Cellular and molecular aspects of the response of the

Cellular and molecular aspects of the response of

the testis to nutrition in sexually mature sheep

Yongjuan Guan

21004548

This thesis is presented for the degree of Doctor of Philosophy of

The University of Western Australia

School of Animal Biology

Institute of Agriculture

March 2015

Declaration

1

Declaration

The work presented in this thesis is original work of the author, and none of the material

in this thesis has been submitted either in full, or part, for a degree at this university or

any other universities or institutions before. The experimental designs and manuscript

preparation were carried out by myself after discussion with my supervisors Prof

Graeme Martin, A/Prof Irek Malecki and Dr Penny Hawken.

Yongjuan Guan

March 2015

Contents

2

Contents

Summary ....................................................................................................................................... 4

Acknowledgements ....................................................................................................................... 8

Publications ................................................................................................................................. 10

Chapter 1: General Introduction ................................................................................................. 12

Chapter 2: Literature Review ...................................................................................................... 16

2.1 Male reproduction ............................................................................................................. 18

2.1.1 Organization of the testis ........................................................................................... 18

2.1.2 Spermatogenesis ........................................................................................................ 21

2.1.3 Physiological control of male reproduction .............................................................. 23

2.1.4 Morphological changes associated with puberty ....................................................... 25

2.2 Environmental factors affecting male reproduction .......................................................... 26

2.2.1 Photoperiod ................................................................................................................ 26

2.2.2 Social-sexual signals .................................................................................................. 28

2.2.3 Stress and temperament ............................................................................................. 29

2.2.4 Nutrition ..................................................................................................................... 30

2.3 Small RNAs affect spermatogenesis and germ cell apoptosis in testis ............................. 35

2.3.1 Small RNA categories ................................................................................................ 35

2.3.2 miRNAs and spermatogenesis .................................................................................... 37

2.3.3 miRNAs and germ cell apoptosis ............................................................................... 37

2.3.4 piRNAs affect spermatogenesis .................................................................................. 38

2.4 Alternative pre-mRNAs splicing affects spermatogenesis and apoptosis ......................... 38

2.4.1 Alternative pre-mRNA splicing and spermatogenesis ................................................ 39

2.4.2 Alternative pre-mRNA splicing and spermatogenesis ................................................ 40

2.5 Conclusions and hypotheses .............................................................................................. 41

Chapter 3: General Materials and Methods ................................................................................ 42

3.1 Experimental location ....................................................................................................... 42

3.2 Experimental animals ........................................................................................................ 42

3.3 Nutrition treatment ............................................................................................................ 42

3.4 Body mass and scrotal circumference ............................................................................... 43

3.5 Semen collection and processing ...................................................................................... 44

3.6 Semen analysis .................................................................................................................. 44

3.7 Tissue collection and preservation .................................................................................... 46

Contents

3

3.8 Sperm concentration in testicular tissue ............................................................................ 46

3.9 Morphometric and histological analysis ........................................................................... 46

3.10 Molecular analysis .......................................................................................................... 50

3.11 Bioinformatics analysis ................................................................................................... 52

Chapter 4: Under nutrition reduces spermatogenic efficiency and sperm velocity, and increases sperm DNA damage in sexually mature male sheep .................................................................. 57

4.1 Abstract ............................................................................................................................. 57

4.2 Introduction ....................................................................................................................... 58

4.3 Materials and methods ...................................................................................................... 60

4.4 Results ............................................................................................................................... 64

4.5 Discussion ......................................................................................................................... 71

Chapter 5: Under-nutrition decreases Sertoli cell function in sexually mature male sheep ....... 77

5.1 Abstract ............................................................................................................................. 77

5.2 Introduction ....................................................................................................................... 78

5.3 Materials and methods ...................................................................................................... 81

5.4 Results ............................................................................................................................... 90

5.5 Discussion ......................................................................................................................... 99

Chapter 6: Roles of small RNAs in the effects of nutrition on apoptosis and spermatogenesis in the adult testis ........................................................................................................................... 104

6.1 Abstract ........................................................................................................................... 104

6.2 Introduction ..................................................................................................................... 105

6.3 Materials and methods .................................................................................................... 107

6.4 Results ............................................................................................................................. 115

6.5 Discussion ....................................................................................................................... 125

Chapter 7: Functional changes in mRNA expression and alternative pre-mRNA splicing associated with the effects of nutrition on apoptosis and spermatogenesis in the adult testis .. 131

7.1 Abstract ........................................................................................................................... 131

7.2 Introduction ..................................................................................................................... 132

7.3 Materials and methods .................................................................................................... 134

7.4 Results ............................................................................................................................. 139

7.5 Discussion ....................................................................................................................... 148

Chapter 8 ................................................................................................................................... 155

General Discussion ................................................................................................................... 155

References ................................................................................................................................. 161

Summary

4

Summary

The reproductive system of small ruminants is affected by a variety of environmental

factors, including socio-sexual signals, photoperiod and nutrition. Genotype controls the

final outcome, but nutrition is particularly important in geographical regions where the

quality and quantity of pasture are poor during the breeding season. In males, the

problem of poor feed availability is exacerbated by a decrease in appetite and the end

result is major losses in both body mass and testis mass, and therefore sperm

production. It is not clear whether the reductions in testis mass and numbers of sperm

are accompanied by changes in the quality of the sperm. Moreover, we know little of

the physiological, cellular and molecular processes involved. In this thesis, these

processes are explored.

Sertoli cells were the focus of attention because they provide nutritional and structural

support for germ cells. We therefore expected the reduction in sperm production by

under-fed sheep to be correlated with decreases in the number or function of the Sertoli

cells. Another possibility is that, in underfed animals, there is an increase in apoptosis of

the germ cells, thus explaining the reductions in sperm output and perhaps explaining

any changes in the quality of the sperm that are eventually ejaculated.

It seems likely that the responses to changes in nutrition are mediated by small RNAs

(including micro-RNAs and piwi-RNAs), mRNAs and alternative pre-mRNA splicing,

within the Sertoli cells. These mRNA-based mechanisms are thought to be associated

with apoptosis and spermatogenesis. In this thesis, therefore, I tested the general

hypothesis that, in adult male sheep, under-nutrition will reduce sperm quality, due to

germ cell apoptosis, that these responses will be explained by reductions in Sertoli cell

Summary

5

function, and that such effects are mediated by changes in the expression of small RNAs

and mRNAs, and by alternative pre-mRNA splicing.

The foundation of my experimental work was a single large study with sexually mature

Merino rams that provided baseline data and tissue samples for cellular and molecular

studies. Three diets (High, Maintenance, Low) were fed for 65 days and induced

changes in testis mass, sperm production and spermatogenic efficiency (sperm per gram

of testis tissue). The quality of ejaculated sperm was analyzed at the end of the

nutritional treatment period. Underfeeding reduced sperm velocity, increased sperm

DNA damage, and decreased spermatogenic efficiency, compared to the other two

dietary treatments. There were no differences in terms of sperm morphology or sperm

viability among the three treatments. Germ cell apoptosis was evaluated by the TUNEL

method and by assessing the expression of some of the major genes that control the

process of apoptosis. The results showed that underfeeding increased the number of

apoptotic germ cells and increased the expression of apoptosis-related genes in

testicular tissue.

Potential effects of nutrition on the number of Sertoli cells were evaluated by counting

cells using GATA4 as a marker for Sertoli cell nuclei, and by evaluating the

proliferation status of the Sertoli cells using immunoreactivity to proliferation cell

nuclear antigen (PCNA). The numbers of Sertoli cells did not differ among the

treatments and was not related to changes in testis mass, although 1% of the Sertoli cells

retained proliferative ability.

The function of the Sertoli cells was evaluated by assessing tight junctions and the

expression of the genes involved in Sertoli cell maturation. Our results showed that,

compared with High and Maintenance diets, the Low diet led to disorganized

localization of the tight junction protein, Claudin 11. In addition, mRNA expression for

Summary

6

Claudin 11 was increased and mRNA expression for ZO1 was decreased. These three

observations are coherent and suggest that, in the Sertoli cells of underfed sheep, tight

junctions are disrupted and there seems to be a reversal of the ‘terminal differentiation’

that is associated with puberty. These cells had lower levels of expression of GATA1, a

marker of mature Sertoli cells, and higher levels of expression of AMH, a marker of

immature Sertoli cells. These observations are consistent with reversal of maturation,

thus explaining the disruption of tight junctions in underfed rams.

To investigate the processes through which nutrition can induce these responses, we

studied the effect of nutrition on the expression of small RNAs, including miRNAs and

piRNAs. We identified 44 miRNAs and 35 putative piRNAs that were differentially

expressed in well-fed and underfed animals. Of particular interest were those related to

reproductive system development, apoptosis (miRNAs), and sperm production and

sperm quality (piRNAs). More importantly, one of the novel miRNAs, miR-144 (a

homologue to miR-98), was found to target three apoptotic genes (TP53, CASP3,

FASL). These observations suggest changes in the expression of miRNAs and piRNAs

were responsible for the effects of under-nutrition on spermatogenesis and germ cell

apoptosis.

We also tested the effect of nutrition on mRNA expression and alternative pre-mRNA

splicing. A total of 2243 mRNAs were differentially expressed in underfed and well-fed

sheep, and functional analysis suggests that they were predominantly related to germ

cells, testis size, apoptosis and spermatogenesis. In addition, 788 genes were spliced

differently in the two dietary treatments, most of which were related to protein

localization, cellular metabolic processes, post-translational protein modification, and

spermatogenesis. These observations suggest that the changes in mRNA expression and

Summary

7

alternative pre-mRNA splicing regulate spermatogenesis and apoptosis and are

responsible for the effects on nutrition on ram fertility.

In conclusion, in sexually mature male sheep, under-nutrition reduces spermatogenic

efficiency and sperm velocity, and increases sperm DNA damage. These processes are

not associated with the changes of Sertoli cell number, but are associated with increased

germ cell apoptosis and disrupted Sertoli cell function (including the disorganization of

tight junctions and reversal of cell maturity). These effects seem to be explained by

changes in small RNAs, mRNAs and alternative pre-mRNA splicing. These discoveries

are major steps towards the development of processes for mitigating the negative effects

of under-nutrition on male fertility.

Acknowledgements

8

Acknowledgements

It was my great honor to do my PhD in the School of Animal Biology in the University

of Western Australia, funded by a Scholarship for International Research Fees (SIRF). I

am indebted to my supervisors Prof Graeme Martin, A/Prof Irek Malecki and Dr Penny

Hawken and I want to express my deepest thanks to them for their strong support,

invaluable guidance, tireless corrections of the manuscripts and their willingness to help

whenever I need them in my research and my life. They will be the ones that I miss

most when I leave Australia and I wish them all the best, especially good health. I will

treasure their great kindness my whole life and look forward to collaborating with them

in the future.

I owe appreciation to those who have contributed to this work. I especially would like to

express my sincere gratitude to Prof Philip Vercoe for his financial support for my

project; Dr John Milton for his nutritional analysis of the diets; Mr Tom Stewart for his

experienced guidance with histological techniques; and the other staff and postgraduate

students in the livestock science group, Dr Trina Jorre de St Jorre, Dr César Rosales

Nieto, Mrs Margaret Blackberry, Mr Sheng Zhang and Mr Fahad Almohsen for their

help during my studies. Thank you to everyone who has shared your experience with

me and contributed to the pleasant atmosphere in the animal science discipline at UWA.

I would like to thank A/Prof Leluo Guan from University of Alberta, Canada, for her

generous financial help and research guidance and also her group members for their

patient help with techniques and data analysis. Dr Sarah Meachem and Mr Seungmin

Ham from Prince Henry’s Institute of Medical Research at Melbourne also provided

excellent guidance and constructive suggestions. Dr Matthew Linden from the Centre

Acknowledgements

9

for Microscopy, Characterisation & Analysis at UWA kindly helped with the flow

cytometry, and Prof Geoff Meyer and Dr Greg Cozens from School of Anatomy and

Human Biology at UWA provided excellent technical support. Dr. Anne Jequier (Pivet

Medical Centre, Perth, Western Australia) made many suggestions for the techniques

for assessment of the testis.

I am very grateful to Mike Carroll Travelling Fellowship Committee for funding the

travel for my collaboration in Canada, and to the UWA Graduates Association for

offering me a Postgraduate Research Travel Award to attend an international conference

in Vancouver.

I would like to say thank you to my beloved boyfriend Guanxiang Liang for his

everlasting love, encouragement and support although he is physically in Canada. I am

thankful to my family in China for their unconditional love and support during the last

30 years.

Finally, the last congratulation goes on to me, potential Dr. Guan. I was a scientific

baby 1276 days ago, now I am proud to say I am a scientific child, at least not a baby

any more. Thanks to my hard work during the last three and half years, I believe the

1276 days’ overseas’ research life will be one of the most beautiful sceneries in my life.

Publications

10

Publications

Refereed journal articles

Guan, Y., Malecki, I.A., Hawken, P.A.R., Linden, M.D. & Martin, G.B. (2014). Under

nutrition reduces spermatogenic efficiency and sperm velocity, and increases sperm

DNA damage in sexually mature male sheep. Animal Reproduction Science 149,

163-172. See Chapter 4.

Guan, Y., Liang, G., Hawken, P.A.R., Meachem, S., Malecki, I.A., Ham, S., Stewart,

T., Guan, L.L. & Martin, G.B. (2014). Nutrition affects Sertoli cell function but not

Sertoli cell numbers in sexually mature male sheep. Reproduction, Fertility and

Development (RD14368, DOI: 10.1071/RD14368). See Chapter 5.

Guan, Y., Liang, G., Hawken, P.A.R., Malecki, I.A., Cozens, G., Vercoe, P.E., Martin,

G.B. & Guan, L.L. (2014). Roles of small RNAs in the effects of nutrition on

apoptosis and spermatogenesis in the adult testis. Scientific Report (Nature

publishing group, SREP-14-10126-B, under revision). See Chapter 6.

Conference papers

Guan, Y., Meachem, S., Malecki, I.A., Hawken, P.A.R., Jequier, A. & Martin, G.B.

(2013). Sperm quality and Sertoli cell function are compromised in underfed

Merino rams. Annual Symposium of the Endocrine and Reproductive Society of WA

(Perth, Australia), page 30.

Guan, Y., Meachem, S., Malecki, I.A., Hawken, P.A.R., Jequier, A. & Martin, G.B.

(2013). Sperm quality and Sertoli cell function are compromised in underfed

Publications

11

Merino rams. Abstracts of the 11th World Conference on Animal Production

(Beijing, China), page 33 (abstract WCAP2013-7-M2).

Almohsen, F., Guan, Y., Malecki, I.A., Hawken, P.A.R. & Martin, G.B. (2012).

Nutrition, testicular mass and sperm viability in the sexually mature male sheep.

Proceedings of the 44th Annual Conference, Society for Reproductive Biology

(Gold Coast, Australia).

Guan,Y., Almohsen F., Malecki, I.A., Hawken, P.A.R. & Martin, G.B. (2012).

Nutrition, testicular mass and sperm viability in the sexually mature male sheep.

Annual Symposium of the Endocrine and Reproductive Society of WA (Perth,

Australia), page 30.

Guan, Y., Almohsen, F., Tawang, A., Malecki, I.A. & Martin, G.B. (2012). Nutrition,

seminal plasma protein concentration and sperm viability in the mature male sheep.

Abstracts of the 17th International Congress on Animal Reproduction (Vancouver,

Canada). Reproduction in Domestic Animals 47 (Supplement 4), 526 (Abstract

2052).

Almohsen F., Guan,Y., Malecki, I.A., Hawken, P.A.R. & Martin, G.B. (2012).

Nutrition, seminal plasma protein concentration and sperm viability in the mature

male sheep. Association for Applied Animal Andrology (Vancouver, Canada).

Chapter 1-General Introduction

12

Chapter 1

General Introduction

The reproductive system of small ruminants is affected by environmental factors such

as socio-sexual signals, photoperiod and nutrition, with genotype controlling the final

outcome (review: Martin and Walkden-Brown 1995). The effects of nutrition on gamete

production have been studied extensively in females (Lozano et al. 2003), but much less

attention has been paid to males. This disparity is surprising because, in sheep and

goats, testis mass is lost during the normal breeding season as a consequence of the poor

quantity and quality of pasture during the autumn in many parts of the world (Martin et

al. 1999). In addition, during the breeding season, male goats and sheep lose their

appetite and their behavioural drive for mating reduces the time available for feeding.

The combination of these circumstances results in major losses in both body mass and

testis mass (Knight et al. 1987) and therefore sperm production (Oldham et al. 1978;

Cameron et al. 1988). Conversely, it is not clear whether the reductions in testis mass

and sperm production in underfed rams are also accompanied by reductions in the

quality of the sperm that are ejaculated. In addition, we do not understand the

physiological, cellular and molecular processes involved. In this thesis, I have explored

these issues.

Spermatogenic efficiency, as indicated by the number of sperm produced per gram of

testicular tissue, is also affected by nutrition (Oldham et al. 1978; Cameron et al. 1988),

probably due to changes in the rate of cell loss during spermatogenesis, perhaps through

effects on the process of apoptosis (Sakkas et al. 1999; Santos et al. 1999). In addition,

changes in testis mass would also be accompanied by changes in blood flow (Setchell et


13

al. 1965) and thus oxidative stress, a major cause of sperm damage (Review:Aitken et

al. 2012). Therefore, germ cell apoptosis is one possible mechanism responsible for the

reduction in sperm production by under-fed sheep.

As germ cells develop, they receive nutritional and structural support from the Sertoli

cells in the testis. However, each Sertoli cell has a fixed capacity for the number of

germ cells it can support (review: Sharpe et al. 2003), so the reduction in sperm

production by under-fed sheep could be related to changes in number or function of

Sertoli cells. In sexually mature male sheep, an assessment of the testes using classical

morphological techniques indicated effects of nutrition on the number and volume of

Sertoli cells (Hötzel et al. 1998). This finding is controversial because it contradicts the

dogma that the number of Sertoli cells in the testis is stable after puberty (Monet-Kuntz

et al. 1984). Therefore, there is a clear need to use more specific techniques to re-

examine the effects of under-nutrition on Sertoli cell number in sexually mature male

sheep.

We know little of the physiological, cellular and molecular processes involved in the

above processes. Recently, regulatory small RNAs including microRNAs (miRNAs)

and PIWI-interacting RNAs (piRNAs) have emerged as important regulators of

spermatogenesis and apoptosis (Ro et al. 2007; Hayashi et al. 2008; Papaioannou and

Nef 2010; Li et al. 2011). miRNAs are small (~22 nucleotides) endogenous RNAs that

negatively regulate gene expression by targeting the 3’-untranslated region (Krutzfeldt

and Stoffel 2006) and/or coding region of mRNAs (Hausser et al. 2013). It has been

reported that a global loss of miRNAs, in germ cells or in Sertoli cells, is detrimental for

male fertility (Niu et al. 2011). By contrast, piRNAs are longer (26–32 nt) than

miRNAs and can bind PIWI proteins, which are spermatogenesis-specific proteins

belonging to the Argonaute protein family (Liu et al. 2012a). The main function of


14

piRNAs is to guide PIWI protein to sites where it can suppress the transposable

elements that protect genomic integrity (Luteijn and Ketting 2013). To date, piRNAs

have been mainly found in the testis in males, suggesting that their roles are specific to

spermatogenesis (Ro et al. 2007). We have therefore postulated that changes in small

RNAs would explain the difference of sperm output in male sheep treated with different

diets.

Another possibility is that the effects of under-nutrition on Sertoli cell function are

mediated by changes in the expression of mRNAs, and by alternative pre-mRNA

splicing. Of particular interest are the mRNAs of genes such as CASP3 and TP53 that

are related to apoptosis (Shaw et al. 1992; Ni et al. 1998), and genes such as Claudin 11

and ZO1 that are associated with spermatogenesis (Tarulli et al. 2008). Alternative pre-

mRNA splicing is being seen as an important mechanism for regulating gene expression

and for increasing transcriptome plasticity and proteome diversity. Indeed, it has been

reported that approximately 60% of human gene products undergo alternative splicing

(Modrek and Lee 2002). It has also been reported that spermatogenesis and many

apoptotic factors are regulated by alternative pre-mRNA splicing (Walker et al. 1996;

Moore et al. 2010), so we expected mRNA expression and alternative pre-mRNA

splicing to be affected by under-nutrition in sexually mature male sheep testis. Rather

than focusing on particular genes, however, we used RNA-Seq for a more general, a

large scale exploration of the expression of mRNAs and alternative splicing events.

In conclusion, the general hypothesis tested in this thesis is that, in adult male sheep,

under-nutrition will decrease sperm quality, and that this effect is associated with

changes in the number or function of Sertoli cells, and with changes of germ cell

apoptosis. To gain an understanding of the processes involved, we studied small RNAs,

mRNAs and alternative pre-mRNA splicing. The experimental work in this thesis


15

therefore focused on the effects of nutrition on sperm quality and testis function in

sexually mature Merino rams and pursued the following aims: 1) To measure the sperm

quality including motility, viability and morphology, as well as sperm DNA damage

and germ cell apoptosis; 2) To explore Sertoli cell number and function; 3) To

investigate the expression of small RNAs, especially miRNAs and piRNAs; 4) To

investigate the expression of mRNAs and functional relationships between miRNAs and

mRNAs; 5) To explore the possible role of alternative pre-mRNA splicing in

spermatogenesis and apoptosis.

Chapter 2-Literature Review

16

Chapter 2

Literature Review

Background

Sheep are seasonal breeders and their breeding season typically falls in the autumn,

when days are getting shorter. In many regions, including south-west Western Australia,

the autumn is also the season when the quantity and quality of pasture are poorest, so

the animals are losing weight whilst mating. For Merino rams, this will mean a loss of

testicular mass as the breeding season progresses (Fig. 2.1).

Figure. 2.1. Seasonal patterns of scrotal

circumference and live weight in Merino

rams in a mediterranean region (south-

Western Australia). Live weight was

corrected for changes in fleece weight, and

these data and those for scrotal circumference

were subjected to a smoothing function to

clarify the patterns. Values are mean ± sem (n

= 8). Daylength (broken line) varies between

10 and 14 h (sunrise to sunset). “M” indicates

a typical time of mating. From (Martin et al.

1994).

At the University of Western Australia, there has been decades of research on the

physiological processes involved (review: Martin et al. 2011), with a particular

emphasis on how acute supplementation with lupin grain can improve testicular mass


17

86%

0

100

200

300

400

Paired testis mass (g)

High diet

Low diet

250%

Spermatozoa (billions per day)

0

2

4

6

8

High diet

Low diet

and daily sperm production. As can be seen in the example in Figure 2.2, the

supplemented animals also show an increase in ‘spermatogenic efficiency’, as

evidenced by the 250% increase in sperm production after only an 86% increase in

testicular mass. This suggests that, in the small testis of underfed rams, there is a high

rate of loss of sperm cells during the process of spermatogenesis. My project will

investigate this idea.

Figure 2.2. Effect of nutrition on testicular growth and the

production of sperm in 1-year-old Merino rams (P < 0.01 for

both). The diets led to liveweight gains of 17 kg (High diet)

and 26 kg (Low diet) after 9 weeks (P < 0.001). Semen was

collected daily with an artificial vagina and the number of

sperm per ejaculate was averaged for the ninth week. The rate

of production of sperm was calculated from numbers of stages

VI, VII and VIII spermatids in testicular homogenates following

castration at the end of the experiment. Testicular mass was determined after slaughter. Redrawn after

(Cameron et al. 1988).

In contrast to the extensive research on the systemic physiology underpinning this

phenomenon, very few studies have dealt with the relationships between nutrition and

the quality of the sperm cells produced in the ejaculate or other aspects of testis

function. This review will describe the physiology of male reproduction and factors that

affect male reproduction, and then place particular emphasis on the changes within the

testis that will be induced by nutritional treatments that are known to lead to gains and

losses in testis mass. The sexually mature male sheep is used as the model in all my

experiments, so this literature review will focus on the sheep and refer to other


18

mammals when information for sheep is lacking or controversial, or where species

comparisons can provide insight.

2.1 Male reproduction

2.1.1 Organization of the testis

The general anatomy of the testis is similar for all mammalian species. The testis

consists of a series of elongated convoluted tubules, among which lie the blood vessels

and Leydig cells, the whole covered by a tough fibrous capsule (Leeson 1974).

a. Seminiferous tubules and the rete testis

i. Seminiferous tubules

The seminiferous tubules are two-ended loops with both ends opening into the rete

testis. Each tubule is extensively convoluted and the number of tubules differs among

species. For example, there are less than five in dasyurid marsupials (Woolley 1975),

approximately 30 in the rat (Tuck et al. 1970), and more in sheep and human. In most

species, the diameter of each tubule is between 200 and 275 µm (Wing and Christensen

1982; Hötzel et al. 1998). The tubules contain various germinal cells and the somatic

Sertoli cells within a compartment bounded by lymphatic endothelium (Fawcett et al.

1973), myoid cells (Maekawa et al. 1996) and acellular elements (Leeson and Forman

1981) that together form a well-defined boundary tissue. There are basement

membranes between the lymphatic endothelial cells and between the myoid cells and

the cells within the tubule. There may be many layers of myoid cells (Maekawa et al.

1996) and they probably provide the motive force for the propulsion of fluid and sperm

along the seminiferous tubules to the rete testis (Russell et al. 1989).

ii. Sertoli cells and the blood-testis barrier


19

These cells were named after Enrico Sertoli who first described them 150 years ago

(Sertoli 1865). They lie inside the boundary tissue of the seminiferous tubules and

surround the undeveloped germinal cells. In the adult, the cytoplasm of the Sertoli cells

extends from the boundary tissue to the lumen of the tubule. There is a complex

nucleus, with infolding or lobulation, with a central mass and two lateral associated

bodies, the perinucleolar spheres (Lipshultz et al. 1982). The mitochondria are

numerous and have an orthodox internal appearance, although they tend to be longer

and thinner than those of germ cells (Bawa 1963). There are multiple separate Golgi

elements and numerous membrane-limited dense bodies of varying size. There is both

rough and smooth endoplasmic reticulum (ER) (Steinberger et al. 1975). The rough ER

is found mainly in the basal part of the cell, in the form of tubules or stacks of cisternae,

whereas some of the smooth ER is arranged in dense masses around the developing

acrosome of the spermatids. Microtubules are also abundant in the Sertoli cells at

certain stages of the spermatogenic cycle (Inoue et al. 2014).

Sertoli cells produce inhibin in response to follicle-stimulating hormone (FSH) released

from the pituitary gland (Bicsak et al. 1987) and are associated with the development of

the germinal cells, but exactly how they provide nutritional and structural support for

germ cells is still unclear. Some studies indicate specific functions, such as secretion of

fluid, phagocytosis, and the maturation of sperm and their release into the tubule lumen

(Valles et al. 2014). Importantly, pairs of adjacent Sertoli cells show specialized

junctions that form the main component of the blood-testis barrier after puberty (Tarulli

et al. 2012). The blood-testis barrier segregates the meiotic and post-meiotic cells into

the immunologically privileged adluminal compartment (Meng et al. 2005) and, when it

is disrupted, the result is germ cell atresia and the cessation of spermatogenesis (Tarulli

et al. 2008). The function and dynamic regulation of the blood-testis barrier are still

poorly understood.


20

It generally accepted that Sertoli cells do not divide after puberty – in the rat, for

example, cessation of Sertoli cell division coincides with differentiation of the germinal

cells up to the early pachytene stage of meiotic prophase during the initial

spermatogenic wave (Steinberger and Steinberger 1971). Similar studies have been

reported in other mammals (Franca et al. 2000; Plant and Marshall 2001) and also in

non-mammalian species, such as the African catfish and Nile tilapia, in which Sertoli

cell proliferation is strongly reduced when germ cells have proceeded into meiosis, and

stops in postmeiotic cysts (Schulz et al. 2005).

Whether Sertoli cells in mature testes can resume division under certain conditions

remains an open question. It was suggested that an increase in the number of Sertoli

cells observed after artificial cryptorchidism in adult rats is due to amitotic divisions

(Clegg 1963), but amitosis is generally not considered to be the mode of cell division in

mammalian tissues (Steinberger and Steinberger 1971).

iii. Lumen and rete testis

The lumen of the tubule is filled with a fluid that is necessary for the development of

germ cells and also for the transport of sperm to the rete testis, efferent ducts and

epididymis (Levine and Marsh 1971). The seminiferous tubules open into short tubuli

recti (or straight tubules) that, in turn, open into the rete testis. The rete testis is the

beginning of the excurrent duct system and, in some species, is embedded in a fibrous

mediastinum. The size and position of the rete varies greatly between species

(Jahnukainen et al. 2011).

b. The interstitial tissue and Leydig cells

The seminiferous tubules are basically cylindrical and these cylinders are stacked,

leading to the formation of a series of 3-sided spaces that contain the interstitial tissue.

The interstitial tissue contains the blood vessels, lymph vessels, fibrocytes, fibroblasts


21

and Leydig cells (Mori and Christensen 1980). Leydig cells are named after Franz

Leydig who described them in 1850 (Roe et al. 1964). These cells have a complex

ultrastructure and contain large amounts of smooth endoplasmic reticulum, plentiful

mitochondria, a prominent Golgi complex, centrioles and a number of lipid droplets

(Kerr et al. 1979). They are responsible for synthesizing and releasing androgens in

response to luteinizing hormone (LH) released from the pituitary gland (Li et al. 2013).

c. The capsule of the testis

The testis is encased in a tough fibrous capsule, usually referred to as the tunica

albuginea. There are really three tunicas: the tunica albuginea in the middle; the visceral

tunica vaginalis on the outside; and the tunica vasculosa nearest to the parenchyma

(Buetow 1995). The main tunica albuginea consists of fibroblasts and bundles of

collagenous fibres running in all directions with a number of smooth muscle cells and

nerve endings. The tunica vaginalis is the peritoneal lining that surrounds the testis and

consists of a single layer of flattened mesothelial cells. The tunica vasculosa can be

considered as a sub-tunical extension of the interstitial tissue and consists of networks

of minute blood vessels held together by delicate areolar tissue (Buetow 1995).

2.1.2 Spermatogenesis

Spermatogenesis is the process in which spermatogonia form sperm (Brinster and

Zimmermann 1994). It may be divided into three phases based on functional

considerations: a) the proliferative phase, b) the meiotic phase, and c) the spermiogenic

phase (Clermont and Morgentaler 1955).

a. Proliferative Phase

Most mammalian species produce millions of sperm each day during their

reproductively active periods and the population of spermatogonia fulfil this need. In


22

the proliferative phase, these relatively immature cells undergo numerous mitoses to

build up a large number of cells that subsequently undergo meiosis and differentiation

to form sperm.

The spermatogonia reside basally within the tubule, generally showing one surface

flattened along the basal lamina and a rounded surface in contact with the Sertoli cells

(Russell 1977). There are three types of spermatogonia: stem cell spermatogonia,

proliferative spermatogonia, and differentiated spermatogonia (Tegelenbosch and de

Rooij 1993; Brinster and Zimmermann 1994). The first two groups are known as

undifferentiated spermatogonia (Shinohara et al. 2000; Ohbo et al. 2003).

Spermatogenesis begins with differentiated spermatogonia that replicate and

differentiate and thus become divided into A, intermediate and B classes. In sheep, A0,

A1, A2, A3, intermediate, B1 and B2 spermatogonia are present (Hochereau-de Reviers

1976).

b. Meiotic Phase

The type B spermatogonia differentiate into primary (preleptotene) spermatocytes

(Yuan et al. 2000) that morphologically resemble the B spermatogonia except that they

are smaller. In addition, slightly less chromatin is seen protruding inward toward the

centre of the nucleus (Costa et al. 2011). The preleptotene spermatocytes undergo two

meiotic divisions during which germ cell number is quadrupled, chromosomes are

recombined, and genetic material is halved in each cell. This whole process can be

divided into two major stages: the first meiotic division (Meiosis I) in which primary

spermatocytes yield secondary spermatocytes, and the second meiotic division (Meiosis

II) yields spermatids (differentiation without proliferation) that differentiate into sperm

(Howard and Pelc 1950). Each major stage is subdivided into prophase, metaphase,


23

anaphase and telophase (Staiger and Cande 1990). The process of differentiation into

sperm is known as spermiogenesis (Li et al. 2014).

c. Spermiogenic Phase

This phase described the process through which the round spermatids differentiate into

elongated spermatids. To be transformed into the very complex sperm, they need to

undergo condensation of the nucleus, formation of the acrosome, virtual elimination of

the cytoplasm, development of a tail, and arrangement of the mitochondria into a helix

that produces the midpiece (Santos et al. 2010). This process occurs without cell

division and is one of the most phenomenal cell transformations in the body. How these

changes are achieved is not fully understood but, as the spermatids develop, they

become closely associated with Sertoli cells (Payne et al. 2010), so it is assumed that

the Sertoli cells are involved.

2.1.3 Physiological control of male reproduction

a. The hypothalamic-pituitary-testicular axis in male sheep

Reproduction is mainly regulated by the neurons in the brain that produce

gonodotrophin-releasing hormone (GnRH). In sheep, the GnRH cell bodies are mainly

located in the preoptico-hypothalamic area, the lateral and medial preoptic area, medial

septum, ventrolateral anterior hypothalamus, lateral hypothalamus and mediobasal

hypothalamus (Caldani et al. 1988). GnRH is synthesized by these neurons and

transported to the organum vasculosum of the lamina terminals (OVLT), where it fulfils

an unknown function, or to the median eminence where it is released into the

hypophyseal portal system. The portal blood transports the GnRH to the anterior

pituitary gland where it interacts with a G-protein-coupled receptor on the surface of the

gonadotrophs, and thus regulates the synthesis and release of the two gonadotrophins,

luteinizing hormone (LH) and follicle stimulating hormone (FSH) (Lincoln 1979). The


24

LH and FSH are released into the bloodstream, transported to the testes, and stimulate

the synthesis and secretion of testosterone and inhibin (Kishi et al. 2000).

Interestingly, GnRH secretion is pulsatile rather than continuous, and this pattern is

reflected in a one-to-one relationship by pulses of LH (Caraty and Locatelli 1988) so it

is possible to use the pattern of LH secretion as a bioassay of GnRH secretion. Unlike

LH, FSH is released in a non-pulsatile pattern, but its secretion still depends on GnRH

(Lincoln 1979), although some studies suggest that the secretion of FSH is independent

on the availability of GnRH (Fraser and McNeilly 1983; Clarke et al. 1986).

b. The internal and external factors controlling the reproductive axis

The hypothalamic-pituitary-testicular axis responds to internal and external factors. The

internal regulatory mechanisms include sex steroids and inhibin (negative feedback) and

metabolic hormones and non-hormonal factors. The external factors include nutrition,

photoperiod, social-sexual signals and stress. Since external factors will be discussed in

details in Section 2.2, here we concentrate on steroids and inhibin (negative feedback).

The primary testicular steroid, testosterone, can be converted to oestradiol-17β by the

action of aromatase (Jones et al. 2003) and to dihydrotestosterone by the action of 5α-

reductase (Trainor and Marler 2002). Both oestradiol-17β and dihydrotestosterone are

thought to inhibit LH secretion by direct effects on the pituitary gland or by indirect

effects on hypothalamic GnRH secretion. In rams, the hypothalamus has been reported

to be the major site for negative feedback (Jackson et al. 1991; Tilbrook et al. 1991) and

responds within minutes (Lincoln and Fraser 1990).

Aromatisation in testis, liver and brain appears to be an important aspect of the

endocrine regulation of reproduction in male sheep (Sharma et al. 2004). For example,

treatment with fadrozole, a non-steroidal aromatase inhibitor, decreases plasma


25

oestradiol-17β concentrations and increases LH pulse frequency in both testis-intact

rams and testosterone-treated castrates, suggesting that non-testicular sites of

aromatization are important in the control of pulsatile LH secretion (Sharma et al.

1999). In addition, LH pulse frequency increases after infusion of fadrozole into the

third ventricle of testis-intact rams (Sharma et al. 1999).

2.1.4 Morphological changes associated with puberty

a. Morphological changes in seminiferous tubules during puberty

During puberty, spermatogenesis is accomplished step by step. At first, spermatogonia

are located within the basement membrane and their number increase. Subsequently,

primary spermatocytes appear in large numbers, followed by secondary spermatocytes,

spermatids, and sperm.

During puberty, a most profound change is seen in the Sertoli cells. In terms of

morphology, immature Sertoli cells show a pseudostratified disposition and they

possess a round-elongated nucleus that has a regular outline and one small nucleolus

close to the nuclear envelope. By contrast, the nuclei of mature Sertoli cells show a

more irregular outline and the nucleoli are more prominent (Sniffen 1952; Nistal et al.

1982). With respect to function, at puberty, Sertoli cells lose their proliferative ability

and form the blood-testis barrier, and so they can perform new functions. These changes

in Sertoli cells at puberty, from fetal to adult phenotype, are termed “maturation” or

“differentiation” (Sharpe et al. 2003).

b. Morphological changes in interstitial tissue during puberty

In the pubertal testis, Leydig cells develop from mesenchymal cells that resemble

fibroblasts. These cells contain neither crystalloids nor pigment, but do contain lipoid

granules. After puberty, Leydig cells gain large amounts of smooth endoplasmic


26

reticulum and mitochondria, reflecting their capability for steroidogenesis (Neaves et al.

1985). In bulls (McCarthy et al. 1979), rats (Odell et al. 1974) and rams (Foster et al.

1978), the ability of LH to increase testosterone concentrations in serum increases at

puberty, indicating acquisition by the Leydig cells of an ability to respond to LH.

2.2 Environmental factors affecting male reproduction

Male reproduction can be affected by several environmental factors, including nutrition,

photoperiod, social-sexual signal and stress. These factors do not act independently but

interact with each other to influence reproductive ability, as discussed here.

2.2.1 Photoperiod

Seasonal changes in testicular mass have been documented in many laboratories over

many decades, especially for sheep and goats. Photoperiod is generally considered to be

the main determinant of seasonal patterns of reproduction through its influences on the

secretion of LH and FSH (Martin et al. 1999). The degree of seasonality depends

largely on genotype and the evolutionary history of the genotype within the

environment in which it evolved (Martin et al. 2011). For this reason, the annual

reproductive cycles of Australian Merino sheep with their Mediterranean origins are

different to those of Suffolk sheep with their temperate origins (Hötzel et al. 2003).

a. The effect of photoperiod on sperm production and sperm quality

In a study of sperm motility and morphology in Suffolk and Lincoln yearling rams

(Mickelsen et al. 1981), the authors observed marked seasonal variations in both breeds.

Reflecting the importance of photoperiod, a change from long days to short days

increases sperm number in rams (Langford et al. 1987). This relationship is not only

seen in sheep but also mammalian species as distant as cats (Nunez Favre et al. 2012).


27

Overall, the evidence is very clear that there is direct relationship between photoperiod

and sperm production and quality.

b. The effect of photoperiod on testis function

In adult hamsters, at least 12.5 hours of light per day are needed to maintain

spermatogenesis and prevent degeneration (Gaston and Menaker 1967), and exposure to

short photoperiods induces germ cell degeneration and drastic involution of testes and

accessory glands (Hoffmann 1974; Russell et al. 1994). Exposure to long photoperiod

increases testis weight, tubular lumen volume, interstitial parameters, and numbers of

preleptotene spermatocytes, although adluminal germ cells did not increase in number

(Russell et al. 1994). In addition, the increase in testis weight was associated with a 65%

decrease in apoptosis in testicular tissue within 5 days (Furuta et al. 1994). The

importance of photoperiod therefore seems to be overwhelming but, in one study, testis

development was similar in the short-day and long-day groups and the authors

suggested that the transition from winter to summer condition is partly based on an

endogenous mechanism which continues even in the absence of stimulating long

photoperiods (Lerchl and Schlatt 1993). The phenomenon, often termed

‘photorefractoriness’ is now considered to explain seasonal cycle in both sexes in many

species, and seems to be linked to the thyroid axis (Dardente et al. 2014).

c. The effect of photoperiod on Sertoli cell number and function

The factors that can affect Sertoli cell number and function are not well documented but

photoperiod has been relatively well studied. In the viscacha, during the annual

reproductive cycle, Sertoli cells exhibit changes in nuclear size and shape, chromatin

texture, and nucleolus characteristics (Munoz et al. 2001). There were only minimal

changes after 4-6 weeks of exposure to short photoperiod but, after the animals were

transferred to long photoperiod, there were increases in the volume of cytoplasm and


28

smooth and rough endoplasmic reticulum (Russell et al. 1994). In the ram, the season of

birth influences the formation of Sertoli cell stocks (de Reviers et al. 1980), and, in the

adult, the testis shrinks and the cross-sectional nuclear area of Sertoli cells becomes

smaller when photoperiod is changed from short to long days, but the number of Sertoli

cells per testis is not affected (Hochereau-de Reviers et al. 1985). In the adult stallion,

by contrast, testis weight and the number of Sertoli cells per testis was reported to be

greater in the breeding season than in the non-breeding season (Johnson and Nguyen

1986). This observation, along with a few others, challenges the dogma that Sertoli cell

number is stable after puberty and suggests that, in these specific settings at least, the

adult Sertoli cell is not terminally differentiated. Even if we accept changes in Sertoli

cell number after puberty, we are left with the mystery of whether it is caused directly

by change in photoperiod or indirectly as a consequence of change in testis size.

Photoperiod also affects Sertoli cell function. In seasonally breeding golden (Syrian)

hamster, Sertoli cell function was virtually shut-down during testicular regression

induced by short photoperiod (Hikim et al. 1989). In addition, the localization of tight

junctions was disordered within the Sertoli cell cytoplasm by short photoperiod (Tarulli

et al. 2006). These studies have generally been restricted to tight junction activity, a

factor that defines mature, differentiated Sertoli cells, but there are few studies of the

effects of photoperiod on the expression of Sertoli cell specific genes. Of particular

interest would be AMH and GATA-1, two genes related to Sertoli cell maturation status

(Rey 1998; Beau et al. 2000).

2.2.2 Social-sexual signals

The sudden introduction of novel rams can induce ovulation in anoestrous ewes through

a phenomenon known as the ‘ram effect’ in which socio-sexual signals from the ram

cause a rapid increase in pulsatile LH secretion in ewes. Similarly, rams show an


29

increase in LH and testosterone concentrations beginning at 12 h after being introduced

to ewes (Ungerfeld and Silva 2004). This response is also mediated by an increase in

pulsatile LH secretion that is induced primarily by the odour of the ewes (Hawken et al.

2009). However, to date, the effect of social-sexual signals on spermatogenic cellular

function in the ram testis has not been studied.

2.2.3 Stress and temperament

Stress affects the secretion of GnRH and gonadotrophins by activating the

hypothalamic-pituitary-adrenal axis, and also impacts on the reproductive axis, mainly

at the level of hypothalamus and pituitary gland (Dobson et al. 1999; Breen et al. 2007;

Hawken et al. 2013). Some previous studies demonstrated that stress had inhibitory

effects on reproduction. For instance, LH secretion is reduced in gonadectomised rams

and ewes that have been isolated and restrained for 3-4h (Matteri et al. 1984). At the

level of the testis, studies in mice have demonstrated that heat stress induces germ cell

apoptosis (Yin et al. 1997; Paul et al. 2009) and that chronic restraint stress decreases

testosterone secretion, an effect that is associated with a decrease in plasma

gonadotropin levels (Lopez-Calderon et al. 1991).

In terms of the relationship between temperament and reproduction, there are reports in

humans, for instance, that high self-confidence, extraversion, and social assertiveness

correspond negatively with male fertility parameters (Hellhammer et al. 1985; Conrad

et al. 2002). However, there are very few studies in sheep, although one interesting

study reported that the least docile rams had higher reproductive success early in life

whereas, at the other extreme, most docile rams attained higher reproductive success

later in life (Reale et al. 2009).


30

Importantly, stress and temperament are not independent of each other because

temperament can modify the responsiveness of an animal to some stressors (Gutteling et

al. 2005). For example, isolation decreases LH secretion in nervous sheep but not in

calm sheep (Hawken et al. 2013) suggesting that animals of calm temperament will

have less reproductive problems when confronted by a stressor.

As with social-sexual signals, the studies of stress and temperament on male

reproduction have not touched the details in the change of testis function, an area for

future study.

2.2.4 Nutrition

The availability of food must be the most important role among all the environment

factors that influence male reproduction (Blache et al. 2003), It is widely accepted that

there is strong and direct relationship between plane of nutrition, testicular mass and the

number of sperm available for ejaculation, for the small ruminants at least. Therefore, in

this review, I will discuss the effect of nutrition in more detail, but with a focus on the

sexually mature ram.

a. The effect of nutrition on testis mass and sperm production

The relationships among nutrition, testis mass and sperm production have been well

documented in rams, bulls and goats (Mori 1959; Moule 1963; Hiroe and Tomizuka

1965; Walkden-Brown et al. 1994a). In sheep, the production of sperm has been shown

to be responsive to nutrition in a number of studies using a variety of techniques

(Salamon 1964; Setchell et al. 1965; Braden et al. 1974; Cameron et al. 1988).

Changing nutrition alters not only testis size, but also the spermatogenic efficiency of

testicular tissue, as evidenced by the observation that changes in sperm production are

relatively greater than the changes in testicular mass. For example, in one study, a 25%


31

increase in testicular size led to an 81% increase in production of sperm (Oldham et al.

1978) and, in another study, an 86% increase in testicular size led to a 250% increase in

production of sperm (Fig.2.2; Cameron et al. 1988). Moreover, it took at least 7 weeks

of nutritional treatment to affect the number of ejaculated sperm (Parker and Thwaites

1972), suggesting that spermatogenic efficiency is affected after the last spermatogonial

division.

b. The effect of nutrition on sperm quality

Compared with the change in sperm production, the studies on the effects of nutrition

on semen and sperm cell quality are scarce. However, some early work demonstrated

the effects of nutrition on the classical measures of semen and sperm quality in both

sheep and goats (Mori 1959; Salamon 1964; Tilton et al. 1964; Hiroe and Tomizuka

1965; Parker and Thwaites 1972). For example, Parker and Thwaites (1972) showed

that sperm count and motility are reduced if under-nutrition lasts longer than 7 weeks.

In Ethiopian highland sheep, these effects can be reversed by dietary supplementation

(Dana et al. 2000; Tufarelli et al. 2011) and, in Sardinian rams, feeding concentrates

results in a greater semen volume and sperm concentration, and lower numbers of

abnormal sperm (Tufarelli et al. 2011). By contrast, in mature Assaf rams, sperm

motility and percentage of live and abnormal cells were not affected by diet (Fernandez

et al. 2004). All these studies were restricted to traditional variables such as sperm

concentration, viability, morphology and subjective motility, and inaccuracy and

imprecision in the techniques might explain the disagreements. There is a clear need to

confirm the effects of nutrition on sperm quality with modern, objective, precise

techniques for semen assessment.

c. The effect of nutrition on testis morphology


32

Gross histological studies have shown that nutrition markedly affects the diameter of

the seminiferous tubules, the relative proportion of testis occupied by the seminiferous

tubules, and the proportion of the seminiferous tubule occupied by the seminiferous

epithelium, the relative proportion of interstitial tissue and total volume of Leydig cells

and tubule length (Hötzel et al. 1998), as shown in Table 2.1. The number of Leydig

cells per testis was not affected by diet, but the total volume of Leydig cells was,

indicating changes in the volume of individual cells. Due to the direct relationship

between Leydig cells and testosterone secretion, therefore, an effect of diet on

testosterone secretion and the peripheral concentrations of testosterone might be

expected, and this is an important consideration because testosterone plays a major role

in spermatogenesis. However, initial studies disagreed in terms of the outcome – for

example, the early study showed a significant effect on testosterone secretion (Setchell

et al. 1965) but recent work showed that nutritional treatments were not associated with

changes in the amplitude of testosterone response to LH (Martin et al. 1994). The

disagreement could due to differences between genotype, age, or methodology, the most

likely explanation is the severity of the nutritional treatments (Hötzel et al. 1998).


33

Table 2.1. Morphometric analysis of the testicular tissue from mature Merino rams (n = 5 per group) fed

a supra-maintenance (high) or a sub-maintenance (low) diet for 69 days (Hötzel et al. 1998). * P < 0.05

Variable High diet Low diet

Body weight (kg) 79 ± 3 48 ± 4*

Mean testis weight (g) 288 ± 14 117 ± 10*

Tubule diameter (µm) 229 ± 6 167 ± 12*

Lumen diameter (µm) 69 ± 3 66 ± 7

Tubule length (m) 3503 ± 104 2378 ± 329*

Leydig cells (x108) per testis 75 ± 8 60 ± 11

Sertoli cells (x108) per testis 120 ± 5 77 ± 6.7*

d. The effect of nutrition on Sertoli cell number

Sertoli cells provide nutritional and structural support for germ cells and each Sertoli

cell has a fixed capacity for the number of germ cells it can support (Sharpe et al. 2003),

so changes in production of sperm may result from alterations in Sertoli cell number.

Unfortunately, very few studies have addressed this question. For instance, the Sertoli

cell numbers appeared to increase in ram lambs fed a nutritional supplement (Bielli et al.

2001), and low maternal nutrition during pregnancy reduced the number of Sertoli cells

in the newborn male lamb (Alejandro et al. 2002). These two studies were both

restricted to sexually immature animals and, so far, there has been only one

comprehensive study investigating whether nutrition associated with Sertoli cell number

in adult rams (Table 2.1; Hötzel et al. 1998). In this study, total volume of Sertoli cell

nucleus and Sertoli cell number per testis were both higher in well-fed adult Merino

sheep than in underfed animals. This finding makes sense when considering the higher

sperm production in well-fed sheep, but it contradicts the dogma that Sertoli cells stop

proliferating at puberty, leaving the number fixed during adult life (Kluin et al. 1984;


34

Monet-Kuntz et al. 1984; Hochereau-de Reviers et al. 1987). This problem could be

dismissed as histological artefact, so there is a clear need to again test whether nutrition

affects Sertoli cell number in the sexually mature male sheep using modern

stereological techniques. Even if the number of Sertoli cells is stable after puberty, it is

possible that a few Sertoli cells retain proliferative ability, but perhaps too few to

change the total number. Clearly, we need to use modern techniques for assessing

proliferative ability if we want to obtain a definitive answer.

e. The effect of nutrition on Sertoli cell function

At around the onset of puberty, Sertoli cells undergo radical changes as they switch

from an immature, proliferative state to a mature, non-proliferative state. Adjacent

Sertoli cells form tight junctions with each other to create a unique adluminal

compartment within which the meiotic and post-meiotic steps of spermatogenesis can

proceed, as well as allowing formation of a fluid-filled lumen. As a result, the germ cells

developing in the adluminal compartment become effectively sealed off from direct

access to many nutrients, so the mature Sertoli cell take on new functions which are

lacking in fetal, proliferating Sertoli cells (McLaren et al. 1993). Studies on the effects

of nutrition on Sertoli cell function are very rare. Peripheral inhibin concentration was

used to reflect the function of Sertoli cells but nutrition treatment did not affect inhibin

concentration (Martin et al. 1994) despite changes in the plasma concentration of FSH

(Hötzel et al. 1998). These observations have become difficult to interpret now that we

know that we were probably measuring inhibin B; it was subsequently reported that

sheep testes produce and secrete only inhibin A (McNeilly et al. 2002). However,

effects of nutrition on other important aspects of Sertoli cell function have not been

investigated, such as the change of Sertoli cell-specific gene expression.


35

2.3 Small RNAs affect spermatogenesis and germ cell apoptosis in testis

Small RNA molecules have recently emerged as potent regulators of gene expression at

the post-transcriptional or translational level. They have diverse biological functions in

the regulation of transcription, RNA stability, and translation (Plasterk 2006). There are

three major categories of small RNA: small interfering RNA (siRNA); microRNA

(miRNA); and piwi-interacting RNA (piRNA).

2.3.1 Small RNA categories

a. Small interfering RNA (siRNA)

The siRNA is a small (~21 nucleotides (nt)) double-stranded RNA (dsRNA) that has

been used in ‘RNA interference’ (RNAi) to block the expression of a gene of interest

(review: He et al. 2009). The technique is seen as an alternative to knocking out genes

in mice because it is less laborious and more economical.

b. microRNAs (miRNAs)

Micro RNAs (miRNAs) contain approximately 22 nucleotides in single-strand non-

coding molecules that bind to target messenger RNAs (mRNAs) and thus inhibit their

expression. MiRNAs were first discovered in 1993 in Caenorhabditis elegans and were

found to regulate the expression of complementary mRNA (Lee et al. 1993; Wightman

et al. 1993). In 2001, miRNAs were also identified in mammals (Lagos-Quintana et al.

2001). miRNAs are highly conserved across species, and importantly, miRNAs appear

to regulate up to 30% of all genes in the human genome (Lewis et al. 2005).

The biogenesis of miRNAs is a multi-step process. Usually, miRNA genes are first

transcribed to primary transcripts (pri-miRNA) by RNA Polymerase II (Kim et al.

2009). Pri-miRNA form specific stem loop structures that undergo cleavage in the

nucleus by the ribonuclease, RNase Drosha, to form isolated hairpin loops (pre-miRNA)


36

(Kim et al. 2009), that are then transported into the cytoplasm via an exportin-5-

dependent mechanism (Yi et al. 2003). In the cytoplasm, the RNase III enzyme (the

Dicer) catalyses the pre-miRNA to form double-stranded miRNAs (Hutvagner et al.

2001). Normally, one strand of the miRNA is loaded into the effector miRNA-induced

silencing complex (miRISC); however, recently, it was reported that the complementary

strand can also be loaded (Mah et al. 2010). The miRISC complex, comprising

Argonaute (AGO) proteins, mediates the post-translational regulation of the mRNA

targets of the loaded miRNA (Shomron and Levy 2009). Most commonly, miRNAs

bind to target sequences in the 3’ untranslated region (3’UTR) of mRNA, but it has

been reported that miRNA could also bind to the 5’ untranslated region (5’UTR) and

open reading frame of a subset of the target mRNA (Moretti et al. 2010). The seed

region, nucleotides 2–7 on the miRNAs, is the most influential factor for target binding

(Bartel 2009). Thousands of miRNAs have been discovered: 2588 in the human and

1915 in the mouse (miRBase Release 21.0, as of June, 2014,

http://www.mirbase.org/index.shtml).

c. piwi-interacting RNA (piRNA)

This newly identified class of small RNAs that are slightly longer (26–32 nt) than

miRNAs and siRNAs. They bind to ‘PIWI’, a spermatogenesis-specific protein

belonging to the Argonaute protein family (Aravin et al. 2006; Girard et al. 2006). The

synthesis of piRNAs is not clear yet, although the “ping pong” mechanism has been

suggested (Liu et al. 2012a). They are distinct from the siRNAs or miRNAs in that they

are 24–30 nt in length and are expressed predominantly in the germline of a variety of

organisms (Klattenhoff and Theurkauf 2008). They are essential for germ cell

maintenance and spermatogenesis in Drosophila and mammals (Thomson and Lin

2009), and so are of interest in our quest to understand the effects of nutrition on

spermatogenesis.


37

2.3.2 miRNAs and spermatogenesis

During spermatogenesis, the spatial and temporal regulation of gene expression is of

vital importance, and translation is periodically silenced in germ cells by miRNAs

(Papaioannou and Nef 2010). The importance of miRNAs for spermatogenesis is

indicated by, for example, the infertility in male mice that follows removal of Dicer1, a

gene necessary for the synthesis of miRNAs (Maatouk et al. 2008). Specifically, in

Dicer1 knock-out mice, only a few tubules contain elongating spermatids and the germ

cells which did differentiate to elongating spermatids exhibited abnormal morphology

and motility. Similar findings were reported in human as well (Hayashi et al. 2008).

Although the functions of miRNAs in the development of male germ cells are still

largely unclear, expression profiling studies have identified a number of miRNAs that

seem to be particularly important in the mammalian testis. For instance, miR-17 and

miR-290 are important for the proliferation of primordial germ cells and spermatogonia

(Hayashi et al. 2008), and miR-122a, could be involved in the posttranscriptional

regulation of mRNAs such as transition protein 2 (Yu et al. 2005).

2.3.3 miRNAs and germ cell apoptosis

Three miRNAs (miR-15 miR-16 and miR-31) are able to induce apoptosis by targeting

the major anti-apoptotic factor, BCL2 (Cimmino et al. 2005; Korner et al. 2013). In

recent years, it has also become clear that apoptosis in male germ cells also involve

regulation by miRNAs. For example, miR-34c was detected in mouse pachytene

spermatocytes and highly expressed in spermatids, and when it was silenced, the Bcl-

2/Bax ratio increased, preventing the induction of germ cell apoptosis by testosterone

deprivation (Liang et al. 2012). In another study, transient inhibition of miR-21 in

spermatogonial stem cell-enriched germ cell cultures increased the number of germ


38

cells undergoing apoptosis (Niu et al. 2011). Clearly, miRNAs could play a major role

in germ cell apoptosis in the shrinking testis of underfed rams.

2.3.4 piRNAs affect spermatogenesis

A role for piRNAs in spermatogenesis is primarily supported by the known functions of

their partners, the Piwi proteins, including MIWI, MIWI2 and MILI, that are known to

be necessary for stem cell self-renewal and the development of male germ cells (Cox et

al. 1998). In Mili-knockout mice, spermatogenesis is disordered at the pachytene

spermatocyte stage (Kuramochi-Miyagawa et al. 2004) and, in Miwi-deficient mice, no

elongated spermatids or mature sperm are observed (Deng and Lin 2002). In addition,

small non-coding RNAs, Nct1 and Nct2, have been reported to be piRNA precursors

and they are expressed predominantly in pachytene spermatocytes in mice (Xu et al.

2008). However, unlike miRNAs, piRNAs are not conserved among species, so might

not be expressed in male germ cells in all mammals. Therefore the functions of piRNAs

in different species require further study, for which the male sheep fed high and low

levels of nutrition is an attractive experimental paradigm.

2.4 Alternative pre-mRNAs splicing affects spermatogenesis and apoptosis

Alternative pre-mRNA splicing (AS) is an important mechanism for regulating gene

expression and for increasing transcriptome plasticity and proteome diversity. It has

been reported that approximately 60% of human gene products undergo alternative

splicing (Modrek and Lee 2002).

The complicated process of generating alternative splicing has been reviewed in detail

(Schwerk and Schulze-Osthoff 2005), so will be addressed only briefly here. In a typical

multiexon mRNA, the splicing pattern can be altered in many ways and, to date, eight

types have been reported. The most common pattern is a cassette exon that can be


39

included in the mRNA or skipped, inserting or deleting a portion of internal sequence

(Gurskaya et al. 2012). Two special cases of paired-cassette exons are mutually

exclusive splicing (only one exon is included) and coordinate cassette exons (both

exons are included). The fourth and fifth patterns are alternative 5' or 3' splice sites, in

which exons can be extended or shortened in length (Fu et al. 1992). The sixth pattern is

alternative first exon, in which transcriptional initiation at different promoters generates

alternative 5'-terminal exons that can be joined to a common 3' exon downstream

(Mironov et al. 1999). Similarly, for the seventh pattern, alternative last exons, with

alternative polyadenylation sites, can be joined to a common upstream exon (Wang et al.

2008). Finally, we have intron retention to leave the retained intronic sequence in the

mRNA (Galante et al. 2004).

2.4.1 Alternative pre-mRNA splicing and spermatogenesis

It has been reported that spermatogenesis is regulated by alternative pre-mRNA splicing

that generates multiple transcript species from a common mRNA precursor. For

example, some specific CREB mRNA isoforms generated by alternative splicing are

expressed at a high level in the adult testis, and these isoforms are expressed after

spermatogenesis has started (Ruppert et al. 1992). In addition, transcripts from several

testis-specific genes that regulate gene expression are themselves alternatively spliced.

For instance, a testis-specific splice of the Sry-related transcription factor, Sox17, which

lacks the exon containing a single high mobility group box near the NH2-terminus,

replaces the normal message during male meiosis, and results in an inactive N-terminal

truncation that lacks the DNA-binding domain in spermatids (Kanai et al. 1996).

Another example is prolactin receptor, a pivotal factor for spermatogenesis in the mouse

– one of its isoform lacks two exons and leads to a down-regulation of the expression of

the full length prolactin receptor, with the potential for explaining the role of prolactin


40

in the annual cycles of testis growth in seasonal breeders such as red deer (Jabbour et al.

1998). These examples are probably the tip of an iceberg, and we expect many more

candidates to be discovered experimentally by, for example, knock-out of testis-specific

splicing factors (Feng et al. 2002).

To date, the effect of environmental factors, such as nutrition, on alternative splicing

within the testis has not been studied and, again, the male sheep fed high and low levels

of nutrition is an attractive experimental paradigm.

2.4.2 Alternative pre-mRNA splicing and apoptosis

Alternative splicing also plays a major role in the control of apoptosis, as evidenced by

its effects on the expression of a huge number of proteins directly involved in the

apoptotic pathways. Moreover, the proteins belonging to each family of apoptotic

factors are alternatively spliced and, normally, the different isoforms produced in this

process have distinct and even opposing functions during apoptosis. For example, by

alternative splicing, C. elegans CED-4 is expressed in two isoforms, CED-4L and CED-

4S, that have opposite functions during apoptosis. Interestingly, splice site mutations in

CED-4 lead to increased expression of anti-apoptotic CED-4L (Shaham and Horvitz

1996). In addition, alternative splicing inhibits apoptosis by removing the intracellular

domain and part of the extracellular domain from FasL (Ayroldi et al. 1999). To date,

there have been no studies of the role of alternative splicing apoptosis in the adult testis.

Although the above findings indicate strong relationships between alternative splicing

and apoptosis, the understanding of this process is in still at the early stage. To gain a

fuller understanding, it would be worthwhile to explore alternative splicing events on a

large scale. This task has become feasible with the recent developments in gene

expression profiling such as microarrays and RNA-Seq.


41

2.5 Conclusions and hypotheses

This literature review has highlighted the external factors that control male sheep

reproduction with particular emphasis on nutrition. Alterations in the plane of nutrition

result in changes in testis size and gross histology, and sperm production. Sertoli cell

number might also be affected, but the concept is controversial so needs verification

with alternative methods. More importantly, we do not know how sperm quality and

Sertoli cell function are affected by nutrition, and we have little idea of the molecular

mechanisms involved.

The general hypothesis tested in this thesis is that, in the sexually mature male sheep,

nutrition will affect sperm quality, due to germ cell apoptosis, that these responses will

be explained by changes in Sertoli cell function, and that effects are mediated by

changes in the expression of small RNAs and alternative pre-mRNA splicing. My PhD

project therefore focused on the effects of nutrition on sperm quality and testis function

in adult Merino rams and pursued the following main aims: 1) To measure sperm

quality, including sperm motility, sperm DNA damage, sperm viability, sperm

morphology, and germ cell apoptosis; 2) To examine number and function of Sertoli

cells; 3) To investigate the expression of small RNAs, especially miRNAs and piRNAs,

and their possible roles in spermatogenesis and apoptosis; 4) To investigate the

functional relationships between miRNAs and mRNAs; 5) To explore the functions of

alternative pre-mRNA splicing in spermatogenesis and apoptosis.

