Estrogen's Impact on the Specialized Transcriptome, Brain, and Vocal

Estrogen’s Impact on the Specialized Transcriptome, Brain, and Vocal Learning Behavior of a Sexually Dimorphic Songbird

Ha Na Choe

Department of Molecular Genetics & Microbiology Duke University

Date:______Approved:

______Erich D. Jarvis, Supervisor

______Hiroaki Matsunami

______Debra Silver

______Dong Yan

______Gregory Crawford

Dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Molecular Genetics & Microbiology in the Graduate School of Duke University

2020

ABSTRACT

Estrogen’s Impact on the Specialized Transcriptome, Brain, and Vocal Learning Behavior of a Sexually Dimorphic Songbird

Ha Na Choe

Department of Molecular Genetics & Microbiology Duke University

Date:______Approved:

______Erich D. Jarvis, Supervisor

______Hiroaki Matsunami

______Debra Silver

______Dong Yan

______Gregory Crawford

2020

Abstract

The song system of the zebra finch (Taeniopygia guttata) is highly sexually dimorphic, where only males develop the neural structures necessary to learn and produce learned vocalizations in adulthood. During early development, both males and females begin to develop their song system in a monomorphic manner, which diverges shortly after the onset of a critical sensory learning phase and results in reduced cell survival and proliferation in females, and accelerated cell proliferation in males. Estrogen has long been known to be involved in coordinating sexual development of the perinatal brain and nestling female zebra finches treated with estrogen do not exhibit this female- specific atrophy of the song system. How estrogen influences the development of the song system, and what it is doing at the molecular level has not been examined utilizing current generation sequencing technology.

In this dissertation, I tested whether estrogen manipulation impacts the transcriptomic profiles of telencephalic song learning nuclei in males and females. I treated animals with either vehicle, exemestane (an estrogen synthesis inhibitor), or 17-

β-estradiol from the moment of hatching until time of sacrifice. I collected the song learning nuclei and their surrounding brain regions during the onset of sensory motor learning for transcriptomic analysis or during adulthood after collecting behavior. I found that of the 4 telencephalic song nuclei examined during the onset of the sensorimotor learning period at post hatch day 30, Area X was the most sexually dimorphic and the most impacted by estrogen administration. HVC was less sexually dimorphic and less impacted by estrogen manipulation. RA and LMAN had limited sexually dimorphic features, with little impact on their transcriptomes with estrogen manipulation.

Additionally, I found that chronic estrogen depletion in males delayed male specific plumage development and resulted in impaired song learning. This supports the notion

that while estrogen is sufficient in preventing atrophy of the song system in female zebra

finches, it is not necessary for the gross development in males and may instead refine

normal song development.

Contents

Abstract ...... iv

Contents ...... vi

List of Tables ...... xi

List of Figures ...... xii

List of Appendix Figures ...... xv

Acknowledgements ...... xvi

Chapter 1: Introduction ...... 1

1.1 Vocal Communication ...... 1

1.1.1 Type of Vocalizations ...... 1

1.1.1.1 Innate Vocalizations ...... 2

1.1.1.2 Learned Vocalizations ...... 2

1.1.2 Types of Auditory-Vocal Learning ...... 3

1.1.2.1 Auditory/Comprehension Learning ...... 3

1.1.2.2 Vocal Usage Learning ...... 4

1.1.2.3 Vocal Production Learning ...... 4

1.2 Animal Models of Vocal Learning ...... 7

1.2.1 Mammalian Models of Vocal Learning ...... 7

1.2.2 Avian Models of Vocal Learning ...... 8

1.3 Zebra Finch Model ...... 10

1.3.1 Sexual Dimorphism in Finches ...... 14

1.4 Sexual Dimorphism & Estrogen ...... 15

1.4.1 Sex Determination ...... 15

1.4.2 Sex Hormones and Sexual Differentiation ...... 18

1.4.2.1 Rodent Brain Sexual Differentiation...... 19

1.4.2.2 Poultry Brain Sexual Differentiation ...... 21

1.4.2.3 Zebra Finch Brain Sexual Differentiation ...... 22

Chapter 2: Pharmacological Manipulation of Estrogen in vivo ...... 25

2.1 Introduction ...... 25

2.2 Methodology ...... 28

2.2.1 Homology Modelling of Exemestane-Aromatase Interaction ...... 28

2.2.2 Drug/Hormone Administration Methods and Timeline ...... 29

2.2.3 Steroid Panel Assay with uHPLC-MS/MS ...... 32

2.2.4 Serum Estradiol Assay with EIA ...... 33

2.2.5 Statistics ...... 33

2.3 Results ...... 34

2.3.1 Human and Zebra Finch Aromatase Protein Structure are Similar ...... 34

2.3.2 Exemestane is a Potent Inhibitor of Estrogen Synthesis in Zebra Finch Blood and Brain ...... 36

2.3.3 Estrogen Suppression Alters Male Plumage Development...... 40

2.3.4 Elevated Estrogen Levels Have Toxic Effects on Bone Strength ...... 42

2.3.5 Hormone Manipulation had No Effects on Nestling Growth ...... 43

2.4 Discussion ...... 44

2.4.1 Reconciliation with Prior Studies Using Fadrozole ...... 45

2.4.2 Caveats of Pharmacological Studies and the Need of Transgenics ...... 46

2.4.3 Unexpected Systemic Changes – Feather Plumage ...... 46

Chapter 3: Estrogen Manipulation Alters Singing Behavior in Males and Females Differentially...... 48

3.1 Introduction ...... 48

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3.2 Methodology ...... 48

3.2.1 Behavior Collection and Analysis ...... 48

3.2.2 Statistics ...... 50

3.3 Results ...... 50

3.3.1 Estrogen Modification Alters Singing Behavior in Males and Females ...... 50

3.3.1.1 Exemestane Treated Males Produced Impoverished Songs and Calls. ..50

3.3.1.2 Estrogen Treated Females Produced Male-Like Songs and Calls ...... 52

3.4 Discussion ...... 55

3.4.1 Estrogen Modulation on Vocal Behavior ...... 55

Chapter 4. Estrogen Manipulation Alters the Neural Architecture and Transcriptome of the Song System in a Sex Dependent Manner ...... 58

4.1 Introduction ...... 58

4.2 Methodology ...... 59

4.2.1 Cresyl Ciolet Histology ...... 59

4.2.2 Chromogenic in-situ Hybridization ...... 60

4.2.3 Imaging and Area Calculations ...... 62

4.2.4 RNA Isolation & Sequencing ...... 63

4.2.5 Statistical Analysis ...... 68

4.3 Results ...... 69

4.3.1 Hormone Manipulation Altered Neural Architecture in Estrogen Treated Females but not Exemestane Treated Males ...... 69

4.3.2 Hormone Manipulation Altered the Specialization of DEGs in the Song System...... 78

4.3.2.1 Estrogen Modulation Impacts the Expression Levels of DEGs in a Sex and Region-Specific Manner ...... 81

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4.3.2.2 Estrogen Modulation Alters the Number of DEGs in a Sex and Region- Specific Manner ...... 84

4.3.2.2.1 Paired Analysis ...... 84

4.3.2.2.2 Unpaired Analysis ...... 87

4.3.2.3 Within Song Capable Animals, a Core Genes May Define Song Nucleus Identity or Development and a Small Subset May Refine Function ...... 89

4.3.2.3.1 Paired Analysis ...... 89

4.3.2.3.2 Unpaired Analysis ...... 91

4.3.2.4 Each Song Learning Nucleus has Specific Functional Molecular Specializations ...... 93

4.3.2.4.1 Paired Analysis ...... 93

4.3.2.4.2 Unpaired Analysis ...... 108

4.3.3 In situ Hybridization Data Corroborate with Results from RNA-seq ...... 121

4.4 Discussion ...... 122

4.4.1 Neuroarchitecture Interpretations ...... 122

4.4.2 Transcriptome Interpretations ...... 124

4.4.2.1 Area X ...... 124

4.4.2.2 HVC ...... 125

4.4.2.3 RA ...... 126

4.4.2.4 LMAN ...... 128

Chapter 5. Conclusions ...... 129

5.1 Some Speculative Conclusions and Future Directions ...... 133

Appendix A – Animal Husbandry ...... 136

Appendix B – Duke Metabolomics Core Protocol for uHPLC-MS/MS for SteroIDQ Panel ...... 138

Appendix C – Full Statistics for E2 Quantification ...... 145

Appendix D – Scripts for Analysis in R...... 153

Appendix E – Software Versions & R packages ...... 163

Appendix F – Genelists from Venn plots (Area X) ...... 164

Appendix G – Genelists from Venn plots (HVC) ...... 169

Appendix H – Genelists from Venn plots (RA) ...... 178

Appendix I – Genelists from Venn plots (LMAN) ...... 187

Appendix J – p-Value Density Plots from Pairwise Analysis ...... 199

Appendix K – Adjusted p-Value (FDR) Density Plots from Pairwise Analysis...... 201

Appendix L – p-Value Density Plots for Non-Pairwise Analysis ...... 203

Appendix M – Adjusted p-Value (FDR) Density Plots for Non-Pairwise Analysis ...... 205

References ...... 207

Biography ...... 222

List of Tables

Table 1: Summary of previous estrogen inhibition studies...... 23

Table 2: Cross validation of top DEGs to online zebra finch mRNA atlas...... 122

List of Figures

Figure 1: Generic timeline for song learning in a closed ended songbird...... 5

Figure 2: Timeline for speech learning in open-ended vocal learning humans...... 6

Figure 3: Convergent connectivity between human and songbird...... 9

Figure 4: Adult zebra finch plumage...... 11

Figure 5: Schematic of zebra finch song system...... 12

Figure 6: Field homology hypothesis of avian and mammalian brain organization...... 13

Figure 7: Timeline of sensory and sensorimotor learning phase in zebra finches...... 14

Figure 8: Gynandromorphic cardinal...... 17

Figure 9: Sex hormones on brain organization and behavior in rodents...... 20

Figure 10: Sex hormones and behavior in quail...... 22

Figure 11: Sex steroid synthesis pathway...... 27

Figure 12: Treatment timeline for pharmacological administration...... 31

Figure 13: Primary sequence alignment between human and zebra finch ARO...... 35

Figure 14: 3-D Homology based model of human and zebra finch ARO with exemestane and HEME cofactor in ligand binding site...... 36

Figure 15: E2 levels in adult animals treated with vehicle or exemestane...... 37

Figure 16: Corticosteroid levels in adult animals treated with vehicle or exemestane. ...38

Figure 17: Sex steroid levels in adult animals treated with vehicle or exemsetane...... 38

Figure 18: E2 levels in juveniles treated with vehicle, exemestane or E2...... 40

Figure 19: Feather plumage changes in exemestane treated males...... 41

Figure 20: Weight gain in estrogen modulated chicks...... 44

Figure 21: Vocalizations from control female and animals treated with exemestane...... 51

Figure 22: Vocalizations from control male and animals treated with E2...... 53

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Figure 23: Syllable clusters of recorded vocalizations...... 54

Figure 24: Comparisons of syllable cluster numbers...... 55

Figure 25: Developmental expression of SLIT1 and ROBO1 in RA...... 59

Figure 26: Samples of laser capture microdissected sections with adjacent cresyl violet stained reference sections...... 65

Figure 27: Area X containing sections from the online zebra finch atlas...... 66

Figure 28: Cresyl violet stained sagittal sections of PHD30 animals...... 70

Figure 29: RA to arcopallium area ratios in PHD30 animals...... 71

Figure 30: HVC to telencephalon area ratios in PHD30 animals...... 72

Figure 31: LMAN to telencephalon area ratios in PHD30 animals...... 73

Figure 32: CADPS2 mRNA expression in sagittal sections of PHD30 animals...... 74

Figure 33: S100B mRNA expression in sagittal sections in males...... 74

Figure 34: Area X to striatum area ratios in PHD30 animals...... 75

Figure 35: Mesopallium to telencephalon area ratios in PHD30 animals...... 76

Figure 36: Striatum to telencephalon area ratios in PHD30 animals...... 77

Figure 37: Arcopallium to telencephalon area ratios in PHD30 animals...... 78

Figure 38: PCA of all genes in all RNA-seq samples of PHD30 animals...... 79

Figure 39: PCA of Area X and MSt RNA-seq samples of PHD30 animals...... 80

Figure 40: Sample to sample distance matrix of all RNA-seq samples of PHD30 animals plotted according to Euclidian distance...... 81

Figure 41: Heatmap of all song nuclei gene expression specializations for all treatment groups...... 83

Figure 42: Venn diagram of pairwise DEGs in each song nuclei between treatment groups stratified by sex...... 86

Figure 43: Volcano plot of DEGs in Area X from pairwise comparisons in PHD30 animals after estrogen modulation...... 86

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Figure 44: Venn diagram of non-pairwise DEGs in each song nuclei between treatment groups stratified by sex...... 88

Figure 45: Volcano plot of DEGs in Area X from non-pairwise comparisons in PHD30 animals after estrogen modulation...... 89

Figure 46: Number of pairwise DEGs in song capable animals...... 91

Figure 47: Number of non-pairwise DEGs in song capable animals...... 92

Figure 48: Top 10 GO terms in Area X for each ontogeny level with pairwise DEGs. ....95

Figure 49: Top 10 GO terms in HVC for each ontogeny level with pairwise DEGs...... 99

Figure 50: Top 10 GO terms in RA for each ontogeny level with pairwise DEGs...... 103

Figure 51: Top 10 GO terms in LMAN for each ontogeny level with pairwise DEGs. ... 107

Figure 52: Top 10 GO terms in Area X for each ontogeny level with non-pairwise DEGs...... 110

Figure 53: Top 10 GO terms in HVC for each ontogeny level with non-pairwise DEGs...... 114

Figure 54: Top 10 GO terms in RA for each ontogeny level with non-pairwise DEGs. . 117

Figure 55: Top 10 GO terms in LMAN for each ontogeny level with Non-pairwise DEGs...... 120

Figure 56: Phylogenetic tree of oscine songbirds and presence or absence of female song...... 132

Figure 57: Possible model of estrogenic rescue of song loss...... 133

xiv

List of Appendix Figures

Appendix Figure 1: Distribution of p-values from pairwise DEG analysis...... 200

Appendix Figure 2: Distribution of FDR adjusted p-values from pairwise DEG analysis ...... 202

Appendix Figure 3: Distribution of p-values from non-pairwise DEG analysis ...... 204

Appendix Figure 4: Distribution of FDR adjusted p-values from non-pairwise DEG analysis...... 206

Acknowledgements

First and foremost, I would like to thank my advisors: Dr. Erich David Jarvis and

Dr. Hiroaki Matsunami. I had a rocky start to my graduate school experience, but I am grateful to Dr. Jarvis for taking a risk and permitting me to not only join his lab, but to design and pursue my own independent project despite the enormity of what I sought to do. I am thankful to Dr. Jarvis for supporting me while I continued my project at Duke

University after his move to Rockefeller University. Even though my reluctance to relocate made the logistics of completing my project much more difficult than it needed to be, it was thanks to his steadfast support and constant guidance that made the completion of my dissertation possible. And this leads me to my second advisor, though not second in my heart. I am deeply grateful to Dr. Matsunami for co-mentoring me, for being a constant presence in my academic life, providing me with a place to do my science, and giving great intellectual guidance and emotional support. I have had so much fun being in the Matsunami lab, and I will treasure my time here. From decade mark birthdays to total solar eclipse viewing road trips, we made many fond memories that I will carry with me forever.

I would also like to thank my committee members Dr. Debby Silver, Dr. Dong

Yan, and Dr. Gregory Crawford, for their continued enthusiasm over the years. I never dreaded my committee meetings, because every meeting with them reignited my love for science and reminded me why I wanted to pursue my project in the first place. Thank you all for cheering me on.

I also acknowledge the office of Diversity and Inclusion, and more specifically the

BioCoRE program. It was through them that I was able to find community and a sense of

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belonging after starting my life over again in North Carolina. It took a long time, but they finally helped me overcome my feelings of inadequacy in academia and helped me come to the realization that success has many forms and that there is no single path to success. Thank you to Dr. Sherilynn Black, Dr. Danny Lew, Dr. Paige Cooper, and all of the fantastic directors and members of the past. I cannot emphasize how important

BioCoRE was to me during my early years of grad school.

And where would I be without my family? To my mother, all of our struggles were worth it mom, your kid is a doctor now. To my found father, Brian: Sometimes, found family is more real than blood family. No matter what, you are always going to be my dad. Thank you for always being there for me.

And of course, to my wife: You have been there through the tears, through all of my many failures, all of the heartache as well as all of my rare successes. You patiently sat through all of my ramblings, my planning, and my many, many practice talks. We married, bought a home together, and built a life together. I love you. Thank you for sticking with me these crazy 7 years.

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Chapter 1: Introduction 1.1 Vocal Communication

Animals communicate in a myriad of different modalities, including through auditory, visual, or olfactory signals. In humans, a key communicative behavior is spoken language. While we have yet to find another animal capable of rivaling human language (Jarvis, 2019), many animals utilize vocal communication to convey complex information to other conspecifics. These vocalizations are used to establish territories, broadcast reproductive state to potential mates, establish status within social hierarchies, and inform others about predators or other environmental hazards. Despite the broad usage of vocal communication in animals, the types of vocalizations and their usage varies dramatically depending on the cognitive capacity of the species (Petkov and Jarvis, 2012).

1.1.1 Type of Vocalizations

The types of vocalizations an animal produces are heavily dependent on both the

physiology of the animal and the environment it lives in. For example, elephants are

capable of producing infrasonic sounds that are inaudible to the human ear but can be

heard by other elephants that are over 2 kilometers away, depending on the terrain

(McComb et al., 2003). Toothed whales can produce a very broad range of

vocalizations, including ultrasonic frequencies that pass more rapidly in water than air,

making them useful for echolocation (Evans, 1973). Mice are incapable of making low

frequency sounds like the elephant, but can produce high pitched squeaks audible to the

human ear and ultrasonic vocalizations that are inaudible to the human ear (Finton et al.,

2017). There are also various types of vocal organs that can be used to produce

vocalizations, including the larynx in terrestrial mammals, the syrinx in birds, and the as

yet unconfirmed nasal/sinus cavity of aquatic toothed whales (Suthers, 2010).

Regardless of how the vocalizations are produced and what their acoustic features are,

animal vocalizations can broadly be organized into two categories: innate vocalizations

and learned vocalizations.

1.1.1.1 Innate Vocalizations

All animals capable of vocalization can produce innate vocalizations, most often

as “innate calls”, but sometimes as “innate songs”. Innate vocalizations do not need to

be learned from another conspecific and are highly stereotyped within a species (Petkov and Jarvis, 2012). Innate calls can be elicited by the emotional or internal state of the animal, which in certain species can be used in specific learned contexts (Petkov and

Jarvis, 2012). Innate vocalizations do not require input from the forebrain in order to be

produced. Mice lacking a large portion of their cortex are still able to produce highly

stereotyped ultrasonic vocalizations that are acoustically similar to those produced from

wild-type animals (Hammerschmidt et al., 2015). Examples of human innate calls include

screams, cries, and laughter. These are all usually produced in response to intense

emotional states but can also be created in a learned context, such as feigned laughter

in response to a bad joke.

1.1.1.2 Learned Vocalizations

Very few animals can produce learned vocalizations. They include humans,

elephants, cetaceans (whales and dolphins), bats, pinnipeds (seals/sea lions),

songbirds, hummingbirds, and parrots (Petkov and Jarvis, 2012). Most of these species

produced learned song, learned calls, and in humans, speech. Learned songs are

similar to other forms of trained motor behavior in that they require active feedback to 2

maintain motor/acoustic fidelity and are susceptible to decay without reinforcement

(Doupe and Kuhl, 1999). Learned vocalizations are not restricted to sounds produced by conspecifics and are open to the development of regional dialects (Marler and Tamura,

1962; Riebel, 2009). For example, parrots are known to readily mimic human speech and baby zebra finches raised with another species will learn the songs and calls of their

foster parents. As for dialects, several species of sparrows have acoustically distinct

learned calls based on their isolated geographic location, and humans have over 7000

different languages (Eberhard et al., 2019).

1.1.2 Types of Auditory-Vocal Learning

In addition to the different types of vocalizations that can be produced, there are three broad categories of auditory-vocal learning interactions: auditory learning, vocal usage learning, and vocal production learning (Petkov and Jarvis, 2012).

1.1.2.1 Auditory/Comprehension Learning

All animals that are capable of perceiving audible sounds are capable of auditory

or comprehension learning. Auditory learning is the ability to identify and learn new

information about a sound stimulus (Petkov and Jarvis, 2012). For example, a dog can

learn the command “sit” and respond with the act of sitting, or a cat can learn to

associate the sound of a can opening with mealtime. This type of learning is widespread

among vertebrates and is often used to convey new information between conspecifics,

like calls made by a sexually receptive adult, calls made by distressed offspring, or

adults establishing the boundary of their territory. All of these calls transmit specific

information between the transmitter and the intended recipients that are not learned and

are instead known “innately” (Breed and Moore, 2010).

1.1.2.2 Vocal Usage Learning

Most animals that vocalize have been found to have vocal usage learning, where

innate or learned vocalizations can be learned to be produce in specific contexts (Petkov

and Jarvis, 2012). For example, a dog can be taught to produce innate barks for a ball or

food. A good case in natural settings is seen with vervet monkeys, who have an

impressive array of innate calls that they learn to use in different contexts to convey

detailed information (Seyfarth et al., 1980). These monkeys may learn to use a particular

call to inform the rest of the social group that there is a snake or a different call to warn

the troupe of a bird of prey. This allows the other members of the group to be wary of

predators on the ground or in the sky, respectively. Younger, inexperienced monkeys

are generally ignored when they make these same calls, as they have not learned the

correct time to produce such calls. Therefore, these innate calls are received differently

depending on the context in which they are made and by whom.

1.1.2.3 Vocal Production Learning

Vocal Production Learning is a type of rare learning, often simply referred to as

Vocal Learning, introduced above in the five mammalian and three avian lineages. Vocal

learning requires that an animal is able to memorize, through auditory learning, sounds

heard and then imitate or modify its imitations through auditory/somatosensory feedback

(Doupe and Kuhl, 1999). Vocal learners can be further classified into “closed-ended

learners” and “open-ended learners.” Closed-ended vocal learners, like the zebra finch,

(Doupe and Kuhl, 1999) have a single plastic learning period that ends around the onset

of sexual maturity. After this learning period closes, the learned vocalization “crystallizes”

and becomes resistant to further changes (Figure 1) (Zevin et al., 2004). Although minor

acoustic modifications, like pitch and amplitude, can be made to this crystalized song

through operant conditioning, the gross acoustic features are fixed. For example, when a

Bengalese finch is interrupted with a blast of white noise when it produces a song that hits a target pitch, over time the animal will shift the pitch of its song to avoid activating the white noise punishment. However, other features of the song, like syllable organization, syllable duration, frequency modulation, and others, stay the same (Tumer and Brainard, 2007).

Figure 1: Generic timeline for song learning in a closed ended songbird. Songbirds have a sensory learning period during early post-hatch life where the young learn their species-specific songs and practice them after the onset of the sensorimotor learning period until sexual maturity or the first breeding season, when this song becomes crystallized. From Doupe and Kuhl (1999).

Open-ended learners will typically undergo rapid learning during a juvenile/adolescent period that ends near the onset of sexual maturity, similar to closed- ended learners (Figure 2). But open-ended learners retain some plasticity in adulthood and can continue adding to their vocal repertoire throughout their lives. Therefore, open- ended learners never truly crystalize their vocal behavior. Humans, parrots (Bradbury and Balsby, 2016), and a few other advanced songbird imitators are open-ended learners (Beecher and Brenowitz, 2005).

Figure 2: Timeline for speech learning in open-ended vocal learning humans. Similar to songbirds, humans have an auditory speech perception period during early post-natal life where the infant can percieve many speech sounds, but not produce them. Thereafter, the infant has a speech production learning period. Unlike closed-ended learners, humans can continue learning new languages well past sexual maturity, albeit less rapidly. From Doupe and Kuhl (1999).

1.2 Animal Models of Vocal Learning

Human spoken language is unparalleled among animals in its complexity and in the breadth of information that can be conveyed. However, this has made it difficult to dissect both the physiological and molecular mechanisms involved in its development and function. To study spoken language production and learning in humans, we have had to rely on the behavioral aftermath from accidental lesions, surgical resections for other morbidities, and strokes (Brainard and Doupe, 2013; Jarvis, 2004, 2007). In more

recent times we have been able to use functional imaging (Price, 2012), and focal

cooling and heating during surgery (Long et al., 2016). On the molecular front, we can

only use post-mortem tissues. As language deficits are so heterogeneous, genetic

studies in humans have been difficult to perform and have relied on rare family case

studies for specific mutations, the most famous being FOXP2 and associated candidates

(Lai et al., 2001; Spiteri et al., 2007), and comparative evolution studies (Mozzi et al.,

2016; Vernes et al., 2011). Therefore, in order to study the molecular underpinnings of

spoken language we have had to study one of its component traits, vocal learning, in the

few rare species with this trait.

1.2.1 Mammalian Models of Vocal Learning

As mentioned in section 1.1.1.2 Learned Vocalizations, only 5 clades of

mammals (elephants, ceteceans, pinnipeds, bats, and humans (but not non-human

primates)) have reliably demonstrated the capacity for vocal production learning (Petkov and Jarvis, 2012). Many of these animals are very large and/or critically endangered, and thus are poorly situated to serve as laboratory models. Mice have limited vocal plasticity and forebrain control of vocalizations as seen in vocal learners (Arriaga et al.,

2012), but this is very rudimentary (Arriaga and Jarvis, 2013) and according to some 7

completely absent (Hammerschmidt et al., 2015). No vocal learning brain circuits have been found yet in bats.

1.2.2 Avian Models of Vocal Learning

Of these three vocal learning bird groups, parrots and songbirds are well adapted

to life in captivity and the bulk of our knowledge on vocal learning have been built on the

zebra finch, a songbird. Thankfully, despite over 300 million years of evolution

separating mammals from avians (Kumar and Hedges, 1998), patterns of convergent

gene expression and brain circuit specializations between vocal learning birds and

humans (Jarvis, 2019; Pfenning et al., 2014) make the songbird a suitable model for

studying vocal learning in vertebrates (Figure 3).

Figure 3: Convergent connectivity between human and songbird. A circuit diagram of the songbird brain, with proposed convergent human brain regions and cell types of the spoken-language brain pathway. Red, anterior pathway necesasry for learning how to produce novel sounds. Orange, posterior pathway necessary to producing learned vocalizations. LMC, Laryngeal motor cortex; HVC, high vocal center; Av, Avalanche; Nif, Nucleus Interface of the Nidopallium; RA, Robust nucleus of the Arcopallium; XIIts, 12th motor nucleus, tracheosyringeal part; RAm, Retroambiguus; RVL, rostral nucleus of the ventral-lateral medulla; MO, Oval nucleus of the Mesopallium; MAN, Magnocellular nucleus of the Anterior Nidopallium; aDLM, anterior dorsolateral medial nucleus of the thalamus. From Jarvis (2019).

1.3 Zebra Finch Model

Zebra finches (Taeniopygia guttata) are small gregarious songbirds from the

Australian outback that are opportunistic breeders, making them well-suited to high density colonies in captivity, similar to mice (Olson et al., 2014). Their breeding status is not controlled by the length of daylight but is instead based on the availability of water, which is a product of the seasonal rains in Australia (Zann et al., 1995). In captivity, so long as they are provided with adequate nutrition and water, they can breed indefinitely.

They also have short generation times, reaching sexual maturity within 3 months as opposed to about 1 year for many other species. This has made the zebra finch the most-used avian model organism. As such, the zebra finch has the best characterized neuroanatomical atlas, complete with publicly available mRNA in-situ hybridization data, and one of the few bird species to have a platinum grade genome (Korlach et al., 2017;

Warren et al., 2010b). Zebra finch development and behavior has also been extensively characterized, with archives of vocal recordings being available in many repositories

(Mello, 2014).

Although zebra finches only live for 5 years in the lab (on average), zebra finches can produce clutches of 3-7 chicks every 60 days, with each clutch weaning at around

45 days. These animals are easily sexed due to their sexually dimorphic plumage: males have bright orange cheek patches, throat striations, and spotted chestnut flank feathers with a grey head and body. Females are a near uniform grey, with lighter grey cheeks

(Figure 4). In addition to plumage differences, their vocal behavior is also sexually dimorphic since only the males are capable of vocal learning. Males learn courtship songs from their father and/or other males, introduce their own variations, and use these songs to attract mates in adulthood (Zann, 1996). 10

Figure 4: Adult zebra finch plumage. Male on the left and female on the right.

The motor component of the zebra finch song system, and songbirds in general,

is comprised of 7 interconnected discrete nuclei; the HVC (high vocal center), RA (robust

nucleus of the arcopallium), LMAN (lateral magnocellular nucleus of the anterior

nidopallium), Area X (proper name), DLM (medial portion of the dorsolateral nucleus of

the thalamus), DM (dorsomedial nucleus of the intercollicular complex), and nXIIts

(tracho-syringeal portion of the 12th motor nucleus) (Figure 3, Figure 5) (Jarvis, 2009).

HVC, RA, and LMAN are in the pallial portion of the avian brain, which is comparable to the mammalian cortex (Jarvis et al., 2013). Area X resides within the striatum, DLM in the thalamus, DM in the midbrain, and nXIIts in the brainstem.

Figure 5: Schematic of zebra finch song system. Brain organization sex differences and experimental paradigm. (A) Adult male zebra finch brain. (B) Adult female zebra finch brain. There are 7 telencephalic song nuclei: HVC, RA, LMAN, Area X, Av, Nif, and MO. Additionally, there are 3 vocal nuclei in the thalamus (DLM) and brainstem (DM and the XIIts motor nucleus). The male brain (A) has all 7 song nuclei, and the female (B) 4 confirmed song nuclei, but is missing Area X, and Av. HVC and RA are also smaller in females. LMAN is not visually different between males and females. Some connections of male song nuclei are shown; medial MAN projects to HVC. HVC, High Vocal Center; RA, Robust nucleus of the Arcopallium; LMAN, Lateral Magnocellular nucleus of the Anterior Nidopallium; Area X, proper name; Av, Avalanche; Nif, Nucleus Interface of the Nidopallium; MO, Oval nucleus of the Mesopallium; DLM, Dorsal Lateral nucleus of the Medial thalamus; DM, Dorsal Medial nucleus; XIIts, 12th motor nucleus, tracheosyringeal part.

LMAN, Area X, and DLM form the anterior cortical-striatal-thalamic (CST) loop which is involved in the learning of motor sequences. This CST loop connects to HVC and RA, the latter of which projects to the nXIIts motor neuron, which in turn controls the syrinx muscles to produce learned vocalizations (Jarvis, 2007). This discrete nuclear organization of the avian brain has made it a highly amenable system to probe the

neural pathways involved in learning and memory. This is in contrast to the layered

cortex of mammals (Jarvis, 2007) (Figure 6).

Figure 6: Field homology hypothesis of avian and mammalian brain organization. (A) Sagital view of a songbird brain diagram, as an avian brain representative. (B) Sagittal view of a rat brain diagram, as a mammalian brain representative. Color code, proposed cell population homologies between the pallial and subpallial subdivisions of the avian and mammalian brains. From Jarvis et al. (2013).

Zebra finches begin forming an auditory template memory of their father’s songs shortly after hatching, during a time period known as the “sensory learning period” which spans from approximately post hatch day (PHD) 5 until 60. Male finch chicks begin practicing their vocalizations from PHD30 until adulthood, during the “sensorimotor learning period”. There are approximately 30 days where the sensory and sensorimotor learning periods overlap, between PHD30 and 60. During this time, new songs can be learned by fostering the male chick with a new father/tutor (Kojima and Doupe, 2007b)

(Figure 7). The learning period for this species ends near the onset of sexual maturity around PHD90, since they are a closed-ended learner as mentioned earlier. At this time, the male will produce the same song for the rest of his adult life (Zevin et al., 2004).

Figure 7: Timeline of sensory and sensorimotor learning phase in zebra finches. The sensory phase begins shortly after hatching around PHD2-5 and ends halfway through the sensorimotor phase around PHD60. The sensorimotor phase begins around fledging at PHD30 and ends during sexual maturation around PHD90. From Kojima and Doupe (2007a).

1.3.1 Sexual Dimorphism in Finches

In contrast to males (Figure 5A), the song system of adult female zebra finches is highly atrophied (Figure 5B) (Nottebohm and Arnold, 1976), and this atrophy occurs late during the sensory learning period (Bottjer et al., 1985; Konishi and Akutagawa, 1985;

Mooney and Rao, 1994). Before then, the song systems of female and male zebra

finches appear to develop in an identical manner, including in all of the pallial regions

and Area X (Garcia-Calero and Scharff, 2013). Beginning PHD15, the inter-nuclear

connections within the song system begin to form. In males, this connection is

established and is subsequently followed by cellular proliferation and expansion of the

connected song nuclei. In females, although these inter-nuclear connections begin, they

never complete, and as a result, the female song nuclei undergo shrinkage. Upon

maturity, the female brain lacks a functional song system, and some song nuclei like

Area X are absent altogether (Nottebohm and Arnold, 1976).

Despite lacking a functional song system, females do have highly refined

auditory perception of male song and also form memories of conspecific songs during

early development. They use this memory to help them select suitors in adulthood

(Riebel, 2009; Riebel et al., 2002). This separation of behaviors likely enhances the

mate selection process to prevent inbreeding in the large flocks formed by the

gregarious zebra finch. Females were found to prefer songs that were within a

“goldilocks” zone in similarity to their own father’s song, preferring the “boy-next-door”

who had similar songs, but not so similar as to hint at paternal relations (Riebel, 2009;

Riebel et al., 2002).

1.4 Sexual Dimorphism & Estrogen

1.4.1 Sex Determination

Vertebrates display a wide diversity of reproductive strategies, with the bulk of species employing a two-sex reproductive system. How these two sexes are determined can vary and range from relying on external cues like temperature (Valenzuela and

Lance, 2004), social cues like hormones (Todd et al., 2016), to internal cues like the

effects of genetics.

Birds and mammals both use the genetically encoded chromosomal sex

determination system. Mammals utilize the XX/XY-female/male system where the sex

determining gene, SRY, is located on the male Y chromosome. The SRY gene is

responsible for the differentiation of the testes from the primordial gonad, and in the

absence of SRY the gonads differentiate into ovaries. To compensate for the second X

chromosome in females, one of the X chromosomes is silenced to ensure that both

males and females have near identical X chromosome gene dosage. Although some

gene products can escape this X chromosome inactivation, sex determination is not

caused by the presence of the entire Y chromosome, or by the presence of two X

chromosomes, but by the presence of the SRY gene, as demonstrated by four core

genotype experiments in which the SRY gene was artificially removed from the Y and

moved to a second X chromosome (Dementyeva and Zakian, 2010; McCarthy and

Arnold, 2011).

Birds utilize the ZZ/ZW-male/female system where the sex determining

mechanism is yet to be resolved. There are 2 distinct possibilities for sex determination

in the avian system:

1) There is a sex determining gene on the heterogametic W found in the

female that drives the development of the ovaries from the primordial

gonad in a similar fashion to the mammalian heterogametic Y. If this is

true, the determinant gene products have not yet been discovered, but

there has been one documented case of a ZZW female bird, which 16

supports the concept of a dominant W sex determining system (Küpper et

al., 2012).

2) Due to the incomplete gene dosage compensation on the ZZ

combination in birds (Dementyeva and Zakian, 2010), excess Z

chromosome gene products may drive development of the testes.

Particularly the DMRT1 gene, present on the Z chromosome, which can

induce male development in turtles after DMRT1 becomes up-regulated

in response to elevated temperature (Ge et al., 2017).

Interestingly, the ZZ/ZW system is capable of producing true gynandromorphs

(Figure 8) where an animal is bisected into male and female halves (Morris et al., 2018), with the opposing sides having weakly differentiated gonads implicating the role of extra- genetic factors, like hormones.

Figure 8: Gynandromorphic cardinal. Gynandromorphic birds are bisected in the middle between male and female halves. Left is genetically female and right is genetically male. From Peer and Motz (2014).

These disparate observations, alongside the lack of genetic tools available in avian models, have made it very difficult to establish the causal mechanism of sex determination in birds.

1.4.2 Sex Hormones and Sexual Differentiation

Sex as a biological factor is intrinsically linked to the actions of steroid hormones.

The effects of sex steroids are wide ranging beyond the reproductive system, influencing the musculoskeletal system (Carson and Manolagas, 2015), the immune system

(Gilliver, 2010), and the nervous system (Schlinger, 1997), among many others.

In the nervous system, the actions of sex hormones can influence the

development of sexually dimorphic neural architecture. It is generally thought that in

males there is a surge of androgens from the newly formed testes, that is rapidly

aromatized into estrogens which then masculinize the brain (McCarthy, 2008; McCarthy

and Arnold, 2011). But these same steroids are also synthesized locally within the brain

(Holloway and Clayton, 2001; McCarthy, 2008). It is also generally accepted that this

brain masculinization is driven by estrogens, not androgens, since when estrogenic

activity is reduced in the brain, the brain undergoes passive feminization. Likewise, when

this estrogenic activity is increased, the brain undergoes active-defeminization and

masculinization (Kurian et al., 2010; McCarthy, 2008; McCarthy and Arnold, 2011; Wu et

al., 2009). This estrogen-controlled masculinization/feminization of the brain has been

best demonstrated in rodents (Wu et al., 2009), poultry (Brunström et al., 2009) and

songbirds (Schlinger, 1997; Simpson and Vicario, 1991b), though interestingly with

opposing effects depending on the model system being examined.

1.4.2.1 Rodent Brain Sexual Differentiation

When neonatal rodent pups are given estrogen for a short period, both male and

female pups display masculinized brain architecture in adulthood with enlarged

hypothalamic brain regions that control innate sexual behavior, and these animals

exhibit male-typical behaviors (Figure 9). These masculinized females are far more

aggressive, resist male reproductive solicitations, attempt mounting and copulating with

other females, and have male-like territorial displays (Baum, 1979; Bayless and Shah,

2016; Wu et al., 2009). These behaviors become stronger when testosterone is administered later, which is not seen when non-masculinized females are given testosterone (Juntti et al., 2010), indicating that testosterone activates circuits that were established through the actions of estrogen during early life.

C D

Figure 9: Sex hormones on brain organization and behavior in rodents. (A) The timeline for when sex hormones exert organizational vs activational effects in rodent brains. Late embryonic/early peri-natal life is when estrogens have an organizational effect on the brain, forming sex-specific circuits which are then activated later in life by either estrogens or androgens to induce specific behaviors. (B) Testosterone is aromatized via a P450 family enzyme (CYP19A1/Aromatase) into estradiol. (C) Luteinizing hormone levels in normal males vs castrated males and normal females vs testosterone treated females. (D) Mounting behavior and lordosis behavior in normal males vs castrated males and normal females vs testosterone treated females. Adapted from McCarthy (2008).

