Pharmacogenetics of Drug Metabolizing Enzymes Involved in Cardiovascular Drug
Treatment
DISSERTATION
Presented in Partial Fulfillment of the Requirements for the Degree Doctor of Philosophy
in the Graduate School of The Ohio State University
By
Jonathan Christian Sanford
Graduate Program in Integrated Biomedical Science Program
The Ohio State University
2014
Dissertation Committee:
Wolfgang Sadee (Advisor)
Mitch Phelps
Amanda Toland
Daren Knoell
Copyright by
Jonathan Christian Sanford
2014
Abstract
Regulatory variants, less well characterized than coding polymorphisms, have the
potential to serve as clinically relevant biomarkers for determining drug efficacy or
toxicity. This study focuses on the identification of functional regulatory single
nucleotide polymorphisms (SNPs) in the candidate genes cytochrome P450 2C19
(CYP2C19), carboxylesterase 1A1 (CES1A1) and angiotensin-I converting enzyme
(ACE), which are involved in the metabolism of many cardiovascular drugs. Using
allelic mRNA ratios (an indicator of functional regulatory variants), classic molecular
genetics and next-generation sequencing technologies, I demonstrate that cis-acting
regulatory polymorphisms in these genes are present and may have utility as
pharmacogenetic biomarkers.
The drug metabolizing enzyme cytochrome P450 2C19 plays a major role in
activation of clopidogrel (Plavix). Ultra-rapid metabolizer phenotype status has been
associated with enhanced transcription caused by promoter allele CYP2C19*17. Here, I
characterize the effect of CYP2C19*17 on gene expression in human liver. CYP2C19*17
heterozygotes and homozygotes show an increase in total CYP2C19 mRNA expression of
1.8-fold (p=0.028) and 2.9-fold (p=0.006), respectively compared to homozygous
reference allele livers. Allelic mRNA ratios of ~1.8 fold (SD±0.6, p<0.005), also
significantly associate with CYP2C19*17. Potential novel regulatory variants were
identified in an individual of African descent whose allelic mRNA ratio (~2-fold) is not
ii
accounted for by CYP2C19*17. This indicates that additional regulatory SNPs may
affect CYP2C19 expression in non-Caucasian populations.
Carboxylesterase 1A1 metabolizes clopidogrel, ACE inhibitors and many other
compounds. Coding polymorphisms have been shown to affect carboxylesterase 1A1
function and decrease clopidogrel efficacy. The previously described CES1A1VAR, a
highly linked block of 11 SNPs in the 5′ region of carboxylesterase 1A1, shows a strong
association with allelic mRNA ratios (~1.35-fold). CES1A1VAR carriers also show a
decrease in total gene expression of 2.65-fold (P = 0.003), and a 1.33-fold decrease in
protein quantity (P= 0.008). Additional study of the carboxylesterase gene locus has
identified a 5 SNP linkage block in the 5′ UTR (termed CES1A1SVAR). Utility as a
biomarker for these variants will require further study.
Previously, our group had shown angiotensin converting enzyme-1 (a target of
ACE inhibitors) harbors enhancer SNPs prevalent in African populations with function in
heart tissue. Performed here, a survey of other tissues indicates that enhancer SNP
function is limited to the heart. Further, allelic mRNA analysis provides evidence of
regulatory variants in Caucasian liver tissue (~2-fold), putamen (~1.7-fold, Hispanic and
African descent) and adipose (RNA-sequencing, ~1.35-fold).
These targeted studies demonstrate the impact genetic variability can have on
gene expression. CES1A1VAR in particular has potential as a pharmacogenetic
biomarker, especially in predicting clopidogrel response. CYP2C19 and ACE studies also
indicate that these genes harbor functional variants active in certain tissues or present in
certain populations. These enzymes affect the metabolism of numerous drugs
iii
(cardiovascular or otherwise) making pharmacogenetic characterization crucial to
effective prediction of drug efficacy or toxicity.
iv
This work is dedicated to my mother, father, sister, brother-in law, extended family and
friends whose constant love and support allowed me to weather the exciting journey that
is graduate school. I dedicate this also to Stephanie, your love, confidence, and
enthusiasm was essential to my success.
v
Acknowledgments
Considerable thanks are given to my advisor Wolfgang Sadee who gave me the
wonderful opportunity to work with others in a cutting edge research area that can rapidly
affect human health. The time and energy he spent mentoring me has developed my
scientific acumen and have motivated me in areas outside the lab. I must also thank my
fellow Sadee lab members and collaborators. All of you acted as excellent sources of
information and discussion for my scientific inquiries. Lastly, I would thank my
committee members Dr. Mitch Phelps, Dr. Amanda Toland and Dr. Daren Knoell for
their participation and advisement in my scientific education.
vi
Vita
May 14th, 1987 ...............................................Born, Rochester NY
2005................................................................Brockport Central High School
2009................................................................B.S. Molecular Genetics, SUNY Fredonia
2009 to present ..............................................Graduate Research Assistant, Department of
Pharmacology, The Ohio State University
Publications
1.
Webb A, Papp AC, Sanford JC, Huang K, Parvin JD, Sadee W. Expression of
mRNA transcripts encoding membrane transporters detected with whole transcriptome
sequencing of human brain and liver. Pharmacogenetics and Genomics. 2013; 23(5):26978. PMID: 23492907
2.
Sanford JC, Guo Y, Sadee W, Wang D. Regulatory polymorphisms in CYP2C19
affecting hepatic expression. Drug Metabolism and Drug Interactions. 2013; 28(1):23-30.
PMID: 23412869
3.
Barrie ES, Smith RM, Sanford JC, Sadee W. mRNA transcript diversity creates
new opportunities for pharmacological intervention. Molecular Pharmacology. 2012;
81(5):620-30. PMID: 22319206
Fields of Study
Major Field: Integrated Biomedical Science Program
vii
Table of Contents
Abstract ............................................................................................................................... ii
Vita.................................................................................................................................... vii
Table of Contents ............................................................................................................. viii
List of Tables ...................................................................................................................... x
List of Figures .................................................................................................................... xi
Chapter 1: Fundamentals of Pharmacogenetics .................................................................. 1
1.1 Introduction ............................................................................................................... 1
1.2 A Brief History of Pharmacogenetics ....................................................................... 2
1.3 Basics of Modern Pharmacogenetics ...................................................................... 11
1.4 Pharmacogenetics of Cardiovascular Drug Metabolizing Enzymes ....................... 18
Chapter 2: Regulatory Polymorphisms in CYP2C19 ........................................................ 22
2.1 Introduction ............................................................................................................. 22
2.2 Materials and Methods ............................................................................................ 28
2.3 Results ..................................................................................................................... 32
2.4 Discussion ............................................................................................................... 44
Chapter 3: Regulation of gene expression at the CES1A1 gene locus .............................. 49
viii
3.1 Introduction ............................................................................................................. 49
3.2 Materials and Methods ............................................................................................ 53
3.3 Results ..................................................................................................................... 62
3.2 Discussion ............................................................................................................... 74
Chapter 4. Evidence of regulatory polymorphisms at the ACE gene locus ...................... 81
4.1 Introduction ............................................................................................................. 81
4.2 Materials and Methods ............................................................................................ 85
4.3 Results ..................................................................................................................... 87
4.4 Discussion ............................................................................................................... 92
Chapter 5. Conclusions and Final Thoughts ..................................................................... 96
References ....................................................................................................................... 103
Appendix A: Tables ........................................................................................................ 123
Appendix B: Figures ....................................................................................................... 135
Appendix C: Abbreviations ............................................................................................ 139
ix
List of Tables
Table 1. Allele frequency and metabolizer status of common CYP2C19 alleles. ........... 25
Table 2. CES1A1 5′ Region SNP #s.................................................................................. 56
Table 3. Primer Sequences and PCR conditions............................................................. 123
Table 4. CYP2C19 SNP genotypes and Hardy-Weinberg Equilibrium. ........................ 130
Table 5. CES1A1 SNPs genotypes and Hardy-Weinberg Equilibrium .......................... 131
Table 6. ACE. Marker SNP and Hardy-Weinberg Equilibrium. .................................... 132
Table 7. CES1A1-pGL3-Basic construct variations. ...................................................... 133
Table 8. Average ACE-fold AEI in RNA-sequencing.................................................... 134
x
List of Figures
Figure 1. Common types of single nucleotide polymorphisms. ....................................... 13
Figure 2. Allelic Expression Imbalance ............................................................................ 17
Figure 3. Cardiovascular Drug/Gene Network ................................................................. 19
Figure 4. Schematic of CYP2C19 gene locus and relevant SNPs. ................................... 23
Figure 5. Association between CYP2C19*17 genotype and total CYP2C19 expression.33
Figure 6. Allelic RNA expression imbalance of CYP2C19 at marker SNP rs17885098
(T>C) in nine individual livers. ........................................................................................ 35
Figure 7. GATA4 gene locus and allelic ratios at rs3729856. ........................................... 38
Figure 8. Allelic expression imbalance in the 3′ UTR of GATA4. ................................... 39
Figure 9. HNF4A gene locus and allelic expression imbalance in at rs6130615. ............. 41
Figure 10. Allelic expression imbalance in the gene AHR. .............................................. 42
Figure 11.Association between CYP2C19*17 and CYP2C19 enzyme activity by
CYP2C19 diplotypes. ........................................................................................................ 46
Figure 12. CES1A1 Gene Structure. ................................................................................. 54
Figure 13. Polymorphisms of CES1A1 and known variants. ........................................... 55
Figure 14. CES1A1 5′ RACE Indicates UTR lengths. ...................................................... 65
Figure 15. CES1A1VAR associates with decreased CES1 gene expression. .................... 66
Figure 16. Allelic expression at rs12149370 .................................................................... 69
xi
Figure 17. Correlation between CES1 protein and RNA, CES1 protein quantity
association with CES1A1VAR. .......................................................................................... 70
Figure 18. CES1A1VAR does not associate with metabolic ability. ............................... 72
Figure 19. Luciferase activity of CES1A1 constructs in HepG2 cells. ............................. 73
Figure 20. 3D Structures of CES1A1 5′ UTR Isoforms. ................................................... 78
Figure 21. Schematic of ACE gene locus and relevant SNPs. .......................................... 83
Figure 22. Allelic mRNA ratios in Prefrontal Cortex. ...................................................... 88
Figure 23. Allelic mRNA ratios in Prefrontal Cortex. ...................................................... 90
Figure 24. Allelic mRNA ratios in human liver. .............................................................. 91
Figure 25. Allelic mRNA ratios determined by RNA-Sequencing Data. ......................... 95
Figure 26. Linkage Disequilibrium of CYP2C19 alleles. ............................................... 135
Figure 27. Linkage of relevant polymorphisms in CES1A1. ......................................... 136
Figure 28. CES1 protein expression in human liver tissue. ............................................ 137
Figure 29. Relative Gene Expression of ACE in Prefrontal Cortex and Putamen. ........ 138
xii
Chapter 1: Fundamentals of Pharmacogenetics
1.1 Introduction
Pharmacogenetics –the use of genetic biomarkers to predict an individual’s
response to drug treatment—is a discipline of genetics that has grown alongside our
understanding of the human genome. The field developed at the intersection of four
areas, inborn errors of metabolism, inheritance, molecular genetics, and the
characterization of new pharmacological drug entities. Seminal studies in these areas
build on one another to create the framework for modern pharmacogenetics. Previously,
pharmacogenetics existed alongside treatment dogma (treating patients according to the
drug). However, understanding the role of genetic variation in drug response has shifted
this focus to the individual patient and their predicted response.
Cardiovascular drugs are a highly prescribed group of compounds with broad
function. These drugs are activated or metabolized by key drug metabolizing enzymes
(DMEs) including cytochrome p450 2C19 (CYP2C19), carboxylesterase-1 (CES1) and
angiotensin-1 converting enzyme (ACE), among others. As such, these genes are strong
candidates for pharmacogenetic study. Here, I will explore the development of modern
pharmacogenetics from a historical perspective and define the important components of a
pharmacogenetic study. This will provide context and background relevant to the chief
aim of this thesis, the identification and characterization of pharmacogenetic biomarkers
in DMEs involved in cardiovascular drug treatments.
1
1.2 A Brief History of Pharmacogenetics
Evolution and Heredity
Understanding evolution and heredity is critical when defining a population’s
ability to metabolize xenobiotics or drugs. Importantly, these mechanisms generate and
maintain the diversity that grants populations the robust metabolic phenotypes
pharmacogenetics seeks to exploit. Charles Darwin’s “On the Origin of the Species” was
the first well researched and highly popularized publication on the theory of evolution.
The public both derided it for its anti-theist message and praised it for its well
documented findings. The sum of his worldly travels, Darwin presented evidence for
significant biodiversity among plant and animal life (Darwin, 1859). He ultimately
concluded that this biodiversity was the product of an organism’s adaptations to its
surroundings. One well known example of this is Darwin’s finches, which were
identified during his visit to the Galapagos Islands. Darwin and others have documented
significant variation in beak shape, function, and even song structure (Lack, 1947;Podos
et. al., 2004). In subsequent years, we have learned that these finches represent a specific
type of evolution known as adaptive radiation (Schluter, 2000). Here, a single ancestral
species rapidly speciates to fill many niches (in this case feeding opportunities). In the
case of the Galapagos Islands, there were many available feeding niches that the finches
adapted to as evidenced by their varied beak shape and function. This example, along
with others, (see Darwin’s thoughts on domestic pigeons) made it clear that adaptation of
ancestor organisms drives speciation ultimately producing biodiversity. Darwin’s
proposed mechanism driving adaptation was gemmules, which function as hereditary
2
particles (Darwin, 1890). Though these are not what we know as genes, they are an early
hypothesis for the transmission of traits, a concept important to pharmacogenetics.
Diverse environmental compound exposure allows a population to adapt heterogeneous
drug metabolizing ability. This qualifies DME genes as strong candidates to contain
genetic biomarkers that predict enzyme activity and therefore drug efficacy or toxicity.
Shortly following Darwin’s work, the friar Gregor Mendel defined the laws of
segregation and dominance (Mendel et. al., 1901). Mendel developed these laws through
crossing various “races” of common pea plants. Though there were many traits, Mendel
focused on simple characteristics such as pea shape or color. Through careful planning
and artificial cross pollination between plants, Mendel was able to observe how traits of
the parent plants would manifest in the offspring. From this he developed the law of
dominance, stating that in hybrids (offspring) the dominant trait would be visible while
the recessive trait would not be. The second, the law of segregation, states that when first
generation hybrids are crossed amongst themselves they will produce offspring which
express all ancestral characteristics. These second generation hybrids will also adhere to
the law of dominance, though some offspring will exhibit no visible dominant traits
allowing recessive traits to be observed (Weldon, 1902). This work pioneered the concept
of discrete, dominant and recessive traits. These advances would define heritability,
which is essential to early pharmacogenetic studies.
Heritability of Inborn Errors of Metabolism
In the early 1900s, the physician and scientist Sir Archibald Garrod observed
variable pigment in the urine of individuals with alkaptonuria (Garrod, 1996). William
3
Bateson –the man who would coin the term genetics– informed Garrod that his
observations indicated that alkaptonuria followed basic Mendelian inheritance. Following
extensive study of alkaptonuria and other variations in metabolism, Garrod published
“Inborn Errors of Metabolism”. Here he stated, “It is to be regretted that so many
observers of these anomalies have paid little or no attention to their heredity… but when
scraps of information available are pieced together there is revealed an underlying
resemblance between them, which is specially evident in their sex-incidence, in their
tendency to occur in several members of one generation of a family, and in the rarity of
their direct transmission from parent to child. If the lack of a special enzyme be in each
instance the underlying factor, it is to be expected that they should behave as Mendelian
recessive characters” (Garrod, 1923). From these experiences Garrod developed the
hypothesis of “Chemical Individuality”, that is that individual chemical variation was
present in all individuals, often with little consequence. Garrod punctuated this
hypothesis throughout his career with exhaustive characterization of many inborn errors
in metabolism and their inheritance patterns. Garrod’s work would act as the basis for
early pharmacogenetic familial studies and solidify him as the father of biochemical
genetics (Harper, 2005).
The first pharmacogenetic study was far from what we imagine today. It started
when A.L Fox at Dupont spilled a sugar substitute he was developing
(phenylthiocarbamide, PTC) sending it into the air. Fox was not bothered by the airborne
powder, but a coworker remarked on the bitter flavor of the substitute. They soon found
that they had significant differences in their taste perception of PTC (Fox,
4
1932;Wooding, 2006). Another researcher, L.H. Snyder was interested in Fox’s
observation and collected 800 families to study the differences in PTC taste perception
and its inheritance. From this study he found that “taste blindness” was inherited in an
autosomal-recessive manner (similar to many inborn errors of metabolism as studied by
Garrod) and that taste blind individuals were of diverse ethnic origin (Snyder,
1931a;Snyder, 1931b). Considered the first large study of a genetic biomarker this would
become a model for future pharmacogenetic studies. Further, it was noted for its
importance in describing the racial heterogeneity of metabolic processes.
The structure of DNA
By the early 1950s it was clear deoxyribose nucleic acid (DNA) was the heritable
molecule through which genetic information was communicated within an organism
(Avery et. al., 1944;Hershey et. al., 1952). However, the obstacle to understanding the
finer points of genetic heritability was the unknown structure of DNA. By 1953, X-ray
crystallography suggested that nucleic acid molecules were helical, though it was unclear
in what way (Franklin et. al., 1953). It was hypothesized that nucleic acid structure must
be stable, replicable and capable of containing a massive amount of information. Early in
1953, Linus Pauling –who defined the protein α-helix structure – and Robert B. Corey
postulated that DNA was a tri-helical molecule with a phosphate tetrahedral at the center
axis and the nucleotides positioned on the outside of the helix (Pauling et. al., 1953).
However, as Watson and Crick would point out in their seminal paper, this conclusion
was likely the result of misinterpretation of x-ray crystallography results. Instead, their
proposed structure was a two chain helical structure, with complementary bases in the
5
center (Watson et. al., 1953). In large part their findings would not have been possible
without the research of others including but not limited to Erwin Chargaff, Rosalind
Franklin and Maurice Wilkins who provided critical information to discerning the
structure of DNA.
Understanding the structure of DNA is a crucial component of pharmacogenetics.
In order to define heritable traits that predict drug response, the organization of genetic
information needed to be understood. Prior to defining the structure of DNA,
chromosome mapping and genes had been identified. However, it wasn’t until the genetic
code had been cracked that pharmacogenetics would truly have the tools to identify
genetic biomarkers. Classical molecular genetics techniques and ultimately DNA
sequencing would drive new pharmacogenetic studies.
Genetics as a driver of drug metabolizing enzyme variability
During the years following, scientists and physicians continued to observe
heritable metabolic variation as described by Garrod. Arno Motulsky collected many of
these studies to examine “drug allergy” and the role of genetics in determining drug
response. The studies collected observed, an increased incidence of hemolytic events in
African Soldiers when compared to Caucasian soldiers receiving anti-malarial drugs
(primaquine), prolonged apnea in select individuals following exposure to
succinylcholine, and variable efficacy of isoniazid therapy (Motulsky, 1957). Though it
would be some time before these differences in drug response would be associated with
specific genetic biomarkers, it was clear that variation in DNA was not only relevant in
inborn errors of metabolism but was also a driving factor in drug response variability.
