Probing the Role of Neuronal Nicotinic Acetylcholine Receptors in Modulating in Vitro Hippocampal Network Dynamics

PROBING THE ROLE OF NEURONAL NICOTINIC ACETYLCHOLINE RECEPTORS IN MODULATING IN VITRO HIPPOCAMPAL NETWORK DYNAMICS

A Dissertation submitted to the Faculty of the Graduate School of Arts and Sciences of Georgetown University in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Pharmacology

Sarra Djemil, M.S.

Washington, D.C. April 5, 2018

PROBING THE ROLE OF NEURONAL NICOTINIC ACETYLCHOLINE RECEPTORS IN MODULATING IN VITRO HIPPOCAMPAL NETWORK DYNAMICS

Sarra Djemil, M.S.

Thesis Advisor: Rhonda Dzakpasu, Ph.D.

ABSTRACT

Nicotinic acetylcholine receptors (nAChRs), the first receptors to be identified, play varying and essential roles throughout the CNS. Both the endogenous ligand, acetylcholine, and exogenous ligand, nicotine, have been found to induce a wide range of effects on neurotransmitters. On the systems level, nAChRs are involved in the maintenance of hippocampal gamma and theta oscillations, and these oscillations are associated with cognitive functions, facilitating attention, learning and memory. As such, the aberrant nicotinic transmission has been implicated in many neuropathological and psychiatric disorders as they impact the dynamics of select neuronal oscillations such as Alzheimer’s disease, Parkinson’s disease, and schizophrenia. In the following studies, I used cultured hippocampal neurons plated on multi-electrode arrays to investigate how nicotine modulates spiking and network bursting activity, the latter of which is necessary for reliable information transmission in the hippocampus.

In the first study, intermediate and high doses of nicotine were used as a tool to investigate the impact of activation and desensitization of nicotinic receptors on network dynamics. My results suggest that the high dose of nicotine activates nAChRs, which enhances firing of action potentials as well as facilitates synchronized bursting activity, whereas the intermediate dose had minimal effects on network spiking activity but appeared to desensitize

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the network to a higher dose. These effects are absent in hippocampal networks where nAChRs were initially treated with a low nicotine concentration, suggesting that stochastic activation of nAChRs may protect the network from pathological synchronization.

The last part of my thesis focuses on determining the nAChR subtypes involved in nicotine’s effect on hippocampal network dynamics. To elucidate their contribution to the observed effects, I blocked the conductance of discrete nAChR subtypes and then stimulated the network with nicotine. My results suggest that β4-containing nAChRs are necessary for the observed increases in spiking, bursting and synchrony, whereas α7 nAChRs play a role in mediating the impact of nicotine. Lastly, to address the role of synaptic N-methyl-D-aspartate receptors (NMDARs) and group I metabotropic glutamate receptors (mGluRs) in these dynamics, I blocked either NMDARs or group I mGluRs, then stimulated the network with nicotine.

ACKNOWLEDGMENTS

“I will never stop being ravenously hungry for science, no matter how well it feeds me.”

Hope Jahren, Lab Girl

Many people have played an instrumental role in my development as a scientist, and to whom I

feel a deep sense of gratitude. To attempt to do justice to the gratitude, I feel towards these individuals would make this section the longest in my thesis. Therefore, these acknowledgments

reflect a sense and not the magnitude of gratitude that I feel.

First and foremost, I would like to thank my thesis advisor, Dr. Rhonda Dzakpasu. Dr.

Dzakpasu’s brimming passion for science makes research in her lab a continuous source of

excitement. She is always willing to consider new ideas and quick to embrace them. I feel that

Dr. Dzakpasu pushed my abilities as a scientist by prompting me to explore, encouraging me to

be independent, and to develop my own ideas and creativity in research. For these reasons, and

for many more, I will always be grateful for her mentorship.

I have been very fortunate to be a member of the Pharmacology and Physiology Department, where research is highly collaborative, and the faculty, staff, and students are incredibly helpful.

In particular, I would also like to thank my thesis committee members Dr. Gerard Ahern, Dr.

Kathy Conant, Dr. Ken Kellar, Dr. Dan Pak, and Dr. Barry Wolfe, whose support and advice was

pivotal to my success. I am especially thankful to Dr. Ahern for teaching me Ca2+ imaging. To

Dr. Conant for graciously agreeing to serve as my outside committee member. To Dr. Kellar for teaching me how to perform radioligand neurotransmitter release assays. To Dr. Pak for teaching

me how to culture primary neurons but also shared with me his hippocampal cultures. To Dr.

Wolfe for his expertise in experimental design and statistical analysis, and for serving as my

committee chair. I also appreciate the contributions of Dr. Bob Yasuda, who as far as I am

concerned served as an additional committee member. Importantly, many of experiments that I conducted could not have been performed without the generosity Dr. Stefano Vicini, who kindly allowed us the use of his MEA2100 system. No words will sufficiently convey my gratitude for

his generosity.

My understanding of pharmacology has grown exponentially since the day that I joined the

Ph.D. program. For this growth, I must thank the late Dr. Jarda Wroblewski, a great teacher, and pharmacologist. I am forever indebted to him and to Dr. Wolfe for teaching me the principles of

pharmacology.

I would also like to specifically thank Dr. Xin Chen, whose substantial insights into neural

network dynamics field have contributed to this project. I am also grateful for the help and

support I received from Dr. Gustavo Rodriguez, who was generally helpful when I first joined

the lab. I will always be grateful to the many friends that I made in Georgetown who made the

good times more joyful, and the difficult times more bearable.

I would also like to thank Dr. Nancy L. Greenbaum, my undergraduate mentor, as well as Dr.

Victoria Luine and Janerie Rodriguez, of the MBRS-RISE program at Hunter College, for

believing in me, and for giving me the opportunity and the means to flourish as a scientist.

My parents, husband, sisters, brothers, and children have been an amazing support system.

They understand my passion for science, and always encourage me to strive to be the best that I can be. I am especially thankful to my parents for teaching me the value of hard work and the art

of preserverence. Above all, Ahmed my husband and best friend has been a constant source of inspiration and support. He understands me like no other and is always there for me when I am in

need. Thank you for all you have done.

DEDICATION

The research and writing of this thesis is dedicated to everyone who helped along the way.

Thank you, Sarra Djemil

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TABLE OF CONTENTS

CHAPTER I: INTRODUCTION ...... 1 Nicotinic Acetylcholine Receptors: Discovery ...... 2 Nicotinic Acetylcholine Receptors: Structure ...... 2 Nicotinic Acetylcholine Receptors: Pharmacology ...... 3 Nicotinic Acetylcholine Receptors: Localization Within the Hippocampus ...... 6 The Functional Role of Hippocampal Nicotinic Cholinergic Transmission ...... 8 The Role of nAChRs in Modulating in vitro Hippocampal Neuronal Network Dynamics ...... 9 Studying Neuronal Networks In Vitro ...... 10 Introduction to Thesis Project ...... 11 CHAPTER II: MATERIALS AND METHODS ...... 17 Cell Culture ...... 18 Multi-electrode Array Recordings ...... 18 Data Acquisition ...... 18 Drugs ...... 19 Drug Application: Chapter III ...... 19 Drug Application: Chapter IV ...... 20 Data Analysis ...... 20 Statistics ...... 22 CHAPTER III: STOCHASTIC ACTIVATION OF NICOTINIC ACETYLCHOLINE RECEPTORS PREVENTS INDUCED SYNCHRONIZATION WITHIN IN VITRO HIPPOCAMPAL NETWORKS ...... 24 Introduction ...... 25 Results ...... 27 Dissociated Embryonic Hippocampal Neurons Cultured on MEA ...... 27 Nicotine Qualitatively Enhances Network Activity ...... 29 Effects of Nicotine on Network Spiking and Bursting ...... 33 Effects of Nicotine on Network Rhythmicity ...... 36 Effects of Nicotine on Network Efficiency and Synchrony ...... 39 Discussion ...... 45 CHAPTER IV: ACTIVATION OF NICOTINIC ACETYLCHOLINE RECEPTORS INDUCES POTENTIATION AND SYNCHRONIZATION WITHIN IN VITRO HIPPOCAMPAL NETWORKS ...... 47 Introduction ...... 48 Results ...... 51

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Concentration-Dependent Effects of Nicotine on Network Potentiation ...... 51 Effects of Nicotine on Network Synchrony ...... 55 Contribution of α7 nAChRs to Nicotine-Mediated Network Potentiation ...... 58 Contribution of Heteromeric nAChRs to Nicotine-Mediated Network Activity...... 63 Contribution of Steady-state Activation of nAChRs to the Long-lasting Effects of Nicotine on Network Spiking ...... 75 Nicotine-Mediated Network Potentiation is Independent of Synaptic NMDAR and Group I mGluR Activation ...... 78 Discussion ...... 86 CHAPTER V: CONCLUSION AND FUTURE DIRECTIONS ...... 92 APPENDIX ...... 104 NMDAR-Mediated Release of [3H] Norepinephrine From Adult Rat Hippocampal Slice ... 105 Methods...... 106 Animal Care and Use ...... 106 Hippocampal Tissue Extraction and Loading with [3H] Norepinephrine ...... 106 [3H] Norepinephrine Release Assay ...... 107 Data Analysis ...... 108 Results ...... 109 Glutamate-Stimulated [3H] Norepinephrine Release ...... 109 MK-801 Attenuates Glutamate-Stimulated [3H] Norepinephrine Release ...... 111 NMDA-Stimulated [3H] Norepinephrine Release...... 113 AT-1001 Does Not Perturb NMDAR-Mediated Release of [3H] Norepinephrine ...... 115 List of Abbreviations ...... 118 REFERENCES ...... 119

LIST OF FIGURES

Figure 1: Representative ISI frequency histogram generated from spontaneous spiking activity recorded from a network of dissociated hippocampal cultures on DIV14...... 21

Figure 2: Spontaneous activity from rat hippocampal neural networks...... 28

Figure 3: Representative raster plots of activity from hippocampal networks 14DIV before and after treatment with a 100 μM of nicotine...... 30

Figure 4: Representative raster plots of activity from hippocampal networks 14DIV after desensitization with 10 μM of nicotine and subsequent treatment with 90 μM of nicotine...... 32

Figure 5: Nicotine significantly increases spiking activity, but high - and not low - concentrations of nicotine significantly increase bursting activity...... 34

Figure 6: A high concentration of nicotine qualitatively enhances network rhythmicity but fails to enhance rhythmicity in desensitized networks...... 37

Figure 7: A high concentration of nicotine decreases “errant” spikes and recruits silent units into bursting activity...... 40

Figure 8: Synchronization is enhanced after the addition of 100 µM and, to a lesser extent after 10 µM, but pretreatment of 10 µM prevents further synchronization when followed by 90 µM. 43

Figure 9: Nicotine potentiates network-wide spiking and bursting, and reorganizes network activity in a concentration-dependent manner...... 53

Figure 10: High, but not intermediate concentrations of nicotine promote network synchrony. . 57

Figure 11: Blocking α7 nAChRs with MLA attenuates nicotine-mediated network potentiation...... 59

Figure 12: Activation of α7 nAChRs is not necessary for nicotine-mediated network synchrony...... 62

Figure 13: Activation of α4β2 nAChR with the partial agonist saz-A is sufficient to enhance network-wide bursting...... 66

Figure 14: Desensitization of α4β2 nAChRs does not occlude nicotine-mediated network synchrony...... 69

Figure 15: Desensitization of β4-containing nAChRs with partial agonist, AT-1001, blocks nicotine-mediated network potentiation...... 71

Figure 16: Activation and subsequent desensitization β4-containing nAChRs blocks nicotine but not gabazine-mediated network synchrony...... 74

Figure 17: Steady-state activation of α7 nAChRs does not contribute to the long-lasting effects of nicotine on spiking...... 76

Figure 18: Steady-state of α4β2 nAChRs does not contribute to the long-lasting effects of nicotine on spiking...... 77

Figure 19: Activation of synaptic NMDARs is not necessary for nicotine-mediated network potentiation...... 79

Figure 20: Activation of group I mGluRs is not necessary for nicotine-mediated network potentiation...... 82

Figure 21: Representative raster plots of activity from hippocampal networks 14DIV depicting the effects of group I mGluRs on spontaneous activity and nicotine-mediated network potentiation...... 84

Figure 22: Role of nAChR activation in modulating in vitro hippocampal network dynamics. 101

Figure 23: Role for pre- and peri-synaptic nAChRs in mediating long-lasting GABA and glutamate release...... 103

Figure 24: Glutamate-stimulated [3H] NE release in adult rat hippocampal slices...... 110

Figure 25: MK-801 attenuates glutamate-stimulated [3H] NE release in adult rat hippocampal slices...... 112

Figure 26: NMDA-stimulated [3H] NE release in adult rat hippocampal slices...... 114

Figure 27: AT-does not perturb glutamate-stimulated [3H] NE release in adult rat hippocampal slices...... 116

Figure 28: Pharmacological analysis of glutamate-stimulated [3H] NE release in adult rat hippocampal slices...... 117

CHAPTER I: INTRODUCTION

Nicotinic Acetylcholine Receptors: Discovery

At the turn of the twentieth century, John Newport Langley designed a seminal study in which he observed that contrary to his what his contemporaries believed, the site of action of nicotine is a “receptive substance” on the target organ itself rather than its nerve endings. In this instance, the organ Langley referred to was striated muscle, and the “receptive substance” was none other than the nicotinic acetylcholine receptor (nAChR). This was the first occasion that receptors were mentioned, making the nAChR the first receptor to be identified. Langley’s groundbreaking observation went beyond identifying the first receptor. Indeed, he was perceptive in also observing in the same studies that nicotine had different actions on avian species versus mammals, and that even within mammals, it had different effects on different tissue types. This astute observation, he reported, suggests the existence of different “receptive substances” (Langley, 1905), and ushered the start of a new chapter in the field of pharmacology: receptor pharmacology.

Although nicotine was the first pharmacological tool used to characterize nAChRs, identifying what activates these receptors physiologically proved to be elusive for more than three decades. In 1936, Sir Henry Hallett Dale who dedicated much of his career to the field of autopharmacology (the study of endogenous substances and their effect on the body) identified acetylcholine (ACh) as the endogenous ligand that acts on nAChRs (Dale et al., 1936).

Nicotinic Acetylcholine Receptors: Structure

Nicotinic acetylcholine receptors (nAChRs) belong to the Cys-loop superfamily of ligand-gated ion channels, which includes γ-aminobutyric-acid (GABAA and GABAC), glycine and serotonin 5-HT3 receptors. They are composed of five symmetrically arranged subunits that

surround a central ion pore that is permeable to Ca2+, sodium, and potassium. In mammals, sixteen distinct subunits (α1-α7, α9, α10, β1- β4, δ, γ, ε) have been identified. The α1, β1, δ, γ and ε subunits are typically found at the end plate of the neuromuscular junction on muscle nAChRs. Within the central nervous system (CNS), the remaining subunits combine to form neuronal nAChRs, and a large variety of nAChR subtypes exists. They can be homomeric, such as the α7 receptor, which includes five identical subunits to form a pentamer. Subtypes can also be heteromeric, in which α and β subunits combine in a pentamer, such as the α3β4 receptor.

The ligand binding domain binds nicotine and acetylcholine at the N-terminal of the α subunit (Brejc et al., 2001). For proper binding to occur, two subunits need to be adjacent to trap the ligand. Studies show that β2 subunits cannot contribute to binding, but β3, α4 and α5 subunits all have been shown to contribute to binding (Harpsoe et al., 2011; Jain et al., 2016;

Mazzaferro et al., 2011; J. Wang et al., 2015). The number of binding sites on a nAChR depends on the subunit composition. For example, since the receptors contain five α subunits, a homomeric receptor contains five ligand binding sites (Rayes et al., 2009). Alternatively, depending on the number of α3 and α4 subunits they contain, heteromeric nAChRs may contain

2 or 3 ligand binding sites.

Nicotinic Acetylcholine Receptors: Pharmacology

Following a brief period of activation, prolonged binding of a ligand to nAChRs results in desensitization (Thesleff, 1955), characterized by a high affinity and non-conducting receptor state (Katz & Thesleff, 1957). Physiologically, this phenomenon is unlikely to happen because, near ACh release sites, acetylcholinesterase (AChE) rapidly breaks down ACh to choline and acetate, precluding desensitization (Tripathi & Srivastava, 2008). In the presence of high-affinity

ligands that bind tightly to the receptor, such as nicotine, nAChRs enter into desensitization

(Quick & Lester, 2002). High concentrations of ligand hasten the onset of desensitization, which in turn terminates upon the removal of the ligand. Alternatively, lower concentrations take longer to result in full desensitization but may cause more robust desensitization that outlasts ligand removal (Fenster et al., 1997; Quick & Lester, 2002).

Although desensitization is a property of the receptor protein itself, some cellular process may modulate the desensitized receptor shifting it towards a more stable desensitized state.

These processes include Ca2+ binding directly to the nAChR, and direct or indirect modulation by peptides and phosphorylation of distinct residues within the receptor by protein kinase C

(Ochoa et al., 1989). Importantly, the subunit composition of nAChRs strongly correlates with the kinetics of desensitization (Quick & Lester, 2002). Finally, nAChRs may enter into and recover from desensitization at varying rates during the continuous application of an agonist, resulting in “smoldering” activation, a stochastic, steady-state activation of nAChRs (Campling et al., 2013).