Chapter 3-General Materials and Methods

42

Chapter 3

General Materials and Methods

3.1 Experimental location

The animal experiment was conducted in the animal house at CSIRO Floreat, Perth,

Western Australia. Perth is located at latitude 31°56’S, longitude 115°E and has a

Mediterranean climate with mild winters and hot, dry summers. Natural day-length

(sunrise to sunset) ranges from 10 h during winter to 14 h during summer. The animals

were kept in individual pens under natural lighting, for 3.5 months, starting in early

May. This is after the end of the breeding season for Merino sheep, but sexually mature

rams of this genotype can respond to nutritional inputs year-round (Martin et al. 2004).

3.2 Experimental animals

Sexually mature Merino rams (n = 24) were obtained from Allandale Farm (UWA).

They were 24 months old, weighed 65.7 ± 4.7 kg and had a scrotal circumference of

31.8 ± 2.5 cm. They were selected from the ‘Allandale Temperament Flock’, sheep that

had been selected for over 18 generations for high and low reactivity to humans and to

isolation (Bickell et al. 2009). To avoid any confounding effects of genetic background,

the treatments used in the current study were balanced for temperament.

3.3 Nutrition treatment

During a 3-week acclimatization period, all rams were fed daily with 750 g oaten chaff

(8.4% crude protein; 8.0 MJ/Kg metabolisable energy) and 200 g lupin grain (35.8%

crude protein; 13.0 MJ/Kg metabolisable energy). Quantitative details of the dietary

components and descriptions of the endocrine responses can be seen in the report by


43

(Boukhliq et al. 1997). The rams were then allocated among three dietary treatments

(high, maintenance, low) with the groups balanced for training success to semen

collection (see below), body mass, scrotal circumference, temperament, polled or

horned, and sperm quality. Animals fed to maintenance requirements were expected to

maintain constant body mass. The high diet was designed to allow the animals to gain

10% live weight over 65 days whereas the low diet was designed to allow 10% loss in

weight. At the start of the treatment period, individual daily allowance was 1.2 kg oaten

chaff plus 0.3 kg lupin grain for the rams in the high-diet group, 0.7 kg chaff and 0.18

kg lupin grain for the maintenance group, and 0.51 kg chaff and 0.13 kg lupin grain for

the low-diet group. Every week, the animals were weighed and the amount of feed

offered to each individual was adjusted to ensure achievement of target live weight. In

Chapters 4 and 5, sheep fed with all three diets were included in the analysis to obtain

the baseline data. However, in Chapters 6 and 7, assessment was only performed in

underfed sheep and well-fed sheep for two reasons: 1) the two extreme groups (high diet

and low diet) offered the opportunity of comparing testes that were shrinking and

growing, the thrust of our hypotheses; 2) we have limited resources, so needed to make

strategic decisions about which measurements would allow the greatest gains in

understanding.

3.4 Body mass and scrotal circumference

Body mass was measured once every week before feeding. Scrotal circumference was

measured every week with a tape measure at the point of maximum diameter when the

skin of the scrotum was stretched taut around both testes.


44

3.5 Semen collection and processing

Attempts were made to train all rams for semen collection with an artificial vagina and a

teaser ewe (Wulster-Radcliffe et al. 2001) and, by the end of the acclimatization period,

training was successful for 18 rams. Semen collected on Day 56 and Day 63 was

immediately immersed in a 37°C water bath. Within 20 min of collection, all samples

were analyzed for sperm concentration, semen volume, sperm motility, sperm viability

and morphology, as described below. A portion of each sample was quickly transferred

to 200 μl straws, labeled and plunged into liquid nitrogen to await DNA fragmentation

analysis.

3.6 Semen analysis

3.6.1 Ejaculate quality

Semen volume was measured with an automatic pipette by carefully drawing the entire

ejaculate into a tip and reading the volume. The sperm concentration was determined by

spectrophotometer (8001 UV-vis Metertech, Inc., Taipei Taiwan). We followed the

protocol described by (Prathalingam et al. 2006): 20 μl semen was diluted 1: 400 (v/v)

with a mixture of phosphate-buffered saline (PBS) and formalin and the suspension was

placed in a spectrophotometer for measurement of absorbance at 580 nm. Sperm

concentration was determined by reference to a standard curve that had been developed

for ram sperm. The total number of sperm per ejaculate was estimated by multiplying

ejaculate volume (ml) by the concentration of sperm (109/ml).

3.6.2 Sperm morphology and viability

Sperm morphology and viability were evaluated after eosin-nigrosin staining (Bjorndahl

et al. 2003): 3 μl semen was mixed with 27 μl eosin-nigrosin mixture, and 5 μl of the

mixture was transferred to a labeled glass microscope slide and smeared using another


45

slide. Two smears were made for each sample. At least 300 sperm per slide were

counted on each of the 2 slides using an oil immersion objective on an Olympus BX60

microscope (Olympus, Australia Pty, Ltd., Mt Waverley, VIC, Australia). The

percentages of live, dead, morphologically normal and abnormal sperm were calculated.

3.6.3 Computer-assisted semen analysis (CASA)

An aliquot (5.5 μl) of sperm suspension was diluted to 20 x 106 sperm/mL with motility

buffer (8 g/L sodium chloride, 0.1% bovine serum albumen, adjusted to pH 8.2 with 1.0

M NaOH) at 37°C, and 5 μl of the mixture was placed in a 20 μm sperm motility slide

chamber (Leja, Nieuw-Vennep, Netherlands). A video image was recorded using a

Basler A602fc digital camera (Basler AG, Ahrensburg, Germany) mounted on Olympus

BX53 microscope (Olympus Optical Co., Australia) equipped with phase contrast optics

and a motorized heated stage (Prior Optiscan II, Prior Scientific Intl.). In each sample,

500-600 sperm were evaluated. The percentages of motile and progressively motile

sperm and straight-line velocity (VSL), curvilinear velocity (VCL) and average path

velocity (VAP) were estimated using CASA software (SCA®, Microptics, V5, SL,

Spain). Sperm with VCL greater than 10 µm/sec were considered motile and sperm with

STR (VCL/VAP x 100) greater than 80% were classified as progressive.

3.6.4 Sperm chromatin structure assay (SCSA)

We followed the protocol described by (Evenson et al. 1999). In brief: frozen aliquots

of semen were placed in a 37°C water bath until just thawed and then diluted to 1-2 x

106 sperm cells per ml with TNE buffer (0.15 M NaCl, 0.01 M Tris-HCl, 0.001 M

disodium EDTA, pH 7.4, 10% glycerol). Aliquots (200 µl) were then mixed with 400 µl

acid-detergent solution (0.08 M HCl, 0.15 M NaCl, 0.1% Triton X-100, pH 1.2). After

30 s, the cells were stained with 1.2 ml acridine orange (AO) solution containing 6 mg


46

AO (Cat. # 04539, Polysciences Inc., Warrington, PA, USA) per ml buffer (0.037 M

citric acid, 0.126 M Na2HPO4, 0.0011 M di-sodium EDTA, 0.15 M NaCl, pH 6.0).

Fluorescence was then measured on 5000 cells per sample by FACSCalibur (Becton

Dickinson, Franklin Lakes, NJ, USA) with an excitation wavelength of 488 nm. AO

bound to double-stranded DNA emits a green signal, collected using a 530/30 nm BP

filter, while AO bound to fragmented, single-stranded DNA emits a red signal, collected

using a 670 nm LP filter. Sperm cells were identified and debris excluded using

characteristic forward and side laser scatter. The percentage DNA fragmentation index

(%DFI = the percentage of cells outside the main sperm population) was calculated

using FlowJo v7.6 software (Tree Star, Inc. USA).

3.7 Tissue collection and preservation

After 65 days, all male sheep were killed with intravenous overdose of sodium

pentobarbitone, and the testes were immediately removed, dissected and weighed. Three

samples were chosen from top, middle and bottom parts of both testes; those from the

right testis were snap-frozen in liquid nitrogen and stored at –80°C for total RNA

preparation; those from the left testis were washed by 0.9% saline and then fixed by 4%

paraformaldehyde for 6 h, then dehydrated and processed for routine embedding in

paraffin wax for histological analysis (Francavilla et al. 2000).

3.8 Sperm concentration in testicular tissue

A sample of tissue (10 g) from the left testis was excised, weighed accurately, and

homogenized in 100 ml of equal volumes 0.9% NaCl plus 0.1% TritonX-100. The

sperm number per gram of testicular tissue was determined using a haemocytometer and

a phase contrast microscope (Olympus BX60, Olympus, Australia).

3.9 Morphometric and histological analysis


47

We used an Olympus BX50 microscope coupled with a digital imaging system (DP2-

BSW) to assess 5 µm paraffin sections. Images of cells and structures were displayed on

a high-resolution colour monitor and were traced using a computerized mouse. Fields

were selected using a systematic random approach. All slides were masked to avoid

personal bias.

3.9.1 Analysis of testicular compartments

Sections were stained with Periodic Acid Schiff (PAS) reagent. The point-counting

method was used to determine the volume fraction of each structure: the number of

points landing on each structure divided by the number of the points hitting the entire

testis (Meachem et al. 1996). The total volume of each structure per testis was

determined from the product of the volume fraction and testicular volume.

Tubule and lumen diameter were estimated for 30-40 tubules per animal, ensuring the

standard error was <10% within each animal. For elliptical profiles, the short axis of the

ellipse was measured. The length of the tubule as calculated as the absolute volume of

tubules per unit area of tubule cross-section (calculated from the estimated tubule

diameter), assuming a cylindrical model. No correction factor for shrinkage or swelling

was applied.

Sertoli cells were identified by positive GATA4 reactivity (McCoard et al. 2003), as

described below. Sertoli cell nuclear volume was measured using the longest and

shortest diameters of the nucleus at magnification of 1000, with the aid of ImageJ

software (Schneider et al. 2012). For each animal, measurements were made on 12

cross-sections of tubules from the top, middle and bottom of the testis (ie, 36 tubules per

animal), and on at least 300 Sertoli cell nuclei, to ensure that the standard error was

<10% within each animal. The volume fraction of Sertoli cell nuclei was determined by


48

the point-counting method as described above. Mean nucleus volume was calculated

using the formula for a prolate spheroid (4/3 πab2, where a = longest radius and b =

shortest radius) (McCoard et al. 2001). The total number of Sertoli cells per testis was

estimated by dividing the absolute volume of Sertoli cell nuclei per testis by the mean

volume of Sertoli cell nucleus (Wreford 1995).

3.9.2 Immunohistochemistry

Immunoreactivity for GATA4, Claudin11 and PCNA was detected, as described before

(Tarulli et al. 2006), 5 µm sections (two adjacent serial sections from male sheep for

GATA4 and PCNA). Three sections per testis were de-waxed in xylene (twice for 3

min) and 100% ethanol (twice for 3 min) and then rehydrated through graded

concentrations of ethanol (90%, 75% and 50%) to deionized water. Antigen retrieval

was then performed by immersing sections in 600 ml 1 mM EDTA-NaOH (pH 8.0) and

heated in an 800-W microwave oven set on high for 5 min and medium for 5 min and

cooled for 1 h in EDTA buffer. After washing with 0.01 M phosphate-buffered saline

(PBS), sections were blocked in 0.3% H2O2 at 37°C for 1 h. Sections were then blocked

in Avidin, Biotin (SP-2001, Vector Laboratories), CAS-Block (Invitrogen, Australia)

with 10% normal goat serum (Vector Laboratories, California, USA), for 20 min each at

room temperature, with a PBS wash between each treatment. Rabbit antibodies to

GATA4, PCNA and Claudin11 (1 µg ml-1, Santa Cruz Biotechnology, Texas, USA)

were then applied for 2 h. The specificity of the primary antibodies was verified by

incubating sections in normal rabbit IgG (1 µg ml-1; Santa Cruz Biotechnology, Texas,

USA). After washing with PBS, the samples were treated with goat anti-rabbit second

antibody for 1 h, followed by application of ABC reagent (Vector Laboratories,

California, USA) (1 drop of A + 1 drop of B in 1ml PBS), then DAB (DAKO,

Australia) for 5 min. The sections were then washed with deionized water for 3 min,


49

counterstained with Mayer’s haemotoxylin (Vendor, Australia) for 2 min, and

dehydrated through graded concentrations of ethanol (50%, 75%, 90%, 100%), and

briefly immersed in xylene. Sections were mounted in Entellan (Merck, Australia)

under 50 mm coverslips (HD Scientific, Australia). To detect Sertoli cells that can

proliferate, the same tubules were located in the serial sections used for GATA4 and

PCNA staining, and cells with double staining were counted in 30 tubule cross-sections

for each animal.

3.9.3 Immunofluorescence

To verify the result of double-staining for GATA4 and PCNA, immunofluorescence

was performed using a protocol adapted from (Tarulli et al. 2006). The primary

antibodies were polyclonal rabbit anti-GATA4 (1 µg ml-1; catalogue number sc-9053,

Santa Cruz Biotechnology, Texas, USA), and monoclonal mouse anti-proliferating cell

nuclear antigen clone pc10 (1 µg ml-1, catalog no. M0879, DAKO, Sydney, Australia).

The primary antibodies were replaced by PBS as a negative control (Anttonen et al.

2003; Salonen et al. 2010). Secondary antibodies used were goat anti-rabbit Alexa 488

(10 µg ml-1, catalog no. A-11034, Molecular Probes) and goat anti-mouse Alexa 546

(10 µg ml-1, catalog no. A-11030, Molecular Probes). In addition, Hoechst 33342

(Invitrogen, Australia) was used as a nuclear stain.

3.9.4 Evaluation of apoptosis

For terminal deoxynucleotidyl transferase mediated dUTP nick-end labeling (TUNEL),

we followed the instructions of the ApopTag plus peroxidase in situ Apoptosis

Detection Kit (Chemicon International, USA). Briefly, deparaffinized tissue sections

(top part of left testes) were incubated with proteinase K (20µg/ml), subjected to 3%

H2O2 at 37°C for 30 min to inhibit endogenous peroxidase, and then incubated with


50

equilibration buffer at room temperature for 1 min. Each section was incubated with

TdT (terminal deoxynucleotidyl transferase) at 37°C for 1 h and then washed in

stop/wash buffer for 10 min. The sections were incubated in anti-Digoxigenin

Peroxidase Conjugate at room temperature for 30 min and were stained with

diaminobenzidine (DAB) as a peroxidase substrate. After counterstaining with methyl

green, numbers of TUNEL-positive cells per tubule were counted in 50 tubules per

animal with the aid of a light microscope. All counting procedures were performed

‘blindly’.

3.10 Molecular analysis

3.10.1 Isolation of RNA and reverse transcription

The trizol protocol was used to isolate total RNA (Hellani et al. 2000). The quality and

quantity of RNA were determined by Agilent 2100 Bioanalyzer (Agilent Technologies,

Santa Clara, CA) and Qubit 2.0 Fluorometer (Invitrogen, Carlsbad, CA) and only RNA

with an integrity number (RIN) > 7.0 was used for further analysis. High capacity RNA-

to-cDNA kits (Applied Biosystems, USA) were used to reverse-transcribe 2 µg RNA to

cDNA in a final volume of 20 µl, according to the manufacturer’s protocol. The absence

of contaminating genomic DNA in total RNA samples was confirmed using reactions in

which reverse transcriptase was omitted.

3.10.2 Quantitative real-time PCR

QPCR was performed using SYBR Green (Fast SYBR® Green Master Mix; Applied

Biosystems) to detect mRNA expression of target genes. Oligonucleotide primer

sequences for these genes were designed using NCBI primer blast

(http://www.ncbi.nlm.nih.gov/tools/primer-blast/index.cgi?LINK_LOC=BlastHome).

Fluorescence signal was detected with StepOnePlus™ Real-Time PCR System (Applied

http://www.ncbi.nlm.nih.gov/tools/primer-blast/index.cgi?LINK_LOC=BlastHome


51

Biosystems). In total, each reaction contained 10 μl Fast SYBR Green Master Mix

(Applied Biosystems), 1 μl of forward primer (20 pmol/μl), 1 μl of reverse primer (20

pmol/μl), 7 μl nuclease-free water, and 1 μl DNA template (50 ng/μl). Samples were

measured in triplicate using the following protocol: 95°C for 10 min for initial

denaturation and then 40 cycles of 95°C for 20 s, followed by annealing/extension for

30 s at 60°C. Analysis of melting curves was used to monitor PCR product purity.

Amplification of a single PCR product was confirmed by agarose gel electrophoresis

and DNA sequencing (data not shown).

3.10.3 Small RNA library sequencing

For each sample, 1.0 µg of total RNA was used to construct miRNA libraries with a

unique index using the TruSeq Small RNA Sample Preparation kit (Illumina, San

Diego, CA) by following the manufacturer’s instructions. PCR amplification was

performed for 11 cycles and gel purification was used to individually purify libraries

with unique indices. Quantitative real-time PCR (qRT-PCR) was performed for library

quantification using the StepOnePlus™ Real-Time PCR System (Applied Biosystems,

Carlsbad, CA) and KAPA SYBR Fast ABI Prism qPCR kit (Kapa Biosystems, Woburn,

MA). Individual libraries were then pooled for sequencing at Génome Québec

(Montréal, Canada) using the HiSeq 2000 system (Illumina) to generate 50 b single

reads. All the reads were de-multiplexed according to their index sequences using

CASAVA version 1.8 (Illumina) and reads that did not pass the Illumina chastity filter

were removed from the dataset. The small RNA sequencing reads with good quality

were subjected to 3’ adaptor sequence trimming, and all reads were mapped by blastn to

the non-coding RNA sequences (Rfam) to remove sequences belonging to tRNA,

snoRNA, rRNA, and other non-coding RNAs.

3.10.4 Construction and sequencing of the RNA-seq library


52

In each sample, total RNA (1.0 µg) was used to construct miRNA libraries with a

unique index, according to the instructions of the TruSeq Small RNA Sample

Preparation kit (Illumina, San Diego, CA). Quantitative real time PCR (qPCR) was

performed for library quantification using the StepOnePlus™ Real-Time PCR System

(Applied Biosystems, Carlsbad, CA) and KAPA SYBR Fast ABI Prism qPCR kit (Kapa

Biosystems, Woburn, MA). Individual libraries were then pooled for sequencing at

Génome Québec (Montréal, Canada) using the HiSeq 2000 system (Illumina).

Sequencing was performed as 100 bp paired-end reads. All reads were de-multiplexed

according to their index sequences with CASAVA version 1.8 (Illumina) and reads that

did not pass the Illumina chastity filter were discarded.

3.11 Bioinformatics analysis

3.11.1 Identification of miRNAs

The miRNAs were identified using the methods outlined by (Liang et al. 2014). Briefly,

known miRNAs were identified by mapping the filtered 18 to 25 nt sequences to

miRbase (miRBase release version 20), and all reads from 16 libraries were pooled to

predict novel miRNA using miRDeep2 based on the reference genome sequence of

OAR3.1 (http://www.livestockgenomics.csiro.au/). Small RNA sequences with a

miRDeep2 score higher than 5 and read numbers larger than 10 were defined as novel

miRNAs in sheep. The novel miRNA precursor sequences were then combined with the

known miRNA precursor sequences to form a new custom reference database.

Sequencing reads from different samples were mapped to the new custom reference

database to get the read number for the known and novel miRNAs for each sample.

Homologous miRNAs were identified with the method described by (Jin et al. 2014).


53

The conservation of known miRNAs was analyzed based on the definitions from

Targetscan (Lewis et al. 2003) for “highly conserved” (conserved across most

vertebrates), “conserved” (conserved across most mammals, but usually not beyond

placental mammals) and “poorly conserved” (not present in the above two groups). In

the present study, ovine-specific miRNAs were defined by using two criteria: 1) they

belong to a poorly conserved group; 2) their seed region sequences have only been

reported previously in sheep.

The genomic location of the miRNAs was searched for using the UCSC Genome

Browser (http://genome.ucsc.edu/) based on the reference genome sequence of OAR3.1

(http://www.livestockgenomics.csiro.au/). The miRNA genes are distributed across

chromosomes either individually, or in “clusters”, groups of miRNA genes located

within a short distance (10 Kb) on the same chromosome, based on the definition in the

miRBase database (http://www.mirbase.org). In the present study, all the known and

novel miRNAs were grouped into various clusters based on their genomic location.

3.11.2 piRNA characterization

To identify piRNAs, sequencing reads that ranged from 26 to 32 nt were mapped to the

ovine genome by Bowtie (version 1.0.1). Reads that could not be perfectly mapped to

the genome were discarded, and those remaining were de-duplicated to unique

sequences. The filtered unique reads were subjected to an online predictor

(http://59.79.168.90/piRNA/analysis.php) to predict piRNA candidates (Zhang et al.

2011). The genomic positions in the ovine genome of piRNA candidates were obtained

by Bowtie. piRNA candidates with multiple genomic locations may confuse the

derivation of piRNAs, so only piRNA candidates with a single location in the genome

were further analyzed. The piRNAs in each library were quantified by blastn and

customized perl scripts. All the sequencing data were deposited in the publicly available

http://www.mirbase.org/

http://59.79.168.90/piRNA/analysis.php


54

NCBI’s Gene Expression Omnibus (GEO) Database

(http://www.ncbi.nlm.nih.gov/geo/).

3.11.3 Identification of differentially expressed (DE) mRNAs, miRNAs and piRNAs

The effects of nutritional treatment on the expression of mRNAs, miRNAs and piRNAs

were determined by analysis of differential expression (DE) using the bioinformatics

tool, edgeR (Robinson et al. 2010), which uses a negative binomial distribution to

model sequencing data. The expression of mRNAs, miRNAs and piRNAs in each

library was normalized to counts per million reads (CPM) by the following formula:

CPM = (number of mRNAs/miRNAs/piRNAs reads/total reads number per library) ×

1,000,000. mRNAs/miRNAs/piRNAs with CPM > 5 in at least 50% of the samples

were subjected to DE analysis. Fold change (FC) was defined as the ratio (low diet/high

diet) of the arithmetic means of CPM values. Significance of the differential expression

of mRNAs, miRNAs and piRNAs was determined by false discovery rate (FDR) < 0.05

based on Benjamini and Hochberg multiple-testing correction (Benjamini et al. 2001) as

well as FC > 1.5 (McCarthy and Smyth 2009).

3.11.4 Validation of miRNA expression using stem-loop qRT-PCR

The TAQMAN miRNA assay was used to validate miRNA expression following the

manufacturer’s recommendation (Applied Biosystems, USA). In brief, cDNAs were

reverse transcribed from 10 ng total RNA, using 5 X specific miRNA RT primer, and

then amplified using a 20 X TAQMAN miRNA assay. StepOnePlus™ Real-Time PCR

System (Applied Biosystems) was used to detect the fluorescence signal. miRNAs with

cycle threshold (Ct) > 35 were considered as having not been expressed. In this study,

U6 snRNA was used as an internal control (Liu et al. 2013) and three biological

replicates were performed. The 2-ΔΔCt method was used to analyze the expression level


55

and all statistical analyses were carried out using SPSS software (Version 20). One-way

ANOVA was used to compare the groups, and P < 0.05 was considered significant.

Data are expressed as Mean ± SEM.

3.11.5 miRNA target prediction and functional analysis

TargetScan Release 6.0 (http://www.targetscan.org/) (Liu et al. 2012b) and miRanda

(http://www.microrna.org/microrna/home.do) (Birney et al. 2006) were used to predict

the target genes for selected miRNAs. The 3'UTR sequences of genes from sheep were

obtained from Ensembl Gene 75 Ovis aries genes (Oar_v3.1)

(http://uswest.ensembl.org/). The target genes predicted by both TargetScan (default

parameters; Bao et al. 2013) and miRanda (Total score >= 145, Total energy <=- 10;

Bao et al. 2014) for each miRNA were further analyzed through ingenuity pathway

analysis (IPA; Ingenuity Systems, www.ingenuity.com). The significance of the

predicted function in IPAs was determined using a corrected P value calculated by the

Benjamini-Hochberg method (FDR: Benjamini et al. 2001). Threshold FDR < 0.05 and

molecule number > 2 were used to enrich significant biological functions for each

miRNA.

3.11.6 miRNA target validation using dual luciferase reporter assay

The entire 3’UTR of target genes was amplified from sheep genomic DNA by PCR.

The details of each primer can be found in Chapter 6. Both PCR products were cloned

into the pmirGLO Dual-Luciferase miRNA Target Expression Vector (Promega) using

the Xho1 and Sal1 restriction sites.

A sheep fetal testis cell line (ATCC® CRL-6546) was cultured in ATCC-formulated

Dulbecco's Modified Eagle's Medium (ATCC, Catalog No. 30-2002), supplemented

with 10% fetal bovine serum (Gibco, Invitro-gen, Carlsbad, CA, USA), in a 37°C

http://www.targetscan.org/

http://www.microrna.org/microrna/home.do


56

incubator with 5% CO2. The 60 nM target miRNA mimics/miRNA mimic negative

control (Ambion) was co-transfected with 200 ng luciferase reporter containing 3’UTR

of target genes using Lipofectamine 2000 reagent (Invitrogen) in 24-well plates. After

transfection for 48 h, the Dual-Glo luciferase assay system (Promega) and SpectraMax

M3 system were used to obtain readouts of firefly and Renilla luciferase. All the firefly

luciferase readouts were normalized to their matching renilla luciferase readouts.

3.11.7 Identification and annotation of alternative splicing (AS) events

TopHat2 was used to predict the splice junctions with the RNA-seq data. Based on the

gene annotation information, splice junctions were classified into known and novel

groups. Splicing analysis was performed for events that had at least 20 total RNA-seq

reads (Wang et al. 2008). JuncBASE (Brooks et al. 2011) was used to annotate all AS

events (cassette exons, alternative 5' splice site, alternative 3' splice site, mutually

exclusive exons, coordinate cassette exons, alternative first exons, alternative last exons,

and intron retention). Values for Percentage Spliced Index (PSI) were calculated using

the formulas provided by (McManus et al. 2014).

3.11.8 Identification of differential AS events

Statistical significance was determined using the software package, R (source). Fisher’s

exact test was used to compare PSI values for pairwise comparison, and the P value was

adjusted to false discovery rate (FDR). In addition, only splicing events with FDR <

0.05 and PSI differences (ΔPSI) > 10% were further considered. The number of

differential AS events per chromosome length was calculated.

Chapter 4-Under nutrition reduces sperm quality

57

Chapter 4

Under nutrition reduces spermatogenic efficiency and sperm velocity,

and increases sperm DNA damage in sexually mature male sheep

4.1 Abstract

We tested whether the quality of sperm from mature male sheep would be affected

during nutrition-induced changes in testicular mass. Merino rams were fed for 65 days

with diets that increased, maintained or decreased body and testis mass (n = 8 per

group). In semen collected on Days 56 and 63, underfed rams had less sperm per

ejaculate than well-fed rams (P < 0.05) and a lower sperm velocity (computer-assisted

semen analysis) than well-fed or maintenance-fed rams (P < 0.05). Sperm chromatin

structure assay revealed more sperm DNA damage in underfed rams than in well-fed

rams (P < 0.05). The amount of sperm DNA damage was inversely correlated with

change in scrotal circumference (r = – 0.6; P < 0.05), the percentages of progressive

motile sperm (r = – 0.8; P < 0.01) and motile sperm (r = – 0.6; P < 0.05), and the

numbers of sperm per gram of testis (r = – 0.55, P < 0.05). In testicular tissue collected

on Day 65, underfed rams had fewer sperm per gram of testis than rams in the other two

groups (P < 0.001). We conclude that, in adult rams, underfeeding reduces

spermatogenic efficiency and that this response is associated with a reduction in sperml

quality.

Key words: ram, sperm number, sperm motility, DNA damage


58

4.2 Introduction

The reproductive system of small ruminants is affected by environmental factors such

as socio-sexual signals, photoperiod and nutrition, with genotype controlling the final

outcome (Martin and Walkden-Brown 1995). With respect to the effects of nutrition on

gamete production, the responses in females (ovulation rate) have been studied

extensively (Lozano et al. 2003), but less attention has been paid to male fertility. For

example, Pubmed lists only 8 research articles on the effect of nutrition on sheep testis

compared to 74 articles for the sheep ovary (http://www.ncbi.nlm.nih.gov/pubmed).

This disparity is surprising considering that testis mass is lost during the normal

breeding season of sheep and goats because, in many environments, the quantity and

quality of pasture are very poor at that time of the year (Martin and Walkden-Brown

1995). In addition, during the breeding season, male goats and sheep lose their appetite

and their behavioural drive for mating reduces the time available for feeding. The

combination of all these circumstances leads to major losses in both body mass and

testis mass, and therefore the capacity for sperm production (Review: Martin et al.

2011).

Reduction of sperm output by under-nutrition has been demonstrated in adult rams

(Salamon 1964; Setchell et al. 1965; Oldham et al. 1978; Cameron et al. 1988), but it is

not clear whether the loss in testis mass and reduction in sperm production are

associated with changes in the quality of the sperm. We have postulated that the sperm

produced when the testis is shrinking are of a lower quality than those produced when

the testis is growing (Review: Martin et al. 2011) because of changes in ‘spermatogenic

efficiency’ – the number of sperm produced per gram of testicular tissue (Cameron et

al. 1988; Walkden-Brown et al. 1994b). Spermatogenic efficiency depends on the rate

of cell loss, probably through apoptosis, during spermatogenesis (Sakkas et al. 1999;


59

Santos et al. 1999). In addition, changes in testis mass would also be accompanied by

changes in blood flow (Setchell et al. 1965) and thus oxidative stress, a major cause of

sperm damage (Review: Aitken et al. 2012).

Semen quality has been studied in respect to under nutrition but there were limitations

to their experimental approach. For example, Parker and Thwaites (1972) showed that

sperm count and motility are reduced if under-nutrition lasts longer than 7 weeks

(Parker and Thwaites 1972), and these effects can be reversed by dietary

supplementation (Dana et al. 2000; Tufarelli et al. 2011). In addition, dietary level with

higher concentrate supplementation result in higher semen volume and sperm

concentration, lower abnormal sperm in Sardinian rams (Tufarelli et al. 2011).

However, these studies were restricted to traditional variables such as sperm

concentration, viability, morphology and subjective motility (Ollero et al. 1998). There

is a clear need to confirm the negative effects of under nutrition on sperm quality with

modern, objective, precise techniques for assessment of sperm cell function, such as

computer-aided semen analysis (CASA), a technology that has been applied to species

as diverse as catfish and humans (Rurangwa et al. 2001; Sigurdson et al. 2007; Gil et al.

2009). The value of CASA motility parameters is evident from studies showing that, for

example, straight-line velocity [VSL], curvilinear velocity [VCL], and average path

velocity [VAP] are related to the number of newborn in pigs (Broekhuijse et al. 2012).

Another important indicator of sperm quality is DNA damage because it can have

severe long-term impacts on fetal development and on the health of the offspring

throughout its life (Evenson and Jost 2000). Consequently, Evenson and colleagues

developed a sperm chromatin structure assay (SCSA), based on computer-aided flow

cytometry, as a means for evaluating DNA damage in sperm cells (Evenson et al. 1999).

The technology has been widely used to assess DNA damage in sperm produced by


60

rams, bulls and humans (Reichart et al. 2000; Bochenek et al. 2001; Boe-Hansen et al.

2005) and is likely to provide a valuable insight into the effects of under-nutrition on

sperm cell quality in rams.

Overall, therefore, we expected the testicular regression and reduced in spermatogenic

efficiency caused by under-nutrition to be associated with degradation of sperml cell

quality. To test this hypothesis, we used computer-assisted sperm analysis (CASA) and

sperm chromatin structure assay (SCSA) as well as conventional methods to assess

sperm quality in sexually mature male sheep in which testicular mass was changing

under the influence of nutrition.

4.3 Materials and methods

The experimental protocol was approved by the Animal Ethics Committee of the

CSIRO Centre for Environment and Life Sciences, Floreat, Western Australia (Project

No.1202).

4.3.1 Animals and treatments

In early May, 24 Merino rams (24 months old, 65.7 ± 4.7 kg, scrotal circumference 31.8

± 2.5 cm) were selected from the ‘Allandale Temperament Flock’. These animals have

been selected for over 18 generations for high and low reactivity to humans and to

isolation (Bickell et al. 2009) so, to avoid any confounding effects of genetic

background, the treatments used in the current study were balanced for temperament.

All the rams were housed in individual pens in a sheep shed with windows allowing

good penetration of natural light (CSIRO Floreat, Western Australia, latitude 31o59’S).

During the 3-week acclimatization period, all rams were fed daily with 750 g oaten

chaff (8.4% crude protein; 8.0 MJ/Kg metabolisable energy) and 200 g lupin grain

(35.8% crude protein; 13.0 MJ/Kg metabolisable energy). Attempts were made to train


61

all rams for semen collection with an artificial vagina and a teaser ewe (Wulster-

Radcliffe et al. 2001) and this training regime was eventually successful for 18 rams.

At the end of May (mid-autumn), rams were allocated among three dietary treatments

(high, maintenance, low) with the groups balanced for training success to semen

collection, body mass, scrotal circumference, temperament, polled or horned, and sperm

quality. Animals fed to maintenance requirements were expected to maintain constant

body mass. The high diet was designed to allow the animals to gain 10% live weight

over 65 days whereas the low diet was designed to allow 10% loss in weight. At the

start of the treatment period, individual daily allowance was 1.2 kg oaten chaff plus 0.3

kg lupin grain for the rams in the high-diet group, 0.7 kg chaff and 0.18 kg lupin grain

for the maintenance group, and 0.51 kg chaff and 0.13 kg lupin grain for the low-diet

group. Every week, the animals were weighed and the amount of feed offered to each

individual was adjusted to ensure achievement of target live weight.

4.3.2 Body mass and scrotal circumference

Body mass was measured once every week before feeding. Scrotal circumference was

measured every week with a tape measure at the point of maximum diameter when the

skin of the scrotum was stretched taut around both testes.

4.3.3 Semen collection and processing

Semen collected on Day 56 and Day 63 was immediately immersed in a warm (37°C)

water bath until analysis for sperm concentration, semen volume, sperm motility, sperm

viability and morphology. All analyses were done within 20 min of collection. A

portion of each sample was quickly transferred to 200 μl straws, labeled and plunged

into liquid nitrogen to await DNA fragmentation analysis.



62

Semen volume was measured with an automatic pipette by carefully drawing the entire

ejaculate into a tip and reading the volume. The sperm concentration was determined by

spectrophotometer (8001 UV-vis Metertech, Inc., Taipei Taiwan). We followed the

protocol described by (Prathalingam et al. 2006): 20 μl semen was diluted 1: 400 (v/v)

with a mixture of phosphate-buffered saline (PBS) and formalin and the suspension was

placed in a spectrophotometer; absorbance was measured at 580 nm and sperm

concentration determined from a standard curve that had been developed for ram sperm.

The total number of sperm per ejaculate was estimated by multiplying ejaculate volume

(ml) by the concentration of sperm (109/ml).

4.3.5 Sperm morphology and viability

Sperm morphology and viability were evaluated after eosin-nigrosin staining (Bjorndahl

et al. 2003): 3 μl semen was mixed with 27 μl eosin-nigrosin mixture, and 5 μl of the

mixture was transferred to a labeled glass microscope slide and smeared using another

slide. Two smears were made for each sample. At least 300 sperm per slide were

counted on each of the 2 slides using an oil immersion objective on an Olympus BX60

microscope (Olympus, Australia Pty, Ltd., Mt Waverley, VIC, Australia). The

percentages of live, dead, morphologically normal and abnormal sperm were calculated.

4.3.6 Computer assisted semen analysis (CASA)

An aliquot (5.5 μl) of sperm suspension was diluted to 20 x 106 sperm/mL with motility

buffer (8 g/L sodium chloride, 0.1% bovine serum albumen, adjusted to pH 8.2 with 1.0

M NaOH) at 37°C, and 5 μl of the mixture was placed in a 20 μm sperm motility slide

chamber (Leja, Nieuw-Vennep, Netherlands). The video image was recorded using a

Basler A602fc digital camera (Basler AG, Ahrensburg, Germany) mounted on Olympus

BX53 microscope (Olympus Optical Co., Australia) equipped with phase contrast optics


63

and a motorized heated stage (Prior Optiscan II, Prior Scientific Intl.). In each sample,

500-600 sperm were evaluated. The percentages of motile and progressively motile

sperm and straight-line velocity (VSL), curvilinear velocity (VCL) and average path

velocity (VAP) were estimated using CASA software (SCA®, Microptics, V5, SL,

Spain). Sperm with VCL greater than 10 µm/sec were considered motile and sperm with

STR (VCL/VAP x 100) greater than 80% were classified as progressive.

4.3.7 Sperm chromatin structure assay (SCSA)

We followed the protocol described by (Evenson et al. 1999). In brief: frozen aliquots

of semen were placed in a 37°C water bath until just thawed and then diluted to 1-2 x

106 sperm cells per ml with TNE buffer (0.15 M NaCl, 0.01 M Tris-HCl, 0.001 M

disodium EDTA, pH 7.4, 10% glycerol). Aliquots (200 µl) were then mixed with 400 µl

acid-detergent solution (0.08 M HCl, 0.15 M NaCl, 0.1% Triton X-100, pH 1.2). After

30 s, the cells were stained with 1.2 ml acridine orange (AO) solution containing 6 mg

AO (Cat. # 04539, Polysciences Inc., Warrington, PA, USA) per ml buffer (0.037 M

citric acid, 0.126 M Na2HPO4, 0.0011 M di-sodium EDTA, 0.15 M NaCl, pH 6.0).

Fluorescence was then measured on 5000 cells per sample by FACSCalibur (Becton

Dickinson, Franklin Lakes, NJ, USA) with an excitation wavelength of 488 nm. AO

bound to double-stranded DNA emits a green signal, collected using a 530/30 nm BP

filter, while AO bound to fragmented single-stranded DNA emits a red signal, collected

using a 670 nm LP filter. Sperm cells were identified and debris excluded using

characteristic forward and side laser scatter. The percentage DNA fragmentation index

(%DFI = the percentage of cells outside the main sperm population) was calculated

using FlowJo v7.6 software (Tree Star, Inc. USA).

4.3.8 Sperm concentration in testicular tissue


64

At the end of the 65-day treatment period, all rams were killed with an intravenous

overdose of sodium pentobarbitone. Testes were collected, dissected and weighed. A

sample of tissue (10 g) from the left testis was excised, weighed accurately, and

homogenized in 100 ml of equal volumes 0.9% NaCl plus 0.1% TritonX-100. The

density of sperm was determined using a haemocytometer and a phase contrast

microscope (Olympus BX60, Olympus, Australia).

4.3.9 Statistical analysis

All statistical analyses were carried out using SPSS software (Version 20). The

differences among the dietary groups were analysed by one-way ANOVA and ANOVA

for repeated measurements followed by LSD’s (least significant different procedure)

multiple range test. For semen parameters, the average of the two collections (Days 56

and 63) was used. For the variables that were not normally distributed, logarithm

and square-root transformations were used where appropriate. P < 0.05 was considered

significant. Data are expressed as Mean ± SEM. In addition, the change in scrotal

circumference during the treatment period (the difference between Day 1 and Day 65

values) was calculated for each animal, pooled for the three groups, and used as an

independent variable in correlations with measures of treatment response. This allowed

us to test whether changes in testicular mass affected the quantity and quality of sperm

produced if dietary treatment could be ignored. By doing the correlation analysis with

the low diet group included and with it excluded, we could assess the possibility that the

outcomes reflected a general response to loss of testis mass or were specifically

associated with undernutrition.

4. 4 Results

4.4.1 Body mass, scrotal circumference and testis mass


65

Body mass increased in rams fed the high diet, decreased in rams fed the low diet, and

remained unchanged in rams fed maintenance diet (Fig. 4.1A). At the end of treatment

(Day 65), there were differences (P < 0.001, Fig. 4.1B) of 17.5 kg between high and low

dietary treatments, 9.5 kg between high and maintenance treatments and 8.0 kg between

maintenance and low dietary treatments. For scrotal circumference (Fig. 4.1C), the

dietary treatments had similar effects after an initial 2-week lag period during which

changes were not detectable. By Day 65, the high and low dietary groups differed by

5.3 cm (P < 0.01, Fig. 4.1D), but differences between the maintenance group and the

other two treatments were not significant (Fig. 4.1D). The outcomes for scrotal

circumference were reflected in paired testis weight at slaughter: 581 ± 31 g for the high

diet, 441 ± 44 g for maintenance, and 349 ± 49 g for the low diet. Testes were heavier

for the high diet than the maintenance or low diets (P < 0.05), but testis weight did not

differ significantly between the maintenance and low diets.


66

Fig. 4.1. Body mass (A, B) and scrotal circumference (C, D) in sexually mature rams fed a high diet

(black circles and columns), a maintenance diet (grey circles and columns), or a low diet (white circles

and columns) for 65 days. All values are mean ± SEM (n = 8). * P < 0.05 for high versus low diet. a-c: P

< 0.05 for different letters.


67


At the end of the treatment period, semen volume (Fig. 4.2A) was smaller for the low

diet than for the high and maintenance diets (P < 0.01, Fig. 4.2A) and smaller for the

maintenance diet than for the high diet (P < 0.05,). There were no differences in sperm

concentration (data not shown). Underfed and maintenance-fed rams produced less total

sperm per ejaculate than high-fed rams (P < 0.01, Fig. 4.2B), but the difference between

underfed and maintenance-fed rams was not significant. Overall, total sperm per

ejaculate was positively correlated with scrotal circumference in all treatments (r = 0.6,

P < 0.05). Increase in scrotal circumference was positively correlated with semen

volume (r = 0.7, P < 0.01), sperm concentration (r = 0.5, P < 0.05) and sperm number (r

= 0.7, P < 0.01) when all three dietary groups were included in the analysis, but not

when the underfed rams were excluded (data not shown).

Fig. 4.2. Semen volume (A) and sperm number per ejaculate (B) in sexually mature rams fed a high diet

(black columns), a maintenance diet (grey columns), or a low diet (white columns). All values mean ±

SEM (n = 6) for samples collected on Day 56 and 63 of the treatment period. Values are. a-c: P < 0.05 for

different letters.

4.4.3 Spermatogenic efficiency


68

There were fewer sperm per gram of tissue in the rams fed the low diet than in the rams

fed the maintenance or high diets (P < 0.001, Fig. 4.3A). Sperm density was higher with

the high diet than with the low diet (P < 0.001, Fig. 4.3B) and the maintenance diet (P <

0.001, Fig. 4.3B). This parameter was also higher in the rams fed at maintenance than

rams fed the low diet (P < 0.05, Fig. 4.3B). Increase in scrotal circumference was

positively correlated with sperm per gram of tissue (r = 0.7, P < 0.01), and sperm

density (r = 0.7, P < 0.01) when all three dietary groups were included in the analysis.

Interestingly, when underfed rams were excluded, positive correlations were still

observed (sperm per gram of testis: r = 0.5, P < 0.05; sperm density: r = 0.55, P < 0.05).

Fig. 4.3. Sperm number per gram of testis (A) and sperm number per testis (B) in sexually mature rams

fed a high diet (black columns), a maintenance diet (grey columns), or a low diet (white columns). Values

are mean ± SEM (n = 8). a-c: P < 0.05 for different letters.

4.4.4. Sperm motility (CASA)

Indicators of sperm velocity; curvilinear velocity (VCL), straight-line (VSL) and

average path velocity (VAP) were lower for sperm from rams fed the low diet than for

sperm from rams fed the high and maintenance diets (P < 0.05, Fig. 4.4). The

differences in these parameters between high and maintenance diets were not significant

(P > 0.05). The percentages of motile and progressive motile sperm did not differ


69

among the three groups (0.05 < P < 0.1). In addition, change in scrotal circumference

was positively correlated with progressive motile sperm percentage (r = 0.8, P < 0.01),

VCL (r = 0.6, P < 0.05), VSL (r = 0.6, P < 0.05) and VAP (r = 0.7, P < 0.01), but not

when the underfed rams were omitted from the analysis (data not shown).

Fig. 4.4. Curvilinear (A), straight-line (B) and the average path (C) velocities of sperm from sexually

mature rams fed a high diet (black columns), a maintenance diet (grey columns), or a low diet (white

columns). VCL = curvilinear velocity; VSL = straight-line velocity; VAP = average path velocity. All

values are means ± SEM (n = 6) for semen collected on Days 56 and 63 of the treatment period. Values

are. a-c: P < 0.05 for different letters.

4.4.5 Sperm cell quality

Sperm morphology and viability: the major abnormalities observed in the sperm were

bent tail, bent mid-piece, broken tail, and coiled tail, detached head and tag defects.

However, eosin-nigrosin staining did not reveal any significant differences among diets

in the percentage of abnormal and live sperm (Fig. 4.5A and Fig. 4.5B).


70

Fig. 4.5. Effect of diet on ejaculate quality as measured by A) percentage of abnormal sperm, and B)

percentage of live sperm in sexually mature rams fed a high diet (black columns), a maintenance diet

(grey columns), or a low diet (white columns). Values are mean ± SEM (n = 6) for semen collected on

Days 56 and 63 of the treatment period. a-c: P < 0.05 for different letters.

DNA fragmentation (%DFI): Figure 4.6 shows representative cytograms depicting

gating of normal double-stranded DNA (A: high green fluorescence, low red

fluorescence), the population with denatured single-stranded DNA (B: low green

fluorescence, high red fluorescence) and high-staining immature sperm (C). The value

for %DFI was higher in sperm from rams fed the low diet (2.23 ± 0.24) than in sperm

from rams fed the high diet (0.72 ± 0.11, P < 0.01); the value for the maintenance-fed

rams (1.39 ± 0.38) was intermediate between the values for these two groups, but did

not differ significantly from them (P > 0.05). There was a negative correlation (P <

0.05) between %DFI and the percentages of progressive motile sperm (r = – 0.8; Fig.

4.7A) and motile sperm (r = – 0.6; Fig. 4.7B), and a negative correlation (P < 0.05)

between %DFI and sperm density in testicular tissue (r = – 0.55; Fig. 4.7C). In addition,

%DFI was negatively correlated with change in scrotal circumference when all three


71

dietary groups were included in the analysis (r = – 0.6; Fig. 4.7D), but not when the

underfed rams were excluded (data not shown).

Fig. 4.6. Representative cytograms from the sperm chromatin structure assay (SCSA) for ejaculated

sperm from sexually mature rams that had been fed a high diet (A), a maintenance diet (B), or a low diet

(C). Mean fluorescence intensity (MFI) of green (488 530/30 nm BP) and red (488 670 nm LP) emission

produced by 5000 acridine orange stained sperm are plotted. Population A has high green fluorescence

and low red fluorescence, representing cells with normal double-stranded DNA; Population B are cells

outside of the main population which have low green fluorescence and high red fluorescence,

representing cells with denatured single-stranded DNA; Population C are cells outside of the main

population with high green staining, representing immature cells. Debris (Population D) was excluded

from analysis by characteristic forward and side light scatter. From these data, the percentage of DNA

fragmentation index (%DFI) is calculated: 100% x (number of sperm with denatured single-stranded

DNA)/(sperm with normal double-stranded DNA + sperm with denatured single-stranded DNA).


72

Fig. 4.7. Correlation between DNA fragmentation index (%DFI) and A) percentage of progressive motile

sperm, B) percentage of motile sperm, C) number of sperm per gram of testis, and D) change in scrotal

circumference, in sexually mature rams. For D: black circles for high diet; grey circles for maintenance

diet; white circles for low diet.

4. 5 Discussion

In this study of sexually mature Merino rams, feeding below the requirements for

maintenance led to reductions in scrotal circumference, testis mass, the density of sperm

in testis homogenates and the numbers of sperm in semen, whereas feeding above the

requirement for maintenance had the opposite effects. These observations confirm

previous studies and demonstrate the major effects of nutrition on spermatogenic

efficiency and the rate of sperm production in sexually mature Merino rams (Oldham et

al. 1978). This experiment thus provided a strong foundation for testing whether the

quality of sperm that are produced in the ejaculate are affected by nutrition, gains or


73

losses in testis mass, and the capacity of the testis to produce sperm. Using several

indicators of sperml quality (CASA-based measures of motility; SCSA-based measures

of sperm DNA damage; eosin-nigrosin-based detection of abnormal and live sperm), we

have shown that under-nutrition decreases sperm velocity and increases DNA damage,

but has little effect on the percentages of abnormal or live sperm.

Underfed rams produced sperm that had a lower curvilinear velocity (VCL), straight-

line velocity (VSL) and average path velocity (VAP), than was observed in sperm from

rams fed the high and maintenance diets. These effects of under-nutrition on objective

measures of motility are supported by previous studies in other laboratories that used

subjective assessment of motility in other breeds of sheep (Chiboka 1980; Dana et al.

2000), as well as in nutritional studies in humans (Review: Sinclair 2000). The reason

why under-nutrition reduces sperm motility is still not clear, although it has been

reported that reduction in sperm motility is correlated with increases in the formation of

reactive oxygen species (ROS) (Agarwal et al. 2003). This suggests that under-nutrition

would increase oxidative stress and further decrease sperm motility (Review: Aitken et

al. 2012). In the present study, we did not test whether the effects of under-nutrition on

sperm velocity are correlated with fertility in sheep, but parameters derived from

CASA, such as VAP, have been shown to be positively correlated (r = 0.87) with

fertility in species as diverse as cattle (Farrell et al. 1998) and humans (Youn et al.

2011). We would therefore expect the negative effect of under-nutrition on sperm

velocity to lead to a reduction in fertility at mating in sheep, a hypothesis that needs to

be tested in further studies.

DNA damage in ram sperm has been investigated previously, with respect to

temperature stress (Malama et al. 2013), but the present study is the first to investigate

the effects of nutrition. The higher levels of DNA fragmentation in sperm from underfed


74

rams than in sperm from well-fed rams adds further support for our hypothesis that

under nutrition reduces sperm cell quality. The values for %DFI in the current study

were similar to those from some previous studies with rams (Kasimanickam et al. 2007;

Peris et al. 2007) but were lower than the values in other reports (Garcia-Macias et al.

2006; Malama et al. 2013), even though all were using the SCSA technique. In the

study by Malama et al. (2013), the %DFI values differed between Chios rams and East

Friesian rams, so variation among genotypes is one possible reason for the lower values

in our Merinos. In addition, semen was collected weekly in Malama’s study and twice

per week in Garcia-Macias’s study, whereas the samples we analyzed were only

collected twice, near the end of the 65-day nutritional treatment. In boars, at least,

collection frequency affects DNA damage (Strzeiek et al. 1995), so this difference in

collection frequency may have contributed to the lower %DFI values in the current

study. Distinct cutoff values for %DFI that are associated with reduced fertility (15%)

and sterility (30%) have been established for human sperm (Evenson et al. 1999) but

not for rams. The %DFI in rams is much lower than humans due to well established

differences between the density of chromatin packaging between species (Evenson et al.

2002). However, in humans, failure to achieve pregnancy occurs even where 70% of the

sperm are not deemed abnormal on the basis of SCSA (Evenson and Jost 2000).

Therefore, it is likely that fertility in low-fed rams may be affected, despite apparently

low values for %DFI. Indeed, we found that %DFI was negatively correlated with the

percentages of motile sperm and progressive motile sperm, and the relationship was

quantitatively similar (r = – 0.39) to that reported for humans (Mahfouz et al. 2010),

suggesting a cause-effect relationship that should be related to fertility. Therefore,

studies involving fertility measurement are needed to test this relationship in rams.

The cause of the DNA damage in ejaculated sperm is still not clear but two possibilities

have been proposed (Sakkas et al. 1999): apoptosis during spermatogenesis and


75

incomplete maturation during spermiogenesis. Apoptosis during spermatogenesis

contributes to spermatogenic efficiency and is thought to facilitate functional

elimination of defective germ cells. This process implicitly involves destruction of

DNA so it is feasible that, in some sperm, the DNA breakdown has been only partially

completed by the time of ejaculation, leading to a higher %DFI in otherwise intact

sperm (Sakkas et al. 1999). Incomplete maturation during spermiogenesis is also a

plausible explanation for the increase in DFI% because of the positive association

between DNA damage and poor chromatin packaging due to under-protamination in

mature sperm (Gorczyca et al. 1993). To test which of these possibilities applies to the

effects of nutrition on ram sperm, we need to study germ cell apoptosis in the testis, and

the transition from round to elongated spermatids.

The percentages of abnormal sperm and dead sperm in the ejaculate are an important

indicator of sperm quality (Ombelet et al. 1995; Januskauskas et al. 2003). However,

neither sperm morphology nor mortality appeared to be affected by under-nutrition in

the present study, in agreement with previous work on Bakhtiary rams (Kheradmand et

al. 2006) and yearling bulls (Ohl et al. 1996). The duration and intensity of nutritional

imposition could be explanatory factors. We chose 65 days of nutritional treatment

because it easily exceeded the duration of the spermatogenic cycle of the ram (48 days:

Zeng et al. 2006), and we targeted 10% loss of body mass in the underfed group

because it approximated seasonal changes in live weight under field conditions (Masters

and Fels 1984). These conditions were sufficient to reduce sperm velocity and DNA

integrity in the underfed group, but a longer and more severe period of under-nutrition

might have led to detectable effects on sperm morphology and viability. For example, in

ram lambs, 86 days of zinc supplementation was needed to change the percentage of

live sperm (Kendall et al. 2000).


76

There were strong correlations between changes in scrotal circumference and some of

the measures of semen production and function when data for all three dietary groups

were included, but not when the data for the under-fed group were omitted from the

analysis. This observation raises the possibility that factors associated with the loss in

testis mass, rather than direct effects of the nutritional treatments, are responsible for the

observed changes in the production and quality of sperm. This concept is supported by a

study in adult fallow deer in which ejaculate volume, sperm density and the percentage

of normal sperm were directly correlated with annual changes in testis volume that were

probably driven by photoperiod rather than nutrition (Gosch and Fischer 1989). If this

were to be the case, then the relationship between testis mass and semen parameters

observed in this study could be applied more generally to other factors that can cause

changes in the testis mass, such as genotype, photoperiod or physical fitness.

In conclusion, the reductions in testis mass and sperm production caused by

underfeeding in sexually mature rams are associated with adverse outcomes for sperml

quality, as indicated by reduced sperm velocity (as indicated by CASA) and increased

DNA damage. Further research is required to determine whether the negative effect of

under-nutrition on these objective measures of sperm quality are associated with a

reduction in fertility.

Chapter 5-Under nutrition decreases Sertoli cell function

77

Chapter 5

Under-nutrition decreases Sertoli cell function in sexually

mature male sheep

5. 1 Abstract

We tested whether the reversible effects of nutrition on spermatogenesis in sexually

mature sheep were mediated by Sertoli cells. Rams were fed with diets designed to

achieve a 10% increase (High), no change (Maintenance) or a 10% decrease (Low) in

body mass after 65 days. At the end of treatment, testes were lighter in the Low than the

High group (P < 0.01). The Maintenance group had intermediate values that were not

significantly different to those of the other two groups. Spermatogenesis (Johnsen

score) was impaired in the Low group, but normal in both other groups. There was no

effect of treatment on Sertoli cell numbers, although 1% of Sertoli cells retained their

ability to proliferate. By contrast, Sertoli cell function was affected by dietary treatment,

as evidenced by differences between the High and Low groups (P < 0.05) in the

expression of seven Sertoli cell-specific genes. Under-nutrition appeared to reverse

cellular differentiation leading to disruption of tight-junction morphology. In

conclusion, in sexually mature sheep, reversible reductions in testis mass and

spermatogenesis caused by under-nutrition were associated with impairment of basic

aspects of Sertoli cell function but not with changes in the number of Sertoli cells.

Key words: Spermatogenesis, tight junction, Sertoli cell number, Sertoli cell activity


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5.2 Introduction

In sexually mature sheep, sperm output is positively correlated with testis mass, and

both sperm output and testis mass are affected by interactions between genotype and

environmental factors such as photoperiod and nutrition, with nutrition being the

dominant factor in genotypes like the Merino (Blache et al. 2003; Hötzel et al. 2003).

These effects are reversible, as demonstrated by annual cycles in testis mass (Martin et

al. 2002). The loss of testis mass with underfeeding involves mild cellular degeneration

and decreases in the diameter of the seminiferous tubules and the volume of the

seminiferous epithelium (Oldham et al. 1978; Hötzel et al. 1998). These outcomes are

not pathological but a reflection of the normal and reversible changes in testis function

that occur naturally under field conditions (Colas et al. 1986; Martin et al. 2002) and are

mediated by normal processes in the control of spermatogenesis.

We would expect the effects of nutrition on germ cell output to be mediated by the

Sertoli cells because there is a strict relationship between Sertoli cell numbers and

sperm production (Sharpe et al. 2003) and because a population of fully functional

Sertoli cells is considered essential for providing the structural and nutritional support

for germ cell development and sperm output (Meachem et al. 1996). Before Sertoli cells

can perform these tasks successfully, they must undergo several maturational changes

during the transition from fetal to adult life, most of which occur around puberty,

including the loss of proliferative activity and the formation of the blood-testis barrier

(Tarulli et al. 2012). With the loss of proliferative ability, the number of Sertoli cells in

the testis should be stable and un-modifiable after puberty but, following a study using

classical morphological techniques, we reported a substantial reduction in both the

number and volume of Sertoli cells in the sexually mature male sheep after nutritional

restriction (Hötzel et al. 1998). This contradiction of a dogma of testis biology might be


79

dismissed as histological artifact, but a growing body of evidence suggests that the adult

Sertoli cell population can be modified (Johnson and Nguyen 1986; Hess et al. 1993;

Tarulli et al. 2006). In these specific settings, at least, it appears that the adult Sertoli

cell might not be terminally differentiated. The implications are profound: if Sertoli cell

number could be changed, it would lead to exciting prospects for basic and clinical

research in testis biology and perhaps realization of the potential to replenish adult

Sertoli cells (Sharpe et al. 2003; Tarulli et al. 2012). We therefore decided to again test

whether nutrition affects the proliferation of Sertoli cells in the sexually mature male

sheep. This time, rather than using conventional histology, we adopted GATA4 as a

marker for Sertoli cells (Ketola et al. 2000) and combined stereological cell counts with

assessment of Sertoli cell differentiation status and activity by analysis of

immunoreactivity to proliferation cell nuclear antigen (PCNA).

Independently of changes in Sertoli cell numbers, we need to test whether nutrition

affects Sertoli cell function, particularly the blood-testis barrier that divides the

seminiferous epithelium into basal and adluminal compartments (Cheng and Mruk

2012; Tarulli et al. 2013). A critical component of the blood-testis barrier is the tight

junctions created basally between Sertoli cells (McCabe et al. 2010), the disruption of

which leads to germ cell atresia and the cessation of spermatogenesis (Tarulli et al.

2008). Sertoli cell tight junctions seem to be regulated by a wide array of signaling

pathways and molecules (Lui et al. 2003) but, for the present study, we have focused on

Claudin11 and ZO1, two proteins that are expressed in Sertoli cells and rarely in other

cell types in testis (Byers et al. 1991; Morita et al. 1999). In men with extreme

conditions, such as primary seminiferous tubule failure, the localization of Claudin11

protein is disorganized (Haverfield et al. 2013). Similar studies have not been reported

for normal, reversible changes in spermatogenesis, such as the response to nutrition, so

we analyzed ram tissue for mRNA expression for Claudin11 and ZO1, and assessed the


80

localization of Claudin11, to test whether they are affected by nutrition-induced growth

and regression of the testis.

We also studied other Sertoli cell-specific genes that are important for spermatogenesis.

For example, the maturity of Sertoli cells can be assessed using two genes, Anti-

Müllerian Hormone (AMH) and GATA1. AMH is expressed throughout fetal life and

decreases to a barely detectable level at the onset of puberty, so it is normally used as a

marker of immature Sertoli cells (Al-Attar et al. 1997; Sharpe et al. 2003). By contrast,

GATA1, a transcription factor that is first expressed in Sertoli cells as they are maturing,

is used as a marker for Sertoli cell maturation (Rey 1998; Beau et al. 2000). Other

specific genes that are crucial for Sertoli cell function and spermatogenesis include:

SRY-box containing gene 9 (SOX9) because it is associated with initiation of testis

development and is essential for the differentiation of Sertoli cells (Sekido et al. 2004);

the membrane-bound form of Kit-ligand (KLm) that is essential for spermatogenesis

(Johnston et al. 2004); Musashi homolog 1 (MSI1) that is involved in the differentiation

of germ cells in a variety of species (Saunders et al. 2002); follicle-stimulating hormone

receptor (FSHR) and Sertoli cell-specific androgen receptor (AR) (Collins et al. 2003;

Allan et al. 2006); and aquaporin 8 (AQP8), a trigger for the onset of spermatogenesis,

controls water secretion and thus fluid-filling of the seminiferous tubular lumen

(Calamita et al. 2001). We hypothesize that the expression of these eight Sertoli cell-

specific genes would decrease in underfed sheep.

The present study, based on nutrition-induced change of spermatogenesis in sheep as a

model of reversible, non-pathological change in spermatogenesis, therefore used a

variety of morphological, histological and molecular approaches to test whether the

responses are associated with changes in several fundamental aspects of Sertoli cell

function: proliferative ability, tight junctions, and cellular differentiation.


81




No.1202).


From May to July (autumn-winter), 24 sexually mature Merino male sheep (24 months

old; 65.7 ± 4.7 kg body mass; scrotal circumference 31.8 ± 2.5 cm) were housed in

individual pens under natural light at Floreat, Western Australia, where the 24-h

photoperiod cycles annually from 10 to 14 h light. They were acclimatized for 3 weeks,

with ad libitum access to water and a daily allocation of 750 g of oaten chaff (8.4%

crude protein; 8.0 MJ/kg metabolizable energy) plus lupin grain (35.8% crude protein;

13.0 MJ/kg metabolizable energy).

After acclimatization, the animals were allocated among three dietary groups, with

stratification based on body weight, scrotal circumference, and sperm viability. The

dietary treatments were designed to alter body weight over 65 days: an increase of 10%

in the high diet group, no change in the maintenance group, and a decrease of 10% in

the low diet group. At the start of the treatment period, the daily allocations for each

animal were 1.24 kg oaten chaff plus 0.31 kg lupin grain (high diet), 0.73 kg of chaff

plus 0.18 kg lupin grain (maintenance diet), and 0.51 kg chaff plus 0.13 kg lupin grain

(low diet). The animals were weighed every week and the amount of feed allocated was

modified for each individual to ensure the target body weights were reached.

Quantitative details of the dietary components and descriptions of the endocrine

responses can be seen in the report by (Boukhliq et al. 1997). The data from the present


82

experiment showing the effects of treatments on body mass, scrotal circumference,

semen quality and sperml quality are presented elsewhere (Chapter 4).

5.3.2 Tissue collection and preservation




right testis were snap-frozen in liquid nitrogen and stored at –80˚C for total RNA

preparation; those from the left testis were washed by 0.9% saline and then fixed by 4%

paraformaldehyde for 6 h, then dehydrated and processed for routine embedding in

paraffin wax for histological analysis (Francavilla et al. 2000).

5.3.3 Morphometric analysis

We used an Olympus BX50 microscope coupled with a digital imaging system (DP2-

BSW) to assess 5 µm paraffin sections stained with Periodic Acid Schiff (PAS) reagent.

Images of cells and structures were displayed on a high-resolution color monitor and

were traced using a computerized mouse. Fields were selected using a systematic

random approach. All slides were masked to avoid personal bias.

5.3.4 Qualitative assessment of spermatogenesis

Spermatogenesis status was scored for 60 tubules from each animal, using a modified

Johnsen scoring system with values that range from 1 to 10 (Johnsen 1970) based on

criteria detailed in Figure 5.1. Two independent observers scored each section to avoid

bias, with all slides being masked. In the current study, we did not observe any

seminiferous tubules with no Sertoli cells (Score 1), so the Johnsen scores ranged from

2 to 10.


