The Immunomodulatory Effects of Nociceptin – Development and Validation of a Biosensor Assay

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THE IMMUNOMODULATORY EFFECTS OF NOCICEPTIN – DEVELOPMENT AND VALIDATION OF A BIOSENSOR ASSAY

Thesis submitted for the degree of

Doctor of Philosophy

at the University of Leicester

Christopher Paul Hebbes MBChB BSc MMedSci FRCA FFICM

Department of Cardiovascular Sciences

February 2021

Supervisors

Professor Jonathan P. Thompson

Professor David G. Lambert

I. Abstract The immunomodulatory effects of nociceptin – development and validation of a biosensor assay

Christopher Hebbes

Opioids have long been associated with increased susceptibility to infectious diseases, and the role of the classical opioids in immunomodulation is a subject of some debate. A fourth opioid-like receptor, sharing significant sequence homology, and Gi mechanism, to the classical MOP, DOP and KOP opioid receptors has emerging evidence for its expression throughout the immune system. Furthermore, observational evidence suggests that the concentration of nociceptin, the endogenous agonist for this receptor increases in the human plasma, synovial fluid and sputum sampled in the context of inflammatory conditions. Administration of exogenous nociceptin worsens mortality in an animal model of sepsis, further suggestive that this non-classical opioid may have an immunomodulatory role in-vivo.

Despite limited observational evidence, localising the source of the increased nociceptin concentration has been challenging, because of its low concentration and problems with existing use of ELISA and RIA tests. Chimeric G-proteins facilitate non-classical coupling between intracellular pathways, allowing the use of new markers as a readout of receptor activation, and therefore local detection of agonist ligands and their concentration.

This work validated the design of a chimeric G-protein based biosensor test to detect nociceptin release from a single cell. In principle, the biosensor facilitated nociceptin receptor coupling via Gq to calcium flux for a non-invasive and non-radioactive readout of receptor activation. The validated test was used to investigate nociceptin release from immunocytes (and subtypes) from healthy individuals and patients admitted to the intensive care unit with sepsis. These data were correlated with additional PCR and immunohistochemistry tests to determine expression of nociceptin, its receptor and their precursors.

In general, mixed granulocytes were found to release nociceptin. In evaluating immunocyte subsets, both nociceptin and its receptor are found in eosinophils and neutrophils from individuals with sepsis, and when incubated in an environment mimicking sepsis, suggesting upregulation of this system in sepsis.

II. Acknowledgements Science is built on the hard work of others in a quest for new knowledge, and this work is no exception. Firstly, I thank my supervisors Jonathan Thompson and Dave Lambert for their patience, thoughtful critique and encouragement. Much of the groundwork for this project was undertaken by Simon Scott and Mark Bird, to whom I am indebted.

As a physician-scientist, the combination of two disciplines has been challenging – and I thank my clinical and basic science colleagues for their support in this endeavour. Notably, I am indebted to the lab team, John, Mark, Laki, Barbara and Jonathan for their careful instruction, advice, and comments, to Jenny for her flow cytometry expertise, and to Clett for his NovoStar tuition.

As a firm believer in open source, I am grateful to colleagues at the data science breakfast club and the #rstats community for introducing me to the world of R. To this end, much of this project is built upon open source software, Python, R, Inkscape, FCSAlyzer.

The work from this project is, in part funded by a BBSRC grant, and the clinical study was supported by the Royal College of Anaesthetists Ernest Leech fund. The use of the confocal microscope was supported by the Advanced Imaging Facility at the University of Leicester.

No man is an island, however much he might wish to be. A heartfelt thanks to my parents, and my partner Benjamin for his love and support – and finally to our dog, Oscar, for reminding me when to stop writing and go for a walk.

III. Contents

I. Abstract ...... i

II. Acknowledgements ...... ii

III. Contents ...... iii

IV. Abbreviations ...... viii

V. List of tables ...... xiii

VI. List of figures ...... xvi

1 Introduction ...... 1

1.1 General introduction ...... 1

1.2 Pharmacology of N/OFQ ...... 1

1.2.1 Structure and localisation of N/OFQ and its receptor ...... 1

1.2.2 Cellular mechanisms following NOP activation ...... 5

1.2.3 In-vivo actions of N/OFQ-NOP ...... 9

1.2.4 Key points...... 14

1.3 The immune system in health and disease ...... 15

1.3.1 Cells of the immune system ...... 18

1.3.2 Innate immunity ...... 25

1.3.3 Inflammation and Inflammatory conditions ...... 25

1.3.4 Sepsis ...... 29

1.3.5 Key points...... 34

1.4 Role of N/OFQ-NOP in the immune system ...... 35

1.4.1 Methods of investigating and characterising the NOP-N/OFQ system .... 36

1.4.2 In-vitro evidence for N/OFQ-NOP localisation ...... 46

1.4.3 Modelling inflammation ...... 53

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1.4.4 Possible mechanisms and targets for NOP-N/OFQ immune signalling .... 58

1.4.5 Key points...... 61

1.5 Aims and objectives ...... 62

2 Materials and Methods ...... 64

2.1 Project overview ...... 64

2.2 Cell Culture ...... 68

2.2.1 Protocol 1 - Maintenance of transfected immortalised cell lines ...... 68

2.2.2 Protocol 2 - Cell counting ...... 70

2.3 Fluorescence-based assays ...... 72

2.3.1 Principles of fluorescence ...... 72

2.3.2 Biological measurements using fluorophores...... 75

2.3.3 Ligands used for characterisation of cell lines and testing ...... 82

2.3.4 Principles of confocal microscopy for live samples ...... 84

2.4 Confocal microscopy ...... 88

2.4.1 Protocol 3 - Preparation of glass coverslips ...... 88

2.4.2 Protocol 4 - Live cell confocal imaging of Calcium Flux in CHO cells ...... 88

2.4.3 Protocol 5 - Immunofluorescent staining of immune cells ...... 92

2.5 Cuvette based fluorometry ...... 96

2.5.1 Protocol 6 - Cell preparation ...... 96

2.5.2 Protocol 7 - Fluorometric measurement of calcium concentration using Fura-2-AM dye ...... 97

2.5.3 Protocol 8 - Data analysis ...... 97

2.6 Extraction of Leucocytes ...... 98

2.6.1 Techniques for density gradient separation ...... 102

2.6.2 Techniques for isolation of immune cell populations by cell surface markers 108

2.7 Characterisation of immune cells by flow cytometry ...... 115

2.7.1 Principles of Flow Cytometry ...... 115

2.7.2 Protocol 14 - Validation of immune cell separation by Flow Cytometry 118

2.8 Luciferase ATP Assay ...... 124

2.8.1 Principles of luciferase-based assays ...... 124

2.8.2 Protocol 15 – Determination of ATP concentration in cell fractions using a bioluminescence-based assay ...... 124

2.9 Reverse Transcriptase Quantitative Polymerase Chain Reaction (RT-qPCR) . 127

2.9.1 Principles of PCR ...... 127

2.9.2 Protocol 16 – RT-qPCR of granulocytes for NOP and ppNoc transcripts 133

3 Cell line tests - Pharmacological characterisation of CHOhNOPGqi5 cells ...... 139

3.1 Background ...... 139

3.1.1 Aims and objectives ...... 140

3.2 Results 1 – Measurements of intracellular calcium mobilisation in whole cell

CHOhNOPGqi5 suspensions by cuvette based fluorimetry ...... 142

3.2.1 Experimental design ...... 142

3.2.2 Response of CHOhNOPGqi5 cells to N/OFQ ...... 144

3.2.3 Response of CHOhNOPGqi5 cells to ATP ...... 145

3.2.4 Response of CHOWT cells to ATP and N/OFQ ...... 146

3.3 Results 2 – Measurements of calcium responses of CHOhNOPGqi5 to test ligands by confocal fluorescence microscopy ...... 149

3.3.1 Experimental design ...... 149

3.3.2 Response of CHOhNOPGqi5 cells to N/OFQ ...... 154

3.4 Discussion and Conclusions...... 166

4 Assay development and validation ...... 172

4.1 Background ...... 172

4.1.1 Aims and objectives ...... 173

4.2 Results 3 - Optimisation and validation of leukocyte extraction ...... 174

4.2.1 Extraction of mixed PMNs ...... 174

4.2.2 Extraction of granulocyte subpopulations ...... 177

4.2.3 Discussion ...... 186

4.3 Results 4 - Optimisation of biosensor conditions ...... 188

4.3.1 CHOhNOPGqi5 seeding density ...... 188

4.3.2 PMN seeding density and distribution ...... 192

4.4 Results 5 - Response of biosensor to mixed immunocyte degranulation ..... 194

4.4.1 EOL-1 (Eosinophil like) ...... 194

4.4.2 Mixed PMN ...... 197

4.5 Results 6 - Blockade of purinergic signalling ...... 202

4.5.1 Response of CHOhNOPGiq cells to purinergic antagonists ...... 202

4.5.2 PMN ATP Luciferase assay ...... 206

4.6 Discussion and Conclusions...... 207

5 Assay application ...... 212

5.1 Background ...... 212

5.1.1 Aims and objectives ...... 212

5.2 Results 7 - Effects of mixed PMN degranulation on biosensor cells in the presence of purinergic antagonists ...... 212

5.2.1 Experimental design ...... 212

5.2.2 Pooled cellular responses ...... 214

5.2.3 Single cell effects ...... 216

5.3 Discussion and Conclusions...... 220

6 Further application and confirmatory testing ...... 224

6.1 Background ...... 224

6.1.1 Aims and objectives ...... 225

6.2 Comparing the release of N/OFQ from granulocyte subsets isolated from healthy volunteers and patients with sepsis using a biosensor-based assay ...... 226

6.2.1 Results 8 – Participant characteristics and cell yields ...... 226

6.2.2 Results 9 – N/OFQ release assay ...... 229

6.2.3 Results 10 – Immunohistochemistry ...... 246

6.2.4 Results 11 – PCR ...... 253

6.3 Conclusions and discussion ...... 255

7 General discussion, conclusions and further work ...... 261

7.1 Discussion ...... 261

7.1.1 Validation and development of a biosensor-based assay to detect N/OFQ release from immunocytes ...... 261

7.1.2 N/OFQ release from and expression in immunocytes ...... 262

7.1.3 NOP expression by granulocyte subpopulations ...... 266

7.1.4 Implications ...... 267

7.2 Conclusions and further work ...... 268

Appendix – Buffers and reagents ...... 271

Appendix – Ethical approvals (University of Leicester) ...... 273

Appendix – Ethical approvals (NHS) ...... 276

Bibliography ...... 281

Publications and grants relating to this thesis ...... 303

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IV. Abbreviations 3T3 ...... 3-day transfer, inoculum 3×105 cells

AIM ...... Absence In Melanoma

APACHE ...... Acute Physiology and Chronic Health Evaluation

APC ...... Allophycocyanin

ARC118925XX...... 5-[[5-(2,8-Dimethyl-5H-dibenzo[a,d]cyclohepten-5-yl)-3,4-dihydro-2- oxo-4-thioxo-1(2H)-pyrimidinyl]methyl]-N-2H-tetrazol-5-yl-2-furancarboxamide

ATP ...... Adenosine triphosphate

B2M ...... β2-microglobulin

BAPTA ...... (1,2-bis(o-aminophenoxy)ethane-N,N,N′,N′-tetraacetic acid)

BRET ...... Bioresonance Energy Transfer

CAM ...... Cellular Adhesion Molecule cAMP ...... 3',5'-cyclic adenosine monophosphate

CCD ...... Charge Coupled Device

CGRP ...... Calcitonin Gene-Related Peptide

CHO ...... Chinese Hamster Ovary

CHOWT ...... Wild-type Chinese Hamster Ovary

CLP ...... Caecal Ligation and Perforation

COS ...... CV-1 (simian) in Origin

Ct ...... Cycle Threshold

CTCF ...... Corrected Total Cell Fluorescence

CV ...... Coefficient of Variation

DAG ...... Diacylglycerol

DAMP ...... Damage Associated Molecular Proteins

DAPI ...... 4′,6-diamidino-2-phenylindole

DMEM ...... Dulbecco’s Minimal Essential Media

DOP ...... Delta Opioid Peptide

EDTA ...... Ethylenediaminetetraacetic acid

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EGTA ...... ethylene glycol-bis(β-aminoethyl ether)-N,N,N′,N′-tetraacetic acid

ELF1 ...... ETS transcription factor 1

ELISA ...... Enzyme Linked Immunosorbent Assay

EOL-1 ...... Eosinophil-like cell line-1

ERK ...... Extracellular Signal-Regulated Kinase

FACS ...... Fluorescent Activated Cell Sorting

FITC...... Fluorescein isothiocyanate fMLP ...... N-Formyl-Met-Leu-Phe

FRET...... Förster Resonance Energy Transfer

FSC ...... Forward-scatter

GADPH ...... Glyceraldehyde 3-phosphate dehydrogenase

GCSF ...... Granulocyte Colony Stimulating Factor gDNA ...... Genomic Deoxyribonucleic Acid

GFP ...... Green Fluorescent Protein

GIRK ...... G protein-gated Inwardly Rectifying Potassium Channels

GM-CSF ...... Granulocyte-Macrophage Colony-Stimulating Factor

GOI ...... Gene Of Interest

GPCR ...... G-Protein Coupled Receptor

Grb2 ...... Growth factor receptor-bound protein 2

GTP ...... Guanosine Triphosphate

HEK ...... Human Embryonic Kidney hNOP ...... Human Nociceptin Opioid Peptide Receptor

IFN- ...... Interferon-

IFNβ ...... Interferon-β

IUPHAR ...... International Union of Pharmacology

J-113397 ...... 1-[(3R,4R)-1-(cyclooctylmethyl)-3-(hydroxymethyl)piperidin-4-yl]-3- ethylbenzimidazol-2-one

JNK...... c-Jun N-terminal kinase

KOP ...... Kappa Opioid Peptide

LSCM ...... Laser Scanning Confocal Microscopy

MAP ...... Mean Arterial Pressure

MAPK ...... Mitogen Activated Protein Kinase

MEK ...... Mitogen-activated protein kinase kinase

MOP ...... Mu Opioid Peptide mRNA ...... Messenger Ribonucleic Acid

MRS2279 ...... (1R*,2S*)-4-[2-Chloro-6-(methylamino)-9H-purin-9-yl]-2- (phosphonooxy)bicyclo[3.1.0]hexane-1-methanol dihydrogen phosphate ester diammonium salt

MyD88 ...... Myeloid Differentiation Primary response gene 88

N/OFQ ...... Nociceptin / OrphaninFQ

18 N/OFQATTO594 ...... [Cys(ATTO 594) ]-N/OFQ-NH2

NET ...... Neutrophil Extracellular Trap

NF- ...... Nuclear Factor-

NK ...... Natural killer

NLR ...... NOD-like receptor

NLRPNucleotide-binding oligomerization domain, Leucine rich Repeat and Pyrin domain containing protein

NMDA ...... N-methyl-D-aspartate

NO ...... Nitric Oxide

NOD ...... Nucleotide Oligomerization Domain-like receptor

NOP ...... Nociceptin Opioid Peptide

ORL-1 ...... Opioid Receptor Like peptide 1

PAMP ...... Pathogen Associated Molecular Proteins

PBMCs ...... Peripheral Blood Mononuclear Cells

PBS ...... Phosphate Buffered Saline

PBST ...... Phosphate Buffered Saline with 0.1% Tween 20

PCR ...... Polymerase Chain Reaction

PE ...... Phycoerythrin

PI-3K ...... Phosphoinositide 3-kinases

PIP2 ...... Phosphatidylinositol 4,5-bisphosphate

PKA ...... Protein Kinase A

PKC ...... Protein Kinase C

PLC ...... Phospholipase C

PMN ...... Polymorphonuclear cells

PMT ...... Photomultiplier tube ppNoc ...... Prepronociceptin

PRRs ...... Pattern Recognition Receptors qPCR ...... Quantitative polymerase chain reaction qSOFA ...... Quick Sequential Organ Failure Assessment

Raf ...... Rapidly Accelerated Fibrosarcoma

RIA ...... Radio-linked Immunosorbent assays

RIG ...... Retinoic acid Inducible Gene

Rluc ...... Renilla luciferase

ROC ...... Receiver-Operating Characteristic

ROCK ...... rho-associated Protein Kinase

ROI ...... Region Of Interest

RPM ...... Revolutions Per Minute

RPMI ...... Roswell Park Memorial Institute

RT-qPCR ...... Reverse Transcriptase Quantitative Polymerase Chain Reaction

SB612111 ...... 7-[[4-(2,6-Dichlorophenyl)-1-piperidinyl]methyl]-6,7,8,9-tetrahydro-1- methyl-5H-benzocyclohepten-5-ol hydrochloride scRNA-seq ...... single cell RNA sequencing

SIGLEC8 ...... Sialic Acid Binding Ig Like Lectin 8

SIRS ...... Systemic Inflammatory Response Syndrome

SNR ...... Signal-to-noise ratio

SOFA ...... Sequential Organ Failure Assessment

SOS ...... Son of Sevenless

Src...... Proto-oncogene tyrosine-protein kinase Src

SSC ...... Side-scatter

TGF- ...... Transforming growth factor-

TLRs ...... Toll-Like-Receptors

TxRed ...... Texas Red

1 14 15 UFP-101 ...... [Nphe ,Arg ,Lys ]Nociceptin-NH2

UFP-505 (3S)-2-[(2S)-2-amino-3-(4-hydroxy-2,6-dimethylphenyl)propanoyl]-N-[2-oxo-2- (phenylmethylamino)ethyl]-3,4-dihydro-1H-isoquinoline-3-carboxamide

UTP ...... Uridine-5'-triphosphate

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V. List of tables

Table 1-1 – Classical and non-classical opioid receptor nomenclature ...... 2 Table 1-2 – Localisation of NOP ...... 3 Table 1-3 - NOP expression in the cardiovascular system ...... 12 Table 1-4 – Lineage and reference ranges for selected immunocytes in human blood 21 Table 1-5 – Inflammatory mediators ...... 28 Table 1-6 – qSOFA scoring system ...... 30 Table 1-7 – Sequential Organ Failure Score ...... 31 Table 1-8 – NOP expression within lymphoid tissues ...... 49 Table 1-9 – NOP expression within immune cells (1/2) ...... 49 Table 1-10 – NOP expression within human immune cells (2/2) ...... 50 Table 1-11 – Studies demonstrating localisation of N/OFQ and encoding mRNA within the immune system ...... 51 Table 2-1 - Project overview (1/2) ...... 66 Table 2-2 – Project overview (2/2) ...... 67 Table 2-3 – Cell culture media and supplements ...... 69 Table 2-4 – Absorption-emission spectra of common fluorophore and dyes used ...... 75 Table 2-5 – Dyes used for quantification of intracellular calcium concentrations ...... 76 Table 2-6 – Fluorescent markers used to detect and identify cells ...... 82 Table 2-7 – Settings for confocal microscopy ...... 90 Table 2-8 – Conditions for immunofluorescent ...... 95 Table 2-9 - Characteristics of separation techniques for neutrophil isolation ...... 100 Table 2-10 – Techniques for cell isolation ...... 102 Table 2-11 – Density gradient media applications and properties ...... 103 Table 2-12 – Reagents for immunomagnetic separation using MACS® MicroBead kits ...... 113 Table 2-13 – Flow cytometry gating strategy ...... 118 Table 2-14 – Antibody staining protocol ...... 119 Table 2-15 – Flow cytometry staining set up for validation of PMN and subcellular separations...... 120 Table 2-16 – Gating strategy for assessment of PMN purity ...... 122

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Table 2-17 – Master mix for reverse transcription reaction ...... 135 Table 2-18 – Reagents for qPCR reaction ...... 136 Table 3-1 – Test compounds for cuvette based fluorimetry ...... 142

Table 3-2 – Percentiles for maximal F/F0 ...... 157 Table 3-3 – Contingency table with threshold for responder cells set at 1.8 ...... 158 Table 3-4 – 2 x 2 contingency table showing the significant difference in pooled, paired responses to N/OFQ and buffer...... 159 Table 3-5 - Intra-assay variability ...... 160 Table 3-6 – Response to N/OFQ and the antagonists SB612111 and TRAP-101 ...... 162

Table 3-7 – Response of CHOhNOPGqi5 to N/OFQ in the presence of the antagonists TRAP-101 and SB612111...... 163 Table 4-1 – Characteristics of mixed PMN separations obtained by Polymorphprep™ separation ...... 176 Table 4-2 – Gating strategy for assessment of PMN subtype purity ...... 180 Table 4-3 – Yield, viability and % target cells obtained from 30mls of blood from healthy volunteers by MACS® MicroBeads based separation techniques ...... 182 Table 4-4 – Characteristics of neutrophil separation by MACSxpress® ...... 184 Table 4-5 – Seeding density and counts after 17-21 hours incubation ...... 189 Table 4-6 – Test for random distribution of CHO cells ...... 191 Table 4-7 – CHO/PMN density distribution and distances between CHO and PMNs .. 192 Table 4-8 – Antagonist concentrations ...... 204 Table 5-1 – Protocol for degranulation experiments ...... 213

Table 5-2 – Summary data showing the proportion of responder CHOhNOPGαqi5 cells coincubated with mixed PMNs ...... 214 Table 6-1 – Participant characteristics...... 227 Table 6-2 – Characteristics of participants diagnosed with sepsis ...... 227 Table 6-3 – Characteristics of cell separations ...... 228 Table 6-4 – Sample usage ...... 229

Table 6-5 – Responses of CHOhNOPGαqi5 cells to neutrophils and eosinophils from healthy volunteers and patients with sepsis in the absence of SB612111 ...... 231

Table 6-6 - Responses of CHOhNOPGαqi5 cells to neutrophils and eosinophils from healthy volunteers and septic patients in the presence of SB612111 ...... 232 xiv

Table 6-7 – Key to representative figures of live cell N/OFQ release assay and controls ...... 235 Table 6-8 – Qualitative immunohistochemistry results ...... 248 Table 6-9 – Gene stability as assessed using the GeneNorm algorithm ...... 253

Table 6-10 – mRNA yields following NanoDrop quantification ...... 253 Table 6-11 – Results of qPCR analysis of mRNA for NOP extracted from eosinophils and neutrophils ...... 254

VI. List of figures

Figure 1-1 – N/OFQ associated peptides ...... 4 Figure 1-2 – Mechanisms of NOP transduction...... 6 Figure 1-3 – Classical G-Protein pathway ...... 7 Figure 1-4 - GPCR general mechanism ...... 8 Figure 1-5 – In-vivo effects attributable to the N/OFQ-NOP system ...... 10 Figure 1-6 – Interplay between innate and adaptive immunity ...... 17 Figure 1-7 – Identification and lineage of cells of the immune system ...... 20 Figure 1-8 – Inflammation ...... 26 Figure 1-9 – Sepsis definitions ...... 33 Figure 1-10 – The basis of ELISA and RIA tests ...... 38 Figure 1-11 – Strategies for studying receptor systems in cells ...... 42

Figure 1-12 – Gq dependent mechanisms...... 46 Figure 2-1 – Cell counting by haemocytometry...... 71 Figure 2-2 – Jablonski diagram ...... 72 Figure 2-3 – Absorption-emission spectrum for the calcium-sensitive dye Fluo-4 ...... 74 Figure 2-4 – Mechanism of action of Fura-2-AM ...... 77 Figure 2-5 – Absorption-emission spectra for Fura-2 ...... 79 Figure 2-6 – Mechanism of action of Fluo-4 ...... 80 Figure 2-7 – Schematic of confocal microscope setup ...... 85 Figure 2-8 – Schematic of density gradient separation techniques ...... 104 Figure 2-9 – Techniques used for immunomagnetic separation ...... 109 Figure 2-10 – Schematic of flow cytometer setup ...... 115 Figure 2-11 – Forward (FSC-A) vs Side scatter (SSC-A) dot plot from flow-cytometric analysis of whole blood ...... 117 Figure 2-12 – Gating strategy ...... 123 Figure 2-13 – Layout of 96 well plate for ATP determination in PMNs ...... 125 Figure 2-14 – Standard curve relating ATP concentration to the luminescence of luciferase (3 point logistical fit) ...... 126 Figure 2-15 – Principles of RT-PCR ...... 129 Figure 2-16 – Fluorescent readouts in real time RT-qPCR ...... 130

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Figure 2-17 – Sample PCR amplification curve ...... 132 Figure 3-1 – Change in fluorescence measured at 340 nm (red) and 380 nm (blue) from

-6 CHOhNOPGαqi5 cells excited at 510 nm following treatment with 10 M N/OFQ...... 143

Figure 3-2 – Change in intracellular calcium concentration in CHOhNOPGαqi5 cells following treatment with 10-6 M N/OFQ ...... 144 Figure 3-3 – Concentration response curve ...... 145 Figure 3-4 - Concentration response curve ...... 146

Figure 3-5 – Increased measured calcium upon treating CHOWT cells to ATP...... 147

Figure 3-6 – Change in calcium concentration following treatment of CHOhNOPGqi5 and

-6 CHOWT cells with 10 M N/OFQ ...... 148 Figure 3-7 – Confocal microscopy protocols ...... 151 Figure 3-8 – Cell segmentation workflow ...... 152 Figure 3-9 – Distribution of regions of interest for fluorescence analysis ...... 153

Figure 3-10 - CHOhNOPGqi5 response to N/OFQ...... 155

-6 Figure 3-11 – Maximal F/F0 for every cell after treatment with 10 M N/OFQ and buffer ...... 156 Figure 3-12 – Frequency distribution of maximal relative fluorescence following

-6 treatment of CHOhNOPGqi5 with 10 M N/OFQ and buffer ...... 157

Figure 3-13 – ROC curve analysis of F/F0 threshold values ...... 158 Figure 3-14 – Proportion of cells classified as responders by maximal relative fluorescence following exposure to a bolus addition of 100 L N/OFQ (final concentration 10-6M) and Buffer ...... 159 Figure 3-15 – Concentration-response curve showing the proportion of responsive

CHOhNOPGqi5 cells (F/F01.8) following exposure to N/OFQ ...... 161 Figure 3-16 – Response to antagonist treatment, buffer, 10-6 M N/OFQ alone and with 10-7 M SB612111 and 10-7 M TRAP-101 ...... 162

-7 Figure 3-17 – Responses of CHOhNOPGqi5 to 10 M N/OFQ +/- NOP antagonists ...... 164

-7 -7 Figure 3-18 - CHOhNOPGqi5 response to TRAP-101 + 10 M NOFQ, compared to 10 M NOFQ following wash out ...... 165 Figure 4-1 – Purity, viability and yield of granulocyte separation by Polymorphprep™ ...... 175

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Figure 4-2 – Representative triple stained dot plot ...... 176 Figure 4-3 – Gating strategy for PMN subtype purity. Doublets/Triplets not discriminated, live/dead stains not used ...... 181 Figure 4-4 – Separation characteristics of eosinophils and basophils ...... 183 Figure 4-5 – Flow cytometry of neutrophil separation by MACSxpress® ...... 185 Figure 4-6 – Optimisation of seeding density ...... 189

Figure 4-7 – CHOhNOPGαqi5 seeding density and final field counts ...... 190

Figure 4-8 – Cell distribution of CHOhNOPGαqi5 seeded at different densities ...... 191

Figure 4-9 – Mixed PMNs layered to CHOhNOPGqi5 – demonstrating poor adhesion ... 193

Figure 4-10 - Mixed PMNs layered to CHOhNOPGqi5 (white arrow)– demonstrating compartmentalisation ...... 193

Figure 4-11 – Representative response of (i) CHOhNOPGqi5 and (ii) CHOWT and cells to coincubation with EOL-1 cells ...... 195 Figure 4-12 – Coincubation of EOL-1 with antagonists ...... 196

Figure 4-13 – Pooled data – response of CHOhNOPGqi5 coincubated with mixed PMN and treated with fMLP and N/OFQ ...... 198

Figure 4-14 - Results from mixed PMN coincubation with CHOhNOPGqi5 cells and treated with fMLP, representative single experiment ...... 199

Figure 4-15 – Results from mixed PMN coincubation with CHOhNOPGqi5 cells and treatment with fMLP ...... 200 Figure 4-16 – Change in relative fluorescence of PMN2 and adjacent CHO16 ...... 201

-6 Figure 4-17 – Response of CHOhNOPGqi5 cells to 10 M ATP in the presence and absence of PPADS and oATP ...... 205 Figure 4-18 – ATP concentration released by lysis of cells ...... 207

Figure 5-1 – Proportion of responsive CHOhNOPGqi5 cells co-incubated with PMNs .... 214

Figure 5-2 – Confocal fluorescent response observed in CHOhNOPGqi5 cells co-incubated with fMLP stimulated mixed PMNs ...... 215

Figure 5-3 – Representative microscopy field of Fluo-4 loaded CHOhNOPGqi5 cells with PMNs ...... 216

Figure 5-4 – Representative graphs showing F/F0 corresponding with Figure 5-3...... 217

-6 Figure 5-5 – Response of CHOhNOPGαqi5 + oATP + PPADS + 10 M SB61211...... 219

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Figure 5-6 – Representative graphs showing F/F0 corresponding with Figure 5-5 ...... 220

Figure 6-1 – Responses of CHOhNOPGαqi5 cells to neutrophils and eosinophils from healthy volunteers and patients with a diagnosis of sepsis ...... 233

Figure 6-2 – Representative photomicrographs (left), and (right) graphs of F/F0 responses of Fluo-4 stained CHOhNOPGαqi5 cells...... 238

Figure 6-3 - Representative photomicrographs (left) and graphs (right) of F/F0

5 responses of Fluo-4 stained CHOhNOPGαqi5 cells and 5 x 10 neutrophils...... 239

Figure 6-4 - Representative photomicrographs (left), and graphs (right) of F/F0 responses of Fluo-4 stained CHOhNOPGαqi5 cells...... 240

Figure 6-5 - Representative photomicrographs (i), and (ii) graphs of F/F0 responses of

5 Fluo-4 stained CHOhNOPGαqi5 cells and 5 x 10 neutrophils...... 241

Figure 6-6 - Representative photomicrographs (left), and (right) graphs of F/F0

5 responses of Fluo-4 stained CHOhNOPGαqi5 cells and 5 x 10 eosinophils...... 243

Figure 6-7 - Representative photomicrographs (left), and (right) graphs of F/F0

5 responses of Fluo-4 stained CHOhNOPGαqi5 cells and 5 x 10 eosinophils in the presence of SB612111...... 244

Figure 6-8 - Representative photomicrographs (left), and (right) graphs of F/F0

-6 responses of Fluo-4 stained CHOhNOPGαqi5 cells to 10 M N/OFQ following washing to remove SB612111...... 245 Figure 6-9 – Representative whole field images stained for CD16/CCR3-VioBlue, Anti-

N/OFQFITC and N/OFQATTO594 ...... 247 Figure 6-10 –Expression of N/OFQ and NOP in native eosinophils and neutrophils and those exposed to LPS / PepG assessed by immunofluorescence ...... 249 Figure 6-11- Representative immunohistochemical staining of neutrophils ...... 250

Figure 6-12 – Selected Z-stack slices from Anti-N/OFQFITC (green), N/OFQATTO594 (red) and CCR3-BioBlue/CD16-VioBlue (blue) stained eosinophils and neutrophils ...... 251 Figure 6-13 – differences in expression of NOP in eosinophils and neutrophils between healthy volunteers, those with sepsis, and cells treated with LPS and native cells ..... 254

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Chapter 1

Introduction

Section 1 Introduction

1 Introduction

1.1 General introduction Opioid use has long been associated with immune deficiency and increased susceptibility to infectious diseases (1). The shared pharmacology of the orphan (fourth) opioid receptor and classical opioid receptors may suggest that opioid-like peptides and their receptors share similar immune modulating properties (2). The fourth opioid receptor, also known as the nociceptin opioid peptide (NOP) receptor, interacts with its ligand nociceptin, or OrphaninFQ (N/OFQ). This chapter reviews the pharmacology of the N/OFQ-NOP system, the physiology of the immune system relevant to N/OFQ and the current evidence that N/OFQ-NOP interacts with the immune system, from cell lines to whole organisms.

1.2 Pharmacology of N/OFQ 1.2.1 Structure and localisation of N/OFQ and its receptor N/OFQ is a heptadecapeptide, the complementary ligand for the orphan opioid receptor. Following cDNA cloning of the classical opioid receptors, MOP, DOP, and KOP, DNA encoding a fourth “opioid-receptor like” (ORL-1) structure was discovered in rat brain (3, 4). ORL-1 has highly conserved structural motifs and >60% sequence homology with the classical opioid receptors, and notably with the  opioid receptor (KOP) (3-6). ORL-1 mRNA transcripts were detected in human (7), rat (3) and mouse brain(8).

Following International Union of Pharmacology (IUPHAR) convention, this receptor, OP4 is named after its ligand, nociceptin opioid peptide (NOP) (9) (Table 1-1). In common with classical opioid receptors, NOP is a G-protein coupled receptor, with a characteristic 7-transmembrane domain structure, coupling to downstream effectors via G-proteins (Figure 1-3 and Figure 1-4). NOP couples to the Gi G-Protein, causing reduced intracellular cyclic-AMP concentrations by inhibition of adenylate cyclase. Other roles include modulation of calcium and potassium channel activity, and additional effects via noncanonical pathways (Figure 1-2) (10).

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Classical opioid receptors Nonclassical opioid receptors Classical    ORL-1 Name Other names OP3 OP1 OP2 OP4 Standard MOP DOP KOP NOP name Endogenous Endomorphin-1 Enkephalin Dynorphin A N/OFQ agonist Antagonist Naloxone Naloxone Naloxone UFP-101 Table 1-1 – Classical and non-classical opioid receptor nomenclature

Classical opioid and NOP peptides, with their corresponding mRNA sequences, are expressed throughout the body, with high concentrations observed in the central nervous system. NOP mRNA expression has also been detected in the immune system, vascular endothelium, gastrointestinal tract and respiratory system (10, 11) (Table 1-2). Distribution and localisation of NOP and N/OFQ have been reviewed extensively elsewhere, and are summarised below (12-15). The distribution of N/OFQ-NOP peptides in immune cells are considered separately in detail (Table 1-8, Table 1-9, Table 1-10).

Although N/OFQ has a cellular mechanism underlying its function as an inhibitory neurotransmitter (see 1.2.2), its physiological role outside of the central nervous system is less certain. The widespread tissue distribution of the NOP receptor suggests a ubiquitous role in a range of physiological processes. Emerging evidence suggests a role in tolerance and reward, pain, inflammation, immune function and vascular reactivity (10) (1.2.3 and Figure 1-5).

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Area Species Signal Author Brain (and spinal cord) Human NOP mRNA Mollereau (7) Mouse NOP Cerebellum Rat NOP mRNA Fukuda (3) Brain (see below) Rat NOP mRNA Bunzow (16) Brain (see below) Rat NOP mRNA Chen (17) Brain (see below) Mouse NOP mRNA Pan (8) Brain (see below) Rat NOP mRNA Wang (4) Brain (see below) Rat NOP mRNA Wick (5) Ileum Porcine NOP mRNA Osinski (18) Human Bronchial Epithelial Cells Human NOP mRNA Singh (19) NOP Human Airway Smooth Muscle Human NOP mRNA Singh (19) NOP Vascular endothelium Human NOP mRNA Granata (20) Rat NOP Table 1-2 – Localisation of NOP. For detailed breakdown of studies see Table 1-8, Table 1-9, Table 1-10)

NOP has a widespread distribution within the brain of rats, mice and humans, areas of

higher NOP mRNA expression (>2 log2) include the hypothalamus, midbrain, medulla and cortex(21).

Analysis of the human transcriptome demonstrates high NOP mRNA expression in the human, pig and mouse cerebral cortex, basal ganglia, cerebellum and hypothalamus(22, 23), although this dataset did not include protein expression data. Similarly, ppNoc mRNA is highly expressed in the cerebral cortex, hypothalamus, midbrain and spinal cord(22, 23).

The complementary agonist for NOP was isolated from porcine pituitary (15) and rat hippocampus (24) simultaneously by two groups using reverse pharmacology. This technique measured the inhibition of forskolin-induced cyclic AMP production in Chinese hamster ovary (CHO) cells transfected with the human NOP receptor when exposed to a library of brain tissue fractions. After purification, the candidate fractions causing the greatest inhibition were isolated and characterised. Through an iterative process, the native NOP agonist was discovered, sequenced, and characterised.

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Intracerebroventricular administration of the purified ligand was found to induce hyperalgesia in mice, leading to the name nociceptin (24). The heptadecapeptide amino acid sequence flanked by phenylalanine (F) and glutamine (Q) amino acids led to its other name, OrphaninFQ (15), henceforth known as N/OFQ.

N/OFQ is released and cleaved from a precursor, prepronociceptin, also yielding nocistatin and NocII which may have regulatory functions (Figure 1-1).

Figure 1-1 – N/OFQ associated peptides

Studies of the Structure-Activity Relationships (SAR) of N/OFQ, and the use of peptide fragments have characterised the “address” (binding) and “message” (activation) domains of this peptide (25). The configuration of these domains differ from those of classical opioids and have been used in the design of peptide and non-peptide agonists

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1 14 15 (e.g. N/OFQ-PWT) and antagonists (e.g. [Nphe ,Arg ,Lys ]N/OFQ-NH2) for study and characterisation of the N/OFQ-NOP system (26, 27). SAR studies have shown the C terminus to be key to the potency of N/OFQ related peptides at NOP receptors (25).

1.2.2 Cellular mechanisms following NOP activation 1.2.2.1 Classical G-Protein mediated pathways NOP is part of the large superfamily of G-protein coupled receptors (GPCRs), sharing a common 7-transmembrane domain structure. The receptor’s intracellular C-terminus binds and interacts with a heterotrimeric G-protein complex (Gi), and the extracellular N-terminal region interacts with the complementary ligand and allosteric modulators (28).

Conformational change of the intracellular portion of the receptor following ligand binding leads to activation of Gi/o (canonical) and a -arrestin (non-canonical) pathway

(29). Activation of Gi/o initiates a downstream cascade resulting in a decreased intracellular concentration of cyclic-AMP and cellular hyperpolarisation through interactions with calcium and potassium channels (29, 30). Activation of -arrestin leads to receptor desensitisation, and activation of other downstream targets, including mitogen-activated protein kinases (MAPK) such as c-Jun N-terminal kinase (JNK), rho- associated protein kinase (ROCK), protein kinases A and C (PKA and PKC) and extracellular signal-regulated kinase (ERK) (29). In neurones, cellular hyperpolarisation decreases release of a range of neurotransmitters which are voltage and calcium dependent. Neuronal hyperpolarisation is a major mechanism underlying N/OFQ’s inhibitory neurotransmitter effects.

The putative pathways through which NOP may couple are shown in Figure 1-2, and expanded below.

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Figure 1-2 – Mechanisms of NOP transduction. Green solid line – activation, Red broken line – inhibition; GRK – G-protein coupled receptor kinase, cAMP – Cyclic-AMP, Arr - -arrestin, P – Phosphate, AC – Adenylate cyclase

NOP agonists promote differential activation of downstream pathways, favouring one or more over others; functional selectivity or biased agonism (31). Biased agonism describes the occurrence of different effects from individual equipotent ligands acting at the same receptor (32). Furthermore, ligands may exhibit differential effects at one or coexistent more receptors, leading to mixed effects as observed with the mixed NOP/MOP agonist Cebranopadol (33). Synergistic effects of both MOP and NOP activation may reduce overall dose requirement and adverse pharmacodynamic effects (33). Various approaches have been used for bi-functional drugs acting at more than one receptor, including the use of linkers (e.g. Targinact combines oxycodone and naloxone), bivalent drugs (e.g. UFP-505), and single molecules with effects at different sites (e.g. Cebranopadol).

These approaches may be used in the development of drugs for characterisation of receptors, or to minimise clinical adverse effects.

1.2.2.2 Signalling via the Gi pathway

In common with classical opioid receptors, NOP couples to the Gi/o system of G-proteins.

The activated Gi subunit inhibits adenylate cyclase in response to receptor activation, the canonical pathway as shown in Figure 1-3 (7, 24).

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Figure 1-3 – Classical G-Protein pathway

At rest, the intracellular C-terminus of NOP in its inactive state binds a heterotrimeric G-protein complex consisting of G, G, G subunits. On agonist binding to NOP, the receptor-ligand complex forms. Upon activation, conformational change switches G bound GDP for GTP (Figure 1-3, 1). The heterotrimeric complex dissociates, interacting with downstream effectors (Figure 1-3, 2). The Gi/o G-proteins complex interacts with adenylate cyclase, calcium, and potassium channels (Figure 1-2). Inhibition of Adenylate Cyclase causes reduced intracellular concentrations of the second messenger cyclic- AMP.

Bound GTP is hydrolysed by intrinsic G GTPase activity. Following hydrolysis, the G- protein components dissociate from downstream effectors, and recombine as an inactive heterotrimer (Figure 1-4 and Figure 1-3, 3, 4). This process is dynamic, amplifies the initial receptor signal and facilitates downstream processes, and regulatory feedback (the NOP signalling cascade is an example of this in Figure 1-2).

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Figure 1-4 - GPCR general mechanism - Activation of Gi/o coupled GPCR causes a conformational change in Gα, promoting GTP binding and dissociation. Gα acts on an effector molecule, hydrolyses GTP and then recombines with the deactivated  complex

Both classical and nonclassical opioid receptor coupling to Gi is demonstrated by

35 observing G-[ S]GTPS binding and inhibition of cAMP production in response to appropriate agonist, reversible with naloxone or the NOP antagonist J-113397 respectively (34). Gi/o is pertussis toxin sensitive. Pre-treatment of NOP expressing neuroblastoma cell membrane preparations with pertussis toxin decouples Gi/o, preventing inhibition of adenylate cyclase via the Gi/o mechanism (35).

The  subunits of Gi/o directly activate G protein-gated inwardly rectifying potassium channels (GIRK), and inhibit N, P and Q-type calcium channels. This causes hyperpolarisation of electrically active tissues, such as guinea pig ileum, mouse vas deferens (36) and neurones , a further readout for NOP activity.

1.2.2.3 Mitogen Activated Protein Kinase Pathway MAPK is a major pathway for downstream phosphorylation of protein targets involved in cell signalling, growth cycle regulation and transcription. This ubiquitous signalling system is the end point of many regulatory pathways. Activation of NOP produces pertussis sensitive MAPK activation, and therefore this suggests that there is a G-protein

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Section 1 Introduction dependent mechanism (37). Much of the early work in transfected CHO cells highlighted a role for Gi in the activation of MAPK (37, 38).