6
Friedrich Vogel would recognize this trend and coin the term pharmacogenetics by 1959
(Vogel, 1959).
Familial studies have been an important component of human molecular genetics
studies, when studying human disease or otherwise. One example of a familial
pharmacogenetic study was the characterization of phenylbutazone metabolism in
monozygotic and dizygotic twins (Vesell et. al., 1968). Here they observed that identical
twins (monozygotic) had similar half-lives for phenylbutazone while drug half-lives in
fraternal twins (dizygotic) varied to a much larger degree. This study provided more
evidence that interindividual variation in drug metabolizing ability was driven by genetic
factors.
Early applications of DNA sequencing to pharmacogenetics
A major contribution to the development of modern pharmacogenetics was the
invention of DNA sequencing technologies (Sanger et. al., 1975;Sanger et. al., 1977).
With evidence of heritable drug metabolizing ability, knowledge of DNA structure, and
DNA sequencing, pharmacogenetics shifts focus to candidate gene studies. Candidate
genes typically encode enzymes or proteins that are directly related to the metabolism or
action of a compound. Here, non-synonymous polymorphisms that affect an enzyme’s
protein sequence have been easily defined –especially when the mutation is deleterious.
The function of these variants can be characterized, defining them as pharmacogenetic
biomarkers which can then be used to guide drug treatments.
One example of developing technologies shaping pharmacogenetic research is in
the stories of debrisoquine and sparteine metabolism. Debrisoquine, an anti-hypertensive
7
drug was the subject of study in 1975 at St. Mary’s Hospital Medical School. To explore
adverse drug effects, Robert L. Smith and four other laboratory members received 32 mg
of the drug. Within a few hours, Smith experienced symptoms of orthostatic
hypertension that lasted for days following drug administration (Goldstein et. al.,
2003;Meyer, 2004). Notably, these symptoms were not experienced by the others who
had taken the drug. Examination of the urine of these initial participants indicated that
individuals with sensitive responses (such as Smith) did not metabolize debrisoquine to
4-hydoxydebrisoquine.
A follow-up study in a group of medical students revealed that
debrisoquine metabolism was bimorphically distributed to poor and extensive
metabolizers (Mahgoub et. al., 1977).
Simultaneously, two researchers were studying the pharmacokinetics of sparteine,
an antiarrhythmic drug. When Michel Eichelbaum and Hans Dengler administered the
drug to a study population two participants reported side effects including nausea, blurred
vision and headaches (Eichelbaum et. al., 1975). These symptoms were indicative of an
overdose and in these individuals the plasma levels of sparteine were 3-4 times higher
than normal (Meyer, 2004). Continued studies showed that sparteine metabolism was
also divided into two distinct phenotypic groups as seen in debrisoquine (Eichelbaum et.
al., 1979).
The observations from both sets of studies confirmed that an individual’s
metabolic ability for debrisoquine and sparteine was following Mendelian inheritance
patterns. It was also believed that the same genetic mutation was responsible for the
polymorphic drug responses. Soon after, the responsible cytochrome P450 enzyme was
8
isolated in human liver microsomes in the lab of F.P. Guengerich (Distlerath et. al.,
1985). Then referred to as P-450DB, this enzyme metabolized debrisoquine, sparteine,
and many other compounds. Following this work, the location of the gene was
determined to be on chromosome 22 (Eichelbaum et. al., 1987). This cytochrome P450
would be named CYP2D6 and was cloned and sequenced in 1988(Gonzalez et. al., 1988).
Upon sequencing, three variant mRNA isoforms were identified and attributed to the
common defective enzyme phenotypes observed for CYP2D6 metabolism. Additional
characterization of the gene locus occurred in following years uncovering over 100
CYP2D6 alleles, including hybrid genes, copy number variations and single nucleotide
polymorphisms (SNPs) (Marez et. al., 1997;Panserat et. al., 1995;Soyama et. al., 2004).
The story of CYP2D6 demonstrates how many disciplines have intersected to identify
pharmacogenetic biomarkers.
The Genomic Era and Pharmacogenetics
The completion of the Human Genome Project (HGP) was another critical
advance in the field of pharmacogenetics. The HGP aimed to map the entire human
genome and was completed in collaboration of 20 groups spanning the globe. Planned as
early as 1984, the project formally began in 1990 and was declared complete in 2003
(Lander et. al., 2001). The first released draft sequence (2001) was predicted to cover
94% of the genome. Upon completion, a predicted 30-40,000 protein coding genes with
complex gene splicing were identified. By 2001, an estimated 1.4 million single
nucleotide polymorphisms (SNPs) had already been identified. The sequence was
relatively high quality, and predicted to have 9 incorrect bases per 1 MB with a
9
significant error every 6 MB (Schmutz et. al., 2004). In the spirit of competition the
Human Genome Project was not the only genome released in 2001. A second genome
was published by Celera Cooperation. Using shotgun sequencing and public data already
obtained by the HGP a privately funded genome of 2.91 billion bases was assembled in a
period of 3 years (Venter et. al., 2001).
With the human genome mapped by two groups, it became clear the extreme
variation of an individual’s genome. An estimated 3-million SNPs exist in any one
genome with an average of 1 SNP per 1000 bases (Cargill et. al., 1999). Of these, an
estimated 1% of SNPs would affect protein function (Venter, 2001). As SNPs occurring
in >1% of the population have been identified, microarray technologies (using targeted
oligonucleotides) have been used to genotype thousands (and sometimes millions) of
SNPs. A major step forward following sequencing of the human genome was genome
wide association studies (GWAS). Here, a large number of SNPs are genotyped in a
clinical population and then statistically associated to a phenotypic endpoint.
Genome Wide Association Studies
One classic genome wide study published in 2008, focused on the drug
simvastatin and its relationship to statin-induced myopathy (Link et. al., 2008). Here,
using a HumanHap300 chip(318,237 SNPs), over 300k SNPs were genotyped and
associated to incidence of statin induced myopathy. Genome wide association indicated
SNP rs4363657 in the gene SLCO1B1 (encoding the transporter OATP1B1), was
associated with myopathy risk (P = 4x10-9). The identified SNP was in high linkage with
rs4149056 (R2= 0.97), a nonsynonymous coding SNP which has been associated with
10
statin metabolism (Kameyama et. al., 2005;Pasanen et. al., 2007;Pasanen et. al., 2006).
Presence of the rs4149056 minor allele increases an individual’s odds ratio of developing
statin induced myopathy to 4.5 and accounts for ~60% of all observed cases. This study
which utilized many post genome techniques is a culmination of advances that have
furthered pharmacogenetics. One of the best examples of genome wide association, this
study accurately identified a biomarker associated with an adverse drug reaction.
The historical advances outlined here are not an exhaustive overview of all the
projects that have contributed to the development of pharmacogenetic research. The
identification of the DNA molecule (prior to its structure), genetic codons for protein
synthesis, mapping of the first disease gene (Huntington’s), invention of polymerase
chain reaction (PCR), and other advances in DNA sequencing have been critical for
continued advancement of the pharmacogenetics.
1.3 Basics of Modern Pharmacogenetics
Drug metabolizing ability is the sum of the environment, age, gender, diet and
genetics of an individual. With the development of genetic and genomic technologies,
pharmacogenetic studies have now been applied to countless genes. As a result, the Food
and Drug Administration has drug label indications for over 150 genetic variants known
to affect drug metabolism or response. Many of these variants are coding polymorphisms
with dramatic effect on an enzymes function. Even common drug treatments such as
Warfarin have seen pharmacogenetic biomarkers in the genes VKORC1 and CYP2C9.
Types of single nucleotide polymorphisms
11
The Human Genome Project revealed the extent of individual variability in our
genomes. With the entire genome sequenced and polymorphisms mapped we are able to
better interrogate potential functional polymorphisms. Single nucleotide polymorphisms
come in three major varieties (Figure 1). Coding SNPs (cSNPs) are located in mature
mRNA and have the potential to change the protein sequence in addition to the mRNA
sequence. Regulatory SNPs (rSNPs) are found in the regulatory regions of a gene,
typically a promoter or enhancer. These variants often change gene transcription through
creation or ablation of transcription factor or enhancer binding sites. Here, the effects of
rSNPs can easily be measured through changes in gene expression. Finally, there are
structural SNPs (srSNPs). These variants are found sometimes in 5′ and 3′ UTRs, exons
(near splice points), or introns. They are typically present in the pre-processed
heteronuclear RNA, and will change mRNA processing, namely splicing, mRNA folding
and more.
Cytochrome P450 2C19 (CYP2C19) is a gene which contains all three types of
SNPs. Here, the common loss of function polymorphism, CYP2C19*2 (srSNP), has been
shown to create an aberrant splice site that creates a stop codon downstream resulting in a
non-functional protein (de Morais et. al., 1994b). The cSNP CYP2C19*10, has been
shown to decrease mephenytoin metabolism (Blaisdell et. al., 2002). Finally, promoter
rSNP, CYP2C19*17, which is also a subject of study in Chapter 2, has been shown to
increase gene transcription (Sim et. al., 2006). These SNPs represent the wide array of
SNP functions that may be present in a gene.
12
Figure 1. Common types of single nucleotide polymorphisms.
Indicates variant name, location in RNA and functional effects typically observed. Some
polymorphisms may fall into multiple categories.
13
Identification of regulatory variants
Many studies have searched for regulatory polymorphisms through expression
Quantitative Trait Loci (eQTL) studies. Here, gene expression from a microarray is
combined with SNP genotype information to identify polymorphisms associating with
increases or decreases in gene expression. In human liver, Schadt et al used gene
expression data and a SNP panel of over 300,000 variants to identify 6000 potential
eQTLs (Schadt et. al., 2008). The corresponding SNPs were characterized as cis- or
trans- depending on their distance to the gene locus itself (trans> 1MB). Similar to the
study on SLCO1B1, both eQTL and GWAS studies can indicate genes experiencing cisregulatory action. The associated SNP may or may not be the functional polymorphism
(they may be in linkage). However, regulatory elements can exist at a large distance from
a gene locus (enhancer region). These studies allow regulatory effects that are likely
present and frequent in the genome to be identified on a genome wide scale.
To identify SNPs with regulatory function, the focus of these studies, the Sadee
lab has traditionally measured allelic mRNA expression in combination with molecular
genetics studies (Wang et. al., 2005;Zhang et. al., 2007;Zhang et. al., 2005). This method
(termed SNaPshot), unlike an eQTL study, interrogates gene expression at an allelic level
allowing the action of a cis-acting polymorphism to be observed on an individual allele
basis (Figure 2). Here, coding SNPs with high frequency are selected as marker SNPs for
analysis. Polymerase chain reactions (PCR) are developed (of similar size) for both
gDNA and mRNA surrounding the selected marker SNP, and then amplified. An
additional amplification (SNaPshot) combines a primer extension primer which (5′>3′)
14
stops adjacent to the marker SNP, and a mix containing fluorescent dideoxy nucleotide
triphosphates (ddNTPs). A single fluorescent base is added for either allele at the marker
SNP and transcription is aborted. In individuals heterozygote for the marker SNP this
results in two populations of PCR product, one for each allele. The gDNA acts as a
control (often with a ratio of 1:1 between alleles), and deviation from the gDNA ratio in
the mRNA is indication that one of the alleles is experiencing the effect of a cis-acting
regulatory polymorphism. Notably, the effect of trans-acting polymorphisms –which by
definition would act upstream of gene expression— cannot be seen because they do not
discriminate on an allelic basis. Still, deviation in the mRNA ratio indicates there is a
functional SNP present and influencing gene expression. These polymorphisms may be
affecting splicing, transcription, mRNA folding and more. Often, candidate genes can be
selected that have seen study previously because regulatory SNP effects may not have
been assessed, particularly at an allelic level. Evidence of cis-acting regulatory
polymorphisms is followed by molecular genetic studies, and SNP screening to identify
and characterize the causative variant.
A natural continuation of the SNaPshot assay is seen in DNA/RNA sequencing.
When sequencing a samples RNA, the obtained “transcriptome” contains the allelic ratio
of heterozygote SNPs at all coding SNPs in all expressed genes. This allows rapid
identification of candidate genes experiencing the effects of regulatory SNPs on total
gene expression, allelic mRNA ratios, splicing and more. From here, detailed molecular
genetic and allelic studies can validate RNA-sequencing findings. Transcriptome
sequencing will drive future discoveries in the field of pharmacogenetics.
15
SNPs mainly exist in haplotypes, which is a group of SNPs that are inherited
together. SNPs existing within the same haplotype block are considered in terms of their
linkage disequilibrium with one-another. Here the terms D′ and R2 are used to measure
the likelihood that two SNPs will appear in the same individual, and on the same allele,
respectively. Haplotype blocks can exist over large regions with SNPs in varying degrees
of linkage. Using these measures; highly, moderately, and poorly linked SNPs are
identified. This is information is critical in pharmacogenetic studies. When a SNP is
associated to changes in gene expression, allelic expression imbalance, or a complex
phenotype (like statin induced myopathy), it may not be the causative SNP but rather
highly linked to the functional variant.
Currently there is some disagreement on what type of polymorphisms
should be focused on when studying disease and pharmacogenetic phenotypes. For
human disease, rare variants can be highly relevant, and they certainly can affect drug
response. Conversely, common variants typically confer a smaller effect and are not often
disease causing. However, pharmacogenetic variants often are only relevant given
exposure to a certain drug. Further, small or large effects of these variants may only be
significant given certain temporal and environmental conditions. This is unlike disease
causing variants where severe phenotypes are easily identified. Because coding
polymorphisms have been well-studied, it is likely that unexplained variability in drug
metabolism is driven by common regulatory variants. These variants will likely have a
wide array of functions (with small to large effect sizes) and potential utility as a
biomarkers for predicting drug metabolizing enzyme expression or activity.
16
Figure 2. Allelic Expression Imbalance
Paternal and Maternal genomic DNA is inherited in a 1:1 ratio. Allelic mRNA ratios
under action of a cis-acting regulatory polymorphism will deviate from this ratio. Allelic
ratios are visualized using the fluorescent output obtained from SNaPshot product run
using ABI 3730
17
This dissertation focuses on 3 candidate genes (CYP2C19, CES1, ACE) related to
one another through the cardiovascular drugs they metabolize (Figure 3). Here, I make
use of DNA-sequencing technologies, SNaPshot and other molecular genetics techniques
in search of cis- acting functional polymorphisms. Due to the frequent administration of
drug metabolized by my enzymes of interest, pharmacogenetic biomarkers in one or more
genes may be relevant in determining drug response.
1.4 Pharmacogenetics of Cardiovascular Drug Metabolizing Enzymes
The focus of this dissertation is the pharmacogenetics of cardiovascular drug
metabolism. Three enzymes which are directly related to the metabolism and action of
multiple cardiovascular drugs are interrogated for presence of cis-acting regulatory
biomarkers (Figure 3). The first, Cytochrome P450 2C19, is a drug metabolizing enzyme
responsible for the metabolism of a large number of clinically prescribed drugs. Its
primary site of action is the liver, and it is involved in the metabolism of proton pump
inhibitors, anti- epileptic drugs, and the anti-platelet prodrug clopidogrel. Clopidogrel, a
prodrug prescribed to over 25 million people, requires activation by CYP2C19 for drug
action. CYP2C19 competes with carboxylesterase 1A1, an enzyme which is the primary
inactivator of clopidogrel. Ultimately, 15% of the administered prodrug is activated, with
the remaining 85% inactivated. The 15% is in part determined by the activity of
CYP2C19. Previously described polymorphisms affecting the coding region and gene
splicing have been associated with poor drug activation and response. Regulator
18
19
Figure 3. Cardiovascular Drug/Gene Network
Cardiovascular drug treatments and their relationship to the candidate enzymes (genes) of interest. These genes are good
candidates for pharmacogenetic study with relevance in multiple treatment environments. Green arrows: Drug activating, red
arrows: Drug inactivating.
19
polymorphisms affecting CYP2C19 gene expression have already been identified.
However, the most common, CYP2C19*17 has been poorly characterized in human liver
tissue, and additional polymorphisms in non-Caucasian populations may be present.
The second gene included in this study is carboxylesterase 1a1. Though this
enzyme is involved in the inactivation of the prodrug clopidogrel, it is also involved in
the metabolism of cardiovascular drugs such as ACE inhibitors (enalapril, imidapril,
trandolapril, ramipril) and the anti-clotting drug dabigatran(Geshi et. al., 2005;Pare et. al.,
2013;Thomsen et. al., 2014). Non-cardiovascular compounds metabolized include
chemotherapeutics (irinotecan, capecitabine), anti-virals (oseltamivir), heroin, and
cocaine (Guichard et. al.;Pindel et. al., 1997;Sai et. al., 2010;Shi et. al., 2006). In the
context of clopidogrel, variability in the speed of inactivation can increase compound
bioavailability for activation by CYP2C19. Previous studies have shown that moderately
frequent polymorphisms which ablate enzyme function can significantly affect
clopidogrel efficacy (Lewis et. al., 2013). It is not currently clear to what extent cisregulatory polymorphisms affect CES1A1 gene expression and protein activity.
CES1A1, which is involved in the activation of some ACE inhibitors, implicates
angiotensin converting enzyme-I as a potential gene to harbor pharmacogenetic variants.
ACE is a ubiquitously expressed gene with a broad range of functions, particularly in the
heart. A well studied insertion/ deletion (I/D) polymorphism has been associated to many
disease phenotypes, though it’s effect as a regulatory polymorphism is unclear(AlRubeaan et. al., 2013;Das et. al., 2013;Dhangadamajhi et. al., 2010). Previous work in
our lab, led by Andrew Johnson, identified three enhancer polymorphisms specific to
20
African ancestry populations that carry a significant risk for non-fatal myocardial
infarction in hypertensive patients (Johnson et. al., 2009). These SNPs and their function
in alternative tissues is still unclear. There has also been limited screening for potential
regulatory SNPs in non-cardiovascular tissues and non-African populations. Addressed
here, functional SNPs in these tissues may relevant in the treatment or development of
Alzheimer’s, diabetes or other disease phenotypes.
Here, I further the field of pharmacogenetics through study of these inter-related
genes. I define CYP2C19*17, a promoter polymorphism, as having a 2-fold increase on
gene expression in the human liver. I also present functional rationale for a CES1A1
genetic variant which decreases gene expression and protein quantity. Finally, I indicate
that ACE likely harbors multiple functional polymorphisms with effects in multiple
tissues while the enhancer SNPs show heart specific function. This work adds to our
understanding of enzymes closely involved with cardiovascular drug treatments and
further defines variants that have potential as pharmacogenetic biomarkers. Moreover,
diverse functional genetic features of these genes indicate that consideration of SNP-SNP
interactions should be a feature of future studies where relevant drugs are prescribed.
21
Chapter 2: Regulatory Polymorphisms in CYP2C19
2.1 Introduction
CYP2C19 is a member of the cytochrome P450 enzyme family. Hepatic CYP
enzymes are main contributors to the metabolism of many endogenous and exogenous
compounds(Guengerich, 2008). The CYP2C19 isoform metabolizes 8%–10% of
prescribed drugs, including clopidogrel, omeprazole and citalopram (Karam et. al.,
1996;Kazui et. al., 2010;Ohlsson Rosenborg et. al., 2008). CYP2C19 consists of nine
coding exons spanning 90,209 bases with a coding region of 1473 bases (Figure 4).
