LIGAND DISCOVERY AND FUNCTIONAL CHARACTERIZATION OF MRGPRX

FAMILY ORPHAN GPCRS

Katherine Lansu

A dissertation submitted to the faculty at the University of North Carolina at Chapel Hill in

partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department

of Pharmacology in the School of Medicine.

Chapel Hill

2017

Approved by:

Robert Nicolas

Bryan L. Roth

Terrance Kenakin

Thomas Kash

Brian K. Shoichet

ii

© 2017

Katherine Lansu

ALL RIGHTS RESERVED

iii

ABSTRACT

Katherine Lansu: Ligand Discovery and Functional Characterization of MRGPRX Family

Orphan GPCRs

(Under the direction of Bryan Roth)

Orphan G protein coupled receptors (oGPCRs) have no known endogenous modulators

and poorly understood function. As GPCRs make useful targets for modern pharmacotherapies,

improving our understanding of oGPCR pharmacology and function could be beneficial for

human health. The primate-exclusive family of Mas-related G protein-coupled receptors

(MRGPRX) are four oGPCRs (MRGPRX1-4) with enriched expression in mast cells and sensory

neurons of the dorsal root and trigeminal ganglia. The MRGPRX receptors are hypothesized to

regulate itch, pain, inflammation, and other sensory modalities. To uncover the functions of these

receptors, I sought to generate selective and potent small molecule modulators of MRGPRX2

and MRGPRX4. I applied a combined in vitro and in silico approach to tackle this problem. I

began by performing a high-throughput small molecule β-arrestin screen to identify novel

chemical matter for MRGPRX2 and MRGPRX4. Using this method, I discovered several novel

MRGPRX4 agonists and that MRGPRX2 is a novel Gαq-coupled opioid receptor activated by

endogenous pro-dynorphin-derived peptides and exogenous opiates, such as morphine and

codeine. Then, utilizing the active and inactive compounds from the small molecule screening,

my collaborators generated computational homology models for MRGPRX2 and MRGPRX4

based on the kappa opioid receptor (KOR). For MRGPRX2, they used the computational model

to predict a pair of nanomolar selective probes to determine that agonism of this receptor leads to

iv

degranulation in a human mast cell line. For MRGPRX4, they used the homology model to

predict essential binding pocket residues for agonist activity. I then synthesized several

iv

micromolar potency probes for MRGPRX4. Lastly, using data from a collaborator’s genome-

wide association study, I characterized an MRGPRX4 coding variant to increase preference for

menthol cigarette smoking in African Americans and identified menthol as a negative allosteric

modulator of MRGPRX4. Altogether, these studies provide novel chemical matter for the

pharmacologically dark receptors MRGPRX2 and MRGPRX4 and suggest that these receptors

have putative functions in the sensory and immune systems.

v

To my family, who provided boundless encouragement during this journey. To my friends, who

helped light the way. Thank you for all of your support.

vi

ACKNOWLEDGEMENTS

First and foremost, I would like to thank my mentor Dr. Bryan Roth for providing an

exceptional training in molecular pharmacology. Dr. Roth taught me how to approach a scientific

project logically and methodically, always remembering to stay excited and “do the killer

experiment” as soon as possible. I also want to thank Dr. Roth for providing me with honest and

helpful advice during times of great frustration and for shaping me into the scientist I am today.

Many thanks to my committee members, Drs. Rob Nicholas, Terry Kenakin, Tom Kash,

and Brian Shoichet for guiding this project and helping me achieve my goals. I hope I can

continue to succeed from all you taught me. I am also tremendously grateful to Dr. Dave Nichols

for his encouragement, and for teaching me the value of thinking like a medicinal chemist. An

additional thank you to Dr. Joel Karpiak for generating the computational receptor models and

docking experiments, which was essential for the completion of this work.

I am grateful to all members of the Roth lab, past and present, for providing an

intellectually stimulating and fun scientific workplace. More specifically, Dr. Wes Kroeze, thank

you for teaching me small molecule screening from my very first day in the Roth lab, and for

making yourself available for conversations about life and succeeding in science. Dr. Kate White,

thank you for encouraging me to join the Roth lab and inspiring me to work hard. Drs. Daniel

Wacker, Patrick Giguere, Eyal Vardy, Justin English, Tao Che, and Brian Krumm – thanks you

all for the experimental, scientific and emotional advice, including coffee breaks and popcorn

chats. A special thanks to Sandy Hufeisen and Estela Lopez for keeping the lab running and

ensuring we had supplies for our research projects.

vii

I would like to thank Dr. John McCorvy in particular for teaching me to be a meticulous

experimentalist, a better friend, and a more authentic person. Dr. McCorvy inspired me to think

more deeply about receptor pharmacology and the big picture. Thank you for the daily scientific

and personal guidance over coffee, and helping me gain skill and confidence as a scientist.

I have sincere thanks for my former scientific mentors, without whom this work would

not have been possible. Thanks to Dr. James Platz for guiding my first scientific research project

and igniting my interest in how chemicals exert their actions. Equally deserving of my gratitude

is Dr. Soochin Cho, who encouraged me to keep trying to become a scientist when I thought I

was not smart enough to succeed. Without your guidance and support, I would have never made

it this far. Thanks to Dr. Saverio Gentile for teaching me to cultivate a love of science and to

perform experiments independently, two skills that served me well in graduate school.

I would like to thank all the friends I made in graduate school for providing love and

emotional support. Thank you to my running partner and friend Samantha Bevill for hours of

scientific advice and for inspiring me to be more dedicated and hard working. Thanks to Dr.

Daniel Dominguez for demonstrating how to be fearless and interested in everything. Thank you

Megan Schertzer, Reid and Maggie Olsen for being great friends to me and helping learn to have

fun while working hard. Thanks to Jeffrey for your unending kindness and support. Thank you

Annie Spikes for your tremendous encouragement and support over many, many cups of tea.

Thanks to my family for love and support, especially my cousin Cheryl for listening to

me and inspiring my work ethic and my brother Mike for providing encouragement and always

making me laugh. Lastly, a very special thank you to my mom and dad for listening to many

phone calls and providing me with help whenever I needed it. Thank you for encouraging me to

pursue my dreams, take risks, ask a lot of questions, and keep trying even when things get hard.

viii

TABLE OF CONTENTS

LIST OF TABLES…………………………………………………………………………..xi

LIST OF FIGURES…………………………………………………………………………...xii

LIST OF ABBREVIATIONS…………………………………………………………….……..xiv

CHAPTER 1: INTRODUCTION…………………………………………………………………1

1.1: BACKGROUND……………………………………………………………………..1

1.2: GPCR SIGNALING AND FUNCTION……………………………………………..5

1.3: OPIOIDS AND OPIOID RECEPTORS……………………………………………...7

1.4: ORPHAN GPCRS………………………………………………………………..…14

1.5: COMPUTATIONAL APPROACHES TO oGPCR PHARMACOLOGY………....18

1.6: MRG RECEPTORS…………………………………………………………………20

CHAPTER 2: IDENTIFICATION OF INITIAL CHEMICAL MATTER

FOR MRGPRX RECEPTORS VIA THE PRESTO-TANGO

β-ARRESTIN SCREENING PLATFORM……………………………………………...28

2.1: INTRODUCTION AND RATIONALE…………………………………………….28

2.2: METHODS………………………………………………………………………….29

2.3: RESULTS………………………………………………………………………..…32

2.3.1: Summary of screening results…………………………………………...32

2.3.2: NCC1 Library screen unveils novel

MRGPRX actives in the GPCRome……………………………………...33

2.3.3: A variety of ligands activate MRGPRX2…………………………………37

2.4: CONCLUSIONS……………………………………………………………………39

ix

CHAPTER 3: IN SILICO DESIGN OF NOVEL PROBES FOR

THE ATYPICAL OPIOID RECEPTOR MRGPRX2…………………………………….…….41

3.1: INTRODUCTION AND RATIONALE…………………………………………...41

3.2: METHODS…………………………………………………………………………43

3.3: RESULTS…………………………………………………………………………..52

3.3.1: Identification of MRGPRX2 agonists…………………………………….52

3.3.2: Preference for dextro-enantiomers and N-methyl scaffolds………………58

3.3.3: Prodynorphin-derived peptides activate MRGPRX2……………………..62

3.3.4: Structure-based docking predicts MRGPRX2-selective ligands………….66

3.3.5: ZINC-3573 as a chemical probe for MRGPRX2………………………….71

3.3.6: MRGPRX2 agonists induce degranulation in human mast cells………….73

3.4: CONCLUSIONS……………………………………………………………………74

CHAPTER 4: DEVELOPMENT OF PROBES FOR MRGPRXX4…………………..………80

4.1: INTRODUCTION AND RATIONALE…………………………………………..80

4.2: METHODS………………………………………………………………………...81

4.3: RESULTS………………………………………………………………………….89

4.3.1: MRGPRX4 screening reveals novel activators………………………….89

4.3.2: Glinides activate MRGPRX4…………………………………………….91

4.3.3: In silico model of MRGPRX4 predicts binding pocket residues………...92

4.3.4: R86 and R95 residues mediate agonist activity………………………….93

4.3.5: (D)-Phenylalanine nateglinide analogs are active at MRGPRX4……….95

4.3.6: (L)-Phenylalanine nateglinide analogs do not

antagonize MRGPRX4…………………………………………………………98

4.3.7: An MRGPRX4 variant confers menthol

Cigarette preference in African Americans………………………...……..99

x

4.3.8: MRGPRX4 N245S mutation attenuates agonist activity………….……..101

4.3.9 (-)-Menthol suppresses MRGPRX4 agonism…………………………….102

4.4: CONCLUSIONS…………………………………………………………………..105

CHAPTER 5: CONCLUSIONS AND FUTURE DIRECTIONS……………………………..109

5.1: CONCLUSIONS………………………………………………………………….109

5.2: FUNCTIONAL IMPLICATIONS FROM MRGPRX2 STUDIES………………..110

5.3: FUNCTIONAL IMPLICATIONS FROM MRGPRX4 STUDIES………………..113

5.4: IMPLICATIONS FROM IN SILICO HOMOLOGY MODELS………………….115

5.5: FINAL COMMENTS……………………………………………………………...118

APPENDIX 1…………………………………………………………………………………..120

APPENDIX 2…………………………………………………………………………….…….126

REFERENCES…………………………………………………………………………………133

xi

LIST OF TABLES

Table 1.1. -- Amino acids among opioid receptor structures……………………………………13

Table 3.1 -- Validation of previously published MRGPRX2 agonists

in PRESTO-Tango and FLIPR intracellular calcium release………………………53

Table 4.1 – Glinide and nateglinide analog functional data at MRGPRX4……………………96

Table 4.2 – Statistics for WT versus variant values in functional assays………………………105

Table 6.1 – Amino acids among opioid receptor structures and MRGPRX models………...…117

xii

LIST OF FIGURES

Figure 1.1 – Basic principles of receptor pharmacology………………………………………….2

Figure 1.2 – GPCR signaling summary…………………………………………………………...6

Figure 1.3 – Typical opioid ligand chemotypes…………………………………………………...9

Figure 1.4 – Opioid peptides……………………………………………………………………..10

Figure 1.5 – MRG expression in DRG and TG neuronal subpopulation………………………...22

Figure 2.1 – PRESTO-Tango screening at MRGPRX1, MRGPRX2,

and MRGPRX4……………………………………………………………………33

Figure 2.2 – Discovery of unique MRGPRX4 nateglinide

activity via parallel GPCRome screening of NCC1 library………………………..34

Figure 2.3 – Concentration-response curves of nateglinide

in Gs and Gi assays in MRGPRX receptor-expressing cells………………………35

Figure 2.4 – Calcium mobilization responses in stable cell lines expressing

MRGPRX receptors….……………………………………………………………36

Figure 2.5 – MRGPRX2 activation by H1Rantagonists…………………………………………37

Figure 2.6 – MRGPRX2 activation by H1R-interacting compounds……………………………38

Figure 3.1 – Validation of MRGPRX2 and MRGPRB2 agonists……………………………….54

Figure 3.2 – MRGPRB2 does not share ligands with MRGPRX2……………………………...56

Figure 3.3 – PRESTO-Tango screening of MRGPRX2 reveals new agonists………………….57

Figure 3.4 – Validation of MRGPRX2 opioid ligand activation………………………………..58

Figure 3.5 – MRGPRX2 is activated by many opioid scaffolds………………………………..59

Figure 3.6 – Sinomenine activity at MOR, KOR, and DOR……………………………………60

Figure 3.7 – MRGPRX2 prefers dextrorotary enantiomers including (+)-TAN-67……………61

Figure 3.8 – MRGPRX2 is not antagonized by traditional opioid antagonists…………………63

Figure 3.9 – MRGPRX2 is preferentially activated by prodynorphin-derived peptides………..64

xiii

Figure 3.10 – Peptide agonism at MRGPRX2 and comparison among related receptors………65

Figure 3.11 – In silico MRGPRX2 homology modeling predicts a selective agonist……….….67

Figure 3.12 – Mutations support in silico model of opioid ligand binding……………….……..68

Figure 3.13 – Modeling the MRGPRX2/Dynorphin binding site……………………….……….69

Figure 3.14 – MRGPRX2 is preferentially activated by R-ZINC-3573……….………………...71

Figure 3.15 – MRGPRX2 selective agonist confirmation……………………………………….72

Figure 3.16 – MRGPRX2 mediates intracellular calcium release and

degranulation in the LAD2 human mast cell line…………………………………75

Figure 3.17 – X2-Activating opiates are not antagonized by naloxone

in LAD2 cells, siRNA knockdown data…………………………………………..77

Figure 4.1 – Small molecule screening reveals novel MRGPRX4 activators………………….90

Figure 4.2 – Activation of MRGPRX4 by glinide drugs……………………………………….91

Figure 4.3 – In silico model of MRGPRX4…………………………………………………….93

Figure 4.4 – Mutagenesis experiments with MRGPRX4 in PRESTO-Tango………………….94

Figure 4.5 – (D)-Phenylalanine nateglinide analogs are agonists of MRGPRX4……………97

Figure 4.6 – (L)-Phenylalanine nateglinide analogs do not antagonize MRGPRX4…………...99

Figure 4.7 – MRGPRX4 rs7102322 is associated with menthol smoking

In African American participants………………………………………………….100

Figure 4.8 – MRGPRX4 N245S variant has dampened signaling

When compared to WT……………………………………………………………102

Figure 4.9 – (-)-Menthol reduces MRGPRX4 WT and N245S+T43T

Arrestin signaling……………………………………………………………….…103

Figure 4.10 – Positive controls for MRGPRX4 Menthol experiments…………………………104

xiv

LIST OF ABBREVIATIONS

7TM – Seven transmembrane receptor

AA – African American

AC – Adenylyl cyclase

ANOVA – Analysis of variance

AUC – Area under the curve

BAM – Bovine adrenal medulla (peptide)

BRET – Bioluminescence resonance energy transfer

cAMP – Cyclic adenosine monophosphate

CMV – Cytomegalovirus

DCQL – DC Tobacco Quitline

DHS – Dallas Heart Study

DMSO – Dimethyl sulfoxide

DNA – Deoxyribonucleic acid

DOR – Delta opioid receptor

DRG – Dorsal root ganglia

DXM – Dextromethorphan

xv

DYN – Dynorphin

EA – European American

EC50 – Half maximal effective concentration

EL2 – Extracellular loop 2

Emax – Maximum response

ENM – Elastic network modeling

ESI – Electrospray ionization

FLIPR – Fluorescence imaging plate reader

GDP – Guanine diphosphate

GPCR – G protein-coupled receptor

GRK – G protein coupled receptor kinase

GTP – Guanine triphosphate

HCS – High-content screening

HPLC – High performance liquid chromatography

HRMS – High-resolution mass spectra

HTS – High-throughput screening

IB4 – Isolectin B4

IgE – Immunoglobulin E

xvi

Kir – Inward rectifying potassium channel

KOR – Kappa opioid receptor

MAF – Minor allele frequency

MAPK – Mitogen-activated protein kinase

MOR – Mu opioid receptor

MRG – Mas-related G protein-coupled receptor

MRGPRX – Mas-related G protein-coupled receptor X

NC – Not calculable

NMDAR – N-methyl-D-aspartate receptor

NMR – Nuclear magnetic resonance

NOP – Nociceptin opioid receptor

NS – Not significant

oGPCR – Orphan G protein-coupled receptor

OR – Odds ratio

PDYN – Prodynorphin

PENK – Proenkephalin

PI – Phosphatidylinositol

PLC – Phospholipase C

xvii

PNOC – Pronociceptin

POMC – Proopiomelanocortin

PRESTO-Tango – Parallel receptor-ome expression and screening via transcriptional output

RLU – Relative luminescence units

SAR – Structure activity relationship

SBDD – Structure-based drug design

SD – Standard deviation

SEM – Standard error of the mean

siRNA – small interfering ribonucleic acids

SNP – Single nucleotide polymorphism

TET – Tetracycline

TEV – Tobacco etch virus protease

TG – Trigeminal ganglia

TM – Transmembrane

TRP – Transient receptor potential channels

tTA – Tetracycline transactivator

VLS – Virtual ligand screening

WT – Wild type

1

CHAPTER 1: INTRODUCTION

1.1 BACKGROUND

The human fascination with drugs and their ability to heal, alter mood, or induce injury

goes back to early civilizations (Merlin, 2003). Identifying helpful or harmful plant preparations

was based on personal experience and observations in animals, with little understanding of how

these enigmatic substances exerted their actions (Crocq, 2007). Without an understanding of the

mechanisms of drug action, there was little progress in improving pharmacotherapies. Today,

pharmacologists use systematic molecular approaches to discover safer and more effective drugs

for the treatment of disease, but the innate human curiosity for drugs and their actions remains as

strong as ever. Fortunately, the ancient exploration of unique chemical compounds provided

modern pharmacologists with an array of molecules that have catalyzed our understanding of the

body at the molecular level.

Despite the historical prevalence of drug use for medical, personal, and spiritual purposes,

specific drug receptors were not identified until the mid-20th

century. It was only in the early

1900s that John Langley and Paul Erlich postulated the existence of “receptive substances”

(Ehrlich, 1908; Langley, 1905). During this time and leading up to the 1940s, pharmacologists

outlined basic theories of drug action by mathematically analyzing drug effects on animal organ

preparations. Without knowing the molecular identity of the receptors, Hill (Hill, 1910), Clark

(Clark, 1937), Gaddum (Gaddum, 1937), Schild (Arunlakshana and Schild, 1959), and other

pharmacologists used potent chemicals such as atropine and ergotamine to define drug theory

2

(reviewed in (Rang, 2006)). Early receptor theory focused on mathematically defining

interactions between ligands and receptors analogous to enzyme kinetics and incorporating the

law of mass action (Fig 1.1) (Arunlakshana and Schild, 1959; Stephenson, 1956). Further

progress developing receptor theory was delayed because many prominent scientists considered

the receptor concept to be an abstraction and remained skeptical about the existence of receptors

in cells (Ahlquist, 1973).

The ability for drugs and hormones to produce physiologic changes was well-known, but

it wasn’t until the 1960s and 1970s that the molecular mechanisms of this began to be understood.

Martin Rodbell and Albert G. Gilman used novel biochemical approaches to identify

heterotrimeric guanine triphosphate (GTP)-binding proteins, or G proteins (Rodbell, 1980; Ross

and Gilman, 1977; Ross et al., 1978), that mediated hormone-induced increases of intracellular

cyclic adenosine monophosphate (cAMP). Using radioligand binding, Robert Lefkowitz and

colleagues then discovered the existence of specific beta-adrenergic receptors that bound to

Figure 1.1: Basic Principles of Receptor Pharmacology. Blue box: Various models of receptor activity

including the law of mass action as a simple starting point, leading to the ternary and extended ternary complex

models. Lower right: definitions of terms for models. Top right: Simulated dose response curve depicting

various ligand types with corresponding labels. Gray line indicates constitutive activity (R*). Modified from

(De Lean et al., 1980; Kenakin, 2004; Samama et al., 1993))

3

norepinephrine and the adrenergic antagonist propanalol (Lefkowitz and Haber, 1971). The

discovery that many molecules exerted their signaling actions via receptors and G proteins

prompted an important change in receptor theory by Andre De Lean, who created the ternary

complex model to explain the allosteric interaction between activated receptor and G protein

leading to signal propagation (De Lean et al., 1980) (Figure 1.1). These G protein-interacting

receptors were deemed G protein-coupled receptors or GPCRs.

From there, Black and Leff generated an operational model of receptor activity (Black

and Leff, 1983), which allowed pharmacologists to quantify a drug’s parameters from functional

and physiological assays. This operational model expanded receptor theory by considering the

propensity of an agonist to elicit a response and the receptor occupancy of the system, which

better explained the non-linear effects observed on tissues with increasing doses of drug

(Kenakin, 2004). Later, De Lean’s ternary model was expanded to the extended ternary complex

model (Samama et al., 1993) (Figure 1.1) to include consideration of non-ligand dependent

active GPCR state (R*), also called constitutive activity (reviewed further in (Wacker et al.,

2017). This model was extended again to the cubic ternary complex model to explain the

coupling of non-signaling G proteins with inactive receptor states (Weiss et al., 1996). In total,

the culmination of biochemical and theoretical expertise allowed the field to quantify how G

proteins, receptors, and ligands functioned in concert with one another to produce cellular

signaling. These advances ultimately stimulated the improvement of ligands and their

pharmacologic parameters.

To further discuss ligands and their actions at GPCRs, it is helpful to outline some basic

pharmacological principles (for a detailed review, see (Kenakin, 2009a)). A ligand possesses

affinity for a receptor of interest, which is a measurement of the strength of the ligand’s

4

molecular interaction with the receptor. The term potency refers to the amount of drug needed to

produce a certain response and is usually provided as an EC50 value, referring to the

concentration needed to produce a half-maximal response. On the other hand, efficacy is the

ability for a ligand to produce a response in a system, which can be pluridimensional (Galandrin

and Bouvier, 2006) and refer to several distinct outputs. Activating compounds, called agonists,

can either be full agonists that produce the maximal response (Emax), or partial agonists that

produce a sub-maximal response (Fig 1.1). Inverse agonists reduce efficacy compared to

constitutive signaling and neutral antagonists inhibit the effects of agonists without altering basal

(R*) activity (Fig 1.1). The variety of ligands and efficacies makes GPCRs complex signaling

transducers capable of producing an array of signaling outputs.

To add to this complexity, the completion of the human genome sequencing project in

2003 revealed the existence of more than 800 GPCR genes, making it the largest category of

membrane proteins in the genome (Fredriksson et al., 2003). About 400 of these receptors are

odorant receptors, with the remaining non-olfactory receptors subdivided into five families:

Rhodopsin (Class A), Secretin (Class B), Glutamate (Class C), Frizzled/Taste (Class F), and the

Adhesion Family (Alexander et al., 2015). The substantial number and diversity of GPCRs

expressed throughout the body, coupled with their relevance to signaling, make these receptors

extremely successful drug targets. In fact, GPCRs are a staple target of modern medicines and

make up the largest category of proteins targeted by FDA-approved drugs (30%) (Overington et

al., 2006). In order to continue generating novel GPCR-targeting drugs, it is important to

understand and apply the molecular mechanisms of GPCR-induced signal transduction, which

will be introduced below.

5

1.2 GPCR SIGNALING AND FUNCTION

GPCRs can be modulated by diverse chemotypes, including ions, hormones, plant

alkaloids, and neurotransmitters (Allen and Roth, 2011). At the molecular level, GPCR agonists

stabilize active receptor conformations leading to structural changes in the Gα subunit of the G

protein and the exchange of guanine diphosphate (GDP) by GTP (Gilman, 1995; Rodbell, 1995).

Active GTP-bound Gα subunits dissociate from the obligate dimer Gβγ and then each subunit

binds separate downstream effectors to initiate intracellular signaling cascades (Preininger and

Hamm, 2004)(Fig 1.2). Gα proteins come in four main subtypes defined by sequence and the

intracellular signaling cascade they modulate: Gαs stimulates cAMP production at adenylyl

cyclases (AC), Gαi/o inhibits cAMP production at ACs, Gαq/11 activates phospholipase C (PLC)

to generate inositol phosphates and the release of intracellular calcium stores, and Gα12/13

activates various cytoskeletal-related processes including actin polymerization (Buhl et al., 1995;

Simon et al., 1991). Each GPCR canonically couples to a particular Gα subtype, although G

protein “switching” may occur, creating the potential for a multiplicity of downstream effects for

each GPCR (Milligan and Kostenis, 2006). Humans also express five distinct Gβ subunits and 12

Gγ subunits that can interact in various combinations to create many potential signaling outputs

(Hurowitz et al., 2000). Following GPCR activation and G protein dissociation, Gβγ directly

modulates multiple effectors depending on the obligate dimer composition, including inward

rectifying potassium (Kir) channels, PLC, ACs, and an array of kinases (Fig 1.2) (see (Khan et al.,

2013) for a comprehensive review).

Active GPCR conformations recruit binding of G protein-coupled receptor kinases

(GRKs) to phosphorylate the receptor and change its conformation, prompting the recruitment of

6

β-arrestin proteins (Lohse et al., 1990). β-arrestin binding initiates receptor internalization via

endocytosis in clathrin-coated pits (Lefkowitz, 1998). This important step in a GPCR’s lifespan

is essential for downregulating a signal and allowing the cell to return to homeostasis (Fig 1.2).

Internalization of the receptor results in endosomal degradation of the receptor or recycling back

to the membrane (Koenig and Edwardson, 1994; von Zastrow and Kobilka, 1994). The

regulation of internalization and membrane expression is essential for proper signaling kinetics

and cellular function. Notably, β-arrestin binding can also initiate distinct, non-G protein-

dependent intracellular signaling (Luttrell et al., 1999).

Not all ligands signal equally though the described effectors, however. Ligands that

preferentially modulate one signaling pathway over another, in comparison to a reference ligand,

have functional selectivity (Urban et al., 2007). Functionally selective ligands often have

different cellular and physiological efficacy than balanced ligands, as each pathway downstream

of a GPCR regulates a distinct functional effect. One way to quantify functionally selective

ligand activity relies on utilizing the Black-Leff operational model and/or the calculation of

relative activity (RA = log(Emax/EC50)) (Kenakin, 2009b). The rational design of functionally

selective ligands is useful for basic scientists needing precise tool compounds and for drug

discovery efforts aiming to reduce unwanted side effects. One such example of a potentially

useful functionally selective agonist is PZM21, a G protein-biased opioid receptor ligand that

displays reduced respiratory depression but maintains analgesic efficacy (Manglik et al., 2016).

GPCRs function to recognize and transduce chemical stimuli into cellular signaling

responses. The diversity of molecules, GPCRs, G proteins, effectors, and tissue expression

profiles results in a seemingly infinite array of efficacies. Further, multitudes of receptors must

translate the barrage of chemical information into a phenotypic response. In order to understand

7

and modulate this process, it is necessary to study receptors at the molecular, cellular, and

physiological level. Thus far, there has been extensive progress in identifying receptors that

mediate essential physiological roles, including the adrenergic, serotonin, and opioid receptors.

Studies on these receptors provide a framework for dissecting GPCR structure, signaling, and

function.

1.3 –OPIOIDS AND OPIOID RECEPTORS

Our understanding of GPCR action is limited by the availability of compounds to

selectively modulate those receptors. Opioids, chemical compounds isolated from the opium

poppy such as morphine and codeine, are some of the oldest drugs used by man and today

include thousands of chemical analogs (Roth and Kroeze, 2015). This surplus of chemical matter

makes it is no surprise that we understand the opioid receptors in great depth. Shortly after the

radioligand binding experiments performed by Lefkowitz to identify the beta-adrenergic GPCRs

(Lefkowitz and Haber, 1971), radiolabeled opioid antagonists were used to discover the presence

of opioid receptors in the brain (Pert et al., 1973). By this time, there were already hundreds of

synthetic opioids with similar scaffolds and varying chemical substituents that were selective for

each receptor (Snyder and Pasternak, 2003), leading to the radioligand characterization of three

separate opioid receptors by 1978 (Kosterlitz and Leslie, 1978; Lord et al., 1977). Today, there

are four known receptors in this family of Class A GPCRs with ~70% sequence identity to one

another: the µ-opioid (MOR), κ-opioid (KOR), δ-opioid (DOR), and the nociceptin/orphanin FQ

peptide receptor (NOP). These receptors are expressed throughout the brain, spinal cord, afferent

sensory nerves, and intestines (Ryall, 1989). Both exogenous opioids and endogenous opioid

peptides modulate these receptors, which in turn mediate an array of psychological and

8

physiological effects including analgesia (Waldhoer et al., 2004), drug addiction/abuse (Kreek,

1996), mood (Lutz and Kieffer, 2013), respiration (Shook et al., 1990), and gut motility (Kurz

and Sessler, 2003).

Determination of each opioid receptor’s physiological contributions and biochemical

characteristics was made easier by the fact that many opiate analogs had already been used in

numerous physiological and clinical experiments before the receptors and their cognate

endogenous peptide agonists were identified (Pasternak and Pan, 2013). A further illustration of

the necessity of opioid small molecules in facilitating research on the opioid receptors is the fact

that NOP was not identified until 1994 (Mollereau et al., 1994). NOP has substantial differences

in pharmacology when compared with the other three opioid receptors (Toll et al., 2016) and

selective NOP modulators were only recently identified (Mustazza and Bastanzio, 2011). The

Figure 1.3: Typical opioid ligand chemotypes. Structures of five common opioid scaffolds are shown,

with the positions numbered in the 4,5α-epoxymorphinans for clarity. Example compounds are listed in

italic below each class. Note that acyclic analgesics can have many other scaffolds.

9

available opioid molecules exposed several key pharmacological parameters that define opioid

receptor pharmacology, which will be useful to understand in Chapter 3.

The four main types of opioid chemical scaffolds are 4,5α-epoxymorphinans, morphinans,

benzomorphans, phenyl-piperidines, and acyclic analgesics (Figure 1.3) (Pasternak and Pan,

2013). Using these compounds, it was determined that each of the four opioid receptors bind to

distinct ligands, but all are Gαi/o-coupled and reduce intracellular cAMP/neuronal transmission.

Analogs of these scaffolds revealed that opioid receptors exhibit stereoselectivity, or a preference

for certain optical isomers over others (Loh et al., 1974). Only the natural levorotary opioid

enantiomers activate the opioid receptors (Pasternak and Pan, 2013; Sromek et al., 2014).

Although the dextrorotary opioid enantiomers are inactive at the canonical opioid receptors,

many retain physiological activity perhaps through alternate receptors including the anti-tussive

dextromethorphan. Another pharmacological characteristic of the opioid receptors is that all four

can be antagonized by naloxone, a 4,5α-epoxymorphinan with an N-allyl substitution (Mollereau

et al., 1994; Waldhoer et al., 2004). Identification of naloxone and other antagonists

demonstrated that, generally, extending the length of the nitrogen substituent in morphinans,

benzomorphans, or 4,5α-epoxymorphinans transforms agonists into opioid antagonists (Lemke et

al., 2013). This is true for the pan-opioid antagonist naloxone and the subtype-selective

antagonists naltrindole, nalbufine, butorphanol, naltrexone, and β-furnaltrexamine, among others.

These compounds were used for upwards of 40 years to characterize the physiologic and

molecular efficacy of opioid receptors, and although recent progress has been made in generating

novel chemotypes for opioid receptors (Manglik et al., 2016), much of how we understand the

actions of these GPCRs is in the context of morphine-based scaffolds.

In addition to opioid alkaloids and synthetic compounds, endogenous opioid peptides also

10

bind to all four opioid receptors. Identification of endogenous opioid peptides was determined

using radioligand binding with selective, high affinity radioactive small molecule opiates

(Hughes et al., 1975; Pasternak et al., 1975; Terenius and Wahlstrom, 1975). All of these opioid

peptides are cleaved from four precursor peptides: pro-proopiomelanocortin (POMC),

proenkephalin (PENK), prodynorphin (PDYN), or pronociceptin/orphanin FQ (PNOC) (Figure

1.4) (Waldhoer et al., 2004). Generally, the peptides formed from these precursor peptides target

the DOR, MOR, KOR, and NOP receptors, respectively, although the peptides lose opioid

subtype specificity at higher concentrations. Of the 27 identified opioid peptides, all 27 (Fig 1.4)

contain a critical N-terminal sequence Tyr-Gly-Gly-Phe-Met/Leu (YGGFM/L) that is required

for opioid receptor binding except for the nociceptin peptide, which contains an N-terminal Phe

(F) instead of a Tyr (Y) (Meunier et al., 1995). The processing and selectivity of these peptides

depends on tissue expression, post-translational modifications, and presence of cleavage

enzymes (Berezniuk and Fricker, 2011). For these reasons, there are still likely some unidentified

Figure 1.4 Opioid peptides. Opioid peptide fragments derived from each of the four pro-peptide genes.

11

opioid peptide fragments and/or opioid peptides with unidentified functions that may contribute

to undiscovered opioid functions.

The determination of the inactive state crystal structures of all four opioid receptors

(Fenalti et al., 2014; Granier et al., 2012; Manglik et al., 2012; Thompson et al., 2012; Wu et al.,

2012) revealed the molecular determinants of classical opioid ligand binding. These structures,

as well as the active-state MOR (Huang et al., 2015a), exposed atomic detail of each opioid

receptor’s unique orthosteric binding pocket and conserved structural features for which to

compare other Class A GPCRs. All residues in this section and later which will be described

according to the Ballesteros-Weinstein numbering system (Ballesteros et al., 1995). This format

designates the amino acid first, followed by a number (1-7) indicating the transmembrane

domain (TM), followed by a third number that refers to the position from the most conserved

residue, which is always designated 50. For example, position D5.46 is an aspartate in TM5, 4

residues from the most conserved residue Pro5.50.

The inactive and active state opioid structures displayed all of the highly conserved

GPCR residues (N1.50, D2.50, R3.50, 4.50, P5.50, P6.50 and P7.50), which make up critical

activation microswitches throughout the GPCR that are altered upon ligand binding and

important for initiating signaling, such as the PIF, CWxP, DRY, and NPxxY motifs (see Table

1.1) (Nygaard et al., 2009; Vardy et al., 2013; Wacker et al., 2013). Additionally, the inactive

state structure of the human DOR (Fenalti et al., 2014) demonstrated a conserved sodium ion

binding site near D2.50, N3.35, and S3.39, providing crystallographic evidence of sodium’s

allosteric effects on opioid ligand binding, which had first been proposed long before the

determination of the opioid crystal structures (Pasternak and Snyder, 1975).

The orthosteric binding pockets are comprised of residues located at the tops of TMs 3, 5,

12

6, and 7, with minor variations of residues between opioid receptors allowing for ligand

specificity among subtypes (Shang and Filizola, 2015)(see Table 1.1). Further receptor

differences can be observed in the KOR and NOP structures, which have ligand binding residues

extending into a pocket defined by TM2, 3, and 7 (Thompson et al., 2012; Wu et al., 2012). The

crystal structures also demonstrate the poorly conserved extracellular loop (EL) 3, located

between TM 6 and TM7, which may act as a lid for the ligand or otherwise be involved in ligand

binding and/or kinetic differences between the receptors (Filizola and Devi, 2013; Manglik et al.,

2012). One of the most important residues identified in the opioid crystal structures is the highly

conserved D3.32, which forms an ionic interaction with the amine of morphinan and other

classical opioid compounds in all four receptors.

In addition to clarifying the molecular mechanisms of receptor activation, the opioid

receptor crystal structures may promote the discovery of novel opioid small molecule

chemotypes using structure-based drug design (SBDD) techniques. Novel chemotypes may have

unique cellular and physiological signaling signatures and/or improved opioid receptor subtype

specificity. As such, SBDD can lead to the development of more precise treatments of pain,

addiction, affective disorders, or other pathologies. Thus far, SBDD approaches have been

successful at the β2-adrenergic receptor (Kolb et al., 2009), KOR (Zheng et al., 2017), and with

MOR, where a combined in vitro/in silico approach was used to identify a novel, G protein-

biased agonist with reduced side effects (Manglik et al., 2016). Opioid pharmacology research

has entered a new era where the availability of good probes, high- resolution structures, and

relevant animal models will continue to provide new information about the molecular and

clinical functions of this interesting class of receptors.

Interacting residues MOP DOP KOP NOP MOTIF MOLECULAR FUNCTION(S)

N/N/N/N 1.50 + + + + Coordination of water molecules (Fenalti et al., 2014); stabilizes other activation motifs

D/D/D/D 2.50 + + + + Coordination of sodium ion (Fenalti et al, 2014)

A/A/V/V 2.53 - - + - Ligand specificity (Wu et al 2012)

T/T/T/T 2.56 - - + - Coordination of water molecules (Wu et al)

Q/Q/Q/Q 2.60 - - + + Hydrogen bonding with ligand (Thompson et al., 2012)

N/K/V/D 2.63 - - + + Ligand specificity (Wu et al 2012; Thompson et al., 2012)

V/V/V/V 3.28 - - + + Hydrophobic interactions with ligands (Wu et al., 2012)

I/L/L/I 3.29 - - + + Hydrophobic interactions with ligands (Wu et al., 2012)

D/D/D/D 3.32 + + + + Salt bridge with amine-containing ligands (Manglik et al., 2012)

Y/Y/Y/Y 3.33 + + + + Coordination of water molecules (Vardy et al., 2013)

N/N/N/N 3.35 + + + + Coordination of sodium ion (Fenalti et al, 2014)

M/M/M/M 3.36 + + + + Hydrophobic interactions with ligands and coordination of water molecules (Thompson et al., 2012)

F/F/F/F 3.37 - - - + Coordination of water molecules (Vardy et al., 2013)

S/S/S/S 3.39 + + + + Coordination of sodium ion (Fenalti et al, 2014)

I/I/I/T 3.40 + + + - PIF Facilitates receptor activation (Vardy et al., 2013)

R/R/R/R 3.50 + + + + DRY Formation of "ionic lock" to stabilize inactive state (Nygaard et al., 2009)

W/W/W/W 4.50 + + + + TM structural integrity (Manglik et al., 2002)

E/D/D/G 5.35 + - - - Ligand specificity (Granier et al, 2012)

K/K/K/A 5.39 + + + - Coordination of water molecules (Wu et al, 2012); Ligand specificty (Thompson et al., 2012).

