A Thesis Entitled Investigation of Two Interacting Neurosteroid Sites on A

Home , Isoguvacine, Loreclezole, Pregnenolone sulfate

A Thesis

entitled

Investigation of two Interacting Neurosteroid Sites on a GABA Receptor by

Mutagenesis and Mathematical Modeling

Lindsay A. Horn

Submitted to the Graduate Faculty as partial fulfillment of the requirements for the Master of Science Degree in Cellular and Molecular Biology

______Dr. Bruce Bamber, Committee Chair

______Dr. Scott Molitor, Committee Member

______Dr. Guofa Liu, Committee Member

______Dr. Sadik Khuder, Committee Member ______Dr. Patricia R. Komuniecki, Dean College of Graduate Studies

The University of Toledo December 2012

An Abstract of Investigation of two Interacting Neurosteroid Sites on a GABA Receptor by Mutagenesis and Mathematical Modeling by Lindsay A. Horn Submitted to the Graduate Faculty as partial fulfillment of the requirements for the Master of Science Degree in Cellular and Molecular Biology The University of Toledo December 2012

Given the utility of GABA receptor ligands as anthelminthics and insecticides, further study of invertebrate GABA receptor modulation promises to help identify new drugs to combat emerging resistance. The C. elegans GABA receptor, UNC-49, is modulated by the neurosteroids pregnenolone sulfate and pregnanolone, identifying neurosteroids as potential lead compounds. Notably, neurosteroid sites are non-conserved between invertebrates and mammals, limiting possible cross-toxicity. This proposal is focused on better understanding neurosteroid modulation of UNC-49.

In several UNC-49 mutants, pregnanolone and sulfated neurosteroids serve as positive allosteric regulators suggesting neurosteroids may activate inhibitory and enhancing pathways simultaneously which contribute to an overall additive effect on the receptor. Our hypothesis is that there are two pathways operating separately and independently: a novel inhibitory pathway acting negatively, and the well-recognized enhancement pathway acting purely positively.

To test this hypothesis, we used data from a receptor mutation, M2 15’, designed to increase efficiency of the enhancing pathway and observed the difference to the balance between inhibition and enhancement. M2 15’ is a universal positive allosteric

iii

residue known for involvement in enhancement of GABA receptors by many modulators.

We mathematically modeled the data to attribute the change in modulation caused by the mutation solely to a change in enhancement. The experiment was performed on multiple receptor chimeras to confirm wider applicability of the model.

Because M2 15’ is known as an enhancing residue, we predict that changes in modulation will be attributable solely to increased activity of the enhancement pathway, thus confirming that enhancement is operating independently of and simultaneously with inhibition.

Receptors showed decreased inhibition and/or increased enhancement when exposed to the neurosteroids consistent with multiple pathways occurring. However, unexpectedly, the mutations to the enhancement pathway also affected the activity of the inhibition pathway. These data suggest M2 15’ is not involved solely in enhancement and may be involved in neurosteroid inhibition. This reveals a new function for well-studied

M2 15’ as a residue involved in both inhibition and enhancement.

In addition, modeling suggests that the neurosteroid directly affects the gating process rather than affecting the process that occurs between ligand binding and channel opening. This narrows down the set of receptor conformational changes that may be allosterically inhibited by neurosteroids. This contributes to an expanded understanding of the allosteric regulatory pathways of GABA receptors and has provided a lead-in toward drug development by adding to the characterization of a potentially useful site for the development of novel pesticides.

Contents

Abstract iii

Contents v

List of Tables x

List of Figures xiii

Abbreviations xviii

1 Introduction 1

1.1 Neurotransmission……………………………………………………………....1

1.2 Cys-loop Ligand Gated Ion Channels…………………………………………...3

1.2.1 Cys-loop LGIC Activation: C. elegans GluCl ……………………..….6

1.2.1.1 Ligand Binding…………………………………………..…..6

1.2.1.2 Transduction of Binding Energy to Channel Opening……....8

1.2.1.3 Mechanisms of Ion Permeation……………………………...9

1.2.2 Cys-loop LGIC Modulation: C. elegans GluCl ……………………...12

1.2.2.1 Picrotoxin Mechanisms……………………………………..12

1.2.2.2 Avermectin Mechanisms…………………………………...14

1.3 Vertebrate GABAA Receptor Allosteric Regulation ……………………..……18

1.3.1 Benzodiazepines………………………………………………………19

1.3.2 Barbiturates……………………………………………………………20

1.3.3 Volatile Anesthetics and Ethanol……………………………………...21

1.4 Targets for Antiparasitic Compounds in Medicine and Agriculture…………..23

1.4.1 Modulation of Invertebrate GABA Receptors: Anthelminthics……...27

1.4.2 Modulation of Invertebrate GABA Receptors: Insecticides…………..29

1.5 GABA Receptor Neurosteroid Binding Sites are Non-Conserved Between

Invertebrates and Vertebrates and Therefore a Potential Target for

Pesticides………………………………………………………………………30

1.5.1 Enhancement of Mammalian GABAA Receptors by Pregnane

Neurosteroids………………………………………………………….33

1.5.2 Modulation of Invertebrate GABA Receptors by Sulfated

Neurosteroids………………………………………………………….36

1.5.2.1 Modulation of Invertebrate GABA Receptors by PS….….36

1.5.2.2 Modulation of Invertebrate GABA Receptors by DHEAS..41

1.5.3 The GABA Receptor Neurosteroid Site is Non-conserved…………...45

1.6 Residues Mediating PS and DHEAS Modulation Also Appear to Mediate

Pregnanolone Action…………………………………………………………..46

1.7 Do Separate Neurosteroid Enhancement and Inhibition Pathways Operate in

UNC-49? ………………………………………………………………………61

1.8 Mathematically Modeling Dose-Response Relationships for GABA receptor

Modulation by Two Interacting Neurosteroid Sites…………………………...63

1.9 The Hill Equation as a Pharmacokinetic-Pharmacodynamic Model to Describe

Dose-Response Relationships………………………………………………...64

1.10 Variations on the Hill Equation to Account for Simultaneous Enhancement and

Inhibition……………………………………………………………………...69

2 Materials and Methods 73

2.1 Data Analysis…………………………………………………………………73

3 Results 74

3.1 Mathematical Modeling to Determine the Role of I300 in Neurosteroid

Modulation of UNC-49……………………………………………………….74

3.2 Mathematically Modeling the X, XY, and Y chimera Exposed to Pregnanolone

With the Multiplicative Equation……………………………………………..75

vii

3.2.1 Mathematical Modeling Proposal 1: The Change in Modulation of

the X, XY, and Y Chimera Caused by I300Q is Solely a Change in

Inhibition……………………………………………………………..75

3.2.2 Mathematical Modeling Examination: A Step-Wise View of Changes

in Enhancement Parameters………………………………………….80

3.2.3 Mathematical Modeling Proposal 2: The Change in Modulation of the

X, XY, and Y Chimera Caused by I300Q is Solely a Change in

Enhancement Quantified by the Multiplicative Equation…………...88

3.2.4 Mathematical Modeling Proposal 3: The Change in Modulation of

the X, XY, and Y Chimera Caused by I300Q is Caused by a

Combination of Changes in Enhancement and Inhibition…………..92

3.3 Examining an Alternative Model: the Additive Equation…………...………97

3.3.1 Mathematical Modeling Examination: A Step-Wise View of Changes

in Enhancement Parameters Using the Additive Equation………….98

3.3.2 Mathematical Modeling Proposal 2: The Change in Modulation of the

X, XY, and Y Chimera Caused by I300Q is Solely a Change in

Enhancement Quantified by the Additive Equation…………….....105

3.3.3 Mathematical Modeling Proposal 3: The Change in Modulation of

the X, XY, and Y Chimera Caused by I300Q is Caused by a

Combination of Changes in Enhancement and Inhibition…………109

viii

3.4 Additional Support for the Additive Model: Outward Currents Generated

From Exposure of the XY Chimera to Pregnanolone……………………114

3.5 Examining the Performance of the Additive Equation with a Different Data

Set: X, XY and Y Chimera Modulated by Pregnenolone Sulfate……..……117

3.5.1 Mathematical Modeling Proposal 2: The Change in Modulation of the

X, XY, and Y Chimera Caused by I300Q is Solely a Change in

Enhancement Quantified by the Additive Equation…………….....117

3.5.2 Mathematical Modeling Proposal 3: The Change in PS Modulation of

the X and XY Chimera Caused by I300Q is Caused by a Combination

of Changes in Enhancement and Inhibition……………..…………120

4 Discussion 124

References 132

List of Tables

1.1 Responsiveness of M1 Chimeras to Pregnanolone……………...……………....57

3.1 Parameter Initial Values for X and XY Chimera Inhibitory Hill Equation Fits...76

3.2 Inhibitory Hill Equation X and XI300Q Parameter Values……..…………...….77

3.3 Inhibitory Hill Equation XY and XYI300Q Parameter Values...... ……………..78

3.4 Inhibitory Hill Equation Y and YI300Q Parameter Values...…………….……..79

3.5 X (Inhibitory Hill Equation) and XI300Q (Multiplicative Equation) Parameter

Values……………………………………………………………………………89

3.6 XY (Inhibitory Hill Equation) and XYI300Q (Multiplicative Equation) Parameter

Values…………………………………………………………………………....90

3.7 Y (Multiplicative Equation) and YI300Q (Multiplicative Equation) Parameter

Values……………...……………...……………...……………...……………...91

3.8 Parameter Initial Values and Constraints for Multiplicative Fits…………….....93

3.9 Parameter Values for Fits Generated to Represent XI300Q (Pregnanolone)

Contributing to a Change in Inhibition, Enhancement, and Both (Multiplicative

Equation) ……………...……………...……………...……………...…………...94

3.10 Parameter Values for Fits Generated to Represent XYI300Q (Pregnanolone)

Contributing to a Change in Inhibition, Enhancement, and Both (Multiplicative

Equation) ……………...……………...……………...……………...…………...95

3.11 Parameter Values for Fits Generated to Represent YI300Q (Pregnanolone)

Contributing to a Change in Inhibition, Enhancement, and Both (Multiplicative

Equation) ……………...……………...……………...……………...…………..96

3.12 Parameter Initial Values and Constraints……………...……………...………....99

3.13 X (Inhibitory Hill Equation) and XI300Q (Additive Equation) Parameter

Values……………………………………………………….………………....106

3.14 XY (Inhibitory Hill Equation) and XYI300Q (Additive Equation) Parameter

Values……………………………………………………..….………………..107

3.15 Y (Additive Equation) and YI300Q (Additive Equation) Parameter Values….108

3.16 Parameter Initial Values and Constraints……………...……………...………..110

3.17 Parameter Values for Fits Generated to Represent XI300Q (Pregnanolone)

Contributing to a Change in Inhibition, Enhancement, and Both……………...111

3.18 Parameter Values for Fits Generated to Represent XYI300Q (Pregnanolone)

Contributing to a Change in Inhibition, Enhancement, and Both……………....112

3.19 Parameter Values for Fits Generated to Represent YI300Q (Pregnanolone)

Contributing to a Change in Inhibition, Enhancement, and Both (Additive

Equation).……………...……………...……………...……………...………….113

3.20 XY Chimera Treated With Pregnanolone Parameter Values…………………..116

3.21 X (Inhibitory Hill Equation) and XI300Q (Additive Equation) Parameter

Values……………………………………………….…………………………119

3.22 XY (Inhibitory Hill Equation) and XYI300Q (Additive Equation) Parameter

Values…………………………………………...…………………………...... 123

3.23 Parameter Initial Values and Constraints……………...……………...………..121

3.24 Parameter Values for Fits Generated to Represent XI300Q (Pregnenolone Sulfate)

Contributing to a Change in Inhibition, Enhancement, and Both……………....122

3.25 Parameter Values for Fits Generated to Represent XYI300Q (Pregnenolone

Sulfate) Contributing to a Change in Inhibition, Enhancement, and Both……..123

xii

List of Figures

1-1 Structural and Functional Components of a 5-HT3 cys-loop LGIC……………...5

1-2 Glutamate Binding Site of Glutamate Receptor……………...……………...……7

1-3 GluCl 1 Ion Channel……………...……………...……………...……………...... 9

1-4 GluCl 1 Ion Channel Selectivity……………...……………...……………...…..11

1-5 Picrotoxin Binding Site of Glutamate Receptor……………...……………...…..13

1-6 Ivermectin Binding Site of Glutamate Receptor……………...……………...…..17

1-7 Phylogenetic Analysis of Vertebrate and Invertebrate Cys-loop Receptor

Subunits……………...……………...……………...……………...…………….24

1-8 Neurosteroid Structure……………...……………...……………...……………..32

1-9 A Model of THDOC Bound to the Subunit Between M1 and M4…………….34

1-10 UNC-49 Splice Variants……………...……………...……………...…………...37

1-11 Proposed Neurosteroid Binding Orientation……………...……………...………44

1-12 Pregnanolone Inhibition of M1 and M2-3 Loop Point Mutants……...………….48

1-13 Pregnanolone Inhibition of the X, Y, and XY Chimeras………………………...50

xiii

1-14 Pregnanolone Modulation of M1 N Terminal Mutants……………...…………..52

1-15 No single UNC-49C Y Segment Mutant Residue is Sufficient to Confer Inhibition

by Pregnanolone to the Level of the XY Chimera………………………....…….54

1-16 No single UNC-49B Y Segment Residue Confers Inhibition Levels to the Extent

of QF-R……...……………...……………...……………...……………...……...56

1-17 Proposed Pregnanolone Binding Orientation……………...…………...………...60

3-1 Pregnanolone Modulation of the X Chimera and XI300Q Chimera…………….77

3-2 Pregnanolone Modulation of the XY Chimera and XYI300Q……………...…...78

3-3 Pregnanolone Modulation of the Y Chimera and YI300Q……………...……….79

3-4 Step-wise Increases in X Chimera EC50 Values (Multiplicative Equation)……..82

3-5 Step-wise Increases in X Chimera aE Values (Multiplicative Equation).…...…..83

3-6 Step-wise Increases in XY Chimera EC50 Values (Multiplicative Equation)…...84

3-7 Step-wise Increases in XY Chimera aE Values (Multiplicative Equation).……..85

3-8 Step-wise Increases in Y Chimera EC50 Values (Multiplicative Equation)…...... 86

3-9 Step-wise Increases in Y Chimera aE Values (Multiplicative Equation).…….....87

3-10 I300Q Multiplicative Equation Fits with Fixed Inhibition Values……….……...89

3-11 I300Q Multiplicative Equation Fits with Fixed Inhibition Values…..………....90

3-12 I300Q Multiplicative Equation Fits with Fixed Inhibition Values…………...... 91 xiv

3-13 I300Q Multiplicative Equation Fit with Non-constrained Enhancement and

Inhibition……………...……………...……………...……………...…………...93

3-14 I300Q Multiplicative Equation Fit with Non-constrained Enhancement and

Inhibition……………...……………...……………...……………...…………...94

3-15 I300Q Multiplicative Equation Fit with Non-constrained Enhancement and

Inhibition……………...……………...……………...……………...…………...95

3-16 Step-wise Increases in X Chimera EC50 Values……………...………..…...…...99

3-17 Step-wise Increases in X Chimera aE Values……….………...…….……...….100

3-18 Step-wise Increases in XY Chimera EC50 Values……………....……..…...….101

3-19 Step-wise Increases in XY Chimera aE Values……….………...….….…...….102

3-20 Step-wise Increases in Y Chimera EC50 Values………………...……..…...….103

3-21 Step-wise Increases in Y Chimera aE Values……….………...……….…...….104

3-22 I300Q Additive Equation Fits with Fixed Inhibition Values……………...…...106

3-23 I300Q Additive Equation Fits with Fixed Inhibition Values……………...... 107

3-24 I300Q Additive Equation Fits with Fixed Inhibition Values……………...…...108

3-25 I300Q Additive Equation Fit with Non-constrained Enhancement and

Inhibition……………………………………………………………………….110

3-26 I300Q Additive Equation Fit with Non-constrained Enhancement and

Inhibition... ……………...………………...…………………………………...111

3-27 I300Q Additive Equation Fit with Non-constrained Enhancement and

Inhibition……………………………………………………………………….112

3-28 The XY Chimera Exposed to Pregnanolone and fit With the Additive

Equation………………………………………………………………………..116

3-29 I300Q Additive Equation Fits with Fixed Inhibition Values (Pregnenolone

Sulfate)……………...……………...... ……………...……………...... 118

3-30 I300Q Additive Equation Fits with Fixed Inhibition Values (Pregnenolone

Sulfate) ……………...……………...... ……………...……………...... 119

3-31 I300Q Additive Equation Fit with Non-constrained Enhancement and Inhibition

(Pregnenolone Sulfate)……………...... ……………...……………...... 121

3-32 I300Q Additive Equation Fit with Non-constrained Enhancement and Inhibition

(Pregnenolone Sulfate)……………...... ……………...……………...... 122

xvi

List of Abbreviations

5-HT ……………………….serotonin

ALLOP………………….....allopregnanolone AMPA……………………...α-amino-3-hydroxy-5-methyl-4-isoxazolepropionic acid

C. briggsae…………………….Caenorhabditis briggsae C. elegans……………………..Caenorhabditis elegans CNS………………………...central nervous system

D. melanogaster………………Drosophila melanogaster DA……………………….…dopamine DDT………………………...dichlorodiphenyltrichloroethane DHEAS…………………….dehydroepiandrosterone sulfate

ECD………………………..extracellular domain

GABA……………………...γ-aminobutyric acid GluCl……………………….glutamate-gated chloride channel GlyR………………………..glycine receptor GPCR………………………G protein-coupled receptor

xvii

ICD…………………………intracellular loop

LGIC……………………….ligand-gated ion channel nAChR……………………...nicotinic acetylcholine receptor NMDA……………………...N-methyl D-aspartate

PNS…………………………peripheral nervous system PS…………………………...pregnanolone sulfate

xviii

Chapter 1

Introduction

1.1 Neurotransmission

Nervous systems contain two types of cells: neurons and glia. Neurons function to receive input from other neurons, process the input, and send the information to other neurons via a synapse. This process, neurotransmission, is responsible for vertebrate motor function, sensory function (seeing, hearing and touching) and cognitive function

(thinking, reasoning, and planning). Chemical neurotransmission occurs when the action potential of a presynaptic cell causes the release of a neurotransmitter sensed by a postsynaptic cell leading to a downstream effect on the postsynaptic cell potential. The postsynaptic cell contains receptors that bind chemical neurotransmitters causing changes in the receptor leading to changes in cellular activity.

Amino acids, biogenic amines, peptides, and various others chemicals serve as neurotransmitters. Neurotransmitters activate a receptor leading to an excitatory or inhibitory effect on the post-synaptic cell. Glutamate, an amino acid, has a diverse effect in the vertebrate nervous system. Glutamate binds several post synaptic receptors including N-methyl D-aspartate (NMDA) and α-amino-3-hydroxy-5-methyl-4- isoxazolepropionic acid (AMPA) receptors. Glutamate receptors are important for learning and memory. Dopamine (DA), a biogenic amine, activates dopamine receptors

in both invertebrates and vertebrates and is important in attention, sleep, cognition, motivation, and learning. Serotonin (5-HT) is a monoamine neurotransmitter that primarily binds G protein-coupled receptors (GPCRs) which mediate both inhibitory and excitatory neurotransmission. Serotonin is involved in appetite, anxiety, aggression and many other neurological processes. Acetylcholine activates muscle in the peripheral nervous system (PNS) and has a variety of effects as a neuromodulator in the central nervous sytem (CNS). Acetylcholine has been linked to many physiological processes including mood and memories and diseases including myasthenia gravis and Alzheimer’s disease.