Following activation of NOP, the G subunit activates a cascade of serine and threonine kinases, PI-3K, Src, SHC, Grb2, SOS, p21ras, Raf, MEK and MAP Kinase, which are linked to the control of receptor expression, of downregulation, and longer term modifications to neuronal function (39).

1.2.2.4 Other pathways and effects

NOP couples to -arrestin2, a negative regulator of GPCRs which promotes internalisation, receptor downregulation and recycling (Figure 1-2) (40, 41).

Both classical and non-classical opioid pathways couple to -arrestins. Administering classical MOP agonists, such as morphine to -arrestin knockout mice prevents the development of tolerance and enhances the duration of analgesic effect. This suggests that opioid receptor downregulation and internalised is a -arrestin dependent phenomenon (42, 43).

1.2.3 In-vivo actions of N/OFQ-NOP The N/OFQ-NOP system is ubiquitous in its distribution and has multi-system effects in-vivo (Figure 1-5). These are expanded in the sections below.

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Figure 1-5 – In-vivo effects attributable to the N/OFQ-NOP system (10) (with permission from the author)

1.2.3.1 Central Nervous System NOP, N/OFQ, and the corresponding mRNA transcripts are found throughout the peripheral and central nervous systems (44). The widespread distribution of components of the NOP-N/OFQ system has led to putative roles in pain, reward, tolerance, stress and anxiety (10). The hyperpolarising, inhibitory mechanisms brought about by NOP activation suggests a role as an inhibitory neurocrine modulator.

Of relevance to pain and analgesia, NOP has a different distribution to the classical opioid receptors although both are found in the brain with roles in nociception (44, 45). NOP binding sites and mRNA are located spinally, and in the nucleus raphe magnus, nucleus accumbens, locus coeruleus and periaqueductal gray matter (44, 45). N/OFQ

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Section 1 Introduction effects differ depending on site of administration, and the presence of other chemical modulators. When administered intrathecally, N/OFQ is antinociceptive (46, 47) in rats and monkeys. In these species, intracerebroventricular administration is pronociceptive, increases nociceptive transmission and reverses the effect of exogenous classical opioid agonists and the effect of endogenous opioids (48). However, more recently, studies in non-human primates have shown the effects of supraspinal N/OFQ to be antinociceptive (49).

NOP is also thought to have a role in regulating tolerance and reward, and has a distribution throughout the limbic system, and the ventral tegmental area. NOP may influence tolerance, addiction and reward-based behaviours via dopaminergic transmission as a downstream messenger (50). Similarly, consistent with an inhibitory neurotransmitter effect, NOP agonists have demonstrated similar anxiolytic properties to benzodiazepines and may be useful in the management of withdrawal and anxiety (51).

1.2.3.2 Cardiovascular In mammals (humans, rats, mice and guinea pigs), there is evidence for central and peripheral expression of NOP mRNA and N/OFQ peptide (Table 1-3). Collectively, these data suggest that there may be a role for N/OFQ-NOP in the control and regulation of the cardiovascular system (52-54).

Although anatomically widespread, expression of functional NOP in vascular tissue appears to be limited to the endothelium (20). NOP has been localised to myocardial membranes in rat and humans (55, 56), although its role remains unclear.

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NOP Expression CNS areas Tissue Species Finding Reference Sympathetic ganglia Rat NOP mRNA Xie (57) Superior cervical Rat NOP mRNA Xie (57) ganglia Brain capillaries Rat NOP mRNA Granata (20) NOP protein Non-CNS tissues Tissue Species Finding Reference Thoracic Aortic Rat NOP mRNA Granata (20) sections (localised to NOP protein endothelium) Myocardium (mixed Rat N/OFQ Dumont (55) membranes) binding Right Atrium Human NOP mRNA McDonald (56) Table 1-3 - NOP expression in the cardiovascular system

Intravenous N/OFQ administration causes bradycardia and hypotension in rats (58), mice (59), and guinea pigs (60) without any associated reflex tachycardia. These responses are not present in NOP knockout mice (61), suggesting a direct, NOP mediated mechanism. One study demonstrated hypertension and tachycardia in sheep following intravenous administration of N/OFQ (62); these differences may be because of differences in resting sympathetic tone between species (as the response observed in sheep is attenuated by sympatholytics), or nonspecific receptor interactions.

The presence of NOP on myocardium, blood vessels, central and peripheral areas of cardiovascular control supports involvement of direct, neurological, or endocrine mechanisms of N/OFQ mediated dilation. These pathways may also involve production of other intermediaries, such as NO, histamine or NMDA.

The local effect of N/OFQ on vascular tissue is vasodilation. This is supported by in-vitro and ex-vivo studies which show dose dependent vasodilation in response to N/OFQ, blocked by NOP antagonists. In the rat, treatment of aorta and brain capillaries with

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N/OFQ caused increases in MAPK, suggesting the presence of functionally coupled NOP (20). In-vitro stimulation of vascular NOP reverses  adrenoceptor induced vasoconstriction in feline renal, mesenteric, carotid and femoral intact vascular rings (63, 64). These vasodilatory effects are mirrored in tissues from rats (65, 66) and pigs (67), and not antagonised by antimuscarinic, anti-CGRP and anti-NO compounds, suggesting a NOP specific mechanism, independent of autonomic influences and of NO and prostaglandins (68). In spite of the presence of NOP on atrial tissue (56), the effects of N/OFQ on myocardium have not been studied.

A role for N/OFQ-NOP in the central control of the cardiovascular system is suggested by NOP expression throughout the autonomic nervous system (57), and in cardiac control centres. Intracerebroventricular, rostral ventrolateral medulla and intrathecal injection of N/OFQ repeatedly produces bradycardia and hypotension in rats, whereas equimolar intravenous doses failed to elicit the same effect (69). The stellate ganglion of rats expresses NOP, and exposure to N/OFQ causes dose dependent pertussis toxin sensitive closure of calcium channels consistent with NOP activation (70). These suggest that N/OFQ has a sympatholytic effect, and this may be responsible for its observed effects. In rats treated with chemical and surgical vagotomy and sympathectomy, the effects of N/OFQ indicated that a reduced sympathetic outflow and increased vagal outflow were responsible for the overall cardiovascular effects outlined (71). Modulation of underlying autonomic tone may also be responsible for some of the differences observed in studies of NOP-N/OFQ on anaesthetised and conscious animals.

Overall, the effects of intravenous or intrathecal N/OFQ are to rapidly produce hypotension and bradycardia, combined with vasodilation and without any reflex tachycardia. These effects, observed in mice and rats, are not reversed by naloxone, supporting a non-classical opioid receptor pathway either locally or centrally mediated.

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1.2.3.3 Urogenital and renal mRNA encoding NOP has been localised to porcine kidney (18), and the receptor detected in the collecting ducts taken from rat renal medulla (72).

In addition to the vascular effects on renal blood flow discussed above, intravenous N/OFQ produces an increase in urine flow with antinatriuretic and potassium sparing effects when administered systemically to rats (73, 74).

The long acting NOP agonist ZP120 has been demonstrated to have long acting diuretic effects, and trialled for resistant hypertension as an aquaretic (75-77).

1.2.3.4 Other N/OFQ-NOP are both expressed throughout the immune system, with evidence of an immunomodulatory effect. These effects are discussed in detail in section 1.4.

1.2.4 Key points N/OFQ is a non-classical opioid peptide found throughout the body, with putative roles in pain, tolerance, addiction, vascular reactivity and more recently immunomodulation. NOP, the receptor for N/OFQ couples to both canonical and non-canonical pathways, activating numerous phosphorylation pathways, resulting in short- and longer-term effects on cell polarisation, transcription, and translation. These diverse pathways may be responsible for the wider effects of N/OFQ over and above its short-term effects as an inhibitory neurotransmitter.

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1.3 The immune system in health and disease The immune system is comprised of physical, cellular, paracrine, and endocrine mediators that collectively mediate a micro- and macroscopic response to foreign (non- self) antigens. Immune responses are initially generic and innate, later activating more specific adaptive mechanisms. Antigenic material may be rendered less harmful through mechanisms involving containment (via antibody mediated opsonisation), neutralisation (antibody mediated) and destruction (phagocytosis and activation of reactive oxygen species) (78). The initial innate response is rapid, although may be nonspecific, whereas the adaptive response is delayed but more precise. These branches of the immune system work synergistically to mount a whole organism response to pathogens. This section presents an overview of the response from initial encounter, the cells and mechanisms involved and finally focusing on inflammation, an area in which there is mounting evidence for early involvement of N/OFQ-NOP.

Key to successful immune containment of pathogens is the distinction of self from non- self. Inability to recognise foreign antigens increases susceptibility to infections, particularly opportunistic pathogens which are of little consequence in health. Failure to recognise self leads to immune mediated damage to healthy tissue, and to autoimmune pathologies. Immune responses are dependent on a balance of pro- and anti-inflammatory mediators to prevent over- and under-activation of the system and avoid deleterious consequences.

The response of the innate branch of the immune system is initiated by the interactions between Pattern Recognition Receptors (PRRs) and Damage or Pathogen Associated Molecular Proteins (DAMPs or PAMPs). Toll-Like Receptors (TLRs), of which there are 10 types, are the largest and most widely studied group of PRR. Other PRR families include retinoid acid-inducible gene I (RIG-I)-like receptors (RLR), nucleotide-binding oligomerization domain (NOD)-like receptors (NLRs) and melanoma differentiation- associated gene 5 (MDA5)(79).

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TLRs recognise lipids or nucleic acids, either directly or through an intermediate binding protein. In relation to sepsis, the most important TLRs are TLR4 (responds to lipopolysaccharide from gram negative bacterial cell walls) and TLR2 (responds to membrane lipids from gram positive cells)(79, 80). TLRs also respond to endogenous molecules, TLR2 and TLR4 may be activated by DAMPs as part of a response to sterile injury including heat shock proteins, amyloid, ATP and fibrinogen, although the mechanism of binding may be different and dependent on cofactors and accessory proteins.

The multi-peptide inflammasome complex is activated by exposure to PAMPs or DAMPs, resulting information of active Caspase 1, and cleavage of precursors to form active cytokines IL-1 and IL-18(81). Nucleotide-binding oligomerization domain, Leucine rich Repeat and Pyrin domain containing protein (NLRP) is upregulated in response to TLR4 activation and forms an inflammasome scaffold. ASC, an adaptor protein facilitates assembly of the inflammasome and subsequent formation of pro-caspase 1, which is then cleaved to active caspase 1(81).

ATP is a DAMP, and promotes activation of the NLRP inflammasome via P2X, which promotes increased intracellular calcium(82). Functionally, this has been shown to be important in liver injury; suppression of ATP reduces the extent of the injury, highlighting its importance(83). In addition to its role in activating the NLRP inflammasome, ATP may act at purinergic receptors present on immunocytes. The P2X7 receptor is an ATP sensitive, ligand gated ion channel, permeable to potassium, calcium, and sodium. This receptor has been found on lymphocytes, monocytes and macrophages, and contributes to elevation of intracellular calcium concentration, and formation of the NLRP inflammasome(84, 85). The role of ATP in immune cell functioning is complex, and includes chemoattraction, phagocytosis and apoptosis. There may be a biphasic role where high concentrations are pro-inflammatory and act as danger signals, whereas chronic, low concentrations are anti-inflammatory(86, 87).

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Figure 1-6 shows how upon recognition of non-self, the innate immune system activates generic mechanisms to contain, kill, neutralise, and process the pathogen for presentation to the adaptive immune system.

Figure 1-6 – Interplay between innate and adaptive immunity - 1) Antigen recognised by Natural Killer (NK) cells, promoting release of cytokines. 2) Macrophages phagocytose pathogen and 3) Present antigenic motifs to T-Helper cells 4) Cytotoxic T-cells are activated and mediate cellular killing 5) B-cells differentiate into plasma and memory cells to produce antibodies 6) Granulocytes migrate to the site of injury and release further cytokines

Innate immunity includes physical, chemical, and physiological barriers prior to entering tissues in addition to the cellular processes shown. Once these barriers are circumvented, innate immunity is orchestrated by macrophages, granulocytes, and natural killer (NK) cells.

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Natural killer cells, granulocytes and macrophages initially encounter the pathogen and later present fragments to the T-helper cells which control the overarching response through release of cytokines and prime the adaptive response. Following immune presentation, the T- lymphocytes command and control the recruitment of other cells, whilst B- lymphocytes produce antibodies, and differentiate into memory cells, which enable later reactivation of the immune response and antibody production if the antigen is encountered again.

Inflammation describes the cascade of cellular and chemical mediators that support immune destruction through an increase in local blood flow, delivery of immune mediators, increased membrane permeability and cellular recruitment. This response is critical to immune functioning but may also cause harm and is discussed in section 1.3.3.

Immune cells secrete cytokines, a broad group of peptide mediators to which immune cells respond via paracrine, endocrine or autocrine mechanisms (88). Cytokines include more specific categories of mediator released from monocytes (monokines), and leukocytes (interleukins), and those with chemotactic effects (chemokines). Depending on the phase of the immune response, or the cell type, these cytokines may be stimulatory or immunosuppressive in nature. Interaction with cytokine release, or receptors may be a possible mechanism for the effects of N/OFQ on inflammation and immunomodulation.

1.3.1 Cells of the immune system Immune cells recognise, kill, and process pathogens and coordinate overall host response to infection. These cells are derived from differentiation of stem cell into myeloid and lymphoid lineages, resulting in a distinctive morphology and expression of cell surface markers (Figure 1-7, Table 1-4).

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There is growing evidence for N/OFQ-NOP involvement in immune responses, and for expression of both within the cells of the immune system. Immune cells have been shown to express NOP receptors and N/OFQ precursors through PCR studies, and functional studies (Figure 1-7, Table 1-4).

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Figure 1-7 – Identification and lineage of cells of the immune system , and summary of N/OFQ-NOP expression

Section

* 1 Median Ct Introduction Cell type Lineage Reference range (human)† Cell surface marker NOP ppNoc Cells x 109 l-1 Monocytes Myeloid 0.2 – 0.8 6.33 8.37

Mixed granulocytes Myeloid 5.93 10.37 Eosinophils Myeloid 0.04 - 0.4 SIGLEC-8 5.70 13.18 Neutrophils Myeloid 1.5 - 7.5 CD16 2.56 - Basophils Myeloid 0.02 – 0.1 FCɛRIα 4.15 - B and T lymphocytes Lymphoid 1.0 – 4.0 CD3, CD4, CD8 6.78 6.28 Table 1-4 – Lineage and reference ranges for selected immunocytes in human blood with evidence for N/OFQ-NOP expression *Unpublished data from our own lab 1-21

†Reference ranges from (89)

Section 1 Introduction

The cells involved in the immune response may be divided into those of myeloid (granulocytes and monocytes) and lymphoid (T- and B- cells) lineages. Myeloid cells have a common precursor and are the hallmarks of the acute inflammatory response. They are recruited to sites of acute inflammation and secrete the mediators responsible for the local tissue response, and for activating and recruiting cells of the adaptive immune system.

Lymphocytes are cells of lymphoid lineage, with wide ranging command and control functions in adaptive immunity through synthesis and release of pro- and anti- inflammatory regulatory cytokines, antibodies and the formation of immune memory and secondary immune responses. Lymphocytes differentiate in the bone marrow from a common progenitor before maturation in the thymus (T-) or bone marrow (B-). These cells have a role in the command and control of the overall immune response and are responsible for the production of antibodies and an immune memory. The role of lymphocytes as part of the overall immune cascade is reviewed comprehensively elsewhere (90).

There is compelling evidence for N/OFQ release from cells of myeloid lineage. Granulocytes have an established role in the synthesis of inflammatory mediators and are candidates for the release of N/OFQ. Release of N/OFQ from granulocytes suggests a role of initiation and control of acute inflammation. The remainder of this section will focus on the cells involved in these inflammatory processes, their role in inflammation, and evidence supporting interactions with the N/OFQ-NOP system.

1.3.1.1 Granulocytes Granulocytes are large polymorphonuclear (PMN) immune cells of myeloid origin, characterised by their cytoplasmic secretory granules and large multilobed nucleus. These cells are recruited to sites of inflammation and release their secretory granules in

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Section 1 Introduction response to activation. The granulocyte family includes the three subtypes of neutrophils, eosinophils, and basophils.

Neutrophils are a major subset of granulocytic polymorphonuclear cells, and constitute 50-70% of circulating leucocytes (91). They are approximately 10 m in diameter, derived from a myeloid lineage, and are distinct by their multilobed nucleus and secretory granules, staining with a neutral stain (Figure 1-7). They express the CD45 leukocyte marker, and the CD66b granulocyte marker. Healthy neutrophils consistently express CD11b, CD16 and CD15 which have been successfully been used to identify them by flow-cytometry (92). They weakly express the Sialic acid-binding Ig-like lectin 8 (SIGLEC-8). Neutrophils are a short-lived cell type, with a half-life of 6-8 hours, upregulated following release of Granulocyte Colony Stimulating Factor (GCSF) from circulating macrophages in response to antigens. These migratory neutrophils are a key component of the innate immune system and may mediate cellular killing by phagocytosis, degranulation and formation of neutrophil extracellular traps (NETs). Following release and maturation from the bone marrow, neutrophils migrate to the site of infection or injury and then enter tissues through rolling, adhesion and extravasation under the control of cytokines and cellular adhesion molecules (CAMs). The characteristic primary (azurophilic), secondary (specific) and tertiary (gelatinase) granules release cytotoxins in response to activation and priming of Pattern Recognition Receptors (PRRs) expressed on the neutrophil. These granules release myeloperoxidase, proteases (elastase, proteinase-3 and matrix metalloproteases) and acidic hydrolases which cause tissue and cellular destruction.

Eosinophils are larger than neutrophils (12-17 m), contain a bilobed nucleus and secretory granules (containing eosinophil peroxidase, eosinophil derived neurotoxin, ribonuclease, and major basic proteins 1 and 2). Their eosinophilic staining and bilobed appearance distinguish eosinophils from neutrophils (91). These rare cells constitute 1- 6% of circulating leukocytes.

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In common with leukocytes, they express CD45, and the granulocyte marker CD66b. The eosinophil cell surface receptor CCR3 is a further distinguishing marker. In contrast to neutrophils, eosinophils strongly express SIGLEC-8.

Eosinophils are classically described as having a role in the destruction of helminth and parasitic infections, and in allergic conditions such as asthma. In common with other granulocytes, they have a chemotactic response to inflammatory mediators. At the site of infection or tissue damage, they degranulate. The contents of eosinophilic granules have a beneficial role in the destruction of parasites although can cause significant tissue damage. Recent evidence indicates that eosinophils may have a greater role in sepsis and the immune response. IL-5, a pro-eosinophil cytokine may be protective in sepsis (93). Similarly, the expression of Toll-Like-Receptors on the surface of eosinophils may have a role in early cellular killing and activation of the immune response (93, 94).

Basophils are a small subset of granulocytes, comprising 0.5-1.5% of circulating leukocytes and containing darkly staining basophilic granules and a bilobed nucleus. They are phenotypically similar to mast cells. Basophils are recognised by their microscopic appearance and the cell surface expression of FcRI (an IgE receptor) and CD123. Basophils release histamine and a range of other inflammatory mediators. Activation of basophils by the cytokine IL-3 causes upregulation, and an expansion of circulating numbers. Activation promotes IL-4 secretion, which may have a role in coordinating and controlling the T-cell response. They are thought to have a role in controlling the allergic response, and an anti-helminthic response.

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1.3.2 Innate immunity Innate immunity is conferred by that cellular and molecular material which the individual is born with (95). This includes the barrier function of skin, mucus membranes and cell-to-cell junctions, cytokines and serum proteins, and immune cell functions of phagocytosis, cellular killing and containment (96). The complement system is a series of proteins which recognise pathogenic motifs, labelling antigens for phagocytosis and recruiting cellular components to the site of injury. The cellular components of innate immunity include polymorphonuclear cells, monocytes and macrophages, all of which are of myeloid lineage (see Figure 1-7) (95). Polymorphonuclear cells include neutrophils, basophils, and eosinophils. Their role is largely in cellular killing, through the release of granules containing cytotoxic proteins. Macrophages are large single nucleated cells, which play a key role in phagocytosis and are derived from monocytes.

Innate immunity is a rapid nonspecific defence system protecting the host from pathogens expressing antigenic signatures recognised by Pattern Recognition Receptors (PRRs). These proteins are a highly conserved series of receptors (e.g. Toll Like Receptors; TLRs), activated by pathogens (Pathogen Associated Molecular Proteins; PAMP), or by cellular derived molecules associated with trauma (Damage Associated Molecular Proteins; DAMP) (95, 97). PAMPs are expressed on pathogens but not self, and, via this mechanism, the innate immune system distinguishes potentially pathogenic non-self molecules. The control of inflammation via these receptors is discussed in section 1.3.3.

1.3.3 Inflammation and Inflammatory conditions Inflammation describes the local tissue response to innate immune system activation and cytokine release. The local inflammatory features of calor, dolor, rubor, and functio laesa, described by Celsus and Galen enhance delivery of immune cells, antibodies and chemical mediators to the site of injury (98).

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Macroscopically, inflammation is initiated following exposure to a noxious/non-self stimulus, is propagated by inflammatory cytokines (Table 1-5) and finally regulated by anti-inflammatory mediators. The balance between pro- and anti-inflammatory mediators causes a microscopic, local, regional, and whole organism response to injury (Figure 1-8).

Figure 1-8 – Inflammation – initiated via innate immune cells responding to PAMPs and DAMPs via PRRs, leading to local and systemic responses to contain and control the pathogenic stimulus

Cells of the innate immune system initiate the inflammatory response through recognition of pathogens interacting with PRRs on their surface. 5 classes of PRR are described on immune cells: Toll Like Receptors (TLRs), nucleotide oligomerization domain-like receptors (NODs or NLRs), retinoic acid-inducible gene (RIG-1)-like receptors, C-type lectin receptors, and absence-in-melanoma 2 (AIM-2)-like receptors. PRRs are activated by PAMPs such as lipopolysaccharide and subsequently interact with intracellular kinase pathways via adaptors such as myeloid differentiation primary response gene 88 (MyD88) and the Toll/IL1 receptor (TIR) containing adapter. The end point of these pathways is to induce interferon- (IFNβ). The complex of PAMP/DAMP- PRR when linked, via an adaptor protein to an effector, such as the caspase 1 enzyme is termed an inflammasome. These multimeric structures are potent stimuli to inflammation.

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Section 1 Introduction

The downstream final common pathway results in activation of mitogen activated protein kinase (MAPK) pathways and subsequently a pro-inflammatory state inducing the transcription factor NF- (97, 99, 100). The pro-inflammatory NF- pathway influences transcription of cytokines, cell survival and apoptosis, and recruitment of immune cells, providing an amplification and bridge to adaptive immunity (Table 1-5). Specifically, precursors to the pro-inflammatory cytokines are produced, and cleaved by the inflammasome, which further induces NF- activation in an amplificatory loop. The major pro-inflammatory cytokines activating and recruiting cells of the adaptive immune system are TNF-, IL-12, and IFN- (101).

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Proinflammatory Cytokines Cytokine Source Effect TNF- Macrophages Cytokine production Fever Adhesion IFN- T-cells Enhance antigen presentation NK Cells GM-CSF Macrophages Induce proliferation and release of granulocytes from marrow Interleukins IL-1 Macrophages Cytokine production Fever Adhesion IL-8 Macrophages Chemotaxis of neutrophils IL-6 Amines Histamine Mast cells Vasodilation Anti-inflammatory Cytokines TGF- T-cells Promotes T-cell survival Suppress NK and dendritic cells Interleukins IL-10 T-cells, Macrophages, B-cells Limits inflammatory response IL-22 T-cells Regulation of apoptosis Other Glucocorticoids Adrenal glands Downregulates NF- activity Table 1-5 – Inflammatory mediators

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Priming and initiation of the immune response occurs following the release of pro- inflammatory cytokines such as TNF- from macrophages. This stimulates the recruitment of PMNs, and expression of cellular adhesion molecules on the endothelium. Adhesion, rolling and migration of PMNs delivers these cells to the site of injury (Figure 1-8). Degranulation of activated PMNs releases myeloperoxidase, proteases (elastase, proteinase-3 and matrix metalloproteases) and acidic hydrolases which cause tissue and cellular destruction, containing the pathogen ahead of the arrival of the lymphocytes, forming the basis of adaptive immunity.

Cessation of inflammation occurs where anti-inflammatory cytokines predominate, the natural apoptosis of immune cells, depletion of cytokines or inflammatory granules, or via negative feedback loops (102). Dysregulation of these control mechanisms can result in autoimmune or inflammatory conditions such as sepsis (see section 1.3.4).

1.3.4 Sepsis Sepsis describes a condition of life threatening organ dysfunction from a dysregulated host response to infection, with a subgroup in septic shock displaying profound circulatory, cellular and metabolic abnormalities associated with a significantly increased risk of mortality than with sepsis alone (103). The mortality of sepsis and septic shock is >10% and >40% respectively (103).

Overall, in the UK, the incidence of sepsis and septic shock nationally is 101.8 and 19.3 per 100000 person years (104), approximately 234,000 cases per year in the UK at an estimated cost of £7.75 million (105). Sepsis is therefore an important clinical problem with widespread implications for public health and economics. Over recent years, trials of interventions have yielded disappointing results in the management of sepsis – and recognition that sepsis is a heterogenous syndrome such that large unselected trials are

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Section 1 Introduction frequently inconclusive. The broad spectrum of presentations of sepsis creates a complex problem of definition and classification which is explored below.

Sepsis is currently diagnosed using the Sepsis-3 criteria based on organ dysfunction with coexistent suspicion of infection (103). Organ dysfunction, defined as an increase of 2 points on the Sequential Organ Failure Assessment (SOFA) score is associated with a mortality in excess of 10% (Table 1-7) (103). This definition has been criticised because of its weighting of the complex SOFA score.

Accurately diagnosing infection in its early stages with certainty requires cultures, which can take 24-48 hours to become diagnostic, delaying recognition and treatment. Empiric antibiotics within the first hour of recognition reduces mortality in severe sepsis (106). Therefore, to avoid some of the challenges of recognition and to facilitate early antibiotics, diagnoses are often clinical, based on suspicion, without microbial diagnosis, and using an abbreviated quick Sequential Organ Failure Assessment (qSOFA) score (103) (Table 1-6).

System Criterion Respiratory Respiratory rate ≥22 Central nervous system Altered mentation Cardiovascular system Systolic blood pressure ≤ 100 mmHg Table 1-6 – qSOFA scoring system (103)

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Score Introduction 0 1 2 3 4 Respiratory * * PaO2/FiO2 ratio mmHg ≥400 (53.3) <400 (53.3) <300 (40) <200 (26.7) <100 (13.3) (kPa)

Coagulation Platelets x 103 l-1 ≥150 <150 <100 <50 <20 Hepatic Bilirubin mg dl-1 <1.2 1.2-1.9 2.0-5.9 6.0-11.9 >12.0 (mol l-1) (20) (20-32) (33-101) (102-204) (204)

1-31 Cardiovascular MAP ≥70 mmHg MAP <70 mmHg Dopamine <5 or Dopamine 5.1-15 or Dopamine >15 or dobutamine epinephrine ≤0.1 or epinephrine >0.1 or (any dose)† norepinephrine ≤0.1† norepinephrine >0.1†

CNS Glasgow Coma Score‡ 15 13-14 10-12 6-9 <6 Renal Creatinine mg dL-1 <1.2 (110) 1.2-1.9 (110-170) 2.0-3.4 (171-299) 3.5-4.9 (300-440) >5.0 (440) Urine output mL day-1 <500 <200 Table 1-7 – Sequential Organ Failure Score (103) *With respiratory support †Catecholamine and vasopressor doses given as g kg-1 min-1 ‡Glasgow Coma Score is between 3 (comatose) and 15 (alert), based on eye (1-4), verbal (1-5), and motor (1-6) responses. Higher scores indicate better function

Section 1 Introduction

The current Sepsis-3 definition is a development of Sepsis-1 and Sepsis-2 (Figure 1-9), and include differences in the definitions of organ dysfunction, and the withdrawal of the Systemic Inflammatory Response Syndrome. All criteria include a central assumption of infection, with criteria for diagnosis ± severity based on degree of organ dysfunction (Figure 1-9).

The criteria for Sepsis-3 are less specific than Sepsis-1 and Sepsis-2, but more sensitive. The different criteria used for the diagnosis of sepsis over time may have contributed to a heterogenous group of patients labelled with a sepsis diagnosis. This complicates the use of historic studies, and any time-based comparisons of treatments or outcomes.

Sepsis-3 criteria are weighted towards changes in SOFA score for diagnosis, however, this has been criticised as SOFA has been validated for prediction of mortality rather than diagnosis. The predictive value of the Sepsis-3 criteria for diagnosis of sepsis is equivalent to the older Sepsis-2 definition. Additionally, the SOFA score is less well known outside of the intensive care unit, further reducing the utility of this definition.

The global criteria for sepsis definitions have been criticised for their over-simplification and static nature. They do not account for changes in the course of sepsis over time. Finally, there has never been any gold standard to use in the validation of these criteria.

Therefore, these criteria are practical, and may predict patients with a high risk of mortality and organ dysfunction. However, criteria such as Sepsis-1, -2, or -3 lack sensitivity and give no indication of time of onset; where such definitions are used, the resultant populations risk overdiagnosis including those with inflammation of a noninfective cause, in addition to those with true sepsis.

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Figure 1-9 – Sepsis definitions *Definitions of sepsis (red) and associated clinical criteria (green) and scoring systems (blue).SIRS – Systemic Inflammatory Response Syndrome

Section 1 Introduction

Various biochemical markers are in evaluation for the diagnosis of sepsis, such as C- Reactive protein and procalcitonin. However, there is currently no marker with sufficient sensitivity and specificity to be used to identify sepsis. A biomarker is “a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes or pharmacological responses to a therapeutic intervention” (107). The use of biomarkers in sepsis are of interest, as their use may enable early detection, and personalised treatment. However, the complexity and heterogeneity of pathophysiological changes in sepsis have prevented the successful adoption and widespread validation of any markers (108).

Treatment of sepsis relies on supporting the patient through the immune mediated pathophysiological changes of systemic inflammation, and targeting the underlying infection with antimicrobial agents, guided by the International Surviving Sepsis guidelines (109). Latterly, immunomodulatory approaches, such as the use of corticosteroids in refractory septic shock have been recommended (109), however there is limited evidence to support this. However, the use of compounds to modify the dysregulated immune function in sepsis may prove to be a useful adjunct to sepsis management. Animal studies discussed in 1.4.3.1 have indicated that N/OFQ-NOP may modulate the immune response to induced sepsis, and therefore this system could be a useful target in the management of sepsis related immune dysfunction.

1.3.5 Key points The immune system provides a host defence against pathogens through its innate and adaptive divisions.

The innate immune system provides an initial, generic, rapid response leading to an increase in several proinflammatory cytokines early in the process which coordinate a

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Section 1 Introduction cellular and biochemical reaction. There is evidence for N/OFQ-NOP interaction within some parts of the immune system

Sepsis is an important example of systemic inflammation. However, the descriptions of Sepsis-1, 2 and 3 define a heterogeneous syndrome, with differences in the criteria for diagnosis over time. This is a challenge in the study of sepsis and requires caution in the interpretation of studies of this patient group.

1.4 Role of N/OFQ-NOP in the immune system Opioids have a longstanding association with increased susceptibility to infection (1). Much of the underlying work in this area is observational and confounded by the coexistence of socioeconomic factors such as deprivation, poverty and substance abuse (110). Opioid administration per se in this context, through shared needles, or unsterile conditions produces a port of entry for infection. Similarly, the stigmata of poverty, poor nutritional state, poor access to healthcare, housing and living conditions also predispose to infection.

Studies of intravenous drug abusers demonstrate an increased incidence of opportunistic infections such as candidaemias compared to controls, suggesting underlying immunosuppression as a result of opioids(111). In the context of current increases in opioid prescriptions for chronic pain, in the UK and USA, termed the “opioid epidemic”, this has concerning implications for healthcare(112, 113).

Many different mechanisms have been proposed to underlie this. Opioid receptors are present on immune cells and may have a direct immunomodulatory effect (macrophages, NK cell, thymocytes, T- and B- cells)(114). Stimulation of opioid receptors in the hypothalamus may cause suppression of the adrenocortical axis (115- 118).

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As a non-classical opioid receptor, NOP-N/OFQ may also have a role in immunomodulation.

Evidence for the presence of NOP and N/OFQ, both as peptides and encoding mRNA throughout the immune system is conflicting, and the results of functional studies are varied as are the methods used. Studies have examined effects at the single cell, tissue, and organism level. Results have been varied, possibly as a result of the detection methodology, and difficulties separating, characterising, and studying individual immune cells. This section presents a critique of detection methods and potential confounders (1.4.1), the available evidence (1.4.2), and a unified putative role for NOP- N/OFQ in inflammatory conditions (1.4.4).

1.4.1 Methods of investigating and characterising the NOP-N/OFQ system As with other peptides, N/OFQ-NOP may be detected as peptide, or by proxy via its precursor or encoding genetic material. The gold standard is detection of functional protein using receptor activation as a readout. Detection of protein can include recognition of specific motifs via antibody binding techniques, or detection based on molecular mass.

1.4.1.1 Detection of target molecules Studies of NOP-N/OFQ localisation in humans have been reviewed above (1.2.1) and elsewhere (13, 119). Localisation of peptide and genetic signals of NOP-N/OFQ in biological tissues has yielded conflicting results. Genetic precursors detected by PCR based techniques may not indicate the presence of functional protein. Assays recognise specific sequences in genetic and peptide material which may give false positive results in the presence of nonspecific binding.

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Molecular detection techniques based on identification of molecular size alone (such as mass spectrometry and western blotting) may produce false positive results in the detection of specific peptides or may lack sensitivity in the discrimination of peptides of similar sizes. Morphine and fentanyl are highly protein bound to albumin and 1-acid glycoprotein; similarly, a protein bound nociceptin molecule may not be detected under these conditions. Nociceptin is broken down by aminopeptidases in plasma and endopeptidases centrally(120). These fragments may be active and have a feedback role, although may not be detected by conventional tests(121).

Specific techniques utilising the lock and key ligand-receptor mechanism or binding of a labelled antibody through recognition of a unique peptidic motif suggest expression, and when combined with a measure of downstream effector activation provide strong evidence of functional peptide expression. Reported concentration ranges of plasma N/OFQ detected in health vary significantly, which may reflect true differences in expression, or methodological problems with the techniques chosen (122). Enzyme linked immunosorbent assay (ELISA) and radioimmunoassays (RIA) are used extensively in the detection of peptides (Figure 1-10). For the detection of N/OFQ these assays have significant differences in performance; RIA consistently detects N/OFQ ng ml-1 compared to the pg ml-1 concentrations detected by ELISA from healthy plasma samples (122).

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Introduction

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Figure 1-10 – The basis of ELISA and RIA tests. In the Direct (A), Indirect (B), and Sandwich (C) ELISA, an antigen is bound by the primary antibody. In the direct test, this antibody is conjugated to an enzyme, and generates a signal, whereas in the indirect test, the primary antibody is conjugated to an intermediary to which the secondary antibody is raised and then generates a signal. In a sandwich ELISA, the capture antibody binds the antigen of interest, which is then processed as for an indirect ELISA. RIA tests use the principle of displacement of labelled antigen of interest by exposure to (unlabelled) antigen in the sample. In both tests, standard concentration-signal curves can be prepared by exposure to standards of known concentration in order to determine an unknown concentration from an experimental sample.

Section 1 Introduction

In principle, ELISA uses the binding of an enzyme-linked antibody raised to the protein of interest to facilitate detection. Antibody-target interaction may be direct (the antibody binds to the antigen of interest), or indirect (one or more intermediaries are involved). The enzyme causes formation of products which are detected (for example by luminescence or fluorescence). Direct ELISA is simple and straightforward, although requires a specific antibody to the peptide of choice, conjugated to an enzyme, which is costly. Where there are multiple antigens within a sample, and a low concentration of target antigen, the sensitivity is reduced.

Indirect ELISA tests use one or more non enzyme-linked primary antibodies, and a secondary antibody raised to the common FC of the primary antibody. This enables labelling of different antigens and avoids conjugating multiple primary antibodies. The sensitivity is greater due to the increased number of binding sites on the primary antibody. A third approach using a sandwich ELISA coats the sample wells with antibody specific to the antigen of interest, retaining it in the well. The antigen of interest is then detected using direct or indirect approaches as above. This is more expensive and time consuming but offers greater sensitivity.

Radioimmunoassay (RIA) uses the competition between a sample and radiolabelled antigen in order to determine the sample concentration. Increased radioactive signal indicates a lower concentration of sample antigen. The major disadvantage of this technique is the requirement for radiation, and relatively short shelf life of these test kits.

Both tests have been used in the detection of N/OFQ and are sensitive to concentrations in the picomolar range. However, distinguishing N/OFQ from the other peptides present in biological samples, and interactions from the products of enzyme activity (from the assay, or in the sample) may lead to the significant variation in reported

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N/OFQ concentrations. In our laboratory, measurements of N/OFQ obtained by EIA were up to 1000 fold greater than those from RIA (which was consistently in agreement with the literature) (122). Some of the reported measurements of N/OFQ from PMNs and cultured human splenocytes using ELISA techniques (123, 124) may reflect inaccuracies in the test methodology (122).

The study of interactions within the immune system presents additional challenges, because of the challenges of harvesting immunocytes in sufficient numbers and activation of and damage to immune cells attributable to the extraction process. PCR based genetic studies may require less cellular substrate for detection, although this does not imply the presence of functional protein in physiological concentrations, nor that this process is viable under physiological conditions. For example, mRNA encoding N/OFQ is detectable in eosinophils within the sputum of asthmatic subjects (19), but to date the detection of N/OFQ peptide from this cell type has not been reported. This may be due to the challenges of cell extraction, the low concentrations, or that the peptide is not synthesised or released.

Conventional tests, based on ELISA or RIA can detect relatively small concentrations of N/OFQ, but the levels produced by single cells, or where the target cell frequency is low may not be detectable using this methodology. The challenges of detecting picomolar concentrations of peptides, combined with the difficulties outlined above of studying immunocytes has confounded investigation of the source and nature of NOP-N/OFQ in cell-cell interactions in the immune system.

Other approaches use the downstream effects of receptor activation as a readout to infer the presence of active receptor, peptide, or both (Figure 1-11). The characteristics of the GPCR system amplify a small signal, which may facilitate detection of small concentrations of peptide, which may fall below the range of detection of other

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Section 1 Introduction techniques The significant amplification associated with GPCRs make such receptor systems useful for detection of low concentrations of candidate peptide – particularly where the readout is downstream. However, intracellular pathways resulting in a downstream readout may be vulnerable to other intra- or extracellular influences.

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Figure 1-11 – Strategies for studying receptor systems in cells

Section 1 Introduction

1.4.1.2 Biosensor based approaches Biosensor based techniques rely on the use of organic material in order to detect the presence of a molecule through the use of isolated enzymes, immunosystems, tissues, organelles or whole cells (125). Fluorescent biosensors are widely used to probe receptor and downstream events non-invasively. This approach does not require radioisotopes and allows the visualisation of cellular events in real time. Biosensors produce a measurable change in some readout (often fluorescent intensity or wavelength) occurring in response to activity at a receptor, which infers ligand-receptor interaction.

NOP natively couples to Gi/o, which, following agonist stimulation, causes reduced cellular concentrations of cAMP and membrane hyperpolarisation. Both cAMP concentrations, and membrane polarity can be measured. However, techniques have been developed permitting NOP to couple via Gq, inducing a calcium flux on agonist stimulation, which is measurable via calcium sensitive fluorophores. A Gi/o to Gq chimeric modification facilitates detection of agonist-receptor interactions using intracellular calcium concentration as a readout for single cell (126), and plate based assays (127) for high throughput screening.

Latterly, individual components of the effector machinery may be investigated to determine protein-protein interactions using the spectral shifts of adjacent fluorophores unmasked by bioresonance energy transfer (BRET).

BRET occurs where the emission of a photon from one fluorophore is of the complementary wavelength to excite and cause emission of a second adjacent fluorophore at a specific wavelength. By inference, this suggests proximity of the two fluorophores (128). The fluorophores luciferase and green-fluorescent protein (GFP) exhibit this phenomenon. Renilla luciferase (Rluc) bioluminesces at a peak wavelength

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Section 1 Introduction of 480 nm, whereas GFP excites at 488 nm and emits and 510 nm. Close physical interaction between NOP–Rluc and the RGFP--arrestin molecule following agonist binding to the parent GPCR results in an increased ratio of fluorescent emission at 510nM relative to that at 488 nm – an observed shift in the wavelength of emitted light.

This interaction has been observed when this system is coupled to 2-adrenoceptors and dopamine receptors (129). Latterly, this technique has unmasked NOP coupling to -arrestin and characterised biased agonism at this receptor (42).

GFP tagging has been utilised extensively in determining the mechanism of GPCR activation, deactivation, internalisation and recycling (130). Modification of targets to express this small fluorescent peptide allows receptor localisation and tracking. Terminal modification of NOP by addition of GFP has facilitated study of intracellular tracking of this receptor in recombinant cell lines (126, 131, 132).

Transfection of non-native proteins and receptor systems with or without fluorescent labels into immortalised cell lines is a common technique used to develop biosensor- based tests. Desirable properties of the cell line of interest include ease of transfection, presence of downstream transduction machinery, and ease of culture. The expression of native receptors must not compete with or confound the transfected receptor system. Existing immortalised cell lines satisfying this requirement includes the Human Embryonic Kidney (HEK), 3-day transfer, inoculum 3×105 cells (3T3), CV-1 (simian) in Origin (COS) and CHO lines.

The CHO cell line is a frequent target for expression of recombinant receptors due to its relative ease of transfection, stability and accessible culture requirements (133). This well characterised cell line is therefore suitable for use as a transfection target for biosensor-based assays. In addition to any transfected receptor systems, CHO cells are

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Section 1 Introduction known to express relatively few endogenous receptors, although do express purinergic (134), muscarinic (135, 136) and serotonergic (137) receptors natively.

