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Identification and transcriptional characterisation of novel inhibitors of NAV1.7 AND TRKB

Hompoonsup, Supanida

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IDENTIFICATION AND TRANSCRIPTIONAL CHARACTERISATION OF NOVEL INHIBITORS OF

NAV1.7 AND TRKB

A thesis for the degree of Doctor of Philosophy

Supanida Hompoonsup

Wolfson Centre for Age-Related Diseases

King’s College London Table of Contents Table of Figures ...... 5 Table of Tables ...... 7 ABSTRACT ...... 8 ACKNOWLEDGEMENTS ...... 10 ABBREVIATIONS ...... 11 DECLARATIONS ...... 12 GENERAL INTRODUCTION ...... 13 A. Phenotypic and Target-Based Screening in Drug Discovery ...... 14 B. The Search for Different Drug Classes ...... 16 Biologics (mAbs) vs Small Molecules ...... 16 Recombinant Antibodies ...... 18 Small Molecules ...... 22

CHAPTER 1: NAV1.7-BASED AFFINITY SELECTION AND TRANSCRIPTIONAL REGULATION26 Introduction ...... 26

A. Voltage-Gated Sodium (Nav) Channel ...... 26 B. Antibody Phage Display ...... 38 C. Objectives ...... 50 Materials and Methods ...... 51 Design of Polypeptides ...... 51 Phage Display Selection ...... 51 1. Library Rescue...... 51 2. Affinity Selection/Biopanning ...... 52 3. Output Screening...... 54

hNav1.7-expressing Cell Lines ...... 58 1. Cell Culture ...... 58 2. Preparation of Cell Membrane Fraction ...... 58 3. Western Immunoblotting ...... 59 4. Immunocytochemistry ...... 60 Scratch Wound Assay ...... 61 mRNA Microarray ...... 62 Results I ...... 63 1. Target site determination ...... 63 1.1 Sequence conservation ...... 63

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1.2 Hydrophobic properties ...... 66 2. Optimisation of phage-display selection for a peptide target ...... 67 2.1 Solid phase selection – Selection I and II ...... 67 2.2 Solution phase selection – Selection III ...... 71 2.3 Further characterisation of putative hits from Selection III ...... 74 2.4 Sequence analysis of putative hits from Selection III ...... 76 2.5 B03, a putative D2C1-selective vNAR ...... 79 2.6 Alternating support matrices - Selection IV ...... 85 Discussions I ...... 87 1. Feasibility of peptides as a target in phage-display antibody selection ...... 87

2. Druggability of the E2 loops on Nav1.7 ...... 89 3. Limitations and Future Directions ...... 93 4. Challenges ...... 94 Results II ...... 96

Non-canonical roles of Nav1.7 ...... 96

1. Transcriptional regulation through Nav1.7 ...... 96

2. Investigating the roles of Nav1.7 in cell migration using a scratch assay ...... 105 Discussions II ...... 109

1. Transcriptional profiling for the screening of novel Nav1.7 inhibitors ...... 109

2. Nav1.7-mediated regulation of cell migration ...... 111 3. Limitations and Future Directions ...... 111 CHAPTER 2: NEUROTROPHIN/RECEPTOR INTERACTIONS AND SMALL MOLECULE MODULATORS ...... 114 Introduction ...... 114 A. Neurotrophin/Receptor Interactions in the Developing and Mature Nervous System ...... 114 B. Consequences of Dysregulated Neurotrophin/Receptor Interactions ...... 121 C. Drug Discovery Perspectives ...... 124 D. Small Molecule Similarity Search ...... 134 E. Objectives ...... 136 Materials and Methods ...... 137 TrkB-NFAT-bla CHO-K1 Assay ...... 137 Tango-CB1 β-arrestin Assay ...... 139 mRNA Microarray ...... 140 Multiplex Assay ...... 141 3

1. RNA Extraction ...... 141 2. Treatment Conditions and Cell Lysate Preparation ...... 141 3. Hybridisation and Signal Amplification ...... 141 Analysis and Predictive Modelling ...... 144 Results I ...... 145 Investigating ANA-12 selectivity using a beta-lactamase reporter assay ...... 145 1. ANA-12 inhibition in the presence of BDNF – an endogenous TrkB agonist ...... 145 2. ANA-12 inhibition in the presence of ionomycin – a Ca2+ ionophore ...... 147 3. ANA-12 inhibition in the presence of thapsigargin – an inhibitor of Ca2+ transporter ...... 149 4. ANA-12 inhibition in an irrelevant (non-Trk) β-lactamase system ...... 151 Discussions I ...... 154 1. ANA-12 Selectivity ...... 154 2. Limitations and Future Directions ...... 158 Results II ...... 159 Transcriptional characterisation of novel TrkB inhibitors ...... 159 1. Building a neurotrophin-specific transcript set ...... 159 2. Validation of the neurotrophin-specific multiplex panel ...... 166 3. Transcriptional modulation by putative TrkB antagonists ...... 174 4. Preliminary work on random forests to predict class membership of small molecules ...... 186 Discussions II ...... 188 1. Invariant Genes ...... 188 2. Utility of Transcriptional Screen ...... 188 3. Limitations and Future Directions ...... 190 GENERAL DISCUSSIONS ...... 193 REFERENCE ...... 195 APPENDIX ...... 228 A. Voltage clamp recordings of ProTx-II activity ...... 228 B. Probe set information of neurotrophin multiplex set ...... 230

