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Systematic analysis of single nucleotide polymorphisms in the human steroid 5-alpha reductase type I gene

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Systematic analysis of single nucleotide polymorphisms in the human steroid 5-alpha reductase type I gene

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Content SYSTEMATIC ANALYSIS OF SINGLE NUCLEOTIDE
POLYMORPHISMS IN THE HUMAN STEROID 5-ALPHA REDUCTASE
TYPE I GENE
by
Troy Phipps
A Dissertation Presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(BIOCHEMISTRY AND MOLECULAR BIOLOGY)
December 2006
Copyright 2006 Troy Phipps
ii
Acknowledgements
I would like to acknowledge the many individuals who have contributed to
my education and this research project.
To my committee members: Drs. Baruch Frenkel, Bob Stellwagen, and Gerry
Coetzee, I greatly appreciate your mentoring on science and writing.
The Reichardt Lab, and others, with particular thanks to:
Dr. Jüergen Reichardt: for mentoring over many years.
Dr. Nick Makridakis: for thoughtful experimental and life advice.
Dr. Laurence Kedes: for providing laboratory space after my mentor
departed.
Brian Pike, Sharon Paul, Warunee Paul, Gillian Little, and Trevor
Pemberton: for providing friendship, much needed scientific advice, and
multiple reagents over the years.
Sara Phipps: for her loving support and council through the long years.
Scott Jouppi: for expanding my horizons and for encouraging exploration of
the natural world.
Finally, I wish to extend heartfelt thanks to all the staff and faculty of the
IGM for forming a unique research environment and making many of us feel
like we are part of an extended second family.
iii
Table of Contents
ACKNOWLEDGEMENTS ii
TABLE OF CONTENTS iii
LIST OF TABLES v
LIST OF FIGURES vi
ABBREVIATIONS vii
ABSTRACT ix
CHAPTER 1: INTRODUCTION 1
1.1 PROSTATE CANCER 2
1.2 THE ASSOCIATION BETWEEN DIET AND PROSTATE CANCER 4
1.3 GENETIC MECHANISMS OF PROSTATE CANCER 7
1.4 GERMLINE PREDISPOSITION TO PROSTATE CANCER 10
1.5 SOMATIC MUTATIONS IN PROSTATE CANCER 12
1.5.1 SINGLE GENES 12
1.5.2 PROSTATE CANCER CYTOGENETICS 13
1.5.3 PROSTATE CANCER EPIGENETIC 16
1.5.4 MICRORNA DYSREGULATION IN PROSTATE CANCER 16
1.5.5 GENE FUSION PRODUCTS IN PROSTATE CANCER 17
1.6 ANDROGEN ACTION IN THE PROSTATE 18
1.7 HUMAN STEROID 5-ALPHA REDUCTASE TYPE 1 AND TYPE 2 24
1.8 EUKARYOTIC MESSENGER RNA STABILITY AND PROCESSING 27
CHAPTER 2: MATERIALS AND METHODS 30
2.1 GENOTYPING SRD5A1 30
2.1.1 PCR OLIGONUCLEOTIDES 30
2.1.2 POLYMERASE CHAIN REACTION CONDITIONS 34
2.1.3 PCR GEL PURIFICATION 36
2.1.4 DNA SEQUENCING 36
2.1.5 HAPLOTYPE RECONSTRUCTION 37
2.2 CLONING AND MUTAGENESIS 38
2.2.1 CLONING LUCIFERASE EXPRESSION CONSTRUCTS 38
2.2.2 HAPLOTYPE MUTAGENESIS 38
2.2.3 CLONING NATIVE EXPRESSION CONSTRUCTS 41
2.3 MAMMALIAN CELL CULTURE 42
2.3.1 TRANSFECTIONS 42
2.3.2 LUCIFERASE ASSAYS 43
iv
2.3.3 TOTAL PROTEIN QUANTITATION 44
2.3.4 BETA-GALACTOSIDASE ASSAYS 44
2.4 RNA ASSAYS 44
2.4.1 TOTAL RNA ISOLATION 44
2.4.2 CDNA SYNTHESIS 45
2.4.3 SEMI-QUANTITATIVE RT-PCR 45
2.4.4 QUANTITATIVE RT-PCR (REAL TIME) 46
CHAPTER 3: SRD5A1 SNPS 47
3.1 CATALOGING SRD5A1 SNPS ACROSS THE GENE 47
3.2 ANALYSIS OF SRD5A1 INDIVIDUAL SNPS WITHIN THE LHMEC COHORT 49
3.3 ANALYSIS OF SRD5A1 SNP COMBINATIONS AS HAPLOTYPES 59
3.4 DISCUSSION 64
CHAPTER 4. SRD5A1 3’UTR BIOCHEMICAL ASSESSMENT OF SNPS
AND COMBINATIONS 66
4.1 INTRODUCTION 66
4.2 PREDICTED RNA SECONDARY STRUCTURES OF INDIVIDUAL SRD5A1 3'UTR SNPS
AND COMBINATIONS 66
4.3 INDIVIDUAL SNPS ALONG THE SRD5A1 3'UTR DO NOT AFFECT ACTIVITY OF A
FUSED LUCIFERASE REPORTER OR RNA STEADY STATE LEVELS. 70
4.4 SNP COMBINATIONS ALONG THE SRD5A1 3'UTR DO NOT AFFECT ACTIVITY OF A
FUSED LUCIFERASE REPORTER OR RNA STEADY STATE LEVELS 76
4.5 NATIVE SRD5A1 EXPRESSION VECTORS CONTAINING SINGLE AND COMBINATION
SNPS DO NOT DEMONSTRATE DIFFERENCES IN RNA LEVELS 79
4.6 DISCUSSION 85
CHAPTER 5 CONCLUSIONS 86
5.1 RNA HAPLOTYPE RESULTS IN THE CONTEXT OF PUBLISHED SRD5A1 IN VIVO DATA
89
5.2 USING SRD5A1 3'UTR SNP 1368 IN ADMIXTURE MAPPING 90
5.3 FUTURE WORK 91
REFERENCES 92
v
List of Tables
Table 1.1 Genes Involved in Prostate Cancer Predisposition and
Development
8
Table 2.1 PCR Oligonucleotides 30
Table 2.2 PCR Conditions to Amplify the SRD5A1 Gene 35
Table 2.3 3’UTR SRD5A1 Mutagenesis Primers 40
Table 3.1 SRD5A1 Allele Frequencies and Statistics 51
Table 3.2 Ethnic distribution of the 3’UTR SNP 1368 in Three
Different Cohorts
56
Table 3.3 SRD5A1 3’UTR SNP Haplotypes and Frequencies 63
vi
List of Figures
Figure 1.1 Ethnic Incidence and Mortality of Prostate Cancer 4
Figure 1.2 Androgen Action in Prostate Cells 23
Figure 1.3 Messenger RNA stability is mediated by the
interactions of 5’ and 3’UTRs
29
Figure 3.1 SRD5A1 SNP Locations Along Chromosome 5 48
Figure 3.2 Ethnic Distribution of 3’UTR SNP 1368 in Three
Different Populations
56
Figure 3.3 Predicted Effects of SNPs on the SRD5A1 Putative
Promoter Region
57
Figure 3.4 Predicted Effects of SNPs on the SRD5A1 3’UTR 58
Figure 3.5 SRD5A1 SNP Haplotypes 60
Figure 4.1 In silico RNA Secondary Structures of Individual
SRD5A1 3’UTR SNPs and Combinations
68
Figure 4.2 Luciferase Reporter Activity and RNA Steady State
Levels of Constructs Containing Individual SRD5A1
3’UTR SNPs
74
Figure 4.3 Luciferase Reporter Activity and RNA Steady State
Levels of Constructs Containing SRD5A1 3’UTR SNP
Combinations
78
Figure 4.4 PCR Confirmation of Proper Intron Splicing from
Native Vectors
81
Figure 4.5 Influence of SRD5A1 3’UTR SNPs on mRNA Steady
State Levels Expressed from Constructs Containing
the Native SRD5A1 Promoter and cDNA as well as a
Heterologous Intron.
83
Figure 4.6 Comparison of RNA Levels between Luciferase and
Native Constructs
84
vii
Abbreviations
ABI, Applied Biosystems, Incorporated
AR, Androgen receptor
BPH, Benign prostatic hypertrophy
CaP, Prostate cancer
CGH, Comparative genomic hybridization
CpG, Adjacent cytosine and guanosine DNA bases on the same strand
cDNA, Complementary DNA
dNTPs, Deoxynucleotide triphosphates
DHT, Dihydrotestosterone
EDTA, Ethylaminediaminetetraacetic acid
FISH, Fluorescence in situ hybridization
GnRH, Gonadotropin releasing hormone
HR-CaP, Hormone refractory prostate cancer
LHMEC, Los Angeles Hawaii multiethnic cohort
LNCaP, Lymph node prostate cancer cell line
NADPH, reduced nicotinomide adenine diphosphate
PABP, Polyadenylate binding protein
viii
PABPC, Polyadenylate binding protein cytosol
PABPN, Polyadenylate binding protein nucleus
PCR, Polymerase chain reaction
SHBP, Steroid hormone binding protein
SKY, Spectral karyotyping
SNPs, Single nucleotide polymorphisms
SRD5A, Steroid 5-alpha reductase
SRD5A1, Steroid 5-alpha reductase type I
SRD5A2, Steroid 5-alpha reductase type II
T, Testosterone
TBE, Tris-Boric Acid-EDTA
UTR, Untranslated region
ix
Abstract
The human steroid 5-alpha reductase type I (SRD5A1) gene was sequenced in
101 men to identify genetic variants that may predispose/protect carriers to prostate
cancer. We uncovered 30 single nucleotide polymorphisms (SNPs) along the gene
length, with 7 polymorphic sites lying in the 3’-untranslated region (3’UTR). All
SNPs were silent, but one 3’UTR SNP (named ‘1368T’) was significantly over
represented two fold in African-American men, possibly making this a useful
admixture mapping marker. To further pursue the relevance of the SNPs in the 3’-
UTR of this gene, we used cell culture assays to test for the potential effects at the
RNA or protein level. Individual and different combinations of SRD5A1 3’-UTR
SNPs (named “RNA haplotypes”) had no effect on luciferase activity when cloned
onto the 3’ end of the luciferase cDNA. Moreover, steady state mRNA levels did not
change when expressed either from luciferase or ‘native’ expression constructs
containing the native SRD5A1 promoter, cDNA, 3’UTR, and a heterologous intron.
Thus SRD5A1 3’UTR SNPs were truly neutral and non-functional in vitro, and are
therefore unlikely to play a significant role in prostate cancer predisposition in vivo.
If functional germline SRD5A1 3’UTR SNPs existed that altered reproductive
fecundity, they were likely selected against by evolution, and may explain why only
neutral SNPs were observed in three modern human populations.
1
Chapter 1: Introduction
Prostate cancer is a major health threat affecting 1 in 6 American men during
their lifetime. This year alone in the United States, over 234,460 men will be
diagnosed with, and 27,350 die from Prostate Cancer (Jemal et al., 2006).
Understanding how biological and environmental components contribute to prostate
cancer is a major goal of researchers worldwide. The highest at-risk population is
African-Americans, followed by Caucasians, Latinos, and then Asian-Americans.
Levels of circulating blood androgens, sex hormone binding globulin, and androgen
breakdown products in populations may follow that of prostate cancer risk: highest
for African-Americans, intermediate for Caucasians and Latinos, and lowest for
Asian-Americans (Ahluwalia et al., 1981);(Ross et al., 1992).
As early as the 1940s, a link between androgens and prostate cancer was
established based on the reduction (or elimination) of prostate carcinoma by
castration, as well as the observed opposite effect caused by injections of
testosterone (Huggins, C.B., Hodges, C.V. 1941). Later work discovered that steroid
hormones bind to receptors and regulate nuclear gene regulation, thus altering cell
and tissue phenotypes (Lefkowitz et al., 1970);(Attramadal et al., 1976). Enzymes
and biochemical pathways that control androgen metabolic flux are of significant
interest to understanding prostate cancer predisposition studies because they
participate in the regulation, distribution, and concentration of the various types of
steroid hormones.
2
Prostate cancer is strongly influenced by genetics, with the estimated
contribution being 42% of the total risk (Lichtenstein et al., 2000). Although most
sporadic cancers are caused by environmental factors, the relatively large genetic
contribution in prostate cancer demonstrates that there are knowledge gaps in
understanding this disease (Lichtenstein et al., 2000). Therefore, an examination of
the different enzymes which catalyze the anabolism of testosterone into
dihydrotestosterone (DHT) might be useful for investigating whether increased
metabolic flux through these pathways impact prostate growth and the likelihood of
tumor development (Davies and Eaton, 1991);(George et al., 1991);(Wilson, et al.
1975). As more evidence is being acquired regarding the interactions of androgens
with growth factors, neuroendocrine cells, as well as different tissues of the prostate,
new hypotheses about the etiology and development of the normal prostate into
benign prostatic hyperplasia (BPH) or adenocarcinoma are being put forth and
tested.
1.1 Prostate Cancer
As the leading cancer diagnosed in men, with the third highest cancer
mortality, understanding and treating prostate cancer in an ever larger, aging
population becomes more important (Jemal et al., 2006). Familial prostate cancer has
an attributable genetic risk of 42% as determined in a large Scandinavian Twin Study
(Lichtenstein et al., 2000), but is overall secondary to (unknown) environmental
factors (Baker et al., 2005). Thus, there are specific genes participating in the disease
progression.
3
In families with a history of BRCA2 mutations, or of familial male breast
cancer, there is an increased risk for developing prostate cancer in related males
(Lorenzo Bermejo and Hemminki, 2004). In families with multigenerational BRCA1
mutations, there is a slight, but noticeable increase in prostate cancer risk,
implicating BRCA1/2 proteins as playing a minor, but measurable role in prostate
carcinoma development (Bermejo and Hemminki, 2005). To highlight additional
genetic components, it was found that when 1, 2, or 3 first-degree relatives were
diagnosed with any cancer type, then the risk for a male relative of developing
prostate cancer increased 2, 5, and 11% respectively (Cussenot et al., 1998).
To date, five risk factors have been reproducibly established for prostate
cancer: sex, age, ethnicity, diet, and familial cancer history (Hsing and
Chokkalingam, 2006). As a man ages, his risk increases dramatically. African men
possess the highest incidence and mortality, followed by Latinos and Caucasians,
while Asians are the lowest (Figure 1.1). Various genes and gene products have
been identified as contributing small effects to prostate cancer predisposition,
progression or metastasis. A variety of genetic mechanisms have been identified as
having a causal role in prostate cancer such as epigenetics and single nucleotide
polymorphisms (SNPs), but new discoveries are still being made (Chapters 1.5.4
and 1.5.5).
4
0
50
100
150
200
250
300
Av erage W hites Blacks Asian-
Am ericans
N ativ e
Am ericans
H ispanics
R ateper 100,0
Incidence
M ortality
Figure 1.1 Ethnic Incidence and Mortality of Prostate Cancer. The left bar
represents prostate cancer incidence and the right bar is mortality per 100,000 men.
Data from Jamal, et al. 2006 was plotted graphically.
1.2 The Association Between Diet and Prostate Cancer
A large portion of prostate cancer relative risk comes from the environment.
Many epidemiological studies have investigated a number of nutrients and cooking
factors. Some interesting findings (that were reproduced) include an inverse
relationship between consumption of tomatoes/lycopenes, red wine, selenium,
vitamin E, and prostate cancer risk. Positive correlations exist for red meat
consumption, along with fats, and calcium in cow’s milk.
Multiple studies of lycopene consumption have demonstrated a 30-40%
relative risk reduction in prostate cancer independent of fruit and leafy vegetable
consumption (Giovannucci et al., 1995);(Giovannucci et al., 2002). In men who have
a family history of CaP, eating cooked tomato products demonstrated a stronger
protective effect against advanced prostate cancer risk as compared to eating raw
Rate/100,000
5
tomatoes, (Etminan et al., 2004);(Kirsh et al., 2006). It was hypothesized that heating
may increase lysis of the plant cell wall, and/or by converting the majority trans-
lycopenes (found in fresh tomatoes), into cis-lycopene, which is more readily
absorbed (Kirsh et al., 2006). The vast majority of prostatic lycopenes are the cis-
isomer, and thus higher levels from the diet may protect tissues from DNA damage
by limiting free radical exposure (Clinton et al., 1996);(Chen et al., 2001).
After adjusting for confounding variables such as age, prostate cancer family
history, smoking status, ethnicity, and body mass index, the consumption of red wine
reduced CaP relative risk 6% per glass/week (Platz et al., 2004);(Schoonen et al.,
2005). Drinking hard liquor or beer showed no such protective effects. The current
hypothesis is that red wine polyphenols act as a protectant (Platz et al., 2004). But
minerals have also been studied for their protective role.
A strong protective effect (50-65%) from ingesting high selenium levels was
found in multiple trials (Combs et al., 1997);(Nelson et al., 1999). But most
importantly, dietary supplementation was shown to reduce prostate cancer risk 50%
in a placebo-controlled, blinded study (Duffield-Lillico et al., 2003). Selenium has
been shown to be anti-proliferative, pro-apoptotic, anti-angiogenic, and to play a role
in antioxidant pathways (Corcoran et al., 2004);(Huang et al., 2003);(Waters et al.,
2003). The exact mechanism for this protection is not known.
Vitamin E, a well known antioxidant, was demonstrated to be protective
against developing prostate cancer in heavy smokers by approximately 30-40%
6
