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From gamete to genome: evolutionary consequences of sexual conflict in house mice
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From gamete to genome: evolutionary consequences of sexual conflict in house mice
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i
From gamete to genome: evolutionary
consequences of sexual conflict in house mice
By
Sara Keeble
A Dissertation presented to the
FACULTY OF THE GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(MOLECULAR BIOLOGY)
MAY 2019
Copyright 2019 Sara Keeble
ii
Acknowledgements
I would like to thank Matt Dean and my lab mates for their support throughout my
PhD. I would also like to thank my committee members, Sergey Nuzhdin, Peter
Calabrese, and Carly Kenkel, for their academic support. This work was supported
by a NSF Graduate Research Fellowship, NSF Graduate Research Opportunities
Worldwide Award, NSF CAREER award 1150259, NIH award 1R01GM098536-
01A1, and Society for the Study of Evolution Rosemary Grant Award.
iii
Table of Contents
ACKNOWLEDGEMENTS ......................................................................................................... II
LIST OF FIGURES .................................................................................................................... IV
LIST OF TABLES ........................................................................................................................ V
ABSTRACT ................................................................................................................................... 1
CHAPTER 1: INTRODUCTION ................................................................................................ 2
1.1 SEXUAL CONFLICT .................................................................................................................. 2
1.2 SPERM COMPETITION .............................................................................................................. 2
1.3 CRYPTIC FEMALE CHOICE ....................................................................................................... 3
1.4 PROTEIN MEDIATORS OF POST-COPULATORY CONFLICT .......................................................... 5
1.5 EVOLUTIONARY CONSEQUENCES OF UNEQUAL REPRODUCTIVE SUCCESS BETWEEN THE SEXES
..................................................................................................................................................... 6
1.6 GOALS OF THIS DISSERTATION ................................................................................................ 7
1.7 SUMMARY OF THE CHAPTERS .................................................................................................. 9
CHAPTER 2: SEX RATIO SHAPES PATTERNS OF VARIATION AND ADAPTIVE
EVOLUTION IN MUS ................................................................................................................ 11
2.1 ABSTRACT ............................................................................................................................ 11
2.2 INTRODUCTION ..................................................................................................................... 12
2.3 MATERIALS AND METHODS .................................................................................................. 15
2.4 RESULTS ............................................................................................................................... 18
2.5 DISCUSSION .......................................................................................................................... 25
2.6 SUPPLEMENTARY MATERIALS .............................................................................................. 29
CHAPTER 3: ADAPTIVE EVOLUTION IN THE CUMULUS-OOCYTE COMPLEX
PROTEOME HIGHLIGHTS INVOLVEMENT OF THE FEMALE IMMUNE SYSTEM
IN FERTILIZATION .................................................................................................................. 36
3.1 ABSTRACT ............................................................................................................................ 36
3.2 INTRODUCTION ..................................................................................................................... 37
3.3 METHODS ............................................................................................................................. 39
3.4 RESULTS ............................................................................................................................... 42
3.5 DISCUSSION .......................................................................................................................... 51
3.6 SUPPLEMENTARY MATERIALS .............................................................................................. 56
CHAPTER 4: CONCLUDING REMARKS ............................................................................. 86
4.1 IMPACT OF MY WORK ............................................................................................................ 86
4.2 FUTURE DIRECTIONS ............................................................................................................. 89
REFERENCES ............................................................................................................................ 91
iv
List of Figures
FIGURE 2.1 .................................................................................................................................. 22
FIGURE 2.2 .................................................................................................................................. 23
FIGURE 2.3 .................................................................................................................................. 23
FIGURE 2.4 .................................................................................................................................. 24
FIGURE 2.5 .................................................................................................................................. 25
FIGURE S2.1 ............................................................................................................................... 29
FIGURE S2.2 ............................................................................................................................... 30
FIGURE S2.3 ............................................................................................................................... 31
FIGURE S2.4 ............................................................................................................................... 32
FIGURE S2.5 ............................................................................................................................... 33
FIGURE S2.6 ............................................................................................................................... 34
FIGURE S2.7 ............................................................................................................................... 35
FIGURE 3.1 .................................................................................................................................. 43
FIGURE 3.2 .................................................................................................................................. 43
FIGURE 3.3 .................................................................................................................................. 45
FIGURE 3.4 .................................................................................................................................. 45
FIGURE 3.5 .................................................................................................................................. 46
FIGURE 3.6 .................................................................................................................................. 47
v
List of Tables
TABLE 2.1 .................................................................................................................................... 19
TABLE 2.2 .................................................................................................................................... 20
TABLE 2.3 .................................................................................................................................... 21
TABLE 3.1 .................................................................................................................................... 44
TABLE 3.2 .................................................................................................................................... 47
TABLE 3.3 .................................................................................................................................... 48
TABLE 3.4 .................................................................................................................................... 49
TABLE 3.5 .................................................................................................................................... 50
TABLE S3.1 ................................................................................................................................. 56
1
Abstract
In many species, males and females have a fundamental conflict of interest over
reproduction due to their differential levels of investment. Males benefit from siring offspring with
as many females as possible, and females benefit from choosing the best male for fertilization.
This sexual conflict generates strong selection for traits that function to increase the fitness of one
sex over the other. In internal fertilizers, the outcome of sexual conflict is often determined by
gamete interactions that take place in the female reproductive tract after mating occurs, however,
the mechanisms involved remain largely unknown. Additionally, sexual conflict often results in
differential reproductive success between males and females. The long-term evolutionary outcome
of unequal breeding sex ratios, however, is unclear.
In this dissertation, I dissect the consequences of sexual conflict on gamete biology and
genomic evolution. In chapter two, I demonstrate that wild populations of mice have a wide range
of breeding sex ratios that can alter the evolutionary rate of the X chromosome relative to the
autosomes. In chapter three, I uncover a potential mechanism of female gamete plasticity in
response to sexual conflict over fertilization rate in mice. I also provide the first global evolutionary
analysis on female gametes in mice and uncover evidence for recurrent positive selection in
proteins that function in both reproduction and immunity.
2
Chapter 1: Introduction
1.1 Sexual conflict
In mammals, reproduction requires differential investment from males and females
(Trivers 1972). Females produce few large gametes that are rich in components necessary for
initial zygote development, gestate embryos until birth, and provide care after birth (Wedell et al.
2006). Males, on the other hand, produce numerous small gametes that provide little more than
DNA to the zygote and most species provide little to no paternal care of offspring (Wedell et al.
2006; Aloise King et al. 2013). Mammals have particularly unequal investment due to long, costly
gestation and lactation periods (Aloise King et al. 2013). This circumstance dictates that males
benefit greatly from mating with multiple females, while females benefit most from choosing the
best male for fertilization (Bateman 1948). These differences set the stage for an inherent conflict
between the sexes, meaning selection favors behaviors and traits that result in an increase in fitness
for an individual at the cost of its mate (Aloise King et al. 2013). The overall goals of my
dissertation research are to understand the consequences of sexual conflict in terms of genomic
variation, plasticity in gamete production, and gamete protein evolution.
1.2 Sperm competition
Male-male competition for access to mates is rampant, both pre- and post-copulation. Pre-
copulatory competition often selects for weaponry (Hunt et al. 2009) that dominant males use to
secure access to mates, or for alternative mating tactics (Dominey 1984) used by subordinate males
to gain paternity without competing physically. Post-copulatory competition continues when
sperm from multiple males overlap in the female’s reproductive tract (Parker 1970, 1979, 1991,
2007; Parker et al. 1997). Such sperm competition introduces strong selection on ejaculate traits,
some of which are plastic (Stockley 2004). A common adaptation to sperm competition is simply
ejaculating greater numbers of sperm to increase chances of fertilization (Parker 1991), and males
have been shown to strategically allocate ejaculates based on presence of rival males (Pound and
Gage 2004; Ramm and Stockley 2007). Producing larger numbers of sperm is usually
accompanied by an increase in testis size (Hosken and Ward 2001). In fact, this pattern is so
3
common that testis mass relative to body mass has been widely considered a good indicator of the
intensity of sperm competition in a species (Stockley 2004). Sperm motility also plays a role in
competition, and relative testis size is correlated with sperm motility in primates (Møller 1988).
As longer sperm swim faster, sperm competition has been thought to introduce selection for
increased sperm length (Gomendio and Roldan 1991), however this does not appear to be the case
in mammals (Firman and Simmons 2010b; Lüpold and Fitzpatrick 2015). Sperm midpiece length,
however, does correlate with increased swimming speed in mice (Firman and Simmons 2010c)
and is correlated with intensity of sperm competition in primates (Anderson and Dixson 2002).
Seminal fluid contains components that influence sperm competitive ability as well, and has been
shown to enhance sperm viability and motility in mammals (Ramm et al. 2008).
While these traits are expected to increase fertilization rate by any one male, they
simultaneously increase the risk of oocytes being fertilized by more than one sperm, referred to as
polyspermy, which induces egg death (Frank 2000). Thus, traits that are adaptive for males may
be maladaptive for females. Female reproductive tracts have molecular mechanisms to bias
paternity toward a given male, called cryptic female choice (Firman et al. 2017). A great deal of
research has focused on the male adaptations to sperm competition, while cryptic female choice,
and especially the mechanisms by which it occurs, have remained largely elusive.
1.3 Cryptic female choice
Multiply mating females must have strong defenses to polyspermy to avoid egg death as
well as mechanisms for exercising control over the fertilizing sperm. These selective events can
occur throughout the entire female reproductive tract, with most sperm being weeded out along
the route through the vaginal canal, cervix, uterus, and uterotubal junction (UTJ) into the oviduct
(Ikawa et al. 2010a). Oviduct length is correlated with testis mass in mammals (Anderson et al.
2006), and sperm from multiple males unequally pass through the UTJ in rabbits, highlighting the
involvement of the oviduct in sperm selection (Holt and Fazeli 2010). The remaining sperm in the
oviduct still have several formidable barriers to cross, starting with the outermost layer called the
cumulus oophorus. This layer consists of thousands of cumulus cells in a thick extracellular matrix
that sperm must digest with endogenous hyaluronidases (Kimura et al. 2009). Cumulus cells
mostly function in support of the oocyte, providing nutrients and hormonal signal transduction
(Chen et al. 2013), but also secrete chemoattractants for sperm (Sun et al. 2005) and improve their
4
fertilizing ability (Chian et al. 1995; Hong et al. 2003; Tanii et al. 2011). Cumulus cells have also
been shown to select the best sperm for fertilization (Carrell et al. 1993; Hong et al. 2009), and
capillary tubes filled with them are used to do so for in vitro fertilization (IVF) (Hong et al. 2004;
Rijsdijk and Franken 2007; Franken and Bastiaan 2009). As most studies of sperm-egg interactions
involved removing the cumulus cell layer prior to experimentation, interactions between sperm
and this layer remain poorly understood.
Following the cumulus cell layer, sperm must cross the zona pellucida, a glycoprotein layer
that plays a critical role in polyspermy avoidance by hardening after fertilization occurs (Ikawa et
al. 2010b; Burkart et al. 2012). The mouse zona pellucida (ZP) is composed of the three
glycoproteins, ZP1, ZP2, and ZP3, but the number of ZP proteins varies even among closely
related species (Claw and Swanson 2012). While the exact modifications to ZP proteins are
unknown, carbohydrate and posttranslational modifications seem to influence sperm binding
(Claw and Swanson 2012). The zona pellucida also plays a role in preventing cross-species
fertilization (Claw and Swanson 2012). The final barrier sperm must cross is the plasma membrane
of the oocyte. The plasma membrane contains receptors that are critical for fertilization such as
CD9 (Bianchi et al. 2014), although some redundancy in these receptors exists (Claw and Swanson
2012). While physical barriers operate more heavily in the earlier parts of the female reproductive
tract, sperm selection occurring at the level of the cumulus-oocyte complex (COC) seems to rely
mostly on protein-protein interactions.
The female immune system also severely limits the number of sperm that reach the egg
vicinity (Birkhead et al. 1993). Phagocytosis induced by an aggressive leucocyte invasion kills a
large proportion of sperm (Phillips and Mahler 1977), and the same immune response is not seen
in response to mating with vasectomized males in rabbits (Tyler 1977), meaning it is a specific
response to antigenic sperm. Anti-sperm antibodies are abundant in mammalian cervical mucus
(Birkhead et al. 1993), and high immune response to sperm is a cause of female infertility in
humans (Shulman 1986). Semen contains immunosuppressive contents that function by activating
T suppressor cells or blocking antibody production (Witkin 1988). Interestingly, estrogen
concentrations during ovulation reduces the immune response to sperm, and non-estrous hormones
restore normal immune system function (Lasarte et al. 2013). Hypotheses explaining the immune
response to sperm include weeding out sperm that lack fertilization capability, avoiding
polyspermy, and gaining control over the sperm that fertilize ova (Birkhead et al. 1993), however,
5
these may be secondary effects of a strong need to protect against infection in the reproductive
tract.
While these female defenses to polyspermy are effective, they are only adaptive when
females mate multiple times. If females fail to mate multiply, strong defenses to polyspermy could
prevent fertilization altogether. Interestingly, Firman and Simmons (2013) demonstrated that
female mice dynamically alter defenses to polyspermy depending on perceived risk of sperm
competition. Females were reared in either a ‘high risk’ or ‘low risk’ of sperm competition
treatment where they received the soiled chaff from 10 males or 1 male, respectively. During in
vitro fertilization (IVF) following these social manipulation experiments, females in the ‘high risk’
treatment group exhibited a slower fertilization rate than ‘low risk’ females, consistent with
predictions of sexual conflict over fertilization. The mechanism behind this plasticity, however,
remains unknown.
1.4 Protein mediators of post-copulatory conflict
The highly conflicted goals of sperm and oocytes are often mediated by protein
interactions, and rapid adaptive evolution of these proteins has been documented in many species
(Swanson and Vacquier 2002c). Selection has regularly favored substitutions in sperm-binding
proteins (Swanson et al. 2001), presumably because it alters binding capacity of sperm (Swanson
and Vacquier 2002b; Vacquier and Swanson 2011). Changes of this nature drive compensatory
change in the corresponding sperm proteins to regain binding, leading to an evolutionary ‘arms
race’ (Vacquier and Swanson 2002). In mammals, evolutionary analyses conducted on proteins
present in sperm (Dorus et al. 2010) and seminal fluid (Ramm et al. 2008) have also found rampant
adaptive evolution occurring. Two zona pellucida proteins on the oocyte, ZP2 and ZP3, have
undergone recurrent adaptive evolution, as well as CD9 on the oocyte membrane (Claw and
Swanson 2012). While targeted studies on these handful of oocyte proteins have generally shown
signs of similar evolutionary patterns as sperm, a global evolutionary analysis has never been
conducted on mammalian oocytes, much less the cumulus cell layer surrounding them.
Understanding how, or if, sexual conflict has driven evolution in the COC proteome is critical for
a better understanding of how post-copulatory sexual selection introduces variance in reproductive
success in males and females.
6
1.5 Evolutionary consequences of unequal reproductive success between the
sexes
While the relative impact of pre- and post-copulatory sexual selection varies between
species, these general themes of males competing and females choosing result in variation in
reproductive success, and this variation is often usually higher for males (Clutton-Brock 1988). In
general, fewer males successfully reproduce than females, leading to a reduction in the effective
population size (Ne) of males as well as the overall Ne of a population. Therefore, relatively more
genomic contributions are from females. As crucial evolutionary forces like migration, drift,
selection, and recombination differ between the sexes (Hedrick 2007b), the time a genome spends
in males versus females can have a dramatic effect on genomic variation and selection. In
mammals, dispersal is primarily done by males, while females generally are philopatric (Pusey
1987). As sperm replicate and divide throughout the entirety of a male’s lifespan, they contribute
many more mutations than females due to replication infidelity (Hedrick 2007a). On the other
hand, recombination rate tends to be higher in females, though there is a high degree of variation
between species (Ptak et al. 2005; Winckler et al. 2005; Hedrick 2007a). Because most of the X
chromosome only recombines in females (Campos et al. 2013), the recombination rate is reduced
relative to the autosomes. Regions of recombination seem to differ between the sexes as well -
more telomeric recombination in males and more centromeric recombination in females has been
demonstrated in humans (Broman et al. 1998). Additionally, sex-specific differences in pairing
and synapsis have been seen in mice (Hedrick 2007a).
The genomic impact of breeding sex ratio is further compounded by the genomic
differences between males and females, namely in the sex chromosomes. As males are hemizygous
for the X chromosome, the effective population size (NeX) is ¾ of the autosomes (NeA) in an equally
breeding population (Hammer et al. 2008). If the Ne of males is reduced relative to that of females,
as is the case in many species (Clutton-Brock 1988), the ratio of NeX/ NeA skews higher than 0.75
due to the increased number of copies of the X chromosome from female contributions, while the
inverse sex ratio skews NeX/ NeA below 0.75 (Hammer et al. 2008). As Ne is positively correlated
with the probability of fixation of adaptive mutations (Kimura 1971; Kimura and Ohta 1971),
increasing NeX relative to NeA sets the stage for more efficient selection on the X chromosome.
Accordingly, a higher rate of divergence of the X relative to the autosomes, called the faster-X
effect, has been demonstrated in many species (Meisel and Connallon 2013; Kousathanas et al.
7
2014). A key element to faster-X evolution is the exposure of recessive X-linked mutations, both
advantageous and deleterious, in males. Selection on these two types of mutations, however,
impacts the X in different ways. Adaptive X-linked mutations in males can sweep to fixation
quickly, but because the average recombination rate of the X chromosome is lower than the
autosomes, these mutations are surrounded by larger swaths of hitchhiking DNA than the
autosomes due to the increased linkage distance (Charlesworth et al. 1987b). As a result, an impact
of strong positive selection is a larger area of reduction in variation on the X chromosome than the
autosomes (Hammer et al. 2008). On the other hand, recessive deleterious mutations on the
autosomes, or on the X in females, can recombine onto many different genomic backgrounds
before experiencing purifying selection. As a consequence, a large amount of variation is lost,
referred to as background selection (Charlesworth 1994). In contrast, X-linked deleterious
mutations in males are often purged before recombining onto other genetic backgrounds, meaning
less variation is lost from background selection on the X chromosome in comparison to the
autosomes (Hammer et al. 2008). Taking these differential effects of NeX/NeA into account, the
breeding sex ratio in a population is critical for understanding the patterns of variation and
evolution in the genome.
1.6 Goals of this dissertation
Given the large impact of breeding sex ratio on genome evolution, I sought to understand the
degree to which it is impacted by post-copulatory sexual selection. Specifically, I was interested
in understanding the following questions:
1) Is there a correlation between the ratio of Ne of the X chromosome and the
autosomes and the intensity of sperm competition?
Intense sperm competition between rival males introduces a great deal of variance into the
reproductive success of males, making the effective population size of females higher than
that of males. As a common adaptation to sperm competition is an increase in testis size to
accommodate increased sperm production, testis size relative to body size is a good
predictor of the intensity of this competition, and should predict the extent of the Ne skew
between the sexes. A positive correlation between NeX/NeA and relative testis size could
indicate that post-copulatory sexual selection heavily influences the long-term breeding
8
sex ratio of a population. A lack of correlation, on the other hand, could indicate that testis
size differences between species evolved recently and do not reflect a long-term change in
breeding sex ratio. Alternatively, because selection before mating can also introduce
variance in reproductive success, a lack of correlation could indicate a combination of pre-
and post-copulatory sexual selection determines breeding sex ratio. As relative testis size
is very commonly used as a proxy for mating system in this way, it is critical to understand
if it is a reliable metric for use in comparative studies.
2) Does the rate of adaptive evolution on the X chromosome relative to the autosomes
vary with breeding sex ratio?
As an increase in Ne is accompanied by an increased efficacy of selection, if the NeX/NeA
ratio is higher than 0.75 due to breeding sex ratio skew, the X chromosome should
experience a decreased effect of drift. On the other hand, if NeX/NeA is lower than 0.75 from
more males breeding, we would expect to see an increase in the rate of adaptive evolution
on the X chromosome relative to the autosomes due to the exposure of recessive mutations.
Post-copulatory sexual selection studies have largely focused on sperm competition and neglected
cryptic female choice. In mammals, a global evolutionary rate analysis has never been conducted
for the oocyte, and the outermost cumulus cell layer of the oocyte has been neglected completely.
I aspired to characterize the evolutionary patterns of the cumulus-oocyte complex and assess their
role in mediating fertilization rate. Specifically, I addressed the following questions:
1) What proteins are present in cumulus cells?
As the proteome of cumulus cells is not fully characterized in mice, it was necessary to
establish a baseline for futher experimentation.
2) What kinds of proteins are adaptively evolving in COCs?
Mouse sperm contain many proteins that function solely in reproduction and are evolving
under positive selection, putatively due to selective pressure from female reproductive
proteins. However, it is completely unclear if cumulus-oocyte complex proteins exhibit
similar patterns.
3) How do evolutionary rates of COC proteins compare to sperm proteins?
9
As sperm have many restricted expression proteins and no critical tasks besides
fertilization, they have less evolutionary constraint compared to COCs that must partition
resources for early development. COCs may contain many proteins with rapid evolutionary
rates, though they are likely to be under stronger purifying selection than sperm.
4) What role does the cumulus cell layer play in fertilization rate?
Female mice reared under a perceived risk of polyspermy ovulate COCs that have slower
in vitro fertilization rates than those reared without a perceived risk of polyspermy. Due to
the dynamic nature of the cumulus cell layer, it seems a likely candidate for modifying
fertilization rate plastically.
5) Do females adjust their COC proteome in response to risk of polyspermy?
As fertilization rate changes could also be modified by changing the expression of sperm-
binding proteins, the differences between COCs of females perceiving a risk, or lack of
risk, of polyspermy could be due to changes in the COC proteome.
To address these questions, I conducted a series of experiments in house mice, a species group
known to mate multiply and give birth to mixed paternity litters (Dean et al. 2006; Firman and
Simmons 2008). Mice are well suited to address these questions as they offer a uniform genetic
background through their tolerance of inbreeding in the lab, outstanding genomic resources, and
relative ease of sampling.
1.7 Summary of the chapters
In chapter 2, I use whole-exome and noncoding sequence captures to quantify the neutral
variation and rates of adaptive evolution across wild populations of four species of mice that vary
in testis size: Mus musculus musculus, M. m. domesticus, M. m. castaneus, and M. spretus. I show
that there is indeed a correlation between NeX/NeA and testis size in these species of mice,
suggesting that post-copulatory sexual selection plays a significant role in modulating breeding
sex ratios. I also find that Ne is correlated with the proportion of sites fixed by positive selection,
but that almost all populations of mice sampled have very low rates of adaptive evolution genome-
wide. I find reduced variation on the X chromosome in all populations sampled, and derived
populations exhibit the strongest reduction in NeX/NeA, as well as reduced Ne overall.
10
In chapter 3, I use mass spectrometry to identify proteins present on cumulus cells and
oocytes in a laboratory strain of M. m. musculus. I then conduct an evolutionary rate analysis of
these identified proteins across 11 species of mice. In addition, I simulate a ‘risk’ of sperm
competition, perceived through urine scents of 10 males, or a lack thereof in wild-caught M. m.
musculus, a treatment previously shown to induce differences in in vitro fertilization rate. To test
whether the cumulus cell layer moderates fertilization rate, I quantified the rate of breakdown of
the cumulus cell layer and performed mass spectrometry on cumulus cell surface proteins from
both treatment groups. I found the COC proteome to be evolving more slowly than sperm proteins
as well as the genome average. Unlike adaptively evolving sperm genes, the COC genes adaptively
evolving did not solely function in reproduction, but most exhibit phenotypes indicating a dual-
function in fertilization and immune response. I found a faster rate of cumulus cell layer breakdown
only in the first minute of enzymatic digestion in the ‘no risk’ treatment group. Additionally, I
found proteins only present in the ‘risk’ treatment cumulus cells that are known biomarkers of
fertilization competency in cycling females, while proteins present only in the ‘no risk’ treatment
group are known biomarkers of prepubescence, indicating these treatments may have induced early
puberty in the ‘risk’ treatment group.
In chapter 4, I discuss future directions of my work and the impact on furthering the
understanding of genome evolution and cryptic female choice in mice.
11
Chapter 2: Sex ratio shapes patterns of variation and adaptive
evolution in Mus
2.1 Abstract
Studies on house mice have revealed a large contribution of the X chromosome to speciation
processes and a faster rate of evolution than the autosomes. However, it is unknown if faster-X
evolution is common among wild populations of mice, and what factors shape this pattern. Sex-
specific demographics can alter the effective population size (Ne) of the X chromosome relative to
the autosomes, setting the stage for evolution to proceed differently across the genome. Here, we
test for differences in breeding sex ratio between Mus musculus musculus, M. m. domesticus, M.
m. castaneus, and M. spretus by calculating the ratio of Ne on the X chromosome and the autosomes
across 12 wild mouse populations from ancestral and derived portions of their ranges. We find that
testis size relative to body size, a proxy for intensity of sperm competition, significantly predicts
breeding sex ratio. We also find the proportion of sites fixed by adaptive evolution (a) is positively
correlated with Ne on the autosomes across all populations and find the same relationship on the X
chromosome in ancestral populations only. Overall, we find a surprisingly low rate of adaptive
evolution genome-wide and an extreme reduction in X-linked variation across all populations.
Additionally, we provide the first estimate of Ne in M. spretus and find it to be larger than expected
based on the ecology of this species.