γ-aminobutyric acid (GABA) is the chief fast inhibitory neurotransmitter in the central nervous system. A-type, or GABAA receptors, are ligand-gated ion channels that mediate the majority of GABA neurotransmission. GABA molecules bind and activate

GABAA receptors triggering the opening of an intrinsic ion selective pore which allows negative chloride ions to flow into the neuron. This chloride influx causes hyperpolarization, resulting in an inhibitory effect on the neuron action potential. GABAA receptors have been linked to many neurological and psychiatric diseases (Macdonald et al., 1994). Activation of the GABAA receptor has physiological implications ranging from anxiolytic effects to sedation. Anxiety and epilepsy have been attributed to reduced function of GABAA receptors and the effects of anesthesia have been linked to activation of GABAA receptors. GABAA and GABAB are the most extensively studied classes of

GABA receptors. GABAB receptors are GPCRs and are involved in the regulation of potassium channels via G-proteins. GABAA-ρ receptors, also known as GABAC receptors, are a subclass of GABAA receptors composed entirely of ρ subunits and are 2

insensitive to typical GABAA receptor allosteric modulators such as benzodiazepines and barbiturates.

1.2 Cys-loop Ligand Gated Ion Channels

GABA receptors, nicotinic acetylcholine receptors (nAChRs), 5-HT3 serotonin receptors (Figure 1-1), glycine receptors (GlyRs) and glutamate-gated chloride channels

(GluCls) belong to the cysteine-loop ligand-gated ion channel (cys-loop LGIC) superfamily. In addition to common structural and functional regions, all members of the cys-loop receptor family share approximately 30% sequence homology (Olsen et al.,

2008).

Cys-loop receptors are pentameric membrane-spanning proteins containing a central pore which forms an ion channel through the cell membrane. Each of 5 subunits contains an extracellular domain (ECD) consisting of an N-terminal hydrophilic region and a relatively short C-terminal domain. The ECD also contains the characteristic cys-loop ligand-binding region containing two cysteine residues separated by 13 amino acids and connected by a disulphide bond. The transmembrane domain contains 4 -helices (M1-

M4) which allow ions to cross the membrane and are involved in modulation by various ligands. M2 forms the lining of the ion channel. The intracellular domain (ICD) is involved in ion access to the conducting pore and receptor modulation and contains the

M3-M4 intracellular loop. The domains, subunits, and general structure of the 5-HT3 cys- loop LGIC are shown in figure 1-1 (Thompson et al., 2010).

The crystal structure of the glutamate-gated chloride channel GluCl 1 from

Caenorhabditis elegans (C. elegans) was recently elucidated and illustrates functional principles that can be applied to the entire superfamily of cys-loop LGICs. The structure reveals the ligand binding site and the binding site for an allosteric regulator, ivermectin, and has greatly clarified our understanding of how these channels work.

Figure 1-1: Structural and Functional Components of a 5-HT3 cys-loop LGIC (Thompson et al., 2010)

The 5-HT3 receptor, a typical cys-loop LGIC, consists of 5 subunits (1-5). Receptor structure is shown with 3 main domains indicated: the intracellular, extracellular and transmembrane domains.

1.2.1 Cys-loop LGIC Activation: C. elegans GluClα Activation by Glutamate

1.2.1.1 Ligand Binding

Cys-loop receptor activation occurs when the receptor ligand binds the receptor ECD at either of two pockets formed between 2 proximal subunits. The pocket consists of 6 loops (loops A-F). One subunit forms the sides of the box-like binding site and β-strands from the adjacent subunit form the base. Loop C has a critical role in ligand binding and adopts a closed confirmation during binding. According to studies based on crystal structures of the C. elegans GluCl glutamate receptor, loop C contains two tyrosine residues that sandwich functional groups of the glutamate molecule. Loop C Tyr200 has a

3.8Å cation-π interaction with the -amino nitrogen of glutamate. Tyr151 contains a backbone carbonyl oxygen that also interacts with the -amino nitrogen of glutamate. In addition, the same -amino nitrogen of glutamate forms a hydrogen bond with the backbone carbonyl oxygen of Ser150 (Figure 1-2).

Figure 1-2: Glutamate Binding Site of Glutamate Receptor

(Hibbs et al., 2011)

A) Extracellular view of glutamate in subunit interface binding pocket with important residues and interactions indicated B) Additional view of glutamate binding site from transmembrane domain. Loop C has been removed. 7

1.2.1.2 Transduction of Binding Energy to Channel Opening

Glutamate binding energy is transferred into mechanical energy causing a shift in protein structure leading to a pore conformation favorable for the passing of chloride ions through the membrane. Conformational changes start at the ligand binding site. The subunit that contributes β-strands to the binding pocket contains positive residues that contact glutamate including Arg56 and Arg37 (Figure 1-2). Arg56 of loop D contacts the glutamate -carboxylate and γ-carboxylate groups. The Arg56 side chain then shifts by

0.5Å to accommodate the glutamate molecule. Tyr200, located in loop C, also shifts by about 0.5Å closer to the ligand. These residues, in addition to Arg37 and the M2-M3 loop, are directly connected to the ion pore. Arg 37 belongs to a region important for binding interactions on another LGIC, the nAChR (Quiram et al., 2000). The pore is lined with M2 -helices (Figure 1-3). These movements lead to a splaying apart of the

M1 and M3 helices in addition to the shift of M2 away from the pore axis causing the opening of the pore.

Figure 1-3: GluCl 1 Ion Channel

(Hibbs et al., 2011)

Internal surface of ion pore including M2 -helices residues lining the pore

1.2.1.3 Mechanisms of Ion Permeation

The mechanisms of ion permeation begin with pore opening and closing caused by protein conformational changes initiated in this case by the binding of glutamate to

GluCl . In addition, ion channels exhibit selection among different ions, control the rate of ion movement, and are ultimately tightly regulated by many simultaneous processes.

For GluCl , ion selectivity is accomplished by electrostatic potential caused by the N- terminal end of the M2 helix dipoles and by pore constriction caused by proline residues.

A positive electrostatic potential was found at the base of the pore and because no residues making up the pore contain a formal charge, this is likely caused by peptide dipoles in the M2 -helices. Hibbs et al. used iodide to identify pore residues important in chloride binding and selectivity. The iodide ions sit in a positive electrostatic potential consisting of M2 2’ Pro residues, 1’ Ala and 3’ Ile residue atoms. The 1’ Ala residue is consistent with other chloride specific receptors which contain either an alanine or glycine at the 1’ position (Figure 1-4, X’ nomenclature is indicated in figure 1-3).

Figure 1-4: GluCl 1 Ion Channel Selectivity

(Hibbs et al., 2011)

A) Electropositive pockets where iodide binds the GluCl 1 pore

B) Chloride binding site and Thr residues that contact the chloride ion

1.2.2 Cys-loop LGIC Modulation: C. elegans GluCl

In addition to the binding of glutamate to the GluCl receptor, many other compounds bind the receptor causing changes in protein function. Allosteric modulation is the regulation of protein function caused by an effector molecule binding the protein at an allosteric site. An allosteric site is a binding site other than the active site. All cys-loop receptors are subject to allosteric modulation by a wide variety of endogenous and synthetic compounds. GABAA receptors are allosterically modulated by drugs such as anaesthetics, alcohols, barbiturates, picrotoxin, ivermectin and benzodiazepines. For example, for GABAA receptors, barbiturates potentiate the effect of GABA by increasing efficiency of gating machinery. Synthetic modulators of GABAA receptors are widely used as drugs targeting the nervous system. All of these compounds target specific allosteric binding sites on the cys-loop receptor. Understanding the mechanisms of cys- loop inhibition and enhancement is important for the continued development of pesticides and other drugs that act on GABA receptors.

1.2.2.1 Picrotoxin Mechanisms

Picrotoxin is an allosteric inhibitor of glutamate receptors. Picrotoxin works as an open-channel blocker of glutamate receptors. The crystal structure of the GluCl receptor bound to ivermectin and soaked in picrotoxin has revealed the mechanism of inhibition that is likely shared by many other cys-loop allosteric modulators. Crystal structures show picrotoxin present on the five-fold axis of molecular symmetry at the intracellular side of the transmembrane pore (Figure 1-5). The fused tri-cyclic picrotoxin rings face 12

out of the cell near the 2’ Thr. The isoprenyl tail faces the cytoplasm next to the -2’ Pro residues. The 2’ Thr hydroxyl groups are in contact with the picrotoxin oxygen molecules and the -2’ Pro non-polar side chains contact the picrotoxin isoprenyl moiety. Picrotoxin resistance is attributed to M2 2’ mutations (Hibbs et al., 2011).

Figure 1-5: Picrotoxin Binding Site of Glutamate Receptor

(Hibbs et al., 2011)

A) Membrane view of picrotoxin in subunit interface binding pocket B) Picrotoxin molecule surrounded by residues important for binding pocket interactions

The mechanisms of allosteric inhibition and enhancement are important to understand for the characterization of cys-loop receptor mechanisms and development of drugs.

Ivermectin is an anti-parasitic drug used to treat parasitic infection and river blindness.

Ivermectin belongs to the avermectin family of anti-parasitic drugs. The crystal structure of the C. elegans GluCl receptor bound to ivermectin has recently revealed the mechanism of modulation that is likely shared by many other cys-loop allosteric activators.

1.2.2.2 Avermectin Mechanisms

Ivermectin activates glutamate gated chloride channels by binding 2 proximal subunits. Ivermectin contacts the M3 -helix of one subunit and the M1 -helix of the other. The binding site is located on the extracellular side of the transmembrane domain.

Ivermectin specifically contacts two turns of the M1 and M3 helices. Ivermectin is centered at residues Leu218 and Ile222 of M1 on one turn of π-helix. The tertiary hydroxyl group on ivermectin forms a hydrogen bond with the carbonyl oxygen of

Leu218 (Hibbs et al., 2011).

Ivermectin forms hydrogen bonds with the M2 and M3 helices as well. The M2 residue that forms a hydrogen bond with ivermectin is M2 15’ or Ser260, which is a key determinant of positive allosteric regulation in many cys-loop receptors including GABA receptors, 5-HT3 receptors and glycine receptors. For GABAA receptors, this serine residue is also important for modulation by alcohols and anesthetics. GluClβ and 7 nAChRs are not directly activated by ivermectin and do not contain this otherwise conserved serine residue. Ser260 specifically forms a hydrogen bond with the secondary

hydroxyl group located on the cyclohexane ring of ivermectin (Figure 1-6). The ivermectin interaction with M2 is suspected to increase the affinity of ivermectin and increase the stability of the receptor ion channel open state produced by ivermectin activation (Hibbs et al., 2011).

Using GluCl crystal structures with ivermectin bound, Hibbs et al. discovered that the separation between Leu218 and Gly281 C atoms increases from 6.4Å to 9.4Å upon ivermectin binding. This indicates a splaying apart of the M1 and M3 -helices. A proline residue at the C-terminal end of the short π-helix is highly conserved and directly contacts ivermectin. This residue is suspected to be responsible for the splaying apart of the M1 and M3 -helices. T285 of M3 forms a hydrogen bond with ivermectin directly.

This splaying leads to a global conformational change causing the M2 -helix to shift away from the pore and subsequently opening the ion conductive pathway. The confirmation of M2 is stabilized by the direct interaction of M2 with ivermectin.

Additional stabilization of the open state may occur by ivermectin disaccharide moiety contact with Ile273 of the M2-M3 loop. In addition to M2 15’, hydrophobic residues in the M2-M3 loop are also have a central role in activation of cys-loop receptors because they are highly conserved and important in receptor modulation (Hibbs et al., 2011).

In addition to activating GluClα, ivermectin renders the receptor susceptible to further activation by glutamate. Ivermectin specifically stabilizes an activated conformation of the glutamate binding site and glutamate stabilizes the open state of the receptor. Overall,

Hibbs hypothesizes “that the global conformational change induced by ivermectin binding is rooted in the splaying apart of the M1 and M3 helices and the movement of the

apical portion of M2 away from the pore axis, towards the periphery of the receptor, opening an ion conductive pathway.” The most important receptor residues for the avermectin modulation mechanism include M2 15’ and the M2-M3 loop.

Figure 1-6: Ivermectin Binding Site of Glutamate Receptor

(Hibbs et al., 2011)

A) Membrane view of ivermectin in subunit interface binding pocket with important residues and interactions indicated B) Additional view of ivermectin binding site from extracellular domain C) Structure of ivermectin with residue interactions indicated 17

1.3 Vertebrate GABAA Receptor Allosteric Regulation

The crystal structure of GluCl provided valuable information on allosteric enhancement mechanisms of cys-loop receptors. The following examples of allosteric modulation of vertebrate GABAA receptors support a fundamental and universal role for

M2-15’ in positive allosteric regulation. We can use this insight to help characterize modulation mechanisms for additional receptors in the same family.

The diversity of cys-loop LGICs is dependent on subunit composition and subunit- encoding genes. Subunit composition determines the properties of the receptor including localization, activities, and clinical and pharmacological effects. Specifically for GABAA receptors, 19 total genes have been identified as subunits and classified into 7 subunit types: (1-6), β (1-3), γ (1-3), δ, ε, θ, π, and ρ(1-3) (Simon et al.,2004). Most synaptic

GABAA receptors mediating phasic inhibition are heteropentamers consisting of 2 , 2β and 1γ ( 2β2γ) subunit (Tretter et al.,1997; Baumann et al.,2002). Many subunit combinations are possible with 19 contributing genes, and at least 20 different combinations of subunits have been experimentally confirmed. Subunit diversity is essential for the development and action of drugs that act on cys-loop receptors.

Mammalian and invertebrate GABA receptors exhibit different pharmacological sensitivities. Vertebrate GABAA receptors are the target of several important drugs such as alcohols and anesthetics. Invertebrate GABA receptors are the targets of several insecticides and anthelminthics such as dieldrin and piperazine. Differences in invertebrate and vertebrate GABA receptors form the basis on which pesticides are developed and function. 18

GABA receptors are modulated by many positive and negative regulators that have binding pockets in various locations on the receptor. A common set of core mechanisms can be found throughout these inhibitory and enhancing allosteric modulation mechanisms. Key residues for activation include M2 15’ serine and the M2-M3 loop. The

M2 2’ residue is often important in inhibition of cys-loop receptors by allosteric modulators. The distinct mechanisms of vertebrate GABAA receptors have been explored due to the potential for the development of beneficial drugs and understanding of human disease.

1.3.1 Benzodiazepines

Benzodiazepines are psychoactive drugs consisting of the fusion of a benzene ring and a diazepine ring. Benzodiazpines have been used for the treatment of anxiety and panic, insomnia, and seizures. Benzodiazepines allosterically potentiate GABAA receptors by inducing a conformational change which causes an increase in the frequency of chloride ion channel opening and results in a greater affinity for GABA.

Benzodiazepines interact with 1βγ2, 2βγ2, 3βγ2, or 5βγ2 GABAA receptors by binding an aromatic portion of the /γ subunit interface. GluCl activation by glutamate and

GABAA receptor activation by GABA both also occur at a subunit interface. Residues important for allosteric modulation include Y235, F236 and T237 at the N-terminus of

M1, T281, and I282 at the C-terminus of M2 and the M2-M3 loop (Jones-Davis et al.,

2005).

1.3.2 Barbiturates

Barbiturates act on GABAA receptors and are anesthetics based on the structure of barbituric acid. Barbiturates have been largely replaced for medical use by benzodiazepines. The dangers of long-term exposure prevent barbiturate use as a therapeutic drug, and thiopental is the only barbiturate that remains in use medically as a short-term anesthetic. At higher doses, thiopental and pentobarbital are used for judicial executions and euthanasia of animals. Barbiturates can activate GABAA receptors in the absence of the GABA molecule. In contrast, benzodiazepines require the presence of

GABA to activate GABAA receptors. This may explain the wide range of barbiturate physiological properties including anesthetic, sedative, anxiolytic, anticonvulsant and hypnotic activities. Thiopental, hexabarbital and pentobarbitial prolong the lifetime of

GABAA currents and stereo-specifically enhance GABAA receptors by directly gating the chloride ion channel at high concentrations (Steinbach et al., 2001). Contrary to benzodiazepines, which increase the frequency of chloride channel, barbiturates increase the duration of chloride channel opening. The conserved M2 15’ residue does not affect barbiturate modulation of GABAA receptors. The M1 residues from the N-terminus to pro241 in the δ subunit of a mammalian GABAA receptor are necessary and sufficient for potentiation by pentobarbital (Feng et al., 2010). This suggests barbiturates do not share the same mechanism of allosteric enhancement as ivermectin and benzodiazepines but work by a distinct binding site and mechanism.

1.3.3 Volatile Anesthetics and Ethanol

Ethanol is a straight chain alcohol and exhibits mild psychoactive effects. Ethanol causes physiological effects partially by enhancement of GABAA receptors. The potency of the effects of alcohols on GABAA receptors increases with alcohol chain length up to

12 carbons (Didly-Mayfield et al., 1996). The first evidence that GABAA receptors mediate ethanol behavioral effects was revealed by a GABAA receptor homozygous subunit mutation, 6R100QQ which caused increased alcohol sensitivity of tonic GABA currents and increased alcohol-induced ataxia. Ro15-4513 is an antagonist of alcohol that binds 4/6β3δ receptors with high affinity. Ro15-4513 binds competitively with ethanol suggesting a specific ethanol binding site that mediates alcohol actions at low to moderate doses (Olsen et al., 2007). High doses of ethanol and long-chain alcohols bind to anesthetic sites and cause anesthetic activity (Mihic et al., 1997).

Residue M2 15’ is essential for action of ethanol on GABAA receptors (Ueno et al.,

1999). M2 15’ transduces many different structurally-diverse compounds, and so is likely to be a broad-spectrum determinant of positive allosteric regulation. In addition to ethanol, etomidate, furosemide, loreclezole, mefenamic acid and isoflurane show changes in receptor sensitivity with the mutation of residues homologous to M2 15’. The M2-M3 loop is also well known as an important region for alcohol modulation of cys-loop receptors.

Volatile anesthetics structure greatly varies and includes a wide variety of chemical bases such as halogenated and fluorinated ethers, trihalomethanes, cycloalkanes and oxides of nitrogen. Current medically used volatile anesthetic ethers include isoflurane, 21

desflurane, and sevoflurane. Ethers enhance GABAA receptor function by increasing receptor affinity for GABA and affecting the opening probability of the receptor. All

GABAA receptors with an subunit are sensitive to one or more volatile anesthetics.

Volatile anesthetics enhance the amplitude of GABA current response to low concentrations of GABA and increase the duration of synaptic inhibition.

Mihic et al. used a structure-function approach to identify residues important for the modulation of GABAA receptors by volatile anesthetics and alcohols. Because GABA receptor ρ1 subunits are not enhanced by anesthetics, chimeras containing ρ1 subunits and other anesthetic-sensitive GABAA receptor subunits were constructed to identify residues involved in modulation by volatile anesthetics and alcohols (Mihic, et al., 1997).

For example, a GABA receptor ρ1 subunit and glycine-receptor 1 subunit chimera is sensitive to enflurane and ethanol suggesting the portion of the chimera from glycine- receptor 1 contains residues responsible for volatile anesthetic and ethanol sensitivity.

GABAA receptor -2 S270I of M2 (M2 15’) and A291W of M3 regulation by isoflurane and enflurane were abolished, but when exposed to propofol, another general anesthetic with a distinct structure, receptor potentiation was not altered suggesting these residues are specific for modulation of ethanol and enflurane (Mihic et al., 1997). S270 is the M2

15’ residue in Xenopus and is an isoleucine residue in invertebrates, specifically Ile307 in the GABAρ1 subunit. Mihic et al. found that in both glycine receptors and GABAA receptors, M2 and M3 were important for modulation by alcohol and volatile anesthetics.

The M2 15’ residue serves an important role in modulation of GABA receptors by alcohols and anesthetics and in general positive allosteric modulation of GABA

receptors. Knowledge of this residue and its role in modulation of GABA receptors will contribute to the development of new drugs targeting GABA receptors.