Similar to other CYP genes, CYP2C19 carries genetic variants with strong effects on
substrate metabolism. Two frequent alleles lacking metabolic ability in vivo and in vitro
are CYP2C19*2 (rs4244285, cryptic splice site) and CYP2C19*3 (rs4986893, premature
stop codon), causing intermediate (heterozygous) or poor metabolizer phenotypes
(homozygous) (de Morais et. al., 1994a;de Morais, 1994b). Minor allele frequencies vary
by race, with CYP2C19*2 frequent in Caucasian and African populations, whereas *3 is
common in Asians and Africans (Table 1A).
The anti-platelet prodrug clopidogrel (Plavix) requires enzymatic activation
mediated mainly through CYP2C19 (Kazui, 2010). Clinical evidence indicates that *2
and *3 carriers fail to benefit fully from clopidogrel therapy, thereby increasing the risk
for reoccurring cardiac events such as myocardial infarction, stroke and stent
22
23
Figure 4. Schematic of CYP2C19 gene locus and relevant SNPs.
Gray boxes indicate coding exons.
23
thrombosis (Hwang et. al., 2011;Mega et. al., 2009;Mega et. al., 2010;Shuldiner et. al.,
2009). With an FDA-issued black box warning regarding clopidogrel treatment in
CYP2C19*2 or*3 carriers, these have emerged as clinical biomarkers to predict the
metabolizer status of CYP2C19 (Table 1B).
Not yet commonly represented in CYP2C19 allele biomarker panels, is the gain of
function allele CYP2C19*17 which has been reported to increase transcription, resulting
in ultra-rapid metabolism of CYP2C19 substrates (Sim, 2006). Clinical association
studies have shown CYP2C19*17 increases clopidogrel efficacy in reducing reoccurring
cardiac events, risk of stent thrombosis and residual platelet aggregation, while enhancing
the risk of a major bleeding event (Pare et. al., 2010;Sibbing et. al., 2010a;Sibbing et. al.,
2010b;Tiroch et. al., 2010). The Clinical Pharmacogenetics Implementation Consortium,
which intends to define how genetic biomarkers should be used clinically, has
classified CYP2C19*17 carriers as ultra-rapid metabolizers in the context of clopidogrel
treatment (Table 1B) (Scott et. al., 2013). However, this classification may not stand for
other CYP2C19 substrates. The effect of CYP2C19*17 has been measured for several
drugs, including omeprazole, amitriptyline, voriconazole, mephenytoin, pantoprazole and
escitalopram(Baldwin et. al., 2008;de Vos et. al., 2011;Dolton et. al., 2011;GawronskaSzklarz et. al., 2012;Goldstein et. al., 1994;Ohlsson Rosenborg, 2008;Sim, 2006;Wang et.
al., 2009). In many cases, CYP2C19*17 does increase enzyme activity, but when
comparing the effect on multiple drugs, CYP2C19*17 is not always a clinically relevant
contributor to drug metabolism. Alternate routes of clearance between drugs may be
responsible for some of these discrepancies, but this does draw into question the use of
24
Table 1. Allele frequency and metabolizer status of common CYP2C19 alleles.
(A) Minor allele frequencies of common CYP2C19 biomarkers and (B) associated
metabolic phenotypes defined by Clinical Pharmacogenetics Implementation Consortium
guidelines for individuals receiving clopidogrel (Scott, 2013)
A.
SNP
Minor allele frequency
Caucasian
African
Asian
CYP2C19*2
0.133
0.169
0.308
CYP2C19*3
0.00
0.021
0.058
CYP2C19*17
0.208
0.271
<0.02
B.
CYP2C19*1
CYP2C19*2
CYP2C19*1
CYP2C19*2
CYP2C19*3
CYP2C19*17
*1/*1(wt)
Extensive
metabolizer
*1/*2
Intermediate
metabolizer
*1/*3
Intermediate
metabolizer
*1/*17
Ultra-rapid
metabolizer
*2/*2
Poor
metabolizer
*2/*3
Poor
metabolizer
*3/*3
Poor
metabolizer
*2/*17
Intermediate
metabolizer
*3/*17
Intermediate
metabolizer
CYP2C19*3
CYP2C19*17
*17/*17
Ultra-rapid
metabolizer
25
the ultra-rapid metabolizer classification outside of clopidogrel treatment. Recent metaanalyses have questioned the role of CYP2C19*17 (and to some extent *2) as a
biomarker (Bauer et. al., 2011;Jang et. al., 2012;Li et. al., 2012), and others propose that
CYP2C19*17 homozygotes should be considered extensive metabolizers, rather than
ultra-rapid(Li-Wan-Po et. al., 2010).
Whereas the impact of *2 and *3 alleles on CYP2C19 function is well
documented, the regulation of gene expression by *17 and possibly other polymorphisms
is less well established. In human liver, CYP2C19*17 carrier status has not been explored
in the context of gene expression, which fails to support the ultra-rapid metabolizer
classification of CYP2C19*17 carriers. However, evidence supporting a gain-offunction for *17 includes electrophoretic mobility shift assays showing enhanced binding
of the CYP2C19*17 allele (-806T) to nuclear receptor cellular fractions and increased
luciferase reporter gene activity in mice (Sim, 2006). Another study in transfected cells
failed to show that CYP2C19*17 is active, instead implicating variants such as
rs4986894, part of a haplotype including two other promoter single-nucleotide
polymorphisms (SNPs), with increased expression. In the case of rs4986894, high linkage
disequilibrium (LD) with *2 (D′=1.00, R2=1.00, 1000 Genomes Project), denoted as
CYP2C19*2C or *21, renders this variant of little clinical consequence (FukushimaUesaka et. al., 2005;Satyanarayana Chakradhara Rao et. al., 2011).
Absent from these analyses is an accurate measure of the effect size of
CYP2C19*17 and its interindividual variability on CYP2C19 expression in human liver.
This is crucial information if *17 were to be used as a predictor of ultra-rapid metabolizer
26
status for a wide range of CYP2C19 substrates. A further confounding factor is the
estimated low linkage disequilibrium of *17 with *2 (D′=1.0, r2 =0.04, 1000 Genomes
Project)(Pedersen et. al., 2010). The high D′, low R2 and different allele frequencies
indicate that *17 resides mainly on the opposite allele compared to *2 (namely, on the
wild- type *1 allele). Therefore, the enhanced transcriptional activity of *17 is not
annulled by residing on the inactive *2 allele in compound heterozygotes, although the
latter case cannot be excluded in some subjects. An additional source of variation in gene
expression may be driven by variable gene expression for transcription factors that
initiate expression of CYP2C19 and other cytochrome p450 enzymes. Functional
polymorphisms within transcription factors may be particularly relevant in the case of
CYP2C19*17 where the functional polymorphism lies in the promoter region and is
predicted to create a binding site for the transcription factor GATA4 (Sim, 2006).
In this study, I determine whether CYP2C19*17 enhances CYP2C19 expression
in target tissues (liver) and to what extent. Further, I discern whether additional promoter
polymorphisms are contributing to variability in CYP2C19 expression and I reexamine
the relationships between *17 and *2 by measuring CYP2C19 total mRNA expression
and enzyme activity in human livers. In addition, I apply allelic mRNA expression
analysis, a more accurate and precise measure of the effect of cis-acting regulatory
variants on mRNA levels. When determined in livers heterozygous for a marker SNP in
the mRNA, a significant deviation of allelic mRNA ratios from the gDNA ratios indicates
the presence of allelic expression imbalance (AEI), a measure of cis-acting regulatory
polymorphisms (Lim et. al., 2007). Whereas the scarcity of frequent exonic CYP2C19
27
marker SNPs limits the scope of AEI analysis, I show that CYP2C19*17 accounts for 2fold enhanced allelic and 1.6-fold increased mRNA expression, with some degree of
interindividual variability. Allelic expression analysis in transcription factor GATA4 and
other select transcription factors showed no indication of strong functional
polymorphisms that may be acting upstream of CYP2C19 expression. Further, AEI not
accounted for by CYP2C19*17 indicates the presence of a candidate regulatory variant(s)
in a sample of African descent. These results indicate that CYP2C19*17 may compensate
for the presence of loss of function alleles such as *2 in compound heterozygous samples,
but this requires further confirmation in a larger number of subjects.
2.2 Materials and Methods
Tissue samples
A total of 125 biopsy or autopsy human liver samples were obtained from the
Cooperative Human Tissue Network (Midwest and Western Division) under approval of
the Ohio State Institutional Review Board. Fifty liver samples were obtained from other
sources that included the Medical College of Wisconsin (Milwaukee, WI, USA), Medical
College of Virginia (Richmond, VA, USA), Indiana University School of Medicine
(Indianapolis, IN, USA) or University of Pittsburgh (Pittsburgh, PA, USA). These livers
were under protocols approved by the appropriate committees for the conduct of human
research. Samples were primarily from Caucasians, with ~15% of African descent. Liver
microsomes were prepared by differential centrifugation using standard procedures (van
28
der Hoeven et. al., 1974) and characterized for protein content by the Lowry
method(Lowry et. al., 1951).
DNA and RNA isolation and genotyping
Genomic DNA and RNA were prepared from liver tissue samples as previously
described (Wang et. al., 2011). DNase I was used for RNA isolation to prevent
contamination of genomic DNA in cDNA synthesis. cDNA was prepared using RNA and
Reverse Transcriptase SSIII (Invitrogen, San Francisco, CA, USA), with controls lacking
reverse transcriptase to test for residual gDNA. SNPs in CYP2C19 were genotyped in
liver samples using a primer extension assay (SNaPshot, Life Technologies) or
fluorescently labeled PCR-restriction fragment length polymorphism (RFLP) analysis as
previously described(Pinsonneault et. al., 2004;Wang, 2005). PCR conditions and
primers for all PCR assays (including genotyping) are given in Appendix A.1. For quality
control, the Hardy-Weinberg equilibrium was assessed for each SNP, and allele
frequencies were compared to existing population genotype data (Appendix A, Table 4).
Measurements of gDNA and mRNA allelic ratios using a primer extension assay
(SNaPshot)
The protocol for SNaPshot has been previously described (Pinsonneault,
2004;Wang, 2011). It uses PCR amplification surrounding a marker SNP in an exonic
region, from a heterozygous sample. This is followed by the addition of fluorescent
ddNTPs at the marker SNP. Next, the SNaPshot product is read out as peaks on an
ABI3730 sequencer (Life Technologies) to provide the relative amounts of each allele.
The average of allelic ratios of gDNA is used to normalize allelic mRNA ratios in each
29
tissue. A finding of significant AEI in each tissue was established with a cutoff of three
standard deviations from the mean of the genomic DNA allele ratios calculated from all
tissues assayed. For the marker SNP in CYP2C19, normalized allelic mRNA ratios,
measured at rs17885098 (T > C) in exon 1, > 1.26-fold in either direction (> 1 or < 1)
were considered an indication of AEI. SNaPshot measurements for CYP2C19 were
completed in duplicate for gDNA and at minimum triplicate for mRNA. To achieve
sufficient accuracy, CYP2C19 mRNA expression needed to be robust (Ct of < 26
measured via qRT-PCR) for a liver to be considered for AEI analysis. Only nine livers
were heterozygous for SNP rs17885098 and passed quality control for AEI analysis. PCR
conditions and primers are available in Appendix A, Table 3. Additional allelic ratios
were surveyed in selected transcription factors. Here, ratios were measured by sample
with duplicates in mRNA and a single measurement in gDNA. Analytically significant
deviation from gDNA was considered 3-standard deviations.
SNP scanning and sequencing of CYP2C19
The CYP2C19 promoter region (4kb) and exons were sequenced to scan for
regulatory variants (for PCR conditions and primer sequences, see Appendix A.1,
(Blaisdell, 2002)). PCR products were quantitated by QUBIT Broad Range DNA
spectroscopy (Invitrogen). Fragments were mixed in equi-molar ratios to generate
libraries that were analyzed by an IonTorrent PGM (Life Technologies) and barcoded for
multiple samples per sequencing run. A total of four runs were performed on 15 samples.
Sequences were analyzed in CLC Bio’s Genomics Workbench (CLC Bio, Katrinebjerg,
Denmark). SNP calling required, at minimum, 20 reads at a given base with 30% reads
30
per allele. Select regions and samples were also sequenced using Sanger sequencing at
OSU’s sequencing facility.
Analysis of mRNA levels with quantitative real-time PCR
mRNA levels of CYP2C19 and transcription factors PXR, RXRα, HNF4α, CAR
and GATA4 were measured in human liver samples using quantitative real-time PCR
(qRT-PCR) (in duplicate) on a 7500 Fast Real Time PCR System (Applied Biosystems,
Carlsbad, CA, USA). GAPDH served as a housekeeping gene yielding expression
representative of overall RNA quality in human liver with low interindividual variability
(for primers, see Appendix A, Table 3)(Barber et. al., 2005). Standard curves were made
using serial dilutions of cDNA for each gene and used for mRNA quantitation.
Measurement of CYP2C19 activity in human liver microsomes
CYP2C19 enzyme activity was measured in 40 liver microsomes using Smephenytoin as the substrate as described previously (Baker JAR, 2010). The formation
of 4’-hydroxy mephenytoin (nmol/min/mg) at a single substrate concentration (≤ Km),
represented by metabolic velocity (V), was used to measure the metabolic activity of
CYP2C19. The data represent duplicate assays, transformed into log scale for analysis by
linear regression and independent t-test.
Data analysis
Linkage disequilibrium (LD) and Hardy-Weinberg equilibrium were analyzed
using Haploview, and Helix Tree SNP and Variation Suite (Golden Helix, Bozeman, MT,
USA), respectively (Appendix A, Table 4; Appendix B, Figure 26). Significant
differences in allelic mRNA ratios from mean allelic gDNA ratios (presence of AEI)
31
were confirmed by t-test. Linear regression was used to test the effects of SNPs and
transcription factor expression on CYP2C19 mRNA expression and protein activity using
SPSS (IBM, Armonk, NY) and Minitab (Minitab, State College, PA), respectively. PValues <0.05 were considered significant for all associations.
2.3 Results
CYP2C19*17 is associated with increased CYP2C19 total mRNA expression
Total mRNA expression for CYP2C19 was measured by qRT-PCR in 83 liver samples.
On average, CYP2C19*17 heterozygotes showed a 1.8-fold increase in mRNA
expression (p=0.028, 95% CI 1.08-fold to 3.00-fold, t-test) over the tissue of *17 noncarriers. CYP2C19*17 homozygotes showed 2.9-fold increased expression (p=0.006,
95% CI 1.5- fold to 5.8-fold, t-test, equal variances not assumed) with large variability
(Figure 5). Known regulators of CYP2C19 expression (transcription factors) including
the pregnane X receptor (PXR), constitutive androgen receptor (CAR) and GATA4
(Chen et. al., 2003;Mwinyi et. al., 2010) were measured by qRT-PCR to determine
whether they significantly contribute to CYP2C19 expression. Hepatic nuclear factor-α
(HNF4α) and retinoid x receptor-α (RXRα), additional factors that do not directly interact
with the CYP2C19 promoter but may affect expression of other CYP2C19 regulators
were also included. CYP2C19 expression was positively correlated with the expression of
GATA4 and negatively correlated with CAR and RXRα mRNA (p<0.05). After adjusting
for the expression of GATA4, CAR and RXRα, CYP2C19*17 carriers remain significantly
associated with increased expression of CYP2C19 mRNA (1.6-fold, p=0.014).
32
Figure 5. Association between CYP2C19*17 genotype and total CYP2C19 expression.
CYP2C19*17 heterozygotes and homozygotes were associated with 1.8-fold (p=0.028)
and 2.9-fold (p=0.006) increases in CYP2C19 mRNA, respectively. Data are presented as
a box plot and whisker plot (box shows median with the 25th and 75th percentiles), and
minimum and maximum values are shown by whiskers. Outliers that do not fall within
the bounds of the plot and that are greater than three box-heights are indicated with o and
*, respectively.
33
CYP2C19*17 is associated with increased allelic expression of CYP2C19 in human
liver tissues
CYP2C19 allelic RNA expression was measured to determine whether and to
what extent CYP2C19*17 increases gene transcription and whether any additional
functional polymorphisms are present. Allelic expression was measured using marker
SNP rs17885098, located in the first exon of CYP2C19 (minor allele frequency=0.083,
1000 Genomes Project). Lack of frequent marker SNPs in CYP2C19 exons limited the
number of livers accessible for allelic RNA expression analysis. Of 175 livers, nine
identified heterozygotes passed the quality controls outlined in Materials and Methods.
Of these nine samples, seven were Caucasian and two were of African ancestry (LL34,
L123). Five samples showed significant AEI using criteria described in the Material and
Methods (Figure 6, AEI >1.26-fold).
To test the association of AEI with CYP2C19*17, I genotyped CYP2C19*17 and
other known functional SNPs CYP2C19*2, CYP2C19*3 and promoter SNP rs4986894
(Satyanarayana Chakradhara Rao, 2011). Functional SNPs must be heterozygous in all
samples showing AEI and absent or homozygous in all samples not showing AEI. Four
samples lacking AEI were also lacking CYP2C19*17. Of the five with AEI, all were *17
heterozygotes except one (L123), which is one of two samples of African descent.
Significant allelic mRNA ratios (AEI-positive) within the CYP2C19*17 carriers ranged
from 1.4- to 2.8-fold, with an average of 1.8-fold (SD±0.68-fold) (Figure 3). These
results support the hypothesis that CYP2C19*17 is functional, increasing RNA
expression ~2-fold and also indicates additional regulatory polymorphisms may be
34
Figure 6. Allelic RNA expression imbalance of CYP2C19 at marker SNP rs17885098
(T>C) in nine individual livers.
The allelic RNA ratio is expressed as a ratio of C/T averaged from a minimum of three
measurements. Three standard deviations of the genomic DNA control (>1.26-fold AEI)
indicate a biologically significant allelic expression imbalance (indicated by arrows),
showing significant difference from the gDNA control at p<0.005. L123 is the only
sample showing AEI and not carrying *17. Data are mean±SD.
35
present in African Americans.
Sample L123 did not contain the functional SNPs for *2 and *3 haplotypes. To
identify any additional polymorphisms causing AEI in L123, CYP2C19 coding exons and
4 kb upstream of the first exon (promoter) were sequenced using the Ion Torrent PGM.
Four additional livers were also sequenced for comparison: L114 (*1/*2, no AEI), LL16
and LL19 (*1/*17, with AEI), and L90, an additional African liver (no AEI data).
CYP2C19*17 was the only allele uniquely associated with AEI in LL16 and LL19, which
is consistent with CYP2C19*17 causing increased gene expression. Comparing
sequencing results of L123 with genotypes of LL34 (an AEI − sample of African descent)
as control, I found a group of multiple SNPs as being potentially causative (rs11568730,
rs7919698, rs710258 and rs56043006); however, additional studies will be needed to
establish the function of these regulatory variants. These observations indicate that there
are additional polymorphisms that may affect the transcription of CYP2C19 in African
American populations.
