Identification and Investigation of Single Nucleotide Polymorphisms

Associated with Cognitive-Emotional Phenotypes in Rats

Li Li

Integrated Program in Neuroscience

Faculty of Medicine

McGill University

August 2020

A thesis submitted to McGill University in partial fulfillment of the requirements of the degree

of Master of Science in Neuroscience

© Li Li 2020

1 Abstract

Genes are the basic building blocks of our development as they transmit information in a sequential order. Genetic variations and their interactions with environmental factors underlie individual differences that ultimately determine our disease susceptibility (Ottman, 1996).

Genome-wide association studies (GWAS) have associated hundreds of genetic variations with biological and behavioral alterations related to mental health disorders (Wray et al., 2012).

However, the underlying biological functions of these significant loci remain unclear. Therefore, we rely on animal models to characterize the functional relevance of these genetic signals in the brain.

Using a behaviorally-characterized cohort of 256 outbred rats, we performed candidate single nucleotide polymorphism (SNP) genotyping based on recent findings from human and rodent GWAS and candidate studies on mental health outcomes. After an extensive literature review, 192 SNPs in 70 genes were chosen from publically available databases including Rat

Ensembl and Rat Genome Database (J. R. Smith et al., 2020). Linear mixed-effect regression models were used to investigate the main effects of SNP genotypes on cognitive-emotional outcomes in rats. Results show 9 SNPs in males and 5 SNPs in females are significantly associated with anxiety-like behaviors. Three SNPs in males and 3 SNPs in females are significantly associated with short term memory. Six SNPs in males and 7 SNPs in females are significantly associated with anhedonia.

Many publications have associated the expression of Group II metabotropic glutamate receptor 2 (mGluR2) with the function of antidepressants (i.e. ketamine) (Zanos et al., 2019) and with the pathophysiology of multiple psychiatric disorders (McEwen et al., 2015). In this project, we investigated the molecular implications of Grm2 (encodes for mGluR2) genetic variations by

2 integrating cross-species genomic and transcriptomic data. We showed that the genotypes of Grm2

SNP, rs107355669, significantly associated with anxiety-like behaviors in the rat novelty suppressed feeding test (NSF), altered transcription patterning across the emotional circuitry, and

Grm2 network expression in the basolateral amygdala (BLA). We also used an expression-based polygenic risk score (ePRS) (Silveira et al., 2017) to analyze the association between Grm2 network expression and emotional and behavioral outcomes in humans. These results highlighted an important glutamatergic network involved in individual differences in glutamate-dependent processes, as well as vulnerability for mood disorders for both rodents and humans.

Overall, findings from this thesis suggested that the outbred rat is a suitable model to investigate whether genetic signals can be causal factors implicated in psychiatric disorders. Future studies should focus on depicting a complete profile of how common genetic variants dynamically influence gene expression across the lifespan and interact with the environment. Such genotype- phenotype studies will shed light on the complex pathophysiology of psychiatric disorders and provide potential therapeutic targets.

3 Résumé

Les gènes sont les éléments de base de notre développement car ils transmettent des informations dans un ordre séquentiel. Les variations génétiques et leurs interactions avec les facteurs environnementaux sous-tendent les différences individuelles qui déterminent en fin de compte notre sensibilité aux maladies (Ottman, 1996). Des études d'association à l'échelle du génome (GWAS) ont associé des centaines de variations génétiques à des modifications biologiques et comportementales liées à des troubles liés à la santé mentale (Wray et al., 2012).

Cependant, les mécanismes biologiques sous-jacents fonctionnent de manière peu claire. Par conséquent, nous nous appuyons sur des modèles animaux pour caractériser la pertinence fonctionnelle de ces signaux génétiques dans le cerveau.

En utilisant une cohorte comportementale caractérisée de 256 rats surpassés, nous avons effectué un génotypage du polymorphisme d'un seul nucléotide (SNP) candidat, basé sur les récentes découvertes de l'étude d'évaluation de l'état de santé mentale chez l'homme et les rongeurs et sur des études candidates sur les résultats de la santé mentale. Après un examen approfondi de la littérature, 192 SNP dans 70 gènes ont été choisis dans des bases de données accessibles au public, notamment Rat Ensembl et Rat Genome Database (J. R. Smith et al., 2020). Des modèles de régression linéaire à effets mixtes ont été utilisés pour étudier les principaux effets des génotypes de SNP sur les résultats cognitifs et émotionnels chez les rongeurs. Les résultats montrent que 9 SNP chez les mâles et 5 SNP chez les femelles sont associés de manière significative à des comportements de type anxieux. Trois SNP chez les mâles et 3 SNP chez les femelles sont significativement associés à la mémoire à court terme. Six SNP chez les hommes et

7 SNP chez les femmes sont significativement associés à l'anhédonie.

4 De nombreuses publications ont associé l'expression du récepteur métabotropique du glutamate 2 du groupe II (mGluR2) aux fonctions des candidats antidépresseurs (c'est-à-dire la kétamine) (Zanos et al., 2019) et à la physiopathologie de multiples troubles psychiatriques

(McEwen et al., 2015). Dans ce projet, nous avons étudié les implications moléculaires des variations génétiques du Grm2 (qui code pour le mGluR2) en intégrant les données génomiques et transcriptomiques inter-espèces. Nous avons montré que les génotypes du SNP de Grm2, rs107355669, sont associés de manière significative à des comportements anxieux dans le test d'alimentation supprimant la nouveauté chez le rat (NSF), qu'ils modifient le schéma de transcription dans les circuits émotionnels et l'expression du réseau Grm2 dans l'amygdale basolatérale (BLA). Nous avons également utilisé une approche translationnelle du score de risque polygénique basé sur l'expression (ePRS) (Silveira et al., 2017) pour analyser l'association entre l'expression du réseau Grm2 et les résultats émotionnels et comportementaux dans les comportements liés à la santé mentale des humains. Ces résultats ont mis en évidence un important réseau glutamatergique impliqué dans les différences individuelles des processus dépendant du glutamate, ainsi qu'une vulnérabilité aux troubles de l'humeur chez les rongeurs et les humains.

Dans l'ensemble, les résultats de cette thèse suggèrent que le rat de race supérieure est un modèle approprié pour étudier si les signaux génétiques peuvent être des facteurs de causalité impliqués dans les troubles psychiatriques. Les études futures devraient s'attacher à dresser un profil complet de la manière dont les variantes génétiques communes influencent dynamiquement l'expression des gènes tout au long de la vie et interagissent avec les variations environnementales.

De telles études génotypes-phénotypes permettront de faire la lumière sur la physiopathologie complexe des troubles psychiatriques et de fournir des cibles thérapeutiques potentielles.

5 Acknowledgement

This master’s project has been a tremendous journey and there are many people whom I want to acknowledge for helping me in different ways.

Firstly, I would like to thank my supervisor, Dr. Michael Meaney. Thank you for always seeing the bigger picture, for taking a chance and believing in me even when I made mistakes, and most importantly, for giving me the space I needed to explore and learn science in my own way.

I would also like to thank the past and the present members of my advisory committee, Dr.

Rosemary Bagot, Dr. Heungsun Hwang, and Dr. Cecilia Flores, for tracking my progress and providing guidance.

I owe a debt of gratitude to my lab members for their guidance. Thank you to Dr. Patricia

Silveira, Dr. Kieran O’Donnell, and Dr. Tie Yuan Zhang for their supervision throughout my thesis.

Thank you to Irina Pokhvisneva and the team of intelligent biostatisticians for their expertise.

Thank you to Dr. Maeson Latsko, Dr. Danusa Arcego, and Dr. Carine Parent for their advice, suggestions, edits, and so much more. To Josie Diorio, for hiring me after my interview and supporting my project. I will of course never forget our beltline of sacrifice in G1256, the shifts of maternal observations and the stories of dams and pups that we shared in our office, the delicious snacks and lovely gifts from Brazil on our office table, the birthday cakes, the lab hangouts all over the city, and the badminton sessions after work. I am forever grateful for everything that made my life at the Meaney Lab an unforgettable experience.

I would also like to thank my previous supervisors and colleagues, Dr. Alain Nepveu, Dr.

Zubaidah Ramdzan, and Dr. Simran Kaur, for their continuous support and encouragement. I must also thank Tianci Wang, who has known me since U0 frosh week and whose friendship has supported me throughout the years. And Myles, for listening to stories of crazy angry rats and not

6 judging. Finally, I would like to dedicate this thesis to my parents and my grandparents. They have believed in me before anyone else did. 谢谢爷爷奶奶爸爸妈妈给我最温暖的家庭和最大的支

持，我永远爱你们！

7 Contribution of Authors

I wrote this thesis and the manuscript in Chapter 2 with editing from Dr. Patricia Silveira and Dr. Michael Meaney.

Animal experiments were conducted mainly by Allison Martel and myself with generous help from lab colleagues for maternal observations and tissue collection. DNA extraction and genotyping were done by me with service from Genome Quebec. Bioinformatics analyses of transcriptomic data were conducted by me with help from Dr. Nick O’Toole. Human analyses were supervised by Dr. Patricia Silveira and Irina Pokhvisneva and conducted by Zihan Wang,

Sachin Patel, and Omar Khedr. The entire project was planned and supported by Dr. Michael

Meaney in collaboration with Dr. Marla Sokolowski and members from her lab.

8 Table of contents Abstract ------2 Résumé ------4 Acknowledgement ------6 Contribution of authors ------7 Chapter 1: Introduction ------10 1.1 Framing the questions ------10 1.2 Genetics of mental illnesses ------13 1.3 Genetic risk factors in anxiety disorders ------15 1.4 Genetic risk factors for depression ------19 1.5 What follows GWAS findings? Challenges and future perspectives ------22 1.6 Animal behavioral tests to model human mental health outcomes ------25 1.7 Limitations in the current rodent genetic approaches ------28 1.8 The influence of early life environmental variations ------32 1.9 Preliminary studies and results from the current project ------36 1.10 The glutamate system and its regulation by glutamate receptors ------37 1.11 The glutamate system in mood disorders ------39 1.12 Glutamate signaling correlates with synaptic plasticity in mood disorders ------42 1.13 Metabotropic glutamate receptor II in psychiatry ------43 Chapter 2: Integration of cross-species genomics and transcriptomics to identify sex- specific glutamatergic gene networks involved in emotional phenotypes ------46 Abstract ------47 Introduction ------48 Method ------51 Results ------67 Discussion ------75 Figure 1 ------81 Figure 2 ------83 Figure 3 ------85 Figure 4 ------89 Chapter 3: Supplemental material ------90 Supplemental Table 1 ------86 Supplemental Table 2 ------92 Supplemental Table 3 ------96 Supplemental Figure 1 ------99 Supplemental Figure 2 ------101 Supplemental Figure 3 ------102 Supplemental Figure 4 ------103 Supplemental Figure 5 ------104 Supplemental Figure 6 ------105 Supplemental Figure 7 ------106 Chapter 4: Discussion ------107 SNP main effects on cognitive-emotional phenotypes ------107 Interactions between SNPs and maternal care on cognitive-emotional phenotypes ------110 Limitations ------112 Future perspectives ------103

9 Chapter 5: Bibliography ------116

10 CHAPTER 1. INTRODUCTION

1.1 Framing the questions

The natural variations embedded in the genome build our developmental foundation, producing unique spectra of phenotypes that constitute individual differences. For complex mental health-related traits, such as depression and anxiety, early life environments are also of relevance.

The interaction between genomic and environmental variations ultimately determines one’s risk for psychopathology.

Genome wide association studies (GWAS) have generated a knowledge repository for increasingly informative genetic risk factors for mental health – related traits. Understanding the mechanisms by which the genetic variations associate with phenotypes is of critical concern for novel therapeutic advances. It was initially thought that the significant “hits” from GWAS were

“non-functional” because most loci are outside of coding regions (Schaub, Boyle, Kundaje,

Batzoglou, & Snyder, 2012) and do not cause null protein (S. L. Edwards, Beesley, French, &

Dunning, 2013). However, numerous studies have shown that many GWAS signals are enriched in regulatory regions, suggesting that variants may affect gene expression (Maurano et al., 2012).

To date, useful and valid genetic markers for mental health disorders have been scarce, due to the inaccessibility of human brain tissue and the subsequent lack of insight into disease mechanisms.

To address the origin of individual differences, further work is paramount to annotate the functional relevance of genetic risk factors. Validation studies rely on the back-translational approach, where animal models provide a unique opportunity to study the influence of population genetics, especially in the brain.

The laboratory rat (Rattus Norvegicus) has been an ideal animal model for human diseases due to its physiological and genetic similarities to humans (Twigger, 2004). However, current genetic

11 techniques, such as knock-out or knock-in, introduce drastic manipulations to gene transcription in rodents, and thus do not accurately reflect the subtle influences of genetic variations. A critical question that will be addressed in this thesis using animal models is whether genetic variants themselves, or in combination with environmental stressors, can influence the region-specific transcription patterns in the brain.

The time window between infancy and adolescence represents sensitive periods in development where the brain is amenable to environmental influences (Moriceau & Sullivan, 2006;

E. J. Yang, Lin, & Hensch, 2012). During these periods, environmental variations can lead to developmental trajectories that significantly vary, in positive or negative ways. Early life adversities, such as parental addiction, neglect and abuse, and mental illnesses can cause devastating changes to neurodevelopment (Murray, Fiori-Cowley, Hooper, & Cooper, 1996). In contrast, positive early life experiences can promote resilience and buffer against stress (Jaffee,

Takizawa, & Arseneault, 2017). One early life environmental variation in rodents that influences neurodevelopment and cognitive-emotional phenotypes in the offspring is the maternal licking and grooming behaviors during the first week after giving birth (Bagot et al., 2012; Caldji et al., 1998;

Liu, Diorio, Day, Francis, & Meaney, 2000; Liu et al., 1997). The combination of genetic variations with the enduring effects of maternal care likely contributes to individual variability in cognitive-emotional phenotypes and vulnerability to psychopathology.

The thesis presented here builds upon the findings of genetic risk factors from human population studies on cognitive-emotional phenotypes. Specifically, we aimed to investigate if rodent models are suitable for back-translating the findings from population genetics studies. We addressed our hypothesis on three levels of analyses to characterize the functional relevance of genetic variants. We first (1) associated candidate SNPs with cognitive-emotional behaviors in a

12 cohort of outbred rats. We then proceeded to (2) characterize the molecular mechanisms of these

SNPs in the emotional circuitry of the brain by analyzing rat brain transcriptome data. Finally, we

(3) explored the interactions between SNP genotypes and maternal licking and grooming behaviors to observe if genetic variations moderate the influences of environmental exposures. The rationale for each of these studies follows:

(1) Results from family and twin studies have shown that genetics contribute to the

pathogenesis of mental health disorders. The biological mechanisms underlying common

genetic risk factors identified by GWAS remain unclear. Current animal genetics

techniques, such as large-scale mutation screens, transgenesis, and knock-out or knock-in

models introduce drastic manipulations that cause stochastic gene disruption, which do not

accurately reflect the subtle influences of common genetic variants. Therefore, we

investigated the suitability of rodent models to study population genetics by associating

SNP genotypes with cognitive-emotional phenotypes in an outbred rat cohort.

(2) SNPs can function as expression quantitative trait loci (eQTL) by influencing the

expression of proximal (cis) and distal (trans) genes (Nica & Dermitzakis, 2013). However,

how genetic variations influence the transcription patterning of the brain is unclear given

that post-mortem human brain tissue is scarce. Furthermore, post-mortem brain tissue from

psychiatric patients are confounded by factors including disease comorbidity, cause of

death, and post-mortem interval. We performed RNA-sequencing of the emotional

circuitry in the brain using a cohort of 48 rats and explored the molecular effects of

candidate SNPs by identifying the differential expression of proximal and distal genes as

well as gene network expression associated with SNP genotypes.

13 (3) Rodent models of early life environmental variation, such as maternal licking and

grooming behaviors, can exert profound influences on the neurodevelopment and

cognitive-emotional phenotypes in the offspring. Elucidating how common genetic

variants interact with early life environmental stressors to alter gene expression and

biological processes within the brain may provide important clues to identifying genes that

are susceptible to the environment. Therefore, we investigated whether candidate SNP

genotypes interact with maternal licking and grooming behaviors and how such

interactions influence cognitive-emotional phenotypes in the offspring.

Currently, there is a gap in the field of animal models to accurately recapitulate the effects of natural genetic variations. Through this project, we hope to connect genotype to phenotype, which will help to improve the current understanding of the biological alterations associated with genetic variations. The following section is aimed at providing a descriptive overview on the efforts to identify genetic risk factors associated with various phenotypes of mood disorders in human population studies, the statuses of genetic technologies and genome mapping in rodents, and maternal licking and grooming behaviors as an early life environmental variation.

1.2 Genetics of mental illnesses

Genetic variations contribute to almost every human disease by conferring susceptibility or resistance and influencing disease severity and progression. The genetic architecture of mental illnesses is enormously complex, as shown when cumulative genetic risk factors only explain partial heritability in most mental health disorders (Gandal et al., 2018; Geschwind & Flint, 2015).

Most disorder-associated alleles do not cause drastic disruptions in gene and protein expression, but rather impose more nuanced effects in the general population (Park et al., 2010). Therefore,

14 elucidating these subtle effects behind the significant loci may provide important clues to the puzzling disease biology, allowing for the development of novel therapeutic targets for mental illnesses.

Much has been learned from candidate gene approaches, in which a priori hypotheses are tested for the linkage between SNPs and certain phenotypes. However, this approach is limited by our current understanding of the pathophysiology of psychiatric disorders, which is far from comprehensive. The availability of high-throughput genotype technologies and genomic resources such as HapMap (www.hapmap.org) and the Psychiatric Genomics Consortium (PGC) (Sullivan et al., 2018) have made it possible to survey the genome without bias and increase the probability of discovering unexpected genetic influences (Duncan, Ostacher, & Ballon, 2019). More recently, biorepositories such as the UK Biobank (Bycroft et al., 2017), 23 & Me, and the Million Veterans

Program (Gaziano et al., 2016) have significantly boosted the identification of genetic risk factors for numerous psychiatric disorders, highlighting the shared genetic etiology despite the uniqueness and heterogeneity of each disorder (Duncan et al., 2019). The use of a quantitative phenotype in combination with a genome-wide screening is advantageous over a case-control design as the former usually has better statistical power by including larger sample sizes. However, the possibility of discovering false-positive signals also increases with the whole-genome approach, where efforts might be wasted focusing on signals that only statistically associate with phenotypes, with no underlying linkage to biological pathways.

The sections below will describe how mental health – related GWAS have evolved over time and enabled the identification of increasing numbers of genetic loci associated with phenotypes of interest. Depressive and anxiety disorders are the most frequent psychiatric disorders in the general population and cause significant disability and global disease burden (Kessler, 2007). Depression

15 and anxiety – related disorders also both have complex etiology and strongly overlap in symptomatology (Cerda, Sagdeo, Johnson, & Galea, 2010; A. R. Mathew, Pettit, Lewinsohn,

Seeley, & Roberts, 2011). Validating the genetic risk factors associated with both disorders using rodent models will improve our understanding of the shared and unique etiology of each disorder.

The next two sections will focus on describing the discovery of genetic risk factors associated with both disorders, which will later be included in the selection of candidate SNPs for this thesis.

1.3 Genetic Risk Factors for Anxiety Disorders

Anxiety disorders have a global prevalence of 30-35% in adults under 60 years (Kessler et al., 2005) and are characterized by excessive, uncontrollable worry and fear in the absence of a stimuli. Family and twin studies indicate that genetics contribute to the etiology of anxiety disorders, with heritability estimates ranging between 30-60% (Hettema, Neale, & Kendler, 2001).

Although anxiety disorders are divided into clinically-distinct subtypes, including generalized anxiety disorder (GAD), social anxiety disorder (SAD), panic disorder (PD), agoraphobia, and specific phobias (Kessler et al., 1994), all these anxiety disorders and other psychiatric disorders share phenotypic and genetic commonalities (Bandelow & Michaelis, 2015) (Craske et al., 2009)

(Waszczuk, Zavos, Gregory, & Eley, 2014). Numerous candidate gene studies of anxiety disorders have been performed, but these associations have not proven robust (Smoller, 2016). As with other disorders of a polygenic nature, non-hypothesis-driven genome-wide association approaches have been applied to elucidate the genetic underpinnings of anxiety disorders, leveraging on the statistical power available from large sample sizes.

The first GWAS for generalized anxiety symptoms was conducted in a community-based sample (N=12,282) of Hispanic and Latin American ancestry and identified the intronic

16 rs78602344 polymorphism on chromosome 6 in the thrombospondin-2 gene (THBS2) as the highest association with GAD symptoms (Dunn et al., 2017). However, this was not replicated in the follow-up meta-analysis (N=7785) (Dunn et al., 2017). One common problem with single- cohort GWAS is the limitation of sample size when investigating a disorder with a heterogeneous profile. Aiming for higher statistical power, the ANGST Consortium reported a meta-analysis comprised of nine GWASs of European ancestry to identify genetic associations based on two anxiety phenotype definitions: 1) a case-control definition based on the five core anxiety disorders, and 2) a quantitative factor score indexing anxiety disorder liability (Otowa et al., 2016). The intronic rs1709393 minor C allele located in an uncharacterized noncoding RNA locus

(LOC152225) on chromosome 3 pleiotropically associated with a lifetime diagnosis of anxiety disorders (N/case=7016, N/control=14,745) (Otowa et al., 2016). The intronic rs1067327 on chromosome 2 within the coding region for calmodulin-lysine N-methyltransferase (CAMKMT) was the most significant SNP associated with an overall latent anxiety disorder factor score

(N=18,186) (Otowa et al., 2016). Results from the meta-analysis for anxiety disorder traits further validated that larger sample sizes can yield increasing numbers of significant loci associated with traits of interest.

