Neuropeptides in the Bed Nucleus Stria Terminalis: Implications in Chronic Stress

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NEUROPEPTIDES IN THE BED NUCLEUS STRIA TERMINALIS: IMPLICATIONS IN CHRONIC STRESS

Catherine Parisien Normandeau

A thesis submitted to the Centre for Neuroscience

In conformity with the requirements for

the degree of Doctor of Philosophy

Queen’s University

Kingston, Ontario, Canada

(August 2018)

For the last 20-30 years, new discovery of pharmacological treatment for psychiatric disorders has been stagnant despite a pressing need to find alternative therapies for patients that are treatment-resistant to the current medication. Neuropeptides are powerful modulators of neural activity that robustly influence a wide-range of behaviours. Notably, their expression profiles and subsequent function can be altered in some mental health illnesses such as anxiety disorders.

However, neuropeptides are rarely studied in clinically relevant contexts. Consequently, there is currently a lack of understanding of how they may predict or contribute to certain pathological states. Pre-clinical models in rodents are useful tools to investigate this. The chronic unpredictable stress (CUS) is one model that mimics the every day stressors that can lead to pathologies in humans. Here, we hypothesize that neuropeptide modulation will be altered after male rats are exposed to CUS resulting in changes in behaviour. Specifically, we look at the effect of neuropeptides in the oval bed nucleus of the stria terminalis (ovBNST), a brain area sensitive to energy homeostasis and the anticipation of aversive stressors. We found that endogenously released neuropeptides robustly modulate both excitatory and inhibitory transmission. Interestingly, CUS altered neuropeptides regulation of synaptic transmission whereby one neuropeptide (neurotensin (NT)) became the dominant modulator of inhibitory transmission. To investigate whether this had behavioural consequences, we pharmacologically blocked NT in the ovBNST and tested rats in the elevated plus maze (EPM) and forced swim test

(FST). We observed changes only in exploratory behaviour in the CUS rats where blockage of

NT increased time spent in the open arm suggesting a reduction in anxiety-like behaviour.

Lastly, knowing that modulation of inhibitory transmission is specifically changed with CUS, we examined how neuropeptides may influence the main inhibitory inputs to the ovBNST: the ii central amygdala (CeA). We found that NT and dynorphin antagonistically modulate CeA-to- ovBNST inputs. Whether this modulation changes in different conditions or animal state remains elusive. Overall, multiple neuropeptides regulate the activity of ovBNST neurons that is dysregulated following a chronically stressful experience (i.e., CUS) but whether these changes observed are adaptive or maladaptive has yet to be determined.

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Co-Authorship

In all cases, I (Catherine P Normandeau) designed the experiments, collected and analyzed data and wrote the manuscripts. Dr. Eric C Dumont contributed to experimental design, data analysis and manuscript preparation.

For Chapter 3:

Paula Ventura-Silva contributed (25%) to data analysis and collection for the behaviour experiment

Emily Hawken contributed (15%) to electrophysiological data collection

Staci Angelis contributed (10%) to behavioural data collection

Calvin Sjaarda and Xudong Liu contributed (50%) to the experimental design, data collection and analysis of mRNA expression experiment

Jose Miguel Pego contributed to the experimental design of the behaviour experiment

The chapter has been published and can be cited as: Normandeau CP, Ventura Silva AP, Hawken ER,

Angelis S, Sjaarda C, Xudong Liu, Pêgo JM, Dumont ÉC (2018) A key role for neurotensin in chronic- stress-induced anxiety-like behaviour in rats. Neuropsychopharmacology. 43(2):285-293.

For Chapter 4:

Maria Luisa Torruella Suárez contributed (50%) to collecting the in situ data and helped with surgeries for the optogenetic mice

Phillippe Sarret donated the selective NTR2 agonist and antagonist

Zoe McElligott contributed to the experimental design and analysis of the optogenetic mice experiment

Acknowledgements

Pour mes parents, Anne-Marie et Charles.

Table of Contents

Abstract ...... ii Co-Authorship ...... iv Acknowledgements ...... v List of Figures ...... ix List of Tables ...... x List of Abbreviations ...... xi Chapter 1 : Introduction ...... 1 Chapter 2 : Literature Review ...... 3 2.1 Neuropeptides and Neuromodulation ...... 3 2.1.1 What is a neuropeptide? ...... 3 2.1.2 Neuropeptide Function ...... 4 2.1.3 Neuropeptide Release ...... 5 2.1.4 Using Electrophysiology to Study Neuropeptides ...... 8 2.1.5 Neuropeptides and Psychiatric Disorders ...... 11 2.2 Chronic Stress ...... 11 2.2.1 What is stress? ...... 12 2.2.2 Animal Models of Chronic Stress (Pre-clinical Models) ...... 13 2.2.3 Animal Behavioural Test of Mood Disorders ...... 15 2.3 Neural Modulation of Chronic Stress ...... 17 2.3.1 HPA Axis ...... 17 2.3.2 HPA Modulation ...... 19 2.3.3 BNST ...... 23 2.3.4 Oval BNST ...... 25 2.3.5 Neuropeptides in the ovBNST ...... 27 2.4 Hypothesis ...... 28 2.4.1 Aim 1: Measure modulatory effects of locally released neuropeptides ...... 28 2.4.2 Aim 2: Investigate chronic stress alteration of modulatory effects of locally released neuropeptides ...... 29 2.4.3 Aim 3: Determine the role of ovBNST neuropeptides in behaviour ...... 29 2.4.4 Aim 4: Expand neurophysiological findings at the circuit level ...... 29 Chapter 3 : A key role for neurotensin in chronic-stress-induced anxiety-like behaviour in rats ...... 30 3.1 Abstract ...... 30

3.2 Introduction ...... 32 3.3 Materials and Methods ...... 33 3.3.1 Rats ...... 33 3.3.2 Slices preparation and electrophysiology ...... 34 3.3.3 Drugs ...... 35 3.3.4 Chronic Unpredictable Stress (CUS) ...... 35 3.3.5 Surgery ...... 36 3.3.6 Behavioural Tests ...... 36 3.3.7 Elevated Plus Maze (EPM) ...... 37 3.3.8 Forced Swim Test (FST) ...... 37 3.3.9 Open Field ...... 38 3.3.10 Histological Procedures ...... 38 3.3.11 RNA extraction and reverse transcription ...... 38 3.3.12 Real time qPCR and data analysis ...... 39 3.3.13 Statistical analyses ...... 39 3.4 Results ...... 40 3.5 Discussion ...... 51 Chapter 4 : Neurotensin and Dynorphin Bi-Directionally Modulates CeA Inhibition of oval BNST Neurons in Male Mice ...... 57 4.1 Abstract ...... 57 4.2 Introduction ...... 58 4.3 Materials and Methods ...... 59 4.3.1 Mice ...... 59 4.3.2 Stereotaxic injections ...... 60 4.3.3 Slices preparation and electrophysiology ...... 60 4.3.4 Drugs ...... 61 4.3.5 Fluorescence In Situ Hybridization (FISH) ...... 62 4.3.6 Statistical analyses ...... 63 4.4 Results ...... 63 4.5 Discussion ...... 74 Chapter 5 : General Discussion ...... 80 5.1 Summary of Findings ...... 80 5.2 Neuromodulatory Role of Neurotensin ...... 81 5.3 General Role of Neurotensin in Behaviour ...... 84 vii

5.4 Neurotensin: Adaptive or Maladaptive? ...... 87 5.5 Neuropeptides: Where to Next? ...... 90 5.6 Closing Remarks ...... 92 References ...... 94 Appendix A : Illustration of mRNA tissue punches...... 121 Appendix B : Real-time qPCR primers ...... 122 Appendix C : Cell distribution for different rat strain and light cycles ...... 124 Appendix D : Effect of NTR and CRFR1 Antagonist ...... 125 Appendix E : Effect of NT on electrophysiological properties ...... 126 Appendix F : Effect of NTR antagonist in the open field ...... 127 Appendix G : Effect of bath application of NT on excitatory transmission ...... 128

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List of Figures

Figure 1: Illustration of modulation by neuropeptides...... 8 Figure 2: Illustration of electrophysiology recording with electrical stimulation...... 10 Figure 3: Illustration of important brain regions regulating activation of the HPA axis that results in glucocorticoid secretion...... 22 Figure 4: Effect of post-synaptic depolarization on GABAA synaptic transmission in the ovBNST of non- stressed (NS) rats...... 42 Figure 5: Effect of post-synaptic depolarization on GABAA synaptic transmission in the ovBNST of chronic unpredictable stress (CUS) exposed rats...... 44 Figure 6: Effect of post-synaptic depolarization on AMPA synaptic transmission in the ovBNST of NS rats...... 46 Figure 7: Effect of post-synaptic depolarization on AMPA synaptic transmission in the ovBNST of CUS rats...... 48 Figure 8: Effect of intra-ovBNST NTR pharmacological blockade on elevated plus maze (EPM) and forced swim test (FST) behaviours in NS and CUS rats...... 50

Figure 9: Endogenous neuropeptides modulation of electrically-evoked ovBNST GABAA-IPSCs...... 65

CeAèovBNST Figure 10: Effect of postsynaptic depolarization on optically-evoked Vgat GABAA-IPSCs...... 67 Figure 11: Expression and co-localization of Pdyn and Nts mRNA in the ovBNST...... 69 Figure 12: Expression and co-localization of NT and NTRs mRNA in the ovBNST and CeA...... 71 Figure 13: Contribution of neurotensin receptors 1 and 2 on exogenous NT-induced modulation of electrically-evoked ovBNST GABAA-IPSCs...... 73 Figure 14: Illustration of NT and Dyn bi-directional modulation of CeA inputs onto ovBNST neurons. . 77 Figure 15: Illustration comparing neuropeptidergic modulation of synaptic transmission in the ovBNST in non-stressed vs. chronically stressed male rodents...... 81 Figure 16: Illustration of mRNA tissue punches...... 121

Figure 17: Effect of NTR antagonist and CRFR1 antagonist on GABAA-IPSCs in the ovBNST...... 125 Figure 18: Effect of intra-ovBNST NTR antagonist injection on locomotion activity in the open field test...... 127 Figure 19: Effect of NT application on AMPA-EPSCs in the ovBNST ...... 128

List of Tables

Table 1: Summary of modulatory effects of neuropeptides expressed in the ovBNST...... 27 Table 2: Real-time qPCR primers for amplifying reference genes and genes of interest...... 122 Table 3: Cell distribution after post-synaptic activation in different rat strain and light cycles...... 124 Table 4: Effects of NT on passive electrophysiological properties of ovBNST neurons...... 126

List of Abbreviations

AD- Anxiety disorders

AVP- Vasopressin

BLA- Basolateral amygdala

CeA- Central amygdala

ChR2- Channel rhodopshin

CRF- Corticotrophin releasing factor

CRFR1- Corticotrophin releasing factor receptor 1

CUS- Chronic unpredictable stress

DBS- Deep-brain stimulation

DCV- Dense core vesicle

Dyn- Dynorphin eCB- endocannabinoid

Enk- Enkaphalin

EPM- Elevated plus maze

EPSC- Excitatory post-synaptic current

FST- Forced swim test fuBNST- fusiform nucleus of the bed nucleus stria terminalis

GABA- γ-Aminobutyric acid

GPCRs- G protein-coupled receptors

GR- Glucocorticoid receptors

HPA- Hypothalamic–pituitary–adrenal

IPSC- Inhibitory post-synaptic current

LH- Lateral hypothalamus

MDD- Major depressive disorder xi

MR- Mineralocorticoid receptor

NPY- Neuropeptide Y

NS- Non-stressed

NT- Neurotensin

NTR1- Neurotensin receptor 1

NTR2- Neurotensin receptor 2

PACAP- Pituitary adenylate cyclase-activating polypeptide

PFC- Prefrontal cortex

PTSD- Post-traumatic stress disorder

PVN- Paraventricular nucleus of the hypothalamus ovBNST- oval bed nucleus stria terminalis

SSRI- serotonin receptor inhbitor

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Chapter 1: Introduction

Starting in the 1950s, specific pharmacological treatments for psychiatric disorders were serendipitously discovered without any knowledge of the physiology underlying these pathologies. For example, chlorpromazine (an anti-psychotic medication) was used as a drug to reduce surgical shock when physicians noticed it had calming effects on mental state and gave it to patients with schizophrenia (Kane and Correll, 2010). These first generation drugs represented a serious advancement in the treatment of mental health disorders that were practically nonexistent until that point. However, it quickly became apparent that many also had unpleasant side effects and significant safety and toxicity issues (Baldwin et al, 2005).

Subsequently, researchers identified some major cellular pathways (serotonin, dopamine, noradrenaline) by which the first generation drugs were bioactive and engineered new drugs that targeted them specifically (Ban, 2001). Notably, selective serotonin receptor inhibitor (SSRIs) were developed during this era (80s-90s) to treat major depression disorder (MDD) (Hillhouse and Porter, 2015). Overall, these drugs were a great success with the majority of patients reporting amelioration of symptoms (Al-Harbi, 2012).

However, a subset of patients (approximately 30%) were identified as treatment-resistant, reporting no change or worsening in symptoms (Al-Harbi, 2012). This precipitated a major push from researchers and pharmaceutical companies to develop alternative treatments for this treatment-resistant population.

In 1998, neuropeptides were identified as a potential alternative target. The blockade of neuropeptide substance P showed promising anti-depressant effects in male rats, and fueled neuropeptide research in general (Griebel and Holsboer, 2012a; Kramer et al,

1998). The therapeutic potentials of neuropeptides were mostly based on the expression of neuropeptides in certain areas/circuits of interest (for example, areas known to be active during stress) and/or their neuromodulatory effects on certain neurotransmission

(for example, neurotensin’s antagonizing effects on dopamine) (Caceda et al, 2006;

Kramer et al, 1998). The pharmaceutical industry keenly developed many compounds targeting neuropeptide systems that could cross the blood-brain barrier and slowed degradation in the stomach (Griebel et al, 2012a). These compounds were then tested in animals (predominantly male rats) and many demonstrated enough potential (for example, significant decreases in anxiety- and depression-like behaviours in rodent) to start clinical trials (Kormos and Gaszner, 2013).

Substance P was included amongst these successful neuropeptides. As such, Merck & Co conducted a clinical trial to test the efficacy of a substance P antagonist to treat depression symptoms (using the HAM-D17) in patients with MDD (Keller et al, 2006).

In 2006, the results were published and the outcome was not as positive as anticipated:

Substance P antagonist had no effects on depression symptoms (using the HAM-D17) in patients with MDD (Keller et al, 2006). Sadly, substance P was not alone and in the last

20 years, no drug targeting neuropeptide systems has made it to the market for the treatment of psychiatric disorders. Thus, all of them failed once applied clinically to humans. What went wrong?

Chapter 2: Literature Review

2.1 Neuropeptides and Neuromodulation Neuropeptides have an early evolution origin. They are found in cnidarians, an aquatic specie with a primitive nervous system, that diverged evolutionarily prior to vertebrates and invertebrates (Durrnagel et al, 2010). Thus, they are hypothesized to have played a key role in the evolution of the first nervous system. Consequently, they are ubiquitously found in vertebrate and invertebrate species.

2.1.1 What is a neuropeptide? Neuropeptide is a broad categorization that represents the largest and most diverse class of signaling molecules in the brain (Burbach, 2011). They are generally defined as small chains of amino acids found in neurons that modulate neuronal activity such as classical neurotransmitters γ-Aminobutyric acid (GABA, inhibitory) and glutamate (excitatory)

(Li et al, 2012; Williams et al, 2014). They are also found outside of the central nervous system (CNS), and serve as important mediators between the CNS and the periphery.

Although neuropeptides were identified as chemical signals in the brain in the 1970s, new ones are still being discovered today (De Wied, 1971; Zhang et al, 2017). As such, our understanding and definition of neuropeptides is still being refined.

From a therapeutic perspective, neuropeptides and their receptors are ideal targets. They can be used to change a wide range of physiological and behavioural functions (Argiolas and Melis, 2013; Griebel and Holsboer, 2012b). They have a high affinity for their own

receptors limiting the possibility of secondary effects due to binding to other bioactive receptors (Nusbaum et al, 2017; van den Pol, 2012). Additionally, they are capable of eliciting biological responses at low concentrations minimizing the risk of toxicity (Salio et al, 2006). They also tend to have slow peak responses (can be up to 40 minutes) that are ideal for psychiatry disorder medication where long-term effects are necessary

(Karhunen et al, 2001). As such, there are many advantages to developing neuropeptides into therapies.

2.1.2 Neuropeptide Function Neuropeptides function through a variety of different receptors. They typically bind to G protein-coupled receptors (GPCRs) causing conformational changes and leading to downstream cellular and genomic effects. Some neuropeptides act on multiple receptors while others are more specific. For example, neuropeptide Y (NPY) binds to five different receptors while kisspeptin only binds to one (Herzog, 2003; Messager et al,

2005). Therefore, neuropeptides’ mechanism of action of is highly variable depending on their affinity for certain receptors.

Furthermore, different concentrations of the same neuropeptide can have different effects.