12
2.2 Introduction
Studies on house mice, comprised of the closely related species Mus musculus musculus,
M. m. domesticus, and M. m. castaneus, have highlighted a disproportionate involvement of the X
chromosome in speciation and reproductive isolation (Payseur et al. 2004; Good et al. 2008b,a,
2010; Larson et al. 2016a). The mouse X chromosome also exhibits more rapid protein evolution
(Kousathanas et al. 2014; Larson et al. 2016c) and less variation (Baines and Harr 2007) than the
autosomes. This faster-X evolution is thought to occur due to the increased exposure of recessive
mutations in males, however, this effect only occurs when advantageous mutations are on average
recessive, and beneficial for males (Charlesworth et al. 1987a; Pool and Nielsen 2008; Vicoso and
Charlesworth 2009). In addition, estimates of recurrent positive selection based on the ratio of
nonsynonymous to synonymous substitution rates on the X can be confounded by an increased
effect of genetic drift due to its smaller effective population size (Ne) (Kousathanas et al. 2014).
Because males are hemizygous for the X chromosome, its Ne should equal approximately 0.75 of
the autosomal Ne if equal numbers of males and females are breeding in a population (Charlesworth
et al. 1987a). However, equal breeding sex ratios are rarely seen in nature, meaning the ratio of
effective population size on the X (NeX) can become skewed relative to the autosomes (NeA) and
further change evolutionary dynamics on the X chromosome. With a higher Ne of females (NeF)
than males (NeM), the NeX/NeA ratio will be skewed >0.75 and could theoretically be as high as
~1.125 (Hammer et al. 2008). Conversely, a ratio <0.75 can occur when NeF < NeM, , or in the case
of a recent demographic event resulting in a greater reduction in diversity on the X chromosome
relative to the autosomes due to its smaller Ne (Powell and Richmond 1974; Wall et al. 2002; Pool
and Nielsen 2008; Ellegren 2009). Though the impact of the X chromosome in the evolution of
mice has been well established, the role of sex-specific demographics in shaping its variation and
efficacy of selection remain unknown.
We expect that mouse species exhibit a range of breeding sex ratios because they vary
widely in testis size relative to body size, a reliable metric for intensity of sperm competition
(Moller 2006; Firman and Simmons 2008). Females mating with multiple males generates
selection for males to ejaculate more sperm during copulation (Parker 1991; Parker et al. 1997),
and this increase in sperm production is often accompanied by an increase in testis size (Hosken
and Ward 2001). Sperm competition is an indicator of breeding sex ratio because it introduces
more variance in male reproductive success than in female reproductive success, meaning fewer
13
males sire disproportionate numbers of offspring, effectively reducing the effective population size
of males relative to females. However, testis size is an ephemeral trait with the ability to evolve
very rapidly (Hosken and Ward 2001; Firman and Simmons 2008). It is unclear if testis size
differences between species persist across time, making it a poor signal of sperm competition
intensity when comparing to traits evolving on a longer time scale. Additionally, sperm
competition after mating is but one component contributing to variance in reproductive success in
a species, and therefore breeding sex ratio. However, calculating the ratio of NeX/NeA from neutral
polymorphism offers a complete quantification of breeding sex ratio and can adequately address
the role of sex-specific demographics in genomic evolution.
Differences in effective population size across the genome cause an unequal opportunity
for adaptive evolution to take place because the efficacy of selection is thought to be positively
correlated with Ne (Kimura and Ohta 1971). Changes in the ratio of NeX / NeA due to sex-specific
gene flow add an extra dimension to genome evolution because males and females differ in the
evolutionary forces they exert. In mammals, males tend to accumulate more mutations than
females due to their high number of sperm cell divisions (Li et al. 2002), while females exhibit a
higher rate of recombination on average (Hammer et al. 2008). In addition, differences exist in
locations of recombination (Broman et al. 1998) and migration patterns (Bronson 1979; Hammer
et al. 2008) between the sexes. Selection also proceeds differently in males than females, and there
is a higher fixation rate on the X chromosome when a recessive adaptive mutation is present in,
and beneficial for, males (Charlesworth et al. 1987a). Similarly, recessive deleterious mutations
can be more efficiently purged on the X chromosome due to exposure in males (Charlesworth et
al. 1987a). Selective sweeps inducing fixation of beneficial mutations drive a reduction in diversity
at linked sites due to hitchhiking (Smith and Haigh 1974; Avery 1984; Kaplan et al. 1989; Fay and
Wu 2000; Andolfatto 2001; Baines and Harr 2007), and larger swaths of the X experience a
reduction in diversity than on the autosomes due to greater linkage distances (Wall et al. 2002).
Due to the evidence of faster-X adaptive evolution in mice, it seems recurrent selective sweeps
could potentially explain the observed reduction in X-linked diversity across many derived
populations (Baines and Harr 2007). However, faster-X in wild populations has only been tested
in a population of M. m. castaneus from India. As M. m. castaneus has the largest demonstrated
Ne in mice (Geraldes et al. 2008, 2011), it is unclear if adaptive evolution is also rampant on the
X chromosome in other species and populations of mice with smaller effective population sizes.
Additionally, rates of autosomal adaptive evolution and Ne have only been estimated from derived
14
populations of M. m. domesticus and M. m. musculus, in addition to the aforementioned population
of M. m. castaneus, meaning these population genetic parameters have never been estimated for
these species in the absence of significant demographic effects. Much like house mice, the closely
related Mus spretus has been studied quite extensively in the laboratory through several inbred
strains (Dejager et al. 2009), yet basic population genomic parameters such as effective population
size remain unknown.
Demographic history can also play a large role in altering NeX/NeA because X-linked loci
experience a larger reduction in diversity from bottlenecks than autosomal loci (Wall et al. 2002).
Population expansion, on the other hand, can increase NeX/NeA (Pool and Nielsen 2007). House
mice are thought to have originated in northern India and subsequently colonized the Middle East,
followed by radiation through the rest of the current ranges (Boursot et al. 1996; Din et al. 1996).
Accordingly, populations of mice closest to the ancestral origin have shown the highest levels of
polymorphism (Boursot et al. 1996; Din et al. 1996; Phifer-Rixey et al. 2012b). Baines and Harr
(2007) demonstrated a reduction in X-linked diversity in derived populations of house mice,
consistent with predictions of a differential effect of founder events on the X chromosome (Pool
and Nielsen 2007, 2008). However, similar results in derived populations of Drosophila have been
shown to not be fully explained by founder effects and are likely influenced by sex-specific
demographics as well (Pool and Nielsen 2008). The study of Baines and Harr (2007) was
conducted on a handful of intronic loci and, as breeding sex ratio inference is heavily influenced
by linkage to selected sites (Hammer et al. 2010), the effect of sex ratio on levels of diversity
cannot be accurately determined from those data.
To assess variation in breeding sex ratio and its influence on genomic evolution across
populations of wild mice, we performed whole-exome and noncoding sequence captures on 64
individuals from four populations, including an ancestral range population of M. spretus from
Northern Africa and derived populations of M. m. domesticus, M. m. musculus and M. spretus from
Western and Central Europe. We combine these data with a recent study from nine wild
populations of house mice and M. spretus (Harr et al. 2016), which includes whole genome
sequencing of M. m. domesticus and M. m. musculus populations close to the ancestral origin of
house mice on the Indian subcontinent (Din et al. 1996) and derived populations in Europe, as well
as a population of M. m. castaneus from India (Halligan et al. 2010). Using genomic data from
127 mice in total, we explore patterns of variation, effective population size, and rates of adaptive
15
evolution on and off the X chromosome in 12 wild populations of house mice and M. spretus
encompassing ancestral and derived portions of their ranges.
2.3 Materials and Methods
Sequence Captures
Mouse tissue samples were generously shared with us by Dr. Jeremy Searle (Mus musculus
domesticus, Portugal), Dr. Paulo Alves (M. m. domesticus, Portugal), Dr. Michael Nachman (M.
m. musculus, Czech Republic, Slovakia, Poland), and Dr. Francois Bonhomme (M. spretus,
France, Spain, Morocco, Tunisia). Genomic DNA was extracted from tissue samples and Illumina
sequencing libraries were prepared and individually indexed following the protocol described in
Meyer and Kircher (2010). Individual libraries were pooled and targeted sequences were captured
using a hybridization based, in-solution capture method (Bainbridge et al. 2010). In order to assess
adaptive evolution, the whole exome was targeted with SeqCap EZ (Roche Sequencing,
Pleasanton, CA) mouse whole-exome capture reactions (Fairfield et al. 2011) and non-coding
regions were targeted using myBaits custom target capture kit reactions (Arbor Biosciences, Ann
Arbor, MI). The exome capture reactions targeted 54.3 Mb of the mouse genome. To capture
neutral variation, we designed custom non-coding capture reactions in putatively neutral genomic
regions, totally approximately 2.8 Mb split relatively equally between the X chromosome and the
autosomes. Chosen regions were at least 100kb from the nearest protein-coding locus to avoid
reductions in diversity due to linked selection (Gregory et al. 2002) and in regions of high
recombination (>1.0 cM/Mb, (Cox et al. 2009)). Additionally, chosen loci were at least 100 kb
away from one another to ensure independent sampling of the genome (Laurie et al. 2007). To
determine the relative neutrality of the site classes used in our analyses, phastCons conservation
scores (Siepel et al. 2005) between all placental mammals for mouse reference genome sites were
downloaded from the “Conservation” track in the UCSC Genome Browser
(http://genome.ucsc.edu) and used to annotate our four site class positions (0-fold degenerate, 4-
fold degenerate, intronic, and noncoding).
Captures were sequenced with 100 bp paired-end runs on an Illumina HiSeq 2000. Our
libraries were sequenced at the University of Southern California Molecular Genomics Core, the
University of Oregon Genomics and Cell Characterization Core Facility, the University of
16
Utah/Huntsman Cancer Institute High-Throughput Genomics Core, and the University of
California Berkeley Vincent J. Coates Genomics Sequencing Laboratory. These data are available
on the SRA through accession number #######. M. m. castaneus and M. famulus sequence data
were accessed from the European Nucleodtide Archive (accession ERP000231, (Halligan et al.
2010)). M. m. domesticus sequence data from France, Germany, and Iran (accession PRJEB9450),
M. m. musculus data from the Czech Republic, Kazhakstan, and Afghanistan (PRJEB14167), and
M. spretus data from Spain (PRJEB11742) were also obtained from the European Nucleotide
Archive (Harr et al. 2016).
Quality Control and Raw Data Processing
Raw sequencing data was pre-processed using the expHTS pipeline (available
from https://github.com/msettles/expHTS). Briefly, this pipeline screens for contaminants,
removes PCR duplicates, trims low-quality bases and adapters, and merges overlapping paired-
end reads. As our mouse species are not equally divergent to the mouse reference genome
(GRCm38), we used an iterative mapping approach to mitigate reference bias by generating a
pseudoreference for each species as described in Sarver et al. (2017). This approach iteratively
maps reads to the mouse reference genome (GRCm38), calls variants, and inserts confidently
called single nucleotide variants (SNVs) back into the reference genome while maintaining
original coordinate positions. As reference bias increases outside of coding regions of the genome
(Sarver et al. 2017), this was important for our study of noncoding sites. Individuals were then
mapped to their respective species pseudoreference using the MEM algorithm in BWA v0.7.12 (Li
and Durbin 2009; Becker et al. 2018). Exome and non-coding sequence data was merged into a
single BAM file for each individual. Duplicate reads were marked and removed using Picard
v.2.1.1. Regions around insertions or deletions (indels) were locally realigned using the
RealignerTargetCreator and IndelRealigner tools implemented in the Genome Analysis Toolkit
(GATK) v3.5. Genotypes were called at both variant and confident reference sites using
UnifiedGenotyper with the EMIT_ALL_CONFIDENT_SITES output mode in the GATK.
Population Genetic Parameter Estimates
Populations for downstream analysis were determined through principle components
analysis (PCA) after thinning sites in linkage disequilibrium (> 0.1 squared correlation) using
PLINK v1.90 (Purcell et al. 2007) and individuals clustering with a different species or population
17
were discarded. We filtered sites further on a population level, keeping sites only if 75% or greater
of individuals had at least 4x coverage and GQ >= 20. As Watterson’s theta (qW, (Watterson 1975))
calculations rely on a site being variant rather than individual genotype calls, we filtered on a
variant quality score (QD > 2 for SNVs or QD = 0 for reference calls) for that portion of analysis.
To assess genomic variation, per-site q W was calculated for sites with passing variant
quality scores using the following equation (Watterson 1975):
"
=
Κ
&
Where Κ is the number of polymorphic sites, either zero or one in this per-site calculation, and
&
= (
1
&+,
-.,
where is the number of chromosomes sampled at each site. As our sampling included unequal
sex ratios across populations, using the correct number of chromosomes for based on the sex of
the sampled individuals was critical for unbiased qW calculations on the X. Effective population
size was calculated for the X chromosome and the autosomes separately (NeX and NeA) using =
4
2
(3
2
for NeX), where is the mean
"
calculated from noncoding regions of the X
chromosome and autosomes for
24
and
25
, respectively, and is the mutation rate. We used
divergence from an outgroup, M. famulus, on the X chromosome and autosomes as a proxy for
in Ne calculations. The resulting values were used to calculate the effective population size ratio
between the X chromosome and autosomes 6
7
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7
8:
;. We computed 95% confidence intervals around
our estimates using 10,000 bootstrap replicates per population. We calculated the distribution of
fitness effects (DFE) and proportion of sites fixed by positive selection (a) using DFE-alpha v2.16
(Keightley and Eyre-Walker 2007; Eyre-Walker and Keightley 2009). We generated folded site
frequency spectra (SFS) for neutral and selected site classes, consisting of 4-fold and 0-fold
degenerate sites, respectively, on the X chromosome and autosomes separately for each
population. In order to determine the appropriate demographic model for each population, we ran
est_dfe within DFE-alpha under epoch models 1, 2, and 3 on neutral SFS and performed likelihood
ratio tests to select the best model. Selected SFS were run through est_dfe to estimate the DFE,
18
followed by est_alpha_omega to estimate a. We bootstrapped sites of the selected SFS 1,000 times
to generate 95% confidence intervals. While correcting for autocorrelation due to shared ancestry
is usually appropriate when testing for correlations between traits among species (Revell 2010),
the three species of house mice are so closely related, in addition to having a discordant phylogeny
(White et al. 2009), that a correction of this nature was not necessary or possible. We defined 10
tissue-specific gene sets (brain, gut, heart, kidney, liver, lung, muscle, spleen, testis, thyroid) from
previously published expression data (Harr et al. 2016), using a threshold of >=90% expression
coming from the tissue of interest. Neutral and selected SFS were generated for each tissue by
pooling across all genes from each tissue, and tissues were only analyzed if >=500 variable sites
existed for all populations. SFS were analyzed using DFE-alpha in the same manner described
above.
2.4 Results
Population Analysis
All four sampled species clustered distinctly from one another based on PC1 and PC2 from our
principle component analysis (PCA) containing all individuals (Figure S2.1). Approximately 42%
and 13% of the variance in the data is explained by PC1 and PC2, respectively. PC1 mostly
separated the M. spretus and M. m. musculus populations from M. m. domesticus, with M. m.
castaneus falling in between the two groups. PC2 then separated M. m. musculus from M. spretus
and M. m. domesticus. We chose populations for individual analysis based on distinct clusters
formed in PCAs from each species separately to avoid biased parameter estimates from subdivided
or introgressed populations (Figures S2.2-S2.4). We analyzed the data as 12 distinct populations
(Table 2.1).
19
Neutrality of Targeted Noncoding Regions
We repeated most of our analyses in four different regions of the genome: 0-fold degenerate sites,
4-fold degenerate sites, introns, and our targeted non-intronic noncoding regions. To assess the
neutrality of each site class, we annotated each with phastCons conservation scores and compared
the distributions of scores (Figure S2.5). Interestingly, 4-fold degenerate sites have a high level of
conservation across placental mammals, similar to 0-fold degenerate sites. Our noncoding sites
generally show the lowest conservation scores, followed closely by introns. We then compared
mean
"
on the autosomes across all four site classes (Figure S2.6). As expected, our lowest
estimates of diversity of each population were calculated from 0-fold degenerate sites. Our highest
estimates of
"
were generally from noncoding sites, however, they were not significantly
different than 4-fold degenerate sites or introns. Taken with our results of distributions of
phastCons scores between site classes, it appears our targeted noncoding regions represent a good
dataset for assessing neutral variation.
Summary Statistics
We compiled existing population genetic studies of wild mice to compare our estimates of
autosomal diversity with published values (Table 2.2). We use mean autosomal theta calculated
from confidently called sites as a proxy for Ne because the mutation rate used to calculate Ne from
the equation =4
2
varies in the literature depending on the number of generations one
assumes per year for mice (Geraldes et al. 2011). The Indian M. m. castaneus population provided
the most comparisons as this population has been analyzed several times. We find almost identical
estimates of
"
across 0-fold, 4-fold, and intronic sites as Halligan et al. (2010), Kousthanas et al.
Species Sampling Location(s) Source Type Number of Individuals
M. m. castaneus India Halligan et al. 2010 WGS 10
M. m. domesticus Portugal This study Exome & noncoding capture 17
M. m. domesticus France Harr et al. 2016 WGS 8
M. m. domesticus Germany Harr et al. 2016 WGS 8
M. m. domesticus Iran Harr et al. 2016 WGS 8
M. m. musculus Czech Republic, Hungary, Poland This study Exome & noncoding capture 16
M. m. musculus Czech Republic Harr et al. 2016 WGS 8
M. m. musculus Afghanistan Harr et al. 2016 WGS 6
M. m. musculus Kazakhstan Harr et al. 2016 WGS 7
M. spretus Morocco & Tunisia This study Exome & noncoding capture 9
M. spretus France & Spain This study Exome & noncoding capture 22
M. spretus Spain Harr et al. 2016 WGS 8
Table 2.1 Analyzed mouse populations from this study, Halligan et al. (2010), and Harr et al. (2016) with
sampling location and type of sequencing (whole-genome or exome and noncoding sequence capture).
20
Table 2.2 Estimates of mean autosomal
"
across 0-fold and 4-fold degenerate site classes, introns, and noncoding regions from our study and
published studies.
Species M. m. castaneus
Sampling Locality India France Germany Portugal Iran Czech Republic Czech, Hungary, Poland Afghanistan Kazakhstan Morocco, Tunisia France, Spain Spain
0-fold sites
This study 0.0021 0.0008 0.0007 0.0006 0.0011 0.0010 0.0006 0.0013 0.0011 0.0015 0.0006 0.0019
Halligan et al 2010 0.0021
Kousathanas et al 2010 (78 genes) 0.0022
4-fold sites
This study 0.0096 0.0029 0.0027 0.0025 0.0045 0.0043 0.0023 0.0055 0.0048 0.0073 0.0027 0.0086
Halligan et al 2010 0.0091
Kousathanas et al 2010 (78 genes) 0.0093
Introns
This study 0.0089 0.0025 0.0023 0.0022 0.0041 0.0039 0.0021 0.0049 0.0043 0.0065 0.0023 0.0077
Halligan et al 2010 0.0083
Baines & Harr 2007 (7 loci) 0.0079 0.0023 0.0013 0.0035 0.0016 0.0016
Geraldes et al 2011 (27 loci) 0.0066 0.0021 0.0015
Kousathanas et al 2010 0.0083
Noncoding (non-intronic)
This study (outside gene linkage distance) 0.0103 0.0028 0.0024 0.0029 0.0048 0.0044 0.0030 0.0056 0.0048 0.0067 0.0024 0.0088
Kousathanas et al 2010 (upstream from genes) 0.0071
Kousathanas et al 2010 (downstream from genes) 0.0069
All sites
This study 0.0088 0.0024 0.0023 0.0016 0.0042 0.0025 0.0015 0.0032 0.0025 0.0043 0.0015 0.0038
Harr et al 2016 0.0074 0.0020 0.0018 - 0.0035 0.0018 - 0.0025 0.0019 - - 0.0029
M. m. domesticus M. m. musculus M. spretus
21
(2010), very similar all-site results as Harr et al. (2016), and even quite similar results to the studies
done by Baines & Harr (2007) and Geraldes et al (2011) on only 7 and 27 intronic loci,
respectively. In accordance with our hypothesis of our noncoding regions being a good estimate
of neutral variation, we found greater
"
values in these regions compared to the Kousthanas et
al. (2010) estimates from regions upstream or downstream from genes (1.03% vs. 0.71% upstream
from genes and 0.69% downstream), likely due to reduced linkage to selected sites. As we
expected, derived populations of all four species have lower levels of diversity than their ancestral
counterparts, probably reflecting a founder effect (Jones and Yamazaki 1974; Powell and
Richmond 1974). We expected M. spretus to have a smaller Ne than house mice because they are
not human commensals (Palomo et al. 2009), have a relatively smaller geographic distribution
(Gray et al. 1998), and do not achieve nearly the same population density (Hurst et al. 1996).
Interestingly, we estimate the Ne of M. spretus to be very similar to M. m. domesticus and larger
than M. m. musculus based upon estimates from the populations of each species closest to their
respective ancestral range, indicating M. spretus has a much larger Ne than anticipated.
Specifically, our estimates of mean autosomal
"
are 0.43% for M. spretus from Morocco and
Tunisia, 0.88% for M. m. castaneus from India, 0.42% for M. m. domesticus from Iran, and 0.32%
and 0.25% for M. m. musculus from Afghanistan and Kazakhstan, respectively.
Adaptive Evolution
We estimated the DFE and a, the proportion of sites fixed by positive selection, on the X
chromosome and autosomes for each population. The DFE is estimated under a gamma
distribution, and the shape
parameter of the DFE, b, was
highly leptokurtic in almost
every population (Table 2.3),
meaning there is a
disproportionately large
contribution of mutations with
large selection coefficients (s)
to the mean Nes (Kousathanas
et al. 2014). Despite a likely
large and differential effect of demography among populations, we found a positive correlation
Table 2.3 Shape parameter estimates (b) of the distribution of fitness
effects for each population.
Species Sampling Location(s) !
M. m. castaneus India 0.17 (0.16-0.18)
M. m. domesticus Portugal 0.08 (0.07-0.10)
M. m. domesticus France 0.05 (0.05-0.05)
M. m. domesticus Germany 0.05 (0.05-0.06)
M. m. domesticus Iran 0.09 (0.09-0.11)
M. m. musculus Czech Republic, Hungary, Poland 0.05 (0.05-0.05)
M. m. musculus Czech Republic 0.05 (0.05-0.05)
M. m. musculus Afghanistan 0.05 (0.05-0.05)
M. m. musculus Kazakhstan 0.05 (0.05-0.05)
M. spretus Morocco & Tunisia 0.12 (0.11-0.14)
M. spretus France & Spain 0.05 (0.05-0.05)
M. spretus Spain 0.05 (0.05-0.05)
22
between autosomal Ne and a (p = 0.010), consistent with predictions from population genetic
theory that the probability of fixation of beneficial mutations increases with Ne (Figure 2.1;
(Kimura and Ohta 1971; Phifer-Rixey et al. 2012b)). Unlike the autosomes, we did not find a
significant relationship between Ne and a on the X chromosome, and our estimates had a much
higher degree of error (Figure 2.2). However, consistent with the disproportionate effect of
demographic events on X-linked sites, we found a marginally significant correlation between Ne
and a on the X when we restricted our analysis to ancestral range populations (p = 0.06; Figure
2.3). We observed stronger relationships using the rate of adaptive substitution relative to neutral
substitution (wa) rather than a (p < 10
-3
autosomes; p = 0.04 ancestral range X chromosome). We
did not find evidence for a high degree of adaptive evolution on the X chromosome or the
autosomes.
Mating Ecology
We found a positive correlation between relative testis mass and
7
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8:
(p = 0.019; Figure 2.4),
indicating testis mass may be a good long-term predictor of breeding sex ratio. We estimate
7
89
7
8:
in
-1.0
-0.5
0.0
0.5
0.00145 0.00154 0.0016 0.00228 0.00241 0.00247 0.00255 0.00323 0.00383 0.00416 0.00432 0.00885
Mean Theta, Autosomes
Alpha, Autosomes
Population
Afghanistan
Czech
Czech_Hungary_Poland
France
France_Spain
Germany
India
Iran
Kazakhstan
Morocco_Tunisia
Portugal
Spain
Species
M. castaneus
M. domesticus
M. musculus
M. spretus
Figure 2.1 Mean autosomal
"
by the proportion of sites fixed by positive selection (a)
with 95% confidence intervals.
23
M. m. castaneus is 0.46, M.
m. musculus ranges from
0.24 in the Czech Republic
to 0.50 in Kazakhstan, M.
m. domesticus ranges from
0.24 in France to 0.39 in
Iran, and M. spretus ranges
from 0.43 in France and
Spain to 0.65 in Morocco
and Tunisia. In each case,
the populations closest to
the ancestral range exhibit
the highest
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7
8:
ratios. This
makes good sense, as
demographic events serve
to reduce variation on the X chromosome to a larger degree than the autosomes (Pool and Nielsen
-1
0
1
2
0.00082 0.00089 0.00094 0.00095 0.00098 0.00102 0.0013 0.00178 0.00195 0.0025 0.0032 0.00485
Mean Theta, X chromosome
Alpha, X chromosome
Population
Afghanistan
Czech
Czech_Hungary_Poland
France
France_Spain
Germany
India
Iran
Kazakhstan
Morocco_Tunisia
Portugal
Spain
Species
M. castaneus
M. domesticus
M. musculus
M. spretus
Figure 2.2 Mean X chromosomal
"
by the proportion of sites fixed by positive selection
(a) with 95% confidence intervals.
0.0015 0.0020 0.0025 0.0030 0.0035 0.0040 0.0045
-0.6 -0.4 -0.2 0.0 0.2 0.4
Mean Theta, ChrX
Alpha, ChrX
Figure 2.3 Mean X chromosomal
"
by the proportion of sites fixed
by positive selection (a) in ancestral range populations.