Within the hippocampus, the major nAChR subtypes are α7, α4β2*, and α3β4* (please note that the asterisk indicates that it is possible that an unknown subunit is part of the receptor complex). The electrophysiological profile of the α7 nAChR was first described in hippocampal cultures by Alkondon and Albuquerque. They showed that the “type IA current” has a fast rise and decay time, and is sensitive to both MLA and the muscle nAChR blocker derived from snake venom, α-bungarotoxin (Alkondon & Albuquerque, 1993; Alkondon et al., 1994). They concluded that these are currents elicited from functional α7 nAChRs characterized by low affinity to Ach and nicotine, and high permeability to Ca2+ (Alkondon & Albuquerque, 2004). In addition to ACh, other endogenous agonists of the α7 nAChR are choline, the precursor of

acetylcholine (Alkondon et al., 1997), and amyloid- beta, the proteolytic cleavage product of the amyloid precursor protein that has been widely implicated in Alzheimer’s disease (AD) (H.

Wang et al., 2000). Finally, α7 nAChRs enter into, and recover from desensitization rapidly

(Quick & Lester, 2002).

The α4β2* nAChRs were identified within the hippocampus as “type II current,” a current sensitive to dihydro-β-erythroidine (DhβE) (Alkondon & Albuquerque, 1993). Studies have demonstrated that two stoichiometric forms of α4β2 nAChRs may form functional channels; a high sensitivity (HS) receptor assembled as (α4)2(β2)3 and low sensitivity (LS) receptor assembled as (α4)3(β2)2 (Nelson et al., 2003). The HS receptors have a high affinity for

ACh and nicotine, are less permeable to Ca2+ than α7 and α3β4* nAChRs, desensitize rapidly, and remain desensitized for a long time (Harpsoe et al., 2011; M. Moroni, Zwart, Sher, Cassels,

& Bermudez, 2006a; Vernino et al., 1992). Alternatively, the LS receptors have a much lower affinity for both ACh and nicotine (Harpsoe et al., 2011; M. Moroni, Zwart, Sher, Cassels, &

Bermudez, 2006b), and are more permeable to Ca2+ (Tapia et al., 2007). Recent studies using sazetidine-A (saz-A), a nAChR agonist that selectively activates the HS but not the LS form of the receptor, show that the LS receptor predominates within the rat motor cortex (DeDominicis et al., 2017). The stoichiometric form that predominates within the hippocampus has yet to be elucidated, but the ability of saz-A to discriminate between the two forms may pave the way for addressing this question.

Like the α4β2 nAChR, α3β4* nAChRs are sensitive to DhβE, but only at high concentrations that would also block α4β2 nAChRs (Harvey et al., 1996). Finally, α3β4* nAChRs are characterized by slow activation and desensitization kinetics (Bohler et al., 2001;

Quick & Lester, 2002), high permeability to Ca2+, and low affinity to both ACh and nicotine

(Alkondon & Albuquerque, 2004; Campling et al., 2013; Fenster et al., 1997; Vernino et al.,

1992).

Nicotinic Acetylcholine Receptors: Localization Within the Hippocampus

Nicotinic cholinergic transmission plays a pivotal role in modulating many important

CNS functions including synaptic plasticity, neurogenesis, neuroprotection (Belluardo et al.,

2000a; Belluardo et al., 2000b; Caldarone et al., 2004; Harrist et al., 2004; Mudo, Belluardo,

Mauro et al., 2007; Mudo, Belluardo, & Fuxe, 2007; Mudo et al., 2009), and orchestrates learning and memory-related circuits in the brain (Drever et al., 2011; Luchicchi et al., 2014).

These roles are dependent on the pharmacology of the receptor subtype, as well as the receptor localization. On the macroscopic scale, nAChRs are wide-spread across the different anatomical regions of the brain, with intermediate expression levels detected within the hippocampus

(Clarke et al., 1985; Paterson & Nordberg, 2000). nAChRs in the hippocampus are present in the cell body, as well as at pre-synaptic, peri-synaptic, and postsynaptic locations. Depending on their localization, the activation of nAChRs may increase excitatory or inhibitory drive, which in turn can impact neural network activity (Albuquerque et al., 2009; Alkondon & Albuquerque,

2002; Bürli et al., 2010; Fabian-Fine et al., 2001; Gray et al., 1996; Khiroug et al., 2003; Tang et al., 2011; Wonnacott, 1997; Yakel & Shao, 2004; Zarei et al., 1999).

The capacity of presynaptic (and in some cases peri-synaptic) nAChRs, perhaps the most widely characterized function of nicotinic receptors in the brain, facilitates the release of neurotransmitters. This function of nAChRs is not unique to the hippocampus; many other brain regions were also shown to have presynaptic nAChRs that control the release of neurotransmitters. For example, in the striatum, α4β2 nAChRs facilitate the release of dopamine

(Exley & Cragg, 2008), and in the prefrontal cortex, nAChRs facilitate the release of dopamine, serotonin, and norepinephrine (Rao et al., 2003). Presynaptic nAChRs of the hippocampus are localized on GABAergic, cholinergic, serotonergic, glutamatergic, and adrenergic axonal terminals, where their activation enhances pre-terminal Ca2+, thereby facilitating the release of

GABA, acetylcholine, serotonin, glutamate, and norepinephrine, respectively (Gray et al., 1996;

Kenny et al., 2000; Lena et al., 1993; MacDermott et al., 1999; Marchi & Grilli, 2010; McGehee

& Role, 1996; Schicker et al., 2008; Wonnacott, 1997). Here, we are particularly interested in the capacity of hippocampal nAChRs to facilitate the release of glutamate and GABA, thereby shifting the balance of excitatory and inhibitory inputs to neurons within the network and their probability to fire action potentials (Burkitt, 2006).

The activation of presynaptic α7 and α4β2, as well as peri-synaptic α3β4 nAChR, enhance the release of GABA (Alkondon & Albuquerque, 2004; Alkondon & Albuquerque, 2002;

Zarei et al., 1999). Notably, perisomatic-targeting parvalbumin-containing GABAergic interneurons express peri-synaptic α3β4 nAChRs. When activated, these α3β4 nAChRs lead to the activation

2+ 2+ of axonal T-Type (Cav3) Ca channels and Ca release from stores, consequently leading to prolonged quantal GABA release from nerve terminals (Tang et al., 2011). Finally, presynaptic α7 nAChRs are reported to enhance glutamatergic transmission (Gray et al., 1996; Radcliffe et al., 1999).

α4β2* and α7 nAChRs are also expressed on the soma of inhibitory interneurons of the hippocampus. Their activation increases the release of GABA in an action potential-dependent manner (Alkondon & Albuquerque, 2004; Barrantes et al., 1995; Ji & Dani, 2000; Zarei et al.,

1999), thereby altering the probability of neurons in the network to fire action potentials. Finally,

α7 nAChRs are expressed on the postsynaptic membrane of glutamatergic and GABAergic synapses. Their functional role in mediating fast synaptic transmission has been described

(Alkondon et al., 1998; Frazier et al., 1998; Hefft et al., 1999), but the replication of these studies has been difficult. This difficulty is primarily due to the diffuse nature of cholinergic inputs to the hippocampusi (Descarries et al., 1997), which makes stimulating cholinergic fibers as a mechanism to detect synaptic transmission technically challenging.

The Functional Role of Hippocampal Nicotinic Cholinergic Transmission

Nicotinic cholinergic transmission modulates a wide variety of physiological functions throughout the CNS and within the hippocampus, such as learning and memory, alertness, and attention. Many of these functions are due to the ability of presynaptic nAChRs to release a wide variety of neurotransmitters (Picciotto et al., 2012). Since nAChRs modulate neurotransmitter systems crucial for physiological functions, it is not surprising that aberrant nicotinic transmission is implicated in several disease states, such as Alzheimer’s disease (AD) and mild cognitive impairment (MCI)ii, Parkinson’s disease (PD)iii, depressioniv, and schizophreniav.

One of the most studied physiological functions of ACh is its role in modulating learning and memory. This function occurs in various brain regions and is achieved by activating presynaptic nAChRs, which in turn enhances excitatory inputs to the hippocampus (Hasselmo,

2006). Additionally, long-term potentiation (LTP), which is widely believed to be one of the molecular mechanisms of learning and memory (Malenka & Bear, 2004), can be enhanced by nicotine in various brain areas (Fujii et al., 1999; Ji et al., 2001; Mansvelder & McGehee, 2000;

Welsby et al., 2006a). Within the hippocampus, nicotine enhances LTP through the activation of presynaptic α7 and α4β2 nAChRs on glutamatergic synapses coupled with the desensitization of

α4β2 nAChRs on GABAergic synapses (Dani et al., 2000; Fujii, Ji et al., 2000; Fujii, Jia et al.,

2000; Matsuyama & Matsumoto, 2003; Stoiljkovic et al., 2016b).

As a consequence of their activation, nAChRs modulate both excitation and inhibition, thereby altering the excitatory-inhibitory (E/I) ratio (S. R. Cobb et al., 1999; Griguoli &

Cherubini, 2012; Klausberger et al., 2003; Klausberger et al., 2004; Somogyi & Klausberger,

2005). Precise regulation of the E/I ratio is crucial for the occurrence of collective phenomena that arise from synchronous bursting within the neuronal circuitry. Neural network oscillations occurring at a variety of frequency bands, across different brain regions, support the brain’s complex cognitive functions (Buzsaki, 2006). For example, hippocampal gamma and theta rhythms are modulated via the activation of α7 nAChRs (Hajos et al., 2005; Siok et al., 2006a;

Y. Wang et al., 2015b). During the past two decades, research on the state of neural network oscillations in multiple neurological diseases has gained momentum. Intriguingly, findings point to altered network dynamics in several diseases in which aberrant nicotinic cholinergic transmission has been implicated. These findings have raised interest in the role of nAChRs in modulating neural network dynamics (Buzsaki & Watson, 2012; Goutagny et al., 2013; Hajos et al., 2005; Levin, 2013; Nimmrich et al., 2015; Oswal et al., 2013; Scott et al., 2012; Uhlhaas &

Singer, 2010).

The Role of nAChRs in Modulating in vitro Hippocampal Neuronal Network Dynamics

While the impact of nAChR activation has been well studied at the receptor level

(Alkondon et al., 2000; Clarke et al., 1985; Dani, 2015; Gray et al., 1996; Lester et al., 2009;

Mansvelder & McGehee, 2000; Nelson et al., 2003), as well as in vivo (Barrass et al., 1969; Fujii

& Sumikawa, 2001; Hulihan-Giblin et al., 1990; K. Kellar & Wonnacott, 1990; Miner & Collins,

1988; Mudo, Belluardo, Mauro et al., 2007; Picciotto et al., 2008; Rezvani & Levin, 2001), less attention has been directed towards its impact on neuronal networks with single-cell resolution.

Several studies show that the activation of nAChRs augments preexisting network dynamics 9

such as busts and network oscillations (S. R. Cobb et al., 1999; Lu & Henderson, 2010; Roshan-

Milani et al., 2003; Y. Wang et al., 2015a). For example, both in vitro and in vivo studies show that the activation of nAChRs results in potentiated theta and gamma frequency bursting of the hippocampus (RW.ERROR - Unable to find reference:183; Siok et al., 2006b). A caveat in these studies is that these studies were confined to studying the impact of nAChR activation on induced network activity (S. R. Cobb et al., 1999; S. R. Cobb & Davies, 2005; Dannenberg et al.,

2017; Palop et al., 2006; Siok et al., 2006b; Teles-Grilo Ruivo & Mellor, 2013). Alternatively, while a role for nAChRs in modulating network dynamics that arise during behavior, such as theta oscillations, has been suggested, there is a lack of studies that directly probe this proposed function (Dannenberg et al., 2015; Dannenberg et al., 2017). This mainly arises from the lack of specificity in the in vivo models utilized, where cholinergic transmission to the hippocampus is investigated by using non-selective AChR agonists or the lesioning septal afferents (Dannenberg et al., 2017; Hasselmo & Sarter, 2011; Janiesch et al., 2011; Lawson & Bland, 1993; M. Lee et al., 1994). Given the role of nicotinic cholinergic transmission in CNS health and disease

(Belluardo et al., 2000b; Leiser et al., 2009; Levin, 2013; Luchicchi et al., 2014; Scott et al.,

2012; Uhlhaas & Singer, 2010), understanding how the activation of nAChRs modulates the integrated response from spontaneously active neuronal networks will inform the development of more effective treatments.

Studying Neuronal Networks In Vitro

We use the multi-electrode array (MEA), an electrophysiological system that performs simultaneous extracellular recordings of action potentials from multiple sites within the neuronal networks. MEAs facilitate both acute and chronic characterization of network dynamics from cultured cell preparations (Chen & Dzakpasu, 2010; S. M. Potter & DeMarse, 2001). Also, the 10

use of pharmacological manipulations with the MEA allows for the study of collective and rhythmic network activity (Bologna et al., 2010; Gandolfo et al., 2010; Niedringhaus et al.,

2012; Segev et al., 2001; Wagenaar et al., 2006). As a reduced model, mature primary cultured hippocampal networks retain many of the functional synapses observed in vivo (K. J. Lee et al.,

2013) and develop complex patterns of spontaneous activity (Li et al., 2017). These characteristics allow the study of neuronal network dynamics, including spiking and bursting activity. Additionally, an advantage of this model is that it facilitates the characterization of the effects of nAChR activation without the confounding contribution of receptor desensitization resulting from the intrinsic and extrinsic cholinergic tone of the intact hippocampus. This advantage is primarily due to the lack the primary source endogenous cholinergic tone that arises from the septum (Feder & Ranck, 1973; Freund & Buzsáki, 1996; Mesulam et al., 1983; Ranck

Jr, 1973). Moreover, there is little consensus regarding the existence of intrinsic cholinergic neurons within the intact hippocampus proper (Blaker et al., 1988; Frotscher et al., 1986;

Lauterborn et al., 1993). Finally, cholinergic neurons do not exhibit high rates of survival in culture (Culmsee et al., 2002; Hartikka & Hefti, 1988; Zarei et al., 1999).

Introduction to Thesis Project

In this body of work, we aim to explore how the activation of nAChRs within the network modulates spiking, network bursting, and collective rhythmic activity. The spatiotemporal organization of spiking is highly indicative of the state of information transmission efficiency within a neural network. For instance, synapses within hippocampus are less reliable at transmitting the signal of a single action potential from a presynaptic locale to a postsynaptic neuron (J. E. Lisman, 1997). However, bursts of action potentials are transmitted reliably due to their ability to facilitate the release of a higher amount of neurotransmitter (J. E. 11

Lisman, 1997). Additionally, postsynaptic bursting is essential for Hebbian synaptic plasticity

(Buzsaki et al., 2002; Pike et al., 1999a). A burst increases the likelihood that another burst will follow, and therefore maintains rhythmicity within a network. This bursting, in turn, ensures the maintenance of network activity (Singer, 1993). Conversely, action potentials that are not part of a burst train can exert a “veto effect” on bursts by decreasing the availability of sodium channels

(Buzsaki et al., 2002; Pike et al., 1999a). These extra-burst spikes are therefore considered an inefficient mode of communication within the network. Finally, it is believed that the interplay between bursts versus spikes is a homeostatic mechanism to ensure stable network activity

(Buzsaki et al., 2002).

The first main objective of this work is to study how desensitizing nAChRs with sequential doses of nicotine manifests at the neural network level. Although similar studies have previously been carried out at the single cell level and the whole animal level, this first objective serves as a proof of concept: by using the MEA, we can detect the well-studied nicotine- mediated phenomenon of receptor desensitization. We add to the existing body of studies by characterizing additional network dynamics that have not previously been reported. Additionally, low concentrations of nicotine, blocking the effects of the higher concentration, serve as a confirmation that the effects observed at the higher concentration of nicotine are indeed a direct result of nAChR activation.

The second main objective of this work is to 1) characterize the concentration-dependent effects of nicotine on the network, 2) identify which of the nAChR subtypes expressed in the rat hippocampus mediate the observed effects, 3) investigate whether steady-state activation of nAChRs contributes to effects of nicotine, and 4) determine whether the activation of N-methyl-

D-aspartate receptors (NMDARs) or group I metabotropic glutamate receptors (mGluRs), two

classes of basally active glutamate receptors that participate in Ca2+ amplification signal transduction cascades, could influence the ability of nicotine to mediate long-lasting network potentiation.