(Heinonen et al., 1998), and was found to reduce the risk of CaP recurrence and
mortality by 40% (Chan et al., 1999);(Goodman et al., 2003).
One of the strongest factors known to increase CaP risk is consuming red
meats. However, the exact mechanism for this is unknown, although recent studies
have indicated that heterocyclic amines like PhIP (2-amino-1-methyl-6-
phenylimidazol[4,5-b]pyridine) and/or benzo(a)pyrenes may be activated by the
endogenous P450 enzymes, followed by additional metabolism by N-
acetyltransferase-2 (SULT2) prior to DNA adduct formation (Kilbane et al., 1990).
Humans have two SULT2 enzymes, leading to either fast or slow metabolic
acetylation of molecules, as well as known functional polymorphisms that account
for the slow acetylation isoform (Costa et al., 2005). African-Americans as a
population tend to be fast acetylators compared to Caucasians, which was correlated
to the increased risk of developing prostate cancer (Nowell et al., 2004);(Yu et al.,
1994).
African-American men who consumed >650g cooked meats/week, had twice
the relative risk, while Caucasians who consumed the same amount of meat showed
no increased risk (Rodriguez et al., 2003). Moreover, African-American men were
twice as likely (50% blacks versus 21% whites) to eat very well cooked red meats
(Rodriguez et al., 2003), and also excrete in their urine twice as much heterocyclic
amine metabolic products as do Caucasians, which correlated with increased CaP
relative risk (Sinha et al., 1998);(Kidd et al., 1999).
7
A high intake of milk (calcium) has been reproducibly shown to be
predisposing for CaP (Rodriguez et al., 2003);(Qin et al., 2004). In one study, there
was a five fold increase in CaP risk when more than 2g of daily calcium was
consumed, as compared to men who ingested less than 0.5g daily calcium
(Giovannucci et al., 1998). The mechanism by which higher calcium levels may
induce prostate cancer formation is not known.
1.3 Genetic Mechanisms of Prostate Cancer
Not all CaP cases arise from the same mechanism per se, but the pathways
discussed below are thought to be etiological genetic factors. Multiple molecular
mechanisms have been identified as participating in prostate cancer development and
progression with two major classes: predisposing germline and acquired somatic
alterations (Table 1.1). Predisposing (germline) genetic factors include DNA lesions
and/or radial damage caused by diet (Chapter 1.2), along with single and multi-gene
alterations (Chapter 1.4). Somatic changes such as single genes (Chapter 1.5.1),
chromosomal structural rearrangements (Chapter 1.5.2), DNA methylation and
altered gene expression (Chapter 1.5.3), RNA antisense/microRNA dysregulation
(Chapter 1.5.4), translocation fusion proteins (Chapter 1.5.5) and individual gene
mutations have been identified (Table 1.1).
8
Table 1.1 Genes Involved in Prostate Cancer Predisposition and Development.
Individual genes identified as etiological in CaP predisposition (germline) or
acquired alterations (somatic) occurring during or after tumor formation are listed
with chromosomal location and alteration type. While no single locus is causitive for
CaP, multiple regions have been identified as participating in the disease causing
process.
9
Gene Name Function Chromosomal Location Malfunction
AR Transcription factor Xq11.2-q12
Amplification,
Mutation,Expansion
ATBF1 Tumor Suppressor 16q22 Deletion
BRCA1 Tumor Suppressor 17q21 Deletion
BRCA2 Tumor Suppressor 13q12 Deletion
CHEK2 DNA Damage Response 22q12.1 Frameshift or Truncation
CYP17 Androgen Anabolism 26q22-31 Functional Polymporphism
CYP1B1 Androgen Metabolism 2p21 Functional Polymporphism
CYP3A1 Androgen Catabolism 12p11 Functional Polymporphism
GSTM1 Carcinogen Catabolism 1p13.3 Promoter Inactivation/Missense
GSTM1 Carcinogen Catabolism 11q13 Functional Polymporphism
GSTT1 Carcinogen Catabolism 22q11.23 Functional Polymporphism
MSR1
Macrophage Membrane
Protein
8p22 Deletion/mutation
p53 Tumor Suppressor 17p13.1 Mutation
PON1 Carcinogen Catabolism 7q21.3 Functional Polymporphism
SRD5A2 Androgen Anabolism 2p23 Mutation
VDR Vitamin D Receptor 12q13.11 Functional Polymporphism
ELAC2
tRNA Processing
Ribonuclease
17p11 Truncation
MLH1 Tumor Suppressor 3p21.3 LOH/Mutation
MSR1
Macrophage Membrane
Protein
8p22 Deletion/mutation
RNASEL Ribonuclease 1q25 Mutation
ANXA7
Calcium activated
GTPase
10q21 LOH/Haploinsufficiency
AR Transcription factor Xq11.2-q12
Amplification,
Mutation,Expansion
ATBF1 Tumor Suppressor 16q22 Deletion
CHEK2 DNA Damage Response 22q12.1 Frameshift or Truncation
COX2 Oxidative Phosphorylation Mitochondria Promoter Silencing
DKK3 Wnt Signaling 11p15.2 Promoter Silencing
EIF3S3 Translation Factor 8q24.11 Amplification
EPHB2 Tumor Suppressor 1p36.1 Nonsense Mutation
GPX3 Oxidative Protection 5q23 Promoter Silencing
KLF5 Transcription factor 13q21 Deletion
KLF6 Tumor Suppressor 10p15 LOH/Mutation
Mitochondrial
DNA
Cellular Respiration Cytosolic Deletion/Mutation
MLH1 Tumor Suppressor 3p21.3 LOH/Mutation
MSR1
Macrophage membrane
protein
8p22 Deletion/Mutation
MYC Transcription factor 8q21 Copy Number Amplification
NKX3-1 Transcription factor 8p21 Deletion
p27 Tumor Suppressor 12p12 Deletion or SNP
p53 Tumor Supressor 17p13.1 Mutation
p57 Tumor Suppressor 11p15.5 Promoter Silencing
PSCA Membrane Glycoprotein 8q24.2 Overexpression
PTEN Tumor Supressor 10q23 Deletion/mutation
RAS Proto-oncogene Family 1p13,11p15,12p12 Mutation
SFRP1 Apoptosis Signaling 8p12-p11.1 Promoter Silencing
SRD5A2 Androgen Anabolism 2p23 Mutation
Germline-Sporatic or Hereditary Germline-Familial Somatic-Sporatic
10
1.4 Germline Predisposition to Prostate Cancer
It is thought that no single gene locus plays a major role in prostate cancer
formation (Nwosu et al., 2001);(Cancel-Tassin et al., 2001). However,
polymorphisms in a wide variety of genes such as AR, PSA, SRD5A2, VDR, and CYP
have been reported to be important for predisposition in familial and sporadic cases,
while genes such as HPC1, PCAP, HPCX, CAPB, HPC20, ELAC2, have been shown
to be susceptibility genes only in familial cases. Other genes are mutated later in the
tumor lifespan, such as the loss of p53 or c-myc which has been associated with
prostate cancer aggressiveness (Qian et al., 2002), as well as unknown genes at
chromosomal regions 5q31-q33, 7q32 and 19q12, thus demonstrating that prostate
cancer etiology is complex. In Chapter 1.4, genes that are known or suspected to
predispose to sporadic and familial prostate cancer are discussed as well as unknown
genes suspected to lie with large regions identified by linkage mapping.
Many single genes known to be predisposing for prostate cancer are listed in
Table 1.1. Not surprisingly, many are housekeeping genes such as those that control
the cell cycle or encode metabolic enzymes that regulate androgen anabolism or
catabolism, the key mitogen in prostate physiology. Perhaps the most important gene
involved in all stages of prostate cancer (from predisposition to aggressive
recurrence) is the androgen receptor (AR). This transcription factor’s biochemistry is
discussed in Chapter 1.6, but the gene is known to undergo copy number
amplification, mutation, and microsatellite expansion of exon 1 leading to altered
11
gene transactivatation (Alvarado et al., 2005), (Gottlieb et al., 2004). Its importance
to prostate physiology cannot be understated.
Other known genes implicated in germline prostate cancer predisposition
include: ATBF1 (Sun et al., 2005; Xu et al., 2006), BRCA1/2 (Rauh-Adelmann et al.,
2000; Rosen et al., 2001), CHEK2 (Wu et al., 2006; Zheng et al., 2006), CYP1B1
(Dong, 2006), CYP17 (Dong, 2006), CYP3A1 (Dong, 2006), GSTM1 (Dong, 2006),
GSTT1 (Dong, 2006), MSR1 (Maier et al., 2006), p53 (Bookstein et al., 1993; Chen
et al., 2005; Navone et al., 1999; Qian et al., 2002), PON1 (Dong, 2006), SRD5A2
(Makridakis et al., 2004; Soderstrom et al., 2002), VDR (Dong, 2006), ELAC2
(Nwosu et al., 2001), MLH1 (Fredriksson et al., 2006; Strom et al., 2001), MSR1
(Lindstrom et al., 2006; Maier et al., 2006; Seppala et al., 2003), and RNASEL
(Dong, 2006; Maier et al., 2005; Nakazato et al., 2003; Wang et al., 2002). A number
of chromosomal regions identified in linkage studies likely harbor single genes
thought to attenuate CaP predisposition (Chapter 1.5.2).
In North America over the past 15-20 generations, breeding between Africans
and Europeans occurred that led to approximately 20% admixture in the African
lineage (Lind et al., 2006), thus forming large haplotype blocks that were of
European ancestry. Because this genetic mixing occurred recently, recombination
has not broken up these large haplotype blocks, meaning that significantly less loci
(100-300 times less markers) need to be genotyped for genome-wide mapping while
still maintaining power (Smith and O'Brien, 2005; Smith et al., 2004). Diseases like
prostate cancer demonstrate striking racial differences in penetrance, incidence, and
mortality. For these diseases, admixture mapping can be a very powerful tool. This
12
new linkage method was utilized to reveal a prostate cancer susceptibility locus in
African-American men (Freedman et al., 2006). A maximum LOD score of 7.1 was
discovered at 8q24, an interval of almost 4 Mb, in African-American men younger
than 72. If older men were included, then the LOD score dropped to 4.1 suggesting
that a younger-age-of-onset predisposing gene lies within the interval. The MYC
gene lies at the distal edge of the site, but it is not known if this is the causative gene.
1.5 Somatic Mutations in Prostate Cancer
1.5.1 Single Genes
A large number of known single genes are mutated or altered as a
consequence of tumorigenesis. Genes such as those in Table 1.1 are suspected to
drive later stages of prostate cancer, but not necessarily start the process. Genes such
as ANXA7 (Srivastava et al., 2003), AR (cited above), ATBF1 (cited above), COX2
(Edwards et al., 2004), DKK3 (Edwards et al., 2004; Lodygin et al., 2005), EIF3S3
(Nupponen and Visakorpi, 2000; Saramaki et al., 2001; Savinainen et al., 2004),
EPHB2 (Dong, 2006; Huusko et al., 2004), GPX3 (Lodygin et al., 2005; Ouyang et
al., 2005), KLF5/6 (Chen et al., 2003a; Chen et al., 2003b; Dong, 2006; Li et al.,
2005), mitochondrial DNA (Gomez-Zaera et al., 2006), MLH1 (Fredriksson et al.,
2006; Strom et al., 2001), MSR1 (Lindstrom et al., 2006; Seppala et al., 2003), MYC
(Bieche et al., 1999; Bubendorf et al., 1999; Jenkins et al., 1997; Nupponen et al.,
1998; Qian et al., 2002; Sato et al., 1999; Savinainen et al., 2004), NKX3-1 (Ouyang
et al., 2005), p27 (Dong, 2006), p53 (cited above), p57 (Lodygin et al., 2005), PSCA
(Reiter et al., 1998), PTEN (Cairns et al., 1997), RAS (Dong, 2006), and SFRP1
13
(Lodygin et al., 2005). Many cytogenetic alterations are also found in most CaP
cases, with abnormalities occurring in regions with no established candidate gene;
yet this genetic heterogeneity is important for understanding CaP molecular
pathology (Chapter 1.5.2).
1.5.2 Prostate Cancer Cytogenetics
Prostate cancer displays a variety of genetic and chromosomal changes as
determined by comparative genomic hybridization (CGH), centromere fluorescence
in situ hybridization (FISH), spectral karyotyping (SKY), and multiplex FISH.
CGH has revealed that primary prostate cancers contain mostly chromosomal
losses at: 6q, 8p, 10q, 13q, 16q, 17p, and 18q, supporting the role for tumor
suppressor loss early in the disease progression (Visakorpi et al., 1995). It should be
noted that prostate cancers exhibit less cytogenetic variability than that observed in
other cancer types (e.g. breast cancer). Advanced prostate cancers usually exhibit
chromosome gains/gene amplifications, suggesting that oncogenes may be
participating in the later disease states (Nupponen and Visakorpi, 2000).
Interestingly, comparisons of early and late prostate cancers revealed that most
chromosomal abnormalities are due to differences between samples, with only some
overlap (i.e. deletions early, with amplifications later). This suggested that the
person-to-person molecular mechanisms of tumor formation and prostate metastasis
were very different, driven by separate genes and mechanisms. Using only primary,
untreated prostate cancer markers found in high Gleason scored CaP is not suggested
for drawing conclusions about early CaP pathology.
14
8p and 13q are chromosomal arms which are most commonly lost in prostate
cancer samples but are also found occurring in prostatic intraepithelial neoplasms
(PINs) as demonstrated by FISH and loss of heterozygosity testing (LOH) (Bova et
al., 1993);(Hyytinen et al., 1999);(MacGrogan et al., 1994);(Qian et al.,
1999);(Vocke et al., 1996). Since PIN is considered a prostate cancer precursor,
heterogeneous chromosomal rearrangements appear very early in prostate cancer
development and may be a general molecular finding through the entire tumor life.
In one study using centromere-interphase FISH, 25 prostate cancers of
different stages were studied along with matched PINs. Most PINs demonstrated low
levels of chromosomal aneuploidy (34%), but had substantial levels of centromere
heterogeneity. True CaP tumors from the same section were analyzed and it was
found that centromere heterogeneity was present, but there was increased
chromosomal aneuploidy as compared to the PIN samples. Advanced CaP showed
centromeric heterogeneity, increased aneuploidy, partial loss of chromosome 10,
partial gain of chromosome 7, and aberrations of chromosome 8 (Alers et al., 1995).
These important results suggest that prostate adenocarcinoma first begins with PIN
initiating chromosome instability, with increasing chromosomal damage as the
cancer progresses.
PTEN (10q23) (Cairns et al., 1997);(Dong et al., 1998);(Feilotter et al.,
1998);(Pesche et al., 1998);(Suzuki et al., 1998);(Wang et al., 1998), p53 (17p13.1)
(Bookstein et al., 1993);(Navone et al., 1993);(Navone et al., 1999), and E-cadherin
(16q22) (Cher et al., 1996);(Nupponen et al., 1998) deletions have been found in a
15
minority of late stage CaP (10-30%), with mutations usually occurring in early CaP,
and are thus thought to be important in cancer development (10-30%).
In hormone refractory prostate cancers (HR-CaP), the following loci are
commonly gained: 1q, 2p, 7p, 8q, 18q, and Xq (Visakorpi et al., 1995);(Nupponen et
al., 1998). Nearly 90% of HR-CaP or distant metastases, but only 5% of untreated
prostate cancers have a gain at 8q (Cher et al., 1996);(Nupponen et al., 1998),
suggesting that an unknown gene critical for allowing prostate cells to adapt to a
hormone insensitive environment lies within that expanded site. Another common
HR-CaP gain is at 18q (30%), which harbors the known oncogene BCL2, which is
often times over expressed in recurrent prostate cancers (Colombel et al., 1993).
Trisomy 7 is associated with prostate cancer progression (Alcaraz et al.,
1994);(Bandyk et al., 1994);(Takahashi et al., 1994), as is a centromeric copy
number gain in chromosome 8 (Sato et al., 1999); (Takahashi et al., 1994),
(Takahashi et al., 1996). The c-MYC gene (8q24.1) is amplified in 8% of primary
prostate cancers, and 11-30% of advanced CaPs (Bubendorf et al., 1999);(Jenkins et
al., 1997);(Nupponen et al., 1998);(Sato et al., 1999). This c-MYC amplification
correlates with poor prognosis in patients with localized CaP (Bieche et al., 1999).
The EIF3S3 gene, located at 8q23 is amplified and over-expressed in 30% of
recurrent HR-CaP, suggesting that this gene product is important for CaP adaptation
into a hormone insensitive state (Nupponen et al., 1999). PSCA (prostate specific cell
antigen) localizes to 8q24.2 and is over expressed in most malignant CaPs and
metastases (Reiter et al., 1998). PSCA is often co-amplified with c-MYC, and these