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Figure 5.1. Representative testis histology (Periodic Acid Schiff) associated with the criteria for the

Johnsen score in sexually mature male sheep: A) Score 10, full spermatogenesis; B) Score 9, slightly

impaired spermatogenesis, many late spermatids; C) Score 8, few late spermatids; D) Score 7, no late

spermatids, many early spermatids; E) Score 6, no sperm, no late spermatids, few early spermatids; F)

Score 5, no sperm or spermatids, many spermatocytes; G) Score 4, no sperm or spermatids, few

spermatocytes; H) Score 3, spermatogonia only; I) Score 2, no germinal cells, Sertoli cells only. Scale bar

represents 50 µm.

5.3.5 Analysis of testicular compartments

Point-counting method (Meachem et al. 1996) was used to determine the volume

fraction of each structure. Volume fraction of each structure = the number of points

landing on each structure /the number of the points hitting the entire testis. The total

volume of each structure per testis was determined from the product of the volume


84

fraction and testicular volume. Tubule and lumen diameter were estimated for 30-40

tubules per animal, ensuring the standard error was <10% within each animal. For

elliptical profiles, the short axis of the ellipse was measured. The length of the tubule =

the absolute volume of tubules/the area of the tubule cross-section (calculated from the

estimated tubule diameter), assuming a cylindrical model. No correction factor for

shrinkage or swelling was applied.

Sertoli cells were identified by positive GATA4 reactivity (McCoard et al. 2003), as

described below. Sertoli cell nuclear volume was measured using the longest and

shortest diameters of the nucleus at magnification of 1000, with the aid of scientific

image analysis software, ImageJ (Schneider et al. 2012). For each animal,

measurements were made on 12 cross-sections of tubules from the top, middle and

bottom of the testis (ie, 36 tubules per animal), and at least 300 Sertoli cell nuclei, to

ensure that the standard error was <10% within each animal. The volume fraction of

Sertoli cell nuclei was determined by the point-counting method as described above.

Mean nucleus volume was calculated using the formula for a prolate sphere (4/3 πab2,

where a = longest radius and b = shortest radius) (McCoard et al. 2001). The total

number of Sertoli cells per testis was estimated by dividing the absolute volume of

Sertoli cell nuclei per testis by the mean volume of Sertoli cell nucleus (Wreford 1995).

5.3.6 Immunohistochemistry

Immunoreactivity for GATA4, Claudin11 and PCNA was detected, as described before

(Tarulli et al. 2006), in 5 µm sections (two adjacent serial sections from male sheep for

GATA4 and PCNA). Three sections per testis were dewaxed in xylene (twice for 3 min)

and 100% ethanol (twice for 3 min) and then rehydrated through graded ethanols (90%,

75% and 50%) to deionized water. Antigen retrieval was then performed by immersing

sections in 600 ml of 1 mM EDTA-NaOH (pH 8.0) and heated in an 800-W microwave


85

oven set on high for 5 min and medium for 5 min and cooled for 1 h in EDTA buffer.

After washing with 0.01 M phosphate-buffered saline (PBS), sections were blocked in

0.3% H2O2 at 37˚C for 1 h. Sections were then blocked in Avidin, Biotin (SP-2001,

Vector Laboratories), CAS-Block (Invitrogen, Australia) with 10% normal goat serum

(Vector Laboratories, California, USA) (20 min each at room temperature), with a PBS

wash between each treatment. Rabbit antibodies to GATA4, PCNA and Claudin11 (1

µg ml-1, Santa Cruz Biotechnology, Texas, USA) were then applied for 2 h. Specificity

of primary antibodies was verified by incubating sections in normal rabbit IgG (1 µg ml-

1; Santa Cruz Biotechnology, Texas, USA). After washing with PBS, the samples were

treated with goat anti-rabbit second antibody for 1 h, followed by application of ABC

reagent (Vector Laboratories, California, USA) (1 drop of A + 1 drop of B in 1ml PBS),

then DAB (DAKO, Australia) for 5 min. The sections were then washed with deionized

water for 3 min, counterstained with Mayer’s haemotoxylin (Vendor, Australia) for 2

min, and dehydrated through graded ethanol (50%, 75%, 90%, and 100%), and briefly

immersed in xylene. Sections were mounted in Entellan (Merck, Australia) under 50-

mm coverslips (HD Scientific, Australia) and observed under Olympus BX50

microscope. To detect Sertoli cells that can proliferate, the same tubules were located in

the serial sections used for GATA4 and PCNA staining, and cells with double staining

were counted in 30 tubule cross-sections for each animal.

5.3.7 Immunofluorescence

In order to verify the result of double staining of GATA4 and PCNA,

immunofluorescence for PCNA and GATA4 on sheep testis tissue was performed using

a protocol adapted from (Tarulli et al. 2006). Primary antibodies used were as follows:

polyclonal rabbit anti-GATA4 (1 µg ml-1; catalogue number sc-9053, Santa Cruz

Biotechnology, Texas, USA), monoclonal mouse anti-proliferating cell nuclear antigen


86

clone pc10 (1 µg ml-1, catalog no.M0879, DAKO, Sydney, Australia). Primary

antibodies were replaced by PBS as negative control (Anttonen et al. 2003; Salonen et

al. 2010). Secondary antibodies used were goat anti-rabbit Alexa 488 (10 µg ml-1,

catalog no. A-11034, Molecular Probes) and goat anti-mouse Alexa 546 (10 µg ml-1,

catalog no. A-11030, Molecular Probes). In addition, Hoechst 33342 (Invitrogen,

Australia) was used for nuclear stain.

5.3.8 Assessment of Claudin11 protein organization

Claudin11 staining was assessed by morphological evaluation of the immuno-staining

pattern (absent, filamentous or punctate), and by localization of staining within the

tubule (Haverfield et al. 2013). Based on these two observations, four different

organizational patterns (I–IV) were identified, as described in Table 5.1. The four

patterns indicate progressive loss of blood-testis junction. Pattern I indicates well

functional blood-testis junction. Pattern 4 is linked to disrupted blood-testis junction.


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Table 5.1: Morphological descriptions of the four staining patterns observed for Claudin-11 in

seminiferous tubules from sexually mature male sheep.

5.3.9 Isolation of RNA and reverse transcription

Staining Pattern Morphologic description of spatial organization

Pattern Ⅰ The protein presented a filamentous staining

pattern around the basal aspect of the tubule,

with no staining in the adluminal aspect.

Pattern Ⅱ The protein presented a filamentous staining


with additional staining present in the

adluminal aspect representing <10% of total

tubule area, appearing as a small cluster

disconnected from the basal staining.

Pattern Ⅲ The protein presented a filamentous staining


with additional staining in the adluminal aspect

representing > 10% of total tubule area.

Pattern Ⅳ The protein staining did not follow a filamentous

pattern; rather, the protein staining was

punctate and cytoplasmic in localization, with

diffuse staining present throughout both basal

and adluminal aspects of the tubule.


88

The trizol protocol was used to isolate total RNA (Hellani et al. 2000). The quality and

quantity of RNA were determined by Agilent 2100 Bioanalyzer (Agilent Technologies,

Santa Clara, CA) and Qubit 2.0 Fluorometer (Invitrogen, Carlsbad, CA) and only RNA

with an integrity number (RIN) > 7.0 was used for further analysis. High capacity RNA-

to-cDNA kits (Applied Biosystems, USA) were used to reverse-transcribe 2 µg RNA to

cDNA in a final volume of 20 µl, according to the manufacturer’s protocol. The absence

of contaminating genomic DNA in total RNA samples was confirmed using reactions in

which reverse transcriptase was omitted.

5.3.10 Quantitive real-time PCR

QPCR was performed using SYBR Green (Fast SYBR® Green Master Mix; Applied

Biosystems) to detect mRNA expression of ten Sertoli cell-specific genes: ZO1,

Claudin11, AMH, GATA1, AR, KLm, FSHR, AQP8, SOX9, MSI1 (Johnston et al. 2004;

Abel et al. 2008; O'Shaughnessy et al. 2008). Oligonucleotide primer sequences for

these genes were designed using NCBI primer blast

(http://www.ncbi.nlm.nih.gov/tools/primer-blast/index.cgi?LINK_LOC=BlastHome)

and the primer for glyceraldehyde 3-phosphate dehydrogenase (GAPDH) was obtained

from a published source (Table 5.2; Yu et al. 2010). Fluorescence signal was detected

with StepOnePlus™ Real-Time PCR System (Applied Biosystems). In total, each

reaction contained 10 μl Fast SYBR Green Master Mix (Applied Biosystems), 1 μl of

forward primer (20 pmol/μl), 1 μl of reverse primer (20 pmol/μl), 7 μl nuclease-free

water, and 1 μl DNA template (50 ng/μl). Samples were measured in triplicate using the

following protocol: 95 ºC for 10 min for initial denaturation and then 40 cycles of 95 ºC

for 20 s, followed by annealing/extension for 30 s at 60 ºC. Analysis of melting curves

was used to monitor PCR product purity. Amplification of a single PCR product was

confirmed by agarose gel electrophoresis and DNA sequencing (data not shown).

http://www.ncbi.nlm.nih.gov/tools/primer-blast/index.cgi?LINK_LOC=BlastHome


89

Table 5.2. Details of primers used for RT-QPCR.

GATA6 is known to be expressed exclusively and constantly by the Sertoli cells, with

little expression in other cell types in testis (Florin et al. 2005; Tarulli et al. 2008). In

addition, in this study, the quantification cycles (Ct) of GATA6 were constant in the

testes from 16 sheep (Data not shown), so GATA6 was used as housekeeper gene. The

ΔΔCt method was used to analyze the relative expression of Sertoli cell-specific genes

(Livak and Schmittgen 2001; Blais et al. 2008). Gene expression (ΔCt value) was

Gene Abbrev. Primer Sequence Product size (bp) Gene Bank

Claudin11 CLDN11 F:CTGATGATTGCTGCCTCCGT 75 XM_004003841.1

R:CATTCGGATGCACGGGAGAA

Tight junction protein 1 ZO1 F:AGCAAGCACCGAATCTTCCA 101 XM_004018080.1

R:ATTGGGCAGGACACCATCAG

Anti-Mullerian homone AMH F: TCCATCTCTCTCTCTGGCCC 150 XM_004009377.1

R:CAGTCCCAGCCTTGCTGAAA

GATA-binding factor 1 GATA1 F: GGACTGCACCACCTTCATCA 148 XM_004003841.1

R:GGGGTTAAGGGCAGAGTTCC

Androgen receptor AR F:GTACAGCCAGTGTGTCCGAA 99 XM_004022146.1

R:AAGAGCAGCAGTGCCTTCAT

Kit ligand, membrane-bound KLm F:GTGGAACAACTGTCAGTCAGC 127 NM_001267888.1

R:CCATGCACTCCACAAGGTCA

Follicle-stimulating hormone receptor FSHR F:TGTCCACACCAAAAGCCAGT 85 NM_001009289.1

R:GACAGTGAAAAAGCCCGCAG

Aquaporin 8 AQP8 F:GGAACAGCACAACAGAAGCG 116 XM_004020851.1

R:AATCGGGGCACAAGAGAAGG

SRY-box containing gene 9 SOX9 F:TAATTCGGAGGCGAACCCTG 126 XM_004013527.1

R:GGAGCGAGGGTTTAGGAAGG

Musashi homolog 1 MSI1 F:GTCTCGAGTCATGCCCTACG 109 XM_004017605.1

R:GAGGCCTGTATAACTCCGGC

Glyceraldehyde 3-phosphate

dehydrogenase GAPDH F:CTGCTGACGCTCCCATGTTTGT

150 NM_001190390.1

R:TAAGTCCCTCCACGATGCCAAA

GATA-binding factor 6 GATA6 F:TCGTTTGGTACACACCTCCG 76 XM_004020625.1

R: CAGTCCTGCAAACCGAGTGA


90

calculated based on quantification cycles (Ct) (ΔCt = Ct target gene – Ct GATA6) and levels

of Sertoli cell transcript expression were calculated relative to maintenance group

(ΔΔCt = mean ΔCt sample – mean ΔCt maintenance). Relative expression (RQ) was

calculated using the StepOnePlus™ Real-Time PCR System (Applied Biosystems) and

the formula RQtarget gene = 2-ΔΔCt. This experiment was tested by a more commonly

employed housekeeper, GAPDH, which is highly and constantly expressed during sheep

testis development (Yu et al. 2010) .

5.3.11 Statistical analysis

All statistical analyses were carried out using IBM SPSS statistics data editor (Version

20). One-way ANOVA was used to test the effect of nutrition on Johnsen score, all the

morphometric data, and Sertoli cell gene expression. For the variables that were not

normally distributed, logarithm and square-root transformations were used. P < 0.05

was considered as statistically significant. Data are expressed as Mean ± SEM.

5.4 Results

5.4.1 Paired testes weight

One-way ANOVA revealed a significant effect of treatment on paired testes weight at

slaughter (P < 0.05). Specifically, rams from the High group had a higher paired testes

weight than rams in the Low group (581 ± 31 g versus 349 ± 49 g; P < 0.01). This

variable did not differ significantly between rams in the Maintenance and Low groups

(441 ± 44 g versus 349 ± 49 g; P = 0.09) or between the High and Maintenance groups

(581 ± 31 g versus 441 ± 44 g; P = 0.06).

5.4.2 Spermatogenesis


91

One-way ANOVA revealed a significant effect of treatment on spermatogenic status, as

indicated by the Johnsen score (P < 0.01). Specifically, rams from the Low group had a

lower Johnson score (6.3 ± 0.3) than rams from both the Maintenance group (8.0 ± 0.2;

P < 0.001) and the High group (8.1 ± 0.3, P < 0.001). Late spermatids were absent in the

Low group and only a few early spermatids were observed, whereas spermatogenesis

was qualitatively normal in the High and Maintenance groups.

One-way ANOVA revealed a significant effect of treatment on the diameter of the

seminiferous tubules (P < 0.01) and the volume fractions and absolute volume of the

seminiferous tubules (P < 0.05) and epithelium (P < 0.05). However, there was no effect

of treatment on tubule length and lumen diameter (P > 0.05; Table 5.3) or in the

absolute volumes of lumen and interstitial tissue (P > 0.05; Table 5.3). Specifically,

rams in the Low group had narrower seminiferous tubules (P < 0.01), lower volume

fractions (P < 0.05) and absolute volume (P < 0.05) of seminiferous tubule and

epithelium (P < 0.05; Table 5.3) than High group and Maintenance group. In contrast,

the volume fraction of the lumen and interstitial tissue was higher in the Low group than

the other two groups (P < 0.05; Table 5.3).

5.4.3 Sertoli cell numbers

Sertoli cell nuclei were detected by GATA4 immunoreactivity (Fig. 5.2). No significant

differences were observed between the three groups for mean Sertoli cell nuclear

volume, Sertoli cell nuclei volume fraction, absolute volume of the Sertoli cell nuclei,

total number of Sertoli cells per testis, or number of Sertoli cells per tubule cross section

(Table 5.4).


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Table 5.3. Morphometric analysis of the testicular tissue from sexually mature male sheep after feeding

for 65 days with a diet that increased (High diet), maintained (Maintenance diet), or reduced (Low diet)

body mass and testis mass. All values are mean ± SEM (n = 8 per treatment). a, b: different letters denote

statistically significant differences among dietary treatments.

Variable High diet Maintenance diet Low diet

Tubule diameter (µm) 216.5 ± 7.2a 203.2 ± 7.7a 170.5 ± 7.9b

Lumen diameter (µm) 93.2 ± 4.4 98.2± 3.2 98.3 ± 4.8

tubule length (m) 5794 ± 383 5102 ± 243 4975 ± 829

Volume fraction (%)

Seminiferous tubules 0.73 ± 0.02a 0.71 ± 0.02a 0.64 ± 0.02b

Tubular lumen 0.14± 0.02a 0.17 ± 0.01a 0.21 ± 0.02b

Seminiferous epithelium 0.59 ± 0.03a 0.53 ± 0.03a 0.40 ± 0.03b

interstitial tissue 0.27 ± 0.02a 0.29 ± 0.02a 0.36 ± 0.02b

Absolute volume of testicular components (cm3)

Seminiferous tubules 211.3 ± 14.1a 167.4 ± 14.4a 115.2± 21.7b

Tubular lumen 40.0± 4.6 38.2 ± 1.6 37.7± 6.4

Seminiferous epithelium 172.5 ± 13.9a 129.0± 14.1a 76.1 ± 16.0b

interstitial tissue 78.6 ± 6.0 68.7 ± 7.4 62.5 ± 7.7


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Table 5.4. Sertoli cell measurements in sexually mature male sheep after feeding for 65 days with a diet

that increased (High diet), maintained (Maintenance diet), or reduced (Low diet) body mass and testis

mass. All values are mean ± SEM (n = 8 per treatment).

5.4.4 Sertoli cell proliferative ability

Cells that were positive to both GATA4 and PCNA, and thus considered to be

displaying proliferative ability, were observed in all dietary groups (Fig. 5.2). Of the 30

tubules counted for each animal, proliferating Sertoli cells were detected in 1.6 ± 0.3

tubules from the High group, 1.8 ± 0.4 tubules from the Maintenance group, and 1.7 ±

0.3 tubules from the Low group, with no significant differences among treatments. This

result was validated by confocal immunofluorescence of GATA4 and PCNA (Fig. 5.3).

PCNA reactivity was detected in 0.83% ± 0.002 of Sertoli cells in High group, 1.0% ±

0.002 of Sertoli cells in Maintenance group, 0.92 % ± 0.001 of Sertoli cells in Low

Variable High diet Maintenance diet Low diet

Nuclear volume (µm3) 199.1± 16.1 206.5 ± 17.21 159.9 ± 15.0

Nuclei volume fraction 0.018 ± 0.001 0.022 ± 0.004 0.025 ± 0.002

Absolute volume of Sertoli cell nuclei per testis (cm3) 5.24 ± 0.49 5.33 ± 1.07 4.24 ± 0.57

Number per testis (x 109) 26.9 ± 2.6 24.9± 4.9 26.5 ± 1.9

Number per tubule cross section 23.8 ± 1.5 22.5 ± 1.2 20.9 ± 0.9


94

group, with no differences among three treatments.

Figure 5.2. Seminiferous tubules from sexually mature male sheep after 65 days of nutritional treatment,

illustrating GATA4 positive cells (A, B, C) and PCNA-positive cells (E, F, G). Two adjacent serial

sections are provided from the testis of a male sheep fed the high diet (A, E), a male sheep fed the

maintenance diet (B, F), and a male sheep fed the low diet (C, G). The arrows indicate the same cell in the

adjacent sections. D, H: Negative control with normal rabbit IgG. Scale bar represents 50 µm.


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Figure 5.3. Immunofluorescence for the Sertoli cell nuclear marker GATA4 and the proliferation marker

PCNA in testis from sexually mature male sheep fed a high diet (A), a maintenance diet (B), or a low diet

(C). Immunofluorescence for GATA4 (green), PCNA (red), Hoechst (blue), coexpression of GATA4 and

PCNA (yellow). D: Negative control with PBS instead of primary antibodies. The arrows indicate cells

coexpressed of GATA4 and PCNA. Scale bar represents 50 µm.

5.4.5 Claudin11 protein localization

A filamentous Claudin11 staining pattern, restricted to the basal aspect of the tubule

(Pattern I) was observed in 81% of tubules in the High group and 61% of tubules in the

Maintenance group (Fig. 5.4 and Fig. 5.5). In contrast, in the Low group, only 37% of


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tubules followed Pattern I and in approximately 36% of tubules, the staining was

punctate and diffusely spread

Figure 5.4. Examples of staining patterns for the tight-junction protein, Claudin11, in sexually mature

male sheep. The scale bar represents 50 µm. Pattern I: a filamentous staining pattern around the basal

aspect of the tubule, no staining in the adluminal aspect; Pattern II: a filamentous staining pattern around

the basal aspect of the tubule, additional staining in the adluminal aspect (<10% of total tubule area)

appearing as a small cluster disconnected from the basal staining; Pattern III: a filamentous staining

pattern around the basal aspect of the tubule, additional staining present in the adluminal aspect (>10% of

total tubule area) appearing as a small cluster disconnected from the basal staining; Pattern IV: protein

staining does not follow a filamentous pattern, but is punctate and cytoplasmic in localization, with

diffuse staining present throughout both basal and adluminal aspects of the tubule.


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throughout both the basal and adluminal aspects of the tubule (Pattern IV; Fig. 5.4 and

Fig. 5.5). No tubules from High group featured Pattern IV staining. The frequency of

Patterns I and IV differed significantly (P < 0.05) between the Low group and both the

High and Maintenance groups (Fig. 5.5). The frequency of Pattern II differed (P < 0.05)

between High and Maintenance group, but not between High and Low group or

between Low and Maintenance group (Fig. 5.5). The frequency of Pattern III differed (P

< 0.05) between the Maintenance group and both the High and Low groups, but not (P >

0.05) between High and Low group (Fig. 5.5).

Figure 5.5. The frequency of each Claudin11 staining pattern (I–IV) in tubules from sexually mature

male sheep fed the high diet (black), maintenance diet (grey) and low diet (white). Data are expressed as

mean ± SEM (n = 8 per treatment). a, b, c: different letters denote statistically significant differences

among dietary treatments.

5.4.6 Expression of Sertoli cell-specific genes


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There were significant effects of treatment on the expression of MSI1, GATA1, KLm,

SOX9, ZO1, AMH and Claudin11 (one-way ANOVA, P < 0.05; Fig. 5.6). Treatment did

not affect the expression of FSHR, AQP8 or AR (P > 0.05; Fig. 5.6). Specifically, the

expression of MSI1 was higher in High group than the Maintenance and Low groups (P

< 0.05, Fig. 5.6), but there was no difference between Maintenance group and Low

group. The expression of GATA1 and KLm were higher in tissues from rams in the High

group and Maintenance group than in tissues from the Low group (P < 0.05, Fig. 5.6),

but there was no difference between the High and Maintenance groups. The expression

of SOX9 and ZO1 was higher in the High group than the other two groups, and the

expression of these two genes was greater in the Maintenance group than in the Low

group. By contrast, the expression of AMH and Claudin11 was greater in tissue from

rams in the Low group than in tissue from the High and Maintenance groups (P < 0.05,

Fig. 5.6), but High and Maintenance groups did not differ. All the genes for which

expression differed between groups were also tested against a more commonly

employed housekeeper, GAPDH, with the same outcomes (data not shown).


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Figure 5.6. mRNA expression for Sertoli cell-specific genes normalized to GATA-6 from sexually mature

male sheep fed the high diet (black), maintenance diet (grey) and low diet (white). MSI1 (Musashi

homolog 1), GATA1 (GATA-binding factor 1), KLm (Kit ligand membrane-bound), SOX9 (SRY-box

containing gene 9), ZO1 (Tight junction protein 1), AMH (Anti- Müllerian hormone), Claudin11, FSHR

(Follicle-stimulating hormone receptor), AQP8 (Aquaporin 8), AR (Androgen receptor). Values are mean

± SEM (n = 8 per treatment). a, b, c: different letters denote statistically significant differences among

dietary treatments.

5.5 Discussion

This is apparently the first comprehensive evaluation of the morphological and

functional processes that underlie a natural, reversible, non-pathological change in

spermatogenesis during gain or loss of testicular tissue, in this case induced by nutrition

in the sexually mature male sheep. The nutritional treatments led to a 10% change in

body mass but a 25% change in testis mass. Within the testis, nutrition changed tubule

diameter, volume of seminiferous epithelium, Johnsen score, germ cell density,

spermatogenic efficiency, sperm output and sperm cell quality in the semen (Chapter 4).


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As we shall discuss below, these responses were associated with changes in the

function, but not numbers, of Sertoli cells.

We did not detect any change in the numbers of Sertoli cells, per testis or per tubule

cross-section, thus directly contradicting our previous observation (Hötzel et al. 1998)

and neutralizing controversy concerning the stability of Sertoli cell number after

puberty (Kluin et al. 1983; Kluin et al. 1984; Monet-Kuntz et al. 1984; Hochereau-de

Reviers et al. 1987). The most obvious explanation of the disagreement is technique –

in our initial study, we used conventional histology and counted Sertoli cell nuclei

based on their shape and location. This approach has several drawbacks: i) the Sertoli

cell nucleus is usually sitting among more prominent nuclei, such as those of germ cells;

ii) the shape of Sertoli cell nuclei can change, perhaps due to nutrition treatment; iii) the

apparent location of the nucleus can be affected by the sectioning process. We can now

avoid most of these problems by using GATA4 as a marker for Sertoli cells, and thus

generate more robust data and interpretations. Indeed, with the benefit of hindsight, the

outcomes of our initial study could be explained by the effects of nutritional treatment

on the ease with which Sertoli nuclei can be definitively identified, reflecting de-

differentiation (see below). The only caveat would be that, in the study by Hötzel et al.

(1998), the dietary treatments were more stringent and had a much larger effect on testis

mass, to the extent that the length of the seminiferous tubules was affected. It is thus

still feasible that Sertoli cell numbers might change under extreme circumstances.

Indeed, in the present study, we detected a few PCNA-positive Sertoli cells, indicating

the retention of some proliferative ability after puberty (Tarulli et al. 2006), although

there were only 1.6 PCNA-positive cells per tubule and this value was not affected by

nutritional treatment and not related to changes in testis mass. The possibility that

Sertoli cells can proliferate after puberty suggests that, in future we might find ways to

replenish damaged testes and restore germ cell production.


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Conversely, Sertoli cell function was clearly affected by nutritional treatment, as

evidenced by changes in the several groups of molecular regulators that reflect critical

Sertoli-cell-specific processes:

1. Tight junctions – The pattern of distribution of the tight junction protein,

Claudin11, was disrupted in testis from underfed animals, as reported for adult

Djungarian hamsters and humans in which spermatogenesis had been severely

disrupted, experimentally or pathologically (Tarulli et al. 2008; Haverfield et al.

2013). In addition, underfeeding, loss of testis mass and disruption of

spermatogenesis, were associated with increased expression of Claudin11 and

decreased expression of ZO1, as reported for some non-reversible, pathological

models of defective spermatogenesis (Singh et al. 2009; Nah et al. 2011). It has

been hypothesized that the increase in Claudin11 expression is caused by an

insufficiency of inhibitory factors such as TGF-β during the later stages of the

spermatogenic wave (Nah et al. 2011) but, in the present study, testicular TGF-β

expression did not differ among treatments (data not shown), so we need to

investigate other factors that regulate Sertoli cell tight junctions. Interestingly,

the localization of Claudin11 protein was disorganized in the testis from

underfed rams, but the level of Claudin11 mRNA was increased, supporting the

hypothesis that the disruption of Sertoli cell tight junctions is due to post-

translational processing (Tarulli et al. 2008; Fink et al. 2009; Chihara et al.

2010).

2. Markers of Sertoli cell maturity – Compared to well-fed sheep, underfed sheep

had lower expression of GATA1, a marker of mature Sertoli cells (Beau et al.

2000) but higher expression of AMH, a marker for immature Sertoli cells (Rey

1998), suggesting that the Sertoli cells in underfed sheep were going through de-


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differentiation and reversal of maturation, thus regaining proliferative ability and

losing tight junctions (Kliesch et al. 1998; Steger et al. 1999; Sharpe et al.

2003). This hypothesis is consistent with our observation of PCNA-positive

Sertoli cells, indicating proliferative ability, and with the data for Claudin11 and

ZO1, indicating disruption of tight junctions. It is also feasible that these

responses lead to difficulty in counting Sertoli cells using classical histology,

thus explaining our earlier observation of changes in Sertoli cell numbers in the

sexually mature male sheep (Hötzel et al. 1998).

3. Other Sertoli cell-specific genes – As expected, KLm, SOX9 and MSI1 were

down-regulated in the testicular tissue from underfed sheep compared to tissue

from well-fed sheep. Comparable outcomes for KLm have been reported for

other experimental models of defective spermatogenesis (Packer et al. 1995;

Kissel et al. 2000), including the FSH β-subunit knock-out mouse (Johnston et

al. 2004). Similarly, testicular function is disrupted in mice lacking the Sox9

protein in their Sertoli cells (Lardenois et al. 2010). Aberrant expression of MSI1

is associated with abnormal spermatogenesis (Sutherland et al. 2014) and, in

mice with abnormal spermatogenesis caused by knock-out of FSH receptors or

androgen receptors, expression of MSI1 is lower than normal (Abel et al. 2008).

It is therefore likely that down-regulation of KLm, Sox9 and MSI1 is among the

causes for reduced spermatogenic efficiency in underfed sheep and, since these

three genes are mainly expressed in Sertoli cells (Abel et al. 2008;

O'Shaughnessy et al. 2008), these observations add further support to our

hypothesis that Sertoli cell function is compromised in underfed rams

experiencing a loss of testicular mass. However, mRNA expression of FSHR,

AQP8 and AR was not affected by nutritional treatment. This finding was not

surprising – in the mouse, it had been reported that germ cell ablation did not


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change the expression of AQP8 and FSHR (O'Shaughnessy et al. 2008) and that

expression of AR was positively correlated with FSHR expression (Johnston et

al. 2004). The expression of FSHR, AQP8 and AR might only be affected by

extreme under-nutrition rather than the relatively benign treatments used in the

present study.

In conclusion, in sexually mature male sheep, underfeeding reduces testicular mass and

leads to a reduction in spermatogenesis, and these responses are associated with a

reduction in the functional ability of the Sertoli cells, apparently by inducing a reversal

of differentiation evidenced by disorganized tight junction, up-regulation of Sertoli cell

immature marker and down-regulation of Sertoli cell mature marker. There was no

reduction in the number of Sertoli cells with the loss of testicular mass, although some

Sertoli cells in sexually mature male sheep do retain proliferative ability, suggesting a

capacity for some regeneration of the population. Further study is required to determine

whether the molecular mechanisms that are responsible for the reversible and non-

pathological change in spermatogenesis induced by under-nutrition can be applied to

other models, such as normal cycles or pathological disruption of gamete production.

Chapter 6-Small RNAs affect apoptosis and spermatogenesis

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Chapter 6

Roles of small RNAs in the effects of nutrition on apoptosis

and spermatogenesis in the adult testis

6.1 Abstract

We tested whether reductions in sperml quality induced by under-nutrition are

associated with increased germ cell apoptosis and disrupted spermatogenesis, and

whether these effects are mediated by small RNAs. Groups of 8 male sheep were fed for

a 10% increase or 10% decrease in body mass over 65 days. Underfeeding increased the

number of apoptotic germ cells (P < 0.05) and increased the expression of apoptosis-

related genes (P < 0.05) in testicular tissue. We identified 44 miRNAs and 35 putative

piRNAs that were differentially expressed in well-fed and underfed males (FDR <

0.05). Some were related to reproductive system development, apoptosis (miRNAs),

and sperm production and quality (piRNAs). Novel-miR-144 (miR-98), was found to

target three apoptotic genes (TP53, CASP3, FASL). The proportion of miRNAs as a

total of small RNAs was greater in well-fed males than in underfed males (P < 0.05) and

was correlated (r = 0.8, P < 0.05) with the proportion of piRNAs in well-fed and

underfed males. In conclusion, the reductions in sperml quality induced by under-

nutrition are caused, at least partly, by disruptions to Sertoli cell function and increased

germ cell apoptosis, mediated by changes in the expression of miRNAs and piRNAs.

Key words: nutrition, spermatogenesis, apoptosis, miRNAs, piRNAs


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6.2 Introduction

Spermatogenesis is regulated by a specific and complex genetic program that is

susceptible to many disorders that can induce infertility (Eddy 2002). In addition to

disease situations, spermatogenesis can be affected by environmental factors, such as

nutrition, that operate through normal physiological processes to profoundly change

testicular mass and the efficiency of sperm production, thus affecting sperm output

(Martin et al. 1994). In the male sheep, for example, 2 months of mild under-nutrition

leads to a reversible reduction in testis mass, impaired spermatogenesis, a reduction in

the numbers of sperm produced per unit mass of testis (efficiency of spermatogenesis),

and a reduction in sperm motility (Chapter 4). These effects seem to be mediated, at

least partly, by changes in the activity of the Sertoli cells (Chapter 5). However, a

thorough understanding of the effects of nutrition on spermatogenesis is elusive because

of the complexity of the process of spermatogenesis, and the breadth of the spectrum of

endocrine and paracrine signals that coordinate events from the initiation of meiosis

through the differentiation of germ cells to the generation of mature sperm (Brinster

2007).

Disruption of spermatogenesis by under-nutrition also seems to involve the pathways of

apoptosis (Martin et al. 2011), a crucial event in many physiological and pathological

conditions (Santos et al. 1999) that was detected long ago in the seminiferous

epithelium and is thought to be an important determinant of sperm output (Billig et al.

1996). The efficiency of spermatogenesis depends on the total number of cells at

successive stages of spermatogenesis (F.M. Cardoso 1985), so is probably regulated by

programmed cell death. Indeed, factors that disrupt spermatogenesis can induce

apoptosis in the testis – for example, suppression of FSH activity, which reduces Sertoli

cell proliferation and germ cell number, leads to loss of germ cells through apoptosis


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rather than through a decrease in proliferation (McLachlan et al. 1995; Meachem et al.

1999). Similarly, selenium deficiency results in apoptosis of germ cells by arresting the

cell cycles (Kaushal and Bansal 2007). Conversely, a relationship between the rate of

apoptosis and the level of nutrition has not been reported, not even for the sheep, a

model in which acute but reversible responses to nutrition are well documented (Martin

and Walkden-Brown 1995). The present study was therefore designed to test whether

nutrition affects germ cell apoptosis in the testis of the sexually mature male sheep. It

was also designed to investigate the molecular regulatory processes involved in the

responses to changes in nutrition.

Among the regulators of spermatogenesis, small RNAs, including microRNAs

(miRNAs) and PIWI-interacting RNAs (piRNAs), have recently become prominent.

miRNAs are small (~22 nucleotides) endogenous RNAs that negatively regulate gene

expression by targeting the 3’-untranslated region (3’UTR)(Krutzfeldt and Stoffel 2006)

and/or coding region (Hausser et al. 2013) of mRNAs. It has been reported that a global

loss of miRNAs, in germ cells or Sertoli cells, is detrimental for male fertility (Niu et al.

2011). By contrast, piRNAs are longer (26–32 nt) than miRNAs and can bind to PIWI,

a spermatogenesis-specific protein belonging to the Argonaute protein family (Lau et al.

2006; Liu et al. 2012a). The main function of piRNAs is to guide PIWI protein to

repress the transposable elements that protect genomic integrity (Luteijn and Ketting

2013). In addition, piRNAs derived from mRNAs play a role in the regulation of gene

expression (Lee et al. 2012). To date, piRNAs have been mainly found in the testis,

suggesting their role specific to spermatogenesis (Ro et al. 2007). Indeed, we have a

confluence of hypotheses here because it has been reported that some miRNAs are

crucial for the process of apoptosis (Li et al. 2011). For example, miR-98 expression is

reduced during apoptosis (Wang et al. 2011), miR-14 is a dose-dependent suppressor of

apoptosis, miR-278 antagonizes apoptosis (Jovanovic and Hengartner 2006), and


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transient inhibition of miR-21 in germ cell cultures increases the rate of apoptosis (Niu

et al. 2011).

As important regulators of spermatogenesis and germ cell apoptosis, miRNAs and

piRNAs should help explain the effects of nutrition on sperm production and sperm

quality in males, for example, male sheep. In the present study, we used sexually mature

male sheep as a model because the reversible effects of nutrition on testis mass, sperm

production and sperm quality are well documented (Cameron et al. 1988), thus

providing a solid foundation for studying the roles of small RNAs. We therefore 1)

profiled miRNAs and piRNAs in sheep testis; 2) investigated the relationships among

miRNA functions, spermatogenesis and germ cell apoptosis, particularly during

responses to nutrition; and 3) explored the potential for gene-derived piRNAs as

regulators of spermatogenesis in the testis of the sexually mature sheep.




No.1202) and all procedures were conducted in accordance with the approved protocol.


From late autumn to mid-winter, 16 Merino male sheep were housed in individual pens

in a building with windows allowing good penetration of natural light (CSIRO Floreat,

Western Australia, latitude 31o59’S). On entry, the rams were 24 months old, weighed

65.7 ± 4.7 kg, and had a scrotal circumference of 31.8 ± 2.5 cm. During a 3-week

acclimatization period, they were all fed daily with 750 g oaten chaff (8.4% crude

protein; 8.0 MJ/Kg Metabolisable Energy) and 200 g lupin grain (35.8% crude protein;

13.0 MJ/Kg Metabolisable Energy). They were then allocated into two dietary


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treatments (‘high’ and ‘low’) with the groups balanced for training success to semen

collection, body mass, scrotal circumference, temperament, poll-horn type, and sperm

quality (the percentage of live and motile sperm, sperm concentration). The high diet

was designed to allow the animals to gain 10% body mass over 65 days whereas the low

diet was designed to allow 10% loss in body mass. At the start of the treatment period,

individual daily allowance was adjusted to provide two dietary groups: animals fed the

High diet were offered 1.2 kg oaten chaff plus 0.3 kg lupin grain; animals fed the Low

diet were offered 0.51 kg chaff and 0.13 kg lupin grain. Every week, the animals were

weighed and the amount of feed offered to each individual was adjusted to ensure

achievement of target change in body mass. The outcomes for body and testis growth,

and for sperm production, have been reported before (Chapter 4).

6.3.2 Tissue collection and preservation

After 65 days of treatment, the animals were killed with an intravenous overdose of

sodium pentobarbitone, and the testes were immediately removed, dissected and

weighed. Three samples were chosen from the top, middle and bottom of both testes;

those from the right testis were snap-frozen in liquid nitrogen and stored at –80˚C for

total RNA preparation; those from the left testis were washed with 0.9% saline and then

fixed with 4% paraformaldehyde for 6 h, dehydrated and processed for routine

embedding in paraffin wax for histological analysis.

6.3.3 Evaluation of apoptosis

Terminal deoxynucleotidyl transferase mediated dUTP nick-end labeling (TUNEL) was

performed under the instruction of the ApopTag plus peroxidase in situ Apoptosis

Detection Kit (Chemicon International, USA). Briefly, deparaffinised tissue sections

(top part of left testes) were incubated with proteinase K (20 µg/ml), subjected to 3%


109

H2O2 at 37˚C for 30 min to inhibit endogenous peroxidase, and then incubated with

equilibration buffer at room temperature for 1 min. Each section was incubated with

TdT (terminal deoxynucleotidyl transferase) at 37˚C for 1 h and then washed in

stop/wash buffer for 10 min. The sections were incubated in anti-Digoxigenin

Peroxidase Conjugate at room temperature for 30 min and were stained with

diaminobenzidine (DAB) as a peroxidase substrate. After counterstaining with methyl

green, numbers of TUNEL-positive cells per tubule were counted in 50 tubules per

animal with the aid of a light microscope. All counting procedures were performed

‘blindly’.

6.3.4 Isolation of RNA and Reverse transcription

The trizol method was used to isolate total RNA (Hellani et al. 2000) with the quality

and quantity of RNA determined by Agilent 2100 Bioanalyzer (Agilent Technologies,

Santa Clara, CA) and Qubit 2.0 Fluorometer (Invitrogen, Carlsbad, CA), RNA with an

integrity number (RIN) higher than 7.0 was used for further analysis. A high capacity

RNA-to-cDNA kit from Applied Biosystems was used for reverse transcription.

6.3.5 Quantitative real-time PCR (qRT-PCR)

QRT-PCR was performed using SYBR Green (Fast SYBR® Green Master Mix;

Applied Biosystems) to detect mRNA relative expression, with primers specifically

targeting sheep fas ligand (FASL), tumor protein p53 (TP53) and caspase3 (CASP3)

genes designed using NCBI primer blast

(http://www.ncbi.nlm.nih.gov/tools/primerblast/index.cgi?LINK_LOC=BlastHome).

Primer sequences for GAPDH were obtained from a published source (Appendix Table

6.1) (Yu et al. 2010). Fluorescence signal was detected with StepOnePlus™ Real-Time

PCR System (Applied Biosystems). The primer specificity was confirmed by PCR

http://www.ncbi.nlm.nih.gov/tools/primerblast/index.cgi?LINK_LOC=BlastHome


110

amplification, agarose gel electrophoresis and amplicon sequencing (data not shown).

The total volume of each reaction contained 10 μl Fast SYBR Green Master Mix

(Applied Biosystems), 1 μl of each primer (20 pmol/μl), 7 μl of nuclease-free water, and

1 μl of DNA template (50 ng/μl). Samples were measured in triplicate using the

following program: 95 °C for 10 min for initial denaturation and then 40 cycles of 95 °C

for 20 s, followed by annealing/extension for 30 s at 60 °C. Analysis of melting curves

was used to monitor PCR product purity. Previous work had shown that the level of

expression of GAPDH is relatively high and consistent during testicular development in

the sheep, indicating its suitability as a housekeeping gene expressed at similar levels in

somatic and germ cell populations (Yu et al. 2010). The ΔΔCt method was used to

analyze relative gene expression (Blais et al. 2008). The same sample was always used

as calibrator. The gene expression (ΔCt value) was calculated on the basis of

quantification cycles (Ct) (ΔCt = Ct target gene – Ct GAPDH). The levels of expression of

apoptosis-related genes were calculated in relation to the calibrator (ΔΔCt = mean ΔCt

sample – mean ΔCt calibrator). Relative expression (RQ) was calculated using

StepOnePlus™ Real-Time PCR System (Applied Biosystems) and the formula: RQtarget

gene = 2-ΔΔCt.


In each sample, 1.0 µg of total RNA was used to construct miRNA libraries with a


Diego, CA) according to the manufacturer’s instruction. PCR amplification was


with unique indices. Quantitative real-time PCR (qRT-PCR) was performed for library

quantification using the StepOnePlus™ Real-Time PCR System (Applied Biosystems,

Carlsbad, CA) and KAPA SYBR Fast ABI Prism qPCR kit (Kapa Biosystems, Woburn,


111

MA). Individual libraries were then pooled for sequencing at Génome Québec

(Montréal, Canada) using the HiSeq 2000 system (Illumina) to generate 50 b single

reads. All the reads were demultiplexed according to their index sequences using


were removed from the dataset. The small RNAs sequencing reads with good quality

were subjected to 3’ adaptor sequence trimming, and all reads were mapped by blastn to

the non-coding RNA sequences (Rfam) to remove sequences belonging to tRNA,

snoRNA, rRNA, and other non-coding RNAs.


The miRNAs were identified using the methods outlined in a previous study (Liang et

al. 2014). Briefly, known miRNAs were identified by mapping the filtered 18 to 25 nt

sequences to miRBase (release version 20), a searchable database of published miRNA

sequences and annotation (Kozomara and Griffiths-Jones 2014). All reads from 16

libraries were pooled to predict novel miRNA using miRDeep2 based on the reference

genome sequence of OAR3.1 (http://www.livestockgenomics.csiro.au/). Small RNA

sequences with a miRDeep2 score higher than 5 and read numbers larger than 10 were

defined as novel miRNAs in sheep. The novel miRNA precursor sequences were then

combined with the known miRNA precursor sequences to form a new custom reference

database. Sequencing reads from different samples were mapped to the new custom

reference database to get the read number for the known and novel miRNAs for each

sample. Homologous miRNAs were identified by the method described previously (Jin

et al. 2014).

The conservation of known miRNAs was analyzed based on the definitions for “highly

conserved”, “conserved”, and “poorly conserved” from Targetscan (Lewis et al. 2003).

More specifically, highly conserved miRNAs are those conserved across most


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vertebrates; conserved miRNAs are those conserved across most mammals, but usually

not beyond placental mammals; poorly conserved miRNAs are those not present in the

above two groups. In this study, ovine-specific miRNAs were defined by using two

criteria: 1) they belong to a poorly conserved group; 2) their seed region sequences have

only been reported previously in sheep.

The genomic location of the miRNAs was searched for using the UCSC Genome

Browser (http://genome.ucsc.edu/) based on the reference genome sequence of OAR3.1

(http://www.livestockgenomics.csiro.au/). The miRNA genes are distributed across

chromosomes either individually, or in clusters. A cluster is a group of miRNA genes

located within a short distance (10 Kb) on the same chromosome, based on the

definition in the miRBase database (http://www.mirbase.org). In the present study, all

the known and novel miRNAs were grouped into various clusters based on their

genomic location.

6.3.8 piRNA characterization

To identify piRNAs, sequencing reads that ranged from 26 to 32 nt were mapped to the

ovine genome by Bowtie (version 1.0.1). Reads that could not be perfectly mapped to

the genome were discarded, and the remainders were de-duplicated to unique

sequences. The filtered unique reads were subjected to an online predictor

(http://59.79.168.90/piRNA/analysis.php), that relies on the training sets from non-

piRNA and piRNA sequences of five model species sequenced: rat, mouse, human, fruit

fly and nematode, to predict piRNA candidates (Zhang et al. 2011). The positions in the

ovine genome of these candidates were obtained by Bowtie and, to avoid confusion

caused by multiple locations, only those with a single location were further analyzed.

The piRNAs in each library were quantified by blastn and customized perl scripts. All

the sequencing data were deposited in a publicly available Gene Expression Omnibus

http://www.mirbase.org/

http://59.79.168.90/piRNA/analysis.php


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database (http://www.ncbi.nlm.nih.gov/geo/), the data are accessible through GEO

accession number GSE62797.

6.3.9 Identification of differentially expressed (DE) miRNAs and piRNAs

Differential expression (DE) of miRNA/piRNAs between the nutritional treatments was

investigated using the bioinformatics tool, edgeR (Robinson et al. 2010), that utilizes a

negative binomial distribution to model sequencing data. The expression of

miRNAs/piRNAs in each library was normalized to counts per million reads (CPM) by

the following formula: CPM = (number of miRNAs/piRNAs reads/total reads number

per library) × 1,000,000. miRNAs/piRNAs with CPM > 5 in at least 50% of the samples

were subjected to analysis of differential expression. Fold change (FC) was defined as

the ratio (low diet/high diet) of the arithmetic means of CPM values. Significant

differential expression was accepted when false discovery rate (FDR) was < 0.05 based

on Benjamini and Hochberg multiple-testing correction (Benjamini et al. 2001), as well

as FC < 0.67 or > 1.5 (McCarthy and Smyth 2009).

6.3.10 Validation of miRNA expression using stem-loop qRT-PCR

The TAQMAN miRNA assay was used to validate miRNA expression following the

manufacturer’s recommendation (Applied Biosystems). In brief, cDNAs were reverse

transcribed from 10 ng total RNA, using 5 X specific miRNA RT primer, and then

amplified using a 20 X TAQMAN miRNA assay. StepOnePlus™ Real-Time PCR

System (Applied Biosystems) was used to detect the fluorescence signal. miRNAs with

cycle threshold (Ct) values > 35 were considered as having not been expressed. In this

study, U6 snRNA was used as an internal control (Liu et al. 2013) and three biological

replicates were performed. The 2-ΔΔCt method was used to analyze the expression level

and all statistical analyses were carried out using SPSS software (Version 20). One-way

http://www.ncbi.nlm.nih.gov/geo/


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ANOVA was used to compare the groups, and P < 0.05 was considered significant.

Data are expressed as Mean ± SEM.

6.3.11 miRNA target prediction and functional analysis

TargetScan Release 6.0 (http://www.targetscan.org/) (Liu et al. 2012b) and miRanda

(http://www.microrna.org/microrna/home.do) (Birney et al. 2006) were used to predict

the target genes for selected miRNAs. The 3'UTR sequences of genes from sheep were

obtained from Ensembl Gene 75 Ovis aries genes (Oar_v3.1)

(http://uswest.ensembl.org/). The predicted target genes by both TargetScan (default

parameters; Bao et al. 2013) and miRanda (Total score ≥145, Total energy ≤ –10;

Bao et al. 2014) for each miRNA were further analyzed through ingenuity pathway

analysis (IPA; Ingenuity Systems, www.ingenuity.com). The significance of the

predicted function in IPAs was determined using a corrected P value calculated by the

Benjamini-Hochberg method (FDR: Benjamini et al. 2001). Threshold with FDR < 0.05

and molecule number > 2 were used to enrich significant biological functions of each

miRNA.

6.3.12 miRNA target validation using dual luciferase reporter assay

The entire 3’UTRs of TP53, BCL2-like 1(BCL2L1), CASP3 and FASL were amplified

from sheep genomic DNA by the method of PCR. All the primers are shown in

Appendix Table 6.2. Both PCR products were cloned into the pmirGLO Dual-

Luciferase miRNA Target Expression Vector (Promega) using the Xho1 and Sal1

restriction sites.

A sheep fetal testis cell line (ATCC® CRL-6546) was cultured in ATCC-formulated

Dulbecco's Modified Eagle's Medium (Catalog No. 30-2002), supplemented with 10%

fetal bovine serum (Gibco, Invitro-gen, Carlsbad, CA, USA), in a 37 °C incubator with

http://www.targetscan.org/

http://www.microrna.org/microrna/home.do


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5% CO2. The 60 nM miR-98 mimics/miRNA mimic negative control (Ambion) was co-

transfected with 200 ng luciferase reporter containing BCL2L1, CASP3 or FASL 3’UTR

using Lipofectamine 2000 reagent (Invitrogen) in 24-well plates. After transfection for

48 h, the Dual-Glo luciferase assay system (Promega) and SpectraMax M3 system were

used to obtain readouts of firefly and Renilla luciferase. All the firefly luciferase

readouts were normalized to their matching renilla luciferase readouts.

6.4 Results

6.4.1 Relationship between nutrition and apoptosis

TUNEL-positive germ cells were observed in all treatments (Fig. 6.1A - Fig. 6.1C), but

most were seen in the early stages of spermatogenesis (spermatogonia and

spermatocytes) and none were seen amongst spermatids or sperm. The number of

TUNEL-positive germ cells per tubule was greater in underfed (1.4 ± 0.3) than in well-

fed males (0.49 ± 0.06; P < 0.05). A relationship between under-nutrition and apoptosis

was further supported by the expression of the apoptosis-related genes, FASL, TP53 and

CASP3, with all three showing greater expression in underfed sheep than well-fed sheep

(P < 0.05, Fig. 6.1D - Fig. 6.1F).


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Figure 6.1. TUNEL-positive cells (arrows) detected in the testis in sexually mature sheep fed the High

diet (A) or the Low diet (B). Negative control without terminal deoxynucleotidyl transferase (C). The

scale bar represents 50 µm. D, E, F: relative mRNA expression for apoptosis-related genes, normalized to

GAPDH. Values are mean ± SE, N = 8 for each treatment. Significant effect of diet: *P < 0.05.

6.4.2 Profiling of small RNAs in the ovine testis

A total of 64 million high-quality small RNA reads were obtained from 16 testes (8

from each dietary group), with an average of 4.2 million reads per library (range: 1.2

million to 7.4 million). There was a bimodal length distribution with two peaks at 22

and 30 nt (Appendix Fig. 6.1). For the miRNA class (18 to 25 nt), a total of 13 million

reads were obtained, of which 10.4 million were mapped to the ovine genome. Mapping

to Rfam database allowed us to remove 1.7 million reads that could be mapped to

snoRNAs, snRNAs, tRNAs, rRNAs or other non-coding RNAs. Among the remaining

8.7 million reads, we identified 1.9 million unknown small RNAs, 5.5 million known

and 1.3 million novel small RNAs, resulting in the identification of 110 known miRNAs

and 194 putative novel miRNA candidates from the ovine testis. All novel miRNA

candidates were mapped to all vertebrate miRNAs in miRBase, a database of published


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miRNA sequences and annotation, to identify the homologues of novel miRNAs

(Appendix Table 6.3). Based on the definition in Targetscan, among the 110 known and

194 novel miRNA candidates identified in sheep testis, 41 known and 62 novel miRNA

candidates were highly conserved, 28 known and 21 novel miRNA candidates were

conserved, and 36 known and 42 novel miRNA candidates were poorly conserved.

From the poorly conserved group, 5 known and 69 novel miRNA candidates were

sheep-specific and found in all of our animals. In the genomic context, 16 clusters of

miRNAs were identified on 9 chromosomes and two large clusters comprising 40

miRNA precursors were identified on Chromosome 18 (Appendix Table 6.4).

The sequences of piRNA were predicted based on previously published databases, as

described above. There were approximately 44 million reads of length 26 to 32 nt, of

which 23.8 million reads mapped perfectly to the ovine genome. In total, 6 million reads

representing 13567 putative piRNAs were identified and named with the prefix “oar-

piR” followed by a number (data deposited in Gene Expression Omnibus). There were

13241 putative piRNA candidates mapping to unique loci, and these putative piRNAs

were selected for direct comparison between the well-fed and underfed sheep.

6.4.3 Identification of differentially expressed (DE) miRNAs and piRNAs

There were 44 DE miRNAs in testicular tissue from underfed and well-fed males, of

which 21 were known and the rest were novel. Among all the DE miRNAs, 20 miRNAs

including novel-miR-144 showed greater expression in underfed than in well-fed males

(Fig. 6.2). For putative piRNAs, a total of 35 were DE in underfed and well-fed male

sheep (Appendix Table 6.5), and among them, two (oar-piR-12568, oar-piR-6442) were

derived from the 3’UTR of mRNAs (FLVCR2, KRTAP10-2). One putative piRNA, piR-

9006, was derived from the 5’UTR of mRNA (ATP2B4), and 11 putative piRNAs were


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derived from introns within 7 genes (MORF4L1, SNX5, STOX1,

ENSOARG00000013508, FAH, CLEC16A, DCAF6) (Appendix Table 6.6).

Figure 6.2. Box plot showing the differentially expressed (DE) miRNAs in testis from sexually mature

sheep fed a high diet (yellow bar) and the low diet (blue bar). Central lines inside the boxes indicate

median values, box width indicates 25% and 75% quartile ranges around the median, “T” indicates the

maximum and minimum values, and black dots represent outliers. N = 8 for each treatment.

In addition, the expression of DE miRNAs detected by the RNA-sequencing data

reflected qRT-PCR expression results. Six known and 6 novel miRNAs were selected

from the DE miRNAs and, for all of them, the qRT-PCR expression results were

consistent with the sequencing data. For example, both the sequencing data and the

qRT-PCR results showed that oar-miR-411b-3p was expressed at a lower level in

underfed males than in well-fed males (Appendix Fig. 6.2). In addition, the expression

of novel-miR-144 was down regulated in well-fed male sheep (Appendix Fig. 6.2).


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6.4.4 Relationship between nutrition and top ten expressed miRNAs

The top ten expressed miRNAs were the same in tissues from underfed males and well-

fed males, and their total expression did not differ between treatments (68% versus and

75% of the total miRNAs) (Appendix Fig. 6.3). However, expression of three of the top

ten miRNAs did differ between treatments – specifically, oar-miR-10b and oar-miR-26a

showed greater expression in underfed than well-fed males, whereas novel-miR-31

showed the opposite effect (Fig. 6.2).

6.4.5 Relationship between nutrition and genomic location of miRNAs

There were two large clusters of miRNAs on Chromosome 18 and they included 10

miRNAs for which expression was significantly lower (FDR < 0.05, FC > 1.5) in

underfed than well-fed males (Appendix Table 6.4).

6.4.6 Relationships between miRNAs and piRNAs

The proportion of miRNAs/piRNAs was defined by the ratio: miRNAs or putative

piRNAs reads number /total small RNAs reads number. Interestingly, the proportion of

putative piRNAs was greater than the proportion of miRNAs in well-fed males (P <

0.05, Fig. 6.3A) whereas the proportion of miRNAs was greater in underfed males (P <

0.05, Fig. 6.3A). In addition, there was a positive correlation between the proportion of

miRNAs and proportion of putative piRNAs in testicular tissue from well-fed males (r =

0.8, P < 0.05, Fig. 6.3B) and underfed males (r = 0.8, P < 0.05, Fig. 6.3C).


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Figure 6.3. Proportions of putative piRNAs (black columns) and miRNAs (white columns) in each

dietary treatment (A). Correlation between proportions of piRNAs and miRNAs in testis from sheep fed

the High diet (B) or the Low diet (C). N = 8 for each treatment. The proportions of miRNAs or piRNAs

were calculated as number of miRNAs or piRNAs reads divided by the total number of small RNA reads.


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6.4.7 Functional analysis of miRNAs

The targets of the miRNAs were predicted with TargetScan and miRanda and it was

found that 74 sheep-specific miRNAs would target 7783 genes. The function of these

targeted genes was analyzed by IPA. The most common functions are listed in Table 6.1

Table 6.1. Functions or diseases linked to 74 sheep-specific miRNAs detected in the testis of sexually

mature sheep. P values indicate the relevance of the function, with lower values suggesting greater

relevance.

Category Function or Disease p-Value

Small Molecule Biochemistry Synthesis of lipid 3.04E-03

Endocrine System Development and Function Synthesis of hormone 8.24E-03

Lipid Metabolism Synthesis of steroid 8.24E-03

Organ Morphology Abnormal morphology of enlarged testis 1.12E-02

Reproductive System Development and Function Abnormal morphology of enlarged testis 1.12E-02

Cell Morphology Size of connective tissue cells 1.26E-02

Small Molecule Biochemistry Steroidogenesis 1.33E-02

Endocrine System Development and Function Steroidogenesis 1.33E-02

Reproductive System Development and Function Production of sperm 2.37E-02

Cellular Function and Maintenance Production of sperm 2.37E-02

Cellular Growth and Proliferation Production of sperm 2.37E-02

Tissue Morphology Quantity of macrophages 2.37E-02

Cell Morphology Size of cells 2.37E-02

Connective Tissue Development and Function Quantity of connective tissue cells 3.82E-02


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where it can be seen that, in general, they are related to synthesis of lipids and

hormones, to testis morphology, and production of sperm. Also, 3567 genes were

predicted to be targeted by the miRNAs clustered on Chromosome 18. Their most

common functions were related to testis morphology (P < 0.05).

In addition, 44 DE miRNAs were predicted to target 1,597 genes (data not shown), and

a total of 14 functional categories were identified, including those involved in the

development and function of the hematological and reproductive systems (Table 6.2).

We identified 11 biological processes that were targeted within the category of

development and function of the reproductive system, including apoptosis and Sertol

cell number (Fig. 6.4). Furthermore, IPA analysis revealed that these DE miRNAs were

also involved in 76 signaling pathways, of which apoptosis signaling, germ cell-Sertoli

cell junction signaling, Sertoli cell-Sertoli cell junction signaling are among the most

relevant pathways (Appendix Fig. 6.4). To test our hypothesis that apoptosis explains

the poor sperm quality in underfed animals, we further analyzed the DE miRNAs in the

apoptosis-signaling pathway. We found that 12 genes involved in apoptosis could be

targeted by 9 DE miRNAs, with novel-miR-144 targeting four of the apoptosis-related

genes (FASL, CASP3, BCL2L1, TP53).


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Table 6.2. The predicted functions of the DE miRNAs analyzed by ingenuity pathway analysis (IPA).

values indicate relevance of the function, with lower values suggesting greater relevance.

Category p-value Number of molecules

Hematological system development and function 1.3E-03-1.3E-03 3

Tissue morphology 1.3E-03-3.31E-02 19

Connective tissue development and function 1.45E-03-3.31E-02 14

Reproductive system development and function 3.49E-03-4.77E-02 40

Organ morphology 3.92E-03-4.77E-02 38

Cell morphology 1.09E-02-1.8E-02 10

Molecular transport 1.13E-02-4.77E-02 7

Small molecule biochemistry 1.19E-02-4.77E-02 6

Cellular growth and proliferation 2.2E-02-4.01E-02 11

Lipid metabolism 3.24E-02-4.77E-02 6

Cell death and survival 3.31E-02-3.31E-02 2

Cellular function and maintenance 3.31E-02-3.31E-02 2

Drug metabolism 4.77E-02-4.77E-02 3

Endocrine system development and function 4.77E-02-4.77E-02 3


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Figure 6.4. The top 11 functions of DE miRNAs related to development and function of the reproductive

system. X-axis represents the –lg (p-value) and indicates the relevance of the function to the DE miRNAs,

with a lower p-value (a higher value of – lg(p-value)) suggesting greater relevance.

6.4.8 Validation of predicted miRNA targets

We validated FASL, CASP3, BCL2L1 and TP53 as direct targets for novel-miR-144 in a

biological process using a dual luciferase reporter assay on a sheep fetal testis cell line.

Co-transfection of novel-miRNA-144 mimics and pmirGLO vector containing 3'UTR of

TP53 decreased normalized luciferase activity by 72% compared to the pmirGLO

vector no-insert control (P < 0.01, Fig. 6.5). There was no difference between the

pmirGLO vector no-insert control and miRNA mimics negative control. Similar results

were observed with CASP3 and FASL for which there were 67% and 74% decreases

compared with the no-insert control (P < 0.01, Fig. 6.5). However, normalized


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luciferase activity of pmirGLO that contained BCL2L1 was not affected by novel-miR-

144 (Fig. 6.5).

Figure 6.5. Normalized luciferase activity. Black column: co-transfection of novel-miRNA-144 mimics

and reconstructed pmirGLO vector (containing 3'UTR of target genes). Grey column: co-transfection of

novel-miRNA-144 mimics and pmirGLO vector (without 3'UTR of target genes). White column: co-

transfection of miRNA mimics negative control and reconstructed pmirGLO vector (containing 3'UTR of

target genes). Values are mean ± SE (N = 8 per treatment). a, b, c: different letters denote statistically

significant differences within each target gene.

6.5 Discussion

This is the first comprehensive description of small RNAs in sheep testis and, by

combining these observations with bio-informatics analysis and experimental

validation, in the context of an experimental model of reversible testis growth in the

sexually mature males, we have been able to identify miRNAs and piRNAs that are

associated with the control of testis function. Importantly, we have also shown how the

expression of these small RNAs changes in response to under-nutrition in association


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with apoptosis in germ cells. Our findings strongly support the hypothesis that the

decline in sperm production and sperm quality induced by under-nutrition (Chapter 4)

in the sexually mature sheep are mediated at least partly by increased apoptosis in germ

cells.

Profile of small RNAs in sheep testis

Approximately 63% of the known miRNAs and 43% of the novel miRNAs that we

detected are conserved or highly conserved, suggesting that their biological functions

are conserved across species (Yan et al. 2009). The present study also revealed 5 known

and 69 novel miRNAs that are sheep-specific, most with functions focused on synthesis

of lipid or hormones, or production of sperm. This differed from the conserved

miRNAs, suggesting that sheep-specific miRNAs may represent an important source of

novel functionalities during evolution, an idea previously raised for the human and the

mouse (Guo et al. 2009). The ten most highly ranked miRNAs are relatively well

conserved across species – for example, four miRNAs (miR-143, let-7a, let-7f, miR-

148a) in pig testis (Li et al. 2011) and two miRNAs (let-7a, let-7f) in human testis

(Yang et al. 2013) ranked within the ten most highly expressed miRNAs found in this

study. This suggests that they play similar roles in the control of testis function for a

variety of mammalian species. To our knowledge, the present study is the first to profile

piRNAs in sheep testis. Approximately 10% of the small RNAs were predicted to be

putative piRNAs, similar to pigs (13%), mice (10%) and humans (9%) (Gan et al. 2011;

Liu et al. 2012a; Yang et al. 2013). However, in contrast to miRNAs, the sequences of

the putative piRNAs are weakly conserved among species (Aravin et al. 2006).