V/V/V/I 5.42 + + + + Form hydrophobic contacts with ligand (Wu et al., 2012)

A/A/A/S 5.46 - - - + Hydrogen bonding with ligand (Thompson et al., 2012)

P/P/P/P 5.50 + + + + PIF Facilitates receptor activation (Vardy et al., 2013)

F/F/F/F 6.44 + + + + PIF Facilitates receptor activation (Vardy et al., 2013)

W/W/W/W 6.48 + + + + CWxP "Rotamer toggle switch"; facilitates receptor activation (Nygaard et al., 2009)

P/P/P/P 6.50 + + + + CWxP "Rotamer toggle switch"; facilitates receptor activation (Nygaard et al., 2009)

I/I/I/V 6.51 + + + + Form hydrophobic contacts with ligand (Manglik et al., 2012)

H/H/H/Q 6.52 + + + + Coordination of water molecules (Manglik et al., 2012); Ligand specificty (Thompson et al., 2012).

V/V/I/V 6.55 + + + + Form hydrophobic contacts with ligand (Manglik et al., 2012); ligand specificity (Wu et al 2012)

K/W/E/Q 6.58 + + - - Ligand specificity (Vardy et al., 2013, Fenalti et al., 2014)

W/L/Y/L 7.35 + + + - Ligand specificity (Manglik et al., 2012, Granier et al., 2012)

I/I/I/T 7.39 + + + + Hydrophobicity; Ligand specificty (Thompson et al., 2012).

G/G/G/G 7.42 - - + - May provide space and stabilize activation switch between 7.43 and 3.32 (Kolinksi et al., 2009)

Y/Y/Y/Y 7.43 + + + + Provides stabilizing hydrogen bonds (Manglik, et al., 2012 Thompson et al., 2012); involved in activation

P/P/P/P 7.50 + + + + NPxxY Facilitates receptor activation (Nygaard et al., 2009)

W/W/W124/W (EL1) - - + - Hydrophobic contact with JDTic; ligand specificity (Wu et al., 2012)Table 1.1: Amino acids among opioid receptor structures. Light gray background: Residues within 4 Å of ligands indicated with a "+" or "-" if present. Dark blue font

indicates identical residues at all four receptors; light blue font indicates identical residues in MOP, DOP, and KOP, but unique to NOP; red font labels divergent residues in

all receptors; orange font indicates unique residues to either MOP, DOP, or KOP. Dark gray background: conserved residues that are involved in receptor function. Blue

background: residues associated with sodium binding. Associated motifs are labeled; all proposed functions are summarized in right-hand column. (Modified from Filizola

and Devi, 2013.)

13

14

1.4 – ORPHAN GPCRs

In contrast to the opioid and aminergic receptors that were characterized predominantly

using ligand binding studies, as many as 120 of the 394 non-olfactory GPCRs have no known

endogenous ligands or suitably selective probes (Fredriksson et al., 2003; Rask-Andersen et al.,

2014). As a result, these understudied or orphan GPCRs (oGPCRs) have mostly unknown

function despite demonstration of their existence via genomic sequencing. Tissue expression

profiling and phylogenetic analyses suggest many oGPCRs are highly conserved in sequence

and/or tissue expression, while others may have species-specific pattern and expression.

Uncovering these receptors’ functions can occur in two main ways: genetic studies or

identification of novel oGPCR modulators. The identification of oGPCR putative function is

especially relevant to generating innovative new drug targets, as GPCRs are highly druggable but

only 10% of the 394 non-olfactory GPCRs are utilized as drug targets (Wacker et al., 2017).

Thus, oGPCR research expands the repertoire of targets that can be utilized for treating an array

of diseases and may also identify the source of unwanted side effects for approved clinical drugs.

While determining the phenotypic functions of oGPCRs can be accomplished using a

variety of genetic approaches, the identification of selective ligands offers differences and

advantages over genetic studies alone (Knight and Shokat, 2007). Genetic ablation studies (e.g. a

knocked-out oGPCR of interest) are commonly employed to detect oGPCR-mediated behavioral

and physiological phenotypes, however, not all receptor knockouts result in measureable

phenotypic changes. Further limiting the utility of knock out studies is the lack of expression of

some primate oGPCRs in rodents and poorly conserved tissue expression across species. In

contrast, selective oGPCR ligands provide the opportunity to test both agonism and antagonism,

which is a more physiologically relevant manipulation than gene knockout since GPCRs evolved

15

to transmit extracellular stimuli into a particular phenotype. Further, receptor knockout

eliminates basal signaling that may be responsible for homeostasis of a particular phenotype.

Selective oGPCRs modulators can also be utilized in a wide range of biochemical, in vitro, and

in vivo studies that enrich our understanding of the receptor’s specific and multifaceted functions.

The general approach for identifying novel oGPCR ligands centers on testing a library of

potential ligands for receptor interactions in an over-expression system. One of the most

important parameters in this experimental framework is the selection of the ligand library

(Janzen, 2014). One option is a focused library of similar chemical scaffolds (Harris et al., 2011),

such as a panel of known neurotransmitters tested at an orphan receptor with known brain

expression; this approach was used to deorphanized the 5HT1A serotonin receptor (Fargin et al.,

1988) and the D4dopamine receptor (Bunzow et al., 1988). Alternately, a more unbiased

approach involves using ligand libraries containing heterogeneous chemotypes (Irwin, 2006),

which can promote exploration of greater chemical space and improve the hit rate.

Another vital parameter for oGPCR ligand screening is the selection of the assay system.

Radioligand binding assays have been the standard for finding novel compounds for established

receptors, but these assays are not suitable for many oGPCRs because there is almost no

chemical matter to start with, including radioligands. Instead, cell-based functional assays have

become the standard approach since there is no requirement for a high affinity radioligand or

tracer. Functional assays rely on measuring downstream signaling and have the advantage of

amplifying the signal to produce a higher signal-to-noise ratio (Thomsen et al., 2005).

Determining which functional assay to use is non-trivial, as GPCR downstream signaling can

occur through G proteins, arrestins, and/or other effectors (DeWire et al., 2007).

16

Standard G protein-based functional assays typically measure intracellular cAMP levels

for Gαs and Gαi-coupled receptors, or intracellular calcium and/or inositol phosphate

accumulation for Gαq/11-coupled receptors. These assays are easy to use and can be optimized for

high-throughput application, however, most oGPCRs have unknown G protein coupling. To

circumvent this issue, one can perform multiple G protein assays or switch to a modified Gαq-

coupled assay relying on chimeric G proteins or forced coupling. Chimeric G proteins are Gαi/o

proteins engineered to have Gαq-coupled C-terminal amino acids, allowing Gαi/o-coupled

receptors to signal through PLCβ and produce intracellular calcium release (Allen and Roth,

2011). Similarly, forced coupling is the overexpression the promiscuous proteins Gα15 and Gα16,

which couple to many receptors and stimulate PLCβ, in order to measure intracellular calcium

release from receptors with unknown coupling. While forced and chimeric approaches can lead

to the discovery of novel oGPCR agonists, there is the potential for these assays to have a high

false positive rate or lead to inaccurate conclusions about a receptor’s role in native systems

(Kostenis, 2001). If used, modified G protein assays should be validated in orthogonal assays

that assess endogenous G protein read-out or other measures of activity.

Arrestin recruitment-based assays may be more broadly applicable for oGPCR ligand

discovery considering the G protein coupling is often unknown and arrestin recruitment is a more

universal read-out for GPCR activation (Luttrell and Lefkowitz, 2002). Common arrestin

recruitment assays rely on bioluminescence resonance energy transfer (BRET) or enzyme

complementation (Zhang and Xie, 2012). Both of these techniques rely on the generation of

either a bioluminescent or chemiluminescent signal following the interaction of a tagged receptor

and tagged arrestin. These assays have been employed for high-throughput screening (HTS), but

may be problematic for receptors that poorly recruit arrestin, such as the chemokine receptor

17

CXCR7, the α1A adrenergic receptor ADRA1A, and the angiotensin II receptor AGTR2 (Kroeze

et al., 2015; Rajagopal et al., 2010). Any functional assay used for HTS (including arrestin-based

assays) carries the risk of missing functionally selective compounds or antagonists (Kenakin,

2013; Ngo et al., 2016). This caveat should not preclude the use of functional assays in HTS

campaigns for oGPCRs, as false negatives are inherent to screening assays (Zhang et al., 1999)

and any discovered and validated oGPCR chemical matter will be useful.

Although less commonly used, small molecule screening platforms in the yeast

Saccharomyces cerevisiae are also suitable for revealing oGPCR ligands. These versatile assays

rely on the heterologous expression of a human GPCR gene in place of the singular S. cerevisiae

GPCR Ste2 (Pausch, 1997); GPCR activation by a ligand prompts activation of a mitogen-

activated protein kinase (MAPK) cascade and transcription of a reporter gene that can be easily

measured. This approach has been successfully used to find orphan receptor ligands (Huang et al.,

2015b), discover oGPCR Gα-coupling (Brown et al., 2003), and identify ligands for established

GPCRs (Stewart et al., 2010).

Since the identification of orphan GPCRs in the 1980s, many receptors have been “de-

orphanized” using one or several of the methods described above (Civelli et al., 2013). “De-

orphanized” receptors have validated endogenous ligand pairings; thus, a receptor may have

several proposed small molecule agonists but still remain an oGPCR. Examples of successfully

deorphanized receptors include the NOP opioid receptor, whose deorphanization in 1995

prompted many studies that have since identified its role in modulation of analgesia, tolerance,

and reward pathways (reviewed in (Toll et al., 2016)). The cannabinoid receptors (CB1 and CB2)

(Matsuda et al., 1990; Munro et al., 1993), the ghrelin receptor (GHSR) (Kojima et al., 1999),

and the orexin receptors (HCRTR1 and HCRTR2) (Sakurai et al., 1998) are three examples of

18

previously oGPCRs that have been identified to be therapeutically relevant to analgesia and

appetite, obesity, and narcolepsy, respectively (Civelli et al., 2006). Importantly, despite this

progress in finding ligand-receptor pairings, over 100 receptors remain categorized as oGPCRs

(Davenport et al., 2013). Additionally, many proposed oGPCR-ligand pairings remain un-

validated following the first publication. To this end, the use of multiple assay platforms and

techniques is extremely important to the deorphanization of these receptors.

1.5 COMPUTATIONAL APPROACHES TO oGPCR PHARMACOLOGY

The advent of computational, cheminformatic, and bioinformatic analyses catalyzed new

approaches to receptor deorphanization such as virtual ligand screening (VLS)(Evers and Klebe,

2004), molecular modeling (Eswar, 2007), and in silico ligand design (Besnard et al., 2012). The

generation of freely available chemical/pharmacological databases and technological advances in

GPCR crystallography of GPCRs propelled advances in these computational methods to make

them exceptionally useful for pharmacological applications. For example, PubChem

(https://pubchem.ncbi.nlm.nih.gov/), ChEMBL (https://www.ebi.ac.uk/chembl/), the Ki Database

(https://kidbdev.med.unc.edu/databases/kidb.php), and ZINC15 (http://zinc15.docking.org/) are

public databases containing chemical structures annotated with efficacy (and sometimes affinity)

at various protein targets, including GPCRs, with chemicals, drugs, and drug-like molecules.

These databases can be utilized to predict novel receptor interactions based on chemical structure

similarity (Hert et al., 2008; Keiser et al., 2007; Mestres and Gregori-Puigjane, 2009) or as

source databases for virtual docking and VLS (Huang et al., 2015b).

High-resolution GPCR crystal structures provide an atomic map of the receptor,

including the binding pocket, which gives insight into the molecular determinants of drug action.

19

Resolving GPCR structures has historically been a challenge (Jazayeri et al., 2015), but advances

in lipidic cubic phase crystallography has enhanced success in this area, leading to the generation

of tens of active and inactive-state GPCR structures from Classes A, B, C, and F. These

structures afford a scaffold for understanding how GPCRs respond at the molecular level to a

chemical stimulus. Leveraging these structures, newer in silico efforts aim to use the structural

information to computationally predict novel ligand-receptor interactions and even design new

therapeutics that exploit a particular molecular characteristic (Carlsson et al., 2011; Cavasotto

and Orry, 2007; de Graaf et al., 2011; Kolb et al., 2009).

As most structures to date require a high affinity ligand to stabilize a receptor

conformation, no orphan GPCR structures are available for performing structure-based in silico

predictions. Homology models, computational models of oGPCRs based on receptors with

known structure and similar sequence, have been employed in lieu of having an oGPCR crystal

structure (Evers and Klebe, 2004). Homology models have a predicted 7TM and binding pocket

structure that use commonly conserved structural motifs as anchor points. VLS campaigns can

then be performed using databases of millions of chemical structures against the homology

model of the receptor to predict novel oGPCR ligands (Huang et al., 2015b).

VLS is useful because it can probe a much wider array of chemically space than would be

possible with in vitro testing using chemical libraries of actual molecules. Additionally, VLS and

other computational methods can easily test molecules that are difficult to synthesize for activity

without having to synthesize them, which is more cost-effective and prompts more high-risk

chemical testing, leading potentially to new chemical scaffolds for targets. Computational

pharmacology approaches should be used carefully and validated using in vitro or in vivo studies

that validate the findings and improve the predictive power of future computational methods.

20

Although there has been great improvement over time using these approaches to accurately

predict activity at oGPCRs, more work needs to be done to improve these models to increase the

success rate and general utility.

1.6 - MRG RECEPTORS

The Mas-Related GPCRs (MRGs) are a large family of Class A oGPCRs named for their

sequence similarity to the MAS1 receptor, an oGPCR that was previously misattributed to be a

proto-oncogene activated by angiotensin peptides (Bader et al., 2014). Approximately 25 Mrg

genes exist in mice and can be categorized according to sequence homology as MrgprA, B, C, D,

E, F, G, and H subfamilies (Bader et al., 2014; Liu et al., 2008). MrgA, B, and C subfamilies are

found only in rodents and contain whole clades of genes (Solinski et al., 2014). In contrast,

MrgD, E, F, G and H subfamilies contain a single gene and are expressed in primates, rodents,

and several other vertebrates (Zylka et al., 2003). More interestingly, there is a primate-exclusive

subfamily of MRGs referred to as MrgprX containing MRGPRX1, 2, 3, and 4 receptors

(Burstein et al., 2006; Lembo et al., 2002).

Mrg gene clusters group together on the same chromosomes; in mice this is on

chromosome 7 and in humans on chromosome 11 (Dong et al., 2001; Liu et al., 2009; Zylka et

al., 2003). The abundance of MRG genes in close proximity to one another is attributed to local

gene duplication events that occurred a result of retrotransposons and non-homologous

recombination near the Mrg loci (Zylka et al., 2003). Phylogenetic analyses suggest Mrg genes

diverged recently in murine and primate evolution, with MrgX genes resulting from gene

amplification and selection events following the primate-mouse divergence (Choi, 2003; Zylka et

al., 2003). MRGRX receptors have approximately 50% sequence identity to the other MRG

receptors but are not strictly orthologous with any rodent-specific MrgA, B, or C genes (Dong et

21

al., 2001). In fact, each MrgprX gene is more similar in sequence to one another than to any of

the MrgprA, MrgprB, or MrgprC genes (Dong et al., 2001; Lembo et al., 2002). The lack of

rodent orthologs has made studying the function of MRGPRX receptors via genetic methods

difficult, although there have been attempts to define functionally orthologous rodent receptors

(McNeil et al., 2015).

Analysis of synonymous and nonsynonymous substitutions among the rodent and primate

MRG genes demonstrates that most of the variation between genes arises in the extracellular

domains and predicted binding pockets as a result of positive evolutionary selection (Choi, 2003).

It follows that despite the myriad of duplicated MRG genes, MRG receptor diversity reflects

adaptive evolution from species-specific pressures (Zylka et al., 2003). Thus, each MRG

subfamily of receptors may be uniquely adapted for species-specific functions and ligands. In

contradistinction, the opioid receptors do not show evidence of positive selection, suggesting that

MRG receptors are distinctively adapted in the sensory system, where both MRG and opioid

receptors are expressed (Choi, 2003).

Almost all MRGs have enriched expression in small-diameter sensory neurons in the

dorsal root ganglia (DRG) and trigeminal ganglia (TG) due to conservation of promoters and

transcriptional regulatory elements among genes (Dong et al., 2001; Lembo et al., 2002; Zylka et

al., 2003). The DRG and TG are clusters of thousands of bipolar neuronal cell bodies whose

axonal projections are directed towards the spine and towards the body (Le Pichon and Chesler,

2014). These ganglia act as the major receiving nodes for peripheral sensory stimuli and function

to relay these messages to the central nervous system for processing (Figure 1.5). Generally

speaking, DRG sensory neurons project to the skin and some peripheral tissues including the

22

intestines(van Diest et al., 2012), whereas TG sensory neurons innervate the head, eyes, cheeks,

teeth and jaws (Haas et al., 2011; Zheng et al., 2014).

The sensory nerves can contain small, medium, and large diameter neurons that convey

different types of information (thermal, mechanical, and/or chemical). MRG subfamilies are

found in distinct small diameter sensory neuron subpopulations in the DRG and TG, which

further differs by species (Zylka et al., 2003 (Liu et al., 2008; Zylka et al., 2003). Small diameter

nociceptive neurons can be peptidergic (expressing CGRP, Substance P, and TRPV1) or non-

peptidergic (marked by the presence of the canonical marker of dorsal root ganglion C fibers

isolectin B4 (IB4) (Fang et al., 2006)); MRG receptors are predominately found in the non-

peptidergic, IB4+ subtype (Figure 1.5) (Liu et al., 2008; Zylka et al., 2003; Zylka et al., 2005).

Figure 1.5: MRG expression in DRG and TG neuronal subpopulations. Diagram depicting known

MRG expression in the DRG and TG, with shades of red indicating non-peptidergic neuron subtypes.

Relevant laminae of spinal cord are labeled (I-III).

23

Rodent Mrg-expressing neurons are further subdivided into those with co-expressed

MRGPRA/B/C receptors or those having only MRGPRD receptors (Figure 1.5) (Bader et al.,

2014). The majority of MRG-expressing neurons studied in rodents innervate the skin and

synapse onto laminae II of the spinal cord, which is predominantly involved in the processing of

pain and temperature (Diaz and Morales, 2016; Zylka et al., 2005). It is also notable that mouse

MRGPRC receptors were identified in vagal sensory afferent nerves, suggesting MRG receptors

could mediate a separate, distinct array of sensory roles, including modulation of organs such as

the lungs, heart, and intestines (Lee et al., 2008). If true in primates, MRGPRX receptors could

mediate the health or sensation of vital internal organs, making these receptors particularly

relevant to druggable physiology.

MRGPRX receptors are also found in IB4+ nociceptive neurons (Lembo et al., 2002),

and while there appears to be conservation of MRGPRD neuronal C-type fibers between

primates and rodents (Wooten et al., 2014), whether MRGPRX receptors have subtype-specific

sensory neuron expression is unknown. Potential differences between rodent MRG and primate

MRG neuronal subtype specificity make transgenic knock-in studies with MRGPRX receptors

problematic. Further, these notable differences in neuronal subpopulation expression combined

with the substantial genetic variance in binding pocket residues suggests each MRG may have

distinct functions and pharmacology. To add to this complexity, there are not validated

endogenous ligands or reliable chemical probes for most MRGs (see guidetopharmacology.org

for an up-to-date list of proposed ligands or Bader et al., 2014). The reported ligands are

frequently non-selective and have potency values in the high micromolar to low millimolar range,

making selective pharmacologic experiments a challenge. Finding novel, selective MRG ligands

is also alluring for pharmaceutical development, as their nociceptive neuronal expression

24

suggests potential roles in sensory, pain, or itch transduction.

Studies in rodents support the notion that MRG receptors may be relevant to a variety of

sensory pathologies. Knockout of MRGPRB4 in mice suggests this receptor mediates the

sensation of light touching on hairy skin (Vrontou et al., 2013). Alternatively, similar knockout

studies indicate mMRGPRA3 mediates chemical-induced itch via modulation of TRP channels

in sensory neurons (Liu et al., 2009). In another study, mMRGPRC activation with the proposed

MRGPRC-selective peptides BAM8-22 and γ-MSH 6-12 promoted not itch but pain-like

behavior and allodynia in rodents (Grazzini et al., 2004; Ndong et al., 2009). In total, these

studies demonstrate that MRGs play an integral role in detecting diverse stimuli and suggest the

primate-exclusive MRGPRX receptors may also be vital for the sensation of innocuous and/or

noxious stimuli. As knockout studies in primates are expensive and difficult, selective chemical

probes for MRGPRX receptors would be useful tools for enabling pharmacologic manipulations

in vitro and in vivo.

Unfortunately, there are very few reported ligands for MRGPRX receptors. Since their

initial discovery in 2001 (Dong et al., 2001; Lembo et al., 2002), a handful of ligands have been

identified for MRGPRX1 and MRGPRX2 but none for MRGPRX3 or MRGPRX4. The first

ligands identified were bovine adrenal medulla peptides BAM1-22 and BAM8-22 for

MRGPRX1, which established that MRGPRX1 is Gαq-coupled (Lembo et al., 2002). Several

other small molecule agonists activate MRGPRX1, including the low affinity agonist

chloroquine (EC50 ≈ 300 µM) (Liu et al., 2009), several benzimidazole agonists (EC50 ≈ 300 nM)

(Malik et al., 2009), and a positive allosteric modulator with unknown specificity for MRGPRX1

that was demonstrated to inhibit spinal nociceptive transmission via Gαi-dependent pathways (Li

et al., 2017).

25

Of the MRGPRX family, MRGPRX2 has the largest number of proposed peptide

agonists, including the peptides Cortistatin-14 (Kamohara et al., 2005; Robas et al., 2003),

PAMP-12 (Kamohara et al., 2005), and Substance P (Tatemoto et al., 2006), but whether any of

these are true endogenous peptide(s) remains to be determined. To date, MRGPRX2 remains

categorized as a Class A orphan receptor according to the IUPHAR classification (Alexander et

al., 2015). There are also a number of proposed MRGPRX2 small molecule agonists including

the delta opioid receptor agonist TAN-67 (Southern et al., 2013), complanadine A (Johnson and

Siegel, 2014), compound 48/80 (McNeil et al., 2015), and numerous basic secretagogues

(McNeil et al., 2015) but none have been demonstrated as selective.

MRGPRX2 is the only MRGPRX receptor expressed in mast cells as well as sensory

neurons (Kashem et al., 2011; Lembo et al., 2002; Subramanian et al., 2011a; Tatemoto et al.,

2006), and this characteristic has helped facilitate numerous studies of endogenous MRGPRX2

function in human mast cell lines. Mast cells are the first responders to chemical signals of

infection and allergy that will canonically release cytokines and inflammatory factors in response

immunoglobulin E (IgE) antibodies cross-linking to IgE receptors in a process called

degranulation (Metcalfe et al., 1997). GPCRs can modulate IgE-induced degranulation, initiate

degranulation upon ligand binding, or abrogate degranulation depending on the receptor

(Gaudenzio et al., 2016; Kuehn and Gilfillan, 2007). Several studies demonstrate MRGPRX2

agonism promotes IgE-receptor independent degranulation, suggesting this receptor may be

important for the primate immune system and the pathology of itch (Subramanian et al., 2011a;

Tatemoto et al., 2006). Most of these studies, however, were performed using MRGPRX2

agonists with unspecified selectivity and low potency, such as substance P (Tatemoto et al.,

2006), compound 48/80 (Kashem et al., 2011; McNeil et al., 2015), atracurium (McNeil et al.,

26

2015), and LL-37 (Subramanian et al., 2011a). The generation of novel and selective MRGPRX2

agonists would promote a more precise determination of the role of this receptor in mast cells

and other tissues.

Despite 15 years of MRG studies, nanomolar potency selective probes for each of the

four MRGPRX receptors do not exist. For MRGPRX3 and MRGPRX4, there are no reported

ligands (Davenport et al., 2013). Most information gathered about MRG receptors is the result of

receptor knockout studies in rodents, which has resulted in valuable information for some MRGs

but also fails to uncover the function of the primate-specific MRGPRXs. A lack of

pharmacologic tools has limited our understanding of this family of receptors and the stimuli

they transduce, which is especially relevant due to the diversity of binding pocket residues

among these genes. It is clear that MRGs regulate a multiplicity of sensations in rodents, and one

cannot help but wonder what sensation the MRGPRX receptors mediate in primates. Further, it is

unclear whether MRGPRX receptors also define distinct neuronal subpopulations of sensory

neurons or whether these receptors are uniquely adapted for primate sensory and/or immune

function. Identification of endogenous ligands and ideal probes for the MRGPRX receptors (as

defined by (Arrowsmith et al., 2015)) would enable a variety of in vivo, in vitro, and other

biochemical studies that will uncover answers to some of these questions.

The research contained in this dissertation aims to identify novel probes for the

MRGPRX family of oGPCRs, specifically MRGPRX2 and MRGPRX4, and assess the putative

functions of these receptors. First, I identified novel MRGPRX ligands using a β-arrestin

screening platform. Then, combining further in vitro screening and in silico modeling/docking, I

identified opioid agonists of MRGPRX2 and generated two MRGPRX2-selective probes. Using

the identified opioid and selective chemical matter, I demonstrated that MRGRX2 functions to

27

mediate mast cell degranulation. Extending this approach to MRGPRX4, I identified new small

molecule MRGPRX4 agonists including a pair of tool compounds. Lastly, I identified menthol as

an allosteric modulator of MRGPRX4 from the results of a genome-wide-association study. The

results of this work will shed more light on this interesting family of orphan receptors and the

sensory functions they regulate.

____________________________ 1Figures 2.1, 2.2, 2.3, and 2.4 were modified from the originals, which were previously published in

Nature Structural and Molecular Biology. The original citation is as follows:

Kroeze, W.K., Sassano, M.F., Huang, X.P., Lansu, K., McCorvy, J.D., Giguere, P.M., Sciaky, N., and

Roth, B.L. (2015). PRESTO-Tango as an open-source resource for interrogation of the druggable human

GPCRome. Nat Struct Mol Biol 22, 362-369.

28

CHAPTER 2: IDENTIFICATION OF INITIAL CHEMICAL MATTER FOR MRGPRX

RECEPTORS VIA THE PRESTO-TANGO β-ARRESTIN SCREENING PLATFORM

2.1 INTRODUCTION AND RATIONALE

Estimates suggest that 30% of the non-olfactory GPCRs are orphan and understudied

(oGPCRs), meaning there are no known endogenous ligands and frequently no available

chemical matter to interrogate their function (Fredriksson et al., 2003; Roth and Kroeze, 2015).

High-throughput screening (HTS) of oGPCRs is a facile way to discover novel chemical matter

for these understudied receptors (Civelli, 2005). Although many approaches have been devised

to best achieve this task, recent progress in generating a β-arrestin screening platform called

PRESTO-Tango allows for the measurement of multiple GPCRs across thousands of compounds

(Kroeze et al., 2015). Arrestin-based approaches are more universal for assessing GPCR activity

(Lefkowitz and Shenoy, 2005; Luttrell and Lefkowitz, 2002), as they do not rely on G protein

coupling, making this format a good fit for oGPCRs with unknown G protein coupling. One such

family of oGPCRs are the Mas-related G protein coupled receptor X family (MRGPRX).

Chemical probes for these receptors would greatly improve progress in identifying their

functions, as these primate-exclusive receptors are not readily testable in rodents (Lembo et al.,

2002; Zylka et al., 2003).

29

To identify chemical matter as a starting point for probe development for MRGPRX

receptors, I utilized the PRESTO-Tango β-arrestin screening platform (Kroeze et al., 2015).

Using this method, I screened 9 small molecule libraries containing 5,695 compounds for

activity at each MRGPRX1, MRGPRX2, and MRGPRX4. This multiplexed screening campaign

allowed me to compare MRGPRX activating compounds across this receptor family, and to other

receptors screened by the Roth lab to determine possible promiscuous agonists and false

positives. The initial traction gained in this screen was helpful in validating the utility of the

PRESTO-Tango platform for oGPCR screening.

In this chapter I will discuss detailed results of the screening performed at the MRGPRX1,

MRGPRX2, and MRGPRX4 receptors, including major conclusions and identified chemical

matter from these initial studies that laid the groundwork for chapters 3 and 4.

2.2 METHODS

Transfections

All transfections were done using the calcium phosphate method (Jordan et al., 1996).

Chemical Compounds

Library screening was performed using nine small molecule libraries: NCC-1 (NIH

Clinical Collection), NCC-2 (NIH Clinical Collection), NIMH Library, Tocris, Prestwick,

LOPAC, Selleck Chemical, Spectrum, and the Roth Lab Collection (in-house).

Constructs

The MRGPRX4-Tango codon-optimized plasmid was made as previously described

(Kroeze et al., 2015), also see Fig 2.1a. The MRGPRX genes from these constructs (without the

30

modified tail) was subcloned into the FLP-IN inducible stable cell lines as per Invitrogen’s

instructions using the FLP/IN construct. See Appendix 2 for all plasmid maps and sequences.

PRESTO-Tango Assay Screening

HTLA cells were maintained in DMEM (Corning) supplemented with 10% FBS, 2

μg/mL puromycin and 100 μg/mL hygromycin B in a humidified atmosphere with 5% CO2 at

37°C. Cells were seeded at 50% confluency and transfected with the MRGPRX4-Tango

construct using the calcium phosphate method (Jordan et al., 1996). The following day,

transfected cells were plated in poly-L-lysine-coated, glass-bottomed white 384-well plates

(Greiner Bio-one) at a density of 20,000 cells/well. Twenty-four hours later, cells were treated

with 10 μM concentrations of small molecules (in quadruplicate) diluted at 3X final

concentration in drug buffer (1X HBSS containing 20 mM HEPES and 0.3% Bovine Serum

Albumin, pH 7.4) and incubated for 18-24 hours. Following drug treatment, media was removed

and Bright Glo (Promega) was added to each well (diluted 20-fold, 20 μL/well) and incubated

for 15 minutes at room temperature. A TriLux luminescence counter was used to collect

luminescence data, which were analyzed as relative luminescent units (RLU) using GraphPad

Prism. Compounds with two-fold or greater increases in luminescence signal were considered

“actives” and were subsequently assayed using concentration-response curves. More detail on

the PRESTO-Tango assay can be found in (Kroeze et al., 2015).

Intracellular Calcium Mobilization Assay

I created a MRGPRX4-expressing tetracycline-inducible stable cell line containing the

MRGPRX4 codon-optimized receptor sequence with an N-terminal FLAG tag using the FLP-

IN/T-REX Core Kit (Invitrogen). MRGPRX4 stable cells were cultured in DMEM containing 10%

FBS, 100 µg/mL hygromycin B, and 15 µg/mL blasticidin. For the assay, cells were seeded onto

31

glass bottomed, poly-L-lysine coated, black 384 well plates at a density of 20,000 cells/well in

medium containing 1% dialyzed FBS, 1 µg/mL tetracycline, 100 IU/mL penicillin, and 100

µg/mL streptomycin and incubated 24 hours. After tetracycline induction, media was removed

and cells were loaded for 1 hour with 20 µL/well of 1X FLIPR calcium dye (Molecular Devices)

and 2.5 mM probenic acid in a humidified environment with 5% CO2 at 37°C. Then, baseline

calcium levels were measured for 10 seconds, followed by drug addition of 10 µL of 3X

concentrated drug in drug buffer (1X HBSS with 20 mM HEPES, pH 7.4) in 16 point

concentration response curves from 0.003 nM to 10 μM. Fluorescence was measured for 200

seconds and data were analyzed using GraphPad Prism. For double addition Schild competition

experiments, potential antagonists were added 10 minutes before addition of agonist.

Inositol Phosphate Hydrolysis:

MRGPRX4 tetracycline-inducible cells were maintained in DMEM containing 10% FBS,

100 µg/mL hygromycin B, and 15 µg/mL blasticidin. For the assay, cells were seeded into poly-

L-lysine coated, glass-bottom 96 well plates at a density of 50,000 cells/well in inositol-free

DMEM (Caisson labs) containing 1 µCi/well of 3H-myo-inositol (Perkin Elmer), 1 µg/mL

tetracycline, and 100 IU/mL penicillin and 100 µg/ml streptomycin and incubated 16-18 hours in

a humidified environment at 37°C with 5% CO2. Following tetracycline induction and labeling,

medium was removed and 200 µl/well of inositol-free DMEM (Caisson labs) was added per well.

Then, 5X drug was added, diluted in drug buffer (1X HBSS with 20 mM HEPES and 0.3%

bovine serum albumin, pH 7.4) in concentration response curves. Cells were incubated with drug

for 1 hour in a humidified environment at 37°C with 5% CO2. At exactly 15 minutes before

lysing, 10 µl/well of 26X concentrated LiCl (15 mM final concentration) was added. After drug

and LiCl incubation, 40 µl/well of 50 mM formic acid was added and cells were incubated at

32

4°C overnight. The next day, 10 µl of lysate was transferred from each well into 96-well flexi-

plates (Perkin Elmer) and combined with 75 µl/well of 0.2 mg/well YSI RNA Binding Beads

(Perkin Elmer). Lysate-bead mixture was incubated on a shaker at room temperature, protected

from light, for 1 hour. Before reading on a TriLux beta counter, plates were centrifuged at 1000 x

g for 2 minutes. CPM data were analyzed with GraphPad Prism.

2.3 RESULTS

2.3.1 Summary of screening results

To perform the PRESTO-Tango screen with the MRGPRX receptors, I used the “Tango-

ized” constructs. In brief, these constructs contain a receptor with an N-terminal HA-signal and

FLAG-tag, followed by a modified C-terminal tail containing the vasopressin (V2) receptor tail

to aid arrestin recruitment, a tobacco etch virus protease cut site, and a tetracycline transactivator

protein (tTA) as previously reported by (Barnea et al., 2008) and (Kroeze et al., 2015) (see Fig

2.1a). The constructs were transfected into HTLA cells expressing a luciferase reporter under

control of a tTA promoter (see methods or (Kroeze et al., 2015)).

Using these constructs, I screened 9 different small molecule libraries containing 9,218

compounds (5,695 of which were unique) against each MRGPRX1, MRGPRX2, and MRGPRX4

receptors (Fig 2.1a,b). To optimize the chemical space, the 9 libraries contained a mixture of

FDA-approved drugs, lead-like compounds, tool compounds, and other various small molecules.

I did not screen MRGPRX3, as we could not optimize the assay for optimal signal to noise for

this receptor for unknown reasons. All compounds that activated the MRGPRX receptors above

two-fold were considered to be potential actives and analyzed further with concentration

response curves (Fig 2.1b). The total number of non-promiscuous active compounds and the hit

33

rate for each receptor from the number of unique compounds was as follows: 39 for MRGPRX1

(0.68% hit rate), 81 for MRGPRX2 (1.4 % hit rate), and 54 for MRGPRX4 (0.95% hit rate) (Fig

2.1c). Interestingly, the majority of the hits for each MRGPRX1, MRGPRX2, and MRGPRX4

were unique to each receptor, with a minority of compounds shared amongst the MRGPRX

receptors (Fig 2.1b-d). A list of which compounds were shared is provided in Figure 2.1d.

2.3.2 NCC1 Library screen unveils novel MRGPRX actives in the GPCRome

In the process of validating the PRESTO-Tango β-arrestin screening platform, Dr. Wes

Kroeze compared the MRGPRX results of the first 446 small molecule screen (the NCC-1

Figure 2.1: PRESTO-Tango screening at MRGPRX1, MRGPRX2, and MRGPRX4. A) Flow chart

depicting Tango construct (modified from (Kroeze et al., 2015)) and experimental outline. B). XY Scatter

plot showing all data points (averaged from quadruplicate wells) for the log2(Fold Change) activation at each

MRGPRX1 (blue), MRGPRX2 (red), and MRGPRX4 (black). C). Bar graph showing the number of

activating compounds (≥2 fold ) for each receptor, with red referring to unique compounds at each receptor

among the three MRGPRX receptors and blue referring to shared active compounds. D). Table depicting a

break down of the shared compounds from C.

34

library) with the results of 91 other GPCR targets tested in this library in PRESTO-Tango by

other members of the lab. One of the most striking results from this screen was activity of

nateglinide at MRGPRX4 (Fig 2.2a,b.). Nateglinide produced a 45-fold increase in luminescence

over basal activity and did not activate the other 90 targets tested with this library (2.2a,b).