two proteins may play an important role in CaP late progression.
16
1.5.3 Prostate Cancer Epigenetics
Disrupted epigenetic mechanisms have been found in low-to-high grade
primary prostate cancers and cells lines (Du145, PC-2, and LNCaP). While global 5-
methylcytosine levels are generally reduced across the genome in most prostate
cancers (29/30 patients), specific gene promoters are hypermethylated and led to
mRNA reduction as validated by expression microarrays (Brothman et al., 2005).
Specifically, GPX3, SFRP1, COX2, DKK3, GSTM1, and p57 promoters had greater
than half of their promoter CpG sites hypermethylated as compared to matched
samples from the same slide section, and were thus under expressed (Lodygin et al.,
2005). Thus, epigenetic mechanisms may play a very important role in prostate
cancer at all stages.
1.5.4 MicroRNA Dysregulation in Prostate Cancer
Complementary DNA (cDNA) microarray analysis of prostate cancer and
normal adjacent RNA from 27 samples with mid-to-high Gleason Scores were
analyzed for novel, intronic RNA transcripts. Twelve intronic sense or anti-sense
RNAs were found to be significantly reduced/increased in prostate cancer, and one
gene, RASSF1 (which was extensively validated), demonstrated reduction of both
sense and anti-sense intronic RNA in high grade prostate cancers from multiple
patients (Reis et al., 2004). The findings that intronic-sense and anti-sense RNAs
were altered in some prostate cancers, which in turn altered the targeted mRNA,
suggested that small, non-coding RNAs are important regulators of gene expression,
and should be further studied for their impact in CaP.
17
1.5.5 Gene Fusion Products in Prostate Cancer
A DNA microarray approach identified ETV1 and ERG transcription factors
as commonly over-expressed in prostate cancer samples. Upon attempting to
characterize the 5’-end of the ETV1 and ERG transcripts (Ets protein family
members) via 5’-RNA ligase-mediated rapid amplification of cDNA ends (RLM-
RACE), the authors discovered that ETV1 or ERG were fused on their 5’-ends with a
second androgen-responsive gene named TMPRSS2. Confirmatory fluorescent in situ
hybridizations and quantitative RT-PCR (qRT-PCR) demonstrated that the fusion
was indeed present in two cells lines (DuCaP, LNCaP), as well as hormone
refractory metastatic primary prostate cancer tissues. The fusion translocation was
not detected in pooled benign prostate tissue, and is the first example of a novel solid
tumor fusion protein product (Tomlins et al., 2005).
It is known that the PI3K-AKT or RAS-MAPK signaling pathways regulate
ETS transcription factor expression, and the ETS proteins can positively and
negatively affect cellular growth and apoptosis (Tomlins et al., 2005). However, a
homozygous deletion of PTEN can induce cellular death (Chen et al., 2005). PTEN
loss (sometimes lost in early CaP), although promoting the PI3K-AKT pathway, can
also trigger a negative feedback loop that down regulates protein tyrosine kinases
(Tremblay and Marette, 2001). Therefore, over-expression of an ETS transcription
factor may down-regulate the PTEN mediated negative feedback loop, leading to
increased cellular proliferation and cell survival via the PI3K-AKT pathway (Shaffer
18
and Pandolfi, 2006), thus explaining why the selection of the fusion event occurs
with such a high frequency in metastatic prostate cancer cells.
This new fusion protein discovery is exciting because there are no prior
descriptions of such fusion proteins from a solid tumor, raising hope that a selective
inhibitor can be designed. Since only prostate cancer cells contain the fusion, an
inhibitor would likely have few side effects. This work highlights that there are still
new discoveries waiting to be revealed in prostate cancer.
1.6 Androgen Action in the Prostate
The androgen metabolic pathway is important for human physiology and in
disease. A 46, XY person with a complete loss-of-function mutation in their
androgen receptor gene (AR), will manifest a sterile female phenotype (Poletti et al.,
2005). And partial inactivation of the androgen receptor is characterized by altered
male external genitalia (Sultan et al., 2002). Thus, androgen metabolism is important
for the maturation of male secondary sex characteristics (Sinisi et al., 2003) but also
early male brain patterning (Fitch and Denenberg, 1998);(Viger et al., 2005).
Alteration of testosterone to dihydrotestosterone levels later in life, may contribute to
male pattern baldness (Ellis et al., 2002), benign prostatic hypertrophy (BPH)
(Untergasser et al., 2005), or even prostate cancer (Soronen et al., 2004). All of these
phenotypes are modulated through the androgen receptor pathway.
Since the 1940s, medical castration proved to be an effective method that led
to prostate cancer remission. However, usually within a few years, most men
relapsed with hormone refractory prostate cancer that was normally very aggressive.
19
It was not until the late 1980s, when the androgen receptor gene was cloned, that the
answers explaining how androgens regulated prostate cancer started to unravel
(Lubahn et al., 1988);(Trapman et al., 1988).
The human androgen receptor (AR) gene lies at Xq11-12, with males being
hemizygous. The gene encompasses 8 exons over 90 kb genomically, with an mRNA
over 10.6 kb long, and containing ~2,800 bp protein coding bases (Lubahn et al.,
1988);(Trapman et al., 1988). The AR protein is approximately 110 kDa as a
monomer, but in vivo interacts as a homodimer until activated. The crystal structure
of the AR protein is solved, revealing that the protein is composed of 919 amino
acids, binds two zinc atoms, and has four domains: transcription regulation, DNA
binding, hinge, and ligand binding (He et al., 2004);(Pereira de Jesus-Tran et al.,
2006);(Shaffer et al., 2004). Exon 1, the largest, exclusively encodes the
transcriptional domain, and is of key interest in human disease because it can
undergo expansion.
Exon 1 contains multiple variable repeat regions named: Gln
12-23
, and Gly
16
.
The length of the glutamine (Gln) repeat number is correlated with CaP risk and
progression. Specifically, the smaller the Gln repeat number, the greater the chance
to develop prostate cancer-regardless of ethnicity (Hsing et al., 2000). This inverse
relationship is established for age of onset (Hardy et al., 1996), likelihood of
developing aggressive CaP, and the odds to relapse (Nam et al., 2000). The
mechanism responsible for the inverse effect is not known, but disrupting
interaction(s) between the AR and its p160 co-activator, is the suspected mechanism
(Irvine et al., 2000). Despite this relationship, there is no link between familial CaP
20
and Gln length (Lange et al., 2000). A recent, large study found (at best) marginal
correlation between CAG repeat length and prostate cancer risk (Freedman et al.,
2005), but did find a weak correlation for linkage disequilibrium between SNPs
around the androgen receptor gene and prostate cancer risk.
The androgen receptor protein is expressed in all tissues except the spleen,
and shuttles between the nucleus and the cytoplasm. As a transcription factor, the
androgen receptor is bound to repressor proteins such as heat shock protein 70
(hsp70) and Hip. Upon androgen binding (usually dihydrotestosterone), the
repressors are lost, a new heat shock protein binds (hsp90), induces an AR
conformational change. The complex dimerizes, the ARs are phosphorylated, and
translocated into the nucleus along microtubules, where they transduce a large
variety of genes involved in proliferation and growth (Prescott and Coetzee, 2006).
The androgen receptor plays a critical role at all stages of prostate cancer
etiology. Disruption of the androgen receptor signaling axis (usually using androgen
antagonists), reduces circulating and intra-tissue androgen levels, and shrink prostate
tumors. The most effective treatment for aggressive or metastatic CaP are those that
ablate androgens (Santen, 1992). But eventually the cancer becomes androgen
insensitive, and the patient relapses usually with poor prognosis (Kozlowski et al.,
1991). How prostate tumors escape this androgen reduction is not fully understood,
but copy number amplification of the AR gene, expansion in exon 1, or mutation
within the ligand binding region allows the AR to bind non-optimal ligands are
implicated mechanisms (Balk, 2002; Brooks, 2002; Buchanan et al., 2001; Feldman
and Feldman, 2001; Jenster, 1999). It appears that the AR is still necessary in
21
hormone refractory disease as demonstrated recently by Zegarra, et al. (Zegarra-
Moro et al., 2002), which found that inactivation of the AR protein using antibodies
or an anti-AR ribozyme prevented cell proliferation in a hormone insensitive cell
line.
In most cases such ‘androgen-independent’ signaling may simply reflect that
the AR is hypersensitive to DHT but continues to signal. Since the androgen receptor
is a transcription factor, it amplifies a hormonal signal. Thus, when more AR protein
is produced, it can have marked effect on cell biochemistry. In fact, increasing AR
mRNA expression was sufficient to change a hormone sensitive cell line into an
insensitive one, thus reaffirming the importance of the AR in prostate cancer
progression (Chen et al., 2004). A prostate cancer mouse model was established into
which a mutant AR transgene was introduced. In all mice, the mutant AR had higher
transcriptional activity in the absence of ligand, suggesting that AR protein level was
the key determinate in the switch between whether cells were hormone sensitive or
insensitive. If AR mRNA or protein levels can be reduced, then it may be possible for
the long term control of prostate cancer, thus reducing mortality.
There is little evidence to implicate the AR in early prostate cancer/BPH
development. However, in 25-30% of androgen insensitive CaPs, the AR gene has
been amplified (Edwards et al., 2003), thus allowing those cells to respond to very
low androgen levels thus escaping androgen abatement treatment. Occasionally, the
AR is mutated into a promiscuous form that more readily binds other sex hormones
such as estrogens, testosterone, etc. AR expansions/gene mutations appear only to
22
occur as adaptive mutations to androgen ablation therapy thus driving later stage CaP
progression (Shi et al., 2002).
Figure 1.2 depicts androgen action in the prostate. Male steroid hormones are
mainly synthesized in the testes, and to a minor extent in the adrenal cortex.
Androgens are mostly bound to albumin and steroid hormone binding proteins in the
blood (SHBP), but diffuse across plasma membranes into the cytoplasm.
Testosterone (T) is the major sex hormone produced in the testes, and this chemical
is reduced into the more active form, dihydrotestosterone (DHT), which has a 10x
higher affinity for androgen receptor binding as compared to testosterone alone
(Deslypere et al., 1992). Recent microarray data suggests that the AR binds to and
trans-activates at least 858 different genes, with at least a least a ±30% change in
expression as compared to controls (Wang et al., 2006). Thus altering androgen
metabolism could have large effects on cellular physiology.
Hormonal modulation of the androgen pathway is the primary
chemotherapeutic for prostate cancer treatment. This is accomplished by using
androgen antagonists, or through the use of specific steroid 5-alpha reductase
enzyme inhibitors (finasteride or dutasteride), which reduces DHT levels in the
prostate and peripheral system, but leads to increased testosterone concentrations
(Gomella, 2005). Gonadotropin releasing hormone (GnRH), an androgen antagonist,
is the first line of hormonal treatment for androgen responsive prostate cancer.
GnRH desensitization of the pituitary gland leads to a reduction of gonadal sex
steroids (Kraus et al., 2006), and cancer remission. Since DHT activates the AR,
lowering testosterone levels lead to prostatic epithelial apoptosis, and gland volume
23
reduction. Orchiectomy also leads to dramatic testosterone and prostate epithelial
thickness reduction. Thus, treatment modalities that target androgen anabolism and
catabolism, have been (and will continue being) useful for modulating prostatic DHT
concentration and physiology.
Figure 1.2 Androgen Action in Prostate Cells. Androgens such as testosterone are
bound in the blood by albumin or steroid hormone binding globulin (SHGB). Once
the androgens dissociate, they diffuse across the plasma membrane and are reduced
by 5-alpha reductases (type 1 or 2) in the cytoplasm into dihydrotestosterone (DHT).
DHT is the primary ligand for the androgen receptor protein, which is activated and
translocated into the nucleus where genes are regulated and modulate cellular
proliferation, survival, and differentiation.
24
1.7 Human Steroid 5-Alpha Reductase Type I & II Genes
There are two known human steroid 5- alpha reductase genes, SRD5A1 and
SRD5A2. They have similar gene structure, and encode similar steroid 5-alpha
reductases, but lie on different chromosomes and exhibit different tissue expression
(Jenkins et al., 1991). The type II reductase was shown to influence prostate cancer
predisposition or progression. Certain SNPs may participate in prostate cancer
predisposition for some racial groups.
Both genes possess 5 exons and 4 introns. Because the size and location of
each intron is similar for both genes, it was hypothesized that they arose via gene
duplication. There is also a processed pseudogene that resembles the SRD5A1
mRNA at Xq24-qter but has a variety of additional SNPs across the cDNA and a
nonsense mutation in codon 147. There is no evidence that the pseudogene is
expressed (Jenkins et al., 1991).
SRD5A1 expresses two different mRNA forms: long (2285 bp) and short
(~1,300 bp), while there is only one mRNA species for SRD5A2 (2,464 bp). Both
mRNAs encode peptides that are 28-29 kDa, but migrate on polyacrylamide gels at
21-27 kDa, and share 47% amino acid identity. Approximately 37% of the amino
acids have hydrophobic side chains, making the proteins membrane associated.
Neither enzyme has been purified to homogeneity, as total activity is lost after
chromatography (Russell and Wilson, 1994).
25
SRD5A1 and SRD5A2 have different tissue specific isoform expression.
SRD5A2 is mainly expressed in the prostate and liver, while SRD5A1 is expressed in
most tissues with highest levels in non-genital skin and brain. Neither type is
expressed in the testes or adrenal glands. SRD5A2 is the predominant 5-alpha
reductase in the prostate, but there is some SRD5A1 activity. Upon a week long oral
administration of finasteride, an irreversible inhibitor of SRD5A2, circulating serum
DHT levels were reduced by ~65%, suggesting that the remaining 35% of serum
DHT was contributed from the activity of SRD5A1 in peripheral tissues (Russell and
Wilson, 1994).
Subcellular localization of SRD5A1/2 using immunocytochemistry
demonstrated that both enzymes are primarily found in the endoplasmic reticulum.
Although in vitro enzyme assays work best at alkaline pH for SRD5A1, and acidic
pH for SRD5A2, there is evidence that SRD5A2 undergoes a conformational change
induced by sample processing, and that the enzyme is most active in vivo at neutral
pH. It is important to note that the in vitro pH optimum gives apparent kinetics,
which may, or may not, reflect in vivo enzyme kinetics (Russell and Wilson, 1994).
Steroid 5-alpha reductases reduce a variety of substrates including
testosterone, progesterone, androstenedione, 20-alpha-hydroxy-preg-4-en-3-one, and
17-alpha-hydroxy-progesterone using only the cofactor NADPH. The reaction is an
ordered bi-bi with the cofactor binding first. The reduction is trans- with the cofactor
