Effects of antidepressants on the ventral dentate gyrus

Elena Carazo Arias

Submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy under the Executive Committee of the Graduate School of Arts and Sciences

COLUMBIA UNIVERSITY

2021

© 2021

Elena Carazo Arias

All Rights Reserved

Abstract

Effects of antidepressants on the ventral dentate gyrus

Elena Carazo Arias

Fluoxetine is a Selective Serotonin Reuptake Inhibitor (SSRI) often prescribed for the treatment of anxiety and depression. Many of its effects are thought to be mediated by the dentate gyrus, but the mechanism by which some patients respond to treatment and some do not remains poorly understood. In this study we have characterized a previously-unknown component of the behavioral response to fluoxetine in the dentate gyrus, using a combination of genomic, behavioral, and imaging techniques. We have found that different components of the opioid system are involved in the treatment efficacy of fluoxetine in the dentate gyrus. Specifically, we have identified a population of anatomically and transcriptionally distinct mature granule cells that are defined by their high levels of proenkephalin expression after fluoxetine treatment.

Furthermore, we have shown that the delta opioid receptor is partly mediating some of the behavioral effects of fluoxetine in a neurogenesis-independent manner. These results open an interesting new avenue for research into the involvement of the opioid system in the behavioral response to SSRIs.

Table of Contents

List of Charts, Graphs, Illustrations ...... iv

Acknowledgments ...... vii

Dedication ...... ix

Preface ...... 1

Chapter 1 : General Introduction ...... 2

1.1 Depression and Anxiety ...... 2

1.1.1 The HPA Axis ...... 4

1.2 Antidepressant Treatment ...... 5

1.2.1 A Historical Perspective ...... 5

1.2.2 Selective Serotonin Reuptake Inhibitors ...... 8

1.3 The Hippocampus ...... 9

1.3.1 Anatomy ...... 10

1.3.2 Differentiation Along the Longitudinal Axis ...... 11

1.4 The Dentate Gyrus ...... 13

1.4.1 Anatomy ...... 13

1.4.2 Semilunar Granule Cells ...... 14

1.4.3 Neurogenesis ...... 16

1.5 Depression, Antidepressants and the Hippocampus ...... 18

1.6 The Opioid System...... 21

1.6.1 A Historical Perspective ...... 21

1.6.2 Organization ...... 23

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1.6.3 Opioid system in the Dentate Gyrus ...... 26

1.6.4 Opioid System and Depression ...... 29

1.7 Behavioral Paradigms in Mood Disorders ...... 32

Chapter 2 : Contribution of the Opioid System to the Antidepressant Effects of

Fluoxetine ...... 36

2.1 Fluoxetine Modulates Dentate Gyrus Function Through the Opioid System ...... 36

2.1.1 Introduction ...... 36

2.1.2 Methods ...... 38

2.1.3 Results and Figures ...... 46

2.1.4 Conclusions ...... 66

2.1.5 Supplementary Figures ...... 72

2.2 The Opioid System Mediates Some of the Behavioral Effects of Fluoxetine...... 76

2.2.1 Introduction ...... 76

2.2.2 Methods ...... 77

2.2.3 Results and Figures ...... 81

2.2.4 Conclusions ...... 87

Chapter 3 : Fluoxetine Mediates Changes in Granule Cell Activity in the Dentate Gyrus .

...... 89

3.1 Introduction ...... 89

3.2 Methods ...... 92

3.3 Results and Figures ...... 97

3.4 Conclusions ...... 109

Chapter 4 : General Discussion ...... 112

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4.1 Summary of Results ...... 112

4.2 Responsiveness to Fluoxetine ...... 113

4.3 Dentate Gyrus Granule Cell Heterogeneity ...... 115

4.4 High Adaptability of the Opioid System ...... 117

4.5 Proposed Mechanism ...... 120

4.6 Future Directions...... 124

References ...... 126

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List of Charts, Graphs, Illustrations

Figure 1.1: The trisynaptic circuit of the hippocampus ...... 11

Figure 1.2: Differential output streams along the dorsal-ventral hippocampus ...... 12

Figure 1.3: Morphology of semilunar granule cells...... 15

Figure 1.4: Important molecular mediators of the effects of SSRIs in the Dentate Gyrus...... 20

Figure 1.5: Organization of opioid system ...... 26

Figure 1.6: Rodent models of mood disorders used in our studies...... 35

Figure 2.1: Chronic corticosterone and fluoxetine administration results in treatment response. 48

Figure 2.2: Chronic corticosterone and fluoxetine treatment results in treatment responder and non-responder mice in the NSF test...... 50

Figure 2.3: Behavioral analysis of only the mice used for gene expression analysis...... 51

Figure 2.4: Ventral DG microarray results show a significant upregulation of Penk in fluoxetine treated mice...... 53

Figure 2.5: Raw gene expression values for genes of interest in the vDG...... 54

Figure 2.6: Significantly correlated genes with Penk...... 55

Figure 2.7: Correlation between ventral Penk gene expression and behavior...... 56

Figure 2.8: Ventral DG microarray results show a significant downregulation of Penk in fluoxetine non-responders compared to responders...... 57

Figure 2.9: Differential expression of opioid related genes in DG...... 58

Figure 2.10: WGCNA cluster dendrogram of all modules found in the analysis...... 59

Figure 2.11: Subset of relevant modules identified through WGCNA...... 60

Figure 2.12: WGCNA analysis of gene expression data identifies Penk containing module as significantly correlated with behavior ...... 61

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Figure 2.13: Penk mRNA expression quantification across brain regions...... 63

Figure 2.14: Subset of fluoxetine upregulated genes localized in a discreet population of mGCs.

...... 65

Supplementary Figure 2.1: Microarray results from fluoxetine treated mice in dorsal DG show a significant upregulation of Penk...... 72

Supplementary Figure 2.2: RNA sequencing confirms microarray results in ventral DG...... 73

Supplementary Figure 2.3: RNA-sequencing confirms microarray results in dorsal DG...... 74

Supplementary Figure 2.4: Heatmap diagram of genes contributing to the single-cell gene expression cluster "Mature GC3"...... 75

Figure 2.15: Behavioral effects of fluoxetine in the FST in MOR-KO but not DOR-KO mice. . 82

Figure 2.16: No behavioral effect of fluoxetine in the NSF in MOR-KO or DOR-KO mice...... 83

Figure 2.17: DOR-KO mice display similar behavior than WT mice after fluoxetine treatment. 84

Figure 2.18: Fluoxetine treatment increases neurogenesis in both WT and DOR-KO mice...... 86

Figure 3.1: Calcium imaging of the ventral dentate gyrus...... 99

Figure 3.2: Markerless pose estimation with DeepLabCut...... 100

Figure 3.3: Example tracking of 5 different body parts during behavior using DeepLabCut. ... 101

Figure 3.4: Behavioral analysis using DeepLabCut...... 102

Figure 3.5: Supervised selection of stretching bouts...... 103

Figure 3.6: Stretching behavior is decreased in the Elevated Plus Maze in fluoxetine treated mice...... 104

Figure 3.7: Stretching behavior is not changed in the Novelty Suppressed Feeding test in fluoxetine treated mice...... 105

Figure 3.8: Fluoxetine treatment attenuates increased firing during stretching behavior...... 106

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Figure 3.9: Fluoxetine decreases dentate gyrus activity during stretching behavior in the Elevated

Plus Maze...... 107

Figure 3.10: Fluoxetine decreases dentate gyrus activity during stretching behavior in the

Novelty Suppressed Feeding...... 108

Figure 4.1: Proposed molecular mechanism of opioid system involvement in the DG circuitry after SSRI treatment...... 121

Figure 4.2: Proposed Dentate Gyrus circuit recruited by chronic fluoxetine treatment...... 123

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Acknowledgments

It takes a village to write a thesis, and I’ve had the great luck of being surrounded by an amazing village of people who have helped me through this process.

First and foremost, I would like to thank my mentor and advisor, Rene Hen. He is not only a brilliant scientist, but also an amazing human being. His infectious enthusiasm for science is only rivaled by his excitement for outdoor activities - from mushroom gathering to skiing - and good cheese (a passion I also share). Rene has been a compassionate and thoughtful mentor. These qualities are mirrored by all lab members, who have made the lab a fun, cooperative work environment powered by scientific curiosity, rigor, and silliness.

I have also had great mentors over the years who have helped me become the scientist I am today. Joachim Frank and Yaser Hashem, my undergraduate mentors at Columbia, encouraged me to start a PhD and helped spark the flame that has carried me through the years. Thank you also to Zoe Donaldson, my first mentor in the Hen Lab, whose unrelenting work ethic and energy inspired me to work harder and smarter. To Chris Anacker, a mentor and sounding board over the years (and occasional karaoke partner), who has always encouraged me to think bigger and take risks. To Marley Kass, whose methodical and positive energy inspired me to be more ambitious in my scientific endeavors. To Jess, Valentine, Jaena, Julia and Hill, for helping me laugh through the never-ending hours of behavioral experiments. And to every member of the

Hen Lab that I have had the pleasure of working with over the years.

I want to specially thank my family for being both the wind at my back and the roots that ground me. My parents, Soledad and Jose Maria, have helped me get up every time I have been down.

My sister, Isabel, has been my sounding board and confidant, and always helps me see things in

vii perspective. My cousins and aunts, who bring joy and balance to my life, and make sure my roots are safely secured.

Thank you to the friends who have been my oasis, a place hidden from space and time, and who never fail to make me laugh – the nerds, the biologists, the commune, the dogs who see the rainbow, the high-school weirdos, my grad school cohort.

To all the amazing women scientists I have met through Women in Science at Columbia

(WISC), may you always charge ahead.

To Jason R., for his ability to help me question my assumptions just by raising a single eyebrow.

I want to give special thanks to all the people behind the scenes in the Hen Lab and the Biology

Department who have made this process possible: Navieta Ramasami, Jay Liriano, Josephine,

Sarah Kim and Ellie Siddens.

I also want to thank the Hope for Depression Research Foundation (HDRF), and the New York

Stem Cell Initiative (NYSTEM) for providing me with the resources to conduct this research.

Finally, I want to thank my thesis committee members, Steve Siegelbaum, Mark Ansorge, John

Pintar and Harmen Bussemaker, for committing the time and effort to read and evaluate this thesis, and for their invaluable input.

viii

Dedication

To my father, for believing in me.

To my mother, for being my cheerleader.

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Preface

I want to advise the reader that, as scientists, we research psychiatric diseases with an objective and scientific approach. However, we are also acutely aware of the awful personal toll these diseases take on individuals, and this knowledge drives our search for answers.

Anxiety and depression are terrible burdens to carry alone – if you think you or a loved one might be suffering from these conditions, don’t hesitate to ask for help.

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Chapter 1 : General Introduction

1.1 Depression and Anxiety

Mood disorders, such as anxiety and depression, are crippling psychiatric diseases that will affect

20-30% of Americans at some point in their lives (Kessler et al., 2012). According to the World

Health Organization, over 260 million people suffer from depression worldwide, and 800,000 people die due to suicide every year, making it the second leading cause of death in 15–29-year- olds worldwide.

The diagnostic criteria for anxiety and depressive disorders are based on behavioral symptomatology, and thus a heterogenous assortment of conditions are grouped under a common clinical presentation. The Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition

(DSM-5) outlines the taxonomic and diagnostic tools which serve as the principal authority for the diagnosis of psychiatric conditions. Under these criteria, Major Depressive Disorder (MDD) is characterized by depressed mood, loss of interest or pleasure in activities previously enjoyable, weight loss or gain, insomnia or hypersomnia, psychomotor agitation or retardation, and fatigue, all of which should be presented consistently for over two weeks and cannot be better explained by another diagnosis. MDD can further be described as ‘with mixed features’ for those cases presenting manic symptoms, or ‘with anxious distress’ when patients also experience anxiety.

Other types of depression are also considered, such as Persistent Depressive Disorder, Bipolar

Disorder, Post-Partum Depression, Premenstrual Dysphoric Disorder, Seasonal Affective

Disorder or Atypical Depression (American Psychiatric Association, 2013). While we won’t be discussing all the nuanced ways in which depressed mood can present in a clinical setting, it is important to emphasize how heterogenous depressive disorders are, which in turn points to the

2 variability of possible molecular, cellular and circuit level dysregulations underlying these diseases.

Anxiety disorders also include a heterogenous assortment of disorders that share features of excessive fear and anxiety resulting in behavioral disturbances. Some of the disorders in this category include Phobias, Social Anxiety Disorder, Panic Disorder and Generalized Anxiety

Disorder (GAD). The DSM-5 criteria for GAD include uncontrollable excessive worrying, restlessness, fatigue, impaired concentration, irritability and difficulty sleeping.

Anxiety and depressive disorders are often presented concurrently. Approximately 50% of patients who meet the criteria for MDD also suffer from an anxiety disorder. Conversely, a patient diagnosed with an anxiety disorder is almost nine times more likely to develop MDD within the following year, making anxiety disorders the largest clinical predictor for the development of depression (Hirschfeld, 2001; Kessler et al., 1996). The concomitant presentation of these disorders often makes for a difficult differential diagnosis, which is further hindered by the fact that these mood disorders often have overlapping symptom criteria.

Furthermore, neuroimaging studies have pointed to similar brain circuits underlying both anxiety and depressive disorders (Williams, 2016). These emotional disorders have been commonly linked to dysregulation in different brain regions such as prefrontal cortex, cingulate cortex, amygdala and hippocampus (Mayberg, 2003; Ressler and Nemeroff, 2000). The overlap between these mood disorders is further emphasized by the fact that common interventions such as selective serotonin reuptake inhibitors and cognitive behavioral therapy are both successful in the treatment of anxiety and depressive disorders. It is therefore important to understand that while symptomatic criteria are useful for the correct diagnosis and treatment of these psychiatric disorders, going forward it may be more useful to develop neurobiological assessment tools that

3 might more closely identify phenotypic differences underlying different clusters of mood disorders.

1.1.1 The HPA Axis

The pathophysiology of mood disorders has been studied extensively, and many different factors have been implicating in the development of these diseases. The most widely studied of these factors are dysregulation of neurotransmitter signaling (serotonin, dopamine and glutamate among others), genetic predisposition, changes in neurogenesis, and dysregulation of the

Hypothalamic-pituitary-adrenal (HPA) axis (Hasler, 2010). The HPA axis acts through the secretion of different factors from the hypothalamus and the pituitary, which then results in the secretion of glucocorticoids (cortisol in humans and corticosterone in rodents) from the adrenal gland. These glucocorticoids, normally released at varying levels during the day, act on many targets throughout the body, including the brain. Both in human and rodent studies, glucocorticoids and their release is sharply increased following a stressor, and this response is considered a crucial adaptive response to stress (Munck et al., 1984). These molecules act on two different receptors: mineralocorticoid receptors (MR) and glucocorticoid receptors (GR). In rodent studies, high corticosterone levels after stress activate GRs (Joëls and Kloet, 1994), which in turn mediate both the effects of stress on different brain regions as well as negative feedback of the HPA axis itself (Herman et al., 1989).

The hippocampus is an important target of these effects due to its high levels of GRs, as well as its involvement in HPA axis regulation (Reul and Kloet, 1985). In the hippocampus, glucocorticoids have been found to be involved in neurogenesis, hippocampal size, memory acquisition and emotional regulation (Pariante and Lightman, 2008). There are many kinds of stressors that can disrupt the regulation of the HPA axis. In rodents, these stressors include

4 psychosocial stress and unpredictable mild stress, among others (Fuchs and Flügge, 1998; Surget et al., 2011).

The HPA axis therefore lies at the intersection between stress and brain functioning, and its role is compromised in depression. In MDD patients compared to control, researchers have found higher cortisol levels, blunted stress reactivity and impaired recovery (Bhagwagar et al., 2005;

Burke et al., 2005). Chronic stress in fact is known to induce depressive states in previously healthy patients, and the effects may persist after the resolution of the stressor.

1.2 Antidepressant Treatment

1.2.1 A Historical Perspective

Throughout the centuries and up until the 1950s, mood disorders were treated with a combination of opioids and amphetamines (Heal et al., 2013; Weber and Emrich, 1988). These treatments, however, presented many challenges, especially regarding their addictive nature and potential side effects. It wasn’t until the 1950s when the discovery of new compounds for the treatment of depression precipitated the first of several waves of antidepressant treatments. In

1951 two chemists working at Hoffman-LaRoche began a clinical trial testing two new compounds for the treatment of tuberculosis: isoniazid and iproniazid. Besides a markedly improvement in tuberculosis outcome, the researchers also noted a general increased vigor and stimulation of the patient’s demeanor after treatment with iproniazid (Selikoff and Robitzek,

1952). Alert to the potential psychostimulant effect of this drug, over the next several years other clinicians around the world reported similar side effects including euphoria, increased appetite and improved sleep (Lopez-Munoz et al., 2007). Max Lauri, a Cincinnati psychiatrist, is believed to have been the first of these clinicians to coin the term “antidepressant” in reference to the

5 effect of these compounds on depressed patients (Healy, 1999). In 1957 Loomer, Saunders and

Kline conducted the first clinical study on depressed patients who did not have tuberculosis, treating them with iproniazid for several weeks. They reported an improvement in 70% of the patients (Loomer et al., 1957). This antitubercular drug was therefore the first off-label treatment used for MDD patients.

Studies into the mechanism of iproniazid demonstrated that this compound inhibited monoamine oxidase (MAO) enzymatic function. MAOs catalyze the oxidation of monoamine compounds such as serotonin, and thus MAO inhibitors (MAOI) result in increased monoamine availability.

Indeed, further studies in animals showed an increase in serotonin levels in the brain following iproniazid treatment (Lopez-Munoz et al., 2007).

The first wave of antidepressant discoveries in the 1950s culminated with the development of

Imipramine, a tricyclic antidepressant (TCA) in 1958 (Kuhn, 1958). Imipramine was developed in an attempt to improve the antipsychotic medication chlorpromazine, but showed little effectiveness in the treatment of psychosis. However, it was serendipitously discovered to be useful in the treatment of depression, and its use was rapidly commercialized in the 1960s.

Although the mechanism was not known at the time, TCAs are now known to inhibit reuptake of norepinephrine and serotonin, as well as blocking some postsynaptic adrenergic, muscarinic and histamine receptors (Hillhouse and Porter, 2015).

These converging lines of evidence in the 1950s planted the seeds for the articulation of the monoamine hypothesis of depression, which postulates that depressed patients have depleted concentrations of monoamines such as serotonin, dopamine and norepinephrine, and treatment with drugs that increase the levels of these neurotransmitters will result in an antidepressant effect (Hillhouse and Porter, 2015).

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The use of iproniazid was recalled in 1961 due to reports of lethal hepatotoxicity, but later development of reversible MAOI made these drugs safer to use. Imipramine resulted in less severe side effects, and thus its use continues to the present day, although use of TCAs has decreased in recent decades after the introduction of Selective Serotonin Reuptake Inhibitors

(SSRIs).

SSRIs were developed in the 1970s, when evidence pointing to the role of serotonin in MDD

(Shaw et al., 1967) prompted pharmaceutical companies to develop ligands that would selectively inhibit the reuptake of serotonin by the serotonin transporters, resulting in increased availability of serotonin. Fluoxetine was the first reported SSRI with antidepressant properties

(Wong et al., 1974), which also had a weak affinity for the norepinephrine transporter (Wong et al., 1975), and it was approved by the FDA in 1987 under the brand name Prozac®. Other SSRIs have been discovered and approved since the introduction of fluoxetine to the market, such as sertraline, paroxetine, citalopram or escitalopram (Hillhouse and Porter, 2015).

In later decades newer antidepressants have been introduced to the market such as dopamine- norepinephrine reuptake inhibitors like bupropion, or serotonin-norepinephrine reuptake inhibitors like venlafaxine or duloxetine.

While many antidepressants have been developed based on the monoamine hypothesis of depression, recent evidence has urged the field to revise this simplistic hypothesis. For example, experimental depletion of monoamines does not affect healthy subjects (Ruhé et al., 2007).

Moreover, while SSRIs and other compounds increase monoamine transmission acutely, their effects on mood improvement take weeks to take effect. This is believed to happen due to secondary neuroplasticity that occurs over longer timescales (Pittenger and Duman, 2008). Thus the monoamine system, while playing an undeniable role in the neuromodulatory mechanisms

7 underlying mood disorders, should be considered as part of a larger system integrating gene- environment interactions, neuronal plasticity, and immunological and metabolic processes

(Krishnan and Nestler, 2008). A better understanding of the pathophysiology of depression, as well as the exact way in which current antidepressants such as SSRIs exert their mood-enhancing effects, will lead the field to the development of better antidepressants.

1.2.2 Selective Serotonin Reuptake Inhibitors

Serotonin (5-HT) is a monoamine neurotransmitter ubiquitously found throughout the brain and body. In the central nervous system, serotonin producing neurons are located within the raphe nuclei of the brain stem (Mohammad-Zadeh et al., 2008). Efferent serotonergic projections from the raphe nuclei innervate the lateral and medial cerebral cortex, hypothalamus, amygdala and hippocampus (Hornung, 2003). Upon depolarization of these neurons, synaptic vesicles deliver serotonin to the synaptic cleft, and serotonin is released onto these different brain targets.

Serotonin then binds to presynaptic serotonin autoreceptors and postsynaptic serotonin receptors

(5-HTR). The serotonin transporter (5-HTT) located on the presynaptic membrane then removes the serotonin from the synaptic cleft back into the presynaptic neuron, where serotonin is recycled back into presynaptic vesicles (Mohammad-Zadeh et al., 2008). SSRIs act by inhibiting the reuptake of serotonin by the 5-HTT, and thus increasing the amount of serotonin in the synaptic cleft available for the 5-HTRs.

5-HTRs are a group of G-protein-coupled-receptors (except for the 5-HT3R) that include 14 different identified subtypes in the central nervous system, all of them with different functions.

Because of the functional and anatomical heterogeneity of 5-HTRs, SSRIs can influence many processes throughout the brain.

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SSRIs are the medication most often prescribed in the treatment of depression (Cipriani et al.,

2018), and they are also commonly prescribed for the treatment of other psychiatric conditions such as eating disorders, obsessive-compulsive disorder, panic disorder, GAD and premenstrual dysphoric disorder. The wide use of this medication is partly due to its high tolerability and lower side effects compared to other antidepressant drugs such as TCAs or MOAIs, as well as for their safety in overdose and low propensity to induce seizures (Feighner, 1999).