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Table of Figures Figure 1. General structures of full IgG, and variable domain...... 18 Figure 2. Hybridomas and in vitro display technologies...... 21 Figure 3. Overview of small-molecule rational drug design...... 25 Figure 4. Structure and voltage-gating modes of a Nav channel...... 28 Figure 5. Four vNAR subtypes, and 3D structure of a vNAR (Type II)...... 39 Figure 6. Life cycle of a filamentous bacteriophage...... 42 Figure 7. Genomic, hybrid, and phagemid systems...... 44 Figure 8. Phagemid construction for phage-display vNAR/antibody platform...... 46 Figure 9. Phage display affinity selection...... 48 Figure 10. The lac operon and induction of recombinant vNAR expression...... 49 Figure 11. Aligned E2 sequences from 4 domains of all hNav isoforms and mNav1.7. .... 64 Figure 12. Summary of Selection I outcomes...... 68 Figure 13. .Summary of Selection II outcomes...... 70 Figure 14. Summary of Selection III outcomes...... 73 Figure 15. Binding of purified B03, E08, and 1B8 clones to biomolecules...... 75 Figure 16. B03 sequence and predicted structure...... 77 Figure 17. Screening result and protein sequences of E08 subclone 1-4...... 78 Figure 18. Protein sequences of E08 subclone 5-8...... 79 Figure 19. Confirmed hNav1.7 expression in inducible hNav1.7-HEK293 cell line...... 80 Figure 20. Cell binding by purified B03 vNARs...... 82 Figure 21. D2C1-grafted vNARs and B03 selectivity for D2C1...... 84 Figure 22. Kyte-Doolittle hydropathy plot of the 4 domains of hNav1.7...... 91 Figure 23. Transcriptional responses in the H460 cells treated with TTX or ProTx-II...... 98 Figure 24. Volcano plots of SH-SY5Y transcriptional responses to veratridine, TTX, or ProTx-II...... 102 Figure 25. Western blot of Nav1.7 in cell membrane samples, and relative rank of SCN9A...... 104 Figure 26. Preliminary experiments for ECM selection...... 106 Figure 27. Migration rates of the H460 cells in different treatment conditions...... 108 Figure 28. Structural configuration of Trk and p75NTR receptors...... 116 Figure 29. Three canonical pathways of Trk signalling...... 119 Figure 30.Crystal structure of TrkB-IgL-d2 in complex with NT-4...... 131 Figure 31. CellSensor Trk-NFAT-bla CHO-K1 reporter assay...... 138 Figure 32. Multiplex Assay Workflow...... 143 Figure 33. BDNF and ANA-12 concentration responses...... 146 Figure 34. Ionomycin and ANA-12 concentration responses...... 148 Figure 35. Thapsigargin and ANA-12 concentration responses...... 150 Figure 36. ANA-12 Inhibition of the ACEA-driven response in a CB1-Tango β-arrestin assay...... 152 Figure 37. Predicted bioactivity score of ANA-12 by Molinspiration...... 157 Figure 38. Volcano plot of BDNF-elicited transcriptional changes in SH-SY5Y...... 160 Figure 39. Selected genes and their grouping in the neurotrophin signalling pathways...... 164 Figure 40. Relative rank of expression of 50 genes measured in two sample types. .... 167 5

Figure 41. Distinguishable expression patterns between cell lines of different origins. 168 Figure 42. Confirmed invariance for the two putatively invariant genes...... 169 Figure 43. BDNF non-response in the low TrkB SH-SY5Y cell line...... 170 Figure 44. TrkB-SHSY5Y transcripts with significant BDNF-induced changes...... 172 Figure 45. Expression values of 19 transcripts in 3 conditions (Ctrl, BDNF, ANA12+BDNF)...... 176 Figure 46. ANA-12 transcriptional effects in the TrkB SH-SY5Y cell line...... 179 Figure 47. ANA-12 effects in the low TrkB SH-SY5Y cell line...... 180 Figure 48. Transcriptional responses in the TrkB SH-SY5Y cell line from BDNF treatment w/wo A3...... 181 Figure 49. A3-induced transcriptional changes in the high- and low-TrkB cell lines. .... 183 Figure 50. A3 and ANA-12 response signatures...... 185 Figure 51. Instances of correct versus incorrect prediction...... 187 Figure 52. Voltage-clamp recordings of net inward current in induced hNav1.7-HEK293 cell...... 229

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Table of Tables Table 1. mAbs vs Small Molecules...... 17 Table 2. hNav α-subunit subtypes, genes, and distribution...... 27 Table 3. Sequences of predicted E2 (and E1) loops on domain 1-4 of hNav1.7...... 65 Table 4. Solubility of peptide mimetics of hNav1.7 E2 loops...... 66 Table 5. Selection Criteria...... 86 Table 6. Successful phage display selections against peptide targets ...... 88 Table 7. Common DE genes in the TTX- or ProTx-II-treated H460 cells...... 99 Table 8. Top eight KEGG 2016 pathways of the top responders from TTX treatment. . 100 Table 9. DE genes in the SH-SY5Y cells following veratridine or TTX treatment...... 101 Table 10. Top-10 transcripts with absolute lfc > 2.as ranked by adjusted p-value...... 161 Table 11. Rank differences of neurotrophin-related genes in the SH-SY5Y cell line...... 162 Table 12. NCBI-GEO neurotrophin experiments included in our analysis...... 163 Table 13. Three invariant genes; FTL, FOXJ2 and HLCS...... 166 Table 14. Transcripts in the multiplex set that show significant BDNF response...... 173

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ABSTRACT

The focus of this thesis is on the discovery of novel neurotherapeutics, more specifically, the identification and characterisation of biologics and small molecules that bind to and selectively inhibit function of two transmembrane proteins that have been validated as disease-modifying targets, i.e., Nav1.7 and TrkB. Importantly, transcriptional profiling has been used in an innovative manner in order to characterise genes that are regulated downstream of these receptors and to establish a bespoke transcriptional signature that may potentially be used to evaluate the efficacy and specificity of putative antagonists.