For example, at low concentrations (<100 nM), oxytocin increases inhibitory transmission, but at higher concentrations (>500 nM) indirectly decreases inhibitory transmission through the release of endocannabinoid (eCB) (Israel et al, 2008; Oliet et al,

2007). This could be due to neuropeptides having different affinity for their own receptors, and engaging different mechanisms of actions accordingly. At low

concentration neuropeptide neurotensin (NT) will predominantly bind to NTR1 engaging

Gαq/11 coupled cellular mechanism while at high concentration it may bind to both NTR1 and NTR2 that is generally Gαi/o coupled (Tschumi and Beckstead, 2018). As such, varying concentration of neuropeptide can alter its physiological effect.

Neuropeptides can also target specific cells and have an effect on the functional output of neuronal circuits. For example, in the hippocampus, cholecystokinin regulates two distinct sources of inhibition: it strongly depolarizes specific parvalbumin-expressing inhibitory interneurons cells while suppressing GABA release from cholecystokinin- expressing inhibitory interneurons (Foldy et al, 2007). Thus, cholecystokinin bolsters one source of hippocampal inhibition by both exciting these cells and suppressing its counter part (Foldy et al, 2007). Taken together, the modulatory functions of neuropeptides are extremely versatile, and can change depending on concentrations, receptor affinity, and specific cell type targets.

2.1.3 Neuropeptide Release

Further adding to their versatility, neuropeptides release is not restricted to the synaptic active zone. They are stored in dense core vesicles (DCV) that can be released from dendrites, soma, and axons (Kombian et al, 1997; Morris and Pow, 1991). The set of proteins that regulate the release of DCVs are not completely understood although, specific ones have been shown (Sieburth et al, 2007). For example, in Caenorhabditis elegans motor neurons protein kinase C-1 is necessary for DCV release but not synaptic vesicles (Sieburth et al, 2007). The exact mechanism by which DCVs release their

content is still not well understood. Neuropeptides also have the capability of volume transmission, and can have long distance effects (Jan and Jan, 1982; Karhunen et al,

2001). Therefore, once released, neuropeptides can affect both local and external neural circuitry.

Generally neuropeptides are released by high or prolonged firing rates resulting in elevated Ca2+ concentrations (Whim and Lloyd, 1989). The amount of neuropeptide released increases with frequency of electrical stimulation up to 20-30Hz (Bondy et al,

1987; Dreifuss et al, 1971; Gainer et al, 1986; Vilim et al, 2000). Increase of electrical stimulation frequency also correlates with increases in intracellular calcium plateauing in the 20 to 30Hz range (Jackson et al, 1991; Muschol and Salzberg, 2000). Notably, neuropeptides release can still occur in the lower frequency ranges (1-2 Hz) but possibly not in the same quantity as higher frequencies (Bondy et al, 1987; Iremonger and Bains,

2009). Additionally, different firing patterns can release different neuropeptides. For example, oxytocin is released after brief high-frequency discharges that terminate quickly, whereas vasopressin neurons exhibit longer-lasting bursts of lower frequency

(Poulain et al, 1977). Overall, neuropeptide release is sensitive to both frequency and stimulation pattern.

Mammalian neuropeptide release has been mostly investigated using the neurohypophysis that may not be completely generalized to neuropeptides in other brain areas. The neurohypophysis neurons were used due to their high density of large DCVs

(180-200nm in diameter) in their axons that are more easily observed using electron

microscopy vs medium sized DCVs (~100nm in diameter) that are found in most neurons in the brain (Nordmann and Morris, 1984). Different-sized DCVs harbour different membrane-trafficking proteins that results in functional differences such as ease of triggering fusion to the membrane (Flasker et al, 2013; Zhang et al, 2011). Furthermore, most DCVs outside of the neurohypophysis are not concentrated in the axon terminals

(Kombian et al, 1997; Morris et al, 1991). Therefore, it is plausible that the stimulation necessary for neuropeptide release for other brain areas may diverge from that of the neurohypophysis.

Importantly, neuropeptides are rarely stored individually, and are mostly released with other compounds (commonly, other neuropeptides or neurotransmitters) (Nusbaum et al,

2017; Tritsch et al, 2016). They can be co-released (two or more neuropeptide/neurotransmitter in the same synaptic vesicle) or co-transmitted

(neuropeptides from different synaptic vesicles) (Nusbaum et al, 2017). This provides neuromodulatory flexibility as illustrated in Figure 1 since their effects can be convergent

(influencing the same neuronal target as another compound) or divergent (influencing different neuronal targets as another compound) as well as having opposing (increase vs. decrease) or, complementary (both increase or decrease) results on neurotransmission (Ju and Han, 1989b; Ptak et al, 2009; Sun et al, 2003; Vaaga et al, 2014; Whitnall et al,

1983). Neuropeptides physiological effects may therefore influence or be influenced by its co-released or co-transmitted compounds. However, historically neuropeptide physiological functions have been studied individually potentially limiting our understanding of their full effects.

CO-RELEASE CO-TRANSMISSION

Neuropeptide 1 VS Neuropeptide 2

CONVERGENT DIVERGENT + +

+ COMPLE- MENTARY + + +

- - OPPOSING

Figure 1: Illustration of modulation by neuropeptides.

2.1.4 Using Electrophysiology to Study Neuropeptides Having multifaceted actions, the full range of neuropeptide modulation can be difficult to study. Electrophysiology is one of the most commonly used methods as it offers a high- temporal resolution and can probe activity at the cellular or network levels. By varying the recording electrode types, one can record individual neuronal activity (single units) all the way up to the entire brain activity (electroencephalograms). Whole-cell patch clamping is a single unit method that enables the measurement of ionic current flow, and

can therefore be used to measure the effect of neuropeptides on specific neurotransmission (Karmazinova and Lacinova, 2010).

Ideally, electrophysiology is performed using a whole brain in vivo experimental approach to keep the neural circuitry intact. A stimulation electrode can be placed in any input areas, and a recording electrode is placed in the region of interest to measure changes in neural activity. This enables the study of neuropeptide’s short and long distance effects. Additionally, specific inputs and outputs effects can be measured using this method. However, these experiments can be difficult to perform especially when investigating the effect of neuropeptide on specific neurotransmission. It also often entails anesthetizing animals, which can have a broad inhibitory effect on the brain.

Therefore, although ideal, the in vivo experimental approach is not always viable.

Alternatively, an ex vivo brain slice approach, where only the brain slice containing the region of interest is used, can be applicable. Neural inputs are electrically stimulated, and changes in current or voltage from an individual neuron are recorded using a micropipette with a recording electrode as illustrated in Figure 2. The effects of neuropeptides on neurotransmission can be more easily measured using this technique.

neurotransmitter neurotransmitter receptor

electrical POSTSYNAPTIC stimulation recording PRESYNAPTIC NEURON electrode INPUTS

Figure 2: Illustration of electrophysiology recording with electrical stimulation.

However, investigating the effect of neuropeptides on circuitry is more difficult using this approach. Some of the neural inputs and outputs are inherently loss due to the slicing.

Moreover, using an electrical electrode stimulates all local fibers, and is not input specific. Although, combining brain slice electrophysiology with optogenetics can help circumvent this limitation. To do this, specific cells can be transfected with opsin proteins

(example channel rhodopsin (ChR2)) that can be stimulated by light (Deisseroth, 2015).

Therefore, specific inputs can be light-evoked in order to measure neuropeptide’s effect on them (Stamatakis and Stuber, 2012). Importantly, using ChR2 to depolarize cells increases calcium influx and the probability of release (Schoenenberger et al, 2011).

Thus, results attained using this technique cannot be blindly equated to normal physiological conditions. Overall, the complex modulatory role of neuropeptides should be taken into consideration when choosing an experimental approach.

2.1.5 Neuropeptides and Psychiatric Disorders One of the biggest shortcomings of pre-clinical neuroscience studies targeting neuropeptides is the omission of clinically relevant animal models. The majority of pre- clinical studies using neuropeptides were tested in healthy male animals rodents (Kehne and Cain, 2010; Kramer et al, 1998). As such, there is currently a lack of understanding of how these regulatory molecules are altered in states promoting psychiatric disorders.

One exception is the study of NPY in post-traumatic stress disorder (PTSD). Repeated exposure to aversive stressors predicts and contributes to PTSD (Deppermann et al,

2014). The efficacy of NPY to decrease the behavioural and physiological changes associated with PTSD was studied using a rat model of chronic stress (Serova et al,

2013). Subsequently, there are currently two promising clinical trials for intranasal delivery of NPY as a treatment for PTSD (NCT 00748956, NCT 01533519). One clinical trial has recently released phase I results showing that a single dose of NPY given nasally decreased anxiety symptoms on the Beck Anxiety Inventory score compared to patients given a placebo (Sayed et al, 2018). This bolsters the need to study neuropeptides in clinically relevant animal models that may more efficiently translate to clinical populations.

2.2 Chronic Stress In humans, exposure to chronic aversive stressors predicts and contributes to major depressive disorder (MDD), and anxiety disorders (AD) (Bidzinska, 1984; Hammen,

2005; Hammen et al, 2004; Hammen et al, 2009; Harkness et al, 2014; Patrick et al,

1986; Paykel et al, 1984; Sheets and Craighead, 2014). Knowing this, many chronic 11

stress rodent animal models were developed to study the biological changes that may precipitate pathologies such as MDD and AD (Heim and Nemeroff, 1999; Mathew et al,

2008; Risbrough and Stein, 2006; Sayed et al, 2018). From these models, it was found that neuropeptide expression and/or function is altered by chronic stress in different brain areas (Glangetas et al, 2013; Hammack et al, 2009; Hirano et al, 2014; Kolasa et al,

2014; Murgatroyd et al, 2015). As such, the use of animal models of chronic stress may be beneficial for the development of neuropeptide as diagnostic and/or therapeutic tools.

2.2.1 What is stress? Stress is often used colloquially as a negative event, or a period of hardship. In fact, stress is the physical and behavioural response to a particular stressor that is necessary for survival. While a stressor is a stimulus challenging homeostasis and/or threatening an individual’s well being. An acute stressor, or the anticipation of, triggers a cascade of biological events that restores homeostasis (body’s stable equilibrium) (Herman, 2013).

This process is defined as allostasis (maintaining stability, or homeostasis, through change), and enables organisms to meet the demands and challenges of their changing environment (Huether, 1996; McEwen, 2004; Sterling, 2012, 2014; Ursin and Olff,

1993).

Long periods of activating the stress response pathways however, can cause pronounced, sometimes permanent, changes in physiology and behaviour (Flak et al, 2012; Herman et al, 1995; Jankord et al, 2011; Ulrich-Lai et al, 2006; Ulrich-Lai et al, 2007). This process is defined as allostatic load, and refers to the physical and psychological load the body

endures to adapt to chronic adverse situations (McEwen, 2000). The ability to respond to these challenges lies at the crossroad of both resilience, and transition into pathology.

Adaptive vs. maladaptive coping mechanisms are difficult to distinguish, and consist of a major limitation in the application of stress research to clinically relevant therapies

(Herman, 2013; Radley et al, 2015). As Hans Seyle once said, “its not stress that kills us, it is our reaction to it”.

2.2.2 Animal Models of Chronic Stress (Pre-clinical Models) The goal of these pre-clinical models is to reproduce a state of allostasic load that more closely resembles the context or physiology that produces pathological human disorders.

The complex manifestations of psychiatric disorders as well as the cognitive and affective differences between humans and laboratory animals (e.g., rats, mice, hamsters, gerbils, guinea pigs) are, however, two major limitations (Kalueff and Tuohimaa, 2004).

Although, pre-clinical models are particularly advantageous when investigating cellular changes that occur with chronic stress that cannot be performed on humans.

There are two main approaches to animal stress models: environmental (subjecting animals to stressors for a set period of time), or genetic (using specific bred strains or mutant animals) (Bali and Jaggi, 2015). Both have their advantages and disadvantages

(Nestler and Hyman, 2010). The genetic approach enables researchers to specifically study a gene, protein, or even, neural circuit of interest, and establish causation with changes in behaviour. Although, in the absence of relevant human genetic evidence, it is unknown whether this approach represents legitimate disease models rather than

interesting phenocopies. For example, it is not feasible to knockout genes in humans and therefore unknown whether this precipitates pathology symptoms per se. The true strength of genetic models lies in their ability to observe effects of specific targets.

The environmental approach, on the other hand, replicates risk factors associated with psychiatric disorders in humans. Unlike the genetic approach, these factors are known to precipitate pathology in humans (Bidzinska, 1984; Hammen, 2005; Hammen et al, 2004;

Hammen et al, 2009; Harkness et al, 2014; Patrick et al, 1986; Paykel et al, 1984; Sheets et al, 2014). However, it lacks specificity, as aversive stressors are predictors and contributors of multiple psychiatric (for example, AD, MDD, PTSD) and metabolic disorders (for example, diabetes and obesity) (Green et al, 2010). Different animal models using an environmental approach diverge from one another due to the duration of stressors (chronic vs. acute), the nature of stressors (physical vs. psychological), and the developmental stage during which the stressors are administered (early vs. late life)

(Campos et al, 2013). There are a wide variety of models that can be used.

The chronic unpredictable (CUS) paradigm is an example of animal model using the environmental approach. It is a mixture of psychological and physical stressors that mimic the variability of stressors encountered in daily life, and is typically performed in adult animals. In this model, animals are randomly exposed for 2 to 5 weeks to a wide range of stressors, including hot air, restraint stress, strobe light, unpleasant noises, changes in the home cage (removal of sawdust, replacement of sawdust with water, heating [37°C] or cooling [4°C] of the home cage) (Cerqueira et al, 2007; Herman et al,

1995). The paradigm results in a sensitized stress response, reduced body weight gain, anhedonia, anxiety-like behaviours, and behavioural despair (Flak et al, 2012; Herman et al, 1995; Jankord et al, 2011; Ulrich-Lai et al, 2006; Ulrich-Lai et al, 2007; Ventura-

Silva et al, 2012b; Willner, 1997). As such the CUS model has been widely used to induce persisting stress-related physiological changes in animals (Mineur et al, 2006).

This paradigm is used in conjunction with certain animal tests to assess changes in behaviour.

2.2.3 Animal Behavioural Test of Mood Disorders There are over 30 different tests that have been developed to facilitate pre-clinical research of AD and MDD. They have produced significant contributions to the discovery of pharmacological therapies, and the understanding of the neurobiology of psychiatric diseases (Bourin et al, 2007). There are two main categories: unconditioned response tests (which require no training and evaluate spontaneous behaviour) and conditioned response tests (which require training and often involve a painful event) (Bourin et al,

2007; Kumar et al, 2013; Rodgers, 1997). These tests attempt to model certain behaviours associated with psychiatric disorders in order to predict the efficacy of a potential treatment (Harris, 1989). Importantly, none have the ability to unequivocally test efficacy for any particular disorder, and as such, are often used in a test battery rather than individually.

The elevated plus maze (EPM) is the most common behavioural test used for pre-clinical

AD drug discovery. It represents approximately 35% of all AD animal studies conducted

between 1960-2012 (Griebel and Holmes, 2013). The test consists of an elevated plus shaped maze with two arms enclosed by walls (closed arms) perpendicular to two arms openly exposed (open arms) (Handley and Mithani, 1984; Pellow et al, 1985). Briefly, animals are placed at the center junction of the four arms, and entries/time spent in each arm is recorded for 5 minutes (Walf and Frye, 2007). The test is an approach-avoidance conflict where the rodents’ natural tendency to explore novel environments conflicts with avoiding exposed places where they can be more easily preyed upon such as the open arms. When animals undergo the CUS paradigm they spend less time in the open arm interpreted as an increase in anxiety-like behaviour (anxiogenic) (Berube et al, 2014;

Bessa et al, 2009; Pego et al, 2008; Sousa et al, 1998; Ventura-Silva et al, 2013a;

Ventura-Silva et al, 2012a; Vyas et al, 2002). Thus, the perception of risk in the open arm may be exaggerated in CUS animals, which is possibly reminiscent of human symptoms of anxiety where the stress response is active without a stimuli (Pego et al, 2008;

Ventura-Silva et al, 2012a). Alternatively, the animal could be appropriately minimizing risk in order to reduce threat in a hostile environment (Dias-Ferreira et al, 2009).

Exposure to classical anxiolytic drugs, such as benzodiazepines, increases time spent in the open arms, and is inferred to be a decrease in anxiety-like behaviour (anxiolytic)

(Pellow and File, 1986).

The forced swim test (FST) is a common behavioural test to assess pre-clinical MDD drug development (Molendijk and de Kloet, 2015). It involves timing active (swimming and climbing/diving) or passive (immobility) behaviour when rodents are forced to swim in a cylinder filled with water from which there is no escape (Porsolt et al, 1977; Slattery

and Cryan, 2012). The original theory behind it being that increased immobility is a sign of behavioural despair as the animal gives up on trying to escape (Porsolt et al, 1977;

West, 1990). However, a study did demonstrate that immobility was actually beneficial, and prevented animals from sinking (Nishimura et al, 1988). As such, the FST is more accurately an assessment of active vs. passive coping strategy (Campus et al, 2015).

Antidepressant drugs consistently reduce the amount of immobility time in the FST

(Cryan and Mombereau, 2004; Cryan et al, 2005; Petit-Demouliere et al, 2005). CUS, on the other hand, increases the time spent immobile (Bessa et al, 2009; Monteiro et al,

2015; Strekalova et al, 2004).

Overall, these tests provide important insight into behavioural changes in animals after undergoing a chronic stress paradigm. As aforementioned, pre-clinical models have elucidated important cellular changes that occur with stress, notably, important brains areas that modulate the stress response.