The mechanisms controlling the sexually dimorphic development of the brain are an area of active research, and recent work by the McCarthy lab has revealed a link between the nervous system and the immune system. It is not the activities of estrogen per se that activates male-typical brain development, but the estrogenic activation of microglia (the resident macrophages in the brain) that induces mild inflammation (Lenz

et al., 2012; McCarthy, 2019). This inflammation correlates strongly with an increase in

male-like dendritic spine density, cell proliferation, and cell survival that results in

enlarged brain regions specific to males.

1.4.2.2 Poultry Brain Sexual Differentiation

Contrary to what has been observed in rodents, the effects of estrogen induce

feminization in the brains of poultry (chicken, quails, and turkeys). When male quails are

treated with estrogen in ovo, they develop female-typical characteristics and behaviors.

Likewise, female quails treated with estrogen synthesis inhibitors in ovo develop male-

typical characteristics and behaviors (Figure 10) (Balthazart et al., 1992). However, like

the rodent model, these effects in poultry are limited to early development (Konishi and

Akutagawa, 1988; Pohl‐Apel and Sossinka, 1984) and sex hormones administered later

in life activate hypothalamic circuits that have already been established (Kern and King,

1972).

Figure 10: Sex hormones and behavior in quail. Estradiol treatment suppressed male behavior and aromatase inhibition with R76713 (racemic vorozole) induced male mating behavior in quails. From Balthazart and Ball (1995).

1.4.2.3 Zebra Finch Brain Sexual Differentiation

Despite zebra finches being more closely related to quails (66 million years) than rodents (300 million years) (Jarvis et al., 2014; Kumar and Hedges, 1998), the hormone sensitivity of the sexually dimorphic zebra finch song system at first glance appears to respond to hormones more similarly to the sexually dimorphic mammalian than avian hypothalamic system. When female zebra finches are treated with estrogen during early post-hatch life, they develop fully functional song systems and can sing like normal males (Simpson and Vicario, 1991a; Simpson and Vicario, 1991b). These masculinized females direct these courtship songs at, and sometimes attempt copulation with, 22

potentially receptive females (Gurney, 1982; Pohl‐Apel and Sossinka, 1984), indicating a

masculinization of other behaviors outside the song system. This estrogen-sensitive

critical period lasts between PHD2-45 (Konishi and Akutagawa, 1988). Outside this

sensitive period, estrogen has reduced or no impact on preserving the song system.

However, the hormone manipulated de-masculinization and feminization of male

songbirds has not been reliably demonstrated (Table 1), indicating that additional

variables may be at play.

Table 1: Summary of previous estrogen inhibition studies. Summary of previous estrogen inhibition studies in zebra finches, including drug used, drug class, duration of treatment, and results from treatment on song system anatomy and behavior, if available. PHD, Post Hatch Day; SERM, Selective Estrogen Receptor Modulator; SERD, Selective Estrogen Receptor Degrader; AI, Aromatase Inhibitor; GPER, G-Protein coupled Estrogen Receptor (GPR30).

Since the female song system does not establish connectivity between PHD15

and PHD30, the pallial nuclei undergo shrinkage by apoptosis (Konishi and Akutagawa,

1985) and Area X does not develop altogether (Bottjer et al., 1985; Garcia-Calero and

Scharff, 2013). The Area X projecting axons from HVC neurons still travel to where Area

X should be anatomically (Shaughnessy et al., 2018), but it is not known what cells

these HVC axons synapse to, if they do at all. This stunted phenotype is prevented in

females treated with early estrogen (Konishi and Akutagawa, 1988).

This estrogen-induced masculinization is not complete since the song nuclei of these masculinized females are still much smaller than those of control males (Grisham

and Arnold, 1995; Gurney, 1982). Despite being smaller, they are fully functional

(Gurney, 1982; Pohl‐Apel and Sossinka, 1984; Simpson and Vicario, 1991a), and these

females can produce courtship songs of comparable quality to their brothers. This

incomplete masculinization may either be due to genetic differences, alluding to the

extra expression of Z chromosomal genes in males (Dementyeva and Zakian, 2010; Itoh

et al., 2007), or due to differences in the hormone milieu impacting expression of

autosomal genes, since exogenous estrogen administration does not take into

consideration other hormones, like androgens, that may be involved in normal male

development (Gahr and Metzdorf, 1999; Kim et al., 2004; Schlinger and Arnold, 1991).

The latter hypothesis is attractive as androgen receptors are more abundant in the song

system than estrogen receptors (Clayton, 1997; Dittrich et al., 1999; Jacobs et al., 1999).

Due to the inconsistencies regarding prior hormone manipulations in male zebra

finches (Table 1) and the uncertainty of estrogens’ role in the development of anatomical

and molecular specializations within the song learning system, I embarked on resolving

these issues in my thesis research. In contrast to past studies, I used a more potent

estrogen synthesis inhibitor that I administered chronically throughout post-embryonic

development, and measured potential impact in behavior, anatomy, and molecular

specializations in the song learning system. In Chapter 2, I describe my new hormone

manipulation approach and its systemic impact. In Chapter 3, I describe impacts on song

behavior. In Chapter 4, I describe impacts on song nuclear anatomy and gene

expression specializations. And in Chapter 5, I present my conclusions.

Chapter 2: Pharmacological Manipulation of Estrogen in vivo 2.1 Introduction

Estrogen supplementation is sufficient in masculinizing the song system, but its

necessity has yet to be reliably demonstrated since prior investigations have reported

contradictory effects of estrogen interference in normal male song development (Table

1). Resolving these contradictory findings with transgenic zebra finches (Abe et al.,

2015; Agate et al., 2009; Velho and Lois, 2014), such as with estrogen receptor or

enzymatic knock outs like what has been done in mice (McCarthy, 2008), would be

beyond the scope of what could be done in my thesis, as methods for generating

transgenic songbirds are not well developed. This makes the use of pharmacological

agents, or drugs, the current best way to manipulate estrogenic activity in vivo in

songbirds. However, the various classes of drugs used have their caveats. Some of the

earliest studies attempting to prevent estrogenic activity in the finch have used tamoxifen

(Mathews et al., 1988), which at the time was classified as an “anti-estrogen” due to its

antagonistic activity in breast tissue. Tamoxifen has since been re-classified as a

selective estrogen receptor modulator (SERM) due to that fact that it, and other SERMs,

can behave as either agonists or antagonists depending on the target tissue they are

acting upon (Hall and McDonnell, 2005; Martinkovich et al., 2014). In the case of the

brain, tamoxifen, raloxifene, and nitromifene all act as “super-estrogens” and have been

shown to hypermasculinize the song system of the zebra-finch (Mathews and Arnold,

1990, 1991; Mathews et al., 1988). Estrogen receptor disruptors (SERDs) are different in

that these drugs can directly antagonize the activity of estrogen receptors, but studies

using SERDs have only reported minor changes specific to zebra finch RA and HVC

(Bender and Veney, 2008), with no observations regarding behavior.

There are 3 main classes of estrogen receptors in the zebra finch brain: two nuclear receptors (NRs), which include estrogen receptor α (ESR1) and estrogen

receptor β (ESR2) (Ball et al., 2002; Perlman and Arnold, 2002), and a G-protein

coupled estrogen receptor (GPER/GPR30) (Acharya and Veney, 2012). ESR1 and

ESR2 can homodimerize or heterodimerize to modulate activating and repressing

activities, which can be modulated by SERMs and SERDs to modulate or repress

activity, respectively (Paige et al., 1999). ESR1 is highly expressed only within and near

HVC (Jacobs et al., 1999), but GPER is expressed widely in the zebra finch brain

(Acharya and Veney, 2012).

One study to date has used a selective GPER antagonist (Tehrani and Veney,

2018), which was found to have demasculinizing effects in HVC and Area X, but again

behavior was not examined. Other attempts have been made at inhibiting the synthesis

of estrogen using pharmacological agents to inhibit aromatase (ARO also known as

CYP19A1), which is the sole enzyme responsible for synthesizing estrogens from

androgens (Figure 11) (Levin et al., 2017; Manna et al., 2016; Shaheenah et al., 2008).

Figure 11: Sex steroid synthesis pathway. Estrogens are terminal metabolites of androgens. Aromatization is irreversible and estrogens cannot be reverted back to androgens. Aromatase is the primary enzyme in the androgen to estrogen conversion. Adapted from Levin et al. (2017).

One such inhibitor is fadrozole, which can successfully demasculinize the song system in vitro (Holloway and Clayton, 2001), but poorly in vivo (Balthazart et al., 1994;

Merten and Stocker-Buschina, 1995; Wade and Arnold, 1994). This may be due to the metabolic and pharmacodynamic properties of fadrozole that make it a poor long-term inhibitor of estrogen synthesis. In vivo in the adult canary, fadrozole inhibition of ARO is resolved within 4 hours (Alward et al., 2016). Fadrozole is a competitive inhibitor of ARO with higher affinity for the binding site than endogenous ligands (androgens). This competitive inhibition is stochastic, and when fadrozole eventually detaches from the active site, it is cleared from the system fairly quickly as fadrozole has a half-life of 10 hours (Kochak et al., 1990). Fadrozole is very efficient at acutely inhibiting estrogen

synthesis, but this drug has fallen out of clinical use due to unacceptable side effects and enzyme stabilizing properties while bound (Campos and Winer, 2003; Harada and

Hatano, 1998). In cell culture, fadrozole treatment actually results in increased estrogen synthesis after drug clearance as there is more intact ARO protein after treatment

(Harada and Hatano, 1998).

For this chapter of my thesis, since the specificity of clinically relevant estrogen receptor disruptors and antagonists for all three classes of estrogen receptors is not known in the zebra finch, I opted to recapitulate prior studies by reducing endogenous levels of estrogen in vivo. Instead of fadrozole, I chose to use a different class of ARO inhibitor, exemestane. Exemestane is a potent 3rd generation steroidal ARO inhibitor,

trademarked by Pfizer as Aromasin. Instead of being a competitive inhibitor, like most

non-steroidal inhibitors, Exemestane irreversibly binds to the ligand binding site of the

ARO enzyme, acting as a “suicide inhibitor”. Exemestane binding alters the confirmation

of ARO, targeting the enzyme for ubiquitin mediated degradation (Lonning and Eikesdal,

2013) without altering the transcription or translation rate of ARO. Exemestane has a

half-life of 24 hours, effectively suppressing estrogen synthesis with greater than 98%

efficiency in humans when given daily (Pfizer, 2018; Wang and Chen, 2006). For these

reasons, I chose exemestane to probe the role estrogen has in the development of the

song system, either in its masculinization or feminization.

2.2 Methodology

2.2.1 Homology Modelling of Exemestane-Aromatase Interaction

The complete amino acid sequences of human ARO (UniprotKB: P11511) and

zebra finch ARO (UniprotKB: Q92112) were taken from the UniprotKB database and

aligned using the UniProt alignment tool (https://www.uniprot.org/align). The model of 28

the finch ARO was built via homology modeling using Modeller (v9.23) based on a mono template of the human experimental structure bound to exemestane and the heme cofactor (Protein Data Bank (PDB) ID: 3S7S). Visualization of the zebra finch ARO bound to exemestane and the heme co-factor was performed using the Visual Molecular

Dynamics (VMD v1.9.3) program.

2.2.2 Drug/Hormone Administration Methods and Timeline

Exemestane (Sigma PHR1634) was dissolved in DMSO (PanReac Applichem

191954) at a concentration of 100mg/mL, which was then suspended in olive oil (Sigma

75343) for a final concentration of either 10mg/mL or 20mg/mL, which is needed for prolong absorption as done with sesame oil in quail (Çiftci, 2012) and rat (Theodorsson et al., 2005). Vehicle was the same DMSO-olive oil solution without exemestane.

Exemestane or vehicle treatments were given daily via subcutaneous injection with a

28.5-gauge needle from PHD0 until sacrifice at PHD30, or until ~PHD60, after which the

treatments were given every other day until sacrifice at ~PHD90 (Figure 12). Doses

were given between 10 and 60ug/g body weight (between 5uL and 30uL) and not

exceeding 100ug/g, as doses over 125ug/g (mg/kg) in mammals was reportedly toxic

(Pfizer, 2018).

Estradiol is the most potent form of estrogen in biological systems. Estradiol

(Sigma E1024-1G) was also dissolved in DMSO (PanReac Applichem 191954) at a

concentration of 100mg/mL, which was suspended in olive oil (Sigma 75343) for a final

concentration of 1mg/mL. Initially in a pilot experiment, I treated chicks with daily subcutaneous estradiol injections at 20ug/g body weight, but this resulted in high mortality even after lowering the dose to 5ug/g body weight. Thereafter, I transitioned to

daily topical treatments with one drop (~30-50uL) of the 1mg/mL solution applied near

the flank as this was the easiest and least invasive route of treatment. Identical topical daily treatments with the estradiol solution or vehicle were given at doses no higher than

50ug of estradiol (or equivalent vehicle volume) until ~PHD14, and then the solution was given every other day until time of sacrifice for animals taken at PHD30, or until PHD20 for animals intended for sacrifice at PHD90 (Figure 12). I found continued daily (or alternating day) topical application past PHD30 resulted in animals with bones too fragile to properly fly or walk. Thus, for the latter group, at ~PHD20, instead of topical application, silastic pellets were implanted. These implants were made by mixing medical grade silicone adhesive (Nusil MED-1037) with estradiol dissolved in DMSO at

100mg/mL or adhesive mixed with DMSO alone as a vehicle control. The mixture was extruded from syringes into ropes that were cured overnight. The resulting pellets were cut, weighed, and kept in sterile conditions at 4°C until use (Gurney, 1982; Sahores et al., 2013; Simpson and Vicario, 1991a). Each silastic implant carried approximately 150-

200ug of estradiol, and vehicle implants were size matched. The silastic pellets were surgically placed subcutaneously on the flank under the wing and retained until sacrifice at ~PHD90. PHD20 was the earliest one could reliably hide the surgical site from their parents, who would peck at the chicks when they could see the surgical site. This approach was less detrimental for animal health.

Figure 12: Treatment timeline for pharmacological administration. (A) PHD30 experimental timeline. Vehicle, estradiol, and exemestane treated animals were dosed daily until PHD30 and taken for brain transcriptome analysis. (B) PHD90 experimental timeline. Estradiol and vehicle animals were dosed daily until PHD20, whereupon they received silastic implants with estradiol or vehicle until sacrifice at PHD90. Exemestane animals were dosed daily until PHD60, and then every other day until behavior collection and sacrifice at PHD90.

To surgically place the implants, the animals were given meloxicam (Metacam

NDC 0010-6013-01, 5mg/mL) intramuscularly at 0.3mg/kg body weight. They were then initially anesthetized with 3-4% isoflurane (Isothesia NDC 11695-6776-1) in 100% oxygen, and then sustained with 1.5-2% isoflurane for the duration of the procedure. The surgical site was plucked of feathers, and the exposed skin was scrubbed with 70% ethanol and 10% povidone-iodine, followed by a shallow incision with a scalpel. A pocket was created under the skin using a blunt hemostat, and the implant was placed in the pocket. The incision was sealed with veterinary adhesive (3M Vetbond, 1469SB or

Henry Schein Vetclose 031477), and bupivacaine (Hospira NDC 0409-1159-01, 0.25%) was applied topically afterwards. The animals were observed continuously for the first 2 hours, and daily afterwards. The animals were also given additional intramuscular

meloxicam 24 hours and 48 hours after surgery to manage pain. When handling estradiol, in addition to normal standard lab protective clothing, experimenters wore respirator mask to avoid inhaling any possible airborne estrogen-contaminated particles.

On the day of sacrifice, all experimental animals were kept in the dark for approximately 1 hour to return most brain gene expression activity to similar baseline levels (Jarvis and Nottebohm, 1997; Whitney et al., 2014). For animals collected at

PHD30, they were separated from their parents for 1 hour to rest in the dark. The conditions of adults are described below in section 3.2.1 Behavior collection and analysis” for vocalization analyses. For both juveniles and adults, the animals were rapidly anesthetized via isoflurane inhalation overdose followed by rapid decapitation, to reduce possible stress-induced brain gene expression. The brains were quickly dissected and embedded in OCT before snap freezing in a slurry of dry ice and ethanol.

Trunk blood was collected from the neck at sacrifice and left at room temperature for approximately 20 minutes to clot before centrifuging at 20,000g for 3 minutes to separate the cells from serum. Gonads were examined post-mortem to confirm sex. The full special animal husbandry protocol to obtain healthy animals for vehicle and hormone manipulations is in Appendix A – Animal husbandry.

2.2.3 Steroid Panel Assay with uHPLC-MS/MS

To test the efficacy of exemestane in zebra finches, exemestane or vehicle was subcutaneously injected in adult animals (60-40ug/g body weight: 600-800ug) for three days, and they were then sacrificed 24 hours after final treatment. Animals were euthanized in the same fashion as outlined above. Serum and whole brain samples were submitted to the metabolomics core facility at Duke university. Sterols were extracted from biological samples before being assayed for a complete steroid panel (Cortisol,

Cortisone, 11-Deoxycortisol, 17α-Hydroxyprogesterone, Progesterone, Aldosterone,

Corticosterone, 11-Deoxycorticosterone, Estradiol, Estrone, Androstenedione,

Androsterone, Dehydroepiandrosterone, Dehydroepiandrosterone sulfate,

Dihydrotestosterone, Etiocholanolone, and Testosterone), using the AbsoluteIDQ

Stero17 kit (BioCrates) on the Xevo TQ-S MS UPLC/MS/MS instrument (Waters

Corporation). The full extraction and assay protocol is included in Appendix B – Duke

metabolomics core protocol for uHPLC-MS/MS for SteroIDQ panel.

2.2.4 Serum Estradiol Assay with EIA

Collected serum was serially diluted in ultrapure water (Invitrogen 10977015) and used directly in a commercial estradiol (E2) enzyme immunosorbent assay (EIA,

Cayman Chemical 582251) following manufacturers protocol (Vedder et al., 2014). Four dilutions were run in triplicate for each exemestane treated animal (1:5, 1:10, 1:20, 1:40) and similar four dilutions for each vehicle or estrogen vehicle treated animal (1:10, 1:20,

1:40, 1:80). Results were obtained using a SpectraMax M3 micro-plate reader

(Molecular Devices) with Softmax Pro software v6.2.1 on a computer operating Windows

7 professional.

2.2.5 Statistics

Estradiol measurements did not fit a normal distribution and were thus analyzed using the non-parametric Kruskal-Wallace tests with post-hoc Steel-Dwass tests for each pair comparison or Wilcoxon rank sum tests. Chick growth rates were compared

using mixed model for repeated measures.

2.3 Results

2.3.1 Human and Zebra Finch Aromatase Protein Structure are Similar

The human and zebra finch ARO primary protein sequence is conserved, sharing

75% amino acid sequence identity (Figure 13). This level of conservation allowed us to use the crystal structure of human ARO to model the zebra finch protein structure based on homology remodeling. In both species, 11 amino acids line the ligand binding site of

ARO, and of these, 10 were conserved between human and zebra finch (Figure 13). The single non-conserved amino acid is a conservative amino acid substitution where a valine (human) was substituted for an isoleucine (zebra finch). Both amino acids belong to the aliphatic amino acid family, indicating they should have similar biochemical interactions.

Figure 13: Primary sequence alignment between human and zebra finch ARO. Conserved residues outside the binding site are in light green, and inside the binding site are in cyan. Non-conserved residues outside the binding site are not highlighted, and the single valine to isoleucine substitution in the binding site is in magenta.

The tertiary structure of both proteins were predicted to fold in similar ways and a close view of the binding site shows that this single substitute amino acid functional group faces away from the binding pocket into the interior of the protein (Figure 14). This raises the confidence that the binding pocket of human and the finch ARO should interact with exemestane in a very similar fashion.

Figure 14: 3-D Homology based model of human and zebra finch ARO with exemestane and HEME cofactor in ligand binding site. Superimposed tertiary structure of human (PDB ID: 3S7S – white ribbon) and finch (built by homology modeling – ice blue ribbon). Binding sites are magnified in right insets. Cyan, conserved residues between human and finch. Magenta, non-conserved amino acid. Black, the cofactor heme (HEM) group. Orange, exemestane molecule.

2.3.2 Exemestane is a Potent Inhibitor of Estrogen Synthesis in Zebra Finch Blood and Brain

Of the 17 different steroids available in the AbsoluteIDQ Stero17 assay, 8 were detectable in the adult samples: Corticosterone (B), Cortisone (E), Testosterone (T),

Dihydrotestosterone (DHT), Progesterone (P), Estradiol (E2), 11-Deoxycorticosterone

(DOC), and Aldosterone (A). We found that in all birds treated with exemestane, there was no detectable E2 in the brain and very low levels in serum, whereas in vehicle

treated animals some of them had moderate to high estradiol levels (Figure 15).

However, this reduction was only significant in females for both brain (p=0.025) and

serum (p=0.0273), likely due to the low number of subjects (n=5 for each group).

Figure 15: E2 levels in adult animals treated with vehicle or exemestane. E2 levels assayed by uHPLC-MS/MS in (A) adult brain samples and (B) adult serum samples. S.E.M. error bars. Wilcoxon test with Steel-Dwass post hoc. n=5 for each treatment and sex.

Analyses of seven other steroids showed a small but statistically significant

decrease of aldosterone in female brains and an increase in cortisone in male serum

(Figure 16) alongside a decrease in progesterone in male serum (Figure 17) but no other

changes. These findings indicate that exemestane is a potent inhibitor of estrogen

synthesis in zebra finches, with limited off target effects.

Figure 16: Corticosteroid levels in adult animals treated with vehicle or exemestane. Corticosteroid levels assayed by uHPLC-MS/MS in (A) adult brain samples and (B) adult serum samples. E- Cortisone, B- Corticosterone, A- Aldosterone, DOC-11- Deoxycorticosterone. S.E.M. error bars. Wilcoxon test with Steel-Dwass post hoc. n=5 for each treatment and sex.

Figure 17: Sex steroid levels in adult animals treated with vehicle or exemsetane. Sex steroid levels assayed by uHPLC-MS/MS in (A) adult brain samples and (B) adult serum samples. T- Testosterone, P- Progesterone, DHT- 5α-Dihydrotestosterone. S.E.M. error bars. Wilcoxon test with Steel-Dwass post hoc. n=5 for each treatment and sex.

I also collected blood from juvenile animals (PHD15-60) that were treated with either vehicle/nothing (naïve), exemestane, or E2. I used a commercially available E2 enzyme immunosorbent assay (EIA) to measure serum E2 levels. As expected, animals given exogenous E2 had hugely elevated levels of E2 in serum, regardless of sex

(Figure 18). Exemestane treated animals had a significant decrease in E2 (p=0.004).

Most exemestane treated juvenile males (n=10) had no detectable serum E2, whereas a

minority (n=4) had levels similar to vehicle controls. A single exemestane treated juvenile

female (n=1) had no detectable serum E2, but some (n=3) had levels that overlapped

with vehicle controls. When we consider all exemestane treated animals at both ages for

both sexes, the majority of them (84%) had very low to no detectable serum E2 levels

when compared to vehicle treated animals. We surmise that the minority of animals

where we did not see a decrease to undetectable levels was due to the treatment

regimen not being optimized for complete aromatase suppression in all animals.

Juveniles treated daily with E2 had ~380-fold elevated serum E2 levels compared to

vehicle controls. Full statistics are in Appendix C – Full statistics for E2 quantification.

Figure 18: E2 levels in juveniles treated with vehicle, exemestane or E2. E2 levels assayed by EIA in juvenile serum samples. S.E.M. error bars. Kruskal-Wallis test with Steel-Dwass post hoc. n=4 to n=14.

Regardless of the age of the animals, tissue, or assays used, there was a wide margin of variability in serum E2 levels in vehicle controls. The independent validation by two different assays suggest the variability observed in E2 is biological in nature and not technical. It is also apparent that EIA is more sensitive to lower levels of E2 than UPLC-

MS/MS, which is a documented phenomenon for other chemicals (Elgarch et al., 2008;

Rubio et al., 2003; Shimoi et al., 2002).

2.3.3 Estrogen Suppression Alters Male Plumage Development

Around PHD55 or so, zebra finches complete their first molt into adult colors, where males begin displaying orange cheek patches, black throat striations, and spotted chestnut flank feathers. Females molt into a gray coat, with cream belly feathers and

light gray cheeks. Males have redder beaks, and females have a pale orange beak instead of the black beaks that both sexes have as juveniles.

I was surprised to observe that males treated with exemestane had reduced male specific coloration when compared to males treated with either vehicle or E2

(Figure 19). By PHD90, despite some recovery of male feather colorations, these males

were not as “masculine” in their plumage as vehicle or estrogen treated males (Figure

19). None of the females displayed male plumage in response to any either of the

pharmacological manipulations.

Figure 19: Feather plumage changes in exemestane treated males. At PHD60, compared to vehicle males (A), exemestane males (B) had diluted male plumage which was more similar to vehicle female (C). By PHD90, vehicle males (D) still had brightly dimorphic cheek patches that were only partially recovered by exemestane treated males (E). E2 treated males (F) did not show plumage defects.

2.3.4 Elevated Estrogen Levels Have Toxic Effects on Bone Strength

Prior studies have used large doses of both synthetically conjugated (Pohl‐Apel and Sossinka, 1984) and unconjugated E2 (Simpson and Vicario, 1991a; Simpson and

Vicario, 1991b) to masculinize female zebra finches. The doses given to animals late in

development were as high as 1500ug of E2 (Simpson and Vicario, 1991b), which I

initially attempted to replicate in this study. However, I discovered that such elevated

estrogen levels for extended periods (> 1 week) caused bones to become severely

weakened in the treated animals, resulting in many developing femoral fractures. These

animals had to be euthanized upon discovery since the injuries occurred shortly after

fledging (PHD28) but long before weaning (PHD45). I had no way of ensuring proper

supportive care when their parents ceased feeding them after the injury, and I had no

way of preventing further injury as they were likely to injure other limbs.

Studies in poultry agriculture have demonstrated that estrogen levels control the

release of calcium from medullary bones in birds, which serves as a labile source of

calcium needed for egg production (Çiftci, 2012). In chickens, elevated estrogen is

correlated with elevated serum calcium and reduced medullary bone density. This in turn

causes severe structural weakening and morbidity in egg-laying hens (Whitehead,

2004). This is opposite the effect that estrogen has on mammalian bone biology, where

estrogen increases calcium deposition in bones and estrogen depletion results in

osteoporosis (Carson and Manolagas, 2015). Prior publications that have used estrogen

to masculinize finches failed to report this bone weakening effect, and I was surprised to

encounter this phenotype.

To remedy this problem, I spent a year titering the maximal amount of E2 that

could be given without eliciting this bone weakening phenotype, while also optimizing

supportive animal husbandry to encourage faster weaning. In the end, instead of using

1500ug E2 pellets in juvenile animals, I used 100-200ug pellets. I also replaced subcutaneous injections like what was done initially (to match treatment conditions with exemestane animals) and instead applied E2 cutaneously. This reduced the handling of chicks, which also made implantation at PHD20 much easier.

For enhanced animal husbandry (also see Appendix A – Animal husbandry), I supplemented the animals’ diet with hardboiled eggs blended with peeled oranges, which were given twice weekly to encourage faster weaning and growth. Water was also supplemented with calcium boroglucanate to increase systemic calcium levels and reduce bone adsorption. With these modified husbandry and treatments, I was able to prevent long bone fractures, but the animals still displayed mild musculoskeletal weakness, later confirmed by a veterinarian to be spraining. These spontaneously resolved within a week as the animals became stronger at flying and perching.

2.3.5 Hormone Manipulation had No Effects on Nestling Growth

Despite the bone weakening phenotype prior to fledging, I saw no other signs of

delayed growth or other obvious developmental defects with E2 treatment. The earliest

cohorts which included animals that were all dosed subcutaneously, were weighed daily

until fledging. I observed that all animals followed the same rate of weight gain (Figure

20). Later cohorts dosed cutaneously with lower amounts of E2 were not weighed to

avoid over-handling the animals. All animals fledged at the same time (~PHD25) and

weaned around the same time as well (~PHD45), though some weaned earlier after I

increased the frequency of their dietary supplements. All birds completed their first adult

molt by PHD60.

Figure 20: Weight gain in estrogen modulated chicks. Subcutaneously treated chicks were weighed daily until they fledged at approximately PHD20. No significant difference in weight gain was observed between treatments via repeated measures mixed model analysis.

2.4 Discussion

I predicted that exemestane should work similarly between zebra finches and humans based on a homology-built model of zebra finch ARO, and this prediction was supported from my steroid quantification assays. I found that in zebra finches, treatment with exemestane resulted in decreased levels of E2 in both serum and brain, and I also

observed changes in feather coloration, indicating a strong systemic effect of drug

treatment. I saw little changes in other detectable hormones, indicating that my

exemestane treatment in vivo had limited off-target effects.

2.4.1 Reconciliation with Prior Studies Using Fadrozole

The role of estrogen in the development of the zebra finch song system has been difficult to resolve due to use of suboptimal pharmacological agents to suppress estrogenic activity in these animals. Prior reports in zebra finches that used fadrozole and related inhibitors (Balthazart et al., 1994; Merten and Stocker-Buschina, 1995;

Wade and Arnold, 1994), presumably were impacted by the then unknown short half-life and rebound effect after drug clearance. Clinically, fadrozole has since been replaced with newer classes of non-steroidal inhibitors that have far longer half-lives in vivo.

However, I chose not to use these newer classes of non-steroidal ARO inhibitors, as anastrozole has been documented to have poor penetration of the blood-brain-barrier in rodents (Miyajima et al., 2013). While this is advantageous in clinical use for treatment of

estrogen sensitive cancers of the breast, ovaries and prostate, this made anastrozole a

poor candidate for my study. Rather, I chose to use the second class of ARO inhibitors,

called suicide inhibitors, like exemestane, which are generally steroidal in nature. Like

other steroids, exemestane readily crosses the blood-brain-barrier due to its small size

and hydrophobic nature.

I was happy to find that exemestane greatly reduced both serum and brain E2

levels 24 hours after administration in both adult and juvenile animals. This contrasts

with what has been reported in adult canaries by Alward et al. (2016), which showed that

ARO inhibition by fadrozole treatment was resolved within 4 hours of treatment, leaving

20 hours between treatments where estrogenic activity is normal/elevated. This makes it

difficult to draw conclusions from prior publications that used daily doses of fadrozole

(Wade and Arnold, 1994).

2.4.2 Caveats of Pharmacological Studies and the Need of Transgenics

Despite my best efforts, the largest caveat with pharmacological inhibition is that drugs do not have complete penetrance. Transgenic manipulations do not have this problem. However, the generation of transgenic zebra finches is labor intensive and low throughput, taking many years and thousands of eggs to create a handful of animals

(Abe et al., 2015; Agate et al., 2009; Velho and Lois, 2014). With the rising popularity of

CRISPR/Cas9 technology, there has been a renewed effort to create a “multi-tool” transgenic bird that can be used to more easily create transgenic animals, however this is still in development (Han and Park, 2018; Jung et al., 2019). In the future, the work contained in this thesis will serve as hypothesis that can be further tested with conditional ARO knock-out animals. Despite the drawbacks of using pharmacological agents, my administration of exemestane drastically reduced the levels of E2 to levels

below the threshold of detection in most of the animals that were assayed.

2.4.3 Unexpected Systemic Changes – Feather Plumage

To my knowledge, my findings is the first account of sex hormones being linked to the development of male plumage in zebra finches. Previous publications using either estrogen receptor antagonists or ARO inhibitors have not reported changes in zebra

finch male plumage. However, it has been shown that sex hormones levels correlate with the type of melanin produced in the feathers of waterfowl (Ralph, 1969; Somes and

Smyth, 1967). The changes I observed in feather color presentation also hints at an

intersection between steroid hormones and genes that may be a rich avenue of study for

future projects. These changes outside of the central nervous system, in addition to my

assay results gave me good confidence that, compared to prior studies, I was able to

substantially reduce the levels of estrogen in vivo.

Chapter 3: Estrogen Manipulation Alters Singing Behavior in Males and Females Differentially 3.1 Introduction

Previous publications have demonstrated that early estrogen administration in

female zebra finches induces development of a male-like song system and the ability to

produce learned courtship songs in adulthood (Gurney, 1982; Konishi and Akutagawa,

1988; Pohl‐Apel and Sossinka, 1984; Simpson and Vicario, 1991a; Simpson and Vicario,

1991b). But as mentioned previously, the inhibition of estrogenic activity during

development has yielded inadequate evidence regarding estrogen’s influence on the

development of the male song system (Balthazart et al., 1994; Merten and Stocker-

Buschina, 1995; Wade and Arnold, 1994). In this chapter, I have been able to replicate

estrogen sufficiency in masculinizing female vocal behavior, and I have also been able

to alter the vocal behavior of males after treatment with an estrogen synthesis inhibitor.

3.2 Methodology

3.2.1 Behavior Collection and Analysis

For animals sacrificed at ~PHD90, vocal behavior was recorded starting at

PHD60, after the animals were weaned and past the sensory learning period of song development (Kojima and Doupe, 2007a). These animals were recorded continuously in

sound isolation chambers where they could neither see nor hear other animals. At

~PHD90, control and experimental animals (males and females) were provided with a

novel female to collect directed songs and/or other vocalizations for longer than 2 hours.

After this period of directed vocalization, the novel females were returned to their home

cage and the treated animals were kept in the dark for approximately 1 hour to fast and

return brain activity to baseline. The animals were then sacrificed. 48

One E2 treated PHD90+ female underwent audio/visual recording with a novel control female as a visual stimulus. Both animals were able to hear the other but were separated by an electrochromic glass that could be turned opaque or transparent remotely. This E2 treated female underwent several sessions for 5 hours, with 30-minute intervals of seeing and 1-hour of not seeing the stimulus female. This female was later returned to her sound isolation home cage overnight before conducting recording behavior the same as all other experimental animals.

Recordings were collected using Earthworks SR69 or SRO microphones and an

Aardvark 24/96 Pro pre-amplifier, connected to a computer operating Windows XP sp3.

Avisoft recording software (Avisoft Bioacoustics) was used to gate and record sounds that were due to vocalizations, namely with an energy threshold of >1%, entropy threshold of <70%, and duration of >3 milliseconds, recording 500 milliseconds before and after the triggering event. Avisoft Recorder v4.2.18 generated sound files were opened in Raven Lite 2.0 interactive sound analysis software (Cornell lab of Ornithology) and examined manually by blinded personnel to remove sound files with excessive cage noise and retain sound files with suitable vocalizations for automated thresholding in subsequent analysis. Syllable characterization and quantification was performed in

Sound Analysis Pro 2011 (SAP2011) (Tchernichovski et al., 2000). Syllables were segmented and had their feature characteristics tabulated through automated batch analysis functions in SAP2011. These characteristics were tabulated across development for all individuals. Syllables were then visualized through Nearest Neighbor

Hierarchical syllable clustering functions provided in SAP2011, and the discrete clusters separated in Euclidian space were identified manually by 2 blinded evaluators.

3.2.2 Statistics

Song-syllable cluster data was normally distributed, and thus analyzed using standard least squares models for a 2-way ANOVA with post-hoc Tukey’s Honest

Significant Difference (HSD) tests for each pair comparison.

3.3 Results

3.3.1 Estrogen Modification Alters Singing Behavior in Males and Females

3.3.1.1 Exemestane Treated Males Produced Impoverished Songs and Calls.

Males treated with exemestane had severely diminished song production ability.

These songs lacked structure: syllables did not arrange into the repeating motifs that define a normal zebra finch song (Figure 21). The songs produced were far simpler in that they had a much smaller repertoire of syllables than either vehicle or estrogen treated males (Figure 23, Figure 24). They were, however, recognizable as attempts at

courtship singing since these bouts were always preceded by introductory notes (Riebel,

2009). I was also surprised to find that exemestane treated females produced unique

long calls that were more similar to those produced by males (Simpson and Vicario,

1990; Zann, 1984). While these exemestane treated females replaced their feminine

long calls with more masculine ones, these new syllables were never arranged in any

repeating motifs that were reminiscent of song and they were never preceded by

introductory notes when in the presence of another female.

Figure 21: Vocalizations from control female and animals treated with exemestane. Directed songs/vocalizations from a vehicle female (A), exemestane female (B), and exemestane male (C). Syllables are named and outlined in yellow. No repeating motifs were identifiable in any of these animals.

3.3.1.2 Estrogen Treated Females Produced Male-Like Songs and Calls

Vehicle treated and E2 treated males learned and produced courtship songs normally in adulthood. However, unlike vehicle control females, E2 treated females also produced courtship songs, which they directed at females. These songs were made of distinct syllables that were arranged in repeating motifs. This organization of 2-6 syllables is characteristic of normal zebra finch courtship songs (Figure 22). These E2 females also engaged in male-typical somatic courtship displays as well: puffing the feathers of the head and flank, beak swiping, standing tall to expose the throat feathers, and offering nesting materials (Video 1).

One of the E2 treated females did not have full masculine behaviors (she failed

to produce full courtship songs despite being able to produce unique syllables), but after

examining her post-mortem, I found that she was missing her steroid implant. It is not

known when this implant was lost, but if it was lost before PHD45, then that may explain

why she had incomplete behavior masculinization. Prior studies by Konishi and

Akutagawa (1988) have shown that the song system becomes resistant to estrogen

induced masculinization by PHD45.

Figure 22: Vocalizations from control male and animals treated with E2. Directed songs/vocalizations recorded from a vehicle male (A), E2 male (B), and E2 female (C). Syllables are named and outlined in yellow and repeating motifs are outlined in red.

Figure 23: Syllable clusters of recorded vocalizations. Samples of binarized cluster plots generated with the clustering feature in SAP2011. All syllables were recorded from directed singing sessions after the introduction of a novel female. Each syllable is plotted in Euclidean space, with axis representing duration vs mean frequency modulation.

Figure 24: Comparisons of syllable cluster numbers. Number of SAP syllable clusters identified by blinded personnel. There is a significant decrease in number of unique syllables produced by exemestane males compared to vehicle males. S.E.M. error bars. 2-way ANOVA with Tukey’s HSD post hoc. n=4 to n=7.

3.4 Discussion

3.4.1 Estrogen Modulation on Vocal Behavior

Normal male zebra finch courtship songs are highly complex, comprising of distinct syllables beginning with repeating introductory notes followed by a repeating

motif, to produce a bout of song. Male zebra finches also learn to modify the acoustic

structure of their long calls, which differentiates them from innate female long calls. The

long calls of females have flat harmonic stacks and have very little variation between

females (Zann, 1984). Short calls like “tet” calls are innate and are the same between

males and females.