1.4 Targets for Antiparasitic Compounds in Medicine and Agriculture

The difference between invertebrate GABA receptors and mammalian GABAA receptors are important because invertebrate LGICs are the target of important pesticides and anthelminthics. The nematode C. elegans has the largest known LGIC family containing 90 genes consisting of nAChRs, GABA receptors, GluRs, and acetylcholine and serotonin gated ion channels, (Bargmann, 1998). Caenorhabditis briggsae has a similar LGIC subunit superfamily with only a few subunits missing relative to C. elegans. Parasitic nematodes, Brugia malyai and Trichinella spiralis have much smaller subunit families than C. elegans and C. briggsae. However, orthologs have been clearly identified between C. elegans and both parasitic nematodes. These genes may belong to a core group of receptor genes required for the function of all nematode nervous systems which explains why genes such as these may be useful as anti-parasitics (Williamson et al., 2007). Similarities in nematode genes suggest studies with model organisms may provide useful insight for the development of drug targets in parasitic nematodes.

Figure 1-7: Phylogenetic Analysis of Vertebrate and Invertebrate Cys-loop

Receptor Subunits

(Bamber et al., 2003)

A) M2 segments B) Percent sequence similarity for C. elegans UNC-49B and UNC-49- C and human cys-loop receptor subunits C) GABA receptor subunit phylogenetic tree. Bootstrap values are indicated above each branch

Insects also have large LGIC families on which drug development has been based. In contrast to vertebrate GABAA receptors, insect GABA receptors are insensitive to bicuculline, have low sensitivity to 3-aminopropanesulfonic acid and high sensitivity to isoguvacine (Hosie et al., 1996) and muscimol (Buckingham et al., 1994). There are similarities between the C. elegans LGIC family and the insect LGIC family. Drosophila melanogaster, for example, has 23 genes that code for nAChRs, GABA receptors, GluRs and histamine anion channels. Many of these subunits are orthologs of nematode subunits. For example, C. elegans UNC-49 GABA receptor and D. melanogaster RDL

GABA receptor are orthologs. C. elegans, similarly to D. melanogaster, contains genes encoding nAChRs, GABA receptors, and GluRs. However, C. elegans contains cys-loop

LGICs gated by acetylcholine and serotonin that have not been identified in insects or vertebrates, and no C. elegans histamine gated channels have been identified. Even so, many of the C. elegans LGIC genes have yet to be classified. Evolutionarily, it appears that the GABA receptor and nAChR families evolved from common ancestors of vertebrates and invertebrates. Indeed, the earliest LGICs evolved in bacteria, such as the frequently studied ELIC and GLIC from the organisms Erwinia chrysanthemi and

Gloeobacter violaceus respectively (Tasneem et al., 2004). Other, more specialized subunits evolved subsequent to the vertebrate and invertebrate split including GluCl receptors and glycine receptors. The nematode and insect evolutionary split caused the development of additional specialized subunits such as the serotonin receptors present in

C. elegans and the histamine receptors present in D. melanogaster. As with vertebrates, invertebrate GABA receptor modulation is based on subunit composition. Restriction to

invertebrate modulation is the basis for the development of useful insecticides and anthelminthic drugs.

The D. melanogaster GABA receptor subunit, RDL, is the target of cyclodiene and phenylpyrazole insecticides. Dieldrin, a cyclodiene, acts as a non-competitive antagonist of RDL. Insecticide resistance has arisen from a single mutation of alanine to serine at the

M2 2’ position in the lumen of the GABA receptor channel. The M2 2’ residue is important for inhibitory modulation of cys-loop receptors. This residue also confers resistance to several other pesticides including fipronil and picrotoxin. Fipronil is important in crop protection including use as a rice pesticide, lawn maintenance, locust control, and to treat tick and flea infections in domestic pets. Fipronil is a leading pesticide currently used world-wide and complete resistance would have devastating effects.

The development of a drug that will act only on invertebrate receptors and will not adversely affect humans allows for the control of pests while maintaining the safety of humans. Infections by helminths including hookworms, whipworms and Ascaris, cause disease in animals, plants, and greater than two billion people worldwide. Human infections can lead to anemia and malnourishment. Animal and plant infections cause severe economic losses in agriculture. Increasing our knowledge of invertebrate and parasite physiology, including several cys-loop LGIC potential drug targets, provides insight for the development of insecticides and anthelminthics. The list of requirements for pesticides is lengthy: safe to humans and the environment, cost-effective, and non- susceptibility to resistance. The rewards for effective pesticides are numerous as well

including saving the potential economic loss and human disease caused by parasites. In addition to the savings of economic loss, pesticides themselves are profitable such as fipronil, for example, which sold $470M in 2009 alone. Studying invertebrate GABA receptors and characterizing modulation allows for a directed and rational search for anthelminthics and insecticides.

For this study, we are interested in characterizing specific modulation mechanisms of invertebrate cys-loop receptors. The identification of key regulatory residues belonging to orthologous nematode and insect receptors can serve as a paradigm for designing novel insecticides and anthelminthics. Specifically, we are interested in the modulation of C. elegans GABA receptor UNC-49 and the drosophila ortholog RDL.

1.4.1 Modulation of Invertebrate GABA Receptors: Anthelminthics

Anthelminthic drugs act by different mechanisms and target different proteins. Most anthelminthics target the nervous system of the parasite. Levamisole and the terahydropyrimidines (pyrantel, morantel) bind to the nicotinic acetylcholine receptors leading to paralysis. As discussed, ivermectin targets GluCl receptors, and piperazine targets GABA receptors. Octadepsipeptides interfere with a Ca2+-activated K+ channel,

SLO1 (Guest et al., 2007). Other anthelminthics do not target the nervous system, such as benzimidazole-based anthelminthics which bind tubulin. Some mechanisms of anthelminthic drug action have yet to be discovered, such as praziquantel, an anthelminthic used primarily for the treatment of schistosomiasis. A relatively new class of anthelmintics, cyclooctadepsipeptides, bind to two GPCR latrophilins (LAT-1 and 27

LAT-2) located presynaptically at the C. elegans neuromuscular junction (Harder et al.,

2003). Most anthelminthics target key inhibitory and enhancing regulatory residues.

Characterizing the inhibitory and enhancing modulation mechanisms of invertebrate

GABA receptors provides insight for the design of better insecticides and anthelminthics.

As agonists of GABA receptors, piperazines were first used as anthelminthics in

1953. The most common anthelminthic piperazines are piperazine hydrate and piperazine citrate. Piperazine activates GABA receptors located on the somatic muscle of nematodes allowing an influx of chloride ions (Martin et al., 1997). This causes a reduction in excitability or relaxation and subsequent paralysis. Paralysis causes detachment from the intestinal lumen and expulsion by the host. Piperazines have been very successful anthelminthics and are still being used today.

Avermectin, ivermectin and doramectin are nematicidal macrocyclic lactones. These drugs are widely used to treat human parasitic diseases including onchocerciasis and lymphatic filariasis and to control parasites in livestock, pets, and agricultural pests. In parasitic worms, the avermectins cause paralysis of somatic worm musculature and block pharyngeal pumping leading to feeding inhibition. Ivermectin alone is the world’s largest selling veterinary drug and is responsible for the eradication of river blindness. As agonists of glutamate gated chloride channels, the macrocyclic lactones increase the open time of receptors located in nematode pharyngeal muscle. The mechanism of ivermectin action is discussed above (section 1.2.2). GluCl activation and ivermectin modulation involve key regulatory residues including the cys-loop M2-M3 loop and M2 15’.

1.4.2 Modulation of Invertebrate GABA receptors: Insecticides

Insecticides with various mechanisms of action have been developed for several reasons including the maintenance of crops by preventing defoliation and disease, prevention of food spoilage, and the prevention of disease infection of humans and domestic animals, including malaria. Insecticides act on several target sites. For example, dichlorodiphenyltrichloroethane (DDT) and pyrethroids target voltage-gated sodium channels and organophosphate and carbamate insecticides inhibit acetylcholinesterase.

Cys-loop receptors serve as the target of several insecticides. Nicotine, nitromethylene and nitroimine heterocycles target nicotinic acetylcholine receptors.

Fipronil, a broad spectrum insecticide, was placed on the market in 1993 and targets

GABA receptors. The fipronil suppression of GABA-induced currents is caused by a decrease in channel open times and a decrease in the frequency of channel openings

(Narahashi et al., 2007). Fipronil is 59 times more potent on cockroach GABA receptors than rat GABAA receptors. GABA receptor M2 2’ residue mutation has been shown to confer resistance to fipronil (Nakao et al., 2010). Lindane, a polychlorocycloalkane, also blocks GABA receptors as an insecticidal mechanism. Lindane has been used as an agricultural insecticide and a pharmaceutical treatment for lice and scabies. Lindane binds insect GABA receptors at the picrotoxin binding site. Blockage of the GABA receptor leads to hyperexcitation of the nervous system.

Similarly to anthelminthics, insecticide resistance has produced a need for the understanding of insecticide target receptor mechanisms and the development of new insecticides. Given the utility of GABA receptor ligands as anthelminthics and

insecticides, further study of the inhibition of invertebrate GABA receptors promises to help identify new drugs useful as pesticides. These compounds rely on differential efficacy toward invertebrate and vertebrate receptors, so non-conserved invertebrate receptor sites are important to characterize. In addition, insect and helminth orthologs allow for the simultaneous characterization of modulation mechanisms and development of pesticides.

1.5 GABA Receptor Neurosteroid Binding Sites are Non-Conserved between

Invertebrates and Vertebrates and Therefore a Potential Target for

Pesticides

Anthelminthics and insecticides are vital commodities. Resistance has been observed for many important pesticides and will continue to develop for all insecticides and anthelminthics. This creates a need to continually develop new compounds to stay ahead of the development of resistance and potential consequences of losing the functionality of pesticides on which our society relies for combating disease. A new avenue for developing the next generation of compounds is the invertebrate GABA receptor neurosteroid site.

Because GABA receptor neurosteroid binding sites seem to be different in invertebrates and mammals, the neurosteroid binding site may serve as a useful target for anthelminthic drug development. Neurosteroids are steroid hormone metabolites that consist of a cholesterol four steroid-ring backbone with a variety of substituents (Figure

1-8). Neurosteroids are endogenous chemicals that exhibit some degree of psychoactivity.

Neurosteroids are involved in many physiological processes and diseases including 30

learning and memory (Belelli et al., 2005), stress, pregnancy, alcohol intoxication, and schizophrenia. Synthetic neurosteroids are well known for their use as anesthetics.

Neurosteroids have the ability to directly modulate cells without steroid receptors.

Neurosteroids are synthesized in neurons and glia from cholesterol. Neurosteroid functional groups determine whether they have an inhibitory or enhancing effect on

GABA receptors. Sulfated neurosteroids, such as pregnenolone sulfate (PS) and dehydroepiandrosterone sulfate (DHEAS), are inhibitors of both mammalian and invertebrate GABA receptors. Understanding the mechanism of neurosteroid modulation of GABA receptors is important for the development of novel drugs. Neurosteroid-related drug treatment has been used clinically for epilepsy and traumatic brain injury and may contribute to the development of drugs to treat depression, schizophrenia, alcoholism, and multiple sclerosis (Morrow et al., 2007). Invertebrate neurosteroid exploration may provide new avenues for the development of safe and effective pesticides.

Figure 1-8: Neurosteroid Structure

(Hosie et al., 2007)

Chemical structures in for selected neurosteroids showing the four steroid ring backbone and functional groups at C3 and C17. Allopregnanolone (A) includes ring identification (A-D), carbon numbering and chiral centers. 32

The understanding of drug mechanisms is achieved through gathering insight from multiple structural studies. Structure-activity studies examine the activity of a wild-type receptor when treated with a ligand and variations of that ligand. Structure-function studies expose mutant variations of a receptor to a drug to examine the drug potency and efficacy. The combination of structure-activity and structure-function studies provides insight into the mechanism of drug action. In addition, functional studies where the pharmacology and kinetics of drug interactions are modeled can contribute to our knowledge of the drug mechanism and provide additional insight.

This approach has been used to elucidate the binding pockets for neurosteroids on both vertebrate and invertebrate GABA receptors with the potential to advance both the development of new therapeutic drugs and new anthelminthics and insecticides. Until

2006, the mechanism for modulation of LGICs by neurosteroids was unknown. Great progress has since been made in understanding neurosteroid interactions with both vertebrate and invertebrate GABA receptors. For the C. elegans GABA receptor, UNC-

49, the binding site for neurosteroids is much different than the binding site in vertebrate

GABAA receptors. This presents an opportunity for characterization and understanding of the invertebrate neurosteroid site in order to development novel anthelminthics and insecticides.

1.5.1 Enhancement of Mammalian GABAA Receptors by Pregnane Neurosteroids

Mammalian neurosteroid binding site characterization is important for the development of insecticides and anthelminthics. Pesticides must only target specialized 33

invertebrate sites and must not have an effect on related or non-related mammalian proteins. In attempt to identify residues important for neurosteroid modulation of mammalian GABAA receptors, Hosie et al., used structure-function studies creating chimeras from D. melanogaster GABA receptor, RDL. RDL is not affected by neurosteroids, and sequences from mammalian 1 and β2 GABAA receptor subunits were swapped into RDL to examine effects on neurosteroid sensitivity. The chimeras were exposed to THDOC and ALLOP leading to receptor potentiation. Hosie et al. has proposed that the C3 hydroxyl group of the neurosteroid A ring binds a hydrogen bond acceptor residue in the M1 domain while the C17 group on the steroid D ring binds a residue in M4 (Hosie et al.,2006) (Figure 1-8, 1-9).

Figure 1-9: A Model of THDOC Bound to the Subunit Between M1 and M4

(Hosie et al., 2006) C17 of the neurosteroid D-ring interacting with potential hydrogen bond acceptor Q241 of M1 and the C3 hydroxyl group of the neurosteroid A- ring interacting with potential M4 hydrogen bond donors N407 and Y410. The hydrophobic side chain of Ile238 lies next to the neurosteroid A- ring. 34

Four residues provided insight into neurosteroid modulation. T236 initiates receptor activation. T236 is located in M1 on the receptor surface and may have a role directly contacting the neurosteroid near the β/ subunit interface. When replaced with non-hydrogen bonding residues, the agonist potency of ALLOP and THDOC was reduced suggesting this residue has hydrogen bonds with the neurosteroid. βY284 is also a direct activation residue and is located in M3 of the β2 subunit. βY284 is suspected to have hydrogen bonding interactions with neurosteroids. Removing the hydroxyl group by replacing tyrosine with phenylalanine reduced THDOC and ALLOP potency. βY284 is a surface exposed residue and lies 15Å from T236, which is about the length of a neurosteroid molecule.

Q241 is located in M1 and mediates receptor potentiation (Fig. 1-9). Q241 lies in a water-filled cavity suspected to be involved in neurosteroid binding. After receptor activation, the depth and volume of the cavity are said to increase and subsequently stabilize the receptor in an active state (Hosie et al., 2006). N407 and Y410 are located in the M4 domain of the 1 subunit. N407 and Y410 are also suspected to be involved in potentiation and are located 15-18Å from Q241, an appropriate distance for a neurosteroid molecule to contact both residues. N407 and Y410 are suspected to donate a hydrogen bond to the C20 ketone of the neurosteroid (Hosie et al., 2006) (Figure

1-9). This binding proposal serves one of the first contributions to the characterization of neurosteroid binding sites and their potential usefulness as targets for insecticide and anthelminthic development.

1.5.2 Modulation of Invertebrate GABA Receptors by Sulfated Neurosteroids

1.5.2.1 Modulation of Invertebrate GABA Receptors by PS

Structure function studies by Hosie et al. have provided advancements of our understanding of enhancing neurosteroid modulation of mammalian GABAA receptors, in addition to the proposal of an orientation based on the chemistry of the side chains. While

Hosie and colleagues were dissecting the neurosteroid modulatory site on mammalian receptors, Wardell et al. were using a similar chimera approach to investigate neurosteroid modulation of an invertebrate GABA receptor. Interestingly, they came up with a very different picture of a neurosteroid modulatory site.

Wardell et al. aimed to identify the residues important for PS modulation of the C. elegans GABA receptor UNC-49. UNC-49 encodes 3 splice variants: A, B and C, which share an amino terminus and differ in carboxy termini (Figure 1-10). The carboxy termini contain a portion of the GABA binding site and all four transmembrane domains. UNC-

49A expression is barely detectible in C. elegans. UNC-49B and UNC-49C are abundant equally. The native receptor in vivo is a heteromer containing UNC-49B and UNC-49C subunits. The UNC-49B/C heteromer is involved in the coordination of locomotion at the neuromuscular junction (Bamber et al., 2005).

Figure 1-10: UNC-49 Splice Variants

Bamber et al., 1999.

Splice variants UNC-49A, UNC-49B and UNC-49C mRNA shown with identical N-terminal regions and C-terminal regions containing 4 transmembrane domains. UNC-49B alternative splice site indicated with a triangle. The right column shows predicted UNC-49 protein orientation in the GABA receptor membrane.

The difference in receptor responses to neurosteroids for splice variants B and C is the basis for identifying residues important in neurosteroid modulation (Figure 1-10).

UNC-49B/C heteromers are inhibited by PS while UNC-49B homomers are inhibited to a much lesser degree. This implies residues in UNC-49C that differ from residues in UNC-

49B may confer sensitivity to PS. Swapping UNC-49C residues into an UNC-49B 37

background to form chimeras and subsequently exposing the mutants to neurosteroids allowed for the identification of segments or residues important for neurosteroid modulation.

Wardell et al. created chimeras to identify these residues, the first of which contained the UNC-49B extracellular domain and M1-M4 from UNC-49C (Figure 1-10).

PS sensitivity beyond the level of the UNC-49B homomer can be attributed to UNC-49C

M1-M4 residues. This chimera was highly sensitive to PS, so progressively smaller

UNC-49C transmembrane regions were tested. A chimera consisting of M1 and the M2-

M3 linker residues from UNC-49C in an UNC-49B background was equally sensitive to

PS. Wardell et al. demonstrated that UNC-49 modulation by PS depends on residues in

M1 and the M2-M3 linker domain of UNC-49C. To further identify important residues, a chimera containing only the M1 and M2 domains was exposed to PS. The (UNC-49C)

M1-M2 chimera showed a significant loss in UNC-49 sensitivity to PS implying the M2-

M3 linker region present in the previously tested chimera played a significant role in modulation. The UNC-49C M1-only chimera did not have reduced sensitivity relative to the M1-M2 chimera allowing the elimination of the M2 domain as essential for sensitivity to PS. This leaves two regions important for PS modulation: M1 and the M2-

M3 linker. Neither the M1 region alone nor the linker residue alone was sufficient to confer sensitivity to the level of the UNC-49B/C heteromers (Wardell et al., 2006).

In the M2-M3 linker region, UNC49-B contains neutral residue N305 while

UNC-49C contains positively-charged residue R305. A subunit containing the UNC-49C

M1 domain and N305R in an UNC-49B background is referred to as M1-R. This subunit increased the sensitivity to PS to the level of a subunit containing M1 and entire M2-M3 38

linker region. Therefore, N305R is the only residue in the M2-M3 linker region required for modulation by PS.

Two M1 residues conserved among mammals and PS-sensitive subunits and also present in UNC-49C are Q259 and F261. These residues are not conserved in UNC-49B.

To test the importance of these residues in modulation of UNC-49 by PS, they were substituted into an UNC-49B background (N259Q and V261F) in combination with linker mutation N305R to form the ‘QF-R’ subunit. This combined mutant was more sensitive to PS than N305R itself but still 6.7-fold less sensitive than the M1-linker chimera. Although Q259 and F261 are important in modulation of UNC-49 by PS, there must be other M1 residues important for modulation by PS (Wardell et al., 2006).