Nav1.7 is an isoform of voltage-gated sodium channels that is implicated in a spectrum of pain disorders and hypothesised to play a role in cancer metastasis via cell fate determination. We synthesised Nav1.7 peptidomimetics as “bait” and identified two hits capable of binding to the peptides from phage-display libraries of single- domain antibodies (vNARs). However, following extensive characterisation we concluded that they did not display significant binding to the native Nav1.7 protein and were not pursued further. We subsequently generated a transcriptional profile of a

Nav1.7-selective inhibitor, ProTx-II, and found that Nav1.7 inhibition elicited moderate transcriptional responses in the H460 cells with enrichment in pathways crucial for cell growth and motility, providing a bespoke transcriptional signature that might be used as a screening tool for future drug discovery programs.

TrkB is one of the three Trk isoforms that exhibits high affinity for BDNF. Dysregulation of TrkB/BDNF has been identified in various CNS pathologies and also leads to cell cycle disruption found in malignant cancers. ANA-12 has been reported as a TrkB-selective small-molecule drug that was discovered via in silico screening targeting the “specificity” patch on the TrkB receptor. A similar strategy has been employed by our lab to identify A3 as a potential TrkB-selective antagonist. To compare the transcriptional effects of ANA-12 and A3, we determined the TrkB transcriptional signature and developed a bespoke multiplex assay which allows for rapid quantification of multiple transcripts. Using this assay, it was found that ANA-12 and A3 induced similar transcriptional responses consistent with a shared mechanism of action. However, the results also showed that at best these compounds were modest

8 antagonists as they were unable to fully block the transcriptional responses elicited by the activated TrkB receptor.

To summarise, we have verified the utility of transcriptional profiling for characterising the effects of selective inhibitors. It is likely that Nav1.7 inhibition affects motility-related transcripts which would explain its integral role in cancer metastases. Furthermore, distinctive transcriptional patterns between the TrkB agonist and antagonists suggest that transcriptional characterisation is useful for assessing compound activity and distinguishing one compound class from another.

Key words: Drug discovery, transcriptional characterisation, selective antagonist, pain, phage display, vNARs, Nav1.7, A3, ANA-12, TrkB, neurotrophin

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ACKNOWLEDGEMENTS

I would like to first and foremost thank my primary supervisor Professor Patrick Doherty for his support. I truly appreciate his guidance, patience, and flexibility. My gratitude also extends to Dr Gareth Williams, my second supervisor, for lending his bioinformatics knowledge and expertise. Thank you, my good friend and colleague, Leanne Lu, for her help and companionship throughout the good and bad times.

My first year was spent doing an internship at Ossianix. This opportunity came about thanks to a generous offer from the founders of the company, Dr Frank Walsh and Dr Lynn Rutkowski. The work on phage display selection would not have been completed without the guidance and wisdom from Dr Julien Häsler. I would also like to thank Yatindra Tirunagari for his advice and help on protein expression and purification. Colleagues at the Wolfson CARD and Ossianix have been incredibly supportive and readily lent me assistance when needed; the late Dr Fiona Howell (our lab manager), Dr David Chambers (microarray), Dr Ramin Raouf (electrophysiology), Dr Emma Williams (NFAT assay), James Justin (ANA-12 work), Dr Krzysztof B Wicher (molecular biology), Dr Fabrizio Comper (structural modelling), Sian Frost (tissue culture), Dr Jarek Szary (molecular biology).

I am eternally grateful for my family. My mother has been my rock throughout the seemingly endless years of studies. Thank you, my three brothers, for their constant support in more ways than one. I truly appreciate the generosity and kindness that has been freely and abundantly offered by my extended family, Barbara. In the context of work, she had graciously agreed to proofread the final draft of this thesis. Special thanks also go to Ayako, who made the time to fact-check the section on sodium channels. I would like to thank every one of my UK landlords and all my friends for making my stay in this foreign country much more enjoyable.

Lastly, I would not have been able to receive college education in the UK (A-level, BA, MSc, and PhD) without the scholarship from the Royal Thai Government. This great opportunity has widened my perspective of the world and shaped me into the person I am today.

Supanida Hompoonsup March 2018 10

ABBREVIATIONS

Abbreviation Name in full BDNF Brain-derived neurotrophic factor bla Beta lactamase CDR Complementarity-determining region CHO Chinese hamster ovary cell CNS Central nervous system DRG Dorsal root ganglion ELISA Enzyme-linked immunosorbent assay FBS Fetal bovine serum HEK 293 Human embryonic kidney cell 293 HSA Human serum albumin Ig Immunoglobulin IMAC Immobilised metal-ion affinity chromatography mAb Monoclonal antibody Nav1.7 Voltage-gated sodium channel, subtype 1.7 NFAT Nuclear factor of activated T cells NGF Nerve growth factor PNS Peripheral nervous system PTM Posttranslational modification RMA Robust multi-array average SAR Structure-activity relationship TM Transmembrane Trk Tropomyosin receptor kinase TTX Tetrodotoxin vNAR Variable fragment, new antigen receptor

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DECLARATIONS

I, Supanida Hompoonsup, declare that the work presented in this thesis is my own and has been generated by me as a result of my original research. Circumstances where contributions were made by others are declared below and are clearly stated in the script where appropriate. All phage display work was carried out at Ossianix, Stevenage site. The rest of the work was performed at Wolfson CARD, Guy’s Campus. None of the work has been published prior to thesis submission.