The use of a plasmid encoding NOP has significantly advanced understanding of this receptor and candidate ligands through establishment of stable transfects in CHO and HEK cells for receptor binding and functional assays. Stable transfection enables high level, inducible expression of the exogenous receptor (138) of the receptor of interest for further study and can be used for non-invasive screening of large libraries to identify and isolate candidate ligands. The human N/OFQ receptor has been stably transfected into CHO cells, in the immortalised CHOhNOP cell line.

1.4.1.2.1 IP3/DAG pathway

NOP does not natively couple to the Gq pathway, although there is evidence suggesting that classical opioid receptors, (sharing a Gi/o mechanism) couple to phospholipase C (PLC) when expressed in recombinant systems (139). Similarly, there is limited evidence for a small increase in PLC activity as a result of NOP agonism, again in recombinant systems (140). The effects reported are small, and the implications for in-vivo expression are unclear. The apparent cross-over between Gi/o and Gq pathways may reflect supraphysiological levels of receptor and G-protein expression permitting receptor promiscuity. The PLC pathway, when activated via chimeric G-proteins is a mechanism of noninvasively studying drug effects at NOP through Gq using change in intracellular calcium concentration as a readout. In CHOhNOP cells cotransfected with the Gqi5 chimera (CHOhNOPGqi5), the measured intracellular calcium concentration increases following NOP stimulation (127).

The Gq family of G-proteins couples via PLC, causing hydrolysis of the membrane phospholipid phosphatidylinositol 4,5-bisphosphate (PIP2) to the second messengers diacylglycerol (DAG) and inositol trisphosphate (IP3) as shown in Figure 1-12. DAG and

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Section 1 Introduction

IP3 increase intracellular Calcium, and activation of Protein Kinase C respectively. This mechanism initially increases cellular excitability, followed by prolonged changes in gene expression, cellular growth, and proliferation.

In addition to inhibition of calcium channels via the GI dimeric subunit, some NOP responses are abolished by Protein Kinase C inhibitors, suggesting a PKC related mechanism (141). Moreover, N/OFQ activation of PKC in a CHO-NOP cell line was abolished by inhibitors of phospholipase and by pertussis toxin, suggesting that this response is both G-Protein controlled, and utilises the IP3/DAG pathway (140). This may relate to  coupling with phospholipases or nonspecific GPCR promiscuity in this recombinant highly expressing system.

Figure 1-12 – Gq dependent mechanisms - Binding of an agonist to the GPCR stimulates G- protein activation of PLC conversion of PIP2 to DAG and IP3. IP3 stimulates release of intracellular calcium from the sarcoplasmic reticulum, DAG causes phosphorylation of calcium channels via PKC.

1.4.2 In-vitro evidence for N/OFQ-NOP localisation N/OFQ-NOP and their mRNA transcripts are present throughout immune tissues in humans and other non-primate species and on immortalised human immune cell lines (Table 1-8, Table 1-9, Table 1-10, Table 1-11).

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Section 1 Introduction mRNA encoding NOP is detectable in whole blood, mixed peripheral blood mononuclear cells (PBMCs), lymphocytes, and polymorphonuclear cells, and monocytes (Figure 1-7) (142-145). Whilst underling mRNA is ubiquitously present, the distribution of functional NOP is more limited, suggesting either that mRNA is not translated, is inactivated, or specific conditions are required to facilitate the synthesis of functional NOP receptors.

The human immune immortalised U937 (kD 21 pM), CEM (kD 14 pM), MOLT-4 (kD 19 pM), and Raji (kD 68.4 pM) cell lines express NOP (146, 147). These cancer derived pathological cell lines may not reflect the true in-vivo receptor expression, may have aberrations of cell cycle regulation and protein synthesis. However, freshly isolated human immune cells do have evidence of NOP expression, correlating earlier evidence from immortalised cell lines.

N/OFQ binding sites, and by inference, NOP receptors have been localised to human polymorphonuclear cells (126, 148), PBMCs (146) and lymphocytes (149). Labelled N/OFQ binds to sites on lymphocytic cell lines (146, 147, 150), monocytes (150), and polymorphonuclear cells (148, 150) in a specific and saturable manner, suggesting the presence of NOP on these species. Exposure of human lymphocytes expressing the NOP receptor to nanomolar concentrations of N/OFQ has been shown to modulate antibody production, activate NF-, and modulate migration and chemotaxis (146, 148, 151).

mRNA encoding N/OFQ and its precursor ppNoc are similarly widely distributed throughout primary and secondary immune tissues, splenic and thymic leukocytes (124, 143, 144), lymphocytes (124, 145), monocytes (152), and polymorphonuclear cells (123, 153). The eosinophil subset of PMNs derived from bronchial tissue of asthmatics has also been shown to express N/OFQ mRNA (154). The expression of NOP mRNA appears to be modified by local stimuli, although the role of stimulatory mediators is conflicting. NOP mRNA expression in human whole blood is downregulated by pre-incubating with

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Section 1 Introduction the pro-inflammatory agent lipopolysaccharide (LPS) (155). Conversely, however, splenic NOP mRNA expression in rats is (nonsignificantly) upregulated by LPS exposure (156).

Despite widespread expression of encoding cDNA and mRNA for both N/OFQ and NOP throughout the immune system, there is comparatively little evidence for translation and synthesis of NOP peptide. N/OFQ peptide is detectable in a mixed PMN population (123), in splenic leukocytes (124) and in lymphocytes (124). Synovial fluid from arthritic patients has been found to contain elevated concentrations of N/OFQ – further supporting the hypothesis that polymorphs associated with the inflammatory response could be a source (123).

The widespread expression of N/OFQ-NOP mRNA is either indicative of a highly conserved genetic sequence, or that other immune cells may synthesis and release N/OFQ under appropriate conditions. The role of this peptide is unclear but may suggest that non-classical opioids may have a role in immunomodulation and/or represent a pharmacological target.

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NOP Expression Lymphoid tissues Tissue Species Finding Reference Spleen (tissue) Porcine NOP mRNA Pampusch(143) Splenic lymphocytes Porcine NOP mRNA Arjomand(145) Thymus Porcine NOP mRNA Pampusch(143) Mesenteric LN* Porcine NOP mRNA Pampusch(143) Inguinal LN* Porcine NOP mRNA Pampusch(143) Spleen Mouse NOP mRNA Goldfarb(144) Thymus Mouse NOP mRNA Goldfarb(144) Table 1-8 – NOP expression within lymphoid tissues - *LN – Lymph nodes

Immune cells (and immune-like cell lines) Tissue Species Finding Reference Mixed whole blood Human NOP mRNA Stamer(157) Al-Hashimi(158) Zhang(155) Mixed PBMC Porcine NOP mRNA Pampusch(143)

Mixed PBMC Human NOP mRNA Williams(146, 159) N/OFQ binding Peluso U937 (lymphoma) Human N/OFQ binding Peluso(146) CEM (lymphoblast) Hom(147) MOLT-4 lymphoblast RAJI Raji Cells Human N/OFQ binding Hom(147) (lymphoma) Lymphocytes Mouse NOP mRNA Halford(6) Table 1-9 – NOP expression within immune cells (1/2)

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NOP Expression Immune cells (and immune-like cell lines) Tissue Finding Cell identification NOP Reference identification Lymphocytes NOP mRNA Density gradient Northern Wick(160) (Human) Cytapheresis NOP mRNA Cytapheresis Northern Peluso(12) NOP mRNA Density PCR/FACS Krüger(150) NOP mRNA gradient/IM Northern Arjomand(145) FACS NOP mRNA qPCR McDonald(161)

Monocytes NOP mRNA Cytapheresis Northern Peluso(12) (Human) NOP mRNA Density gradient Binding Serhan(148) Northern NOP mRNA FACS qPCR McDonald(161) NOP mRNA Cytapheresis PCR/FACS Krüger(150) PMNs NOP mRNA Density gradient Binding Serhan(148) (Human) Northern NOP mRNA Density gradient Northern Fiset(123)

N/OFQATTO594 Density gradient Binding Bird(126) binding

Anti-NOP Density gradient Binding Serhan(148) binding Functional Peluso(12) NOP mRNA Cytapheresis PCR/FACS Krüger(150)

Eosinophils NOP mRNA Immunomagnetic qPCR Singh(19) (Human) Table 1-10 – NOP expression within human immune cells (2/2)

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N/OFQ, ppNoc expression Lymphoid tissues Tissue Species Finding Reference Spleen Porcine ppNoc Pampusch(143) Thymus Porcine ppNoc Pampusch(143) Spleen Rat ppNoc Thomas(162) Rat N/OFQ Miller(124) Spleen Mouse ppNoc Goldfarb(144) Thymus Mouse ppNoc Goldfarb(144) Immune cells (and immune-like cell lines) Tissue Species Finding Reference Mixed whole blood Human ppNoc mRNA Zhang(163) ppNoc mRNA Stamer(157) Mixed PBMC Human ppNoc mRNA Williams(152) Lymphocytes Human ppNoc mRNA Arjomand(145) Lymphocytes Rat N/OFQ Miller(124) Polymorphonuclear Human ppNoc mRNA Thompson(153) cells Human ppNoc mRNA, Fiset(123) N/OFQ Eosinophils Human ppNoc mRNA Singh(19) EOL-1 (Eosinophil- Human ppNoc mRNA Thomas(162) like) Table 1-11 – Studies demonstrating localisation of N/OFQ and encoding mRNA within the immune system

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Single cell mRNA sequencing has given further insights into the transcriptome of immune cell, and the expression of cell surface markers in different states of activation and pathology. The evidence presented above demonstrates the expression of NOP from a wide range of immune cells, and immune cell lineages. However, there are significant phenotypic differences between the cell types, cell surface markers and expression(164).

Analysis of the transcriptome of healthy individuals demonstrates high NOP mRNA expression in granulocytes and monocytes, with detectable but low-level expression in lymphocytes(22, 23). There are no specific studies of the NOP expression in activated immunocytes.

Granulocyte expression of NOP when analysed by single cell RNA sequencing (scRNA- seq) is highly variable, although appear to be localised to eosinophils and neutrophils, consistent with previous functional data(23, 165). Neutrophil subsets within the peripheral blood have been identified by scRNA-seq techniques. These experiments have demonstrated 3 distinct families of neutrophils within the peripheral blood where immunocytes are older, differentiated and targeted to specific functions(166).

Of note, there is a significant variability in the maximal NOP expression within the dataset for neutrophils and eosinophils, with consistent lack of expression in basophils, both within and between datasets. This may indicate differences within the underlying cell populations that have not been investigated,

In contrast to scRNA-seq studies of NOP expression, ppNoc mRNA has high levels of expression in B-lymphocytes, with limited expression in other immune cell types. Expression is consistently high in memory B-cells, and moderate in naive and exhausted B-cells(23, 165).

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Section 1 Introduction

Study of the transcriptome suggests that B cells may be a major site of N/OFQ secretion, and granulocytes a site of action at the NOP receptor. However, the significant variability between and within datasets may also suggest that other factors such as immunocyte activation and other circulating mediators may play a role.

1.4.3 Modelling inflammation Sepsis is life threatening organ dysfunction caused by a dysregulated host response to infection(103). The widespread immune dysfunction a useful model for a whole organism inflammatory response, formally known as the Systemic Inflammatory Response Syndrome (SIRS). Experimental sepsis is induced in an organism by exposure to either a pathogenic organism, an antigen (such as LPS), or to proinflammatory mediators (such as TNF-), activating a cascade of cellular and whole organism responses (Figure 1-8). This approach has been used in the study of in-vitro cell lines, ex-vivo tissues from animals or humans, or in whole animals.

Study of responses at the single cell or tissue level, through immortalised cell lines or ex-vivo samples has a role in localising encoding DNA, mRNA, or NOP-N/OFQ peptide. However, study at the cell or tissue level do not always accurately represent whole organ responses, and localisation of peptides gives no information about the underlying receptor functional coupling or cross-interactions with other organs and systems. The whole organism inflammatory response is more complex. Cell lines may originate from healthy mammalian cells, although many are derived from neoplastic cell lines, which may not exhibit the same behaviours as healthy cells in-vivo.

Macroscopic approaches using animal models of sepsis, or ex-vivo tissue models are sometimes used to overcome these shortcomings. A gold standard approach would be to study inflammation and sepsis in humans. However, data obtained from human

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Section 1 Introduction studies are frequently observational. This approach, in a heterogeneous patient population with multiple interacting comorbidities is pragmatic but challenging. This section critiques the approaches used to study inflammation in animal and human models.

1.4.3.1 Animal models N/OFQ-NOP receptor, ligand and encoding mRNA and DNA have been detected in mammalian species using ex-vivo samples from dogs, pigs, rats, and mice as a proxy for human study (Table 1-10, Table 1-11).

For investigation of the role of N/OFQ in disease states, animal models provide a way of experimentally inducing the condition of interest and manipulating the underlying expression of the receptor or its cognate ligand through gene knockout studies. This work facilitates study of receptor and peptide localisation and provides some evidence of functional coupling and underlying mechanisms, adding to the underlying in-vitro evidence.

Under controlled conditions, there are a number of models of experimentally induced sepsis in animals, which have been reviewed extensively (2). The caecal ligation and perforation (CLP) (167) model exposes the whole organism to a stimulus mimicking bacterial peritonitis. Other models of inflammation include dextran induced colitis as a mimic for ulcerative colitis. These distinguish “infective” inflammatory stimulus from noninfective stimuli, which may be significant when considering the role of N/OFQ-NOP in inflammation.

Treatment of rats with exogenous N/OFQ following experimental peritonitis induced by CLP increases mortality, reversed by treatment with the antagonist UFP-101 (167, 168).

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Section 1 Introduction

Whilst this finding is suggestive of an immunomodulatory role of N/OFQ-NOP, there are other potential explanations for the differences in mortality observed, and caution in the application to humans. Animal models are not subject to the same interventions that humans are – such as fluids and antibiotics. Moreover, animal models have a defined time of initiation for the septic insult, whereas the time of initiation is unknown in humans. Therefore, although animal evidence is suggestive that N/OFQ has an immunomodulatory effect, further investigation of the nature of these interactions at a cellular level is required.

Migration of leucocytes into bronchoalveolar and peritoneal fluid was increased by N/OFQ and reduced by the NOP antagonist UFP-101 (167). Similarly, in an experimental colitis model in rats, clinical symptoms and plasma concentrations of inflammatory markers decreased significantly following treatment with the NOP antagonist SB612111 (169). In the same colitis model, NOP knockout mice show less histological evidence of colitis than their wild type counterparts (170).

Collectively, these animal studies show some evidence that activation of N/OFQ-NOP has a pro-inflammatory effect in the face of established experimentally induced inflammation.

Animal studies enable experimental induction of sepsis in organisms under controlled conditions. Higher order mammalian models used for the study of sepsis, such as the pig and the dog do reproduce some of the changes in physiology associated with sepsis in humans, although rodents differ. Major criticisms of animal models of sepsis include the physiology of the animals, the timecourse of the sepsis and the lack of intervention in animal sepsis as compared to human models. In humans, sepsis has an onset of weeks (and often an unknown initiation time), whereas animal models have a clear “time zero”, and onset of hours to days. Animal models do not have access to the treatments,

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Section 1 Introduction resuscitation antibiotics and vasopressor support afforded to humans treated for sepsis and are often younger and without significant comorbidities. Therefore, whilst a useful tool, animal models do not always fully represent the situation in the critically ill human(171). A number of historic interventions for sepsis, whilst initially promising in animal and laboratory studies have lacked efficacy in clinical trials, such as activated protein C, now discontinued due to an lack of evidence, concerns about the PROWESS trial protocol, and increased thrombotic risks(172).

1.4.3.2 Human studies Human studies based on observational data have shown the NOP-N/OFQ system to be modulated in inflammatory conditions; asthma (19), colitis (173), sepsis (153, 157, 174), arthritis (123) and post cardiopulmonary bypass (CPB) (153).

Plasma concentrations of N/OFQ increase in humans with sepsis, post CPB and after major gastrointestinal surgery (153, 174). Increased N/OFQ concentrations have also been detected in the synovial fluid associated with arthritis (123) and in the sputum of patients with asthma, correlated with disease severity (19).

Fiset detected free N/OFQ in the ng ml-1 range in the acellular synovial fluid of arthritis sufferers by ELISA, in significantly higher concentrations in patients with rheumatoid and osteoarthritis than those with gouty or psoriatic arthritis (123). Arthritic patients did not have detectable levels of N/OFQ in plasma. There were no healthy controls in this study, although the differences between the types of arthritis suggest that the N/OFQ concentrations may be related to the underlying inflammatory pathology. Singh detected N/OFQ in the pg ml-1 range in sputum of healthy and asthmatic volunteers by RIA, with significantly higher concentrations detected in those patients with more severe asthma (19). Using the same RIA based assay, Williams detected plasma N/OFQ

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Section 1 Introduction concentrations in the pg ml-1 range for patients with sepsis, with higher concentrations correlating with 30-day mortality (174).

The observed increased plasma N/OFQ concentrations in sepsis (174), and peripheral blood NOP mRNA (157) have been associated with increased mortality in a cohort of critically ill patients with sepsis. Surviving critically ill patients with sepsis had reduced ppNoc (N/OFQ precursor) mRNA expression in peripheral blood (157, 175). This may reflect increased expression of functional peptide utilising ppNoc mRNA.

However, increased plasma N/OFQ concentrations are not always associated with increased mortality (153). Increases in plasma N/OFQ concentration during sepsis were associated in preliminary studies with an increased mortality, although, in later work, proinflammatory states caused by sepsis or cardiopulmonary bypass were associated with increases in N/OFQ, but not mortality(153, 174). Increases in N/OFQ were associated with mortality in the sepsis group (more so with intrabdominal sepsis), but not postoperatively (after cardiopulmonary bypass). These findings may be explained by the sterile inflammatory response caused by CPB, compared to the response observed in sepsis. This may represent the heterogeneity of sepsis, and a normal immune response that has not become dysregulated.

Observational studies in humans provide evidence of the whole organism human response to illness. However, the heterogeneity of patients enrolled in these studies can complicate interpretation of these data. Sepsis is a heterogeneous condition, and its clinical course may be a factor of the underlying characteristics of the patients. Many enrolled to these studies may not necessarily reflect the breadth of human physiology, and those at extremes of age, weight, or with certain conditions (such as cancer) may be excluded. The nature of studying the N/OFQ-NOP system in the critically ill patient

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Section 1 Introduction may reveal effects not present in health, due to changes in the N/OFQ-NOP system itself, or due to the interactions with the general effects of critical illness.

1.4.4 Possible mechanisms and targets for NOP-N/OFQ immune signalling The mechanism and roles of NOP-N/OFQ in the control of the immune system are a focus of active research. Evidence discussed above (1.4.2) indicate that N/OFQ-NOP may act as a cytokine, promoting chemotaxis (148), modulating immune cell proliferation(146) and production of other cytokines via NF- (151). There is further work suggesting that N/OFQ-NOP has a direct effect on blood vessels, promoting vasodilation (64).

As a vasodilator and chemoattractant, N/OFQ-NOP may be associated with the vasodilation and recruitment of immune cells seen in the early stages of inflammation. An early involvement in inflammation suggests that N/OFQ may be released by the initial cells to encounter a pathogen, typically antigen presenting cells, macrophages, polymorphonuclear cells and natural killer cells. Further evidence for an early role in inflammation is supported by improvements in mortality seen in the CLP model attributed to the NOP antagonist UFP-101, which occurred within the first 2 days (167). This suggests that the role of N/OFQ may be in the priming and initiation of an immune response. There is further evidence suggesting that granulocytes may be the source for this early peak in N/OFQ through PCR data demonstrating increased ppNoc mRNA expression in this cell type (Figure 1-7).

Previous studies have detected ng ml-1 concentrations of N/OFQ by ELISA in the supernatant of neutrophils obtained from plasma of arthritis sufferers, separated over a density gradient and degranulated by treatment with fMLP (123). The finding of nociceptin in both plasma and synovial fluid of patients with arthritis has also been demonstrated more recently, although at a lower (pg ml-1) concentration(176).

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Section 1 Introduction

However, in sputum from patients with asthma, Singh demonstrated a correlation between eosinophil number and N/OFQ concentration(19), in a pg ml-1 range suggesting that these cells may be a source. Furthermore, Singh was able to detect mRNA encoding ppNoc in peripheral blood eosinophils (isolated by immunomagnetic separation)(19). This suggests that eosinophils transcribe and store N/OFQ, releasing it at sites of inflammation (in this case, within the inflamed lung of patients with asthma).

Elevated plasma N/OFQ concentration is associated with increased plasma concentrations of the pro-inflammatory cytokines TNF-α, IL-8 and IL-10 in sepsis and CPB raising the possibility that it is acting as a DAMP under these conditions (153). Increased secretion and release of the same pro-inflammatory cytokines has been observed from T-Lymphocytes exposed to exogenous N/OFQ in-vitro (177). Whole blood mRNA encoding NOP is upregulated following exposure to LPS, whereas mRNA encoding ppNoc falls. The converse is true where IL-10, an anti-inflammatory cytokine is added. This suggests that N/OFQ has an important role as a mediator in sepsis, modulating cytokine release, and, is itself regulated by the inflammatory cascade (163). Increased proinflammatory cytokine synthesis may occur upstream at the level of transcription; activation of NOP endogenously expressed in SH-SY5Y cells caused upregulation of the pro-inflammatory NF- transcription factor (151).

The effects of N/OFQ on immune cells may also modulate antibody production. Blockade of NOP expression in mouse CD4+CD8+ and CD4-CD8- lymphocyte populations reduced the LPS stimulated production of both IgG and IgA, and lymphocyte proliferation (6). Conversely, the production of antibodies by cultured mouse splenocytes in response to sheep serum decreased following treatment with exogenous N/OFQ (178).

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Section 1 Introduction

N/OFQ also appears to modulate cellular migration. Increased plasma N/OFQ concentrations are associated with chemotaxis of polymorphonuclear cells (148). Monocytic exposure to N/OFQ appears to reduce the production of chemoattractants CCL2 and CCL5 (179). Similarly, exposure of glioblastoma cells to N/OFQ reduces inflammatory signalling and migration (180).

The mechanisms for the observed whole organism and tissue effects of N/OFQ appear to include modulation of cytokine and antibody transcription and release, immune cell proliferation and chemotaxis. The observed associations between NOP, ERK, and JNK may explain an increase in NF- activity, and subsequent transcription of pro- inflammatory cytokines following NOP stimulation.

Overall, the majority of pre-clinical, animal and cell-line evidence suggests that stimulation of NOP by N/OFQ has a pro-inflammatory effect on immune cells. Initial tissue evidence following N/OFQ exposure demonstrates vasodilation and microvascular inflammation through prostacyclin (177) and histamine (64) release, reversed by the NOP antagonist UFP-101 (181). This is supported by evidence in animal studies, and appears to be supported by studies of inflammatory and immune conditions such as sepsis and asthma in humans (19).

The relationship between N/OFQ-NOP and immune functioning is complex; studies have demonstrated both pro- and anti-inflammatory effects from N/OFQ-NOP exposure in different immune tissues from a range of mammals. The study of immune system is challenging, complicated by difficulties obtaining pure, unaltered immune tissues.

Conditions such as sepsis evolve in individual patients, and the effects of N/OFQ-NOP may differ throughout this journey, notwithstanding the emerging evidence that sepsis 1-60

Section 1 Introduction is a heterogeneous condition with numerous subtypes yet to be defined (182). The effects of N/OFQ-NOP on inflammation observed at a whole organism or tissue level are likely to be due to a combination of immediate immune-cell interactions (activation of NF- through ERK and JNK), and coincidental direct effects on target organs (vasodilation) and be dependent on the prevailing balance of pro- and anti- inflammatory mediators.

1.4.5 Key points There are numerous studies demonstrating the involvement of N/OFQ-NOP in the immune response, collected from immortalised cell lines, animal, and human models. These systems all have challenges associated with them, and none have completely uncovered the responses at a single cell level.

N/OFQ concentrations in plasma appear to increase early and rapidly in inflammation, suggesting that those cells of innate immunity may be responsible for its release.

There is conflicting evidence for N/OFQ (and/or) precursor presence in polymorphonuclear cells. There are challenges both in the separation of immune cells and reliable detection of functional peptide. Therefore, there is currently no clear evidence for the cell of origin.

A bioassay-based technique may enable measurement of single cell N/OFQ release and provide confirmation of the cell of origin to inform future studies.

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1.5 Aims and objectives There is widespread evidence for the involvement of N/OFQ-NOP in the inflammatory immune response. Early autoradiographical and binding studies, and latterly PCR demonstrate NOP-N/OFQ, precursors and encoding mRNA within granulocytes.

However, PCR data does not necessarily demonstrate the presence of functional N/OFQ peptide, and in the absence of a sensitive, specific test for N/OFQ the role of this molecule remains unclear.

Therefore, the overall aim of this thesis is to provide further insight into the role of the N/OFQ-NOP system in the immune response.

Based on in-vitro and clinical evidence, I hypothesise that N/OFQ is differentially released in granulocyte subsets, and that release is modulated by inflammatory conditions such as sepsis.

The specific objectives to achieve this aim are to

1) Develop and validate a live cell assay to detect N/OFQ release from granulocytes to detect functional N/OFQ peptide 2) Characterise N/OFQ peptide presence and release from granulocyte subsets 3) Characterise the presence of the NOP receptor on granulocyte subsets

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Chapter 2

Materials and Methods

Section 2 Materials and Methods

2 Materials and Methods

2.1 Project overview This section has been divided according to the high-level project objectives (1.5). The materials and methods are organised by objective and chapter for cell testing, assay development, validation, application of the test to clinical samples and subsequently confirmatory testing.

As this work was exploratory and iterative in nature, the calculation of power and sample size was not possible during the cell line tests, or for assay optimisation. Therefore, for wet lab work, cell line test and validation, a target of 5 independent experiments was used.

Table 2-1 and Table 2-2 show the organisation of this section, and where each protocol was used. For reference, the protocols are indexed below.

2.2.1 Protocol 1 - Maintenance of transfected immortalised cell lines ...... 68 2.2.2 Protocol 2 - Cell counting ...... 70 2.4.1 Protocol 3 - Preparation of glass coverslips ...... 88 2.4.2 Protocol 4 - Live cell confocal imaging of Calcium Flux in CHO cells ...... 88 2.4.3 Protocol 5 - Immunofluorescent staining of immune cells ...... 92 2.5.1 Protocol 6 - Cell preparation ...... 96 2.5.2 Protocol 7 - Fluorometric measurement of calcium concentration using Fura- 2-AM dye ...... 97 2.5.3 Protocol 8 - Data analysis ...... 97 2.6.1.1 Protocol 9 – Venepuncture ...... 104 2.6.1.2 Protocol 10 – Separation by Polymorphprep™ ...... 106 2.6.1.3 Protocol 11 – Separation by Ficoll-Paque™ ...... 107 2.6.2.1 Protocol 12 – Immunomagnetic separation of eosinophils and neutrophils by negative selection from whole blood (MACSxpress®) ...... 111

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Section 2 Materials and Methods

2.6.2.2 Protocol 13 – Immunomagnetic labelling and separation after density gradient separation...... 112 2.7.2 Protocol 14 - Validation of immune cell separation by Flow Cytometry ...... 118 2.8.2 Protocol 15 – Determination of ATP concentration in cell fractions using a bioluminescence-based assay ...... 124 2.9.2 Protocol 16 – RT-qPCR of granulocytes for NOP and ppNoc transcripts ...... 133

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Section Project progress / section Overview Techniques Cell line Assay Assay Application Confirmatory Protocols 2

tests development validation testing Materials and MethodsMaterials and Ch. 3 Ch. 4 Ch. 4 Ch. 5 Ch. 6 Cell culture Maintenance 1, 2 Cell counting

Validation of Cuvette based 3, 4, 5 CHOhNOPGqi5 cell line Fluorimetry 6, 7, 8 Confocal microscopy

Extraction of mixed Density gradient 9, 10, 11 polymorphonuclear extraction of cells leucocytes 2-66 Characterisation of immune cells by flow cytometry Testing and Density gradient 4, 9, 10, optimisation of extraction of 11, 15 granulocyte leucocytes degranulation assay in Luciferase ATP assay mixed PMNs Confocal microscopy Table 2-1 - Project overview (1/2) – shaded sections highlight where specified techniques and protocols are used, for navigation

Section Project progress / section Overview Techniques Cell line Assay Assay Application Confirmatory Protocols 2

tests development validation testing Materials and MethodsMaterials and Ch. 3 Ch. 4 Ch. 4 Ch. 5 Ch. 6 Extraction and Density gradient 9, 10, 11, validation of extraction of 12, 13, 14 granulocyte subsets leucocytes Immunomagnetic separation Flow cytometry

Testing and Extraction of 4, 9, 10, optimisation of leucocytes 11, 12, 13, granulocyte Immunomagnetic 14 2-67 degranulation assay in separation granulocyte subsets Confocal microscopy Application of Extraction of 4, 9, 10, granulocyte leucocytes 11, 12, 13, degranulation assay to Confocal microscopy 14 investigate release of N/OFQ from healthy volunteers and patients with sepsis Confirmatory testing Extraction of 4, 5, 9, 10, leucocytes 11, 12, 13, Confocal microscopy 14, 16 Immunofluorescence qPCR Table 2-2 – Project overview (2/2) – shaded sections highlight where specified techniques and protocols are used, for navigation

Section 2 Materials and Methods

2.2 Cell Culture 2.2.1 Protocol 1 - Maintenance of transfected immortalised cell lines

Wild-type CHO cells (CHOWT), CHO cells transfected with the NOP Gqi5 chimera

(CHOhNOPGqi5), and the eosinophil-like EOL-1 cell line were maintained in appropriate media, with addition of antibiotic to stock culture flasks to ensure plasmid selection

(Table 2-3). All cells were maintained in 5.0% CO2, humidified air, at 37C, and routinely subcultured after 2-3 days when ≥ 90% confluence had been attained. Stock flasks containing transfected cells were maintained in selection media containing an appropriate antibiotic favouring the transfected clones. Experimental flasks were maintained in serum containing feed media (Table 2-3). Feed media was refreshed every 2-3 days.

All cell culture procedures were carried out under strict asepsis, and within a Class 2 cabinet for containment to minimise the risk of contamination by mycoplasma and other organisms. If cells were found to have abnormal morphology, or an abnormal macroscopic appearance, cultures were discarded and replaced with fresh stocks.

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Section Cell line Feed media Selection media

CHOhNOPGqi5 1:1 Ham’s F12 nutrient mix and dulbecco’s minimal essential 1:1 Ham’s F12 nutrient mix and dulbecco’s minimal 2

media (DMEM) (Invitrogen, UK) essential media (DMEM) (Invitrogen, UK) MethodsMaterials and 10% fetal calf serum 10% fetal calf serum 100 IU ml-1 penicillin 100 IU ml-1 penicillin 100 IU ml-1 streptomycin 100 IU ml-1 streptomycin 2.5 g ml-1 fungizone 2.5 g ml-1 fungizone 200 g ml-1 geneticin, G418 200 g ml-1 hygromycin B CHOWT 1:1 Ham’s F12 nutrient mix and dulbecco’s minimal essential N/A media (DMEM) (Invitrogen, UK) 2-69 100 IU ml-1 penicillin 100 IU ml-1 streptomycin 2.5 g ml-1 fungizone EOL-1 RPMI*-1640 plus 2% fetal bovine serum (Invitrogen, UK) N/A Table 2-3 – Cell culture media and supplements for feeding experimental passages and selection of stock cells - *RPMI - Roswell Park Memorial Institute

Section 2 Materials and Methods

Cells were subcultured using standard techniques (183). Stock flasks were incubated with 0.05% Trypsin / EDTA (Life Sciences, UK) for 2-3 minutes at 37°C. Once treated with trypsin, flasks were agitated to detach adherent cells, the suspension was aspirated and the flask washed with appropriate neutralising media 10 mls (Table 2-3) into a sterile universal container. The cell count was determined (2.2.2), and a new flask inoculated, or coverslip seeded depending on requirements. For routine subculture, flasks were inoculated with a ratio of 1-part cell suspension to 10 parts media. Experimental flasks and coverslips were maintained in feed media (Table 2-3).

2.2.2 Protocol 2 - Cell counting Cell counting and resuspension was performed using a standard haemocytometer based technique, with viability determined by trypan blue exclusion (183).

50 l of cell suspension was mixed with 50 l of trypan blue (Life Sciences). The resultant mixture was agitated to avoid clumping and obtain a homogenous single cell suspension. The prepared suspension was transferred to the chamber of an improved Neubauer haemocytometer by capillary action and examined under a light microscope. Using this method, live cells appear unstained and dead cells stain blue when viewed using brightfield microscopy.

The haemocytometer is divided into 0.05 x 0.05 mm squares, with larger 1 x 1mm squares in each corner. The depth is 0.1 mm, therefore each 1mm corner square has a volume of 1 x 1 x 0.1 ml (100 nl). The number of cells counted in 100 nl is 10/10000th of the number of cells in 1 ml. When corrected by a scaling factor of 104, this gives the number of cells in a 1 ml suspension (Figure 2-1).

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Section 2 Materials and Methods

Count all of the stained and unstained cells in each of the corner 3 x 3 squares (1 mm2 = 0.1 ml outlined in red) and average the results

Cells touching the double lines (left and bottom in the shaded example), were excluded.

Figure 2-1 – Cell counting by haemocytometry (1 mm2 is represented by the red shaded area)

The number of cells is determined using Equation 2-1.

푛 푐 =2 × 0.0001

Equation 2-1 – determination of total cell number by haemocytometry (where c and n are cell concentration in cells ml-1, and number of cells in 0.1ml respectively)

This value is corrected to account for the dilution with trypan blue.

Viability is defined as the proportion of live cells (unstained by trypan blue) from the total cell count and is expressed as a percentage of live (unstained) cells relative to the total cell count (Equation 2-2).

푡표푡푎푙 − 푠푡푎푖푛푒푑 푣푖푎푏푖푙푖푡푦 = 100 × 푡표푡푎푙

Equation 2-2 – calculation of viability (%) by exclusion of trypan blue stained cells

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Section 2 Materials and Methods

2.3 Fluorescence-based assays 2.3.1 Principles of fluorescence Fluorescence describes rapid and short-lived emission of light following absorption of light of a shorter wavelength (184), in comparison to the prolonged emission of phosphorescence. Substances which exhibit fluorescence are known as fluorophores. Bioluminescence implies emission of light from living organisms by chemical reaction without any prior absorption of light (185). Bioluminescence is the basis for tests using formation of light emitting molecules as a readout, such as the luciferase ATP assay discussed in 2.8.1.

The Jablonski diagram describes transitions in energy state for a given molecule (Figure 2-2). When a molecule absorbs a photon, it undergoes transition to a higher energy state. The molecule then decays from the higher energy state and emits photons. The time taken for energy transfer between states results in characteristic time periods during which emission for fluorescence (rapid) and phosphorescence (slow) occur.

Figure 2-2 – Jablonski diagram, demonstrating the changes in energy state during absorption, fluorescence, and phosphorescence

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Section 2 Materials and Methods

When exposed to light of a given wavelength, the energy state of a fluorophore increases from S0 to S2, after which energy is dissipated, leaving the fluorophore at energy state S1. There may also be change in energy state due to nonradiative transition without the emission of photons. After a variable time period (nanoseconds), the molecule returns from S1 to ground state S0, emitting a photon of a characteristic wavelength (186). The time delay between absorption and emission of photons is characteristic of fluorescence or phosphorescence.

ℎ푐 퐸 = 휆

Equation 2-3 – Planck-Einstein relationship between energy (E), frequency (h), wavelength () and the speed of light (c)

Velocity, energy, wavelength and the speed of light are associated by the Planck- Einstein relation: photon energy (E) is directly proportional to frequency (h) and inversely proportional to wavelength (λ) (Equation 2-3). The photon emitted during decay to a lower energy ground state therefore has a longer wavelength. The separation between wavelengths of absorbed and emitted light is known as the Stokes shift (Figure 2-3). Fluorophores are highly efficient in this process. Other molecules may also naturally fluoresce, this is known as autofluorescence.

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Section 2 Materials and Methods

Figure 2-3 – Absorption-emission spectrum for the calcium-sensitive dye Fluo-4, showing the Stokes shift between excitation and emission wavelengths

Use of a filter to exclude all but the emission wavelength facilitates detection of fluorescent molecules. Therefore, a key component of design for experiments where fluorescence is a readout is the separation between excitation, emission and autofluorescence wavelengths; these and other factors are considered below.

The absorption-emission spectra for a given fluorophore is characteristic, although may overlap with other fluorescent molecules. Where multiple fluorophores are used this leads to the possibility of interactions between molecules. Absorption of emitted light (quenching) and excitation of other, adjacent fluorophores (Förster Resonance Energy Transfer; FRET) may affect the measured fluorescence.

Experimental design is affected by the spectra of the fluorophores used, the presence of any autofluorescence and interactions with other molecules. Therefore, where multiple fluorophores are used, such as for cell marker characterisation by flow

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Section 2 Materials and Methods cytometry, or immunofluorescence, the markers and emission filters are chosen to avoid quenching, FRET or overlapping spectra.

The fluorophores used within the scope of this project are shown in Table 2-4.

 (nm) Fluorophores and dyes Emmax Exmax Fluorescein Isothiocyanate (FITC) 518 494 Allophycocyanin (APC) 650 660 VioBlue 452 400 Phycoerythrin (PE) 575 565 4′,6-diamidino-2-phenylindole (DAPI) 461 358 ATTO594 626 603 Fluo-4* 516/516 491/494 Fura-2* 512/505 362/335 Table 2-4 – Absorption-emission spectra of common fluorophore and dyes used in this project *Calcium free/bound (187, 188)

The transition cycle between ground and excited state may theoretically be repeated infinitely, although the intensity of the light emitted from fluorophores falls irreversibly with time and with exposure to light (bleaching). Reversible loss of fluorescence can occur where emitted photons interact with nearby molecules, preventing the detection of the emitted light (quenching) (184). Both bleaching and quenching reduce the detectable fluorescence, degrading the quality of any image.

2.3.2 Biological measurements using fluorophores. Fluorescent techniques permit non-invasive real-time quantification of intracellular messengers, molecules, and ions in biological systems. Fluorescent techniques label markers or intracellular molecules with a fluorophore.

Calcium is a common intracellular second messenger, and the techniques for fluorescent quantification of this system have been reviewed extensively (189-191), and are summarised below.

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Section 2 Materials and Methods

Calcium-sensitive dyes have been used extensively to monitor this system, which is an end point and readout for many biological processes. The first use of modern dyes with a technique to load cells noninvasively, and with high sensitivity was recorded in 1982 using the dye Quin2 (192). This dye has since been superseded by other, more sensitive molecules with a greater change in fluorescent properties in response to calcium binding, such as Fluo-3 and its derivative, Fluo-4 (Table 2-5) (190). The Fluo-based dyes are modified forms of the calcium chelating agents ethylene glycol-bis(β- aminoethyl ether)-N,N,N′,N′-tetraacetic acid (EGTA) and (1,2-bis(o- aminophenoxy)ethane-N,N,N′,N′-tetraacetic acid) (BAPTA). The ability of these compounds to buffer intracellular calcium leads to an equilibration with free calcium.

Calcium free Calcium bound (nm)

 Exmax (nm)  Emmax  Exmax (nm)  Emmax Kd (nM) (nm) (nm) Fura-2 362 512 335 505 224 Fluo-3 503 526 506 526 400 Fluo-4 491 516 494 516 345 Table 2-5 – Dyes used for quantification of intracellular calcium concentrations (190)

Calcium-sensitive dyes undergo change in their fluorescent properties on binding free calcium, detectable by imaging techniques. Calcium within a biological system is in equilibrium between the bound and free states, and with the indicator dye. The properties of this equilibrium are described by the Kd, with the calcium effectively buffered by the dye (Equation 2-4). Binding is affected by temperature, pH, the presence of other calcium binding species and cofactors such as magnesium, all of which are controlled to minimise their effect on measured fluorescence.

퐵표푢푛푑[퐶푎]⇌ 퐹푟푒푒 [퐶푎] ⇌ 퐷푦푒[퐶푎]⇌ 퐷푦푒

Equation 2-4 – Equilibrium between bound and free calcium, and indicator dye

Fluorophores are large polar molecules and not membrane permeable. Fluo-4 and Fura- 2 dyes are presented as acetoxymethyl esters, which form a hydrophobic, lipid soluble

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Section 2 Materials and Methods molecule which diffuses freely across cell membranes and into cytoplasm during incubation. The ester is readily cleaved by intracellular esterases to form a negatively charged molecule, trapped (loaded) within the cell (Figure 2-4). Efficiency of cell loading depends on incubation time, pH, temperature, and intracellular concentration of other ions, such as magnesium.

Figure 2-4 – Mechanism of action of Fura-2-AM

Once loaded, dyes are subject to decay, bleaching and quenching, all of which reduce the number of intracellular fluorescent molecules and therefore measured fluorescent intensity. Over time, the dye may leak from a cell, further reducing measured fluorescence. Furthermore, some cell types, particularly CHO cells actively extrude dye, a process inhibited by co-incubation with the transport inhibitor probenecid (193). The combined effect of bleaching, decay, quenching, and dye extrusion is a decay in the baseline fluorescence over time and with exposure to light.

Fluorophores are described as ratiometric or non-ratiometric based on the change in their excitation-emission spectra upon exposure to the compound of interest. Non- ratiometric fluorophores have a single excitation wavelength, and the fluorescent

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Section 2 Materials and Methods intensity is variable dependent on concentration of the molecule of interest bound to the dye. Differences between the fluorescence of the bound and unbound states for non-ratiometric calcium sensitive dyes can be 100 fold or more (193, 194). However, the absolute fluorescence is a factor of the dye concentration, pH, and presence of other ions (notably magnesium).

Ratiometric dyes exhibit a shift in either excitation or emission spectra upon exposure to the molecule of interest. The ratio of fluorescent intensity at peak excitation or emission is used to negate the effect of dye loading, quenching, leakage, or bleaching. Using Fura-2 as an example of a fixed emission dye with a high affinity for calcium (Figure 2-4), the fluorophore is excited by light of alternating wavelengths (340 nm and 380 nm), but the peak emission is measured at a fixed wavelength (510 nm). The converse is true for fixed excitation dyes. Fixed emission ratiometric dyes have a different peak excitation wavelength between the bound and unbound states. Therefore, by determining the ratio of emission when excited at alternating wavelengths and calibrating to a maximal and minimal concentration of the ion of interest, the sample concentration can be derived, independent of dye concentration, pH, bleaching and other variables.