To validate nateglinide’s activity at MRGPRX4, I performed 12-point concentration

Figure 2.2: Discovery of unique MRGPRX4 nateglinide activity via parallel GPCRome screening of NCC1

Library. Results of screening of the 446-compound NCC-1 library at 91 GPCR targets in the Tango assay and

follow-up studies are shown. A) Heat map of the entire matrix (red, stimulation of luminescence over

background). B) Close-up view of a section of the heat map in a, showing the activity of nateglinide at

MRGPRX4. C) Concentration-response curve of nateglinide at MRGPRX4 in the Tango assay (n = 4). D)

Concentration-response curve of nateglinide at MRGPRX4 by PI hydrolysis (n = 3) Data in concentration

response curves are shown as mean ± s.e.m. of technical replicates. This figure was modified from the original,

which can be found in the PRESTO-Tango resource paper (Kroeze et al., 2015)

35

response curves with MRGPRX4 and nateglinide in the PRESTO-Tango assay and found an

averaged EC50 of 9.1 µM (Fig 2.2c). As the screening results suggested, nateglinide was also

inactive at the other MRGPRX receptors (Fig 2.3a). From there, I generated a tetracycline

inducible MRGPRX4 stable cell line and tested this receptor in three G protein assays to evaluate

its canonical coupling (Fig 2.3b-c). Assessment of MRGPRX4 activity with Nateglinide in PI

hydrolysis (Fig 2.2d) and GloSensor cAMP assays revealed that MRGPRX4 does not modulate

Figure 2.3: Concentration-response curves of nateglinide in Gs and Gi assays in MRGPRX receptor-

expressing cells. A) Gs response to nateglinide in HEK293T cells expressing MRGPRX receptors, showing a Gs

response only in cells expressing MRGPRX4 receptors at high concentrations of nateglinide. B) Comparison of

the concentration-response curves in Gs assays of nateglinide and isoproterenol in MRGPRX4-expressing

HEK293T cells. C) Concentration-response curve in Gi assay of nateglinide in MRGPRX4-expressing

HEK293T cells. Data are expressed as mean ± SEM of triplicate or quadruplicate determinations, and curves

were fitted using Graphpad Prism. This figure was modified from the original, which can be found in the

PRESTO-Tango resource paper (Kroeze et al., 2015)

36

intracellular cAMP levels (Fig 2.6b,c) and instead promoted the hydrolysis of inositol

phospholipids, indicating this receptors couples to Gαq (Fig 2.2d) and not Gαi or Gαs.

To determine of other MRGPRX receptors might also couple to Gαq, I made

tetracycline-inducible stable cell lines for MRGPRX1 and MRGPRX2 and tested them for

activity with MRGPRX1 positive control peptide BAM8-22 (Lembo et al., 2002) and

MRGPRX2 positive control SB 205, 607 (also known as TAN-67) (Southern et al., 2013) (Fig

2.4). These experiments revealed that in HEKT cells MRGPRX1 and MRGPRX2, like

Figure 2.4: Calcium mobilization responses in stable cell lines expressing MRGPRX receptors. (a,c,e)

Concentration-response curves; data are expressed as mean ± SEM, and curves were fitted using Graphpad

Prism. (b,d,f) Time course of responses, showing representative curves of experiments done in triplicate. (a,

b) Responses of MRGPRX1-expressing cells to BAM8-22. (c,d) Responses of MRGPRX2-expressing cells

to SB 205,607. (e,f) Responses of MRGPRX4-expressing cells to nateglinide. TRAP is an agonist for

endogenous PAR1 and serves as an internal control for the calcium mobilization assay. This figure was

modified from the original, which can be found in the PRESTO-Tango resource paper (Kroeze et al., 2015)

37

MRGPRX4, are Gαq-coupled receptors that promote intracellular calcium release, in line with

previous reports from (Lembo et al., 2002) (Fig 2.4)

2.3.3 A variety of ligands activate MRGPRX2

The MRGPRX2 screening campaign revealed the largest number of active compounds

compared to the MRGPRX receptors. Out of the 81 total activating compounds, there were

several chemotypes that emerged to suggest common MRGPRX2 scaffolds. One of these

chemotypes was histamine receptor 1 (H1R) antagonists (Fig 2.5). From the initial screening, I

tested four H1R antagonists clemastine, azelastine, cyclizine, and pizotifen in concentration

Figure 2.5: MRGPRX2 activation by H1R antagonists. A) Concentration-response curves with H1R

antagonists at MRGPRX2 in Tango; data are expressed as mean ± SEM of quadruplicate wells (n=1), and

curves were fitted using Graphpad Prism. B) Chemical structures of compounds from A. C) Schild

experiment to test histamine activity at MRGPRX2, with 3 or 30 μM histamine incubated with postitive

control agonist TAN-67. Data are expressed as mean ± SEM of quadruplicate wells (n=1).

38

response curves in PRESTO-Tango to validate their screening activity and found all four had

varying degrees of agonism at MRGPRX2 (Fig 2.5a,b).

These data, coupled with MRGPRX2’s known expression in mast cells (Kashem et al.,

2011; Subramanian et al., 2011b), generated the hypothesis that the endogenous compound

histamine might interact with MRGPRX2. To test this, I performed a Schild analysis experiment

with 3 and 30 μM of histamine added to 12-point concentration response curves of positive

control compound TAN-67 (Fig 2.5c). I found that histamine did not alter TAN-67 agonism or

basal signaling and thus was neither an agonist or antagonist for MRGPRX2 (Fig 2.5c).

The activity of the four H1R antagonists from the screening prompted me to compare all

Figure 2.6: MRGPRX2 activation by H1R-interacting compounds. Graph depicting compounds

annotated to interact with H1R from the original screening libraries and their activity at MRGPRX2 at 10

μM in PRESTO-Tango. Data is expressed as log 2 of the fold change. Note that as not all libraries were

annoted, this information is from a subset of the total number of compounds.

39

of the predicted H1R-interacting libraries from the original libraries to see if any structure

activity relationship for MRGPRX2 and this scaffold was apparent. After graphing the fold-

change activity from the initial PRESTO-Tango screen at MRGPRX2 across all potential H1R-

directed compounds, I found that the length of the N-substituent appeared to correlate with

agonism at MRGPRX2 (Fig 2.6). Future studies that parse out the full details of this SAR will be

needed to confirm these assertions.

At least four of the ligands identified from the PRESTO-Tango screen of MRGPRX2

were opioid or opioid-like and these data will be discussed in detail in the following chapter.

2.4 CONCLUSIONS

Here, I demonstrated the utility of the PRESTO-Tango screening assay (Kroeze et al.,

2015) for discovering chemical matter for the oGPCRs MRGPRX1, MRGPRX2, and

MRGPRX4. After screening 5,695 unique compounds for activity at each receptor with nine

chemically diverse libraries, I validated activating compounds in concentration response curves

and orthologous assays. In total, the pooled screening data and analyses show that these receptors

predominantly couple to Gαq and that there are substantial differences in MRGPRX

pharmacology, as previously predicted by phylogenetic analyses (Choi and Lahn, 2003).

Of the MRGPRX receptors, I gained the most chemical traction with MRGPRX2 and

MRGPRX4, which were activated by opioids, antihistamines, and the FDA-approved

antidiabetic nateglinide. The Tango construct for MRGPRX3 did not produce a robust assay, and

the screening actives for MRGPRX1 turned out to be either non-specific, not very potent, or

were unable to be replicated. Future studies that could optimize these assays with changes to the

DNA constructs or the diversity of the chemical libraries might improve screening results.

40

Alternately, as we and others have shown the MRGPRX receptors to be Gαq-coupled (Kamohara

et al., 2005; Lembo et al., 2002), future studies looking for MRGPRX1 and MRGPRX3

modulators could screen compounds in the intracellular calcium release assay.

Caveats of this study include the difficulty in determining antagonist compounds from the

initial screen. Thus far, no antagonists for these receptors have been identified and future efforts

could use this screen in “antagonist mode” or use the intracellular calcium assay to uncover

novel antagonist scaffolds for MRGPRX receptors. These compounds would be useful as probes

for biochemical and pharmacological assays and could potentially lead to the development of

novel therapeutics.

These data also provide a basis for further SAR studies that will occur in the next two

chapters. For MRGPRX2, I pursued the opioid compounds from the initial screening instead of

the antihistamine scaffold based on potency of the compounds in the concentration response

studies. Future studies assessing further modifications to the antihistamine scaffold and

functional implications of MRGPRX2 activation by antihistamines should be explored, since

MRGPRX2 expression in mast cells (Kashem et al., 2011; McNeil et al., 2015; Subramanian et

al., 2011b) poise it to be involved in inflammatory roles and potential regulatory feedback loops.

For MRGPRX2 and MRGPRX4, this initial PRESTO-Tango screen promoted the development

of novel probes and suggests this assay is broadly useful for oGPCR probe discovery.

____________________________ 2This chapter was previously published in Nature Chemical Biology. The original citation is as follows:

Lansu, K., Karpiak, J., Liu, J.,Huang, X. P.,McCorvy, J. D.,Kroeze, W. K.,Che, T.,Nagase, H.,Carroll, F.

I.,Jin, J.,Shoichet, B. K., and Roth, B. L. In silico design of novel probes for the atypical opioid receptor

MRGPRX2. Nat Chem Biol 13, 529-536 (2017).

41

CHAPTER 3: IN SILICO DESIGN OF NOVEL PROBES FOR THE ATYPICAL OPIOID

RECEPTOR MRGPRX2

3.1 INTRODUCTION AND RATIONALE

G protein-coupled receptors (GPCRs) are seven transmembrane receptors that transduce

extracellular signals into biological responses via heterotrimeric G-proteins and β-arrestins

(Allen and Roth, 2011). GPCRs are involved in nearly every known biological system and,

unsurprisingly, GPCR-targeting small molecules represent the largest target class for FDA-

approved drugs (Overington et al., 2006). Despite their therapeutic utility, only 10% of GPCRs

function as therapeutic targets for FDA approved drugs (Overington et al., 2006), while ~120 of

394 non-olfactory GPCRs represent “orphan receptors” or understudied GPCRs

(“oGPCRs”(Huang et al., 2015b)) without useful probes and, frequently, without validated

endogenous ligands (Fredriksson and Schioth, 2005; Rask-Andersen et al., 2014). The process of

discovering specific and potent probes for oGPCRs yields useful research tools and can also

illuminate previously unrealized drug interactions, potentially establishing new drug targets.

To identify oGPCR ligands, we have developed and tested new physical and

computational approaches for screening these receptors. The first physical method, dubbed

“PRESTO-Tango,” involves high-throughput, massively parallel screening of potential

42

modulators (including small molecules, bioactive peptides, and other reagents) for their ability to

stimulate β-arrestin recruitment at most non-olfactory receptors in the GPCRome (Kroeze et al.,

2015). The second orthogonal and complementary approach relies on the principle that over-

expressed G proteins facilitate a partially active state for most GPCRs enabling the discovery of

both allosteric and orthosteric modulators for oGPCRs using engineered yeast (Huang et al.,

2015b) . Encouraged by the success of other structure-based drug design methods in GPCRs

(Isberg et al., 2014; Mason et al., 2012; Ngo et al., 2016), our in silico approach leverages the

physical screens to develop comparative structural models of the receptors, and then

computationally screens a much wider chemical space—typically several million commercially

available molecules—to find specific ligands for the oGPCRs.

We apply this strategy to the Mas-related G protein-coupled receptor X2 (MRGPRX2) --

a primate-exclusive (Dong et al., 2001; Zylka et al., 2003) class A orphan GPCR expressed in

mast cells and small diameter neurons in the dorsal root and trigeminal ganglia (Kamohara et al.,

2005; Lembo et al., 2002; Robas et al., 2003; Subramanian et al., 2013; Tatemoto et al., 2006).

Several unrelated peptides are reported to activate MRGPRX2 including Cortistatin-14 (Robas et

al., 2003), Substance P (Tatemoto et al., 2006), and PAMP(9-20) (Kamohara et al., 2005),

whether any proposed peptides are endogenous MRGPRX2 agonists is unknown. MRGPRX2

remains an oGPCR in part because no convincing rodent ortholog has been validated (see

Discussion) and because MRGPRX2-selective nanomolar potency probes are unavailable.

Although several selective agonists are reported for MRGPRX2 (Johnson and Siegel, 2014;

Malik et al., 2009), the compounds are not easily obtained and have not been validated for

specificity or potency. The identification of demonstrably selective, potent MRGPRX2 agonist

probes represents an essential step toward illumination of its function in vitro and in vivo.

43

Here, we describe how an integrated approach combining our PRESTO –Tango method

and our modeling and docking platforms led to the identification of MRGPRX2 agonists,

including exogenous and endogenous opioids and a selective MRGPRX2 probe ZINC-3573. We

confirmed ZINC-3573’s selectivity for MRGPRX2 via testing at 315 class A GPCRs using

PRESTO-Tango, binding assays performed by the National Institute of Mental Health

Psychoactive Drug Screening Program (NIMH-PDSP), and by testing the parent scaffold using a

commercial (DiscoverX) kinome screen. Using ZINC-3573, we show MRGPRX2 activation

induces intracellular calcium release and degranulation in a human mast cell line. We also

demonstrate that MRGPRX2 represents a novel Gαq-coupled opioid-like receptor that could

mediate some peripheral side effects of commonly prescribed opiate medications. This discovery

of the specific MRGPRX2 agonist ZINC-3573, matched with an inactive enantiomer, provides

the community with a pair of chemical probes by which the in vivo function of this fascinating

target may be investigated with exquisite specificity and control.

3.2 METHODS

Cell Lines

HTLA cells were a gift from Dr. Richard Axel (Columbia University) and are maintained

at low passage batches. FLP-IN/T-REX HEK-293-T cells were purchased from Invitrogen for

ease of inducible stable cell line generation and were certified as HEK-T and mycoplasma-free

by Invitrogen. FLP-IN/T-REX cells were made into inducible stable cell lines according to the

manufacturer’s instruction. The LAD2 human mast cell line was obtained from cells’ original

source lab of Dr. Arnold Kirshenbaum and Dr. Dean Metcalfe (Kirshenbaum et al., 2003) at the

Laboratory of Infectious Diseases at the National Institutes of Health in Bethesda, MD and were

44

maintained as per the their instructions. No further characterization of HTLA or LAD2 cells was

performed, as the cells came from the source labs

Chemical Compounds

The nine libraries used in screening were the NCC-1 (NIH Clinical Collection), NCC-2

(NIH Clinical Collection), NIMH Library, Tocris, Prestwick, LOPAC, Selleck, Spectrum, and

the Roth Lab Collection (in-house). Opioid ligands were obtained from either Sigma Aldrich or

synthesized.

Constructs

MRGPRX-Tango plasmids for MRGPRX1, MRGPRX2, or MRGPRX4 were made as

was previously described (Kroeze et al., 2015) MRGPRX2 constructs were also “de-Tangoized”

to form the WT-MRGPRX2 construct by inserting a STOP codon before the 3’ TEV site for

transient transfection. MRGPRB2 insert was obtained from Dharmacon cDNA and subcloned

into the PRESTO-Tango backbone. See Appendix 2 for all plasmid maps and sequences.

PRESTO-Tango Assay Screening

HTLA cells (HEK-T cells stably expressing a β-arrestin2-TEV fusion protein and a tTa-

dependent luciferase reporter) were maintained in DMEM (Corning) containing 10% FBS, 2

μg/mL puromycin and 100 μg/mL hygromycin B in a humidified atmosphere at 37°C with 5%

CO2. In brief, cells were plated to 50% confluency and transfected with a codon-optimized

MRGPRX-Tango construct using the calcium phosphate method (Jordan et al., 1996) . The next

day, transfected cells were transferred to glass-bottomed, poly-L-lysine-coated white 384-well

45

plates at a density of 20,000 cells/well. The following day, cells were treated with 10 μM

concentrations of small molecules (in quadruplicate) diluted in drug buffer (1X HBSS with 20

mM HEPES and 0.3% Bovine Serum Albumin, pH 7.4) and incubated for 18-24 hours. After

drug incubation, medium was removed and 20 μL of Bright Glo (Promega) (diluted 20-fold) was

added to each well and incubated 15 minutes at room temperature. Luminescence was measured

on a TriLux luminescence counter and analyzed as relative luminescent units (RLU) using

GraphPad Prism. Compounds that increased the luminescence signal by two fold or greater were

considered “actives” and were analyzed further using concentration-response curves. More

detailed discussion of the PRESTO-Tango assay can be found in (Kroeze et al., 2015). Total

number of dose response replicates for each compound can be found in Appendix 2 Table 1.

Intracellular Calcium Mobilization Assay

We generated a MRGPRX2-expressing tetracycline-inducible stable cell line containing

the MRGPRX2 codon-optimized receptor sequence with a N-terminal FLAG tag using the FLP-

IN/T-REX Core Kit (Invitrogen) following the manufacturer’s instructions. MRGPRX2 stable

cells were maintained in DMEM containing 10% FBS, 100 µg/mL hygromycin B, and 15 µg/mL

blasticidin. For the calcium mobilization assay, cells were plated into glass bottomed, poly-L-

lysine coated, black 384 well plates at a density of 20,000 cells/well in medium containing 1%

dialyzed FBS, 1 µg/mL tetracycline, 100 IU/mL penicillin and 100 µg/ml streptomycin and

incubated 24 hours. Following tetracycline induction, medium was removed and cells were

loaded with 20 µl/well of 1X FLIPR Calcium dye (Molecular Devices) and 2.5 mM probenic

acid, for 1 hour in a humidified environment at 37°C with 5% CO2. For mast cell calcium

experiments, cells were seeded at a density of 50,000 cells/well in Tyrode’s buffer containing 1X

46

calcium dye and incubated for 1 hour before treatment and analysis. After dye loading, baseline

was measured for 10 seconds before drug treatment, and then cells were treated with 10 µL of 3x

concentrated drug in drug buffer (1X HBSS with 20 mM HEPES and 2.5 mM probenic acid, pH

7.4) in 16 point concentration response curves from 10 μM to 0.003 nM. Fluorescence was

measured for an additional 120 seconds and data was analyzed using GraphPad Prism. Total

number of dose response replicates for each compound can be found in Appendix 2 Table 1.

β-hexosaminidase Assay

LAD2 cells were maintained in Stem-Pro34 medium (GIBCO) supplemented with

StemPro34 Supplement (GIBCO), 2 mM L-Glutamine, 100 U/mL penicillin/streptomycin, and

recombinant human stem cell factor (100 ng/ml) (Peprotech) as described in(Kirshenbaum et al.,

2003) . For the assay, adapted from (Staats et al., 2013) , cells were incubated in fresh medium

for 18 hours prior to the experiment. If testing IgE activation, cells were also incubated with

biotin-labeled IgE during this time. Then, cells were washed twice in Tyrode’s buffer (20 mM

HEPES with 134 mM NaCl, 5 mM KCl, 1.8 mM CaCl2, 1 mM MgCl2, 5.5 mM glucose, and 0.3%

bovine serum albumin, pH 7.4) and seeded at 10,000 cells per well of V-bottom clear 96 well

plates in 90 μL of Tyrode’s buffer. Drugs were diluted at single concentrations or in 8-point

concentration-response curves at a 10X working concentration in Tyrode’s buffer and 10 μL

drug, or streptavidin for biotin-labeled IgE samples, and were added in triplicate to the 90 μL

mast cell suspension. Drugs and cells were incubated for 30 minutes in a humidified incubator

without CO2 at 37°C. After drug incubation, plates were centrifuged at 250g for 5 minutes and

30 μl supernatant was added to new 96 well plates containing 10 μl of 10 µM 4-nitrophenyl N-

acetyl-β-D-glucosaminide (NAG) diluted in 0.1M Citrate Buffer (49.5% 0.1M citric acid and

47

50.5% 0.1M sodium citrate, pH 4.5). The remaining supernatant was discarded and cells were

lysed by adding 100 μl of Tyrode’s buffer with 0.1% Triton-X-100 to the pellets and mixed.

Thirty μL of lysate was added to 10 μL of 10 μM NAG. All plates were incubated for 90 minutes

in a humidified incubator without CO2 at 37°C. Lastly, 100 μL of bicarbonate buffer (88 mM

Na2CO3, 40 mM NaHCO3, pH 10) was added to stop the reaction, inducing a color change that

was measured on an absorbance plate reader (BMG Labtech POLARstar Omega) at 405 nm.

Results were analyzed by dividing the released fraction by the total fraction (released + lysed)

and multiplying by 100 to get a percentage degranulation. To correct for spontaneous

degranulation, average percent degranulation for vehicle-treated wells was subtracted from drug-

treated wells. Total number of dose response replicates for each compound is reflected in figures

and figure legends.

siRNA Knockdown and qPCR Analysis

Using TransIT 20/20 transfection reagent (Mirus), 25 nM of each siRNA (Dharmacon #

EQ-005666-00-0002, set of 4 distinct MRGPRX2 siRNAs) or the non-targeting siRNA control

(Dharmacon # D-001910-10-05) was prepared in 250 µL of OPTI-MEM with 5 μL TransIT per

reaction. After 30 minutes, the transfection mixture was added dropwise to 1.2 million mast

cells/well of a 6-well plate and allowed to incubate 72 hours. After 72 hours, 200,000 cells were

removed for use in the β-hexosaminidase assay and 1 million cells were assayed using qRT-PCR

analysis with the High-Capacity cDNA Reverse Transcription Kit (Life Technologies) following

the manufacturer’s instructions and TaqMan Universal Master Mix II with UNG (Life

Technologies) and TaqMan Gene Expression Assay for MRGPRX2 (Life Technologies

#4331182, Hs00365019_s1) following the manufacturer’s suggested protocol.

48

Significance calculations of siRNA experiments

To assess significance, we used GraphPad Prism to perform a Two-way ANOVA and

Sidak post-hoc with an alpha value of 0.05. Total number of replicates is reflected in figure

legends and figures.

Homology Modeling

The alignment for the construction of the MRGPRX2 models was generated using

PROMALS3D (Pei et al., 2008) , and homology models were built with MODELLER-9v8

(Eswar, 2007) using the crystal structure of the κ-opioid receptor (PDB code 4DJH) as the

template. The homology models were aligned against the entire MRGPRX and opioid receptor

family. The alignment was manually edited to remove the amino and carboxy termini that

extended past the template structure, to remove the engineered T4 lysozyme, and to create

different alignments of the flexible and non-conserved second extracellular loop (EL2); the final

resulting sequence alignment between the template opioid structure and the MRGPRX receptors

is shown in Fig 3.10c. Three models were built from each of 180 elastic network models (ENMs),

produced by the program 3K-ENM (Yang and Sharp, 2009), for a total of 540 models built

during each iterative round of model refinement. Although EL2 is significantly shorter in

MRGPRX2 than in other GPCRs, and it lacks the conserved disulfide bond between TM3 and

EL2, an additional 540 models were also built forcing a putative disulfide between Cys168 in

EL2 and Cys180 in TM5.

49

Model evaluation

Models were ranked on the basis of prioritizing active opioids (dextromethorphan) over

the rest of the inactive PRESTO-Tango library that was used in the β-arrestin screen, as well as

over property-matched decoy small molecules. We further insisted that in its docked pose,

dextromethorphan engaged in an ion pair between its charged nitrogen and an anionic side chain

in MRGPRX2. The entire PRESTO-Tango library was docked to analogous 4DJH ligand

binding site in the modeled MRGPRX2 receptors for several rounds of iterative binding site

refinement. In each round, the top-ranked models were examined for a binding pose that made

hydrophobic and electrostatic interactions with the receptor, including the key positive-negative

charge coordination. Residues within 6 Å of the dextromethorphan pose were then minimized

around the docked ligand with PLOP (Jacobson, 2002). The PRESTO-Tango library was then re-

docked into this optimized binding site for each model. This refinement continued for several

cycles until the top-ranked models all converged to the same dextromethorphan pose, with the

top-scoring model chosen as the final one. Structural models (PDB files) of MRGPRX2-

modelled complexes (with dextromethorphan and ZINC-9232) are shown in the Supplementary

Data online with published manuscript.

Virtual screens

We used DOCK 3.6 to screen the ZINC database (http://zinc15.docking.org/). The

flexible ligand sampling algorithm in DOCK 3.6 superimposes atoms of the docked molecule

onto binding site matching spheres, which represent favorable positions for individual ligand

atoms. Forty-five matching spheres were used, with each respectively starting from the previous

refinement round’s best pose of dextromethorphan. The degree of ligand sampling is determined

50

by the bin size, bin size overlap, and distance tolerance, set at 0.4 Å, 0.1 Å and 1.5 Å,

respectively, for both the matching spheres and the docked molecules. The complementarity of

each ligand pose was scored as the sum of the receptor–ligand electrostatic and van der Waals’

interaction energies and corrected for context-dependent ligand desolvation using QNIFFT point-

charge Poisson-Boltzmann electrostatics models (Sharp, 1995). Partial charges from the united-

atom AMBER (Meng, 1992) force field were used for all receptor atoms; ligand charges and

initial solvation energies were calculated using AMSOL (Chambers, 1996; Li, 1998)

(http://comp.chem.umn.edu/amsol/). The best scoring conformation of each docked molecule

was then subjected to 100 steps of rigid-body minimization.

Selection of potential ligands for testing

We docked the approximately 3.7 million commercially available molecules of the lead-

like subset of the ZINC database to the final MRGPRX2 models. The full hit list was

automatically filtered to remove molecules that possess high internal energy, non-physical

conformations, which are not well modeled by our scoring function. The reported rankings

reflect this filtering. From the top 0.13% (~5,000 molecules) of the docked ranking list, 20

compounds were chosen for testing, based on complementarity to the binding site and presence

of predicted charge interactions with Glu1644.60

and Asp1845.36

, mimicking those predicted for

dextromethorphan.

Chemistry General Procedures.

HPLC spectra for all compounds were acquired using an Agilent 1200 Series system with

DAD detector. Chromatography was performed on a 2.1×150 mm Zorbax 300SB-C18 5 μm

51

column with water containing 0.1% formic acid as solvent A and acetonitrile containing 0.1%

formic acid as solvent B at a flow rate of 0.4 mL/min. The gradient program was as follows: 1%

B (0−1 min), 1−99% B (1−4 min), and 99% B (4−8 min). High-resolution mass spectra (HRMS)

data were acquired in positive ion mode using an Agilent G1969A API-TOF with an electrospray

ionization (ESI) source. Nuclear Magnetic Resonance (NMR) spectra were acquired on eith a

Bruker DRX-600 spectrometer (600 MHz 1H, 150 MHz

13C). Chemical shifts are reported in

ppm (δ). HPLC was used to establish the purity of target compounds. All final compounds had >

95% purity using the HPLC methods described above.

7-Chloro-5-phenylpyrazolo[1,5-a]pyrimidine. The title compound was synthesized

according to a known literature procedure (Paruch et al., 2010). To a solution of 3-

aminopyrazole (2.0 g, 24 mmol) in acetic acid (15 mL) was added ethylbenzoylacetate (4.7 mL,

27 mmol). The resulting reaction mixture was refluxed for 3 h before being cooled to rt and

concentrated. The solid residue was diluted with EtOAc and filtered to afford 5-

phenylpyrazolo[1,5-a]pyrimidin-7(4H)-one (4.19 g, 83%) as white solid. This intermediate (1.0

g, 4.7 mmol) was dissolved in POCl3 (5.0 mL, 55 mmol). To the resulting solution was added

pyridine (0.25 mL, 3.1 mmol) at rt. After being stirred for 3 days at rt, the reaction mixture was

diluted with Et2O (50 mL) and filtered. The solid was rinsed with Et2O (2 x). The combined Et2O

solution was cooled to 0 °C and treated with ice and water. The organic layer was washed with

water (2 x) and brine (2 x). The organic layer was dried (Na2SO4) and concentrated to give the

tittle compound (0.82 g, 76%) as pale yellow solid. 1H NMR (600 MHz, CDCl3) δ 8.23 (d, J =

2.3 Hz, 1H), 8.12 – 8.04 (m, 2H), 7.58 – 7.48 (m, 3H), 7.45 (s, 1H), 6.84 (d, J = 2.3 Hz, 1H). 13

C

NMR (151 MHz, CDCl3) δ 156.2, 149.8, 146.1, 139.2, 136.5, 131.0, 129.2 (2C), 127.5 (2C),

105.8, 98.8. HRMS (ESI-TOF) m/z: [M+H]+ calcd for C12H9ClN3, 230.0485; found: 230.0484.

52

(R)-N,N-Dimethyl-1-(5-phenylpyrazolo[1,5-a]pyrimidin-7-yl)pyrrolidin-3-amine

((R)-ZINC-3573). To a solution of 7-chloro-5-phenylpyrazolo[1,5-a]pyrimidine (0.069 g, 0.3

mmol) in dioxane (2 mL) was added DIEA (0.19 mL, 0.6 mmol), followed by (R)-(+)-3-

(dimethylamino)pyrrolidine (0.038 g, 0.33 mmol). The resulting solution was stirred for 16 h at

rt before being concentrated and purified by silica gel column to provide the title compound

(0.070 g, 76%). 1H NMR (600 MHz, CDCl3) δ 8.02 – 7.97 (m, 2H), 7.95 (d, J = 2.3 Hz, 1H),

7.51 – 7.40 (m, 3H), 6.48 (d, J = 2.3 Hz, 1H), 6.12 (s, 1H), 4.30 (t, J = 8.8 Hz, 1H), 4.23 (t, J =

9.8 Hz, 1H), 4.02 (q, J = 9.4 Hz, 1H), 3.83 (t, J = 9.5 Hz, 1H), 2.90 – 2.82 (m, 1H), 2.36 (s, 6H),

2.29 – 2.21 (m, 1H), 2.01 – 1.91 (m, 1H). 13

C NMR (151 MHz, CDCl3) δ 157.5, 151.8, 147.9,

143.8, 139.2, 129.6, 128.7 (2C), 127.3 (2C), 94.9, 86.2, 65.2, 55.0, 50.1, 44.5 (2C), 30.1. HRMS

(ESI-TOF) m/z: [M+H]+ calcd for C18H22N5, 308.1875; found: 308.1871.

(S)-N,N-Dimethyl-1-(5-phenylpyrazolo[1,5-a]pyrimidin-7-yl)pyrrolidin-3-amine

((S)-ZINC-3573). The title compound (0.075 g, 81%) was prepared using the same synthetic

procedure for the preparation of (R)-ZINC-3573 with (S)-(+)-3-(dimethylamino)pyrrolidine

(0.038 g, 0.33 mmol). 1H NMR (600 MHz, CDCl3) δ 8.05 – 7.97 (m, 2H), 7.95 (d, J = 2.3 Hz,

1H), 7.52 – 7.39 (m, 3H), 6.48 (d, J = 2.3 Hz, 1H), 6.12 (s, 1H), 4.30 (t, J = 8.8 Hz, 1H), 4.23 (t,

J = 9.8 Hz, 1H), 4.02 (q, J = 9.3 Hz, 1H), 3.83 (t, J = 9.5 Hz, 1H), 2.91 – 2.81 (m, 1H), 2.36 (s,

6H), 2.30 – 2.21 (m, 1H), 2.02 – 1.91 (m, 1H). 13

C NMR (151 MHz, CDCl3) δ 157.5, 151.8,

147.9, 143.8, 139.2, 129.6, 128.7 (2C), 127.3 (2C), 94.9, 86.2, 65.2, 55.0, 50.1, 44.5 (2C), 30.1.

HRMS (ESI-TOF) m/z: [M+H]+ calcd for C18H22N5, 308.1875; found: 308.1872.

53

Table 3.1: Validation of previously published MRGPRX2 agonists in PRESTO-Tango and

FLIPR intracellular calcium release. pEC50s given with +/- SEM; Compounds with pEC50s

over 4.5 are annotated as < 4.5 and NT = Not Tested because compounds are not commercially

available or available via contact through source paper. Number of replicates of experiments in

each assay, performed in triplicate, are shown beside each pEC50.

3.3 RESULTS

3.3.1 Identification of MRGPRX2 agonists

We initially attempted to replicate prior reports of potential MRGPRX2 agonists to

determine if any might prove suitable as leads for probe development. Unfortunately, we could

not replicate the activities of most reported MRGPRX2 agonists when tested at the highest

concentrations possible for our assays (Fig 3.1, Table 3.1). Thus, of the many putative

MRGPRX2-activating peptides and peptide-like compounds, we could replicate activities only

54

Figure 3.1: Validation of MRGPRX2 and MRGPRB2 Agonists. (a–d) Average concentration–response

curves in the Fluorescent imaging plate reader (FLIPR) intracellular calcium release assay (n = 3

experiments in triplicate wells) in MRGPRX2-inducible cells, designated +X2 or –X2 for the presence or

absence of tetracycline-induced receptor expression, for previously published MRGPRX2 agonists. The y

axis shows the fold change of calcium release over the basal receptor signaling. (e) Concentration–response

curves in FLIPR intracellular calcium release assay for stable cell lines expressing either human MRGPRX2

(solid lines) or the proposed mouse ortholog MRGPRB2 (dotted lines) with X2-activating peptides

cortistatin-14 and substance P and small-molecule agonist TAN-67. Y axis is fold-change calcium release

over basal (n = 3 in quadruplicate) (f) Concentration–response curves for cortistatin-14, substance P, and

small-molecule agonist TAN-67 with MRGPRX2-Tango (solid lines) and MRGPRB2-Tango (dotted lines)

in the PRESTO-Tango arrestin recruitment assay (n = 3 in triplicate). The y axis shows the fold change

response over basal luminescent signal. RLU, relative luminescent units. All error bars represent s.e.m.

for substance P, cortistatin-14 and PAMP (9-20) (Fig 3.1a,b). Mastoparan, octreotide, leuprolide,

and kallidin were inactive or only marginally so (e.g. Gαq EC50 = 11.5 µM for kallidin) at human

MRGPRX2 (Fig 3.1c, Table 3.1).The putative MRGPRX2 agonist mastoparan (McNeil et al.,

55

2015) was active in cells with and without MRGPRX2 expression suggesting non-specific

activity (Fig 3.1d, Table 3.1).

Of the more than one dozen non-peptide compounds reported to activate MRGPRX2, we

could only replicate four (TAN-67 (Southern et al., 2013), compound 48/80 (McNeil et al., 2015),

cetrorelix (McNeil et al., 2015) and the proposed selective agonist complanadine A (Johnson and

Siegel, 2014)) and even these had low potencies or have other known receptor targets (Fig 3.1c,d,

Table 3.1). Notably, we could not validate several recently reported secretagogue agonists for

MRGPRX2 including the THIQ motif-containing octreotide, rocuronium, ciprofloxacin,

atracurium, moxifloxacin, and levofloxacin (McNeil et al., 2015) even when tested up to 100 μM

(Fig 3.1c,d, Table 3.1). Likewise, although the proposed nanomolar MRGPRX2-selective agonist

complanadine A (Johnson and Siegel, 2014) was active in our assays, we measured a Gαq EC50 of

18 µM and could not obtain a β-arrestin EC50 due to apparent cytotoxicity above 1 µM using

PRESTO-Tango, a luciferase reporter-based β-arrestin screening platform (Kroeze et al., 2015)

(Fig 3.1c,d, see Table 3.1 for a full list of validated compounds).

As MRGPRX2 is expressed only in primates, finding a rodent analog would enable the

use of genetic techniques to probe for the receptor’s functional roles, with the caveat that GPCRs

may often be knocked down without incurring phenotypes that recapitulate their roles in

pharmacology. The suggested MRGPRX2 rodent ortholog MRGPRB2 (McNeil et al., 2015) only

shares 52% sequence identity with MRGPRX2 (Fig 3.2a) and we could not substantiate any

proposed shared ligands except for Cortistatin-14, which had high micromolar activity at

MRGPRB2 (Fig 3.1e, f; Fig 3.2b). Thus, although there have been prior studies of MRGPRX2

and/or MRGPRB2’s pharmacology, many remain insufficiently robust for facile replication; and

no independent studies have emerged that replicate these prior findings. Accordingly, we chose

56

Figure 3.2: MRGPRB2 does not share ligands with MRGPRX2. A) Sequence alignment of MRGPRX2

(top) with MRGPRB2 (bottom), blue highlights conserved residues. B-C) Average concentration response

curves in PRESTO-Tango for MRGPRB2 (n=2, triplicate) using ligands for MRGPRX2 or MRGPRB2

positive control ligands Cortistatin-14, PAMP-12, Compound 48/80, and Substance P reported in [23] in

PRESTO-Tango and in FLIPR (D-F) using the MRGPRB2 stable cell line and 100 µM ATP as a positive

control, n=3 in triplicate. G) Depicts B2 stable cell line without TET for compounds observed in F), n=1 in

triplicate. All error bars represent s.e.m.

an unbiased approach to identify reliable MRGPRX2 probes. We recently proposed parallel

screening strategies (Huang et al., 2015b; Kroeze et al., 2015) using small libraries of drugs and

drug-like compounds as fruitful initial approaches to discover active compounds at oGPCRs

(Kroeze et al., 2015). Here we screened 5,695 unique compounds for agonist activity at three

members of the MRGPRX family (MRGPRX1, MRGPRX2, and MRGPRX4) using the

57

Figure 3.3: PRESTO-Tango Screening of MRGPRX2 reveals new agonists. (a) Venn diagram depicting

pooled PRESTO-Tango screening actives for MRGPRX1, MRGPRX2, and MRGPRX4 for all hits with

greater than two-fold activation over basal (known false positives and duplicates excluded). (b,c) Average

concentration–response curves (n = 3 in triplicate wells for all, except ADL5859, for which n = 1) for the

five compounds from the screening show low micromolar activation of MRGPRX2. (d) Concentration–

response curve for previously published MRGPRX2 peptide agonists at the MRGPRX2-Tango construct. y

axes are shown as percent of TAN-67 activity. All error bars represent s.e.m.

PRESTO-Tango platform. Our strategy was to screen against three MRGPRX family receptors

in parallel to find active compounds with selectivity within the family.