2.3 Neural Modulation of Chronic Stress

2.3.1 HPA Axis Stressors ultimately activate two major pathways: the hypothalamic–pituitary–adrenal

(HPA) axis, and the sympathetic adrenomedullary system (Herman, 2013). Upon stimulation, neurosecretory neurons of the paraventricular nucleus of the hypothalamus

(PVN) secrete corticotrophin-releasing factor (CRF) and arginine-vasopressin (AVP) that then, through a chain of interactions with the pituitary and the adrenal cortex, causes the release of glucocorticoids in the blood stream (Herman, 2013; Herman and Cullinan,

1997; Keller-Wood and Dallman, 1984). Release of glucocorticoids enables redistribution of metabolism for increased demand of energy, coordination of the circadian rhythm, suppression of innate immunity in immune organs, and negative feedback for the HPA axis (Oster et al, 2017; Rhen and Cidlowski, 2005). Overall, this promotes vigilance and physical readiness for a challenging environment, also known as the “flight or fight” response.

Negative feedback of glucocorticoids occurs in two phases: fast and delayed feedback as illustrated in Figure 3. First, the PVN CRF neurons are rapidly shut off by membrane actions of glucocorticoids that cause the release of eCB inhibiting excitatory afferent inputs to the PVN (Di et al, 2003). This occurs within minutes (Keller-Wood et al, 1984).

Then, slow glucocorticoid feedback is mediated by endogenous glucocorticoid receptors present in key HPA-regulatory brain regions, and returns glucocorticoid levels to baseline

(Ahima and Harlan, 1990; Aronsson et al, 1988; Arriza et al, 1988; Fuxe et al, 1987;

Reul and de Kloet, 1986). Both of these feedback mechanisms occur through binding to glucocorticoid receptors.

There are two glucocorticoid receptors: glucocorticoid receptors (GR) and mineralocorticoid receptor (MR) that has an approximately 5–10 fold greater affinity

(Reul and de Kloet, 1985). The GR receptor is only bound in response to intermediate to high glucocorticoid secretion (for example, following a stressor) mediating the negative feedback whereas the MR regulates HPA tone (De Kloet et al, 1998). A decreased sensitivity to glucocorticoids negative feedback is a hallmark of mood disorders (Alt et

al, 2010; De Kloet and Reul, 1987; Furay et al, 2008; Keller-Wood et al, 1984; Klok et al, 2011; Matsubara et al, 2006; Patel et al, 2008; Perlman et al, 2004; Sapolsky et al,

1983a, 1984; Sapolsky et al, 1983b). As such, MR and GR have an integral role in the proper functioning of HPA axis regulation.

Despite the clear importance of glucocorticoids in feedback regulation, the HPA axis can also be inhibited in a glucocorticoid-independent way. There are both phasic and tonic inhibitory GABAergic neuronal inputs onto CRF neurons of the PVN (Camille Melon and Maguire, 2016). The relationship between glucocorticoid and neural stimulation of inhibitory circuits is still not fully understood. Overall, the controlled feedback of HPA axis is mediated by multiple mechanisms, and when impaired can have pathological consequences.

2.3.2 HPA Modulation Multiple neuronal inputs are responsible for the activation of PVN neurons in reaction or in anticipation to a stressor. Physiological stressors, consisting of a more specific and eminent challenge to homeostasis tend to activate direct inputs onto the PVN to evoke rapid reflexive activation (Cascio et al, 1987; Ganong, 2000; Ulrich-Lai and Herman,

2009). Emotional (or psychological) stressors, more closely associated with the anticipatory aspect of stress activate multiple brain regions, many of which do not have a direct input onto the PVN (Herman et al, 2003). This enables integration of cognitive

(previous memories) and affective (positive vs. negative valence) information with the ongoing physiological state. 19

The amygdala, hippocampus, and prefrontal cortex (PFC) are important modulators of the HPA axis as illustrated in Figure 3. Generally, HPA activation is facilitated by stimulation of the amygdala (involved in aversive learning and fear conditioning) while the hippocampus (involved in contextualization and necessary for HPA-negative feedback) and PFC (involved in cognitive control, emotional regulation and working memory) attenuate it (Radley et al, 2015). GR activation in these areas reduces HPA axis response (Furay et al, 2008; McKlveen et al, 2013). As such, these brain areas play an integral role in ensuring appropriate activation of the HPA axis.

Chronic exposure to emotional stressors is associated with the long-term anatomical and functional alterations in the PFC, hippocampus, and amygdala (Conrad, 2008; Frodl et al,

2002; Soares et al, 2012). Chronic stress reduces expression of GR protein, and decreases

GR binding affinity in the PFC and hippocampus (Chiba et al, 2012; Guidotti et al, 2013;

Sasuga et al, 1997). On the other hand, chronic stress facilitates HPA axis activation by the main output nucleus of the amygdala (central amygdala, CeA), and increases excitability (increase in excitatory drive, reduction in tonic inhibition, more sustained excitatory activity) in the basolateral amygdala (BLA) nucleus that inputs onto the CeA

(Padival et al, 2013; Prewitt and Herman, 1997; Suvrathan et al, 2014; Van de Kar et al,

1991). Overall, chronic stress reduces negative feedback efficacy from the PFC and hippocampus while enhancing stimulation from the amygdala resulting in sustained HPA axis activation. However, none of these directly contact the PVN and all work through

inhibition or disinhibition of relay centers such as the bed nucleus of the stria terminalis

(BNST) (Cullinan et al, 1993; Prewitt and Herman, 1998; Radley et al, 2009).

PFC Hippocampus

+ + Amygdala - BNST -

PVN

CHR AVP

Pituatary

ACTH

Adrenal Cortex

Glucocorticoids [Cort]

FAST DELAYED FEEDBACK FEEDBACK

Figure 3: Illustration of important brain regions regulating activation of the HPA axis that results in glucocorticoid secretion.

2.3.3 BNST The BNST is an important source of inhibitory input onto the PVN outside of other hypothalamic regions (dorsomedial and lateral hypothalamus and medial preoptic area)

(Cullinan et al, 1996; Cullinan et al, 1993; Roland and Sawchenko, 1993; Wu et al,

2015). The BNST is a coordination center for information regarding potential changes in homeostasis from cortical areas and current homeostatic state from brainstem areas

(Dumont, 2009). It is crucial for the anticipation of an aversive stressor and its activity is enhanced by the proximity, unpredictability and controllability of a stressor (Alvarez et al, 2011; Mobbs et al, 2010; Somerville et al, 2010; Straube et al, 2007). In rats, sustained fear is modulated by the BNST (Davis et al, 2010). Thus, this area mediates general apprehension to threat and loss of control to prevent that threat.

This is particularly pertinent for psychiatric disorders such as anxiety disorders where

“fear or anxiety [are] excessive or persisting beyond developmentally appropriate periods” according to the DSM-V. Patients with AD have increased BNST activity while performing stress-inducing tasks such as gambling task with low probability of winning or the presentation of a phobogenic visual stimuli (Straube et al, 2007; Yassa et al, 2012).

Deep-brain stimulation (DBS) of the BNST is also currently under clinical trial in treatment-resistant MDD patients (Raymaekers et al, 2017). Preliminary findings were inconclusive due to the small sample size (7 patients) but did show some decrease in

HAM-D scores of MDD patients that received DBS in the BNST (Raymaekers et al,

2017). The exact role of the BNST in both or either disorder is however poorly understood.

Chronic stress does have morphological and physiological effects in the BNST. It robustly alters excitatory synaptic transmission in the BNST, and results in dendritic remodeling (Conrad et al, 2011a; Dabrowska et al, 2013; Glangetas et al, 2013; Hubert and Muly, 2014; McElligott et al, 2010; Pego et al, 2008; Vyas et al, 2003). Thus, chronic stress induced changes in the BNST may modify its modulatory effects on the

PVN. Accordingly, chronic stress decreases inhibitory innervation in the PVN suggesting

BNST inhibitory modulation may be less efficient or even altered (Cullinan, 2000;

Verkuyl et al, 2004).

Notably, the BNST is often treated as an entity when it comprises of 18 subregions in the rat brain. Each subregion has distinctive neuronal inputs and outputs and different effects on anxiety-like behaviour (Bota et al, 2012a; Choi et al, 2007; Kim et al, 2013). One of the main subregion that outputs to the PVN is the fusiform nucleus (fuBNST) that increases HPA axis activation (Choi et al, 2007). This is mainly a GABAegic nuclei but it receives dense inhibitory inputs from the oval nucleus (ovBNST) (Dong et al, 2001;

Poulin et al, 2009). As such, fuBNST pro-HPA activation may be mediated by its disinhibition from the oval. The ovBNST also has a high density of MR that colocalizes with CRF, and is perhaps an important site for negative feedback and termination of HPA activation (Ahima et al, 1990; Fuxe et al, 1987). Overall, the ovBNST may be an important relay center for the stress response.

2.3.4 Oval BNST The ovBNST is a hub that is important for energy homeostasis, and promoting appetitive behaviours. It is sensitive to both an imminent and non-imminent threat, and activation decreases exploratory behaviour while increasing food intake (Jennings et al, 2013b; Kim et al, 2013; Lebow and Chen, 2016). As such, the ovBNST is plausibly an important control center for foraging behaviour in rodents in unpredictable environment with potential predation or limited access to energy sources where heightened awareness is necessary. Its function may be hijacked in pathological compulsive states, such as drug addiction, where consummatory behaviours become exaggerated (Krawczyk et al,

2013a).

Outputs from the oval are entirely inhibitory as it contains only GABAergic neurons

(Nguyen et al, 2016). It projects to the lateral hypothalamus where it has been shown to increase feeding behaviour (Jennings et al, 2013a). It also inputs onto brain stem regions such as the nucleus of solitary tract that controls respiratory behaviour, and parabrachial nucleus that is important for taste processing (Dong et al, 2001). As aforementioned, ovBNST projects to the fuBNST that is an important pro-HPA axis nuclei (Dong et al,

2001). Overall, activation of ovBNST neurons may promote appetitive behaviours.

The major excitatory inputs to the ovBNST originate from both forebrain and brainstem regions. It is densely innervated from brain regions related to arousal (noradrenaline from locus coeruleus) or activated by stimulus with appetitive or positive/aversive emotional valences (paraventricular thalamus) (Alden et al, 1994; McDonald et al, 1999; Moga et

al, 1995; Pickel et al, 1974). It also receives information about the rewarding salience of a stimulus through dopaminergic inputs from the periaqueductal gray and sensory smell information from the piriform cortex (Freedman and Cassell, 1994). From brainstem regions, it receives information related to blood sugar levels, body temperature, and sleep wake cycles (Alden et al, 1994; Eiden et al, 1985; Pickel et al, 1974; Vertes, 1991).

Therefore, excitatory inputs to the ovBNST contain contextual information about the current environment from forebrain regions, and physiological state information from brainstem regions.

The major inhibitory input to the oval consists of the central amygdala (CeA) (the major output nuclei of the amygdala). This inhibitory modulation is however bi-directional as the oval also projects inhibitory inputs to the CeA (Dong et al, 2001; Petrovich and

Swanson, 1997). Thus, the two may have opposing roles. Behaviorally, the CeA is involved in phasic-cued fear response while the ovBNST is activated during sustained fear response (Walker et al, 2009a). Therefore, the two areas may be responsible for the balance between active fear response (CeA) and anticipatory fear response (ovBNST).

The bi-directional inhibitory relationship likely becomes altered with chronic stress, and neuropeptides may play a role in this. For example, CRF mRNA expression is increased after chronic stress in both areas (Kim et al, 2006). Notably, ovBNST and CeA express a very similar array of neuropeptides including CRF, NT, enkaphalin, dynorphin (Dyn), to name a few (Day et al, 1999). At this point, there is limited knowledge about how

neuropeptides may be coordinating their functions to modulate both nuclei. As such, it is unknown whether their modulatory functions become altered after chronic stress.

2.3.5 Neuropeptides in the ovBNST The ovBNST contains a variety of different neuropeptides (Bota et al, 2012b). In the ovBNST, neuropeptides modulate inhibitory and excitatory neurotransmission (described in Table 1).

Table 1: Summary of modulatory effects of neuropeptides expressed in the ovBNST.

Neuropeptides Modulatory Effect(s) CRF Increases inhibitory transmission in the BNST through a post-synaptic mechanism (Kash and Winder, 2006) Dyn Inhibits excitatory and inhibitory transmission in the BNST (Crowley et al, 2016; Li et al, 2012) NT Increases inhibitory tansmission in the ovBNST through a pre-synaptic mechanism (Krawczyk et al, 2013a) Enk Decreases excitatory transmission in the ovBNST (unpublished data) PACAP Unknown Somatostatin Unknown

Some neuropeptides expressed in this area highly co-localize like CRF and NT suggesting they may cooperate (Ju et al, 1989b). While others like NT and Enk seem to have no overlap suggesting these may have independent modulatory roles (Day et al,

1999). There is currently a paucity of information regarding endogenously released neuropeptides in this area, how they work as a network, and which neuronal inputs are sensitive to which neuropeptides. Additionally, neuropeptides in this area have only been studied using pharmacological application of individual neuropeptide in non-stressed rodents (Kash et al, 2006; Krawczyk et al, 2013a). Answering some of these questions 27

may enable us to better understand how neuropeptide modulation occurs in a brain region that both effects and is affected by chronic stress.

2.4 Hypothesis Overall, the ovBNST is an important region for homeostasis, and has a high concentration of neuropeptides that likely modulates proper neural functioning of this area. Notably, these neuropeptides may be working cooperatively or antagonistically to fine tune specific inputs/outputs. Since the ovBNST is a key relay center for the stress response, the function of these neuropeptides may also be changed by chronic stress. We hypothesize that neuropeptides modulate neurotransmission in the ovBNST and will be altered by exposure to chronic aversive stressors resulting in changes in behaviour. We will test our hypothesis using the following 4 specific aims.

2.4.1 Aim 1: Measure modulatory effects of locally released neuropeptides Endogenously released neuropeptides likely have an important role in shaping neural activity especially in areas, such as the ovBNST, that have a dense concentration of them

(Day et al, 1999). We will locally release neuropeptides using a repetitive depolarization protocol in order to better understand how these co-released and co-transmitted modulators work as a system to influence neurotransmission. Using whole-cell brain slice electrophysiology, we can measure changes in inhibitory and excitatory transmission, and identify specific neuropeptide’s role with pharmacological antagonists.

2.4.2 Aim 2: Investigate chronic stress alteration of modulatory effects of locally released neuropeptides Repeated aversive stressors cause morphological changes in BNST neurons, and alter excitatory transmission (Conrad et al, 2011b; Pego et al, 2008). To investigate whether neuropeptide modulation of neurotransmission is altered in the ovBNST, we will repeat experiments from aim 1 with animals who underwent the CUS paradigm. This enables us to better understand whether neuropeptide modulation in this area is sensitive to allostatic load.

2.4.3 Aim 3: Dissociation of specific neuropeptide function in ovBNST on anxiety- like and behavioural despair in stressed and non-stressed conditions The CUS paradigm results in robust behavioural changes in rats such as decreased time spent exploring the open arm of the EPM (Ventura-Silva et al, 2013b). We will investigate whether neuropeptide(s) altered by chronic stress have behavioural effects using the EPM and FST. To do this, we will use intracranial cannulation in order to specifically administer neuropeptide antagonist to the ovBNST in both non-stressed and

CUS rats.

2.4.4 Aim 4: Expand neurophysiological findings at the circuit level Neuropeptides have the capacity to target specific neural circuits. We hypothesize that neuropeptide(s) will affect the main inhibitory input to the ovBNST: the CeA. We will use an optogenetic approach to isolate specific CeA inputs to the ovBNST and measure endogenously released neuropeptides modulatory effects.

Chapter 3: A key role for neurotensin in chronic-stress-induced anxiety-

like behaviour in rats

3.1 Abstract Chronic stress is a major cause of anxiety disorders that can be reliably modeled pre- clinically, providing insight into alternative therapeutic targets for this mental health illness. Neuropeptides have been targeted in the past to no avail possibly due to our lack of understanding of their role in pathological models. In this study we used a rat model of chronic stress-induced anxiety-like behaviours and hypothesized neuropeptidergic modulation of synaptic transmission would be altered in the Bed Nucleus of the Stria

Terminalis (BNST), a brain region suspected to contribute to anxiety disorders. We used brain slice neurophysiology and behavioural pharmacology to compare the role of locally released endogenous neuropeptides on synaptic transmission in the oval (ov) BNST of non-stressed (NS) or chronic unpredictably stressed (CUS) rats. We found that in NS rats, post-synaptic depolarization induced the release of vesicular neurotensin (NT) and corticotropin releasing factor (CRF) that co-acted to increase ovBNST inhibitory synaptic transmission in 59% of recorded neurons. CUS bolstered this potentiation (100% of recorded neurons) through an enhanced contribution of NT over CRF. In contrast, locally-released opioid neuropeptides decreased ovBNST excitatory synaptic transmission in all recorded neurons, regardless of stress. Consistent with CUS-induced enhanced modulatory effects of NT, blockade of ovBNST neurotensin receptors completely abolished stress-induced anxiety-like behaviours in the elevated plus maze paradigm. The role of NT has been largely unexplored in stress and our findings highlight its potential contribution to an important behavioural consequence of chronic stress, that 30

is, exaggerated avoidance of open space in rats.