Impact of undernutrition on miRNAs and apoptosis


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The predicted targets of the 44 miRNAs that were differentially expressed between

well-fed and under-fed sheep were related to tissue morphology and development of the

reproductive system. Among all the DE miRNAs, there were 24, including novel-miR-

31, that were more highly expressed in well-fed sheep than in under-fed sheep. Novel-

miR-31 is homologous with miR-34 which has been shown to enhance germ cell

phenotype during the late stages of spermatogenesis in other species (Bouhallier et al.

2010). Under-nutrition was associated with greater expression of 20 miRNAs, including

miR-99a, which reduces the expression of the tight-junction-related protein, ZO-1

(Turcatel et al. 2012).

The predicted targets of top ten expressed miRNAs were functionally related to diseases

of the reproductive system, connective tissue function and development, tissue

morphology and cellular growth and proliferation, suggesting a critical role in

regulating reproductive performance. Under-nutrition did not affect the composition of

the top ten, but did affect the expression of three of them - specifically, underfed males

showed higher expression of oar-miR-10b and oar-miR-26a, and lower expression of

novel-miR-31 (miR-34c), than well-fed males. The genes that were up-regulated in

underfed males have been implicated in the induction of apoptosis (miR-26a: Kota et al.

2009) and testis dysfunction (miR-10b: Abu-Halima et al. 2014), whereas novel-miR-

31(miR-34c), which was up-regulated in well-fed males, enhances the expression of

germ cell-specific genes in late spermatogenesis (Bouhallier et al. 2010). These roles

are consistent with the conclusion that, during the loss of testis mass with under-

nutrition, apoptosis is induced and spermatogenesis is disrupted.

We had hypothesized that increased apoptosis is one of the main causes of the negative

effect of under-nutrition on sperm production and spermatogenic efficiency (Chapter 4).

Underfed sheep had more TUNEL-positive germ cells and higher expression of


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apoptosis-related genes (FASL, TP53, CASP3: Stuart et al. 1997; Janicke et al. 1998;

Benard et al. 2003) than well fed sheep, thus supporting our hypothesis. Interestingly,

TUNEL-positive germ cells were observed only in the early stages of spermatogenesis

(spermatogonia and spermatocytes) and not in spermatids or sperm. This observation

agrees with reports for other species (Blanco-Rodriguez and Martinez-Garcia 1996;

Rodriguez et al. 1997; Weinbauer et al. 2001), suggesting that spermatogenic cells are

mostly eliminated before the first meiotic division. However, there are a few reports of

apoptosis in spermatids or sperm (Heninger et al. 2004). It is therefore possible that our

TUNEL assay was not able to adequately label spermatids or sperm due to either the

compact nature of their DNA or their limited production of mRNA, generally thought to

be necessary for apoptotic death (Heninger et al. 2004).

We focused our investigation on novel-miR-144 (homolog: miR-98) for two reasons:

first, novel-miR-144 was up-regulated in underfed sheep; second, novel-miR-144

targets four of the apoptosis-related genes (FASL, CASP3, TP53 and BCL2L1: Wang

and Lee 2009; Wang et al. 2011). Our working hypothesis, that the negative effects of

under-nutrition on spermatogenic efficiency are mediated by increased apoptosis, is

supported by studies in mice (Siragam et al. 2012) where the homologue to novel-miR-

144 has been shown to be pro-apoptotic. However, our finding contradicts some

previous reports showing that miR-98 is up-regulated in small-cell lung cancer (Du et

al. 2009) and breast cancer (Deng et al. 2014), two conditions associated with cell

proliferation rather than apoptosis. We also found a positive correlation between

expression of novel-miR-144 and the apoptosis-related genes, FASL, CASP3 and TP53,

and therefore contradict the conventional wisdom that the expression of miRNAs is

negatively correlated with their target genes (Iorio et al. 2005). However, this type of

positive correlation has been reported previously in mice (Nunez et al. 2013) and could

be interpreted as miRNAs playing a role in homeostatic mechanisms that maintain


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stability within the organism. The underfed sheep had increased TUNEL staining

indicating increased apoptosis, most due to increased expression of the apoptosis-related

genes, FASL, CASP3 and TP53. miRNAs are thought to ‘fine tune’ the physiological

balance within an organism, so it is possible that the increased expression of novel-miR-

144 was a response to the increase in apoptosis, rather than a cause of the apoptosis.

Novel-miR-144 relationships are obviously complex, but we propose that miR-98, and

thus its sheep homologue (novel-miR-144), is both pro-apoptotic and anti-apoptotic

depending on the physiological condition of organism.

Impact of nutrition on putative piRNAs

Of the 35 differentially expressed piRNAs found in well-fed and underfed sheep, 60%

were derived from intergenic and repeated regions of the genome. Based on

observations in pigs, these DE piRNAs may have specific germline functions, including

the repression of transposons and other repetitive elements (Liu et al. 2012a). The

remaining 40% of the piRNAs that were differentially expressed in the current study

were derived from genes. It is difficult to predict the specific functions of the piRNAs

identified in the present study because our current understanding of their function is

limited and their sequence identities are poorly conserved among species. We therefore

focused only on the gene-derived DE piRNAs and predicted their functions based on

previous references – for example, feline leukemia virus subgroup C receptor-related

protein 2 (FLVCR2), which produces piR-12568 and functions as a calcium transporter

and affects reproduction and respiration (Meyer et al. 2010). In addition, P-type Ca2+-

ATPase isoform 4 of the plasma membrane (ATP2B4), which produces piR-9006, is

responsible for sperm motility (Gong et al. 2009). Furthermore, five piRNAs (piR-

10216, piR-10217, piR-10729, piR-10730, piR-10731) that showed higher expression in

underfed than well-fed sheep, were derived from MORF4L1, a gene that is highly


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expressed during male meiosis and spermatogenesis (Smirnova et al. 2006) and seems

likely to play a crucial role in male reproduction. To date, the mechanism through which

piRNAs regulate sperm production and sperm quality is not clear. However, there has

been a suggestion that a piRNA pathway was active in Sertoli cells (Lim et al. 2013)

and, as we have shown, Sertoli cell function is reduced in underfed males (Chapter 5).

We therefore expect that piRNAs affect male reproduction by regulating the function of

Sertoli cells, a hypothesis that needs to be tested in further studies.

Relationships between miRNAs and piRNAs

In well-fed males, the proportion of putative piRNAs, as a percentage of total small

RNAs, was greater than the proportion of miRNAs. This relationship was reversed in

underfed males. These observations suggest that nutrition has distinct, differential

effects on the expression of small RNAs in sheep testis. This relationship could be

explained by the higher rate of sperm production in well-fed males compared to

underfed males (Chapter 4), because piRNAs are specifically expressed in germ line

cells (Girard et al. 2006). Interestingly, there was a positive correlation between the

proportions of miRNAs and putative piRNAs in testicular tissue, for both underfed and

well-fed males, indicating a synergistic relationship between these classes of small

RNAs. Further studies are needed to test this hypothesis.

In conclusion, under-nutrition is associated with increased germ cell apoptosis, as

evidenced by increases in TUNEL-positive germ cells and in the expression of genes

and miRNAs that are related to apoptosis. Furthermore, in under-fed males, the

differential expression of miRNAs and piRNAs is likely to help explain the negative

effects of nutrition on spermatogenesis, spermatogenic efficiency and sperm quality.

Chapter 7-mRNAs and alternative pre-mRNA splicing affect apoptosis and spermatogenesis

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Chapter 7

Functional changes in mRNA expression and alternative pre-mRNA

splicing associated with the effects of nutrition on apoptosis and

spermatogenesis in the adult testis

7.1 Abstract

In this study, we tested whether defective spermatogenesis and increased germ cell

apoptosis induced by under-nutrition is associated with a change of mRNA expression

and pre-mRNAs alternative splicing in the genes of testis. Groups of 8 male sheep were

fed for a 10% increase or 10% decrease in body mass over 65 days. We identified 2243

mRNAs, including TP53 and Claudin 11 were differentially expressed in underfed and

well-fed sheep (FDR < 0.1), and found they were predominantly related to germ cells,

testis size, cell cycle and spermatogenesis. Furthermore, 940 miRNA-mRNA pairs (48

miRNAs, 269 mRNAs) were indentified based on the target prediction and the negative

regulatory effect miRNAs on mRNA expression levels. Their functions are involved in

abnormal morphology of reproductive system, apoptosis and male infertility. Nutrition

did not affect the total number of alternative splicing junctions between treatments, but

it affected 1040 alternative splicing events (FDR < 0.05, ∆PSI > 10%). In total of 788

genes, including CREM, MAP2, HIPK3 and TRa2β, were differentially spliced between

dietary treatments, the functions of these genes were related to protein localization,

cellular metabolic process, post-translational protein modification and spermatogenesis.

We conclude that the changes of mRNAs and pre-mRNA alternative splicing induced

by under-nutrition are, at least partly, contributing to disrupted spermatogenesis and

increased germ cell apoptosis.


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Key words: nutrition, spermatogenesis, apoptosis, mRNAs, pre-mRNA alternative

splicing

7.2 Introduction

The development of mature haploid sperm from diploid spermatogonial cells (Hecht

1998) can be affected by many factors, including photoperiod, hormones, temperature

and nutrition. The effects of nutrition on testis mass in the sexually mature male have

long been known, as has the direct relationship between testicular mass and sperm

production (Oldham et al. 1978). In addition, with change in testicular size, the

efficiency of sperm production also changes (Walkden-Brown et al. 1994b). We have

been investigating the cellular and molecular processes of the testis response to

nutrition, and we have found that under-nutrition despaired spermatogenesis in adult

sheep (Chapter 5).

Within the testis, spermatogenesis is a strictly regulated process, at both the

transcriptional and the post-transcriptional level (review: Papaioannou and Nef 2010).

In recent years, a novel mechanism of post-transcriptional control, mediated by

microRNAs (miRNAs), has emerged as an important regulator of spermatogenesis

(review: Papaioannou and Nef 2010). miRNAs (miRNAs) are small (~22 nucleotides)

endogenous RNAs that negatively regulate gene expression by targeting the

3’untranslated region (3’UTR) (Krutzfeldt and Stoffel 2006) and/or coding region

(Hausser et al. 2013) of mRNAs. We have recently found that the expression of a

number of miRNAs is affected by nutrition in sexually mature male sheep, and most of

the predicted targets of the differentially expressed miRNAs were mainly involved in

reproductive system development and function (Chapter 6). However, the regulatory

relationship between these miRNAs and their corresponding mRNAs targets in testis

remains to be verified. We therefore decided to profile mRNA expression in the testes


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of well-fed and underfed male sheep using RNA-seq so we could explore the

relationships between the miRNAs we had identified and their putative targets.

In addition to the disruption of spermatogenesis, under-nutrition of sexually mature

male sheep increased apoptosis in germ cells (Chapter 6). In this situation, we were

especially interested in miR-98 because it is critical for apoptosis (Wang et al. 2011),

and observed higher levels of expression in underfed sheep than in well-fed sheep

(Chapter 6). Surprisingly, miR-98 expression was positively correlated with the

expression of apoptosis-linked genes (FASL, CASP3, TP53), contradicting the

conventional view that there is always a negative correlation between miRNA

expression and the expression of their target genes (Iorio et al. 2005). A positive

correlation between miRNAs and their corresponding targets has been reported in

humans and mice (Nunez-Iglesias et al. 2010; Nunez et al. 2013). Since the molecular

mechanisms through which miRNAs regulate the expression of apoptosis-related genes

are still controversial, we decided to explore these processes further using our nutrition

model.

It has also been reported that spermatogenesis and a large number of apoptotic factors

are regulated by alternative pre-mRNA splicing that generates multiple transcript

species from a common mRNA precursor and thus raises protein diversity and allows

the system to cope with the increasingly broad spectrum of functional and behavioural

complexity (Walker et al. 1996; Schwerk and Schulze-Osthoff 2005). To date, eight

types of alternative splicing have been reported: cassette exon, alternative 5' splice site,

alternative 3' splice site, mutually exclusive exon, coordinates cassette exons, alternative

first exon, alternative last exon and intron retention (Ding et al. 2014). We therefore

also tested the hypothesis that nutritional treatment will induce differences in alternative


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splicing, and these changes will be related to the regulation of spermatogenesis and

germ cell apoptosis in the testis.

Overall, this study used testicular tissue from under-fed and well-fed sexually mature

sheep to pursue three objectives: 1) To investigate the differences of the expression of

mRNAs; 2) To investigate the influence of miRNAs on spermatogenesis and the

expression of apoptosis-related genes; 3) To investigate the relationships between

alternative pre-mRNA splicing and spermatogenesis and apoptosis.




No.1202).


From May to July (autumn-winter), 16 Merino male sheep (age 24 months, body mass

65.7 ± 4.7 kg, scrotal circumference 31.8 ± 2.5 cm) were housed in individual pens in a

building with windows that allowed good penetration of natural light at Floreat,

Western Australia. During the 3-week acclimatization period, all sheep were fed daily

with 750 g oaten chaff (8.4% crude protein; 8.0 MJ/Kg metabolisable energy) and 200 g

lupin grain (35.8% crude protein; 13.0 MJ/Kg metabolisable energy). At the start of the

treatment period (end of May; mid-autumn), the animals were allocated into two dietary

treatment groups (high and low) balanced for training success to semen collection, body

mass, scrotal circumference, temperament, poll-horn type, and sperm quality (the

percentage of live and motile sperm, sperm concentration). The high diet was designed

to allow a gain or a loss of 10% live weight over 65 days: animals fed the High diet

were initially offered 1.2 kg oaten chaff plus 0.3 kg lupin grain, whereas those fed the


135

Low diet were initially offered 0.51 kg chaff and 0.13 kg lupin grain. Every week, the

animals were weighed and the amount of feed offered to each individual was adjusted to

ensure the target change in body mass was achieved. The methodology details and

outcomes for body and testis growth, and for sperm production, have been reported

elsewhere (Chapter 4).

7.3.2 Tissue Collection and preservation




right testis were snap-frozen in liquid nitrogen and stored at –80˚C for the present study.

7.3.3 Isolation of RNA

The trizol method was used to isolate total RNA (Hellani et al. 2000) from testis

samples. The quality and quantity of the RNA were determined by Agilent 2100

Bioanalyzer (Agilent Technologies, Santa Clara, CA) and Qubit 2.0 Fluorometer

(Invitrogen, Carlsbad, CA). Only RNA with an integrity number (RIN) greater than 7.0

was used for further analysis.


For each sample, 1.0 µg of total RNA was used to construct miRNA libraries with a


Diego, CA) according to the manufacturer’s instructions. PCR amplification was


with unique indices. Individual libraries were then pooled for sequencing at Génome

Québec (Montréal, Canada) using the HiSeq 2000 system (Illumina) to generate 50 bp

single reads. All the reads were de-multiplexed according to their index sequences using


136


were removed from the dataset. Good quality sequencing reads of small RNAs were

subjected to 3’ adaptor sequence trimming, and all reads were mapped by blastn to the

non-coding RNA sequences (Rfam) to remove sequences belonging to tRNA, snoRNA,

rRNA, and other non-coding RNAs.


The miRNAs were identified using the methods outlined previously (Liang et al. 2014).

Briefly, known miRNAs were identified by mapping the filtered 18 to 25 nt sequences

to miRbase (miRBase release version 20) and all reads from 16 libraries were pooled to

predict novel miRNA using miRDeep2 based on the reference genome sequence,

OAR3.1 (http://www.livestockgenomics.csiro.au/). Small RNA sequences with a

miRDeep2 score higher than 5 and read numbers greater than 10 were defined as novel

miRNAs in sheep. The novel miRNA precursor sequences were then combined with

known miRNA precursor sequences to form a new custom reference database.

Sequencing reads from different samples were mapped to the new custom reference

database to obtain the read number for the known and novel miRNAs for each sample.

7.3.6 Construction and sequencing of the RNA-seq library

In each sample, total RNA (1.0 µg) was used to construct miRNA libraries with a

unique index, according to the instructions of the TruSeq Small RNA Sample

Preparation kit (Illumina, San Diego, CA). Quantitative real time PCR (qPCR) was

performed for library quantification using the StepOnePlus™ Real-Time PCR System

(Applied Biosystems, Carlsbad, CA) and KAPA SYBR Fast ABI Prism qPCR kit (Kapa

Biosystems, Woburn, MA). Individual libraries were then pooled for sequencing at

Génome Québec (Montréal, Canada) using the HiSeq 2000 system (Illumina).

Sequencing was performed as 100 bp paired-end reads. All reads were de-multiplexed


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according to their index sequences with CASAVA version 1.8 (Illumina) and reads that

did not pass the Illumina chastity filter were discarded.

7.3.7 Mapping and annotation of RNA-seq reads

RNA-seq reads were aligned to the ovine genome (OAR 3.1) using Tophat 2.0.10 with

default parameters (Kim et al. 2013). Each BAM output file from the TopHat2

alignment, along with the GTF file from ENSEMBL (http://uswest.ensembl.org/) ovine

gene annotation v75.30 were used in the htseq-count (http://www-

huber.embl.de/users/anders/HTSeq/) to determine the number of reads mapped to each

gene.

7.3.8 Identification of differentially expressed (DE) miRNAs and mRNAs

DE miRNAs and mRNAs were investigated with the bioinformatics tool, edgeR

(Robinson et al. 2010) that utilizes a negative binomial distribution to model sequencing

data. The expression of miRNAs and mRNAs in each library was normalized to counts

per million reads (CPM) with the formula: CPM = (reads number/total reads number per

library) × 1,000,000. miRNAs and mRNAs with CPM > 5 in at least 50% of the

samples were subjected to DE analysis. Fold changes were defined as ratios of

arithmetic means of CPM within each comparison group. Significant DE miRNAs and

mRNAs were determined by an adjusted P value (False discovery rate, FDR < 0.1)

based on Benjamini and Hochberg multiple testing correction (Benjamini et al. 2001) as

well as fold change > 1.5 (McCarthy and Smyth 2009).

7.3.9 Construction of a miRNA-mRNA regulatory network

The results for miRNAs were all obtained from a previous study using the same

samples (Chapter 6). The predicted regulatory relationships between miRNAs and

mRNAs were identified on the basis of two criteria: negative correlation and


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computational target prediction. Genes targeted by miRNAs were predicted by

TargetScan Release 6.0 (http://www.targetscan.org/) and miRanda

(http://www.microrna.org/microrna). Target genes predicted by both TargetScan

(default parameters) and miRanda (total score > 145, total energy < –10 kcal/mol) were

used. Pairwise Pearson correlation coefficient (R) was computed for each miRNA and

their predicted target genes, and multiple testing corrections were done by calculating

FDR (Benjamini et al. 2001). Significant miRNA-mRNA pairs were defined as R < –

0.9 and FDR < 0.1.

7.3.10 Functional analysis for DE mRNAs/mRNAs in the miRNA-mRNA regulatory

network

The identities of the DE mRNAs/mRNAs in the miRNA-mRNA regulatory network

were uploaded into IPA software (Ingenuity Systems, www.ingenuity.com) to detect the

top functions. A threshold of P < 0.01 was applied to enrich significant biological

functions. The IPA regulation z-score algorithm was used to predict the direction of

change for a given function (increase or decrease), with a z-score > 2 suggesting a

significant increase whereas a z-score < –2 suggesting a significant decrease.

The GO terms defined and the KEGG pathways enriched using Database for

Annotation, Visualization and Integrated Discovery (DAVID,

http://david.abcc.ncifcrf.gov) (Huang da et al. 2009). For each analysis, the functional

annotation clustering option was used and significant GO terms and KEGG pathways

were declared at P < 0.05 and molecule number > 2.

7.3.11 Identification and annotation of alternative splicing (AS) events

TopHat2 was used to predict the splice junctions. Based on the gene annotation

information, splice junctions were classified into known and novel groups. Splicing


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analysis was performed for events that had at least 20 total RNA-seq reads (Wang et al.

2008). JuncBASE (Brooks et al. 2011) was used to annotate all AS events (cassette

exons, alternative 5' splice site, alternative 3' splice site, mutually exclusive exons,

coordinate cassette exons, alternative first exons, alternative last exons, and intron

retention). Values for Percentage Spliced Index (PSI) were calculated using the

formulas provided by (McManus et al. 2014).

7.3.12 Identification of differential AS events

Statistical significance was determined using the software package, R (source). Fisher’s

exact test was used to compare PSI values for pairwise comparison, and the P value was

adjusted to false discovery rate (FDR). In addition, only splicing events with FDR <

0.05 and PSI differences (ΔPSI) > 10% were further considered. The number of

differential AS events per chromosome length was calculated.

7.4 Results

7.4.1 High Quality RNA-seq data were obtained from all samples

More than 350 million sequenced paired-end reads were obtained from 16 libraries, of

which an average of 76% could be mapped to OAR3.1

(http://www.livestockgenomics.csiro.au/). The genomics region of reads, the RNA-seq

3'/5' bias and the sequencing depth were analysed to evaluate the quality of the RNA-

seq data. Approximately 81% of the reads were derived from exonic regions, intronic,

gene upstream and downstream regions, whereas 19% were derived from intergenic

regions (Fig. 7.1A and Fig. 7.1B). In general, the coverage of reads along each

transcript revealed no obvious 3'/5' bias, indicating good quality in the sequencing

libraries (Fig. 7.1C). As can be seen in Fig. 7.1D, the number of transcripts detected

http://www.livestockgenomics.csiro.au/


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increased as the number of the sequencing reads increased, and plateaued, suggesting

that almost all of the expressed transcripts were obtained in this study.

Figure 7.1. Quality and features of the RNA-seq datasets obtained from testis of male sheep. Distribution

of the RNA-seq reads from High Diet group (A) and Low Diet group (B) along the annotated features of

the sheep genome. (C) Relationship between the RNA-seq read coverage and the length of the

transcriptional unit. The x-axis indicates the relative length of the transcripts. (D) Saturation curve for

gene detection. Randomly sampled reads are plotted against the expressed genes.

7.4.2 Profile of mRNAs in sheep testis

An average of 13,980,416 (SD = 2,930,788) reads from high diet and 11,014,809 (SD =

2,524,631) from low diet were mapped to Ensembl gene annotation database (P < 0.05).

A total of 13,859 genes were detected in testicular tissue from the low diet group,

compared to 14,561 from the high diet group. In total, 11,748 genes were expressed in


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all 16 animals. The most abundant transcript (~2% of total reads) was from the 7SK

gene, a small nuclear RNA involved in pre-mRNA splicing and processing. Functional

analysis with DAVID software revealed that the most highly expressed 3000 genes

were mainly related to cell cycles, protein catabolic processes, and spermatogenesis

(Table 7.1). Only genes (14,385) that were expressed in at least 8 libraries were used for

further analysis.

Table 7. 1. The 15 functions most commonly related to the most highly expressed 3000 genes by DAVID

software. P value indicates the relevance of the function (lower value means greater relevance).

Term Gene number P-Value

Cell cycle 232 5.49E-35

Cell cycle process 185 2.43E-33

Modification-dependent macromolecule catabolic process 177 2.43E-28

Modification-dependent protein catabolic process 177 2.43E-28

Proteolysis involved in cellular protein catabolic process 181 1.05E-27

Cellular protein catabolic process 181 2.03E-27

Protein catabolic process 184 4.97E-27

M phase 121 7.18E-27

Intracellular transport 190 1.39E-26

Male gamete generation 114 1.30E-25

Spermatogenesis 114 1.30E-25

Cellular macromolecule catabolic process 200 3.66E-25

Cell cycle phase 135 3.62E-24

Macromolecule catabolic process 208 4.51E-24

Sexual reproduction 144 5.26E-24

7.4.3 Effects of nutrition on mRNA expression


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In total, 2,243 mRNAs were found to be differentially expressed when comparing

underfed with well-fed male sheep (Appendix Table 7.1), of which, 1,081 were

expressed more in underfed than well-fed sheep (eg, TP53 and Claudin 11) and 1,162

were expressed less in underfed than well-fed sheep (eg, CYP51A1 and SPATA4).

IPA analysis revealed that the functions of most of the DE mRNAs are related to

quantity of germ cells, testis size, quantity of Sertoli cells, and quantity of connective

tissue cells (Fig. 7.2). Among all the genes that were related to these functions, some

were particularly important because they were related to more than one function.

Specifically, the expression of IGF1R and INHBA was higher in well-fed sheep than

Figure 7.2. The 20 functions most commonly related to the mRNAs differentially expressed,in testis from

underfed and well-fed male sheep, as determined by IPA software. P value indicates the relevance of the

function (lower value means greater relevance).


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under-fed sheep, in contrast with TP53 in which the opposite was observed.

Another functional analysis, DAVID, produced largely the same outcome, indicating

most common functions of DE mRNAs to be cell cycle, spermatid development,

spermatogenesis, and DNA replication (Appendix Table 7.2). Importantly, one gene,

PIWIL1 (MIWI) was associated with all of these functions.

7.4.4 miRNA-mRNA regulatory network

Putative miRNA-mRNA pairs were identified on the basis of target prediction and the

negative regulatory effect of miRNAs on mRNA expression levels. A total of 940

miRNA-mRNA pairs (48 miRNAs, 269 mRNAs) were identified. We focused on oar-

novel-miR-33 and oar-novel-miR-31, because they paired with the highest number of

mRNAs: oar-novel-miR-33 paired with 68 mRNAs and oar-novel-miR-31 paired with

52 mRNAs (Fig. 7.3).

Figure 7. 3. Regulatory networks for two pairs of miRNAs and mRNAs that were differentially expressed

in sheep testis following nutritional treatment: oar-novel-miR-33 with 68 mRNAs, and oar-novel-miR-31

with 52 mRNAs.


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IPA analysis indicated that the mRNAs in the negative pairs were mainly involved with

organization of cytoplasm, morphology of cells, abnormal morphology of reproductive

system, cell death and male infertility (Fig. 7.4A). Among the 15 most common

functions, we focused on genes that are related to at least 8 functions: AR, FOXO3,

PMS2 and PTEN. The expression of these genes was higher in well-fed sheep than

underfed sheep, except for PTEN in which the opposite was observed. In addition, these

mRNAs were also involved in 76 signalling pathways, of which Sertoli cell-Sertoli cell

junction signalling, germ cell-Sertoli cell junction signalling, androgen signalling,

apoptosis signalling were among the 15 most relevant (Fig. 7.4B). Again, DAVID was

used to further confirm the analysis and identified 62 clustered functions, the 10 most

relevant including regulation of apoptosis, cell cycle, development of germ cells

(Appendix Table 7.3).

7.4.5 Identification of alternative splicing events

We initially obtained 42,945 exon-exon junctions from the 16 RNA-seq libraries with

the Tophat software. For further analysis, we focused on alternative splicing junctions

that could be detected in at least 8 animals and also be matched to genes. This process

provided a total of 19,370 junctions and 4,584 unique mRNAs. Among these junctions,

only 5,858 (30.2%) were previously annotated in the Ensembl Database. In terms of the

alternative splicing event, we identified 3,876 cassette exons, 3,496 alternative 5' splice

sites, 3,848 alternative 3' splice sites, 41 mutually exclusive exons, 612 coordinate

cassette exons, 1,204 alternative first exons, 428 alternative last exons, and 5,865 intron

retentions, from the 16 libraries.


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Figure 7.4. The 20 functions (A) and the 20 signalling pathways (B) most commonly related to the

mRNAs in miRNA-mRNA regulatory network by IPA software. P value indicates the relevance of the

function (lower value means greater relevance).


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7.4.6 Effects of nutrition on alternative splicing

We found 16,650 ± 451 (Mean ± SD) junctions in the High Diet group and 15,847 ±

857 junctions in the Low Diet group (not significant). Each type of alternative splicing

event was counted for each treatment and, again, there were no significant differences

between treatments (Fig. 7.5). The Fisher’s Exact Test on the PSI value indicated that

1,040 alternative splicing events (284 alternative acceptor, 279 alternative donor, 164

alternative first exon, 52 alternative last exon, 218 cassette, 14 coordinate cassette exon,

25 intron retention events, 4 mutually exclusive exon) were affected by nutrition (FDR

< 0.05, ∆PSI > 10%). Underfeeding increased the PSIs of 608 alternative splicing

events and decreased the PSIs of the remaining 432 events (Appendix Table 7.4). Since

one mRNA can have more than one alternative splicing junction, a total of 788 unique

genes were identified as differential alternative splicing genes, including CREM, HIPK3

and MAP2 (Appendix Table 7.5). Interestingly, Chromosomes 11, 7 and 3 had the

greatest number of differential alternative splicing events per chromosome length

(Appendix Table 7.6). In addition, Wilcoxon tests indicated that the coefficient of

variation of the alternative splicing events was higher in underfed sheep than in well-fed

sheep (0.13 vs 0.06, P < 0.001), indicating a greater rate of change in underfed testis.


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Figure 7.5. The counts of each type of alternative splicing event in testicular tissues from underfed and

well-fed male sheep. Values = Mean ± SEM (n = 8). Most common alternative splicing events were

cassette exon, intron retention, alternative first exon and alternative last exon. Conversely, four types

were not as common, they were alternative 3' splice site, mutually exclusive exons, alternative 5' splice

site, coordinate cassette exons.

DAVID functional analysis revealed the most common functions of differential

alternative splicing events were related to protein localization, cellular metabolic

process, post-translational protein modification, mRNA processing and spermatogenesis

(Fig. 7.6).


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Figure 7.6. The 15 functions most commonly related to the differential alternative splicing events by

DAVID software. P value indicates the relevance of the function (lower value means greater relevance).

7.5 Discussion

This study appears to be the first to profile the whole transcriptome in sheep testis, to

construct the network between miRNAs and mRNAs in testis, and to explore the

relationships between pre-mRNA alternative splicing and testis function. In the context

of an experimental model of reversible testis growth in the sexually mature male, we

have been able to identify mRNAs that are associated with testis function and, more

importantly, apoptosis in germ cells. These findings strongly support the hypothesis that

the decline in spermatogenesis and increase in germ cell apoptosis induced by under-


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nutrition in the sexually mature male sheep are, at least, partially due to changes in

mRNA expression and pre-mRNA alternative splicing.

The most abundant transcript was 7SK, as reported for mouse testis (Laiho et al. 2013).

The function of 7SK in the testis is still unknown, but 7SK is a RNA polymerase III type

III promoter, and a small nuclear RNA involved in pre-mRNA splicing and processing

(Cummins et al. 2008). It is therefore likely that 7SK regulates testis function by

affecting small RNAs and pre-mRNA splicing.

We found over 2000 mRNAs that were differentially expressed between treatments,

with over 1000 mRNAs, including TP53 and Claudin 11, that were more highly

expressed in underfed than in well-fed sheep. This result supports our previous

observations based on qPCR (Chapter 5). A high level of TP53 indicates more cells

going through apoptosis (Li and Jogl 2009), so we conclude that under-nutrition

increases apoptosis in germ cells. On the other hand, Claudin-11 is a tight junction

protein expressed in Sertoli cells and rarely in other cell types in the testis (Morita et al.

1999) and plays a central role in the formation of tight junctions (Gow et al. 1999;

Wolburg et al. 2001). In testicular tissue from underfed sheep, expression of Claudin 11

is increased and the localization of Claudin 11 protein is disorganized (Chapter 5)

strongly indicating impairment of tight junctions. In addition, in the present study, over

1000 mRNAs showed lower expression in underfed than in well-fed sheep, including

CYP51A1 and SPATA4. CYP51A1 is a member of the cytochrome P450 family and is

expressed strongly by germ cells (Rozman and Waterman 1998), illustrating its crucial

role in spermatogenesis. Therefore, the lower level of CYP51A1 expression in underfed

sheep is coherent with the decrease in numbers of germ cells and defective

spermatogenesis caused by undernutrition (Chapter 4 and 5). SPATA4 has also been

reported to be testis-specific and associated with spermatogenesis, and involved in


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maintaining spermatogenesis (Liu et al. 2004). Therefore, the reduced expression of

SPATA4 in underfed sheep is also consistent with compromised spermatogenesis with

under-nutrition.

The functional analysis using IPA revealed that, for mRNAs that are differentially

expressed between nutritional treatments, the most common functions are quantity of

germ cells, testis size, quantity of Sertoli cells and quantity of connective tissue cells.

Therefore, the differentially expressed transcriptomes are consistent with the reductions

of testis mass and sperm production in underfed rams (Chapter 4). Among all the genes

that were related to these functions, we considered the most important to be those

related to more than one function, such as IGF1R, INHBA and TP53. In the testes of

adult mice lacking IGF1R in their Sertoli cells, there is a reduction in testis size and

daily sperm production (Pitetti et al. 2013), indicating a role for IGF1R in control of

sperm production by Sertoli cells. A protein product of the INHBA gene, activin A, is an

important regulator of testicular cell proliferation (Archambeault and Yao 2010). As

indicated above, TP53 regulates spermatogenesis by inducing apoptosis (Chen et al.

2012). We conclude that, the expression of IGF1R, INHBA and TP53 may be used as

biomarkers of sperm production.

Functional analysis based on DAVID indicated a similar range of functions for the

differentially expressed mRNAs, but highlighted one gene, PIWIL1 (MIWI) associated

with all the above functions. It has been reported that PIWI 1 (MIWI) encodes a

cytoplasmic protein specifically expressed in spermatocytes and spermatids (Bak et al.

2011). In addition, Miwinull mice display spermatogenic arrest at the beginning of the

round spermatid stage (Deng and Lin 2002). In recent years, piRNAs have been

reported to play important roles in spermatogenesis (Liu et al. 2012a), although the

exact mechanism of action have not yet been elucidated. One possibility is that piRNAs


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and PIWI proteins form a complex, the PIWI-interacting RNA complex (piRC), that

triggers gene silencing, a possibility supported by the finding that piRC can be extracted

and purified from rat testis (Lau et al. 2006). The roles of piRNAs in spermatogenesis

are indicated by the known functions of their partner, the PIWI proteins, one of the most

important of which is MIWI (Beyret and Lin 2011). Therefore, one of the explanations

for the reduced spermatid development and spermatogenesis in underfed sheep is that

under-nutrition changes the function of piRNAs by affecting the expression of MIWI.

Based on target prediction and the negative effects of miRNAs on the expression of

mRNAs, a total of 940 miRNA-mRNA pairs were indentified, including 48 miRNAs

and 269 mRNAs. Of particular importance are oar-novel-miR-33 and oar-novel-miR-31

because they paired with the greatest number of mRNAs, indicating a crucial role in

testis function. Novel-miR-33 is homologous to miR-296 that is specific to embryonic

stem cells and has been reported to be highly conserved between species (Gangaraju

and Lin 2009). So far, there is no direct evidence for a role for miR-296 in testis

function. However, one study proved that miR-296 was more highly expressed in

mature testis than in immature testis, indicating a pivotal role in spermatogenesis in the

adult. In addition, miR-296 was also defined as anti-apoptotic (Cheng et al. 2005).

Therefore, the reduced expression of novel-miR-33 (miR-296) in underfed sheep

(Chapter 6) illustrates decreased testis function and increased cell apoptosis. By

contrast, novel-miR-31 is homologous with miR-34 which has been shown to enhance

the expression of germ cell-specific genes in late spermatogenesis in other species

(Bouhallier et al. 2010). In the current study, therefore, the lower level of novel-miR-31

in underfed sheep is coherent with the loss of germ cell function (Chapter 4).

Approximately 33% of genes in the sheep testis were alternatively spliced, a percentage

that agrees with the 21% found in the whole bovine transcripts (Chacko and


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Ranganathan 2009). Approximately 70% of the junctions have not been annotated

previously, so they represent novel isoforms of known genes. Nutritional treatment did

not affect the total number of alternative splicing junctions, in contrast with some

previous reports of nutritional effects on other biological processes (Salati et al. 2004;

Kulseth et al. 2010). The lack of effect of nutritional treatment on the total number of

alternative splicing junctions suggests that alternative splicing is a fine-tuner in the

testis that stabilizes testis function, as suggested for other tissues (Elton and Martin

2003; Wu et al. 2013).

On the other hand, with respect to specific genes, we found almost 800 that were

differentially spliced between high diet and low diet groups. Functional analysis of

these genes indicated their roles in protein localization, cellular metabolic process, post-

translational protein modification, mRNA processing and spermatogenesis, and

suggests that nutrition affects protein localization and spermatogenesis by changing pre-

mRNA splicing. These findings may help us to explain the disrupted localization of the

tight junction-related protein, Claudin 11, and the reduced expression of the tight

junction-related gene, ZO-1, in testicular tissue from underfed sheep (Chapter 5).

However, it is difficult to define the specific gene(s) that control the localization of a

protein like Claudin 11, so future studies on verification of alternative splicing activity,

possibly involving construction of a shortened ‘minigene’ containing the regulated

exons and splicing signals (Stoss et al. 1999), will be required to better understand this

process.

Among the differentially spliced genes, we focused on CREM, MAP2, HIPK3 and

TRa2β, because the differences between nutritional treatments in their percentage

splicing index were higher than for other genes and their variant transcripts were crucial

in testis function. For example, the alternative splicing type for CREM (cAMP response


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element modulator) is alternative last exon. It has been reported that CREM mRNA

exhibits a remarkable array of alternative splice variants (Sanborn et al. 1997). For

example, during male meiosis, the inactive CREM variant is switched to active CREM

variant (by incorporation of transactivating domains) directed by alternative splicing.

Therefore, in the mature male sheep, it is possible that nutrition affects the number of

active CREM isoforms, potentially explaining the disruption of spermatogenesis in

underfed sheep. Considering MAP2, previous studies suggest that a low molecular

weight isoform, resulting from alternative splicing of the MAP2 gene, is the

predominant isoform in the testis (Loveland et al. 1999). According to the PSI formula,

the higher number of reads of exclusion junctions results in a lower PSI value

(McManus et al. 2014), so the lower PSI value in underfed sheep suggests that most of

the MAP in testicular tissue in these animals is the exon-exclusion isotype and that this

isotype is responsible for the defective spermatogenesis. The third gene of interest,

alternatively spliced at the 5’ site, is homeodomain-interacting protein kinase (HIPK3),

a member of a gene family that has been implicated in apoptosis (Curtin and Cotter

2004). It has been reported that the splicing of HIPK3 is testis-specific (Venables et al.

2005), and thus plays an important role in spermatogenesis. Importantly, the alternative

splicing of HIPK3 is thought to be regulated by the fourth gene of interest, Transformer-

2 Protein Homolog Beta2 (TRa2β) (Venables et al. 2005), a gene that is known to be

highly expressed in testis (Venables et al. 2000). In the current study, nutritional

treatment did not affect the total expression of TRa2β, so differences in the splicing of

TRa2β rather than the amount of expression, might explain the differences in HIPK3

alternative splicing between treatments, and follow-on effects for spermatogenesis and

apoptosis.

In conclusion, we have identified two molecular mechanisms that could explain the

effect of nutrition on spermatogenesis and germ cell apoptosis in the adult male: 1)


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nutrition-induced changes in the expression of mRNAs in sheep testis, the functions of

the differentially expressed mRNAs are mainly related to spermatogenesis and germ

cell apoptosis, an important regulator in these processes are the networks between

miRNAs and mRNAs; 2) nutritional treatment causes differences in pre-mRNA

alternative splicing, and these changes are closely involved in spermatogenesis and

germ cell apoptosis in testis. Some differentially spliced genes including CREM, MAP2,

HIPK3 and TRa2β should be able to work as potential biomarkers for spermatogenesis

and apoptosis. To move the study moving forward, confirming the predicted functions

of these genes using in vivo and in vitro experiments are required.

Chapter 8-General Discussion

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Chapter 8

General Discussion

The general hypothesis of this thesis was that, in adult male sheep, under-nutrition will

reduce the quantity and quality of sperm produced, due to germ cell apoptosis, that

these responses will be explained by reductions in the number and function of Sertoli

cells, and that such effects are mediated by changes in the expression of small RNAs

and mRNAs, and by alternative pre-mRNA splicing. My observations support all of

these hypotheses except the change of Sertoli cell number.

The dietary treatments induced major changes in testis mass, sperm production and

spermatogenic efficiency. Importantly, these responses are reversible and non-

pathological, demonstrating the power of our experimental model for investigating the

cellular and molecular mechanisms that control the quantity and quality of sperm

produced in the ejaculate. The effects of under-nutrition on sperm number had been

known for some time, but it is now clear that sperm quality, including sperm velocity

and sperm cell DNA damage, are also affected. How this translates to fertility in the

field, with underfed rams mating in commercial sheep flocks, is yet to be determined.

Interestingly, when we used change in scrotal circumference as an independent variable,

combining the data for all dietary groups, strong correlations between some measures of

sperm production and function could be detected, but they disappeared when the data

for the under-fed group were omitted from the analysis. This observation suggests that

factors associated with a loss in testis mass, rather than direct effects of the nutritional

treatments, are responsible for the changes in the production and quality of sperm.

Moreover, in contrast with well-fed and maintenance-fed rams, spermatogenesis in


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testicular tissue of under-fed rams was generally disrupted, as evidenced by lower

Johnsen Score, narrower seminiferous tubules, and a smaller proportion of seminiferous

epithelium. Therefore, we conclude that nutrition affects sperm production by altering

spermatogenesis, and that the relationships between testis mass and semen parameters

could be applied more generally to other non-nutritional factors that affect testis mass,

such as genotype, photoperiod or physical fitness.

In contrast with the finding by (Hötzel et al. 1998), there was no evidence that the

number of Sertoli cells was affected by diet. The possible reasons for the contrasting

results have been comprehensively discussed in Chapter 5 and, in brief, it seems likely

that the histological techniques used in the original study were unsuitable for this

experimental model. Therefore, observations based on the effect of nutrition on the

testis of the sexually mature male sheep no longer contradict the dogma that Sertoli cell

number is stable and changes little after puberty. Conversely, the detection of

proliferating cell nuclear antigen (PCNA) in a number of Sertoli cells does suggest that

some of the cells retain an ability to divide after puberty, although the number of

PCNA-positive Sertoli cells was neither affected by nutritional treatment nor related to

changes in testis mass.

In contrast with the lack of change in Sertoli cell number, Sertoli cell function was

clearly affected by nutritional treatment, as evidenced by changes in cell tight junction

and maturation status, with loss of testis mass and under-nutrition apparently inducing a

reversal of these aspects of terminal differentiation. These changes in Sertoli cell

function seem likely to explain the outcomes for sperm quality and production with

underfeeding. At the level of spermatogenesis, the consequences seem to be an increase

in the rate of apoptosis in germ cells, an observation consistent with the reduction in

spermatogenic efficiency in the testis of under-fed sheep.


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To explore in the possible molecular mechanisms that might underpin the responses in

spermatogenesis and apoptosis, we investigated small RNAs and the combination of

this concept with our experimental model has opened up an entire new area of testicular

biology, for all species, not just sheep. In total, we found 44 miRNAs and 35 piRNAs

that were differentially expressed (DE) with the high and low diets. The targets of the

DE miRNAs are mainly involved in the synthesis of lipids and hormones, the

morphology of enlarged testis, and the production of sperm. Most importantly, we

found some potential biomarkers that were highly specific to spermatogenesis and

apoptosis. For example, novel-miR-144 (homologous with miR-98) is linked with

apoptosis, although we still do not know whether the increased expression of miR-144

in underfed rams is the cause or the consequence of enhanced apoptosis. In addition,

novel-miR-31 (homologous with miR-34) seems likely to be a biomarker of germ cell

phenotype during the late stages of spermatogenesis (Bouhallier et al. 2010). With

respect to the DE piRNAs, it is even more difficult to predict specific functions in sheep

because their sequences are poorly conserved among species. We therefore only focused

on the gene-derived DE piRNAs and predicted their functions based on previous

references – for instance, feline leukemia virus subgroup C receptor-related protein 2

(FLVCR2), which produces piR-12568 and functions as a calcium transporter, is know

to affect reproduction (Meyer et al. 2010). Overall, it is clear that changes in the

expression of small RNAs can help explain how nutrition affects sperm production and

quality, but it is early days for this field and there is room for a lot more work.

RNA-seq allowed us to profile gene expression in testis tissue that has been affected by

nutrition. More than 2200 mRNAs were differentially expressed in underfed and well-

fed sheep, and functional analysis suggests that they were predominantly related to

germ cells, testis size, apoptosis and spermatogenesis. Indeed, two of these mRNAs

code for the proteins that we used to reveal the effects of nutrition on Sertoli cell tight


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junctions (Claudin 11) and germ cell apoptosis (TP53). Meanwhile, based on target

prediction and the inhibition of mRNA expression by miRNAs, a total of 940 miRNA-

mRNA pairs were identified, including 48 miRNAs and 269 mRNAs. Functional

analysis indicates that the mRNAs inhibited by miRNAs are mainly involved with cell

morphology, abnormal morphology in the reproductive system, cell death and male

infertility. We therefore conclude that, in addition to changing small RNAs, under-

nutrition changes the expression of mRNAs that affect the quantity and quality of the

sperm produced.

In addition, approximately 800 genes, including CREM, HIPK3 and MAP2, were

spliced differentially in the dietary treatments. These genes were mostly related to

protein localization, cellular metabolic processes, post-translational protein modification

and spermatogenesis. It is therefore possible that changes in alternative pre-mRNA

splicing are responsible for the effects on nutrition on spermatogenesis and germ cell

apoptosis.

In conclusion, in sexually mature male sheep, under-nutrition reduces spermatogenic

efficiency and sperm velocity, and increases sperm DNA damage. These processes are

not associated with the changes of Sertoli cell number, but are associated with increased

germ cell apoptosis and disrupted Sertoli cell function. It seems very likely that these

outcomes are mediated by changes in three RNA-based processes: the expression of

small RNAs, the expression of mRNAs, and the alternative pre-mRNA splicing. Our

findings have led us to develop a working hypothesis that explains how nutrition affects

testis function (Fig. 8.1). Specifically, under-nutrition leads to a reduction in FSH

secretion (Zhang et al. 2004), a key hormone associated with the function of Sertoli

cells and, changes in Sertoli cell function, including the disorganization of tight

junctions and reversal of cell maturity (Chapter 5). The outcome is impaired


159

spermatogenesis and reductions in the number of germ cells per unit mass of testis, and

Figure 8.1. A working hypothesis of the mechanisms through which nutrition affects testis function in the

sexually mature sheep, indicating roles that could be played by small RNAs. Stimulation is indicate by

“+ve” and inhibition is indicated by “–ve”. The effects of undernutrition on each regulatory factor are

indicated by vertical arrows. Solid lines denote pathways supported by strong evidence whereas broken

lines indicate pathways where the evidence is still accumulating. Nutritional and metabolic signals do not

seem to affect the proliferation of Sertoli cells but they do affect Sertoli cell function, notably the

organization of tight junctions and cellular maturation. The Sertoli cell responses, combined with

nutrition-induced changes in germ cell apoptosis, affect the quantity and quality of sperm produced.

Several small RNAs are strongly affected by undernutrition: miR-34c, miR-10b, piR-9006 and piR-12568

are directly associated with spermatogenesis; miR-98 and miR-26a regulate apoptosis of germ cells; miR-

99a is predicted to regulate tight junctions by targeting ZO-1 expression. In addition, there are

correlations between the proportions of miRNAs and piRNAs, although it is not clear whether this

reflects mutual regulation, and there appears to be a feedback loop linking apoptosis and miR-98

expression.


160

increases in germ cell apoptosis that dramatically reduced the quantity and quality of

sperm (Chapter 4). Small RNAs seem to be involved in these processes. Specifically,

miR-98 appears to regulate germ cell apoptosis by changing the expression of CASP3,

TP53 and FASL in parallel with the direct effects of miR-26. Similarly, changes in

expression of miR-99a seem to affect the organization of Sertoli-cell tight junctions by

targeting ZO-1 (Turcatel et al. 2012). The other small RNAs that we have detected

(miR-26, miR-34c, miR-10b, piR-9006, piR-12568) are involved in the control of

reproductive function in other species, so might also contribute to the effects of

nutrition on spermatogenesis in the sexually mature sheep. The role of piRNAs in this

process is far from clear, but the distinct differences between well-fed and under-fed

males in piRNA expression, and in the proportion of piRNAs as a total of small RNAs,

suggest that they also play an important, possibly synergistic, role.

This new understanding of the control of Sertoli cell function and spermatogenesis will

contribute to efforts to find ways to mitigate the negative effects of under-nutrition on

male fertility.

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Appendix Figures and Tables

Appendix Fig. 6.1. Length distribution of small RNAs in testis from sexually mature male sheep (n = 16). Small RNAs displayed a bimodal length distribution with two peaks at 22 nt and 30 nt.

Appendix Fig. 6.2. Expression of DE miRNAs in testis from sexually mature sheep fed the High diet or

the Low diet, as detected by qRT-PCR and miRNA-seq. Measurement of relative expression by qRT-

PCR is shown by line graphs and right Y-axis. Measurement of expression by miRNA-seq is shown by

bar graphs and the left Y-axis (values are log2 of normalized number of reads). A, B, C: different letters

denote significant difference in the relative expression detected by qRT-PCR; a, b, c: different letters

denote significant difference in the expression detected by miRNA-seq. Values are mean ± SE (N = 8 per

treatment).

Appendices

191

Appendix Fig. 6.3. Top 30 highly expressed miRNAs detected in the testis of male sheep fed a high diet

(HD) and a low diet (LD). N = 8 for each treatment.

Appendix Fig. 6.4. The top 9 signaling pathways of DE miRNAs analyzed by ingenuity pathway analysis

(IPA). The X-axis is –lg(p-value) and indicates the relevance of the pathway to the DE miRNAs, with a

lower p-value (a higher value of – lg(p-value)) suggesting greater relevance.

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Appendix Table 6.1 Details of primers used for RT-QPCR

Note: * GAPDH primers were obtained from Yu et al. 2010.

Appendix Table 6.2 Details of primers used for amplification of 3'UTR of the predicted targets of novel-

miR-144.

Gene Primer Sequence Product size (bp) Gene Bank FASL F: CTCGAGAGCACTCTGGGATTCTCTCC

694 NM_004013705.1

R: GTCGACTGCCCTTCCCAATTTCCACAT CASP3 F: CTCGAGCCCAAGGCAAGAAGCTCCA

520 XM_004021690.1

R: GTCGACGGGCTGACATTCAGGGATGG BCL2L1 F: CTCGAGTTCATCCCCACCCTCCAAGA

985 NM_001009226

R: GTCGACAGCTGGAAAAAGTGTGGGCT TP53 F: GTCATCTAGCGTCCGACCTC

895 NM_001009403.1 R: TGTCTCCTGACTCAGAGGGG

Gene Primer Sequence Product size

(bp) Gene Bank FASL F:CCACGTGGCTGGTATCAACT

108 NM_001123003.1

R:GGCTGACAGCAAAACAGGTG TP53 F:GTCATCTAGCGTCCGACCTC

147 NM_001009403.1

R:TGTCTCCTGACTCAGAGGGG CASP3 F:GGCTCTGAGTGTTTGGGGAA

135 XM_004021690.1

R:AGCTCCTGGACAAAGTTCCG GAPDH F:CTGCTGACGCTCCCATGTTTGT

150 NM_001190390.1* R:TAAGTCCCTCCACGATGCCAAA

Appendices

193

Appendix Table 6.3 Identification of homologues of novel miRNAs in testis from sexually mature male

sheep.

Novel miRNAs homolog Conservation Novel-miR-1 ggo-miR-320a conserved Novel-miR-5 cgr-miR-181c-5p highly conserved Novel-miR-6 mse-miR-100 highly conserved Novel-miR-7 cgr-miR-193b-3p highly conserved Novel-miR-8 pma-miR-129a-3p poorly conserved Novel-miR-11 cgr-miR-1839-5p poorly conserved Novel-miR-14 bta-miR-363 highly conserved Novel-miR-16 aca-miR-214-3p poorly conserved Novel-miR-18 pol-miR-140-3p conserved Novel-miR-19 bta-miR-1247-5p poorly conserved Novel-miR-20 rno-miR-504 conserved Novel-miR-22 bta-miR-6119-5p poorly conserved Novel-miR-23 ssc-miR-219 poorly conserved Novel-miR-26 cgr-miR-222-3p highly conserved Novel-miR-27 cgr-miR-423-3p poorly conserved Novel-miR-28 ggo-miR-138 highly conserved Novel-miR-29 ssc-miR-31 highly conserved Novel-miR-30 cgr-miR-34b-5p highly conserved Novel-miR-31 cgr-miR-34c-5p highly conserved Novel-miR-33 ssc-miR-296-3p conserved Novel-miR-34 ggo-miR-491 conserved Novel-miR-38 bta-miR-1388-3p poorly conserved Novel-miR-39 cgr-miR-215-5p highly conserved Novel-miR-40 ggo-miR-502b poorly conserved Novel-miR-41 ggo-miR-628 poorly conserved Novel-miR-43 cgr-miR-186-5p conserved Novel-miR-44 ssc-miR-20b highly conserved Novel-miR-45 cgr-miR-93-5p highly conserved Novel-miR-46 aca-miR-490-3p highly conserved Novel-miR-47 bta-miR-224 conserved Novel-miR-51 ggo-miR-424 highly conserved Novel-miR-52 cgr-miR-497-5p highly conserved Novel-miR-54 ppy-miR-301b highly conserved Novel-miR-55 ccr-miR-130a poorly conserved Novel-miR-56 ggo-miR-188 poorly conserved Novel-miR-57 cgr-miR-532-5p poorly conserved Novel-miR-58 rno-miR-1306-5p poorly conserved Novel-miR-60 ggo-miR-142 poorly conserved Novel-miR-63 bta-miR-3431 poorly conserved Novel-miR-65 eca-miR-326 conserved Novel-miR-69 bta-miR-2483-5p poorly conserved Novel-miR-71 ssc-miR-1468 poorly conserved Novel-miR-72 ggo-miR-192 highly conserved

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194

Novel-miR-73 ssc-miR-874 conserved Novel-miR-74 cgr-miR-328 conserved Novel-miR-75 ccr-miR-129 poorly conserved Novel-miR-76 bta-miR-6529 poorly conserved Novel-miR-78 ppy-miR-873 conserved Novel-miR-80 ggo-miR-331 poorly conserved Novel-miR-83 eca-miR-345-5p poorly conserved Novel-miR-84 bta-miR-503-3p highly conserved Novel-miR-87 sha-miR-101 highly conserved Novel-miR-88 ggo-miR-877 poorly conserved Novel-miR-89 cgr-miR-505-3p poorly conserved Novel-miR-90 pma-miR-145-5p highly conserved Novel-miR-91 aca-miR-147 poorly conserved Novel-miR-93 ccr-miR-20a-5p highly conserved Novel-miR-95 cgr-miR-141 poorly conserved Novel-miR-96 rno-miR-212-3p highly conserved Novel-miR-97 ccr-miR-18a highly conserved Novel-miR-98 ccr-miR-429 highly conserved Novel-miR-99 ccr-miR-365 highly conserved Novel-miR-101 bta-miR-660 poorly conserved Novel-miR-103 eca-miR-508-5p poorly conserved Novel-miR-104 oan-miR-18-3p poorly conserved Novel-miR-107 ccr-miR-29a poorly conserved Novel-miR-108 cgr-miR-195 highly conserved Novel-miR-109 cgr-miR-15a-5p highly conserved Novel-miR-110 cgr-miR-15b-5p highly conserved Novel-miR-111 ccr-miR-196a highly conserved Novel-miR-112 dre-miR-196d highly conserved Novel-miR-113 ggo-miR-454 highly conserved Novel-miR-115 ccr-miR-183 highly conserved Novel-miR-116 ggo-miR-135b highly conserved Novel-miR-117 aca-miR-135-5p highly conserved Novel-miR-118 cgr-miR-455-5p highly conserved Novel-miR-120 cgr-miR-32-5p highly conserved Novel-miR-121 ccr-miR-92a highly conserved Novel-miR-122 eca-miR-105 highly conserved Novel-miR-123 bta-miR-105b poorly conserved Novel-miR-124 ccr-miR-128 highly conserved Novel-miR-127 cgr-miR-148b-3p highly conserved Novel-miR-128 ccr-miR-338 highly conserved Novel-miR-129 ssc-miR-339 conserved Novel-miR-131 cgr-miR-615-3p conserved Novel-miR-132 ggo-miR-486 conserved Novel-miR-134 bta-miR-342 conserved Novel-miR-135 ppy-miR-330-5p conserved Novel-miR-138 pol-miR-9b-5p highly conserved Novel-miR-140 ggo-miR-146b highly conserved

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195

Novel-miR-141 bta-miR-146a highly conserved Novel-miR-142 bta-miR-769 poorly conserved Novel-miR-143 ppy-let-7e highly conserved Novel-miR-144 cgr-miR-98 highly conserved Novel-miR-145 cgr-miR-190a highly conserved Novel-miR-146 ggo-miR-190b highly conserved Novel-miR-147 eca-miR-507 highly conserved Novel-miR-149 ppy-miR-767-5p poorly conserved Novel-miR-151 bta-miR-6123 poorly conserved Novel-miR-154 cgr-miR-744-5p poorly conserved Novel-miR-156 ccr-miR-7a highly conserved Novel-miR-157 ccr-miR-1c poorly conserved Novel-miR-158 cgr-miR-184 highly conserved Novel-miR-161 ppy-miR-449a highly conserved Novel-miR-162 ccr-miR-34 highly conserved Novel-miR-163 cgr-miR-24-3p highly conserved Novel-miR-165 bta-miR-30f highly conserved Novel-miR-166 sha-miR-30e highly conserved Novel-miR-169 ggo-miR-542-3p conserved Novel-miR-171 cgr-miR-19a highly conserved Novel-miR-173 ccr-miR-499-5p highly conserved Novel-miR-174 ppy-miR-155 highly conserved Novel-miR-175 mml-miR-1296 poorly conserved Novel-miR-176 cgr-miR-340-5p conserved Novel-miR-177 ggo-miR-361-5p conserved Novel-miR-178 rno-miR-3585-5p poorly conserved Novel-miR-179 ggo-miR-27b highly conserved Novel-miR-180 ggo-miR-197 conserved Novel-miR-181 cgr-miR-204 highly conserved Novel-miR-183 pma-miR-153-3p highly conserved Novel-miR-186 ppy-miR-592 conserved Novel-miR-188 mmu-miR-670-3p poorly conserved Novel-miR-191 pol-miR-133-3p poorly conserved Novel-miR-193 cgr-miR-450a poorly conserved Novel-miR-194 mmu-miR-335-3p poorly conserved

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196

Appendix Table 6.4 Identification of clustered miRNAs in testis from sexually mature male sheep. Note:

Fold change (FC) = CPM of low diet group/CPM of high diet group. CPM (counts per million) =

(piRNAs reads number/total reads number per library) × 1,000,000. The significant DE piRNAs were

determined by false discovery rate (FDR) < 0.05.