Materials Contributor/Help Nav1.7 Chapter Ossianix OsX-3 and OsX-4 nurse shark vNAR synthetic library Dr Julien Häsler Size exclusion chromatography data Yatindra Tirunagari Tetracycline-inducible hNav1.7-HEK293 cell line Sian Frost vNAR grafting Dr Fabrizio Comper Electrophysiological recordings Dr Ramin Raouf (KCL)

Trk Chapter Wolfson CARD, KCL Microarray readouts Dr David Chambers Database search Dr Gareth Williams Trk-NFAT-bla assay Dr Emma Williams

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GENERAL INTRODUCTION

This thesis explores drug intervention strategies against two key biological targets that are implicated in pain and cancer. We started by investigating the voltage- gated sodium channel subtype 1.7 (Nav1.7), a well-established target for chronic pain intervention. We carried out numerous attempts to identify novel single-domain shark antibodies, i.e., vNARs that selectively bind Nav1.7 peptidomimetics using a phage display platform. We next investigated the putative Nav1.7 roles in cancer cell migration, and control of gene expression. Essentially, transcriptional profiling was used to determine if the channel is involved in the regulation of genes that are implicated in metastatic properties of a human lung cancer cell line, H460. We generated a transcriptional profile of a Nav1.7-selective inhibitor, ProTx-II, and found that Nav1.7 inhibition led to moderate transcriptional responses in the H460 cells with enrichment in pathways crucial for cell growth and motility. These findings support the hypothesis that Nav1.7 plays a key role in cell fate determination and subsequently cancer metastasis and offer a defined transcriptional signature that might be used as a screening tool for future drug discovery programs.

For the second part of the thesis, we explored the use of virtual screening of small-molecule libraries to identify drug-like molecules that antagonise the function of a different protein family, i.e., the tropomyosin receptor kinase (Trk). Trk consists of three closely related members, TrkA, TrkB, and TrkC. The receptors are the binding partner of different neurotrophic factors and their interaction plays a crucial role in establishing the fully-functioning vertebrate nervous system. Dysregulation of the Trk/neurotrophin interactions are implicated in various pathological conditions, including pain, cancer and neurological abnormalities. We started by investigating a proclaimed TrkB-selective antagonist, ANA-12. ANA-12 is a small-molecule drug that was discovered via in silico screening that targeted the extracellular “specificity” patch on the TrkB receptor. We employed a similar strategy and identified A3 to be a potential TrkB-selective antagonist. To compare the transcriptional effects of ANA-12 and A3, we determined the Trk/neurotrophin transcriptional signature and developed a bespoke multiplex assay which enables rapid quantification of multiple transcripts. This assay revealed that ANA-12 and A3 modulate the bespoke transcripts in a similar manner consistent with a common mechanism of action. Interestingly, these compounds were unable to fully 13 reverse the transcriptional responses elicited by the activated TrkB receptor, suggesting that at best they are modest antagonists.

A. Phenotypic and Target-Based Screening in Drug Discovery In the past, keen eyes and sometimes the help of serendipities have led to discoveries of a number of natural products that inherently possess therapeutic effects. The most famous serendipitous discovery is arguably that of penicillin by Alexander Fleming in 1928. Fleming spotted areas of no bacterial growth around the moulds in a Staphylococcus culture dish. He then isolated and identified the anti-bacterial moulds to belong to a Penicillium strain (Williams, 2014). Another notable natural product is morphine – an extremely potent analgesia, extracted from pods of the poppy seeds. Morphine is highly addictive which imposes restriction on its continuous usage (Nestler, 2004). These two cases are examples of phenotypic screening – even though the term was not yet coined back then. It describes the identification of potential drugs by looking at the abilities of the compound to change the disease profiles or phenotypes. One advantage of phenotypic screening is that one can be certain that the compound has desired efficacy. The main hurdle resides in the optimisation step. When one does not know through which mechanisms the compound exerts the observed effects, it is almost impossible to attempt to improve its efficacy. If the compound is not already potent, target deconvolution is the obvious next step in the development pipeline – often an extremely cumbersome process. This type of screen used to be the mainstream of drug discovery prior to rapid advances in molecular biology, and genomics which kicked in around 1980s (Kotz, 2012). These two fields combined with the already existing synthetic chemistry developed backed in the early 1900s diverted the screening track towards a target-oriented regime which has largely stuck to this day. Nevertheless, an increasing number of scientists are pushing for a combination approach utilising both types of screening, as many suspect the slowness of drug discovery progress in the past decades to be attributable to serious flaws in target- based screening application (Wagner, 2016). Target-based screening is heavily reliant upon the concept of single target engagement. By specifying a target of interest as having a confirmed biological link to the disease, the focus is to identify hits that engage the target and attempt to improve the binding and pharmacokinetics properties down the line. This reductionist approach is elegantly simplistic and allows rapid optimisation

14 of the lead compounds often due to the knowledge about the structures of both binding partners. The simplicity however comes at a cost and is usually reflected in failures of the compounds to work in system biology during animal testing or clinical trials (Sams-Dodd, 2005).

We hoped, in part, to bridge the gap between the phenotypic and target-based screen by establishing a transcriptional tool that sheds light on the effects of distinct compound classes at a genetic level. Coming in from the target-based approach, genetic understanding can reveal whether the compounds largely affect the same target and possibly identify the key players that are largely responsible for the phenotypic outcomes. This notion is essentially another interpretation of the central dogma, that if a compound can effectively reverse the clinicopathological symptoms, there must be underlying changes at a molecular level, from proteins down to the genetic codes (Iorio et al., 2013). These changes can be measured and utilised for reverse engineering. The concept of deciphering gene expression profiles of an immortalised cell line as an indicator of small molecules’ activities is by no means novel, and may be categorised under many broad descriptors - one such paradigm is ‘network pharmacology’ (Hopkins, 2008). Transcriptional profiling is the crux of the Connectivity MAP (CMap) project conducted by the Broad Institute. The project gathered gene expression profiles of different cell types that had undergone treatment with ~5000 small molecules and ~3000 genetic reagents (Lamb et al., 2006). Later the L1000 technology was developed to reduce the time and cost of processing the full expression profiles. L1000 consists of ~1000 so-called landmark genes that are representative of the entire transcriptome. Expressions of the omitted genes can be estimated using the model built upon real expression data that carry the information on how different landmark genes relate to their respective ‘dependent’ genes (Duan et al., 2014).