Another technique to relax the stringent thresholds of GWAS is to target a broader range of phenotypes to ensure a sufficient sample size. The iPSYCH study reported a GWAS of anxiety- and stress-related disorders (N/case=12,655, N/control=19,225) (S. Meier et al., 2018). Case subjects were defined as individuals whose records documented at least one international classification of disease (ICD) -10 diagnosis for an anxiety disorder or a stress-related disorder

(including acute stress disorder, PTSD, and adjustment disorder) (S. Meier et al., 2018). The authors found a significant association in camp-specific 3’,5’-cyclic phosphodiesterase 4B

17 (PDE4B), a gene previously implicated in schizophrenia. In vivo experiments showed reduced

PDE4B expression in mice exhibiting anxiety-related behaviors in response to chronic social defeat stress (CSDS) (S. M. Meier et al., 2019).

Studies that combined lenient thresholds and large sample sizes generally obtained more genetic loci associated with phenotypes of interest. A larger anxiety disorders GWAS was conducted using the UK Biobank dataset, a community-based prospective cohort of 500,000 participants. The authors examined two case definitions of anxiety phenotypes: 1) lifetime anxiety disorder (N=83,565) based on self-reported diagnosis of the five core anxiety disorders and/or likely generalized anxiety disorder based on a self-report measure of anxiety, and 2) additional current anxiety symptoms (N=77,125) (Purves et al., 2019). Five genome-wide significant loci were identified. Out of the five, one locus is in the neurotrophic receptor tyrosine kinase 2 (NTRK2) gene (chromosome 9), which encodes for the brain-derived neurotrophic factor (BDNF) receptor.

NTRK2 and BDNF regulate both short-term synaptic functions and long term potentiation of brain synapses (OMIM *600456; https://www.omim.org/entry/600456). NTRK2 has been widely investigated in a range of neuropsychiatric traits and disorders (Chen et al., 2006; Kohli et al.,

2010; Spalek et al., 2016). Another locus is in the transmembrane protein 106B (TMEM106B) gene on chromosome 7, which associates with lysosomal enlargement and cell toxicity that are implicated in depression and coronary artery disease (Howard et al., 2018; van der Harst & Verweij,

2018). With the increase in samples sizes of GWAS, more genetic signals have been associated with disease-relevant phenotypes, which could potentially provide tissue-specificity and cell type

– specificity information of the genes associated with these genetic signals.

The largest GWAS conducted to date on anxiety disorder phenotypes used the Million

Veteran Program (MVP), a homogenous observational study of one million U.S. military veterans

18 (Levey et al., 2019). Two anxiety definitions were examined: 1) anxiety score derived from self- report measures of generalized anxiety and worry symptoms during the past 2 weeks (n=199,611) and, 2) self-reported history of “anxiety reaction/panic disorder” diagnosis (n=224,330) (Levey et al., 2019). Based on the anxiety score, six significant loci were identified, five from European

American ancestry, and one from African American ancestry. Based on the case-control phenotype, two additional loci were significantly associated. The strongest anxiety score-associated SNP was proximal to the special AT-rich sequence-binding protein-1 (SATB1) (Balamotis et al., 2012).

SATB1 regulates the transcription and chromatin structure of several other genes involved in neuronal development (Balamotis et al., 2012). One of SATB1’s target genes is corticotrophin- releasing hormone (CRH), a key component of the hypothalamic-pituitary-adrenal (HPA) axis stress response (Zorrilla, Valdez, Nozulak, Koob, & Markou, 2002). Another genome-wide significant variant was found in an intron of the estrogen receptor gene (ESR1), which was extensively studied in anxiety/fear behaviors in animal models (Borrow & Handa, 2017) and implicated in the sex differences found in anxiety disorders (Haskell et al., 2010). Another locus was in the intron of MAD1L1 associated with the case-control anxiety phenotype and was replicated in two other psychiatric disorder GWAS on a gene-level, suggesting pleiotropic effects

(Cichon et al., 2011; Schizophrenia Working Group of the Psychiatric Genomics, 2014; Wray et al., 2018). It can be seen in these well-powered studies that large sample sizes yield greater numbers of significant associations even with a disorder consisting of extreme heterogeneous profiles.

The identification of genetic loci associated with anxiety-related traits is still in the early stages of development. GWAS improved this identification process by revealing numerous signals that were not considered as “candidate genes” previously. These signals provide us with important

19 clues regarding the physical locations in the genome that associate with anxiety-related traits.

Throughout the years, the increases in cohort size and the comprehensive profiling of anxiety- related traits enabled more genetic loci to be recognized. However, it is equally as important to dig deeper into the biological mechanisms associated with the loci described in this section. The purpose of this thesis is to explore whether anxiety GWAS loci can also be found in rodent orthologs, and if so, do these loci associate with cognitive-emotional phenotypes in rodents?

1.4 Genetic Risk Factors for Depression

Major depressive disorder (MDD) is the most common affective disorder characterized by episodes of depressed mood, decreased drive, and loss of interest in pleasuring activities

(Association, 1952). Other accessory symptoms can also occur with these core symptoms, which contribute to the heterogeneous phenotypes of depressive episodes (Association, 1952). In most western countries, major depressive disorder (MDD) has a lifetime prevalence of around 11.7% among adolescents and 16.6% among adults and its prevalence is twofold higher in women

(Kessler et al., 1994; Kessler & Ustun, 2004). An important etiological clue related to MDD is its familial aggregation with a summary odds ratio of approximately 2.84 (95% CI: 2.31-3.49) amongst first-degree relatives compared with controls, as well as heritability estimates of 31-42%

(Sullivan, Neale, & Kendler, 2000). A common issue in the search for risk factors associated with both anxiety and depressive disorders is the co-morbidity of their phenotypes (Middeldorp, Cath,

Van Dyck, & Boomsma, 2005). One way to uncover the commonality is to discover and examine the shared and the unique genetic factors that associate with these two disorders.

The first GWAS of a large representative sample of MDD (1738 MDD patients, 1802 controls) found no SNP that surpassed genome-wide significance after replication (Sullivan et al.,

20 2009). The maximum significance was found for rs2715148 (p=7.7x10-7) near the piccolo presynaptic cytomatrix protein (PCLO) gene, which was confirmed in some subsequent GWASs

(Mbarek et al., 2017; Verbeek et al., 2012). PCLO is localized in the cytoplasmic matrix of the presynaptic active zone and plays a significant role in brain monoaminergic neurotransmission

(Woudstra et al., 2012; Woudstra et al., 2013). The first GWAS that reported genome-wide significance was for the SNP rs1545843 in the solute carrier family 6, neutral amino acid transporter member 15 gene (SLC6A15), which is involved in transporting neutral amino acids

(Kohli et al., 2011). Risk allele carriers rs1545843 in humans and chronically stressed mice were associated with a downregulation of the expression of SLC6A15 in the hippocampus and an overall reduction in hippocampal volume (Kohli et al., 2011). Early GWASs were also mostly underpowered due to the use of a heterogeneous population and small effect sizes associated with most SNPs. To enhance power, the Psychiatric Genomics Consortium (PGC) conducted mega- analyses, pooling data from many studies for an association study of MDD phenotypes in

European ancestry (N/cases=9240, N/controls=9519) (Major Depressive Disorder Working Group of the Psychiatric et al., 2013). Although this study had the largest sample size at the time, no SNP reached genome-wide significance in the discovery phase or the replication phase. The authors used this result to highlight the heterogeneity of MDD etiology and the complexity of this disorder’s genetic architecture. GWAS that used larger sample sizes and lenient, easy-to-measure depression phenotypes had more success in identifying genetic variants associated with MDD. A

GWAS from Okbay et al. published in 2016 looked at subject well-being (N=298,420), depressive symptoms (N=161,460), and neuroticism (N=170,911) phenotypes and identified three, two, and

11 variants associated with each phenotype, respectively (Okbay et al., 2016). These three phenotypes were highly genetically correlated, which strengthens the GWAS and enables the

21 identification of additional novel variants. Through enrichment analyses, loci associated with all three phenotypes were significantly enriched in the central nervous system (CNS) and surprisingly also in the adrenals and the pancreas (Okbay et al., 2016). Other than using broad phenotypes, restricting the dataset to a homogeneous population can also increase statistical power. By restricting the population to a homogeneous European descent subset (Hyde et al., 2016), Hyde et al. meta-analyzed the 23andMe dataset and previously-published MDD GWAS statistics by categorizing self-reported clinical diagnosis of depression as case subjects (N=75,607) and no history of depression as control subjects (N=231,747) (Hyde et al., 2016). After replication, they found 17 independent SNPs from 15 regions with genome-wide significance. Out of these, NEGR1 associated with rs11209948, TMEM161B associated with rs454214, OLFM4 associated with rs12552, and MEIS2-TMCO5A associated with rs8025231 were the most significant hits. This study also showed that significant SNPs were enriched in genes expressed in the CNS and function in transcriptional regulation related to neurodevelopment. Other than increasing the sample sizes of human cohorts to increase the identification of genetic risk factors, another method to increase statistical power is by combining multiple GWAS in a meta-analysis.

A meta-analysis from Howard et al. used 807,553 individuals from the three largest GWAS of depression to date and identified 102 independent variants located across 269 genes significantly associated with depression, of which 87 replicated in an independent sample (n=1,306,354)

(Howard et al., 2019). As expected, functional analyses of the genes associated with these 87 genetic variants revealed enrichment in synaptic structure and neurotransmission of prefrontal brain regions. Comparing significant associations with previous publications, the authors found that the genetic variations in DRD2 (dopamine receptor 2), CUGBP ELAV-like family member 4

(CELF4) and ELAV-like RNA binding protein 2 (ELAVL2) replicated. DRD2 is associated with

22 the regulation of mood and emotional processing related to the function of cortical brain regions

(Quarto et al., 2017), and its genetic variations were found in numerous studies of schizophrenia and depression (Whitmer & Gotlib, 2012). CELF4 plays a key role in coordinating synaptic function in excitatory neurons (Wagnon et al., 2012). ELAVL2 is involved in the regulation of gene expression pathways related to neurodevelopment (Fogel et al., 2012). This meta-analysis identified the largest number of significant genetic associations with depressive phenotypes, providing valuable insights into the tissue- and cell type specificity of these genetic loci.

Significant associations from GWAS not only offer novel insights into the genetic background of depression, they also suggest a polygenic architecture consisting of many common variants each exerting only weak individual effects. However, GWAS encompass several weaknesses that will be discussed in the next section. To circumvent the caveats, we can investigate the translatability of the significant SNPs identified using GWAS by studying orthologs of these SNPs in rodent models.

1.5 What Follows GWAS Findings? Challenges and Future Perspectives

Several valuable insights on the etiology of psychiatric disorders have emerged from human studies. First is the considerable contribution of genetic risk factors (Brainstorm et al.,

2018). Genetic predisposition to one disease might also associate with the increase in risk for other disorders. Secondly, most mental health disorders usually encompass heterogeneous genetic profiles composed of modest-effect genetic variants, instead of large-effect rare mutations

(Manolio et al., 2009). The increase in the minor allele frequency (MAF) in the genetic risk factors typically associates with the decrease in the estimated odds ratio (Schizophrenia Working Group of the Psychiatric Genomics, 2014). Thirdly, most genetic risk factors for psychopathology are

23 involved in neural development processes such as synapse formation and plasticity (T. Walsh et al., 2008), which can be further explored in detail. Fourthly, different haplotypes in the same mutation may result in different phenotypic outcomes, heterozygous alleles may exhibit no outcome while homozygous alleles may cause disease onset (C. A. Walsh & Engle, 2010). Lastly, the eventual predisposition for psychopathology is susceptible to non-genetic factors, including early life adversity or inherent biological variations (Mitchell, 2007).

Incorporating these novel findings regarding the genetic architecture of complex mental illnesses in therapeutic interventions remains distant due to small individual effect sizes and a lack of a direct causal relationship with disease for each variant. With a few exceptions, details of the functional mechanisms responsible for the association signals are not yet known. It is fair to say that most of these significant loci were not hypothesized as candidate risk genes, therefore the underlying biology behind these hits remains to be explored. Unfortunately, while hundreds of genetic loci have been identified as risk factors for mental illnesses, functional studies aimed at delineating the biological mechanisms underlying these statistical associations have lagged far behind.

Associational findings are just the tip of the iceberg. One important limitation of GWAS results is that the identified variants merely flag associated genomic regions without revealing a connection to biological mechanisms (Altshuler, Daly, & Lander, 2008). The association of a locus with disease does not specify which variant(s) within that locus are the actual causal factors (Boyle,

Li, & Pritchard, 2017). This is because variants within the same locus can be in linkage disequilibrium (LD), meaning that based on their shared evolutionary history, alleles at nearby

SNPs are often correlated (Goldstein, 2009; Psychiatric et al., 2009). Therefore, one of the major

24 challenges now is to ‘drill down’ into the associated regions to define the causal variants and to uncover how they contribute to disease.

The more familiar Mendelian paradigm involved in many human diseases suggests that functional mutations act through truncating or altering a protein. By contrast, there is growing evidence implying that most functional variants associated with GWAS exert their effects through gene regulation rather than changing the final products (E. P. Consortium, 2012). This theory is based on the observation that most signals do not locate in gene coding regions, but rather in regulatory regions or introns (Hindorff et al., 2009). These signals also overlap with expression quantitative trait loci (eQTLs), where disease-associated variants are more likely to associate with mRNA expression levels related to one or more genes rather than expected by chance (Nicolae et al., 2010). Various bioinformatics analyses may be used to explore cellular tissue types, networks, or pathways where these significant loci are preferentially expressed (Gallagher & Chen-Plotkin,

2018). However, the ultimate evidence linking SNPs with mental health disorders comes from the functional exploratory analysis of the brain.

Challenges related to delineating the functional relevance of genetic risk factors in mental health disorders lie within the complexity and the heterogeneity in root causes and symptoms of these disorders. The use of peripheral tissues and non-invasive techniques to decipher the complexity of genetic associations has limited value in human studies (Gallagher & Chen-Plotkin,

2018). Using post-mortem human brain tissue can overcome some limitations, however, the availability of such tissue is scant and the tissue samples often associate with confounding variables such as antidepressant treatments, different post-mortem intervals, and comorbidity with other diseases. In addition, the degree of genetic susceptibility to disease should only be studied in the context of cellular interactions and environmental variations, as gene by gene and gene by

25 environment interactions can partially explain for the missing heritability when only looking at genetic risk factors (Frazer, Murray, Schork, & Topol, 2009). However, human experiences are hard to characterize and standardize; therefore, unequivocal evidence of biological mechanisms to establish causal relationships between genotype and phenotype will come from the use of animal models and the analysis of rodent brain tissue.

1.6 Animal Behavioral Tests to Model Human Mental Health Outcomes

Rodent models represent a unique opportunity to back-translate associational findings from clinical patients to biological mechanisms. The laboratory rat (Rattus Norvegicus) was the first mammalian species domesticated for scientific research due to its size, ease of handling, and breeding characteristics (Jacob, 2010). Rats are especially advantageous in their capacity to model human mental health disorders, due to their physiological similarities with the human central nervous and endocrine systems (Huang, Tong, et al., 2011). Inbred rat strains were primarily developed for trait-specific selective breeding, where quantitative trait loci (QTL) were associated with physiological phenotypes in inbred strains (Huang, Ashton, Kumbhani, & Ying, 2011). An example is the hypertension QTL mapped near the angiotensin-converting enzyme (Ace) gene in rats, which occurred years before the mapping of the human hypertension QTL in the same region

(Jacob et al., 1991; Soubrier et al., 2002). Conserved QTLs across rat and human were also identified for drug addiction (Bice et al., 1998), cancer (Fijneman, de Vries, Jansen, & Demant,

1996; Nagase, Bryson, Fee, & Balmain, 1996), and autoimmune-related disorders (Merriman et al., 2001) in more than 200 inbred strains, highlighting the genetic conservation across species.

Major challenges to model phenotypes of human neuropsychiatric diseases using rodents include that many characteristics are fundamentally intrinsic to humans (e.g. suicidal thoughts,

26 feelings of guilt, and sadness) and most diseases are comorbid with other psychiatric, metabolic, and immune illnesses (Brainstorm et al., 2018). Therefore, rather than developing an animal model that recapitulates a complex human disorder in its entirety, a more practical approach is to model different components of an illness that may account for clusters of co-varying symptoms

(Belovicova, Bogi, Csatlosova, & Dubovicky, 2017); (Baker, 2011; Powell & Miyakawa, 2006;

Slattery & Cryan, 2017). Multiple animal behavioral tests have been developed based on behaviors analogous to human tasks used in population studies, with the aim of generating cross-species translatability (Bussey et al., 2012; Humby & Wilkinson, 2011).

Many behavioral tests have been designed to assess anxiety-like behaviors in rodents

(Gould, Dao, & Kovacsics, 2009). Two main paradigms are typically used: forcing rodents to encounter a new environment or placing rodents in a conflicting situation (Lezak, Missig, &

Carlezon, 2017). The novelty suppressed feeding (NSF) test was initially developed to study the efficacy of chronic and sub-chronic treatments with anxiolytic drugs and antidepressants (Nestler

& Hyman, 2010). The NSF test detects hyponeophagia, i.e. the innate fear of rodents to novelty induces inhibition of feeding behavior (Bodnoff, Suranyi-Cadotte, Aitken, Quirion, & Meaney,

1988). This test measures the time that the rodent takes to overcome the conflict between an anxiogenic environment and hunger-induced appetite and involves the measurement of time taken to begin feeding in the novel setting (Stedenfeld et al., 2011). Rodents showing longer latencies to initiate feeding in the novel arena are regarded as exhibiting greater anxiety-related behavior compared to rats that took less time to begin feeding. The novelty suppressed feeding test will be used in this thesis to study anxiety-like behaviors in rodents.

According to the criteria of the DSM-IV, one of the major symptoms of depression is anhedonia, which is the loss of interest or pleasure in daily activities (Treadway & Zald, 2011). In

27 rodents, a two-bottle choice test measuring the preference in nutrients intake has been applied in diet-intake studies for decades (Ellenbroek & Youn, 2016; Hasegawa & Tomita, 1986). The ratio of a given solution intake relative to the total solution intake and water is considered as a measurement of taste preference. Rodents have a natural tendency to consume palatable liquid when given a two-bottle free-choice test of sucrose solution and regular water. A reduction in the consumption of sweet solution is generally considered a measurement of anhedonia related to depression (Figueroa, Sola-Oriol, Manteca, Perez, & Dwyer, 2015; Willner, Towell, Sampson,

Sophokleous, & Muscat, 1987). The sucrose preference test will be used in this thesis to study anhedonic behaviors in rodents.

Cognitive dysfunction is a core pathological feature of affective disorders, which has been shown to persist even when the affective symptoms are in remission (McIntyre et al., 2013). In humans, memory is accessed through spoken or written language. Memory, attention, and flexibility deficits and learning difficulties are observed in depressed patients (McDermott &

Ebmeier, 2009). In animals, cognitive functions are accessed through different kinds of experimental models of memory and learning. The ability to recognize a previously presented stimulus constitutes the core of animal models of human amnesia (Baxter, 2010). The novel object recognition test (NORT) is a well-established test of memory that relies on rodents’ natural tendency to explore novel objects without external stimulus (Ennaceur & Delacour, 1988).

Behavioral results in the NORT can vary depending on the aim of each study. Different indexes can be calculated, including the discrimination index, index of global habituation, or preference index (Ennaceur & Delacour, 1988; Gaskin et al., 2010; Hammond, Tull, & Stackman, 2004).

Object recognition can be measured by the difference in the exploration time of novel objects and familiar objects, which is influenced by the time elapsed between the training period and the testing

28 period. The discrimination of novelty against familiarity requires more cognitive skills than emotional responses, and thus provides a relatively clear measurement of memory and cognition

(Silvers, Harrod, Mactutus, & Booze, 2007). The novel object recognition test will be used in this thesis to test short term memory in rodents.

Standardized behavioral tests are commonly used to screen through drug libraries or experimental paradigms for novel therapeutic compounds or to test the efficacy of existing medications (Moore, 2010). Drawbacks in doing so include the validity of the approaches — receptor blockage or brain lesioning are not the actual underlying causes of psychiatric disorders.

Instead of looking for behavioral differences caused by certain manipulations, it is equally as important to characterize the behaviors on a baseline level to look for causes of phenotypic variations at a genetic level. Therefore, in this thesis, we will study the main effects of candidate

SNPs on anxiety-like behaviors, short term memory, and anhedonia using the novelty suppressed feeding test, novel object recognition test, and sucrose preference test, respectively.

1.7 Limitations in Current Rodent Genetic Approaches

The roots and mechanisms of mental health disorders are inarguably complex and heterogeneous (Risch, 1990). From the perspective of human genetics, many different genes contribute to the development of abnormal psychological functions (Hyman, 2018). One way to develop animal models that accurately recapitulate human genetic variations would be to insert or delete human disease-associated alleles in rodents and observe the consequences. In the past decades, techniques that aim to knockout (KO) or overexpress individual genes have become increasingly popular to understand gene function and the etiopathogenesis of psychiatric disorders.

The following section is a brief historical overview of the genetic techniques developed in rats.