To identify additional non-conserved residues of M1 important for UNC-49 sensitivity to PS modulation, the M1 domain was divided into 3 segments: X (residues

255-264), Y (residues 265-273), and Z (residues 274-282) of which chimeras were constructed (Figure 1-13). All chimeras contain mutant residues QF-R (N259Q, V261F, and N305R) already shown to be necessary for PS sensitivity. When exposed to PS, the X chimera was more sensitive to modulation than QF-R. The XY chimera was even more sensitive to PS. The Y chimera, however, showed decreased sensitivity to PS. This implies the residues in the X segment and the Y segment work together to confer inhibition sensitivity. To further characterize modulation of UNC-49 by PS, Wardell et al. identified the individual X segment residues that provide maximal PS sensitivity by substituting UNC-49C X segment residues into a Y chimera background. Residues

M258, T257, S264 and I262 differ between UNC-49C and UNC-49B and were swapped

to the corresponding UNC-49C residues in all possible combinations. All receptor chimeras contained N259Q, V261F, and N305R (QF-R) substitutions in addition to single to triple mutations of the remaining residues. Results indicated for a single residue mutation, T257F increased PS sensitivity most, and the maximum sensitivity occurred for the double mutation of residues 257 and 264 relative to Y chimera sensitivity to PS. The combined mutation of 264, 257 and 258 was not sufficient to confer inhibition sensitivity to the level of the XY chimera which implies that all 4 residues are required for modulation by PS. Additionally, several mutants were non-functional including the 262 single mutation, 257/264 double mutant, 258/262 double mutant, 258/262/264 triple mutant, 257/262/264 triple mutant and 257/258/262 triple mutant (Wardell et al., 2006).

Interestingly, the single mutation of residue 258 and double mutation of residues

258 and 264 conferred a biphasic response to PS (Wardell, et al.,2006) meaning high levels of PS (100µM) caused inhibition and moderate levels (10µM) enhanced GABA induced currents. This suggests residue 258 reveals an enhancing site that may function in combination with the inhibitory site. Two distinct sites acting together suggest modulation of the current is caused by a combination of enhancing and inhibiting effects.

The overall effect is a combination of enhancement and inhibition, which are potentially completely separate effects that are additive or interact in some way to produce a total effect and may originate from separate binding sites. This experiment was the first insight into the possibility of two separate pathways for inhibition and enhancement. This information forms the basis of a hypothesis tested explicitly in further studies involving the addition of a commonly known enhancing pathway via the mutation of M2 15’ to explore whether enhancement is occurring independently of inhibition (see section 1.7). 40

To identify Y segment residues that confer sensitivity to PS, Wardell et al. identified 5 residues that differ between UNC-49B and UNC-49C and created single residue mutants in an X chimera background. When exposed to PS, only residue 265

(IC50=3.43±0.6) was sufficient to confer PS sensitivity to the level of the XY chimera

(IC50=3.7±0.5) (Wardell, et al.,2006). This suggests that this serine residue is important for modulation by PS. In all, the mutation of 7 residues of UNC-49B to their UNC-49C counterparts resulted in a 58-fold increase in PS sensitivity.

The inhibitory residues identified by Wardell et al. comprise a different modulatory site than is typically utilized by allosteric inhibitors. Most cys-loop inhibition works through the M2 domain and specifically the M2 2’ residue. Picrotoxin is an allosteric regulator of GABA receptors and binds M2 2’, deep in the pore. Dieldrin, fipronil, and picrotoxin resistance come from M2 2’ mutations. Picrotoxin did not show parallel changes in sensitivity compared to PS. Wardell et al. have provided evidence of a different site and mode of action for inhibition of UNC-49 by a sulfated neurosteroid than the typical M2 involved inhibitory mechanism. It appears PS modulation of UNC-49 overlaps with ivermectin modulation of the GluClα channel.

1.5.2.2 Modulation of Invertebrate GABA Receptors by DHEAS

To better understand the interaction between sulfated neurosteroids and UNC-

49 GABA receptors, Twede et al. compared how mutations important for PS inhibition affect the action of a closely-related neurosteroid, DHEAS. The structures of PS and

DHEAS are identical aside from a carbonyl oxygen on C17 of the DHEAS steroid D ring 41

which is replaced with an acetyl group in PS (Figure 1-8). DHEAS and PS are inhibitory, sulphated neurosteroids. To further characterize the effect of sulfated neurosteroids on invertebrate GABA receptors, Twede et al. identified residues important for modulation of UNC-49 by DHEAS using chimera structure-function studies with the same mutants used by Wardell et al. to characterize modulation by PS. Modulation of UNC-49 by

DHEAS and PS rely on a broadly parallel set of residues and exhibit only slight differences in inhibition sensitivity. Therefore, it is likely DHEAS binds UNC-49C in a similar orientation and modulates UNC-49C with a similar mechanism.

First, the response of UNC-49B and UNC-49C to DHEAS was examined and

UNC-49C was more sensitive to DHEAS as was the case for PS. This was followed by exposure of the chimera containing only the N305R mutation and the chimera containing

UNC-49C M1 and N305R to DHEAS. Like for PS, DHEAS sensitivity was increased at high concentrations for the single N305R mutation and also increased for the UNC-49C

M1 and N305R combination (Twede et al., 2007).

Next, the QF-R mutant was exposed to DHEAS. DHEAS sensitivity was restored to the extent of the M1-R mutation indicating the residues N259Q, V261F, and

N305R are alone sufficient for modulation of UNC-49C by DHEAS (Twede et al., 2007).

Modulation by PS was also dependent on these residues, but other UNC-49C M1 residues were required for full sensitivity of UNC-49B.

When exposed to the X, Y, and XY chimera, the XY chimera had the highest sensitivity to DHEAS as was the case with PS. In regards to single residue mutations,

265, 257 and 262 had the same effect on sensitivity to PS as DHEAS, but the mutation of residues 264 and 258 were different. S264A reduced DHEAS sensitivity but had little

effect on sensitivity to PS. M258L only slightly changed the sensitivity of UNC-49B to

DHEAS whereas enhancement was observed upon exposure to PS (Twede et al., 2007).

Twede et al. found residues in M1 play an important role in the receptor’s ability to distinguish between DHEAS and PS. Because M1 is required for modulation by

DHEAS and PS and the only difference between DHEAS and PS is the substituent on the neurosteroid D ring, Twede et al. claim it is possible that the M1 domain of UNC-49 contacts the neurosteroid D ring. Although this does not constitute direct data on binding site contact residues, the simplest interpretation is that D contacts M1, since the effects of altering ligand are most likely to be felt in the local environment of those altered atoms, and the effects of a mutation are most likely to be felt strongest by the ligand atoms making contact (Twede et al.,2007). Positive residue N305R of the M2-M3 loop also plays a significant role in modulation by both DHEAS and PS. Based on these data and residues important for modulation by PS, we propose an orientation of neurosteroid binding where the negatively charged sulfate group on the A ring of the sulfated neurosteroid contacts the positively charged arginine residue of the M2-M3 loop (Figure

1-11). In addition, it is also possible that the neurosteroid contacts M1 and M2-3 of the same subunit.

Figure 1-11: Proposed Neurosteroid Binding Orientation

Combined studies suggest the GABA receptor M1 region interacts with the neurosteroid D-ring and the M2-M3 linker region interacts with the A ring. Cylinders are UNC-49 transmembrane -helices, view is parallel to membrane.

1.5.3 The GABA Receptor Neurosteroid Site is Non-conserved

The current data suggest that the neurosteroid binding site on invertebrate GABA receptors is different than the heterotrimeric GABAA neurosteroid binding site which provides an important opportunity for a potential anthelminthic or insecticide drug target site. Phylogenetic analysis revealed that the UNC-49B and ρ1-3 GABAC receptor subunits cluster as a separate branch from the GABAA receptor α and γ subunits (see figure 1.7,

Bamber et al., 2003). Baker et al. compared mammalian and invertebrate mechanisms for inhibitory steroids and in support of phylogenetic analyses, found them to be quite different. Results indicated that the mechanism of inhibition of UNC-49 by the neurosteroid PS does not rely on the same set of residues in mammalian GABAA receptors. As described above, PS sensitivity was localized to a 19 residue segment of

UNC-49 that can be mutated to increase or decrease PS sensitivity. The UNC-49B residues within this stretch conferred PS insensitivity while a minimum of seven UNC-

49C residues conferred 58-fold higher sensitivity. Substituting UNC-49B residues from this segment into mammalian 1, β1 and γ2 subunits, which are normally sensitive to modulation by PS, did not cause the reduced sensitivity expected to occur if the UNC-

49C and the mammalian receptors had a similar binding site and modulation mechanism.

Considering this, it is not likely a conserved PS pocket is formed in this region of the mammalian receptor. In summary, the combined data suggest that the C. elegans UNC-

49 inhibitory modulation mechanism differs from vertebrate inhibitory modulation mechanisms.

1.6 Residues Mediating PS and DHEAS Modulation Also Appear to Mediate

Pregnanolone Action

The newest data continue and extend the structure-activity and structure-function approach by examining the roles of M1 and M2-3 loop residues on pregnanolone modulation. Whereas PS and DHEAS differed at the D loop, PS and pregnanolone have identical D loops, and differ at the A loop and nearby B loop saturation. Unpublished studies further characterize the modulation of GABA receptors by neurosteroids by comparing the modulation mechanisms of PS and pregnanolone on C. elegans UNC-49 using structure-function studies. What are the similarities and differences in the mechanisms of modulation by PS and pregnanolone and how can these differences be linked to structural differences to provide insight into the mechanism of neurosteroid modulation of GABA receptors?

Our lab used the panel of mutant and chimeric receptors used to characterize PS modulation of UNC-49 by Wardell et al. to further characterize neurosteroid modulation of UNC-49 by pregnanolone. Pregnanolone was found to be an inhibitor of UNC-49 even though it is an enhancer of mammalian GABAA receptors (Morris and Amin, 2004). This provides further evidence of a lack of conservation in neurosteroid modulation mechanisms between invertebrates and vertebrates supporting the potential for anthelminthic and insecticide drug development. We additionally found that mutants that altered DHEAS and PS sensitivity showed broad similarities when treated with pregnanolone. Some differences were observed suggesting although the sulfated and non- sulfated neurosteroid modulation sites are overlapping, they are non-identical.

For pregnanolone, the N305R mutation caused a decrease in inhibition. The decrease returned to wild-type levels with the addition of N259Q and V261F (QF-R chimera) (Figure 1-12, Table 1.1). For PS, N305R conferred increased sensitivity to PS and the addition of N259Q and V261F (QF-R) further increased sensitivity to PS

(Wardell et al., 2006).

Figure 1-12: Pregnanolone Inhibition of M1 and M2-3 Loop Point Mutants (A) Pregnanolone inhibition of wild type, N305R and QF-R receptors (B) Pregnanolone dose-reponse curves of UNC-49B and QF-R and associated traces

To further compare the residues important for pregnanolone modulation with those important for PS and DHEAS modulation, additional mutants were examined.

Overall, all chimeras had parallel effects when treated with PS or pregnanolone (Figure

1-13). In the X chimera, pregnanolone sensitivity was unchanged and PS sensitivity was slightly increased. In the Y chimera, pregnanolone became an enhancer and PS inhibition was almost completely abolished. In the XY chimera, both pregnanolone and PS were more potent inhibitors (Wardell et al., 2006). Exposure of the XY Chimera to pregnanolone and GABA causes large outward currents (Figure 1-13C). A similar effect on mammalian GABAA receptors containing the UNC-49C XY segment has been observed. Exposing a wild-type mammalian GABAA receptor to PS in the absence of

GABA caused large outward currents (Baker et al., 2010). It is possible the outward current is caused by the blockade of a chloride leak current. PS inhibits spontaneous activity that occurs in the absence of GABA meaning PS can function independently of

GABA leading to reduced open probability of the channel regardless of the GABA activation state. Other studies have reported receptors in Xenopus oocytes that produce a constitutive chloride current capable of being inhibited by bicucculine, picrotoxinin, furosemide and Zn2+ (Hadley et al., 2007).

Figure 1-13: Pregnanolone Inhibition of the X, Y and XY Chimeras (A) Subdivision of the M1 domain into 3 segments: X (residues 255- 264), Y (residues 265-273) and Z (residues 274-282). Black dots represent UNC-49B QF-R mutations at positions 259, 261, and 305. UNC-49C sequences corresponding to the X, Y, and combined XY segments have been swapped into the QF-R subunit. (B) Pregnanolone dose-response curve of chimeras X, Y, and XY generated at EC50 GABA. (C) Positive current trace derived during exposure of the XY chimera to GABA and 10 µM pregnanolone 50

The point mutants within the X segment of M1 that had been tested with PS and

DHEAS were also tested with pregnanolone to see whether the parallels between the neurosteroids extend to the single amino acid level. Again, similar results were observed upon mutant exposure to both PS and pregnanolone (Figure 1-14). Similarly to PS, no single X segment residue conferred pregnanolone inhibition to the extent of the XY chimera. All chimeras containing mutation T257F had increased sensitivity to inhibition by both PS (Wardell et al., 2006) and pregnanolone (Table 1.1). The Y chimera is enhanced by pregnanolone and when residue 257 is a phenylalanine, the enhancement is reversed to inhibition. This implies that residue 257 is involved in receptor response to neurosteroids contributing to an inhibitory or enhancing pathway. In a Y chimera M258L or M258L/M264A background, threonine leads to enhancement and phenylalanine leads to inhibition. This suggests modulation by pregnanolone may not consist of simple inhibition or enhancement. Inhibition and enhancement instead might be separate effects occurring simultaneously and combining or interacting in some way to produce total current.

Finally, all four UNC-49C residues are required for full sensitivity to inhibition.

Because the triple mutant chimera (M258L/T257F/S264A) did not confer inhibition to the level of the XY chimera, residue 264 must play a role in inhibition as well.

Figure 1-14: Pregnanolone Modulation of M1 N Terminal Mutants (A) Pregnanolone sensitivity of X segment mutations in the Y chimera background. Chimeric M1 domain depicted as in figure 1-13. Grid indicates the residue present at each position that is divergent between UNC-49B and C. Closed boxes and white letters are UNC-49C. Open boxes and white letters are UNC-49B. (B) Pregnanolone dose-response curves of chimeras containing mutant residues shown in part (A). All pregnanolone IC50 values and dose response curves generated at EC50 GABA. 52

Y leads to enhancement in the context of Y alone, Y potentiates X in the XY context, and Y causes a leakage current in the context of XY. To identify residues important for modulation near the intracellular face of the membrane, Y segment residues that differ between UNC-49B and UNC-49C were individually mutated in an X chimera background. In an X background, Y potentiates X, and residues T269V and V273I were sufficient to confer XY chimera inhibition to the X chimera (Figure 1-15, Table 1.1). For

PS and DHEAS, the deepest residue that altered inhibition was residue 265, which was alone sufficient to recreate the effect of the intact Y segment (Twede et al. 2007; Wardell et al., 2006). By contrast, the I270L mutation altered pregnanolone inhibition, consistent with deeper membrane interactions between pregnanolone and the GABA receptor.

Figure 1-15: No single UNC-49C Y Segment Mutant Residue is Sufficient to Confer Inhibition by Pregnanolone to the Level of the XY Chimera Pregnanolone sensitivity of Y segment mutations in the X chimera background. Chimeric M1 domain depicted as in figures 1-13 and 1-14. Grid indicates the residue present at each position that is divergent between UNC-49B and C. Closed boxes and white letters are UNC-49C. Open boxes and white letters are UNC-49B.

To determine the Y segment residues important for enhancement of UNC-49 by pregnanolone, single Y segment residues were mutated in a Y chimera background.

Results indicate that enhancement of the Y chimera by pregnanolone requires several Y 54

segment residues (Figure 1-16). L270I and I273V rendered the chimera insensitive to pregnanolone while V267I had no effect on enhancement by pregnanolone. S265I and

V269T converted pregnanolone enhancement to inhibition but not to the extent of QF-R.

This supports pregnanolone contact with deeper M1 residues, as the loss of residues 270 and 273 caused insensitivity to pregnanolone but the loss of 267 had no effect on modulation.

Outward currents observed are consistent with blockade of a chloride leak current and were observed by Baker et al., 2010 and in figure 1-13. We suspect that the XY chimera has a high open probability in the absence of GABA. Inhibition by the neurosteroid reduces the open probability and leads to currents below baseline. Because no point mutant receptor had the outward current seen indicating this property, all 5 Y segment residues are required for this effect and for modulation by pregnanolone. In addition, this suggests inhibition occurs independently of GABA activation state because

GABA is not required for the inhibitor to have an effect on spontaneous opening of the channel.

Figure 1-16: No single UNC-49B Y Segment Residue Confers Inhibition Levels to the Extent of QF-R (A) Pregnanolone sensitivity of Y segment mutants in the Y chimera background. Chimeric M1 domain depicted as in figures 1-13, 1- 14, and 1-15. Grid indicates the residues present that are different between UNC-49B and UNC-49C. White letters represent UNC- 49C and black letters represent UNC-49B. Bars at left represent M1 and M1-M2 linker region depicted above.

(B) Pregnanolone dose-response curves for the Y chimera and all point mutations defined in (A). Pregnanolone inhibition was measured at GABA EC50 for each mutant.

Table 1.1: Responsiveness of M1 Chimeras to Pregnanolone

Pregnanolone half-max Subunit Slope Coefficient concentration (μM)

UNC-49B IC50 2.940.22 (n=4) 1.20.04 (n=4)

N305R IC50 98.83.3 (n=4) 1.50.05 (n=4)

QF-R IC50 4.20.47 (n=4) 1.70.10 (n=4)

X IC50 4.3570.71 (n=7) 1.350.15 (n=7)

Y EC50 3.1420.35 (n=13) 0.830.30 (n=13)

XY IC50 2.580.24 (n=3) 2.630.13 (n=3)

Y(M258L) EC50 1.830.66 (n=4) 2.310.75 (n=4)

Y(M258L/S264A) EC50 5.934.0 (n=4) 3.332.1 (n=4)

Y(T257F/S264A) IC50 8.712.3 (n=4) 1.180.7 (n=4)

Y(T257F/M258L) IC50 8.441.64 (n=4) 1.080.13 (n=4)

Y(T257F/M258L/S264A) IC50 6.630.97 (n=4) 1.210.05 (n=4)

X(I265S) IC50 6.220.42 (n=5) 1.840.18 (n=5)

X(I267V) IC50 3.891.96 (n=4) 0.910.14 (n=4)

X(T269V) IC50 2.310.13 (n=3) 1.090.03 (n=3)

X(I270L) IC50 11.012.72 (n=4) 1.760.14 (n=4)

X(V273I) IC50 1.800.43 (n=4) 1.230.11 (n=4)

Overall, the most striking difference for modulation by pregnanolone was observed in the M2-M3 loop. Whereas for PS and DHEAS, introducing a positive charge increased 57

inhibition, for pregnanolone, an increased M2-3 loop positive charge eliminated inhibition (Figure 1-12). This reinforces the proposed binding orientation for PS and

DHEAS. PS and pregnanolone differ in structure at the opposite end, where both PS and

DHEAS contain a sulfate moiety and pregnanolone contains an α-hydroxyl group (Figure

1-8). Since pregnanolone differs from the sulfated neurosteroids at the steroid A ring, the differential effect of the M2-3 loop residues is consistent with A ring moieties contacting the M2-3 loop, as described above (Figure 1-11). Furthermore, the specific functional requirements for each steroid suggest potential modes of interaction: the sulfated neurosteroids contain negatively-charged A ring substituents, and require positively- charged M2-3 loop residues for maximal inhibition, consistent with electrostatic interactions. Conversely, the A-ring moiety of pregnanolone is a hydroxyl group, a hydrogen bond donor, which may form a hydrogen bond with the asparagine carbonyl oxygen. Pregnanolone data suggest an orientation where the C3 moiety contacts the M2-

M3 loop, the neurosteroid A ring protrudes extracellularly, and the D ring interacts with

M1 (Figure 1-11, 1-17).