I found that males treated with exemestane produced songs with significantly

fewer unique syllables when compared to vehicle or E2 treated males. These syllables

were of poor acoustic quality and were not arranged in any identifiable motif, or

repeatable units that could be classified as a song bout. Instead, strings of 1-2 syllables

were arranged in variable orders, preceded by introductory notes. This is the first report,

to my knowledge, of male song learning being impacted by estrogen synthesis inhibition.

This is contrary to results from prior studies that have used fadrozole, since those prior

studies reported that males treated with fadrozole did not show any deficits in song

learning or in song production as adults. These discrepancies likely lie with the

differences in the pharmacological agent used. As mentioned in the previous chapter,

fadrozole was likely a poor choice to reduce estrogen synthesis in vivo, due to the short

half-life.

Despite changes in song, unique long calls of these exemestane males were

retained, showing frequency modulation in their harmonic stacks. I was however,

surprised to see that exemestane treatment also altered female vocalizations.

Exemestane treated females were able to produce unique long call-like syllables that

were strung into a series of rapid repetitions, without any noticeable organization that

could be identified as a motif, similar to what was seen in exemestane treated males.

However, unlike exemestane treated males, these exemestane treated female “bouts”

had no identifiable introductory notes.

Like prior reports, E2 treated females were also capable of producing courtship songs and long calls that were similar to those from males, and different from vehicle or exemestane treated females. None of my vehicle treated females were capable of

singing or producing unique long calls.

The changes observed in song learning behavior of exemestane and E2 treated animals could be due to changes in the auditory or motor components of the song

system. Auditory regions harbor far more estrogen sensitive neurons than the motor

regions (Jacobs et al., 1999; Krentzel and Remage-Healey, 2015; Schlinger, 1997), and

it has been shown that acutely modulating estrogen impacts song perception (Krentzel

et al., 2019) and can influence female song preferences (Maney and Pinaud, 2011;

Remage-Healey et al., 2010; Vyas et al., 2009; Yoder and Vicario, 2012). However, HVC

has high levels of ESR1, and Area X has high levels of androgen receptor. Thus, in the

next chapter I performed experiments to test if the hormone treatments that impacted

song learning behavior were associated with changes in neural architecture and

specialized gene expression of the song learning nuclei.

Chapter 4. Estrogen Manipulation Alters the Neural Architecture and Transcriptome of the Song System in a Sex Dependent Manner 4.1 Introduction

The song systems of vocal learning animals have specialized neural architecture

and specialized gene expression patterns (Feenders et al., 2008; Jarvis and Nottebohm,

1997; Jarvis et al., 2013; Pfenning et al., 2014). Since female zebra finches do not utilize their atrophied song system in the same ways as males, it was presumed that their song system did not have the same degree of gene expression specializations. In fact, prior work from our lab has shown that at PHD35, genes that showed specialized expression in male zebra finches, like SLIT1 in RA and ROBO1 in HVC, and human laryngeal motor cortex, do not have the same expression patterns in females (Wang et al., 2015) (Figure

25). Instead, these two genes in female zebra finches have an expression pattern more

like vocal non-learning species, like doves and mice. However, this is only two genes, a

ligand and receptor respectively, involved in axon guidance. We know that axons from

HVC innervate RA in males, but not in females unless treated with estrogen (Holloway

and Clayton, 2001; Mooney and Rao, 1994). In this chapter, I test the impact of chronic

estrogen manipulation on song nuclei presence versus absence, size, and gene

expression specializations at the beginning of the sensorimotor learning period (PHD30),

where sex differences in vocal behavior begin to become more apparent (Bottjer et al.,

1985; Mooney and Rao, 1994; Nixdorf‐Bergweiler, 1996). I performed transcriptomic

analysis of all 4 motor song regions in animals treated with and without endocrine

modulators.

Figure 25: Developmental expression of SLIT1 and ROBO1 in RA. SLIT1 and ROBO1 mRNA expression in RA versus adjacent LAi in male and female zebra finches between PHD20 and >PHD90 (adult). *P<0.05; **P<0.01; ***P<0.001. From Wang et al. (2015).

4.2 Methodology

4.2.1 Cresyl Ciolet Histology

The left hemisphere of brains from PHD30 treated animals were frozen in OCT, sagittally sectioned over 9 serial slides at 14-16um on a microtome cryostat (Leica

CM1850), thaw mounted onto charged borosilicate slides (Fisherbrand Superfrost #12-

550-15) and stored at -80°C until use. One series was dehydrated and rehydrated in graded ethanols (0%, 50%, 70%, 95% 100%), stained in 0.3% cresyl violet acetate

(Sigma C5042), defatted in mixed xylenes (Fisher X5), cover-slipped with permount

(Fisher SP15) mounting media and cured for one week in a chemical cabinet prior to 59

imaging on a stereomicroscope (Zeiss Stemi 305) equipped with a color camera (Zeiss

Axiocam 105). Images were obtained on a computer operating Windows 7 using Zeiss

Zen Blue 2.0 software.

4.2.2 Chromogenic in-situ Hybridization

Previously cloned CADPS2 (Accession: DV955943) and S100B (Accession:

DV950377) plasmids were grown from bacterial stock and collected via miniprep

columns (Qiagen 27104). CADPS2 and S100B was verified for sequence identity and

orientation using Sanger sequencing services provided by Eton Biosciences or

GeneWiz. Using these clones, I performed a modified version of the in-situ hybridization protocol as first described by Takatoh et al. (2013) (Biegler et al., in preparation).

Template DNA was PCR amplified from plasmids using M13 forward and reverse primers and Phusion high-fidelity DNA polymerase (Thermofisher F530S). The target product was gel purified using NucleoSpin mini-spin columns (Machery-Nagel

740609.50). DIG-labelled (Roche 11277073910) RNA probes were transcribed from 1ug of purified DNA template and cleaned via ethanol-salt purification with GenElute linear polyacrylamide (Sigma 56575-1ML) as a neutral carrier. The probe pellet was rehydrated in 100uL of 90% formamide, and frozen in 5uL aliquots at -80°C. Probes were only freeze-thawed once to prevent RNA degradation.

Slides with fresh-frozen mounted sections were fixed in freshly prepared 4%

PFA/1x PBS for 5 minutes at room temperature. The slides were then washed in 1x PBS and acetylated in 0.1M triethylamine + acetic acid for 10 minutes before dehydration in a series of graded ethanols. The opaque tissue sections were outlined with hydrophobic marker (Thermofisher 008899) and then prehybridized (50% formamide

[ThermoScientific 15515026], 5X SSC [ThermoScientific AM9763], 1X Denhardt’s

solution [Sigma D2532], 250ug/mL Brewer’s yeast tRNA [Roche 10109495001],

500ug/mL herring sperm DNA [ThermoScientific 15634017]) with parafilm coverslips at room temperature for 1 hour in a humidified chamber. Hybridization solution with a 1:100 probe dilution (from frozen aliquots outlined above) in the hybridization buffer (300mM

NaCl, 20mM Tris-HCl pH 8.0, 5mM EDTA, 10M Na2HPO4 pH 7.2, 10% dextran sulfate,

1X Denhardt’s solution [Sigma D2532], 500ug/mL Brewer’s yeast tRNA [Roche

10109495001], 200ug/mL Herring sperm DNA [ThermoScientific 15634017], 50% formamide [ThermoScientific 15515026]) was hydrolyzed at 80°C for 6 minutes and then chilled on ice before application. After prehybridization was complete, the parafilm coverslips were removed, and the excess prehybridization buffer was removed with a kimwipe. The prepared hybridization solution was applied and then the slides were coverslipped with glass coverslips (VWR 48393106). The slides were then incubated at

65°C in a hybridization oven overnight (>16 hours) in a humidified chamber.

Humidification can be done using either RNase free water or using a solution of 5X SSC,

50% formamide. Humidification chambers may be purchased or made using RNase decontaminated plastic food storage containers made of polypropylene large enough to hold 150mm petri-dishes with stripettes to hold the slides.

After hybridization, the coverslips were gently removed at room temperature in a

5X SSC bath. The hydrophobic pen residues were wiped off using kimwipes. The slides were then washed in 5X SSC at 68°C for 10 minutes, and then washed for 30 minutes in

0.2X SSC at 68°C, repeated 4 times. After the final wash, the container holding the 0.2X

SSC and slides were allowed to cool to room temperature. The slides were washed once more in fresh 0.2X SSC at room temperature.

After the high stringency washes, the slides were washed in Buffer B1 (0.1M Tris pH=7.5, 0.15M NaCl) for 5 minutes at room temperature before being transferred to slide mailers. The slides were then incubated in blocking Buffer B2 (10% Sheep serum

[Sigma S3772] in Buffer B1) at room temperature for a minimum of one hour. After blocking, the slides were incubated with AP-conjugated anti-DIG antibody ([Roche

11093274910] diluted 1:2000 in 1% sheep serum in Buffer B1) overnight at 4°C.

Following antibody incubation, the slides were washed for 10 minutes in Buffer

B1 at room temperature 3 times and then equilibrated in 100mM Tris-HCl pH-9.5 for 5

minutes at room temperature. The slides were then incubated in a working solution of

NBT/BCIP (Vector labs sk-5400), prepared according to the manufacturer’s protocol, for

16 hours in the dark at room temperature.

When the NBT/BCIP signal was optimal, the reaction was stopped in 1X PBS,

and the slides were washed for 5 minutes in 1X PBS at room temperature for 3 washes.

The slides were rinsed in diH2O and counterstained in a 1:3 diluted solution of Nuclear

fast red (Vector labs H-3403) for no longer than 3 minutes. The slides were then rinsed

with diH2O and then dipped in 100% histology grade ethanol (no more than 10 times)

and left to air dry. The dried sections should be opaque. After drying, the slides were

mounted with Vectamount permanent mounting solution (Vector labs H-5000) and dried

overnight at room temperature in a dark, dry location before imaging.

4.2.3 Imaging and Area Calculations

Cresyl violet and in-situ hybridized sections were imaged on a stereomicroscope

(Zeiss Stemi 305) equipped with a color camera (Zeiss Axiocam 105). Images were obtained on a computer operating Windows 7 using Zeiss Zen Blue 2.0 software.

Images were saved as .czi files (Zeiss) and area size values were obtained using the

“region of interest” tools available in Zen Blue 3.0 software (Zeiss). The area of song nuclei (Area X, HVC, LMAN and RA) and several brain subdivisions (striatum, arcopallium and mesopallium) were obtained from sections stained with either cresyl violet or CADPS2 as a marker gene. Sections were selected based on anatomical landmarks to compare across individuals. Song nuclei areas and brain subdivision areas were divided by the area of the whole telencephalon within each respective section.

Brain subdivisions were determined using boundaries referenced in the zebra finch histological atlas from the Oregon Health and Science University

(http://www.zebrafinchatlas.org) and established molecular markers by Feenders et al.

(2008) and Jarvis et al. (2013).

4.2.4 RNA Isolation & Sequencing

The right hemisphere of brains frozen in OCT were sectioned coronally at 14um and thaw mounted onto polyethylene naphthalate (PEN) membrane slides (Applied

Biosystems LCM0522) spanning LMAN/lateral anterior nidopallium (Nido), Area

X/medial striatum (MSt), HVC/HVC shelf and RA/lateral intermediate arcopallium (LAi).

As soon as the sections dried, the slides were promptly stored at -80°C until use.

Only processing one slide at a time, sections containing the target song nucleus and its immediate surrounding region were dehydrated in a series of alcohols before laser capture microscopy (LCM) using an ArcturusXT LCM system (Nikon) with CapSure

Macro LCM caps (Applied Biosystems LCM0211). Care was done to protect samples from RNase degradation by using only RNase-free materials/reagents and cleaning all reusable equipment with RNase Zap (Invitrogen AM9780). The slide for LCM processing was first taken out of -80oC and submerged in -20oC 75% ethanol. The slide then was

dehydrated in ice cold graded ethanols: 75%, 95%, 95% (second rinse), 100%, 100%

ethanol (second rinse), for 10 dips each, before incubating in fresh xylenes twice for 5 minutes each incubation. After the second xylene incubation, the slide was air dried and the desired regions were collected using the Arcturus LCM system. The core regions of the song nuclei and size matched surrounding areas were collected. All collection was done within 35 minutes for each slide upon exposure to open air. Adjacent Nissl stained slides were used as reference (Figure 26). Females do not have an Area X and thus I

could not laser dissect out an Area X. However, the location of Area X is highly

stereotyped: it is located at the anterior most portion of the striatum near the boundary

between striatum and nidopallium, and always ventral to LMAN (Figure 27) (Karten et

al., 2013). As LMAN is a non-dimorphic song nucleus, this nucleus was used to orient

the location of an Area X analogous region in females who lack an Area X.

Figure 26: Samples of laser capture microdissected sections with adjacent cresyl violet stained reference sections. Example images of LCM sections from PHD30 animals. 14um coronal sections. All images taken under brightfield. Red dashed lines outline the song nucleus.

Figure 27: Area X containing sections from the online zebra finch atlas. Area X is always ventral to LMAN at the anterior most portion of the striatum, and occupying the same planes laterally. From Karten et al. (2013).

RNA was isolated from the LCM collected tissues using the Arcturus Picopure kit

(Applied Biosystems KIT0204) following manufacturer’s instructions. RNA quality was determined using an Agilent 2100 bioanalyzer with the high sensitivity RNA 6000 pico kit

(Agilent 5067-1513). Only samples with RIN numbers higher than 5 were used for further processing.

cDNA was synthesized using the SMART-Seq v4 Ultra Low input RNA Kit

(Takara 634892) following the manufacturer’s protocol. The cDNA product was validated

using an Advanced Analytical fragment analyzer (Agilent) with the HS NGS 1-6000

fragment kit (Agilent DNF-474). Sequencing libraries were created using the NEBNext

Ultra II DNA Library Prep kit for Illumina sequencing (New England Biolabs E7645L). All

cDNA and library clean-up was done using SPRIselect beads (Beckman Coulter

B23317).

Sequencing services were conducted by Novogene Co., Ltd. on the Novaseq

6000 platform (Illumina) via the s4 flow cell for 150bp paired end reads. The resulting

reads were aligned to the TaeGut 3.2.4 zebra finch genome assembly (Warren et al.,

2010a) (GCA_000151805.2 in NCBI) using two independent approaches in custom

designed pipelines: Kallisto (Bray et al., 2016) with reads aligned to cDNA transcripts

from Ensembl; and the splice aware STAR (Dobin et al., 2013) to the annotated zebra

finch genome. In both analyses, the output of read counts for all genes were called. The

read counts were comparable with both methods, but there were some differences for a

small number of genes. I only included in my analyses genes with consistent read count

differences using both methods.

4.2.5 Statistical Analysis

From the cresyl violet staining and CADPS2 expression patterns, song nuclei:surround or song nuclei:telencephalon area ratio measurements were analyzed using Aligned-ranks transformed ANOVA (Wobbrock et al., 2011) with post-hoc

Wilcoxon tests for each pair.

For the RNA-seq analyses, to compare samples across all genes, including those without formal gene names, I normalized the data by variance stabilization transformation and then performed principle component analysis (PCA). To identify significant differentially expressed genes (DEGs) between the song nuclei and their surrounding brain subdivisions, I took the Kallisto and STAR generated read counts and applied them to the DESeq2 package (Love et al., 2014) in R and performed two types of DESeq2 analysis using the Wald test: non-pairwise group comparisons between brain regions for each of the six groups of birds (Area); and pairwise comparisons between brain regions for each bird (Subject + Area). The unpaired analysis only detected DEGs that were most robustly differentially expressed as averaged across our biological replicates, raising our risk of committing a type II error. The paired analysis was more sensitive at identifying DEGs within our replicates, raising our risk of committing a type I error. For both non-pairwise and pairwise DE analyses, genes with FDR ≤ 0.05 were considered significantly DEGs. DEG log2-fold change values greater than 2 were binned at |2| for generating heatmaps. I also removed genes identified as singing-regulated in each song nucleus (40-67 genes) reported by Whitney et al. (2014), to remove potential differences associated with vocalizing behavior in the hours before sacrifice as opposed to real baseline differences. Scripts for DESeq2 and downstream analysis are in

Appendix D – Scripts for analysis in R. All statistical functions not included in the default

DESeq2 software as-is, were carried out using one of two software/programming packages: JMP 13 or R Studio with various packages (Appendix E – Software Versions

& R packages).

Gene ontology (GO) terms enriched for DEGs were identified using the

clusterProfiler package (Yu et al., 2012) in R. I had initially attempted to use a zebra

finch annotation database made via the AnnotationForge package (Carlson and Pagès),

however this database was incomplete and I deferred to the more complete human

annotation database (Carlson, 2019).

4.3 Results

4.3.1 Hormone Manipulation Altered Neural Architecture in Estrogen Treated Females but not Exemestane Treated Males

When viewed using common histological dyes, like cresyl violet, normal males had the expected large, easily identifiable HVC, RA, and Area X song nuclei (Nixdorf‐

Bergweiler, 1996) while normal females had regressed HVC and RA, and no detectable

Area X. LMAN was visually the same between males and females (Figure 28).

Figure 28: Cresyl violet stained sagittal sections of PHD30 animals. Nissl stained 16-14um sagittal sections from PHD30 animals. A- Arcopallium, P- Pallidum, St- Striatum, N- Nidopallium, M- Mesopallium, H- Hyperpallium. All sections contain HVC, LMAN, and Area X (if present). RA is present in more lateral sections.

For the posterior song nuclei, in vehicle treated animals, RA relative to the surrounding arcopallium was larger in males than in females. E2 and exemestane had no effects on the size of RA in females, but both E2 and exemestane treatment decreased the size of RA in males with values that approached near significance, after the removal of an outlier in the exemestane treated animals (p=0.057) (Figure 29).

Similarly, HVC (relative to the whole telencephalon as an internal control) was larger in males than in females. HVC size remained unchanged in males treated with either E2 or exemestane. Likewise, exemestane treatment did not alter the size of HVC in females.

However, E2 treated females had significantly larger HVC than vehicle treated females

(p=0.0159), approaching sizes seen in males (Figure 30).

Figure 29: RA to arcopallium area ratios in PHD30 animals. RA size as ratio between RA area and Arcopallium area within sections matched using cresyl violet staining and neuroanatomical landmarks. RA size is smaller in exemestane and E2 treated males, approaching significance. No difference seen between RA size in females in response to treatment. *All data points are shown, including outliers which were removed for p-value calculation. S.D. error bars. Aligned ranks transformed ANOVA with Wilcoxon post hoc. n=3 to n=5.

Figure 30: HVC to telencephalon area ratios in PHD30 animals. HVC size as ratio between HVC area and Telencephalon area within sections matched using CADPS2 mRNA localization and neuroanatomical landmarks. HVC size is smaller in females. E2 females had a significantly larger HVC than vehicle females, approaching sizes typical for males. No change in HVC in males in response to treatment. S.D. error bars. Aligned ranks transformed ANOVA with Wilcoxon post hoc. n=3 to n=5.

For the anterior song nuclei, LMAN was largely non-dimorphic and identifiable in both sexes using cresyl violet, regardless of treatment (Figure 31). For Area X, we did not detect the nucleus in vehicle treated females, and it was sometimes difficult to detect in some of the PHD30 males. Thus, we used CADPS2 as a marker since in adults it is highly expressed in Area X, HVC, and mesopallium (Jarvis et al., 2013). I found that this mRNA distribution was also true for PHD30 males (Figure 32). However, Area X was visible in E2 treated females both by cresyl violet staining (Figure 28) and by CADPS2 in situ hybridization (Figure 32). Other markers for LMAN, HVC and RA, like S100B (Jarvis et al., 2013), did not work as well at this age (Figure 33), supporting prior observations 72

that some gene specializations are developmentally regulated within the song system

(Gahr, 1996; Jacobs et al., 1999; Olson et al., 2015; Tang and Wade, 2010; Wang et al.,

2015). Using CADPS2 as a marker, Area X size relative to the striatum was not changed by exemestane or E2 treatment in males, but caused the presence of Area X in E2 treated females that was close to half the size seen in males (Figure 34). This suggests

that estrogen affects Area X development in a sex dependent manner, with different

responses to estrogen in males versus females.

Figure 31: LMAN to telencephalon area ratios in PHD30 animals. LMAN size as ratio between LMAN area and Telencephalon area within sections matched using cresyl violet staining and neuroanatomical landmarks. No difference seen in LMAN size in any groups, regardless of sex or treatment. S.D. error bars. Aligned ranks transformed ANOVA with Wilcoxon post hoc. n=3 to n=5.

Figure 32: CADPS2 mRNA expression in sagittal sections of PHD30 animals. 16-14um sections from PHD30 animals. White is mRNA signal. Adjacent sections to cresyl violet stained slides in Figure 28. A- Arcopallium, P- Pallidum, St- Striatum, N- Nidopallium, M- Mesopallium, H- Hyperpallium. All sections contain HVC, LMAN, and Area X (if present). RA is present in more lateral sections.

Figure 33: S100B mRNA expression in sagittal sections in males. 16-14um sections. White is mRNA signal. LMAN and Area X are both visible in the adult male (left), but only Area X is visible in the PHD30 male (right).

Figure 34: Area X to striatum area ratios in PHD30 animals. Area X size as ratio between Area X area and Striatum area within sections matched using CADPS2 mRNA localization and neuroanatomical landmarks. No difference seen in Area X size in males, regardless of sex or treatment. Vehicle and exemestane females have no Area X, and Area X was induced in E2 females. S.D. error bars. Aligned ranks transformed ANOVA with Wilcoxon post hoc. n=3 to n=5.

For the brain subdivisions I used as controls, the mesopallium (Figure 35), the striatum (Figure 36), and the arcopallium (Figure 37), there were no differences between the sexes, regardless of treatment. Therefore, the changes observed in song nuclei sizes are specific to the nuclei and not to global changes in brain subdivision sizes.

Figure 35: Mesopallium to telencephalon area ratios in PHD30 animals. Mesopallium size as ratio between Mesopallium area and Telencephalon area within sections matched using CADPS2 mRNA localization and neuroanatomical landmarks. No difference seen in Mesopallium size in any groups, regardless of sex or treatment. S.D. error bars. Aligned ranks transformed ANOVA with Wilcoxon post hoc. n=3 to n=5.

Figure 36: Striatum to telencephalon area ratios in PHD30 animals. Striatum size as ratio between Striatum area and Telencephalon area within sections matched using cresyl violet staining and neuroanatomical landmarks. No difference seen in Striatum size in any groups, regardless of sex or treatment. S.D. error bars. Aligned ranks transformed ANOVA with Wilcoxon post hoc. n=3 to n=5.

Figure 37: Arcopallium to telencephalon area ratios in PHD30 animals. Arcopallium size as ratio between Arcopallium area and Telencephalon area within sections matched using cresyl violet staining and neuroanatomical landmarks. No difference seen in Arcopallium size in any groups, regardless of sex or treatment. Near significant difference seen between exemestane females and E2 females, likely due to low variance in exemestane females. S.D. error bars. Aligned ranks transformed ANOVA with Wilcoxon post hoc. n=3 to n=5.

4.3.2 Hormone Manipulation Altered the Specialization of DEGs in the Song System

PCA analyses of the expression levels for all annotated genes (18,618) across all brain regions, separated the samples according to their specific brain subdivision, then further by their respective song nucleus and surround (Figure 38). The Area X- analogous regions were more similar to the surrounding MSt than true Area X, and this was visible when all striatal samples were plotted using PCA (Figure 39).

Figure 38: PCA of all genes in all RNA-seq samples of PHD30 animals. Left striatum, right is pallial. Pallial samples are further separated into nidopallium (bottom right) and arcopallium (top right). HVC, Nido, and shelf are highly similar, with strong overlap between Nido and shelf. LMAN is distant from surrounding Nido. RA and LAi have weak overlap.

Figure 39: PCA of Area X and MSt RNA-seq samples of PHD30 animals. Area X analogous samples dissected from vehicle females (black filled, salmon triangles) and exemestane females (black filled, salmon diamonds) are more similar to MSt samples (cyan) on the left than true Area X samples on the right.

When sample to sample similarity (144 total) was plotted using Euclidean distances (Figure 40), unique patterns became apparent: HVC, HVC shelf, and Nido were most similar to each other, but HVC samples were still specialized from the rest of the nidopallium. All striatal samples were very distinct from the nidopallial and arcopallial samples, but true Area X was unique from the rest of the striatum. RA and LAi were very distinct from each other and did not overlap with any of the nidopallial samples. LMAN was the most unique nucleus, separating distantly from other nidopallial samples.

Overall, the sample to sample distance plot indicates that the transcriptomes of the surrounding regions were highly similar to one another, regardless of sex or treatment.

This allowed us to compare differentially expressed genes (DEGs) across subjects, using the read counts of surrounding regions as internal controls for each subject. 80

Figure 40: Sample to sample distance matrix of all RNA-seq samples of PHD30 animals plotted according to Euclidian distance. Dark blue indicates greater similarity and red indicates greater dissimilarity.

4.3.2.1 Estrogen Modulation Impacts the Expression Levels of DEGs in a Sex and Region-Specific Manner

Unpaired statistical analysis for differentially expressed genes (DEG) over- or under-expressed in each song nucleus relative to their surrounding brain subdivision revealed that, as in adults (Pfenning et al., 2014), vehicle treated males at PHD30 already had hundreds of such DEGs (Figure 41). The heatmaps generated using the

paired results were visually very similar.

Within Area X, vehicle and exemestane treated females had much weaker

expression levels of those genes that were specialized within vehicle control males. After

females underwent E2 treatment, the expression patterns for these DEGs became more

like those of males. Conversely, males treated with exemestane had similar DEG

patterns as Veh and E2 treated males despite their poor singing ability (Figure 41A). 81

This indicated that in females, Area X was highly responsive to increases in estrogen levels, but in males Area X was resistant to decreases in estrogen. This suggests a sex- dependent effect of estrogen on the “masculinization” of Area X.

Interestingly, vehicle treated male DEG expression patterns in HVC were most categorizable by sex (Figure 41B). These results revealed that HVC is most likely

sexually dimorphic, and not functionally dimorphic like Area X. This was very interesting

since HVC is the only song nucleus with nuclear receptor class (NR) estrogen receptors

during development in zebra finches (Ball et al., 2002; Gahr, 1996; Gahr and Konishi,

1988).

In RA, the patterns of DEG expression levels were highly similar across all

groups, regardless of sex and treatment as evidenced by the branch lengths of the

hierarchical clustering heatmap (Figure 41C). This suggested that there was very limited

DEG dimorphism in RA, despite what we know from behavior and anatomy.

Likewise, DEG expression level patterns in LMAN between males and females, across

all treatments, were also very similar (Figure 41D).

Figure 41: Heatmap of all song nuclei gene expression specializations for all treatment groups. Shown are genes with significant differential expression in male song nuclei of PHD30 animals, and their profiles in all other groups. (A) Vehicle male Area X had 326 DEGs. Note vehicle and exemestane treated females had hardly any specialized gene expression of the genes found in males. (B) Vehicle male HVC had 818 DEGs. Regardless of treatment, DEG patterns separated based on sex with males showing far greater fold changes for the 818 genes than females. (C) Vehicle male RA had 202 DEGs. Vehicle treated animals clustered with exemestane treated animals within sex, however estradiol treatment animals were most similar to each other and then more similar to females than males. (D) Vehicle male LMAN has 1023 DEGs. Vehicle and exemestane treated males were most similar to each other. Heatmaps show differential expression in log2 fold change (log2FC) values for each gene (row) by experimental group (column). Values with log2FC greater than |2| were binned at |2|. Red, increased expression in song nuclei relative to the surround. Blue, decreased expression.

4.3.2.2 Estrogen Modulation Alters the Number of DEGs in a Sex and Region- Specific Manner

4.3.2.2.1 Paired Analysis The number of DEGs identified from Area X/Area X-analogous regions were

different between animals that had a visible Area X and those that did not. Those that

had a visible Area X (males and E2 females) had many DEGs from pairwise comparisons and those without Area X (vehicle and exemestane females) had very few genes (Figure 42A,B). The few DEGs that the vehicle and exemestane treated females

had did not have the same scale of fold-change differences as was seen in those

animals with a true Area X (Figure 43). The full genelist is in Appendix F – Genelists

from Venn plots (Area X).

In HVC, vehicle treated males had the greatest number of DEGs and the largest

number of exclusive DEGs. Far more than any other treatment group, regardless of sex

or treatment. On the other end of the spectrum, vehicle treated females had the fewest

total DEGs. I found that despite their increased HVC size, E2 treated females had far

fewer DEGs than vehicle males, although the number of DEGs in E2 treated females did

reach close parity with E2 treated males (802 and 751, respectively) (Figure 42C,D). The

full genelist is in Appendix G – Genelists from Venn plots (HVC).

Conversely, females had many more DEGs than males in RA (Figure 42E,F).

Males treated with vehicle had the fewest DEGs, and males treated with E2 had the

most. However, E2 treated females had the fewest number of DEGs and females treated

with exemestane had the most. Similar to HVC, E2 treatment seems to equalize the

number of DEGs between males and females (804 and 879, respectively). The full

genelist is in Appendix H – Genelists from Venn plots (RA).

LMAN was the most transcriptionally distinct song nucleus and had the largest number of differentially expressed genes, conferring a unique transcriptional identity that was very different from all the other song nuclei, including those that are also within the nidopallium, like HVC. Exemestane treated males had the largest number of DEGs and exemestane treated females had the fewest (Figure 42G,H). Unlike HVC and RA, treatment with E2 did not bring DEG numbers to reach parity between males and females. In concordance with the general monomorphism we see in DEG expression levels (Figure 41D) and the neuroanatomy of this song nucleus (Figure 31), this nucleus seems resistant to both hormone modulation and genetic sex. The full genelist is in

Appendix I – Genelists from Venn plots (LMAN).

For all song nuclei, P-value and FDR distribution density plots are in Appendix J

– p-Value Density Plots for Pairwise Analysis and Appendix K – Adjusted p-Value (FDR)

Density Plots for Pairwise Analysis. The adjusted p-value (FDR) distribution indicates that a significance cutoff of 0.1 could have been sufficient to identify significant changes, but due to the small sample size and the low power of my analysis, I chose a more conservative significance cutoff of 0.05. Although these parameters raise our chances of committing a type II error, we determined that committing type I error was more undesirable in this context. Therefore, both pairwise and non-pairwise analysis was performed using the FDR < 0.05 cutoff.

Figure 42: Venn diagram of pairwise DEGs in each song nuclei between treatment groups stratified by sex.

Figure 43: Volcano plot of DEGs in Area X from pairwise comparisons in PHD30 animals after estrogen modulation. All genes from vehicle males (A), vehicle females (B), exemestane males (C), exemestane females (D), E2 males (E), and E2 females (F). Y axis is -log10 transformed FDR values on a log10 scale. Higher on the Y axis indicates greater significance. X-axis is log2 fold change values with MSt enriched genes appearing to the left, and Area X enriched genes appearing to the right. Marked in red is CADPS2.

4.3.2.2.2 Unpaired Analysis The unpaired analysis was far more stringent in identifying the number of DEGs present in each song nuclei, only identifying genes that were robust in their differential analysis as averaged across the three biological replicates. This raises the chance of performing a Type II error but gave greater confidence that the identified genes were truly DEGs. In the Area X/Area X-analogous regions between animals with and without a visible Area X, a few genes passed the FDR<0.05 cutoff in the pairwise analysis of vehicle treated and exemestane treated females (Figure 42B), but in non-pairwise

comparisons there were none (Figure 44B, Figure 45B,D). Furthermore, fewer DEGs

were present in the E2 treated female. However, our known positive control, CADPS2,

still passed the FDR<0.05 threshold (Figure 45F).

In HVC, similar to our pair-wise analysis, vehicle treated males had the greatest

number of DEGs and the largest number of exclusive DEGs and vehicle treated females

had the fewest total DEGs. Unlike the pairwise analysis, E2 treated females did not

reach close parity with E2 treated males (104 and 555, respectively) (Figure 42C,D).

In the unpaired analysis, females still had many more DEGs than males in RA

(Figure 42E,F). The number of DEGs in E2 treated females were more similar to males

treated with vehicle than males treated with E2 (286, 202 and 392, respectively).

Non-pairwise analysis of LMAN still had largest number of DEGs, albeit far less

than what was seen in the pairwise analysis (1023 and 1530 in vehicle treated males,

respectively). Exemestane treated males still had the largest number of DEGs, but

exemestane and E2 treated females both had the lowest (Figure 42G,H). Reinforcing the

observation that drug treatment did not alter the number of DEGs between males and

females. P-value and FDR distribution density plots are in Appendix L – p-Value Density

Plots for Non-Pairwise Analysis and Appendix M – Adjusted p-Value (FDR) Density Plots

for Non-Pairwise Analysis.

Figure 44: Venn diagram of non-pairwise DEGs in each song nuclei between treatment groups stratified by sex.

Figure 45: Volcano plot of DEGs in Area X from non-pairwise comparisons in PHD30 animals after estrogen modulation. All genes from vehicle males (A), vehicle females (B), exemestane males (C), exemestane females (D), E2 males (E), and E2 females (F). Y axis is -log10 transformed FDR values on a log10 scale. Higher on the Y axis indicates greater significance. X-axis is log2 fold change values with MSt enriched genes appearing to the left, and Area X enriched genes appearing to the right. Marked in red is CADPS2.

4.3.2.3 Within Song Capable Animals, a Core Genes May Define Song Nucleus Identity or Development and a Small Subset May Refine Function

4.3.2.3.1 Paired Analysis When I compared the DEGs of Area X from all of 4 of the song capable groups

(all males and females treated with E2), after excluding DEGs that were shared with

song incapable groups (females treated with vehicle or exemestane), I found an overlap

of 108 DEGs between all singing groups. These 108 could be the “core” genes involved

in Area X development or identity as of PHD30 (Figure 46A). When I excluded

exemestane treated males, who had poor singing ability, only 6 DEGs were shared

between vehicle treated males, E2 treated males, and E2 treated females. If Area X is

the source of the song proficiency seen in the behavioral experiments, then these 6 genes may represent those most important for “refining” Area X function in addition to the 108 “core” genes in Area X (Appendix F – Genelists from Venn plots (Area X)).

Likewise, in HVC there were 124 “core” DEGs and 18 “refining” DEGs (Figure

46B). Since HVC is thought to control song syllable sequencing (Hahnloser et al., 2002;

Long and Fee, 2008), these 18 genes may important for modulating the coding

sequence of syllables, in addition to the 124 genes that may be important for the

expansion and maturation of HVC (Appendix G – Genelists from Venn plots (HVC)).

Compared to the other nuclei, RA had very few genes remaining after I filtered

out the non-singing female groups (Figure 46C). There was only 1 “core” DEG and 2

“refining” DEGs in RA, one of which was SLIT1 (Appendix H – Genelists from Venn plots

(RA)).

LMAN retained the largest portion of DEGs (67%) after removing genes from

non-singing females. After this filtering, I saw 16 “core” DEGs and 3 “refining” DEGs

(Figure 46D)(Appendix I – Genelists from Venn plots (LMAN)).

Figure 46: Number of pairwise DEGs in song capable animals. Venn diagram of “singing” DEGs identified in (A) Area X, (B) HVC, (C) RA and (D) LMAN. Outlined in yellow are all genes common between singing capable animals and outlined in red are all genes common between “proficient” singing capable animals.

4.3.2.3.2 Unpaired Analysis There were far fewer DEGs in the unpaired analysis and even fewer after filtering

out DEGs from song incapable groups (females treated with vehicle or exemestane).

Area X had 11 “core” DEGs and 1 “refining” DEG (Figure 47A). HVC had 52 “core”

DEGs and 1 “refining” DEGs (Figure 47B). RA retained its single “core” DEG but lost all

“refining” DEGs (Figure 47C). Lastly, LMAN had 7 “core” DEGs and 2 “refining” DEGs

(Figure 47D).

Figure 47: Number of non-pairwise DEGs in song capable animals. Venn diagram of “singing” DEGs identified in (A) Area X, (B) HVC, (C) RA and (D) LMAN. Outlined in yellow are all genes common between singing capable animals and outlined in red are all genes common between “proficient” singing capable animals.

4.3.2.4 Each Song Learning Nucleus has Specific Functional Molecular Specializations

4.3.2.4.1 Paired Analysis Although we use the term “masculinization” and “feminization” in respects to the

zebra finch, these terms may be incorrect. In the Area X of vehicle treated males, the top

biological processes were for synaptic functions, neuronal development, and axon

development (Figure 48). In E2 females, the top terms were associated with ATP

metabolism and ion channels, indicating that her Area X was not “masculinized” so much

as “functionalized”.

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Figure 48: Top 10 GO terms in Area X for each ontogeny level with pairwise DEGs. Top Area X GO terms for (A) vehicle males, (B) E2 males, (C) exemestane males, and (D) E2 females. X-axis indicates the ratio of genes with specialized expression out of the total list of genes, which contribute to each value. Count, number of DEGs that contributed to each category. BP, Biological Process; CC, Cell Compartment; MF, Molecular Function. Genelist is from all vehicle male DEGs for each region with FDR <0.05. GO enrichment results include p.adjusted value <0.1. Heatmap scale, level of significance. 95

Likewise, the top GO terms for HVC in vehicle males were in neuron

differentiation, synaptic function, and axonogenesis (Figure 49A). E2 treated female had processes involved in neuron projection development, axon development, and synaptic

function (Figure 49F) which is very similar to the vehicle treated male. Exemestane

treated females had terms involved with neuron differentiation and synaptic signaling

(Figure 49D). This, as well as the average DEG expression patterns, suggest that

although HVC is sexually dimorphic, similar biological functions are taking place in the

HVC of both males and females.

Vehicle treated females were enriched for terms like cell/stem cell development,

particularly for tissues that derive from the ectoderm (eye and neural crest). This was

possibly due to artifacts from LCM dissection, since there is a high possibility that some

ependymal cells were captured alongside HVC. HVC rests just below the ventricle and,

based on the enrichment for stem cell terms and the small number of genes being

enriched for these terms, the contamination of ependymal cells likely changed the GO

enrichment analysis.

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Figure 49: Top 10 GO terms in HVC for each ontogeny level with pairwise DEGs. Top HVC GO terms for (A) vehicle males, (B) vehicle females, (C) exemestane males, (D) exemestane females, (E) E2 males, (F) E2 females.

Within RA, except for vehicle treated females, all groups had the similar top GO enrichment terms. These included neurotransmitter transport and synaptic functions

(Figure 50). Top GO terms for vehicle treated females were in ATP and nucleic acid metabolism. These results suggest that RA may be bimodal in its response to estrogen levels independently from sex, responding either to estrogen depletion or estrogen surplus.