The screening revealed MRGPRX1 had the fewest number of actives (39), followed by

MRGPRX4 (54), and MRGPRX2 (81) (Fig 3.3a). We were most interested in compounds

showing selective MRGPRX2 agonism without apparent activity at other MRGPRX receptors.

Among the 67 compounds that activated MRGPRX2, and neither MRGPRX1 nor MRGPRX4,

were five opioid-related ligands ADL-5859, sinomenine, dextromethorphan, dextromethorphan’s

58

metabolite dextrorphan, and the previously reported MRGPRX2 ligand TAN-67 (Southern et al.,

2013), a delta opioid receptor agonist.

Confirmatory concentration response curves using the PRESTO-Tango platform

indicated the five opioid-like compounds had low micromolar potency (Fig 3.3b, c). To confirm

the MRGPRX2-Tango construct performed similarly to the unmodified wild-type (WT) receptor,

we tested previously reported MRGPRX2 agonists TAN-67 (Southern et al., 2013), cortistatin-14

(Robas et al., 2003), substance P (Tatemoto et al., 2006), and compound 48/80 (McNeil et al.,

2015) and found all activated the MRGPRX2-Tango receptor at similar reported potencies for

Gαq assays at the WT receptor (Fig 3.3d).

Figure 3.4: Validation of MRGPRX2 opioid ligand activation. A tetracycline-inducible HEK-T cell line was

created for MRGPRX2 WT inducible expression to test opioid and opioid-like ligands from PRESTO-Tango screen.

A, B) Average concentration response curves in the intracellular calcium release assay with dextromethorphan,

dextrorphan, and TAN67 (n=3, triplicate) in tetracycline-induced (a) and non-induced (b) cells. Error bars depict

SEM.

To confirm MRGPRX2 Gαq-mediated functional activity of agonists, we used a

tetracycline-inducible WT MRGPRX2 stable HEK-T cell line to test for intracellular calcium

release. Dextromethorphan, dextrorphan, sinomenine, and TAN-67 promoted intracellular

calcium release when MRGPRX2 expression was induced by tetracycline (1 µg/ml; Fig 3.4a) but

not in the absence of tetracycline (e.g, MRGPRX2 is not expressed, Fig 3.4b).

59

3.3.2 Preference for dextro-enantiomers and N-methyl scaffolds

As several dextrorotatory opiate ligands activated MRGPRX2, we initially investigated

ligand stereochemistry, aware that classical opioid receptors prefer levorotary morphinans and

benzomorphans (Sromek et al., 2014). We assayed levorphanol and levallorphan, enantiomers of

the screening hits dextrorphan and dextromethorphan, respectively, for activity at MRGPRX2 in

β-arrestin recruitment and calcium mobilization assays. Levorphanol was approximately ten-fold

less potent at MRGPRX2 than dextrorphan and levallorphan was completely inactive up to 100

µM (Fig 3.5a). Likewise the dextrorotary morphinan sinomenine had comparable potency to

dextrorphan and dextromethorphan at MRGPRX2 (Appendix 2 Table 1), more potent than

sinomenine’s purported activity at the opioid receptors (Wang et al., 2008) (Fig 3.6). MRGPRX2

also preferred dextrorotary benzomorphan compounds, as (-)-cis-normetazocine was ten-fold less

potent compared to (+)-cis-normetazocine (Fig 3.5b, Appendix 2 Table 1).

Figure 3.5: MRGPRX2 is activated by many opioid scaffolds. (a–d) Average concentration–response curves (n =

3 in triplicate) for structurally related compounds, morphinans (a), benzomorphans (b), morphine and analogs (c),

and codeine and analogs (d), in an intracellular calcium release assay in which the y axis is fold-change calcium

release over baseline. Error bars represent s.e.m. (e) Summary of major findings in structure–activity relationships

60

for opioid scaffolds at MRGPRX2, including size of N-substituent, stereochemistry of chiral centers, and bulk on the

3-position of the morphinan.

To determine if other opioids activated MRGPRX2 with similar stereochemical

preferences, we tested a panel of 11 morphine and codeine analogues for activity in PRESTO-

Tango and calcium mobilization assays, including the most common metabolites of morphine

and codeine. Both (+)- and (-)-morphine enantiomers activated MRGPRX2 with comparable

potency in PRESTO-Tango and calcium assays, as did (+)- and (-)-codeine and thebaine (Fig

3.5c,d, Appendix 2 Table 1). For the metabolites, we observed differential effects of substitutions

on C-3 and C-6 positions in the morphine scaffold, as replacement of morphine’s 3-hydroxy with

a 3-methoxy (e.g. codeine and thebaine) had little effect on affinity or efficacy at MRGPRX2

whereas replacement of the morphine’s 3-hydroxy with gluconuride to yield morphine-3β-

gluconuride eliminated MRGPRX2 agonist activity (Fig 3.5c,d, Appendix 2 Table 1). In the C-6

Figure 3.6: Sinomenine activity at MOR, KOR, and DOR. A-C) Sinomenine activity (red) in the Glo Sensor

assay for Gαi activity in comparison to other known potent opioid receptor agonists. Average n=3 in triplicate

for all shown, error bars represent SEM. D-F) Competitive binding data for sinomenine (red) versus other potent

opioid receptor ligands, average of two trials each in duplicate.

61

position, gluconuride or acetyl modifications to morphine and codeine all retained agonist

activity, albeit with loss in potency compared to (-)-morphine and (-)-codeine, respectively (Fig

3.5c,d, Appendix 2, Table 1). These preliminary opioid structure-activity relationships (SARs)

demonstrate that larger modifications on C-6 are more tolerated than on C-3.

We then explored the effect of substituents on the cationic nitrogen in the morphinans on

MRGPRX2 activation. N-methyl substituted scaffolds, as in codeine, morphine, and metazocine,

conferred receptor agonism while the N-unsubstituted norcodeine was inactive up to 100 µM

(Fig 3.5b-d, Appendix 2 Table 1). Morphinans and benzomorphans with substituents larger than

N-methyl, such as the N-allyl of levallorphan and N-allyl-normetazocine, and the N-cyclopropyl

of naltrexone and cyclazocine, were inactive at MRGPRX2 (Fig 3.5a,b, Appendix 2 Table 1).

The inactive morphinans and benzomorphans showed no antagonist activity against MRGPRX2,

suggesting a tight SAR around substitution of this position.

As mentioned, TAN-67 is a δ-opioid receptor agonist known to activate MRGPRX2

(Southern et al., 2013). The active enantiomer at the δ-opioid receptor is (-)-TAN-67 whereas

Figure 3.7: MRGPRX2 prefers dextroratory enantiomers including (+)-TAN-67. A) Concentration

response in PRESTO-Tango for (+/-)-TAN-67 versus the purified enantiomer (+)-TAN-67. Y axis is percent

of (+/-)-TAN-67, average n=3 in triplicate. B) Concentration response curves in FLIPR for (+/-)-TAN-67

versus the purified enantiomer (+)-TAN-67, (average of n=3 in triplicate). Y axis is fold change intracellular

calcium release over baseline. Error bars for both graphs reflect the SEM for each point.

62

(+)-TAN-67 displays pro-nociceptive activity in vivo through an unidentified receptor (Nagase et

al., 2001) . We tested (+)-TAN-67 for MRGPRX2 activity in PRESTO-Tango and calcium

mobilization assays and found (+)-TAN-67 was more potent than racemic (+/-)-TAN-67 (Gαq

EC50 = 290 nM vs 740 nM) (Fig 3.7), demonstrating that MRGPRX2 prefers dextro-enantiomers

at most chemical scaffolds with some exceptions. Neither (-)- nor (+)-naloxone antagonized (+/-

)-TAN-67 agonist activity (Fig 3.8a, b) and no other canonical opioid antagonists, including

naltrindole, naltrexone, and β-chlornaltrexamine, inhibited (+/-)-TAN-67 activity. (Fig 3.8c-e).

These SARs suggests that MRGPRX2, despite its distant sequence relationship to the four classic

opioid receptors (transmembrane region sequence identity no greater than 26%), is an opioid-

responding oGPCR with important differences in ligand recognition patterns.

Figure 3.8: MRGPRX2 is not antagonized by traditional opioid antagonists. A-D) Representative concentration

response curves in the PRESTO-Tango assay using (-)- and (+)-Naloxone, β-chlornaltrexamine (BCNAL), Naltrindole

(NTD), or Naltrexone (NTX), bottom right. N=3 in triplicate for (+)- or (-)- naloxone experiments, n=1 in triplicate for

BCNAL, NTD, NTX in TANGO. Error bars represent s.e.m.

63

3.3.3 Prodynorphin-derived peptides activate MRGPRX2

A key question is whether MRGPRX2 is also activated by endogenous opioid ligands.

Accordingly, we tested a panel of 20 endogenous opioid peptides for activity in PRESTO-Tango

and intracellular calcium assays. MRGPRX2 was preferentially activated by prodynorphin-

derived peptides (Fig 3.9a, b) and only minimally activated, if at all, by other opioid peptides

(Fig 3.9c,d). The full length dynorphin A (1-17) and several truncated prodynorphin peptides

activated MRGPRX2 (Fig 3.9a). C terminal amino acid truncation of dynorphin peptides reduced

agonist potency until dynorphin A (1-7) and dynorphin A (1-6) fragments, which were

completely inactive (Fig 3.9a, Fig 3.10a).

To determine if the C terminal portion of dynorphin A was sufficient for MRGPRX2

activation, we assayed dynorphin A (13-17) and dynorphin A (6-17) and found that dynorphin A

(6-17) retained minimal activity (EC50 = 39.6 µM) and dynorphin A (13-17) was inactive (Fig

3.9b), suggesting both the N terminal YGGF motif and C terminal cationic tail are important for

MRGPRX2 activation (Fig 3.10a). Dynorphin B (1-13) and α- and β-neoendorphin also activated

MRGPRX2, with dynorphin B and α-neoendorphin having almost identical µM potency (Fig

3.9b). Intriguingly, EC50 values for the dynorphin peptides were right-shifted in the PRESTO-

Tango assay, indicating perhaps modest G protein bias, similar to observations made for

dynorphin peptides at the κ-opioid receptor (White et al., 2014).

To determine whether more closely related receptors might share peptide ligands with

MRGPRX2, we used the GPCRdb (gpcrdb.org) to identify class A receptors with greater than or

equal to 30% sequence similarity to MRGPRX2 in the class A binding pocket (Fig 3.10b). We

identified 14 receptors, 11 of which had established probes (10 ligands total). 3 receptors were

oGPCRs with no known ligands. We tested the 10 established ligands for activity and found only

64

one ligand, somatostatin-14, activated MRGPRX2 (Supplementary Fig 3.10b,d; EC50 = 380 nM).

This is consistent with previous reports that somatostatin-14, somatostatin-28, and Cortistatin-14

are MRGPRX2 agonists (Robas et al., 2003; Southern et al., 2013). Interestingly, the canonical

opioid receptors and somatostatin receptors are closely related in binding site-sequence space,

but not in small molecule-chemical space (Lin et al., 2013). Our data, along with previously

proposed peptide ligands, suggest that although MRGPRX2 can be activated by many peptide

ligands, this cannot be predicted from sequence similarity alone.

Figure 3.9: MRGPRX2 is preferentially activated by prodynorphin-derived peptides. (a,b) Average

concentration response curves (in triplicate wells; n = 3 for all except Dyn A fragments below 7 amino acids in

length and α-neoendorphin for which n = 2 ; see Supplementary Table 2) for pro-dynorphin-derived peptides

in MRGPRX2 Tet-On cells, in which the y axis is fold-change calcium release over baseline. Error bars

represent s.e.m. (c,d) Average concentration–response curves (in triplicate wells; n = 3 for all except nociceptin

and BAM(8–22) for which n = 2 and β-endorphin and BAM-22 for which n =1 ; see Supplementary Table 2)

depicting nonprodynorphin-derived peptides with minimal activity compared to dynorphins in A and B. Error

bars represent s.e.m.

65

Figure 3.10: Peptide agonism at MRGPRX2 and comparison among related receptors. A) Table

showing peptide sequence alignment and Gα functional EC50 values in nM for MRGPRX2 and κ-OR for

reference. MRGPRX2 values were measured in FLIPR intracellular calcium assay whereas κ-OR values

were determined using the Glo sensor assay. κ-OR values are average of n=3, MRGPRX2 values are average

of n=2 or 3 (see Supplementary Table 2 for details). B.) Results of sequence similarity search in GPCRdb for

all class A receptors based on the class A binding pocket. Note the green highlights the only ligand active at

MRGPRX2. C) Sequence alignment for all four members of MRGPRX family receptors aligned with κ-

opioid receptor (OPRK1) and nociceptin opioid receptor (OPRX1). Dark blue illustrates full identity residues

and light blue indicates similar residues. D) Dose response curve, average of 2 trials, in FLIPR calcium assay

for somatostatin at MRGPRX2 in MRGPRX2-expressing cells (left) and cells without MRGPRX2 (right).

E.) Heat map matrix showing percentage sequence similarity (left) or sequence identity (right) for the entire

sequences of the four opioid receptors and somatostatin 4 receptor in comparison to MRGPRX2. Red

indicates lower percentage and green indicates higher percentage of similarity or identity. Modified from

GPCRdb. F) Heat map matrix showing percentage sequence similarity (left) or sequence identity (right) for

the Class A binding pocket residues only between the four opioid receptors and somatostatin 4 receptor in

comparison to MRGPRX2. Red indicates lower percentage and green indicates higher percentage of

similarity or identity. Modified from GPCRdb.

66

MRGPRX2 is an opioid-like oGPCR that responds to endogenous pro-dynorphin-derived

opioid peptides, binds to many well-known synthetic opioid agonists, but which differs from

classic opioid receptors in its unique preference for dextro-morphinans, dextro-benzomorphans

and its inability to be antagonized by potent and classic opioid receptor antagonists. Although

none of these compounds are suitable as a selective probe or tool, the chemical matter identified

was useful to launch an in silico campaign to identify novel chemotypes active at MRPGRX2.

3.3.4 Structure-based docking predicts MRGPRX2-selective ligands

We next turned to our computational approach using a 1000-fold larger compound library

than that used in the physical screen—the over 3.7 million commercially available “lead-like”

Figure 3.11: In silico MRGPRX2 homology modeling predicts a selective agonist. Workflow depicting

MRGPRX2 homology model construction (top left) followed by identification of a putative binding site (top

middle) that was confirmed by testing the mutations E164Q and D184N (top right, average dose response, n =

3 in triplicate, shown with dextromethorphan (DXM). Then, ~3.7 million molecules were docked to predict

the agonist ZINC-9232 (bottom left). Further iteration and testing revealed the tool and selective compounds

(S)- and (R)-ZINC-3573 (bottom middle and right). Docking pose of selective compound is supported with

mutation experiments in PRESTO-Tango (bottom right, average dose–response curve, average of n = 4 in

triplicate). Error bars on graphs shown is representative of s.e.m.

67

molecules then in the ZINC database (Irwin and Shoichet, 2005). Our strategy was to calculate

many 3D MRGPRX2 models, select those few that recapitulated the ligand recognition patterns

we observed experimentally, and use these to template a final model to screen the full, larger

compound library for novel molecules unrelated to opioid ligands but selective for MRGPRX2.

The general approach for modeling and docking has been recently described (Huang et al.,

2015b), and we summarize it here (see Methods for more details). We first sought GPCRs of

known structure with MRGPRX2 sequence similarity; intriguingly, this led to the κ-opioid

receptor, which only shares 23.3% sequence identity in the transmembrane regions with

MRGPRX2. We calculated an initial MRGPRX2 structure using Modeller (Eswar, 2007) and

expanded on this using elastic network modeling to increase the amount of sampled backbone

conformations (Yang and Sharp, 2009). From the initial model, we calculated 360 further

structures using the Elastic Network Modeling program ENM; half of these had an extracellular

disulfide bond imposed and half did not. Each of these was expanded three-fold with Modeller

(i.e., using the ENM models as templates for Modeller). Overall, 1,080 models were calculated.

We docked the 5,695 unique compounds from physical screening against all 1080 models

(i.e., over 6 million compound-receptor docking calculations, and over 1012

individual

configurations of the library molecules in the receptor). We looked for models that best enriched

the discovered opioid agonists over the vast number of inactive decoy molecules. The top-

enriching models were inspected visually for binding poses that captured sensible ion-pairing

interactions with the conserved aminergic group of the opioid agonists. This prioritized a model

that had a background-corrected logAUC for enrichment (Mysinger and Shoichet, 2010;

Mysinger et al., 2012) of 8.42—which happened to be the top-enriching model (this is not

always the case, as sometimes the top-enriching model does not have sensible geometries

68

Figure 3.12: Mutations support in silico model of opioid ligand binding. Average concentration response

curves in PRESTO-Tango depicting the effect of D184 mutations and E164 mutations on opioid ligand activity

where A) is (-)-morphine, B) is (+)-morphine, C) is dextromethorphan and D) is Dynorphin A (1-13). Y axis

depicts fold change over basal signal, average n=2 in triplicate. Error bars represent SEM. E). Graph showing

23 different rotamers of Asp184 (blue circles) and 27 different rotamers of Glu164 (orange squares) versus

dextromethorphan RMSD in Å (Y axis). X axis is amino acid rotamer RMSD in Å.

69

(Carlsson et al., 2011), though usually the model chosen is among the best enriching models).

Residues within 6 Å of the dextromethorphan pose were minimized around the docked ligand

with PLOP (Jacobson et al., 2002), and the 5,695 molecules re-docked. Two rounds of docking

and PLOP-based refinement led to the final modeled receptor conformation, which predicts the

opioid agonists making ionic interactions with Glu1644.60

and Asp1845.36

(Fig 3.11, showing

dextromethorphan). The exact rotamer state of these residues allowed some flexibility; i.e.,

several different rotamers of the Asp/Glu led to productive placement of the known agonists (Fig

3.12e). To test the predicted ion pair between the receptor and the ligands, we made a series of

mutations at Glu1644.60

and Asp1845.36

. E164Q and D184N substitutions retained steric

properties of the WT residues but removed their negative charges and both resulted in a loss of

activity for dextromethorphan, (-)-morphine, and related opioids in the PRESTO-Tango assay

(Fig 3.11, Fig 3.12a). We then tested the importance of the length of the acidic residue via

E164D and D184E mutations and observed that E164D increased the Emax and reduced the

potency of both dextrorphan and morphine (Fig 3.12a,c) while D184E ablated activity of all

opioids tested (Fig 3.11, Fig 3.12a-d). The substitutions support the importance of the proposed

ionic interaction and the putative binding site predicted by the in silico model, suggesting both

Glu1644.60

and Asp1845.36

are necessary for MRGPRX2 opioid activation.

As an aside, dynorphin A (1-13) activity was lost with the D184N but not the E164Q

mutation, suggesting only one of these residues is required for the opioid peptide interaction (Fig

3.12d). We modeled the putative MRGPRX2-dynorphin binding site by overlaying the

MRGPRX2 homology model with the previously published κ-opioid receptor docked with

dynorphin A (1-13) (O'Connor et al., 2015). The N-terminal Tyr1 of dynorphin A (1-13) is

accommodated within a negatively charged aromatic pocket, whereas Arg7 and Phe4 appear to

70

Figure 3.13: Modeling the MRGPRX2/Dynorphin Binding Site. A) Overlay of MRGPRX2 model (cyan)

and KOR (brown) with dynorphin (1-13) (DYN, orange) docked based on NMR experiments. Dashed lines

between DYN and MRGPRX2 show negatively charged residues pairing with positively charged DYN side

chains. The charged N- and C-termini are very flexible, with the Tyr1 side chain occupying the sodium

allosteric site near Asp2.50

; the N-terminus is thought to partially interact with Asp3.32

, the main ligand

recognition site in opioid and aminergic receptors (in MRGPRX2, it is a Met). B) Surface representation

shows the Tyr1 side chain is accommodated in an aromatic pocket in MRGPRX2. C) The positive charge and

aromatic ring of dextromethorphan (green) and ZINC-3573 (magenta) overlap with the positive charge and

aromatic ring of Arg7 and Phe4, interacting with Asp184 and Phe. D) Left: Overlay of all 5 KOR-docked

DYN models in MRGPRX2 show the flexibility of N- and C-termini; Right: Arg7 side chains of each DYN

model show a positioning preference for Asp184 over Glu184, supporting the mutagenesis studies. E)

Positive (blue) and negative (red) electrostatic solvent-accessible surfaces of MRGPRX2 (top) and KOR

(bottom). The large exposed negative surface allows for cross-recognition of DYN-like peptides containing

multiple positively charged amino acids.

interact with Asp1845.36

(Fig 3.13 a-c). The predicted orientation of dynorphin A (1-13) at

Asp1845.36

and Glu1644.60

shows a clear preference for charged interactions between dynorphin A

and Asp1845.36

, a result supported by the mutagenesis results (Fig 3.13d).

71

With experimental support for the MRGPRX2 comparative model, we proceeded to

virtually screen the ZINC library for new ligands. Over 3.7 million commercially available

molecules were docked, each in an average of 926 orientations and 558 conformations; overall

1.9 trillion docked complexes were sampled and scored (for details, see Methods). We selected,

purchased, and assayed 20 compounds from the 0.13% of top-ranked compounds for activity at

MRGPRX2. As there is little scoring difference among the top-ranked molecules, we chose

molecules following our usual strategy (Irwin and Shoichet, 2016) and prioritized those with

diverse, non-opioid chemotypes. We deprioritized molecules with apparently high internal

energies, which the docking scoring function does not well account for(Irwin and Shoichet,

2016), and prioritized those that made ionic interactions with the Asp1845.36

/Glu1644.60

pair,

similar to the modeled opioids. Of the 20 ZINC compounds tested, one, ZINC-72469232 (1, here

on referred to as ZINC-9232), had substantial activity below 10 M in PRESTO-Tango and

Figure 3.14: MRGPRX2 is preferentially activated by R-ZINC-3573. A) PRESTO-Tango average

concentration response curve (n=4, triplicate) showing arrestin recruitment with (R) or (S)-ZINC-3573

(EC50 of R enantiomer = 760 nM); B) Average concentration response curve in FLIPR calcium assay (n=5,

triplicate), showing Gq activity with (S) or (R)-ZINC-3573 (EC50 of R enantiomer = 1 μM). Error bars

represent s.e.m.

72

calcium release assays (Fig 3.11). To improve potency, 22 analogs of ZINC-9232 topologically

similar to the lead were obtained and tested. Of these 22, 7 were active, with the most potent

ZINC-72453573 (2, from here on referred to as ZINC-3573) having sub micromolar potency

(EC50 = 760 nM, Fig 3.11, Fig 3.14). We then assessed whether ZINC-3573 interacts with the

predicted residues Asp1845.36

and Glu1644.60

using E164Q and D184N mutations and found that

both eliminated ZINC-3573 agonism at MRGPRX2 (Fig 3.11), consistent with the modeling.

3.3.5 ZINC-3573 as a chemical probe for MRGPRX2

To confirm MRGPRX2 selectivity, we tested ZINC-9232 and ZINC-3573 activity at 315

other human GPCRs using our PRESTO-Tango GPCRome assay. ZINC-9232 and ZINC-3573

showed minimal agonist efficacy at receptors other than MRGPRX2 at 10 μM (Fig 3.15 a,b).

ZINC-9232 was also screened against a panel of 97 representative human kinases using the

DiscoverX KINOMEscan diversity panel; only three kinases were modestly inhibited, with IC50

values between 20-30 µM (Appendix 1 Figure 1).

These results encouraged us to synthesize both enantiomers of the more potent ZINC-

3573, originally supplied as a racemic mixture, in an effort to create a pair of differentially active

molecules that could jointly be used as a chemical probe pair (Appendix 2 Scheme 1). The R-

isomer (3) retained an EC50 of 740 nM in PRESTO-Tango and a similar EC50 value of 1 µM in

the calcium mobilization assay (Fig 3.14). The S-isomer (4) had little activity below

concentrations of 100 µM (Fig 3.14). The separate activity of the enantiomers makes them

highly attractive probes, as one can distinguish MRGPRX2 activity (owing to the R-isomer) from

general, non-specific activity owing to the scaffold and its physical properties (due to either

isomer). Each enantiomer was further tested using the PRESTO-Tango GPCRome screening

73

platform (Fig 3.15c,d), which indicated 21 receptors may be activated at 2-fold or higher by (R)-

and (S)-ZINC-3573; however, subsequent concentration-response studies showed that no other

receptor was significantly activated by either compound (Appendix 2 Figure 1).

3.3.6 MRGPRX2 agonists induce degranulation in human mast cells

MRGPRX2 has been implicated in IgE-independent inflammatory responses in mast cells

using peptide ligands and other non-specific agonists (McNeil et al., 2015; Subramanian et al.,

2013; Tatemoto et al., 2006), prompting us to investigate whether MRGPRX2-selective agonists

induce degranulation and mobilize intracellular calcium in the LAD2 human mast cell line.

ZINC-9232 and ZINC-3573 induced intracellular calcium release and degranulation in LAD2

mast cells at comparable potencies to assays performed in MRGPRX2-expressing HEK-T cells

Figure 3.15: MRGPRX2 selective agonist confirmation. A-D) PRESTO-Tango GPCR-ome screening

results for 315 GPCRs against 10 µM of either A) ZINC-9232, B) (R,S)-ZINC-3573, C) (R)-ZINC-3573, or

D) (S)-ZINC-3573 where X axis is individual GPCR and Y axis is fold change over baseline for each GPCR.

MRGPRX2 is highlighted on each graph. All hits over two-fold activation were followed up in PRESTO-

Tango concentration response curves in Appendix 2 Fig 1.

74

(Fig 3.16a,b). For the stereochemical pair, only (R)-ZINC-3573 promoted degranulation in

LAD2 mast cells (Fig 3.16b), consistent with the HEK-T in vitro activity. As expected,

MRGPRX2 siRNA significantly reduced (R/S)-ZINC-3573-induced degranulation (Fig 3.16c,

p=0.01).

We then considered whether the opioid ligands might activate endogenously expressed

MRGPRX2 to induce degranulation in mast cells. This is important, in part, because of the well-

known but enigmatic ‘histamine flush’ associated with many opioids, which is not due to

engagement of classical opioid receptors. We found (+/-)-TAN-67, (+)-morphine, (-)-morphine

and dynorphin A (1-13) produced robust intracellular calcium release in the mast cells while the

structurally similar κ-opioid receptor agonist (-)-cyclazocine, which does not activate

MRGPRX2, did not (Fig 3.16d). Correspondingly, structurally unrelated agonists for the κ, δ,

and μ opioid receptors (Salvinorin A, BW373U86 and DAMGO, respectively) were inactive (Fig

3.16e). To determine whether MRGPRX2-activating opioids induce degranulation, we first

tested 10 μM (+/-)-TAN-67 in the presence or absence of biotin-labeled IgE antibodies and

found that with or without IgE-biotin antibody activation via streptavidin, the MRGPRX2-ligand

(+/-)-TAN-67 produced 70% degranulation, indicating MRGPRX2-mediated degranulation is

IgE-independent (Fig 3.16f). MRGPRX2 agonists (+/-)-TAN-67, (+)-morphine, (-)-morphine,

and dynorphin A (1-13) all promoted degranulation in LAD2 cells (Fig 3.16g) while the classical

opioid agonists salvinorin A, BW373U86 and DAMGO did not (Fig 3.16h). 10 µM naloxone

pretreatment did not influence the EC50 or Emax values of MRGPRX2 opioid agonists (Fig

3.17a,b), whereas MRGPRX2 siRNA significantly attenuated (+/-)-TAN-67, dynorphin A (1-13),

and (-)-morphine-induced degranulation (Fig 3.16c, Fig 3.17c,d). These data suggest MRGPRX2,

not the canonical opioid receptors, mediates opioid-induced degranulation in human mast cells.

75

Figure 3.16: MRGPRX2 mediates intracellular calcium release and degranulation in the LAD2 human mast

cell line. (a) Average concentration response for MRGPRX2-selective agonists ZINC-9232 and ZINC-3573 in the

calcium mobilization assay in LAD2 mast cells (n = 3 in triplicate). (b) Average concentration response (n = 3 in

triplicate) for MRGPRX2 probes ZINC-3573 (R) and ZINC-3573 (S) in the β-hexosaminidase degranulation assay

in LAD2 cells. (c). Bar graph depicting fold change percent degranulation (baseline is average DMSO of all plates)

induced by EC80 concentration of drug following 25 nM MRGPRX2 siRNA transfection. NT, non-targeting pool;

siRNA 2 + 3, MRGPRX2 siRNA pool. Statistics were calculated using two-way ANOVA with a Sidak post hoc test

(P < 0.05 = *; P = 0.031, 0.031, 0.033, and 0.017 for TAN, dynorphin A (Dyn A), morphine, and ZINC-3573,

respectively). n = 3 for all except ZINC-3573, for which n = 2. All replicates in triplicate wells. (d) Concentration–

76

response curves for MRGPRX2-activating opioid ligands and the structurally related, κ-opioid receptor ligand (−)-

cyclazocine in the calcium mobilization assay (n = 3 in triplicate). (e) Average concentration response in

intracellular calcium release with MRGPRX2-activating (+/−)-TAN-67 and canonical opioid receptor ligands

DADLE, DAMGO, salvinorin A, and BW373U86 (n = 3 in triplicate). (f) Bar graph depicting baseline normalized

percent degranulation induced for each 10 μM (+/−)-TAN-67, DMSO, and calcium ionophore ionomycin (IONO) in

the presence of absence of biotin-labeled IgE antibodies. (streptavidin in all wells, average of n = 2 in triplicate.) (g)

Average concentration–response curves for MRGPRX2-activating opiates (n = 3 in triplicate) in the β-

hexosaminidase degranulation assay. (h) Degranulation concentration response with canonical opioid receptor

agonists and MRGPRX2-activating (+/−)-TAN-67, (n = 3 in triplicate). All error bars demonstrate s.e.m.

3.4 CONCLUSIONS

Our MRGPRX2 results underscore two major findings. First, the oGPCR MRGPRX2

responds to opioid drugs and endogenous pro-dynorphin peptides at potentially physiologically

relevant concentrations and mediates opioid-induced degranulation in a human mast cell line.

Although this receptor is not in the opioid receptor family by sequence similarity, its agonism by

opioid drugs and peptides qualifies it as an atypical opioid receptor that responds to morphinan-

based opioids and transmitters. A second key result is the structure-based discovery of the

selective, sub-micromolar MRGPRX2 agonist (R)-ZINC-3573. This agonist promotes

degranulation in mast cells, has no measurable agonism at over 315 other GPCRs, and the parent

scaffold has little activity against 97 representative kinases. The inactive enantiomer (S)-ZINC-

3573 and the active (R)-ZINC3573 are an effective and internally controlled probe-pair for

investigating the biology of this intriguing primate-exclusive receptor. The probes will be made

available to the community from Sigma-Aldrich (#SML1699 and #SML1700).

SAR results show MRGPRX2 is distinct from the canonical opioid receptors. MRGPRX2

prefers dextro-enantiomers and N-methyl substituted opioid scaffolds whereas opioid receptors

prefer levo-enantiomer opioids (Jacquet et al., 1977; Sromek et al., 2014) and tolerate a wider

array of N-substituents. In further contradistinction to the canonical opioid receptors, MRGPRX2

is Gαq- rather than Gαi-coupled. Thus, MRGPRX2 may be classified as an atypical opioid

77

recognizing receptor, arguably more an example of convergent evolution within the GPCR

family than the divergent evolution that relates the four canonical opioid receptors.

MRGPRX2 activation by opiates may be relevant physiologically and therapeutically.

We found MRGPRX2, not the canonical opioid receptors, mediates morphine and dynorphin A

(1-13)-induced mast cell degranulation. Morphine and structurally similar analgesics induce mast

cell histamine release (Baldo and Pham, 2012; Rosow et al., 1982) in humans, resulting in

Figure 3.17: X2-activating opiates are not antagonized by naloxone in LAD2 cells, siRNA knockdown

data. A) Representative concentration response curve depicting X2-activating opioids in LAD2 mast cells in the

presence of 10 uM (-)-Naloxone as measured by intracellular calcium release. Y axis is fold change calcium

release over baseline, n=1 in triplicate, error bars depict SEM for triplicate wells. B) Average concentration

response curve depicting X2-activating opioids in LAD2 mast cells in the presence of 10 uM (-)-Naloxone as

measured by Beta-hexosaminidase release. Values shown are normalized by subtracting baseline degranulation,

average of n=3 in triplicate. C) Representative quantitative real time PCR data for MRGPRX2 knockdown in the

presence of 25 nM of each siRNA (n=1). SiRNAs 2 and 3 were combined for use in knockdown experiments

along with the non-targeting pool control (NT). D). Average quantification of knockdown, as measured by

relative quantification (RQ) method to housekeeping gene GAPDH, for combination siRNA treatment vs non-

target control, (n=3).

78

pruritus (Kumar and Singh, 2013), vasodilation, and hypotension that is poorly reversed by

naloxone (Baldo and Pham, 2012; Hutchinson et al., 2011); these effects are not seen for opioid

analgesics lacking MRGPRX2 activity, such as fentanyl (Rosow et al., 1982). MRGPRX2 may

also be involved in the efficacy of sinomenine, which is used to treat rheumatoid arthritis due to

its histamine-releasing properties (Yamasaki, 1976) . Dextromethorphan’s potency at

MRGPRX2 is also greater or equal to reported Ki values for μ, κ, δ, NMDA, and σ2 receptors

(Sromek et al., 2014), suggesting MRGPRX2 could contribute to dextromethorphan’s side effect

profile which includes itch at high doses (Zajac et al., 2013). Definitive studies in non-human

primates are needed to address these hypotheses.

The micromolar potency of pro-dynorphin peptides at MRGPRX2 suggests in vivo

receptor activation might require close proximity to high local concentrations of dynorphin.

Local synaptic concentrations of neuropeptides can reach millimolar range (Scimemi and Beato,

2009) and dynorphin is expressed in MRGPRX2-expressing regions (Podvin et al., 2016;

Rojewska et al., 2014; Sweetnam et al., 1982). At such concentrations, dynorphin could activate

synaptic GPCRs or those expressed in mast cells, which can localize to nerve terminals

(Bienenstock et al., 1991). Thus, it is conceivable that MRGPRX2 is exposed to activating

concentrations of dynorphin in vivo.

Structural modeling enabled the discovery of a new scaffold, represented by ZINC-9232

and ZINC-3573, unrelated to classical opioids topologically and structurally. The unusual

specificity of ZINC-3573 against essentially the entire GPCRome and the relevant kinome, and

the availability of an inactive enantiomer makes this molecule a uniquely useful MRGPRX2

probe. Methodologically, we previously used the combination of an initial physical compound

screen followed by a much larger docking screen to discover probes for oGPCRs (Huang et al.,

79

2015b). Identification of ZINC-3573 by a similar approach against a wholly different receptor

family suggests this approach has broad utility in structure-based drug design.

The modelled MRGPRX2 provided intriguing insights to this receptor’s unique opioid

pharmacology. MRGPRX2 shares no more than 26% sequence identity with the μ-, κ-, δ- or

nociceptin-opioid receptors, and its modeled orthosteric binding differs from that of the

canonical opioid receptors. The cationic nitrogen of the morphinans is recognized by an aspartate

in the opioid receptors, located in transmembrane (TM) helix 3, position 3.32. The residue for

cationic nitrogen recognition in MRPRX2 appears instead to be in TM5, Asp1845.36

(see Fig.

3.8c). Many other recognition residues do not overlap in sequence or structural placement.

Indeed, the entire region surrounding Asp1845.36

and Glu1644.60

of the MRGPRX2 putative

binding site is closed off from ligand contacts in the classical opioid binding site by Tyr3.33

– this

is a smaller Thr110 in MRGPRX2. These seeming mismatches reflect that different receptor

environments can recognize related ligand chemistry and the capacity for ligands to interact with

receptors from different evolutionary families (Barelier et al., 2015; Lin et al., 2013) and

different G protein coupling.

Certain caveats merit airing. We have not determined the structure of MRGPRX2 in

complex with any of the ligands discussed here, and the modeling that we have used to interpret

activity and to predict new molecules—however successfully—must remain tentative. Until the

opioid actions at this receptor can be probed in vivo, so, too, must the physiological implications.

We note that a prior study also suggested that MRGPRX2 is a novel “morphine receptor,” that

mediates some of morphine’s analgesic activity (Akuzawa, 2007). Whereas this report certainly

shares some similarity with our observations, we find, in contrast, that MRGPRX2 is more

80

potently activated by (+)-, rather than (-)-, morphine, suggesting MRGRPX2 is unlikely to confer

analgesia, as (+)-morphine is devoid of such activity (Jacquet et al., 1977; Wu et al., 2007).

MRGPRX2 is a novel Gαq-coupled opioid-like receptor activated by endogenous

prodynorphin-derived peptides and opioid compounds, including FDA-approved drugs and their

metabolites. The discovery of selective and relatively potent MRGPRX2 agonist (R)-ZINC-3573

and its inactive (S)-isomer provides researchers with a chemical probe pair to specifically

modulate this receptor, illuminating its role in pathological reactions such as itch and potentially

revealing a path for therapeutic design.

81

CHAPTER 4: DEVELOPMENT OF PROBES FOR MRGPRX4

4.1 INTRODUCTION AND RATIONALE

The MRGPRX4 receptor is an understudied, primate-exclusive oGPCR expressed in the

dorsal root and trigeminal ganglia (Davenport et al., 2013; Dong et al., 2001; Lembo et al., 2002).