3.2 Introduction While the stress response is integral for survival, prolonged exposure to stressors can have damaging consequences. Repeated exposure to aversive stressors predicts and contributes to mental illnesses such as generalized anxiety disorders (GAD), major depressive disorder (MDD), or post-traumatic stress disorder (PTSD) (Deppermann et al,

2014; Gosselin and Laberge, 2003; Hammen et al, 2009). However, the biochemical imbalances caused by repeated stress in the brain remain elusive and animal models of chronic stress are essential to elucidate these mechanisms (Conrad et al, 2011b).

Repeated aversive stressors result in increased volume and dendritic branching as well as long-term alterations of excitatory synaptic transmission in the bed nucleus of the stria terminalis (BNST) (Conrad et al, 2011b; Dabrowska et al, 2013; Glangetas et al, 2013;

Hubert et al, 2014; McElligott et al, 2010; Pego et al, 2008; Vyas et al, 2003).

Surprisingly, the effects of chronic stress on local γ-aminobutyric acid (GABA) transmission, imperative for fine-tuning neuronal output, have been largely unexplored in the BNST. Neuropeptides are potent modulators of GABA transmission in the BNST, but whether their function is altered in chronically stressed rats has never been investigated

(Crowley et al, 2016; Kash et al, 2006; Krawczyk et al, 2013b). Neuropeptides in the

BNST may be affected by chronic stress due to their involvement in the modulation of stress- or aversion-related phenomena (Lezak et al, 2014; Walker et al, 2009b).

Specifically, the oval nucleus of the BNST (ovBNST) contains high concentrations of many different neuropeptides and activation of this specific nucleus increases anxiety-

like behaviours suggesting it may be sensitive to chronic stress (Kim et al, 2013).

Therefore, we hypothesized that chronic stress would change neuropeptide modulation of synaptic transmission in the ovBNST. We used the chronic unpredictable stress (CUS) paradigm to test this hypothesis, a preclinical model that mimics every day stressors and invariably increases anxiety-like behaviours in rats (Cerqueira et al, 2007). Interestingly, the neuromodulatory effects of neurotensin (NT), but not of corticotropin releasing factor

(CRF), became sensitized after one-month exposure to CUS. Accordingly, in vivo pharmacological blockade of ovBNST NT receptors had an anxiolytic effect in CUS rats.

The neuropeptide NT is therefore a significant contributor to ovBNST promotion of anxiety-like behaviour in chronically stressed animals.

3.3 Materials and Methods

3.3.1 Rats One hundred and thirty-three adult male rats (Charles River Laboratories, Canada/Spain) weighing 350-450g were included in the electrophysiology experiments (Wistar and

Long Evans rats, n=63) and behavioural experiments (Wistar rats, n=70). The rats were maintained on an artificial 12h light/dark cycle (8:00 A.M. lights on/8:00 P.M. lights off) or 12h dark/light cycle (8:00 A.M. lights off/8:00 P.M. lights on).

The rats acclimatized for a minimum of 1 week upon arrival to the facility. Rat chow and water were provided ad libitum in the home cages. Sixty-three rats were used for electrophysiology (Canada), 33 rats performed the elevated plus maze (Portugal), and 37 rats performed the elevated plus maze and the forced swim test (Canada). All 33

experiments were conducted in accordance with the guidelines from the Canadian

Council on Animal Care in Science and approved by the Queen's University Animal Care

Committee and with Portugal local regulations (European Union Directive 86/609/EEC).

3.3.2 Slices preparation and electrophysiology Rats were anesthetized with isoflurane (5% at 5 L/min) and their brains removed into ice- cold artificial cerebral spinal fluid (aCSF) containing (in mM): 126 NaCl, 2.5 KCl, 1.2

MgCl2, 6 CaCl2, 1.2 NaH2PO4, 25 NaHCO3, and 12.5 D-glucose equilibrated with 95%

O2/5% CO2. Brains were cut in 2°C aCSF into coronal slices (250 µm) with a vibrating blade microtome (VT-1000; Leica). We use the slice corresponding to -0.26mm from bregma. Slices were incubated at 34°C for 60 minutes and transferred to a chamber perfused (3 ml/min) with aCSF at 34°C. Remaining slices were kept in aCSF at room temperature until further use. Whole-cell voltage-clamp recordings were made using glass microelectrodes (3-5 MΩ) filled with (in mM): 70 K+-gluconate, 80 KCl, 1 EGTA,

5 HEPES, 2 MgATP, 0.3 GTP, and 1 P-creatine. We recorded lateral from an imaginary line drawn vertically across the lateral ventricle and medial to the internal capsule. In the dorso-lateral plan, we only recorded dorsally to an imaginary horizontal line drawn half- way between the ventral tip of the lateral ventricle and the top of the anterior commissure as illustrated in our previous publications (Krawczyk et al, 2011a; Krawczyk et al,

2011b). Paired electrical stimuli (10–100 µA, 0.1 ms duration, 20 Hz) were applied at 0.1

Hz. Excitatory or inhibitory post-synaptic currents (E/IPSCs) were evoked by local fiber stimulation with tungsten bipolar electrodes while neurons were voltage-clamped at −70 mV. GABAA-IPSC and AMPA-EPSC were pharmacologically isolated with 6,7-

dinitroquinoxaline-2,3-dione (DNQX; 50 µM) or picrotoxin (100µM), respectively. To induce local endogenous neuropeptide release, post-synaptic neurons were repetitively depolarized in voltage clamp from -70 to 0 mV (100 ms) at a frequency of 2 Hz for 5 mins (Iremonger et al, 2009).

We defined long-lasting post-synaptic depolarization-induced changes in EPSCs or

IPSCs peak amplitude as a >20% deviation from baseline, 25 mins following the end of the repetitive depolarization protocol. Recordings were made using a Multiclamp 700B amplifier and a Digidata 1440A (Molecular Devices Scientific). Data were acquired and analyzed with Axograph X running on Apple computers.

3.3.3 Drugs Stock solutions of SR 142948 (10 mM) and naloxone (1 mM) were prepared in double- distilled water and stock solutions of DNQX (100 mM), NBI-27914 (50 mM),

Concanamycin A (1mM) were prepared in DMSO (100%). All drugs were further dissolved in the physiological solutions or 0.9% saline at the desired concentrations

(DNQX 50µM, SR-142948 5-10 µM, NBI-27914 1 µM, Concanamycin A 5 µM, naloxone 1 µM) and the final DMSO concentration never exceeded 0.1%. Drugs were obtained from Sigma-Aldrich or R&D Systems.

3.3.4 Chronic Unpredictable Stress (CUS) Rats were singly-housed and randomly assigned to non-stressed (NS) or CUS groups.

Rats in the NS group were handled regularly over 4 weeks. Rats in the CUS group were 35

exposed to 4 weeks of daily exposure to one stressor (5-60 min/day) at different times, as described previously (Cerqueira et al, 2007). Stressors presentation was randomized and included one of the following aversive stimuli: cold water immersion (18°C, 60 mins), home cage shaking (5 mins), restraining (60 mins), overcrowding (3-4 rats/cage, 60 mins), and exposure to hot air stream (5 mins).

3.3.5 Surgery Rats were positioned in a stereotaxic instrument and secured by non-rupture ear bars under isoflurane (2-3%, 5L/min) or ketamine/medetomidine anaesthesia. Double guide cannulas (Plastics One) were bilaterally implanted 1 mm above the upper limit of the oval region of the dorsal BNST (−0.26 A.P., ±1.9 M.L., −6.5 D.V.). Injector cannulas (Plastics

One) were placed into the guide cannulas (7.5 mm length). All stereotaxic coordinates were relative to bregma. The head attachment was secured in place via four 0.08 × 0.125 in jeweler screws and dental acrylic cement. The guide cannulas were fitted with an autoclaved 30 Ga stylet and covered with a screw-on dust cap. Following surgery, the rats recovered for one week and then were randomly assigned to six experimental groups: NS

(saline, n=11); NS SR 5 (SR-142948 5 µM, n=8), NS SR 10 (SR-142948 10 µM, n=6),

CUS saline (saline, n=16), CUS SR 5 (SR-142948 5 µM, n=12) and CUS SR 10 (SR

142948 10 µM, n=17). Three rats (2 in the NS SR5 group and 1 in the NS SR 10 group) were euthanized before the forced swim test due to health issues.

3.3.6 Behavioural Tests The rats were placed in the NS groups or the CUS groups receiving a 300nL injection of either saline, SR-142948 5 µM, or SR-142948 10 µM 30 mins before testing (Binder et 36

al, 2001). Behavioural testing was done on 3 consecutive days, starting with the elevated- plus maze (EPM) followed by the forced swim test (FST).

3.3.7 Elevated Plus Maze (EPM) Rats were tested for 5 mins in the EPM using a black polypropylene “plus”-shaped maze

(Med Associates) as previously described (Pego et al, 2008). The maze consisted of two facing open arms (50.8 x 10.2 cm) and two closed arms (50.8 x10.2 x 40.6 cm), 72 cm above the floor. Testing was performed under bright white light (≅ 40 lux). The time spent in the open arms, junction area and closed arms, as well as the number of entrances and explorations in each section were recorded using a system of infrared photo beams, the crossings of which were monitored by a computer. The times spent in each of the compartments of the EPM are presented as percentage of the total duration of the trial.

3.3.8 Forced Swim Test (FST) Rats were introduced to a cylindrical container filled with 30 cm of water (23-25°C) for

15 minutes during pre-test and 5 mins during testing. The rats’ behaviour was categorized as 1) immobile, 2) swimming and 3) climbing (included diving). We defined immobile as the absence of directed movements, climbing as vertical movement of the forepaws and swimming as horizontal movement in the swim chamber. The predominant behaviour over each 5-sec period of the 300-sec test was rated over a total score of 60 by an experimenter blind to the pharmacological treatment or the stress group.

3.3.9 Open Field Animals were individually tested for 5 min each in an open field (OF) arena (43.2 × 43.2 cm) that had transparent acrylic walls and a white floor (model ENV-515, MedAssociates

Inc, St. Albans, VT 05478). Each subject was initially placed in the center of the arena and horizontal activity and instant position were registered, using a system of two 16- beam infrared arrays connected to a computer. Total distances were used as indicators of locomotor activity.

3.3.10 Histological Procedures Following behavioural testing, the rats were anaesthetized with pentobarbital or isoflurane. Extracted brains were submerged in fresh paraformaldehyde for 2 days and switched to 30% sucrose paraformaldehyde for cryoprotection. The brains were kept at

−80°C until histology. Thirty µm coronal sections were sliced and stained with cresyl violet to assess the location of the central injections (Figure 5B,C).

3.3.11 RNA extraction and reverse transcription Brain sections containing the dorsal BNST or the central amygdala (Appendix A) were collected from RNAlater (ThermoFisher Scientific) solution with a sterile tissue puncher and submerged in 100 uL of lysis/binding buffer from the Dynabeads mRNA Direct

Micro Kit (ThermoFisher Scientific). The tissue was immediately homogenized in microcentrifuge tubes using a disposable pestle (Fisherbrand). mRNA was purified using the Dynabeads mRNA Direct Micro Kit (ThermoFisher Scientific) following the manufacturer’s recommended protocol for mRNA isolation from tissues. mRNA

concentration was determined using the Qubit Fluorometer 2.0 (ThermoFisher

Scientific). 40 ng of mRNA from each sample was reverse transcribed using the

SuperScript IV First-Strand Synthesis kit (Invitrogen) in an Applied Biosystems

GeneAmp PCR System 9700 (ThermoFisher Scientific).

3.3.12 Real time qPCR and data analysis The cDNA was amplified in the ViiA7 Real-Time PCR machine (ThermoFisher

Scientific) with a two-step PCR protocol (95°C for 10 minutes followed by 40 cycles of

95°C for 15 seconds and 60°C for 1 minute) using the Power SYBR Green PCR Master

Mix (ThermoFisher Scientific) and KiCqStart SYBR Green Primers (Sigma-Aldrich)

(Appendix B). Each reaction was performed in triplicate and dissociation curves were generated for all reactions to ensure primer specificity. All target genes were normalized to 3 reference genes (Sdha, Actb and Hprt) and the relative quantification using the comparative Ct method was determined using the DataAssist Software Version 3.01

(ThermoFisher Scientific).

3.3.13 Statistical analyses Changes in E/IPSCs peak amplitude were measured from baseline and are shown as percentages as follows: (Peak amplitudepost−Peak amplitudebaseline/Peak amplitudebaseline)*100. Data are reported as means ± SEM and each data point represents the average of values in 1 min bins (6 evoked E/IPSCs) across recorded neurons.

Two-way ANOVAs were used to compare multiple means of parametric data and

Kruskal-Wallis H Test for non-parametric. A Bonferroni correction was used for multiple 39

comparisons. Mann Whitney U test was used to compare specific means with an adjusted p-value according to the number of test performed. Fisher’s exact tests and Chi Squares analyzed contingency tables of the neuronal response distribution. All statistical analyses were done with SPSS Statistics Version 23 (SAS Institute) or Prism 6.

3.4 Results Post-synaptic activation of ovBNST neurons (0mV, 100msec, 2Hz, 5 mins) resulted in robust long-lasting depolarization-induced enhancement of inhibition (l-DEI) in 59% of recorded neurons (time x group, F1,32=7.9, P<0.0001, n=20/34 cells l-DEI from 21 rats;

Figure 4A,F). The addition of the v-ATPase inhibitor concanamycin A (5µM) to the intracellular recording solution completely ablated ovBNST l-DEI that was thus vesicular release-dependent (Fisher’s Exact Test (NS-aCSF vs. NS-Conc), P=0.04, n=0/4 cells l-

DEI from 2 rats; Figure 4B,F). Additionally, rat strain and light cycle had no effect on l-

2 DEI cell response (X (2,n=34)=4.69, P=0.1; Appendix C).

The ovBNST is exclusively populated with GABA neurons that also contain the neuropeptides NT, CRF, dynorphin, or enkephalin (Day et al, 1999; Ju et al, 1989c;

Poulin et al, 2009). NT and CRF both increase ovBNST GABAA-inhibitory post-synaptic currents (IPSCs) through either NT receptors (NTR) or CRF receptors 1 (CRFR1), respectively, and we hypothesized that one or both could be responsible for l-DEI (Kash et al, 2006; Krawczyk et al, 2013b). As such we used a non-selective NTR antagonist

(SR-142948, 10µM) and a CRFR1 selective antagonist (NBI-27914, 1µM). Blocking

NTR did not significantly block the l-DEI cell response (Fisher’s Exact Test (NS-aCSF

vs. NS-SR), P=0.1, n=6/18 cells l-DEI from 11 rats; Figure 4C,F). Likewise, l-DEI was largely unaltered (56% of neurons) by the CRF antagonist (Fisher’s Exact Test (NS-aCSF vs. NS-NBI), P=1.0, n=5/9 cells l-DEI from 6 rats; Figure 4D,F). However, co-application of SR-142948 and NBI-27914 completely eliminated l-DEI indicating cooperation between NT and CRF in producing l-DEI, which is consistent with their co-localization in ovBNST neurons (Fisher’s Exact Test (NS-aCSF vs. NS-SR/NBI), P=0.004, n=0/8 cells l-DEI from 4 rats; Figure 4E,F)(Ju and Han, 1989a). Interestingly, bath application of NTR antagonist but not CRFR1 antagonist resulted in a reversible depression of

GABAA-IPSCs suggesting constitutive NTR activity (Appendix D). Additionally, neither

NT bath application or repetitive depolarization changed holding current or input resistance indicating membrane potential and channels were not changed by the neuropeptide (Appendix E).

A 1 2 B

%59 DEI No DEI %41 change 100 %100 No 100 change

50 50 -IPSC (%) -IPSC (%) A A 0 0 ∆ GABA ∆ GABA Conconamycin

1 2 -50 -50 0 10 20 30 40 0 10 20 30 40 Time (mins) Time (mins) C D %33 DEI %56 DEI No No %67 change %44 change 100 100

50 50 -IPSC (%) -IPSC (%) A A 0 0 ∆ GABA ∆ GABA SR-142,948 NBI-27,914 -50 -50 0 10 20 30 40 0 10 20 30 40 E Time (mins) Time (mins) 100 F DEI * %100 No 100 change 50 l l -IPSC (%) e A

C 50 0 % ∆ GABA SR + NBI 0 -50 R I I 0 10 20 30 40 NS-S NS-NB NS-aCSF NS-Conc Time (mins) NS-SR/NB Figure 4: Effect of post-synaptic depolarization on GABAA synaptic transmission in the ovBNST of non-stressed (NS) rats.

Effect of post-synaptic activation on the amplitude of electrically-evoked GABAA-IPSCs over time in the ovBNST of NS rats in (A) aCSF (B) with intracellular concanamycin (5µM) (C) with extracellular SR-142948 (10µM) (D) with extracellular NBI-27914 (E) with extracellular NBI- 27914 (1µM) and SR-142948 (10µM). Insets in panels A-E show representative ovBNST GABAA-IPSCs before and after post-synaptic activation followed by the proportion of responding neurons. Bar scale: 250 pA and 25 ms. Double arrows represent post-synaptic depolarization (0mV, 100ms at 2Hz, 5 mins) (F) Histogram summarizing the proportion of responding neurons to post-synaptic depolarization across different pharmacological treatments. Asterisk; p<0.05.