Finally, since the Psychoactive Drug Screening Program (PDSP) reports that AT-1001 binds NMDARs, we test whether this binding impacts NMDAR function. To this end, we utilize adult rat hippocampal slices as biosensors to test the impact of AT-1001 on the NMDAR- mediated release of norepinephrine. Since these studies are indirectly related to the second major topic of the present dissertation, they are presented in the appendix.

i The major source of ACh to the hippocampus is extrinsic, mainly arriving from the medial septum, with a minor contribution arising from the diagonal band (Descarries et al., 1997). The existence of intrinsic cholinergic neurons within the hippocampus is a topic that has received less attention and has not arrived at a consensus (Blusztajn &

Rinnofner, 2016; Freund & Buzsáki, 1996; Frotscher et al., 1986). Cholinergic transmission is widely accepted to be crucial for the cognitive tasks of the hippocampus (Ballinger et al., 2016; Luchicchi et al., 2014). The majority of septal cholinergic inputs are thought to act via volume transmission (Dani & Bertrand, 2007; Descarries et al.,

1997), yet the evidence for this hypothesis is not conclusive. A competing hypothesis is that phasic transmission of

ACh is responsible for the effects of the cholinergic system on cognition (Sarter et al., 2009). ii An estimated 5.5 million people live with AD, which has a rapid rate of incidence. A new case is diagnosed every 66 seconds, making it the most prevalent type of dementia (Alzheimer's Association, 2017). Given that the greatest risks factors of developing AD are aging and the apolipoprotein E (APOE) 4 allele (which occurs naturally in many populations), the urgency of AD has made it a major focus for research. This neurodegenerative disease is characterized by the accumulation of amyloid-β (Aβ), Tau neurofibrillary tangles, and the loss of basal forebrain cholinergic neurons (BFCN) that project to the cortex and hippocampus, choline acetyltransferase activity and nAChRs (Albuquerque et al., 2009; Bierer et al., 1995; Gsell et al., 2004; K. Kellar & Wonnacott, 1990; Nordberg,

1992; Walsh & Selkoe, 2004). Interestingly, in patients suffering from MCI (a state of decreased cognition that may progress to AD), no decrease in cholinergic neurons, α4β2 nAChRs or choline acetyltransferase (ChAT, the

enzyme responsible for synthesizing ACh) has been detected; rather, the cholinergic neurons that are present are dysfunctional (Schliebs & Arendt, 2011). Several symptomatic treatment strategies for AD (and in some cases

MCI) that target the nicotinic cholinergic system have been proposed and investigated, with some positive results depending on the severity of the symptoms (Auld et al., 2002). These include ACh precursors, AChE inhibitors and nicotinic agonists (Moreno, Maria De Jesus Moreno, 2003; P. A. Newhouse et al., 1988; P. Newhouse et al.,

2012; Rogers et al., 1998; Terry & Buccafusco, 2003). In addition to improving AD symptoms, nicotinic agonists standout as a treatment due to their neuroprotective properties, which have been shown in epidemiological studies, as well as in preclinical studies, and may be well suited for prophylactic use in persons who are at higher risk of developing AD (P. Newhouse et al., 2012; Picciotto & Zoli, 2008; Ryan et al., 2001). Finally, studies show that soluble Aβ activates α7 nAChRs with high affinity, which creates a link between the amyloid-deposit hypothesis and the cholinergic hypothesis (H. Wang et al., 2000).To test the effects of α7 nAChR activation in vivo, 5xFAD mice that overproduce Aβ were treated with an α7 nAChR agonist, a surprising finding was that in comparison to wild-type mice, the 5xFAD mice had decreased hippocampal theta oscillation power which was normalized by treatment with α7 nAChR agonists (Stoiljkovic et al., 2016a). iii Parkinson’s disease (PD), a neurological disorder that results in tremors and many cases dementia (Parkinson’s disease with dementia, PDD), is characterized by the progressive loss of dopaminergic neurons in the nigrostriatal pathway, and the accumulation of Lewy bodies (A. J. Hughes et al., 1992). In addition to the severe loss of dopaminergic neurons, PDD patients suffer from cholinergic deficits within the hippocampus and other cortical regions, indicating loss of cholinergic neurons (Bohnen & Albin, 2011; Court et al., 2000; Lange et al., 1993;

Piggott et al., 1999; Pimlott et al., 2004). Loss of cholinergic neurons in PD patients was similar in severity to patients with early-onset AD (Kuhl et al., 1996). Additionally, epidemiological studies have been consistent in showing a negative correlation between smoking and PD, leading to the idea that nicotine, the active ingredient in smoked cigarettes, is neuroprotective by preserving dopaminergic and cholinergic neurons, halting the occurrence of PD (Picciotto & Zoli, 2008). Finally, several studies have reported altered neural network dynamics in animal models of PD, and although these were mainly observed in the dopaminergic pathway, they nevertheless coincide with areas where cholinergic transmission is dysfunctional (Oswal et al., 2013). Given that there are common

dysfunctions of the cholinergic pathways in both AD and PDD, some of the same drugs nicotinic targeting drugs for AD may be useful for treating PDD. iv Major depressive disorder (MDD) is a psychiatric disorder that is characterized by unremitting despondency that has no external cause or is disproportionate to its causes and lingers long after the cause dissipates (Belmaker &

Agam, 2008). The cholinergic hypothesis of depression postulates that negative affective moods predominate as a result of a cholinergic overdrive acting mainly via nAChRs (D. Janowsky et al., 1972). This is supported by the observation that orchardists that are exposed to AChE inhibitors are more prone to depression (Gershon & Shaw,

1961), and by preclinical studies showing that the systemic administration of AChE results in depressive-like symptoms (D. S. Janowsky et al., 1974; Overstreet & Russell, 1982; Risch et al., 1981). Epidemiological studies show a strong representation of the MDD patients in the smoker population; on the other hand, smokers who abstain from smoking report depressive-like symptoms, which led to the hypothesis that people suffering from depression smoke to self-medicate (Mineur & Picciotto, 2010). Further evidence supporting this hypothesis comes from preclinical tests that report that nicotine improves symptoms of depression (Djurić et al., 1999; Semba et al.,

1998; Tizabi et al., 1999). More recently, studies aiming for a more direct approach to support the cholinergic hypothesis show that the knockdown of AChE within the hippocampus induces anxiety- and depression-like behavior that is alleviated by treatment with fluoxetine, a selective serotonin reuptake inhibitor that has also increased AChE activity (Mineur et al., 2013). Taken together, these observations strongly implicate aberrant cholinergic transmission in the development of depression, suggesting that blunting nicotinic cholinergic transmission may be a viable mechanism to treat depression. v Schizophrenia is a mental disorder that is characterized by a difficulty to distinguish between reality and hallucinations. Patients typically display positive (delusions and hallucinations), negative (perturbed psychosocial state accompanied by loss of motivation and altered emotional expression) and cognitive (impaired executive function, and deficits in thought organization, attention, and working memory) symptoms (Hogarty et al., 2004; Kay et al., 1987)(Wong and Van Tol, 2003). While antipsychotics work relatively well to alleviate the positive symptoms, the negative and cognitive symptoms remain a problem regarding treatment and are the main focus for drug development for treating schizophrenia (Hogarty et al., 2004; Javitt et al., 2008; Parikh et al., 2016). One of the most promising targets for drug development for the negative and cognitive symptoms of Schizophrenia are 15

nAChRs. This debilitating disorder was first linked to a deficit in the nicotinic cholinergic system by observational studies reporting that nearly 50-88% of people who suffer from schizophrenia are smokers, leading to the hypothesis that schizophrenics smoke to self-medicate (Dalack et al., 1998; J. R. Hughes et al., 1986; Ziedonis &

George, 1997). This prompted further investigations into the links between the cholinergic system and the manifestation of schizophrenia, which have linked a mutation in the α7 nAChR (CHRNA7) that decreases its function (Freedman et al., 1997) and results in aberrant gamma oscillations (Freedman et al., 1997; Uhlhaas &

Singer, 2010). Clinical trials in humans have shown an alleviation of impaired cognition during treatment with nicotine (Dalack et al., 1998; Freedman et al., 1997), paving the way for further research into the potential efficacy of nicotinic agonists, especially those acting on α7 nAChRs, in the treatment of Schizophrenia.

CHAPTER II: MATERIALS AND METHODS

Cell Culture

All experiments were carried out in agreement with the Georgetown University Animal

Care and Use Committee (GUACUC). Hippocampal tissue was harvested from embryonic day

18 Sprague-Dawley rats using a protocol modified from (Pak et al., 2001). Briefly, neural tissue was digested with 0.1% trypsin and by mechanical trituration. Cells were plated onto multi- electrode arrays (MEA, Multi Channel Systems MCS GmbH, Reutlingen, Germany) that were previously treated with poly-d-lysine and laminin (Sigma, St. Louis, MO) at an approximate density of 600 cells/mm2. Cultures were maintained in Neurobasal A medium with B27

(Invitrogen, Carlsbad, CA) with bi-weekly changes and stored in a humidified 5% CO2 and 95%

O2 incubator at 37°C. Experiments were performed on cultures at 14 days in vitro (DIV).

Multi-electrode Array Recordings

Data Acquisition

Spontaneous extracellular electrical activity was recorded using an MEA (MEA2100,

Multi Channel Systems MCS GmbH, Reutlingen, Germany). The MEA is composed of 59 titanium nitride electrodes, arranged on an 8x8 square array, accompanied by one reference electrode and four auxiliary analog channels. Each electrode is 30 µm in diameter, and the inter- electrode distance is 200 µm. Upon plating, cells adhere to the poly-d-lysine, and laminin treated silicon nitride substrate of the MEA, and spontaneous electrical activity is detected after seven days. Electrical activity is detected and amplified and the time series are sampled at a 10 kHz acquisition rate to allow the detection of spikes. Data were digitized and stored on a Dell personal computer (Round Rock, TX) for offline analysis. Possible exposure to fluctuations in osmolality and pH and contaminants were significantly reduced during the data acquisition

period by covering the MEA with a hydrophobic membrane that is permeable to CO2 and O2 (S.

M. Potter & DeMarse, 2001). Recordings were performed on a heated stage at 37°C on 14 days in vitro (14DIV). This is a time point during development where the network displayed vigorous spontaneous electrical activity, and network connectivity is well-established (Wagenaar et al.,

2006). To ensure the reproducibility of results, all reported experimental groups were derived from multiple experimental preparations.

Drugs

Methyllycaconitine citrate (MLA), (MPEP), (+)-MK-801 (maleate), and α-Amino-5- carboxy-3-methyl-2-thiopheneacetic acid (3-MATIDA) were purchased from Tocris Bioscience

(Minneapolis, MN, USA). (-) Nicotine hydrogen tartrate was purchased from Sigma-Aldrich (St.

Louis, MO, USA). Sazetidine-A (saz-A) tartrate was synthesized by Drs. Milton L. Brown,

Mikell A. Paige, and Brian E. McDowell (Georgetown University) and kindly provided by Dr.

Ken Kellar (Georgetown University, Washington, DC). AT-1001 [N-(2-bromophenyl)-9- methyl-9-azabicyclo [3.3.1] nonan-3-amine] was a gift from Astraea Therapeutics LLC

(Mountain View, CA, USA). 4-[6-imino-3-(4-methoxyphenyl)pyridazin-1-yl] butanoic acid hydrobromide (Gabazine) was purchased from (R&D Systems, Minneapolis, MN, USA).

Drug Application: Chapter III

The nAChRs agonist, nicotine, was dissolved in purified water to make a 10mM stock solution. This was added directly to the conditioned media of each MEA for a final concentration of 10 µM or 100 µM. Baseline and drug effects were recorded for 15 minutes. The data presented is from the last 3 minutes of each 15-minute recordings.

Drug Application: Chapter IV

Each drug was dissolved in ultrapure water to make a 10mM stock solution. 500 µL of media was removed from each MEA (conditioned media), and an aliquot of the stock drug solution was mixed with the conditioned media for the desired final concentration. The final solution was added to the MEA. In the experiments using MLA, saz-A, AT-1001, and MPEP &

3-MATIDA, each MEA was first incubated in the respective drug(s) for 15 minutes. Next, the cultures were stimulated with nicotine. Network activity was recorded after drug incubation as well as after stimulation with nicotine. Unless otherwise indicated, all data presented is the last 3 minutes of the 15-minute drug incubation and the last 3 minutes of the 15-minute recording after nicotine stimulation.

Data Analysis

MEA traces were high-pass filtered at 200 Hz to remove low frequency components.

Recorded spikes from these traces were detected using Offline Sorter (Plexon Inc., Dallas TX) and thresholded at −4.5σ of the mean biological noise for each channel. As this study investigated responses from the network rather than from individual units, we did not discriminate and sort spikes by electrode since the signal from each electrode represents a collective response.

Network activity was analyzed with custom software written in MATLAB (MathWorks,

Natick, MA). First, to investigate changes in overall network activity, we calculated the total number of spikes over a 3-minute window for each electrode within the MEA. Next, we isolated bursts, which are a common temporal feature of cultured networks and can occur across the entire network. Each electrode had a resulting spike train, 휏푆푇(푡), expressed as:

푁 휏푆푇(푡) = ∑푛=1 훿(푡 − 푡푛),

where N is the total number of spikes, 푡푛is the time of the nth spike and 훿푡 is a delta function that indicates a spike taking place at time 푡 = 푡푛. The inter-spike interval (ISI) between spike n and spike n-1 (푛 > 1) is:

퐼푆퐼 휏푛 = 푡푛 − 푡푛−1,

For all experimental groups, a burst recorded from each electrode was defined to consist of no fewer than four spikes (Lisman 1997) with a maximum ISI of 100 ms. This value was selected because it represents the temporal boundary between a distribution of ISIs thought to be within bursts and the intervals between bursts within our networks (Niedringhaus et al., 2012).

Figure 1: Representative ISI frequency histogram generated from spontaneous spiking activity recorded from a network of dissociated hippocampal cultures on DIV14.

A plot of the log[ISI] characterized by a bimodal distribution whose curves (from left to right) decay at ~100 ms and 100 ms, respectively. The decay of the curve whose bins cluster at

the short time intervals represents the temporal boundary between a distribution of ISIs thought to be within bursts and the intervals between bursts (inter-burst intervals, IBIs) within our networks.

Lastly, the correlation coefficient was calculated as a measure of synchronized activity between pairs of electrodes. In this study, correlation coefficients was introduced to identify whether the activity in a pair of electrode are correlated to each other, which results in a measure to define the synchrony of activity. Correlation Coefficient (r) can be calculated from spike/burst trains of any pair of active electrodes.

∑ 푋 ∑ 푌 ∑ 푋푌 − 푟 = 푁 (∑ 푋)2 (∑ 푌)2 √ ∑ 2 ∑ 2 ( 푋 − 푁 ) ( 푌 − 푁 )

X,Y are two spike waveform trains (μV) from same/or different active electrodes and N is the number of elements of a spike train. While the timestamps of spike waveforms are important for synchrony analysis, the sign and amplitude of the waveform are not. As such, we consider the absolute values of correlation coefficients which range from 0 (perfectly weak correlation) to 1

(perfectly strong correlation). We reduced the noise and sporadic, low amplitude spikes by reducing the sampling frequency of a 60 second segments of raw data to 1kHz with Butterworth low pass filter. The output preserved bursts and large amplitude voltage transients. Lastly, to better distinguish the network correlation level (synchrony) between cultures with different drug treatments, we calculated the absolute value of the mean and standard error of the mean (SEM) of correlation coefficients from each pair of electrodes of MEA.

Statistics 22

Statistical tests were performed using GraphPad Prism 7 (GraphPad Software, San

Diego, CA). Data are expressed as before-after graphs or as means ± SE, and differences were considered significant at P < 0.05. Multiple t-tests paired (correlation coefficients) and unpaired

(burst analyses) followed by a Holm-Bonferroni correction were used to determine significance.

CHAPTER III: STOCHASTIC ACTIVATION OF NICOTINIC ACETYLCHOLINE RECEPTORS PREVENTS INDUCED SYNCHRONIZATION WITHIN IN VITRO HIPPOCAMPAL NETWORKS

Introduction

Rolling activation of nAChRs with nicotinic ligands, such as nicotine, leads to the accumulation of nAChRs into the desensitized state over time (Ochoa et al., 1989; Quick &

Lester, 2002), resulting in a “time-averaged antagonism” (Hulihan-Giblin et al., 1990).

Desensitization of nAChRs is well studied at the single channel (Sakmann et al., 1980), single cell (Katz & Thesleff, 1957; Quick & Lester, 2002), and in vivo level (Hulihan-Giblin et al.,

1990; Picciotto et al., 2008), yet the impact of nicotine on nAChR desensitization has not been examined on the neural network level.

In the following study, cultured primary hippocampal neurons were utilized to study how nicotine modulates spiking and network bursting activity. We examined how nicotine impacts network dynamics during rolling activation where, in the presence of a slowly applied intermediate concentration of ligand, only a few receptors at a time are briefly activated and subsequently desensitized. If the network response is minimal, this can be considered to be a desensitized network state; integration of activity over the network in the presence of nicotine does not result in a measurable effect. On the other hand, treatment with a high concentration of ligand -likely to activate the low-affinity nAChRs (α7, α3β4, and LSα4β2) - will activate a more substantial number of receptors before their desensitization (Albuquerque et al., 2009; Eaton et al., 2014; Mazzaferro et al., 2011). With the resulting receptor activation expected to be collective and global, what is the ensuing network response?

We bath applied either a single dose (intermediate or high) or two successive doses of increasing concentration of nicotine and recorded neural spiking activity. We used these two concentrations to ask: 1) which concentrations has a significant impact on network activity and

2) is the dose that had a minimal effect on network dynamics capable of desensitizing the network to the larger concentration, thereby showing that the effects of the higher concentration are via activation of nAChRs? We show that the intermediate dose has minimal effects on network spiking activity and we call this a network-desensitized state; it desensitizes to a higher dose. When the high dose significantly impacts both spiking and bursting activity, we call this a network-activated state. Overall network reorganization, as well as network efficiency, increased with the higher concentration of nicotine. However, when an intermediate concentration nicotine was applied before the high concentration, we show that changes in network activity were comparable to the vehicle.

Results

Dissociated Embryonic Hippocampal Neurons Cultured on MEA

We cultured embryonic hippocampal neurons (E18) onto treated MEAs to study the effects of nicotine on neuronal network activity. By DIV7, processes can be visualized, and several cell bodies are proximal to electrodes (Fig. 2A). Robust, spontaneous network-wide activity is apparent on DIV14 (Fig. 2B), with a rich mixture of spiking and bursting activity as seen from a single representative electrode (Fig. 2C). After recording baseline activity for 15 minutes, we bath applied either 100 µM nicotine or 10 µM nicotine directly into the conditioned medium of each culture recorded each for 15 minutes. After a 15 minute incubation in 10 µM nicotine, we followed with an application of 90 µM of nicotine to each MEA and recorded for an additional 15 minutes.