To date, there are no known rodent orthologues of the human MRGPRX4 that would enable

genetic or behavioral studies assessing this receptor’s putative functions. Studies of other MRG

receptors, combined with MRGPRX4’s tissue expression profile, suggest this receptor may be

important for sensation of itch, pain, or other sensory stimuli (Solinski et al., 2014). Thus, small

molecule probes for this receptor might enable new studies that directly test the function of this

receptor in sensory physiology, which may have relevance to human health.

Despite progress in identifying small molecule and/or peptide ligands for other human

MRGPRX receptors (e.g. MRGPRX1, MRGPRX2)(Johnson and Siegel, 2014; Lansu et al., 2017;

Li et al., 2017; Liu et al., 2009), the only known modulator of MRGPRX4 is nateglinide

(Starlix®)(Kroeze et al., 2015). Identified in the Roth lab using the PRESTO-Tango small

molecule screening platform, nateglinide is an FDA-approved anti-diabetic drug and a

micromolar potency agonist for MRGPRX4. Nateglinide was selective for MRGPRX4 among 91

total GPCR targets tested, suggesting MRGPRX4 selectivity among GPCRs (Kroeze et al, 2015).

This compound is not a selective MRGPRX4 agonist, however, as its anti-diabetic activity is

attributed to blockade of potassium channels in pancreatic β-cells (Akiyoshi et al., 1995; Fujitani

82

and Yada, 1994). It remains to be seen whether MRGPRX4 mediates any nateglinide’s clinical

effects.

In order to identify small molecule probes for MRGPRX4 that would be useful for the

scientific community, I first screened 5,695 unique small molecules for activity at MRGPRX4

using the PRESTO-Tango system (Kroeze et al., 2015). After identifying several novel agonists

for this receptor, I then generated chemical analogs and tested them for activity and selectivity.

Using these data, my collaborator Joel Karpiak generated an in silico homology model of the

MRGPRX4 to visualize a putative binding pocket, which I validated with mutagenesis

experiments. Here, I demonstrate several novel non-selective small molecule MRGPRX4

agonists, initial SAR, and propose two micromolar potency agonists as tool compounds that can

be used for future studies of this receptor. Lastly, based on genome-wide association studies

performed by collaborators at the National Institutes of Health, I characterized a coding variant

N245S in MRGPRX4 that is associated with increased preference for menthol cigarettes in

African Americans and identified (-)-menthol as a negative allosteric modulator of MRGPRX4.

4.2 METHODS

Cell Lines

HTLA cells (a HEK-T stable cell line expressing a β-arrestin2-TEV fusion protein and a

tTa-dependent luciferase reporter) was a gift from Dr. Richard Axel and were maintained at low

passage. FLP-IN/T-REX HEK-293-T cells (Invitrogen, Cat# R78007) were obtained from

Invitrogen in order to make the inducible stable cell line. Stable cell lines were generated from

the FLIP-IN cells as per manufacturer’s instructions.

83

Chemical Compounds

Library screening was performed using nine small molecule libraries: NCC-1 (NIH

Clinical Collection), NCC-2 (NIH Clinical Collection), NIMH Library, Tocris, Prestwick,

LOPAC, Selleck Chemical, Spectrum, and the Roth Lab Collection (in-house). For follow-up

studies, nateglinide was purchased separately from Tocris (#4231). Repaglinide (#R9028) and

mitiglinide (#SML0234) were purchased from Sigma Aldrich .

Constructs

The MRGPRX4-Tango codon-optimized plasmid was made as previously described

(Kroeze et al., 2015). The MRGPRX4 gene from this construct (without the modified tail) was

subcloned into the FLP-IN inducible stable cell lines as per Invitrogen’s instructions using the

FLP/IN construct. MRGPRX4-R86M, MRGPRXR-R95M, MRGPRX4-N245S, MRGPRX4-

T43T, and MRGPRX4-T43T+N245S Tango constructs were generated using PrimeStarMAX

(Takara) as per the manufacturer’s instructions. Mutations were confirmed by Sanger sequencing

using primers V2tail forward, CMV forward BGHR, and TEV tail reverse primers. See

Appendix 2 for all plasmid maps and sequences.

PRESTO-Tango Assay Screening

HTLA cells were maintained in DMEM (Corning) supplemented with 10% FBS, 2

μg/mL puromycin and 100 μg/mL hygromycin B in a humidified atmosphere with 5% CO2 at

37°C. Cells were seeded at 50% confluency and transfected with the MRGPRX4-Tango

construct using the calcium phosphate method (Jordan et al., 1996). The following day,

transfected cells were plated in poly-L-lysine-coated, glass-bottomed white 384-well plates

84

(Greiner Bio-one) at a density of 20,000 cells/well. Twenty-four hours later, cells were treated

with 10 μM concentrations of small molecules (in quadruplicate) diluted at 3X final

concentration in drug buffer (1X HBSS containing 20 mM HEPES and 0.3% Bovine Serum

Albumin, pH 7.4) and incubated for 18-24 hours. Following drug treatment, media was removed

and Bright Glo (Promega) was added to each well (diluted 20-fold, 20 μL/well) and incubated

for 15 minutes at room temperature. A TriLux luminescence counter was used to collect

luminescence data, which were analyzed as relative luminescent units (RLU) using GraphPad

Prism. Compounds with two-fold or greater increases in luminescence signal were considered

“actives” and were subsequently assayed using concentration-response curves. More detail on

the PRESTO-Tango assay can be found in (Kroeze et al., 2015).

Intracellular Calcium Mobilization Assay

I created a MRGPRX4-expressing tetracycline-inducible stable cell line containing the

MRGPRX4 codon-optimized receptor sequence with an N-terminal FLAG tag using the FLP-

IN/T-REX Core Kit (Invitrogen). MRGPRX4 stable cells were cultured in DMEM containing 10%

FBS, 100 µg/mL hygromycin B, and 15 µg/mL blasticidin. For the assay, cells were seeded onto

glass bottomed, poly-L-lysine coated, black 384 well plates at a density of 20,000 cells/well in

medium containing 1% dialyzed FBS, 1 µg/mL tetracycline, 100 IU/mL penicillin, and 100

µg/mL streptomycin and incubated 24 hours. After tetracycline induction, media was removed

and cells were loaded for 1 hour with 20 µL/well of 1X FLIPR calcium dye (Molecular Devices)

and 2.5 mM probenic acid in a humidified environment with 5% CO2 at 37°C. Then, baseline

calcium levels were measured for 10 seconds, followed by drug addition of 10 µL of 3X

concentrated drug in drug buffer (1X HBSS with 20 mM HEPES, pH 7.4) in 16 point

85

concentration response curves from 0.003 nM to 10 μM. Fluorescence was measured for 200

seconds and data were analyzed using GraphPad Prism. For double addition Schild competition

experiments, potential antagonists were added 10 minutes before addition of agonist.

Whole Cell ELISA:

To compare expression of the FLAG-tagged MRGPRX4 receptor and the MRGPRX4

mutant N245S, I performed immunohistochemistry on cells plated in 384-well plates as

described in (Sato et al., 2016). Briefly, transfected cells or stably expressing MRGPRX4 cells

were seeded at a density of 10,000 cells/well in poly-lysine-coated 384 well plates. After 16-18

hours, cells were fixed with 20 ul/well of 4% paraformaldehyde solution for 10 minutes at room

temperature. Cells were then washed twice with 40 ul/well of phosphate-buffered saline (PBS),

pH 7.4. Cells were then blocked with 5% normal goat serum in PBS for 30 minutes at room

temperature. After removing blocking solution, 20 ul/well of anti-FLAG–horseradish

peroxidase–conjugated antibody (Sigma-Aldrich, diluted 1/10,000) was added and incubated at

room temperature for 1 hour. Following antibody incubation, cells were washed 2x with 80

ul/well of PBS. Then, 20 ul/well of SuperSignal Enzyme-Linked Immunosorbent Assay Pico

Substrate (Sigma-Aldrich) was added and the resulting luminescence was measured using a

MicroBeta Trilux luminescence counter. Data was analyzed using GraphPad Prism, replicates

were averaged by taking fold change compared to WT receptor expression. Each experiment was

performed with 64 wells per receptor.

Inositol Phosphate Hydrolysis:

MRGPRX4 tetracycline-inducible cells were maintained in DMEM containing 10% FBS,

100 µg/mL hygromycin B, and 15 µg/mL blasticidin. For the assay, cells were seeded into poly-

86

L-lysine coated, glass-bottom 96 well plates at a density of 50,000 cells/well in inositol-free

DMEM (Caisson labs) containing 1 µCi/well of 3H-myo-inositol (Perkin Elmer), 1 µg/mL

tetracycline, and 100 IU/mL penicillin and 100 µg/ml streptomycin and incubated 16-18 hours in

a humidified environment at 37°C with 5% CO2. Following tetracycline induction and labeling,

medium was removed and 200 µl/well of inositol-free DMEM (Caisson labs) was added per well.

Then, 25 µl of 10X concentrated (-)-menthol diluted in drug buffer (1X HBSS with 20 mM

HEPES and 0.3% bovine serum albumin, pH 7.4) was added followed by 25 µl of 10X

concentrated Nateglinide in concentration response curve. Cells were incubated with drug for 1

hour in a humidified environment at 37°C with 5% CO2. At exactly 15 minutes before lysing, 10

µl/well of 26X concentrated LiCl (15 mM final concentration) was added. After drug and LiCl

incubation, 40 µl/well of 50 mM formic acid was added and cells were incubated at 4°C

overnight. The next day, 10 µl of lysate was transferred from each well into 96-well flexi-plates

(Perkin Elmer) and combined with 75 µl/well of 0.2 mg/well YSI RNA Binding Beads (Perkin

Elmer). Lysate-bead mixture was incubated on a shaker at room temperature, protected from

light, for 1 hour. Before reading on a TriLux beta counter, plates were centrifuged at 1000 x g for

2 minutes. CPM data were analyzed with GraphPad Prism. Normalized data (fold change over

basal) were averaged to analyze replicates.

Homology Modeling

These models were generated by Joel Karpiak in the same fashion as those of the

MRGPRX2; for further details please see (Lansu et al., 2017). In brief, the alignment for

generating the MRGPRX4 models was performed in PROMALS3D (Pei et al., 2008) and

homology models were constructed with MODELLER-9v8 (Eswar, 2007) using the crystal

structure of the κ-opioid receptor (PDB code 4DJH) as a template. The initial alignment included

87

the entire MRGPRX and opioid receptor family. The alignment was manually edited to remove

the N- and C- termini, which extended past the template KOR structure, and the engineered T4

lysozyme. He also created alternate alignments of the flexible and non-conserved second

extracellular loop (EL2). Three models were built from each of 180 elastic network models

(ENMs) generated by the 3K-ENM program (Yang and Sharp, 2009) for a total of 540 models

for each iterative cycle of model refinement.

Model evaluation

Models were ranked by Joel Karpiak to prioritize active compounds such as nateglinide

over the rest of the inactive PRESTO-Tango β-arrestin screening compounds. The entire

PRESTO-Tango library was docked to equivalent 4DJH ligand binding site in the modeled

MRGPRX4 receptors for several cycles of iterative binding site refinement. In each round, the

top-ranked models were examined for a binding pose that made hydrophobic and electrostatic

interactions with the receptor, including the key positive-negative charge coordination. Then,

residues within 6 Å of the nateglinide pose were minimized around the docked ligand with PLOP

(Jacobson, 2002). The PRESTO-Tango library was then re-docked into this optimized binding

site for each model. This refinement continued for several cycles until the top-ranked models all

converged to the same nateglinide pose, with the top-scoring model chosen as the final one.

Chemistry General Procedures

N-Cycloheptanecarbonyl-D-phenylalanine (D-KL1)

To a vial containing a solution of D-phenylalanine methyl ester (215.7 mg, 1.0 mmol)

88

and cycloheptane carboxylic acid (177.8 mg, 1.3 mmol) in 5.0 mL of CH2Cl2 was added Et3N

(0.30 mL, 2.3 mmol), followed by propylphosphonic anhydride (0.80 mL, 800 mg). The

resulting reaction mixture was stirred on ice for 2 h and then was washed 2X with a mixture of

water (4.0 mL) and 1 N NaOH (0.50 mL), discarding the aqueous washes each time. The washed

organic layer was then extracted 5X with a mixture of water (4.0 mL) and 1 N HCl (0.50 mL) to

remove any unreacted amino acid. The resulting solution was washed 1X with saturated brine,

dried overnight at 4° C over Na2SO4, then filtered and concentrated by rotary evaporation to

afford 302 mg (99.5%). The resulting oil was hydrolyzed by stirring with MeOH (3.0 mL) and 2

N NaOH (1.2 mL) at 50°C for 5 h. The pH was adjusted to 2-3, and the mixture was

concentrated by rotary evaporation. The resulting solid was crystallized from MeOH-H2O to

give a white solid; 70 mg (23%); mp 109-110°C.

N-Cycloheptanecarbonyl-L-phenylalanine (L-KL1)

The title compound was prepared following the same conditions as for N-

cycloheptanecarbonyl-D-phenylalanine, except using L-phenylalanine methyl ester (215.7 mg,

1.0 mmol). The resulting white solid weighed 80 mg (26%); mp 102-104°C.

(R) and (S)-N-1,2,3,4-tetrahydronapthalene-2-carbonyl-D-phenylalanine

The title compounds were prepared following the method employed for N-

cycloheptanecarbonyl-D-phenylalanine, except using 1,2,3,4-tetrahydro-2-napthoic acid (220.3

mg, 1.3 mmol). In order to separate the diastereoisomers following hydrolysis, the oil was

separated by centrifugal thin layer chromatography (Chromatatron, Harrison Research) using a 2

mm silica gel rotor, eluting first with CH2Cl2, then 10% MeOH in CH2Cl2 for the first product

89

fraction, and 12% MeOH in CH2Cl2 to obtain the second product fraction. The mixtures were

concentrated and crystallized spontaneously to give white crystalline solids (100 mg, 125 mg,

31%, 38%. mp >280°C for each). Because this chromatron rotor had been used previously under

an ammonia atmosphere, it was discovered that the plate had retained sufficient ammonia to

convert the eluting products into their ammonium salts. Two products were produced, KL2a or

2b (also called F2 and F3) to refer to the fraction number from purification on the chromatatron.

We assumed these to be distinct isomers based on their molecular weight and differential

fractionation.

4-phenylcyclohexane-1-carbonyl-D-phenylalanine (D-KL3)

Following the same general procedure as before D-phenylalanine methyl ester (107.9 mg,

0.5 mmol) and 4-phenylcyclohexane carboxylic acid (100 mg, 0.50 mmol) were converted into

the title compound to produce a white solid (100 mg, 57%); mp 163-165°C.

(4-Hydroxycyclohexane-1-carbonyl)-D-phenylalanine (D-KL5)

The title compound was prepared following the same synthetic method as

cycloheptanecarbonyl-D-phenylalanine, except using 4-hydroxycyclohexane acid (180.0 mg, 1.3

mmol). (42 mg, 15%)

Genome-wide association study

Dr. Denis Drayna and colleagues at the National Institutes of Health performed the

genome-wide association study. Briefly, whole exome sequencing was performed on a subset

(N=389) of 561 self-identified smokers from the Dallas Heart Study (DHS) and the Dallas

90

Biobank (Victor et al., 2004) and aligned to a human reference genome (GRCh37) to identify

variants using the Genome Analysis Toolkit (McKenna et al., 2010). Dr. Drayna and colleagues

genotyped Illumina Infinium HumanExome BeadChip v12.1 data from the DHS and Dallas

Biobank to call genetic markers and variants. To determine if variants identified in the Dallas

cohort were present in other populations, Sanger sequencing was performed on DNA samples

from the saliva of 718 smokers in a second cohort (the Shroeder population; enrolled in the DC

Tobacco Quitline (DCQL) (Kirchner et al., 2013)). To determine exome-wide associations,

ancestry of the Dallas cohorts was inferred using principle component analysis (Price et al.,

2006). Exome-wide association analysis was performed with the PLINK v1.90p program (Chang

et al., 2015) and tested for significance using a likelihood-ratio test (Xing et al., 2012) in R

(www.r-project.org) with a plug-in function for logistic regression. The resulting analysis was

adjusted for gender, age, and six top principle components of ancestry. All variants with a

significance of P < 1x10-4

(determined in PLINK) in the Dallas cohort were tested in the

Schroeder cohort.

4.3 RESULTS

4.3.1 MRGPRX4 screening reveals novel activators

To identify novel chemical matter for MRGPRX4, I initially screened 5,695 unique

compounds at 10 µM concentration for agonist activity against three members of the MRGPRX

family (MRGPRX1, 2, and 4) using the PRESTO-Tango β-arrestin screening platform as

described previously (Kroeze et al., 2015; Lansu and Karpiak et al., 2017). Focusing on

compounds with greater than 2-fold activation at MRGPRX4 that did not activate either

MRGPRX1 or MRGPRX2 (n=35), I found three primary compounds of interest including the

91

FDA-approved anti-diabetic compound nateglinide (as reported previously in Kroeze et al.,

2015). Additionally, the angiotensin 2 receptor (AT2R) antagonist PD123319 and the

neurotensin1/2 receptor (NTSR1, NTSR2) antagonist SR-142948 activated MRGPRX4 by 6-fold

and 9-fold over basal levels, respectively (Fig 4.1a). The chemical structures of these compounds

varied substantially in size and chemotype, with carboxylic acid substituents as the most obvious

shared substituent among these three compounds (Fig 4.1b).

I confirmed the activity of the three compounds using 12-point concentration response

curves in the PRESTO-Tango assay (Fig 4.1c). This revealed apparent potency (EC50) values of

8.8 µM for nateglinide, 33.4 µM for PD123319, and 1.7 µM for SR-142948 (Fig 4.1c, Table 4.1).

Figure 4.1: Small molecule screening reveals novel MRGPRX4 activators. A) Scatter plot showing the

activity of ~8,000 compounds (5900 unique) at MRGPRX4 in the PRESTO-Tango assay. Arrows indicate

locations of agonists nateglinide (red, appears in multiple libraries), PD123319 (blue, appears in multiple

libraries) or SR-142948 (Black arrow). The Y axis is log2 of the fold change. B) Structures for compounds

identified with arrows in A. C) 12-point concentration-response curves for nateglinide, PD, and SR in

PRESTO-Tango arrestin assay and D). FLIPR intracellular calcium release assay. Error bars for C and D are in

s.e.m.

92

The PRESTO-Tango concentration-response curves also revealed that PD123319 and SR-

142948 have partial agonist activity in comparison to the apparent full agonist nateglinide (Fig

4.1c). MRGPRX4 was previously reported to couple to Gαq (Kroeze et al., 2015). Thus, to

validate the activity of the discovered compounds in an orthogonal assay, I tested each small

molecule in 12-point concentration-response curves in the FLIPR intracellular calcium assay

(Fig 4.1d) using a tetracycline-inducible MRGPRX4 HEK-T cell line (for details, see methods).

The EC50 values for these compounds in the FLIPR assay were comparable to those determined

by PRESTO-Tango: 10.3 µM for nateglinide, 33.2 µM for PD123319, and 3.7 µM for SR-

142948 (Fig 4.1d, Table 4.1).

Figure 4.2: Activation of MRGPRX4 by glinide drugs. A, B) 12-point concentration response

curves for the glinide drugs nateglinide, mitiglinide, and repaglinide in PRESTO-Tango or FLIPR,

respectively. Data shown are the average of multiple experiments (n = 3 for nateglinide and

mitiglinide in Tango and FLIPR; n = 2 for repaglinide in Tango; n = 1 for repaglinide in FLIPR).

Error bars are in s.e.m.

93

4.3.2 Glinides activate MRGPRX4

Nateglinide is an FDA-approved anti-diabetic drug. Several other glinide compounds

with similar chemical structure are also FDA-approved. To determine whether MRGPRX4 could

be activated by other, structurally and clinically related glinides, I tested the activity of

repaglinide (Prandin ®) and mitiglinide (Glufast ®) in PRESTO-Tango and FLIPR intracellular

calcium assays and found that both compounds readily activated MRGPRX4 in both assays.

Repaglinide had reduced efficacy, but similar potency, when compared with nateglinide in

PRESTO-Tango (Fig 4.2a, Table 4.1). In the intracellular calcium assay, nateglinide and

mitiglinide had comparable efficacy and potency, whereas repaglinide had elevated efficacy in

this assay (Fig 4.2.b, Table 4.1). In total, these assays support this chemotype as activating at

MRGPRX4 and demonstrate that further major modifications are well-tolerated and retain

activity.

4.3.3 In silico model of MRGPRX4 predicts binding pocket residues

Using a similar approach as I employed for MRGPRX2 (Lansu et al., 2017), Joel Karpiak

(UCSF) generated an in silico homology model of MRGPRX4 using the Modeller program

(Eswar, 2007) based this receptor’s structure on the structure of the human KOR receptor in

complex with the antagonist JDTic (Wu et al., 2012). He chose KOR based on a search for

receptors with known structure that had the highest homology with MRGPRX4 (in this case,

26%). The initial model was expanded using 300+ further structures using the elastic network

modeling program ENM. Each of these models were further expanded with Modeller to generate

over 1,000 MRGPRX4 models. Using the PLOP program (Jacobson et al., 2002), he then

iteratively docked the actives from the 5,695 small molecule screen against each of the 1,000+

models, using the inactive screening compounds as decoys, to predict an orthosteric binding site.

94

He looked for models that best enriched nateglinide over the decoy molecules. The resulting

model is shown in Figure 4.3 and shows two arginines R86 and R95 that appear to anchor the

carboxylic acid of nateglinide.

4.3.4 R86 and R95 residues mediate agonist activity

To assess potential binding pocket residues responsible for nateglinide activity in the

MRGPRX4 binding pocket, I mutated the R86 and R95 that were predicted to interact with the

carboxylic substituents of nateglinide. I generated R86M and R95M mutations and tested them

against nateglinide, SR-142948, PD123319, and repaglinide in the PRESTO-Tango assay (Fig

4.4) and found that the R86M mutation substantially reduced nateglinide’s efficacy from 85.1

fold activation at the wild type (WT) to 18.1 fold (Fig 4.4a). In contrast, the R95M mutation had

almost no effect on nateglinide’s efficacy, but slightly reduced the potency of nateglinide (Fig

Figure 4.3: In silico model of MRGPRX4. (Left) Flow chart showing the generation of the in silico model of

MRGPRX4, based on the KOR inactive JDTic structure (Wu et al., 2012). (Right) Close-up of the predicted

binding MRGPRX4 pocket shown docked with nateglinide (orange). R86, R95, and various TMs and

extracellular loops (ELs) are labeled.

95

4.4a).

Surprisingly, the much larger MRGPRX4 agonist SR-142948 also showed a decrease in

efficacy from 34.3-fold at the WT to only 9.9-fold activation at the R86M mutation (Fig 4.4b).

Further, both the R86M and R95M mutations reduced SR-142948’s potency to 5.0 µM and 31.4

µM, respectively, when compared to the WT EC50 value of 1.8 µM (Fig 4.4b). This finding

suggests that perhaps the carboxylic acid contained in the structures of nateglinide and SR-

142948 may be interacting with similar residues despite their difference in size.

Interestingly, the R86M mutation improved the potency of PD123319 from an EC50 of 38

Figure 4.4: Mutagenesis experiments with MRGPRX4 in PRESTO-Tango. A-D) 12 point

concentration-response curves in PRESTO-Tango for MRGPRX4 WT, R86M, or R95M, separated by

ligand (nateglinide, SR, PD, and repaglinide, respectively). Data are normalized to fold over basal signal.

Average of n = 2 in triplicate, error bars are in s.e.m. Structures of compounds are shown above each graph.

96

µM at the wild type to 515 nM, albeit with substantially reduced fold activation (Fig 4.4c). In

contrast, the R95M mutation did not affect PD123319’s potency, but increased the Emax only at

30 µM (Fig 4.4c). These differences seem to suggest PD123319 may engage different residues

than nateglinide or SR-142948, as the mutated arginines in these experiments had little effect on

PD123319’s activity at MRGPRX4.

Repaglinide, another glinide compound with antidiabetic activity in humans, had notable

changes in activity at the MRGPRX4 R86M and R95M mutations. Repaglinide is a partial

agonist at the WT receptor (EC50 = 5.1 µM; Emax = 31.6 fold) and like nateglinide and SR-

142948, the R86M mutation drastically reduces efficacy of this compound (Fig 4.4d).

Surprisingly, the R95M mutation improves repaglinide’s efficacy up to 170-fold activation of

MRGPRX4, with an EC50 improved to 1.7 µM (Fig 4.4d). Thus, it appears that R86 is important

for activation of MRGPRX4 by nateglinide, SR-142948, and repaglinide, with R95 being

important for the interaction of some, but not all, agonists tested here.

4.3.5 (D)-phenylalanine nateglinide analogs are active at MRGPRX4

Nateglinide is a D-phenylalanine derivative with high bioavailability and good

pharmacokinetic parameters (McLeod, 2004; Shinkai et al., 1988). Due to these optimal drug-

like properties and nateglinide’s MRGPRX4 activity, I sought to generate analogs that would

retain MRGPRX4 agonism while losing potassium channel antagonism. I based the initial

analogs on the nateglinide medicinal chemistry literature (Shinkai et al., 1989), focusing on those

nateglinide analogs that lacked hypoglycemic activity. The first non-hypoglycemic compound

that I synthesized was cycloheptanecarbonyl-D-phenylalanine, or (D)-KL1, which was active at

MRGPRX4 in PRESTO-Tango and the FLIPR intracellular calcium assay with EC50 values of

97

17.2 µM and 8.5 µM, respectively (Fig 4.5a,b, Table 4.1). To assess the importance of the

stereochemistry of the phenylalanine component of the analogs, I synthesized

cycloheptanecarbonyl-L-phenylalanine ((L)-KL1) and tested it for activity at MRGPRX4. (L)-

KL1 was inactive in the PRESTO-Tango and FLIPR intracellular calcium assays (Fig 4.5a,b,

Table 4.1). A second L-isomer analog with a different R group, (R/S)-1,2,3,4-

tetrahydronapthalene-2-carbonyl-L-phenylalanine ((L)-KL2) was also inactive at MRGPRX4 in

PRESTO-Tango and intracellular calcium assays (Fig 4.5c,d, Table 4.1). These data suggest that

the D isomer of phenylalanine is important for the activity of these nateglinide analogs.

To explore the chemical space and find molecules with improved MRGPRX4 potency, I

generated a series of analogs with a D-phenylalanine base and varying R groups (Fig 4.5c-e,

Table 4.1: Glinide and nateglinide analog functional data at MRGPRX4. Table showing compound names

with pEC50 values for Tango and FLIPR (each are average of n = 3 in triplicate wells except repaglinide and

mitiglinide n=2 in Tango, and for repaglinide n=1 in FLIPR). NA = Not applicable (due to inactivity). Emax

values are provided as a percentage of nateglinide activity. Note that KL2a or 2b are also called F2 or F3 to

refer to the fraction number from purification on the chromatatron (see methods). We assumed these to be

distinct isomers based on their molecular weight and differential fractionation.

Analog Name TANGO pEC50TANGO Emax (%

Nateglinide)

FLIPR

pEC50

FLIPR Emax (%

Nateglinide)

Nateglinide -5.23 99.26 -5.33 99.31

Mitiglinide -4.53 129.5 -4.96 92.59

Repaglinide -5.32 45.7 -4.95 143.9

(D)-KL1 -4.765 95.69 -5.07 94.14

(L)-KL1 NA 5.77 NA 9.2

(D)-KL2a [or F2] -4.465 120.7 -4.922 102.4

(D)-KL2b [or F3] -4.467 110.3 -4.939 101

(D)-KL3 -4.5 46.5 -4.542 114

(D)-KL5 -3.873 50.84 -4.157 144

(L)-KL2 NA 4.121 NA 3.236

98

Figure 4.5: (D)-phenylalanine nateglinide analogs are agonists of MRGPRX4. A-D) 12 point

concentration-response curves in PRESTO-Tango for nateglinide analogs, with structures shown in

E). Data are normalized to % of nateglinide. Average of n=3 in triplicate, error bars are in s.e.m.

99

Table 4.1). (R) and (S)-1,2,3,4-tetrahydronapthalene-2-carbonyl-D-phenylalanine ((D)- Table

4.1). (R) and (S)-1,2,3,4-tetrahydronapthalene-2-carbonyl-D-phenylalanine ((D)-KL2-F2 and

(D)-KL2-F3) were both MRGPRX4 agonists in PRESTO-Tango and FLIPR assays with similar

potencies for the (R)- and (S)-tetrahydronaphthoic moiety (Tango EC50 = 34.2 µM; FLIPR EC50

= 12.0 µM) (Fig 4.5c,d, Table 4.1). This finding suggests that the stereochemistry of the

tetrahydronaphthalene was not vital for activity of this compound (Fig 4.5c,d). Adding larger or

more polar substituents to the R position, as with 4-phenylcyclohexane-1-carbonyl-D-

phenylalanine ((D)-KL3) and (4-hydroxycyclohexane-1-carbonyl)-D-phenylalanine ((D)-KL5)

reduced potency and efficacy at MRGPRX4 in PRESTO-Tango (Fig 5.5c). Similarly, (D)-KL3

and (D)-KL5 had reduced potency compared to nateglinide in FLIPR, but retained full agonism

(Fig 4.5d, Table 4.1). In a similar trend as nateglinide (Fig 4.4a), all of the generated nateglinide

analog agonists displayed reduced affinity and/or potency when tested against the R86M and

R95M mutations (data not shown).

4.3.6 (L)-phenylalanine nateglinide analogs do not antagonize MRGPRX4

To test whether the (L)-phenylalanine nateglinide analogs (L)-KL1 and (L)-KL2 could

function as MRGPRX4 antagonists, I performed a Schild competition experiment in PRESTO-

Tango (Fig 4.6a, b) and FLIPR (Fig 4.6c, d). Using the test agonist nateglinide, neither (L)-KL1

or (L)-KL2 competitively or non-competitively reduced agonist activity (Fig 4.6a-d). These data

suggest (L)-KL1 and (L)-KL2 are not antagonists and do not interact with the receptor up to

concentrations of 100 µM.

100

4.3.7 An MRGPRX4 variant confers menthol cigarette preference in African Americans

Menthol is an additive in cigarettes that increases addiction liability and makes quitting

more difficult (Anderson, 2011; Ferris Wayne and Connolly, 2004). Furthermore, there is an

unequal distribution of menthol smoking among demographics, and elevated preference for

smoking mentholated cigarettes in young adults and African Americans (Caraballo and Asman,

2011; Giovino et al., 2004; Substance Abuse and Mental Health Services Administration and

Figure 4.6: (L)-phenylalanine analogs do not antagonize MRGPRX4. A-D) 12 point concentration-response

curves of nateglinide in PRESTO-Tango (A,B) or FLIPR intracellular calcium (C,D) against MRGPRX4 WT in

the presence of increasing concentrations of either (L)-KL1 or (L)-KL2. Data shown as either relative

luminescent units (RLU) or fold change calcium over basal signal. n = 1 in quadruplicate, error bars are in

s.e.m.

101

Figure 4.7: MRGPRX4 rs7102322 is associated with menthol smoking in African American

Participants. A) Table showing the minor allele frequency (MAF) and association with mentholated

smoking in the four groups genotyped. Data in this table gathered and generated by Dr. Drayna and

colleagues at NIH. B) Image generated from the MRGPRX4 homology model (generated by Joel Karpiak)

depicting the predicted location of N245S residue in the receptor, including labels of receptor TMs and ELs.

Office of Applied Studies, 2009; Tobacco Products Scientific Advisory Committee, 2011). To

determine whether there is a genetic basis for this disparity between demographic groups,

collaborators at the NIH performed a genome-wide association study in two cohorts of smokers

(for further details, see Methods). One variant, rs7102322 in the gene MRGPRX4 increased the

odds of smoking mentholated cigarettes by 5-8 fold among smokers and met the criteria for

genome wide signficance (P<=1.6x10-3

). The rs7102332 variant results in a coding change

N245S and was observed exclusively in African American participants with a frequency of 5-8%

in the two cohorts (for cohort details, see methods) (Fig 4.7a). This rs7102332 SNP was in

complete linkage disequilibrium with a nonsynonymous codon variant rs61733596 (T43T) in

102

MRGPRX4.

To determine where this variant occurs in MRGPRX4, I turned to the homology model

generated by our collaborators and found N245 is predicted to be in extracellular loop 3 (EL3)

(Fig 4.7b). This asparagine was not predicted to be a glycosylation site for MRGPRX4 by the

NetNGluc1.0 prediction software (http://www.cbs.dtu.dk/services/NetNGlyc/). Thus, we

hypothesized that this variant could have effects on signaling.

4.3.8 MRGPRX4 N245S mutation attenuates agonist activity

To determine the functional and signaling effects of the N245S variant (in concert with

the T3T nonsynonymous variant), I generated N245S+T3T mutations in the FLAG-tagged

MRGPRX4-Tango construct and made tetracycline-inducible stable cell lines expressing a

FLAG-tagged N245S+T43T variant. I first tested whether WT and N245S+T43T variants had

differential membrane expression for the Tango constructs and stable cell line and found no

substantial difference in receptor expression as measured by whole cell ELISA (Fig 4.8a,b).

Then, to determine if there were differences in signaling capacity or ligand activation

between WT and the N245S+T43T variant, I tested each receptor for agonism with the

previously published agonist nateglinide (Kroeze et al., 2015) in the PRESTO-Tango β-arrestin

recruitment assay. I found that nateglinide had equal potency at the WT and N245S+T43T

receptors, but there was a 66% reduction in maximal β-arrestin recruitment at the N245S+T43T

in comparison to WT (p<0.001, Fig 4.8c, Table 4.2). Then, I tested the effect of this mutant on

nateglinide-induced PI hydrolysis, a measurement of Gαq signaling. Similar to the results from

the Tango assay, I found little difference in potency between variant and WT but found maximal

PI hydrolysis was reduced by 44% in cells expressing the N245S+T43T variant compared to WT

103

Figure 4.8: MRGPRX4 N245S variant has dampened signaling when compared to WT. A) Receptor

expression as calculated by whole cell ELISA in HTLA cells transfected with N245S+T43T or WT receptor

with mock transfected shown as negative control (n=2, 64 wells per experiment; y axis is fold expression

normalized to WT). B) Receptor expression as calculated by whole cell ELISA in tetracycline inducible WT or

N245S+T43T cells with non-tetracycline-induced cells shown as negative control (n=2, 64 wells per

experiment; y axis depicts fold expression normalized to WT). C) Average concentration response curves for

nateglinide in PRESTO-Tango arrestin assay with WT and N245S+T43T receptors, (n=4, in quadruplicate; y

axis is in fold response over basal 588 signaling). D) Average concentration response curves for nateglinide in

PI hydrolysis assay with WT and N245S+T43T tet-inducible cell lines, (n=3, in duplicate; y axis is in fold

response over basal signaling). * indicates P

(P<0.001, Fig. 4.8d, Table 4.2).

4.3.9 (-)-Menthol suppresses MRGPRX4 agonism

To determine if (-)-menthol, the additive in cigarettes, could modulate MRGPRX4 WT or

the N245S+T43T variant, I tested (-)-menthol as an agonist in concentration response curves up

to 1 mM in the PRESTO-Tango assay but found no activation of WT or variant (Figure 4.10a,b).

Then, I tested whether (-)-menthol could modulate MRGPRX4 WT or N245S + T43T receptors

104

by incubating concentration response curves of agonist nateglinide in the presence of increasing

concentrations of (-)-menthol in PRESTO-Tango and PI hydrolysis assays (Figure 4.9). I found

that 100 and 300 µM (-)-menthol significantly reduced the Emax of nateglinide on the WT

Figure 4.9: (-)-Menthol reduces MRGPRX4 WT and N245S+T43T arrestin signaling. A,B) Average

concentration response curves for nateglinide in MRGPRX4-WT-Tango (A) or MRGPRX4-N245S+T43T-Tango

(B) following 100 µM or 300 µM (-)-menthol addition, (n=3, in triplicate, y axis is % nateglinide). C,D) Average

concentration response curves for nateglinide-induced PI hydrolysis in MRGPRX4-WT (C) or MRGPRX4-

N245S+T43T (D) tetracycline inducible cells following 100 µM or 300 µM (-)-menthol addition, (n=3, in

duplicate, y axis is % nateglinide). E) Plot showing the effect of agonist treatments (x 600 axis) nateglinide (gray

circles), nateglinide + 100 µM (-)-menthol (gray squares) and nateglinide + 300 µM (-)-menthol (gray triangles)

between WT and N245S+T43T variant shown as ΔΔLog(Emax/EC50) values (y axis). Y vales > 0 indicate

increased effect for agonist at WT and values < 0 indicate increased effect for agonist at N+T variant. Bars depict

95% confidence intervals. F) Average concentration response curves for fold change activation with nateglinide

in MRGPRX4-WT-Tango or MRGPRX4- N245S+T43T-Tango following 300 µM or 300 µM (-)-menthol

addition, (n=3, in quadruplicate). For all: NS = not significant, * p

105

(P<0.001) and N245S+T43T (P<0.001) in the PRESTO-Tango assay (Fig. 4.9a,b) but not in the

PI hydrolysis assay (Fig. 4.9c,d).