24
2007, 2008; Ellegren 2009). Interestingly,
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8:
ratios are strikingly reduced in all populations, and
would result in a breeding sex ratio interpretation of male-biased (~0.50) sex ratios, which is
almost certainly not the case given behavioral observations of house mice (Bronson 1979) and
many cases of multiple paternity in wild litters (Dean et al. 2006; Firman and Simmons 2008).
More likely these results are due to the previously demonstrated reduction in X-linked diversity in
house mice (Baines and Harr 2007), perhaps due to demographic events. Because we did not find
evidence of widespread adaptive evolution on the X chromosome across populations, the reduction
in diversity is unlikely to be caused by selective sweeps. If we interpret these data in relation to
one another, rather than in an absolute context, it does seem that breeding sex ratios estimated
from genomic data track fairly well with assumptions based on relative testis size, indicating post-
copulatory events could account for most reproductive variance in mice.
We also compared rates of adaptive evolution in genes specifically expressed in 10
different tissues to determine if there was a relationship between relative testis size and a in testis-
expressed genes. We found no correlation between these estimates (Figure 2.5), but mean a for
testis-expressed genes (0.31) was higher than all tissues except liver (0.47) and thyroid (0.33)
Figure 2.4 NeX/NeA ratios with 95% confidence intervals by relative testis size.
0.2
0.3
0.4
0.5
0.6
0.7
0.3 0.5 0.7 0.9 1.1
Relative Testis Size
NeX/NeA
Location
Afghanistan
Czech
Czech_Hungary_Poland
France
France_Spain
Germany
India
Iran
Kazakhstan
Morocco_Tunisia
Portugal
Spain
Species
M. castaneus
M. domesticus
M. musculus
M. spretus
25
(Figure S2.7). With the exception of genes with heart-biased expression (-1.0), all tissues showed
evidence of high rates of adaptive evolution.
2.5 Discussion
In this study we sought to explore the amount of population-level variation in genomic
diversity, effective population size, and rate of adaptive evolution of the X chromosome relative
to the autosomes across four species of mice. We found a positive correlation between relative
testis size and
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8:
in our analyzed populations, meaning sex-specific gene flow likely plays a role
in patterns of genomic variation. However, diversity is lower than we would expect on the X
chromosome given our assumptions of breeding sex ratios of these species. They exhibit a range
of testis sizes, with M. m. castaneus having relatively small testes, M. spretus quite large, and M.
m. musculus and M. m. domesticus intermediate (Gómez Montoto et al. 2011). These differences
are assumed to be due to varying intensities of sperm competition, and several sperm quality
metrics are in good agreement with this inference. For example, M. m. castaneus makes ~30%
Figure 2.5 The proportion of adaptive substitutions (a) in testis-expressed genes
by relative testis size.
0.2
0.4
0.6
0.8
0.3 0.5 0.7 0.9 1.1
Relative Testis Size
Alpha, Testis Expressed Genes
Population
Afghanistan
Czech
Czech_Hungary_Poland
France
France_Spain
Germany
India
Iran
Kazakhstan
Morocco_Tunisia
Portugal
Spain
Species
M. castaneus
M. domesticus
M. musculus
M. spretus
26
non-motile sperm, M. m. musculus ~28%, M. m. domesticus makes approximately 10%, while M.
spretus only has ~1% non-motile sperm (Gómez Montoto et al. 2011). While the rank order of the
calculated
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for each species follows our expectations, the absolute values suggest many of
these populations are monogamous, or even have male-biased sex ratios. The general social
structure of house mice is dictated by territories defended by a single male, usually containing
several breeding females and some subordinate males (Lloyd 1975; Bronson 1979; Wolff 1985;
Potts et al. 2013). Multiple paternity has also been well documented in the wild (Dean et al. 2006;
Firman and Simmons 2008), meaning females often mate with more than one male during a single
estrous cycle. These observations make the possibility of our calculations reflecting true breeding
sex ratios very unlikely. The global reduction in X-linked diversity in mice is likely due to effects
of selection and/or demography.
As studies have pointed to a faster-evolving X chromosome in mice (Kousathanas et al.
2014; Larson et al. 2016c), it is possible our observations of low diversity are due to recurrent
selective sweeps reducing large swaths of variation on the X due to greater exposure of recessive
beneficial mutations in males and larger linkage distances than the autosomes (Smith and Haigh
1974; Wall et al. 2002). Two populations of M. m. domesticus from Iran and Germany, in addition
to M. m. castaneus, had positive estimates of a on the X chromosome, but adaptive evolution does
not seem to be common on the X as a whole in house mice or M. spretus. Selective sweeps seem
unlikely to be responsible for reduced X-linked variation in these populations, unless they are not
adequately detected by the method we used to detect positive selection (a). As some studies
finding evidence for faster-X evolution in mice have relied on calculations of the ratio of
nonsynonymous to synonymous substitution rates (Larson et al. 2016b), the estimates of recurrent
positive selection may have been confounded by an increased effect of drift due to the reduced Ne
of the X chromosome.
Similar to selection on adaptive mutations, recessive deleterious mutations can be more
efficiently purged on the X chromosome due to exposure in males (Charlesworth et al. 1987a).
The shorter sojourn time means these mutations recombine onto different genetic backgrounds less
frequently, resulting in less reduction in diversity on the X chromosome due to background
selection (Baines and Harr 2007; Hammer et al. 2008). Because background selection ought to
leave the X chromosome more polymorphic than the autosomes, it seems highly unlikely to play
a role in the pattern we have observed. Depressed X-linked variation can also be explained by
recent demographic events, such as bottlenecks and founder events (Powell and Richmond 1974;
27
Wall et al. 2002; Pool and Nielsen 2007, 2008; Ellegren 2009), that affect the X chromosome to a
larger degree than the autosomes. Demographic events almost certainly play a role in the derived
populations from Europe as these populations exhibit the lowest
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values in our dataset.
Interestingly, variation on the X is greatly reduced in European M. spretus compared to the
population from Northern Africa, a pattern that has also been demonstrated in Drosophila
melanogaster, D. simulans (Betancourt et al. 2004), and humans (Payseur and Nachman 2002).
Additionally, the reduced
7
89
7
8:
values in derived populations of Drosophila are thought to be due
to a combination of effects of female multiple mating and founder events, so the same effect could
be responsible for the observations in this study.
Previous studies of the Indian M. m. castaneus population have estimated ~40% of sites
are fixed by positive selection across the genome (Halligan et al. 2010), leading to the prediction
that mice in general have a high rate of adaptive evolution. A re-analysis of adaptive evolution
using a slightly different method of the same M. m. castaneus population, in addition to two
derived populations of M. m. musculus and M. m. domesticus, also found a significant signature
of positive selection in M. m. castaneus but not in the other two species (Phifer-Rixey et al. 2012a).
As this analysis was conducted on derived populations, however, it was unclear if this was a result
of founder events or a property of M. m. musculus and M. m. domesticus in general due to their
smaller estimated Ne. It appears the latter explanation is likely, because while we estimate a very
similar value of a as previous studies on the M. castaneus population from India, it is the sole
population in our analysis of both ancestral and derived populations with a positive value of a on
the autosomes. This was somewhat surprising, but ultimately is reflective of positive selection not
being as common in house mice as previously thought. This population also had the largest value
of b, the shape parameter of the gamma-distributed DFE, meaning the mean selective effects are
not overwhelmed by strongly deleterious mutations (Kousathanas et al. 2014). Perhaps this
population is the only one sampled with a high enough Ne to provide good efficacy of selection. It
is also possible that adaptive evolution is occurring at a higher rate but in a manner that escapes
quantification by a. However, we did find a positive correlation between a and Ne on the
autosomes across all populations, and also on the X chromosome in ancestral range populations.
When we analyzed a on a smaller scale by restricting to genes with biased gene expression in 10
tissues, all but the heart had a large, positive mean estimate across all populations, suggesting
adaptive evolution is proceeding rapidly in mice in genes with restricted expression.
28
In summary, we find a significant signature of sex-specific demographics across 12
populations of M. m. musculus, M. m. domesticus, M. m. castaneus, and M. spretus. These
differences in Ne between populations predict the proportion of sites fixed by positive selection on
the autosomes, and on the X chromosome in ancestral range populations, suggesting mating system
evolution could have a large impact on genomic evolution in mice. We also find a global reduction
in X-linked diversity that cannot be readily explained by rampant positive selection. This study
highlights how much variation exists in several genomic characteristics in mice and calls for
greater sampling to inform our understanding of evolution in these species, as well as to inform
the laboratory studies on mice derived from these populations.
29
2.6 Supplementary Materials
-0.10
-0.05
0.00
0.05
0.10
0.15
-0.05 0.00 0.05 0.10
PC1: 42% variance
PC2: 13% variance
Population
Afghanistan
Czech_Hungary_Poland
Czech_Republic
France
France_Spain
Germany
India
Iran
Kazakhstan
Morocco_Tunisia
Portugal
Spain
Species
M. castaneus
M. domesticus
M. musculus
M. spretus
Figure S2.1 Principal components 1 and 2 from principal components analysis on
chromosome 1 after thinning sites in linkage disequilibrium
30
-0.2
0.0
0.2
-0.3 -0.2 -0.1 0.0 0.1
PC1: 11% variance
PC2: 9% variance
Species
M. domesticus
Population
France
Germany
Iran
Portugal
Figure S2.2 Principal components 1 and 2 from principal components analysis on
chromosome 1 from Mus musculus domesticus populations
31
-0.3
-0.2
-0.1
0.0
0.1
0.2
-0.2 -0.1 0.0 0.1 0.2
PC1: 13% variance
PC2: 8% variance
Population
Afghanistan
Czech Republic
Czech_Hungary_Poland
Kazakhstan
Species
M. musculus
Figure S2.3 Principal components 1 and 2 from principal components analysis on
chromosome 1 from Mus musculus musculus populations
32
-0.3
-0.2
-0.1
0.0
0.1
0.2
-0.2 -0.1 0.0 0.1 0.2
PC1: 25% variance
PC2: 9% variance
Population
France_Spain
Morocco_Tunisia
Spain
Species
M. spretus
Figure S2.4 Principal components 1 and 2 from principal components analysis on
chromosome 1 from Mus spretus populations
33
0.03 0.09 0.15 0.21 0.27 0.33 0.39 0.45 0.51 0.57 0.63 0.69 0.75 0.81 0.87 0.93
PhastCons Score
0.00 0.05 0.10 0.15 0.20
0-fold
4-fold
introns
noncoding
Figure S2.5 Distributions of PhastCons conservation scores calculated from all placental
mammals across 0-fold and 4-fold degenerate sites, introns, and noncoding regions
34
0-fold 4-fold Introns Noncoding
0.002 0.004 0.006 0.008 0.010
Mean Theta, Autosomes
Figure S2.6 Mean autosomal qw from all populations across 0-fold and 4-fold degenerate
sites, introns, and noncoding regions
35
-1.0
-0.5
0.0
0.5
Brain Gut Heart Kidney Liver Lung Muscle Spleen Testis Thyroid
Mean Alpha
Figure S2.7 Mean proportion of sites fixed by positive selection (a) across all populations for genes with
biased expression in 10 different tissues
36
Chapter 3: Adaptive evolution in the cumulus-oocyte complex
proteome highlights involvement of the female immune system in
fertilization
3.1 Abstract
In internally fertilizing species, competition between sperm of rival males in the female
reproductive tract selects for sperm traits that increase fertilization rate. As fertilization by more
than one sperm is fatal for ova in many species, females must utilize barriers to slow fertilization
rate. However, strong defenses to polyspermy are only adaptive when females mate multiple times
and could prevent fertilization altogether when multiple mating does not occur. Firman and
Simmons (2013) showed female house mice dynamically alter ova defensiveness based on their
perceived risk of sperm competition from male urinary scents and encounters, however, the
mechanism of this plasticity is unknown. As these experiments were conducted via in vitro
fertilization, fertilization rate changes must have occurred from modifications of the oocyte’s
plasma membrane, zona pellucida, and/or the cumulus cell layer surrounding the oocyte. Here, we
test the involvement of the cumulus cell layer in plastically modifying fertilization rate in response
to sperm competition risk. We find an increased rate of breakdown in the first minute of enzymatic
digestion of the cumulus cell layer from females with a low perceived risk of sperm competition,
indicating it could play a role in the phenotype previously described. We test for surface protein
changes between treatment groups and find evidence suggesting females perceiving a high risk of
sperm competition may be induced into earlier puberty. As sexual conflict over fertilization has
been shown to drive rapid coevolution in gamete binding proteins, we also conduct the first global
evolutionary analysis of the cumulus-oocyte complex proteome in mice. We find evidence of
adaptive evolution in many COC proteins that seem to function in both reproduction and immunity,
potentially providing a glimpse into the nature of gamete reproductive evolution in mice.
37
3.2 Introduction
In many internally fertilizing species, females mate with more than one male during a
single estrous cycle, introducing the opportunity for female mate choice to continue after
copulation (Eberhard 1996; Jennions and Petrie 2000) and for sperm from different males to
compete with one another for fertilization (Parker 1991; Simmons 2005). Sperm competition
generates selection for ejaculate traits, such as increased sperm production (Parker 1991; Gómez
Montoto et al. 2011; Firman et al. 2015) and motility (Anderson and Dixson 2002; Gomendio et
al. 2006), that function to increase fertilization rate (Birkhead and Pizzari 2002; Firman and
Simmons 2013; Firman et al. 2014). As fertilization by more than one sperm is lethal for ova in
many species (Fraser and Maudlin 1978), females employ a series of barriers that slow fertilization
rate (Frank 2000). Accordingly, studies of marine invertebrates (Levitan et al. 2007) and mice
(Martín‐Coello et al. 2009) have demonstrated correlations between the intensity of sperm
competition and ova defensiveness. In addition, experimental evolution of mice under
monogamous or polyandrous mating regimes results in more defensive ova in polyandrous females
(Firman et al. 2014), and defensiveness is also higher in mouse populations with high frequencies
of multiple paternity litters (Firman and Simmons 2008). While effective, these strong defenses
could result in fertilization failure when females do not mate with multiple males. Selection
resulting from sexual conflict over fertilization rate must strike a balance between selecting for
traits that benefit one sex at the cost of the other and ensuring reproduction still occurs normally.
Interestingly, Firman and Simmons (2013) demonstrated that female mice dynamically alter the
defensiveness of their ova depending on their perceived risk of sperm competition, indicating at
least some of the blocks to polyspermy are plastic. However, the mechanism behind this
phenotype, and female adaptations to sexual conflict in general, remain unknown.
In existing studies on post-copulatory sexual conflict, elevated divergence in gamete-
binding proteins has been repeatedly shown (Swanson et al. 2001; Swanson and Vacquier 2002a;
Vacquier and Swanson 2002, 2011; Moy et al. 2008; Dapper and Wade 2016; Wilburn and
Swanson 2016). Changes in sperm-binding proteins on ova are thought to aid in polyspermy
prevention by limiting the number of compatible sperm, subsequently driving compensatory
changes in sperm proteins and rapid co-evolution of binding pairs (Swanson et al. 2003; Vacquier
and Swanson 2011). Sequence evolution of this nature cannot dynamically modify polyspermy
defenses, however, changes in the abundance of sperm-binding proteins could potentially mediate
38
plastic responses to sperm competition, but this has yet to be explored. In mammals, evolutionary
studies of sperm proteins (Swanson et al. 2003; Dorus et al. 2010) and seminal proteins (Dean et
al. 2008, 2009) have revealed adaptively evolving proteins putatively involved in evading female
barriers to fertilization. However, with the exception of studies on a few targeted genes (Swanson
et al. 2001, 2003), evolution in female gametes and the female reproductive tract is remarkably
uncharacterized.
In mammals, ejaculated sperm must pass through the cervix and uterotubal junction before
reaching the oocyte surrounded by a layer of cumulus cells in a viscous extracellular matrix (Ikawa
et al. 2010b). As the experiments of Firman et al. (2013) were conducted via in vitro fertilization
(IVF), plasticity in polyspermy defense must be due to the oocyte plasma membrane, zona
pellucida, and/or the cumulus cell layer. Sperm possess endogenous hyaluronidases necessary to
enzymatically digest the cumulus layer (Kimura et al. 2009), and one of the two primary sperm
hyaluronidases is rapidly evolving (Dorus et al. 2010; Prothmann et al. 2012). Cumulus cells
demonstrate a variety of fertilization-promoting interactions with sperm, including secreting
chemoattractants (Tesařik et al. 1988; Sun et al. 2005) and inducing changes that increase
fertilizing ability (Hong et al. 2003; Jin et al. 2011; Tanii et al. 2011; Chen et al. 2013). In fact, co-
incubating cumulus cells with sperm is widely used to improve IVF success (Chian et al. 1995;
Hong et al. 2009; Ikawa et al. 2010b) and capillary tubes filled with cumulus cells are used to
select good quality sperm (Franken and Bastiaan 2009; Hong et al. 2009). The cumulus layer seems
a likely candidate for preventing polyspermy and facilitating female choice, yet the molecular
interactions between sperm and cumulus cells are largely uncharacterized, and these cells have
never been studied in an evolutionary context.
Given these known sperm interaction phenotypes exhibited by the cumulus cell layer, we
postulated it was likely to exert an effect on the dynamic change in fertilization rate observed by
Firman and Simmons (2013). Involvement of the cumulus cell layer could be mediated by physical
modifications, such as an increase in the amount of extracellular matrix produced or in cell density,
or even through modifications in proteins necessary for increasing fertilization ability of sperm.
Here, we specifically test whether the cumulus cell layer is responsible for the shifts in fertilization
potential observed by Firman and Simmons (2013). We repeat their exposure experiments and test
for cumulus cell surface protein composition changes between treatments, as well as whether the
cumulus layer dissociates at different rates depending on perceived risk of sperm competition. As
the mouse cumulus cell proteome is largely uncharacterized, we employ shotgun proteomics to
39
identify proteins present on cumulus cells as well as the whole COC. In order to test for recurrent
positive selection and to better understand the evolutionary patterns in COCs, we compute
evolutionary rates (dN/dS) for genes encompassing the entire COC proteome in concert with the
previously published sperm proteome (Dorus et al. 2010) across 11 mouse species. Collectively
our study identifies new mouse cumulus-oocyte proteins, uncovers a potential mechanism of
polyspermy defense plasticity in mice, and conducts the first global evolutionary analysis on the
mouse cumulus-oocyte complex proteome.
3.3 Methods
Protein Identification
For our cumulus-oocyte complex (COC) proteomics experiments, we induced ovulation in
sexually mature female mice using intraperitoneal injections of 0.1 mL Pregnant Mare Serum
Gonadotropin (PMSG) followed by Human Chorionic Gonadotropin 48 hours later (Fowler and
Edwards 1957). We then dissected cumulus-oocyte masses from the oviducts and washed them in
phosphate buffered saline (PBS) before protein extraction. We sought to identify surface proteins
that are most likely to interact with sperm during fertilization, so we chose to use the Pierce Cell
Surface Protein Isolation kit (ThermoFisher Scientific) to biotinylate proteins on the surface of
live cells before lysing them and affinity purifying the labelled proteins using Streptavidin agarose.
We concentrated the proteins with a centrifugal 10K filter, ran them in a single lane of an SDS-
PAGE gel briefly to avoid separation of proteins, and cut the band out of the gel for mass
spectrometry. As the cumulus cell proteome is less characterized than the oocyte proteome in mice,
we initially retained the oocytes in the COCs for protein extraction from three C57BL/6N mice to
serve as a positive control of our extraction method. Mass spectrometry was performed by the
Biomedical Mass Spectrometry Center at the University of Pittsburgh by digesting the sample with
trypsin followed by nano reverse phase HPLC. The tandem mass spectra (MS/MS) were analyzed
against the mouse protein database with the MASCOT (Matrix Science) search engine, with
further validation done using Scaffold v.4 (Proteome Software). In total, we generated 524 COC
surface proteins of high confidence (protein identification threshold >=95%, minimum unique 2
peptides with >=90% identification threshold). In this mass spectrometry dataset we identified
40
known oocyte surface proteins, such as all three major protein components of the zona pellucida
(Primakoff and Myles 2002), which we considered a validation of our chosen extraction protocol.
As these mass spectrometry runs were performed on cumulus-oocyte masses with an intact
extracellular matrix, it was still unclear what fraction of the cumulus cell surface proteome we had
uncovered. We repeated our extraction protocol on six C57BL/6N mice after digesting the
hyaluronic acid extracellular matrix using hyaluronidase, removing the oocytes, and combining
the cumulus cells from all females. Mass spectrometry was performed on this sample at the Keck
School of Medicine Proteomics Core at the University of Southern California. In total we identified
151 surface proteins from cumulus cells only with high confidence. As we identified a large
number of keratin proteins (29) indicative of dissection contamination, we repeated our protocol
with an additional two wash steps in PBS before protein biotin labelling and extraction. LC-
MS/MS was performed by ITSI Biosciences (Johnstown, PA), followed by protein matching in
Proteome Discoverer v1.4 (ThermoFisher Scientific) and peptide spectrum match validation using
Percolator (The et al. 2016), yielding 42 high confidence cumulus cell surface proteins. We then
used a fractionation-based protein extraction method to complement our existing surface protein
dataset. We induced ovulation and dissected cumulus-oocyte masses as described above, and
fractionation into cytoplasmic, nuclear, and membrane proteins followed by LC-MS/MS was
performed by ITSI Biosciences. As we were expanding our dataset from surface-only COC
proteins, the fractionation experiment yielded many more high confidence proteins than our
previous experiments, with 364 identified from the membrane fraction, 333 from the nuclear
fraction, and 346 from the cytoplasm. We performed a gene ontology analysis by annotating our
identified proteins using Jackson Lab’s Mouse Genome Database (Smith et al. 2018). We used the
PANTHER database v.14.0 (Mi et al. 2017) to test for overrepresentations of protein categories.
Social Manipulation
House mice were trapped on Rat Island off the coast of Western Australia and housed at
the University of Western Australia. These animals were bred in the lab for two generations, and
F2 animals were used in our experiments. Social manipulation experiments were carried out in the
same fashion as published in Firman & Simmons (2013). Briefly, 24 females were weaned at 21
days of age and females from the same litter were split into two treatment groups, resulting in 12
females in each treatment. These treatment groups simulated a high and low risk of sperm
competition, respectively, predominantly through urine scent cues known to have an effect on the
41
development and reproduction of mice. Females in the “risk” treatment group were housed singly
in a room with singly housed sexually mature males, while females in the “no risk” group were
singly housed in a separate room with sexually mature females. “Risk” females received mixed
soiled chaff donated from 10 sexually mature males once per week, while “no risk” females
received soiled chaff from the same male once per fortnight. To control for the possibility of an
effect of simply placing chaff into the cage regardless of the source, “no risk” females received
clean chaff on the weeks when they did not receive soiled chaff. Focal females in both treatment
groups “encountered” a caged mouse every fortnight inside a large plastic tub, allowing them to
receive stimulation without physical contact. “Risk” females encountered a different sexually
mature male each fortnight, while “no risk” females encountered a sexually mature female. This
social manipulation technique has been shown to result in phenotypic plasticity in fertilization rate,
with “risk” females exhibiting a slower fertilization rate (Firman and Simmons 2013). Because the
mechanism of this plasticity is unknown, we sought to understand if the cumulus cell layer was
playing a role, perhaps through producing more extracellular matrix, increasing cellular
proliferation, or altering availability of receptors needed for fertilization to occur. After at least 8
weeks of our exposure experiments, we induced ovulation in focal females and sacrificed them to
collect cumulus-oocyte masses as described above. To assess the rate of breakdown of the cumulus
layer, prior to protein extraction we placed the cumulus-oocyte masses into 1mL of PBS and added
5uL of hyaluronidase to standardize the concentration of the enzyme, then obtained time-lapse
images as digestion progressed every 5 seconds for 5 minutes. In some cases, the cell mass was
not entirely dissociated by the end of this time period, so an additional 5uL of hyaluronidase was
added and time-lapse imaging continued. Once the cumulus layer was entirely dissociated, we
removed the oocytes and proceeded with surface protein extraction from cumulus cells as
previously described. ITSI Biosciences performed LC-MS/MS on these samples to identify
proteins potentially mediating fertilization rate change. As we lacked the sample sizes to find
quantitative differences in cell surface proteins, we simply identified high confidence proteins
present in one treatment group but not the other.
We measured the COCs blind to female ID and treatment in Adobe Photoshop using the
magnetic lasso tool to offer some degree of standardization in drawing the boundary of the cell
mass. After selecting the COCs we measured the area, perimeter, and circularity of each one and
analyzed the data using custom code in R. In a few cases, the COCs drifted partially out of view
during the time-lapse imaging, so we manually estimated the proportion off-screen to adjust the
42
measurements accordingly, again done blind to treatment and female ID. For each COC, we
estimated two different aspects of the rate of breakdown: the time until maximum area was
achieved and the rate of area increase during the first minute. The timeframe of the latter
measurement was chosen because dissociation proceeds rapidly during the first minute following
the addition of hyaluronidase, then plateaus.
Evolutionary Analysis
To identify COC proteins that might be evolving under recurrent positive selection, we
compiled multispecies alignments from 11 mouse species (Mus platythrix, M. Pahari, M.
minutoides, M. caroli, M. cervicolor, M. cookii, M. spretus, M. spicilegus, M. macedonicus, M. m.
musculus, and M. m. domesticus) of canonical transcripts for our proteins identified in this study
in addition to a published mouse sperm proteome (Dorus et al. 2010) and oocyte proteome (Zhang
et al. 2009) and analyzed them using the Phylogenetic Analysis by Maximum Likelihood (PAML)
software v.4.9 (Yang 2007). We used the published resolved mouse phylogeny (Sarver et al. 2017)
to dictate species relationships and branch lengths in our data. We randomly selected 6,000 non-
COC or sperm genes to represent the ‘background’ rate of evolution in the mouse genome. To
identify proteins that may contain sites evolving under positive selection, we ran each transcript
under the M7, M8, and M8a site models using codeml within PAML to test for positive selection.