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Figure 50: Top 10 GO terms in RA for each ontogeny level with pairwise DEGs. Top RA GO terms for (A) vehicle males, (B) vehicle females, (C) exemestane males, (D) exemestane females, (E) E2 males, (F) E2 females.

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In LMAN, the top GO enrichments in vehicle treated males were related to axon development, synaptic function, and neuron development and the top terms for vehicle treated females were related to nucleic acid metabolism, cellular respiration and transsynaptic signaling (Figure 51). However, despite the different biological functions, the molecular function of many of these DEGs were involved in ion channel activity and were also enriched for involvement at the site of the synapse.

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Figure 51: Top 10 GO terms in LMAN for each ontogeny level with pairwise DEGs. Top LMAN GO terms for (A) vehicle males, (B) vehicle females, (C) exemestane males, (D) exemestane females, (E) E2 males, (F) E2 females.

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4.3.2.4.2 Unpaired Analysis The top biological processes in the unpaired analysis of Area X in males remained the same: synaptic functions, neuronal development, and axon development

(Figure 48A-C, Figure 52A-C). However, in E2 females, the top terms changed from ATP

metabolism and ion channels (Figure 48D) to regulation of apoptosis and neuron death

(Figure 52D). Far fewer genes were classified as DEGs in the non-pairwise analysis and

only 4 genes attributed to these GO enrichment terms, decreasing the confidence that

this is indicative of a true biological phenomenon. However, this is still in alignment with

the previously mentioned hypothesis that E2 does not “masculinize” Area X so much as

“functionalize” it. Even if that “functionalization” is in preventing or modifying

programmed cell death.

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Figure 52: Top 10 GO terms in Area X for each ontogeny level with non-pairwise DEGs. Top Area X GO terms for (A) vehicle males, (B) E2 males, (C) exemestane males, and (D) E2 females.

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In the non-pairwise analysis, vehicle treated females lost their enrichment for cell/stem cell development in HVC (Figure 49B) and gained more general immunology terms like NF-κB signaling (Figure 53B). In RA, the other treatment/sex groups retained their same GO enrichment terms and vehicle treated females were the only group to change their GO enrichment; going from ATP and nucleic acid metabolism to axon development and synaptic function, which is more similar to that seen in all other treatment/sex groups. (Figure 50, Figure 54). In LMAN, the top GO enrichments did not change between pairwise and non-pairwise analysis (Figure 51, Figure 55).

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Figure 53: Top 10 GO terms in HVC for each ontogeny level with non-pairwise DEGs. Top HVC GO terms for (A) vehicle males, (B) vehicle females, (C) exemestane males, (D) exemestane females, (E) E2 males, (F) E2 females.

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Figure 54: Top 10 GO terms in RA for each ontogeny level with non-pairwise DEGs. Top RA GO terms for (A) vehicle males, (B) vehicle females, (C) exemestane males, (D) exemestane females, (E) E2 males, (F) E2 females.

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Figure 55: Top 10 GO terms in LMAN for each ontogeny level with Non-pairwise DEGs. Top LMAN GO terms for (A) vehicle males, (B) vehicle females, (C) exemestane males, (D) exemestane females, (E) E2 males, (F) E2 females.

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4.3.3 In situ Hybridization Data Corroborate with Results from RNA- seq

I evaluated the mRNA localization of a subset of genes identified through non- pairwise analysis by in-situ hybridization (36 genes across 4 song nuclei) on PHD30

animals performed in our lab, from the online zebra finch gene expression atlas on

control adult males (http://www.zebrafinchatlas.org) and our past adult studies (Jarvis et

al., 2013). I found that 104/144 (72%) of song nucleus/DEG expression patterns were

congruent with the in-situ hybridization data available online (Table 2). This validation

rate is presumably lower than what it really is, as we know differences exist between

DEGs in juveniles (this study) and adults (past studies).

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Table 2: Cross validation of top DEGs to online zebra finch mRNA atlas. Blue filled cells indicate agreement and orange filled cells indicate disagreement between mRNA localization in adult males and DEG expression values from RNA-seq in PHD30 males.

4.4 Discussion

4.4.1 Neuroarchitecture Interpretations

My song nuclei size measurements show that there is a nucleus and sex specific response to E2. HVC and Area X were the most responsive to estrogen elevation, where in females they grew or appeared, respectively. In males, these nuclei were unresponsive to E2 elevation and did not hyper-masculinize or change in any other way.

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These nuclei were also unresponsive to estrogen depletion in both males and females and exemestane treatment did not cause feminization or hyper-feminization,

respectively. RA size responded to both E2 elevation and depletion by shrinking and

approaching a more feminine phenotype in males. RA did not change in any of the

treated females. LMAN was unresponsive to either estrogen elevation or depletion in

either sex.

The responsive-ness of female HVC and Area X is interesting since HVC is the

only song nucleus that contains estrogen receptors. The prevailing current theory is that

HVC is a gateway nucleus, whose “activation” paves the way for other song nuclei to

develop, since lesioning HVC prevents E2 induced masculinization in females

(Herrmann and Arnold, 1991). Estrogen-induced Area X projecting neurons from HVC

may innervate the proto-Area X capsule described by Garcia-Calero and Scharff (2013)

to coalesce this proto-region into a true Area X in males and in E2 treated females.

Without induction, these projecting neurons may not form correct synapses and instead

terminate in the analogous region (Shaughnessy et al., 2018) without differentiating the

proto-nucleus to form a true Area X. Likewise, RA projecting neurons from HVC that

synapse onto RA neurons may promote proliferation and prevent the apoptosis that

occurs in females (Konishi and Akutagawa, 1985).

This hypothesis however does not account for why there is a decrease in RA size

in males and females treated with E2, since in vitro studies suggest that E2 treatment

should cause RA to be innervated by projections from HVC (Holloway and Clayton,

2001). Furthermore, exemestane treatment in males decreased the size of RA and had

no effect in Area X. It is possible estrogen may work in tandem with androgens to fully

develop the HVC-RA connection and RA protection, as androgen receptors are plentiful

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around RA (Ball et al., 2002; Kim et al., 2004; Tang and Wade, 2010). Regardless, these sex-specific and hormone-specific differences in RA and Area X indicate that further investigation by circuit tracing will need to be done in developing and adult animals treated with E2 and exemestane to see what role estrogen modulation has on the connectivity of the song circuit and whether that accounts for the changes seen in the song nuclei size.

4.4.2 Transcriptome Interpretations

It has long been known that estrogen can masculinize the song system in the zebra finch, but the molecular mechanisms driving this change are unknown. To begin

addressing this, I performed transcriptomic analyses of the song nuclei and their

surrounding brain regions using LCM dissected tissues from males and females treated

with vehicle, E2, or exemestane. In contrast to the neuroanatomical phenotypes, the

transcriptomic profiles of specific nuclei in these same animals were more binary in

nature than expected. Furthermore, I observed that transcriptomic changes in response

to hormone modulation and biological sex were unique to the song nucleus being

examined. Below I present interpretations for each song nucleus.

4.4.2.1 Area X

Neuroanatomical and DEG changes were concordant in Area X: males with E2

or exemestane treatment showed no overt effects of treatment on the size or

transcriptomic profile of Area X compared to males with vehicle treatment. Females with

E2 treatment developed Area X and became more transcriptionally similar to vehicle

males than vehicle females. Exemestane treated females were very similar to vehicle

treated females regarding their neuroanatomy and DEG profile.

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This was not entirely unexpected, however the extent to which Area X became specialized was very interesting when I examined my pairwise analyzed data: Vehicle treated females had only 3 DEGs that reached significance, which we presume could be false positives, and exemestane females had 19, but E2 treated females had over 200

DEGs, approaching the 400 DEGs seen in vehicle treated males and the 300 DEGs seen in E2 males. What surprised me was that males treated with exemestane had over an astounding 700 DEGs in the pairwise analysis. I had expected the DEG profile of

Area X to be strongly binary from my histological data, which had an all-or-nothing spectrum, but the extant of this binarization was unexpected (3 vs 700 DEGs).

The top GO terms for Area X (regulation of synapses, axon development, and

neuron differentiation) were shared among all groups that had an Area X (E2 females, vehicle males, E2 males, and exemestane males). The top GO terms for the two female groups without Area X were different, likely due to the low number of genes that were differentially expressed in this region. For exemestane treated females, the top terms were associated with aging and dopamine activity, with 4 or fewer genes contributing to this enrichment. For vehicle treated females, the top terms were in chemokine production and secretion, with 2 genes contributing to this enrichment. These GO term analyses for exemestane, and vehicle treated females are meaningless, however, as

there were only 19 and 3 genes that could contribute to GO enrichment, respectively.

4.4.2.2 HVC

I had expected HVC to be the most responsive to estrogen modulation since this

is the only song nucleus that contains estrogen receptors. But this was not the case, as

there weren’t any noted drastic changes in HVC’s DEG expression pattern in response

to estrogen modulation, and the DEG profiles of the animals were instead segregated by

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sex. These observed patterns also did not correlate with the neuroanatomical finding

that females treated with E2 had expanded HVC with nucleus sizes more like those seen

in vehicle males.

Similarly, males treated with exemestane did not show a difference in DEGs

when compared to males treated with E2 or vehicle. This was interesting as it was both

expected and unexpected; since HVC is the only song nucleus with estrogen receptors, I

expected there to be more of a response to estrogen depletion, but this was not the

case. This does, however, correlate well with the lack of neuroanatomical change in

HVC size. This merits further investigation since lesioning HVC or severing the RA-

projecting afferents from HVC prevents RA expansion (Herrmann and Arnold, 1991),

and changes in these compartments would not be detected using CADPS2 in situ

hybridization and cresyl violet staining alone.

Interestingly, females treated with exemestane produced what appeared to be

novel vocalizations. If control of these novel calls reside within the HVC, and estrogen

depletion plays a role in establishing the connection between HVC and RA, then this

suggests a bimodal effect of estrogen, either within HVC or in RA, to permit this

innervation. It is also possible that long calls are a variant of innate vocalizations, as

exemestane females did not have an Area X but were still able to produce unique long

calls like exemestane treated males.

4.4.2.3 RA

The minor effect of elevated estrogen on the transcriptomic profile in RA did not

correlated with the size reduction seen. It was very interesting to see that both elevated

and depleted estrogen levels caused RA to shrink in males while having no effect on

females whatsoever. It is possible that changes in a very small subset of effector genes

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are driving this difference in RA neuroanatomy, and this would escape detection from the more global view of overall trends. The SLIT1 specialization in RA of singing

proficient animals and absence in males treated with exemestane, suggest that estrogen

may have altered the necessary axon guidance cues needed for the HVC-RA projection

to develop. However, this does not explain why there is a decrease in RA size in males

and females treated with E2, as we know from in vitro studies that in both of these

instances, RA should become innervated by HVC (Holloway and Clayton, 2001) and

following innervation there has always been an observed increase in RA size in vivo

(Bottjer et al., 1985; Mooney and Rao, 1994).

There may be two reasons for this: Estrogenic signaling may permit pioneer HVC

efferents to enter RA, forming synapses which then induce activity-dependent gene

expression changes. These molecular changes may alter axon guidance cues like

SLIT1, permitting more efferents to penetrate RA, strengthening this HVC-RA

connection which then promotes further differentiation and maturation of RA. Since

exemestane treated males, like vehicle females, did not have this downregulation of

SLIT1 within RA, this may have strongly repulsed these HVC secondary axons.

Alternatively, it is possible that estrogen levels may work in tandem with androgens to

fully develop this HVC-RA connection, as androgen receptors are plentiful around HVC

and RA (Ball et al., 2002; Frankl-Vilches and Gahr, 2018; Kim et al., 2004). Regardless,

these differences, like SLIT1 in RA, may point towards other genes involved in

axonogenesis and axon guidance, which may explain the unique vocalizations produced

by animals treated with exemestane.

The top GO terms in RA of all groups except female vehicle are for the regulation

of synapse function, regulation of neuron projection development, axon development,

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and neurotransmitter regulation, this supports the possibility that estrogen modulation alters the balance of repulsive and attractive cues for axon guidance in RA. This could include projections that RA sends to the brainstem or that it receives from HVC. This will

have to be readdressed in future studies.

4.4.2.4 LMAN

Though the function of LMAN in females is unknown, my findings are consistent

with previous studies where normal adult males and females were found to have nearly

identical LMAN (Nixdorf‐Bergweiler, 1996). Despite this monomorphic nature of LMAN, I

had expected at least minor differences between males and females since the female

LMAN does not serve the same role in the song circuit as the male LMAN. In males,

LMAN projects to RA, and this input from LMAN is thought to introduce variability in the

songs produced, which in turn aids in the motor learning process (Scharff and

Nottebohm, 1991; Zevin et al., 2004). LMAN being a source of variability is interesting

since LMAN afferents also enter the RA in females (Mooney and Rao, 1994). This high

degree of similarity between female and male LMAN suggests that this specific song

nucleus is not the source of the deficient RA size seen in males treated with

exemestane. Although very few changes were detected in LMAN DEGs in response to

hormone modulation, these changes in conjunction with changes in RA and Area X (for

males treated with exemestane) may influence the behavioral phenotypes I observed in

my exemestane treated males and females.

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Chapter 5. Conclusions

The goal of this dissertation was to perform rigorous estrogen manipulations and evaluate its role in the development of the sexually dimorphic song system and behavior in a songbird. To do this, I first had to find and validate a drug that could effectively and sustainably suppress estrogen synthesis in the zebra finch. Prior studies utilized faulty aromatase inhibitors with poor pharmacodynamic properties to inhibit estrogen

synthesis. In chapter 2, I tested the efficacy of exemestane in zebra finches in vivo.

Exemestane is a clinically current, steroidal, highly specific and effective aromatase

inhibitor that suppresses estrogen synthesis by irreversibly binding to aromatase and

modifying the tertiary structure of the enzyme for ubiquitin-mediated degradation. By

using two independent assays, I was able to confirm that exemestane was very effective

at reducing serum and brain E2 levels in both adult and juvenile birds. From what could

be detected, exemestane had a negligible impact on the levels of other corticosteroids and sex steroids in the brain and serum of male and female finches.

I also found that administering E2 at doses far lower than what was previously published by Simpson and Vicario (1991a) was just as effective at masculinizing the

female song system. I explored the systemic effects of these manipulations in chapter 3,

where I examined the behavior in male and female zebra finches following hormone

manipulation. I found that despite having chronically reduced serum estrogen levels,

males treated with exemestane still developed song nuclei of comparable size to vehicle

treated males, excluding RA. However, contrary to prior publications, I found that males

treated with exemestane had diminished song quality reflected by a significantly reduced

syllable repertoire and visible deterioration in syllable sequencing/motif organization. In

agreement with the prior literature (Simpson and Vicario, 1991b), I found that females

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treated with E2 did not have their neuroanatomy masculinized to the same degree as seen in males. However, these E2 masculinized females, without being given testosterone, were still capable of producing complex learned songs and engaging in

courtship behavior like vehicle control males.

In chapter 4, to uncover the molecular pathways that may be involved in this estrogen induced masculinization, I examined the transcriptome of four song motor nuclei in PHD30 animals, near the onset of the sensorimotor learning phase. I performed

differential gene expression analysis of these song nuclei, comparing them to their

surrounding brain regions to see if/how they changed in response to estrogen

manipulation. I discovered that estrogen modulation impacted the song system

transcriptome in a sex-dependent manner, with many of the differences representing a

change in “functional dimorphism” as opposed to “sexual dimorphism”. I observed that

despite HVC having exclusive expression of ESR1, it was not affected by estrogen

modulation nearly as strongly as Area X, which paradoxically does not have ESR1/ESR2

anywhere, including near its immediate vicinity. I also was able to establish that, save for

some effector genes like SLIT1/ ROBO1 (Wang et al., 2015) in RA, the transcriptomic

profile of RA is modestly dimorphic in the number of DEGs and not their expression

levels, while LMAN is generally monomorphic and not delineated by either sex, function,

or estrogenic state.

Taking all results together, they support the hypothesis (Konishi and Akutagawa,

1985; Odom et al., 2014) that vocal learning in the zebra finch is dimorphic due to its

development being suppressed in the female and not due to special properties within the

male. This suppression is incomplete, and likely influences a small handful of effector

molecules that ultimately inhibit the maturation of the song system, driving it towards

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atrophy-mediated decay. The molecularly homogeneous nature of LMAN supports the idea that female selective decay is not solely due to reduced Z gene product in the

heterogametic female or the actions of steroid hormones, suggesting that the expansion

of the song system in males is indeed the passive developmental trajectory.

The mechanistic cause of this female selective decay is heretofore unknown, but

the transcriptome data from this project alongside the rapidly diverging nature of avian

sex chromosomes (Xu et al., 2018; Zhang et al., 2014; Zhou et al., 2014), suggest that

the loss in females is an evolutionarily active process. Although additional analysis

needs to be performed, the current work in this thesis is in agreement with the

hypothesis that non-sexually dimorphic vocal learning is the ancestral trait of all

songbirds, and that independent losses of singing in females is a recent development in

the evolution of certain songbird species (Figure 56) (Odom et al., 2014).

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Figure 56: Phylogenetic tree of oscine songbirds and presence or absence of female song. 323/1,141 species from 34/44 songbird families for which the author could identify female song. Female song was present in 229 species (32 families; red terminal nodes) and absent in 94 species (19 families; blue terminal nodes). Prevalence of female song suggests that the ancestral songbird likely had singing females. From Odom et al. (2014)

From what I have observed, estrogen may incidentally play a dual role in serving as a labile control mechanism for: 1) refining the development of the song system in

males, and; 2) overturning the active decay of the song system which may be encoded

within the female W chromosome (Figure 57).

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Figure 57: Possible model of estrogenic rescue of song loss. Alternative hypothesis of female song loss in sexually dimorphic songbirds. The song permissive state is the ancestral trait of songbirds (blue line), the rapid degeneration of the W chromosome either alone or in tandem with rapid evolution of the Z chromosome may contribute to the active restriction of the development of the song system in sexually dimorphic songbirds (red line). Estrogen may work downstream of this induced atrophy to rescue the nascent song system (green line).

5.1 Some Speculative Conclusions and Future Directions

Estrogen modulation could have affected various sites in the song system to result in the changes observed in behavior, anatomy, and DEGs through 2 mechanisms:

1) Estrogenic activity could have exerted strong changes on individual song nuclei or other brain regions that then influenced the other song nuclei; or 2) Estrogenic activity could have exerted minor changes on all of the nuclei, which in turn influenced each other through efferent connections. The former possibility has been highly favored by other investigators due to the nature of ESR1 localization being restrained to the HVC.

Both scenarios may explain why the critical window for estrogen application is not the entirety of the vocal learning period. The estrogen sensitive period lasts from PHD2-45 in the zebra finch (Konishi and Akutagawa, 1988), this constrains it to the sensory learning phase of song system development, when the song system is being established and the connections are being made (Bottjer et al., 1985; Mooney and Rao, 1994). This 133

estrogen sensitive period appears to end shortly after the onset of sensorimotor learning, when the internuclear connections have established and are undergoing expansion/pruning. This may also explain why the female song system becomes

resistant to estrogenic “rescue” after PHD45 since the song nuclei may shift dependence

from estrogen-induced to activity-driven survival and expansion.

Some possible scenarios of estrogen-induced remodeling may involve RA

becoming more permissive to afferents from HVC and LMAN, as evidenced by the

changes in SLIT1 transcripts seen in song proficient animals. In concert with this,

estrogen may also permit HVC to expand, increasing the number of Area X and RA

projecting neurons and raising the number of pioneer neurons to increase the likelihood

of Area X and RA becoming innervated. Estrogen may also make cells of the striatum

more open to synapsing with these projections from HVC, allowing Area X to coalesce

into a discrete nucleus. All of these scenarios are purely speculative, but they could be

assessed in future projects. I examined the gross morphology of song nuclei in this

study, but I did not examine the connectivity or activity of different song nuclei. I

analyzed transcriptomic changes from bulk tissues, leaving the identity of estrogen

responsive cell types unknown. Additionally, my work was performed in PHD30 animals,

but it would be informative to go earlier and examine PHD15 animals, before the onset

of sensorimotor learning when the song system is monomorphic between the sexes.

Beyond the zebra finch model, it would be very interesting to examine other

species that are also sexually dimorphic, like the Bengalese finch (Tobari et al., 2005), to

see if what I observed is true in all sexually dimorphic vocal learning songbirds, or if it is

unique to zebra finches. Partially sexually dimorphic species, where both sexes sing with

unequal distribution of singing, like canaries (Nottebohm, 1980; Nottebohm and Arnold,

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1976), and non-sexually dimorphic species where both sexes sing equally well, like bay wrens (Brenowitz and Arnold, 1986), cardinals (Jawor and MacDougall-Shackleton,

2008) and crows (Wang et al., 2009), should also be included in future studies to identify

the source of female selective decay. Because of the rapid radiation in the speciation of

songbirds (Jarvis et al., 2014; Odom et al., 2014), and the rich repertoire of complex

behaviors, the cause of sexual dimorphism as it pertains to vocal learning is a rich vein

of study in comparative evolutionary genomics.

Of course, specific estrogen signaling pathways will also need to be dissected to

understand what role estrogen signaling plays in song system development. The three

classes of estrogen receptors exert different effects on the cell (Hall and McDonnell,

2005; Maggiolini and Picard, 2010). These receptors bind to common and unique

ligands. Selective agonists and antagonists, and their interactions, will need to be

isolated, manipulated, and examined in further detail to learn how these song-selective

brain regions differentiate during development. My study has also identified candidate

genes that were common to all song-capable animals. These candidate genes may help

us discover the molecular ontogeny of the song system.

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Appendix A – Animal Husbandry

Zebra finches from our breeding colony at Duke University were kept on a 12:12 light/dark cycle at 23-29°C and 30-70% humidity. Fortified finch seed mixture (Kaytee), enriched grit (Higgins), poultry feed (Purina), cuttlefish bone and water were provided ad-libetum. For animals used in this experiment, to obtain high quality eggs and hearty chicks, this normal diet was supplemented with hardboiled eggs and oranges given twice weekly. Water was also supplemented with liquid calcium borogluconate (Morning Bird).

Baths were given weekly with cage pan changes, and cage enrichments were provided and exchanged during cage changes (every 3 months). Breeding animals were kept in pairs, and native or foster offspring were kept with their parents until the time of sacrifice at post PHD30 or until PHD60 when the offspring have completed their sensory learning period and they are able to feed on their own. Nest boxes were cleared out and replenished with fresh nesting material between clutches.

To synchronize embryo development from clutches of multiple parents, eggs were collected daily from nest boxes within a 2-week period and placed in developmental stasis at 15°C with 80% humidity on a 30° angle rotator set to rotate once every 2 hours. Eggs were kept for no longer than 3 weeks in a P-008A BIO incubator

(Showa Furanki Corp). Egg collection from nesting pairs chosen for fostering was ceased 3 days prior to artificially incubating the synchronized eggs, permitted these foster parents to brood a clutch of 3-4 eggs. After synchronizing a cohort of eggs in the low temperature incubator, these eggs were then moved to a higher temperature

incubator at 37.5°C with ~50% humidity on a 30° angle rotator set to rotate once per

hour and incubated for up to 14-15 days. When chicks started to pip (crack the

eggshell), usually beginning around incubation day 13, all eggs were transferred from

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the incubator’s egg rotators to the hatch plate. Within 16 hours of hatching, animals were tagged and transferred to the chosen foster nests in the aviary. To tag them, a distal toe joint of the newly hatched animals were removed with sterile forceps and a scalpel; the removed sample was used to determine the sex of the animal by PCR-genotyping with degenerate P2 (TCTGCATCGCTAAATCCTTT) and P8

(CTCCCAAGGATGAGRAAYTG) primers as previously described by Adam et al. (2014).

Chicks born natively under their biological parents had their downy feathers removed in

unique patterns for identification and were later sexed using cells collected from buccal

swabs during banding (~PHD 10). Nests were limited to no more than 5 chicks to reduce

nestling mortality. Animals were separated into two colonies: one for animals treated

with exemestane or estradiol, and the main colony for those treated with vehicle and

general breeding. To prevent cross contamination of drugs by contact between animals,

exemestane and estradiol treatments were not done concurrently and treatment with the

opposing drug was not conducted until 48 hours had passed following the removal of the

active compound and the cage was replaced with a cleaned and sanitized one.

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Appendix B – Duke Metabolomics Core Protocol for uHPLC-MS/MS for SteroIDQ Panel

Report from Duke Proteomics Core: Lisa St. John-Williams (sample preparation, data collection, data analysis, report writing), Will Thompson (study design, scientific oversight, report writing), and Arthur Moseley (scientific oversight, report writing).

The AbsoluteIDQ Stero17 assay quantifies 17 steroid hormones. The Stero17 kit includes all requisite calibration standards, internal standards, and QC samples. The use of these standards according to the detailed analysis protocol, which was validated in Biocrates’ lab in

Austria, assures assay harmonization and standardization within a project, across projects, and across laboratories. Selective analyte detection is accomplished by use of a triple quadrupole tandem mass spectrometer operated in Multiple Reaction Monitoring (MRM) mode in which specific precursor to product ion transitions are measured for every analyte and stable isotope labeled internal standard. There are two separate tandem mass spectrometric analyses of each sample. Sample analysis is performed by a UPLC (ultra-high pressure liquid chromatography) tandem MS method using a reversed phase analytical column for analyte separation (LC-MS/MS,

Figure 1). A second UPLC/MS-MS method is used to acquire data for DHEAS (chromatogram not shown).

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22 retention time scheduled transitions 17 Steroids 15 stable-isotope internal standards

Figure 1. LC-MS/MS chromatogram showing a base-peak ion chromatogram of the analysis of steroids using the Biocrates Stero17 kit.

The calibration standards provided in the Biocrates AbsoluteIDQ kit were used for quantitation.

Seven calibration standards are used for highly accurate and reproducible quantitation of the steroids as shown in Figure 2. Calibration standards were fit with a linear regression using 1/x2 weighting. Figure 2 also shows a schematic and representative example of how the calibration curve would be used to back-calculate QC and sample concentrations for the different analytes.

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LC-MS “Quantitative” 9 y = 0.0205x + 0.0089 R² = 0.9978 8

3 Ratio to Internal Standard 2 7-point standard curve, 1 establish linearity

0 0 100 200 300 400 Concentration (uM)

Std. Curve QC standards Samples

Figure 2. Schematic depicting the quantitative calibration methodology used in the Biocrates Stero17 kit for LC-MS/MS analysis

The samples were prepared in a 96-well plate format using the layout shown in Figure 3.

96-well Plate Layout double blank Calibration Matrix ‘zero’ sample Calibration curve Low, Mid, High QC Samples Global Reference QC SPQC Study Samples

Figure 3. Schematic depicting the 96-well plate layout for the analysis of study samples including: blanks, calibration standards, and QC samples from Biocrates. Two additional QC samples were analyzed: the DPMCF Global Reference QC and the brain study sample pool QC (SPQC).

Brain Sample Preparation: Brain tissue samples were transferred into Precellys soft tissue homogenizing CK14 tubes (Bertin Technologies, Montigny-le-Bretonneux, France) and weighed.

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Each sample was diluted with 3 volumes of tissue extraction buffer (85:15 Ethanol:10mM

Phosphate Buffer, v:v). For example, use 90µL tissue extraction buffer for 30mg brain tissue.

Samples were then homogenized using 3x10-second pulses in the Precellys Evolution, between which samples were cooled for 60 seconds using the Cryolys function. The samples were then sonicated in an ice bath for 5 minutes and were stored at -80C until the day of sample extraction.

Plasma Sample Preparation: The typical volume of plasma extracted for steroid analysis is 500µL to allow the detection low abundance steroids. The volume of the plasma samples for this study ranged from 82µL to 377µL. Therefore, the sensitivity of the assay is as much as 6-fold less than expect due to sample volume limitation.

Sample Extraction: On the day of Stero17 sample extraction the plasma samples were thawed and vortexed. The brain homogenates were subjected to a single 10 second burst in the Precellys followed by centrifugation at 4°C for 10 minutes at 10,000rpm. The samples were then stored on ice until addition to the kit plate.

Samples were prepared using the AbsoluteIDQ® Stero17 kit (Biocrates Innsbruck,

Austria) in accordance with their detailed protocol. A proprietary 96-well solid phase extraction

(SPE) plate was provided with the kit. The SPE plate was washed with 1mL dichloromethane, 1 mL acetonitrile, 1mL methanol, and 1mL water. Blank Calibration Matrix, Biocrates calibration standards, Biocrates QC samples, brain study samples, and the SPQC sample were added to an empty 2mL 96-well plate in 500µL aliquots. Plasma samples were added to the plate in their entirety since they were all provided in aliquots of less than 500µL. After the addition of 10uL of the supplied Stero17 internal standard to the appropriate wells 400µL water was added to each well. Samples in the 2mL plate were mixed with 3 aspirate/dispense cycles using an 8-channel pipette then transferred to the appropriate wells of the SPE plate. Samples were allowed to elute by gravity for 10 minutes after which a gentle vacuum was applied. After the SPE plate was washed with 500µL water, it was dried under a stream of nitrogen while full vacuum was applied for one hour to ensure complete drying. Steroids were eluted from the SPE plate with 500µL dichloromethane into a 1mL 96-well collection plate. The eluent was dried under a stream of 141

nitrogen at 50°C for 10 minutes. The dichloromethane elution and drying was repeated into the same wells of the 1mL plate. The dried dichloromethane eluents were reconstituted in 50µL 25% methanol in water, capped, sonicated in a water bath for 1 minute, and then shaken at 600rpm for

5 minutes.

Elution of DHEAS was then accomplished by adding two aliquots 200µL acetonitrile to the SPE plate and collecting the extracts in a second 96-well plate. The acetonitrile samples were diluted with 200µL water then shaken at 400rpm for 5 minutes.

A pool of equal volumes of all 20 brain homogenate samples analyzed was created (5226

SPQC). The pooled sample was prepared and analyzed in the same way as the study samples.

From the plate, this sample was injected once before and once after the study samples in order to measure the performance of the assay across the sample cohort. The analyses of this pool can be used to assess potential batch effects. The order of injection of the samples is shown in

Figure 4. There was insufficient volume of the plasma samples to prepare a plasma study pool.

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Sample Injection Sequence

double blank Calibration Matrix ‘zero’ sample Calibration curve Low to High Low, Mid, High QC Samples Global Reference QC SPQC Plasma Samples 1 to 20 Mid QC Sample Brain Samples 1 to 20 Global Reference QC SPQC High, Mid, Low QC Samples Calibration Curve Low to High

Figure 4. Schematic depicting the injection order of the samples for UPLC analysis. Dichloromethane extracts of each sample were injected alternately with the acetonitrile extracts of the same sample. Note that the Global Reference QC was prepared once and analyzed two times giving a measure of the analytical variability of the MS/MS analyses. The SPQC sample was also prepared one time and analyzed two times giving an additional measure of analytical variability.

Sample Analysis: UPLC separation of steroids (except for DHEAS) in the dichloromethane extracts was performed using a Waters (Milford, MA) Acquity UPLC with a proprietary column fitted with a proprietary guard column, both supplied by Biocrates. Analytes were separated using a gradient from a 30% proprietary aqueous solution to 85:10:5 Acetonitrile:Methanol:Water

(v:v:v). Total UPLC analysis time was approximately 6 minutes per sample. DHEAS was analyzed by UPLC with total analysis time of approximately 6 minutes per sample using the same column and mobile phases. Using electrospray ionization in positive mode, samples were introduced directly into a Xevo TQ-S triple quadrupole mass spectrometer (Waters) operating in the Multiple Reaction Monitoring (MRM) mode. MRM transitions (compound-specific precursor to 143

product ion transitions) for each analyte and internal standard were collected over the appropriate retention time. The UPLC-MS/MS data were imported into Waters application TargetLynx™ for peak integration, calibration, and concentration calculations. The UPLC-MS/MS data from

TargetLynx™ were analyzed using Biocrates MetIDQ™ software.

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Appendix C – Full Statistics for E2 Quantification

Rank Sums: Kruskal-Wallis Test/Wilcox Level Count Score Sum Expected score Score Mean ChiSquare Df pValue Adult Brain Estradiol Male Veh 5 35 27.5 7 3.716 1 0.0539 Exem 5 20 27.5 4 Female Veh 5 37.5 27.5 7.5 5.539 1 0.0186 Exem 5 17.5 27.5 3.5 All (Both Sexes) Veh 10 140 105 14 9.668 1 0.0019 Exem 10 70 105 7

Adult Serum Estradiol Male Veh 5 26 27.5 5.2 0.101 1 0.7511 Exem 5 29 27.5 5.8 Female Veh 5 38.5 27.5 7.7 5.345 1 0.0208 Exem 5 16.5 27.5 3.3 All (Both Sexes) Veh 10 124 105 12.4 2.101 1 0.1472 Exem 10 86 105 8.6

Juvenile Serum Estradiol Male Veh 6 92 87 15.33 20.434 2 <0.0001 Exem 14 118 203 8.43 E2 8 196 116 24.5 Female Veh 10 84 115 8.4 15.281 2 0.0005 Exem 4 21 46 5.25 E2 8 148 92 18.5 All (Both Sexes) Veh 16 375 408 23.44 36.269 2 <0.0001 Exem 18 224 459 12.44 E2 16 676 408 42.25

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Steel-Dwass Pairs (Post-hoc)

Level (n) Level (n) Score Mean Diff Std Err Dif Z pValue Df Lower Cl Upper Cl Adult Brain Estradiol Male Exem (5) Veh (5) -2.8 1.556 -1.799 0.072 1 -0.006 0 Female Exem (5) Veh (5) -3.8 1.699 -2.236 0.025 1 -0.006 0 All (Both Sexes) Exem (10) Veh (10) -6.9 2.251 -3.064 0.0022 1 -0.005 0

Adult Serum Estradiol Male Exem (5) Veh (5) 0.4 1.892 0.211 0.833 1 -1.03 0.034 Female Exem (5) Veh (5) -4.2 1.903 -2.207 0.027 1 -0.96 0 All (Both Sexes) Exem (10) Veh (10) -3.7 2.622 -1.411 0.158 1 -0.217 0.001

Juvenile Serum Estradiol Male E2 (8) Exem (14) 10.9 2.741 3.978 0.0002 2 0.439 48.382 E2 (8) Veh (6) 6.9 2.259 3.034 0.007 2 0.282 411.137 Exem (8) Veh (6) -6.8 2.702 -2.512 0.032 2 -0.021 0.001 Female E2 (8) Veh (10) 8.9 2.532 3.509 0.0013 2 2.741 139.553 E2 (8) Exem (4) 5.8 2.208 2.632 0.023 2 2.116 187.964 Exem (4) Veh (10) -2.9 2.475 -1.202 0.45 2 -0.937 0.061 All (Both Sexes) E2 (16) Exem (18) 16.9 3.364 5.037 <0.0001 2 3.828 48.382 E2 (16) Veh (16) 15.4 3.316 4.655 <0.0001 2 2.903 48.375 Exem (18) Veh (16) -10.7 3.363 -3.177 0.004 2 -0.078 -0.008

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Rank Sums: Wilcoxon Test Score Expected Score Level Count ChiSquare Df pValue Sum score Mean Adult Brain Pan-Steroids Male: Cortisone (E) Veh 5 28 27.5 5.6 0.0112 1 0.9158 Exem 5 27 27.5 5.4 Female: Cortisone (E) Veh 5 25 27.5 5 1 1 0.3173 Exem 5 30 27.5 6 Both Sex: Cortisone (E) Veh 10 98.5 105 9.85 0.3076 1 0.5792 Exem 10 111.5 105 11.15 Male: Corticosterone (B) Veh 5 35 27.5 7 2.4545 1 0.117 Exem 5 20 27.5 4 Female: Corticosterone (B) Veh 5 35 27.5 7 2.4545 1 0.1172 Exem 5 20 27.5 4 Both Sex: Corticosterone (B) Veh 10 135 105 13.5 5.1429 1 0.0233 Exem 10 75 105 7.5 Male: Aldosterone (A) Veh 5 31.5 27.5 6.3 0.7024 1 0.402 Exem 5 24.5 27.5 4.7 Female: Aldosterone (A) Veh 5 37.5 27.5 7.5 4.3902 1 0.0361 Exem 5 17.5 27.5 3.5 Both Sex: Aldosterone (A) Veh 10 133.5 105 13.35 4.6589 1 0.0309 Exem 10 76.5 105 7.65

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Male: 11-Deoxycorticosterone (DOC) Veh 5 31 27.5 6.2 0.5378 1 0.4633 Exem 5 24 27.5 4.8 Female: 11-Deoxycorticosterone (DOC) Veh 5 31 27.5 6.2 0.5841 1 0.4447 Exem 5 24 27.5 4.8 Both Sex: 11-Deoxycorticosterone (DOC) Veh 10 122 105 12.2 1.7106 1 0.1909 Exem 10 88 105 8.8 Male: Testosterone (T) Veh 5 36 27.5 7.2 3.5876 1 0.0582 Exem 5 19 27.5 3.8 Female: Testosterone (T) Veh 5 27.5 27.5 5.5 0 1 1 Exem 5 27.5 27.5 5.5 Both Sex: Testosterone (T) Veh 10 121 105 12.1 2.5268 1 0.1119 Exem 10 89 105 8.9 Male: Progesterone (P) Veh 5 35 27.5 7 2.6299 1 0.1049 Exem 5 20 27.5 4 Female: Progesterone (P) Veh 5 35 27.5 7 3.7156 1 0.0539 Exem 5 20 27.5 4 Both Sex: Progesterone (P) Veh 10 133.5 105 13.35 5.5664 1 0.0183 Exem 10 76.5 105 7.65 Male: Dihydrotestosterone (DHT) Veh 5 31 27.5 6.2 0.8092 1 0.3684 Exem 5 24 27.5 4.8 Female: Dihydrotestosterone (DHT) Veh 5 27.5 27.5 5.5 0 1 1 Exem 5 27.5 27.5 5.5 Both Sex: Dihydrotestosterone (DHT) Veh 10 111 105 11.1 0.5323 1 0.4656 Exem 10 99 105 9.9