approaching from below (Bjorkhem et al., 1992). There is no evidence of the reverse
5-alpha oxidization reactions occurring in vivo with either enzyme (Russell and
26
Wilson, 1994). It is presumed that as soon as DHT is created, it is immediately
bound by the androgen receptor or degraded.
The steroid 5-alpha reductase type II enzyme (E.C. 1.3.99.5), was shown to
have at least ten single amino acid substitutions and three double mutations, all of
which occur in normal, healthy males (Makridakis et al., 2000). Biochemical
analyses of these variant enzymes demonstrated an activity spectrum that ranged
from slower to faster reaction kinetics relative to the wild-type enzyme (Makridakis
et al., 1999);(Makridakis et al., 2000). One mutation, which changes an alanine at
amino acid 49 into a threonine, boosts the relative risk in African-American men of
developing prostate cancer by 7.2-fold. Meanwhile, the same mutation imparted a
3.6 fold boost for Latinos (Makridakis et al., 1999). Some mutant SRD5A2 enzymes
also demonstrated a 32-fold range of inhibition to finasteride (Makridakis et al.,
2004). In the prostate cancer prevention trial (PCPT), daily oral dosing of finasteride
decreased prostate cancer risk by roughly 25%, while those that did develop cancer
and who had taken finasteride were at a 15% higher risk for more aggressive, higher
grade cancers (Thompson et al., 2003). Therefore, studying the related SRD5A1 gene
for any contribution to prostate cancer progression is a next logical step.
The best way to begin this study was to catalog the SRD5A1 single nucleotide
polymorphisms (SNPs) using samples from a case-control cohort composed of
different ethnicities, with the expectation that one or more SNPs would be
discovered that would alter an amino acid in the protein coding region. If all of the
SNPs turned out to be silent, then it still might be worthwhile to consider how that
SNP may alter promoter transcription or mRNA biochemistry. Since the majority of
27
the RNA length is the 3’-untranslated region, it is likely that multiple SNPs would be
found there. These SNPs could change the RNA steady state or half-life and thus
SRD5A1 protein levels, which would be useful for understanding the role of the
SRD5A1 message in prostate cancer progression. But how exactly can single
nucleotide polymorphisms change mRNA levels?
1.8 Messenger RNA Stability and Processing
Until the early 1980s, very little research was conducted on eukaryotic
mRNA stability (Jacobson and Favreau, 1983). It was not until the 1990s that
detailed molecular biology studies of 5’ and 3’ UTRs began unraveling the
interesting interplay between the distal mRNA ends (Imataka et al., 1998). While
most work was completed using a yeast model system (Tarun and Sachs, 1996), later
experiments in mammals uncovered similar findings (Imataka et al., 1998; Skalweit
et al., 2003). The results revealed that after transcription, the polyadenylated pre-
mRNA were coated with a number of nuclear polyA binding proteins (PABPN, see
Figure 1.3). These PABPN bound the 3’UTR, participated in mRNA export, protein
translation, mRNA turnover (by acting as an antagonist to the ERF1/ERF3 complex),
and by providing a scaffold for other RNA binding proteins (Mangus et al., 2003).
Upon nuclear export, cytoplasmic PABP (PABPC) binds the polyA region
and stimulates interaction with the eIF4G protein subunit. The eIF4G protein
interacts to form a complex with other proteins bound to the 5’UTR, thus bringing
both mRNA ends together, while preventing ribonuclease digestion. These circular
RNAs are further complexed by proteins including the 40S ribosomal subunit, which
28
ultimately leads to protein translation (Figure 1.3) (Imataka et al., 1998). Disruption
of mRNA secondary structure by the addition of artificial RNA stem-loop structures
(of various sizes) to the 5’ or 3’UTRs of luciferase constructs resulted in decreased
protein translation, but did not alter mRNA half-life (stability), thus demonstrating
that protein translation was sensitive to mRNA secondary structure (Niepel et al.,
1999).
Prostate cancer etiology is complex and not governed purely by gene
mutations, but rather by a contribution of both environmental and familial factors,
some of which are unknown. Not all men who are at high risk for developing the
disease manifest significant symptoms over their lifetime, and so further research is
needed in order to completely understand the enigma behind this devastating disease.
We chose to evaluate SRD5A1 for missense or nonsense SNPs. Thus, the hypothesis
that SRD5A1 variants alter enzyme levels and predispose to prostate cancer was
tested.
29
Figure 1.3. Messenger RNA stability is mediated by the interactions of 5’ and
3’UTRs. Proteins coat and protect the mRNA from degradation and to mediate
transport into the cytoplasm. The distal ends of mRNA are then circularized while in
the cytoplasm so that both ends interact with proteins. Altering either RNA
secondary structure can change protein translation or even RNA stability. This figure
is based after Mangus, et al. 2003.
30
Chapter 2: Materials and Methods
2.1 Genotyping SRD5A1
2.1.1 PCR Oligonucleotides
Table 2.1. PCR Oligonucleotides. The below DNA oligos were used to PCR
amplify and sequence the human SRD5A1 gene. The primers were designed
using online software at the MIT/Whitehead Institute (primer3). All primers
were blasted to confirm their specificity to the correct genomic region.
31
Primer Name SEQUENCE (5'->3')
PF1 5’-AGG GCT GTA AAA GGG AGC TG-3’
PF2 5’-ACC TCC AGG TCC TCC AAT TC-3’
PF3 5’-TGT AAC TAC AAA GAT GGA GGG GG-3’
PF4 5’-TTG GAC CCT GTG CTC CCC AC-3’
PF5 5’-GGC CGC TGC TGT TGC TGG AG-3’
PF6 5’-AGG AAT AAG CCC AAA GCG CAC AAC-3’
PF7 5’-AGC CCT GAG GAA GGA AAG AG-3’
PR1 5’-GGG TTC TCA AGT CAG GCT TCT-3’
PR2 5’-CAC GTG GGG CCT TGG TTT-3’
PR3 5’-TCT TTC CTT CCT CAG GGC TG-3’
PR4 5’-GTG GGG AGC ACA GGG TCC AA-3’
1F2 5’-CGC CGC CCT ATA TGT TGC-3’
1F3 5’-TCA GAC GAA CTC AGT GTA CGG-3’
1F4 5’-CTA CCC CGG AGA AGC CTG-3’
32
Table 2.1 Continued
1R 5’-GAG GGG AGG GTC GGA GAG-3’
1R2 5’-GGA TCT GGC AGG GAG AGG-3’
1R3 5’-GCC GTA CAG TGA GTT CGT CTG-3’
1R4 5’-CCG TAG TGG ACG AGG AAC AT-3’
2F1 5’-CCC AAA TCA TTT AAG ATA GGA TTA C-3’
2R 5’-GCT GCT GCT TTC TCT GTT GTC-3’
3F 5’-TGG CAT ACC TGA CAA CAG TCC-3’
3R 5’-ATG GCA AGC AAC TTT CAC AG-3’
4F 5’-CCG TTC TTG AAT TTA TGT TCT CC-3’
4R 5’-CAA AAA TCT AGC CTG AAA AAT GG-3’
5F 5’-TGG TTA AAT GTC TAA GCG ACA GA-3’
5F2 5’-CAC CTT TAG GCC ATG GGT C-3’
5F3 5’-TCA ACT GCA GTG TTG CTT CC-3’
5F5 5’-CCC TCA TAG CCT GTA CCT GT-3’
5F6 5’-TTT CCA ATG GCG CTT CTC TAT-3’
5F7 5’-GGG GAT AGA GGA GGA AGC T-3’
33
Table 2.1 Continued
5F8 5’-CTG GCA TTG CTT TGC CTT AT-3’
5R 5’-ATA GAG AAG CGC CAT TGG AA-3’
5R2 5’-GTA AAA GAA TTT GAG CTA CCT TGT-3’
5R3 5’-AGC TTC CTC CTC TAT CCC C-3’
5R4 5’-CAG GTA CAG GCT ATG AGG G-3’
5R5 5’-GAC CCA TGG CCT AAA GGT G-3’
5R6 5’-GGA AGC AAC ACT GCA GTT GA-3’
cDNAF_1 5’-GCA GGA GCT GCC CTC GCT GG-3’
cDNAF_2 5’-ATG TTG ATA AAC ATC CAT TC-3’
cDNAR_1 5’-CCA GCG AGG GCA GCT CCT GC-3’
cDNAR_2 5’-GAA TGG ATG TTT ATC AAC AT-3’
RT_SRD5A1_F1 5’-CAT GGA GTG GTG TGG CTA TG-3’
RT_SRD5A1_R1 5’-ACA CAG CAC CTG ACA CGA AG-3’
HBB_IVS_F_2 5’-GCA TCA GTG TGG AAG TCT CAG GAT-3’
HBB_IV2_R_2 5’-GCA GAA TGG TAG CTG GAT TGT AGC-3’
-925F_HindIII 5'-CCC CCA AGC TTC ATG AAC TGG CCC CAC TC-3'
-128R_HindIII
5'-CCC CAA GCT TTG CAG AAA GGG TTC TCA
AGT-3'
GAPDH_f1 5'-CTG TGG CGT GAT GGC CGC GGG GCT C-3'
GAPDH_r1 5'-GAC TGA GTG TGG CAG GGA CTC CCC A-3'
34
2.1.2 Polymerase Chain Reaction (PCR) Conditions
Polymerase chain reactions (PCRs) were conducted in a final volume of 50
µL, with 1.5 mM MgCl
2
(Roche), 0.4-0.5 µM primers (see DNA Oligos above,
Integrated DNA Technologies), 2-2.5 units AmpliTaqGold DNA Polymerase
(Roche) (Vosberg, 1989) and 0.2 mM of each of the four dNTPs (Takara) (Mullis et
al., 1992). Thermocycling was performed on an MJ PTC-225 multiblock machine
with the following conditions: 95°C, 12 minutes for 1 cycle (enzyme activation),
95°C 30 seconds (denaturation), annealing temperature (see table below) for 30
seconds, with an elongation at 72°C for 30-60 seconds for a total of 44 cycles, and a
final 10 minute hold at 72°C.
All DNA samples were electrophoresed on agarose gels in 1X TAE buffer,
and stained with ethidium bromide. Visualization was through an ultraviolet
transilluminator system. Positive (genomic DNA) and negative (water) controls were
co-amplified and electrophoresed with the other samples.
35
Table 2.2. PCR Conditions to Amplify the Human SRD5A1 Gene. This table lists
the amplicon sizes, and PCR conditions necessary for obtaining the seven amplicons
covering the putative promoter through the 3’UTR. All of the PCRs used 1.5 mM
Mg
2
Cl
2
.
SRD5A1
Fragment
Primer
Combinations
% Final
DMSO
Annealing
(Centigrade)
Amplicon
Size
1A 1F2/1R4 5 58.4 400
1B 1F3/1R 5 54 264
2 2F/2R 0 54 372
3 3F/3R 0 54 317
4 4F/4R 0 51 332
5 5F/5R 0 58.4 176
3'UTR 5F/5R2 5 60 1383
36
2.1.3 Gel Purification of PCR Amplicons
All PCR amplicons were purified on 4% high melt agarose, 1X TAE gels
stained with ethidium bromide for detection. Qiagen’s gel extraction kit was used to
isolate the DNA from the agarose slivers (Weil and Hampel, 1973). The DNA was
eluted in 20 µL water or elution buffer (10 mM tris-Cl pH 7.5). Eluted DNAs were
then dried on medium heat in a speed-vacuum for 15 minutes. Samples were
resuspended in 10 µL water, with 1 µL used to quantitate concentration using a
Nanodrop Spectrophotometer (Nanodrop Technologies).
2.1.4 DNA Sequencing
DNA sequencing reactions consisted of DNA template (15-50 ng for PCR
amplicons and 250-1000 ng for plasmids), 3.2 pmol primer, 2-8μL BigDye v3.1
chain terminator mix (ABI), 1-2 µL 5x dilution buffer (1x=80 mM tris-Cl pH 9.0, 2
mM MgCl
2
), and water to 10 µL final volume. Samples were cycle sequenced using
an MJ PTC-225 thermocycler with the following conditions: 95°C for 30 seconds,
50°C for 15 seconds, and 60°C for 4 minutes for a total of 26 cycles. (Innis et al.,
1992; Sanger et al., 1977)
To the sequenced samples, SDS was added to a final concentration of 0.2%
v/v, mixed, and heated to 98°C for 5 minutes, followed by 10 minutes at room
temperature. Samples were then size exclusion purified on Sephadex 50 resin either
over individual columns (Amersham), or in a 96-well format using resin in filter
37
plates (Amersham) following the manufacturer’s recommendations. The purified
DNAs were then dried in an oven for 5 minutes, followed by the addition of 10 µL
de-ionized formamide, mixed, and then heat denatured in an oven for at least 10
minutes prior to snap cooling on ice. Samples were loaded onto an ABI 3100
automatic DNA sequencing machine (Bernat et al., 2002) (Szantai et al., 2006).
2.1.5 Haplotype Reconstruction
DNA sequencing electropherograms were printed in color and manually
examined for variants. The SeqScape Multiple Alignment Software package (ABI)
was further utilized to analyze the data. Both manual and computer alignments and
data were used in cataloging SNPs.
SNP data and physical location on the chromosome were entered into Phase
2.0.2 (Stephens et al. 2001; Stephens and Donnelly 2003), Haploview, and JLIN
(http://www.genepi.com.au/projects/jlin/) computer software packages for haplotype
reconstruction. Linkage disequilibrium values, as assessed by the D’ algorithm was
calculated to determine if SNPs were linked. Hardy-Weinberg Equilibrium (HWE)
was determined by using the chi-square statistic to measure how the observed
genotypes compared to the mathematically expected allele frequencies, with a cutoff
of 3.8 to determine if the data was in HWE.
38
2.2 Cloning and Mutagenesis
2.2.1 Cloning Luciferase Vectors
The base backbone vector used for cloning was pGL3 Promoter (Promega).
The backbone vector was prepared by first digesting the plasmid with BamHI and
FseI to remove the SV40 ploy adenylation site. The backbone was gel purified,
blunted, and then dephosphorylated prior to ligation. The full length SRD5A1 3’UTR
was digested using SpeI from the p4.5 vector (kindly provided by Dr. David
Russell), and then gel purified. The insert was ligated at a 10:1 insert:vector ratio.
Three endotoxin-free independent clones were maxiprepped for transfections
(Qiagen).
For cloning of the putative SRD5A1 promoter region, the pGL3 basic vector
was digested with HindIII and dephosphorylated. The promoter insert was PCR
amplified from human genomic lymphocyte DNA using the –925F_HindIII and
–128R_HindIII primer set (see DNA Oligo table), followed by HindIII digestion.
Three independent clones were prepared for transfections (Auyeung et al., 2003).
2.2.2 Haplotype Mutagenesis
Mutagenesis was accomplished using Stratagene’s Quickchange Mutagenesis
II kit according to the manufacturer’s recommendations. In short, 25-45 bp oligos
were synthesized containing the mutant base in the oligo middle and had a GC
content above 50% and a melting temperature higher than 75°C. Reactions were
39
carried out using 20 ng of plasmid with the following cycling conditions: 1 cycle of
95°C for 30 seconds, 12 cycles of: 95°C for 30 seconds, 55°C for 1 minute, and 68°C
for 6 minutes and 10 seconds.
An aliquot was removed and analyzed via an agarose gel, then DpnI treated
for 1 hour at 37°C. Following digestion of the methylated parental plasmid, another
aliquot was removed and analyzed on an agarose gel. One microliter was
transformed into XL1-Blue E.coli cells. Plasmids were DNA sequenced to confirm
the mutation, and then was restriction digested to check for other structural changes
(Li et al., 1999).
40
Primer Name Sequence 5’->3’
T1202G Sense 5’-GCT ATG TCT TGC CAA GTG GGT ATG AGA CTA GAC TTT AC-3’
T1202G Anti-Sense 5’-GTA AAG TCT AGT CTC ATA CCC ACT TGG CAA GAC ATA GC-3’
T1368C Sense 5’-GCC CTC TCT CGG AGG CCA CAG AGG CTG GGG GTA GCC ATT GTG CAG-3’
T1368C Anti-Sense 5’-CTGCACAATGGCTACCCCCAGCCTCTGTGGCCTCCGAGAGAGGGC-3’
G1441A Sense 5’-GTG TCA GGT GCT GTG TAT AAG TGG AGA ACT TGG G-3’
G1441A Anti-Sense 5’-CCC AAG TTC TCC ACT TAT ACA CAG CAC CTG ACA C-3’
A1526G Sense 5’-TGT TCC AGC CCG GCC CAC CGG GTG-3’
A1526G Anti-Sense 5’-CAC CCG GTG GGC CGG GCT GGA ACA-3’
G1535A Sense 5’-TGT TCC AGC CCA GCC CAC CGA GTG ACA TCA CCG GGC AGG GAG GGG-3’
G1535A Anti-Sense 5’-CCC CTC CCT GCC CGG TGA TGT CAC TCG GTG GGC TGG GCT GGA ACA-3’
T1578C Sense 5’-GGG GTG CTG GTG GTG GTT CAT ACG GAG TAA GCT GCT CTG CCT GTG-3’
T1578TC Anti-Sense 5’-CACAGGCAGAGCAGCTTACTCCGTATGAACCACCACCAGCACCCC-3’
C1757T Sense 5’-CGT ATG GAT ATA GTA GAG ATT GTT GTC TGT GAA ATT TCT CTT TTG TAG-3’
C1757T Anti-Sense
5’-CTA CAA AAG AGA AAT TTC ACA GAC AAC AAT CTC TAC TAT ATC CAT
ACG-3’
Table 2.3. List of Mutagenesis Primers. Sense and Anti-sense DNA oligos used to
create single and combination SNPs in the human SRD5A1 3’UTR.
41
2.2.3 Cloning Native SRD5A1 Expression Vectors
Sub-cloning the promoter, cDNA, and intron into maintenance vectors prior
to assembly was the key to successfully create the native expression vector.
The full length SRD5A1 cDNA was provided by Dr. David Russell in a
Bluescript cloning vector (p45.1). The Full length cDNA was removed using NotI,
and subcloned into a gutted pUC18 backbone containing only the origin replication,
and beta-lactamase gene (Martinez et al., 1988).
A 1072 bp fragment of the upstream SRD5A1 region was PCRed from human
genomic DNA using the -925F_NdeI and -128R_NheI oligos, and cloned into the
TOPO pCR-4 vector (Invitrogen). This construct was double digested with NdeI and
NheI, klenow blunted, and the promoter fragment was gel purified for cloning.
The human beta-globin intron II (750 bp) (Reed and Maniatis, 1985; Rubin et
al., 2000) was obtained from PCRing human genomic DNA with HBB_IVS_F_2and