In our Nav1.7 transcriptional studies that will be described in detail later, we found Nav1.7 inhibition to be associated with gene enrichment in motility-related pathways consistent with the integral role of Nav1.7 in cancer metastasis. As there are no simple assays for sodium channel activity, this defined set of transcripts has the potential to be used as a bespoke signature for straightforward screening of novel

Nav1.7 modulators. We then evaluated the target-based screening by considering published and in-house virtual screening data that identified drug-like molecules that 15 potentially interact with an extracellular site on the TrkB receptor. Once efficacy against TrkB was confirmed in an industry standard reporter cell assay, we extended the study to examine selectivity against the TrkA and TrkC receptors. To assess the efficacy and selectivity of a drug in a more physiologically relevant system, we have developed a bespoke gene multiplex to report on the NT/Trk interactions. Gene multiplexing simultaneously detects multiple transcripts, which allows us to investigate the effects of the potential drug candidates at a transcriptional level particularly on the key regulatory genes. Similarities between profiles likely reflect some shared underlying mechanisms, e.g., pathways and/or targets. Results can also shed light on the possible off-target effects that are not easily detected in the single-endpoint binding assay.

B. The Search for Different Drug Classes Not only did we evaluate the merit of transcriptional profiling for compound screening, we also explored two distinct classes of therapeutics, i.e., antibodies and small molecules. Both types bring their own advantages and disadvantages and different approaches are recruited for their discovery. In this section, the differences in their properties are first reviewed, after which relevant detail of the discovery strategies are given. In our studies, we used a phage display platform to identify novel single- domain antibodies – the working of which will be fully described in chapter 1. Our lab also tackled small molecule discovery and enlisted in silico screening tools for the purpose. After rounds of similarity screening and docking, A3 was identified as a candidate TrkB-selective small molecule antagonist. This work and the follow-up experiments are explained in greater detail in chapter 2.

Biologics (mAbs) vs Small Molecules Biologics refer to a large family of therapeutics which are distinctive from chemically derived drugs such as small molecules. Two general attributes that are inherent to biologics are that they can only be produced in a living system, and that they are complex molecules – often large polypeptides (Morrow and Felcone, 2004). Monoclonal antibodies (mAbs) constitute a class of protein-based biologic agents which include hormone, interferon, growth factor, protein, and vaccine (Leader et al., 2008). mAbs offer an advantage of high specificity and affinity to the target as compared to other classes of biologics. In addition, mAbs are particularly amenable to structural engineering to improve affinity and specificity, as well as attenuate immunogenicity. 16 mAbs and small molecules have been the main agents under scrutiny in the process of drug discovery in the past decades. These two types of drug agents differ markedly in their properties and pharmacokinetics (see Table 1 for details). Small molecules contribute up to 90% of the drugs in the market today.(Torre and Albericio, 2017). The reasons behind this large share of the market are mainly attributable to the fact that they have been around for much longer and that their small size facilitates large-scale production, chemical modification, and oral bioavailability. Several other forms of therapeutics have emerged in recent years including gene therapy (Waehler et al., 2007), optogenetics (Song and Knopfel, 2016), and nanomedicines (Shi et al., 2016).

Table 1. mAbs vs Small Molecules.

Source: Imai and Takaoka (2006), Wang et al. (2008b), Banks (2009). The table is not exhaustive and only provides a broad overview of drug-relevant properties. Properties mAbs Small Molecules Physicochemical/Pharmacological MW ~150 kDa (Intact IgGs) ~500-1000 Da Specificity Highly specific for a single Often less specific than mAbs, target, bi-specificity possible reports of cross-reactivity Water Solubility Limited, < 100 mg/mL Variable, clogP < 5 Route of Intravenous (IV), Oral, IV administration subcutaneous (SC), intramuscular (IM), intravitreal injection Immunogenicity Low for chimeric and fully- Minimal human Abs (~1-10%) Cell permeability Low, normally via fluid-phase Good, diffusion endocytosis BBB permeability Low Moderate to high (size- dependent) Pharmacokinetics Oral Bioavailability Poor Good Absorption Slow, maximal plasma Rapid, within hours concentration reached within 2-8 days Distribution Low Vd, often confined within High Vd, easily diffused into plasma/extracellular fluid organs/tissues Half-life Long, ~3-25 days (human IgG) Short, 1-2 days Toxic metabolites Low risk of bioconversion Moderate to high likelihood Elimination Mainly a result of intracellular Primarily through renal and catabolism, and convective hepatic clearance elimination clearance

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Recombinant Antibodies

A. Structure and functions of monoclonal antibodies Drug discovery attempts have cleverly hijacked the unique ability of an antibody to recognise and generate a complementary paratope against an epitope on the antigen. Antigen binding is mainly regulated by complementarity determining regions

(CDRs) on the variable domains of Ig antigen binding fragment (Fab) (Schroeder and

Cavacini, 2010). Fab is made up of two light chain domains VL/CL, and two heavy chain domains VH/CH1 (Figure 1). Heavy-chain CH2 and CH3 domains constitute a portion of IgG known as Fc which aids binding to Fc receptors and complement-dependent cytotoxicity

(CDC). Two Fab fragments and a single Fc are the resulting products of IgG cleavage by papain. Anti-CD3 monoclonal antibody (mAb), Orthoclone OKT3, was the first mAb approved for human use which became commercially available in 1986 (Smith, 1996). Since then around three to five new mAb products have been approved annually, giving a total of 58 approved mAbs in Europe and/or the US as of 2014 (Ecker et al., 2015).

These products include full-length mAbs, bispecfic Abs, antigen-binding fragment (Fab), and Fc-fusion proteins. Most of the currently approved therapeutic antibodies belong to IgG1 subclass (Irani et al., 2015).

A B

Figure 1. General structures of full IgG, and variable domain.