Calibrating to a maximal concentration typically requires cell lysis, releasing intracellular stores, followed by chelation. Calibration is a destructive process, changing cell morphology and imaging properties, precluding repeated measurements or single cell imaging.

In the calcium-free state, Exmax is 382 nm, and when bound to calcium, Exmax is 335 nm.

Emmax is 505-512 nm (Figure 2-5, Table 2-5). The measured concentration of calcium bound to the dye is proportional to the ratios of emission at 510 nm following alternating excitation at 340 nm and 380 nm via fixed instrument filters. The system

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may be calibrated by measuring Em510 at Ex340 and Ex380 in the presence of maximal and minimal concentrations of calcium (usually following cell lysis and chelation).

The Grynkiewicz equation (Equation 2-5) relates the calcium concentration to the Kd, measured, maximal and minimal Em510 ratio at Ex340/480 and baseline at 380nm under calcium free and bound conditions (195). The Kd for calcium binding to Fura-2 at 37C is 225 nM.

푅−푅 [퐶푎 ] =퐾 × 푆푓푏 푅 −푅

Equation 2-5 - Grynkiewicz equation relating calcium concentration to maximal, minimal and measured fluorescent ratios measured at Em510 at Ex340/480 for ratiometric dyes, where Kd = dissociation constant, R, Rmax, Rmin = measured, maximal and minimal Em510 ratio at Ex340/480 and sfb = baseline Em510 at Ex380 under calcium free and bound conditions

Figure 2-5 – Absorption-emission spectra for Fura-2 in the bound and unbound state, with alternate excitation at 350 nm and 380 nm (red lines), and emission at 510 nm (blue line).

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Fluo-4 is a fluorescent non-ratiometric dye with a moderate affinity for calcium (Kd 335 nM). Emission and excitation maxima in the calcium bound state are 516 nm and 494 nm; the dye is nonfluorescent in the unbound state (196). The ester form, Fluo-4-AM is cell permeable, and becomes de-esterified after cell loading, which significantly reduces leakage from the intracellular compartment (Figure 2-6).

Figure 2-6 – Mechanism of action of Fluo-4

Fluorescent intensity at 516 nm following stimulation at 494 nm is directly proportional to the concentration of Fluo-4 molecules in the calcium bound state, and therefore calcium concentration. Measured intracellular calcium concentration depends on free calcium available to bind with Fluo-4 and is therefore affected by other calcium binding agents (which may compete with Fluo-4), cellular compartmentalisation, and pH. Observed fluorescence may be dependent on other characteristics of the measurement system, including loading anomalies (available Fluo-4 for binding), autofluorescence, BRET and FRET. Autofluorescence for CHO cells is negligible within a Fura-2 based calcium assay (193).

Calcium change is inferred by measuring relative fluorescence (F). This measurement normalises measured fluorescence (F) to a stable baseline (F0) and highlights threshold change (Equation 2-6).

퐹 ∆퐹 = 퐹

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Equation 2-6 – Relative fluorescence (F), normalised to baseline, where F = measured fluorescence, F0 = mean baseline fluorescence

In experiments on cells in suspension, fluorescence is normalised to maximal calcium concentration by cell lysis and release of a saturating calcium concentration. Changes in morphology prevent measurement of this within the narrow focal plane of the

-6 confocal microscope. Therefore, exposure of the CHOhNOPGqi5 cells to 10 M N/OFQ was used as a positive control, and Krebs HEPES buffer as a negative control.

Fluorescence measurements are based on whole cell suspensions or individual cells. A whole suspension approach compared to single cell microscopy reduces inter-cell variability in loading, focal plane, and local background measurement. However, measuring fluorescence from a cell suspension does not give insight into single cell interactions.

Measurements of individual cell fluorescence may vary during single cell microscopy because of the orientation, morphology, or spatial organisation of the cells on the microscope slide. The 2-dimensional representation on the microscope may give a false reading if the dye is concentrated into a smaller area due to changes in cellular morphology or intracellular compartmentalisation. An approach to correct for this uses the measured area of the cell to correct for the observed fluorescence (CTCF, Corrected Total Cell Fluorescence), as has been described by various authors (Equation 2-7) (197).

퐶푇퐶퐹 = 퐹 −(퐴 − 퐹)

Equation 2-7 – Corrected Total Cell Fluorescence (CTCF) , where F = measured fluorescence, A = area, FB = mean background fluorescence

Fluorophores bound to antibodies directed against cell surface markers allow quantification and localisation of these molecules by measuring emitted fluorescence excited by light of a specified wavelength (Table 2-6). Nonspecific binding was minimised by the use of a blocking buffer containing bovine serum albumin (BSA), negating the use of isotype controls, which in themselves are controversial(198).

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Cell marker Fluorescent Exmax Emmax Emission Isotype probe filter CD16 VioBlue 400 452 DAPI Mouse IgMκ Siglec-8 APC 652 657 TxRed Recombinant human IgG1 FcɛRiα FITC 495 519 FITC Mouse IgG2bκ, Mouse IgG2b CD45 FITC 495 519 FITC Recombinant human IgG1 N/OFQATTO594 ATTO594 603 626 TxRed (non antibody) CD66 PE 565 573 TxRed Recombinant human IgG1 CCR3 VioBlue 400 452 DAPI Recombinant human IgG1 CD123 VioBlue 400 452 DAPI Recombinant human IgG1 Table 2-6 – Fluorescent markers used to detect and identify cells

Antibodies raised to the marker of interest, or ligands for the receptor system are conjugated to a fluorophore. The markers are incubated with the specimen, allowed to bind, and the excess washed off before imaging to localise either receptors or cell-based markers.

2.3.3 Ligands used for characterisation of cell lines and testing CHO cells are a well characterised cell line, capable of stable transfection, and expressing relatively few endogenous receptors. In order to characterise this cell line and verify its sensitivity to detect N/OFQ, experimental NOP agonists and antagonists were used. CHO cells are known to express purinoceptors, and therefore the response to ATP and to a series of purinergic antagonists was assessed. Furthermore, as ATP is a DAMP, released in response to cellular damage, and from immune cells, effective purinergic antagonism was important in order to exclude a false positive test for N/OFQ in the presence of activated immunocytes.

2.3.3.1 Ligands targeted at the N/OFQ-NOP system N/OFQ was the only agonist used. Previous studies have demonstrated CHOhNOP cells

-7 -12 to respond within the range 10 to 10 M (KD 9.91±0.04), which was used for concentration-response studies (199). For positive control experiments in the

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Section 2 Materials and Methods granulocyte release assay, a saturating concentration of 10-6 M N/OFQ was used to account for damage to the biosensor cells during exposure to immune cells and the laser, and to demonstrate cell viability.

To demonstrate a specific response to N/OFQ, the nonpeptide NOP antagonist SB61211 was used. SB612111 is a high affinity nonpeptide pure NOP antagonist, with high antagonist potency (pA2 8.2-8.5)(200, 201). Reversibility of a response in the presence of NOP antagonism provides strong evidence of a NOP dependent response. SB612111 was used in a concentration of 10-6 M, determined empirically.

2.3.3.2 Ligands acting at purinergic receptors ATP is a DAMP, present at the site of cellular damage, and is released by immune cells through damage cause by the extraction process. CHO cells express endogenous purinergic receptors, and therefore, ATP was used to demonstrate CHO cell viability, and the presence of an increased intracellular calcium concentration following stimulation, and to optimise the concentration of purinergic antagonists, and to confirm that any response seen was due to N/OFQ and not ATP.

In testing the response of CHOhNOPGqi5 cells to ATP, a range of concentrations between 10-8 M and 10-4 M were used, determined empirically. In testing combinations of purinergic antagonists, a saturating concentration of 10-6 M was used to demonstrate maximal receptor blockade. The concentrations used were equivalent to the final ATP concentration released during immune cell lysis as tested in the luciferase assay.

Blockade at this concentration supports the hypothesis that any observed CHOhNOPGqi5 response is due to N/OFQ and not ATP.

In order to block the multiple purinergic receptor subtypes present on CHOhNOPGqi5 cells, combinations of purinergic antagonists were used.

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Suramin is a nonpeptide nonselective P2 purinergic antagonist with low potency. Although used in early studies of purinergic pharmacology, its low antagonist potency

(pA2 5) and interactions with G-protein kinetics and a variety of proteins (including proteases, protein kinases, GABA, and 5-HT) preclude it’s use where other G-protein mediated systems are required (such as the CHOhNOPGqi5 cells used in this work)(202).

MRS2279 is a selective, non-competitive antagonist at the P2Y1 receptor, also known to be expressed endogenously in CHO cells. However, this did not provide complete blockade of the endogenous CHO receptors, and therefore was unsuitable for use(203, 204).

PPADS is a nonselective P2 antagonist with effects at both P2X and P2Y receptors and no crossover to muscarinic, histamine, adenosine, or adrenergic receptors. This nonselective blockade effectively antagonises both major purinergic subtypes endogenously expressed in CHO cells(205).

oATP is a specific antagonist at the P2X7 receptor, expressed endogenously on CHO cells, used to augment the weak antagonist effect of PPADS(206, 207).

The combination of PPADS and oATP reliably antagonised 10-6 M ATP, determined empirically, consistent with expected maximal ATP concentration after release from any immunocytes present.

2.3.4 Principles of confocal microscopy for live samples Laser scanning confocal microscopy (LSCM) is a form of fluorescent microscopy which enables high resolution, rapid imaging of fluorescent samples. The technique allows 2-84

Section 2 Materials and Methods optical sectioning to accurately localise fluorescence within the sample in a three- dimensional plane.

In a confocal microscope, the light path passes through two pinholes. The illuminating light is focused on the specimen by the first pinhole. The reflected light passes through a dichroic (semi-silvered) mirror and filter, through a second pinhole and to a detector (Figure 2-7). The first pinhole focuses the light on the specimen, whereas the second excludes out of focus light, resulting in an image of a precise focal plane in high resolution. In order to build up an image, the computer-controlled laser scans the sample in X, Y and Z axes.

Figure 2-7 – Schematic of confocal microscope setup

The system may acquire multiple images across several spatial planes and/or time, giving variable temporal and spatial resolution. This can generate 3- or 4- dimensional images of fluorescent intensity by time, plane, and using different excitation and emission filters and sources, depending on the system set-up.

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Confocal microscopy systems are limited by theoretical maximal temporal and spatial resolutions, the smallest unit of time, and the smallest object that may be resolved, or recorded by the system. Maximal temporal resolution, the recording of images over time is determined by the time to record an individual image. This is affected by the number of pixels in an image, scan speed, dwell time (time to focus on each pixel), exposure, and frame size.

Spatial resolution is a property of the optical light path and the capability of the detector system. The resolution is the minimum separation between two points that can be resolved with the optical configuration. The light path from a single point is represented optically by a point spread function, a bell-shaped curve of intensity around that point. Where points are close together, beyond the limit of resolution, the point spread functions overlap, and the points are indistinguishable as separate points. The point spread function, or airy disc is a function of the optical path of the microscope and is fixed. A further consideration is the Nyquist theorem; this defines the sampling interval (or number of pixels) required in order to reproduce a given point accurately. Therefore, the signal generated through any microscope system is determined by the optical path, objective and detector type.

These variables must be optimised in order to obtain an accurate image, and measurement of fluorescence. Localisation of subcellular particles may be limited by the spatial limits of the microscope system.

2.3.4.1 Optimisation of image quality for confocal applications Overall image quality is affected by the light source, light path, sample, and detection system. Image quality is commonly described by signal-to-noise ratio (SNR). These components are considered in turn, including the theoretical optimal settings.

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The laser light source illuminates the specimen using light of specific wavelength, appropriate to the fluorophore. Laser power can be independently adjusted. High power improves the SNR, although causes heating of the specimen, and can result in increased bleaching of fluorophores. Long, or repeated imaging, particularly with a high laser power can be harmful to biological samples.

In a confocal system, the pinhole size affects resolution, fluorescent intensity, and optical sectioning. The pinhole excludes out of plane light, allowing optical sectioning and improving SNR. However, the image is darker, requiring higher gain or laser power. The converse is true for larger pinholes.

Detector systems are either based on photomultiplier tubes (PMTs) or charge coupled devices (CCDs). A PMT produces a voltage proportional to the light that reaches it with an approximately linear relationship between luminous intensity and voltage within working range. The overall output of the PMT is increased by the gain. Increasing the gain may enable a weak fluorophore or dark image to be visible, but amplifies all outputs, which may worsen SNR. CCDs are semiconductor devices organised in arrays which store photo-electrons with high efficiency. These devices are suitable for low light levels (such as those in confocal microscopy), may be filtered by wavelength and organised into dense arrays for very high spatial and temporal resolution (208). However, the use of CCDs is limited by their considerable expense.

In summary, the detection of fluorescence by a confocal microscope is affected by the laser power, pinhole size, gain and detector type. The optimal image would be obtained with a small pinhole, high laser power and long exposures with a high resolution. However, the use of live cells and fluorophores with susceptibility to bleaching requires a pragmatic approach to limit the duration and intensity of laser exposure.

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The LSCM used for this work was a Nikon Eclipse T1Si, equipped with four laser lines, 4 PMTs, a 60x plan apochromat VC objective and a motor driven stage.

2.4 Confocal microscopy 2.4.1 Protocol 3 - Preparation of glass coverslips 28mm #1 glass coverslips (Thermo Scientific, Loughborough, UK) were dip sterilised by immersion in absolute ethanol, washed in sterile deionised water and allowed to air dry.

Coverslips were coated in filter sterilised NaHCO3 (0.1 M) with 15 L of Cell-Tak™ tissue adhesive (1g ml-1) suspension (Corning 354240) each and allowed to adsorb for 30 minutes at 36.5°C. Cell-Tak™ suspension was aspirated and the coverslips washed with filter sterilised water and allowed to air dry. Plates were stored at 5°C for up to 2 weeks prior to seeding as per the protocol below.

28mm #1 glass coverslips (Thermo Scientific, Loughborough, UK) in 6 well plates were seeded with 25000-50000 CHOhNOPGiq5 or CHOWT cells (obtained by trypsinisation of stock CHO cells at confluency – see 2.2), and incubated in selection media (Table 2-3) for 12-18 hrs at 5% CO2, 37°C prior to microscopy.

2.4.2 Protocol 4 - Live cell confocal imaging of Calcium Flux in CHO cells Prepared coverslips were incubated with Fluo-4-AM dye (Invitrogen, UK) 2.5 g ml-1 in Krebs HEPES buffer for 30-60 minutes at room temperature in the dark before mounting in a perfusion chamber, temperature maintained at 37°C using a PDMI-2 micro- incubator (Harvard Apparatus) and perfused at approx. 5 ml min-1 (4.81-4.96 ml min-1) with Krebs buffer via a peristaltic pump to maintain a constant volume of approx. 1 ml (0.81-1.10 ml) within the chamber. Light was minimised within the microscopy room.

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The perfusion chamber was mounted on the stage of a Nikon Eclipse T1Si inverted Confocal Laser Scanning Microscope, and image acquisition controlled using Nikon EZC1Si software.

2.4.2.1 Image acquisition Settings for excitation sources and emission filters were fluorophore dependent as per Table 2-7. Optimal settings were determined empirically.

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Marker Fluorophore Excitation Filter Pinhole Power* Gain* laser set ( nm) Fluo-4 FITC 488 FITC Large Per Per sample sample N/OFQ Atto-594 594 TxRed Large Per Per sample sample Anti-CD16 VioBlue 405 DAPI Large Per Per sample sample Table 2-7 – Settings for confocal microscopy - *Gain and power balanced to obtain best initial fluorescence of >500 RFU and obtain a linear relationship within the working range of the PMT and to account for differences in loading

Test compounds were added carefully to the chamber by pipette, avoiding direct mechanical stimulation of cells within the field of view.

2.4.2.2 Image analysis Images exported from the Nikon EZC1Si software in Image Cytometry Data and Image Cytometry Standard formats (.ics, .icd) were analysed in ImageJ software, using the Bioformats plugin (209-211).

The number of pixels, and range for each is a factor of the camera, PMT or spectral detector on the microscope system (the system used for this work generates a 12-bit image). In the 12-bit system, each pixel of an image is denoted by a value between 0 and 4095 to represent intensity. The total number of pixels in an image is determined by the camera resolution, and the number and size of steps in the scan process, which limits the scan speed and prolongs imaging. The ICS format stores multiple images, in several sequences (stacks) by channel (emission filter), time, and plane (Z). To balance spatial and temporal resolution, live-cell images were 12-bit with 512 x 512 pixels, and a maximal image acquisition speed (temporal resolution) of 1 frame per second.

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Following acquisition, images were segmented to select cells as regions of interest, based on a fluorescence threshold. The mean fluorescence within each region of interest was determined for each frame of the time stack.

Segmentation is the process of selecting areas of interest (cells) from an image, manually or automatically, based on appearance, fluorescent intensity, shape, or area (212). Thresholding generates a binary image based on original pixel intensity relative to a predefined threshold value; each pixel is 1 or 0 depending on whether the intensity was greater or less than the threshold value. Thresholding highlights areas of increased intensity which may be segmented into regions of interest to represent cells. The coordinates of these defined regions of interest (ROI) are superimposed to the original image and used to measure mean fluorescent intensity within cells.

The mean fluorescent intensity of each ROI was measured in each frame using the protocols discussed above (2.4.2). Relative fluorescence was determined using the average fluorescence for each cell for the first 5 frames as a baseline (F0). For each subsequent frame, relative fluorescence (F/F0) was determined using Equation 2-6.

Bioassays may be subject to natural variability, depending on cellular morphology, optical plane or loading. This variability was measured using the coefficient of variation (CV).

휎 퐶푉 = 휇

Equation 2-8 – Coefficient of variation(where  is standard deviation,  is mean)

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2.4.3 Protocol 5 - Immunofluorescent staining of immune cells Direct immunostaining uses fluorophore-conjugated antibodies directed against proteins of interest in order to detect and localise protein expression (213). Immunocytes may be distinguished by their cell surface markers, for which fluorescent probes are available (Table 2-6).

Standard immunofluorescent methods were used to fix, permeabilise and incubate samples with the antibodies of interest, before washing to remove unbound antibodies (213). Immunofluorescent staining was performed on immune cells in order to localise N/OFQ and NOP to eosinophils and neutrophils extracted as described (2.6).

Eosinophil and neutrophils were obtained by negative immunomagnetic separation from healthy volunteers and patients with sepsis. Extracted samples were incubated overnight in plain RPMI media (native), or RPMI with lipopolysaccharide (2 x 10-6 g ml-1) and peptidoglycan G (2 x 10-5 g ml-1) (LPS/PepG) in an environment mimicking sepsis (214). Samples were used for both immunofluorescence and for PCR studies after overnight incubation. Extracted cell numbers varied according to extraction yield and losses during the overnight incubation. At least 1 x 106 cells were stored for each cell type, under each set of conditions. Two thirds of this suspension (by volume) was utilised for immunofluorescence (estimated 6 x 105 cells per well). Cells were not counted on the day of staining to maximise the number of cells available for immunofluorescence and PCR.

Cell-Tak™ coated coverslips were prepared as described (2.4.1). Cell-Tak™ (Corning) is a tissue adhesive comprised of polyphenolic proteins extracted from the marine mussel Mytilus edulis, promoting adhesion of nonadherent cells to surfaces(215). Alternative approaches include coating coverslips with polylysine and fibronectin. From previous experience in our group, Cell-Tak™ produces a clearer image with less attenuation

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Section 2 Materials and Methods during confocal microscopy than polylysine and is more economical than fibronectin. However, as a protein, the use of Cell-Tak™ risks activation of immune cells, and abnormal adhesion (negating their physiologic expression of cell surface adhesion molecules). The implications of Cell-Tak™ on cell activation in immunocytes are unclear and has not been studied

These suspensions were allowed to adhere to the prepared coverslips for 30-60 minutes at room temperature, then washed twice with PBS. The cells were fixed in 4% (w/v) Paraformaldehyde for 20 minutes, and then washed 3 times in PBS with 0.1% Tween 20 (PBST). Cells were permeabilised (in 0.5% Triton X100 for 5 minutes) and washed a further 4 times in PBST. Cells were incubated for 30 minutes in blocking buffer (PBS with 3% Bovine Serum Albumin and 10% Fetal Calf Serum).

All antibody dilutions were made in blocking buffer. Stains were added, with negative controls anti-N/OFQ (to exclude anti-N/OFQ-FITC binding and autofluorescence), and

SB612111 (to exclude nonspecific N/OFQATTO594 binding) to a final total assay volume of 500 l (Table 2-8). Dilutions and concentrations of stains and antibodies were determined according to the manufacturer’s instructions and previous data within our laboratory. Preparations were incubated overnight at 5°C in the dark.

After incubation, the coverslips were washed 3 times in PBST, and 4 times in deionised water before imaging as soon as possible. Prepared coverslips were stored at room temperature, in the dark before imaging.

During imaging, unstained controls were used to calibrate gain and laser power in order to prevent false positive detection. For each coverslip, images were recorded showing the whole field, and a magnified image of one cell from that field. Images were recorded using the frame lambda function. Images in each channel (FITC, TxR and DAPI) were 2-93

Section 2 Materials and Methods recorded and saved as an image stack at 512 x 512 pixel resolution to balance duration of laser exposure and bleaching. Final images were scaled to the same magnification, and a scale bar added using ImageJ (209).

Images were assessed by two blinded assessors, and graded 0 (no staining), 1 (equivocal or little staining), 2 (strong staining). Inconsistencies were assessed by mutual agreement between assessors.

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Eosinophils (final in assay concentration M / dilution) MethodsMaterials and Triple stain Anti-N/OFQ negative control SB612111 negative control Native CCR3-VioBlue (1:50) CCR3-VioBlue (1:50) CCR3-VioBlue (1:50) N/OFQATTO594 (1 M) Anti-N/OFQ (1:250) N/OFQATTO594 (1 M) Anti-N/OFQ-FITC (1:500) Anti-N/OFQ-FITC (1:500) SB612111 (10 M) Native CCR3-VioBlue (1:50) CCR3-VioBlue (1:50) CCR3-VioBlue (1:50) +LPS/PepG N/OFQATTO594 (1 M) Anti-N/OFQ (1:250) N/OFQATTO594 (1 M) Anti-N/OFQ-FITC (1:500) Anti-N/OFQ-FITC (1:500) SB612111 (10 M)

Neutrophils (final in assay concentration M / dilution) Triple stain Anti-N/OFQ negative control SB612111 negative control Native CD16-VioBlue (1:50) CD16-VioBlue (1:50) CD16-VioBlue (1:50) 2-95 N/OFQATTO594 (1 M) Anti-N/OFQ (1:250) N/OFQATTO594 (1 M) Anti-N/OFQ-FITC (1:500) Anti-N/OFQ-FITC (1:500) SB612111 (10 M) Native CD16-VioBlue (1:50) CD16-VioBlue (1:50) CD16-VioBlue (1:50) +LPS/PepG N/OFQATTO594 (1 M) Anti-N/OFQ (1:250) N/OFQATTO594 (1 M) Anti-N/OFQ-FITC (1:500) Anti-N/OFQ-FITC (1:500) SB612111 (10 M) Table 2-8 – Conditions for immunofluorescent staining of eosinophils and neutrophils for N/OFQ and NOP

Section 2 Materials and Methods

2.5 Cuvette based fluorometry 2.5.1 Protocol 6 - Cell preparation

CHOhNOPGiq5 cells were maintained and subcultured as described (2.2.1). For cuvette based fluorimetry, after subculturing, a T175 flask (per 5 determinations) was seeded with cells and incubated for 48-72 hours in cell culture conditions (2.2.1) until 80-100% confluency.

For details of the Krebs-HEPES and harvest buffers used for this experiment, see Appendix – Buffers and reagents.

On the day of the experiment, for each series of 5 determinations, the media in a T175 flask was replaced with harvest buffer, cells detached, washed, and resuspended in Krebs HEPES buffer 20 mls. The resultant suspension was centrifuged at 1500 g for 3 minutes and resuspended in Krebs-HEPES buffer 20-30 mls three times. The final resuspension was into Krebs HEPES buffer 2 mls.

1 mM Fura-2-AM in DMSO 10 l (Sigma-Aldrich, Dorset, UK) was added to the 2 ml suspension for a final assay concentration of 5 μM. Cells were incubated in the dark for 30 minutes at room temperature to allow loading. The loaded cells were suspended in Krebs HEPES buffer 20-30 mls at 18-20°C in the dark for a further 20 minutes to allow de-esterification.

The resultant suspension was centrifuged and washed 3 times in Krebs HEPES buffer before resuspension in Krebs HEPES buffer 10 mls. 2 mls of this suspension was used per determination.

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2.5.2 Protocol 7 - Fluorometric measurement of calcium concentration using Fura-2-AM dye Fluorimetry was performed using a Perkin-Elmer LS50B fluorimeter (Beaconsfield, UK) using a standard method as described (193). Fluorescent emission was measured at 510 nm, with alternate excitation at 340 nm and 380 nm.

Briefly, 2 mls of cell suspension was introduced into a quartz cuvette and stirred using a magnetic stirrer, maintained at 37°C using a water jacket fed from a thermostatically controlled water bath in the dark. Fluorescence was measured for 180 seconds prior to the introduction of 50 l of test compound (40x in-assay concentration), and then until restabilisation of the signal. Following the final sample (per flask), the loading was calibrated to maximal and minimal fluorescence using 0.1% Triton X-100 50 l (Sigma Aldrich; Missouri, USA) followed by 4.5 mM EGTA 150 l, pH>8 (Sigma Aldrich; Missouri, USA).

Test compounds were maintained on ice, and cells were maintained at room temperature, protected from light throughout.

2.5.3 Protocol 8 - Data analysis Data were analysed using the fluorescence data manager software associated with the LS50B fluorimeter (Perkin-Elmer, Beaconsfield, UK). Raw fluorimetry data were processed using per-batch calibration, and values substituted into the Grynkiewicz equation (Equation 2-5).

For each concentration, the [Ca2+] was calculated by subtracting the mean of 3 baseline calcium concentrations from the maximal calcium concentration following the

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Section 2 Materials and Methods addition of the ligand. Log[Ligand]-[Ca2+] curve fitting was performed in GraphPad Prism (216), and figures generated using the ggplot2 package in R (217, 218).

Experiments were performed in duplicate and repeated on ≥3 sets of cells from different passages.

Concentration-response graphs are presented as mean ± SD for each point to illustrate spread. Summary LogEC50 data are presented as mean (CI95). Normality was assessed using the D'Agostino–Pearson omnibus test.

2.6 Extraction of Leucocytes Leucocytes are fragile cells, present in low numbers in health (Figure 1-7). PMNs are a smaller subfraction of leucocytes with an ex-vivo survival of up to 7 hours. Techniques for cell isolation from blood are based either on positive or negative selection by combinations of cell surface marker expression, or by separation according to size and density (219, 220). The major considerations in the technique chosen for cell isolation are yield, purity, viability, and effects on cell function.

Density gradient based extraction methods separate cells based on differential rates of sedimentation when exposed to centrifugal force through one or more layers of media of known density (221) (2.6.1). Labelling cell surface markers or proteins distinguishes target cells of interest. These may be isolated from unlabelled cells by immunomagnetic, agglutination, binding or preparative fluorescent activated cell sorting (FACS) based techniques (2.6.2).

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Following either density gradient or immunomagnetic separation, excess erythrocyte contamination is removed either by sedimentation or hypotonic lysis (222). The presence of erythrocytes in cell isolates can affect immunocyte function (223). Hypotonic lysis employs a solution of lower tonicity than the erythrocytes, causing cellular swelling and destruction; this is either achieved with hypotonic saline, water, or ammonium chloride. Sedimentation fractionates the erythrocytes from other cells using a polymer-based solution, such as dextran.

Density gradient separation techniques for mixed PMNs (2.6.1) produce higher yields than immunomagnetic techniques, but with more contamination from non-target cells and debris (Table 2-9). Purity is higher where positive selection based on cell surface markers is used (Table 2-9). The cost of immunomagnetic methods exceeds that of density gradient techniques and the yield is comparatively lower. The increased costs associated with immunomagnetic techniques are offset by reduced cell handling and time (224). Increased purity in positive selection is a particular advantage for rarer cell types or where contamination is likely to be a significant problem (224).

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Technique Yield Purity Reference

(x 105 cell ml-1 blood)* (%)*

Ficoll-Paque™ Density gradient 16  6.3 96.2  1.5 (224)

Ficoll-Paque™ (water lysis) Density gradient 120  31.0 95  2.6 (225)

Ficoll-Paque™ (KCl lysis) Density gradient 100  15.0 95  2.8 (225)

Polymorphprep™ Density gradient 14.8  4.3 91.4  4.9 (224)

Polymorphprep™ Density gradient 47.0  12.6 94.1  4.9 (222) 2-100 Percoll® 65-75% Density gradient 140  46.0 98  4.1 (225)

Percoll® 60-70% Density gradient 160  28.0 92  5.0 (225)

Percoll® 50-70% Density gradient 50  20 98  4.3 (225)

Immunomagnetic positive selection (CD15) Immunomagnetic positive selection 9.6  7.9 99.5 0.5 (224)

Spontaneous sedimentation Sedimentation 120  42.0 77  3.2 (225)

Table 2-9 - Characteristics of separation techniques for neutrophil isolation *Mean ± SD

Section 2 Materials and Methods

Several studies have shown the morphology, cell surface markers and function of polymorphonuclear granulocytes to be affected by isolation techniques (223, 226).

Use of density gradient (227) and immunomagnetic techniques (224) for cell isolation changes the expression of cell surface markers in granulocytes. Other aspects of cellular function, respiratory burst and cellular killing are also impaired by density gradient based methods (225). Immunomagnetic techniques based on antibody binding to cell surface markers may activate and prime cells. Erythrocyte depletion of cell isolates by hypotonic lysis can affect the morphology of secretory granules and induce functional changes in PMNs (224, 226, 228).

The high sensitivity of PMNs to handling, isolation techniques, and activation of cell surface receptors necessitates isolation based on rapid techniques and with minimal handling for study of cell function. As activation of cell surface markers affects the presence and release of secretory granules, techniques to yield “untouched” cells including negative selection and/or the avoidance of cell-based markers against the cells of interest will be used. However, this may reduce purity, and require additional validation of cell separation.

Therefore, in order to balance yield and purity and to minimise handling, density gradient techniques were used to isolate mixed PMNs, and immunomagnetic whole- blood techniques (where available) to extract PMN subpopulations (Table 2-10).

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Cell type Technique Rationale Mixed PMN Polymorphprep™ density gradient separation Single step (Axis-shield, Dundee) technique Minimise cell handling Neutrophils Ficoll-Paque™ followed by immunomagnetic (-) High yield MACSxpress® Increased purity GE Healthcare Life Sciences (Buckinghamshire, Avoid contamination UK) Avoid erythrocyte Miltenyi Biotech GmbH (Bergisch Gladbach, lysis step/activation Germany) Eosinophils Ficoll-Paque™ followed by immunomagnetic (+) High yield Whole blood (MACSxpress®) Increased purity GE Healthcare Life Sciences (Buckinghamshire, Avoid contamination UK) Avoid erythrocyte Miltenyi Biotech GmbH (Bergisch Gladbach, lysis step/activation Germany) Basophils Ficoll-Paque™ followed by immunomagnetic No Express kit enrichment (-), then immunomagnetic selection available (+)* Only technique GE Healthcare Life Sciences (Buckinghamshire, available UK) Miltenyi Biotech GmbH (Bergisch Gladbach, Germany) Table 2-10 – Techniques for cell isolation - MACS® MicroBeads and MACSxpress® are immunomagnetic techniques (Miltenyi Biotech GmbH, Bergisch Gladbach, Germany) *No whole blood technique currently exists to extract basophils

The protocols for density gradient (2.6.1) and immunomagnetic (2.6.2) separation are expanded below.

2.6.1 Techniques for density gradient separation Separating blood by centrifugal force, layered over an inert medium containing sterile polymers of specific density and osmolality generates characteristic bands of cells according to their size and density (Table 2-11). The tonicity of the media minimises osmotic lysis and any resultant cell damage. The cell bands formed by separation are recovered by careful pipetting and stored on ice, separated over further gradients, or labelled for separation using immunomagnetic techniques.

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The first described application of density gradient separation was extraction of erythrocytes, granulocytes, lymphocytes, and monocytes from whole blood. The blood was layered over a solution with a density of 1.077 g ml-1, and subject by centrifugal force to yield an erythrocyte and granulocyte pellet and lymphocyte and monocyte layer (221, 229).

Density Density Composition Notes separation (g ml-1) media Polymorphprep™ 1.113 Sodium diatrizoate and Separates neutrophil layer from (Axis-shield) Polysaccharide PBMC and erythrocytes Ficoll-Paque™ 1.077 Ficoll PM400 – Sucrose Pellet of erythrocytes, (GE Life sciences) based polymer eosinophils, and neutrophils; (225) basophils are in PBMC layer Percoll® 1.077 Silica Adjusted according to desired (GE Life sciences) density and osmolality to (225) optimise separation of specific cell types. Table 2-11 – Density gradient media applications and properties

Separation through density gradients (over Polymorphprep™ or Ficoll-Paque™) has successfully been used either as a primary or preparative separation method for purifying mixed PMNs (230), basophils (231), eosinophils (232), and neutrophils (222, 233). However, these two techniques differ in the recovery of PMN subsets, requirements for erythrocyte lysis and likely contaminants.

Following Ficoll-Paque™ based separation, the erythrocytes, neutrophils, and eosinophils form a pellet, which requires an erythrocyte lysis step. The neutrophils, eosinophils, basophils, and monocytes may be recovered using this technique (Figure 2-8). In a Polymorphprep™ based separation, the neutrophils and eosinophils form a separate PMN band away from the erythrocyte pellet, avoiding the requirement for

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Section 2 Materials and Methods erythrocyte lysis (Figure 2-8). PBMCs form a separate band. Basophils cannot be recovered using this separation technique.

Figure 2-8 – Schematic of density gradient separation techniques

2.6.1.1 Protocol 9 – Venepuncture Ethical approval was granted for this work from the University of Leicester ethics committee (for healthy volunteers), and from the NHS Research Ethics Committee (for patients with a diagnosis of sepsis) (Appendix – Ethical approvals (University of Leicester) and Appendix – Ethical approvals (NHS)). After obtaining consent or declaration from a personal consultee as appropriate, up to 30 ml blood was sampled from volunteers or patients using a standard aseptic technique.

Using protective gloves, once an appropriate vein was identified, the skin surface was decontaminated with isopropylethanol 2% and allowed to dry. Blood was sampled using a 21 gauge needle into a 30 ml syringe, decanted into 7.5 ml collection tubes containing liquid EDTA anticoagulant (1.6 mg ml-1 blood, Sarstadt Monovette K3E 01.1605.004) and mixed well by inversion.

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Where blood was sampled from patients with indwelling vascular access lines, the hub of the access line was cleaned with isopropylethanol 2%, allowed to dry, and blood sampled from a 3-way access port after discarding up to 10 ml of blood and saline flush from the dead-space of the access line to avoid dilution and contamination of the sample. After sampling the line was flushed clear of blood using 0.9% saline, 10 ml to prevent clotting and infection.

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2.6.1.2 Protocol 10 – Separation by Polymorphprep™ Polymorphprep™ was used to isolate mixed PMNs from whole blood (Figure 2-8). Polymorphprep™ based separation was performed according to the manufacturer’s instructions as described below (234).

For details of the Krebs-HEPES, PBS and lysis buffers, see Appendix – Buffers and reagents.

Blood was sampled by venepuncture as described previously (2.6.1.1) and layered over Polymorphprep™ (Axis-Shield) in equal volumes in 15 ml or 50 ml Falcon CentriStar tubes and centrifuged (600 g for 45 minutes). Following centrifugation, the PMN layer (Figure 2-8) was removed, resuspended in at least 3 volumes of PBS, and centrifuged at 500 g for 10 minutes at room temperature. The resultant pellet containing PMNs was resuspended and diluted in PharmaLyse ammonium chloride lysis buffer (Becton Dickinson, San Jose, US) and incubated at room temperature for 10 minutes, with gentle intermittent agitation to lyse any remaining erythrocytes. Finally, the suspension was centrifuged at 300 g for 10 minutes, resuspended in Krebs HEPES buffer and stored on ice for immediate use. Yields and viability were assessed using trypan blue exclusion and haemocytometry (2.2.2).

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2.6.1.3 Protocol 11 – Separation by Ficoll-Paque™ Ficoll-Paque™ based separation was used as a preseparation step to extract neutrophils, eosinophils, and basophils from whole blood prior to immunomagnetic isolation. Ficoll- Paque™ based separation was performed according to the manufacturer’s instructions (235).

For details of the Krebs-HEPES, PBS and lysis buffers, see Appendix – Buffers and reagents.

Blood was sampled by venepuncture as described previously (2.6.1.1) and mixed 1:1 with sterile, filtered PBS and layered in equal volumes over Ficoll-Paque™ (GE Healthcare). The resultant suspension was centrifuged at 600 g for 30 minutes with no brake applied, leaving a monocyte/basophil layer, and the granulocyte pellet (Figure 2-8).

The monocyte/basophil layer was harvested, washed in sterile PBS, 50 ml and centrifuged at 300 g for 10 minutes before counting in a haemocytometer. This layer was then used for immunomagnetic isolation of basophils (2.6.2.2).

The granulocyte/erythrocyte pellet was resuspended in PharmaLyse ammonium chloride lysis solution (Becton Dickinson, San Jose, US) and incubated at room temperature for 10 minutes, with gentle intermittent agitation to lyse any remaining erythrocytes. Finally, the suspension was sedimented by centrifugal force at 300 g for 10 minutes, resuspended in Krebs buffer, and counted using a haemocytometer (2.2.2). Viability was assessed using trypan blue exclusion.

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2.6.2 Techniques for isolation of immune cell populations by cell surface markers Immune cell populations express a multitude of cell surface receptors and markers (Figure 1-7), to which antibodies bearing labels may be directed. Antibodies directed against cell surface markers enable separation by various techniques such as paramagnetism, sedimentation, aggregation, preparative FACS, or lysis (219).

Immunomagnetic techniques separate cells from blood or a mixed cell isolate based on the binding of antibodies directed against cell surface markers conjugated to ferromagnetic beads (236). These techniques are either used on whole blood (as in a MACSxpress® system), or following pre-separation, or enrichment by density gradient techniques. Extraction from whole blood reduces time to obtain cells, handling, activation, and apoptosis, although a whole blood-based technique may sacrifice purity and is more expensive than pre-separation.

Commercial systems available using magnetic based separation include MACS® (Miltenyi Biotech GmbH, Bergisch Gladbach, Germany), and EasySep™ (Stemcell technologies), either from whole blood or following pre-separation of granulocyte layers. These work on the principle that antibodies conjugated to a magnetic bead are raised to the cell marker of interest, and after adhesion, retained within a magnetic field, separating labelled from non-labelled cells. The labelled cells may be eluted outside the magnetic field.

Cell isolation may utilise a positive or negative selection strategy (Figure 2-9 ). Positive selection describes the use of an antibody directed to markers on the cell of interest. This cell is retained in the magnetic field and eluted outside of the field, yielding cells, labelled with the antibody. Negative selection raises antibodies to all except the cell of

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Section 2 Materials and Methods interest, the unlabelled cells are not retained in the magnetic field and are “untouched”, avoiding activation (Figure 2-9 ). Using a negative selection strategy leaves cell surface markers unbound, and these can then be used to quantify cells of interest in flow cytometry to assess purity and yield. However, for very rare cell types (such as basophils), the reduced number of the target cell of interest may favour positive selection techniques to avoid contamination and maximise recovery.

Figure 2-9 – Techniques used for immunomagnetic separation

The use of a combined density gradient and immunomagnetic separation technique has been described for cells present in small numbers, such as eosinophils (purity >95%, 2-109

Section 2 Materials and Methods viability >98%) (237), and basophils (238). Cells present in high numbers (such as neutrophils) are less prone to contamination artefact, and therefore do not require pre- enrichment using this process. Separation methodologies are currently limited by the availability of appropriate antibodies and conjugates.

Because of the limitations of cell numbers, availability of antibody labelling and preference for untouched cell isolation, the techniques used for isolation are as shown in Table 2-10, and include whole blood, and density gradient pre-separation for different cell types.

Both whole blood and pre-separation techniques were evaluated in this work in order to obtain optimal separation purity and viability.

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2.6.2.1 Protocol 12 – Immunomagnetic separation of eosinophils and neutrophils by negative selection from whole blood (MACSxpress®) Neutrophils and eosinophils were isolated separately via MACSxpress® kits (Miltenyi, Neutrophil 130-104-434, Eosinophil 130-104-446). The extraction protocols were followed in parallel for each cell type. Both kits use an immunomagnetic negative selection process without pre-separation by density gradient techniques.

Extraction was performed according to the manufacturer’s protocol (239). Up to 30 ml blood was sampled from volunteers using venepuncture or sampling from indwelling vascular access lines into 7.5ml collection tubes containing K3-EDTA (Sarstadt Monovette K3E 01.1605.004) and mixed well.

For details of the Krebs-HEPES, and PBS buffers, see Appendix – Buffers and reagents.

Immunomagnetic antibody complex was reconstituted in proprietary buffer (A). To one volume of blood, 0.25 volume of the reconstituted antibody complex and Buffer B was added, before mixing for 5 minutes on a tube rotator at 5 RPM.

The blood-antibody cocktail was placed into the separation magnet for 15 minutes. Labelled cells (of interest) are attracted to the magnet, and erythrocytes sediment, leaving the cell of choice in the supernatant. These cells were recovered by gentle pipetting along the front wall of the tube.

Following removal, the recovered cells were centrifuged at 300g for 10 minutes and resuspended in appropriate Krebs-HEPES (where used for the degranulation bioassay)

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Section 2 Materials and Methods or PBS (where used for flow cytometry) buffers, counted on a haemocytometer to assess viability, and used immediately.

2.6.2.2 Protocol 13 – Immunomagnetic labelling and separation after density gradient separation Eosinophils were isolated by negative selection (from the granulocyte pellet), and basophils by depletion and then positive selection (from the monocyte layer) following Ficoll-Paque™ separation of whole blood (2.6.1.3).