HBB_IVS_R_2 primers (see oligo list), using 50°C annealing, for 35 cycles in 1.5
mM MgCl
2
, and the Pfx polymerase (Invitrogen). The intron fragment was then
cloned into the TOPO-pCR4 vector prior to sub-cloning.
To assemble the native SRD5A1 cassette, the SRD5A1 promoter fragment
was cut with NaeI, blunted, then subcloned into the blunted NaeI restriction site in
the pUC18+SRD5A1 cDNA construct. Promoter orientation was confirmed with
restriction digestions and partial DNA sequencing. In order to add the intron to the
promoter+cDNA construct, the beta-globin intron was cut from its vector using
42
PvuII, and gel purified prior to ligation. The intron was ligated into an existing PvuII
site near the location of the endogenous SRD5A1 intron 4. Intron orientation was also
confirmed using restriction analysis and partial DNA sequencing. Proper intron
splicing was confirmed after transfection into HEK-293 and Cos-7 cells and
confirmed via DNA sequencing and PCR using the primers RT_SRD5A1_F1 and
RT_SRD5A1_R1 (Table 2.1) at 60°C annealing, 2 mM MgCl
2
, 0.4 µM primers, 5%
v/v DMSO, 2.5 U/reaction AmpliTaq Gold DNA Polymerase, for a total of 34
cycles.
2.3 Mammalian Cell Culture
African Green Monkey kidney cells (Cos-7) or human embryonic kidney
cells (HEK-293) were grown in high glucose DMEM supplemented with 5% fetal
calf serum, and 1% penicillin/streptomycin/amphotericin B at 37°C in 5% carbon
dioxide. The cells were fed every 2-3 days, and passaged weekly (Baudet et al.,
2006; Lee et al., 2006).
2.3.1 Transfections
On day -1, confluent cells were trypsinized and 2-2.5 X 10
5
cells were seeded
into six well 3.5 cm dishes (Corning). The cells were allowed to grow overnight in
complete medium prior to transfection. Late log phase cells (75% confluent) were
transfected with lipofectamine and lipofectamine plus reagents (Invitrogen)
following the manufacturer’s recommendations and using 500 ng luciferase
expression vector and 50-55 ng pCMV (which expresses bacterial beta-
43
galactosidase) (Thompson et al., 1999). Cells were lipofected in plain high glucose
Dulbecco’s Modified Eagle’s Medium (DMEM) for 5 hours at 37°C and 5% CO
2
.
The media was then removed and 3 mL of complete DMEM was added, and the cells
were allowed to grow for 48 hours prior to harvesting.
All dishes were 100% confluent at harvest time, when the media was
removed, cells rinsed once with 1 mL ion-free phosphate buffered saline, and then
scraped into clean tubes. The cells were pelleted by a 1 minute, 600g centrifugation
spin, aspirated, and resuspended in 600 µL of buffer N (10 mM potassium phosphate
pH 8, 150 mM potassium chloride, and 1 mM EDTA).
The tubes were placed on ice, and allowed to cool before sonication.
Sonication was conducted using a VirSonic 60 sonicator three times for 10 seconds
each time, on a setting of 3. The tubes were allowed to cool at least 5 minutes
between bursts to minimize protein denaturation. The samples were immediately
processed, and not frozen for later use.
2.3.2 Luciferase Assays
Twenty microliters of cell extract were mixed with 100 µL of luciferase
substrate from Promega’s Luciferase Assay Kit on a Victor3V 1420 Multi-label
Counter (Perkin-Elmer) Luminometer, with 1 second integration (Takacs and
Abbott, 2006).
44
2.3.3 Total Protein Determination
Total protein concentration in whole cell extracts was determined using Bio-
Rad’s protein assay reagent (modified Bradford reagent) (Bradford, 1976). Micro-
protein assays in 96 well plates were run according to the manufacturer’s protocol,
using bovine serum albumin as a control. Samples were read on a luminometer at
595 nm wavelength. Unknown sample concentrations were fit to a regression line
based on the control protein range.
2.3.4 Beta-Galactosidase Assays
Between 5-20 μg of whole cell protein extract were used to assess beta-
galactosidase activity. The extracts were diluted in buffer N (1x=10 mM potassium
phosphate pH 7.0, 150 mM KCl, 1 mM EDTA) to a final volume of 30 µL, and the
reaction was carried out at 37°C for 20-60 minutes in 0.88 mg/mL ONPG substrate,
1 mM MgCl
2
, 45 mM beta-mercaptoethanol, and 0.1 mM sodium phosphate buffer at
pH 7.5. Reactions were quenched by increasing the pH with 500 µL 1M NA
2
CO
3
,
prior to reading the samples at 420 nm on a Beckman 420 DU Spectrophotometer
(Bianco and Weinstock, 1994).
2.4 RNA Assays
2.4.1 Total RNA Isolation
RNA was prepared using the TRIZOL reagent (Invitrogen) (Chomczynski
and Sacchi, 1987) and treated with DNaseI (Roche) or with the Aurum Mini RNA
45
Prep Kit (Bio-rad) according to the manufacter’s protocol (Guarino et al., 1997).
2.4.2 cDNA Synthesis
One microgram of total RNA was 1st strand transcribed using the
Transcriptor Reverse Transcriptase Polymerase (Roche) using a final concentration
of 2.5 µM oligo dT, 1.25 mM dNTPS, 200U SuperRNAse inhibitor, in 1x RT buffer
at 55°C for 1 hour in a final volume of 20 µL. 1/10 volume of cDNA was used in
subsequent multiplex relative RT-PCR, or diluted 1/10 into water for Real Time RT-
PCR reactions (Rokuhara et al., 2006).
2.4.3 Semi-quantitative (relative) RT-PCR
One microgram of total RNA was 1st strand transcribed using the
Transcriptor Reverse Transcriptase Polymerase (Roche) using oligo dT for priming
at 55°C for 1 hour. Multiplex relative quantitative PCR was performed with
AmpliTaq Gold DNA Polymerase (Applied Biosystems) and primers specific for
human GAPDH and SRD5A1 (GAPDH=GAPDH_f1/GAPDH_r1,
SRD5A1=5F7/5R5) (Dheda et al., 2004). The annealing temperature used in the
multiplex PCR was 58.4°C, for 23 cycles. Expected amplicon sizes were: 520 bp for
GAPDH, and 210 bp for SRD5A1. Samples were quantitated using the GeneTools
software package from Syngene Corporation. Values for SRD5A1 were divided by
GAPDH to obtain a normalized, relative value for each sample. Samples were run at
least in triplicate, averaged, and standard deviations reported.
46
2.4.4 Real Time Quantitative RT-PCR
cDNA was prepared as described above. Samples were diluted (1/10) and
two microliters were used as template in the reaction. Samples were PCR amplified
using primers 5F7/5R5 (SRD5A1 3’UTR), GAPDH_f1/GAPDH_r1, or b-gal_RT_F
(5’-TTG CAG TGC ACG GCA GAT AC-3’)/b-gal_RT_R (5’-TTT GAC ACC AGA
CCA ACT GG-3’) and with the iQ SYBR Green Supermix (Bio-rad) for 30 cycles
on a MJ Research Real Time PCR Machine using the appropriate standard curve and
negative controls at 50°C (Bustin, 2002; Valasek and Repa, 2005). SRD5A1 values
were corrected to GAPDH or beta-galactosidase mRNA.
47
Chapter 3: SRD5A SNPs
3.1 Cataloging SRD5A1 SNPs across the gene
The SRD5A2 gene is known to harbor germline mutations that directly affect
prostate cancer risk (Makridakis et al., 1997). Furthermore, SRD5A1 and SRD5A2
genes have similar gene structure and enzymatic properties. Therefore in this study,
the SRD5A1 gene was sequenced to determine if it also contained predisposing SNPs
that may explain the differential racial trends of prostate cancer incidence and
mortality as seen in Figure 1.1. The cohort comprised 118 blinded male lymphocyte
DNA samples from three ethnicities (African-Americans, Japanese-Americans, and
Caucasians) originally collected as part of the Los Angeles-Hawaii Multiethnic
Cohort (Kolonel et al., 2000), although only 101 yielded results. After PCR
amplification and DNA sequencing, a total of 30 SNPs were discovered at the
SRD5A1 locus (Figure 3.1). Thirteen were 5’ of the transcriptional start site, 6 silent
SNPs in the protein coding region, 3 in introns near splice sites, and 8 in the 3’-
untranslated region (3’UTR). These SNPs and SNP combinations were next analyzed
to determine which ones might be functionally relevant to prostate cancer.
48
Figure 3.1. SRD5A1 SNP Locations Along Chromosome 5. SNPs are numbered off the cDNA relative to the AUG codon (NCBI
Accession: XM_003859). The arrow represents the putative promoter, with the direction indicating transcription off the sense
strand. The grey boxes represent the 5’ and 3’UTRs, and the black boxes are protein coding exons. The four large introns were
removed, leaving only the exon/intron boundary regions denoted with hash marks.
49
3.2 Analysis of SRD5A1 individual SNPs within the
LHMEC cohort.
The Los Angeles/Hawaii Multiethnic cohort (subsequently referred to as the
LHMEC) genotyping data was used to calculate allele frequencies, Hardy-Weinberg
Equilibrium (HWE) via the chi-square statistic, and haplotype blocks (D’). Table 3.1
lists SRD5A1 genotypes, allele frequencies, chi-square, HWE, and whether the SNP
is cataloged at NCBI’s dbSNP (http://www.ncbi.nlm.nih.gov/SNP/). The cut-off chi-
square value for p <0.05 was 3.84 (with one degree of freedom). A value greater than
3.84 indicated that the SNP was not in HWE.
Surprisingly, eight out of the thirteen 5’-regulatory SNPs were novel to the
LHMEC, with three of the eight SNPs also not being in HWE. Since SNPs -525,
-290, and -258 are found only in the LHMEC cohort, and are not in HWE, they may
functionally alter SRD5A1 gene transcription, and therefore be important to
androgen metabolism. Only SNP -243 occurred within a CpG dinucleotide and thus
may be attributable to methyl-cytosine deamination. Consistent with other reports,
82% of the SNPs were transitions suggesting an in vivo mechanism is responsible
(Jiang and Zhao, 2006).
A novel single base deletion at SNP-382 occurred in approximately 12.5% of
the cohort as detected by DNA sequencing and was verified by restriction fragment
length polymorphism (RFLP, data not shown) analysis using the restriction enzyme
BfaI (cutting amplicons containing the deletion).
50
The nine SNPs occurring in coding exons and introns were all in HWE and
were recorded at dbSNP. An additional interesting finding was that being
heterozygous at position 436 was always found paired with being heterozygous at
position 476. In other words, being heterozygous at SNP 436, meant that SNP 476
was also heterozygous. However, whether this has any functional significance is not
known.
51
Table 3.1. SRD5A1 Allele Frequencies and Statistics. Allele frequencies for
SRD5A1 SNPs, HWE Chi-Squared values, and whether the SNP was also found at
dbSNP (http://www.ncbi.nlm.nih.gov/SNP/).
52
Region SNP Genotypes
Frequencies
(Observed/Total) Allele
Allele
Frequency
#
Chromos
omes
2
HWE In dbSNP
G/G 46/85 G 0.72
G/A 30/85 A 0.28
A/A 9/85
G/G 28/85 G 0.51
G/A 30/85 A 0.49
A/A 27/85
T/T 31/85 T 0.55
T/G 31/85 G 0.45
G/G 23/85
C/C 48/87 C 0.72
C/T 30/87 T 0.28
T/T 9/87
A/A 82/86 A 0.98
G/A 4/86 G 0.02
A/A 0/86
G/G 85/86 G 0.99
G/T 1/86 T 0.01
T/T 0/86
G/G 82/85 G 0.98
G/A 2/85 A 0.02
A/A 1/85
C/C 51/86 C 0.77
C/T 31/86 T 0.23
T/T 4/86
A/A 76/87 A 0.94
A/del 11/87 del 0.06
del/del 0/87
A/A 86/87 A 0.99
A/G 0/87 G 0.01
G/G 1/87
C/C 93/97 C 0.98
G/C 4/97 G 0.02
G/G 0/97
G/G 82/87 G 0.97
G/A 4/87 A 0.03
A/A 1/87
C/C 48/85 C 0.85
C/T 24/85 T 0.15
T/T 13/85
G/G 31/101 G 0.51
G/C 41/101 C 0.49
C/C 29/101
G/G 91/98 G 0.96
G/A 7/98 A 0.04
A/A 0/98
No
No
No
No
No
No
No
No
rs8192125
rs248793
rs2307270
rs10076470
rs571550
rs193743
rs10062086
No
Yes
Yes
8.7
3.56
0.13
No
Yes
No
87
0.04
8.33
0.067
0.4
No
No
Yes
Yes
Yes
No
Yes
Yes
1.63
0.05
0.003
20.2
1.41 Yes
7.34
5.93
PROMOTER
EXON 1
217G>C
202
INTRON 1
IVS1-
17G>A
196
-258G>A
174
170
-290A>G
174
-278C>G
194
-243C>T
-477C>T
172
-382delA
174
172
-571G>T
172
-525G>A
170
-618A>G
-815G>A
170
-781G>A
170
-748T>G
170
-648C>T
174
53
Table 3.1 (continued).
A/A 49/99 A 0.73
G/A 46/99 G 0.27
G/G 4/99
G/G 56/99 G 0.76
G/A 39/99 A 0.24
A/A 4/99
T/T 95/98 T 0.98
T/C 3/98 C 0.02
C/C 0/98
C/C 91/100 C 0.95
T/C 8/100 T 0.05
T/T 1/100
A/A 98/100 A 0.99
G/A 2/100 G 0.01
G/G 0/100
G/G 57/100 G 0.76
G/A 37/100 A 0.25
A/A 6/100
C/C 98/103 C 0.98
T/C 5/103 T 0.02
T/T 0/103
T/T 108/109 T 0.99
T/G 1/109 G 0.01
G/G 0/109
T/T 23/100 C 0.57
T/C 40/100 T 0.43
C/C 37/100
G/G 88/95 G 0.96
G/A 6/95 A 0.04
A/A 1/95
A/A 88/99 A 0.93
G/A 8/99 G 0.07
G/G 3/99
G/G 94/100 G 0.97
G/A 6/100 A 0.03
A/A 0/100
C/C 92/99 C 0.93
C/T 6/99 T 0.07
T/T 1/99
T/T 97/97 T1
T/C 0/97 C0
C/C 0/97
T/T 99/103 T 0.98
T/C 4/103 C 0.02
C/C 0/103
EXON 2
436A>G
198
475G>A
198
INTRON 2
INV2+8T>C
196
IVS2-
30C>T
200
198
EXON 3
604A>G
200
607G>A
200
1758C>T
194
EXON 4
706C>T
206
1368C>T
200
1441G>A
190
1526A>G
2062T>C
206
1202T>C
1578C>T
218
3'UTR
198
1535G>A
200
2.88
0.787
0.02
2.49
Yes
Yes
Yes
Yes
Yes
Yes <0.001
0.01
0.062
0.002
3.39
4.47
14.68
0.1
4.7
NA
0.04
Yes
Yes
Yes
No
No
Yes
No
NA
Yes
rs3822430
rs8192186
rs8192187
rs8192207
rs8192208
rs736316
rs523854
No
No
No
rs8192257
rs3297
rs1042150
rs13974
rs8192254
54
SNPs 1202, 1578, and 1758 were all in HWE but not found in dbSNP. The
3’UTR SNPs: 1368, 1535, and 2062 were in HWE, but also were found in dbSNP.
SNPs: 1441, 1526, and 1578 were not in HWE and not in the national database.
However, 1578 is both out of HWE and not in dbSNP, suggesting that this 3’UTR
variant may play some role in SRD5A1 gene transcription or mRNA metabolism as
it relates to prostate cancer. SNP 1758 was not found in dbSNP because it occurred
only in the original SRD5A1 cDNA cloned by Dr. David Russell’s lab. Except for
SNP 1368, the allele frequencies in the 3’UTR were low (Table 3.1).
Because prostate cancer demonstrates striking racial incidence and mortality
rate differences (Figure 1.1), evaluating SNPs for allele frequency differences in
different racial sub-groups may indicate which variants are functionally relevant to
CaP predisposition. SNPs 1368, 1441, 1526, 1535, and 2062 have allele frequencies
in at least two or more cohorts (LHMEC plus additional data stored in dbSNP). SNP
1368 was analyzed because it lies in the 3’UTR and has significant allele frequencies
for both SNPs.
When the LHMEC was stratified by ethnicity at SNP 1368, both the
Japanese-American and Caucasian individuals showed similar allele frequencies
compared to each other, but demonstrated striking differences compared to African-
Americans (Table 3.2). A pure Japanese cohort of 616 men (YUSUKE) showed
similar alleles frequencies compared to the Asian-American and Caucasians in the
LHMEC. This contrasts to the allele frequencies in the LHMEC African-American
sub-population, which has an over-representation of the 1368T allele by a factor of
55
two and is also not in HWE. The allele frequency differences found in pure Japanese,
Japanese-American, and Caucasian men versus African-Americans was interesting,
because the former have significantly lower incidence and mortality rates for prostate
cancer than the latter. (Figure 1.1). The ancestral allele is 1368C as determined by
comparing DNA sequences from Chimps and Rhesus Monkeys. Since the 1368T
allele arose only in human evolution and is massively overrepresented in African-
Americans, this SNP was anticipated to be functional. Even if ‘1368T’ is truly
neutral, it may still be useful for prostate cancer admixture mapping.
I hypothesized that the allele carrying T at this SNP, which was over-
represented in African Americans relative to other racial-ethnic groups, is associated
with increased CaP risk, because it results in stabilization of the mRNA and
increased steady state levels of mRNA and protein, giving the cells a higher
reductase activity and increased DHT levels, which in turn increases the risk for
malignant transformation. On the other hand, the ‘1368T’ SNP may not be functional
but instead, be in tight linkage disequilibrium with another functional region. To
address if the SNP itself was functional or in LD with another SNP, biochemical
assays and haplotype analysis were conducted.
56
1368 Allele
Frequency
YUSUKE LHMEC LHMEC LHMEC LHMEC
North
American
PDR90
Ethnicity Japanese
Japanese-
American
Caucasian
African-
American
Combined Combined
C 0.73 0.72 0.67 0.32 0.57 0.54
T 0.27 0.28 0.33 0.68 0.43 0.46