Because no two steroids share identical structure-function relationships, it is likely that both ends of the neurosteroid interact with the receptor. PS and DHEAS differ in response to mutated residues present deep in M1 which, based on this binding orientation, would be near C17 where the two neurosteroids differ in structure. The mutation of several residues near the extracellular domain of M1 (T257F, N259Q, and

V261F) caused an increase in UNC-49 sensitivity to inhibition when exposed to all 3 neurosteroids (DHEAS, PS, and pregnanolone). Because all neurosteroids contain the 4- ring steroid structure, the steroid rings may contact these residues of the M1 domain 58

leading to the increase in inhibition, which is supported by the proposed binding orientation. Pregnanolone data support this theory as well placing the D-ring of the neurosteroid oriented toward M1 and the A ring near the M2-M3 linker residue. These data support a shared binding pocket for pregnanolone, PS and DHEAS that includes key interactions of the neurosteroid with M1 and the M2-M3 loop. This site is distinct from the mammalian neurosteroid binding site where the neurosteroid seems to bind in the opposite orientation. These data additionally support that the invertebrate neurosteroid binding site has potential as a target site for the development of pesticides.

Figure 1-17: Proposed Pregnanolone Binding Orientation

1.7 Do Separate Neurosteroid Enhancement and Inhibition Pathways Operate in

UNC-49?

Several mutations in UNC-49 M1 chimeras revealed the ability of sulfated neurosteroids to act as positive allosteric regulators by enhancing UNC-49. Specifically, the single mutation of residue 258 and double mutation of residues 258 and 264 conferred a biphasic response to PS (Wardell, et al., 2006) and pregnanolone. This suggests residue

258 reveals an enhancing site that may function in combination with the inhibitory site.

This experiment was the first insight into the possibility of two separate pathways for inhibition and enhancement. Two distinct sites acting together suggest modulation of the current is caused by a combination of enhancing and inhibiting effects. The overall effect is a combination of enhancement and inhibition, which are potentially completely separate effects that are additive or interact in some way to produce a total effect and may originate from separate binding sites. (section 1.5.2.1).

Studies with pregnanolone further support the theory of separate inhibition and enhancement pathways. Y chimeras containing mutations M258L or M258L/S264A and

T257F have increased sensitivity to inhibition by both PS and pregnanolone (Wardell et al., 2006). Y-M258L and Y-M258L/S264A are enhanced by pregnanolone and when residue 257 is a phenylalanine, the enhancement is reversed to inhibition. This implies that T257 is involved in receptor response to neurosteroids contributing to an inhibitory or enhancing pathway. In this case, the original threonine residue at position 257 plays a critical role by coupling neurosteroid binding to an enhancing or inhibitory pathway:

threonine leads to enhancement and phenylalanine leads to inhibition. This also suggests modulation by pregnanolone may not consist of simple inhibition or enhancement.

The overall effect of the neurosteroid on the receptor reflects the contribution of two pathways acting additively. For example, if relatively strong inhibition and relatively weak enhancement were operating over the same concentration range, the overall result would be somewhat weaker inhibition than would have been observed if the inhibitory effect alone were present. These residues appear to have an important role in determining whether neurosteroid binding will contribute to an inhibitory or enhancing pathway.

Structure activity studies such as this provide valuable insight and raise many questions including how these residues interact with and depend on each other to exhibit specific directions and levels of modulation. One way to tease apart two independent opposing modulatory effects is to use mathematical modeling.

Based on the above observations of pregnanolone modulation of UNC-49, it seems that PS and pregnanolone may act as enhancers as well as inhibitors, but the effect was masked when M258 was a methionine residue rather than leucine. These mutations and data hint at dual enhancing and inhibitory sites leading to the hypothesis that there are separate enhancing and inhibitory pathways present.

To test this hypothesis directly, a key residue for enhancement was mutated in C. elegans UNC-49 to increase the enhancement pathway and unmask this potential mechanism. Chimeras with a mutated residue (M2 15’) well known for controlling enhancement in anesthetic and alcohol modulation of GABAA receptors and neurosteroid modulation of ρ1 GABAc receptors were created (section 1.3.3). In addition, this residue

is part of the ivermectin binding site in GluClα1 receptors (section 1.2.2.2). The residue was changed from isoleucine to glutamine in the X, Y, and XY chimera background. If the neurosteroids are activating the canonical enhancement pathway, the I300Q mutation should increase the enhancement component of the overall interaction. Stronger enhancement should be observed, or if inhibition is initially observed and inhibition and enhancement are additive, a decrease in inhibition will be observed. Because an increase in enhancement could look like a simple decrease in inhibition, we can use mathematical modeling to see if the effects might be explainable as differential weighting of the two pathways. Mathematical modeling will allow us to reveal parallel enhancement and inhibition that are expressed as simultaneous additive effects in a dose-response curve.

We will explore whether we can explain the M2 15’mutation data as an increase in the efficacy of the enhancement pathway, whether viewed as an increase in enhancement or a decrease in inhibition. Finally, we can test the applicability of the model by using it to analyze PS data as well.

1.8 Mathematical Modeling to Determine the Role of I300 in Neurosteroid

Modulation of UNC-49

In order to mathematically test the hypothesis that separate inhibition and enhancement pathways are occurring during modulation of UNC-49 by neurosteroids, it is essential to understand dose-response curves and graphical analysis. The effect of drug modulation of any receptor can be quantified with a dose-response curve. A dose- response curve relates the concentration of the drug to the response of the receptor.

Analysis of drug concentration and the associated receptor response are instrumental to the development of safe and effective drugs used to treat both humans and pests.

Parameters are quantities that relate functions containing common variables when the relationship would otherwise be difficult to compare. Dose-response parameters allow for the comparison of different drugs and their effects on the modulated protein. Parameters can be calculated from a dose-response curve. For example, the parameter EC50 represents the half maximal effective concentration of a drug. The EC50 can be determined by defining the dose-response curve baseline and maximum drug concentration and subsequently identifying the half maximal drug concentration. EC50 is commonly used and known as a measurement of a drug’s potency. Mathematical analysis and modeling of drug/receptor systems is instrumental in comparing the modulation of

GABA receptors by various ligands.

1.9 The Hill Equation as a Pharmacokinetic-Pharmacodynamic Model to Describe

Dose-Response Relationships

To use mathematical modeling to see if a change in modulation of UNC-49 by neurosteroids may involve combined inhibition and enhancement pathways, we used a variation on the traditional Hill model. Theoretical biology or biomathematics is the use of applied mathematical techniques to develop a mathematical model of a biological process. A model represents a system of equations describing the behavior of a biological system at equilibrium or over time. Models often make assumptions about the nature of what occurs in the in the system and the system itself. The accuracy of the model is

supported when actual results generated fit the model well assuming realistic parameter values have been generated.

In addition to the Hill equation, an examination of alternative pharmacodynamics equations would be beneficial. The Hill equation is based on the formation of a ligand/receptor complex. According to our observations, this simple assumption may not be true for all of our experiments. Steady state analysis was considered but inappropriate because in these experiments, drug intake is not in dynamic equilibrium with elimination.

A model that better represents what is occurring during our experiments would be more appropriately suited to provide insight for the characterization of molecular mechanisms.

The Hill equation was originally developed in 1909 by Archibald Hill, an English physiologist, to explain the binding curve of oxygen to hemoglobin. The Hill equation was created as a quantitative method for binding cooperativity. The receptor binds n ligands simultaneously to form complex C:

Therefore the dissociation constant equals

[ ][ ]

[ ]

θ= fraction of occupied sites

[ ] [ ]

1- θ= fraction of non-occupied sites providing the ratio

[ ] [ ]

[ ]

The logarithm, linear for n, equals

[ ] [ ]

Where:

n=slope and = intercept or plotting [ ] vs. [ ] where slope = number of

ligands n binding cooperatively at ligand concentration [L].

Cooperativity is a phenomenon displayed by receptors and enzymes with multiple binding sites that change in affinity according to the number of molecules already bound.

When n > 1 –Positively cooperative reaction: affinity for additional ligand molecules increases

When n < 1 – Negatively cooperative reaction: affinity for additional ligand molecules decreases

When n = 1 – Noncooperative reaction: affinity for additional ligands is not dependent on whether or not ligand molecules are already bound

The Hill equation provides a practical analysis of binding equilibria in ligand- receptor interaction. The Hill coefficient describes ligand affinity for a receptor in terms of the fraction of receptor saturated by ligand as a function of ligand concentration. The

success of the Hill equation can be attributed to its flexibility and ease of use in fitting experimental data.

For a general dose response curve, the Hill equation is defined as:

Response = Bottom+

[ ]

Because you want to find the best fit value of LogEC50 and after defining Y to be the response and X to be the logarithm of [Drug] gives:

Response = Bottom+

( )

= Bottom+

For LGIC dose-response curves, x is defined as [drug] and response or Y is current. In this case, Bottom = 0, Top = Imax or often 1 and simplify so that the Hill equation now becomes

( ( ) )

Where:

I = current at a given drug concentration, Imax = current at saturating drug concentrations,

EC50 is the concentration required to produce half-maximal enhancement, and n is the slope coefficient.

EC50 is the effective concentration of agonist that half maximally activates the channel giving the half-maximal response. A curve of agonist versus response will have a positive slope because as the concentration of agonist (X-axis) increases, the current response (Y-axis) increases as well.

The parameter “n” refers to the Hill slope, Hill coefficient, slope coefficient or a slope factor. The steepness of a dose-response curve is defined by the Hill slope. A standard Hill slope value is 1.0.

The inhibitory version of the Hill equation contains parameters IC50 and nI and has a negative slope to represent an inhibitory drug dose response curve. The lack of drug

(X-axis) will lead to a maximal response (Y-axis) whereas increasing drug concentration leads to a decreased response producing a negative slope. IC50 is the concentration of an inhibitor at half maximal response.

( ( ) )

Where:

= current in the presence of inhibitor relative to GABA alone, x = ligand

concentration, IC50 = concentration of inhibitor required to block 50% of the current and n = slope coefficient or Hill coefficient.

The parameters IC50 and EC50 simply represent the concentration of agonist required to provoke a response halfway between the baseline and maximum responses.

These parameters are based on the “top” and “bottom” of the dose-response curve and are 68

simply a relative concentration required to block 50% of current or half-maximally increase current respectively. The IC50 does not represent the Kd for the binding of an agonist to a receptor. The Kd is defined as the saturation of 50% of receptors. Obtaining the value of Kd is difficult because, for example, for membrane receptors, the diffusion of the drug into the membrane may be the rate limiting step. Regardless, IC50 is often used as a reflection of Kd with the assumption that the rank order of IC50 will follow the rank order of Kd.

1.10 Variations on the Hill Equation to Account for Simultaneous Enhancement and

Inhibition

While characterizing the residues important for PS modulation of UNC-49,

Wardell et al. observed a biphasic response caused by expressing M258L combination with other specific mutations. Moderate levels (10uM) of PS enhance GABA-induced

UNC-49 currents and high levels of PS (100uM) inhibit currents. As the curves could not be fit with the standard inhibitory Hill equation, Wardell et al. proposed an equation representing the product of an enhancing and inhibitory Hill equation. Wardell et al. suggest that the M258L mutation may reveal an enhancing site which functions simultaneously with the inhibitory site (Section 1.5.2.1).

[ ] ( ( ) ) ( ( ) ) [ ] ( ( )) ( )

Where:

I=Normalized Current, A=amplitude of I above 1, x = drug concentration, EC50 and IC50 are the half-maximal enhancing and inhibitory concentrations of x respectively and nE and nI are the respective slope coefficients.

The Multiplicative Hill Equation models the inhibitor as an antagonist of the activation process stimulated by GABA and the neurosteroid. For this equation, inhibition reduces the maximal current produced by the joint actions of GABA and the

neurosteroid by a specific fraction determined by ( ) In other words, the [ ] ( ( ) )

enhancement portion ( ) is equivalent to Imax in a standard inhibitory Hill

( ( ) ) [ ] equation.

An alternative approach is also possible, which we designate the Additive Hill

Equation. The additive Hill equation is the sum of an inhibitory and enhancing dose- response function and provides both inhibition and enhancement parameters (IC50, nI,

EC50, nE). The additive equation represents a system in which dose-dependent inhibition is functioning parallel to dose-dependent enhancement with total modulation equal to the summation of the two. Although inhibition is modeled in the form of the activating Hill equation, its effect is inhibitory because it is subtracted from the total.

( ( ) ) ( ( ) ) ( ) ( )

Where: aE = maximal current above 1, aI = maximal extent of inhibition, x = drug concentration,

EC50 and IC50 = half-maximal enhancement and inhibitory concentrations of x respectively, nE and nI = corresponding slope coefficients for enhancement and inhibition respectively

The additive equation models inhibition as an independent pathway that can close the open pore regardless of the GABA activation site. The additive equation consists of inhibition that arises completely independently of activation and that is why the activation term stands alone. Only in this scenario can the current be less than zero, as was observed for outward current traces for both PS (Baker et al., 2010) and pregnanolone (see Figure 1-13 and associated text). The mutant has a high open probability in the absence of GABA and the inhibitor reduces this open probability and forcing the channel to close causing the current to go in the opposite direction.

Based on recent neurosteroid modulation of UNC-49 studies, we hypothesize PS and pregnanolone may have the ability to act as enhancers and inhibitors. By increasing the efficacy of a generally utilized enhancement pathway by creating the I300Q mutant, we have attempted to amplify an enhancing component of the steroid interaction. We predicted that an I300Q mutation in UNC-49 would further increase PS and pregnanolone enhancement directly or indirectly by decreasing inhibition. We used mathematical 71

modeling of variations on the Hill equation to evaluate the inhibition/enhancement model. We predicted that our model would have the ability to account for mutant curves with enhancement activity operating independently, superimposed on the inhibition. We tested several equations and provided supporting evidence for the simultaneous operation of enhancing and inhibitory allosteric regulatory pathways, but surprisingly, we observed that I300Q is likely involved in both inhibition and enhancement mechanisms. This was unexpected in the view of the accepted role for the anesthetic site residue as strictly an enhancing residue, suggesting novel functions for this critical residue.

Chapter 2

Materials and Methods

2.1 Data Analysis:

Dose-response curves were fit with MATLAB (The MathWorks, Natick, MA,

USA) using custom written routines (R. Layne and L. Horn, University of Toledo,

Toledo, Ohio, USA). Initial values and constraints were set to viable values considering the model in use and inferences based on the direction of raw data.

A trust-region algorithm method was used with minimum change in variables for finite difference derivatives set to 1.0e-8 and the corresponding maximum set to 0.1.

Maximum function evaluations were set to 600 and maximum iterations were set to 400.

Tolerances on the objective function were set to 1.0e-6 and tolerances on the decision variables were set to 1.0e-6. All error bars are S. E. M., and statistical significance was assessed using the Student’s T test for pair-wise comparisons and ANOVA for multiple comparisons.

Figures were generated using GraphPad Prism (GraphPad Software, Inc., La Jolla,

CA, USA). Curves were plotted from drug concentration x = [0-30μM] with a resolution of 0.05 μM (L. Horn).

Chapter 3

Results

3.1 Mathematical Modeling to Determine the Role of I300 in Neurosteroid

Modulation of UNC-49

M2 15’ (I300) was discovered as an allosteric residue that increases efficacy of alcohol and volatile anesthetic enhancement of GABAA receptors. In our two-site mechanism of modulation of UNC-49 by neurosteroids, introducing the I300Q mutation will change only the enhancement portion of modulation and the inhibition component will remain the same. All mutations for this study were made and characterized by previous members of the Bamber laboratory and this work presents an analysis to explore modulation mechanisms. I hypothesize that neurosteroids modulate UNC-49 GABA receptors through two independent pathways – a novel inhibitory pathway and the canonical enhancement pathway. To test this hypothesis, the receptor was mutated to increase efficiency of the enhancing pathway so that we could observe the difference to the balance between inhibition and enhancement. I mathematically modeled the data to evaluate whether M2 15’ mutations caused their overall effects solely by increasing the enhancement component in a two-pathway model of neurosteroid action, using equations that assumed either 1) antagonism of the receptor activation process (multiplicative equation) or 2) direct interaction with the receptor gating machinery independent of 74

ligand-induced activation (additive equation). The experiment was performed on multiple receptor forms to confirm wider applicability of the model. We chose the X, XY, and Y chimeras for the I300Q mutation because they span the largest range of receptor behavior from extreme sensitivity to pregnanolone inhibition to pregnanolone enhancement. If making this mutation is going to provide insight, the data will be best interpreted if observed over the extremes of receptor behavior. My prediction is that the mutations will reduce overall inhibition or increase overall enhancement and the change will be attributed solely to increased activity of the enhancement pathway, thus confirming that enhancement is operating independently, alongside inhibition. Ultimately, the receptors showed decreased inhibition and/or increased enhancement by neurosteroids, consistent with multiple pathways. However, unexpectedly, the mutations to the enhancement pathway also affected the activity of the inhibition pathway.

3.2 Mathematically Modeling the X, XY, and Y chimera Exposed to Pregnanolone

With the Multiplicative Equation

3.2.1 Mathematical Modeling Proposal 1: The Change in Modulation of the X, XY, and Y Chimera Caused by I300Q is Solely a Change in Inhibition

We first modeled the I300Q data as though the I300Q mutation affected only inhibition. This involved fitting the X, XY and Y chimera with the Inhibitory Hill equation to generate inhibition parameter values and also fitting the corresponding

XI300Q, XYI300Q, and YI300Q mutants with the Inhibitory Hill equation. By comparing X, XY, and Y chimera inhibition parameter values to corresponding I300Q chimera inhibition parameter values, the change in inhibition is quantified. However, for modulation by pregnanolone, the I300Q chimeras could not be fit to the traditional

Inhibitory Hill equation suggesting the change in modulation caused by the I300Q mutation may involve more than just simple inhibition (R2=.51 for XI300Q, .84 for

XYI300Q, 0 for YI300Q).Data were fit to the standard Inhibitory Hill equation:

( ( ) )

Where I = current, x = ligand concentration, IC50 = dissociation constant or concentration required to produce half-maximal current and n = slope coefficient or Hill coefficient.

For this analysis, IC50 values were constrained from zero to infinity because negative values represent negative concentration values which are not viable with the intended model. No upper constraint is necessary. The IC50 initial value was set to zero because the raw data appear to have a negative slope. Initial values for nI were set to the standard Hill slope value of 1.0 and constrained from 0.0 to 2.0.

Table 3.1: Parameter Initial Values for X and XY Chimera Inhibitory Hill Equation Fits

Parameter Initial Value Lower Constraint Upper Constraint

IC50 0 0 ∞

nI 1 0 2

1.2

1.0

0.8

0.6

0.4

Normalized Current Normalized 0.2 X X I300Q

0.0 1 10 100 [P] (M)

Figure 3-1: Pregnanolone Modulation of the X Chimera and XI300Q Chimera The X chimera (solid black line) and XI300Q (dashed red line) exposed to pregnanolone and fit to the Inhibitory Hill equation to represent a loss in inhibition caused by the mutation of residue 300.

Table 3.2: Inhibitory Hill Equation X and XI300Q Parameter Values

2 Subunit Drug IC50 nI R X chimera P 4.026 1.183 .91 XI300Q (change INH) P 218.4 .8885 .51

1.2

1.0

0.8

0.6

0.4 XY XY I300Q

Normalized Current Normalized 0.2

0.0 1 10 100 [P] (M)

Figure 3-2: Pregnanolone Modulation of the XY Chimera and XYI300Q The XY chimera (solid black line) and XYI300Q (dashed red line) exposed to pregnanolone and fit to the Inhibitory Hill equation to represent a loss in inhibition caused by the mutation of residue 300.