However, treatment with SSRIs still results in many side effects, including sexual dysfunction, sleep disturbance, gastro-intestinal dysregulation and weight fluctuations, which in many cases results in treatment discontinuation (Ferguson, 2001). Despite being so widely used, only one- third of patients experience remission (absence of symptoms) and roughly 47% of patients respond (reduction of at least 50% in baseline symptom levels) to treatment after up to 8 weeks of treatment with SSRIs (Rush et al., 2006; Trivedi et al., 2006). Remission rates decrease with successive interventions, ranging from 10-25% for those patients who are non-responsive to two interventions (Warden et al., 2007). Treatment resistance also increases with subsequent depressive episodes, with 80% of people who have recovered from two or more episodes having an additional recurrence (Burcusa and Iacono, 2007).

1.3 The Hippocampus

The hippocampus1 is a bilateral brain structure located in the inner region of the temporal lobe, and is involved in memory formation and spatial navigation. This region is also a component of the limbic system and plays a role in mood regulation. The hippocampus is a highly conserved structure across

1 Derived from the Greek hippokampus (hippos, meaning “horse,” and kampos, meaning “sea monster”), referring to its resemblance in shape to that of a sea horse. 9 mammalian species both in its connectivity and function, and it is therefore often studied in rodent animal models with the purpose of understanding memory and mood regulation.

1.3.1 Anatomy

The hippocampus encompasses four main structures crucial to its function: the dentate gyrus (DG), cornu ammonis 3 (CA3), CA2, and CA1 2. The hippocampal complex also includes two other associated structures posterior to the hippocampal proper: the subiculum and the entorhinal cortex

(EC). The subiculum, along with CA1, provides the main hippocampal output, while the EC provides the main glutamatergic input into the hippocampus (Amaral, 1993). Other inputs include the mammillary bodies (excitatory input) (Kiss et al., 2000), the raphe nucleus (serotonergic input)

(Moore and Halaris, 1975), the medial septum (cholinergic input) (Amaral and Kurz, 1985), the locus coeruleus (noradrenergic input) (Swanson and Hartman, 1975) and the ventral tegmental area

(dopaminergic input) (Gasbarri et al., 1997).

The hippocampus information processing pathway has been classically described through the trisynaptic circuit (Andersen et al., 1971), which describes three main sequential connections through the hippocampal structures: First, the perforant path projections from the EC to the DG; second, the mossy fiber projections from DG to CA3; and lastly the Schaffer collaterals projection from CA3 to

CA1 (Figure 1.1). This classic circuit, while useful for the conceptualization of the information processing pathways in the hippocampus, is overly simplistic and leaves out other important projections, like EC projections to CA3 and CA1, CA1 projections to the subiculum, as well as interneurons and mossy cells which project within and between subregions (Amaral, 1993; Amaral et al., 2007).

2 Terms coined by the Spanish neuroscientist Rafael Lorente de Nó in 1934, most famous disciple of Ramón y Cajal. 10

Figure 1.1: The trisynaptic circuit of the hippocampus(from (Deng et al., 2010)). The main input into the hippocampus originates from the Entorhinal Cortex (EC) through lateral and media perforant path stimulation (PP) of dentate gyrus granule cells, which in turn synapse onto pyramidal neurons in the CA3 area. Lastly, CA3 projects to CA1 pyramidal neurons, which then project axons to many output regions of the hippocampus such as the EC.

1.3.2 Differentiation Along the Longitudinal Axis

The hippocampus presents distinct properties along its longitudinal axis. In primates, the hippocampus can be subdivided into the posterior and the anterior regions, which in rodents correspond to the dorsal and ventral hippocampus, respectively (Fanselow and Dong, 2010). Since most of the anatomical and functional studies around this subject have been conducted in rodent studies, including the data presented in this thesis, we will refer to the dorsal and ventral poles of the rodent hippocampus and, unless explicitly mentioned, the presented data will be referring to rodent studies. While the internal synaptic connectivity is mostly conserved throughout the longitudinal axis of the hippocampus, there are marked distinctions between the dorsal and ventral hippocampus in relation to anatomical connectivity (Swanson and Cowan, 1977), genetic expression and function.

Regarding its anatomical connectivity differentiation, studies have found important differences in both the afferent and efferent projections of the hippocampus. The dorsal hippocampus projects to

11 regions known for the role in navigation and locomotion such as the anterior thalamus, the mammillary nuclei (Taube, 2007) and the ventral tegmental area (Luo et al., 2011). Conversely, the ventral hippocampus projects to regions believed to be relevant for emotional processing and motivated behavior, such as the hypothalamus, the amygdala or the bed nucleus of the stria terminalis

(Figure 1.2) (Fanselow and Dong, 2010). There is also evidence pointing to a differentiation of gene expression along the dorsoventral axis of the hippocampus (Leonardo et al., 2006), including the expression of serotonin receptors (Tanaka et al., 2012).

Figure 1.2: Differential output streams along the dorsal-ventral hippocampus(from (Tannenholz et al., 2014)). Differential connectivity of the dorsal and ventral hippocampus connectivity. The dorsal hippocampus projects preferentially to areas related to context- dependent processing such as the retrospenial area (RSP) and the septum. The ventral hippocampus projects preferentially to mood-related limbic structures such as the hypothalamus, amygdala, medial prefrontal cortex (mPFC), nuccleus accumbens (nACC), ventral tegmental area (VTA) and the bed nucleus of the stria terminalis (BNST).

The first evidence of the dorsoventral functional differentiation of the hippocampus was found in rodent lesion studies. These studies showed that dorsal lesions resulted in impaired performance on cognitive-related tasks such as fear conditioning, radial arm maze or the Morris water maze (Moser et al., 1995; Pothuizen et al., 2004), while ventral lesions resulted in changes in anxiety-like behaviors in tasks such as the elevated plus maze (Kjelstrup et al., 2002; McHugh et al., 2004).

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This differentiation along the longitudinal axis is also apparent in specific structures within the hippocampus, such as the DG.

Projection input into the dorsal and ventral DG originates from different regions of the EC (Dolorfo and Amaral, 1998), and the ventral DG receives more dense serotonergic input from the raphe nucleus (Gage and Thompson, 1980). Optogenetic studies in the DG have also shown the dorsal DG to be more important for learning related tasks, while the ventral DG seems to be more relevant for expression of anxiety behaviors (Kheirbek et al., 2013).

1.4 The Dentate Gyrus

1.4.1 Anatomy

The DG is generally separated into three layers: the molecular layer, the granule cell layer, and the polymorphic layer (or hilus). The molecular layer is occupied by the dentate gyrus granule cell dendrites, fibers from the EC perforant path, as well as a small number of interneurons. The granule cell layer is largely made up of densely packed granule cells, as well as a small number of interneurons. The granule cell layer encloses the polymorphic layer, where several different cell types can be found, the most prominent of which are the mossy cells. Transversally, the dentate gyrus presents a distinct V or U shape, which separates the suprapyramidal (above CA3) and infrapyramidal (below CA3) blades (Amaral et al., 2007).

There are different distinct cell types within the DG. Granule cells (GC), the primary cell type of the DG, are glutamatergic neurons that project mossy fiber axons to CA3 as well as axon collaterals to cells in the polymorphic layer, such as mossy cells. Mossy cells in the polymorphic layer are large glutamatergic neurons that innervate both GC and GABAergic interneurons along the longitudinal axis of the DG. Inhibitory GABAergic interneurons are found throughout the

13 different layers of the DG and include several different kinds of interneurons (Amaral et al.,

2007).

1.4.2 Semilunar Granule Cells

For the purpose of our studies, we want to emphasize the existence of an understudied class of neurons in the DG, the semilunar granule cells (SLGC). SLGC were first described by Ramón y

Cajal in 1911 (Bergey, 1995) as densely spiny, granule cell-like cells in the inner molecular layer, with a dendritic arbor shaped like a half-moon. SLGCs are believed to be found across mammalian species, although further studies into the characterization of this cell type is needed.

Cells consistent with the dendritic and morphological properties of SLGCs have been observed in primates (Duffy and Rakic, 1983) and rabbits (Sancho-Bielsa et al., 2012), although most data available to date originates from rodent studies.

Research into this population of neurons in the DG has uncovered morphological and physiological distinctions between SLGC and regular GC. Both regular GC and SLGC are glutamatergic neurons with polarized dendrites that project towards the ML and an axon that crosses the granule cell layer and ramifies into the hilus. However, SLGC, unlike GC, also generate axonal collaterals into the inner ML (IML), extend their dendrites over a larger lateral distance, have a significantly different cell body shape, and are located closer to the IML border

(or in the IML layer itself) (Figure 1.3).

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Figure 1.3: Morphology of semilunar granule cells(from (Save et al., 2019)). Semilunar granule cells (SLGCs) have been traditionally identified based on their distinct morphological features. On the left, image of a regular mature granule cell. On the right, image of a SLGC displaying some of its main morphological characteristics: cell body in the inner molecular layer (ml) instead of the granule cell layer (gl) and dendritic arborization with a large lateral span.

SLGC also have distinct electrophysiological properties. These neurons, compared to GC, receive a stronger excitatory input from hilar mossy cell axons, show prolonged firing, have lower spike frequency adaptation, and lower input resistance (Gupta et al., 2012; Save et al.,

2019; Williams et al., 2007). Upon perforant path stimulation from the EC, SLGCs respond with persistent firing lasting several seconds, which in turn results in increased hilar cell activity from mossy cells and interneurons termed “hilar up-states”. These up-states in turn lead to feedback inhibition of the general granule cell population (Larimer and Strowbridge, 2010). Because of these characteristics, it has been hypothesized that these neurons could integrate perforant path and mossy cell input over longer time intervals (Williams et al., 2007).

Developmentally, SLGCs and GCs share a common lineage and neural precursor pool with GCs and are estimated to comprise 3% of this pool. However, SLGC ontogenesis originate around

15 embryonic age E12-15, about 1 week before regular GCs (Save et al., 2019), and around the same period as mossy cells (Li et al., 2008). SLGCs and GCs embryonically labeled on the same day are morphologically distinct, further demonstrating that these two populations are developmentally distinct, rather than different maturation stages of the same cell type.

Interestingly, several studies have consistently reported that GCs located in the outer third of the granule cell layer present distinct dendritic arbors and fields similar to those reported in SLGCs

(Green and Juraska, 1985; Kerloch et al., 2019; Sun et al., 2013). These results hint at the exciting possibility that the outer third of the granule cell layer could consist of a heterogenous population of GCs and SLGCs. However, current identification of SLGCs is predominantly based on morphological characteristics (Gupta et al., 2020), and thus analysis of their role in the

DG remains limited. Further research should be conducted with the aim of identifying distinct genetic and neurochemical markers of this cell population to allow for a better functional understanding of their role within the larger context of the DG.

1.4.3 Neurogenesis

The DG is considered the gateway into the information processing pathway of the hippocampus, receiving sensory input from the EC. The DG has been involved in memory processing, spatial navigation, pattern separation, long term potentiation and mood disorders. Part of its versatility is driven by the ability of the DG to produce new neurons throughout adulthood, termed adult hippocampal neurogenesis. This process has been observed both in rodents and humans.

The first report of human adult hippocampal neurogenesis appeared in the 1990s, after researchers injected bromodeoxyuridine (a compound that incorporates into the DNA of dividing cells) into the brains of terminal cancer patients (Eriksson et al., 1998). Further studies using a

16 variety of techniques such as Carbon-14 provided further evidence of the existence of adult-born neurons in the human hippocampus (Spalding et al., 2013; Spalding et al., 2005). In fact, recent studies have discovered that human adult hippocampal neurogenesis persist through aging in the adult brain (Boldrini et al., 2018).

Most of the electrophysiological and functional studies of neurogenesis have been performed in rodents. These studies showed that during maturation these young granule cells exhibit increased excitability (Schmidt-Hieber et al., 2004), and they have been proposed to decrease the overall activity in the DG via inhibitory interneurons (Lacefield et al., 2012). A recent study into the role of adult-born neuron in the regulation of GC activity has underlined the nuanced way in which these neurons can modulate DG activity. Adult-born GCs can monosynaptically inhibit mature

GCs upon lateral EC input (carrying contextual information), but they can also directly excite these same neurons after medial EC input (carrying spatial information) (Luna et al., 2019).

Hippocampal neurogenesis also plays a role in mood regulation. Neurogenesis levels in rodents are decreased after different kinds of stressors such as treatment with the stress hormone corticosterone (Cameron and Gould, 1994), predator odor (Tanapat et al., 2001), or repeated restraint stress (Pham et al., 2003). Similar results were found after psychosocial stress in monkeys (Gould et al., 1998).

Conversely, antidepressant treatment, environmental enrichment and exercise all increase the number of adult-born neurons (Kempermann et al., 1997; Malberg et al., 2000; van Praag et al.,

2005). Furthermore, a recent study revealed that increased neurogenesis can confer stress resilience to chronically stressed mice through the inhibition of mature GC activity in the ventral

DG (Anacker et al., 2018). Alternate depression therapies such as transcranial magnetic

17 stimulation (Czéh et al., 2002) and electroconvulsive therapy (Madsen et al., 2000) also increase neurogenesis in rodents.

1.5 Depression, Antidepressants and the Hippocampus

Several lines of evidence point to the involvement of the hippocampus in the pathogenesis of mood disorders, including MDD. Connectivity studies investigating the brain networks that are dysregulated in depression have identified several involved brain regions including the amygdala, anterior cingular cortex, prefrontal cortex and, most importantly, the hippocampus

(Frodl et al., 2008). The hippocampus along with several other brain regions has also been identified in the pathophysiology of mood disorders in neuroimaging, neuropathological and lesion studies (Drevets et al., 2008).

Patients who suffer from depression often show impaired cognitive functions, the most pronounced of which is memory impairment (Zakzanis et al., 1998), which is strongly dependent on hippocampal function. In fact, smaller hippocampal grey matter volume is associated with poor memory performance in non-clinical samples (Gatt et al., 2009).

Stressful psychological or psychosocial events, which themselves predict subsequent depressive episodes (Kessler, 1997), are associated with structural changes in the hippocampus such as decreased cell survival and proliferation of adult hippocampal neurogenesis (Malberg and

Duman, 2003; Pham et al., 2003; Thomas et al., 2007; Vermetten and Bremner, 2002).

Furthermore, several structural brain imaging studies have reported hippocampal volumes 4-6% smaller in MDD patients compared to matched controls (Campbell et al., 2004; Sheline et al.,

1996; Videbech and Ravnkilde, 2004). Human post-mortem studies have also reported fewer

18 mature GCs in the anterior DG of untreated MDD patients compared to controls and treated patients (Boldrini et al., 2013).

Antidepressant treatment has been shown to reverse some of the effects caused by MDD in the hippocampus. Hippocampal volume is increased in MDD patients treated with antidepressants

(Boldrini et al., 2009; Frodl et al., 2008), and similar results have been found in monkey (Perera et al., 2007) and rodent studies (Malberg et al., 2000).

Rodent studies have shown that treatment with the SSRI fluoxetine increases adult hippocampal neurogenesis by speeding up the maturation and increasing synaptic plasticity of adult-born neurons (Wang et al., 2008). This increase in adult hippocampal neurogenesis is necessary for some of the behavioral effects of fluoxetine in rodent (Santarelli et al., 2003) and non-human primate studies (Perera et al., 2011). The requirement of neurogenesis in the effects of fluoxetine is only required for some, but not all rodent behaviors improved by fluoxetine treatment. When neurogenesis in mice was eliminated through x-irradiation, the efficacy of fluoxetine was abolished in the Novelty Suppressed Feeding (NSF) test, but not the Forced Swim Test (FST)

(David et al., 2009), pointing to distinct pathways recruited by these behaviors.

Further studies have specifically pointed to the ventral DG in the mediation of the effects of fluoxetine. In mice, deletion of both 5-HTR1A serotonin receptors and adult-born GC of the ventral DG have independently been found to prevent the effects of fluoxetine in the NSF test

(Samuels et al., 2015; Wu and Hen, 2014) (Figure 1.4).

Other cell types in the DG have also been reported to be important in the mediation of the chronic effects of fluoxetine, such as mossy cells (Oh et al., 2020) and 5-HTR5A-mediated signaling in parvalbumin+ interneurons (Sagi et al., 2020) (Figure 1.4).

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Figure 1.4: Important molecular mediators of the effects of SSRIs in the Dentate Gyrus.Left insert represents the serotonergic input from the Raphe Nucleus that projects to the DG. SSRIs block the serotonin transporter (5-HTT), and increase the available serotonin (5-HT) that binds to postsynaptic serotonin receptors known to be involved in the response to SSRIs, such as 5-HTR4 and 5-HTR1A located in mature granule cells in the granule cell layer. Adult- born granule cells are also located in the granule cell layer, and neurogenesis is increased by chronic SSRI treatment. Mossy cells and interneurons in the hilus are also involved in the effects of SSRIs, mediated through different receptors like the 5-HTR5A receptor expressed in parvalbumin interneurons. In the molecular layer, the DG receives the perforant path input from the Entorhinal Cortex, and projects its output onto CA3 through hilar projections. Image created with BioRender.com.

While the exact mechanism by which fluoxetine works in the DG is still unknown, evidence has pointed to the Brain Derived Neurotrophic Factors (BDNF) as an important mediator of its effects. BDNF, a member of the neurotrophin family, is expressed on mGCs of the DG. BDNF binds to the tropomyosin-related kinase B receptor (TrkB) (Soppet et al., 1991), and expression of BDNF is regulated by the transcriptional regulator CREB (calcium/cyclic-AMP responsive- element binding protein) (Conti et al., 2002; Tao et al., 1998). TrkB-BDNF signaling can induce phosphorylation of CREB (Finkbeiner et al., 1997; Ying et al., 2002), which in turn is crucial to activate cAMP response element-containing genes (Montminy et al., 1990). Both BDNF and 20

CREB are downregulated after stress and upregulated after antidepressant treatment (Alfonso et al., 2006; Nibuya et al., 1996). Furthermore, loss of BDNF in the DG attenuates the effects of

SSRIs in the FST (Adachi et al., 2008). The involvement of the BDNF in the effects of antidepressants in the DG is further confirmed by human post-mortem studies, showing increased BDNF expression in the DG of antidepressant-treated patients (Chen et al., 2001).

1.6 The Opioid System

1.6.1 A Historical Perspective

Opium is derived from the opium poppy Papaver somniferum and its use was widespread for thousands of years both for medical and recreational use due to its euphoric and analgesic properties. The first recorded use of opiates in human history was in 8,000-year-old clay tablets from the Sumarian cultures, who called this plant “the plant of happiness”. Since then, references to compounds believed to be opiates have been found in ancient Greek3, Egyptian, Chinese and

Arab cultures, all the way through the middle ages until the present day (Wright, 1968).

In 1805, Friedrich Sertüner identified morphine4 as the main active ingredient present in opium

(Macht, 1915). Morphine and codeine, another compound found in opium, are considered prototypical opioids and have been widely used for centuries as anesthetics and in the treatment of pain. However, these drugs presented several problems such as severe side effects, strong abuse potential, physical dependence and development of tolerance to the drug. In the search for a safer opiate that would not cause addiction, heroin (diacetylmorphine) was synthesized from

3 Some scholars believe Homer was referencing opiates when he wrote in the Odyssee: "Presently she cast a drug into the wine of which they drank to lull all pain and anger and bring forgetfulness of every sorrow." The Odyssey, Homer (Ninth century B.C.). 4 Named after Morpheus, the Greek god of dreams. 21 morphine in 1898 and used as a cough suppressant (Sneader, 1998). Like many attempts after this one, the new compound proved to be as problematic as the previous ones and is a drug of abuse to the present day.

Over the years, efforts were made to develop different antagonists that would counteract the effects of opiates, and several theories were developed regarding possible opioid receptor function. Radioligand binding assays allowed for the development of highly selective ligands and the identification of different opioid binding sites (Goldstein et al., 1971; Pert and Snyder,

1973; Simon et al., 1973; Terenius, 1973).

The mu, delta, and kappa opioid receptors were characterized as distinct receptors by the 1980s

(Handa et al., 1981; Mosberg et al., 1983; Von Voigtlander et al., 1983). Using autoradiography in the rat brain, Mansour et al. (Mansour et al., 1987) demonstrated that these receptors were differentially anatomically distributed throughout the brain, but also overlapped in their expression in some regions.

With the advent of molecular biology, the three main opioid receptor subtypes were cloned in the span of three years (Evans et al., 1992; Kieffer et al., 1992; Meng et al., 1993; Thompson et al.,

1993; Wang et al., 1993), and the receptors were mapped to specific chromosomal locations

(Befort et al., 1994; Wang et al., 1994). A fourth member of the opioid receptor family, the

“Opioid Receptor-Like” or ORL, was also cloned soon thereafter (Bunzow et al., 1994;

Mollereau et al., 1994).

In the late 1970s, pharmacological and receptor binding assays revealed the existence of endogenous opioid peptides that bind to the opioid receptors. These were termed methionine([Met])- and leucine ([Leu])-enkephalin (Hughes et al., 1975; Pasternak et al., 1975;

Terenius and Wahlström, 1975). Some years later, the nucleotide sequence of the

22 preproenkephalin precursor mRNA was reported, demonstrating that this precursor mRNA generated several different endogenous opioid peptides (Gubler et al., 1982; Noda et al., 1982).

In the pursuit of understanding the opioid system, several KO mice lines have been created for the different components of the opioid system. Two different groups have generated mice lacking the preproenkephalin gene by targeting the enkephalin-coding 5’ region of exon 3

(König et al., 1996; Ragnauth et al., 2001). Mice lacking the delta opioid receptor have also been generated by either targeting exon 1 (Filliol et al., 2000) or exon 2 (Zhu et al., 1999) of the gene sequence, and this deletion does not affect the expression of mu or kappa opioid receptors. This lack of a compensatory mechanism was also seen in mu receptor KO mice, which have been generated either by deleting exon 1 (Schuller et al., 1999; Sora et al., 1997), exon 2 (Matthes et al., 1996) or exons 2 and 3 (Loh et al., 1998).

1.6.2 Organization

The opioid system plays a key role in pain processing, endocrine function and mood regulation amongst others (Browne and Lucki, 2019). These effects are mediated by three main types of opioid receptors: mu (MOR), kappa (KOR) and delta (DOR). Opioid receptors display a very high expression throughout the central and peripheral nervous system, including the spinal cord, substantia nigra, brainstem, hypothalamus, thalamus, striatum, neocortex, and hippocampus

(Emery and Akil, 2020). The regional expression patterns of these receptors in the brain show a particular degree of overlap in regions relevant to pain, reward, and emotionality (Mansour et al.,

1988).

