!
Sperm Molecular Alterations are Biomarkers of Testicular Injury and Dysfunction
By
Sara Ellen Pacheco
B.S., University of Massachusetts, Amherst, 2007
Dissertation
Submitted in partial fulfillment of the requirements for the
Degree of Doctor of Philosophy in Biology and Medicine at Brown University
PROVIDENCE, RHODE ISLAND
MAY 2012
© Copyright 2012 by Sara Ellen Pacheco
This dissertation by Sara Ellen Pacheco is accepted in its present form by the
Department of Biology and Medicine as satisfying the dissertation requirement
for the degree of Doctor of Philosophy.
Date______________
________________________________
Kim Boekelheide, M.D., Ph.D., Advisor
Recommended to the Graduate Council
Date______________
________________________________
Mary Hixon, Ph.D., Reader
Date______________
________________________________
Karl T. Kelsey, M.D., M.O.H., Reader
Date______________
________________________________
Mark Sigman, M.D., Reader
Date______________
________________________________
Robert Chapin, Ph.D., Outside Reader
Approved by the Graduate Council
Date______________
________________________________
Peter M. Weber, Ph.D., Dean of the
Graduate School
iii
CURRICULUM VITAE
Sara Ellen Pacheco
Education
2007-2012
Brown University
Providence, RI
Ph.D. in Medical Science, Pathobiology Graduate Program
Mentor: Kim Boekelheide, M.D., Ph.D.
2003-2007
University of Massachusetts
Amherst, MA
B.S. in Microbiology, Commonwealth College Scholar
Departmental Honors, Summa Cum Laude
Publications
1. Sara E. Pacheco, Edward Dere, and Kim Boekelheide
Rat sperm microRNA profiling identifies microRNAs important for reproductive
function and embryonic development. Manuscript in preparation.
2. Sara E. Pacheco, Linnea M. Anderson, Moses A. Sandrof, Marguerite M.
Vantangoli, Susan J. Hall, Kim Boekelheide
Sperm mRNA transcripts are biomarkers of sub-chronic low dose Sertoli cell
injury in the rat. Manuscript in preparation.
3. Sara E. Pacheco, Linnea M. Anderson, and Kim Boekelheide
Optimization of a filter-lysis protocol to purify rat testicular homogenates for
automated spermatid counting. J Androl. 2012 Jan 12. [Epub ahead of print]
iv
4. Sara E. Pacheco, E. Andres Houseman, Brock C. Christensen, Carmen J.
Marsit, Karl T. Kelsey, Mark Sigman, and Kim Boekelheide
Integrative DNA methylation and gene expression analyses identify DNA
packaging and epigenetic regulatory genes associated with low motility sperm.
PLoS One. 2011;6(6):e20280. Epub 2011 Jun 2.
5. Sarah N. Campion, Natasha Catlin, Nicholas Heger, Elizabeth V. McDonnell,
Sara E. Pacheco, Camelia Saffarini, Moses A. Sandrof, and Kim
Boekelheide
Male reprotoxicity and endrocrine disruption. Molecular, Clinical and
Environmental Toxicology, Volume 3: Environmental Toxicology. Birkhäuser
Publishing, Basel, Switzerland. In press.
Awards and Accolades

Superfund Research Trainee-Highlight (Spring 2012)

Rhode Island Hospital’s 19th Annual Research Celebration’s New Investigator
Award (Clinical Research), Finalist (Fall 2011)

Northeast Society of Toxicology, Poster Competition, 3rd Place (Fall 2011)

Northeast Society of Toxicology, Oral Presentation Finalist (Fall 2011)

Pathobiology Program Retreat Poster Competition, 3rd Place (Fall 2010)

Levy Pre-doctoral Travel Award for Ph.D. Students in BioMed (2009)

Sheridan Center Program 1 Teaching Certificate (2009)

UMass Amherst Alumni Association: 5 under 25 (2009)

Graduate Training Grant: “Training in Environmental Pathology” #ES007272 (Fall
2008-Fall 2011)

Jr. Fellows Program (Fall 2006-Spring 2007)
v

CBR-REU Intern (Summer 2006)

William Field Alumni Scholar (Spring 2006)

Golden Key International Honor Society (Fall 2005)

Alpha Lambda Delta National Honor Society (Spring 2004)

Dean’s List (Fall 2003-Spring 2007)
Invited Lectures

American Society of Andrology 2012 Basic Science Workshop “Diagnosing Male
Reproductive Capacity in the Laboratory”, Tuscon, AZ (04/21/12). “Tools for
detecting testicular injury in rodent models”

Superfund Research Program Student Roundup, Brown University, Providence,
RI (03/21/12). “Sperm mRNA transcripts as biomarkers of low dose testicular
injury in the rat”

Pathobiology Admissions Weekend, Brown University, Providence, RI (02/11/12).
“Hidden messages: sperm mRNA transcripts as biomarkers of low dose testicular
injury in the rat”

2012 Pathobiology Seminar Series, Brown University, Providence, RI (01/26/12).
“Sperm molecular signatures: reading and writing the paternal transcriptome and
epigenome”

Brown BioMed Office of Graduate and Postdoctoral Studies Summer Graduate
Student Research Series, Brown University, Providence, RI (07/21/11).
“Integrating clinical semen parameters with sperm molecular biomarkers”
vi
Conferences and Abstracts


American Society of Andrology Annual Meeting
o
Poster – 2012, 2011, 2010, and 2009
o
Podium – 2012 and 2011
Rhode Island Hospital’s 19th Annual Research Celebration
o

Northeast Society of Toxicology Regional Chapter Meeting
o

Poster – 2011 and 2009
American Association for Cancer Research Annual Meeting
o

Poster and Podium – 2011
American Society of Andrology Testis Workshop
o

Poster – 2011
Poster – 2007
Massachusetts Annual Undergraduate Research Conference
o
Poster – 2007 and 2004
Teaching and Leadership Experience

Peer Reviewer, Journal of Andrology

Trainee-Representative Elect, American Society of Andrology Council

Member, American Society of Andrology Trainee Affairs Committee

Special Lecturer of Biology, Providence College

Graduate Student Educator, Advancing RI Science Education

Guest Lecturer, Brown University

Graduate Student Mentor, Brown University Pathobiology Program

Member, Pathobiology Graduate Student Steering Committee, Brown University
vii
ACKNOWLEDGEMENTS
The work highlighted in this dissertation could not have come to fruition
without the support and encouragement from a number of people. I would like to
take this opportunity to express my gratitude to the following individuals:

Kim Boekelheide – Thank you for being my mentor and providing the
environment necessary for me to succeed. You had high expectations for my
work from day one and that really motivated me to push myself harder than I
probably would have on my own. You cultivated my research and critical
thinking skills by allowing me to ask questions and speak my mind. I have
learned so much during these past 5 years – I really couldn’t have asked for a
better experience.

Karl Kelsey, Mark Sigman, and Bob Chapin – Thank you for being part of my
thesis brain-trust. I really value all of your guidance and feedback.

Mary Hixon – Thank you for being you. I truly believe that you are the reason
that I am where I am today. You are my guardian angel and your
benevolence will never be forgotten. You have had a profound impact on my
life and when I think about the kind of teacher that I would like to be someday,
I think of you.

Team Testis – Thank you all for everything. Sue, thank you for always
listening to me, especially when things were difficult for me both in and out of
viii
lab. I couldn’t have made it through without your endless support. Moses,
thank you for being my work-husband – one of my favorite parts of graduate
school was being your bay-mate. Camelia, you have been a true friend for all
these years. Thank you for consistently picking me back up whenever I was
down. I am really going to miss our heart-to-hearts. Nick, grad school would
not have been the same without you. I am glad we got to navigate through
this process together. Maggie, I could not have asked for a better
replacement bay-mate! You are the little sister I never had and I know we will
keep in touch. Thanks for editing this beast. Natasha and Linnea, I still think
you are both nuts, but I think I am “half” crazy, too. Thank you for encouraging
me to do the unthinkable and accomplish one of the greatest feats of my
lifetime. Liz, thank you for being an extra set of hands when I needed them.
You were always willing to help me and I really appreciate it. Ed, you are a
computer genius. Thank you for making all of my figures aesthetically
pleasing and educating me about all of the intricacies of Microsoft Word. The
thesis writing process would not have gone as smoothly without you. Dan,
I’ve really enjoyed our conversations about random facts. They were a muchneeded distraction from all of this writing.

Jodie Pietruska – I am so grateful that fate put us in the same place at the
same time. You will always be more than just my work-wife and I look forward
to continuing our friendship after we leave the 5th floor.
ix

My wolf pack – Thank you for all of the laughs. I am going to miss our daily
doses of coffee and gossip.