We performed model selection using likelihood ratio tests between M7 and M8 as well as M8 and
M8a, then adjusted the p-values using a false discovery rate (Benjamini and Hochberg 1995)
method.
3.4 Results
Protein Identification
In total, we identified 1,007 high confidence proteins present in cumulus oocyte cell masses
(Table S3.1). As some of our proteomics experiments contained only cumulus cells, we compared
43
the overlap between proteins identified using whole
COCs, cumulus cells only, and the previously
published oocyte only proteome ((Zhang et al.
2009); Fig. 3.1). We identified approximately 42%
of the proteins found in the oocyte proteome, while
identifying 769 additional proteins from cumulus
cells and oocytes. While we likely did not recover
the entirety of the COC proteome, we did identify
all three major protein components of the zona
pellucida (Zp1, Zp2, and Zp3), the outermost layer
of the oocyte, using surface protein extraction from
the entire COC and in the membrane fraction of our
fractionation experiment, while none of those
proteins were present in our cumulus cell only
experiments, serving as a good positive control.
We used the PANTHER (Mi et al. 2017) classification system for gene ontology (GO)
annotation of all COC proteins. In GO annotations specific to cellular component, the largest
category was simply ‘cell’, followed by organelle, protein-containing complex, and membrane
(Fig. 3.2a). Overrepresentation tests yielded an exhaustive list of enriched functions, so we
mention only the categories with the highest enrichment. The molecular function GO results from
our dataset were dominated by binding and catalytic activity, with most proteins in the binding
category functioning in a protein binding context (Fig. 3.2b). While reproduction was an annotated
297
622
199
112
1
35
18
Cumulus Cumulus_Oocyte Oocyte
Figure 3.1 Overlap of high-confidence proteins
identified in our experiments from cumulus cells
and cumulus-oocyte complexes and the published
mouse oocyte proteome.
Cell
Organelle
Protein-containing
complex
Membrane
Supramolecular
complex
Extracellular
region
Synapse
Cell junction
0
10
20
30
40
Binding
Catalytic activity
Structural molecule
activity
Transporter activity
Molecular function
regulator
Transcription regulator
activity
Molecular transducer
activity
Translation regulator
activity
0
10
20
30
40
metabolic process
cellular component organization
biological regulation
localization
cellular process
response to stimulus
developmental process
multicellular organismal process
biological adhesion
immune system process
signaling
reproduction
cell proliferation
growth
locomotion
multi-organism process
biological phase
pigmentation
0
10
20
30
40
A B C
Figure 3.2 Gene ontology classifications of high-confidence cumulus-oocyte complex proteins in (a) cellular
component, (b) molecular function, and (c) biological process.
44
biological process for several proteins, far more proteins in the dataset fall generally into metabolic
process, biogenesis, and biological regulation (Fig. 3.2c).
Social Manipulation
Our cumulus cell protein extraction of socially manipulated females yielded 17 high
confidence surface proteins from the ‘no risk’ treatment group and 18 from the ‘risk’ group, 12 of
which were present in both groups (Table 3.1). We identified Apolipoprotein A1, Desmoplakin,
Junction plakoglobin, Phosphatidylethanolamine-binding protein 1, and Peroxiredoxin 1 only in
‘no risk’ females.
We also found six
proteins only in
‘risk’ mice, namely
Enolase 1, Histone
cluster 1 H2bj, Heat
shock protein 90
alpha class B
member 1, Pyruvate
kinase, and Serine
peptidase inhibitor
clade A members 1B and 1A. The number of proteins identified was reduced relative to our
previous cumulus cell surface protein experiments, which yielded 42 and 122 high confidence
proteins, due to small sample sizes. Following our social manipulation experiments, there was a
high degree of variation in inducing ovulation among females. Some of the females that ovulated
produced relatively few oocytes, while some experimental females failed to ovulate entirely,
reducing our sample size and protein yield. Interestingly, the females that failed to ovulate were
all in the ‘risk’ treatment group, despite being closely related to the females in the ‘no risk’ group
and undergoing hormonal injection at the same times.
Table 3.1 High confidence cumulus cell surface proteins identified from females
reared with ‘risk’ or ‘no risk’ of sperm competition.
Actb Beta actin Actb Beta actin
Alb Albumin Alb Albumin
Anxa2 Annexin A2 Anxa2 Annexin A2
Apoa1 Apolipoprotein A1 Eef1a1 Eukaryotic translation elongation factor 1 alpha 1
Dsp Desmoplakin Eno1 Enolase 1
Eef1a1 Eukaryotic translation elongation factor 1 alpha 1 Hist1h2al Histone cluster 1, H2al
Hist1h2al Histone cluster 1, H2al Hist1h2bj Histone cluster 1, H2bj
Hist1h4a Histone cluster 1, H4a Hist1h4a Histone cluster 1, H4a
Hspa5 Heat shock protein 5 Hsp90ab1 Heat shock protein 90 alpha, class B member 1
Jup Junction plakoglobin Hspa5 Heat shock protein 5
Krt2 Keratin 2 Krt2 Keratin 2
Pebp1 Phosphatidylethanolamine binding protein 1 Pkm Pyruvate kinase
Prdx1 Peroxiredoxin 1 Serpina1b Serine peptidase inhibitor, clade A, member 1B
Tuba1c Tubulin, alpha 1C Serpinb1a Serine peptidase inhibitor, clade A, member 1A
Tubb5 Tubulin, beta 5 class 1 Tuba1c Tubulin, alpha 1C
Ubc Ubiquitin C Tubb5 Tubulin, beta 5 class 1
Vapa Vesicle-associated membrane protein Ubc Ubiquitin C
Vapa Vesicle-associated membrane protein
"No Risk" HC proteins "Risk" HC proteins
45
After measuring the
COC time-lapse images blind
to treatment group, we
analyzed the rate of cumulus
cell layer breakdown for each
COC mass. Following the
addition of hyaluronidase, the
cumulus layer breaks down
extremely rapidly then
plateaus around one minute
(Fig. 3.3). We sought to
understand the rate of
breakdown in this initial phase
by fitting a line to this part of the data and recording the slope, and also recorded the time it took
for each COC mass to reach its maximum area for a look into the slower rate of breakdown after
the first minute. While we found
no difference between groups in
the time it took for each mass to
reach their maximum area, we did
find a significant difference in the
rate of area expansion during the
first minute after adding
hyaluronidase (p=0.001, weighted
t-test=3.7; Fig. 3.4), suggesting
the rate of breakdown in the
cumulus layer could have
contributed to the differences in
fertilization rate previously seen
by Firman and Simmons (2013).
0 5 10 15 20 25 30 35
150000 250000 350000 450000
Time points (1 point = 5 seconds)
Area
Figure 3.3 Representative plot of COC mass area by time point during
enzymatic digestion with hyaluronidase. Each time point represents 5
seconds.
0 10000 20000 30000 40000 50000 60000
No Risk Risk
Rate of Dissociation
Figure 3.4 Rate of dissociation of the cumulus cell layer in the first
minute of enzymatic digestion of cumulus-oocyte complexes from
females reared with ‘risk’ or ‘no risk’ of sperm competition.
46
Reproductive Protein Evolution
We included all of our high confidence identified proteins across all experiments in our
evolutionary analysis, along with the published oocyte (Zhang et al. 2009) and sperm (Dorus et al.
2010) proteomes, and approximately 6,000 genes randomly chosen from all non-COC and non-
sperm genes to represent a background comparison. After performing likelihood ratio tests
between PAML site models M7 and M8 and between M8 and M8a to test for positive selection,
we found 240 genes with sites evolving under positive selection, 168 of which were background
genes. The COC proteome has a significantly lower evolutionary rate (dN/dS) than the sperm
proteome and the control genes (p=4.709e
-06
; p=2.2e
-16
, Fig. 3.5). The sperm proteome did not
have a significantly different
dN/dS than the control genes.
Within the COC proteome, we
found membrane proteins to have a
higher dN/dS than both the nucleus
and cytoplasm (p=3.85e
-07
;
p=0.0011; Fig. 3.6), consistent
with results seen in many other
studies (Julenius and Pedersen
2006; Dorus et al. 2010). 30 COC
genes were found to contain sites
under positive selection (Table
3.2). Relative to the mouse
reference genome, these proteins are enriched for cellular response to stress (27.71 fold
enrichment, FDR p=2.56 e
-02
). We also identified the top 5% of whole-gene dN/dS values from
the COC proteome to compare with the adaptively evolving protein set. We found 14/30 of these
adaptively evolving genes were also in the top 5% of dN/dS values (Table 3.3). The genes with
the highest 5% dN/dS values in the COC are overrepresented in protease activity, specifically
Cumulus/Oocyte Sperm Background
0.0 0.5 1.0 1.5 2.0
dN/dS
Figure 3.5 dN/dS ratios of proteins from the cumulus-oocyte
complex, sperm, and background genes computed from 11 mouse
species.
47
endopeptidase inhibitor (p=3.88e-
02), endopeptidase regulator
(p=3.50e-02), peptidase regulator
(p=2.91e-02), protease binding
(p=2.51e-02), serine-type
endopeptidase (p=5.50e-05), and
serine hydrolase (p=1.56e-02)
activities. They are also enriched
for enzyme regulator (p=3.36e-02)
and catalytic (p=3.-6e-02) activity.
We found 42 sperm genes
with evidence of positive
selection (Table 3.4), 11 of which
were also identified as adaptively evolving in the Dorus et al. (2010) analysis from mouse, rat, two
primates out of human, chimpanzee, and macaque and either dog or cow. These adaptively
evolving sperm genes are overrepresented in the complement component (> 100 fold enrichment,
FDR p=2.12 e
-02
), metalloprotease (12.59 fold enrichment, FDR p=1.11 e
-02
), protease (4.83 fold
Nucleus Cytoplasm Membrane
0.0 0.5 1.0 1.5
dN/dS
Figure 3.6 dN/dS ratios of cumulus oocyte proteins annotated as
being present in the nucleus, cytoplasm, and membrane of the cell.
Table 3.2 Mouse cumulus-oocyte proteins evolving under positive
selection with dN/dS (w) values.
Gene Symbol Protein Name Whole-gene ! Prop. Sites !>1 Selected Sites !
Akr1c14 Aldo-keto reductase family 1, member C14 0.299 0.027 5.837
Ank2 Ankyrin 2 0.144 0.013 4.228
Anxa1 Annexin A1 0.371 0.143 2.583
Apcs Serum amyloid P-component 0.981 0.056 9.029
Bod1l Biorientation of chromosomes in cell division 1-like 0.400 0.004 11.137
Chchd6 Coiled-coil-helix-coiled-coil-helix domain containing 6 1.001 0.004 137.978
Dnmt1 DNA methyltransferase 1 0.200 0.019 4.503
Eif3c Eukaryotic translation initiation factor 3, subunit C 0.027 0.002 10.890
Fbxw16 F-box and WD-40 domain protein 16 0.917 0.074 6.040
Fsip2 Fibrous sheath-interacting protein 2 0.406 0.029 3.844
Gcn1l1 General control of amino-acid synthesis 1-like 1 0.038 0.021 1.432
Gm45713 Predicted gene 45713 0.389 0.042 6.716
Gm813 Predicted gene 813 (Ferritin) 1.712 0.112 8.975
Hsd3b1 Hydroxy-delta-5-steroid dehydrogenase, 3 beta- and steroid delta-isomerase 1 0.604 0.048 8.750
Hspa8 Heat shock protein 8 0.030 0.002 13.421
Itih1 Inter-alpha trypsin inhibitor, heavy chain 1 0.210 0.042 3.616
Itih3 inter-alpha trypsin inhibitor, heavy chain 3 0.172 0.001 32.080
Ldlr Low density lipoprotein receptor 0.186 0.028 3.973
Ly9 Lymphocyte antigen 9 0.805 0.255 2.541
Mki67 Antigen identified by monoclonal antibody Ki 67 0.797 0.256 2.324
Nlrp4f NLR family, pyrin domain containing 4F 0.731 0.131 3.604
Nlrp9b NLR family, pyrin domain containing 9B 0.762 0.109 4.167
Pdia3 Protein disulfide isomerase associated 3 0.121 0.011 8.296
Pecr Peroxisomal trans-2-enoyl-CoA reductase 0.553 0.010 15.593
Rnf213 Ring finger protein 213 0.422 0.022 5.132
Tagln2 Transgelin 2 0.069 0.012 4.945
Tars Threonyl-tRNA synthetase 0.058 0.003 8.718
Tfrc Transferrin receptor 0.343 0.015 7.819
Ttn Titin 0.079 0.007 2.574
Vtn Vitronectin 0.474 0.047 5.415
48
enrichment, FDR p=4.04 e
-
02
), and hydrolase (3.57
fold enrichment, FDR
p=4.10 e
-02
) protein classes,
as well as fertilization and
reproduction reactome
pathways (59.83 fold
enrichment, p=3.79 e
-02
;
57.61 fold enrichment,
p=2.10 e
-02
). Of these
genes, 23 are in the top 5%
of global dN/dS values
from the sperm proteome
(Table 3.5). Protease
function was enriched in
the highest 5% dN/dS
values in the sperm
proteome, with
overrepresentation in
serine protease inhibitors
(p=8.81e-04),
metalloproteases (p=4.93e-
03), and protease inhibitors
(p=3.93e-02).
Interestingly, the highest
5% dN/dS genes from both
COCs and sperm were
enriched for the neutrophil
degranulation, innate
immune system, and
platelet degranulation
reactome pathways.
Table 3.3 Mouse cumulus-oocyte complex proteins with the highest 5% w
ratios
Gene Symbol Protein Name !
Ahsg Alpha-2-HS-glycoprotein 0.4
Akr1cl Aldo-keto reductase family 1, member C-like 0.4
Apcs Serum amyloid P-component 0.981
Apoa1 Apolipoprotein A-I 0.4
Bcl2l10 Bcl2-like 10 0.5
Bod1l Biorientation of chromosomes in cell division 1-like 0.4003
Ccdc117 Coiled-coil domain containing 117 0.5152
Ccr1 Chemokine (C-C motif) receptor 1 0.4
Cenpf Centromere protein F 0.4674
Chchd6 Coiled-coil-helix-coiled-coil-helix domain containing 6 1.0009
Crb1 Crumbs family member 1, photoreceptor morphogenesis associated 0.4012
D6Ertd527e DNA segment, Chr 6, ERATO Doi 527, expressed 0.4709
Dppa3 Developmental pluripotency-associated 3 0.5162
E330017A01Rik RIKEN cDNA E330017A01 gene 0.6
Epb42 Erythrocyte membrane protein band 4.2 0.4
Fbxw14 F-box and WD-40 domain protein 14 1
Fbxw16 F-box and WD-40 domain protein 16 0.9171
Fbxw19 F-box and WD-40 domain protein 19 0.6
Fbxw24 F-box and WD-40 domain protein 24 0.6504
Fga Fibrinogen alpha chain 0.4
Flg2 Filaggrin family member 2 0.6
Fsip2 Fibrous sheath-interacting protein 2 0.4064
Gm813 Predicted gene 813 1.7117
Hsd3b1 Hydroxy-delta-5-steroid dehydrogenase, 3 beta- and steroid delta-isomerase 1 0.6041
Iigp1 Interferon inducible GTPase 1 0.5
Khdc3 KH domain containing 3, subcortical maternal complex member 0.6
Ly9 Lymphocyte antigen 9 0.8052
Map4 Microtubule-associated protein 4 0.6
Milr1 Mast cell immunoglobulin like receptor 1 0.7
Mki67 Antigen identified by monoclonal antibody Ki 67 0.7965
Naca Nascent polypeptide-associated complex alpha polypeptide 0.7097
Nlrp2 NLR family, pyrin domain containing 2 0.5
Nlrp4f NLR family, pyrin domain containing 4F 0.7314
Nlrp9b NLR family, pyrin domain containing 9B 0.7624
Nxf1 Nuclear RNA export factor 1 0.4
Oas1e 2'-5' oligoadenylate synthetase 1E 0.5
Ooep Oocyte expressed protein 0.5201
Ovgp1 Oviductal glycoprotein 1 0.5
Padi6 Peptidyl arginine deiminase, type VI 0.4118
Pcna-ps2 Proliferating cell nuclear antigen pseudogene 2 1
Pecr Peroxisomal trans-2-enoyl-CoA reductase 0.5526
Pin1rt1 Protein (peptidyl-prolyl cis/trans isomerase) NIMA-interacting 1, retrogene 1 1
Pla2g4c Phospholipase A2, group IVC (cytosolic, calcium-independent) 0.6
Prdx5 Peroxiredoxin 5 0.4634
Prss1 Protease, serine 1 (trypsin 1) 1
Rfpl4 Ret finger protein-like 4 0.4486
Rnf213 Ring finger protein 213 0.4221
Rsl1d1 Ribosomal L1 domain containing 1 0.5
Serpina1b Serine (or cysteine) preptidase inhibitor, clade A, member 1B 1
Serpina1d Serine (or cysteine) peptidase inhibitor, clade A, member 1D 0.7265
Serpina3k Serine (or cysteine) peptidase inhibitor, clade A, member 3K 0.6
Serpinb6a Serine (or cysteine) peptidase inhibitor, clade B, member 6a 0.4
Slc10a5 Solute carrier family 10 (sodium/bile acid cotransporter family), member 5 0.5191
Sox15 SRY (sex determining region Y)-box 15 0.4
Tas2r104 Taste receptor, type 2, member 104 0.5728
Tcl1b2 T cell leukemia/lymphoma 1B, 2 1
Tle6 Transducin-like enhancer of split 6 0.6
Ube2l3 Ubiquitin-conjugating enzyme E2L 3 1
Utp14b UTP14B small subunit processome component 0.4
Vcan Versican 0.4377
Vtn Vitronectin 0.4743
Zbed3 Zinc finger, BED type containing 3 0.4042
Zp2 Zona pellucida glycoprotein 2 0.4351
49
Table 3.4 Mouse sperm proteins evolving under positive selection with dN/dS
(w) values.
Gene Symbol Protein Name Whole-gene ! Prop. Sites !>1 Selected Sites !
Adam1b A disintegrin and metallopeptidase domain 1b (fertilin ") 0.7305 0.02748 7.89976
Adam2 A disintegrin and metallopeptidase domain 2 (fertilin #) 0.6979 0.06298 5.12964
Adam24 A disintegrin and metallopeptidase domain 24 (testase 1) 1.0947 0.09285 7.20556
Adam3 A disintegrin and metallopeptidase domain 3 (cyritestin) 0.6517 0.0979 4.54289
Adam5 A disintegrin and metallopeptidase domain 5 0.8707 0.06504 7.63754
Alms1 Centrosome and basal body associated 0.9393 0.0436 6.13337
Atp2b4 ATPase, Ca++ transporting, plasma membrane 4 0.3533 0.0262 6.3022
B2m Beta-2 microglobulin 0.8033 0.03889 7.96851
BC051142 cDNA sequence BC051142 0.841 0.0363 11.70426
Car4 Carbonic anhydrase 4 0.8051 0.16811 3.30439
Ccdc27 Coiled-coil domain containing 27 0.4845 0.03188 5.74215
Cd46 CD46 antigen, complement regulatory protein 0.7897 0.16864 4.03461
Chchd6 Coiled-coil-helix-coiled-coil-helix domain containing 6 1.0009 0.00428 137.97813
Cubn Cubilin (intrinsic factor-cobalamin receptor) 0.2998 0.0471 2.90421
Dnajb6 DnaJ heat shock protein family (Hsp40) member B6 0.5181 0.03865 10.73448
Efhc2 EF-hand domain (C-terminal) containing 2 0.5724 0.17709 3.01229
Fhl4 Four and a half LIM domains 4 0.8454 0.05295 8.69761
Fn1 Fibronectin 1 0.0465 0.00483 3.67761
Fscb Fibrous sheath CABYR binding protein 0.9708 0.04844 6.45199
Fsip2 Fibrous sheath-interacting protein 2 0.4064 0.02923 3.84407
Gm884 Predicted gene 884 1.0195 0.04855 6.3408
Gsto1 Glutathione S-transferase omega 1 0.4308 0.07044 4.86624
Hc Hemolytic component 0.5461 0.01401 9.90736
Hspa8 Heat shock protein 8 0.0303 0.00226 13.42145
Itgb2 Integrin beta 2 0.2775 0.0369 5.31206
Izumo1 Izumo sperm-egg fusion 1 0.9765 0.03328 11.91336
Ltf Lactotransferrin 0.3587 0.00729 12.84132
Ms4a14 Membrane-spanning 4-domains, subfamily A, member 14 0.8857 0.26892 2.47809
Pdia3 Protein disulfide isomerase associated 3 0.1208 0.0108 8.2958
Plb1 Phospholipase B1 0.8789 0.08952 5.4949
Prm2 Protamine 2 1.3848 0.04299 23.31435
Psmb7 Proteasome (prosome, macropain) subunit, beta type 7 0.1624 0.02586 6.27799
Pzp Alpha-2-macroglobulin like (pregnancy zone protein) 0.491 0.0151 6.99284
Qsox1 Quiescin Q6 sulfhydryl oxidase 1 0.316 0.00732 10.47803
Spag17 Sperm associated antigen 17 0.3705 0.06564 3.23294
Spam1 Sperm adhesion molecule 1 0.5839 0.02155 8.93398
Tagln2 Transgelin 0.0688 0.01241 4.94517
Tmem190 Transmembrane protein 190 0.6554 0.14619 4.42234
Trf Transferrin 0.4022 0.01563 7.21885
Ttn Titin 0.0792 0.00732 2.57446
Xpo7 Exportin 7 0.0096 0.00108 8.92999
Zan Zonadhesin 0.3816 0.02182 3.95786
50
Gene Symbol Protein Name !
1700001O22Rik RIKEN cDNA 1700001O22 gene 0.5096
1700014D04Rik RIKEN cDNA 1700014D04 gene 0.6723
4930435E12Rik RIKEN cDNA 4930435E12 gene 0.7
4931408C20Rik RIKEN cDNA 4931408C20 gene 0.8
Adam1b A disintegrin and metallopeptidase domain 1b (fertilin ") 0.7305
Adam2 A disintegrin and metallopeptidase domain 2 (fertilin #) 0.6979
Adam24 A disintegrin and metallopeptidase domain 24 (testase 1) 1.0947
Adam3 A disintegrin and metallopeptidase domain 3 (cyritestin) 0.6517
Adam5 A disintegrin and metallopeptidase domain 5 0.8707
Alms1 ALMS1, centrosome and basal body associated 0.9393
Apof Apolipoprotein F 0.544
Art3 ADP-ribosyltransferase 3 0.6
B2m Beta-2 microglobulin 0.8033
BC051142 cDNA sequence BC051142 0.841
Car4 Carbonic anhydrase 4 0.8051
Cd46 CD46 antigen, complement regulatory protein 0.7897
Ceacam2 Carcinoembryonic antigen-related cell adhesion molecule 2 1
Chchd6 coiled-coil-helix-coiled-coil-helix domain containing 6 1.0009
Crisp1 Cysteine-rich secretory protein 1 0.6761
Csnk2b Casein kinase 2, beta polypeptide 0.6382
Dnajb6 DnaJ heat shock protein family (Hsp40) member B6 0.5181
Efhc2 EF-hand domain (C-terminal) containing 2 0.5724
Fhl4 Four and a half LIM domains 4 0.8454
Fscb Fibrous sheath CABYR binding protein 0.9708
Glipr1l2 GLI pathogenesis-related 1 like 2 0.58
Gm1673 Predicted gene 1673 0.7662
Gm884 Predicted gene 884 1.0195
Hc Hemolytic complement 0.5461
Izumo1 Izumo sperm-egg fusion 1 0.9765
Ms4a14 Membrane-spanning 4-domains, subfamily A, member 14 0.8857
Plb1 Phospholipase B1 0.8789
Prm2 Protamine 2 1.3848
Rnls Renalase, FAD-dependent amine oxidase 0.6318
Satl1 Spermidine/spermine N1-acetyl transferase-like 1 0.6
Serpina1b Serine (or cysteine) preptidase inhibitor, clade A, member 1B 1
Serpina1d Serine (or cysteine) peptidase inhibitor, clade A, member 1D 0.7265
Serpina1e Serine (or cysteine) peptidase inhibitor, clade A, member 1E 0.5477
Serpina3k Serine (or cysteine) peptidase inhibitor, clade A, member 3K 0.6
Smcp Sperm mitochondria-associated cysteine-rich protein 0.7
Spaca5 Sperm acrosome associated 5 0.6
Spam1 Sperm adhesion molecule 1 0.5839
Them4 Thioesterase superfamily member 4 0.5065
Tmem190 Transmembrane protein 190 0.6554
Table 3.5 Mouse sperm proteins with the highest 5% w ratios
51
3.5 Discussion
Fertilization rate plasticity
Postcopulatory sexual selection, including competition between sperm of rival males and
female choice resulting in non-random paternity, is a balancing act in maintaining proper
fertilization dynamics despite the conflict of interest between mates. Sperm traits under positive
selection in promiscuous species put a large burden on females to both prevent polyspermic
fertilization and maintain control over which sperm achieve fertilization. While many studies have
unearthed ejaculate adaptations to sperm competition in mammals (Gomendio et al. 2006; Firman
and Simmons 2009, 2010a; Ramm and Stockley 2009; Gómez Montoto et al. 2011), studies
demonstrating cryptic female choice, much less the mechanisms responsible, are lacking. Firman
and Simmons provided one of the first pieces of empirical evidence in support of sexual conflict
operating post-copulation in mammals, demonstrating that female mice adjust fertilization rate
based on their perceived risk of polyspermy. As these experiments were conducted via IVF, the
mechanism responsible for this difference must be due to the oocyte plasma membrane, zona
pellucida, and/or cumulus cell layer, rather than interactions with the female reproductive tract.