Figure 2: Spontaneous activity from rat hippocampal neural networks.

(A) A differential interference contrast (DIC) micrograph of a 7DIV culture of hippocampal neurons plated on the microelectrode array (MEA). (B) A screenshot of network electrical activity from the MEA at 14DIV. Each box corresponds to one-second of activity with a voltage range of ± 200 μV. The dynamics consist of spontaneous spiking and network-wide bursting activity. (C) Three representative 1-second traces of filtered activity from an MEA. Within a single electrode, a wide range of different types of bursts is observed, where the burst can be of long, intermediate or short duration.

Nicotine Qualitatively Enhances Network Activity

Figure 3 is a representative raster plot of one minute of activity to illustrate activity patterns on the network level. Each row in the raster plot corresponds to one electrode and spikes are represented by vertical ticks. The left panel depicts spontaneous, baseline activity, with the inset above illustrating a 20-second segment of the activity. This baseline is characterized by tonic spiking in a small fraction of the electrodes with the majority of active electrodes displaying a mix of spiking interspersed with arrhythmic network-wide bursting. The right panel shows spiking activity fifteen minutes after the initiation of treatment with 100 µM nicotine. The rhythmic activity observed during the baseline activity is enhanced after a high concentration of nicotine is applied. The inset above highlights the changes in the network after application of a high concentration of nicotine as described below.

Figure 3: Representative raster plots of activity from hippocampal networks 14DIV before and after treatment with a 100 μM of nicotine.

Each tick mark represents one spike. (Left) Spontaneous activity present in the baseline of cultures (20 s shown in insert) on DIV14 is characterized by arrhythmic synchronous bursts, and errant spikes not in bursts, with a few electrodes showing tonic spiking activity. (Right)

Reorganized network activity in the presence of 100 µM nicotine can be seen as characterized by rhythmic, synchronous network activity (quantified in Fig. 8), decreased inter-burst intervals (see frequency histograms in Fig. 6A1-A2), increased total number of spikes (quantified in Fig. 5A), and decreased fraction of errant spikes (quantified in Fig. 7A).

It is widely accepted that in the continued presence of a ligand, nAChRs desensitize within seconds of activation (Dani, 2015; Levin, 2013; Quick & Lester, 2002). Despite this however, the high concentration of nicotine applied on the MEAs resulted in sustained, reorganized network activity characterized by rhythmic, synchronous bursts, decreased inter- burst intervals (quantified in Fig. 6 A1-A2), increased total number of spikes (quantified in Fig. 5

A), and decreased fraction of errant spikes (quantified in Fig. 7 A) for at least 15 minutes after the initiation of treatment. This altered network state led us to hypothesize that the brief, but widely synchronous activation of nAChRs is necessary for the observed reorganization of network activity. To test this hypothesis, we applied 10 μM of nicotine, a dose that is believed to only moderately impact spiking activity (Fujii et al., 1999), to desensitize the nAChRs within the network. Subsequently, we applied 90 μM of nicotine to the network, resulting in a final concentration of 100 μM nicotine. Network activity was recorded after each step. The representative raster plots in figure 4 show that there is no change in spiking activity in both the

10 μM or 10 + 90 μM nicotine-stimulated networks (quantified in Fig.5B).

Figure 4: Representative raster plots of activity from hippocampal networks 14DIV after desensitization with 10 μM of nicotine and subsequent treatment with 90 μM of nicotine.

Each tick mark represents a spike. Spontaneous activity in networks that have been desensitized with 10 µM nicotine (left) and after the addition of 90µM (right) to the desensitized networks on

DIV14. After the application of 10 µM nicotine, activity is characterized by arrhythmic synchronous bursts and errant spikes, not in bursts. There are a few electrodes with tonic spiking activity. The network activity in desensitized cultures to which 90 µM nicotine has been added

(right) shows no significant change in network dynamics, with no reorganization that was present in naïve cultures treated with 100 µM nicotine.

Effects of Nicotine on Network Spiking and Bursting

To quantify the differences in the effects of nicotine on naïve versus desensitized networks, we calculated overall network spiking and bursting activity as a function of the concentration of nicotine applied. The application of 100 μΜ nicotine in naïve cultures elicited a robust increase in the total number of spikes (Fig. 5A). In contrast, the final concentration of 100

μM nicotine on desensitized networks (i.e., pre-treated with 10 μM nicotine) resulted in a negligible (but significant) increase in the total number of spikes. Notably, this increase is comparable to that of the vehicle treatment (Fig. 5A).

Bursts - high frequency firing of action potentials - are believed to be critical for efficient signaling at central synapses because they will increase the likelihood of transmitter release, and the occurrence of a postsynaptic action potential, both of which are vital for reliable synaptic transmission (J. E. Lisman, 1997). Given the unique role of nAChRs in modulating the release of several neurotransmitters at hippocampal synapses (Wonnacott, 1997), we sought to investigate how nicotine modulates bursting dynamics within in vitro hippocampal networks. We found that while a large concentration of nicotine applied to naïve cultures resulted in a significant increase in the number of bursts when high concentrations of nicotine were applied to desensitized networks, the number of bursts remained unchanged (Fig. 5B).

Figure 5: Nicotine significantly increases spiking activity, but high - and not low - concentrations of nicotine significantly increase bursting activity.

(A) Effects of nicotine on the total number of number of spikes during a 3-minute recording.

MEAs treated with 100 µM nicotine (green) had a robust and significant increase in the total number of spikes. In comparison with their respective pretreatment activity, MEAs treated with vehicle (red), 10 µM nicotine (light blue) or 100 µM nicotine (dark blue) post-desensitization

(due to pre-incubation in 10 µM nicotine for 15 minutes) had a small (but significant) increase in the total number of spikes. A paired t-test was used to test statistical significance in the 100 µM nicotine-treated group, and an ANOVA followed by a Bonferroni’s post hoc was used for the desensitization measure group. (B) Effects of nicotine on the total number of number of bursts

during a 3-minute recording. MEAs treated with 100 µM nicotine had a significant increase in the total number of bursts (green). In contrast with their respective pretreatment activity, MEAs treated with vehicle (red), 10 µM nicotine (light blue) or 100 µM nicotine (dark blue) post- desensitization (due to pre-incubation in 10 µM nicotine for 15 minutes) had no significant increase in the total number of bursts. A paired t-test was used to test statistical significance in all groups except for the desensitization measure group, where ANOVA followed by a Bonferroni’s post hoc was used (* significant at the 0.05 probability level).

Effects of Nicotine on Network Rhythmicity

We generated histograms of inter-burst intervals (IBI) to assess the effects of nicotine on network rhythmicity (Fig. 6). In contrast to the broad, Gaussian-like distribution observed in the baseline (Fig. 6A1), application of 100 µM nicotine narrows the IBI distribution. The number of intervals in the shorter bins is reduced while a cluster of IBIs aggregates around longer intervals.

The peak of this distribution is centered around 5-7 seconds and is suggestive of an enhancement of rhythmic activity (Fig. 6A2). Networks that experienced the desensitization step did not display a change in the IBI distribution (Fig. 6B). This suggests that desensitizing nAChRs abrogates the nicotine-mediated increase in network rhythmicity. Moreover, the enhancement in rhythmicity in 6A is due to the direct action of nicotine on nAChRs and not due to the result of non-specific effects.

Figure 6: A high concentration of nicotine qualitatively enhances network rhythmicity but fails to enhance rhythmicity in desensitized networks.

(A) Representative frequency histograms of inter-burst intervals pre- and post-100 µM nicotine.

Normal-like inter-burst interval distribution during baseline displays a wide range of interval from 100 ms to 100 s, with a peak at ~5 s (A1). Inter-burst interval distribution during the application of 100 µM nicotine showing a shift of the distribution with a narrower range of ~2-75 s (number of shorter IBIs is nearly negligible) (A2). The peak of the distribution is unchanged

from the baseline, but there is an increase in the number of IBIs that occur at the peak of the distribution, suggesting more rhythmicity within those networks (A2). (B) Representative frequency histograms of inter-burst intervals in networks that have been desensitized with 10 µM nicotine before and after the addition of 90µM to the desensitized networks. A bi-modal normal- like inter-burst interval distribution post desensitization is present with a wide range of intervals from 100 ms - 100 s (B1). Inter-burst interval distribution after the addition of 90µM to the desensitized networks (B2). There is no significant shift in the range of the distribution, which remains between 100 ms to 100 s (B2).

Effects of Nicotine on Network Efficiency and Synchrony

Central synapses, including those of the cortex and hippocampus, have been shown to be unreliable because an action potential in the presynaptic cell might fail to elicit a postsynaptic response (J. E. Lisman, 1997). Given that hippocampal spikes that do not participate in bursts are considered an inefficient mode of communication, they might, therefore, be considered errant.

We quantified the total number of spikes not in bursts, i.e., errant spikes, and observed that while

100 μM nicotine significantly reduced the fraction of errant spikes, 100 μM nicotine post- desensitization had no significant effect (Fig. 7A).

If bursts within hippocampal networks are considered to be a more efficient mode of network communication, we can study how nicotine impacts the transition from spiking to a bursting network. To this end, we identified channels whose activity patterns consisted of only spikes and not bursts during baseline recordings. We call these burst-less channels, silent units.

We compared the change in the number of silent units after nicotine treatment in both naïve and desensitized networks. Interestingly, in naïve networks, but not desensitized networks, a small number of these silent units was recruited into network-wide bursting events when a high concentration of nicotine was applied (Fig. 7B).

Figure 7: A high concentration of nicotine decreases “errant” spikes and recruits silent units into bursting activity.

(A) Effects of nicotine on the fraction of extra-burst or errant spikes. The fraction of spikes not in bursts was significantly decreased in the 100 µM nicotine-treated group (green). In comparison with their respective pretreatment activity, MEAs treated with vehicle (red), 10 µM nicotine

(light blue) or 100 µM nicotine (dark blue) post-desensitization (due to pre-incubation in 10 µM nicotine for 15 minutes) had no significant change in the fraction of spikes not in bursts. A paired t-test was used to test statistical significance in the 100 µM nicotine-treated group, and an

ANOVA followed by a Bonferroni’s post hoc was used for the desensitization measure group

(*** significant at the 0.001 probability level). (B) Effects of nicotine on the number of channels with bursting activity during a 3-minute recording. The number of channels with bursting

activity was significantly increased in the 100 µM nicotine-treated group (green). In comparison with their respective pretreatment activity, the number of channels with bursts did not significantly change in the vehicle (red), 10 µM nicotine (light blue), 100 µM nicotine (dark blue) post-desensitization (due to pre-incubation in 10 µM nicotine for 15 minutes) treated groups. A paired t-test was used to test statistical significance in the 100 µM nicotine-treated group, and a repeated measures ANOVA followed by a Bonferroni’s post hoc was used for the desensitization measure group (* significant at the 0.05 probability level).

Synchronous, rhythmic bursting gives rise to oscillatory activity that supports many of the cognitive tasks of the hippocampus, and when impaired, oscillations can lead to disease

(Bland & Colom, 1993; Buzsáki & da Silva, 2012; Nimmrich et al., 2015). The narrowing of the inter-burst interval distribution observed in figure 6A2 suggests an increase in rhythmicity, and likely synchrony, after treatment of 100 μM nicotine. To directly examine whether the observed shift in IBIs promotes network synchrony, we used the correlation coefficient; pairwise correlations were calculated between the time of spiking activity recorded from active electrodes within the network. Figure 8(A-D) depicts representative spatial maps of correlation coefficients of the 15th minute of activity recorded at baseline or during stimulation with nicotine (quantified in Fig. 8E). There is a significant increase in the correlation coefficient after treatment of 100 μM nicotine, indicating the emergence of synchronous activity. The pretreatment of the network with

10 μM nicotine does not significantly impact network synchrony but is capable of blocking the effects of a subsequent 90 μM application. This strongly suggests that when applied to in vitro hippocampal networks, an intermediate concentration of nicotine is sufficient to protect against induced synchronization via a subsequent, high dose of nicotine.

Figure 8: Synchronization is enhanced after the addition of 100 µM and, to a lesser extent after

10 µM, but pretreatment of 10 µM prevents further synchronization when followed by 90 µM.

Correlation coefficients are a direct measure of synchrony and use the pairwise temporal correlation in spike times between active electrodes of the network. (Left) Displayed are representative spatial maps of correlation coefficients between active electrodes of the network depicts representative spatial maps of correlation coefficients of the 15th minute of activity recorded at baseline or during stimulation with nicotine. There is an increase in the correlation coefficient for the 100 µM treatment (B) as compared to baseline (A) and a modest increase in the correlation coefficient after the addition of 10 µM that is not statistically significant (C).

However, when the networks are first treated with 10 µM, there is no further change in the correlation coefficient after the addition of 90 µM (D). (E) Quantification of the correlation coefficient shows an increase in synchrony after 100 µM and protection from synchrony after 10

µM. The correlation coefficient significantly increased in the 100 µM nicotine-treated group, but not in the 10 µM nicotine-treated group. Additionally, a pre-treatment of 10 µM prevented the

increase in synchrony when followed by a dose of 90 µM (cumulative concentration = 100 µM) nicotine. Statistical significance was assessed by carrying out multiple paired t-tests followed by a Holm-Bonferroni post hoc test (* significant at the 0.05 probability level).

Discussion

We sought to investigate the network effects due to modulation of nAChRs when a high or a low concentration of nicotine is applied. To this end, we showed that when exposed to high concentrations of nicotine, naïve hippocampal networks exhibited a substantial increase in spiking, bursting and synchronous network activity.

We also asked whether an intermediate dose of nicotine could desensitize the network to a subsequent higher dose. Although this question has been answered at the single cell level and the whole animal level (Campling et al., 2013; Hulihan-Giblin et al., 1990; Quick & Lester,

2002), these studies served as a proof of concept, showing that the well-studied phenomenon of nAChR desensitization can be reproduced at the neural network level using the MEA, allowing for the study of collective phenomena with single cell resolution.

Rolling receptor activation resembles a stochastic process (Colquhoun & Hawkes, 1981;

Colquhoun & Hawkes, 1982). On the level of the neural circuit, with millions of nAChRs, an absence of a global network response might lead to the conjecture that desensitization can occur without activation taking place. Accordingly, we observed that stochastic activation of receptors during the desensitization step, i.e., pre-incubation with intermediate concentrations that were shown to efficiently desensitize neurons to subsequent stimulations (Alkondon et al., 2000;

Sakmann et al., 1980) differs from global activation that we observe in naïve cultures.

As the concentration of ligand increases, stochasticity of receptor activation will decrease and receptor activation will manifest more collectively. This coordinated action of the nAChRs will stimulate a global network response, facilitating information transmission and

synchronization within the neural circuit. We observed that naïve hippocampal networks treated with a high dose of nicotine were driven towards a state of enhanced efficiency; reliable communication in the form of bursts was strengthened, and unreliable communication in the form of spikes not participating in bursts was attenuated. We speculate that these phenomena facilitated the recruitment of previously silent units. These effects were abrogated in hippocampal networks where nAChRs had been previously desensitized. This suggests that the observed phenomena are primarily mediated by nicotinic cholinergic receptors. Finally, we suggest that stochastic activation of nAChRs might prevent a pathological network synchronization of neuronal assemblies. Subsequent studies elucidate the nAChR subtypes that participate in the effects of nicotine on the network.

CHAPTER IV: ACTIVATION OF NICOTINIC ACETYLCHOLINE RECEPTORS INDUCES POTENTIATION AND SYNCHRONIZATION WITHIN IN VITRO HIPPOCAMPAL NETWORKS

Introduction

Within the hippocampus the most highly expressed nAChR subtypes are α7 and those containing α4β2 (Alkondon & Albuquerque, 2004; Alkondon & Albuquerque, 1993; Dani,

2015), both of which localize on the soma of inhibitory interneurons (Alkondon &

Albuquerque, 2001; Zarei et al., 1999). Depending on the type of synapses these interneurons make, their activation is poised to produce either inhibition or disinhibition of excitatory pyramidal neurons (Alkondon & Albuquerque, 2001; Ji & Dani, 2000). The activation of presynaptic α7 nAChRs that localize on glutamatergic or GABAergic terminals directly enhances the release of glutamate or GABA within the hippocampus, respectively (Fabian-Fine et al.,

2001; MacDermott et al., 1999; Marchi & Grilli, 2010; Radcliffe et al., 1999; Schicker et al.,

2008; Sher et al., 2004; Wonnacott, 1997; Wonnacott et al., 2006). Additionally, despite their lower expression levels, α3β4 nAChRs are reported to modulate neurotransmission within the hippocampus (Alkondon & Albuquerque, 1993; Alkondon et al., 1994; Lomazzo et al., 2010), resulting in the prolonged release of GABA (Alkondon & Albuquerque, 2002; Tang et al., 2011).