29 As previously described, early works related to identifying quantitative trait loci (QTL) associated with certain phenotypes consisted of in-breeding or out-breeding, meaning breeding the same strain or breeding two different strains for multiple generations, respectively. However, such linkage analyses provide only a rough estimation of gene localization as large segments of DNA remain un-recombined (Solberg Woods, 2014), usually spanning hundreds of genes. As a result, pinpointing causal variants or genes can be challenging (Flint, Valdar, Shifman, & Mott, 2005).

Moreover, in terms of genetics and transgenic models, rats lagged far behind mice for several decades due to the lack of authentic rat embryonic stem cells. To circumvent this problem, several alternative approaches have been developed to manipulate the rat genome.

Early classic genetic approaches first began by introducing random mutations across the rats’ genome to observe functional consequences. One example includes N-ethyl-N-nitrosourea

(ENU) (Mashimo et al., 2008). ENU introduces artificial point mutations throughout the genome by transferring an ethyl group to oxygen or nitrogen nucleophilic groups of nucleobases (van

Boxtel, Gould, Cuppen, & Smits, 2010). The modified DNA would then undergo nucleotide substitutions such as A-T base transversions (Acevedo-Arozena et al., 2008). As ENU introduces mutations randomly at multiple sites throughout the genome, a high throughput screening method is essential for identifying mutations in a predetermined gene of interest. To date, KO rats generated by ENU have become valuable research tools in neurobehavioral studies (Homberg,

Mul, de Wit, & Cuppen, 2009; Homberg et al., 2007). Another example of genetic manipulation is transposon mutagenesis. This technology uses a DNA transposon that can “cut-and-paste” itself into the open reading frame (ORF) of a gene and cause gene disruption. This technology only introduces a few insertions per genome, which can be identified efficiently using polymerase chain reaction (PCR). Most early-phase genetic manipulations have aimed at introducing random

30 mutations across the genome and observing the consequences (Huang, Ashton, et al., 2011).

Recent technologies such as zinc finger nucleases (ZFNs), TALENs, and clustered regularly interspaced short palindromic repeat (CRISPR/Cas) system have vastly improved in targeting specificity (Meek, Mashimo, & Burdon, 2017). These techniques generate double strand breaks

(DSBs) into a target gene that relies on one of two DNA repair mechanisms: non-homologous end joining (NHEJ) or homologous recombination (HR). DNA repair by NHEJ generates knock-out mutations resulting from introducing random insertions or deletions (indels), whereas DNA repair by HR leads to the desired modification being inserted. Over the years, the advancements in genetic technologies have improved in target-specificity and efficiency in disruption or insertion, and thus have helped improving our understanding of when and where a given gene product exerts its effects and provided useful insights to the pathogenesis of developmental conditions (Morozov,

2008). However, genetic models based on common variants of small effect should be treated with skepticism. The effect on risk for psychopathology is incremental for each common variant, and thus in most cases one variation does not produce any disease-relevant phenotypes. Animal models of single genetic polymorphisms may exhibit interesting neurobiological properties, but in the end, it is premature to say that these models are reflective of single base-pair mutations in isolation.

Most rodent research on the stress response and neuro-behaviors use inbred strains to reduce outcome variation. However, single inbred strains often fail to sample sufficient genetic diversity to capture the etiology of complex phenotypes related to mood disorders. Instead, laboratory outbred strains such as the Sprague-Dawley, Long-Evans, and Wistar rats are promising tools for trait-genetic studies. Outbred rats are more genetically diverse than inbred rats and therefore hold translational relevance by reflecting the myriad of gene products that may produce behaviors reflecting a range of psychopathological phenotypes (Rat Genome et al., 2013). Several

31 studies have shown that outbred rats exhibit significant behavioral variations in traits related to anxiety-like behavior, fear, depression-like behavior, and physiological responses to stress, a characteristic attributed to their large degree of genetic variability. Therefore, instead of manually introducing genetic deviations, we can leverage on the natural genomic variability of outbred rats to connect genotypes with phenotypes. To date, several projects have launched to sequence the genome of outbred rats for comparative analysis.

By launching the Rat Genome Project in the 2000s, the genome sequence of the Brown

Norway (BN) rat strain was completed in 2004 (Gibbs et al., 2004), which is also the third completed mammalian genome following human and mouse. By using a combination of random whole-genome shotgun sequencing and a bacterial artificial chromosome (BAC) contig-building approach, a high-quality draft of the Brown Norway rat sequence, covering over 90% of the genome, has been achieved by the Rat Genome Sequencing Project Consortium. Comparison between the human, rat, and mouse genomes revealed that they code for similar numbers of genes

(Zhao et al., 2004). The rat genome is 2.75 gigabases (Gb), which is smaller than the human genome (2.9Gb), but larger than that of the mouse (2.6Gb). Global comparison of the three genomes reveals large orthologous chromosomal regions have been inherited with minimal rearrangement of gene order from the common ancestor. These intact regions have become interspersed during large-scale chromosomal rearrangements since the separation of primate and murid ancestors occurred approximately 75 million years ago, and the split between rat and mouse occurred 12-24 million years ago. An anxiety-related QTL was identified via the rat genome sequencing project, where a single candidate gene, Ctnnd2 (Catenin Delta 2), showed significant association with exploratory behaviors in the open field test (Rat Genome et al., 2013). This was further supported in a Ctnnd2-KO mouse model in a contextual fear conditioning paradigm (Israely

32 et al., 2004) and human associational studies (Nivard et al., 2014). These studies have shown that with sufficient sample size, natural polymorphisms in rats can associate with neuro-behaviors and parallel human findings. We can also leverage on such convergence with human findings by further investigating the biological relevance of genetic polymorphisms using rat models.

Natural DNA variations that persist in rat genomes enable genetics research using rat models. Single nucleotide polymorphisms (SNPs) are the most common type of variation to study due to their ubiquity and the ease to genotype. SNP genotypes in rodents can inform us on multiple levels: the association of certain locus with phenotypes, the functional relevance of SNPs in different tissue types, and the heritability and chromosomal mechanisms associated with the SNPs.

In the first objective of this thesis, we will select candidate SNPs from the rat genome based on human anxiety and depression GWAS and study the association between these SNPs and cognitive-emotional phenotypes in rats.

1.8 The Influence of Early Life Environmental Variations

Genetics alone is not enough to be the key determinant of mental health. Mental health disorder onset relies on the complex interactions between genetic and environmental factors (Caspi

& Moffitt, 2006). Most psychiatric disorders stem from neurodevelopmental origins, highlighting the importance of early life environments in conferring disease susceptibility (L. Newman et al.,

2016). The theory of gene by early life environment interactions has been supported at the molecular level. Extensive research has shown that early life stress, adversity, and trauma present prominent risks for the later development of psychopathology as neurons exhibit the greatest plasticity during this time window and are prone to epigenetic modulations including methylation

33 (Vaiserman, 2015). Therefore, to determine the risk for psychopathology, environmental variations especially during early life need to be considered.

To date, studies on gene by environment interactions typically follow two key theories that consider both environment and genetics when determining the risk for psychopathology

(Gluckman & Hanson, 2004; Gluckman, Hanson, & Pinal, 2005). These theories include the diathesis stress framework (Monroe & Simons, 1991) or the differential susceptibility framework

(Belsky, 1997). The diathesis-stress framework postulates that an individual’s vulnerability to psychopathology is associated with his/her own biological context and the encountered adversity.

The differential susceptibility hypothesis modifies this framework by including positive environmental influences, suggesting that an individual’s biological context is sensitive to both negative and positive experiences. Therefore, the concept of “vulnerability” has been slowly shifted to “plasticity/malleability”. For example, the serotonin transporter (SLC6A4) gene polymorphism (5HTTLPR) has been shown to interact with both adverse and supportive environments (Caspi et al., 2003; Pluess et al., 2011). Carriers of 5HTTLPR short (s) allele are more likely to have depressive episodes in stressful environments, but are also less likely to be depressed in low-stress environments (Eley et al., 2004), confirming that genetic variations confer sensitivity rather than vulnerability to environmental influences. Therefore, when determining an individual’s risk for psychopathology, the interactions between genetic variations and the spectrum of environmental factors including both positive and negative experiences needs to be considered.

One of the most critical features of the early life environment is the quality and quantity of maternal care received, as the quality of care directly contributes to the shaping and the growth of the offspring brain circuitry (Belsky & de Haan, 2011; L. K. Newman, Harris, & Allen, 2011).

The infant brain grows in direct response to stimulation and activation (Murray et al., 1996).

34 Therefore, the quality of early care and emotional regulation in the context of caregiving determines the emergence of core neuropsychological characteristics such as affect regulation, attention, stress regulation, and interpersonal functioning in the offspring (Frankel et al., 2012;

Neece, Green, & Baker, 2012). Child abuse, maltreatment, and neglect impose complex effects on brain development including the alteration of levels of cortisol and catecholamines (De Bellis et al., 1999), delays in myelination (Glaser, 2000), and abnormalities in neuronal pruning (Murray &

Cooper, 1997). These effects confer susceptibility and are directly associated with infant anxiety

(Cohn & Tronick, 1989), child affective symptoms (Hammen et al., 1987), and cognitive impairment (Dunham, Dunham, Hurshman, & Alexander, 1989). Given that early life care imposes significant alterations on the offspring, the biological influences can be further investigated using animal models.

The influence of environmental variations has been extensively modeled in rodents through the implementation of various stress paradigms. Rats are born at an immature state of development, and early life stress shapes the behavioral phenotypes of the offspring in adulthood (Andersen,

2015). Variations in maternal care have been observed in the naturally-occurring, spontaneous behaviors of lactating dams (Liu et al., 1997). After giving birth, a mother usually performs caring behaviors including body contact, active nursing, and intermittent licking and grooming (LG) of the offspring. The tactile stimulation provided by these behaviors is essential to promote homeostasis and growth of the offspring. One of the more widely-used paradigms to induce early life stress is maternal separation. Various versions of maternal separation protocols exist, but in general, the dam is separated from the offspring for 3 hours daily from postpartum day 2 to 14

(Vetulani, 2013). This manipulation induces deficits in learning and memory, depressive-like behaviours, and anxiety-like behaviours (Aisa, Tordera, Lasheras, Del Rio, & Ramirez, 2007;

35 Garner, Wood, Pantelis, & van den Buuse, 2007; Marais, van Rensburg, van Zyl, Stein, & Daniels,

2008). At the molecular level, this paradigm induces decreases in BDNF levels, increases in the signaling of corticotrophin releasing factor (CRF) , and increased activities related to enhanced stress responses in the brain (Holmes et al., 2005; Tractenberg et al., 2016).

Another model of early life stress takes advantage of the naturally-occurring variations in maternal care (Liu et al., 1997). LG behaviors are highly variable between mothers and remain stable over multiple litters (Meaney, 2001). The frequency of LG behaviors usually falls into a normal distribution within a cohort of lactating mothers, and the offspring can then be assigned to

“high” or “low” LG groups if their mothers exhibited LG frequencies that are one standard deviation above or below the mean of that cohort, respectively. The number of pups that survive until weaning age and their weights at weaning day do not significantly differ between offspring from low LG and high LG mothers (Champagne, Francis, Mar, & Meaney, 2003), suggesting that

LG behaviors represent a normal range of variability in maternal care that do not interfere with offspring survival, and thus are more reflective of human early care behaviours.

The behavioral variations in maternal care have profound and enduring effects on neurodevelopment and phenotypic variations persisting through the adulthood period of the offspring (Higley, Hasert, Suomi, & Linnoila, 1991; Meaney, 2001) and this resembles observations from human studies. Offspring of low LG mothers show hyper neuroendocrine and behavioral responses to stress paradigms compared to offspring of high LG mothers (Caldji et al.,

1998). These responses were mediated by alterations in the hippocampal glucocorticoid receptor expression and hypothalamic-pituitary-adrenal axis feedback (Hellstrom, Dhir, Diorio, & Meaney,

2012). The behavioral and physiological outcomes can be reversed by cross-fostering offspring of low LG mothers to a high LG mother, confirming the causal effects of maternal care in long-term

36 neurodevelopment (Liu et al., 2000). Overall, studies from both humans and rodents indicate that early life stress, such as history of childhood abuse or maternal neglect, has long-lasting consequences with increased risk for psychopathology. Therefore, for this thesis, I will also consider the interactions between candidate SNP genotypes and maternal licking and grooming behaviors.

1.9 Preliminary Studies and Results From the Current Project

Studies on the effects of animal genetics on cognitive-emotional phenotypes have not considered the subtle influences of naturally-occurring common genetic variations. The present thesis examines the influence of common genetic variations identified in previous human and animal studies in a cohort of outbred rats. By using publicly available databases such as the

Rat Ensembl (Rnor_6.0) and Rat Genome Database, 192 SNPs spanning 72 genes were selected based on their coordinates within the previously identified genes from GWAS. Since these databases are still at the infancy stage of development, critical information such as minor allele frequency (MAF) and linkage disequilibrium (LD) block within the Long-Evans strain were not available. After sequencing, we excluded SNPs with a minor allele frequency less than 0.05 (5%) and in linkage disequilibrium with other SNPs. Supplementary table 1 represents a complete list of 59 SNPs that passed the filtering thresholds and their corresponding gene name, gene ID, and allelic variations.

SNP genotyping described above were performed in a cohort of behaviorally-characterized outbred rats. Between postnatal day 1 to 6, maternal licking and grooming behaviors that these rats received as pups were scored. Between postnatal day 75 to 100, the novelty suppressed feeding test, the novel object recognition test, and the sucrose preference test were performed. Using linear

37 mixed-effect regression models as described in Method section of Chapter 2, the main effect of

SNP genotypes and the interaction effects between SNP genotypes and maternal LG scores on the outcomes of these behavioral tests were evaluated while adjusting for fixed covariates including weight, litter size, maternal licking and grooming scores and a random covariate of litter ID. Sex- specific preliminary results are presented in Supplementary table 2 and 3 including the SNP ID, gene name, SNP location, and the p value.

Based on the data presented in Supplementary table 2, it can be observed that the 3’ untranslated region (URT) SNP rs107355669 in Grm2 gene (encodes for mGluR2 or Group II metabotropic glutamate receptor 2) is significantly associated with the behaviors measured in the

NSF test, including latency to food, latency to feed, and survival test measuring the cumulative probability of not feeding during the NSF test. Given the importance of the glutamate system in psychiatric disorders and previous publications linking mGluR2 with antidepressant functions and stress susceptibility in rodent models, I would like to dive deeper into the glutamate system in the following sections and focus on the influence of mGluR2 polymorphisms on the transcriptomic regulation in the brain.

1.10 The Glutamate system and its Regulation by Glutamate Receptors

Glutamate is the major excitatory neurotransmitter in the brain and controls synaptic excitability and plasticity in most brain circuits (S. J. Mathew, Keegan, & Smith, 2005), which renders itself essential in processes such as learning and memory (Malenka & Nicoll, 1999).

Glutamatergic neurotransmission requires strict and complex regulation to be maintained under physiological conditions.

38 There are two types of glutamate receptors that govern glutamate transmission: ionotropic ligand-gated ion channels (voltage sensitive) and metabotropic (ligand sensitive) G-protein coupled receptors (Reiner & Levitz, 2018). Ionotropic glutamate receptors are highly expressed in cortical and limbic regions and are divided into three subtypes, including N-methyl-D-aspartate

(NMDA) receptors, a-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptors, and kainate receptors (Asztély & Gustafsson, 1996). The ionotropic receptors are fast-acting. Once activated by agonist binding, the receptors receive a large influx of Na+ and membrane depolarization occurs in the post-synaptic cell to induce signal transmission. NMDA receptors are blocked under resting conditions by Mg2+ ions. However, once the surrounding membrane is depolarized by the AMPA or kainate receptors, NMDA receptors are activated upon the binding of two glutamate and two glycine or D-serine. NMDA receptor activation marks converging excitatory inputs and produces excitation over long periods of time. Dysregulated and excessive activation of ionotropic glutamate receptors leads to excitotoxicity with increased calcium ion entry and neuronal death.

Metabotropic glutamate receptors are slower-acting by exerting indirect effects through gene expression and protein synthesis (Pin & Duvoisin, 1995). These effects often enhance glutamate cell excitability, regulate neurotransmission, and contribute to synaptic plasticity (Lesage &

Steckler, 2010). The metabotropic receptors are highly expressed in the hippocampus, prefrontal cortex, and amygdala regions. The binding of glutamate to a metabotropic receptor activates a post-synaptic membrane-bound G-protein, which, in turn, triggers a second messenger system that opens a membrane channel for signal transmission. Metabotropic glutamate receptors are divided into three groups based on sequence homology: Group I (mGluR1 and mGluR5), Group II

(mGluR2 and mGluR3), and Group III (mGluR4, mGluR6, mGluR7, and mGluR8). Group I

39 metabotropic receptors are largely expressed on the postsynaptic membrane and have been implicated in learning and memory problems, addiction, motor regulation, and Fragile X syndrome

(Niswender & Conn, 2010). Group II metabotropic receptors are situated on both pre-synaptic and post-synaptic cells to suppress glutamate transmission. The dual locations allow stronger modulation of glutamate signaling. Dysfunction of Group II metabotropic receptors have been implicated in anxiety, schizophrenia, and Alzheimer’s disease (Swanson et al., 2005). Group III metabotropic receptors, like Group II, are pre-synaptic and inhibit neurotransmitter release. They are found within the hippocampus and hypothalamus and may play a role in Parkinson’s disease and anxiety disorders (Swanson et al., 2005).

1.11 The Glutamate System in Mood Disorders

The intricate excitatory-inhibitory balance between glutamate and y-aminobutyric acid

(GABA) is essential for the homeostasis of the CNS. Excitotoxicity caused by disrupted glutamate regulation leads to multiple CNS disorders, including Alzheimer’s disease and Huntington’s disease (Choi, 1994), as well as psychiatric disorders, including depression (Bechtholt-Gompf et al., 2010). Therefore, although greater focus has been given to monoamine neurotransmission in understanding the pathophysiology of mood disorders and therapeutics development in the past

(Schildkraut, 1965), recent evidence from molecular mechanisms of antidepressants (Berman et al., 2000; Trullas & Skolnick, 1990; Zarate et al., 2006), clinical post-mortem brain tissue studies, neuroimaging studies, and genetic studies point to disrupted glutamatergic neurotransmission in psychiatric patients as a potential source of pathogenesis.

Early evidence supporting the involvement of the glutamatergic system in the pathophysiology of mood disorders came from the clinical antidepressant studies of tianeptine and

40 ketamine (C. T. Li et al., 2016). Tianeptine has been shown to exert antidepressant-like effects by restoring normal neuroplasticity of limbic regions as well as reducing stress-induced impairment of glutamate neurotransmission by blocking glutamate receptor activity (McEwen et al., 2010). In the hippocampus, tianeptine restores the stress-induced fluctuations in NMDA-receptor excitatory postsynaptic currents (EPSCs) in the CA3 pyramidal neurons (McEwen et al., 2010). In the amygdala, tianeptine normalizes stress-induced disruptions in synaptic concentrations of glutamate (McEwen et al., 2010).

Ketamine is another drug that shows acute antidepressant-like effects by altering the glutamate system. Pre-clinical studies validated the antidepressant-like effects of ketamine in various animal behavioral models of depression, including the forced swim test and tail suspension test (Chindo, Adzu, Yahaya, & Gamaniel, 2012; Engin, Treit, & Dickson, 2009). Similarly in humans, ketamine alleviated depressive symptoms within 72 hours with long-lasting effects in treatment-resistant MDD patients (Berman et al., 2000; Zarate et al., 2006) and bipolar depression patients (Diazgranados et al., 2010). Molecular and cellular evidence from preclinical models show that ketamine stimulates a signaling cascade that enhances glutamatergic neurotransmission in the prefrontal cortex (PFC), with downstream consequences including increased synaptic protein synthesis and increased dendritic spine density (N. Li et al., 2010). A variety of cellular pathways have been implicated to elicit such effects, one of which is the mammalian target to rapamycin

(mTOR) pathway (N. Li et al., 2011). mTOR is a serine/threonine protein kinase involved in translation regulation and synaptic plasticity, processes that are downregulated in numerous neuropsychiatric disorders (Hoeffer & Klann, 2010). Ketamine has also been associated with increased synthesis of brain derived neurotrophic factor (BDNF) in the hippocampus. The role of

BDNF in depression-like behaviors has been confirmed in multiple rodent behavioral tests using

41 knockout models (BDNF -/-) and knock-in models (Vall66Met) (Laje et al., 2012; N. Li et al.,

2011; Shimizu, Hashimoto, & Iyo, 2004). One possible mechanism has been attributed to the blockade of NMDA receptors at rest which leads to reduced eukaryotic elongation factor (Eef2) phosphorylation and increased BDNF translation (Autry et al., 2011). Therefore, based on the preclinical and clinical evidence compiled using tianeptine and ketamine, it is clear that the function of the glutamate system closely intertwines with antidepressant functions.

Disrupted glutamatergic signaling in psychiatric disorders is also supported by the histological and molecular findings from post-mortem studies (McOmish, Pavey, et al., 2016).

Glutamate abnormalities were observed in the plasma (Kim, Schmid-Burgk, Claus, & Kornhuber,

1982), and cerebrospinal fluid (CSF) (Levine et al., 2000) with an undetermined origin of pathophysiology. To better differentiate peripheral and central glutamate levels, instead of peripheral tissues, brain tissues revealed clearer information. Decreased glutamate receptor subunit expression (Gray, Hyde, Deep-Soboslay, Kleinman, & Sodhi, 2015) and elevated levels of glutamate and its neuronal reservoir, glutathione, were identified in the frontal cortex of depressed and bipolar disorder patients (Hashimoto, Sawa, & Iyo, 2007). This evidence supports the hypothesis that the glutamate system is disrupted in psychiatric disorders, which is supported by the peripheral alterations in glutamate levels.