The two fractions were processed in parallel using Eosinophil (130-092-010) and Basophil (130-092-662) kits respectively (Miltenyi). Venepuncture (2.6.1.1), and Ficoll- Paque™ based density gradient separation (2.6.1.3) was performed as described, according to the manufacturer’s instructions. The monocyte layer was carefully removed for basophil extraction, and the granulocyte pellet resuspended and used for eosinophil isolation.

For details of the Krebs-HEPES, PBS and column buffers, see Appendix – Buffers and reagents. Both kits use depletion of non-target cells, and the basophil kit has a second, positive selection step because of the low number of target cells. The proprietary kits, in principle, contain antibodies conjugated to biotin targeted at cell surface markers, MicroBeads conjugated to antibodies targeting biotin, a blocking solution to prevent nonspecific binding, and column buffer. The basophil kit has MicroBeads targeted to the CD123 basophil cell surface marker, for the second, positive selection step.

The resultant suspensions were centrifuged at 300 g for 10 minutes, supernatant removed, resuspended in column buffer, counted (2.2.2), before further centrifugation at 300 g for 10 minutes. The isolation protocol was performed as per Table 2-12.

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Volume (L) Per 107 cells Volume (L) Per 108 cells Eosinophil Basophil Depletion of non- Depletion of non- eosinophils basophils Initial suspension in 40 300 Column buffer FcR Blocking reagent - 100 Biotin-antibody cocktail 20 100 Mix and incubate (10 minutes 2-8C) Column buffer 30 300 Anti-biotin MicroBeads 20 200 Mix and incubate (15 minutes 2-8C) Centrifuge 300 g x 10 minutes Wash in 2000 20000 Column buffer Centrifuge 300 g x 10 minutes Resuspend in column 500 500 buffer Column separation (see text) Eosinophils Basophils No second stage Positive selection of basophils Centrifuge 300 g x 10 minutes Resuspend in - 100 CD123 MicroBeads Mix and incubate (15 minutes 2-8C) Wash in - 2000 Column buffer Centrifuge 300 g x 10 minutes Resuspend in column - 500 buffer Column separation (see text) Table 2-12 – Reagents for immunomagnetic separation using MACS® MicroBead kits

Magnetically labelled fractions were separated over an immunomagnetic column and magnet according to total and labelled cell numbers according to the manufacturer’s specification. An appropriate immunomagnetic column and magnet were assembled and equilibrated with column buffer, before adding the labelled cell suspension and washing with further column buffer. For the initial negative separation, the eluted suspension represented unlabelled cells; the eosinophils, or the enriched basophils. For

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Section 2 Materials and Methods the second stage (positive) basophil separation, after unlabelled cells were eluted, the column was removed from the magnet and washed with column buffer to elute the labelled basophil fraction.

The final basophil and eosinophil suspensions were centrifuged at 300 g for 10 minutes, resuspended, counted, and stored on ice before overnight storage in RPMI or immediate use in the live cell degranulation assay.

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2.7 Characterisation of immune cells by flow cytometry 2.7.1 Principles of Flow Cytometry Flow cytometry enumerates particles passing through a single cell stream according to physical properties (size, complexity, and granularity). This technique can be enhanced by the addition of fluorescent antibodies targeted to cell surface markers, classifying cell populations based on marker expression (240, 241). Fluorescence-activated cell sorting (FACS) physically separates cells (using charge or air streams) based on these properties. The flow cytometer consists of a system of fluidics, an energy (laser) source, light collector, and analysis software.

The fluidics system runs a pressurised stream of buffer, within which the cells flow as a single stream. As it passes through the system, each cell is exposed to one or more lasers. A detection system collects scattered and emitted light signals and can use various excitation/emission filters to determine fluorescent antibody binding (Figure 2-10).

Figure 2-10 – Schematic of flow cytometer setup

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The flow cytometer records the optical and fluorescence properties of cells within the stream. Cell types exhibit characteristic scatter properties, which infer morphology and identity. Binding of antibodies directed at cell surface markers and conjugated to fluorophores gives a fluorescence reading of a given wavelength. The combination of scatter, morphology and cell surface binding are used to identify a cell population.

Gating describes the threshold properties for each variable used to define a population. Gates are set using known populations, and then applied to mixed samples to determine the frequency, identity and purity of the constituent cell types.

2.7.1.1 Scatter Scatter is useful to differentiate cells based on size and complexity (intracellular organelles and granules). Scatter is measured at 90 (side-scatter, SSC) and 180 (forward scatter, FSC) and corresponds to intracellular complexity and size respectively.

Plots of FSC vs SSC obtained from samples of whole blood show characteristic cell populations (Figure 2-11). Granulocytes are large and complex, therefore have high FSC and SSC, whereas monocytes and lymphocytes have correspondingly lower complexity and size (Figure 2-11). Debris are typically subcellular, small and lack complexity (low FSC and SSC). In order to conserve blood for staining, only 1ml was used for the unstained whole blood plot.

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Figure 2-11 – Forward (FSC-A) vs Side scatter (SSC-A) dot plot from flow-cytometric analysis of whole blood from a healthy volunteer. Sample (1ml) was treated with lysis buffer and was unstained. Excluding debris (black), this plot differentiates populations of granulocytes (dark blue, 61.2%), monocytes (green, 6.84%) and lymphocytes (light blue, 31.95%)

Signal within the granulocyte region (cloud) represents neutrophils, eosinophils and basophils. Granularity (as measured by SSC) is variable depending on the separation and activation state of the cells. Differentiating granulocytes based on granularity alone is unreliable, requiring the supplemental use of marker antibodies directed against cell surface markers.

The FSC/SSC plot enables “typical” cell populations to be delineated from cellular debris as an initial gating strategy and can confirm expected staining of subcellular populations when combined with antibody techniques.

2.7.1.2 Gating by fluorescence Fluorescence emitted by fluorophore conjugated antibodies bound to cell surface markers can be used in combination with FSC/SSC to define cell populations by both their physical properties and expression of cell surface markers.

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The set gates are applied to a sample stained with all the antibodies to delineate the different populations of cells within a sample.

2.7.2 Protocol 14 - Validation of immune cell separation by Flow Cytometry Separation of mixed PMN and subcellular populations was verified and optimised using flow cytometry. The yields and viability for each separation were determined by counting on a haemocytometer and trypan blue exclusion (see section 2.2.2).

Gates were set for cell populations based on both physical characteristics (FSC/SSC), and the expression of cell surface markers (Table 2-13).

Cell type Strategy Positive control Negative control Cells vs Debris FSC/SSC Whole blood Granulocytes FSC/SSC, CD66abce Mixed PMN Unstained Monocytes Neutrophils CD16+ CD16+ Neutrophils CD16+ Eosinophils Basophils FcRi+ FcRi+ Basophils CD16+ Neutrophils Eosinophils SIGLEC-8+Hi SIGLEC-8+ SIGLEC-8+ Eosinophils Basophils Leucocytes CD45+ CD45+ PMN Table 2-13 – Flow cytometry gating strategy

Immunocyte populations were extracted from the whole blood of healthy volunteers as described above (2.6), and stained using the manufacturer’s standard protocol (242).

For details of the Krebs-HEPES, binding and PBS buffers, see Appendix – Buffers and reagents.

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In brief, following extraction and erythrocyte lysis, cell suspensions were counted (2.2.2), and viability determined by trypan blue exclusion. PBS and appropriate antibody were added to each sample as per Table 2-14 and Table 2-15.

Volume (per 107 cells) L Binding Buffer 100 Antibody 10 Incubate for 10 minutes at 5°C in the dark Binding Buffer 2000 Table 2-14 – Antibody staining protocol

The resultant suspension was centrifuged at 300g for 10 minutes and resuspended in filter-sterilised PBS for flow cytometry, final volume >200 l, with a total count of >106 cells per sample tube. Experimental setup was as per Table 2-15.

Prepared samples were counted on a FACS Aria II (Becton Dickinson) flow cytometer, and the data collected on the integrated FACSDiva™ software (Becton Dickinson). These data were exported and analysed using FCSAlyzer (243). Compensation for autofluorescence was determined by measuring fluorescence from unstained samples. Gain and laser intensity were set using stained positive control samples, and the gating strategy set accordingly.

For all samples, fluorescence (appropriate to staining antibody), FSC and SSC were measured. Events falling within the pre-set gates were categorised according to cell type. Frequencies of cell types were reported as a percentage of total events, where an event is a signal detected within the sample stream of the flow cytometer. Data were reported as total cell count, viability and percentage of neutrophils, eosinophils, and basophils. Viability was assessed separately using exclusion by trypan blue. Live-dead stains were not, doublets/triplets were not processed.

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Section

Tube Cell type Antibodies Description Tube Cell type Antibodies Description MethodsMaterials and 1 PMN Unstained 13 Eosinophils Unstained 2 PMN CD16-VioBlue 14 Eosinophils CD16-VioBlue 3 PMN Sig8-APC 15 Eosinophils Sig8-APC Sig8 Gate setting / Positive control 4 PMN CD66abce-PE Positive control 16 Eosinophils CD66abce-PE 5 PMN FCϵRIα-FITC Negative control 17 Eosinophils FCϵRIα-FITC Negative control 6* PMN CD16-VioBlue Test sample 18* Eosinophils CD16-VioBlue Test sample Sig8-APC Sig8-APC CD66abce-PE CD66abce-PE FCϵRIα-FITC FCϵRIα-FITC

2-120 7 Neutrophils Unstained 19 Basophils Unstained 8 Neutrophils CD16-VioBlue CD16 gate setting / 20 Basophils CD16-VioBlue Positive control

9 Neutrophils Sig8-APC 21 Basophils Sig8-APC Negative control 10 Neutrophils CD66abce-PE 22 Basophils CD66abce-PE 11 Neutrophils FCϵRIα-FITC Negative control 23 Basophils FCϵRIα-FITC FCϵRIα Gate setting / Positive control 12* Neutrophils CD16-VioBlue Test sample 24* Basophils CD16-VioBlue Test sample Sig8-APC Sig8-APC CD66abce-PE CD66abce-PE FCϵRIα-FITC FCϵRIα-FITC Table 2-15 – Flow cytometry staining set up for validation of PMN and subcellular separations *Sample tubes 6, 18, 12 and 24 were used for quantification of sample purity and yield. Viability was assessed separately by Trypan blue exclusion

Section 2 Materials and Methods

2.7.2.1 Flow cytometry strategy for assessment of purity Antibodies directed against CD66, Siglec-8 and CD45 and conjugated to the fluorophores FITC, APC, and PE were used to verify gating and quantify numbers of granulocytes, eosinophils and leucocytes respectively in samples. Final sample concentration was 1 x 106 cells ml-1 prior to flow cytometry. Samples were processed on a Becton-Dickinson FACS Aria II counter (BD Biosciences, San Jose, USA) and compensated for autofluorescence.

The acquired cytometry data were exported and analysed using FCSAlyzer 0.9.15 software (243). Aggregate data were analysed using GraphPad Prism (216), R and ggplot2 (217, 218). Viability and purity are presented as mean %  standard deviation. Yield is presented as total cells ml-1 whole blood processed.

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Sample Cell type CD66-PE SIGLEC-8-APC CD45-FITC Notes 1 PMN Unstained PMN 2 Monocytes  CD45 Control 3 PMN  Gating Control 4 PMN  Gating Control 5 PMN  Gating Control 6 PMN    Triple stain Table 2-16 – Gating strategy for assessment of PMN purity

Gates were defined by staining individually; monocytes for CD45 (2), PMNs for Siglec-8, CD66 and CD45 (3, 4, 5). The unstained samples were used to delineate debris from whole cells (Table 2-16). Mixed PMN sample 1 (Table 2-16) was used to define gates for nucleated cell-like events based on FSC/SSC profile (Figure 2-12A-C). Gating for leucocytes was defined based on CD45 expression in sample 5 (Figure 2-12D-G), granulocytes based on CD66-PE binding in sample 4 (Figure 2-12H-K) and eosinophils based on SIGLEC-8-APC binding in sample 3 (Figure 2-12L-O). Leucocytes were defined as nucleated cells with CD45-FITC binding (samples 1 and 5), and eosinophils were nucleated cells with CD66-PE and SIGLEC-8-APC binding (samples 1, 3 and 4). Unstained samples were used to compensate for autofluorescence, and these corrections were applied to experimental samples.

The gates were applied to a triple stained PMN sample 6 (Figure 2-12P-Q) to determine purity (% target cell type of all nucleated cells) and yield (total final cell count per ml whole blood processed). The composition of the mixed granulocyte cloud (neutrophils

Hi and eosinophils) was determined using SIGLEC-8 and SIGLEC-8Low binding in granulocytes.

Additionally, contamination from other leucocytes and non-leucocytes were assessed using the comparators below.

 Leucocytes (CD45+) vs debris/nonleucocytes (CD45-)  Granulocytes (CD66+) vs Non-granulocytes (CD66-)  Eosinophils (CD66+Sig8hi) vs Non-eosinophilic granulocytes (CD66+Sig8low)

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Materials and MethodsMaterials and

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Figure 2-12 – Gating strategy A-C. Nucleated cells are gated using FSC/SSC profile; D-G. Leucocytes are gated based on CD45-FITC binding. H-K. Granulocytes are gated using CD66-PE binding. L-O. Eosinophils are gated by strong Sig8-APC binding. The gates (B, E, I, M) are applied to a triple stained PMN sample (Q). Doublets/Triplets not discriminated

Section 2 Materials and Methods

2.8 Luciferase ATP Assay 2.8.1 Principles of luciferase-based assays Luciferin is an enzyme present in the firefly, which exhibits bioluminescence when exposed to ATP (Equation 2-9). Bioluminescence describes biochemical emission of luminescence related to a chemical reaction (and in contrast to fluorescence or phosphorescence, does not require an excitatory light source) (185).

ATP + D-Luciferin + O2  Oxyluciferin + AMP + PPi + CO2 + Light

Equation 2-9 – Bioluminescence reaction catalysed by the luciferin enzyme

The luminescence emitted following exposure of luciferin to samples of known ATP concentration can be plotted on a standard curve. The measured bioluminescence emitted where luciferin is exposed to samples can then be used to determine the sample ATP concentration with reference to the standard curve for the assay.

2.8.2 Protocol 15 – Determination of ATP concentration in cell fractions using a bioluminescence-based assay ATP determinations were carried out using the Abcam Luminescent ATP detection kit (Abcam, ab113849) according to the manufacturer’s instructions.

For details of the PBS buffer, see Appendix – Buffers and reagents.

PMNs obtained by Polymorphprep™ based density gradient separation (2.6.1.2) were diluted to 50000, 2500 and 5000 cells per 100 L of PBS to which 50 L of the supplied detergent was added for cell lysis. The preparation was agitated at 500 RPM on an orbital shaker for 10 minutes.

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Sterile 96 well plates containing luciferin were exposed to cell lysates from 50000, 25000 and 5000 PMNs, and to a serial dilution of ATP. EOL-1 cells were used as an example of positive control cells containing ATP (see Figure 2-13).

Number of cells added (x103 cells ml-1) -Log10[ATP] PMN PMN EOL-1 -Log10[ATP] (M) S1 S2 (M) 1 2 3 4 5 6 7 8 9 A -4 50 50 50 -4 B -5 50 50 50 -5 C -6 25 25 25 -6 D -7 25 25 25 -7 E -8 5 5 5 -8 F -9 5 5 5 -9 G -10 -10 H Blank Blank Figure 2-13 – Layout of 96 well plate for ATP determination in PMNs - S1/S2 – Samples 1 and 2, EOL1 – EOL1 control, SC – Standard Curve ATP in concentrations stated. Columns 10-12 not used

Luminescence was read following dark adaption for 10 minutes at 37C on a NOVOstar (BMG Labtech, Aylesbury, UK) microplate fluorimeter, sequentially, for 1 second per well. Duplicate readings were averaged.

The ATP concentration of the cellular samples was determined with reference to the standard curve generated (Figure 2-14), fitted using GraphPad Prism (216).

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Figure 2-14 – Standard curve relating ATP concentration to the luminescence of luciferase (3 point logistical fit)

Using this standard curve, the measured luminescence can be used to determine the ATP concentration within the well.

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2.9 Reverse Transcriptase Quantitative Polymerase Chain Reaction (RT- qPCR) 2.9.1 Principles of PCR Reverse Transcriptase Quantitative Polymerase Chain Reaction (RT-qPCR) describes a technique to detect, quantify and analyse messenger RNA (mRNA) present in samples of biological material. The process results in an exponential amplification of the genetic material present, enabling detection of small quantities of mRNA in the starting sample. The quantification of the gene of interest (GOI) is expressed relative to a constitutively expressed housekeeper. RT-qPCR is used in this thesis to quantify differences in the expression of the precursor to N/OFQ (ppNoc) and the N/OFQ receptor (NOP) in eosinophils and neutrophils between healthy individuals and patients with a diagnosis of sepsis. The immunocyte extraction process may lead to inadvertent cell activation and stress; therefore a quantitative rather than binary process was used to investigate differences in expression.

The sample containing RNA is processed to extract the RNA and remove genomic DNA, thereby leaving only the single stranded mRNA, which is reverse transcribed to cDNA. Genomic DNA (gDNA) contains the entire genome for an organism. Contamination with gDNA could result in a false positive PCR result, amplifying a GOI that is present but in a non-coding region of the genome for that organism.

The purified single stranded RNA is incubated with nucleotide fragments and the reverse transcriptase enzyme resulting in double stranded copy DNA (cDNA). The cDNA undergoes the cyclical PCR reaction (Figure 2-15). The double stranded cDNA is separated by exposure to high temperatures (denaturing). Oligonucleotide primers specific for the GOI bind to the separated cDNA strands (annealing). The final extension step occurs where a thermostable DNA polymerase enzyme catalyses the 5’ to 3’ extension from the annealed primers, resulting in double stranded cDNA specific for the

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GOI (extension). This cycle is repeated, resulting in exponential amplification of the GOI, with the quantity of each fragment doubling with each cycle.

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Materials and MethodsMaterials and

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Figure 2-15 – Principles of RT-PCR – 1) Denaturing cDNA, 2) Strands separate, 3) Primers anneal to the single stranded cDNA, 4) Reverse transcriptase catalyses stepwise extension of primers, 5) and 6) cDNA is denatured, total content doubles per cycle

Section 2 Materials and Methods

Real-time quantitative PCR adds a detection step to this process, using two differing methodologies, SYBR-green and TaqMan®. Both are based on a fluorescent signal signalling the quantity of genetic material (Figure 2-16).

® TaqMan

® Green ® SYBR

Figure 2-16 – Fluorescent readouts in real time RT-qPCR Mechanism of action of TaqMan® (top), and SYBR® Green (bottom)

The TaqMan® system uses an oligonucleotide probe, complementary to the GOI, with a fluorophore at one end and a quencher covalently bound at the opposite end (Figure 2-16, top). During extension of the amplicons, Taq Polymerase enzyme causes breakdown of the TaqMan® probe, separating the fluorophore and quencher. When no-longer quenched, the fluorescent emission becomes detectable and is therefore proportional to GOI amplification.

SYBR® Green (Figure 2-16, bottom) undergoes conformational change when it binds in the minor groove between the strands of double stranded cDNA following the amplification step of PCR. The conformational change causes an increased fluorescent emission. Only the bound SYBR® Green fluoresces, and therefore the fluorescence is proportional to the number of cDNA molecules present.

2-130 Section 2 Materials and Methods

The TaqMan® system uses specific probes, and therefore, the measured fluorescence is proportional to the number of cDNA molecules complementary to the probe. As SYBR® Green will bind to any cDNA molecule, and several SYBR® green molecules may bind to one cDNA molecule, this method is less specific. SYBR® Green is also less sensitive to low copy numbers, although this method is preferred where specific TaqMan® probes do not exist for the GOI. TaqMan® probes are available for both ppNoc and NOP, therefore the TaqMan® system was used for this series of experiments due to the low cell yield, and for increased sensitivity.

In either system, the fluorescence is measured in real time using appropriate filter sets and compared to the fluorescence compared between the housekeeper and GOI samples in order to measure relative expression of the target, plotted as an amplification curve (Figure 2-17).

At a given fluorescent threshold, the amplicon is detectable, and corresponds to a given quantity of DNA. The thermal cycle number during which threshold is reached is known as the Ct, or cycle threshold. Where the quantity of starting material is high, the Ct would be low and vice-versa. Therefore, to avoid comparative differences due to sampling, Ct values of the gene of interest (GOI) are normalised to an endogenous control – a gene with stable expression, unaffected by the experimental conditions. The endogenous controls are often constitutively expressed housekeeper genes, such as

ELF1. When normalised, CtGOI < CtControl indicates higher expression of the GOI (as threshold is reached in fewer cycles). However, the absolute value is dependent on the quantity of starting material, efficiency of gDNA removal, and errors through contamination.

2-131 Section 2 Materials and Methods

Comparative Ct values may be compared between GOIs, endogenous controls and in the presence of conditions thought to regulate expression. The differences in Ct values between the GOI and endogenous control is known as the Ct.

Figure 2-17 – Sample PCR amplification curve - Rn = relative fluorescence reporter/reference, GAPDH – sample housekeeper gene, GOI – gene of interest

Comparing Ct values for treated and untreated variations of housekeeper genes and GOI quantifies differences in expression under different conditions.

Differences between the expression of a control housekeeper in the treated and untreated states are compared to the gene of interest (Equation 2-10). Using the amplification curve from Figure 2-17 as an example, GAPDH is a sample housekeeper gene, and GOI is the gene of interest. RT-qPCR is conducted on both samples with and without a treatment which may affect gene expression. The Ct will determine the effect (if any) of that treatment on expression.

2-132 Section 2 Materials and Methods

Ct = Ctexperimental - Ctcontrol

Ct = Ct(GOItreated – GAPDHtreated) - Ct(GOIuntreated – GAPDHuntreated)

Equation 2-10 - Ct calculation to compare gene expression

The fold change is a useful representation and is derived from the Ct as per (Equation 2-11).

Fold change = 2(-Ct)

Equation 2-11 – Calculation of fold change of gene expression using the Ct method

RT-qPCR is a useful technique to quantify mRNA expression of target sequences (244). However, an RNA based test is upstream of protein expression, and does not guarantee expression of the functional protein, nor the influence of any regulatory processes on translation. Furthermore, the results from PCR analysis may be subject to error as discussed above – therefore, for the investigation of ppNoc and NOP expression, RT- qPCR has been used as a confirmatory assay, in combination with the results of the granulocyte release live cell assay and immunohistochemistry.

2.9.2 Protocol 16 – RT-qPCR of granulocytes for NOP and ppNoc transcripts 2.9.2.1 RNA extraction The RNA extraction step yields RNA from granulocytes following extraction by MACSxpress® (Miltenyi Biotech GmbH, Bergisch Gladbach, Germany) immunomagnetic techniques, and overnight incubation with RPMI or RPMI with Peptidoglycan G and Lipopolysaccharide as a model of an environment mimicking sepsis (245) (1 g ml-1 of each, final concentration).

Immunocytes were extracted from whole blood, counted, and resuspended as described (see Protocols 9-13). Cell suspensions were sedimented by centrifugal force

2-133 Section 2 Materials and Methods at 12000 g for 10 minutes at 4C. The resultant cell pellet was lysed by addition of Tri- reagent (Sigma Aldrich; Missouri, USA) (1 ml per 1 x 107 cells) to cell suspension and homogenising using a pipette. At this stage, the samples were stored at -80C and processed as a batch to extract clean, contaminant free RNA.

After removal of supernatant ethanol 70% 1 ml was added, and the pellet resuspend by vortexing. The resultant suspension was sedimented into a clean RNA pellet by centrifugal force at 12000 g for 10 minutes at 4C. The pellet was dried by evaporation and resuspended in 100 l PCR grade water before quantification using the NanoDrop ND2000 spectrophotometer (ThermoFisher Scientific, Leicestershire, UK).

2.9.2.2 Clean RNA using DNAse The extracted RNA sample was processed using the Turbo DNAse kit (Invitrogen) according to the manufacturer’s “rigorous DNAse treatment” protocol to yield gDNA free RNA (246).

Sample, DNAse reaction buffer, DNAse and water were added and incubated at 37C for 30 minutes, before addition of inactivation agent. The sample was incubated and intermittently vortexed for 5 minutes, and then centrifuged (10,000 g for 90 seconds).

The supernatant contained RNA without gDNA, and was stored at -80C. The reverse transcription stage was performed on all samples as a batch.

2-134 Section 2 Materials and Methods

2.9.2.3 Reverse transcription Reverse transcription generates complimentary cDNA to the clean RNA, by stepwise 5’ extension of primers to the gene of interest. The resultant cDNA is used in the qPCR reaction.

For each participant, samples were performed in duplicate, and with non-template control without reverse transcriptase (Table 2-17). Any amplification in the nontemplate control implies contamination with gDNA.

Reverse transcription was performed using the High-Capacity cDNA Reverse Transcription Kit (Life Technologies, Carlsbad) according to the manufacturer’s protocol. A master mix was prepared and added to the gDNA free samples in equal volumes (Table 2-17).

Positive Nontemplate (RT+) control (RT-) Multiscribe reverse transcriptase 1 l  RNAse inhibitor 1 l 1 l dNTP mix 0.8 l 0.8 l Reverse transcription buffer 2 l 2 l Random primers 2 l 2 l PCR grade water 3.2 l 4.2 l Sample 10 l 10 l Total volume 20 l 20 l Table 2-17 – Master mix for reverse transcription reaction (From manufacturer’s instructions High-Capacity cDNA Reverse Transcription Kit (Life technologies, Carlsbad))

Samples were incubated in a thermocycler at 25C for 10 minutes, 37C for 2 hours, and 85C for 5 minutes, and held at 4C. The resultant samples contained cDNA for qPCR.

2-135 Section 2 Materials and Methods

2.9.2.4 qPCR cDNA was analysed using the TaqMan® system and a StepOne realtime PCR system according to the manufacturer’s protocol (Applied Biosystems). The cDNA sample, with TaqMan® probes, DNA polymerase and free nucleotides undergoes denaturing, annealing and extension phases controlled by thermal cycling (Figure 2-15). The TaqMan® probes contain a fluorophore linked to a quencher and bind to the single strands of cDNA prior to extension. During extension, the Taq polymerase causes the quencher and fluorophore to be separated. This results in fluorescent emission from the fluorophore (Figure 2-16).

qPCR reagents were added to the clean cDNA samples obtained from reverse transcription (Table 2-18).

Volume TaqMan® Gene expression mastermix 10 l Housekeeper 1 l GOI primer (ppNoc or N/OFQ) 1 l PCR grade water 6 l Sample 2 l Total volume 20 l Table 2-18 – Reagents for qPCR reaction (Using the TaqMan® Gene Expression system, according to manufacturer’s protocol (Life technologies, Carlsbad))

The prepared samples were processed using the StepOne qPCR system, using a standard run time, Comparative CT, ELF-1 and ELF-B as housekeeper genes. The system cycled through 50C for 2 minutes, 95C for 10 minutes followed by 40 cycles of 95C for 15 s and 60C for 1 minute.

2.9.2.5 Data analysis From the amplification curve (Figure 2-17), the Ct is the difference between Ct values for two curves – two genes, or a gene under differing conditions of expression. Ct is the difference between the Ct values for the GOI and housekeeper gene for the

2-136 Section 2 Materials and Methods purposes of this thesis. In this context, the housekeeper gene is the geometric mean of the two most stable housekeeper genes (by the GeneNorm algorithm – see below).

The expression between immunocyte types is compared using the mean Ct values. The fold change, as measured using the delta-delta Ct method (2-Ct) is also used and is a convenient way to show differences in gene expression (247).

From each sample, mean CT for housekeepers were determined. A combination of two housekeeper genes (ELF1 and ELF2) were used for stability (as determined using the GeneNorm algorithm (248)), and the geometric mean of the CT values (Equation 2-12) used to determine the CT of the GOI (Equation 2-13).

퐶푡퐶표푛푡푟표푙 = 퐶퐴 × 퐶퐵

Equation 2-12 – Determination of the control cycle threshold (CtControl) where two endogenous control genes are used (CTA and CTB are the cycle thresholds for both different endogenous control genes)

∆퐶 = 퐶퐺푂퐼 − 퐶퐶표푛푡푟표푙

Equation 2-13 – Determination of the difference in cycle threshold (CT) where the CTGOI and CTControl are the cycle thresholds for the gene of interest and endogenous control respectively

To determine the Ct, the two Ct values are subtracted (Equation 2-14).

∆∆퐶푡 = ∆퐶푡(푡푟푒푎푡푒푑) − ∆퐶푡(푐표푛푡푟표푙)

Equation 2-14 – Ct

The fold change is derived from the Ct as in Equation 2-15.

퐹표푙푑 푐ℎ푎푛푔푒 = 2∆∆

Equation 2-15 – Fold change

2-137

Chapter 3

Results 1

Cell line tests

Section 3 Cell line tests - Pharmacological characterisation of CHOhNOPGqi5 cells

3 Cell line tests - Pharmacological characterisation of

CHOhNOPGqi5 cells

3.1 Background

NOP is negatively coupled to adenylate cyclase via its native Gi G-protein (1.2.2.2). Gi activation opens potassium channels and closes voltage gated calcium channel. The readouts from this, measurements of cyclic-AMP or potassium concentrations as endpoints are not easily achieved non-invasively or non-destructively, limiting the use of CHOhNOP in a biosensor live-cell based system. Moreover, CHO cells are not electrically excitable.

The C-terminal structure of the G subunit confers specificity of downstream interactions. Substitution in the C-terminal region modulates downstream activity. For example, Pertussis toxin mediated C-terminal ribosylation of Gi inhibits the interaction between G and adenylate cyclase, effectively uncoupling this pathway (249). Pertussis toxin is used extensively in the study of GPCR transduction mechanisms.

Substitution of 3 C-terminal Gαi amino acids with those from the same region of Gαq confers activity at Phospholipase C following Gαi activation (250). Substitution of longer amino acid chains from Gαq to Gαi increases the observed stimulation of PLC/DAG following stimulation of the associated receptor. This effect has been observed in α- adrenoceptors, A1, and D2 receptors (250). Gαqi1-23 describes a series of amino acid substitutions that have variable PLC activity and receptor selectivity. Gαqi5 is a 5 amino acid substitution (from Gαq EYNLV to Gαi DCGLF). The Gαqi5 chimera has been extensively studied in the NOP-N/OFQ system (126, 127, 251).

The Gαqi5 chimera can be stably transfected into CHOhNOP cells. The resultant

CHOhNOPGqi5 cells exhibit a measurable increase in calcium mobilisation from

3-139 Section 3 Cell line tests - Pharmacological characterisation of CHOhNOPGqi5 cells

intracellular stores following agonist stimulation (127). The Giq5 substitution is used in high throughput screening and drug discovery (252) and has been used to investigate opioids (253), N/OFQ-NOP (251) and other Gi coupled systems including cannabinoid (254) and melatonin receptors (255). CHO cells have been extensively used as recipients for vectors encoding chimeric G proteins for drug discovery, high throughput and receptor characterisation applications, because of their relative ease of stable transfection and culture, availability, cost, and lack of endogenous receptor expression.

In CHO cells transfected with both hNOP and Gqi5, measurements of receptor efficacy following stimulation with exogenous N/OFQ in a microplate fluorimeter based assay

(pEC50 9.54; 9.27–9.81) (251) showed no significant difference compared to a cAMP

35 assay (pIC50 9.78; 9.74-9.77) (256). The GTP[ S] assay, however, demonstrated significantly greater efficacy (pEC50 8.24 ± 0.01) (257). In the Gqi5 chimeric system, exposure to N/OFQ in nanomolar concentrations results in an increased intracellular calcium concentration, detectable via different assay systems, and comparable with other assays of G-protein function. The amplification intrinsic to G-protein based systems increases the signal-to-noise ratio when a downstream marker such as calcium concentration is used as a readout. However, it also increases the potential for other interactions with the pathway.

3.1.1 Aims and objectives Overall, the aim of the work in this section is to test the hypothesis that CHO cells cotransfected with the Gαqi5 chimera and the human NOP receptor (CHOhNOPGαqi5) will respond with a measurable increase in intracellular calcium concentration when

-12 -7 exposed to N/OFQ within the range 10 to 10 M to which CHOhNOP is sensitive.

The secondary aim is to study the differences in receptor-ligand interactions using calcium mobilisation as a readout as measured by cuvette based fluorimetry and confocal microscopy.

3-140 Section 3 Cell line tests - Pharmacological characterisation of CHOhNOPGqi5 cells

This will be achieved by

1. Measuring the change in intracellular calcium mobilisation in CHOhNOPGαqi5 cells by cuvette based fluorimetry of whole cell suspensions following N/OFQ stimulation and in the presence of known antagonists

2. Measuring the single cell increase in calcium mobilisation in CHOhNOPGαqi5 by

confocal microscopy fluorimetry of plated CHOhNOPGαqi5 following N/OFQ stimulation and in the presence of known antagonists

3-141 Section 3 Cell line tests - Pharmacological characterisation of CHOhNOPGqi5 cells

3.2 Results 1 – Measurements of intracellular calcium mobilisation in

whole cell CHOhNOPGqi5 suspensions by cuvette based fluorimetry

3.2.1 Experimental design Cuvette based fluorimetry was performed as per Protocols 6, 7 and 8 (2.5), using the compounds in the concentrations below (Table 3-1).

Test compound Concentration range N/OFQ 10-11 - 10-6 M ATP 10-8 – 10-4 M Table 3-1 – Test compounds for cuvette based fluorimetry

CHOhNOPGαqi5 cells were harvested at >80% confluence. Cells harvested from each 175 cm2 flask were loaded with Fura-2, and finally resuspended in 10 mls of cold Krebs Buffer and stored on ice, in the dark. 2 ml of the cell suspension was used for each determination.

Using an LS350B fluorimeter (Perkin-Elmer, Beaconsfield, UK), fluorescent emission (at 510nm) was measured every second at alternating excitation at 330 and 380 nm. After the fluorescence readings stabilised, the test compounds were added to give the final concentrations stated. Following addition of an agonist test compound, the emission at 340 nm increases, with a corresponding fall in the measured emission at 380 nm, as the concentration of calcium bound Fura-2 increases, and the F380/F340 ratio (R) increases (Figure 3-1). Fluorescent recording continued until a clear, stable maximal reading had been obtained.

3-142 Section 3 Cell line tests - Pharmacological characterisation of CHOhNOPGqi5 cells

Figure 3-1 – Change in fluorescence measured at 340 nm (red) and 380 nm (blue) from -6 * CHOhNOPGαqi5 cells excited at 510 nm following treatment with 10 M N/OFQ.

The final sample from each flask was used for per-batch calibration in the presence of maximal and minimal calcium concentration by addition of Triton X-100 (0.1%, 50 l) followed by EGTA (150 l). The resultant fluorescent ratios (RMAX and RMIN) are input with experimental R, to the Grynkiewicz equation (Equation 2-5) to derive the Calcium concentration at each timepoint (Figure 3-2). Maximal change in calcium concentration ([Ca2+]) for a given ligand was defined as the difference between the 3 averaged calcium concentrations immediately prior to the addition of test compound, and the maximal calcium concentration observed.

* Dotted line indicates addition of test compound, with some adjacent interference when the chamber is opened. Recording starts at 50 s to exclude artefact

3-143 Section 3 Cell line tests - Pharmacological characterisation of CHOhNOPGqi5 cells

Figure 3-2 – Change in intracellular calcium concentration in CHOhNOPGαqi5 cells following treatment with 10-6 M N/OFQ - Dotted line indicates addition of test compound. Recording starts at 50 s to exclude artefact

Experiments were performed to n ≥ 3, in at least duplicate in order to generate pilot data. The response of CHOhNOPGαqi5 to agonist is presented as a semilogarithmic concentration-response curve showing the maximal increase in calcium concentration measured, fitted to a sigmoid curve in GraphPad Prism (216) to derive LogEC50. Graphical data are plotted using the ggplot2 package in R (217, 218), numeric data are presented as mean (CL95%), and normality tested using the D'Agostino–Pearson omnibus test. Concentration-response graphs are semi-logarithmic and show mean ± SD for each concentration point.

3.2.2 Response of CHOhNOPGqi5 cells to N/OFQ

Following exposure of CHOhNOPGqi5 cells to escalating concentrations of N/OFQ, there was an increase in intracellular calcium concentration as detected by change in Fura-2 fluorescence.

3-144 Section 3 Cell line tests - Pharmacological characterisation of CHOhNOPGqi5 cells

Exposure to increasing concentrations of N/OFQ results in a stepwise increase in [Ca2+], with maxima and minima at 10-6 and 10-10 M respectively (Figure 3-3). These data were normally distributed (p 0.74, D'Agostino–Pearson omnibus test). After fitting to a log[dose] response curve in GraphPad Prism, pEC50 was estimated as 7.23 (7.74 to 6.81), r2 = 0.75.

Figure 3-3 – Concentration response curve demonstrating increases in intracellular calcium concentration following treatment of CHOhNOPGqi5 cells with N/OFQ - pEC50 7.23 (7.74 to 6.81), r2=0.75. n5 for each concentration point. Data are mean and error bars denote ± SD

3.2.3 Response of CHOhNOPGqi5 cells to ATP

Following exposure of Fura-2 loaded CHOhNOPGqi5 cell suspensions to increasing concentrations of ATP, there was stepwise increase in [Ca2+] as measured by fluorimetry. Maxima and minimal responses were obtained when exposed to ATP concentrations of 10-4 and 10-8 M respectively (n  5 for each concentration point) (Figure 3-4). These data were normally distributed (p 0.41, D'Agostino–Pearson omnibus test). After fitting to a log[dose] response curve in GraphPad Prism, pEC50 was estimated as 6.22 (6.56 to 5.91), r2=0.90.

3-145 Section 3 Cell line tests - Pharmacological characterisation of CHOhNOPGqi5 cells

Figure 3-4 - Concentration response curve demonstrating increases in measured calcium concentration following treatment of CHOhNOPGqi5 cells with ATP - pEC50 6.22 (6.56 to 5.91). n5 for each concentration point. Data are mean and error bars denote ± SD

3.2.4 Response of CHOWT cells to ATP and N/OFQ

Fura-2 loaded CHOWT cell suspensions were exposed to increasing concentrations of both ATP and N/OFQ and the calcium concentration measured by fluorimetry. There was a concentration dependent increase in measured calcium upon exposure to ATP

2 (pEC50 4.7; 4.51-4.87, r =0.98, Figure 3-5), but not to N/OFQ (Figure 3-6). These data were normally distributed (p 0.87, D'Agostino–Pearson omnibus test).

3-146 Section 3 Cell line tests - Pharmacological characterisation of CHOhNOPGqi5 cells

Figure 3-5 – Increased measured calcium upon treating CHOWT cells to ATP - pEC50 4.7 (4.51 to 4.87) n3 for each concentration point. Data are mean and error bars denote ± SD

-6 CHOWT cells (n=3) were treated with 10 M N/OFQ and elicited no significant calcium release compared to that observed when CHOhNOPGqi5 cells (n=5) were exposed to the same concentration (Figure 3-6, [Ca2+] 17.81 ± 30.9 nM compared to 344.67 ± 90.1 nM, p<0.05, independent samples t-test).

3-147 Section 3 Cell line tests - Pharmacological characterisation of CHOhNOPGqi5 cells

Figure 3-6 – Change in calcium concentration following treatment of CHOhNOPGqi5 and CHOWT cells with 10-6M N/OFQ - Statistically significant difference between groups, by independent samples t-test p<0.05

This series of experiments has demonstrated that measurements of intracellular calcium concentration in whole CHOhNOPGqi5 cell suspensions increase in a concentration dependent manner following exposure to both N/OFQ and ATP. The measured calcium concentration in CHOWT cell suspensions also increases in response to exposure to exogenous ATP. There was no significant increase in calcium concentration in CHOWT cells following treatment with N/OFQ compared to CHOhNOPGqi5 cells (p<0.05).

3-148 Section 3 Cell line tests - Pharmacological characterisation of CHOhNOPGqi5 cells

3.3 Results 2 – Measurements of calcium responses of CHOhNOPGqi5 to test ligands by confocal fluorescence microscopy

3.3.1 Experimental design

CHOhNOPGiq5 cells were cultured, harvested, counted by haemocytometry and seeded on to 28mm #1 glass coverslips (Thermo Scientific, Loughborough, UK), prepared as described (2.4.1). Cells were seeded at a density of 2500 cells per coverslip and covered with feed media, 2 ml (Table 2-3). The prepared coverslips were incubated at 37C in

5% CO2 overnight to allow adhesion and recovery from harvesting.

On the day of the experiment, coverslips were removed from media, washed twice in Krebs-HEPES buffer before incubating with Fluo-4-AM dye (Invitrogen, UK) 2.5 g ml-1 at room temperature in the dark (30 – 60 minutes). After loading, coverslips were washed and then perfused with Krebs-HEPES buffer on the stage of a Nikon C1Si inverted confocal microscope.

The perfusion system was calibrated in two separate experiments. The perfusion chamber volume at equilibrium was measured by perfusing Krebs-HEPES buffer at 37C through a peristaltic pump (Gilson, UK) at a set rate of 5 ml min-1 for 5 minutes, then weighing the chamber and subtracting the tare weight. The flow rate was measured by measuring the pump outflow into a sterile universal container and subtracting the tare weight.

Seeded, loaded coverslips were perfused at 5 ml min-1 (range of measured rates 4.81- 4.96 ml min-1, n = 5) at 37C using a PDMI-2 micro-incubator (Harvard Apparatus). A constant volume of 1 ml (range of volumes 0.81-1.10 ml, n = 5) was maintained within the chamber on the microscope stage.

3-149 Section 3 Cell line tests - Pharmacological characterisation of CHOhNOPGqi5 cells

Following loading, prepared coverslips were used as soon as possible, and kept covered from light until imaging. Confocal microscopy was performed as described previously (2.4.2). Coverslips were imaged using an Eclipse C1Si inverted Confocal Laser Scanning Microscope, using EZC1Si control software (Nikon Inc, Japan), excitation via a 488 nm laser, and emission recorded at 513 – 556 nm using an oil immersion CFI Plan Apochromat VC 60x objective.