These receptors belong to the superfamily of G-protein coupled receptors (GPCR), and the rhodopsin-line class A GPCR sub-family. These receptors have seven transmembrane domains

23

(Piros et al., 1996), which show a 70% sequence homology across the three receptors. What largely differentiates these three receptors are the N- and C- terminal domains, as well as the extracellular loops 2 and 3, which show no sequence homology across receptors (Kane et al.,

2006). The sequence homology of these receptors across rodents and humans is over 90%

(Knapp et al., 1995).

Opioid receptors act both through G-protein dependent pathways as well as second and third messenger signaling systems (Al-Hasani, 2011). The heterogeneity of their effects in cell signaling and subsequent pain and emotional states can be partly explained by the many ways in which these receptors can be targeted. Besides ligand-directed signaling (Pradhan et al., 2011), opioid receptors can be modulated by positive and negative allosteric compounds such as endogenous neurotransmitters and neurohormones (Kathmann et al., 2006; Livingston et al.,

2018; Meguro et al., 2018), and they also display biased agonism for different endogenous opioid compounds (Emery and Akil, 2020).

Availability of pharmacological and genetic tools in the last two decades have allowed for the investigation of the role of each opioid receptor in the central nervous system (Gianoulakis,

2009; Lutz and Kieffer, 2013; Sauriyal et al., 2011; Shippenberg et al., 2008).

MOR activation mediates euphoric states and analgesia, and this pathway is commonly used for pain management (Zöllner and Stein, 2007). However, due to the addictive properties of MOR agonists and their key role in reward processing, these drugs are commonly abused (Le Merrer et al., 2009). On the other hand, the KOR has an opposing effect on hedonic homeostasis, while also being important for the treatment of pain (Chavkin, 2011). The activation of the DOR also reduces persistent pain (Gavériaux-Ruff and Kieffer, 2011), and has been implicated in drug reward and addiction as well as emotional processes.

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Under natural conditions, opioid receptors are stimulated by several different endogenous opioid peptides which bind to opioid receptors with varying affinities. There are three main neuropeptide families of endogenous opioids: β-endorphins, enkephalins, and dynorphin. Each of these broad peptide families include multiple different peptides which bind to opioid receptors with different binding affinities (Mansour et al., 1995). KORs display a high affinity for dynorphins (Chavkin et al., 1982), MORs display high affinity for endorphins (Zadina et al.,

1997), and both MORs and DORs interact with enkephalins, although DORs display a 10-fold higher affinity for enkephalins than MORs do (Corbett et al., 1984).

These opioid peptides are in turn derived from different precursor proteins: β-endorphins are derived from the precursor pro-opiomelanocortin, dynorphins are derived from prodynorphin

(Pdyn), and enkephalins are derived from both Pdyn and proenkephalin (Penk).

Each of these precursor proteins undergoes post-translational modifications resulting in the formation of several different active peptides. All of these peptides share what is termed the opioid motif, a common N-terminal sequence of a Tyr-Gly-Gly-Phe-Met/Leu peptide, followed by different C-terminal extensions that results in peptides 5-31 aminoacids long. Penk-derived opioid peptides primarily include four different copies of [Met5]-enkephalin, one copy of [Leu5]- enkephalin as well as several other copies of C-terminally extended forms of [Met5]-enkephalin

(Gubler et al., 1982; Noda et al., 1982). PDYN derived opioid peptides include [Leu5]- enkephalin as well as several other peptides that all begin with the same sequence as the [Leu5]- enkephalin peptide, including dynorphin A, dynorphin B, leumorphine and β-endorphins

(Kakidani et al., 1982) (Figure 1.5).

The diversity of opioid peptide affinities coupled with the overlapping distribution of opioid receptors results in a highly flexible and adaptive system (Emery and Akil, 2020).

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Figure 1.5: Organization of opioid system(from (Emery and Akil, 2020)). The three opioid receptors (mu, green; delta, blue; kappa, red) show varying affinities for endogenous opioid peptides. The thickness of the connecting lines represents the strength of the affinity for each peptide, and the values represent the KD equilibrium dissociation constant for each ligand as reported by (Mansour et al., 1995). These opioid peptides are in turn cleaved from the propeptides Pomc, Penk, and Pdyn. The kappa opioid receptor shows a high affinity for dynorphin peptides, while delta opioid receptors have higher affinities with enkephalin products.

1.6.3 Opioid system in the Dentate Gyrus

Several of the processes mediated by the hippocampus are affected by the opioid system, such as hippocampal excitability (Simmons and Chavkin, 1996), long-term potentiation (Pu et al., 2002), seizure activity, and learning (Nestler, 2001; Sandin et al., 1998).

In the DG, the most prevalent endogenous opioid peptides are enkephalins (specifically [Met5]- enkephalin and [Leu5]-enkephalin) and dynorphins, which exert opposing effects (Gall et al.,

1981; Hong et al., 1980). These molecules are not active at the synapse, but rather reach nearby cellular targets through extrasynaptic diffusion, and are modulators of fast neurotransmission

26

(Drake et al., 2007). Both dynorphins and enkephalins are stored in specialized organelles in the cell called dense-core vesicles (Drake et al., 1994; Pierce et al., 1999), and it’s believed that once released, enkephalin peptides are less stable than dynorphin ones (Wagner et al., 1991).

Enkephalins can be found in DG neurons and afferents across species, including mice, rats and humans (Gall, 1988; Rees et al., 1994). They are expressed by approximately 15% of granule cells (Johnston and Morris, 1994), and are normally present in mossy fibers (Gall et al., 1981).

Enkephalins are also contained in segments of the LPP (Fredens et al., 1984) and in scattered

GABAergic interneurons. Enkephalin biosynthesis and posttranslational proteolytic processes are regulated in a region-specific manner, such that production of enkephalins in the mossy fibers could elicit distinct functions compared to other subregions, depending on the peptides produced. Based on anatomical data, enkephalins in the DG have been proposed to modulate inhibitory postsynaptic transmission.

[Leu5]-enkephalin immunoreactivity is present in small mossy fiber terminals in the hilus as well as large complex mossy terminals (Commons and Milner, 1995). [Leu5]-enkephalin is believed to have a relevant role in the regulation of inhibition through GABAergic neurons due to their colocalization with GABA-immunoreactive terminals in the molecular layer as well as

GABAergic neurons in the hilus (Commons and Milner, 1996).

Dynorphin immunoreactivity is also present in large mossy fiber terminals and smaller mossy fiber collaterals in the hilus and CA3 (McGinty et al., 1983; Pierce et al., 1999), although the majority of labeled dense-core vesicles have been found in granule cell dendrites (Drake et al.,

1994). The time course of the release of these vesicles has been suggested to be consistent with local release in the outer molecular layer.

27

Enkephalins and dynorphins typically exert opposing effects through their differential affinities for opioid receptors. The DOR and MOR are the preferential target of enkephalins, while the

KOR is bound preferentially by dynorphins.

The KOR displays very little immunoreactivity in the rat DG (Mansour et al., 1988; Mansour et al., 1986), but has been found consistently expressed in guinea pig studies. These studies suggest that the KOR is expressed in presynaptic afferents to the DG both from PP terminals and suprammamilar afferents. In fact, dynorphins and other KOR agonists have been shown to reduce LTP induction in the hippocampus. It is believed therefore that KOR in presynaptic afferents help modulate information flow into the DG in the outer molecular layer (Drake et al.,

2007).

In rodent studies, the DOR is distributed throughout the DG, partly overlapping with enkephalin- containing regions. Its distribution seems to be high in the hilus, and lower in the GCL. The

DOR is mostly expressed in GABAergic interneurons (Bausch et al., 1995; Commons and

Milner, 1996; Stumm et al., 2004), and in situ hybridization and immunocytochemical studies have estimated that over 90% of parvalbumin positive interneurons in the GCL express this receptor, compared to the 11% of somatostatin positive interneuron. The presence of the DOR in these interneuron subtypes suggests a role in the inhibition of granule cells both at the distal dendrites as well as their output (Drake et al., 2007). Interestingly, mRNA studies revealed there was no observed overlap between the DOR and Penk expressing neurons in the DG, pointing to a lack of autoregulation of DOR containing interneurons (Stumm et al., 2004).

In rodent studies, MOR expression is scattered throughout the DG (Arvidsson et al., 1995) and its expression seems to be higher in GABAergic interneurons. Interestingly, the MOR seems to be important in adult hippocampal neurogenesis in the DG. Chronic morphine or heroin

28 treatment inhibits neurogenesis in an opioid-receptor dependent manner, and this effect is independent of stress hormones (Eisch et al., 2000).

The MOR and DOR receptors produce a mostly disinhibitory effect in the hippocampus through the hyperpolarization of inhibitory GABAergic neurons (Zieglgansberger et al., 1979). This effect has been observed both through direct MOR and DOR activation, which blocked

GABAergic input (Neumaier et al., 1988), as well as through the effects of enkephalin, which was found to hyperpolarize GABAergic interneurons (Madison and Nicoll, 1988).

1.6.4 Opioid System and Depression

Several psychiatric diseases have been associated with a dysregulation of the opioid system, including post-traumatic stress disorders, personality disorders (Bandelow and Wedekind, 2015;

Prossin et al., 2010), Alzheimer’s disease (Torres-Berrio and Nava-Mesa, 2019), and mood disorders such as anxiety (Bruchas et al., 2010) and depression (Lutz and Kieffer, 2013; Pecina et al., 2019).

The use of opiates to enhance mood has been around since ancient times, reaching its apex during the 19th century, when the “opiate cure” was widely used by psychiatrists to treat mood disorders (Weber and Emrich, 1988). These treatments were discontinued when their addictive properties became burdensome, and new antidepressants were discovered. More recently, new evidence appeared showing that existing modern antidepressants can act through the opiate system. For example, tianeptine acts primarily via the MOR receptor (Samuels et al., 2017), and it has been recently suggested that ketamine may recruit the opioid system downstream of the activation of glutamate receptors (Williams et al., 2019).

29

There is increasing evidence of the involvement of opioid receptors in the regulation of emotionality. MOR agonists have been shown to produce euphoria, and acute pharmacological activation of the MOR has been shown to reduce depressive-like behaviors (Berrocoso et al.,

2013; Besson et al., 1996). In contrast, KOR agonists promote depressive-like behaviors while

KOR antagonists induce antidepressant-like effects (Mague et al., 2003). In fact, KOR antagonists are currently being tested in clinical trials for their efficacy to treat anxiety and depression.

Out of the three opioid receptors, the DOR has been the most implicated in the regulation of mood disorders and antidepressant effects. Genetic and pharmacological studies have further supported the involvement of the DOR in emotional processing. Both the number and function of

DOR is modulated by different stressors. DOR-KO mice display higher baseline anxiety- and depressive- like behaviors compared to WT mice, while this phenotype was not present in MOR-

KO or KOR-KO mice. Specifically, DOR-KO mice showed an increased immobility in the

Forced Swim Test, and higher anxiety-like behavior in the elevated plus maze and the light-dark box test (Filliol et al., 2000). Blocking the DOR through the administration of a selective antagonist increased anxiety- like behaviors in rat and mouse studies (Perrine et al., 2006; Saitoh et al., 2004). In contrast, it has been consistently shown in mouse and rat studies that the activation of the DOR through administration of different selective agonists reduced anxiety-like and depressive-like behaviors in a large number of behavioral paradigms, including the Forced

Swim Test (Naidu et al., 2007; Pradhan et al., 2011; Saitoh et al., 2004; Torregrossa et al., 2006;

Vergura et al., 2008). Interestingly, after administration of a DOR agonist, BDNF expression is increased in the rat basolateral amygdala, frontal cortex and hippocampus (Torregrossa et al.,

30

2004). Based on this evidence, some DOR agonists have been tested in clinical trials as potential novel antidepressants, although with not much success (Richards et al., 2016).

Enkephalins, which bind preferentially to the DOR and, to a lesser extent, the MOR, have also been implicated in stress susceptibility and mood related pathologies. Enkephalin is known to modulate the HPA-axis (Pechnick, 1993), and enkephalin mRNA levels are modified after several different stressors (Bertrand et al., 1997; Christiansen et al., 2011; Dziedzicka-

Wasylewska and Papp, 1996; Mansi et al., 2000).

Penk-KO mice, lacking the pre-proenkephaline precursor gene, also exhibit increased levels of anxiety under normal conditions (Bilkei-Gorzo et al., 2008; Bilkei-Gorzo et al., 2004; König et al., 1996), as well as increased anxiety- and depressive-like phenotypes after stress in a large number of testing conditions (Kung et al., 2010; Ragnauth et al., 2001). Furthermore, anxiolytic and antidepressive-like effects have been observed upon systemic administration of enkephalin analogs, as well as after administration of compounds that inhibit enkephalin degradation

(termed enkephalinase inhibitors) (Baamonde et al., 1992; Broom et al., 2002; Javelot et al.,

2010; Jutkiewicz et al., 2006; Randall-Thompson et al., 2010; Tejedor-Real et al., 1995).

Enkephalin peptides have previously been studied in the context of anxiety and depression in several brain regions: FST stress was shown to decrease [Leu]5-Enkephalin levels in the hypothalamus and [Met]5-Enkephalin in the striatum (Nabeshima et al., 1992); [Met]5 and [Leu]5-

Enkephalin levels in the Nucleus Accumbens (NAc) were decreased in susceptible mice in the chronic social defeat stress paradigm, but treatment with enkephalinase inhibitors or a DOR agonist induced a resilient phenotype (Nam et al., 2019); and prolonged predator odor and acute restraint stress decreased [Leu]5-Enkephalin levels and increased [Met]5-Enkephalin in the rat hippocampus (Li et al., 2018).

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These results underlie the importance of the DOR/enkephalinergic system in the modulation of anxiety- and depressive-like behaviors. In the human and rodent central nervous systems, the

DOR is expressed in cortical and limbic structures implicated in mood regulation, including the basal ganglia, hypothalamus, amygdala and hippocampus (Peckys and Landwehrmeyer, 1999;

Peng et al., 2012; Simonin et al., 1994). Stereotactic injection of different DOR agonists has been shown to reduce anxiety in the hippocampus, cingulate cortex and amygdala (Narita et al., 2006;

Randall-Thompson et al., 2010; Solati et al., 2010). These results suggest that the DOR/ enkephalinergic system in the amygdala-cortico-hippocampal circuit is involved in the modulation of emotional processes.

1.7 Behavioral Paradigms in Mood Disorders

Animal models have often been used to study the mechanism of action of therapeutic drugs used in the treatment of psychiatric disorders such as anxiety and depression. The common use of these models is due to the high anatomical and functional homology to humans, as well as to the variety of behaviors they exhibit. This approach however is contingent on the accuracy of the animal model used, since it requires that some aspect of the treatment response is incorporated.

The validity of animal models of mood disorders has been a subject of discussion for decades, but researchers have come to general agreements of the criteria used to determine whether a model is relevant for human pathology. In 1984 Willner (Willner, 1984) proposed the three criteria of validity that are currently considered the gold standard (Belzung and Lemoine, 2011): predictive validity, face validity, and construct validity.

Predictive validity refers to whether a model can correctly identify diverse antidepressant treatments and can accurately predict the phenomenon of interest. While some authors use this

32 definition in a broad sense, using it to identify any variable that correctly predicts a similar effect in humans, it most often refers to a model’s ability to accurately identify and respond to pharmacological treatment (Belzung and Lemoine, 2011; Dulawa et al., 2004). An example of a model commonly used for antidepressant drug discovery is the forced swim test (FST) (Figure

1.6). The FST is one of the behaviors with the highest predictive validity for antidepressants

(Cryan et al., 2002; Porsolt et al., 1977), and is commonly used to test depressive-like endophenotypes measuring active versus passive coping strategies. In this paradigm, rodents are placed in a water tank and forced to swim for 6 minutes. During this time, the animals either swim actively, climb, or passively float (immobility). Immobility is reliably reduced by acute treatment with antidepressants that are also known to be clinically effective, but not by compounds that are only anxiogenic. This test also displays high reliability, since the behavioral response is consistent across experiments. The FST, however, doesn’t exhibit predictive validity for the specific time-course of the effects of SSRI treatment. SSRIs require chronic administration over weeks in order to exert behavioral effects in humans, probably due to neurobiological adaptations that occur over time such as neural plasticity (Blier, 2003).

However, mice in the FST respond to acute as well as chronic administration of SSRIs (Cryan and Mombereau, 2004).

Hyponeophagia-based behavioral paradigms are amongst the few current models that are sensitive to chronic, but not acute, SSRI treatment, and thus demonstrate strong predictive validity of not only the effects of drug treatment, but also their time-course. Hyponeophagia refers to a reduction in feeding when a mouse is placed in a novel environment. These paradigms are successful in identifying the anxiolytic effects of a multitude of compounds used for the treatment of anxiety in humans such as SSRIs (Dulawa et al., 2004). One of the most common

33 hyponeophagia tests used is the Novelty Suppressed Feeding (NSF) test (Figure 1.6). The NSF test is a conflict-based approach-avoidance test in which a food-deprived animal is confronted with the choice of consuming a desirable food in the center of a novel, brightly light arena, or avoiding the novel environment. Besides its strong predictive validity, this paradigm is also considered ethologically relevant because it takes advantage of the animal’s natural aversion to bright, open spaces.

Face validity refers to whether the model resembles features of depression or anxiety in humans, both related to treatment and symptomatic aspects (Willner, 1984). It has also been proposed that rather than a model studying the entirety of the disorder (for example, depression), it should focus on modeling one specific dimension of the disorder (for example, anhedonia). More recently, it has been proposed that face validity should encompass not only behavioral aspects

(ethological validity) such as anhedonia, but also biological aspects (biomarker validity) such as elevated corticosterone (Belzung and Lemoine, 2011).

Construct validity refers to an animal model that can accurately assess the feature of depression or anxiety being modeled (Willner, 1984). Construct validity requires that there is an analogy in the etiological causes between humans and animals causing a behavioral change. Absent an etiological cause, construct validity can also refer to a common genetic or neurobiological mechanism that underlies the similarity in the observed behavioral outcome. The methods that can fulfill this criterion for anxiety- and depressive- like behaviors can be separated into environmental manipulations such as maternal separation or chronic mild stress, and pharmacological manipulations such as chronic corticosterone treatment (Sartori et al., 2011).

The chronic corticosterone treatment model is often used based on evidence of the relationship between elevated glucocorticoid levels caused by chronic stress and anxiety (Young et al., 2008).

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Figure 1.6: Rodent models of mood disorders used in our studies.The elevated plus maze, open field and novelty suppressed feeding tests are free exploration conflict approach- avoidance based tests which include a perceived dangerous (center, open arms) and safe zone (periphery, closed arms). The novelty suppressed feeding test is a free exploration hyponeophagia-based test with strong predictive validity for the assessment of the effects of selective serotonin reuptake inhibitors. The forced swim test is a task measuring active versus passive coping strategies in an assessment of a depression-like phenotypical characteristic, which also displays strong predictive validity. Image created with BioRender.com.

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Chapter 2 : Contribution of the Opioid System to the

Antidepressant Effects of Fluoxetine

2.1 Fluoxetine Modulates Dentate Gyrus Function Through the Opioid

System

2.1.1 Introduction

Anxiety and depressive disorders have a comorbidity of 60% and together they are the most common mental illnesses in the US (Kessler et al., 2012). The medications most often prescribed to treat depressive disorders are Selective serotonin reuptake inhibitors (SSRIs), such as fluoxetine. Despite being widely used, only one-third of patients experience remission after up to four months of treatment with SSRIs, and symptom remission has a delayed onset of effects of at least a month (Trivedi et al., 2006).

Several studies have specifically pointed to different molecular and cellular mechanisms in the ventral DG that mediate the behavioral effects of fluoxetine. In mice, deletion of both 5-HTR1A serotonin receptors and adult-born granule cells from the ventral DG have independently been found to prevent the effects of fluoxetine in the NSF test (Samuels et al., 2015; Wu and Hen,

2014). Other serotonin receptors like the 5-HTR4 receptor in mature granule cells (mGCs) have also been implicated in the effects of fluoxetine that are mediated by the DG (Samuels et al.,

2016). Several cell types in the DG have also been reported to be important in the mediation of the chronic effects of fluoxetine, such as mossy cells (Oh et al., 2020) and 5-HTR5A-mediated signaling in parvalbumin+ interneurons (Sagi et al., 2020).

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In order to gain further insight into the downstream effects of SSRIs, in this Chapter we have focused on the study of the opioid system’s involvement in fluoxetine’s mechanism of action.

The opioid system modulates pain processing, endocrine function and mood regulation amongst others (Browne and Lucki, 2019). These functions are mediated through three major opioid receptor subtypes: mu (MOR), delta (DOR) and kappa (KOR), each of them exhibiting unique influences on mood regulation.

Opioid receptors are stimulated by several different endogenous opioid peptides which bind to opioid receptors with varying affinities. Two types of these endogenous opioid peptides, enkephalins and dynorphin, exhibit opposing effects. [Met5]-Enkephalin and [Leu5]-Enkephalin bind preferentially to the DOR and also exhibit a 10 fold lower affinity for the MOR (Mansour et al., 1995). Dynorphin, on the other hand, binds preferentially to the KOR (Benarroch, 2012).

These peptides are generated from the maturation of long mRNA precursors which give rise to preproteins, namely prodynorphin (Pdyn), which gives rise to [Leu5]-Enkephalin and dynorphin peptides among others, and proenkephalin (Penk), which gives rise to [ Met5]- and [Leu5]-

Enkephalin peptides among others.

Penk has been previously shown to be involved in mood regulation. Penk-KO mice exhibit exaggerated responses to fearful and anxiety-provoking stimuli (Ragnauth et al., 2001).

Furthermore, administration of an enkephalinase cocktail (which increases endogenous enkephalin levels) can alleviate the effects of chronic social defeat stress (Nam et al., 2019), and its anti-depressive-like effects are blunted in Penk-KO mice (Noble et al., 2008).

While there is accumulating evidence implicating the opioid system in antidepressant mechanisms, it is still poorly understood how this system may be involved in the antidepressant effects of SSRIs.

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In this section, we analyzed gene expression data from the DG of fluoxetine treated mice in order to better understand the differential transcriptional changes between fluoxetine responder and non-responder mice after fluoxetine treatment, and identify gene networks involved in this effect.

Using various gene expression analysis tools, we identified Penk and other opioid related genes as important players in the response to fluoxetine treatment. We also show that fluoxetine increases gene expression of Penk and other related genes in a subset of mature granule cells, both anatomically and transcriptionally distinct.

2.1.2 Methods

Mice

Procedures were conducted in accordance with the US National Institutes of Health Guide for the

Care and Use of Laboratory Animals and New York State Psychiatric Institute Institutional

Animal Care and Use Committee at Columbia University and the Research Foundation for

Mental Hygiene.