To determine if (-)-menthol’s could modulate the variant and wild type receptors

differently, I calculated ΔΔlog(Emax/EC50) (Kenakin et al., 2012) using nateglinide as the

reference agonist +/- 100 or 300 µM (-)-menthol and found that (-)-menthol there was no

significant difference between menthol’s negative modulatory effect on WT versus

N245S+T43T variant (Fig 4.9e). When I compared the fold change activation of β-arrestin

recruitment, this revealed that 300 µM (-)-menthol significantly reduces nateglinide-induced fold

Figure 4.10: Positive Controls for MRGPRX4 Menthol Experiments. A,B) Average concentration response

curves for Nateglinide or (-)-menthol in agonist mode for MRGPRX4-WT-Tango or MRGPRX4- N245S+T43T-

Tango (n=2, in triplicate, y axis is % nateglinide). C) Average concentration response curves for D2 receptor

agonist quinpirole in D2-Tango 100 µM or 300 µM (-)-menthol addition, (n=3, in triplicate, y axis is %

Quinpirole). D) Average concentration response curves for Nateglinide-induced PI hydrolysis in MRGPRX4-

WT tetracycline inducible cells without tetracycline addition (ie, no receptor expression) following 100 µM or

300 µM (-)-menthol addition, (n=3, in triplicate, y axis is relative luminescent counts (RLU).

106

activation when compared to WT (P<0.001, Fig 4.9f), similar to the differences in fold change

for non-menthol conditions (Fig 4.8c). The unrelated D2 dopamine receptor was not modulated

by (-)-menthol in the PRESTO-Tango assay (Fig. 4.10c). Similarly, (-)-menthol and nateglinide

had no effect on PI hydrolysis in cells where tetracycline was not added (i.e., with no

MRGPRX4 receptor expression).

4.4 CONCLUSIONS

This work identifies three major findings for the oGPCR MRGPRX4. First, I discovered

several low micromolar potency small molecule agonists for this receptor, including the

NTSR1/2 antagonist SR-142948, the AT2R antagonist PD123319, and two FDA-approved

glinide drugs mitiglinide and repaglinide. Furthermore, I generated seven nateglinide analogs to

ascertain basic SAR of this scaffold and found that two of these compounds ((D)-KL1 and (L)-

KL1) may be suitable as MRGPRX4 probes (pending assays for selectivity). Using the chemical

matter identified here, I demonstrated that residues R86 and R95 are important for MRGPRX4

agonist activity. Lastly, I identified (-)-menthol as an allosteric modulator of MRGPRX4 at WT

and a coding variant associated with menthol cigarette preference. This work uncovers novel

chemical matter for this understudied receptor that may suggest putative MRGPRX4 function

and lays groundwork for the generation of improved MRGPRX4-selective compounds.

Table 4.2: Statistics for WT versus variant values in functional assays. Curve fits were compared between

constructs for Figure 2C and 2D to determine significant differences in fold change and potency. P values were

calculated via F-test comparing statistical difference between either EC50 or Emax values between WT and

variant. Yellow highlights significant P values.

107

MRGPRX4 is agonized by three FDA-approved glinide drugs (nateglinide, repaglinide,

and mitiglinide) and this finding could provide insight to this receptor’s putative functions in

vivo. Glinides canonically antagonize potassium channels located in pancreatic β cells (McLeod,

2004). Type 2 diabetic patients take glinides with every meal to induce insulin secretion and

proper glucose regulation (Rosenstock et al., 2004). Pharmacokinetic data for oral dosing of

nateglinide indicate maximum plasma concentrations reach between 3 and 20 micromolar, well-

within the appropriate range of activating MRGPRX4 in humans (McLeod, 2004).

MRGPRX4 is expressed in sensory neurons of the DRG and TG (Dong et al., 2001;

Lembo et al., 2002), which jointly innervate the skin and peripheral tissues, the forehead, eyes,

cheeks, teeth and jaws (Haas et al., 2011; Zheng et al., 2014). Additionally, one study suggests

MRG receptors are also expressed in vagal sensory afferents, some of which innervate the lungs

(Lee et al., 2008). Vagal neuron expression would be especially interesting in light of the

associated MRGPRX4 N245S variants associated with smoking African Americans. Further,

MRGPRX4’s expression and the high in vivo concentration of nateglinide suggest MRGPRX4

could mediate some of the off-target effects of this class of drugs, which include upper

respiratory infection, sinusitis, constipation, itch/rash, and headache (Rosenstock et al., 2004;

Twaites et al., 2007). As we have not determined these effects experimentally, further studies

will need to be performed to test these hypotheses directly.

The in silico homology model and mutagenesis experiments suggest R86 and R95

mediate the agonist activity of glinides and other D-phenylalanine derivatives at MRGPRX4.

Interestingly, these mutations in EL1 residues suggest that like KOR, MRGPRX4 may utilize an

extended TM2/EL1 binding pocket (Wu et al., 2012). R86M and R95M reduced efficacy and/or

potency of these molecules at MRGPRX4 despite variations in the R group (as shown in Fig 5.5e)

108

or substantial structural differences between the glinides, SR-142948, and PD123319 (Fig 5.3).

Our data suggest these two arginines, especially R86, are likely essential for the interaction of

the shared carboxylic acid component of these structures with the receptor. Future studies to

develop novel ligands for MRGPRX4 may want to consider engaging these residues.

The SAR determined here for MRGPRX4 demonstrate a limitation on the size of the R

group attached to the D-phenylalanine and a preference for dextrorotary amino acid derivatives.

These observations are similar to the agonist preferences of the related receptor MRGPRX2,

which prefers dextrorotary opioid molecules to the levorotary isomers (Lansu et al., 2017). This

suggests there may be some shared structural features in the binding pockets of this family of

receptors, however, we also note that the determined chemical matter for MRGPRX4 and

MRGPRX2 (see (Lansu et al., 2017) or Chapter 2) are distinct and non-overlapping. This is in

line with previous reports that suggest this family of receptors experienced positive selection and

divergence within the binding pocket residues (Choi and Lahn, 2003).

Two of the generated MRGPRX4 agonists, (D)-KL1 and (L)-KL1, will likely have

minimal potassium channel antagonism based on the medicinal chemistry studies performed by

Shinkai and colleagues (Shinkai et al., 1989) although future studies will need to be performed to

test this directly. The active ((D)-KL1) and inactive ((L)-KL1 molecules provide initial tool

compounds to assess MRGPRX4 activity until better analogs of these compounds can be

synthesized.

I demonstrate here that (-)-menthol negatively modulates MRGPRX4 at 100 and 300 µM

concentration. Although this concentration is high, it has been shown that cigarettes can contain

up to 19 mg (-)-menthol per cigarette, depending on the brand (Ai et al., 2016). It remains to be

demonstrated what the actual concentrations of menthol in the face and lungs are for chronic and

109

acute mentholated smoking, although the high menthol concentration in cigarettes may poise the

saliva to reach high µM concentrations capable of interacting with MRGPRX4.

This work expanded the known chemical matter for the oGPCR MRGPRX4 from 1 to 9

agonists and two inactive control compounds. The identified agonists have varying efficacies in

β-arrestin and Gαq, including the partial agonists SR-142948 and PD123319, and at least three

full agonists nateglinide, mitiglinide, and (D)-KL1. Furthermore, I defined putative SAR for the

D-phenylalanine derivatives and identified two important binding pocket residues for their

activity. Despite their micromolar potency, these compounds will be valuable tools for assessing

MRGPRX4 function in vitro and in vivo. The molecules, homology model, and mutagenesis

experiments generated in this study also provide a roadmap for future MRGPRX4 probe or drug

development. Lastly, while the physiological role of MRGPRX4 remains to be determined

experimentally, the glinide agonists identified here and their adverse effects may shed light on

some potential functions of this receptor.

110

CHAPTER 5: CONCLUSIONS AND FUTURE DIRECTIONS

“Fortunate is he who is able to know the causes of things.” -- Virgil

5.1 CONCLUSIONS

Our world is encoded by chemical information. GPCRs are primary translators of

chemical stimuli into biological instructions for cellular and physiological phenomena. Thus,

much of how we perceive and experience the world is mediated via GPCRs, including sight,

smell, touch, pain, and various motivational or euphoric states. Despite the great complexity of

these systems, humans have learned to modulate GPCR action and our experience of the world

for thousands of years with drugs. As we continue to learn about the molecular and physiological

details of GPCR action, we will undoubtedly improve our understanding of these systems and

our ability to modulate them.

The research contained in this dissertation focuses on the primate-exclusive MRGPRX

family of orphan GPCRs expressed in sensory neurons and immune cells, two sensitive and

diverse systems fine-tuned for detection and protection of the organism. In total, I generated

novel chemical probes for the pharmacologically dark receptors MRGPRX2 and MRGPRX4

using the PRESTO-Tango β-arrestin screening platform in combination with in silico homology

models and genome-wide association studies. Then I demonstrated that endogenous opioid

peptides and exogenous opiates activate MRGPRX2, and that this receptor mediates

degranulation when agonized by the selective probes and opioids in a human mast cell line.

111

Lastly, I identified (-)-menthol as a negative allosteric modulator of MRGPRX4 and

characterized a functional variant associated with menthol cigarette preference in African

Americans. As mentioned during the introduction, the generation of selective oGPCR ligands

catalyzes the discovery of receptor function. The experiments contained in Chapters 2-5 and the

chemical probes I discovered provide some insight into putative functional roles of the

MRGPRX receptors in vivo and will be discussed below.

MRGPRX receptors are likely important for transducing sensory information and have

several similarities to other important genes in the sensory system including the olfactory and

vomeronasal receptors. For example, all of these genes occur in gene family clusters on similar

chromosomes, and appear to have undergone species-specific expansion, duplication, and

positive selection events (Choi and Lahn, 2003; Zylka et al., 2003). The evolution of sensory-

expressed receptors to meet species-specific needs has the advantage of optimizing sensory

capabilities for specific environments to provide survival or fitness benefits (Liman, 2012). But

what potential advantages could MRGPRX receptors provide primates? While this question

cannot be answered today, one can hypothesize potential roles for these receptors in primates

based on the expression pattern and pharmacological characteristics of the MRGPRX receptors.

5.2 FUNCTIONAL IMPLICATIONS FROM MRGPRX2 STUDIES

MRGPRX receptor expression in small diameter, nociceptive neurons of the DRG and

TG innervate cutaneous layers of the skin (Bader et al., 2014). Cutaneous sensory afferents are

vital detectors of potentially injurious chemical, thermal, and mechanical stimuli (Smith and

Lewin, 2009). As I showed in Chapter 3, MRGPRX2 is activated by many plant-derived

alkaloids such as sinomenine, morphine, codeine, and berberine (data not shown); suggesting

112

MRGPRX receptors may detect many natural product-based scaffolds. If true, these receptors

may function to detect the presence of various plants in primate environments. Depending on the

neuronal subpopulation of MRGPRX expression, this could either alert the body to

helpful/pleasurable plant alkaloids, as has been shown with MrgB receptor stimulation in rodents

(Vrontou et al., 2013), or unpleasant itch sensations similar to the MrgA receptors (Liu et al.,

2009).

MRGPRX2 receptors could alternately be involved in detecting microbes present on the

skin. Almost all MRGPRX2 agonists contained amines or basic amino acids, which are known to

be pro-inflammatory and indicative of bacterial or viral infection products (Hassan et al., 2012).

Similarly, it is possible that the evolution of primate skin led to changes in the cutaneous

microbiome (Council et al., 2016) that necessitated positive selection of the MRGs to detect

alternate types of bacteria or pathogens. In this way, MRGPRX would be fine-tuned to prevent

inflammation and disease while maintaining healthy bacterial balance.

There are at least four proposed endogenous peptides for MRGPRX2, including

Cortistatin-14(Robas et al., 2003), PAMP(9-20)(Kamohara et al., 2005), Substance P(Tatemoto

et al., 2006), and Dynorphin A (1-13)(Lansu et al., 2017). Substance P, Dynorphin A, and

MRGPRX2 are all expressed in various sensory afferent subtypes, along with canonical

Substance P receptors NK1/2, and the canonical Dynorphin receptor KOR (Ryall, 1989). Thus,

MRGPRX2 could function as a simple feedback trigger on the sensory neurons where it is

expressed. For example, as dynorphin levels rise, which can occur in pathologic pain and itch

states (Podvin et al., 2016), peptide selectivity for KOR would shift to include MRGPRX2. The

resulting shift from inhibitory Gαi to excitatory Gαq signaling would providing important

feedback onto this system and could either increase or attenuate neuronal signaling depending on

113

the synaptic location of MRGPRX2. Future studies that demonstrate MRGPRX2 synaptic

expression and co-occurance with other sensory receptor subtypes would be useful to determine

how MRGPRX receptors function in these neuronal circuits.

The complexity of peptide and receptor expression in the sensory afferents makes

MRGPRX2’s peptide promiscuity especially interesting, suggesting that this receptor could

integrate multiple signals by functioning as a coincidence detector. Coincidence detection in

neuroscience is the integration of multiple input events occurring temporally close together but

in different spatial locations (Bi and Poo, 2001). In this way, the same signals (e.g. peptides and

small molecules) can encode a much larger set of functional outcomes depending on the

temporal order and/or spatial location in which they occur (Spitzer, 2004).

The sensory system is a prime example of the utilization of coincidence detection

because it is constructed to encode multiple senses in a variety of ways, allowing for a large

number of complex outputs stemming from a limited stimulus set. In contrast to gustatory

detection, where tastants are perceived in a switch-like manner leading to either aversion or

attraction (Julius, 2013), pain, temperature, and itch encode graded responses by activating

multiple distinct proteins (TRP channels, GPCRs, etc) on distinct neuronal subtypes to produce a

variety of intensities and qualities of the experience. The number of sensory neurons that are

known to encode purely itch is minimal (Baraniuk, 2012) and the fact that MRG receptors are

expressed in both overlapping and non-overlapping sensory neuron subsets is exemplary of this

type of coding. When considering how the MRGPRX receptors fit into this picture, it is

important to recall that they appear to be adapted to unique ligand sets for each gene and species,

leading to receptor and species-specific sensory modalities.

114

To support this idea, work by other labs demonstrated mMRGRPA3 agonism produces

itch that is dependent on transient receptor potential (TRP) channel expression (Liu et al., 2009),

demonstrating that the sensory output downstream of MRG receptors depends on the expression

of other sensory proteins within those neurons. Note that TRP ion channels regulate neuronal

excitability and can be activated by their own array of chemical, temperature, and mechanical

stimuli (Le Pichon and Chesler, 2014). It is likely that MRGPRX receptors also modulate

downstream TRP channels in the sensory afferents where they are expressed and that the

convergence of signals on MRGPRX and TRP channels will encode a specific itch or pain

response.

5.3 FUNCTIONAL IMPLICATIONS FROM MRGPRX4 STUDIES

Thus far, there are still no proposed endogenous ligands for MRGPRX4, making it

difficult to hypothesize about specific in vivo implications. However, in chapter 4 I characterized

a coding variant N245S in MRGPRX4 that confers preference for mentholated cigarettes in

African Americans and identified (-)-menthol as a negative allosteric modulator of MRGPRX4.

Canonically, (-)-menthol produces cooling sensations by activating TRPM8 (Bautista et al., 2007)

and TRPA1 (Karashima et al., 2009)channels. Activated Gαq-coupled GPCRs hydrolyze PIP2, a

negative regulator of TRP channels, and promote TRP channel activation or increased sensitivity

to ligands (Julius, 2013). As mentioned above, MRGPRX receptor signaling upstream of TRP

channels may lead to the coding of specific sensory outputs and MRGPRX4 is poised to be

activated by menthol-containing cigarettes since TG sensory neurons innervate the head and

mouth (Zheng et al., 2014). Previous studies have shown, however, that TRPM8 and TRPA1 are

115

not expressed in the non-peptidergic C fibers where MRG receptors are found (Le Pichon and

Chesler, 2014), making direct TRPM8/A1/MRGPRX4 interaction less likely.

Nevertheless, it is tempting to speculate that the cooling sensations elicited by (-)-

menthol may be a product of the ratio of TRP channel activation and MRGPRX4 signal

dampening in some fashion, which is dependent on (-)-menthol concentration. Furthermore, the

genetic association study suggests that a reduction of MRGPRX4 signaling promotes increased

menthol preference, potentially in the form of decreased aversion or increased enjoyment.

Whether or not the central nervous system is engaged following MRGPRX4 modulation by

menthol remains unanswered. Future studies to assess which neurons express MRGPRX4 would

better illuminate the neuronal circuitry involved and begin to tease out the function of this unique

receptor.

Alternately, at least one study suggests MRG receptors can be expressed in vagal

afferent neurons(Lee et al., 2008). The vagal nerve innervates a variety of internal organs and

acts in concert with the sympathetic nervous system to sense internal states. One of the major

sites of vagal innervation is the lungs, where the sensory afferents regulate breathing. Thus, it

would be interesting to see if MRGPRX4 receptors are found in primate vagal pulmonary

afferents where it could mediate breathing or the sensation of menthol and other MRGPRX4-

interacting ligands.

Established GPCRs in the sensory and immune systems, including opioid, serotonin, and

purinergic receptors, have extremely effective modulatory capacity and potently induce

inflammation, analgesia and/or itch upon ligand-stimulation. In contrast, orphan receptors of the

sensory and immune systems have largely unknown functions. While this is primarily due to the

lack of chemical matter to interrogate these receptors, it is also likely that oGPCRs in these

116

systems are responsible for fine-tuning of these responses. One example of this is the oGPCR

GPR65, which is expressed in a small subset of vagal afferents to the intestines and detects

nutrients and gut motility (Williams et al., 2016). As we continue to uncover functions of

oGPCRs using chemical and genetic tools, it is likely that we will improve our ability to make

more subtle modifications of physiological systems. Continued studies of MRGPRX function in

the sensory and immune systems will elucidate the intricacies of these receptors’ functions and

uncover the complexities of sensory encoding in primates.

5.4 IMPLICATIONS FROM IN SILICO HOMOLOGY MODELS

This work generated in silico homology models of MRGPRX2 and MRGPRX4, for

which there are no solved crystal structures. Both homology models were based on the structure

of the KOR in complex with antagonist JDTic (Wu et al., 2012). MRGPRX2 and MRGPRX4

share no more than 26% sequence identity with KOR. Despite these drastic sequence differences,

MRGPRX2 and KOR share ligands, most notably the cyclazocines and prodynorphin derived

peptides. In contrast, MRGPRX4 is not activated by any opiates tested. Even more surprisingly,

and MRGPRX2 have 63% sequence identity to one another and yet do not share ligands. These

homology models provide critical insights to the putative binding sites of these oGPCRs and

prompt several intriguing interpretations.

MRGPRX2 and MRGPRX4 sequences contain all of the highly conserved Class A

residues (N1.50, D2.50, R3.50, W4.50, P6.50, P7.50, sodium binding site residues) except for

the highly conserved proline in TM5 (P5.50) (See Table 6.1 below). P5.50 is a component of the

P-I-F activation motif in the opioid receptors and appears to be an important component of

receptor activation and TM shape (Vardy et al., 2013). In MRGPRX2, this residue is a

117

methionine and in MRGPRX4, a valine (Table 6.1). Likewise, MRGPRX2 and MRGPRX4 have

leucines rather than isoleucines in position 3.40 of the P-I-F motif (Table 6.1), suggesting both of

these receptors may not utilize this motif as an essential component for activation. Future studies

that determine the structures of both MRGPRX2 and MRGPRX4 could assess whether these

receptors have a compensatory P-I-F motif formed from alternate residues not observed in these

models or if it is unnecessary for MRGX activation.

Further comparison of the MRGPRX homology models with the opioid receptor

structures demonstrates that most of the residues in the canonical opioid receptor orthosteric

binding site differ in both MRGPRX2 and MRGPRX4 (Table 6.1). Importantly, as discussed in

Chapter 2, the D3.32 that is forms a salt bridge with the amine of opioid ligands and the opioid

receptors is a methionine (M3.32) in MRGPRX2. Instead, MRGPRX2’s binding pocket appears

to be in a reverse orientation, with D5.36 forming an ionic interaction between receptor and

amine, leading to a completely different opioid ligand orientation in this receptor. This appears to

explain how KOR and MRGPRX2 can be activated by the same ligands despite low amino acid

similarity. Trace amine receptors (TAARs) also have a non-classical salt bridge between ligand

and receptor mediated by an aspartate in TM5 (D5.42), and it has been suggested that this non-

traditional amine recognition site occurs in receptors as a result of rapid evolutionary events

where amine recognition was lost and then regained (Li et al., 2015). Furthermore, the evolution

of detecting reverse-oriented amine ligands in binding pockets of TAAR and MRGPRX

receptors allow organisms to detect divergent stimuli and may lead to novel ways of detecting

complex arrays of chemicals (Li et al., 2015)..

MRGPRX4 also has a Met in position 3.32 and D5.36 (Table 6.1); however, the docking

models do not predict D5.36 to interact with the identified chemical matter. Instead, MRGPRX4

118

Table 6.1: Amino acids among opioid receptor structures and MRGPRX models. As before, opioid receptors are

shown with the following: light gray background - residues within 4 Å of ligands indicated with a "+" or "-" if present;

Dark blue font indicates identical residues at all four receptors; light blue font indicates identical residues in MOP, DOP,

and KOP, but unique to NOP; red font labels divergent residues in all receptors; orange font indicates unique residues to

either MOP, DOP, or KOP; dark gray background - conserved residues involved in receptor function; blue background -

associated with sodium binding. Motifs are labeled. (Modified from Filizola and Devi, 2013.) For MRGPRXRs, actual

residue is listed and are shown with the following: dark orange background - different from opioid receptors but

consistent among XRs; dark green – unique to MRGPRX4; light orange – different from opioid but similar residue; light

green – different from X2 but similar to opioid residue.

Interacting residues MOP DOP KOP NOP X2 X4 MOTIF

N/N/N/N 1.50 + + + + N N

D/D/D/D 2.50 + + + + D D

A/A/V/V 2.53 - - + - F F

T/T/T/T 2.56 - - + - F F

Q/Q/Q/Q 2.60 - - + + N R

N/K/V/D 2.63 - - + + V L

V/V/V/V 3.28 - - + + F L

I/L/L/I 3.29 - - + + T V

D/D/D/D 3.32 + + + + M M

Y/Y/Y/Y 3.33 + + + + T T

N/N/N/N 3.35 + + + + A P

M/M/M/M 3.36 + + + + Y Y

F/F/F/F 3.37 - - - + L F

S/S/S/S 3.39 + + + + G G

I/I/I/T 3.40 + + + - L L PIF

R/R/R/R 3.50 + + + + R R DRY

W/W/W/W 4.50 + + + + W W

E/D/D/G 5.35 + - - - Q E

K/K/K/A 5.39 + + + - F F

V/V/V/I 5.42 + + + + A V

A/A/A/S 5.46 - - - + I I

P/P/P/P 5.50 + + + + M V PIF

F/F/F/F 6.44 + + + + F F PIF

W/W/W/W 6.48 + + + + G G CWxP

P/P/P/P 6.50 + + + + P P CWxP

I/I/I/V 6.51 + + + + F F

H/H/H/Q 6.52 + + + + G G

V/V/I/V 6.55 + + + + W G

K/W/E/Q 6.58 + + - - I I

W/L/Y/L 7.35 + + + - H Y

I/I/I/T 7.39 + + + + V M

G/G/G/G 7.42 - - + - S S

Y/Y/Y/Y 7.43 + + + + S S

P/P/P/P 7.50 + + + + P P NPxxY

W/W/W124/W (EL1) - - + - - -

119

appears to capitalize on the extended binding pocket near TM2 and EL1. R86 and R95 were

predicted from the homology model to interact with the carboxylic acids of nateglinide and other

MRGPRX4 ligands. Mutations of these two arginines reduced MRGPRX4 activation by glinides,

(D)-phenylalanine analogs, SR-142948, and PD123319.

Most of the residues near the top of TM2 and into EL1 differ between MRGPRX2 and

MRGPR4 (Table 6.1). Further distinctions between MRGPRX2 and MRGPRX4 residues occur

near the top of TM7 (Table 6.1). In the opioid receptors, many of these residues in TM2, TM7,

and EL1 were important for determining ligand specificity among the opioid receptors and thus it

is no surprise that alterations in these residues between MRGPRX2 and MRGPRX4 may confer

MRGPRX-subtype specificity of ligands. Future studies that seek to generate MRGPRX

subtype-specific ligands should aim to engage one or more of these residues and/or the residues

in TMs 4 and 5 discussed earlier.

The homology models of these receptors expose further evidence of their diverse

pharmacologic profiles and demonstrate the importance of subtle alterations in binding pocket

residue to ligand specificity and functional divergence across species. Of course, to fully

determine the extent of the atomic level differences in MRGPRX receptors, it will be necessary

to solve the crystal structures of these receptors. Hopefully, advances in ligand development for

MRGPRX receptors or in X-ray crystallography techniques will enable the determination of

these structures soon.

5.5 FINAL COMMENTS

The research contained within this dissertation provides multiple novel small molecule

probes for MRGPRX oGPCRs, greatly expanding the repertoire of small molecules for these

receptors and providing the scientific community with valuable tools to elucidate their functions.

120

This work is a model of how novel chemical probes can be used to determine biochemical and

functional properties of GPCRs. As mentioned, however, GPCR signaling is multi-faceted. As

the library of selective chemical matter for GPCRs grows, it will be interesting to determine the

varieties of GPCR ligand recognition and activation mechanisms, as well as test the contributions

of multiple receptors to phenotypic outputs. For the MRGPRX receptors, it is likely that these

GPCRs will become increasingly important for an assortment of sensations, behaviors, and

circuits that make primates uniquely suited to tackle daily life.

121

APPENDIX 1

Compound pEC50 SEM Emax SEM n pEC50 SEM Emax SEM n

1 (-)-Bremazocine NC NC 2.38 0.87 3 NC NC 15.74 7.08 4

2 (-)-Cis-Normetazocine NC NC 24.58 7.55 3 4.30 0.21 40.44 10.47 3

3 (-)-Codeine 5.00 0.02 69.50 5.58 3 5.17 0.11 88.11 8.14 3

4 (-)-Cyclazocine NC NC 1.32 1.05 3 NC NC 8.69 2.21 3

5 (-)-Metazocine 4.82 0.09 68.17 3.60 3 4.72 0.26 43.27 5.42 3

6 (-)-Morphine 5.18 0.12 84.23 4.81 3 5.21 0.05 57.41 7.48 6

7 (-)-N-allyl-Nortmetazocine NC NC 1.34 0.88 3 NC NC 5.92 5.41 4

8 (-)-Naloxone NC NC 1.07 0.70 3 NC NC 9.39 1.46 3

9 (-)-Pentazocine NC NC 1.90 1.16 3 NC NC 12.32 1.48 3

10 (-)-Phenazocine NC NC 7.04 1.27 3 NC NC 54.95 5.69 4

11 (+)-Alpha-propoxyphene 4.50 0.08 55.83 3.46 3 4.82 0.23 65.09 2.52 3

12 (+)-Bremazocine NC NC 0.67 0.40 3 NC NC 5.28 5.14 4

13 (+)-Cis-N-Normetazocine 4.87 0.09 69.49 5.69 3 4.84 0.14 54.72 6.64 8

14 (+)-Codeine 4.86 0.07 59.05 7.14 3 5.26 0.18 88.45 5.52 3

15 (+)-Cyclazocine NC NC 1.80 0.56 3 NC NC 8.54 5.98 5

16 (+)-Dezocine NC NC 3.46 1.08 3 NC NC 49.48 12.52 3

17 (+)-Metazocine 5.03 0.07 81.85 0.09 3 5.14 0.18 53.15 8.59 7

18 (+)-Morphine 5.30 0.11 88.39 5.84 3 5.47 0.12 67.20 7.14 6

19 (+)-N-allyl-N-Normetazocine NC NC 1.03 0.55 3 NC NC 18.14 6.62 3

20 (+)-Naloxone NC NC 0.91 0.13 3 NC NC 9.08 2.49 3

21 (+)Pentazocine NC NC 0.92 0.38 3 NC NC 20.79 11.08 3

22 (+)-Phenazocine NC NC 13.90 2.41 3 NC NC 54.26 0.68 3

23 (+)-TAN-67 6.54 0.04 100.44 5.16 3 6.37 0.28 82.18 16.63 3

24 (+/-) Methadone NC NC 7.24 0.97 3 4.36 0.13 67.43 11.48 3

25 4FBNTI NC NC 5.05 3.22 3 5.25 0.27 39.29 13.48 3

26 6-Acetyl-Codeine 4.60 0.05 53.07 5.06 3 4.72 0.13 77.21 4.18 3

27 6-Acetyl-Morphine 4.68 0.03 52.87 4.10 3 4.92 0.15 80.47 9.54 3

28 ADL-5859 NT NT NT NT 0 5.29 0 64.78 NC 1

29 Alpha-neoendorphin 5.37 0.14 84.69 3.90 2 NC NC 54.12 19.01 2

30 BAM-12 NC NC 27.48 11.07 3 NC NC 11.82 5.89 3

31 BAM-22 NC NC 14.22 0.00 1 NC NC 2.53 NC 1

32 BAM8-22 5.05 0.22 72.13 7.02 2 4.95 0.73 55.73 11.77 3

33 Beta-endorphin NC NC 42.92 0.00 1 NC NC 23.46 NC 1

34 Beta-neoendorphin NC NC 51.86 22.11 3 NC NC 35.62 15.54 3

35 BUFFER NC NC 0.37 0.99 3 NC NC 4.94 0.21 3

36 Buprenorpine NC NC 0.17 0.74 3 NC NC 9.81 2.04 3

37 BW373U86 NC NC 2.03 0.25 3 NC NC 18.51 11.31 3

38 CM2-183 NC NC 63.52 16.57 3 NC NC 9.09 1.77 3

39 Codeine-6G NC NC 12.10 2.34 3 4.36 0.35 65.79 5.96 3

40 Cortistatin-14 6.38 0.13 112.69 12.09 4 6.84 0.35 33.16 6.54 3

41 Dermorphin NC NC 1.29 0.44 3 NC NC 16.97 6.11 3

42 Dextrabenzylorphan NC NC 3.80 1.99 3 NC NC 32.76 11.78 3

Averaged Averaged

FLIPR TANGO

122

Table 1: Table of all tested opioids at MRGPRX2 shown in TANGO and FLIPR.

Compounds are numbered with name listed in Column 2. Emax is given as a percentage of (+/-)-

TAN-67 activity, a full agonist. Abbreviations: NC = Not calculable in Prism because curve did

not saturate at concentrations up to 100 µM, NT = Not tested.

43 Dextromethorphan 5.23 0.17 88.03 1.68 4 5.49 0.12 62.34 6.26 7

44 Dextrorphan 5.41 0.19 86.89 1.13 3 5.38 0.05 64.55 9.15 6

45 Dihydrocodinone 4.96 0.05 70.17 6.31 3 4.73 0.14 49.72 7.54 3

46 Diprenorphine NC NC 0.22 1.33 3 NC NC 12.45 1.05 3

47 Dynorphin A (1-13) 5.98 0.09 91.85 3.96 2 4.52 0.06 67.07 3.16 3

48 Dynorphin A (1-17) 5.64 0.09 88.84 3.82 5 4.68 0.49 53.64 16.34 3

49 Dynorphin A (13-17) NC NC 1.44 0.10 2 NC NC 11.43 4.95 2

50 Dynorphin A (1-6) NC NC 2.34 0.67 2 NC NC 16.84 2.11 2

51 Dynorphin A (1-7) NC NC 0.98 0.59 2 NC NC 25.19 6.46 2

52 Dynorphin A (1-8) NC NC 22.66 11.28 3 NC NC 16.35 11.60 3

53 Dynorphin A (1-9) 4.81 0.22 64.62 11.87 3 NC NC 20.91 7.89 3

54 Dynorphin A (2-8) NC NC 4.62 0.77 3 NC NC 21.31 9.92 3

55 Dynorphin A (6-17) NC NC 34.26 16.79 2 NC NC 15.72 3.70 2

56 Dynorphin B (1-13) 5.19 0.19 83.67 3.50 4 4.48 0.10 41.69 15.26 3

57 Endomorphin-1 NC NC 6.75 3.01 3 NC NC 35.20 13.13 3

58 Endomorphin-2 NC NC 3.29 1.00 3 NC NC 29.89 6.78 3

59 Etorphine NC NC 0.37 0.83 3 NC NC 11.51 8.82 3

60 Fedotozine NC NC 0.51 0.77 3 NC NC 24.73 2.44 3

61 Fentanyl NC NC 0.37 0.81 3 NC NC 10.79 5.31 3

62 IBNTXA NC NC 4.65 1.13 3 NC NC 16.23 10.98 3

63 JDTic NC NC 2.12 0.10 3 NC NC 106.67 22.23 3

64 KNT-127 NC NC -0.44 2.32 4 4.73 0.71 35.42 7.94 4

65 Leu-Enkephalin NC NC 1.67 0.06 3 NC NC 26.04 11.07 3

66 Levallorphan NC NC 4.50 1.88 4 NC NC 22.02 13.81 3

67 Levorphanol 4.90 0.14 62.30 7.77 5 4.65 0.06 56.78 8.31 6

68 Meperidine NC NC 14.06 5.26 4 NC NC 27.95 7.79 3

69 Met-enkephalin NC NC 1.86 0.25 3 NC NC 21.04 6.18 3

70 Morphine-3G NC NC 0.60 0.28 3 4.56 0.03 33.15 7.50 3

71 Morphine-6G NC NC 12.84 2.48 3 4.72 0.22 72.54 8.73 3

72 Nalfurafine NC NC 0.50 0.27 3 NC NC 20.32 4.50 3

73 Nalorphine NC NC 1.12 0.10 3 NC NC 10.93 2.51 3

74 Naltrexone NC NC 1.65 0.39 3 NC NC 10.57 1.51 3

75 Norcodeine NC NC 4.23 1.25 3 4.79 0.21 54.72 5.36 3

76 RB64 NC NC 0.43 0.85 3 NC NC 44.15 9.68 3

77 RTI-4356-2 NC NC 17.07 7.69 4 5.19 0.16 63.33 12.55 6

78 RTI-4612-105 NC NC 33.79 7.86 4 4.66 0.33 46.34 2.19 3

79 RTI-4612-118 NC NC 14.96 4.51 4 4.83 0.10 52.71 12.89 3

80 RTI-4612-158 NC NC 5.02 1.77 4 4.67 0.04 21.02 2.24 3

81 RTI-4612-96 5.17 0.10 83.81 4.27 4 5.09 0.06 56.77 8.78 3

82 RTI-4612-97 5.19 0.21 84.05 3.98 3 5.04 0.10 52.11 5.91 3

83 Salvinorin A NC NC 2.18 0.72 3 NC NC 10.32 3.85 3

84 Sinomenine 5.40 0.18 92.08 3.29 3 5.27 0.09 61.35 10.03 4

85 Substance P 5.97 0.16 106.00 5.47 8 5.29 0.11 49.05 2.49 5

86 TAN-67 (racemic) 6.19 0.06 100.00 0.00 16 5.74 0.09 100.00 0.00 20

87 Thebaine 4.12 0.05 35.13 4.37 3 4.95 0.22 90.84 5.23 3

88 U50 NC NC 2.63 1.48 3 NC NC 19.46 3.77 3

123

Table 2: Small Molecule Screening Information

Supplementary Table 3. Small molecule screening data

Category Parameter Description

Assay Type of assay PRESTO-TANGO cell-based β-arrestin recruitment assay

Target GPCR activation as measured by transcription of a luciferase reporter

Primary measurement Detection of luminescence

Key reagents Luciferin (Bright-Glo)

Assay protocol Kroeze, et al., Nat Struct Mol Bio. 22(5):362-9 (2015)

Additional comments

Library Library size 9 separate libraries were used containing 5,695 unique compounds (9,128 compounds in total)

Library composition Small molecules, FDA-approved drugs

Source Libraries used: NIH Clinical Collection NCC-1 and NCC-2, NIMH X-901, Tocris Tocriscreen Plus, Prestwick Chemical Library, Sigma Aldrich LOPAC®1280, Selleck BioActive Compound Library, Microsource SPECTRUM collection, and the Roth Lab Collection (in-house).

Additional comments

Screen Format 384 well clear bottom plates coated with poly lysine, drugs were added in quadruplicate wells

Concentration(s) tested 10 µM

Plate controls Buffer-only control in four columns (64 wells) per plate.

Reagent/ compound dispensing system Compounds were dispensed manually using a repeating multichannel pipettor (Thermo Scientific Finnpipette)

Detection instrument and software The Perkin Elmer MicroBeta TriLux 1450 LSC & Luminescence Counter was used in combination with the Microbeta Workstation software to detect and export raw data in the form of Excel spreadsheets.

Assay validation/QC Z’ score = 0.71 +/- 0.20 (average Z’ score for 15 plates +/- standard deviation)

Correction factors NA

Normalization NA

Additional comments

Post-HTS analysis Hit criteria Compounds with 2-fold or greater activation above the plate median were considered hits and were selected for further analysis. Known false positives (from previous assays or from cross-checking in PubChem) were excluded.

Hit rate Raw hit rate = 1.86% Hit rate with duplicate compounds and known false positives excluded = 0.86%

Additional assay(s) An orthogonal fluorescence dye-based intracellular calcium release assay was used as a secondary assay for hit confirmation and dose response curves.

Confirmation of hit purity and structure Compounds were re-purchased commercially from a variety of vendors for validation assays. Key probe candidates were confirmed by mass spectrometry and resynthesized.

Additional comments

124

125

Figure 1: Off target activity for (R)-ZINC-3573 and (S)-ZINC-3573 at GCPRs and Kinases.