2
Unknown 1.60 2.30 4.10 3.40 <0.01
HWE Unknown Yes Yes No Yes Yes
2N= 1232 64 72 64 200 142
Table 3.2. Ethnic distribution of the 3’UTR SNP 1368 in Three Different
Cohorts. The SRD5A1 3’UTR SNP, 1368, demonstrated similar allele frequencies
for a pure Japanese population (YUSUKE), Japanese-Americans, and Caucasians
from the LHMEC Multiethnic cohort. LHMEC African-Americans demonstrated
allele skewing compared to the other populations. The Japanese cohort comprised
752 anonymous, unrelated volunteers (of which 616 gave data), while the samples
drawn from the Los Angeles-Hawaiian Multiethnic cohort was a mixed population
consisting of 36 Caucasians, 32 African-Americans, and 32 Japanese-Americans
(101 combined). A Mixed North American Cohort (PDR90) contained an unknown
mixture of the same ethnicities as the LA/HI Multiethnic Cohort. 2N is the number
of genotyped chromosomes. X
2
is the chi-squared value, using a cut-off of 3.8 to
evaluate if a population was in Hardy-Weinberg Equilibrium (HWE).
57
Individual SNPs in the putative promoter were analyzed using the predictive
transcription factor (TF) binding software program, TFSearch
(http://www.cbrc.jp/research/db/TFSEARCH.html). Figure 3.3 displays a cartoon of
the SRD5A1 putative promoter region. Predicted TFs binding, DNA motifs, and the
locations of some of the promoter SNPs are indicated in the figure. Only SNP -648T
was predicted to create an Elk-1 TF, whereas no other SNP was predicted to create or
destroy a putative transcription factor (TF) site, or alter DNA motifs. The unstudied
NSUN2 gene shares a promoter with SRD5A1 (as it lies in a head-to-head orientation
with only 142 bp separation). Little functional work on the putative promoter was
undertaken, as the 3’UTR was deemed more interesting.
Figure 3.3. Predicted effects of SNPs on the SRD5A1 putative promoter region.
The transcriptional start is indicated by the arrow. SNPs were tested for potential
effects on transcription factor binding using the TFSearch computer program, but
only a subset of the SNPs is boxed in blue. Only the -648T SNP was predicted to
create a transcription factor binding site (for Elk-1). The ‘ARE’ is a theoretical AR
target site.
58
Multiple SRD5A1 3’UTR SNPs were predicted to change putative DNA-
binding proteins, as well as altering microRNA binding, or endogenous cis-elements.
Drawn in Figure 3.4, SNPs 1368U, 1441A, 1578U, and 1757C were predicted to be
functional. Note that SNP 1368 previously demonstrated allelic frequency
differences in African-Americans (Table 3.3), and that the 1578 allele was not in
HWE and was unique to the LHMEC. Having 1368U was predicted to change RNA
structure and thus disrupt the second (of 8) interferon translational repressor
elements (GAIT) (Sampath et al., 2003); whereas a message containing 1578U may
completely destroy the microRNA binding site for hsa-miR-197
(http://regrna.mbc.nctu.edu.tw/html/references.html). Destroying a GAIT element is
expected to weaken the translational repression due to this element, and destroying a
microRNA binding site may have developmental consequences.
Figure 3.4. Predicted Effect of SNPs on the SRD5A1 3’UTR. The diagram depicts
the full length SRD5A1 mRNA 3’UTR. The eight predicted interferon translational
repression domains (GAIT) and K-box RNA elements are indicated with different
colored boxes. The four theoretical microRNA binding sites are marked above their
predicted binding sites, and the eight SNPs along the 3’UTR are indicated along with
comments about predicted effects caused by the less common allele. The RegRNA
software program was used to find 3’UTR RNA motifs.
59
3.3 Analysis of SRD5A1 SNP Combinations as Haplotypes
Because SNPs may functionally interact with each other, it was important to
determine which SNP combinations occurred in the LHMEC, as well as their
frequencies. To this end, DNA haplotype blocks were calculated using the JLIN
(http://www.genepi.com.au/projects/jlin/) and PHASE2
(http://www.bioinf.man.ac.uk/resources/phase/) software packages. SNPs along the
entire SRD5A1 gene were used in the analysis which is graphically displayed in
Figure 3.5. Pair-wise linkage disequilibrium (LD) values as measured by the D’
value are marked with different patterns. Values of ±1 indicate that two SNPs are
found more often co-segregating than expected based on their allele frequencies,
whereas values lower than 1 and larger than -1 mean that the two SNPs are found
less frequently linked than is expected. D’ values less than 1 and larger than -1
indicate that the two SNPs have arisen via mutation and then have been slowly
disrupted over multiple generations by recombination or gene conversion (Abecasis
et al., 2005). However, because the SRD5A1 gene spans such a small physical
distance along chromosome 5 (0.02% of the length), the odds of a recombination
event occurring between two SRD5A1 SNPs is very small, thus the incomplete LD
observed in the SRD5A1 promoter region and between 3’UTR SNPs 1441 and 1578
are puzzling, suggestive that these two regions may contain selected mutations that
arose recently in human evolution via mutation.
60
Figure 3.5. SRD5A1 SNP Haplotypes. The above picture was generated using the
JLIN computer program. Pair-wise D’ values are shown across the SRD5A1 gene
with physical distance indicated on the bottom scale. SNP numbering is based on the
AUG codon from the NCBI XM_003859 accession number.
61
What is striking about the D’ values, is that many of the putative promoter
SNPs are not in LD with other very close SNPs, at -165, -258, -392, -571, and -618.
It is unknown what (if any) affect they may have on SRD5A1 gene transcription.
Also of note is the 3’UTR, which has high D’ values except between SNPs 1441 and
1578. This is surprising, as the expectation is that these close SNPs should always be
in virtual complete LD (co-segregate). Since SNP 1441 and 1578 are not in HWE,
(and 1578 is also not in dbSNP), this points to SNP 1578 as warranting biochemical
study.
Since it was impracticable to create and test all possible SNP combinations
along the SRD5A1 3’UTR, haplotypes calculated from Figure 3.5 were used to
determine the frequency of RNA haplotypes that occurred in the SRD5A1 3’UTR.
The details of the SRD5A1 3’UTR RNA haplotype list are shown in Table 3.3. The
haplotype analysis revealed two major 3’UTR haplotypes that we named ‘Alpha’ and
‘1368T.’ The ‘Alpha’ haplotype is a conglomeration of the 7 most common SNPs at
each position and accounts for over half of all SRD5A1 3’UTR haplotypes; followed
by the ‘1368T’ haplotype which differs from ‘Alpha’ by having a thymine
substitution at 1368, and is found in approximately a quarter of SRD5A1 RNA
messages. Haplotypes arbitrarily named ‘Beta’ and ‘Omega’ were composed of two
different SNPs compared to ‘Alpha,’ and accounted for approximately 5% and 3% of
all 3’UTR haplotypes respectively. These four most common haplotypes comprised
85.5% of all SRD5A1 3’UTR sequences. The remaining haplotypes (‘1202G,’
‘1441A,’ ‘1526G,’ ‘1535A,’ and ‘1578T’) form a small fraction of total SRD5A1
3’UTR haplotypes, but were tested to determine if any of the rare combinations or
62
individual SNPs were functional. Two SNP combinations: ‘Gamma’ and
‘Beta/Omega’ do not occur in the cohort, but were necessary to systematically study
some combinations.
A careful analysis of SNPs across the SRD5A1 gene provided justification for
studying 3’UTR SNPs individually and in combination (known as RNA haplotypes).
Because little is known about how 3’UTR SNPs alter RNA stability, and protein
translation, data might be useful in learning whether any of the germline SRD5A1
SNPs are associated with some of the ethnic differences in prostate cancer incidence
and mortality.
63
Haplotype 1202 1368 1441 1526 1535 1578 1758
% Haplotype
Frequency
Alpha T C G A G C T 54.4
1368T T T G A G C T 23.8
Beta T T G A G T T 4.8
Omega T T G A A C T 2.5
1578T T C G A G T T 0.60
1526G T C G G G C T 0.09
1535A T C G A A C T 0.09
1202G G C G A G C T 0.06
1441A T C A A G C T 0.02
Gamma T T G G A C T 0
Beta/Omega T T G A A T T 0
Table 3.3. SRD5A1 3’UTR SNP Haplotypes and Frequencies. Each SRD5A1 3’UTR haplotype is listed with the DNA
sequence at the polymorphic site and the estimated haplotype frequency in the context of the native SRD5A1 mRNA. The
most common haplotype is named ‘Alpha,’ followed by ‘1368T,’ and so forth. Haplotypes ‘Gamma’ and ‘Beta/Omega’
are not found in the study cohort, but were constructed to study the effects of specific SNP combinations.
64
Chapter 3.4 Discussion
The human SRD5A1 was DNA sequenced in over 100 men comprising three
ethnicities and the allele frequencies calculated (Table 3.1). The entire gene except
for introns was analyzed for single nucleotide polymorphisms. Thirty SNPs were
found in the putative promoter region, exons, or flanking intron-exon boundaries
(Figure 3.1). All the SNPs were silent and did not change universal donor or
acceptor sites. However, about a third of the SNPs were novel, with many not in LD
or in HWE (Table 3.1). It has been estimated that approximately one third of
eukaryotic promoter SNPs are functional (Hoogendoorn et al., 2003) and are often
mutated giving rise to low D’ values (Camp et al., 2005). The DNA haplotype
linkage disequilibrium blocks suggested that recent mutation occurred in the putative
promoter region (Figure 3.3, 3.5, and 3.6), with many SNPs being novel to this
study (Table 3.1). It is likely that a subset of them will be functional in luciferase
biochemical assays; however, there is extensive literature on eukaryotic promoters,
and the existence of an upstream gene (NSUN2) in a head-to-head orientation made
this region more complicated to study.
African American men demonstrated a 2 fold over-representation of the
1368T allele located within the 3’UTR as compared to the other two ethnicities
(Table 3.2). Thus, this SNP may be useful in admixture mapping or in linkage
studies involving blacks (Freedman et al., 2006). Since both alleles at this locus are
common, substantial power can be obtained in a prostate cancer case-control study
65
with less samples, thus saving time and money. A case-control study including SNP
1368 may be useful in a predisposition prostate cancer association study.
Additional SNPs in the 3’UTR may also be functional as predicted by in
silico (Genebee at Moscow State University) binding software packages (Macke et
al., 2001) (Figure 3.4). MicroRNAs, as well as gamma interferon (Sampath et al.,
2003) were predicted to bind directly the 3’UTR, with SNPs 1368U and 1578U
expected to inhibit these mechanisms. Because not as much knowledge is known
about 3’UTR and the effect of SNPs on RNA and protein levels, this region of the
gene was chosen for further testing.
66
Chapter 4. Functional Assessment of SRD5A1 3’UTR SNPs
4.1 Introduction
Because missense, nonsense, or splice-site SNPs were not discovered that
would result in mutant steroid 5-alpha reductase type I enzymes, variants in the
3’UTR were considered for biochemical testing using luciferase reporters fused to
the SRD5A1 3’UTR. Because 3’UTRs are known to mediate RNA stability, and
protein translation through a variety of mechanisms (such as altered secondary
structure, or changes in protein or microRNA binding), this region was ideal to study
the effects of SNPs on mRNA function (Chen et al., 2006b). As an added bonus,
since little was known about how 3’UTR SNPs alter RNA, it was likely that any
findings would be novel and lead to valuable lessons about how SNPs can alter
mRNA metabolism.
4.2 Predicted RNA Secondary Structures of Individual
SRD5A1 3'UTR SNPs and Combinations
Prior to conducting empirical experiments accessing the functional role of
SRD5A1 3’UTR SNPs, we used the Genebee software program
(http://www.genebee.msu.su/) (Brodsky, et al. 1995) to estimate the most stable
RNA secondary structure with each SNP. The full length human SRD5A1 mRNA
containing the most common SNPs along the 3’UTR was used as a control, while a
single base was changed at the known polymorphic sites. As shown in Figure 4.1
SNPs at 1368, 1535, and 1578 were predicted to alter SRD5A1 RNA secondary
67
structures (arrows indicate the polymorphic base). The most dramatic change was
predicted for the 1368U SNP, which destabilized a stem loop into a bifurcated fork
structure. SNPs 1535A and 1578U created small stem loop structures not seen in the
‘Alpha’ haplotype. While the RNA folds are not predictive for functional effects, at
least the RNA structures might change with the less common SNP. SNP
combinations like those in the ‘Beta’ haplotype contain the additive structural
changes from each of the two SNPs.
68
Figure 4.1. In silico RNA Secondary Structures of Individual SRD5A1 3'UTR
SNPs and Combinations. Using the Genebee RNA secondary prediction software,
single stranded, full length SRD5A1 mRNA structure was estimated with either the
common allele (left panel) or the rarer allele (right panel). SNPs at 1368, 1535, 1578,
‘Alpha,’ and ‘Beta,’ are listed. Boxes indicate where RNA structural changes were
predicted.
69
1368C 1368U
1535G
1535A
1578C 1578U Alpha Beta
1
70
4.3 Individual SNPs along the SRD5A1 3'UTR do not affect
a fused luciferase reporter at either the RNA or protein level
To address whether SNPs in the 3’UTR of the human SRD5A1 gene were
functional, we created luciferase-SRD5A1 fusion constructs containing either the
most common SRD5A1 3’UTR sequence (named ‘Alpha’) or such that contain
individual SNPs at seven polymorphic positions along the SRD5A1 3’UTR (Figure
4.2A). The SV40 3’UTR and polyadenylation sites present in the parent construct
were removed prior to insertion of the SRD5A1 sequences to eliminate undesirable
interactions between the tested 3’UTRs and that from SV40 (Chao et al., 1999).
The ‘Alpha’ construct and those containing less common individual SNPs
were transiently transfected initially into HEK293 human embryonic kidney cells
(Figure 4.2B). The pCMV plasmid, containing the ß-galactosidase gene, was co-
transfected as an internal control. Each construct was tested using three independent
plasmid preparations, transfected multiple times. As shown in Figure 4.2B, the
‘Alpha’ construct was expressed at levels comparable to those observed with pGL3P,
which contains the SV40 3’UTR. As expected, luciferase activity was hardly
detectable when the ‘Alpha’ 3’UTR sequence was cloned in the reverse orientation
(Figures 4.2B, 4.2C, ‘RTU’). Disappointingly, activity of each of the constructs
‘1202G‘, ‘1368T’, ‘1441A’,’ ‘1526G,’ ‘1535A,’ ‘1578T’ ‘1758C’ and
‘1368T/1757C’ was not significantly different from that of the ‘Alpha’ construct
(Figure 4.2B).
71
While the exact mechanism explaining why the ‘RTU’ construct exhibited
severely reduced luciferase activity is unknown, we speculated that the loss of both
native SRD5A1 3’UTR polyadenylation sites resulted in destabilized RNA that was
exosome degraded and only weakly translated (Fitzgerald and Shenk, 1981; Ikonen
et al., 1992; Mangus et al., 1998; Wilusz et al., 1989). If eukaryotic mRNAs are
indeed circularized, the addition of a random 3’UTR sequence would be reasonably
expected to disrupt secondary structure and the 5’ and 3’UTRs interactions
(Mazumder et al., 2001). It is also possible that a cryptic polyadenylation cis-
sequence was used from somewhere inside the DNA plasmid construct (Milligan et
al., 2005; Torchet et al., 2002), thus leading to destabilized RNA.
The ‘Alpha’ SRD5A1 3’UTR and the four most frequent SNPs, ‘1368T,’
‘1526G,’ ‘1535A,’ and ‘1578T,’ along with the empty vector (pGL3P) and RTU
were also tested in Cos-7 African Green Monkey kidney cells. Cos-7 cells were used
because they exhibited contact inhibition and would stop growing when confluent,
whereas HEK-293 cells do not. Additionally, Cos-7 cells strongly activate DNA
constructs containing an SV40 origin of replication. The luciferase constructs used in
these experiments contain such an origin within the SV40 promoter, which was
expected to dramatically increase luciferase expression. Cos-7 cells consequently
demonstrated more reproducible protein concentration well-to-well, and thus more
reliable data overall. Most data presented here was derived from transfections from
Cos-7 cells. As a side note, raw luciferase data was 3-5 times higher from constructs
transfected into Cos-7 cells as compared to HEK-293 cells.
72
The luciferase results from Cos-7 cells showed no significant difference
between the activity of the ‘Alpha’ construct and those containing individual SNPs
(Figure 4.2C). On a minor note, the Luciferase/SRD5A1 constructs exhibited slightly
reduced activity as compared to the parent construct containing the SV40 3’UTR in
Cos-7 cells, but this was not statistically significant (Figure 4.2C). Thus, when
evaluated separately, none of the SRD5A1 3’UTR SNPs appeared to have a
functional role in the luciferase assays.
An important issue in any kind of analysis is the experimental sensitivity. For
all of the luciferase activity and RNA steady state levels, a retrospective ad-hoc
power calculation (Aaron and Hays, 2004) was used to determine the power for each
experiment. This value utilized the mean, standard deviations, and replicate numbers.
Then the power value was input in the standard power formula (Moher et al., 1994)
to solve for the detectable difference. The luciferase activity and RNA steady state
level assays were able to discriminate a 10-11% difference between single or
combination SNPs and the ‘Alpha’ construct. Thus, the methods employed were
likely to reveal a 10-11% difference caused by a particular RNA haplotype. It is
therefore unlikely that any of the RNA haplotypes demonstrate a biological affect, as
the sensitivity was quite good.
In order to evaluate whether single SNPs might alter RNA, steady state RNA
levels were measured. As seen in Figure 4.2D, there was no significant difference in
RNA levels for any of the tested constructs as compared to ‘Alpha.’ SNP 1526G was
measured using semi-quantitative RT-PCR, but all other results were conducted
73
using a quantitative RT-PCR method (Chapter 2). SNP combinations were then
tested for luciferase activity and RNA levels.
74
Figure 4.2 Luciferase Reporter Activity and RNA Steady State Levels of
Constructs Containing Individual SRD5A1 3’UTR SNPs. (A). Schematic
illustration of the luciferase/SRD5A1 3’UTR fusion constructs used in transfections.
The positions of seven SNPs are indicated by arrowheads. In the ‘Alpha,’ each of
these positions is occupied by the most common SNP. In each of the other
LUC/SRD5A1 constructs, a single base pair was mutated (italicized and underlined)
to recapitulate the respective SNP. The constructs illustrated in (A) were transfected
in triplicate into HEK-293 cells (B) or Cos-7 cells (C) along with pCMV, and
luciferase activity was measured and corrected for ß-Gal activity. (D) RNA steady
state levels of luciferase constructs containing a single SNP in Cos-7 cells as
measured by quantitative RT-PCR or relative RT-PCR. RNA was corrected for
either GAPDH or beta-galactosidase RNA levels and normalized to the ‘Alpha’
construct. Bars represent Means ± standard deviation relative to the activity of
‘Alpha,’ which was defined in each experiment as 1. The number of measurements is
indicated in parentheses within each bar. pGL3P is the parent construct containing
the SV40 3’UTR. RTU refers to the SRD5A1 UTR inserted in the reverse orientation.
75
0
0.2
0.4
0.6
0.8
1
1.2
1.4
pG L3P Alpha 1202G 1368T 1441A 1526G 1535A 1578T 1757C 1368T /1757C U T R -
0
0.2
0.4
0.6
0.8
1
1.2
1.4
pG L3P Alpha 1368T 1526G 1535A 1578T R T U
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Alpha 1368T 1526G 1535A 1578T
G C G A G C T Luciferase
SV40
T T G A G C T Luciferase
SV40
T C A A G C T Luciferase
SV40
T C G G G C T Luciferase
SV40
T C G A A C T Luciferase
SV40
1202G
1368T
1441A
1526G
1535A
1578T
SV40
Luciferase T C G A G T T
1202
SV40
Luciferase