Allelic expression in transcription factors interacting with CYP2C19 and other CYP
450s
To determine the affect transcription factors may have in trans- on the expression
of CYP2C19 or other drug metabolizing enzyme gene loci, allelic mRNA ratios were
measured. Allelic mRNA ratios were measured at GATA4, AHR and HNF4α. GATA4 has
been shown to associate with an eQTL (rs809204) and also induce expression of
CYP2C19 (Mwinyi, 2010;Schadt, 2008). Transcription factors, due to their upstream
36
(trans-) action on a gene locus will require a robust allelic expression imbalance to
significantly affect gene expression and ultimately drug metabolizing ability.
Allelic mRNA expression was measured at 3 SNPs in the coding region of
GATA4. Marker SNPs were selected based on their relative allele frequency and their
linkage to a GATA4 eQTL (rs809204). Marker SNPs selected for analysis are rs3729856,
rs904018 (eQTL LD, R2= 0.9414) and rs884662 (eQTL LD, R2= 0.400). Marker SNP
rs3729856 located in exon 5 exhibited low allelic mRNA ratios ranging from 0.9 to 1.09fold, with significant allelic expression considered > 1.38-fold (Figure 7). Though there is
slight variation in allelic mRNA ratios it is not analytically significant. Further it is not of
an adequate magnitude to have a significant effect on a downstream target such as
CYP2C19.
Previous studies have implicated rs809204 as a GATA4 eQTL (Schadt, 2008). As
described, rs884662 and rs904018 were selected due to their linkage with the eQTL
which may increase likelihood of identifying significant allelic expression imbalance.
Marker SNP rs884662 showed analytically significant allelic mRNA ratios with an
average 1.18-fold change in 8 of 18 samples (Figure 8A). When allelic mRNA levels
were measured at rs904018, 12 of 24 samples showed analytically significant allelic
expression mRNA ratios of averaging at ~1.23 fold (Figure 8B).
HNF4α was selected due to its role as a central regulator of drug metabolism
genes (Watt et. al., 2003). As previously described, allelic mRNA ratios were assessed in
HNF4α at marker SNP rs6130615 to determine if any cis-acting functional
polymorphisms were present (Figure 9A). Analytically significant allelic mRNA ratios
37
A
B
rs3729856
1.5
mRNA fold
1.38
1
1.38
L30
L74
L11
L26
L47
L40
L14
L37
L35
L31
L43
L85
L61
L57
1.5
Sample
Figure 7. GATA4 gene locus and allelic ratios at rs3729856.
(A) Exons indicated by grey boxes, UTRs indicated by hatched regions, and introns
indicated by white space. (B) The allelic RNA ratio is expressed as a ratio of
Major/Minor alleles surveyed from two measurements. Three standard deviations of the
genomic DNA control (>1.38-fold AEI) indicate significant allelic expression imbalance.
Data are mean±SD.
38
rs884662
A
mRNA fold
1.5
1.11
1
1.11
L43
L91
L02
L47
L24
L01
L27
L25
L78
L61
L81
L49
L05
L14
L53
L72
L76
L62
1.5
Sample
B
rs904018
mRNA Fold
1.5
1.14
1
1.14
L20
L43
L05
L51
L28
L64
L27
L47
L49
L01
L33
L24
L71
L11
L63
L02
L91
L68
L62
L25
L74
L66
L61
L50
1.5
Sample
Figure 8. Allelic expression imbalance in the 3′ UTR of GATA4.
(A & B) The allelic RNA ratio is expressed as a ratio of Major/Minor averaged from a
minimum of two measurements at marker SNPs in GATA4. Three standard deviations of
the genomic DNA control (>1.11 and 1.14-fold AEI, respectively) indicate significant
allelic expression. Data are mean±SD.
39
were considered greater than 3 standard deviations of the gDNA control (1.17-fold).
Though there was some minor variability in allelic mRNA ratios, no robust functional
regulatory polymorphisms are indicated (Figure 9B).
Allelic mRNA ratios were interrogated in aryl hydrocarbon receptor (AHR) due
to this transcription factor’s involvement in the initiation of gene expression for CYP1A1
and CYP1A2(Nebert et. al., 2004). Further, AHR contains polymorphisms implicated
with variation in caffeine metabolism (Sulem et. al., 2011). Here, a single exonic
polymorphism was selected for allelic mRNA analysis (rs2066853, Figure 10A).
Analytically significant AEI was observed as high as 1.34-fold (average 1.24-fold), in 5
of 15 samples screened indicating that there is likely a cis-acting regulatory
polymorphism acting at the gene loci (Figure 10B). Of the transcription factors (TFs)
studied here no large effects were observed, making it unlikely that a variant would
exhibit any effect in trans- on CYP2C19 or other DME gene expression.
CYP2C19*17 association with CYP2C19 enzyme activity
To confirm the function of CYP2C19*17, I tested whether *17 was associated
with enzyme activity. CYP2C19 enzyme activity (V) was measured in 40 human liver
microsomes using S-mephenytoin as substrate. To determine the gene dosage effect of
CYP2C19*17 on enzyme activity, CYP2C19 enzyme activity was compared between
diplotypes using a t-test (Figure 4). In *1/*2 carriers (intermediate metabolizers, n =9),
the mean activity was 2.3-fold lower (V=0.0091 nmol/min/mg, SD±0.0086) than the
average of wild-type samples (V=0.021 nmol/ min/mg, SD±0.022,*1/*1, extensive
metabolizers, n=11) (p=0.07, Figure 11). Although not significant at p=0.05, this
40
Allelic mRNA Ratio
(Major/Minor)
HNF4A rs6130615
1.17
1
1.17
L90 L53 L25 L65 L45 L51 L26 L63 L12
Sample
Figure 9. HNF4A gene locus and allelic expression imbalance in at rs6130615.
(A)Exons indicated by black boxes, UTRs indicated by grey boxes with introns indicated
by white space. (B) The allelic RNA ratio is expressed as a ratio of C/T averaged from a
minimum of three measurements. Three standard deviations of the genomic DNA control
(>1.17-fold) indicate a biologically significant allelic expression imbalance. Data are
mean±SD.
41
A
B
rs2066853
mRNA Fold
1.5
1.11
1
1.11
1.5
L76 L68 L11 L3 L54 L37 L18 L26 L8 L31 L56 L45 L34 L75 L70
Sample
Figure 10. Allelic expression imbalance in the gene AHR.
(A) Gene schematic of the AHR gene locus. UTR and coding regions indicated according
to schematic legend. Marker SNP rs2066853 indicated in exon 10. (B) Allelic mRNA
ratios measured in human liver. Analytically significant measurements were considered
> 1.11-fold. Data are mean±SD.
relationship is consistent with current expectations of genotype/phenotype associations.
The average activity for *2/*17 (V=0.025 nmol/min/mg, SD±0.018, n=6) carriers did not
42
significantly differ from wild-type samples, with a 2.7-fold increase in the average
activity over *1/*2 carriers (V=0.0091 nmol/min/mg, SD±0.0086, n=9) (p=0.075, Figure
11). Comparing the average enzyme activity between CYP2C19*17 carriers (both
heterozygote and homozygote) and non-carriers showed no significant difference.
However, this comparison does indicate a borderline (p=0.06) 2.3-fold increase in
carriers over non-carriers of CYP2C19*17. This result suggests that *17, which increases
allelic expression 2-fold may compensate for the CYP2C19*2 nonfunctional allele in
individuals heterozygous for both *17 and *2. This implies that *2 and *17 are likely to
be on different alleles, consistent with an inverse LD between *17 and *2 (Appendix
C.1). Comparisons between CYP2C19*1/*1 (wild-type) and CYP2C19*17 heterozygotes
or homozygotes show these groups are not significantly different (p=0.47 and p=0.512,
respectively). However, the small sample size limits generalizations that could be drawn
from this analysis.
One liver (LL44, *2/*17) displayed extremely high enzyme activity, exhibiting
a velocity of 0.41 nmol/min/ mg, more than 12-fold higher than the average level in
our cohort. I hypothesized that LL44 would contain a rare variant responsible for the
high activity. To test this, I sequenced the promoter (4 kb) and coding regions of sample
LL44 and nine control samples using Ion Torrent PGM. Sequencing confirmed the
presence of both *2 and *17 alleles in LL44. Sample LL44 showed 14 polymorphisms in
the regions sequenced. Compared to nine control samples, no SNPs were unique to LL44
that could explain high enzyme activity, which indicates non-genetic factors or SNPs
located in proximal regulatory regions may be responsible. A larger cohort of well43
phenotyped liver tissues will be needed to investigate variants associated with extremely
high CYP2C19 enzyme activity.
2.4 Discussion
The results of this study show that CYP2C19*17 is associated with a 2-fold
increase in mRNA expression in human liver. Sequencing and SNP scanning further
support the conclusion that CYP2C19*17 is a functional SNP causing enhanced
CYP2C19 transcription. However, the presence of an additional regulatory variant was
indicated by an allelic mRNA expression imbalance in a liver from a sample of African
descent. Further regulatory variants may exist, as indicated by exceptionally high
CYP2C19 metabolic activity not accounted for by *17 alone in a Caucasian liver.
Although the regulation of CYP enzyme expression is complex, use of allelic mRNA
expression analysis in human liver tissues proved a valuable tool for assessing the effect
size of CYP2C19*17. It also prompted the continued search for additional regulatory
variants at the CYP2C19 gene locus. Because of the absence of frequent marker SNPs for
AEI analysis, large collections of liver tissues will be needed to complete this analysis.
Individual CYP mRNA expression and drug metabolizing ability are highly variable
(Furukawa et. al., 2004). Our initial analysis of the association between *17 and total
CYP2C19 mRNA revealed a significant 2-fold increase for *17 heterozygotes and a 3fold increase for *17 homozygotes. To account for confounding effects of a trans-acting
source in CYP2C19 expression, I also measured the expression of five transcription
factors. A positive correlation of CYP2C19 mRNA expression with GATA4 mRNA and
44
negative correlations with CAR and RXRα confirm trans-regulation by diverse
processes. After controlling for the expression of these transcription factors,
CYP2C19*17 carriers remained significantly associated with a 1.6-fold increase in
CYP2C19 mRNA expression, which indicates that CYP2C19*17 increases mRNA
transcription independent of these transcription factors. CYP2C19 allelic RNA
expression and enzyme activity results further support CYP2C19*17 as a gain-of-function
allele. It is possible that there is an interaction between the expression of transcription
factors and *17, accounting for the lower estimated effect size when transcription factor
expression is taken into account, but the sample size was too small to test this hypothesis
further.
CYP2C19*17 effect size when considering the variability in CYP2C19 allelic
mRNA levels, may be the result of variation upstream in transcription factors. Screening
GATA4, the most likely candidate for interacting with the CYP2C19*17 loci, for allelic
expression imbalance indicated low to no significant variation. In fact, the other survey
TFs (AHR, and HNF4α), showed similar results with either low or no biologically
relevant variation detected. Trans- effects exerted by transcription factors would require
a substantial change in gene expression, allelic or otherwise, for marked differences in
target gene expression. Lack of robust AEI may be due to the fact that TFs are tightly
regulated due to their role in the expression of many genes. It is likely that TFs are
subject to some regulatory polymorphisms, but their study is outside of the scope of this
work.
The finding that CYP2C19*17 results in a 2-fold increase in CYP2C19 expression
45
Figure 11. Association between CYP2C19*17 and CYP2C19 enzyme activity by
CYP2C19 diplotypes.
CYP2C19 enzyme activity was measured by conversion of S-mephenytoin to 4hydroxymephenytoin in human liver microsomes. Data are presented as a box plot and
whisker plot (box shows median with the 25th and 75th percentiles), and minimum and
maximum values are shown by whiskers.
46
should be considered in the context of clinical CYP2C19 biomarker tests. Currently, the
primary utility of CYP2C19*17 is in the context of monitoring treatment and managing
risk (clopidogrel treatment). Any variation of CYP2C19*17 effect size between
individuals poses a challenge if *17 is to be used as a biomarker. Shown here, the 2-fold
variability in expression (in human liver) with allelic mRNA ratios of 1.4- to 2.8-fold is
within a sufficiently narrow range for predicting metabolizer status. To fully define the
effect size of *17, more samples are needed to quantitate differences in expression.
Recently, improved metabolic phenotyping supports the hypothesis that *2/*17
compound-heterozygote metabolizer status is equivalent to an intermediate-metabolizer
phenotype, suggesting that CYP2C19*17 may not completely compensate for a loss of
function allele (Scott, 2013). For certain CYP2C19 substrates, where ultra-rapid
metabolizer status prediction by CYP2C19*17 is questionable, alternate
phenotype/genotype tables may be required. Further, this study shows that CYP2C19*17
does not explain all instances of AEI or increased enzyme activity, requiring a continued
search for regulatory variants affecting CYP2C19. These additional marker SNPs will be
needed to assure adequate prediction of the CYP2C19 metabolic phenotype in any single
individual.
In conclusion, I have shown that CYP2C19*17 increases CYP2C19 expression
approximately 2-fold in human liver. The defined effect size measured here supports the
notion that CYP2C19*17 can serve as a biomarker, refining treatment decisions based
on genotype, and solidifies clinical use in the context of risk management for bleeding
events during clopidogrel treatment regimens. Further, in two samples with evidence of
47
enhanced transcription or increased enzyme activity, respectively, the CYP2C19*17
allele cannot account for this finding or CYP2C19*17 alone is insufficient. Therefore,
additional regulatory polymorphisms may alter a patient’s response to drug therapy and
their identification requires further study.
48
Chapter 3: Regulation of gene expression at the CES1A1 gene locus
3.1 Introduction
Carboxylesterase 1A1 (CES1A1) is a member of the CES1 family and is active
primarily in the liver. CES1A1 plays a significant role in the metabolism of drugs, esters
and amides (fatty acyls, cholesterol esters). The drugs metabolized by CES1 include the
anti-platelet prodrug clopidogrel(Lins et. al., 1999), anti-virals (oseltamivir)(Shi,
2006;Suzaki et. al., 2013), ADHD medications (methylphenidate)(Sun et. al., 2004),
chemotherapeutic agents (irinotecan) (Humerickhouse et. al., 2000), ACE inhibitors
(imidapril, enalapril, trandolapril, ramipril)(Geshi, 2005;Song et. al., 2002;Thomsen,
2014) and others. Therefore, CES1 has a significant role in the metabolism and response
to these drugs. More specifically, CES1A1 is of interest because of its high expression
relative to the pseudogene CES1P1, which expresses at less than 2% of total CES1 gene
expression (Fukami et. al., 2008). Spanning 30,312 bases, CES1A1 has not typically been
considered in a pharmacogenetic context. However, recent studies have shown that
polymorphisms within CES1A1 can have significant effect on enzyme function and drug
efficacy (clopidogrel) (Lewis, 2013;Zhu et. al., 2008). Due to CES1A1’s major role in
compound metabolism in the human liver, it is imperative to understand the genetics
behind gene expression and enzyme function.
The role of CES1A1 in treatment response is drug dependent. In the case of
clopidogrel – a prodrug which requires activation by CYP2C19 for therapeutic effect –
49
CES1A1 inactivates the drug, thereby lowering prodrug bioavailability and decreasing
drug efficacy. I aim to determine if CES1A1 exhibits evidence of regulatory variation
that affects gene expression or enzyme activity. These variants could have potential
utility as pharmacogenetic biomarkers in predicting CES1A1 substrate response.
Previous studies have characterized the structure of the CES1 gene family locus
(Fukami, 2008). The main features are two genes which are defined as CES1A1 and
CES1P1 (Figure 12). The CES1P1 gene locus is considered a polymorphic pseudogene.
It can be either CES1A2, a low expressing functional isoform, or CES1A3 a truncated
isoform. CES1P1 is important due to high homology with CES1A1, differing only at
bases in the 5′ UTR, exon 1 and intron 1 regions between the two (Figure 13, Table 2).
The CES1A1 gene locus is also polymorphic with at least two alternative isoforms
identified (Tanimoto et. al., 2007). The major isoform at this locus is CES1A1VAR.
Here, the CES1A1 5′ UTR, exon 1 and intron 1 sequence matches the sequence of
CES1P1 (Figure 12, Figure 13, Table 2). This variation (CES1A1VAR) is marked by 11
highly linked SNPs which cause CES1A1VAR mRNA to be identical to CES1P1 full
length mRNA (the CES1A2 isoform). A major difference in this case is that CES1A1VAR
(which matches CES1P1) is being driven by the CES1A1 promoter region instead of the
pseudogene promoter. A previous study indicates a similar variant besides CES1A1VAR
where CES1P1 sequence is present at exon 1 of CES1A1, though this is not observed in
this study (Tanimoto, 2007). Previous studies have suggested that CES1A1VAR affects
gene expression (Fukami, 2008). However, it not well characterized and has been applied
50
to pharmacogenetic studies in only a handful of substrates (irinotecan, isoniazid) (Sai,
2010;Yamada et. al., 2010).
Multiple association studies implicate CES1 polymorphisms in human disease and
drug response. Previous work has utilized genome wide association studies to identify
potential variants and searched for association between known variants and complex
phenotypes. A CES1P1 SNP (rs3785161) has been shown to have increased promoter
activity as well as increased clopidogrel and imidapril response (Geshi, 2005;Xie et. al.,
2014;Zou et. al., 2014). However, haplotype analysis has shown this variant is in high
linkage with the non-functional CES1A3 isoform (of the CES1P1 locus) suggesting an
associated functional variant may be responsible (Sai, 2010).
Additionally, CES1A1
SNP rs3815583 shows association with appetite reduction during methylphenidate
treatment (Bruxel et. al., 2013). Further, CES1A1 SNP rs2244613 shows association with
increased efficacy of the anticoagulation drug dabigatran (Pare, 2013) as well as the
incidence of sadness during methylphenidate treatment(Johnson et. al., 2013).
Molecular genetic studies testing variants within CES1A1 for function or
association with changes in gene and protein expression are lacking. A previous study on
CPT-11 toxicity seems to indicate that CES1A2 gene expression (as a function of
CES1A1VAR and CES1P1) is relevant to overall toxicity, but this association is unclear
(Tanimoto, 2007). In fact, other studies testing association of major CES1 variants (such
as CES1A1VAR), have not found significant associations (irinotecan, isoniazid) to drug
response or toxicity (Sai, 2010;Yamada, 2010). This may be due to the numerous other
enzymes involved in their metabolism, and in some cases lack of analytical stringency.
51
Previous work by a collaborator identified a coding polymorphism within
CES1A1 that significantly affects enzyme activity (Zhu, 2008). This is achieved through
an amino acid change in the catalytic binding site of CES1A1. The polymorphism
(rs71647871, G143E) was identified through sequencing and shown to ablate CES1
metabolic activity for the majority of substrates (Zhu et. al., 2012). Use as a biomarker
was assessed in a clinical cohort of individuals receiving clopidogrel anti-platelet therapy
(Lewis, 2013). In the case of clopidogrel, CES1 is the primary inactivator of all forms of
the drug (including the active metabolite). Typically, 85% of the drug is inactivated
(Lins, 1999). However, decreased CES1A1 activity increased clopidogrel efficacy. This
indicates that CES1A1 functional SNPs can have a significant affect on clopidogrel
treatment.