Husbandry

Mice were housed in groups of 3–5 per cage with free access to food and water on a 12:12-h light/dark cycle. All behavioral testing was conducted during the light period.

Experimental Mice

C57BL/6J wildtype mice were purchased from Taconic Farms (Germantown, NY) and used for behavioral testing and subsequent Microarray analysis. C57BL/6J wildtype mice were purchased from Jackson Laboratories (Bar Harbor, ME) and used for in situ hybridization studies. All mice were 7–8 weeks old and weighed 23–35 g at the beginning of the treatment.

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Drug Treatments

Corticosterone Administration

Corticosterone (Sigma, St. Louis, MO) (35 ug/ml) was first mixed in a solution of 0.45% beta- cyclodextrin (Sigma, St Louis, MO), which is used to mask the taste of corticosterone. In order to dissolve the corticosterone properly, the mixture was sonicated three times for 5 minutes. The solution was then added to autoclaved drinking water and distributed into opaque drinking bottles to protect from the light. Corticosterone was delivered alone or in the presence or antidepressant. Corticosterone solution was available ad libitum in the drinking water. Bottles and caps were changed every three days.

Fluoxetine Administration

Fluoxetine hydrochloride (160 ug/ml) was purchased from Anawa Trading (Zurich, Switzerland) and delivered in the presence of corticosterone in opaque bottles to protect from light. The fluoxetine and corticosterone solution was available ad libitum in the drinking water.

Behavioral Testing

All behavioral experiments were conducted in male mice 16-20 weeks of age.

Novelty Suppressed Feeding (NSF)

Wildtype C57BL/6J mice were food deprived for 22-24 hours prior to the beginning of the test.

During this time water was provided ad libitum. The NSF test was conducted in a plastic box (50 cm long x 28 cm wide x 15 cm deep) covered with regular mouse bedding. Before the test began, a single food pellet was secured on a platform in the center of the arena, which was brightly lit from above (~1,200 lux). The mouse was placed on the corner of the arena at the same time a stopwatch was started. Latency to feed was measured as the time passed from the moment the

39 mouse was placed in the enclosure to the moment the mouse took a bite from the pellet at the center of the arena while sitting on its haunches and biting the pellet with the use of its forepaws.

After biting the pellet, the latency to feed as appeared on the stopwatch was recorded. If the mouse failed to bite the pellet, it was removed from the arena after 8 minutes. The bedding in the arena was emptied every three runs, and the arena was cleaned with Sanicloth. After the test, mice were placed in their homecage for 5 minutes, where a single food pellet had been previously placed, and latency to feed from this pellet was also recorded. This pellet was weighted before and after the 5 minutes, in order to record the amount of food consumed. Each mouse was weighted before food deprivation and then again at the end of the test, in order to calculate the percent body weight lost.

Forced Swim Test (FST)

The FST was performed in plastic buckets (19 cm diameter, 23 cm deep) filled halfway with 25

ºC water. Mice were placed into a bucket and left for 6 minutes, during which the software automatically collected data of mouse movement. After 6 minutes mice were retrieved, patted dry and returned to their homecage. The water in the buckets was replaced every other run. Time spent immobile vs swimming and climbing was measured. Immobility was defined as the periods of time when an animal was floating with no attempt at swimming.

The NSF test was done first, followed by the FST 4 days later.

Gene Expression

Microarray Analysis

The gene expression data of the fluoxetine effects in the dentate gyrus were obtained using the

Affymetrix gene expression analysis platform. Sample collection and data preprocessing from

40 this microarray experiment have been previously described by Samuels et al., 2014 (Samuels et al., 2014). Briefly, mice were sacrificed one week after their last behavioral experiment in order for the acute stress related effects to subside, while still maintaining their drug regimen. Mice brains were dissected and placed into chilled ACSF solution for 5 minutes. In order to obtain dorsal and ventral dentate gyrus samples, the whole hippocampus was first dissected, taking care to preserve the dorso-ventral orientation. Then the molecular and granular layers of the dentate gyrus were microdissected through a transverse slice cut through the septotemporal axis of the hippocampus. Dorsal and ventral dentate gyrus samples were collected into RNase free microcentrifuge tubes, and combined with the equivalent contralateral sample. All samples were then flash frozen and stored at -80 ºC. The Quiagen RNeasy kit was then used according to the manufacturer’s instructions in order to isolate the RNA from the samples. 500 ng of high-quality

RNA was isolated per sample and then submitted to Expression Analysis

(expressionanalysis.com) for microarray processing. All samples were processed in parallel and hybridized in a single batch using Affymetrix 430_2 39 expression arrays. Data processing for expression levels was performed with Robust Multiarray Analysis (RMA) and MicroArray

Analysis Suite 5 (MAS5). Present calls were obtained from MAS5 analysis. Data were deposited in the Gene Expression Omnibus (GEO) accession number GSE43261.

Differential Expression Analysis

A differential expression analysis was conducted using the GenePattern software’s

ComparativeMarkerSelection tool (Reich et al., 2006) (Reich et al., 2006). Significant genes were selected based on Q value (p < 0.05) and Fold Change (FC > 2).

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Analysis of Penk Co-Expression

For the analysis, the data were filtered on the Presence/Absence calls to remove the features called Absent in more than half of the replicates per group, and transformed into the binary log scale. The gene expression signals were standardized via z-score transformation and the gene expression profiles matching Penk were obtained for the ventral and dorsal dentate gyrus using either pairwise Pearson correlation or Euclidean distance, utilizing base R function cor() and function genefinder() from R genefilter package (Gentleman et al., 2020), respectively. Both approached yielded identical results.

Weighted Gene Coexpression Network Analysis (WGCNA)

WGCNA is a gene expression analysis tool which allows the user to construct weighted gene correlation networks used to describe correlation patterns across a set of genes in a microarray sample. A detailed description of WGCNA has been described elsewhere (Langfelder and

Horvath, 2008; Zhang and Horvath, 2005), and in this study the WGCNA algorithm was used as described by the authors. Briefly, the first step of this analysis is the construction of an adjacency matrix measuring the connection strength between a pair of genes. In order to calculate this adjacency matrix, the correlation between each pair of genes raised to a given power is calculated. The power value used for this calculation amplifies disparities between strong and weak correlations, and the correct selection of this value is crucial for further analysis. For this analysis, we selected the power value of 5, which represented the lowest power for which the scale-free topology index curve flattened at a value of roughly 0.85. Secondly, a network of genes is constructed based on the calculated adjacency matrix. In this gene network, the nodes

42 are the genes and the correlation values are the edges between the nodes. Because the network is weighted, a connectivity measure is calculated for each gene as the sum of the weight of all edges connecting to this gene. Third, a Topological Overlap Matrix (TOM) is calculated in order to create a dissimilarity matrix, a crucial step in the module construction. A TOM represents a pairwise similarity measure between network nodes (genes), and its value is high if two genes have many shared neighbors (this is, overlap of their network neighbors is large). Thus, a high

TOM value implies that genes have similar expression patterns. Fourth, a clustering dendrogram is calculated based on these measures, and hierarchical clustering is used in order to identify modules or groups of genes which are highly correlated with each other. In this study, the module construction was carried out as specified by the WGCNA algorithm, using the function blockwiseModules() with the following parameters: power = 5, TOMType = "unsigned", minModuleSize = 30.

These modules of genes can then be cross-correlated to traits of interest. In our case, our traits of interest were immobility in the FST, latency to feed in the NSF test, treatment received

(fluoxetine or vehicle), treatment responsiveness (fluoxetine responders, fluoxetine non- responders, and no treatment), and DG location (dorsal or ventral DG). Module-trait correlations were calculated between each module eigengene and the specified traits. For each gene within the module, the gene significance (GS) and module memberships (MM) were calculated. GS refers to the absolute value of the correlation between the gene expression profile and the trait.

MM refers to the correlation of the gene expression profile and the module eigengene. A heatmap of correlation values was created for all modules in the sample using labeledHeatmap(), and a subset of these modules were hand-picked to be shown in Figure 2.11.

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Single-cell Analysis of Penk Expression

Mapping of the Penk expression to the previously published single-cell dentate data (Habib et al.,

2016) was performed in R using Seurat v3.1.5 (Stuart et al., 2019) and custom scripts. Data were transformed from log to linear scale and previewed for artifacts. After the preview, data were normalized, scaled, and processed for dimensional reduction, clustering, and differential expression using the standard analytical pipeline from the Seurat package with default parameters. Clustering and UMAP were performed using data dimensionally reduced via principal component analysis (McInnes and Healy, 2018). Differential expression between the identified single-cell clusters was assessed via the ROC test. Cell types were assigned to the gene expression clusters by mapping cluster-specific differentially expressed genes to a reference dataset (Hochgerner et al., 2018).

RNA Sequencing

5HT1A-KO and 5HT1A+ mice (mice where the 5HT1A receptor expression was rescued only in the dentate gyrus granule cells) were treated chronically with vehicle or fluoxetine for 28 days, and then tested in the NSF and FST behavioral paradigms. Tissue from the dorsal and ventral

DG were collected with a biopsy punch, and then RNA was extracted using and RNA purification kit (Norgen, ON, Canada).

RNA-Sequencing was performed by the Columiba Sulzberg Genome Center. They used a poly-

A-pull-down for mRNA enrichment (200ng-1ug per sample, RIN>8 required) and proceed on library preparation by using the Illumina TruSeq RNA prep kit. Libraries were then sequenced using the using Illumina HiSeq2000 instrument. Samples were sequenced together and multiplexed in each lane, and the single-end read length was 100bp for each sample, performing

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30 million reads. Reads were mapped to a reference genome using Tophat version 2.1.0, and the relative abundance of genes was performed using Cufflinks version 2.0.2.

In Situ Hybridization (ISH)

Brains from mature male mice were collected and immediately flash-frozen in 2-methylbutane.

Brains were stored at -80 ºC until sectioning. Brains were coronally sectioned (Leica CM3050 S cryostat) at a thickness of 20 µm, and the slices were thaw mounted on freeze safe slides. Each slide contained 3 brain slices from 3-4 different mice, and all slices within a slide roughly reflected the same anterior-posterior coordinates. Slides were stored at -80 ºC. Slides representing the dorsal dentate gyrus were selected for ISH. ISH was performed using the RNAscope®

Fluorescent Multiplex Detection Assay (CAT# 320851) and ready-made probes against Penk

(CAT# 318761) (Advanced Cell Diagnostics, Newark, CA). The assay was performed according to manufacturer’s instructions.

Briefly, slides were pre-fixed in chilled 4% paraformaldehyde, followed by a series of dehydration steps in 50%, 70% and 100% ethanol. A hydrophobic barrier was drawn around each sample on the slide in order to better contain liquid within it for further steps. A protease

(Protease IV, RNAscope® Fluorescent Multiplex Reagent Kit) was then used to permeabilize the tissue. Slides were then washed in 1 x PBS (phosphate buffer saline), followed by probe hybridization for 2 hours at 40 ºC inside a humidity chamber. The probe was then removed and washed twice in 1 X Wash Buffer (RNAscope® Fluorescent Multiplex Reagent Kit) for 2 minutes. The probe signal was amplified in a series of steps inside a humidity chamber using proprietary amplifying probes, with a wash step twice in 1 X Wash Buffer in between every step:

Amp1-FL for 30 min at 40 ºC, Amp2-FL for 15 min at 40 ºC, Amp3-FL for 30 min at 40 ºC,

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Amp4-FL-ALT B for 15 min at 40 ºC. Samples were then stained with DAPI (4′,6-diamidino-2- phenylindole) for 30 seconds before being covered with Vectashield mounting medium and coverslipped for imaging analysis.

Cell Quantification

Images were acquired through confocal microscopy using a 10x or 20x objective (Leica TCS

SP8). A whole section tile scan was obtained and exported for quantification. Fluorescence was quantified using the Fiji software (Schindelin et al., 2012).

Images were first transformed into 8bit images, then inverted and background subtracted. Images were then thresholded automatically using the OTSU Thresholding tool. A region of interest

(ROI) was then selected around each of the brain regions where fluorescence was to be measured, in roughly similar shapes across brain slices. From each ROI a list of pixels was produced using the histogram tool, generating a value of the total black and white pixels found in a given image. Black pixel value was used as a proxy for fluorescence, and then pixel values were normalized by the total area selected for each ROI in order to account for slight changes in total area selected across samples.

2.1.3 Results and Figures

Fluoxetine treatment in the corticosterone model for chronic stress results in responder and non-responder mice

C57BL/6J WT mice were administered corticosterone (5 mg/kg/day) for 4 weeks, which resulted in a behavioral phenotype similar to that elicited by chronic-stress (David et al., 2009). They were subsequently treated with either fluoxetine (Flx, 18 mg/kg/day) or vehicle (Veh) for an

46 additional 4 weeks while corticosterone administration continued. These mice were then run in the Novelty Suppressed Feeding test (NSF) and the Forced Swim Test (FST), behavioral assays for anxiety-like and depressive-like phenotypes, respectively. A random subset of these mice was selected for ventral (vDG) and dorsal (dDG) DG dissection and extraction, and the samples were then run on a Microarray Affymetrix gene expression assay (Figure 2.1A).

Chronic fluoxetine treatment resulted in a significant decrease in the latency to feed in the NSF test (p<0.0001) (Figure 2.1B) and a significant decrease in the time spent immobile in the FST

(Figure 2.1C). The chronic corticosterone model for chronic stress followed by fluoxetine treatment is often used because it consistently elicits a spectrum of behavioral responses to fluoxetine treatment, ranging from complete response to a lack of response (Dieterich et al.,

2019; Yohn et al., 2020). In our results we also see a range of response levels from fluoxetine treated mice using this paradigm (Figure 2.1). Interestingly, the fluoxetine-treated mice that display a low response to treatment in the NSF test (represented as high latency to feed), also tend to present a low treatment response in the FST (represented as high immobility) (Figure

2.1D).

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Figure 2.1: Chronic corticosterone and fluoxetine administration results in treatment response.A) Experimental timeline of treatment administration. B) Total latency to feed per mouse (left) and survival curve (right) of vehicle (grey) and fluoxetine-treated mice (blue). Gray dotted line represents the cutoff point for the definition of treatment responsiveness. Log-rank (Mantel-Cox) test: Chi square = 34.91, ****p <0.0001; n = 27,33. C) Total (6 min) immobility in FST. Unpaired t-test Vehicle Vs Fluoxetine ***p= 0.0007. D) Scatterplot distribution of latency to feed in the NSF and total (6 min) immobility in FST of the fluoxetine-treated mice (blue). While the distribution of behavioral response in our dataset seems to be continuous, we have chosen to classify mice as fluoxetine responders and non-responders in order to parallel the way in which human psychiatric patients are often classified (Li et al., 2020).

Mice were defined as fluoxetine non-responders if their latency to feed was higher than the lowest latency to feed of the vehicle-treated group (latency = 240s) (Figure 2.1B). We used the

48 latency to feed in the NSF test to calculate the response cutoff value because this test is sensitive to chronic, but not acute, fluoxetine treatment, which more closely resemble the conditions of human fluoxetine treatment response.

By using this definition, we have clustered the fluoxetine-treated mice into fluoxetine-responders

(Flx-R, 69.7%) and fluoxetine non-responders (Flx-NR, 30.3%) (Figure 2.2A, C). We then used this classification to analyze the behavioral response in the FST. We observed that the time spent immobile was significantly different between vehicle and Flx-R mice (p<0.0001), as well as between Flx-R and Flx-NR mice (p=0.0013), but it was not significantly different between vehicle and Flx-NR mice. However, we still observed a number of mice that were classified as fluoxetine-responders based on the NSF test, but upon visual inspection they were ambiguous in their response in the FST (Figure 2.2B). Therefore, we can conclude that the classification of

Flx-R and Flx-NR mice based on the NSF test only partly agrees with the treatment response in the FST (Figure 2.2C).

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Figure 2.2: Chronic corticosterone and fluoxetine treatment results in treatment responder and non-responder mice in the NSF test. A) Total latency to feed per mouse (left) and survival curve (right) of mice after responsiveness classification. B) Total (6 min) immobility in FST. One-way ANOVA: F (2, 57) = 14.77, ****p<0.0001, post-hoc multiple comparison Turkey test Veh vs. Flx-R ***adj.p<0.0001, Flx-R vs. Flx-NR **adj.p=0.0013. C) Scatterplot distribution of latency to feed in the NSF and total (6 min) immobility in FST across Flx-R (dark blue), and Flx-NR (light blue) mice. Insert represents proportion of Flx-R and Flx- NR mice from all the fluoxetine-treated mice.

Out of all the mice previously shown, only a small subset was selected for gene expression analysis using a Microarray assay. The fluoxetine-treated mice used in the gene expression analysis were specifically selected based on their behavioral response, such that the Flx-NR mice selected for gene expression analysis were consistently non-responders across both behavioral tasks (Figure 2.3). Only one of the mice classified as a Flx-R in the NSF test displayed a high immobility time in the FST (Figure 2.3C).

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Figure 2.3: Behavioral analysis of only the mice used for gene expression analysis.A) Total latency to feed per mouse (left) and survival curve (right). B) Total (6 min) immobility in FST. One-way ANOVA: F (2, 17) = 13.25, ***p=0.0003, post-hoc multiple comparison Turkey test Veh vs. Flx-R ***adj.p=0.0003, Flx-R vs. Flx-NR *adj.p=0.0156. C) Scatterplot distribution of latency to feed in the NSF and total (6 min) immobility in FST across Flx-R (dark blue), and Flx- NR (light blue) mice.

These results support using the chronic corticosterone model for chronic stress followed by chronic fluoxetine treatment as a mouse behavioral paradigm that recapitulates some features of the antidepressant response in humans, where about 50% of patients fail to respond to treatment with fluoxetine or other SSRIs (Samuels et al., 2011; Trivedi et al., 2006).

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Chronic fluoxetine treatment increases proenkephalin levels in the dentate gyrus

Previous studies have pointed to the DG as an important region in the mediation of the behavioral response to antidepressants (Breitfeld et al., 2017; David et al., 2009; Samuels et al.,

2015), so we have focused our gene expression analysis in this region. Following chronic corticosterone and fluoxetine treatment, dorsal and ventral DG tissue samples were dissected and processed for subsequent Afymetrix Microarray gene expression analysis. These data have been partially analyzed before (Samuels et al., 2014); here, we report the results of a series of new analyses that have not been previously published.

Differential gene expression analysis was conducted using the GenePattern software’s

ComparativeMarkerSelection tool (Reich et al., 2006). In both the vDG and dDG, we identified over 200 genes that significantly changed their expression levels up or down (Foldchange FC >

1.5, FDR < 0.05) when Veh and Flx-R treatments were compared.

Penk, an opioid peptide precursor gene, exhibited the highest upregulation in the vDG in Flx-R mice compared to Veh (FC = 12, FDR = 0.0101) (Figure 2.4) and was the fourth-highest upregulated gene in the dDG (FC = 6.4, FDR = 0.009) (Supplementary Figure 2.1A).

Interestingly, Penk was significantly upregulated in the vDG in Flx-R compared to Veh-treated mice (p=0.0011), but showed no significant differences between Flx-NR and Veh-treated mice both in the ventral (Figure 2.5) and the dorsal DG (Supplementary Figure 2.1C).

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Figure 2.4: Ventral DG microarray results show a significant upregulation of Penk in fluoxetine treated mice. Heatmap representing a subset of significantly differentially expressed genes (FC > 2, q value < 0.05) between Vehicle and Flx-R mice in the vDG. Each row represents the expression of a specific gene z-score, and genes are organized from largest (top) to smallest FC value within each panel. Top panel shows the first 20 most upregulated genes, bottom panel shows the 15 most downregulated genes. Each column represents an individual mouse.

Pdyn, another opioid peptide precursor, was significantly downregulated in the vDG (Figure

2.5). Other genes previously found to be relevant for the antidepressant response in the DG were also identified by this analysis: in Flx-treated mice, BDNF (brain derived neurotrophic factor) and Rgs4 (regulator of G-protein 4) were significantly upregulated in both the ventral and dorsal

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DG (Figure 2.5); Htr5a (serotonin receptor 5A) and Htr4 were significantly upregulated in the ventral DG, and Htr1a (serotonin receptor 1a) was significantly downregulated in the ventral DG

(Figure 2.5).

Figure 2.5: Raw gene expression values for genes of interest in the vDG. A) Gene expression of select genes of interest: Penk one-way ANOVA: F (2, 17) = 9.695, p=0.0016, post- hoc multiple comparison Tukey test Veh vs. Flx-R **p=0.0011. Pdyn one-way ANOVA: F (2, 17) = 7.906, p=0.0037, post-hoc multiple comparison Tukey test Veh vs. Flx-R **p=0.0054, Veh vs. Flx-NR *p=0.0237. Rgs4 one-way ANOVA: F (2, 17) = 10.18, p=0.0012, post-hoc multiple comparison Tukey test Veh vs. Flx-R ***p=0.0009. Bdnf one-way ANOVA: F (2, 17) = 10.37, ****p<0.0001, post-hoc multiple comparison Tukey test Veh vs. Flx-R ***p=0.0008. B) Gene expression of serotonin receptors Htr1A, Htr4 and Htr5A. Htr1A one-way ANOVA: F (2, 17) = 11.34, p=0.0007, post-hoc multiple comparison Tukey test Veh vs. Flx-R ***p = 0.0005. Htr4 one- way ANOVA: F (2, 17) = 4.973, p=0.0199, post-hoc multiple comparison Tukey test Veh vs. Flx-R *p=0.0421, Veh vs. Flx-NR *p=0.0432. Htr5A one-way ANOVA: F (2, 17) = 11.34, p=0.0007, post- hoc multiple comparison Tukey test Veh vs. Flx-R ***p=0.0005.

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We then identified genes whose expression patterns closely match that of Penk using Pearson’s correlation. Among these genes was Htr4 (serotonin receptor 4), which had an expression pattern similar to Penk’s in the ventral DG (Figure 2.6). Importantly, BDNF and Rgs4, which were both significantly upregulated after Flx treatment (Figure 2.6), had expression patterns that highly correlated with the Penk pattern both in the ventral (Figure 2.6) and dorsal DG (Supplementary

Figure 2.1B).

Figure 2.6: Significantly correlated genes with Penk.The values are represented as the standardized units (z-score) of the gene expression of Rgs4, Bdnf and Htr4. Top left corner value in each square represents the R correlation value for each pair.

Further analysis also showed a correlation between Penk gene expression and latency to feed in the NSF of all the fluoxetine-treated mice (Figure 2.7). These results combined indicate that

Penk seems to be upregulated in a response-dependent manner.