Cluster miRNA precusors Chromosome Start End FDR

1 oar-let-7c 1 138828281 138828348 0.12

1 oar-mir-99a 1 138829044 138829104 0.00

2 oar-let-7d 2 27312334 27312420 0.12

2 oar-let-7f 2 27314541 27314619 0.82

3 oar-mir-23a 2 31139258 31139316 0.17

3 oar-mir-23b 2 31139258 31139316 0.01

4 oar-mir-29a 4 94625756 94625815 0.00

4 oar-mir-29b 4 94626148 94626212 0.15

5 Novel-mir-97 10 66183231 66183294 0.72

5 Novel-mir-171 10 66183379 66183435 0.25

5 Novel-mir-93 10 66183541 66183600 0.82

5 Novel-mir-121 10 66183794 66183853 0.29

6 oar-mir-200a 12 49397310 49397370 0.06

6 oar-mir-200b 12 49397879 49397938 0.07

7 Novel-mir-151 13 56861602 56861671 0.12

7 Novel-mir-33 13 56861865 56861922 0.02

8 oar-mir-431 18 64480275 64480365 NA

8 oar-mir-433-3p 18 64481132 64481245 NA

8 oar-mir-127 18 64482256 64482312 1.00

8 oar-mir-432 18 64483745 64483814 0.11

8 oar-mir-136 18 64483932 64483988 0.32

9 oar-mir-299-5p 18 64620136 64620188 0.04

9 oar-mir-299-3p 18 64620136 64620188 NA

9 oar-mir-380-3p 18 64621337 64621394 0.06

9 oar-mir-411b-3p 18 64621498 64621554 0.02

9 oar-mir-1197-5p 18 64621814 64621942 NA

9 oar-mir-1197-3p 18 64621814 64621942 0.05

9 oar-mir-323a-3p 18 64622016 64622072 0.00

9 oar-mir-758-3p 18 64622312 64622370 0.06

9 oar-mir-329b-3p 18 64623030 64623089 0.59

9 oar-mir-494-3p 18 64626212 64626270 0.02

9 oar-mir-543-3p 18 64628562 64628619 0.60

9 oar-mir-495-3p 18 64629999 64630057 0.81

9 oar-mir-3958-5p 18 64631942 64631998 0.32

9 oar-mir-3958-3p 18 64631942 64631998 0.01

9 oar-mir-376b-3p 18 64634621 64634681 0.79

9 oar-mir-376c-3p 18 64634992 64635050 0.89

9 oar-mir-376d 18 64635363 64635421 0.94

9 oar-mir-376e-3p 18 64635739 64635798 0.07

9 oar-mir-376a-3p 18 64636085 64636187 0.05

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197

9 oar-mir-1185-5p 18 64638058 64638118 0.03

9 oar-mir-1185-3p 18 64638058 64638118 0.02

9 oar-mir-381-3p 18 64639738 64639801 0.06

9 oar-mir-381-3p 18 64639738 64639801 0.06

9 oar-mir-487b-3p 18 64640234 64640291 0.24

9 oar-mir-539-3p 18 64641003 64641061 0.36

9 oar-mir-544-5p 18 64642597 64642697 0.04

9 oar-mir-655-3p 18 64643523 64643583 0.82

9 oar-mir-3959-5p 18 64645543 64645597 0.26

9 oar-mir-3959-3p 18 64645543 64645597 0.90

9 oar-mir-487a-3p 18 64645957 64646015 0.06

9 oar-mir-382-5p 18 64648908 64648965 0.30

9 oar-mir-382-3p 18 64648908 64648965 0.25

9 oar-mir-134-5p 18 64649271 64649334 0.04

9 oar-mir-485-5p 18 64649969 64650028 0.82

9 oar-mir-485-3p 18 64649969 64650028 0.27

10 Novel-mir-7 24 13219122 13219181 0.72

10 Novel-mir-99 24 13223800 13223862 0.30

11 Novel-mir-101 X 52158377 52158433 0.45

11 Novel-mir-40 X 52160489 52160551 0.58

11 Novel-mir-56 X 52175957 52176017 0.91

11 Novel-mir-57 X 52176280 52176339 0.10

12 Novel-mir-123 X 78722115 78722173 0.69

12 Novel-mir-149 X 78723038 78723094 0.07

12 Novel-mir-122 X 78724306 78724365 0.29

13 Novel-mir-47 X 79046399 79046467 0.12

13 Novel-mir-63 X 79048337 79048399 0.11

14 Novel-mir-105 X 84537948 84538004 0.20

14 Novel-mir-17 X 84538978 84539035 0.04

15 Novel-mir-51 X 95343715 95343773 0.33

15 Novel-mir-84 X 95344046 95344109 1.00

15 Novel-mir-169 X 95348844 95348903 0.58

15 Novel-mir-193 X 95350011 95350066 0.13

16 Novel-mir-104 X 95607969 95608031 0.47

16 Novel-mir-44 X 95608208 95608268 0.82

16 Novel-mir-121 X 95608483 95608544 0.29

16 Novel-mir-14 X 95608637 95608702 0.94

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198

Appendix Table 6.5 Differentially expressed piRNAs in testis from sheep fed a low or high diet (N = 8

for each treatment). Note: Fold change (FC) = CPM of low diet group/CPM of high diet group. CPM

(Counts per million) = (piRNAs reads number/total reads number per library) × 1,000,000. The

significant DE piRNAs were determined by false discovery rate (FDR) < 0.05.

logFC logCPM PValue FDR

oar-piR-789 3.64 6.85 6.67E-17 3.33E-13 oar-piR-3085 -3.79 5.96 1.76E-14 4.39E-11 oar-piR-11578 3.50 4.07 6.88E-13 1.15E-09 oar-piR-6442 4.29 3.59 1.88E-11 2.35E-08 oar-piR-606 2.19 6.72 2.32E-10 2.32E-07 oar-piR-12439 -1.94 8.65 1.30E-09 1.08E-06 oar-piR-13207 2.10 5.03 1.32E-07 9.43E-05 oar-piR-2287 -1.98 8.89 2.06E-07 1.28E-04 oar-piR-1866 -2.06 4.89 3.68E-07 1.93E-04 oar-piR-6716 -2.57 4.41 3.85E-07 1.93E-04 oar-piR-3404 -2.91 5.14 5.10E-07 2.30E-04 oar-piR-11406 1.73 5.75 5.84E-07 2.30E-04 oar-piR-1617 -2.38 6.06 5.99E-07 2.30E-04 oar-piR-2194 -1.56 5.21 7.53E-07 2.69E-04 oar-piR-3337 -3.28 5.07 1.35E-06 4.50E-04 oar-piR-10216 2.45 3.93 1.95E-06 5.75E-04 oar-piR-10217 2.45 3.93 1.95E-06 5.75E-04 oar-piR-10729 2.13 4.19 3.02E-06 7.54E-04 oar-piR-10730 2.13 4.19 3.02E-06 7.54E-04 oar-piR-10731 2.13 4.19 3.02E-06 7.54E-04 oar-piR-2327 -2.20 8.53 3.36E-06 8.00E-04 oar-piR-300 -1.47 6.89 3.63E-06 8.24E-04 oar-piR-12568 0.56 6.63 3.90E-06 8.48E-04 oar-piR-2936 1.54 5.18 4.56E-06 9.50E-04 oar-piR-664 1.61 7.59 2.14E-05 4.28E-03 oar-piR-9120 -2.26 4.63 3.90E-05 7.49E-03 oar-piR-9006 -1.05 5.34 4.68E-05 8.53E-03 oar-piR-6573 1.54 6.39 4.78E-05 8.53E-03 oar-piR-223 -0.93 11.98 5.02E-05 8.65E-03 oar-piR-644 -1.98 4.11 6.04E-05 1.01E-02 oar-piR-2747 1.52 5.23 7.48E-05 1.21E-02 oar-piR-1189 -0.89 10.55 1.33E-04 2.07E-02 oar-piR-2886 1.13 9.77 1.42E-04 2.14E-02 oar-piR-7322 1.38 5.99 1.59E-04 2.34E-02 oar-piR-1248 -1.06 5.35 3.32E-04 4.74E-02

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199

Appendix Table 6.6 Gene-derived DE piRNAs in testis from sexually mature male sheep fed a high or

low diet (N = 8 for each treatment).

piRNA Genomic Location Gene full name Gene abbreviation name

oar-piR-12568 3'UTR feline leukemia virus subgroup C cellular receptor family, member 2 FLVCR2

oar-piR-6442 3'UTR keratin associated protein 10-2 KRTAP10-2

oar-piR-9006 5'UTR ATPase, Ca++ Transporting, Plasma Membrane 4 ATP2B4

oar-piR-10216 Intron Mortality Factor 4 Like 1 MORF4L1





oar-piR-11406 Intron sorting nexin 5 SNX5

oar-piR-1248 Intron Storkhead-Box Protein 1 STOX1

oar-piR-2194 Intron ENSOARG00000013508 ENSOARG00000013508

oar-piR-2936 Intron fumarylacetoacetate hydrolase FAH

oar-piR-7322 Intron C-Type Lectin Domain Family 16, Member CLEC16A

oar-piR-9006 Intron DDB1 And CUL4 Associated Factor 6 DCAF6

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200

Appendix Table 7.1 Differentially expressed mRNAs in testis from sheep fed a low or high diet (N = 8

for each treatment). Note: Fold change (FC) = CPM of low diet group/CPM of high diet group. CPM

(Counts per million) = (mRNAs reads number/total reads number per library) × 1,000,000. The

significant DE mRNAs were determined by false discovery rate (FDR) < 0.05.

ID logFC logCPM PValue symbol

ENSOARG00000007324 -3.156 4.693 7.57E-12 SETD9

ENSOARG00000012186 -1.990 6.099 2.10E-09 CATSPERB

ENSOARG00000000285 1.627 4.305 4.19E-08 INSR

ENSOARG00000003592 1.415 5.905 3.92E-08 LRP2

ENSOARG00000020906 1.907 4.181 7.21E-08 ENSOARG00000010318 1.597 4.435 1.43E-07 SVOPL

ENSOARG00000011728 1.776 2.432 1.14E-07 BCL9L

ENSOARG00000018288 1.946 4.219 1.35E-07 NCKAP5L

ENSOARG00000010474 1.852 3.191 2.24E-07 HR

ENSOARG00000003366 1.774 2.857 2.61E-07 PCNXL2

ENSOARG00000016970 1.766 2.696 3.79E-07 TPP1

ENSOARG00000002932 1.859 5.252 4.65E-07 ADCY9

ENSOARG00000009190 1.272 7.349 6.91E-07 ENSOARG00000012814 1.205 9.032 6.66E-07 ARHGAP36

ENSOARG00000004917 1.382 6.919 8.31E-07 SH3TC2

ENSOARG00000001626 1.299 4.256 9.66E-07 PPM1K

ENSOARG00000008998 1.462 5.272 1.16E-06 INADL

ENSOARG00000017603 1.300 4.872 1.26E-06 ENSOARG00000009323 1.622 4.356 1.58E-06 BCAM

ENSOARG00000009750 1.528 3.100 1.74E-06 NPNT

ENSOARG00000020222 1.405 5.593 2.06E-06 ITGB5

ENSOARG00000006648 1.422 2.409 2.37E-06 SLFN14

ENSOARG00000019276 -1.721 8.685 2.47E-06 SLC9C1

ENSOARG00000008143 -1.387 2.591 2.92E-06 SMPDL3A

ENSOARG00000011479 1.643 4.080 2.99E-06 CRTAC1

ENSOARG00000010005 -1.652 5.304 3.50E-06 IGF1R

ENSOARG00000007029 1.406 5.731 3.73E-06 SPOCK2

ENSOARG00000003922 1.500 2.757 4.70E-06 MXRA8

ENSOARG00000012369 1.659 5.789 4.59E-06 SLC4A5

ENSOARG00000014423 1.708 4.196 4.32E-06 DAB2IP

ENSOARG00000015885 1.455 3.469 4.74E-06 TOX

ENSOARG00000001269 -1.515 4.677 5.40E-06 STAR

ENSOARG00000003390 1.266 4.721 5.25E-06 ENSOARG00000020503 1.165 6.317 5.15E-06 COL4A4

ENSOARG00000012200 1.155 5.010 6.04E-06 MPZL1

ENSOARG00000012398 1.482 4.788 6.16E-06 ENSOARG00000016047 1.199 4.616 6.28E-06 TGFBR3

ENSOARG00000019011 1.354 5.014 6.19E-06 LRRFIP1

ENSOARG00000011536 1.393 5.299 6.47E-06 NCKAP5

ENSOARG00000005403 1.470 3.209 6.96E-06 BACE1

ENSOARG00000014938 1.650 2.661 6.82E-06 ZNF395

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201

ENSOARG00000010607 1.804 3.571 7.96E-06 CCDC8

ENSOARG00000012805 1.049 5.856 8.69E-06 TMEM2

ENSOARG00000014559 1.645 5.106 8.50E-06 LAMB2

ENSOARG00000014425 1.484 4.212 9.04E-06 MERTK

ENSOARG00000016334 1.209 4.740 9.28E-06 WT1

ENSOARG00000012739 1.339 5.895 9.98E-06 TANC2

ENSOARG00000020475 1.498 4.100 1.05E-05 LEPREL1

ENSOARG00000005659 -1.325 5.100 1.13E-05 ENSOARG00000010736 1.184 5.608 1.14E-05 DFNA5

ENSOARG00000011943 -1.341 8.826 1.11E-05 SLC9C2

ENSOARG00000020387 1.391 6.150 1.17E-05 TYRO3

ENSOARG00000005188 1.388 8.024 1.27E-05 MAP1B

ENSOARG00000012886 1.454 2.707 1.27E-05 THRA

ENSOARG00000016447 1.247 7.214 1.28E-05 KIAA1210

ENSOARG00000019687 1.614 3.478 1.32E-05 ARHGEF40

ENSOARG00000002130 -1.501 8.316 1.49E-05 ENSOARG00000004562 1.147 3.666 1.50E-05 ZADH2

ENSOARG00000005166 -1.339 8.276 1.47E-05 TEX30

ENSOARG00000009528 1.415 4.734 1.42E-05 AFF2

ENSOARG00000019074 1.703 4.752 1.48E-05 NYNRIN

ENSOARG00000005362 -1.456 7.646 1.56E-05 C1orf101

ENSOARG00000007820 -1.489 7.552 1.59E-05 MEIG1

ENSOARG00000018590 1.339 2.375 1.63E-05 MID2

ENSOARG00000019070 1.051 7.032 1.61E-05 PDGFRA

ENSOARG00000019400 1.304 3.671 1.53E-05 RAPGEF3

ENSOARG00000004441 1.715 3.625 1.75E-05 SLC41A1

ENSOARG00000008504 -1.503 5.174 1.75E-05 LRRC72

ENSOARG00000014858 -1.340 7.893 1.74E-05 PBK

ENSOARG00000016713 1.141 5.428 1.75E-05 TKT

ENSOARG00000005533 1.568 2.997 1.80E-05 UBQLN2

ENSOARG00000015255 -1.537 4.366 1.85E-05 ENSOARG00000019474 -1.553 8.053 1.86E-05 TDRD15

ENSOARG00000020961 1.218 4.612 1.85E-05 MARCKSL1

ENSOARG00000018517 1.458 3.670 1.92E-05 CORO2B

ENSOARG00000007688 1.071 5.287 1.95E-05 SLC25A6

ENSOARG00000000649 -1.429 6.344 2.02E-05 ENSOARG00000009155 1.151 4.022 2.05E-05 CCBL1

ENSOARG00000010354 0.999 5.164 2.12E-05 LRRC8D

ENSOARG00000019206 1.107 4.455 2.12E-05 PHLDB2

ENSOARG00000011436 1.090 6.486 2.16E-05 MGAT5

ENSOARG00000005688 1.181 3.060 2.23E-05 RAB30

ENSOARG00000016106 1.906 3.202 2.27E-05 OTOA

ENSOARG00000016294 -1.485 6.108 2.32E-05 CCDC172

ENSOARG00000020201 1.188 6.201 2.43E-05 MLCK

ENSOARG00000012066 1.835 3.248 2.51E-05 SEMA4B

ENSOARG00000005930 1.369 3.170 2.57E-05 FIGN

ENSOARG00000015492 1.251 3.079 2.59E-05 ABHD17C

Appendices

202

ENSOARG00000009984 1.115 4.665 2.70E-05 PLAC9

ENSOARG00000013070 1.225 3.058 2.64E-05 C1orf115

ENSOARG00000017582 1.636 3.538 2.67E-05 TMEM63A

ENSOARG00000001255 -1.299 6.757 2.81E-05 ENSOARG00000005421 1.241 4.624 2.78E-05 NDRG1

ENSOARG00000010528 1.503 2.997 2.78E-05 SLCO3A1

ENSOARG00000009875 -1.376 7.909 2.92E-05 UBE2U

ENSOARG00000002475 1.383 6.111 3.04E-05 PEG10

ENSOARG00000014992 1.619 3.569 2.98E-05 CAMKK2

ENSOARG00000020239 1.458 5.011 3.02E-05 OBSL1

ENSOARG00000009918 1.309 5.700 3.24E-05 DOCK5

ENSOARG00000011850 1.739 2.998 3.25E-05 PXN

ENSOARG00000020883 1.229 4.749 3.27E-05 CERS2

ENSOARG00000005706 1.033 4.838 3.31E-05 USP27X

ENSOARG00000000480 1.500 3.698 3.56E-05 MOCS1

ENSOARG00000000603 1.347 2.674 3.50E-05 FLT4

ENSOARG00000001432 1.386 4.188 3.50E-05 NDRG4

ENSOARG00000016281 1.759 3.447 3.53E-05 SOGA1

ENSOARG00000005832 1.009 5.958 3.60E-05 PJA1

ENSOARG00000006607 1.148 5.457 3.72E-05 PLD3

ENSOARG00000009757 1.411 4.616 3.71E-05 MARK4

ENSOARG00000005537 -1.407 7.406 3.80E-05 PIGF

ENSOARG00000013753 1.577 4.048 3.85E-05 EGFR

ENSOARG00000002209 0.943 6.102 3.89E-05 ENSOARG00000017682 1.382 3.553 4.11E-05 RGAG4

ENSOARG00000003499 -1.270 7.628 4.22E-05 DNAJB9

ENSOARG00000003570 -1.564 4.158 4.29E-05 LGI1

ENSOARG00000011569 -1.337 7.237 4.33E-05 DEPDC1

ENSOARG00000002708 1.071 4.835 4.50E-05 ENSOARG00000014811 1.422 5.142 4.48E-05 SCARA3

ENSOARG00000000789 1.352 3.469 4.71E-05 SPOCD1

ENSOARG00000002375 -1.199 3.419 4.76E-05 SLC17A1

ENSOARG00000008576 1.246 6.072 4.87E-05 VCL

ENSOARG00000012584 1.366 4.710 4.80E-05 COL18A1

ENSOARG00000014199 1.084 4.560 4.82E-05 ADFP

ENSOARG00000024085 2.063 9.352 4.85E-05 U1

ENSOARG00000002162 1.189 4.641 4.98E-05 ETNK2

ENSOARG00000017397 1.046 4.902 5.02E-05 RHOBTB3

ENSOARG00000013240 -1.273 5.956 5.14E-05 ACER3

ENSOARG00000019527 1.319 3.090 5.21E-05 TMBIM1

ENSOARG00000004843 -1.353 6.181 5.27E-05 C1GALT1

ENSOARG00000007142 1.411 2.511 5.37E-05 ENSOARG00000007743 -1.331 7.474 5.38E-05 DNAJC28

ENSOARG00000018989 1.046 6.467 5.38E-05 NEO1

ENSOARG00000008860 1.337 4.242 5.46E-05 NFIA

ENSOARG00000011871 -1.015 7.041 5.47E-05 ENSOARG00000002101 -1.242 9.566 5.62E-05 ADAM2

Appendices

203

ENSOARG00000000327 1.341 7.555 5.91E-05 LAMA1

ENSOARG00000003586 1.721 3.118 5.97E-05 IGF2

ENSOARG00000005297 -1.267 8.059 5.78E-05 ENSOARG00000006178 1.199 3.020 6.03E-05 FAM20C

ENSOARG00000006760 1.149 4.301 5.91E-05 LPAR1

ENSOARG00000007866 1.152 4.317 5.76E-05 PSMB9

ENSOARG00000013541 1.133 5.466 5.98E-05 GSTT1

ENSOARG00000014988 1.095 10.283 5.94E-05 AHNAK

ENSOARG00000020060 1.004 7.056 6.04E-05 PABPC4

ENSOARG00000014682 -1.080 8.540 6.11E-05 ENSOARG00000013473 1.443 4.110 6.42E-05 NHS

ENSOARG00000018210 0.953 6.962 6.45E-05 TIMP3

ENSOARG00000002785 1.437 6.611 6.56E-05 KIAA1217

ENSOARG00000009144 1.030 3.565 6.68E-05 JAZF1

ENSOARG00000012327 -1.377 9.566 6.67E-05 ENSOARG00000013610 1.594 6.493 6.52E-05 MYO7A

ENSOARG00000016797 -1.507 7.048 6.60E-05 SPATA22

ENSOARG00000000338 1.789 4.291 6.77E-05 ABCA2

ENSOARG00000000692 1.138 5.227 6.96E-05 VILL

ENSOARG00000001888 -1.263 8.978 7.29E-05 ADAM32

ENSOARG00000004896 1.096 3.662 7.19E-05 SYTL2

ENSOARG00000005436 1.339 4.950 7.02E-05 FAM65B

ENSOARG00000011877 1.476 2.599 7.20E-05 ENSOARG00000013071 -1.051 7.472 7.29E-05 ENSOARG00000013365 1.278 5.826 7.14E-05 USP11

ENSOARG00000016052 0.996 6.936 7.33E-05 AOX1

ENSOARG00000016567 -1.290 6.860 7.20E-05 ZSWIM2

ENSOARG00000017517 1.068 6.227 7.03E-05 SERINC5

ENSOARG00000018262 1.308 3.547 7.25E-05 NKIRAS2

ENSOARG00000001507 1.106 3.531 7.38E-05 SETD6

ENSOARG00000009599 1.231 5.653 7.43E-05 ALDH1A3

ENSOARG00000006679 1.471 5.380 7.52E-05 ARHGEF11

ENSOARG00000020797 1.010 5.887 7.53E-05 TPM1

ENSOARG00000019806 1.388 5.293 7.64E-05 PLXNB2

ENSOARG00000020078 -1.462 4.174 7.69E-05 EAF2

ENSOARG00000006117 1.397 2.555 7.92E-05 SLC41A3

ENSOARG00000008570 1.171 3.394 7.99E-05 RBMS2

ENSOARG00000010071 1.040 4.412 7.95E-05 NKX3-1

ENSOARG00000010118 1.027 4.893 7.91E-05 VWA5A

ENSOARG00000014746 1.321 4.752 8.12E-05 MAP3K15

ENSOARG00000010288 1.681 5.821 8.21E-05 MAP1A

ENSOARG00000020192 -1.505 6.577 8.37E-05 MORN2

ENSOARG00000007061 1.233 3.439 8.48E-05 ENSOARG00000013044 0.954 6.700 8.59E-05 ZO2

ENSOARG00000014585 -1.318 5.832 8.68E-05 ENSOARG00000011766 1.260 4.413 8.75E-05 CDCA7L

ENSOARG00000013942 -1.551 7.266 8.89E-05

Appendices

204

ENSOARG00000004561 1.095 4.428 9.25E-05 TMTC4

ENSOARG00000007392 0.942 6.184 9.33E-05 MAP3K1

ENSOARG00000009531 -1.112 9.183 9.14E-05 LDHC

ENSOARG00000012117 1.016 5.236 9.24E-05 RAB11FIP3

ENSOARG00000015487 1.249 4.684 9.13E-05 ZBTB38

ENSOARG00000019354 1.224 3.709 9.29E-05 SLC7A8

ENSOARG00000020003 -1.472 4.356 9.37E-05 LYRM5

ENSOARG00000020999 -1.280 7.771 9.36E-05 FAM227B

ENSOARG00000000178 -1.450 8.757 9.49E-05 PPP1R42

ENSOARG00000014725 1.218 6.103 9.45E-05 APLP2

ENSOARG00000001975 -1.241 10.092 9.66E-05 ENSOARG00000003537 1.037 8.191 9.67E-05 SYNE1

ENSOARG00000008657 -1.141 6.418 9.84E-05 ZNF572

ENSOARG00000018805 -1.287 6.758 9.95E-05 FBXL13

ENSOARG00000002163 -1.171 6.593 0.000101358 ADAM18

ENSOARG00000001381 1.061 4.060 0.000102085 PSEN2

ENSOARG00000011347 1.636 3.191 0.000106031 FBRSL1

ENSOARG00000011582 1.075 4.868 0.000108399 CYP26B1

ENSOARG00000014016 1.365 5.807 0.000108589 TIAM1

ENSOARG00000005151 1.046 6.166 0.000111484 HMCN1

ENSOARG00000014525 1.117 4.878 0.000111891 AR

ENSOARG00000007281 1.467 4.817 0.000112983 SRGAP3

ENSOARG00000007332 1.575 7.480 0.000113611 LRP1

ENSOARG00000015806 -1.317 8.124 0.000113657 PIH1D2

ENSOARG00000000291 -1.307 3.800 0.000115762 FGB

ENSOARG00000001142 1.410 3.829 0.000115688 ITPKB

ENSOARG00000007284 0.973 6.342 0.000118708 ARHGAP10

ENSOARG00000019810 1.331 3.133 0.000120327 GPR55

ENSOARG00000020849 -1.456 7.696 0.0001202 HORMAD1

ENSOARG00000003402 -1.271 6.151 0.00012186 BBS5

ENSOARG00000006859 -1.295 5.600 0.000122305 C11orf70

ENSOARG00000005365 1.218 4.579 0.000124471 FHOD3

ENSOARG00000015849 -1.287 7.601 0.000124123 MRPL42

ENSOARG00000020243 1.255 5.183 0.000123896 INHA

ENSOARG00000013562 1.139 3.295 0.000126952 SCUBE2

ENSOARG00000006748 1.111 3.232 0.000134301 CTSO

ENSOARG00000008224 1.267 2.939 0.000134514 DAB1

ENSOARG00000010114 -1.278 3.435 0.000133138 ENSOARG00000012558 -1.262 6.478 0.000133036 ENSOARG00000017729 1.274 2.527 0.000134876 PCED1B

ENSOARG00000016465 -1.353 4.758 0.000136234 LIPI

ENSOARG00000019436 0.920 6.004 0.000137013 ABHD4

ENSOARG00000014769 0.920 6.287 0.000138417 OCRL

ENSOARG00000000803 1.551 3.690 0.000141707 SOGA2

ENSOARG00000013073 1.598 3.487 0.000141477 RIN3

ENSOARG00000017985 1.245 4.009 0.000142258 ARHGDIA

ENSOARG00000003224 1.218 2.448 0.000146016 SMPDL3B

Appendices

205

ENSOARG00000009486 -1.122 7.183 0.000146196 NME7

ENSOARG00000010461 -1.186 7.081 0.000145636 PPIL6

ENSOARG00000015897 1.829 3.395 0.000147357 SYNE3

ENSOARG00000003156 -1.423 2.936 0.000150052 C5orf58

ENSOARG00000007713 1.042 3.877 0.000151798 NAB2

ENSOARG00000020250 -1.157 6.906 0.00015263 C12orf60

ENSOARG00000008205 1.341 3.718 0.000155233 IGSF8

ENSOARG00000006306 1.484 3.887 0.000157021 BRPF1

ENSOARG00000007465 1.450 3.464 0.000157715 PLXNA1

ENSOARG00000012167 -1.186 7.035 0.000160235 ZPBP2

ENSOARG00000016290 1.041 3.903 0.000160238 APOBEC3F

ENSOARG00000016920 0.871 5.845 0.000160242 ENSOARG00000005802 -1.291 4.621 0.000164786 C1orf100

ENSOARG00000008297 1.345 5.368 0.000165638 ATP1A2

ENSOARG00000010501 -1.295 7.709 0.000166163 ENSOARG00000012800 -1.391 7.327 0.000163704 ASB17

ENSOARG00000019135 1.588 4.170 0.000165179 CELSR2

ENSOARG00000018357 -1.143 5.855 0.000167748 CRLS1

ENSOARG00000001759 -1.130 7.208 0.000171166 FANCL

ENSOARG00000018002 -1.216 2.663 0.000171776 SPO11

ENSOARG00000014193 -1.193 5.897 0.000172997 PIGP

ENSOARG00000015394 0.979 6.624 0.000173347 P4HA2

ENSOARG00000009728 0.941 4.820 0.00017535 PTN

ENSOARG00000020177 0.999 5.727 0.000175008 PDIA5

ENSOARG00000009869 -1.187 6.012 0.000184919 LRRC69

ENSOARG00000010650 1.027 7.118 0.000186296 ENSOARG00000012393 1.052 4.136 0.000185728 GNAQ

ENSOARG00000014651 1.298 3.699 0.000183545 MOB3B

ENSOARG00000019175 -1.137 8.690 0.000185027 PVRL3

ENSOARG00000008974 1.498 4.440 0.000188108 ADAM11

ENSOARG00000011972 -1.098 7.958 0.000189051 ENSOARG00000007525 1.269 5.066 0.000192267 AXL

ENSOARG00000011147 1.354 3.347 0.000192633 DDR1

ENSOARG00000019106 1.408 2.910 0.000192445 RBCK1

ENSOARG00000021089 -1.205 7.678 0.000194559 DLGAP5

ENSOARG00000004403 0.947 5.940 0.000196968 ARID5B

ENSOARG00000015689 -1.365 4.491 0.000197176 PARPBP

ENSOARG00000008398 1.149 8.139 0.00020082 FAT1

ENSOARG00000013347 1.013 5.691 0.000201076 CHGA

ENSOARG00000018452 0.945 4.927 0.000200284 GRAMD3

ENSOARG00000001823 1.045 3.366 0.00020703 TNNT1

ENSOARG00000004522 1.427 4.344 0.000206231 AFF1

ENSOARG00000005208 1.343 7.468 0.000204047 FLNA

ENSOARG00000011189 -1.279 6.843 0.000206057 NUF2

ENSOARG00000012588 -1.217 5.510 0.000206448 C9orf41

ENSOARG00000017976 1.822 3.970 0.000203374 ATP2A3

ENSOARG00000020900 -1.407 3.698 0.000206825 C15orf65

Appendices

206

ENSOARG00000009896 -1.184 5.093 0.000208403 GTF3C6

ENSOARG00000004632 1.497 6.310 0.000212338 IGF2R

ENSOARG00000006836 -1.173 6.587 0.000213468 DPY19L4

ENSOARG00000003333 1.100 3.339 0.000219235 ZBTB7B

ENSOARG00000008975 1.164 7.622 0.000219048 MYH11

ENSOARG00000010884 0.926 4.512 0.000219358 RBMX

ENSOARG00000016774 1.425 3.174 0.000216903 SLC44A2

ENSOARG00000019706 1.201 4.273 0.000218847 NDRG2

ENSOARG00000010755 -1.163 4.902 0.000222525 CYP39A1

ENSOARG00000011687 1.060 4.056 0.000223293 GAMT

ENSOARG00000012252 1.453 2.730 0.000223129 CNNM1

ENSOARG00000018356 -1.161 7.784 0.000221725 SLCO6A1

ENSOARG00000018437 -1.108 6.221 0.000225462 GIN1

ENSOARG00000017897 1.451 3.727 0.000227884 PFKFB4

ENSOARG00000018405 0.855 7.533 0.000227656 PAM

ENSOARG00000025176 -1.153 9.520 0.00022672 ENSOARG00000017505 -1.188 7.759 0.000229878 SUGCT

ENSOARG00000017940 1.300 3.174 0.000229896 SRC

ENSOARG00000010379 -2.329 4.743 0.000230835 NRG3

ENSOARG00000011104 -1.223 2.844 0.000232297 ENSOARG00000015228 1.559 3.632 0.000232195 GATA4

ENSOARG00000008626 1.246 3.333 0.000233088 PRRG3

ENSOARG00000006832 1.505 4.692 0.000235829 PRRC2B

ENSOARG00000017220 -1.586 3.914 0.000235986 IL13RA2

ENSOARG00000002232 -1.279 7.420 0.000239356 CCDC79

ENSOARG00000005218 -1.018 7.867 0.000239032 EPCAM

ENSOARG00000012214 -1.401 7.043 0.000240107 LRRIQ3

ENSOARG00000014621 -1.356 5.358 0.000237827 CLCA2

ENSOARG00000013326 -1.232 8.114 0.000242064 DNAJB4

ENSOARG00000012148 -1.063 6.387 0.000246444 MANEA

ENSOARG00000017409 0.992 3.754 0.000245256 ENSOARG00000017596 -1.413 9.414 0.000245943 ENSOARG00000004237 1.546 4.944 0.00024856 G6PD

ENSOARG00000020321 -1.084 7.043 0.000250784

ENSOARG00000017040 0.886 5.188 0.000252045 FAM47E-STBD1

ENSOARG00000003563 -1.475 3.239 0.000253518 ENSOARG00000016498 1.647 3.877 0.000254289 SLC27A1

ENSOARG00000007151 -1.139 6.986 0.000257509 TTK

ENSOARG00000005495 -1.161 7.192 0.000261563 UQCRB

ENSOARG00000011948 -1.336 4.426 0.00026389 TPRKB

ENSOARG00000009905 1.016 2.890 0.000268328 MBOAT1

ENSOARG00000007201 1.175 4.463 0.00027162 SLC6A8

ENSOARG00000001243 1.194 4.273 0.000282372 RAB31

ENSOARG00000008915 -1.063 7.604 0.000278244 HNRNPLL

ENSOARG00000014317 1.537 3.974 0.000280211 ST5

ENSOARG00000014697 -1.557 7.315 0.000280373 GPR160

ENSOARG00000015665 -1.032 4.464 0.000281559 MAD2L1

Appendices

207

ENSOARG00000016966 0.920 4.934 0.000280145 NONO

ENSOARG00000018632 1.404 4.418 0.000281842 ENSOARG00000020442 1.374 6.431 0.000277343 NOTCH2

ENSOARG00000020613 -0.990 8.612 0.000277651 KLHDC2

ENSOARG00000009014 -1.135 4.579 0.000283372 KPNA5

ENSOARG00000017791 1.585 3.397 0.000286844 FGD5

ENSOARG00000018306 1.165 4.425 0.000286876 ARHGAP17

ENSOARG00000011475 -1.262 5.970 0.000289817 CNBD1

ENSOARG00000004439 -1.451 5.394 0.000294342 C1orf185

ENSOARG00000016505 0.883 6.336 0.000293781 CPT1A

ENSOARG00000017896 -1.066 5.712 0.000292722 FAM228A

ENSOARG00000006855 1.659 3.468 0.000295866 KCNA4

ENSOARG00000017914 -1.433 3.170 0.000296986 MCMDC2

ENSOARG00000015129 0.918 4.074 0.000297971 ENSOARG00000008240 -1.088 7.305 0.000303165 NME8

ENSOARG00000012389 1.311 3.554 0.00030452 GPRC5B

ENSOARG00000008891 0.938 4.518 0.000307965 NTRK2

ENSOARG00000009732 0.881 5.484 0.000308074 FYN

ENSOARG00000011801 -1.003 5.818 0.000312967 C3orf14

ENSOARG00000013006 1.310 3.042 0.000313233 MAPK13

ENSOARG00000011021 1.308 4.811 0.000315893 HEPACAM

ENSOARG00000015548 -1.323 3.574 0.000317092 ENSOARG00000003299 1.065 3.628 0.000318047 PEPT1

ENSOARG00000003730 1.258 4.244 0.000326935 STK10

ENSOARG00000012075 1.064 3.709 0.000324216 CDON

ENSOARG00000015194 1.031 3.062 0.000326203 CRYAB

ENSOARG00000015259 1.220 5.365 0.000326408 MAP7D2

ENSOARG00000019000 -1.092 7.862 0.000323046 RBM44

ENSOARG00000019413 1.545 3.204 0.000327396 FAM84A

ENSOARG00000004477 -1.062 6.631 0.000330518 EED

ENSOARG00000006623 -1.358 6.394 0.000332974 C9orf84

ENSOARG00000016690 -1.081 5.617 0.000333108 MEIOB

ENSOARG00000007102 1.063 6.181 0.000334622 PHF8

ENSOARG00000009294 1.002 6.244 0.000335072 MAGED1

ENSOARG00000018981 -1.030 7.606 0.000336923 GPSM2

ENSOARG00000010373 1.456 2.259 0.000339225 MICAL1

ENSOARG00000016324 -1.478 3.762 0.000339188 TEX12

ENSOARG00000000208 1.023 6.692 0.000342549 CLDN11

ENSOARG00000002758 -1.371 6.619 0.000350942 CLHC1

ENSOARG00000003669 1.429 4.735 0.000352999 ZFHX3

ENSOARG00000005598 -1.338 4.856 0.000349404 ENSOARG00000011004 -1.408 7.342 0.00035059 C1orf141

ENSOARG00000013771 0.941 3.568 0.000347122 L3MBTL3

ENSOARG00000013791 1.303 2.941 0.000354714 CPNE5

ENSOARG00000015356 1.420 2.930 0.000351819 ELF4

ENSOARG00000015569 1.267 3.478 0.000352426 PALLD

ENSOARG00000016755 -1.109 10.452 0.000354512

Appendices

208

ENSOARG00000016832 -1.027 7.775 0.000353036 ATP6V1C1

ENSOARG00000009812 1.386 3.766 0.000360691 FREM2

ENSOARG00000012437 1.055 4.462 0.00036029 DHDH

ENSOARG00000023607 1.470 3.711 0.000360986 SNORA71

ENSOARG00000008120 1.062 3.697 0.000363472 FBXL19

ENSOARG00000014078 -1.406 2.900 0.000363484 FAM177B

ENSOARG00000006066 0.995 4.010 0.000367191 JAKMIP1

ENSOARG00000016387 -1.037 6.756 0.000367767 SLC25A32

ENSOARG00000020283 -1.221 7.363 0.000368343 C2orf74

ENSOARG00000009376 0.958 3.906 0.000371015 USF1

ENSOARG00000020518 1.009 4.467 0.000370054 GGCX

ENSOARG00000019557 1.070 3.440 0.000374366 CTDSP1

ENSOARG00000014849 1.070 4.469 0.000375441 NEDD9

ENSOARG00000020662 -1.188 9.262 0.000377348 DCUN1D1

ENSOARG00000007689 -1.120 7.035 0.000379853 SLC38A9

ENSOARG00000018420 -1.122 4.909 0.000380916 ENSOARG00000004976 1.071 2.443 0.000384205 RPL10

ENSOARG00000007780 -1.223 9.702 0.000383749 CABS1

ENSOARG00000001511 0.965 4.894 0.000385534 STAT3

ENSOARG00000002365 1.380 5.096 0.000394223 USP22

ENSOARG00000012185 1.075 2.976 0.000392855 RCSD1

ENSOARG00000017962 1.474 6.838 0.000394493 MYO9B

ENSOARG00000005335 1.039 5.400 0.000397679 PGD

ENSOARG00000019547 -1.063 6.927 0.000397233 SPDYA

ENSOARG00000000633 -1.186 7.484 0.00040242 IQUB

ENSOARG00000007924 1.037 3.692 0.000401602 PDLIM3

ENSOARG00000014575 -1.170 7.857 0.00040117 ENSOARG00000016794 0.992 5.889 0.000402216 ZMYM3

ENSOARG00000009363 -1.088 5.932 0.000414072 GLRX2

ENSOARG00000012405 0.912 5.754 0.000416247 MBNL3

ENSOARG00000007105 -1.000 6.231 0.000420308 PSMA8

ENSOARG00000008571 -1.056 9.013 0.000420036 CCT6B

ENSOARG00000016868 1.147 2.850 0.000420438 NYAP1

ENSOARG00000020695 -1.044 6.803 0.000419971 TMX1

ENSOARG00000001836 1.179 3.427 0.000424361 PTRF

ENSOARG00000003898 1.203 4.596 0.000423459 SH3PXD2B

ENSOARG00000004649 1.151 2.708 0.000426503 RND2

ENSOARG00000013461 1.516 2.524 0.000428986 ITPK1

ENSOARG00000014181 -1.175 8.647 0.000428113 HAUS6

ENSOARG00000020362 1.028 4.308 0.000427476 CTNNA2

ENSOARG00000020525 0.920 6.588 0.000431875 COL4A3

ENSOARG00000009341 1.414 3.644 0.000435501 DKK3

ENSOARG00000018234 -1.033 5.983 0.000436895 ENSOARG00000008164 1.420 3.320 0.000438443 GRN

ENSOARG00000003046 -1.176 8.346 0.000446701 TFAM

ENSOARG00000003190 1.254 3.553 0.000447001 STK24

ENSOARG00000012723 -1.236 6.485 0.000446401 MSH4

Appendices

209

ENSOARG00000013258 1.133 5.854 0.000446144 TBC1D8

ENSOARG00000020761 1.143 5.708 0.000443736 TNIK

ENSOARG00000000690 -1.139 6.529 0.00044936 LRRC34

ENSOARG00000015354 1.549 4.096 0.000449566 ENSOARG00000014792 -1.024 4.727 0.000450947 FAM151B

ENSOARG00000018224 0.825 5.862 0.000454145 TAB3

ENSOARG00000010789 0.989 4.732 0.000455896 BTBD3

ENSOARG00000011822 0.785 6.693 0.000462195 PTPRG

ENSOARG00000014053 -1.032 9.603 0.000461999 BTBD1

ENSOARG00000019695 -1.011 8.179 0.000463397 ST7L

ENSOARG00000007585 1.354 3.494 0.000464795 TANC1

ENSOARG00000021127 -1.715 4.716 0.000469452 C14orf39

ENSOARG00000004652 1.011 3.861 0.000471841 ZNF792

ENSOARG00000006907 0.825 6.270 0.000471722 CDH2

ENSOARG00000008688 -1.149 8.927 0.000476834 C17orf104

ENSOARG00000005784 -1.214 9.088 0.000482029 BCAP29

ENSOARG00000006610 1.119 3.258 0.000481456 BCAR1

ENSOARG00000016442 1.145 4.625 0.00048405 USP31

ENSOARG00000003065 0.992 5.236 0.000489805 MTHFD1L

ENSOARG00000003694 -0.979 7.800 0.000489309 FNTA

ENSOARG00000005960 1.083 4.419 0.000486669 ACTR3B

ENSOARG00000008600 -1.074 8.011 0.000490451 PAIP1

ENSOARG00000015497 -1.009 5.899 0.00048922 CDKL2

ENSOARG00000002801 1.271 3.819 0.000492125 SLC4A2

ENSOARG00000014542 -1.171 6.196 0.000494353 TSPAN8

ENSOARG00000014663 -1.217 8.210 0.000495279 TBC1D15

ENSOARG00000020213 0.842 7.646 0.000493522 ATP1A1

ENSOARG00000020368 -1.500 5.307 0.000497619 YEATS4

ENSOARG00000001746 -1.225 9.783 0.000499922 CCDC54

ENSOARG00000021604 1.140 3.159 0.000503968 NEAT1_1

ENSOARG00000007794 -1.043 6.491 0.000509463 WDR64

ENSOARG00000020678 -1.201 7.796 0.000508685 CCDC39

ENSOARG00000003451 1.303 3.487 0.000513197 SLC20A2

ENSOARG00000002207 0.948 3.795 0.00051929 CLTB

ENSOARG00000008614 1.392 2.458 0.000520598 PHLDB3

ENSOARG00000013141 -1.006 9.329 0.000529751 MS4A13

ENSOARG00000000549 1.134 5.380 0.000531858 PTPRM

ENSOARG00000010792 -1.200 7.326 0.000537257 WDR78

ENSOARG00000011328 0.815 7.204 0.000538364 VIM

ENSOARG00000008503 -1.126 7.454 0.00054406 C7orf62

ENSOARG00000012262 0.912 4.887 0.000546613 GPC4

ENSOARG00000016151 1.687 3.846 0.000543384 CLUH

ENSOARG00000016654 -1.409 5.590 0.000545301 LYRM7

ENSOARG00000020344 -1.099 4.298 0.000545785 ENSOARG00000016983 -1.131 4.489 0.000550181 ENSOARG00000019852 -1.052 6.533 0.000550759 ASUN

ENSOARG00000020226 0.882 5.237 0.000551214 BAHD1

Appendices

210

ENSOARG00000005076 -0.998 6.915 0.00055393 DNA2

ENSOARG00000010804 -0.956 4.532 0.000557394 ENSOARG00000016483 1.134 2.851 0.000556254 MAPK15

ENSOARG00000004016 -1.050 7.171 0.000567665 NCAPG

ENSOARG00000007346 -1.204 8.237 0.000567542 CEP70

ENSOARG00000010741 1.072 5.455 0.000568862 FOXO3

ENSOARG00000012717 -1.062 11.526 0.000568582 CLGN

ENSOARG00000013376 1.230 3.849 0.000565079 ARHGAP32

ENSOARG00000013909 -0.938 6.774 0.000569088 BBS7

ENSOARG00000015455 -1.238 8.257 0.000563237 C12orf50

ENSOARG00000015546 -1.248 5.956 0.00056273 PKD2L2

ENSOARG00000015691 -1.101 8.548 0.000571691 PLK4

ENSOARG00000018034 1.566 3.727 0.000573268 ZYX

ENSOARG00000004567 -1.073 8.153 0.000577475 ENSOARG00000009866 0.933 5.367 0.000580839 TBL1Y

ENSOARG00000006172 -0.952 5.608 0.000582983 ENSOARG00000016899 1.427 2.772 0.000590223 TMEM132C

ENSOARG00000020185 1.003 3.630 0.000590941 DES

ENSOARG00000009951 -1.180 4.807 0.000595275 ENSOARG00000015422 0.779 5.468 0.000596829 AIFM1

ENSOARG00000017471 -1.156 5.650 0.00059989 PFDN1

ENSOARG00000020935 1.173 2.964 0.000598987 SEMA6C

ENSOARG00000005146 -1.041 5.000 0.000601842 COX20

ENSOARG00000004110 -1.046 6.475 0.000603987 LACTB2

ENSOARG00000006167 -0.844 8.115 0.000610977 HORMAD2

ENSOARG00000011736 -0.998 6.125 0.000611675 FAM8A1

ENSOARG00000021074 -1.068 5.931 0.000610642 CGRRF1

ENSOARG00000014750 -1.051 6.026 0.000613847 STK17B

ENSOARG00000019623 -1.096 7.009 0.000621022 ERGIC2

ENSOARG00000012535 1.338 5.143 0.000624262 TNS3

ENSOARG00000004252 1.059 3.451 0.000625623 ILVBL

ENSOARG00000002149 1.192 6.650 0.00063626 SND1

ENSOARG00000002579 -0.948 5.394 0.000627989 C10orf67


ENSOARG00000017133 -0.978 7.488 0.00063141 PCNA

ENSOARG00000017240 0.979 4.492 0.000633262 HECW1

ENSOARG00000017989 -1.498 2.513 0.000633432 ENSOARG00000019404 1.484 4.382 0.000635088 FBLN1

ENSOARG00000014794 -1.056 4.846 0.000637598 CLCA4

ENSOARG00000007141 1.093 3.446 0.000639129 PLTP

ENSOARG00000006124 -1.190 7.846 0.000641403 RNF138

ENSOARG00000015794 -1.177 5.089 0.000642278 NME5

ENSOARG00000016739 1.032 4.554 0.000645637 ENSOARG00000004758 1.265 3.619 0.00064804 ENC1

ENSOARG00000008868 -1.082 4.259 0.000648225 FBXO4

ENSOARG00000012182 1.198 4.091 0.000651558 STX3

Appendices

211

ENSOARG00000001499 1.094 3.766 0.000661578 ADCK3

ENSOARG00000007299 -1.163 7.602 0.000661053 PPP2R3C

ENSOARG00000008789 1.321 2.765 0.000661218 PC

ENSOARG00000011007 -1.085 5.142 0.000663891 PLA2G7

ENSOARG00000016938 -1.225 4.918 0.000663031 CXorf58

ENSOARG00000001434 1.267 2.838 0.000671695 KCNH2

ENSOARG00000007742 1.205 2.515 0.0006717 ENSOARG00000015272 -1.087 6.683 0.000669451 RCHY1

ENSOARG00000016675 -1.307 11.587 0.000669305 FSIP2

ENSOARG00000021107 0.767 8.293 0.000667528 C14orf37

ENSOARG00000012994 -0.987 7.243 0.000676163 CRYZL1

ENSOARG00000014872 -0.900 6.605 0.000677943 ENSOARG00000002674 1.229 2.918 0.000680838 SHE

ENSOARG00000001057 1.595 2.406 0.000689208 TMEM43

ENSOARG00000001763 -1.119 6.595 0.000683097 HAT1

ENSOARG00000004389 -0.918 6.457 0.000685221 SYPL1

ENSOARG00000010186 -1.052 7.080 0.00068701 C8orf88

ENSOARG00000015325 -1.062 8.442 0.000688837 CEP44

ENSOARG00000019253 1.320 3.781 0.000685448 CARD10

ENSOARG00000016730 -0.933 8.479 0.000692842 HCFC2

ENSOARG00000017686 -1.061 5.804 0.000695871 YAE1D1

ENSOARG00000007344 1.021 7.709 0.000700242 ABCA1

ENSOARG00000012515 1.036 4.747 0.00070011 PCSK5

ENSOARG00000018066 1.314 4.223 0.000698615 QSOX1

ENSOARG00000001290 -0.973 7.028 0.000701628 PEX13

ENSOARG00000006514 1.321 3.885 0.000709736 FRMD4A

ENSOARG00000013552 1.191 5.252 0.000708756 INPP4A

ENSOARG00000020882 -1.243 7.202 0.000708492 TEX9

ENSOARG00000008188 1.013 5.302 0.000711156 ANXA6

ENSOARG00000012940 -1.103 8.095 0.000713403 CISD1

ENSOARG00000008801 -1.661 5.610 0.000716127 CCDC152

ENSOARG00000009522 1.384 5.823 0.000718917 ABCC1

ENSOARG00000016926 -1.023 7.830 0.000719937 FAM216A

ENSOARG00000005949 0.915 4.834 0.000724973 TMC6

ENSOARG00000007862 -1.394 10.312 0.0007257 CCDC110

ENSOARG00000020583 1.000 3.665 0.000725565 ST3GAL5

ENSOARG00000019142 -1.164 9.529 0.000729302 SHCBP1L

ENSOARG00000008430 -1.264 4.816 0.0007369 AMICA1

ENSOARG00000009370 0.927 5.447 0.000737097 PVRL2

ENSOARG00000012070 -1.139 7.743 0.000734793 C7orf63

ENSOARG00000008658 -0.938 4.850 0.00074263 SPRYD7

ENSOARG00000017669 -1.273 4.785 0.000743289 LRRD1

ENSOARG00000000942 0.995 8.831 0.000744692 RNF213

ENSOARG00000011666 -1.050 8.598 0.000750634 KIFAP3

ENSOARG00000006099 -1.195 4.700 0.000758112 ENSOARG00000007270 1.421 3.579 0.000756625 LAMC3

ENSOARG00000010624 -1.194 6.436 0.000759529 CEP57L1

Appendices

212

ENSOARG00000010632 1.068 3.599 0.000759173 TET2

ENSOARG00000021073 -0.933 4.937 0.000760347 GMFB

ENSOARG00000024133 1.039 4.218 0.000756039 NEAT1_2

ENSOARG00000007534 -0.955 6.839 0.0007693 LACC1

ENSOARG00000019457 -1.066 5.411 0.000769297 SLC38A4

ENSOARG00000000192 -0.940 7.831 0.000777836 ENSOARG00000000998 0.895 4.670 0.000774108 DHX35

ENSOARG00000002860 1.456 3.136 0.000778957 FHOD1

ENSOARG00000005088 -1.102 5.918 0.000783876 TCTE3

ENSOARG00000007003 -1.121 5.860 0.00077722 ENSOARG00000009880 0.776 5.114 0.000780996 ZEB2

ENSOARG00000017031 -1.469 5.317 0.000784702 ENSOARG00000020743 -1.145 4.421 0.000779413 KIAA0101

ENSOARG00000021031 -1.025 7.060 0.000783073 ENSOARG00000017507 -1.004 3.631 0.000787078 TMPRSS12

ENSOARG00000000982 1.228 2.593 0.000789566 TMEM180

ENSOARG00000001964 1.533 2.791 0.000794594 NUP210

ENSOARG00000014790 1.073 9.512 0.000802437 ENSOARG00000017475 -1.168 3.322 0.00080162 INHBA

ENSOARG00000021140 -1.268 4.293 0.000809897 ENSOARG00000000983 -1.109 4.467 0.00081413 WDR89

ENSOARG00000010850 -1.085 7.411 0.000813033 ENSOARG00000007824 -1.045 3.842 0.000821154 HINT3

ENSOARG00000016617 1.356 5.457 0.000822558 TRANK1

ENSOARG00000017203 -1.146 9.808 0.000825924 ALS2CR11

ENSOARG00000017552 1.340 4.596 0.000828608 DSP

ENSOARG00000019032 -1.192 3.236 0.000831928 ENSOARG00000019651 -0.992 9.784 0.000833244 DDX20

ENSOARG00000015150 -1.105 7.800 0.000837421 PDZD9

ENSOARG00000018674 -1.073 4.921 0.000840034 ENSOARG00000004832 0.931 3.115 0.000842957 PI3

ENSOARG00000016349 -0.992 8.755 0.000844416 ANKAR

ENSOARG00000015013 -1.154 4.964 0.000851686 ENSOARG00000000210 -1.075 7.566 0.000869921 ENSOARG00000007085 -0.947 7.601 0.000868565 HSPA14

ENSOARG00000010078 -1.148 6.550 0.000867376 C1orf227

ENSOARG00000021123 -1.067 6.822 0.000875977 LRRC9

ENSOARG00000002770 -1.136 4.586 0.000881461 MRPS18C

ENSOARG00000019733 1.149 5.248 0.000880069 MOV10

ENSOARG00000009965 -0.936 4.041 0.000889332 NR1I3

ENSOARG00000002951 0.989 2.750 0.000896066 CFB

ENSOARG00000011267 -0.941 5.735 0.00089471 CSMD3

ENSOARG00000013713 1.275 2.459 0.000891802 SEMA4C

ENSOARG00000019660 -1.109 6.636 0.000897245 CCDC91

ENSOARG00000020541 0.987 2.707 0.000896095 TMEM150A

ENSOARG00000000116 -1.040 9.373 0.000908816 ADAD1

ENSOARG00000005546 -1.089 4.909 0.000905947

Appendices

213

ENSOARG00000007900 -0.999 8.473 0.000908964 LRRC63

ENSOARG00000013671 0.978 3.270 0.000908791 GADD45B

ENSOARG00000018503 1.236 4.625 0.000911397 SIN3B

ENSOARG00000013260 1.119 2.655 0.000918989 PRR12

ENSOARG00000012433 -1.073 9.754 0.000921216 VPS13A

ENSOARG00000018567 -1.115 8.769 0.000925864 SPESP1

ENSOARG00000000388 -1.230 3.995 0.000936621 ENSOARG00000010733 1.027 5.641 0.000937598 MAGI1

ENSOARG00000011396 -1.012 5.440 0.000928349 THOC7

ENSOARG00000016313 -0.991 5.109 0.000938538 ORMDL1

ENSOARG00000018323 -0.961 7.980 0.000937677 DCUN1D4

ENSOARG00000018851 0.848 4.330 0.000936371 MORC4

ENSOARG00000019298 1.015 2.605 0.000929616 CD200

ENSOARG00000019757 -1.012 7.754 0.000931113 SMCO2

ENSOARG00000010385 -0.968 6.227 0.000941784 ENSOARG00000011327 1.265 3.085 0.000941869 GSE1

ENSOARG00000017530 -1.041 5.403 0.0009457 GNPNAT1

ENSOARG00000015713 -1.212 8.148 0.000956176 IFT81

ENSOARG00000014641 -0.986 9.599 0.000963157 RNF17

ENSOARG00000003329 1.009 5.336 0.000966482 SULF1

ENSOARG00000019955 1.166 2.794 0.000965237 ANKZF1

ENSOARG00000011114 1.038 3.010 0.000969456 VGLL1

ENSOARG00000009943 0.966 3.195 0.00097529 CD3EAP

ENSOARG00000004381 0.899 2.885 0.00097705 TMEM26

ENSOARG00000001546 1.006 4.333 0.000980432 NAA10

ENSOARG00000001591 1.009 4.062 0.000980535 FUCA2

ENSOARG00000000939 0.804 5.270 0.001007963 MAVS

ENSOARG00000002395 0.911 3.618 0.001008187 SLC14A1

ENSOARG00000004512 0.937 5.251 0.001021418 MRO

ENSOARG00000004546 -1.045 4.716 0.00101449 SERP1

ENSOARG00000005041 1.488 2.588 0.001016917 ZNF333

ENSOARG00000005329 -0.891 4.069 0.001013331 ZNF566

ENSOARG00000012370 -1.448 3.302 0.001016527 FMO6P

ENSOARG00000013628 -1.052 2.957 0.001021189 IL33

ENSOARG00000014782 -0.967 4.293 0.00102103 GLIPR1L2

ENSOARG00000016351 0.983 3.155 0.001012707 PGLS

ENSOARG00000016646 -0.902 8.472 0.001016241 PDHA2

ENSOARG00000018458 1.202 3.594 0.001010226 STX6

ENSOARG00000019334 1.095 3.454 0.001023878 EPS8L3

ENSOARG00000002377 -1.007 7.353 0.001032828 UGGT2

ENSOARG00000007434 -0.957 6.931 0.001032875 SUV39H2

ENSOARG00000016898 -0.934 6.950 0.001029731 SLC41A2

ENSOARG00000000924 -0.916 6.280 0.00103708 ENSOARG00000002298 -1.071 7.339 0.001039574 NAE1

ENSOARG00000016126 -1.239 4.924 0.001043297 MSMO1

ENSOARG00000019437 -1.048 7.491 0.001042771 ZMYM1

ENSOARG00000015812 -1.190 6.488 0.001045917 HFM1

Appendices

214

ENSOARG00000015078 1.175 3.393 0.001052606 IRF1

ENSOARG00000015328 0.881 4.972 0.001050001 MTA2

ENSOARG00000018990 1.164 3.208 0.001052425 RAMP1

ENSOARG00000005437 -1.315 5.703 0.001056968 ENSOARG00000008882 1.005 3.421 0.001059857 TNKS1BP1

ENSOARG00000014910 0.900 4.472 0.001070984 ELP3

ENSOARG00000001692 -1.031 9.693 0.001077755 RGS22

ENSOARG00000003935 1.300 2.554 0.001078472 FAM198A

ENSOARG00000006012 1.315 3.337 0.001084731 FOXO1

ENSOARG00000006957 0.969 8.330 0.001082059 PSAP

ENSOARG00000008575 -1.028 5.414 0.001083279 TRAPPC6B

ENSOARG00000010651 0.820 5.129 0.001074286 RAB5B

ENSOARG00000012831 0.972 3.895 0.001082066 LRRC14

ENSOARG00000019416 0.944 3.252 0.001080892 METTL20

ENSOARG00000008983 -1.223 2.934 0.001095569 ZNF404

ENSOARG00000010853 -0.881 6.169 0.001092921 MEMO1

ENSOARG00000012418 -0.903 6.457 0.001094501 GDPD1

ENSOARG00000020514 -1.168 8.197 0.001092775 PLCZ1

ENSOARG00000000434 -1.047 4.390 0.001107913 ENSOARG00000004706 1.301 2.282 0.001111892 FLOT2

ENSOARG00000009218 -0.991 8.068 0.001112925 ADAM29

ENSOARG00000013116 1.201 2.879 0.001109873 CA3

ENSOARG00000020182 -0.874 5.644 0.001114822 SEC22A

ENSOARG00000004736 1.062 4.167 0.001120496 LSR

ENSOARG00000014122 -0.992 6.837 0.001119696 C11orf88

ENSOARG00000001889 -1.474 7.355 0.0011243 PDCL2

ENSOARG00000018982 1.286 5.528 0.001123563 SLC52A3

ENSOARG00000020540 -1.030 5.176 0.001129939 HAUS2

ENSOARG00000016821 -1.187 4.624 0.001132699 ENSOARG00000007073 -0.983 6.102 0.001137746 ERCC8

ENSOARG00000009823 -0.989 5.861 0.001137278 ORC4

ENSOARG00000007712 1.146 5.507 0.00115164 ENSOARG00000012527 0.971 4.191 0.001150422 SFT2D2

ENSOARG00000020045 1.242 3.947 0.001148374 KIAA0754

ENSOARG00000010873 -0.900 5.939 0.001155408 DDX43

ENSOARG00000011789 0.777 6.540 0.001159057 PRDX6

ENSOARG00000012419 -1.279 4.378 0.001158444 C2orf76

ENSOARG00000013465 1.431 2.853 0.001156172 CAPN1

ENSOARG00000021115 -1.260 5.557 0.00116016 ENSOARG00000010415 1.242 4.188 0.001161862 WWC3

ENSOARG00000001527 -0.995 6.586 0.001179074 POT1

ENSOARG00000003020 -1.014 6.380 0.00117456 ENSOARG00000004650 -1.106 5.297 0.001192249 SCOC

ENSOARG00000005375 1.051 3.677 0.001183821 FGFR2

ENSOARG00000006006 1.433 4.862 0.001185951 MYO18A

ENSOARG00000008827 1.113 5.376 0.001173939 NCSTN

ENSOARG00000010180 1.393 5.980 0.001169916 ITPR3

Appendices

215

ENSOARG00000010204 -0.995 4.710 0.001188816 TMEM225

ENSOARG00000010803 -1.170 7.051 0.0011911 NBN

ENSOARG00000012529 -0.827 6.664 0.001193105 CENPL

ENSOARG00000013189 0.886 3.534 0.001188974 TXLNG

ENSOARG00000015147 0.986 2.570 0.001193636 ENSOARG00000016384 0.890 4.015 0.001167996 CDK14

ENSOARG00000017186 -0.868 7.467 0.001169355 PAPD5

ENSOARG00000017452 -0.942 5.947 0.001184882 MTX2

ENSOARG00000017656 1.213 2.937 0.001180334 TAF6

ENSOARG00000017905 -0.943 7.216 0.001170108 ENSOARG00000019402 -0.952 6.398 0.001186555 AMN1

ENSOARG00000004154 1.071 5.401 0.001197759 PEX5

ENSOARG00000009438 1.034 5.562 0.001200644 APOE

ENSOARG00000012065 -0.834 8.509 0.001206037 TCFL5

ENSOARG00000017034 -0.893 8.686 0.001203753 STK17A

ENSOARG00000017759 0.888 3.957 0.001204763 PAK3

ENSOARG00000020000 -1.123 5.281 0.001206053 C12orf40

ENSOARG00000020082 -1.365 7.298 0.001207123 SYCP1

ENSOARG00000007312 1.009 3.388 0.001210503 SCPEP1

ENSOARG00000015458 -1.248 9.388 0.001208886 SYCP2

ENSOARG00000017819 -1.054 5.797 0.001214376 ENSOARG00000002608 1.101 2.296 0.001231671 CITED2

ENSOARG00000010868 -0.794 8.004 0.001252612 ENSOARG00000012076 -1.018 4.839 0.001250261 SKA2

ENSOARG00000012406 0.830 4.417 0.00124589 GNA14

ENSOARG00000013865 -1.018 8.677 0.001251026 CDKL3

ENSOARG00000014762 1.353 3.811 0.001251413 AFAP1L2

ENSOARG00000004901 -1.116 4.844 0.001254959 EFCAB2

ENSOARG00000012972 1.241 6.450 0.001260396 MYO10

ENSOARG00000010097 0.879 5.365 0.001271731 CLC5

ENSOARG00000017733 -1.211 9.842 0.001274758 CCDC7

ENSOARG00000018979 -1.019 5.679 0.001274418 ASZ1

ENSOARG00000020827 -0.973 5.745 0.001274302 GTF2A2

ENSOARG00000001405 1.094 2.635 0.001283422 FRMPD2

ENSOARG00000005639 1.086 4.806 0.001283693 PLCB3

ENSOARG00000018479 0.869 5.341 0.001282671 AQP8

ENSOARG00000004834 -1.283 5.411 0.001289339 STARD6

ENSOARG00000016076 -1.158 9.025 0.001288895 BRDT

ENSOARG00000003400 1.234 5.955 0.00129687 RALGAPA2

ENSOARG00000012008 -0.906 7.388 0.001296418 GDE1

ENSOARG00000014853 -0.922 7.486 0.001298202 STK33

ENSOARG00000011941 1.174 2.339 0.001299986 ZO3

ENSOARG00000001491 -1.161 8.271 0.001302067 ZBBX

ENSOARG00000007432 -0.914 5.403 0.001304922 DNAJC15

ENSOARG00000015717 -0.999 5.131 0.001315288 ENSOARG00000008249 -1.315 3.590 0.001317345 ASPN

ENSOARG00000018793 -1.008 8.666 0.001325528 FASTKD2

Appendices

216

ENSOARG00000004103 0.747 8.132 0.001334395 PTPN13

ENSOARG00000021020 -1.344 4.072 0.001334397 CLEC1B

ENSOARG00000020905 -1.032 4.402 0.001343929 PIGB

ENSOARG00000003548 -1.145 6.982 0.001349844 ENKUR

ENSOARG00000011063 -0.970 8.285 0.001348482 STK31

ENSOARG00000015277 -0.982 5.939 0.001348899 ENSOARG00000017832 -1.084 5.445 0.001351272 ENSOARG00000001287 -0.870 5.424 0.001358284 ENSOARG00000007338 -0.984 4.881 0.001359122 C1orf27