In this study, we sought to evaluate the role of the cumulus cell layer in the aforementioned
fertilization rate plasticity. We found an increased rate of breakdown during the first minute of
enzymatic digestion of the cumulus cell layer in ‘no risk’ females, suggesting differences in the
extracellular matrix and/or cellular composition of this layer likely occur as a result of this
treatment. Interestingly, we found differences between the cumulus cell proteins of ‘risk’ and ‘no
risk’ mice that correspond to biomarkers of puberty in porcine COCs (Paczkowski and Krisher
2010). Corresponding to the metabolic shift to glycolysis in cumulus cells of cycling females, they
found an increase in abundance of Enolase 1 and Pyruvate kinase in cycling females, two of the
proteins we identified only in ‘risk’ females, while Apolipoprotein A1, a protein only seen in ‘no
risk’ females, was decreased in cycling females relative to prepubertal females. Given that
previous studies have shown that male urinary proteins and physical interaction with males can
induce puberty and ovulation earlier in female mice (Bronson 1979), it seems likely that the ‘risk’
treatment induced females into early puberty. Despite the fact that sibling females were split into
both treatment groups and induced to ovulate at the same times, half of the females in the ‘risk’
treatment group completely failed to ovulate following hormone injection, while none in the ‘no
risk’ group did. If ‘risk’ females had already begun cycling when we induced ovulation, a
mismatch in timing could have resulted in our observed ovulation failures. While our low protein
52
yield limits our ability to make definitive inferences regarding proteomic differences between
treatment groups, our results do suggest that the sperm competition ‘risk’ treatment induced
puberty earlier than in ‘no risk’ mice. Especially given that Apolipoprotein A1 plays a large role
in sperm capacitation as a cholesterol acceptor (Leahy and Gadella 2015), this finding could be
responsible for the differences previously found in in vitro fertilization rates between the two
treatment groups (Firman and Simmons 2013). In addition to uncovering the potential mechanism
behind fertilization differences based on perceived risk of sperm competition, our results suggest
the same biomarkers of fertilization competency used in porcine COCs could potentially be
utilized in mice.
Evolution in cumulus-oocyte proteins
Though it increases the risk for polyspermic fertilization, mating with multiple males also
gives females the opportunity to continue mate choice after copulation. Cryptic female choice has
been shown to be mediated by selective binding of sperm by proteins on oocytes in external
fertilizers (Vacquier and Swanson 2011), and substitutions in these oocyte proteins induce
compensatory change in sperm proteins (Swanson et al. 2001; Clark et al. 2009). In mammals,
many studies have examined evolutionary rates in sperm (Dean et al. 2008; Dorus et al. 2010;
Prothmann et al. 2012) and seminal fluid (Dean et al. 2008) proteins, finding similar patterns of
pervasive adaptive evolution. With the exception of a few targeted gene studies (Swanson et al.
2001, 2003), not much is known regarding the evolution of mammalian oocyte proteins, and no
study has examined cumulus cell proteins in an evolutionary framework. Considering cumulus
cells are known to select the best sperm for fertilization (Chian et al. 1995; Nandi et al. 1998;
Franken and Bastiaan 2009; Hong et al. 2009; Ikawa et al. 2010b), and sperm hyaluronidases
needed to digest through the cumulus layer are adaptively evolving (Swanson et al. 2003;
Prothmann et al. 2012), closing this gap in knowledge is likely to shed light on the mechanisms of
cryptic female choice in mammals.
In this study we used both surface protein extraction and protein fractionation to generate
a more complete proteome of the COC in mice and to annotate proteins definitively found in
cumulus cells. To further understand the evolutionary patterns in the COC, we conducted tests for
positive selection using PAML on our identified proteins, as well as randomly chosen control
genes and the mouse sperm proteome (Dorus et al. 2010), using sequence data from 11 mouse
species. Given the critical range of functions the COCs must perform, it was not surprising to find
53
that the proteome is evolving slower than our randomly chosen control genes and the sperm
proteome. However, we identified evidence of positive selection in 30 COC proteins that
collectively are enriched for stress response. Unlike adaptively evolving sperm proteins, we did
not find an abundance of proteins functioning solely in fertilization. However, many adaptively
evolving COC proteins do have demonstrated reproductive phenotypes, and many seem to exhibit
dual-functionality in reproduction and immune response. I now discuss two main functional
categories among adaptively evolving COC proteins: gamete modulation and immune response,
the latter case especially emphasized among complement pathway genes.
Cumulus-oocyte proteins involved in sperm interaction
Once sperm reach the uterotubal junction (UTJ), several proteins have been shown to be
required for sperm to cross into the oviduct, including the adaptively evolving Adam3 (Ikawa et
al. 2010b). Once inside the oviduct, noncapacitated sperm bind to the epithelium to extend viability
in a quiescent state until ovulation occurs and they are gradually released following capacitation.
Both the oviductal epithelium and semen play a role in suppressing sperm capacitation until the
right time. Anxa1, an annexin protein adaptively evolving in COCs, has been found to bind bovine
sperm in the oviductal epithelium and is also secreted in high amounts into human semen from the
prostate (Ignotz et al. 2007). Sperm can induce epithelial cells to upregulate heat shock proteins,
and we found the heat shock protein Hspa8 to be adaptively evolving in our dataset. Hspa8
enhances sperm survival when bound, and has been shown to improve fertilization rate when co-
incubated (Holt and Fazeli 2015). Annexins and Hspa8 both help reduce polyspermy (Ignotz et al.
2007; Holt and Fazeli 2015). Transferrin and its receptor are found in sperm, semen, follicular
fluid (Aleporou-Marinou et al. 2002; Shimada et al. 2013) and cumulus cells and were both found
to be adaptively evolving. Transferrin increases sperm motility and induces hyperactivation as well
as capacitation (Shimada et al. 2013), and the concentration in follicular fluid is correlated with
follicular maturity (Aleporou-Marinou et al. 2002). Two integrin-bound glycoproteins, vitronectin
(Vtn) and fibronectin (Fn), were found to be adaptively evolving and are present, along with their
receptors, on both sperm and COCs (Fusi et al. 1996; Thys et al. 2009). Both fibronectin and
vitronectin appear to help facilitate initial binding to the oocyte (Fusi et al. 1996; Thys et al. 2009),
and the addition of fibrinogen exogenously during IVF inhibits sperm-egg binding (Thys et al.
2009), while exogenous vitronectin improves fertilization efficiency (Boissonnas et al. 2010),
except at high concentrations where it induces sperm agglutination (Fusi et al. 1996; Thys et al.
54
2009). The vitronectin integrin receptor is found on the inner acrosomal membrane and coincides
with Cd46. Interestingly, anti-vitronectin antibodies inhibit fertilization in cumulus-intact and zona
pellucida-intact eggs only, meaning the interaction occurs in the cumulus cell layer and/or the zona
pellucida (Boissonnas et al. 2010). In summary, adaptively evolving COC proteins could possibly
mediate fertilization rate, consistent with the predictions of sexual conflict theory.
Cumulus-oocyte proteins with dual-functionality in immune response and reproduction
Many adaptively evolving proteins in both COCs and sperm are involved in immune
response. Nlrp4f and Nlrp9b are involved in immune system function and inflammation and
though their exact function is unknown, they are thought to repress immune system response to
DNA during fertilization (PENG et al. 2015). Nlrps have experienced extensive gene duplications,
and in fact Nlrp4 and Nlrp9 gene families are only found in mice and exhibit oocyte-specific
expression (Tian et al. 2009). Interestingly, most of these genes are found near vomeronasal1
receptor genes, the organ responsible for detecting reproductive chemicals like pheromones, have
a similar duplication history, and are evolving rapidly (Tian et al. 2009). An overwhelming theme
throughout our adaptively evolving gene set was an involvement in the complement pathway of
the innate immune system that functions to clear pathogens and promote inflammation. The
complement pathway proceeds in a cascade-like fashion through a series of complement
components and can be initiated through three different pathways (Jarkovska et al. 2010), resulting
in targeted cell death. When follicles mature, the blood-follicle barrier becomes more permeable
and plasma proteins infiltrate the follicular fluid, generating a similar proteome between the two
fluids that includes many complement components (Jarkovska et al. 2010). Serum amyloid P-
component (Apcs) from COCs is a pentraxin capable of activating complement and is closely
related C reactive protein, shown to activate complement on the surface of acrosome reacted sperm
(Malm et al. 2008). However, the complement cascade initiated on sperm rarely proceeds to cell
death. Ptx3 is another pentraxin protein shown to be necessary for COC extracellular matrix
formation (Malm et al. 2008), and is closely related to two adaptively evolving COC proteins,
inter-alpha trypsin inhibitor heavy chains 1 and 3 (Itih1, Itih3). These two proteins function to
inhibit several proteases as well as construct the extracellular matrix by complexing with
hyaluronan (Zhuo and Kimata 2008). They too can interact with complement factors to stop the
cascade (Zhuo and Kimata 2008). Cd46 is an isoform of the membrane cofactor protein, a
complement regulator, that is found only on sperm in mice and protects cells from attack by serving
55
as a cofactor for inactivation of early complement components (Riley-Vargas et al. 2004).
Interestingly, Cd46 is located on the inner acrosomal membrane (Riley-Vargas et al. 2005),
meaning it is not exposed until sperm undergo the acrosome reaction. As Cd46 has been known to
be present on sperm, and only on sperm in some species, it has been suggested to function to allow
sperm to evade the female immune system (Dorus et al. 2010). However, our adaptively evolving
COC proteins with dual-functionality in immune response and reproduction mostly also work to
inhibit sperm cell death by inhibiting parts of the complement cascade. While speculative based
on the indirect evidence presented here, it seems far more likely that complement activation before
fertilization is an orchestrated interaction involving molecular cooperation between sperm and
COCs. The rapid evolution of immune system proteins involved in reproduction would serve two
roles: responding to a wider array of pathogens and limiting the number of sperm that reach the
egg to prevent polyspermy. As more knowledge is gained surrounding the molecular interactions
leading up to fertilization, it may turn out to be the case that the rapid evolution of immune system
proteins and reproductive proteins are one in the same.
Collectively, our study adds to known proteins in cumulus-oocyte complexes, provides the
first global evolutionary exploration of the cumulus-oocyte complex, and demonstrates the first
glimpse into mechanisms of polyspermy avoidance in mice. Rather than adaptively evolving
proteins functioning solely in fertilization, we found the COC proteome to contain many dual-
functioning proteins under positive selection. Our study highlights the need for further
experimentation in the cumulus cell layer to elucidate its role in sperm selection and prevention of
polyspermy.
Acknowledgements
This project is supported by NSF CAREER award 1150259, NIH award 1R01GM098536-01A1,
and used the UPCI Cancer Biomarkers Facility that is supported in part by award P30CA047904.
56
3.6 Supplementary Materials
Table S3.1 High confidence cumulus-oocyte complex proteins from surface protein extraction and protein
fractionation identified via mass spectrometry in this study
Gene Name Source Cell Type
Acadvl surface protein cumulus_oocyte
Acin1 surface protein cumulus_oocyte
Acly surface protein cumulus_oocyte
Aco2 surface protein cumulus_oocyte
Acsl1 surface protein cumulus_oocyte
Actg1 surface protein cumulus_oocyte
Actn1 surface protein cumulus_oocyte
Ahnak surface protein cumulus_oocyte
Ahsg surface protein cumulus_oocyte
Aifm1 surface protein cumulus_oocyte
Alb surface protein cumulus_oocyte
Alcam surface protein cumulus_oocyte
Aldh18a1 surface protein cumulus_oocyte
Aldh2 surface protein cumulus_oocyte
Aldoa surface protein cumulus_oocyte
Ank1 surface protein cumulus_oocyte
Ank2 surface protein cumulus_oocyte
Anxa1 surface protein cumulus_oocyte
Anxa2 surface protein cumulus_oocyte
Ap1b1 surface protein cumulus_oocyte
Api5 surface protein cumulus_oocyte
Arcn1 surface protein cumulus_oocyte
Atp1a1 surface protein cumulus_oocyte
Atp2a2 surface protein cumulus_oocyte
Atp5a1 surface protein cumulus_oocyte
Atp5b surface protein cumulus_oocyte
Atp6v1a surface protein cumulus_oocyte
Atp6v1b1 surface protein cumulus_oocyte
Bclaf1 surface protein cumulus_oocyte
Bicd1 surface protein cumulus_oocyte
Bsg surface protein cumulus_oocyte
C3 surface protein cumulus_oocyte
Cad surface protein cumulus_oocyte
Cald1 surface protein cumulus_oocyte
Cand1 surface protein cumulus_oocyte
Canx surface protein cumulus_oocyte
Caprin1 surface protein cumulus_oocyte
Cbs surface protein cumulus_oocyte
Cct2 surface protein cumulus_oocyte
Cct3 surface protein cumulus_oocyte
Cct4 surface protein cumulus_oocyte
Cct5 surface protein cumulus_oocyte
Cct6a surface protein cumulus_oocyte
Cct7 surface protein cumulus_oocyte
Cct8 surface protein cumulus_oocyte
Cenpf surface protein cumulus_oocyte
Chd4 surface protein cumulus_oocyte
Chtop surface protein cumulus_oocyte
Ckap4 surface protein cumulus_oocyte
Ckap5 surface protein cumulus_oocyte
Clptm1 surface protein cumulus_oocyte
Cltc surface protein cumulus_oocyte
Cndp2 surface protein cumulus_oocyte
57
Cnot1 surface protein cumulus_oocyte
Copa surface protein cumulus_oocyte
Copb1 surface protein cumulus_oocyte
Copb2 surface protein cumulus_oocyte
Copg1 surface protein cumulus_oocyte
Cul1 surface protein cumulus_oocyte
Cul4b surface protein cumulus_oocyte
Cyfip1 surface protein cumulus_oocyte
D6Ertd527e surface protein cumulus_oocyte
Dars surface protein cumulus_oocyte
Ddb1 surface protein cumulus_oocyte
Ddost surface protein cumulus_oocyte
Ddx1 surface protein cumulus_oocyte
Ddx17 surface protein cumulus_oocyte
Ddx21 surface protein cumulus_oocyte
Ddx3x surface protein cumulus_oocyte
Ddx5 surface protein cumulus_oocyte
Ddx6 surface protein cumulus_oocyte
Dek surface protein cumulus_oocyte
Dhx15 surface protein cumulus_oocyte
Dhx9 surface protein cumulus_oocyte
Dlat surface protein cumulus_oocyte
Dnaja1 surface protein cumulus_oocyte
Dnaja2 surface protein cumulus_oocyte
Dnm2 surface protein cumulus_oocyte
Dnmt1 surface protein cumulus_oocyte
Dnmt3a surface protein cumulus_oocyte
Dsp surface protein cumulus_oocyte
Dync1h1 surface protein cumulus_oocyte
Dync1i2 surface protein cumulus_oocyte
Dync1li1 surface protein cumulus_oocyte
Edc4 surface protein cumulus_oocyte
Eef1a1 surface protein cumulus_oocyte
Eef1g surface protein cumulus_oocyte
Eef2 surface protein cumulus_oocyte
Eftud2 surface protein cumulus_oocyte
Eif3a surface protein cumulus_oocyte
Eif3d surface protein cumulus_oocyte
Eif3l surface protein cumulus_oocyte
Eif4a3 surface protein cumulus_oocyte
Eif4g1 surface protein cumulus_oocyte
Eif4g2 surface protein cumulus_oocyte
Emc1 surface protein cumulus_oocyte
Eno1 surface protein cumulus_oocyte
Epb42 surface protein cumulus_oocyte
Eprs surface protein cumulus_oocyte
Ermp1 surface protein cumulus_oocyte
Esyt1 surface protein cumulus_oocyte
Etf1 surface protein cumulus_oocyte
Etv6 surface protein cumulus_oocyte
Ezr surface protein cumulus_oocyte
Fam98a surface protein cumulus_oocyte
Far1 surface protein cumulus_oocyte
Farsa surface protein cumulus_oocyte
58
Farsb surface protein cumulus_oocyte
Fasn surface protein cumulus_oocyte
Fbxw24 surface protein cumulus_oocyte
Fdxr surface protein cumulus_oocyte
Fga surface protein cumulus_oocyte
Flg2 surface protein cumulus_oocyte
Flna surface protein cumulus_oocyte
Fndc3b surface protein cumulus_oocyte
Fxr1 surface protein cumulus_oocyte
G6pdx surface protein cumulus_oocyte
Gc surface protein cumulus_oocyte
Gcn1l1 surface protein cumulus_oocyte
Gfpt1 surface protein cumulus_oocyte
Gfpt2 surface protein cumulus_oocyte
Glg1 surface protein cumulus_oocyte
Gm5422 surface protein cumulus_oocyte
Gm5619 surface protein cumulus_oocyte
Gm7964 surface protein cumulus_oocyte
Golga2 surface protein cumulus_oocyte
Golgb1 surface protein cumulus_oocyte
Golim4 surface protein cumulus_oocyte
Gtf2i surface protein cumulus_oocyte
Hadha surface protein cumulus_oocyte
Hadhb surface protein cumulus_oocyte
Hdlbp surface protein cumulus_oocyte
Hist1h1b surface protein cumulus_oocyte
Hist1h2ab surface protein cumulus_oocyte
Hist1h2bf surface protein cumulus_oocyte
Hk2 surface protein cumulus_oocyte
Hnrnpl surface protein cumulus_oocyte
Hnrnpm surface protein cumulus_oocyte
Hnrnpr surface protein cumulus_oocyte
Hnrnpu surface protein cumulus_oocyte
Hnrnpul2 surface protein cumulus_oocyte
Hp1bp3 surface protein cumulus_oocyte
Hsd17b4 surface protein cumulus_oocyte
Hsp90aa1 surface protein cumulus_oocyte
Hsp90ab1 surface protein cumulus_oocyte
Hsp90b1 surface protein cumulus_oocyte
Hspa4 surface protein cumulus_oocyte
Hspa5 surface protein cumulus_oocyte
Hspa8 surface protein cumulus_oocyte
Hspa9 surface protein cumulus_oocyte
Hspd1 surface protein cumulus_oocyte
Hsph1 surface protein cumulus_oocyte
Hyou1 surface protein cumulus_oocyte
Iars surface protein cumulus_oocyte
Igf2bp2 surface protein cumulus_oocyte
Ilf3 surface protein cumulus_oocyte
Immt surface protein cumulus_oocyte
Ints3 surface protein cumulus_oocyte
Ipo5 surface protein cumulus_oocyte
Iqgap1 surface protein cumulus_oocyte
Itga2 surface protein cumulus_oocyte
59
Itgav surface protein cumulus_oocyte
Itgb1 surface protein cumulus_oocyte
Itih1 surface protein cumulus_oocyte
Itih2 surface protein cumulus_oocyte
Itih3 surface protein cumulus_oocyte
Jup surface protein cumulus_oocyte
Kars surface protein cumulus_oocyte
Khdc3 surface protein cumulus_oocyte
Khsrp surface protein cumulus_oocyte
Kif5b surface protein cumulus_oocyte
Kpnb1 surface protein cumulus_oocyte
Krt1 surface protein cumulus_oocyte
Krt10 surface protein cumulus_oocyte
Krt13 surface protein cumulus_oocyte
Krt14 surface protein cumulus_oocyte
Krt16 surface protein cumulus_oocyte
Krt17 surface protein cumulus_oocyte
Krt18 surface protein cumulus_oocyte
Krt2 surface protein cumulus_oocyte
Krt4 surface protein cumulus_oocyte
Krt42 surface protein cumulus_oocyte
Krt5 surface protein cumulus_oocyte
Krt7 surface protein cumulus_oocyte
Krt73 surface protein cumulus_oocyte
Krt75 surface protein cumulus_oocyte
Krt76 surface protein cumulus_oocyte
Krt8 surface protein cumulus_oocyte
Krt80 surface protein cumulus_oocyte
Ktn1 surface protein cumulus_oocyte
Lars surface protein cumulus_oocyte
Lbr surface protein cumulus_oocyte
Lcp1 surface protein cumulus_oocyte
Ldlr surface protein cumulus_oocyte
Letm1 surface protein cumulus_oocyte
Lig4 surface protein cumulus_oocyte
Lmna surface protein cumulus_oocyte
Lmnb1 surface protein cumulus_oocyte
Lmnb2 surface protein cumulus_oocyte
Lpcat3 surface protein cumulus_oocyte
Lrp1 surface protein cumulus_oocyte
Lrpprc surface protein cumulus_oocyte
Macf1 surface protein cumulus_oocyte
Man2a1 surface protein cumulus_oocyte
Map1b surface protein cumulus_oocyte
Map4 surface protein cumulus_oocyte
Marcks surface protein cumulus_oocyte
Mars surface protein cumulus_oocyte
Matr3 surface protein cumulus_oocyte
Mcm5 surface protein cumulus_oocyte
Mcm6 surface protein cumulus_oocyte
Mcm7 surface protein cumulus_oocyte
Mfge8 surface protein cumulus_oocyte
Mki67 surface protein cumulus_oocyte
Mogs surface protein cumulus_oocyte
60
Mthfd1 surface protein cumulus_oocyte
Mtmr14 surface protein cumulus_oocyte
Mvp surface protein cumulus_oocyte
Mybbp1a surface protein cumulus_oocyte
Myh10 surface protein cumulus_oocyte
Myh11 surface protein cumulus_oocyte
Myh9 surface protein cumulus_oocyte
Myo1b surface protein cumulus_oocyte
Myo1e surface protein cumulus_oocyte
Myo6 surface protein cumulus_oocyte
Ncapg surface protein cumulus_oocyte
Ncl surface protein cumulus_oocyte
Ncln surface protein cumulus_oocyte
Ndufs1 surface protein cumulus_oocyte
Nlrp14 surface protein cumulus_oocyte
Nlrp2 surface protein cumulus_oocyte
Nlrp4f surface protein cumulus_oocyte
Nlrp5 surface protein cumulus_oocyte
Nlrp9b surface protein cumulus_oocyte
Nnt surface protein cumulus_oocyte
Nomo1 surface protein cumulus_oocyte
Nono surface protein cumulus_oocyte
Nop56 surface protein cumulus_oocyte
Nop58 surface protein cumulus_oocyte
Nsf surface protein cumulus_oocyte
Nt5dc2 surface protein cumulus_oocyte
Numa1 surface protein cumulus_oocyte
Nup133 surface protein cumulus_oocyte
Nup155 surface protein cumulus_oocyte
Nup160 surface protein cumulus_oocyte
Nup205 surface protein cumulus_oocyte