1368
1441
1535
1526
1578
1758
RTU
1758C
SV40
Luciferase T C G A G C C
SV40 Poly A Luciferase
Luciferase
SV40
SV40
T C G A G C T
pGL3P
Alpha
A
B
Relative Luciferase
Relative Luciferase
C
(3) (3) (3) (3) (3) (3) (3) (3) (3) (3) (3)
(6) (39) (27) (3) (30) (18) (6)
D
Fold RNA Change
(15) (6) (3) (9) (6)
RTU
76
4.4 SNP combinations along the SRD5A1 3'UTR do not
affect activity of a fused luciferase reporter or RNA steady
state levels
Because none of the individual SRD5A1 3’UTR SNPs had a significant
influence on luciferase activity (Figures 4.2B and 4.2C), and because most of these
SNPs are rarely found individually (Figure 3.7), we went on to test the influence of
3’UTR SNP combinations using luciferase and RNA assays. We initially compared
the three most common 3’UTR SNP combinations found in our cohort, i.e.,
haplotypes ‘Alpha,’ ‘Beta’ and ‘Omega’ (Figure 4.3A). Luciferase plasmids
containing these 3’UTR haplotypes were constructed and transfected into Cos-7
cells. None of the reporter constructs’ 3’UTRs expressed luciferase at levels
significantly different from those observed with the most common ‘Alpha’ 3’UTR
haplotype (Figure 4.3B). Thus, even SNP combinations did not alter luciferase
activity.
Since SNPs 1368U, 1535A, and 1578U were predicted to change RNA
secondary structure (Figure 4.1), we decided to test all three SNPs together to
determine if they would change luciferase activity. To this end, we constructed a
luciferase reporter containing all three SNPs by generating a G-to-A mutation at
position 1535 in the ‘Beta’ construct (Figure 4.3A). As shown in Figure 4.3B, the
resultant ‘Beta/Omega’ luciferase construct also demonstrated no difference in
77
luciferase activity. These results suggest that the commonly occurring SNP
combinations along the 3’UTR, in the context of a luciferase reporter, did not alter
luciferase activity in Cos-7 cells.
To test the hypothesis that SNP combinations might decrease RNA steady
state levels, yet be compensated by increased protein translation, RNA levels were
quantitated from transfected Cos-7 cells (Figure 4.3C). If the RNA steady state
levels demonstrated significant changes compared to the ‘Alpha’ construct, then it
was possible that one or more of the SNPs were functional. However, there was no
difference in the RNA steady state levels by using the combination constructs
compared to ‘Alpha’ (Figure 4.3C). Because there was no difference in RNA levels
using ‘Beta’ or ‘Omega,’ the combination construct ‘Beta/Omega’ was not tested.
Thus, none of the SNPs (or combinations) had any effect on the RNA levels, and the
hypothesis that some of the SRD5A1 polymorphisms were functional was
consequently rejected.
78
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Alpha Beta O m ega Beta/O m ega
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
Alpha Beta O m ega
R elativeL u c iferaseA ctivity
(30) (15)
(15) (3)
B
C
(45) (36) (9)
F o ldR N A
Figure 4.3. Luciferase Reporter Activity and RNA Steady State Levels of
Constructs Containing SRD5A1 3’UTR SNP Combinations. (A)
Luciferase/SRD5A1 3’UTR fusion constructs containing various SNP combinations.
Mutagenized bases are in italics and are underlined. (B) The constructs illustrated in
(A) were transfected into Cos-7 cells and luciferase activity was measured and
corrected as described Chapter 2. (C) Luciferase construct RNA steady state levels
corrected to -galactosidase and normalized to the ‘Alpha.’ Bars represent means ±
standard deviation relative to the activity of ‘Alpha,’ which was defined in each
experiment as 1. The number of measurements is indicated in parentheses within
each bar.
T T G A G T T Luciferase
SV40
T T G A A C T Luciferase
SV40
T T G A A C T Luciferase
SV40
Beta
Omega
1758
1578
1535
1526
1441
1368
1202
Alpha
SV40
Luciferase T C G A G C T
SV40
pGL3P
Luciferase SV40 Poly A
Beta/Omega
79
4.5 Native SRD5A1 Expression Vectors containing single
and combination SNPs do not demonstrate differences in
RNA levels
The luciferase/SRD5A1 3’UTR constructs described in Figures 4.2 and 4.3
are highly artificial. The expressed messenger RNA possesses an SV40 5’UTR, a
firefly luciferase cDNA, a human 3’UTR, and no intron. It was possible that
chimeric mRNA from these constructs masked subtle changes in protein or RNA
levels.
To test this possibility, mammalian expression vectors containing the native
human SRD5A1 promoter, full length SRD5A1 cDNA, a human beta-globin intron 2,
and full length SRD5A1 3’UTR were constructed (Figure 4.5A). These native
constructs were delineated from luciferase constructs by the addition of an ‘N’ after
the SNP name (e.g. ‘1368T-N’). The pieces were sub-cloned and then assembled
together using endogenous restriction sites so as to not introduce any extra bases that
could alter the expressed RNA sequence (Chapter 2). Even the intron was placed
only four bases away from where the endogenous intron 4 lies. When expressed, the
globin intron was properly spliced out leaving an uninterrupted full length human
SRD5A1 cDNA as determined by DNA sequencing from RT-PCRed transfected
Cos-7 cells (Figure 4.4). The final mRNA did not contain viral or non-human
80
features, and was expected to be identical to the SRD5A1 message transcribed from
the chromosome in human cells.
81
Figure 4.4. PCR Confirmation of Proper Intron Splicing from Native Vectors.
PCR was carried out as described in Chapter 2 using the ‘Alpha-N’ DNA construct
in the (+) lane or the cDNA from this construct from Cos-7 cells (-). The marker is
indicated (M). The cDNA with proper human beta-globin intron 2 splicing gave an
amplicon of 705 bp, while the parent DNA construct gave a 1,555 bp band. The
constructs were also DNA sequenced to confirm the presence or absence of the
heterologous intron.
M
+
Intron
-
705 bp
1555 bp
82
As shown in Figure 4.5B, there was no difference in RNA steady state levels
between the native constructs ‘Beta-N,’ ‘1368T-N,’ or ‘1535A-N’ as compared to the
‘Alpha-N’ construct. Thus it was concluded that all the SRD5A1 SNPs tested were
neutral under the experimental conditions employed, including SNP 1368T which
was over represented in African-American men.
Additionally, the native promoter was strongly expressed in Cos-7 cells
(Figure 4.6). Luciferase and Native expression vectors were co-transfected with the
pCMV plasmid (containing a CMV promoter) for comparing RNA expression
levels (Figure 4.6A). Surprisingly, the native expression vectors exhibited
approximately 6 times higher SRD5A1 expression as compared to either luciferase
construct when SRD5A1 RNA levels had been corrected for beta-galactosidase
(Figure 4.6B). Moreover, there was a reduction of beta-gal mRNA in cells also
transfected with the native expression vectors (Figure 4.6C) as compared to those
transfected with luciferase. This suggested that promoter competition (squelching)
for general transcription factors occurred between either the CMV and SV40
promoters (from the luciferase co-transfection) or SRD5A1 and CMV promoters
(from the native co-transfection). Based on statistical calculations, the RNA assays
should have been able to discriminate differences of 16-23% in RNA steady state
levels. Thus we were unable to detect RNA level changes that were less than 23% as
compared to the ‘Alpha’ construct. Although it is theoretically possible for an RNA
level change of 10-15% to be biologically relevant, the luciferase data supports that
the native constructs are also functionally neutral.
83
SRD5A1 cDNA
INTRON
T C G A G C T
SRD5A1 cDNA T T G A G T T
SRD5A1 cDNA T T G A G C T
SRD5A1 cDNA T C G A A C T
1202
1368
1526
1578
1535
1758
1441
Alpha-N
Beta-N
1535A-N
SRD5A1
SRD5A1
SRD5A1
SRD5A1
INTRON
INTRON
INTRON
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Alpha-N Beta-N 1368T -N 1535A-N
Figure 4.5 Influence of SRD5A1 3’UTR SNPs on mRNA Steady State Levels
Expressed from Constructs Containing the Native SRD5A1 Promoter and
cDNA as well as a Heterologous Intron. (A) DNA constructs used to transfect Cos-
7 cells. Italics indicate mutagenized nucleotide(s). (B) RNA steady-state measured
via Real Time Quantitative RT-PCR. Bars represent Means ± Standard deviation
relative to the activity of ‘Alpha,’ which was defined in each experiment as 1. The
number of measurements is indicated in parentheses within each bar.
B
1368T-N
A
(12) (9) (12) (9)
Fold RNA Change
84
0
1
2
3
4
5
6
7
Alpha-L Beta-L Alpha-N 1368T -N
0
0.2
0.4
0.6
0.8
1
1.2
1.4
Alpha-L Beta-L Alpha-N 1368T -N
T T G A G T T Luciferase
SV40
1202
1441
1535
1758
1526
1578
1368
SV40 Luciferase T C G A G C T
Beta-L
Alpha-L
SRD5A1 cDNA
INTRON
T C G A G C T
SRD5A1 cDNA T T G A G C T
Alpha-N
1368T-N
SRD5A1
SRD5A1
INTRON
A
(27) (27) (9) (9)
(27) (27) (9) (9)
B
C
Figure 4.6. Comparison of RNA Levels between Luciferase and Native
Constructs. (A) Luciferase and Native DNA constructs used to transfect Cos-7 cells.
Italics indicate mutagenized nucleotide(s). (B) SRD5A1 RNA steady-state measured
via Real Time Quantitative RT-PCR corrected for co-transfected beta-galactosidase
mRNA. (C) Beta-galactosidase mRNA levels. Bars represent means ± standard
deviation relative to the activity of ‘Alpha-L,’ which was defined in each experiment
as 1. The number of measurements is indicated in parentheses within each bar.
Fold RNA Change
Fold RNA Change
85
4.6 Discussion
Biochemical measurement of luciferase activity and RNA steady state levels
in both construct types failed to demonstrate any significant differences compared to
the most common ‘Alpha’ haplotype (Figures 4.2, 4.3, and 4.5) even though RNA
secondary structure was predicted to change with some SNPs (Figure 4.1).
Transfection using the native constructs gave significantly more RNA as compared
to the luciferase constructs (Figure 4.6) probably due to the presence of an intron
(Reed and Magni, 2001). It is possible that small differences of 10-25% could not be
detected based on ad-hoc power calculations (Anatol et al., 1997), or that the effect is
cell type specific (Hoogendoorn et al., 2003). It is known that most multifactorial
diseases such as prostate cancer are caused in part by the contribution of small
effects from many genes (Hirschhorn and Daly, 2005). If the contribution of a single
gene to a particular complex disease is on the order of 5-10%, then functionally
demonstrating such a small contribution will be very difficult to detect over
background using current molecular methods (Newton-Cheh and Hirschhorn, 2005).
Thus, it is possible that some of the haplotypes identified and tested here may
actually be biologically functional.
86
Chapter 5: Conclusions
The human steroid 5-alpha reductase type I gene (SRD5A1) was sequenced to
catalog existing germline single nucleotide polymorphisms. Thirty SNPs were found
along the gene, with all being silent. Over thirty percent of the SNPs were novel to
the LHMEC (11/30 SNPs), and were thus not found in the national SNP database
(dbSNP). SNPs along the 3’UTR were studied for a possible functional role in
altering SRD5A1 mRNA or protein translation and RNA haplotypes were analyzed
for whether individual SNPs or their combinations affected RNA secondary
structure, created/destroyed DNA transcription factor sites, disrupted RNA motifs, or
altered microRNA binding. Several 3’UTR SNPs were predicted to affect one or
more of the above mechanisms, but none were demonstrated to be functional using
in vitro biochemical assays.
Despite this finding, the 1368T SNP was found to have dramatic allele
skewing (out of Hardy-Weinberg Equilibrium) in the African-American sub-
population as compared to Caucasians, Japanese, and Japanese-Americans, and arose
only in human evolution. Additionally, the ‘U’ RNA allele was predicted to alter the
native SRD5A1 mRNA secondary structure more than any other 3’UTR SNP. This
RNA structural change occurred near a putative interferon gamma responsive-
translational repression motif (GAIT) (Sampath et al., 2003). Messenger RNA
containing the 1368U allele may have an altered GAIT element (Sampath et al.,
2003), leading to more SRD5A1 protein translation (when interferon gamma is
87
present) as compared to messages containing the 1368C allele. The increased
amounts of SRD5A1 enzymes might then result in a higher concentration of reduced
androgens which would then activate the androgen receptor and thus to cell
proliferation and more DNA errors over a lifetime when coupled with other
environmental factors (diets high in red meats/fats) (for a review see (Bertram,
2000)). Thus, SRD5A1 mRNAs containing the 1368T allele were hypothesized to be
predisposing to prostate cancer.
If 1368T was functional, it would be of great consequence as the ‘T’ allele is
found in a substantial number of men. Even a weak contribution from 1368U could
partially explain why African-American men have a higher risk for developing and
dying from prostate cancer as compared to the other ethnicities. Even though 1368T
did not demonstrate a biochemical difference in this study, it is possible that the SNP
is in LD with another functional region, that kidney cells lack a prostate specific
protein which must be present to observe an affect, that the SNP is important in
female physiology (Timmons and Mahendroo, 2006), or that the difference in
luciferase activity or RNA levels was so small that the differences could not be
detected (<10% difference in luciferase activity, <10% for luciferase steady state
RNA, and <23% for native steady state RNA levels). Future experiments looking for
association between SRD5A1 SNPs and human disease should be considered.
The 1578 SNP was also of interest as it is out of Hardy-Weinberg
Equilibrium and is only described in this cohort. Moreover, this SNP is only in
partial linkage disequilibrium with the close SNP 1441, suggesting a functional role
88
at 1578. In silico and motif searches suggested that the SNP at 1578 may be
functional. However, the biochemical data presented in Chapter 4 does not support
a functional role for any individual or combination SRD5A1 3’UTR SNPs. However,
a series of papers have associated SRD5A1 silent SNPs with a variety of phenotypes
(Ellis et al., 2005; Goodarzi et al., 2006; Klotsman et al., 2004)(see Chapter 5.1),
thus supporting a functional role for either SRD5A1 or a nearby gene. The purely
biochemical approach used here (rather than an association study) is valid as
SRD5A1 has already been associated with some human phenotypic changes.
Two major expression vector classes were tested to determine if SRD5A1
3’UTR SNPS (alone and in combination) were functional. The first constructs were
standard luciferase fusions plasmids that contained the full length human SRD5A1
3’UTR cloned 3’ of the luciferase gene (Auyeung et al., 2003). None of the single or
combination SNPs demonstrated a difference in luciferase activity compared to the
most common haplotype in two different mammalian kidney cell lines. Additionally,
the steady state RNA levels were also unchanged, demonstrating that all of the SNPs
were silent and non-functional in vitro. A possible confounding variable was the lack
of an intron (Buratti and Baralle, 2004). Using luciferase constructs is assumed to
mimic the native mRNA, but there are no literature reports confirming this (Chen et
al., 2006a). This study is the first known attempt to directly compare luciferase
mammalian expression vectors alongside native expression vectors. It is interesting
to note that both vectors gave similar results, thus validating that luciferase vectors
may be used to estimate how a native mRNA functions.
89
5.1 RNA Haplotype Results in the Context of Published
SRD5A1 in vivo data
Even though there was no significant difference in luciferase activity or RNA
steady state levels from any SRD5A1 RNA haplotypes tested (Figures 4.2, 4.3, and
4.5), it is still possible that some SRD5A1 SNPs may be functional or be in linkage
disequilibrium with another functional locus. Therefore, an association study
involving cases and prostate cancer controls using SNPs in the putative promoter
region or 1368T might be useful to determining if any of the tested SNPs are truly
neutral in a larger population (McCarthy et al., 2005; Tang et al., 2002).
The two cell lines employed in this study are not from a prostate source and
therefore may not possess the correct tissue specific proteins that may be critical in
discriminating between different SRD5A1 3’UTR haplotypes (Brooks, 2002; Cazares
et al., 2002). The putative promoter region contains multiple SNPs (specifically -781,
-748, and -382) that are in weak LD, and also have similar allele frequencies for both
bases. Several recent studies have implicated SRD5A1 in a variety of phenotypes:
increases in female hirsutism (Goodarzi et al., 2006), correlations in DHT/T ratio
(Ellis et al., 2005), and the severity of BPH symptoms (Klotsman et al., 2004).
Therefore, it is possible that if functional SNPs are not within SRD5A1, that those
SNPs are in LD with a causative region.
When the SRD5A1 homolog was deleted in male mice (Steers, 2001), there
was no abnormal phenotype, and only a parturition defect in females. The
90
experiments described here provides no functional role for the 3’UTR SNPs at the
protein or RNA level, and thus casts doubt about SRD5A1’s role in prostate cancer
predisposition or protection. It is possible that the SRD5A1 gene plays a role in
general female urogenital physiology or female cancers which rely on hormones
(ovarian and breast) (Lewis et al., 2004; Timmons and Mahendroo, 2006). It is also
possible that a gene involved in reproduction, like SRD5A1, might contain
exclusively neutral SNPs (Bhatti et al., 2006). Individuals possessing any mutation
making them less fit would be evolutionarily selected against (Keightley and Otto,
2006). Thus, it makes sense that a gene involved in male secondary sex virilization
and the onset of female birthing would not contain any deleterious germline
mutations.
5.2 Using SRD5A1 3’UTR SNP 1368 in Admixture
Mapping
An interesting finding was revealed when the LHMEC samples were
stratified at SNP 1368 based on ethnicity. This SNP was chosen for stratification
because it lay in the SRD5A1 3’UTR, had the highest allele frequencies of both bases
in the 3’UTR, and was barely in Hardy-Weinberg Equilibrium (HWE). The 101
LHMEC samples genotyped were composed of three different ethnicities: Japanese-
Americans, Caucasians, and African-Americans. While both the Japanese-American
and Caucasian individuals had similar allele frequencies compared to each other,
they demonstrated a striking difference compared to African-Americans (Table 3.2).
91
Since the 1368T allele arose only in human evolution and is overrepresented in
African-Americans, this SNP was anticipated to be functional. Although the
experiments here revealed that ‘1368T’ is neutral, it may yet be useful in prostate
cancer admixture mapping of germline disease loci in African American men
(Freedman et al., 2006; Smith et al., 2004).
5.3 Future Work
Future work on the SRD5A1 gene might involve studying whether any of the
putative promoter SNPs (or the existing 3’UTR RNA haplotype constructs) alter
luciferase protein and/or RNA levels in prostate cancer cells lines. Since the
promoter region contains many SNPs, some of which are out of HWE, are unique to
the LHMEC cohort, or possess low LD, it is possible that some promoter SNPs
might impact SRD5A1 expression. As an added bonus, there is no published study on
the SRD5A1 promoter region or associated transcription factors.
Another possible project would be to use the SNPs characterized here to
conduct an association study with breast, ovarian, or prostate cancers. Since new
SNPs were uncovered in this study that could, in theory, affect SRD5A1
biochemistry, an epidemiological study could use the data presented here in
determining which SNPs are linked to human disease (Ellis et al., 2005).
92
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Abstract (if available)