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Rank Sums: Wilcoxon Test Score Expected Score Level Count ChiSquare Df pValue Sum score Mean Adult Serum Pan-Steroids Male: Cortisone (E) Veh 5 16 27.5 3.2 5.7709 1 0.0163 Exem 5 39 27.5 7.8 Female: Cortisone (E) Veh 5 22 27.5 4.4 1.32 1 0.2506 Exem 5 33 27.5 6.6 Both Sex: Cortisone (E) Veh 10 68 105 6.8 7.8287 1 0.0051 Exem 10 142 105 14.2 Male: Corticosterone (B) Veh 5 32 27.5 6.4 0.8836 1 0.3472 Exem 5 23 27.5 4.6 Female: Corticosterone (B) Veh 5 35 27.5 7 2.4545 1 0.1172 Exem 5 20 27.5 4 Both Sex: Corticosterone (B) Veh 10 135 105 13.5 5.1429 1 0.0233 Exem 10 75 105 7.5 Male: Aldosterone (A) Veh 5 27.5 27.5 5.5 0 1 1 Exem 5 27.5 27.5 5.5 Female: Aldosterone (A) Veh 5 32 27.5 6.4 0.8836 1 0.3472 Exem 5 23 27.5 4.6 Both Sex: Aldosterone (A) Veh 10 116 105 11.6 0.693 1 0.4051 Exem 10 94 105 9.4

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Male: 11-Deoxycorticosterone (DOC) Veh 5 30 27.5 6 0.2727 1 0.6015 Exem 5 25 27.5 5 Female: 11-Deoxycorticosterone (DOC) Veh 5 27 27.5 5.4 0.0109 1 0.9168 Exem 5 28 27.5 5.6 Both Sex: 11-Deoxycorticosterone (DOC) Veh 10 110 105 11 0.1429 1 0.7055 Exem 10 100 105 10 Male: Testosterone (T) Veh 5 33 27.5 6.6 1.32 1 0.2506 Exem 5 22 27.5 4.4 Female: Testosterone (T) Veh 5 26 27.5 5.2 0.0988 1 0.7533 Exem 5 29 27.5 5.8 Both Sex: Testosterone (T) Veh 10 112.5 105 11.25 0.3222 1 0.5703 Exem 10 97.5 105 9.75 Male: Progesterone (P) Veh 5 39 27.5 7.8 5.7709 1 0.0163 Exem 5 16 27.5 3.2 Female: Progesterone (P) Veh 5 35 27.5 7 2.4545 1 0.1172 Exem 5 20 27.5 4 Both Sex: Progesterone (P) Veh 10 144 105 14.4 8.6914 1 0.0032 Exem 10 66 105 6.6 Male: Dihydrotestosterone (DHT) Veh 5 30.5 27.5 6.1 0.3951 1 0.5296 Exem 5 24.5 27.5 4.9 Female: Dihydrotestosterone (DHT) Veh 5 28 27.5 5.6 0.0109 1 0.9168 Exem 5 27 27.5 5.4 Both Sex: Dihydrotestosterone (DHT) Veh 10 108 105 10.8 0.0516 1 0.8203 Exem 10 102 105 10.2

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Steel-Dwass Pairs (Post-hoc) Level (n) Level (n) Score Mean Diff Std Err Dif Z pValue Df Lower Cl Upper Cl Adult Brain Pan-Steroids Male: Cortisone (E) Exem (5) Veh (5) 0 1.891 0 1 1 -51 53 Female: Cortisone (E) Exem (5) Veh (5) 0.8 1 0.8 0.424 1 0 35 Both Sexes: Cortisone (E) Exem (10) Veh (10) 1.2 2.344 0.512 0.609 1 -13 22 Male: Corticosterone (B) Exem (5) Veh (5) -2.8 1.915 -1.46 0.144 1 -1873 1400 Female: Corticosterone (B) Exem (5) Veh (5) -2.8 1.915 -1.462 0.144 1 -1389 586 Both Sexes: Corticosterone (B) Exem (10) Veh (10) -5.9 2.646 -2.23 0.026 1 -1079 -170 Male: Aldosterone (A) Exem (5) Veh (5) -1.4 1.909 -7.333 0.463 1 -44 27 Female: Aldosterone (A) Exem (5) Veh (5) -3.8 1.909 -1.99 0.047 1 -67 1 Both Sexes: Aldosterone (A) Exem (10) Veh (10) -5.6 2.641 -2.121 0.034 1 -38 0 Male: 11-Deoxycorticosterone (DOC) Exem (5) Veh (5) -1.2 1.909 -0.629 0.530 1 -27 22 Female: 11-Deoxycorticosterone (DOC) Exem (5) Veh (5) -1.2 1.832 -0.655 0.512 1 -11 6 Both Sexes: 11-Deoxycorticosterone (DOC) Exem (10) Veh (10) -3.3 2.600 -1.269 0.204 1 -8 4 Male: Testosterone (T) Exem (5) Veh (5) -3.2 1.795 -1.783 0.746 1 -29 4 Female: Testosterone (T) Exem (5) Veh (5) 0 0 0 1 1 0 0 Both Sexes: Testosterone (T) Exem (10) Veh (10) -3.1 2.013 -1.540 0.124 1 -11 0 Male: Progesterone (P) Exem (5) Veh (5) -2.8 1.850 -1.514 0.130 1 -205 25 Female: Progesterone (P) Exem (5) Veh (5) -2.8 1.556 -1.799 0.072 1 -145 0 Both Sexes: Progesterone (P) Exem (10) Veh (10) -5.6 2.416 -2.318 0.021 1 -75 0 Male: Dihydrosterone (DHT) Exem (5) Veh (5) -1.2 1.556 -0.771 0.441 1 -815 12 Female: Dihydrosterone (DHT) Exem (5) Veh (5) 0 0 0 1 1 0 0 Both Sexes: Dihydrosterone (DHT) Exem (10) Veh (10) -1.1 1.645 -0.669 0.504 1 0 0

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Steel-Dwass Pairs (Post-hoc) Level (n) Level (n) Score Mean Diff Std Err Dif Z pValue Df Lower Cl Upper Cl Adult Serum Pan-Steroids Male: Cortisone (E) Exem (5) Veh (5) 4.4 1.915 2.298 0.022 1 28 11264 Female: Cortisone (E) Exem (5) Veh (5) 2 1.915 1.044 0.296 1 -157 264 Both Sexes: Cortisone (E) Exem (10) Veh (10) 7.3 2.645 2.76 0.006 1 38 249 Male: Corticosterone (B) Exem (5) Veh (5) -1.6 1.915 -0.836 0.403 1 -9837 3035 Female: Corticosterone (B) Exem (5) Veh (5) -2.8 1.915 -1.462 0.144 1 -37262 8012 Both Sexes: Corticosterone (B) Exem (10) Veh (10) -5.9 2.646 -2.23 0.026 1 -14427 -510 Male: Aldosterone (A) Exem (5) Veh (5) 0 1.909 0 1 1 -65 76 Female: Aldosterone (A) Exem (5) Veh (5) -1.6 1.915 -0.836 0.403 1 -252 159 Both Sexes: Aldosterone (A) Exem (10) Veh (10) -2.1 2.643 -0.795 0.427 1 -135 27 Male: 11-Deoxycorticosterone (DOC) Exem (5) Veh (5) -0.8 1.915 -0.418 0.676 1 -981 633 Female: 11-Deoxycorticosterone (DOC) Exem (5) Veh (5) 0 1.915 0 1 1 -1190 895 Both Sexes: 11-Deoxycorticosterone (DOC) Exem (10) Veh (10) -0.9 2.646 -0.340 0.734 1 -596 352 Male: Testosterone (T) Exem (5) Veh (5) -2 1.915 -1.044 0.296 1 -218 243 Female: Testosterone (T) Exem (5) Veh (5) 0.4 1.909 0.209 0.834 1 -9 9 Both Sexes: Testosterone (T) Exem (10) Veh (10) -1.4 2.643 -0.53 0.596 1 -27 9 Male: Progesterone (P) Exem (5) Veh (5) -4.4 1.915 -2.298 0.022 1 -3411 -140 Female: Progesterone (P) Exem (5) Veh (5) -2.8 1.915 -1.462 0.144 1 -11550 328 Both Sexes: Progesterone (P) Exem (10) Veh (10) -7.7 2.646 -2.91 0.004 1 -3411 -745 Male: Dihydrosterone (DHT) Exem (5) Veh (5) -1 1.909 -0.524 0.600 1 -958 143 Female: Dihydrosterone (DHT) Exem (5) Veh (5) 0 1.915 0 1 1 -23 193 Both Sexes: Dihydrosterone (DHT) Exem (10) Veh (10) -0.5 2.642 -0.189 0.850 1 -53 54

152

Appendix D – Scripts for Analysis in R

#Make column data matrix #Make a much larger version for all samples when creating PCA plot/distance matrix. Instead of one Sex, one Tx, 2 areas, 3 subjects (6 samples), used two sex, three Tx, 8 areas, 3 subjects (144 samples). diffExpression_labels_SexTx <- data.frame( FileName = c("Sex1_Tx_Surr","Sex2_Tx_Surr","Sex3_Tx_Surr", "Sex1_Tx_Nuc","Sex2_Tx_Nuc","Sex3_Tx_Nuc"), Subject = c("A","B","C","A","B","C"), Area = c(0,0,0,1,1,1))

#Run DESeq2 on the matrix of count data to output results as a data frame and a dds object #Ran two sets of DE comparisons: #Non-pairwise comparisons of area for average comparisons. Generates more stringent list for comparisons across group averages # design = ~Area #Pairwise comparisons of area within subject for individual comparisons. Generates less stringent list for between group comparisons # design = ~Subject + Area

DiffExp2 <- function(Labels, Output_of_aggregation) dds <- DESeq2::DESeqDataSetFromMatrix(Output_of_aggregation, Labels, design = ~Subject + Area); dds <- DESeq(dds) res <- results(dds) tabl <- as.data.frame(res) tabl <- rownames_to_column(tabl, var = "id") result <- merge(Output_of_aggregation, tabl, by.x=0, by.y="id",sort=F) colnames(result)[1] <- "id" return(result) }

DDS <- function(Labels, Output_of_aggregation) { dds <- DESeq2::DESeqDataSetFromMatrix(Output_of_aggregation, Labels, design = ~Subject + Area); dds <- DESeq(dds) return(dds) }

#Get gene names from BioMart mart <- useMart('ensembl') TaeGutEnsembl <- useMart("ensembl", dataset = "tguttata_gene_ensembl") genelist <- getBM(attributes = c("ensembl_gene_id", "external_gene_name"), mart = TaeGutEnsembl) 153

#Run DESEQ2 vst pipeline (see original documentation) on all of (144) the samples, and then run the PCA data with ggplot to see if samples cluster #Repeat for Area X & MSt only by using Area X and MSt samples (36 samples)

DEGall <- DESeq2::DESeqDataSetFromMatrix(MergedCounts_byGroup, diffExpression_labels, design = ~Subject+Area) DEGall <- DESeq(DEGall) vDEGall <- vst(DEGall, blind=FALSE) pcaAll <- plotPCA(vDEGall, intgroup=c("Area", "Sex","Treatment"), returnData=TRUE) percentVar <- round(100 * attr(pcaAll, "percentVar")) PCA<- ggplot(pcaAll, aes(PC1, PC2, stroke=1.5)) + geom_jitter(aes(shape=Treatment, color=Area, fill=Sex, size=Treatment)) + scale_shape_manual(values = c(21,24,23)) + scale_size_manual(values = c(4,4,4)) + scale_fill_manual(values = c("Black","Red")) + guides(fill = guide_legend(override.aes=list(size=3, color=c("Black","Red"))), shape = guide_legend(override.aes = list(size=3, stroke=1.5)), color = guide_legend(override.aes = list(size=1, stroke=2))) + xlab(paste0("PC1: ",percentVar[1],"% variance")) + ylab(paste0("PC2: ",percentVar[2],"% variance")) + stat_ellipse(aes(color=Area), type = "euclid", level = 2) + theme(legend.title = element_text(size=12), legend.text = element_text(size=10), axis.title.x=element_text(size=15), axis.title.y=element_text(size=15), axis.text.x=element_text(size=15), axis.text.y=element_text(size=15)) + theme(panel.background = element_rect(fill = 'gray95')) coord_fixed()

#Each list of DEGs between STAR and Kallisto is combined to generate the common gene list, will include single example. Repeat for Kallisto_Combined_X. Important to keep ordering the same.

STAR_Combined_X <- list( "MalVeh_X" = DEG_MV_mstX_paird, "MalE2_X" = DEG_ME_mstX_paird, "MalExem_X" = DEG_MX_mstX_paird, "FemVeh_X" = DEG_FV_mstX_paird, "FemE2_X" = DEG_FE_mstX_paird, "FemExem_X" = DEG_FX_mstX_paird)

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#Function to filter and only keep DEG results with unique gene names (not just ensembl gene names) #This version deletes duplicated entries #To see the distribution of p-values & p-adjusted values in a density plot, use the commented-out line instead.

STAR_Filter_List_DEG <- function(List_Combined_DEG) { Filtered_list <- list() for (i in 1:length(List_Combined_DEG)) { current_tbl <- List_Combined_DEG[[i]] current_tbl <- subset(current_tbl, !duplicated(external_gene_name)) filtered_tbl <- dplyr::filter(current_tbl, external_gene_name !="") %>% dplyr::select(external_gene_name, log2FoldChange, padj) # dplyr::select(external_gene_name, pvalue, log2FoldChange, padj) Filtered_list[[i]] <- filtered_tbl } names(Filtered_list) <- names(List_Combined_DEG) return(Filtered_list) }

Kallisto_Filter_List_DEG <- function(List_Combined_DEG) { Filtered_list <- list() for (i in 1:length(List_Combined_DEG)) { current_tbl <- List_Combined_DEG[[i]] current_tbl <- subset(current_tbl, !duplicated(id)) filtered_tbl <- dplyr::filter(current_tbl, id !="") %>% dplyr::select(id, log2FoldChange, padj) # dplyr::select(id, pvalue, log2FoldChange, padj) Filtered_list[[i]] <- filtered_tbl } names(Filtered_list) <- names(List_Combined_DEG) return(Filtered_list) }

#Filter and keep genes that are FDR <0.05 in each Sex/Tx/Brain region to create list of genes that are uniquely DE for each sex/tx/region. #Will also pull the log2FC and pAdj from the STAR aligned data. These are not useful for making the common genes list but used later for volcano plots. #To generate the p-value and p-Adjusted value distribution density plots, do not use this “Sig_genes_Only” function.

Sig_genes_Only <- function(List_Filtered_list){ Sig_list <- list() for (i in 1:length(List_Filtered_list)) {

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current_tbl <- List_Filtered_list[[i]] current_tbl <- dplyr::filter_at(current_tbl, vars(starts_with("pad")), any_vars(. < 0.05)) Sig_list[[i]] <- current_tbl } names(Sig_list) <- names(List_Filtered_list) return(Sig_list) }

#Only pull out genes that are common between the STAR and Kallisto lists

Pull_common <- function(STAR_list, Kallisto_list) { STAR_kallisto <- list() for (i in 1:6) { current_STAR <- STAR_list[[i]] current_Kallisto <- Kallisto_list[[i]] current_STAR_kallisto <- semi_join(current_STAR, current_Kallisto, by = c("external_gene_name" = "id")) STAR_kallisto[[i]] <- current_STAR_kallisto } names(STAR_kallisto) <- names(STAR_list) return(STAR_kallisto) }

#Each item(dataframe) in the list will have genes that are DE with FDR<0.05 for each sex, tx and brain region. Male Veh DE genes are used as the standard for all downstream analysis, except for those that require DE genes of specific sex/tx/region. #Example output

#Remove activity dependent genes (ADGs) identified in the Whitney et Al 2014 paper. Will need to supply the genelist separately as a .csv or .tsv removeADG <- function(common_list, ADG){ no_ADG <- list()

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for (i in 1:6) { current <- common_list[[i]] current <- current[!current$external_gene_name %in% ADG$gene, ] no_ADG[[i]] <- current } names(no_ADG) <- names(common_list) return(no_ADG) }

#To get p-value or p-adjusted distribution, you need to turn the merge columns from the lists into a large dataframe/table. #Turn each list (group) into a column with pvalue for each gene (row) Join_DEG_list <- function(List_Combined_DEG){ Joined_tbl <- List_Combined_DEG[[1]] for (i in 2:length(List_Combined_DEG)) { current_tbl <- (List_Combined_DEG[[i]]) Joined_tbl <- full_join(Joined_tbl, current_tbl, by = "external_gene_name") } Joined_tbl <- column_to_rownames(Joined_tbl, var = "external_gene_name") colnames(Joined_tbl) <- paste(rep(names(List_Combined_DEG)[1:length(List_Combined_DEG)], each = 3), colnames(dplyr::select(List_Combined_DEG[[1]], pvalue, log2FoldChange, padj)), sep = ".") Joined_tbl <- rownames_to_column(Joined_tbl, var = "external_gene_name") return(Joined_tbl) }

#Only keep the gene name and pvalues or pAdj #Omitted any genes with "NA" Pvalue_mutate <- function(Joined_tbl) { tbl <- Joined_tbl[c(1,2,5,8,11,14,17)] tbl <- column_to_rownames(tbl, var = "external_gene_name") tbl <- na.omit(tbl) tbl <- tbl %>% t() %>% as.data.frame() %>% mutate(SexTx = colnames(tbl)) %>% gather(gene, value, -SexTx) return(tbl) }

#Need to mutate the values, AKA make all data into a long dataframe/table with columns for the gene, the pvalue/padj value, and the sex/treatment pAdj_mutate <- function(Joined_tbl) { tbl <- Joined_tbl[c(1,4,7,10,13,16,19)] tbl <- column_to_rownames(tbl, var = "external_gene_name") tbl <- na.omit(tbl)

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tbl <- tbl %>% t() %>% as.data.frame() %>% mutate(SexTx = colnames(tbl)) %>% gather(gene, value, -SexTx) return(tbl) }

#Function for density plot & export to png DP_export <- function(sig_tbl, region, type) { ggplot(sig_tbl, aes(x=value)) + geom_density(aes(fill=factor(SexTx), color=factor(SexTx)), alpha=0.1, size=0.5) + scale_x_continuous(breaks=seq(0,1,0.1)) + labs(title = sprintf("%s_%s Distribution", region, type), x=sprintf("%s",type), color="SexTx") + guides(fill=FALSE) ggsave(sprintf("%s_%s_unpaird_densityplot.png", region, type), width = 20, height = 10, units = "cm") }

#For insets that need a zoomed in image in due to extreme skewing from FV & FX. #Example is for Area X of unpaired analysis below ggplot(pAdj_X, aes(x=value)) + geom_density(aes(fill=factor(SexTx), color=factor(SexTx)), alpha=0.1, size=0.5) + scale_x_continuous(breaks=seq(0,1,0.1), limits = c(0, 0.5)) + labs(title = "AreaX_pAdj_0-0.5_Distribution", x="pAdj", color="SexTx") + guides(fill=FALSE) + ggsave("AreaX_pAdj_0-0.5_unpaird_densityplot.png", width = 20, height = 10, units = "cm")

#DDS_rlog is for normalizing reads for individual DEG analysis #This function will output the normalized reads for each region (both nucleus and surround) for each animal in the Sex/Tx group. Will only keep genes that are in the provided “GeneList”. Use any prior DEG results to bridge the ensembl symbols used in the STAR output (“Output_of_aggregation”) to the external_gene_names.

DDS_rlog <- function(Labels, Output_of_aggregation, GeneList, priorDEG) { Area <- factor(Labels$Area);#DESEQ2 dds <- DESeq2::DESeqDataSetFromMatrix(Output_of_aggregation, Labels, design = ~Subject + Area); dds <- DESeq(dds) lds <- rlog(dds) sa <- SummarizedExperiment::assay(lds) ensembl_symbol <- left_join(GeneList, priorDEG, by= "external_gene_name") ensembl_DEGs <- as.data.frame(dplyr::select(ensembl_symbol, external_gene_name, ensembl_gene_id)) sa <- as.data.frame(sa)

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sa <- rownames_to_column(sa, "ensembl_gene_id") msa <- left_join(ensembl_DEGs, sa, by = "ensembl_gene_id") return(msa) }

#LOG2(A/B) = LOG2(A) - LOG2(B) #LOG2FC (nucleus vs Surround) = LOG2(nucleus) - LOG2(surround) #Subtract matching columns (6$3, 7&4, 8&5) and write to new table with just the subject name #Order of samples from individual areas are always the same, as defined by the diffExpression_labels made above doMath <- function(DDS_results){ DDS_results$Sub1 <- DDS_results[,6] - DDS_results[,3] DDS_results$Sub2 <- DDS_results[,7] - DDS_results[,4] DDS_results$Sub3 <- DDS_results[,8] - DDS_results[,5] Subnames <- substr(colnames(DDS_results[1,]), start = 1, stop = 8) Subonly <- DDS_results %>% dplyr::select(external_gene_name, Sub1, Sub2, Sub3) colnames(Subonly) <- Subnames[c(1, 3:5)] return(Subonly) }

#Merge the LOG2FC generated with the doMath function #This will give a large dataframe of columns (subjects). Made of 2 sex, 3 subjects, 3 Tx mergeMath <- function(list_of_Mathed_subregion) { FC_individuals <- doMath(list_of_Mathed_subregion[[1]]) for (i in 2:length(list_of_Mathed_subregion)) { FC_next <- doMath(list_of_Mathed_subregion[[i]]) FC_individuals <- left_join(FC_individuals, FC_next, by = " external_gene_name ") } return(FC_individuals) }

#Make values into buckets for heatmaps. bucket <- function(DEG) { rownames(DEG) <- c() DEG <- column_to_rownames(DEG, var = "gene") DEG[DEG < -2] <- -2 DEG[DEG > 2] <- 2 return(DEG) }

159

#Generate heatmap for rlog normalized DESeq2 results with each individual’s fold change values, with values higher than 2log2FC binned at 2log2FC by the bucket function above. #Turn off seriation to try and keep male samples to the left heatmaply(Nuc_res_FC_bucket, scale_fill_gradient_fun = scale_fill_gradient2( low="blue", high="red", mid="white", midpoint=0, limits = c(-2,2)), seriate = "none", Colv = TRUE, col_side_colors = c(rep(c("FV", "FE", "FX", "MV", "ME", "MX"), each=3)), side_color_layers = scale_fill_manual(values = c( "FV"="firebrick1", "FE"="darkorange", "FX"="gold", "MV"="purple", "ME"="blue", "MX"="green")), file = "Nuc_res_common_individuals_paird.html")

#Generate heatmap for rlog normalized DESeq2 results with group averages fold change values. Same as individual. Also no seriation. heatmaply(Nuc_res_bucket, scale_fill_gradient_fun = scale_fill_gradient2( low="blue", high="red", mid="white", midpoint=0, limits = c(-2,2)), seriate = "none", Colv = TRUE, col_side_colors = c("MV", "ME", "MX", "FV", "FE", "FX"), side_color_layers = scale_fill_manual(values = c( "MV"="purple", "ME"="blue", "MX"="green", "FV"="firebrick1", "FE"="darkorange", "FX"="gold")), file = "Nuc_res_bucket_colors.html")

#Make Volcano plots using the log2FoldChange and pAdj columns from the common gene list made above. Using all genes specifically DE to Sex/Tx/region. Not using the vehicle male as a standard.

VP_html <- function(DEG_FDR, SexTxNucleus) { VP <- plot_ly(DEG_FDR, x = ~log2FoldChange, y= ~-log10(padj), type = "scatter", text = ~paste("ID: ", external_gene_name)) %>% layout(title = sprintf("%s", SexTxNucleus)) htmlwidgets::saveWidget(VP, sprintf("%s_VP_paird.html", SexTxNucleus)) }

#Make VennEuler plots using the common gene list made above. Use all the genes to compare what is DE between groups. #VE plots are useless after 4 groups, so make several by a common variable: sex/Tx/song

Export_plot <- function(List_Venn, SexNucleus){ Venn <- plot(venn(List_Venn)) ggsave(sprintf("%s_Venn_paird.svg", SexNucleus), plot = Venn, width = 10, height = 7)

160

ggsave(sprintf("%s_Venn_paird.png", SexNucleus), plot = Venn, width = 10, height = 7) }

#Make a list of character vectors of genes from each Sex/Tx #This format is needed for the clusterprofiler functions

MakeGL <- function(common_list){ GL_list <- list() for (i in 1:6) { GL_list[[i]] <- common_list[[i]][,1] } names(GL_list) <- names(common_list) return(GL_list) }

#Need to load the database made from NCBI data using AnnotationForge #Similar to loading a library package, but from a local .sqlite file #This makes an OrgDB type object that is required to run all the functions in clusterProfiler #If you need to make/update this .sqlite source file, then run: # makeOrgPackageFromNCBI(version = "1.0", author = "yourname", maintainer = "yourname", outputDir = "C:/Users/name/Documents/", tax_id = "59729", genus = "Taeniopygia", species = "guttata") #See AnnotationForge/AnnotationHub/AnnotationDbi documentation ZForgDB <- loadDb("./org.Tguttata.eg.sqlite")

#Did not use finch OrgDb due to incomplete annotation. Had to default to human library(org.Hs.eg.db)

#Function to run enrichGO on each character vector in the list #Run the enrichGO function to get the GO terms associated with the genelist given enrich_GList <- function(GL){ enrich_GO_list <- list() for (i in 1:6) { enrich_GO_list[[i]] <- enrichGO(gene = GL[[i]], OrgDb = org.Hs.eg.db, keyType = "SYMBOL", ont = "ALL", pAdjustMethod = "BH", pvalueCutoff = 0.05, qvalueCutoff = 0.1) } names(enrich_GO_list) <- names(GL) return(enrich_GO_list) }

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#Make dotplots for each Sex/Tx

MakeDotplot_list <- function(enrichGO_list){ for (i in 1:6) { Label <- names(enrichGO_list[i]) dotplot(enrichGO_list[[i]], split = "ONTOLOGY", title = Label, showCategory = 10) + facet_grid(ONTOLOGY~., scale = "free") + ggsave(sprintf("%s_GO_dotplot_paird.svg", Label), width = 10, height = 7) + ggsave(sprintf("%s_GO_dotplot_paird.png", Label), width = 10, height = 7) } }

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Appendix E – Software Versions & R packages

Package Name Repository (if not Release number R Citations CRAN) Mixtools 1.1.0 Benaglia, 2009 Seqinr 3.4-5 Charif, 2007 Ape 5.3 Paradis, 2018 DESeq2 Bioconductor 1.24 Love, 2013 Lattice 0.20-38 Sarkar, 2008 MASS 7.3-51.4 Venables, 2002 Gplots 3.0.1.1 Warnes, 2016 Calibrate 1.7.2 Graffelman, 2006 Plotly 4.9.0 Sievert, 2018 Tidyverse 1.2.1 Wickham, 2019 Ggrepel 0.8.1 Slowikowski bioMart Bioconductor 2.40.0 Durinck, 2009 Heatmaply 0.16.0 Galili, 2017 Clusterprofiler Bioconductor 3.12.0 Yu, 2012 enrichPlot Bioconductor 1.4.0 Yu, 2019 AnnotationHub Bioconductor 2.16.0 Morgan, 2019 AnnotationDbi Bioconductor 1.46.0 Pagès, 2019 AnnotationForge Bioconductor 1.26.0 Carlson, 2019 Rcompanion 2.3.7 Magniafico FSA 0.8.25 Ogle, 2019 ARTool 0.10.6 Kay, 2019 Emmeans 1.4.2 Length, 2019 Eulerr 6.0.0 Larsson, 2019 Hmisc 4.3-0 Harrell, 2019

163

Appendix F – Genelists from Venn plots (Area X)

FE_AreaX : FX_AreaX : ME_AreaX : MV_AreaX : MX_AreaX 5 CARHSP1 IGF1 LAMP5 MAP2K1 NDNF

FE_AreaX : FX_AreaX : ME_AreaX : MX_AreaX 1 SNCA

FE_AreaX : ME_AreaX : MV_AreaX : MX_AreaX 108 ADAM23 CDC14A GAS2 KLHL18 NRP1 RBBP7 TMEM8B ALCAM CFTR GDA LRRTM1 NSG2 RGS20 TMPRSS9 ANO4 CHST2 GDPD5 MCF2L2 NSMF RYR2 TNC AQP3 CLSTN3 GJA1 MCUB OLFM1 S100B TRIM9 ARPC5 CNGA3 GPR22 MLC1 OSBPL1A SAMD11 TRPC7 ASIC4 CNTN5 GRIA4 MLYCD P2RX6 SEMA3A TTC7B ATP2B1 CST3 GRIN2A MME PAM SEMA3E TXLNG C11orf87 DYNC1I1 HCRT MOB2 PARD6A SH3KBP1 VTN C1QTNF4 EPHA3 HECTD2 MRPL44 PCMTD1 SLIT2 WFS1 CACNA1I EPHX4 HTR2A MTCL1 PCSK2 STXBP5L ZC3H12C CACNG3 ETV5 INTU NCS1 PIK3C2A SYNJ1 ZMYND11 CACNG4 FGF14 ITM2C NDUFA5 PIP5K1B SYT1 ZRANB1 CADPS2 FHDC1 KATNAL1 NECAB3 PVALB SYT6 CAMK2B FLRT2 KCNA4 NGEF R3HDM1 TBL1X CBARP GABRB3 KCNJ4 NPFFR1 RAB11FIP2 TIAM1 CBLN2 GABRG3 KCNT2 NR3C2 RASSF5 TMEM173

FV_AreaX : FX_AreaX : MV_AreaX : MX_AreaX 1 MBP

FX_AreaX : ME_AreaX : MV_AreaX : MX_AreaX 3 APOD CAMK4 NMU

FE_AreaX : ME_AreaX : MX_AreaX 15 ADGRV1 ATP9A NETO1 PIK3R1 SLC35F1 AKAP5 KCNK5 PDE1A RASD2 TRPC4 ANKRD44 LMO3 PENK RGCC UBQLN4

FE_AreaX : ME_AreaX : MV_AreaX 6 CCN3 CRTAC1 FABP7 JUN MATK THADA

FE_AreaX : MV_AreaX : MX_AreaX 20 AGFG1 CNDP2 KCNQ5 PCCA PTPDC1 SGK3 VCAM1 BRMS1L GDNF MGLL PLEKHA1 RIMKLB SH3BP5 WWC1 CALM1 KCNH8 NRN1 PSD SGCG VAT1L

FX_AreaX : MV_AreaX : MX_AreaX 2 CNP PLP1

FV_AreaX : ME_AreaX : MX_AreaX 164

1 POSTN

ME_AreaX : MV_AreaX : MX_AreaX 112 ACTN1 CNIH3 GCHFR KITLG PAN3 SEZ6L VIT ADARB2 CPNE2 GFRA1 LAMA4 PDE2A SFN XYLT1 AGR3 CYP27A1 GGACT LZTR1 PLCH2 SHISA9 ZDHHC23 ANOS1 DACH2 GJC1 LZTS1 PLCL1 SLC22A3 ZFPM2 ANXA6 DNAJC10 GMPPA MAPK8IP1 PNMT SLC4A4 APBA2 DUSP14 GPD1 MBNL2 PPP1R16B SLC5A12 ASS1 EDIL3 GPR3 MCF2 PPP1R9A SNX13 AZIN2 EMP2 GRIA1 MEF2D RAC1 SOSTDC1 B4GALNT3 EPHB1 GRIN1 MEGF9 RASL11A SPHKAP C2orf40 ERC2 HACD2 MTR RBM38 SRF CACNA1D ESYT3 HACD3 MYCN RGS14 SSTR1 CALHM5 FAR2 HPGDS MYO9B RHOBTB2 ST18 CDH4 FAT1 IAPP NET1 RNF144B SV2B CDH9 FGD6 INPP5D NKX6-2 RPS6KA3 SYT12 CDKN2C FN1 JCAD NRG1 SBK2 TFPI CDO1 FRZB KCNG1 NUAK1 SEMA3F TNFAIP8L1 CEMIP FSTL4 KCNJ2 NXNL2 SEMA4G TYR CLIP4 GALNT17 KIAA1324 PAK5 SEMA5B UBE2A

FE_AreaX : FX_AreaX 1 IVNS1ABP

FE_AreaX : ME_AreaX 10 ABCA3 CAP1 CRH OMG STC2 C1QTNF5 COX7A2L HPCAL4 RGS12 SULT4A1

FE_AreaX : MX_AreaX 20 ACBD7 CDC42BPB DDX10 MPPED1 OLFM3 PTPRN UTP25 B3GAT2 CDH2 DGKB MXD4 PCDH17 SLIT3 ZNF521 CAMK2D CEP290 EHD3 NALCN PTGFR SYT14

FE_AreaX : MV_AreaX 12 AKR1D1 COMTD1 HSD17B12 MT-ND3 MYH11 SEC61G BRS3 ELOVL2 MT4 MTPN PCDH8 SLC7A4

FX_AreaX : MX_AreaX 1 UCK2

ME_AreaX : MX_AreaX 66 ABHD6 DRD5 GLIS1 KCNJ11 NTRK2 RBP5 TMEM94 ACADM ELFN2 GMNN KCNV1 OPRK1 RXRG TRAF1 BGLAP EPB41L2 GNG7 LMCD1 PEAK1 SCN2B UNC13C C3orf18 EPB41L4A GPM6A LMO2 PLCD4 SESN1 VCAN CACNG2 FAAH2 GRM3 MAN1C1 PRDM5 SIGLEC15 VSTM2L CEP78 FGF16 HSPA4L MAST3 PRDX6 SLC13A5 WDR90 CTTNBP2 FSTL5 ID2 MYBPC3 PTN SUB1 DACT2 GALNT10 ITFG1 NEGR1 PTPN9 SYNDIG1L 165

DCX GALNT15 JAZF1 NEU2 RAB21 TM4SF18 DDX5 GALNT18 KAT2B NPY2R RAB40C TMEM63B

ME_AreaX : MV_AreaX 15 COCH DCLK3 EPHB2 PROK2 SRRM4 COL4A1 DOC2B KAT7 RASGRF1 TMC3 CORO1C DUSP4 MGP RRAD UBASH3B

MV_AreaX : MX_AreaX 113 AIF1L CHRNA5 FA2H KCNG2 NTNG2 RPS6KA6 TNIP2 AMIGO2 CLVS1 FLRT3 KLHDC8B NWD2 RRBP1 TRIM24 AQP4 CNTN2 FMN1 KNTC1 PACRGL RRM2 TRUB1 ARHGAP22 CPM GDF10 LRRN1 PANK3 SERPIND1 TTC38 ARHGAP40 CPN1 GJB1 LYPD1 PFKP SH3RF2 TTL ARHGEF12 CPNE4 GNG10 MED27 PGPEP1L SLC12A2 UBLCP1 ARID1B CRYAB GPR156 MEI4 PLEKHH2 SLC25A5 UBTD1 ATP12A CSRP2 GPR21 MELTF PRDX4 SLC27A6 UGT8 ATP1A1 DBF4 GPR39 MFSD2A RAB11FIP4 SLC35F3 UNC13B BARX2 DBX2 GRIN3A MICAL2 RASEF SNTA1 VIP BATF DGLUCY GSN MYOM1 RASGRP3 SORCS3 WSCD2 BHLHE40 DLG3 HCN1 NCK2 RASL11B SPOCK3 CAPZB DOCK4 HECA NECAB2 RFX2 STRN CDH11 EEF2K HMOX1 NINJ2 RHOB SYT16 CDH19 ELOVL1 ISL1 NIPA2 RNLS TLCD2 CELF2 EPCAM JDP2 NRIP3 RPH3A TMEM120B CELF4 ERBB4 KCNF1 NT5DC3 RPL19 TMEM123

FE_AreaX 38 AGAP3 CDK17 CYTB ND1 NDUFS4 RPL27A UQCR11 AIMP1 COPS9 FILIP1L ND2 NDUFS6 RPS16 UQCRQ ATP5ME COX2 HVCN1 ND4 PPFIBP2 TCERG1L ATP6 COX3 LRRTM3 ND4L PPL TCF7L2 C10H15orf6 1 COX6A1 MANBA ND5 RAB3B TMEM255B CCDC91 COX7A2 MRPS5 NDUFB1 RBX1 UQCR10

FX_AreaX 5 ARFGAP2 CCL20 COL12A1 GABRA3 HTR1B

FV_AreaX 1 LHFPL5

ME_AreaX 40 ADAMTS5 CAT CRYBB3 HERPUD1 NAPRT RHBDL3 UNC5A ANGPT2 CCN6 DIPK1C HS3ST4 NCOR2 SDK2 VSNL1 ANKRD29 CCNB2 DOK4 KCNJ9 NEURL1 SKIL ZNF366 APBB1IP CISD2 FAM163B KIF13A NPY5R SPARC ZNF804B ARHGDIB COL5A1 FGF20 MAPK1 PDGFA TAGLN3 C15orf62 CPLX3 GIT1 MTHFD1 RAB41 TENM1