My family – Nick, Mom, and Dad, I could not have done this without the love
and support from all three of you. Nick, you are an amazing person and I
could not have chosen a better life partner. You stuck by me when things got
tough and never let me give up. I am a better person because you are in my
life. Mom and Dad, I am so lucky that you are my parents. You have never
stopped believing in me. You say I can do anything that I set my mind to, but
everything I have accomplished is a direct result of your unconditional love
and support. I would never have made it this far without you.
x
TABLE OF CONTENTS
CURRICULUM VITAE ......................................................................................... IV
ACKNOWLEDGEMENTS ................................................................................. VIII
LIST OF TABLES ............................................................................................ XIV
LIST OF FIGURES .......................................................................................... XVII
CHAPTER 1. INTRODUCTION ............................................................................ 1
Dissertation Significance and Innovation ...................................................................... 2
Hypothesis Statement ................................................................................................... 4
Dissertation Goal ........................................................................................................... 4
Specific Aims ................................................................................................................. 4
Testis Structure ............................................................................................................. 5
Testis Function .............................................................................................................. 8
Epididymis Structure and Function.............................................................................. 11
Sperm .......................................................................................................................... 11
Testicular Toxicity........................................................................................................ 23
Model Testicular Toxicants and Their Targets ............................................................ 25
Evaluating Male Reproductive Capacity...................................................................... 31
Sperm Biomarkers of Spermatogenic Abnormalities................................................... 42
References .................................................................................................................. 45
CHAPTER 2. OPTIMIZATION OF A FILTER-LYSIS PROTOCOL TO PURIFY
RAT TESTICULAR HOMOGENATES FOR AUTOMATED SPERMATID
COUNTING ........................................................................................................ 54
xi
Abstract ....................................................................................................................... 56
Introduction.................................................................................................................. 57
Methods....................................................................................................................... 58
Results ........................................................................................................................ 62
Discussion ................................................................................................................... 75
References .................................................................................................................. 77
CHAPTER 3. SPERM mRNA TRANSCRIPTS ARE BIOMARKERS OF SUBCHRONIC LOW DOSE SERTOLI CELL INJURY IN THE RAT ........................ 79
Abstract ....................................................................................................................... 81
Introduction.................................................................................................................. 82
Materials and Methods ................................................................................................ 84
Results ........................................................................................................................ 95
Discussion ................................................................................................................. 105
References ................................................................................................................ 112
CHAPTER 4. RAT SPERM MICRORNA PROFILING IDENTIFIES MICRORNAS
IMPORTANT FOR REPRODUCTIVE FUNCTION AND EMBRYONIC
DEVELOPMENT .............................................................................................. 124
Abstract ..................................................................................................................... 126
Introduction................................................................................................................ 127
Materials and Methods .............................................................................................. 129
Results ...................................................................................................................... 134
Discussion ................................................................................................................. 147
References ................................................................................................................ 150
xii
CHAPTER 5. THE OPTIMIZATION OF ADDITIONAL TOXICANT EXPOSURES
FOR SPERM RNA PROFILING ....................................................................... 167
Abstract ..................................................................................................................... 168
Introduction................................................................................................................ 169
Materials and Methods .............................................................................................. 171
Results ...................................................................................................................... 177
Discussion ................................................................................................................. 185
References ................................................................................................................ 192
CHAPTER 6. INTEGRATIVE DNA METHYLATION AND GENE EXPRESSION
ANALYSES IDENTIFY DNA PACKAGING AND EPIGENETIC REGULATORY
GENES ASSOCIATED WITH LOW MOTILITY SPERM .................................. 194
Abstract ..................................................................................................................... 196
Introduction................................................................................................................ 197
Materials and Methods .............................................................................................. 198
Results ...................................................................................................................... 206
Discussion ................................................................................................................. 219
References ................................................................................................................ 223
CHAPTER 7. DISCUSSION ............................................................................. 233
Synopsis .................................................................................................................... 234
Conclusions, Limitations, and Future Directions ....................................................... 235
Applications and Implications .................................................................................... 251
Final Remarks ........................................................................................................... 256
References ................................................................................................................ 257
xiii
LIST OF TABLES
CHAPTER 2
Table 1. Correlation Coefficients and p-values Calculated for Manual Counts
........................................................................................................................ 65
Table 2. Optimization Experiment: Correlation Coefficients and p-values ...... 68
Table 3. Application Experiment: Average Body and Testis Weights after
Toxicant Exposure .......................................................................................... 72
CHAPTER 3
Table 1. Preliminary Study: Functional Analysis of Microarray Data .............. 98
Table 2. Application Study: Average Body and Organ Weights,
Histopathological Observations, and Inhibin B .............................................. 103
Table 3. Application Study: Fold Change Ratios for Altered Transcripts ...... 104
Supplemental Table 1. Candidate Transcripts Selected from Microarray for
qRT-PCR ...................................................................................................... 116
Supplemental Table 2. Preliminary Study: Transcripts Altered in Sperm after
2,5-Hexanedione Exposure via Microarray Analysis ..................................... 118
Supplemental Table 3. Time Course Study: Average Weights and Apical
Endpoints ...................................................................................................... 121
Supplemental Table 4. Time Course Study: Fold Change Ratios for
Transcripts Significantly Changed after 2,5-Hexanedione Exposure ............ 122
xiv
CHAPTER 4
Table 1. Sperm miRNA with Multiple Precursors .......................................... 136
Table 2. Genes Targeted by Multiple miRNAs Present in the Sperm ........... 138
Table 3. miRNAs in the Sperm that have Multiple Validated Gene Targets . 140
Table 4. Sperm miRNAs Associated with Reproductive System Disease
Enriched in Ingenuity Pathways Analysis...................................................... 143
Table 5. Pathway Analysis of the Genes Targeted by Multiple miRNAs....... 144
Table 6. miRNA Families Overlapping between Mouse and Rat Sperm ...... 146
Supplemental Table 1. miRNA Families Identified in Rat and Mouse Sperm
...................................................................................................................... 156
Supplemental Table 2. Rat Sperm miRNAs Detected by Affymetrix Array . 158
Supplemental Table 3. Rat Sperm miRNAs Detected by qRT-PCR ........... 159
Supplemental Table 4. miRNA Base Pair Frequencies............................... 162
Supplemental Table 5. Sperm miRNAs Associated with Reproductive System
Disease Enriched in Ingenuity Pathways Analysis ........................................ 163
CHAPTER 5
Table 1. DNB Experiment: Fold Changes and p-values for Altered Transcripts
...................................................................................................................... 180
Table 2. DBCP Experiment: Average Weights ............................................. 181
Table 3. DBCP Experiment: Histological Examination.................................. 183
Table 4. Sertoli Cell Biomarker Candidate Analysis using LIMMA after DBCP
Exposure ....................................................................................................... 184
xv
Table 5. Comparison of Transcripts Altered by 3 Month Exposure to HD, CBZ,
and DNB ....................................................................................................... 187
CHAPTER 6
Table 1. Semen Parameters of Subjects Examined ..................................... 201
Table 2. Examples of Known Imprinted Genes with Aberrant DNA Methylation
...................................................................................................................... 214
Table 3. Genes Associated with Spermatogenesis and Epigenetic Regulation
with Aberrant DNA Methylation ..................................................................... 215
Supplemental Table 1. Imprinted Genes ..................................................... 228
Supplemental Table 2. Genes Associated with Spermatogenesis and
Epigenetic Regulation ................................................................................... 230
Supplemental Table 3. Aberrant CpGs in Low Motility Sperm .................... 231
Supplemental Table 4. Aberrant mRNA Transcripts in Low Motility Sperm 232
xvi
LIST OF FIGURES
CHAPTER 1
Figure 1. Testis Structure ................................................................................. 7
Figure 2. Spermatogenesis ............................................................................ 10
Figure 3. Sperm Chromatin ............................................................................ 15
Figure 4. Testicular Toxicants and Their Target Cell Types ........................... 24
Figure 5. Testis Histopathology ...................................................................... 36
CHAPTER 2
Figure 1. Filter-Lysis Optimization .................................................................. 63
Figure 2. Optimization Experiment: Number of Homogenization Resistant
Spermatid Heads (HRSH) per Testis .............................................................. 70
Figure 3. Application Experiment: Number of Homogenization Resistant
Spermatid Heads (HRSH) per Testis after Toxicant Exposure ....................... 74
CHAPTER 3
Figure 1. Experimental Paradigms ................................................................. 87
Figure 2. Preliminary Study: Weights and Retained Spermatid Heads (RSH)
........................................................................................................................ 96
Figure 3. Time Course Study: Transcript Changes ...................................... 100
Figure 4. Time Course Study: Heatmap Displaying Heirarchical Clustering of
qRT-PCR Data .............................................................................................. 101
Figure 5. Venn Diagram Highlighting Transcript Profiles for HD and CBZ ... 108
xvii
CHAPTER 4
Figure 1. Chromosomal Ideogram................................................................ 137
Figure 2. Comparison of miRNA Families in Rat and Mouse Sperm............ 145
CHAPTER 5
Figure 1. Experimental Paradigms ............................................................... 173
Figure 2. Proposed Mechanisms of Toxicant Exposure on Epididymal Sperm
mRNA Transcripts......................................................................................... 191
CHAPTER 6
Figure 1. Unsupervised Clustering of the 1,000 Most Variable CpG Loci
Average Beta Values .................................................................................... 208
Figure 2. Heatmap Displaying the Methylation Status of CpG Loci Related to
Known and Predicted Imprinted Genes ........................................................ 209
Figure 3. RPMM Classes ............................................................................. 211
Figure 4. Boxplots Comparing DNA Methylation Profiles and Gene Expression
Values for the 3 Epigenetic Regulators. ........................................................ 218
xviii
CHAPTER 1. INTRODUCTION
1
CHAPTER 1. INTRODUCTION
Dissertation Significance and Innovation
The goal of this dissertation research was to utilize high throughput
genome-wide approaches to identify specific alterations to the rat sperm
transcriptome and human sperm transcriptome and DNA methylome, which may
serve as sensitive biomarkers of toxicant exposure and infertility. Currently, no
method exists that easily and reliably compares testicular toxicant responses in
pre-clinical laboratory animals and humans. Histological examination is the gold
standard for assessing testicular toxicity in animal models, while the semen
analysis and hormones are the routine assessments for investigating human
male reproductive effects. Traditional semen analysis measures sperm
concentration, motility, morphology, and semen volume. These methods are poor
predictors of fertility, demonstrating remarkable intra- and inter-individual
variability. Because of these limitations, effort has been devoted to developing
sperm molecular biomarkers that may better and more stably reflect sperm
function.
We hypothesized that applying “omics” techniques to pure sperm
populations would be a useful approach for developing molecular biomarkers of
testicular injury or dysfunction and allowing for improved hazard identification and
a more robust risk assessment. Aim 1 focused on identifying transcript
biomarkers of testis damage within rat sperm after sub-chronic low dose
exposures to classic testicular toxicants. This aim took advantage of the wealth
of data on the specific dose-response and mechanistic action of these model
2
toxicants and utilized the rat model because of its direct applicability in safety
screening for drugs and chemicals with potential reproductive toxicity. This study
was novel because it was the first to assess whether sperm mRNA content was
altered after sub-chronic low dose exposures to model testicular toxicants. Aim 2
focused on developing molecular signatures of human sperm function. It was the
first study to assess whole-genome molecular profiles for mRNA content and
DNA methylation in the same sperm samples. The results of this study indicate
that subcellular markers within sperm have the potential to advance our
understanding of the molecular features of sperm associated with fertility status.
This research builds on our existing knowledge of toxicant specific toxicity
in animal models and develops the foundation required to extrapolate these
observations to human samples. This has a broad range of applications including
toxicity testing, exposure biomonitoring, and fertility assessment. For example,
sperm molecular biomarkers will be of importance for biological monitoring of
male reproductive effects in therapeutically, occupationally, and environmentally
exposed men. In this scenario, sperm molecular profiles from these men can be
directly compared to sperm isolated from animals with parallel exposures.
Overall, augmenting the routine assessments with subcellular sperm biomarkers
will provide a more comprehensive analysis of adverse health effects and clinical
diagnostics.
3
Hypothesis Statement
Sperm molecular alterations are biomarkers of testicular injury and
dysfunction.
Dissertation Goal
Develop tools to identify biomarkers of testicular injury and dysfunction
translatable between animal models and man.
Specific Aims
Aim 1 – Animal Models
1.1 Characterize rat lowest-observable-adverse-effect-level exposures to Sertoli
cell toxicants and determine their effects on sperm mRNA content.
1.2 Identify the miRNAs present in rat sperm during normal spermatogenesis.
Aim 2 – Humans
Characterize mRNA and DNA methylation profiles in human sperm to determine
their utility as translatable biomarkers of testicular dysfunction.
4
Testis Structure
The testes are firm, oval shaped glands found in the mammalian scrotum
(Figure 1A). The adult testis is a complex organ whose two major functions,
spermatogenesis and steroid hormone production, are highly dependent upon
the coordinated regulation of interacting cell types, namely, Leydig cells,
peritubular myoid cells, Sertoli cells, and germ cells. Structurally, the testis is
covered by a layer of connective tissue called the tunica albuginea. The testis
contains two major compartments – the seminiferous tubules and the interstitial
spaces. Blood vessels, lymphatic vessels, and Leydig cells are found in the
interstitial space. The Leydig cells are the interstitial cells directly adjacent to the
seminiferous tubules and they produce and release testosterone. The
seminiferous tubules, covered by peritubular myoid cells, are finely coiled tubes
organized in loops throughout the testis that connect to the excurrent duct
system and contain both Sertoli cells and germ cells (Figure 1, panels B and C).
The Sertoli cells form the blood-testis-barrier and act as “nurse” cells that provide
the nutrients and environment necessary for spermatogenesis. They also
phagocytose both apoptotic germ cells during normal spermatogenesis and the
residual spermatid cytoplasm during spermiogenesis [1]. Additionally, Sertoli
cells secrete fluid that forms a tubular lumen and transports sperm to the
epididymis. Typically, Sertoli cells do not proliferate once they are fully
differentiated, so the Sertoli cell population is fixed in number by the time
spermatogenesis begins [1]. Spermatogonial germ cells line the basement
membrane of the seminiferous tubules and act as sperm progenitor cells; they
5
maintain close contact with the Sertoli cells and move inward toward the lumen
as they proliferate and differentiate (Figure 1C). The seminiferous tubules
converge at the rete testis; from which, the immature sperm formed in the testis
are transported through the efferent ducts to the caput epididymis (Figure 1B).
6
Figure 1. Testis Structure
(A.) External structure of rat testis (t) with epididymis attached (e). (B.) Cartoon of
the internal structure of the rat testis (grey) surrounded by the tunica albuginea
(dark grey) which contain the seminiferous tubules (yellow) and rete testis (red).
The attached epididymis (dark blue) contains coiled tubes (light blue). (C.)
Cartoon of a seminiferous tubule with pink Leydig cells, yellow Sertoli cells, and
blue germ cells.
7
Testis Function
The testes are critical end-organ components of an endocrine feedback
system responsible for testosterone synthesis. Testosterone is produced by a
highly regulated pathway that begins with the secretion of gonadotropin releasing
hormone (GnRH) by the hypothalamus. GnRH stimulates the anterior pituitary to
secrete luteinizing hormone (LH) and follicle stimulating hormone (FSH) [1]. LH
receptors on Leydig cells are sensitive to FSH-induced upregulation, making the
cells more responsive to LH. Leydig cells respond to LH stimulation by enhancing
cholesterol desmolase activity, which converts cholesterol to pregnenolone,
leading to testosterone synthesis and secretion [1]. Testosterone is necessary for
normal spermatogenesis because it activates pathways in Sertoli cells that
promote the differentiation of spermatogonia.
Spermatogenesis occurs in three phases: spermatogoniogenesis, meiosis,
and spermiogenesis
(Figure 2)
[2].
During
spermatogoniogenesis,
the
spermatogonia in the basal compartment undergo multiple mitoses to build a
large population of cells for subsequent meiosis and differentiation. In humans,
there are three subtypes of spermatogonia: Type A(d) cells, Type A(p) cells and
Type B cells [1]. Type A(d) cells have dark nuclei and replicate to ensure a
constant supply of spermatogonia to fuel spermatogenesis. Type A(p) cells have
pale nuclei and divide by mitosis to produce Type B cells. Type B cells divide to
give rise to primary spermatocytes. Each primary spermatocyte moves into the
adluminal compartment of the seminiferous tubule, where it enters the second
phase of spermatogenesis by undergoing meiosis I to produce two secondary
8
spermatocytes [1]. These maturational steps are sources of genetic variation in
the resulting gametes, which occurs by various events, including chromosomal
crossover or random inclusion of either parental chromosome. Secondary
spermatocytes rapidly enter meiosis II and divide to yield haploid spermatids.
These spermatids differentiate into spermatozoa through a process called
spermiogenesis, during which the spermatids grow a tail and develop a thickened
mid-piece where the mitochondria gather and form an axoneme [1]. In addition,
the haploid nucleus is streamlined and the Golgi apparatus surrounds the
condensed nucleus to form the acrosome. The nuclear cytoplasm is eliminated
and chromatin compaction occurs, whereby the somatic and testis-specific
histones are replaced with transition proteins and protamines [3]. Sperm
maturation takes place under the influence of testosterone. The excess
cytoplasm of the spermatids forms into residual bodies and is phagocytosed by
surrounding Sertoli cells. The resulting spermatozoa, mature but lacking motility,
are released from the Sertoli cells into the lumen of the seminiferous tubule in a
process called spermiation [1]. The non-motile spermatozoa are transported to
the epididymis by peristaltic contractions, where they gain motility and become
capable of fertilization. The duration of spermatogenesis differs in rats and
humans, approximately 58 and 74 days, respectively [1].
9
Figure 2. Spermatogenesis
Spermatogenesis is the process by which haploid sperm (1N) are generated from
their precursor diploid stem cells (2N) via a series of multiple mitoses
(spermatogoniogenesis) to build a large population for subsequent meiosis and
differentiation (spermiogenesis).
10
Epididymis Structure and Function
The fluid in the seminiferous tubule moves the sperm to the rete testis,
through the efferent ducts, to the epididymis. The epididymis is a complex coiled
tube that connects the testis to the vas deferens and functions to transport,
nurture, and mature the sperm. Its structure is designed to facilitate these
processes both hormonally and physically. The epididymis is divided into three
sections - the caput, the corpus, and the cauda - each of which plays a role in the
development of the sperm as it travels through the organ. The two initial
segments, the caput and corpus, are involved with sperm maturation, whereas
the cauda is the site of sperm storage. Secretory products produced by the
epididymal epithelium lead to numerous functional changes of the sperm that
include acquisition of motility and increased capacity to fertilize an egg [4, 5]. The
duration of both human and rat epididymal transport is approximately 8 days;
however, sperm transport in humans varies with daily sperm production rate [6,
7].
Sperm
Mature sperm consist of a haploid nucleus, a propulsion system, and an
acrosomal sac of enzymes that enable the nucleus to enter the oocyte [1]. Sperm
were originally regarded as a vessel for the transportation of the male genome to
the oocyte, devoid of translational activity (due to their lack of cytoplasm and
ribosomal RNAs); the oocyte, on the other hand, was believed to be responsible
for producing all of the mRNA and proteins necessary for fertilization and
11
embryogenesis [8]. However, recent data suggest that sperm play a greater role
in these processes than originally believed. It is now evident that in addition to
their genome, sperm transmit mRNA and provide the oocyte with vital organelle
and male-specific proteomic components [8].
Sperm DNA
Chromatin Structure in the Sperm Nucleus
The human sperm cell is haploid, and its 23 chromosomes join the 23
chromosomes of the female egg upon fertilization to form a diploid cell. There is
no detriment associated with having a haploid genome because spermatids are
connected by cytoplasmic bridges, which allow for the sharing of products
generated by other spermatids. Sharing eliminates any disequilibrium resulting
from the random inheritance of a defective allele during meiosis [9]. For example,
the sharing of protamine transcripts via the intracellular bridges between
connected spermatids has been observed in mice over-expressing the protamine
cluster. In this model, there was no statistical difference in the expression of
protamine-1 in the testis, despite the chromosomal imbalance of the protamine
gene cluster [10]. It has been speculated that other haploid-expressed transcripts
can be shared in a similar fashion between adjacent spermatids [10].
The sperm DNA is transcriptionally silent and highly condensed to protect
the DNA from damage caused by outside agents. Mature sperm do not contain
sufficient machinery to repair DNA, so any damage is problematic and may be
12
detrimental to the developing embryo [11]. DNA is physically protected from
insult by the process of chromatin compaction, which is facilitated by the removal
of histones and their replacement with protamines (Figure 3). Protamines are
highly-basic sperm specific proteins that package the DNA 6-20 times tighter
than histones by condensing the DNA into tight toroid structures [11]. Stacking
these toroids allows for a more efficient packaging of the paternal genome,
thereby reducing the size of the sperm nucleus to an absolute minimum, which is
crucial because head shape and size are known to affect sperm motility and
function [12].
Histones are not completely removed from mature sperm, and it has been
estimated that their contribution to sperm chromatin can range from 1% (in
mouse) to 15% (in humans) [12]. This histone retention may be a result of
inefficient replacement machinery or an unknown regulatory mechanism.
However, some evidence suggests that histone retention is programmatic in
nature, in that histone-bound DNA is retained at regions of high genetic
importance for the early embryo, such as developmental gene promoters,
imprinted loci, and microRNA clusters [11]. For example, the embryonically
expressed - and - globin genes are located in histone-enriched regions, while
the postnatally expressed -globin gene is protamine-enriched [10]. These
histone-enriched areas are DNase sensitive, which suggests a more open
conformation and a transcriptionally ready state. However, for these genes to be
truly primed for transcriptional activity, histone modifications need to be present
to turn the genes toward activation (i.e. H3/H4 acetylation and H3K4
13
methylation). Notably, an increase in H3K4 methylation is seen at many
important developmental gene promoters and this data suggests that sperm are
prepared to contribute to the epigenetic state of the embryo [11].
14
Figure 3. Sperm Chromatin
During spermiogenesis, histones (green circles) are replaced by protamines,
condensing the DNA into tightly packaged toroids (red doughnut). Each toroid
can be stacked to conserve space. The DNA strands that link the protamine
toroids are nuclease sensitive (inset, grey). Some large tracts of DNA are not
replaced by protamines and retain histones (green solenoid). Both histone
solenoids and toroids are attached to the nuclear matrix at matrix attachment
regions (MAR, blue). Note, this figure was adapted from [13].
15
DNA Methylation and Imprinting
Epigenetic mechanisms, and specifically DNA methylation, play an
important role in regulating genes during development. Methylation is a heritable
yet reversible epigenetic mark that influences gene expression without altering
the underlying DNA sequence. Mammalian DNA methylation is catalyzed by a
family of DNA-methyltransferases and occurs at the 5-position of cytosine
residues almost exclusively at CpG dinucleotides in CpG islands located in the
promoter regions of genes [14]. DNA methylation modifies the function of the
mammalian genome, and typically results in repression of gene expression. It is
essential for normal development [15, 16], and the epigenetic reprogramming
that occurs during development may be a sensitive window for disruption of the
epigenome. It has been suggested that alterations in the epigenetic
reprogramming processes during development can lead to adult-onset disease
[17].
In the developing embryo, primordial germ cells become demethylated as
they migrate along the genital ridge towards the fetal gonad. During gamete
maturation, the methylation profile is re-established in the germ line [18],
resulting in a pattern of DNA methylation that reflects both inherited imprints and
environmental conditions. CpG dinucleotides are under-represented in the
genome, but over-represented in promoter regions. Hypomethylated promoter
regions establish an open chromatin structure that allows for the initiation of gene
transcription, while hypermethylated promoter regions lead to a closed chromatin
structure, blocking transcription factor binding and silencing gene expression
16
[19]. In addition to regulating gene expression, DNA methylation silences
repetitive elements and is important for the stability of the mammalian genome.
In diploid organisms, autosomal genes are expressed by two copies of
each allele, one from each parent. However, a small number (<200) of all
autosomal genes are thought to be imprinted and are expressed solely on one
allele [20]. If a paternal gene is imprinted (silenced) then the maternal gene is
expressed and vice versa. The methylation status of the imprinting control
regions is established during gametogenesis via de novo methylation according
to the sex of the embryo [18, 21]. In the male, the acquisition of DNA methylation
patterns begins before birth and is completed for most sequences after birth,
before the pachytene phase of germ cell meiosis. For example, in mouse male
germ cells, the paternally imprinted gene H19 first becomes methylated between
15.5-18.5 days gestation. The methylation at H19 continues to increase in
postnatal
germ
cells
and
is
complete
by
the
pachytene
stage
of
spermatogenesis, persisting in mature spermatozoa [22].
Sperm RNA
It has been estimated that rodent sperm contain approximately 100
femtograms (fg) of RNA per cell whereas human spermatozoa carry just 10-20 fg
of RNA [10]. The pool of RNAs in the mature spermatozoa represent the
significant proportion of the RNA synthesized prior to transcriptional arrest [23].
An intact sperm nuclear matrix is required for fertilization and it has been
17
confirmed using RNase treatment that sperm RNA is part of the nuclear matrix
[23]. It is now understood that sperm RNA, histones, and potentiated genes are
localized at the periphery of the nucleus, close to the nuclear envelope; however,
recent evidence also identifies the midpiece as another site of RNA storage [10].
In this segment, the RNAs are localized to the mitochondria [10]. The sperm RNA
population includes mRNAs, miRNAs, piRNAs, and antisense transcripts but
does not consist of 18S or 28S ribosomal RNAs [8].
The sperm RNAs are delivered to the oocyte at fertilization, however, their
functions have yet to be determined. Many functions have been proposed for
these sperm transcripts, including roles in sperm structure and stress response,
de novo translational replacement of degraded proteins, oocyte fertilization,
embryogenesis and morphogenesis, and epigenetic regulation and establishment
and maintenance of the parental imprint [24, 25].
Chromatin Remodeling
It has been speculated that sperm RNAs are involved in chromatin
packaging [25, 26]. The majority of the DNA is wrapped around protamines (85%
in humans and 99% in mice) while the remaining fraction of the genome is
packaged by histones. These histone-bound compartments are associated on
the nuclear periphery forming a shell. Due to the fact that RNA is a known
component of the nuclear envelope, it is possible that sperm RNAs act to
stabilize an interaction between the envelope and the histone-bound DNA. This
18
interaction may be a passive requirement of RNA or RNA might be actively
marking those DNA sequences for the packaging by histones [26].
De novo Translation of Sperm RNA and Sperm Maturation
It was recently demonstrated that capacitating spermatozoa are capable
of incorporating [35S] methionine, [35S] cysteine, and BODIPY-lysine-tRNALys into
newly synthesized peptides, which can be visualized by autoradiography and
fluorescence microscopy [10, 27].
This incorporation was inhibited by
mitochondrial translation inhibitors but not by a cytoplasm translation inhibitor. In
addition, inhibition of protein translation by a mitochondrial translation inhibitor
reduced sperm motility, capacitation, and in vitro fertilization rate [10, 27]. This
suggests that sperm are capable of using mRNAs to synthesize new proteins
needed for capacitation, to replace those that are degraded during transit in the
female reproductive tract prior to fertilization.
Delivery of Sperm RNAs to the Oocyte
It is now known that the sperm’s entire cargo is released into the oocyte at
fertilization. Most components, such as the sperm tail and mitochondria, are
degraded, but some are required for further development. Six transcripts have
been indentified in human sperm and not in unfertilized eggs: CLU, AKAP4,
PRM2, HSBP1, FOXG1B, and WNT5A [10, 28]. The hamster sperm penetration
assay consistently detected CLU and PRM2 in the zygotes 30 min and 3h post
19
fertilization [29]. PLC- mRNA has been detected in human spermatozoa [30]
and injection of human, mouse, and pig oocytes with the mRNA for PLC-
causes calcium oscillations and egg activation [10, 23, 31].
RNA-mediated Epigenetic Effects on the Embryo
Mature spermatozoa contain miRNAs and antisense RNAs [10, 32] and it
has been proposed that sperm can deliver these small RNAs to the oocyte and
participate in early post-fertilization events, inhibiting gene expression in the
developing embryos [10, 32]. For example, miRNAs antisense for IGF2R and
DKK2 have been identified in sperm and those genes are implicated in the
regulation of growth and development [10]. IGF2, the ligand for the IGF2R, is
maternally imprinted and normally expressed only from the paternally derived
allele. IGF2R is normally biallelically expressed, and it is possible that antisense
RNAs mark genes for uniparental downregulation or repression. This would be a
novel mechanism for downregulation of non-imprinted alleles [9].
In an innovative study, two haploid genomes isolated from two eggs were
brought together to generate viable zygotes and offspring [10, 33]. However,
successful development only occurred after balancing out the epigenomes by
inactivating the H19 gene of the donor genome, as it would be in sperm [33]. The
large number of dead animals at birth (80%) and the very low overall success
rate in producing the parthenogenic mice (0.3%), suggests that development is
20
impaired by their altered expression profiles. It has been hypothesized that these
results may be due to the absence of sperm RNA in the gynogenotes [10].
Another elegant study demonstrated RNA-mediated non-mendelian
inheritance of an epigenetic change in the mouse [34]. In paramutation, an allele
in one generation heritably affects the other allele in future generations, even if
the allele causing the change is itself not transmitted. Paramutation violates
Mendel’s first law, which states that during gametogenesis, the allelic pairs
separate, one going to each gamete, and that each allele remains completely
uninfluenced by the other [34]. In this paramutation model, KIT-heterozygous
mutant mice have a white tail tip and white feet. However, when two
heterozygous mice mate, some of the genotypically wild type mice display the
white tailed phenotype. The modified phenotype results from a decrease in KIT
mRNA levels and abnormally high expression of non-coding c-KIT RNA in the
brain, testis, and sperm. The transmission of the white tailed trait was
successfully recreated in wild type crosses augmented by the intra-oocyte
injection of testis, sperm, or brain RNA from mutated mice. Delivery of the noncoding RNA disrupts the c-KIT expression, leading to the anomaly. In addition,
the injection of miRNAs specific for KIT into fertilized mouse embryos induced a
heritable mutant phenotype in the offspring, similar to the paramutated
phenotype [34].
21
Latent Nuclear Activity
Sperm contain a population of normally dormant enzymes, which include
nucleases,
topoisomerases,
DNA
and
RNA
polymerases,
and
reverse
transcriptases that can be activated under certain conditions [10]. An
endogenous Mn
2+
/ Ca
2+
-activated nuclease has been found in hamster, mouse
and human sperm that interacts with topoisomerase IIB [35]. It has been
hypothesized that topoisomerase II-mediated breaks in sperm induce the
degradation of the paternal pronuclear DNA in fertilized oocytes and this is linked
to the initiation of DNA synthesis in zygotes [10, 36, 37].
When epididymal sperm are incubated with exogenous RNA molecules,
the sperm reverse transcriptases can retro-transcribe cDNA copies that can be
transferred into eggs during in vitro fertilization [10, 38]. A similar response
occurs when epididymal sperm are incubated with exogenous DNA. In a novel