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Figure 2.7: Correlation between ventral Penk gene expression and behavior.A) Correlation between vPenk gene expression and immobility time (sec) in the FST of all fluoxetine-treated mice, regardless of response status. Pearson’s correlation r= -0.3311, p= 0.1466. B) Correlation between vPenk gene expression and the latency to feed (sec) in the NSF of all fluoxetine-treated mice. Pearson’s correlation r=-0.5242, *p=0.0401. Gray dotted line represents the cutoff point for the definition of treatment responsiveness used in previous graphs.

Furthermore, differential gene expression analysis between Flx-R and Flx-NR mice identified

Penk as a significantly upregulated gene in Flx-R compared to Flx-NR (Foldchange FC > 2,

FDR < 0.05) (Figure 2.8).

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Figure 2.8: Ventral DG microarray results show a significant downregulation of Penk in fluoxetine non-responders compared to responders.Heatmap representing all the significantly differentially expressed genes (FC > 2, q value < 0.05) between Flx-NR and Flx-R mice in the ventral DG. Each row represents the Z-score of the gene expression, and genes are organized from largest (top) to smallest FC value. Each column represents an individual mouse.

We then investigated specifically the differential expression of opioid-related genes: Pdyn,

Oprd1 (opioid receptor delta), Oprk1 (opioid receptor kappa) and Oprm1 (opioid receptor mu).

Oprd1 and Pdyn exhibited a significant differential expression between Veh and Flx-R in both ventral and dorsal DG (Figure 2.9).

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Figure 2.9: Differential expression of opioid related genes in DG.Differential expression values between Vehicle and Fluoxetine Responder mice in the ventral (A) and dorsal (B) DG for the Delta Opioid Receptor (Oprd1), Kappa Opioid Receptor (Oprk1), Mu Opioid Receptor (Oprm1), Prodynorphin (Pdyn) and Proenkephalin (Penk). Left tables display the calculated values for the differential expression analysis of the given genes, including logFold Change (FC), p-value, and the False Discovery Rate (FDR), which is the significance value adjusted for multiple comparisons. Right graphs show the logFC for each of the genes.

We also performed an unbiased Weighted Gene Co-expression Network Analysis (WGCNA)

(Langfelder and Horvath, 2008) to independently and in an unsupervised way identify clusters

(modules) of genes with similar patterns of expression. The WGCNA analysis yielded a large number of identified modules in our sample, not all of them relevant for the purposes of our analysis (Figure 2.10).

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Figure 2.10: WGCNA cluster dendrogram of all modules found in the analysis. Top panel represents cluster dendrogram produced by WGCNA hierarchical clustering. Bottom panel indicated the different color identifiers for each module for better visualization.

A subset of the identified modules strongly correlated with behavioral outcomes as measured by latency to feed in the NSF and immobility in the FST (Figure 2.11). An in-depth analysis of the different modules discovered is beyond the scope of this Chapter, but it is worth mentioning the wealth of data obtained from this analysis. For example, Module I appears to be strongly correlated with DG location (dorsal or ventral), pointing to this group of genes as important in the dorsoventral differentiation of the DG response to fluoxetine (Figure 2.11). Future studies will be focused on further analyzing differently correlated modules.

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Figure 2.11: Subset of relevant modules identified through WGCNA.Correlation of a subset of identified modules through WGCNA analysis. Top values in each square represents correlation value between a given module (y axis) and a trait (x axis), bottom value represents the significance of this correlation. Traits displayed on the x axis: Immobility in the FST, latency to feed in the NSF, Treatment (Fluoxetine or Vehicle), Response (Flx-R, Flx-NR, or no treatment), DG (Dorsal or Ventral DG sample). Each square is colored based on the correlation value as depicted on the right-hand scale: Red indicates a direct correlation, green indicates an inverse correlation. For example, in module A, higher latency to feed is correlated with lower expression.

For the purpose of our analysis, we identified module A as containing Penk, Rgs4, Htr4 and

Necab3. Within this module, Penk displayed a high module membership as well as a high gene significance (respectively, p < 0.0001 and p=0.00012) (Figure 2.12), which measures the correlation of the gene’s expression with a trait of interest, in our case, latency to feed in the NSF

60 and immobility in the FST. This data indicates that expression patterns of Penk, Htr4, and Rgs4 are strongly correlated with the behavioral response to fluoxetine.

Figure 2.12: WGCNA analysis of gene expression data identifies Penk containing module as significantly correlated with behaviorA-B) Scatter plot of genes belonging to module A as a function of the gene significance for latency to feed in the NSF (A) and immobility in the FST (B). The X axis represents the module membership values for each gene within the module. Highlighted genes: Green: Necab3, Red: Penk, Blue: Rgs4, Purple: Htr4.

These results taken together indicate that the opioid system may be involved in the effects of fluoxetine, and that these effects may be mediated by an upregulation of Penk in the DG.

Penk shows increased expression levels in fluoxetine responder mice as compared to non- responders, and their expression patterns significantly correlate with behavioral outcomes, suggesting that upregulation of Penk contributes to treatment responsiveness.

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Fluoxetine upregulates genes in a subpopulation of mature granule cells in the dentate gyrus

We performed RNAscope in situ hybridization on mice treated with vehicle or fluoxetine in order to quantify how Penk mRNA expression varies across brain regions (Figure 2.13A). Penk was significantly upregulated after chronic fluoxetine treatment in the upper and lower blades of the DG (p = 0.00016 and p = 0.00009, respectively) (Figure 2.13B), as well as, to a smaller extent, in the Entorhinal Cortex (p = 0.00343), but not in the Piriform cortex, Basolateral

Amygdala, Lateral Hypothalamus, CA1 or CA3 (Figure 2.13D). This region-specific bias suggests that the significant increase in Penk transcription we observe in the DG after fluoxetine treatment is not due to a generalized increase of Penk throughout the brain, but rather a targeted effect in the DG and a select few other regions.

We also observed that, both in vehicle and fluoxetine treated mice, the expression level of Penk within the DG was strongest in the upper blade and mostly limited to the uppermost layer of the granule cell layer (GCL) closest to the inner molecular layer (IML), as well as in some cells within the IML itself (Figure 2.13C). Such a pattern is reminiscent of the localization of a recently described population of highly active granule cells that are likely to be semilunar granule cells (Erwin et al., 2020; Williams et al., 2007). Interestingly, four genes that were recently associated with this distinct population of granule cells, namely Penk, Rgs4, Necab3, and Col6a1 (Erwin et al., 2020), were all strongly upregulated by chronic fluoxetine in our experiments (Figure 2.4A), and 3 of them belong to module A identified through the WGCNA analysis (Figure 2.12).

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Figure 2.13: Penk mRNA expression quantification across brain regions.A) Representative 10x slices of Penk expression measured through RNAscope of a vehicle and a fluoxetine treated mouse. DG: Dentate Gyrus, Ent: Enthorhinal Cortex; Pir: Piriform Cortex; BLA: Basolateral Amygdala; LH: Lateral Hypothalamus. Green: Penk ; Blue: DAPI. B) Representative 20x fluorescent images of the dorsal DG of vehicle and fluoxetine treated mice. C) Close up representative image of Penk pattern of expression in the DG. White arrows represent mature granule cell bodies in the inner molecular layer. D) Quantification of Penk expression across the brain regions marked in Figure 2.13A. Unpaired t-test after FDR correction (Benjamin, Krieger, Yekutieli): Upper DG ***p=0.000158; Lower DG ***p=0.00009; Ent *p=0.003427.

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In order to map Penk and other genes of interest to specific cellular subpopulations within the

DG, we re-analyzed a previously published single-cell RNA-Seq dataset (Habib et al., 2016) and identified 6 main cell clusters corresponding to distinct cell types within the DG (Figure 2.14A).

We found that three of these clusters corresponded to mature granule cells and identified Penk as a marker of one of them, namely, the “mature GC3” cluster (Figure 2.14B). According to our data, this cell cluster is also prominently represented by Rgs4, Necab3 and Col6a1 genes (Figure

2.14C-E), as well as other genes we have found to be upregulated by fluoxetine such as Nptx2, and Gpr83 (Supplementary Figure 2.4). In contrast, other genes affected by fluoxetine showed a different distribution of their expression among the identified cell clusters. For example, BDNF was expressed at the same level in most of the granule cells (Figure 2.14F).

Our findings related to the spatial localization of Penk transcription within the DG are in a good agreement with a recent report (Erwin et al., 2020) and point to the existence of a specialized subpopulation of mature GCs expressing many other genes we found to be upregulated by fluoxetine. Together with the anatomically distinct clustering of GCs expressing Penk (Figure

2.13C), our data suggest that fluoxetine upregulates a specific transcriptional program (most prominently Penk, RGS4, Necab3 and Col6a1) in an anatomically and transcriptionally distinct subpopulation of mature granule cells.

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Figure 2.14: Subset of fluoxetine upregulated genes localized in a discreet population of mGCs.A) Identification of cell population clusters using the dimensionality reduction algorithm UMAP on a previously published single-cell RNA sequencing DG dataset (Habib et al., 2016). Cell types assignments for each identified cluster are shown in the figure legend. B-E) Expression of fluoxetine upregulated genes localized in a specific population of mGCs belonging preferentially to cluster “mature GC3”. Insert in each image represents RMA- normalized gene expression values for individual genes from the microarray analysis. F) Bdnf expression distributed across clusters.

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2.1.4 Conclusions

Despite its widespread use, treatment with SSRI still presents many challenges such as unwanted side effects, delayed onset of effect, and treatment non-responsiveness. In fact, up to two-thirds of patients do not experience remission of symptoms after first line treatment with SSRIs

(Trivedi et al., 2006). Therefore, it is crucial to elucidate the mechanism by which some patients respond to treatment and some do not, in order to better apply therapeutic interventions in the future.

In this study, we aimed to better understand what molecular mechanisms underpin treatment efficacy. We have found that the endogenous opioid peptide long precursor gene Penk, as well as other opioid and non-opioid related genes, might be at the center of a targeted transcriptional response in the DG that mediates the response to fluoxetine. This targeted effect seems to be preferentially carried out by a subset of transcriptionally and anatomically defined population of mGCs.

We show here that corticosterone model for chronic stress followed by fluoxetine treatment in

C57BL/6J mice appears to be a useful tool to induce a mixed antidepressant response that mimics human responsiveness and non-responsiveness to treatment (Samuels et al., 2011). In our studies, ~70% of treated mice were responders (according to the NSF test) and ~30% are non- responders. Similar results were recently reported in (Gergues et al., 2021). While these percentages yield a greater number of responder mice than what is commonly seen with SSRIs treatment response in humans, the paradigm still provides a great tool to study responsiveness in a mouse model of depression. It is worth noting that while we use this chronic stress paradigm to model anxiety- and depressive-like phenotypes, there has been recent discussion regarding the accuracy of these terms. The National Institute of Mental Health (NIMH) recently emitted a

66 notice (Notice Number NOT-MH-19-053) discouraging the description of animal behaviors in terms of emotions and thought processes that are accessible only in humans, and promoting the use of other terms such maladaptive (or negative) affective state instead (Dieterich et al., 2019).

For the purpose of this study in the context of antidepressant treatment we have determined that the use of terms such as anxiety- and depressive-like more closely represents our overall goal and closely tracks with the current state of our field, all the while being conscientious that these terms could be interchangeable with other, less emotional ones.

We first studied the differences in gene expression between fluoxetine responder and non- responder mice, with the aim of identifying specific gene regulatory pathways that set these two groups apart. Microarray gene expression analysis in the DG samples revealed that Penk is significantly upregulated in both the ventral and dorsal DG in responder mice, but not in non- responders. In fact, the upregulation of Penk in the hippocampus after chronic SSRI treatment has been reported before (Sillaber et al., 2008). In a 2008 study, Sillaber et al. treated male

DBA/2OlaHsd mice chronically with the SSRI paroxetine (10 mg/kg) for 29 days, and then performed a microarray experiment on whole hippocampal samples followed by radioactive in situ hybridization of selected genes. They found Penk, Rgs4, and Col6a1 to be upregulated by paroxetine in their whole hippocampal samples. Furthermore, radioactive in situ hybridization of

Penk in the hippocampus of paroxetine treated mice revealed a significant upregulation of Penk specifically in the DG (Sillaber et al., 2008).

We have further confirmed our findings in an independent RNA-Seq experiment. In this experiment, 5HTR1A-KO and 5HTR1A+ mice (5HTR1A expression is rescued only in the DG) were treated with chronic fluoxetine without previous corticosterone treatment. We found that, in line with our previous results, Penk was significantly upregulated in fluoxetine-treated mice as

67 compared to vehicle- treated mice, independently of genotype (Supplementary Figure 2.2,

Supplementary Figure 2.3). Furthermore, a similar upregulation of Penk was observed after environmental enrichment, which is another paradigm shown to elicit anxiolytic- and antidepressant-like effects (Zhang et al., 2018).

All these results taken together clearly demonstrate Penk upregulation in the DG as a crucial part of the mechanism leading to behavioral improvement of different antidepressive-like interventions, regardless of the mouse strain or stress model used. The striking pervasiveness of this finding highlights the importance of the study into the mechanism of action of the Penk gene expression network in the DG. Future experiments should study the effects of chronic corticosterone on Penk expression, in order to better understand the effects of stress on the Penk gene expression network, as well as to understand possible baseline gene expression differences that might affect the subsequent response to fluoxetine treatment.

Further analysis of the gene expression changes in the DG after fluoxetine treatment revealed a number of genes which are highly correlated with Penk’s gene expression pattern such as Rgs4,

Htr4 and Bdnf, which are known components of the fluoxetine response in the DG (Figure 2.4B).

In order to study correlated gene networks in an unbiased and unsupervised way, we performed a

WGCNA on all of our samples (Figure 2.10). This method allows for the discovery of modules of genes correlated with each other, and also allows for the identification of modules highly correlated with a trait of interest, such as latency to feed in the NSF and immobility in the FST

(Figure 2.11). This analysis revealed several modules correlated with both of these behaviors, such as module A. We identified Penk, Rgs4, Htr4 and Necab3 as belonging to this module of highly correlated genes. This analysis allowed us to conclude not only that in fact these genes seem to correlated together and thus potentially belong within the same gene expression network,

68 but also identified them as important for the behavioral outcomes of fluoxetine treatment (Figure

2.12). Alternatively, genes clustered in the same module could also be correlated because they belong to the same cell type within the DG, rather than the same gene expression network.

Future experiments including single-cell RNA-seq would clarify the possible interpretation of these results. Further analysis will be focused on the identification of the gene expression network these genes belong to as well as its molecular and functional properties, and how it might be linked to the behavioral effects of fluoxetine.

In-situ hybridization using RNAscope revealed that Penk expression is indeed highest in the DG and appears to be restricted to a subpopulation of granule cells located preferentially within the upper blade of the DG, close to the border between the GCL and the IML. This biased localization becomes stronger in fluoxetine-treated mice (Figure 2.13B). These results stand in contrast to the long-held view that mGCs in the DG are homogenous in their properties and anatomy (Amaral et al., 2007), and point to the need for a better understanding of mGC complexity. Our results as well as recently published studies (Erwin et al., 2020; Rao-Ruiz et al.,

2019) support the hypothesis that mGCs have heterogenous properties that contribute distinctly to the functional processes of the DG. In fact, the biased pattern of expression we found in our study is highly reminiscent of that displayed by the often under-studied semilunar granule cells

(SLGCs) (Williams et al., 2007). Furthermore, a recent study finding Penk to be a marker for highly active granule cells in the DG also highlighted the parallels between Penk+ neurons and

SLGCs (Erwin et al., 2020), pointing to an overlap between these two populations of neurons.

This population of Penk+ neurons is not only anatomically distinct, but transcriptionally differentiated as well. Our analysis of a previously published single-cell RNA-Seq dataset

(Habib et al., 2016) reveals that Penk+ neurons belong to a distinct transcriptional cluster of

69 mGC, which we termed “mature GC3” (mGC3). This mGC3 cluster not only preferentially expresses Penk, but also a number of other unique marker genes we have found to be upregulated by fluoxetine such as Necab3, Col6a1, Rgs4, Nptx2, and Gpr83 (Figure 2.14)

(Supplementary Figure 2.4).

We therefore hypothesize that the antidepressant effect of chronic fluoxetine is mediated by upregulation of Penk expression in this highly specialized group of Penk+ mGCs preferentially recruited by a variety of behavioral paradigms (Erwin et al., 2020) (Rao-Ruiz et al., 2019). This notion is further supported by the correlated upregulation of other markers of this mGC3 subgroup of granule cells that we observe in the ventral DG (Figure 2.4A, Figure 2.12 and Figure

2.14). According to our WGCNA analysis, Necab3, Rgs4 and Penk are also present in module A

(Figure 2.12B), and together with Col6a1, they all display a high correlation with the Penk expression level. This further links the expression patterns of these gene markers with the fluoxetine response.

Rgs4 regulates the activity of G-protein signaling, and it is a negative allosteric modulator of the

DOR (Georgoussi et al., 2006) as well as different serotonin receptors. It is likely however that the increased expression of Rgs4 we observe after fluoxetine is not happening in the same neurons which are expressing the DOR. The increase in Rgs4 expression is most predominant in the mGC population, which is corroborated by its significant presence in the mGC3 cluster.

However, the DOR is primarily expressed in PV interneurons (Erbs et al., 2012), and thus it’s possible that the increase in Rgs4 expression is not functionally implicated in DOR activity, but perhaps in serotonin receptor activity in the mGC3 cluster.

SLGCs have been described as possessing distinctive anatomical and electrophysiological properties (Gupta et al., 2020). However, this class of granule cells has not yet been functionally

70 characterized within the larger context of DG function. In this study we have shown a potential overlap between SLGCs and Penk+ mGCs in the DG which seem to be implicated in the behavioral effects of fluoxetine. Further study of the properties of this population of neurons as well as the relevance of the Penk gene expression network within SLGCs will yield a better understanding of how these granule cells are involved in the general function of the DG as well as the specific implications regarding antidepressant treatment response.

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2.1.5 Supplementary Figures

Supplementary Figure 2.1: Microarray results from fluoxetine treated mice in dorsal DG show a significant upregulation of Penk. A) Heatmap representing significantly differentially expressed genes (FC > 2, q value < 0.05) between vehicle and Flx-R mice in the dorsal DG. Each row represents the Z-score of the gene expression, and genes are organized from largest (top) to smallest FC value within each block. Top panel shows the first 20 most upregulated genes, bottom panel shows the 15 most downregulated genes. B) Significantly correlated genes with Penk in the dorsal DG, expressed as a Z-score of the gene expression. C) Sample raw gene expression values (in RMA units) for genes of interest in the dDG. Penk one- way ANOVA: F (2, 17) = 5.419, p=0.0151, post-hoc multiple comparison Tukey test Veh vs. Flx-R *p=0.0114. Rgs4 one-way ANOVA: F (2, 17) = 6.238, p=0.0093, post-hoc multiple comparison Tukey test Veh vs. Flx-R **p=0.0068. D) Pdny one-way ANOVA: F (2, 17) = 3.232, p=0.0646. E) Gene expression of serotonin receptors Htr1A, Htr4 and Htr5A. Htr1A and Htr5A one-way ANOVA not significant. Htr4 one-way ANOVA: F (2, 17) = 3.690, p=0.0467, post-hoc multiple comparison Tukey test Veh vs. Flx-R *p=0.0371. F) Gene expression of opioid receptors Oprd1, Oprm1 and Oprk1. One-way ANOVAs not significant.

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Supplementary Figure 2.2: RNA sequencing confirms microarray results in ventral DG. A-F) Fragments Per Kilobase of transcript per Million (FPKM) values expression in the ventral DG for 5HT1A-KO and 5HT1A+ mice following an RNA Sequencing experiment confirm microarray results; n = 5,5,5,6. A) ventral Penk Two-way ANOVA: interaction F (1, 17) = 0.7601, p = 0.3955; genotype F (1, 17) = 0.8346, p = 0.3737; treatment F (1, 17) = 12.60, **p=0.0025; Bonferroni multiple comparisons, 5HT1A-KO vehicle versus fluoxetine *p=0.0142, 5HT1A+ vehicle versus fluoxetine p=0.1394. B) ventral Rgs4 Two-way ANOVA: interaction F (1, 17) = 0.9025, p = 0.3554; genotype F (1, 17) = 1.224, p=0.2840; treatment F (1, 17) = 13.01, **p=0.0022; Bonferroni multiple comparisons, 5HT1A-KO vehicle versus fluoxetine *p=0.0116, 5HT1A+ vehicle versus fluoxetine p=0.1433. C) ventral Pdyn Two-way ANOVA: interaction F (1, 17) = 1.013, p=0.3283; genotype F (1, 17) = 0.04444, p=0.8355; treatment F (1, 17) = 3.523, p=0.0778; Bonferroni multiple comparisons, 5HT1A-KO vehicle versus fluoxetine p>0.9999, 5HT1A+ vehicle versus fluoxetine p=0.105. D) ventral Bdnf Two-way ANOVA: interaction F (1, 17) = 0.1420, p=0.7110; genotype F (1, 17) = 0.02508, p=0.8760; treatment F (1, 17) = 11.32, **p=0.0037; Bonferroni multiple comparisons, 5HT1A-KO vehicle versus fluoxetine *p=0.0381, 5HT1A+ vehicle versus fluoxetine p=0.0907. E) ventral Col6a1 Two-way ANOVA: interaction F (1, 17) = 0.03799, p=0.8478; genotype F (1, 17) = 6.768e-005, p= 0.9935; treatment F (1, 17) = 36.74, ****p<0.0001; Bonferroni multiple comparisons, 5HT1A-KO vehicle versus fluoxetine **p=0.0016, 5HT1A+ vehicle versus fluoxetine ***p=0.0006. F) ventral Necab3 Two-way ANOVA: interaction F (1, 17) = 0.04892, p= 0.8276; genotype F (1, 17) = 0.004120, p= 0.9496; treatment F (1, 17) = 20.50, ***p=0.0003; Bonferroni multiple comparisons, 5HT1A-KO vehicle versus fluoxetine *p=0.0168, 5HT1A+ vehicle versus fluoxetine **p=0.0063.