Regulatory polymorphisms can be a significant source of variation in the
expression, splicing, or translation efficiency of a gene. If regulatory variants are
affecting CES1A1 gene expression, they may have an effect on protein levels and
therefore enzyme activity. In the case of clopidogrel, variants affecting enzyme activity
in this way may have utility as clinical biomarkers similar to the G143E coding
polymorphism. Even a moderate effect on gene expression could have a significant
effect on active metabolite levels, due to the compounding of CES1A1 enzyme activity
on all metabolites (prodrug, intermediate and active metabolites). Further, the
associations of variants in the CES1A1 gene locus to drug response indicate that CES1A1
is a strong candidate gene for harboring functional regulatory polymorphisms.
52
CES1A1 has been screen for regulatory SNPs using a comprehensive approach
developed by our lab. Here we show that the variant CES1A1VAR, associates with
decreased overall CES1 mRNA expression, the presence of allelic expression imbalance
(1.3-1.5 fold), and decreased protein quantity in human liver. Traditional Sanger
sequencing further define the CES1A1VAR into a “short” and “long” subtype, where the
“long” is the traditional CES1A1VAR allele while the “short” does not contain the exon 1
differences (CES1A1SVAR, Figure 13, Table 2). Screening for additional polymorphisms
in the promoter of CES1A1 did not identify SNPs that better associated with changes in
gene expression. No significant association of CES1A1VAR to metabolic capability was
seen for enalapril or clopidogrel. Luciferase assay demonstrates CES1A1VAR is
associated with a decrease in protein activity in HepG2 cells. Further, mRNA structure
analysis of the various 5′ regions of CES1A1 in the luciferase construct indicate that the
WT construct has significantly lower free energy with a more defined 3D folding
structure when compared to variant alleles. This evidence indicates CES1A1VAR has
function as a biomarker, though it may only be relevant during clopidogrel therapy where
CES1 is a primary inactivator at all stages of metabolism.
3.2 Materials and Methods
Tissue samples
A cohort of 125 biopsy or autopsy human liver samples were obtained from the
Cooperative Human Tissue Network (Midwest and Western Division) under approval o
53
54
Figure 12. CES1A1 Gene Structure.
(A) CES1 gene loci on chromosome 16. CES1P1 can exist as a truncated isoform CES1A3 (truncated at //), or a full length
isoform (CES1A2). The green region in the 5′ end of CES1P1 represents sequence that can appear as a group of 11 highly
linked SNPs at the CES1A1 gene locus creating the CES1A1VAR isoform. (B) A closer schematic of CES1A1. The 5′ region
which can match CES1P1 sequence creating CES1A1VAR is indicated by a green bracket. Additional sequence
information can be viewed in Figure 13 and Table 2.
54
55
Figure 13. Polymorphisms of CES1A1 and known variants.
5′UTR and exon 1 of CES1A1 compared to CES1A1VAR, CES1A1SVAR and CES1P1. Yellow bases indicate polymorphic
sites and blue bases indicate the start of exon 1. Annotated 5′UTR and an intermediate 5′ UTR are indicated in text. Numbers
correspond to SNP rs#s from Table 2.
55
Table 2. CES1A1 5′ Region SNP #s.
CES1A1VAR and CES1A1SVAR are comprised of highly linked SNPs which match the sequence of CES1P1. In the case of
CES1P1, these variants would not be SNPs, but rather the major alleles
56
Figure #
rs#
Figure #
rs#
Figure #
rs#
0
rs3815583
4
rs12149370
8
rs201577108
1
rs12149373
5
rs12149368
9
rs114788146
2
rs12149371
6
rs111604615
10
rs12149366
3
rs12149322
7
Unknown
11
rs12149359
56
the Ohio State Institutional Review Board. An additional fifty liver samples were
obtained from other sources that included the Medical College of Virginia (Richmond,
VA, USA), Medical College of Wisconsin (Milwaukee, WI, USA), Indiana University
School of Medicine (Indianapolis, IN, USA) or University of Pittsburgh (Pittsburgh, PA,
USA). All livers were used under protocols approved by the appropriate committees for
the conduct of human research. Cohort composition is primarily Caucasian, with ~15% of
African ancestry.
DNA and RNA isolation and genotyping
Genomic DNA and RNA were prepared from liver tissue samples using either
nuclei lysis buffer or Trizol respectively (Life Technologies, Carlsbad, CA). Nucleic
acid isolation has been previously described (Wang, 2011). Briefly, following tissue
lysis genomic DNA samples were phenol chloroform purified, ethanol washed and
resuspended in TE. RNA isolation used SpinSmart Columns (Denville, Metuchen, NJ)
with DNaseI to prevent contamination of genomic DNA in cDNA synthesis. cDNA was
prepared using RNA and Reverse Transcriptase SSIII (Invitrogen, San Francisco, CA,
USA), with controls lacking reverse transcriptase or template to test for residual gDNA
contamination. 5′RACE product was synthesized using the FirstChoice RLM-RACE
RNA Ligase Mediate RACE kit in a CES1A1WT/WT individual (Life Technologies).
SNPs in CES1A1 were genotyped in liver samples using a primer extension assay
(SNaPshot, Life Technologies), GC-clamp real time-PCR assay or fluorescently labeled
PCR-restriction fragment length polymorphism (RFLP) analysis as previously described
57
(Pinsonneault, 2004;Wang, 2005). PCR conditions and primers for all PCR assays
(including genotyping) are given in Appendix A, Table 3. Allele frequencies were
checked for Hardy-Weinberg equilibrium (Appendix A, Table 5).
Measurements of gDNA and mRNA allelic ratios by primer extension (SNaPshot)
The SNaPshot protocol has been previously described (Pinsonneault, 2004;Wang,
2011). In short, PCR amplification surrounding a marker SNP in an exonic region, from
a heterozygous sample is visualized using a primer extension primer and fluorescent
ddNTPs. SNaPshot is quantitated by peaks on an ABI3730 sequencer (Life
Technologies) that provide the relative amounts of each allele. The average of the allelic
ratios of gDNA is used to normalize allelic mRNA ratios in each tissue. The primary
marker SNP for CES1A1 was rs1714370 a feature of CES1A1VAR. Due to CES1A1’s
homology to the CES1P1 gene locus, the genomic DNA control PCR product was larger
than the mRNA product. This allowed specific allelic gDNA ratios, though the product
size difference prevents a definitive statistical analysis or 3-standard deviation cutoff for
significance as previously described in Chapter 2. Despite this limitation, One-Way
ANOVA indicates 13 of 28 samples show significant allelic mRNA ratios when
compared to gDNA allelic ratios (P <0.05, data not pictured). A more defined cutoff for
significant allelic mRNA ratios was set at 1.2-fold for which SNaPshot is sufficient in
measuring. Note, mRNA measurements targeted both CES1A1 and CES1A1VAR
isoforms. This was acceptable because it has been shown that CES1P1, which is
identical to CES1A1VAR, gene expression is <2% of total CES1 gene expression
58
(Fukami, 2008). All SNaPshot measurements were completed in duplicate for gDNA.
Allelic mRNA ratios were measured in at minimum in triplicate for 26 of 28 samples,
while the remaining were measured in duplicate (L36 and L120). Primers for this assay
can be viewed in Appendix A, Table 3.
Determination of CES1 mRNA by quantitative real-time PCR
CES1 mRNA expression was measured using quantitative real time PCR (qRTPCR) on a 7500 Fast Real Time PCR System (Applied Biosystems, Carlsbad, CA, USA).
Amplification of CES1 mRNA captured CES1A1 and CES1P1 (<2% total mRNA) locus
mRNA but also allowed CES1A1VAR mRNA to be amplified. Relative quantitation used
GAPDH as a housekeeping gene where expression is representative of overall RNA
quality in human liver and has low interindividual variability (Barber, 2005).
Measurement of CES1 protein quantity and metabolic ability
CES1 protein was isolated from human liver tissue and western blot was
performed using CES1 antibodies (Bascom, Cambridge, MA). Using equivalent input
quantities, total CES1 protein expression was determined using UVP VisionWorksLS
(Upland, CA) and normalization to a representative sample. All CES1 protein quantities
were measured in duplicate. CES1 enzyme activity was measured using isolated S9
fractions. Protein content was measured by Pierce BCA Assay Kit (Pierce, Rockford,
IL). Equivalent protein S9 protein quantities were incubated with clopidogrel or enalapril
for 90 minutes at 37oC. Following incubation, LC MS/MS was used to measure the
59
concentration of the hydroxyl metabolites for each sample. All metabolic activities were
performed in triplicate.
Sequencing of the CES1A1 Promoter Region
The CES1A1 promoter region (5kb) was sequenced to scan for regulatory
variants. PCR products were visualized on agarose gel to determine relative quantity.
Products were mixed and fragmented on a Covaris S220 (Woburn, MA). NEBNext Fast
DNA Library Prep Set for Ion Torrent (New England Biosciences, Ipswich, MA) was
used to generate barcoded libraries that were run on an IonTorrent PGM (Life
Technologies). Sequencing results were analyzed in CLC Bio’s Genomics Workbench
(CLC Bio, Katrinebjerg, Denmark), and SNP calling required, at minimum, 20 reads at a
given base with 20% reads per allele.
Infusion cloning and construct preparation
Luciferase assay vectors were prepared using pGL3-Basic (Promega, Madison
WI) and a CES1A1 promoter region-5′UTR-exon 1 region insert (Figure 18). The
Infusion Cloning System (Clontech, Mountain View, CA) was used to insert the fragment
at the HindIII multiple cloning site of pGL3-Basic. Completed reactions were
transformed into XL-2 (Agilent, Santa Clara, CA) cells and plated on LB agar with
carbenicillin. Individual clones were screened and plasmids isolated by the Zyppy Mini
Prep kit (Zymo Research, Irvine, CA). Plasmids were screened for inserts by HindIII
digestion and sequenced for confirmation of the insert present in frame with the
luciferase gene. Positive clones with haplotypes of interest were subcloned into DH5α
60
cells and screened for insert presence. Three positive subclones per haplotype were then
combined and cultured via a Qiagen MidiPrep Kit (Qiagen, Valencia, CA). Stock
cultures were frozen in a 1:1 ratio of bacteria to glycerol for future use.
Luciferase Assay
Luciferase assay was performed using pGL3-Basic vector prepared as described.
HepG2 cells were selected due to their high endogenous expression of CES1
(www.biogps.org)(Wu et. al., 2009). Prior to transfection, cells were plated in 12-well
plates and grown to ~60% confluence. Transfection plasmid mixes of CES1A1-pGL3B
and pRL (Promega), a background Renilla fluorescence vector, were prepared in a ratio
of 1ug to 0.2 ug in OptiMem Media (Life Technologies). Plasmid mix was added to an
equivalent OptiMem mix of transfection reagent FuGene HD (Promega) at 1ug pDNA/ 3
ul reagent. Six hours post transfection media containing antibiotics was added to
transfected wells (Penicillin/Streptomycin, Life Technologies). Following incubation for
24 hours luciferase activity was measured by Dual-Glo Luciferase Assay System
(Promega), and read out on a Packard Fusion plate reader (PerkinElmer Life and
Analytical Sciences, Shelton, CT). Luciferase activity was determined as a ratio of
luciferase fluorescence over renilla fluorescence. Luciferase was measured in 3
experiments, with 3-replicates and two measurements per replicate.
Data Analysis
Significant association of CES1A1VAR to gene expression and protein quantity
was performed in SPSS (IBM, Armonk, NY). Stepwise linear regression including
61
covariates, gender, race, rs3815583 carrier status, rs2244613 carrier status, and
CES1A1VAR carrier status were included in these analyses. Correlation statistics for
RNA-Protein relationship, One-way ANOVA for AEI and luciferase activity were
performed in GraphPad Prism. Figures were generated in Graph Pad, or designed in
Photoshop/PowerPoint. P-values < 0.05 were considered significant for all associations.
Hardy-Weinberg equilibrium and linkage for CES1A1VAR and other variants of interest
was assessed using Helix Tree SNP and Variation Suite (Golden Helix, Bozeman, MT,
USA) (Appendix A, Table 5; Appendix B, Figure 27).
Generation of CES1A1 mRNA folding structure
Structure (2D) and free energy was assessed using mFOLD, with sequence based
on the intermediate length 5′ UTR, CES1A1 exon, and the first 18 bases of pGL3-Basic
downstream of the insertion site. Folding structures and free energy was recorded and
compared between the various constructs by One-way ANOVA (GraphPad Prism). To
best visualize the overall difference in the predicted structures, three dimensional models
were generated using RNAFold and RNAComposer. Based on the medium 5′ UTR
length, the rs3815583 construct was identical to the WT and was omitted from analysis.
3.3 Results
An alternative 5′ variant of CES1A1
To understand the makeup of CES1A1 and its relationship to CES1P1, Sanger
sequencing of the CES1A1 gene locus was performed. Previous studies characterized the
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gene loci for CES1A1 and CES1P1 identifying CES1A1VAR (where CES1P1 “replaces”
CES1A1). Multiple CES1A1 isoforms have been previously described, though the
number of variants in unclear. Sequencing samples of CES1A1 homozygotes,
CES1A1VAR heterozygotes and CES1A1VAR homozygotes defined the major regions
affected by CES1A1VAR. The 5′ boundary begins between rs3815583 (unaffected) and
rs12149373, and ends in intron 1 (Figure 13). Screening for CES1A1VAR SNPs, while
using CES1A1 specific primers identified 3 samples containing only CES1A1VAR 5′UTR
associated SNPs. This shorter variant, referred here as CES1A1SVAR was confirmed by
sequencing and represents 5′ sequence variation seen at the CES1A1 gene locus.
CES1A1 5′ UTR Usage
To measure the most abundant mRNA isoforms for allelic mRNA analysis,
CES1A1 5′ UTR length was tested by 5′ RACE. In a CES1A1 homozygote, fluorescent
CES1A1-RACE PCR product indicates that the primary length of CES1A1 mRNA in
human liver is ~ 50 bases shorter than mRNA annotated by RefSeq and the UCSC
Genome Browser (Figure 14). qPCR confirms abundance of the intermediate 5′ UTR
length mRNA and indicates the annotated 5′ UTR measures at roughly 1-2% of total
CES1 gene expression (data not pictured). A shorter, exon 1 coding region is also likely
present based on RACE analysis, though in small quantity. Based on these observations,
the medium 5′ UTR is an abundant mRNA isoform for SNaPshot analysis.
CES1A1 associates with decreased total mRNA expression
63
Previous studies have shown that CES1A1 gene expression is highly variable,
though it is not clear to what extent it associates with any CES1A1 variants. Total CES1
mRNA was measured in 57 human liver samples. Analysis was performed by stepwise
linear regression including gender, race, rs3815583, rs224816 and CES1A1VAR carrier
statuses. For individuals with at least one copy of CES1A1VAR (heterozygotes and
homozygotes) total CES1 expression was 2.65-fold lower (p =0.003) than in non-carriers
(Figure 15). No other covariates were significantly associated with gene expression.
CES1A1VAR is moderately associated with allelic expression imbalance.
Allelic mRNA ratios were measured determine if the CES1A1 gene locus is being
affected by CES1A1VAR or a cis- acting functional regulatory polymorphism(s). Again,
due to the minor contribution of CES1P1 to total gene expression, allelic mRNA ratios
have been measured using CES1 primers. Allelic expression was measured at
rs12149370 in the 5′UTR of CES1A1 (included in the intermediate length mRNA
transcript). This SNP is included in both CES1A1VAR (minor allele frequency = 0.17)
and CES1A1SVAR (MAF < 2%) isoforms. Allelic mRNA ratios were screened in 28
human liver samples. Statistical analyses indicate 13 of these show significantly different
allelic mRNA ratios at P < 0.05. However, due to the differences in gDNA and mRNA
PCR amplification, a cutoff for potential allelic expression imbalance was set at >1.2fold. Based on this, 18 samples showed allelic expression imbalance greater than 1.2fold (1.23 to 1.4-fold, with a 2.23-fold outlier, Figure 16). It is clear that there is some
degree of allelic expression imbalance in the majority of the samples screened.
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Figure 14. CES1A1 5′ RACE Indicates UTR lengths.
5′UTR length was assessed by 5′RACE. Medium 5′ UTR transcript is indicated by
arrows consists of 2 similarly sized transcripts flanking the defined “---“labeled Med 5′
UTR in Figure 13. Data also suggests a small population of CES1A1 transcript with no 5′
UTR, and no annotated 5′ UTR transcript product was observed.
65
CES1 Total mRNA
P = 0.003
12.5
10.0
dCT
7.5
5.0
2.5
0.0
W/W
W/V and V/V
CES1A1 Allele
Figure 15. CES1A1VAR associates with decreased CES1 gene expression.
CES1A1VAR carrier status was the only significant predictor of gene expression when
analyzed with gender, race, rs2244613 and rs3815583, decreasing gene expression 2.65fold when compared to non-carriers (P = 0.003; WT, n = 29; VAR, n = 31). Data are
presented as a box plot and whisker plot (box shows median with the 25th and 75th
percentiles), and minimum and maximum values are shown by whiskers.
66
CES1A1SVAR carriers, L50, L51, and LL10 all show some degree of AEI, though it
varies as in CES1A1VAR carriers. Because the majority of samples screened show some
degree of AEI, this indicates that CES1A1VAR (and CES1A1SVAR) may be a functional
variant, or is highly linked to the functional variant(s). Notably, allelic mRNA ratios in
one case measured at ~2-fold, and in other case at ~5-fold (data not pictured), suggesting
uncommon variants may also be present.
Sequencing of the CES1A1 Promoter Region
One possible conclusion of the allelic expression assay is that CES1A1VAR is in
linkage with another functional polymorphism. To test this, 5kb of the promoter region
was screened for polymorphisms that associate with instances of allelic expression
imbalance. No single SNP associated with allelic mRNA ratio presence, though
rs38135583 was present in all AEI – samples (n = 4), and only present in 1 of 6 AEI+
samples. This SNP is located in the 5′UTR of CES1A1, is not associated with
CES1A1VAR and is associated to appetite reduction during methylphenidate therapy.
However, genotyping revealed no association with AEI status (or other measured
phenotypes).
CES1A1VAR association with Protein Quantity
Based on the association of CES1A1VAR with decreased total CES1 gene
expression, I sought to determine if these effects were present at the protein level. CES1
protein quantity was measured by western blot and quantitated using protein input and a
sample for normalization (arbitrary unit) (Appendix B, Figure 28). To determine the
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effect of CES1A1VAR on protein quantity, carriers (heterozygotes n=15, and
homozygotes n=4) were compared as a single group against non-carriers. CES1A1VAR
carrier protein quantity was 1.33-fold lower (1.018 units, SD 0.07277 N= 19) when
compared to non-carriers (1.34 units, SD 0.08228 N = 24). When analyzed by stepwise
linear regression including gender, race, rs3815583 carrier status and rs224816 carrier
status, CES1A1VAR was the only significant contributor to decreased protein quantity (p
= 0.008) (Figure 17). To strengthen the association between CES1A1VAR carrier status
and CES1 protein quantity, correlation between CES1 RNA and protein was analyzed.
In this cohort, gene expression and protein quantity were not significantly associated
where both data were available (Figure 17, p = 0.065, N = 46).