study, EGFP DNA was transcribed into a complementary RNA using an RNA
polymerase. A primary transcript lacking introns was generated and reversetranscribed into stable cDNA copies [39]. Some of the cDNAs integrated into
DNase-I sensitive portions of the chromatin, while others were delivered to the
oocyte at fertilization where they propagated in the offspring’s tissues as extrachromosomal structures [10, 40].
22
Testicular Toxicity
The testis is a target organ for many environmental toxicants, yet the
multiple cell types within the testis can respond differently to these insults
(Figure 4). For example, Sertoli cells are sensitive to 2,5-hexanedione, whereas
germ cells are susceptible to 1,2-dibromo-3-chloropropane toxicity. The resulting
injury can manifest in different ways including retained spermatid heads, germ
cell sloughing, Sertoli cell vacuolization, altered seminiferous tubule diameter,
and multinucleated gonocytes. The damage ultimately culminates in germ cell
apoptosis and aberrant spermatogenesis. High dose exposures to these
toxicants can lead to testicular atrophy, oligospermia (reduced sperm numbers),
azoospermia (no sperm in semen), and infertility.
23
Figure 4. Testicular Toxicants and Their Target Cell Types
Toxicants that target the Sertoli cell (A.) include 2,5-hexanedione (HD),
carbendazim (CBZ), 1,3-dinitrobenzene (DNB), and di-(2-ethylhexyl)phthalate
(DEHP). Germ cells (B.) and Leydig cells (C.) are also targeted by toxicants
including
1,2-dibromo-3-chloropropane
(DBCP)
and
ethylene-1,2dimethanesulfonate (EDS), respectively.
24
Model Testicular Toxicants and Their Targets
Sertoli Cell Toxicants
The Sertoli cells form the blood-testis barrier and are the “nurse” cells that
provide the environment necessary for proper spermatogenesis.
2,5-Hexanedione
2,5-Hexanedione (HD) is the active metabolite of the common industrial
solvent n-hexane. Although the most significant exposures occur in occupational
settings, humans are ubiquitously exposed to low levels of n-hexane as a
chemical component of gasoline [41]. HD is both a neuronal and testicular
toxicant with clear neurotoxic clinical manifestations and subtle indications of
testicular injury. Exposure to HD is unique because there is a latency period
between toxicant administration and injury [42]. The mechanism of delayed
toxicity may be due to sequential steps of pyrrole formation, oxidation, and
crosslinking that are necessary events in the pathogenesis of HD-induced
testicular injury [42, 43]. HD testicular injury is dose-rate sensitive and
independent of total dose [44]. Extensive HD dose response studies have been
conducted in our laboratory and the severity of testicular injury (including
decreased testicular weights and increased sloughing, germ cell apoptosis, and
stage-specific retained spermatid heads) consistently increases with dose [42,
44-46].
25
Animal studies indicate that HD targets Sertoli cell microtubule assembly
by the induction of tubulin cross-linking both in vitro and in vivo [41]. HD
exposure is characterized by rapid assembly and enhanced stability of
microtubules [47-49]. HD exposure causes altered microtubule-dependent
transport in Sertoli cells and disturbs the germ cell niche by impeding
seminiferous
tubule
fluid
secretion.
Disruption
of
the
Sertoli
cell
microenvironment stimulates germ cell apoptosis and ultimately results in
testicular atrophy [41].
Carbendazim
Carbendazim (CBZ) is the active metabolite of benomyl, a benzimidazole
fungicide used to prevent and eliminate fungal plant diseases [46, 50]. Mammals
are exposed to CBZ orally and it is readily absorbed and metabolized rapidly.
Overall, CBZ has low acute toxicity but has many negative effects on the male
reproductive system [46, 50]. CBZ is a Sertoli cell toxicant that binds to the betatubulin subunit of the tubulin heterodimer and inhibits microtubule polymerization.
This ultimately decreases the rate and stability of microtubule assembly [46].
Acute exposure of adult male Fischer 344 rats to CBZ results in increased testis
weights and seminiferous tubule diameters as well as increased rates of germ
cell sloughing, retained spermatid heads, and apoptotic germ cells one hour
post-exposure in a dose dependent manner [46]. Sub-chronic exposure of adult
male Wistar rats to CBZ resulted in many histopathological changes of the testis
including atrophic seminiferous tubules, decreased germ cells, and increased
26
sloughing in a dose dependent manner [50]. These rats had smaller testes,
decreased epididymal sperm counts and motility, and mating studies
demonstrated a decreased fertility index, which was dose-dependent [50]. Flow
cytometric analysis of the testicular tissue suggested aberrant spermatogenesis,
showing a dose-dependent increase in primary spermatocytes and a decrease in
the number of spermatogonia, spermatids, and DNA synthesizing cells [50].
Overall, CBZ disrupts proper Sertoli cell function and spermatogenesis and
ultimately reduces fertility in male rats [46, 50].
1,3-Dinitrobenzene
1,3-Dinitrobenzene (DNB) is used as an intermediate in organic synthesis
reactions and in the production of explosives, dyes, industrial solvents, and
pesticides [51]. Male reproductive toxicity of DNB has been demonstrated in
laboratory animals, the effects of which include decreased testicular weights,
degeneration of germ cells, decreased testicular sperm head counts, decreased
epididymal sperm counts, and reduced fertility [51-53]. When male SpragueDawley rats were given 0, 0.75, 1.5, 3, or 6 mg/kg, 5 days/week, for 12 weeks
the lowest-observable-effect-level dose for male reproductive toxicity was 1.5
mg/kg (1.07 mg/kg/d) and the no-observable-effect-level was 0.75 mg/kg (0.54
mg/kg/d) [52]. DNB directly targets the Sertoli cells in the testis with germ cell
injury occurring as a secondary event [53, 54]. Recent evidence has suggested
that DNB induces germ cell apoptosis in rats via the mitochondrial pathway [54].
27
Di-(2-ethylhexyl)phthalate
Di-(2-ethylhexyl)phthalate (DEHP) is a commonly used plasticizer found in
biomedical devices, consumer products, and food-packaging materials [55]. This
endocrine disrupter is rapidly metabolized to mono-(2-ethylhexyl)phthalate
(MEHP), which is more toxic than the parent compound. DEHP and MEHP are
testicular toxicants that target Sertoli cells [55, 56] and there have been many
mechanisms of action proposed for the effects of phthalates on the male
reproductive system including gene expression dysregulation, estrogen-receptor
mediated mechanisms, peroxisome proliferator activation, and alteration of
enzymes responsible for sperm maturation [55]. However, recent evidence
suggests that DEHP induces oxidative stress, and it was found that rats with a
defect in testicular redox equilibrium and cellular antioxidant defense (due to a
selenium deficiency) are more sensitive to DEHP induced injury [55].
Germ Cell Toxicant
Germ cells are the sperm progenitor cells that line the basement
membrane of the seminiferous tubules.
1,2-Dibromo-3-chloropropane
1,2-Dibromo-3-chloropropane (DBCP) is a nematocide that has been
shown to reduce fertility and induce sterility in humans exposed in occupational
settings [57]. DBCP was used from 1950 to 1979, at which time it was banned in
28
the United States due to the adverse health effects that had been identified a few
years earlier. In 1977, workers at the Dow chemical company complained of
deleterious health effects in connection with the chemical, and published
laboratory findings of these workers showed that they suffered from
oligospermia, azoospermia and elevated hormone levels [57, 58]. Animal
experiments have also revealed the testicular toxicity of DBCP; exposed rats
display altered seminiferous tubule morphology, malformations of the sperm, and
reduced sperm counts [59]. Studies also indicate that DNA is the subcellular
target of this toxicant. DBCP can be converted to reactive metabolites both by
cytochrome P450 and glutathione S-transferase-dependent (GST) pathways [60],
and these resulting metabolites can induce single-strand breaks in DNA. The
amount of GSTs in the spermatogenic cell types increases with spermatogenic
cell development [60], which may indicate a higher potential to activate DBCP in
germ cells at later stages of development. In addition, cells in S-phase are more
susceptible to DBCP-induced apoptosis than cells in the growth phases [60]. In
accordance with the model, proliferating and differentiating spermatogenic cells
(e.g. round spermatids) are the cells most sensitive to DBCP-induced apoptosis.
Because round spermatids have less compacted DNA than elongating/elongated
spermatids, the DNA in these cells is a more accessible target.
Leydig Cell Toxicant
Leydig cells manufacture and release testosterone and are the interstitial
cells located adjacent to the seminiferous tubules.
29
Ethylene-1,2-dimethanesulfonate
Ethylene-1,2-dimethanesulfonate (EDS) is a toxicant that selectively and
temporarily destroys the adult Leydig cell population through apoptosis via the
Fas/FasL pathway [61]. An intraperitoneal (IP) injection of 75 mg/kg in adult rats
decreases testicular and serum testosterone and increases the pituitary secretion
of LH and FSH [62]. Overall, EDS induces a depletion of germ cells in the
seminiferous epithelium. Leydig cells disappear from the interstitial space by 7
days post-IP injection, as evidenced by histological examination. Additionally,
there is a decrease in elongating (step 9-13) spermatids in late stage
seminiferous tubules (IX-XIII) [62], although the early stages remain intact. Single
new Leydig cells with characteristics of progenitor type Leydig cells are observed
at 2 weeks post-exposure. There is also a loss of all stages of elongated
spermatids as well as germ cell sloughing into the lumen [62]. At 3 weeks postexposure, there is an expansion of immature Leydig cells and signs of recovering
spermatogenesis, including the presence of elongated spermatids in late but not
early stages [62]. At 5 weeks post-exposure, mature adult type Leydig cells are
visible in the interstitial space. The late stages are almost completely recovered
(there is evidence of all germ cell types), although there are still no elongated
spermatids found in early stages [62]. By 7 weeks post-exposure, the Leydig cell
population has resumed a normal histological appearance. Spermatogenesis
appears recovered in most of the tubules, although some of tubules still contain
only Sertoli cells [62]. There is a close relationship between germ cell and Leydig
cell changes; on the subcellular level, it is believed that the EDS-induced
30
testosterone reduction causes apoptosis of germ cells, particularly haploid germ
cells, and causes the temporary arrest of spermatogenesis [62].
Evaluating Male Reproductive Capacity
A battery of assessments has been developed to evaluate the male
reproductive system. Most of the tests are invasive and limited to animal use,
and thus unacceptable for use in humans. The sensitivity of these parameters
varies considerably and there are advantages and limitations of a number of
these standard procedures. The human male is often considered to be more
susceptible to toxic agents than commonly utilized laboratory species because
baseline gonadal function is lower in humans. In rats, for example,
spermatogenesis must be decreased by at least 90% to affect the number of
progeny produced [1]. Therefore, any indication of male reproductive toxicity in
preclinical study is considered relevant to human reproductive safety.
Non-histopathologic Endpoints
Testis Weights
A strong correlation exists between testis weights and the number of germ
cells present in the testis.
31
Homogenization Resistant Spermatid Counts
The number of elongated spermatids in the testis can be determined and
these values can be used to calculate sperm production rates.
Epididymis Weights
A strong correlation exists between epididymis weights and the number of
sperm present in the epididymis.
Evaluations of the Caudal Sperm
Caudal sperm parameters including count, motility, and morphology are
often evaluated to assess abnormalities.
Histopathological Endpoints
Histopathological examination of the male reproductive tract after a 4
week good laboratory practice study is considered the “gold standard” approach
determining the potential of a compound to cause human reproductive toxicity
[63]. However, direct examination of testicular tissue gives very little information
about the quality of the sperm that are being produced. Histology provides a
qualitative assessment of effects on the spermatogenic process and/or
quantitative information on sperm production [1]. Toxic injury to sperm may occur
without histologic testicular or epididymal changes and may be detectable via
32
semen analyses or functional/fertility testing [63]. Common histological lesions of
the seminiferous tubule include:
Hypospermatogenesis
All stages of spermatogenesis are present throughout the testis but are
reduced to a varying degree.
Maturation Arrest
There is a complete arrest at a particular stage, most often at
spermatogonial or primary spermatocyte stage.
Delay or Failure to Release Spermatids
The presence of elongated spermatids in stage IX (or later) seminiferous
tubules indicates a delay or failure of release (spermatids should be released
during stage XIII and are absent in stage IX).
Testicular Atrophy
Seminiferous tubules are shrunken and may have peritubular and
interstitial fibrosis and germ cell loss.
33
Multinucleated Germ Cells
Symplasts are formed by opening intracellular bridges between germ
cells, collapsing their structure and forming one giant cell.
DNA Breaks (TUNEL)
TUNEL staining can identify DNA breaks, which non-selectively labels the
nuclei of apoptotic, necrotic, and viable cells undergoing DNA repair and active
gene transcription (Figure 5A).
Vacuoles
Vacuoles larger than 16 µm in diameter are indicators of Sertoli cell injury
and can be detected within one cell layer in the basement membrane of the
seminiferous tubule (Figure 5B).
Sertoli Cell-only Tubules
These are characterized by a complete absence of germ cells in the
tubules; only Sertoli cells are present.
34
Sloughing
Sloughing is characterized by the premature release of germ cells from
the seminiferous epithelium into the lumen and transport into the epididymis
(Figure 5C).
Sperm Retention and Phagocytosis
The number of spermatid heads along the basement membrane in stages
IX-XI tubules can be quantified (Figure 5D).
Seminiferous Tubule Diameter
The length of the short axis of those seminiferous tubules with a 1:1.5 ratio
(short axis: long axis) can be measured to determine seminiferous tubule atrophy
due to loss of germ cells or swelling due to fluid retention.
35
Figure 5. Testis Histopathology
Toxicant induced testicular injury can be manifested as (A.) DNA breaks, which is
detectible by TUNEL staining (brown stain); (B.) Sertoli cell vacuolization along
basement membrane (H&E, arrows); (C.) germ cell sloughing into the lumen
(H&E, arrow); and (D.) spermatid head retention (PASH, arrows). Scale bar in AC is 100 µm.
36
Serum Hormones
Serum hormones (testosterone, follicle stimulating hormone, luteinizing
hormone, and inhibin B) can also be used as a male reproductive endpoint.
However, disruptions in hormone signaling are difficult to identify due to their
pulsatile release patterns and other such cofounding factors as age, stress, and
inter-individual variability [63].
Semen Analysis
Men are included in the clinical development phase of a drug discovery
program; early Phase I and II studies utilize normal healthy volunteers and
patients, respectively, to measure safety and tolerability of a compound.
However, proper fertility studies are not often conducted until the longer Phase III
studies [63]. The clinical monitoring of male fertility typically includes evaluation
of semen parameters. The semen analysis focuses on the concentration, motility,
and morphology of the sperm in the ejaculate, with semen volume also noted.
However, recent publications have questioned the value of semen
analysis in clinical scenarios [64], as they can vary within individual men and are
dependent on geographical location [65, 66]. Cutoff values for semen parameters
found in the World Health Organization (WHO) manual were derived from
populations of healthy men and are not necessarily indicative of the absolute
minimal values necessary for conception to occur. In addition, there is no
consensus as to which semen parameter is the best predictor of fertility [64]. The
37
WHO recently established an editorial committee to address concerns with
performing consistent semen analyses and updated their manual to describe
methods in greater detail and define new reference values to improve global
comparisons [67].
Toxicogenomics
Toxicogenomics is the study of the response of a genome to hazardous
substances, using emerging “omics” technologies, such as transcriptomics,
proteomics, and metabolomics, in concert with bioinformatic methods and
conventional toxicology [68, 69]. In general, toxicogenomics experiments follow
molecular alterations after exposure of cells, tissues, or organisms to a toxicant,
with the goal of producing molecular signatures that can differentiate treatment
from control groups. The strength of these signatures is increased when they are
linked to a phenotypic endpoint, and dose-response and time course studies can
further identify cause-and-effect relationships between changes in molecular
profiles after toxicant exposure. Ultimately, these microarray experiments set up
the foundation for understanding, characterizing, and predicting target-organ
toxicity [70].
Gene expression measurements are more sensitive than traditional
toxicology methods in predicting histopathological changes and can be
reproducibly measured weeks before the traditional toxicology endpoints [71, 72].
The evaluation of molecular profiles at ambient exposure levels has the potential
38
to address uncertainty about possible adverse effects at no-observable-adverseeffect-levels. It is not feasible to explore the shape of the dose response curve at
low exposure levels using traditional animal studies because the statistical
resolving power of the studies is not high enough to detect very small changes in
the incidence of apical effects [71]. Predictive toxicology will utilize the gene
expression profiles to elucidate the shape of the dose-response curve at
exposure levels below the no-observable-adverse-effect-level, which is important
for risk assessment.
It has been shown that functional and mechanistic similarities between
small molecules can be appreciated through similarities of their gene expression
profile [71, 72]. Within any particular target tissue, the molecular alterations are
specific for the mode of action of the toxicant. Assessing mechanism will allow for
1.) chemical prioritization; 2.) customizing test strategies for deciphering
mechanism of action; 3.) serving as a basis for read-across assessments; 4.) and
facilitating dose/response evaluation [71]. Read-across methods group chemicals
into classes based on common chemical structure and biological activity. Rather
than test the new compounds empirically, it may be possible to compare the
pattern of gene expression induced by the new compound with the ones that
have been studied. If the results are similar, then it may be possible to rely on the
tested chemicals as the surrogate for assessing the risk of the new chemical. For
example, one study profiled testicular gene expression after in utero exposure to
developmentally toxic and non-toxic phthalates [73]. The phthalates with known
developmental toxicity altered a common panel of genes; however, there were no
39
changes in gene expression for the non-toxic phthalates. A clear understanding
of the mechanism of action and toxicity of a compound or class of compounds at
the molecular level would improve the success of the drug development process.
Transcriptional alterations induced by drug candidates can be put in the
context of gene expression changes caused by reference toxicants through the
comparison to a reference database. This will distinguish class effects from
compound-specific effects and elucidate mechanisms of action and toxicity [72].
DrugMatrix is a toxicogenomics reference database that has data from short-term
repeat dose rat studies for numerous toxicants, reference standards, and
marketed and withdrawn drugs. DrugMatrix contains microarray data for many
tissues, including: liver, kidney, bone marrow, thigh muscle, spleen, intestines,
and primary hepatocytes. In addition to microarray data, the database contains
additional histological and weight information [72]. Iconix has developed liver in
vitro screening tools to generate molecular signatures and compare to in vivo
responses.
Overall, toxicogenomics will decrease the turn around time required to
detect and explain histopathological endpoints, provide insight into mechanism of
action, and aid in the development of biomarkers of toxicity and activity [72].
Testicular Toxicogenomics
The testis is susceptible to injury by a variety of therapeutic agents and
environmental toxicants. However, the injury may be subtle and histopathological
40
changes may be undetectable at early time points. In addition, data from serum
hormone and semen analyses are not strong enough to detect early changes in
both pre-clinical studies and clinical trials [70]. Effort has been spent investigating
the utility of inhibin B as a biomarker of testicular injury, but it appears that it is
not a sensitive biomarker in rodents [70]. With this in mind, several studies have
used toxicogenomics approaches to screen compounds for testicular toxicity [70].
Of note, one study utilized microarray analysis after acute exposures to four
model testicular toxicants. The results suggested that even though there were no
histopathological changes to the testis after the exposure, the gene expression
changes were robust and reproducible, with some genes differentially expressed
in all treatment groups [70]. This is a significant finding that highlights the
capability of transcriptomic profiling to identify different toxicant responses in the
testis.
Sperm Toxicogenomics
Utilizing toxicogenomics approaches to develop biomarkers of testicular
toxicity is a valuable tool for the pharmaceutical industry, which will use these
molecular profiles to identify and eliminate potential compounds in the early
stages of drug development. However, the heterogeneity of the testis, in addition
to the spatial-temporal intricacy of spermatogenesis, makes it a very complex
tissue to study. Moreover, assessing gene expression in the testis is an
unrealistic endpoint when comparing pre-clinical animal studies and clinical trials
41
because of the invasive techniques required for sample acquisition. Sperm are
considered the best substitute for the evaluation of spermatogenic function [74].
Toxicogenomics approaches provide the potential to identify new and
improved biomarkers for testicular toxicity, which could enhance sensitivity and
predictivity over current methods, and may ultimately prove to be useful as
translatable markers across laboratory species and into the clinic [63]. Other
applications include biomonitoring, specifically the monitoring of adverse health
effects following drug approval or risk assessment at exposure sites.
Sperm Biomarkers of Spermatogenic Abnormalities
Messenger RNA
The use of human sperm mRNAs as potential biomarkers of fertility is an
emerging area of research. Ostermeier et al. found that a genetic fingerprint of
normal fertile men can be generated from mRNA present in sperm [28], and that
studying ejaculated sperm is a convenient method for investigating testis-specific
infertility. Wang et al. used microarray technology to analyze gene expression
differences between testis-specific and sperm-specific genes in fertile men [75].
By examining the expression of 5 sperm motility-related genes identified in this
profile by reverse transcriptase polymerase chain reaction (qRT-PCR), they
found the expression of two genes, TXP1 and LDHC, to be significantly altered
between normal and motility-impaired semen samples. Again, their results
indicated that clinical assessments of sperm quality can be made from differential
42
sperm mRNA content patterns. Additionally, human studies have indicated that
abnormal protamine ratios exist in infertile men and functional evidence has
demonstrated that male protamine knockout mice are infertile [76, 77]. With this
in mind, Steger et al. investigated the potential use of protamine ratios and BCL2
expression as biomarkers of infertility using qRT-PCR [78]. They found aberrant
protamine ratios and increased BCL2 mRNA in ejaculates and in testicular
biopsies of infertile men compared to controls. These results encourage the use
of mRNA expression levels as predictive biomarkers of fertility status. As of yet,
only one laboratory has focused on identifying biomarkers of fertility in animal
models; Klinefelter et al. have studied expression of SP22, a sperm membrane
protein, and its correlation with caudal epididymal sperm fertility after exposure to
testicular and epididymal toxicants [79]. In addition, they have confirmed that
post-meiotic germ cells express a testis-specific SP22 transcript. The same
group is currently examining the potential use of this protein as a diagnostic
marker of human infertility [79].
microRNA
Recent studies suggest that abnormal sperm miRNA content is associated
with semen parameters in both animals and humans [80, 81]. Curry et al.
compared the levels of 10 miRNAs in pig sperm with low motility and abnormal
morphology and they identified 2 miRNAs (let-7d and let-7e) elevated in the low
motility sperm and 4 miRNAs (let-7a, let-7d, let-7e, and miR-22) elevated in the
sperm with abnormal morphology [80]. Wang et al. generated seminal plasma
43
miRNA profiles from normal, azoospermic, and asthenospermic men. Using
Solexa sequencing they identified 19 miRNAs aberrant in the seminal plasmal
from abnormal men compared to controls [81]. Men with no sperm in their semen
(azoospermia) had 5 upregulated and 14 downregulated miRNAs while the men
with abnormal motility (asthenozoospermia) had 12 upregulated and 5
downregulated miRNAs. They confirmed 7 of these miRNA alterations between
the 3 groups using qRT-PCR (miR-34c-5p, miR-122, miR-146b-5p, miR-181a,
miR-374b, miR-509-5p, and miR-513a-5p) [81].
In addition, to the seminal
plasma, the authors measured the levels of 4 miRNAs (miR-34c-5p, miR-122,
miR-146b-5p,
and
miR-181a)
in
sperm
isolated
from
normal
and
asthenozoospermic men and found that they were all increased in the low motility
group, with miR-34c-5p and miR-146b-5p reaching statistical significance [81].
Overall, the results of these two studies strongly support the utility of sperm
miRNAs as biomarkers of effect.
DNA Methylation
Both human and animal studies indicate that abnormal sperm DNA
methylation patterns are associated with subfertility, including aberrant
methylation of both imprinted [82-90] and non-imprinted genes [83, 91, 92] in
oligospermic men. Houshdaran et al. examined the global methylation pattern of
sperm in semen samples collected from male members of 69 couples referred for
infertility analysis and found that in poor quality sperm, the methylation state of
numerous sequences in the DNA was elevated [83]. They hypothesized that the
44
mechanism behind the epigenetic change may be aberrant erasure of DNA
methylation during epigenetic reprogramming of the male germ line [83]. A recent
study compared genome-wide DNA methylation profiles for men that have
demonstrated poor in vitro fertilization embryogenesis and abnormal sperm
chromatin compaction [93]. Their results identified a subset of patients with
genome-wide DNA methylation defects, with imprinted regions more prone to
error than the entire genome [93].
In addition, Pathak et al. examined the effects of tamoxifen exposure on
DNA methylation patterning in rat spermatozoa [94]. The authors measured
global sperm DNA methylation, the methylation state of the IGF2-H19 imprinting
control region (ICR), and embryo post-implantation loss. Although no changes in
global methylation were seen, methylation was reduced at the IGF2-H19 ICR.
Mating experiments showed a significant increase in post-implantation loss,
which positively correlated with the reduced ICR methylation. The authors
suggest that errors in paternal imprints could affect embryo development and that
methylation patterns could be useful as biomarkers for evaluating male fertility
[94].
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53
CHAPTER 2. OPTIMIZATION OF A FILTER-LYSIS PROTOCOL TO PURIFY
RAT TESTICULAR HOMOGENATES FOR AUTOMATED SPERMATID
COUNTING
54
CHAPTER 2. OPTIMIZATION OF A FILTER-LYSIS PROTOCOL TO PURIFY
RAT TESTICULAR HOMOGENATES FOR AUTOMATED SPERMATID
COUNTING
Sara E. Pacheco, Linnea M. Anderson, and Kim Boekelheide
Department of Pathology and Laboratory Medicine, Brown University,
Providence, RI, USA, 02912
J Androl. 2012 Jan 12. [Epub ahead of print] PMID: 22240558
Declaration of authors’ roles
SEP designed and performed the experiments and analyses presented in
this paper. LMA assisted in the manual counting experiments and performed the
automated counts. KB contributed to study design and data analysis. Everyone
contributed to the organization and writing of the manuscript and approved the
final version to be published.
55
Abstract
Objectives: Quantifying testicular homogenization resistant spermatid
heads (HRSH) is a powerful indicator of spermatogenesis. These counts have
traditionally been performed manually using a hemocytometer, but this method
can be time consuming and biased. We aimed to develop a protocol to reduce
debris for the application of automated counting, which would allow for efficient
and unbiased quantification of rat HRSH. Findings: We developed a filter-lysis
protocol that effectively removes debris from rat testicular homogenates. After
filtering and lysing the homogenates, we found no statistical differences between
manual (classic and filter-lysis) and automated (filter-lysis) counts using one-way
ANOVA with Bonferroni’s multiple comparison test. In addition, Pearson’s
correlation coefficients were calculated to compare the counting methods and
there was a strong correlation between the classic manual counts and the filterlysis manual (r = 0.85, p = 0.002) and the filter-lysis automated (r = 0.89, p =
0.0005) counts. We also tested the utility of the automated method in a low dose
exposure model known to decrease HRSH. Adult Fischer 344 rats exposed to
0.33% 2,5-hexanedione (HD) in the drinking water for 12 weeks demonstrated
decreased body (p = 0.02) and testes (p = 0.002) weights. In addition, there was
a significant reduction in the number of HRSH per testis (p = 0.002) when
compared to control. Conclusions: A filter-lysis protocol was optimized to purify
rat testicular homogenates for automated HRSH counts. Automated counting
systems yield unbiased data and can be applied to detect changes in the testis
after low dose toxicant exposure.
56
Introduction
Quantification of testicular homogenization resistant spermatid heads
(HRSH) can be used to estimate daily sperm production rates and is a commonly
used method in studies of toxicant-induced testicular injury or dysfunction [1-5].
The use of a hemocytometer in this method necessitates that multiple counts be
recorded due to the high levels of variation and error inherent in the technique. It
has been previously reported that mean differences of greater than 20% are
common in manual sperm counts, even when the counts are performed by the
same individual [6, 7], highlighting the need for a reliable automated protocol.
Previous work describes the use of the Coulter Counter for automated
semen analysis, but cellular contaminants within the semen tend to inflate the
counts [8]. In addition, the CASA technology has been applied to enumerate
rodent testicular spermatids, however, the CellSoft system also overestimates
spermatid number by misidentifying testicular debris as spermatid heads [9].
Through the addition of filtration and somatic cell lysis steps and the use of an
automated counter that can identify Trypan blue stained cells, the classic
protocol can be modified for automatic quantification of testicular spermatid
heads.
Here we describe a novel update to the classic protocol for counting
testicular HRSH to eliminate cellular debris and purify spermatid heads. Using
the pure lysates in an automated counting system produces efficient, reliable,
and unbiased results that can be applied to detect low dose toxicant-induced
57
testicular injury.
Methods
Chemicals
2,5-Hexanedione (CAS# 110-13-4) used in the application study was
purchased from Sigma Aldrich (St. Louis, MO).
Animals
Adult male Fischer 344 rats weighing 175-225 grams (Charles River
Laboratories, Wilmington, MA) were maintained in a temperature and humidity
controlled vivarium with a 12 hour alternating light-dark cycle. All rats were
housed in community cages with free access to water and Purina Rodent Chow
5001 (Farmer’s Exchange, Framingham, MA). The Brown University Institutional
Animal Care and Use Committee approved all experimental animal protocols in
compliance with National Institute of Health guidelines.
Preparation of Testes and Homogenization Procedures
Body weights were recorded at the time of necropsy and the testes were
removed and weighed. The right testis was detunicated and one third of the
parenchyma was weighed, flash frozen, and stored at -80C for later evaluation.
58
At the time of processing, each sample was thawed on ice and homogenized
using a Brinkmann Kinematica Homogenizer Polytron PT 10/35 (Brinkmann
Instruments, Westbury, NY) in saline-merthiolate-triton (SMT) buffer following a
previously published protocol [3]. Briefly, testis samples were homogenized in 25
mL SMT at maximum speed (27,000 rpm) for 2 minutes and used immediately
for counting.
Additional Filter and Lysis Protocol
After the homogenization procedure, the testis homogenates were filtered
through 10 μm nylon mesh (Dynamic Aqua-Supply Ltd., Surrey, Canada). The
filtered homogenates were then combined in a 1:1 ratio with an optimized
somatic cell lysis buffer (0.3% SDS and 1% Triton-X 100) derived from a protocol
used for lysing somatic cell contamination in human semen [10]. Each sample
containing the homogenate and lysis buffer mixture was incubated on wet ice for
5 minutes prior to counting. The lysis of debris was confirmed using phase
contrast microscopy and photographs of Trypan blue (Invitrogen, Eugene, OR)
stained homogenates and lysates were taken using the Nikon Diaphot
microscope (40X) and Nikon D40 digital camera.
59
Manual Testicular Spermatid Head Counts
Testis homogenates or lysates were combined with 0.2% Trypan blue in a
1:1 ratio and 10 μL was loaded into both chambers of 2 hemocytometers,
resulting in 4 counts per sample that were averaged together to obtain one value.
The hemocytometers were placed in a humidified chamber for 5 minutes prior to
counting and the samples were counted according to previously published
methods (Blazak et al., 1993).
Automated Testicular Spermatid Head Counts
Testis homogenates or lysates were combined with 0.2% Trypan blue in a
1:1 ratio and 10 μL was loaded to both sides of a cell counting chamber slide
(Invitrogen, Eugene, OR), resulting in 2 counts per sample that were averaged
together to obtain one value. HRSH were counted using the Countess
Automated Cell Counter (Invitrogen, Eugene, OR) following manufacturer
guidelines. The gating parameters “sensitivity”, “minimum size”, “maximum size”,
and “circularity” were optimized for rat testicular spermatid heads and were
determined to be 5, 5 μm, 10 μm, and 30%, respectively.
Optimization Experiment
Control testis samples (n = 10) were homogenized. Each homogenate
was divided and one half was left as is while the other half was subjected to the
60
additional filter-lysis steps described above. Both preparations of each sample
were counted manually by two individuals (SP and LA), and automatically using
the automated counter. Coefficients of variation were calculated for each
approach ((standard deviation/mean) x 100) and the manual counts from SP and
LA were averaged to obtain one value to reduce variability. This resulted in four
counts for each sample: 1) classic manual, 2) classic automated, 3) filter-lysis
manual, and 4) filter-lysis automated. One-way ANOVA with the Bonferroni’s
multiple comparison test was applied to determine if there was a statistical
difference in the average number of HRSH per testis between the four counting
approaches. Correlation between the groups was determined using Pearson’s
correlation coefficients. Results from both tests were considered significant if p <
0.05. A scatter diagram of the number of HRSH obtained using the classic
manual method versus the filter-lysis automated method was fitted with a Deming
regression line. In addition, a Bland-Altman plot was generated to determine the
agreement between the classic manual and filter-lysis automated approaches [7].
Application Experiment
To determine if the adapted protocol was capable of detecting changes in
the testes after low dose toxicant exposure, rats were exposed to either water
(control, n=8) or 0.33% 2,5-hexanedione in the drinking water (HD, n=10) for 12
weeks. We chose this dose of HD because it induces minimal, but detectable
testicular injury in Fischer 344 rats [11] and pilot experiments in our laboratory
61
indicated that HD decreased the number of HRSH in the testis when counted
manually
(unpublished
data).
The
testes
were
homogenized
and
the
homogenates were filtered, lysed, and counted automatically as described
above. The total number of HRSH in the testis of the control and HD rats were
calculated and compared using a two-tailed Student’s t-test and the results were
considered significant if p < 0.05.
Statistical Analyses
All statistical analyses were performed using the Prism 5 software (GraphPad
Software, La Jolla, CA).
Results
Optimization Experiments
The additional filter-lysis protocol removes testicular debris
A comparison of the samples prepared using the classic protocol (Figure
1, panels A and B), to those following an additional filter-lysis (Figure 1, panels
C and D), clearly demonstrated the efficacy of the updated method to remove the
majority of cellular contaminants that would inflate automated counting methods.
62
Figure 1. Filter-Lysis Optimization
Testis homogenates were prepared using each of the two methods described (AB, classic; C-D, filter-lysis) and stained with Trypan blue for visualization. White
arrowheads indicate rat spermatid heads while white arrows indicate cellular
debris. Photomicrographs were taken at 40X and the scale bar = 20 μm.
63
Manual counting has high inter-observer variability compared to automated
counts
Correlation coefficients were calculated among the 4 sets of manual
counts and there was poor correlation between the two individuals doing the
counting (Table 1). The correlation coefficient calculated comparing “Classic
Manual #1” vs “Filter-Lysis Manual #2” was low (0.425). Likewise, the correlation
coefficient calculated comparing “Filter-Lysis Manual #2” and “Filter-Lysis Manual
#1” was also low (0.438). These findings suggest that the variability in the counts
was mainly due to the variation between the two individuals counting, and not the
methods used to prepare the samples. The CVs for the mean manual counts
(“Classic Manual #1”, “Classic Manual #2”, “Filter-Lysis Manual #1”, and “FilterLysis Manual #2”) for all 10 samples were 32.2%, 28.6%, 26.4%, and 33.7%,
respectively. However, the “Filter-Lysis Automated” had less variation (CV =
25.5%). To reduce variability we averaged the two individual counts for the
classic manual and filter-lysis manual approaches and the CVs were reduced to
28.72% and 25.43%, respectively.
64
Table 1. Correlation Coefficients and p-values Calculated for Manual
Counts
Classic
Classic
Filter-Lysis
Filter-Lysis
Manual #1
Manual #2
Manual #1
Manual #2
Classic
0.010
0.0005
0.220
Manual #1
Classic
0.767
0.0215