Due to the inevitable shortcomings in post-mortem studies, such as limited access to brain tissues and varying duration of post-mortem interval, in vivo non-invasive approaches such as neuroimaging provides a better understanding of glutamate pathophysiology in the living brain.

Glutamate content can be measured as “Glx” by combining signals from glutamate with glutamine.

The Glx signal is significantly reduced in the anterior cingulate (ACC) and dorsolateral prefrontal cortex (PFC), the amygdala, and the hippocampus of major depressive disorder (MDD) patients

42 (Hasler et al., 2007; Luykx et al., 2012; Yuksel & Ongur, 2010). Conversely, in bipolar disorder,

Glx levels are usually elevated in the grey matter areas of the ACC, the PFC, the occipital cortex, and the hippocampus (Yuksel & Ongur, 2010). Studies have also shown increased Glx levels in remitted patients and in the occipital lobe of patients with the melancholic subtype of depression

(Sanacora et al., 2004) and in the frontal cortex of patients with late-life depression and post-stroke depression (Binesh, Kumar, Hwang, Mintz, & Thomas, 2004; Glodzik-Sobanska et al., 2006). As suggested by these studies, due to the complexity of glutamatergic dysfunction, changes in levels of glutamate are irregular and far more complex than a simple increase or decrease. While the direction and magnitude in the level of glutamate may differ based on brain regions and disease subtypes, this converging evidence generally indicates alterations in glutamate in mood disorders.

1.12 Glutamate signaling correlates with synaptic plasticity in mood disorders

Brain region-specific volumetric changes are associated with the stress response and emotional processing (McEwen & Gianaros, 2010). Given that the increase in glutamatergic signaling can lead to cellular toxicity and neurodegeneration, it is possible that the combination of reduced extracellular glutamate clearance and excessive glutamate release to the synaptic cleft could provide a causal linkage between alterations in glutamate abnormalities, reductions in brain volume, and cognitive-emotional behaviors.

Based on the evidence that depressed patients had volume reductions in certain brain regions, the effects of stress on structural remodeling of the brain has been investigated using rodent models. Different stress paradigms, acute and chronic, have been shown to induce structural changes in brain regions that are also affected in depressed patients. Chronic stress causes neurons in the medial prefrontal cortex (mPFC) (Liston et al., 2006) to undergo dendritic and spine density

43 reduction, whereas the same stress paradigms increase dendritic length and spine density in the basolateral amygdala (BLA) and orbitofrontal cortex (OFC) (Diamond, Campbell, Park, Halonen,

& Zoladz, 2007; Vyas, Mitra, Shankaranarayana Rao, & Chattarji, 2002). Most changes seem reversible, except in the BLA, where the changes in glutamate signaling persist for at least 30 days after stress exposure (Vyas, Pillai, & Chattarji, 2004). Acute stress paradigms can also cause new spines to grow in the BLA (Mitra, Jadhav, McEwen, Vyas, & Chattarji, 2005). These results highlight the alterations of neuronal remodeling as a response to stress paradigms, where in the

BLA and OFC seem to have opposite remodeling patterns compared to the mPFC.

Dendritic/structural remodeling in key brain regions implicated in cognition, emotion, and memory (e.g. PFC, hippocampus, amygdala) is thought to play a significant role in depression and anxiety (Gorman & Docherty, 2010; Pittenger & Duman, 2008). Glutamate is essential for the normal development of dendritic branching, and thus it has been speculated that excessive glutamatergic neurotransmission causes dendritic retraction and loss of spines (Gorman &

Docherty, 2010; L. J. Lee, Lo, & Erzurumlu, 2005). Such changes would effectively limit the number of exposed glutamate receptors and as a result, drugs that reduce glutamatergic neurotransmission may prevent dendritic retraction and protect brain synapses (Bessa et al., 2009).

Due to the close connection between glutamatergic signaling, neuronal remodeling, and stress responses in psychiatric disorders, it is important to identify the regulators of the glutamate system, as this may offer novel targets for therapeutic interventions.

1.13 Metabotropic glutamate receptor 2 in psychiatry

In comparison to the ubiquitous expression of ionotropic glutamate receptors, metabotropic glutamate receptor subtypes are more unevenly distributed in the brain, giving rise to the

44 possibility that manipulations of its activity, expression, and functions may be used to target glutamatergic activity in certain brain circuits. The group II metabotropic glutamate receptors, mGluR2 and mGluR3, have emerged as attractive pharmacological targets for the treatment of multiple mental health-related disorders. Up to now, mGluR2 and mGluR3 have been thought to have similar functions: they share great sequence homology and signaling pathways, as well as function in providing negative feedback to reduce glutamate signaling. Presynaptic mGluR2 inhibits glutamate release, while glial mGluR3 increases glutamate uptake by regulating excitatory amino acid transporters (EAATs). Most studies have focused on the pharmacological effects of antidepressants targeting both mGluR2 and mGluR3. However, few studies have shown that since mGluR2 is expressed primarily pre-synaptically, it has a more significant role in the regulation of glutamate (Saitoh, Wakatsuki, Tokunaga, Fujieda, & Araki, 2016).

As previously described, ketamine exerts its antidepressant-like effects by modulating glutamatergic signaling. One important limitation to the clinical use of ketamine is its abuse liability concern for drug users (Hillhouse & Porter, 2015). In vivo, ketamine is rapidly metabolized to norketamine and then hydroxylated to produce hydroxynorketamines (HNKs). The

(R)-ketamine enantiomers appear to have reduced abuse liability and longer-lasting antidepressant-like effects compared to (S)-ketamine enantiomers (C. Yang et al., 2017; C. Yang et al., 2015; Zanos et al., 2016). One enantiomer, (2R,6R)-HNK, is present in the plasma and the brain after ketamine administration, and elicits potent, putative rapid-acting antidepressant-like effects as ketamine through glutamatergic synapses (Abdallah, Sanacora, Duman, & Krystal, 2018;

Autry et al., 2011; Narimatsu, Kawamata, Kawamata, Fujimura, & Namiki, 2002). mGlu2 receptors, but not mGlu3 receptors, are involved in the antidepressant-like effects of (2R,6R)-HNK as the antidepressant-like effects were absent in mice KOmodels lacking the Grm2 gene, but not

45 the Grm3 gene (Zanos et al., 2019). Behaviorally, (2R,6R)-HNK exerts rapid and sustained antidepressant-like effects reflected across a battery of behavioral tests including the forced swim test (FST) (Chou et al., 2018), learned helplessness test, and NSF test (Fukumoto et al., 2019;

Highland et al., 2019; Lumsden et al., 2019). Furthermore, another study has shown that Grm2 KO mice also exhibited cognitive deficits (De Filippis et al., 2015). Similar observations were made with a drug named N-acetylcysteine (NAC) that alter glutamate release in the cortico-limbic circuitry to attenuate stress-related symptoms such as anxiety and depression. NAC acts on cysteine-glutamate antiporter, a protein that releases glutamate into the extra-synaptic space.

Raising glutamate levels could stimulate mGluR2 and reduce the synaptic release of glutamate, therefore exhibiting antidepressant-like effects in patients diagnosed with mental health disorders such as bipolar disorder and depression (Berk et al., 2008; Krystal, 2008). These studies provided further evidence that certain antidepressant may act through mGluR2 to exert effects.

Given the recent clinical and pre-clinical studies, it can be speculated that mGluR2 may be a key player in the regulation of the glutamatergic system, and the expression of mGluR2 may influence brain circuitry closely implicated in psychiatric disorders. Therefore, Chapter 2 presents a story that is built upon the glutamate system and investigates the genetic variations in the Grm2 gene that encodes for mGluR2. By characterizing the influence of Grm2 genetic variations on multiple levels, including animal behaviors, transcriptomic patterns, and network expression, we gain a more comprehensive understanding of this gene and the regulation of the glutamate network.

46

CHAPTER 2: Integration of cross-species genomics and transcriptomics to identify sex-

specific glutamatergic gene networks involved in emotional phenotypes

Li Li 1, Zihan Wang 3, Sachin Patel 3, Omar Khedr 3, Allison Martel 3, Oscar Vasquez 2, Maria

Aristizabal 2, Irina Pokhvisneva 3, Nick O’Toole 3, Tie Yuan Zhang1,3, Josie Diorio 3, Kieran J.

O’Donnell 1,3, Marla B Sokolowski 2, Patricia Pelufo Silveira 1,3, Michael J Meaney 1,3,4.

Institution affiliation:

1 Integrated Program of Neuroscience, McGill University, Montreal, Quebec, Canada.

2 Department of Ecology & Evolutionary Biology, University of Toronto, Toronto, Ontario,

Canada.

3 Ludmer Centre for Neuroinformatics and Mental Health, Douglas Hospital Research Centre,

McGill University.

4 Singapore Institute for Clinical Sciences, Agency for Science, Technology and Research

(A*STAR)

47 Abstract:

Abnormal glutamatergic neurotransmission underlies the development of several mood disorders.

Relatively little is known about the health consequences of common genetic variations on glutamatergic genes. By integrating cross-species genomic and transcriptomic approaches, we examined the associations between rs107355669, a genetic variant in the 3’ UTR of the Grm2 gene

(which encodes for metabotropic glutamate receptor 2), and mood-related phenotypes, Grm2 transcription patterns across the brain emotional circuitry, and Grm2 co-expression networks. We found sex differences in the associations between rs107355669 genotypes and anxiety-like phenotypes in outbred rats, Grm2 mRNA expression, global differential transcription, and basolateral amygdala-specific expression of Grm2 network. We implemented a novel translational approach by constructing an expression-based polygenic risk score (ePRS) to reflect the expression of human GRM2 network derived from rat transcriptome, and found sex differences in the association between BLA-specific ePRS-GRM2 and mental health-related phenotypes in different human cohorts. These results highlight an important network involved in individual differences in glutamate-dependent processes, as well as vulnerability to mood disorders.

48 Introduction:

Glutamate is the primary excitatory neurotransmitter in the brain and contributes to synaptic plasticity, learning, and memory(Danbolt, 2001; McEntee & Crook, 1993). Glutamate neurotransmission and excitatory-inhibitory balance are implicated in the pathogenesis of stress- related psychiatric disorders and in antidepressant functions (Gao & Penzes, 2015; Nasca et al.,

2018; Nasca et al., 2013; Ohgi, Futamura, & Hashimoto, 2015; Swanson et al., 2005; Thompson et al., 2015). Although multiple genome-wide association studies (GWASs) identified glutamatergic gene variants as risk loci, the biological mechanisms underlying these associations remain unclear (Forstner et al., 2017; Howard et al., 2018; P. H. Lee et al., 2012; H. Zhou et al.,

2018).

Type 2 metabotropic glutamate receptor (mGluR2), a G protein-coupled transmembrane autoreceptor that functions as a key regulator of synaptic glutamate release, has been identified to play a role in mood disorder-related behaviors (Lorrain, Baccei, Bristow, Anderson, & Varney,

2003; Nicoletti et al., 2011; S. Wang, Chen, Kurada, Huang, & Lei, 2012). mGluR2 is widely distributed throughout the central nervous system (CNS), and highly expressed in brain regions implicated in cognition and emotion (McOmish, Demireva, & Gingrich, 2016). Sex differences in mGluR2 expression have been observed at baseline levels as well as in association with suicidal behaviors (Dean, Duncan, & Gibbons, 2019) and major depressive disorder (MDD) (Y. Wang et al., 2015),(Gray et al., 2015; McOmish, Pavey, et al., 2016). The mGluR2 gene, also known as

GRM2, is localized on chromosome 3 in human and on chromosome 8 in rats. Grm2 genetic polymorphisms were identified in Wistar rat sub-strains that were selectively-bred for phenotypic traits such as anxiety-like behaviors (Ceolin et al., 2011), alcohol preference, and risk-taking behaviors (Wood et al., 2017).

49 Genes do not act in isolation, but in concert with other genes in functional networks, which could inform biologically relevant, tissue-specific changes underlying disease neurobiology (Hari

Dass et al., 2019; Miguel et al., 2019; Silveira et al., 2017). In this context, the network hub genes

(genes with high intra-network connectivity and whose variations in the expression drive the entire network activity)(Langfelder, Mischel, & Horvath, 2013) are key players. A plethora of research has linked mGluR2 to mental health; however, little is known about the interaction between common genetic variations of Grm2 and other genes, and whether gene networks having Grm2 as a hub gene have a special role in modulating emotional responses. In this study, we identified, in a sex-specific manner, the genotypic associations between Grm2 polymorphism and 1) mood disorder-related behaviors in rodents; 2) Grm2 mRNA expression across brain’s emotional circuitry, including cingulate cortex (CC), ventral dentate gyrus (vDG), nucleus accumbens shell

(NAcc), and basolateral amygdala (BLA); 3) global transcriptomic signatures and Grm2 network expression. Moreover, we demonstrated the cross-species preservation of the Grm2-hub gene network in the BLA, and its ability to inform the vulnerability of individuals to emotional behaviors in different human samples. These analyses highlight the importance of natural genetic polymorphisms in a sex-specific manner, and shed light on the tissue-specific glutamatergic system implicated in phenotypes of mood disorders.

50 Methods:

Animals: All animal procedures were performed in accordance with the Canadian Council on

Animal Care (CCAC) guidelines and approved by the University Animal Care Committee at

McGill University. The animals used in these studies were male and female Long-Evans offspring from dams bred at the Neurophenotyping Centre (Douglas Mental Health University Institute)

(Champagne et al., 2003). Maternal licking and grooming (LG) observations were performed on the dams between postnatal day (PND) 1 and PND 6 (Champagne et al., 2003). Observations were performed 5 times per day, 3 times in the light phase at 10:00 AM, 1:00 PM, and 5:00 PM, and 2 times in the dark phase at 7:00 AM and 8:00 PM. Each observation session was 72 minutes in duration consisting of 25 observations in 3-minute increments. Dams were observed in their home cage and undisturbed for the duration of the observation period. Dams were scored on maternal behaviors such as no contact (x), contact (c), licking and grooming pups (lc), arched-back nursing

(level 1 to level 4), passive nursing (p), split-litter (s), and retrieval (r). The frequency score of pup

LG for each dam was based on a total of 750 observations (25 observations/session x 5 rounds/day x 6 days= 750) and expressed as a percentage occurrence (number of occurrences /750*100).

Litters that received maternal LG scores greater than 20 were excluded from future analyses as outliers of the normal distribution (3 standard deviation above the mean). The categorization of a high or low LG mothers was based on pups receiving LG scores that were one standard deviation above or below the mean of that cohort, respectively. The remaining dams were classified as mid

LG mothers. Male and female offspring were separated and pair-housed with litter-mates 21 days after birth. Husbandry was maintained with minimal handling under controlled conditions of light

(lights ON: 08:00hours, lights OFF: 20:00 hours), temperature (21-23°C), and humidity (35-45%) and ad libitum access to standard rat chow and water.

51

Animal cohorts: Forty-four litters (N=115/male, N=124/females) were used in the analyses of genotypic associations with phenotypes measured from behavioral tests. Two males and two females were used from each litter if the dam was characterized as mid LG, but the entire litter was considered in the case of high and low LG dams. There were eight high LG dams, 31 mid LG dams, and five low LG dams. Out of 115 male rats, 34 were from high LG dams, 22 from low LG dams, and 59 from mid LG dams. Out of 124 female rats, 37 were from high LG dams, 16 were from low LG dams, and 71 were from mid dams. Behavioral tests were performed according to the experimental timeline after the offspring reached maturity (PND 70-PND 120) (Fig. 1a). All behavioral tests were performed in the light cycle between 09:00 and 15:00 hours by an experimenter blind to the LG group and the genotype group. There was at least a 24-hour interval between each test. An independent rat cohort consisting of 48 Long-Evans outbred rats

(N=25/male, N=23/female) from 19 litters was used for RNA sequencing.

Novelty suppressed feeding test (NSF): The NSF test was adapted from previous work and measures anxiety-like behaviors by eliciting a conflict between an appetite stimulus and the fear of a novel environment (Bodnoff et al., 1988). The animals were food-deprived for 24 hours before the testing day, and habituated to the testing room for at least 30 minutes before the test. During testing, a single food pellet was placed in the middle of an illuminated arena (28 in x 28 in x 24 in). Animals were placed in the top left-hand corner of the arena. The test was stopped when the animal began to feed or after 10 minutes. The arena was cleaned after each test with hydrogen peroxide solution (0.5%). Each test was video-recorded. ‘Latency to food’ (seconds) and ‘latency to feed’ (seconds) were assessed manually post-test by video scoring. ‘Latency to food’ measures

52 the time that the animal took to poke its nose on the food pellet. Latency to feed measures the time the animal took to start feeding during test. Home cage feeding test was measured immediately following the NSF test to ensure that there is no difference in appetite between the animals. This task was performed in the rat’s home cage in the testing room whereby a piece of chow was placed on top of the bedding material at the back of the cage. Latency to feed (in seconds) in the home cage was also recorded.

Open field habituation and novel object recognition test (NORT): The NORT evaluates the rodent’s ability to recognize a novel object in a familiar environment (Ennaceur & Delacour, 1988).

Animals were habituated to an open field for 10 minutes each day for two days prior to NORT.

On the testing day, during training period, animals were placed back into the aforementioned open field for 5 minutes in the presence of two identical objects (training objects) for free exploration.

The animals were returned to their home cage for a 15-minute interval. After the interval, the animals were immediately returned to the same open field for testing. During the testing session, the animals were allowed free exploration of a training object and a novel object for 5 minutes.

The objects were placed in the same location as in the training period. To prevent preference for object type, the objects used as novel or training objects were randomly assigned between subjects.

The time that each animal spent exploring the training object and the novel object during the testing session was measured. Novel object preference percentage was calculated by: time spent exploring novel object during testing session / (time spent exploring novel object during testing session + time spent exploring training object during testing session) * 100. Both the open field and the objects were cleaned with hydrogen peroxide solution (0.5%) between each animal trial. The behaviors were assessed manually post-test by video scoring.

53

48-hour sucrose preference test (SPT): This test is widely used to assess anhedonia behaviors in animals (Nielsen, Arnt, & Sanchez, 2000). Animals were habituated to have two bottles of water in their home cage from day 1 to day 4. On day 5, animals were habituated to having free access to two bottles of sucrose solution (1%) for 24 hours. Then, starting on day 6, animals were singly- housed and presented with one bottle of water and one bottle of 1% sucrose solution simultaneously on the home cage starting at 09:00 hours. Bottle positions were switched after 24 hours (day 7) to avoid side preferences, and the weights of the bottles were measured at 0 hours,

24 hours, and 48 hours. The bottles were topped up and re-weighed if needed. Total consumption of each fluid was recorded at 24 hours and 48 hours. Total sucrose preference was calculated by: total consumption of sucrose / (total consumption of sucrose + total consumption of water) * 100.

Animals were pair-housed with their original cage mates after the test was completed with ad libitum access to standard rat chow and water.

Tissue collection and Deoxyribonucleic Acid (DNA) extraction: Animals were sacrificed via live decapitation 2-4 weeks after the behavioral testing between 09:00 hours and 13:00 hours. Ear snips were immediately dissected, stored in Eppendorf tubes, and flash-frozen on dry ice. Frozen ear snips were stored in -80 ºC until extraction. The Qiagen DNeasy Blood & Tissue Kit (QIAGEN,

Ontario, Canada. Catalog number: 69506) was used to extract genomic DNA from the frozen ear snips. Genomic DNA was quantified using a SpectroPhotometer® (Implen, California, USA) and stored at -20 ºC until use.

54 Grm2 candidate single nucleotide polymorphisms (SNPs): Knowing the modulatory roles of metabotropic glutamate receptor (mGluR2) in the excitatory neurotransmission, and how its imbalance mediates the pathogenesis of multiple psychiatric disorders and functions of antidepressants. We selected genetic polymorphisms associated with the Grm2 gene (encodes for the mGluR2). These SNPs were chosen from the Ensembl Rat Database (Rnor_6.0) based on their coordinates. Strain-specific linkage disequilibrium data and allelic frequency data were not available for the SNPs located in Grm2. More information is displayed in Supplementary table 1.

Sequenom iPLEX Gold Assay: Genomic DNA samples of at least 10 ng/µl in a 30µl volume were processed at McGill University and Genome Quebec Innovation Centre (Montreal, Quebec,

Canada) using the Sequenom® iPLEX® Gold genotyping. Flanking sequences of each SNP for the primer design were taken from Rat Ensembl Database (Rnor_6.0). Every sample was sequenced for the SNPs selected from Grm2. The genotyping reaction is based on a multiplex polymerase chain reaction (PCR) followed by a template-directed single base extension (SBE) using a probe. The products were then separated and detected by mass spectrometry (MALDI-

TOF MS), where the genotypes are clustered and identified.

Ribonucleic acid (RNA) extraction and sequencing: Tissue from cingulate cortex (CC), ventral dentate gyrus (vDG), basolateral amygdala (BLA), and nucleus accumbens shell (NAcc) were collected from coronal and transversal brain slices at 200µm in the RNA sequencing cohort. All animals were genotyped for rs107355669 (Grm2 SNP with the best minor allele frequency (MAF)), and there were N=7/GA and N=12/GG females; N=12/GA and N=12/GG in males. RNA was extracted using the QIAGEN RNA Mini kit (QIAGEN, Ontario, Canada. Catalog

55 number:74104). We performed on-column DNase I treatment during RNA extraction. RNA quality and purity were verified using a Bioanalyzer 2100 (Agilent Technologies, Santa Clara,

USA). RNA libraries were prepared using the TruSeq Stranded Total RNA Library Prep Kit with

Ribo-Zero Gold (Catalog number: RS-122-2301, Illumina Canada Ulc.). Paired-end, 100bp read- length RNA-sequencing was performed using a HiSeq 4000 sequencer at a depth of 25 M sequencing. The rat RNA-seq data were aligned to the Rattus Norvegicus rn6 genome with STAR

Aligner to obtain gene counts (Dobin et al., 2013).

