple antibiotic or probiotic. Therefore, the therapeutic potentials are much greater. However, as this study 139
has shown, the genome and natural microbiome of an individual has an influence over how they will respond to antibiotic treatment. Therefore, this study provides evidence for a new potential therapeutic, but also offers caution and highlights the importance of personalized medicine.
140
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