Top: All 21 receptors with greater than two-fold activity from original GPCRome screening (see

Supplemental Figure 8) were tested with both (R)- and (S)-ZINC-3573 in PRESTO-Tango

concentration response curves with gene names listed above each curve. Where possible, positive

control compounds were used or DMSO (vehicle) for reference. N=1 in triplicate for each

receptor, error bars show SEM for average value of triplicate wells. Bottom: results of Kinome

screen for 10 µM of ZINC-9232 yielded only three potential kinases (listed). Follow-up kinase

assays determined the Kd of ZINC-9232 for each BTK, JNK1, and JNK3.

126

Scheme 1. Synthesis of (R)- and (S)-ZINC-3573.

127

APPENDIX 2

PLASMID MAPS:

Plasmid map for MRGPRX1-Tango construct. Includes schematic at top, vector is

PCDNA3.1. Note the presence of a stop codon following the TEV protease cut site and tTA

(listed here as “TEV CUT SITE). MRGPRX1 coding sequence (CDS) is swapped out for other

codon optimized MRGPRX sequences in other constructs, as expected. Sequences for all

receptors and mutations will be provided below.

128

Plasmid map for pcDNA5/FRT/TO for FLP-IN Constructs. . Vector (provided by

Invitrogen) with annotated cut sites and location of MRGPRX gene insertion. MRGPRX

receptors were swapped out into this location to generate each of the tet-inducible stable cell

lines. Sequences for all receptors and mutants will be provided below. For all inducible stable

cell lines, the codon-optimized CDS for each receptor from the Tango construct was inserted,

minus the extended C-terminal tail (e.g. the V2 tail, TEV site, and tTA).

129

CODING SEQUENCES FOR RECEPTORS

1. MRGPRX1

Atggacccgacaatcagcacactcgacactgaactgacgcccatcaatggcacagaggaaacactttgttacaagcaaacact

gtccttgactgtcctcacctgtattgtaagtctggtggggctgaccggaaatgcggtagttctctggctgctgggttgccggatgcg

acggaatgctttctccatttacatactgaacctcgccgctgctgatttccttttcctctcagggagacttatctacagccttttgtctttta

tcagcattccacatacaatttccaagatcctgtacccggtcatgatgttttcttacttcgccggcctgtcctttttgagtgcagtctcca

cggaaagatgtctgtctgtcctctggccgatttggtataggtgtcataggccgactcacctgagtgccgtcgtgtgtgtgctgctgt

gggccctctcccttctgcggtctatactggagtggatgctgtgtggatttctctttagtggtgccgactctgcatggtgtcagaccag

tgattttatcactgtcgcctggcttattttcctctgtgtggtgctgtgtgggtcatctttggtgctgctcattaggatactgtgtggcagc

cgcaaaatcccactgacacgcctgtacgtgaccatccttctgacggtattggtgttcctcctgtgtggtttgcctttcgggatccaatt

cttcctcttcctctggatacacgtggatcgggaagtgcttttctgccacgttcacctggtgagtatctttctgtccgcactgaatagca

gcgcaaatcccattatctattttttcgtagggtcatttagacagaggcagaatagacagaaccttaaacttgttctccaacgcgctttg

caagacgcctctgaagtggatgaggggggtggccaactgcccgaggagatcttggaactgtccggcagccggttggaacag

2. MRGPRX2

Atggatcccacaaccccggcatggggaaccgaatctacaacagtgaacggaaacgatcaggcactgctcctgctgtgtggca

aggaaaccctcataccagtatttcttattctctttattgcactggtggggctggtggggaatggttttgtgttgtggcttctgggatttc

ggatgcggcgcaacgctttcagcgtctacgtactgtccctggcaggcgccgatttcctgttcctttgcttccagataattaattgcct

cgtttacttgtccaatttcttctgctccatttccataaatttccctagttttttcactactgttatgacctgtgcatatctggccggcctgtct

atgttgagtactgtgagcacagagcgatgcctgagtgtgctttggcctatatggtatcggtgtcgcagacctagacacttgtccgc

agtggtgtgtgttcttctgtgggccctttcactgctcctgagcatcctggagggaaaattctgtgggttcctgttcagcgacgggga

ctccggctggtgtcagacttttgactttatcaccgcggcatggctgatcttcttgttcatggtgctgtgcggaagctcactggcgctg

ctggttcgcatcctctgcggatcccgaggccttccactcactcggctgtatctgacgatcctgctgacagtactcgtgttcctcctgt

gcggcctccctttcggcattcaatggttccttatactgtggatttggaaagattccgacgtgctgttttgccacattcatcccgttagc

gtggtgctgtcttcactgaatagttctgccaaccccattatctacttttttgtaggttcttttcggaaacagtggcggcttcagcagcct

atccttaaactggccctgcaaagggccttgcaagacatcgccgaagtcgaccatagcgagggttgctttaggcagggtacccca

gagatgtctaggtccagcttggta

3. MRGPRX4

Atggatcctactgtgcctgtgtttggtacaaagctcacccctattaacgggagagaggaaactccctgctataatcagaccctctc

tttcaccgtgctcacatgtatcatcagtctggtcgggctgacaggcaacgcagtggtactttggttgctggggtatagaatgcgac

gcaatgccgtgagtatttatatccttaatttggcagccgcagattttctgtttctctccttccaaatcatccgcctgcctctgcgactcat

taatatctctcaccttattcggaagatcctcgtgtcagtgatgacatttccctatttcacaggtctgagcatgctctcagccattagcac

cgaacggtgtttgagtgtcctgtggcccatatggtaccgatgcagaagacccactcacctgtccgcagtggtttgtgtgcttctgtg

gggcctgtcactgctgttctccatgctggaatggcgcttctgtgactttctcttctccggagctgactccagctggtgcgagacctc

cgattttattccggtcgcttggctcatctttctttgtgtagtactttgtgtgtcatccttggtactgctcgtcagaattctgtgtgggtcca

gaaagatgccgctcacacggttgtacgtcacaattttgctcacagttctggtatttcttctgtgtgggcttccgttcgggattctcgga

gccctcatctacaggatgcacctgaacctggaggtgctctactgtcacgtttacctggtgtgtatgtctctgtctagcctcaactcta

gcgctaacccaatcatctatttctttgtggggagcttccggcagcgacagaaccgccaaaacttgaaactcgttcttcagcgagca

ctccaagataaacccgaagtggataaaggggagggccagctgcctgaggagtccctggaactgtctggttcaagactcggacc

c

130

4. MRGPRB2

Atgagtggagatttcctaatcaagaatctaagcacctcagcctggaaaacgaacatcacagtgctgaatggaagctactacatc

gatacttcagtttgtgtcaccaggaaccaagccatgattttgctttccatcatcatttccctggttgggatgggactaaatgccatagt

gctgtggttcctgggcatccgtatgcacacgaatgccttcactgtctacattctcaacctggctatggctgactttctttacctgtgct

ctcagtttgtaatttgtcttcttattgccttttatatcttctactcaattgacatcaacatccctttggttctttatgttgtgccaatatttgctta

tctttcaggtctgagcattctcagcaccattagcattgagcgctgcttgtctgtaatatggcccatttggtatcgctgtaaacgtccaa

gacacacatcagctatcacatgttttgtgctttgggttatgtccttattgttgggtctcctggaagggaaggcatgtggcttactgttta

atagctttgactcttattggtgtgaaacatttgatgttatcactaatatatggtcagttgttttttttggtgttctctgtgggtctagcctcac

cctgcttgtcaggatcttctgtggctcacagcgaattcctatgaccaggctgtatgtgactattacactcacagtcttggtcttcctga

tctttggtcttccctttgggatctattggatactctatcagtggattagcaatttttattatgttgaaatttgtaatttttatcttgagatactat

tcctatcctgtgttaacagctgtatgaaccccatcatttatttccttgttggctccattaggcaccgaaggttcaggcggaagactctc

aagctacttctgcagagagccatgcaagacacccctgaggaggaacaaagtggaaataagagttcttcagaacaccctgaaga

actggaaactgttcagagctgcagc

5. MRGPRX2 E164Q

Atggatcccacaaccccggcatggggaaccgaatctacaacagtgaacggaaacgatcaggcactgctcctgctgtgtggca

aggaaaccctcataccagtatttcttattctctttattgcactggtggggctggtggggaatggttttgtgttgtggcttctgggatttc

ggatgcggcgcaacgctttcagcgtctacgtactgtccctggcaggcgccgatttcctgttcctttgcttccagataattaattgcct

cgtttacttgtccaatttcttctgctccatttccataaatttccctagttttttcactactgttatgacctgtgcatatctggccggcctgtct

atgttgagtactgtgagcacagagcgatgcctgagtgtgctttggcctatatggtatcggtgtcgcagacctagacacttgtccgc

agtggtgtgtgttcttctgtgggccctttcactgctcctgagcatcctgCAGggaaaattctgtgggttcctgttcagcgacgggg

actccggctggtgtcagacttttgactttatcaccgcggcatggctgatcttcttgttcatggtgctgtgcggaagctcactggcgct

gctggttcgcatcctctgcggatcccgaggccttccactcactcggctgtatctgacgatcctgctgacagtactcgtgttcctcct

gtgcggcctccctttcggcattcaatggttccttatactgtggatttggaaagattccgacgtgctgttttgccacattcatcccgttag

cgtggtgctgtcttcactgaatagttctgccaaccccattatctacttttttgtaggttcttttcggaaacagtggcggcttcagcagcc

tatccttaaactggccctgcaaagggccttgcaagacatcgccgaagtcgaccatagcgagggttgctttaggcagggtacccc

agagatgtctaggtccagcttggta

6. MRGPRX2 E164D

Atggatcccacaaccccggcatggggaaccgaatctacaacagtgaacggaaacgatcaggcactgctcctgctgtgtggca

aggaaaccctcataccagtatttcttattctctttattgcactggtggggctggtggggaatggttttgtgttgtggcttctgggatttc

ggatgcggcgcaacgctttcagcgtctacgtactgtccctggcaggcgccgatttcctgttcctttgcttccagataattaattgcct

cgtttacttgtccaatttcttctgctccatttccataaatttccctagttttttcactactgttatgacctgtgcatatctggccggcctgtct

atgttgagtactgtgagcacagagcgatgcctgagtgtgctttggcctatatggtatcggtgtcgcagacctagacacttgtccgc

agtggtgtgtgttcttctgtgggccctttcactgctcctgagcatcctgGACggaaaattctgtgggttcctgttcagcgacgggg

actccggctggtgtcagacttttgactttatcaccgcggcatggctgatcttcttgttcatggtgctgtgcggaagctcactggcgct

gctggttcgcatcctctgcggatcccgaggccttccactcactcggctgtatctgacgatcctgctgacagtactcgtgttcctcct

gtgcggcctccctttcggcattcaatggttccttatactgtggatttggaaagattccgacgtgctgttttgccacattcatcccgttag

cgtggtgctgtcttcactgaatagttctgccaaccccattatctacttttttgtaggttcttttcggaaacagtggcggcttcagcagcc

tatccttaaactggccctgcaaagggccttgcaagacatcgccgaagtcgaccatagcgagggttgctttaggcagggtacccc

agagatgtctaggtccagcttggta

131

7. MRGPRX2 D184N

Atggatcccacaaccccggcatggggaaccgaatctacaacagtgaacggaaacgatcaggcactgctcctgctgtgtggca

aggaaaccctcataccagtatttcttattctctttattgcactggtggggctggtggggaatggttttgtgttgtggcttctgggatttc

ggatgcggcgcaacgctttcagcgtctacgtactgtccctggcaggcgccgatttcctgttcctttgcttccagataattaattgcct

cgtttacttgtccaatttcttctgctccatttccataaatttccctagttttttcactactgttatgacctgtgcatatctggccggcctgtct

atgttgagtactgtgagcacagagcgatgcctgagtgtgctttggcctatatggtatcggtgtcgcagacctagacacttgtccgc

agtggtgtgtgttcttctgtgggccctttcactgctcctgagcatcctggagggaaaattctgtgggttcctgttcagcgacgggga

ctccggctggtgtcagacttttAATtttatcaccgcggcatggctgatcttcttgttcatggtgctgtgcggaagctcactggcgc

tgctggttcgcatcctctgcggatcccgaggccttccactcactcggctgtatctgacgatcctgctgacagtactcgtgttcctcct

gtgcggcctccctttcggcattcaatggttccttatactgtggatttggaaagattccgacgtgctgttttgccacattcatcccgttag

cgtggtgctgtcttcactgaatagttctgccaaccccattatctacttttttgtaggttcttttcggaaacagtggcggcttcagcagcc

tatccttaaactggccctgcaaagggccttgcaagacatcgccgaagtcgaccatagcgagggttgctttaggcagggtacccc

agagatgtctaggtccagcttggta

8. MRGPRX2 D184E

Atggatcccacaaccccggcatggggaaccgaatctacaacagtgaacggaaacgatcaggcactgctcctgctgtgtggca

aggaaaccctcataccagtatttcttattctctttattgcactggtggggctggtggggaatggttttgtgttgtggcttctgggatttc

ggatgcggcgcaacgctttcagcgtctacgtactgtccctggcaggcgccgatttcctgttcctttgcttccagataattaattgcct

cgtttacttgtccaatttcttctgctccatttccataaatttccctagttttttcactactgttatgacctgtgcatatctggccggcctgtct

atgttgagtactgtgagcacagagcgatgcctgagtgtgctttggcctatatggtatcggtgtcgcagacctagacacttgtccgc

agtggtgtgtgttcttctgtgggccctttcactgctcctgagcatcctggagggaaaattctgtgggttcctgttcagcgacgggga

ctccggctggtgtcagacttttGAGtttatcaccgcggcatggctgatcttcttgttcatggtgctgtgcggaagctcactggcgc

tgctggttcgcatcctctgcggatcccgaggccttccactcactcggctgtatctgacgatcctgctgacagtactcgtgttcctcct

gtgcggcctccctttcggcattcaatggttccttatactgtggatttggaaagattccgacgtgctgttttgccacattcatcccgttag

cgtggtgctgtcttcactgaatagttctgccaaccccattatctacttttttgtaggttcttttcggaaacagtggcggcttcagcagcc

tatccttaaactggccctgcaaagggccttgcaagacatcgccgaagtcgaccatagcgagggttgctttaggcagggtacccc

agagatgtctaggtccagcttggta

9. MRGPRX4 N245S + T43T

atggatcctactgtgcctgtgtttggtacaaagctcacccctattaacgggagagaggaaactccctgctataatcagaccctctct

ttcaccgtgctcacatgtatcatcagtctggtcgggctgACGggcaacgcagtggtactttggttgctggggtatagaatgcga

cgcaatgccgtgagtatttatatccttaatttggcagccgcagattttctgtttctctccttccaaatcatccgcctgcctctgcgactc

attaatatctctcaccttattcggaagatcctcgtgtcagtgatgacatttccctatttcacaggtctgagcatgctctcagccattagc

accgaacggtgtttgagtgtcctgtggcccatatggtaccgatgcagaagacccactcacctgtccgcagtggtttgtgtgcttct

gtggggcctgtcactgctgttctccatgctggaatggcgcttctgtgactttctcttctccggagctgactccagctggtgcgagac

ctccgattttattccggtcgcttggctcatctttctttgtgtagtactttgtgtgtcatccttggtactgctcgtcagaattctgtgtgggtc

cagaaagatgccgctcacacggttgtacgtcacaattttgctcacagttctggtatttcttctgtgtgggcttccgttcgggattctcg

gagccctcatctacaggatgcacctgAGTctggaggtgctctactgtcacgtttacctggtgtgtatgtctctgtctagcctcaac

tctagcgctaacccaatcatctatttctttgtggggagcttccggcagcgacagaaccgccaaaacttgaaactcgttcttcagcga

gcactccaagataaacccgaagtggataaaggggagggccagctgcctgaggagtccctggaactgtctggttcaagactcg

gaccc

132

10. MRGPRX4 R86M

Atggatcctactgtgcctgtgtttggtacaaagctcacccctattaacgggagagaggaaactccctgctataatcagaccctctc

tttcaccgtgctcacatgtatcatcagtctggtcgggctgacaggcaacgcagtggtactttggttgctggggtatagaatgcgac

gcaatgccgtgagtatttatatccttaatttggcagccgcagattttctgtttctctccttccaaatcatccgcctgcctctgATGctc

attaatatctctcaccttattcggaagatcctcgtgtcagtgatgacatttccctatttcacaggtctgagcatgctctcagccattagc

accgaacggtgtttgagtgtcctgtggcccatatggtaccgatgcagaagacccactcacctgtccgcagtggtttgtgtgcttct

gtggggcctgtcactgctgttctccatgctggaatggcgcttctgtgactttctcttctccggagctgactccagctggtgcgagac

ctccgattttattccggtcgcttggctcatctttctttgtgtagtactttgtgtgtcatccttggtactgctcgtcagaattctgtgtgggtc

cagaaagatgccgctcacacggttgtacgtcacaattttgctcacagttctggtatttcttctgtgtgggcttccgttcgggattctcg

gagccctcatctacaggatgcacctgaacctggaggtgctctactgtcacgtttacctggtgtgtatgtctctgtctagcctcaactc

tagcgctaacccaatcatctatttctttgtggggagcttccggcagcgacagaaccgccaaaacttgaaactcgttcttcagcgag

cactccaagataaacccgaagtggataaaggggagggccagctgcctgaggagtccctggaactgtctggttcaagactcgga

ccc

11. MRGPRX4 R95M

Atggatcctactgtgcctgtgtttggtacaaagctcacccctattaacgggagagaggaaactccctgctataatcagaccctctc

tttcaccgtgctcacatgtatcatcagtctggtcgggctgacaggcaacgcagtggtactttggttgctggggtatagaatgcgac

gcaatgccgtgagtatttatatccttaatttggcagccgcagattttctgtttctctccttccaaatcatccgcctgcctctgcgactcat

taatatctctcaccttattATGaagatcctcgtgtcagtgatgacatttccctatttcacaggtctgagcatgctctcagccattagc

accgaacggtgtttgagtgtcctgtggcccatatggtaccgatgcagaagacccactcacctgtccgcagtggtttgtgtgcttct

gtggggcctgtcactgctgttctccatgctggaatggcgcttctgtgactttctcttctccggagctgactccagctggtgcgagac

ctccgattttattccggtcgcttggctcatctttctttgtgtagtactttgtgtgtcatccttggtactgctcgtcagaattctgtgtgggtc

cagaaagatgccgctcacacggttgtacgtcacaattttgctcacagttctggtatttcttctgtgtgggcttccgttcgggattctcg

gagccctcatctacaggatgcacctgaacctggaggtgctctactgtcacgtttacctggtgtgtatgtctctgtctagcctcaactc

tagcgctaacccaatcatctatttctttgtggggagcttccggcagcgacagaaccgccaaaacttgaaactcgttcttcagcgag

cactccaagataaacccgaagtggataaaggggagggccagctgcctgaggagtccctggaactgtctggttcaagactcgga

ccc

133

12. ENTIRE MRGPRX2-TANGO SEQUENCE WITH N TERMINAL HA, FLAG,

AND C TERMINAL V2 TAIL AND TEV/TTA

gcggccgcgccaccatgaagacgatcatcgccctgagctacatcttctgcctggtattcgccgactacaaggacgatgatgacg

ccagcatcgatatggatcccacaaccccggcatggggaaccgaatctacaacagtgaacggaaacgatcaggcactgctcctg

ctgtgtggcaaggaaaccctcataccagtatttcttattctctttattgcactggtggggctggtggggaatggttttgtgttgtggctt

ctgggatttcggatgcggcgcaacgctttcagcgtctacgtactgtccctggcaggcgccgatttcctgttcctttgcttccagata

attaattgcctcgtttacttgtccaatttcttctgctccatttccataaatttccctagttttttcactactgttatgacctgtgcatatctggc

cggcctgtctatgttgagtactgtgagcacagagcgatgcctgagtgtgctttggcctatatggtatcggtgtcgcagacctagac

acttgtccgcagtggtgtgtgttcttctgtgggccctttcactgctcctgagcatcctggagggaaaattctgtgggttcctgttcagc

gacggggactccggctggtgtcagacttttgactttatcaccgcggcatggctgatcttcttgttcatggtgctgtgcggaagctca

ctggcgctgctggttcgcatcctctgcggatcccgaggccttccactcactcggctgtatctgacgatcctgctgacagtactcgt

gttcctcctgtgcggcctccctttcggcattcaatggttccttatactgtggatttggaaagattccgacgtgctgttttgccacattca

tcccgttagcgtggtgctgtcttcactgaatagttctgccaaccccattatctacttttttgtaggttcttttcggaaacagtggcggctt

cagcagcctatccttaaactggccctgcaaagggccttgcaagacatcgccgaagtcgaccatagcgagggttgctttaggcag

ggtaccccagagatgtctaggtccagcttggtaatcgataccggtggacgcaccccacccagcctgggtccccaagatgagtc

ctgcaccaccgccagctcctccctggccaaggacacttcatcgaccggtgagaacctgtacttccagctaagattagataaaagt

aaagtgattaacagcgcattagagctgcttaatgaggtcggaatcgaaggtttaacaacccgtaaactcgcccagaagctaggtg

tagagcagcctacattgtattggcatgtaaaaaataagcgggctttgctcgacgccttagccattgagatgttagataggcaccata

ctcacttttgccctttagaaggggaaagctggcaagattttttacgtaataacgctaaaagttttagatgtgctttactaagtcatcgc

gatggagcaaaagtacatttaggtacacggcctacagaaaaacagtatgaaactctcgaaaatcaattagcctttttatgccaaca

aggtttttcactagagaatgcattatatgcactcagcgctgtggggcattttactttaggttgcgtattggaagatcaagagcatcaa

gtcgctaaagaagaaagggaaacacctactactgatagtatgccgccattattacgacaagctatcgaattatttgatcaccaagg

tgcagagccagccttcttattcggccttgaattgatcatatgcggattagaaaaacaacttaaatgtgaaagtgggtccgcgtacag

ccgcgcgcgtacgaaaaacaattacgggtctaccatcgagggcctgctcgatctcccggacgacgacgcccccgaagaggc

ggggctggcggctccgcgcctgtcctttctccccgcgggacacacgcgcagactgtcgacggcccccccgaccgatgtcagc

ctgggggacgagctccacttagacggcgaggacgtggcgatggcgcatgccgacgcgctagacgatttcgatctggacatgtt

gggggacggggattccccgggtccgggatttaccccccacgactccgccccctacggcgctctggatatggccgacttcgagt

ttgagcagatgtttaccgatgcccttggaattgacgagtacggtgggtagctcgag

134

REFERENCES

Ahlquist, R.P. (1973). Adrenergic receptors: a personal and practical view. Perspect Biol Med 17, 119-

122.

Ai, J., Taylor, K.M., Lisko, J.G., Tran, H., Watson, C.H., and Holman, M.R. (2016). Menthol Content in

US Marketed Cigarettes. Nicotine Tob Res 18, 1575-1580.

Akiyoshi, M., Kakei, M., Nakazaki, M., and Tanaka, H. (1995). A new hypoglycemic agent, A-4166,

inhibits ATP-sensitive potassium channels in rat pancreatic beta-cells. Am J Physiol 268, E185-193.

Akuzawa, N., Obinata, H., Izumi, T., and S. Takeda (2007). Morphine is an exogenous ligand for MrgX2,

a G protein-coupled receptor for cortistatin. J Cell Anim Biol 2, 004-009.

Alexander, S.P., Davenport, A.P., Kelly, E., Marrion, N., Peters, J.A., Benson, H.E., Faccenda, E.,

Pawson, A.J., Sharman, J.L., Southan, C., et al. (2015). The Concise Guide to PHARMACOLOGY

2015/16: G protein-coupled receptors. Br J Pharmacol 172, 5744-5869.

Allen, J.A., and Roth, B.L. (2011). Strategies to discover unexpected targets for drugs active at G protein-

coupled receptors. Annu Rev Pharmacol Toxicol 51, 117-144.

Anderson, S.J. (2011). Marketing of menthol cigarettes and consumer perceptions: a review of tobacco

industry documents. Tob Control 20 Suppl 2, ii20-28.

Arrowsmith, C.H., Audia, J.E., Austin, C., Baell, J., Bennett, J., Blagg, J., Bountra, C., Brennan, P.E.,

Brown, P.J., Bunnage, M.E., et al. (2015). The promise and peril of chemical probes. Nat Chem Biol 11,

536-541.

Arunlakshana, O., and Schild, H.O. (1959). Some quantitative uses of drug antagonists. Br J Pharmacol

Chemother 14, 48-58.

Bader, M., Alenina, N., Andrade-Navarro, M.A., and Santos, R.A. (2014). MAS and its related G protein-

coupled receptors, Mrgprs. Pharmacol Rev 66, 1080-1105.

Baldo, B.A., and Pham, N.H. (2012). Histamine-releasing and allergenic properties of opioid analgesic

drugs: resolving the two. Anaesth Intensive Care 40, 216-235.

Ballesteros, J.A., Ballesteros, J.A., and Weinstein, H. (1995). [19] Integrated methods for the construction

of three-dimensional models and computational probing of structure-function relations in G protein-

coupled receptors. Methods in neurosciences 25, 366-428.

Baraniuk, J.N. (2012). Rise of the sensors: nociception and pruritus. Curr Allergy Asthma Rep 12, 104-

114.

Barelier, S., Sterling, T., O'Meara, M.J., and Shoichet, B.K. (2015). The Recognition of Identical Ligands

by Unrelated Proteins. ACS Chem Biol 10, 2772-2784.

135

Barnea, G., Strapps, W., Herrada, G., Berman, Y., Ong, J., Kloss, B., Axel, R., and Lee, K.J. (2008). The

genetic design of signaling cascades to record receptor activation. Proc Natl Acad Sci U S A 105, 64-69.

Bautista, D.M., Siemens, J., Glazer, J.M., Tsuruda, P.R., Basbaum, A.I., Stucky, C.L., Jordt, S.E., and

Julius, D. (2007). The menthol receptor TRPM8 is the principal detector of environmental cold. Nature

448, 204-208.

Berezniuk, I., and Fricker, L.D. (2011). Endogenous Opioids. In The opiate receptors, G.W. Pasternak,

ed. (New York: Springer Science+Business Media, LLC).

Besnard, J., Ruda, G.F., Setola, V., Abecassis, K., Rodriguiz, R.M., Huang, X.P., Norval, S., Sassano,

M.F., Shin, A.I., Webster, L.A., et al. (2012). Automated design of ligands to polypharmacological

profiles. Nature 492, 215-220.

Bi, G., and Poo, M. (2001). Synaptic modification by correlated activity: Hebb's postulate revisited. Annu

Rev Neurosci 24, 139-166.

Bienenstock, J., MacQueen, G., Sestini, P., Marshall, J.S., Stead, R.H., and Perdue, M.H. (1991). Mast

cell/nerve interactions in vitro and in vivo. Am Rev Respir Dis 143, S55-58.

Black, J.W., and Leff, P. (1983). Operational models of pharmacological agonism. Proc R Soc Lond B

Biol Sci 220, 141-162.

Brown, A.J., Goldsworthy, S.M., Barnes, A.A., Eilert, M.M., Tcheang, L., Daniels, D., Muir, A.I.,

Wigglesworth, M.J., Kinghorn, I., Fraser, N.J., et al. (2003). The Orphan G protein-coupled receptors

GPR41 and GPR43 are activated by propionate and other short chain carboxylic acids. J Biol Chem 278,

11312-11319.

Buhl, A.M., Johnson, N.L., Dhanasekaran, N., and Johnson, G.L. (1995). G alpha 12 and G alpha 13

stimulate Rho-dependent stress fiber formation and focal adhesion assembly. J Biol Chem 270, 24631-

24634.

Bunzow, J.R., Van Tol, H.H., Grandy, D.K., Albert, P., Salon, J., Christie, M., Machida, C.A., Neve,

K.A., and Civelli, O. (1988). Cloning and expression of a rat D2 dopamine receptor cDNA. Nature 336,

783-787.

Burstein, E.S., Ott, T.R., Feddock, M., Ma, J.N., Fuhs, S., Wong, S., Schiffer, H.H., Brann, M.R., and

Nash, N.R. (2006). Characterization of the Mas-related gene family: structural and functional

conservation of human and rhesus MrgX receptors. Br J Pharmacol 147, 73-82.

Caraballo, R.S., and Asman, K. (2011). Epidemiology of menthol cigarette use in the United States. Tob

Induc Dis 9 Suppl 1, S1.

Carlsson, J., Coleman, R.G., Setola, V., Irwin, J.J., Fan, H., Schlessinger, A., Sali, A., Roth, B.L., and

Shoichet, B.K. (2011). Ligand discovery from a dopamine D3 receptor homology model and crystal

structure. Nat Chem Biol 7, 769-778.

Cavasotto, C.N., and Orry, A.J. (2007). Ligand docking and structure-based virtual screening in drug

discovery. Curr Top Med Chem 7, 1006-1014.

136

Chambers, C.C., Hawkins, G.D., Cramer, C.J., and D.G. Truhlar (1996). Model for aqueous solvation

based on class IV atomic charges and first solvation shell effects. J Phys Chem 100, 16385–16398

Chang, C.C., Chow, C.C., Tellier, L.C., Vattikuti, S., Purcell, S.M., and Lee, J.J. (2015). Second-

generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7.

Choi, S.S., and Lahn, B.T. (2003). Adaptive evolution of MRG, a neuron-specific gene family implicated

in nociception. Genome Res 13, 2252-2259.

Civelli, O. (2005). GPCR deorphanizations: the novel, the known and the unexpected transmitters. Trends

Pharmacol Sci 26, 15-19.

Civelli, O., Reinscheid, R.K., Zhang, Y., Wang, Z., Fredriksson, R., and Schioth, H.B. (2013). G protein-

coupled receptor deorphanizations. Annu Rev Pharmacol Toxicol 53, 127-146.

Civelli, O., Saito, Y., Wang, Z., Nothacker, H.P., and Reinscheid, R.K. (2006). Orphan GPCRs and their

ligands. Pharmacol Ther 110, 525-532.

Clark, A.J. (1937). General Pharmacology. In Heffner's Handbuch der Experimentaellen Pharmacokogie

(Berlin: Springer-Verlag).

Council, S.E., Savage, A.M., Urban, J.M., Ehlers, M.E., Skene, J.H., Platt, M.L., Dunn, R.R., and

Horvath, J.E. (2016). Diversity and evolution of the primate skin microbiome. Proc Biol Sci 283.

Crocq, M.A. (2007). Historical and cultural aspects of man's relationship with addictive drugs. Dialogues

Clin Neurosci 9, 355-361.

Davenport, A.P., Alexander, S.P., Sharman, J.L., Pawson, A.J., Benson, H.E., Monaghan, A.E., Liew,

W.C., Mpamhanga, C.P., Bonner, T.I., Neubig, R.R., et al. (2013). International Union of Basic and

Clinical Pharmacology. LXXXVIII. G protein-coupled receptor list: recommendations for new pairings

with cognate ligands. Pharmacol Rev 65, 967-986.

de Graaf, C., Kooistra, A.J., Vischer, H.F., Katritch, V., Kuijer, M., Shiroishi, M., Iwata, S., Shimamura,

T., Stevens, R.C., de Esch, I.J., et al. (2011). Crystal structure-based virtual screening for fragment-like

ligands of the human histamine H(1) receptor. J Med Chem 54, 8195-8206.

De Lean, A., Stadel, J.M., and Lefkowitz, R.J. (1980). A ternary complex model explains the agonist-

specific binding properties of the adenylate cyclase-coupled beta-adrenergic receptor. J Biol Chem 255,

7108-7117.

DeWire, S.M., Ahn, S., Lefkowitz, R.J., and Shenoy, S.K. (2007). Beta-arrestins and cell signaling. Annu

Rev Physiol 69, 483-510.

Diaz, E., and Morales, H. (2016). Spinal Cord Anatomy and Clinical Syndromes. Semin Ultrasound CT

MR 37, 360-371.

Dong, X., Han, S., Zylka, M.J., Simon, M.I., and Anderson, D.J. (2001). A diverse family of GPCRs

expressed in specific subsets of nociceptive sensory neurons. Cell 106, 619-632.

137

Ehrlich, P. (1908). Nobel Lecture: Partial cell functions. In Nobel Lectures, Physiology or Medicine

1901-1921 (Amsterdam: Elsevier Publishing Company).

Eswar, N., Webb, B., Marti-Renom, M.A., Madhusudhan, M.S., Eramian, D., Shen, M.Y., Pieper, U.,

Sali, A. (2007). Comparative protein structure modeling using MODELLER. In Curr Protoc Protein Sci

Evers, A., and Klebe, G. (2004). Ligand-supported homology modeling of g-protein-coupled receptor

sites: models sufficient for successful virtual screening. Angew Chem Int Ed Engl 43, 248-251.

Fang, X., Djouhri, L., McMullan, S., Berry, C., Waxman, S.G., Okuse, K., and Lawson, S.N. (2006).

Intense isolectin-B4 binding in rat dorsal root ganglion neurons distinguishes C-fiber nociceptors with

broad action potentials and high Nav1.9 expression. J Neurosci 26, 7281-7292.

Fargin, A., Raymond, J.R., Lohse, M.J., Kobilka, B.K., Caron, M.G., and Lefkowitz, R.J. (1988). The

genomic clone G-21 which resembles a beta-adrenergic receptor sequence encodes the 5-HT1A receptor.

Nature 335, 358-360.

Fenalti, G., Giguere, P.M., Katritch, V., Huang, X.P., Thompson, A.A., Cherezov, V., Roth, B.L., and

Stevens, R.C. (2014). Molecular control of delta-opioid receptor signalling. Nature 506, 191-196.

Ferris Wayne, G., and Connolly, G.N. (2004). Application, function, and effects of menthol in cigarettes:

a survey of tobacco industry documents. Nicotine Tob Res 6 Suppl 1, S43-54.

Filizola, M., and Devi, L.A. (2013). Grand opening of structure-guided design for novel opioids. Trends

Pharmacol Sci 34, 6-12.

Fredriksson, R., Lagerstrom, M.C., Lundin, L.G., and Schioth, H.B. (2003). The G-protein-coupled

receptors in the human genome form five main families. Phylogenetic analysis, paralogon groups, and

fingerprints. Mol Pharmacol 63, 1256-1272.

Fredriksson, R., and Schioth, H.B. (2005). The repertoire of G-protein-coupled receptors in fully

sequenced genomes. Mol Pharmacol 67, 1414-1425.

Fujitani, S., and Yada, T. (1994). A novel D-phenylalanine-derivative hypoglycemic agent A-4166

increases cytosolic free Ca2+ in rat pancreatic beta-cells by stimulating Ca2+ influx. Endocrinology 134,

1395-1400.

Gaddum, J.H. (1937). The quantitative effects of antagonistic drugs. Journal of Physiology London 89, 7-

9.

Galandrin, S., and Bouvier, M. (2006). Distinct signaling profiles of beta1 and beta2 adrenergic receptor

ligands toward adenylyl cyclase and mitogen-activated protein kinase reveals the pluridimensionality of

efficacy. Mol Pharmacol 70, 1575-1584.

Gaudenzio, N., Sibilano, R., Marichal, T., Starkl, P., Reber, L.L., Cenac, N., McNeil, B.D., Dong, X.,

Hernandez, J.D., Sagi-Eisenberg, R., et al. (2016). Different activation signals induce distinct mast cell

degranulation strategies. J Clin Invest 126, 3981-3998.

Gilman, A.G. (1995). Nobel Lecture. G proteins and regulation of adenylyl cyclase. Biosci Rep 15, 65-97.

138

Giovino, G.A., Sidney, S., Gfroerer, J.C., O'Malley, P.M., Allen, J.A., Richter, P.A., and Cummings,

K.M. (2004). Epidemiology of menthol cigarette use. Nicotine Tob Res 6 Suppl 1, S67-81.

Granier, S., Manglik, A., Kruse, A.C., Kobilka, T.S., Thian, F.S., Weis, W.I., and Kobilka, B.K. (2012).

Structure of the delta-opioid receptor bound to naltrindole. Nature 485, 400-404.

Grazzini, E., Puma, C., Roy, M.O., Yu, X.H., O'Donnell, D., Schmidt, R., Dautrey, S., Ducharme, J.,

Perkins, M., Panetta, R., et al. (2004). Sensory neuron-specific receptor activation elicits central and

peripheral nociceptive effects in rats. Proc Natl Acad Sci U S A 101, 7175-7180.

Haas, E.T., Rowland, K., and Gautam, M. (2011). Tooth injury increases expression of the cold sensitive

TRP channel TRPA1 in trigeminal neurons. Arch Oral Biol 56, 1604-1609.

Harris, C.J., Hill, R.D., Sheppard, D.W., Slater, M.J., and Stouten, P.F. (2011). The design and

application of target-focused compound libraries. Comb Chem High Throughput Screen 14, 521-531.

Hassan, M., Kjos, M., Nes, I.F., Diep, D.B., and Lotfipour, F. (2012). Natural antimicrobial peptides from

bacteria: characteristics and potential applications to fight against antibiotic resistance. J Appl Microbiol

113, 723-736.

Hert, J., Keiser, M.J., Irwin, J.J., Oprea, T.I., and Shoichet, B.K. (2008). Quantifying the relationships

among drug classes. J Chem Inf Model 48, 755-765.

Hill, A.V. (1910). The possible effects of the aggregation of the molecules of haemoglobin on its

dissociation curves. Journal of Physiology 40, iv-vii.

Huang, W., Manglik, A., Venkatakrishnan, A.J., Laeremans, T., Feinberg, E.N., Sanborn, A.L., Kato,

H.E., Livingston, K.E., Thorsen, T.S., Kling, R.C., et al. (2015a). Structural insights into micro-opioid

receptor activation. Nature 524, 315-321.