We next determined whether l-DEI might be altered in the ovBNST of chronically stressed rats. CUS significantly facilitated l-DEI (time, F1,10=5.0, P=0.009, n=11/11 cells l-DEI from 7 animals; Figure 5A) that was now measurable in all tested neurons

2 compared to the NS group (X (1,n=45)=6.6, P=0.01; Figure 5E). The NTR antagonist significantly reversed CUS-induced facilitation of l-DEI suggesting that NT took over modulation of ovBNST inhibitory synaptic transmission in stressed conditions (Fisher’s

Exact Test (CUS-aCSF vs. CUS-SR), P=0.0002, n=2/10 cells l-DEI from 4 rats, Figure

5B,E). In contrast, CRFR1 blockade had no effect on l-DEI in CUS rats (Fisher’s Exact

Test (CUS-aCSF vs. CUS-NBI), P=1.0, n=11/12 cells l-DEI from 3 rats, Figure 5C,E) although both CRF and NT antagonists were necessary to completely eliminate l-DEI

(Fisher’s Exact Test (CUS-aCSF vs. CUS-SR/NBI), P=0.0001, n=0/8 cells l-DEI from 5 rats, Figure 5D,E).

We then investigated changes in mRNA expression of CRF, NT and their receptors in the dorsal BNST (dBNST) and, the central amygdala (CeA) that has strong inhibitory inputs onto the ovBNST and a similar neuropeptide array (expressing both CRF and NT) (Day et al, 1999). In support of CUS-induced changes in the NT system, CUS significantly and selectively reduced dBNST Ntsr1 mRNA levels compared to NS (P=0.05, Figure 5F). In contrast, CUS had no significant effect on other stress-related transcripts in either the dBNST or the CeA (Figure 5F,G).

Figure 5: Effect of post-synaptic depolarization on GABAA synaptic transmission in the ovBNST of chronic unpredictable stress (CUS) exposed rats.

Effect of post-synaptic activation on the amplitude of electrically-evoked GABAA-IPSCs over time in the ovBNST of CUS rats in (A) aCSF, (B) with extracellular SR-142948 (10µM), (C) with extracellular NBI-27914 (1µM), (D) with extracellular NBI-27914 (1µM) and SR-142948 (10µM). Insets in panels A-D show representative ovBNST evoked GABAA-IPSCs before and after post-synaptic activation followed by the proportion of responding neurons. Bar scale: 250 pA and 25 ms. Double arrows represent post-synaptic depolarization (0mV, 100ms at 2Hz, 5 mins) (E) Histogram summarizing the proportion of responding neurons to post-synaptic depolarization across different pharmacological treatments. (F, G) Histogram showing fold change in mRNA expression of Crhr1, Crhr2, Crh, Ntsr1, Ntsr2 and Nts in CUS compared to NS rats in the dBNST and CeA. Asterisks, p<0.05.

In NS animals, post-synaptic depolarization resulted in long-lasting depolarization- induced reduction of excitatory synaptic transmission (l-DRE) in all tested neurons (time,

F1,7=12.2, P<0.0001, n=9/9 cells l-DRE from 6 rats; Figure 6,C). The broad-spectrum opioid receptor antagonist naloxone (Nal, 10µM) abolished l-DRE suggesting that post- synaptic depolarization triggered the local release of endogenous opioids (Fisher’s Exact

Test (NS-aCSF vs. NS-Nal), P=0.002, n=2/9 cells l-DRE from 4 rats, Figure 6B,C).

A B C %22 * 1 2 50 DRE 100 50 No DRE %88 change %100 No 25 change 25 l 50 %Cel 0 0

∆ AMPA-EPSC (%) AMPA-EPSC ∆ -25 -25 0 ∆ AMPA-EPSC (%) AMPA-EPSC ∆ 1 2 Naloxone -50 -50 NS-Nal NS-aCSF 0 10 20 30 40 0 10 20 30 40 Time (mins) Time (mins)

Figure 6: Effect of post-synaptic depolarization on AMPA synaptic transmission in the ovBNST of NS rats.

Effect of post-synaptic activation on the amplitude of electrically-evoked AMPA-EPSCs over time in the ovBNST of NS rats in (A) aCSF and (B) with extracellular Naloxone (1 µM). Insets in panels in A and B show representative ovBNST evoked AMPA-EPSCs before and after post- synaptic activation followed by the proportion of responding neurons. Bar scale: 250 pA and 25 ms. Double arrows represent post-synaptic depolarization (0mV, 100ms at 2Hz, 5 mins) (C) Histogram summarizing the proportion of responding neurons to post-synaptic depolarization across different pharmacological treatments. Asterisk; p<0.05.

The effect of post-synaptic activity on excitatory transmission was largely unaffected by

CUS and still resulted in robust l-DRE in the vast majority of recorded ovBNST neurons

(time, F1,5=4.2, P=0.05, n=6/7 cells l-DRE from 3 rats; Fisher’s Exact Test (NS vs. CUS),

P=0.4, Figure 7A,C). Similar to NS conditions, Nal completely blocked l-DRE (Figure

7B), supporting the involvement of locally released endogenous opioids in this response

(Fisher’s Exact Test (CUS-aCSF vs. CUS-Nal), P= 0.005, n=0/6 cells l-DRE from 3 rats,

Figure 7C).

A 1 2 B C * 100 DRE DRE 50 %86 50 %100 No No change

change l %14 25 25 50 %Cel

0 0

-25 0 ∆ AMPA-EPSC (%) AMPA-EPSC ∆ -25 ∆ AMPA-EPSC (%) AMPA-EPSC ∆ 1 2 Naloxone

-50 -50 CUS-Nal 0 10 20 30 40 0 10 20 30 40 CUS-aCSF Time (mins) Time (mins)

Figure 7: Effect of post-synaptic depolarization on AMPA synaptic transmission in the ovBNST of CUS rats.

Effect of post-synaptic activation on the amplitude of electrically-evoked AMPA-EPSCs over time in the ovBNST of CUS rats in (A) aCSF and (B) with extracellular Naloxone (1µM). Insets in panels A and B show representative ovBNST evoked AMPA-EPSCs before and after post- synaptic activation followed by the proportion of responding neurons. Bar scale: 250 pA and 25 ms. Double arrows represent post-synaptic depolarization (0mV, 100ms at 2Hz, 5 mins) (C) Histogram summarizing the proportion of responding neurons to post-synaptic depolarization across different pharmacological treatments. Asterisk, p<0.05.

CUS increases avoidance of open arms in the elevated plus maze (EPM) and immobilization in the forced swim test (FST) (Bessa et al, 2009). Converging evidence suggests that the BNST plays a key role in these chronic stress-induced anxiety- and depression-like phenomena (Daniel and Rainnie, 2016). Since CUS altered the neuromodulatory effect of NT in the ovBNST, we hypothesized that in vivo pharmacological blockade of ovBNST NTR might reverse CUS-induced avoidance of open arms in the EPM and immobility in the FST. As expected, CUS significantly reduced the percentage of time spent in the open arms in saline-treated rats (U=5, p=0.0002, Figure 8D). Intra-ovBNST SR-142948 (5-10µM/side) had no effect on EPM behaviours in NS but significantly increased the percentage of time spent in the open

2 arms in CUS (Kruskal-Wallis H test, X 5=18.2, P=0.003, Figure 8D). SR-142948 (5-

10µM/side) dose-dependently reversed this effect in CUS rats (U=7, P=0.0001, Figure

8D). SR-142948 had no effect on the number of open arms entries (Kruskal-Wallis H

2 test, X 5=5.3, P=0.4, Figure 8E) and did not affect total distance travelled in the open field

(F(3,32)=0.7, p=0.6, Appendix F) therefore did not affect locomotion. In our conditions, intra-ovBNST NTRs blockade had no effect on immobility scores in the FST in either NS

2 or CUS conditions (Kruskal-Wallis H test, X 5=8.6, P=0.1, Figure 8F).

Figure 8: Effect of intra-ovBNST NTR pharmacological blockade on elevated plus maze (EPM) and forced swim test (FST) behaviours in NS and CUS rats.

(A) Experimental timeline. (B) Photomicrograph showing a representative accurate bilateral cannula placement. (C) Drawing showing all intracranial cannula placements. (D) Bar graph representing the percentage of time spent in the EPM open arms (OA) across experimental groups. (E) Bar graph representing the number of OA entry across experimental groups. (F) Bar graph representing immobility scores in the FST across experimental groups. Sal, saline; SR5, SR 142948 (5 µM); SR 10, SR-142948 (10 µM), Accl./Surg., acclimatation/surgeries; Hist, histology. Asterisks, p<0.05.

3.5 Discussion We used brain slice whole-cell voltage-clamp recordings and discovered that in the ovBNST of NS rats, post-synaptic activation resulted in long-lasting depolarization- induced enhancement of inhibitory GABAA- and reduction of excitatory AMPA synaptic transmission that we termed l-DEI and l-DRE, respectively. NT and CRF both produced l-DEI while opioids were fully responsible for l-DRE. CUS facilitated l-DEI through an enhanced contribution of NT whereas l-DRE was not affected. Pharmacological blockade of ovBNST NT receptors abolished CUS-induced reduction in open arm avoidance in the elevated plus maze, suggesting that NT may contribute to anxiety disorders (Laszlo et al,

2010b; Saiz Ruiz et al, 1992).

In NS rats, post-synaptic activation produced l-DEI in slightly over half (59%) of recorded ovBNST neurons. There is clear evidence of various ovBNST neurons subpopulations with distinct morphological, neurochemical, or electrophysiological signatures that could explain this dichotomy (Day et al, 1999; Hammack et al, 2007; Ju et al, 1989c; Larriva-Sahd, 2006; Poulin et al, 2009). The neuron-specific expression of l-

DEI may be tightly linked with specific neuropeptidergic profiles (Iremonger et al, 2009;

Ludwig and Pittman, 2003). NT and CRF are highly concentrated in ovBNST neurons and both neuropeptides robustly potentiate GABAA-mediated synaptic transmission although through distinct pre- and post-synaptic loci, respectively (Day et al, 1999; Ju et al, 1989a; Kash et al, 2006; Krawczyk et al, 2013b). A study combining brain slice electrophysiology and single-cell PCR showed that 60% of ovBNST neurons contain

CRF which is precisely the percentage of l-DEI response we obtained, supporting a role

for CRF in l-DEI (Dabrowska et al, 2011). Importantly, CRF and NT co-localize in the ovBNST and pharmacological blockade of both CRFR1 and NTR was necessary to completely abolish l-DEI (Ju et al, 1989a). Application of either neuropeptide antagonist alone did not block l-DEI suggesting a cooperative mechanism where one neuropeptide activity can compensate for the blockage of the other. The exact functional link however remains elusive.

Post-synaptic activation also resulted in opioid-dependent l-DRE in all recorded ovBNST neurons in NS rats, mitigating the possibility of sub-population effects. Only 41% of ovBNST neurons seem to express detectable amounts of enkephalins mRNA which poorly co-localizes with CRF or NT (Day et al, 1999). Dynorphin is also abundant in the rat ovBNST and may have contributed to the opioid-dependent l-DRE we measured

(Poulin et al, 2009). Enkephalin and dynorphin are potent inhibitors of excitatory synaptic transmission in the brain, supporting opioid-dependent l-DRE (Crowley et al,

2016). Opioid neuropeptides also modulate inhibitory transmission but we did not detect this response likely due to their short-lasting effects that we did not include in our analyses (Crowley et al, 2016; Dumont and Williams, 2004).

Altogether, our data show that blocking CRFR1, NTR, and opioid receptors completely abolished post-synaptic activation-induced modulation of synaptic transmission in the rat ovBNST. This does not preclude that other stimulation patterns may trigger local synthesis and/or release of other neuromodulators (Puente et al, 2010) or of other neuropeptides expressed in ovBNST neurons (Woodhams et al, 1983). In addition, we

focused on long-lasting changes in synaptic transmission but short-duration phenomena have also been reported (Puente et al, 2010). Unquestionably, numerous other neuropeptides, monoamines, or other molecules originating outside the ovBNST also robustly modulate synaptic transmission in this area (Dumont et al, 2004; Kash et al,

2006; Krawczyk et al, 2013b; Krawczyk et al, 2011b; Li et al, 2012; McElligott and

Winder, 2008; Shields et al, 2009). Nevertheless, the objective of the study was to determine whether local neuropeptidergic synaptic modulation was affected by chronic stress.

The neurophysiological mechanisms responsible for chronic stress-induced increase in anxiety-like behaviours are still largely unknown. Here, CUS facilitated l-DEI and NT was responsible for this effect whereas the contribution of CRF was mitigated by stress.

This is a novel observation considering that NT has been largely overlooked as a potential contributor in the pathological consequences of stress, compared to CRF (Saiz

Ruiz et al, 1992). Alteration of NT function could be due to an increase in NT synthesis, release or receptor membrane expression, binding or coupling. Under normal physiological conditions, NT increases inhibitory transmission by binding pre- synaptically to NTRs in the ovBNST (Krawczyk et al, 2013b). NT increases excitability and firing rate in other brains areas but we did not detect post-synaptic changes in the membrane potential or membrane channels opening suggesting these were not altered in the ovBNST (Jassar et al, 1999; Xiao et al, 2014)). Interestingly, NTR receptor antagonist acted as an inverse agonist when applied without repetitive depolarization, depressing GABAA-IPSCs, suggesting constitutive NTR activity. NTR1 is a Gq coupled

receptor while NTR2 exhibits constitutive activity on inositol phosphate production

(Richard et al, 2001). Thus, the inverse agonist activity is likely due to NTR2 that is also highly expressed in the BNST GABA neurons specifically (Mazzone et al, 2016).

However, it is still unknown whether the l-DEI is specific to or a combination of NTR1 and NTR2 activity and whether this is altered with CUS.

When we investigated mRNA expression, only Ntsr1 mRNA, and not Nts or Ntsr2, was decreased in CUS rats compared to NS. Our findings corroborate other studies showing reduction of Ntsr1 mRNA following maternal separation or CRF-overexpressing mice

(Peeters et al, 2004; Toda et al, 2014a). CUS decreasing Ntsr1 mRNA expression may not result in a reduction of the NTR1 receptor expression at the cellular membrane. A decrease in Ntsr1 mRNA could be due to an increase in mRNA stability or a compensatory mechanism to reduce increased NT activity. The latter explanation could indicate that NTR1 expression is actually increased with CUS and may be responsible for the changes cell response. Future studies investigating protein level expression is necessary to fully understand the neurophysiological changes occurring with CUS.

The CUS paradigm in this study utilizes variable and uncontrollable stress where the animal does not habituate to the repeated stressors over time (Herman, 2013). As predicted from previous studies, we found that rats that underwent the CUS paradigm spent significantly less time in the open arms compared to their NS counterparts. Whether this behaviour is “adaptive” or “maladaptive” is unclear. In the context of our EPM test, chronically stressed animals could be mounting an adaptive response to a possible threat.

Alternatively, their response could be interpreted as maladaptive as the animal limits their exploration and possibility of finding resources in the absence of an immediate threat.

Since the rats had no controllability over the stressors, it is impossible to distinguish whether they were mounting a contextually appropriate or inappropriate response.

Intra-ovBNST micro-injections of a NTR antagonist did however robustly modify CUS rats behaviours in the EPM, tying the sensitized NT neurophysiological response in ovBNST neurons of stressed rats with their anxiety profile. NTR blockade reversed the anxiogenic effect of CUS without affecting normal EPM exploratory behaviour displayed by NS rats. This is consistent with the fact that NT seems particularly important in mounting physiological and behavioural responses to face potentially extreme conditions

(e.g. store fat, seek rich and highly rewarding nutrients, increase vigilance)(Deutch et al,

1987; Geisler et al, 2006; Krawczyk et al, 2013b; Li et al, 2016; Luttinger et al, 1982).

We also tested the effect of CUS on immobility in the FST but we did not find any changes in behaviour previously reported (Bessa et al, 2009). This discrepancy may be due to the shorter duration of the stress paradigm although our data showed that NT in the ovBNST may not contribute to this behaviour, regardless of the stress condition (Crestani et al, 2010).

Overall, these findings elucidate a clear role for NT in chronic stress although we cannot conclude exactly how NT-induced increase of GABA transmission in the ovBNST translates into anxiety-like behaviour in the EPM. However, anatomical studies enable us to speculate how a NT-induced decrease of ovBNST activity could affect the HPA axis.

The ovBNST has strong GABAergic outputs onto the fusiform nucleus of the BNST

(fuBNST) that has direct inhibitory inputs onto the paraventricular nucleus of the hypothalamus (Dong et al, 2001). Lesion of the fuBNST attenuates HPA axis response suggesting it enhances PVN activity (Choi et al, 2007). As such, a NT-mediated reduction of ovBNST inhibition output to the fuBNST could promote HPA axis excitation and result in a decrease of EPM open arm exploration. Parallel to this, in the

PVN, blocking NT receptor during stress counteracts the increase of plasma corticosterone levels (Geisler et al, 2006). Additionally, decrease activity in ovBNST inhibitory projections could increase fear/anxiety (CeA), vigilance and arousal (substantia innominata), respiration (parabrachial nucleus) and, defensive response (periaqueductal gray) (Dong et al, 2001). At this point however, we cannot discern the exact output of the ovBNST and whether it is affecting local or extrinsic circuitry.