Nup210 surface protein cumulus_oocyte
Nup93 surface protein cumulus_oocyte
Nxf1 surface protein cumulus_oocyte
Ogdh surface protein cumulus_oocyte
Ovgp1 surface protein cumulus_oocyte
P4hb surface protein cumulus_oocyte
Pa2g4 surface protein cumulus_oocyte
Pabpc1 surface protein cumulus_oocyte
Padi6 surface protein cumulus_oocyte
Paip1 surface protein cumulus_oocyte
Parp1 surface protein cumulus_oocyte
Pdcd6ip surface protein cumulus_oocyte
Pdia3 surface protein cumulus_oocyte
Pdia4 surface protein cumulus_oocyte
Pdia6 surface protein cumulus_oocyte
Pfkl surface protein cumulus_oocyte
Pgk1 surface protein cumulus_oocyte
Pgs1 surface protein cumulus_oocyte
Phgdh surface protein cumulus_oocyte
Picalm surface protein cumulus_oocyte
Pkm surface protein cumulus_oocyte
Pla2g4c surface protein cumulus_oocyte
Plec surface protein cumulus_oocyte
61
Plod2 surface protein cumulus_oocyte
Ppp2r1a surface protein cumulus_oocyte
Prpf19 surface protein cumulus_oocyte
Prpf6 surface protein cumulus_oocyte
Prpf8 surface protein cumulus_oocyte
Psip1 surface protein cumulus_oocyte
Psmd1 surface protein cumulus_oocyte
Psmd12 surface protein cumulus_oocyte
Ptbp1 surface protein cumulus_oocyte
Ptx3 surface protein cumulus_oocyte
Qars surface protein cumulus_oocyte
Rad50 surface protein cumulus_oocyte
Ranbp2 surface protein cumulus_oocyte
Rangap1 surface protein cumulus_oocyte
Rars surface protein cumulus_oocyte
Rbbp4 surface protein cumulus_oocyte
Rbm14 surface protein cumulus_oocyte
Rbm25 surface protein cumulus_oocyte
Rbm39 surface protein cumulus_oocyte
Rbmxl1 surface protein cumulus_oocyte
Rcc1 surface protein cumulus_oocyte
Rhot1 surface protein cumulus_oocyte
Rpl13 surface protein cumulus_oocyte
Rpl14 surface protein cumulus_oocyte
Rpl3 surface protein cumulus_oocyte
Rpl4 surface protein cumulus_oocyte
Rpl6 surface protein cumulus_oocyte
Rpl7 surface protein cumulus_oocyte
Rpn1 surface protein cumulus_oocyte
Rpn2 surface protein cumulus_oocyte
Rps2 surface protein cumulus_oocyte
Rps3 surface protein cumulus_oocyte
Rps9 surface protein cumulus_oocyte
Rrbp1 surface protein cumulus_oocyte
Ruvbl1 surface protein cumulus_oocyte
Ruvbl2 surface protein cumulus_oocyte
Sacm1l surface protein cumulus_oocyte
Serbp1 surface protein cumulus_oocyte
Serpina1d surface protein cumulus_oocyte
Serpina3k surface protein cumulus_oocyte
Sf3a1 surface protein cumulus_oocyte
Sf3b1 surface protein cumulus_oocyte
Sf3b3 surface protein cumulus_oocyte
Sfpq surface protein cumulus_oocyte
Shmt2 surface protein cumulus_oocyte
Slc16a3 surface protein cumulus_oocyte
Slc25a12 surface protein cumulus_oocyte
Slc25a13 surface protein cumulus_oocyte
Slc25a3 surface protein cumulus_oocyte
Slc25a4 surface protein cumulus_oocyte
Slc2a1 surface protein cumulus_oocyte
Slc3a2 surface protein cumulus_oocyte
Slc4a1 surface protein cumulus_oocyte
Smarca4 surface protein cumulus_oocyte
62
Smarca5 surface protein cumulus_oocyte
Smarcc1 surface protein cumulus_oocyte
Smarce1 surface protein cumulus_oocyte
Smc1a surface protein cumulus_oocyte
Smc2 surface protein cumulus_oocyte
Smc3 surface protein cumulus_oocyte
Smc4 surface protein cumulus_oocyte
Snd1 surface protein cumulus_oocyte
Snrnp200 surface protein cumulus_oocyte
Soat1 surface protein cumulus_oocyte
Sorbs2 surface protein cumulus_oocyte
Spta1 surface protein cumulus_oocyte
Sptan1 surface protein cumulus_oocyte
Sptb surface protein cumulus_oocyte
Sptbn1 surface protein cumulus_oocyte
Strip1 surface protein cumulus_oocyte
Stt3a surface protein cumulus_oocyte
Surf4 surface protein cumulus_oocyte
Syne2 surface protein cumulus_oocyte
Tardbp surface protein cumulus_oocyte
Tars surface protein cumulus_oocyte
Tcp1 surface protein cumulus_oocyte
Tfrc surface protein cumulus_oocyte
Thrap3 surface protein cumulus_oocyte
Tjp1 surface protein cumulus_oocyte
Tjp2 surface protein cumulus_oocyte
Tkt surface protein cumulus_oocyte
Tle6 surface protein cumulus_oocyte
Tln1 surface protein cumulus_oocyte
Tmpo surface protein cumulus_oocyte
Tmtc3 surface protein cumulus_oocyte
Tomm70a surface protein cumulus_oocyte
Top2a surface protein cumulus_oocyte
Top2b surface protein cumulus_oocyte
Tpm1 surface protein cumulus_oocyte
Tpr surface protein cumulus_oocyte
Trim28 surface protein cumulus_oocyte
Ttn surface protein cumulus_oocyte
Tuba1c surface protein cumulus_oocyte
Tubb2a surface protein cumulus_oocyte
Tubb4b surface protein cumulus_oocyte
Tubb5 surface protein cumulus_oocyte
Tubgcp3 surface protein cumulus_oocyte
U2af2 surface protein cumulus_oocyte
Uba1 surface protein cumulus_oocyte
Ubap2l surface protein cumulus_oocyte
Ugdh surface protein cumulus_oocyte
Uhrf1 surface protein cumulus_oocyte
Upf1 surface protein cumulus_oocyte
Uqcrc1 surface protein cumulus_oocyte
Uqcrc2 surface protein cumulus_oocyte
Uso1 surface protein cumulus_oocyte
Usp9x surface protein cumulus_oocyte
Vars surface protein cumulus_oocyte
63
Vcan surface protein cumulus_oocyte
Vcp surface protein cumulus_oocyte
Vim surface protein cumulus_oocyte
Xpo1 surface protein cumulus_oocyte
Yars surface protein cumulus_oocyte
Ybx1 surface protein cumulus_oocyte
Ybx2 surface protein cumulus_oocyte
Zbtb2 surface protein cumulus_oocyte
Zp1 surface protein cumulus_oocyte
Zp2 surface protein cumulus_oocyte
Zp3 surface protein cumulus_oocyte
Actb surface protein cumulus_oocyte
Hist1h2al surface protein cumulus_oocyte
Hist1h3b surface protein cumulus_oocyte
Hist1h4a surface protein cumulus_oocyte
Hist3h2ba surface protein cumulus_oocyte
Hnrnpk surface protein cumulus_oocyte
Krt12 surface protein cumulus_oocyte
Krt6a surface protein cumulus_oocyte
Rdx surface protein cumulus_oocyte
Rpl18 surface protein cumulus_oocyte
Serpinh1 surface protein cumulus_oocyte
Tuba1b surface protein cumulus_oocyte
Actb surface protein cumulus
Alb surface protein cumulus
Anxa2 surface protein cumulus
Apoa1 surface protein cumulus
Eef1a1 surface protein cumulus
Krt2 surface protein cumulus
Pebp1 surface protein cumulus
Prdx1 surface protein cumulus
Tuba1c surface protein cumulus
Ubc surface protein cumulus
Dsp surface protein cumulus
Jup surface protein cumulus
Hist1h2al surface protein cumulus
Hist1h4a surface protein cumulus
Hspa5 surface protein cumulus
Tubb5 surface protein cumulus
Vapa surface protein cumulus
Serpina1b surface protein cumulus
Eno1 surface protein cumulus
Hist1h2bj surface protein cumulus
Hsp90ab1 surface protein cumulus
Pkm surface protein cumulus
Serpinb1a surface protein cumulus
Krt10 surface protein cumulus
Krt1 surface protein cumulus
Krt4 surface protein cumulus
Kxd1 surface protein cumulus
Tubb4b surface protein cumulus
Abcd3 membrane fraction cumulus_oocyte
Acaa1a membrane fraction cumulus_oocyte
Acaa2 membrane fraction cumulus_oocyte
64
Acadm membrane fraction cumulus_oocyte
Acads membrane fraction cumulus_oocyte
Acadvl membrane fraction cumulus_oocyte
Acat1 membrane fraction cumulus_oocyte
Acly membrane fraction cumulus_oocyte
Aco2 membrane fraction cumulus_oocyte
Acta2 membrane fraction cumulus_oocyte
Actb membrane fraction cumulus_oocyte
Adpgk membrane fraction cumulus_oocyte
Aifm1 membrane fraction cumulus_oocyte
Alcam membrane fraction cumulus_oocyte
Aldh2 membrane fraction cumulus_oocyte
Aldh3a2 membrane fraction cumulus_oocyte
Aldoa membrane fraction cumulus_oocyte
Alg2 membrane fraction cumulus_oocyte
Anxa2 membrane fraction cumulus_oocyte
Asph membrane fraction cumulus_oocyte
Astl membrane fraction cumulus_oocyte
Atl3 membrane fraction cumulus_oocyte
Atp1a1 membrane fraction cumulus_oocyte
Atp2a2 membrane fraction cumulus_oocyte
Atp5a1 membrane fraction cumulus_oocyte
Atp5b membrane fraction cumulus_oocyte
Atp5c1 membrane fraction cumulus_oocyte
Atp5d membrane fraction cumulus_oocyte
Atp5f1 membrane fraction cumulus_oocyte
Atp5h membrane fraction cumulus_oocyte
Atp5j membrane fraction cumulus_oocyte
Atp5j2 membrane fraction cumulus_oocyte
Atp5k membrane fraction cumulus_oocyte
Atp5l membrane fraction cumulus_oocyte
Atp5o membrane fraction cumulus_oocyte
Bcl2l13 membrane fraction cumulus_oocyte
Calr membrane fraction cumulus_oocyte
Canx membrane fraction cumulus_oocyte
Cbx3 membrane fraction cumulus_oocyte
Cct2 membrane fraction cumulus_oocyte
Cct3 membrane fraction cumulus_oocyte
Cct6a membrane fraction cumulus_oocyte
Cct7 membrane fraction cumulus_oocyte
Cd81 membrane fraction cumulus_oocyte
Cdc42 membrane fraction cumulus_oocyte
Cdh2 membrane fraction cumulus_oocyte
Cdk1 membrane fraction cumulus_oocyte
Ckap4 membrane fraction cumulus_oocyte
Clec10a membrane fraction cumulus_oocyte
Clta membrane fraction cumulus_oocyte
Cltc membrane fraction cumulus_oocyte
Copa membrane fraction cumulus_oocyte
Copb2 membrane fraction cumulus_oocyte
Cox4i1 membrane fraction cumulus_oocyte
Cox5a membrane fraction cumulus_oocyte
Csl membrane fraction cumulus_oocyte
Ctnna1 membrane fraction cumulus_oocyte
65
Cyb5a membrane fraction cumulus_oocyte
Cyb5r3 membrane fraction cumulus_oocyte
Cyc1 membrane fraction cumulus_oocyte
Cyp11a1 membrane fraction cumulus_oocyte
Cyp2s1 membrane fraction cumulus_oocyte
Cyp51 membrane fraction cumulus_oocyte
Ddost membrane fraction cumulus_oocyte
Ddx1 membrane fraction cumulus_oocyte
Ddx3x membrane fraction cumulus_oocyte
Ddx5 membrane fraction cumulus_oocyte
Dhx9 membrane fraction cumulus_oocyte
Dlat membrane fraction cumulus_oocyte
Dld membrane fraction cumulus_oocyte
Dnaja2 membrane fraction cumulus_oocyte
Dnajb11 membrane fraction cumulus_oocyte
Dnmt1 membrane fraction cumulus_oocyte
Dync1h1 membrane fraction cumulus_oocyte
Eci1 membrane fraction cumulus_oocyte
Eef1a1 membrane fraction cumulus_oocyte
Eef1d membrane fraction cumulus_oocyte
Eef1g membrane fraction cumulus_oocyte
Eef2 membrane fraction cumulus_oocyte
Eif4a2 membrane fraction cumulus_oocyte
Ephx2 membrane fraction cumulus_oocyte
Erlin2 membrane fraction cumulus_oocyte
Etfa membrane fraction cumulus_oocyte
Fam162a membrane fraction cumulus_oocyte
Far1 membrane fraction cumulus_oocyte
Fasn membrane fraction cumulus_oocyte
Fdx1 membrane fraction cumulus_oocyte
Fdxr membrane fraction cumulus_oocyte
Fh1 membrane fraction cumulus_oocyte
Flna membrane fraction cumulus_oocyte
G3bp1 membrane fraction cumulus_oocyte
Ganab membrane fraction cumulus_oocyte
Gapdh membrane fraction cumulus_oocyte
Gdpd1 membrane fraction cumulus_oocyte
Gfpt2 membrane fraction cumulus_oocyte
Gja1 membrane fraction cumulus_oocyte
Glud1 membrane fraction cumulus_oocyte
Gm10036 membrane fraction cumulus_oocyte
Gm11361 membrane fraction cumulus_oocyte
Gm45713 membrane fraction cumulus_oocyte
Gnai2 membrane fraction cumulus_oocyte
Got2 membrane fraction cumulus_oocyte
Gpi1 membrane fraction cumulus_oocyte
H2-D1 membrane fraction cumulus_oocyte
H2-K1 membrane fraction cumulus_oocyte
H2afv membrane fraction cumulus_oocyte
H2afx membrane fraction cumulus_oocyte
H2afy membrane fraction cumulus_oocyte
Hadh membrane fraction cumulus_oocyte
Hadha membrane fraction cumulus_oocyte
Hadhb membrane fraction cumulus_oocyte
66
Hba-a2 membrane fraction cumulus_oocyte
Hist1h2bp membrane fraction cumulus_oocyte
Hist1h4a membrane fraction cumulus_oocyte
Hist2h2aa1 membrane fraction cumulus_oocyte
Hist3h2a membrane fraction cumulus_oocyte
Hmox2 membrane fraction cumulus_oocyte
Hnrnpa1 membrane fraction cumulus_oocyte
Hnrnpa2b1 membrane fraction cumulus_oocyte
Hnrnpa3 membrane fraction cumulus_oocyte
Hnrnpc membrane fraction cumulus_oocyte
Hnrnph1 membrane fraction cumulus_oocyte
Hnrnpk membrane fraction cumulus_oocyte
Hnrnpl membrane fraction cumulus_oocyte
Hnrnpm membrane fraction cumulus_oocyte
Hnrnpu membrane fraction cumulus_oocyte
Hsd17b12 membrane fraction cumulus_oocyte
Hsd17b4 membrane fraction cumulus_oocyte
Hsd3b1 membrane fraction cumulus_oocyte
Hsp90aa1 membrane fraction cumulus_oocyte
Hsp90ab1 membrane fraction cumulus_oocyte
Hsp90b1 membrane fraction cumulus_oocyte
Hspa5 membrane fraction cumulus_oocyte
Hspa8 membrane fraction cumulus_oocyte
Hspa9 membrane fraction cumulus_oocyte
Hspd1 membrane fraction cumulus_oocyte
Hspe1 membrane fraction cumulus_oocyte
Hyou1 membrane fraction cumulus_oocyte
Idh1 membrane fraction cumulus_oocyte
Idh2 membrane fraction cumulus_oocyte
Idh3a membrane fraction cumulus_oocyte
Idh3b membrane fraction cumulus_oocyte
Immt membrane fraction cumulus_oocyte
Itga2 membrane fraction cumulus_oocyte
Itgav membrane fraction cumulus_oocyte
Itgb1 membrane fraction cumulus_oocyte
Itgb5 membrane fraction cumulus_oocyte
Itih1 membrane fraction cumulus_oocyte
Itih2 membrane fraction cumulus_oocyte
Itih3 membrane fraction cumulus_oocyte
Jup membrane fraction cumulus_oocyte
Khdc3 membrane fraction cumulus_oocyte
Kpnb1 membrane fraction cumulus_oocyte
Krt1 membrane fraction cumulus_oocyte
Krt15 membrane fraction cumulus_oocyte
Krt16 membrane fraction cumulus_oocyte
Krt42 membrane fraction cumulus_oocyte
Krt77 membrane fraction cumulus_oocyte
Krt78 membrane fraction cumulus_oocyte
Krt8 membrane fraction cumulus_oocyte
Ktn1 membrane fraction cumulus_oocyte
Kxd1 membrane fraction cumulus_oocyte
Lamb1 membrane fraction cumulus_oocyte
Ldha membrane fraction cumulus_oocyte
Ldhb membrane fraction cumulus_oocyte
67
Ldlr membrane fraction cumulus_oocyte
Letm1 membrane fraction cumulus_oocyte
Lman1 membrane fraction cumulus_oocyte
Lman2 membrane fraction cumulus_oocyte
Lmna membrane fraction cumulus_oocyte
Lmnb1 membrane fraction cumulus_oocyte
Lmnb2 membrane fraction cumulus_oocyte
Lonp1 membrane fraction cumulus_oocyte
Lrp1 membrane fraction cumulus_oocyte
Lrpprc membrane fraction cumulus_oocyte
Lrrc59 membrane fraction cumulus_oocyte
Magt1 membrane fraction cumulus_oocyte
Man2a1 membrane fraction cumulus_oocyte
Map1b membrane fraction cumulus_oocyte
Matr3 membrane fraction cumulus_oocyte
Mdh2 membrane fraction cumulus_oocyte
Me2 membrane fraction cumulus_oocyte
Mgst1 membrane fraction cumulus_oocyte
Mogs membrane fraction cumulus_oocyte
Myh10 membrane fraction cumulus_oocyte
Myh9 membrane fraction cumulus_oocyte
Myl6 membrane fraction cumulus_oocyte
Myo1b membrane fraction cumulus_oocyte
Ncl membrane fraction cumulus_oocyte
Ndufa4 membrane fraction cumulus_oocyte
Ndufa9 membrane fraction cumulus_oocyte
Ndufb10 membrane fraction cumulus_oocyte
Ndufs1 membrane fraction cumulus_oocyte
Ndufs2 membrane fraction cumulus_oocyte
Ndufs3 membrane fraction cumulus_oocyte
Ndufs7 membrane fraction cumulus_oocyte
Nlrp14 membrane fraction cumulus_oocyte
Nlrp5 membrane fraction cumulus_oocyte
Nomo1 membrane fraction cumulus_oocyte
Nono membrane fraction cumulus_oocyte
Npm1 membrane fraction cumulus_oocyte
Nt5dc2 membrane fraction cumulus_oocyte
Nucb1 membrane fraction cumulus_oocyte
Numa1 membrane fraction cumulus_oocyte
Oat membrane fraction cumulus_oocyte
Ogdh membrane fraction cumulus_oocyte
Ooep membrane fraction cumulus_oocyte
Ovgp1 membrane fraction cumulus_oocyte
P4ha1 membrane fraction cumulus_oocyte
P4hb membrane fraction cumulus_oocyte
Pabpc1 membrane fraction cumulus_oocyte
Padi6 membrane fraction cumulus_oocyte
Pcbp1 membrane fraction cumulus_oocyte
Pcbp2 membrane fraction cumulus_oocyte
Pcyox1 membrane fraction cumulus_oocyte
Pdia3 membrane fraction cumulus_oocyte
Pdia4 membrane fraction cumulus_oocyte
Pdia6 membrane fraction cumulus_oocyte
Pfn1 membrane fraction cumulus_oocyte
68
Pgk1 membrane fraction cumulus_oocyte
Phb membrane fraction cumulus_oocyte
Phb2 membrane fraction cumulus_oocyte
Picalm membrane fraction cumulus_oocyte
Pitrm1 membrane fraction cumulus_oocyte
Pkm membrane fraction cumulus_oocyte
Plec membrane fraction cumulus_oocyte
Plod3 membrane fraction cumulus_oocyte
Por membrane fraction cumulus_oocyte
Ppia membrane fraction cumulus_oocyte
Ppib membrane fraction cumulus_oocyte
Prdx1 membrane fraction cumulus_oocyte
Prkar2b membrane fraction cumulus_oocyte
Prkcsh membrane fraction cumulus_oocyte
Procr membrane fraction cumulus_oocyte
Prpf19 membrane fraction cumulus_oocyte
Psma2 membrane fraction cumulus_oocyte
Psmc4 membrane fraction cumulus_oocyte
Ptbp1 membrane fraction cumulus_oocyte
Ptgis membrane fraction cumulus_oocyte
Ptgs2 membrane fraction cumulus_oocyte
Ptx3 membrane fraction cumulus_oocyte
Rab10 membrane fraction cumulus_oocyte
Rab14 membrane fraction cumulus_oocyte
Rab18 membrane fraction cumulus_oocyte
Rab1a membrane fraction cumulus_oocyte
Rab7 membrane fraction cumulus_oocyte
Rack1 membrane fraction cumulus_oocyte
Raly membrane fraction cumulus_oocyte
Ranbp2 membrane fraction cumulus_oocyte
Rhoa membrane fraction cumulus_oocyte
Rpl10 membrane fraction cumulus_oocyte
Rpl10a membrane fraction cumulus_oocyte
Rpl12 membrane fraction cumulus_oocyte
Rpl13 membrane fraction cumulus_oocyte
Rpl14 membrane fraction cumulus_oocyte
Rpl15 membrane fraction cumulus_oocyte
Rpl17 membrane fraction cumulus_oocyte
Rpl18 membrane fraction cumulus_oocyte
Rpl19 membrane fraction cumulus_oocyte
Rpl21 membrane fraction cumulus_oocyte
Rpl22 membrane fraction cumulus_oocyte
Rpl23 membrane fraction cumulus_oocyte
Rpl23a membrane fraction cumulus_oocyte
Rpl24 membrane fraction cumulus_oocyte
Rpl27a membrane fraction cumulus_oocyte
Rpl3 membrane fraction cumulus_oocyte
Rpl32 membrane fraction cumulus_oocyte
Rpl36 membrane fraction cumulus_oocyte
Rpl4 membrane fraction cumulus_oocyte
Rpl5 membrane fraction cumulus_oocyte
Rpl6 membrane fraction cumulus_oocyte
Rpl7 membrane fraction cumulus_oocyte
Rpl7a membrane fraction cumulus_oocyte
69
Rpl8 membrane fraction cumulus_oocyte
Rpl9-ps6 membrane fraction cumulus_oocyte
Rplp0 membrane fraction cumulus_oocyte
Rplp2 membrane fraction cumulus_oocyte
Rpn1 membrane fraction cumulus_oocyte
Rpn2 membrane fraction cumulus_oocyte
Rps12 membrane fraction cumulus_oocyte
Rps13 membrane fraction cumulus_oocyte
Rps16 membrane fraction cumulus_oocyte
Rps2 membrane fraction cumulus_oocyte
Rps23 membrane fraction cumulus_oocyte
Rps24 membrane fraction cumulus_oocyte
Rps25 membrane fraction cumulus_oocyte
Rps3 membrane fraction cumulus_oocyte
Rps4x membrane fraction cumulus_oocyte
Rps5 membrane fraction cumulus_oocyte
Rps6 membrane fraction cumulus_oocyte
Rps7 membrane fraction cumulus_oocyte
Rps8 membrane fraction cumulus_oocyte
Rps9 membrane fraction cumulus_oocyte
Rrbp1 membrane fraction cumulus_oocyte
Rsf1 membrane fraction cumulus_oocyte
Scp2 membrane fraction cumulus_oocyte
Sdha membrane fraction cumulus_oocyte
Sec22b membrane fraction cumulus_oocyte
Sec23a membrane fraction cumulus_oocyte
Serpinh1 membrane fraction cumulus_oocyte
Sf3b1 membrane fraction cumulus_oocyte
Sfxn1 membrane fraction cumulus_oocyte
Shmt2 membrane fraction cumulus_oocyte
Skp1a membrane fraction cumulus_oocyte
Slc25a12 membrane fraction cumulus_oocyte
Slc25a13 membrane fraction cumulus_oocyte
Slc25a3 membrane fraction cumulus_oocyte
Slc25a5 membrane fraction cumulus_oocyte
Slc3a2 membrane fraction cumulus_oocyte
Snrnp200 membrane fraction cumulus_oocyte
Soat1 membrane fraction cumulus_oocyte
Spcs2 membrane fraction cumulus_oocyte
Sptbn1 membrane fraction cumulus_oocyte
Srsf3 membrane fraction cumulus_oocyte
Ssr1 membrane fraction cumulus_oocyte
Ssr4 membrane fraction cumulus_oocyte
Stoml2 membrane fraction cumulus_oocyte
Stt3b membrane fraction cumulus_oocyte
Tardbp membrane fraction cumulus_oocyte
Timm50 membrane fraction cumulus_oocyte
Tkt membrane fraction cumulus_oocyte
Tle6 membrane fraction cumulus_oocyte
Tm9sf4 membrane fraction cumulus_oocyte
Tmed10 membrane fraction cumulus_oocyte
Tmed4 membrane fraction cumulus_oocyte
Tmed9 membrane fraction cumulus_oocyte
Tmem43 membrane fraction cumulus_oocyte
70
Tmpo membrane fraction cumulus_oocyte
Tmx1 membrane fraction cumulus_oocyte
Tnfaip6 membrane fraction cumulus_oocyte
Tomm22 membrane fraction cumulus_oocyte
Tpi1 membrane fraction cumulus_oocyte
Trap1 membrane fraction cumulus_oocyte
Trim28 membrane fraction cumulus_oocyte
Tuba1a membrane fraction cumulus_oocyte
Tuba1b membrane fraction cumulus_oocyte
Tubb2a membrane fraction cumulus_oocyte
Tubb4a membrane fraction cumulus_oocyte
Tubb5 membrane fraction cumulus_oocyte
Tubb6 membrane fraction cumulus_oocyte
Tufm membrane fraction cumulus_oocyte
Uchl1 membrane fraction cumulus_oocyte
Uggt1 membrane fraction cumulus_oocyte
Uhrf1 membrane fraction cumulus_oocyte
Uqcrb membrane fraction cumulus_oocyte
Uqcrc1 membrane fraction cumulus_oocyte
Uqcrc2 membrane fraction cumulus_oocyte
Vamp3 membrane fraction cumulus_oocyte
Vapa membrane fraction cumulus_oocyte
Vcan membrane fraction cumulus_oocyte
Vcp membrane fraction cumulus_oocyte
Vdac1 membrane fraction cumulus_oocyte
Vdac2 membrane fraction cumulus_oocyte
Vdac3 membrane fraction cumulus_oocyte
Vim membrane fraction cumulus_oocyte
Vnn1 membrane fraction cumulus_oocyte
Vtn membrane fraction cumulus_oocyte
Ybx1 membrane fraction cumulus_oocyte
Ywhaz membrane fraction cumulus_oocyte
Zp1 membrane fraction cumulus_oocyte
Zp2 membrane fraction cumulus_oocyte
Zp3 membrane fraction cumulus_oocyte
Acta2 nuclear fraction cumulus_oocyte
Ahnak nuclear fraction cumulus_oocyte
Pgam1 nuclear fraction cumulus_oocyte
Hspa9 nuclear fraction cumulus_oocyte
Psma7 nuclear fraction cumulus_oocyte
Ddx5 nuclear fraction cumulus_oocyte
Krt77 nuclear fraction cumulus_oocyte
Shmt2 nuclear fraction cumulus_oocyte
Naca nuclear fraction cumulus_oocyte
Atp5b nuclear fraction cumulus_oocyte
Npm2 nuclear fraction cumulus_oocyte
Pcbp2 nuclear fraction cumulus_oocyte
Rpl17 nuclear fraction cumulus_oocyte
Sf3b2 nuclear fraction cumulus_oocyte
Eif3a nuclear fraction cumulus_oocyte
Stip1 nuclear fraction cumulus_oocyte
Rpl9-ps6 nuclear fraction cumulus_oocyte
U2af1 nuclear fraction cumulus_oocyte
Matr3 nuclear fraction cumulus_oocyte
71
Hnrnpul2 nuclear fraction cumulus_oocyte
Pdcd11 nuclear fraction cumulus_oocyte
Hdlbp nuclear fraction cumulus_oocyte