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Supplementary Figure 2.3: RNA-sequencing confirms microarray results in dorsal DG.A-F) Fragments Per Kilobase of transcript per Million (FPKM) values expression in the ventral DG for 5HT1A-KO and 5HT1A+ mice following an RNA Sequencing experiment confirm microarray results; n = 5,5,5,6. A) dorsal Penk Two-way ANOVA: interaction F (1, 17) = 0.1492, p = 0.7041; genotype F (1, 17) = 0.4863, p = 0.4950; treatment F (1, 17) = 3.636, p= 0.0736; Bonferroni multiple comparisons, 5HT1A-KO vehicle versus fluoxetine p= 0.2617, 5HT1A+ vehicle versus fluoxetine p= 0.5738. B) dorsal Rgs4 Two-way ANOVA: interaction F (1, 17) = 0.06595, p = 0.8004; genotype F (1, 17) = 0.1843, p= 0.6731; treatment F (1, 17) = 9.951, **p= 0.0058; Bonferroni multiple comparisons, 5HT1A-KO vehicle versus fluoxetine p= 0.0608, 5HT1A+ vehicle versus fluoxetine p= 0.1029. C) dorsal Pdyn Two-way ANOVA: interaction F (1, 17) = 0.0007, p= 0.9790; genotype F (1, 17) = 0.0309, p= 0.8626; treatment F (1, 17) = 6.390, *p= 0.0217; Bonferroni multiple comparisons, 5HT1A-KO vehicle versus fluoxetine p= 0.1899, 5HT1A+ vehicle versus fluoxetine p= 0.1765. D) dorsal Bdnf Two-way ANOVA: interaction F (1, 17) = 0.0841, p= 0.7754; genotype F (1, 17) = 0.04642, p= 0.8320; treatment F (1, 17) = 3.452, p= 0.0806; Bonferroni multiple comparisons, 5HT1A-KO vehicle versus fluoxetine p= 0.3107, 5HT1A+ vehicle versus fluoxetine p= 0.5453. E) dorsal Col6a1 Two-way ANOVA: interaction F (1, 17) = 5.484, *p= 0.0316; genotype F (1, 17) = 0.0084, p= 0.9283; treatment F (1, 17) = 1.024, p= 0.3257; Bonferroni multiple comparisons, 5HT1A-KO vehicle versus fluoxetine p= 0.0659, 5HT1A+ vehicle versus fluoxetine p= 0.6997. F) dorsal Necab3 Two-way ANOVA: interaction F (1, 17) = 0.1839, p= 0.6735; genotype F (1, 17) = 0.1131, p= 0.7408; treatment F (1, 17) = 6.927, *p= 0.0175;

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Bonferroni multiple comparisons, 5HT1A-KO vehicle versus fluoxetine p= 0.0983, 5HT1A+ vehicle versus fluoxetine p= 0.2592.

Supplementary Figure 2.4: Heatmap diagram of genes contributing to the single-cell gene expression cluster "Mature GC3".Averaged expression of only the mGC3 cluster marker genes, showing their expression in each predicted cell type. Top colored bar represents identified cellular clusters in Figure 2.14A. Rows indicate Z-scored gene expression.

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2.2 The Opioid System Mediates Some of the Behavioral Effects of Fluoxetine

2.2.1 Introduction

The opioid system has been extensively studied in the context of pain and addiction, but its involvement in mood disorders and mood regulation has received less attention. In the past decade, increasing efforts have been invested into understanding the interplay between this system and mood-related processed with the goal of not only furthering our understanding of mood regulation, but also with the hope that it will lead to better, more sophisticated treatment strategies (Lutz and Kieffer, 2013)

All three main types of opioid receptors, MOR, DOR and KOR, have been shown to be involved in mood dysregulation. MOR agonists produce euphoria, and acute pharmacological activation of the MOR has been shown to reduce depressive-like behaviors (Berrocoso et al., 2013; Besson et al., 1996). In contrast, KOR agonists promote depressive-like behaviors, while KOR antagonists induce antidepressant-like effects (Mague et al., 2003). DOR activation through peptidergic and non-peptidergic agonists produces anxiolytic and antidepressive-like effects

(Naidu et al., 2007; Saitoh et al., 2004; Torregrossa et al., 2006; Vergura et al., 2008).

Furthermore, mice lacking the DOR gene display higher anxiety-like behavior in the elevated plus maze and the light-dark box test, while mice lacking the MOR gene show a decrease in anxiety levels in those same tasks (Filliol et al., 2000).

Opioid receptors are therefore considered a potential target for the mediation of antidepressant effects. In fact, tianeptine, which is an antidepressant commonly used in Europe and South

America, has recently been shown to be an agonist of MOR (Gassaway et al., 2014), and our lab has shown that the antidepressant-like effects of tianeptine require the MOR (Samuels et al.,

2017). Ketamine, which promotes rapid antidepressant effects (Zarate et al., 2006) and is now

76 approved for treatment-resistant depression, has also been recently suggested to recruit the opioid system (Williams et al., 2018). In addition, clinical trials evaluating the efficacy of DOR and MOR agonists, or KOR antagonists as antidepressants are currently underway.

These extensive findings involving opioid receptors in antidepressant efficacy beg the question of whether they might also be involved in the function of other well-established antidepressants such as SSRIs. Treatment with SSRIs cause a wide range of effects all throughout the brain, perhaps the most widely studied being neurogenesis (Santarelli et al., 2003), but many of the processes involved in improving behavioral outcome remain elusive. Based on the findings described in the previous section of this chapter regarding opioid related gene regulation after fluoxetine treatment, we hypothesize that opioid receptors might be mediating some of the behavioral effects of fluoxetine.

In this section, we aim to further understand the role of the opioid system in the mediation of fluoxetine’s effect through opioid receptor modulation. We measured anxiety- and depressive- like behaviors in mice lacking the MOR (MOR-KO) and mice lacking the DOR (DOR-KO) after chronic corticosterone and fluoxetine treatment. Our results show that the DOR, but not the

MOR, is important in the mediation of some of the antidepressant-like effects of fluoxetine in a neurogenesis-independent manner.

2.2.2 Methods

Mice

Procedures were conducted in accordance with the US National Institutes of Health Guide for the

Care and Use of Laboratory Animals and New York State Psychiatric Institute Institutional

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Animal Care and Use Committee at Columbia University and the Research Foundation for

Mental Hygiene.

Husbandry

Mice were housed in groups of 3–5 per cage with free access to food and water on a 12:12-h light/dark cycle. All behavioral testing was conducted during the light period.

Experimental Mice

MOR-KO and DOR-KO mice were obtained from Dr John Pintar, and then bred in house. MOR-

WT and DOR-WT littermates from heterozygote breedings were used as WT controls.

Behavioral Testing

Novelty Supressed Feeding (NSF): The NSF test was generally conducted as described in

Chapter 2. For DOR-KO and MOR-KO mice, the test was carried out for 6 min, and mice were food deprived for 18 hours.

Forced Swim Test (FST): The FST was conducted as described in Chapter 2. Immobility duration in the FST was manually scored using the Ethovision XT10 software.

Open Field Test (OF): Mice were placed in a square, acrylic, open chamber 40 cm long x 40 cm wide x 40 cm wide x 37 cm under 300-600 lux light. Mice were placed in the box for 15 minutes and movement was collected using the Limelight software according to manufacturer’s instructions. The box was cleaned with Sanicloths between runs. Total distance moved was recorded.

Elevated Plus Maze (EPM): The EPM was conducted on a four-arm maze, elevated 34cm from the floor, each arm 63.5cm long and 5cm wide. Two of the arms were open, and two arms were flanked by 18cm tall walls. The test was conducted under 400-600 lux light and recorded using a

78 handheld camera on a tripod. The mouse was placed in the center of the maze, and the test was run for 5 minutes before the mouse was placed back into their homecage. The maze was cleaned with Sanicloths between runs. Time spent in the open arms of the maze was scored using the

Ethovision XT10 software.

Contextual Fear Conditioning (CFC): Conditioning took place over 3 days in fear conditioning boxes that contained one clear plexiglass wall, three aluminum walls and a stainless-steel grid floor (Med Associates). Context A (Day 1) included a houselight and fan, white noise, exposed shock floor, box doors closed, and anise scent was placed under the grid floor. 180s into the trial, mice received a single 2s footshock of 0.75mA. Mice were taken out of the box 15s after the shock and returned to their home cage. On Day 2, mice were placed in the same context (Context

A’) without a shock. On day 3 mice were placed in the same boxes but presented with different contextual cues (Context B): floor bars and walls were covered with an insert, the box doors were open, chamber lights and white noise were off, lemon scent was placed under the grid floor, and no shock was administered. The boxes were cleaned with Sanicloths between runs. Digital video cameras recorded the session and freezing behavior was analyzed using FreezeFrame and

FreezeView softwares (Actimetrics).

Immunohistochemistry

DOR-KO test mice were sacrificed a week after the end of behavioral testing. Mice were deeply anesthetized with a ketamine/xylazine (150 mg/kg & 10 mg/kg) intraperitoneal injection and transcardially perfused with 1x PBS followed by 4% PFA. Brains were dissected and post-fixed in 4% PFA overnight at 4°C, followed by three days in a 30% sucrose solution for cryoprotection. Brains were subsequently frozen in OCT (optimum cutting temperature

79 compound) and stored at -80°C. Sections of 30 µm coronal brain slices of the whole dentate gyrus were collected (Leica CM3050S cryostat) and stored in 1x PBS (phosphate-buffered saline). Sections were washed three times in 1x PBS for 10 minutes, then heated for 5 seconds in the microwave in a sodium citrate buffer solution (pH 8) for antigen retrieval. Slides were then placed for 30 minutes in an 80°C water bath. The sections were washed again before being incubated in 10% NDS (normal donkey serum) blocking buffer in 0.2% PBST (PBS and 0.3%

Triton X-100) for 2 hours at room temperature. The slices were incubated overnight at 4°C in

10% NDS blocking buffer in 0.2% with the primary antibody (rabbit-anti-doublecortin 1:600,

Cell Signaling). The following day slices were washed three times in 1 x PBS for 10 minutes, and the incubated in 0.2% PBST with the secondary antibody for 2 hours at room temperature.

Slices were again washed three times in 1 x PBS for 10 minutes and then washed with DAPI

(300nM) for 5 minutes. Sections were then mounted on glass slides and coverslipped with

Vectashield mounting medium.

Cell Quantification

Cell quantification was conducted as described in Chapter 2. For the purpose of DCX quantification, the region of interest (ROI) was selected to include the whole of the granule cell layer for each sample.

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2.2.3 Results and Figures

The DOR contributes to the behavioral effects of fluoxetine in the FST

The Penk gene transcript gives rise to enkephalin peptides which bind preferentially to the DOR and, to a lesser extent, to the MOR (Mansour et al., 1995). Since in the previous section we showed that chronic fluoxetine treatment increases Penk expression in the DG, we wondered whether the DOR and MOR receptors play a role in the mediation of fluoxetine’s antidepressant effects. MOR-KO and DOR-KO mice were treated chronically with corticosterone, followed by fluoxetine (or vehicle) and then subjected to the NSF and FST behavioral tasks, measuring anxiety- and depression-like phenotypes respectively (Figure 2.15A). As shown in the previous section, treatment with chronic corticosterone followed by fluoxetine in WT mice robustly elicits a decreased immobility in the FST and decreased latency to feed in the NSF test.

Both MOR-KO and WT control littermates displayed a decreased immobility in the FST after chronic fluoxetine treatment [Two-way ANOVA with significant effect of treatment factor

(*p=0.0132) but not genotype factor (p=0.2847) or interaction (p=0.9711)] (Figure 2.15B). In contrast, DOR-KO mice had an attenuated response to fluoxetine in the FST compared to their

WT control littermates [Two-way ANOVA with significant effect of treatment factor

(***p=0.0003) and genotype factor (*p=0.0133), but no interaction (p=0.1062)] (Figure 2.15C).

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Figure 2.15: Behavioral effects of fluoxetine in the FST in MOR-KO but not DOR-KO mice.A) Timeline of corticosterone and fluoxetine treatment for MOR-KO and DOR-KO mice. B) Antidepressant-like effect of fluoxetine in MOR-KO mice in the FST. Two-way ANOVA: interaction F(1,30) = 0.001, p=0.9711; genotype F(1,30) = 1.187, p=0.2847; treatment F(1,30) = 6.937, *p=0.013; n = 8,9,10,7. Left: average immobility time per minute, right: Total immobility during the last 4 minutes of the FST. C) No antidepressant-like effect of fluoxetine in DOR-KO mice in the FST. Two-way ANOVA: interaction F(1,74) = 2.675, p = 0.1062; genotype F(1,74) = 6.433, *p = 0.013; treatment F(1,74) = 14.71, ***p=0.0003; planned comparison t-test, WT vehicle versus fluoxetine, **p=0.0016; n = 15,15,26,22. Left: average immobility time per minute, right: Total immobility during the last 4 minutes of the FST.

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In order to better understand the possible involvement of these two receptors on behavior, we also tested mice in the NSF test. Both MOR-KO and DOR-KO mice displayed a decreased latency to feed after fluoxetine treatment to levels comparable of those of WT control littermates

(Figure 2.16).

Figure 2.16: No behavioral effect of fluoxetine in the NSF in MOR-KO or DOR-KO mice. A) Latency to feed in the NSF test in both WT (left) and MOR-KO (middle) mice represented as a survival curve and as the total value (right). Log-rank (Mantel-Cox) test: WT Chi square = 3.83, p=0.0504; MOR-KO Chi square = 11.57, ***p=0.0007; n = 8,9,10,7. B) Latency to feed in the NSF test in both WT (left) and MOR-KO (middle) mice represented as a survival curve and as the total value (right). Log-rank (Mantel-Cox) test: WT Chi square = 8.941, **p=0.0028; DOR-KO Chi square = 31.54, ****p<0.0001; n = 15,15,26,22.

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After discovering the involvement of the DOR in the behavioral effects of fluoxetine in the FST, we performed further behavioral tests in order to determine the extent of the contribution of this receptor in the response to fluoxetine. We tested DOR-KO mice in the Open Field (OF) test,

Elevated Plus Maze (EPM) and Contextual Fear Conditioning (CFC) test. The DOR does not seem to be involved in the behavioral effects of fluoxetine in any of these behaviors (Figure

2.17).

Figure 2.17: DOR-KO mice display similar behavior than WT mice after fluoxetine treatment. A) Total distance moved in OF test. Two-way ANOVA: interaction F(1, 78) = 0.5770, p=0.4498; genotype F(1, 78) = 1.666, p=0.2007; treatment F (1, 78) = 11.95, ***p=0.0009; planned comparisons t-test WT vehicle versus fluoxetine *p=0.0112. B) Time in Open Arms in EPM test. Two-way ANOVA: interaction F (1, 78) = 0.2361, p=0.6284; genotype F (1, 78) = 0.1941, p=0.6607; treatment F (1, 78) = 10.75, **p=0.0016; planned comparisons t-test WT vehicle versus fluoxetine *p=0.032. C) Contextual Fear Conditioning % time freezing. WT Repeated Measures ANOVA: interaction F (2, 47) = 4.986 , *p=0.0109; day F (1.664, 39.10) = 24.62, ****p<0.0001; treatment F (1, 24) = 3.220, p=0.0853. DOR-KO Repeated Measures ANOVA: interaction F (2, 66) = 1.022, p=0.3653; day F (1.269, 41.87) = 23.02, ****p<0.0001; treatment F (1, 33) = 3.036, p=0.0908; n = 17,18,26,22.

These results taken together point to the contribution of DOR to the behavioral effects of fluoxetine only in the FST, but not in any other behavioral task analyzed.

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Fluoxetine treatment increases neurogenesis in both WT and DOR-KO mice

Chronic SSRI treatment increases neurogenesis in the DG, and that neurogenesis mediates some of the behavioral effects of fluoxetine (Breitfeld et al., 2017; Santarelli et al., 2003). Previous work has also shown that ablating neurogenesis blocks the behavioral effect of fluoxetine in the

NSF test, but not in the FST (David et al., 2009). Therefore, in order to assess whether the DOR mediates the behavioral effects of fluoxetine through the modulation of neurogenesis, we quantified neurogenesis levels in the DG of fluoxetine-treated WT and DOR-KO mice.

We performed an immunohistochemical analysis of the immature neuron marker doublecortin

(DCX) in DOR-KO and WT control littermates that had been treated with chronic corticosterone and fluoxetine (or vehicle) (Figure 2.18A). In the ventral DG, fluoxetine increased neurogenesis both in the DOR-KO and WT control littermates [Two-way ANOVA with significant effect of treatment factor (*p=0.01) but not genotype factor (p=0.7292) or interaction (p = 0.7095)]

(Figure 2.18B). The same effect of treatment (but not genotype) on the neurogenesis level was observed in the dorsal DG [Two-way ANOVA with significant effect of treatment factor

(*p=0.03) but not genotype factor (p=0.5919) or interaction (p=0.7086)] (Figure 2.18C).

This result suggests that the contribution of the DOR to the behavioral effects of fluoxetine in the

FST is not mediated by neurogenesis, corroborating previous evidence showing that the FST, as opposed to the NSF, is a neurogenesis-independent behavior (David et al., 2009).

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Figure 2.18: Fluoxetine treatment increases neurogenesis in both WT and DOR-KO mice.A) Confocal 20x images of ventral (top 4) and dorsal (bottom 4) DCX antibody staining in the DG. B, C) DCX (green) fluorescence represented as a normalized pixel count value. B) Ventral Two-way ANOVA: interaction F (1, 11) = 0.1462, p=0.7095; genotype F (1, 11) = 0.1261, p=0.7292; treatment F (1, 11) = 9.419, *p=0.0107. C) Dorsal DG Two-way ANOVA: interaction F (1, 11) = 0.1471, p=0.7086; genotype F (1, 11) = 0.3048, p=0.5919; treatment F (1, 11) = 6.262, *p=0.0294; n = 3,5,2,5.

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2.2.4 Conclusions

Previous studies have pointed to the opioid system in the mediation of some behavioral effects of antidepressants: the effects of tianeptine are mediated through the MOR (Gassaway et al., 2014)

(Samuels et al., 2017), and studies have pointed to the MOR being engaged in the antidepressant effects of ketamine (Williams et al., 2018). However, we did not find the MOR to be involved in the antidepressant effects of fluoxetine.

We found that the DOR may be mediating the behavioral effects of fluoxetine in the FST but not in the NSF test, and that the DOR does not appear to mediate the effects of fluoxetine on neurogenesis (Porsolt et al., 1977). While both the NSF test and the FST are behavioral tasks with established predictive validity in the study of antidepressant efficacy (Lucki, 1997)

(Santarelli et al., 2003), we have previously shown that the behavioral effects of fluoxetine require adult hippocampal neurogenesis in the NSF test, but not in the FST (David et al., 2009).

The negative results from the EPM, OF and CFC behavioral tests suggest that the DOR is not involved in mediating the anxiogenic effects of fluoxetine.

These studies suggest therefore that the behavioral effects of fluoxetine in the NSF and FST tests are mediated by distinct mechanisms. There are in fact several ongoing efforts focusing on understanding the underlying biological substrates that differentiate the vast heterogeneity of depressive subtypes, with the goal of identifying potential targets of biomarker development

(Drysdale et al., 2017) (Grosenick et al., 2019).

In our experiments we used a constitutive DOR-KO mouse model, where the DOR is not present anywhere in the brain all throughout development. It is therefore not clear where in the brain the observed DOR contribution to fluoxetine effect occurs, but we can point to several likely candidates: The DG, NAc and Prefrontal Cortex all display high levels of DOR receptors while

87 being targets of serotonergic input from the Dorsal Raphe Nucleus, and are also known to be involved in the behavioral outcome of the FST (Lutz and Kieffer, 2013).

Based on the results presented in the previous section, we would tentatively hypothesize that the

DG is a very likely candidate for the mediation of these effects. Our gene expression analysis points to the DOR as being involved in the chronic response to fluoxetine in the DG. A differential expression analysis of our DG samples revealed that the DOR was significantly upregulated by fluoxetine, although it resulted in a small foldchange difference (Figure 2.9).

Future studies should be focused on specifically targeting different brain regions in order to further understand how and where the DOR is contributing to the antidepressant effects of fluoxetine, starting with modulation of hippocampal DOR signaling after fluoxetine treatment by administration of a DOR antagonist.

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Chapter 3 : Fluoxetine Mediates Changes in Granule Cell Activity in

the Dentate Gyrus

3.1 Introduction

The study of the effect of fluoxetine on the DG has been extensively studied at the molecular level. Previous research has underlined the importance of neurogenesis for some of the behavioral effects of fluoxetine (Santarelli et al., 2003), as well as the mediation of behavioral effects through 5HTR-1A (Samuels et al., 2015) and 5HTR-4 serotonin receptors signaling on mature granule cells (Samuels et al., 2016). Other studies have pointed to 5HTR-5A serotonin receptors on parvalbumin interneurons (Sagi et al., 2020) as well as hilar mossy cell activity (Oh et al., 2020) as important mediators of the chronic effects of fluoxetine. Furthermore, the results presented in Chapter 2 also point to the opioid system as a relevant network involved in the behavioral effects of fluoxetine. These results highlight the large number of factors mediating fluoxetine’s effects in the DG, which emphasizes the complexity of the DG signaling network.

While our understanding of the cellular and molecular components of the behavioral response to fluoxetine continues to grow, it is still debated what the net effect of these pathways are in the overall neuronal activity and output of the DG.

Recent evidence points to an inhibitory effect of fluoxetine and neurogenesis when a mouse is confronted with an acute stressor. Shuto et al. (2020) observed that when mice were exposed to a novelty stress, there was a temporary increase in 5-HT levels in the DG in placebo-treated mice.

However, this novelty-induced increase was not observed in fluoxetine-treated mice (Shuto et al., 2020). Furthermore, Anacker et al. (2018) showed that when a mouse with regular levels of

89 neurogenesis was attacked by an aggressor mouse during social defeat stress, the test mouse showed an increased firing rate of vDG granule cells. However, when mice with increased levels of neurogenesis were confronted with the same stressor, this increase in firing rate was reduced, leading to a stress resilient behavioral phenotype (Anacker et al., 2018). These results point to the exciting possibility that fluoxetine might achieve its anxiolytic effects through the dampening of a stress-evoked increase in activity of DG granule cells.

In order to test this hypothesis, we chose to study the stretch-attend posture (stretching), a behavior that can be identified across several different behavioral paradigms, and thus allowing us to confirm possible findings in different behavioral settings.

The stretching posture is widely observed in several rodent species (Grant and Mackintosh,

1963) and can be identified by the forward elongation of the body of an animal when performing risk-assessment behaviors. Therefore, stretching is considered an ethologically-relevant behavioral measure. Risk assessment behaviors are thought to be important indicators of anxiety in mice and rats (Blanchard and Blanchard, 1989), and in fact this behavior has been shown to be modulated by anxiety in a novel environment (Garbe et al., 1993) and in the EPM test in mice

(Cole and Rodgers, 1994; Kaesermann, 1986; Molewijk et al., 1995). Several studies have shown that stretching behavior is decreased after treatment with anxiolytic drugs such as diazepam, and increased after treatment with anxiogenic drugs in conflict-based anxiety behaviors such as the zero-maze or the I-maze (Gilhotra et al., 2015; Grewal et al., 1997). In fact, acute fluoxetine (5 mg/kg) seems to decrease stretching behavior in the I-maze (a modified version of the EPM) in mice (Gilhotra et al., 2015). Stretching has been identified to varying degrees in several mouse strains, and this behavior has been found to be significantly correlated with other anxiety-related

90 behaviors (Kim et al., 2002). For these reasons, we have chosen to study this behavior in the context of fluoxetine treatment effects in the vDG.