Abstract The human steroid 5-alpha reductase type I (SRD5A1) gene was sequenced in 101 men to identify genetic variants that may predispose/protect carriers to prostate cancer. We uncovered 30 single nucleotide polymorphisms (SNPs) along the gene length, with 7 polymorphic sites lying in the 3'-untranslated region (3'UTR). All SNPs were silent, but one 3'UTR SNP (named '1368T') was significantly over represented two fold in African-American men, possibly making this a useful admixture mapping marker. To further pursue the relevance of the SNPs in the 3'-UTR of this gene, we used cell culture assays to test for the potential effects at the RNA or protein level. Individual and different combinations of SRD5A1 3'-UTR SNPs (named "RNA haplotypes") had no effect on luciferase activity when cloned onto the 3' end of the luciferase cDNA. Moreover, steady state mRNA levels did not change when expressed either from luciferase or 'native' expression constructs containing the native SRD5A1 promoter, cDNA, 3'UTR, and a heterologous intron. Thus SRD5A1 3'UTR SNPs were truly neutral and non-functional in vitro, and are therefore unlikely to play a significant role in prostate cancer predisposition in vivo. If functional germline SRD5A1 3'UTR SNPs existed that altered reproductive fecundity, they were likely selected against by evolution, and may explain why only neutral SNPs were observed in three modern human populations.

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Asset Metadata

Creator Phipps, Troy (author)

Core Title Systematic analysis of single nucleotide polymorphisms in the human steroid 5-alpha reductase type I gene

School Keck School of Medicine

Degree Doctor of Philosophy

Degree Program Biochemistry and Molecular Biology

Degree Conferral Date 2006-12

Publication Date 10/31/2006

Defense Date 10/18/2006

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag haplotypes,OAI-PMH Harvest,prostate cancer,single nucleotide polymorphisms,SRD5A1

Language English

Advisor Frenkel, Baruch (committee chair), Coetzee, Gerhard A. (committee member), Stellwagen, Robert H. (committee member)

Creator Email tphipps@usc.edu

Permanent Link (DOI) https://doi.org/10.25549/usctheses-m119

Unique identifier UC1321310

Identifier etd-Phipps-20061031 (filename),usctheses-m40 (legacy collection record id),usctheses-c127-28700 (legacy record id),usctheses-m119 (legacy record id)

Legacy Identifier etd-Phipps-20061031.pdf

Dmrecord 28700

Document Type Dissertation

Rights Phipps, Troy

Type texts

Source University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Repository Name Libraries, University of Southern California

Repository Location Los Angeles, California

Repository Email cisadmin@lib.usc.edu