MX_AreaX 346 166

ABCB6 CABP1 EEPD1 IGFBP7 NDRG1 RALYL STON2 ABCC8 CACNA1B ELL INSYN2B NEK2 RAP1A STX12 ABI2 CACNA1G ENPP6 ITGA4 NKAIN3 RAPGEF5 SV2C ABR CACNG5 EPHA1 JAK3 NME7 RASD1 SYAP1 ABRACL CALCB FABP5 JAKMIP1 NONO RASL10B SYN2 ACADL CALM2 FAM118B JPT1 NRP2 RASSF6 SYNGR3 ACTR3 CAPN1 FAM161A KCNC1 NT5DC2 REEP1 TBC1D14 ADA2 CCDC82 FAM207A KCND2 NTN1 RERE TCF12 ADAM22 CD151 FAM20C KCNG3 NTN4 RERGL THBS2 ADAMTS9 CD93 FAM210B KCNH6 NTS RHBDF1 TIMP2 ADAMTSL2 CDCP2 FAM43A KCNJ10 OPCML RIC8A TM7SF3 ADH5 CDH17 FBLN1 KCNK17 OTOG RIMBP2 TMEM121 ADM CDH8 FBXO45 KDR PABPC1L RIT2 TMEM144 ADORA2A CDHR1 FGF12 KIAA1217 PALLD RNF103 TMEM159 AHSG CDKL5 FTH1 KIF3B PCDH18 RNF130 TMEM50A ALDH1A2 CEBPD FUNDC1 KLF10 PCLO SAT1 TMTC3 ALG11 CFAP36 FXN KLF13 PCMTD2 SCARB1 TPM4 ANK2 CHL1 GAREM1 KLHL29 PCSK1 SCG5 TPX2 ANKRD6 CHORDC1 GGT5 LAPTM4A PDE10A SDCBP TRIM39 ANO3 CHST15 GLCCI1 LAPTM4B PGM1 SERPINH1 TRPC1 ANXA11 CISH GNE LGI1 PHACTR3 SERTM1 TSPAN13 APCDD1L CKB GPC6 LIPG PHKB SETD3 TSPAN2 AREL1 CLTA GPCPD1 LPL PHKG1 SFRP2 TSPAN3 ARFGAP3 CNR1 GPM6B LRRC4C PIP4K2A SGSM2 TSPO ARID5B CNTN3 GPR78 LRRC73 PITPNM3 SH3BP2 TTC39A ARL6IP5 CNTNAP2 GPX6 LRRK1 PKM SHC4 TTC39B ARX COLEC12 GRHPR LTBP2 PLCB1 SHPRH TTC39C ASAP1 COX1 GRIK2 LTF PLCL2 SIGMAR1 TTC7A ASPM CRACR2A GRM1 LYPLAL1 PLPPR4 SIPA1L2 UACA ATP1B1 CRY1 GRM2 MAN1A2 PLPPR5 SLC1A7 UBALD1 ATP2A2 CUTC HADHA MCFD2 PLXDC2 SLC25A29 UGP2 B3GLCT CYGB HAPLN4 ME1 PLXNA2 SLC29A4 USE1 B4GALT6 DACH1 HCN4 MED16 PLXNC1 SLC35A1 USH1C BACE1 DBI HDDC2 MERTK PMP2 SLC4A11 USP12 BAG2 DDHD1 HEATR5B METTL9 POC1A SLC5A6 USP34 BASP1 DEPDC1 HIGD1A MGST1 PPFIBP1 SLC9A3 USP7 BAZ1A DEPDC7 HK1 MGST3 PPP1R12A SLITRK2 VAMP4 BDH1 DEPTOR HLF MMP17 PPP4R2 SMC3 VIM BNIP3 DGAT2 HMGCLL1 MOGAT2 PRADC1 SNRNP40 VWC2 BRCA2 DGCR6L HNRNPU MON2 PRMT8 SNRPD3 YIPF5 BRINP1 DIP2A HORMAD1 MSMO1 PTDSS2 SNU13 YRDC BRINP3 DIRAS2 HPCAL1 MTNR1B PTK2 SNX9 ZDHHC14 BTBD17 DLK1 HPF1 MTUS1 PTPRD SORCS2 ZEB1 C11orf49 DNAJC12 HS6ST1 MYBL1 PXDN SOX8 ZMIZ2 C12orf57 DOCK11 HSPA2 MYO16 PYGB SPOPL ZNF277 C18orf21 DPP10 HSPB8 MYO1E QRFPR SPRED1 ZSWIM5 C1orf21 DPP8 IARS MYO3B QSOX1 SPTSSA C1R DPYSL3 ICA1 MYO6 RAB33A ST3GAL2 CA2 DRAXIN ID4 NCBP3 RAD23B ST8SIA2 CA7 DUSP26 IFT80 NCOR1 RALGAPA2 ST8SIA3

MV_AreaX 51 ABCC4 CYP19A1 ERO1B GDF2 L3MBTL3 SLC1A6 TMEM97 ST6GALNA ABCG8 CYP26A1 F13B HRH1 LPAR1 C3 TNNT3 167

ADGRD1 DCN FAM102B IFI6 LRMP SYT7 TSPAN14 AOAH DESI2 FAM131B IRF2BP2 NEURL1B TDRP APC2 DOK7 FOSL2 ITSN2 NTNG1 TLR3 B3GALT5 DYRK2 GABRA5 KCNK13 PLCE1 TMC7 BPHL EIF4E3 GAK KCNMB4 PLK3 TMCC3 CHRNA3 ELF1 GALNT13 KLHL4 RNF19A TMEM125

168

Appendix G – Genelists from Venn plots (HVC)

FE_HVC : FV_HVC : FX_HVC : ME_HVC : MV_HVC : MX_HVC 14 CALB2 CCDC82 CNTN5 GPR78 NRP1 SYNDIG1L TMSB4X CBARP CDH4 ELMOD1 ITM2C R3HDM1 TMSB15B ZEB2

FE_HVC : FV_HVC : FX_HVC : ME_HVC : MX_HVC 1 RET

FE_HVC : FV_HVC : FX_HVC : ME_HVC : MV_HVC 1 CDH7

FE_HVC : FV_HVC : FX_HVC : MV_HVC : MX_HVC 5 CST3 LRTM2 NPY PRRG3 S100B

FE_HVC : FX_HVC : ME_HVC : MV_HVC : MX_HVC 170 ABHD6 CHRDL2 FAM163B JCAD PDE2A SHC3 THSD7B ACAN CHRM3 FGF14 KCNJ4 PDLIM1 SHC4 TMC5 ADCYAP1 CHRNA3 FGL2 LAMP5 PDZRN3 SHF TMEFF2 AMIGO2 CKAP2 FKBP1B LDB2 PLCH2 SIDT1 TMEM150C ANKRD33B CLSTN2 FLRT2 LIN7A PLPPR4 SIX2 TMEM45B ARHGDIB COL12A1 FMOD LMO3 PLXNC1 SLC15A5 TMEM8B ARHGEF9 CPNE8 FSTL5 LMO7 POMGNT2 SLC30A3 TNFAIP8L3 ASZ1 CTXN1 GABRA5 LRP2 POSTN SNCA TOX ATF1 CTXN2 GALNT15 LRRTM2 PPFIBP1 SNTG1 TRIB2 ATP8A1 CYP19A1 GALNT16 MAP2K1 PRELP SNX9 UNC5C B3GNT2 DACH1 GALNT9 MAPK6 PRKAR1B SOX5 USP12 BCAP29 DGKI GDPD1 MATK PTN SOX6 UTP25 BMP2 DIP2A GFRA2 MCF2 PTPRN2 SPRED1 UTS2B BRINP1 DLG3 GHR MMD PTPRR SRD5A2 WSCD1 BTBD6 DLGAP1 GLRA4 NCAM2 PVALB SSTR3 WWC1 CADPS2 DLGAP2 GPR63 NCKIPSD RASGRF1 STRN ZDHHC22 CAMTA1 DLK2 GRIA4 NEFL RASGRP1 SV2C ZMAT4 CAP2 DPYSL3 GRM1 NEGR1 RASL11B SYN2 ZNF365 CDH22 EDIL3 GRM5 NELL1 RGS11 SYT10 ZNF423 CDH6 EPHA4 GSG1 NPY2R RGS14 SYT14 ZNF503 CDH9 EPHB6 GUCY1A2 NR3C2 RPRML TENM1 CDK17 ETV1 HPCAL1 NRSN1 RUNX1T1 TENM4 CDO1 ETV5 HS3ST5 NSMF SACS TET1 CFTR FAAH2 IKZF2 NWD2 SEMA6D TFAP2D CH25H FAM135B INSM1 PDE1A SERPINE2 THSD7A

FE_HVC : FV_HVC : ME_HVC : MV_HVC : MX_HVC 5 LRMP NTS RASL12 SEMA3A TH

FV_HVC : FX_HVC : ME_HVC : MV_HVC : MX_HVC 2 PROK2 TMEM196

FE_HVC : FV_HVC : FX_HVC : MX_HVC 2 169

NCS1 VIM

FE_HVC : FX_HVC : ME_HVC : MX_HVC 14 AMOTL1 BCAR3 CDH12 FERMT1 IRF1 PCDH1 SHISAL1 BARX2 BDNF DPP10 FNDC3A KCNJ9 RTN4RL1 ZFPM2

FE_HVC : FX_HVC : ME_HVC : MV_HVC 11 ADGRG2 AFF2 C1QL3 COMTD1 LAPTM4B SIPA1L2 AFF1 AQP9 CACNG3 DUSP14 MCF2L2

FE_HVC : FX_HVC : MV_HVC : MX_HVC 41 ACKR3 CDCA7L GLT1D1 LPL MYO1E PPP1R14C SH3BP5 B2M CFAP97 HOMER1 LRRC8B NGEF PRMT8 SLC35F1 BDH1 CHN1 KCTD12 LTF NRXN1 PTCHD1 SYNPR CAMK2A CPE KIAA1147 LZTS1 P2RY1 RASSF5 TMEM184B CAP1 DNAJB6 LANCL1 MAP7 PDIA6 RGS12 UBE2A CCDC126 G3BP1 LNX1 MCFD2 PHF24 SDF2L1

FE_HVC : FV_HVC : ME_HVC : MX_HVC 1 KL

FE_HVC : FV_HVC : MV_HVC : MX_HVC 4 FABP7 LGALS1 LYPD1 SEMA4G

FE_HVC : ME_HVC : MV_HVC : MX_HVC 124 ADCY8 CDH18 ENTPD3 INPP4A NDUFA5 RHOB SSTR5 ADCYAP1R 1 CDK14 EPB41L3 JUN NKAIN3 RHOBTB2 ST8SIA6 ADGRB3 CELF4 EPHB1 KCNA2 NR0B1 RPA1 STC2 ANKDD1A CHORDC1 FAAH KCNF1 OLFM3 SCG2 STRBP AQP11 CHST1 FAM102B KCNK5 OTOG SCNN1B STRIP2 AR CLTA FAM160A1 KIAA1211 PCDH17 SERPINB5 TBR1 ARRDC2 CLVS1 FBXW7 LRFN2 PEAK1 SESN1 TIAM1 ASAH1 CNTNAP1 FGF9 LRRN3 PFKP SETBP1 TMEM233 B3GAT2 COX7C FST MAP1B PHACTR2 SFMBT2 TMTC1 BACE2 CPNE2 GALNT18 MCU PIANP SGCD TRPC4 BOK CSMD3 GAS6 MCUB PLLP SHISAL2A TRPM2 BRINP3 CTTN GFRA1 MDFIC2 POMC SLC25A4 TSPAN12 C11orf87 DACT2 GJC1 MMD2 PPP4R2 SLC4A4 UXS1 CACNB2 DPY30 GLRA2 MPC1 PRXL2C SNRK VTN CACNG4 EDA2R GPM6B MRAS PTX3 SNX10 WDR19 CALB1 ELFN1 GRM8 MTNR1A RASL10B SOD2 WRB CD99L2 ELOVL2 HERC4 MYOZ2 RASSF7 SPOPL CDC14A ELOVL6 HTR1F NDNF RGS7BP SPTSSA

FV_HVC : FX_HVC : ME_HVC : MX_HVC 1 STAC

FV_HVC : FX_HVC : MV_HVC : MX_HVC 1 SYT12

170

FX_HVC : ME_HVC : MV_HVC : MX_HVC 16 ABCC8 CYP7B1 LMO2 RAB26 TMC7 WIF1 ADARB2 DENND2C NLK SERTM1 TNIP2 APMAP DRAXIN PQLC1 SFRP2 TRIM67

FV_HVC : ME_HVC : MV_HVC : MX_HVC 1 CACNG5

FE_HVC : FV_HVC : FX_HVC 1 ANXA5

FE_HVC : FX_HVC : ME_HVC 11 ASCL3 GPR139 KITLG SCD5 SOX8 ZNF106 CCDC88A KCNT2 PITPNM1 SHISA9 SRF

FE_HVC : FX_HVC : MX_HVC 15 ABHD12 CAVIN1 DNM1 KIF1A PCDH19 ABR CHMP7 GNG5 NDRG1 SPATS2L APCDD1L COL21A1 GPC1 OMG TMEM63C

FE_HVC : FX_HVC : MV_HVC 22 ACTR2 DCUN1D5 MAF NAV2 PINK1 SOX11 AFDN EIF4A2 MB21D2 NHSL1 RGS20 TMEM163 AREL1 HMCN1 MLC1 NPY1R SELENOF ATP6V0A1 HSD17B12 MTX2 PIGK SLC25A25

FE_HVC : FV_HVC : MV_HVC 1 IQCA1

FE_HVC : ME_HVC : MX_HVC 19 ADAM22 ANGPT2 BAALC MAST3 NOX1 SLC4A11 WDR36 ADCY2 ARHGAP24 C2CD4C NADSYN1 RGS4 STK32C AGR2 ASTN1 HCRT NEXMIF SATB2 USH1C

FE_HVC : ME_HVC : MV_HVC 18 ACTN1 COP1 GARNL3 NIM1K PRR16 SLC29A4 B4GALT4 CRH GPR3 NTNG1 PXDN SYNJ1 CCN2 DCBLD1 KIAA0408 NXPH2 SLC10A1 ZNF385B

FE_HVC : MV_HVC : MX_HVC 48 ACLY CDH2 DGKB GUCY1A1 NOL4 POU1F1 TLNRD1 ACSL4 CHST11 DOK4 KAT2B NTAN1 RIOK3 TMEM169 ACVR1 CLIP4 DRG2 KCNC2 ORMDL3 RNF182 UBASH3B ARHGAP22 CLMN EIF5B KCTD8 OTUD1 SERINC1 UBE2J1 ARPC5 COL25A1 ENO2 LRRN4 PDCL SGCE UCK1 AZIN2 CYB5D2 FREM2 MAPK1 PITPNM3 SLC35E3 USE1 BTBD10 DACH2 GAD2 MPPED1 PLXNA2 TCTN2

FV_HVC : FX_HVC : MV_HVC 171

1 GRM2

FX_HVC : ME_HVC : MX_HVC 9 C5orf34 DTX2 KCNH8 NDE1 WNT4 CBLN2 GNAT2 NACC2 TRPM3

FX_HVC : ME_HVC : MV_HVC 2 CNGA3 CPA6

FX_HVC : MV_HVC : MX_HVC 16 CPNE4 GABBR2 KCNG3 NRN1 SETD3 TRPC1 EXT1 GNAZ LINGO3 PALLD THY1 FBXL15 IPMK LRRTM3 RALY TOLLIP

FV_HVC : ME_HVC : MV_HVC 1 INHBA

ME_HVC : MV_HVC : MX_HVC 154 ADAM23 CAPN1 DRD5 IAPP ME1 PPP1R27 SLC35G2 ADAMTSL2 CCDC134 ELFN2 IGDCC4 MERTK PRDX3 SLC7A2 ADORA1 CCDC146 EPB41L2 IL1RAPL2 MGAT4C PRPSAP1 SNAP25 AIG1 CCNB2 ESYT3 INSR MGST3 PTCHD4 SORCS1 AKR1D1 CD82 EVC JPH1 MMP15 PTGER4 SOSTDC1 ANKRD29 CDH8 EXOC3L1 KCNA1 MMRN2 PTPRA STK39 AOAH CDK6 EXTL3 KCNJ10 MOGAT2 PTPRU SULF1 ASS1 CHGA FAM185A KCNQ3 MOXD1 RAB11FIP4 TBC1D19 ATP11B CHRNB2 FGF10 KDM7A MYLK4 RAP1GDS1 TBCA ATP1A1 CIT GAS2 KERA NALCN REEP5 TG ATP8A2 CNIH3 GDAP1L1 KLHL4 NANS REL TGFB3 AVEN COL5A1 GMNN LDHA NECAB2 RFX3 TMEM100 BAG5 CPLX1 GPC3 LGALS2 NEURL1B RHOBTB3 TNFAIP8L1 BARX1 CYP1B1 GPD1 LHFPL6 NKAIN2 RHPN1 TTC39A BAZ1A DAB1 GPR155 LSP1 NME7 RPS6KA6 TUFT1 BGLAP DBNDD2 GRB14 LZTS3 OPRM1 RRAS2 TWSG1 BTG1 DEPDC7 GRIA1 MAN1C1 PCDHAC2 RSPH3 UBLCP1 CA8 DERA GRIK1 MAP1LC3C PLCL2 RSPO4 USB1 CAB39L DGCR6L GRIK2 MAPK11 PLCXD3 SEMA3E VAMP1 CABLES1 DIPK1A HABP4 MASP1 PLD5 SEMA5A VDAC1 CALCB DOCK4 HDAC9 MBNL2 PPFIBP2 SLC13A1 WDR66 CAMK2D DRC3 HMMR MCM10 PPL SLC16A3 ZRANB1

FE_HVC : FX_HVC 74 ABHD17A CD74 FLRT3 LRRC73 OGT SGSM3 UCK2 ABLIM1 CEP290 GJA1 MAPK8IP1 PDP1 SLC17A6 UQCRFS1 ADGRL2 CETN3 HPCAL4 MATN3 PEBP1 SSTR1 VPS51 AGAP3 CLTB ID4 MCTP1 PLCD1 ST8SIA3 VSNL1 AP1M1 DAGLB IFITM10 MDN1 PTPRD TACC2 WSCD2 ATP6AP2 DCX IQGAP1 MLLT10 PTPRN TET2 YWHAH BSPRY DHCR24 KAT6B MPP5 RAB41 TMEM30A ZFHX4 C15orf62 DOCK11 KIF26A MTSS1 RALYL TRIM9 ZNRF1 C1QB EPHX4 KLHDC2 MYF6 RASA3 TTC28 172

CAMK2B EPN3 LCMT1 NELL2 RGS6 UBQLN4 CAMKV FDFT1 LINGO1 NKTR SGSM2 UBTD1

FE_HVC : FV_HVC 4 ANKRD66 SLC13A3 TMEM255B TREM2

FE_HVC : ME_HVC 20 ABCC4 CDH10 CHRM2 GPR17 PAK1 RTN1 TBC1D8B ADK CDK8 CSMD1 LRRC28 RASD1 SLC26A4 TTLL4 AGPAT3 CEP170 FKBP5 NECTIN1 RBL2 SYNGR3

FE_HVC : MX_HVC 21 ASPHD2 BTBD11 CLPTM1L DNER GBA2 LAMA4 RALGAPA2 ATP12A C3orf70 CNN3 EEF1A1 KCNQ5 LRRC3 SMOC1 ATP8B1 CDKL5 CRYAB EMP2 KIAA1324 MCRIP1 UBXN6

FE_HVC : MV_HVC 39 ARX DCAF6 HSPA5 MINPP1 PPME1 SPOCK2 VPS35 B4GALT2 DYNLL2 INPP5A MSH4 PRADC1 STARD3NL WIPI2 C11orf49 EIF2B5 KCTD3 MT4 PROM1 TECPR1 WWC2 CENPU EVI5 KLHL5 MYOM2 PSMD5 TMEM243 CLCN7 GALNT17 LAPTM4A PHETA1 RAP2A TMEM50A CTNNB1 GPR22 LRP11 PIP4K2A SHQ1 TRIM63

FV_HVC : FX_HVC 4 CAMK4 GADD45G IFI30 SLC6A13

FX_HVC : ME_HVC 6 DGLUCY EIF4E3 NCAPG NRIP3 NTN4 TSPAN6

FX_HVC : MX_HVC 11 AKAP5 COCH KCNMB4 OTULINL TPM4 TYRO3 BMPR2 DKK3 KIAA0232 PCDH8 TRPC7

FX_HVC : MV_HVC 31 ABRACL ATPAF1 GABRB3 GRM3 PGAM5 SRCIN1 TSPAN3 ADAMTS2 CELF5 GALNT1 HSPA2 PLK2 STRADA AGPAT4 CISH GCHFR JUP PTHLH SYT11 AQP4 DTNBP1 GDA MFSD13A SLC24A2 TCAIM ART1 EXOSC2 GPR162 NRIP2 SNU13 TPST2

FV_HVC : ME_HVC 2 FOXC2 RARRES1

FV_HVC : MX_HVC 3 CPNE9 MMP2 TAC1

FV_HVC : MV_HVC 4 173

C1QTNF4 DSP ITIH2 OGN

ME_HVC : MX_HVC 35 CACHD1 DAGLA FIBCD1 HS6ST1 MED27 PGD SESTD1 CCT5 DIRAS2 FN3KRP KCNV1 MN1 RAB40C SFN CEMIP DOP1A GBE1 LHX9 NEK3 RFX2 TK1 CHEK1 DYNLT3 GMDS LRRC49 NEURL1 RIPK4 TUBB4B CRISPLD1 EHD3 GPHN LSS NGB SAMD3 ZMPSTE24

ME_HVC : MV_HVC 38 ABCB11 C11orf96 HAPLN4 LRRC2 PRPH SNAP91 ZNF704 ST6GALNA AMZ1 CD81 HMGCS1 MAMDC2 PRSS35 C5 ZRANB3 ANKRD6 EPSTI1 HS1BP3 MEMO1 RBMS1 TSPAN9 ASIC4 FOXP2 KCND2 MYLIP SLC5A8 UAP1L1 ATPSCKMT GPC5 KLHL14 MYO16 SLIT3 VIP BMP7 GRIN2B LCAT NOG SMYD1 VSX1

MV_HVC : MX_HVC 171 ACAT2 CDKN2C DPP8 IFI6 NEU4 PTK2 STXBP5L ACBD7 CELF2 DPYD IGFBP7 NFATC1 PTPDC1 STXBP6 ACOT7 CFAP20 EEPD1 ITSN2 NMBR QPCT TAPT1 ACOT8 CHAMP1 EIF2S2 KCNIP1 NPTX2 RAB3B TCF20 ACYP1 CHSY3 ENOX1 KCNK12 NRIP1 RASGEF1B TENM3 ADGRA1 CLVS2 ENTPD2 KPNA2 NSG2 REPS2 THNSL1 ADGRV1 CNTN3 ERICH1 LARP6 NT5C3A RIDA TIMP2 ANKRD44 CNTNAP2 FAM118B LHFPL5 NTNG2 SAMD11 TMEM130 AQP3 CNTRL FAM20C LRP8 NUDT8 SAP30L TMEM173 ARHGEF7 COL4A1 FBXL3 LRRN1 OLFM1 SBNO1 TMEM200A ARNT2 COMMD4 FBXO2 LRRTM4 PARK7 SEC14L1 TNKS ATP1B1 CORO6 FH MAN1A1 PCYT1A SEMA3F TRAF1 ATP2A3 COX7A2 FHL3 MAPK1IP1L PDE10A SERPINH1 TRUB1 ATP5MC3 CREB3L2 FN1 MAPKBP1 PDE3A SF3B3 TTC7A ATP6V0E1 CRIP2 FNIP2 MBNL3 PGM1 SIVA1 USP14 ATRN CSDC2 GABRA1 MDGA1 PGM3 SLC35C2 UST B3GALT2 CSRNP3 GNB4 MED30 PHYHIPL SLC41A1 VSTM5 BRINP2 CXCR4 GPR26 MORN3 PLA2G4A SLC8A1 WNT5B MPHOSPH C17orf67 DAPP1 HBEGF 10 PLA2G7 SLITRK2 YIPF4 C6orf203 DBI HECW1 MRPL33 PLCB1 SNN ZDHHC8 CAMSAP1 DCN HERPUD1 MRPS25 PLCL1 SPATA2 ZNF804A CAPN10 DHRSX HEY2 MTPN PLEKHA5 SPECC1 CCN1 DLG2 HIVEP2 MVD PRICKLE1 SRA1 CCND3 DNAJB4 HMG20A MYCN PRKCA SRP68 CDC42SE2 DOK7 HPGDS NCALD PSD STX17

FE_HVC 116 ABCA3 CCND1 EPHA6 KATNAL1 PAK5 SEMA3G TMPRSS7 ADD1 CD9 FAM120A KCNG2 PARP6 SFSWAP TNC AGK CETN2 FAM189A1 KLF13 PCSK1 SGSM1 TNR ANKRD49 CHD7 FGFR2 LCP2 PMEPA1 SIGMAR1 TRAF3 APC2 CHURC1 FILIP1 MAN2A2 POFUT2 SLC12A5 TRIO API5 COPS9 FMNL2 MANBA POMGNT1 SLC16A6 TXNDC16 174

ARHGAP21 COTL1 FRMD4A MGAT4A PPM1H SLC2A12 UNC5A ARRB1 CREB5 FSCN1 MGST1 PPP1R12B SLC38A8 VPS13B ATP10A D2HGDH GAPDH MRPS6 PRDM16 SLC6A7 VRK1 ATP5MF DACT1 GDF10 MTFR1L PRSS12 SLC7A14 WDCP ATP6V0B DECR2 GGA3 MYCBP2 RBP5 SSR1 YRDC ATP6V1C1 DHX36 HMGN3 NAPEPLD RIMS1 STK25 ZBTB20 ATXN7L1 DUSP16 HSPA9 NDFIP1 RNF123 TBC1D16 ZNF385D BACE1 ECHDC1 HSPH1 NET1 RTN4 TBC1D2 ZNF609 BTBD2 EFEMP1 HYOU1 NEUROD6 RXRA TBL1X C1R ELAVL4 KAT2A NPR2 SCG5 TCF7L2 CAMK1 EMC1 KATNA1 NR1D2 SDC4 THADA

FX_HVC 117 ALDH1L2 CD200 EPB41L4A HIP1 MMP16 RCOR1 SOCS4 ANO4 CDA EPHA3 IGF1 MMS19 REV3L ST8SIA2 ARHGAP42 CGNL1 EPHA5 IL13RA2 NAPB RNF10 SVEP1 ARIH1 CHST8 ESR1 ITGA8 NCOA3 RSU1 SYNDIG1 ATP6V1B2 CORO1C F13A1 ITGA9 OCSTAMP SALL1 SYT17 ATP9A CPNE3 FAM43A ITGAV PACSIN1 SAT1 TBC1D2B B3GALT5 CTBP1 FNBP1L KCNK2 PCSK2 SC5D THSD1 BAZ2B DAAM2 GAA KCTD16 PHACTR1 SELENOS TM9SF2 BIRC7 DDIT4 GALNT10 KDM1B PID1 SH3GL3 TMEM98 BTC DENND4A GINM1 KHDRBS2 POLK SLC22A16 TMTC4 C1QA DGKH GLIPR2 KLHL18 PPFIA1 SLC24A5 TXNRD1 C1QTNF5 DIRAS3 GNG10 LRFN5 PRKDC SLC6A6 TYR CACUL1 DOK5 GPD2 LRP1B PRXL2A SLC7A3 UBR5 CARF EGF GRIA2 LURAP1 PTP4A1 SLC7A4 ZEB1 CASR EIF2AK3 GRIP1 MAPK10 RABGAP1L SLITRK1 ZMYND8 CCBE1 EIF4H H1F0 MBNL1 RAC1 SMAP1 CCNG1 EML1 H2AFY MGP RBM20 SMARCA2

FV_HVC 14 C1S CFAP221 GSN LY86 MYB S100A4 SOD3 CELSR1 DNALI1 HBAA MEI4 PLD4 SERPINF1 VILL

ME_HVC 63 ADGRD1 CCDC91 DRD1 LGALS3 NOS1 SH3GL1 TLL2 APBA2 CDH19 DRD2 LMTK2 ONECUT1 SLC6A2 TMEM132D ARHGAP28 CHMP2B EYA4 LPAR4 ORC3 SLC9A5 TOX3 ARHGEF17 COX1 GALR1 LSM11 PACSIN2 SLIT2 TRIM24 ATG10 CXCL12 GRAMD2B MOCOS PTPRQ SMOC2 TSHZ2 ATP11C CYGB HIC2 MSTN RIMKLB SRRM4 TTLL12 BCHE DDX58 HTR2A MSX2 RNF144B ST6GAL2 VEPH1 BMP4 DLK1 KLHL34 NETO1 SDCBP TAGAP WDR48 BRS3 DLX1 LBH NINJ1 SGMS2 THBS2 ZBTB17

MX_HVC 180 ACOT13 CHAC1 FLNC IRF8 NDUFB9 RASGRP3 TLR3 ACSL1 CHRNA1 FRK ITGB5 NDUFS4 RBX1 TM4SF18 ACYP2 CHST2 FRMPD3 KDR NKX2-1 REEP1 TMOD1 ADAMTS15 CKS2 FRMPD4 KHDRBS3 NSD2 RIOX2 TRMT11 ADH5 CLIP2 GADL1 LAYN NSMCE1 RPS24 TRPS1 ADM CPA1 GNG4 LEF1 NT5C1A RPS6KL1 TTC25 175

AGO3 CSTB GNMT LGI1 NTF3 SART3 TTC39B AK4 CTIF GPI LMLN NUTF2 SBK2 TUBGCP5 ANXA1 CTTNBP2 GPR12 LPAR1 OSBPL3 SERGEF TVP23A APOB CYB561 GPX6 MAN2A1 PBLD SERPIND1 TWIST1 ARHGAP10 DHCR7 GRAMD4 MAP3K20 PDXK SETD9 TXN ARL6IP1 DNAJC12 GSG1L MC4R PFKFB3 SGTB UACA ATP6AP1L DNAJC16 GSTK1 MED10 PGM2 SH2B2 UBE2D1 AUH DNAJC18 GTF3C4 MGAT5B PGM2L1 SKAP2 UBE2T B3GLCT DOLPP1 GYPC MICAL3 PLCD3 SLC12A4 UNC13B BCL11B EFTUD2 HDGFL3 MMP11 PLPP1 SLC1A6 UQCRB C20orf194 EIF2AK2 HECA MRPS36 PLXDC2 SLC30A4 VPS53 C8G ELAVL2 HELLS MRPS9 PPDPF SLC32A1 WNT2 CALD1 ENDOG HIGD1A MSS51 PREX1 SNX2 YPEL1 CAPN9 ENPP1 HLF MTURN PRICKLE2 STARD4 ZC3H12B CBFA2T2 ENY2 HMOX1 MTX3 PSKH1 STON2 ZC3H12C CCK EPHA7 HTR1A MXD4 PSMA7 SUCLG2 ZDHHC23 CD2AP ERBB4 IGSF11 MYH9 PSMD14 TBP ZNF462 CDC20 ERO1B IGSF3 NAV3 PYGB TDRD7 ZNF516 CDH11 EZR IQGAP3 NCAM1 QDPR TEAD4 CETP FAM184A IRF2BPL NDUFAF4 RAB33A TIMM21

MV_HVC 575 AAK1 CDC42EP3 EIF4B HYPK NDUFAB1 RAB3GAP1 SYT2 ABCB7 CDCA7 EIF4G1 IBA57 NDUFB1 RAB4A SYT7 ABHD14B CDK1 EIF4G2 IDUA NDUFB2 RAB6A SYTL2 ABHD3 CELF1 EIF4G3 IFFO1 NDUFB5 RABGGTB TAB3 ABI1 CENPH EIF5 IGSF9B NDUFS8 RABL6 TAF7 ABLIM3 CENPP ELF2 ILK NECAP1 RAI14 TARS ACSF2 CENPW ELK4 IMPDH2 NEK6 RALGPS2 TBC1D13 ACTR6 CFAP36 EMC4 INSIG2 NFASC RANBP10 TCP11L1 ACTR8 CFL2 ENTPD5 IPO13 NFIB RANBP3L TDG ADAMTS9 CGRRF1 ENTPD6 IQSEC1 NFIC RAPGEF2 TELO2 ADARB1 CHCHD2 EPAS1 ISOC1 NFX1 RARS TET3 ADGRG6 CHP1 EPS8L2 ITFG1 NFYC RASSF8 TFAP2A AFMID CHPF2 ERC1 JAGN1 NIPSNAP1 RBFOX1 THAP4 AK1 CHRNA4 ERC2 JARID2 NLE1 RBP1 THSD4 ALAS1 CIPC ERCC8 KATNB1 NPBWR2 REXO2 THYN1 ALDH5A1 CIRBP ESRP2 KCNC1 NT5E RFC5 TKFC ALG11 CITED2 ETF1 KCNG4 NTRK2 RGP1 TKT ALG12 CKB ETFA KCNJ11 NUAK1 RIMBP2 TLL1 AMOT CLCN2 ETFB KCNK1 NUCKS1 RLF TM7SF3 ANKRD55 CLDND1 EXOC3 KCNK17 NUP62 RNLS TMEM106B ANKRD9 CLPX EYA1 KCNN2 NUP93 RPL13 TMEM120B ANP32E CMPK2 FAM117B KCTD2 OAZ2 RPL26 TMEM184C ANXA6 CMTM4 FAM161A KCTD20 OCIAD2 RPN1 TMEM185A AP1G1 CNNM1 FAM168A KIF21B ODC1 RPP30 TNFRSF21 AP2S1 CNOT7 FAM219A KLHDC3 ODR4 RRN3 TNIK AP3B1 COG4 FAM234B KLHDC8B OGDH RSPO1 TNMD AP3M2 COL19A1 FAM53A KLHL2 OGFRL1 RTCA TNRC6A APLP2 COL6A1 FAR2 KPNA4 OPCML RTCB TOB1 APOPT1 COL6A3 FAT2 L3MBTL3 PAK2 RUFY3 TOM1L1 ARF6 COMMD9 FBXO3 LANCL3 PARD3 SAPCD2 TPPP3 ARHGAP6 COPS8 FDPS LARP1 PARN SATB1 TRAIP ARL6IP5 COQ6 FERMT2 LCP1 PARP12 SCAMP3 TRHR ARL8B CORO1B FEZ1 LGALSL PARP16 SCAMP5 TRIM2 176

ARMC1 COX4I1 FEZF2 LHX8 PCBP2 SCG3 TRIP11 ASB10 CPM FGD3 LPAR6 PDK3 SCN4B TRMT1L ASB8 CPSF4 FGF1 LRIG3 PENK SCN8A TRMT9B ATG13 CRCP FHDC1 LRP1 PEX11B SCRG1 TSEN2 ATG4C CRMP1 FHOD3 LRRC38 PGS1 SEC23A TSHZ1 ATP2A2 CRTAC1 FKBP3 LRRK1 PHB SEH1L TSN ATP2C1 CRTC1 FKTN LRRTM1 PHKG1 SERBP1 TTC32 ATP5F1A CRY1 FUCA1 M6PR PHLDB1 SETD2 TTC7B ATP5IF1 CSNK2A2 FUNDC2 MAP1LC3A PIAS4 SEZ6 TUBG1 ATP5MD CTH GALNT3 MAP3K4 PIGL SF3B6 TULP4 ATP5ME CTR9 GATAD1 MAPRE3 PIGS SGK3 TUT4 ATP5PB CTSB GDPD5 MAPT PIN4 SIRT3 TXNL1 ATP5PF CXXC5 GFOD2 MBD5 PIP5K1B SIRT5 UBA2 ATP5PO CYB5R4 GLB1 MCMBP PKD1 SIX3 UBE2E2 ATP6V0C CYBC1 GLRA3 MED23 PKM SLAIN1 UBE2H B3GALT1 CYCS GNAI1 MEF2A PKP4 SLC13A5 UBE2O B4GALT5 DAAM1 GNAO1 MEF2C PLA2R1 SLC1A3 UBP1 BAG2 DAD1 GNG2 MEF2D PLBD2 SLC20A1 UBR4 BAP1 DBN1 GNG7 MEGF11 PLEC SLC22A23 UCHL1 BBS4 DBR1 GNPAT METRNL PLPPR5 SLC25A11 UFD1 BBS9 DCTN4 GOT1 METTL4 PNRC2 SLC25A22 UGP2 BCAS2 DDX10 GPM6A METTL7A POLD2 SLC25A48 UIMC1 BICD1 DDX17 GPR37L1 METTL8 PPHLN1 SLC30A5 USP25 BICDL1 DDX31 GPSM2 MIF PPIB SLC35A1 UTP18 BOC DEGS1 GPX4 MIGA2 PPM1E SLC38A4 VMA21 BPGM DENR GRB10 MIOS PPP1CC SLC38A7 VPS18 BSG DEPTOR GRB2 MLX PPP1R7 SLC6A1 VPS41 BTF3L4 DGAT2 GREM1 MMP24 PRDX6 SLC7A5 VSTM2L BZW1 DIABLO GRHPR MON1A PRKCI SLC9A1 VWC2 C12orf57 DIPK1C GRIK4 MPC2 PRKCZ SMCO4 WBP4 C16orf70 DLAT GRN MRPL38 PRNP SMOX WDFY2 C17orf75 DLGAP4 GRPEL2 MRPL43 PRR5 SMYD4 WDR73 C1orf43 DMGDH GSK3B MRPS2 PSAT1 SNCG WFS1 C5orf24 DNAJC5 GSKIP MRPS5 PSEN2 SNX12 WNK2 CA2 DNAJC6 GTF2E1 MRVI1 PSMA4 SOCS5 XBP1 CACNA1B DNAL4 H2AFV MTCL1 PSMA5 SOD1 XRN2 CACTIN DPH3 HACD3 MXRA5 PSMB4 SOX9 YIPF7 CADM1 DPYSL4 HCN1 MYL9 PSMB7 SP3 YPEL2 CALM2 DPYSL5 HDAC3 MYLK3 PSMD4 SPOCK3 ZBTB33 CAMK1G DRC7 HDDC3 MYO5A PSMD6 SRP9 ZDHHC7 CAMLG DTD1 HDGF MYO6 PSMD8 SRRM1 ZMAT2 CARMIL2 DTD2 HELT NBAS PTGES3 SRSF11 ZNF385C CARS DUSP7 HES1 NBR1 PTPRK SSBP3 ZNF518A CAVIN4 DYRK3 HINT3 NCAPD2 PTPRO STX12 ZYX CCDC92 EAPP HMGB1 NCOR2 QSOX1 SUMO1 CCDC93 EBF3 HNRNPLL ND1 RAB11A SVOP CCN4 ECEL1 HSBP1 ND2 RAB14 SYNC CCNDBP1 EEF1A2 HSD17B10 ND6 RAB27A SYT1 CCT6B EFR3A HSPA14 NDFIP2 RAB34 SYT13 CDADC1 EIF2D HTR2C NDRG3 RAB39B SYT16

177

Appendix H – Genelists from Venn plots (RA)

FE_RA : FV_RA : FX_RA : ME_RA : MV_RA : MX_RA 205 ABCC8 CACUL1 DIRAS2 HSD17B12 NETO2 PTPRQ SYNDIG1 ADAMTS9 CADM2 DKK3 IGF1 NFYB R3HDM1 SYT1 ADAMTSL3 CALB1 DMRT2 IL1RAPL2 NIPSNAP2 RAB3GAP1 SYT12 ADCYAP1 CAMK1G DOC2B INPP5A NKAIN2 RALYL SYT17 ADCYAP1R 1 CBARP DYNLT3 IQGAP1 NRIP3 RAP1GAP2 SYT6 ADRA2A CCND1 ELF1 ITFG1 NRSN1 RGS20 TCERG1L ADSS CDA ENO2 ITPR2 NSF RSPO3 TFPI2 AFDN CDC42SE2 EPHA3 KCNG1 NSG1 RSU1 TMEM132D ALDH1A2 CDH11 EPHA5 KIF26A NSMF RTN4 TMEM163 ANGPT1 CDH12 EPHA6 KLHL18 NTN1 SCG2 TNFAIP8L3 ANO4 CDH18 EPHA8 LARGE1 OXR1 SELENOS TOLLIP APBA2 CDH7 FAT1 LDHA PALD1 SEZ6L TOX APLP2 CDH8 FGF22 LIMK2 PALLD SGSM2 TRPC4 ARHGAP22 CDK14 FLRT2 LRP4 PBX1 SHC3 TRUB1 ATP6V0A1 CDO1 FLRT3 LRP8 PCDH17 SHISAL1 TSNARE1 ATP6V0B CELF4 FN1 LRRTM3 PDCD10 SKI TTLL7 ATP6V0D1 CENPW FNDC3B LYPD1 PDZRN3 SLC17A6 TUFT1 ATP6V1B2 CETN2 GABRA1 MAFB PDZRN4 SLC25A29 UBE2QL1 ATP6V1C1 CHGA GCHFR MAN1A1 PEBP1 SLC35E3 UCHL1 B3GNT2 CHRDL2 GFRA2 MCFD2 PHACTR1 SLC4A11 USB1 BDNF CHRNA4 GLT1D1 MINDY3 PHF24 SLC4A3 VPS35 BHLHE40 CIT GPC3 MRAS PLCB1 SLC8A3 VSNL1 BRINP1 CKB GPI MRVI1 PLS1 SLITRK4 WBP4 BRINP2 CLSTN2 GPR22 MTPN PLXDC2 SMAP1 XYLT1 BRINP3 COL25A1 GPR39 NAPB POSTN SNTG1 ZBTB1 C11orf49 CORO1C GRHL1 NAPEPLD PRDM5 SPOCK2 C1QL3 CPLX1 GRIK1 NCAM2 PRR5 SPOCK3 C1QTNF4 CTXN1 GRIN3A NCEH1 PTCHD4 SRD5A2 CACNB2 DACT2 GSG1L NDNF PTPRN ST8SIA3 CACNG3 DCX HS3ST5 NECAP1 PTPRN2 SV2C