Table 3.3: Inhibitory Hill Equation XY and XYI300Q Parameter Values

2 Subunit Drug IC50 nI R XY chimera P 2.497 2 .98 XYI300Q (change INH) P 9.027 1.013 .84

Fitting the Y chimera and YI300Q with the Inhibitory Hill equation is not possible. The data appear to have a positive slope and current values are greater than 1.

R2 values provided were both negative.

2.0

1.5

1.0

0.5 YI300Q Normalized Current Normalized Y

0.0 1 10 100 [P] (M)

Figure 3-3: Pregnanolone Modulation of the Y Chimera and YI300Q The Y chimera (solid black squares) and YI300Q (white squares) exposed to pregnanolone. The data appear to have a positive slope and cannot be fit with the traditional Inhibitory Hill equation.

Table 3.4: Inhibitory Hill Equation Y and YI300Q Parameter Values

2 Subunit Drug IC50 nI R Y chimera P 2282 2 0 YI300Q (change INH) P 1670 2 0

The traditional Inhibitory Hill equation was not adequate to fit the data from the

X, Y, and XY I300Q mutants, suggesting the underlying mechanisms of neurosteroid allosteric regulation are more complex than simple allosteric inhibition.

3.2.2 Mathematical Modeling Examination: A Step-Wise View of Changes in

Enhancement Parameters

The previous analysis rejected exclusive inhibition as an explanation for the change in modulation caused by the mutation, I300Q. This change in modulation may be caused by a decrease in inhibition or an increase in enhancement or a combination of both. We have further explored the causes for this change in modulation by examining how changes in enhancement parameters, specifically EC50, would change the shape of the X and XY chimera dose-response and whether or not these changes appear to lead to I300Q data.

For this analysis, we used a multiplicative version of the Hill equation. The Hill equation cannot account for the effect of inhibition and enhancement occurring simultaneously to produce total measured current. The Hill equation attributes current produced to only one rate constant rather than the combination of inhibition and enhancement rate constants occurring simultaneously. An alternative model was necessary to fit the data.

Wardell et al. proposed a multiplicative Hill equation representing the product of an enhancing and inhibitory Hill equation which provides both inhibition and enhancement parameters (IC50, nI, EC50, nE). This model suggests inhibition is

downstream of enhancement and causes a change in enhancement equal to the reduction created by the product of enhancement and the inhibition factor.

[ ] ( ) ( ) ( [ ] ) ( ) ( )

Where:

I=Normalized Current, A=amplitude of I above 1, EC50 and IC50 are the half-maximal enhancing and inhibitory concentrations of x respectively, nE and nI are respectively the slope coefficients for enhancement and inhibition.

In order to vary enhancement values, initial enhancement values for the X and XY chimeras had to be generated. These were found by fitting the X and XY chimera data with the multiplicative equation and fixing the inhibition values to those found using the

Inhibitory Hill equation. This causes the generation of enhancement parameters representing minimal enhancement for use as a starting point for examination of an increase in enhancement because the modulation represented by the curve is already accounted for by the fixed inhibition parameters. I then manually plotted several additional curves with step-wise decreasing EC50 values to see how these changes in enhancement change curve shape and if it seems these changes could lead to a curve generated from the I300Q data.

1.2

1.0

0.8 EC50=.1 EC =.25 0.6 50 EC50=.5

EC50=1 0.4 EC50=5

Normalized Current Normalized 0.2 X X I300Q

0.0 1 10 100 [P] (M)

Figure 3-4: Step-wise Increases in X Chimera EC50 Values (Multiplicative Equation) Pregnanolone dose-response curve for the X chimera (solid black line) and the X chimera with varied EC50 values (blue lines)

Original X Chimera EC50 value: 1020

Decreasing the EC50 for the X chimera caused a shift toward the I300Q data

(Figure 3-4). However, this shift did not allow for a sufficient decrease in slope to reach the level of the mutant curve. This suggests a gain in enhancement alone is not sufficient to explain the change in pregnanolone modulation of XI300Q relative to the X chimera.

Of the varied EC50 fits, I chose the fit closest to the mutant data (EC50=.5) and subsequently varied aE values to explore the capability of a change in aE to shift the fit toward the mutant curve.

1.2

1.0

0.8 EC50=.5 aE=.2 aE=.5 0.6 aE=1 aE=1.5 aE=2 0.4

Normalized Current Normalized 0.2 X X I300Q

0.0 1 10 100 [P] (M)

Figure 3-5: Step-wise Increases in X Chimera aE Values (Multiplicative Equation) Pregnanolone dose-response curve for the X chimera (solid black line) and the X chimera with varied aE values (red lines) Original X Chimera aE value: .2

Increasing the aE values for the X chimera caused a shift toward the I300Q data

(Figure 3-5). However, this shift did not allow for a sufficient decrease in slope to reach the level of the mutant curve.

1.2

1.0 EC50=.1 EC50=.25

EC50=.5 0.8 EC50=1

EC50=5 0.6

0.4 XY XY I300Q

Normalized Current Normalized 0.2

0.0 1 10 100 [P] (M)

Figure 3-6: Step-wise Increases in XY Chimera EC50 Values (Multiplicative Equation) Pregnanolone dose-response curve for the XY chimera (solid black line) and the XY chimera with varied EC50 values (blue lines)

Original XY Chimera EC50 value: 160

Decreasing the EC50 for the XY chimera caused a shift toward the I300Q data

(Figure 3-6). However, this shift did not allow for a sufficient decrease in slope to reach the level of the mutant curve. This suggests a gain in enhancement alone is not sufficient to explain the change in pregnanolone modulation of XYI300Q relative to the XY chimera.

Again, of the varied EC50 fits, I chose the fit closest to the mutant data (EC50=.5) and subsequently varied aE values to explore the capability of a change in aE to shift the fit toward the mutant curve. 84

1.4

1.2

EC50=.5 aE=.2 1.0 aE=.3 aE=.4 0.8 aE=.5 aE=.6 0.6

0.4 XY

XY I300Q Normalized Current Normalized 0.2

0.0 1 10 100 [P] (M)

Figure 3-7: Step-wise Increases in XY Chimera aE Values (Multiplicative Equation) Pregnanolone dose-response curve for the XY chimera (solid black line) and the XY chimera with varied aE values (red lines) Original XY Chimera aE value: .2

Increasing the aE value did cause a shift toward the I300Q data (Figure 3-7).

However, this shift did not allow for a sufficient decrease in slope to reach the level of the mutant curve.

2.0

1.5

1.0 EC50=0

EC50=.25

EC50=.5 YI300Q 0.5 EC =1 Normalized Current Normalized Y 50 EC50=2.5

0.0 1 10 100 [P] (M)

Figure 3-8: Step-wise Increases in Y Chimera EC50 Values (Multiplicative Equation) Pregnanolone dose-response curve for the Y chimera (solid black line) and the Y chimera with varied EC50 values (blue lines)

Original Y Chimera EC50 value: 5.431

Decreasing the EC50 for the Y chimera caused a shift toward the I300Q data

(Figure 3-8). However, this shift did not allow for a sufficient increase in slope to reach the level of the mutant curve. This suggests a gain in enhancement alone is not sufficient to explain the change in pregnanolone modulation of YI300Q relative to the Y chimera.

Again, of the varied EC50 fits, I chose the fit closest to the mutant data (EC50=0) and subsequently varied aE values to explore the capability of a change in aE to shift the fit toward the mutant curve.

2.0

1.5

1.0 EC50=0 aE=.63 aE=.7 aE=.8 aE=.9 0.5 YI300Q Normalized Current Normalized Y aE=1

0.0 1 10 100 [P] (M)

Figure 3-9: Step-wise Increases in Y Chimera aE Values (Multiplicative Equation) Pregnanolone dose-response curve for the Y chimera (solid black line) and the Y chimera with varied aE values (red lines) Original Y Chimera aE value: .63

Increasing the aE value did cause a shift toward the I300Q data (Figure 3-9).

However, this shift did not allow for a sufficient increase in slope to reach the level of the mutant curve.

Overall, manual shifts of EC50 and aE did not lead to the corresponding I300Q curve for the X, XY, or Y chimera. This suggests more than enhancement may be changing when the I300Q mutation is introduced.

3.2.3 Mathematical Modeling Proposal 2: The Change in Modulation of the X, XY and Y Chimera Caused by I300Q is Solely a Change in Enhancement Quantified by the Multiplicative Equation

To further examine the potential of I300Q causing a gain only in enhancement leading to the shift in modulation of UNC-49 by pregnanolone, I have modeled the mutant data with the multiplicative equation allowing only enhancement parameters to change relative to the non-mutant data. The X and XY chimera were fit with the

Inhibitory Hill equation and the inhibition values generated were then fixed for multiplicative equation fitting of the associated I300Q mutant data leaving only EC50 and nE as variable parameters. The Y chimera was initially fit with the multiplicative equation because it cannot be fit with the Inhibitory Hill equation. The inhibition parameter values generated were then fixed for multiplicative equation fitting of the associated I300Q mutant data leaving EC50 and nE as variable parameters as with the X and XY chimera. By generating inhibition values for the X, XY, and Y chimera and fixing the corresponding inhibition values of the associated I300Q mutant, we can generate EC50 and nE values representing a loss or gain in enhancement and no change in inhibition. This examines the possibility of the activation of independent inhibitory and enhancing transduction pathways and quantifies the contribution of neurosteroid enhancement in the presence of I300Q.

1.2

1.0

0.8

0.6

0.4

Normalized Current Normalized 0.2 X X I300Q

0.0 1 10 100 [P] (M)

Figure 3-10: I300Q Multiplicative Equation Fits with Fixed Inhibition Values Pregnanolone dose-response curve for the X chimera (solid black line) and XI300Q (dashed blue line) generated with inhibition values fixed to the values generated for the X chimera fit.

Table 3.5: X (Inhibitory Hill Equation) and XI300Q (Multiplicative Equation) Parameter Values

2 Subunit Drug Ae EC50 nE IC50 nI R X chimera P ------4.026 1.183 .91 XI300Q P .2 .366 2 4.026 1.183 0

1.2

1.0

0.8

0.6

0.4 XY XY I300Q

Normalized Current Normalized 0.2

0.0 1 10 100 [P] (M)

Figure 3-11: I300Q Multiplicative Equation Fits with Fixed Inhibition Values Pregnanolone dose-response curve for the XY chimera (solid black line) and XYI300Q (dashed blue line) generated with inhibition values fixed to the values generated for the XY chimera fit.

Table 3.6: XY (Inhibitory Hill Equation) and XYI300Q (Multiplicative Equation) Parameter Values

2 Subunit Drug aE EC50 nE IC50 nI R XY Chimera P ------2.497 2 .98 XYI300Q P .2 .8196 2 2.497 2 .27

2.0

1.5

1.0

0.5 YI300Q Normalized Current Normalized Y

0.0 1 10 100 [P] (M)

Figure 3-12: I300Q Multiplicative Equation Fits with Fixed Inhibition Values Pregnanolone dose-response curve for the Y chimera (solid black line) and YI300Q (dashed blue line) generated with inhibition values fixed to the values generated for the Y chimera fit.

Table 3.7: Y (Multiplicative Equation) and YI300Q (Multiplicative Equation) Parameter Values

2 Subunit Drug aE EC50 nE IC50 nI R Y Chimera P .63 5.431 .82 750.6 .68 .22 YI300Q P 1.03 1.213 2 750.6 .68 .9

Fixing inhibition values for the I300Q mutants did not allow viable fitting with the multiplicative Hill equation. R2 values were 0, .27 and .9 for XI300Q, XYI300Q and

YI300Q respectively. Although the YI300Q fit had a high R2 value, the inhibition values used to generate this fit had an R2 value of .22 (as generated with the Y chimera). The multiplicative equation with fixed inhibition cannot account for the pregnanolone data.

The change in modulation caused by the mutation of residue 300 may involve more than a simple gain in enhancement.

3.2.4 Mathematical Modeling Proposal 3: The Change in Modulation of the X, XY, and Y Chimera Caused by I300Q is Caused by a Combination of Changes in

Enhancement and Inhibition

Because several of the I300Q mutants cannot be fit with the Inhibitory Hill equation, it seems the change in modulation may involve more than a decrease in inhibition. Additionally, because some of the I300Q mutants cannot be fit with the multiplicative equation with inhibition parameters fixed to the corresponding X, XY, or

Y chimera values, it seems that enhancement is not independently responsible for the change in modulation of UNC-49 by pregnanolone. Another possibility is the simultaneous change of both inhibition and enhancement. The overall change in modulation could be caused by any combination of increasing enhancement and decreasing inhibition leading to the mutant data. To test this proposal, I fit the I300Q mutant data with the multiplicative equation with no initial parameter values fixed. The goodness of fit for this proposal can be compared to the goodness of fit for the analysis representing changes in inhibition and enhancement exclusively.

Table 3.8: Parameter Initial Values and Constraints for Multiplicative Fits

Parameter Initial Value Lower Constraint Upper Constraint

aE 0 0 .2

EC50 50 0 ∞

IC50 0 0 ∞

nE and nI 1 0 2

1.2

1.0

0.8

0.6

0.4

Normalized Current Normalized 0.2 X X I300Q

0.0 1 10 100 [P] (M)

Figure 3-13: I300Q Multiplicative Equation Fit with Non-constrained Enhancement and Inhibition Pregnanolone dose-response curve for the X chimera (solid black line) and XI300Q (dashed purple line) generated with inhibition and enhancement parameters non-constrained.

Table 3.9: Parameter Values for Fits Generated to Represent XI300Q (Pregnanolone) Contributing to a Change in Inhibition, Enhancement, and Both (Multiplicative Equation)

2 Subunit Drug aE EC50 nE IC50 nI R X chimera P ------4.026 1.183 .91 XI300Q (change ENH) P .2 .366 2 4.026 1.183 0 XI300Q (change INH) P ------218.4 .8885 .51 XI300Q (change ENH and INH) P .2 40.91 2 121.3 .9945 .52

1.2

1.0

0.8

0.6

0.4 XY XY I300Q

Normalized Current Normalized 0.2

0.0 1 10 100 [P] (M)

Figure 3-14: I300Q Multiplicative Equation Fit with Non-constrained Enhancement and Inhibition Pregnanolone dose-response curve for the XY chimera (solid black line) and XYI300Q (dashed purple line) generated with inhibition and enhancement parameters non-constrained.

Table 3.10: Parameter Values for Fits Generated to Represent XYI300Q (Pregnanolone) Contributing to a Change in Inhibition, Enhancement, and Both (Multiplicative Equation)

2 Subunit Drug aE EC50 nE IC50 nI R XY chimera P ------2.497 2 .98 XYI300Q (change ENH) P .2 .8196 2 2.497 2 .27 XYI300Q (change INH) P ------9.027 1.013 .84 XYI300Q (change ENH and INH) P .2 27.81 1.999 8.58 1.047 .84

2.0

1.5

1.0

0.5 YI300Q Normalized Current Normalized Y

0.0 1 10 100 [P] (M)

Figure 3-15: I300Q Multiplicative Equation Fit with Non-constrained Enhancement and Inhibition Pregnanolone dose-response curve for the Y chimera (solid black line) and YI300Q (dashed purple line) generated with inhibition and enhancement parameters non-constrained. aE upper limit was increased to 5 due to the max Y value of YI300Q data.

Table 3.11: Parameter Values for Fits Generated to Represent YI300Q (Pregnanolone) Contributing to a Change in Inhibition, Enhancement, and Both (Multiplicative Equation)

2 Subunit Drug aE EC50 nE IC50 nI R Y chimera P .63 5.43 .82 5.431 .6832 .22 YI300Q (change INH) P ------1670 2 0 YI300Q (change ENH) P 1.03 1.213 2 750.6 .68 .9 YI300Q (change ENH and INH) P 2.02 2.07 1.31 61.45 .50 .92

The X chimera I300Q mutant data could not be fit with the Inhibitory Hill

equation (R2=.51), the multiplicative model representing a change only in enhancement

(R2=0) or the multiplicative model representing a change in both enhancement and

inhibition (R2=.52). An alternative model may be required to fit the data.

The XY chimera I300Q mutant data fit the inhibition model better than the

enhancement model with R2 values of .84 and .27 respectively. The combined

2 enhancement and inhibition model had an R value of .84 with the EC50 of 27.81 and an

IC50 value of 8.58 relative to the X chimera IC50 value of 2.497. Therefore, the

combination model was best fit suggesting the change caused by the I300Q mutation was

primarily a change in inhibition but may have also included a minimal change in

enhancement.

The Y chimera I300Q mutant data could not be fit to the inhibition model and fit

the enhancement model with an R2 value of .9. The R2 value increased with the

2 combination model producing an R value of .92 with an IC50 value of 61.45. Although

this appears to primarily be a change in enhancement, a better fit is achieved with a slight change in inhibition suggesting both may be occurring simultaneously.

The multiplicative equation with roaming inhibition and enhancement can account for the pregnanolone data. Overall, the analyses suggest changes in both inhibition and enhancement and did not attribute the change in pregnanolone modulation caused by the I300Q mutation to a single direction of modulation.

3.3 Examining an Alternative Model: the Additive Equation

We repeated our analyses with an additive Hill equation model to examine how each equation contributes to the model, the stability of the models, and what each model suggests about the behavior of the system.

The additive Hill equation is the sum of an inhibitory and enhancing dose-response function and provides both inhibition and enhancement parameters (IC50, nI, EC50, nE).

The additive equation represents a system in which inhibition is functioning parallel to enhancement with total modulation equal to the summation of the two.

( ) ( )

Where:

I=Normalized Current, aE or aI=amplitude of I > 1, x=drug concentration [PS or pregnanolone], EC50 and IC50 = half-maximal enhancement and inhibitory concentrations of x respectively, nE and nI = corresponding slope coefficients for enhancement and inhibition respectively

3.3.1 Mathematical Modeling Examination: A Step-Wise View of Changes in

Enhancement Parameters Using the Additive Equation

To visualize a step-wise gain in enhancement by the X, XY and Y chimera, X and

XY chimera inhibition parameters are again determined using a simple Inhibitory Hill equation fit. Initial enhancement values were found by fitting the X and XY chimera data with the additive equation and fixing the inhibition values to those found using the

Inhibitory Hill equation as in section 3.2.2. Curves with step-wise decreasing EC50 values were plotted to see if changes in enhancement change curve shape and if these changes could lead to a curve generated from the I300Q data. If I300Q causes a gain in enhancement, the direction of the curves with decreasing EC50 values should lead to the

I300Q mutant data. In addition, the Y chimera was again fit with the additive equation initially because it cannot be fit with the Inhibitory Hill equation. Enhancement parameters were varied from this fit.

For all fits except Y, aE was set to .2 to avoid the tendency to fit at a value of zero which prevents changes in EC50 to cause the curve to shift. In addition, parameter constraints and initial values for all mutants were set to the following:

Table 3.12: Parameter Initial Values and Constraints

Parameter Initial Value Lower Constraint Upper Constraint

aE .2 .2 .2

EC50 50 0 ∞

nE 1 0 2

1.2

1.0

0.8 EC50=.25 EC =.5 0.6 50 EC50=1

EC50=5 0.4 EC50=15

Normalized Current Normalized 0.2 X X I300Q

0.0 1 10 100 [P] (M)

Figure 3-16: Step-wise Increases in X Chimera EC50 Values Pregnanolone dose-response curve for the X chimera (solid black line) and the X chimera with varied EC50 values (blue lines)

Original X Chimera EC50 value: 560.7

Decreasing the EC50 for the X chimera caused a shift toward the I300Q data

(Figure 3-13). However, this shift did not allow for a sufficient decrease in slope to reach the level of the mutant data. This suggests a gain in enhancement alone is not sufficient to explain the change in pregnanolone modulation of XI300Q relative to the X chimera.