The NT system in different brain areas could be working in concert to stimulate the HPA axis during stress conditions. Future studies should explore whether the magnitude of

NT-activation of the HPA axis could possibly correlate with maladaptive vs adaptive behaviour.

Chapter 4: Neurotensin and Dynorphin Bi-Directionally Modulates CeA

Inhibition of oval BNST Neurons in Male Mice

4.1 Abstract Neuropeptides are often co-expressed in neurons but their neurophysiological effects are commonly studied individually. Multiple neuropeptides may therefore be simultaneously released to coordinate proper neural circuit function. Here, we triggered the release of endogenous neuropeptides in brain slices from male mice to better understand the modulation of central amygdala (CeA) inhibitory inputs onto oval (ov) BNST neurons.

We found that locally-released neurotensin (NT) and dynorphin (Dyn) antagonistically regulated CeA inhibitory inputs onto ovBNST neurons. NT and Dyn respectively increased and decreased CeA-to-ovBNST inhibitory inputs through NT receptor 1

(NTR1) and kappa opioid receptor (KOR). Additionally, NT and Dyn mRNAs were highly co-localized in ovBNST neurons suggesting that they may be released from the same cells. Together, we showed that NT and Dyn are key modulators of CeA inputs to ovBNST, paving the way to determine whether different conditions or states can alter the neuropeptidergic regulation of this particular brain circuit.

4.2 Introduction Neuropeptides are frequently co-expressed in individual neurons within the nervous system; their cooperative role may enable flexible neuromodulation and proper function of neural circuits (Griebel et al, 2012b; Kormos et al, 2013; Valentino and Aston-Jones,

2010). However, neuropeptides are often studied individually, neglecting the functional outcomes resulting from their coordinated actions (Ptak et al, 2009; Sun et al, 2003). In brain slices prepared from male rats, post-synaptic activation of oval bed nucleus of the stria terminalis (ovBNST) neurons triggers the release of various neuropeptides, such as neurotensin (NT), corticotrophin releasing factor (CRF) and dynorphin (Dyn) which in turn robustly modulate excitatory and inhibitory synaptic transmission onto the same neurons (Normandeau et al, 2018). Notably, endogenously-released NT produces an increase in inhibitory transmission in the rat ovBNST, that is further enhanced by chronic stress (Normandeau et al, 2018). What remains unknown is whether this coordinated modulation of synaptic transmission is circuit-specific.

Although still under debate, the ovBNST may be devoted to energy homeostasis, promoting foraging behaviours and food intake (Jennings et al, 2013a; Li and Kirouac,

2008; Moga et al, 1995). The ovBNST is robustly and reciprocally connected with the central nucleus of the amygdala (CeA) and because these bi-directional connections are exclusively GABAergic, the ovBNST and the CeA most likely inhibit each other

(Petrovich et al, 1997). This connection may be integral in the balance between aversive or appetitive behaviours (Davis et al, 2010; Dong et al, 2001; Jennings et al, 2013a).

Whether neuropeptides are important modulators of this particular brain circuit is largely

undetermined. Nonetheless, it is known that the opiate dynorphin (Dyn) released from

GABA neurons specifically decreases CeA-to-dorsal BNST inhibitory inputs in male mice (Crowley et al, 2016; Li et al, 2012). Whether Dyn cooperates with other neuropeptides in the ovBNST to regulate CeA inhibition is currently unknown.

Using brain slice electrophysiology in male mice, we triggered endogenous release of neuropeptides and discovered that release of NT and Dyn opposingly modulated CeA-to- ovBNST synaptic inhibition through neurotensin receptors 1 (NTR1) and kappa opioid receptors (KOR), respectively. NT-mediated enhancement of inhibitory transmission in the ovBNST overshadowed the effect of Dyn, paving the way to determine whether changes in condition/state may affect neuropeptidergic regulation of synaptic transmission in this particular neural circuit.

4.3 Materials and Methods

4.3.1 Mice 46 mice (>8 weeks old) were group housed on a reverse 12-hour light/dark cycle (lights

OFF at 8:00 A.M) with ad libitum access to chow and water. C57BL/6J adult male mice

(n=29) used for electrophysiology were obtained from Charles River Laboratories (St-

Constant, QC, Canada) and Vgat-Cre adult male mice (n=17) used for electrophysiology and in situ hybridization were bred in house in the McElligott lab (University of North

Carolina, Chapel Hill, NC, USA). All experiments were conducted in accordance to the guidelines from the Canadian Council on Animal Care in Science, approved by Queen's

University, and were in accordance with the National Institutes of Health guidelines for

animal research with the approval of the Institutional Animal Care and Use Committee at the University of North Carolina at Chapel Hill.

4.3.2 Stereotaxic injections Adult mice (>8 weeks) were deeply anesthetized with 5% isoflurane (vol/vol) in oxygen, placed into a stereotactic frame (Kopf Instruments, Tujunga, CA, USA) and maintained at 1.5–2.5% isoflurane during surgery. A hole was drilled in the skull using CeA coordinates ML: ±2,95, AP: -1.15, DV: -4.75 from Bregma. Microinjections of 300 nL of virus (AAV5-EF1a-DIO-ChR2-mCherry or AAV5-EF1a-DIO-ChR2-eYFP) were made bilaterally using a 1 µl Neuros Hamilton syringe (Hamilton, Reno, NV, USA) at a rate of

100 nl /minute. After infusion, the needle was left in place for at least an additional

5 minutes to allow complete diffusion of the virus before being slowly withdrawn. After surgery, all mice were returned to group housing, and recovered for at least 6 weeks prior to the start of experiments.

4.3.3 Slices preparation and electrophysiology Mice were anesthetized with isoflurane (5% at 5 L/minute) and their brain removed into ice-cold artificial cerebral spinal fluid (aCSF) containing (in mM): 126 NaCl, 2.5 KCl,

1.2 MgCl2, 6 CaCl2, 1.2 NaH2PO4, 25 NaHCO3, and 12.5 D-glucose equilibrated with

95% O2/5% CO2. Brains were cut in 2°C aCSF into coronal slices (300 µm) with a vibrating blade microtome (VT-1000; Leica Canada, Concord, ON, Canada). The ovBNST slice of interest for this study corresponded to -0.26mm from Bregma. Slices were incubated at 34°C for 60 minutes and transferred to a chamber perfused (2-3 mls/minute) with aCSF at 34°C. Remaining slices were kept in aCSF at room 60

temperature until further use. Whole-cell voltage-clamp recordings were made using glass microelectrodes (3-5 MΩ) filled with (in mM): 70 K+-gluconate, 80 KCl, 1 EGTA,

5 HEPES, 2 MgATP, 0.3 GTP, and 1 P-creatine. Electrical stimuli (10–100 µA, 0.1 ms duration) or optical stimuli (490 nm LED intensity 2-100%, 0.1 ms duration) were applied at 0.1 Hz. Inhibitory post-synaptic currents (IPSCs) were evoked by local fiber stimulation with tungsten bipolar electrodes or by a 490 nm LED via the objective while neurons were voltage-clamped at −70 mV. GABAA-IPSCs were pharmacologically isolated with 6,7-dinitroquinoxaline-2,3-dione (DNQX, 50 µM). To induce local endogenous neuropeptide release, post-synaptic neurons were repetitively depolarized in voltage clamp from -70 to 0 mV (100 ms) at a frequency of 2 Hz for 5 minutes

(Normandeau et al, 2018). We quantified peak amplitude and defined 3 possible outcomes to postsynaptic depolarization or drug bath application: 1-long-term potentiation (LTPGABA; >20% deviation from baseline after 20 minutes), 2-long-term depression (LTDGABA; <20% deviation from baseline after 20 minutes) or 3-no change

(NC, within 20% deviation from baseline after 20 minutes). Recordings were made using a Multiclamp 700B amplifier and a Digidata 1440A (Molecular Devices LLC, San Jose,

CA, USA). Data were acquired and analyzed with Axograph X running on Apple computers and Clampfit on Windows computers.

4.3.4 Drugs Stock solutions of SR142948 (10 mM), Norbinaltorphimine (Nor-BNI, 100 mM), NT

(1mM) and JMV431 (1mM) were prepared in distilled water. Stock solutions of DNQX

(100 mM), and NTRC844 (1mM) were prepared in DMSO (100%). All drugs were

further dissolved in the physiological solutions at the desired concentrations (DNQX

50µM, SR142948 10 µM, Nor-BNI, 100 nM, JMV431 100nM, NTRC844 100nM,

SR48692 1µM, NT 1µM) and the final DMSO concentration never exceeded 0.1%.

4.3.5 Fluorescence In Situ Hybridization (FISH) Immediately after removal, brains were placed on a square of aluminum foil on dry ice to freeze for 5 minutes before wrapping to prevent tissue damage. Brains were then placed in a −80 °C freezer for no more than 1 week before slicing. In all, 12-µm slices containing the CeA and ovBNST were obtained on a Leica CM3050S cryostat (Leica

Biosystems, Wetzlar, Germany) and placed directly on coverslips. FISH was performed using the Affymetrix ViewRNA 2-Plex Tissue Assay Kit with custom probes for Nts,

Ntsr1, and Ntsr2 designed by Affymetrix (Santa Clara, CA, USA). FISH was also done using the Advanced Cell Diagnostics (ACD) HybEZ(TM) II Hybridization System with custom probes for Nts and PDyn designed by ACD (Newark, CA, USA). Slides were coverslipped with SouthernBiotech DAPI Fluoromount-G. (Birmingham, AL, USA). z-

Stack (3 × 5 tiled; 8 optical sections comprising 10.57 µm in total) were obtained on a

Zeiss 800 confocal microscope. All images were preprocessed with stitching and maximum intensity projection. Quantification of probe colocalization was hand counted using the cell counter plugin in FIJI (ImageJ, NIH, Bethesda, MD, USA). For all studies, cells were classified into three groups: probe 1+, probe 2+ or probe 1+ and 2+. Only cells positive for a probe were considered.

4.3.6 Statistical analyses Changes in IPSCs peak amplitude were measured from baseline and are shown as percentages as follows:

(Peakamplitudepost−Peakamplitudebaseline/Peakamplitudebaseline)*100. Data are reported as means ± SEM and each data point represents the average of values in 1-minute bins (6 evoked IPSCs) across recorded neurons. ANOVAs were used to compare multiple means and the appropriate post-hoc statistical tests for multiple comparisons conducted when

ANOVAs deemed significance. All statistics from the within-subjects ANOVAs were reported using the Greenhouse-Guesser correction for violations in sphericity, and degrees of freedom for Greenhouse-Giesser values were rounded up to the nearest whole number. A Bonferroni correction was used for multiple comparisons. Fisher’s exact tests analyzed contingency tables of the neuronal response distribution. All statistical analyses were done with SPSS Statistics Version 23 (SAS Institute) or Prism 6 (Graph Pad).

4.4 Results Multiple neuropeptides are usually co-represented in individual neurons in the brain, suggesting concerted roles in coordinating proper neural circuit function. However, whether and how multiple neuropeptidergic systems interact neurophysiologically to regulate neural circuits is largely unknown since neuropeptides are often studied in isolation. Here, we triggered release of endogenous neuropeptides in brain slices to investigate how neuropeptides can regulate CeA to ovBNST inhibitory circuit in male mice.

Bi-directional modulation of electrically-evoked GABA-IPSCs by NT and Dyn in the ovBNST of male mice

Electrical stimulation of local fibers evoked GABAA-IPSCs (eIPSCs) in ovBNST neurons and peak amplitudes were measured. Upon stable baseline of eIPSCs (minimum

5 minutes), post-synaptic ovBNST neurons were activated for 5 minutes by repetitive depolarization (0mV, 100msec, 2Hz), which resulted in long-term potentiation

GABA (LTP ) of eIPSCs in 90% of recorded neurons (time x group, F3,8=9.5, p=0.002;

Figure 9B). The addition of non-selective NTRs antagonist (SR-142948, 10µM) diminished LTPGABA cell response indicating that NT acts as an important modulator of inhibitory transmission in mice (Fisher’s exact test (aCSF vs. SR); p=0.04; Figure 9C, E).

Blocking NT receptors also uncovered LTDGABA that was ablated by the co-application of the selective KOR antagonist nor-NBI (100nM) with SR (time x group, F1,9=31.6, p=0.0003; Figure 9D, E). As such, locally released NT and Dyn inversely regulate inhibitory transmission in the ovBNST.

Figure 9: Endogenous neuropeptides modulation of electrically-evoked ovBNST GABAA- IPSCs.

A, Schematic illustrating stimulating and recording electrodes placement in mice brain slices containing the ovBNST. Recordings were restricted to the displayed shaded ovBNST area. B-D, Effects of postsynaptic depolarization (double arrow symbol) on binned (1 minute, 6 events) electrically-evoked GABAA-IPSCs in (B) aCSF (n=10 cells/6 mice), (C) the presence of the non- selective NTR antagonist SR142948 (10µM, n=8 cells/5 mice) or (D) SR142948 + the KOR antagonist norNBI (100nM, n=11 cells/4 mice). Insets in B-D are representative electrically- evoked GABAA-IPSCs before and after postsynaptic depolarization (double arrows). E, Histogram summarizing the proportion of responding neurons to post-synaptic activation across different pharmacological treatments. Blue LTP, grey no change and orange LTD. Asterisks, p<0.05.

Locally released NT and Dyn target CeA-to-ovBNST GABA-IPSCs

Next, using Vgat-Cre male mice injected with DIO-ChR2-eYFP/mCherry in the CeA, we light-activated (LED 490nm) specific CeA inputs and evoked optical

VgatCeAèovBNSTGABA-IPSCs (opIPSCs) in ovBNST neurons. Post-synaptic activation of ovBNST neurons resulted in 54% LTPGABA of opIPSCs and 8% LTDGABA (time x group,

F5,20=4.1, p=0.01; Figure 10C). Application of the non-selective NTRs antagonist robustly reduced LTPGABA (8%) and revealed further LTDGABA (42%) (Fisher’s exact test (aCSF vs. SR), p=0.04; Figure 10D, F). Combined application of NTRs and KOR antagonist completely eliminated LTPGABA and LTDGABA cell responses (Fisher’s exact test (SR vs.

SR/norNBI), p=0.04; Figure 10E, F). Therefore, NT and Dyn are dominant modulators of

CeA inputs onto ovBNST in mice.

CeAèovBNST Figure 10: Effect of postsynaptic depolarization on optically-evoked Vgat GABAA- IPSCs.

A, Illustration demonstrating ChR2 bi-lateral injections into the CeA of Vgat-Cre mice. B, Illustration demonstrating optically stimulated recordings of ovBNST neurons. C-E, Post- synaptic depolarization in male Vgat-Cre mice injected with ChR2 in the CeA and recorded from ovBNST brain slices in (C) aCSF (n=13 cells/6 mice), (D) in the presence of the non-selective NTR antagonist SR142948 (10µM, n=12 cells/5 mice), (E) SR142948 + the KOR antagonist norNBI (100nM, n=8 cells/2 mice). Insets in C-E are representative optically-evoked GABAA- IPSCs before and after postsynaptic depolarization (double arrows). F, Histogram summarizing the proportion of responding neurons to post-synaptic activation across different pharmacological treatments. Blue LTP, grey no change and orange LTD. Asterisks, p<0.05.

Expression of Nts and Pdyn mRNA co-localized in the ovBNST

Because NT- and Dyn-mediated modulation of inhibitory transmission was detectable in a vast majority of ovBNST neurons, we hypothesized that both neuropeptides might be highly co-localized. Dual fluorescent in situ hybridization (FISH) revealed co- localization of preprodynorphin (Pdyn) and NT (Nts) mRNA in ovBNST neurons (66% of neurons, Figure 11).

Figure 11: Expression and co-localization of Pdyn and Nts mRNA in the ovBNST.

A, Representative image of dual fluorescent in situ hybridization for Nts/Pdyn (Nts (green), Pdyn (purple), and DAPI (blue)). B, Distribution of Nts, Pdyn and co-co-localizing (both) mRNA expressing cells (n=4 mice, 4 slices/mouse).

NTR1 and NTR2 bi-directionally modulated electrically evoked GABA-IPSCs in the ovBNST

Lastly, NT binds to two different G-protein receptors: NTR1 and NTR2 (Vincent et al,

1999). Both Ntsr1 and Ntsr2 mRNA were present in the ovBNST and CeA (Figure 12A-

H). However, neither Ntsr1 nor Ntsr2 co-localized extensively with Nts mRNA suggesting that NT acted pre-synaptically or on another population of Ntsr-positive neurons in the ovBNST.

Figure 12: Expression and co-localization of NT and NTRs mRNA in the ovBNST and CeA.

Representative image of dual fluorescent in situ hybridization (FISH) for Nts/Ntsr1 (Nts (purple), Ntsr1 (green), DAPI (blue) in the ovBNST (A) and CeA (B). Distribution of Nts, Ntsr1 and co- localizing mRNA expression cells (n=2 mice, 2 slices/mouse) in the ovBNST (C) and CeA (D). Representative image of dual FISH for Nts/Ntsr2 (Nts (purple), Ntsr2 (green), DAPI (blue)) in the ovBNST (E) and CeA (F). Distribution of Nts, Ntsr2 and co-co-localizing (both) mRNA expressing cells (n=2 mice, 2 slices/mouse) in the ovBNST (G) and CeA (H).