FE_RA : FV_RA : FX_RA : ME_RA : MX_RA 67 AARS CLTB FAM83G HDAC11 NDUFA9 PSME3 TENM1 ACO2 CNTN5 FBXO34 HIGD1A NEDD4 RAB11A TENM3 AFF1 CPLX3 FGF9 INIP NGF RERE TMEM200A AKAP6 CTH FGFR1OP KCNMB4 NR2F1 REV3L TUBB4B ANOS1 CYP7B1 FHOD3 KLHL29 ORC3 RUBCNL UQCRC1 ARHGAP15 DHRS3 FNDC4 KLHL3 PINK1 SBDS VDAC1 ARPC5 ELAVL1 GAPDH LINGO3 PLD5 SEC14L1 VSTM2L ATP6V1H ELMO1 GNMT MATK PMEPA1 SETBP1 BAZ2B EMX2 GPC6 ME1 PPP1R7 SLC35A1 ST6GALNA CIB2 EPB41L2 HAGH NDUFA10 PPP3CA C5

FE_RA : FV_RA : FX_RA : ME_RA : MV_RA 31 ATP6AP2 FERMT2 ITM2C NXPH2 SLC35F3 TENM4 UNC119 C11orf87 FNDC5 LAMP5 PAM SLCO5A1 TM9SF2 CRTAC1 GRIA4 LDLRAD3 PCSK2 SLIT3 TMEFF2 DIRAS3 GRIN1 LMO3 PRKCA SUSD4 TMEM116 178

DOK5 GUCY1A2 LRRK1 SEMA6D TAGLN3 TMEM8B

FE_RA : FV_RA : FX_RA : MV_RA : MX_RA 14 ABAT DGKB DUSP7 LRTM2 SESTD1 SULT4A1 TMPRSS7 BDH1 DPP10 LGALS1 NGEF SHC4 SVIL TRMT9B

FE_RA : FX_RA : ME_RA : MV_RA : MX_RA 16 CHRM3 FHL3 KCNQ3 LLGL2 OSBPL10 SPHKAP CLMN GPR63 KIF15 MEF2C RBM38 CNR1 GRIA1 KLF4 NFIA RSPH1

FE_RA : FV_RA : ME_RA : MV_RA : MX_RA 3 CDH10 PCDH8 RAB26

FV_RA : FX_RA : ME_RA : MV_RA : MX_RA 19 ACKR3 CCN4 CYP1B1 KLHL14 MAP3K20 ST18 YBX1 ADORA1 CHRM4 ESYT3 LAPTM4B OTULINL TMEM196 CCN2 CRH KLF6 MAMDC2 PCDH19 VAPB

FE_RA : FV_RA : FX_RA : ME_RA 62 ABHD6 CYB5B FAM20C KIAA1109 NELL2 PSMC3 SLC45A4 ACSBG2 DDIT4 FBXO25 KITLG NUP62 QSOX1 SMPDL3A ACTR1A DECR2 FERMT1 KLHDC8B PACSIN1 RAB41 SUCLA2 ARHGAP21 DGCR6L FGF1 LPGAT1 PARVB RAB6A THY1 ARMC9 DHCR24 HDAC4 MECR PCYT2 RGS14 TSHZ3 BRD3 DLAT IMPDH2 MPP1 PID1 RSF1 VPS4B BTBD10 DNM1 KAT6B MYCBP2 PKM RUNX1T1 ZEB2 CABIN1 EGR1 KCNK10 NDRG4 PPP1R12A SERPINB5 ZRSR2 CCDC126 FAF1 KCTD17 NDUFV1 PROSER1 SHISA3

FE_RA : FV_RA : FX_RA : MX_RA 23 CACNA1G DLD GHITM LSP1 PRXL2A STX12 CDC123 DPYSL3 GOT1 MFSD13A RANGAP1 TET3 CGRRF1 ETV1 IDH3A MKKS REEP5 VPS54 CISD1 FUNDC2 LNPK PPME1 SLC25A13

FE_RA : FV_RA : FX_RA : MV_RA 6 ANXA5 AUH GDPD5 GPR26 RPS2 ST8SIA2

FE_RA : FX_RA : ME_RA : MX_RA 7 B4GALT1 BTBD3 DDHD1 DUSP26 MGAT4C RIOK3 SAMD3

FE_RA : FX_RA : ME_RA : MV_RA 17 ACSL4 DUSP4 GNG10 MDGA1 NEURL1 SRGAP1 ADCY1 ERICH1 GRM3 MMP17 PUS7 TTLL4 DHRSX FGF16 MCRIP1 MOGAT1 SPAG6

FE_RA : FX_RA : MV_RA : MX_RA 3 CH25H VMA21 ZFAT 179

FE_RA : FV_RA : ME_RA : MX_RA 12 DCLK3 MDN1 NFIC NR0B1 PIP5K1B RGS4 LHFPL3 MTCL1 NIPA2 PHLPP2 RASL12 VIP

FE_RA : FV_RA : ME_RA : MV_RA 2 COL12A1 TRIO

FE_RA : FV_RA : MV_RA : MX_RA 3 GRB14 KCNK13 SERINC1

FE_RA : ME_RA : MV_RA : MX_RA 1 MAP1B

FV_RA : FX_RA : ME_RA : MX_RA 14 CLYBL EXT1 KLHL2 LDHB NRIP2 RELL2 TAB1 CTSB HCFC2 L3MBTL3 NRG2 PLS3 SYT14 TBC1D8

FV_RA : FX_RA : ME_RA : MV_RA 4 CHRM5 IGSF9B LRRC42 RPH3A

FV_RA : FX_RA : MV_RA : MX_RA 10 CARS GPR6 PHYHIPL PPP1CC S100B CRYM MAP1LC3B PLXDC1 PXYLP1 TLR3

FX_RA : ME_RA : MV_RA : MX_RA 14 AMZ1 DNAJC19 HMGN1 LMO2 SNX5 TMEM74 UNC13B CREBL2 FGF10 ITPK1 MMP15 SPEF2 UBR3 ZNF365

FV_RA : ME_RA : MV_RA : MX_RA 5 GPX1 KCNF1 KCNQ5 RRAD VDAC2

FE_RA : FV_RA : FX_RA 60 ABCA10 BZW1 CHP1 FGF8 MERTK PSMD14 SMYD4 ABRACL C1QB CNRIP1 GLRB MTX2 QDPR SOX11 ALG12 C6orf62 CTNNBIP1 GRB10 NCOA3 RAB11FIP2 TMEM184B AMPH CASP10 DCUN1D4 HNRNPDL NKTR RARRES1 TMEM98 AP2B1 CCDC28A DRAXIN KCNS1 P2RX6 ROGDI TTC27 ATF7IP CCT6B DUSP12 KIAA1191 PFN2 SCAMP5 UCHL3 ATP5F1A CDH9 ERP29 KMT2E PIP4K2A SCARB1 ATP6V1E1 CDKN1B FANCL MBNL3 PLK2 SH3GL3 AZIN2 CHL1 FDFT1 MDH2 PRDX1 SLC25A4

FE_RA : FX_RA : ME_RA 36 ADGRL2 CST3 GRID1 MAPK6 NEO1 RIMBP2 ATRX CTDP1 HS3ST1 MEGF9 NR3C2 SIGMAR1 BACE1 DGKI HSPA4L MMD PPFIA4 STYK1 BAG5 DLG5 KCNJ4 MON1A PTPRA UBQLN4 180

CAP2 EPN3 LZTR1 NCS1 PTPRK UBTD1 CHMP7 FBXL15 MAGI1 NECAB3 RGS7BP USP6NL

FE_RA : FX_RA : MX_RA 7 CDCA7L CHST1 EXTL3 PLA2G4A RTCB SPTSSB WHRN

FE_RA : FX_RA : MV_RA 10 ADAMTS10 DLK1 GAD2 LRRC8B STX7 B3GLCT EDIL3 GNG5 SNCA UBE2A

FE_RA : FV_RA : ME_RA 8 CAMTA1 DYNC1I1 MADD PHIP CSMD1 HSPA5 MYO16 STK16

FE_RA : FV_RA : MX_RA 5 CDH6 COX20 PCF11 SNX12 SYNPR

FE_RA : FV_RA : MV_RA 1 ODR4

FE_RA : ME_RA : MX_RA 6 CBLB FCHSD2 MYCN NPY1R RNF149 TRPM7

FE_RA : ME_RA : MV_RA 2 FAM189A1 SLIT1

FE_RA : MV_RA : MX_RA 1 CSNK1G1

FV_RA : FX_RA : ME_RA 41 ACOT11 COL19A1 G3BP2 INPP5J NUP93 PXDN USF3 ACOT7 DGLUCY GBE1 KCNJ5 P4HA2 RAB33A UXS1 ADAM10 DRAM1 GPR78 LRGUK PAM16 SHOC2 VGLL4 AK1 EEF2K GPX4 MAPKAP1 PITPNM1 SOD1 YARS ATP5F1B ENOX1 HAPLN4 MGST3 PRADC1 STUM ZMIZ1 CDH13 FOSL2 IGF1R NBL1 PSMD2 TSPAN4

FV_RA : FX_RA : MX_RA 14 ACOT13 ARL3 DNAJC11 HCCS MAFA NPTX2 TIMM17A ANKRD6 BPHL GALC LRRC49 NDUFB1 SMS TUSC3

FV_RA : FX_RA : MV_RA 5 FAM13C FAXC GALNT9 HPCA SYT2

FX_RA : ME_RA : MX_RA 3 PTK7 PTPRZ1 SH3BGRL3

FX_RA : ME_RA : MV_RA 181

14 AGFG1 DGAT2 KIF1A MRPS6 PRXL2C SLA SYNE1 CNN3 FABP4 METRNL PCDH18 RIT2 SLC8A1 TRIM9

FX_RA : MV_RA : MX_RA 8 BGLAP DCBLD1 NIPA1 VCAN CHSY3 HSPB8 NSUN6 YPEL2

FV_RA : ME_RA : MX_RA 3 CDK10 SHF UNC5C

FV_RA : ME_RA : MV_RA 6 P4HTM PPM1E PRPSAP1 SORCS1 STK32A ZADH2

FV_RA : MV_RA : MX_RA 3 ANGPTL7 TMEM150C TPX2

ME_RA : MV_RA : MX_RA 9 ADRA1A FABP7 HBAA PVALB TRIM63 CCK FAM49A MEMO1 TBC1D2

FE_RA : FX_RA 70 ACAD8 C3orf14 EPHX4 KCNA4 NPL RIN2 TARBP1 AGAP3 CAB39 EPSTI1 KIAA0513 OGT RPL37 TECPR1 ANKRD46 CDH23 F3 LAMA4 OLFML2B RPS5 TMCC2 AP1M1 CENPM FBRSL1 LOX P2RY1 SEMA3E TMEM30A API5 CEP290 FTH1 MCTP1 PCDH15 SHH TREM2 AREL1 CLIP4 GLRX5 MINPP1 PCMT1 SNAP91 TRPC3 ARNT2 CYB5D2 GOT2 MT4 PPIG SNPH TTL ATF4 DPYSL2 IFI30 MTMR2 PRKAR1B SOX5 VCAM1 BTBD2 EHBP1 ITGAV MTSS1 RBM5 SYNGR3 VIM C1R EIF2B5 JAKMIP1 NARS RGS5 SYTL4 WDR3

FE_RA : FV_RA 39 ACTR6 CREBBP GMDS PKD1 RTN4RL1 TCTN2 UBE2J1 AGR3 CYP51A1 KCNIP1 PLXNA2 SEMA3A TMEM159 UBTF ASH1L ENTPD6 LATS1 PNRC1 SERPIND1 TMSB15B ZIC1 ATP6V1D EP300 LRCH2 RAB2A SLC7A3 TNC CFAP77 FAAH2 LRP11 RET ST14 TRAPPC3 COPS8 GDF10 PDCD2 RPS6KL1 STC1 UAP1

FE_RA : ME_RA 21 ANKRD29 CEP170 EIF2AK3 GOLGA4 MCUB THADA UBE2H ARID5B CFAP97 FAT4 HCN1 RPRML TMEM45B UROD ARX DST FOXRED1 LZTS1 TBR1 TNNI1 WSCD2

FE_RA : MX_RA 8 ANXA4 CAPN5 IFT80 PTCD2 BRCA2 FKTN MCM10 TRIP13

182

FE_RA : MV_RA 8 CAMK2D NELL1 TFDP1 UNC5A CDHR1 RPS6KA1 TG ZNF385B

FV_RA : FX_RA 67 ZFHX4 PPIF IGFBP5 SNAP25 PRCP ATP2A2 ATP10A SARS TSPAN13 PRLR DYNC2H1 DUS2 DTD1 ABTB1 CTSK NAA25 RBBP6 FUNDC1 LCMT1 SKIL UBE2L3 USH1C ADAMTS14 NAV2 IGFBP2 ECE1 SDCBP SHISAL2A PRDX3 LGALS8 MAPK11 TRNT1 PTPN6 SLC25A5 OXSR1 FBXO9 ZMPSTE24 DENND2D MGAT5B OGDH CDC42SE1 NAP1L4 FBXW4 TMEM97 LMO1 ZNF804B CDH2 PRSS12 IFITM10 MMS19 NDUFB5 NEXMIF TBC1D8B MPI IGSF3 ULK4 RXYLT1 AP3M1 ADAMTS7 SLC24A2 PNPT1 MIGA1 ATP5MC3 SV2B CNTNAP1 FXN TRPM2

FX_RA : ME_RA 34 APCDD1L CREB3L2 FUT10 LDB2 RFLNA SMYD1 TMCC1 ATP1B1 DHDDS GABRD LPAR6 RGS6 SUPV3L1 USP2 C11orf96 DNAJC10 GGA3 OPCML SGSM3 TBC1D14 ZMYND11 CALB2 ETF1 HMGB3 PPP2R2B SLC25A25 TEX264 ZNF593 CPN1 FBXO11 ID4 PTPRU SMOC1 THNSL2

FX_RA : MX_RA 18 AGT FAM126A GRM1 LRRC3B PEX7 SERPINF1 AP2A2 FBXO2 INPP4B MMUT PLEKHB2 TAT DNALI1 GABRB3 KIDINS220 NOS2 PTGFRN WIPI2

FX_RA : MV_RA 16 AFAP1L2 C1QTNF5 LRFN2 NCOR1 RAPGEF4 YBX3 ANGPTL2 CLSTN3 LRIG3 PPP1R12B SLC5A3 ARHGAP24 EFEMP1 METTL9 PPP1R9A TSEN2

FV_RA : ME_RA 18 ACTN1 CHURC1 FLAD1 LRP1B PRAG1 SUGT1 CDK9 CYP19A1 GRM8 MXD4 REV1 TET1 CHST15 DGKH HCRT NETO1 SLC25A11 UROS

FV_RA : MX_RA 21 ACAT2 DPAGT1 GALNT17 LRMP PHACTR4 SLC6A1 TTR ARHGDIB GAB2 GFRA1 MRPS27 RTTN TMEM120B WIF1 CACNA1E GALK2 JUP NDRG3 SLC38A7 TMEM184C ZFPM2

FV_RA : MV_RA 6 C15orf62 DBNDD2 MAP3K5 MYH11 PLCD3 UNC13C

ME_RA : MX_RA 12 ADRA1D CLVS2 FNIP2 HERC4 RASL10B ST6GAL1 ATP6AP1L DERA FST MOGAT2 RND3 TTC28

183

ME_RA : MV_RA 19 ACO1 CDS1 CTBP1 GAS2 KLHDC3 SLC2A13 VTN ANO2 COCH ELOVL2 KAZN OSBPL11 TBL1XR1 ARHGEF9 CORO2A FABP5 KIAA1549L SH3GL2 TGFB3

MV_RA : MX_RA 26 ABCG2 CDC20 ENPP1 MAD2L1 NEK2 SMC2 TXNRD1 B3GALT2 CDK1 GJC1 MELK NKIRAS1 SORCS2 VEGFD CALD1 CKAP2 IFI6 MGP OPRM1 STC2 CCPG1 COL4A1 KPNA2 MYL9 PSD3 TMEM108

FE_RA 127 ACTR3 CMPK2 FOXN3 LIN52 OLFM3 RND2 TASOR ADAMTS2 CPTP FSTL5 LRFN5 PAPSS1 RPL31 TMEM130 ADCY2 CTCF GCAT LURAP1 PCDH1 RPL36AL TMEM132C ADPGK CXXC5 GDA MAF PDK1 SARAF TRPC1 AMIGO2 DAAM1 GPHN MANBA PHF3 SATB2 TSHZ2 ARMC3 DCAF6 GRM2 MAP1LC3A PLPBP SEC31B WDFY3 ATP1B3 DIABLO HDAC9 MAP2K1 PLPPR4 SEC62 WEE1 BBS4 DIS3L HMGCLL1 MAP4K4 PRKAG2 SETD3 WNK1 BRMS1L DTNBP1 HMGCS1 MAT2B PRKCZ SFRP2 WWC1 C1QA DUSP16 IPMK MCPH1 PRRG3 SLC25A22 XRN1 C1S ELAC2 JAG1 MLNR RAB30 SLC30A3 ZDHHC14 CADM1 ELMO3 KAT6A MRPL17 RAB33B SLC49A3 ZNF800 CCDC50 ETFDH KBTBD11 MTHFD1L RAF1 SLC6A7 ZYX CCDC88A FAAH KCNA2 NHSL1 RAP2A SLCO3A1 CCNI FAM163B KCNG3 NKX2-2 RASD1 SMG1 CDH4 FAM234B KCTD2 NOL4 RASL11B SMG6 CDKL5 FASTK KDSR NT5C3A RBPJL SMYD3 CHD7 FBXO16 KLHL5 NTM REXO5 SQLE CHN1 FILIP1 LAYN NUDT14 RIPK1 TAF1D

FX_RA 298 AASDHPPT CHST13 FSTL4 LCAT PAK1 RREB1 TBC1D30 ABLIM1 CLCN5 FUCA1 LCP1 PARD3B RRM2B TBL2 ADAM22 CLSTN1 GABRA3 LIAS PDHX RSPH3 TCIRG1 ADAM23 CNNM1 GALNT18 LITAF PDLIM3 SALL1 THOC3 ADAM8 COG6 GNAI3 LMBR1 PDXK SAP30L THSD7A AGPAT3 COL11A1 GNG2 LMBRD2 PENK SCAMP3 TIMP3 AKAP5 COL23A1 GPCPD1 LRRN4 PGM2L1 SCD TMEM100 ALS2 COLEC12 GPER1 LRSAM1 PGM5 SCPEP1 TMEM129 AMOTL1 COX10 GPM6A LY86 PHLPP1 SCRG1 TMEM173 ANO1 CPEB4 GPR158 MAPK8IP1 PLAG1 SDC2 TMEM63C AP3D1 CRABP1 GPR37 MC5R PLCB2 SDC4 TMOD2 APBA1 CRIP2 GPX6 MEF2D PLCD1 SELENOI TMSB4X AQP11 CUTC GRM5 MEX3A PLPP1 SEMA3F TPCN1 ARFGEF3 DAB2 GTF2IRD1 MGST1 PLPPR3 SEMA4B TPPP ARL8B DDX5 H3F3A MIB2 PMPCA SERPINE2 TUT4 ARPP21 DENND2C HACD3 MLC1 PNPLA3 SGSM1 UBAP2 ASPHD2 DHX38 HCN2 MMP2 POGLUT1 SIN3B UBE2I ASXL3 DLX1 HDLBP MPEG1 POMGNT1 SLC25A27 UBE4A ATP5IF1 DNAJB13 HELQ MRTFB POMGNT2 SLC27A4 UCK2

184

UHRF1BP1 ATP5MD DNAJB14 HES1 MTCH1 PPDPF SLC2A12 L ATP6V0A4 DNAJC12 HIP1 MTMR6 PPIP5K2 SLC35F5 UQCRFS1 ATP8A2 DNASE1L3 HIVEP2 MVD PPP6R3 SLC6A13 USP15 ATXN7L1 DOCK8 HPS3 MYH10 PRKAR2B SLC6A6 USP34 BCAT1 DOK4 HSPA12A MYO1E PRKCQ SLC7A4 VAMP1 BCL6 DOK6 HSPB1 NAA35 PSAT1 SNAPC1 VASH1 BEGAIN DYNLT1 HTR1F NALCN PSMA4 SNX17 VAV3 BIN1 E2F5 HVCN1 NAPG PTPA SOD3 VPS26B BMP4 EDA2R IBTK NAPRT PTPRE SOX6 VPS53 C2CD2L EDEM3 IGF2R NDOR1 RAB28 SPARC VRK1 CACNG4 EEF1B2 IRF2BPL NDRG1 RAC2 SPATS2L WASF1 CALHM5 EGF ITGB2 NEDD9 RANBP10 SPOPL WDR7 CANX ELMO2 ITGB5 NEU2 RASSF8 SPTBN1 WDR78 CARD11 ELOVL6 JPH1 NFX1 RBM34 SRGAP3 WNT2 CARF EPHA7 KATNB1 NPY2R RBM39 SRM WNT5A CAVIN4 EXTL2 KCNB1 NTN4 RECQL5 SRPRA WNT5B CCND3 FAM171A1 KCNG4 NUAK1 RGS12 SRSF7 XKR6 CD99L2 FAM217B KCNIP4 NUP37 RNF10 ST3GAL2 ZBTB25 CDC42BPB FBXL7 KCNK5 OCSTAMP RNF144B STAT1 ZC4H2 CDCA7 FGFR4 KCNMA1 OSBP2 RPL12 STK17A ZFP36L1 CDK19 FKBP1B KIAA1211 OSBPL8 RPL3 STXBP4 ZNF618 CFAP410 FKBP4 KTN1 P2RY10 RPS24 SUMF2 CHAC1 FNDC3A KY P2RY6 RPS3A SYPL2 CHRNB2 FRS3 LAPTM5 PACRGL RPS6 TBC1D2B

FV_RA 168 ACAD9 C1orf21 DNAJA2 GSE1 MMP9 PSMA6 SPRYD7 ACADL C20orf194 DNAJB6 GUK1 MPC1 PSMD10 SRRM1 ACOX2 CCDC181 DNAJC17 H2AFY MRPL22 PYGB SRSF11 ADARB1 CCN1 DNAJC6 HIVEP3 MRPS7 RARS SUCLG1 AHCY CCT2 DOCK4 HMOX2 MRPS9 RASGEF1B TFAP2D AIG1 CCT5 DOT1L HPCAL1 MTO1 RBM33 TIAM1 AIMP2 CD9 DYRK1A IDH3B MYO9B RIBC2 TMEM206 ANAPC2 CDC42 EHD4 IL13RA2 NANS RO60 TMEM242 ANKRD66 CFDP1 EIF2AK2 INSM1 NDUFB8 RPL7L1 TNRC6A AP3M2 CNTRL ELL IQCA1 NDUFV2 RPN1 TOMM22 AP3S2 COA3 ELP6 IRF1 NIPSNAP1 RUNX2 TRANK1 ARF1 COL22A1 ENTPD2 ITPR3 NKAIN4 S100A4 TRPC7 ART1 COQ2 FAIM2 IVD NT5DC2 SBK2 TRPV2 ASNS COX1 FAM184A JAZF1 NT5E SDF2L1 TYRO3 ASPG CPNE4 FAM83H KCNT1 NTF3 SEC22B UACA ATG4C CPSF3 FCHO2 KLF7 NTPCR SELENOT UBE2V2 ATN1 CRTC1 FH LIPT2 PAFAH1B2 SLC10A1 UQCRC2 ATP2A3 CTIF FLNC LMBRD1 PARP6 SLC12A3 USP14 ATP5F1C CX3CR1 FNBP1L LTF PGD SLC13A5 VILL ATP5PB CYCS GLRX2 MAN2A2 PIK3C2A SLC35A5 WDR90 ATP5PD DDR2 GMPPB MAP3K2 POLK SLC35C2 ZHX1 ATPAF2 DEGS1 GPR137B MBIP POMT1 SLC44A5 ZNF516 BAIAP2 DERL3 GPR37L1 MCF2L2 PPP1R17 SNU13 ZNF609 BICD1 DLST GRK7 METTL7A PRDX6 SNX14 ZNFX1

ME_RA 93 ADRA2C CTTNBP2 GABRA5 KIFAP3 PANK4 RFESD TMEM240 ADRM1 CYP26A1 GAP43 LHFPL6 PFKFB2 RGS7 TPM4 185

AKAP11 DDX58 GDNF LINGO1 PGR RNPEP TRAK1 ALKAL2 DLGAP2 GLG1 LRRN3 PIANP RPUSD3 TSR3 ARHGDIA DLL4 HACD2 MAK PLCZ1 SFXN3 UBE2B BAHD1 DNAJA1 HHIPL1 MCU PPFIBP1 SH3BP5 UVRAG BMPR2 DOCK9 HPCAL4 MGAT4A PRMT8 SLC16A14 VPS13A C5orf30 EDN3 HSPB11 MYLIP PTCHD1 SLC22A23 ZFAND5 CACNG5 ELFN1 IDE NDUFA5 PTK2 SPAST ZNF644 CADPS2 EMC1 ISCU NPY5R PTPRD STRBP CCN3 EMC2 ISM1 NRG1 PUS10 SYAP1 CENPK ETV5 ITM2B NYAP2 RALY TBC1D19 CHPF FAM117B KIAA0895L OSGIN1 RAMP2 TBXT CSRNP3 FIG4 KIAA1143 PAH RBFOX1 TMEM131

MX_RA 78 ADARB2 CHRNA5 HMGB2 MFSD2A PPARGC1A SATB1 UFD1 AKT3 COL13A1 HTR1B MREG PRKD3 SAV1 UHRF1 ASMT CRCP HUNK NEXN PWWP2A SERTM1 UQCR11 BARHL2 CTXN2 IVNS1ABP NPC1 PYGL SESN1 VWC2 BCAR3 DBN1 KCTD20 NSUN3 RAB19 SLC26A4 VWF BNIP3 DUSP22 KIF20B PARP16 RAB34 SLC41A1 XPOT BRWD3 FKBP6 LAPTM4A PEX11B RACGAP1 SNX24 CACNA2D3 GABRG3 LDAH PGAM5 RIOK2 STRADA CCDC125 GDAP1 LMNA PHLDB1 RNF114 STXBP6 CCDC186 GGACT LRRC6 PIGO RPS6KA2 SVEP1 CCNB2 GRIK2 MAPT PLCH2 RRM2 TLL1 CHCHD1 GYPC MCCC2 PLK1 RTKN2 TMTC1

MV_RA 50 ADM CKS2 DRD3 IGFBP7 MEGF11 SERPINH1 TMEM233 ANXA6 CNMD DUSP10 KCNK12 MMRN2 SGTB ZNF804A ARC COL6A3 EEPD1 KDR NLRC3 SLC29A4 BST1 CPNE9 EMP2 KHDRBS2 PDP1 SLC39A8 CA8 DAPP1 ERC2 KIAA0408 PDS5B SYT4 CERKL DBI FAM135A KIAA0930 PI4KB THBS2 CETP DCN GNG4 KLHL4 PPFIBP2 TK1 CFAP58 DENND2A GPM6B LMO7 RAB3B TMEM185A

186

Appendix I – Genelists from Venn plots (LMAN)

FE_LMAN : FV_LMAN : FX_LMAN : ME_LMAN : MV_LMAN : MX_LMAN 637 AAMDC CCDC88A EEA1 HMG20A MOB2 PMP2 SNAP25 AARS CCDC92 EFTUD2 HMOX1 MOXD1 PPARGC1A SOD1 ABHD12 CCN2 EIF1B HMOX2 MPC1 PPFIBP1 SOD2 ABHD6 CCND3 EIF2B5 HOMER1 MPPED1 PPIB SORCS2 ACADM CCSAP EIF4G3 HPCAL1 MRPS25 PPP1R14C SOX6 ACBD7 CD99L2 ELFN1 HPS1 MRPS36 PPP1R7 SPEF2 ACOT7 CDC14A ELMOD1 HS1BP3 MT-ND3 PQLC1 SPRED1 ACOT8 CDH10 ELOVL6 HS3ST5 MTNR1A PRDX3 SPRED2 ACSL4 CDH13 ELOVL7 HSDL2 MTPN PRDX6 SPTSSA ACTN1 CDH2 ENC1 HSPH1 MTUS1 PRICKLE1 SRPK1 ACTR2 CDH22 ENO2 HTR1B MYCN PRKAR1B SSTR1 ACYP1 CDH9 EPB41L3 IDH3B MYLIP PRKCA SSTR5 ADAM23 CDK14 EPB41L4A IGFBP3 MYO6 PRKCI ST3GAL4 ADARB1 CDK17 EPHB1 ING3 MYOM2 PRMT8 ST3GAL6 ADCYAP1 CDKN1B EPHB6 INSM1 MYOZ2 PRPSAP1 ST6GAL2 ADGRB3 CDO1 ERC2 INSR NAAA PSAT1 ST8SIA3 ADGRV1 CELF4 ESRRG IPPK NADSYN1 PTCHD1 STC2 ADRA1D CELF5 ESYT3 ISCA1 NCALD PTN STK39 ADRA2A CEP170B FAAH IVNS1ABP NCS1 PTPRD STRADA AGTRAP CFAP97 FABP5 JAGN1 ND1 PTPRF STRIP2 AIFM3 CHAC1 FAM102B KCNA1 NDUFA5 PTPRN2 STUM AK1 CHCHD2 FAM110C KCNA2 NDUFAB1 PTPRO STXBP5L AKR1A1 CHCHD4 FAM135B KCNC1 NDUFAF2 PVALB STXBP6 ALDH1A2 CHRDL2 FAM163B KCNG3 NDUFAF3 RAB11FIP4 SULF1 ALG12 CHRM2 FAM189A1 KCNH5 NDUFB1 RAB3C SYN2 AMDHD1 CHRM3 FBXO2 KCNIP2 NDUFB2 RAB4A SYNE1 AMPD3 CHRNA4 FGD3 KCNIP4 NDUFB3 RAB9B SYT10 AMZ1 CHST11 FGF12 KCNJ4 NDUFB8 RAF1 TBC1D13 ANKRD10 CKAP2 FGF9 KCNK1 NDUFB9 RALGPS2 TBC1D19 ANKRD27 CKB FGFR2 KCNV1 NDUFS4 RALY TCF12 ANKRD44 CLYBL FH KCTD12 NDUFS7 RAP1A TCF20 ANKRD6 CNIH1 FHDC1 KCTD17 NDUFS8 RASL11B TDG ANKRD9 CNIH3 FIGN KCTD2 NECTIN1 RASL12 TENM1 ANP32E CNNM1 FILIP1L KCTD3 NEFL RASSF9 TENM3 ANXA4 CNR1 FKBP1B KCTD8 NELL1 RBM22 TFAP2D ANXA6 CNRIP1 FMN1 KIAA0895 NELL2 RBX1 TFPI2 AOAH CNTNAP1 FNDC4 KIAA1147 NET1 RCAN2 TG AP3B1 CNTNAP5 FNIP2 KIAA1841 NEURL1 RET TH AQP9 COBLL1 FZD1 KIDINS220 NFASC RGS12 TIMM10 ARHGEF3 COL21A1 G3BP1 KITLG NFIC RGS14 TIMM17A ARHGEF9 COL4A1 GABBR2 KL NFYC RGS20 TLCD2 ARPC5 COLEC10 GABRD KLHL14 NIM1K RIDA TMEFF2 ASRGL1 COMMD4 GABRG1 KLHL2 NINJ2 RIMKLB TMEM130 ATF1 COMTD1 GABRG3 KLHL4 NKAIN2 RND2 TMEM14A ATG10 COX4I1 GALNT15 KTN1 NKAIN3 RNF10 TMEM184B ATP1A1 COX6A1 GALNT16 LANCL1 NKTR RNF144B TMEM196 ATP1B1 COX7C GALNT17 LAPTM4B NKX6-2 RNF165 TMEM240 ATP2B1 CPE GALNT9 LCP1 NMRK2 RPA1 TMEM8A ATP5IF1 CPLX1 GALNTL6 LDHB NOL4 RPRML TMTC1 ATP5MC3 CPNE2 GALR1 LDLRAD4 NPBWR2 RRBP1 TNFAIP8L1 ATP5MD CPNE4 GAS2 LGALS8 NPR2 RYR2 TNIP1 187

ATP5ME CPNE8 GDA LGALSL NPTX2 SAMD11 TRIB2 ATP5PB CRIP2 GDPD4 LGI2 NR3C2 SAMD3 TRIM36 ATP5PD CRMP1 GDPD5 LIFR NRP2 SAT1 TRPM3 ATP5PO CRYAB GFRA2 LMO2 NSG2 SCGN TRUB1 ATP6V0A1 CSNK2A2 GJB1 LNX1 NSMF SEMA3A TSHZ3 ATP6V0E1 CSRNP3 GLIPR2 LPAR1 NT5DC1 SEMA3E TSPAN12 ATP8A1 CTNNB1 GLT1D1 LRMP NT5E SEMA5B TSPAN4 B3GAT2 CTSB GNAI1 LRP2 NTF3 SEMA6A TTC39A B3GNT2 CTXN1 GNG11 LRRC49 NTM SEMA6D TUBGCP5 B4GALT7 CTXN2 GNG5 LRRTM2 NTN3 SERPINB5 TVP23A BARX2 CYB5D2 GNMT LRRTM4 NTNG1 SESN1 TWSG1 BBS4 CYP7B1 GOT1 LSM11 NTS SF3B3 TYRO3 BDH1 CYTH3 GPC6 LSP1 NUAK1 SFRP2 UBE2J1 BDNF DACT2 GPCPD1 LTF NUP93 SH3BP5 UCK1 BEGAIN DAPK2 GPD1 LYPD1 NWD2 SHF UGT8 BEND6 DBI GPM6B LZTS1 OPRM1 SHISA9 UNC13B BOK DBNDD2 GPR162 MAP1B OXSR1 SHISAL2A UQCR10 BRINP1 DCAF6 GPR39 MAP1LC3A P2RY1 SIDT1 UQCR11 BRINP3 DDIT4 GPR63 MAP2K1 PAK1 SIKE1 UQCRB BTBD11 DERA GPX4 MAP3K4 PAM16 SLC12A2 UQCRQ BZW1 DGCR6L GRAMD4 MAP3K5 PAPPA SLC1A1 USH1C C11orf96 DGKI GRB14 MAPK11 PCDH1 SLC20A1 VAMP1 C17orf75 DIP2A GRIA1 MAPK8IP1 PCDH17 SLC22A23 VTN C1QL3 DIRAS2 GRIA2 MAPRE2 PDE1A SLC24A2 WDR66 C2CD4C DISP1 GRIA4 MASP1 PDE2A SLC25A27 WNT9A C3orf70 DISP2 GRID1 MAST3 PDIA6 SLC25A33 WWC1 CAB39L DLGAP1 GRID2 MATK PEAK1 SLC25A4 XK CABIN1 DLGAP2 GRIK1 MATN1 PFKFB2 SLC29A4 XKR6 CACNA1D DLK2 GRIK2 MBP PHB SLC2A1 XYLT1 CACNA1G DOC2B GRIN3A MED30 PHEX SLC30A3 YAF2 CACNG3 DOK5 GRM1 MEF2C PITPNM3 SLC30A4 ZDHHC6 CACNG4 DPAGT1 GRM5 MEGF9 PKM SLC35F1 ZFPM2 CADM1 DPP10 GSE1 MEIS2 PLCB4 SLC35F4 ZMAT4 CALCB DPYD GSG1 MERTK PLCL2 SLC38A7 ZNF365 CAMK1G DRC3 GSG1L MGAT4C PLCXD3 SLC4A3 ZNF385B CAMK2A DTD2 HAPLN4 MGAT5B PLP1 SLC6A7 ZNF385D CAMTA1 DUSP14 HCCS MGST3 PLPP1 SLITRK1 ZNF423 CASP10 DUSP3 HIGD1A MMD PLPPR4 SLITRK2 ZNF503 CBLN2 DUSP4 HIVEP2 MMD2 PLXDC2 SLITRK4 ZNF593 CCDC126 DUSP7 HMBOX1 MMP15 PLXNC1 SMYD1 ZNF827

FE_LMAN : FV_LMAN : FX_LMAN : ME_LMAN : MX_LMAN 87 ABHD17A B2M CSTB ITM2B NDUFS6 RBFOX1 STAM2 ADCYAP1R 1 C6orf203 CYCS KDM1B NDUFV2 RTCA TBCA AGAP3 CALD1 CYP19A1 LZTR1 NECAB2 RTN4IP1 TCEA2 AIG1 CAMSAP1 DNAJC28 MAPK1IP1L NEO1 RTN4R TIAM1 APOO CDH4 DNM1 MLEC NEXMIF RTN4RL1 TWF2 ARL6IP1 CELF2 EMC8 MVB12B OGT SAP30 UBLCP1 ARPC2 CHAMP1 FBXO41 NCKIPSD PARK7 SCG2 UQCRC1 ARRB1 CHERP FUNDC1 NDUFA1 PCNA SDCBP VDAC1 ASB2 CLTA GPR27 NDUFA10 PDCD6 SGPP2 VSTM2L ATP2A3 COX2 HECTD2 NDUFA6 PHF24 SKIL ATP5F1C COX3 HSPE1 NDUFA9 PIP4K2B SLC32A1 ATP5MG COX7A2 ID4 NDUFAF4 PRICKLE2 SLC7A14 188