Ligands were added as a bolus by pipette, smoothly, to an area adjacent to the field of view to minimise artefact. Final (within assay) concentrations are given, assuming a perfusion chamber volume of 1 ml. N/OFQ was tested at final in-assay concentrations of between 10-6 and 10-12 M. Where tested, NOP antagonist ligands SB612111 and TRAP-101 were added during Fluo-4 loading, and to the perfusion chamber at a final saturating concentration of 10-7 M and allowed to equilibrate for 10 minutes prior to agonist challenge and imaging (Figure 3-7). The chamber was not perfused during ligand addition.

Experiments were performed to n ≥ 3, in order to generate pilot data.

3-150 Section 3 Cell line tests - Pharmacological characterisation of CHOhNOPGqi5 cells

Figure 3-7 – Confocal microscopy protocols - Ag+ Addition of agonist, An+ Addition of antagonist

After incubation and measurement of basal fluorescence (for 30 seconds), cells were exposed to agonist (200 l), delivered as a bolus, and the fluorescence recorded every 2 seconds in a time lapse sequence, stored per channel and time in standard Image Cytometry Standard (.ICS) format, with associated metadata.

3.3.1.1 Image processing Images were segmented to select cells as individual regions of interest, using a threshold technique. Thresholding describes the use of an intensity cut-off to generate a binary image, where each pixel is either above (1) or below (0) the threshold (258). Tracing the resultant image divides areas of high/low intensity, separating cells from background (Figure 3-8).

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Figure 3-8 – Cell segmentation workflow - A – Summed image stack, B – binary image following application of threshold technique, C – result of automated segmentation based on threshold, D – final regions of interest after manual correction

Resultant regions of interest (ROI) are stored, and used for cell counting, measurement of area, fluorescence and distribution (Figure 3-9). The Y coordinates used for analysis are reversed (i.e. Figure 3-9 is vertically flipped compared to Figure 3-8) by convention.

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Figure 3-9 – Distribution of regions of interest for fluorescence analysis - Centroids of regions of interest (cells) for spatial analysis combined with maximal F/F0 and responder classification. labels for each point correspond with ROIs in Figure 3-8 (Y axis reversed by convention)

The distribution is tested for complete spatial randomness by application of the Chi- Squared Dispersion Test based on quadrat counts. In the above example (Figure 3-9), p=0.09, suggesting that the distribution does not differ significantly from complete

3-153 Section 3 Cell line tests - Pharmacological characterisation of CHOhNOPGqi5 cells spatial randomness (although this is limited because of the relatively small number of cells) (259). This implies that each cell is acting individually.

Using ImageJ (209), and the BioFormats plugin (210), the time series of stacked images were processed using the threshold tool used to highlight cells based on fluorescent intensity relative to the background. The threshold was adjusted manually to highlight individual cells. These areas were checked manually to ensure that all cells were included and not grouped. Final ROIs were stored for fluorescent measurement. Raw fluorescence data were processed using R (218) and the ggplot2 (217) and spatstat (259) packages for graphing and geospatial analysis (217, 218). Curve fitting for concentration response curves was undertaken in GraphPad Prism (216).

Raw fluorescence data were normalised to the baseline (F0; the mean of the first 5 measurements for each cell). These were used to evaluate changes in relative fluorescence (F/F0).

3.3.2 Response of CHOhNOPGqi5 cells to N/OFQ 3.3.2.1 Single cell responses

Under the microscope, the fluorescence of Fluo-4 loaded CHOhNOPGqi5 cells increases following exposure to a saturating concentration (100 L, final in-assay concentration 10-6 M) of N/OFQ (Figure 3-10), with no significant fluorescent response to 100 L of buffer.

3-154

Section 3 line Cell tests i ii -

Pharmacological characterisation of CHO 3-155

Figure 3-10 - CHOhNOPGqi5 response to N/OFQ , (i) – change in individual (red) and overall average (blue) relative fluorescence over time showing (ii) hNOPG Micrograph corresponding to (i) showing Basal-A, Maximal-B, Return to basal-C, Segmentation-D. Representative experiment of 5 total  qi5

cells

Section 3 Cell line tests - Pharmacological characterisation of CHOhNOPGqi5 cells

There is significant variability in the recorded fluorescence between cells (Figure 3-10- i).

3.3.2.2 Cellular population-based responses

Across 5 experiments, the overall variation in maximal F/F0 for all cells is shown when treated with 10-6 M N/OFQ and buffer (Figure 3-11).

-6 Figure 3-11 – Maximal F/F0 for every cell after treatment with 10 M N/OFQ and buffer - n=5 experiments, total 343 cells, p<0.05 (independent samples t-test). Error bars denote mean ± SD

Using buffer as a negative control, and N/OFQ as a positive control, the separation of maximal F/F0 values are shown in Figure 3-12 with the percentiles in Table 3-2.

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% cells below threshold F/F0 25% 50% 75% 95% 10-6M N/OFQ 3.88 4.93 6.26 11.11 Buffer 1.05 1.09 1.17 2.15

Table 3-2 – Percentiles for maximal F/F0 for a prototypical positive control, 100 L N/OFQ (final concentration 10-6M), and a negative control, 100 L buffer

Figure 3-12 – Frequency distribution of maximal relative fluorescence following treatment of -6 CHOhNOPGqi5 with 10 M N/OFQ and buffer - The threshold point of F/F0  1.8 is shown

Receiver-Operating Characteristic (ROC) analysis of these data indicate the optimal threshold for discrimination (Figure 3-13).

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Figure 3-13 – ROC curve analysis of F/F0 threshold values - Area under the curve (AUC) 0.98, p < 0.0001

Maximal relative threshold value of 1.8 was chosen as this is sensitive and gives a low “false positive” rate (6.7%) and high sensitivity (97.1%) (Table 3-3).

Compound Buffer NOFQ Total Max F/F0 <1.8 320 (TN) 10 (FN) 340 >1.8 23 (FP) 333 (TP) 343 Total 343 343 Table 3-3 – Contingency table with threshold for responder cells set at 1.8

Where maximal F/F0 meets or exceeds a threshold of 1.8, individual cells were classified “responders”, the remainder classified “non-responders”. On this basis, using a threshold relative fluorescence of 1.8, the paired responses of cells exposed to 100 l boluses of N/OFQ (final concentration 10-6 M) and Krebs-HEPES Buffer from 5 experiments were classified as responders or non-responders (Figure 3-14, Table 3-4).

3-158 Section 3 Cell line tests - Pharmacological characterisation of CHOhNOPGqi5 cells

There was a significantly greater proportion of responsive cells to N/OFQ compared to buffer (2, p < 0.05).

Figure 3-14 – Proportion of cells classified as responders by maximal relative fluorescence following exposure to a bolus addition of 100 L N/OFQ (final concentration 10-6M) and Buffer - Error bars represent mean ± SD

Responders Non-responders Maximal F/F01.8 Maximal F/F0<1.8 N/OFQ† 333 10 Buffer 23 320 Table 3-4 – 2 x 2 contingency table showing the significant difference in pooled, paired responses to N/OFQ and buffer - †Significantly greater proportion of responsive cells compared to buffer (2, p < 0.05)

3.3.2.3 Concentration-responses curves to N/OFQ Within an individual assay, there was significant cell-to-cell variability in response (Table 3-5), with a correlation between Log[Concentration] and proportion of responder cells (R2=0.89).

3-159 Section 3 Cell line tests - Pharmacological characterisation of CHOhNOPGqi5 cells

Variation between cells (intra-assay variation) was assessed by analysis of the coefficient of variation (Equation 2-8) for per-cell maximal F/F0 values (i.e. the standard deviation and mean of maximal F/F0 for each cell in one assay). Variation between assays (inter-assay variability) was assessed by the standard deviation of the maximal mean F/F0 values following exposure to a given concentration of N/OFQ (Table 3-5).

Maximum Coefficient of F/F0 Variation Log[Concentration] n Total Responders Mean SD Mean SD N/OFQ (M) cells (%) F/F01.8 -6 8 289 93.77 2.99 0.71 23.21 4.29 -7 3 55 76.36 1.70 0.56 23.64 13.70 -8 6 161 68.32 2.15 0.69 33.18 12.34 -9 5 111 17.12 1.60 0.82 26.53 26.20 -10 3 79 2.53 1.68 1.11 11.55 17.19 -11 2 57 3.51 1.09 0.05 20.86 16.12 -12 7 258 7.36 1.27 0.41 14.31 15.89 Table 3-5 - Intra-assay variability (as expressed by mean coefficient of variation), and Inter- assay variability (as expressed by standard deviation of F/F0)

Fitting the proportion of responders to a semilogarithmic concentration response curve gives pEC50 of 8.3 (8.57 to 8.06) (Figure 3-15). This differs from that reported in a plate reader based fluorimetry system for the same cell line, of 9.54 (127).

3-160 Section 3 Cell line tests - Pharmacological characterisation of CHOhNOPGqi5 cells

Figure 3-15 – Concentration-response curve showing the proportion of responsive CHOhNOPGqi5 2 cells (F/F01.8) following exposure to N/OFQ - pEC50 = 8.3, R = 0.89

3.3.2.4 Responses to NOP antagonists When exposed to NOP antagonists (SB612111, TRAP-101), or buffer alone there was no fluorescent response (Figure 3-16, Table 3-6), and similarly, exposure to N/OFQ in the presence of antagonists resulted in an attenuated response.

3-161 Section 3 Cell line tests - Pharmacological characterisation of CHOhNOPGqi5 cells

Figure 3-16 – Response to antagonist treatment, buffer, 10-6 M N/OFQ alone and with 10-7 M SB612111 and 10-7 M TRAP-101 - Error bars represent mean (%) ± SD

10-6 M N/OFQ 10-6 M N/OFQ 10-6 M N/OFQ (n=10) 10-7 M TRAP-101 10-7 M SB612111 (n=5) (n=5) Responders 467 29 1 Nonresponders 24 297 165 % responders 95.11 8.90 0.60 Mean max F/F0 3.84 1.27 1.04 Standard 1.60 0.13 0.04 deviation CV 41.82 10.48 3.93 Table 3-6 – Response to N/OFQ and the antagonists SB612111 and TRAP-101

NOFQ was tested alone and in combination with known peptide and nonpeptide antagonists; the results are shown in Table 3-7.

3-162 Section 3 Cell line tests - Pharmacological characterisation of CHOhNOPGqi5 cells

10-7M 10-7M NOFQ 10-7M 10-7M NOFQ SB612111 (post TRAP-101 (post + 10-7M antagonist +10-7M antagonist N/OFQ wash out) N/OFQ wash out) n 3 3 8 8 Responders 37 84 107 482 Nonresponders 55 8 419 44 % response 40.22 91.30 20.34 91.63 Mean max F/F0 1.74 2.78 1.44 4.54 SEM 0.53 0.84 0.26 1.53 CV 30.51 30.24 18.14 33.66

Table 3-7 – Response of CHOhNOPGqi5 to N/OFQ in the presence of the antagonists TRAP-101 and SB612111

The pooled data demonstrate an inhibited response in the presence of antagonists (Figure 3-17), although this did not reach statistical significant for SB612111.

A representative CHOhNOPGqi5 response to N/OFQ in the presence of the peptide antagonist TRAP-101, and then following removal of the agonist by washing is shown below in Figure 3-18.

3-163 Section 3 Cell line tests - Pharmacological characterisation of CHOhNOPGqi5 cells

-7 Figure 3-17 – Responses of CHOhNOPGqi5 to 10 M N/OFQ +/- NOP antagonists - Paired tests in the presence and absence of 10-7 M SB612111 and TRAP-101. n3

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Section 3 line Cell tests i ii -

Pharmacological characterisation of CHO 3-165

hNOPG

 qi5

-7 -7 cells Figure 3-18 - CHOhNOPGqi5 response to TRAP-101 + 10 M NOFQ, compared to 10 M NOFQ following wash out , (i) – change in overall average (red) relative

fluorescence over time showing (ii) Basal with antagonist-A, Maximal with antagonist-B, Basal post-wash-C, Maximal post-wash-D, Return to basal-E responses. Segmentation and location of cells is F. Representative experiment of 5 total

Section 3 Cell line tests - Pharmacological characterisation of CHOhNOPGqi5 cells

3.4 Discussion and Conclusions The intracellular calcium concentration measured by changes in fluorescence of Fura-2 or Fluo-4 loaded CHOhNOPGqi5 cells increased significantly following exposure to N/OFQ in both the cuvette based and confocal microscopy assay, with maximal and minimal responses observed at 10-6 and 10-10 M in each assay.

In cuvette based fluorometry of whole cell suspensions and confocal microscopy of single cells, fluorescence of Fura-2 and Fluo-4 loaded CHOhNOPGqi5 cells increased following exposure to N/OFQ. Cuvette based fluorometry also demonstrated a

CHOhNOPGqi5 response to ATP. In the cuvette-based assay, CHOWT cells responded to ATP, but not to N/OFQ. During testing in the confocal based assay, addition of buffer to

CHOhNOPGqi5 cells failed to elicit a response, suggesting that the observed fluorescence to N/OFQ is a cell-specific, receptor mediated response, and not due to injection artefact.

-4 -5 -6 As demonstrated, following exposure of CHOhNOPGiq cells to ATP at 10 , 10 , 10 M, there was a clear increase in intracellular calcium concentration as measured by Fluo-4 fluorescence. This is likely to represent activity at purinoceptors endogenously expressed on the CHO-K1 cell line. The response to ATP has been described previously in this cell line, with a pEC50 of 6 (127). My results indicate a narrow concentration response curve before saturation occurs. This may be because of a low purinergic receptor number, or an underlying characteristic of the ligand gated ion channel. This assay demonstrates a response at concentrations of ATP > 10-7M.

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Section 3 Cell line tests - Pharmacological characterisation of CHOhNOPGqi5 cells

Addition of N/OFQ to whole cell CHOhNOPGqi5 suspensions in the cuvette-based assay produces a rapid increase in measured calcium concentration. There is significant variation in the fluorescent response observed between single cells, and between tests in the confocal assay following N/OFQ exposure, as demonstrated by the coefficient of variation.

When a Fluo-4 loaded single CHOhNOPGqi5 cell is exposed to N/OFQ, there is a clear increase in fluorescence, which decays to baseline following washing, and cells may then be stimulated again. All aspects of the fluorescence/time profile for an individual cell are variable; the initial increase and peak time dependent on the site of test compound addition, the speed of dissolution (temperature, viscosity, and concentration gradient), and any perfusion. However, where a cell shows no response, there is no characteristic peaked maxima.

Therefore, the division between responsive and nonresponsive cells can be determined based on maximal F/F0 exceeding a given threshold. The proportion of responsive cells is proportional to N/OFQ concentration, although individual or pooled maximal F/F0 values alone do not correlate to N/OFQ concentration in the confocal assay. This may be explained by the effect of a bolus of N/OFQ diffusing across the field of view. Low N/OFQ concentrations may be inadequate to stimulate those cells further away from the site of injection. Therefore, the local concentration at the biosensor cell NOP receptor may be an important influence in the observed response.

Using these principles, concentration-response curves were obtained for calcium increase and proportion of responsive (maximal F/F0  1.8) cells after N/OFQ exposure in the cuvette and confocal assays respectively. The estimated pEC50 values were 7.23

(CL95% 7.74 to 6.81) and 8.3 (CL95% 8.57 to 8.06) for the cuvette and confocal assays

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Section 3 Cell line tests - Pharmacological characterisation of CHOhNOPGqi5 cells

respectively. The published pEC50 for this cell line, in a microplate based Fluo-4 based assay after exposure to N/OFQ is 9.09 (CL95% 8.84 – 9.34), and the lowest concentration of N/OFQ at which a response was observed was 10-10M (127).

It is likely that the differences in the pEC50 values are due to variation within the assay and differences between the phenotypes of adherent and non-adherent cells. A further consideration is that the confocal assay is non-ratiometric and cannot be calibrated to account for differences in cell loading. Moreover, small differences in perfusion and the timing of injection may affect the true concentration within the incubator chamber, therefore affecting the observed response. The confocal microscopy assay is a 2- dimensional representation of a 3-dimensional cellular structure; the observed fluorescent response may depend on the 3-dimensional orientation of the cell on the coverslip, the optical section observed and any compartmentalisation of Fluo-4 and calcium within the cell. CHO cells leak Fura-2 and this may account for some of the difference observed between tests in the cuvette based assay, and when comparing with the confocal microscopy based assay (193).

The sources of variation in bioassays are large, and can include measurement error, or environmental factors (260). There is some natural cell-to-cell variability in receptor, G- protein expression, and intracellular environment, all of which will affect the measured response, particularly for a downstream readout such as calcium concentration.

In live cell assays, the use of destructive calibration, and the need to avoid cell damage by minimising laser screening and power precludes the use of a ratiometric dye, therefore fluorescence is recorded and normalised to baseline as a readout; however, this precludes calibration to calcium concentration. The use of higher laser power to increase fluorescence, whilst improving image quality would also risk cellular damage,

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Section 3 Cell line tests - Pharmacological characterisation of CHOhNOPGqi5 cells therefore a balance between increased pinhole size, more out-of-focus light and variation, and lower laser power is made.

A pragmatic approach to controlling as many factors as possible and, in order to accept this compromise, the measured variability (as measured by coefficient of variation; CV), is presented. CV<40% is considered an acceptable source of variation in bioassays. Therefore, whilst cells may be responders or non-responders, the absolute values for maximal or minimal fluorescence may be artefacts of the test. Therefore, a liberal threshold F/F0 was set to categorise responsiveness. As demonstrated, this had high sensitivity and discriminatory value (3.3.2).

The use of confirmatory methodologies to verify and validate the results of the confocal based assay is therefore important to account for unavoidable variation.

In summary

 CHOhNOPGqi5 cells respond to N/OFQ with a measurable increase in intracellular calcium concentration when exposed to N/OFQ, consistent with the introductory hypothesis

 CHOhNOPGqi5 cells exhibit a sensitive increase in intracellular calcium upon exposure to N/OFQ both as single cell and mixed suspensions which is not due to injection artefact (consistent with published data)

 Absolute cell maximal and minimal F/F0 values from confocal microscopy of

CHOhNOPGqi5 cells show significant variability. However, use of a threshold F/F0 reliably separates cells into responder and non-responder populations

 The proportion of CHOhNOPGqi5 cells responding to N/OFQ in a cell based confocal microscopy assay is comparable to the overall calcium measurement

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Section 3 Cell line tests - Pharmacological characterisation of CHOhNOPGqi5 cells

obtained in cuvette based fluorimetry of whole cell suspensions and in published

data, with similar pEC50 values

 N/OFQ may be antagonised by preincubation of the CHOhNOPGqi5 cells with NOP antagonists TRAP-101 or SB612111

 ATP produces a significant rise in intracellular calcium in both CHOhNOPGqi5 and

CHOWT cells and is a possible source of confounding within a bioassay

The next chapter presents work building on these principles to develop a biosensor- based assay for N/OFQ release, and to test in mixed immune cells.

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Chapter 4

Assay development and validation

Section 4 Assay development and validation

4 Assay development and validation

4.1 Background

This section describes the comparison of techniques for leukocyte extraction (4.2), optimisation of biosensor conditions (4.3), preliminary testing of the granulocyte release assay (4.4) and subsequent blockade of confounding signals from purinergic receptors (4.5).

Polymorphprep™ was used to isolate a sample of mixed PMNs from whole blood (predominately neutrophils and eosinophils). In order to extract individual subpopulations of basophils and eosinophils, blood was pre-separated over a Ficoll-Paque™ density gradient. This generated two phases, one containing monocytes and basophils, the other eosinophils and neutrophils (Figure 2-8), each suitable for subsequent immunomagnetic selection in order to maximise purity (Figure 2-9).

The time and increased handling for multiple immunomagnetic column separations (for eosinophils, neutrophils and basophils) may affect the viability and function of the extracted immunocytes (223). Therefore, a more rapid method, with less handling was used. A MACSxpress® one-step technique was employed, which does not require prior density gradient separation. The yield, purity and viability of these methods are evaluated in this section.

After initial PMN extraction by Polymorphprep™, these cells were layered over the biosensor and used for preliminary testing of granulocyte release (4.3, 4.4). Initial observations demonstrated considerable variability in CHOhNOPGqi5 response. This necessitated further testing of the purity of leukocyte extraction, and to exclude confounding by ATP release from either lysed or degranulated PMNs (4.5).

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Section 4 Assay development and validation

4.1.1 Aims and objectives

The aims of this chapter are to characterise a reliable and reproducible method for extraction of mixed PMNs and PMN subsets, and to optimise the conditions for co- incubating immune cells with CHOhNOPGqi5 cells in the granulocyte release bioassay.

The aim of the work in this section is to test the hypothesis that, after optimisation in the granulocyte release assay, CHOhNOPGqi5 cells will respond with a sensitive and specific, measurable increase in intracellular calcium concentration (using fluorescence as a readout) in the presence of N/OFQ released from adjacent cells (PMNs or EOL1 cells).

This will be achieved by

1. Analysis of purity, viability, and yield of immunocyte extraction by density gradient (for PMN separations), and immunomagnetic techniques (for granulocyte subfractions) from whole blood taken from healthy volunteers 2. Testing and optimising the granulocyte release assay using immortalised immune-cell like cells lines (EOL-1) as a proxy for freshly isolated immunocytes 3. Testing the degranulation of immune cells isolated from healthy volunteers in the granulocyte release assay

4. Optimising conditions for blockade of purinergic signalling in CHOhNOPGqi5 cells to avoid confounding by co-release of ATP from immunocytes

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Section 4 Assay development and validation

4.2 Results 3 - Optimisation and validation of leukocyte extraction

4.2.1 Extraction of mixed PMNs Polymorphprep™ (Table 2-11) was utilised for the extraction of mixed PMNs as described (2.6.1.2). This protocol has been reported to give PMN purity of >90% (261). Previous studies within our group have demonstrated granulocyte purity of >83.8% by FACS (153) using the manufacturer’s protocol (234, 262).

University ethical approval was granted for sampling of blood from healthy volunteers from the Department of Cardiovascular Sciences, University of Leicester (Appendix – Ethical approvals).

Blood was sampled from healthy volunteers by venepuncture (2.6.1.1), and separated by Ficoll-Paque™ (2.6.1.3), or Polymorphprep™ (2.6.1.2). The resultant cell suspension was counted by haemocytometry (2.2.2), viability assessed by trypan blue exclusion, and stained with antibodies against appropriate cell surface markers (2.7.2) before counting on a Becton Dickson FACS Aria flow cytometer (BD Biosciences, San Jose, USA). Data were collected in the associated BD FACS-Diva software (BD Biosciences, San Jose, USA) and exported in Flow Cytometry Standard format for analysis in FCSAlyzer (243).

4.2.1.1 Results Blood was collected by venepuncture from 6 healthy volunteers (18.75  4.10 ml per volunteer), and mixed PMNs obtained as described above. Total yields were 1.39 ± 0.92 x 106 ml-1 blood, with a viability of 83.17% ± 16.59 (Figure 4-1).

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Section 4 Assay development and validation

Figure 4-1 – Purity, viability and yield of granulocyte separation by Polymorphprep™ - Error bars represent mean ± SD

After exclusion of non-nucleated debris, overall granulocyte purity was 81.76 ± 5.1%, with evidence of some contamination by non-granulocyte leucocytes as shown in Table 4-1. Flow cytometric data presented as a dot plot from a representative triple stained PMN sample in Figure 4-2.

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Section Cell number* % Gated

(x 10 cells 4

ml-1) development Assay and validation

Total cells 1.39

Nucleated (FSC/SSC) 1.09 78.76% ± 12.1 (n=6)

Leucocytes (Nucleated and CD45+) 1.02 93.75% ± 4.3 (n=3)

Granulocytes (Nucleated and CD66+) 0.08 81.76% ±

4-176 5.1 (n=5)

Eosinophils (Granulocytes and 0.007 7.69% ±

Sig8High) 11.4 (n=5)

Neutrophils (By subtraction) 0.82 92.31% ± 11.4 (n=5) Figure 4-2 – Representative triple stained dot plot gated (FSC/SSC) for Non-granulocyte Leucocytes 0.007 6.75% ± nucleated cells showing leucocytes (pale blue), eosinophils(red) and 2.35 (n=3) granulocytes (dark blue). Doublets/Triplets not discriminated (Nucleated and CD45+ and CD66-)

Table 4-1 – Characteristics of mixed PMN separations obtained by Polymorphprep™ separation . * Cell number derived from total cells and presence of subtypes by FACS gating

Section 4 Assay development and validation

4.2.2 Extraction of granulocyte subpopulations Individual subpopulations of basophils and eosinophils were initially extracted following pre-separation over a Ficoll-Paque™ based density gradient. The resultant basophil/PBMC layer and the granulocyte pellet (containing neutrophils and eosinophils) (Figure 2-8) were then processed by immunomagnetic separation using MACS® MicroBeads through magnetic columns (2.6.2.2). The granulocyte pellet was first subject to red cell lysis to reduce erythrocyte contamination.

Poor yields and concerns about the viability and function of cells following density gradient separation, red cell lysis, and positive selection techniques (for basophils) led to a separate evaluation of immunomagnetic techniques without a pre-separation and erythrocyte lysis step. Therefore, neutrophils and eosinophils were extracted by MACSxpress® in one step from whole blood to minimise handling. The yields of basophils were low – and no kit exists for separation without pre-enrichment, and a positive selection protocol (which risks stimulation). Therefore, the use of basophils was deemed impractical following this evaluation.

Granulocyte separation by column-based MACS® MicroBeads methods (for eosinophils and basophils) was from a single blood sample per volunteer. Separation by MACSxpress® (for neutrophils and eosinophils) was also from a single sample per volunteer in a separate series of experiments.

4.2.2.1 Flow cytometry strategy for assessment of purity The purity of granulocyte fractions was assessed using flow cytometry as discussed above, using the gating strategy for neutrophils, eosinophils and basophils shown in Figure 4-3 and Table 4-2. Final subfractions (basophils, eosinophils, and neutrophils)

4-177 Section 4 Assay development and validation were used to define gates. Cell suspensions were stained using the general method above (2.7.2).

The antibody stains added to each sample tube are shown in Table 4-2. For each cell type – mixed PMNs (#1-6), neutrophils (7-12), eosinophils (#13-18), basophils (#19-24), and lysed whole blood (#25). Cell debris were excluded by gating based on FSC/SSC of unstained PMNs. Fluorescent gates were defined based on antibody staining of positive samples – neutrophils (#8, CD16), basophils (#23, FCϵRIα-FITC), eosinophils (#15, SIGLEC-8-APC). These gates were applied to the triple stained samples mixed PMNs (#6), neutrophils (#12), eosinophils (#18), basophils (#24) to derive the fraction of stained vs unstained cells, or purity (Equation 4-1).

푛푢푚푏푒푟 표푓 푡푎푟푔푒푡 푐푒푙푙푠 푃푢푟푖푡푦 (%) = × 100 푡표푡푎푙 푝표푝푢푙푎푡푖표푛

Equation 4-1 – calculation of purity (%)

Yield (cells per ml of blood processed) was determined based on the final cell count in the extracted fraction corrected according to dilution and initial blood sampling volume (Equation 4-2).

푛푢푚푏푒푟 표푓 푡푎푟푔푒푡 푐푒푙푙푠 푌푖푒푙푑 = 푣표푙푢푚푒 표푓 푏푙표표푑 푝푟표푐푒푠푠푒푑

Equation 4-2 – calculation of yield (cells ml-1)

CD66-PE, SIGLEC-8-APC, CD16-VioBlue and FcRia-FITC were used to verify gating for and quantify granulocytes, eosinophils, neutrophils, and basophils respectively. Final sample concentration was 1 x 106 cells ml-1 prior to flow cytometry. Samples were processed on a Becton-Dickinson FACS Aria II counter and compensated for autofluorescence. The acquired cytometry data were exported and analysed using FCSAlyzer 0.9.15 software (243). Aggregate data were analysed using GraphPad Prism (216), and plotted using R and ggplot2 (217, 218). Viability and purity are presented as %  standard deviation. Yield is presented as total cells ml-1 whole blood processed.

4-178 Section 4 Assay development and validation

The dot plots from samples used to set gates are shown in Figure 4-3. An initial gate is set to exclude debris, from an unstained PMN sample (Table 4-2, #1) and FSC/SSC profile plotted (Figure 4-3, A). The gate excludes subcellular non-nucleated debris (Figure 4-3,

B, C). A Siglec-8 stained mixed PMN sample (Table 4-2, #3) is gated to select nucleated cells with high levels of Siglec-8 binding, consistent with eosinophils (Figure 4-3, D-G). A

CD16 stained mixed PMN sample (Table 4-2, #2) is gated to select nucleated cells with high levels of CD16 binding, consistent with neutrophil cell surface expression (Figure

4-3, H-K). A basophil gate was determined from mixed PMNs (Table 4-2, #5) based on

FCϵRiα binding to nucleated cells (Figure 4-3, L-O).

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Section # Cell type CD16- SIGLEC- FCϵRiα- CD66- # Cell type CD16- SIGLEC- FCϵRIα- CD66- VioBlue 8-APC FITC PE VioBlue 8-APC FITC PE 4

1 PMN 13 Eosinophil development Assay and validation

2 PMN  14 Eosinophil 

3 PMN  15 Eosinophil 

4 PMN  16 Eosinophil 

5 PMN  17 Eosinophil 

6 PMN     18 Eosinophil    

4-180 7 Neutrophil 19 Basophil

8 Neutrophil  20 Basophil 

9 Neutrophil  21 Basophil 

10 Neutrophil  22 Basophil 

11 Neutrophil  23 Basophil 

12 Neutrophil     24 Basophil    

25 Whole blood

Table 4-2 – Gating strategy for assessment of PMN subtype purity

Section 4

Assay development Assay and validation 4-181

Figure 4-3 – Gating strategy for PMN subtype purity. Doublets/Triplets not discriminated, live/dead stains not used

Section 4 Assay development and validation

4.2.2.2 Eosinophil and Basophil extractions by MACS® MicroBeads following Ficoll- Paque™ pre-separation Blood was sampled from healthy volunteers (n=5, 30 mls), and pre-separated over a Ficoll-Paque™ gradient. The PBMC / basophil layer was used for basophil isolation, and the granulocyte pellet for eosinophil isolation (2.6.2.2).

The resultant fractions were counted, viability assessed by trypan blue exclusion, and then evaluated for purity and yield by flow cytometry (Table 4-3, Figure 4-4).

n Total yield % viability % target (corrected initial count) x 108 cells l-1 Basophil 5 0.01 99.02 65.93 Eosinophil 5 1.04 97.16 72.98 Table 4-3 – Yield, viability and % target cells obtained from 30mls of blood from healthy volunteers by MACS® MicroBeads based separation techniques

4-182

Section i ii – Representative triple stained sample from eosinophil separation gated for eosinophils (red), granulocytes (blue), and leucocytes

(green) 4

Assay development Assay and validation

4-183 Iii - Representative triple stained sample from basophil separation

gated for basophils (purple) and leucocytes (green)

Figure 4-4 – Separation characteristics of eosinophils and basophils (n=5) I – pooled data for viability, purity and yield, ii – Flow cytometry dot-plot of representative eosinophil separation, iii – Flow cytometry dot-plot of representative basophil separation. Doublets/Triplets not discriminated

Section 4 Assay development and validation

4.2.2.3 Neutrophil extractions by MACSxpress® from whole blood

Blood was sampled from healthy volunteers (n=7, 4.57  1.51 ml), and neutrophils extracted as above. Both yields (1.47  0.53 x 106 cells ml-1 peripheral blood) and viability (90.53%  10.33) were high using this procedure (Table 4-4, Figure 4-5).

Volunteer Total yield Viability Purity x 106 cells ml-1 (%) (% Neutrophils) blood processed 1 2.33 75.3% 98% 2 1.38 80% 99.1% 3 0.80 84.4% 98.2% 4 1.00 97.5% 99% 5 1.75 100% 97.3% 6 1.23 98.0% 99.8% 7 1.83 98.6% 99.7% Table 4-4 – Characteristics of neutrophil separation by MACSxpress®

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Section I – Separation characteristics of neutrophils by MACSxpress® ii- Representative stained sample from neutrophil separation gated for neutrophils (yellow), granulocytes (blue), eosinophils (red), and

leucocytes (green) 4

Assay development Assay and validation 4-185

Figure 4-5 – Flow cytometry of neutrophil separation by MACSxpress® (n=7) I – pooled data for viability, purity and yield, ii – Flow cytometry dot-plot of representative neutrophil separation. Doublets/Triplets not discriminated

Section 4 Assay development and validation

4.2.3 Discussion Separation of mixed PMNs from whole blood by Polymorphprep™ results in a suspension with a consistently high yield (1.39 x 106 cells ml-1), viability (83.17% ± 16.59) and moderate purity (81.76% ± 5.1%) as determined by CD66 binding (Table 4-1). Yields are consistent with other reported studies using the same methodology (224). The viability of the isolated PMNs (83.17% ± 16.59) is lower than that reported elsewhere (typically >99%) (222, 224), which may relate to the use of hypotonic red cell lysis in this method.

Granulocyte purity (81.76% ± 5.1) was similar to that reported elsewhere (91.4% ± 4.9) (224). Purities of samples obtained by density gradient separation alone are universally lower than when combined with negative or positive selection, although overall yields are higher (224). The leucocyte nongranulocyte contamination observed (6.75% ± 2.35) may be due to monocytic, T- or B- lymphocytes mixing with the PMN layer either as a result of centrifugal force, or whilst unloading the separated cell layers.

The granulocyte fraction consisted of predominately neutrophils (92.31% ± 11.4), with small numbers of eosinophils (7.69% ± 11.4), reflecting low circulating numbers of this cell type in healthy humans (Figure 1-7). Of note, this method produced a cell suspension with a small but important source of contamination from non-granulocytic leucocytes, and from non-leucocytes. This may represent nonspecific binding of the CD45 antibody, or cellular debris as artefact from the extraction process.

The source of non-leucocytes in the sample is unclear and may represent noncellular debris or fragments which had escaped exclusion by gating, or other cell types, such as erythrocyte contamination.

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Initially, basophils and eosinophils were separated by a combination of density gradient methods and immunomagnetic separation. The purity of the recovered samples for both species was high. However, there was significant eosinophil contamination with neutrophils. Additionally, the yields from the basophil samples were very low. Additionally, the process for separation using this technique is long, and requires significant cell handling, which has been shown to affect function (223). As the yields for basophils were so low – even from a large volume of blood, this technique was deemed unfeasible because of the significant risk of contamination from other cell types, and the high volume of blood required in order to recover a viable number of cells. Furthermore, techniques for basophil isolation use a positive selection methodology which prevents the isolation of “untouched” cells, risking priming and receptor activation.

The MACSxpress® method was evaluated for separation of neutrophils from whole blood and showed very high purity, viability, and yields, with minimal cell handling and time to deteriorate. Therefore, this method was adopted for isolation of neutrophils. Eosinophil extraction was performed using a similar minimal handling, whole blood methodology.

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4.3 Results 4 - Optimisation of biosensor conditions In order for a biosensor to detect released N/OFQ, the concentration at the receptor must be adequate. Testing the working hypothesis that granulocytes are a source for N/OFQ depends on the total amount of N/OFQ released, and the proximity to biosensor cells (and hence local concentration). Therefore, the CHOhNOPGαqi5 / immune cell seeding densities were optimised in order to maximise the probability of a high local concentration of N/OFQ at the hNOP receptor, and to allow immunocyte adhesion to the coverslip.

During testing, the possible spurious activation of CHOhNOPGqi5 by the effects of ATP and immune cell metabolites became apparent. In order to prevent a false positive result, immune cells were assayed to determine local ATP concentration, and the concentration of purinergic antagonists in the granulocyte release assay required to prevent confounding was optimised.

4.3.1 CHOhNOPGqi5 seeding density

CHOhNOPGqi5 were maintained as stated, harvested and seeded on to Number 1, 0.25mm Cell-Tak™ coated glass coverslips in 6 well plates (50000, 25000, 12500, 5000,

500 per well) and incubated overnight (17-21 hours, 37C, 5% CO2) in selection media.

On the day of the experiment, the cells were loaded with Fluo-4, imaged and segmented as described. The centroids of the resultant regions of interest were determined using ImageJ (209), and processed in R via the spatstat, sf and sp packages to determine cell count, distribution (via quadrat counts), and distance to nearest neighbour (217, 218, 259, 263, 264).

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Section i-tabulated data – initial seeding density, and cell counts per field of Ii – representative merged fluorescence / brightfield images vision after 17-21 hours incubation 4

Initial 50000 25000 12500 5000 500 development Assay and validation seeding n=19 n=12 n=12 n=6 n=6 density (cells per coverslip) Cells per 10.9 8.3 6.3 6.3 2.00 field (6.30) (2.31) (1.61) (4.18) (2.68) mean (SD) Quadrat 0.44 0.33 0.25 0.25 0.08 Cells per (0.25) (0.09) (0.64) (0.17) (0.11)

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mean (SD) Distance to 30.91 31.16 35.17 - -

nearest (7.98) (4.56) (10.59) neighbour m mean (SD) Table 4-5 – Seeding density and counts after 17-21 hours incubation

Figure 4-6 – Optimisation of seeding density I-tabulated data, ii-representative merged fluorescence / brightfield microscopy A – 50000 cells/coverslip, B- 25000 cells/ coverslip, C-12500 cells / coverslip, D – 5000 cells / coverslip (Nikon inverted T1Si confocal microscope, 30x PLAN-FLUOR oil immersion objective)

Section 4 Assay development and validation

Overall cell counts, quadrat counts and distance between nearest neighbours are presented above in Table 4-5 (m, mean ± SD), with representative photomicrographs in Figure 4-6. The graph below (Figure 4-7) shows average counts. The sample distribution of 2 seeding experiments at each density is shown in Figure 4-8, where each point represents a centroid of a region of interest (a cell).

Figure 4-7 – CHOhNOPGαqi5 seeding density and final field counts - Error bars are mean ± SD

Cells are distributed across the field of view in a random distribution (Table 4-6).

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Figure 4-8 – Cell distribution of CHOhNOPGαqi5 seeded at different densities – rows represent two example experiments (top = 1, bottom = 2)

The distributions shown above were tested for spatial randomness (Table 4-6).

p-value (chi squared test of complete spatial randomness) Initial seeding density (number of cells per coverslip) n 500 5000 12500 25000 50000 1 0.867 0.139 0.131 0.685 0.002 2 0.302 0.302 0.278 0.118 0.032

Table 4-6 – Test for random distribution of CHO cells

At low cell numbers, the p-values were high, suggesting randomness, although the low overall counts prevent robust statistical analysis. At higher counts, the p-values tend towards significance, suggesting that the distribution is not random. This may reflect clumping or clustering, because of the effect of the Cell-Tak™.

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4.3.2 PMN seeding density and distribution

CHOhNOPGqi5 were maintained as stated, harvested and seeded on to Number 1, 0.25mm Cell-Tak™ coated glass coverslips in 6 well plates at a seeding density of 5000 cells per well and incubated overnight (17-21 hours, 37C, 5% CO2) in selection media.

On the day of the experiment, the CHOhNOPGqi5 cells were loaded with Fluo-4. PMNs were added and allowed to adhere to the plate either 1 x 105, 1 or 2 x 106 cells in total. The resultant plates were imaged as described. Regions of interest were selected by thresholding, and manually checked and categorised as “PMNs” or “CHOs” based on morphology.

The centroids of the resultant regions of interest were determined using ImageJ (209), and processed in R via the spatstat, sf and sp packages to determine cell count, distribution (via quadrat counts), and distance between CHOs and PMNs (217, 218, 259, 264).

PMN density (total number of cells added) 2 x 106 1 x 106 1 x 105 N=3 N=5 N=5 Mean number of 64.3 36.8 75.2 PMNs in field Mean number of 7.6 13.2 19.4 CHOs in field Mean shortest 16.81 ± 5.45 25.95 ± 5.9 24.4 ± 13.93 CHO-PMN distance Table 4-7 – CHO/PMN density distribution and distances between CHO and PMNs

As a qualitative assessment of the effects of seeding density and incubation, it was evident that within 5-10 minutes of seeding, there was a visible deterioration in the

CHOhNOPGqi5 morphology (Figure 4-9, Figure 4-10).

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Figure 4-9 – Mixed PMNs layered to CHOhNOPGqi5 – demonstrating poor adhesion

Figure 4-10 - Mixed PMNs layered to CHOhNOPGqi5 (white arrow)– demonstrating compartmentalisation, and changes associated with apoptosis. PMNs shown (black arrow) were mobile between images

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4.4 Results 5 - Response of biosensor to mixed immunocyte degranulation 4.4.1 EOL-1 (Eosinophil like) Eosinophil-like cells (EOL-1) are a leukaemia cancer cell line, with similar characteristics to eosinophils. This cell line has been readily used for studies of eosinophils, used to overcome the limitations of low endogenous cell numbers, susceptibility to cell damage and priming during extraction and isolation techniques. Human eosinophils are likely to be a source for N/OFQ based on available published data. EOL-1 cells share many characteristics with human eosinophils and were a readily available and stable cell line for testing and assay optimisation.

CHOhNOPGqi5 cells were cultured and seeded on to coverslips (5000 per well) as described. On the day of experiment, CHOhNOPGqi5 cells were loaded with Fluo-4 and perfused with Krebs-HEPES buffer in a PDMI micro-incubator on the stage of a Nikon T1Si inverted microscope.

EOL-1 cells were harvested, pelleted by centrifugal force, resuspended in ice-cold Krebs- HEPES buffer and counted by haemocytometry. The resultant suspension was added directly to the plate on the microscope stage, whilst recording change in fluorescence of the CHOhNOPGqi5 cells. Fluorescent and brightfield images were merged in order to visualise the (unstained) EOL-1 cells (Figure 4-11).

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Section i – CHOhNOPGqi5 – coincubated with EOL-1 without antagonists 4

Assay development Assay and validation

ii - CHOWT - coincubated with EOL-1 without antagonists 4-195

Figure 4-11 – Representative response of (i) CHOhNOPGqi5 and (ii) CHOWT and cells to coincubation with EOL-1 cells (A/C – basal, B/D – maximal stimulation, white arrows show CHO cells, black arrows show unstained EOL-1 cells)

Section i – CHOhNOPGqi5 – coincubated with EOL-1 in Krebs-HEPES buffer with ii – CHOhNOPGqi5 – coincubated with EOL-1 in Krebs-HEPES buffer PPADS and oATP with PPADS, oATP and SB612111 4

Assay development Assay and validation

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Figure 4-12 – Coincubation of EOL-1 with antagonists , I - CHOhNOPGqi5 + EOL-1 with PPADS and oATP, ii - CHOhNOPGqi5 + EOL-1 with PPADS, oATP, and SB612111 (A/C – basal, B/D – maximal, E – N/OFQ postwash [different region to avoid bleaching]

Section 4 Assay development and validation

Coincubation of CHOhNOPGqi5 with the purinergic antagonists PPADS and oATP, and the NOP antagonist SB612111 in a preliminary experiment provided complete blockade of all fluorescent response, which was reversible after washing with buffer for 10 minutes (Figure 4-12-ii).