CES1A1VAR association with protein activity
The effect of CES1A1VAR on CES1 protein levels was further interrogated using
human liver S9 fractions. The velocity of hydrolysis of two CES1 substrates, clopidogrel
and enalapril were measured and tested for association with CES1A1VAR carrier status by
linear regression. Hydrolysis of clopidogrel in CES1A1VAR individuals was not
significantly decreased when compared to wild-type (WT: 329.7 +-41.7 pmole/min/mg N
= 24 vs. VAR: 277.7 +- 32.07 pmole/min/mg N = 19) (Figure 18). Similar results were
obtained when comparing enalapril hydrolysis between CES1A1WT and CES1A1VAR
individuals (WT: 65.91+- 10.3 pmole/min/mg, N =24 vs VAR: 59.8 +-7.342
pmole/min/mg, N = 19) (Figure 18). In both cases, no covariates (gender, race,
rs3815583, rs224613 and rs71647871 (G143E) were significantly associated with protein
68
1.5
1.3
1.2
1
1.2
1.3
LL31
L68
L102
L81
L120
LL11
L51
L42
LL16
L27
L26
L63
L78
L48
L107
L72
LL24
LL30
L6
L77
LL10
L50
L100
L36
LL39
L37
L35
L18
Allelic mRNA Ratio (Major/Minor)
5` UTR rs12149370
Samples
Figure 16. Allelic expression at rs12149370
The allelic RNA ratio is expressed as a ratio of major/minor alleles. Allelic expression is
considered significant when >1.2-fold. AEI+ samples have an average of ~1.35-fold
allelic mRNA ratio (18 of 28 samples). An outlier showing ~5-fold allelic expression
imbalance was also identified (data not pictured). Data are mean±SD.
69
P = 0.008
70
Figure 17. Correlation between CES1 protein and RNA, CES1 protein quantity association with CES1A1VAR.
(A) CES1 mRNA and protein level do not significantly correlate with one another (R2 =0.05134, P=0.065, N = 46). (B)
CES1 protein levels associate with a 1.33-fold decrease in protein expression when an individual has at least one copy of
CES1A1VAR (P = 0.008). Horizontal lines indicate population mean.
70
activity. Notably, including both CYP2C19*2 and CYP2C19*17 (due to the role of
CYP2C19) with the above covariates results in rs3815583 carrier status as a significant
predictor of clopidogrel metabolic ability (though only when analyzed in a non-stepwise
manner, data not pictured).
CES1A1 5′ UTR activity by luciferase in HepG2
To determine the function of the various CES1A1 5′ UTRs, luciferase assay was
performed in HepG2 cells. Constructs contained 5′UTR regions corresponding to
CES1A1, CES1A1VAR, CES1A1SVAR and CES1A1WT + rs3815583 (a complete list of
construct structure and variation can be viewed in Figure 19A and Appendix A, Table 7,
respectively). CES1A1VAR showed a significant 35% decrease in luciferase activity
when compared to CES1A1WT (P <0.01) (Figure 19B).
Interestingly, CES1A1SVAR showed a 36% increase in luciferase activity when
compared to CES1A1WT (P <0.01), though there was considerable variation in activity
between replicates. CES1A1+rs3815583 showed no significant difference in luciferase
activity. Deviation of CES1A1VAR and CES1A1SVAR suggest that these two variants
may be functional. Notable significant differences are indicated by * (WT vs. VAR) or **
(WT vs. SVAR) (Figure 19).
RNA folding in the 5′ UTR of CES1A1
To explore the potential mechanism for which 5′ UTRs have exhibited functions
in this study, they were put into various RNA folding programs. The CES1A1WT
construct showed 2 potential folding patterns, with an average final ΔG = -27.74. The
CES1A1VAR construct yielded 4 different structures with an average ΔG = -40.58. The
71
Figure 18. CES1A1VAR does not associate with metabolic ability.
Gender, race, rs3815583, rs2244613 and CES1A1VAR do not significantly associate with
metabolic ability for (A) clopidogrel or (B) enalapril. Horizontal lines indicate population
mean.
72
A
B
200
Relative Expression
(Luciferase/Renilla)
**
150
100
*
50
0
WT
VAR
rs3815583
shortVAR
Figure 19. Luciferase activity of CES1A1 constructs in HepG2 cells.
(A) Construct schematic, specific construct differences can be viewed in Appendix A,
Table 7. (B) CES1A1VAR luciferase activity was significantly lower than CES1A1WT
(35%, P < 0.01). CES1A1SVAR was significantly higher than CES1A1WT activity (36%,
P < 0.01). Data are presented as the average of 3 transfection experiments. Significant
differences when compared to WT are indicated by * (CES1A1VAR) and **
(CES1A1SVAR).
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CES1A1SVAR construct yielded 3 structures with an average ΔG = -40.68. When
compared to the WT, both VAR and SVAR constructs had significantly lower free energy
(P <0.001, One-way ANOVA). Though there is some overlap in the predicted structures,
the difference in ΔG indicates that CES1A1VAR and CES1A1SVAR, which also showed
significant differences in luciferase activity in cell culture, are more stable in their
predicted structures when compared to the WT. RNAFold outputs confirmed mFOLD
free energy predictions. The RNAFold 2-D structure was visualized in 3-D using
RNAComposer. Here, drastically different 3-D structures can be seen between the WT,
VAR and SVAR constructs (Figure 20). These indicate overall structural differences that
may affect transcription or translation.
3.2 Discussion
The results of this study indicate that CES1A1 contains regulatory polymorphisms
that decrease mRNA and protein expression in human liver. This conclusion is supported
by total gene and allelic mRNA expression data, as well as protein quantitation and
luciferase activity. The primary variant associating with a change in mRNA and protein
expression is CES1A1VAR,which consists of 11 SNPs in the 5′ UTR and Exon 1 of
CES1A1 (Figure 13, Table 2). Present at a minor allele frequency of ~17%, this variant
results in CES1P1 (CES1A2) mRNA expression from the CES1A1 gene locus. In
addition to this potential pharmacogenetic biomarker, I also better define CES1A1 5′
UTRs and their role in gene expression.
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Measurement of the average 5′ UTR length by 5′RACE (in a CES1A1/CES1A1
individual) indicates that the most frequent UTR length is shorter than the RefSeq
annotation. Interestingly, this intermediate length 5′ UTR is considered an exonic coding
region at the CES1P1 gene locus (where no 5′ UTR is annotated). Targeted sequencing
also identified an uncommon (MAF < 2%) alternative mRNA isoform referred to as
CES1A1SVAR, where only the 5′ UTR region at CES1A1 contains CES1P1 variants
(Figure 13). Interestingly, CES1A1SVAR and a previous study suggest that there are
likely numerous, uncommon CES1A1 variations in addition to CES1A1VAR (Tanimoto,
2007).
Decreases in CES1 mRNA transcription are supported by our human liver protein
data. These results are further recapitulated as CES1A1VAR decreases protein expression
through luciferase activity in HepG2 cells (while CES1A1SVAR may increase protein
activity). The three dimensional structures based on the 5′ UTRs of CES1A1 indicate that
significantly different free energy and folding conformations which may affect
transcription or translation. If translation is also negatively affected by CES1A1VAR this
would indicate two mechanisms both acting in the same phenotypic direction.
Due to the complex nature of the CES1 gene loci, gene expression is difficult to
measure. Therefore, the majority of this study has focused on the promoter and 5′ region
of the gene where the few differences between the two isoforms exist. In this study, in
addition to CES1A1VAR carrier status other relevant SNPs associated with CES1A1
phenotypes, such as rs2244613 (associated to dabigatran metabolism and
methylphenidate side effects), and the 5′ UTR SNP rs3815583 (associated to
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methylphenidate side effects) were included. Notably, these polymorphisms were not
considered significant contributors to mRNA expression or CES1 protein quantity.
Further, inclusion of rs71641730 (G143E) showed no significant associates with protein
activity, though this may be due to small sample size.
The CES1P1 variant rs3785161 has not yet been assessed in relationship to the
work here. Though function has been shown in cell culture, the true functional effect of
this polymorphism is unclear. If it does truly increase gene expression it would have to
be have a large effect size to increase gene expression from <2% of total CES1 mRNA to
a significant amount. This could potentially interfere with our allelic or total CES1
mRNA measurements, but would indicate a ~15+ fold increase in gene expression.
Moreover, an increase in gene expression would be counter intuitive to the decreases in
gene and protein expression observed. The original association to imidapril response for
this variant is what is expected –increased promoter activity, CES1 activity, drug
activation and efficacy. However, the two recent studies testing for association with
clopidogrel efficacy take opposing views. Increases in CES1 activity as a result of this
variant should decrease clopidogrel efficacy, as more of the drug is inactivated as seen in
Xie, et. al. (Xie, 2014). However, the publication by Zou et. al. suggests that variant
presence improves clopidogrel efficacy, which is unexpected given the role of CES1 in
the inactivation of clopidogrel (Zou, 2014). Notably, this takes into account supports the
defined linkage of rs3785161 with the non-functional CES1A3 (at the CES1P1 locus)
(Sai, 2010) and suggests that rs3785161 is likely a variant in high linkage with a
functional SNP. The relationship between rs3785161 ( or associated functional SNP),
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CES1A1VAR, and any potential effects on mRNA or protein expression warrant further
exploration.
CES1 gene expression is highly variable as evidenced by our quantitative PCR
and protein quantity data. Carboxylesterase, having a diverse role in the liver is
undoubtedly under the influence of both trans- genetic and environmental factors, which
contribute to variability. This variability may have a significant effect on observing
significant associations of CES1A1VAR to metabolic activity. Clopidogrel and enalapril
metabolism was not significantly associated with CES1A1VAR carrier status. It has
previously been shown that CES1A1VAR does not associate with irinotecan and isoniazid
metabolic ability, though CES1 is not the only involved DME (Sai, 2010;Whirl-Carrillo
et. al., 2012;Yamada, 2010). One explanation for the lack of association in the protein
activity experiments is additional enzymes interacting within the S9 fraction with either
clopidogrel or enalapril. Due to well-defined CYP2C19 genetic variant alleles, linear
regression was performed with the other available covariates, CYP2C19*2 and
CYP2C19*17 to determine if any of the genotyped variants affected clopidogrel
metabolism. Interestingly, rs3815583 carrier status was associated only in a nonstepwise linear regression. However, no significant association is seen with mRNA or
protein expression and this variant. One explanation is that this variant is marking a
functional SNP in the coding region of CES1A1. This requires further study to determine
if rs3815583 has any effect on enzyme function.
CES1A1VAR is not completely associated with the presence of allelic mRNA
ratios (average ~1.35-fold). Still, CES1A1VAR associates with decreases in total mRNA
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A
B
C
78
Figure 20. 3D Structures of CES1A1 5′ UTR Isoforms.
Three dimensional structures are based on the intermediate CES1A1 5′UTR and a portion of the following pGL3-Basic vector.
Structures for WT (A), VAR (B) and SVAR (C) include pink dNTPs which represent positions where variation occurs between
isoforms. In all cases CES1A1 exonic region differences are located in the upper helix, while 5′ region variation is in the lower
region.
78
expression, protein quantity, and lower luciferase activity. One explanation is that AEI
may be driven by enzyme induction, in some cases producing larger allelic mRNA ratios
such as the ~2-fold AEI as seen in L18. An outlier sample was also identified in this
study, with allelic mRNA ratios at~5-fold (data not pictured). Though this sample is a
carrier of CES1A1VAR it is likely that either gene duplication or an unrelated SNP is
responsible. To determine if any candidate SNPs were associating with typical AEI or
changes in gene or protein expression, the promoter region was sequenced. In 5kb of the
promoter region, no SNPs strongly associating with AEI + samples were observed, and a
moderate association was seen with rs3815583 and AEI – samples (present in only a
single AEI + sample). This is interesting considering association of the polymorphism
with variation in methylphenidate treatment (Bruxel, 2013). However, genotyping and
subsequent analyses indicate this SNP is not clearly associated with CES1A1 AEI status,
total mRNA or protein expression and does not alter luciferase activity. Functional
polymorphisms might exist outside the screened region, but the density of variation in the
5′UTR of CES1A1, and the subsequent findings demonstrate rationale for CES1A1VAR as
a functional variant.
CES1A1VAR’s utility as a pharmacogenetic biomarker is not clear, though here I
have gathered evidence for a function in human liver tissue. Interestingly, CES1A1
shows a higher free energy when compared to both variant alleles, implying it may be
more receptive to translation. It has already been shown that rs71641730 (G143E) can
have a significant effect on clopidogrel efficacy. With this loss of function allele,
heterozygote carriers have their metabolic ability halved. Though CES1A1VAR does not
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have as strong an effect in decreasing gene expression or protein quantity it may be
relevant in clopidogrel therapy. Clopidogrel isoforms, which consists of a prodrug,
intermediate and active metabolite are always inactivated by CES1. These three points of
entry for CES1 may allow the modest effect of CES1A1VAR to have a measurable effect
on clopidogrel efficacy. Distinct from hydroxylation as measured in this study, a clinical
population with endpoints such as platelet aggregation, incidence of bleeding or coronary
stent thrombosis would be a more informative measure of clinical utility.
Here I show that the previously described CES1A1VAR decreases gene and
protein expression in the human liver by at least ~30%. I also indicate the primary 5′
UTR length for CES1A1, and identify an uncommon alternative 5′ UTR sequence
(CES1A1SVAR). It remains to be seen if CES1A1VAR will have utility as a clinical
biomarker, as it has already been shown to not associate with drug response in select
compounds. However, given a substrate where CES1 is the primary metabolizer
(clopidogrel) and relevant clinical endpoints are available, there may be a significant
association. It is worth considering that, CES1A1 expression is variable and may harbor
additional regulatory variants (as evidenced by outliers), though these are likely
uncommon.
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Chapter 4. Evidence of regulatory polymorphisms at the ACE gene locus
4.1 Introduction
Angiotensin I-converting enzyme (ACE) regulates blood pressure through the
renin-angiotensin-system (RAS), among other physiological functions. Renin facilitates
the formation of angiotensin-I from the peptide angiotensinogen. ACE hydrolyzes
angiotensin I to angiotensin II (vasopressor) and inactivates bradykinin (vasodilator)
(Soffer, 1976). In turn this increases blood pressure. Hypertension (high blood pressure)
increases risk of stroke and heart failure (Wolf et. al., 1991). Due to its role in the RAS,
ACE is an attractive drug target for managing blood pressure. ACE inhibitors are widely
prescribed, lower blood pressure and decrease adverse cardiac events in individuals with
hypertension and heart failure (Wing et. al., 2003;Yusuf et. al., 2000). A 25 exon gene
spanning ~25 kb, ACE is ubiquitously expressed and implicated in numerous diseases in
addition to hypertension (Figure 21). These include myocardial infarction, diabetic
neuropathy, and amyloid plaque formation in Alzheimer’s disease (Cambien et. al.,
1992;Lewis et. al., 1993;Yang et. al., 2008). The aim of this study is to survey ACE for
evidence of cis-regulatory polymorphisms affecting ACE gene expression in non-cardiac
tissues while assessing the function of previously identified variants on gene expression
in these tissues. Both classic molecular genetics and next-generation sequencing methods
will be applied to assess these questions.
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Early familial studies have determined ACE activity is highly heritable (Cambien
et. al., 1988). The vast majority of genetic studies have focused on an insertion/deletion
(I/D, rs1799752, Figure 21) polymorphism located in intron 16. However, in vivo studies
are unable to identify an effect of the I/D variant on gene expression or splicing (Lei et.
al., 2005;Rosatto et. al., 1999). Further, meta-analyses have not consistently associated
the I/D polymorphism to hypertension(Sayed-Tabatabaei et. al., 2006). However,
significant associations have been reported for Alzheimer’s, ischemic stroke and diabetic
nephropathy suggesting that the I/D polymorphism is a surrogate marker for a functional
variant (Fujisawa et. al., 1998;Lehmann et. al., 2005;Ng et. al., 2005;Zhang et. al., 2012).
Our group has found an association between three enhancer SNPs located ~3kb
upstream of ACE and a 4-fold decrease in cardiac ACE expression. These enhancer SNPs
also increase risk for primary outcomes in hypertensive individuals with coronary disease
(mostly myocardial infarction with an odds ratio of ~6) (Johnson, 2009) . The
polymorphisms (rs4290, rs7213516, rs7214530) are in high linkage disequilibrium with
each other (but not with the I/D variant). They are common in African populations (MAF
~17%) and to a lesser degree in Hispanics and Caucasians (~4% and 0.2% MAF,
respectively). Reporter gene assays fail to definitively identify which of the variants is
active though it is clear these account for a significant decrease in ACE mRNA
expression in the heart.
African populations are known to have high risk for cardiac events and adverse
drug reactions (ADRs) from ACE inhibitors (angioedema, dry cough, and hypotension)
(Gibbs et. al., 1999;Israili et. al., 1992;Spinar et. al., 2000). These adverse reactions are
82
83
Figure 21. Schematic of ACE gene locus and relevant SNPs.
Gray boxes indicate UTR and black boxes indicate coding exons. SNPs of interest are indicated with arrows. Enhancer SNPs
rs4290, rs7214530 and rs7213516 are located ~3 kb upstream from exon 1 of ACE.
83
not limited to African populations but are less frequent in Caucasians (Israili et. al.,
1995;Murray et. al., 1998). Notably, the I/D polymorphism is present in similar
frequencies among populations, implying the variant responsible for increasing ADRs is
unrelated and specific to African populations. These ADRs can be therapy limiting
prompting alternative antihypertensive treatments. Further, while the enhancer SNPs may
affect ADRs, additional variants may be active at the ACE gene locus and affect drug
response and efficacy.
Although the I/D variant is poorly characterized and unlikely to have function per
se, thousands of clinical studies continue to rely on it as the sole variant representing
genetic factors in ACE. Lacking frequent nonsynonymous SNPs of proven effect, ACE is
likely to harbor regulatory variants such as the enhancer SNPs (Johnson, 2009). Our
group has developed a comprehensive approach to discovery of regulatory variants that I
apply here in non-cardiovascular tissues. In short, allelic mRNA ratios are measured at a
coding SNP in both mRNA and gDNA by SNaPshot. Deviation of the mRNA ratio from
the gDNA ratio is termed allelic expression imbalance (AEI), and indicates a cis-acting
functional polymorphism is present.
The work here expands on our previous study and aims to determine whether the
enhancer SNPs have activity in other tissues, and if evidence of regulatory variants are
present in alternative tissues. Here I determine that the enhancer SNPs show no notable
effect on gene expression in liver and two brain tissues (putamen, prefrontal cortex).
This is supported by their existence in a MEF2A cardiac transcription factor binding site
(Johnson, 2009). A survey of different tissues (liver, prefrontal cortex, putamen, adipose
84
and heart) for indication of cis-acting regulatory polymorphisms by allelic mRNA ratios,
identifies liver (~2-fold AEI), putamen (~1.7-fold), and adipose (1.4-fold) as tissues
where cis-acting polymorphisms are active. This was achieved in two ways, by allelic
mRNA ratios and RNA-sequencing transcriptomes which allow the capture of allelic
ratios for all the SNPs in ACE coding regions.
4.2 Materials and Methods
Tissues
Allelic mRNA survey was performed on 50 heart tissues, obtained under an existing IRB
protocol from heart transplant patients (collaboration with Dr. P. Binkley, OSU).
Additional tissues were analyzed from IRB approved cohorts of 125 liver tissues, and
over 200 brain tissues (prefrontal cortex and putamen).