Of the varied EC50 fits, I chose the fit closest to the mutant data (EC50=.25) and subsequently varied aE values to explore the capability of a change in aE to shift the fit toward the mutant curve.

1.4

1.2

1.0

EC50=.25 aE=.2 0.8 aE=.3 aE=.4 0.6 aE=.5 aE=.6 0.4

Normalized Current Normalized X 0.2 X I300Q

0.0 1 10 100 [P] (M)

Figure 3-17: Step-wise Increases in X Chimera aE Values (Additive Equation) Pregnanolone dose-response curve for the X chimera (solid black line) and the X chimera with varied aE values (red lines) Original X Chimera aE value: .2

100

Increasing the aE value did cause a shift toward the I300Q data (Figure 3-17).

However, this shift did not allow for a sufficient decrease in slope to reach the level of the mutant curve.

1.2

1.0 EC50=.25

EC50=.5 0.8 EC50=1 EC50=5 EC =15 0.6 50

0.4 XY XY I300Q

Normalized Current Normalized 0.2

0.0 1 10 100 [P] (M)

Figure 3-18: Step-wise Increases in XY Chimera EC50 Values Pregnanolone dose-response curve for the XY chimera (solid black line) and the XY chimera with varied EC50 values (blue lines)

Original XY Chimera EC50 value: 595.4

Decreasing the EC50 for the XY chimera caused a shift toward the I300Q data

(Figure 3-18). However, this shift did not allow for a sufficient decrease in slope to reach the level of the mutant data. This suggests a gain in enhancement alone is not sufficient to explain the change in pregnanolone modulation of XYI300Q relative to the XY chimera.

101

Again, of the varied EC50 fits, I chose the fit closest to the mutant data (EC50=1) and subsequently varied aE values to explore the capability of a change in aE to shift the fit toward the mutant curve.

1.2

aE=.05 1.0 aE=.1

EC50=1 aE=.2 0.8 aE=.3 aE=.4 0.6

0.4 XY XY I300Q

Normalized Current Normalized 0.2

0.0 1 10 100 [P] (M)

Figure 3-19: Step-wise Increases in XY Chimera aE Values (Additive Equation) Pregnanolone dose-response curve for the XY chimera (solid black line) and the XY chimera with varied aE values (red lines) Original XY Chimera aE value: .2

aE values were both increased and decreased in an attempt to reach mutant data and mutant slope. Increasing the aE value did cause a shift toward the I300Q data (Figure

3-19). However, this shift did not allow for a sufficient decrease in slope to reach the level of the mutant curve.

102

2.0

1.5

EC50=.5 1.0 EC50=.25

EC50=1

EC50=2 0.5 YI300Q Normalized Current Normalized Y EC50=3

0.0 1 10 100 [P] (M)

Figure 3-20: Step-wise Increases in Y Chimera EC50 Values Pregnanolone dose-response curve for the Y chimera (solid black line) and the Y chimera with varied EC50 values (blue lines)

Original Y Chimera EC50 value: 4.031

The R2 value of the Y chimera additive fit is only .22 as it was for the multiplicative fit. Decreasing the EC50 for the Y chimera caused a shift toward the I300Q data (Figure 3-20). However, this shift did not allow for a sufficient increase in slope to reach the level of the mutant data. This suggests a gain in enhancement alone is not sufficient to explain the change in pregnanolone modulation of YI300Q relative to the Y chimera.

103

2.0

1.5

1.0 EC50=.5 aE=.49 aE=.6 YI300Q aE=.7 0.5 Y

Normalized Current Normalized aE=.8 aE=.9

0.0 1 10 100 [P] (M)

Figure 3-21: Step-wise Increases in Y Chimera aE Values (Additive Equation) Pregnanolone dose-response curve for the Y chimera (solid black line) and the Y chimera with varied aE values (red lines) Original Y Chimera aE value: .49

Increasing the aE value did cause a shift toward the I300Q data (Figure 3-21).

However, this shift did not allow for a sufficient decrease in slope to reach the level of the mutant curve.

104

3.3.2 Mathematical Modeling Proposal 2: The Change in Modulation of the X, XY, and Y Chimera Caused by I300Q is Solely a Change in Enhancement Quantified by the Additive Equation

As in section 3.2.3, generating Inhibitory Hill inhibition values for the X and XY chimera and fixing the corresponding inhibition values of the associated I300Q mutant allow generation of EC50 and nE values representing a loss or gain in enhancement and no change in inhibition. This examines the possibility of the activation of independent inhibitory and enhancing transduction pathways and quantifies the contribution of neurosteroid enhancement in the presence of I300Q. If fixing inhibition values for the

I300Q mutants does not allow viable fitting with the additive Hill equation, the change in modulation caused by the mutation of residue 300 may involve more than a simple gain in enhancement. In addition, the Y chimera was fit with the additive equation because it cannot be fit with the Inhibitory Hill equation as in section 3.2.3.

105

1.2

1.0

0.8

0.6

0.4

Normalized Current Normalized 0.2 X X I300Q

0.0 1 10 100 [P] (M)

Figure 3-22: I300Q Additive Equation Fits with Fixed Inhibition Values Pregnanolone dose-response curve for the X chimera (solid black line) and XI300Q (dashed blue line) generated with inhibition values fixed to the values generated for the X chimera fit

Table 3.13: X (Inhibitory Hill Equation) and XI300Q (Additive Equation) Parameter Values

2 Subunit Drug Ae EC50 nE IC50 nI R X chimera P ------4.026 1.183 .91 XI300Q P .2 .3857 2 4.026 1.183 0

106

1.2

1.0

0.8

0.6

0.4 XY XY I300Q

Normalized Current Normalized 0.2

0.0 1 10 100 [P] (M)

Figure 3-23: I300Q Additive Equation Fits with Fixed Inhibition Values Pregnanolone dose-response curve for the XY chimera (solid black line) and XYI300Q (dashed blue line) generated with inhibition values fixed to the values generated for the XY chimera fit

Table 3.14: XY (Inhibitory Hill Equation) and XYI300Q (Additive Equation) Parameter Values

2 Subunit Drug aE EC50 nE IC50 nI R XY Chimera P ------2.497 2 .98 XYI300Q P .2 .8218 2 2.497 2 .74

107

2.0

1.5

1.0

0.5 YI300Q Normalized Current Normalized Y

0.0 1 10 100 [P] (M)

Figure 3-24: I300Q Additive Equation Fits with Fixed Inhibition Values Pregnanolone dose-response curve for the Y chimera (solid black line) and YI300Q (dashed blue line) generated with inhibition values fixed to the values generated for the Y chimera fit

Table 3.15: Y (Additive Equation) and YI300Q (Additive Equation) Parameter Values

2 Subunit Drug aE EC50 nE aI IC50 nI R Y Chimera P .49 4.03 .85 .06 175.1 1.63 .22 YI300Q P .92 1.10 2 .06 175.1 1.63 .88

Fixing inhibition values for the I300Q mutants did not allow viable fitting with the additive Hill equation. R2 values were 0, .74 and .88 for XI300Q, XYI300Q and

108

YI300Q respectively. Although the YI300Q fit had a relatively high R2 value, the inhibition values used to generate this fit had an R2 value of .22 (as generated with the Y chimera). These analyses suggest that the change in modulation caused by the mutation of residue 300 may involve more than a simple gain in enhancement.

3.3.3 Mathematical Modeling Proposal 3: The Change in Modulation of the X, XY and Y Chimera Caused by I300Q is Caused by a Combination of Changes in

Enhancement and Inhibition

X, XY or Y chimera, it seems that enhancement is not independently responsible for the change in modulation of UNC-49 by pregnanolone. Another possibility is the simultaneous change of both inhibition and enhancement. The overall change in modulation could be caused by any combination of increasing enhancement and decreasing inhibition leading to the mutant data. To test this proposal, I fit the I300Q mutant data with the additive equation with no initial parameter values fixed. The goodness of fit for this proposal can be compared to the goodness of fit for the analysis representing changes in inhibition and enhancement exclusively.

The following constraints and initial values were used:

109

Table 3.16: Parameter Initial Values and Constraints

Parameter Initial Value Lower Constraint Upper Constraint

aE/aI 0 0 .2

EC50/ IC50 50 0 ∞

nE and nI 1 0 2

1.2

1.0

0.8

0.6

0.4

Normalized Current Normalized 0.2 X X I300Q

0.0 1 10 100 [P] (M)

Figure 3-25: I300Q Additive Equation Fit with Non-constrained Enhancement and Inhibition Pregnanolone dose-response curve for the X chimera (solid black line) and XI300Q (dashed purple line) generated with inhibition and enhancement parameters non-constrained.

110

Table 3.17: Parameter Values for Fits Generated to Represent XI300Q (Pregnanolone) Contributing to a Change in Inhibition, Enhancement, and Both

2 Subunit Drug aE EC50 nE aI IC50 nI R X chimera P ------4.026 1.183 .91 XI300Q (change ENH) P .2 .3857 2 -- 4.026 1.183 0 XI300Q (change INH) P ------218.4 .8885 .51 XI300Q (change ENH and INH) P .2 .28 2 1.27e-11 193.3 .31 .63

1.2

1.0

0.8

0.6

0.4 XY XY I300Q

Normalized Current Normalized 0.2

0.0 1 10 100 [P] (M)

Figure 3-26: I300Q Additive Equation Fit with Non-constrained Enhancement and Inhibition Pregnanolone dose-response curve for the XY chimera (solid black line) and XYI300Q (dashed purple line) generated with inhibition and enhancement parameters non-constrained.

111

Table 3.18: Parameter Values for Fits Generated to Represent XYI300Q (Pregnanolone) Contributing to a Change in Inhibition, Enhancement, and Both

2 Subunit Drug aE EC50 nE aI IC50 nI R XY chimera P ------2.497 2 .98 XYI300Q (change ENH) P .2 .3857 2 -- 2.497 2 .74 XYI300Q (change INH) P ------9.027 1.013 .84 XYI300Q (change ENH and INH) P .2 1.507 2 3.72e-14 4.095 1.443 .87

2.0

1.5

1.0

0.5 YI300Q Normalized Current Normalized Y

0.0 1 10 100 [P] (M)

Figure 3-27: I300Q Additive Equation Fit with Non-constrained Enhancement and Inhibition Pregnanolone dose-response curve for the Y chimera (solid black line) and YI300Q (dashed purple line) generated with inhibition and enhancement parameters non-constrained. aE upper limit was increased to 5 due to the max Y value of YI300Q data.

112

Table 3.19: Parameter Values for Fits Generated to Represent YI300Q (Pregnanolone) Contributing to a Change in Inhibition, Enhancement, and Both (Additive Equation)

2 Subunit Drug aE EC50 nE aI IC50 nI R Y Chimera P .49 4.03 .85 .06 175.1 1.63 .22 YI300Q (change ENH) P .92 1.10 2 .06 175.1 1.63 .88 YI300Q (change ENH and INH) P 1.12 1.46 1.65 .27 52.14 1.59 .92

Similarly to the multiplicative equation model, the XI300Q data were not fit well

with the exclusive enhancement or exclusive inhibition models. But the combined

enhancement and inhibition additive model did account for the pregnanolone data (R2 =

.63) as was the case with the multiplicative equation (R2=.52) with combination R2 values

higher than those generated for either enhancement or inhibition alone. Neither of the

models strongly suggests a pregnanolone modulation mechanism for XI300Q.

For XYI300Q, the multiplicative enhancement model had an R2 value of .27 and

the additive enhancement model had an R2 value of .74. The additive model suggests a

chance in enhancement more than the multiplicative model. The additive inhibition

model (R2=.84) fit better than the additive enhancement model. Combination modulation

for the multiplicative and additive models had R2 values of .87 and .84 respectively.

Because the inhibition and combination models had the same fit, this suggests the change

in modulation is equally likely for both models. IC50 for the XY chimera is 2.497 which

is increased to 9.027 for the inhibition model and to 4.095 for the combination model,

which also includes an EC50 value of 1.507.

113

For YI300Q, both the enhancement and combination models for the additive and multiplicative equation had similar R2 values. Although the enhancement models had high R2 values (.90 and .88), the combination had higher R2 values at .92 for both. The inhibition incorporated in the combination equation had IC50 values of 52.14 and 61.45.

This suggests that in addition to a change in enhancement, a slight change in inhibition may be occurring because of the I300Q mutation for the Y chimera.

Overall, the additive equation produced a similar analysis to the multiplicative equation and it not apparent that one is clearly a better representation of modulation than the other. However, the combination inhibition and enhancement model provides consistently better fits than either enhancement or inhibition alone regardless of the model used.

3.4 Additional Support for the Additive Model: Outward Currents Generated From

Exposure of the XY Chimera to Pregnanolone

Two of the current values generated when the XY chimera was exposed to pregnanolone were less than zero. For previous analyses, these data points were set to zero for simplicity, however, outward current values are of value to our examination of two separate models. These data provide an opportunity to independently test our prediction of the additive model.

The depression of current below baseline can be explained by inhibition of spontaneous channel opening in the absence of GABA. In the additive model, the

114

neurosteroid can promote more time in the shut state reducing the current below the non-

GABA baseline. For the multiplicative model, the neurosteroid only has the ability to return current to the non-GABA baseline and is irrelevant to spontaneous opening.

The additive and multiplicative models had similar R2 values. However, further analyses of the models provide additional support for the additive model. For the additive model, separate inhibition and activation pathways could cause a negative current when added together if there was a larger negative inhibition value than positive enhancement value. For the multiplicative equation, the inhibition reduces activation by a factor or percentage. This would never allow the generation of negative current values. Therefore, these data support the proposed theory of activation and inhibition as separate pathways summed to lead to total current as generated with the additive equation because of the negative current values generated.

115

Figure 3-28: The XY Chimera Exposed to Pregnanolone and fit With the Additive Equation Pregnanolone dose-response curve for the XY chimera (solid black line) Table 3.20: XY Chimera Treated With Pregnanolone Parameter Values

2 Subunit Drug aE EC50 nE aI IC50 nI R XY chimera P 2.907 47.94 1.895 2.137 6.933 1.699 .94

The ability to fit the dose-response data for the XY chimera treated with pregnanolone and generate a high R2 value provides independent validation that our interpretation of the additive equation representing separate activation and inhibition pathways accurately reflects the physical reality of the channel mechanism.

116

3.5 Examining the Performance of the Additive Equation with a Different Data Set:

X, XY and Y Chimera Modulated by Pregnenolone Sulfate

Close examination of the multiplicative and additive models reveals the additive model may be more reliable for analysis of these data. To examine the performance of the model with another set of data, I will repeat the same analysis using the X and XY chimeras with the I300Q mutation treated with pregnenolone sulfate. The Y chimera with the M2 15’ mutation is not included for this analysis because it did not have the expected effects, as if enhancement was already at maximum levels.

3.5.1 Mathematical Modeling Proposal 2: The Change in Modulation of the X and

XY Chimera Caused by I300Q is Solely a Change in Enhancement Quantified by the Additive Equation

Again, the mutant data have been modeled with inhibition values fixed to that of the corresponding mutant allowing only enhancement parameters to change. The X and

XY chimera were fit with the Inhibitory Hill equation. By generating inhibition values for the X and XY chimera and fixing the corresponding inhibition values of the associated

I300Q mutant, we can generate EC50 and nE values representing a loss or gain in enhancement and no change in inhibition. This examines the possibility of the activation of independent inhibitory and enhancing transduction pathways and quantifies the contribution of neurosteroid enhancement in the presence of I300Q.

117

1.2

1.0

0.8

0.6

0.4

Normalized Current Normalized 0.2 X (PS) X I300Q 0.0 1 10 100 [PS] (M)

Figure 3-29: I300Q Additive Equation Fits with Fixed Inhibition Values (Pregnenolone Sulfate) Pregnenolone sulfate dose-response curve for the X chimera (solid black line) and XI300Q (dashed blue line) generated with inhibition values fixed to the values generated for the X chimera fit.

Table 3.21: X (Inhibitory Hill Equation) and XI300Q (Additive Equation) Parameter Values

2 Subunit Drug aE EC50 nE IC50 nI R X chimera PS ------2.255 .8981 .96 XI300Q PS .2 .0003 1.18 2.255 .8981 .61

118

1.2

1.0 XY I300Q XY (PS) 0.8

0.6

0.4

Normalized Current Normalized 0.2

0.0 1 10 100 [PS] (M)

Figure 3-30: I300Q Additive Equation Fits with Fixed Inhibition Values (Pregnenolone Sulfate) Pregnenolone sulfate dose-response curve for the XY chimera (solid black line) and XYI300Q (dashed blue line) generated with inhibition values fixed to the values generated for the XY chimera fit

Table 3.22: XY (Inhibitory Hill Equation) and XYI300Q (Additive Equation) Parameter Values

2 Subunit Drug aE EC50 nE IC50 nI R XY chimera PS ------1.045 1.425 .99 XYI300Q PS .2 .2052 2 1.045 1.425 .74

119

Fixing inhibition values for the I300Q mutants did not allow viable fitting with the additive equation for XI300Q and XYI300Q with R2 values of .61 and .74 respectively. The change in modulation caused by I300Q for the X and XY chimera may involve more than a simple gain in enhancement. It is advantageous to examine additional possibilities for the X and XY chimeras.

3.5.2 Mathematical Modeling Proposal 3: The Change in PS Modulation of the X and XY Chimera Caused by I300Q is Caused by a Combination of Changes in

Enhancement and Inhibition

Because some of the I300Q mutants cannot be fit with the Inhibitory Hill equation, it seems the change in modulation may involve more than a decrease in inhibition. Additionally, because some of the I300Q mutants cannot be fit with the multiplicative and additive equation with inhibition parameters fit to the corresponding X or XY chimera, it seems that enhancement is not independently responsible for the change in modulation of UNC-49 by pregnenolone sulfate. Another possibility is the simultaneous change of both inhibition and enhancement. To test this proposal, I fit the

I300Q mutant data with the additive equation with no initial parameter values fixed. The goodness of fit for this proposal can be compared to the goodness of fit for the analysis representing changes in inhibition and enhancement exclusively.

The following constraints and initial values were used:

120

Table 3.23 Parameter Initial Values and Constraints

Parameter Initial Value Lower Constraint Upper Constraint

aE/aI 0 0 .2

EC50/ IC50 50 0 ∞

nE and nI 1 0 2

1.2

1.0

0.8

0.6

0.4

Normalized Current Normalized 0.2 X (PS) X I300Q 0.0 1 10 100 [PS] (M)

Figure 3-31: I300Q Additive Equation Fit with Non-constrained Enhancement and Inhibition (Pregnenolone Sulfate) Pregnenolone Sulfate dose-response curve for the X chimera (solid black line) and XI300Q (dashed purple line) generated with inhibition and enhancement parameters non-constrained.

121

Table 3.24: Parameter Values for Fits Generated to Represent XI300Q (Pregnenolone Sulfate) Contributing to a Change in Inhibition, Enhancement, and Both

2 Subunit Drug aE EC50 nE aI IC50 nI R X chimera PS ------2.255 .8981 .96 XI300Q (change ENH) PS .2 .0003 1.18 -- 2.255 .8981 .61 XI300Q (change INH) PS ------13.62 2 .98 XI300Q (change ENH and INH) PS .03 17.29 6.10e-5 .2 16.52 1.89 .99

1.2

1.0 XY I300Q XY (PS) 0.8

0.6

0.4

Normalized Current Normalized 0.2

0.0 1 10 100 [PS] (M)

Figure 3-32: I300Q Additive Equation Fit with Non-constrained Enhancement and Inhibition (Pregnenolone Sulfate) Pregnenolone Sulfate dose-response curve for the XY chimera (solid black line) and XYI300Q (dashed purple line) generated with inhibition and enhancement parameters non-constrained.