4.4.2 Mixed PMN Mixed PMNs were extracted from male and female healthy adult volunteers using a

Polymorphprep™ based extraction technique, and coincubated with CHOhNOPGqi5 cells for 10-15 minutes, in Krebs-HEPES buffer, at 36C, in the absence of any antagonists.

Following adhesion to the Cell-Tak™ coated plates, the PMNs were treated with 10-6 M fMLP to induce granulation. Resultant changes in fluorescence of the CHOhNOPGqi5 cells were recorded. The whole preparation was subsequently treated with 10-6 M N/OFQ as a positive control.

Some of the cells within the field of view responded following fMLP addition, although the response was more pronounced following N/OFQ addition (Figure 4-13).

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Figure 4-13 – Pooled data – response of CHOhNOPGqi5 coincubated with mixed PMN and treated with fMLP and N/OFQ - Points represent % cells with maximal F/F01.8, bars represent mean, and error bars mean ± SD. n=4 separate experiments

There was significant variability in CHO response following fMLP addition when considering all cells present (Figure 4-14). However, examination of single CHO cells, temporally related to stimulated PMNs did show an association between PMN and CHO fluorescence (Figure 4-15, Figure 4-16).

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Figure 4-14 - Results from mixed PMN coincubation with CHOhNOPGqi5 cells and treated with fMLP, representative single experiment - Solid lines represent averages, shaded areas are maxima and minima to demonstrate variability. CHOhNOPGqi5, (blue), PMN (red). F/F0 1.8 is marked

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Section

i- Responses of CHOhNOPGqi5 cells exposed to mixed PMNs (in the presence ii-representative single experiment (PMN – black arrows, CHO – white of fMLP). Cell 16 has a response following degranulation (this is the arrow). A – baseline, B – PMN stimulated, C – CHO stimulated

marked cell in ii) 4

Assay development Assay and validation 4-200

Figure 4-15 – Results from mixed PMN coincubation with CHOhNOPGqi5 cells and treatment with fMLP (i, CHOhNOPGqi5 Mean F/F0 vs time, ii – photomicrograph showing representative single experiment)

Section 4 Assay development and validation

The F/F0 graph of CHO16, and PMN2 shows the sequential increase in fluorescence, initially of the PMN, and subsequently of the CHO.

Figure 4-16 – Change in relative fluorescence of PMN2 and adjacent CHO16

The response of the whole CHOhNOPGqi5 population when exposed to degranulating mixed PMNs is variable, either using average F/F0 and proportion of responders as signal markers. However, on analysis of individual cellular response, some biosensor cells where there are adjacent PMNs have a delayed increase in fluorescence following PMN degranulation (Figure 4-15, Cell 16). This may represent a fluorescent response to substances released by the adjacent PMNs (or a subset of the PMN population).

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4.5 Results 6 - Blockade of purinergic signalling

4.5.1 Response of CHOhNOPGiq cells to purinergic antagonists

A CHOWT fluorescent response was observed following coincubation with EOL-1 cells.

As CHOWT cells are lacking the hNOP receptor, this may have been due to release of other substances from EOL-1 cells to which CHO cells endogenously respond.

Furthermore, the fluorescent response observed in CHOhNOPGqi5 to EOL-1 cells was entirely blocked by coincubation with the NOP antagonist SB612111, and the purinergic antagonists PPADS and oATP (4.4.1).

In addition to their transfects, CHO-K1 cells endogenously express other receptors. Those associated with an increased calcium flux include purinergic (134), thrombin, adrenergic and calmodulin receptors (265). The profile of substances released by EOL- 1 cells is not documented, although it is likely that ATP is released either in a secretory manner, or as a result of cell metabolism, deterioration or apoptosis.

In early studies of CHO-K1, studies of calcium flux, using Fura-2 demonstrated an increased intracellular calcium concentration following exposure to ATP and UTP (134). This response was neither typical of the P2X (ligand gated) or P2Y (GPCR) receptor type.

The CHO-K1 purinergic receptor was initially classified P2U, a GPCR (134, 266) and later reclassified as P2Y2 following genetic sequencing (267). This GPCR may interact with Gq and may be responsible for the observed response (268). Further studies of the CHO- K1 cell line suggests both functional and genetic evidence of expression of a P2X7 receptor, associated with calcium influx in addition to its P2Y2 receptor (269).

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SAR analysis of nucleoside based purinergic ligands has led to the generation of specific agonists and antagonists. However, there is a relative lack of available ligands, complicated by incompatibilities, solubility, toxicity and cost (270). Furthermore, full blockade of P2 receptor subtypes requires a combination of ligands. This is true of the CHO-K1 cell line, which expresses both P2Y2 and P2X7 receptors.

The non-specific purinergic antagonists suramin and reactive blue 2 (266) are antagonists at the P2Y2 receptor. However, both ligands lack potency (266, 270), and suramin directly inhibits Gq coupling activity (271), inhibiting other receptor pathways, including the recombinant hNOPGqi5 biosensor.

Both ARC118925XX and MRS2279 are selective, competitive antagonists at the P2Y2 receptor, although are expensive and lack solubility (272). The synthetic compound PPADS is known to have antagonist activity at P2X1, P2X2, P2X3, P2X5, P2Y2 and P2Y4 receptors (although not at P2X7).

There is a lack of available ligands to block the P2X7 receptor, although oxidised ATP (oATP, Sigma-Aldrich, Dorset, UK) is an irreversible antagonist (with low potency) at this receptor (206).

Therefore, in order to prevent purinergic stimulation upon addition of immune cells to the biosensor, the combination of PPADS and oATP was tested to block both P2X7 and

P2Y2 purinergic receptors in the biosensor CHOhNOPGqi5 cells. In order to conserve immune cells, for repetition and to avoid damaging CHO cells by prolonged exposure, the antagonist response was evaluated in the presence of exogenous ATP, and not from immunocyte addition.

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The nonspecific P2 receptor antagonists PPADS (Tocris, 0625) and oATP (Sigma 71997- 40-5) were evaluated in the final concentrations specified (Table 4-8) against a saturating concentration of 10-6 M ATP.

PPADS oATP ATP

Final 5 x 10-3 M 8 x 10-4 M 1 x 10-6 M concentration

Table 4-8 – Antagonist concentrations

The antagonists PPADS and oATP, prepared in deionised water, were added to the perfusion chamber on the microscope (final concentrations 5 x 10-3 M and 8 x 10-4 M, determined empirically). The preparation equilibrated for 10 minutes prior to addition of ATP, final concentration 1 x 10-6 M. PPADS is bright orange in colour, and therefore the transmitted light from the 488-laser line is completely undetectable, and the fluorescent image degraded. Therefore, in order to record a transmitted light image, the 604-laser line was added. Imaging was limited to reduce the exposure of the live cells to the laser and limit their deterioration.

Addition of the purinergic antagonists oATP and PPADS (800 M and 5 mM) to

CHOhNOPGqi5 resulted in a significantly reduced response to exogenous ATP, 1 M (Figure 4-17), suggesting complete blockade of purinergic signalling under these conditions.

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-6 Figure 4-17 – Response of CHOhNOPGqi5 cells to 10 M ATP in the presence and absence of PPADS and oATP

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4.5.2 PMN ATP Luciferase assay

The non-specific response of both CHOhNOPGqi5 and CHOhNOPWT cells to adjacent EOL-1 cells and mixed PMNs implies release of a ubiquitous compound to which the cells are responding. The known expression of both P2Y2 and P2X7 purinergic subtypes on CHO cells, and its ubiquity makes ATP a likely candidate. As demonstrated above, the ATP response observed in CHOhNOPGqi5 cells was antagonised by exposure to the purinergic antagonists PPADS and oATP (4.5.1).

A series of experiments was conducted in order to determine the final concentration of ATP within the biosensor assay when exposed to EOL-1, and mixed PMN cells.

Mixed PMN cells were isolated from four separate healthy volunteers as described, layered on to 96 well plates, and lysed by exposure to detergent. EOL-1 cells were added to two plates as controls. ATP concentration was determined with reference to a standard curve of the luminosity (RLU) of ATP serially diluted in duplicate (Figure 2-14). Samples included measures of ATP released from lysed PMNs and EOL-1 cells, total n=4 (Figure 4-18).

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Figure 4-18 – ATP concentration released by lysis of cells - Dotted error bars are mean ± SD. PMN: n=4, EOL-1 n=2. Bars represent means

4.6 Discussion and Conclusions Immunocyte extraction and isolation was a challenge due to the low cell numbers and fragility of these cells. Consistent with previous studies, the isolation method affected the yield and purity of cells, with immunomagnetic methods giving greater purity, with slightly lower yields than density gradient methods alone.

As shown in 4.2, isolation of mixed PMNs via Polymorphprep™, and eosinophil and neutrophil subsets via immunomagnetic MACSxpress® gave high yields, purity and viability with minimal cell handling.

Local concentration of ligand at the biosensor cell may be an important variable determining the assay result. Therefore, the cell seeding density was evaluated for both biosensor CHOhNOPGqi5 cells and immunocytes (4.3). There was a significant variation in

4-207 Section 4 Assay development and validation the distance between nearest neighbour, suggesting that the cells were not evenly distributed on the slide, likely due to the distribution of the Cell-Tak™ and a tendency for cells to cluster together. In order to allow adequate space on the slide for PMNs and to prevent clumping, a seeding density of 5000 was used for all subsequent experiments, determined empirically. The local concentration at the biosensor depends on proximity to releasing granulocyte. An optimal count of 1 x 106 cells per application was used, as this gave an adequate number and spread of PMNs with CHOhNOPGqi5 cells, whilst allowing sufficient replicates for average cell yields. The deleterious effects of PMN application to the biosensor (Figure 4-9, Figure 4-10) precluded the use of very high cell numbers, and prolonged adhesion times.

Addition of EOL-1 and PMNs to both CHOhNOPGqi5 cells and to CHOWT cells produced a clear increase in fluorescence, indicating that the cells were being stimulated (4.4). Prior experiments confirmed that this was not injection artefact, as no response was seen to buffer only. CHO-K1 cells endogenously express both P2Y2 and P2X7 purinergic receptor types. The response of the CHOWT cells was likely to be due to purinergic stimulation, either as a result of cell lysis during the extraction process, or through release and degranulation from immunocytes.

Studies of the ATP content of PMNs and EOL-1 cells confirmed this to be in the 10-6 – 10- 5 M range (4.5). When released in close proximity to a CHO cell, this is within the range of concentrations detectable by these cells (3.2). In order to prevent confounding by the release of ATP, a series of studies utilised the purinergic antagonists PPADS, and oATP to block purinergic signalling in CHOhNOPGqi5 cells. A combination of PPADS (5 mM) and oATP (800 M) attenuated the response to ATP and was used for subsequent assays.

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The combination of PPADS and oATP reliably and consistently blocked the CHOhNOPGqi5

CHOhNOPGqi5 response to ATP, confirming that responses seen are due to N/OFQ rather than ATP, released by damaged immune cells. However, ATP acts as a DAMP and immunomodulator, and immune cells are responsive to purinergic ligands via their P2 receptors.

PPADS is a P2X receptor antagonist. It is bright orange and relatively poorly soluble; this impairs detection FLUO-4 fluorescence and causes image degradation. Furthermore, there is evidence that PPADS impairs ADP hydrolysis(273). The significance of this is unclear, although ATP and its breakdown products may have a role in immune activation and regulation. Purinergic receptors are present on immune cells, mediating their response to ATP as a DAMP, and stimulating the formation of the NLRP inflammasome. Blockade of these receptors may therefore modulate the function of the immunocytes used in this assay, altering their sensitivity to fMLP, or their activation status. The effect of purinergic blockade on immune functioning may be a limitation of the current study. The presence of endogenous purinergic receptors on CHO cells is a limitation, mitigated using cell lines without endogenous purinoceptors, or the use of silencing techniques.

The work in this chapter has demonstrated the optimal biosensor and PMN seeding, and concentration of purinergic antagonists for the granulocyte degranulation assay.

Under these conditions, CHOhNOPGqi5 cells respond temporally to PMN degranulation with an increased fluorescence. This is antagonised in the presence of the N/OFQ antagonist SB612111, suggesting that it is specific to N/OFQ release, consistent with the opening hypothesis.

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In summary

 Isolation of PMNs by density gradient separation over Polymorphprep™ produces reliably high yields with high purity and viability  Isolation of untouched neutrophils and eosinophils by MACSxpress® produces cells with very high purity and viability. This technique reduces cell handling and the time for cell deterioration prior to use compared with separation over columns  Isolation of basophils in significant numbers was unfeasible, even when a large volume of blood was used

 CHOhNOP seeding of approx. 5000 cells per plate gave an adequate area for immune cells to adhere without overcrowding

 CHOhNOPGqi5 (and CHOWT) cells show an increased fluorescence following exposure to EOL-1 and mixed PMN cells. This is likely to represent ATP release, from lysed cells or degranulation with effect at endogenous P2Y2 and P2X7 receptors.  The response to mixed PMN degranulation is variable, suggesting that only some of the PMNs are releasing N/OFQ on degranulation. This requires an individual approach, rather than pooled averages of cellular fluorescence

 The CHOhNOPGqi5 response to ATP is attenuated by a combination of the antagonists PPADS (5 mM) and oATP (800 M).

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Chapter 5

Assay application

Section 5 Assay application

5 Assay application

5.1 Background The evidence suggesting N/OFQ release from mixed PMNs is from populations of cultured cells (123). However, there are no studies to date examining the single cell release of N/OFQ from freshly extracted immune cells.

5.1.1 Aims and objectives The aims of this section are to use the optimised assay conditions in order to test the release of N/OFQ from mixed PMN cells obtained from healthy volunteers and to test the hypothesis that when coincubated with PMNs, CHOhNOPGqi5 cells will respond to N/OFQ release from granulocytes with a measurable increase in intracellular calcium concentration antagonised by the NOP antagonist SB612111.

This will be achieved by

 Extraction of mixed PMNs from healthy volunteers using a Polymorphprep™ based extraction technique  Use of the extracted, mixed PMNs in the N/OFQ release assay, with PPADS and oATP to minimise confounding by ATP release

5.2 Results 7 - Effects of mixed PMN degranulation on biosensor cells in the presence of purinergic antagonists 5.2.1 Experimental design

Coverslips coated with Cell-Tak™ were seeded with 5000 CHOhNOPGqi5 cells 24 hours prior to imaging, incubated at 37C in 5% CO2 and maintained in culture media. Mixed PMNs were extracted using Polymorphprep™ according the manufacturer’s instructions (2.6), stored on ice-cold Krebs buffer and used immediately.

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On the day of the experiment, immediately prior to use, CHOhNOPGqi5 seeded, Cell-Tak™ coated coverslips were incubated with Fluo-4-AM dye (Invitrogen, UK) 2.5 g ml-1 for 45 minutes – 1 hour in the dark. The loaded coverslips were transferred to a Harvard Apparatus PDMI perfusion chamber on the stage of a Nikon T1Si inverted confocal microscope at 37C and perfused with Krebs buffer for 5 minutes.

PPADS and oATP were added to the coverslips (final concentration 1 M and 800 M) and allowed to equilibrate for 10 minutes. Mixed PMNs (7.5 x 105 – 1 x 106 absolute count) were carefully seeded on to coverslips, allowed to adhere for 20 minutes followed by washing with Krebs HEPES buffer for 5 minutes to remove debris, nonadherent cells and PMN metabolites.

Subsequent imaging in the presence of antagonists was as per the protocol below (Table

5-1). Mixed coincubated CHOhNOPGqi5 and PMNs were imaged (time-stack, 1 image per 2 seconds, settings as per 2.4.2), and fMLP (1 M) applied after stabilisation of basal fluorescence (30 – 60 sec, until peak).

Following this, the cells were perfused with buffer for 10 minutes. For the positive control experiment, PMNs were imaged (time-stack, 1 image per 2 seconds, settings as per 2.4.2), and N/OFQ (1 M) applied after stabilisation of basal fluorescence (30 – 60 sec, until peak). Perfuse 10

1 mins no– 2 imaging Experiment Degranulation REST – Control Antagonists PPADS (5 mM) None oATP (800 M) Post wash Test ligands fMLP (1 M) N/OFQ (1 M) Table 5-1 – Protocol for degranulation experiments

The acquired images were segmented, and F/F0 determined for all cells at all timepoints.

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5.2.2 Pooled cellular responses

Cells were classified as responsive if maximal F/F0 exceeded 1.8. When co-incubated with mixed PMNs, PPADS and oATP as above, the proportion of CHOhNOPGαqi5 cells responding increases following degranulation, and when treated with N/OFQ as a positive control (Table 5-2).

Responder cells (%) (n=7) Mean ± SD CV fMLP 50.54 ± 33.55 66.60 N/OFQ 58.01 ± 36.37 62.69

Table 5-2 – Summary data showing the proportion of responder CHOhNOPGαqi5 cells coincubated with mixed PMNs following treatment with fMLP or N/OFQ - No significant difference in responsiveness between fMLP and N/OFQ groups

These data show significant variability in response both in the fMLP and N/OFQ treated group (Figure 5-1). However, there is no significant difference in the proportions of responsive cells between the fMLP and N/OFQ stimulated tests (p=0.7, paired samples t-test).

Figure 5-1 – Proportion of responsive CHOhNOPGqi5 cells co-incubated with PMNs and PPADS and oATP (1 M and 800 M) with (left) fMLP (1 M), and with (right) N/OFQ (1 M) - Error bars mean % responders ± SD. n=7. No significant difference by paired t-test (p=0.70)

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Figure 5-2 – Confocal fluorescent response observed in CHOhNOPGqi5 cells co-incubated with fMLP stimulated mixed PMNs (±SB612111) . Error bars represent mean (%) ± SD. PPADS/oATP n=7, +SB n=3

Upon exposure to fMLP stimulated mixed PMNs, in the presence of PPADS and oATP, there is an increased fluorescence of the CHOhNOPGαqi5 cells, reversibly antagonised by the presence of the N/OFQ antagonist SB612111 (Figure 5-2).

These data from populations of cells demonstrate a CHOhNOPGqi5 response when coincubated with fMLP stimulated mixed PMNs, similar to that observed with N/OFQ alone. This response is significantly lower in the presence of the NOP antagonist SB612111 and is fully reversible. However, significant variability in fluorescent response was observed within and between experiments.

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5.2.3 Single cell effects

The significant variation in response observed as fluorescence in the CHOhNOPGqi5 cells was investigated by examining single cell responses of both the PMNs and the biosensor cells (Figure 5-3, Figure 5-4).

fMLP Basal PMN stimulation CHO Maximum

10-6 M ATP Basal PMN stimulation CHO Maximum

10-6 M N/OFQ Basal PMN stimulation CHO Maximum

Figure 5-3 – Representative microscopy field of Fluo-4 loaded CHOhNOPGqi5 cells with PMNs (in the presence of PPADS and oATP) treated with 10-6 M fMLP (A-C), 10-6 M ATP (D-F), and 10-6M N/OFQ (G-I). PMNs are highlighted in red, CHOs in white. A/D/G basal, B = PMN response, E/H = max CHO response, F/I return to basal CHO response

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Assay application Assay

5-217

-6 -6 -6 Figure 5-4 – Representative graphs showing F/F0 corresponding with Figure 5-3. (top) 10 M fMLP, (middle) 10 M ATP, (bottom) 10 M N/OFQ . Solid lines represent averages, shaded areas are maxima and minima to demonstrate variability. CHOhNOPGqi5, (blue), PMN (red). F/F0 1.8 is marked

Section 5 Assay application

-6 When Fluo-4 loaded CHOhNOPGqi5 cells are layered with PMNs and treated with 10 M fMLP in the presence of oATP and PPADS (Figure 5-3 A-C, Figure 5-4, top), there is a rapid increase in fluorescence observed in the PMNs (red), followed by a selective increase in F/F0 in the CHO cells with a variable profile.

When the same field is later treated with 10-6 M ATP (Figure 5-3 D-F, Figure 5-4 middle), there is a minimal CHOhNOPGqi5 response, indicating that the initial response is not due to purinergic stimulation. Subsequent treatment with 10-6 M N/OFQ (Figure 5-3 G-I, Figure 5-4 C) produces a larger CHO response – however this is attenuated due to the deleterious effects of the PMNs and bleaching from repeated imaging.

Repeating this experiment in the presence of the NOP antagonist SB612111 attenuates the response observed. In Figure 5-5 A-C, the fluorescent response of the (yellow) CHO cells following PMN (red) stimulation by fMLP in the presence of the NOP antagonist

SB612111 is minimal (graphed in Figure 5-6, top), and does not reach the F/F0 threshold of 1.8. Figure 5-5 D-F demonstrates how, after perfusing in Krebs HEPES buffer for 5 minutes to remove the antagonist, the CHO cells (yellow) showed a significant response to 10-6M N/OFQ (graphed in Figure 5-6, bottom).

5-218 Section 5 Assay application

CHOhNOPGqi5 + oATP + PPADS + SB612111 + fMLP

Basal PMN CHO maximum

CHOhNOPGqi5 Postwash + N/OFQ

Basal CHO maximum CHO basal

-6 Figure 5-5 – Response of CHOhNOPGαqi5 + oATP + PPADS + 10 M SB612111 to fMLP (A-C), and, after 5 minutes perfusion with Krebs buffer to N/OFQ (D-F). A/D – basal, B – PMN stimulation, C/E – CHO maximal, F – CHO return to basal.

5-219 Section 5 Assay application

-6 Figure 5-6 – Representative graphs showing F/F0 corresponding with Figure 5-5 . (top) 10 M fMLP in the presence of SB612111, (bottom) 10-6 M N/OFQ after washing . Solid lines represent averages, shaded areas are maxima and minima to demonstrate variability. CHOhNOPGqi5, (blue), PMN (red). F/F0 1.8 is marked. PMNs not shown for N/OFQ plot B. Outliers excluded (see text)

Figure 5-6 (top) shows that, despite a PMN response to 10-6 M fMLP, there is a minimal CHO response only, in the presence of the NOP antagonist SB612111. After washing off SB612111, the CHO response is restored (bottom). The significant mobility of the PMNs created several artificially high F/F0 values (>200), which were excluded.

5.3 Discussion and Conclusions

Treatment of co-incubated populations of Fluo-4 loaded CHOhNOPGαqi5 cells and mixed PMNs with fMLP and N/OFQ in the presence of the purinergic antagonists oATP and PPADS causes an increase in fluorescence, initially of the PMNs, followed by the

CHOhNOPGαqi5 cells. This response is attenuated in the presence of the NOP antagonist SB612111.

5-220 Section 5 Assay application

The responses observed are variable, in time, maximal fluorescence and proportion of responder cells. Variable fluorescence in response to a standard N/OFQ stimulus is discussed in section 3.4, and these factors of diffusion, differences in loading, and receptor expression are important variables which may explain variation observed in the PMN degranulation assay. The variability observed may explain the failure of a statistically significant difference in the proportion of responsive cells across groups (when comparing PPADS+oATP, NOFQ alone, PPADS+oATP+SB612111, N/OFQ post wash), despite a clearly attenuated response in the PPADS+oATP+SB612111 group.

The characteristics of PMNs may introduce further variability over and above that observed when testing with a fixed concentration of N/OFQ, both related to the intrinsic properties (cell cycle state, metabolism and storage of N/OFQ, activation) and the properties of the assay (cell distribution). Temporal variation in CHO response may be related to the distribution of PMNs relative to biosensor cells. This cannot be fully evaluated using the confocal microscope as the pinhole effectively excludes out of focus fluorescence from other focal planes. Therefore, the origin of the N/OFQ to which a biosensor is responding may not always be visualised.

The combined effects of co-incubation of CHOhNOPGαqi5 with PMNs and confocal laser imaging causes bleaching, deterioration in the signal and cellular apoptosis, another possible source of variation.

However, as demonstrated PPADS and oATP in the concentrations used effectively prevent purinergic signalling. The clear temporal relationship between fMLP stimulation, PMN degranulation and biosensor response is suggestive that N/OFQ is released by the PMN cells. This relationship is further strengthened by the lack of CHO

5-221 Section 5 Assay application response to PMN degranulation in the presence of the NOP antagonist SB612111. The observed response is due to N/OFQ and not due to the physical stimulation caused by pipetting (as shown in section 3.3).

The relatively small proportion of CHOhNOPGαqi5 cells responding following PMN degranulation may indicate that only some of the mixed PMNs are releasing N/OFQ, consistent with the hypothesis that this is due to a subpopulation of immunocytes.

In summary

 1 M fMLP stimulation of PMNs coincubated with CHOhNOPGαqi5 cells and in the presence of 1 M and 800 M PPADS and oATP causes an increase fluorescence first of PMNs, followed by CHO cells.  The observed response is attenuated by the presence of the N/OFQ antagonist SB612111. This attenuation is reversible by washing with Krebs buffer for 5 minutes.  CHO response is not uniform across the field of vision, and may be associated with PMN subtypes, or regional density.  This demonstrates the release of N/OFQ from mixed PMNs, stimulating adjacent

CHOhNOPGαqi5 biosensor cells.

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Chapter 6

Further application and confirmatory testing

Section 6 Further application and confirmatory testing

6 Further application and confirmatory testing

6.1 Background As demonstrated in Chapter 5, the fluorescence of a proportion of Fluo-4 loaded

CHOhNOPGαqi5 cells increased when coincubated with fMLP stimulated mixed PMNs from healthy volunteers. This was attenuated by the NOP antagonist SB612111 and was not related to artefact from injection or ATP. There was significant variability in the fluorescent response, related to the properties of the assay (cell density around

CHOhNOPGqi5 cells, optical plane), or to the PMNs (cell type, activation state, cell cycle state, or N/OFQ storage and metabolism).

Chapter 1 examines the evidence supporting an increase in N/OFQ concentrations in the plasma of patients with inflammatory conditions. Observed coexistent increases in both N/OFQ concentration in plasma and tissue fluids and immunocyte number may be due to primary release from immunocytes or due to chemotactic and pro-inflammatory effects of N/OFQ.

Where N/OFQ may be produced and released by granulocytes, the releasing cell type may be basophils, eosinophils, neutrophils, or a combination of these. However, the basophil count within a mixed PMN sample is so low that they are unlikely to be responsible for the observed response. As demonstrated in Chapter 4, basophil isolation and testing would require a significant volume of blood (>30mls), and therefore this was deemed unfeasible.

The role of the N/OFQ-NOP system in eosinophils and neutrophils from healthy volunteers (both native and with induced sepsis), and from patients with sepsis was investigated using a combination of the N/OFQ release assay, immunohistochemistry, and PCR.

6-224 Section 6 Further application and confirmatory testing

Eosinophils and neutrophils were extracted from healthy volunteers and patients admitted to the intensive care unit with a diagnosis of sepsis. Following extraction and isolation, immunocytes may have a short lifespan and abnormal physiology in functional assays (223). Therefore, for real-time testing in the granulocyte isolation assay, native cells from healthy volunteers and patients with sepsis were used immediately. For PCR and immunohistochemistry based tests, immunocytes were incubated overnight, half of each exposed to LPS/PepG, and half in plain RPMI media to facilitate recovery and re- synthesis of N/OFQ, NOP and the RNA precursors ppNoc and NOP, and to expose to a supramaximal stimulus mimicking sepsis to mitigate any attenuation.

6.1.1 Aims and objectives The aim of the work described in this section is to test for N/OFQ-NOP expression and synthesis from PMN subsets (neutrophils and eosinophils) to test the hypothesis that the nociceptin system is modulated in one or more immunocyte subsets in sepsis as evidenced by differences in N/OFQ release, immunohistochemistry and PCR.

This will be achieved by

 Extraction and isolation of eosinophil and neutrophil subsets from healthy volunteers and patients with a clinical diagnosis of sepsis  Simulating the effects of sepsis on immunocytes by exposing cells extracted from healthy volunteers to the proinflammatory substances LPS and PepG, mimicking sepsis  Testing eosinophils and neutrophils from healthy volunteers, and from patients with sepsis in the granulocyte N/OFQ release assay  Immunohistochemical staining of eosinophils and neutrophils from healthy volunteers (native and in an environment mimicking sepsis), and from patients with sepsis for N/OFQ and NOP

6-225 Section 6 Further application and confirmatory testing

 PCR of eosinophils and neutrophils from healthy volunteers (native and in an environment mimicking sepsis), and from patients with sepsis for RNA encoding ppNoc and NOP

6.2 Comparing the release of N/OFQ from granulocyte subsets isolated from healthy volunteers and patients with sepsis using a biosensor- based assay

Differences reported in the expression of NOP, and its endogenous ligand, N/OFQ support the hypothesis that the N/OFQ-NOP system may be modulated in inflammatory conditions such as sepsis (1.4.4). The significant variability in CHOhNOPGαqi5 response observed to mixed PMN degranulation suggests that a subpopulation of granulocytes may be responsible for N/OFQ release. Studies detecting increased N/OFQ in the sputum of patients with asthma (274), and in fMLP stimulated neutrophils (123) suggest that eosinophils and neutrophils may be the source of the observed N/OFQ.

Ethical approval (Appendix – Ethical approvals (NHS)) was obtained in order to harvest and separate eosinophils and neutrophils from healthy volunteers and patients diagnosed with sepsis. These granulocytes were tested in order to compare the release of N/OFQ from these cells and investigate the presence of genetic and peptide material encoding N/OFQ and NOP within the target cells.

6.2.1 Results 8 – Participant characteristics and cell yields 6 patients and 8 healthy volunteers were recruited to the study between April 2016- June 2019 (Table 6-1). The mean ages of the characteristics of healthy volunteers (healthy group) and participants with a clinical diagnosis of sepsis (septic group) differed significantly (by 41 years). Similarly, there was a difference in sex ratios between the groups.

6-226 Section 6 Further application and confirmatory testing

Mean (range) Group n M:F Age Volume bled (mls) Healthy 8 2:6 34.5 (26-47) 22.5 (12-24) Volunteers Septic 6 3:3 75.5 (55-85) 24 ± (24-24) Total 14 5:9 52.1 (26-85) 23.1 ± (12-24) Table 6-1 – Participant characteristics

Participants with a clinical diagnosis of sepsis were recruited within 48 hours of admission to the intensive care unit at the Leicester Royal Infirmary. Composite physiological illness severity scores (APACHE-2 and SOFA) were calculated for these patients and are presented (Table 6-2) with selected individual markers of illness (white cell count, blood lactate concentration on the first arterial blood gas after admission to ICU). The 30-day mortality in this group was 50%. n=6 Mean (range) SOFA (Sequential Organ Failure Assessment) 9.33 (5-12) APACHE-2 (Acute Physiology and Chronic Health Evaluation) 19.8 (6-31) Lactate (mmol l-1) 2.47 (1.1-3.6) White Cell Count (x 109 l-1) 16.9 (6.9-29) Age (years) 75.5 (55-85) Table 6-2 – Characteristics of participants diagnosed with sepsis

Blood was obtained by venepuncture as described and separated by MACSxpress® immunomagnetic protocols into eosinophils and neutrophils. Yields, viability, and derived initial cell count are as shown below (Table 6-3). Purity of the separation technique was assessed in 4.2, and was not repeated during this series of experiments.

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Further application and Further confirmatorytesting

Eosinophils Neutrophils Mean (range) Mean (range) Group Raw Count Corrected Initial Viability (%) Count Corrected Initial Viability (%) (x107 cells ml-1) count (x107 cells ml-1) count (x106 cells ml-1) (x106 cells ml-1) Healthy 2.57 1.61 99.37 1.52 2.05 ± 0.77 98.74 Volunteers (8.5 – 39.0) (0.53-2.44) (98.4-100) (0.75-2.72) (0.94-3.40) (93.3-100) (n=8)

6-228 Septic 1.57 0.98 96.92 4.06 5.07 ± 1.43 97.89 (n=6) (6.4-30.4) (0.4-1.9) (90.9-100) (2.66-5.12) (3.33-6.40) (95.5-100) Table 6-3 – Characteristics of cell separations (n=12 – full data unavailable for 2 experiments).

Section 6 Further application and confirmatory testing

Of the extracted cells, half were incubated overnight at 5% CO2, 37C split equally between plain RPMI media, an induced sepsis environment (LPS / PepG) for imaging by immunohistochemistry and PCR. The remaining cells not incubated were placed on ice and used immediately for the N/OFQ live cell release assay.

As the immunocyte yields were low, samples were divided as in Table 6-4 in order to maximise the utility of the tests within the limits of the blood available.

Participant Live cell Immunohistochemistry PCR HV1    HV2    HV3    HV4    HV5    HV6  HV7  HV8  SEP1  SEP2    SEP3  SEP4  SEP5    SEP6    Table 6-4 – Sample usage

6.2.2 Results 9 – N/OFQ release assay

The following data show a comparison of the CHOhNOPGqi5 response to eosinophil (EO) and neutrophil (N) degranulation, from patients with sepsis, and healthy volunteers. Purity of the separation technique was assessed in 4.2, and was not repeated during this series of experiments.

6-229 Section 6 Further application and confirmatory testing

Even in the presence of Cell-Tak™, the time taken for immunocyte adhesion frequently led to biosensor deterioration. Therefore, to avoid this, limiting exposure, fluorescent responses of CHOhNOPGαqi5 cells were recorded during addition of the immunocytes (drop-on), and in response to fMLP stimulation (fMLP), both in the presence of the purinergic antagonists PPADS and oATP. Where a positive response was observed, the experiment was repeated with the addition of the NOP antagonist SB612111. To avoid repeated imaging, bleaching, and deterioration of the biosensor cells through prolonged exposure to immunocytes, experiments were repeated using fresh coverslip preparations. Poor adhesion prevented monitoring of PMN response to fMLP, as the immunocytes were mobile, and the short incubation time prevented adequate uptake of the Fluo-4 dye by the PMNs (therefore these cells were visible via transmitted light imaging, but it was not possible to observe fluorescent responses). The fluorescent absorption of the PPADS further degraded the image quality.

N/OFQ control responses were recorded before and after addition of immunocytes.

Where the peak F/F0 exceeded the threshold of 1.8, the CHO cell was classified as a responder.

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Section Without N/OFQ antagonist Cell* Test† Total number of responder Total number of Percentage responders Mean percentage

‡ 6

cells nonresponder cells (%) responders • application and Further confirmatorytesting Max F/F0≥1.8 Max F/F0<1.8 mean (range) EO D 24 84 22.22 25.13 (0-90) EO F 8 100 7.41 10.71 (0-15.99) EO N 76 133 36.36 39.97 (0-100)

N D 36 66 35.29 32.34 (0-62.50) N F 11 112 8.94 11.57 (0-40.00) Healthy (N=5) Healthy N N 38 163 18.91 17.07 (0-88.89) EO D 26 48 35.14 34.26 (7.69-83.33) EO F 10 64 13.51 14.78 (7.69-33.33) 6-231 EO N 56 84 40.00 44.17 (9.09-100)

N D 15 49 23.44 25.73 (8.33-72.73)

Septic (N=3) Septic N F 9 52 14.75 19.73 (6.25-60.00) N N 67 51 56.78 48.67 (9.09-100.00) * Table 6-5 – Responses of CHOhNOPGαqi5 cells to neutrophils and eosinophils from healthy volunteers and patients with sepsis in the absence of SB612111 . EO – Eosinophil, N – Neutrophil (purity assessed in 4.2); †D-drop on, F – fMLP stimulated, N – N/OFQ control; ‡% responders overall – pooled data across replicates (all responders  all cells); •Mean % responders (average of all % responses per experiment)

Section

With N/OFQ antagonist (SB612111) application and Further confirmatorytesting Cell* Test† Total number of responder Total number of Percentage responders Mean percentage cells nonresponder cells (%)‡ responders • Max F/F0≥1.8 Max F/F0<1.8 mean (range) EO D 14 84 14.29 17.87 (0-42.86) EO F 6 100 5.66 8.00 (0-28.57) EO N 41 124 24.85 31.78 (0-100)

N D 11 75 12.79 12.88 (0-62.5) N F 10 68 12.82 12.97 (0-40.0) Healthy (N=5) Healthy N N 48 109 30.57 31.68 (0-88.89) 6-232 EO D 1 16 5.88 5.88 (5.88-5.88) EO F 1 16 5.88 5.88 (5.88-5.88) EO N 18 16 52.94 52.94 (17.64-88.24)

N D 2 21 8.70 8.71 (8.33-9.09)

Septic (N=3) Septic N F 2 21 8.70 8.71 (8.33-9.09) N N 17 29 36.96 36.93 (8.33-66.67) * Table 6-6 - Responses of CHOhNOPGαqi5 cells to neutrophils and eosinophils from healthy volunteers and septic patients in the presence of SB612111 . EO – Eosinophil, N – Neutrophil; †D-drop on, F – fMLP stimulated, N – N/OFQ control; ‡% responders overall – pooled data (all responders  all cells); •Mean % responders (average of all % responses per experiment)

Section 6

Further application and Further confirmatorytesting 6-233

Figure 6-1 – Responses of CHOhNOPGαqi5 cells to neutrophils and eosinophils from healthy volunteers and patients with a diagnosis of sepsis in the presence and absence of SB612111 (note N/OFQ + SB is after washing off SB612111 as a positive control)

Section 6 Further application and confirmatory testing

As shown in Table 6-5, Table 6-6 and Figure 6-1, there was significant variability in the responses of CHOhNOPGαqi5 cells following exposure to neutrophils and eosinophils taken from patients diagnosed with sepsis, and from healthy volunteers.

Coincubation of CHOhNOPGqi5 cells separately with neutrophils and eosinophils from healthy individuals and those diagnosed with sepsis was associated with an increased fluorescent response in the biosensor. The observed responses were, in general, attenuated by coincubation with the N/OFQ antagonist SB612111, suggesting that this response is due to N/OFQ rather than physical stimulation or the release of other mediators. However, the blockade observed with SB612111 was variable, due to the limitations of the assay.

Representative confocal photomicrographs (merged transmitted light and fluorescence) and corresponding graphs demonstrating fluorescent changes in

CHOhNOPGαqi5 cells are shown in Figure 6-2, Figure 6-3, Figure 6-4 and Figure 6-5 for a neutrophil sample from a patient diagnosed with sepsis (SEP5) (see Table 6-7) and explained below.

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Section Experiment Conditions Figure Graph N/OFQ control without antagonists - Figure 6-2 A, B Figure 6-2 (top)

5 6

Addition of 5 x 10 neutrophils PPADS, oATP Figure 6-2 C, D Figure 6-2 (bottom) Further application and Further confirmatorytesting Addition of fMLP PPADS, oATP Figure 6-3 A, B Figure 6-3 (top) Neutrophils N/OFQ control without antagonists (post wash) - Figure 6-3 C, D Figure 6-3 (bottom) N/OFQ control without antagonists - Figure 6-4 A, B Figure 6-4 (top) 5

Neutrophils Neutrophils Addition of 5 x 10 neutrophils PPADS, oATP, SB612111 Figure 6-4 C, D Figure 6-4 (bottom) Addition of fMLP PPADS, oATP, SB612111 Figure 6-5 A, B Figure 6-5 (top) Neutrophils N/OFQ control without antagonists (post wash) - Figure 6-5 C, D Figure 6-5 (bottom) Addition of 5 x 105 eosinophils PPADS, oATP Figure 6-6 A,B Figure 6-6 (top) Addition of fMLP PPADS, oATP Figure 6-6 C, D Figure 6-6 (bottom) 6-235 Eosinophils Addition of 5 x 105 eosinophils PPADS, oATP, SB612111 Figure 6-7 A,B Figure 6-7 (top)

Addition of fMLP PPADS, oATP, SB612111 Figure 6-7 C,D Figure 6-7 (bottom) Eosinophils Eosinophils Eosinophils

N/OFQ control without antagonists (post wash) - Figure 6-8 A, B Figure 6-8 (top) Table 6-7 – Key to representative figures of live cell N/OFQ release assay and controls

Section 6 Further application and confirmatory testing

Following exposure to 10-6M N/OFQ, there was a modest increase in fluorescence

-5 observed in the CHOhNOPGαqi5 cells (Figure 6-2 – A, B, top). Addition of 5 x 10 neutrophils in the presence of the purinergic antagonists PPADS and oATP caused a variable increase in CHO cell fluorescence (Figure 6-2 – C, D, bottom). Increases in CHOhNOPGqi5 fluorescence also occurred following treatment of the neutrophil/biosensor preparation with fMLP (Figure 6-3, A, B, top) and 10-6 M N/OFQ (Figure 6-3, C, D, bottom). Little or no fluorescent response was observed in response to addition of 5 x 105 neutrophils (Figure 6-4, C, D, bottom), nor to fMLP treatment in the presence SB612111 (Figure 6-5, A, B, top), although the response to N/OFQ was completely restored after washing to remove the NOP antagonist (Figure 6-5, C, D).

The observed CHOhNOPGqi5 response was variable – both the range of fluorescent responses within assays (for example Figure 6-2 graphs) and between assays (Figure 6-1). Optical artefact in fluorescent signal is caused by the mobile immunocytes and attenuation by the strong colouration of the PPADS. The responses of the CHO cells were attenuated over time, due to bleaching or to the effect of the coincubation with immunocytes, which resulted in a lower observed F/F0.

For quantitative analysis, the “responder” threshold of F/F0≥1.8 was used, although this is likely to result in an underestimate of responsive cells due to the signal degradation. This may explain the variable, and low numbers of responsive cells seen. Furthermore, the production and release of N/OFQ as a dynamic process may be influenced by a stress response, such as the extraction process. Observed N/OFQ release may be affected by intracellular stores – an apparent lack of N/OFQ may represent N/OFQ depletion by prior degranulation (or a lack of synthesis). As sepsis is a heterogenous condition, and the patients in this study were heterogenous, with an indeterminate time of onset of illness, this is difficult to characterise. Furthermore, it is difficult to exclude confounding by the effects of immunocyte isolation and extraction which may mask any significant differences between those with sepsis and healthy volunteers.