The activation of nAChRs leads to the opening of the receptor channel, allowing for the flow of sodium, potassium, and Ca2+ down their respective concentration gradients (Dani,

2015). As a consequence of their activation, nAChRs modulate both excitation and inhibition, thereby altering the excitatory-inhibitory (E/I) ratio (S. R. Cobb et al., 1999; Griguoli &

Cherubini, 2012; Klausberger et al., 2003; Klausberger et al., 2004; Somogyi & Klausberger,

2005). Precise regulation of the E/I ratio is essential for the occurrence of collective phenomena that arise from synchronous bursting within the neuronal circuitry. Several of these phenomena, such as hippocampal network oscillations (S. Cobb et al., 1995; Freund & Buzsáki, 1996; Miles et al., 1996) and sharp wave ripples (Buzsáki, 1986) are believed to support hippocampus- 48

dependent cognitive tasks (Buzsaki & Draguhn, 2004; Buzsaki & Watson, 2012; Feder &

Ranck, 1973; Otto et al., 1991; Ranck Jr, 1973; Singer, 1993). The ability of nAChRs to modulate the E/I ratio makes them are necessary for maintaining proper brain function

(Belluardo et al., 2000b; Leiser et al., 2009; Levin, 2013; Luchicchi et al., 2014). This is supported by the observation that the disruption of E/I ratio-dependent network dynamics coexists with nicotinic cholinergic transmission dysfunction in various neurological disorders and disease models (Goutagny et al., 2013; Hajos et al., 2005; Leiser et al., 2009; Levin, 2013;

Scott et al., 2012; Uhlhaas & Singer, 2010). While the impact of nAChR activation has been well studied at the receptor level, as well as in vivo (Dani, 2015; K. J. Kellar & Xiao, 2007), less attention has been directed towards the effects of nicotine and the activation of nAChRs on neuronal networks with single-cell resolution (S. R. Cobb et al., 1999; S. R. Cobb & Davies,

2005; Palop et al., 2006; Teles-Grilo Ruivo & Mellor, 2013). We propose that understanding how nAChRs modulate the integrated response from neuronal networks will help bridge the information gap between the effects of nAChR experimental therapeutics on single cells and behavior, thereby informing the development of more effective treatments for the various CNS disorders presenting with aberrant nicotinic cholinergic transmission.

To this end, in the present study, we bath-apply single doses of increasing concentrations of nicotine and record neural spiking activity from in vitro hippocampal networks cultured on

MEA. We use these concentrations to ask: (1) Does nicotine have a concentration-dependent impact on network activity? If so, (2) which nAChR subtypes mediate the effects of nicotine?

(3) Does steady-state activation of nAChRs contribute to the effects of nicotine? Finally, given that glutamate is the primary excitatory neurotransmitter in the CNS, and nicotine has been shown to modulate NMDAR-dependent long-term potentiation (Mann & Greenfield, 2003) as

well as group I mGluR activity (Welsby et al., 2006b), (4) what role do these glutamate receptors play in mediating the effects of nicotine?

Results

Concentration-Dependent Effects of Nicotine on Network Potentiation

Embryonic hippocampal neurons (E18) were cultured onto treated MEAs to study the effects of nicotine on neuronal network activity. To determine how nicotine impacts basal network activity, we observed and quantified changes in action potential firing after nicotine application. We removed 70% of the conditioned media from each culture and dissolved nicotine into it, then reapplied the media to naive cultures. The five final concentrations ranged from 0.1

µM to 90 µM. We record baseline activity and the effects of nicotine for 15 minutes. Unless otherwise stated, we report on the last 3 minutes of the15-minute recording in the presence of nicotine normalized to baseline activity. This time point was chosen to show the long-lasting effect of nicotine after receptor activation, and subsequent desensitization would have taken place.

To quantify the effects of nicotine on network dynamics, we calculated overall network spiking and bursting activity as a function of the concentration of nicotine applied. Nicotine concentrations in the range between 10 and 90 µM increase action potential firing as well as bursting activity (Fig. 9A-B). Most significantly, this range of nicotine concentration increases the fraction of spikes within bursts, resulting in a reorganized pattern of action potential firing

(Fig. 9C). Bursts are believed to facilitate information transmission within hippocampal networks. This is because bursts increase the probability of the postsynaptic neurons to fire action potentials (Csicsvari et al., 1998; Izhikevich et al., 2003; J. E. Lisman, 1997; Miles &

Wong, 1987; Thomson, 2000) and the likelihood of synaptic potentiation to occur (Paulsen &

Sejnowski, 2000; Pike et al., 1999a). Therefore, we refer to the nicotine-mediated increase in

bursting and fraction of spikes within bursts as nicotine-mediated network potentiation (Fig. 9B-

C). No significant change in bursting activity or spikes within bursts was observed in 0.1-1 µM nicotine and vehicle-treated networks (Fig. 9B-C).

Figure 9: Nicotine potentiates network-wide spiking and bursting, and reorganizes network activity in a concentration-dependent manner.

Effects of nicotine on the fractional change in (A) spikes, (B) bursts and (C) spikes within bursts during a 3-minute recording. (A) Nicotine concentrations between 10 and 90 µM increased spiking activity. (B) MEAs treated with nicotine concentrations of 10 µM and higher show a significant increase bursting activity. (C) MEAs treated with nicotine concentrations of 10 µM and higher show a significant increase in the fraction of spikes within bursts. Data expressed as fractional change that has been normalized to baseline ((stimulation mean- baseline mean)/

baseline mean). Baseline values reported as mean ± SEM (N=5 for each treatment), Spikes: nicotine- 0.1 µM {23120 ± 6564}, 1 µM {11383 ± 993}, 10 µM {28681 ± 5244}, 50 µM {11596

± 2360}, 90 µM {21909 ± 4741}, VEH {9555 ± 1966}; Bursts: nicotine- 0.1 µM {1296 ± 386} ,

1 µM {737 ± 84}, 10 µM {1767 ± 308}, 50 µM {768 ± 188}, 90 µM {1245 ± 290}, VEH {646

± 97}; Spikes within bursts (as a fraction of the total number of spikes): nicotine- 0.1 µM {0.51

± 0.09} , 1 µM {0.52 ± 0.04}, 10 µM {0.60 ± 0.05}, 50 µM {0.54 ± 0.06}, 90 µM {0.43 ± 0.02},

VEH {0.59 ± 0.03}. Statistical significance was assessed by carrying out multiple t-tests followed by a Holm-Bonferroni post hoc test (*, **, *** significant at the 0.05, 0.01 and 0.001 probability level, respectively).

Effects of Nicotine on Network Synchrony

Although excessive neural network synchrony can lead to pathological states such as seizures, a moderate degree of synchrony is physiologically necessary (RW.ERROR - Unable to find reference:157; Buzsaki, 2006; Buzsaki & Draguhn, 2004; Buzsaki & Draguhn, 2004;

Buzsaki & Watson, 2012; Jefferys et al., 1996; Traub & Jefferys, 1994). For example, synchronous firing of converging afferents is necessary for proper signal transmission to postsynaptic neurons (J. E. Lisman, 1997). Given that concentrations of nicotine within the range of 10-90 µM enhanced network parameters that give rise to more efficient information transmission, such as increased bursting activity as well as the number of spikes within bursts, we hypothesized that concentrations of nicotine within this range might also increase network synchrony. To this end, we used the correlation coefficient as a measure of synchrony; pairwise temporal correlations were calculated between the time of spiking activity recorded from active electrodes within the network. Figure 10 depicts representative spatial maps of correlation coefficients calculated from 1-minute recordings of either baseline during the 15th minute of the recording, or in the presence of nicotine during the 1st and 15th minute of the recording. There is a significant increase in the correlation coefficient indicating the emergence of synchronous activity at one minute after treatment with 90 μM of nicotine that is sustained at the fifteenth minute of nicotine application (Fig. 10A). There is no increase in the correlation after the application of 10 μM of nicotine (Fig. 10B). Vehicle-treated networks also did not show a significant change in correlation coefficients (Fig. 10C).

Figure 10: High, but not intermediate concentrations of nicotine promote network synchrony.

As a measure of synchrony, we calculate correlation coefficients which are the pairwise correlations between the spiking times of active electrodes within the network. (A)

Representative spatial maps of correlation coefficients between active electrodes of the network when 90 µM nicotine is applied. There is an increase in the correlation coefficient one minute after the application of nicotine that lasts until the 15th minute of recording as compared to baseline. Quantification of the correlation coefficient shows a persistent increase in synchrony after 90 µM. (B) Representative spatial maps of correlation coefficients between active electrodes of the network when 10 µM nicotine is applied. Quantification of the correlation coefficient shows a slight non-significant increase in the correlation coefficient one minute after the application of nicotine that lasts up until the 15th minute of recording as compared to baseline. (C) Correlation coefficients measured in the presence and absence of vehicle during the

1st and 15th minute of application. There is no significant increase in correlation coefficients.

Statistical significance was assessed by carrying out multiple paired t-tests followed by a Holm-

Bonferroni post hoc test (** significant at the 0.01 probability level).

Contribution of α7 nAChRs to Nicotine-Mediated Network Potentiation

Since α7 nAChRs predominate in the hippocampus, we sought to address their role in facilitating nicotine-mediated network potentiation. To this end, we treated hippocampal networks with 30 nM of MLA, a highly selective and potent competitive antagonist of the α7 nAChR, followed by stimulation with 90 µM of nicotine. The activity that ensues as a result of the 90 µM of nicotine treatment is an increase in spiking (Fig. 11A), bursting (Fig. 11B), and spikes within bursts (Fig. 11C) in the networks where α7 nAChRs are blocked. However, the observed nicotine-mediated potentiation is smaller in the MLA-treated networks as compared to naïve networks. Importantly, MLA attenuated but did not abrogate nicotine-mediated network potentiation, suggesting that α7 nAChR activation contributes to, but is not necessary for nicotine-mediated network potentiation. Finally, the most pronounced effect we observed in the

MLA-treated nicotine-stimulated networks is a decrease in the fraction of spikes within bursts.

Figure 11: Blocking α7 nAChRs with MLA attenuates nicotine-mediated network potentiation.

Effects of α7 nAChR antagonist, MLA, (before and during stimulation with nicotine) on the fractional change in (A) spikes, (B) bursts and (C) spikes within bursts during a 3-minute recording. After a 15-minute incubation, MLA does not significantly change spiking, bursting, or spikes within bursts. In contrast, 90 µM nicotine alone significantly increases spiking, bursting and spikes within bursts. Lastly, stimulation with 90 µM nicotine in the continued presence of

MLA results in attenuated spiking bursting and spikes within bursts. Data is expressed as

fractional change that has been normalized to baseline ((stimulation mean- baseline mean)/ baseline mean). Baseline values reported as mean ± SEM (N=5 for each treatment), Spikes: VEH

{9555 ± 1966}, 30 nM MLA {10773 ± 1613}, 30 nM MLA + 90 µM nicotine {10773 ± 1613},

90 µM nicotine {21909 ± 4741}; Bursts: VEH {646 ± 97}, 30 nM MLA {530 ± 125}, 30 nM

MLA + 90 µM nicotine {530 ± 125}, 90 µM nicotine {1245 ± 290}; Spikes within bursts (as a fraction of the total number of spikes): VEH {0.59 ± 0.03}, 30nM MLA {0.50 ± 0.07}, 30 nM

MLA + 90 µM nicotine {0.50 ± 0.07}, 90 µM nicotine {0.43 ± 0.02}. Statistical significance was assessed by carrying out multiple t-tests followed by a Holm-Bonferroni post hoc test (*, **,

**** significant at the 0.05, 0.01 and 0.0001 probability level, respectively).

Since stimulating networks in which α7 nAChRs are blocked with MLA results in a marked attenuation of spikes within bursts, we hypothesized that synchronization would be impacted negatively in these networks. Therefore, we sought to assess the role of α7 nAChRs in nicotine-mediated synchrony. To this end, in figure 12 we calculated correlation coefficients in

MLA-treated networks before and after stimulation with 90 µM of nicotine (quantified in Fig.

12B). We find that 30 nM of MLA does not significantly change network synchrony from its baseline levels (Fig. 12A), and it does not block nicotine-mediated network synchrony (Fig. 12A-

B), suggesting that the activation of α7 nAChRs is not necessary for nicotine-mediated network synchrony (the statistical analysis conducted does not assess whether blocking α7 nAChRs with

MLA attenuates nicotine-mediated network synchrony, therefore we cannot discount the possibility that the activation of α7 nAChRs contributes to nicotine-mediated network synchrony).

Figure 12: Activation of α7 nAChRs is not necessary for nicotine-mediated network synchrony.

Correlation coefficients measured at baseline, the 1st and 15th minute of MLA application, and during stimulation with 90 μM nicotine (15th minute). (A) Representative spatial maps of correlation coefficients between active electrodes of the network during baseline, when 30 nM

MLA and 90 µM nicotine are added. There is no increase in the correlation coefficient during the application of MLA as compared to baseline. MLA does not block the effects of 90 μM which causes an increase in the correlation coefficient. (B) Quantification of the correlation coefficient shows an increase in synchrony after the application of 90 µM nicotine. Statistical significance was assessed by carrying out multiple paired t-tests followed by a Holm-Bonferroni post hoc test

(* significant at the 0.05 probability level).

Contribution of Heteromeric nAChRs to Nicotine-Mediated Network Activity

There is a dearth of antagonists that are specific to distinct heteromeric nAChRs.

Specifically, the high concentrations of the α4β2 nAChR antagonist DHβE that are needed to block their activation with 90 µM nicotine are also capable of blocking α3β4 nAChRs (Harvey et al., 1996). This lack of specificity makes distinguishing the contributions of the two receptor subtypes a challenge. Consequently, to distinguish the contributions of α4β2 and β4-containing nAChRs to network activity, we used two different drugs: Saz-A to study the role of the α4β2 nAChR and AT-1001 to study the role of the β4-containing nAChR subtype in potentiating hippocampal networks (Fig. 13-16). The prolonged application time will desensitize the target nAChR receptor, essentially giving rise to a "time-averaged antagonism” (Hulihan-Giblin et al.,

1990).

Contribution of α4β2 nAChRs to nicotine-mediated network potentiation and synchronization

Saz-A is a partial agonist at rat α4β2 and α7 nAChRs (Brown & Wonnacott, 2015;

DeDominicis et al., 2017; Tuan, 2014; Zwart et al., 2008). 1 μM of saz-A was chosen so that it can elicit the maximal degree of activation and desensitization at α4β2 nAChRs while causing limited activation of α7 nAChRs (1 μM saz-A causes ~60% increase in current flow at α4β2 nAChRs, and ~4% increase in Ca2+ at α7 nAChRs). It potently desensitizes rat α4β2, and partially desensitizes α7 nAChRs (1 μM saz-A blocks ~98% and ~70% of nicotine-mediated

Ca2+ increase via α4β2 and α7 nAChRs, respectively) but not rat α3β4 nAChRs. (Brown &

Wonnacott, 2015; Xiao et al., 2006; Zwart et al., 2008).

We applied 1µM of saz-A onto hippocampal networks and recorded the activity for 15 minutes; this treatment was performed to elucidate the contribution of α4β2 nAChR activation to the effects of nicotine network activity. After a 15-minute incubation in saz-A (during which

α4β2 nAChRs presumably became desensitized), we stimulated the network with 90 µM of nicotine in the continued presence of saz-A; this treatment was to investigate the impact that desensitizing α4β2 nAChRs has on nicotine-mediated network potentiation (Fig. 13).

We find that neither the activation of α4β2 nAChRs with saz-A is sufficient to increase spiking nor is their desensitization effective in blocking the nicotine-mediated increase in spiking activity (Fig.13A). Similarly, neither the activation of α4β2 nAChRs with saz-A is sufficient to increase the fraction of spikes within bursts nor is their desensitization effective in blocking the nicotine-mediated increase in the fraction of spikes within bursts (Fig. 13C). If saz-A appreciably activated α7 nAChRs, we would have expected a significant increase in network activity that is opposite to the effects of MLA (attenuated nicotine-mediated network potentiation). We

observed no significant change in spiking or spikes within bursts (Fig. 13A, C), suggesting that saz-A acting on α7 nAChRs has no significant effect on these network parameters. Interestingly, the activation and subsequent desensitization of α4β2 nAChRs and α7 nAChRs caused an increase in bursting that occluded nicotine-mediated increase in bursting (Fig. 13B).

Figure 13: Activation of α4β2 nAChR with the partial agonist saz-A is sufficient to enhance network-wide bursting.

Effect of saz-A (before and during stimulation with nicotine) on the fractional change in (A) spikes, (B) bursts and (C) spikes within bursts during a 3-minute recording. After a 15-minute incubation, saz-A enhances bursting (B) but does not significantly increase spiking (A) and spikes within bursts (C). Stimulation with 90 µM nicotine in the continued presence of saz-A results in no further increase in spiking and bursting and increases spikes within bursts whereas

90 µM nicotine alone significantly increases spiking, bursting and spikes within bursts. Data are expressed as fractional change that is normalized to baseline ((stimulation mean- baseline mean)/ baseline mean). Baseline values reported as mean ± SEM (N=5 for each treatment), Spikes:

VEH {9555 ± 1966}, 1 µM saz-A {22300 ± 5658} , 1 µM saz-A + 90 µM nicotine {22300 ±

5658}, 90 µM nicotine {21909 ± 4741}; Bursts: VEH {646 ± 97}, 1 µM saz-A {1199 ± 326}, 1

µM saz-A + 90 µM nicotine {1199 ± 326}, 90 µM nicotine {1245 ± 290}; Spikes within bursts

(as a fraction of the total number of spikes): VEH {0.59 ± 0.03}, 1µM saz-A {0.49 ± 0.06} , 1

µM saz-A + 90 µM nicotine {0.49 ± 0.06}, 90 µM nicotine {0.43 ± 0.02}. Statistical significance was assessed by carrying out multiple t-tests followed by a Holm-Bonferroni post hoc test (*, ** significant at the 0.05 and 0.01 probability level, respectively).

Since the activation of α4β2 nAChRs caused an increase in bursting, this led us to speculate whether this change would be sufficient to promote network synchrony. To this end, we calculated the correlation coefficients of saz-A treated networks (Fig. 14). We find that saz-A does not cause a significant change in network synchrony (Fig. 14B). Moreover, after the activation and subsequent desensitization of α4β2 nAChRs with saz-A, 90 µM nicotine is still capable of increasing network synchrony (Fig. 14B), suggesting that α4β2 nAChRs are not necessary for the nicotine-mediated increase in network synchrony (the statistical analysis conducted does not assess whether desensitizing α4β2 nAChRs with saz-A attenuates nicotine- mediated network synchrony, therefore we cannot discount the possibility that the activation of

α4β2 nAChRs contributes to nicotine-mediated network synchrony).