ENSOARG00000017227 0.855 6.352 0.001359926 APPL2

ENSOARG00000020462 0.963 2.536 0.001360934 IL1RAP

ENSOARG00000012496 -1.058 7.714 0.001364795 RFK

ENSOARG00000017531 -0.922 7.126 0.001374852 PTBP2

ENSOARG00000017921 -1.129 6.926 0.001373995 SASS6

ENSOARG00000018975 1.187 6.167 0.001373406 KMT2D

ENSOARG00000000230 1.044 3.072 0.001386259 SKI

ENSOARG00000001631 -1.189 7.228 0.001390033 TIGD7

ENSOARG00000003012 0.968 5.296 0.001394541 PLEKHG1

ENSOARG00000003277 -0.852 5.339 0.001408863 MRPS10

ENSOARG00000004510 1.375 3.393 0.001400902 ZNF516

ENSOARG00000004745 0.956 3.184 0.001404197 USF2

ENSOARG00000005268 -1.072 7.579 0.001385375 G2E3

ENSOARG00000006758 -0.957 6.074 0.001403958 SNX6

ENSOARG00000007877 0.764 4.937 0.001407277 C1orf168

ENSOARG00000008619 0.869 7.333 0.001395356 FATE1

ENSOARG00000009391 0.973 5.098 0.001382265 TAPBP

ENSOARG00000012519 -1.110 6.188 0.001403835 TIPRL

ENSOARG00000013279 1.035 6.482 0.001379724 TRIO

ENSOARG00000013563 -1.268 5.836 0.001398882 ACTR6

ENSOARG00000013607 1.264 4.966 0.00140638 MICAL3

ENSOARG00000014124 1.132 2.688 0.001404415 PTPRE

ENSOARG00000018803 0.713 6.041 0.001408631 PGK1

ENSOARG00000020686 0.816 5.683 0.001409196 PYGL

ENSOARG00000015941 -0.965 11.371 0.001412292 HSPA4L

ENSOARG00000002269 1.080 3.831 0.001421399 IRF2BPL

ENSOARG00000007051 -1.113 8.992 0.001420493 KIAA1377

ENSOARG00000010969 0.822 5.227 0.001425585 GPAM

ENSOARG00000008231 -0.793 7.663 0.001428408 PRMT3

ENSOARG00000000493 1.029 4.968 0.001431389 KDM4A

ENSOARG00000003472 0.904 4.336 0.00143756 MARK2

ENSOARG00000002685 0.756 5.106 0.001447128 MBNL2

ENSOARG00000005116 -0.990 7.234 0.0014502 SSMEM1

ENSOARG00000010041 -1.439 3.478 0.001446891 POSTN

ENSOARG00000011685 -1.008 7.581 0.001453583 SLC35F5

ENSOARG00000011927 -1.044 7.762 0.001453118 EFCAB3

ENSOARG00000016176 1.087 4.873 0.001449095 CHD7

ENSOARG00000017013 -0.931 7.463 0.001445245 PAIP2

Appendices

217

ENSOARG00000021149 -1.091 8.840 0.001444648 ENSOARG00000001249 1.205 3.521 0.001466947 SCD5

ENSOARG00000003580 -1.088 6.957 0.001462403 ENSOARG00000006633 0.884 4.164 0.001465959 YIPF1

ENSOARG00000019661 -1.143 6.122 0.001467068 TWF1

ENSOARG00000015201 -0.971 7.073 0.001473286 TAF1B

ENSOARG00000013705 0.722 5.352 0.001483068 VDAC1

ENSOARG00000006841 -1.124 7.662 0.001486859 ENSOARG00000015604 0.908 3.500 0.001491944 SATB1

ENSOARG00000021187 1.172 2.819 0.001492963 KIAA0247

ENSOARG00000017967 1.058 4.459 0.001495515 SMARCD1

ENSOARG00000005976 1.207 4.124 0.001503132 HCFC1

ENSOARG00000000859 1.353 2.885 0.001508424 SLC22A23

ENSOARG00000009090 0.935 3.170 0.001513197 CYP1B1

ENSOARG00000018765 -0.919 7.993 0.001512218 LRRC49

ENSOARG00000016852 0.828 3.739 0.001521453 CFLAR

ENSOARG00000008136 0.825 5.049 0.001523771 SUSD3

ENSOARG00000006826 1.290 4.009 0.001535144 LTBP4

ENSOARG00000016834 0.775 6.973 0.001538063 SSFA2

ENSOARG00000017138 -0.959 8.960 0.001537705 C3orf38

ENSOARG00000009344 -1.229 2.438 0.001544568 FABP4

ENSOARG00000008936 0.929 5.466 0.001549199 KDM5C

ENSOARG00000020586 1.184 4.397 0.001547971 CLCN2

ENSOARG00000003246 1.231 2.661 0.001553875 IKKBETA

ENSOARG00000003263 1.038 4.981 0.001562194 FAM129A

ENSOARG00000021116 -1.376 5.145 0.00156644 ENSOARG00000008330 1.135 4.170 0.001568563 WDR91

ENSOARG00000007445 -0.950 5.829 0.001581484 RAD18

ENSOARG00000008327 -0.943 7.521 0.001582718 TBC1D32

ENSOARG00000011461 -1.041 7.892 0.001579651 SEL1L2

ENSOARG00000012480 1.198 5.360 0.001584794 EP400

ENSOARG00000014814 0.932 5.712 0.001592392 ACTB

ENSOARG00000013164 -0.885 6.575 0.001599883 ENSOARG00000008315 -1.002 7.479 0.001603857 CDKL4

ENSOARG00000013645 1.227 4.246 0.001604153 DAG1

ENSOARG00000009319 0.773 5.247 0.001619341 SLC29A1

ENSOARG00000012294 -1.188 5.835 0.001618719 C11orf65

ENSOARG00000016238 1.217 3.586 0.001616866 MED12

ENSOARG00000000891 -1.012 7.767 0.001631106 NDUFV2

ENSOARG00000006480 -1.027 5.816 0.001629576 ENSOARG00000006820 -1.050 7.745 0.00163146 DYNC2LI1

ENSOARG00000012129 1.635 3.807 0.001628465 SLC7A5

ENSOARG00000016672 1.243 2.580 0.001628468 ATXN7L3B

ENSOARG00000004723 1.187 2.526 0.001641573 CENPB

ENSOARG00000017042 -0.954 4.514 0.001644771 RAD9B

ENSOARG00000017028 -0.928 4.375 0.001649317 DEPDC7

ENSOARG00000001577 -0.833 7.726 0.001654415 FBXO43

Appendices

218

ENSOARG00000008843 -0.864 4.431 0.001663249 GALM

ENSOARG00000007484 -0.844 7.438 0.001668884 WDSUB1

ENSOARG00000019427 1.129 2.601 0.001669507 PPARA

ENSOARG00000000048 0.928 4.962 0.001691678 PIGS

ENSOARG00000002110 -1.035 7.746 0.001693005 IFT80

ENSOARG00000012853 -0.973 6.333 0.001690205 PIGK

ENSOARG00000015258 -1.064 4.416 0.001687451 SYCP3

ENSOARG00000016812 0.760 4.690 0.001683121 AGFG2

ENSOARG00000019804 -1.044 5.162 0.001686111 ENSOARG00000020651 -1.139 3.612 0.001691209 PTPRO

ENSOARG00000012026 0.879 4.706 0.00169551 ABHD11

ENSOARG00000006729 1.198 3.421 0.001700628 TACC2

ENSOARG00000007721 -0.831 5.632 0.001706958 CKS2

ENSOARG00000009491 -1.193 2.212 0.00172802 DTHD1

ENSOARG00000005496 -0.933 6.307 0.00173013 ENSOARG00000020301 1.132 3.909 0.001733227 TNK2

ENSOARG00000009711 -0.837 7.423 0.001743608 CERS3

ENSOARG00000006128 -1.014 4.699 0.001752819 RNF125

ENSOARG00000019619 1.032 4.610 0.00175389 ZBTB20

ENSOARG00000004699 -1.055 8.284 0.001769627 CCDC83

ENSOARG00000005193 0.929 7.577 0.001769852 KMT2C

ENSOARG00000010654 0.941 6.430 0.001766878 CTNND1

ENSOARG00000015796 0.839 6.083 0.001769561 KAT2B

ENSOARG00000018168 1.385 3.180 0.00177587 RREB1

ENSOARG00000016802 -0.900 6.044 0.001788389 PDE1A

ENSOARG00000008016 1.080 3.180 0.001792202 TNIP1

ENSOARG00000014147 -1.005 7.454 0.001799428 ENSOARG00000015196 -1.030 7.142 0.001800193 CHPT1

ENSOARG00000003556 -1.001 6.641 0.00180859 EFCAB10

ENSOARG00000006519 -0.907 7.553 0.001807942 HSDL2

ENSOARG00000007771 1.150 3.483 0.001804442 SEMA4D

ENSOARG00000014195 -1.296 3.439 0.001807955 TMEFF2

ENSOARG00000016346 -0.933 7.605 0.001813984 ENSOARG00000008353 -1.047 3.142 0.001824385 LINC00998

ENSOARG00000019578 0.845 5.219 0.001826998 ANO6

ENSOARG00000008756 1.144 2.392 0.001834446 H6PD

ENSOARG00000004238 -0.953 6.453 0.001839197 GPR87

ENSOARG00000020021 -1.115 8.072 0.001840197 CASC1

ENSOARG00000005420 -0.936 5.764 0.001852957 ZNF420

ENSOARG00000015316 -0.932 9.109 0.00185584 OXR1

ENSOARG00000007283 -0.876 5.894 0.001861144 FSD1L

ENSOARG00000020924 0.998 2.744 0.001861278 WDR72

ENSOARG00000021013 1.183 5.040 0.001862284 CGN

ENSOARG00000020330 -0.765 6.608 0.001876887 EXD1

ENSOARG00000003193 1.137 3.915 0.001882491 C1QTNF1

ENSOARG00000006604 1.076 3.033 0.001889733 FKBP9

ENSOARG00000004660 1.227 4.123 0.001917991 GRAMD1A

Appendices

219

ENSOARG00000013993 -1.111 7.731 0.001911801 LRRCC1

ENSOARG00000014440 -1.234 2.875 0.001917881 VNN1

ENSOARG00000015296 -0.945 7.780 0.001913536 MRPL39

ENSOARG00000019270 -0.884 6.145 0.001915681 SPAG16

ENSOARG00000020260 -0.831 6.658 0.001909008 ZFP69

ENSOARG00000001504 1.315 3.798 0.001922502 SREBF1

ENSOARG00000004909 0.979 2.285 0.001928749 ENSOARG00000002499 -0.946 9.547 0.001941945 MLF1

ENSOARG00000003818 -1.123 5.433 0.001943381 EFCAB9

ENSOARG00000004340 0.701 5.726 0.001941799 FSHR

ENSOARG00000009091 1.261 3.555 0.001937693 SMTN

ENSOARG00000015644 -0.907 8.519 0.001937826 BOLL


ENSOARG00000015191 0.865 3.881 0.001978205 SHBG

ENSOARG00000011878 1.228 3.260 0.001987867 ILDR2

ENSOARG00000011975 -0.930 6.581 0.001992096 MOB1B

ENSOARG00000003182 1.059 4.314 0.002013145 CD81

ENSOARG00000003593 0.910 4.578 0.002001187 ZNF805

ENSOARG00000005337 -1.024 7.185 0.002006685 VPS26A

ENSOARG00000006692 -0.999 7.806 0.00200119 CLK4

ENSOARG00000006896 -1.075 7.015 0.002004192 TXNDC8

ENSOARG00000014175 1.142 3.108 0.002010543 SLC9A7

ENSOARG00000016063 -0.933 8.369 0.002007984 USP44

ENSOARG00000017631 1.070 3.832 0.002011111 PRKCB

ENSOARG00000009128 -0.969 5.224 0.002024048 C7

ENSOARG00000019611 -0.868 8.118 0.002022352 CLIP4

ENSOARG00000009604 -0.867 5.926 0.002037906 NDC80

ENSOARG00000018769 -0.989 5.086 0.002036194 PIH1D3

ENSOARG00000001265 0.939 2.860 0.002059902 SLC27A3

ENSOARG00000002279 -0.796 8.673 0.002061124 TSPAN6

ENSOARG00000003916 0.994 3.510 0.002055339 SMARCD3

ENSOARG00000006126 -1.113 4.920 0.002056945 FAM46D

ENSOARG00000014908 0.859 4.244 0.002061631 LGR4

ENSOARG00000002545 0.857 4.020 0.00207384 EPB41

ENSOARG00000016737 1.196 4.278 0.002071932 TENC1

ENSOARG00000013264 -1.016 8.318 0.002077749 DBF4

ENSOARG00000015341 -1.245 5.493 0.0020789 TSPAN19

ENSOARG00000003150 0.740 5.098 0.002087052 MPP1

ENSOARG00000011952 -0.881 6.942 0.002082948 CCDC38

ENSOARG00000012955 -0.887 7.726 0.002086014 SPACA1

ENSOARG00000006146 0.904 8.483 0.002093379 ENSOARG00000011482 1.149 3.219 0.002103519 GRHPR

ENSOARG00000011806 -1.029 6.231 0.002095869 ENSOARG00000015571 1.189 2.126 0.002101953 TP53

ENSOARG00000017555 -1.169 3.782 0.002095867 ENSOARG00000019765 1.233 2.470 0.002103978 TEP1

Appendices

220

ENSOARG00000020578 -0.882 7.098 0.002098479 DAW1

ENSOARG00000011838 0.773 4.031 0.002108186 CAP2

ENSOARG00000004160 -0.880 6.026 0.002114312 ENSOARG00000007049 1.361 2.959 0.002125668 ABCD1

ENSOARG00000002721 1.022 3.810 0.002131162 CBX2

ENSOARG00000016547 -1.031 2.498 0.002135489 CALCRL

ENSOARG00000010862 0.788 5.212 0.002139706 ENSOARG00000001199 -0.841 6.599 0.00215355 KIAA1841

ENSOARG00000014022 -0.937 2.868 0.002154545 ENSOARG00000016907 0.952 2.523 0.002151916 UNC5C

ENSOARG00000017950 1.052 6.368 0.002155925 TNRC6A

ENSOARG00000019939 -0.841 5.609 0.00214744 RABL3

ENSOARG00000013845 0.749 5.314 0.002160834 FADS2

ENSOARG00000012454 1.247 3.388 0.002166596 MYO1C

ENSOARG00000004743 1.126 4.501 0.002182022 ATP6AP1

ENSOARG00000008746 -0.798 7.524 0.002202809 SAMD8

ENSOARG00000001979 1.263 2.904 0.002231258 NOS3

ENSOARG00000014489 -0.998 6.203 0.002235542 IZUMO3

ENSOARG00000016013 -0.909 6.094 0.002233861 TMEM161B

ENSOARG00000014785 -0.942 6.678 0.002238759 IDI1

ENSOARG00000004577 1.112 6.404 0.002247565 INPPL1

ENSOARG00000010531 0.779 5.507 0.002251198 KDM1B

ENSOARG00000013446 1.032 3.980 0.002255097 SFMBT2

ENSOARG00000005237 1.034 5.712 0.002263769 SEC61A1

ENSOARG00000007244 1.109 3.579 0.002262083 ACOT11

ENSOARG00000011447 1.101 3.349 0.002275314 FBXO10

ENSOARG00000015614 -0.946 5.211 0.002274118 XRCC4

ENSOARG00000020295 -1.198 3.764 0.002278703 VTCN1

ENSOARG00000008916 -0.905 5.735 0.002286527 TATDN1

ENSOARG00000007213 1.040 5.080 0.002289594 ZNF462

ENSOARG00000013210 -0.832 6.359 0.002292065 SEC11A

ENSOARG00000019441 1.022 2.783 0.002295955 RHOB

ENSOARG00000000135 0.731 4.739 0.002310106 PBX2

ENSOARG00000004845 -1.094 7.248 0.002324709 C18orf54

ENSOARG00000000420 -0.784 6.546 0.002344904 NDFIP2

ENSOARG00000007220 -0.916 8.642 0.002348111 ENSOARG00000006289 -0.919 6.407 0.002365175 PPWD1

ENSOARG00000006700 0.800 3.331 0.002357325 MXRA7

ENSOARG00000009170 -1.088 5.372 0.00236459 RMDN2

ENSOARG00000015847 1.126 3.230 0.002358768 MYL9

ENSOARG00000016915 -0.968 8.346 0.002363666 FAM81B

ENSOARG00000017842 1.235 4.166 0.002360419 RGAG1

ENSOARG00000000594 0.977 3.923 0.002368844 FAM199X

ENSOARG00000016311 -0.998 5.927 0.002371237 ENSOARG00000015400 1.003 2.694 0.002374653 WNT5A

ENSOARG00000006975 -0.997 3.948 0.002379664 CHST9

ENSOARG00000004668 0.843 3.966 0.002383022 PRP

Appendices

221

ENSOARG00000009336 -1.488 3.060 0.002394742 ENSOARG00000018662 -1.217 3.330 0.002395834 ENSOARG00000000754 0.854 3.469 0.002411163 PROSC

ENSOARG00000004967 1.079 2.892 0.002412876 DOK6

ENSOARG00000009476 1.347 4.470 0.002416503 DLG5

ENSOARG00000011417 1.163 5.744 0.002418671 FRMPD1

ENSOARG00000018928 -0.793 6.322 0.002421552 NAPEPLD

ENSOARG00000015814 0.831 3.685 0.002427979 METAP1

ENSOARG00000005806 1.371 2.462 0.002430983 MECP2

ENSOARG00000018255 -1.024 4.954 0.002434011 FAM174A

ENSOARG00000006994 -0.958 5.949 0.002440406 MTRF1

ENSOARG00000013890 -0.875 9.410 0.002438916 ENSOARG00000015718 0.793 4.695 0.002445648 PTPRC

ENSOARG00000003296 0.925 4.184 0.002450839 KLC2

ENSOARG00000019318 -0.921 7.091 0.002455267 ATG3

ENSOARG00000001193 -0.955 8.180 0.002458683 ENSOARG00000014119 0.984 3.905 0.002462216 RARA

ENSOARG00000007666 0.723 5.641 0.002473383 SLC25A4

ENSOARG00000018107 0.715 5.935 0.002481796 CFL1

ENSOARG00000002586 0.834 6.801 0.002491504 APOA1

ENSOARG00000003590 -1.050 7.326 0.002494032 C2orf73

ENSOARG00000014712 0.694 8.241 0.002498021 KIDINS220

ENSOARG00000009433 -0.897 7.645 0.002509788 CNOT7

ENSOARG00000019829 -0.945 7.052 0.002529537 FGFR1OP2

ENSOARG00000005682 -1.119 3.010 0.002533113 ENSOARG00000000869 1.094 4.560 0.002549867 LMTK2

ENSOARG00000016026 -0.903 6.235 0.002552931 ENSOARG00000001771 1.117 4.366 0.002563902 CAMSAP3

ENSOARG00000009584 -0.942 4.912 0.002564186 FMR1NB

ENSOARG00000011353 0.772 3.949 0.00257907 NAGK

ENSOARG00000019590 -1.010 7.284 0.002580919 C2orf88

ENSOARG00000003665 -0.795 10.359 0.002587492 DYNC2H1

ENSOARG00000008706 1.264 3.060 0.002585848 DAPK1

ENSOARG00000015202 -1.077 3.326 0.002602204 ENSOARG00000018238 1.160 3.664 0.002610521 TEF

ENSOARG00000017077 -0.901 7.335 0.00261401 ENSOARG00000019938 0.847 4.015 0.002621681 ZNF132

ENSOARG00000000357 1.195 4.767 0.002638554 NCOR2

ENSOARG00000020314 -0.879 7.778 0.002636603 ENSOARG00000013003 -0.884 6.815 0.002643238 RARS2

ENSOARG00000006529 -1.090 4.757 0.002654071 CIR1

ENSOARG00000009730 -1.153 3.298 0.002663882 CCDC169

ENSOARG00000007655 0.897 2.539 0.002682226 MSS51

ENSOARG00000009472 -0.772 4.643 0.002681733 ENSOARG00000012962 -1.140 7.373 0.002680462 ENSOARG00000007557 1.079 5.265 0.002706522 UBTF

ENSOARG00000002335 0.729 4.603 0.002725946 PKD2

Appendices

222

ENSOARG00000003850 1.254 3.190 0.002724952 FITM2

ENSOARG00000007857 -0.952 9.668 0.002720516 GK2

ENSOARG00000015330 -0.926 5.615 0.002727836 THAP6

ENSOARG00000001078 0.941 2.522 0.002734963 ENSOARG00000007429 -0.953 9.174 0.002741271 PSMA6

ENSOARG00000015211 -0.993 5.804 0.002743979 KIF18A

ENSOARG00000006375 0.833 5.988 0.002752682 FKBP15

ENSOARG00000020455 -0.863 6.171 0.002753538 ENSOARG00000013679 0.995 4.097 0.002756452 URB1

ENSOARG00000001299 -1.044 5.937 0.002766783 PDCD10

ENSOARG00000002903 -0.866 8.509 0.002786486 ENSOARG00000008667 -0.805 6.684 0.002784085 NUS1

ENSOARG00000013343 1.449 3.058 0.00278425 PIEZO1

ENSOARG00000020611 -0.944 4.642 0.002780903 KLHDC1

ENSOARG00000005714 -0.874 7.563 0.00278923 ATF7IP2

ENSOARG00000007238 0.660 5.396 0.002794959 PAK1

ENSOARG00000011138 1.099 4.141 0.002803951 ZDHHC7

ENSOARG00000016108 0.811 3.572 0.002804186 LZTS2

ENSOARG00000003256 0.881 2.902 0.002813803 LCAT

ENSOARG00000014457 0.703 5.412 0.002814188 RPL18A

ENSOARG00000003572 0.777 4.650 0.002825329 RPS6KA5

ENSOARG00000019707 1.108 3.668 0.002828925 ARHGAP31

ENSOARG00000017542 -1.033 5.567 0.002843217 STYX

ENSOARG00000017568 1.161 3.527 0.002847166 SSH1

ENSOARG00000003408 -0.928 6.060 0.002860913 ENSOARG00000008357 -0.843 7.136 0.002869312 STAM2

ENSOARG00000012034 0.982 3.495 0.002871523 FAM107A

ENSOARG00000018000 0.751 5.655 0.002883305 ZKSCAN8

ENSOARG00000021120 -1.196 4.547 0.002880796 CCDC175

ENSOARG00000016889 0.909 3.010 0.002887839 TSC22D4


ENSOARG00000016635 -1.280 2.946 0.00289883 PPIL3

ENSOARG00000011478 0.788 3.608 0.002905141 STARD3

ENSOARG00000012445 -0.909 6.479 0.002912907 PTP4A1

ENSOARG00000013168 0.894 3.106 0.002912876 IFNGR2

ENSOARG00000016136 1.022 4.467 0.002913066 GIGYF1

ENSOARG00000018344 0.760 4.452 0.002916325 ALDH7A1

ENSOARG00000006033 0.769 3.412 0.002933737 FAM198B

ENSOARG00000013995 0.873 5.556 0.002952063 EPM2AIP1

ENSOARG00000020263 -0.944 8.452 0.002964678 IQCG

ENSOARG00000001357 0.854 3.030 0.002969762 ENSOARG00000016080 -0.940 7.328 0.002984919 ENSOARG00000020247 -1.005 8.274 0.003012898 CASC5

ENSOARG00000004565 -0.984 8.873 0.003017018 USP15

ENSOARG00000016603 1.161 2.900 0.003021738 TAB1

ENSOARG00000017304 -0.966 7.283 0.003019957 SPATA9

Appendices

223

ENSOARG00000007184 0.708 7.232 0.003033037 BCAP31

ENSOARG00000005043 -0.857 6.714 0.003041212 PACRGL

ENSOARG00000013920 1.438 2.611 0.003040067 WDR81

ENSOARG00000006857 1.160 2.603 0.003048033 SVEP1

ENSOARG00000006838 -1.041 7.454 0.003057542 CFL2

ENSOARG00000007710 1.181 3.249 0.003055484 RNF157

ENSOARG00000014789 0.776 4.207 0.003056523 MBOAT2

ENSOARG00000018864 -1.000 3.661 0.00306678 CXorf30

ENSOARG00000004533 -0.824 8.806 0.003079852 UBA2

ENSOARG00000016937 0.847 4.557 0.003080866 F2R

ENSOARG00000008185 -0.935 4.440 0.00308567 PRIM1

ENSOARG00000012304 -0.891 6.235 0.003089436 ANKRD45

ENSOARG00000019607 -0.857 7.323 0.003096752 ENSOARG00000013748 0.847 2.749 0.003102345 HSPB1

ENSOARG00000019897 -1.008 5.352 0.003119777 APLF

ENSOARG00000013963 -0.917 7.808 0.003144136 TTC39B

ENSOARG00000007397 -0.816 7.100 0.003149252 FAR1

ENSOARG00000020524 0.825 2.733 0.003173053 FMO5

ENSOARG00000001559 -1.210 7.186 0.003181493 CYLC1

ENSOARG00000008990 -1.067 9.081 0.003197443 GKAP1

ENSOARG00000018795 -0.874 4.679 0.003199348 NTNG1

ENSOARG00000011279 0.906 4.151 0.003250295 PHF13

ENSOARG00000006804 -1.116 4.999 0.003255711 SPCS3

ENSOARG00000020547 0.662 6.087 0.00325894 CHD1L

ENSOARG00000010883 -0.894 4.486 0.003264541 OSTM1

ENSOARG00000013962 -0.886 6.768 0.003274571 FAM103A1

ENSOARG00000020567 -0.774 9.079 0.003288718 AGFG1

ENSOARG00000009072 -0.859 8.901 0.003296843 RNF139

ENSOARG00000006180 -0.870 4.864 0.003305008 CCNE2

ENSOARG00000012949 -0.955 7.161 0.003305475 C9orf135

ENSOARG00000001004 1.105 4.213 0.003315402 RAB11FIP1

ENSOARG00000004339 0.962 2.691 0.003316007 RHPN2

ENSOARG00000008817 0.955 4.151 0.003310259 HSP70

ENSOARG00000011889 0.718 4.827 0.003321465 TPM2

ENSOARG00000019207 1.003 3.852 0.003319026 ENSOARG00000002223 0.775 6.514 0.003329466 MYOF

ENSOARG00000011793 1.171 2.813 0.003338181 POGK

ENSOARG00000014323 0.838 3.704 0.003358701 ECHDC3

ENSOARG00000018756 -0.856 5.687 0.003357191 PPP1CC

ENSOARG00000019389 -0.719 4.795 0.003364508 CCDC121

ENSOARG00000014058 0.845 3.295 0.003377314 LPAR3

ENSOARG00000008588 1.093 3.793 0.003383744 GAS6

ENSOARG00000012922 0.978 2.708 0.003388708 ACOX3

ENSOARG00000003588 -0.861 6.428 0.00340306 THNSL1

ENSOARG00000013977 -0.923 8.905 0.003400389 SSX2IP

ENSOARG00000007899 -0.783 6.502 0.003419098 ENSOARG00000015049 0.833 5.110 0.00341945

Appendices

224

ENSOARG00000015905 -0.823 7.658 0.003416931 EIF4E

ENSOARG00000007644 -0.812 9.417 0.003424335 DDX4

ENSOARG00000003834 0.833 7.390 0.003441757 NSD1

ENSOARG00000018970 -0.894 7.776 0.003447742 KIAA1524

ENSOARG00000000263 1.200 4.360 0.003457752 SZT2

ENSOARG00000011215 0.631 7.557 0.003463253 ENSOARG00000014685 0.758 4.696 0.003467699 SLC2A12

ENSOARG00000001408 1.247 2.894 0.003489275 ACVR2B

ENSOARG00000003943 0.899 5.843 0.00348921 GLTSCR1L

ENSOARG00000004623 0.730 5.473 0.003488277 GDI1

ENSOARG00000017841 -0.788 5.884 0.003496102 PFN4

ENSOARG00000015555 -1.170 5.832 0.003500729 NCAM2

ENSOARG00000005228 1.016 5.422 0.00351562 ABAT

ENSOARG00000006205 1.348 4.745 0.003514459 ENSOARG00000000456 0.693 6.744 0.003533625 CTTN

ENSOARG00000003937 0.983 2.675 0.003543474 GBA

ENSOARG00000007413 -0.732 5.524 0.003540534 ENSOARG00000005893 -0.982 5.087 0.00354676 ENSOARG00000010324 0.906 3.035 0.003551621 ENSOARG00000008329 -0.824 6.466 0.003558699 RAB28

ENSOARG00000021109 -0.892 6.253 0.003565493 PSMA3

ENSOARG00000019608 -0.849 5.493 0.003581108 ENSOARG00000004104 -0.935 4.211 0.003588475 SPC25

ENSOARG00000006321 -1.367 4.602 0.003596733 CENPK

ENSOARG00000013650 0.946 4.282 0.003604031 MDH2

ENSOARG00000011001 -0.963 7.734 0.003611831 ENSOARG00000014201 0.917 5.053 0.003621481 MTCH1

ENSOARG00000008399 1.357 2.402 0.003627286 CCDC69

ENSOARG00000003602 1.155 2.665 0.003635219 ENSOARG00000012855 0.954 3.341 0.003636659 ST3GAL4

ENSOARG00000009419 0.863 5.300 0.003648503 ENSOARG00000020736 -0.828 5.716 0.003650852 ECT2

ENSOARG00000000634 -0.893 4.946 0.003679482 DNAL1

ENSOARG00000001930 -0.844 7.279 0.003670991 PTPN2

ENSOARG00000007255 -0.861 7.829 0.003659195 UBA6

ENSOARG00000007537 -0.878 7.691 0.003666968 CENPU

ENSOARG00000007619 -0.739 7.950 0.003687951 BTBD10

ENSOARG00000007648 -1.063 5.782 0.003654444 CCDC148

ENSOARG00000009602 -0.826 8.928 0.003689204 LDHA

ENSOARG00000016393 0.887 2.393 0.003686296 ATP5G2

ENSOARG00000018552 1.253 3.533 0.003683349 ENSOARG00000018747 -0.876 3.637 0.003689197 RAD21L1

ENSOARG00000019534 -0.716 6.739 0.003679277 IPO8

ENSOARG00000020445 -0.674 8.294 0.003669864 KCMF1

ENSOARG00000015144 0.836 9.081 0.003695805 SERPINA5

ENSOARG00000007139 -0.830 7.807 0.003711806 THUMPD3

ENSOARG00000013664 -0.831 5.871 0.003732681 SKA3

Appendices

225

ENSOARG00000004058 1.084 2.541 0.003738884 IKBKG

ENSOARG00000010989 -0.825 6.798 0.003754845 SOBP

ENSOARG00000018481 -0.816 5.957 0.003766951 RBM48

ENSOARG00000020665 -0.775 8.340 0.003774544 ATP11B

ENSOARG00000012489 0.828 2.626 0.00378783 AIP

ENSOARG00000002742 1.066 2.913 0.00381688 RAP2A

ENSOARG00000002873 -0.998 5.473 0.003815739 ENSOARG00000005743 -0.946 5.308 0.003804843 MRPS36

ENSOARG00000008268 1.151 2.571 0.003798029 AMOTL2

ENSOARG00000013652 -0.964 4.569 0.003814867 VAMP4

ENSOARG00000014393 0.970 4.516 0.003807452 BACH1

ENSOARG00000018220 0.770 4.012 0.003796866 CPED1

ENSOARG00000019700 0.868 3.183 0.003813451 B4GALT4

ENSOARG00000012911 -0.787 6.662 0.003837597 RNGTT

ENSOARG00000004647 0.898 2.948 0.003850165 ZNF385D

ENSOARG00000005356 1.068 3.045 0.003854577 MVP

ENSOARG00000005791 0.801 4.921 0.003869952 ACTN4

ENSOARG00000008734 1.207 2.966 0.003871972 TPCN1

ENSOARG00000013496 -0.820 7.582 0.003881552 MGAT4A

ENSOARG00000020500 -0.965 3.785 0.003880145 ENSOARG00000021090 -1.189 3.078 0.003888358 ENSOARG00000017793 0.879 3.583 0.003893839 IRF2BP2

ENSOARG00000001634 0.699 4.797 0.00391044 PHACTR2

ENSOARG00000013921 -0.976 7.186 0.003908605 UBE2B

ENSOARG00000014786 -0.912 7.945 0.003909757 EIF3E

ENSOARG00000015956 -0.888 4.494 0.003903703 DNAJC24

ENSOARG00000006055 0.922 3.408 0.003913859 SORBS1

ENSOARG00000000769 0.886 4.170 0.003951199 ENSOARG00000001745 -0.948 4.521 0.003955746 CDH8

ENSOARG00000019042 -0.779 4.052 0.003966064 BMP3

ENSOARG00000009374 -0.771 8.324 0.003984398 FBXL5

ENSOARG00000010163 0.773 3.230 0.003988153 GCDH

ENSOARG00000008725 0.826 4.202 0.004015521 LCOR

ENSOARG00000018763 0.842 4.362 0.004016516 UNC119

ENSOARG00000009087 -0.937 5.109 0.004025894 ZUFSP

ENSOARG00000012407 -0.913 9.208 0.004026466 C1orf173

ENSOARG00000006840 -0.844 5.420 0.004037394 DIMT1

ENSOARG00000015089 -0.806 6.784 0.004041323 OTOGL

ENSOARG00000018010 -0.780 7.476 0.004038028 TOMM70A

ENSOARG00000021161 -0.996 6.411 0.004038451 ENSOARG00000009536 0.750 4.130 0.004050611 PLCB4

ENSOARG00000015539 -1.001 2.706 0.004068754 ENSOARG00000015392 -0.763 5.104 0.004085822 SHCBP1

ENSOARG00000008151 1.080 2.742 0.00409635 ORAI3

ENSOARG00000014767 -0.908 7.149 0.004099695 GLIPR1L1

ENSOARG00000009819 -1.074 4.002 0.00410602 ENSOARG00000001850 0.751 6.708 0.004127962 GNB1

Appendices

226

ENSOARG00000014549 -1.105 6.150 0.004124973 ODF2L

ENSOARG00000015831 -0.759 6.730 0.004136407 ENSOARG00000007411 0.943 3.156 0.004149226 ASAP3

ENSOARG00000010805 -0.685 6.700 0.004148383 SNX3

ENSOARG00000005827 0.870 3.071 0.004162159 C1orf85

ENSOARG00000011637 -1.133 2.823 0.004162348 SAGE1

ENSOARG00000015999 -0.932 4.991 0.004164142 IMMP1L

ENSOARG00000013061 -0.916 5.114 0.004174011 UBLCP1

ENSOARG00000005856 -1.085 4.365 0.004192117 C6orf229

ENSOARG00000020386 0.716 6.148 0.004193504 APOD

ENSOARG00000000732 1.247 4.358 0.004198064 XPC

ENSOARG00000007621 -0.893 6.531 0.004201521 C21orf140

ENSOARG00000006192 -0.770 8.449 0.004208021 ATR

ENSOARG00000021063 -1.122 3.183 0.004212093 CD69

ENSOARG00000009317 -0.714 6.470 0.004226337 PRKAA1

ENSOARG00000003649 -0.871 5.393 0.004234335 ENSOARG00000014272 -0.998 5.028 0.004235 LIPJ

ENSOARG00000005569 1.066 4.663 0.004254405 ZNF532

ENSOARG00000009449 -0.839 5.073 0.004249711 ENSOARG00000020667 -0.898 5.044 0.004253397 FCGR1A

ENSOARG00000005629 -0.864 7.697 0.004259425 RAD17

ENSOARG00000017799 1.161 3.585 0.004282796 KANK2

ENSOARG00000018280 -0.860 3.264 0.004282519 SLCO4C1

ENSOARG00000016985 -0.808 6.553 0.004302558 ENSOARG00000021152 -0.865 7.892 0.004303487 PPP1R36

ENSOARG00000008130 -0.757 8.908 0.004317291 CCDC62

ENSOARG00000004868 -0.836 6.068 0.004321054 SPINK2

ENSOARG00000012388 -0.745 6.468 0.004330304 ENSOARG00000012639 -1.036 6.210 0.004334459 ENSOARG00000006486 -1.075 4.532 0.004343785 SREK1IP1

ENSOARG00000012773 -0.898 8.413 0.004355106 ATP5O

ENSOARG00000003774 0.842 3.912 0.004367883 SETD8

ENSOARG00000000299 -1.023 3.840 0.004380103 ENSOARG00000021045 0.623 6.890 0.004376918 GATM

ENSOARG00000004695 -0.866 9.326 0.004388624 GALNTL5

ENSOARG00000008666 -0.806 4.409 0.004390919 KCNRG

ENSOARG00000009824 -1.122 5.418 0.004395851 ENSOARG00000015002 1.046 3.667 0.00441899 ENSOARG00000018062 1.062 3.209 0.004422307 GUCY2F

ENSOARG00000020206 -0.930 8.356 0.004420779 TMCO2

ENSOARG00000011950 0.835 4.771 0.004439953 KIF16B

ENSOARG00000012207 0.869 2.550 0.004434272 EPHA7

ENSOARG00000016291 -1.151 3.445 0.004438164 POLR3G

ENSOARG00000014321 -0.772 9.003 0.004448825 TOP2A

ENSOARG00000017943 0.906 7.173 0.004448633 EP300

ENSOARG00000005015 0.905 4.272 0.004453874 PHB2

ENSOARG00000019701 -1.001 3.088 0.004462176

Appendices

227

ENSOARG00000006936 1.047 2.480 0.004469376 ABCC10

ENSOARG00000010076 -1.011 4.474 0.004474071 ENSOARG00000016782 -0.851 8.250 0.004472119 DNAJC10

ENSOARG00000002431 -0.968 7.718 0.004487707 ERICH2

ENSOARG00000014397 -0.744 5.721 0.004490421 TMEM217

ENSOARG00000002363 -0.772 7.531 0.004533744 EXOC1

ENSOARG00000010558 -0.879 8.384 0.004531613 SHOC2

ENSOARG00000006112 -0.830 3.694 0.004546895 ENSOARG00000007894 0.667 6.350 0.004548641 GAPDH

ENSOARG00000016406 -0.776 7.936 0.004543972 RBM46

ENSOARG00000000967 -0.857 5.716 0.00455561 ENSOARG00000004782 -0.951 5.394 0.004555936 NACA2

ENSOARG00000000324 -0.843 5.103 0.004562405 SCRN3

ENSOARG00000001811 0.807 5.266 0.004564424 TNNI3

ENSOARG00000011382 -0.942 3.166 0.004582789 ENSOARG00000020006 -0.999 6.118 0.004581014 ABCD2

ENSOARG00000001056 -0.834 7.202 0.00461253 ENSOARG00000014717 1.258 4.965 0.004621517 KIAA1462

ENSOARG00000010086 -0.786 6.934 0.004642833 UBA3

ENSOARG00000015309 -0.971 7.871 0.004650209 DIS3

ENSOARG00000000211 -1.103 3.401 0.00465669 ENSOARG00000004559 -1.005 4.636 0.004658799 DCUN1D5

ENSOARG00000007768 0.860 8.386 0.00466335 HUWE1

ENSOARG00000010768 -0.810 6.561 0.004675259 LACE1

ENSOARG00000003775 0.654 5.683 0.004688491 FOLH1

ENSOARG00000013789 0.896 4.537 0.004707396 ENSOARG00000019446 1.024 5.410 0.004716037 TNS1

ENSOARG00000017971 0.900 5.245 0.004729778 ZKSCAN1

ENSOARG00000002060 -0.759 5.786 0.004737767 DCAF17

ENSOARG00000005904 -0.982 7.314 0.004738589 CCDC178

ENSOARG00000006495 -0.772 7.476 0.004748431 ATP1B3

ENSOARG00000013295 1.462 3.016 0.004745729 SDK2

ENSOARG00000006944 -1.094 4.133 0.00476416 LSM5

ENSOARG00000019650 -1.201 4.826 0.004763224 PHOSPHO2

ENSOARG00000018060 -0.997 3.486 0.004793969 GPR128

ENSOARG00000016031 -1.046 7.663 0.004800518 SGOL2

ENSOARG00000021108 -0.814 7.690 0.004816976 ACTR10

ENSOARG00000015475 -0.780 5.129 0.00482188 ATG10

ENSOARG00000003653 1.374 2.932 0.004829498 MGRN1

ENSOARG00000004461 -0.825 5.741 0.004833679 RTKN2

ENSOARG00000002448 1.210 3.972 0.004846284 ATP8B2

ENSOARG00000011731 1.321 3.285 0.004846099 CCDC88C

ENSOARG00000017689 -0.869 4.937 0.004842168 UBXN2A

ENSOARG00000009408 -0.796 4.919 0.004868519 UEVLD

ENSOARG00000012601 -0.790 6.302 0.004881385 C9orf40

ENSOARG00000005048 0.926 4.405 0.004885187 RNF214

ENSOARG00000017055 -0.742 7.335 0.004893815 WDR41

Appendices

228

ENSOARG00000001375 0.889 3.175 0.004919828 PSMG4

ENSOARG00000008578 1.042 4.126 0.004919508 PTCH

ENSOARG00000020512 -0.789 7.008 0.004913804 RFC4

ENSOARG00000005933 -0.922 7.571 0.004933214 C17orf105

ENSOARG00000012040 0.745 4.811 0.004934637 DDX18

ENSOARG00000015477 0.731 6.539 0.004930599 GLB1

ENSOARG00000017449 1.503 4.146 0.004948595 CACNA1S

ENSOARG00000020389 -0.846 9.363 0.004965346 PPP1R2

ENSOARG00000017477 -0.765 7.898 0.004985402 CYP51A1

ENSOARG00000000637 0.925 4.555 0.004997619 EHMT1

ENSOARG00000018274 0.827 7.488 0.005000584 SORL1

ENSOARG00000015733 -0.879 3.959 0.005023294 ENSOARG00000015760 0.709 4.194 0.005021257 SLC25A14

ENSOARG00000007160 -1.019 8.416 0.005027527 SRP54

ENSOARG00000002888 -0.661 6.080 0.00505546 SPDL1

ENSOARG00000007206 -0.771 6.025 0.005043694 FAM177A1

ENSOARG00000009743 -0.804 5.901 0.005056787 EFCAB7

ENSOARG00000014545 -0.933 7.624 0.005058268 IFT74

ENSOARG00000016935 0.723 4.779 0.005049188 BCAR3

ENSOARG00000015824 -0.792 6.393 0.005090985 ENSOARG00000010707 -0.757 6.929 0.005097994 RAN

ENSOARG00000019628 -0.654 5.852 0.005100871 ENSOARG00000005869 0.807 3.655 0.005111981 BEND6

ENSOARG00000003158 -0.850 6.303 0.005121041 CEP55

ENSOARG00000016247 -1.157 6.006 0.005132478 CETN3

ENSOARG00000017292 0.714 5.445 0.005129871 CDS2

ENSOARG00000007767 -0.791 7.027 0.005142994 LRP2BP

ENSOARG00000010082 0.804 5.664 0.00518446 EGFLAM

ENSOARG00000003608 1.036 5.155 0.005203365 AMBRA1

ENSOARG00000019637 -0.919 7.803 0.005208552 ENSOARG00000006660 0.857 4.959 0.005220656 YAP1

ENSOARG00000008206 -0.818 6.312 0.005241395 CENPN

ENSOARG00000012574 -0.793 4.907 0.005235847 NMRK1

ENSOARG00000015462 -0.867 5.898 0.005242737 COQ10B

ENSOARG00000017457 -0.857 5.717 0.005245171 PIP

ENSOARG00000013374 1.036 2.716 0.005295217 CAPN5

ENSOARG00000007720 -0.620 6.424 0.005300911 PPAP2A

ENSOARG00000021079 0.772 3.574 0.005308499 S100A11

ENSOARG00000008620 1.166 4.215 0.00532688 LTF

ENSOARG00000003048 -0.908 2.972 0.005336186 ENSOARG00000012678 -0.691 6.993 0.00534509 ENSOARG00000015916 -1.014 6.579 0.005365166 CCDC41

ENSOARG00000001379 0.890 3.767 0.005390387 CAV2

ENSOARG00000003104 -0.810 4.793 0.005388001 AP1AR

ENSOARG00000000609 -0.857 7.417 0.005421622 RPGR

ENSOARG00000007529 -0.798 5.491 0.005429934 SVIP

ENSOARG00000002633 0.847 3.113 0.00544602 CDK5

Appendices

229

ENSOARG00000004854 -0.955 4.164 0.005454074 ENSOARG00000019451 -0.884 5.839 0.005451377 RPAP3

ENSOARG00000005787 1.318 2.924 0.005464253 WFS1

ENSOARG00000020696 -0.724 6.079 0.005470613 ACTL6A

ENSOARG00000010523 1.290 2.864 0.005481889 JAG1

ENSOARG00000004852 0.847 2.806 0.005496916 SCN1A

ENSOARG00000007048 0.734 4.783 0.005509637 ANKRD10

ENSOARG00000007473 -0.858 9.533 0.005505099 LUZP2

ENSOARG00000010256 -0.835 9.041 0.005521491 DYDC1

ENSOARG00000013675 -0.944 6.033 0.005508095 ENSOARG00000014002 -0.886 8.141 0.005520611 PSIP1

ENSOARG00000015776 -0.795 8.418 0.005520134 C21orf91

ENSOARG00000001463 -0.812 6.473 0.005529191 ENSOARG00000019156 -0.885 7.676 0.005529596 ZBTB26

ENSOARG00000015875 -0.828 7.654 0.005535497 DCDC1

ENSOARG00000000928 -0.651 5.876 0.005554672 ECT2L

ENSOARG00000008592 -0.876 6.689 0.005569797 ENSOARG00000012694 1.200 2.634 0.005559556 ARHGAP39

ENSOARG00000012858 -0.752 7.603 0.005565061 NSUN7

ENSOARG00000013104 -1.005 7.783 0.005561932 C6orf163

ENSOARG00000015526 1.122 3.436 0.005571049 ACHE

ENSOARG00000016586 -0.903 2.758 0.005558835 ENSOARG00000019140 0.973 4.230 0.005587553 PBR

ENSOARG00000003750 0.734 6.350 0.005604245 MTR

ENSOARG00000004122 0.847 2.734 0.005603542 RAB24

ENSOARG00000009818 0.793 7.556 0.005609207 NCOA3

ENSOARG00000001016 0.810 4.970 0.005615601 EPN1

ENSOARG00000003498 -0.944 5.430 0.005626754 RMI1

ENSOARG00000015327 -0.842 4.480 0.00563566 KRT10

ENSOARG00000016569 -0.870 4.020 0.005636715 FAM69A

ENSOARG00000014780 0.673 5.376 0.005641615 KIAA1324L

ENSOARG00000008840 -0.814 7.942 0.005647819 CEP57

ENSOARG00000013476 -0.774 8.091 0.005662398 UHRF1BP1L

ENSOARG00000015621 -0.805 6.551 0.005680359 RFTN2

ENSOARG00000020440 -0.793 7.486 0.00568417 HRASLS

ENSOARG00000007846 -0.868 6.141 0.005700066 C4orf47

ENSOARG00000009280 0.766 3.797 0.005696296 F11R

ENSOARG00000006701 0.940 2.973 0.005710403 PLD2

ENSOARG00000020023 0.846 9.871 0.005711983 MACF1

ENSOARG00000003229 1.246 3.631 0.005733293 TRAK1

ENSOARG00000008636 -0.740 4.750 0.005761328 C5orf34

ENSOARG00000012942 -0.652 7.996 0.005762169 RABEPK

ENSOARG00000014625 0.779 4.052 0.005771096 TMEM19

ENSOARG00000005327 -0.787 9.273 0.005776159 DPY19L2

ENSOARG00000019679 0.884 2.278 0.005779477 ZNF219

ENSOARG00000010266 -0.729 6.155 0.005788665 SPOPL

ENSOARG00000005094 1.108 5.098 0.0057998 CUL7

Appendices

230

ENSOARG00000019975 -0.781 6.731 0.00580258 IFLTD1

ENSOARG00000013284 0.608 6.735 0.005817296 NT5E

ENSOARG00000022521 0.783 4.502 0.005818359 NEAT1_3

ENSOARG00000005279 0.989 3.179 0.005824043 UBQLN4

ENSOARG00000017427 0.885 4.139 0.005851111 LDB1

ENSOARG00000007925 0.693 5.521 0.005865778 PCCB

ENSOARG00000005283 1.096 5.833 0.005886225 CDC42BPB

ENSOARG00000013887 0.860 3.324 0.005892939 NFIB

ENSOARG00000014278 -0.832 4.589 0.005897323 C1orf52

ENSOARG00000017508 0.655 5.276 0.005900783 ENSOARG00000014617 -0.725 6.645 0.005905612 PPP4R4

ENSOARG00000014238 -0.900 7.910 0.005915705 WDR63

ENSOARG00000016859 -0.787 7.842 0.005911887 AXDND1

ENSOARG00000000907 0.613 7.209 0.005931978 MUM1L1

ENSOARG00000017451 -0.802 3.712 0.00593083 GDPGP1

ENSOARG00000005290 -0.990 4.920 0.005945701 USMG5

ENSOARG00000012523 0.963 7.544 0.00594722 DCTN1

ENSOARG00000013666 -0.737 8.465 0.005956413 SCYL2

ENSOARG00000020690 -0.828 6.213 0.005956582 NDUFB5

ENSOARG00000004258 -0.778 4.613 0.005984616 ENSOARG00000015359 -0.966 6.943 0.005988292 LRRIQ1

ENSOARG00000007254 -0.668 4.847 0.006016112 MCU

ENSOARG00000014845 -0.725 5.562 0.006004942 ESCO2

ENSOARG00000016227 -0.729 6.735 0.006009902 DCAF13

ENSOARG00000019099 0.791 3.015 0.006015128 ENSOARG00000017309 -0.913 7.389 0.006028156 DNAJC5B

ENSOARG00000010759 -0.847 6.682 0.006056861 PPP2R5A

ENSOARG00000019369 0.679 5.787 0.006078954 AHCYL1

ENSOARG00000017503 -0.811 8.646 0.006099123 ENSOARG00000018434 0.932 3.409 0.006102218 NRP2

ENSOARG00000005102 -0.927 2.957 0.006117348 TMEM126B

ENSOARG00000005715 -0.855 7.333 0.00612977 DPY19L1

ENSOARG00000011313 0.674 5.223 0.006126546 ENSOARG00000013903 -0.841 10.510 0.006131996 STAT1

ENSOARG00000007414 0.679 6.431 0.006141031 ENSOARG00000004435 -0.796 6.538 0.006155672 ENSOARG00000020345 -0.849 6.466 0.006155097 WDR53

ENSOARG00000010135 0.748 3.596 0.006163655 F5

ENSOARG00000020554 -0.788 8.812 0.00617699 MFF

ENSOARG00000009208 -0.813 4.595 0.006186566 ENSOARG00000020936 -1.029 2.992 0.006197276 MAGOHB

ENSOARG00000004318 -1.190 4.448 0.006203866 CDC2

ENSOARG00000017168 1.092 6.392 0.006215865 FASN

ENSOARG00000005425 -0.813 5.200 0.006220494 ENSOARG00000012992 -0.708 5.829 0.006229831 ORC3

ENSOARG00000008434 0.635 4.948 0.006307549 ABHD3

ENSOARG00000018595 0.818 2.375 0.00632328

Appendices

231

ENSOARG00000006789 -0.786 4.578 0.00633697 IRAK1BP1

ENSOARG00000001687 0.634 5.134 0.006350713 ARMCX3

ENSOARG00000016413 1.110 2.838 0.006347856 ENSOARG00000017821 -0.753 7.810 0.006343011 CLPX

ENSOARG00000012863 -0.711 8.084 0.006359354 SRSF12

ENSOARG00000000237 1.064 4.673 0.006390449 DAAM2

ENSOARG00000010347 -0.710 5.654 0.006404832 C5orf54

ENSOARG00000010822 0.626 7.761 0.00642731 C17orf47

ENSOARG00000015017 0.927 4.827 0.006434064 INTS9

ENSOARG00000001956 1.179 3.790 0.006472548 SNX33

ENSOARG00000005515 1.197 3.306 0.006466809 PDPR

ENSOARG00000005900 -0.834 4.771 0.006480589 GMNN

ENSOARG00000009556 -0.949 5.337 0.006480267 MICU3

ENSOARG00000009814 1.010 2.659 0.006460123 ZMIZ1

ENSOARG00000011525 -0.824 7.576 0.006450252 SPATA17

ENSOARG00000014754 -0.731 4.759 0.006463095 CAPS2

ENSOARG00000019549 -0.887 5.235 0.006477394 ENSOARG00000003944 -0.761 8.521 0.006494867 TRAM1

ENSOARG00000013050 0.912 4.024 0.006514814 SLC9A2

ENSOARG00000000109 0.625 5.662 0.006522191 SLAIN1

ENSOARG00000003314 -0.808 8.551 0.006569655 POLB

ENSOARG00000005338 -0.765 5.500 0.006588592 ZNF345

ENSOARG00000002356 -0.792 5.986 0.006613854 RARRES1

ENSOARG00000005343 0.951 5.677 0.006606407 SRGAP2

ENSOARG00000006719 -0.812 6.147 0.006609891 EAPP

ENSOARG00000012384 0.792 4.812 0.006631021 ENSOARG00000016551 -0.698 4.394 0.006625163 STPG2

ENSOARG00000018225 -0.690 5.630 0.006634776 ZWILCH

ENSOARG00000020993 1.166 2.476 0.006627088 SELENBP1

ENSOARG00000006155 -0.983 3.647 0.006667367 ENSOARG00000016066 -0.793 6.194 0.006659826 ENSOARG00000016523 1.012 6.000 0.006664102 CAMSAP2

ENSOARG00000017414 -0.856 6.283 0.006679706 ENSOARG00000020605 1.032 3.043 0.006691137 PIAS3

ENSOARG00000005690 0.934 4.133 0.006701089 FBXW2

ENSOARG00000015653 1.144 4.422 0.00672786 EEF2K

ENSOARG00000012031 -0.723 8.347 0.006741627 CSE1L

ENSOARG00000000647 -0.769 6.720 0.006747279 ENSOARG00000006187 1.037 3.448 0.006806424 MAPKAPK2

ENSOARG00000011378 -0.853 5.629 0.006811304 SEC61G

ENSOARG00000018461 -0.994 4.561 0.006807411 ENSOARG00000001391 0.802 3.634 0.006820878 ENSOARG00000020535 -0.787 5.028 0.006817106 SNAP23

ENSOARG00000017948 1.035 4.956 0.006846245 DOCK6

ENSOARG00000001330 -0.813 5.335 0.006861159 NRG4

ENSOARG00000012362 -0.680 6.243 0.00685775 TC2N

ENSOARG00000007806 -0.856 4.136 0.006865935 ANKRD37

Appendices

232

ENSOARG00000020878 -0.936 7.103 0.006871446 MNS1

ENSOARG00000020998 -1.183 4.608 0.006877593 DTWD1

ENSOARG00000010011 -0.755 5.625 0.006890099 ENSOARG00000012703 -0.779 4.427 0.006894475 ZNF791

ENSOARG00000011769 1.125 2.754 0.006906835 PYROXD2

ENSOARG00000012612 -0.744 6.556 0.00691737 SLC44A5

ENSOARG00000015721 0.783 3.220 0.006917228 B3GAT3

ENSOARG00000012754 -0.837 7.187 0.006938805 ATXN3

ENSOARG00000002869 -0.902 4.640 0.006965783 ENSOARG00000008180 1.091 5.222 0.006966532 PTPRS

ENSOARG00000021111 -0.861 4.052 0.006967571 TOMM20L

ENSOARG00000010839 -0.690 7.355 0.006995337 MPP6

ENSOARG00000000772 1.058 3.219 0.007005776 PLCD1

ENSOARG00000000451 -0.825 6.131 0.007019116 ENSOARG00000005644 0.904 2.492 0.007034356 ENSOARG00000004426 -0.848 3.521 0.007052657 NSA2

ENSOARG00000007668 -1.157 2.739 0.007086081 GRM7

ENSOARG00000008254 -0.812 7.023 0.007091608 TTC6

ENSOARG00000015533 -0.780 6.586 0.007094049 CCDC53

ENSOARG00000020534 -0.684 7.302 0.007091778 TBCCD1

ENSOARG00000018154 0.856 3.844 0.007126053 CNP

ENSOARG00000018833 -0.776 4.474 0.00713598 ENSOARG00000019375 1.046 2.387 0.007133114 HDAC7

ENSOARG00000008955 -0.806 4.557 0.007143393 TM2D1

ENSOARG00000014482 -0.677 5.612 0.007154416 ENSOARG00000021162 0.670 5.209 0.007155572 GPHN

ENSOARG00000002195 -0.881 7.434 0.007181637 RNMT

ENSOARG00000005209 1.079 2.406 0.007183533 SDC4

ENSOARG00000014614 1.340 3.148 0.007186662 ENSOARG00000013492 -0.947 7.335 0.00720285 INSL6

ENSOARG00000003731 -0.807 8.413 0.007209992 PSME3

ENSOARG00000013178 -0.858 4.867 0.007239344 CCDC126

ENSOARG00000021101 -0.798 7.885 0.007244332 EXOC5

ENSOARG00000009688 0.763 5.359 0.007259326 RERE

ENSOARG00000015046 -0.906 4.591 0.007255425 GNPDA2

ENSOARG00000001833 1.008 2.624 0.007276336 ENSOARG00000015300 -0.768 8.794 0.007290482 LARP1B

ENSOARG00000000810 0.773 5.097 0.007304865 NHSL1

ENSOARG00000012153 -0.987 4.917 0.007311631 CCDC160

ENSOARG00000001254 1.138 2.937 0.007339792 TNXB

ENSOARG00000014555 1.166 3.960 0.007338918 BCR

ENSOARG00000014904 0.855 4.018 0.007352793 PAPD7

ENSOARG00000001870 0.903 3.723 0.007386685 VSTM4

ENSOARG00000002026 -0.805 8.274 0.007406689 COPS4

ENSOARG00000005374 -0.783 5.298 0.007409399 ENSOARG00000007738 0.911 3.834 0.007402348 TAP1

ENSOARG00000008310 -0.781 7.191 0.00741928 TTC29

Appendices

233

ENSOARG00000011500 0.940 4.434 0.007463182 DYSF

ENSOARG00000002953 -0.641 8.099 0.007479432 LRRC36

ENSOARG00000005346 -0.785 6.098 0.007537369 SCFD1

ENSOARG00000008048 0.756 4.926 0.007545958 FAM120A

ENSOARG00000013466 -0.792 9.587 0.007574131 TSGA10

ENSOARG00000019759 -0.742 7.160 0.00757915 GMCL1

ENSOARG00000006752 -0.699 7.650 0.007588976 SPATA4

ENSOARG00000013951 -1.088 4.306 0.007584436 NRIP3

ENSOARG00000013417 -0.814 5.859 0.007595858 TXNDC9

ENSOARG00000001068 -0.724 6.285 0.007613111 ADAM21

ENSOARG00000000519 1.049 4.923 0.007636334 PCDHAC2

ENSOARG00000015716 -0.943 2.570 0.007645878 COMMD8

ENSOARG00000016435 -0.834 5.596 0.00765332 ENSOARG00000006851 -0.805 6.409 0.007701879 LZIC

ENSOARG00000007704 1.037 3.657 0.007713103 ALS2CL

ENSOARG00000006805 -0.724 9.178 0.007727291 WDR96

ENSOARG00000008800 0.855 4.103 0.007733922 HSP70

ENSOARG00000012967 -0.835 6.492 0.007727388 AKIRIN2

ENSOARG00000016410 -0.748 7.097 0.007733275 WDR75

ENSOARG00000003080 -0.734 6.648 0.007747496 STK3

ENSOARG00000005287 -1.042 4.408 0.007764928 APOBEC4

ENSOARG00000007039 1.341 3.923 0.007768505 AGRN

ENSOARG00000020342 -0.745 5.838 0.007770229 OIP5

ENSOARG00000002528 0.953 3.976 0.007788405 TTYH1

ENSOARG00000009320 -0.749 7.268 0.007786763 MMADHC

ENSOARG00000003388 -0.860 4.233 0.007803794 KLHL41

ENSOARG00000015154 -0.724 7.726 0.007820942 ZPBP

ENSOARG00000019335 -0.895 6.766 0.007822015 PSIP1

ENSOARG00000018957 -0.889 4.623 0.007875368 AKNAD1

ENSOARG00000000347 1.009 8.332 0.00790089 TRRAP

ENSOARG00000005722 0.887 5.359 0.00792317 EPAS1

ENSOARG00000019744 -0.866 3.893 0.007934076 SNRNP27

ENSOARG00000016137 0.750 2.741 0.007948985 CTBP1

ENSOARG00000006724 -0.742 6.512 0.007961806 ENSOARG00000003276 -0.798 5.249 0.007989319 ENSOARG00000011351 -0.752 6.896 0.007998923 WDR76

ENSOARG00000007486 -0.947 5.012 0.00801086 CCDC122

ENSOARG00000012067 -0.800 5.036 0.008024283 TEFM

ENSOARG00000007764 -0.749 7.687 0.008039069 SKIV2L2

ENSOARG00000013165 0.669 5.738 0.008050871 ENSOARG00000011408 0.789 4.472 0.008073671 FRY

ENSOARG00000014007 0.839 3.988 0.008077851 JADE2

ENSOARG00000011541 -0.766 3.537 0.008085781 MELK

ENSOARG00000010350 0.791 3.326 0.008097841 QDPR

ENSOARG00000002606 -0.721 6.962 0.008103579 CCDC67

ENSOARG00000012299 0.932 3.623 0.008127989 TLE1

ENSOARG00000020093 -0.776 7.944 0.008146857 UBE2N

Appendices

234

ENSOARG00000001541 -0.731 5.394 0.008172247 PEX3

ENSOARG00000005736 0.847 6.150 0.008169347 GLG1

ENSOARG00000016793 -0.745 4.909 0.008171858 GGH

ENSOARG00000010875 -0.870 6.439 0.008181708 CUL5

ENSOARG00000020072 -0.795 7.668 0.008186378 FSIP1

ENSOARG00000017232 1.307 2.623 0.008238425 ACVR1B

ENSOARG00000020650 0.667 4.639 0.008237295 REEP1

ENSOARG00000000897 0.604 5.706 0.008260349 CUEDC2

ENSOARG00000006481 -0.855 6.984 0.008257838 TMEM30A

ENSOARG00000002762 0.950 2.654 0.008268853 TUBG2

ENSOARG00000000818 0.883 2.717 0.008278722 TRIM68

ENSOARG00000006293 -0.728 4.896 0.008292755 ENSOARG00000004487 0.627 6.729 0.00832923 YWHAB

ENSOARG00000013778 0.971 3.995 0.008321846 YWHAG

ENSOARG00000017713 0.805 4.817 0.008330819 TRAF7

ENSOARG00000014990 -0.673 6.700 0.008368813 ZNF613

ENSOARG00000015373 -0.759 7.107 0.008374908 EIF1AX

ENSOARG00000009729 -0.944 3.586 0.008407132 ITGB3BP

ENSOARG00000011659 -0.908 4.322 0.008406752 LYPLAL1

ENSOARG00000013745 0.764 3.641 0.008408763 FADS1

ENSOARG00000020153 -1.012 3.967 0.008413537 DTX3L

ENSOARG00000020022 -0.871 8.926 0.00841889 TMCO5A

ENSOARG00000016048 -0.760 5.474 0.008426857 C11orf57

ENSOARG00000018954 -0.743 5.346 0.008432188 SNX2

ENSOARG00000004081 0.927 2.236 0.008451071 ENSOARG00000004469 0.991 4.027 0.008459513 TAGAP

ENSOARG00000002282 0.874 4.944 0.008482876 SETBP1

ENSOARG00000006270 0.721 5.277 0.008478499 KDSR

ENSOARG00000009996 0.848 5.467 0.008502951 CACHD1

ENSOARG00000012812 1.095 2.468 0.008501266 CYB561A3

ENSOARG00000014400 -0.835 7.909 0.008510815 ENSOARG00000020803 0.979 4.805 0.008506504 TLN2

ENSOARG00000021070 -0.830 7.235 0.00851201 CDKN3

ENSOARG00000011071 1.260 3.243 0.008535789 FAM102A

ENSOARG00000001927 -0.906 3.072 0.008567646 ENSOARG00000020903 -0.894 9.096 0.00856417 CCPG1

ENSOARG00000013077 -0.710 6.617 0.008582122 FAM134B

ENSOARG00000002398 0.794 4.812 0.008591968 COLGALT2

ENSOARG00000019010 -0.796 5.740 0.008653158 DZIP3

ENSOARG00000016444 -0.832 5.722 0.008683954 ENSOARG00000003802 -0.610 8.060 0.008695604 SCCPDH

ENSOARG00000005402 -0.832 4.556 0.008694005 ZNF569

ENSOARG00000000938 -0.694 5.892 0.008715788 SOCS4

ENSOARG00000018707 0.786 5.415 0.008711831 SLC38A10

ENSOARG00000002484 0.875 6.216 0.008725397 CREBBP

ENSOARG00000013631 1.246 2.835 0.008753056 FAM65C

ENSOARG00000018627 0.731 3.155 0.00875151 TSC22D3

Appendices

235

ENSOARG00000013254 1.057 2.620 0.008772469 ARAF

ENSOARG00000014979 -0.770 6.802 0.00876984 GTF2B

ENSOARG00000005814 0.960 2.172 0.008782644 ENSOARG00000012135 -0.789 6.694 0.008811067 MROH9

ENSOARG00000013579 -1.022 6.263 0.008831224 ENSOARG00000016685 -1.048 6.912 0.00882738 CCDC18

ENSOARG00000007348 -0.663 4.609 0.008840941 P4HA1

ENSOARG00000017558 -0.795 5.920 0.008842812 ENSOARG00000009416 0.946 5.727 0.008849495 PTBP1

ENSOARG00000016837 -0.767 6.066 0.008861019 ZNF624

ENSOARG00000006664 0.799 2.862 0.008884992 LITAF

ENSOARG00000020626 1.052 2.902 0.008890599 SLC16A14

ENSOARG00000016831 -0.845 4.897 0.008898315 NDUFB3

ENSOARG00000011576 0.708 3.368 0.008921882 ENSOARG00000016153 0.666 4.039 0.00892067 CPE

ENSOARG00000019299 -0.812 4.969 0.008932263 ENSOARG00000018404 0.870 2.936 0.008937941 ENSOARG00000013149 0.762 4.890 0.008971659 HIST1H2BJ