Differential Expressed Genes (DEGs) analysis: DEGs between rs107355669 GG and GA genotypes (AA genotype excluded) were identified to first determine if genotypes were significantly associated with Grm2 mRNA expression as a cis-eQTL per brain region and per sex

(McCarthy, Chen, & Smyth, 2012). Genes with counts >1 in at least three samples were retained for the analysis. CalcNormFactors function was used to normalize the data using trimmed mean of M values (TMM). EstimateDisp function was used to estimate common dispersion and tagwise dispersions. TopTags was used to probe differentially expressed genes. GlmQLFit and glmQLFTest were used for quasi-likelihood F-test for a more conservative and rigorous type I error rate control. For the trans-eQTL effects of rs107355669 genotypes, A log2(fold change) (LFC) threshold was set at > 30% (LFC > |0.3875|) and uncorrected p value < 0.05 for each comparison of DEGs. We observed 1-15% overlap of DEGs between males and females in each of the four brain regions included in this study, which is consistent with the very small overlap previously seen in the differential gene expression analyses of depressed brains between males and females in both human and rodent cohorts(Labonte et al., 2017; Pena et al., 2019; Seney et al., 2018).

56 Rank-Rank Hypergeometric Overlap analysis (RRHO): To evaluate the sex differences in the unique transcriptional signatures of the differentially expressed genes between GG and GA genotypes of rs107355669, RRHO analysis was conducted on the full differential expression gene lists with no significance thresholds applied (Bagot et al., 2016; Plaisier, Taschereau, Wong, &

Graeber, 2010). Gene lists were ranked by the LFC values from positive to negative. A four- quadrant version of this analysis was used to test for coincident and opposite enrichment (Cahill,

Huo, Tseng, Logan, & Seney, 2018). RRHO maps were produced for different brain regions (CC, vDG, BLA, NAcc) comparing male and female DEGs between GG and GA genotypes of rs107355669. The comparison is done by calculating the normal approximation of difference in log odds ratio and standard error of overlap between each comparison for each pixel (Pena et al.,

2019). Pixels represent the overlap between the transcriptome of each comparison as noted, with the significance of overlap (-log10(p-value) of a hypergeometric test) color coded. Lower left quadrant includes co-upregulated genes, upper right quadrant includes co-downregulated genes, and upper left and lower right quadrant include oppositely regulated genes (up-down and down- up, respectively).

Weighted Gene Co-expression Network Analysis (WGCNA) (signed network): WGCNA R package was used to detect Grm2 gene networks that correlate with the GG and GA genotypes of rs107355669 (Langfelder & Horvath, 2008; Zhang & Horvath, 2005). Genes with raw RNA read counts less than 10 in more than 90% of samples were excluded. Filtered raw RNA read counts were normalized to read per kilobase per million mappable reads (RPKM) and then transformed to log2 values. Data was checked for excessive missingness. A similarity matrix was constructed by calculating the Pearson correlations of all pairs of genes to define the “scale-free” adjacency

57 matrix. Network topology for thresholding powers from 1 to 20 were analyzed and scale independence and mean connectivity were validated. Threshold of 0.9 was used for all analysis.

Adjacency was transformed into a topological overlap matrix (TOM) by using TOM similarity.

Then the corresponding dissimilarity (dissTOM) was also calculated. Network construction and module detection were analyzed by hierarchical clustering methods of “automatic module detection”, “blockwise module detection” and “manual block detection” to ensure for robust and consistent gene module detection. The parameters for module detection include: maxBlockSize =

24000 for automatic, 12000 for blockwise, minModuleSize = 30, deepSplit = 2, and merCutHeight

= 0.25. Genes of similar expression profiles were clustered into modules. Each module was assigned a unique color and contains a unique gene network. Color “grey” denotes genes that failed to segregate into a specific module.

Relating Grm2 network to rs107355669 genotypes: The first principal component (PC) of each module’s gene expression matrix is referred to as the module eigengene, a single value that represents the highest percent of variance for expression values for all genes in a module. We calculated the correlation between the module eigengene of Grm2 network and rs107355669 genotypes. GG genotype was set as 0 and GA genotype was set at 1 for both sexes (AA genotype was excluded). Afterwards, module membership significance (kME), defined as the log10 transformation of the p-value in the linear regression between gene expression and rs107355669 genotypes, was calculated for of every gene in the Grm2 networks. |kME| > 0.5 was used as a cut- off to filter for important genes of the Grm2 networks when appropriate (Langfelder et al., 2013).

|kME| > 0.8 was used as a cut-off to determine if Grm2 is a hub gene in the network. Supplementary table 2 and Figure 3a explain summary statistics of Grm2 networks from WGCNA per sex and per

58 brain region. Grm2 networks from male and female BLA were selected for further investigation due to their uniquely opposite associations to rs107355669 genotypes and high kME value of

Grm2 (0.88 and 0.87, respectively).

Module preservation analysis from WGCNA: A WGCNA integrated function

(modulePreservation) was used to calculate module preservation statistics of Z summary score (Z score) to evaluate whether a module was preserved in other brain regions of the same sex. Grm2-

BLA network was used as a reference network and other brain regions as test networks. Parameters include: nPermutations = 20. Z scores less than 2 indicated that the module was not preserved. Z scores between 2-10 indicated low to moderate preservation. Z scores greater than 10 indicated strong preservation.

Functional enrichment analysis: To gain further insight into the function of genes in Grm2 networks, we performed the Gene Ontology (GO) enrichment analysis using EnrichR package

(Kuleshov et al., 2016). Biological processes, cellular component, and molecular functions were explored. Functional Mapping and Annotation of Genome-Wide Association Studies (FUMA: https://fuma.ctglab.nl/ ) was used for further exploration such as tissue specificity analysis and hallmark gene set analysis(Gandal et al., 2018; Watanabe, Taskesen, van Bochoven, & Posthuma,

2017). Adjusted p value of 0.05 or FDR of 5% were regarded as significant. Transcription factor binding analysis was done in MetaCoreTM (Clarivate Analytics). Cell-type enrichment analysis used curated gene lists in which a gene was considered a cell-type-specific marker if the average expression met the stringent criteria of > 10X the average background expression levels of the remaining cell types in that region(Mancarci et al., 2017). Enrichment of WGCNA network genes

59 with these cell-type marker lists was determined as an odds ratio of list overlap using GeneOverlap tool in R(Shen L, 2019).

Expression based-polygenic risk score (ePRS)-GRM2-BLA calculation:

The steps for the generation of expression-based polygenic risk score (ePRS) were previously described (Hari Dass et al., 2019; Miguel et al., 2019; Silveira et al., 2017) . The expression-based polygenic risk score of the metabotropic glutamate receptor 2 (ePRS-GRM2) gene network was constructed as follows:

1. We used WGCNA to find co-expressed genes in the Grm2 network of CC, vDG, NAcc,

and BLA separately for male and female rats (Supplementary table 2). After that, we

scanned each Grm2 network for its correlation with rs107355669 genotypes and whether

Grm2 is a hub gene in the network (kME > |0.8|) (Langfelder et al., 2013). Only Grm2-

BLA networks in males and females met these criteria. A cutoff of kME > |0.5| was applied

to filter out background genes in the Grm2-BLA networks.

2. We used BioMart R package to find the human orthologs (GRCh37.p13) for genes in

Grm2-BLA networks (Smedley et al., 2009). To further filter for brain region-specific

genes, we performed a differential search on BrainSpan and only kept the human orthologs

that were overexpressed in the BLA using a threshold of r > 1.2-fold in donor ages from

20 to 40 years old (adulthood) (Miller et al., 2014) for age-specificity and region-specificity.

3. Based on their functional annotation in the National Center for Biotechnology Information,

U. S. National Library of Medicine (NCBI Variation Viewer) and expression data from

Genotype-Tissue Expression project (GTEx), we retrieved all SNPs located within the base

60 pair window of the genes, beta values (ß) that correlate genotypes of each SNP to the tissue-

specific gene expression, and p values of the beta (ß) correlation (G. T. Consortium, 2013).

4. We subjected the list of SNPs to linkage disequilibrium (LD) clumping, which uses the

lowest association p-values from GTEx and removed highly correlated SNPs across 500

kb regions at r2 < 0.2 to ensure that only independent functional SNPs contributed to the

ePRS score.

5. Final ePRS was obtained by summation of all SNPs accounting for the sign of correlation

coefficient between the gene expression and rs107355669 genotypes from WGCNA.

GWAS enrichment analysis: Summary statistics from GWAS meta-analyses of attention deficit hyperactivity disorder (ADHD) (Demontis et al., 2019), anxiety (Otowa et al., 2016), bipolar disorder 2019 (Stahl et al., 2019), Converge major depressive disorder (MDD) (C. consortium,

2015), MDD 2013 (Major Depressive Disorder Working Group of the Psychiatric et al., 2013),

MDD 2018 (Wray et al., 2018), multi-trait analysis of GWAS 2018 (MTAG) (Turley et al., 2018),

Psychiatry Genomics Consortium (PGC) UKBioBank 23andMe MDD 2019 (Howard et al., 2019), schizophrenia (SCZ) 2014 (Schizophrenia Working Group of the Psychiatric Genomics, 2014), suicide attempt in MDD (Mullins et al., 2019), UKBioBank MDD 2019 (Howard et al., 2018), and fasting insulin (Heid & Winkler, 2016) were downloaded from the PGC website

(https://www.med.unc.edu/pgc/downloads). After translating the genes in the Grm2-BLA networks into human orthologs as described above, Spearman correlation was used to correlate the kME values of each gene with the p-value of the corresponding gene from each GWAS summary statistics.

61 Participants: We used data from two population-based studies: UK Biobank (Sudlow et al., 2015) and ALSPAC [Avon Longitudinal Study of Parents and Children] (Boyd et al., 2013; Fraser et al.,

2013; Golding, Pembrey, Jones, & Team, 2001).

The UK Biobank is a UK population study with a total of 501,726 community-dwelling participants between 37 and 73 years of age recruited between 2006 and 2010 through the United

Kingdom National Health Service patient registers (response rate=5.47%, 23 centers). Extensive phenotypic data are available for UK Biobank participants from health records to questionnaires.

We analyzed data on 339,584 individuals (N=157,463/males, N=182,121/females) who had available genotyping data after quality control. The UK Biobank study was conducted under generic approval from the NHS National Research Ethics Service (reference 11/NW/0382). All participants gave full informed written consent. This research has been conducted using the UK

Biobank Resource under application number 41975.

ALSPAC is a geographically based prospective cohort study investigating the health and development of children. Pregnant women residing in three health districts in the South West of

England with an expected date of delivery between 1 April 1991 and 31 December 1992 were eligible to enroll. We analyzed data on 6159 children participants (N=3131/males,

N=3028/females). Ethical approval of the study was obtained from the ALSPAC Law and Ethics

Committee and three Local Research Ethics Committees.

Behavioral outcomes:

In the UK Biobank, extensive phenotypic data were collected from participants using health records, biological sampling, physical measures, and touchscreen tests and questionnaires.

We closely examined participants who had diagnostic records across hospital inpatient records in

62 either the primary or secondary position. Diagnoses are coded according to the International

Classification of Disease version 10 (ICD-10). We focused on diagnosis pertaining to mental and behavioral disorders from F30 to F48 in UK Biobank field #41270, which include diagnosis of mood [affective] disorders and neurotic, stress-related and somatoform disorders, respectively.

In ALSPAC, the Strengths and Difficulties Questionnaire (SDQ) was used. This is a well- validated assessment of child emotional and behavioral problems that show good predictive validity of clinician-related mental health disorders (Goodman, 1997, 2001). SDQ consists of 25 items in five categories: emotional symptoms (e.g. many fear, easily scared), conduct problems

(e.g. often lies or cheats), hyperactivity/inattention (e.g. restless, overactive, cannot stay still for long), peer relationship problems (e.g. picked on or bullied by other children), and prosocial behaviors (e.g. considerate of other people’s feelings). Participants of ages between 47 months and

16.5 years were included in the analysis. The participants were rated on a 3-point scale ranging from 0 (not true) to 2 (certainly true). SDQ prosocial behaviors and total difficulties were visualized.

Genotyping:

Genotyping of UK biobank samples was performed using custom-made arrays. The

Affymetrix UK BiLEVE Axiom was used to sequence 49,950 participants and the Affymetrix UK

Biobank Axiom was used to sequence the remaining participants (438,427). The two arrays have

95% of markers in common. Imputation was performed by Wellcome Trust Centre for Human

Genetics using a combination of the Haplotype Reference Consortium (HRC), 1000 Genomes phase 3 and the UK10K haplotype resources. This yields a total of 92.6 million imputed variants.

More details on genotyping, quality control, and imputation procedures can be found on the UK

63 Biobank website (http://www.ukbiobank.ac.uk/scientists-3/genetic-data/) and Sudlow et al

(Bycroft et al., 2017; Sudlow et al., 2015). The population structure of UK Biobank cohort was evaluated using fastPCA (Galinsky et al., 2016) algorithm for principal component analysis.

ALSPAC offspring were genotyped using the Illumina HumanHap550 quad chip genotyping platforms by 23andMe subcontracting the Wellcome Trust Sanger Institute,

Cambridge, UK and the Laboratory Corporation of America, Burlington, NC, US. Following quality control (individual call rate > 0.97, SNP call rate > 0.95, MAF > 0.01, HWE > 1E-7, cryptic relatedness within offspring IBD < 0.1, non-European clustering individuals removed). Imputation was performed using Impute V2.2.2 against all 2186 reference haplotypes (including non-

Europeans) in the December 2013 release of the 1000 genomes reference haplotypes (Version 1

Phase 3).

The population structure of ALSPAC cohort was evaluated using principal component analysis (SMARTPCA) (Patterson, Price, & Reich, 2006) of all autosomal SNPs that passed the quality control: genotyped SNPs with low allele frequency (MAF) > 5% were subjected to linkage disequilibrium pruning using PLINK 1.9 (Chang et al., 2015) with the following parameters – a window size of 100 kilobases, a step size of 5 SNPs and a variance inflation factor (VIF) threshold of 1.01.

Gene network construction: The Search Tool for the Retrieval of Interacting Genes (STRING: https://string-db.org/) database was used to analyze functional interactions between the corresponding proteins in GRM2-BLA gene network lists (Szklarczyk et al., 2015).

64 Gene expression heat maps: We used publicly available gene expression data from BrainSpan

(https://www.brainspan.org/) to analyze the correlation between the expression of all genes included in the ePRS-GRM2 in the human amygdala at two different time points (Miller et al.,

2014): perinatal (8 pcw to 10 months, N=12/male, N=5/female) and adulthood (20 years to 40 years, N=4/female, N=5/male). Same number of genes is maintained for all comparison. Pearson correlation was performed in R using the heatmaply package (Galili, O'Callaghan, Sidi, & Sievert,

2018).

Statistical analysis:

Animals: Genotype data were received from Genome Quebec Innovation Centre. Minor allele frequency (MAF) for each Grm2 SNP was calculated. SNPs were pruned for linkage disequilibrium (LD) by noting the correlation of genotypic frequency of each SNP (R2 calculated).

The SNP with the highest MAF in the behavioral cohort was chosen for further analyses

(rs107355669). AA genotype was removed from analysis because it is the least frequent genotype.

However, we also conducted the analysis on the full dataset by combining AA and GA genotypes, and the results remained similar. All analyses were performed using the R statistical software, version 3.1.5 (R Development Core Team, 2010). Linear mixed-effect models (LMM) were applied to the behavioral data using the nlme package to investigate SNP main effects. Analyses were performed separately for each sex (N=115/male, N=124/female). Latency to food and latency to feed from the NSF test, novel object exploration from the NOR test, and total sucrose preference from SPT were examined. Body weight, litter size, and maternal LG scores were considered as fixed-effect covariates in all analyses. Time of the day for the NSF test and training objects used for the NOR test were added as fixed effect covariates for their respective models. Litter ID was

65 included as a random effect in all models. Influential points with high change in fitted values were removed before applying LMM. We used a general genetic model (that retains distinct genotype categories: GG and GA). Where appropriate (ANOVA p threshold < 0.05), pairwise post hoc comparisons were completed using the Tukey HSD. Several animals did not feed during the NSF test, and their latency to feed was assigned to 600 seconds (right censoring). A survival analysis

(a Cox proportional hazards model) was conducted to calculate the cumulative probability of not feeding during the NSF test using the Survminer package in R (T, 2020). Body weight, maternal

LG scores, litter size, time of the day for NSF test, and litter ID were included as covariates.

Humans: The statistical analysis of baseline characteristics from UK Biobank and ALSPAC cohorts was performed using t-test and Chi-squared test (Supplementary table 3 to 4). Significance levels for all measures were set at p < 0.05. All models were adjusted for population stratification and analyses were run split by sex. Based on the inspection of the scree plot, the first three principal components were the most informative of population structure in both cohorts and were included in all subsequent analyses. Linear regression is applied to test the association between ePRS-

GRM2 and mental health-related diagnosis in UK Biobank. Generalized estimating equation (GEE) is applied to test for population-average effect of ePRS-GRM2 on mental health outcomes for

ALSPAC children.

66 Result:

Long Evans outbred rats are polymorphic for Grm2 SNPs

Rats from a genotyping cohort were polymorphic for all four SNPs chosen from the Grm2 gene (Fig. 1b). Rs8171225 is in LD with rs199137038 and rs8170799 with rs107355669 (R2 =1)

(Fig. 1b). Since rs107355669 showed higher minor allele frequency (MAF) of 23.67% in the behavioral cohort, the subsequent analyses were focused on this SNP. Out of 256 rats behaviorally assessed, 239 were genotyped successfully and had maternal LG scores < 20. In this cohort, 138

(58%) were homozygous for the G allele (referred here as GG), 89 (37%) were heterozygous (GA), and 12 (5.0%) were homozygous for the A allele (AA). AA genotype group was excluded from the analyses since it was the most infrequent group, but the same analyses presented below were repeated by combining AA and GA genotype and in general provided similar results.

Grm2 SNP rs107355669 associates with anxiety-like behaviors

Behavioral tests were performed according to timeline outlined in (Fig. 1a). Linear mixed- effect regression models were used to assess the association between genotypes of rs107355669 and the behaviors tested for males and females separately (see Methods). For novelty suppressed feeding test (NSF), there was significant main effect of genotypes in males and trending significance in females (Fig. 1c). Male GG genotype had reduced latency to food compared to male GA genotype (beta=-19.19, F (1,61) =5.719, p <0.05) (Fig. 1c). Female GG genotype had increased latency to food compared to female GA genotype with trending significance (beta=14.89,

F (1,70) =3.85, p = 0.053) (Fig. 1c). Significant genotype effects were validated in latency to feed for males (beta=-50.02, F (1,60) = 3.20, p < 0.05), but not in females (beta=17.28, F (1,74) = 0.59, p=0.44) (Fig. 1d). Cox proportional-hazards ratio regression models were used to assess genotypic

67 differences in the cumulative probability of not feeding during NSF test between GG and GA genotypes while accounting for the covariates described. Male rats show significant genotypic difference in the cumulative probability of not feeding during NSF test (Hazards ratio: 0.527, p=0.0129, CI = (0.3179,0.8732)), but not females (Hazards ratio:1.266, p=0.306) (Fig. 1g, h).

For novel object recognition test (NORT), no genotype difference was observed for male rats and female rats in percentage of novel object exploration (p > 0.05 for all) (Fig. 1e). For sucrose preference test (SPT), no genotype difference was observed for male and female total sucrose preference (p > 0.05 for all) (Fig. 1f). Overall, genotypes of rs107355669 significantly associated with anxiety-like behaviors in NSF test, but not short term memory in NORT or anhedonia in SPT.

Rs107355669 genotypes associate with broad transcription patterns

To investigate if rs107355669 genotypes correlate with transcriptional patterns, we performed RNA sequencing from whole tissue punches of adult male and female CC, vDG, BLA, and NAcc in an independent cohort. A SNP located within or near a gene that significantly associates with the gene’s mRNA expression defines a cis-acting expression quantitative trait loci

(cis-eQTL). We performed differential expression analysis to compare rs107355669 genotypes with Grm2 mRNA expression in each brain region (Fig. 2a, d, g, j). LFC of Grm2 is defined as log2(fold change) of Grm2 gene when comparing GG genotype to GA genotype. In CC and vDG,

GG genotype had reduced Grm2 mRNA expression compared to GA genotype in both males and females (Male LFC(Grm2) in CC: 0.6177, p=0.0019. Female LFC(Grm2) in CC: 1.0155, p=0.001.

Male LFC(Grm2) in vDG: 0.6349, p=0.0005. Female LFC(Grm2) in vDG: 0.5345, p=0.0005) (Fig.

2a, d). Interestingly, in nucleus accumbens shell (NAcc) and basolateral amygdala (BLA), female

68 Grm2 mRNA expression were lower in GG genotype compared to GA genotype, but not in males

(Male LFC(Grm2) in NAcc: 0.291, p=ns. Female LFC(Grm2) in NAcc: 0.7375, p=0.0026. Male

LFC(Grm2) in BLA: -0.12, p=ns. Female LFC(Grm2) in BLA: 1.2638, p=0.0006). (Fig. 2g, j). To validate that this observation is not exclusive to rats, we queried the GTEx portal (G. T.