Genotyping
SNPs of interest were genotyped using primer extension primers, GC-clamp realtime PCR, and fluorescent restriction fragment length polymorphism. This includes the
three enhancer SNPs, and rs4343, the marker SNP used for allelic expression ratios. The
enhancer SNP rs4290 was used as the primary indicator of enhancer SNP status in all
tissues due to the high linkage of these polymorphisms. Primer sets and cycling
conditions can be viewed in Appendix A, Table 3. Genotypes for rs4343 were assessed
for Hardy-Weinberg Equilibrium using Helix Tree SNP and Variation Suite (Golden
Helix, Bozeman, MT, USA) (Appendix A, Table 6).
Allelic mRNA ratios
85
SNaPshot as previously described (Pinsonneault, 2004;Wang, 2011), uses PCR
amplification surrounding a marker SNP in an exonic region, from a heterozygous
sample. The SNaPshot reaction outputs fluorescent peaks on an ABI3730 sequencer
(Life Technologies) that provides the relative amounts of each allele. Average gDNA
allelic ratios are used to normalize allelic mRNA ratios in each tissue. Deviation of
allelic mRNA ratios from unity following normalization is termed allelic expression
imbalance (AEI), and indicates a cis-acting regulatory SNP. In ACE, normalized allelic
mRNA ratios are measured at rs4343. Significant mRNA AEI in each tissue is considered
three standard deviations from the mean of the genomic DNA.
Allelic ratios from RNA-sequencing data
RNA-sequencing transcriptomes were obtained from human prefrontal cortex,
putamen, heart, adipose and liver tissues. Transcriptome alignment was completed by
either TopHat or LifeScope. The allelic ratios of all SNPs in the coding region of ACE
were extracted from the aligned data. Allelic mRNA ratios were selected for analysis
that had a minimum of 30 reads at the variant’s position. Variants that met the read depth
criteria were all aligned to the genome using TopHat and an IUPAC reference genome
(which counts SNPs as mismatched bases). To account for potential loss of minor allele
reads introduced into allelic ratios by this method I employed the correction methods
previously outlined by our group(Smith et. al., 2013). Briefly, all allelic ratios are
converted to their absolute fold change (e.g. 60%VAR = 1.5-fold, 40%VAR = 0.667 with
an overall change of 1.5-fold). Absolute fold-changes were adjusted for IUPAC bias
86
with a reduction of 0.3 per 1 fold (e.g. 2-fold increase would subtract 0.6-fold equaling
1.4-fold final ratio).
4.3 Results
Allelic mRNA ratios in Prefrontal Cortex
Our group previously identified enhancer SNPs that were shown to decrease ACE
gene expression in human heart. To test the effects of these polymorphisms in prefrontal
cortex, and determine if any additional cis-acting polymorphisms are present, allelic
mRNA ratios were assessed. Significant allelic ratios were considered >1.3-fold as
determined by gDNA ratios. Sample MB145, an individual of African descent and
heterozygote carrier of the enhancer SNPs shows no significant allelic expression
imbalance. In the other samples assayed, a single sample showed border-line allelic
expression imbalance (~1.3-fold) (Figure 22). Significant standard deviation was
observed in some samples. This is likely due to the low average expression of ACE in
prefrontal cortex (Appendix B, Figure 29). Further, there is no observed association of
allelic mRNA ratios to the I/D variant.
Allelic mRNA ratios measured in human putamen.
Due to potential function of ACE, and use of ACE inhibitors in Alzheimer’s
disease, I surveyed putamen for evidence of cis-acting polymorphisms and enhancer SNP
function (Hu et. al., 2001;Ohrui et. al., 2004). Putamen is a good candidate due to high
expression of ACE when compared to prefrontal cortex expression (Appendix B, Figure
87
Allelic RNA ratio (A/G, fold)
2
1.5
1.3
*
1
1.3
1.5
D/D
Sample
A
N
gD
B4
M
B1
52
M
M
B1
45
B1
2
M
B2
1
M
B6
6
M
B3
6
M
M
B3
3
2
D/D
Figure 22. Allelic mRNA ratios in Prefrontal Cortex.
The allelic RNA ratio is expressed as a ratio of major/minor alleles averaged from a
survey of two measurements. Three standard deviations of the genomic DNA control
(>1.3-fold AEI) indicate a biologically significant allelic expression imbalance. Minor
allelic ratios are present in at least one sample (MB33). ACE I/D carrier status is
heterozygous unless otherwise indicated. No I/D association with AEI was observed.
MB145, indicated by *, is a carrier of the enhancer SNPs and shows no significant AEI.
Variability may be driven by low expression of ACE in the PFC. Data are mean±SD.
88
28). Significant allelic mRNA ratios were considered >1.2-fold, with some large standard
deviation observed. Of samples assessed, 2 of 19 showed significant allelic expression
averaging 1.73-fold, indicating that cis-acting regulatory polymorphisms are likely
present in these individuals (Figure 23). These samples, MB72, and MB80 are of African
and Hispanic ancestry, respectively. Sample MB55 is a heterozygote carrier of the
enhancer SNPs, though no functional effect is observed (as seen in PFC). No association
between allelic mRNA ratios and the I/D polymorphism was observed.
Allelic mRNA ratios in human liver.
To continue to explore the incidence of allelic expression imbalance in human
tissues, allelic mRNA ratios were measured in the liver. Here, allelic mRNA ratios were
considered significant when >1.15-fold (3-standard deviations of gDNA). No African
samples were observed that carried our marker SNPs and the enhancer SNPs. Here, 3 of
7 samples of Caucasian ancestry show allelic expression imbalance of ~1.83-fold, with at
least one additional sample showing borderline significance as determined by 3-standard
deviations (Figure 24). There is no observed association of allelic mRNA ratios to the
I/D polymorphism.
Allelic mRNA ratios from RNA-sequencing.
To survey additional tissues for indication of cis-acting functional
polymorphisms, RNA-sequencing transcriptomes were utilized to obtain allelic mRNA
ratios in many tissues. Following application of the criteria for observing allelic ratios in
RNA-sequencing, only heart and adipose tissue were left for analysis. This may be due
to lower expression of ACE in the other tissues when compared to adipose and heart.
89
Allelic Fold Ratio (Major/Minor)
2
1.5
*
1.5
D/D
D/D
MB80B
MB73B
MB4B
MB46B
MB54B
MB9B
MB65B
MB58B
MB74B
MB67B
MB47B
MB79B
MB84B
MB69B
MB66B
MB12B
MB68
MB55B
MB72B
2
I/I
Sample
Figure 23. Allelic mRNA ratios in Prefrontal Cortex.
The allelic RNA ratio is expressed as a ratio of major/minor alleles averaged from a
survey of two measurements. Three standard deviations of the genomic DNA control
(>1.2-fold AEI) indicate a biologically significant allelic expression imbalance. Though
there is significant variation in some samples, two non-Caucasian individuals (MB72 and
MB80) show significant AEI of ~1.73-fold. ACE I/D carrier status is heterozygous
unless otherwise indicated. No I/D association with AEI was observed. MB55, indicated
by *, is a carrier of the enhancer SNPs and shows no significant AEI. Data are
mean±SD.
90
Liver
Allelic RNA ratio (A/G, fold)
3
2
1.5
1.15
1
1.15
1.50
2
3
L66
L57
L71
L86
L70
L76
L89
gDNA
Sample
Figure 24. Allelic mRNA ratios in human liver.
The allelic RNA ratio is expressed as a ratio of major/minor alleles averaged from a
survey of two measurements. Three standard deviations of the genomic DNA control
(>1.15-fold AEI) indicate a biologically significant allelic expression imbalance.
Caucasian samples L66, L76 and L89 show average AEI of 1.83-fold. ACE I/D carrier
status is heterozygous unless otherwise indicated. No I/D association with AEI was
observed. Data are mean±SD.
91
Allelic mRNA ratios were collected and analyzed as described in Materials and Methods.
Corrected allelic mRNA ratios are grouped by sample in Figure 25. Averaging all
absolute allelic mRNA ratios indicates select adipose tissues (3) with overall AEI of
~1.35-fold. The I/D polymorphism was not assessed in these samples due to lack of
gDNAl. Still, this indicates presence of a functional polymorphism acting on ACE
expression in adipose.
4.4 Discussion
The results of this survey of allelic mRNA ratios at ACE in non-cardiac tissue
indicate that the previously identified enhancer SNPs only have function in the heart. It
also demonstrates that moderate allelic expression imbalance is present in putamen, liver
and adipose tissue. Further, the commonly studied ACE I/D polymorphism has no
association with instances of AEI. This demonstrates that ACE is under influence from
additional regulatory polymorphisms with tissue specific function.
The tissues in this study were selected based primarily on their availability.
Prefrontal cortex, which does show low AEI in a single sample, is a poor expresser of
ACE. However, even at a low level of gene expression the lack of enhancer SNP
function can still be observed. Interestingly, the putamen has ties to Alzheimer’s amyloid
plaque associated memory loss (de Jong et. al., 2008). Due to the role of ACE in breaking
down amyloid plaque protein, this is an excellent target tissue (though not a direct
application to cardiovascular drug metabolism) (Hu, 2001;Ohrui, 2004). Here, non92
Caucasian (Hispanic and African ancestry) samples exhibit AEI of ~1.73-fold. It is not
clear what a modest change in gene expression might do to ACE activity in the putamen,
or if it would affect memory loss or Alzheimer’s progression.
Interestingly, ACE also showed significant allelic mRNA expression in human
liver. To some extent ACE in the liver is affected by ACE inhibitors, as they are
activated here by carboxylesterases (Thomsen, 2014). Moreover, the AEI identified here
is in Caucasian samples, a subset of individuals where AEI is not seen in the heart.
Though functional polymorphisms may be acting in this population in the liver it is not
clear their relevance to drug treatments or other disease phenotypes.
Using RNA-sequencing data to screen multiple allelic mRNA ratios at SNPs in
ACE allows rapid identification of AEI. ACE plays a role in cardiovascular risk in
adipose, where an RAS is active. It has also been shown that ACE in adipose may
regulate body weight (Engeli et. al., 2005;Fleming, 2006). Following, analysis 3 samples
showed an average of ~1.35-fold AEI. Importantly, in these samples standard deviation
is relatively high, which may be a product of the IUPAC alignment correction, or the
modest 30-read depth coverage. Additional follow up including targeted allelic
expression assays or RNA-sequencing could further confirm the presence of AEI in
adipose tissue. Though some heart was included in this RNA-sequencing analysis no
striking allelic mRNA ratios were observed.
This study presents multiple tissues with evidence of functional cis-acting
regulatory polymorphisms acting on ACE. It is not clear the role this AEI and associated
variants might have in drug treatment or human disease progress. I also show a lack of
93
function for the ACE enhancer SNPs, which is expected because these SNPs exist in a
MEF2A cardiac transcription factor binding site. Notably, the ACE I/D variant does not
show any association with allelic mRNA ratios, which provides evidence that it is not
functional and may associate with a trans- factor (if any at all). These results indicate
that targeted studies to identify functional genetic variants affecting ACE should be
completed in alternative (non-cardiac) tissues and diverse populations.
94
Allelic mRNA Fold Ratio
IUPAC Corrected Fold by SNP
2
1.5
1.3
1
1.3
Adipose 14
Adipose 13
Adipose 12
Adipose 11
Adipose 10
Adipose 9
Adipose 8
Adipose 7
Adipose 6
Adipose 5
Adipose 4
Adipose 3
Adipose 2
Heart 7
Adipose 1
Heart 6
Heart 5
Heart 4
Heart 3
Heart 2
95
Heart 1
1.5
Sample
Figure 25. Allelic mRNA ratios determined by RNA-Sequencing Data.
Allelic mRNA ratios that have been corrected for lack of IUPAC correction are presented here. Bars indicate observed
allelic mRNA ratios at a SNP, while groupings indicate all the SNPs in a particular sample. In some cases significant
variability between intra-sample SNPs is observed. Average allelic mRNA ratios between all SNPs indicate that at least 3samples may exhibit an average of ~1.35-fold allelic expression balance in adipose tissue (Data summarized in Appendix
B.4).
95
Chapter 5. Conclusions and Final Thoughts
The work herein illustrates the complexity of regulatory influence on the
expression and activity of candidate genes involved in the metabolism of cardiovascular
drug metabolism in the human liver (and other relevant tissues). This regulatory control
is presented as a promoter variant increasing gene transcription in cytochrome p450 2C19
(CYP2C19). In carboxylesterase-1A1 (CES1A1), decreases in gene expression and
protein quantity are regulated by a polymorphic 5′UTR which significantly alters mRNA
folding. Additionally regulatory variation is seen in the ubiquitously expressed
angiotensin-I converting enzyme (ACE) which exhibits moderate allelic mRNA ratios in
a wide array of tissues. Further, a known group of enhancer SNPs show tissue specific
function in the heart. Though, functional ACE SNPs have not been identifed outside the
heart, it is clear that they are present in both a population and tissue specific manner. In
the same way 90% of gene loci undergo alternative splicing (Wang et. al., 2008), the
majority likely experience some degree of regulatory variation. In fact, alternative
splicing and regulatory variants are linked by srSNPs, which may drive the presence or
abundance of alternative splice forms. Further demonstrated here, robust allelic
phenotypes, though relevant (especially in drug metabolism) are less common than
regulatory variants with moderate effects. This work presents evidence for wide-spread
regulatory and structural variation in DME genes. Though this may not significantly
96
affect clinical drug efficacy, it does contribute to the cadre of genetic variation that drives
the drug metabolism interindividual variability.
A person’s metabolic capabilities are often irrelevant until they are prescribed a
drug. As a system, drug metabolism is redundant, with multiple enzymes that have
overlapping substrate specificity. Additionally, throughout an individual’s life, their drug
metabolizing ability varies due to environment, gender, age, race and more. As a result
this creates a population with a diverse spectrum of drug metabolizing ability which
contributes to a dynamic response to new environmental compounds.
This diversity is
strengthened by the polymorphic nature of drug metabolism enzyme gene loci, which
contributes to metabolic variation. This abundant genetic variation and the need to
predict individual drug response drives pharmacogenetic studies.
As described, drug metabolism is subject to influence from non-genetic and
genetic factors. Transcription factors (TFs) such as the central regulator of liver gene
expression hepatocyte nuclear factor-alpha (HNF4α) shows little evidence of regulatory
variants by allelic expression imbalance (AEI). Nuclear receptors (NRs), a class of TFs
which induce drug metabolizing enzyme (DME) expression when binding certain
ligands, have been shown to experience extensive alternative splicing(Lamba et. al.,
2005). Though cis- acting variants may be present in these genes, large effect sizes are
needed to observe significant changes in DME ability. Taken together, these transelements, (plus age, race and gender), are mechanisms for exerting environmental and
genetic variation on drug metabolism. Understanding these different factors is a
challenge pharmacogeneticists needs to address to predict drug response. Unlike some
97
pharmacogenetic applications, where a drug/compound and its target are the subject of
interest, the huge number of variables involved in drug metabolism can make
identification of clinically actionable biomarkers difficult.
This brings the field to an impasse. As “common” pharmacogenetics biomarkers
with strong effect sizes (regulatory or coding variant sites) have been discovered, new
biomarkers with large effect become more difficult to identify. Certainly there is merit in
identification of these variants as predictors of metabolic ability, but as demonstrated
here, moderate regulatory effects are also common in critical drug metabolizing enzymes
(DMEs). Does a modest effect size invalidate a cis-acting polymorphism as a
pharmacogenetic biomarker? I would venture that it does not. It does indicate that
targeted studies are required to determine clinical relevance of these variants, particularly
in relationship to other known functional SNPs. This approach will be essential as more
functional SNPs are identified. Pharmacogeneticists must examine our ability to assess
the true contribution of a variant (particularly of modest effect) to drug metabolizing
ability.
An important application of these biomarkers is the manner in which their
contributions to a phenotype are considered. Typically, polygenic models “add” the
effects of identified variants to generate risk predictions in development of a phenotype.
A model polygenic trait is height, where multiple gene loci have been shown to
contribute to an increase height in an additive method (Weedon et. al., 2008).
Alternatively, epistatic interactions are beginning to be considered when studying the
effects of multiple SNPs. Here, variants are assessed in the context of one another, and
98
may have differing function in the presence or absence of a variant(s). The elucidation of
SNP-SNP epistatic interactions is a large computational and statistical task. However,
focused studies may be able to utilize previously characterized SNPs in disease or
metabolic pathways, and assess any epistatic interactions present. A previous study by
our lab showed an epistatic interaction between SNPs in dopamine receptor D2 (DRD2)
and dopamine transporter (DAT). Here, homozygotes for a non-functional DAT variable
number tandem repeat (VNTR, rs3836790) augmented the odds ratio for risk of cocainerelated death in the presence of the functional DRD2 srSNP rs2283265 to an odds ratio of
7.5 (Sullivan et. al., 2013). This study demonstrates that targeted studies can identify
epistatic interactions, and that even non-functional variants can carry significant risk in a
particular genetic context.
The same can be predicted for drug metabolism, though these studies are limited.
In yeast, metabolic gene deletion was used to broadly define epistatic interaction within
yeast metabolism. Importantly, they conclude each phenotype contains its own specific
set of epistatic interactions. They also highlight the complexity of metabolic networks,
stating that apparently unrelated metabolic processes may affect a phenotype in an
epistatic and nonlinear fashion (Snitkin et. al., 2011). As such this highlights limitations
on current pharmacogenetic studies. If we accept that complex genetic interactions with
possible nonlinear effects on drug metabolism are prevalent, then assessment of a few
well-defined SNPs in the context of drug metabolism may be inadequate. If SNP-SNP
interactions are pervasive, then all SNPs regardless of effect size may be clinically
relevant in a genetic or substrate specific context.
99
Twenty genotypes were assessed for their ability to predict development of type 2
diabetes when compared to, or in addition to phenotype based risk models. Using
cumulative allele counts or odds ratios, genetic prediction was considered inferior to
phenotype based models. Further, when combined with these models, no significant
increase in risk prediction was observed (Talmud et. al., 2010). Though perhaps genetic
prediction is not effective here, any present SNP-SNP interactions would be represented
to some extent in phenotype risk models while an additive genetic model would not.
Importantly, fields such as cancer are taking an epistatic approach to both predict disease
risk and drug response (Chu et. al., 2014;Weigelt et. al., 2014).
Epistatic interactions could also have a role in studies adjacent to this body of
work. CYP2C19 has been the primary focus of study in clopidogrel metabolism because
it is responsible for the majority of prodrug activation. Notably, clopidogrel’s main two
enzymes; CYP2C19 (drug activation) and CES1A1 (drug inactivation) have numerous
variants that have not been tested for epistatic interaction. Additionally, of the 8 enzymes
involved in clopidogrel metabolism, 6 harbor variants that are moderately associated with
a drug’s efficacy or toxicity (not necessarily clopidogrel). Here the large numbers of
involved enzymes and variants have not been assessed for epistatic interaction. The same
holds true for the enzymes (and SNPs) involved in many therapeutic interventions.
Eventually, the SNP-SNP approach will be refined; allowing accurate prediction
of patient metabolic ability. Ultimately, polymorphisms with modest effect size may find
clinical relevance in these studies. Still, these analyses will be limited by an inability to
account for non-genetic sources of metabolic variation. However, a shift towards
100
epistatic interactions should address missing heritability –unaccounted genetic
contributions to a phenotype. In the case of CYP2C19*2, only ~12% of variation of
clopidogrel response is accounted for by the loss of function allele (Shuldiner, 2009). If a
clearly functional polymorphism has a small overall effect as seen here, epistatic
interactions have potential to increase the predictive power of genetic biomarkers.