122

Table 3.25: Parameter Values for Fits Generated to Represent XYI300Q (Pregnenolone Sulfate) Contributing to a Change in Inhibition, Enhancement, and Both

2 Subunit Drug aE EC50 nE aI IC50 nI R XY chimera PS ------1.045 1.425 .99 XYI300Q (change ENH) PS .2 .2052 2 -- 1.045 1.425 .74 XYI300Q (change INH) PS ------6.333 1.164 .99 XYI300Q (change ENH and INH) PS .2 115.6 1.89 .18 9.36 .97 .99

For XI300Q, the inhibition model fit much better than the enhancement model (R2

= .98 and .61 respectively). The combination model increased the R2 value to .99 by

incorporating a change in enhancement (EC50= 17.29) suggesting inhibition may be

involved as well.

For XYI300Q, the inhibition model again fit better than the enhancement model

with R2 = .99 and .74 respectively. The combination model had an R2 value of .99

making it as likely as the inhibition model. The combination model included an inhibition

component decreasing IC50 from 1.045 to 9.36 and an EC50 value of 115.6.

Overall, the additive equation with roaming inhibition and enhancement was the

best option for the analysis of pregnanolone data and is mostly generalizable to another

steroid, pregnenolone sulfate because similar results were observed. This provides more

evidence that the M2 15' residue is more than just an enhancement residue.

123

Chapter 4

Discussion

Understanding modulation of invertebrate and vertebrate cysteine loop ligand gated ion channels is important for the development of new anthelminthics and insecticides.

New drugs can combat resistance and also potentially decrease the unintended effects of pesticides on humans and the environment. GABA receptors are the targets of pesticides and many drugs used for the treatment of human disease; however the mechanisms of modulation of GABA receptors are largely unknown. Here, I have attempted to contribute to the characterization of the neurosteroid modulation mechanism of a C. elegans GABA receptor, UNC-49. Using structure-function and structure-activity studies, allosteric regulation by several neurosteroids has been studied including PS (Wardell et al., 2006), DHEAS (Twede et al., 2007) and pregnanolone. In addition, Hibbs et al., have recently elucidated a crystal structure and identified a GluCl ivermectin binding site that potentially overlaps with the GABA receptor neurosteroid binding site. Examining the modulation of GABA receptors by neurosteroids will provide a better understanding of how GABA receptors are regulated and contribute to pesticide drug development.

While studying the effects of neurosteroids on UNC-49, Wardell et al. found that the mutation of only one residue caused the direction of modulation of UNC-49 by pregnanolone to be reversed from inhibition to enhancement. In a Y-M258L and Y-

124

M258L/M264A background, T257F unmasks an enhancing component in the overall inhibitory neurosteroid action that is undetectable when 258 is a methionine residue. In this case, the original threonine residue at position 257 plays a critical role by coupling neurosteroid binding to an enhancing or inhibitory pathway: threonine leads to enhancement and phenylalanine leads to inhibition. This suggests modulation by pregnanolone may not consist of simple inhibition or enhancement but instead a combination of enhancement or inhibition combining or interacting to produce total current. The mutation of additional UNC-49 residues important for neurosteroid modulation may provide more insight into the inhibition and enhancement pathway mechanisms.

Based on these studies and insights, our hypothesis is that there are two pathways operating separately and independently: a novel inhibitory pathway acting negatively, and the well-recognized enhancement pathway acting purely positively. Our approach is to mutate the enhancement pathway residue M2 15’ (I300Q) and see if resulting neurosteroid modulation can be mathematically modeled as a change strictly in the enhancement component. We picked three mutants that span the full range of pregnanolone action: strong inhibition (XY), weak inhibition (X), and enhancement (Y), and asked whether improving the universal enhancement pathway would lead to increased pregnanolone enhancement in all 3 cases.

We chose the M2 15’ residue because it controls GABA receptor enhancement by many structurally-diverse compounds and is a broad-spectrum determinant of positive allosteric regulation. M2 15’ is important for enhancement of mammalian GABAA receptors by anesthetics and alcohols (Mihic et al., 1997, Ueno et al., 1999). Specifically,

125

ethanol, etomidate, furosemide, loreclezole, mefenamic acid and isoflurane show changes in receptor sensitivity with the mutation of residues homologous to M2 15’. M2 15’ has also been shown to control modulation of GABAc receptors by neurosteroids (Morris and

Amin, 2004). And finally, M2 15’ forms a portion of the ivermectin binding site in

GluClα1 (S260) (Hibbs et al., 2011), rendering it likely positioned to effect positive neurosteroid modulation in UNC-49.

By mutating M2 15’, we are attempting to increase enhancement. Because an increase in enhancement could look like a simple decrease in inhibition, we can use mathematical modeling to see if the effects might be explainable as differential weighting of the two pathways. Mathematical modeling will allow us to reveal parallel enhancement and inhibition that are expressed as simultaneous additive effects in a dose-response curve.

We developed two combination functions that combine separate enhancement and inhibition functions either multiplicatively or additively leading to total current. This allows for contributions to two directions of modulation to be quantified from one current value and provides insight for the underlying mechanisms of modulation. The multiplicative equation represents the product of inhibition and enhancement. The multiplicative model suggests inhibition is downstream of enhancement and causes a change in enhancement equal to the reduction or increase created by the product of enhancement and the inhibition factor. So, the multiplicative equation essentially models the inhibitor as an antagonist of the activation process stimulated by GABA and the neurosteroid. The additive equation represents the summation of inhibition and enhancement and a system in which inhibition is functioning parallel to enhancement with total modulation equal to the summation of the two. In this model, inhibition is an

126

independent pathway that can reduce the probability of the pore opening regardless of

GABA activation state. Because the pore may open spontaneously in the absence of activation by GABA, the additive equation has the flexibility to model suppression of receptor resting currents (i.e. in the absence of added GABA), which we observe for certain combinations of mutations and ligands.

In addition to identifying the model that most appropriately represents the data, we are interested in whether the M2 15’ mutation can be attributed to enhancement.

Because M2 15’ is known as an enhancing residue from previous studies, I predicted that the change in modulation caused by the I300Q mutation could be attributed to a change in only enhancement and not inhibition. If this is true, I should be able to mathematically account for the change in modulation that occurs with the I300Q mutation with only the enhancing portion of a mathematical model while the inhibition portion will remain unchanged. Dose-response data for the I300Q mutant chimeras underwent 3 separate analyses representing (1) a change only occurring in inhibition, (2) a change only occurring in enhancement or (3) a change occurring in both inhibition and enhancement.

The 3 analyses were done with both the additive and multiplicative combination equations.

To test method (1), a change only in inhibition, both the non-mutant and XI300Q chimera were fit with the standard Inhibitory Hill equation. Several mutants provided poor fits suggesting more than a change only in inhibition is occurring. To accomplish option (2) (a change only in enhancement) the Inhibitory Hill equation was used to generate inhibition parameter values for the X, XY and Y chimeras. The inhibition portion of the additive and multiplicative combination Hill equation was fixed to these 127

values and the data were fit allowing only enhancement parameters to change. R2 values for these fits did not support the mutation being attributed only to enhancement. Option

(3) represents I300Q leading to a change in both inhibition and enhancement. The I300Q data were fit to a combination equation with all parameters open to roam to theoretically plausible values leading to the highest R2 values. Overall, my analysis rejected my hypothesis that I300Q caused a simple quantifiable change in only enhancement. The insight provided by this study is that M2 15’ does not just play a role solely in enhancement as previously thought. Because the data cannot be accounted for by models representing exclusive changes in enhancement, these results suggest that inhibition must be changing too. This gives M2 15’ a novel role increasing the canonical enhancement pathway and additionally reducing inhibitory efficacy.

In addition to discovering a new role for M2 15,’ we have gained insight into the interaction of inhibition and enhancement with the use of two distinct combination Hill equations. Overall, the additive and multiplicative equations provided similar R2 values.

For analyses representing both inhibition and enhancement changing, the multiplicative equation produced R2 values of .52, .84, and .92 for the X chimera, XY chimera and Y chimera treated with pregnanolone respectively. For the same simulation using the additive equation, R2 values were .63, .87 and .92 respectively. For analyses representing fixed inhibition with only enhancement changing, the multiplicative equation produced

R2 values of 0, .27, and .9 for the X chimera, XY chimera and Y chimera respectively.

For the same simulation using the additive equation, R2 values were 0, .74, and .88. So for one of six simulations, the data were better fit using the multiplicative equation. For one of six simulations, the data fit the multiplicative and additive equations equally as

128

well, and for the remaining four of six simulations, the additive equation produced a better fit. Overall, the additive equation produced slightly better fits than the multiplicative equation but not considerable enough to conclude that it is a better model based only on these fits.

To further explore the additive and multiplicative combination equations, we performed an analysis on an independent data set. Exposure of the XY chimera to pregnanolone leads to outward currents (Figure 1-13 and 3-28). These data were fit with the additive equation producing an R2 value of .94. The depression of current below baseline can be explained by inhibition of spontaneous channel opening in the absence of

GABA. For the multiplicative model, the neurosteroid would only have the ability to return current to the non-GABA baseline and would not have a direct effect on the pore and associated spontaneous opening allowing the generation of currents below baseline.

But in the additive model, the neurosteroid can promote more time in the shut state reducing the current below the non-GABA baseline. This implies inhibition and enhancement by pregnanolone are independent processes and suggest the additive equation is a better indication of the interaction of inhibition and enhancement than the multiplicative equation.

In summary, enhancement was increased in all 3 cases, but our results suggest that the conventional enhancement pathway does not operate independently of inhibition as we hypothesized; instead the two pathways appear to overlap structurally revealing a novel function for M2 15’. Additionally, our results suggest the pregnanolone inhibitory pathway modifies the gating process directly, independent of GABA activation

129

reminiscent of a barbiturate, rather than modifying GABA action, reminiscent of a benzodiazepine.

Our data support the interaction of both ends of the steroid with the receptor.

Pregnenolone sulfate and pregnanolone differ in B ring saturation and the A ring C3 moiety as a sulfate group for pregnenolone sulfate which is absent for pregnanolone.

Treatment of the same UNC-49 I300Q chimera with pregnenolone sulfate and pregnanolone produced similar results. For all graphs, the dose-response curve was shifted up except one. For YI300Q treatment with pregnenolone sulfate, enhancement was slightly decreased, which is different than pregnanolone. This may be caused by enhancement reaching a maximum capability for the Y chimera alone and the I300Q mutation no longer had the ability to increase maximized enhancement. In regards to all other experiments, we have proposed a binding orientation in which the A ring protrudes extracellularly, the C3 moiety interacts with the M2-M3 loop and the D ring contacts M1.

These data support the binding orientation because the I300Q mutant is in M2 and the portions of pregnenolone sulfate and pregnanolone that contact this region are identical.

Both the additive and multiplicative models have limitations, potential improvements and require future experiments and data gathering missions. For example, the more complex models that allowed more parameters to roam often produced a better fit and may therefore provide insight into these modulation mechanisms. However, it is important to consider that more complex systems have inherently higher probabilities of matching given data sets. To follow up, it is important to test independent predictions of the models. In this example, we attempted to accomplish this by modeling a data set with spontaneous opening (and outward current values) for independent validation that our 130

interpretation of the additive model involving separate activation and inhibition pathways accurately reflects the channel mechanism.

To follow up this study, it would be beneficial to generate data using additional neurosteroids. It would also be beneficial to repeat the experiment with the mutation of other residues important for neurosteroid modulation. In addition, it would be helpful to test the combination inhibition and enhancement models with a system where inhibition and enhancement contributions have been previously quantified.

This study contributes to the structural vision of neurosteroid modulation of

UNC-49. In addition to structure-function and structure-activity studies, mathematical modeling allows us to further explore the mechanism of modulation by neurosteroids. All of these studies are useful toward designing drugs and this new invertebrate binding site is a lead-in toward drug development, providing information on a promising part of the

UNC-49 GABA receptor and its homologs in insects and other nematodes.

131

References

1. Baker C, Sturt BL, Bamber BA. Multiple roles for the first transmembrane

domain of GABAA receptor subunits in neurosteroid modulation and spontaneous

channel activity. Neuroscience Letters, 2010, 473:3.

2. Bamber BA, Beg AA, Twyman RE, Jorgensen EM. The Caenorhabditis elegans

unc-49 locus encodes multiple subunits of a heteromultimeric GABA receptor.

The Journal of Neuroscience, 1999, 19(13):5348-5359.

3. Bamber BA, Twyman RE, Jorgensen EM. Pharmacological Characterization of

the homomeric and heteromeric UNC-49 GABA receptors in C. elegans. British

Journal of Pharmacology, 2003, 138: 883-893.

4. Bamber, BA, Richmond, JE, Otto JF, Jorgensen EM. Composition of the GABA

receptor at the Caenorhabditis elegans neuromuscular junction. Br. J.

Pharmacol., 2005, 144: 502–509.

5. Bargmann CI. Neurobiology of the Caenorhabditis elegans Genome. Science,

1998, 282: 2028.

6. Baumann SW, Baur R, Sigel E. Forced subunit assembly in alpha1beta2gamma2

GABAA receptors. Insight into the absolute arrangement. J. Biol. Chem., 2002,

277: 46020-46025.

7. Belelli D and Lambert JJ. Neurosteroids: endogenous regulators of the GABAA

Receptor. Nature Reviews Neuroscience, 2005, 6: 565-575. 132

8. Buckingham SD, Hosie AM, Roush RL, and Sattelle DB. Actions of agonists and

convulsant antagonists on a Drosophila melanogaster GABA receptor (rdl)

homo-oligomer expressed in Xenopus oocytes. Neurosci Lett, 1994, 181: 137-140.

9. Dildy-Mayfield JE, Mihic SJ, Liu Y, Deitrich RA, Harris RA. Actions of long

chain alcohols on GABAA and glutamate receptors: relation to in vivo effects. Br

J Pharmacol., 1996, 118(2):378-84.

10. Feng H, Macdonald R. Barbiturates require the N terminus and first

transmembrane domain of the δ subunit for enhancement of α1β3δ GABAA

receptor currents. Journal of Biological Chemistry, 2010, 285(31): 23614-21.

11. Guest M, Bull K, Walker RJ, Amliwala K, O’Connor V, Harder A, Holden-Dye

L, Hopper NA. The calcium-activation potassium channel, SLO-1, is required for

the action of the novel cyclo-octadepsipeptide anthelminthic, emodepside, in

Caenorhabditis elegans. International Journal for Parasitology, 2007, 37:1577-88.

12. Hadley SH, Amin J. Rat α6β2δ GABAA receptors exhibit two distinct and

separable agonist affinities. J. Physiol., 2007, 581: 1001-18.

13. Harder A, Schmitt-Wrede HP, Krucken J, Marinovski P, Wunderlich F, Wilson J,

Amliwala K, Holden-Dye L, Walker R. Cyclooctadepsipedtides – an

anthelmintically active class of compounds exhibiting a novel mode of action.

International Journal of Antimicrobial Agents, 2003, 22(3):318-31.

14. Hibbs R, Gouaux E. Principles of activation and permeation in an anion-selective

Cys-loop receptor. Nature, 2011, 474:54-62.

15. Hosie AM, Sattelle DB. Agonist pharmacology of two Drosophila GABA receptor

splice variants. Br J Pharacol, 1996, 119:1577-1585.

133

16. Hosie AM, Wilkins ME, da Silva HM, and Smart TG. Endogenous neurosteroids

regulate GABAA receptors through two distinct transmembrane sites. Nature,

2006, 444(7118): 486-9.

17. Hosie AM, Wilkins ME, Smart TG. Neurosteroid binding sites on GABAA

receptors. Pharmacology and Therapeutics, 2007, 116: 7-19.

18. Jones-Davis DM, Song L, Gallagher MJ, Macdonald RL. Structural Determinants

of Benzodiazepine Allosteric Regulation of GABAA Receptor Currents. The

Journal of Neuroscience, 2005, 25(35):8056-65.

19. Macdonald RL and Olsen RW. GABAA receptor channels. Annu Rev Neurosci,

1994, 17:569–602.

20. Martin RJ, Robertson AP, Bjorn H. Target sites of anthelminthics. Parasitology,

1997, 114: S111-S124.

21. Mihic SJ, Ye Q, Wick MJ, Koltchine VV, Krasowski MD, Finn SE, Mascia MP,

Valenzuela CF, Hanson KK, Greenblatt EP, Harris RA, Harrison NL. Sites of

alcohol and volatile anesthetic action on GABAA and glycine receptors. Nature,

1997, 389:385-389.

22. Morris KDW, Amin J. Insight into the Mechanism of Action of Neuroactive

Steroids. Molecular Pharmacology, 2004, 66(1): 56-69.

23. Morrow AL. Recent developments in the significance and therapeutic relevance

of neuroactive steroids--Introduction to the special issue. Pharmacol. Ther., 2007,

116(1):1–6.

134

24. Nakao T, Naoi A, Kawahara N, Hirase K. Mutation of the GABA receptor

associated with fipronil resistance in the whitebacked planthopper, Sogatella

furcifera. Pesticide Biochemistry and Physiology, 2010, 97:3 262-6.

25. Narahashi T, Zhao X, Ikeda T, Nagata K, Yeh JZ. Differential actions of

insecticides on target sites: basis for selective toxicity. Human & Experimental

Toxicology, 2007, 26: 361-366.

26. Olsen RW, Hanchar HJ, Meera P, Wallner M. GABAA Receptor Subtypes: The

‘One Glass of Wine’ Receptors. Alcohol, 2007, 41(3): 201-209.

27. Olsen RW and Sieghart W. International Union of Pharmacology. LXX. Subtypes

of γ-Aminobutyric AcidA Receptors: Classification on the Basis of Subunit

Composition, Pharmacology, and Function. Pharmacol. Rev., 2008, 60:243-260.

28. Quiram PA, McIntosh JM, Sine SM. Pairwise interactions between neuronal α7

acetylcholine receptors and α-conotoxin PnIB. The Journal of Biological

Chemistry, 2000, 275:4889-4896.

29. Simon J, Wakimoto H, Fujita N, Lalande M, Barnard EA. Analysis of the set of

GABA(A) receptor genes in the human genome. J. Biol. Chem., 2004, 279: 41422-

41435.

30. Steinbach JH, Akk G. Modulation of GABA(A) receptor channel gating by

pentobarbital. J. Physiol., 2001, 537: 715-733.

31. Tasneem A, Iyer LM, Jakobsson E, Aravind L. Identification of the prokaryotic

ligand-gated ion channels and their implications for the mechanisms and origins

of animal Cys-loop ion channels. Genome Biology, 2004, 6:R4.

135

32. Thompson AJ, Lester HA, Lummis SCR. The structural basis of function in Cys-

loop receptors. Quarterly Review of biophysics, 2010, 1-51.

33. Tretter V, Ehya N, Fuchs K, Sieghart W. Stoichiometry and assembly of a

recombinant GABAA receptor subtype. J. Neurosci., 1997, 17: 2728-2737.

34. Twede VD, Covey DF, Tartaglia AL, Bamber BA. The neurosteroids

dehydroepiandrosterone S and P S inhibit the UNC-49 GABA receptor through a

common set of residues. Mol. Pharmacol., 2007, 72:1322-9.

35. Ueno S, Wick MJ, Ye Q, Harrison NL, Harris RA. Subunit mutations affect

ethanol actions on GABA(A) receptors expressed in Xenopus oocytes. British

Journal of Pharmacology, 1999, 127:377-82.

36. Wardell B, Marik PS, Piper D, Rutar T, Jorgensen EM, and Bamber BA. Residues

in the first transmembrane domain of the Caenorhabditis elegans GABAA

receptor confer sensitivity to the neurosteroid pregnenolone sulfate. S. British

Journal of Pharmacology, 2006, 148: 162-172.

37. Williamson SM, Walsh TK, Wolstenholme AJ. The cys-loop ligand-gated ion

channel gene family of Brugia malayi and Trichinella spiralis: a comparison with

Caenorhabditis elegans. Invertebrate Neuroscience, 2007, 7:219-226.

136