0.019
Manual #2
Filter-Lysis
0.891
0.710
0.206
Manual #1
Filter-Lysis
0.425
0.720
0.438
Manual #2
Note: Correlation coefficients are shown in black cells (with white font) and pvalues are shown in gray cells (with black font). Data was generated comparing
the average number of homogenization resistant spermatids per testis.
65
There is high correlation between the classic manual and the filter-lysis
automated approaches to counting testicular spermatid heads
One-way ANOVA determined that there were no significant differences
between the observer-averaged manual (both classic and filter-lysis) and the
filter-lysis automated methods. However, there was a significant increase in the
total number of HRSH counted using the classic automated method compared to
the other three protocols (Figure 2A).
These results demonstrate that the
additional filter-lysis steps did not alter overall testicular HRSH numbers and that
the automated cell counter produced accurate HRSH counts. Furthermore,
Pearson’s correlation coefficients were calculated to compare the similarity
among the four preparation techniques. The classic manual protocol was highly
correlated with the filter-lysis manual (r = 0.85, p = 0.002) and automated
protocols (r = 0.89, p = 0.0005), but not the classic automated counts (r = 0.65, p
= 0.04) (Table 2). The strongest correlation occurred when the filter-lysis manual
and automated counts were compared (r = 0.90, p = 0.0004) (Table 2). Scatter
plots with a fitted Deming regression line (Figure 2B), and Bland-Altman plots
(Figure 2C) were used to get the best overview of comparative data generated
using the classic manual and filter-lysis automated methods. Deming regression
was applied to describe the relationship between the two methods that were both
generated with error, taking into account the analytical imprecision of each
method. This regression analysis yielded a best-fit line with a slope and Yintercept (when X = 0) of 0.84 ± 0.15 (95% CI: 0.50 to 1.18) and 0.20 ± 0.25
(95% CI: -0.37 to 0.78), respectively (p = 0.005).
66
The Bland-Altman plot
calculated a bias of 0.06 when comparing the classic manual and the filter-lysis
automated approaches (Figure 2C, dashed line) with a standard deviation of
0.21. The standard deviation of the differences between the two assay methods
was used to calculate the limits of agreement according to the formula: bias 
1.96 x SD. Our 95% limits of agreement are between -0.35 and 0.47 (Figure 2C,
solid lines). As expected, the two methods give very similar results on average
and the level of agreement among the samples is good, with 90% of the data
falling within the limits.
67
Table 2. Optimization Experiment: Correlation Coefficients and pvalues
Classic
Classic
Filter-Lysis
Filter-Lysis
Manual
Automated
Manual
Automated
Classic
0.044
0.002
0.0005
Manual
Classic
0.645
0.007
0.0066
Automated
Filter-Lysis
0.846
0.790
0.0004
Manual
Filter-Lysis
0.894
0.789
0.897
Automated
Note: Correlation coefficients are shown in black cells (with white font)
and p-values are shown in gray cells (with black font). Data was
generated comparing the average number of homogenization resistant
spermatids per testis.
68
69
Figure 2. Optimization Experiment: Number of Homogenization Resistant
Spermatid Heads (HRSH) per Testis
(A) The average number of HRSH per testis were calculated for 10 rats using
each protocol and values were graphed as follows: classic manual (black circle);
classic automated (open circle); filter-lysis manual (black square); and filter-lysis
automated (open square). The light gray boxes represent the range of values
and the dark gray boxes contain the SEM, with the mean as the line in the middle
of the dark gray box. Statistical differences between the average number of
spermatids per testis among groups was determined using one-way ANOVA with
the Bonferroni’s multiple comparisons test; *** = p <0.001 relative to all other
methods. (B) Scatter diagram of the number of HRSH obtained using the classic
manual method versus the filter-lysis automated method, with Deming regression
line fitted (solid line). (C) Bland-Altman absolute bias plot of the number of HRSH
obtained using the classic manual method versus the filter-lysis automated
method showing the average bias (dashed line) and limits of agreement (solid
lines).
70
Application Experiment
Exposure to 0.33% HD decreases body and testes weights
Rats were exposed to either water (control) or 0.33% HD in the drinking
water for 12 weeks and all rats were weighed at the time of necropsy (Table 3).
Rats exposed to HD displayed significantly decreased body weights when
compared to control rats (p = 0.02). Testis weights were also recorded during the
necropsy and rats exposed to HD had significantly decreased testis weights
compared to control (p = 0.002) (Table 3).
71
Table 3. Application Experiment: Average Body and Testis Weights
after Toxicant Exposure
Treatment
Body Weights (g)
Testis Weights (g)
Control (n = 8)
315.8 ± 6.1
1.58 ± 0.02
HD (n = 10)
286.9 ± 8.9*
1.48 ± 0.02**
Note: Testis weight is the mean of the average left and right testis weights.
Values are presented as mean ± SEM. HD = 2,5-Hexanedione; * = p = 0.02
and ** = p =0.002 relative to the control.
72
Exposure to HD decreases the average number of homogenization resistant
spermatids in the testis
Following optimization of the technique, the utility of the automated cell
counter was tested in the context of toxicant exposure that we had previously
seen reduce the number of HRSH in the testis when counted manually
(unpublished data). Testes from HD and control rats were prepared with the
additional filter-lysis steps and counted using the automated cell counter. The
total number of HRSH per testis was calculated for each sample and consistent
with the significant decrease in testis weights, HD rats displayed significantly
decreased HRSH counts (p = 0.002) relative to control rats (Figure 3).
73
Figure 3. Application Experiment: Number of Homogenization Resistant
Spermatid Heads (HRSH) per Testis after Toxicant Exposure
Testicular spermatid head counts were obtained from HD (n=10; black circle) and
control rats (n=8; black square) using the filter-lysis automated technique and
graphed as the total number of HRSH per testis. The light gray boxes represent
the range of values and the dark gray boxes contain the SEM, with the mean as
the line in the middle of the dark gray box. ** = p = 0.002 relative to control.
74
Discussion
The most recent World Health Organization laboratory manual for the
examination and processing of human semen emphasizes the necessity for
automated systems for analyzing sperm because they have the potential for
greater objectivity, precision, and reproducibility than manual systems [12]. The
CASA system evolved because of the need to obtain objective and bias-free data
when analyzing sperm motility, but has been incorporated into other aspects of
the semen analysis [13]. Unfortunately, not all laboratories with interests in male
fertility have access to a CASA system, and these laboratories have to rely on
manual methods to examine sperm parameters. Here we present a novel update
to the classic protocol for quantification of rat testicular homogenization resistant
spermatid heads (HRSH). This update utilizes a filtration step followed by
somatic cell lysis of the homogenate in order to rid the sample of debris while
preserving spermatid heads. These additional steps allow for the use of an
automated cell counter, rather than a hemocytometer, to quantify HRSH.
Optimization of the protocol was performed using normal rat testes, and
four sample preparation/counting methods were compared to demonstrate the
effectiveness and reproducibility of the newly modified protocol. We were able to
show that automated counts of classically prepared samples produced an
artificially high HRSH count due to inclusion of cellular debris, but with the
addition of filtration and lysis steps, the automated counts were no different from
those obtained using a hemocytometer. In fact, we saw the strongest correlation
between the filter-lysis manual and automated protocols. These results suggest
75
that the additional filter-lysis steps are beneficial for both manual and automated
counting and this may be because the lysates are cleaner, which reduces the
misidentification of debris as HRSH or prevents debris from masking the HRSH.
Additionally, the automated method can be applied to detect differences in
testicular HRSH counts following low dose exposure to a known testicular
toxicant. Previous work in our lab has documented the deleterious effects of HD
on the rat testis, including reductions in testis weights and histological alterations
to the seminiferous tubules, including germ cell sloughing, Sertoli cell
vacuolization, and retained spermatid heads [11]. These manifestations of
testicular injury can reduce the number of spermatids in the testis, which we have
observed in HD-exposed testes after performing manual HRSH counts
(unpublished data). This is consistent with the results presented here that show
decreased body and testis weights and decreased testicular HRSH with HDexposure, thereby verifying the utility of the automated protocol in animal studies
of testicular injury.
This applicability of the pure testicular lysates to an automated basic cell
culture counting platform represents a significant improvement to the established
manual protocol for counting testicular HRSH, a procedure that provides an
important measure of spermatogenic capacity in animal models. Manual counts
using the hemocytometer are variable, time consuming, and susceptible to bias.
We have developed a newly modified protocol that is effective and sensitive,
allowing testicular HRSH counts to be obtained more efficiently and reliably.
76
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Blazak, W.F., K.A. Treinen, and P.E. Juniewicz, Application of testicular
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Chapin, Heindel, Jerrold J., Editor. 1993, Academic Press. p. 86-94.
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of rat sperm motility. J Androl, 1987. 8(5): p. 330-7.
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Goodrich, R., G. Johnson, and S.A. Krawetz, The preparation of human
spermatozoal RNA for clinical analysis. Arch Androl, 2007. 53(3): p. 161-7.
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Moffit, J.S., et al., Dose-dependent effects of Sertoli cell toxicants 2,5hexanedione, carbendazim, and mono-(2-ethylhexyl) phthalate in adult rat
testis. Toxicol Pathol, 2007. 35(5): p. 719-27.
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78
CHAPTER 3. SPERM mRNA TRANSCRIPTS ARE BIOMARKERS OF SUBCHRONIC LOW DOSE SERTOLI CELL INJURY IN THE RAT
79
CHAPTER 3. SPERM mRNA TRANSCRIPTS ARE BIOMARKERS OF SUBCHRONIC LOW DOSE SERTOLI CELL INJURY IN THE RAT
Sara E. Pacheco, Linnea M. Anderson, Moses A. Sandrof, Marguerite M.
Vantangoli, Susan J. Hall, Kim Boekelheide
Department of Pathology and Laboratory Medicine, Brown University,
Providence, Rhode Island, United States of America, 02912
Declaration of authors’ roles
SEP designed and performed the experiments and analyses presented in
this paper. LMA performed the TUNEL analysis and homogenization resistant
spermatid head counts. MAS and SJH assisted with animal necropsy, dosing,
and tissue preparations. MMV performed the retained spermatid head counts on
the carbendazim treated animals. KB contributed to study design and data
analysis. Everyone contributed to the design and writing of the manuscript and
approved the final version to be published.
80
Abstract
Current human reproductive risk assessment methods rely on semen and
serum hormone analyses, which are not easily comparable to the apical
endpoints and mating studies used in animal toxicity testing. Because of these
limitations, there is a need to develop sensitive biomarkers that reliably reflect
male reproductive function. This study aimed to: 1) identify biomarkers of testis
damage within rat sperm after sub-chronic (3-month) low dose exposure to the
Sertoli cell (SC) toxicant 2,5-hexanedione (HD) using microarrays; 2) expand on
the HD-induced transcript changes in a comprehensive time course study using
qRT-PCR arrays; and 3) apply these sperm biomarkers to another 3-month SC
toxicant exposure, carbendazim (CBZ). Microarray analysis of HD-treated adult
Fischer 344 rats identified 128 altered sperm mRNA transcripts when compared
to control using LIMMA (q < 0.05). However, all transcript alterations disappeared
after 3 months of recovery. In the time course study, time-dependent alterations
were observed for 12 candidate transcripts selected from the microarray for fold
change and biological relevance, and 8 of these transcripts remained significantly
altered after the 3-month recovery period (p < 0.05). In the application study, 8
candidate transcripts changed after exposure to CBZ (p < 0.05). The two SC
toxicants produced distinct molecular signatures with only 4 overlapping
transcripts between them, each occurring in opposite directions. Conclusions:
The developed biomarker panel provides a sensitive molecular screen that is
capable of discerning the molecular signatures of different SC toxicants that elicit
a response through distinct mechanisms of action.
81
Introduction
Toxicogenomics is the convergence of emerging technologies with
conventional toxicological assays to identify molecular signatures resulting from
toxic insult [1, 2]. The strength of these signatures is increased when they are
linked to a phenotypic endpoint and dose-response and time course studies can
further identify cause and effect relationships between changes in molecular
profiles after toxicant exposure. For example, microarrays can measure gene
transcript levels of the entire genome simultaneously and provide the foundation
for understanding, characterizing, and predicting target-organ toxicity [3].
The testis is susceptible to a variety of therapeutic agents and
environmental toxicants. Injury may be subtle and histopathological changes are
undetectable at early time points, while serum hormones and semen analyses
are not able to detect early changes in both pre-clinical studies and clinical trials
[3]. Effort has been spent investigating the utility of inhibin B as a biomarker of
testicular injury, but it appears that it is not a sensitive endpoint in rodents [3].
With this in mind, several studies have used toxicogenomic approaches to
screen compounds for testicular toxicity [3]. Of note, one study utilized
microarray analysis after acute exposures to four model testicular toxicants. The
results suggested that even though there were no histopathological changes to
the testis after the exposure, the gene expression changes were robust and
reproducible, with some genes differentially expressed in all treatment groups [3].
This is a significant finding that highlights the capability of transcriptomic profiling
to identify different toxicant responses in the testis.
82
Utilizing toxicogenomic approaches to develop biomarkers of testicular
toxicity is a valuable tool for the pharmaceutical industry, which can eliminate
potentially toxic compounds in the early stages of drug development based on
their elicited molecular profiles. However, the heterogeneity of the testis, in
addition to the spatial-temporal intricacy of spermatogenesis, makes it a very
complex tissue to study. Furthermore, assessing gene expression in the testis is
an unrealistic endpoint when comparing pre-clinical animal studies and clinical
trials because of the invasive techniques required for sample acquisition. Sperm,
however, are considered the best substitute for the evaluation of spermatogenic
function [4]. In addition to their genome, sperm transmit mRNA, and provide the
oocyte with vital organelles, as well as male-specific proteomic components [5].
Moreover, it is understood that the quantity and types of sperm mRNA transcripts
may indicate the quality and productivity of spermatogenesis [6].
Spermatogenesis relies on carefully orchestrated microtubule dynamics
within SC cytoskeleton for spermatogenesis to occur properly [7]. Two toxicants,
2,5-hexanedione (HD) and carbendazim (CBZ), can induce alterations in
microtubule assembly and disrupt germ cell (GC) development. HD is the active
metabolite of the common industrial solvent, n-hexane. Although the most
significant exposures occur in occupational settings, humans are ubiquitously
exposed to low levels of n-hexane as a chemical component of gasoline [8]. HD
targets the SC and promotes rapid microtubule assembly by inducing tubulin
cross-linking, altering microtubule-dependent transport in these cells [8]. This
disturbs the GC niche by impeding seminiferous tubule fluid secretion. Disrupting
83
the SC microenvironment leads to GC apoptosis and ultimately testicular atrophy
[7-10]. CBZ is the toxic metabolite of the benzimidazole fungicide, benomyl, and
it inhibits microtubule polymerization and formation. The testicular toxic effects of
CBZ include the induction of atrophic seminiferous tubules due to increased GC
apoptosis and sloughing. Previous studies have characterized the dose-response
of HD and CBZ exposure on the testis [7, 11-13], making them model toxicants
with predictable male reproductive effects.
Taking advantage of the wealth of data on the specific dose-response and
mechanistic action of these model toxicants, we hypothesized that applying
toxicogenomic techniques to pure sperm populations would be a useful approach
for identifying molecular biomarkers of testicular injury that are translatable
across species and will allow for improved hazard identification and a more
robust risk assessment. The goals of this project were to further develop a subchronic, low dose exposure paradigm for the model SC toxicant HD, to utilize
toxicogenomic approaches to identify alterations to the caudal epididymal sperm
mRNA transcriptome, and to test a panel of sperm transcripts as sensitive
biomarkers of effect, using another SC cytoskeletal toxicant, CBZ.
Materials and Methods
Animals
Adult male Fischer 344 rats weighing 175-225 grams (Charles River
Laboratories, Wilmington, MA) were maintained in a temperature and humidity
84
controlled vivarium with a 12 hour alternating light-dark cycle. All rats were
housed in community cages with free access to water and Purina Rodent Chow
5001 (Farmer’s Exchange, Framingham, MA). The Brown University Institutional
Animal Care and Use Committee approved all experimental animal protocols in
compliance with National Institute of Health guidelines.
Chemicals
HD (CAS# 110-13-4), CBZ (CAS# 10605-21-7), and all other chemicals
were purchased from Sigma Aldrich (St. Louis, MO) unless otherwise noted.
Dose Selection
Lowest-observable-adverse-effect-level doses of HD and CBZ were
chosen to produce minimal but detectable testicular injury. Based on previous
studies, 0.33% HD in the drinking water was expected to produce some
morphological evidence of injury in the rat testis [7, 11-13]. Our previous work
with CBZ led us to select a dose of 50 mg/kg/d in corn oil vehicle via oral gavage
to induce minor testicular damage [7].
Experimental Design
Three experiments were designed to examine the utility of sperm mRNAs
as biomarkers of SC-toxicant induced testicular injury: 1) a preliminary study
85
using whole-genome profiling to identify a list of robust genes comparing control
and HD-exposed sperm; 2) a more comprehensive time course study examining
the temporal effects of HD on candidate sperm mRNAs; and 3) an application
study extending this approach to evaluate CBZ, another SC toxicant (Figure 1).
In each of the three experiments, rats were exposed to a SC toxicant for 3
months. Rat spermatogonial commitment through spermiogenesis takes
approximately 8 weeks, so the 3-month exposure-window guaranteed that the
sperm being evaluated would have experienced a disrupted environment
throughout development, including the time needed for epididymal transport. Two
of the experimental paradigms allowed rats to recover following exposure for up
to 3 additional months. Rats were euthanized by carbon dioxide asphyxiation and
the body weights and reproductive organ weights were recorded at necropsy.
Left testes were fixed in 10% neutral-buffered formalin for histological
examination, and a portion of each animal’s right testis was detunicated and
snap frozen for the automated determination of homogenization resistant
spermatid head (HRSH) counts [14]. The epididymides were weighed and the
caudal regions of the epididymides were used immediately for sperm isolation
and RNA extraction. The following experiments were conducted:
86
Figure 1. Experimental Paradigms
For the Preliminary experiment (A), rats were treated with 0.33% HD in the
drinking water or water control, for 3 months. The recovery group received
control water for 3 additional months. For the Time Course experiment (B), the
same study design was used as in (A), except with the addition of monthly
assessments during HD exposure and post-exposure recovery.
For the
Application experiment (C), rats were exposed to 50 mg/kg/d CBZ in corn oil
vehicle via oral gavage (dotted lines) for 3 months. Grey arrowheads indicate
euthanization time points.
87
Preliminary Study
Rats were randomly assigned to four groups: water, HD, water-recovery,
and HD-recovery (n = 18-20/group). HD was administered as a 0.33% solution in
drinking water ad libitum for 3 months (HD and HD-recovery groups). Rats were
necropsied after 3 months of exposure (water and HD) or after 3 months of
exposure plus 3 months of additional post-exposure recovery (water-recovery
and HD-recovery) (Figure 1A). In addition to the common endpoints assessed in
all exposure paradigms (weights and testis HRSH), testis histology (including GC
apoptosis, seminiferous tubule diameter, and spermatid head retention) was
evaluated in these animals and the sperm transcriptome was characterized by
microarray analysis.
Time Course Study
Rats (n = 4-6/group) were randomly assigned to seven groups classified
by the duration of the exposure and recovery [Months (+ Recovery); 0, 1, 2, 3,
3+1, 3+2, 3+3]. The same exposure paradigm was used here as in the
preliminary study, except rats were euthanized at 1-month intervals (Figure 1B).
Serum was also collected from each animal at necropsy and archived for inhibin
B measurement. The sperm transcript changes were confirmed using qRT-PCR
array analysis.
88
Application Study
Rats were randomly assigned to two groups, corn oil (n = 9) and CBZ (n =
6). CBZ-exposed rats received 50 mg/kg/d CBZ as a slurry in corn oil via oral
gavage for 3 months (Figure 1C). Testis spermatid head retention and serum
inhibin B levels were measured in these animals and the sperm transcript
changes were confirmed using qRT-PCR array analysis.
Histological Examination
Two cross sections from the center of the formalin-fixed testes were
embedded in glycol methacrylate (Technovit 7100; Heraeus Kulzer GmBH,
Wehrheim, Germany) for histological examination of stage-specific retained
spermatid heads (RSH) (Preliminary and Application Studies) or embedded in
paraffin for detection of apoptosis by TUNEL staining with concurrent
measurement of seminiferous tubule diameter (Preliminary Study only). The
Aperio Scan Scope (Aperio Technologies, Vista, CA) was used to visualize and
analyze all histological endpoints.
For the enumeration of RSH, sections (3 m) of testes from 6 randomly
selected rats per treatment group were stained with periodic acid-Schiff’s reagent
followed by hematoxylin counter stain (PASH). Each cross section was evaluated
for seminiferous tubules in spermatogenesis stages IX-XI, each of which was
required to have a major:minor axis of less than 1.5:1 [13]. The number of RSH
along the basal compartment was recorded for each stage-specific seminiferous
89
tubule, and the counts were averaged together on an individual rat basis. The
counts were log-transformed to assure normally distributed errors prior to
statistical analysis.
For the evaluation of apoptosis, paraffin sections (5 m) of testes from the
subset of rats utilized above were stained using the ApopTag Peroxidase In Situ
Apoptosis Detection Kit (Chemicon, Temecula, CA) following the manufacturer’s
protocol and were counterstained with methyl green. Apoptotic cells were
counted in 50 random seminiferous tubules having a major:minor axis of less
than 1.5:1. The percentage of seminiferous tubules containing TUNEL positive
cells was assessed. The minor axis diameter was also recorded.
Serum Isolation and Inhibin B Measurements (Time Course and Application
Studies)
Blood was collected at necropsy via cardiac puncture, samples were
allowed to clot before centrifugation, and serum was isolated following standard
protocols. Serum samples were stored at -80C prior to shipment to Pfizer
(Groton, CT). Serum inhibin B (pg/mL) was measured using the ACTIVE Inhibin
Gen II ELISA assay kit (REF A81301, Diagnostic Systems Laboratories, Inc,
Webster, TX) following the manufacturer’s instructions. Samples were run in
duplicate and values were averaged together to obtain one measurement.
90
Sperm Isolation and RNA Extraction (All experiments)
The cauda epididymides were punctured repeatedly with 30 and 26 gauge
needles and put into micro-centrifuge tubes containing phosphate buffered saline
(1X PBS, GIBCO REF 10010-023, Life Technologies, Grand Island, NY). The
samples were incubated in a water bath at 37C for 10 minutes to aid sperm
diffusion from the tissue. Following centrifugation for 3 minutes at 300 x g to
pellet the epididymal pieces, the supernatant was removed and centrifuged for 5
minutes at 2000 x g to pellet the sperm. A previously optimized lysis buffer (0.15
M ammonium chloride, 10 mM potassium bicarbonate, 0.1 mM EDTA (Thermo
Fischer Scientific Inc., Pittsburgh, PA)) was added to the sperm pellet, incubated
on the bench top for 30 seconds and centrifuged at 16,100 x g for 1 minute to
remove somatic cell contamination (data not shown). The pellet was washed with
PBS and centrifuged again at 16,100 x g for 1 minute. RNA was extracted from
the
fresh
sperm
using
the
mirVana
miRNA
Isolation
Kit
(Applied
Biosystems/Ambion, Austin, TX).
RNA Cleanup and Concentration Protocols
mRNAs were further purified and concentrated into a smaller volume. In
the Preliminary Study, mRNAs were cleaned and concentrated using ammonium
acetate and ethanol precipitation. RNAs were resuspended in 10 µl RNAse-free
water. In the Time Course and Application Studies, mRNAs were DNase treated,
and processed using Qiagen’s RNase-free DNase and RNeasy MinElute
91
Cleanup kits (Qiagen Sciences, Germantown, MD) following the manufacturer’s
protocol.
mRNA Analyses
Sperm mRNA content was assessed for each of the three experiments
using microarrays or qRT-PCR arrays. The microarray data discussed in this
publication is MIAME compliant and the raw data has been deposited in NCBI's
Gene Expression Omnibus (GEO) database [15] as detailed in the MGED
Society website http://www.mged.org/Workgroups/MIAME/miame.html. These
data files are accessible through GEO accession number GSE34614.
Preliminary Study
Using the Brown Genomics Core Facility, the isolated sperm mRNA (160
ng/sample) from each rat (n = 18-20/group) was processed and hybridized to
Affymetrix GeneChip Rat Gene 1.0 ST Arrays (Affymetrix, Santa Clara, CA). The
probe cell intensity data from the Affymetrix GeneChips was normalized,
annotated, and analyzed following previously published methods [16]. The
microarray data were also analyzed using Ingenuity Pathways Analysis (IPA)
(Ingenuity® Systems, www.ingenuity.com). The IPA Functional Analysis program
identified the molecular and cellular functions that were significantly associated
with the sperm mRNA transcripts. Only transcripts meeting the q-value cutoff of <
0.05 and absolute fold change values > 1.5 that were recognized in Ingenuity's
92
Knowledge Base were considered for the analysis. Twenty-nine differentially
present mRNA transcripts were selected from the microarray data based on the
strength of association, magnitude of change, and biological function
(Supplemental Table 1).
Time Course and Application Studies
Sperm mRNAs from the Time Course and Application Studies (4-9
rats/group) were used on a SABiosciences Rat RT2 Profiler Custom qRT-PCR
array (SABiosciences, a Qiagen Company, Frederick, MD), which profiled the
expression of 32 genes x 12 samples per 384 well plate. The 32 genes included
29 candidate genes selected above plus three plate controls (GAPDH,
NM_017008; RGDC, U26919; RTC, SA_00104). The 384 well PCR plates were
loaded using an epMotion 5075 LH robot (Eppendorf North America, Hauppauge,
NY). The qRT-PCR reactions were run following the manufacturer’s instructions
using an ABI 7900 HT thermocycler (Applied Biosciences, Life Technologies
Corporation, Carlsbad, CA). Raw CT values were normalized to the average of
the GAPDH and RGDC control CT values and expression was analyzed using
the CT method [17] following ABI’s guidelines. The upper and lower fold
change ratio limits were generated using the formula 2
-CT ± SE
, with the
standard error generated after calculating the average CT values for each
transcript.
93
The fold changes determined in the qRT-PCR analysis for the 12
transcripts altered in the Time Course Study were uploaded into Multi-experiment
Viewer (MeV version 10.2) [18]. Gene tree hierarchical clustering was performed
using Manhattan Distance and Average Linkage Clustering.
Statistical Analyses
Preliminary Study
For body and organ weights and histological endpoints, Student’s
unpaired two-tailed t-tests were performed using the Prism 5 software (GraphPad
Software, La Jolla, CA) to determine statistical differences (p < 0.05) between the
treatments and their respective controls.
The linear models for microarray
analysis (LIMMA) procedure [19] (R package limma) was used to fit a linear
regression model for each transcript on the Affymetrix GeneChip Array for each
treatment and its control. The p-values were adjusted for multiple comparisons
with the qvalue package in R [20], and the log2 expression ratios were
transformed into fold change values. Transcripts were considered statistically
significant when q-value < 0.05. IPA performed right-tailed Fisher's exact test to
calculate a p-value determining the probability that each biological function
assigned to the sperm transcripts was due to chance. These p-values were
adjusted for multiple comparisons within the IPA software using the BenjaminiHochberg correction, which is IPA’s preferred method. The high-level functional
categories with adjusted p-values < 0.05 were considered significant.
94
Time Course Study
One way-ANOVA with Dunnett’s correction for multiple comparisons was
performed using the Prism 5 software for the weights, the histological endpoints,
and the mRNA qRT-PCR array data. Data was considered significant when p <
0.05.
Application Study
Student’s unpaired two-tailed t-tests were performed using the Prism 5
software to determine statistical differences (p < 0.05) in weights, histological
endpoints, and mRNA qRT-PCR array data.
Results
Preliminary Study
There was a significant decrease in average body, testis, and epididymis
weights after 3 months of exposure to HD; this effect resolved after 3 months of
post-exposure recovery (Figure 2, panels A and C).
While various
histopathological endpoints were assessed to determine the severity of testicular
injury, the only effect observed after 3 months of exposure was a 14.53 fold
increase in stage-specific spermatid head retention, which resolved after 3
months of recovery (Figure 2D).
95
Figure 2. Preliminary Study: Weights and Retained Spermatid Heads (RSH)
In the Preliminary experiment, body (A); epididymis (B); and testis (C) weights
were recorded at necropsy for the four treatment groups (water and HD (black
bars) and water-recovery and HD-recovery (water-R and HD-R; white bars). In
addition, histological analyses of the testis were performed and the average
number of RSH per stage-specific seminiferous tubule was determined (D). Data
is presented as mean ± SE, and significance (* = p < 0.001) was calculated for
each pair of treatment and controls using Student’s unpaired two-tailed t-test.
The RSH data was log-transformed prior to statistical analysis.
96
The Affymetrix array platform provided whole-transcript coverage of
27,342 genes by ~26 probes spread across the length of each gene. Utilizing the
probe cell intensity values for the entire array, LIMMA analysis identified 128
transcripts with significantly altered levels in sperm after HD exposure and 112 of
these had well-characterized gene annotations (Supplemental Table 2).
Furthermore, 106 of the transcripts representing 95% of the well-annotated
genes were elevated in the sperm, with a maximum fold change value of 2.64.
No transcripts were significantly altered after 3 months of post-exposure recovery
(data not shown).
Ingenuity Pathway Analysis (IPA) of the list of 112 well-annotated
significant transcripts found that 47 transcripts met the significance and fold
change cutoffs (q < 0.05 and |fold change| > 1.5) and these were subsequently
used
for
downstream
functional
analysis.
After
adjusting
for
multiple
comparisons, a list of 17 significant high-level functional categories was
generated that included cell death, cell cycle, cellular assembly and organization,
and cellular growth and proliferation (Table 1). Twenty-nine differentially present
mRNA transcripts were selected from the microarray data based on the strength
of association, magnitude of change and biological function (Supplemental
Table 1).
97
Table 1. Preliminary Study: Functional Analysis of Microarray Data
Category
Number of qRT-PCR Candidates
with This Function
Cell Death
12
Cellular Assembly and Organization
9
Cellular Movement
9
Cell-To-Cell Signaling and Interaction
7
Cellular Development
7
Cellular Growth and Proliferation
7
Small Molecule Biochemistry
7
Cellular Compromise
6
Cellular Function and Maintenance
5
Cell Cycle
4
Lipid Metabolism
4
Molecular Transport
4
Carbohydrate Metabolism
3
Antigen Presentation
2
Post-Translational Modification
2
Free Radical Scavenging
1
Gene Expression
1
Note: IPA found that these functions were enriched in the microarray study (p <
0.05 after adjusting for multiple comparisons). Candidates were selected from the
microarray data based on the magnitude of their fold change and biological
significance.
98
Time Course Study
As indicated by the Preliminary Study results (Figure 2, panels A and B),
body and epididymis weights increased over the time course study when
compared to time point 0 (Supplemental Table 3). In addition, there were no
changes in testicular weights, HRSH, or serum inhibin B levels at any time point
when compared to time point 0 (Supplemental Table 3).
Analysis of the qRT-PCR-array data found that 12 of the 29 candidate
transcripts were significantly altered at any given time point when compared to
time point 0, with fold-changes ranging from -20 to 20 (Figure 3 and
Supplemental Table 4). Few transcripts (n = 3, 2, and 6) were altered during the
3-month exposure window at time points 1, 2, and 3, respectively. However,
there was a robust response to HD in the recovery phase (n = 10, 11, and 8) at
time points 3+1, 3+2, and 3+3, respectively (Figure 3 and Supplemental Table
4). CLU and LYZ2 were decreased during exposure while the other 10 transcripts
were increased.
Heirarchical clustering of the 12 significant transcripts from qRT-PCRarray data identified a major bifurcation in the transcript data (Figure 4), where
transcripts were separated by the direction of the fold change. The upregulated
cluster was further divided into two sub-clusters that were associated with the
timing of the onset of the transcript change. Early changing transcripts were
altered during the toxicant exposure (SOD3, PTGDS, TBC1D5, and IFT81), and
late changing transcripts were altered in sperm during post-exposure recovery
(LRRC6, SCLT1, SIL1, STRBP, LRRC69, and DNAJB4).
99
Figure 3. Time Course Study: Transcript Changes
Twelve of the 29 transcripts selected from the Preliminary Experiment showed
significant alterations by qRT-PCR. CT values were compared to time point 0
using one-way ANOVA with Dunnett’s correction for multiple comparisons (*** =
p < 0.001, ** = p < 0.01, and * = p < 0.05). Data is presented as mean ± limits;
the upper and lower fold change limits were generated using the formula 2
± SE
. Black bars = treatment and white bars = recovery.
100
-CT
Figure 4. Time Course Study: Heatmap Displaying Heirarchical Clustering
of qRT-PCR Data
The fold changes determined in the qRT-PCR analysis (relative to time point 0)
for the 12 transcripts altered at any of the six time points (columns) were
clustered using Manhattan Distance. The 12 genes (rows) were separated via
the direction of fold change (downregulated = red dendrogram). The upregulated
genes segregated into two groups based upon the onset of statistically different
transcript expression; early = blue and late = orange dendrograms.
101
Comparison of the qRT-PCR data from the Time Course Study with the
Affymetrix array data from the Preliminary Study after 3 months of HD-exposure
found that both platforms exhibited similar responses for the majority of
transcripts. However, the qRT-PCR array had a greater dynamic range (-2.74 to
11.16 fold compared to -2.11 to 2.37 fold; Supplemental Table 5), which is
consistent with the signal compression commonly observed in microarray
platforms. Only 6 of the 29 candidates were significantly altered at 3 months
when compared to time point 0 via one-way ANOVA and Dunnett’s correction for
multiple comparisons (Supplemental Table 5). Analyzing the data using
Student’s unpaired two-tailed t-test comparing 0 and 3 months resulted in 10
significantly altered transcripts, with 4 additional transcripts approaching
significance (p < 0.09) (Supplemental Table 5).
Application Study
Body, testis, and epididymis weights of CBZ-exposed rats were similar to
those of corn oil controls (Table 2). There were no changes in the number of
HRSH in the testis after CBZ-exposure; however, CBZ increased the number of
RSH in the testis approximately 3.43 fold. In addition, serum inhibin B levels were
decreased in CBZ-exposed animals (Table 2).
Eight of the 29 candidate
transcripts were altered in sperm after exposure to CBZ, with an additional two
nearly significant (p < 0.06). The fold changes for these transcripts ranged from 2.70 to 21.58 (Table 3). Of these ten transcripts, four corresponded with the
transcripts that were altered in the Time Course Study after HD exposure.
102
Table 2. Application Study: Average Body and Organ
Weights, Histopathological Observations, and Inhibin B
Corn Oil
CBZa
Body (g)
318.8 ± 6.12
324.8 ± 7.39
Testes (g)
1.52 ± 0.02
1.43 ± 0.06
Epididymides (mg)
465.6 ± 6.76
442.3 ± 20.03
HSRH (x108)
2.29 ± 0.36
1.60 ± 0.29
c
RSH
0.58 ± 0.11
1.99 ± 0.91*
Inhibin B (pg/mL)
91.46 ± 6.08
55.03 ± 12.01**
Note: Data presented as mean ± SE; CBZ = 50 mg/kg/d
carbendazim; HSRH = automated homogenization resistant
spermatid head counts; RSH = number of retained spermatid
heads per stage-specific seminiferous tubule; * p = 0.03 after
student’s unpaired two-tailed t-test; and **p = 0.01 after student’s
unpaired two-tailed t-test.
103
Table 3. Application Study: Fold Change Ratios for Altered Transcripts
Transcript
Mean (upper limit, lower limit)
p-values
21.58 (26.94, 17.30)
<0.0001
CLU
0.37 (0.51, 0.27)
0.0032
SIL1
2.44 (3.50, 1.70)
0.014
FANK1
2.50 (2.92, 2.15)
0.016
ABI2
2.69 (3.96, 1.83)
0.020
BAG1
2.29 (3.35, 1.56)
0.026
MFAP3L
0.47 (0.65, 0.33)
0.039
IFT81
0.46 (0.63, 0.34)
0.047
PTGDS
2.26 (3.51, 1.46)
0.058
BCL2L14
1.95 (2.71, 1.40)
0.058
PIM1
Note: The upper and lower fold change ratio limits were generated using the
-CT ± SE
formula 2
tailed t-test.
. p-values were generated using Student’s unpaired two-
104
Discussion
The present study demonstrated the utility of toxicogenomic approaches
to develop sperm biomarkers of testicular injury. The transcript changes initially
detected by microarray analysis were confirmed and expanded upon using qRTPCR arrays in two additional SC toxicant exposure paradigms. This data builds