Figure 14: Desensitization of α4β2 nAChRs does not occlude nicotine-mediated network synchrony.

Correlation coefficients measured at baseline, during the 1st and 15th minute of saz-A application, and during stimulation with 90 μM nicotine (15th minute). (A) Representative spatial maps of correlation coefficients between active electrodes of the network during baseline, when 1 µM saz-A and 90 µM nicotine are added. There is no significant increase in the correlation coefficient during the application of saz-A as compared to baseline. Saz-A does not block the effects of 90 μM nicotine, which causes an increase in the correlation coefficient. (B)

Quantification of the correlation coefficient shows an increase in synchrony after the application of 90 µM nicotine. Statistical significance was assessed by carrying out multiple paired t-tests followed by a Holm-Bonferroni post hoc test (* significant at the 0.05 probability level).

Contribution of β4-containing nAChRs to nicotine-mediated network potentiation and synchronization

AT-1001 is a partial agonist at rat β4-containing nAChRs that is known to potently desensitize rat β4-containing and human α4β2 nAChRs (Tuan, 2014; Tuan et al., 2015). We utilized AT-1001 to investigate the role of β4-containing nAChRs in nicotine-mediated network potentiation. The concentration of AT-1001 used in these studies was chosen based on the maximal activation and desensitization the drug would cause at its target receptor (Tuan, 2014;

Tuan et al., 2015; Xiao et al., 2006).

Approximately one minute into the application of 20 µM AT-1001, a long-lasting decrease in overall network activity, likely due to the activation of β4-containing receptors, ensued. This decrease in activity is characterized by a sharp decrease in spiking, bursting, and the fraction of spikes within bursts (Fig. 15 A-C). Additionally, desensitizing β4-containing receptors via a 15-minute incubation in AT-1001 blocks the impact of 90 µM nicotine on spiking, bursting, and the fraction of spikes within bursts (Fig. 15 A-C), suggesting that β4- containing nAChRs are necessary for the emergence nicotine-mediated network potentiation.

Figure 15: Desensitization of β4-containing nAChRs with partial agonist, AT-1001, blocks nicotine-mediated network potentiation.

Effects of α3β4 nAChR partial agonist, AT-1001, (before and during stimulation with nicotine) on the fractional change in (A) spikes, (B) bursts and (C) spikes within bursts during a 3-minute recording. After a 15-minute incubation, AT-1001 decreases spiking, bursting, and spikes within bursts. Lastly, in contrast to 90 µM nicotine alone, stimulation with 90 µM nicotine in the continued presence of AT-1001, in which α3β4 nAChRs are presumably desensitized, does not

result in enhanced spiking, bursting or spikes within bursts. Data expressed as fractional change that has been normalized to baseline ((stimulation mean- baseline mean)/ baseline mean).

Baseline values reported as mean ± SEM (N=5 for each treatment), Spikes: VEH {9555 ±

1966}, 20 µM AT-1001 {14338 ± 1496}, 20 µM AT-1001 + 90 µM nicotine {14338 ± 1496},

90 µM nicotine {21909 ± 4741}; Bursts: VEH {646 ± 97}, 20 µM AT-1001 {1065 ± 148}, 20

µM AT-1001 + 90 µM nicotine {1065 ± 148}, 90 µM nicotine {1245 ± 290}; Spikes within bursts (as a fraction of the total number of spikes): VEH {0.59 ± 0.03}, 20 µM AT-1001 {0.52

± 0.04}, 20 µM AT-1001 + 90 µM nicotine {0.52 ± 0.04}, 90 µM nicotine {0.43 ± 0.02}.

Statistical significance was assessed by carrying out multiple t-tests followed by a Holm-

Bonferroni post hoc test (*, *** significant at the 0.05 and 0.001 probability level, respectively).

Correlation coefficient calculations for the AT-1001 treated networks show that after one minute of incubation, AT-1001 does not significantly alter network synchrony in comparison to baseline (Fig. 16B). We note that during the fifteenth minute of AT-1001 application, there is a marked de-synchronization within the network and subsequent stimulation of 90 µM of nicotine does not rescue the effect (Fig. 16). These data suggest that β4-containing nAChRs are necessary for nicotine-mediated network synchrony. Lastly, activation of α3β4 nAChRs within the hippocampus has been shown to support inhibitory transmission. We hypothesized that the observed desynchronization might be a result of enhanced GABAergic transmission that is due to the activation of α3β4 nAChRs within the in vitro hippocampal network. To test this, we applied 10 µM of gabazine, a GABAAR antagonist, and observed the return of synchrony within the networks (in the continued presence of both AT-1001 and nicotine) (Figs. 16). Since the

GABAA antagonist re-established synchrony within the network, the activation of β4-containing nAChRs likely enhances GABAergic transmission.

Figure 16: Activation and subsequent desensitization β4-containing nAChRs blocks nicotine but not gabazine-mediated network synchrony.

Correlation coefficients measured at baseline, during the 1st and 15th minute of AT-1001 application, during stimulation with 90 μM nicotine and 10 μM gabazine (15th minute). (A)

Representative spatial maps of correlation coefficients between active electrodes of the network during baseline, when 20 µM AT-1001, 90 µM nicotine, and 10 μM gabazine are added. (B)

Quantification of the correlation coefficient shows a non-significant increase (p= 0.059) increase in synchrony during the 1st minute of AT-1001. At the 15th minute of AT-1001 application, there is a sharp decrease in the correlation coefficient. AT-1001 blocks the effects of 90 μM nicotine, which causes no appreciable increase in the correlation coefficient. Gabazine, a GABAR antagonist, added in the continued presence of both AT-1001and nicotine causes a robust increase in the correlation coefficient. Statistical significance was assessed by carrying out multiple paired t-tests followed by a Holm-Bonferroni post hoc test (*, ** significant at the 0.05 and 0.01 probability level, respectively).

Contribution of Steady-state Activation of nAChRs to the Long-lasting Effects of Nicotine on

Network Spiking

Nicotine caused a sustained increase in activity in both naïve networks and those treated with either MLA or saz-A. This long-lasting increase in activity may be attributed to either steady-state, i.e., “smoldering,” activation of a population of receptors that are not fully desensitized by nicotine (Campling et al., 2013) or a process downstream of nAChR activation, or to both.

To examine the possible contribution of steady-state activation, we tested whether an increase in spiking that is mediated by nicotine can be attenuated or blocked by specific nAChR antagonists. Briefly, after recording baseline activity for 15 minutes, we applied nicotine to increase network activity and recorded network spiking for 12 minutes. We followed with either

MLA, DHβE, or vehicle and recorded spiking activity for 6 minutes. If steady-state activation of

α7 or α4β2 nAChRs contributes to the increase in network spiking during stimulation with nicotine, then we would expect a decrease in spiking the during the application of MLA, or

DHβE, respectively (Fig. 17-18).

We find that neither the application of MLA nor vehicle cause a significant change in spiking during the first or the last 3 minutes of application (Fig. 17). This suggests that the steady-state activation via the α7 nAChRs does not contribute to the nicotine-mediated increase in spiking activity. Similarly, we find that applying DHβE or vehicle does not result in a significant change in spiking, suggesting that steady-state activation via the α4β2 nAChRs also does not contribute to the nicotine-mediated increase in spiking activity.

Figure 17: Steady-state activation of α7 nAChRs does not contribute to the long-lasting effects of nicotine on spiking.

Effects of 90 μM nicotine on the fractional change in spiking (before and during co-application with the α7 nAChR antagonist, MLA [blue] or vehicle [black]). 90 μM nicotine increases in spiking (min. 9-12). When applied at the 13th minute of stimulation with 90 μM nicotine, both

MLA [blue] and vehicle [black] do not significantly change the spiking. Baseline spiking values presented as mean ± SEM: MLA-treated group {0.584829± 0.1309, N=5}; vehicle-treated group

{0.637065± 0.140104, N=3}. Statistical significance of the treatment with 90 μM nicotine was calculated via a one-sample t-test. Statistical difference between the effect of nicotine before or after the application of MLA or vehicle was assessed by a repeated-measures ANOVA, followed by a Tukey’s post hoc. (*, *** significant at the 0.05 and 0.001 probability level, respectively)

Figure 18: Steady-state of α4β2 nAChRs does not contribute to the long-lasting effects of nicotine on spiking.

Effects of 10 μM nicotine on the fractional change in spiking (before and during co-application with the α4β2 nAChR antagonist, DHβE [green] or vehicle [black]). 10 μM nicotine increases in spiking (min. 9-12). When applied at the 13th minute of stimulation with 10 μM nicotine, both

DHβE [green] and vehicle [black] do not significantly change the spiking. Baseline spiking values presented as mean ± SEM: DHβE -treated group {0.359944± 0.045189, N=4}; vehicle- treated group {0.371782± 0.074619, N=3}. Statistical significance of the treatment with 10 μM nicotine was calculated via a one-sample t-test. Statistical difference between the effect of nicotine before or after the application of DHβE or vehicle was assessed by a repeated-measures

ANOVA, followed by a Tukey’s post hoc. (*, *** significant at the 0.05 and 0.001 probability level, respectively).

Nicotine-Mediated Network Potentiation is Independent of Synaptic NMDAR and Group I mGluR Activation

NMDARs and group I mGluRs are two classes of glutamate receptors that are part of signal transduction cascades leading to the amplification of Ca2+ signaling (Berridge et al.,

2003). Additionally, cooperative activity between these two receptor classes and nAChR activation has been previously reported (Mann & Greenfield, 2003; Welsby et al., 2006a). Since activation of nAChRs is brief and steady-state activation does not seem to contribute to the long- lasting network potentiation (~15 minutes), we hypothesized that the long-lasting network potentiation we observe might be dependent upon one or both of the glutamate receptors. To test this, we blocked synaptic NMDAR with 10 µM MK-801 and challenged the network with 90

µM nicotine (Fig. 19). The blockade of synaptic NMDARs resulted in de-potentiation characterized by reduced spiking, bursting and the fraction of spikes within bursts (Fig. 19 A-C).

The observed decrease in activity after blocking NMDARs indicates that they are basally active.

Blocking synaptic NMDARs failed to prevent the effects of nicotine on spiking, bursting and the fraction of spikes within bursts (Fig. 19 A-C). These data suggest that synaptic NMDARs are not necessary for nicotine-mediated network potentiation and that activation of nAChRs may rescue excitatory-inhibitory ratio deficits that arise due to NMDAR hypofunction.

Figure 19: Activation of synaptic NMDARs is not necessary for nicotine-mediated network potentiation.

Effects of NMDAR channel blocker, MK-801, (before and during stimulation with nicotine) on the fractional change in (A) spikes, (B) bursts and (C) spikes within bursts during a 3-minute recording. After a 15-minute incubation, MK-801 decreases spiking, bursting and spikes within

bursts. Stimulation with 90 µM nicotine in the continued presence of MK-801 results in the recovery of spiking, bursting and spikes within bursts whereas 90 µM nicotine alone significantly increases spiking, bursting and spikes within bursts. Data expressed as fractional change that has been normalized to baseline ((stimulation mean- baseline mean)/ baseline mean).

Baseline values reported as mean ± SEM (N=4 for each treatment), Spikes: VEH {9555 ± 1966},

10 µM MK-801 {7336 ± 704}, 10 µM MK-801 + 90 µM nicotine {7336 ± 704}, 90 µM nicotine

{21909 ± 4741}; Bursts: VEH {646 ± 97}, 10 µM MK-801 {376 ± 69}, 10 µM MK-801 + 90

µM nicotine {376 ± 69}, 90 µM nicotine {1245 ± 290}; Spikes within bursts (as a fraction of the total number of spikes): VEH {0.59 ± 0.03}, 10 µM MK-801 {0.37 ± 0.07}, 10 µM MK-801 +

90 µM nicotine {0.37 ± 0.07}, 90 µM nicotine {0.43 ± 0.02}. Statistical significance was assessed by carrying out multiple t-tests followed by a Holm-Bonferroni post hoc test (*, **, *** significant at the 0.05, 0.01 and 0.001 probability level, respectively).

To assess the role of group I mGluRs in nicotine-mediated network potentiation, we co- applied specific antagonists, 3-MATIDA and MPEP, at concentrations that would result in the maximal blockade of their respective receptors, mGluR1 and mGluR5 (Gasparini et al., 1999; F.

Moroni et al., 2002). As with blocking NMDARs, blocking group I mGluRs results in a deceased

E-I ratio that is accompanied by decreased spiking (Fig. 20A), bursting (Fig. 20B) and the fraction of spikes within bursts (Fig. 20C). Additionally, blocking group I mGluRs failed to prevent nicotine-mediated network potentiation (Fig. 20), suggesting that group I mGluRs do not contribute to the effects of a high concentration of nicotine on the network. Although their activation is not necessary for nicotine-mediated network potentiation, we observed that blocking group I mGluRs results in an epileptic-like discharge that occurred once every 3 minutes and lasted for approximately 5 seconds (Fig. 21). These epileptic-like discharge are reminiscent of superbursts, i.e., small burst trains occurring in tight clusters (Wagenaar et al., 2006). These results suggest that basally active group I mGluRs prevent excessive network excitability arising from the activation of nAChRs.

Figure 20: Activation of group I mGluRs is not necessary for nicotine-mediated network potentiation.

Effects of group I mGluR antagonists MPEP -mGluR1 antagonist and 3-MATIDA -mGluR5 antagonist (before and during stimulation with nicotine) on the fractional change in (A) spikes,

(B) bursts and (C) spikes within bursts during a 3-minute recording. After a 15-minute

incubation, group I mGluR antagonists (1 µM MPEP + 100 µM 3-MATIDA) decreased spiking, bursting and spikes within bursts. Stimulation with 90 µM nicotine in the continued presence of group I mGluR antagonists results in the recovery of spiking, bursting and spikes within bursts whereas 90 µM nicotine alone significantly increases spiking, bursting and spikes within bursts.

Data expressed as fractional change that has been normalized to baseline ((stimulation mean- baseline mean)/ baseline mean). Baseline values reported as mean ± SEM (N=4 for each treatment), Spikes: VEH {9555 ± 1966}, 1 µM MPEP & 100 µM 3-MATIDA {12100 ± 11529}

, 1 µM MPEP & 100 µM 3-MATIDA + 90 µM nicotine {12100 ± 11529}, 90 µM nicotine

{21909 ± 4741}; Bursts: VEH {646 ± 97}, 1 µM MPEP & 100 µM 3-MATIDA {778 ± 132}, 1

µM MPEP & 100 µM 3-MATIDA + 90 µM nicotine {778 ± 132}, 90 µM nicotine {1245 ±

290}; Spikes within bursts (as a fraction of the total number of spikes): VEH {0.59 ± 0.03}, 1

µM MPEP & 100 µM 3-MATIDA {0.56 ± 0.02}, 1 µM MPEP & 100 µM 3-MATIDA + 90 µM nicotine {0.56 ± 0.02}, 90 µM nicotine {0.43 ± 0.02}. Statistical significance was assessed by carrying out multiple t-tests followed by a Holm-Bonferroni post hoc test (*, ** significant at the

0.05 and 0.01 probability level, respectively).

Figure 21: Representative raster plots of activity from hippocampal networks 14DIV depicting the effects of group I mGluRs on spontaneous activity and nicotine-mediated network potentiation.

Effects of group I mGluR antagonists MPEP -mGluR1 antagonist and 3-MATIDA -mGluR5 antagonist before and during stimulation with 90 µM nicotine (1 µM MPEP + 100 µM 3-

MATIDA). Each tick mark represents one spike. (A) Spontaneous activity present basally in cultures on14DIV is characterized by arrhythmic synchronous bursts and spikes, not in bursts

(quantified in Fig. 20). (B) After a 15-minute incubation, group I mGluR antagonists (1 µM

MPEP + 100 µM 3-MATIDA) decreased spiking, bursting and spikes within bursts (quantified in Fig. 20). (C) Stimulation with 90 µM nicotine in the continued presence of group I mGluR 84

antagonists results in the recovery of spiking, bursting and spikes within bursts (quantified in

Fig. 20), and the emergence of superbursting (10s inset shown in D). (D) Inset depicts a superbursting event that lasts approximately 5 seconds.

Discussion

We show that nicotine can potentiate in vitro hippocampal network dynamics in a concentration-dependent manner. Increasing concentrations of nicotine not only enhance action potential firing, but they reorganize the temporal pattern of spiking towards network dynamics that support information transmission; we observe more bursts that contain a larger fraction of spikes.

The observed nicotine-induced network activity - on the order of several minutes - is significantly longer than the time needed for nAChRs to become desensitized, which is on the order of tens of milliseconds (Quick & Lester, 2002). The initial increase may likely be due to the influx of cations contributing to the depolarization of neurons resulting from activation of nAChRs (Dani & Bertrand, 2007). As the receptors desensitize within seconds after activation, their contribution to the sustained activity becomes less clear. It is known that nAChRs in the hippocampus are located at both synaptic and non-synaptic sites, and they are capable of influencing synaptic plasticity due to their ability to conduct Ca2+ (Lena et al., 1993; Zarei et al.,

1999). Activation of nAChRs results in both membrane depolarization as well as the initiation of intracellular Ca2+ release from stores leading to prolonged neurotransmitter release (Sharma &

Vijayaraghavan, 2003).