Pharmacological application of NT (1 µM) for 5 minutes resulted in LTPGABA of eIPSCs in 80% of recorded neurons (time, F1,46=8.3, p=0.002; Figure 13A). Bath application of the NTR2 selective antagonist (NTRC844, 100nM) did not significantly alter cell responses to bath-applied NT with 57% of cells responding with LTPGABA (Fisher’s exact test (NT vs. NT+NTRC844), p=0.6; Figure 13B, E) (Thomas et al, 2016). Bath application of the NTR1 selective antagonist (SR48692, 1µM) however, reduced the percentage of neurons responding with LTPGABA (Fisher’s exact test (NT vs. NT

+SR48692), p=0.002; Figure 13C, E), suggesting that NTR1 are required to mediate the post-synaptic activation induced by NT. NTR1 blockade also uncovered LTDGABA in

80% of neurons that could be NTR2 mediated. Accordingly, bath application of the

NTR2 selective agonist (JMV431, 100nM) for 5 minutes resulted in a LTDGABA in 38% of neurons (time x group, F6,35=3.4, p=0.04; Figure 13D) (Thomas et al, 2014). Overall,

NTRs have opposing modulatory roles on inhibitory transmission in the ovBNST, although the NTR1 component was predominant in response to endogenous NT release

(see Figure 9 and Figure 10).

Figure 13: Contribution of neurotensin receptors 1 and 2 on exogenous NT-induced modulation of electrically-evoked ovBNST GABAA-IPSCs.

A, Effect of a 5-minute bath application of NT (1uM) (black horizontal bar) on the peak amplitude of eIPSCs (n=8 cells/4 mice). Effect of NT (1µM, black horizontal bar) on eIPSCs in the presence of (B) the NTR2-selective antagonist NTRC844 (100 nM, n=7 cells/3 mice) or the (C) NTR1-selective antagonist SR48692 (1 µM, n=5 cells/3 mice) D, Effect of a 5-minute bath application of NTR2 agonist JMV431 (100nM, n=8 cells/4 mice) (black horizontal bar) on the peak amplitude of eIPSCs in the ovBNST. Insets in B-D are representative electrically-evoked GABAA-IPSCs before and after bath application of NT or JMV431. Blue LTP, grey no change and orange LTD. Asterisks, p<0.05.

4.5 Discussion Post-synaptic depolarization of ovBNST neurons in male mice brain slices resulted in the putative release of the neuropeptides NT and Dyn, respectively increasing and decreasing inhibitory inputs originating in the CeA. FISH revealed that Nts and Pdyn mRNA significantly overlapped in single ovBNST cells demonstrating the co-localization of NT and Dyn in the ovBNST. Locally released NT and Dyn modulated CeA inhibition of ovBNST neurons through NTR1 and KOR, respectively. Together, our data shed new light unto the coordinated action of co-localized neuropeptides in fine-tuning neural circuit function.

Postsynaptic activation of voltage-clamped ovBNST neurons resulted in a robust and long-lasting modulation of inhibitory postsynaptic GABAA currents in a vast majority (up to 90%) of recorded neurons. We hypothesized that these could be neuropeptides since they are generally released after high or prolonged postsynaptic depolarization, and have long-lasting effects (Karhunen et al, 2001; Whim et al, 1989). Pharmacological blockade of NTR and KOR receptors confirmed that NT and Dyn were fully responsible for the bi- directional regulation of ovBNST inhibitory inputs from the CeA. In male rats, the same post-synaptic activation protocol (2Hz, 5 minutes) also triggered local endogenous neuropeptides release and similar modulation of inhibitory synaptic transmission in the ovBNST, suggesting a conserved mechanism across both species (Normandeau et al,

2018). Although our data do not preclude the presence of other neuromodulators (for example endocannabinoids, growth factors, or other neuropeptides), the modulation of inhibitory synaptic transmission was largely eliminated by the co-application of NTR and

KOR antagonists. Thus, these observed effects were predominantly NT and Dyn dependent.

Postsynaptic activation resulted in a strikingly homogenous NT-mediated enhancement of inhibitory transmission in the ovBNST. This is consistent with the prevalence of NT- positive neurons in the ovBNST (Allen Mouse Brain Atlas, Ju et al, 1989c). This homogenous neurophysiological outcome we measured here in mice and previously in rats, may seem to contrast with the various neurophysiological signatures reported in the rat dorsal BNST neurons (Hammack 2007, Dabrowska, 2013). But yet, a majority of dorsal BNST neurons express the neuropeptide CRF and further co-express NT regardless of their neurophysiological signature, perhaps explaining the homogenous neurophysiological outcome we measured upon postsynaptic depolarization (Dabrowska et al, 2013; Hammack et al, 2007; Ju et al, 1989b). Importantly, we purposefully restricted our recordings to the oval subregion of the dorsolateral BNST (see Figure 9A and Figure 10B) and that may have enabled us to target a population of neurons.

Nonetheless, the majority of neurons in this sub-nuclei are similar morphologically and we suspect the ovBNST may function in a coordinated network fashion (Larriva-Sahd,

2006).

Narrowing our investigation to CeA-mediated optically-driven (op)IPSCs revealed that post-synaptic depolarization correspondingly resulted in robust modulation of inhibitory inputs onto ovBNST neurons. Although at a lower proportion than non-specific (eIPSC) input stimulation, a majority of neurons (54%) displayed LTPGABA of similar magnitude

and duration. In contrast, LTDGABA was more readily observable with opIPSCs (8% of neurons) without NTRs blockade. Similar to eIPSC, NTR blockade fully revealed KOR- mediated LTDGABA. Co-application of NTR and KOR antagonists completely abolished the modulation of opIPSCs whereas a residual LTPGABA remained with electrical stimulation (45%). In male rats, a similar residual eIPSC LTPGABA was mediated by CRF, and we suspect this neuropeptide may also be responsible for the residual LTPGABA observed in mice (Normandeau et al, 2018). Nonetheless, our findings strongly suggest that NT and Dyn are fully responsible for bi-directional modulation of CeA inhibitory inputs to ovBNST neurons in male mice (Li et al, 2012). Both Pdyn and Nts mRNA were highly co-localized in the ovBNST suggesting that they could be released from the same cells to have their modulatory effects. In contrast, Kor mRNA is only expressed in the

CeA while Ntsr1 and Ntsr2 mRNA were expressed in both ovBNST and CeA neurons

(Poulin et al, 2009; Torruella-Suarez, 2018). Neither neuropeptides mRNA (NT or Dyn) co-localized significantly with their respective receptors mRNA (Torruella-Suarez,

2018). Therefore, we suggest that NT and Dyn found in ovBNST neurons act directly onto pre-synaptic axon terminals originating from the CeA (illustrated in Figure 14).

Though, we cannot exclude the possibility that NT may be working through interneurons since we identified Ntsr mRNA in the ovBNST.

KOR NTR1 GABA Dyn GABA - CeA ovBNST

Figure 14: Illustration of NT and Dyn bi-directional modulation of CeA inputs onto ovBNST neurons.

Bath-application of exogenous NT resulted in LTPGABA in 80% of recorded ovBNST neurons, similar to our previous observation in rats (Krawczyk et al, 2013b). NTR1

(SR48692), but not NTR2 (NTRC844), blockade, significantly interfered with NT- induced LTPGABA. Interestingly, the NTR2 specific agonist JMV431 resulted in LTDGABA in 38% of recorded neurons, indicating that both NT receptors may have opposing modulatory roles on inhibitory transmission in the ovBNST. NT has a higher affinity to

NTR1 than NTR2, suggesting that post-synaptic activation may release low to moderate amounts of endogenous NT that may only activate NTR1s (Tschumi et al, 2018).

Additionally, NTR2 are preferentially expressed intra-cellularly whereas NTR1s are expressed at the cell membrane, potentially increasing NT’s ability to bind and activate

NTR1s (Perron et al, 2007).

NT and Dyn may independently modulate inhibition in the ovBNST by acting at their respective receptors. However, they may also interact directly since KOR and NTR1 can

form heterodimers that alter KOR-mediated signaling (Liu et al, 2016). In fact, KOR and

NTR1 are both expressed in the CeA and could co-localize pre-synaptically in ovBNST inputs (Boudin et al, 1996; Mansour et al, 1987). While the exact functional link between

NT and Dyn remains elusive, it is clear that the two neuropeptides have strong cooperative, although antagonistic, potential to regulate inhibition in the ovBNST.

With either stimulation of local fibers or CeA inputs, the blockade of NTR1 uncovered

Dyn-induced LTDGABA. Consequently, NT seems to be the primary modulator of CeA inhibitory inputs onto ovBNST neurons. Our stimulation paradigm (2Hz, 5minutes) could particularly favor NT over Dyn release, as previous research suggests that different firing patterns can release different neuropeptides (Poulain et al, 1977). Alternatively, the relative availability of neuropeptides and receptor expression or function may be state- dependent. Here, the mice were sated, minimally stressed, and brain slices prepared approximately 2 hours within the active (dark) phase of the mice’s circadian cycle.

Because the ovBNST seems important for energy homeostasis and is a one of the brain’s circadian clock, it will be critical to determine whether Dyn-mediated LTDGABA may not become the primary response in different conditions (Amir et al, 2004; Dong et al, 2001).

Anatomical studies suggest that the CeA and ovBNST robustly inhibit one another such that their physiological roles are possibly antagonistic (Dong et al, 2001; Petrovich et al,

1997). The CeA is instrumental in promoting aversive learning and fear response whereas the dorsolateral BNST might be more related to appetitive behaviours (Davis et al, 2010;

Jennings et al, 2013a). Energy homeostasis may be an important factor in the

neuromodulatory effects of NT vs. Dyn on the CeA-to-ovBNST circuit. For instance, they have opposing effects on foraging behaviours: NT and Dyn respectively decrease and increase food intake in both rats and mice (Cooke et al, 2009; Lambert et al, 1993;

Levine et al, 1983; Luttinger et al, 1982; Sainsbury et al, 2007). Intriguingly, NT reduces

Dyn-induced feeding in rats (Levine et al, 1983). Furthermore, NT and Dyn expression and release are metabolically state-dependent. In rats, NT release into the hepatic-portal circulation occurs immediately after cessation of eating (George et al, 1987). In contrast,

Dyn expression increases significantly in the rat hypothalamus after a 72-hour food deprivation (Przewlocki et al, 1983). Therefore, NT may promote CeA inhibition of the ovBNST to decrease foraging behaviour.

In sum, our data strongly suggest that NT and Dyn cooperate to fine-tune CeA inhibitory inputs onto ovBNST neurons. We have described one circuit, as well as one pair of behaviorally-relevant neuropeptides that could serve as a source of bi-directional modulation in vivo: the physiological and/or behavioural relevance of this mechanism remains to be elucidated. Our study paves the way to investigate whether this phenomenon occurs in other neuropeptidergic systems, or can be generalized outside of the extended amygdala.

Chapter 5: General Discussion

5.1 Summary of Findings We initially hypothesized that endogenous neuropeptides in the ovBNST would regulate neurotransmission and become altered after animals were exposed to CUS. Indeed, we discovered that locally released neuropeptides robustly modulated synaptic transmission of ovBNST neurons in male mice and rats. Precisely, NT and CRF increased inhibitory transmission while opioid neuropeptides decreased excitatory transmission in non- stressed rats. In non-stressed mice, NT and Dyn bi-directionally modulated CeA inhibitory inputs onto ovBNST neurons through NTR1 and KOR respectively. Overall, in non-stressed rodents, neuropeptides play an integral role in shaping neuronal activity, and have the ability to target specific neural circuits.

In rats that underwent the CUS paradigm, NT became the dominant neuromodulatory peptide of inhibitory transmission as illustrated in Figure 15. Changes in Ntsr1 mRNA expression after CUS substantiate our electrophysiological findings, providing further evidence that aversive stressors modify the NT system. When applied to behaviour in vivo, pharmacological blockade of NTRs in the ovBNST reversed CUS-induced exploratory behaviours in rats. As such, not only was NT activity in the ovBNST changed by chronic stress, but this also influenced behaviour. Up to now, NT’s role in chronic stress and anxiety-like behaviours has largely been overlooked but clearly should be further investigated.

NON STRESSED RODENTS CHRONICALLY STRESSED RODENTS

CeA CeA GABA (+) GABA (+)

(+) (+)

NT NT (-) CRF (-) CRF Dyn Dyn Enk Enk ovBNST ovBNST

Glutamate Glutamate (-) (-)

Figure 15: Illustration comparing neuropeptidergic modulation of synaptic transmission in the ovBNST in non-stressed vs. chronically stressed male rodents.

5.2 Neuromodulatory Role of Neurotensin Like all other neuropeptides, NT is a modulator of neuronal activity. Depending on the brain area, it can excite or inhibit neuronal activity based on the locus of action (whether pre-synaptic or post-synaptic) and concentration of the neuropeptide (Tschumi et al,

2018). For example, in the nucleus solitary tract, low concentration of NT increased pre- synaptic glutamate and GABA release, and at higher concentrations it depolarized the membrane of the post-synaptic neuron (Ogawa et al, 2005). In the ovBNST, the effect of endogenously released NT was exclusive to inhibitory transmission, and previous studies demonstrated its effect was pre-synaptic (Krawczyk et al, 2013a). However, NT bath application (1 µM) also decreases excitatory transmission in the ovBNST (Appendix G).

Thus, the overall effect of NT in this area seems to be to potently increase inhibitory synaptic transmission by both increasing GABA release and decreasing AMPA excitatory transmission. 81

Since neuropeptide release is positively correlated with frequency, it is plausible that with a higher frequency post-synaptic activation protocol, more NT could be released to have different effects than those observed here (Bondy et al, 1987; Dreifuss et al, 1971; Gainer et al, 1986; Vilim et al, 2000). NT has varying affinity to four different receptors: two G- protein receptors (NTR1 and NTR2) and two sorting receptors (NTR3 and NTR4)

(Chalon et al, 1996; Mazella et al, 1998; Tanaka et al, 1990). NT binds with highest affinity to NTR1 and NTR3 (0.1-0.3 nM) followed by NTR2 (1-5 nM) and lastly NTR4

(30 nM) (Jacobsen et al, 2001; Mazella et al, 1988; Pelaprat, 2006). These receptors are ubiquitously expressed throughout mice and rat brains, and they activate different intracellular pathways (Boudin et al, 1996; Geisler et al, 2006; Wang et al, 2005).

Therefore, higher release of NT could potentially activate receptors other than NTR1 that was the predominant effect observed after our post-synaptic activation protocol.

The role of NTR2 at this point remains elusive; although, we found it does modulate inhibitory transmission contrary to NTR1 in the ovBNST. The two receptors may affect different cell types, as DAPI is not a specific neuron stain, resulting in opposing modulatory functions. For example, in the ventral tegmental area of male mice, NTR1 are expressed in dopamine neurons that project to the nucleus accumbens and olfactory tubercle while NTR2 were mostly expressed in glial cells (Woodworth et al, 2017a).

Alternatively, the two receptors may co-localize in the same cells since both are expressed in the ovBNST and CeA although this has never been shown. Notably, the heterodimerization of NTR1 and NTR2 in COS-7 cells increased internalization of NTR1 receptors typically found at the plasma membrane (Perron et al, 2007). Therefore,

different states or conditions may promote expression of NTR1 or NTR2 that could facilitate the activation of one over the other. There is currently a paucity of information regarding the relationship between NTR1 and NTR2. Varying the concentration of NT or studying the effect of NTRs on specific cells may be an important next step to elucidate this.

Our electrophysiological approach did not discriminate between different cell types

(either with different chemical or electrical properties), and may explain the variety of cell responses observed. Using an approach that enables specific population tagging (for example, reporter mice) could elucidate the specificity of these modulators on certain cell populations. We did however target a specific inhibitory input that NT and Dyn regulated. We cannot exclude the possibility that this effect may be due to an increase in calcium influx and probability of release that occurs when using ChR2 to evoke cell depolarization (Schoenenberger et al, 2011). Although, the effects observed using optogenetics were similar to electrical stimulation suggesting this may not be the case.

Importantly, our post-synaptic activation protocol was not neuropeptide specific, and likely released a variety of neuromodulatory molecules. Although, we focused on long- term effects that are typical of neuropeptides, this does not exclude the possibility that other neuromodulators may have confounded the effects observed. ECb have been shown to decrease GABA release through cannabinoid type 1 receptor, and estrogen increases

GABA release through E2 receptors in the ovBNST (unpublished data). Whether and

how these modulators work in concert with the neuropeptides in the ovBNST remains elusive.

Other neuromodulatory molecules that co-localize with NT may strongly affect its physiological role. Indeed, our findings demonstrate a cooperative relationship between

NT and CRF to modulate ovBNST inhibitory transmission in non-stressed rats. The two neuropeptides seem to be tightly linked in other areas as well where chronic administration of NTR1 antagonist resulted in a decrease of CRF protein expression in the PVN and the median eminence (Rowe et al, 1997). Additionally, NTR2 may also have some affinity for Dyn (Huidobro-Toro et al, 1984; Pettibone et al, 1988). Clearly, the modulatory effects of NT, Dyn and CRF are highly intertwined and may vary based on animal state or environmental conditions.