upon our existing knowledge of testicular cell-type specific toxicity in animal
models and develops the foundation required to extrapolate these observations
from animal experiments to human samples. This has valuable implications for
biological
monitoring
of
male
reproductive
effects
in
therapeutically,
occupationally, and environmentally exposed men. In this scenario, sperm
molecular profiles from these men can be directly compared to sperm isolated
from animals with paralleled exposures.
A time course experiment was performed to validate the alterations in
sperm transcripts and to examine the time dependence of the HD effect. Over
the time course of HD exposure and post-exposure recovery, we observed
dynamic changes in sperm transcript content. Significant differences in steady
state transcript levels for 12 of the 29 candidates were identified at various time
points, with the most robust response occurring in the post-exposure recovery.
This was an unexpected finding because the microarray data suggested that the
phenotypic and transcriptomic indicators of injury resolved 3 months after
exposure cessation. The inconsistency between the microarray and qRT-PCR
recovery data is most likely due to the sensitivity of the platforms, with the qRT-
105
PCR arrays being far more robust than the microarray chips [21, 22]
(Supplemental Table 5).
Heirarchical clustering suggested a possible time-dependence for the
transcript changes. These alterations may be due to the targeting of specific
signaling mechanisms to distinct GC populations during spermatogenesis, with
early changing transcripts disrupted in spermatids or spermatoyctes and late
changing transcripts altered in spermatogonia. It is also possible that the
epididymal epithelial cells or SCs are transferring transcripts to the sperm
directly, and HD exposure affects these processes. It has been previously
hypothesized that mRNA can be taken up by sperm released by the testis and
that CLU may be one of these transcripts [5]. Future work will investigate whether
the epididymal epithelial cells or SCs directly provide the developing sperm with
transcripts, or if the GC alter their own transcript content in response to
environmental changes.
Additional experiments were performed to determine whether the changes
that occurred with HD exposure were found with another SC toxicant, CBZ. No
changes in body or reproductive weights after the low dose sub-chronic exposure
to CBZ were observed, but histological (an increase in RSH) and serum analyses
(a decrease in inhibin B) provided evidence of subtle testicular injury. The HD
transcript panel was only partially predictive of testicular injury resulting from CBZ
exposure, with 10 of the 29 transcripts altered.
106
Although HD and CBZ both target the same cell-type within the testis, the
qRT-PCR data identified toxicant specific alterations in the sperm transcriptome
(Figure 5). This result is most likely due to the opposing actions these two
toxicants have on the SC microtubules, and it highlights the qRT-PCR array
panel’s capabilities to discern the transcriptional signature of different toxicants.
HD promotes and stabilizes microtubule assembly by cross-linking tubulin, while
CBZ inhibits microtubule assembly and decreases it’s stability by binding the tubulin subunit of the -tubulin heterodimer [7]. Even though the toxicant
mechanisms of actions differ, they ultimately produce similar phenotypic
alterations in the seminiferous epithelium, including sloughing and RSH [7]. In
this study, RSH were increased 14.53 fold by HD and 3.43 fold by CBZ. Our
results suggest that RSH is an appropriate phenotypic anchor for determining SC
toxicity due to low dose sub-chronic exposures. However, the comparison of the
RSH for the two toxicants highlights the fact that the doses we selected for each
toxicant were not equipotent. The differences in the severity of the injury may
also explain why the two toxicants produced dissimilar transcript profiles. Lowdose HD extrapolation experiments are now underway in our laboratory to test
this hypothesis.
107
Figure 5. Venn Diagram Highlighting Transcript Profiles for HD and CBZ
A comparison of the sperm molecular profiles for HD and CBZ identified 4
transcripts overlapping between the exposures; however, the fold changes were
in opposing directions. The direction of the arrowhead is indicative of the
direction of change of the transcript by that toxicant: up = upregulated; and down
= downregulated.
108
Exposure to low dose HD induced alterations in sperm transcripts
associated with stress response and apoptosis inhibition, possibly as an adaptive
mechanism to maintain the developing GC. For example, SOD3 is an
extracellular superoxide dismutase responsible for the removal of reactive
oxygen species, and recent studies have suggested that SOD3 has an important
role in regulating cellular signaling networks that reduce the development of
injury and apoptosis [23]. In addition, DNAJB4 is a member of the DNAJ/HSP40
protein family and DNAJs prepare the HSPA folding machine by supplying the
substrate and ultimately determining the specificity of the HSPA chaperone [24].
This hypothesis is consistent with our histopathology results that showed no
changes in GC apoptosis after the 3-month exposure to HD.
Low dose CBZ exposure altered sperm transcripts associated with cell
junctions and apoptosis. At higher doses, CBZ induces GC apoptosis in the testis
and increases sloughing, which is the premature release of the GC from the SC
crypts. ABI2 is important for dynamic actin cytoskeleton remodeling at adherens
junctions, which are important for SC-GC interactions including the movement of
GC from the basement membrane to the lumen of the seminiferous tubules
during spermatogenesis [25, 26]. BAG1 plays many roles in promoting cell
survival and can interact with proteosomes and heat shock proteins to prevent
cell death [27]. Our data showed an induction of these two transcripts and this
suggests that the SCs are protecting the GC from injury and apoptosis.
The qRT-PCR studies identified 4 transcripts (CLU, PTGDS, SIL1, and
IFT81) that overlapped between HD and CBZ, with functions important for stress
109
response and spermatogenesis. CLU is a glycoprotein involved in many
biological processes, including protecting cells from injury, mediating apoptosis,
and influencing the differentiation and maturation of GC [28]. CLU mRNA has
previously been detected by qRT-PCR in porcine spermatozoa, and sperm are
hypothesized to deliver the CLU mRNA transcript to the oocyte to support
subsequent development [29, 30]. PTGDS catalyzes conversion of prostaglandin
H2 to prostaglandin D2, a major prostaglandin that regulates many bodily
functions including sleep, body temperature, hormone release, and odor
responses [31]. PTGDS mRNA have been measured in SC and GC, and are also
in the epididymis [31], where the protein product is important component in the
seminal fluid and may have a role in fertilization [32, 33]. SIL1 is an adenine
nucleotide exchange factor for the heat-shock protein 70 member HSPA5/BiP
[34, 35]. Defects in this gene have been implicated in Marinesco–Sjögren
syndrome, which commonly presents with hypogonadism [35]. Heat-shock
proteins are important for proper gametogenesis and embryogenesis, and it has
been previously hypothesized that an induction of heat-shock expression due to
environmental factors is probably at the benefit to the embryo but loss of
expression could be detrimental [36]. IFT81, also known as CDV-1, is necessary
for the assembly and maintenance of the eukaryotic cilia and flagella [37]. CDV1R mRNA is predominantly expressed in the testis with expression increasing
with male sex maturation and onset of spermatogenesis [38]. In addition, CDV1R mRNA has been localized to the epididymis and may play an important role in
sperm maturation [39]. Interestingly, the transcript direction of change for all 4
110
transcripts differed between the two toxicants, and this may be due to the
opposing actions of HD and CBZ on the microtubules within the SC. Future
research in our laboratory will further characterize these mRNA biomarkers using
comprehensive dose response studies and test additional testicular toxicants.
Our data demonstrates that 18 out of 29 sperm transcripts identified by
microarrays as indicators of testicular toxicity were verified by subsequent qRTPCR array analysis. We were unable to observe statistically significant changes
for the other 11 sperm transcripts identified by microarray, and this may be due
to the smaller sample size for the qRT-PCR studies (n = 4-9 compared to the
~18-20 used in the Preliminary Study). In addition, it is possible that these targets
may have been good candidates that had transient changes in expression, but
were missed at the time points selected for necropsy.
In summary, sub-chronic low dose exposure to SC toxicants produced
alterations to the cauda epididymal sperm transcriptome in the rat. These novel
findings indicate that sperm mRNA content is an informative biomarker of
testicular injury at low doses, especially as these alterations persisted for months
after exposure cessation. Moreover, these selected biomarkers are promising
tools for screening and categorizing additional Sertoli cell toxicants based on
their molecular sperm signatures.
111
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115
Supplemental Table 1. Candidate Transcripts Selected from Microarray
for qRT-PCR
Gene
Refseq #
Gene Ontology
NM_199376
binding; unfolded protein binding
SIL1
NM_001013047
hydrolase activity; intermediate filament
MTM1
binding; phosphatase activity
NM_001025659
unknown
LRRC6
NM_001037788
hydrolase activity; protein
STYXL1
tyrosine/serine/threonine phosphatase
activity; intracellular signal transduction
NM_024129
collagen binding; extracellular matrix
DCN
binding; glycosaminoglycan binding;
protein N-terminus binding
XM_574454
apoptotic process; cell cycle; cell cycle
GAS2
arrest; cellular component
XM_217868
unknown
TCP10B
NM_001012049
integral to membrane; plasma membrne
MFAP3L
NM_001024338
protein kinase binding; regulation of
BCL2L14
apoptosis
NM_017034
cell cycle; cell proliferation
PIM1
XM_001055887
unknown
LRRC69
NM_012771
hydrolase activity; lysozyme activity
LYZ2
XM_220877
phosphatase activity
PHOSPHO1
NM_001004199
kinase binding; anti-apoptosis
TAX1BP1
XM_231184
SH3 domain binding; synaptic vesicle
DENND1A
endocytosis
NM_001013076
heat shock protein binding; protein
DNAJB4
folding
NM_053021
misfolded protein binding; antiCLU
apoptosis; cellular response to
transcription factor stimulus
XM_216377
chaperone binding; anti-apoptosis
BAG1
NM_013015
fatty acid biosynthesis; prostaglandin
PTGDS
biosynthesis
NM_001013125
metal ion binding; anti-apoptosis
BFAR
NM_022922
isomerase activity; carbohydrate
TPI1
metabolic process
NM_001008347
unknown
FANK1
NM_012880
metal
ion
binding;
superoxide dismutase
SOD3
activity
NM_031140
intermediate filament organization
VIM
NM_199120
cell differentiation; spermatogenesis
IFT81
NM_053416
RNA binding; cell differentiation; cell
STRBP
component movement;
spermatogenesis
116
TBC1D5
SCLT1
XM_576503
NM_153740
ABI2
NM_173143
Rab GTPase activator activity
clathrin binding; sodium channel
regulator activity
SH3 domain binding; cellular
component movement; cell migration
117
Supplemental Table 2. Preliminary Study: Transcripts Altered in
Sperm after 2,5-Hexanedione Exposure via Microarray Analysis
Transcript
p-value
q-value
Fold Change
0.000000085
0.0012
2.64
RGD1304694
0.00000052
0.0025
2.37
MTM1
0.0000016
0.0040
2.29
SIL1
0.000002
0.0045
2.13
LOC685184
0.000013
0.011
2.09
DCN
0.000019
0.014
2.07
STYXL1
0.0000044
0.0059
2.02
GAS2
0.0000094
0.0095
1.97
LRRC6
0.000023
0.015
1.95
MFAP3L
0.000004
0.0059
1.94
TCP10B
0.00013
0.036
1.93
LYZ2
0.000024
0.015
1.91
BCL2L14
0.000031
0.018
1.90
PIM1
0.000052
0.025
1.89
LRRC69
0.00007
0.028
1.87
DENND1A
0.000019
0.014
1.85
PHOSPHO1
0.0000044
0.0059
1.85
RGD1560258
0.0000017
0.0041
1.84
RGD1566314
0.00008
0.030
1.81
TAX1BP1
0.000091
0.030
1.80
BFAR
0.000068
0.027
1.80
RGD1563680
0.000028
0.016
1.80
ZFP407
0.00014
0.038
1.78
DNAJB4
0.000078
0.029
1.77
RGD1564140
0.00013
0.038
1.74
CCDC46
0.00015
0.040
1.73
RGD1308023
0.000066
0.027
1.70
TPI1
0.000076
0.029
1.69
TBC1D5
0.000016
0.012
1.68
SCLT1
0.000056
0.026
1.67
IFT81
0.000024
0.015
1.65
POU2F1
0.00000077
0.0025
1.63
SLC10A7
0.0000044
0.0059
1.62
ERC1
0.000092
0.030
1.62
UBN2
0.00000034
0.0025
1.62
S100Z
0.00022
0.049
1.61
LOC688916
0.00000017
0.0017
1.61
SPPL3
0.000018
0.014
1.61
LOC313149
0.00011
0.035
1.60
ABI2
0.000093
0.030
1.59
TMEM192
0.000089
0.030
1.58
RGD1309931
0.000064
0.027
1.57
LOC498330
118
LOC500959
TPI1
FCER1G
SLC10A7
LOC681849
PLEKHK1
KIF6
CTXN1
RGD1307526
LOC500700
LOC307974
DNAJC15
ELOF1
CD4
PPP1R9A
WDR7
P2RY5
ARMC4
GIMAP4
LOC311026
ACER1
RGD1308023
RGD1561537
CCR2
CLEC7A
RGD1305469
SPATS1
RGD1564887
RGD1308023
KCNG1
EAF2
TMEFF1
FMN1
ALG9
NOL4
ARMC2
EFCAB5
HSF2BP
PTAR1
ZNF532
GPATC2
PTPRK
SPSB3
CX3CR1
C3
0.000071
0.000025
0.00014
0.00019
0.0000090
0.00021
0.00012
0.000000069
0.000063
0.00020
0.0000035
0.00015
0.00019
0.00012
0.000087
0.00000049
0.000026
0.000064
0.000039
0.0000072
0.0000026
0.000089
0.000041
0.000024
0.00021
0.000023
0.000082
0.000058
0.0000099
0.000012
0.000063
0.000099
0.00014
0.000012
0.000061
0.000043
0.00012
0.00012
0.0000039
0.0000080
0.000092
0.000094
0.000031
0.0000044
0.0000050
0.028
0.016
0.038
0.047
0.0094
0.049
0.035
0.0012
0.027
0.048
0.0059
0.038
0.047
0.035
0.030
0.0025
0.016
0.027
0.021
0.0080
0.005
0.030
0.022
0.015
0.048
0.015
0.030
0.026
0.0096
0.010
0.027
0.032
0.038
0.010
0.027
0.022
0.035
0.036
0.0059
0.0086
0.030
0.030
0.018
0.0059
0.0063
119
1.57
1.57
1.57
1.55
1.54
1.54
1.54
1.52
1.52
1.50
1.50
1.48
1.48
1.47
1.46
1.45
1.42
1.42
1.39
1.39
1.39
1.39
1.37
1.37
1.37
1.36
1.36
1.35
1.35
1.35
1.34
1.33
1.32
1.31
1.30
1.30
1.30
1.29
1.29
1.28
1.28
1.27
1.27
1.27
1.26
RGD1560273
CACNG7
AEBP2
EYA1
PCCA
RPS6KA5
KLF12
NARS2
GBP5
NEK4
LOC687517
ERCC5
RIMS2
SNAPC1
RGD1564943
AXL
GLI2
TMEM116
RHOBTB2
STMN1
UQCRC2
HDGFL1
SH2D4A
ALOX15B
CLU
0.00020
0.000011
0.00019
0.00011
0.00013
0.0000013
0.000043
0.000014
0.00019
0.00015
0.00021
0.00000075
0.000039
0.00010
0.000082
0.000055
0.00019
0.00012
0.000088
0.00015
0.00015
0.000050
0.000038
0.000021
0.000061
0.048
0.010
0.047
0.035
0.037
0.0037
0.022
0.011
0.047
0.040
0.049
0.0025
0.021
0.033
0.030
0.026
0.047
0.035
0.030
0.038
0.038
0.024
0.021
0.015
0.027
120
1.26
1.25
1.24
1.23
1.21
1.21
1.21
1.21
1.20
1.20
1.20
1.20
1.19
1.18
1.17
1.17
1.16
1.15
1.14
-1.17
-1.18
-1.25
-1.27
-1.41
-2.11
Supplemental Table 3. Time Course Study: Average Weights and Apical Endpoints
0
1
2
3
3+1
Body (g)
237.2  1.70
*
1.44  0.02
358.5  33.52
262.0  5.58
***
1.32  0.08
350.6  13.87
279.7  4.30
***
1.35  0.06
356.8  12.49
303.7  5.53
***
1.44  0.04
386.5  12.90
336.2  1.99
***
1.52  0.02
463.4  10.13**
3+2
3+3
367.3  9.55
***
1.50  0.03
470.6 14.24
***
377.5  11.49
***
1.40  0.10
458.5  25.67
**
121
Testis (g)
Epididymis
(mg)
1.71  0.21
2.10  0.31
1.46  0.32
1.41  0.19
1.90  0.27
2.53  0.21
1.97  0.25
HRSH (x108)
50.55  1.21
43.50  7.03
50.30  7.89
54.05  10.17
54.28  6.31
69.52  7.93
58.48  11.85
Inhibin B
(pg/mL)
Note: Data presented as mean  SE; and HRSH = Automated homogenization resistant spermatid head counts;
*, p < 0.05 when compared to time point 0 via one- way ANOVA with Dunnett’s multiple comparison test
**, p < 0.01 when compared to time point 0 via one- way ANOVA with Dunnett’s multiple comparison test
***, p < 0.001 when compared to time point 0 via one- way ANOVA with Dunnett’s multiple comparison test.
121
Supplemental Table 4. Time Course Study: Fold Change Ratios for Transcripts Significantly Changed after 2,5Hexanedione Exposure
1
2
3
3+1
3+2
3+3
0.05***
0.69
0.38
0.69
0.04***
0.07***
CLU
PTDGS
SOD3
IFT81
TBC1D5
SCLT1
LRRC6
122
SIL1
STRBP
LRRC69
LYZ2
DNAJB4
(0.09, 0.03)
6.88***
(8.21, 5.76)
2.61
(3.78, 1.80)
3.52
(5.46, 2.27)
4.59*
(8.61, 2.45)
2.08
(3.36, 1.29)
2.46
(3.82, 1.59)
2.17
(4.68, 1.00)
2.00
(2.90, 1.38)
1.77
(4.05, 0.78)
0.34
(0.45, 0.26)
2.03
(2.85, 1.45)
(0.79, 0.60)
9.47***
(11.88, 7.55)
11.98**
(16.75, 8.57)
2.71
(3.32, 2.22)
3.78
(5.29, 2.70)
1.85
(2.17, 1.57)
1.74
(2.30, 1.31)
1.18
(1.46, 0.96)
1.75
(1.96, 1.55)
2.23
(2.97, 1.68)
0.35
(0.41, 0.31)
2.16
(2.54, 1.84)
(0.47, 0.31)
8.24***
(11.22, 6.05)
11.16**
(13.67, 9.12)
6.02**
(8.09, 4.47)
7.86**
(10.01, 6.17)
3.57*
(4.56, 2.97)
4.57*
(5.78, 3.61)
3.35
(4.83, 2.33)
2.51
(2.99, 2.10)
3.34
(4.70, 2.38)
0.37
(0.47, 0.29)
3.22
(4.10, 2.52)
(0.88, 0.54)
14.82***
(16.69, 13.16)
17.64***
(27.95, 11.14)
11.94***
(13.60, 10.49)
12.48***
(14.06, 11.07)
8.60***
(10.45, 7.08)
8.96**
(10.34, 7.76)
7.41*
(8.81, 6.23)
6.35***
(7.75, 5.20)
6.07*
(7.08, 5.21)
0.39
(0.48, 0.32)
4.87**
(6.18, 3.83)
(0.06, 0.03)
11.17***
(13.61, 9.17)
8.75*
(11.41, 6.71)
11.70***
(13.96, 9.81)
13.19***
(15.90, 10.95)
8.32***
(10.07, 6.87)
9.71**
(11.43, 8.25)
11.36 **
(13.41, 9.62)
6.03***
(7.33, 4.96)
7.25*
(9.06, 5.80)
0.45
(0.51, 0.40)
3.94*
(5.05, 3.08)
(0.09, 0.05)
11.95***
(13.97, 10.21)
5.69
(11.07, 2.93)
7.22**
(11.70, 4.45)
8.81**
(10.34, 7.50)
6.20**
(8.34, 4.61)
4.08
(8.01, 2.08)
6.81*
(10.22, 4.54)
4.39**
(6.23, 3.10)
4.56
(6.50, 3.20)
0.21*
(0.42, 0.11)
2.28
(3.73, 1.39)
Note: Data is presented as mean (upper limit, lower limit) fold change ratios. The upper and lower fold change ratio limits
-CT ± SE
. CT values were compared to time point 0 using by one-way ANOVA with
were generated using the formula 2
Dunnett’s correction for multiple comparisons: ***, p <0.001; **, p < 0.01; and *, p <0.05.
122
Supplemental Table 5. Time Course Study: Fold Change Comparisons After
3 Months of 2,5-Hexanedione Exposure
Transcripts
qRT-PCR
Array
3.35 *, ††
2.29
SIL1
-1.05
2.37
MTM1
4.57 *, †
1.97
LRRC6
2.85 ††
2.07
STYXL1
-2.07
2.09
DCN
-2.74
2.02
GAS2
2.12 ††
1.94
TCP10B
1.78
1.95
MFAP3L
1.72
1.91
BCL2L14
1.31
1.90
PIM1
3.34 †
1.89
LRRC69
-2.70 ††
1.93
LYZ2
-1.12
1.85
PHOSPHO1
-1.02
1.81
TAX1BP1
-1.16
1.87
DENND1A
3.22 *, †
1.78
DNAJB4
-2.63 *, †
-2.11
CLU
1.88
1.73
BAG1
8.24 **, †
1.73
PTGDS
-1.64
1.80
BFAR
1.68
1.70
TPI1
1.68
1.66
FANK1
11.16 **, †
1.56
SOD3
-1.53
1.64
VIM
6.02 **, †
1.67
IFT81
2.51 *, †
1.65
STRBP
7.86 **, †
1.69
TBC1D5
3.57 *, †
1.68
SCLT1
1.07
1.60
ABI2
Note:
* , p < 0.05 at any time point when compared to 0 via one-way ANOVA and
Dunnett’s correction for multiple comparisons;
**, p <0.05 at 3 months when compared to 0 via one-way ANOVA and Dunnett’s
correction for multiple comparisons
†, p < 0.05 using student’s unpaired two-tailed t-test comparing 0 and 3
††, p < 0.09 using student’s unpaired two-tailed t-test comparing 0 and 3.
123
CHAPTER 4. RAT SPERM MICRORNA PROFILING IDENTIFIES MICRORNAS
IMPORTANT FOR REPRODUCTIVE FUNCTION AND EMBRYONIC
DEVELOPMENT
124
CHAPTER 4. RAT SPERM MICRORNA PROFILING IDENTIFIES MICRORNAS
IMPORTANT FOR REPRODUCTIVE FUNCTION AND EMBRYONIC
DEVELOPMENT
Sara E. Pacheco1, Edward Dere1,2, and Kim Boekelheide1,
1
Department of Pathology & Laboratory Medicine, Brown University, Providence,
RI
2
Division of Urology, Rhode Island Hospital, Providence, RI
Declaration of authors’ roles
SEP performed the animal work, array analyses, and functional analyses.
ED executed the miRNA genomic characterization analyses, including the
chromosome locations, base pair frequencies, and miRNA downstream targets.
Both contributed to the species comparison and to the design, analysis, and
writing of the manuscript. KB contributed to study design and data analysis.
Everyone approved the final version to be published.
125
Abstract
MicroRNAs (miRNAs) are small non-coding RNAs with essential roles in
numerous biological processes and aberrant miRNA expression has been
implicated in various disease states. Previous studies have demonstrated that
miRNAs are required for proper spermatogenesis. However, the characterization
of miRNAs present in mammalian sperm has been limited to date, with some
data available for humans, mice, and pigs. The goal of this work was to identify
the sperm miRNA profile for rats, a widely used animal model. Utilizing two highthroughput array platforms, Affymetrix GeneChips and SABiosciences’ PCR
arrays, we identified 90 miRNAs present in sperm isolated from Fischer 344 rats
(n = 36). Functional analyses of the miRNAs found that they have important roles
in regulating reproductive function and embryonic development. These results
provide a foundation under normal spermatogenic conditions for examining and
understanding the role of miRNA content during disrupted spermatogenesis and
infertility.
126
Introduction
MicroRNAs (miRNAs) are ~22 nucleotide single stranded, non-coding
RNAs that are involved in regulating gene expression through sequence-specific
base pairing with target gene transcripts [1] and have critical regulatory roles in a
variety of disease states [2-5]. In animals, miRNAs are phylogenetically
conserved and primarily inhibit transcript translation by binding in the 3´
untranslated region (UTR) sequences of targeted gene transcripts [1, 6, 7].
However, there is growing evidence that miRNAs are also able to regulate posttranscriptionally via complementary binding in the 5´ UTRs [8-10] and coding
regions of target gene transcripts [11, 12]. Recent studies have also
demonstrated that miRNAs can bind at promoter sequences to regulate target
gene expression at the transcriptional level [13, 14]. miRNAs are expressed
endogenously within cells and it has been hypothesized that every animal cell
type at each developmental stage may carry a specific miRNA profile to regulate
its transcriptome [1].
The differentiation program of male germ cells requires multiple finely
tuned levels of gene regulation (as reviewed in [15]). During spermiogenesis,
mRNAs are abundant within round spermatids prior to transcriptional arrest [15]
and chromatin compaction. These transcripts are stored and later encode
important sperm proteins that are translated during the later stages of
spermiogenesis [16, 17]. Stored mRNAs have potentially critical roles in haploid
mammalian germ cells where abundant transcripts are present in free
ribonucleoprotein particles awaiting translational activation [17, 18].
127
It has been previously shown that miRNAs have critical roles during
spermatogenesis. Loss of miRNAs in the testes due to conditional knockdowns
of the miRNA machinery in both germ cells and Sertoli cells has resulted in
aberrant effects on spermatogenesis. Germ cells lacking Dicer, a protein involved
in the maturation of miRNA from precursor miRNA, have reduced miRNA levels
resulting in late-onset adult subfertility and infertility due to defects in both
spermatogonial proliferation/differentation and aberrant transitions from round to
elongating spermatids [19, 20]. Additionally, loss of Dicer in Sertoli cells results in
a complete depletion of spermatozoa and ultimately culminates in testicular
degeneration [21]. These studies clearly indicate the critical importance of
miRNAs in the normal development of spermatozoa.
In addition to miRNA expression in the testes, they are also found in
mature spermatozoa [22-26]. It has been proposed that sperm deliver these
small RNAs to the oocyte, and they participate in early post-fertilization
processes, inhibiting gene expression in the developing embryos [25, 27-33]. For
example, miRNAs antisense for IGF2R and DKK2 genes have been identified in
sperm and those genes are implicated in the regulation of growth and
development [30, 34-36]. In addition, the injection of miRNAs specific for KIT or
SOX9 into fertilized mouse embryos induced paramutation, a heritable mutant
phenotype in the offspring [29, 37]. Comparison of miRNAs levels in the sperm
with those of unfertilized oocytes found that sperm have relatively low amounts of
miRNA and, therefore, their contribution at fertilization may be limited [22, 25].
However, some miRNAs appear to be sperm-specific, including hsa-miR-34c,
128
hsa-miR-375, hsa-miR-252, and hsa-miR-25 [25], indicating that there is a
possible role for these sperm-specific miRNAs during fertilization.
To further delineate the role of sperm miRNAs, it is important to first
identify miRNAs that are present in the sperm. The goal of this study was to
generate the naïve sperm miRNA profiles in Fischer 344 rats using two highthroughput approaches and determine their functional significance by examining
their gene targets. Furthermore, these miRNA signatures were compared to
previously published reports describing other mammalian sperm profiles [22-26]
to identify species-conserved miRNAs.
Materials and Methods
Animals
Adult male Fischer 344 rats weighing 175-225 grams (Charles River
Laboratories, Wilmington, MA) were maintained in a temperature and humidity
controlled vivarium with a 12 hour alternating light-dark cycle. All rats were
housed in community cages with free access to water and Purina Rodent Chow
5001 (Farmer’s Exchange, Framingham, MA). The Brown University Institutional
Animal Care and Use Committee approved all experimental animal protocols in
compliance with National Institute of Health guidelines.
129
Sperm Extraction and RNA Isolation
The cauda epididymides were punctured repeatedly with 30 and 26 gauge
needles and put into micro-centrifuge tubes containing phosphate buffered saline
(1X PBS, GIBCO REF 10010-023, Life Technologies, Grand Island, NY). The
samples were incubated in a water bath at 37C for 10 minutes to aid sperm
diffusion from the tissue. Following centrifugation for 3 minutes at 300 x g to
pellet the epididymal pieces, the supernatant was removed and centrifuged for 5
minutes at 2000 x g to pellet the sperm. Lysis buffer (0.15 M ammonium chloride,
10 mM potassium bicarbonate, 0.1 mM EDTA (Thermo Fischer Scientific Inc.,
Pittsburgh, PA)) was added to the sperm pellet, incubated on the bench top for
30 seconds and centrifuged at 16,100 x g for 1 minute. The pellet was washed
with PBS and centrifuged again at 16,100 x g for 1 minute. miRNA was extracted
from the fresh sperm using the mirVana miRNA Isolation Kit (Applied
Biosystems/Ambion, Austin, TX).
Affymetrix Array Platform
Sperm miRNAs (200 ng/sample) from the 12 randomly selected rats were
labeled with biotin using the Genisphere FlashTag Biotin HSR RNA labeling Kit
for Affymetrix GeneChip miRNA Arrays (Affymetrix, Santa Clara, CA, USA). The
Enzyme Linked Oligosorbent Assay was used to confirm that the miRNA was
labeled properly and was performed following manufacturer’s instructions
(Affymetrix). The Brown Genomics Core Facility hybridized the labeled miRNA to
the GeneChips, providing a complete measurement of over 6,703 miRNA
130
sequences (71 organisms) from the Sanger miRNA database (version 11) and an
additional 922 sequences of Human snoRNA and scaRNA from the Ensembl
database and snoRNABase. The miRNA QC Tool (Affymetrix, version 1.0.33.0)
was used for the array data summarization, normalization, and quality control for
GeneChip miRNA Arrays following the workflow recommended by Genisphere.
The miRNA QC Tool exported a file containing the expression value for each
transcript on each array (.CEL file), the p-value calculated for each transcript,
and whether the transcript was detected on the array (TRUE/FALSE).
Our
analysis focused on the rat miRNAs only, and miRNAs were considered present
in rat sperm when they were listed as TRUE for all 12 samples.
SABiosciences Rat miRNome RT2 miRNA PCR Array
Sperm miRNAs (200 ng/sample) from an additional 24 rats were used on
the SABiosciences Rat miRNome RT2 miRNA PCR array (SABiosciences, a
Qiagen Company, Frederick, MD) which profiles the expression of the 370 most
abundantly expressed and best characterized microRNA sequences in the rat
genome as annotated by the Sanger miRBase Release 14. The PCR reactions
were run following the manufacturer’s instructions using an ABI 7900 HT
thermocycler (Applied Biosciences, Life Technologies Corporation, Carlsbad,
CA). Raw CT values were averaged for each miRNA for all samples and miRNAs
with CT values < 35 were considered present in rat sperm.
131
miRNA Sequence Analysis
The entire set of mature rat miRNA sequences were retrieved from the
mirBase database (version 18) [38]. The sequences were scanned and used to
calculate the individual and dinucleotide base pair frequencies for the rat miRNAs
present in the sperm and throughout the entire genome.
Positional Analyses of miRNA Precursors and Validated Gene Targets
The genomic precursor miRNA and validated target gene positions were
obtained from the mirBase database (version 18) [38] and UCSC Genome
Browser (rn4) [39], respectively. Validated target transcripts were determined
based on information from mirBase derived from either miRTarBase [40] or
TarBase [41]. The positions of the precursor miRNAs and gene targets were
mapped on to a circular chromosome ideogram using Circos [42].
Functional Analyses of miRNA Gene Targets
Functional analyses of miRNA data was performed using Ingenuity
Pathways Analysis (IPA) (Ingenuity® Systems, www.ingenuity.com). The IPA
Functional Analysis program identified the biological functions and/or diseases
that were significantly associated with the sperm miRNA transcripts. Only
significant transcripts meeting the CT value cutoff of < 35 cycles that were
associated with biological functions and/or diseases in Ingenuity's Knowledge
Base were considered for the analysis. Right-tailed Fisher's exact test was used
132
to calculate a p-value determining the probability that each biological function
and/or disease assigned to the sperm transcripts was due to chance. These pvalues were adjusted using the Benjamini-Hochberg correction and the high-level
functional categories with adjusted p-values <0.05 were considered significant.
Genes targeted by multiple sperm miRNAs were also analyzed using IPA
core analysis to identify networks and pathways that were significantly enriched.
Right-tailed Fisher's exact test was used to calculate a p-value determining the
probability that each pathway assigned to the transcript targets was due to
chance. These p-values were adjusted using the Benjamini-Hochberg correction
and the high-level pathways with adjusted p-values <0.05 were considered
significant.
Rat and Mouse miRNA Comparison
For the mouse comparison, CT values from 3 sperm replicates listed in
the supplemental dataset from Liu et al. were averaged together and CT values <
35 were considered present [26]. The lists of mature rodent sperm miRNAs were
consolidated using the IPA software to highlight the specific mammalian miRNA
families present in sperm, and one mouse miRNA (mmu-miR-464) was excluded
from the consolidation process because it was not recognized by the software
(Supplemental Table 1). Two miRNA families (miR-678 and miR-409-3p) were
eliminated from the analysis because of their rat specificity. Subsequently, these
133
list of mouse and rat miRNA families were directly compared to identify
functionally conserved epigenetic regulators using IPA.
Results
The Affymetrix GeneChip miRNA array was used for 12 rats and the
Affymetrix miRNA QC tool identified 20 miRNAs consistently detected in all of the
sperm samples (Supplemental Table 2). SABiosciences Rat miRNome miRNA
PCR Array was used for an additional 24 rat samples and identified 90 miRNAs
present in sperm (average raw CT values < 35) (Supplemental Table 3), with
only 16 of the 20 miRNAs identified by the Affymetrix platform confirmed. rno-mir337, rno-mir-let7b*, and rno-mir-881 were not detected using the PCR array and
was considered to be a false positive while rno-mir-494 was not on the PCR
array. Comparison of the individual and dinucleotide base pair frequencies of the
sperm miRNA sequences with the entire set of rat miRNAs found minimal
differences (Supplemental Table 4).
The genomic locations of the precursors for the miRNAs present in the
sperm were retrieved from the miRBase database [38] and analyzed for
positional hotspots or clusters within the genome. Their genomic positions failed
to identify clusters of miRNAs when plotted on a chromosomal ideogram (Figure
1; circles), consistent with previous studies, which illustrated that human miRNAs
are isolated and not clustered together [43]. Interestingly, no miRNAs originated
from chromosomes 14 or 16; there is currently no available sequence for
134
chromosome Y in the latest rat genome assembly (rn4). Further analysis of the
sperm miRNAs found that 13 of the 90 miRNAs have multiple genomic
precursors (Table 1). For example, the mature sequence for rno-let-7a can be
derived from two different genomic sequences found on chromosomes 1 and 17.
One miRNA, rno-miR-124*, corresponded to genomic sequences found on
chromosomes 2, 3 and 15. These miRNAs with multiple genomic origins may
provide a redundant mechanism to ensure downstream epigenetic regulation.
The gene targets of the identified sperm miRNAs were also mapped to
their genomic locations using the UCSC Genome Browser database [39] in order
to identify hotspots of targeted genes. Figure 1 depicts the 51 miRNA that
possess validated gene targets as determined by either the miRTarBase [40] or
TarBase [41] databases, connected to their targets (arrowheads); the remaining
39 miRNAs currently do not have any validated targets. As with the distribution of
precursor miRNA locations across the genome, the genomic locations of the
gene targets are widespread, except that none are present on chromosomes 9
and 16. Examination of each target gene transcript found that 19 of the 51
transcripts are targeted by multiple miRNAs (Table 2), suggesting that there are
redundant or fail-safe mechanisms to ensure the epigenetic regulation of those
targeted genes. The reciprocal relationship found that 19 miRNAs target more
than one gene (Table 3), illustrating the potential of these miRNAs to
epigenetically regulate multiple biological pathways simultaneously.
135
Table 1. Sperm miRNA with Multiple Precursors
miRNA
Genomic Location of Precursor miRNA
rno-let-7a
chr8: 44525769 – 44525864
chr17: 22119767 – 22119860
rno-let-7c
chr7: 123700790 – 123700884
chr11: 16398264 – 16398357
rno-let-7f
chr17: 22120135 – 22120223
chrX: 41376943 – 41377025
rno-miR-7a
chr1: 134607264 – 134607358
chr17: 12204082 – 12204178
rno-miR-17-5p
chr15: 99853735 – 99853818
chrX: 139741444 – 139741521
rno-miR-19b
chr15: 99854320 – 99854406
chrX: 139740932 – 139741027
rno-miR-24
chr17: 7350971 – 7351038
chr19: 25638430 – 25638537
rno-miR-30c
chr5: 141362120 – 141362208
chr9: 22162630 – 22162713
rno-miR-92a
chr15: 99854442 – 99854519
chrX: 139740802 – 139740893
rno-miR-103
chr3: 118996602 – 118996687
chr10: 20695027 – 20695112
rno-miR-124*
chr2: 102699466 – 102699574
chr3: 170004571 – 170004657
chr15: 43944536 – 43944620
rno-miR-125b-5p
chr8: 44570155 – 44570241
chr11: 16443666 – 16443753
rno-miR-664
chr13: 101253993 – 101254051
chr18: 47881354 – 47881412
136
Figure 1. Chromosomal Ideogram
Chromosomal ideogram with the locations of the precursor miRNA sequences
and validated gene targets. Each arc segment represents a chromosome in the
rat genome and precursor miRNA positions (circles) are plotted on the inner
edge of the arcs. miRNAs are connected to their validated gene targets
(arrowheads), where the color of the connecting line and arrowhead represents
the chromosome that the miRNA originated from.
137
Table 2. Genes Targeted by Multiple miRNAs Present in the Sperm
Average
Gene
Entrez
miRNA CT
Gene Name
miRNA
Symbol
GeneID
Value
ACVR1
AQP4
BCL2
CAPN8
CAV2
HMOX1
INSIG1
KCNJ16
MCL1
MMP9
activin A receptor, type I
79558
aquaporin 4
25293
B-cell CLL/lymphoma 2
calpain 8
24224
170808
caveolin 2
363425
heme oxygenase
(decycling) 1
24451
insulin induced gene 1
64194
potassium inwardlyrectifying channel,
subfamily J, member 16
29719
myeloid cell leukemia
sequence 1
60430
matrix metallopeptidase
9
81687
138
rno-miR-30a
33.90 ± 0.42
rno-miR-30c
29.31 ± 0.26
rno-miR-30e
33.34 ± 0.47
rno-miR130a
34.75 ± 0.36
rno-miR-99a
31.18 ± 0.28
rno-miR-100
31.74 ± 0.34
rno-miR-15b
27.88 ± 0.21
rno-miR-16
26.44 ± 0.24
rno-let-7e
30.75 ± 0.28
rno-miR-7a
28.50 ± 0.24
rno-miR-21
30.01 ± 0.26
rno-miR-34a
32.11 ± 0.24
rno-miR-132
34.05 ± 0.49
rno-miR301a
34.87 ± 0.47
rno-miR-29a
33.02 ± 0.44
rno-miR-29c
34.72 ± 0.58
rno-let-7a
29.05 ± 0.32
rno-let-7d
32.61 ± 0.26
rno-let-7e
30.75 ± 0.28
rno-miR-29a
33.02 ± 0.44
rno-miR-29c
34.72 ± 0.58
rno-let-7e
30.75 ± 0.28
rno-let-7f
34.75 ± 0.39
rno-miR-21
30.01 ± 0.26
rno-miR-29a
33.02 ± 0.44
rno-miR-29c
34.72 ± 0.58
rno-miR-132
34.05 ± 0.49
rno-miR-664
33.61 ± 0.45
NEUROD1
NOX4
SEPT3
STX1A
SYT4
TAGLN
TPM1
VIM
VSNL1
neurogenic differentiation
1
29458
NADPH oxidase 4
85431
septin 3
syntaxin 1A (brain)
56003
116470
synaptotagmin IV
64440
transgelin
25123
tropomyosin 1, alpha
24851
vimentin
81818
visinin-like 1
24877
139
rno-miR-30a
33.90 ± 0.42
rno-miR-30c
29.31 ± 0.26
rno-miR-25
29.00 ± 0.20
rno-miR-92b
34.72 ± 0.40
rno-miR-23a
32.52 ± 0.25
rno-miR-23b
33.56 ± 0.50
rno-miR-24
33.18 ± 0.36
rno-miR-29a
33.02 ± 0.44
rno-miR-29c
34.72 ± 0.58
rno-miR-497
34.75 ± 0.49
rno-miR-30a
33.90 ± 0.42
rno-miR-30e
33.34 ± 0.47
rno-let-7a
29.05 ± 0.32
rno-let-7b
33.17 ± 0.53
rno-let-7e
30.75 ± 0.28
rno-miR-34a
32.11 ± 0.24
rno-miR-21
30.01 ± 0.26
rno-miR-29c
34.72 ± 0.58
rno-let-7a
29.05 ± 0.32
rno-let-7b
33.17 ± 0.53
rno-let-7c
31.48 ± 0.31
rno-let-7i
33.74 ± 0.56
rno-miR-92a
31.78 ± 0.25
rno-miR-25
29.00 ± 0.20
rno-miR-92a
31.78 ± 0.25
rno-miR-374
34.60 ± 0.44
Table 3. miRNAs in the Sperm that have Multiple Validated Gene Targets
Average
Gene
Entrez
miRNA
miRNA CT
Gene Name
Symbol
GeneID
Value
rno-let-7a
29.05 ± 0.32
rno-let-7b
30.21 ± 0.23
rno-let-7e
30.75 ± 0.28
rno-miR-7a
28.50 ± 0.24
rno-miR-18a
33.81 ± 0.40
MYC
PRMT5
rno-miR-21
30.01 ± 0.26
CAPN8
FASLG
HMOX1
TAGLN
VIM
TAGLN
VIM
CAPN8
HMOX1
KCNJ16
TAGLN
CAPN8
DHCR24
SLC17A7
KCNJ16
PDCD4
PELI1
PTEN
TIAM1
rno-miR-23a
32.52 ± 0.25
rno-miR-25
29.00 ± 0.20
rno-miR-29a
33.02 ± 0.44
rno-miR-29c
34.72 ± 0.58
rno-miR-30a
33.90 ± 0.42
TPM1
GAD1
SEPT3
TRIM63
NOX4
VSNL1
CAV2
INSIG1
MCL1
PMP22
STX1A
CAV2
INSIG1
MCL1
STX1A
TPM1
ACVR1
NEUROD1
SYT4
TP53
heme oxygenase (decycling) 1
transgelin
vimentin
transgelin
vimentin
calpain 8
heme oxygenase (decycling) 1
potassium inwardly-rectifying
channel, subfamily J, member 16
transgelin
calpain 8
24-dehydrocholesterol reductase
solute carrier family 17 (sodiumdependent inorganic phosphate
cotransporter), member 7
myelocytomatosis oncogene
protein arginine methyltransferase
5
calpain 8
Fas ligand (TNF superfamily,
member 6)
potassium inwardly-rectifying
channel, subfamily J, member 16
programmed cell death 4
pellino 1
phosphatase and tensin homolog
T-cell lymphoma invasion and
metastasis 1
tropomyosin 1, alpha
glutamate decarboxylase 1
septin 3
tripartite motif-containing 63
NADPH oxidase 4
visinin-like 1
caveolin 2
insulin induced gene 1
myeloid cell leukemia sequence 1
peripheral myelin protein 22
syntaxin 1A (brain)
caveolin 2
insulin induced gene 1
myeloid cell leukemia sequence 1
syntaxin 1A (brain)
tropomyosin 1, alpha
activin A receptor, type I
neurogenic differentiation 1
synaptotagmin IV
tumor protein p53
140
24451
25123
81818
25123
81818
170808
24451
29719
25123
170808
298298
116638
24577
364382
170808
25385
29719
64031
305549
50557
304109
24851
24379
56003
140939
85431
24877
363425
64194
60430
24660
116470
363425
64194
60430
116470
24851
79558
29458
64440
24842
rno-miR-30c
29.31 ± 0.26
rno-miR-30e
33.34 ± 0.47
rno-miR-34a
32.11 ± 0.24
ACVR1
CTGF
NEUROD1
ACVR1
SYT4
CAPN8
E2F3
GRM7
MYCN
rno-miR-92a
31.78 ± 0.25
rno-miR-99a
31.18 ± 0.28
rno-miR-132
34.05 ± 0.49
rno-miR-145
33.92 ± 0.62
rno-miR-200c
31.78 ± 0.28
NOTCH1
SIRT1
TAGLN
SLC12A5
VIM
VSNL1
AQP4
FGF16
CAPN8
MECP2
MMP9
ARHGAP32
KLF5
SOD2
ZEB1
ZEB2
activin A receptor, type I
connective tissue growth factor
neurogenic differentiation 1
activin A receptor, type I
synaptotagmin IV
calpain 8
E2F transcription factor 3
glutamate receptor, metabotropic
7
v-myc myelocytomatosis viral
related oncogene, neuroblastoma
derived (avian)
notch 1
sirtuin 1
transgelin
solute carrier family 12
(potassium-chloride transporter),
member 5
vimentin
visinin-like 1
aquaporin 4
fibroblast growth factor 16
calpain 8
methyl CpG binding protein 2
matrix metallopeptidase 9
Rho GTPase activating protein 32
Kruppel-like factor 5
superoxide dismutase 2,
mitochondrial
zinc finger E-box binding
homeobox 1
zinc finger E-box binding
homeobox 2