(A) Crystal structure of an intact murine IgG1, PDB ID 1igy (Harris et al., 1998). IgG molecule consists of 2 heavy chains and 2 light chains. Fc fragment consists of CH2 and CH3 domains. VH/CH1 and VL/CL constitute the Fab fragment. (B) Representative picture of a variable domain with β-sandwich folds held together by a disulphide bond. Three hypervariable loops (in magenta) on each VH or VL are key to determining antigen binding and are known as complementarity determining regions (CDRs: CDR1, CDR2, CDR3). Images from Berg et al. (2002).

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B. In vitro production of recombinant mAbs With ex vivo antibody production made possible by the invention of hybridomas technology by Kohler and Milstein (1975), significant progress has been made in the field of antibody therapy. In vivo antibody discovery begins with injecting an animal (usually rodents) with purified antigens of interest (

Figure 2A). B-lymphocytes are extracted from the spleen or lymph nodes and fused with myeloma cells to generate immortal hybrid cells, i.e., hybridomas. Since these hybridomas originate from a pool of different antigen-producing B cells, the secreted antibodies in the culture are polyclonal. To obtain monoclonal antibodies, individual clones are isolated in separate wells and undergo repeated screening against target antigens (Liu, 2014). One attractive quality of choosing this route of discovery is that the heavy lifting is supposedly carried out by the host body, i.e., in vivo affinity maturation. In the natural system, one of the key features of the adaptive immune system is an extensive repertoire of antigen receptors generated by combinatorial genetics (Lerner, 2016). Critically, the adaptive immune system dictates a direct genotype-phenotype link through antibody presentation on the B cell as a receptor. Upon exposure to an antigen, the cell becomes activated and replicates. Over time, the affinity-matured antibody clones with selection advantage become predominant. These detailed underpinnings illustrate two fundamental components of the system, diversity and searchability by selection – both of which are amenable to manipulation by in vitro genetic engineering.

A major breakthrough in antibody discovery arrived in the form of display technologies, which similarly work on the principle of direct linkage between the phenotype and the corresponding genotype in a package capable of replication. Display technologies allow affinity-based selection to be carried out in vitro, and consequently may prove to be more time efficient than the hybridoma method. Additionally, species cross-reactivity is also made possible (Bradbury et al., 2011). As a result of immune tolerance, cross-reactive antibodies are difficult to raise using hybridoma methods (Zhou et al., 2009). Various display technologies (

Figure 2B) make use of different biological systems including bacteriophage (Smith, 1985), yeast (Boder and Wittrup, 1997), and genetic machineries, such as, DNA (Sepp et al., 2002, Odegrip et al., 2004), RNA (Roberts and Szostak, 1997), and ribosome 19

(Mattheakis et al., 1994, Hanes and Pluckthun, 1997). Not all display platforms have been proven to work well for antibody discovery however. Properly-folded antibodies are more likely to be present in the display systems that make use of higher or lower eukaryotes with more evolved protein synthesis machinery, compared to phage-display antibodies that are produced by bacteria (Doerner et al., 2014). Specifically, functional assembly of the antibody molecule is largely dependent on the correct formation of disulphide bonds and post-translational glycosylation of the Fc region (Vazquez- Lombardi et al., 2015). The reducing nature of bacterial cytoplasm can severely hinder disulphide formation, although this issue can be circumvented with periplasmic expression which allows folding of the antibody fragments in an oxidising environment (Rouet et al., 2012). Another integral component for in vitro antibody selection is the generation of a combinatorial antibody library that mimics that of the natural repertoire. The combinatorial library is built based on the sequence information of antibody molecules from naïve or immunised animals, or can be fully synthetic (Sheehan and Marasco, 2015). For yeast and phage display the size of the library is mostly limited by transformation efficiency. However, this is not applicable to in vitro technologies i.e., ribosome, RNA, and DNA display where libraries can contain as many as 1014 members (Amstutz et al., 2001). The magnitude of the combinatorial antibody libraries and the robustness of the selection methodologies have undoubtedly potentiated a rapid progression in the development of target-selective antibodies for applications in biotechnology and medicine.

Phage display strategy is widely used for antibody display and has successfully yielded many target-binding antibodies (Frenzel et al., 2016). Prominent antibody types that are broadly used in phage display are single-chain variable fragments (scFvs), and to a lesser extent, single-domain antibodies. As the name suggests, single-domain antibody consists of a single protein domain which possesses the ability to recognise an antigen with high specificity and affinity. The first discovered single-domain antibodies,

VHH, were derived from heavy-chain antibodies found in camelids (Harmsen and De Haard, 2007). Cartilaginous fish, e.g., sharks, also produce antibodies that are devoid of light chains, and the variable domain vNAR has similarly been engineered for biotechnology applications (Feige et al., 2014). We have utilised phage-display vNAR

20 libraries in our search for Nav1.7-selective antibodies. The full detail of our study will be described later.

21

A B

Figure 2. Hybridomas and in vitro display technologies.

(A) Hybridoma technology. A mouse is immunised with antigens of interest, following which spleen cells are harvested. The spleen cells are then fused with myeloma cells. The resulting hybrid cells known as hybridomas are screened for antigen selectivity, and subsequently isolated for mAb production. Image from Ruigrok et al. (2011). (B) Diversity of display platforms. Macromolecules or peptides of interest may be displayed on a cellular platform, e.g., yeast/bacterial surface, or phage capsid. Acellular approaches make use of a cell-free environment and do not require transformation of genetic materials into the cell. These approaches include ribosome, mRNA, in vitro compartmentalisation (IVC) or CIS display. Image from Ullman et al. (2011).