A recent study has investigated the effects of fluoxetine in the activity of the DG using activity- dependent cFos immunostaining as well as chemogenetic DREADD-mediated inhibition and excitation of the DG granule cell population (Yohn et al., 2020). This study revealed that fluoxetine treatment decreases granule cell activation compared to non-treated mice as measured by cFos immunostaining, and that chronic inhibition of mature granule cells in the vDG results in decreased latency to feed in the NSF (Yohn et al., 2020).

However, it has not yet been studied how fluoxetine influences vDG granule cell activity in-vivo during freely-moving behavior. There are several challenges to the study of these effects in vivo.

First, the vDG is a brain area which is hard to access optically due to its deep location within the brain. Second, in order to assess anxiety- and depressive-like behaviors, it is necessary to record brain activity while the animal is freely-moving, which makes traditional head-fixed 2-photon

Ca2+ imaging challenging. And finally, traditional methods of measuring brain activity in vivo, such as in vivo electrophysiology, do not allow for the possibility of spatially tracking the same neurons over time in order to understand long-term coding patterns of these cells.

In order to tackle these issues, we have used a gradient refractory index (GRIN) lens coupled with a 1-photon miniature microscope (2 grams in weight), which allows for in vivo Ca2+ imaging of deep structures in freely-moving mice (Ghosh et al., 2011; Ziv et al., 2013). Using this method, the implantation of the GRIN lens above the fluorescently-labeled granule cell layer of the vDG allows for the recording of Ca2+ activity during freely-moving behavioral exploration. We have identified a stress-related behavior, stretching, which is present across behavioral tasks and is partly reduced by fluoxetine treatment in the EPM. Preliminary evidence

91 from our analysis points to a potential protective effect of fluoxetine through the inhibition of a stress-induced increase in activity in the vDG.

3.2 Methods

Mice

Procedures were conducted in accordance with the US National Institutes of Health Guide for the

Care and Use of Laboratory Animals and New York State Psychiatric Institute Institutional

Animal Care and Use Committee at Columbia University and the Research Foundation for

Mental Hygiene.

Husbandry: Mice were housed in groups of 3–5 per cage with free access to food and water on a

12:12-h light/dark cycle. All behavioral testing was conducted during the light period. After stereotactic surgery, mice were singly housed for the rest of the experimental timeline in a satellite facility.

Experimental Mice: 129S6/SvEvTac wildtype mice were purchased from Taconic Farms

(Germantown, NY). All mice were 7–8 weeks old and weighed 23–35 g at the beginning of the treatment.

Virus Construct

In order to perform Ca2+ imaging, we ordered a replication deficient viral construct from UPenn

Vector Core (AAV9-CamKII-GCaMP6f) at a titer of ~4.3x10-13 GC/ml. 3ul aliquots of the virus were diluted before use to reach a final concentration of ~0.5x10-13 GC/ml.

Stereotactic Surgeries

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Surgical procedures were conducted in accordance with IACUC guidelines in a sterile field. All tools were sterilized prior to surgery. Male mice were anesthetized with a 2-0.5% concentration of an isoflurane and oxygen via a nose cone, with a flow rate of 1L/min. They were then head- fixed in a stereotactic frame (David Kopf, Tujunga, CA), and their temperature was kept stable at

37°C with the use of a warm water recirculator (Stryker, Kalamazoo, MI). Eyes were lubricated with ophthalmic ointment to keep them from drying, and the body temperature and breathing were regularly monitored. In order to prevent dehydration, a subcutaneous saline injection was provided during the surgery, as well as a carpofen injection in order to ease the pain upon waking up from the surgery.

First, the fur on the heard was cropped and the skin was sterilized with betadine followed by

70% Ethanol. Then, a midline incision was performed to expose the skull. We identified the proper coordinates to target the ventral DG (vDG) unilaterally, and dentist’s drill and trephine drill bit was used to create a ~1mm craniotomy in the skull in that position.

The dura layer surrounding the brain was removed, and the brain surface profusely cleaned with sterile saline and absorptive spears (Fine Science Tools (FST), Foster City, CA) in order to remove all debris and create a clean field. A pulled glass needle containing the virus was then targeted to the same region and lowered into the vDG coordinates (Nanoject, Drummond

Scientific). Viral injection coordinates were (in mm, from begma) -3.6 AP, 2.8 ML, -3.85, -3.50,

-2.7 DV. An electronic pump connected to the syringe delivered a total volume of 300 nL at a rate of 0.1 µl min-1, injected at successive depths (from -3.3 to -2.6 DV) in order to reach the whole vDG area. Three metal screws (FST, Foster City, CA) were evenly inserted into the skull around the craniotomy site. A previously-sterilized GRIN lens (Gradiante Refractory Index lens,

Inscopix, Palo Alto, CA) (0.5 mm in diameter, 6.1 mm in length) was then targeted over the

93 same coordinates as the virus injections. The GRIN lens was then slowly lowered into the vDG coordinates, using small 0.1 mm dorsoventral incremental steps over a period of 10 min in order to allow for pressure equilibration. The lens was then affixed to the skull using dental cement

(Dentsply Sinora, Philadelphia, PA). The skin around the dental cement was then closed with

VetBond tissue adhesive, and the lens protected with a liquid rubber mix (Smooth-On, Lower

Macungie, PA). A Bupivicaine ointment was administered to the area of incision to minimize discomfort, and a Carprofen analgesic (5mg/kg s.c.) was administered for three days following surgery. The mice were then singly housed while they recovered from surgery for three weeks in order to allow for virus expression.

After 3-4 weeks, mice were anesthetized and head-fixed again, and fluorescence was confirmed using a miniature microscope as a guide (Inscopix, Palo Alto, CA). If fluorescence was detected through the lens, a magnetic baseplate was attached to the skull with dental cement. This baseplate is an attachment plate to which the microscope is screwed on during behavior. When not preforming behavior, the baseplate and lens were protected with a baseplate cover. This allowed for re-imaging of the same field of view for consecutive weeks.

Fluoxetine Administration

Fluoxetine hydrochloride was purchased from Anawa Trading (Zurich, Switzerland). Mice were gavaged daily for 28 days (18 mg/kg/day).

Freely Moving Calcium Imaging

The miniature microscope and focal plane were kept consistent during each imaging session.

Before each awake-behaving imaging session mice were briefly anesthetized and the miniature

94 microscope was attached to the baseplate. After 30 minutes of recovery, mice were run in a different behavioral task each day. The microscope was attached to a processing computer, and

Ca2+ videos were acquired with the nVista software (Inscopix, Palo Alto, CA). In order to allow for simultaneous acquisition, the nVista software was triggered through a TTL pulse created from the EthoVision XT10 software and Noldus IO box system. Acquisition settings were kept consistent across mice at 15 fps. LED power was kept consistent for each mouse across imaging session, and the optimal power was chosen visually based on fluorescence expression in the field of view.

Histology

Mice were transcardially perfused as described in Chapter 2, except the brains were post-fixed in

4% PFA for 1 week with the head-caps still attached in order to properly fix the location of the lens in the brain. After brains were placed in a 30% sucrose solution, the head cap was removed and brains were subsequently frozen in OCT (optimum cutting temperature compound) and stored at -80°C. Sections of 50 µm coronal brain slices of the vDG were collected (Leica

CM3050S cryostat) and stored in 1x PBS (phosphate-buffered saline). Brain slices were mounted in anterior-posterior order and for each mouse the exact location of the bottom of the GRIN lens was determined by examining the section on an epiflourescent microscope.

Image Processing and Analysis

Calcium videos were collected for each behavioral imaging session and processed using the

Mosaic Data Processing software (Inscopix, Palo Alto, CA). Videos were spatially downsampled by a factor of 4, and then motion corrected using a single frame of reference and high-contrast

95 features. After cropping the videos to delete black borders produced by the XY translation during motion correction, videos were imported into Matlab, where cell segmentation was performed using CNMF-E (Constrained Non-negative Matrix Factorization for microendoscopic data)

(Pnevmatikakis et al., 2016; Zhou et al., 2018). Cells with an estimated diameter of 18 pixels were segmented and then manually inspected and sorted to identify neurons presenting the correct spatial conformation and neuronal Ca2+ signal dynamics. Ca2+ traces were z-scaled to allow for comparisons, and then Ca2+ events were identified as activity transients larger than

2s.d. of amplitude from a 0.5 s.d. baseline. Using CNMF-E, we then performed a spike deconvolution in order to estimate the action potentials underlying these Ca2+ events.

Analysis of the spike data was done using custom Python functions. These functions were used to calculate the firing rate of extracted spikes during different behaviors, such as stretching.

Behavior Scoring

DeepLabCut is a markerless pose estimation method used for the tracking of several body parts of an animal at once. This method uses transfer learning and deep neural networks in order to track regions with a small amount of training data and labeled reference frames. The structure of the algorithm and how the training works has been thoroughly described elsewhere (Mathis et al., 2018), and the implementation code is freely available

(https://github.com/AlexEMG/DeepLabCut). For each mouse, we tracked the position of the tail base, the head, the center of the body, and the left and right ears. The number of frames extracted from each video to manually label was 10, and they were iteratively adjusted. This data was then used to train a deep convolution network which extracted the desired tracked positions from each video frame. This method allows for the prediction of the probability that a given label is located

96 in a specific pixel, which we then used as a cutoff for the selection of frames to include. Frames with a likelihood probability <0.99 were deleted and then their position interpolated. The position of the four body parts was converted from pixels to cm as defined by the distance of the behavioral chamber. In order to calculate the stretching of the mouse body, we calculated the distance between the head and the tail base of the animal using the formula for the calculation of the distance between two points (x,y):

2 2 퐷𝑖푠푡푎푛푐푒 = √(ℎ푒푎푑푥 − 푡푎𝑖푙푥) + (ℎ푒푎푑푦 − 푡푎𝑖푙푦)

3.3 Results and Figures

Calcium imaging of the ventral dentate gyrus

Some of the molecular aspects of the response to fluoxetine treatment in the DG have been elucidated over the years, including the results presented in Chapter 2. However, it is still unknown how the overall activity of the DG is affected after treatment. In order to answer this question, we performed in vivo Ca2+ imaging of the vDG in freely moving mice using a miniature microscope (Figure 3.1A) (Ziv and Ghosh, 2015). It is worth noting that the results presented in this Chapter remain very preliminary due to the low sample size.

Wildtype 129S6/SvEvTac mice were used because they present with a higher baseline anxiety, and chronic effects of fluoxetine treatment are seen in the Novelty Supressed Feeding test without the need of a previous treatment with corticosterone (Santarelli et al., 2003). Mice first underwent a virus injection surgery, which delivered the Ca2+ reporter GCamP6f to neurons of the vDG. A GRIN lens was then implanted in this location and cemented to the skull. The correct location of the lens was verified after the experimental procedures were done by perfusing the

97 mice and collecting brain slices of the vDG (Figure 3.1B). Mice were then gavaged for four weeks with fluoxetine (18 mg/kg/day) or vehicle, which also allowed for expression of the virus.

Oral gavage was used in order to provide an additional mild daily stressor during treatment with fluoxetine.

Mice were then imaged in the EPM and the NSF behavioral tasks while we recorded both the behavior using Ethovision XT10 and the neuronal calcium activity using the integrated miniscope software. Calcium videos were processed and segmented in order to identify individual neurons, and the calcium activity traces were extracted and deconvoluted using

CNMF-E (Figure 3.1C). Viable mice for analysis were selected based on the visual confirmation of correct lens implantation site as well as having >20 neurons identified using cell segmentation. We identified three vehicle and three fluoxetine-treated mice that fit these criteria, and only used these mice for further analysis.

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Figure 3.1: Calcium imaging of the ventral dentate gyrus.A) Experimental timeline. B) GCamP6f labeling in the vDG of a sample brain slice shows the position of the lens implantation site. C) Cell segmentation of a calcium video correctly identifies individual neurons in the field of view. Example calcium traces of identified neurons are shown on the right panel. X axis represents the length of a behavioral task, Y axis represents relative fluorescence.

Pose estimation using DeepLabCut

In order to automatically track the position of the mice in behavioral videos, we used the

DeepLabCut algorithm (Mathis et al., 2018; Nath et al., 2019), which allows for the estimation of the position of different body parts in a video. Using this method, we tracked the position of five

99 body parts in each behavioral video: the base of the tail, the center of the body, the head, the left ear and the right ear (Figure 3.2).

Figure 3.2: Markerless pose estimation with DeepLabCut.We tracked the position of the base of the tail (red), the center of the body (orange), the head (dark blue), the left ear (light blue) and the right ear (green) using DeepLabCut both in the Novelty Suppressed Feeding (A) and the Elevated Plus Maze (B). Shown above, example frames of the labeled body parts.

This method allows for the prediction of the probability that a given label is located in a specific pixel, which we then used as a cutoff for the selection of frames to include. Frames with the prediction probability <0.99 that a given label was located in a specific pixel (likelihood) were deleted, and then the label position was interpolated from the known positions in adjacent frames. Our analysis resulted in the successful tracking of the designated body parts in 95% of frames. Using this method, we tracked the location of the head of the mouse in the EPM and

NSF arenas, as well as the tail base, center of the body, and ears (Figure 3.3).

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Figure 3.3: Example tracking of 5 different body parts during behavior using DeepLabCut.DeepLabCut allowed us to track the position of the tail base (brown), body (orange), right ear (green), left ear (light blue) and head (dark blue) in the NSF (A) and EPM (B) arenas with a high degree of accuracy.

Using the location of the head to track the position of the mouse in the arena, we found that in the EPM, both vehicle and fluoxetine treated mice spent a very small amount of time in the open arms (Figure 3.4A). In the NSF test, fluoxetine treated mice seemed to spend more time in the center of the arena compared to vehicle treated mice (Figure 3.4B). Because of the small sample size of this experiment, none of the results were significant.

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Figure 3.4: Behavioral analysis using DeepLabCut.For behavioral analysis, the tracking of the head was used. A) Time in the center of the NSF arena seems to be increased in fluoxetine treated mice compared to vehicle treated mice (n=3). B) Time spent in the open arms of the EPM in vehicle and fluoxetine treated mice (n=3).

The low amount of time all mice spent in the open arms led us to consider the analysis of other fluoxetine-affected behaviors which could be found both in the NSF and EPM tests. The stretch- attend posture in mice has been linked to increased anxiety (Molewijk et al., 1995), and it is decreased after treatment with diazepam or with a 5-HT1A receptor agonist (Grewal et al.,

1997). We have selected the stretch-attend posture for our analysis because it is the only anxiety- related behavior we have reliably identified across our two behavioral paradigms, and thus allowing for the comparison of EPM and NSF behaviors.

In order to automatically define stretching bouts, we calculated the distance between the base of the tail and the head of each mouse based on the position provided by the DeepLabCut algorithm. We then visually examined the behavioral videos against the calculated body distance, in order to determine which distance value corresponded with stretching, as well as assessing the viability of this method. We found that most of the visually identified stretching occurred when the distance between the head and tail base was larger than 6.5 cm, so we selected that value as

102 the cutoff for defining a stretching bout. Therefore, stretching is defined as those frames for which the distance between the head and tail base is > 6.5 cm. This procedure was followed for the analysis of both EPM and NSF behaviors, yielding similar results (Figure 3.5).

Figure 3.5: Supervised selection of stretching bouts.Calculation of the distance between the head and the base of the tail over the length of the NSF (A) and EPM (B) behaviors. The red line indicates the 6.5 cm threshold that was used to determine stretching after visually inspecting the videos at those time points.

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Using the above-defined definition of stretching bouts, we calculated stretching frequency

(number of stretching bouts) and stretching time (sum of the length in seconds of every stretching bout) for all mice both in the EPM and the NSF tests. Stretching behavior seems to be decreased in the EPM in fluoxetine-treated mice, although due to the low number of animals included in the analysis (n=3), this decrease did not reach significance (Figure 3.6). No change was observed in stretching behavior in the NSF (Figure 3.7).

Figure 3.6: Stretching behavior is decreased in the Elevated Plus Maze in fluoxetine treated mice.Number of stretching bouts (A) and total time spent stretching (B) seems to be decreased in fluoxetine treated mice compared to vehicle treated mice. Because of the low sample size (n=3), the experiment does not have enough power to reach significance.

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Figure 3.7: Stretching behavior is not changed in the Novelty Suppressed Feeding test in fluoxetine treated mice.In the Novelty Suppressed Feeding (NSF) test, behavior was stopped after mice took a bite from the pellet, leading to a variability in the behavior lengths of this test. For this reason, stretching frequency and total stretching times were normalized for each mouse by diving each value by their respective behavior lengths. Both normalized stretching frequency (A) and normalized time spent stretching (B) seems to remain constant in fluoxetine treated mice compared to vehicle treated mice.

Increased firing rate during stretching is attenuated by fluoxetine treatment

We then investigated how the overall activity of the vDG was affected by fluoxetine treatment in the EPM and NSF tests. In order to be able to compare the activity across both behaviors, we focused on the analysis of stretching behaviors in both behavioral tasks. We first performed a spike deconvolution in order to infer the underlying event spikes from the Ca2+ transients obtained using the CNMF-E algorithm (Pnevmatikakis et al., 2016; Zhou et al., 2018). Event rates were calculated (Inferred spikes / second) for stretching and non-stretching behavioral bouts. We observed that both in the EPM and NSF tests, vehicle-treated mice experienced a sharp increase in activity during the stress-related stretching behavior compared to non-

105 stretching bouts. However, this increase in activity during stretching was attenuated in fluoxetine-treated mice, both in the EPM and the NSF tests (Figure 3.8).

Figure 3.8: Fluoxetine treatment attenuates increased firing during stretching behavior.During non-stretching bouts, there is no difference in firing rate between vehicle and fluoxetine-treated mice. Both in the Novelty Suppressed Feeding (A) and the Elevated Plus Maze tests (B), stretching behavior results in an increased firing rate in vehicle-treated mice. This increase is attenuated in fluoxetine-treated mice (p<0.05).

We next aimed to assess the extent to which neuronal activity was increased in vehicle-treated mice during specific stretching bouts as compared to fluoxetine-treated mice. We found that in the EPM, stretching bouts seem to coincide with periods of larger cumulative activity in vehicle- treated mice, but this effect was not apparent in fluoxetine-treated mice (Figure 3.9). In the NSF test we also visually observed an increased overlap between stretching bouts and periods of increased vDG activity in vehicle-treated mice compared to fluoxetine-treated mice (Figure

3.10), albeit with more modest effects than those observed in the EPM.

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Figure 3.9: Fluoxetine decreases dentate gyrus activity during stretching behavior in the Elevated Plus Maze. Two example mice either treated with vehicle (A) or fluoxetine (B). Within each panel, the top section shows a raster plot of inferred activity spikes, where each line is an individual neuron. The bottom section represents the cumulative spike activity added over 10 second bins, for a period of 10 minutes. The overlayed pink shading indicates the identified stretching bouts. In vehicle treated mice (A), stretching bouts occur concurrently with periods of large neuronal activity. In the Elevated Plus Maze (EPM), total stretching time and stretching behaviors are decreased in fluoxetine-treated mice, but when stretching does occur, these bouts do not overlap with periods of large neuronal firing.

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Figure 3.10: Fluoxetine decreases dentate gyrus activity during stretching behavior in the Novelty Suppressed Feeding.Two example mice either treated with vehicle (A) or fluoxetine (B). Information depicted as described in Figure 3.9. In vehicle treated mice (A), stretching bouts often occur concurrently with periods of large neuronal activity. In the Novelty Supressed Feeding (NSF) test, total stretching time and stretching behaviors are not decreased in fluoxetine-treated mice, but these bouts overlap to a lesser extent with periods of large neuronal firing.

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3.4 Conclusions

Here we have identified preliminary evidence of the dampening effects of fluoxetine in the activity of mature granule cells of the vDG during stress-evoked behaviors. While they are very preliminary, these results stand in agreement with previously published data pointing to the anxiolytic effects of decreased mGC activity (Anacker et al., 2018; Yohn et al., 2020). Ongoing work from our lab has also shown an increased activity of mGCs in anxiogenic environments, corroborating the findings presented here (Berry et al., in preparation).

In this study we have used the recently developed DeepLabCut algorithm (Mathis et al., 2018) to track the position of different body parts during a behavioral task. In the EPM, mice spent a majority of the time in the center and the closed arms of the arena. The lack of exploration of the open arms of the maze could be due to the combination of the higher baseline anxiety state of

129S6/SvEvTac mice and the added anxiety of the weight of the miniature microscope, both of which should be addressed in future experiments in order to obtain higher behavioral sampling.

In the NSF test, fluoxetine treated mice seemed to spend more time in the center of the arena compared to vehicle treated mice, although this difference was not significant.

We then used the DeepLabCut algorithm to automatize the detection of stretching behavior in mice. Stretching behavior has been shown to be reduced in the EPM after treatment with anxiolytic drugs such as diazepam, and after acute treatment with fluoxetine (5 mg/kg) (Gilhotra et al., 2015; Grewal et al., 1997). In our results, we have observed a trend towards decreased stretching frequency in the EPM after chronic fluoxetine treatment (18 mg/kg), suggesting that stretching behavior may be a reliable measure for the anxiolytic effects of chronic fluoxetine treatment in the EPM. However, a larger sample size is needed in order to reach more conclusive results. We did not observe a trend towards lower stretching behavior after fluoxetine treatment

109 in the NSF test. Stretching behavior is considered to be an anxiety-related risk assessment behavior (Blanchard and Blanchard, 1989), but it is possible that stretching behavior in the NSF test is confounded by the appetitive stimulus at the center of the arena, which might draw the mice to stretch based on olfactory cues as well as risk assessment. Future experiments should include a control NSF test without food in the arena in order to account for the appetitive nature of the task.

Both in the EPM and NSF tests we observe a decrease of stretching over time, where after a few minutes the length of the body of the mouse remains constant. This effect is largely due to an overall decrease in mobility over the course of the behavior. In order to avoid this caveat in the future, we should consider using a different strain of mouse, like C57BL/6J, as well as decreasing the anxiogenic components of the behavioral task such as the luminosity.