Tpi1 nuclear fraction cumulus_oocyte
Eif3h nuclear fraction cumulus_oocyte
Hp1bp3 nuclear fraction cumulus_oocyte
Rps25 nuclear fraction cumulus_oocyte
Tubb5 nuclear fraction cumulus_oocyte
Hnrnpa3 nuclear fraction cumulus_oocyte
Copg1 nuclear fraction cumulus_oocyte
Cacybp nuclear fraction cumulus_oocyte
Snu13 nuclear fraction cumulus_oocyte
Rps24 nuclear fraction cumulus_oocyte
Ovgp1 nuclear fraction cumulus_oocyte
Vim nuclear fraction cumulus_oocyte
Snrnp200 nuclear fraction cumulus_oocyte
Khdc3 nuclear fraction cumulus_oocyte
Rpl19 nuclear fraction cumulus_oocyte
Rpl26 nuclear fraction cumulus_oocyte
Gm11361 nuclear fraction cumulus_oocyte
Astl nuclear fraction cumulus_oocyte
Eif2s3x nuclear fraction cumulus_oocyte
Psmc3 nuclear fraction cumulus_oocyte
Eif5 nuclear fraction cumulus_oocyte
Eprs nuclear fraction cumulus_oocyte
Krt78 nuclear fraction cumulus_oocyte
Rbm3 nuclear fraction cumulus_oocyte
Puf60 nuclear fraction cumulus_oocyte
Rrbp1 nuclear fraction cumulus_oocyte
Hspa5 nuclear fraction cumulus_oocyte
Dnmt1 nuclear fraction cumulus_oocyte
Nmt1 nuclear fraction cumulus_oocyte
Hspd1 nuclear fraction cumulus_oocyte
Rpl18 nuclear fraction cumulus_oocyte
Cfl1 nuclear fraction cumulus_oocyte
Copa nuclear fraction cumulus_oocyte
Elavl1 nuclear fraction cumulus_oocyte
Ddx17 nuclear fraction cumulus_oocyte
Gtf2i nuclear fraction cumulus_oocyte
Rps27a nuclear fraction cumulus_oocyte
Hnrnpc nuclear fraction cumulus_oocyte
Csrp2 nuclear fraction cumulus_oocyte
Srsf1 nuclear fraction cumulus_oocyte
Nop56 nuclear fraction cumulus_oocyte
Poldip3 nuclear fraction cumulus_oocyte
Cct6a nuclear fraction cumulus_oocyte
Serbp1 nuclear fraction cumulus_oocyte
Vcp nuclear fraction cumulus_oocyte
Acsl4 nuclear fraction cumulus_oocyte
Cct2 nuclear fraction cumulus_oocyte
Hnrnpa1 nuclear fraction cumulus_oocyte
Supt16 nuclear fraction cumulus_oocyte
Pabpc1 nuclear fraction cumulus_oocyte
Srrm2 nuclear fraction cumulus_oocyte
72
Ddx3x nuclear fraction cumulus_oocyte
Pcbp1 nuclear fraction cumulus_oocyte
Hnrnpm nuclear fraction cumulus_oocyte
Ooep nuclear fraction cumulus_oocyte
Rtraf nuclear fraction cumulus_oocyte
Ilf3 nuclear fraction cumulus_oocyte
Cpsf6 nuclear fraction cumulus_oocyte
Map4 nuclear fraction cumulus_oocyte
Rpl21 nuclear fraction cumulus_oocyte
Gfpt2 nuclear fraction cumulus_oocyte
Eif4g1 nuclear fraction cumulus_oocyte
Capzb nuclear fraction cumulus_oocyte
Ddx6 nuclear fraction cumulus_oocyte
Rcc2 nuclear fraction cumulus_oocyte
Rps13 nuclear fraction cumulus_oocyte
Rpl7 nuclear fraction cumulus_oocyte
Ywhaz nuclear fraction cumulus_oocyte
Hspa8 nuclear fraction cumulus_oocyte
Pspc1 nuclear fraction cumulus_oocyte
Srsf9 nuclear fraction cumulus_oocyte
Eef1a1 nuclear fraction cumulus_oocyte
Mapre1 nuclear fraction cumulus_oocyte
Prpf19 nuclear fraction cumulus_oocyte
Hnrnpr nuclear fraction cumulus_oocyte
Rsl1d1 nuclear fraction cumulus_oocyte
Actb nuclear fraction cumulus_oocyte
Parp1 nuclear fraction cumulus_oocyte
Tpm3 nuclear fraction cumulus_oocyte
Nap1l1 nuclear fraction cumulus_oocyte
Hnrnpa0 nuclear fraction cumulus_oocyte
Tuba1c nuclear fraction cumulus_oocyte
Ssb nuclear fraction cumulus_oocyte
Eef2 nuclear fraction cumulus_oocyte
Itih2 nuclear fraction cumulus_oocyte
Kpnb1 nuclear fraction cumulus_oocyte
Rps27 nuclear fraction cumulus_oocyte
Rars nuclear fraction cumulus_oocyte
Anp32b nuclear fraction cumulus_oocyte
Pdia4 nuclear fraction cumulus_oocyte
Rps28 nuclear fraction cumulus_oocyte
Rbmx nuclear fraction cumulus_oocyte
Rpl35 nuclear fraction cumulus_oocyte
Ptx3 nuclear fraction cumulus_oocyte
Qars nuclear fraction cumulus_oocyte
Hist3h2a nuclear fraction cumulus_oocyte
Spin1 nuclear fraction cumulus_oocyte
Nono nuclear fraction cumulus_oocyte
Hnrnph2 nuclear fraction cumulus_oocyte
Eif4g2 nuclear fraction cumulus_oocyte
Sf3b3 nuclear fraction cumulus_oocyte
Eif6 nuclear fraction cumulus_oocyte
Mdh1 nuclear fraction cumulus_oocyte
Ptbp2 nuclear fraction cumulus_oocyte
Rps15a nuclear fraction cumulus_oocyte
73
Hnrnpd nuclear fraction cumulus_oocyte
Itih3 nuclear fraction cumulus_oocyte
Tubb4b nuclear fraction cumulus_oocyte
Psmc6 nuclear fraction cumulus_oocyte
Caprin1 nuclear fraction cumulus_oocyte
E330017A01Rik nuclear fraction cumulus_oocyte
Snrpd2 nuclear fraction cumulus_oocyte
Uhrf1 nuclear fraction cumulus_oocyte
Rbm39 nuclear fraction cumulus_oocyte
Hnrnpdl nuclear fraction cumulus_oocyte
Hyou1 nuclear fraction cumulus_oocyte
Eef1d nuclear fraction cumulus_oocyte
Myl6 nuclear fraction cumulus_oocyte
Pdia3 nuclear fraction cumulus_oocyte
Cct3 nuclear fraction cumulus_oocyte
Tardbp nuclear fraction cumulus_oocyte
Cycs nuclear fraction cumulus_oocyte
Rpl10a nuclear fraction cumulus_oocyte
Pa2g4 nuclear fraction cumulus_oocyte
Itih1 nuclear fraction cumulus_oocyte
Rps4x nuclear fraction cumulus_oocyte
Ncl nuclear fraction cumulus_oocyte
Mybbp1a nuclear fraction cumulus_oocyte
Fubp3 nuclear fraction cumulus_oocyte
Dync1h1 nuclear fraction cumulus_oocyte
Hnrnpa2b1 nuclear fraction cumulus_oocyte
Copb1 nuclear fraction cumulus_oocyte
P4hb nuclear fraction cumulus_oocyte
Tle6 nuclear fraction cumulus_oocyte
Mcm2 nuclear fraction cumulus_oocyte
Rpl13 nuclear fraction cumulus_oocyte
Acin1 nuclear fraction cumulus_oocyte
Myo1b nuclear fraction cumulus_oocyte
Hsp90aa1 nuclear fraction cumulus_oocyte
Arhgdia nuclear fraction cumulus_oocyte
Pgk1 nuclear fraction cumulus_oocyte
Alyref nuclear fraction cumulus_oocyte
Rtcb nuclear fraction cumulus_oocyte
Arpc1b nuclear fraction cumulus_oocyte
Rpl22 nuclear fraction cumulus_oocyte
Eif3c nuclear fraction cumulus_oocyte
Cbx3 nuclear fraction cumulus_oocyte
Ssrp1 nuclear fraction cumulus_oocyte
Rpl4 nuclear fraction cumulus_oocyte
Dhx9 nuclear fraction cumulus_oocyte
Rpsa nuclear fraction cumulus_oocyte
Rpl8 nuclear fraction cumulus_oocyte
Tpt1 nuclear fraction cumulus_oocyte
Pkm nuclear fraction cumulus_oocyte
Hsp90b1 nuclear fraction cumulus_oocyte
Top1 nuclear fraction cumulus_oocyte
Cstf2 nuclear fraction cumulus_oocyte
Acly nuclear fraction cumulus_oocyte
Srsf6 nuclear fraction cumulus_oocyte
74
Rpl23 nuclear fraction cumulus_oocyte
Rpl24 nuclear fraction cumulus_oocyte
Rpl5 nuclear fraction cumulus_oocyte
Tomm34 nuclear fraction cumulus_oocyte
Plbd1 nuclear fraction cumulus_oocyte
Myef2 nuclear fraction cumulus_oocyte
Alb nuclear fraction cumulus_oocyte
Iqgap1 nuclear fraction cumulus_oocyte
Dnajc9 nuclear fraction cumulus_oocyte
Rpl14 nuclear fraction cumulus_oocyte
Srsf5 nuclear fraction cumulus_oocyte
Zp2 nuclear fraction cumulus_oocyte
Drg1 nuclear fraction cumulus_oocyte
Sptbn1 nuclear fraction cumulus_oocyte
Rplp0 nuclear fraction cumulus_oocyte
Sf3a1 nuclear fraction cumulus_oocyte
Nlrp5 nuclear fraction cumulus_oocyte
Dhx15 nuclear fraction cumulus_oocyte
Nxf1 nuclear fraction cumulus_oocyte
Chd4 nuclear fraction cumulus_oocyte
Fubp1 nuclear fraction cumulus_oocyte
Sfpq nuclear fraction cumulus_oocyte
Clta nuclear fraction cumulus_oocyte
Trim28 nuclear fraction cumulus_oocyte
Rpl30 nuclear fraction cumulus_oocyte
Fkbp3 nuclear fraction cumulus_oocyte
Fus nuclear fraction cumulus_oocyte
G3bp2 nuclear fraction cumulus_oocyte
Rps14 nuclear fraction cumulus_oocyte
Ptbp1 nuclear fraction cumulus_oocyte
Rpl36 nuclear fraction cumulus_oocyte
Hsp90ab1 nuclear fraction cumulus_oocyte
Rpl3 nuclear fraction cumulus_oocyte
Rps23 nuclear fraction cumulus_oocyte
Lonp1 nuclear fraction cumulus_oocyte
Srsf3 nuclear fraction cumulus_oocyte
Rps6 nuclear fraction cumulus_oocyte
Prdx1 nuclear fraction cumulus_oocyte
Ilf2 nuclear fraction cumulus_oocyte
Rps17 nuclear fraction cumulus_oocyte
Snrpa nuclear fraction cumulus_oocyte
Rps19 nuclear fraction cumulus_oocyte
Atp5a1 nuclear fraction cumulus_oocyte
Srsf10 nuclear fraction cumulus_oocyte
Fkbp4 nuclear fraction cumulus_oocyte
Rnps1 nuclear fraction cumulus_oocyte
Tcp1 nuclear fraction cumulus_oocyte
Nop2 nuclear fraction cumulus_oocyte
Eif3b nuclear fraction cumulus_oocyte
Hist1h1d nuclear fraction cumulus_oocyte
Tuba4a nuclear fraction cumulus_oocyte
Calr nuclear fraction cumulus_oocyte
Snd1 nuclear fraction cumulus_oocyte
Rpl7a nuclear fraction cumulus_oocyte
75
Gapdh nuclear fraction cumulus_oocyte
Serpinh1 nuclear fraction cumulus_oocyte
Tkt nuclear fraction cumulus_oocyte
Uchl1 nuclear fraction cumulus_oocyte
Eif3d nuclear fraction cumulus_oocyte
Arcn1 nuclear fraction cumulus_oocyte
Rpl27a nuclear fraction cumulus_oocyte
Hist1h1e nuclear fraction cumulus_oocyte
Numa1 nuclear fraction cumulus_oocyte
Rbm14 nuclear fraction cumulus_oocyte
Rpl23a nuclear fraction cumulus_oocyte
Capza1 nuclear fraction cumulus_oocyte
Rps7 nuclear fraction cumulus_oocyte
Hmgb1 nuclear fraction cumulus_oocyte
Hnrnph3 nuclear fraction cumulus_oocyte
Npm1 nuclear fraction cumulus_oocyte
Eif4a1 nuclear fraction cumulus_oocyte
Ppia nuclear fraction cumulus_oocyte
Fdxr nuclear fraction cumulus_oocyte
Rps9 nuclear fraction cumulus_oocyte
Cdv3 nuclear fraction cumulus_oocyte
Hist1h4a nuclear fraction cumulus_oocyte
Hist1h1a nuclear fraction cumulus_oocyte
Rps11 nuclear fraction cumulus_oocyte
H2afv nuclear fraction cumulus_oocyte
Ldhb nuclear fraction cumulus_oocyte
Mdh2 nuclear fraction cumulus_oocyte
Acat1 nuclear fraction cumulus_oocyte
Pdia6 nuclear fraction cumulus_oocyte
Rack1 nuclear fraction cumulus_oocyte
Purb nuclear fraction cumulus_oocyte
Ezr nuclear fraction cumulus_oocyte
Flna nuclear fraction cumulus_oocyte
Hmgb3 nuclear fraction cumulus_oocyte
Hnrnpab nuclear fraction cumulus_oocyte
Rplp1 nuclear fraction cumulus_oocyte
Syncrip nuclear fraction cumulus_oocyte
Srp68 nuclear fraction cumulus_oocyte
Rpl31 nuclear fraction cumulus_oocyte
Ldha nuclear fraction cumulus_oocyte
Rps3 nuclear fraction cumulus_oocyte
Nlrp14 nuclear fraction cumulus_oocyte
Tln1 nuclear fraction cumulus_oocyte
Prdx2 nuclear fraction cumulus_oocyte
Ran nuclear fraction cumulus_oocyte
Myh10 nuclear fraction cumulus_oocyte
Rpl27 nuclear fraction cumulus_oocyte
Snrpa1 nuclear fraction cumulus_oocyte
Hist1h1b nuclear fraction cumulus_oocyte
Hist1h2bp nuclear fraction cumulus_oocyte
Eef1g nuclear fraction cumulus_oocyte
Hnrnpk nuclear fraction cumulus_oocyte
Nop58 nuclear fraction cumulus_oocyte
Khsrp nuclear fraction cumulus_oocyte
76
Gm43738 nuclear fraction cumulus_oocyte
Map1b nuclear fraction cumulus_oocyte
G3bp1 nuclear fraction cumulus_oocyte
Ppib nuclear fraction cumulus_oocyte
Hnrnpl nuclear fraction cumulus_oocyte
Eftud2 nuclear fraction cumulus_oocyte
Lmna nuclear fraction cumulus_oocyte
Cox6b1 nuclear fraction cumulus_oocyte
Dis3 nuclear fraction cumulus_oocyte
Slc3a2 nuclear fraction cumulus_oocyte
Gm10036 nuclear fraction cumulus_oocyte
Hnrnph1 nuclear fraction cumulus_oocyte
Srsf7 nuclear fraction cumulus_oocyte
Rps12 nuclear fraction cumulus_oocyte
Pfn1 nuclear fraction cumulus_oocyte
Rps10 nuclear fraction cumulus_oocyte
Rps8 nuclear fraction cumulus_oocyte
Dek nuclear fraction cumulus_oocyte
Padi6 nuclear fraction cumulus_oocyte
Hmgb2 nuclear fraction cumulus_oocyte
Akr1b3 nuclear fraction cumulus_oocyte
Tnfaip6 nuclear fraction cumulus_oocyte
Idh1 nuclear fraction cumulus_oocyte
Rps16 nuclear fraction cumulus_oocyte
Fau nuclear fraction cumulus_oocyte
Rps20 nuclear fraction cumulus_oocyte
Nlrp4f nuclear fraction cumulus_oocyte
Rpl6 nuclear fraction cumulus_oocyte
Myh9 nuclear fraction cumulus_oocyte
Aldoa nuclear fraction cumulus_oocyte
Aco2 nuclear fraction cumulus_oocyte
Ddx21 nuclear fraction cumulus_oocyte
Vcan nuclear fraction cumulus_oocyte
Rps5 nuclear fraction cumulus_oocyte
Rpl12 nuclear fraction cumulus_oocyte
Rplp2 nuclear fraction cumulus_oocyte
Eif5a nuclear fraction cumulus_oocyte
Rdx nuclear fraction cumulus_oocyte
Safb2 nuclear fraction cumulus_oocyte
Rps2 nuclear fraction cumulus_oocyte
Cct4 nuclear fraction cumulus_oocyte
Cndp2 cytosolic fraction cumulus_oocyte
Tubb6 cytosolic fraction cumulus_oocyte
Papss2 cytosolic fraction cumulus_oocyte
Ganab cytosolic fraction cumulus_oocyte
Dpp3 cytosolic fraction cumulus_oocyte
Pgam1 cytosolic fraction cumulus_oocyte
Pcna-ps2 cytosolic fraction cumulus_oocyte
Hspa9 cytosolic fraction cumulus_oocyte
Srsf2 cytosolic fraction cumulus_oocyte
Psap cytosolic fraction cumulus_oocyte
Prps1l3 cytosolic fraction cumulus_oocyte
Fbxw19 cytosolic fraction cumulus_oocyte
Ddx5 cytosolic fraction cumulus_oocyte
77
Naca cytosolic fraction cumulus_oocyte
Atp5b cytosolic fraction cumulus_oocyte
Psmd14 cytosolic fraction cumulus_oocyte
Aars cytosolic fraction cumulus_oocyte
Galk1 cytosolic fraction cumulus_oocyte
Pcbp2 cytosolic fraction cumulus_oocyte
Fasn cytosolic fraction cumulus_oocyte
Eif3a cytosolic fraction cumulus_oocyte
Stip1 cytosolic fraction cumulus_oocyte
Matr3 cytosolic fraction cumulus_oocyte
Prdx6 cytosolic fraction cumulus_oocyte
Hdlbp cytosolic fraction cumulus_oocyte
Sept2 cytosolic fraction cumulus_oocyte
Mif cytosolic fraction cumulus_oocyte
Tpi1 cytosolic fraction cumulus_oocyte
Pgd cytosolic fraction cumulus_oocyte
Tubb5 cytosolic fraction cumulus_oocyte
Hnrnpa3 cytosolic fraction cumulus_oocyte
Copg1 cytosolic fraction cumulus_oocyte
Sec31a cytosolic fraction cumulus_oocyte
Ovgp1 cytosolic fraction cumulus_oocyte
Vim cytosolic fraction cumulus_oocyte
Aldh2 cytosolic fraction cumulus_oocyte
Lap3 cytosolic fraction cumulus_oocyte
Ugdh cytosolic fraction cumulus_oocyte
Ywhaq cytosolic fraction cumulus_oocyte
Uba1 cytosolic fraction cumulus_oocyte
Astl cytosolic fraction cumulus_oocyte
Anp32a cytosolic fraction cumulus_oocyte
Anxa7 cytosolic fraction cumulus_oocyte
Map2k2 cytosolic fraction cumulus_oocyte
Psmb1 cytosolic fraction cumulus_oocyte
Psmc3 cytosolic fraction cumulus_oocyte
Eif5 cytosolic fraction cumulus_oocyte
Eprs cytosolic fraction cumulus_oocyte
Krt78 cytosolic fraction cumulus_oocyte
Spr cytosolic fraction cumulus_oocyte
Hsph1 cytosolic fraction cumulus_oocyte
Puf60 cytosolic fraction cumulus_oocyte
Krt8 cytosolic fraction cumulus_oocyte
Hspa5 cytosolic fraction cumulus_oocyte
Mtmr14 cytosolic fraction cumulus_oocyte
Gmps cytosolic fraction cumulus_oocyte
Usp5 cytosolic fraction cumulus_oocyte
Glud1 cytosolic fraction cumulus_oocyte
Hspd1 cytosolic fraction cumulus_oocyte
Ranbp1 cytosolic fraction cumulus_oocyte
Cfl1 cytosolic fraction cumulus_oocyte
Copa cytosolic fraction cumulus_oocyte
Ckb cytosolic fraction cumulus_oocyte
Kpna2 cytosolic fraction cumulus_oocyte
Ppid cytosolic fraction cumulus_oocyte
Tubb2a cytosolic fraction cumulus_oocyte
Cct7 cytosolic fraction cumulus_oocyte
78
Pygb cytosolic fraction cumulus_oocyte
Gstp1 cytosolic fraction cumulus_oocyte
G6pdx cytosolic fraction cumulus_oocyte
Cct6a cytosolic fraction cumulus_oocyte
Vcp cytosolic fraction cumulus_oocyte
Acsl4 cytosolic fraction cumulus_oocyte
Cct2 cytosolic fraction cumulus_oocyte
Nsfl1c cytosolic fraction cumulus_oocyte
Ptms cytosolic fraction cumulus_oocyte
Ipo5 cytosolic fraction cumulus_oocyte
Kxd1 cytosolic fraction cumulus_oocyte
Nap1l5 cytosolic fraction cumulus_oocyte
Ddx3x cytosolic fraction cumulus_oocyte
Tuba1a cytosolic fraction cumulus_oocyte
Pcbp1 cytosolic fraction cumulus_oocyte
Psmd5 cytosolic fraction cumulus_oocyte
Atic cytosolic fraction cumulus_oocyte
Sec23ip cytosolic fraction cumulus_oocyte
Vars cytosolic fraction cumulus_oocyte
Nme1 cytosolic fraction cumulus_oocyte
Nap1l4 cytosolic fraction cumulus_oocyte
Adss cytosolic fraction cumulus_oocyte
Pebp1 cytosolic fraction cumulus_oocyte
Fam49b cytosolic fraction cumulus_oocyte
Rnh1 cytosolic fraction cumulus_oocyte
Rpl7 cytosolic fraction cumulus_oocyte
Ruvbl1 cytosolic fraction cumulus_oocyte
Ywhaz cytosolic fraction cumulus_oocyte
Hspa8 cytosolic fraction cumulus_oocyte
Eef1a1 cytosolic fraction cumulus_oocyte
Mapre1 cytosolic fraction cumulus_oocyte
Ddx39b cytosolic fraction cumulus_oocyte
Actb cytosolic fraction cumulus_oocyte
Anxa4 cytosolic fraction cumulus_oocyte
Tpm3 cytosolic fraction cumulus_oocyte
Isyna1 cytosolic fraction cumulus_oocyte
Hnrnpu cytosolic fraction cumulus_oocyte
Copz1 cytosolic fraction cumulus_oocyte
Eef2 cytosolic fraction cumulus_oocyte
Fscn1 cytosolic fraction cumulus_oocyte
Tpm4 cytosolic fraction cumulus_oocyte
Itih2 cytosolic fraction cumulus_oocyte
Cand1 cytosolic fraction cumulus_oocyte
Kpnb1 cytosolic fraction cumulus_oocyte
Ahcy cytosolic fraction cumulus_oocyte
Anp32b cytosolic fraction cumulus_oocyte
Pdia4 cytosolic fraction cumulus_oocyte
Rps28 cytosolic fraction cumulus_oocyte
Ugp2 cytosolic fraction cumulus_oocyte
Psmd1 cytosolic fraction cumulus_oocyte
Hist3h2a cytosolic fraction cumulus_oocyte
Anxa2 cytosolic fraction cumulus_oocyte
Cnpy2 cytosolic fraction cumulus_oocyte
Nans cytosolic fraction cumulus_oocyte
79
Oat cytosolic fraction cumulus_oocyte
Hnrnph2 cytosolic fraction cumulus_oocyte
Mdh1 cytosolic fraction cumulus_oocyte
Pgm2 cytosolic fraction cumulus_oocyte
Ube2l3 cytosolic fraction cumulus_oocyte
Plin3 cytosolic fraction cumulus_oocyte
Rbbp4 cytosolic fraction cumulus_oocyte
Clic1 cytosolic fraction cumulus_oocyte
Npepps cytosolic fraction cumulus_oocyte
Ola1 cytosolic fraction cumulus_oocyte
Itih3 cytosolic fraction cumulus_oocyte
Psmc6 cytosolic fraction cumulus_oocyte
Uhrf1 cytosolic fraction cumulus_oocyte
Dnm1l cytosolic fraction cumulus_oocyte
Phgdh cytosolic fraction cumulus_oocyte
Hyou1 cytosolic fraction cumulus_oocyte
Srm cytosolic fraction cumulus_oocyte
Eef1d cytosolic fraction cumulus_oocyte
Myl6 cytosolic fraction cumulus_oocyte
Pdia3 cytosolic fraction cumulus_oocyte
Cct3 cytosolic fraction cumulus_oocyte
Actn1 cytosolic fraction cumulus_oocyte
Tardbp cytosolic fraction cumulus_oocyte
Farsb cytosolic fraction cumulus_oocyte
Psmd3 cytosolic fraction cumulus_oocyte
Cct8 cytosolic fraction cumulus_oocyte
Xpnpep1 cytosolic fraction cumulus_oocyte
Paics cytosolic fraction cumulus_oocyte
Cycs cytosolic fraction cumulus_oocyte
Adh5 cytosolic fraction cumulus_oocyte
Fermt2 cytosolic fraction cumulus_oocyte
Tagln2 cytosolic fraction cumulus_oocyte
Pa2g4 cytosolic fraction cumulus_oocyte
Itih1 cytosolic fraction cumulus_oocyte
Ncl cytosolic fraction cumulus_oocyte
Actc1 cytosolic fraction cumulus_oocyte
Psmc5 cytosolic fraction cumulus_oocyte
P4ha1 cytosolic fraction cumulus_oocyte
Esd cytosolic fraction cumulus_oocyte
Cnn3 cytosolic fraction cumulus_oocyte
Dync1h1 cytosolic fraction cumulus_oocyte
P4hb cytosolic fraction cumulus_oocyte
Tle6 cytosolic fraction cumulus_oocyte
Ptma cytosolic fraction cumulus_oocyte
Psmb2 cytosolic fraction cumulus_oocyte
Ywhae cytosolic fraction cumulus_oocyte
Psmc4 cytosolic fraction cumulus_oocyte
Hsp90aa1 cytosolic fraction cumulus_oocyte
Arhgdia cytosolic fraction cumulus_oocyte
Pgk1 cytosolic fraction cumulus_oocyte
Psmd11 cytosolic fraction cumulus_oocyte
Csl cytosolic fraction cumulus_oocyte
Eif3c cytosolic fraction cumulus_oocyte
Rpl4 cytosolic fraction cumulus_oocyte
80
Rpsa cytosolic fraction cumulus_oocyte
Tpt1 cytosolic fraction cumulus_oocyte
Pin1rt1 cytosolic fraction cumulus_oocyte
Psmb3 cytosolic fraction cumulus_oocyte
Pkm cytosolic fraction cumulus_oocyte
Hsp90b1 cytosolic fraction cumulus_oocyte
Tsc22d1 cytosolic fraction cumulus_oocyte
Fh1 cytosolic fraction cumulus_oocyte
Acly cytosolic fraction cumulus_oocyte
Huwe1 cytosolic fraction cumulus_oocyte
Rpl23 cytosolic fraction cumulus_oocyte
Cdk1 cytosolic fraction cumulus_oocyte
Plod2 cytosolic fraction cumulus_oocyte
Ephx2 cytosolic fraction cumulus_oocyte
Pgls cytosolic fraction cumulus_oocyte
Dscaml1 cytosolic fraction cumulus_oocyte
Fkbp5 cytosolic fraction cumulus_oocyte
Alb cytosolic fraction cumulus_oocyte
Dpysl2 cytosolic fraction cumulus_oocyte
Actr3 cytosolic fraction cumulus_oocyte
Nasp cytosolic fraction cumulus_oocyte
Mvk cytosolic fraction cumulus_oocyte
Bpgm cytosolic fraction cumulus_oocyte
Dbi cytosolic fraction cumulus_oocyte
Psma4 cytosolic fraction cumulus_oocyte
Rplp0 cytosolic fraction cumulus_oocyte
Bzw1 cytosolic fraction cumulus_oocyte
Pdcd5 cytosolic fraction cumulus_oocyte
Psmd6 cytosolic fraction cumulus_oocyte
Park7 cytosolic fraction cumulus_oocyte
Xpo1 cytosolic fraction cumulus_oocyte
Prkar2b cytosolic fraction cumulus_oocyte
Ruvbl2 cytosolic fraction cumulus_oocyte
Got1 cytosolic fraction cumulus_oocyte
Psma8 cytosolic fraction cumulus_oocyte
Mthfd1 cytosolic fraction cumulus_oocyte
Sfpq cytosolic fraction cumulus_oocyte
Ppa1 cytosolic fraction cumulus_oocyte
Uggt1 cytosolic fraction cumulus_oocyte
Akr1a1 cytosolic fraction cumulus_oocyte
Trim28 cytosolic fraction cumulus_oocyte
Vcl cytosolic fraction cumulus_oocyte
Ptbp1 cytosolic fraction cumulus_oocyte
Hsp90ab1 cytosolic fraction cumulus_oocyte
Lonp1 cytosolic fraction cumulus_oocyte
Tpr cytosolic fraction cumulus_oocyte
Prdx1 cytosolic fraction cumulus_oocyte
Pabpc6 cytosolic fraction cumulus_oocyte
Hist2h2aa1 cytosolic fraction cumulus_oocyte
Rps17 cytosolic fraction cumulus_oocyte
Hba-a2 cytosolic fraction cumulus_oocyte
Atp5a1 cytosolic fraction cumulus_oocyte
Akr1c14 cytosolic fraction cumulus_oocyte
Ahsa1 cytosolic fraction cumulus_oocyte
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Uqcrc1 cytosolic fraction cumulus_oocyte
Tcp1 cytosolic fraction cumulus_oocyte
Ftl1 cytosolic fraction cumulus_oocyte
Ppp2r1a cytosolic fraction cumulus_oocyte
Cmpk1 cytosolic fraction cumulus_oocyte
Tuba4a cytosolic fraction cumulus_oocyte
Calr cytosolic fraction cumulus_oocyte
Snd1 cytosolic fraction cumulus_oocyte
Gapdh cytosolic fraction cumulus_oocyte
Mcm7 cytosolic fraction cumulus_oocyte
Serpinh1 cytosolic fraction cumulus_oocyte
Tkt cytosolic fraction cumulus_oocyte
Uchl1 cytosolic fraction cumulus_oocyte
Eif3d cytosolic fraction cumulus_oocyte
Stmn1 cytosolic fraction cumulus_oocyte