As more functional SNPs in drug metabolism are characterized, scientists and
clinicians will seek to identify these interactions. Databases such as PharmGKB or The
Human Cytochrome P450 Allele Nomenclature Database, indicate the large number of
variants defined (cSNPs, srSNPs, and rSNPs), in addition to current evidence for clinical
implementation (www.pharmgkb.org, www.cypalleles.ki.se) (Whirl-Carrillo, 2012).
Further, considering these polymorphisms in addition to the SNPs with large enough
effect sizes to prompt FDA black box warnings, SNP-SNP interactions predicting drug
response seem likely. This will elevate pharmacogenetic studies, drug response
prediction, and should be reflected in guidelines as defined by the Clinical
Pharmacogenetics Implementation Consortium (CPIC) or other similar groups.
This study utilizes cardiovascular drug metabolism as a representation of how
multiple genes that interact at the point of treatment each have their own complex
molecular genetic mechanisms. Though studied individually here, they represent a group
of genes where SNP-SNP interactions may exist given the proper substrate (clopidogrel,
ACE inhibitors). Here, I define CYP2C19*17 in human liver, and the functional effect
of CES1A1VAR. I also present evidence for cis-acting regulatory SNPs acting on ACE in
multiple races and tissues (with lack of ACE enhancer SNP function in heart).
101
CES1A1VAR has potential for utility as a pharmacogenetic biomarker, and the
characterization of CYP2C19*17 should improve classification as a biomarker. The
evidence for cis-acting functional regulatory variants in multiple drug metabolizing
enzyme genes (as seen here), is not only relevant to cardiovascular drugs but also a wide
array of therapeutic interventions. Though variants observed in this study have a modest
effect, they may be relevant clinically in certain treatments, or when considered as part of
a SNP-SNP interaction. In the future, as genomic data is gathered, functional molecular
genetic studies will provide rationale in the inclusion of variants into accurate metabolism
prediction algorithms.
102
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Appendix A: Tables
Table 3. Primer Sequences and PCR conditions.
Sequencing, quantitation, genotyping and SNAPshot primers are included. Primers are
presented with their use, name, sequence and run conditions. Run conditions are
annotated as (Annealing temp)PCR(HS=Hotstart)(# of cycles if >30). Extension times are
1 minute or default for the Taq specified.
CYP2C19 Primer List
1. CYP2C19 Promoter and Exonic regions for Ion Torrent Sequencing
Promoter ~-4KB to -2KB
63.5PCRHS, 2 min 30 sec extension
2C192kbTL1F1, GACCGGAGCTGTTCGTATTTGGC
2C19P2kbTL1R1, TTGACTAGATTGGGGGTCAGAAGAGTTTG
Promoter -2KB to start of Exon 1
63.5PCRHS, 2 min 30 sec extension
2C19P2kbTL2F2, CATTGTGATACTTTGTCTCACTGAGTCA
2C19P2kbTL2R2, CTTACATTGGTTAAGGATTTGCTGACA
***Promoter -3KB to Exon 1
63.5PCRHS_35
2C19_3kbP_to_Ex1_F, TTATTTGTTGCTAGGGCTCGTG
2C19_3kbP_to_Ex1_R, CTTACTGTTTACCCTCAGCC
Continued
123
Table 3 continued
***Exon 2 to Exon 3
60PCRHS
2C19_Ex2_to_Ex3_F, AGGTAGACACAAGAGTGCTGA,0.025,desalt
2C19_Ex2_to_Ex3_R, TTCTCTGGTGACATGTTCTGGA,0.025,desalt
***Exon 4 to Exon 5
60PCRHS_35
2C19_Ex4_to_Ex5_F, CCATTATTTAACCAGCTAGGC,0.025,desalt
2C19_Ex4_to_Ex5_R, TCCTATCCTGACATCCTTATTG,0.025,desalt
Exon 6
60PCRHS
2C19 I5F1, GACAAACCCACAGCCAATATCATACT
2C19 I6R1, GGGACAGATTACAGCTGCGG
Exon 7
60PCRHS
2C19_Ex7_F, AATGCTGAAGTGGGTTGTTG,0.025,desalt
2C19_Ex7_R, ACCCTGACAGAAATTCTAGCCC,0.025,desalt
Exon 8
64.1PCRHS, requires gel purification of ~951 bp band
CYP2C19I7F1, GTGCTGCACCCATTAACTCATC
CYP2C19I8R1, TTTCCAAACACAGAAGTGAGCCTC
Exon 9
59.3PCRHS
CYP2C19I8F1, CCATCCATTCATCCATTAATCCT
CYP2C193UPR1, CATTATGTGGCACTCAATGTAACTATTAT
*** From Blaisdell J, et al.
Continued
124
Table 3 continued
2. Quantitation Primers
GATA4QF, AGCCTGGCCTGTCATCTCACTA
GATA4QR, GACATCGCACTGACTGAGAACGT
CARquan-F, CACATGGGCACCATGTTTGA
CARquan-R, AAGGGCTGGTGATGGATGAA
RXRA-F, GAGCGGCAGCGTGGCAAGG
RXRA-R, GGCAAATGTTGGTGACAGGG
PXRquan-F, CAAGCGGAAGAAAAGTGAACG
PXRquan-R,CACAGATCTTTCCGGACCTG
HNF4A1A-F, ACATGGACATGGCCGACTAC
HNF4A1A-R, CTCGAGGCACCGTAGTGTTT
GAPDHquan-F1, ACTCCTCCACCTTTGACGCT
GAPDHquan-R1, GGTCCACCACCCTGTTGC
CYP2C19_E8F2, GTCACTTTCTGGATGAAGGTGTA
CYP2C19_E9_R, AGGTCCTTTGGGTCAATCAGAG
3.SNaPshot/Genotype primers
rs12248560(CYP2C19*17)
60PCRHS
2C19 Promoter F1, GCCCTTAGCACCAAATTCTCTG
2C19 Promoter R1, AACACCTTTACCATTTAACCCC
CYP2C19_rs560_PEF, CAAATTTGTGTCTTCTGTTCTCAAAG
rs4244285 (CYP2C19*2)
60PCR
CYP2C19Int4F, ATCTTATATTTCAAGATTGTAGAGAAGAATTGTTG
CYP2C19int5R, CATCCGTAGTAAACACAAAACTAGTCAATG
2C19rs285PEFT10, T(10)CCCACTATCATTGATTATTTCCC
Continued
125
Table 3 continued
rs17885098 (marker SNP)
60PCRHS
2C19 E1DNA-R1, CTTACATTGGTTAAGGATTTGCTGACA
2C19 E1RNA-R1, AGAGATTGGTTAAGGATTTGCTGACA
2C10 rs098PER2, ATTTCCAATCACTGGGAGAGGAGT
rs4986894(-98T>C)
60PCRHS
F Primer, [6FAMTGCATTGGAACCACTTGGGTTA
R Primer, CCTACATTGGTTAAGGATTTGCTGACA
Cut with BtsCI at 50C in Buffer 4 (New England Biosciences, Ipswich MA)
rs4986893(2C19*3)
60PCRHS
F Primer, GAATGAAAACATCAGGATTGTAAGCAC Tm: 60
R Primer, GGATTTCCCAGAAAAAAAGACTGT Tm: 58
Primer Extension R, CAAAAAACTTGGCCTTACCTGGAT Tm: 60.5
4. Sanger sequencing primers for promoter region
2C19P7F1, CGGAGCTGTTCGTATTTGGC
2C19P7F2, AATAAGATGACAAGACACAGACTGGGA
2C19P7F3, AGAAATGAACTATCAAGCCATGAAAAGA
2C19P7F4, CATTGTGATACTTTGTCTCACTGAGTCA
2C19P7F5, CCTCATTCCTGAGATGGGTCAAT
2C19P7F6, GTGGTTCTATTTAATGTGAAGCCTGTT
2C19P7F7, TTAGCTATTTCATGTTTAGGCTGCTGTAT
CES1 Primer List
1. Genotyping and allelic mRNA expression assays
60PCRHS
CES1A1 gDNA amplification
CES1A1_gDNA_F2, AGTAGTGAGTTCTCTGACACCGATG
CES1A1_gDNA_R2, GTAAGGGGACTAATTAAGGGACAGC
Continued
126
Table 3 continued
CES1A1 mRNA amplification for SNaPshot
60PCRHS
CES1A1Qmedalt_F, CAGAGACCTCGCAGGCCC
CES1A1Qshort_R, CAAGCCGCGGAAGCAG
Primer extension primers for preceding allelic mRNA ratios and gDNA reactions
CES1A1 rs12149370 PER,TGGAAGGGCGACAGTTC
CES1A1 rs3815583 PER, TGCTGTCCAGCCCTGG
CES1 rs2244613 genotyping
60PCRHS
CES1A1_rs2244613_FAM_F,(6FAM)AGACAGCCATGTCACTCCTGTG
CES1A1_rs2244613_R,GGACACCAAACATCACATCTGCT
Digest with SmaI in NEB CutSmart Buffer(Cuts at minor allele 'C').
CES1A1VAR genotyping
7500 Fast, SYBR Default Cycling
Fukami_1A1_FR_F,CAAGGGTGAACCCTTATGTA,0.025,desalt
and
CES1A1WT
Fukami_1A1_ex1AS, AGAGAGTGGCCAGGATAAAG
or
CES1A1VAR
Fukami_1A2_3_ex1AS, CGAGAGTGGCCAGGACAAGA
Genotype by presence or absence of product in parallel reactions.
From Fukami et al. 2008.
2. mRNA quantitation and notable sequencing primers
CES1 mRNA quantitation
7500 Fast, SYBR Default Cycling
CES1_RNA_Q_F,GTTCCGCGGCTTGG
CES1_RNA_Q_R,CGGCTTGGCAAAAGGGATT
See GAPDH quant primers from CYP2C19
Continued
127
Table 3 continued
5kb promoter region amplification
61.8_PrimeStarTaq
See CES1A1_gDNA_F2
CES1A1_5K_F,ATGACGGCGATCAAGGCT
Infusion Cloning Primers for insertion into pGL3B
59.8_ADVHD Taq
CES1A1_Inf2_HIII_F,
CGATCTAAGTAAGCTTTGCTCTTTGTGTACAAGCTTTTGTG
CES1A1_Inf3_HIII_R, CCGGAATGCCAAGCTTCAAGCCGCGGAAGCA
Promoter Sanger Sequencing Primer
CES1A1Qlong_F,AGCGCAGGGCGGTAACT
ACE Primer List
1. Genotyping and allelic mRNA primers
ACE (I/D, rs13447447)
60PCR
ACE_ID_R_FAM,(6FAM)GTGGCCATCACATTCGTCAG
ACE_ID_common_F,CCCATCCTTTCTCCCATTTCT
ACE_ID_specific_F,GACCTCGTGATCCGCCC
Insertion= peaks at 191 bp and 462
Deletion = 175 bp peak
rs4343
7500 Fast, SYBR Default Cycling
ACE_rs4343_gcG_F,CTGACGAATGTGATGGCAACG
ACE_rs4343gc_gcA_F,CGCGCCCGCCGGCGCGCGGCTGACGAATGTGATGGCCG
CA
ACE_rs4343_gc_R,GATGGCTCTCCCCGCC
rs4343 allelic mRNA ratio
60PCR
ACEI_rs4343_DNA_F,CCCTTACAAGCAGAGGTGAGCTAA
ACEI_rs4343_RNA_F,ACCACCTACAGCGTGGCC
ACEI_rs4343_R,CATGCCCATAACAGGTCTTCATATT
ACEI_rs4343_PEF,GACGAATGTGATGGCCAC
Continued
128
Table 3 continued
rs7214530
7500 Fast, SYBR Default Cycling
ACE_rs7214530_F1,CATTTCTGGACTCTCAATTCTATTCCA
ACE_rs7214530R_A,CACCAGTTACCACAGGAGAGAAGAA
ACE_rs7214530gcR_C,GGCGCGGGCGCGCGGCACCAGTTACCACAGGAGAGAC
AAC
Use half concentration of allele A compared to allele C
rs4290
60PCR
ACE_rs4290_RFLP_F, TCTTATATTATTCCTGCAAAAGGCTG
ACE_rs4290_RFLP_FAM_R,(6FAM)CACCTCCCTCACTGTAACGCTT
Digest with Taq1α in NEB CutSmart Buffer, cuts with major 'C' allele
rs7213516
60PCR
ACE_rs7213516_RFLP_FAM_F,(6FAM)TGCACCCAGCCCCAAAT
ACE_rs7213516_RFLP_R,CATCTCTCTTCCAGGAAGTACAGGT
268 BP fragment, Digest with HinF1 in NEB CutSmart Buffer
A allele cuts at 163 bp
G allele cuts at 175 bp
2. Quantitation Primers
PGK1F,CTGTGGTCCTGAAAGCAGCA
PGK1R,TTAGCCCGAGTGACAGCCTC
ACE_Q_F,CCCCTTCCCGCTACAACTT
ACE_Q_R,TCCCCTGATACTTGGTTCGAA
129
Table 4. CYP2C19 SNP genotypes and Hardy-Weinberg Equilibrium.
Varying allele frequencies between populations may cause lack of rs17885098 Hardy-Weinberg Equilibrium. (MAF = 0.083
Caucasians, MAF = 0.180 in Africans).
rs17885098
rs4244285
rs4986894
rs12248560
Minor
Allele
G
A
C
T
Minor Allele
Frequency
0.128
0.174
0.149
0.26
Major
Allele
A
G
T
C
130
130
Major Allele
Frequency
0.872
0.826
0.851
0.74
HWE-P
2.20E-08
0.576
0.72
0.856
LOG10
HWE P
7.658
0.239
0.143
0.068
Table 5. CES1A1 SNPs genotypes and Hardy-Weinberg Equilibrium
Minor Allele
Frequency
0.345
Major
Allele
A
Major Allele
Frequency
0.655
HWE-P
LOG10 HWE P
rs3815583
Minor
Allele
C
0.795
0.010
VAR
B
0.176
A
0.824
0.215
0.670
rs2244613
C
0.221
A
0.779
0.184
0.735
rs71647871
A
0.017
G
0.983
0.850
0.071
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Table 6. ACE. Marker SNP and Hardy-Weinberg Equilibrium.
rs4343 was genotyped and enhancer (rs4290) and I/D variants were genotyped on a by sample basis in rs4343 carriers.
Marker
Minor Allele
rs4343
A
Minor Allele
Frequency
0.424
Major
Allele
G
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132
Major Allele
Frequency
0.576
HWE P
0.289
-log10 HWE P
0.539
Table 7. CES1A1-pGL3-Basic construct variations.
CES1A1VAR and CES1A1SVAR primary SNPs are represented in Table X in the text.
Additional SNPs are present, with rs3859102 and rs334428341 minor alleles present in
all constructs. Allele rs28759040 is present in CES1A1WT and CES1A1WT+rs3815583
which show no significant difference in luciferase assay, indicating that likely nonfunctional.
CES1A1WT
CES1A1VAR
CES1A1SVAR
CES1A1WT+rs3815583
SNP Minor Allele
rs3859102
rs334428341
rs28759040
rs3859102
rs28779040
Table 2 SNPs:1-11
rs3859102
rs334428341
rs28759040
Table 2 SNPs:1-5
rs3859102
rs28779040
rs3815583
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Table 8. Average ACE-fold AEI in RNA-sequencing.
Absolute fold-change is computed and corrected for the lack of IUPAC alignment, which
when not performed causes an allelic bias in the number of minor alleles mapped to the
genome. Average fold is presented along with standard deviation to represent the overall
allelic mRNA ratio at n SNPs in ACE.
Heart 1
Heart 2
Heart 3
Heart 4
Heart 5
Heart 6
Heart 7
Adipose 1
Adipose 2
Adipose 3
Adipose 4
Adipose 5
Adipose 6
Adipose 7
Adipose 8
Adipose 9
Adipose 10
Adipose 11
Adipose 12
Adipose 13
Adipose 14
Fold-AEI
1.122
1.094
0.871
1.225
0.975
1.211
1.235
0.925
0.976
0.914
1.378
0.990
0.969
0.933
0.862
1.324
0.813
0.939
0.907
1.342
1.078
Standard Deviation
0.318
0.433
0.073
0.247
0.331
0.612
0.233
0.175
0.157
0.057
0.364
0.198
0.076
0.245
0.079
0.273
0.112
0.240
0.082
0.303
0.310
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# of SNPs
5
2
3
2
2
4
2
3
5
3
4
5
4
4
4
2
4
4
4
3
4
Appendix B: Figures
Figure 26. Linkage Disequilibrium of CYP2C19 alleles.
Included *17(rs12248560), *2(rs4244285), marker SNP (rs17885098), rs4986894 and *3
(rs4986893) where data was available. LD expressed as a measure of r2 . *2 and
rs4986894 are in high LD as expected. Our marker SNP and *3 are also in high LD
however no *3 carriers were detected in this study. Linkage plots generated in
Haploview using data from 1K Genomes Project.
135
Figure 27. Linkage of relevant polymorphisms in CES1A1.
Polymorphisms included are CES1A1VAR, rs3815583, rs71647871 and rs2244613. LD
is expressed as a measure of r2. Linkage plots are developed from genotyping data and
generated in Helix Tree SNP and Variation Suite.
136
A
B
Figure 28. CES1 protein expression in human liver tissue.
CES1 protein quantity was measured by normalization to input of L100 (arbitrary unit,
Sample #32). Quantitation was performed in duplicate and averages were used for
analysis.
137
Putamen vs PFC qPCR
8
7
6
dCT
5
4
3
2
1
0
Putamen
Prefrontal Cortex
Figure 29. Relative Gene Expression of ACE in Prefrontal Cortex and Putamen.
ACE mRNA expression normalized to PGK1 housekeeping gene in human prefrontal
cortex and putamen. In many cases, prefrontal cortex samples showed no measurable
gene expression (not pictured).
138
Appendix C: Abbreviations
ACE - angiotensin-I converting enzyme
ACE I/D - ACE insertion/deletion polymorphism
AEI - allelic expression imbalance
AHR - aryl hydrocarbon receptor
CAR - constitutive androstane receptor
cDNA- complimentary deoxyribonucleic acid
CES1A1- carboxylesterase-1A1
CES1P1 - carboxylesterase pseudogene 1 (also CES1A2/CES1A3)
CYP2C19 - cytochrome P450 2C19
DAT- dopamine transporter
DME - drug metabolizing enzyme(s)
DRD2- dopamine receptor D2
eQTL- expression quantitative trait loci
GATA4 - GATA binding protein 4
gDNA- genomic deoxyribonucleic acid
HGP- Human Genome Project
139
HNF4α - hepatic nuclear factor α
mRNA- messenger ribonucleic acid
NR - nuclear receptor
PCR- polymerase chain reaction
PXR - pregnane X receptor
qRT PCR- quantitative real-time polymerase chain reaction
RACE- rapid amplification of cDNA ends
RT- reverse transcriptase
RXRα - retinoid x receptor α
SNP - single nucleotide polymorphism
TF - transcription factor
140