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Small Molecules Small molecules possess many positive attributes, e.g., they are relatively easier to synthesise compared to recombinant antibodies, and the cost of screening can be much less with the availability of in silico docking tools to narrow down potential hits for biochemical screening. At the initial stage, small molecule screening is often a laborious task. Once a few hits emerge, the chemists can then make use of the backbones as the starting point for chemical modification to achieve better efficacy or safety profiles. This time-consuming process of manual modification has been revolutionised by combinatorial chemistry and cheminformatics. Disappointingly, combinatorial chemistry did not drastically speed up the drug discovery process as originally expected. One of the reasons is that it focuses on expanding the compound library by combining the given building blocks in any way possible and does not take into consideration how the actual structure might affect activities. Cheminformatics, on the contrary, was developed to overcome these issues. Essentially, initial hits are stripped down to their basic ‘molecular descriptors’ which are used as building blocks to rapidly expand the hit space by virtue of similarity measurement. Similarity measurement is the crux of structure-activity relationship (SAR) which posits that the chemical or 3D structure of a molecule is directly related to its biological activity (Frye, 1999). By focusing the search on the molecules with similar structure, the time and cost are reduced and the likelihood of uncovering new molecules with similar or better activity is much enhanced. One obvious drawback of this method is that it eliminates the chance of identifying compounds that do not obey the SAR assumption. To get the best of both worlds, both combinatorial chemistry and cheminformatics are often utilised in parallel. Critically, in silico screening has resulted in the discovery of novel tool compounds, including inhibitors of GPCRs (Becker et al., 2004), and antiviral agents (Kampmann et al., 2009).

A. Ligand-based Drug Design Molecular descriptors are standardised chemical representations based on the counts of molecular fragments, or the presence or absence of a defined set of chemical groups (i.e., chemical fingerprints). This standardisation provides unambiguous and objective criteria, which enable chemical comparisons to be performed computationally in the QSAR studies and drug discovery attempts. Chemical fingerprinting can be

23 categorised into 2D (topological) or 3D (configurative) substructural fragmentation. Specifically, a molecule is modelled in a 2D plane or 3D space, and all atoms, bonds, branching points, and cyclic patterns are discerned. The detected fragments are represented in a bit string (using Boolean logic; 0 for absent and 1 for present) of a defined length and order as specified by a dictionary (one-to-one mapping) or hashing (many-to-many) scheme (Leach and Gillet, 2007). Based on these formal definitions, similarity parameters such as distance-based and angle-based descriptors are formulated (for extensive review, see Nikolova and Jaworska (2003)). Similarity is often quantified by calculating numerical equivalence of the overlap between fingerprints in the form of similarity coefficients. These methods do not require structural knowledge of the target protein and solely depend on the information presented by the ligands; hence are sometimes referred to as ligand-based rational drug design. Despite the difference in how similarities are approximated, divergent methods hold the SAR assumption to be true. Tanimoto coefficient is arguably the most popular similarity index given the molecules are described by 2D fingerprints. The simplified formula is 훾 (퐴, 퐵) = ; α is the substructure count of A, β is the substructure count of B, γ is 훼+훽−훾 the count of substructures that are in both A and B (Rogers and Tanimoto, 1960, Xu and Hagler, 2002). The performance of Euclidean-based similarity search methods appears to be largely dependent on the broad class of descriptors (i.e., circular fingerprints, circular fingerprints considering counts, path-based and keyed fingerprints, and pharmacophoric descriptors) rather than individual parameterisations (Bender et al., 2009).

B. Structure-based Drug Design Any forms of similarity searches and classifications can be regarded as virtual screening as they effectively predict molecular properties in silico. One other common virtual screening approach that has not been described is structure-based drug design, i.e., molecular docking. Available 3D structure of the target is a prerequisite for docking. Docking was pioneered in the early 1980s, and is essentially an iterative two-step process of sampling the molecular poses in the binding pocket, and ranking how good the fit is via a scoring algorithm (Meng et al., 2011). Various combinations of ligand’s and receptor’s rigidity are available for implementation. Both ligand and receptor may be considered as a rigid entity or one with limited conformational flexibility, so that

24 docking does not induce conformational change to the structure of either binding partner. This imposes limitations on the search space and can speed up the docking process significantly. This methodology is used by the first generation of automated docking software, including DOCK (Kuntz et al., 1982), and FLOG (Miller et al., 1994). More intuitively, binding partners likely adopt different conformations when they interact as per the induced-fit paradigm. Incorporation of molecular flexibility into the docking simulations starts off with permitting ligand flexibility while keeping the receptor rigid to keep the computational time within a sensible window. Currently, this methodology is adopted as a default mode in almost all docking programs, including most versions of AutoDock (Morris et al., 2009), and FlexX (Kramer et al., 1999). Modelling flexible ligand and flexible receptor docking has remained a great challenge in molecular docking but considerable progress has been made to overcome the grave limitations imposed by inadequate sampling issue and high computational cost. Accurate modelling is necessary but not sufficient for accurate prediction of binding affinities, i.e., scoring and ranking. It seems that scoring functions continue to be one of the main limiting factors of the success rate in molecular docking (Kitchen et al., 2004).

There comes a point where the outcomes of all virtual screenings converge; that point being synthesis of the compounds to be used in biochemical screening. Despite the usefulness of computational tools in early-stage drug discovery, they can only provide predictive measures based on previously known results. Biochemical, behavioural assays and animal models will always remain integral for determining the actual properties and efficacy of the molecules.

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Figure 3. Overview of small-molecule rational drug design.

There are two main approaches for development of small-molecule therapeutics; ligand-based and structure-based drug design. Heavily reliant on the SAR assumption, LBDD searches the chemical space, often limited-size libraries, for molecules that show similarity to previously known hits/leads/drugs. Similarity can be defined based on pharmacophore matching or chemical fingerprint scoring. SBDD models ligand poses in a defined region on the known structure of the target protein/receptor. Often, probable ligands are first screened via LBDD prior to docking to limit the number of virtual processing required, i.e., the computational cost. Virtual hits that display high similarity or docking scores are then synthesised and tested in relevant biochemical assays to confirm their in vitro activities. Adapted from Cozza (2017).