6-236 Section 6 Further application and confirmatory testing

However, even accounting for this, there are instances where CHOhNOPGαqi5 cells do exhibit a significant fluorescent response to degranulation of both eosinophils and neutrophils, with minimal responses in the presence of the NOP antagonist SB612111, suggesting that both cell types store and release N/OFQ.

6-237

Section Basal Maximal 6

Further application and Further confirmatorytesting N/OFQ N/OFQ 6-238

Neutrophils Neutrophils

Figure 6-2 – Representative photomicrographs (left), and (right) graphs of F/F0 responses of Fluo-4 stained CHOhNOPGαqi5 cells. (A, B, top graph) Basal, maximal and response to N/OFQ, (C, D, bottom graph) Basal, maximal and response to addition of 5 x 105 neutrophils in the presence of PPADS and oATP (Sample = SEP5). Solid lines represent averages, shaded areas are maxima and minima to demonstrate variability. CHOhNOPGqi5, (blue). F/F0 1.8 is marked

Section Basal Maximal 6

Further application and Further confirmatorytesting Neutrophils fMLP + Neutrophils 6-239

N/OFQ N/OFQ

5 Figure 6-3 - Representative photomicrographs (left) and graphs (right) of F/F0 responses of Fluo-4 stained CHOhNOPGαqi5 cells and 5 x 10 neutrophils. (A, B, top graph) Basal, maximal and response to fMLP in the presence of PPADS and oATP, (C, D, bottom graph) Basal, maximal and response to addition of 10-6M N/OFQ post wash (Sample = SEP5). Solid lines represent averages, shaded areas are maxima and minima to demonstrate variability. CHOhNOPGqi5, (blue). F/F0 1.8 is marked

Section

Basal Maximal 6

Further application and Further confirmatorytesting N/OFQ N/OFQ 6-240

Neutrophils+ SB Neutrophils+

Figure 6-4 - Representative photomicrographs (left), and graphs (right) of F/F0 responses of Fluo-4 stained CHOhNOPGαqi5 cells. (A, B, top graph) Basal, maximal and response to N/OFQ, (C, D, bottom graph) basal, maximal and response to addition of 5 x 104 neutrophils in the presence of PPADS, oATP and SB612111 (Sample = SEP5). Solid lines represent averages, shaded areas are maxima and minima to demonstrate variability. CHOhNOPGqi5, (blue). F/F0 1.8 is marked

Section Basal Maximal 6

Further application and Further confirmatorytesting Neutrophils+fMLP+SB Neutrophils+fMLP+SB 6-241

N/OFQ (post-wash) N/OFQ

5 Figure 6-5 - Representative photomicrographs (i), and (ii) graphs of F/F0 responses of Fluo-4 stained CHOhNOPGαqi5 cells and 5 x 10 neutrophils. (A, B, top graph) basal, maximal and response to fMLP in the presence of PPADS, oATP and SB612111, (C, D, bottom graph) Basal, maximal and response to addition -6 10 M N/OFQ post wash (Sample = SEP5). Solid lines represent averages, shaded areas are maxima and minima to demonstrate variability. CHOhNOPGqi5, (blue). F/F0 1.8 is marked

Section 6 Further application and confirmatory testing

Similar results were obtained from eosinophils. The following series of images shows a representative addition of 5 x 105 eosinophils and fMLP (Figure 6-6), and the same experiment repeated in the presence of the NOP antagonist SB612111, followed by stimulation with 10-6 M N/OFQ after washing for 5 minutes to remove the antagonist.

Upon addition of the eosinophils, there is a global increase in fluorescence as the eosinophils move across the field of view. However, when stimulated with fMLP, there is a focused increase in fluorescence of a single CHO cell with a cluster of eosinophils adjacent to it (Figure 6-6, D). There is no response when eosinophils are added and stimulated in the presence of SB612111 (Figure 6-7), which is reversed after washing the antagonist (Figure 6-8).

The CHOhNOPGqi5 fluorescent response observed following addition and fMLP stimulation of eosinophils and neutrophils from healthy volunteers followed a similar pattern, although was attenuated (Figure 6-1).

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Section Basal Maximal 6

Further application and Further confirmatorytesting Eosinophils Eosinophils 6-243

Eosinophils fMLP + Eosinophils

5 Figure 6-6 - Representative photomicrographs (left), and (right) graphs of F/F0 responses of Fluo-4 stained CHOhNOPGαqi5 cells and 5 x 10 eosinophils. (A, B, top graph) Basal, maximal and response to addition 10-6M fMLP in the presence of oATP and PPADS (C, D, bottom graph) (Sample = SEP3) . Solid lines represent averages, shaded areas are maxima and minima to demonstrate variability. CHOhNOPGqi5, (blue). F/F0 1.8 is marked

Section Basal Maximal 6

Further application and Further confirmatorytesting Eosinophils SB + Eosinophils 6-244

Eosinophils + fMLP + SB fMLP+ + Eosinophils

5 Figure 6-7 - Representative photomicrographs (left), and (right) graphs of F/F0 responses of Fluo-4 stained CHOhNOPGαqi5 cells and 5 x 10 eosinophils in the presence of SB612111. (A, B, top graph), Basal, maximal and response to addition 10-6M fMLP in the presence of SB612111, oATP and PPADS (C, D, bottom graph) (Sample = SEP3) . Solid lines represent averages, shaded areas are maxima and minima to demonstrate variability. CHOhNOPGqi5, (blue). F/F0 1.8 is marked

Section Basal Maximal 6

Further application and Further confirmatorytesting N/OFQ (post-wash) N/OFQ

6-245

-6 Figure 6-8 - Representative photomicrographs (left), and (right) graphs of F/F0 responses of Fluo-4 stained CHOhNOPGαqi5 cells to 10 M N/OFQ following washing to remove SB612111. (A, B, graph) (Sample = SEP3). Solid lines represent averages, shaded areas are maxima and minima to demonstrate variability. CHOhNOPGqi5, (blue). F/F0 1.8 is marked

Section 6 Further application and confirmatory testing

These data demonstrate a biosensor response to addition of both eosinophils and neutrophils from healthy volunteers, with an attenuated response observed in the presence of SB612111.

The biosensor also responded to neutrophils and eosinophils extracted from patients with a clinical diagnosis of sepsis.

6.2.3 Results 10 – Immunohistochemistry Eosinophils and neutrophils were extracted as described above, and incubated overnight at 37C, 5% CO2, in plain RPMI media, or RPMI with LPS / PepG in an environment mimicking sepsis.

Eosinophils and neutrophils were harvested after 24hrs (pseudoseptic and healthy for each cell type). Half of each group were utilised for immunohistochemistry, seeded on to Cell-Tak™ coated coverslips and stained as per protocol (2.4.3, Table 2-8). Due to the low yields for PCR, samples were divided as in Table 6-4 (some samples were used exclusively for PCR). Purity of the separation technique was assessed in 4.2, and was not repeated during this series of experiments.

Immunohistochemistry demonstrated variable expression of NOP and N/OFQ between healthy volunteers (in healthy immunocytes and those exposed to LPS and PepG), and patients admitted with sepsis (Table 6-8).

For each coverslip, a whole field image (Figure 6-9) was obtained using the 30x objective only and no additional magnification in order to identify cells of interest and observe staining (DAPI, FITC and Texas Red channels). Individual cells were identified and

6-246 Section 6 Further application and confirmatory testing subject to digital magnification for subsequent still (Figure 6-11) and z-stacked (Figure 6-12) images.

Figure 6-9 – Representative whole field images stained for CD16/CCR3-VioBlue, Anti-N/OFQFITC and N/OFQATTO594 from A) Neutrophils and B) Eosinophils

Of 22 merged images, there were 10 discrepancies, resolved by discussion and mutual agreement. The results are shown in Table 6-8, and Figure 6-10.

6-247

Section Eosinophils Neutrophils -LPS/PepG +LPS/PepG* -LPS/PepG +LPS/PepG* 6

native native Further application and Further confirmatorytesting # N/OFQATTO594 Anti- N/OFQATTO594 Anti- N/OFQATTO594 Anti- N/OFQATTO594 Anti- N/OFQFITC N/OFQFITC N/OFQFITC N/OFQFITC HV1 2 2 2 2 1 2 0 2 HV2 1 2 2 2 0 1 0 1 HV3 1 2 1 2 0 1 1 2 HV4 1 1 2 2 0 1 1 1 HV5 1 2 0 1 1 2 2 2 SEP2 2 0 1 2 1 2 1 2 SEP5 2 1 1 0 1 0 0 0 6-248 SEP6 2 1 1 0 2 0 2 2 Table 6-8 – Qualitative immunohistochemistry results . 2 - strong signal, 0 - no signal, 1 - equivocal signal; *Cells exposed to LPS / PepG for 12-18 hours in

RPMI media

Section 6 Further application and confirmatory testing

Figure 6-10 –Expression of N/OFQ and NOP in native eosinophils and neutrophils and those exposed to LPS / PepG assessed by immunofluorescence (by anti-N/OFQFITC binding), and NOP (by N/OFQATTO594 binding). Expression scale 0 – no binding (red), 1 – equivocal (green), 2 – strong binding (blue). Highlighted points represent median values

6-249

Section A CD16-VioBlue D 6

N/OFQATTO594 Further application and Further confirmatorytesting

Anti-

N/OFQFITC

B CD16-VioBlue

Anti-N/OFQ

6-250 Anti-

N/OFQFITC

DAPI – Blue C CD16-VioBlue Anti-N/OFQFITC – Green

N/OFQATTO594 N/OFQATTO594 - Red SB612111

Figure 6-11- Representative immunohistochemical staining of neutrophils(participant SEP5), showing strong N/OFQ-FITC binding (A, D, green), limited Anti- N/OFQFITC binding (B, green, in the presence of competing unlabelled anti-N/OFQ), and no N/OFQATTO594 binding in the presence of competing SB612111 (C, green).

Section 6 Further application and confirmatory testing

Z-stack images were obtained for representative samples of eosinophils and neutrophils. Selected slices are shown in Figure 6-12, highlighting (A, C) Healthy volunteer eosinophil with internalised (white arrow) NOP receptor, and clusters of N/OFQ adjacent to the membrane (black arrow – A, C), and in internal vesicles from healthy volunteers (black arrow, C). N/OFQ was also demonstrated in healthy neutrophils by binding of anti-N/OFQ-FITC (B).

Eosinophils Neutrophils

A B

Figure 6-12 – Selected Z-stack slices from Anti-N/OFQFITC (green), N/OFQATTO594 (red) and CCR3- BioBlue/CD16-VioBlue (blue) stained eosinophils and neutrophils . (black arrows – Anti- N/OFQFITC, white arrows – N/OFQATTO594).

The qualitative results (Table 6-8, Figure 6-10), suggest that eosinophils constitutively express both N/OFQ and NOP when sampled from healthy volunteers, patients with

6-251 Section 6 Further application and confirmatory testing sepsis and after exposure to LPS/PepG. The only category with limited evidence of N/OFQ expression were eosinophils treated with LPS/PepG from patients diagnosed with sepsis. In general, the expression of both NOP and N/OFQ was reduced in eosinophils from patients diagnosed with sepsis, compared to those from healthy volunteers. This may indicate receptor turnover, internalisation, and recycling, and provide a suggestion of a mechanistic link between sepsis, inflammation and N/OFQ- NOP.

Native neutrophils, in contrast to eosinophils showed weak binding of N/OFQATTO594 to

NOP sites when sampled from healthy volunteers. Binding of N/OFQATTO594 to eosinophil binding sites increased in those sampled from patients with sepsis, and to healthy native neutrophils exposed to LPS/PepG. This suggests that the receptor may be upregulated in both eosinophils and neutrophils in the presence of an environment mimicking sepsis.

Anti-N/OFQFITC appeared to bind strongly to almost all samples from healthy volunteers but was reduced in native neutrophils taken from patients diagnosed with sepsis.

These data, although variable, are suggestive of a stepwise increase in N/OFQATTO594 binding and a fall in anti-N/OFQFITC binding to eosinophils and neutrophils from healthy volunteers, exposed to an environment mimicking sepsis, and from patients diagnosed with sepsis.

6-252 Section 6 Further application and confirmatory testing

6.2.4 Results 11 – PCR Samples for PCR were utilised from 14 participants. One sample (HV4) had insufficient RNA to proceed with analysis and was excluded from further analysis.

Expression of housekeeper genes (GADPH, B2M, HRT1, ELF1 and ELF2B1) was analysed from the samples using the GeneNorm algorithm(248) (Table 6-9). The most stable single gene was ELF1 (stability value 0.046). The geometric mean of the combined ELF1 and ELF2B1 genes (stability value 0.034) were used as housekeepers for sample analysis to calculate Ct values.

Gene name Stability value GAPDG 0.089 B2M 0.114 HPRT1 0.088 ELF1 0.046 ELF2B1 0.057 Table 6-9 – Gene stability as assessed using the GeneNorm algorithm

After extraction, RNA was quantified using a NanoDrop™ spectrophotometer. Yields are shown in Table 6-10.

Target cell Sample type RNA yield mean ± SD (g l-1) Eosinophil Control 0.17 ± 0.30 Eosinophil Pseudoseptic 0.13 ± 0.17 Neutrophil Control 0.17 ± 0.27 Neutrophil Pseudoseptic 0.18 ± 0.28 Table 6-10 – mRNA yields following NanoDrop quantification mRNA was analysed for both ppNoc and NOP. ppNoc was not consistently detected in any samples. The data for expression of RNA encoding NOP as measured by qPCR are presented in Table 6-11. The negative Ct value (calculated from Equation 2-13) indicates a higher Ct for the control than the GOI. Values of zero indicate similar levels of expression in both the GOI and control.

6-253 Section 6 Further application and confirmatory testing

Cell Group Test Ct Ct (mean ± SD) (mean ± SD) (n) Eosinophils Healthy LPS 0.60 ± 6.15 27.23 ± 1.62 (4) Native -0.56 ± 1.06 33.27 ± 1.49 (6)

Septic LPS -2.06 ± 1.18 30.23 ± 4.17 (5) Native -2.27 ± 0.51 28.45 ± 3.5 (5) Neutrophils Healthy LPS 1.01 ± 2.79 34.78 ± 2.72 (7) Native -5.52 ± 12.7 33.54 ± 2.31 (5) Septic LPS -2.03 ± 1.36 29.64 ± 5.59 (5) Native -2.47 ± 0.99 27.87 ± 4.31 (5) Table 6-11 – Results of qPCR analysis of mRNA for NOP extracted from eosinophils and neutrophils . Ct values comparing housekeeper genes ELF1 and ELF21 with GOI. Each assay performed in duplicate per volunteer

These data show significant variability, with no significant differences between eosinophils and neutrophils, nor between native and LPS treated cells from healthy volunteers or patients with sepsis (Figure 6-13).

Figure 6-13 – differences in expression of NOP in eosinophils and neutrophils between healthy volunteers, those with sepsis, and cells treated with LPS and native cells

6-254 Section 6 Further application and confirmatory testing

These data demonstrate that RNA encoding ppNoc was not present in any samples. The RNA encoding NOP was consistently detected in extracts from eosinophils and neutrophils natively, from healthy volunteers, after exposure to an environment mimicking sepsis, and when extracted from patients with a diagnosis of sepsis.

The lack of ppNoc may be explained by downregulation following NOP stimulation. Following extraction, all immunocytes were incubated overnight, either in plain RPMI, or in the presence of LPS and PepG. There is evidence of ppNoc encoding, N/OFQ synthesis and release demonstrated by the N/OFQ release assay and PCR. Overnight incubation in the presence of N/OFQ may promote negative feedback and reduce transcription. Similarly, the method of immunocyte extraction may result in inconsistent results in PCR studies(275).

6.3 Conclusions and discussion The aim of the work described in this section was to investigate differences in expression of both N/OFQ and NOP (and their precursors) between eosinophils and neutrophils taken from healthy volunteers and patients with a diagnosis sepsis, using both native cells, and those exposed to an environment mimicking sepsis. The primary methodology used a live cell-based assay, with confirmatory immunofluorescence and PCR.

This work used a combination of methods to investigate genetic expression and translation of functional protein. Whilst demonstrating N/OFQ release from granulocytes was a gold standard, the variability within this assay, poor cell adhesion, short lifespan and likelihood of immune cell activation during handling and extraction led to inconsistent results (223).

6-255 Section 6 Further application and confirmatory testing

Because of their short lifespan and to minimise handling of the immunocytes, cells were imaged during their addition to the assay (drop-on), and on adding fMLP. The deleterious effects of the immunocytes on the biosensor cells did not allow enough time to adhere, nor absorb Fluo-4, which prevented full tracking of cell degranulation.

However, despite these limitations, the live cell assay demonstrated release of N/OFQ from both eosinophils and neutrophils from healthy individuals, patients with a diagnosis of sepsis and cells exposed to an environment mimicking sepsis. This response was attenuated in the presence of SB612111. The observed responses appeared to be attenuated in eosinophils taken from healthy volunteers compared to those taken from patients with sepsis, or from either neutrophil group. No group showed a significant response to fMLP. In all cases, the response observed was less than that of N/OFQ, possibly because of immune mediated deterioration of the biosensor.

The methodological challenges and low sampling numbers preclude confirmatory statistics. An alternative approach may have been to use flow cytometry and staining to analyse the entire population of a given cell, with CD markers and N/OFQ-NOP staining. However, this approach was limited by cost.

Despite the uncertainties surrounding these data, there is a suggestion that N/OFQ may be present in both neutrophils and eosinophils, supporting previous data from our group and others. The lack of response to fMLP relative to drop-on may be explained by the mobility of the cells. When adding the cells, any N/OFQ present in the suspension will also be added, and this could explain the high number of responder cells. The number of responder cells is a function of the concentration of N/OFQ (as demonstrated in Chapter 3), and this supports the presence of N/OFQ in these samples, where neutrophils and eosinophils were very mobile, and it was impossible to track the response of adjacent biosensor cells.

6-256 Section 6 Further application and confirmatory testing

There was significant variability in responses. The fluorescent responses observed during coincubation of some cells was minimal, and there was no clear differentiation between sepsis and healthy volunteers. This may reflect the activated state induced in cells during isolation and extraction procedures, and the variable timecourse of patients diagnosed with sepsis. Although not undertaken in this work, the use of flow cytometry to demonstrate the presence of markers of immunocyte activation such as CD11b could further clarify the effect of activation state on the N/OFQ-NOP system (276).

Patients with sepsis, or with activated immunocytes may have N/OFQ stored in vesicles; these may be depleted or upregulated, accounting for the variable response seen.

The graphs (Figure 6-2 to Figure 6-8) highlight the range of responses. As a positive response may reasonably be given by single CHO cell, the maximal F/F0 (rather than mean) is useful as a screening tool, and these data and associated micrographs also suggest that N/OFQ is ubiquitously present.

Immunohistochemistry was performed to further investigate eosinophils and neutrophils for the presence of N/OFQ (via anti-N/OFQFITC binding), and NOP (via

N/OFQATTO594 binding). N/OFQ appeared to be expressed by both eosinophils and neutrophils from healthy volunteers, those exposed to an environment mimicking sepsis, and those from patients with a diagnosis of sepsis (although binding was reduced in native neutrophils from those with sepsis). There was also significant variability in the immunofluorescence assay, which may be because of partial activation of cells during the extraction process, and the varied time course of sepsis for patient samples.

6-257 Section 6 Further application and confirmatory testing

PCR was similarly variable in response, limited by cell yields and low levels of mRNA obtained, which are consistent with previous studies(277). However, there was widespread expression of mRNA encoding NOP in both eosinophils and neutrophils. 12- 18-hour incubation with LPS/PepG made little difference to the expression, although the timing window may have been insufficient to see results. The short lifespan of PMNs and susceptibility to apoptosis may have influenced these data during the incubation period.

There was negligible expression of ppNoc across all groups, which is surprising given the evidence that N/OFQ is ubiquitously expressed. However, this may represent a downregulation, cellular depletion, or degradation as a result of cell activation as part of the extraction process.

These data are exploratory and did indicate some differences between cell types and cells in an environment mimicking sepsis compared with native cells. However, the data are inconclusive and do not clearly demonstrate the nature of such differences.

In summary

 Functional N/OFQ does appear to be expressed ubiquitously in both neutrophils and eosinophils. Live cell biosensor responses to immunocyte addition are attenuated in the presence of SB, and this is supported by immunofluorescence

data demonstrating binding of Anti-N/OFQFITC. PCR data does not support this conclusion, although this may be complicated by low mRNA yields and the effects of cell activation during extraction.  The fluorescent response observed in the live cell assay is variable. This may reflect difficulties in recording fluorescence due to the attenuating properties of PPADS and oATP, or due to the deleterious effects of immunocytes on the biosensor cells.

6-258 Section 6 Further application and confirmatory testing

 NOP mRNA appears to be expressed in both neutrophils and eosinophils in the PCR assay. In the immunofluorescence assay, eosinophils appeared to express NOP constitutively.  It is possible that the effects of cell separation have masked some differences between patients with sepsis and healthy volunteers. However, these data suggest widespread expression of N/OFQ.

6-259

Chapter 7

General discussion

Section 7 General discussion, conclusions and further work

7 General discussion, conclusions and further work The work described in this thesis aimed to provide further insight into the role of the N/OFQ-NOP system in the immune response by investigating N/OFQ release and NOP receptor expression on granulocytes. This was achieved by refining and validating a live cell biosensor-based assay and implementing this to investigate differences between patients with a diagnosis of sepsis and healthy volunteers. The observed results were combined with other confirmatory tests, PCR and immunofluorescence.

7.1 Discussion 7.1.1 Validation and development of a biosensor-based assay to detect N/OFQ release from immunocytes

In common with other studies using the CHOhNOPGqi5 chimera, this work demonstrated that the biosensor signal increased reliably and reproducibly following exposure to  10- 6 M N/OFQ in both the single cell Fluo-4 and suspension Fura-2 assays.

The Fluo-4 single-cell based microscopy assay was used in initial testing of the

CHOhNOPGqi5 cell line, with increase in biosensor fluorescence as a readout. The biosensor responded to N/OFQ and to ATP, but not to buffer, nor to the direct application of NOP antagonists SB612111, or TRAP-101. The measured fluorescence increased following exposure to N/OFQ concentrations between 10-6 and 10-10 M. This sensitivity is consistent with other microplate studies of CHOhNOPGqi5 cells(127). The change in fluorescence measured in CHO cells even when exposed to fixed standard concentrations is highly variable – due to dye leakage, differences in loading and the effects of optical sectioning.

Cell-to-cell and between-assay variability is common in bioassays, due to cell mediated factors, or to the properties of the assay (260). Confocal microscopy induces heating and bleaching which may affect the recorded signal. Furthermore, when co-incubated

7-261 Section 7 General discussion, conclusions and further work with immune cells (and contaminants in the supernatant), this work demonstrated morphological changes in CHO cells, with deterioration in the recorded signal seen with even short (<5 minute) incubations.

The absolute magnitude of fluorescence was an unreliable readout for this assay and calibrating to determine intracellular calcium concentration was similarly unreliable, further complicated by morphological changes induced by cell lysis. Therefore, the proportion of responsive cells was used as a readout for subsequent testing, with a threshold response set at F/F01.8, based on response to exogenous N/OFQ to minimise false negative results as a screening test to prompt further investigation. This was found to be reliable in testing with exogenous N/OFQ, and yielded LogEC50 values consistent with previous studies (127). The ROC curve for this test demonstrated that this method was both reliable and sensitive.

CHO cells (both CHOhNOPGqi5 and CHOWT) responded to ATP, confirming the presence of endogenous purinergic receptors on CHO cells. Activity at these was effectively suppressed using a combination of the purinergic antagonists PPADS and oATP (5mM and 800 M respectively), which in combination have effects against P2Y2 and P2X7 receptor subtypes. CHO cells are generally considered suitable cells for recombinant transfects and bioassays because of their relative lack of endogenous receptors. Purinergic receptors have been previously described within this cell line previously, although there has been some controversy regarding the subtypes present. This work provides evidence for the presence of P2Y2 and P2X7 receptors.

7.1.2 N/OFQ release from and expression in immunocytes There is existing evidence suggesting that N/OFQ is expressed in and released from immunocytes (Table 1-11), with granulocytes being a potential source and delivery mechanism in inflammatory states. This hypothesis was evaluated in eosinophils, neutrophils, and mixed PMNs.

7-262 Section 7 General discussion, conclusions and further work

Poor cell yields precluded assessment of basophils, due to their low endogenous cell numbers. Extraction of basophils from other granulocytes required a two-step separation process (non-basophil depletion, followed by positive selection), increasing handling and potentially resulting in increased losses and effects on cell function and the risk of erroneous results. When trialling this process, the low yield, and purity of only 65.93% rendered the extraction unfeasible.

The live cell degranulation assay demonstrated N/OFQ release from mixed PMNs (5.2). Analysis of individual cell responses demonstrated that approximately 50% of

CHOhNOPGαqi5 cells were classified as responders when exposed to mixed PMNs, although with significant variability. This was comparable to the response observed to treatment with exogenous N/OFQ under the same conditions. Although the response rate of the biosensor cells in the presence of mixed PMNs was low, the similarities with the N/OFQ response under the same conditions is likely to suggest that a significant proportion of the mixed PMNs are releasing N/OFQ. Repeating the live cell assay in subpopulations of eosinophils and basophils demonstrated that both eosinophils and neutrophils were releasing N/OFQ, from native cells from healthy volunteers and patients with sepsis.

A comparable study, using neutrophil samples and stimulating degranulation of a whole population using fMLP also demonstrated N/OFQ release based on an ELISA assay (123). Of note, Fiset used 1 x 107 cells ml-1, resulting in an N/OFQ concentration in the ng ml-1 range measured in supernatant. The cell density used by Fiset exceeds that used in this work and may account for some of the low response rates observed in this thesis. Local work has demonstrated the ELISA assay to be unreliable and subject to nonspecific binding, potentially leading to false positives and overestimation of the observed concentration. However, Fiset’s study demonstrated N/OFQ in inflammatory exudates which correlates with my findings of N/OFQ released from neutrophils sampled from patients with sepsis(123).

7-263 Section 7 General discussion, conclusions and further work

The live cell release assay was subject to significant variability, both within the cells on an individual coverslip and between assays. Variability is a characteristic of many bioassays. However, this was a particular problem because of the limited scope for repeated imaging, the deleterious effects of immunocytes on the biosensor cells, and difficulty imaging because of the optical properties of PPADS. The fluorescent responses observed to N/OFQ used as controls during immunocyte tests (5.2) were much lower than those observed during initial cell-line tests and validation (3.3.2). This may have been due to the effects of repeated imaging and bleaching on the CHOhNOPGαqi5 cells, or because of the deleterious effects of the immunocytes. As demonstrated during cell line testing, the biosensor response is proportional to local concentration – and therefore any observed response is likely to be related to the density of PMNs added rather than to the quantity of N/OFQ released. The concentration released from any given PMN is likely to be too small to be detected, outside of the range for this assay (10-6 – 10-9 M).

Immunocyte extraction procedures are known to affect their function – and particularly degranulation. This may have led to inadvertent activation, or priming of immunocytes prior to the assay, masking some of the differences between healthy and septic cells. The comparison between cells taken from participants with a diagnosis of sepsis, and from healthy volunteers had few differences. This may have reflected the heterogeneity of the patients with sepsis, that they were of low illness severity (with a low SOFA score), or early in the course of their disease. It is difficult to estimate the time of sepsis onset, and so phase of the immune response that the patient is undergoing at the time of sampling.

Despite problems with variability, the live cell assay did suggest that N/OFQ was released from neutrophils and eosinophils, and that this signal was effectively suppressed by the NOP antagonist SB612111.

7-264 Section 7 General discussion, conclusions and further work

The present findings support the hypothesis that, at a single cell level, degranulation of eosinophils and neutrophils may release N/OFQ. Conditions promoting degranulation (such as sepsis, surgery or other inflammatory stimulus) may increase N/OFQ concentrations locally by promoting N/OFQ release. This may explain Fiset’s findings of N/OFQ in the synovial fluid removed from the joints of patients with inflammatory arthropathies(123), in plasma sampled post cardiopulmonary bypass, and in sepsis (153). The sputum of asthmatics has an elevated concentration of N/OFQ, which may due to from eosinophilic or neutrophilic release(278).

Immunofluorescence data support the findings from the live cell assay. N/OFQ (as detected by Anti-N/OFQFITC binding) was present in neutrophils and eosinophils from healthy volunteers, patients with sepsis and from cells cultured in an environment mimicking sepsis. This study did demonstrate N/OFQ in clusters within the cell, suggesting that it is released as part of a coordinated degranulation process. NOP was variable but present in the majority of samples and appeared to be upregulated in samples from those with sepsis or cultured in an environment mimicking sepsis. There was significant variability between groups for both N/OFQ and NOP. It is impossible to exclude subclinical infection in healthy volunteers, or that the immune cells may have been activated during extraction. Furthermore, although samples were taken from patients with a diagnosis of sepsis, within 24 hours of admission to the intensive care, it was impossible to determine the onset of the septic insult. Therefore, from a small sample, it is difficult to draw conclusions about the mechanism of NOP-N/OFQ involvement in sepsis.

PCR was used as a supplemental test for mRNA encoding the N/OFQ precursor ppNoc, and for NOP in samples of neutrophils and eosinophils. The yields of mRNA were very low, and this was further complicated by the low number of cells recovered. The results obtained were mixed. ppNoc was not detected in significant quantities in any sample,

7-265 Section 7 General discussion, conclusions and further work in contrast to the results from the immunofluorescence or the live cell assay, and to other studies.

Previous studies have reported mixed results; plasma concentrations of ppNoc either increase (153) or decrease (157) following inflammatory stimuli. Opposing theories to explain these results suggest that either immunocytes are the source for N/OFQ, and that the ppNoc is depleted as it is utilised, or that ppNoc is upregulated to support N/OFQ synthesis and account for the increased plasma concentrations observed. Similar to this study, there is evidence for expression of the functional NOP peptide in neutrophils, and lymphocytes(148, 160).

The heterogeneity of sepsis, with an unclear time of onset could lead to sampling of blood at any point in the septic process, and it is feasible that patients were entering a depleted or immunocompromised state at the time of sampling. Furthermore, the yields of mRNA were small, and this led to relatively high Ct and threshold values for the housekeeper gene which may risk error and underreporting of mRNA present in very low concentrations (as with ppNoc).

Overall, however, these data suggest that N/OFQ is present in both eosinophils and neutrophils, and is released by degranulation, which correlates with previously observed data in mixed granulocytes (123), the EOL-1 cell line (4.4.1), and in clinical studies in inflammatory conditions, sepsis(153), and asthma(278).

7.1.3 NOP expression by granulocyte subpopulations

NOP expression was assessed by immunofluorescence of N/OFQATTO594 binding to NOP, and by PCR.

7-266 Section 7 General discussion, conclusions and further work

Immunofluorescence data, generated by our group using the novel ligand N/OFQATTO594 demonstrated N/OFQ binding to mixed PMNs(126). Data within this thesis show similar widespread binding of N/OFQATTO594 to both eosinophils from healthy volunteers and patients with sepsis. The binding of N/OFQATTO594 occurred within the native septic neutrophil samples, but not those from healthy volunteers, suggesting that it is upregulated in inflammatory conditions.

PCR was used as a supplemental test for NOP mRNA in samples of neutrophils and eosinophils. Previous studies have demonstrated the presence of increased free plasma NOP mRNA in samples taken from patients with sepsis, compared to healthy volunteers

(153). NOP was detected in all samples, although the expression is variable, and the Ct is similar to that of the housekeeper genes used.

It is difficult to draw conclusions about the timing and mechanism of NOP regulation in sepsis, but these data indicate that it is likely to have a role. Mechanistic studies have demonstrated that NOP may have a role in regulation of antibody production, and chemotaxis, again supporting the role of this receptor in immune regulation.

7.1.4 Implications These studies have demonstrated that both N/OFQ and the associated NOP receptor are present in immune cells, and that N/OFQ is released by degranulation of PMNs, from both neutrophils and eosinophils.

The expression of both N/OFQ and NOP are likely to be modulated in the presence of inflammatory conditions. However, the significance of this system in inflammatory states is unclear.

7-267 Section 7 General discussion, conclusions and further work

Both neutrophils and eosinophils produce N/OFQ. The effects of this neuropeptide mirror the inflammatory response, promoting vasodilation and capillary leak (64), activating and recruiting immune cells (148, 279) and stimulating antibody production (177). Once stimulated, these cells may undergo a feedback downregulation, preventing harm from overactivation of the inflammatory response (157). Failure of this endogenous regulation – or application of exogenous N/OFQ is associated with increased mortality in the rat CLP model (167), and observed decreases in plasma N/OFQ in those surviving intensive care admission with sepsis compared to these that died (174). The increased mortality in the rat CLP model is reversed by treatment with the N/OFQ antagonist UFP-101 (167), which further supports a role for N/OFQ-NOP in the pathogenesis of inflammatory conditions.

The profile of N/OFQ-NOP activation discussed above is analogous to the classical model of sepsis, in which the immune system undergoes a state of hyperactivation followed by a state of relative immunosuppression, which may lead to death.

This leads to the hypothesis that N/OFQ-NOP may have a pro-inflammatory effect in the initial part of the inflammatory response, recruiting and signalling to immune cells, and that dysregulation is associated with death through failure to ameliorate the effects of uncontrolled inflammation. If this hypothesis were true, other conditions associated with dysregulation of N/OFQ-NOP would also be associated with dysregulation of inflammation. One such example is the use of a colitis model in NOP knockout mice who were unaffected compared to their wildtype counterparts (170).

7.2 Conclusions and further work This work provides further evidence that N/OFQ-NOP is associated with the immune system and has a role in inflammatory conditions.

7-268 Section 7 General discussion, conclusions and further work

Combined with previous studies, there is a compelling argument that N/OFQ is released by granulocytes, and that both N/OFQ and its receptor are modulated in inflammatory conditions. These findings have been replicated to some degree in in-vitro, animal, and observational human clinical studies. However, there are some inconsistencies between studies. In-vitro studies are complicated by the effects of immunocyte extraction on the immune cells. Clinical studies are complicated by differences between patients with a diagnosis of sepsis. As sepsis is a heterogenous clinical syndrome, responses observed in one subtype may not be observed elsewhere because of differences between patients which are not yet apparent.

The granulocyte degranulation assay produced variable results in this study, and was limited by yields, the deleterious effects of immunocytes on the biosensor, and the harmful effects of bleaching and laser heating. The CHO response was related to the concentration of ligand at the receptor (which may explain why only a proportion of cells responded, and only when they were in close proximity to an immunocyte). To avoid this, a further approach may be to extract N/OFQ from immunocytes and then assay for N/OFQ – eliminating these problems. The bioassay was a reliable test for N/OFQ if present in sufficient concentrations, and therefore, this assay could be used to test supernatant obtained from degranulated immunocytes. Other testing methods such as RIA and ELISA have proved inaccurate and subject to error, and there is not currently another reliable test for N/OFQ. Other testing methodologies such as mass spectrometry and HPLC for detection of low concentrations of N/OFQ could also be used in this context.

Some of variable response observed may be addressed by a robust in-vitro model of sepsis. Existing models are limited by our understanding of the condition, or groups of conditions considered under the umbrella of sepsis. Sepsis is characterised by an immune response, followed by a relative suppression of immune functioning. The intensity of the immune reaction, and subsequent immunosuppression may be

7-269 Section 7 General discussion, conclusions and further work responsible for mortality, and may herald differing underlying pathophysiology, in which NOP-N/OFQ may have a role.

The process of extracting immune cells for later study is known to cause activation and phenotypically different cells from those in-vivo. Immunocytes have limited ex-vivo survival, and it is unlikely that culture would allow recovery to return them to the in- vitro state and exclude the confounding effects of activation. Low cell recovery was also a limiting factor of this work. Although the yields obtained are comparable to those expected, isolation of rare cells such as basophils would require larger volumes of blood, or the use of pooled samples, which were impractical for this study.

There is currently emerging evidence of subtypes of sepsis using artificial intelligence to analyse physiological parameters. Isolating cells from patients diagnosed with a specific subtype of sepsis may reveal differences in the role of N/OFQ-NOP which are not apparent without this stratification. Identifying underlying subtypes may highlight those patients in which treatment with a NOP antagonist may prove beneficial (such as UFP-101 in the rat CLP studies).

In summary, this work has validated a test for N/OFQ and demonstrated its release from eosinophils and neutrophils. Both NOP and N/OFQ are likely to be present in eosinophils and neutrophils. However, because of the significant variability in response, further work is needed to understand the interactions between NOP and N/OFQ and the immune system.

7-270 Appendix – Buffers and reagents

Appendix – Buffers and reagents

Krebs-HEPES Buffer

+ + 2+ 2+ - – 143.3 mM Na ,4.7 mM K , 2.5 mM Ca , 1.3 mM Mg , 125.6 mM Cl , 25 mM HCO3 ,

– 2– 1.2 mM H2PO4 , 1.2 mM SO4 , 11.7 mM glucose, and 10 mM HEPES, pH 7.4 titrated with 10 M NaOH

Binding buffer

Phosphate-buffered saline (PBS), pH 7.2, 0.5% bovine serum albumin (BSA), and 2 mM EDTA

Phosphate Buffered Saline (PBS)

0.01 M phosphate buffer, 0.0027 M potassium chloride and 0.137 M sodium chloride, pH 7.4

PBST (Immunofluorescence)

PBS with 0.1% Tween 20

Blocking buffer (Immunofluorescence)

PBS with 3% Bovine Serum Albumin and 10% Fetal Calf Serum.

Triton X100 (Immunofluorescence)

PBS with 0.5% Triton X100

271 Appendix – Buffers and reagents

Harvest Buffer (for cuvette based fluorimetry)

10mM HEPES buffered 0.9% saline plus 0.05% EDTA, pH 7.4

Triton X-100 (for cuvette based fluorimetry)

0.1% Triton X-100 (50l added to 2ml cell suspension in cuvette)

Miltenyi Column buffer (for immunomagnetic separation)

PBS pH 7.2, 0.5% Bovine Serum Albumin (BSA) and 2 mM EDTA

272 Appendix – Ethical approvals (University of Leicester)

Appendix – Ethical approvals (University of Leicester)

University Ethics Sub-Committee for Medicine and Biological Sciences

08/09/2016

Ethics Reference: 7815-cph16-cardiovascularsciences

TO:

Name of Researcher Applicant: Christopher Hebbes

Department: Cardiovascular Studies

Research Project Title: Use of human blood in opioid pharmacology: Development of an assay to determine single cell immunocyte release of nociceptin V2

Dear Christopher Hebbes,

RE: Ethics review of Research Study application

The University Ethics Sub-Committee for Medicine and Biological Sciences has reviewed and discussed the above application.

1. Ethical opinion

The Sub-Committee grants ethical approval to the above research project on the basis described in the application form and supporting documentation, subject to the conditions specified below.

2. Summary of ethics review discussion

273 Appendix – Ethical approvals (University of Leicester)

The Committee noted the following issues:

There are no concerns with this study. My only observation is that collected blood will be used with a day or so. If blood, or other relevant material such as primary non- divided cells, were to be stored or frozen for over 1 week then the stored material would come under the Human Tissue Act. The relevant PD would need to be notified.

3. General conditions of the ethical approval

The ethics approval is subject to the following general conditions being met prior to the start of the project:

As the Principal Investigator, you are expected to deliver the research project in accordance with the University’s policies and procedures, which includes the University’s Research Code of Conduct and the University’s Research Ethics Policy.

If relevant, management permission or approval (gate keeper role) must be obtained from host organisation prior to the start of the study at the site concerned.

4. Reporting requirements after ethical approval

You are expected to notify the Sub-Committee about:

 Significant amendments to the project  Serious breaches of the protocol  Annual progress reports  Notifying the end of the study

5. Use of application information

Details from your ethics application will be stored on the University Ethics Online System. With your permission, the Sub-Committee may wish to use parts of the application in an anonymised format for training or sharing best practice. Please let me know if you do not want the application details to be used in this manner.

274 Appendix – Ethical approvals (University of Leicester)

Best wishes for the success of this research project.

Yours sincerely,

Dr. Chris Talbot

Chair

275 Appendix – Ethical approvals (NHS)

Appendix – Ethical approvals (NHS)

276 Appendix – Ethical approvals (NHS)

277 Appendix – Ethical approvals (NHS)

278 Appendix – Ethical approvals (NHS)

279 Appendix – Ethical approvals (NHS)

280 Bibliography

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274. Singh SR, Sullo N, D'Agostino B, Brightling CE, Lambert DG. The effects of nociceptin peptide (N/OFQ)-receptor (NOP) system activation in the airways. Peptides. 2013;39:36- 46.

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276. Abdel-Salam BKA, Ebaid H. Clinical immunology Expression of CD11b and CD18 on polymorphonuclear neutrophils stimulated with interleukin-2. Cent Eur J Immunol. 2014;2:209-15.

277. Scott S. N/OFQ and the Granulocyte [dissertation]. University of Leicester; 2019.

278. Singh SR, Sullo N, Matteis M, Spaziano G, McDonald J, Saunders R, et al. Nociceptin/orphanin FQ (N/OFQ) modulates immunopathology and airway hyperresponsiveness representing a novel target for the treatment of asthma. Br J Pharmacol. 2016;173(8):1286-301.

279. Trombella S, Vergura R, Falzarano S, Guerrini R, Calo G, Spisani S. Nociceptin/orphanin FQ stimulates human monocyte chemotaxis via NOP receptor activation. Peptides. 2005;26(8):1497-502.

302 Publications and grants relating to this thesis

Publications and grants relating to this thesis Grants

Royal College of anaesthetists, Ernest Leech fund (£2,422.63, 2016)

Publications

Bird M. F. Hebbes C. P. Scott S. W. M. Willetts J. Thompson J. P. Lambert D. G., A bioassay to detect Nociceptin/Orphanin FQ release from live human granulocytes. (pre- submission)

Presentations

Hebbes, C. Lambert, D. Thompson, J. Detecting nociceptin release from polymorphonuclear cells. (Anaesthetic Research Society, 2017)

Hebbes, C., Bird, M., Scott, S., Willets, J., Thompson, J. P., Lambert, D. G, Single cell nociceptin release from human immunocytes (Academy of Medical Sciences meeting, Leicester, 2016)

303