Huang, X.P., Karpiak, J., Kroeze, W.K., Zhu, H., Chen, X., Moy, S.S., Saddoris, K.A., Nikolova, V.D.,

Farrell, M.S., Wang, S., et al. (2015b). Allosteric ligands for the pharmacologically dark receptors GPR68

and GPR65. Nature 527, 477-483.

Hughes, J., Smith, T.W., Kosterlitz, H.W., Fothergill, L.A., Morgan, B.A., and Morris, H.R. (1975).

Identification of two related pentapeptides from the brain with potent opiate agonist activity. Nature 258,

577-580.

Hurowitz, E.H., Melnyk, J.M., Chen, Y.J., Kouros-Mehr, H., Simon, M.I., and Shizuya, H. (2000).

Genomic characterization of the human heterotrimeric G protein alpha, beta, and gamma subunit genes.

DNA Res 7, 111-120.

Hutchinson, M.R., Shavit, Y., Grace, P.M., Rice, K.C., Maier, S.F., and Watkins, L.R. (2011). Exploring

the neuroimmunopharmacology of opioids: an integrative review of mechanisms of central immune

signaling and their implications for opioid analgesia. Pharmacol Rev 63, 772-810.

Irwin, J.J. (2006). How good is your screening library? Curr Opin Chem Biol 10, 352-356.

139

Irwin, J.J., and Shoichet, B.K. (2005). ZINC--a free database of commercially available compounds for

virtual screening. J Chem Inf Model 45, 177-182.

Irwin, J.J., and Shoichet, B.K. (2016). Docking Screens for Novel Ligands Conferring New Biology. J

Med Chem 59, 4103-4120.

Isberg, V., Andersen, K.B., Bisig, C., Dietz, G.P., Brauner-Osborne, H., and Gloriam, D.E. (2014).

Computer-aided discovery of aromatic l-alpha-amino acids as agonists of the orphan G protein-coupled

receptor GPR139. J Chem Inf Model 54, 1553-1557.

Jacobson, M.P. (2002). Epilepsy in Aging Populations. Curr Treat Options Neurol 4, 19-30.

Jacobson, M.P., Friesner, R.A., Xiang, Z., and Honig, B. (2002). On the role of the crystal environment in

determining protein side-chain conformations. J Mol Biol 320, 597-608.

Jacquet, Y.F., Klee, W.A., Rice, K.C., Iijima, I., and Minamikawa, J. (1977). Stereospecific and

nonstereospecific effects of (+)- and (-)-morphine: evidence for a new class of receptors? Science 198,

842-845.

Janzen, W.P. (2014). Screening technologies for small molecule discovery: the state of the art. Chem Biol

21, 1162-1170.

Jazayeri, A., Dias, J.M., and Marshall, F.H. (2015). From G Protein-coupled Receptor Structure

Resolution to Rational Drug Design. J Biol Chem 290, 19489-19495.

Johnson, T., and Siegel, D. (2014). Complanadine A, a selective agonist for the Mas-related G protein-

coupled receptor X2. Bioorg Med Chem Lett 24, 3512-3515.

Jordan, M., Schallhorn, A., and Wurm, F.M. (1996). Transfecting mammalian cells: optimization of

critical parameters affecting calcium-phosphate precipitate formation. Nucleic Acids Res 24, 596-601.

Julius, D. (2013). TRP channels and pain. Annu Rev Cell Dev Biol 29, 355-384.

Kamohara, M., Matsuo, A., Takasaki, J., Kohda, M., Matsumoto, M., Matsumoto, S., Soga, T., Hiyama,

H., Kobori, M., and Katou, M. (2005). Identification of MrgX2 as a human G-protein-coupled receptor

for proadrenomedullin N-terminal peptides. Biochem Biophys Res Commun 330, 1146-1152.

Karashima, Y., Talavera, K., Everaerts, W., Janssens, A., Kwan, K.Y., Vennekens, R., Nilius, B., and

Voets, T. (2009). TRPA1 acts as a cold sensor in vitro and in vivo. Proc Natl Acad Sci U S A 106, 1273-

1278.

Kashem, S.W., Subramanian, H., Collington, S.J., Magotti, P., Lambris, J.D., and Ali, H. (2011). G

protein coupled receptor specificity for C3a and compound 48/80-induced degranulation in human mast

cells: roles of Mas-related genes MrgX1 and MrgX2. Eur J Pharmacol 668, 299-304.

Keiser, M.J., Roth, B.L., Armbruster, B.N., Ernsberger, P., Irwin, J.J., and Shoichet, B.K. (2007).

Relating protein pharmacology by ligand chemistry. Nat Biotechnol 25, 197-206.

Kenakin, T. (2004). Principles: receptor theory in pharmacology. Trends Pharmacol Sci 25, 186-192.

140

Kenakin, T. (2009a). Quantifying biological activity in chemical terms: a pharmacology primer to

describe drug effect. ACS Chem Biol 4, 249-260.

Kenakin, T. (2013). New concepts in pharmacological efficacy at 7TM receptors: IUPHAR review 2. Br J

Pharmacol 168, 554-575.

Kenakin, T., Watson, C., Muniz-Medina, V., Christopoulos, A., and Novick, S. (2012). A simple method

for quantifying functional selectivity and agonist bias. ACS Chem Neurosci 3, 193-203.

Kenakin, T.P. (2009b). '7TM receptor allostery: putting numbers to shapeshifting proteins. Trends

Pharmacol Sci 30, 460-469.

Khan, S.M., Sleno, R., Gora, S., Zylbergold, P., Laverdure, J.P., Labbe, J.C., Miller, G.J., and Hebert,

T.E. (2013). The expanding roles of Gbetagamma subunits in G protein-coupled receptor signaling and

drug action. Pharmacol Rev 65, 545-577.

Kirchner, T.R., Cantrell, J., Anesetti-Rothermel, A., Ganz, O., Vallone, D.M., and Abrams, D.B. (2013).

Geospatial exposure to point-of-sale tobacco: real-time craving and smoking-cessation outcomes. Am J

Prev Med 45, 379-385.

Kirshenbaum, A.S., Akin, C., Wu, Y., Rottem, M., Goff, J.P., Beaven, M.A., Rao, V.K., and Metcalfe,

D.D. (2003). Characterization of novel stem cell factor responsive human mast cell lines LAD 1 and 2

established from a patient with mast cell sarcoma/leukemia; activation following aggregation of

FcepsilonRI or FcgammaRI. Leuk Res 27, 677-682.

Knight, Z.A., and Shokat, K.M. (2007). Chemical genetics: where genetics and pharmacology meet. Cell

128, 425-430.

Koenig, J.A., and Edwardson, J.M. (1994). Kinetic analysis of the trafficking of muscarinic acetylcholine

receptors between the plasma membrane and intracellular compartments. J Biol Chem 269, 17174-17182.

Kojima, M., Hosoda, H., Date, Y., Nakazato, M., Matsuo, H., and Kangawa, K. (1999). Ghrelin is a

growth-hormone-releasing acylated peptide from stomach. Nature 402, 656-660.

Kolb, P., Rosenbaum, D.M., Irwin, J.J., Fung, J.J., Kobilka, B.K., and Shoichet, B.K. (2009). Structure-

based discovery of beta2-adrenergic receptor ligands. Proc Natl Acad Sci U S A 106, 6843-6848.

Kostenis, E. (2001). Is Galpha16 the optimal tool for fishing ligands of orphan G-protein-coupled

receptors? Trends Pharmacol Sci 22, 560-564.

Kosterlitz, H.W., and Leslie, F.M. (1978). Comparison of the receptor binding characteristics of opiate

agonists interacting with mu- or kappa-receptors. Br J Pharmacol 64, 607-614.

Kreek, M.J. (1996). Opiates, opioids and addiction. Mol Psychiatry 1, 232-254.

Kroeze, W.K., Sassano, M.F., Huang, X.P., Lansu, K., McCorvy, J.D., Giguere, P.M., Sciaky, N., and

Roth, B.L. (2015). PRESTO-Tango as an open-source resource for interrogation of the druggable human

GPCRome. Nat Struct Mol Biol 22, 362-369.

141

Kuehn, H.S., and Gilfillan, A.M. (2007). G protein-coupled receptors and the modification of

FcepsilonRI-mediated mast cell activation. Immunol Lett 113, 59-69.

Kumar, K., and Singh, S.I. (2013). Neuraxial opioid-induced pruritus: An update. J Anaesthesiol Clin

Pharmacol 29, 303-307.

Kurz, A., and Sessler, D.I. (2003). Opioid-induced bowel dysfunction: pathophysiology and potential new

therapies. Drugs 63, 649-671.

Langley, J.N. (1905). On the reaction of cells and of nerve-endings to certain poisons, chiefly as regards

the reaction of striated muscle to nicotine and to curari. J Physiol 33, 374-413.

Lansu, K., Karpiak, J., Liu, J., Huang, X.P., McCorvy, J.D., Kroeze, W.K., Che, T., Nagase, H., Carroll,

F.I., Jin, J., et al. (2017). In silico design of novel probes for the atypical opioid receptor MRGPRX2. Nat

Chem Biol 13, 529-536.

Le Pichon, C.E., and Chesler, A.T. (2014). The functional and anatomical dissection of somatosensory

subpopulations using mouse genetics. Front Neuroanat 8, 21.

Lee, M.G., Dong, X., Liu, Q., Patel, K.N., Choi, O.H., Vonakis, B., and Undem, B.J. (2008). Agonists of

the MAS-related gene (Mrgs) orphan receptors as novel mediators of mast cell-sensory nerve interactions.

J Immunol 180, 2251-2255.

Lefkowitz, R.J. (1998). G protein-coupled receptors. III. New roles for receptor kinases and beta-arrestins

in receptor signaling and desensitization. J Biol Chem 273, 18677-18680.

Lefkowitz, R.J., and Haber, E. (1971). A fraction of the ventricular myocardium that has the specificity of

the cardiac beta-adrenergic receptor. Proc Natl Acad Sci U S A 68, 1773-1777.

Lefkowitz, R.J., and Shenoy, S.K. (2005). Transduction of receptor signals by beta-arrestins. Science 308,

512-517.

Lembo, P.M., Grazzini, E., Groblewski, T., O'Donnell, D., Roy, M.O., Zhang, J., Hoffert, C., Cao, J.,

Schmidt, R., Pelletier, M., et al. (2002). Proenkephalin A gene products activate a new family of sensory

neuron--specific GPCRs. Nat Neurosci 5, 201-209.

Lemke, T.L., Williams, D.A., Roche, V.F., and Zito, S.W. (2013). Foye's Principles of Medicinal

Chemistry: Seventh Edition.

Li, J., Zhu, T., Cramer, C.J., and D.G. Truhlar (1998). New class IV charge model for extracting accurate

partial charges from wave functions. J Phys Chem A 102, 1820-1831.

Li, Q., Tachie-Baffour, Y., Liu, Z., Baldwin, M.W., Kruse, A.C., and Liberles, S.D. (2015). Non-classical

amine recognition evolved in a large clade of olfactory receptors. Elife 4, e10441.

Li, Z., Tseng, P.Y., Tiwari, V., Xu, Q., He, S.Q., Wang, Y., Zheng, Q., Han, L., Wu, Z., Blobaum, A.L.,

et al. (2017). Targeting human Mas-related G protein-coupled receptor X1 to inhibit persistent pain. Proc

Natl Acad Sci U S A 114, E1996-E2005.

142

Liman, E.R. (2012). Changing senses: chemosensory signaling and primate evolution. Adv Exp Med Biol

739, 206-217.

Lin, H., Sassano, M.F., Roth, B.L., and Shoichet, B.K. (2013). A pharmacological organization of G

protein-coupled receptors. Nat Methods 10, 140-146.

Liu, Q., Tang, Z., Surdenikova, L., Kim, S., Patel, K.N., Kim, A., Ru, F., Guan, Y., Weng, H.J., Geng, Y.,

et al. (2009). Sensory neuron-specific GPCR Mrgprs are itch receptors mediating chloroquine-induced

pruritus. Cell 139, 1353-1365.

Liu, Y., Yang, F.C., Okuda, T., Dong, X., Zylka, M.J., Chen, C.L., Anderson, D.J., Kuner, R., and Ma, Q.

(2008). Mechanisms of compartmentalized expression of Mrg class G-protein-coupled sensory receptors.

J Neurosci 28, 125-132.

Loh, H.H., Cho, T.M., Wu, Y.C., and Way, E.L. (1974). Stereospecific binding of narcotics to brain

cerebrosides. Life Sci 14, 2231-2245.

Lohse, M.J., Benovic, J.L., Codina, J., Caron, M.G., and Lefkowitz, R.J. (1990). beta-Arrestin: a protein

that regulates beta-adrenergic receptor function. Science 248, 1547-1550.

Lord, J.A., Waterfield, A.A., Hughes, J., and Kosterlitz, H.W. (1977). Endogenous opioid peptides:

multiple agonists and receptors. Nature 267, 495-499.

Luttrell, L.M., Ferguson, S.S., Daaka, Y., Miller, W.E., Maudsley, S., Della Rocca, G.J., Lin, F.,

Kawakatsu, H., Owada, K., Luttrell, D.K., et al. (1999). Beta-arrestin-dependent formation of beta2

adrenergic receptor-Src protein kinase complexes. Science 283, 655-661.

Luttrell, L.M., and Lefkowitz, R.J. (2002). The role of beta-arrestins in the termination and transduction

of G-protein-coupled receptor signals. J Cell Sci 115, 455-465.

Lutz, P.E., and Kieffer, B.L. (2013). Opioid receptors: distinct roles in mood disorders. Trends Neurosci

36, 195-206.

Malik, L., Kelly, N.M., Ma, J.N., Currier, E.A., Burstein, E.S., and Olsson, R. (2009). Discovery of non-

peptidergic MrgX1 and MrgX2 receptor agonists and exploration of an initial SAR using solid-phase

synthesis. Bioorg Med Chem Lett 19, 1729-1732.

Manglik, A., Kruse, A.C., Kobilka, T.S., Thian, F.S., Mathiesen, J.M., Sunahara, R.K., Pardo, L., Weis,

W.I., Kobilka, B.K., and Granier, S. (2012). Crystal structure of the micro-opioid receptor bound to a

morphinan antagonist. Nature 485, 321-326.

Manglik, A., Lin, H., Aryal, D.K., McCorvy, J.D., Dengler, D., Corder, G., Levit, A., Kling, R.C., Bernat,

V., Hubner, H., et al. (2016). Structure-based discovery of opioid analgesics with reduced side effects.

Nature 537, 185-190.

Mason, J.S., Bortolato, A., Congreve, M., and Marshall, F.H. (2012). New insights from structural

biology into the druggability of G protein-coupled receptors. Trends Pharmacol Sci 33, 249-260.

143

Matsuda, L.A., Lolait, S.J., Brownstein, M.J., Young, A.C., and Bonner, T.I. (1990). Structure of a

cannabinoid receptor and functional expression of the cloned cDNA. Nature 346, 561-564.

McKenna, A., Hanna, M., Banks, E., Sivachenko, A., Cibulskis, K., Kernytsky, A., Garimella, K.,

Altshuler, D., Gabriel, S., Daly, M., et al. (2010). The Genome Analysis Toolkit: a MapReduce

framework for analyzing next-generation DNA sequencing data. Genome Res 20, 1297-1303.

McLeod, J.F. (2004). Clinical pharmacokinetics of nateglinide: a rapidly-absorbed, short-acting

insulinotropic agent. Clin Pharmacokinet 43, 97-120.

McNeil, B.D., Pundir, P., Meeker, S., Han, L., Undem, B.J., Kulka, M., and Dong, X. (2015).

Identification of a mast-cell-specific receptor crucial for pseudo-allergic drug reactions. Nature 519, 237-

241.

Meng, E.C., Shoichet, B.K. and Kuntz, I.D. (1992). Automated docking with grid-based energy

evaluation. . J Comp Chem 13, 505-524.

Merlin, M.D. (2003). Archaeological evidence for the tradition of psychoactive plant use in the old world.

Economic Botany 57, 295-323.

Mestres, J., and Gregori-Puigjane, E. (2009). Conciliating binding efficiency and polypharmacology.

Trends Pharmacol Sci 30, 470-474.

Metcalfe, D.D., Baram, D., and Mekori, Y.A. (1997). Mast cells. Physiol Rev 77, 1033-1079.

Meunier, J.C., Mollereau, C., Toll, L., Suaudeau, C., Moisand, C., Alvinerie, P., Butour, J.L., Guillemot,

J.C., Ferrara, P., Monsarrat, B., et al. (1995). Isolation and structure of the endogenous agonist of opioid

receptor-like ORL1 receptor. Nature 377, 532-535.

Milligan, G., and Kostenis, E. (2006). Heterotrimeric G-proteins: a short history. Br J Pharmacol 147

Suppl 1, S46-55.

Mollereau, C., Parmentier, M., Mailleux, P., Butour, J.L., Moisand, C., Chalon, P., Caput, D., Vassart, G.,

and Meunier, J.C. (1994). ORL1, a novel member of the opioid receptor family. Cloning, functional

expression and localization. FEBS Lett 341, 33-38.

Munro, S., Thomas, K.L., and Abu-Shaar, M. (1993). Molecular characterization of a peripheral receptor

for cannabinoids. Nature 365, 61-65.

Mustazza, C., and Bastanzio, G. (2011). Development of nociceptin receptor (NOP) agonists and

antagonists. Med Res Rev 31, 605-648.

Mysinger, M.M., and Shoichet, B.K. (2010). Rapid context-dependent ligand desolvation in molecular

docking. J Chem Inf Model 50, 1561-1573.

Mysinger, M.M., Weiss, D.R., Ziarek, J.J., Gravel, S., Doak, A.K., Karpiak, J., Heveker, N., Shoichet,

B.K., and Volkman, B.F. (2012). Structure-based ligand discovery for the protein-protein interface of

chemokine receptor CXCR4. Proc Natl Acad Sci U S A 109, 5517-5522.

144

Nagase, H., Yajima, Y., Fujii, H., Kawamura, K., Narita, M., Kamei, J., and Suzuki, T. (2001). The

pharmacological profile of delta opioid receptor ligands, (+) and (-) TAN-67 on pain modulation. Life Sci

68, 2227-2231.

Ndong, C., Pradhan, A., Puma, C., Morello, J.P., Hoffert, C., Groblewski, T., O'Donnell, D., and Laird,

J.M. (2009). Role of rat sensory neuron-specific receptor (rSNSR1) in inflammatory pain: contribution of

TRPV1 to SNSR signaling in the pain pathway. Pain 143, 130-137.

Ngo, T., Kufareva, I., Coleman, J., Graham, R.M., Abagyan, R., and Smith, N.J. (2016). Identifying

ligands at orphan GPCRs: current status using structure-based approaches. Br J Pharmacol 173, 2934-

2951.

Nygaard, R., Frimurer, T.M., Holst, B., Rosenkilde, M.M., and Schwartz, T.W. (2009). Ligand binding

and micro-switches in 7TM receptor structures. Trends Pharmacol Sci 30, 249-259.

O'Connor, C., White, K.L., Doncescu, N., Didenko, T., Roth, B.L., Czaplicki, G., Stevens, R.C.,

Wuthrich, K., and Milon, A. (2015). NMR structure and dynamics of the agonist dynorphin peptide

bound to the human kappa opioid receptor. Proc Natl Acad Sci U S A 112, 11852-11857.

Overington, J.P., Al-Lazikani, B., and Hopkins, A.L. (2006). How many drug targets are there? Nat Rev

Drug Discov 5, 993-996.

Paruch, K., Dwyer, M.P., Alvarez, C., Brown, C., Chan, T.Y., Doll, R.J., Keertikar, K., Knutson, C.,

McKittrick, B., Rivera, J., et al. (2010). Discovery of Dinaciclib (SCH 727965): A Potent and Selective

Inhibitor of Cyclin-Dependent Kinases. ACS Med Chem Lett 1, 204-208.

Pasternak, G.W., and Pan, Y.X. (2013). Mu opioids and their receptors: evolution of a concept.

Pharmacol Rev 65, 1257-1317.

Pasternak, G.W., and Snyder, S.H. (1975). Identification of novel high affinity opiate receptor binding in

rat brain. Nature 253, 563-565.

Pasternak, G.W., Wilson, H.A., and Snyder, S.H. (1975). Differential effects of protein-modifying

reagents on receptor binding of opiate agonists and antagonists. Mol Pharmacol 11, 340-351.

Pausch, M.H. (1997). G-protein-coupled receptors in Saccharomyces cerevisiae: high-throughput

screening assays for drug discovery. Trends Biotechnol 15, 487-494.

Pei, J., Kim, B.H., and Grishin, N.V. (2008). PROMALS3D: a tool for multiple protein sequence and

structure alignments. Nucleic Acids Res 36, 2295-2300.

Pert, C.B., Pasternak, G., and Snyder, S.H. (1973). Opiate agonists and antagonists discriminated by

receptor binding in brain. Science 182, 1359-1361.

Podvin, S., Yaksh, T., and Hook, V. (2016). The Emerging Role of Spinal Dynorphin in Chronic Pain: A

Therapeutic Perspective. Annu Rev Pharmacol Toxicol 56, 511-533.

Preininger, A.M., and Hamm, H.E. (2004). G protein signaling: insights from new structures. Sci STKE

2004, re3.

145

Price, A.L., Patterson, N.J., Plenge, R.M., Weinblatt, M.E., Shadick, N.A., and Reich, D. (2006).

Principal components analysis corrects for stratification in genome-wide association studies. Nat Genet

38, 904-909.

Rajagopal, S., Kim, J., Ahn, S., Craig, S., Lam, C.M., Gerard, N.P., Gerard, C., and Lefkowitz, R.J.

(2010). Beta-arrestin- but not G protein-mediated signaling by the "decoy" receptor CXCR7. Proc Natl

Acad Sci U S A 107, 628-632.

Rang, H.P. (2006). The receptor concept: pharmacology's big idea. Br J Pharmacol 147 Suppl 1, S9-16.

Rask-Andersen, M., Masuram, S., and Schioth, H.B. (2014). The druggable genome: Evaluation of drug

targets in clinical trials suggests major shifts in molecular class and indication. Annu Rev Pharmacol

Toxicol 54, 9-26.

Robas, N., Mead, E., and Fidock, M. (2003). MrgX2 is a high potency cortistatin receptor expressed in

dorsal root ganglion. J Biol Chem 278, 44400-44404.

Rodbell, M. (1980). The role of hormone receptors and GTP-regulatory proteins in membrane

transduction. Nature 284, 17-22.

Rodbell, M. (1995). Nobel Lecture. Signal transduction: evolution of an idea. Biosci Rep 15, 117-133.

Rojewska, E., Makuch, W., Przewlocka, B., and Mika, J. (2014). Minocycline prevents dynorphin-

induced neurotoxicity during neuropathic pain in rats. Neuropharmacology 86, 301-310.

Rosenstock, J., Hassman, D.R., Madder, R.D., Brazinsky, S.A., Farrell, J., Khutoryansky, N., Hale, P.M.,

and Repaglinide Versus Nateglinide Comparison Study, G. (2004). Repaglinide versus nateglinide

monotherapy: a randomized, multicenter study. Diabetes Care 27, 1265-1270.

Rosow, C.E., Moss, J., Philbin, D.M., and Savarese, J.J. (1982). Histamine release during morphine and

fentanyl anesthesia. Anesthesiology 56, 93-96.

Ross, E.M., and Gilman, A.G. (1977). Resolution of some components of adenylate cyclase necessary for

catalytic activity. J Biol Chem 252, 6966-6969.

Ross, E.M., Haga, T., Howlett, A.C., Schwarzmeier, J., Schleifer, L.S., and Gilman, A.G. (1978).

Hormone-sensitive adenylate cyclase: resolution and reconstitution of some components necessary for

regulation of the enzyme. Adv Cyclic Nucleotide Res 9, 53-68.

Roth, B.L., and Kroeze, W.K. (2015). Integrated Approaches for Genome-wide Interrogation of the

Druggable Non-olfactory G Protein-coupled Receptor Superfamily. J Biol Chem 290, 19471-19477.

Ryall, R. (1989). Pain and analgesia. In Mechanisms of Drug Action on the Nervous System (Cambridge:

Cambridge University Press).

Sakurai, T., Amemiya, A., Ishii, M., Matsuzaki, I., Chemelli, R.M., Tanaka, H., Williams, S.C.,

Richardson, J.A., Kozlowski, G.P., Wilson, S., et al. (1998). Orexins and orexin receptors: a family of

hypothalamic neuropeptides and G protein-coupled receptors that regulate feeding behavior. Cell 92, 573-

585.

146

Samama, P., Cotecchia, S., Costa, T., and Lefkowitz, R.J. (1993). A mutation-induced activated state of

the beta 2-adrenergic receptor. Extending the ternary complex model. J Biol Chem 268, 4625-4636.

Sato, S., Huang, X.P., Kroeze, W.K., and Roth, B.L. (2016). Discovery and Characterization of Novel

GPR39 Agonists Allosterically Modulated by Zinc. Mol Pharmacol 90, 726-737.

Scimemi, A., and Beato, M. (2009). Determining the neurotransmitter concentration profile at active

synapses. Mol Neurobiol 40, 289-306.

Shang, Y., and Filizola, M. (2015). Opioid receptors: Structural and mechanistic insights into

pharmacology and signaling. Eur J Pharmacol 763, 206-213.

Sharp, K.A. (1995). Polyelectrolyte electrostatics: Salt dependence, entropic, and enthalpic contributions

to free energy in the nonlinear Poisson–Boltzmann model. Biopolymers 36, 227-243.

Shinkai, H., Nishikawa, M., Sato, Y., Toi, K., Kumashiro, I., Seto, Y., Fukuma, M., Dan, K., and

Toyoshima, S. (1989). N-(cyclohexylcarbonyl)-D-phenylalanines and related compounds. A new class of

oral hypoglycemic agents. 2. J Med Chem 32, 1436-1441.

Shinkai, H., Toi, K., Kumashiro, I., Seto, Y., Fukuma, M., Dan, K., and Toyoshima, S. (1988). N-

acylphenylalanines and related compounds. A new class of oral hypoglycemic agents. J Med Chem 31,

2092-2097.

Shook, J.E., Watkins, W.D., and Camporesi, E.M. (1990). Differential roles of opioid receptors in

respiration, respiratory disease, and opiate-induced respiratory depression. Am Rev Respir Dis 142, 895-

909.

Simon, M.I., Strathmann, M.P., and Gautam, N. (1991). Diversity of G proteins in signal transduction.

Science 252, 802-808.

Smith, E.S., and Lewin, G.R. (2009). Nociceptors: a phylogenetic view. J Comp Physiol A Neuroethol

Sens Neural Behav Physiol 195, 1089-1106.

Snyder, S.H., and Pasternak, G.W. (2003). Historical review: Opioid receptors. Trends Pharmacol Sci 24,

198-205.

Solinski, H.J., Gudermann, T., and Breit, A. (2014). Pharmacology and signaling of MAS-related G

protein-coupled receptors. Pharmacol Rev 66, 570-597.

Southern, C., Cook, J.M., Neetoo-Isseljee, Z., Taylor, D.L., Kettleborough, C.A., Merritt, A., Bassoni,

D.L., Raab, W.J., Quinn, E., Wehrman, T.S., et al. (2013). Screening beta-arrestin recruitment for the

identification of natural ligands for orphan G-protein-coupled receptors. J Biomol Screen 18, 599-609.

Spitzer, N.C. (2004). Coincidence detection enhances appropriate wiring of the nervous system. Proc Natl

Acad Sci U S A 101, 5311-5312.

Sromek, A.W., Provencher, B.A., Russell, S., Chartoff, E., Knapp, B.I., Bidlack, J.M., and Neumeyer,

J.L. (2014). Preliminary pharmacological evaluation of enantiomeric morphinans. ACS Chem Neurosci 5,

93-99.

147

Staats, H.F., Kirwan, S.M., Choi, H.W., Shelburne, C.P., Abraham, S.N., Leung, G.Y., and Chen, D.Y.

(2013). A Mast Cell Degranulation Screening Assay for the Identification of Novel Mast Cell Activating

Agents. Medchemcomm 4.

Stephenson, R.P. (1956). A modification of receptor theory. Br J Pharmacol Chemother 11, 379-393.

Stewart, G.D., Sexton, P.M., and Christopoulos, A. (2010). Detection of novel functional selectivity at

M3 muscarinic acetylcholine receptors using a Saccharomyces cerevisiae platform. ACS Chem Biol 5,

365-375.

Subramanian, H., Gupta, K., Guo, Q., Price, R., and Ali, H. (2011a). Mas-related gene X2 (MrgX2) is a

novel G protein-coupled receptor for the antimicrobial peptide LL-37 in human mast cells: resistance to

receptor phosphorylation, desensitization, and internalization. J Biol Chem 286, 44739-44749.

Subramanian, H., Gupta, K., Lee, D., Bayir, A.K., Ahn, H., and Ali, H. (2013). beta-Defensins activate

human mast cells via Mas-related gene X2. J Immunol 191, 345-352.

Subramanian, H., Kashem, S.W., Collington, S.J., Qu, H., Lambris, J.D., and Ali, H. (2011b). PMX-53 as

a dual CD88 antagonist and an agonist for Mas-related gene 2 (MrgX2) in human mast cells. Mol

Pharmacol 79, 1005-1013.

Substance Abuse and Mental Health Services Administration, and Office of Applied Studies (2009). The

NSDUH Report: Use of Menthol Cigarettes. (Rockville, MD.).

Sweetnam, P.M., Neale, J.H., Barker, J.L., and Goldstein, A. (1982). Localization of immunoreactive

dynorphin in neurons cultured from spinal cord and dorsal root ganglia. Proc Natl Acad Sci U S A 79,

6742-6746.

Tatemoto, K., Nozaki, Y., Tsuda, R., Konno, S., Tomura, K., Furuno, M., Ogasawara, H., Edamura, K.,

Takagi, H., Iwamura, H., et al. (2006). Immunoglobulin E-independent activation of mast cell is mediated

by Mrg receptors. Biochem Biophys Res Commun 349, 1322-1328.

Terenius, L., and Wahlstrom, A. (1975). Search for an endogenous ligand for the opiate receptor. Acta

Physiol Scand 94, 74-81.

Thompson, A.A., Liu, W., Chun, E., Katritch, V., Wu, H., Vardy, E., Huang, X.P., Trapella, C., Guerrini,

R., Calo, G., et al. (2012). Structure of the nociceptin/orphanin FQ receptor in complex with a peptide

mimetic. Nature 485, 395-399.

Thomsen, W., Frazer, J., and Unett, D. (2005). Functional assays for screening GPCR targets. Curr Opin

Biotechnol 16, 655-665.

Tobacco Products Scientific Advisory Committee (2011). Menthol Cigarettes and Public Health: Review

of the Scientific Evidence and Recommendations (Rockville, MD: Center for Tobacco Products, Food

and Drug Administration).

Toll, L., Bruchas, M.R., Calo, G., Cox, B.M., and Zaveri, N.T. (2016). Nociceptin/Orphanin FQ Receptor

Structure, Signaling, Ligands, Functions, and Interactions with Opioid Systems. Pharmacol Rev 68, 419-

457.

148

Twaites, B., Wilton, L.V., Layton, D., and Shakir, S.A. (2007). Safety of nateglinide as used in general

practice in England: results of a prescription-event monitoring study. Acta Diabetol 44, 233-239.

Urban, J.D., Clarke, W.P., von Zastrow, M., Nichols, D.E., Kobilka, B., Weinstein, H., Javitch, J.A.,

Roth, B.L., Christopoulos, A., Sexton, P.M., et al. (2007). Functional selectivity and classical concepts of

quantitative pharmacology. J Pharmacol Exp Ther 320, 1-13.

van Diest, S.A., Stanisor, O.I., Boeckxstaens, G.E., de Jonge, W.J., and van den Wijngaard, R.M. (2012).

Relevance of mast cell-nerve interactions in intestinal nociception. Biochim Biophys Acta 1822, 74-84.

Vardy, E., Mosier, P.D., Frankowski, K.J., Wu, H., Katritch, V., Westkaemper, R.B., Aube, J., Stevens,

R.C., and Roth, B.L. (2013). Chemotype-selective modes of action of kappa-opioid receptor agonists. J

Biol Chem 288, 34470-34483.

Victor, R.G., Haley, R.W., Willett, D.L., Peshock, R.M., Vaeth, P.C., Leonard, D., Basit, M., Cooper,

R.S., Iannacchione, V.G., Visscher, W.A., et al. (2004). The Dallas Heart Study: a population-based

probability sample for the multidisciplinary study of ethnic differences in cardiovascular health. Am J

Cardiol 93, 1473-1480.

von Zastrow, M., and Kobilka, B.K. (1994). Antagonist-dependent and -independent steps in the

mechanism of adrenergic receptor internalization. J Biol Chem 269, 18448-18452.

Vrontou, S., Wong, A.M., Rau, K.K., Koerber, H.R., and Anderson, D.J. (2013). Genetic identification of

C fibres that detect massage-like stroking of hairy skin in vivo. Nature 493, 669-673.

Wacker, D., Stevens, R.C., and Roth, B.L. (2017). How Ligands Illuminate GPCR Molecular

Pharmacology. Cell 170, 414-427.

Wacker, D., Wang, C., Katritch, V., Han, G.W., Huang, X.P., Vardy, E., McCorvy, J.D., Jiang, Y., Chu,

M., Siu, F.Y., et al. (2013). Structural features for functional selectivity at serotonin receptors. Science

340, 615-619.

Waldhoer, M., Bartlett, S.E., and Whistler, J.L. (2004). Opioid receptors. Annu Rev Biochem 73, 953-

990.

Wang, M.H., Chang, C.K., Cheng, J.H., Wu, H.T., Li, Y.X., and Cheng, J.T. (2008). Activation of opioid

mu-receptor by sinomenine in cell and mice. Neurosci Lett 443, 209-212.

Weiss, J.M., Morgan, P.H., Lutz, M.W., and Kenakin, T.P. (1996). The cubic ternary complex receptor-

occupancy model. III. resurrecting efficacy. J Theor Biol 181, 381-397.

White, K.L., Scopton, A.P., Rives, M.L., Bikbulatov, R.V., Polepally, P.R., Brown, P.J., Kenakin, T.,

Javitch, J.A., Zjawiony, J.K., and Roth, B.L. (2014). Identification of novel functionally selective kappa-

opioid receptor scaffolds. Mol Pharmacol 85, 83-90.

Williams, E.K., Chang, R.B., Strochlic, D.E., Umans, B.D., Lowell, B.B., and Liberles, S.D. (2016).

Sensory Neurons that Detect Stretch and Nutrients in the Digestive System. Cell 166, 209-221.

149

Wooten, M., Weng, H.J., Hartke, T.V., Borzan, J., Klein, A.H., Turnquist, B., Dong, X., Meyer, R.A., and

Ringkamp, M. (2014). Three functionally distinct classes of C-fibre nociceptors in primates. Nat

Commun 5, 4122.

Wu, H., Wacker, D., Mileni, M., Katritch, V., Han, G.W., Vardy, E., Liu, W., Thompson, A.A., Huang,

X.P., Carroll, F.I., et al. (2012). Structure of the human kappa-opioid receptor in complex with JDTic.

Nature 485, 327-332.

Wu, H.E., Schwasinger, E.T., Terashvili, M., and Tseng, L.F. (2007). dextro-Morphine attenuates the

morphine-produced conditioned place preference via the sigma(1) receptor activation in the rat. Eur J

Pharmacol 562, 221-226.

Xing, G., Lin, C.Y., Wooding, S.P., and Xing, C. (2012). Blindly using Wald's test can miss rare disease-

causal variants in case-control association studies. Ann Hum Genet 76, 168-177.

Yamasaki, H. (1976). Pharmacology of sinomenine, an anti-rheumatic alkaloid from Sinomenium

acutum. Acta Med Okayama 30, 1-20.

Yang, Q., and Sharp, K.A. (2009). Building alternate protein structures using the elastic network model.

Proteins 74, 682-700.

Zajac, M., Andrzejczyk, A., Kuich, A., Tyranska-Fobke, A., Waldman, W., and Sein Anand, J. (2013).

[Recreational usage of dextromethorphan--analysis based on internet users experiences]. Przegl Lek 70,

525-527.

Zhang, J.H., Chung, T.D., and Oldenburg, K.R. (1999). A Simple Statistical Parameter for Use in

Evaluation and Validation of High Throughput Screening Assays. J Biomol Screen 4, 67-73.

Zhang, R., and Xie, X. (2012). Tools for GPCR drug discovery. Acta Pharmacol Sin 33, 372-384.

Zheng, J.P., Song, M., Zhan, X.X., Li, C.Z., Zong, X.Y., and Zhang, Y.Z. (2014). Endoscopic approach to

the trigeminal nerve: an anatomic study. J Craniomaxillofac Surg 42, 674-682.

Zheng, Z., Huang, X.P., Mangano, T.J., Zou, R., Chen, X., Zaidi, S.A., Roth, B.L., Stevens, R.C., and

Katritch, V. (2017). Structure-Based Discovery of New Antagonist and Biased Agonist Chemotypes for

the Kappa Opioid Receptor. J Med Chem 60, 3070-3081.

Zylka, M.J., Dong, X., Southwell, A.L., and Anderson, D.J. (2003). Atypical expansion in mice of the

sensory neuron-specific Mrg G protein-coupled receptor family. Proc Natl Acad Sci U S A 100, 10043-

10048.

Zylka, M.J., Rice, F.L., and Anderson, D.J. (2005). Topographically distinct epidermal nociceptive

circuits revealed by axonal tracers targeted to Mrgprd. Neuron 45, 17-25.