141
79558
64032
29458
79558
64440
170808
291105
81672
298894
25496
309757
25123
171373
81818
24877
25293
60464
170808
29386
81687
315530
84410
24787
25705
311071
The list of the 90 rat miRNAs was analyzed using IPA, which removed
duplicate miRNA family members and condensed the list to 57 miRNA
cluster/family identifiers. IPA recognized 30 of these identifiers as important for
reproductive system disease, with a significant emphasis on azoospermia (n =
15; p = 3.35 x 10-19) (Table 4). Functional analysis of the 19 genes targeted by
multiple miRNAs identified two networks: 1) cell death, free radical scavenging,
and cellular development and 2) lipid metabolism, small molecule biochemistry,
and cellular growth and proliferation, that integrated 11 and 6 of the 19 targets,
respectively. In addition, 10 significant pathways were identified from the set of
sperm miRNAs, including IL-8 signaling and apoptosis signaling (Table 5).
The list of 93 mouse sperm miRNAs identified in Liu et al. 2012 was also
analyzed using IPA, which consolidated the list to 64 miRNA families. IPA
identified 42 of these miRNAs with roles vital to reproductive disease, including
nonobstructive azoospermia (n = 19; p = 5.26 x 10-25). Comparison of the sperm
miRNA in rats and mice identified 25 miRNA families present in both (Figure 2;
Table 6) and functional analysis of these overlapping miRNA families produced
similar results to the rat and mouse miRNA analyses, with 76% of the families
involved in reproductive system disease, 9 of which are implicated in
nonobstructive azoospermia (p = 1.53 x 10-12). These results suggest that even
though rodent sperm contain different miRNA profiles, they ultimately consist of
comparable miRNA families that perform analogous functions.
142
Table 4. Sperm miRNAs Associated with Reproductive
System Disease Enriched in Ingenuity Pathways
Analysis
# of
Functional Category
p-value
miRNAs
Present in
Functional
Category
5.32 x10 -22
16
-19
15
gynecological disorder
2.07x10-15
24
endometrial ovarian cancer
3.28x10-15
10
-14
19
cervical carcinoma
nonobstructive azoospermia
uterine cancer
3.35 x10
1.85x10
genital tumor
1.2210
-10
-09
endometriosis
19
2.34x10
13
polycystic kidney disease
2.15x10-07
7
prostate cancer
8.53x10-07
12
uterine leiomyoma
1.74x10-06
8
-04
3
-02
1
-02
3
ovarian endometriosis
7.98x10
recurrent ovarian cancer
1.49x10
endometrial cancer
3.56x10
Note: ** = includes other family members.
143
Table 5. Pathway Analysis of the Genes Targeted by Multiple miRNAs
Pathway
p-value
Ratio*
Targets
IL-8 Signaling
0.0021
4/193
BCL2, HMOX1,
MMP9, NOX4
Apoptosis Signaling
0.0039
3/96
BCL2, CAPN8, MCL1
PI3K/AKT Downstream Genes
from IPA
0.0048
3/140
BCL2, HMOX1,
NOX4
cAMP/PKA Downstream
Genes
0.0048
4/414
BCL2, CAV2,
HMOX1, MMP9
Increased By cAMP or PKA
0.0048
3/176
BCL2, CAV2,
HMOX1, MMP9
Wikipathways: FAS Pathway
and Stress Induction
0.023
2/83
BCL2, MCL2
Rat Wikipathway: Apoptosis
0.023
2/84
BCL2, MCL2
Rat Wikipathway: Apoptosis
Mechanisms
0.023
2/90
BCL2, MCL2
STREBP1 Target Genes_All
0.024
2/103
HMOX1, INSIG1
Srebp1/2- dependent genes
0.036
2/148
HMOX1, INSIG1
Note: Ratio = number of genes in list/total number of genes in pathway.
144
Figure 2. Comparison of miRNA Families in Rat and Mouse Sperm
Mature rat miRNAs (our data) and the mouse miRNAs characterized by Liu et al.,
2012 were consolidated into miRNA families using IPA, resulting in 57 and 64
families, respectively. The miRNA families were directly compared to identify
conserved sperm miRNAs.
145
Table 6. miRNA Families Overlapping
between Mouse and Rat Sperm
let-7a/let-7f/let-7c **
miR-100/miR-99a/miR-99b
miR-103/miR-103a/miR-107
miR-10a/miR-10b/miR-10a-5p
miR-125b-5p/miR-125a-5p/miR-125b **
miR-130a/miR-130b/miR-301a **
miR-132/miR-212/miR-212-3p
miR-140*/miR-140-3p
miR-145
miR-150/miR-5127
miR-16/miR-497/miR-195 **
miR-19b/miR-19a
miR-203
miR-20a/miR-106b/miR-17-5p **
miR-24
miR-26a/miR-26b
miR-27b/miR-27a
miR-30c/miR-30a/miR-30d **
miR-342-3p
miR-429/miR-200b/miR-200c
miR-449a/miR-34a/miR-34c **
miR-451
miR-463
miR-471/miR-471-5p
miR-92a/miR-92b/miR-32 **
Note: ** = includes others.
146
Discussion
The present study utilized high-throughput techniques to identify and
characterize rat sperm miRNAs that may play important roles in normal
spermatogenesis and/or embryo development. Our analysis identified 90
miRNAs consistently expressed in the sperm of Fischer 344 rats. It has been
estimated that at least a third of miRNA families are conserved across species,
with 60% of miRNAs conserved between human and mouse [38]. Our species
comparison identified 44% of rat sperm miRNAs also found in mouse sperm.
Functional analysis of the individual miRNA families and the overlapping subset
yield nearly identical results, emphasizing the functional importance of the rodent
sperm miRNAs. Of note, the let-7 family of miRNAs is conserved in sperm across
all mammalian species assessed, including mouse, pig, rat, and humans, with
let-7e present in all 4 species [22-26]. The let-7 family is associated with sperm
function, with aberrant expression of let-7e in sperm with low motility and
abnormal morphology [24].
The set of miRNAs present in rat sperm included miRNAs that regulate
the epigenetic machinery (epi-miRNAs; miR-29, miR-301, miR-140, and
miR449a), miRNAs important for spermatogenesis (miR-34b/c, miR-125a, and
the let-7 and miR-9 families), and miRNAs necessary for embryogenesis (miR34c) [26]. Epi-miRNAs can control the epigenetic machinery by directly targeting
its enzymatic components (as reviewed in [44]). For example, the de novo DNA
methyltransferases DNMT3A and DNMT3B are direct targets of the miR-29
family, while the maintenance DNA methyltransferase DNMT1 is regulated by
147
miR-301 [45]. In addition, histone deacetylases HDAC1 and HDAC4 are
controlled by miR-449a [46] and miR-140, respectively [47, 48]. The presence of
these miRNAs in the sperm supports the hypothesis that these miRNAs function
to modify gene expression in embryos, and may be contribute to the
demethylation events that occur in the embryo prior to implantation [49-53].
It is well recognized that the expression of miRNAs is crucial for
spermatogenesis (as reviewed in [54, 55]). For example, miR-125a and the let-7
miRNA family are increased in male germ cell development [19]. The let-7 family
members regulate the RAS oncogenic [56] and FAS-regulated apoptosis
pathways [59], which are important signaling networks involved in balancing cell
proliferation and cell death during spermatogenesis [60-63]. miR-34c is highly
expressed in the late stages of spermatogenesis and influences germ cell fate by
promoting the germinal phenotype in cells already committed to the lineage [64].
In addition, miR-34c is sperm-specific and absolutely critical for the first zygotic
cleavage in the mouse [26]. Interestingly, miR-34c was one of the most
abundantly expressed miRNAs in rat sperm, highlighting its functional importance
during fertilization. Functional analysis of the sperm miRNAs further emphasized
the importance of these miRNAs in non-obstructive azoospermia. A recent study
investigated the differential expression of miRNA in abnormal pig sperm and
found that low motility sperm and sperm with abnormal morphology have altered
molecular profiles compared to control sperm [24]. Sperm with abnormal
morphology had increased let-7a, let-7d, let-7e, and miR-22 and decreased
expression of miR-15b, while low motility sperm had decreased let-7d and let-7e
148
expression [24]. Overall, these findings highlight the utility of sperm molecular
profiles to discern subfertile cohorts and support the possible use of sperm
miRNA analysis in other instances where spermatogenesis may be disrupted by
environmental stressors.
Analysis of the relationship between miRNAs present in the sperm and
their transcript targets found that 19 of the 51 transcripts are targeted by multiple
miRNAs. This implies that there is a critical need to ensure the epigenetic
regulation of those targeted transcripts by multiple epigenetic mechanisms.
Functional analysis of these 19 targets found enrichment for pathways involved
in IL-8 signaling, apoptosis, PI3K/AKT signaling, and cAMP/PKA signaling.
Interestingly, these 4 pathways have important roles in fertilization and
embryogenesis. PI3K/AKT [65, 66] and cAMP/PKA [67] signaling are two
independent pathways responsible for capacitation and the acrosome reaction.
The ability of a sperm to fertilize an egg relies on the precise timing of
capacitation, hyperactivation, and the acrosome reaction, and it is possible that
the key players in these pathways are regulated in the developing germ cells via
miRNAs to ensure their proper expression in sperm [68]. Throughout
embryogenesis, apoptotic and inflammatory pathways need to be controlled to
guarantee normal development. Blastocyst implantation requires control of
specific chemokines, including IL-8 [69]. The maternal endometrium is
responsible for producing this chemokine, as the developing blastocyst does not
produce detectable levels of IL-8 [69]. miRNAs may regulate this pathway to
maintain a proper balance of these cytokines between mother and embryo. In
149
addition, miRNAs have been implicated in regulating apoptosis in Drosophila
embryos and aberrant regulation can result in birth defects and embryo lethality
[70]. Thus, our data suggests that sperm miRNAs could regulate pathways
essential for proper fertilization and embryogenesis.
In summary, these data confirm the presence of a diverse population of
miRNAs in rat sperm. Functional analyses of the miRNAs indicate they are
important for reproductive function and embryonic development. The high degree
of miRNA sequence homology across species suggests that their functional roles
are also conserved. Identifying and classifying the miRNAs present in sperm
under normal spermatogenic conditions will further elucidate the potential roles of
these small RNAs and provide the appropriate context for examining miRNA
content during disrupted spermatogenesis and infertility.
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Supplemental Table 1. miRNA Families Identified in Rat and Mouse Sperm
Rat
Mouse
let-7a // let-7f //
miR-30c // miRlet-7a // let-7f //
miR-222 // miRlet-7c **
30a // miR-30d **
let-7c **
221 // miR-1928
miR-100 // miRmiR-342-3p
let-7d*
miR-24
99a // miR-99b
miR-103 // miRmiR-347
miR-100 // miRmiR-26a // miR103a // miR-107
99a // miR-99b
26b
miR-10a // miRmiR-34c* // miRmiR-101 // miRmiR-27b // miR10b // miR-10a-5p
34c-3p // miR101a // miR-101b
27a
34b* **
miR-10b
miR-365
miR-103 // miRmiR-291a-5p //
103a // miR-107
miR-291b-5p
miR-1224 // miRmiR-370
miR-10a // miRmiR-297a // miR1224-5p
10b // miR-10a-5p
297
miR-124*
miR-374 // miRmiR-125b-5p //
miR-30c // miR374a // miR-374b
miR-125a-5p //
30a // miR-30d **
miR-125b **
miR-103 // miRmiR-347
miR-100 // miRmiR-26a // miR103a // miR-107
99a // miR-99b
26b
miR-125b-5p //
miR-375 (human,
miR-127 // miRmiR-30e* // miRmiR-125a-5p //
mouse, rat)
127-3p
30a* // miR-30d*
miR-125b **
miR-130a // miRmiR-377* // miRmiR-130a // miRmiR-31
130b // miR-301a
672
130b // miR-301a
**
**
miR-132 // miRmiR-409-3p (rat)
miR-132 // miRmiR-324-5p
212 // miR-212-3p
212 // miR-212-3p
miR-140* // miRmiR-425 // miRmiR-133a // miRmiR-339-5p //
140-3p
489
133b
miR-3586-5p
miR-145
miR-429 // miRmiR-140* // miRmiR-340-3p //
200b // miR-200c
140-3p
miR-340*
miR-150 // miRmiR-449a // miRmiR-140 // miRmiR-341
5127
34a // miR-34c **
876-3p // miR140-5p
miR-151-5p //
miR-451
miR-141 // miRmiR-342-3p
miR-151b // miR200a
151
miR-16 // miR-497
miR-463
miR-142-3p
miR-344 // miR// miR-195 **
344a-3p // miR344c
miR-18a // miRmiR-471 // miRmiR-145
miR-344d // miR18b // miR-4735471-5p
410
3p
156
miR-190 // miR190b
miR-503
miR-191
miR-664 (human,
mouse, rat)
miR-671-3p //
miR-671
miR-196a* // miR196a-2*
miR-19b // miR19a
miR-203
miR-678 (rat)
miR-20a // miR106b // miR-17-5p
**
miR-21 // miR590-5p
miR-22
miR-743a // miR743b-3p // miR743b
miR-7a // miR-7 //
miR-7b
miR-872
miR-23b // miR23a // miR-23c **
miR-24
miR-872*
miR-25*
miR-26a // miR26b
miR-27a*
miR-743a
miR-146a // miR146b // miR-146b5p
miR-150 // miR5127
miR-152 // miR148b // miR-148a
**
miR-16 // miR-497
// miR-195 **
miR-181a // miR181b // miR-181d
**
miR-185 // miR3473 // miR-4306
**
miR-186
miR-196a // miR196b
miR-199a-3p
miR-92a // miR92b // miR-32 **
miR-935
miR-30c // miR30a // miR-30d **
miR-27b // miR27a
miR-296-5p //
miR-296*
miR-29b // miR29c // miR-29a
Note: ** = includes others.
miR-350
miR-376a // miR376b // miR-376b3p
miR-382 (human,
mouse, rat)
miR-411 (human,
mouse, rat)
miR-429 // miR200b // miR-200c
miR-449a // miR34a // miR-34c **
miR-451
miR-463
miR-199a-5p
miR-467a
miR-19b // miR19a
miR-203
miR-470
miR-20a // miR106b // miR-17-5p
**
miR-210 (human,
mouse, rat)
miR-211 // miR204
miR-217
157
miR-346
miR-471 // miR471-5p
miR-761 // miR214 // miR-36195p
miR-9
miR-92a // miR92b // miR-32 **
miR-96 // miR1271
Supplemental Table 2. Rat Sperm miRNAs Detected by Affymetrix Array
Affymetrix
Sequence
Sequence Length
Transcript ID
rno-let-7b
UGAGGUAGUAGGUUGUGUGGUU
22
rno-let-7b*
CUAUACAACCUACUGCCUUCCC
22
rno-let-7c-1 // rnoUGAGGUAGUAGGUUGUAUGGUU
22
let-7c-2
rno-mir-103-1 //
AGCAGCAUUGUACAGGGCUAUGA
23
rno-mir-103-2
rno-mir-16
UAGCAGCACGUAAAUAUUGGCG
22
rno-mir-17-1 // rno- CAAAGUGCUUACAGUGCAGGUAG
23
mir-17-2
rno-mir-191
CAACGGAAUCCCAAAAGCAGCUG
23
rno-mir-19b-1 //
UGUGCAAAUCCAUGCAAAACUGA
23
rno-mir-19b-2
rno-mir-20a
UAAAGUGCUUAUAGUGCAGGUAG
23
rno-mir-26a
UUCAAGUAAUCCAGGAUAGGCU
22
rno-mir-337
UUCAGCUCCUAUAUGAUGCCUUU
23
rno-mir-34c
AGGCAGUGUAGUUAGCUGAUUGC
23
rno-mir-34c*
AAUCACUAACCACACAGCCAGG
22
rno-mir-425
AAUGACACGAUCACUCCCGUUGA
23
rno-mir-449a
UGGCAGUGUAUUGUUAGCUGGU
22
rno-mir-494
UGAAACAUACACGGGAAACCU
21
rno-mir-743a
GAAAGACGCCAAACUGGGUAGA
22
rno-mir-881
AACUGUGGCAUUUCUGAAUAGA
22
rno-mir-92b
UAUUGCACUCGUCCCGGCCUCC
22
rno-mir-93
CAAAGUGCUGUUCGUGCAGGUAG
23
158
Supplemental Table 3. Rat Sperm miRNAs Detected by qRT-PCR
Mature ID
Average CT
rno-miR-34b
25.78 ± 0.25
rno-miR-34c
25.91 ± 0.22
rno-miR-16
26.44 ± 0.24
rno-miR-195
27.46 ± 0.21
rno-miR-15b
27.88 ± 0.21
rno-miR-19b
28.41 ± 0.23
rno-miR-7a
28.50 ± 0.24
rno-miR-19a
28.66 ± 0.24
rno-miR-503
28.76 ± 0.81
rno-miR-25
29.00 ± 0.20
rno-miR-125b-5p
29.04 ± 0.22
rno-let-7a
29.05 ± 0.32
rno-miR-34c*
29.07 ± 0.22
rno-miR-20a
29.11 ± 0.21
rno-miR-375
29.24 ± 0.30
rno-miR-191
29.28 ± 0.24
rno-miR-449a
29.29 ± 0.30
rno-miR-30c
29.31 ± 0.26
rno-miR-425
29.48 ± 0.26
rno-miR-17-5p
29.54 ± 0.33
rno-miR-30b-5p
29.85 ± 0.25
rno-miR-21
30.01 ± 0.26
rno-miR-7b
30.21 ± 0.23
rno-miR-20b-5p
30.22 ± 0.27
rno-miR-125a-5p
30.50 ± 0.17
rno-miR-93
30.70 ± 0.30
rno-let-7e
30.75 ± 0.28
rno-miR-99a
31.18 ± 0.28
rno-let-7c
31.48 ± 0.31
rno-miR-151
31.52 ± 0.22
rno-miR-100
31.74 ± 0.34
rno-miR-196a*
31.77 ± 0.22
rno-miR-92a
31.78 ± 0.25
rno-miR-200c
31.78 ± 0.28
rno-miR-10a-5p
31.91 ± 0.44
rno-miR-34a
32.11 ± 0.24
rno-miR-27a*
32.31 ± 0.19
rno-miR-672
32.44 ± 0.56
rno-miR-1224
32.47 ± 0.41
rno-miR-296*
32.49 ± 0.46
rno-miR-22
32.52 ± 0.32
rno-miR-26a
32.52 ± 0.55
rno-miR-23a
32.52 ± 0.25
159
rno-miR-124*
rno-let-7d
rno-miR-743a
rno-miR-140*
rno-miR-743b
rno-miR-103
rno-miR-29a
rno-let-7b
rno-miR-24
rno-miR-30e
rno-miR-150
rno-miR-370
rno-miR-23b
rno-miR-664
rno-let-7i
rno-miR-671
rno-miR-678
rno-miR-30d
rno-miR-18a
rno-miR-10b
rno-miR-30a
rno-miR-145
rno-miR-342-3p
rno-miR-347
rno-miR-203
rno-miR-132
rno-miR-935
rno-miR-872
rno-miR-106b
rno-miR-26b
rno-miR-25*
rno-miR-365
rno-miR-471
rno-miR-409-3p
rno-miR-463
rno-miR-374
rno-miR-92b
rno-miR-29c
rno-miR-130a
rno-let-7f
rno-miR-497
rno-miR-872*
rno-miR-451
rno-miR-301a
rno-miR-27b
32.55 ± 0.13
32.61 ± 0.26
32.76 ± 0.46
32.77 ± 0.25
32.91 ± 0.29
33.00 ± 0.63
33.02 ± 0.44
33.17 ± 0.53
33.18 ± 0.36
33.34 ± 0.47
33.40 ± 0.50
33.53 ± 0.29
33.56 ± 0.50
33.61 ± 0.45
33.74 ± 0.56
33.75 ± 0.17
33.75 ± 0.29
33.79 ± 0.35
33.81 ± 0.40
33.88 ± 0.45
33.90 ± 0.42
33.92 ± 0.62
33.95 ± 0.48
33.96 ± 0.27
34.02 ± 0.45
34.05 ± 0.49
34.13 ± 0.26
34.20 ± 0.44
34.31 ± 0.43
34.33 ± 0.27
34.41 ± 0.52
34.52 ± 0.38
34.53 ± 0.37
34.53 ± 0.31
34.58 ± 0.55
34.60 ± 0.44
34.72 ± 0.40
34.72 ± 0.58
34.75 ± 0.36
34.75 ± 0.39
34.75 ± 0.49
34.86 ± 0.49
34.86 ± 0.62
34.87 ± 0.47
34.91 ± 0.56
160
rno-miR-212
rno-miR-190b
34.93 ± 0.35
34.94 ± 0.55
161
Supplemental Table 4. miRNA Base Pair Frequencies
Individual Bases
Dinucleotide Pairs
A
C
G
U
AA AC AG AU CA CC CG CU GA GC GG GU UA UC UG UU
All
24% 24% 25% 28% 5% 6% 6% 5% 7% 6% 2% 7% 5% 5% 6% 7% 5% 6% 9% 7%
miRNAs
Sperm
25% 19% 28% 28% 5% 6% 8% 5% 5% 5% 3% 4% 6% 4% 7% 9% 8% 3% 9% 7%
miRNAs
Note: A = adenine, C = cytosine, T = thymine, and U = uracil.
162
162
Supplemental Table 5. Sperm miRNAs Associated with Reproductive
System Disease Enriched in Ingenuity Pathways Analysis
Functional
p-value
IPA miRNA Cluster/Family
# of
Category
Identifier
miRNAs
Present in
Functional
Category
-22
cervical
5.32 x10
let-7a/let-7f/let-7c **
16
carcinoma
miR-10a/miR-10b/miR-10a-5p
miR-130a/miR-130b/miR-301a **
miR-132/miR-212/miR-212-3p
miR-145
miR-150/miR-5127
miR-16/miR-497/miR-195 **
miR-19b/miR-19a
miR-20a/miR-106b/miR-17-5p **
miR-203
miR-21/miR-590-5p
miR-26a/miR-26b
miR-29b/miR-29c/miR-29a
miR-30c/miR-30a/miR-30d **
miR-449a/miR-34a/miR-34c **
miR-92a/miR-92b/miR-32 **
-19
nonobstructive 3.35 x10
let-7a/let-7f/let-7c **
15
azoospermia
miR-130a/miR-130b/miR-301a **
miR-132/miR-212/miR-212-3p
miR-145
miR-190/miR-190b
miR-19b/miR-19a
miR-20a/miR-106b/miR-17-5p **
miR-21/miR-590-5p
miR-23b/miR-23a/miR-23c **
miR-29b/miR-29c/miR-29a
miR-374/miR-374a/miR-374b
miR-425/miR-489
miR-429/miR-200b/miR-200c
miR-449a/miR-34a/miR-34c **
miR-92a/miR-92b/miR-32 **
gynecological
2.07x10-15
let-7a/let-7f/let-7c **
24
disorder
miR-103/miR-103a/miR-107
miR-10a/miR-10b/miR-10a-5p
miR-130a/miR-130b/miR-301a **
miR-132/miR-212/miR-212-3p
miR-145
163
endometrial
ovarian cancer
3.28x10-15
uterine cancer
1.85x10-14
miR-150/miR-5127
miR-16/miR-497/miR-195 **
miR-191
miR-19b/miR-19a
miR-203
miR-20a/miR-106b/miR-17-5p **
miR-21/miR-590-5p,
miR-23b/miR-23a/miR-23c **
miR-26a/miR-26b
miR-27b/miR-27a
miR-29b/miR-29c/miR-29a
miR-30c/miR-30a/miR-30d **
miR-365
miR-425/miR-489
miR-429/miR-200b/miR-200c
miR-449a/miR-34a/miR-34c **
miR-503,
miR-92a/miR-92b/miR-32 **
miR-103/miR-103a/miR-107
miR-10a/miR-10b/miR-10a-5p
miR-130a/miR-130b/miR-301a **
miR-191
miR-203
miR-20a/miR-106b/miR-17-5p **
miR-23b/miR-23a/miR-23c **
miR-429/miR-200b/miR-200c
miR-449a/miR-34a/miR-34c **
miR-92a/miR-92b/miR-32 **
let-7a/let-7f/let-7c **
miR-10a/miR-10b/miR-10a-5p
miR-130a/miR-130b/miR-301a **
miR-132/miR-212/miR-212-3p
miR-145
miR-150/miR-5127
miR-16/miR-497/miR-195 **
miR-19b/miR-19a
miR-203
miR-20a/miR-106b/miR-17-5p **
miR-21/miR-590-5p
miR-23b/miR-23a/miR-23c **
miR-26a/miR-26b
miR-27b/miR-27a
miR-29b/miR-29c/miR-29a
miR-30c/miR-30a/miR-30d **
164
10
19
genital tumor
1.2210-10
endometriosis
2.34x10-09
polycystic
kidney disease
2.15x10-07
miR-449a/miR-34a/miR-34c **
miR-503
miR-92a/miR-92b/miR-32 **
let-7a/let-7f/let-7c **
miR-100/miR-99a/miR-99b
miR-103/miR-103a/miR-107
miR-10a/miR-10b/miR-10a-5p
miR-125b-5p/miR-125a-5p/miR125b **
miR-130a/miR-130b/miR-301a **
miR-145
miR-16/miR-497/miR-195 **
miR-191
miR-203
miR-20a/miR-106b/miR-17-5p **
miR-21/miR-590-5p
miR-23b/miR-23a/miR-23c **
miR-26a/miR-26b
miR-29b/miR-29c/miR-29a
miR-30c/miR-30a/miR-30d **
miR-429/miR-200b/miR-200c,
miR-449a/miR-34a/miR-34c **
miR-92a/miR-92b/miR-32 **
miR-100/miR-99a/miR-99b
miR-125b-5p/miR-125a-5p/miR125b **
miR-145
miR-150/miR-5127
miR-16/miR-497/miR-195 **
miR-20a/miR-106b/miR-17-5p **
miR-23b/miR-23a/miR-23c **
miR-29b/miR-29c/miR-29a
miR-34c*/miR-34c-3p/miR-34b* **
miR-365
miR-425/miR-489
miR-429/miR-200b/miR-200c
miR-449a/miR-34a/miR-34c **
miR-100/miR-99a/miR-99b
miR-203
miR-20a/miR-106b/miR-17-5p **
miR-21/miR-590-5p
miR-30c/miR-30a/miR-30d **
miR-449a/miR-34a/miR-34c **
miR-7a/miR-7/miR-7b
165
19
13
7
prostate
cancer
8.53x10-07
uterine
leiomyoma
1.74x10-06
ovarian
endometriosis
7.98x10-04
recurrent
ovarian cancer
endometrial
cancer
1.49x10-02
let-7a/let-7f/let-7c **
miR-100/miR-99a/miR-99b
miR-125b-5p/miR-125a-5p/miR125b **
miR-145
miR-16/miR-497/miR-195 **
miR-191
miR-21/miR-590-5p
miR-26a/miR-26b
miR-29b/miR-29c/miR-29a
miR-30c/miR-30a/miR-30d **
miR-429/miR-200b/miR-200c
miR-449a/miR-34a/miR-34c **
let-7a/let-7f/let-7c **
miR-132/miR-212/miR-212-3p
miR-21/miR-590-5p
miR-23b/miR-23a/miR-23c **
miR-27b/miR-27a
miR-29b/miR-29c/miR-29a
miR-30c/miR-30a/miR-30d **
miR-92a/miR-92b/miR-32 **
miR-20a/miR-106b/miR-17-5p **
miR-365
miR-425/miR-489
miR-429/miR-200b/miR-200c
3.56x10-02
miR-16/miR-497/miR-195 **
miR-29b/miR-29c/miR-29a
miR-503
Note: ** = includes other family members.
166
12
8
3
1
3
CHAPTER 5. THE OPTIMIZATION OF ADDITIONAL TOXICANT EXPOSURES
FOR SPERM RNA PROFILING
167
CHAPTER 5. THE OPTIMIZATION OF ADDITIONAL TOXICANT EXPOSURES
FOR SPERM RNA PROFILING
Abstract
Objective: In Chapters 3 and 4, we investigated the effects of toxicant
exposure on sperm mRNA content and catalogued the miRNAs present in
epididymal rat sperm under normal conditions. The goals of this study were to: 1)
examine the effect of 2,5-hexanedione (HD) on sperm miRNAs archived from the
Chapter 3 studies using two high-throughput platforms; 2) investigate whether
our biomarker panel can detect alterations after exposure to other Sertoli cell
(SC) toxicants, 1,3-dinitrobenzene (DNB) and di-(2-ethylhexyl)phthalate (DEHP);
and 3) determine whether sperm RNA content is altered after exposure to the
germ cell (GC) toxicant, 1,2-dibromo-3-chloropropane (DBCP) using highthroughput arrays. Findings: HD had a very limited effect on sperm miRNA
content, with only 1 miRNA altered after the 3-month post-exposure recovery
period. DEHP and DNB had no effect on the apical endpoints examined in this
study. DEHP had no influence on sperm mRNAs, but DNB altered a few
transcripts when compared to control. DBCP decreased body and epididymal
weights but had no effect on the sperm mRNAs using LIMMA. DBCP had a slight
effect on miRNAs, with 2 altered after the 3-month post-exposure recovery.
Conclusions: These results suggest that the biomarker panel is capable of
detecting DNB induced SC injury at doses that do not produce any pathological
indicators of testicular injury. Moreover, SC toxicants appear to elicit a different
molecular profile compared to GC toxicants. In addition, the miRNA data
168
supports their potential use as biomarkers of testicular injury. Unfortunately,
these results cannot fully confirm the above hypotheses; however, they provide
the preliminary foundation for future studies in our laboratory investigating
toxicant effects on sperm RNA content.
Introduction
In Chapter 3, we determined that sub-chronic low dose exposure to the
two SC toxicants, 2,5-hexanedione (HD) and carbendazim altered sperm mRNA
content. This novel finding indicated that sperm mRNA content is an informative
biomarker of testicular injury at low doses. We generated a panel of 29
transcripts to assess SC injury and our findings highlight the utility of these
selected biomarkers as promising tools for screening and categorizing additional
SC toxicants based on their molecular sperm signatures.
In Chapter 4, we identified and characterized the population of miRNAs in
normal rat sperm. We found a random chromosomal distribution of miRNAs and
their mRNA targets. In addition, pathway analysis suggested the sperm miRNAs
play important roles in spermatogenesis, fertilization, and embryogenesis. This is
consistent with the finding that many of the miRNAs were conserved in the sperm
of mouse and rat, emphasizing their functional importance. In this study we
wanted to determine whether sub-chronic low dose HD exposure disrupted
sperm miRNA content. This was addressed using both Affymetrix and PCR
arrays on sperm miRNAs isolated in the preliminary study described in Chapter
3.
169
We also wanted to test our PCR biomarker panel on two additional SC
toxicants that do not target microtubules, DNB and DEHP. DNB is used as an
intermediate in organic synthesis reactions and in the production of explosives,
dyes, industrial solvents, and pesticides [1]. DNB directly targets the SCs in the
testis with GC injury occurring as a secondary event [2, 3]. Male reproductive
toxicity of DNB has been demonstrated in laboratory animals including
decreased testicular weights, degeneration of GCs, decreased testicular sperm
head counts, decreased epididymal sperm counts, and reduced fertility [2, 3].
Previous research suggests that DNB treatment produces testicular toxicity via
the SC mitochondria, either inducing oxidative stress generated by redoxcycling,
and/or the inhibition of aldehyde dehydrogenase [4-6]. DEHP is a commonly
used plasticizer in biomedical devices, consumer products, and food-packaging
materials [7]. This endocrine disrupter is rapidly metabolized to mono-(2ethylhexyl)phthalate (MEHP), which is more toxic than the parent compound.
Lastly, we wanted to determine whether a GC toxicant, DBCP, altered
sperm mRNA and miRNA content. DBCP is a nematocide that has been shown
to reduce fertility and induce sterility in humans exposed in occupational settings
[9]. Studies also indicate that DNA is the subcellular target of this toxicant. DBCP
can be converted to reactive metabolites and these resulting metabolites can
induce single-strand breaks in DNA. The differentiating spermatogenic cells are
the cells most sensitive to DBCP-induced apoptosis. Round spermatids have
less compacted DNA than elongating/elongated spermatids which makes the
DNA in these cells is a more accessible target [10].
170
Materials and Methods
Animals
Adult male Fischer 344 rats weighing 175-225 grams (Charles River
Laboratories, Wilmington, MA) were maintained in a temperature and humidity
controlled vivarium with a 12 hour alternating light-dark cycle. All rats were
housed in community cages with free access to water and Purina Rodent Chow
5001 (Farmer’s Exchange, Framingham, MA). The Brown University Institutional
Animal Care and Use Committee approved all experimental animal protocols in
compliance with National Institute of Health guidelines.
Chemicals
2,5-Hexanedione (CAS# 110-13-4), 1,2-dibromo-3-chloropropane (CAS#
96-12-8), and di-(2-ethylhexyl)phthalate (dioctyl phthalate, CAS# 117-81-7) were
purchased from Sigma Aldrich (St. Louis, MO). 1,3-Dinitrobenzene (CAS # 9965-0) was purchased from Alfa Aesar (Ward Hill, MA).
Dose Selection
Doses of HD, DBCP, DEHP, and DNB were selected to produce minimal
but detectable testicular injury. Based on previous studies we selected doses of
0.33% HD in the drinking water, 5 mg/kg/d DBCP in corn oil via subcutaneous
injection, and 150 mg/kg/d DEHP and 0.75 mg/kg/d DNB in corn oil via gavage
[11-14].
171
Experimental Design
The experiments presented in this chapter are organized by the testicular
cell types targeted by these toxicants: SCs and GCs (Figure 1). Each study was
based on the 3-month exposure paradigm discussed in Chapter 3 and rats were
euthanized via carbon dioxide asphyxiation.
SC Toxicants
Two experiments used SC toxicants (Figure 1A). The first examined
miRNA expression after exposure to a SC toxicant that we know disrupts mRNA
expression (HD) and the second was to assess mRNA content after exposure to
two new SC toxicants (DEHP and DNB). For the HD miRNA analysis, archived
samples were used from the preliminary study described in Chapter 3. For the
SC toxicant sensitivity study, rats were assigned into 3 groups: corn oil, DNB,
and DEHP (n = 7-9/group). Rats were weighed daily and dosed with either corn
oil vehicle or toxicant by oral gavage for 3 months. Rats were euthanized after
the exposure and the body weights and reproductive organ weights were
recorded at necropsy. Blood was collected via cardiac puncture and serum was
extracted for inhibin B measurements. A portion of the right testes was
detunicated and snap frozen for homogenization resistant spermatid head
counts. The epididymides were used immediately for sperm isolation.
172
Figure 1. Experimental Paradigms
The experiments performed in this chapter are organized by the testicular cell
type targeted by the model toxicants (A = Sertoli cell and B = germ cell). For the
first Sertoli cell toxicant experiment (A.1) rats were treated with 0.33% HD in the
drinking water or water control, for 3 months. The recovery group received
control water for 3 additional months. For the second Sertoli cell toxicant
experiment (A.2), rats were exposed to either 150 mg/kg/d DEHP in corn oil
vehicle, 0.75 mg/kg/d DNB in corn oil vehicle, or corn oil control, for 3 months.
For the germ cell toxicant experiment (B), rats were exposed to 5 mg/kg/d DBCP
in corn oil vehicle or corn oil control via subcutaneous injection for 3 months. The
recovery group did not receive any injections. Grey arrowheads indicate
euthanization time points.
173
GC Toxicants
One experiment used a GC toxicant (Figure 1B). Rats were randomly
assigned to 4 groups: corn oil, DBCP, corn oil–recovery, and DBCP-recovery
(n=18-20/group). DBCP was administered as a 5/mg/kg subcutaneous injection
in corn oil vehicle daily for 3 months (DBCP and DBCP-recovery groups). Rats
were weighed weekly and the amount of DBCP that was administered was
adjusted for each rat. The groups were euthanized after 3 months of exposure
(corn oil, and DBCP) or after 3 months of exposure plus 3 months of additional
post-exposure recovery (corn oil-recovery, and DBCP-recovery). The body
weights and reproductive organ weights were recorded at necropsy. Left testes
were fixed in 10% neutral-buffered formalin for histological examination, and a
portion of the right testes was detunicated and snap frozen for homogenization
resistant spermatid head counts. The epididymides were weighed and the caudal
epididymides were used immediately for sperm isolation.
Sperm Isolation and RNA Extraction (All)
Sperm were isolated from the caudal epididymides following the protocol
outlined in Chapter 3 and RNA was extracted from the fresh sperm using the
mirVana miRNA Isolation Kit (Applied Biosystems/Ambion, Austin, TX, USA),
which separated the RNA into large (mRNA) and small (miRNA) fractions per
manufacturer’s instructions.
174
Histological Examination (DBCP)
Two middle cross sections of the formalin-fixed testes were embedded in
glycol methacrylate (Technovit 7100; Heraeus Kulzer GmBH, Wehrheim,
Germany) for histological examination of stage-specific retained spermatid heads
(RSH) or embedded in paraffin for detection of apoptosis by TUNEL staining with
concurrent measurement of seminiferous tubule diameter as described in
Chapter 3. The Aperio Scan Scope (Aperio Technologies, Vista, CA) was used to
analyze all histological endpoints.
Homogenization Resistant Spermatid Head Counts (DEHP, DNB, and
DBCP)
Testicular
homogenization
resistant
spermatid
head
counts
were
performed following the methods described in Chapter 2.
Affymetrix Microarray Preparation for miRNA Analysis (HD and DBCP
experiments)
Sperm miRNAs were prepared for Affymetrix analysis following the
protocols used in Chapter 4.
175
SABiosciences Rat miRNome RT2 miRNA PCR Array (HD and DBCP)
Sperm miRNAs were prepared for PCR analysis following the protocols
used in Chapter 4.
Affymetrix Microarray Preparation for mRNA Analysis (DBCP)
Sperm mRNAs were prepared for Affymetrix analysis following the
protocols used in Chapter 3.
SABiosciences Custom RT2 Profiler PCR Array (DNB and DEHP)
Sperm mRNAs were prepared for PCR analysis following the protocols
used in Chapter 3.
Statistical Analyses
For the HD miRNA study, LIMMA was used to fit a linear regression model
for each miRNA transcript on the Affymetrix GeneChips for each pair of treatment
and controls following the procedures outlined in Chapter 3. Transcripts were
considered statistically significant when q-value <0.05. For the PCR array,
student’s unpaired two-tailed t-tests were performed using the Prism 5 software
(GraphPad Software, La Jolla, CA) to determine statistical differences (p < 0.05)
within the two pairs of treatments and controls: 1.) water and HD, and 2.) waterrecovery and HD-recovery.
176
For the DEHP and DBCP study, one-way ANOVA with the Dunnett’s
Multiple Comparison Test was used for the weights, inhibin B measurements,
and the ∆CT values using the Prism 5 software. Student’s unpaired two-tailed ttests were also performed on the ∆CT values comparing the corn oil and DNB.
Significance was determined when p < 0.05.
For the DBCP study, student’s unpaired two-tailed t-tests were used to
determine statistical differences (p <0.05) within the two pairs of treatments and
controls: 1.) corn oil and DBCP, and 2.) corn oil-recovery and DBCP-recovery for
the weights, the histological endpoints, and the miRNA PCR array data. LIMMA
was used to analyze each gene or miRNA transcript on the Affymetrix
GeneChips for each pair of treatment and controls. An additional analysis using
the biomarker candidates in Chapter 3 was performed using LIMMA for both
pairs of treatment and controls. Transcripts were considered statistically
significant when q-value <0.05.
Results
HD miRNA Analysis
miRNA Affymetrix Microarray Data
LIMMA analysis identified one miRNA
(rno-miR-299) as differentially
present in sperm exposed to HD. This miRNA was no longer altered after 3
months of post-exposure recovery (data not shown).
177
miRNA PCR Array Data
No miRNAs were altered after 3 months of exposure to HD (data not
shown). However, one miRNA was altered after the 3-month post-exposure
recovery. Rno-miR-150 was decreased 3.60 fold (p = 0.015). This miRNA was
previously identified in sperm (Chapter 4).
DEHP and DNB mRNA Analysis
Body and Reproductive Organ Weights
Body, testis, and epididymis weights were recorded during necropsy and
there were no differences compared to control for DNB or DEHP (data not
shown).
Homogenization Resistant Spermatid Heads and Inhibin B
There were no differences in either testicular homogenization resistant
spermatid head counts or serum inhibin B levels compared to control for DNB or
DEHP (data not shown).
mRNA PCR Array Data
No mRNAs were significantly altered after exposure to DEHP. However,
one transcript (MTM1) was significantly altered with DNB exposure, after
adjusting for multiple comparisons. Focusing on DNB only, 5 transcripts were
178
statistically altered compared to control using t-tests and 4 transcripts
approached significance (Table 1).
DBCP mRNA and miRNA Analysis
Body and Reproductive Organ Weights
Body, testis, and epididymis weights were recorded during necropsy
(Table 2). There was a significant decrease in body and epididymis weights after
3 months of exposure to DBCP. This effect resolved after the additional 3-month
recovery period.
179
Table 1. DNB Experiment: Fold Changes and p-values for Altered
Transcripts
Transcript
Mean (upper limit, lower limit)*
p-values
-3.13 (-3.45, -2.70)
0.030
MTM1**
1.53 (1.44, 1.63)
0.033
MFAP3L
-2.33 (-2.44, -2.17)
0.033
DENND1A
1.46 (1.38, 1.55)
0.035
LRRC69
1.60 (1.51, 1.70)
0.046
PIM1
1.53 (1.45, 1.62)
0.057
TBC1D5
1.69 (1.56, 1.83)
0.088
LYZ2
1.69 (1.57, 1.81)
0.12
DCN
1.32 (1.23, 1.40)
0.16
TCP10B
Note: *, The upper and lower fold change ratio limits were generated using the
-CT ± SE
. ** = Transcript was significantly altered using ANOVA. Pformula 2
values were generated using Student’s unpaired two-tailed t-test.
180
Table 2. DBCP Experiment: Average Weights
Corn Oil
DBCP
Corn Oil
Recovery
375.8  4.06
1.59  0.01
DBCP
Recovery
369.1  3.95
1.59  0.02
Body (g)
344.4  3.55 330.9  4.03*
Testes (g)
1.52  0.02
1.48  0.02
Epididymides
439.7 
(mg)
6.60***
475.0  7.21
489.7  4.34 478.4  6.09
Note: DBCP = 5 mg/k/d 1,2-dibromo-3-chloropropane. Values were recorded as
mean  SEM. * = p < 0.05 when compared to appropriate control and *** = p <
0.001 when compared to appropriate control using student’s unpaired two-tailed
t-test.
181
Testicular Histopathology
Various histopathological endpoints were assessed to determine the
severity of the toxicant-induced testicular injury (Table 3). There was no
statistical difference in any of the histopathological endpoints after 3 months of
DBCP exposure; however, there was an increasing trend of apoptosis in the GCs
in these rats after 3 months DBCP treatment (p < 0.06).
mRNA Affymetrix Microarray Data
DBCP exposure had no effect on the mRNA transcriptome at any time
point assessed when analyzed utilizing the entire array (data not shown) or the
candidates selected in Chapter 3 for each pair of treatments and controls using
LIMMA (Table 4).
182
Table 3. DBCP Experiment: Histological Examination
Corn Oil
DBCP
Corn Oil
DBCP
Recovery
Recovery
RSH
0.92 ± 0.20
1.59 ± 0.31
0.62 ± 0.13
0.63 ± 0.08
TUNEL
16.28  2.06
22.94  2.37
20.66  1.70
21.98  2.54
ST
263.7  10.26 270.4  5.58
274.0  7.74
279.3  7.02
DIAMETER
HRSH
2.14  0.14
1.93  0.18
2.41  0.15
2.55  0.13
Note: DBCP = 5 mg/kg/d 1,2-dibromo-3-chloropropane; RSH = average # of
retained spermatid heads per stage-specific seminiferous tubule; TUNEL = % of
seminiferous tubules with > 0 TUNEL positive cells; ST DIAMETER = average
seminiferous tubule diameter (µm); and HRSH = # of homogenization resistant
spermatid heads per testis (x108). Data are presented as mean + SEM; *** =
p<0.001 when compared to the appropriate control using student’s unpaired twotailed t-test.
183
Table 4. Sertoli Cell Biomarker Candidate Analysis using LIMMA after
DBCP Exposure
Fold Change
Transcript
Log2
p-value
q-value
Ratios
0.30
0.02
0.23
0.81
CLU
-0.35
0.02
0.23
1.28
LYZ2
0.24
0.08
0.37
0.84
BCL2L14
0.27
0.07
0.37
0.83
FANK1
0.22
0.11
0.42
0.86
TPI1
0.22
0.14
0.46
0.86
MTM1
0.22
0.12
0.46
0.86
PIM1
0.18
0.19
0.48
0.88
MFAP3L
0.16
0.25
0.52
0.90
BAG1
0.17
0.24
0.52
0.89
PHOSPHO1
0.14
0.30
0.52
0.91
ABI2
0.14
0.32
0.52
0.91
STRBP
0.14
0.31
0.52
0.91
TAX1BP1
0.13
0.34
0.52
0.92
SCLT1
0.12
0.37
0.53
0.92
TCP10B
0.11
0.40
0.53
0.93
IFT81
-0.10
0.47
0.59
1.07
DCN
0.09
0.52
0.59
0.94
DNAJB4
-0.08
0.52
0.59
1.06
VIM
0.08
0.55
0.60
0.94
LRRC6
0.05
0.73
0.70
0.97
GAS2
0.05
0.77
0.70
0.97
LRRC69
0.06
0.72
0.70
0.96
PTGDS
0.04
0.75
0.70
0.97
TBC1D5
0.03
0.84
0.71
0.98
SIL1
0.03
0.84
0.71
0.98
STYXL1
0.02
0.91
0.71
0.99
DENND1A
-0.02
0.89
0.71
1.01
SOD3
0.01
0.94
0.71
0.99
BFAR
184
miRNA Affymetrix Microarray Data
DBCP exposure had no effect on the miRNA transcriptome at either time
point (data not shown).
miRNA PCR Array Data
No miRNAs were altered after 3 months of exposure to DBCP (data not
shown). However, two miRNAs were significantly altered after the 3-month postexposure recovery. Rno-miR-743a was increased 2.15 fold, while rno-miR-18a
was decreased 2.23 fold (p = 0.037 and 0.039, respectively). Both of these
miRNAs are normally expressed in sperm (Chapter 4).
Discussion
The present study investigated the utility of sperm RNAs as a sentinel of
testicular injury. It focused on identifying biomarkers of testis damage within rat
sperm after sub-chronic low dose exposures to cell-type specific testicular
toxicants. This builds on our existing knowledge of cell-type specific toxicity in
animal models and develops the foundation required to extrapolate these
observations to human samples.
To test the effects of exposures on sperm mRNA content, male rats were
given sub-chronic low dose exposures of three cell type specific testicular
toxicants (DEHP, DNB, and DBCP) for 3 months. DEHP and DNB are SC
toxicants and DBCP is a GC toxicant. DEHP had no effect on any of the apical
185
endpoints we assessed or sperm mRNA content and this was most likely
because the dose selected was too low to induce any testicular injury. DNB on
the other hand, produced no evidence of injury but had disrupted mRNA content
when analyzed using ANOVA (MTM1) and t-tests (MTM1, MFAP3L, DENND1A,
LRRC69, and PIM1). We did not quantify retained spermatid heads for DNB
treated testes, which is needed to confirm detection of transcript alterations prior
to histopathological changes. Comparing the transcripts altered by HD, CBZ, and
DNB after 3 months of exposure, each toxicant produced a slightly different
profile, with few overlapping transcripts (Table 5). DNB and HD both upregulated
LRRC69, while DNB and CBZ both upregulated MFAP3L and PIM1. As
mentioned in Chapter 3, the discrepancies between the toxicant profiles may be
due to toxicant mechanism of action or the varying degrees of toxicity.
186
Table 5. Comparison of Transcripts Altered by 3 Month Exposure to HD,
CBZ, and DNB
HD
CBZ
DNB
LRRC6
CLU
MTM1