Current efforts are focused on manually scoring the behavioral videos for the EPM and NSF tasks, in order to validate the automatic detection using DeepLabCut. Preliminary results point to an agreement between the manually scored and the automatically collected data.

In order to acquire more behavioral data, we are also currently focused on optimizing the NSF task for imaging experiments. In this modified NSF task, the food pellet at the center of the arena is enclosed in a plastic cage with holes large enough for the mouse to smell and see the pellet, but far out of reach enough that the mouse is not able to bite into the pellet. This behavioral setup allows for the continuous imaging of brain activity for 10 minutes without the added variable of consummatory behavior, as well as the option to analyze several behaviors during the test besides the latency to feed. Preliminary evidence of this modified behavioral task suggests that there are several other behaviors that are quantifiable and are modified by fluoxetine treatment,

110 such as latency to first bite of the plastic cage, number of attempts to bite through the cage, and total time spent interacting with the cage.

Future efforts will also be focused on studying granule cell activity changes over time comparing activity of individual neurons before fluoxetine treatment, after acute fluoxetine treatment, and after chronic treatment in the same behavioral tasks.

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Chapter 4 : General Discussion

4.1 Summary of Results

The DG is a crucial mediator of the antidepressant effects of SSRIs such as fluoxetine. Both rodent and human studies have shown that the DG is modified by chronic SSRI treatment in relation to volume, granule cell density, synaptic plasticity, and adult hippocampal neurogenesis

(Boldrini et al., 2013; Santarelli et al., 2003). Furthermore, there is ample rodent literature involving several molecular components of the DG in the response to fluoxetine, including the serotonin receptors HTR-1A and HTR-4 (Samuels et al., 2015; Samuels et al., 2016) in mGCs, the dopamine receptor Drd1 in mGCs (Shuto et al., 2020), HTR-5A receptors in PV interneurons

(Sagi et al., 2020), as well as several signaling molecules such as Bdnf (Bjorkholm and

Monteggia, 2016) and Activin A (Gergues et al., 2021). In this study we have further characterized a previously-unknown component of the fluoxetine effect in the DG using a combination of genomic, behavioral, and imaging techniques.

First, we have used the chronic corticosterone model for anxiety coupled with chronic fluoxetine treatment in order to produce a population of mice that can be separated into fluoxetine- responders and non-responders based on the behavioral response in the NSF test. This model highlights the importance of using a stress paradigm that resembles the human response to

SSRIs, where one third of patients do not respond to fluoxetine treatment. Using this model allows us to study the differences that might underly the response efficacy.

We have found that different components of the opioid system are involved in the treatment efficacy of fluoxetine in the DG. Specifically, we have identified a population of anatomically and transcriptionally distinct mature granule cells that are defined by their high levels of Penk

112 expression after fluoxetine treatment. Interestingly, Penk expression seems to be upregulated specifically in those mice responding to fluoxetine treatment, but not in non-responder mice, and it belongs to a group of genes correlated with a successful response to fluoxetine. Furthermore, we have shown that the DOR is partly mediating some of the behavioral effects of fluoxetine in a neurogenesis-independent manner. These results open an interesting new avenue for research into the involvement of the opioid system in the behavioral response to SSRIs.

Finally, a preliminary analysis of the activity of DG neurons suggests that chronic fluoxetine treatment contributes to decreased anxiety through the dampening of a stress-induced increase in the activity of mGCs. This possibility would further our understanding of the functional role DG activity plays in mood regulation.

4.2 Responsiveness to Fluoxetine

One of the main drawbacks of treatment with SSRIs is the fact that up to one third of patients do no respond to treatment. Furthermore, patients who do not respond to first line of treatment with

SSRIs are less likely to respond to successive rounds of treatment, in many cases leading to treatment resistant depression (Akil et al., 2018). In order to better understand what underlying processes are involved in treatment responsiveness, it is crucial to use an animal model that reflects this response disparity. To this end, we have used the chronic corticosterone model of stress, which has been previously validated by several groups, including our own.

In our study, we were able to identify a group of mice which failed to respond to fluoxetine in the NSF test. We found a group of genes that were significantly differentially expressed between responder and non-responder mice. Among these genes we identified Penk, and its expression seems to be correlated with behavioral outcome. It would be of interest in the future to

113 experimentally manipulate Penk expression in order to understand its functional relevance within treatment responsiveness. This could be accomplished through modulation of enkephalinase activity. Enkephalinases are the enzymes in charge of converting, degrading and modifying enkephalin peptides, and their activity can be manipulated through treatment with enkephalinase inhibitors such as RB101 (Baamonde et al., 1992; Jutkiewicz et al., 2006).

This type of manipulation of a gene involved in treatment responsiveness has been recently reported by Gergues et al., (2021). In their study, the enhancement of Activin A signaling in the

DG transformed fluoxetine non-responders into responders, and the reverse effect was found when Activin A signaling was inhibited (Gergues et al., 2021). In fact, in our own results we find that the expression of Inhba, which encodes a proprotein that gives rise to a subunit of Activin A, is significantly upregulated in fluoxetine-responders, but not in non-responders, compared to vehicle treated mice (not shown). Inhba has also been implicated in the signaling of BDNF (Lau et al., 2015), which could suggest the possibility of an interesting mechanism of fluoxetine responsiveness implicating Activin A, BDNF, and Penk, pointing to potential novel therapeutic areas.

A recent study into the effects of fluoxetine demonstrated that fluoxetine-responder mice have a decreased activation of GCs in the DG compared to fluoxetine non-responder mice, and that in fact inhibition of mGC activity could convert non-responder mice into responders (Yohn et al.,

2020). In our imaging analysis we also saw a decrease in stress-evoked mGC activity in fluoxetine treated mice, but due to the low power of our experiment we were not able to discern between responder and non-responder mice. Follow up imaging experiments including a larger cohort of animals could provide the first in vivo freely moving Ca2+ imaging data studying single neuron resolution of the mGC activity in responder and non-responder mice.

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4.3 Dentate Gyrus Granule Cell Heterogeneity

The hippocampus is involved in a diverse range of functional processes such as spatial navigation (O'Keefe and Nadel, 1978), emotional processing (Kjelstrup et al., 2002), episodic memory (Scoville and Milner, 1957) and the general representation of internal and external states

(Aronov et al., 2017). The diverse functionality of the hippocampus lies on several different cell types defined either by their functional properties or their anatomical localization. Such cell types include granule cells (GC) in the DG or pyramidal cells in the CA3 and CA1 regions.

However, studies into these hippocampal cell types have revealed they are more heterogenous than previously thought. Distinct subsets of CA1 Pyramidal neurons in the ventral hippocampus, for example, have been shown to target different brain regions and to activate different brain circuits (Jimenez et al., 2018; Soltesz and Losonczy, 2018).

On the other hand, not a lot of information is known about heterogeneity within the mature GC

(mGC) population. Some studies hint at the possibility of differential recruitment of mGC in the

DG. For example, some studies suggest that GCs in the suprapyramidal blade (or upper blade) are differentially recruited compared to those in the infrapyramidal (or lower) blade (Liu et al.,

2012; Ramirez et al., 2013; Redondo et al., 2014). Following exposure to a novel stimulus, GCs in the suprapyramidal blade show increased activation as compared to the infrapyramidal blade

(Chawla et al., 2018; Penke et al., 2011; Ramírez-Amaya et al., 2005), and active GCs exhibit a higher dendritic complexity than other GCs (Diamantaki et al., 2016).

A recent study has found that a wide range of behavioral paradigms elicit blade-specific activation of mGCs, and was able to identify underlying morphological, electrophysiological and transcriptional properties of these neurons mapping to a specific subpopulation of mGCs. They

115 also identified Penk as a transcriptional marker of this subtype of GCs. Interestingly, this Penk+ subpopulation comprises only 5% of the total GC population, but it seems to account for 70-80% of the observed behaviorally recruited GCs, resulting in a 10-fold higher recruitment of Penk+ mGCs (Erwin et al., 2020). Penk+ mGCs have also been shown to be enriched in DG memory engram cells (Rao-Ruiz et al., 2019), suggesting that this population is potentially contributing to many of the functional properties of the DG. In our results, we have found that Penk expression in the DG appears to be restricted to a subpopulation of granule cells located preferentially within the upper blade of the DG, close to the border between the GCL and the IML. This biased localization becomes even more apparent in fluoxetine-treated mice. We have further identified

Penk+ mGCs as an independent transcriptional cluster within the DG. The subpopulation of GC that we found in our results are very likely to overlap with those found preferentially recruited by behavior as well as by memory engrams (Erwin et al., 2020; Rao-Ruiz et al., 2019). Since our results have shown that the fluoxetine-induced increase in Penk expression is correlated with a behavioral response to treatment, we now can draw a connection between this subpopulation of mGCs and anxiolytic behavioral outcomes. However, it is still unclear what the functional properties of this subpopulation of GCs is. Necab3, which codes for a calcium binding protein, and Col6a1, which partly codes for a collagen chain, have not been previously studied in the context of psychiatric disease. Ongoing work is focused on studying the anatomical localization of these other mGC3 cluster upregulated genes, in order to determine if they overlap with Penk expression in the IML.

Semilunar Granule Cells (SLGCs) are a class of mGCs that have been sparsely studied (Williams et al., 2007). SLGC are densely spiny, granule cell-like cells with distinct morphological and

116 physiological characteristics compared to regular GCs. SLGCs are found in the inner molecular layer (IML) as well as the top portion of the granule cell layer (GLC) itself, much like our identified Penk+ population. Classic identification of SLGCs is predominantly based on morphological characteristics (Gupta et al., 2020), but Erwin et al. (2020) have recently suggested that Penk could be used as a cell-type marker for some SLGCs based on the shared electrophysiological and morphological properties between SLGCs and the Penk+ subpopulation they characterized. While we did not characterize our identified mGC3 population based on electrophysiology or morphology, we believe our results stand in agreement with the suggestion of using Penk as a SLGC cell-type marker, and future work will be focused on further characterization of this group of neurons.

Interestingly, some electrophysiological studies of SLGCs have suggested there could be a developmentally-created circuit in the DG by which perforant path stimulation leads to persistent plateau-potentials firing from SLGCs, which in turn result in increased mossy cell activity. This increase in activity may in turn lead to feedback inhibition of the general granule cell population

(Larimer and Strowbridge, 2010). Future work will be focused on studying and characterizing this circuit in the context of fluoxetine treatment.

4.4 High Adaptability of the Opioid System

The opioid system is a highly complex and adaptive system that sits at the intersection of several different brain functions including sexual behavior, immune function, feeding behaviors, mood and emotionality. The opioid system regulates the emotional circuitry of the brain by providing fine tuning of emotional states at any given point in time, thus being able to calibrate affective responses to positive and negative stimuli (Drolet et al., 2001; Kennedy et al., 2006; Koepp et al.,

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2009). The ability to influence such complex systems in a moment-to-moment basis is provided by the opioid system’s integration of many compensatory and regulatory mechanisms. While this high level of complexity allows for high adaptability, it also results in the system being vulnerable to disturbance that can lead to the dysregulation of any of the processes it is involved in regulating, leading to maladaptive outcomes (Emery and Akil, 2020).

Some of the mechanisms that provide the opioid system with such a high degree of flexibility include the differential expression of opioid peptides across brain tissues due to variance in their synthesis, processing and release. The processing of opioid peptides is highly variable between brain structures and depends on several catalytic enzymes that can convert, degrade or modify these peptides at varying rates depending on factors such as neural activity. The combination of the different precursor genes giving rise to a mix of different peptides which can be in turn processed differentially by enzymes depending on many factors, leads to many possible combinations of neuropeptides in varying concentrations released into a given structure.

Opioid receptor expression and regulation is also highly flexible. These receptors have different expression profiles across brain regions. They also present different temporal dynamics of chronic and acute activation (Emery and Eitan, 2019), can form functional heterodimers with each other (Zhang et al., 2020), and they bind to peptides with varying affinities. Furthermore, their expression, desensitization and internalization can vary greatly depending on tissue activity and opioid peptide availability (Emery and Akil, 2020).

The high flexibility of the opioid system affects the interpretation of the possible implications of

Penk and Pdyn regulation. We have observed a significant increase in Penk and Oprd1 expression in the DG of fluoxetine-responder mice, as well as a significant decrease of Pdyn expression in all fluoxetine treated mice compared to vehicle. Pdny gives rise to [Leu]5-

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Enkephalin and Dynorphin amongst other peptides, and Penk gives rise to four copies of [Met]5-

Enkephalin and one copy of [Leu]5-Enkephalin, among other products. Dynorphins preferentially bind to the KOR, while enkephalins preferentially bind to the DOR. These two systems have classically been described to have opposing effects: KOR activation results in depressive behaviors, while DOR activation results in antidepressive-like behaviors. In the context of the hippocampus, it is very likely that increased Penk expression results in increased enkephalin release from mGCs, which then binds to the DOR which is expressed in PV interneurons (Erbs et al., 2012). This signaling pathway seems to be upregulated in fluoxetine-treated mice, in agreement with the antidepressive-like properties of DOR stimulation.

Our results did not provide information on which specific enkephalin is modulating this effect, but previous studies in the rat hippocampus suggest that predator odor and acute restraint stress decreased [Leu]5-Enkephalin levels and increased [Met]5-Enkephalin (Li et al., 2018). It is therefore possible that chronic fluoxetine treatment has the opposite effect, increasing [Leu]5-

Enkephalin release.

On the other hand, downregulation of Pdyn by fluoxetine also agrees with an antidepressive-like effect, due to the potential depressive effects if KOR was activated. In fact, the release of dynorphins in the ML of the DG has been shown to reduce excitatory transmission from the perforant path, probably largely due to the presence of KOR in these projections (Drake et al.,

2007; Drake et al., 1994). A decrease in Pdyn expression after fluoxetine treatment would result in decreased dynorphin availability, which in turn would prevent the reduction in perforant path stimulation, and therefore allowing increased input into the SLGC population in the DG. This increased input into the SLGC population would further promote the activation of the SLGC- mossy cell circuit described in the previous section of this Chapter.

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In order to better understand this mechanism, future work should be aimed at specifically measuring opioid peptide levels such as dynorphin, [Met]5- and [Leu]5-Enkephalin in the different cell types of the DG through immunostaining analysis; however, the enkephalin antibodies we tested so far lack the resolution and specificity necessary to correctly measure enkephalin levels.

4.5 Proposed Mechanism

Molecular Model

After the inhibition of serotonin reuptake caused by fluoxetine, several postsynaptic serotonin receptors are involved in mediating the immediate effects of this increase in serotonin availability. Our data shows that Htr5a and Htr4 are upregulated after chronic fluoxetine treatment. Htr4 is expressed in mGCs in the DG and have also been shown to contribute to the effects of fluoxetine (Samuels et al., 2015; Samuels et al., 2016). Based on the high correlation between Htr4 and Penk expression in the vDG, we believe the Htr4 upregulation is also likely to be located in the same mGC3 cluster where Penk is upregulated. Htr4 is a Gs excitatory coupled receptor, and its activation leads to an increase in cAMP/CREB signaling, which in turn result in increased expression of Penk (Qi et al., 2008). We hypothesize that this signaling cascade, which includes Activin A and BDNF as described in a previous section of this chapter, would be strongly upregulated after chronic fluoxetine treatment in the mGC3 cluster in the DG.

Upregulation of Penk in this specialized sub-group of GCs may lead to an increased secretion of enkephalins from these granule cells, which will in turn activate postsynaptic DOR located primarily in GABAergic PV interneurons (Erbs et al., 2012). PV interneurons in the DG also express the fluoxetine-upregulated inhibitory receptor Htr5a, which contributes to the

120 antidepressant effects of fluoxetine in the hippocampus through the hyperpolarization of PV interneurons (Sagi et al., 2020). Both the DOR and 5-HTR5A receptors are coupled to inhibitory

Gi proteins, and thus activation of these receptors might lead to a lower activity of PV interneurons (Figure 4.1).

Figure 4.1: Proposed molecular mechanism of opioid system involvement in the DG circuitry after SSRI treatment. SSRIs block reuptake of serotonin by the serotonin transporter and thus increases synaptic availability of serotonin (left insert). Increased serotonin binds to 5- HT4R postsynaptic serotonin receptor located in the mGC3 cluster granule cells. 5-HT4R, an excitatory Gs coupled receptor, activates the cAPM/CREB signaling cascade. CREB upregulation then promotes the increased expression of Penk in that same mGC3 cluster of granule cells. Penk is then post-transcriptionally modified to give rise to enkephalin peptides, which are released from these neurons extrasynaptically, and then binds to DORs located in PV interneurons (right insert). Activation of DOR receptors by enkephalin and 5-HT5AR by serotonin converges in PV interneurons. Both of these receptors are coupled to inhibitory Gi proteins, which likely lead to a hyperpolarization of PV interneurons. Figure created with BioRender.com.

Circuit Model

Semilunar granule cells (SLGCs) axon collaterals mono-synaptically excite mossy cells, and they in turn receive mono-synaptic input from mossy cells, potentially giving rise to “reverberatory circuits” (Williams et al., 2007). SLGCs generate long-duration plateau potentials in response to

121 excitatory synaptic input from the perforant path, which in turn leads to persistent firing in hilar mossy cells. This increased hilar firing then triggers functional inhibition of regular mGCs

(Larimer and Strowbridge, 2010).

Under the assumption that mGC3 cells indeed overlap in their identity with SLGCs, we propose that the activation of mGC3 by fluoxetine treatment activates the same circuit as SLGC activation. Therefore, the activation of mGC3 neurons after fluoxetine treatment could lead to an inhibition of the larger mGC population, in accordance with recently published studies (Yohn et al., 2020). In fact, our imaging results confirm this hypothesis. Our Ca2+ imaging data shows that, under normal conditions, mGCs show an increased activity in response to stress, but that this increase is dampened in fluoxetine-treated mice. It is possible that the Penk+ population of neurons we have termed mGC3, which seem to overlap with the population of SLGCs, becomes more activated after chronic fluoxetine treatment, and thus causing hilar up-states that then lead to increased inhibition of the rest of mGCs (Figure 4.2). This decrease activity of the larger mGC population would in turn lead to a decreased output onto CA3.

The observed decrease in Pdyn after fluoxetine treatment also likely contributes to an increased perforant path input into the ML, as described in a previous section of this chapter, therefore further contributing to the activation of this circuit.

The proposed circuit mechanism would involve the upregulation of interneurons caused by

SLGC activation, leading to the overall decrease in activity of the general mGC population.

However, our proposed molecular mechanism would involve the activation of 5-HTR5A and

DOR inhibitory receptors in PV interneurons, which would lead to hyperpolarization of PV interneurons, causing a disinhibition of mGCs. These two models therefore present conflicting

122 hypothesis regarding the activation or inhibition of DG interneurons in order for fluoxetine to have a behavioral effect.

It is possible that the interneurons involved in the “hilar up-states” (Larimer and Strowbridge,

2010) that cause overall mGC activity decrease are not PV interneurons, but rather other of the many interneuron cell types located in the DG. In this context, PV interneuron hyperpolarization caused by DOR and 5-HT5AR activation could contribute to this circuit in a yet unknown way.

Further electrophysiological studies should be carried out in order to elucidate the functional contribution of DG interneurons to the overall activity of the DG after fluoxetine treatment.

Figure 4.2: Proposed Dentate Gyrus circuit recruited by chronic fluoxetine treatment.Chronic SSRI treatment leads to increased serotonergic input into Penk+ GCs, which are likely to overlap in their identity with Semilunar Granule Cells (SLGCs). This effect leads to increased activation of SLGCs, which likely leads to increased activation of mossy cells in the hilus. This activation, in turn, leads to the overall inhibition of the general mGC population via inhibitory interneurons, which results in decreased output onto CA3.

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Our hypothesis might also provide insight into the different mechanisms involved in fluoxetine- responder compared to non-responder mice. In our results, we have demonstrated that Penk expression is upregulated in responder mice compared to non-responders, and in fact correlates with behavioral outcomes, and also that Penk-expressing mGCs are an anatomically and transcriptionally distinct population of mGCs. This Penk+ population of mGC has been previously shown to increase their activity when recruited by different behaviors (Erwin et al.,

2020), so it is likely that by increasing Penk expression in fluoxetine responder mice in our experiments, we are pharmacologically causing an increase in SLGC activity in responder mice, leading to decreased activity of the general mGC population, which is not found in fluoxetine non-responder mice. This hypothesis would stand in agreement with the recent published results

(Yohn et al., 2020).

Our data therefore provide an interesting framework for the understanding of fluoxetine treatment responsiveness, and provides a population of Penk-expressing mGCs which could be potentially targeted to convert non-responders into responders.

4.6 Future Directions

There are several avenues of research that can arise from the work presented in this thesis.

Firstly, future work should be focused on further elucidating the mechanism by which the DOR is involved in mediating some of the behavioral effects of fluoxetine. Since we only find the

DOR to be involved in mediating the effects of fluoxetine in the FST, we should focus further investigation of this mechanism on brain regions known to be involved in this behavioral outcome (Chau et al., 2011; Choi et al., 2013; Hamani et al., 2010; McKlveen et al., 2013;

Molendijk and de Kloet, 2019; Warden et al., 2012). Since we find an upregulation in the

124 expression of the DOR in the DG of fluoxetine-treated mice, and the hippocampus is implicated in the behavioral effects in the FST, we believe the hippocampus will be an important area of study for the modulation of these behavioral effects. Local infusion of a DOR antagonist into the hippocampus of fluoxetine-treated mice would elucidate the involvement of this receptor in the behavioral effects of fluoxetine. If we do not find significant effects in the hippocampus, we can point to other likely candidates: Nucleus accumbens and Prefrontal cortex both display high levels of DOR receptors, are targets of serotonergic input from the Dorsal Raphe Nucleus, and are also known to be involved in the behavioral outcomes of the FST (Chau et al., 2011; Warden et al., 2012), which also makes them likely candidates for the mediation of the observed effects.

The identification of a specialized group of granule cells (mGC3), which seems to be modulated by fluoxetine treatment, provide an avenue of research of great interest. Future work should be focused on characterizing the overlap between Penk+ GCs and SLGCs, and specifically study the functional role of Penk in this population of neurons, in order to better ascertain the use of

Penk as a marker of SLGCs. In order to understand the role of these granule cells in the response to fluoxetine, there are available Penk-Cre mouse line which could be used to specifically study the involvement of these cells in the behavioral response to fluoxetine; these Penk-Cre lines could also be used to carry out further imaging studies.

Understanding the function of the mGC3 Penk+ cells could provide insights into whether these neurons can be targeted to produce antidepressant effects in patients who do not respond to

SSRIs.

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