Consortium, 2013). Similar patterns of Grm2 expression were observed between our rat transcriptome data and human RNA expression data from GTEx on a brain region-specific level

(Supplemental Fig. 7,8). Grm2 mRNA expression in CC was the highest in both rats and humans.

GTEx also revealed multiple cis-eQTLs in GRM2 gene regulating its expression levels in hypothalamus and nucleus accumbens. This suggests that the expression and the cis-eQTL regulation of Grm2 are shared observations between rats and humans.

We extended the transcriptome analysis by looking at the significant DEGs comparing GG genotype and GA genotype at uncorrected p value < 0.05 and log2(fold-change (FC) > |0.3875|)

(LFC, corresponding to FC > 30%) for broad transcriptional pattern identification and sex difference (McCarthy et al., 2012; Pena et al., 2019). SNPs that regulate the expression of genes located further away on the same or different chromosomes are termed trans-eQTLs. There was 4-

13 % overlap in DEGs between sexes for all brain regions investigated (Fig. 2b, e, h, k). Both male and female BLA had the highest number of DEGs with the highest percentage of overlap (8.4% and 13%, respectively) (Fig. 2m, n). We also investigated if there are common differentially- expressed genes between all brain regions. Females had six commonly differentially expressed genes and males had four (Fig. 2m, n).

We complemented the DEG analysis with a two-sided rank-rank hypergeometric overlap

(RRHO) analysis to identify patterns and strength of genome-wide overlap in a threshold-free manner (Cahill et al., 2018). RRHO revealed broad transcriptional patterns and sex differences in

69 differentially expressed genes across all brain regions. No directional specificity in the overlap of

DEGs was observed between male and female in CC and in vDG (Fig. 2c, f). There is a strong overlap in genes regulated in the same direction between DEGs in male and female NAcc, indicating similar directional regulation in transcription of genes by genotypes of rs107355669

(Fig. 2i). In BLA, the overlap in gene expression regulation occurred in opposite directions between sex (Fig. 2l), as indicated in upper left and lower right quadrants. Overall, these unique transcriptional patterns and sex differences associated with rs107355669 genotypes suggest altered global gene expression in the emotional circuitry. Overlapping patterns in DEGs associated with rs107355669 genotypes observed in the BLA can be further investigated for anxiety-like behaviors due to the strongest sex difference.

Rs107355669 genotypes associate with Grm2 gene network in BLA

We hypothesized that if genotypes of rs107355669 alter broad patterns of transcription, then the expression of Grm2 gene network could also be influenced. Using Weighted Gene Co- expression Network Analysis (WGCNA), we clustered genes into networks based on their expression and correlated the Grm2 network to rs107355669 genotypes (Supplementary table 2).

All Grm2 networks positively associated with genotypes of rs107355669 except for male Grm2 network in the BLA (Fig. 3a). Grm2 also has the highest absolute kME values in male and female

BLA Grm2 networks (0.88 and 0.87, respectively) (Fig. 3a), indicating that Grm2 is a driver gene.

Overlap analysis shows that genes in male and female Grm2-BLA networks significantly overlap

(p < 0.0001) (Fig. 3b).

In female BLA, Grm2 is in the red module (1163 genes) that positively correlated with rs107355669 genotypes (R=0.42, uncorrected p < 0.0001). This indicates that the Grm2 network

70 expression is upregulated in GA genotype compared to GG genotype in females. In male BLA,

Grm2 is in the turquoise module (2861 genes) that negatively correlated with rs107355669 genotypes (R=-0.32, uncorrected p < 0.0001). This indicates that the module is downregulated in

GA genotype compared to GG genotype in males. Overlap of male and female Grm2 networks in the BLA indicate 727 genes in common (Fig. 3c). 497 out of 727 genes (68%) have kME > 0.5, indicating an overlap between the hub genes of both networks. However, 484/727 genes are upregulated (97%) and 13/727 genes are downregulated (2.6%) in female Grm2 network; 476/727 genes are downregulated (95.7%) and 21/727 genes are upregulated (4.2%) in male Grm2 network.

This validates that the Grm2-BLA networks have opposite expression patterns associated with the genotypes of rs107355669 depending on sex.

WGCNA module preservation analysis were done to explore if the Grm2-BLA network is preserved in other brain regions of the same sex. In both males and females, Grm2-BLA networks are weakly preserved in CC (Zsummary < 3), moderately preserved in vDG (Zsummary < 10), and strongly preserved in NAcc (Zsummary > 10) (Supplementary Fig. 1a-f).

Grm2-BLA networks converge on functional pathways but are distinct in gene components

We used three complementary analyses to assess how genotypes of rs107355669 affect molecular regulation in the BLA: (1) enrichment annotation for gene ontology of biological processes, molecular functions, and cellular components, (2) cell-type specificity marker overlap to see cell-type specific transcriptional regulation, (3) transcription factor binding site analysis to predict differential transcriptional regulators. We hypothesized that although similar biological processes may be affected, gene components of each network might differ between male and female BLA. Gene ontology enrichment of Grm2 network in males and females shows functional

71 convergence with known biochemical functions of the glutamate receptor. Overlapping biological processes include modulation of chemical synaptic transmission and transmembrane receptor protein signaling pathway (adjusted p-value < 0.05) (Fig. 3f, i). Molecular functions include kinase activities, glutamate receptor activities, and transporter binding activities (adjusted p-value < 0.05)

(Fig. 3f, i). Cellular components are enriched for cation channel complex and dendrites (adjusted p-value < 0.05) (Fig. 3f, i).

We asked if Grm2 rs107355669 genotypes associate with any cell-type specific transcriptional changes. Enrichment analysis using curated cell type-specific marker lists

(Mancarci et al., 2017) in the amygdala revealed significant overlap between both Grm2 networks and glutamatergic- and pyramidal- cells in the BLA (Fig. 3j). We next conducted transcription factor (TF) binding analysis in MetaCore TM to evaluate both modules for overrepresentation of known TF binding sites in silico. TF binding analysis revealed little overlap in TF binding sites between both modules (Fig. 3e, h). Male Grm2-BLA network genes are enriched for Creb1, Nrf2, and Sox17 binding sites, which play important roles in cell survival and extensively implicated in human MDD and animal depression modules (Blendy, 2006; Hashimoto, 2018). Female Grm2-

BLA network genes are most commonly enriched for Bmal1 binding site, a TF known to co-bind other circadian rhythm genes such as CLOCK to regulation gene expression (Bellet, Vawter,

Bunney, Bunney, & Sassone-Corsi, 2011). Disruption in Bmal1 activity is found in the development of psychiatric disorders and metabolic comorbidity (G. Y. Li, Wang, & Chen, 2019).

Overall, TF binding analysis suggest distinct transcription regulation between both modules.

Grm2 gene network is preserved in humans

72 We examined if Grm2-BLA networks identified in rat transcriptome are preserved after translating to human orthologs. Enrichment analysis of male and female GRM2-BLA networks

(1862 genes and 635 genes after filtering for |kME| >0.5, respectively) on several genome-wide association studies (GWAS) related to psychiatry disorders was performed (Fig. 4a). The female

GRM2 network was significantly enriched in genes identified in Psychiatric Genomics

Consortium(PGC)-UKBioBank-23AndMe-major depressive disorder (MDD) 2019 (Spearman, p=0.04), MDD 2013 (Spearman, p=0.02), and schizophrenia (SCZ) 2013 (Spearman, p=0.0002)

(Fig. 4a). The male GRM2 network was significantly enriched in genes identified in Bipolar

Disorder 2018 (Spearman, p=0.036), PGC-UKBioBank-23AndMe-MDD 2019 (Spearman, p=0.025), MDD 2013 (Spearman, p=0.015), Suicide attempt in MDD 2019 (Spearman, p=0.016),

Schizophrenia 2013 (Spearman, p=0.002), and UKB-MDD 2019 (Spearman, p=0.008) (Fig. 4a).

Together, these results suggest that both GRM2-BLA networks contain genes enriched in major depressive disorder and schizophrenia (lowest p-value), with the male GRM2 network enriched for more psychiatric disorders than females.

We used BrainSpan expression data to filter for genes over-expressed in the amygdala at

LFC > 1.2 (corresponding for FC > 130%) and over-expressed in adulthood (20-40 years old) to ensure region- and age-specificity, which reduced the networks to 127 genes for females and 279 genes for males for ePRS-GRM2-BLA construction. Overexpression in amygdala was confirmed by tissue specificity analysis in FUMA (Supplementary Fig. 2) (Watanabe et al., 2017). Gene expression heat maps confirmed strong co-expression patterns in both male and female GRM2 networks with relatively little moderation between infancy and adulthood (Fig. 4b-e) (Miller et al.,

2014). Hallmark gene set analysis reveals that both networks are enriched for genes in mTORC1 signaling pathway and estrogen response pathway (FDR < 0.0001) (Supplementary Fig. 3).

73 STRING confirmed more significant protein-protein interactions (PPI) in both networks than the background, suggesting the existence of a cohesive network (Supplementary Fig. 4). PPI predicts that epidermal growth factor receptor (EGFR) as the hub gene in both networks (Supplementary

Fig. 4). A differential search using GTEx validates the correlation of expression between EGFR and GRM2 in the amygdala (Supplementary Fig. 4). EGFR is a transmembrane post-synaptic receptor and it plays an essential role in downstream signaling cascades that promotes DNA synthesis and cell proliferation (Wee & Wang, 2017).

ePRS-GRM2-BLA predicts mental health problems

We constructed expression-based polygenic risk scores (ePRS) for male and female

GRM2-BLA networks to capture the expression of the network (Supplementary Fig. 5, methods).

Baseline characteristics between low and high ePRS-GRM2-BLA groups (median split) were compared by testing for differences in means of the main childhood confounding variables using

Student’s t-test for independent samples for continuous variables and Chi-square test for categorical variables. There were no significant differences between the ePRS-GRM2-BLA groups on main childhood confounding variables for both cohorts except for male participants in UK

Biobank: breastfed as a baby and maternal smoking around birth significantly differed between ePRS-GRM2-BLA groups (Supplementary table 3-6).

We first asked whether ePRS-GRM2-BLA would predict mental health-related problems in the UK Biobank dataset in a sex-specific manner as we observed in our rodent cohorts, as the age of participants in this dataset best matches the age range in rodents. We used ICD-10 clinical diagnosis of F30-F39 and F40-F48 disorders (mood [affective] disorders and Neurotic, stress- related and somatoform disorders, respectively). We observed that in females, low ePRS-GRM2-

74 BLA significantly associated with higher risk for ICD-10 F30-F39 mood [affective] disorder clinical diagnosis (retrieved from data field 41270) (OR=1.049, 95% CI = [1.003, 1.096], p < 0.05, n=182,121), but not with F40-F48 neurotic, stress-related and somatoform disorder clinical diagnosis (p > 0.05) (Fig. 4f). However, in males, ePRS-GRM2-BLA did not significantly associate with any mental health disorder-related diagnosis from F30 to F48 (p > 0.05 for all) (Fig.

4g).

Based on the gene expression heat maps constructed for GRM2-BLA networks during the perinatal and adulthood periods using expression data from GTEx, we observed a consistency in the expression patterns of both networks throughout the life course (Fig. 4b-e). We then asked if ePRS-GRM2-BLA exerts influences on relevant mental health-related behaviors during developmental periods by testing the association of ePRS-GRM2-BLA in the younger participants of the ALSPAC [Avon Longitudinal Study of Parents and Children] cohort. Using a generalized estimation equation (GEE) model on Strengths and Difficulties Questionnaire (SDQ) outcomes, we observed that the female ePRS-GRM2-BLA significantly associated with prosocial social scores (beta=-0.13, se=0.046, p < 0.01, n=3,028) (Fig. 4h). Non-significant associations were found for all SDQ outcomes with male ePRS-GRM2-BLA (p > 0.05 for all) (Fig. 4i). Translational analyses of relevant human behaviors across different stages of life course suggested the importance of the glutamatergic network in the vulnerabilities to mood disorders in a sex-specific manner.

75 Discussion:

The glutamatergic system is implicated in the pathophysiology of multiple psychiatric disorders. A key regulator of presynaptic glutamate release is the metabotropic glutamate receptor

(mGluR2), encoded by the GRM2 gene. Several studies have shown the importance of mGluR2 in antidepressant functions (Zanos et al., 2019), stress susceptibility and reactivity (Aujla, Martin-

Fardon, & Weiss, 2008; Jaramillo, Randall, Frisbee, Fisher, & Besheer, 2015), and psychiatric disorder-related behaviors (Yoshimizu, Shimazaki, Ito, & Chaki, 2006). Elucidating the transcriptomic regulations of the glutamatergic system can provide important clues underlying disease neurobiology and provide novel targets of therapeutic developments. Here, we performed behavioral phenotyping, SNP genotyping, and transcriptional profiling across four brain regions in Long-Evans outbred rats. On a single polymorphism level, we first demonstrated that SNP rs107355669 in the Grm2 gene associate with anxiety-like behaviors in standardized rodent test.

At transcriptomic level, we identified sex-specific cis- and trans-eQTL influences of rs107355669 in the emotional circuitry. We extended the analyses by showing that an independent, cohesive

BLA-based Grm2 gene network was associated with rs107355669 genotypes and conserved functionally cross-species. Finally, we used the Grm2 network to construct an expression-based polygenic risk score (ePRS) in human samples, and showed that the BLA-specific ePRS-GRM2 network is key in identifying individual vulnerability to mood disorders.

Our rodent study suggested that the genotypes of rs107355669, GG and GA, significantly associated with anxiety-like behaviors in measurements including latency to food, latency to feed, and cumulative probability of not feeding during the NSF test. This is not surprising since multiple previous studies have discovered a Grm2-associated cys407* mutation in Wistar rats that are selectively-bred for alcohol dependence, anxiety-like, and risk-seeking behaviors. Although

76 rs107355669 is located further downstream to the cys407* mutation, it is possible that the two variations are in partial linkage disequilibrium (LD). Interestingly, we also observed sex differences in genotypic associations of these behaviors, suggesting that the genotypes of rs107355669 may have sex-specific biological implications. Previous studies have shown that eQTLs tend to be either tissue-specific or act universally across the brain, with few eQTLs regulating in some but not all tissues. Using transcriptomic profiling, our data suggests that rs107355669 can act as brain region- and sex-specific cis- and trans-eQTL across the emotional circuitry, by not only influencing the mRNA levels of Grm2 but also the expression of distant genes. To our surprise, Grm2 is a hub gene of its network in the BLA, which suggests that variations in expression levels of Grm2 may exert functional influence by driving the activity of a brain region-specific network. We decided to focus on the eQTL effects of rs107355669 in the

BLA, because we identified sex differences in the genotypic associations with Grm2 mRNA level, global differential expression patterns (Fig. 2j, l), and Grm2 network activity, which may provide some explanation for the sex-specific genotype associations in NSF test outcomes.

The biological process enrichments of the Grm2-BLA networks in both sexes highly converged and paralleled the known biochemical roles of the glutamate receptor. This is not a surprising result, as the BLA is a key relay structure in the emotional circuitry, and 95% of BLA neurons are glutamatergic and their abnormal hyperactivity from BLA to central nucleus of amygdala (CeA) has been implicated in the etiology of several mood disorders, including depression and anxiety (Roozendaal, Koolhaas, & Bohus, 1997),(Prager, Bergstrom, Wynn, &

Braga, 2016). Interestingly, from WGCNA module preservation analysis, BLA-based Grm2 network is highly conserved in nucleus accumbens shell (NAcc) for both sexes. As a functional central structure between multiple brain regions, NAcc plays a modulative role in the flow of

77 information from the amygdala to other regions. It is also extensively involved in regulatory functions associated with motivation and efforts (McCool, Christian, Diaz, & Lack,

2010),(Shirayama & Chaki, 2006). The involvement of Grm2-BLA networks in the output of BLA was validated in the cell-type marker specificity overlap analysis (Fig. 3j), where both male and female Grm2-BLA networks are not only enriched for markers of glutamatergic cells but also pyramidal cells, a key output cell type of the amygdala. Alterations in the expression of BLA- specific Grm2 network may influence its output to NAcc in relation to cognitive, emotional, and psychomotor functions, although this needs to be further tested. In contrast to the functional convergence, sex differences between the Grm2-BLA networks were highlighted by the opposite correlations between network expression in association with rs107355669 genotypes and the enrichment for different transcription factor binding sites. It is important to note that major psychiatric disorders, such as major depressive disorder and anxiety, are associated with highly sex-specific characteristics in phenotypes and gene expression.

A prominent feature of our study is the cross-species conservation in the functional relevance of the GRM2 network shown in the GWAS enrichments. The expression-based polygenic risk score (ePRS) method is a novel translational approach that captures the expression of the GRM2 network and its region- and age-specific functional relevance. We highlight that this approach is focused on alterations of biological processes that underlie the endophenotypic differences in healthy, community-based samples. We showed that in the UK Biobank cohort, female subjects with low ePRS-GRM2-BLA network expression have higher probability of a clinical diagnosis of mood [affective] disorders. These results are aligned with our findings of anxiety-like behaviors in rodents, where GG genotyped female rats displayed lower Grm2-BLA network expression and elevated anxiety-like behaviors in the latency to food outcome in NSF test.

78 Functional relevance of the GRM2-BLA network during the developmental periods were explored using the ALSPAC cohort. EPRS-GRM2-BLA significantly associated with prosocial outcome of the SDQ in female subjects between 47 months to 16.5 years of age, suggesting that this network may contribute in shaping the developmental trajectory of mental health-related behaviors from birth. Our results highly correspond to the current publications emphasizing sex differences in mood disorder susceptibility, with possible causal mechanisms attributed to a higher sensitivity to glutamate in females, resulting in hyper-responsiveness to anxiety-provoking stimuli (Wickens,

Bangasser, & Briand, 2018).

Cross-species analysis like the one performed here are limited on the capacity and meaningfulness of the equivalence of human and rat gene expression. Beyond species specificities, other factors such as heterogeneous phenotypes, environmental variations, and lack of network preservation are among the many possible explanations for divergences observed between male rodents and human participants. However, the approach used in this study – translating from a rat transcriptome Grm2 co-expression network to human orthologs through the implementation of bioinformatics and observing the conservation of the network on a functional level – is noteworthy.

The fact that we could identify a single gene of importance whose variant acts as a main effect in rodent behaviors and whose network shows functional preservation and main effect associations with mood disorders in multiple independent human cohorts testifies the importance of GRM2 in the regulation of the glutamatergic system in the BLA specifically for females. The integration of genomic and transcriptomic techniques used in this study offers a thorough investigation of potential association between genetic variations and gene expression, which may suggest a much broader impact for potential therapeutic targets in stress and mood disorders. Rather than directly targeting ionotropic glutamate receptors which would result in the mediation of glutamatergic

79 signaling with fast changes in neuronal excitability and known adverse effects (i.e. ketamine)

(Kavalali & Monteggia, 2012; Short, Fong, Galvez, Shelker, & Loo, 2018), the metabotropic glutamate receptors could offer a much safer profile and rapid therapeutic actions through the modulation of the same signaling system.

Recent advances in sequencing technologies have linked hundreds of genetic variants to the phenotypes of psychiatric disorders in human cohorts. Therefore, it is our primary interest to understand the biological mechanisms of how these genetic variants influence brain development without introducing drastic genetic manipulations. Our rodent findings only show a static snapshot of regulatory differences in brain transcription associated with rs107355669 genotypes. Future studies should focus on investigating the dynamic expression of gene networks across lifespan and environmental variations. Genotypes can influence epigenetic regulations, and these alterations can happen early in life, interacting with environmental adversities and enrichments during a critical time window long before the display of phenotypes. This will ultimately result in individual differences in vulnerability and susceptibility to mental health disorders. Therefore, a thorough characterization of baseline-level behaviors, genome, and transcriptome in rodent populations would be essential to dissect the relationship between genetic variants, epigenetic modifications, gene expression, environmental variations, and the final display of psychiatric phenotypes. As we move forward in the era of sequencing, studies that focus on the biological importance of common genetic variants and the comorbidities between psychiatric disorders on genotype- and genetic- levels will shed light on the underlying complex pathophysiology.

80 Figures:

Maternal A observation Weaning Behavior tests Sacrifice Tissue collection

Birth PND 1 PND 6 PND 21 PND 70 PND 120 PND 125 +

Novelty suppressed Open field Novel object Sucrose preference feeding test habituation recognition test test

PND 70 PND 72 - PND 73 PND 74 PND 76 - PND 78 B

SNP ID Rs107355669 Rs8170799 Rs199137038 Rs8171225 R2 1 1 0.14 0.14

C D G

E F H

Male GA genotype Male GG genotype Female GA genotype Female GG genotype

81 Figure 1: Rs107355669 genotypes associate with anxiety-like behaviors, but not short term memory or anhedonia.

A. Schematic timeline of behavioral test cohort. B. Schematic DNA structure of rat Grm2 gene with positions of candidate SNPs labeled in red arrows. The table below represent genotype frequencies from previously unpublished data. R2 indicates squared correlation between each SNP in comparison to Rs107355669. Novelty suppressed feeding test results in C. Latency to food

(seconds), D. Latency to feed (seconds), G, H. Cumulative probability of not feeding during test.

E. Novel object recognition test. F. Sucrose preference test. Blue bars = male rats (N=69/GG,

N=38/GA). Orange bars = female rats (N=69/GG, N=51/GA). *: Tukey p-value < 0.05.