LRRC69
SIL1
MFAP3L



LYZ2
FANK1
DENND1A



DNAJB4
ABI2
LRRC69



CLU
BAG1
PIM1



PTGDS
MFAP3L


SOD3
IFT81


IFT81
PTGDS


STRBP
BCL2L14


TBC1D5
PIM1


SCLT1

Note: HD = 2,5-hexanedione; CBZ = carbendazim; DNB = 1,3-dinitrobenzene; =
upregulated; and  = downregulated.
187
DBCP altered some apical endpoints but had no effect on mRNA. This
was true after performing LIMMA on the entire array and on the 29 biomarker
candidates selected in Chapter 3. One reason that we are not detecting mRNA
alterations after analyzing all 27,000 transcripts on the array could be because
the signals were weak and washed out after adjusting for multiple comparisons.
However, LIMMA analysis using a different panel of candidate transcripts may
elucidate some differences between control and DBCP. Another possibility is that
low dose DBCP does not elicit any mRNA alterations. This may be because the
GCs were targeted directly by the toxicant, and aberrant GCs would undergo
apoptosis and the sperm that did survive maintain a normal transcript profile.
However, additional studies using higher doses of DBCP need to be performed
to confirm this hypothesis.
In contrast to mRNA content, sperm miRNAs do not appear to be easily
influenced by toxicant exposure. The initial miRNA analysis using LIMMA had
identified one altered sperm miRNA (rno-miR-299) after 3 months of HD
treatment but no changes were observed after recovery. PCR analysis failed to
identify miRNA alterations after the 3-month exposure, but rno-miR-150 was
aberrant after the post-exposure recovery. Similarly, miRNA alterations were not
observed after DBCP exposure using LIMMA for either the exposure or recovery.
PCR analysis did not detect any alterations after the 3-month exposure but two
miRNAs, rno-miR-743a and rno-miR-18a, were altered after the post-exposure
recovery. Our limited results may be because miRNAs are tightly controlled at
both the transcriptional and functional level and the doses that we used may
188
have been too low to adequately disrupt this regulation [15]. In addition, the small
sample size (n = 6) could have limited the ability to observe slight alterations in
miRNA transcript content. Another limiting factor could be the timing of these
alterations. The time points we selected (3 month exposure and 3 month postexposure recovery) may not have been appropriate to detect changes, because
as seen with the mRNA data in Chapter 3, the majority of transcripts were altered
during the three months of recovery. To address this question, PCR arrays can
be performed using the archived miRNAs from Chapter 3’s HD time course.
In summary, the data in this chapter provide preliminary insights about the
effect of toxicant exposure on sperm RNA content. Our results suggest that
sperm mRNA content is altered after low dose exposures to SC toxicants and not
GC toxicants. In this study, exposure to a very low dose of DNB altered mRNA
content, with no apparent toxicity detected. However, DBCP did not alter mRNA
content, even though evidence of toxicity was present. Analyzing the microarray
data using the candidates selected in Chapter 3 suggested that the molecular
profiles for DBCP exposure are different than those of SC toxicants. As alluded
to previously, this could be because SC toxicants disrupt the microenvironment
necessary for spermatogenesis, altering mRNA content in sperm, while GC
toxicants disrupt the GCs directly by inducing apoptosis, leaving the surviving
sperm unharmed (Figure 2). However, more work is needed to confirm this
hypothesis. The miRNA analyses highlight the potential utility of sperm miRNAs
as biomarkers of toxicant induce injury. As observed with the mRNAs in Chapter
3, the PCR arrays were more sensitive in detecting molecular alterations
189
compared to the Affymetrix arrays. Unfortunately, the exposure paradigms
utilized in this study were not optimal for assessing miRNA alterations because
we only saw a few miRNAs change after the 3-month post-exposure recovery.
Additional studies with larger sample sizes and more time points will provide
greater insight to these miRNA effects.
190
Figure 2. Proposed Mechanisms of Toxicant Exposure on Epididymal
Sperm mRNA Transcripts
(A.) Sertoli cells (SC) support developing germ cells by creating a nurturing
microenvironment that facilitates spermatogenesis. Normal spermatogenesis
yields sperm that contain a normal transcriptome and are subsequently prepared
for embryogenesis. (B.) The SC microenvironment is not affected by DBCP.
Spermatogenesis is directly disrupted because DBCP targets spermatocytes and
round spermatids. The apoptosis resistant sperm have normal transcript content.
(C.) SC toxicants (HD and CBZ) alter this microenvironment and disrupt
spermatogenesis. These sperm contain abnormal transcript content and are
prepared for embryogenesis in a stressful environment.
191
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1.
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Irimura, K., et al., Collaborative work to evaluate toxicity on male
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epididymides of rats with 2-week daily repeated dosing. J Toxicol Sci,
2000. 25 Spec No: p. 251-8.
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Muguruma, M., et al., Molecular mechanism on the testicular toxicity of
1,3-dinitrobenzene in Sprague-Dawley rats: preliminary study. Arch
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Reeve, I.T., J.C. Voss, and M.G. Miller, 1,3-Dinitrobenzene metabolism
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Reeve, I.T. and M.G. Miller, 1,3-Dinitrobenzene metabolism and protein
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Strandgaard, C. and M.G. Miller, Germ cell apoptosis in rat testis after
administration of 1,3-dinitrobenzene. Reprod Toxicol, 1998. 12(2): p. 97103.
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Erkekoglu, P., et al., The Effects of Di(2-Ethylhexyl)Phthalate Exposure
and Selenium Nutrition on Sertoli Cell Vimentin Structure and Germ-Cell
Apoptosis in Rat Testis. Arch Environ Contam Toxicol, 2011.
8.
Richburg, J.H. and K. Boekelheide, Mono-(2-ethylhexyl) phthalate rapidly
alters both Sertoli cell vimentin filaments and germ cell apoptosis in young
rat testes. Toxicol Appl Pharmacol, 1996. 137(1): p. 42-50.
9.
Bjorge, C., et al., In vitro toxicity of 1,2-dibromo-3-chloropropane (DBCP)
in different testicular cell types from rats. Reprod Toxicol, 1995. 9(5): p.
461-73.
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Pearson, P.G., et al., Metabolic activation of 1,2-dibromo-3-chloropropane:
evidence for the formation of reactive episulfonium ion intermediates.
Biochemistry, 1990. 29(20): p. 4971-81.
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Ahmad, N., J.R. Wisner, Jr., and D.W. Warren, Morphological and
biochemical changes in the adult male rat reproductive system following
long-term treatment with 1,2-dibromo-3-chloropropane. Anat Rec, 1988.
222(4): p. 340-9.
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12.
Bryant, B.H., et al., Spermatid head retention as a marker of 2,5hexanedione-induced testicular toxicity in the rat. Toxicol Pathol, 2008.
36(4): p. 552-9.
13.
Linder, R.E., R.A. Hess, and L.F. Strader, Testicular toxicity and infertility
in male rats treated with 1,3-dinitrobenzene. J Toxicol Environ Health,
1986. 19(4): p. 477-89.
14.
Moffit, J.S., et al., Dose-dependent effects of sertoli cell toxicants 2,5hexanedione, carbendazim, and mono-(2-ethylhexyl) phthalate in adult rat
testis. Toxicol Pathol, 2007. 35(5): p. 719-27.
15.
Krol, J., I. Loedige, and W. Filipowicz, The widespread regulation of
microRNA biogenesis, function and decay. Nat Rev Genet, 2010. 11(9): p.
597-610.
193
CHAPTER 6. INTEGRATIVE DNA METHYLATION AND GENE EXPRESSION
ANALYSES IDENTIFY DNA PACKAGING AND EPIGENETIC REGULATORY
GENES ASSOCIATED WITH LOW MOTILITY SPERM
194
CHAPTER 6. INTEGRATIVE DNA METHYLATION AND GENE EXPRESSION
ANALYSES IDENTIFY DNA PACKAGING AND EPIGENETIC REGULATORY
GENES ASSOCIATED WITH LOW MOTILITY SPERM
Sara E. Pacheco1, E. Andres Houseman2, Brock C. Christensen2, Carmen J.
Marsit1, Karl T. Kelsey1,2, Mark Sigman3, and Kim Boekelheide1
Department of Pathology and Laboratory Medicine1, Department of Community
Health2, Division of Urology3, Brown University, Providence, RI, USA, 02912
PLoS One. 2011;6(6):e20280. Epub 2011 Jun 2. PMID: 21674046
Declaration of authors’ roles
SEP performed all experiments and contributed to the design, analysis,
and writing of the manuscript. EAH directed and designed the statistical analysis
and assisted in writing the manuscript. BCC assisted with methylation
experiments and also contributed to the design, analysis, and writing of the
manuscript. CJM, KTK, and KB contributed to the design, analysis, and writing of
the manuscript.
MS was involved in study design, patient recruitment and
sample and clinical data acquisition. Everyone approved the final version to be
published.
195
Abstract
Background: In previous studies using candidate gene approaches, low
sperm count (oligospermia) has been associated with altered sperm mRNA
content and DNA methylation in both imprinted and non-imprinted genes. We
performed a genome-wide analysis of sperm DNA methylation and mRNA
content to test for associations with sperm function. Methods and results:
Sperm DNA and mRNA were isolated from 21 men with a range of semen
parameters presenting to a tertiary male reproductive health clinic.
DNA
methylation was measured with the Illumina Infinium array at 27,578 CpG loci.
Unsupervised clustering of methylation data differentiated the 21 sperm samples
by their motility values. Recursively partitioned mixture modeling (RPMM) of
methylation data resulted in four distinct methylation profiles that were
significantly associated with sperm motility (P=0.01).
Linear models of
microarray analysis (LIMMA) was performed based on motility and identified
9,189 CpG loci with significantly altered methylation (Q<0.05) in the low motility
samples. In addition, the majority of these disrupted CpG loci (80%) were
hypomethylated. Of the aberrantly methylated CpGs, 194 were associated with
imprinted genes and were almost equally distributed into hypermethylated
(predominantly paternally expressed) and hypomethylated (predominantly
maternally expressed) groups. Sperm mRNA was measured with the Human
Gene 1.0 ST Affymetrix GeneChip Array. LIMMA analysis identified 20 candidate
transcripts as differentially present in low motility sperm, including HDAC1 (NCBI
3065), SIRT3 (NCBI 23410), and DNMT3A (NCBI 1788). There was a trend
196
among altered expression of these epigenetic regulatory genes and RPMM DNA
methylation class. Conclusions: Using integrative genome-wide approaches we
identified CpG methylation profiles and mRNA alterations associated with low
sperm motility.
Introduction
Traditional semen analysis measures sperm concentration, motility,
morphology, and semen volume, and is acknowledged to be a poor predictor of
fertility, demonstrating remarkable intra- and inter-individual variability [1, 2].
Because of these limitations, effort has been devoted to developing sperm
molecular biomarkers that may better and more stably reflect sperm function.
DNA methylation is the stable, covalent addition of a methyl group to
cytosine that can represent response to environmental cues or exposures that
may modify gene expression. Both human and animal studies indicate that
abnormal sperm DNA methylation patterns are associated with subfertility,
including aberrant methylation of both imprinted [3-11] and non-imprinted genes
[4, 12, 13] in oligospermic men.
In addition to DNA methylation, significant effort is being devoted to
developing human sperm mRNAs as biomarkers of infertility [14-30].
The
discovery of mRNAs in mature sperm shook the long-held belief that the sole
purpose of sperm was to deliver its DNA to the egg [14]. Recent evidence
indicates that some of these transcripts may be intentionally transported to the
197
oocyte to aid embryogenesis, since some sperm mRNAs are found to persist in
the zygote and are functionally important [14, 27, 28]. In addition, remnant sperm
mRNAs provide a record of the spermatogenic environment and may have
clinical applications as novel biomarkers of fertility status [15-26].
In the present study, we utilized high-density array techniques to
investigate the hypothesis that alterations to the pattern of sperm DNA
methylation or mRNA content are associated with sperm function.
Materials and Methods
Ethics Statement
The Committee on the Protection of Human Subjects: Rhode Island
Hospital Institutional Review Board 2 (Committee #403908) approved the study
and written informed consent was obtained from all participants. Clinical
investigation was conducted according to the principles expressed in the
Declaration of Helsinki.
Microarray DataSets
The microarray data discussed in this publication is MIAME compliant and
the raw data has been deposited in NCBI's Gene Expression Omnibus (Edgar et
al.,
2002)
as
detailed
in
the
MGED
Society
website
http://www.mged.org/Workgroups/MIAME/maime.html. This data is accessible
198
through
GEO
Series
accession
number
GSE26982
(http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE26982).
Patient Population, Semen Analysis, and Sperm Isolation
Study subjects presented for semen evaluation at Rhode Island Hospital’s
tertiary male reproductive health clinic. Samples were collected from 21 men with
unknown fertility status and a range of semen characteristics (Table 1). During
the semen analysis, morphology was scored using Kruger strict criteria and total
motility was calculated as described in the WHO laboratory manual (2010) [31].
After clinical analysis the samples were divided into one quarter and three
quarter aliquots for DNA and RNA isolations, respectively. Each group was
processed through an optimized Percoll (GE Healthcare, Uppsala, Sweden)
gradient to eliminate debris, non-sperm cells, and dead sperm [32]. Briefly, 1 ml
of the fresh semen was applied to a monolayer of 50% Percoll. After
centrifugation, the upper and interface layers containing the dead sperm and
other somatic contaminants were aspirated off, leaving the sperm enriched
fraction. The sperm fraction was washed with phosphate buffered saline and the
purified sperm samples were processed immediately for mRNA and DNA
isolation.
Prior to processing the 21 samples, sperm purity was confirmed by the
absence of somatic cell contaminants using bright phase microscopy and by the
199
absence of 18/28S ribosomal RNA peaks by RNA gel electrophoresis (data not
shown) [19, 21].
200
Table 1. Semen Parameters of Subjects Examined
Subject ID
Motility (%) Morphology (%)
Count (x106/ml)
16
77
11
132
8
74
4.5
18
26
70
6
80
5
64
8
44
22
64
5.5
67
29
63
7
174
35
62
8
103
13*
60
8
26
28
60
10
72
10
57
10
58
31*
57
4
30
11
52
8
49
36
51
3
44
37
49
1
9
32
46
8.5
23
23
45
1
67
33*
41
6
1.6
34
39
4
20
40
34
4
72
9
30
3.5
7
41
21
0
15
Note: Patients with * were excluded from the mRNA analysis due to
low yield.
201
DNA Isolation, Bisulfite Modification, and Illumina Infinium
HumanMethylation27 BeadChip Array
DNA was isolated from the sperm of the 21 men using a modified protocol
in which sperm pellets were lysed for 16 hours in a solution containing Tris
(Fisher Scientific, Pittsburgh, PA, USA), DTT (Promega Corporation, Madison,
WI, USA), NaCl (EMD Chemicals, Inc., the North American Affiliate of Merck
KGaA, Darmstadt, Germany), EDTA (Fisher Scientific, Pittsburgh, PA, USA),
SDS (Fisher Scientific, Pittsburgh, PA, USA), Proteinase K (Promega
Corporation, Madison, WI, USA), and beta-mercaptoethanol (Sigma-Aldrich, St.
Louis, MO, USA) [33]. The DNA was then extracted using phenol/chloroform
(Sigma-Aldrich, St. Louis, MO, USA), ethanol precipitated, and bisulfite modified
using the EZ DNA Methylation kit (Zymo Research Corporation, Orange, CA,
USA). Genome-wide scanning for DNA methylation was performed using the
Illumina Infinium HumanMethylation27 BeadChip assay (Illumina, Inc., San
Diego, CA, USA) to determine the methylation state at 27,578 CpG sites
spanning more than 14,000 genes; and on this array, there are 616 CpGs
associated with 187 imprinted genes identified using the array’s annotation file
(HumanMethylation27_270596_v.1.2,
www.Illumina.com).
Multiple
groups
including ours have previously demonstrated the validity of Illumina methylation
array data using several different approaches [34-39].
202
Imprinted Genes
A list of 187 imprinted genes in the human genome was compiled based
on information from three sources: (1) experimentally determined imprinted
genes listed in two databases (http://www.geneimprint.com/databases/ and
http://igc.otago.ac.nz/home.html) (n=62); (2) imprinted genes identified using the
ChIP-SNP method (n=27) [40]; and (3) protein-coding genes from the 156
putatively imprinted sequences that correspond to known genes listed by NCBI
(n=106) [41]. Taken together, a final list of 187 imprinted genes is identified from
these three sources (Table S1).
mRNA Isolation and Affymetrix GeneChip Human Gene 1.0 ST Array
Sperm mRNA was extracted from 18 of the 21 men using a modified Stat
60 (IsoTex Diagnostics, Inc., Friendswood TX, USA) protocol in addition to
components of Qiagen’s RNeasy kit (Qiagen Sciences, Germantown, MD, USA).
Using the Brown Genomics Core Facility, the isolated sperm mRNA was
processed and hybridized to Affymetrix GeneChip Human Gene 1.0 ST Arrays
(Affymetrix, Santa Clara, CA, USA), providing whole-transcript coverage of
28,869 genes by ~26 probes spread across the length of each gene. The probe
cell intensity data from the Affymetrix GeneChips was normalized and annotated
using Affymetrix Expression Console as recommended by the manufacturer. The
application uses the RMA-Sketch workflow analysis as the default to create CHP
files. The CHP log2 expression files were then merged in Expression Console
with the annotation file and the annotated log2 results were exported as a text file
203
for third-party downstream analysis.
Statistical Analyses
Aside from array normalization procedures, the R software environment (R
Foundation for Statistical Computing, Vienna, Austria) was used for all statistical
analysis.
Recursively Partitioned Mixture Modeling
Recursively partitioned mixture modeling (RPMM) profiles were fit to the
entire Infinium array using previously described methods [42]. This method builds
classes of samples based upon the similarity of methylation profiles by
recursively splitting samples into parsimoniously differentiated classes. The
classes are identified by pattern of branching into right (R) or left (L) arms.
Permutation tests (5,000 permutations run with the Kruskal-Wallis [KW] test
statistic) were used to test associations between RPMM class and the 3 clinical
fertility variables: count, motility and morphology, using the values in Table 1. Our
test statistic was the maximum of the KW test statistic, and the null distribution
for this test statistic was obtained by the permutation. Semen parameters were
considered significantly associated with RPMM profiles when P<0.02, after
Bonferroni correction for multiple comparisons.
204
Quantitative Analysis of the DNA Methylation Status of All CpGs
The LIMMA procedure [43] (R package limma) utilized a matrix design
containing the 21 samples and their corresponding percent motility values listed
in Table 1 to fit a simple linear regression model for each CpG dinucleotide. This
univariately tests each CpG for association between methylation and sperm
motility. LIMMA results provided estimates of strength and direction of
association between CpG methylation and sperm motility and were adjusted for
multiple comparisons with the qvalue package in R [44]. CpGs with positive
slopes were interpreted as hypomethylated in low motility sperm and CpGs with
negative slopes were interpreted as hypermethylated in low motility sperm.
mRNA Content Analysis of Candidate Transcripts
The transcript presence of the 276 candidate genes was tested using the
same statistical strategy as the CpG analysis except here the design matrix was
limited to the 18 samples with array data and the slopes were transformed into
fold change values. The Affymetrix platform yielded a dataset with ~28,000
transcripts to assess. However, sperm contain a limited transcriptome (~5000
transcripts) with few (~400) consistently expressed in sperm [22]. Therefore, we
assessed 276 genes where an a priori hypothesis for association with subfertility
existed based on previous reports. The analysis included 177 imprinted genes
(10 of the 187 potential imprinted genes were not present on the Affymetrix
array) as well as 99 candidate genes with biallelic expression (Supplemental
Table 1 and Supplemental Table 2) [10, 11, 13, 24, 26, 29, 45-49].
205
Statistical Analysis Comparing Associations Among RPMM Classes and
Candidate Genes
Associations among the RPMM classes and the normalized gene
expression values for candidate transcripts were calculated with the KW test
statistic utilizing the strategy employed previously. Messenger RNAs were
considered significantly associated with RPMM class when P<0.02, after
adjusting for multiple comparisons using the Bonferroni correction.
Results
Sperm DNA Methylation Profiles Cluster by Motility
Unsupervised clustering of sperm DNA methylation data for the 1,000
most variable CpG loci on the array highlights the methylation differences among
the 21 individual men (Figure 1). As shown in the column annotation track, the
clustering differentiated men based upon the motility of their sperm, with high
motility samples (dark purple) clustering together and low motility samples (dark
orange) clustering together, with intermediate shades between.
methylation
of
CpGs
within
imprinted
genes
is
The DNA
established
during
spermatogenesis and maintained in mature spermatozoa. In addition, several
laboratories have shown alterations at imprinted loci to occur more frequently in
men with sperm abnormalities [3-8, 10, 11]. Thus, we hypothesized that
imprinted loci may be specifically targeted for aberrant methylation in low motility
sperm and separately clustered the 616 CpG loci associated with the 187
206
imprinted genes present on the array. We observed the same overall trend, with
high motility samples clustering together and low motility samples clustering
together (Figure 2).
207
Figure 1. Unsupervised Clustering of the 1,000 Most Variable CpG Loci
Average Beta Values
The dendrograms above the heatmap show unsupervised clustering based on
the methylation data alone, using a Euclidean metric with Ward’s method of
hierarchical clustering. Patients are represented by column (n=21) and CpG loci
(n=1000) by row. Each cell represents the CpG level of methylation for one site in
one sample. The methylation scale indicates the level of methylation: yellow =
sample predominantly unmethylated (0-49% methylated); black = 50% of the
sample is methylated; blue = sample predominantly methylated (51-100%). The
column annotation track shows motility: orange = low, purple = high.
208
Figure 2. Heatmap Displaying the Methylation Status of CpG Loci Related
to Known and Predicted Imprinted Genes
The dendrograms above the heatmap show unsupervised clustering based on
the methylation data alone, using a Euclidean metric with Ward’s method of
hierarchical clustering. Patients are represented by column (n = 21) and CpG loci
by row (n=616). Each cell represents the CpG level of methylation for one site in
one sample. The methylation scale indicates the level of methylation: yellow =
sample predominantly unmethylated (0-49% methylated); black = 50% of the
sample is methylated; blue = sample predominantly methylated (51-100%). The
column annotation track shows motility: orange = low, purple = high. The row
annotation track shows type of imprinting: pink = maternally expressed, light blue
= paternally expressed, light green = not determined.
209
Sperm DNA Methylation Profiles are Significantly Associated with Motility
Recursively partitioned mixture modeling (RPMM) was performed on raw
methylation data to organize the sperm samples into methylation classes based
on similarity. The algorithm first separated the 21 sperm profiles into two different
branches left (L) and right (R) and then further subdivided each branch into right
and left branches resulting in 4 total classes: left left (LL), left right (LR), right left
(RL) and right right (RR) (Figure 3, A). In Figure 3 (B) we plotted methylation
class-specific sperm motility values: samples in methylation class RR had the
lowest median motility, and methylation class was significantly associated with
motility after adjusting for multiple comparisons (P=0.01). The association
between RPMM methylation class and sperm morphology approached statistical
significance (P=0.09), though methylation class was not associated with sperm
count (P=0.29).
210
Figure 3. RPMM Classes
(A) RPMM displaying 1,000 most variable CpGs by class. Each column
represents a class generated by RPMM (left left (LL), left right (LR), right left
(RL), and right right (RR)). The width of the column represents the number of
patients distributed in that class. The rows represent the average beta values for
each CpG within the class. The methylation scale indicates the level of
methylation: yellow = sample predominantly unmethylated (0-49% methylated);
black = 50% of the sample is methylated; blue = sample predominantly
methylated (51-100%).(B) Boxplot comparing the motility values for each class.
211
Thousands of CpG Loci are Significantly Altered in Low Motility Sperm
Linear models of microarray analysis (LIMMA) was used to univariately
test each CpG for association with motility. 9,189 of 27,578 CpGs (34%) had
significantly altered methylation associated with motility after adjusting for
multiple comparisons (Q<0.05) (Supplemental Table 3). Of these, 1,827 CpGs
(20%) were hypermethylated in the low motility samples, whereas 7,362 CpGs
(80%) were hypomethylated.
Because establishing proper methylation marks within imprinted genes
during spermatogenesis is critical, we next restricted our analysis to CpGs
associated with imprinted genes. Of the 616 CpGs associated with imprinted
genes, 194 CpGs (31.5%) had significant associations with motility, similar to the
distribution of the array overall. Amongst these loci, 47% (n=92) were
hypermethylated in the low motility samples, whereas 53% (n=102) were
hypomethylated. The majority of hypomethylated CpGs were on maternally
expressed genes (45%), followed by paternally expressed (33%) and those with
undetermined parent of expression (22%). Conversely, the majority of
hypermethylated CpGs were associated with paternally expressed genes (70%),
with the remainder maternally expressed (26%), and of undetermined parental
expression (4%). The 194 loci corresponded to 92 genes, with 11 genes showing
both hyper- and hypomethylated loci (Table 2).
Aberrant promoter methylation in genes related to spermatogenesis and
epigenetic regulation have recently been identified in sperm from men with poor
212
semen quality [3-13] Thus, we next performed an analysis restricted to array
CpGs associated with genes related to spermatogenesis and epigenetic
regulation. Of the 147 CpGs on the array associated with genes involved in
spermatogenesis, 39% (n=58) were significantly altered in low motility sperm
(similar to the 34% of CpGs associated with low motility in array-wide tests, Table
2). Among these 58 CpG loci, 71% (n=41) were hypomethylated and 29% (n=17)
were hypermethylated in low motility samples. There were 50 CpG loci
associated with epigenetic regulatory genes identified on the array, and only 26%
(n=13) had significantly altered methylation in low motility sperm samples. Of
these,
61.5%
(n=8)
were
hypomethylated
hypermethylated (Table 3).
213
and
38.5%
(n=5)
were
Table 2. Examples of Known Imprinted Genes with Aberrant DNA
Methylation
Gene
E
#
MS
Gene
E # MS
N
1
N 2
BMPR2
MAPK12
N
1
M 3
+
CCNE1
MEG3*
N
2
P 3
-/+
CD44
MEST*
M
3
P 1
+
CDKN1C
MKRN3
P
2
P 3
+
COPG2
NDN
N
2
+
N 1
CTAG2
NEDD9
N
1
N 1
CTNND2
NGFB
N
1
P 5
+
CYR61
NNAT
P
11
+
N 2
DIRAS3*
PCNA
P
1
+
P 8
-/+
DLK1
PEG10
M
3
-/+
M 4
DLX5
PHLDA2
P
1
P 5
+
GABRA5
PLAGL1*
P
1
P 1
+
GFI1
SDHD
N
5
-/+
P 2
+
GRB10
SGCE
M
4
-/+
M 1
H19*
SHANK2
P
1
M 7
-/+
HYMAI
SLC22A18
P
1
P 6
+
IGF2*
SNRPN*
P
4
M 3
+
IGF2AS
TCEB3C
N
2
M 2
-/+
IL1B
TP73
N
2
M 1
ILK
UBE3A
M
2
+
P 9
-/+
KCNQ1DN
WT1
P
2
+
P 5
+
L3MBTL
ZIM2
N
2
M 3
-/+
LASS4
ZNF264
N
1
P 1
LMO1
ZNF331
P
3
+
MAGEL2
Note: E = parent with expressed allele; # = number of significantly altered loci
in low motility samples; MS = methylation status of the loci; M = maternally
expressed; P = paternally expressed; N = parent of origin not determined; - =
loci hypomethylated in low motility samples; + = loci hypermethylated in low
motility samples; -/+ = more than two loci were altered and some of the loci
were hypomethylated and some of were hypermethylated. Genes with * have
been previously reported differentially methylated in sperm.
*This is just a sampling of genes. If you’d like to see the whole table please
refer to the published manuscript.
214
Table 3. Genes Associated with Spermatogenesis and Epigenetic
Regulation with Aberrant DNA Methylation
Gene
# MS
Gene
# MS
Gene
# MS
Gene
#
MS
ADAM2
1
+
CSF1
2
-
KIT
1
-
SIRT5
1
-
ADAMTS
2
AR
1
-
CTCF
1
-
LIMK2
1
-
SIRT7
2
-
4
-/+
CTCFL
1
+
MLH1
2
+
SLC12A2
1
-
ATM
5
-/+
DAZL*
1
+
MORC1
2
+
SPO11
2
+
BCL2
2
-/+
DDX4
1
+
MSH5
1
-
STRBP
1
-
BCL2L2
1
-
DHH
1
-
MTHFR*
1
+
STYX
1
-
BCL6
2
-
DMC1
1
-
PCSK4
1
-
SYCP3
2
+
BRDT
1
+
DNMT3A
1
-
PIK3CG
1
-
TBPL1
1
-
BMP8B
1
+
DNMT3B
1
+
PMS2
2
-
TERT
1
-
BSG
1
-
EGR4
2
-/+
RXRB
1
-
TUSC2
2
-
CCNA1
1
-
HCLS1
1
-
SIAH1
2
-
VDAC3
1
+
CDKN2C
1
-
HDAC2
1
-
SIRT1
2
-/+
CREM
2
-
INPP5B
1
-
SIRT2
1
-
Note: # = Number of significantly altered loci in low motility samples; MS =
methylation status of the loci; - = loci hypomethylated in low motility samples; + =
loci hypermethylated in low motility samples; -/+ = more than two loci were
altered and some of the loci were hypomethylated and some of were
hypermethylated. Genes with * have been previously reported differentially
methylated in sperm.
215
mRNA Content is Altered in Low Motility Sperm
Focusing on imprinted mRNAs and candidate biallelic mRNAs, LIMMA
analysis
was
performed
to
identify
differentially
expressed
transcripts,
conditioning on motility. Twenty genes were identified as significant after
adjusting for false discovery rate (Q<0.05) (Supplemental Table 4). These
included 11 imprinted genes (GLI3 (NCBI 2737), APAB1 (NCBI 320), CTNND2
(NCBI 1501), FERMT2 (NCBI 10979), PHPT1 (NCBI 29085), SNRPN (NCBI
6638), PPP1R9A (NCBI 55607), CDH18 (NCBI 603019), ALDH1L1 (NCBI
10840), LDB1 (NCBI 8861), and PEX10 (NCBI 5192)), six genes associated with
spermatogenesis (SERPINA5 (NCBI 5104), ACE (NCBI 1636), FANCC (NCBI
2176), PCSK4 (NCBI 57460), CYP19A1 (NCBI 1588), and FAS (NCBI 355)), and
three epigenetic regulatory genes (HDAC1, DNMT3A, and SIRT3). HDAC1,
DNMT3A, LDB1 and FAS showed increased mRNA content in the low motility
samples, whereas the remaining 16 showed decreased mRNA content.
Integration of Epigenetic and Transcript Data
It is known that major modifications in chromatin organization occur in
spermatid nuclei during spermatogenesis, leading to the high degree of
packaging in the sperm head. Chromatin compaction ensues when the histones
surrounding the DNA are replaced by protamines, and this occurs in parallel with
transcriptional arrest [45]. Therefore, nuclear packaging and transcript content
are interrelated. To determine whether altered expression of epigenetic
regulatory genes was associated with methylation profiles we plotted the
216
methylation class-specific gene expression values for the three epigenetic
regulatory genes (HDAC1, SIRT3, and DNMT3A) with significantly altered
expression in low motility sperm (Figure 4).
Among methylation classes,
expression values for HDAC1, SIRT3, and DNMT3A were most altered in class
RR, the class with lowest motility sperm (increased expression for HDAC1 and
DNMT3A, and decreased expression for SIRT3). For all three genes, the
association between mRNA expression level and methylation class membership
approached significance after adjusting for multiple comparisons (HDAC1,
P=0.03; SIRT3, P=0.06; and DNMT3A, P=0.07).
217
Figure 4. Boxplots Comparing DNA Methylation Profiles and Gene
Expression Values for the 3 Epigenetic Regulators
Each panel of boxplots (A-C) compares expression data for a particular gene:
(A) HDAC1; (B) SIRT3; and (C) DNMT3A. The x-axis represents RPMM classes.
The y-axis represents normalized gene expression values (NEV).
218
Discussion
Currently, the evaluation of male infertility relies upon physical exam and
semen and hormone analyses; although quick and relatively inexpensive, these
physiologic measurements often do not explain the underlying cause of infertility
nor predict the usefulness of various therapeutic interventions. Therefore, new
approaches are needed to identify the etiologies of male infertility. Recent data
suggest that sperm DNA methylation abnormalities and alterations in sperm
mRNA content are found in infertile men [3-8, 10, 11, 13-30, 50]. Here we extend
these studies by performing integrative analysis of sperm DNA methylation and
mRNA
content
using
genome-wide
approaches
to
identify
significant
associations among these profiles and semen parameters.
Due to the unreliable nature of classifying men into abnormal and normal
groups during a semen analysis, we used a data driven approach to first
qualitatively assess associations among sperm DNA methylation and our patient
population. Unsupervised clustering indicated that there was an association
between DNA methylation and motility status. This was true both for all of the
CpGs on the array and the imprint-only subset.
RPMM separated the 21 men into four classes based on similarity of DNA
methylation array data. The median motility values were calculated for each class
and the results suggested that the methylation profiles were associated with
motility. Comparing the DNA methylation heatmap to the class versus motility
boxplot indicates that the low motility class has the most aberrantly methylated
CpGs. Overall, these data suggest that low motility sperm have increased
219
hypomethylation relative to high motility sperm. We used LIMMA to identify the
significantly altered CpGs conditioned on changes in motility for all CpGs on the
array: over one-third of the CpGs (and almost half of the genes represented on
the array) were significantly differentially methylated in the low motility samples
and the majority of these were hypomethylated. The high prevalence of
aberrantly methylated CpGs suggests a genome-wide DNA methylation defect in
the low motility sperm. It has been previously hypothesized that the aberrant
sperm DNA methylation could be due to abnormal chromatin compaction,
inefficient DNA methyltransferases, and/or failure to maintain or acquire the
correct methylation marks during spermatogenesis and our results are consistent
with this literature [11-13, 29].
We initially focused on CpGs mapping to imprinted genes because of their
plasticity during spermatogenesis, biological relevance following conception and
development, and because previous studies have identified imprinted loci as
aberrantly methylated in abnormal sperm [3-8]. In our data, the distribution of
significantly hyper- and hypomethylated imprinted loci was nearly equal.
Expanding the imprinting analysis to the gene level identified 92 genes with
altered CpG methylation, seven of which (DIRAS3 (NCBI 9077), H19 (NCBI
283120), IGF2 (NCBI 3481), MEST/PEG1 (NCBI 4232), PLAGL1/ZAC (NCBI
5325), MEG3/GTL2 (NCBI 55384), and SNRPN) have already been noted as
aberrantly methylated in abnormal sperm [3-8, 10, 11]. The methylation status of
two genes, (PEG3 (NCBI 5178) and LIT1/KCNQ1OT1 (NCBI 10984)) has been
inconsistently reported in the literature [3, 8, 10]. We observed no statistical
220
differences for these genes between the low and high motility sperm, which is
consistent with the results published by Sato, et al. [10]. In fact, our study
confirmed all of the DNA methylation results reported in the aforementioned
study.
To further clarify the potential functional alterations to imprinted genes and
critical epigenetic regulatory genes, we evaluated sperm mRNA content of 177
imprinted genes and 99 other transcripts where an a priori hypothesis for
association with male subfertility or epigenetic regulation exists. Twenty genes
were identified as demonstrating significantly altered transcript levels in low
motility sperm. All of the mRNAs except HDAC1, DNMT3A, LBD1, and FAS
were present in decreased amounts in low motility sperm, and we did not
observe altered mRNA content for BRDT, which was previously reported to have
increased expression in subfertile patients [29].
Integration of epigenetic and expression data revealed a relationship
between transcript content of three epigenetic regulatory genes (HDAC1, SIRT3,
and DNMT3A) and methylation class. HDAC1 is the predominant histone
deacetylase (HDAC) during spermatogenesis. Histone hyperacetlyation is
required for the histone to protamine exchange and is facilitated by the
degradation of HDAC1 in elongated spermatids [51]. If HDAC1 is in excess, one
could hypothesize that the histones are not being replaced by protamines,
leading to an “immature” sperm chromatin structure, with less compact DNA.
Therefore, incomplete or incorrect nuclear compaction may influence overall
sperm maturation and be reflected in the physiological endpoint of motility.
221
SIRT3 is a class III histone deacetylase and this HDAC family is similar to
the yeast Sir2 protein which has been associated with chromatin silencing and
also plays roles in cellular metabolism and aging [46]. In mammals, however,
SIRT3 is targeted to the mitochondria and functions to induce the expression of
the antioxidant MnSOD to eliminate reactive oxygen species (ROS) generated
during oxidative phosphorylation [52]. Recent studies have found that increased
ROS in sperm have deleterious effects on sperm motility parameters which
ultimately have adverse effects on fertility [53]. Therefore, the decrease in SIRT3
mRNA in the low motility sperm may reflect reduced MnSOD and increased
intracellular ROS during spermatogenesis, leading to a diminished fertility
potential.
The literature also suggests that oxidative stress itself can impede the
process of DNA methylation, resulting in a hypomethylated phenotype [54].
Interestingly, we observed global hypomethylation in the low motility sperm even
though we saw increased DNMT3A transcript presence in the low motility sperm.
Because DNMT3A is the DNA methyltransferase responsible for de novo
methylation, our data suggests a failure of the low motility sperm to acquire the
proper methylation patterns.
Although we were limited by sample size, we used a powerful integrative
approach to simultaneously examine sperm DNA methylation and mRNA content
utilizing two high density array techniques. We found that: (1) low motility sperm
have genome-wide DNA hypomethylation that may be due to a failure of the