It is conceivable that although the open-time of nAChRs is a short-term phenomenon,

Ca2+ entry through the channel allows for a long-term action of Ca2+-regulated enzymes and other processes. This is consistent with studies demonstrating that nicotine causes a prolonged (~

15 minutes) transcription of cFos (Greenberg et al., 1986), an immediate early gene product that is transcribed when its regulatory transcription factor, CREB, is activated when membrane

depolarization that triggers Ca2+ influx (Abraham et al., 1991; Frank & Greenberg, 1994; Sheng et al., 1990).

Additionally, King and colleagues report a direct coupling of α7 nAChRs to G proteins that enables a downstream Ca2+ response to persist past the expected time course of channel activation. The coupling to G proteins may play a role in maintaining the effects of nicotine.

Moreover, this G protein coupling process depends on the binding of Gαq at the G protein- binding cluster (GPBC) located in the M3-M4 loop of the receptor. Although it has yet to be further examined, the same study proposes the existence of a GPBC in other nAChR subunits based on sequence homology at the M3-M4 loop of other nAChR subunits (Kabbani & Nichols,

2018; King & Kabbani, 2016; King et al., 2015), which may also contribute to a G protein- mediated maintenance of the effects.

We investigated the contribution of steady-state activation of nAChRs to the maintenance of nicotine-mediated network potentiation due to the presence of nicotine throughout the recording. We show that once nicotine increases spiking, neither blocking α7 or α4β2 nAChRs with MLA or DHβE, respectively, impacts spiking. The inability of the blockers to negatively impact nicotine-mediated spiking suggests that steady-state activation of α7 and α4β2 nAChRs does not contribute to nicotine-mediated increased spiking. However, due to the lack of specific

α3β4 nAChR antagonists, the contribution of steady-state activation of this receptor subtype was not examined. Finally, steady-state activation may also maintain a small current that may contribute to the observed excitability (Campling et al., 2013; Papke, 2014).

To delineate the nicotinic receptor subtypes that are involved in the effect of nicotine on network potentiation, we used various pharmacological tools. Interestingly, we observed that the

time-averaged effect of activating β4-containing nAChRs is network de-potentiation that is not recoverable via stimulation with nicotine but is recoverable via blocking GABAARs. This β4- containing nAChR-dependent effect is consistent with evidence that shows that these receptors are found on the axonal terminals of GABAergic interneurons and that the release of GABA via this mechanism is slow, but long-lasting (Tang et al., 2011). Moreover, AT-1001, which selectively activates β4-containing receptors, was recently shown to have anxiolytic properties

(Cippitelli et al., 2015; Yuan et al., 2017), further suggesting a role for GABA in mediating the effects of AT-1001.

We also show that the pretreatment of networks with the α7 nAChR antagonist MLA attenuates the effects of nicotine on network potentiation. This attenuation of nicotine-mediated network potentiation arising from blocking α7 nAChRs is consistent with a previously described role of α7 nAChRs in supporting increased hippocampal action potential firing (Sharma &

Vijayaraghavan, 2003). The expected degree of desensitization of α7 nAChRs after incubation in saz-A is approximately 70%. We hypothesized that 90μM nicotine would have an attenuated effect on the saz-A desensitized network like MLA, yet this was not the case. This inconsistency is possibly due to the activation of the remaining pool of α7 nAChRs that are not desensitized by saz-A (~30%). Alternatively, it is possible that saz-A does not desensitize α7 nAChRs in rat hippocampal cultures as effectively as it does in mouse cortical cultures (Brown & Wonnacott,

2015). Additionally, we observe that the activation of α4β2 nAChRs is sufficient to enhance bursting; saz-A at this concentration results in ~5% activation of α7 nAChRs, therefore their contribution to this effect may be minimal. Since postsynaptic bursts are crucial for proper information transmission within hippocampal networks (Izhikevich et al., 2003; J. E. Lisman,

1997), the occurrence Hebbian synaptic plasticity in vitro (Pike et al., 1999b), and for

homeostatic maintenance of network excitability in vivo (Buzsaki et al., 2002). This effect of

α4β2 nAChRs on network bursts could potentially play a significant role in the ability of nicotine to act as a cognitive enhancer at the concentrations used in this study. Support for this comes from observations that the selective activation of α4β2 nAChRs improves cognition (Dunbar et al., 2007; Howe et al., 2010; Parikh et al., 2008; Parikh et al., 2010; A. Potter et al., 1999; Wilens

& Decker, 2007). Additionally, nicotine concentrates in the rodent brain between four- to tenfold within 15 minutes of administration (Ghosheh et al., 2001; Hussmann et al., 2014; Ilbäck &

Stålhandske, 2003). Furthermore, the accumulation of nicotine has also been observed in humans

(Henningfield et al., 1993); therefore, a brain nicotine concentration of 10 μM may be achieved.

It is possible that unlike saz-A, 10 μM nicotine does not occlude bursting due to the ability of saz-A to potently desensitize α7 nAChRs, which were shown to counteract the procognitive effects of α4β2 nAChR activation (Parikh et al., 2010).

Synchronization of neural activity is necessary for several normal and pathological functions that rely on oscillatory activity (RW.ERROR - Unable to find reference:157; Buzsaki

& Draguhn, 2004; Buzsaki & Watson, 2012; Jefferys et al., 1996; Traub & Jefferys, 1994;

Uhlhaas & Singer, 2006; Uhlhaas & Singer, 2010). Studies have shown that cholinergic innervation plays a role in both physiological oscillatory states such as the hippocampal theta rhythm, which is necessary for learning and memory (Bland & Colom, 1993; Buzsáki, 2002;

Kramis et al., 1975; Williams & Kauer, 1997), as well as in hypersynchrony (Bui et al., 2015;

Traub & Jefferys, 1994; Williams & Kauer, 1997). We hypothesized that the activation of nAChRs that leads to increased bursting detected at the single electrode level from small subpopulations of neurons could increase the likelihood of synchronous bursting across the network. To assess this, we analyzed correlation coefficients as a direct measure of synchrony

and found that increased bursting alone -via the activation of α4β2 nAChRs - is not sufficient to promote network synchrony. Moreover, we find that only networks treated with a high concentration of nicotine can cause a significant amount of synchrony. The absence of synchrony in networks treated with low to intermediate concentrations of nicotine suggests that network synchrony manifests only after a certain degree of network potentiation will result in significant network synchrony. In agreement with the observation that activation of nAChRs within the hippocampus augments the power of pre-existing oscillatory states (S. R. Cobb et al., 1999;

Griguoli et al., 2009; Griguoli & Cherubini, 2012; Teles-Grilo Ruivo & Mellor, 2013; Williams

& Kauer, 1997), we find that networks that entered into a desynchronized state after β4- containing nAChR activation were incapable of being synchronized by nicotine.

Using the parameters that we have described, neither the activation of NMDAR nor group I mGluR is necessary for network potentiation mediated by a high concentration of nicotine. The lack of dependency on the activation of these glutamate receptors distinguishes of nicotine-mediated network potentiation from previously described synaptic potentiation mediated by nicotine (Mann & Greenfield, 2003; Welsby et al., 2006b). This is likely due to the property of nAChRs, especially the α7 subtype, to be highly permeable to Ca2+, to a level that is equal to, or exceeds that of the NMDAR (Bertrand et al., 1993; Seguela et al., 1993). Moreover, nAChRs, unlike NMDARs, nAChRs are not voltage-dependent, and therefore do not require the depolarization of the postsynaptic membrane to pass Ca2+. Importantly, the observation that nicotine acts independently of NMDARs to increase network activity might have implications for disease states with known NMDAR hypofunction such as schizophrenia.

In summary, we show that nicotine significantly potentiates basal network activity in a concentration-dependent manner. This action of nicotine involves multiple nAChRs, such as α7,

α4β2, and α3β4. These studies contribute to a better understanding of the role of nAChRs as modulators of information transmission within hippocampal networks. Future studies using Ca2+ imaging will help elucidate the role of Ca2+ in mediating the effects of nicotine on neural network potentiation and synchrony.

CHAPTER V: CONCLUSION AND FUTURE DIRECTIONS

To summarize, the goals of my work were to 1) test whether the phenomenon of nAChR desensitization can be recapitulated at the neural network level; 2) characterize the effects of nAChR activation on spontaneous network activity; 3) elucidate which nAChR subtypes contribute to nicotine-mediated network potentiation (which we define as a network state that emerges as a result of nAChR activation leading to more reliable information transmission); 4) investigate whether steady-state activation of nAChRs contributes to effects of nicotine; and 5) determine whether the activation of NMDARs or group I mGluRs could influence the ability of nicotine to mediate long-lasting network potentiation. To arrive at these goals, I used the MEA to record the activity of in vitro cultured hippocampal networks. Since we desired to study the effects of nAChR activation on spontaneous activity, hippocampal cultures were used instead of slices. Because cultures do not preserve the anatomy of the hippocampus, the results in this study were confined to network dynamics that do not depend on intact hippocampal connections.

First, to study the effects of nAChR activation at the network level, I directly bath applied either 10 or 100 μM nicotine to the conditioned media of the cultured neural network. The application of the 100 μM nicotine resulted in decreased extra-burst spikes accompanied by increased bursting. These results are consistent with the observation that single spikes in hippocampal networks decrease the likelihood that a burst will follow (Buzsaki 2006; Lisman

1997). Furthermore, it increased synchrony and the number of electrodes in which bursts were detected. Notably, these effects lasted for the duration of the experiment (15 minutes). However, the intermediate concentration of 10 μM nicotine failed to perturb network dynamics. Next, to generate the effect of desensitization at the network level, I added 90 μM nicotine to the networks that were incubated with 10 μM nicotine (the cumulative concentration is 100 μM). In contrast to naïve networks, the resulting high concentration of nicotine in these networks failed

to reorganize network activity. The incubation of hippocampal networks with an intermediate concentration of nicotine blocked the increase spiking, bursting, and synchrony that was typically induced by a high concentration of nicotine.

These data suggest that activating low-affinity nAChRs with a high concentration of nicotine leads to the emergence of efficient networks that supports information transmission, which in turn results in the recruitment of previously silent units into network bursts.

Importantly, through these experiments, we were able to demonstrate nAChR desensitization in in vitro neuronal networks. Moreover, by showing that a lower concentration of nicotine can block the effect of a subsequent, higher concentration, we show that the effects observed at the high concentration of nicotine are due to the activation of nAChRs rather than non-specific effects. Finally, to our knowledge, this is the first report that nicotine recruitments previously silent units into network activity; this effect may contribute to nicotine-induced seizures (Damaj et al., 1999).

In chapter IV, I sought to characterize further the effects of nAChR activation on spontaneous network activity. To this end, I utilized a range of concentrations of nicotine to identify the concentration at which the network becomes significantly potentiated. Note that the parameter of spikes within bursts is the converse of extra-burst spikes as defined in chapter III.

In this chapter, we chose to concentrate on the spikes within bursts to better describe the manner in which nicotine alters network bursts, but in every instance that nicotine increased the spikes within bursts, it decreased the number of extra-burst spikes, thereby decreasing unreliable signal transmission. We find that concentrations in the range between 10-90 μM cause a significant increase in network bursts, spikes, and spikes within bursts. These results are inconsistent with those from that in chapter III where 10 μM nicotine did not cause a significant change in network

dynamics. This difference is likely attributable to the difference in the nicotine delivery method.

Stochastic activation of nAChRs manifested as a result of the slower delivery of the 10 μM nicotine in chapter III versus the collective activation of the receptors in chapter IV where the delivery method was immediate and global.

Although the range of concentrations of nicotine is vast, the low to intermediate concentrations of nicotine in this study can be insightful to the effects of nicotine during smoking. Studies carried out by Hussmann supports this and others show that nicotine concentrates in the brain of rats to nearly two-fold the plasma concentration at steady-state, and nearly tenfold during acute (~ 15 minutes) nicotine administration (Ghosheh et al. 2001; Ilbäck and Stålhandske 2003; Hussmann et al. 2014). Importantly, the accumulation of nicotine is also observed in vivo in the brain of smokers (Henningfield et al. 1993). Given that its reported plasma concentration range of 300 nM to 1 µM (US Department of Health and Human Services

1988), it is possible that nicotine can reach as high as 3-10 µM in the brain of smokers (Gahring and Rogers 2008).

Next, I set to identify the nAChR subtypes that are involved in mediating the effects of nicotine on network dynamics that we observed. To achieve this goal, I utilized several pharmacological agents to either block or desensitize nAChRs before stimulating them with 90

µM of nicotine, a concentration of nicotine that was capable of inducing both network potentiation and synchrony. AT-1001 abolished the nicotine-mediated network potentiation and synchronization, yet alone, it was not able to induce these effects on the network. The absence of network potentiation and synchronization in AT-1001-treated networks before and after stimulation with nicotine suggests that the activation of β4-containing nAChRs is necessary, but not sufficient to induce network potentiation. The most striking observation is that when we only

activate β4-containing nAChRs, the predominant effect is quiescence. Gabazine, a GABAA antagonist, reversed the observed quiescence, suggesting that the activation of β4-containing nAChRs potentiates GABAergic transmission. This GABAergic transmission potentiating feature of nicotinic transmission that may potentially prove as a potential treatment for diseases in which GABAergic transmission is compromised, such as epilepsy or anxiety. Indeed, preclinical studies report anxiolytic effects in mice treated with AT-1001 (Cippitelli et al. 2015).

The decrease in activity as a result of β4-containing nAChR activation is in sharp contrast to the network potentiation evoked by the non-specific activation of nAChRs within the network.

This suggests that nicotine-induced network potentiation emerges from the coordinated activation of multiple nAChR subtypes. To assess the contribution of other nAChRs to the potentiating effects of nicotine on network activity, I pre-incubated networks in MLA, a highly potent and specific α7 nAChR antagonist, then stimulated with 90 μM of nicotine. In these

MLA-treated networks, I observed a marked decrease in spiking, bursting, and spikes within bursts, suggesting that α7 nAChRs while contributing to nicotine-induced network potentiation, their activation is not necessary for its induction (Fig. 22).

I also sought to probe the contribution of α4β2 nAChR activation and desensitization in mediating network potentiation and synchrony. To this end, I pre-incubated the network with the

α4β2 nAChR agonist, saz-A, then stimulated with 90 μM of nicotine. I found that activating

α4β2 nAChRs with saz-A occludes nicotine-mediated increase in bursting, yet has no significant effect on the total number of spikes and the number of spikes within bursts. Remarkably, while maximal bursting was not induced by 10 μM of nicotine, which is well above the maximally effective concentration for many effects linked to the activation of HSα4β2 nAChRs (M. Moroni,

Zwart, Sher, Cassels, & Bermudez, 2006b), 1 μM saz-A, which potently activates the HS but not

the LSα4β2 nAChRs (Zwart et al., 2008), was sufficient to induce maximal bursting. Studies show that the activation of α7 nAChRs counters the procognitive effects of α4β2 nAChR activation (Parikh et al., 2010). This marked difference in the effectiveness of saz-A and 10 μM of nicotine in enhancing bursting activity may be attributed to the complex pharmacology of saz-

A. Unlike saz-A, which activates HSα4β2 much more effectively than α7 nAChRs (Brown &

Wonnacott, 2015), nicotine activates both receptor subtypes, albeit to varying degrees.

Nevertheless, the ability of saz-A, but not 10 μM nicotine to occlude bursting needs further probing with alternate α4β2 nAChR agonists, such as A-85380.

Follow-up studies using knockout mice lacking specific nAChR subtypes would be useful to confirm the effects observed as a result of incubating the networks in nicotine in the presence of MLA, saz-A, or AT-1001. Stimulating cultures obtained form α7-, β2-, or β4- knockout mice will elucidate the contribution of each subunit to nicotine –mediated network potentiation, respectively. Additionally, since our data suggest that the activation of β4- containing nAChRs that cluster on PV-expressing interneurons is necessary for nicotine- mediated network potentiation, to confirm these results, we may harness the power of optogenetics. Using cultures obtained from PV-Cre mice and injected with a vector carrying the channelrhodopsin-2 (ChR2) gene, we may first activate ChR2 using 473 nm laser, and follow with 90 μM of nicotine. Since our data suggest that AT-1001-mediated activation of on β4- containing nAChRs leads to enhanced GABAergic tone and abrogates the effects of nicotine, we expect to observe similar results in these sets of experiments, where stimulating ChR2 will lead to network quiescence that is not reversed by 90 μM of nicotine, but is reversed by gabazine.

Consistently, we observed that network potentiation persists on the time scale of minutes, which is longer than the time-scale of nAChR desensitization (milliseconds). This prompted us

to ask whether the steady-state activation of nAChRs that recover from desensitization contributes to the maintenance of the nicotine-induced network up-state. To this end, we first stimulated spiking activity within the network of neurons with nicotine for 12 minutes; then we added either an α7 or α4β2 nAChR antagonist. We were able to conclude that once increased, spiking activity is not attenuated by blocking either the α7 or α4β2 nAChRs, suggesting that steady-state activation of nAChRs does not contribute to maintaining increased firing of action potentials in the network. These observations lead us to hypothesize that the long-lasting effects of nicotine on network activity arise from a cooperation between nAChRs and glutamate receptors that linked signal transduction cascades that involve the amplification of Ca2+ signaling, such as NMDARs and group I mGluRs (Berridge et al., 2003).