82

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Figure 2: Rs107355669 genotypes associate with broad transcriptional patterns within male and female emotional circuitry.

Exploratory analyses of cis-eQTL and trans-eQTL effects of rs107355669 GG and GA genotypes in A-C: CC D-F: vDG, G-I: NAcc, and J-L: BLA. Sex-split Grm2 mRNA expression comparison

83 between rs107355669 genotypes in A: CC, D: vDG, G: NAcc, and J: BLA. Differential gene expression analysis (uncorrected p < 0.05, LFC > 30%) of rs107355669 genotypes (GG and GA) in Venn diagrams (B: CC, E: vDG, H: NAcc, K: BLA). N.s.=Non-significant. *0.01

0.001

(up-down and down-up, respectively). M, N: Gene along each axis are sorted from most to least significantly regulated. Venn diagrams of common differentially expressed genes between GG and

GA genotypes of rs107355669 in M, females and N, males for all four brain regions.

84 A

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Male GRM2 Female GRM2 network network

85 D E

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86 J Female Grm2-BLA

Male Grm2-BLA

Figure 3: Rs107355669 genotypes associate with Grm2 network expression in BLA.

Bubble graph representing summary results from WGCNA (A). Each bubble represents a Grm2 network. Bubble size indicates number of genes in each Grm2 network. X axis: Grm2 kME value in each Grm2 network. Y-axis: Correlation of each network to rs107355669 genotypes. Male

Grm2 networks are blue. Females are red. NAcc: nucleus accumbens shell. vDG: ventral dentate gyrus. CC: cingulate cortex. BLA: basolateral amygdala. B: Heat map displaying the odds ratio

(color scale) and p-value (displayed number) of the overlap of genes between each Grm2 network.

C: Common genes between male and female BLA Grm2 network. D, G: Scatterplots of Grm2 networks in male and female BLA. Each gene is represented by a dot. X-axis: Gene significance within network. Y-axis: Correlation with rs107355669 genotypes. Regression line: correlation between gene expression in Grm2 network and rs107355669 genotypes. F, I: Predicted biological processes, molecular functions, and cellular components terms enriched in male and female Grm2-

BLA networks. Top female biological processes: (GO:0050770) Regulation of axonogenesis,

(GO:0007169) Transmembrane receptor protein tyrosine kinase signaling pathway, (GO:0007411)

Axon guidance, (GO:0007268) Chemical synaptic transmission, (GO:0048846) Axon extension involved in axon guidance. Top female molecular functions: (GO:0004714) Transmembrane receptor protein tyrosine kinase activity, (GO:0031434) Mitogen-activated protein kinase kinase binding, (GO:0004709) MAP kinase kinase kinase activity, (GO:0008066), Glutamate receptor activity (GO:0030695) GTPase regulator activity. Top female cellular components: (GO:0030424)

87 Axon, (GO:0030425) Dendrite, (GO:0034703) Cation channel complex, (GO:0031410)

Cytoplasmic vesicle, (GO:0034774) Secretory granule lumen. Top male biological processes:

(GO:0090382) Phagosome maturation, (GO:0048013) Ephrin receptor signaling pathway,

(GO:0007411) Axon guidance, (GO:0051452) intracellular pH reduction, (GO:0007409)

Axonogenesis. Top male molecular functions: (GO:0046961) Proton-transporting ATPase activity,

(GO:0036442) Hydrogen-exporting ATPase activity, (GO:0015078) Hydrogen ion transmembrane transporter activity, (GO:0019905) syntaxin binding, (GO:0070567)

Cytidylytransferase activity. Top male cellular components: (GO:0099513) Polymeric cytoskeletal fiber, (GO:0005874) Microtubule, (GO:0034703) Cation channel complex,

(GO:0005765) Lysosomal membrane, (GO:0030424) Axon. E, H: Top predicted transcription factor binding sites enriched in male and female Grm2-BLA networks. J: Enrichment of the Grm2-

BLA network genes in cell-type specific markers of BLA. Significance of each enrichment (p- value adjusted) is indicated in each cell and shaded by degree of odds ratio.

88 A

B C

D E

89

Figure 4: GRM2-BLA networks preserved in human

A: Enrichment of GRM2-BLA networks in different GWAS summary statistics. Red line at p value < 0.05. B-E: Heat maps of GRM2-BLA network co-expression in B. male perinatal period

(top left), C. male adulthood period (top right), D. female perinatal period (bottom left), and E. female adulthood (bottom right). Expression data extracted from BrainSpan. F, G: Measurements clinical diagnosis using ICD-10 for F30-F39: mood [affective] disorders, F32: depressive disorder,

F40-F48: Neurotic, stress-related and somatoform disorders in F. female participants and G. male participants of the UK Biobank cohort. Female BLA-based ePRS-GRM2 significantly associated with F30-F39: mood [affective] disorders, whereas male ePRS-GRM2-BLA did not. N/females=

182,121. N/males=157,463. H, I: Measurements of SDQ outcomes (i.e. prosocial and total difficulties) between 47 months to 16.5 years of age for H. female children and I. male children of the ALSPAC cohort. Female BLA-based ePRS-GRM2 significantly associated with prosocial outcome, whereas male ePRS-GRM2-BLA did not. N/females=3,131, N/females=3,028.

90 CHAPTER 3: SUPPLEMENTARY MATERIAL

Supplemental Methods:

Calculation of NSF ratio: NSF ratio = latency to feed during NSF / latency to feed during home cage feeding test.

Supplemental Table 1: Information on 59 SNPs that passed filtering thresholds.

SNP ID Gene name Function Chr: Coord Gene ID Alleles

rs198916801 Abcb4 splice region 4:22397643 ENSRNOG00000008012 G/A

rs8174265 Ace missense 10:94173365 ENSRNOG00000062101 G/A

rs13453359 intron 20:20449326 ENSRNOG00000053288 C/T Ank3 rs65559393 intron 20:20366334 ENSRNOG00000053288 A/C

rs64550502 Avpr1a downstream 7:67340449 ENSRNOG00000004400 A/T

rs107254030 Cacna2d1 upstream 4:16132563 ENSRNOG00000033531 G/A

rs13448419 Creb3l1 synonymous 3:80893478 ENSRNOG00000005413 C/G

rs105692493 Creb3l2 splice region 4:64874047 ENSRNOG00000012826 A/G

rs105623791 Ctnnd2 intron 2:83576310 ENSRNOG00000010649 C/T

rs105765196 Dcc synonymous 18:66645651 ENSRNOG00000033099 A/G

rs105271298 Drd1 upstream 17:11100256 ENSRNOG00000023688 A/T

rs104907678 downstream 8:53745708 ENSRNOG00000008428 A/T Drd2 rs107017253 3 prime UTR 8:53742670 ENSRNOG00000008428 A/G

rs197303797 5 prime UTR 7:41475527 ENSRNOG00000023896 G/T Dusp6 rs8163278 3 prime UTR 7:41478691 ENSRNOG00000023896 A/G

rs107326493 Elavl2 downstream 5:109498767 ENSRNOG00000006853 G/A

rs8165911 Esr1 3 prime UTR 1:41593812 ENSRNOG00000019358 A/C

rs8161939 Fkbp5 3 prime UTR 20:7976923 ENSRNOG00000022523 C/T

rs197882879 synonymous 5:143631602 ENSRNOG00000008992 C/T Grik3 rs64047160 intron 5:143689539 ENSRNOG00000008992 G/A

rs106033600 intron 1:81921821 ENSRNOG00000020310 G/T Grik5 rs198765613 synonymous 1:81942493 ENSRNOG00000020310 A/G

rs107355669 3 prime UTR 8:115345314 ENSRNOG00000013171 A/G Grm2 rs199137038 5 prime UTR 8:115356380 ENSRNOG00000013171 A/G

rs13453062 Grm5 downstream 1:151783605 ENSRNOG00000016429 A/G

rs66072185 Lrfn5 5 prime UTR 6:83255457 ENSRNOG00000005550 C/T

rs198565239 MaoA intron X:6576398 ENSRNOG00000002848 G/C/A/T

rs13451047 Mef2c 3 prime UTR 2:11819958 ENSRNOG00000033134 A/C

rs65866395 Msra intron 15:47537832 ENSRNOG00000012440 T/C

rs106440339 Nap1l4 3 prime UTR 1:216715987 ENSRNOG00000020615 G/T

rs104902078 Negr1 3 prime UTR 2:263650157 ENSRNOG00000021410 A/G

91 rs198862086 Nr3c1 3 prime UTR 18:31732120 ENSRNOG00000014096 A/T rs106335124 Olfm4 downstream 15:62433754 ENSRNOG00000013280 A/G rs105026499 Oprm1 downstream 1:43705042 ENSRNOG00000018191 A/G rs198098398 Otx2 downstream 15:25513284 ENSRNOG00000056186 C/T rs105578346 Pclo intron 4:16609824 ENSRNOG00000005726 A/T

rs8171128 Penk non-coding exon 5:17063370 ENSRNOG00000008943 C/T

rs13454221 Pik3ca 3 prime UTR 2:118859425 ENSRNOG00000056371 A/G rs107056754 Pik3cg synonymous 6:51483890 ENSRNOG00000009385 A/C rs106516137 Ppp2r1a splice region 1:60733083 ENSRNOG00000011282 A/G

rs8143394 Ppp2r2a 3 prime UTR 15:43674150 ENSRNOG00000011158 G/C rs198664367 Prkaa1 3 prime UTR 2:54890546 ENSRNOG00000012799 A/C

rs8156095 Prkaca 3 prime UTR 19:25118521 ENSRNOG00000005257 A/G rs105552381 Prkg1 intron 1:248997326 ENSRNOG00000060655 C/T

rs8173337 Rims1 downstream 9:28440533 ENSRNOG00000011000 C/T rs106838668 intron 3:51733192 ENSRNOG00000005018 T/C Scn2a rs107365153 splice region 3:51785022 ENSRNOG00000005018 A/G rs106922835 Sdk1 upstream 12:15099165 ENSRNOG00000001103 C/G rs106093165 5 prime UTR 10:102576655 ENSRNOG00000024711 A/G Sdk2 rs197246478 synonymous 10:102349102 ENSRNOG00000024711 A/G rs198535812 Slc4a1 intron 10:90301669 ENSRNOG00000020951 A/G rs106116247 Slc6a15 intron 7:45374877 ENSRNOG00000027468 A/G rs106755836 Slc6a3 3 prime UTR 1:32321923 ENSRNOG00000017302 A/G

rs8154473 Slc6a4 synonymous 10:63171823 ENSRNOG00000003476 A/G rs106136002 Sorcs3 synonymous 1:268516290 ENSRNOG00000028832 C/T

rs8174549 Sox5 synonymous 4:178349975 ENSRNOG00000027869 G/A rs106927726 Tcf4 intron 18:65492582 ENSRNOG00000012405 A/G rs105972323 Th synonymous 1:216073400 ENSRNOG00000020410 C/T

rs66150982 Vrk2 intron 14:110789883 ENSRNOG00000007864 T/C

92 Supplementary Table 2: SNP main effect associations with cognitive-emotional phenotypes

Male Female Behavioral tests Outcomes SNP ID Gene Location ANOVA P SNP ID Gene Location ANOVA P

rs105578346 Pclo intron 0.035

Latency to rs107355669 Grm2 3 prime UTR 0.02

food (seconds) rs198535812 Slc4a1 intron 0.034

rs106093165 Sdk2 5 prime UTR 0.049

rs106922835 Sdk1 upstream 0.005 rs13448419 Creb3l1 synonymous 0.0092

Latency to rs106838668 Scn2a intron 0.013 rs8161939 Fkbp5 3 prime UTR 0.0474 Novelty feed (seconds) rs105026499 Oprm1 downstream 0.045 rs107365153 Scn2a splice region 0.0155 suppressed feeding rs107355669 Grm2 3 prime UTR 0.033 test rs197882879 Grik3 synonymous 0.03 rs197882879 Grik3 synonymous 0.007 NSF ratio rs104902078 Negr1 3 prime UTR 0.02 rs64047160 Grik3 intron 0.0041

rs106838668 Scn2a intron 0.035 rs13448419 Creb3l1 synonymous 0.001

rs106922835 Sdk1 upstream 0.006 rs8161939 Fkbp5 3 prime UTR 0.01 Survival NSF rs107355669 Grm2 3 prime UTR 0.013

rs107365153 Scn2a splice region 0.039

rs106516137 Ppp2r1a splice region 0.032 rs107326493 Elavl2 downstream 0.0086 Novel object Novel object rs107017253 Drd2 3 prime UTR 0.039 rs197882879 Grik3 synonymous 0.016 recognition test exploration rs8173337 Rims1 downstream 0.044 rs199137038 Grm2 5 prime UTR 0.045

rs106516137 Ppp2r1a splice region 0.0021 rs105026499 Oprm1 downstream 0.023

rs106755836 Slc6a3 3 prime UTR 0.022 rs106116247 Slc6a15 intron 0.0079

rs107365153 Scn2a splice region 0.0059 rs106516137 Ppp2r1a splice region 0.0023 Sucrose preference Total sucrose rs13453359 Ank3 intron 0.034 rs197303797 Dusp6 5 prime UTR 0.017 test preference rs65559393 Ank3 intron 0.015 rs65866395 Msra intron 0.027

rs8174549 Sox5 synonymous 0.0053 rs8154473 Slc6a4 synonymous 0.035

rs8174549 Sox5 synonymous 0.049

Behavioral tests were performed according to timeline depicted in Chapter 2: The

Manuscript Figure 1A. The 192 SNPs selected for the current study were subjected to minor allele frequency (>5%) and linkage disequilibrium (100%) filtering thresholds. Only 59 SNPs passed the filters as listed in the Supplemental Table 1. Linear mixed-effect regression models were used to assess the associations between genotypes of each SNP and the behaviors tested for males and females separately (see Chapter 2 Methods). Information regarding the significant SNPs for each phenotype is presented in Supplemental Table 2 (see above).

93 For novelty suppressed feeding test (NSF), four SNPs significantly associated with latency to food in males but no SNP was significant in females. The SNP rs107355669 in the metabotropic glutamate receptor 2 (Grm2) gene showed the most significant association with latency to food in males (beta=-19.19, F (1,61) = 5.72, p=0.019). This is followed by SNPs located in solute carrier family 4 member 1 gene (Slc4a1) (beta=15.76, F (1,62) = 4.68, p=0.034), and then by piccolo presynaptic cytomatrix protein (Pclo) (beta=-24.91, F (1,67) =4.63, p=0.035), and lastly by sidekick cell adhesion molecule 2 gene (Sdk2) (beta=15.59, F (1,64) = 3.99, p=0.049).

Four SNPs and three SNPs significantly associated with latency to feed measured in the

NSF test in males and females, respectively. SNP rs106922835 located in the upstream region of sidekick cell adhesion molecule 1 gene (Sdk1) showed the most significant association (beta=-

88.85, F(1,67)=8.35, p=0.0052) in latency to feed in males, which was followed by rs106838668 in the sodium voltage-grated channel alpha subunit 2 gene (Scn2a) (beta=-68.24, F(1,65)=6.43, p=0.014), then by rs107355669 in the glutamate receptor 2 gene (Grm2) (beta=-50.02,

F(1,60)=3.20, p=0.033), and lastly by rs105026499 in the opioid receptor mu 1 gene (Oprm1)

(beta=-96.32, F(2,65)=3.252, p=0.045). In females, rs13448419 in the CAMP responsive element binding protein 3 like 1 gene (Creb3l1) showed the most significant association with latency to feed (beta=-92.21, F(2,75)=4.99, p=0.0092). This is followed by rs107365153 in the sodium voltage-grated channel alpha subunit 2 gene (Scn2a) (beta=105.28, F(2,76)=4.41, p=0.016), and then by rs8161939 in the FKBP prolyl isomerase 5 gene (Fkbp5) (beta=82.69, F(2,62)=3.20, p=0.047).

Two SNPs and two SNPs significantly associated with NSF ratio in males and females, respectively. In males, rs104902078 in the neuronal growth regulation 1 gene (Negr1) showed the most significant association (beta=-1.39, F(1,65)=5.58, p=0.02), which is followed by

94 rs197882879 in the glutamate ionotropic receptor kainite type subunit 3 gene (Grik3) (beta=-0.99,

F(1,59)=4.87,p=0.03). In females, both significantly associated SNPs in the glutamate ionotropic receptor kainite type subunit 3 gene (Grik3). The first one is rs64047160 (beta=-1.06, F(1,59)=8.93, p=0.004), and the second one is rs197882879 (beta=-1.03, F(1,68)=7.61, p=0.007).

Cox-proportional hazards ratio regression models were applied to measure feeding behaviors in the NSF test. Four SNPs and two SNPs significantly associated with the cumulative probability of not feeding during the NSF test in males and females, respectively. In males, the most significantly associated SNP is rs106922835 in the sidekick cell adhesion molecule 1 gene

(Sdk1) (hazards ratio: 2.168, p=0.006), followed by rs107355669 in the glutamate receptor 2 gene

(Grm2) (hazards ratio:1.898, p=0.013), and then by rs106838668 and rs107365153 in the sodium voltage-grated channel alpha subunit 2 gene (Scn2a) (hazards ratio:1.732, p=0.035, hazards ratio:

0.512, p=0.039, respectively). In females, the SNP rs13448419 in the CAMP responsive element binding protein 3 like 1 gene (Creb3l1) showed the most significant association with the cumulative probability of not feeding (hazards ratio:4.072, p=0.001), followed by rs8161939 in the FKBP prolyl isomerase 5 gene (Fkbp5) (hazards ratio: 0.444, p=0.01).

For novel object recognition test, three SNPs significantly associated with novel object recognition percentage in males and three different SNPs significantly associated in females.

Rs106516137 in protein phosphatase 2 scaffold subunit alpha gene (Ppp2r1a) showed the most significant association with male novel object recognition percentage (beta=-5.94, F (2,65) =3.64, p = 0.03), followed by rs107017253 in dopamine receptor 2 gene (Drd2) (beta=7.76, F(1,66)=4.43 , p=0.039), and then by rs8173337 in the regulating synaptic membrane exocytosis 1 gene (Rims1)

(beta=6.12, F(1,62)=4.23, p=0.044). In females, rs107326493 in the ELAV like RNA binding protein 2 gene (Elavl2) shows the most significant association with novel object recognition

95 percentage (beta=11.95, F (1,74) =7.28, p=0.0086), which is followed by rs197882879 in the glutamate ionotropic receptor 3 gene (Grik3) gene (beta=-7.855, F(1,70)=6.13, p=0.016), and then by rs199137038 in the glutamate receptor 2 gene (Grm2) (beta=6.07, F(1,72)=4.17, p=0.045).

For total sucrose preference, six SNPs significantly associated with males and seven SNPs significantly associated with females. For males, rs106516137 in the protein phosphatase scaffold

2 subunit alpha gene (Ppp2r1a) shows the most significantly association (beta=-10.89, F (2,65)

=6.77, p=0.002). This is followed by rs8174549 in the SRY-Box transcription factor 5 gene (Sox5)

(beta=-16.19, F (2,65) =5.68, p=0.005), and then by rs107365153 in the sodium voltage-gated channel alpha subunit 2 gene (Scn2a) (beta=-14.51, F(2,65)=5.55, p=0.006). Two SNPs in the

Ankyrin 3 gene (Ank3), rs65559393 and rs13453359, were also significantly associated with total sucrose preference (beta=12.35, F (1,62)=6.25, p=0.015, beta=9.12, F(2,66)=3.57, p=0.034, respectively). Lastly, rs106755836 in the solute carrier family 6 member 3 gene (Slc6a3) was also associated (beta=12.05, F (1,43) =5.66, p=0.022). In females, the most significant association also came from rs106516137 in the protein phosphatase 2 scaffold subunit alpha gene (Ppp2r1a)

(beta=-14.02, F(2,75)=6.62, p=0.002). This is followed by rs106116247 in the solute carrier family

6 member 15 gene (Slc6a15) (beta=7.92, F(1,74)=7.46, p=0.008), and then by rs197303797 in the dual specificity phosphatase 6 gene (Dusp6) (beta/TG=2.86, beta/TT=-11.71, F(2,76)=4.31, p=0.017), and then by rs105026499 in the opioid receptor mu 1 gene (Oprm1) (beta/GA=-12.23, beta/GG=-5.51,F(2,77)=3.94, p=0.023), rs65866395 in the methionine sulfoxide reductase A gene

(Msra) (beta/CT=-9.85, beta/TT=-1.76, F(2,67)=3.82, p=0.027), rs8154473 in the solute carrier family 6 member 4 gene (Slc6a4) (beta=-6.89, F(1,78)=4.59, p=0.035), and lastly rs8174549 in the SRY-Box transcription factor 5 gene (Sox5) (beta/GA=-1.24, beta/GG=-9.05, F(2,77)=3.15, p=0.049).

96 Supplemental Table 3: Interaction between SNP genotypes and maternal licking and grooming scores on cognitive-emotional phenotypes.