sperm to complete chromatin compaction properly because of increased HDAC1
222
presence; (2) low motility sperm have reduced SIRT3 mRNA content which might
be related to increased subcellular ROS during spermatogenesis leading to the
abnormal motility phenotype; and (3) this oxidative stress may be impeding the
ability of DNMT3A to set the correct methylation marks which would also
contribute to the hypomethylated phenotype. Our results suggest that additional
integrative studies including larger sample sizes as well as prospective studies of
fertility following these integrated molecular assessments have great potential to
advance our understanding of the molecular features of sperm associated with
fertility status.
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Supplemental Table 1. Imprinted Genes
Experimentally Tested
ATP10A
BMPR2
CCDC86
CCNE1
CD44
CDKAL1
CDKN1C
COPG2
COPG2IT1*
CPA4
CTAG2
CTNNA3
CTNND2
CYR61
DHCR24
DIRAS3
DLGAP2
DLK1
DLX5
DOK7
E2F7
EPS15
GABRA5
GABRB3
GABRG3
GDNF
GFI1
GNAS
GRB10
GRIA1
H19
HTR2A
HYMAI*
IGF2
IGF2AS
Computationally
Predicted
LASS4
ABCC9
HOXC9
LMO1
ABCG8
HSPA6
MAGEL2
ACD
IFITM1
MAGI2
ADAMTS16 KBTBD3
MAPK12
ALDH1L1
LDB1
MEG3
ANKRD11
LILRB4
MEST
APBA1
LMX1B
MESTIT1*
B4GALNT4
LY6D
MKRN3
BMP8
MYEOV2
NDN
BRP44L
MZF1
NEDD9
BRUNOL4
NDUFA4
NGFB
BTNL2
NKAIN3
NLRP2
CCBL2
NKX6-2
NNAT
CCDC85A
OBSCN
OR11L1
CDH18
OSBPL1A
OSBPL5
CDK4
OTX1
PCNA
CHMP2A
PAOX
PEG3
CHST8
PEX10
PEG10
COL9A3
PHPT1
PHLDA2
CSF2
PKP3
PLAGL1
CYP1B1
PPAP2C
PPP1R9A
DGCR6
PRDM16
SDHD
DUX2*
PTPN14
SGCE
DVL1
PURG
SHANK2
EGFL7
PYY2
SLC22A18
EVX1
RAB1B
SLC22A18AS
FAM50B
RBP5
SLC22A3
FAM59A
RPL22
SNORD64
FAM70B
RTL1
SNORD107*
FAM132A
SALL1
SNORD108*
FAM174A
SIM2
SNORD109B*
FASTK
SLC22A2
SNORD116@*
FERMT2
SLC26A10
SNRPN
FGFRL1
SLC4A2
SNURF
FOXF1
SOX8
228
IL1B
ILK
INS*
KCNK9
KCNQ1
KCNQ1DN
KCNQ1OT1*
KLF14
L3MBTL
TCEB3C
TFPI2
TP73
UBE3A
WT1
ZIM2
ZNF264
ZNF331
FOXG1
SPON2
FUCA1
TIGD1
GATA3
TMEM52
GLI3
TMEM60
GPT
TMEM88
HES1
TSHZ3
HIST3H2BB
VAX2
HOXA2
VENTX2
HOXA3
WDR8
HOXA4
ZFP36L2
HOXA5
ZIC1
HOXA11
ZNF225
HOXB2
ZNF229
HOXB3
ZNF550
HOXC4
ZNF738
Note: Genes with * were excluded from the LIMMA
mRNA analysis because they were not on the Affymetrix
array.
229
Supplemental Table 2. Genes Associated with Spermatogenesis and
Epigenetic Regulation
ACE
CTCF
HDAC8
RXRB
ADAM2
CTCFL
HMGB2
SERPINA5
ADAM3A*
CYP19A1
HSPA1B
SIAH1
ADAMTS2
DAZL
INHA
SIRT1
AGFG1*
DDX4
INPP5B
SIRT2
APAF1
DHH
KIT
SIRT3
APOB
DMC1
KITLG
SIRT4
AR
DMRT1
LHCGR
SIRT5
ATM
DNAH1*
LIMK2
SIRT6
BAX
DNMT1
MAN2A2
SIRT7
BCL2
DNMT3A
MLH1
SLC12A2
BCL2L2
DNMT3B
MORC1
SPO11
BCL6
DNMT3L
MSH5
STRBP
BMP8B
EGR4
MTHFR
STYX
BDRT
EMHT1*
MYBL1
SYCP3
BSG
FANCA*
NCOA1
TBPL1
CAMK4
FANCC*
NCOA3
TERT
CCNA1
FANCG*
PCSK4
THEG
CDKN2C
FAS
PIK3CG
TNP1
CLDN11
GJA1
PMS2
TNP2
CLGN
HAT1
PPP1CC
TRDMT1*
CPEB1*
HCLS1
PRM1
TUSC2
CREM
HDAC1
PRM2*
UBE2B
CSF1
HDAC2
PRM3*
VDAC3
CSNK2A2
HDAC3
ROS1
Note: Genes with * were not on the Illumina Infinium Array.
230
Supplemental Table 3. Aberrant CpGs in Low Motility Sperm
This table has been excluded from this dissertation due to its large size. If
necessary, please download separately at www.PLoSOne.org.
231
Supplemental Table 4. Aberrant mRNA Transcripts in
Low Motility Sperm
Gene
Slope (log2)
Fold Change
q-value
2.5
-5.7
0.016
GLI3
3.7
-13.0
0.016
SERPINA5
1.9
-3.7
0.020
ACE
1.5
-2.8
0.020
APBA1
-3.2
9.2
0.020
HDAC1
2.1
-4.3
0.028
CTNND2
2.2
-4.6
0.028
FERMT2
2.3
-4.9
0.028
CYP19A1
2.1
-4.3
0.028
PHPT1
2.7
-6.5
0.028
SNRPN
2.3
-4.9
0.030
FANCC
1.6
-3.0
0.036
SIRT3
-1.0
2.0
0.041
DNMT3A
2.1
-4.3
0.041
PPP1R9A
2.1
-4.3
0.042
CDH18
1.5
-2.8
0.046
ALDH1L1
-3.0
8.0
0.046
LDB1
2.6
-6.1
0.047
PCSK4
2.0
-4.0
0.047
PEX10
-2.6
6.1
0.049
FAS
Note: Genes with a negative slope (-) have increased
transcript presence in the low motility samples. Genes with a
positive slope (+) have decreased transcript presence in the
low motility samples.
232
CHAPTER 7. DISCUSSION
233
CHAPTER 7. DISCUSSION
Synopsis
This dissertation highlights the potential use of sperm molecular
signatures as biomarkers of testicular injury and dysfunction. Mature sperm
provide the paternal contribution to the fertilized oocyte, which includes genomic,
epigenomic, transcriptomic, and proteomic components [1]. Emerging evidence
suggests that all of these factors have an influence on sperm function and fertility
potential [2-19]. Animal and human studies suggest that paternal exposures may
induce alterations in sperm and that these changes can lead to poor reproductive
outcomes [20-22]. It is important to note that no semen assessment can fully
predict fertility, and in fact, the underlying cause of infertility is unknown in
roughly two-thirds of men with non-obstructive spermatogenic defects with
seemingly normal semen quality [23]. Since the sperm must contain all of the
components needed to fertilize a normal egg and form the zygote, nonazoospermic male infertility must be due to a deficiency in the sperm itself.
Because of this, new approaches to assessing male reproductive function must
be developed. Analyzing sperm subcelluar content has broad applications in
assessing male reproductive function, including: (1) toxicity testing and drug
development; (2) biomonitoring of environmental, occupational, and therapeutic
exposures; (3) and clinical evaluations of fertility. These applications will be
discussed further at the end of this chapter.
234
Conclusions, Limitations, and Future Directions
Aim 1 - Animal Models
1.1
Characterize rat lowest-observable-adverse-effect-level exposures to
Sertoli cell toxicants and determine their effects on sperm mRNA content.
The focus of Aim 1 was to develop the foundation required to identify
sperm molecular signatures of testicular toxicant exposure in the rat. Chapter 2
described an automated update to a classic testicular toxicity assessment
protocol to enumerate homogenization resistant spermatid heads in an efficient
and unbiased manner. Chapter 3 utilized the automated counting protocol
developed in Chapter 2, in concert with other standard endpoints, to characterize
low dose sub-chronic exposure models to two Sertoli cell (SC) toxicants. The
potential role of sperm mRNA content as a biomarker of testicular injury was also
evaluated in this chapter using Affymetrix and PCR arrays. Because miRNAs are
another RNA population within sperm, Chapter 4 discussed the identification and
characterization of sperm miRNAs during normal spermatogenesis utilizing
Affymetrix and PCR arrays. This study served as a benchmark for assessing
these small RNAs in sperm after toxicant exposure, which is discussed in
Chapter 5. Chapter 5 highlighted preliminary studies investigating the roles of
other testicular toxicants on sperm RNA content, for future use as a point of
reference for upcoming studies in our laboratory.
235
The major finding of Aim 1.1 was that sub-chronic low dose exposure to
SC toxicants produced alterations to the cauda epididymal sperm transcriptome
in the rat (Chapter 3). These novel findings indicate that sperm mRNA content is
an informative biomarker of toxicant-induced testicular injury at low doses,
especially as these alterations persisted for months after exposure cessation. In
addition, our results suggest that these biomarkers are able to discern toxicantspecific responses that can be used to elucidate the differing mechanisms of
action of the selected SC toxicants, 2,5-hexanedione (HD) and carbendazim
(CBZ). Moreover, these selected biomarkers are promising tools for screening
and categorizing additional SC toxicants based on their molecular sperm
signatures. Complementary studies were performed using the PCR array panel
for two other SC toxicants, di-(2-ethylhexyl)phthalate and 1,3-dinitrobenzene
(DNB); and the DNB results suggest that our transcript panel can detect SC
injury (Chapter 5). In addition, we performed a similar study analyzing sperm
mRNAs after sub-chronic low dose exposure to the germ cell (GC) toxicant 1,2dibromo-3-chloropropane (DBCP) using microarrays (Chapter 5). However, no
changes were observed analyzing the entire array or the 29 candidate transcripts
selected in Chapter 3 using LIMMA. These results suggest that GC toxicants
generate a different sperm molecular signature than the SC toxicants or do not
alter mRNA content. With this in mind, we collaborated with Boehringer
Ingleheim and obtained sperm RNA from rats exposed to DBCP at a higher dose
with a shorter dosing paradigm (25 mg/kg/d for 1 month, with an additional 1
236
month recovery arm). These samples have not yet been analyzed, but will be
tested using the PCR array panel to determine its efficacy.
1.2
Identify the miRNAs present in rat sperm during normal spermatogenesis.
Aim 1.2 characterized the rat sperm miRNA population during normal
spermatogenesis (Chapter 4). In this study, the small RNAs were isolated from
sperm and arrayed using both the Affymetrix and PCR array platforms, which
identified a subset of universally present miRNAs. Subsequent analyses showed
no chromosome preferences for the miRNAs or their confirmed targets and IPA
identified
miRNAs
important
for
reproductive
function
and
embryonic
development. Sperm miRNA content was analyzed after sub-chronic low dose
exposure to HD and DBCP and no treatment-related changes were observed
using the PCR array (Chapter 5). Interestingly, three miRNAs were altered after
post-exposure recovery for HD and DBCP (HD, n = 1 and DBCP, n = 2) using the
PCR array, but these findings were not investigated further. The overall lack of
miRNA alterations after toxicant exposure may be due to their tight regulation or
the intricate timing of spermatogenesis. It is possible that we did not select
appropriate time points to assess changes in miRNA content. To address this
issue, miRNA PCR arrays will be performed for miRNAs isolated and archived
from the HD time course study (Chapter 3).
237
A major limitation to this aim was the duration of the exposures. Rats were
exposed to the toxicants for 3 months, which is the appropriate length of time to
assess toxicant effects on spermatogenesis and sperm function, but is too long
for most good laboratory practice animal studies and Phase I human trials. Our
HD time course data suggests a time dependence for the transcript alterations,
with the most robust response occurring after 3 months of exposure, extending
into the recovery phase (Chapter 3). Therefore, sperm toxicogenomics analyses
are most likely best suited for extended Phase III trials and large Phase II trials
designed solely for reproductive toxicity testing. It is possible that acute
exposures to higher doses of these toxicants may show a transcript effect,
however, this hypothesis needs further exploration.
Another limitation to this
study is the smaller samples sizes in the HD time course and application studies.
Due to the inter-animal variability, this may have given non-significant results,
especially for the apical endpoints and perhaps for some of the PCR data as
well.
Although the studies presented in Aim 1 had some limitations, the
resulting data generated many questions regarding toxicant effects on sperm
molecular signatures, all of which are worthy of further exploration. These
questions are listed here and discussed below.
1.
Are sperm mRNAs a more sensitive biomarker of testicular injury
than histological analyses?
238
2.
Is the biomarker panel capable of discerning mechanisms of
toxicant action or is it specific to SC exposures at equipotent
doses?
3.
Do SC and GC toxicants produce different molecular signatures?
4.
What does the sperm mRNA profile look like after co-exposure
to two toxicants that have opposing mechanisms of action (HD
and CBZ)?
5.
Do these aberrant mRNAs disrupt normal fertilization and early
embryogenesis?
6.
Are sperm DNA methylation profiles altered after toxicant
exposure?
1. Are sperm mRNAs a more sensitive biomarker of testicular injury than
histological analysis?
Based on our results sperm biomarkers can be used to assess testicular
injury. We observed molecular changes at doses that induced histological
changes, which highlights their potential utility as biomarkers, but is not indicative
of their sensitivity. Histology is currently the gold standard for determining
testicular injury, but recent evidence in other tissues suggests that molecular
changes happen prior to histological alterations [24]. Thus, it is necessary to
determine whether sperm molecular changes occur before histological alterations
appear in the testis. This will be useful for evaluating the dose response curve
and raises the question of whether the sperm molecular changes are adverse.
239
This question can easily be addressed by doing a low dose extrapolation for HD,
quantifying RSH and testing the PCR biomarker panel. If molecular changes are
detected at doses in which no phenotypic alterations are observed, then our
biomarkers are in fact more sensitive.
2. Is the biomarker panel capable of discerning toxicant mechanisms of action or
is it specific to SC exposures at equipotent doses?
We know that our biomarker panel can measure SC toxicity, but we do not
know how specific it is for determining mechanism of action. To address this
question, dose response studies for CBZ and HD need to be executed to
determine equipotent doses of these two toxicants. Equipotency can be
measured utilizing RSH as the phenotypic anchor. Sperm mRNA profiles need to
be generated using our PCR biomarker panel at these equipotent doses. If the
molecular profiles are similar in direction and magnitude, then the biomarker
panel is specific to determining SC injury; however, if the biomarker panel yields
unique molecular signatures, then it is sensitive enough to discern the differing
mechanisms of action of the toxicants.
3. Do SC and GC toxicants produce different molecular signatures?
The initial hypothesis investigated was whether cell-type specific toxicants
generate different molecular signatures. However, after the initial microarray
240
studies, we decided to pursue the more robust HD effect. This raises the
question of whether DBCP elicits an effect on sperm mRNA content. The
preliminary analysis using microarrays did not detect any changes when
examining the entire array or the HD candidates using LIMMA. However, this
does not mean that the sperm are not altered. Microarrays are not the most
sensitive platform for assessing transcript changes (the absolute fold changes
were around 2-fold with HD), and adjusting for multiple comparisons for all
27,000 transcripts most likely washed out any potential signals. The candidate
approach suggested that the profiles generated after targeting SCs and GCs
were different. Going forward, the transcripts indicative of GC toxicity need to be
identified. The dose we had selected for DBCP was strong enough to reduce
epididymal weights, which suggests that it was a dose high enough to cause
disruptions in spermatogenesis; however DBCP had no detectable effect on
sperm mRNA. This could be because the GCs were targeted directly by the
toxicant, with aberrant GCs undergoing apoptosis, while the surviving sperm
maintain a normal transcript profile during the exposure. A DBCP dose response
study would determine whether the dose that we selected was high enough to
induce the appropriate levels of injury and alterations to the mRNA content. In
addition, a time course study would elucidate the timing of any transcript
changes, in case a more robust response occurs post-exposure.
4. What does the sperm mRNA profile look like after co-exposure to two toxicants
that have opposing mechanisms of action (HD and CBZ)?
241
Previous studies in our laboratory have investigated the effects of HD and
CBZ co-exposure on the testis [25, 26]. While the two toxicants have opposing
actions on the SC microtubules, they have the same physiological effect of
inhibiting microtubule dependent functions in SC. Co-exposure of HD and CBZ in
vitro demonstrated molecular antagonism of the two toxicants on their
microtubule target; however, in vivo the toxicants had a synergistic effect,
producing testicular injury greater than that of the single toxicant exposures [25].
Recent testicular toxicogenomics analysis of the HD and CBZ co-exposure
indicated that there was an agonistic effect on gene expression [26]. With this in
mind, it would be interesting to see whether the co-exposure has synergistic
effects on the sperm mRNA content as well. One way to examine this would be
to co-expose rats to these toxicants at their individual no-observable-adverseeffect level doses for the molecular endpoints and see if the two toxicants in
concert produce significant molecular alterations.
5.
Do
these
aberrant
mRNAs
disrupt
normal
fertilization
and
early
embryogenesis?
It has been suggested that some of the sperm mRNAs play a role in early
fertilization and embryogenesis [1, 6, 11-15, 19]. However, an important step in
strengthening the correlation between the presence of sperm mRNA and
embryogenesis is to verify whether sperm that contain aberrant mRNA profiles
yields embryos with decreased developmental potential [19]. Clusterin, in
242
particular, is hypothesized to be essential for both gametogenesis and
embryogenesis, playing a bioprotective role against heat shock and oxidative
stress [27, 28]. Our sperm molecular signatures showed a 20-fold increase of
clusterin mRNA after CBZ and a 20-fold decrease after HD exposure (Chapter
3). We hypothesize that this drastic alteration in mRNA content will have a
downstream effect on fertilization and embryogenesis. With this in mind, we
predict that CBZ will have a positive effect on embryogenesis, supporting
fertilization and embryo survival and HD will have the opposite effect, hindering
survival. These hypotheses can be investigated using a rat model for in vitro
fertilization (IVF) [29].
6. Are sperm DNA methylation profiles altered after toxicant exposure?
A recent study investigated alterations in rat sperm DNA methylation
following a 9 week exposure to the clinical testicular cancer treatment bleomycin,
etoposide, and cis-platinum (BEP). The authors showed dose dependent hyperand hypomethylation at various loci, but not at imprinting regions [2]. However,
Pathak et al. examined the effects of tamoxifen exposure on DNA methylation
patterning in rat spermatozoa and although no changes in global methylation
were seen, methylation was reduced at the IGF2-H19 imprinted region [30].
These findings suggest that exposures can alter the sperm DNA methylome.
Many approaches can be taken to address whether sub-chronic exposures to our
model toxicants HD, CBZ, and DBCP alter sperm DNA methylation. Ideally, DNA
243
methylation arrays like the one used in Chapter 6 could be used. However, only
limited high-throughput DNA methylation arrays exist for the rodent, such as
those utilizing methylation dependent chromatin immunoprecipitation (Me-DIP
ChIP). Other approaches, such as restriction landmark genomic scanning,
quantitative analysis of DNA methylation using real-time PCR (qAMP), and
bisulfite sequencing are more routinely used [2]. In any case, after an approach
has been selected, archived sperm samples from the experiments presented in
Chapters 3 and 5 can be used for DNA isolation and subsequent DNA
methylation analysis.
Aim 2 – Humans
Characterize mRNA and DNA methylation profiles in human sperm to
determine their utility as translatable biomarkers of testicular dysfunction.
The goal of Aim 2 was to determine whether sperm molecular profiles are
associated with sperm function (Chapter 6). In this study, sperm mRNA and DNA
were isolated from a cohort of men undergoing clinical fertility assessment at
Rhode Island Hospital. These men had a range of semen parameters and their
fertility status was unknown. High-throughput microarray analyses and
bioinformatics approaches were utilized to develop molecular signatures for each
semen sample and the results were integrated with the clinical data to determine
relationships with sperm function.
244
One of our major findings was that the low motility sperm samples in our
cohort had a hypomethylated phenotype. An initial observation using hierarchical
clustering suggested that the DNA methylation profiles of the samples parsed out
by sperm motility. This conclusion was confirmed using RPMM and permutation
testing, which determined a significant association among the DNA methylation
classes and motility. There were no correlations between the DNA methylation
data and the other semen parameters. LIMMA analysis identified thousands of
CpG loci that were significantly altered in the low motility sperm, with the majority
being
hypomethylated.
These
results
suggested
a
genome-wide
DNA
methylation defect in the low motility sperm.
We investigated whether the mRNA content was also aberrant in these
semen samples, specifically for transcripts with an a priori hypothesis for
epigenetic regulation and spermatogenesis. LIMMA identified 20 transcripts with
altered presence in the low motility sperm, including three important epigenetic
regulators, HDAC1, SIRT3, and DNMT3A. HDAC1 needs to be degraded in
elongated spermatids for proper chromatin compaction to ensue and we
observed an increase in this transcript in the low motility sperm. SIRT3 is
important for the clearance of reactive oxygen species and we found a decrease
in this transcript in low motility sperm. The de novo DNA methyltransferase,
DMNT3A was increased in the low motility sperm.
The main limitation to this study was sample size. Sample acquisition was
slow at the beginning of the collaboration with Rhode Island Hospital, but
fortunately, this is no longer an issue. This will allow us to do larger scale studies
245
to further investigate the relationships among low motility sperm and molecular
profiles. We were also limited by the lack of information we had on these
patients, particularly fertility status. Ideally, the next step would be to focus on Dr.
Sigman’s and Dr. Hwang’s patients. For example, we could prospectively recruit
men and ultimately compare sperm molecular signatures with fertility potential,
monitoring time to pregnancy, success with IVF, embryo development,
implantation rates, and spontaneous abortion.
Although this study had limitations, our data generated many questions
regarding sperm molecular components and their effect on sperm function, all of
which are worthy of further exploration. These hypotheses are listed here and
discussed below.
1.
Is the hypomethylated phenotype seen in low motility sperm due
to a failure of sperm to complete chromatin compaction properly
because of increased HDAC1 presence?
2.
Does the level of SIRT3 protein alter the levels of ROS in the
sperm, and does this influence sperm motility?
3.
Does oxidative stress hinder the ability of DNMT3A to set the
proper methylation marks?
1. Is the hypomethylated phenotype seen in low motility sperm due to a failure of
sperm to complete chromatin compaction properly because of increased HDAC1
presence?
246
To begin to address this question, the state of chromatin compaction in
the low motility sperm needs to be determined. Chromatin quality can be
determined in sperm using three complimentary staining techniques, including
Aniline Blue (AB), Acridine Orange (AO), and Chromomycin A3 (CMA3) [31].
CMA3 competes with protamines for binding to the minor groove of DNA and
detects protamine deficiency in loosely packed chromatin; AB binds to histones
and indirectly infers the amount of protamines in the sperm nucleus; AO reflects
sperm DNA denaturation via the detection of single and double stranded breaks
[31]. A previous study found that progressive motility was negatively correlated
with CMA3 and AB staining, suggesting that low motility sperm do have altered
chromatin compaction [10]. The next step is to determine the role of HDAC1 in
this process. HDAC1 negatively regulates the histone-to-protamine transition,
and needs to be degraded so the histones can become hyperacetylated and
replaced [32]. Sperm HDAC1 protein levels can be measured using a Western
blot. If the low motility sperm show an increased level of HDAC1 protein
compared to high motility sperm, then HDAC1 may be influencing chromatin
structure. If HDAC1 is preventing chromatin compaction it may be possible
measure histone acetylation in sperm prior to and after treatment with a histone
deacetylase inhibitor. If HDAC1 is still present in the mature sperm, then HDAC
inhibitors should increase histone acetylation. If HDAC1 is degraded then there
should be no effect with the addition of the inhibitor.
247
2. Does the level of SIRT3 protein alter the levels of ROS in the sperm, and does
this influence sperm motility?
To address this question, the levels of ROS need to be measured in the
low motility sperm. ROS can be measured using dihydroethidine solution, which
is specific for the detection of oxygen metabolites like superoxide [33]. Due to the
fact that SIRT3 induces the enzymatic antioxidant MnSOD, the levels of SIRT3
and MnSOD protein need to be measured. If ROS levels are influenced by
SIRT3, then low motility sperm would have decreased SIRT3 and MnSOD
proteins compared to high motility sperm. In addition, if low motility/high ROS
sperm are treated with additional antioxidants and their motility improves, then
ROS is influencing sperm motility. Conversely, if low motility sperm are treated
with hydrogen peroxide and do worse than high motility sperm treated with
hydrogen peroxide, then this may be due to deficient levels of MnSOD.
3. Does oxidative stress hinder the ability of DNMT3A to set the proper
methylation marks?
A previous report found that oxidative DNA damage impairs global sperm
DNA methylation in fertile men [34]. They found that the administration of an
antioxidant to their infertile cohort reduced sperm ROS and DNA damage and
increased
sperm
DNA
methylation.
However,
to
truly
correlate
DNA
hypomethylation and oxidative stress, the extent of ROS needs to be measured
in concert with DNA methylation and the ROS-induced DNA base adduct, 8248
hydroxyl-2’-deoxyguanosine (8-OH-dG) [34, 35]. The presence of 8-OH-dG one
or two nucleotides 3’ from the cytosine on the same strand diminishes the ability
of the methyltransferase to methylate a target cytosine [35]. If ROS causes DNA
hypomethylation, then there will be increased adducts present in the DNA.
Integration of Aims 1 and 2
One possible way to integrate the information obtained from Aims 1 and 2
is to identify sperm molecular profiles in men exposed to an environmental insult
or a therapeutic agent known to cause testicular injury and compare with animal
models of a similar exposure. This could be utilized for many exposures,
including, but not limited to, pesticides, heavy metals, chemotherapies, or statins.
For example, exposure to statins is a chronic low dose exposure in adults that
affects approximately 18% of men ages 45-64 [36]. The statins are a class of
drugs that lower cholesterol levels in people at risk of cardiovascular disease.
They reduce cholesterol by inhibiting the enzyme HMG-CoA reductase, which is
the rate-limiting enzyme of the mevalonate pathway of cholesterol synthesis. The
inhibition of this enzyme in the liver results in decreased cholesterol synthesis
and increased synthesis of low-density lipoprotein (LDL) receptors. This results in
an increased clearance of LDL from the bloodstream. The reduction of
cholesterol biosynthesis can affect normal steroid hormone production and could
adversely affect male gonadal function. The testes require a continuous supply of
cholesterol for testosterone synthesis and this can be derived from de novo local
synthesis or LDL-receptor mediated uptake.
249
A few publications have addressed the issue of statin exposures and
testicular function in human patient populations. K. Purvis et al. monitored
exposure to simvastatin (40 mg/d) for 14 weeks in a cohort of 19 men [37]. They
examined sperm quality, the seminal plasma concentration of various sex gland
products, and serum concentrations of testosterone, cortisol, follicle-stimulating
hormone (FSH), luteinizing hormone (LH), and prolactin. The authors concluded
that the short term reduction in circulating LDL cholesterol had no marked effect
on testicular function or sperm quality [37]. However, the study did not address
the fertility status of these men. A few years later, C. Azzario et al. studied the
effect of a lower dose of simvastatin (20 mg/d) for 3, 6, and 12 months in 8
patients [38]. The authors observed a significant reduction in free testosterone;
however, it still remained within the normal range. This study concluded that the
drug caused a mild decline in free testosterone without any clinical sign of
testicular dysfunction [38]. The authors solely measured the gonadal function
based on patient complaints, of which there were none. However, no semen
analysis was performed, which makes their results inconclusive. This study was
followed up by A. Dobs et al., who examined various doses of simvastatin and
pravastatin for 24 weeks [39]. The authors found no significant differences in
testosterone, hCG-stimulated testosterone, free testosterone index, FSH, LH, or
sex hormone binding globulin. In addition they observed no significant changes in
sperm concentration and motility or ejaculate volume, and concluded that they
found no evidence of clinically relevant effects on gonadal function [39].
Conversely, C. Neiderberger addressed the issue of atorvastatin and male
250
infertility and made the observation that men on Lipitor® have low sperm motility
[40]. It has been hypothesized that even subtle effects on the testes would be of
concern, especially if the male in question has an underlying reproductive defect
to which small alterations in reproductive potential may be confounding [40]. In
addition, a recent study reported a possible negative effect of co-administration
of amlodipine and atorvastatin on semen volume and sperm count in men [41].
However, they did not see an effect with atorvastatin alone.
There has been notable discrepancy in the literature amongst the effects
of statins observed in animal studies [40, 42, 43]. Therefore, use of the
multifaceted approach developed in Aims 1 and 2 may provide greater insight to
the effects of statins on sperm function. Sperm molecular signatures could be
generated in rats exposed to atorvastatin (Lipitor®) and compared to men taking
Lipitor®, to determine whether transcriptomic and epigenomic alterations exist in
these sperm. Conventional studies have not seen an effect of atorvastatin on rat
fertility [43], but humans have lower baseline gonadal function, possibly making
them more susceptible to atorvastatin-induced injury. Nonetheless, investigating
sperm molecular signatures in men exposed to statins may identify cohorts of
susceptible men who are sensitive to atorvastatin due to an underlying condition.
Applications and Implications
As extensively discussed in Chapter 1, the intended application of this
research is for toxicity testing, with the aim of generating molecular signatures to
251
discern the effects of different toxicants on the testis. Sperm is a surrogate cell
type for investigating testicular dysfunction and examining sperm molecular
components should provide greater information about abnormal testicular
function and fertility potential. Sperm is an easily accessible and translatable
endpoint between laboratory animals and humans, which will allow for the
comparison between sperm molecular signatures generated in vivo in controlled
animal studies and human molecular signatures generated in Phase III trials.
However, assessing testicular toxicity and dysfunction is not limited to the
pharmaceutical industry. The etiology of many adverse reproductive outcomes
among humans is not well understood. Most research focuses on maternal
factors, with paternal effects left understudied. However, a number of animal
studies as well as human epidemiological studies have demonstrated that preconception exposures of males to various agents can result in abnormal
reproductive, pregnancy, and/or progeny outcomes [20-22]. In animal models, for
example, cyclophosphamide given chronically at low doses similar to clinical
regimens had no impact on various male reproductive endpoints. However, it led
to increases in pre- and post- implantation loss and an increase in abnormal and
growth stunted fetuses when exposed males were mated with untreated females
[20]. In humans, paternal occupational exposure to rubber, plastics, and solvents
leads to an excess of spontaneous abortions [20]. It has been recognized that
there are three main mechanisms of male reproductive toxicity: 1) nongenetic; 2)
genetic; and 3) epigenetic, and it is hypothesized that many of these paternal
252
exposures are acting epigenetically to cause adverse paternally mediated
developmental outcomes [20, 22].
With this in mind, it is important to develop better assays for monitoring
fertility potential after occupational, environmental, and therapeutic exposures
because the standard semen analysis is not designed to detect epigenetic
alterations. One example where this would be particularly useful is the evaluation
of reproductive function after chemotherapy. As previously mentioned, exposure
to the clinical testicular cancer treatment regimen BEP for 9 weeks induced DNA
methylation alterations in rat sperm and altered mRNA content in round
spermatids [2, 5]. Unfortunately, these studies did not examine the ability of rats
to recover from the molecular injury; however, our mRNA results indicate that
epigenetic alterations can persist for months after the exposure ends (Chapter 3).
Humans undergoing chemotherapy are advised to delay conception for at least 6
months after all therapy ceases [20]. Unfortunately, no method currently exists to
do a proper subcellular examination of these sperm to determine whether and/or
when the epigenetic defects ameliorate.
Previous clinical reports have identified relationships between sperm
quality and successful IVF [44-46]. For example, fertility potential using
intracytoplasmic sperm injection (ICSI) is influenced by sperm motility [45]. In
addition, fertilization using low motility sperm results in diminished blastocyst
development and quality [44]. Overall, the quality of sperm may influence embryo
implantation and pregnancy outcomes [46]. These findings allude to the potential
253
relationships among sperm molecular alterations and adverse reproductive
outcomes.
Evidence suggests that both genetic (DNA fragmentation) and epigenetic
(chromatin compaction and DNA methylation) abnormalities play a role in
infertility and IVF outcomes [2-19]. Chromatin compaction is vital for sperm
maturation and is facilitated by the replacement of histones by protamines.
Aberrant protamine ratios are associated with decreased sperm count and
motility, abnormal head morphology, higher frequency of DNA fragmentation, a
lower sperm penetration assay score, reduced fertilization rates and decreased
clinical pregnancy rates [47]. In humans, an increased protamine ratio is
correlated with decreased fertilization capacity but no change in embryo quality;
while decreased protamine ratio is associated with decreased fertilization and
poor preimplantation stage embryo morphology [19]. A recent study identified a
relationship between chromatin condensation, DNA integrity, and quality of
ejaculated sperm from infertile men [10].
Sperm DNA fragmentation, characterized by single and double strand
breaks, not only results from the abnormal chromatin remodeling mentioned
above, but also abortive apoptosis and oxidative stress [47]. DNA fragmentation
has been associated with altered reproductive outcomes, including decreased
sperm count and motility, abnormal sperm morphology, decreased fertilization
and implantation rates, high spontaneous abortion rate, and unexplained
recurrent pregnancy loss [47].
254
A clinical correlation has been established between DNA methylation,
sperm quality, and pregnancy rates [47]. However, a novel study in animal
models established a link between disrupted DNA methylation in sperm and
altered embryogenesis [19, 48]. In this study, rats were treated with the DNA
methylation inhibitor 5-azacytidine, which inhibited normal GC methylation in
male rats. Fertilization occurred via normal mating, but severe embryo
fragmentation was observed after the first division [48].
DNA methylation is a dynamic process during embryo development and
spermatogenesis. The primordial GC genome is demethylated during embryonic
development, which erases the parental imprinting marks. The paternally
imprinted marks are then reestablished after birth during spermatogenesis and
are maintained in mature sperm. Following fertilization, the paternal genome is
actively demethylated, with the exception of imprinted genes. At this point, the
maternal genome is passively demethylated and this restores the totipotency of
the fertilized egg. Remethylation of the embryonic genome takes place after
implantation. It is important to note that the imprinted loci are not affected during
these two stages of demethylation. Thus, any imprinting errors that occurred
during spermatogenesis are maintained in the developing embryo. An increase in
the incidence of imprinting disorders, such as Beckwith-Wiedeman (BWS) and
Angelman syndrome, have been observed following ICSI [47, 49-51]. A 2003
prospective study estimated at least a 6-fold increase in BWS in children born
after assisted reproduction, compared to the general U.S. population [52]. In
addition, a recent study has found an association with widespread epigenetic
255
alterations
in
phenotypically
normal
children
conceived
with
assisted
reproduction, and that these modifications may increase the risk of adverse
cardiometabolic outcomes [53].
The correlation between sperm mRNA content and its effects on fertility
and embryogenesis have yet to be extensively studied. Different molecular
signatures have been identified distinguishing normal and abnormal sperm and
this supports the idea that abnormal transcriptomes may affect embryogenesis
[47]. In addition, some sperm-specific transcripts have been identified that have
roles in embryogenesis. This suggests that the fertilized oocyte could utilize
these transcripts for the initial phase of embryo development. Conversely, the
presence of paternal transcripts deleterious to embryo growth has been
validated, including the protamine transcripts. Fortunately, these transcripts are
selectively degraded in the early embryo, which may preserve normal
embryogenesis [19].
Final Remarks
This dissertation developed the foundation necessary to generate sperm
molecular signatures for toxicity testing. Prior to the execution of this work, little
research examined toxicant effects on sperm molecular components. While
certain aspects of this project required considerable optimization, including such
seemingly trivial tasks as sample handling and DNA and RNA preparation, the
final product is a promising tool to assess testicular toxicity. In addition, the data
256
presented in Aims 1 and 2 spawned many questions that will not only impact
biomarker development, but also provide molecular insight into the roles of
sperm DNA and RNA in fertilization and embryogenesis.
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