Accordingly, I tested the hypothesis that cooperation between NMDARs or group I mGluRs and nAChRs is necessary for nicotine-mediated network potentiation. Results suggest that blocking either NMDARs or group I mGluRs does not abrogate nicotine-mediated network potentiation. Two key points may be derived from these experiments. First, since nicotine- mediated network potentiation occurs in the absence of NMDAR activation, nAChRs activation can potentially alleviate symptoms via this mechanism in disorders that present with NMDAR hypofunction, especially in the case of schizophrenia where nAChRs are also less active (Parikh et al., 2016). Furthermore, because their activation of is neuroprotective, nAChRs are likely superior therapeutic targets to NMDARs, whose activation can lead to neurotoxicity. Second, the application of a high concentration of nicotine while blocking group I mGluRs did not prevent nicotine-mediated network potentiation. Instead, it resulted in the emergence of epileptic-like discharges similar to superbursting, a phenomenon where a train of small bursts occurs in a tight cluster (Wagenaar et al. 2006). Given the role of group I mGluRs in mediating LTD (Snyder et

al. 2001) and homeostatic synaptic scaling (Hu et al. 2010), this observation supports a role for group I mGluRs activation as negative feedback for increased network activity.

Since nicotine-mediated network potentiation of hippocampal networks does not require of the activation of NMDARs and group I mGluRs and does not involve the steady-state activation of nAChRs, then the maintenance of the potentiated network state is likely due to one or more other mechanisms known to be involved in sustained increases in spiking activity. These mechanisms can either be pre- or postsynaptic.

Presynaptically, the facilitation of neurotransmitter release has been linked to the activation of nAChRs (Marchi & Grilli, 2010). This mode of facilitation requires an increase in

Ca2+ at the presynaptic membrane, which can be achieved directly through activating nAChRs, yet the rapid desensitization of nAChRs points to the interaction of nAChRs with other receptors.

One class of receptors that potentially participates in the sustained increase in Ca2+ are voltage- gated calcium channels, such as the T-type or L-type calcium channels. The activation of both of these VGCC subtypes leads to the activation of the ryanodine receptor, resulting in calcium- induced calcium release (CICR) via (see Fig. 23) (Sharma & Vijayaraghavan, 2003; Tang et al.,

2011). Ca2+ imaging experiments conducted in the presence of blockers of these pathways, as well as ones conducted with nicotinic agonists after the depletion of Ca2+ stores, are warranted to investigate these mechanisms. An ideal experimental approach would be one where MEA recordings and Ca2+ imaging can be performed simultaneously. Regrettably, a drawback is that

MEA experiments must be conducted in conditioned media, which is not an ideal medium Ca2+ imaging. Nevertheless, using the insights gained from the MEA experiments, we can investigate the aforementioned Ca2+ pathways, and assess network level phenomena such as the synchronization of Ca2+ spikes, which are believed to arise from bursts of action potentials.

Finally, it is possible that the activation of postsynaptic nAChRs may lead to the activation of Ca2+/calmodulin-dependent kinase (CAMKII), which in turn leads to changes in α- amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid (AMPA) receptor trafficking to the membrane and phosphorylation that supports larger conductance can also lead to increased membrane depolarization (J. Lisman et al., 2002). To test the possible contribution of this mechanism to nicotine-mediated network potentiation, we can block CAMKII then stimulate the network with 90 μM of nicotine, and observe whether nicotine-mediated network potentiation ensues. We can also probe for changes membrane-bound AMPARs as a result of stimulating with nicotine by biotinylating the AMPARs, which causes the AMPARs to become cross-linked to the membrane, then immunoblotting. If nicotine-treated cultures display a greater degree of biotinylated AMPARs in comparison to control cultures, this would indicate that nicotine does indeed increase membrane-bound AMPARs. Additionally, we can assess whether the phosphorylation of AMPARs at serine 831, which leads to increased channel conductance (J.

Lisman et al., 2002), occurs as a result of stimulation with nicotine. The contribution of S831P can be assessed by determining the ratio of phospho-S831 AMPARs to total AMPAR via immunoblotting.

In conclusion, using MEA recordings of hippocampal network activity, I was able to describe multiple dynamical effects that support information transmission as a result of nAChR activation. These results demonstrate the potential of the MEA system to gain insights into the dynamical processes that arise due to targeted pharmacological perturbations. While investigating neural network dynamics in cultures has limitations, the dynamical processes that ensue can give us clues regarding how different components of the network can interact with each other to maintain a balanced level of network activity. Gaining a better understanding of the

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principles by which neurons interact will guide our study of diseases that present with neural network dysfunction and ultimately will inform the search for treatments and cures.

Figure 22: Role of nAChR activation in modulating in vitro hippocampal network dynamics.

Graphic abstract of proposed role for nAChR activation on modulating spiking and bursting activity within in vitro hippocampal networks. Gray receptors denote nAChRs not activated. (A)

Non-selective activation of nAChRs results in network potentiation characterized by a robust

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increase in spiking, bursting and spikes within bursts (SWB). (B) Nicotine-mediated network potentiation is attenuated in the absence of α7 nAChR activation. (C1) Activation of α4β2 nAChRs increases network bursting. (C2) Activation and subsequent desensitization of α4β2 nAChRs occludes network bursting, but does not abrogate nicotine-mediated network potentiation. (D1) Activation of β4-containing nAChRs produces network de-potentiation. (D2)

Activation and subsequent desensitization of β4-containing nAChRs abolishes nicotine-mediated network potentiation.

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Figure 23: Role for pre- and peri-synaptic nAChRs in mediating long-lasting GABA and glutamate release.

Schematic depicting the various Ca2+ pathways that may be recruited to enhance neurotransmitter release as result of nAChR activation. The activation of β4-containing nAChRs enhances GABA release from PV-expressing interneurons, resulting in pronounced and long- lasting inhibition (~minutes) (Tang et al., 2011). The activation of α7 nAChRs leads to the activation of voltage-gated calcium channels (VGCC), and causing a rise in Ca2+ which leads to calcium-induced calcium release (CICR) via the activation of ryanodine receptors (RyRs), leading to which enhances neurotransmitter release (~10 min.) (Sharma et al., 2003).

Alternatively, the activation of α7 nAChRs is linked to G protein-mediated increase in IP3 and the activation of IP3 receptors (IP3Rs), which may lead to long-lasting release of neurotransmitter (Kabbani et al., 2018). The activation of α4β2 nAChRs leads to GABA release via depolarization (Alkondon et al., 2004).

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APPENDIX

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NMDAR-Mediated Release of [3H] Norepinephrine From Adult Rat Hippocampal Slice

The Psychoactive Drug Screening Program (PDSP) reports that at the concentrations that were used in these studies, AT-1001 has an excess of 50% binding at the NMDAR, yet no data of AT-1001 function on NMDARs was reported. To control for this confound, we sought to assess whether AT-1001 can perturb NMDAR function. To this end, we used adult rat hippocampal slices as biosensors to test whether NMDAR-mediated hippocampal [3H] norepinephrine (NE) release is perturbed by AT-1001 (Jones et al., 1987).

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Methods

Animal Care and Use

Adult male Sprague Dawley rats were purchased from Harlan (Frederick, MD). Animals were housed in Association for Assessment and Accreditation of Laboratory Animal Care facilities at

Georgetown University (Washington, DC). All experimental procedures were performed in accordance with the National Institutes of Health (USA) guidelines for the ethical use of animals in research under the approval of the Georgetown University Institutional Animal Care and Use

Committee.

Hippocampal Tissue Extraction and Loading with [3H] Norepinephrine

Rats (weighing 220-250 g) were anesthetized with isoflurane then decapitated, and the hippocampus was rapidly dissected on ice. Experimental conditions for release of [3H]NE from brain slices were chosen according to the methods described by Snell et al. (1987), Vezzani et al.

(1987) and Monnet et al. (1995) with some modifications (Jones et al., 1987; Monnet et al.,

1995; Vezzani et al., 1987). Hippocampal pairs were mechanically chopped in the transverse plane at a thickness of 200 μm x 200 μm. The slices were suspended in 10 ml of oxygenated

Krebs-bicarbonate buffer (OKB), pH 7.5, containing ascorbic acid (0.2 mM L-ascorbic acid).

Tissue was incubated in OKB for 5 min at 0 ºC and gently shaken to disperse tissue evenly, then allowed to settle at the bottom of the tube. The supernatant was removed and replaced with 10 mL of fresh OKB. After 10-min pre-incubation at 25 ºC where the tube containing the tissue was rotated, the tissue was allowed to settle, and the supernatant was removed and replaced with 2 mL of 0.1 μM L-[3H] Norepinephrine (20 Ci/mmol; radiochemical purity 90%; NEN, Boston,

MA, U.S.A.), and the slices were incubated in a shaker for another 20 min at 37 ºC. The

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[3H]NE-containing supernatant was decanted off, and the slices were washed three times with 10 mL of fresh OKB. The first wash was followed by a 5-min incubation, and the subsequent washings were followed by a 2-min incubation. The supernatant from the last wash was decanted, and the tissue was re-suspended to 1:1 tissue to fresh OKB. The tissue was then gravity-packed for 10 min. The slices (~20 mg wet weight/basket) were transferred to nylon- nitrex mesh bottom baskets immersed in OKB-containing 12-well cell culture plates, with each well containing 3 mL OKB and incubated at 37°C. In instances of drug stimulation, the drug was contained in a separate well with the proper drug diluted into the buffer.

[3H] Norepinephrine Release Assay

Two sets of 6 12-well plates were used for each experiment, and each experiment was run in duplicate at least twice (using different animals). Two sets of four baskets were welded together in order to move them along the wells at a similar rate. All experiments from this point on were conducted in a temperature regulated incubator. Tissue was allowed to settle in the first well of the first plate for 5 min, then for 10 min for the second and third wells (total incubation period of 25 minutes in the 1st plate). In the second plate, the tissue was incubated for 2 min in each of the 3 wells (total incubation period of 6 min- this plate served as our basal release plate for the glutamate/NMDA stimulation). In the third plate, which contained glutamate/NMDA in the second well, the tissue was incubated for 2 min in the first well which contained buffer, then for 2 min in the glutamate/NMDA containing well, followed by a 15 second incubation in the 3rd well of the third plate (total incubation period of 4 minutes and 15 seconds in the 3rd plate). In the fourth plate, the tissue was incubated in each of the 3 wells for 2 minutes (total incubation period of 6 minutes in the 4th plate- this plate served as an assessment as to whether the release returns to basal levels, as well as the basal release to which stimulation with 30 mM potassium chloride

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was normalized). In the fifth plate, which contained the 30mM potassium chloride in the second well, the tissue was incubated in each well for 2 minutes (total incubation period of 6 minutes in the 5th plate- this plate served as a positive control to assess the health of the tissue). In the sixth plate, the tissue was incubated for 2 minutes in the first and second well, and then for 30 minutes in the last well which contained -0.1N NaOH (total incubation period of 30 min- used to lyse the tissue in order to ascertain the total amount of the remaining [3H] NE). At the end of the experiment, 1 mL of the buffer from each well was removed at the end of the experiment to measure [3H] NE release. (Add something about adding 5mL scintillation cocktail and counted in liquid scintillation counter). Radioactivity was assessed in the various fractions by liquid scintillation spectrometry.

Data Analysis

Fractional release was calculated as DPM released into the medium during each 2-min interval as a percentage of the DPM content of the slices during that interval. The effect of the drugs was calculated as S/B: the fractional release during drug application (S) divided by the baseline of spontaneous release (B). Spontaneous basal (B) release was assessed as the average of the three samples before drug application. Stimulated (S) release was assessed as the average of the samples collected during drug application. Values were assessed using appropriate statistical tests using GraphPad Prism 5.

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Results

Glutamate-Stimulated [3H] Norepinephrine Release

In figure 24, we stimulated hippocampal slices with 1 mM of glutamate to induce [3H]

NE release. At baseline, the amount of [3H] NE that was released is minimal. After stimulation with glutamate, [3H] NE release is increased, then returns to basal levels after a short (2-minute) recovery period. We then followed by a high potassium stimulation to assess whether the slices were still healthy. We find that the high potassium (30 mM) buffer is capable of mediating a robust release of [3H] NE (Fig. 24-quantified in figure 28).

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Figure 24: Glutamate-stimulated [3H] NE release in adult rat hippocampal slices.

Hippocampal slices (10 mg of tissue from anesthetized rat) that were loaded with [3H] NE were incubated in Krebs buffer for four 2-minute epochs for basal release measurements, then stimulated with 1 mM glutamate (dissolved in Krebs buffer). Stimulation with glutamate results in enhanced [3H] NE release. When the hippocampal slices are replaced in normal Krebs buffer,

[3H] NE release returns to basal levels gradually. Stimulation with 30 mM potassium (dissolved in Krebs buffer) results in robust [3H] NE release that gradually returns to baseline.

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MK-801 Attenuates Glutamate-Stimulated [3H] Norepinephrine Release

In figure 25, we sought to verify that NMDARs are indeed what mediates the release of

[3H] NE. To this end, we incubated hippocampal slices in the NMDAR antagonist MK-801 before and during stimulation with 1 mM of glutamate. We find that MK-801 attenuates the amount of [3H] NE typically released by 1 mM of glutamate (see figure 24), suggesting that

NMDARs do indeed mediate the glutamate-stimulated [3H] NE release (Fig. 25- quantified in figure 28).

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Figure 25: MK-801 attenuates glutamate-stimulated [3H] NE release in adult rat hippocampal slices.

Hippocampal slices (10 mg of tissue from anesthetized rat) that were loaded with [3H] NE were incubated in Krebs buffer containing 10 μM MK-801 for four 2-minute epochs for basal release measurements, then stimulated with 1 mM glutamate (dissolved in Krebs buffer). Stimulation with glutamate in the continued presence of MK-801 results in an attenuated [3H] NE release.

When the hippocampal slices are replaced in normal Krebs buffer, [3H] NE release returns to basal levels gradually. Stimulation with 30 mM potassium (dissolved in Krebs buffer) results in robust [3H] NE release that gradually returns to baseline.

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NMDA-Stimulated [3H] Norepinephrine Release

As a final positive control that [3H] NE is mediated by NMDARs, we directly stimulated hippocampal slices with NMDA, an NMDAR specific agonist. We find that like glutamate,

NMDA is resulted in [3H] NE release (Fig. 26- quantified in figure 28). These data unequivocally show that as others have shown before, NE can be released from hippocampal slices in an NMDAR-dependent manner (Jones et al., 1987).

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Figure 26: NMDA-stimulated [3H] NE release in adult rat hippocampal slices.

Hippocampal slices (10 mg of tissue from anesthetized rat) that were loaded with [3H] NE were incubated in Krebs buffer for four 2-minute epochs for basal release measurements, then stimulated with 1 mM NMDA (dissolved in Krebs buffer). Stimulation with NMDA results in enhanced [3H] NE release. When the hippocampal slices are replaced in normal Krebs buffer,

[3H] NE release returns to basal levels gradually. Stimulation with 30 mM potassium (dissolved in Krebs buffer) results in robust [3H] NE release that gradually returns to baseline.

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AT-1001 Does Not Perturb NMDAR-Mediated Release of [3H] Norepinephrine

Finally, we incubated hippocampal slices in 20 μM AT-1001 before and during stimulation with 1 mM of glutamate. We find that AT-1001 does not perturb either basal or

NMDAR-mediated [3H] NE release (Fig. 27- quantified in figure 28). These data suggest what although AT-1001 is reported to bind to the NMDAR, it does not alter the function of the

NMDARs that we tested.

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Figure 27: AT-does not perturb glutamate-stimulated [3H] NE release in adult rat hippocampal slices.

Hippocampal slices (10 mg of tissue from anesthetized rat) that were loaded with [3H] NE were incubated in Krebs buffer containing 20 μM AT-1001 for four 2-minute epochs for basal release measurements, then stimulated with 1 mM glutamate (dissolved in Krebs buffer). Stimulation with glutamate in the continued presence of AT-1001results in [3H] NE release. When the hippocampal slices are replaced in normal Krebs buffer, [3H] NE release returns to basal levels gradually. Stimulation with 30 mM potassium (dissolved in Krebs buffer) results in robust [3H]

NE release that gradually returns to baseline.

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Figure 28: Pharmacological analysis of glutamate-stimulated [3H] NE release in adult rat hippocampal slices.

Hippocampal slices (10 mg of tissue from anesthetized rat) that were loaded with [3H] NE were incubated in Krebs buffer, then stimulated with 1 mM glutamate, 1 mM NMDA, 1 mM glutamate + 10 μM MK-801, or 1 mM glutamate + 20 μM AT-1001. Glutamate, NMDA stimulate the release of a comparable amount ofq1 glutamate + AT-1001 to release a comparable amount [3H] NE. MK-801 significantly attenuates the amount of [3H] NE released by glutamate, whereas AT-1001 has no significant effect on the amount of [3H] NE released by glutamate. A repeated measures ANOVA followed by a Tukey’s post hoc was used to test statistical significance (* significant at the 0.05 probability level).

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LIST OF ABBREVIATIONS

ACh Acetylcholine AChE Acetylcholine esterase AD Alzheimer’s disease ANOVA Analysis of variance CNS Central nervous system DHβE Dihydro-beta-ethroidine DIV Day in vitro E/I Excitatory-inhibitory GABA Gamma-Aminobutyric acid GPBC G protein-binding cluster HS High sensitivity IBI Inter-burst interval ISI Inter-spike interval LS Low sensitivity MCI Mild cognitive impairment MEA Multi-electrode array MLA Methyllycaconitine mGluRs Metabotropic glutamate receptors nAChR Nicotinic acetylcholine receptor [3H] NE [3H] norepinephrine NMDARs N-methyl-D-aspartate OKB Oxygenated Krebs-bicarbonate buffer PD Parkinson’s disease Saz-A Sazetidine-A SEM Standard error of the mean

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