ENSOARG00000015536 1.024 4.090 0.008984425 KIAA1199

ENSOARG00000000113 -0.914 6.556 0.009022782 FAM204A

ENSOARG00000005311 -0.700 5.556 0.009009223 C2orf61

ENSOARG00000010252 -0.739 8.656 0.009017546 AK9

ENSOARG00000018927 0.973 2.596 0.009012552 XK

ENSOARG00000014024 0.824 5.844 0.009059557 SPG7

ENSOARG00000008564 0.998 2.684 0.00906691 MAPKAPK3

ENSOARG00000006852 -0.804 3.451 0.009075418 NEIL3

ENSOARG00000000577 -0.783 7.765 0.009094714 ENSOARG00000001844 -0.692 6.204 0.009096337 VRK2

ENSOARG00000008738 0.794 4.275 0.00908796 SCRN1

ENSOARG00000017468 0.771 3.753 0.009116774 EPHX1

ENSOARG00000000898 -0.750 9.652 0.009129503 CCDC176

ENSOARG00000010098 -0.703 6.459 0.00913526 CDC40

ENSOARG00000018138 -0.731 6.776 0.009136101 RIOK2

ENSOARG00000011833 0.616 5.543 0.009157386 CREB3

ENSOARG00000010108 0.680 3.612 0.009193737 FEZ2

ENSOARG00000019802 -0.771 5.179 0.00920258 STK38L


ENSOARG00000017760 -0.941 5.422 0.009263551 ENSOARG00000021096 -0.959 10.491 0.009262777 KTN1

ENSOARG00000021259 -1.031 11.739 0.009275606 U2

ENSOARG00000004263 0.808 2.468 0.009282017 ZDHHC14

ENSOARG00000011277 1.311 2.688 0.009318159 PRICKLE2

ENSOARG00000011981 0.764 2.763 0.009320221 FAM122B

ENSOARG00000015967 -0.711 7.125 0.009317736 NR2C1

ENSOARG00000012699 0.926 3.294 0.00932775 WASF3

ENSOARG00000003327 0.942 3.650 0.009345757 SIPA1L2

Appendices

236

ENSOARG00000005162 0.901 3.623 0.00933469 TRIP10

ENSOARG00000016030 -0.954 3.814 0.009342681 C6orf52

ENSOARG00000011342 1.046 3.439 0.009376415 ARRB1

ENSOARG00000002597 0.918 6.584 0.009400932 CHD6

ENSOARG00000003915 0.674 5.149 0.009437778 SRGAP1

ENSOARG00000006415 -0.889 4.140 0.009482 ENSOARG00000008158 -0.891 8.466 0.009474 PRPF40A

ENSOARG00000016649 0.703 4.667 0.009465845 CDC42SE2

ENSOARG00000019180 0.809 6.082 0.009478284 LAMC1

ENSOARG00000014098 -0.806 8.257 0.009511412 ATAD1

ENSOARG00000007249 -0.743 4.195 0.009533891 TMEM38B

ENSOARG00000007691 -0.783 5.554 0.009541913 RAD54B

ENSOARG00000018260 -0.767 4.433 0.009537714 ST8SIA4

ENSOARG00000023308 0.984 3.414 0.009557063 SCARNA16

ENSOARG00000007258 1.138 3.989 0.009575895 UBE2O

ENSOARG00000013924 1.039 5.854 0.009580179 ANKRD11

ENSOARG00000015317 -0.781 7.346 0.009569395 SRR

ENSOARG00000021193 -0.863 4.481 0.009583523 COX16

ENSOARG00000020674 -0.779 10.065 0.00967086 FXR1

ENSOARG00000004249 -1.105 4.719 0.009700292 ENSOARG00000011033 -0.693 5.630 0.009696974 MED7

ENSOARG00000013828 -0.855 9.004 0.009701016 DNAJB8

ENSOARG00000009863 -0.877 7.228 0.009730943 ENSOARG00000010050 0.824 3.454 0.009736405 GALNT18

ENSOARG00000014319 0.731 4.814 0.009749661 DDAH1

ENSOARG00000021006 -0.960 6.673 0.009791194 COPS2

ENSOARG00000009238 -0.804 7.712 0.009802868 THOC1

ENSOARG00000020774 0.700 3.730 0.009811151 EIF2AK3

ENSOARG00000006723 0.755 4.987 0.009832616 TXNDC11

ENSOARG00000009768 -0.940 7.876 0.009846786 CCDC181

ENSOARG00000009056 1.097 3.444 0.009855444 ATP7B

ENSOARG00000021126 -0.804 8.695 0.009859272 PPM1A

ENSOARG00000010931 -0.771 6.276 0.00989431 GOSR1

ENSOARG00000012096 0.983 2.446 0.009892269 SUV39H1

ENSOARG00000020899 -0.843 5.600 0.009887589 DYX1C1

ENSOARG00000004086 0.828 3.824 0.009925356 BICC1

ENSOARG00000018426 -0.731 6.353 0.00992966 ZNF184

ENSOARG00000006095 1.115 4.299 0.009955024 MINK1

ENSOARG00000008203 0.726 3.146 0.009956467 CD99

ENSOARG00000020978 1.040 3.916 0.009956269 ZNF687

ENSOARG00000016132 -0.735 9.137 0.009982948 PRKAR2A

ENSOARG00000005126 -0.925 3.183 0.010006588 EMR3

ENSOARG00000009659 0.602 6.128 0.010020198 EPHX2

ENSOARG00000010622 -0.703 5.354 0.010023454 YIPF4

ENSOARG00000014455 -0.806 7.006 0.010016306 ENSOARG00000007234 -0.568 6.748 0.010080592 PLK2

ENSOARG00000010127 0.780 5.551 0.01008491 CRIM1

Appendices

237

ENSOARG00000015809 -0.700 6.427 0.010108194 PSMD12

ENSOARG00000006311 0.581 5.814 0.010130295 HNRNPAB

ENSOARG00000011799 -0.845 6.419 0.010138101 TEX26

ENSOARG00000010020 -0.702 4.739 0.010154612 ERI1

ENSOARG00000010700 0.810 3.091 0.010149081 MSANTD2

ENSOARG00000000220 -0.798 5.128 0.010219355 LIN9

ENSOARG00000018855 -0.689 4.496 0.010216954 LNX1

ENSOARG00000017720 0.966 2.715 0.010237979 CASP2

ENSOARG00000020986 -0.842 8.816 0.010243313 USP50

ENSOARG00000012052 -0.685 5.770 0.010271743 UBE2K

ENSOARG00000021166 -0.790 3.287 0.010270087 EIF2S1

ENSOARG00000007660 0.635 4.297 0.010293944 KIAA0408

ENSOARG00000009665 0.700 4.115 0.010296153 MTPN

ENSOARG00000021174 1.061 4.526 0.010300295 ZFYVE26

ENSOARG00000012421 -0.724 5.039 0.01033119 HUS1

ENSOARG00000005937 0.897 4.533 0.010344173 MAST4

ENSOARG00000000726 -0.687 5.610 0.010355221 ENSOARG00000003335 -0.726 5.413 0.010365158 PDE6C

ENSOARG00000011197 -0.712 6.228 0.010383538 RIPK2

ENSOARG00000012536 -1.152 4.010 0.010368791 ENSOARG00000016006 0.959 4.643 0.010371505 TBCD

ENSOARG00000019665 -0.727 10.957 0.010381864 RPGRIP1

ENSOARG00000020238 0.985 3.837 0.0103876 IGSF3

ENSOARG00000003379 -0.833 6.384 0.010428525 EFCAB11

ENSOARG00000015267 0.937 2.817 0.010435227 MCAM

ENSOARG00000004097 -0.680 6.071 0.010460358 ENSOARG00000015582 0.685 4.684 0.010506187 TBC1D4

ENSOARG00000015976 0.930 5.407 0.010515715 SUN2

ENSOARG00000016012 0.982 3.044 0.010518551 TYK2

ENSOARG00000018821 -0.834 4.594 0.010515177 CHDC2

ENSOARG00000006337 -0.853 5.601 0.010539013 TMEM70

ENSOARG00000013957 -0.685 6.346 0.010538401 PPEF1

ENSOARG00000019620 -0.672 6.721 0.010529442 ENSOARG00000001863 0.922 4.318 0.010562357 SEMA6A

ENSOARG00000001667 0.667 5.224 0.010575458 EIF2AK1

ENSOARG00000002221 0.782 6.079 0.010579043 SIN3A

ENSOARG00000008248 1.198 2.954 0.010583619 HSPG2

ENSOARG00000007942 0.718 3.846 0.010606931 KIAA0226L

ENSOARG00000014643 -0.876 7.412 0.010654831 EQTN

ENSOARG00000000172 -1.026 2.436 0.010666998 ENSOARG00000015966 -0.735 5.573 0.010671354 ENSOARG00000019676 -0.726 2.476 0.010690849 GALNT14

ENSOARG00000002806 1.069 2.436 0.010697081 MBOAT7

ENSOARG00000013755 0.631 6.064 0.010798615 PTPRD

ENSOARG00000018491 -0.855 4.916 0.01086122 MTERFD3

ENSOARG00000019154 1.219 3.400 0.010859893 TTLL12

ENSOARG00000013893 -0.817 4.442 0.010887866 MGARP

Appendices

238

ENSOARG00000015846 0.989 6.731 0.010905841 PLEC

ENSOARG00000015122 -0.707 7.243 0.01091953 C11orf1

ENSOARG00000006202 0.790 7.992 0.010932234 ENSOARG00000013281 0.731 4.996 0.010929114 HIST2H2AC

ENSOARG00000006902 0.848 4.519 0.010944436 HDAC5

ENSOARG00000011626 0.710 6.798 0.010966157 TUBB

ENSOARG00000014615 -0.783 4.321 0.010970668 ZCWPW2

ENSOARG00000021133 -0.746 4.638 0.010971063 SLC38A6

ENSOARG00000005310 -0.701 5.222 0.010991816 CCDC90B

ENSOARG00000013256 -0.753 5.499 0.010990101 ERI2

ENSOARG00000004275 0.955 6.295 0.011035097 ARID1A

ENSOARG00000008013 -0.749 7.877 0.011035294 PNPLA8

ENSOARG00000003557 -0.808 5.304 0.01107576 EFHC2

ENSOARG00000004114 -0.831 6.042 0.011114723 ENSOARG00000005183 -0.662 4.568 0.011140587 DLG2

ENSOARG00000006144 -0.786 5.770 0.011215645 TRAPPC13

ENSOARG00000000582 0.812 2.877 0.011263257 SLC25A53

ENSOARG00000013080 0.802 5.524 0.01126 DOCK1

ENSOARG00000017509 1.001 4.052 0.01125844 PPRC1

ENSOARG00000022778 1.050 3.333 0.01129021 SNORA54

ENSOARG00000004438 0.752 3.121 0.011314972 ENSOARG00000014536 0.715 4.949 0.011318405 RPS12

ENSOARG00000009299 0.748 4.370 0.011330976 ENSOARG00000020818 0.657 5.411 0.011332126 PSD4

ENSOARG00000003489 -0.694 5.769 0.011416646 GGPS1

ENSOARG00000006863 -0.727 5.461 0.011420664 AGA

ENSOARG00000007045 0.683 4.723 0.01140482 TAF4B

ENSOARG00000009681 0.883 4.606 0.011404397 RRP12

ENSOARG00000011864 0.954 3.232 0.011403848 PFKL

ENSOARG00000012701 0.755 4.369 0.011410761 CA5B

ENSOARG00000012869 -0.835 3.896 0.011389081 ENSOARG00000016453 0.829 4.061 0.01137515 MAP3K12

ENSOARG00000019222 1.043 4.932 0.011363546 CAD

ENSOARG00000020652 -0.618 7.761 0.011382699 ATL1

ENSOARG00000021144 -0.713 6.031 0.011416753 PPP2R5E

ENSOARG00000008274 -0.745 7.613 0.011434445 HSF2

ENSOARG00000016372 -0.565 7.189 0.011439722 ART3

ENSOARG00000017769 0.723 5.323 0.011440669 CITED1

ENSOARG00000013480 -0.800 6.875 0.011507768 JAK2

ENSOARG00000011888 -0.615 8.824 0.01152282 MAEL

ENSOARG00000007481 -0.664 6.493 0.011541989 RWDD2A

ENSOARG00000016121 -0.577 8.703 0.011548513 MTL5

ENSOARG00000010417 -0.598 7.509 0.011568112 HSF5

ENSOARG00000008511 -0.954 3.961 0.01160355 DENR

ENSOARG00000003862 0.745 4.944 0.011633897 PHLPP2

ENSOARG00000008539 0.796 4.315 0.011637996 GREB1L

ENSOARG00000012141 1.077 2.739 0.01164406 ATXN1

Appendices

239

ENSOARG00000015416 -0.840 5.730 0.011632421 PIBF1

ENSOARG00000017911 -0.809 7.081 0.011636443 TMEM30C

ENSOARG00000001411 0.977 3.593 0.011670748 PRKAB1

ENSOARG00000002400 -0.785 10.036 0.011675058 CCDC88A

ENSOARG00000003647 0.717 3.126 0.011672764 EFNA1

ENSOARG00000002954 0.839 5.841 0.011698908 KAT6A

ENSOARG00000018582 -0.712 6.848 0.011701484 ZNF706

ENSOARG00000017662 -0.917 3.215 0.011738625 ENSOARG00000020246 -0.596 6.763 0.011740502 OSBPL11

ENSOARG00000014528 -0.812 5.781 0.011747845 ENSOARG00000006586 0.744 2.407 0.011761666 PDGFC

ENSOARG00000013437 -0.738 5.737 0.011795629 CDC37L1

ENSOARG00000017748 0.637 5.996 0.011789724 SLC3A2

ENSOARG00000008651 -0.713 4.992 0.011809948 UBE2D2

ENSOARG00000002642 -0.858 6.335 0.011819337 SLIRP

ENSOARG00000007744 -0.745 5.532 0.011833738 CENPW

ENSOARG00000016045 0.705 3.685 0.011827536 NAAA

ENSOARG00000000333 1.225 3.039 0.011905007 ADAMTS7

ENSOARG00000001861 -0.627 7.957 0.011902074 CEP76

ENSOARG00000002703 -0.735 2.598 0.011876261 VEPH1

ENSOARG00000002841 -0.634 6.405 0.011920128 NUP43

ENSOARG00000003199 0.957 3.637 0.011880065 SHC1

ENSOARG00000004075 0.827 6.449 0.011893357 NISCH

ENSOARG00000005768 0.795 3.001 0.011854595 ZNF397

ENSOARG00000006371 -0.640 5.531 0.011915902 ADAMTS6

ENSOARG00000014131 -0.704 6.593 0.011926398 ENSOARG00000014146 -0.708 7.826 0.011886403 PTEN

ENSOARG00000014557 -0.742 6.608 0.011931121 RARS

ENSOARG00000016714 -0.979 4.457 0.011870265 CCDC73

ENSOARG00000017269 0.866 5.400 0.011923891 SCN8A

ENSOARG00000020209 0.914 3.886 0.011895231 CHPF

ENSOARG00000023833 0.963 8.435 0.011877348 SNORD17

ENSOARG00000003508 -0.741 8.624 0.011955832 DHX36

ENSOARG00000016911 0.654 3.279 0.011956784 TTC9C

ENSOARG00000003800 -0.798 6.343 0.011995899 C19orf18

ENSOARG00000004979 0.893 2.442 0.011993048 CTSD

ENSOARG00000018316 0.858 3.045 0.012015378 TMCO6

ENSOARG00000008376 0.696 4.578 0.0120906 KIAA0196

ENSOARG00000005969 0.709 7.618 0.012097171 DST

ENSOARG00000015254 0.646 4.575 0.012110483 FBXO9

ENSOARG00000020746 -0.651 7.623 0.012119124 ENSOARG00000019782 -0.689 5.000 0.012135445 CCNB1IP1

ENSOARG00000007361 -0.635 5.712 0.012176613 ENSOARG00000016778 -0.764 5.514 0.012182268 POLK

ENSOARG00000008787 0.628 5.832 0.012193901 DDX39B

ENSOARG00000008479 0.732 2.655 0.012204726 SUMF1

ENSOARG00000021027 -0.772 7.930 0.012209752 MYEF2

Appendices

240

ENSOARG00000014356 0.955 4.222 0.012305758 HIVEP1

ENSOARG00000018778 -0.829 4.107 0.01232631 CXorf22

ENSOARG00000010908 -0.765 6.940 0.0123414 ERP44

ENSOARG00000021023 -0.674 4.812 0.012337977 ENSOARG00000003581 -0.787 4.892 0.012352778 CMPK1

ENSOARG00000013491 0.747 2.793 0.01236104 GRIA3

ENSOARG00000008241 0.939 2.530 0.012391617 XG

ENSOARG00000014500 -0.682 8.618 0.012390959 CCT8

ENSOARG00000019982 -0.725 4.804 0.012380935 ARHGAP11B

ENSOARG00000018007 0.973 5.018 0.012443644 SLC24A1

ENSOARG00000007109 -0.645 4.762 0.012451968 ELOVL4

ENSOARG00000008705 0.857 5.980 0.012472272 MICAL2

ENSOARG00000012427 0.646 6.390 0.012506926 NUCB1

ENSOARG00000006116 -0.601 6.047 0.012525696 ENSOARG00000021010 -0.604 6.909 0.01255905 SHC4

ENSOARG00000008971 -0.736 9.856 0.012587424 KIF27

ENSOARG00000017241 -0.601 5.677 0.012599252 DNAJC18

ENSOARG00000023720 1.158 4.293 0.012593557 SNORA74

ENSOARG00000002639 0.918 3.234 0.01261956 PIK3C2B

ENSOARG00000021112 -0.779 4.083 0.012639285 TIMM9

ENSOARG00000008814 0.549 5.998 0.012653417 HP1BP3

ENSOARG00000014599 -0.781 5.510 0.012667482 EMC2

ENSOARG00000019257 -0.808 2.839 0.012665369 C3orf52

ENSOARG00000016043 -0.737 7.910 0.012684772 METAP2

ENSOARG00000004929 0.744 5.496 0.012706675 ENSOARG00000014059 0.995 3.746 0.012789265 BNC2

ENSOARG00000014852 -0.776 4.770 0.01278501 BBS10

ENSOARG00000015319 0.928 3.146 0.012791072 KLF11

ENSOARG00000014495 0.790 3.267 0.012801201 ENSOARG00000016738 0.789 3.991 0.01282064 LRCH4

ENSOARG00000003650 -0.781 6.989 0.012874682 C12orf56

ENSOARG00000003971 -0.641 10.192 0.012905907 SPATA6

ENSOARG00000011718 0.587 4.829 0.012927612 DDX26B

ENSOARG00000011894 -0.684 7.905 0.012979157 ATM

ENSOARG00000014339 0.571 5.870 0.012976433 ENSOARG00000005557 -0.649 4.217 0.012987941 ENSOARG00000016119 -0.633 5.716 0.012995291 SLC25A31

ENSOARG00000000725 0.724 2.966 0.013053606 ENSOARG00000003284 -0.845 4.501 0.013050431 FASTKD1

ENSOARG00000004501 1.056 3.456 0.013060416 PCSK7

ENSOARG00000006800 0.785 2.323 0.013075205 ENSOARG00000013603 -0.790 6.261 0.013078966 CACYBP

ENSOARG00000014967 -0.615 7.603 0.013081993 AHI1

ENSOARG00000009517 -0.871 3.515 0.013104216 ENSOARG00000017357 -0.836 4.755 0.013139971 ENSOARG00000010129 0.849 2.086 0.013148047 BACE2

ENSOARG00000007444 0.820 2.997 0.013161394 MRAS

Appendices

241

ENSOARG00000013330 0.804 5.385 0.013176724 DNMBP

ENSOARG00000020068 -0.689 6.198 0.013193384 IQCB1

ENSOARG00000002533 0.617 5.783 0.0132033 VIPAS39

ENSOARG00000017299 -0.571 8.231 0.01320987 TDRD5

ENSOARG00000018277 -0.821 5.005 0.013225799 CD28

ENSOARG00000008213 0.786 4.253 0.013244239 VEZF1

ENSOARG00000012410 1.050 3.678 0.013257773 UNC45A

ENSOARG00000006848 0.678 2.743 0.013302905 ENSOARG00000020374 -0.679 7.960 0.0133254 ACSL3

ENSOARG00000003126 0.691 4.899 0.013405606 PBXIP1

ENSOARG00000011176 -0.846 6.144 0.013427671 TEX36

ENSOARG00000020302 0.957 3.060 0.013466417 VPS18

ENSOARG00000000638 0.840 2.345 0.013488445 CTDSPL

ENSOARG00000006509 0.938 3.310 0.013573855 IGF2BP1

ENSOARG00000009177 -0.750 9.924 0.01356861 ENSOARG00000015785 0.655 6.171 0.013568912 CYFIP1

ENSOARG00000010287 0.720 3.235 0.013609157 FAM213A

ENSOARG00000010576 -0.780 10.008 0.013658921 SP17

ENSOARG00000016835 -0.602 4.848 0.01365545 SLC35B3

ENSOARG00000017608 0.730 4.030 0.0136496 D2HGDH

ENSOARG00000017653 0.880 2.938 0.013682396 FAM117B

ENSOARG00000000757 -0.692 6.698 0.013714712 MYNN

ENSOARG00000002667 -0.869 3.717 0.01371161 KBTBD3

ENSOARG00000017837 -0.723 9.098 0.013715603 TOR1AIP1

ENSOARG00000006151 0.746 3.789 0.013734932 ANKFN1

ENSOARG00000012172 -0.618 6.007 0.013733885 ZNF330

ENSOARG00000017318 0.826 3.924 0.013737593 CTNS

ENSOARG00000000900 -0.608 3.781 0.013769438 GCNT3

ENSOARG00000004033 -0.673 4.560 0.013772796 TMEM5

ENSOARG00000018082 0.946 5.264 0.013758581 SART3

ENSOARG00000019330 -0.661 7.628 0.013765834 SLC35A5

ENSOARG00000012223 -0.777 4.720 0.013789416 SNRPB2

ENSOARG00000009272 0.658 5.201 0.013838945 SLC7A2

ENSOARG00000012669 0.759 2.657 0.013856112 INO80B

ENSOARG00000008766 0.718 3.516 0.01388791 RXRB

ENSOARG00000019386 -0.805 8.296 0.013894936 SMC1B

ENSOARG00000019714 0.581 5.814 0.013906996 METTL17

ENSOARG00000020712 -1.022 4.244 0.01390381 ERP27

ENSOARG00000012371 -0.635 6.799 0.013926566 CEP78

ENSOARG00000008014 0.637 2.661 0.013941354 LRCH1

ENSOARG00000017974 -0.795 5.211 0.013940839 NAA38

ENSOARG00000005538 -0.709 8.518 0.014000355 POLR2B

ENSOARG00000010903 -0.788 7.885 0.013997579 MIER1

ENSOARG00000011233 -0.652 7.940 0.014006465 NCBP1

ENSOARG00000017003 -0.869 3.652 0.014020748 ENSOARG00000020631 0.922 3.450 0.014024392 ABCC5

ENSOARG00000021036 -0.785 5.448 0.014054637 RIIAD1

Appendices

242

ENSOARG00000016355 -0.856 5.932 0.014062074 CCDC112

ENSOARG00000012354 0.592 5.823 0.014071526 PSAT1

ENSOARG00000008420 0.689 4.645 0.014086179 HSD17B10

ENSOARG00000013136 1.006 4.262 0.014099098 PITRM1

ENSOARG00000002727 0.812 6.994 0.014120847 SUPT6H

ENSOARG00000011690 -0.604 7.086 0.014134957 DIABLO

ENSOARG00000012522 0.899 2.550 0.014142542 FOXRED1

ENSOARG00000015629 0.819 4.539 0.014153527 TP53BP2

ENSOARG00000018579 -0.906 8.003 0.01415709 CCDC146

ENSOARG00000000843 -0.831 2.458 0.014190359 ENSOARG00000003856 1.150 2.835 0.014313989 ENSOARG00000000510 -0.718 5.397 0.01434099 IFNGR1

ENSOARG00000011415 0.910 6.224 0.014326526 PRR14L

ENSOARG00000019320 0.757 3.875 0.014340381 ATG16L1

ENSOARG00000007778 -0.572 6.127 0.014406862 ANO5

ENSOARG00000008209 0.563 5.831 0.014419274 FNBP1

ENSOARG00000016223 -0.630 7.539 0.014404594 YTHDC2

ENSOARG00000020244 -0.814 9.838 0.01441548 CAPZA3

ENSOARG00000014646 -0.765 8.204 0.014433244 TBPL1

ENSOARG00000020826 -0.732 5.574 0.014439415 BNIP2

ENSOARG00000011470 -0.624 4.460 0.014446841 RPE65

ENSOARG00000001009 -0.886 2.397 0.014461892 ENSOARG00000019034 0.791 3.119 0.014458805 PRKAG1

ENSOARG00000016040 -0.874 4.455 0.01451444 HECTD2

ENSOARG00000009671 -0.600 7.357 0.014575296 GTF2H1

ENSOARG00000001354 -0.823 5.068 0.014693529 COMMD3

ENSOARG00000003436 0.727 2.554 0.014657429 REXO4

ENSOARG00000007757 0.735 4.137 0.014697548 RBM15B

ENSOARG00000009763 0.907 2.570 0.01468087 PCGF2

ENSOARG00000010269 0.518 5.849 0.014696825 FAM114A1

ENSOARG00000014844 -0.715 7.708 0.014653273 KIF3A

ENSOARG00000017813 -0.902 5.808 0.014675236 MRPL1

ENSOARG00000018057 0.622 4.537 0.01466913 RLIM

ENSOARG00000016598 0.872 4.468 0.014710989 ABL2

ENSOARG00000016728 -0.726 6.651 0.014719134 NUP35

ENSOARG00000007180 0.856 6.597 0.014752987 SRCAP

ENSOARG00000016263 -0.569 9.040 0.014748284 PIWIL1

ENSOARG00000000956 -0.856 2.542 0.014765428 ENSOARG00000019579 -0.710 7.054 0.014768843 ENSOARG00000001809 -0.850 5.758 0.014806557 LARP7

ENSOARG00000016422 -0.620 6.971 0.014817199 EVI5

ENSOARG00000018044 -0.723 5.339 0.014802977 ENSOARG00000019219 0.579 4.729 0.014820587 PSME1

ENSOARG00000020692 -0.751 4.895 0.014800177 MRPL47

ENSOARG00000012232 0.610 7.322 0.014828221 WBSCR22

ENSOARG00000009770 0.723 5.656 0.014839559 DPYSL2

ENSOARG00000018752 -0.618 6.597 0.014849381 MDH1B

Appendices

243

ENSOARG00000010231 -1.054 3.489 0.014886818 MX2

ENSOARG00000004051 0.876 2.562 0.014909412 DVL1

ENSOARG00000003189 -0.620 5.762 0.014996872 INPP5F

ENSOARG00000013462 0.906 2.568 0.015005956 ENSOARG00000006313 0.736 5.229 0.015027087 ALDH18A1

ENSOARG00000010616 -0.877 3.389 0.015026002 ENSOARG00000007681 1.021 4.067 0.01506303 ZNF335

ENSOARG00000015368 -0.874 4.226 0.01507118 ENSOARG00000020991 -0.643 5.437 0.01508626 NRF-2

ENSOARG00000013864 -0.609 7.024 0.015157393 ENSOARG00000005197 1.021 3.060 0.015223147 CLIP3

ENSOARG00000006726 -0.988 3.430 0.015226624 HSPB11

ENSOARG00000008276 -0.754 5.074 0.015205416 ENSOARG00000010161 0.630 4.976 0.015214371 CHMP7

ENSOARG00000012137 -1.015 3.245 0.015203919 CENPQ

ENSOARG00000018612 0.588 4.693 0.015196005 NXPE3

ENSOARG00000007694 0.770 7.440 0.015252345 LAMA3

ENSOARG00000009203 -0.859 9.221 0.015271814 USP1

ENSOARG00000016123 0.810 2.582 0.015257533 ENSOARG00000016164 1.100 2.164 0.015271041 NT5DC3

ENSOARG00000020671 -0.601 7.226 0.015281238 CAB39

ENSOARG00000003004 -0.721 5.134 0.015297153 MND1

ENSOARG00000004704 0.678 4.170 0.01538793 LRIG3

ENSOARG00000006075 0.668 3.116 0.015398277 NLN

ENSOARG00000007314 -0.622 6.963 0.015408465 MIER3

ENSOARG00000011740 -0.633 5.696 0.015370556 LIAS

ENSOARG00000011803 -0.556 7.049 0.015410598 MEDAG

ENSOARG00000012889 -0.659 7.846 0.015375678 CPSF2

ENSOARG00000013173 0.747 4.256 0.015380714 DOCK8

ENSOARG00000016500 -0.670 5.521 0.015390594 ENSOARG00000006082 -0.664 6.863 0.01547227 ENSOARG00000017434 -0.667 7.380 0.015463257 CMYA5

ENSOARG00000020036 0.571 6.484 0.01546784 KIF21A

ENSOARG00000019841 -0.595 7.962 0.01548036 PHTF1

ENSOARG00000004957 0.593 4.702 0.015518475 PRKAR2B

ENSOARG00000020648 -0.650 8.622 0.015516092 KLHL24

ENSOARG00000000488 0.924 4.092 0.015540196 RPTOR

ENSOARG00000004731 -0.840 4.959 0.015603821 ENSOARG00000018772 0.874 3.222 0.015609224 ENSOARG00000015503 -0.884 6.871 0.015625609 KIF20B

ENSOARG00000006699 -0.674 5.072 0.015691483 PSMD14

ENSOARG00000016272 0.806 4.383 0.015698165 GNB2

ENSOARG00000019331 0.697 3.810 0.015693404 SLC22A17

ENSOARG00000004495 -0.748 5.821 0.0157123 HELLS

ENSOARG00000015895 0.648 6.607 0.015717028 PLXNC1

ENSOARG00000007389 0.963 2.468 0.015729169 ENSOARG00000002890 -0.717 8.108 0.015759667 PCMT1

Appendices

244

ENSOARG00000004713 -0.552 7.144 0.015759834 ENSOARG00000006977 0.698 6.549 0.015778974 SETD5

ENSOARG00000001397 0.714 2.748 0.015817032 GPR126

ENSOARG00000010912 -0.630 9.460 0.015850537 PDIA3

ENSOARG00000002309 -0.767 8.554 0.015880423 ENSOARG00000011086 0.844 3.947 0.0158931 ABR

ENSOARG00000016530 0.784 7.412 0.015887559 ZO1

ENSOARG00000009714 -0.626 6.657 0.015913042 ABCE1

ENSOARG00000016339 -0.626 4.883 0.015949084 ZNF214

ENSOARG00000017443 0.638 4.483 0.016027872 PDLIM5

ENSOARG00000014127 0.755 3.411 0.016058001 HLCS

ENSOARG00000020732 -0.736 9.719 0.016051514 SPATA16

ENSOARG00000008132 0.779 6.368 0.016072511 ITPR1

ENSOARG00000002440 -0.968 3.248 0.016159597 ENSOARG00000002998 -0.647 6.076 0.016195493 DDX52

ENSOARG00000003115 1.187 3.381 0.016194522 GALNT2

ENSOARG00000004362 0.931 3.202 0.016123994 KIF1C

ENSOARG00000008448 -0.693 7.127 0.01620574 CAB39L

ENSOARG00000009189 0.788 3.322 0.016160938 DDI2

ENSOARG00000010229 -0.725 6.377 0.016168923 EXOSC8

ENSOARG00000011135 0.817 3.660 0.016148794 ATXN1L

ENSOARG00000012192 -0.650 6.924 0.01613099 FAM126A

ENSOARG00000013802 -0.697 5.936 0.016204191 ENSOARG00000014977 -0.646 6.391 0.01614589 FZD3

ENSOARG00000017648 -0.675 6.189 0.016176258 TXNDC16

ENSOARG00000023519 -0.837 2.847 0.016159523 ENSOARG00000002919 -0.826 5.416 0.016231281 LLPH

ENSOARG00000005710 -0.636 5.902 0.016224992 C11orf82

ENSOARG00000013803 -0.620 3.921 0.016229548 MIS18A

ENSOARG00000013505 0.799 4.615 0.016269181 BCL2L13

ENSOARG00000003383 -0.886 3.191 0.01629111 ASB4

ENSOARG00000005980 -0.603 8.357 0.016296893 TRIM13

ENSOARG00000008193 -0.700 8.504 0.016284232 CEP63

ENSOARG00000001918 0.736 2.630 0.016320705 TK2

ENSOARG00000015932 0.688 4.071 0.016323015 ATP10D

ENSOARG00000015884 0.604 7.115 0.01635627 HELZ

ENSOARG00000017581 0.820 4.762 0.016349757 KDM6B

ENSOARG00000004391 -0.732 6.106 0.016391673 C10orf107

ENSOARG00000009636 -0.713 8.783 0.016404343 BLZF1

ENSOARG00000006823 -0.869 5.742 0.016417669 SUGT1

ENSOARG00000001333 -0.648 6.334 0.016470611 MDM1

ENSOARG00000003163 0.921 5.368 0.016474607 PTPN21

ENSOARG00000012113 -0.715 6.294 0.016490001 UFL1

ENSOARG00000010124 0.822 2.972 0.016500191 MYO1D

ENSOARG00000014090 -0.767 3.657 0.016517066 BTG4

ENSOARG00000003234 -0.724 4.518 0.016587401 C6orf211

ENSOARG00000016729 -0.675 6.705 0.016580375 KIAA0825

Appendices

245

ENSOARG00000009922 -0.602 6.058 0.016611274 C12orf4

ENSOARG00000017271 -0.609 6.728 0.016678736 CSTF3

ENSOARG00000011358 -0.651 8.575 0.016715203 RFC1

ENSOARG00000012785 0.722 3.474 0.016733193 DCPS

ENSOARG00000012880 0.902 3.291 0.016729767 COL6A2

ENSOARG00000015056 0.554 7.791 0.016735099 APP

ENSOARG00000019029 -0.803 6.353 0.016704511 DNAJC2

ENSOARG00000014876 -0.729 8.174 0.016808416 OSBPL8

ENSOARG00000009575 -0.892 5.704 0.016856992 MIS18BP1

ENSOARG00000009848 -0.714 4.999 0.016851838 ENSOARG00000000718 -0.909 2.988 0.016922228 TGDS

ENSOARG00000001384 0.643 4.723 0.016943337 PMS2

ENSOARG00000005526 -0.671 7.292 0.016916614 MCFD2

ENSOARG00000014126 -0.804 5.031 0.016929687 HMMR

ENSOARG00000015515 -0.736 5.990 0.016944968 ARL14EP

ENSOARG00000007130 0.960 7.783 0.016979161 CLSTN1

ENSOARG00000005160 -0.745 8.514 0.016996236 CCDC168

ENSOARG00000012402 0.664 4.015 0.017003726 SRSF9

ENSOARG00000020009 -0.624 8.102 0.017013534 POLQ

ENSOARG00000015288 -0.575 8.363 0.017026148 CNOT10

ENSOARG00000016933 -0.688 5.211 0.017033407 VPS29

ENSOARG00000018256 0.873 3.066 0.017041079 ENSOARG00000005558 0.997 5.903 0.017049499 PKN1

ENSOARG00000007819 0.561 6.258 0.017086662 CALD1

ENSOARG00000015763 -0.750 7.739 0.017105401 USO1

ENSOARG00000002153 -0.653 6.427 0.017128565 YIPF5

ENSOARG00000007849 0.678 3.039 0.017142137 PLEKHB1

ENSOARG00000024340 1.707 11.347 0.017141913 Metazoa_SRP

ENSOARG00000018122 -0.747 5.424 0.017248475 RTCA

ENSOARG00000011501 -0.626 6.307 0.017261865 GRIK2

ENSOARG00000020170 -0.694 8.188 0.017274176 FAM161A

ENSOARG00000012710 -0.702 3.747 0.01731476 ENSOARG00000005719 -0.721 6.846 0.017344815 CDK7

ENSOARG00000008584 -0.686 5.296 0.017333871 TBC1D19

ENSOARG00000013799 -0.958 2.641 0.01734413 SLC25A40

ENSOARG00000004315 0.726 3.132 0.017365685 ACSF2

ENSOARG00000008887 -0.606 9.542 0.017367888 INTS6

ENSOARG00000017866 1.133 3.455 0.017438565 ENSOARG00000017882 0.693 2.837 0.017428236 ENSOARG00000021059 -0.582 9.077 0.017440693 ENSOARG00000001704 0.644 4.354 0.017458707 PLEKHA2

ENSOARG00000003387 -0.666 8.718 0.017501066 UIMC1

ENSOARG00000008537 1.049 4.423 0.017495418 NAV2

ENSOARG00000008928 -0.681 3.814 0.01750607 ENSOARG00000009956 -0.631 5.647 0.017502125 GSTCD

ENSOARG00000003386 -0.678 8.755 0.01753243 ENSOARG00000003742 0.730 2.765 0.017528245

Appendices

246

ENSOARG00000020954 -0.631 8.640 0.017542335 MAPK6

ENSOARG00000011863 0.849 7.943 0.017682419 TLN1

ENSOARG00000018201 0.716 2.391 0.017697914 ARFIP2

ENSOARG00000000664 0.628 6.701 0.017725894 ACSBG1

ENSOARG00000008468 0.755 4.534 0.017783117 EMB

ENSOARG00000008905 -0.637 6.008 0.017779551 ACAD11

ENSOARG00000020937 0.569 5.128 0.017867883 UXS1

ENSOARG00000007186 -0.746 6.608 0.017883537 RWDD4

ENSOARG00000004948 -0.684 7.296 0.017928262 BTF3L4

ENSOARG00000017933 -0.621 6.923 0.017952987 CAGE1

ENSOARG00000020661 -0.753 3.877 0.017961684 ENSOARG00000012638 0.654 6.511 0.018042644 BAZ1B

ENSOARG00000019956 -0.700 6.123 0.018052369 ENSOARG00000012436 -0.752 10.199 0.018083065 CRISP2

ENSOARG00000016520 -0.713 9.412 0.018092785 BZW1

ENSOARG00000016957 0.576 5.922 0.018101919 N4BP1

ENSOARG00000001482 -0.596 4.959 0.018137707 ZNF200

ENSOARG00000003749 0.857 2.447 0.018138606 ZNF154

ENSOARG00000004329 -0.582 9.167 0.018191197 SRP72

ENSOARG00000013588 1.114 3.840 0.018223858 MED25

ENSOARG00000013516 -0.624 6.264 0.018233061 UNC50

ENSOARG00000005979 0.789 3.756 0.01825261 TBC1D10B

ENSOARG00000016332 -0.661 5.431 0.018296083 PGGT1B

ENSOARG00000019513 -1.040 3.559 0.018340019 GRAMD1C

ENSOARG00000020622 0.600 6.242 0.01833779 EPS8

ENSOARG00000014306 -0.715 6.760 0.018350368 CAMLG

ENSOARG00000017694 -0.572 6.873 0.018357133 CLDND1

ENSOARG00000014117 0.694 4.930 0.018434245 BEND4

ENSOARG00000006819 0.645 5.026 0.018465371 RBM33

ENSOARG00000020698 0.650 3.537 0.018464542 ARHGDIB

ENSOARG00000018825 0.902 4.840 0.018481234 FAM160A2

ENSOARG00000013955 -0.644 6.911 0.018490441 H2AFZ

ENSOARG00000013535 0.886 3.823 0.018500457 ITIH5

ENSOARG00000020763 0.693 2.642 0.018561263 GUCY2C

ENSOARG00000005128 0.616 4.124 0.018602955 CDK2AP1

ENSOARG00000008422 0.621 6.144 0.018599587 ENSOARG00000000131 -0.799 9.577 0.018620556 TEX15

ENSOARG00000006746 0.799 3.755 0.018659176 SHKBP1

ENSOARG00000015613 -0.662 5.016 0.018724992 NUP37

ENSOARG00000016001 0.784 2.991 0.018734326 CA8

ENSOARG00000001055 0.721 3.168 0.018760705 RARRES2

ENSOARG00000001261 -0.627 8.163 0.018826436 SPAG1

ENSOARG00000008556 0.732 3.745 0.01893168 RCBTB1

ENSOARG00000019643 0.688 7.223 0.018971456 CHD8

ENSOARG00000019963 0.558 5.387 0.018966168 GJA1

ENSOARG00000009480 -0.644 6.440 0.01900028 PSMG1

ENSOARG00000020673 0.657 4.214 0.019079724 ITM2C

Appendices

247

ENSOARG00000008641 -0.754 4.123 0.019090652 C5orf28

ENSOARG00000018760 0.976 5.064 0.019105957 MYH9

ENSOARG00000001946 -0.686 5.230 0.019136839 ORC5

ENSOARG00000002508 0.792 2.787 0.019214356 TPRN

ENSOARG00000006235 -0.786 5.431 0.019228093 MAGOH

ENSOARG00000001011 -0.734 5.896 0.019246923 MRPL19

ENSOARG00000001621 0.591 5.695 0.019258789 MED14

ENSOARG00000013096 0.638 3.853 0.0192619 IL18R1

ENSOARG00000005030 0.704 4.383 0.019295707 KCTD13

ENSOARG00000015472 -0.748 7.066 0.019362301 C12orf29

ENSOARG00000017349 1.127 2.399 0.0193967 ENSOARG00000023190 1.032 3.470 0.019415971 SNORA84

ENSOARG00000013876 0.490 7.450 0.019439855 EPB41L2

ENSOARG00000000805 -0.569 7.762 0.019461007 ACTRT3

ENSOARG00000004199 0.608 5.462 0.019467199 ANKRD27

ENSOARG00000015887 1.042 3.184 0.019456761 TGIF2

ENSOARG00000016944 -0.822 3.965 0.019535779 CT83

ENSOARG00000003255 -0.764 7.618 0.019581534 ENSOARG00000001555 0.938 4.763 0.019615964 NFATC2IP

ENSOARG00000002161 0.688 2.894 0.019631571 ENSOARG00000015590 -0.708 5.311 0.019657714 DMC1

ENSOARG00000008848 -0.785 8.508 0.019674891 FAM92A1

ENSOARG00000017330 -0.782 6.167 0.019699613 MRPL32

ENSOARG00000018731 0.827 3.079 0.019712602 ARPC1B

ENSOARG00000002894 -0.564 6.804 0.019776087 RTN4

ENSOARG00000005762 -0.681 6.822 0.019794419 TMEM144

ENSOARG00000008856 -0.619 6.305 0.019797928 FBXO33

ENSOARG00000019929 -0.647 4.543 0.019793988 ENSOARG00000011051 0.950 2.643 0.019833225 CRISPLD2

ENSOARG00000010039 -0.565 10.413 0.019853293 AKAP4

ENSOARG00000013154 -0.641 4.863 0.019852599 TMEM50B

ENSOARG00000012943 0.729 4.352 0.019874691 CRTC3

ENSOARG00000013515 -0.753 4.365 0.019868993 ENSOARG00000008854 1.101 2.503 0.01990506 FAM73B

ENSOARG00000021007 0.658 5.525 0.019962076 POGZ

ENSOARG00000017139 -0.556 6.758 0.019993005 SCAMP1

ENSOARG00000015560 -0.691 8.927 0.02002492 ZNF280C

ENSOARG00000017584 0.602 5.567 0.02003034 GSTA1

ENSOARG00000006704 0.782 4.411 0.02008192 ETV3

ENSOARG00000010381 0.893 4.273 0.020089931 PPIP5K1

Appendices

248

Appendix Table 7.2. The 10 most related function clusters of differentially expressed mRNAs by DAVID analysis. The lower p value indicates greater relevance.

Cluster Number Term PValue

1 Cell cycle 6.34E-07

1 Cell cycle process 9.66E-07

1 Cell cycle phase 2.23E-06

1 M phase 2.38E-06

1 Mitotic cell cycle 5.08E-04

1 Nuclear division 5.40E-03

1 Mitosis 5.40E-03

1 M phase of mitotic cell cycle 7.09E-03

1 Cell division 8.12E-03

1 Organelle fission 9.81E-03

2 Spermatid development 1.02E-04

2 Spermatid differentiation 1.98E-04

2 Reproductive cellular process 5.26E-03

2 Germ cell development 1.37E-02

2 Reproductive developmental process 2.37E-02

3 Sexual reproduction 6.03E-04

3 Male gamete generation 7.12E-04

3 Spermatogenesis 7.12E-04

3 Reproduction 8.46E-04

3 Reproductive process 1.02E-03

3 Reproductive cellular process 5.26E-03

3 Gamete generation 5.94E-03

3 Reproductive process in a multicellular organism 1.44E-02

3 Multicellular organism reproduction 1.44E-02

3 Reproductive developmental process 2.37E-02

4 Regulation of DNA replication 4.31E-05

4 Regulation of DNA metabolic process 3.77E-04

4 Positive regulation of DNA replication 1.35E-02

4 Negative regulation of DNA replication 1.35E-02

4 Positive regulation of DNA metabolic process 1.47E-02

4 Negative regulation of DNA metabolic process 7.11E-02

5 Signal complex assembly 2.49E-03

5 Receptor clustering 4.12E-03

5 Receptor metabolic process 2.34E-02

6 Chromosome organization 1.34E-04

6 Covalent chromatin modification 2.81E-04

6 Histone modification 4.21E-04

6 Chromatin modification 6.59E-04

6 Chromatin organization 1.48E-02

6 Protein amino acid acetylation 2.25E-02

6 Histone acetylation 3.43E-02

6 Protein amino acid acylation 5.42E-02

6 Histone H2A acetylation 9.78E-02

6 Histone H4 acetylation 2.93E-01

Appendices

249

7 Macromolecule localization 2.37E-04

7 Protein localization 4.74E-04

7 Establishment of protein localization 1.35E-03

7 Protein transport 1.50E-03

7 Intracellular transport 1.81E-03

7 Establishment of localization in cell 3.80E-03

7 Cellular localization 5.47E-03

7 Cellular protein localization 5.49E-02

7 Cellular macromolecule localization 6.08E-02

7 Establishment of localization 6.45E-02

7 Transport 6.76E-02

7 Localization 7.36E-02

7 Intracellular protein transport 7.80E-02

8 Peptidyl-amino acid modification 5.26E-03

8 Peptidyl-tyrosine phosphorylation 9.71E-03

8 Peptidyl-tyrosine modification 1.31E-02

9 Cellular component assembly 1.19E-04

9 Cellular component biogenesis 1.38E-03

9 Protein complex biogenesis 4.40E-03

9 Protein complex assembly 4.40E-03

9 Macromolecular complex subunit organization 3.41E-02

9 Macromolecular complex assembly 4.91E-02

9 Cellular macromolecular complex subunit organization 3.72E-01

9 Cellular macromolecular complex assembly 5.40E-01

10 Positive regulation of cell communication 7.57E-03

10 Positive regulation of I-kappaB kinase/NF-kappaB cascade 9.26E-03

10 Regulation of signal transduction 1.25E-02

10 Positive regulation of signal transduction 1.31E-02

10 Regulation of cell communication 1.92E-02

10 Regulation of I-kappaB kinase/NF-kappaB cascade 2.33E-02

10 Positive regulation of protein kinase cascade 2.56E-02

10 Regulation of protein kinase cascade 1.02E-01

Appendices

250

Appendix Table 7.3. The 10 most related function clusters of mRNAs in miRNA-mRNA regulatory

network by DAVID software. The lower p value indicates greater relevance.

Cluster Number Term Count PValue

Cluster 1 Cellular component organization 59 6.86E-06

Cluster 1 Protein complex assembly 17 1.19E-03

Cluster 1 Protein complex biogenesis 17 1.19E-03

Cluster 1 Cellular component assembly 24 1.64E-03

Cluster 1 Cellular component biogenesis 26 1.73E-03

Cluster 1 Macromolecular complex subunit organization 20 2.97E-03

Cluster 1 Macromolecular complex assembly 19 3.39E-03

Cluster 1 Protein oligomerization 7 2.90E-02

Cluster 2 Positive regulation of nitrogen compound metabolic process 19 2.41E-03

Cluster 2 Positive regulation of cellular process 39 3.86E-03

Cluster 2 Positive regulation of cellular biosynthetic process 19 4.61E-03

Cluster 2 Positive regulation of RNA metabolic process 15 5.10E-03

Cluster 2 Positive regulation of biosynthetic process 19 5.35E-03

Cluster 2 Positive regulation of biological process 41 6.50E-03

Cluster 2 Positive regulation of macromolecule biosynthetic process 18 6.50E-03

Cluster 2 Positive regulation of transcription, DNA-dependent 14 1.15E-02

Cluster 2 Positive regulation of cellular metabolic process 21 1.35E-02

Cluster 2 Positive regulation of macromolecule metabolic process 20 2.01E-02

Cluster 2 Positive regulation of metabolic process 21 2.11E-02

Cluster 2 Regulation of transcription from RNA polymerase II promoter 17 3.35E-02

Cluster 2 Positive regulation of transcription 14 3.87E-02

Cluster 2 Positive regulation of gene expression 14 4.71E-02

Cluster 2 Regulation of nitrogen compound metabolic process 46 1.12E-01

Cluster 2 Positive regulation of transcription from RNA polymerase II promoter 9 1.23E-01

Cluster 2 Regulation of RNA metabolic process 30 1.69E-01

Cluster 2 Regulation of transcription, DNA-dependent 29 1.93E-01

Cluster 3 Response to estradiol stimulus 5 5.74E-03

Cluster 3 Response to estrogen stimulus 6 1.31E-02

Cluster 3 Regulation of DNA replication 4 4.96E-02

Cluster 3 Response to steroid hormone stimulus 6 1.14E-01

Cluster 3 Regulation of DNA metabolic process 4 1.94E-01

Cluster 3 Response to ethanol 3 2.10E-01

Cluster 3 Response to cytokine stimulus 3 2.84E-01

Cluster 3 Response to inorganic substance 5 2.91E-01

Cluster 3 Protein amino acid autophosphorylation 3 3.13E-01

Cluster 4 Response to vitamin 4 5.78E-02

Cluster 4 Aging 5 5.94E-02

Cluster 4 Response to extracellular stimulus 7 7.42E-02

Cluster 4 Response to nutrient 5 1.17E-01

Appendices

251

Cluster 4 Response to nutrient levels 6 1.23E-01

Cluster 4 Response to organic cyclic substance 4 2.18E-01

Cluster 5 Intracellular receptor-mediated signaling pathway 4 7.84E-02

Cluster 5 Androgen receptor signaling pathway 3 8.28E-02

Cluster 5 Steroid hormone receptor signaling pathway 3 1.81E-01

Cluster 6 Cell motion 13 2.53E-02

Cluster 6 Cell migration 8 7.65E-02

Cluster 6 Cell motility 8 1.16E-01

Cluster 6 Localization of cell 8 1.16E-01

Cluster 6 Locomotion 9 2.17E-01

Cluster 6 Focal adhesion 5 4.35E-01

Cluster 7 Cell death 18 1.56E-02

Cluster 7 Death 18 1.66E-02

Cluster 7 Regulation of apoptosis 17 7.05E-02

Cluster 7 Regulation of programmed cell death 17 7.55E-02

Cluster 7 Regulation of cell death 17 7.75E-02

Cluster 7 Apoptosis 12 1.80E-01

Cluster 7 Programmed cell death 12 1.92E-01

Cluster 7 Positive regulation of apoptosis 9 2.16E-01

Cluster 7 Positive regulation of programmed cell death 9 2.21E-01

Cluster 7 Positive regulation of cell death 9 2.24E-01

Cluster 7 Induction of apoptosis 7 2.54E-01

Cluster 7 Induction of programmed cell death 7 2.57E-01

Cluster 7 Negative regulation of apoptosis 7 3.33E-01

Cluster 7 Negative regulation of programmed cell death 7 3.43E-01

Cluster 7 Negative regulation of cell death 7 3.47E-01

Cluster 7 Induction of apoptosis by extracellular signals 3 4.41E-01

Cluster 8 M phase 9 7.30E-02

Cluster 8 Nuclear division 6 1.71E-01

Cluster 8 Mitosis 6 1.71E-01

Cluster 8 M phase of mitotic cell cycle 6 1.81E-01

Cluster 8 Cell cycle phase 9 1.88E-01

Cluster 8 Organelle fission 6 1.90E-01

Cluster 8 Cell cycle process 10 3.40E-01

Cluster 8 Mitotic cell cycle 6 5.52E-01

Cluster 8 Cell division 5 5.56E-01

Cluster 8 Cell cycle 11 5.89E-01

Cluster 9 Reproductive cellular process 6 6.57E-02

Cluster 9 Reproductive developmental process 7 1.38E-01

Cluster 9 Germ cell development 4 1.52E-01

Cluster 9 Spermatid development 3 1.62E-01

Cluster 9 Spermatid differentiation 3 1.76E-01

Cluster 9 Sexual reproduction 9 2.67E-01

Cluster 9 Reproduction 13 3.25E-01

Cluster 9 Male gamete generation 6 3.92E-01

Cluster 9 Spermatogenesis 6 3.92E-01

Cluster 9 Gamete generation 7 4.31E-01

Appendices

252

Cluster 9 Reproductive process 12 4.36E-01

Cluster 9 Multicellular organism reproduction 7 6.34E-01

Cluster 9 Reproductive process in a multicellular organism 7 6.34E-01

Cluster 10 Reproductive developmental process 7 1.38E-01

Cluster 10 Development of primary sexual characteristics 3 5.06E-01

Cluster 10 Sex differentiation 3 6.00E-01

Appendices

253

Appendix Table 7.4. The differences of Percentage Splicing Index (PSI) in testis from sheep fed a low or

high diet (N = 8 for each treatment).