Arcn1 cytosolic fraction cumulus_oocyte
Pfkp cytosolic fraction cumulus_oocyte
Serpinb6a cytosolic fraction cumulus_oocyte
U2af2 cytosolic fraction cumulus_oocyte
Set cytosolic fraction cumulus_oocyte
Rps7 cytosolic fraction cumulus_oocyte
Hmgb1 cytosolic fraction cumulus_oocyte
Hspa4 cytosolic fraction cumulus_oocyte
Dld cytosolic fraction cumulus_oocyte
Npm1 cytosolic fraction cumulus_oocyte
Eif4a1 cytosolic fraction cumulus_oocyte
Ppia cytosolic fraction cumulus_oocyte
Fdxr cytosolic fraction cumulus_oocyte
Fabp5 cytosolic fraction cumulus_oocyte
Hist1h4a cytosolic fraction cumulus_oocyte
Aldh9a1 cytosolic fraction cumulus_oocyte
Ldhb cytosolic fraction cumulus_oocyte
Tnpo1 cytosolic fraction cumulus_oocyte
Mdh2 cytosolic fraction cumulus_oocyte
Cyp11a1 cytosolic fraction cumulus_oocyte
Psma5 cytosolic fraction cumulus_oocyte
Pdia6 cytosolic fraction cumulus_oocyte
Rack1 cytosolic fraction cumulus_oocyte
Serpinb1a cytosolic fraction cumulus_oocyte
Dpysl3 cytosolic fraction cumulus_oocyte
Pfkl cytosolic fraction cumulus_oocyte
Ppp1cc cytosolic fraction cumulus_oocyte
Flna cytosolic fraction cumulus_oocyte
Rplp1 cytosolic fraction cumulus_oocyte
Ldha cytosolic fraction cumulus_oocyte
Acat2 cytosolic fraction cumulus_oocyte
Rps3 cytosolic fraction cumulus_oocyte
Nlrp14 cytosolic fraction cumulus_oocyte
Tln1 cytosolic fraction cumulus_oocyte
Prdx2 cytosolic fraction cumulus_oocyte
Ran cytosolic fraction cumulus_oocyte
Myh10 cytosolic fraction cumulus_oocyte
Hist1h2bp cytosolic fraction cumulus_oocyte
Eef1g cytosolic fraction cumulus_oocyte
82
Hnrnpf cytosolic fraction cumulus_oocyte
Hnrnpk cytosolic fraction cumulus_oocyte
Etfa cytosolic fraction cumulus_oocyte
Khsrp cytosolic fraction cumulus_oocyte
Anxa5 cytosolic fraction cumulus_oocyte
Alad cytosolic fraction cumulus_oocyte
Map1b cytosolic fraction cumulus_oocyte
Gars cytosolic fraction cumulus_oocyte
Fdps cytosolic fraction cumulus_oocyte
Hnrnpl cytosolic fraction cumulus_oocyte
Dnpep cytosolic fraction cumulus_oocyte
Mtap cytosolic fraction cumulus_oocyte
Cct5 cytosolic fraction cumulus_oocyte
Idh3a cytosolic fraction cumulus_oocyte
Gm10036 cytosolic fraction cumulus_oocyte
Sec23a cytosolic fraction cumulus_oocyte
Hnrnph1 cytosolic fraction cumulus_oocyte
Akr1cl cytosolic fraction cumulus_oocyte
Rps12 cytosolic fraction cumulus_oocyte
Pfn1 cytosolic fraction cumulus_oocyte
Tubb4a cytosolic fraction cumulus_oocyte
Uba2 cytosolic fraction cumulus_oocyte
Gdi2 cytosolic fraction cumulus_oocyte
Prdx5 cytosolic fraction cumulus_oocyte
Me1 cytosolic fraction cumulus_oocyte
Padi6 cytosolic fraction cumulus_oocyte
Hcfc1 cytosolic fraction cumulus_oocyte
Akr1b3 cytosolic fraction cumulus_oocyte
Usp14 cytosolic fraction cumulus_oocyte
Psmc1 cytosolic fraction cumulus_oocyte
Tnfaip6 cytosolic fraction cumulus_oocyte
Idh1 cytosolic fraction cumulus_oocyte
C3 cytosolic fraction cumulus_oocyte
Sae1 cytosolic fraction cumulus_oocyte
Prep cytosolic fraction cumulus_oocyte
Fdx1 cytosolic fraction cumulus_oocyte
Psma3 cytosolic fraction cumulus_oocyte
Myh9 cytosolic fraction cumulus_oocyte
Aldoa cytosolic fraction cumulus_oocyte
Aco2 cytosolic fraction cumulus_oocyte
Ddx21 cytosolic fraction cumulus_oocyte
Ddx39 cytosolic fraction cumulus_oocyte
Psmc2 cytosolic fraction cumulus_oocyte
Vcan cytosolic fraction cumulus_oocyte
Rps5 cytosolic fraction cumulus_oocyte
Rpl12 cytosolic fraction cumulus_oocyte
Txndc5 cytosolic fraction cumulus_oocyte
Mat2a cytosolic fraction cumulus_oocyte
Rplp2 cytosolic fraction cumulus_oocyte
Psma2 cytosolic fraction cumulus_oocyte
Calm1 cytosolic fraction cumulus_oocyte
Rps2 cytosolic fraction cumulus_oocyte
Cct4 cytosolic fraction cumulus_oocyte
Grhpr cytosolic fraction cumulus_oocyte
83
Eif3f cytosolic fraction cumulus_oocyte
Krt5 surface protein cumulus
Krt14 surface protein cumulus
Krt17 surface protein cumulus
Krt6a surface protein cumulus
Krt6b surface protein cumulus
Cps1 surface protein cumulus
Krt42 surface protein cumulus
Krt75 surface protein cumulus
Krt16 surface protein cumulus
Krt19 surface protein cumulus
Spint2 surface protein cumulus
Krt13 surface protein cumulus
Krt73 surface protein cumulus
Krt79 surface protein cumulus
Krt15 surface protein cumulus
Krt77 surface protein cumulus
Krt76 surface protein cumulus
Atp8b3 surface protein cumulus
Krt8 surface protein cumulus
Mas1 surface protein cumulus
Acta2 surface protein cumulus
Krt20 surface protein cumulus
Krt71 surface protein cumulus
Slc10a5 surface protein cumulus
Actbl2 surface protein cumulus
Ahcy surface protein cumulus
Hist1h2ab surface protein cumulus
Krt7 surface protein cumulus
Krt72 surface protein cumulus
Krt74 surface protein cumulus
Krt84 surface protein cumulus
Ly9 surface protein cumulus
Ttn surface protein cumulus
Aldob surface protein cumulus
Alppl2 surface protein cumulus
Ass1 surface protein cumulus
Cfap69 surface protein cumulus
Cpeb2 surface protein cumulus
Crb1 surface protein cumulus
Cyb561d2 surface protein cumulus
Ddr2 surface protein cumulus
Fam3a surface protein cumulus
Gstm1 surface protein cumulus
Hspa8 surface protein cumulus
Krt18 surface protein cumulus
Nefh surface protein cumulus
Slc38a4 surface protein cumulus
Slc39a3 surface protein cumulus
Ssr2 surface protein cumulus
St8sia3 surface protein cumulus
Zzz3 surface protein cumulus
Abca5 surface protein cumulus
Adamts15 surface protein cumulus
84
Aldh2 surface protein cumulus
Ap1m2 surface protein cumulus
Arap3 surface protein cumulus
Atp5a1 surface protein cumulus
Bod1l surface protein cumulus
C2cd5 surface protein cumulus
Cacna1s surface protein cumulus
Cacna2d1 surface protein cumulus
Cartpt surface protein cumulus
Ccr1 surface protein cumulus
Cdh16 surface protein cumulus
Cnnm3 surface protein cumulus
Cpeb4 surface protein cumulus
Ctc1 surface protein cumulus
Cyp2b9 surface protein cumulus
Dmap1 surface protein cumulus
Dnah12 surface protein cumulus
Dnah3 surface protein cumulus
Dnah5 surface protein cumulus
Dnah8 surface protein cumulus
Dsg1a surface protein cumulus
Dync2h1 surface protein cumulus
Dzip3 surface protein cumulus
Eef1a2 surface protein cumulus
Ehmt1 surface protein cumulus
Emc9 surface protein cumulus
Ezr surface protein cumulus
Fam198a surface protein cumulus
Fancd2 surface protein cumulus
Fgf14 surface protein cumulus
Fpr-rs3 surface protein cumulus
Fsip2 surface protein cumulus
Galnt10 surface protein cumulus
Gcc2 surface protein cumulus
Gfap surface protein cumulus
Gmeb1 surface protein cumulus
Gstp1 surface protein cumulus
H2afz surface protein cumulus
H3f3c surface protein cumulus
Hdac9 surface protein cumulus
Hist1h2bf surface protein cumulus
Hspa2 surface protein cumulus
Hspa9 surface protein cumulus
Hydin surface protein cumulus
Il17rd surface protein cumulus
Inhbe surface protein cumulus
Isoc1 surface protein cumulus
Itga2 surface protein cumulus
Itgal surface protein cumulus
Kcna1 surface protein cumulus
Kif4 surface protein cumulus
Kntc1 surface protein cumulus
Krt25 surface protein cumulus
Krt35 surface protein cumulus
85
Lmna surface protein cumulus
Lrrk2 surface protein cumulus
Map2 surface protein cumulus
Mfsd3 surface protein cumulus
Milr1 surface protein cumulus
Nudt18 surface protein cumulus
Pard6a surface protein cumulus
Paxip1 surface protein cumulus
Pomt1 surface protein cumulus
Pusl1 surface protein cumulus
Rnf139 surface protein cumulus
Rnf213 surface protein cumulus
Shisa6 surface protein cumulus
Shkbp1 surface protein cumulus
Slc12a4 surface protein cumulus
Slc12a7 surface protein cumulus
Slc35f3 surface protein cumulus
Slc38a1 surface protein cumulus
Slc47a1 surface protein cumulus
Stab2 surface protein cumulus
Tas2r104 surface protein cumulus
Tmod1 surface protein cumulus
Tmprss13 surface protein cumulus
Trove2 surface protein cumulus
Trpm6 surface protein cumulus
Ttc7b surface protein cumulus
Tuba1b surface protein cumulus
Ubiad1 surface protein cumulus
Vps13c surface protein cumulus
Vwa2 surface protein cumulus
Rnf103 surface protein cumulus
86
Chapter 4: Concluding Remarks
4.1 Impact of my work
My PhD work has contributed novel insight into the outcome of genomic and gametic
evolution as a result of sexual conflict in mice. I have addressed the following questions regarding
genomic variation and adaptive evolution as a result of sexual selection:
1. Is there a correlation between the ratio of Ne of the X chromosome and the autosomes and
the intensity of sperm competition?
In chapter two, I found a significant relationship between a morphological proxy for sperm
competition intensity, testis size relative to body size, and the ratio of Ne on the X
chromosome versus the autosomes (NeX/ NeA) across 12 populations of mice encompassing
four species. While relative testis size is regularly used as a proxy for mating system in
comparative studies, it is only a signal of the post-copulatory component of male-male
competition and cannot be used to predict breeding system as a whole. Testis size also
evolves rapidly, meaning it is not adequate for correlation testing with traits evolving on a
longer timescale. On the other hand, NeX/ NeA calculated from neutral variation offers a
comprehensive quantification of long-term breeding sex ratio. Our results suggest post-
copulatory sexual selection is responsible for most variance in male reproductive success
in mice and that differences in breeding sex ratio between M. m. musculus, M. m.
domesticus, M. m. castaneus, and M. spretus evolved long enough ago to be reflected in
the genome.
2. Does the rate of adaptive evolution on the X chromosome relative to the autosomes vary
with breeding sex ratio?
As recessive mutations on the X chromosome are exposed more often in males, a male-
skewed sex ratio should allow for more rapid adaptive evolution on the X chromosome.
However, male biased sex ratios also reduce the effective population size of the X
chromosome, meaning there should be less efficacy of selection than on the autosomes. In
87
chapter 2, I did not find a relationship between the proportion of sites fixed by positive
selection (a) on the X chromosome and breeding sex ratio, as calculated by NeX/ NeA. I did
find a significant relationship between NeA and a on the autosomes, consistent with the
theoretical prediction that efficacy of selection increases with effective population size. I
did not find the same results on the X chromosome across all ancestral and derived
populations, however, I did when I restricted the analysis to ancestral populations only.
This is likely reflective of the disproportionate effect of demographic events on the X
chromosome. A high rate of genome-wide adaptive evolution has previously been reported
in the M. m. castaneus population that I analyzed in my study, but surprisingly I found it
to be the only population with this characteristic out of all 12 populations. Collectively it
seems adaptive evolution is not as rampant across the genome in mice as previously
thought, and that faster-X evolution may not be adequately captured by a if it is indeed
occurring in mice.
3. What proteins are present in cumulus cells in mice?
While the whole-oocyte proteome has been well established in mice, the proteome of the
cumulus cells surrounding oocytes has been neglected. In chapter 3 I identified 1,007 high
confidence proteins present in cumulus-oocyte complexes (COCs) in mice, 769 of which
had not been previously found in oocyte protein studies. Identification of more proteins
present, on cumulus cells in particular, will build a better foundation for studies in assisted
reproduction and sexual selection.
4. What kinds of proteins are adaptively evolving in COCs?
I found 30 COC proteins adaptively evolving from an evolutionary rate analysis across 11
mouse species. These proteins are enriched for cellular response to stress. It was surprising
to not find many proteins involved solely in fertilization evolving under positive selection,
as is the case in the mouse sperm proteome. Instead, these 30 genes contained a large
number of proteins with published functionality in both fertilization and immune response.
As this study was the first global analysis of female gamete proteins in mammals, to my
knowledge, it has provided a glimpse into the role female gametes play in post-copulatory
88
sexual selection. The cumulus-oocyte complex is under more evolutionary constraint than
sperm, but the involvement of the immune system in fertilization could mean adaptive
evolution can proceed to maintain control over infection and fertilization simultaneously.
5. How do evolutionary rates of COC proteins compare to sperm proteins?
In chapter 3, I found COC proteins to be evolving significantly slower than both sperm
proteins and control proteins. As COCs have many more critical tasks to perform than
sperm, a slower evolutionary rate was in agreement with my predictions.
6. What role does the cumulus cell layer play in fertilization rate?
I performed social manipulation experiments that generated a perceived ‘risk’ of sperm
competition for female mice, previously shown to result in the ovulation of COCs with a
slower in vitro fertilization rate compared to females in the ‘no risk’ treatment. I tested for
an influence of the cumulus cell layer in this phenotype and found a faster rate of enzymatic
digestion of the cumulus cell layer in ‘risk’ mice during the first minute of breakdown.
While the sample size was quite small, it seems the cumulus cell layer could, in fact, play
a role in fertilization rate plasticity.
7. Do females adjust their COC proteome in response to risk of polyspermy?
While I did not identify many surface proteins on cumulus cells due to the small sample
size of experimental animals mentioned above, I did find 5 proteins only present in ‘no
risk’ females and 6 proteins only present in ‘risk’ females. While I cannot make definitive
statements regarding changes in cumulus cell surface proteins in response to sperm
competition risk, several of these protein differences between treatment groups are
biomarkers of puberty in porcine COCs, suggesting this treatment and subsequent
fertilization rate differences could have been due to the induction of earlier puberty and
natural ovulation in ‘risk’ females. Consistent with this idea, half of the ‘risk’ females
failed to ovulate, while none of the ‘no risk’ females did, despite being siblings and
undergoing ovulation induction at the same times. As male urinary scent and physical
89
contact with males has been shown to induce early puberty and ovulation in female mice,
it seems likely that females in the ‘risk’ group were induced into early puberty after
receiving more male urine, and more frequently, along with physically “encountering”
males. If this finding is accurate, it would indicate that ova undergo changes making them
more difficult to fertilize during puberty, uncovering the mechanism behind fertilization
rate plasticity in mice.
Other impacts
Beyond answering the questions listed above, my dissertation research greatly adds to the
amount of available data for mice. We add genomic data from 64 individuals, encompassing three
species and four populations, including the first genomic data from an ancestral range population
of M. spretus, substantially expanding resources for population genomics research in mice. Despite
being studied in the lab for approximately 40 years, we provide the first estimate of the effective
population size of M. spretus, which was substantially larger than expected. Together, this lays
groundwork for further investigation of this species. I also add hundreds of new proteins present
on COCs in mice, and denote proteins found only on cumulus cells.
4.2 Future directions
Chapter two
Although the study in chapter two is the largest population genomic study of mice
conducted to date, it has limitations in its ability to make inferences on the true relationship
between relative testis size and breeding sex ratio, as calculated by NeX/ NeA. While I did find a
correlation between these values, we did not have access to population-level measurements of
relative testis size and had to rely on species-level values. It could be the case that this relationship
is spurious, and calls for greater morphological sampling to be used in concert with the genomic
data we and others have collected to better determine the relationship between breeding sex ratio
and relative testis size in mice.
In our calculations of NeX/ NeA, all of the populations of mice sampled seem to show a
reduced amount of variation on the X chromosome, consistent with previous studies. As we did
90
not find rampant positive selection on the X chromosome that could result in the pattern of
variation observed, further and explicit exploration into the cause of the reduction in diversity is
warranted. Additionally, it could be that positive selection on the X chromosome is rampant but
that a does not accurately capture it, meaning quantifying adaptive evolution with another method
would be informative. A natural following analysis would be to test for recurrent positive selection
on the X chromosome by calculating the ratio of nonsynonymous to synonymous substitution rates
(dN/dS) in each of the populations analyzed. Studies of this nature are likely to be biased by the
greater effect of drift on the X chromosome due to the extremely reduced NeX/ NeA values we found
in all populations. It would be interesting to test for a correlation between NeX/ NeA and dN/dS on
the X chromosome in our populations as an indirect method for assessing the extent of bias due to
effective population size.
Chapter three
Though my study in chapter three suggests induction of early puberty and a change in the
cumulus cell layer could explain fertilization rate plasticity as a result of sperm competition risk,
it is very limited by small sample sizes. In addition to half of the ‘risk’ females failing to ovulate,
the mice that were induced into ovulation produced relatively few eggs, resulting in the
identification of only ~1/6-1/2 the number of proteins I found on cumulus cells in laboratory mice.
It would be informative to not only repeat this study, but also change it slightly. If ‘risk’ females
truly are induced into earlier puberty, testing this hypothesis would require checking for natural
estrous rather than inducing ovulation with injections. The assessment of rate of breakdown of the
cumulus cell layer is also hampered by sample size, as well as a methodological issue that occurred
where cells would drift partially off-screen during time-lapse imaging. A more controlled approach
using equipment designed for flow-through experiments would more accurately assess the role of
cumulus cell layer breakdown in fertilization rate. Additionally, with an increased sample size it
would be informative to quantitatively compare proteins between groups to more accurately test
for protein abundance differences between treatments.
91
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Abstract (if available)
Abstract
In many species, males and females have a fundamental conflict of interest over reproduction due to their differential levels of investment. Males benefit from siring offspring with as many females as possible, and females benefit from choosing the best male for fertilization. This sexual conflict generates strong selection for traits that function to increase the fitness of one sex over the other. In internal fertilizers, the outcome of sexual conflict is often determined by gamete interactions that take place in the female reproductive tract after mating occurs, however, the mechanisms involved remain largely unknown. Additionally, sexual conflict often results in differential reproductive success between males and females. The long-term evolutionary outcome of unequal breeding sex ratios, however, is unclear. In this dissertation, I dissect the consequences of sexual conflict on gamete biology and genomic evolution. In chapter two, I demonstrate that wild populations of mice have a wide range of breeding sex ratios that can alter the evolutionary rate of the X chromosome relative to the autosomes. In chapter three, I uncover a potential mechanism of female gamete plasticity in response to sexual conflict over fertilization rate in mice. I also provide the first global evolutionary analysis on female gametes in mice and uncover evidence for recurrent positive selection in proteins that function in both reproduction and immunity.
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Creator
Keeble, Sara Maria
(author)
Core Title
From gamete to genome: evolutionary consequences of sexual conflict in house mice
School
College of Letters, Arts and Sciences
Degree
Doctor of Philosophy
Degree Program
Molecular Biology
Publication Date
04/26/2019
Defense Date
03/11/2019
Publisher
University of Southern California
(original),
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Tag
Evolution,Mice,OAI-PMH Harvest,population genomics,sexual conflict,sexual selection
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Electronically uploaded by the author
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Dean, Matthew (
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), Calabrese, Peter (
committee member
), Kenkel, Carly (
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), Nuzhdin, Sergey (
committee member
)
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keeble@usc.edu,sara.keeble9@gmail.com
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Tags
population genomics
sexual conflict
sexual selection