5.3 General Role of Neurotensin in Behaviour NT’s effects on behaviour are diverse but likely have a coordinated function.

Vasodilation in rats after intravenous injection was the first observed property of NT

(Carraway and Leeman, 1973). Since then, NT has been identified to have important effects on thermoregulation, energy balance, locomotion, pain, and social behaviour

(Bissette et al, 1976; Lafrance et al, 2010; McHenry et al, 2017; Vadnie et al, 2014;

Woodworth et al, 2017b). Overall, NT seems to prepare the body for a challenging environment by minimizing energy expenditures while promoting social behaviours. It may be a neuropeptide specifically engaged in times of hardship that also requires reproduction for specie survival.

NT is a neuropeptide released in times of extreme conditions such as an unpredictable environment. It is a potent modulator of the HPA axis after exposure to aversive stressors but not under non-stressed (control) conditions. Specifically, blocking NTR1 decreased stress-induced elevation of ACTH and corticosterone plasma levels in male rats (Nicot et al, 1997; Rowe et al, 1997). As such, NT plays an integral role in the upregulation of the

HPA axis that occurs during stress conditions. Conversely, glucocorticoids exerted a negative control on Nts mRNA production in the PVN in male rats (Loum-Ribot et al,

2006). After CUS or maternal separation stress in rodents, Ntsr1 mRNA decreased and

NT serum levels also decreased after 1-hour restraint (Normandeau et al, 2018; Tache et al, 1979; Toda et al, 2014b). Therefore, NT is a crucial participant in the stress response, and is readily downregulated by glucocorticoids.

Furthermore, NT curtails energy expenditure by reducing locomotion and decreasing body temperature (Bissette et al, 1976; Vadnie et al, 2014). This is matched with retention of nutritional energy whereby NT enhanced the absorption of dietary lipids and delayed gastric emptying (Armstrong et al, 1986; Blackburn et al, 1980; Li et al, 2016).

Additionally, NT has powerful antinociceptive effects mostly mediated by NTR2 (Sarret et al, 2005). Specifically, spinal NTR2 receptor activation increased antinociceptive activity induced by stress factors (in this case by cold-water swim stress) (Lafrance et al,

2010). As such, lessening the sensation of pain and conserving energy is important when navigating a challenging environment where cognitive faculties should be focused on finding food and reproducing.

NT however minimizes foraging behaviours by suppressing food intake in food-deprived states or during rodent’s (male mice and rats) active dark phase (Cooke et al, 2009;

Levine et al, 1983; Luttinger et al, 1982). Given chronically (4 days intraperionally), NT has long-term effects on food intake (up to 15 days) and decreases weight gain (Boules et al, 2000). Central injection of NT in the nucleus of the tractus solitaries or in the PVN suppressed feeding demonstrating that the role of NT inside and outside the brain is coordinated even though NT does not cross the blood brain barrier (de Beaurepaire and

Suaudeau, 1988). NT’s role in feeding behaviour is highly linked with leptin where

NTR1 antagonist completely reversed the inhibitory effects of leptin on feeding (Sahu et al, 2001). In the lateral hypothalamus (LH), most neurons expressing leptin receptors co- localize with NT and activation of these specific neurons reduced food intake in fasted mice (Leinninger et al, 2011; Woodworth et al, 2017b). NT therefore may be involved in high-risk contexts, for example in high predator prevalence environments where foraging should be limited.

Conversely, for social behaviours, NT seems to promote reproductive courtship along with defensive behaviours. In female mice, stimulation of NT neurons from medial preoptic area projecting to ventral tegmental area increased approach behaviour towards male odor; these neurons preferentially encoded attractive male cues compared to non- social appetitive stimuli (McHenry et al, 2017). In European male starling birds, NT expression was correlated with vocal courtship, non-vocal courtship and agonistic behaviour (territorial behaviour) (Merullo et al, 2015). When contact was forced between a threat stimulus (rat) and a subject animal (mouse), NTR1 antagonist reduced attack

reactions (biting) (Griebel et al, 2001). It is unclear whether protecting offspring motivated these defensive behaviours although NT is necessary for the discrimination between novel or non-novel juvenile rats (Feifel et al, 2009). Thus, NT may be involved in the assessment of “us” vs. “other” that is crucial for initiating social behaviours in dangerous environments.

Importantly, NT behavioural studies have largely overlooked the role of the neuropeptide in stressed conditions. Our findings demonstrated that NT contributes to the stress- induced decrease in exploratory behaviour in the EPM that is coherent with NT’s role of minimizing energy expenditure. Furthermore, NT modulation is most predominant during chronic stress suggesting its activity may be specific to unpredictable states that requires vigilance. Indeed, in NS rats NTR blockade in the ovBNST had no effects on time spent in the open arm of the EPM nor did bilateral injection of NT in the CeA (Laszlo et al,

2010a). However, it is possible that NT is deregulated by CUS and is no longer functioning as it should under normal physiological conditions.

5.4 Neurotensin: Adaptive or Maladaptive? At this point, we cannot define whether the CUS induced upregulation of NT modulation is adaptive or maladaptive. Animals that undergo CUS cannot be equated to a pathological state since chronic stress responses predominantly entail attempts at adaptation to minimize the allostatic load. NT changes could therefore be part of stress habituation.

Stress habituation is an important adaptive mechanism that diminishes the stress response following repeated challenges. Recurring exposure to different stressors results in habituation of HPA activity in both rodents and humans (Grissom and Bhatnagar, 2009).

Whether a stressor is homotypic (the same stressor as previously experience) or heterotypic (different stressor than previously experienced) habituation rate does not change (Dumont et al, 2000; Grissom et al, 2009). However, it is dependent on severity of the stressor whereby immobilization by wooden boards provoked a higher HPA axis response compared to the restraint test in male rats (Garcia et al, 2000). Additionally, habituation is sensitive to the predictability of the stressors. Female prairie voles exposed to an unpredictable immobilization stressor had elevated plasma corticosterone levels compared to those exposed to a predictable immobilization stressor (Smith et al, 2013).

Thus, both severity and predictability of a stressor will affect the rate of habituation.

Habituation still occurs with unpredictable repeated stress paradigms, like CUS, but not to the same extent as scheduled repeated stress. Rate of body weight loss is most profound in the first 3 days of variable stressors, and adrenal hypertrophy and thymic atrophy peaks between 1-2 weeks of CUS exposure (Paskitti et al, 2000; Tamashiro et al,

2007). However, males rats exposed to CUS have lower body weights compared to controls emphasizing that the stress habituation does not represent a return to physiological control conditions (Ventura-Silva et al, 2013b). Indeed, ACTH and corticosterone responses to novel stressors in CUS male rats are sensitized (Ulrich-Lai et al, 2006). Thus, physiological changes due to CUS provide a potential to crossover to maladaptative physiology and behaviours but by no means implies it.

Importantly, the majority of humans exposed to chronic stress do not develop any pathology (Green et al, 2010). The switch between maladaptive and adaptive stress responses is likely highly dependent upon individual differences, whether genetic or environmentally driven, to cope with stressors. Certain genetic differences that make animals more or less capable of coping with aversive stressors have been identified. For example, male mice that underwent CUS were categorized into high-stress vs. low-stress groups based on their immobility time in the forced swim test and sucrose preference

(Nasca et al, 2015). Only the high-stress group correlated with a lower expression of glutamate receptors in the hippocampus and prefrontal cortex (Nasca et al, 2015). It would be interesting to correlate either NT modulatory effects or NTR1 expression to different behaviours in order to better understand individual differences. We predict that high-stress groups would correlate with higher NT modulatory effects or lower NTR1 expression.

Whether NT is involved in active coping behaviour may enable us to better define if the upregulation observed is adaptive or maladaptive. A known coping approach to aversive stressors is controllability. For example, male rats that do not have control over their electric shock stressor will later fail to learn to escape in a different apparatus such as a shuttle box (Maier and Watkins, 2010). Additionally, they are less active in the presence of aversive stimuli, not as aggressive or dominant, and will eat and drink less (Maier et al, 2010). However, controllability does not affect the HPA axis response (Helmreich et al, 1999; Maier et al, 1986). Therefore, even if the physiological reaction to the stressor was the same (i.e. ACTH and corticosterone levels), the behavioural outcome was

different based on previous controllability experience. An escapable and yoked tail shock stressor stimulated similar increases in Nts mRNA in the PVN of male rats (Helmreich et al, 1999). Thus, NT may not be sensitive to controllability of a stressor, but this should be further investigated.

5.5 Neuropeptides: Where to Next? Neuropeptides remain an interesting potential target for disorders due to their potent effects on behaviours. However, our understanding of their contribution to the transition between adaptation and pathology are poorly understood. In fact, this lack of knowledge goes beyond neuropeptides, as specific biomarkers underlying this switch in psychiatric disorders remain elusive (Chiao et al, 2017). There are major hurdles to overcome when researching therapeutic targets for psychiatric disorders particularly. First, the diagnosis for the diseases have fluid end points that are based almost entirely on behavioural symptomatology rather than on mechanistic understanding of the underlying biology.

Second, signs and symptoms of such conditions often reflect motivations, emotions, and thought processes not realistically attributable to animals such as rats and mice. Thus, researchers are dependent upon behavioural test to quantify adaptive vs. pathological behaviour.

The EPM is certainly restricted in its translational potential. There is very little evidence that time spent in the open arm can be correlated to pathological behaviour other than the currently available drugs alter exploratory behaviour (Pellow et al, 1985). The EPM is also more sensitive to benzodiazepine anxiolytics than to SSRIs that are more effective at

treating AD (Haller et al, 2013; Rodgers, 1997). This is a major concern considering the

EPM is the most commonly used pre-clinical test for AD, and may be biasing research towards finding “benzodiazepine-like” treatments (Griebel et al, 2013). Although, the test contains interesting behavioural information, there is a lack of scrutiny in terms of its potential to translate to human pathologies. Consequentially, it may be very difficult to find new treatment for patients that are treatment-resistant using the classical behavioural test.

While the EPM offers limited amounts of data during a brief snapshot of time, new approaches can provide constant (24/7) data streams over long periods of time (Lezak et al, 2017). One example is the development of large-scale behaviour-based systems that can track multiple measures (example, locomotion, trajectory complexity, pauses) for each animal in their home cage (Alexandrov et al, 2015; Wiltschko et al, 2015). This may enable the discovery of behavioral patterns that are impossible to detect solely by human observation, and avoids the confounding effect of having an observer present. The main obstacle being the quantity of data collected (upwards of 500 000 data points per subject) that is extremely difficult to manage and interpret. Nonetheless, it may be an important step forward to using a more holistic approach to observing changes in behaviour.

An underappreciated approach to identifying physiological biomarkers with translational relevance may be examining sleep architecture. Most AD are associated with disruptions in sleep pattern (decreases in active waking and deep sleep, and disruptions of REM sleep), and chronic mild stress produces sleep pattern alterations in rodents that are

similar to humans (Boland and Ross, 2015; Cheeta et al, 1997; Moreau et al, 1995).

Electroencephalogram (EEG) data can provide empirical insight into sleep states for both rodents and humans. For rodents, some systems are wireless enabling continuous measurement of freely moving animals for periods up to several weeks. These data are also easily translatable to humans, and provide a more attainable end point that is independent of anthropomorphism.

5.6 Closing Remarks In sum, neuropeptides are powerful neuromodulators of neural activity in the ovBNST that have behavioural relevance. Particularly, NT is involved in changes that occur after being exposed to chronic aversive stressors. Whether this change has pathological implication remains elusive. Notably, exclusively in women, proneurotensin was related to incidents of diabetes, breast cancer, total mortality, and cardiovascular mortality

(Melander et al, 2012). A major caveat in animal research currently is the male bias that is most prominent in neuroscience compared to other biomedical sciences. Single-sex studies of male animals outnumber those of females 5.5 to 1 in neuroscience (Beery and

Zucker, 2011). Therefore, most pre-clinical neuropeptide studies conducted so far have been on male animals.

It is paradoxical that most of the research on AD and MDD is conducted on male animals since in Canada, anxiety and depression disorders are more prevalent in females

(O'Donnell et al, 2016). Sex differences in stress responses can be found at all stages of life. Males are more susceptible to develop mental health disorder due to early-life stress

while females are more vulnerable later in life during and post their reproductive years

(Bale and Epperson, 2015). A common rationalization for not studying female animals is to avoid the confounding effect of fluctuating hormone levels. However, it is concerning that none of the drugs developed are tailored to females even though they are most affected by some of the psychiatric disorders. It is clearly imperative moving forward to rectify the gender bias, and ensure experiments are conducted on both males and females.

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Appendix A: Illustration of mRNA tissue punches.

Figure 16: Illustration of mRNA tissue punches.

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Appendix B: Real-time qPCR primers

Table 2: Real-time qPCR primers for amplifying reference genes and genes of interest.

Gene Gene name RefSeq ID Primer sequence symbol Hprt1 Hypoxanthine NM_012583 F: 5’-GAC TAT AAT GAG CAC TTC phosphoribosyltransferase AGG-3’ 1 R: 5’-TAG AAG GTT CAT GCA AAA GC-3’ Actb Actin, beta NM_031144 F: 5’-AAG ATC ATT GCT CCT CCT G- 3’ R: 5’-AAA GAA AGG GTG TAA AAC GC-3’ Sdha Succinate dehydrogenase NM_130428 F: 5’-ACT ATT ATT GCT ACT GGG complex flavoprotein GG-3’ subunit A R: 5’-CTG AAC AAA TTC TAA GTC CTG G-3’ Crhr1 Corticotropin Releasing NM_030999 F: 5’-CAC TGA CTA CAT CTA CCA Hormone Receptor 1 GG-3’ R: 5’-TGT ACT GAA TGG TCT CAG ATG-3’ Crhr2 Corticotropin Releasing NM_022714 F: 5’-ACT CTA CTA TGA GAA TGA Hormone Receptor 2 GCA G-3’ R: 5’-TGA CGA TGT TGA ACA GAA AC-3’ Crh Corticotropin Releasing NM_031019 F: 5’-GAA AGG GGA AAG GCA AAG- Hormone 3’ R: 5’-CTC TCT TCT CCT CCC TTG-3’ Ntsr1 Neurotensin Receptor 1 NM_001108967 F: 5’-TCA TCC AGG TTA ACA CCT TC-3’

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R: 5’-CAT GAC TGT CAG TTT GTT GG-3’ Ntsr2 Neurotensin Receptor 2 NM_022695 F: 5’-ACC AGA GTT GAA GAA TAG GAA C-3’ R: 5’-GCA CCG GGT TAA AAT TTA TTG-3’ Nts Neurotensin NM_001102381 F: 5’-ACA CTG GGA GAT AAT TCA GG-3’ R: 5’-CCT TCT GGG TTT ATT CTC ATA G-3’

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Appendix C: Cell distribution for different rat strain and light cycles

Table 3: Cell distribution after post-synaptic activation in different rat strain and light cycles.

Rat Breed & Light Cycle #Neurons Chi Square Potentiation No change Long Evans Lights ON PM 9 2 Long Evans Lights ON AM 4 7 X2(2,n=34)=4.69, Wistar Lights ON AM 7 5 p=0.1

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Appendix D: Effect of NTR and CRFR1 Antagonist

A 100 1 2

50 SR -IPSC (%) A

50 ms 100 pA 0 1 2 ∆ GABA

-50 0 10 20 30 Time (mins)

B 100 1 2

50 NBI -IPSC (%) A 100 pA 2 50 ms 0 1 ∆ GABA -50 0 10 20 30 Time (mins)

Figure 17: Effect of NTR antagonist and CRFR1 antagonist on GABAA-IPSCs in the ovBNST.

Effect of 10 minutes bath application of (A) SR29148 10 µM and (B) NBI27914 1 µM. Representative traces on the right.

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Appendix E: Effect of NT on electrophysiological properties

Table 4: Effects of NT on passive electrophysiological properties of ovBNST neurons.

Treatment Mean Holding Current (pA) Mean Input Resistant (mΩ)

Pre Post Paired t-test Pre Post Paired t-test NT -61.26 -60.06 t(6)=0.10, 709.14 829.26 t(6)=1.02, application ±59.01 ±30.87 p=0.92 ±442.00 ±700.04 p=0.35

Repetitive -9.38 -14.37 t(14)=1.27, 434.96 426.77 t(14)=0.88, Depolarization ±19.09 ±26.77 p=0.22 ±230.66 ±224.03 p=0.40

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Appendix F: Effect of NTR antagonist in the open field

NS Saline CUS Saline CUS SR5 CUS SR10

Figure 18: Effect of intra-ovBNST NTR antagonist injection on locomotion activity in the open field test.

Total distance travelled by non-stressed rats injected with saline in the ovBNST (white), and CUS rats injected with saline (black), SR29148 5 µM (squared), and SR29148 10 µM (striped) in the ovBNST.

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Appendix G: Effect of bath application of NT on excitatory transmission

2 100 pA 50 ms 1

1 2

25 NT

-25 ∆ AMPA-EPSC (%) AMPA-EPSC ∆

-50 0 10 20 30 40 Time (mins)

Figure 19: Effect of NT application on AMPA-EPSCs in the ovBNST

Effect of 5 minutes bath application of NT 1 µM on AMPA-EPSCs in the ovBNST. Representative traces above.

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