Behavioral tests Outcomes Male Female

SNP ID Gene Location ANOVA P SNP ID Gene Location ANOVA P

rs105972323 Th synonymous 0.0012 rs105692493 Creb3l2 splice region 0.0259

rs8174549 Sox5 synonymous 0.0044 rs64047160 Grik3 intron 0.0351

rs106033600 Grik5 intron 0.0048 rs106755836 Slc6a3 3 prime UTR 0.0374

rs107326493 Elavl2 downstream 0.0066 rs8171128 Penk Non-coding 0.0396 transcript exon rs107254030 Cacna2d1 upstream 0.0091 rs13448419 Creb3l1 synonymous 0.0485

rs105692493 Creb3l2 splice region 0.0115

rs197882879 Grik3 synonymous 0.0132

rs105578346 Pclo intron 0.0133

rs105552381 Prkg1 intron 0.0205

rs64047160 Grik3 intron 0.0215 Latency to food rs105623791 Ctnnd2 intron 0.0233 (seconds) rs8154473 Slc6a4 synonymous 0.0250

rs13453062 Grm5 downstream 0.0271

rs105271298 Drd1 upstream 0.0273

rs106335124 Olfm4 downstream 0.0279

rs198862086 Nr3c1 3 prime UTR 0.0316

Novelty rs65866395 Msra intron 0.0378 suppressed feeding test rs198765613 Grik5 synonymous 0.0413

rs8143394 Ppp2r2a 3 prime UTR 0.0417

rs107017253 Drd2 3 prime UTR 0.0465

rs107326493 Elavl2 downstream 0.0018 rs13453359 Ank3 intron 0.0231

rs107056754 Pik3cg synonymous 0.0031 rs198098398 Otx2 downstream 0.0341

rs105578346 Pclo intron 0.0062 rs106335124 Olfm4 downstream 0.0464

rs107017253 Drd2 3 prime UTR 0.0133 Latency to feed rs107254030 Cacna2d1 upstream 0.0137 (seconds) rs105972323 Th synonymous 0.0239

rs66150982 Vrk2 intron 0.0441

rs198664367 Prkaa1 3 prime UTR 0.0453

rs13448419 Creb3l1 synonymous 0.0053 rs106838668 Scn2a intron 0.0011

rs105578346 Pclo intron 0.0109 rs107017253 Drd2 3 prime UTR 0.0037

rs66150982 Vrk2 intron 0.0135 rs104907678 Drd2 downstream 0.0074 NSF ratio rs8173337 Rims1 downstream 0.0182 rs13448419 Creb3l1 synonymous 0.0098

rs198664367 Prkaa1 3 prime UTR 0.0218 rs8154473 Slc6a4 synonymous 0.0131

97 rs64550502 Avpr1a downstream 0.0259 rs13451047 Mef2c 3 prime UTR 0.0291

rs105026499 Oprm1 downstream 0.0322 rs8174265 Ace missense 0.0337

rs8154473 Slc6a4 synonymous 0.0395 rs8174549 Sox5 synonymous 0.0394

rs105026499 Oprm1 downstream 0.0448

rs198098398 Otx2 downstream 0.0299 rs106922835 Sdk1 upstream 0.0080

rs105972323 Th synonymous 0.0366 rs105623791 Ctnnd2 intron 0.0123

rs105765196 Dcc synonymous 0.0221

rs106927726 Tcf4 intron 0.0327 Novel object Novel object exploration recognition test rs106033600 Grik5 intron 0.0409 (%) rs13448419 Creb3l1 synonymous 0.0439

rs13453062 Grm5 downstream 0.0339 rs198565239 MaoA intron 0.0051

rs106335124 Olfm4 downstream 0.0430 Total sucrose Sucrose preference preference test rs64550502 Avpr1a downstream 0.0450 (%) rs104907678 Drd2 downstream 0.0464

Linear mixed-effect regression models were used to examine if there is any interaction between SNP genotypes and maternal licking and grooming behaviors separately in males and females. Fixed covariates including litter size and animal weights were adjusted for all models. A random covariate of litter ID was also included. The table above (Supplemental Table 3) contains the summary results of this analysis.

For novelty suppressed feeding test results in males, 20 SNPs, 8 SNPs, and 8 SNPs significantly interacted with maternal licking and grooming scores for latency to food, latency to feed, and NSF ratio, respectively. The SNP rs105578346 in the piccolo presynaptic cytomatrix protein gene (Pclo) showed significantly interactions with maternal LG scores for all three phenotypes. Simple slope analysis showed that for TT genotyped male rats, latency to food significantly decreases with increasing maternal licking and grooming scores (beta= -4.62, p =

98 0.009, n = 102), whereas the AT genotyped male rats showed a non-significant correlation with maternal LG scores. In latency to feed and the NSF ratio tests, AT genotyped males showed a positive correlation with maternal LG scores (beta=43.602, p = 0.008, n=10; beta=0.801, p = 0.002, n = 12, respectively). There is no overlap in significant interactions between SNPs and maternal care for female measurements from the NSF test.

In novel object recognition test, 2 SNPs and 6 SNPs showed significant interactions with maternal LG scores for males and females, respectively. In males, the SNP in the orthodenticle homeobox 2 (Otx2) gene has the most significant interaction (p=0.0299). However, dissection of this significant interaction via simple slope analysis showed that none of the correlations was significant. In females, the most significantly interacting SNP is in the sidekick cell adhesion molecule 1 gene (Sdk1) (p = 0.008). Simple slope analysis showed that for CG genotyped females, novel object exploration increases as maternal licking and grooming score increases (beta=4.289, p = 0.006, n=14).

In sucrose preference test, 4 SNPs and 1 SNP significantly interacted with maternal care for males and females, respectively. In males, the most significant interaction is from glutamate metabotropic receptor 5 gene (Grm5). Simple slope analysis showed that the most significantly correlated slope comes from the GA genotype. As maternal licking and grooming increases, the total sucrose preference of GA genotype group increases significantly (beta=3.31, p= 0.036, n=41).

In females, the only significant interaction is from the monoamine oxidase A gene (MaoA)

(p=0.005). The AA genotype group from MaoA gene showed significant increase in total sucrose preference as maternal licking and grooming score increases (beta=2.803, p=0.013, n=18); however, the CA group showed no significant trend.

99 Supplemental Figure 1:

A C

B D

E

F

Supplemental Figure 1: WGCNA module preservation analysis for Grm2-BLA networks.

Male Grm2-BLA network is represented by the turquoise circle. Female Grm2-BLA network is

100 represented by red circle. Blue dash line represents medium preservation. Red dash line represents strong preservation. Z preservation summary plots are provided for:

A, B: Male Grm2-BLA network versus male Grm2-CC network (A). Female Grm2-BLA network versus female Grm2-CC network (B).

C, D: Male Grm2-BLA network versus male Grm2-vDG network (C). Female Grm2-BLA network versus female Grm2-vDG network (D).

E, F: Male Grm2-BLA network versus male Grm2-NAcc network (E). Female Grm2-BLA network versus female Grm2-NAcc network (F).

101

Supplemental Figure 2: Tissue specificity analysis (FUMA) of the genes included in ePRS-

GRM2-BLA to validate over-expression in the amygdala.

102 A

B

Supplemental Figure 3: Hallmark gene set analysis (FUMA) in genes included in ePRS-GRM2-

BLA. Top (A): female. Bottom (B): male.

103 A B C

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Supplemental Figure 4: Protein-protein interaction prediction from STRING for genes included in the ePRS calculation of A. female GRM2-BLA, B. Male GRM2-BLA. C: Validation from

GTEx showing that the amygdala expression of GRM2 and EGFR are positively correlated.

104

Supplemental Figure 5: Construction process of an expression-based polygenic risk score (ePRS) of BLA-specific GRM2 network.

105

Supplemental Figure 6: RPKM counts of Grm2 gene expression in CC, vDG, NAcc, and BLA in the brain emotional circuitry of rat transcriptome.

106

Supplemental Figure 7: TMM counts of GRM2 gene expression in human tissues downloaded from GTEx.

107 CHAPTER 4: DISCUSSION

In the current study, leveraging on the previous GWASs and candidate studies from humans and rodents, we selected 192 candidate SNPs from 70 genes previously associated with outcomes related to mental health disorders. After sequencing a cohort of outbred rats, we found that 59 out of 192 SNPs were independently polymorphic, meaning that there were 59 SNPs that have at least 5% MAF and are not in 100% LD with any other SNPs. We tested for SNP main effects on cognitive-emotional behaviors by associating the genotypes of these SNPs with the baseline outcomes measured in the NSF test, the NOR test, and the SPT test. We also tested the interactions between SNP genotypes and maternal licking and grooming scores on similar behavioral outcomes. Lastly, we extended our analyses for the SNP in the Grm2 gene by investigating the molecular influences of Grm2 SNPs on transcriptional regulation in the brain.

SNP main effects on cognitive-emotional behaviors

There was no overlap between sexes in the genotypic associations measured in the phenotypes analyzed in response to the NSF test, including latency to reach the food, latency to feed, and the survival test. For the NSF ratio, SNP rs197882879 in the Grik3 gene was significantly associated with the NSF ratio in both sexes. These results suggest sex-specific biological mechanisms influencing anxiety-like behaviors in rodents, where the SNPs that contribute to the risk for psychopathology are different depending on the sex. This is supported by numerous studies that showed sex differences in the susceptibility, onset, symptoms, and transcriptional patterning of the brain for multiple psychiatric disorders (Kendler et al., 1995; Labonte et al., 2017; Tolin &

Foa, 2006).

108 Few SNPs were consistently significant in the behavioral measurements of the NSF test.

Rs107355669 in the Grm2 gene, rs106922835 in the Sdk1 gene, and rs107365153 in the Scn2a gene were significantly associated with latency to feed and the survival test. Sdk1 (sidekick cell adhesion molecule 1) is a member of the immunoglobulin superfamily. The Sdk1 gene has been associated with schizophrenia (Sakai et al., 2015) and autism (Connolly, Glessner, & Hakonarson,

2013). Altered levels of Sdk1 methylation have been observed in post-mortem brain tissue from

MDD patients (Kaut et al., 2015). Furthermore, Bagot et al. investigated the transcriptional regulation of stress-susceptibility in mice and found that Sdk1 was associated with the social interaction test and anxiety behaviors and was a driver gene part of a susceptibility transcriptomic network (Bagot et al., 2016). It was also a significant gene locus associated with anxiety-related traits identified by GWAS using the iPSYCH human data set (S. Meier et al., 2018). Scn2a (sodium voltage-gated channel alpha subunit 2) mediates the voltage-dependent sodium ion permeability of excitable membranes (Kasai et al., 2001). Allelic variants of this gene are associated with autism spectrum disorder (Sanders et al., 2018) and intellectual disability (Yokoi, Enomoto, Tsurusaki,

Naruto, & Kurosawa, 2018). Rodent studies have shown that Scn2a haploinsufficient mice display a spectrum of phenotypes affecting anxiety, sociability, and memory flexibility (Tatsukawa et al.,

2019). Surprisingly, there is an overlap in sex for the association between the NSF ratio and the

SNP in the glutamate ionotropic receptor 3 gene (Grik3). In a recent GWAS of 106,000 individuals,

Grik3 has shown significant associations with neuroticism while adjusting by sex (D. J. Smith et al., 2016).

We observed no overlap between the sexes in the percentage of novel object exploration in the NOR test. For males, the most significantly associated SNP is in the protein phosphatase 2 scaffold subunit alpha (Ppp2r1a) gene. This gene is involved in the negative control of cell growth

109 and division. Ppp2r1a is associated with mental retardation (Reynhout et al., 2019), pervasive developmental disorders (Reynhout et al., 2019), and Alzheimer’s disease (Miron et al., 2019).

This second most significantly associated SNP is in the dopamine receptor 2 (DRD2) gene. Drd2 is a G-protein coupled receptor for dopamine, which is crucially involved in motivated behavior

(Richter et al., 2013). Dysfunctional dopaminergic neurotransmission has been implicated in the pathophysiology of neuropsychiatric disorders such as schizophrenia and substance dependence

(Heinz & Schlagenhauf, 2010). The most significantly associated SNP with novel object exploration during NOR test is in the ELAV-like RNA binding protein 2 (Elavl2) gene in females.

This is a neural-specific RNA-binding protein known to bind 3’ untranslated regions. The transcriptional regulation influences human neurons linked to neurodevelopment and autism

(Berto, Usui, Konopka, & Fogel, 2016). Interestingly, SNPs in the Grik3 gene and Grm2 gene were also significantly associated with novel object exploration in females. In combination with the observation that both genes were also significantly associated with phenotypes measured in the

NSF test for males, this suggests the pleiotropic effects of genetic variations in different biological processes vary depending on sex (Gratten & Visscher, 2016; Pickrell et al., 2016). Moreover, it can be observed that the genes significantly associated with short term memory during the NOR test, including Grik3, Grm2, and Ppp2r1a, were also significantly associated with anxiety-like behaviors and anhedonia, highlighting that cognitive deficits associate with affective disorders, both in rodents and in humans (Marvel & Paradiso, 2004).

SNPs in Ppp2r1a and Sox5 were significantly associated with averaged sucrose preference across two days in both sexes. This further suggests that SNPs can exert pleiotropic influences on different behaviors as Ppp2r1a was significantly associated with short term memory in the NORT in males. Ppp2r1a was identified as a significant locus in a MDD GWAS mega-analysis by PGC

110 in both the discovery and the replication phase (Ripke et al., 2013). Similarly, the Sox5 gene was also identified as a significant genetic locus in depression and substance addiction GWAS (A. C.

Edwards et al., 2012), and recent whole-genome sequencing studies have shown that this gene is intolerant to functional nonsynonymous variants (Lelieveld et al., 2016). Interestingly, the Dusp6 gene showed significant association with average sucrose preference in females. This corresponds with a recent transcriptional analysis conducted in postmortem human brains, where Dusp6 was identified as a female-specific hub gene that is downregulated in the prefrontal cortex of depressed patients (Labonte et al., 2017). Genes such as Slc6a15 (Kohli et al., 2011), Sox5 (Wray et al.,

2018), Ank3 (Takata et al., 2011), and Oprm1 (Hang Zhou et al., 2019) were all previously identified in human mental health-related GWAS.

Overall, these results validate that outbred rats can be used to model population genetic findings from human cohorts. We saw that SNPs can be associated with cognitive-emotional phenotypes, yet no SNP was commonly associated with all behaviors. These observations highlight the heterogeneity and sex differences underlying the cognitive-emotional behaviors.

However, a certain degree of overlap between tests were observed, such as the association between genotypes of Ppp2r1a gene and novel object exploration and averaged sucrose preference, as well as the association between genotypes of the Grik3 gene and NSF ratio and novel object exploration.

Furthermore, overlap between sexes were also noted for SNPs in the Grik3 for the NSF ratio and

Ppp2r1a gene for averaged sucrose preference. These observations highlight the speculation that different phenotypes or behaviors can share certain biological pathways.

Interactions between SNPs and maternal care in cognitive-emotional phenotypes

111 Genetic studies have provided valuable insights into the biological underpinnings of mental health-related phenotypes. However, both nature and nurture play important roles in the genesis of psychopathology. In this case, we also investigated the interaction between SNP genotypes and maternal licking and grooming, an early life environmental marker of maternal care.

Numerous SNPs from this study also interacted with maternal licking and grooming scores.

Many of these SNPs have been previously shown to interact with the early life environment in human and rodent studies. For an example, in the novel object recognition test, Otx2 genotypes showed significant interactions with maternal LG scores in male rats. A recent genome-wide by environment interaction study identified that one of the regulators of Otx2, Piwi like RNA- mediated gene silencing 4 gene (PIWIL4), interacts with the environment to influence depressive behaviors during adulthood (Arnau-Soler et al., 2019). PIWIL4 has also been shown to interact with the mother’s warmth to influence the risk for attention deficit hyperactivity disorder (ADHD)

(Sonuga‐Barke et al., 2008). PIWIL4 is involved in chromatic modification in the brain (Sugimoto et al., 2007) and is critical for forebrain development and the coordination of cortical circuits integration during critical periods, suggesting its involvement in stress responses and depressive- like behaviors (H. H. C. Lee et al., 2017). In this thesis, another example of a SNP by maternal LG interaction comes from the SNP in the Grm5 gene and its association with the averaged sucrose preference test scores for male rats. Grm5 has been implicated in MDD (Krystal et al., 2010), obsessive compulsive disorder (OCD) (Akkus et al., 2014), and addiction (Chiamulera et al., 2001).

In addition, the observation of the interaction between the MaoA SNP and averaged sucrose preference was also found in previous studies. MaoA has been extensively studied as a candidate gene for depressive symptoms in humans (Brummett et al., 2007). Interaction of this gene with

112 childhood adversities has been shown to dysregulate its epigenetic programming (Byrd & Manuck,

2014; Naoi, Maruyama, & Shamoto-Nagai, 2018).

Most significant SNP genotype by maternal LG behavior interaction findings from this thesis are novel, especially in the field of animal models. This study provides clues to the genes that are susceptible to environmental variations, and thus gene by environment interaction effects must be taken into account when studying disease susceptibility.

Limitations

Validating previous findings using completely naturalistic animal models – on both a genetic and environmental level – can provide valuable insight to the field about gene by environment interactions in rodents. The gene by environment animal model described in this thesis more accurately captures the subtle influences of natural genetic variations and measures natural variations in transcription within brain circuitry

While the significant associations and interactions identified in this study replicate previously published findings from candidate studies and genome-wide association studies, the causal mechanisms behind these signals remain unclear. The manuscript presented in Chapter 2 represents a possible pipeline for identifying the transcriptomic influences related to genetic variation in the brain. We first identified the behavioral associations between a SNP in the Grm2 gene and cognitive-emotional phenotypes in rodents. Then, we explored for cis-eQTL or trans- eQTL effects of the Grm2 SNP in relevant brain regions via bioinformatics techniques such as differential gene expression analysis and rank-rank hypergeometric overlap analysis (RRHO).

Then, we elucidated the gene network of Grm2 and correlated it with the SNP. Lastly, we

113 introduced the translatability of our glutamate network by predicting relevant human behaviors using the expression of this network.

Possible limitations of this study include the lack of available data in current rat genomic databases (i.e. LD and MAF data), which limited the number of SNPs that passed the quality control thresholds in this study. From the 192 candidate SNPs that were chosen for this thesis, only

59 passed the quality control thresholds. Furthermore, many SNPs show differential or opposite associations with cognitive-emotional phenotypes between sexes; however, most are not statistically significant due to the sample size of this study. This is due to the unequal distribution of genotypes for each SNP, where one homozygous genotype typically is dominant in the sample, which is followed by the heterozygous genotype, then by the least frequent homozygous genotype.

In addition, the transcriptomic data of the rodent brain used in this study is only a static snapshot of brain transcription during adulthood. Many SNPs influence the temporal and spatial expression of genes early in life, and thus capturing the dynamic effects of genetic variations would significantly improve our understanding on where and when SNPs exert influences on transcription. There are many other mechanisms by which a SNP can influence brain activity and circuitry, including alternative splicing (Kasowski et al., 2010), changing the secondary structure

(Nackley et al., 2006), shifting open reading frames (Cai et al., 1992), or affecting start codon recognition (Kozak, 1986) of the gene. Future directions should focus on characterizing the effects of SNPs in different brain regions — creating large transcriptomic and proteomic databases so that the interactions between the inherited genetic variations and exposure to different environmental factors can be better understood.

Future perspectives

114 The benefits of incorporating animal models into population genetic findings from human studies include (1) the molecular mechanisms of SNPs in different brain regions can be characterized and (2) the environmental variations are much easier to control and standardize. This thesis leveraged on these two advantages and showed that rats can be used to validate population genetic findings from human studies. Furthermore, using RNA-sequencing data from different brain regions, we introduced a pipeline of transcriptomic analysis to explore the functional relevance of genetic variants.

One future direction is to expand upon the current transcriptomic data. A SNP does not necessarily only exert transcriptomic influence during adulthood. Some SNPs may be more active during infancy, while other SNPs may only be functional during adulthood, depending on when or where the gene is needed. In addition, SNPs can exert region-specific influences in the brain, as shown with the Grm2 SNP rs107355669 in Chapter 2, where sex difference in the cis-eQTL effects of rs107355669 was only observed in the Nacc and BLA, but not in the CC and vDG. Therefore, by obtaining a set of brain transcriptomic data across different brain regions and at different stages of the life span, we can then use the same analysis pipeline as presented in Chapter 2 and explore for region-specific and time-specific influences of SNPs.

The ultimate confirmation comes from direct manipulation. Traditional genetic techniques are not suitable even for SNP validation, because most SNPs do not exert drastic influences.

Furthermore, traditional genetic techniques rely on creating double stranded breaks (DSBs) at targeted sites, which are repaired by an error-prone non-homologous end-joining (NHEJ) pathway.

During the repair, random insertions and deletions (indels) are commonly observed at target sites.

This is less desirable when mutations consisting of one base-pair is studied. Therefore, one technique to focus on is the CRISPR/Cas9 base editing approach (Gaudelli et al., 2017). Instead

115 of causing a DSB, CRISPR/Cas9 only creates a single-strand nick. This technique increases target specificity and reassures that only one base pair is replaced. Promising examples of this technique include the APOBEC1 (apolipoprotein B editing complex 1) or AID (activation-induced deaminase) (Komor, Kim, Packer, Zuris, & Liu, 2016). When either of these two enzymes are linked to a catalytically deficient Cas9 (dCas9), it generates a base substitution by replacing a cytosine with a thymine or an adenine with a guanine (Gaudelli et al., 2017; Ma et al., 2016). This has been demonstrated successfully in yeast, cultured mammalian cells, plants, and mice (Villiger et al., 2018; Zong et al., 2017). Therefore, we believe that developing this DNA base-editing technology to precisely edit the rat genome at a single base-pair efficiency can help us establish the final causal link between genetic variants and their functional relevance.

In conclusion, as the number of signals accumulates in human population genetic studies, the functional relevance of these signals must be characterized to prioritize them for therapeutic compound development. This thesis represents the very first step to validate the biological mechanisms of genetic variants using a rodent model. In the future, with the combination from multi-level datasets, a comprehensive profile on the influence of genetic variants can be obtained.

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