AUH CST3 IFI6 NDUFB5 PTPRS SMDT1

FE_LMAN : FV_LMAN : FX_LMAN : ME_LMAN : MV_LMAN 39 ADAMTSL2 COL6A3 DCX NAB1 PTPRN SLC25A30 VPS53 AQP11 CPNE9 DGAT2 NPFFR1 RAB6A SLC49A3 VWF ST6GALNA ARRDC2 CRABP1 EIF4G2 P2RX6 RAPGEF4 C5 ZNF804A BTC CYGB FMOD PARVB RORA TECPR1 CBARP DAP GREB1 PCSK1 SERPINE2 TYR CHL1 DCLK2 HECW1 PLTP SETBP1 UNC5C

FE_LMAN : FV_LMAN : FX_LMAN : MV_LMAN : MX_LMAN 27 AGPAT4 CDH19 ELOVL1 LINGO1 RAPGEF5 SLC35E3 TBC1D2 ANKDD1A DACH1 GLS PDE10A RASD2 SOSTDC1 TCF15 ARPP19 DENND6A GPR3 POMGNT1 SCRG1 ST18 TPM4 BACE2 DNAJB4 IGF1 PRAG1 SHC3 TAC1

FE_LMAN : FX_LMAN : ME_LMAN : MV_LMAN : MX_LMAN 29 AP2B1 DLG3 KCNJ10 PMEPA1 SHCBP1 TNIK BGLAP GLRB KLHDC1 PRKDC SLC38A2 TOX BRI3BP GPC3 KLHL7 RGS5 SLC9A7 UXS1 CCK GREM1 MAPK9 SACS SLK ZRANB3 CPN1 ISOC1 NR0B1 SGSM2 SPAG9

FE_LMAN : FV_LMAN : ME_LMAN : MV_LMAN : MX_LMAN 58 ACVR1 CHRM5 HDAC9 MT4 PHYHIPL SEC61A1 TBC1D5 ADGRA1 CIPC KCNF1 NCOR2 PSMB4 SHROOM2 TRPC1 AMPH CYP1B1 KPNA3 NDNF PSTPIP1 SLC9A3 WIF1 API5 DNAJB6 LRFN5 NDUFA8 RAB3B SPATA2 YBX3 BORCS5 ESRP2 MAPKBP1 NDUFV1 RAMP3 SRD5A2 BRMS1L GRPEL2 MBTPS2 ORMDL3 RHOB SSTR3 CA2 GTF2IRD1 MEGF11 OTUD1 RORB ST6GAL1 CCNG1 H1F0 MPC2 PDP2 RP2 STK25 CDC42EP3 HBAA MRPL17 PHACTR2 SEC23A SYNM

FV_LMAN : FX_LMAN : ME_LMAN : MV_LMAN : MX_LMAN 101 ACSBG2 DLG2 GPR22 LRFN2 PPP2R5D SAP30L TPD52L1 APMAP DPH3 GRM8 LRRCC1 PRELID3A SEH1L TRPC7 ARHGAP15 DTNBP1 GYG1 LTBP1 PRNP SFN TTL ARHGAP21 EDA H3F3A MBNL2 PTPN2 SIRT5 UBE2A ASZ1 EPHA7 HCN1 MDFIC2 PTPRR SLC16A3 UBE2E2 BSG FA2H HSBP1 NEIL3 QSOX1 SLC25A34 YIPF3 BTBD6 FAAP24 IFFO1 NIPSNAP1 RAC1 SLC35G2 YWHAH BTF3L4 FAM53A IPMK OGG1 RBM20 SLC4A4 ZBTB1 CDC42BPB FEZF2 ISCU OTOG RBPJ ST8SIA6 ZEB2 CIRBP FGFBP2 KHDRBS3 PAPOLG RELL2 SUGT1 ZMYND11 CNP FITM2 KIAA1211 PARN RND3 TBL1XR1 ZNF516 CNTN5 GDAP1L1 KLHDC8B PDLIM1 RNF157 TEC COPG2 GMPR LIN7A PPM1J RPAP1 THY1 CRIM1 GPM6A LINGO3 PPME1 RSPO3 TLL1 DGKB GPR143 LMO1 PPP1R12A RTKN2 TMCC2

FE_LMAN : FV_LMAN : FX_LMAN : ME_LMAN 189

39 AHSA2P CNBP FAM174B KBTBD11 PODN SDC3 TRAPPC4 AP1M1 DDRGK1 FDFT1 LAMTOR2 PODXL2 SH3GL2 UNC119 ARHGAP22 DHCR24 GRB10 MCUB PRDX1 SS18L2 ZMIZ2 ATP5MF DNALI1 GRIN2B MRPL2 PRKAR1A STK32C BNIP3 EDF1 HK1 MRPL58 RSPH1 SYAP1 CFDP1 EXT1 IFIH1 NCAM2 SCP2 TMEM132D

FE_LMAN : FV_LMAN : FX_LMAN : MX_LMAN 7 CDCA7L CLSTN2 GRIK4 LAMP5 PLCB1 PLPPR5 SYNDIG1L

FE_LMAN : FV_LMAN : FX_LMAN : MV_LMAN 7 CTSA ITPK1 JAG2 KIF26B NDE1 PRSS23 UTS2B

FE_LMAN : FX_LMAN : ME_LMAN : MX_LMAN 30 AGO4 CELSR3 DHRS3 GLB1 NCOA3 SLIT1 AP3S1 COPA DNAJC21 IPO13 ND4L SRF CADM2 CREB3L2 DYNC1H1 METTL8 PALD1 TAGAP CCDC186 CSMD3 EXTL3 MRPL44 PRRG3 TM2D3 CDK8 DCDC2 FGF14 NAA30 RGS4 TTC32

FE_LMAN : FX_LMAN : ME_LMAN : MV_LMAN 5 ANO4 CUX1 DCBLD1 LUZP2 PREX2

FE_LMAN : FX_LMAN : MV_LMAN : MX_LMAN 7 KCTD6 KLHL32 NLE1 PRR5 RRAD TMEM25 TOMM34

FE_LMAN : FV_LMAN : ME_LMAN : MX_LMAN 35 ADSL D2HGDH FRYL HNRNPLL MRPS14 PDE4B TCERG1L AGFG1 ENDOG GJA1 LZTS3 MRPS5 PLPPR3 TM4SF18 AREL1 EXOC5 GTF3C4 MAPK1 NEURL1B PLXDC1 TUBB4B ATG13 FAM234B HDDC2 MFSD13A NOVA1 RPSA TXNL1 CHCHD1 FOXK1 HINT1 MOCS2 OAZ1 SLC25A5 WBP4

FE_LMAN : FV_LMAN : ME_LMAN : MV_LMAN 16 ALDH5A1 ATP5F1B DMGDH NFIA SCN8A VSX1 ANGPT2 CHRNA5 HPCAL4 NRIP1 SGCE ATAD3A COPE KCNG2 PSD SGSM1

FE_LMAN : FV_LMAN : MV_LMAN : MX_LMAN 16 ABCC6 EFCAB10 FDPS MYO1E TMBIM1 UCK2 ATP12A ENPP6 GJC1 S100B TSPAN2 DRD1 ENTPD3 GTPBP1 SERINC5 UBA2

FE_LMAN : ME_LMAN : MV_LMAN : MX_LMAN 16 TNFRSF11 ARNT2 FAM131B INSYN2B LGALS1 RHAG B CCN3 GABRA5 KCNG1 MCPH1 RHBG ECHDC1 GOLM1 LDAH NXNL2 TAF5L

190

FV_LMAN : FX_LMAN : ME_LMAN : MX_LMAN 15 CAMKK1 CHN1 JPH1 PECAM1 SAMD10 CFL2 DAB1 ND6 PSMD5 TBP CHGA IGFBP7 NLGN3 RASSF7 TMEM255A

FV_LMAN : FX_LMAN : ME_LMAN : MV_LMAN 10 BARD1 FLRT2 KLF10 PLA2G12A TBC1D8B BTBD3 GCHFR NRIP2 RAP1GDS1 UCHL3

FV_LMAN : FX_LMAN : MV_LMAN : MX_LMAN 34 AIF1L CACNA2D3 EPHA4 LARP6 PKD1 RNF182 SYNPO2 AMIGO2 CACNB2 FAP NCAPD2 PLLP RNPS1 TBC1D30 ARHGDIB DAAM2 FBLN1 NDRG4 PTPRU RPS27L TMEM125 BRINP2 DMTF1 IGFBP4 PALLD RASGEF1B RUNX1T1 TTLL7 C1orf21 DOK7 KIF21B PGM1 RILP SLC16A6

FX_LMAN : ME_LMAN : MV_LMAN : MX_LMAN 23 BMP2 DTD1 KCNH7 MIPOL1 PSMD10 TMEM50A CBFA2T2 HMGN1 KIAA0232 MPP1 SLC25A11 TRIM63 CD82 HSD17B12 KSR1 PLXNB2 SLC35A1 UBE2O DENND2C HSPA2 LRR1 PPEF1 TMEM131L

FV_LMAN : ME_LMAN : MV_LMAN : MX_LMAN 43 ACAN CAMLG GPC1 MDGA1 NPM3 SHC4 ZDHHC13 ADAMTS10 CASP3 INTS7 METTL7A NR5A2 SLAIN1 ADCY8 CHD6 IQSEC3 MTA1 POLE2 SLC16A14 AKAP7 DECR2 KCNIP1 NDFIP2 POSTN SVEP1 ANKRD46 FANCI KIF2A NDRG1 PROM1 TMEM100 ATP5F1A FBXL3 LMO3 NECAP1 RRN3 VPS8 CA8 GNAI2 LRP12 NINJ1 RSU1 WDR73

FE_LMAN : FV_LMAN : FX_LMAN 14 AGK ATP6 IQUB NEGR1 NRSN1 PPP3CA WNT4 AP2A2 CXXC5 MALSU1 NFX1 NTRK2 RASSF5 ZBTB43

FE_LMAN : FX_LMAN : ME_LMAN 15 BACE1 CREM GPD2 MMP17 RASGRP1 CAP1 DPP7 LDB1 PHKB STAT3 COMMD8 DPY30 LRRC40 PIP5K1A ZNF609

FE_LMAN : FX_LMAN : MX_LMAN 8 CDH11 GK5 MTURN PPM1G CRISPLD1 IGSF3 PENK PRIM2

FE_LMAN : FX_LMAN : MV_LMAN 8 AKR1D1 DRC7 GCLC RETREG1 CROT E2F7 PRKCQ SLC12A8

FE_LMAN : FV_LMAN : ME_LMAN

191

44 ABR COPS9 EPHA3 MRPL34 POMP SMS URM1 ABRACL CYB5A FAM49B NDUFA12 PSMA7 ST8SIA4 VDAC2 ANKRD50 DCTN6 GLRA4 PCDH19 PSMD7 THSD7A ARL3 DIPK2A INPP5A PFDN1 REXO2 TIMM22 ATP5PF EIF5B ITM2C PFDN4 ROMO1 TMEM132C BAP1 EMC2 MDH2 PIN4 RPL14 TRIP11 CALM1 EPCAM MICOS10 POLR2F SLTM TSPAN3

FE_LMAN : FV_LMAN : MX_LMAN 17 ACBD6 CD9 EIF2AK2 HSD11B2 MIGA2 TRAF3 ACYP2 CRTAC1 FUNDC2 IGDCC4 SOCS5 TTC28 CCND1 DCN GOLGA4 LRP1B SPECC1

FE_LMAN : FV_LMAN : MV_LMAN 4 CALCRL GPX6 PLCH2 RAMP2

FE_LMAN : ME_LMAN : MX_LMAN 26 ALAS1 CCDC91 EIF5 HEATR6 NECAB1 SLC1A3 SYNRG ARMC6 CFAP44 EPAS1 IL21R PDGFD SLC7A4 TIMM21 ASPG CUX2 FABP7 KIAA1549L RHOBTB2 SSC4D BVES DST FAM160A1 MMP11 SH2B2 STAU1

FE_LMAN : ME_LMAN : MV_LMAN 3 KRT23 PIANP ZNF804B

FE_LMAN : MV_LMAN : MX_LMAN 7 CCNYL1 CELF1 CGGBP1 FBLN7 GSN RAP2A ZSWIM5

FV_LMAN : FX_LMAN : ME_LMAN 14 ACO2 ATP8A2 CHMP2B EIF4EBP1 MRPL12 RGS7 TRIM2 AFDN CDA COQ9 FSTL4 NSD2 TBC1D22B XPR1

FV_LMAN : FX_LMAN : MX_LMAN 16 ADAMTS1 DOCK4 GMNN PFKP SMARCAD1 SYNGR2 ALKBH3 EPHX4 LSAMP PRKAR2B SMPDL3A CTR9 FGF1 PAPPA2 SFMBT2 SORCS3

FV_LMAN : FX_LMAN : MV_LMAN 11 ACAD8 HAPLN1 OPN3 PROK2 SMOX TWF1 AHSG HPCA PACS2 SH3BP4 SYNC

FX_LMAN : ME_LMAN : MX_LMAN 22 AKIRIN2 DLD FLT1 JUN PCCB STRN C5orf34 DRD5 GARS LCMT1 PTPN7 WTAP CIAPIN1 E2F4 GFRAL OLFM3 RB1 CSNK1G1 EFCAB14 IRF2BP2 OSTF1 RHOBTB3

FX_LMAN : ME_LMAN : MV_LMAN 7 192

C15orf62 DDX31 FKBP8 HSPB1 MAPK10 PHF21B SLC45A4

FX_LMAN : MV_LMAN : MX_LMAN 33 ABI1 CBY1 EIF4A2 LIMCH1 RAD54L2 SASH1 WWOX ACTR3 CEP112 ELMO1 MC4R RAI14 SEPSECS ZEB1 BASP1 CRADD EPB41L2 MVD RARS SP3 ZNF800 CALM2 DLG5 GLRA3 OSBPL1A RASL11A TNFAIP8L3 CARS DYNLL1 JCAD PTPRK RMDN3 USP12

FV_LMAN : ME_LMAN : MX_LMAN 36 AIMP2 ENTPD6 GATD1 HDLBP LRRC8B PSMD8 AKT1 FAM110B GFOD2 IAH1 MGLL RPS28 CAPN10 FAM13C GOLGB1 IFITM10 MRPL21 SCG3 CFAP20 FAM173A GPT2 ITPA NTN1 SNRK CIAO2B FMC1 GSTK1 KCNK10 PCNX4 TRMT1L CIDEC FN1 GTF2E1 LHFPL6 PLXNA2 ZBTB25

FV_LMAN : ME_LMAN : MV_LMAN 23 ABHD14B EGLN3 MYBPC3 PTPRZ1 SLC18A3 UGDH APAF1 ETFA OGDH RAB11A ST8SIA2 ZBTB18 APBB2 HIC2 PLEC REPS2 SYTL2 ZC4H2 BTG1 KLHDC10 PTHLH RHPN1 TMEM181

FV_LMAN : MV_LMAN : MX_LMAN 41 APCDD1L CHKA ELFN2 LMO7 NYX SCARB1 TEX264 ARFGAP3 CHST15 EMP2 MAPRE1 PCMTD2 SEMA4D THNSL1 C8G CLCN2 ETV1 MCFD2 PTBP2 SHISA5 TMEM185A CDC42SE2 CNTNAP2 GALNT3 MMADHC RBP5 SVOP TRIM32 CENPH CTTNBP2 GJC2 MRPL38 RNF4 TAPT1 TTLL12 CHD7 DEPDC1B GSKIP NFIL3 SART3 TCF7L2

ME_LMAN : MV_LMAN : MX_LMAN 37 ABLIM1 CETN3 HSPA12A PPP1CC SAMSN1 TM9SF2 ZNF704 BAALC CHST13 KIFC3 PRXL2A SERTM1 TMEM200A BAG5 CKS2 L3MBTL3 PTPRA SH3BGRL TOM1L1 BAMBI CNOT6 MSH4 RAB11FIP2 SLC4A11 TOR2A CCDC181 FHL3 NFIB RAPGEF2 SRP9 TRAIP CDC42BPA HDGFL3 PGAM5 RIMBP2 SYT17 WDCP

FE_LMAN : FX_LMAN 21 ADAMTS15 CABP1 COL5A1 GPER1 ND4 PTPRE TMEM169 ATP8 CGRRF1 ENOX2 HS3ST1 NPY SSBP3 TRH BLK CNGA3 FN3KRP KIF1A PPL STX3 ZFYVE19

FE_LMAN : FV_LMAN 36 ADORA1 CLCN3 GRHPR MAP1LC3C NUBP1 SLC8A1 ATP9A DENND1A GRIP1 MAU2 OAZ2 SNCA B4GALT1 EFEMP1 HEYL MGAT3 PGM2L1 TEK BNIP3L EIF2S2 IGF1R MLC1 PLA2G15 TMEM167A CALN1 FOXO1 LRP11 NAPRT PTGES3 UBE2H CC2D1B GPATCH11 LRRC8D NSG1 SLC2A13 UBTF 193

FE_LMAN : ME_LMAN 44 ABCC8 BTBD2 DNAJC18 KCNK15 PSMB2 SIPA1L2 UFL1 ABLIM3 CBX4 EFNB1 KCTD10 RAB41 SLC9A6 VPS26B ADAMTS14 CH25H EPB42 MAEA RASGRF1 SRCIN1 ASPHD2 CHMP7 EPHB2 MMP9 RFESD SRI ATXN7L1 CNN3 ERP29 MTRF1 RNF123 STMN4 B4GALT2 COP1 FAM92A NAV1 RUSC2 TLL2 BCKDHB CREG1 IRF2BPL OASL SIN3B TXNDC16

FE_LMAN : MX_LMAN 19 AGBL5 EGFL7 MET NF2 POMGNT2 SLC35D3 ZNFX1 ATP2A2 LARGE1 MMRN2 OTOGL RXFP3 SPAG6 CHPT1 LDB2 NAV2 PIK3AP1 SEC14L1 TNFAIP3

FE_LMAN : MV_LMAN 9 BHLHE40 FBN2 PUSL1 ST3GAL2 TIFA FAM49A IQSEC1 RAB11FIP3 TENT2

FV_LMAN : FX_LMAN 14 ARHGAP10 CDC34 DRD3 MATN3 NCOA4 PHACTR1 POP5 BRS3 CLCN5 ITPKB MYH11 OXR1 PLPPR1 SYT4

FX_LMAN : ME_LMAN 21 AGO3 ANGPT1 COTL1 JPT1 MEI4 PTPN9 SATB1 AGPAT5 CDH6 ELAVL2 KLHDC2 MRRF RAB40C SSRP1 ALS2 CLVS2 GNPAT KPNA6 NR1D2 RTN4 TMEM50B

FX_LMAN : MX_LMAN 21 BRF1 CHRNA1 DLC1 FMNL2 HNRNPM RAB33A TRMT9B C10H15orf6 1 CSDC2 EPHA6 FUBP1 MEAK7 RNF146 TTYH2 CALB1 CSRP1 ETF1 GALK2 PTS TMEM206 WDR5B

FX_LMAN : MV_LMAN 13 ABTB1 CHRNA9 PRR16 SGSH UIMC1 AQP3 IFT140 RASGRP3 SOX9 C3orf14 LPGAT1 RERGL THAP4

FV_LMAN : ME_LMAN 75 ACOT13 COA5 HYPK MRPL35 PITHD1 RPL26 TSEN15 AGT CRTAP INIP MRPL42 PLS1 RPL37A TXN ARMC1 DIP2B IRS2 MRPL51 POLR2H SEC62 TXNDC17 BPGM DNAJC1 ITGBL1 MRPS12 PPT1 SQLE TYW5 BUD31 DPH5 KAT6B MRPS33 PSMB6 SRSF10 UAP1L1 C1QTNF4 ENTPD8 KCTD7 MTA3 PSMB7 STMN2 UQCRFS1 CBR4 EXOSC5 KIAA2013 MYF6 PSMD14 SUCLG1 WDR19 CERS6 GATAD1 LMBRD1 NUDCD2 PSMD4 TAF11 WDR61 CISD2 GLOD4 MECR NUTF2 RFLNA TIPARP ZFAND4 CISH GLRA2 MRPL22 OGFOD2 RGMA TMTC4 194

COA3 HDGF MRPL28 PDAP1 RPL21 TRAPPC6B

FV_LMAN : MX_LMAN 34 ANAPC1 C3orf38 GPR52 KCNA4 PHKG1 SERINC1 TMEM135 ANXA5 CAMK2D H2AFV MCRIP2 PIK3IP1 SMOC1 TMEM19 AP1G1 FKBP3 HAGH NALCN PTK2 SNX20 TNFRSF19 ARHGEF7 GABRA4 HRH3 NBEA RALYL SNX4 USE1 BCAP29 GNB4 IGSF9B NKX2-2 RHBDL3 TCTN2

FV_LMAN : MV_LMAN 26 ADIPOR1 CYP46A1 GPATCH8 ITGA4 SLA SNX10 ZADH2 BIRC7 DEPTOR GRAMD2B LGR5 SLC39A11 TRIM13 ZFYVE9 C2CD2 DIRAS3 GRM2 PPIP5K1 SLC49A4 TSHZ1 CYBC1 FOXN3 HTR3A SIK3 SLIT3 WEE1

ME_LMAN : MX_LMAN 52 ADH5 DESI1 KLB NOG QDPR TAS1R1 VAT1 AMD1 DNAJC15 LAMA4 NUP133 RPS6KA3 TM9SF4 VMA21 ATP11B EXOSC1 LRRC73 PALM SLC13A5 TMEM131 WDR24 BPHL HRH1 MCRIP1 PIGG SLC2A5 TMOD1 WNT5B CAMKV IDH3A MDH1B PLEKHG1 SLC48A1 TRIM24 CDHR1 IKZF2 ME1 PTP4A1 SRP68 TTBK2 COCH ITGA1 MYH9 PUS3 SSR1 UPF3A CTH KCNN2 NBL1 PWWP2B SYNPR VASH2

ME_LMAN : MV_LMAN 19 ARHGAP5 HMGN5 KCNK2 LVRN PPIL2 TKFC ZBTB47 GTDC1 INHBA KDSR MAPK8IP3 SYT1 YWHAQ HMCN1 KCNJ11 LGALS3 PNPLA4 TAOK1 ZBTB20

MV_LMAN : MX_LMAN 83 ABCC1 CCN4 ENPP4 HMGXB4 MPPED2 POP4 SLC41A2 ACAT2 CCNB2 ERICH1 KCNC4 MYL9 PSEN2 SNCG AGL CD81 ETFB KCNJ3 NAV3 PUS7 SPSB1 ANOS1 CDS1 FAM76A KCNK17 NEK1 RAB26 STARD10 ARC CLDN14 FOXP2 KIAA1328 NFYB RABGAP1L TNNT3 ARFGEF2 CLDND1 GMFB LDLRAD3 NOL9 RALGPS1 TNR ARHGEF28 CNTFR GNG2 LIMD2 NOTCH1 RBMS1 TSR3 ASAP1 COL19A1 GPR160 MCM3 NTN4 RFX3 TTC7B BEND5 COQ6 GPR37L1 MED23 OTOA RIPPLY2 YWHAG C5orf30 DENND2A GPSM2 MFSD4A PARP8 RNF6 ZFP36L1 CA9 DRAXIN HCN2 MME PLCD4 SAMD12 ZNF366 CCDC167 EDA2R HMGB3 MOGAT2 PLOD2 SDK2

FE_LMAN 130 AAK1 CEP162 FAM83H KRAS NEUROD2 RPH3A SNX24 ABCB11 CLCN7 FASTK KREMEN1 NSMAF RPL7L1 SNX9 ABHD17C CLSTN1 FBXL15 LASP1 OGFRL1 RPTOR SRGAP3 ABHD8 CORO2B FSCN1 LAYN OSGIN1 RUBCN TCIM ADAM20 CORO6 FZD4 LHFPL4 PACSIN1 SCNN1B TEKT1 AGR2 CREB5 FZR1 LMBR1 PBX1 SELENON TMEM116 AHCYL1 CYP39A1 GAP43 LPL PCCA SEMA3F TMEM63C 195

AKAP6 DCUN1D2 GGA3 LRRFIP1 PFN2 SEMA4A TMEM94 ALKBH5 DDHD2 GM2A LRRN2 PHF10 SH3RF2 TPPP ARFIP2 DIXDC1 GTPBP2 MAN1C1 PI4KB SLC16A12 TTC1 ARL8A DNAJC6 GUSB MAPT PIP5K1B SLC24A5 UBXN10 ASIC4 DPYSL4 HACD2 MBNL1 PLD4 SLC25A22 UPB1 ASTN1 DUS1L HIP1 MCF2 PPP1R12B SLC7A5 VPS51 ATM EEF1D IGFBP1 MFSD12 PTCHD4 SLC9A2 XDH ATP6AP1L EMC3 KAZN MGP RAB3GAP1 SLCO1C1 YPEL2 B3GALNT2 ENAH KCNT1 MN1 RAB3IL1 SLITRK5 ZNF407 BMP7 EPN3 KIAA0408 MTHFSD RAI1 SMARCD1 CALB2 ETV5 KLHL11 MYO3A RMND1 SNORC CD74 FAM135A KLHL26 NETO1 RPAP3 SNTG1

FX_LMAN 63 ACOX2 DYNC1LI2 GLCCI1 MYO9B POR SEMA5A TMEM159 ADGRF5 ECI2 GPR21 ND5 PPP2R5E SHE TMEM171 BAHD1 ENG HPDL NMBR PRRC2B SHOC2 TMEM209 C1QTNF5 FHOD3 HTR1F OCIAD2 RBBP4 SLC26A11 TMEM82 CSGALNAC T1 FIBCD1 ITGB1BP1 OCSTAMP RBL2 SLC35B3 TRPC4 CTBP1 FNDC5 KLHL29 PARD3 RIC8A SNU13 UBTD1 CYTB FOSL2 MAGI1 PCDHB3 RIN3 SOX11 USP53 DEPDC7 FZD10 MAN2B2 PCYOX1L SC5D SUPT20H WASF1 DGKZ GABRA1 MREG PLCE1 SCN2B SYT12 ZNF770

FV_LMAN 179 ACVR1B CLCN4 FRRS1L LRRC3B NOL11 RGS9BP TMEM109 ADAMTS9 CLIC6 FSTL5 LUM NPY5R RHBDF1 TMEM170B AKAP11 CNPY3 GABRB3 MAF NRG1 RHOH TMEM45B ANKRD40 COL6A2 GDPD1 MAP3K13 OCIAD1 RIOK3 TMSB15B ANXA11 COQ7 GEMIN8 MAT2B PCBP2 RPAIN TMTC2 AP3S2 CPEB3 GMPPB MCTP1 PCOLCE2 RPL13A TOMM22 ARAP3 CPLX3 GNAZ MFGE8 PDP1 RPLP1 TPT1 ARF6 CWC25 GOLGA5 MIOS PHB2 RPLP2 TRIM45 ARFGAP1 CYP26A1 GPR182 MMP16 PIP4P2 RPS20 TTC25 ARPC5L CZH18orf32 GPR37 MORF4L1 PLBD2 RRAS2 TTC33 ASXL3 DAP3 GRIP2 MOSMO PLXND1 SCNN1A UBR2 ATRAID DCTD GRK3 MRPL16 PNRC2 SCYL2 USP14 B3GALT1 DGKH HIKESHI MRPL18 PPID SGTB VPS26A B3GALT5 ECHS1 HMGCR MRPL33 PPM1H SH3BGRL3 VSTM2A BAIAP2L1 EEF1A2 HSCB MRPL40 PRKRIP1 SLC13A3 WDR17 BCAR3 EFR3A HTR2C MRPS7 PSIP1 SLC17A6 YY1 BMPER EIF2B2 IBSP MRTFB PSMA4 SLC24A1 ZBTB37 C12orf57 EIF4G1 INTS4 MTCH2 PSMA5 SMPD3 ZC3H15 C1QBP EPSTI1 KBTBD8 NAA20 PSMD12 SNN ZC3H7A CAMSAP2 EXT2 KIF6 NAA25 PTPRM SNX17 ZCCHC17 CCDC58 FABP4 KXD1 NAIF1 QRFPR SOCS6 ZDHHC17 CDC123 FAM149A LANCL3 NBR1 QRICH1 SOX21 ZMYND8 CDK19 FAM192A LCLAT1 NDFIP1 RAD51B SPAG7 ZNF711 CDK5RAP1 FANCL LMTK2 NIPA2 RASGEF1C TKT CHORDC1 FBXO31 LONRF1 NKD1 REEP1 TLNRD1 CHRNA3 FKBP4 LPCAT3 NKX2-1 RFC2 TM2D2

ME_LMAN 196 196

ACVRL1 CLMN FGF3 KIAA1191 PCIF1 RPS6 THUMPD3 ADGRG2 CLUL1 FOXK2 LRRC28 PEMT RTRAF TMEM132A ADGRL2 CMTR2 FST LRRK1 PHC2 RWDD1 TMEM98 AIDA CRYM GABRA2 LRRN4 PITPNB S1PR1 TMOD4 CSGALNAC ALCAM T2 GADL1 LSG1 PLA2G6 SBK1 TNKS ARF1 CTNNBIP1 GALT MAP1S PLA2G7 SELENOK TOMM20 ASB6 CUEDC2 GAPDH MAPKAP1 PLEKHA1 SESTD1 TPMT ATF2 CYP20A1 GBE1 MCM2 PLEKHO1 SH3D19 TPRA1 ATP6V0B DCTN3 GDI2 MCU PLXNB1 SH3RF3 TTBK1 ATP6V0D1 DCUN1D4 GGPS1 MEMO1 POLB SHPRH TTC29 ATP6V1C2 DCUN1D5 GMPPA MINPP1 POLR2E SIAH1 TUBGCP2 ATP6V1E1 DDX17 GNG10 MLST8 POLR2G SLC15A4 TUT7 ATPAF1 DLK1 GPHN MORN5 POMC SLC35C1 UBC AUTS2 DNAJC5 GPN1 MRPL20 PPHLN1 SLC39A10 UBFD1 B3GNT4 EEPD1 GTF2H5 MRPL41 PRELP SLCO3A1 UQCRC2 BEND3 EFCAB1 HDAC3 MRPL45 RAB2A SMARCE1 USP10 BOC EIF4E HEY1 MRPS22 RACGAP1 SNAPC3 USP31 BRD7 ELAC2 HNRNPA0 MTMR9 RBPMS SNRNP48 VAMP7 BSDC1 ELOVL4 HSD3B1 MTX2 REPS1 SPTBN1 VAV2 C15orf41 ENY2 HTR2A NAA10 RIMS1 SRBD1 VPS18 CACYBP EWSR1 IL17REL NDRG3 RIOK1 ST8SIA5 VPS36 CASR EXTL2 INPP5D NELFCD RPL23 STN1 VWA5B1 CCBE1 FAM169B IQCG NMRK1 RPL24 STX6 YIPF4 CCDC40 FAM219A ITPRIP NOL6 RPL27A SUCLA2 YTHDF3 CDH18 FAM89A JOSD1 NPY1R RPL34 SUPT4H1 ZBTB17 CDH23 FAM8A1 JUP NTMT1 RPL35A TBC1D16 ZDHHC2 CENPW FBXO11 KAT2B NXPH1 RPS11 TBC1D22A ZDHHC7 CHCHD3 FGD5 KDM2B OSBPL6 RPS24 THOC7 ZNF106

MX_LMAN 262 ACKR3 CCDC146 EMC4 HSF2 MYADML2 PXYLP1 TIMP3 ADAM8 CDC25A ENPP1 HSPB8 MYLK R3HDM1 TLK1 ADAMTS20 CDCA7 EPHA5 IFT122 MYO10 RBP4 TMEM30A ADAMTS8 CDH7 EPHB3 IFT43 MYO5A REEP2 TMEM45A ADCY2 CDH8 EPS8 IQGAP1 NANP REEP3 TMEM52 AGR3 CDKN2AIP EVC IRF8 NBAS REEP5 TMEM62 AICDA CEBPD FAM118A KATNAL1 NEDD4 RGL1 TPST2 AKAP5 CGNL1 FAM126A KBTBD4 NETO2 RGS10 TRERF1 ALG11 CHRM4 FAM221A KCNJ9 NGEF RHCG TRIM14 ALG8 CHST2 FBXO34 KCTD16 NRAS RNLS TRMT61B AMN CHST8 FES KIAA0319L NTPCR RPS6KL1 TSPAN1 ANKRD29 CHSY3 FKBP5 KIF13A NUP62 RRM2 TSPAN9 ANO9 CLPX FLII KLHL42 NYAP2 SCAMP2 TTC39B APBA2 CNMD FRK LFNG ORAI2 SCD TTC7A APOPT1 COA7 FRMPD3 LHFPL5 OSBPL11 SDC4 TTYH3 ARL14EPL COL26A1 FUBP3 LIMS2 OTUD7B SEMA4B TXNDC5 ARL4A COLEC11 FUCA1 LINGO2 OTULINL SGCD UBE2QL1 ARPP21 CORO2A GALNT18 LIPA P3H2 SGK1 UROD ASS1 CRY1 GAS6 LOX PAPSS2 SGMS1 USP35 ATG5 CUL4A GFRA1 LPP PARP12 SIGLEC15 VAC14 ATP11C CYB5R4 GGT5 LRRC38 PCSK5 SLC25A21 VCAM1 ATP2C1 DACH2 GJA5 LRRC4C PCYT1A SLC25A24 VEGFA ATP6V0A2 DAPP1 GJD2 LRRC59 PCYT1B SLC26A4 WAPL BACH2 DBX2 GPR157 LRRTM1 PDIK1L SLC52A3 WASL 197

BAG2 DDHD1 GPR26 MAB21L4 PDXK SLCO5A1 WDYHV1 BCL11B DDX51 GPR75 MAP2K5 PEX7 SMCO4 WFS1 BFSP2 DHCR7 GPR78 MAPK6 PGF SMTNL2 WWTR1 BMP4 DISC1 GPX1 MARS2 PGGT1B SPOCK3 XRN2 C9orf64 DLL4 GRM3 MEAF6 PIK3R1 SRD5A3 XYLB C9orf78 DPP8 GSS MFSD2A PLCL1 STARD8 ZDHHC14 CA13 DSP GTPBP10 MIDN PMPCA STK24 ZDHHC23 CA6 DUSP1 GUCY1A1 MLYCD PPDPF STYX ZNF438 CAP2 DUSP10 HCRT MMAB PPM1M SUCLG2 ZNF518A CAPN1 DUSP23 HDDC3 MORN3 PPP1R9A TDRD7 ZSWIM6 CAPN9 E2F3 HEATR5A MSANTD1 PPP2R5C TECTB CAT ECI1 HIC1 MSN PRKN TEX10 CAV2 EFNA5 HMCES MSRB3 PRPH TFDP1 CBX3 EIF2D HMGCS1 MTMR1 PTBP3 TGFB2

MV_LMAN 138 ABCA2 CORO1B GAPVD1 INPP5J ODC1 RUBCNL TALDO1 AMT CPSF4 GASK1B ITGAV OMG SARS TBR1 ATIC CRHBP GCN1 KCNJ6 P3H1 SCAMP3 TBXT ATP13A4 CYP4V2 GLIS3 KLHL24 PAIP1 SCML4 TCP11L1 BAZ1A DENND4C GLUL LIMK1 PLD5 SCRT2 THYN1 BICD2 DLAT GNE LURAP1 POLR2D SEZ6L TLCD1 BICDL1 DLGAP5 GNG4 MAFK PPIP5K2 SLC9A1 TNFRSF21 BTBD10 DLST GOLT1B MAGI2 PPWD1 SLC9A3R2 TPPP3 C16orf87 DNAJB5 GOT2 MAMLD1 PRKAB2 SMAP1 TUBG1 C17orf67 DUSP6 GPRC5B MDK PRLR SOCS2 UBE2B C9orf85 DYNC1LI1 GRN MED16 PRUNE1 SOGA1 UBXN2A ST6GALNA CACNG2 DYNLT3 H3F3B MEF2A PYGB C2 UCHL1 ST6GALNA CCNC DYRK3 HEATR5B MFAP3L RAB30 C3 UPF1 CDIP1 EDIL3 HERC4 MOGAT1 RBM34 STIM2 VILL CERS2 ERCC8 HERPUD1 NACC2 RBM46 STX19 VLDLR CFAP61 F2RL2 HINT3 NEU4 RERE SUSD1 WDR35 CIB1 FAM107A HNMT NFKB1 RGN SV2B ZC3H12C CLSTN3 FGL2 HTR2B NIPA1 RNF130 SYNDIG1 ZFYVE27 CMPK2 FNDC3B IGFBP2 NME5 RPL3 SYT11 COPS7A FUT8 IL6ST NPL RPS16 TAF7

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Appendix J – p-Value Density Plots from Pairwise Analysis

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Appendix Figure 1: Distribution of p-values from pairwise DEG analysis. Shown are the distribution plots of p-values from pairwise analysis in a probability density plot for (A) Area X, (B) HVC, (C) RA and (D) LMAN. Y axis is the probability density, or the probability per unit of the X axis which is the possible p-value score that can be assigned to a DEG. Without false discovery rate (FDR) adjustment/correction, there is a large inflation of p-values < 0.05. Even prior to FDR adjustment, this p-value inflation is not seen in (A) Area X of females treated with vehicle or exemestane.

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Appendix K – Adjusted p-Value (FDR) Density Plots from Pairwise Analysis

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Appendix Figure 2: Distribution of FDR adjusted p-values from pairwise DEG analysis Distribution of FDR corrected p-values (p-Adj) from pairwise analysis as a probability density plot for (A) Area X, (B) HVC, (C) RA and (D) LMAN. Inset in (A) shows probability density for Area X FDR p-values between 0 and 0.5.

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Appendix L – p-Value Density Plots for Non-Pairwise Analysis

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Appendix Figure 3: Distribution of p-values from non-pairwise DEG analysis (A) Area X, (B) HVC, (C) RA and (D) LMAN. Without FDR adjustment/correction, there is a large inflation of p-values < 0.05, though much smaller than that seen in pairwise DEG analysis (Appendix Figure 1). Like pairwise analysis, p-value inflation is not seen in (A) Area X of females treated with vehicle or exemestane even before FDR correction.

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Appendix M – Adjusted p-Value (FDR) Density Plots for Non-Pairwise Analysis

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Appendix Figure 4: Distribution of FDR adjusted p-values from non-pairwise DEG analysis. Shown is the distribution of p-Adj from non-pairwise analysis as a probability density plot for (A) Area X, (B) HVC, (C) RA and (D) LMAN. Insets in (A) Area X, (B) HVC and (C) RA show probability densities for p-Adj values between 0 and 0.5.

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Biography

Ha Na Choe attended the Rochester Institute of Technology from 2008-2011 and graduated magna cum laude with a Bachelor of Science in biotechnology & molecular biology.

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