Symbol AS_event_type Chromosome High diet Low diet P value

TRA2B coord_cassette chr1 46.47 40.02 0.000

NDUFV3 cassette chr1 21.23 51.30 0.001

CC2D1B alternative_acceptor chr1 2.43 8.87 0.002

GOLGB1 cassette chr1 96.78 84.67 0.003

DCAF6 alternative_first_exon chr1 80.29 87.61 0.003

MIER1 alternative_acceptor chr1 71.36 54.16 0.003

OPA1 cassette chr1 52.92 35.43 0.003

GMPS cassette chr1 96.65 99.49 0.005

SEC62 alternative_donor chr1 22.87 6.94 0.005

TBC1D23 cassette chr1 24.55 38.01 0.005

NA alternative_acceptor chr1 1.09 3.93 0.006

ATP11B alternative_donor chr1 9.56 4.33 0.007

CFAP45 alternative_donor chr1 3.72 1.05 0.007

GOLGB1 cassette chr1 44.61 59.78 0.007

CFAP44 alternative_acceptor chr1 3.18 0.00 0.007

ST7L cassette chr1 64.05 74.00 0.007

USP24 alternative_donor chr1 0.58 4.71 0.008

ALDH9A1 alternative_donor chr1 1.89 8.36 0.008

GPSM2 alternative_first_exon chr1 94.29 88.44 0.008

DGKD alternative_donor chr1 0.94 3.92 0.008


LRRC40 alternative_donor chr1 2.43 7.99 0.010

ARHGAP29 alternative_acceptor chr1 93.15 98.46 0.011

TTC4 alternative_first_exon chr1 11.50 6.40 0.011

CHMP2B alternative_acceptor chr1 3.42 6.87 0.012

FXR1 alternative_first_exon chr1 10.38 1.76 0.012

ENSOARG00000002284 alternative_last_exon chr1 57.22 31.11 0.013

CRYBG3 alternative_donor chr1 5.79 11.30 0.013

FUBP1 alternative_acceptor chr1 18.37 11.59 0.013

SPATA16 cassette chr1 74.71 81.39 0.015

ENSOARG00000000941 alternative_acceptor chr1 0.62 2.97 0.015

ENSOARG00000019579 coord_cassette chr1 96.12 98.03 0.016

GPSM2 alternative_first_exon chr1 5.71 11.15 0.016

DCAF6 cassette chr1 0.95 3.18 0.019

RPRD2 cassette chr1 44.12 53.90 0.019

CDV3 alternative_donor chr1 1.55 3.80 0.020

MACF1 alternative_donor chr1 4.34 0.73 0.020

MGST3 alternative_donor chr1 28.45 69.44 0.020

NUP210L cassette chr1 95.90 100.00 0.022

MINA alternative_donor chr1 1.16 4.86 0.022

PRPF3 alternative_acceptor chr1 3.59 7.15 0.022

RYK cassette chr1 99.79 95.57 0.023

VPS8 alternative_donor chr1 6.67 1.77 0.024

Appendices

254

MRPS6 alternative_first_exon chr1 29.55 18.09 0.024

SLC9C1 cassette chr1 98.34 100.00 0.024

NRD1 alternative_acceptor chr1 1.01 3.30 0.025

KAT2B alternative_last_exon chr1 20.82 36.36 0.025

RBM44 alternative_acceptor chr1 1.96 0.00 0.026

RAB5A cassette chr1 98.63 92.94 0.026

DR1 alternative_donor chr1 1.16 2.99 0.027

PSMD4 alternative_donor chr1 7.16 4.88 0.027

BBX cassette chr1 9.70 15.72 0.027

NIT2 alternative_donor chr1 4.61 11.10 0.027

ATP2C1 alternative_donor chr1 1.20 2.88 0.028

KAT2B alternative_donor chr1 1.72 4.14 0.028

BTF3L4 alternative_acceptor chr1 4.05 7.15 0.028

SEC22A cassette chr1 90.27 98.04 0.029

PPM1J alternative_last_exon chr1 65.63 44.56 0.030

UAP1 alternative_donor chr1 90.44 94.86 0.031


TPM3 alternative_acceptor chr1 59.41 40.37 0.032

EPS15 cassette chr1 95.71 99.55 0.032

SMC4 alternative_donor chr1 2.92 10.36 0.032

DCAF6 alternative_donor chr1 1.36 3.77 0.033

BBX alternative_first_exon chr1 26.65 7.54 0.033

ENSOARG00000001661 alternative_donor chr1 5.67 2.49 0.033

SPATA6 alternative_donor chr1 29.61 34.73 0.034

CRYZL1 alternative_donor chr1 3.44 7.39 0.034

LRRIQ3 alternative_first_exon chr1 4.87 15.10 0.036

MACF1 alternative_donor chr1 94.32 97.68 0.036

SETD4 alternative_donor chr1 2.09 7.58 0.037

FXR1 cassette chr1 90.93 87.55 0.037

HIPK1 alternative_last_exon chr1 70.92 58.00 0.037

UBAP2L alternative_donor chr1 8.85 4.28 0.037

PEX11B alternative_last_exon chr1 100.00 97.95 0.038

TBC1D5 alternative_donor chr1 5.47 10.48 0.038

COPA intron_retention chr1 6.33 13.44 0.039

DNAJB4 alternative_first_exon chr1 96.59 98.91 0.039

U2SURP cassette chr1 95.35 90.09 0.039

KALRN alternative_first_exon chr1 28.50 17.24 0.039

SCAMP3 alternative_acceptor chr1 0.37 3.86 0.039

HIPK1 cassette chr1 94.32 98.30 0.039

LRP8 alternative_acceptor chr1 6.38 9.42 0.039

THRAP3 alternative_donor chr1 15.21 8.86 0.040

SENP2 alternative_acceptor chr1 3.34 5.24 0.041

TF cassette chr1 100.00 97.10 0.042

SERBP1 alternative_acceptor chr1 33.19 27.85 0.042


ATP13A3 cassette chr1 17.82 34.22 0.043

KPNA1 alternative_acceptor chr1 21.19 12.09 0.043

Appendices

255

USP40 cassette chr1 59.80 67.21 0.043

TBC1D5 alternative_acceptor chr1 0.95 2.96 0.044

SEC62 alternative_donor chr1 3.58 0.00 0.044

PDIA5 alternative_acceptor chr1 1.37 4.33 0.046

FNDC3B alternative_donor chr1 0.00 3.34 0.046


KIAA1324 alternative_last_exon chr1 5.11 1.61 0.047

ENSOARG00000017506 alternative_first_exon chr1 15.64 6.05 0.047

MORC3 cassette chr1 89.06 95.98 0.048

ATP2C1 cassette chr1 99.42 97.33 0.049

TOPBP1 alternative_first_exon chr1 96.42 100.00 0.049

CDC16 alternative_donor chr10 6.47 14.42 0.001

DGKH alternative_first_exon chr10 6.49 17.31 0.004


ZMYM2 alternative_donor chr10 6.03 10.85 0.005

PSPC1 cassette chr10 97.51 100.00 0.006



ZDHHC20 cassette chr10 60.02 47.78 0.009


TPP2 cassette chr10 11.37 17.55 0.010

PAN3 alternative_acceptor chr10 24.81 40.63 0.016


KATNAL1 alternative_last_exon chr10 17.79 10.05 0.020

MYCBP2 alternative_acceptor chr10 8.34 4.30 0.020

MICU2 alternative_acceptor chr10 2.16 3.92 0.023

ENSOARG00000012842 cassette chr10 90.02 79.92 0.024


TEX26 alternative_acceptor chr10 64.07 55.31 0.027

MICU2 alternative_acceptor chr10 2.53 0.00 0.028

DGKH cassette chr10 16.72 24.55 0.029

MTMR6 alternative_last_exon chr10 7.18 12.66 0.030

NBEA alternative_acceptor chr10 90.63 98.72 0.037

ZDHHC20 alternative_donor chr10 6.93 10.81 0.039

MYCBP2 cassette chr10 74.74 83.17 0.040

SLAIN1 alternative_last_exon chr10 0.00 3.92 0.042

PAN3 alternative_donor chr10 9.15 14.15 0.042

ZDHHC20 alternative_acceptor chr10 1.60 5.07 0.042


SLAIN1 alternative_donor chr10 4.38 13.97 0.047

PAN3 cassette chr10 68.30 57.42 0.047

GTF3A mutually_exclusive chr10 0.00 5.82 0.048

KAT7 cassette chr11 61.01 70.96 0.002

KANSL1 alternative_first_exon chr11 14.23 7.80 0.002

PRPSAP1 alternative_donor chr11 95.44 91.68 0.002

BPTF cassette chr11 30.20 53.42 0.004

DDX5 alternative_donor chr11 13.81 20.15 0.007

Appendices

256

MED13 alternative_donor chr11 3.52 1.08 0.007

ALKBH5 alternative_acceptor chr11 1.98 4.34 0.008

TADA2A alternative_donor chr11 88.25 80.66 0.009

NDEL1 alternative_last_exon chr11 29.48 14.54 0.009


MFSD11 alternative_donor chr11 2.53 5.56 0.010


ULK2 alternative_donor chr11 5.29 8.55 0.011

GPATCH8 alternative_acceptor chr11 67.17 77.13 0.011

NT5C cassette chr11 90.14 99.53 0.011


SAP30BP alternative_first_exon chr11 0.93 6.07 0.014

EXOC7 coord_cassette chr11 80.53 67.72 0.014

TOM1L1 coord_cassette chr11 70.19 79.53 0.017


NA alternative_first_exon chr11 13.12 20.73 0.019

CAMTA2 alternative_donor chr11 0.00 3.43 0.021

SMURF2 alternative_donor chr11 1.41 4.29 0.021

PRPSAP1 alternative_donor chr11 3.06 5.09 0.022

MFSD11 alternative_acceptor chr11 6.58 12.66 0.024

RAD51C alternative_donor chr11 4.73 14.89 0.025

HELZ alternative_acceptor chr11 24.83 16.33 0.026

LSM12 cassette chr11 96.95 99.15 0.026

NUP88 alternative_acceptor chr11 3.27 6.75 0.026

TTLL6 cassette chr11 91.90 98.37 0.026

TOM1L1 cassette chr11 58.61 71.36 0.026

ZNF207 alternative_donor chr11 5.64 11.68 0.027

TIMM22 alternative_donor chr11 4.12 8.66 0.028

SYNRG alternative_acceptor chr11 89.31 77.76 0.029

LRRC46 alternative_acceptor chr11 91.75 96.53 0.030

HSF5 alternative_acceptor chr11 91.98 94.43 0.030

SRSF1 alternative_acceptor chr11 4.25 5.92 0.030


DNAJC7 alternative_donor chr11 1.77 3.64 0.032

ACOX1 cassette chr11 66.41 80.87 0.033

40238 cassette chr11 98.87 95.89 0.033

SRSF1 alternative_acceptor chr11 95.52 93.72 0.034


SPAG9 intron_retention chr11 3.74 0.18 0.036

NFE2L1 cassette chr11 36.78 27.35 0.037

SOCS7 alternative_acceptor chr11 10.10 6.69 0.037

SPEM1 alternative_first_exon chr11 18.05 20.64 0.038

DNAJC7 alternative_acceptor chr11 5.53 13.96 0.039


KIAA0100 intron_retention chr11 14.61 27.79 0.040

ALKBH5 alternative_acceptor chr11 3.42 5.52 0.040

UTP18 cassette chr11 94.62 100.00 0.041

Appendices

257

MAPT cassette chr11 4.14 0.70 0.042

ZPBP2 alternative_donor chr11 2.13 4.33 0.042

NBR1 alternative_donor chr11 97.84 94.18 0.042


PTGES3L-AARSD1 alternative_first_exon chr11 0.77 3.59 0.044

PSME3 alternative_first_exon chr11 78.61 69.68 0.047

TRIM37 alternative_acceptor chr11 6.06 4.20 0.048

ZSWIM7 alternative_donor chr11 21.72 26.21 0.048

YPEL2 alternative_first_exon chr11 95.25 85.52 0.048

GOSR1 alternative_donor chr11 1.50 5.88 0.048

NBR1 alternative_donor chr11 0.62 2.40 0.048

CDC27 alternative_acceptor chr11 8.40 3.85 0.049


GPATCH8 alternative_donor chr11 17.36 12.64 0.050

C1orf101 cassette chr12 93.96 99.07 0.000

ETNK2 alternative_acceptor chr12 0.84 5.13 0.002

NVL cassette chr12 88.35 97.00 0.002

TRMT1L alternative_first_exon chr12 99.22 87.86 0.003

RGL1 cassette chr12 81.60 93.95 0.007

NVL alternative_donor chr12 1.85 4.93 0.008

RABGAP1L alternative_first_exon chr12 64.40 40.14 0.011

VPS13D alternative_acceptor chr12 4.97 9.24 0.012

PRRC2C alternative_donor chr12 10.78 22.19 0.015

SMG7 alternative_donor chr12 6.54 2.72 0.017

CEP170 alternative_acceptor chr12 19.36 26.18 0.019

KIF1B cassette chr12 1.31 4.29 0.020

GPATCH2 alternative_donor chr12 16.68 9.90 0.021

AKT3 alternative_donor chr12 2.23 5.44 0.022

UBE2T alternative_acceptor chr12 1.94 3.79 0.022

UCHL5 alternative_acceptor chr12 3.64 6.55 0.025

PRRC2C alternative_donor chr12 11.67 6.36 0.025

CAPN2 alternative_donor chr12 38.34 13.14 0.025

KLHL12 alternative_acceptor chr12 2.51 6.34 0.028


RGL1 alternative_donor chr12 4.38 7.01 0.033

AKT3 cassette chr12 94.28 98.14 0.034

SLC9C2 alternative_last_exon chr12 97.25 94.05 0.036

RPL22 alternative_acceptor chr12 2.17 4.45 0.037

CEP170 alternative_first_exon chr12 64.44 74.82 0.041

CYB5R1 cassette chr12 98.06 95.47 0.043


CAPN2 alternative_first_exon chr12 14.39 8.64 0.045


PPP2R5A alternative_acceptor chr12 97.20 90.78 0.048

CREM alternative_last_exon chr13 5.86 11.89 0.000

SVIL alternative_first_exon chr13 58.50 35.49 0.001

GNAS alternative_first_exon chr13 5.37 1.87 0.002

Appendices

258

NELFCD cassette chr13 78.22 94.55 0.002

NELFCD coord_cassette chr13 87.90 96.86 0.003

ZMYND11 alternative_first_exon chr13 42.58 24.62 0.004


RBM39 intron_retention chr13 61.82 74.86 0.005

USP6NL alternative_donor chr13 4.36 9.88 0.005

RALY alternative_donor chr13 1.57 4.02 0.006

USP6NL alternative_donor chr13 0.57 4.45 0.008

TPD52L2 cassette chr13 91.12 78.75 0.008

SEC23B alternative_donor chr13 9.57 3.84 0.009

PHF20 alternative_donor chr13 27.94 17.82 0.011

RPN2 cassette chr13 56.13 49.29 0.013

PSMF1 alternative_first_exon chr13 6.63 4.01 0.015

ENSOARG00000000526 intron_retention chr13 0.48 3.46 0.018

ZMYND11 cassette chr13 72.12 62.23 0.018

RALY alternative_donor chr13 93.34 88.56 0.018

C20orf194 alternative_donor chr13 0.68 3.78 0.020

RAE1 alternative_acceptor chr13 1.75 4.25 0.025

UQCC1 alternative_acceptor chr13 5.08 10.06 0.026

RALGAPB alternative_first_exon chr13 94.22 97.95 0.028

ARMC3 alternative_donor chr13 74.77 83.05 0.029

ARMC4 cassette chr13 97.07 99.66 0.030

C20orf96 alternative_last_exon chr13 10.06 1.79 0.031

NCOA6 alternative_donor chr13 17.91 9.70 0.031

ANKRD60 alternative_first_exon chr13 7.41 1.80 0.033

ABI1 alternative_donor chr13 93.93 96.62 0.034

CRLS1 alternative_first_exon chr13 6.91 11.84 0.035

WDR37 alternative_acceptor chr13 14.72 19.28 0.036

PTPRA alternative_first_exon chr13 7.12 2.97 0.036

C20orf194 alternative_acceptor chr13 4.06 9.28 0.038

NCOA6 alternative_acceptor chr13 11.74 6.65 0.038

RAE1 alternative_donor chr13 1.65 4.02 0.039

C20orf85 alternative_first_exon chr13 1.38 2.94 0.039


CTNNBL1 alternative_last_exon chr13 1.30 7.15 0.042


NDRG3 alternative_acceptor chr13 5.27 6.55 0.046

PTPRA cassette chr13 2.53 9.07 0.046

TM9SF4 alternative_donor chr13 0.75 3.05 0.047


RBM38 alternative_last_exon chr13 69.26 49.26 0.047

OPTN alternative_acceptor chr13 18.03 10.32 0.047

YWHAB alternative_donor chr13 1.53 3.49 0.048

ADRM1 alternative_donor chr13 12.40 22.52 0.049

ABI1 alternative_donor chr13 4.07 1.79 0.049

HSPA14 alternative_first_exon chr13 2.36 8.61 0.049

UBOX5 cassette chr13 93.17 98.44 0.049

Appendices

259


RPS5 alternative_first_exon chr14 60.73 79.13 0.000

RPS5 alternative_first_exon chr14 37.18 20.30 0.000



COX6B1 alternative_first_exon chr14 90.03 95.36 0.009


AP1G1 cassette chr14 44.95 63.03 0.011

GPI alternative_acceptor chr14 1.55 4.24 0.020

GGN alternative_acceptor chr14 15.80 23.26 0.020

AARS cassette chr14 73.81 86.06 0.020

DYNC1LI2 alternative_acceptor chr14 3.22 9.60 0.021

CATSPERG cassette chr14 83.33 75.00 0.022

ZNF569 alternative_last_exon chr14 5.44 1.87 0.022



VRK3 alternative_acceptor chr14 2.81 5.22 0.028


NUCB1 intron_retention chr14 49.60 30.85 0.031

SF3B3 alternative_acceptor chr14 0.32 2.19 0.033

PSMB10 alternative_donor chr14 53.78 48.03 0.037

DUS2 alternative_donor chr14 16.87 10.59 0.038

CEP89 alternative_acceptor chr14 4.86 0.86 0.040

CHD9 alternative_first_exon chr14 89.28 80.52 0.043

NUCB1 alternative_donor chr14 24.43 14.29 0.044

CSNK2A2 alternative_acceptor chr14 3.52 6.42 0.048

PRMT1 alternative_first_exon chr14 63.63 72.47 0.049

LRRC36 cassette chr14 82.87 77.35 0.049

PAPD5 alternative_acceptor chr14 2.92 5.87 0.049

HIPK3 alternative_donor chr15 1.22 5.22 0.000

CELF1 alternative_last_exon chr15 32.43 51.11 0.002


FAR1 cassette chr15 20.54 36.65 0.008

HSD17B12 alternative_first_exon chr15 85.37 97.24 0.010


F2 cassette chr15 95.95 93.57 0.013

CLPB coord_cassette chr15 90.68 98.63 0.016


PIK3C2A alternative_donor chr15 95.05 90.57 0.019

KMT2A alternative_acceptor chr15 97.47 100.00 0.023

CKAP5 alternative_donor chr15 2.76 5.63 0.024

FNBP4 alternative_last_exon chr15 44.90 52.88 0.028

SOX6 alternative_donor chr15 3.29 0.00 0.030

MS4A13 alternative_first_exon chr15 95.95 91.10 0.033

ARFGAP2 alternative_donor chr15 2.80 0.00 0.034


TRAPPC4 alternative_donor chr15 7.27 13.71 0.041

Appendices

260

PIK3C2A alternative_acceptor chr15 2.34 6.83 0.045


WDR70 alternative_donor chr16 3.09 0.00 0.000

ATP6V0E1 alternative_last_exon chr16 14.95 9.56 0.001

TNPO1 alternative_first_exon chr16 8.08 22.99 0.001

WDR70 mutually_exclusive chr16 2.46 0.00 0.005

GPBP1 cassette chr16 28.55 43.93 0.007

PARP8 alternative_acceptor chr16 7.65 14.30 0.008

RAD17 alternative_acceptor chr16 87.23 80.87 0.010

PARP8 alternative_donor chr16 7.00 18.19 0.014

DDX4 cassette chr16 86.53 91.24 0.015

RANBP17 cassette chr16 95.45 100.00 0.027

IPO11 alternative_donor chr16 2.30 4.78 0.030

DDX4 cassette chr16 90.22 93.43 0.035

BDP1 alternative_acceptor chr16 4.60 0.54 0.036

PARP8 alternative_donor chr16 2.83 4.99 0.036

TAF9 alternative_first_exon chr16 4.54 1.73 0.039

ZFR alternative_acceptor chr16 4.11 1.53 0.040

NA alternative_first_exon chr16 5.72 15.56 0.046

SKIV2L2 cassette chr16 95.18 99.29 0.047

LMBRD2 alternative_acceptor chr16 4.19 0.79 0.047

SUDS3 cassette chr17 96.72 100.00 0.001

CIT alternative_donor chr17 5.99 2.89 0.002



GTF2H3 alternative_donor chr17 3.37 7.18 0.010

FGF2 alternative_donor chr17 6.38 4.10 0.011


PLK4 alternative_acceptor chr17 80.90 74.56 0.015

MMAA alternative_first_exon chr17 5.51 2.01 0.016


TTC29 alternative_first_exon chr17 33.53 22.52 0.020

CIT alternative_last_exon chr17 51.92 39.32 0.020

ZCCHC8 coord_cassette chr17 79.26 70.52 0.021

KIAA1109 cassette chr17 23.42 31.45 0.023

KIAA1109 alternative_first_exon chr17 2.67 0.72 0.025


BRAP alternative_donor chr17 20.01 9.15 0.031

AACS alternative_donor chr17 0.28 2.34 0.033

PLK4 alternative_acceptor chr17 94.37 98.91 0.034

NAA15 alternative_donor chr17 4.28 6.91 0.036

GLT1D1 cassette chr17 94.65 97.31 0.037

PGRMC2 alternative_first_exon chr17 21.76 39.16 0.038

MORC2 alternative_acceptor chr17 22.75 14.91 0.040

TTC29 alternative_acceptor chr17 3.99 1.94 0.041

FBXO21 alternative_donor chr17 19.41 9.89 0.042

CDK2AP1 alternative_first_exon chr17 46.67 25.94 0.045

Appendices

261

ENSOARG00000016579 coord_cassette chr18 87.28 78.63 0.000

ARHGAP5 alternative_first_exon chr18 31.26 13.50 0.002


TP53BP1 alternative_acceptor chr18 6.08 1.97 0.002

ACSBG1 alternative_donor chr18 1.18 5.18 0.003


SNPRA1 intron_retention chr18 1.69 14.62 0.009

ACSBG1 alternative_donor chr18 1.18 6.70 0.011


SETD3 alternative_acceptor chr18 1.24 3.86 0.015

CERS3 cassette chr18 3.68 6.40 0.016

EAPP alternative_first_exon chr18 13.47 6.81 0.016

CRABP1 alternative_first_exon chr18 16.84 28.89 0.017


UBE3A alternative_acceptor chr18 3.56 1.67 0.020


MARK3 alternative_donor chr18 5.77 1.75 0.023

SETD3 alternative_donor chr18 3.37 5.12 0.024

ACSBG1 alternative_last_exon chr18 68.85 54.11 0.024


SETD3 intron_retention chr18 1.44 5.13 0.025

AKAP13 alternative_donor chr18 3.41 8.24 0.027


MAN2C1 alternative_donor chr18 15.62 8.56 0.030


TARSL2 cassette chr18 83.65 91.73 0.035

SETD3 coord_cassette chr18 95.35 99.45 0.036

APOPT1 alternative_acceptor chr18 2.47 0.69 0.042

HECTD1 cassette chr18 91.27 94.72 0.045

NA alternative_last_exon chr18 12.86 5.75 0.045

BAZ1A alternative_donor chr18 2.22 3.57 0.045

BAZ1A alternative_acceptor chr18 93.82 88.82 0.046

CRABP1 alternative_first_exon chr18 68.77 57.75 0.050

PLXNB1 alternative_first_exon chr19 37.95 27.22 0.001

CNBP alternative_donor chr19 73.62 64.40 0.001

GOLGA4 cassette chr19 96.49 89.41 0.001

SMARCC1 alternative_donor chr19 3.62 0.58 0.001

PTPRG alternative_first_exon chr19 34.35 17.12 0.002

DENND6A alternative_acceptor chr19 7.05 1.74 0.002

KIAA1143 alternative_donor chr19 22.17 13.44 0.004

CCDC174 alternative_donor chr19 0.57 2.94 0.005


MLH1 alternative_acceptor chr19 2.23 4.78 0.008

PPP4R2 alternative_acceptor chr19 4.08 0.85 0.009

PPP4R2 alternative_first_exon chr19 4.13 0.29 0.010

CCDC37 alternative_acceptor chr19 1.67 3.64 0.012

FANCD2 alternative_acceptor chr19 4.75 1.35 0.012

Appendices

262

LANCL2 alternative_acceptor chr19 98.50 96.47 0.016

DYNC1LI1 cassette chr19 93.00 98.70 0.020

LANCL2 alternative_acceptor chr19 1.50 3.34 0.022

CMTM6 alternative_donor chr19 1.87 5.72 0.023

THUMPD3 alternative_acceptor chr19 97.36 94.74 0.026

LRRFIP2 cassette chr19 7.92 13.53 0.026

PLXNB1 alternative_first_exon chr19 9.84 23.62 0.026

ARL8B alternative_donor chr19 1.63 4.35 0.031

CRELD1 alternative_donor chr19 3.00 0.95 0.033


OXSR1 alternative_acceptor chr19 22.77 13.15 0.039

NCKIPSD cassette chr19 96.49 100.00 0.040

VPRBP alternative_last_exon chr19 90.25 80.56 0.040


NEK4 alternative_acceptor chr19 5.57 8.64 0.041

ITPR1 alternative_first_exon chr19 43.19 23.90 0.042


AZI2 cassette chr19 96.29 100.00 0.044

THUMPD3 alternative_donor chr19 1.38 3.71 0.047

NISCH alternative_first_exon chr19 1.19 5.85 0.048

RAD18 alternative_donor chr19 2.50 5.49 0.049

MAP2 cassette chr2 85.61 63.65 0.000

SYK alternative_first_exon chr2 93.30 100.00 0.000

KANK1 cassette chr2 69.97 94.27 0.000

DENND4C alternative_last_exon chr2 26.14 14.78 0.001

H-FABP alternative_acceptor chr2 8.13 22.69 0.002


SRRM1 alternative_acceptor chr2 98.52 94.66 0.003

VPS13A cassette chr2 94.53 98.62 0.003

FAM126B cassette chr2 4.07 8.21 0.004

MFF cassette chr2 6.89 14.84 0.005


SPATA6L alternative_acceptor chr2 41.42 51.34 0.006

SPATA6L alternative_acceptor chr2 58.59 48.67 0.006

GKAP1 cassette chr2 97.55 100.00 0.006

MTFR1L alternative_acceptor chr2 3.51 8.75 0.006

CLASP1 alternative_acceptor chr2 99.84 96.50 0.006

SMARCA2 alternative_first_exon chr2 28.71 58.45 0.006


UBQLN1 cassette chr2 27.59 35.43 0.008

H-FABP alternative_donor chr2 6.30 15.05 0.008

NEK1 alternative_acceptor chr2 20.37 10.22 0.008

CARF alternative_acceptor chr2 6.82 1.97 0.008

CLASP1 alternative_acceptor chr2 0.17 2.61 0.008

TTC39B cassette chr2 83.68 92.44 0.008

BAZ2B alternative_donor chr2 3.81 9.16 0.009

TTC21B alternative_donor chr2 2.31 5.03 0.009

Appendices

263

DCTN3 alternative_acceptor chr2 3.09 7.09 0.010

CCDC171 cassette chr2 92.00 99.41 0.012

SMC5 cassette chr2 45.12 59.08 0.013

KIF27 alternative_donor chr2 96.89 99.55 0.013

BIN3 alternative_acceptor chr2 3.61 8.79 0.014

RFX3 alternative_first_exon chr2 27.29 13.94 0.014

CAB39 alternative_acceptor chr2 1.35 3.67 0.015


HERC2 alternative_first_exon chr2 39.12 25.46 0.016

SMARCA2 alternative_acceptor chr2 73.60 46.44 0.017

MYO1B cassette chr2 6.13 10.36 0.019

S100PBP alternative_first_exon chr2 10.22 4.77 0.020

PTPN4 alternative_donor chr2 90.40 97.64 0.021

PTAR1 cassette chr2 99.67 94.67 0.022


RGP1 alternative_acceptor chr2 11.71 21.44 0.023

SMARCA2 alternative_first_exon chr2 18.47 6.75 0.023

ALS2CR11 alternative_last_exon chr2 78.22 85.44 0.024

KIAA2026 cassette chr2 97.90 91.54 0.024


RAB3GAP1 alternative_acceptor chr2 5.59 9.17 0.027



TTC39B cassette chr2 93.95 98.06 0.029


CEP85 alternative_donor chr2 5.98 10.65 0.032

FSIP2 cassette chr2 95.71 98.38 0.033


PTPN4 alternative_donor chr2 8.94 2.36 0.036

MAP2 intron_retention chr2 7.12 7.92 0.037

ARHGEF39 alternative_donor chr2 79.93 90.39 0.037

SNAPC3 cassette chr2 62.42 51.11 0.038

RAB3GAP1 alternative_donor chr2 6.12 2.86 0.038

39142 alternative_last_exon chr2 11.47 30.14 0.040

RAB3GAP1 alternative_first_exon chr2 20.56 13.58 0.040

ZO2 cassette chr2 96.78 88.43 0.040


PTP4A2 alternative_donor chr2 5.86 2.88 0.042


ARHGEF39 alternative_donor chr2 7.84 3.20 0.043

MAP2 alternative_first_exon chr2 3.13 3.24 0.044

TSSK3 alternative_first_exon chr2 4.36 5.94 0.046

MFF alternative_acceptor chr2 2.29 4.06 0.046

SPAG16 cassette chr2 94.68 84.62 0.046

SPAG16 coord_cassette chr2 97.64 90.24 0.047

CDC37L1 alternative_donor chr2 9.21 5.33 0.047

AGTPBP1 alternative_acceptor chr2 2.08 5.25 0.048

Appendices

264

IWS1 cassette chr2 96.94 99.32 0.049

HSDL2 cassette chr2 95.21 98.43 0.049

STK36 alternative_acceptor chr2 0.00 1.67 0.050

JARID2 alternative_acceptor chr20 1.31 5.32 0.000

TDP2 alternative_donor chr20 99.05 94.97 0.001

DST alternative_first_exon chr20 51.09 31.94 0.001

FARS2 alternative_last_exon chr20 9.09 0.86 0.003

TDP2 alternative_donor chr20 0.95 4.54 0.007

LY6G6C alternative_first_exon chr20 91.23 98.81 0.007

ABHD16A cassette chr20 96.26 91.19 0.007

SNRPC alternative_first_exon chr20 5.10 1.18 0.008

NELFE cassette chr20 95.91 93.00 0.011

CAGE1 cassette chr20 21.09 13.08 0.014

MRPS18A alternative_donor chr20 4.44 9.85 0.016

ENPP5 alternative_acceptor chr20 96.37 98.12 0.018

CAGE1 cassette chr20 89.37 94.78 0.021

POLR1C alternative_donor chr20 40.12 47.78 0.022

ATF6B alternative_donor chr20 8.68 1.67 0.024


ZNRD1 cassette chr20 71.28 82.40 0.031

ENPP5 alternative_acceptor chr20 3.28 1.88 0.034

FLOT1 cassette chr20 35.69 42.99 0.036

GPR116 cassette chr20 41.41 67.28 0.037

SLC29A1 alternative_acceptor chr20 0.00 2.02 0.039

ARMC12 alternative_acceptor chr20 7.26 9.61 0.040

FKBP5 alternative_first_exon chr20 2.90 0.00 0.040

SLC26A8 cassette chr20 85.18 77.11 0.044

BAG6 cassette chr20 2.69 4.29 0.046



MRPS18A alternative_donor chr20 2.67 6.49 0.049

ZNF451 alternative_last_exon chr20 8.81 14.67 0.049

ATL3 alternative_first_exon chr21 88.12 73.73 0.008

ZBTB44 alternative_acceptor chr21 5.98 9.10 0.008

NA alternative_donor chr21 7.62 5.13 0.012

HPS5 alternative_acceptor chr21 7.70 1.56 0.017

ZBTB44 alternative_acceptor chr21 89.86 85.88 0.018

HPS5 alternative_donor chr21 9.18 4.11 0.018

PRMT3 alternative_acceptor chr21 3.97 7.32 0.019

PICALM cassette chr21 26.44 20.16 0.020



OTUB1 intron_retention chr21 3.00 6.38 0.027

PPP6R3 alternative_acceptor chr21 5.99 10.25 0.028



CDCA5 alternative_acceptor chr21 5.51 9.03 0.031

Appendices

265

CDCA5 alternative_acceptor chr21 94.49 90.97 0.031

PICALM cassette chr21 4.75 9.50 0.032

CTTN cassette chr21 56.19 64.01 0.033

FIBP alternative_acceptor chr21 68.52 75.05 0.041

SUV420H1 alternative_acceptor chr21 5.64 14.63 0.041

GANAB cassette chr21 42.48 30.67 0.043

FAM178A alternative_first_exon chr22 65.88 86.72 0.000


MXI1 alternative_acceptor chr22 1.49 6.42 0.002

ENTPD1 cassette chr22 94.83 98.85 0.003

INPP5A alternative_donor chr22 3.60 11.85 0.004

ATE1 cassette chr22 88.38 82.15 0.005

RAB11FIP2 alternative_acceptor chr22 83.61 75.86 0.005

ADD3 cassette chr22 63.99 49.99 0.008

PDZD8 alternative_donor chr22 2.73 5.11 0.009

ATE1 cassette chr22 10.98 15.60 0.011

PDCD4 alternative_acceptor chr22 90.88 95.30 0.015

BTBD16 cassette chr22 75.51 59.84 0.016

RAB11FIP2 cassette chr22 84.80 92.58 0.017

TM9SF3 cassette chr22 95.24 99.57 0.017

INPP5A alternative_acceptor chr22 4.35 11.77 0.017

FAM178A cassette chr22 98.64 96.20 0.018


MGEA5 coord_cassette chr22 86.63 94.91 0.020

TM9SF3 alternative_acceptor chr22 97.78 93.37 0.020

GOT1 alternative_donor chr22 2.93 4.75 0.022

CPEB3 alternative_acceptor chr22 95.42 91.38 0.023

EDRF1 alternative_acceptor chr22 3.12 10.17 0.023




TCTN3 cassette chr22 94.59 86.81 0.029

INPP5A alternative_donor chr22 98.46 95.93 0.029



MORN4 alternative_donor chr22 6.02 2.29 0.040


SEH1L alternative_acceptor chr23 12.87 6.23 0.001

LAMA3 alternative_first_exon chr23 67.68 48.71 0.002

SEH1L cassette chr23 95.93 88.99 0.006

KATNAL2 alternative_acceptor chr23 37.56 28.04 0.007

CFAP53 alternative_donor chr23 2.49 5.72 0.007

MBD2 alternative_first_exon chr23 30.48 11.79 0.007

TIMM21 alternative_first_exon chr23 15.16 7.95 0.008

NARS alternative_donor chr23 4.57 10.46 0.008

MBD1 alternative_last_exon chr23 53.84 61.44 0.011

NARS alternative_acceptor chr23 8.76 14.35 0.012

Appendices

266

MBD1 alternative_last_exon chr23 22.46 14.23 0.015

CDH2 alternative_first_exon chr23 39.63 17.48 0.019

TIMM21 alternative_acceptor chr23 3.44 6.39 0.019

LMAN1 alternative_acceptor chr23 0.95 6.36 0.020

KDSR alternative_donor chr23 0.00 3.03 0.026

SEH1L alternative_last_exon chr23 64.35 49.98 0.029

RAB27B alternative_first_exon chr23 8.50 28.01 0.029

RNF125 alternative_first_exon chr23 49.63 38.45 0.032

SMAD4 alternative_first_exon chr23 84.58 78.76 0.032

PIK3C3 coord_cassette chr23 3.90 1.21 0.037

SEH1L coord_cassette chr23 95.80 90.41 0.039

POLI alternative_donor chr23 4.97 8.53 0.040

SMCHD1 alternative_first_exon chr23 93.70 97.23 0.042

MBD2 alternative_first_exon chr23 7.60 2.96 0.044

GLYR1 alternative_donor chr24 4.57 11.06 0.002

GTF2I alternative_donor chr24 1.78 4.41 0.010

ECI1 cassette chr24 22.40 30.95 0.012

CUX1 alternative_last_exon chr24 42.21 33.78 0.013

41153 coord_cassette chr24 97.58 100.00 0.019

DNAH3 alternative_acceptor chr24 6.04 2.34 0.019


GDE1 cassette chr24 94.58 98.88 0.026

TRRAP alternative_donor chr24 0.21 2.17 0.026

PDILT alternative_acceptor chr24 92.99 98.81 0.026

TEKT5 alternative_acceptor chr24 3.51 5.94 0.027

UBN1 alternative_donor chr24 4.11 1.36 0.030

SLX4 alternative_acceptor chr24 6.10 3.32 0.031

GTF2I alternative_donor chr24 7.14 14.58 0.033

FAM57B alternative_donor chr24 3.25 8.41 0.033

SLX4 alternative_acceptor chr24 93.91 96.57 0.035


STAG3 cassette chr24 97.26 99.37 0.037

TMC7 cassette chr24 78.20 85.03 0.044

PRSS21 alternative_acceptor chr24 92.05 97.77 0.047

STAG3 intron_retention chr24 1.20 3.71 0.049


CLEC16A alternative_acceptor chr24 2.39 4.69 0.050

DNA2 alternative_donor chr25 6.49 1.74 0.006

BMS1 alternative_first_exon chr25 98.24 95.10 0.006

TMEM254 alternative_first_exon chr25 66.37 45.52 0.006


CCAR1 alternative_donor chr25 17.60 8.04 0.022

GNPAT alternative_donor chr25 0.94 2.48 0.022


SIRT1 alternative_donor chr25 97.97 100.00 0.042

WAPAL intron_retention chr25 3.10 0.23 0.044

PSD3 cassette chr26 96.47 92.70 0.005

Appendices

267

PCM1 alternative_acceptor chr26 67.06 80.23 0.005

ADAM32 cassette chr26 93.94 90.22 0.007

PCM1 alternative_acceptor chr26 24.42 14.48 0.012


HOOK3 alternative_first_exon chr26 79.66 65.64 0.023

LETM2 alternative_acceptor chr26 4.23 1.89 0.023


PCM1 alternative_donor chr26 7.12 3.63 0.028

UBXN8 alternative_first_exon chr26 96.71 99.64 0.032

ADAM2 alternative_donor chr26 1.63 3.61 0.039


SORBS2 alternative_last_exon chr26 2.37 5.46 0.041

SNX25 cassette chr26 95.63 97.73 0.048

RABGAP1 alternative_acceptor chr3 1.03 4.07 0.000

CHD4 alternative_donor chr3 0.00 4.16 0.001

ERGIC2 cassette chr3 80.12 89.51 0.001

KIDINS220 alternative_donor chr3 2.92 7.95 0.002

MYPT1 alternative_first_exon chr3 4.47 0.65 0.002


CTDSP2 alternative_donor chr3 96.35 89.46 0.004

SMCO2 cassette chr3 95.42 99.16 0.005

AK8 alternative_first_exon chr3 29.58 40.33 0.006

EIF4B intron_retention chr3 2.19 8.04 0.007

EML4 cassette chr3 60.85 86.19 0.007


AMN1 alternative_first_exon chr3 8.31 2.03 0.008

CTDSP2 alternative_acceptor chr3 2.69 7.71 0.008

MAT2A alternative_first_exon chr3 83.55 88.39 0.008




SLC4A1AP alternative_donor chr3 88.13 79.22 0.009

CDK17 alternative_first_exon chr3 70.71 51.82 0.009

CAPRIN2 alternative_first_exon chr3 58.90 37.93 0.010

CAPRIN2 cassette chr3 96.05 91.04 0.011

DDX31 alternative_acceptor chr3 2.11 4.98 0.011

SPTBN1 alternative_first_exon chr3 70.22 53.79 0.011

USP34 alternative_first_exon chr3 56.11 41.36 0.011

SMEK2 alternative_acceptor chr3 19.72 26.22 0.012

IMMT cassette chr3 38.64 46.09 0.012

ASB8 cassette chr3 94.62 99.04 0.014

LMBR1L alternative_acceptor chr3 0.62 3.01 0.015


C2CD5 cassette chr3 76.69 90.14 0.016

USP34 alternative_donor chr3 2.83 5.05 0.016

EFR3B alternative_first_exon chr3 97.29 92.33 0.016


Appendices

268

SMC6 alternative_acceptor chr3 96.95 91.26 0.018

PUM2 alternative_donor chr3 6.36 1.04 0.018

REV1 alternative_donor chr3 0.32 3.83 0.019

PPHLN1 cassette chr3 42.64 62.94 0.019

FNBP1 alternative_first_exon chr3 57.83 39.80 0.019

SETX alternative_donor chr3 12.47 9.37 0.020

GOLGA1 alternative_acceptor chr3 2.55 0.00 0.021

SLC41A2 alternative_donor chr3 2.03 0.00 0.021

PAWR alternative_acceptor chr3 2.26 6.88 0.023

RABGAP1 alternative_donor chr3 8.12 14.88 0.023

PRDM4 alternative_first_exon chr3 24.84 10.31 0.024



GOLGA1 alternative_donor chr3 1.16 4.37 0.025

OS9 cassette chr3 94.51 90.13 0.025

ASXL2 alternative_acceptor chr3 23.02 11.02 0.025

ODF2 alternative_donor chr3 62.49 57.95 0.025

TTC7A alternative_donor chr3 2.51 5.54 0.025


HNRNPLL cassette chr3 54.92 64.18 0.026


DCTN1 intron_retention chr3 5.41 15.08 0.026

SMCO2 alternative_acceptor chr3 4.84 2.38 0.027


MGAT4A alternative_donor chr3 0.41 2.32 0.027

UNC50 alternative_acceptor chr3 2.01 4.11 0.027

ARID2 alternative_acceptor chr3 1.62 4.90 0.027


PAWR alternative_acceptor chr3 94.69 86.11 0.028

FBXO7 alternative_acceptor chr3 0.00 4.61 0.028

UNC50 alternative_acceptor chr3 2.70 8.49 0.029

APPL2 alternative_donor chr3 11.36 17.20 0.029

GOLGA1 alternative_acceptor chr3 4.88 2.40 0.029

SOX5 alternative_acceptor chr3 5.55 3.29 0.030

C2CD5 cassette chr3 74.52 89.90 0.030


MYPT1 cassette chr3 45.45 57.66 0.030

FUBP3 alternative_first_exon chr3 19.66 40.12 0.031

AGBL5 alternative_donor chr3 98.95 95.19 0.032

C12ORF29 alternative_donor chr3 32.29 22.59 0.032

MYPT1 mutually_exclusive chr3 100.00 93.33 0.032

CPSF6 alternative_acceptor chr3 15.50 12.12 0.033

AK8 alternative_donor chr3 1.08 3.33 0.033

ASXL2 alternative_donor chr3 23.70 11.33 0.033


PAWR alternative_donor chr3 0.60 3.57 0.034


Appendices

269


RTN4 alternative_donor chr3 4.17 6.17 0.035

MYL6 alternative_last_exon chr3 1.74 6.16 0.035


EP300 alternative_donor chr3 31.65 19.29 0.035

YIPF4 alternative_donor chr3 1.14 4.36 0.035

ATXN10 alternative_acceptor chr3 3.55 6.20 0.035


HADHB alternative_acceptor chr3 3.46 6.10 0.036

MRPL51 alternative_first_exon chr3 14.43 20.15 0.036

DNAH6 alternative_donor chr3 2.53 0.00 0.036

RIC8B alternative_acceptor chr3 9.69 4.25 0.037

ODF2 alternative_acceptor chr3 85.04 86.62 0.037


MAPRE3 alternative_donor chr3 17.94 24.69 0.039

BRE alternative_donor chr3 3.28 7.87 0.039

PRPF40B alternative_last_exon chr3 19.75 13.62 0.039

FAM186B alternative_donor chr3 1.75 5.56 0.040

AUP1 alternative_acceptor chr3 5.00 8.54 0.040

NBAS alternative_acceptor chr3 3.04 7.78 0.040

MYPT1 mutually_exclusive chr3 100.00 91.82 0.041

CEP290 cassette chr3 82.08 69.10 0.041

AK8 alternative_first_exon chr3 58.21 48.99 0.041


PLCZ1 cassette chr3 90.99 87.92 0.043

RABL6 alternative_acceptor chr3 1.70 6.15 0.044


MYPT1 cassette chr3 100.00 96.24 0.044

HSP90B1 alternative_first_exon chr3 90.63 94.58 0.044


CAPRIN2 cassette chr3 67.16 75.82 0.045

ARL1 alternative_donor chr3 0.95 4.23 0.046

DYRK4 cassette chr3 91.08 98.85 0.047

EHBP1 alternative_acceptor chr3 96.91 98.38 0.049

SNRNP200 alternative_acceptor chr3 0.33 1.89 0.049

SOS1 alternative_acceptor chr3 5.68 1.73 0.050

DUSP16 alternative_donor chr3 2.52 5.19 0.050

ANKMY2 alternative_first_exon chr4 51.89 31.26 0.001

COPG2 alternative_donor chr4 6.45 0.27 0.001

PEG10 alternative_donor chr4 5.85 12.59 0.003

TSGA13 alternative_acceptor chr4 90.27 73.07 0.004

TNPO3 alternative_acceptor chr4 7.37 11.87 0.007

DFNA5 alternative_acceptor chr4 0.00 3.11 0.008

HNRNPA2B1 cassette chr4 43.16 51.67 0.012

COPG2 alternative_donor chr4 4.28 7.08 0.013

TAX1BP1 alternative_acceptor chr4 1.41 3.99 0.013

LMBR1 cassette chr4 95.03 98.64 0.013

Appendices

270

COPG2 alternative_acceptor chr4 97.08 100.00 0.013

CDK13 alternative_donor chr4 1.97 5.86 0.014

BRAF alternative_acceptor chr4 6.62 3.80 0.017

GSAP alternative_acceptor chr4 3.62 7.17 0.019

GALNTL5 cassette chr4 95.56 92.62 0.020

TNPO3 alternative_first_exon chr4 34.90 25.38 0.020

GPNMB alternative_donor chr4 0.96 5.76 0.021

SUN3 alternative_first_exon chr4 71.25 79.62 0.021

PHTF2 alternative_last_exon chr4 17.11 9.43 0.023

PPP1R9A cassette chr4 100.00 95.79 0.024

NUDCD3 alternative_donor chr4 5.30 10.90 0.024

COA1 alternative_acceptor chr4 6.71 12.28 0.025

PHF14 alternative_donor chr4 0.37 4.66 0.027

NAMPT alternative_acceptor chr4 12.12 5.89 0.027

VPS41 alternative_donor chr4 4.47 7.58 0.029


SUN3 cassette chr4 96.81 98.58 0.031

SKAP2 alternative_first_exon chr4 0.00 2.81 0.036

DMTF1 alternative_acceptor chr4 5.11 7.31 0.039

BRAF alternative_acceptor chr4 95.22 100.00 0.039

UBE3C intron_retention chr4 1.94 10.47 0.041



PEX1 alternative_first_exon chr4 19.09 12.06 0.047

HNRNPM cassette chr5 92.46 84.91 0.000

MIER2 alternative_donor chr5 8.90 2.20 0.001

SLCO6A1 cassette chr5 95.23 99.56 0.001

FAM13B alternative_first_exon chr5 35.92 19.47 0.002

AFF4 alternative_first_exon chr5 24.60 36.14 0.002


NSD1 alternative_last_exon chr5 60.37 44.95 0.003

CLINT1 alternative_acceptor chr5 57.92 44.26 0.004


LNPEP cassette chr5 60.18 78.48 0.008

AFF4 alternative_first_exon chr5 28.76 14.10 0.008

PCSK4 alternative_acceptor chr5 2.15 4.35 0.011


FAF2 alternative_acceptor chr5 0.93 6.55 0.011

FAM81B alternative_acceptor chr5 5.82 0.00 0.011

CANX alternative_first_exon chr5 55.54 35.92 0.011



PAM cassette chr5 64.48 50.35 0.014

PFDN1 alternative_acceptor chr5 4.85 12.17 0.014


PPIP5K2 cassette chr5 99.54 90.88 0.016

MAPK9 alternative_acceptor chr5 7.43 14.54 0.016

Appendices

271


THG1L alternative_first_exon chr5 80.49 64.56 0.020



PFDN1 alternative_donor chr5 0.91 3.44 0.023

FAM13B alternative_first_exon chr5 31.28 46.67 0.023

MATR3 cassette chr5 97.68 91.49 0.024


MATR3 alternative_acceptor chr5 34.40 23.04 0.029


PPP2R2B cassette chr5 36.91 49.35 0.029

PFDN1 alternative_first_exon chr5 1.46 6.03 0.030

DNM2 cassette chr5 19.17 24.69 0.030

PCBD2 cassette chr5 82.32 92.48 0.032

REXO1 alternative_acceptor chr5 16.36 8.09 0.033

PLIN3 alternative_acceptor chr5 2.75 7.16 0.035

RAB11B alternative_donor chr5 0.38 2.23 0.037

PLIN3 alternative_donor chr5 2.40 10.34 0.039

CTNNA1 alternative_acceptor chr5 2.10 0.00 0.039

FBN2 alternative_acceptor chr5 9.70 4.82 0.039


TMEM161B alternative_donor chr5 2.63 5.71 0.040


GRAMD3 intron_retention chr5 0.00 6.36 0.041


ATP8B3 cassette chr5 88.51 79.00 0.043

STK11 alternative_acceptor chr5 5.97 9.65 0.045


FAM114A2 alternative_donor chr5 0.65 3.38 0.048

FER alternative_acceptor chr5 2.42 6.51 0.048

RNF145 alternative_acceptor chr5 2.02 4.70 0.049

PDS5A alternative_donor chr6 11.43 3.61 0.002

FIP1L1 cassette chr6 56.84 70.45 0.007

RFC1 alternative_acceptor chr6 97.94 95.91 0.007

TMEM33 alternative_donor chr6 10.51 16.93 0.008

RFC1 alternative_acceptor chr6 1.38 3.49 0.010

CCDC158 alternative_last_exon chr6 49.60 35.69 0.012

HNRNPD cassette chr6 45.46 37.11 0.012

SEC31A cassette chr6 69.75 83.19 0.014

EXOC1 alternative_donor chr6 3.15 8.62 0.015

PRDM5 cassette chr6 96.30 84.91 0.017

SMIM20 alternative_first_exon chr6 43.66 58.06 0.019


WDR19 alternative_donor chr6 22.53 30.37 0.024

CDS1 alternative_donor chr6 3.21 5.96 0.026



Appendices

272

FBXL5 alternative_acceptor chr6 4.19 14.16 0.031


ENOPH1 cassette chr6 96.47 99.60 0.035

RAP1GDS1 alternative_donor chr6 4.63 8.51 0.036


PPA2 alternative_donor chr6 3.16 5.99 0.040

TSPAN5 alternative_acceptor chr6 1.13 4.36 0.041


PDE5A cassette chr6 94.33 99.51 0.043

SMIM20 alternative_first_exon chr6 49.30 36.08 0.044

PTPN13 cassette chr6 100.00 97.39 0.045

LRRC74A alternative_donor chr7 11.23 19.00 0.000



SPESP1 alternative_acceptor chr7 25.62 44.02 0.001

BNIP2 alternative_first_exon chr7 70.76 85.77 0.001

ALDH6A1 intron_retention chr7 93.97 87.98 0.001

YTHDC2 alternative_first_exon chr7 17.33 4.02 0.001

WDR41 cassette chr7 76.41 89.65 0.002

CGRRF1 alternative_first_exon chr7 45.37 64.58 0.002

BNIP2 alternative_first_exon chr7 27.21 13.85 0.002

ALDH6A1 alternative_donor chr7 87.21 75.93 0.002

TTLL5 alternative_donor chr7 94.45 97.59 0.002

AHSA1 alternative_first_exon chr7 13.79 5.39 0.003

AHSA1 alternative_first_exon chr7 75.90 87.52 0.003


TEX9 cassette chr7 98.92 94.62 0.006

ANGEL1 alternative_acceptor chr7 6.81 11.79 0.007

DDHD1 alternative_acceptor chr7 3.49 1.42 0.007

MYO9A cassette chr7 11.48 18.55 0.007

AQR alternative_first_exon chr7 52.61 26.17 0.008

DENND4A alternative_first_exon chr7 23.58 16.75 0.009


ALDH6A1 alternative_acceptor chr7 33.00 40.90 0.012

FUT8 alternative_first_exon chr7 89.41 98.54 0.013

IQGAP2 cassette chr7 37.34 46.11 0.013

DCAF5 alternative_acceptor chr7 1.65 4.87 0.014

DDHD1 alternative_donor chr7 3.99 1.61 0.014

BCL2L2-PABPN1 alternative_first_exon chr7 5.89 2.08 0.016

AHSA1 alternative_donor chr7 5.57 9.08 0.017

HOMEZ alternative_first_exon chr7 2.24 10.24 0.018

YLPM1 alternative_donor chr7 32.22 18.74 0.019

BCL2L2-PABPN1 intron_retention chr7 12.14 21.02 0.020

GALK2 alternative_acceptor chr7 1.66 4.26 0.022

CLPX alternative_donor chr7 4.93 3.29 0.025


GTF2A1 alternative_acceptor chr7 8.16 11.02 0.027

Appendices

273

BBS4 alternative_acceptor chr7 1.04 5.72 0.027

SNX1 alternative_first_exon chr7 9.32 15.84 0.027

MPP5 intron_retention chr7 14.70 4.18 0.029



FERMT2 cassette chr7 41.33 19.65 0.030

KTN1 cassette chr7 15.43 19.65 0.030



CGRRF1 alternative_first_exon chr7 30.66 18.54 0.033

TPM1 intron_retention chr7 6.90 15.43 0.035

RAB8B alternative_acceptor chr7 1.13 4.73 0.035

AP3B1 alternative_acceptor chr7 2.37 4.86 0.037

ZNF106 alternative_acceptor chr7 7.59 15.13 0.037

ANGEL1 intron_retention chr7 4.15 10.67 0.037

MYO9A cassette chr7 12.50 25.25 0.037

HOMEZ alternative_first_exon chr7 54.21 42.49 0.038



OIP5 alternative_donor chr7 4.66 0.56 0.041

SYNE2 alternative_acceptor chr7 3.14 6.54 0.041

FUT8 alternative_donor chr7 92.36 98.05 0.041


IFT43 alternative_acceptor chr7 0.64 2.56 0.041


WDR41 cassette chr7 92.76 88.60 0.042

FUT8 alternative_donor chr7 3.70 0.67 0.042

FEM1C alternative_first_exon chr7 8.69 3.02 0.043


ALDH6A1 intron_retention chr7 87.71 81.37 0.044

FUT8 alternative_first_exon chr7 4.73 0.57 0.045



SERINC5 cassette chr7 100.00 98.01 0.046

ANGEL1 alternative_donor chr7 9.20 12.93 0.046

CDKN3 cassette chr7 89.21 81.13 0.047

GALK2 alternative_donor chr7 2.11 4.88 0.049

RNGTT alternative_donor chr8 83.07 94.83 0.002


ATG5 alternative_last_exon chr8 2.96 0.00 0.005

PPIL6 alternative_first_exon chr8 82.45 65.87 0.007

PPIL6 alternative_first_exon chr8 17.38 33.96 0.007

DYNLT1 alternative_last_exon chr8 95.28 98.03 0.010

HSF2 cassette chr8 78.05 67.30 0.010

SNX9 alternative_first_exon chr8 63.17 44.67 0.013

RNGTT alternative_donor chr8 8.42 2.06 0.013

KLHL32 alternative_acceptor chr8 0.93 5.40 0.013

Appendices

274

MAP3K7 alternative_acceptor chr8 1.04 9.01 0.014

SENP6 alternative_first_exon chr8 24.79 9.16 0.019

FHL5 alternative_acceptor chr8 99.28 97.22 0.020

REPS1 alternative_first_exon chr8 14.22 7.78 0.021

MYO6 alternative_acceptor chr8 9.66 6.65 0.024

CEP85L cassette chr8 80.01 86.61 0.033

DYNLT1 alternative_last_exon chr8 2.92 0.72 0.034

RNGTT alternative_first_exon chr8 4.89 0.00 0.034

TIAM2 alternative_acceptor chr8 0.79 4.27 0.035

SOBP cassette chr8 91.48 88.17 0.039


TCTE3 alternative_donor chr8 2.71 0.00 0.040

SNX9 alternative_donor chr8 99.19 97.21 0.040

MANEA alternative_acceptor chr8 0.29 6.02 0.040


MANEA alternative_acceptor chr8 99.59 93.98 0.044

REPS1 alternative_first_exon chr8 5.35 0.36 0.045

MAP3K7 cassette chr8 49.30 39.20 0.046

PPIL6 alternative_donor chr8 3.32 13.26 0.049

PGM3 alternative_last_exon chr8 1.55 6.98 0.049


RIMS2 cassette chr9 70.55 33.69 0.001

PPP1R42 cassette chr9 93.77 88.46 0.004

ARFGEF1 alternative_acceptor chr9 6.01 14.22 0.008

EFR3A alternative_first_exon chr9 6.71 0.75 0.010

EMC2 alternative_acceptor chr9 4.82 1.82 0.011

TP53INP1 alternative_acceptor chr9 3.70 6.50 0.016


EFR3A alternative_donor chr9 5.33 8.77 0.019



VPS13B alternative_donor chr9 14.46 8.68 0.023

PHF20L1 alternative_acceptor chr9 14.30 6.84 0.025


TSTA3 alternative_donor chr9 1.14 5.13 0.033


FAM92A1 alternative_acceptor chr9 1.44 3.73 0.036

CSPP1 alternative_acceptor chr9 4.13 7.88 0.036

KHDRBS3 cassette chr9 94.80 97.04 0.036

TAF2 alternative_acceptor chr9 3.45 1.20 0.036

ENPP2 cassette chr9 97.93 100.00 0.038

LMBRD1 alternative_donor chr9 3.30 9.65 0.041

EYA1 alternative_last_exon chr9 98.58 94.06 0.041

FAM91A1 alternative_first_exon chr9 62.48 48.72 0.042

FAM91A1 cassette chr9 92.34 95.82 0.043


INTS8 cassette chr9 92.85 85.58 0.048

Appendices

275

INTS8 cassette chr9 91.98 97.50 0.048

MROH5 alternative_donor chr9 48.73 31.67 0.049

ATP11C cassette chrX 91.98 81.66 0.003

LAS1L alternative_donor chrX 8.97 21.55 0.010

PRDX4 alternative_first_exon chrX 38.83 46.72 0.034

FMR1 cassette chrX 28.06 44.78 0.040

PHF8 alternative_donor chrX 9.03 13.55 0.041

MOSPD2 alternative_acceptor chrX 100.00 98.11 0.043

ENSOARG00000016540 alternative_donor chrX 5.33 1.50 0.044

PDK3 alternative_donor chrX 94.24 85.89 0.047

Appendices

276

Appendix Table 7.5. Differential alternative spliced genes in testis from sheep fed a low or high diet (N

= 8 for each treatment).

MAP2 PARP8 ENPP5 ARMC12 ENSOARG00000013702

CREM MLH1 NCKIPSD VRK3 ENSOARG00000016755

LRRC74A ARFGEF1 REV1 FBXO7 ENSOARG00000002534

C1orf101 UBQLN1 PPHLN1 BTF3L4 ENSOARG00000014576

TRA2B ADD3 CDH2 MPP5 ENSOARG00000003198

RPS5 AMN1 PRMT3 SEC22A ENSOARG00000013996

SYK USP24 SMIM20 FAM186B ENSOARG00000007116

HIPK3 AQR 41153 SYNRG ENSOARG00000016579

RBM25 ZBTB44 KIAA0100 TCTN3 VPRBP

KANK1 ALDH9A1 YLPM1 ARMC3 ZFR

HNRNPM SNRPC SENP6 APPL2 AUP1

FAM178A TIMM21 DNAH3 CEP89 TMEM161B

TCTE3 DFNA5 GSAP RAB27B MORC2

JARID2 NARS PIK3C2A PPP2R2B CCDC36

WDR70 TMEM33 MYO1B VPS41 THRAP3

RABGAP1 TPD52L2 RPRD2 PPA2 NBAS

TDP2 MAT2A FNBP1 MTMR6 MORN4

MIER2 GPSM2 CEP170 UBN1 FKBP5

C15orf41 NEK1 THG1L MAN2C1 MANEA

CHD4 CARF TTC29 ARMC4 FMR1

DST DGKD GALNTL5 PPM1J POLI

ANKMY2 CCDC38 LMAN1 LRRC46 ZO2

ATP6V0E1 TTC39B CDV3 SOX5 ENSOARG00000021149

SPESP1 FAR1 MACF1 LMBRD1 OIP5

PLXNB1 LNPEP GPI SOX6 SYNE2

SLCO6A1 ALKBH5 MGEA5 HSF5 UTP18

CDC16 COX6B1 C20orf194 FERMT2 TSPAN5

CNBP DENND4A FHL5 TRAPPC4 ENSOARG00000016418

TNPO1 PDZD8 S100PBP TRIM24 ENSOARG00000006929

SUDS3 TADA2A KIF1B CEP290 ENSOARG00000003810

BNIP2 BAZ2B GGN IPO11 ENSOARG00000004224

COPG2 SNPRA1 EYA1 KTN1 ENSOARG00000003552

ALDH6A1 TTC21B AARS SRSF1 ENSOARG00000017636

TMEM260 SLC4A1AP UBE3A DNM2 ENSOARG00000015593

ERGIC2 PPP4R2 KATNAL1 BRAP PHF8

RIMS2 NDEL1 DYNC1LI1 C20orf96 GRAMD3

SVIL ZDHHC20 C15orf26 NUCB1 SENP2

DENND4C SEC23B SETX ZNRD1 ENSOARG00000003373

GOLGA4 CDK17 MYCBP2 NEK4 SORBS2

SMARCC1 IFT43 PICALM FBXL5 UBE3C

NDUFV3 MFSD11 MGST3 ARL8B SUV420H1

SEH1L DYNLT1 GOLGA1 NCOA6 SMCHD1

YTHDC2 FIBP REPS1 FUBP3 FBXO21

LAMA3 HSD17B12 DYNC1LI2 C11orf80 SLAIN1

Appendices

277

CC2D1B LRRC40 ZCCHC8 SLX4 SPATA21

KAT7 TPP2 CAMTA2 UAP1 MAPT

RNGTT LAS1L GPATCH2 CDCA5 FAM91A1

WDR41 EFR3A SMURF2 CCDC150 TF

PTPRG DCTN3 PTPN4 DNAJC7 SERBP1

ETNK2 HSF2 GPNMB RNF125 CTNNBL1

PDS5A GTF2H3 SLC41A2 CEP85 ZPBP2

ARHGAP5 GTF2I SUN3 PCBD2 NBR1

DENND6A RAD17 CCAR1 ENSOARG00000000434 PTP4A2

CGRRF1 CAPRIN2 AKT3 AGBL5 ENSOARG00000003035

FAM13B APOPT1 GNPAT TPM3 ENSOARG00000007328

KIDINS220 ITPR1 NUP210L C15orf48 ENSOARG00000017846

GLYR1 ULK2 ENSOARG00000015269 SMAD4 ENSOARG00000000526

GNAS PCSK4 GALK2 EPS15 ENSOARG00000004778

CELF1 RABGAP1L UBE2T SMC4 FEM1C

H-FABP NELFE POLR1C C12ORF29 PDE5A

NVL SIRT1 PTAR1 UBXN8 CYB5R1

TTLL5 DDX31 GOT1 TSTA3 CHD9

MXI1 EMC2 CATSPERG MS4A13 ATP13A3

C9orf116 GPATCH8 ZNF569 FSIP2 KPNA1

CIT SPTBN1 MINA ACOX1 MOSPD2

TP53BP1 MBD1 PRPF3 AACS USP40

MYPT1 FAF2 PHTF2 SF3B3 GANAB

KANSL1 ARHGAP29 CPEB3 CPSF6 PLCZ1

AFF4 TTC4 MARK3 REXO1 ATP8B3

PRPSAP1 FAM81B VPS13B ANKRD60 CDC73

NELFCD FGF2 PAWR CRELD1 RABL6

GOLGB1 CANX EDRF1 CTTN ENSOARG00000019964

AHSA1 NT5C CMTM6 ENSOARG00000001661 WAPAL

CCDC91 AZI2 RYK ENSOARG00000008621 ENSOARG00000016540

ATP11C USP34 RGP1 FAM57B PTGES3L-AARSD1

ACSBG1 PHF20 KIAA1109 CEP85L ENSOARG00000014131

FARS2 AP1G1 MICU2 40238 ENSOARG00000002284

DCAF6 SMEK2 KMT2A ABI1 ENSOARG00000010650

SRRM1 CCDC37 HOOK3 SPATA6 ENSOARG00000005912

VPS13A FANCD2 LETM2 CRYZL1 TMC7

PEG10 CCDC158 MATR3 PRDX4 PTPN13

TRMT1L CCDC171 VPS8 ARFGAP2 HECTD1

MIER1 CHMP2B MRPS6 ENSOARG00000014575 STK11

SLC26A8 HNRNPA2B1 PPP1R9A CRLS1 NCOA5

ENTPD1 FXR1 MYO6 ENSOARG00000010853 CDK2AP1

OPA1 VPS13D ALS2CR11 TPM1 BAZ1A

HSP90B1 HNRNPD SLC9C1 TIAM2 ARL1

NSD1 IMMT ENSOARG00000012842 RTN4 PDIA5

BPTF ECI1 WDR19 ENOPH1 TSSK3

FAM126B CUX1 NUDCD3 MYL6 BAG6

PPP1R42 RPN2 CKAP5 ENSOARG00000020039 ZNF76

Appendices

278

INPP5A TEX37 ATF6B EP300 FNDC3B

DGKH FUT8 PRDM4 RAB8B NDRG3

CLINT1 SNX9 KIAA2026 YIPF4 SERINC5

CTDSP2 IQGAP2 NCOA1 PLIN3 SPAG16

ZMYND11 SMC5 UCHL5 TARSL2 CDKN3

PUM2 TAX1BP1 C19orf44 ATXN10 DYRK4

KIAA1143 LMBR1 COA1 ENSOARG00000010149 PRSS21

TSGA13 KIF27 NRD1 SLC9C2 PSME3

CCDC174 F2 ENSOARG00000020917 FAM92A1 SKIV2L2

GMPS CRYBG3 PHF20L1 CSPP1 KIAA1324

PSD3 KLHL32 KAT2B NAA15 LMBRD2

SEC62 TRIM11 RAD51C BDP1 TM9SF4

RBM39 FUBP1 WDR88 HADHB ENSOARG00000017506

ATE1 CDC37L1 ENSOARG00000007319 LRRIQ3 ENSOARG00000017106

PDK3 DCAF5 RAE1 KHDRBS3 ENSOARG00000001675

ZMYM2 CDK13 OS9 FLOT1 ENSOARG00000011009

TBC1D23 SAP30BP ASXL2 SPAG9 PEX1

PCM1 BIN3 CLPX RAP1GDS1 RBM38

SMCO2 CAGE1 ODF2 WDR37 OPTN

MFF PAM TTC7A SKAP2 AGTPBP1

RAB11FIP2 SEC31A CAPN2 MRPL51 CSNK2A2

ATG5 MAP3K7 ZNF276 DNAH6 NISCH

USP6NL RFX3 ENSOARG00000018278 PTPRA TRIM37

C9orf43 PFDN1 THUMPD3 TAF2 ZSWIM7

C19orf45 C20orf144 HELZ AP3B1 YPEL2

C11orf58 EXOC7 LSM12 ZNF106 FAM114A2

SPATA6L ASB8 ENSOARG00000021161 GPR116 INTS8

NA PLK4 GDE1 GLT1D1 GTF3A

PSPC1 LMBR1L LRRFIP2 RPL22 ENSOARG00000001195

RALY PDCD4 RBM44 PIK3C3 FER

RBM26 DDX4 CDS1 STAG3 ENSOARG00000006886

DNA2 SETD3 HNRNPLL NFE2L1 ENSOARG00000006777

GKAP1 PSMF1 KDSR SOCS7 ENSOARG00000000941

GOSR1 SPATA16 NUP88 SETD4 ENSOARG00000020362

BMS1 YWHAB TRRAP ARHGEF39 C11orf65

TMEM254 PRRC2C DCTN1 PSMB10 SNX25

AK8 CAB39 TTLL6 NBEA IWS1

PPP2R5A MORC3 PDILT RIC8B CDC27

MTFR1L EXOC1 RAB5A HIPK1 MROH5

CLASP1 C2CD5 UQCC1 UBAP2L ADRM1

TEX9 CERS3 GTF2A1 RAB11B HSDL2

SMARCA2 LANCL2 RAB3GAP1 DUS2 HSPA14

KATNAL2 TP53INP1 DR1 ENPP2 RAD18

ATP11B CLPB BBS4 PEX11B RNF145

GPBP1 HERC2 TBC1D15 SPEM1 ZNF451

EIF4B MRPS18A PHF14 TBC1D5 UBOX5

CFAP45 LRP8 RANBP17 SNAPC3 PRMT1

Appendices

279

RGL1 EFR3B TEX26 PGRMC2 TOPBP1

CFAP44 PPIP5K2 SNX1 SCAMP1 LRRC36

ANGEL1 PAN3 ZNF207 DMTF1 EHBP1

PPIL6 FAM49B AKAP13 SLC29A1 PGM3

ADAM32 BTBD16 MGAT4A ADAM2 PAPD5

CFAP53 MMAA UNC50 ENSOARG00000008041 SNRNP200

DDX5 BCL2L2-PABPN1 ARID2 OXSR1 STK36

TNPO3 MYO9A PSMD4 COPA ENSOARG00000014682

LRRC63 MAPK9 BBX MAPRE3 SOS1

LY6G6C SMC6 TEKT5 DNAJB4 DUSP16

PRPF40B EAPP NAMPT TAF9 CLEC16A

ABHD16A SMG7 OTUB1 SOBP RFC1

MBD2 TOM1L1 NIT2 C20orf85 HOMEZ

DDHD1 HPS5 RALGAPB U2SURP TIMM22

FIP1L1 PRDM5 FBN2 CTNNA1 ENSOARG00000019579

EML4 TM9SF3 ATP2C1 KALRN ENSOARG00000014353

C12orf50 BRAF LRRC1 SCAMP3 ENSOARG00000008531

ST7L CRABP1 KLHL12 TBC1D1 ENSOARG00000005386

MED13 PPP6R3 FNBP4 BRE ENSOARG00000016858

ATL3

Appendices

280

Appendix Table 7.6. The number of differential alternative splicing events per chromosome length.

Chromosome chromosome length AS-number ratio

chr11 62248096 66 10.60

chr7 100079507 73 7.29

chr13 83079144 51 6.14

chr22 50832532 31 6.10

chr19 60464314 35 5.79

chr20 51176841 29 5.67

chr24 42034648 23 5.47

chr3 224283230 118 5.26

chr5 107901688 54 5.00

chr18 68604602 33 4.81

chr14 62722625 28 4.46

chr21 50073674 21 4.19

chr23 62330649 24 3.85

chr12 79100223 30 3.79

chr1 275612895 101 3.66

chr17 72286588 26 3.60

chr10 86447213 31 3.59

chr8 90695168 31 3.42

chr26 44077779 14 3.18

chr2 248993846 76 3.05

chr9 94726778 28 2.96

chr4 119255633 34 2.85

chr16 71719816 19 2.65

chr15 80923592 20 2.47

chr6 117031472 27 2.31

chr25 45367442 9 1.98

chrX 135437088 8 0.59

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Cellular and molecular aspects of the response of the