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CHAPTER 1: NAV1.7-BASED AFFINITY SELECTION AND TRANSCRIPTIONAL REGULATION

Introduction The initial aim of the thesis was to use a phage-display vNAR platform to select antibodies that can bind and inhibit the function of Nav1.7. The first part of the introduction will explain the importance of Nav1.7 in the nervous system, and why

Nav1.7 is the key target for chronic pain intervention. This will then be followed by review of the less known roles of Nav1.7 in cancer metastasis. The second part of the introduction handles the principles and technicalities behind phage display technology to give an overview of the platform that we used in our investigation.

A. Voltage-Gated Sodium (Nav) Channel

Nav channel subtypes and functions Voltage-gated sodium channels are Na+-selective ion channels in the cell membrane that open or close in response to changes in membrane potential. Fully functioning channel in vivo requires both the pore-forming α-subunit, and one or two auxiliary β-subunits which modify expression and gating properties of the pore domain (Isom et al., 1994). Evidence from heterologous expression studies suggests that the α- subunit is also functional on its own (Yu and Catterall, 2003). Often the term Nav channel exclusively refers to the α-subunit. In humans, nine closely-related α-subunit isoforms have been identified which are numerically labelled Nav1.1 to Nav1.9 (Catterall, 2000). These isoforms are encoded by SCN superfamily genes (Table 2), and exhibit subtype-specific cellular localisation and gating behaviours (Wood et al., 2004). In mammals the β-subunit exists in five different isoforms (β1, β1B, β2, β3, β4) encoded by SCN1B – SCN4B (Brackenbury and Isom, 2011). β1B is a secreted protein, while the rest are members of the immunoglobulin superfamily with the characteristic Ig-like β- sandwich folds in the extracellular domain. β1 and β3 subunits non-covalently interact with the α-subunit, while covalent disulphide bonds are formed between the α-subunit and its β2 or β4 partner (Laedermann et al., 2013).

Vertebrate Nav α-subunit is a single polypeptide chain of ~2000 nucleotides, with approximate molecular mass of 260 kDa. The polypeptide repeatedly traverses the membrane bilayer to form four homologous domains (DI – DIV), each with six α-helical

27 membrane-spanning segments S1 – S6 (Figure 4A). The domains are arranged to surround a central aqueous pore which is ion-permissive (Figure 4B). S4 is known as a voltage-sensing segment and contains positively-charged residues that form an alignment on one side of the helical structure. Changes in membrane potentials during depolarisation are sensed by these residues, which then respond to push the S4 segment outward. In doing so, conformational changes are induced which open the activation gate situated on the inner face of the pore (Figure 4C). The four voltage sensors are not structurally symmetric and exhibit distinct kinetics. S4 of DI – DIII respond quickly to fluctuations in the membrane potential, whereas S4 of DIV displays slower kinetics (Chanda and Bezanilla, 2002). Outward movement of the sensor exposes the hydrophobic clusters within S4-S5 in DIII and DIV, i.e., docking sites, to the cytoplasmic DIII-DIV linker containing a critical isoleucine-phenylalanine-methionine IFM motif of the inactivation gate (Goldin, 2003). Interaction between the motif residues and the docking sites pull the linker towards the intracellular aperture and occlude the pore. This process is often referred to as a hinged-lid mechanism. When the so-called inactivation loop is in place, the channel becomes inactivated and non-conducting. DIV- S4 is the only voltage sensor that is coupled to the selectivity filter, and is implicated in its gating transition, as well as the initiation of slow inactivation (Capes et al., 2012).

Table 2. hNav α-subunit subtypes, genes, and distribution.

In human, there are 9 distinct isoforms of Nav-channel α-subunit, all of which are encoded by the SCN gene superfamily. These Nav isoforms are distinctly localised throughout the body, including the heart, muscle, PNS and CNS. Nav1.7, 1.8, and 1.9 are the predominant PNS subtypes. In addition, Nav channels are conventionally categorised in terms of their TTX sensitivity (nM ~ sensitive, µM ~ resistant). Source: Dib-Hajj et al. (2010), Eijkelkamp et al. (2012) Isoforms Gene Major locations TTX sensitivity Nav1.1 SCN1A CNS, PNS Sensitive Nav1.2 SCN2A CNS, PNS Sensitive Nav1.3 SCN3A Embryonic CNS, PNS Sensitive Nav1.4 SCN4A Skeletal muscle Sensitive Nav1.5 SCN5A Cardiac muscle Resistant Nav1.6 SCN8A CNS, PNS, smooth muscle Sensitive Nav1.7 SCN9A PNS Sensitive Nav1.8 SCN10A PNS.(DRG, trigeminal) Resistant Nav1.9 SCN11A PNS Resistant

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Figure 4. Structure and voltage-gating modes of a Nav channel.

(A) Cartoon representations of the two subunits that constitute a Nav channel; pore-forming α- subunit, and auxiliary β-subunit. There are 9 α-subunit, and 4 β-subunit isoforms in human. Image from Fraser et al. (2014b), (B) Top and side views of the crystal structure of bacterial NavAb (Payandeh et al., 2011). The main α-subunit channel protein is a single polypeptide that repeatedly crosses the membrane and forms 4 homologous domains with a pore in the centre. Each domain has 6 membrane-spanning segments, S1-S6. Changes in membrane potentials are detected by S4, i.e., voltage sensor. Image from Namadurai et al. (2015), (C) Three modes of channel gating. At rest, the activation gate is closed, and the channel is non-conducting. When the membrane is depolarised, the channel becomes activated and allows selective Na+ influx through the selectivity filter. Channel inactivation occurs via a hinged-lid mechanism, with the intracellular loop moving towards and occluding the mouth of the inner pore.

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Roles of Nav in nociception Pain or nociception is a sensory embodiment of potential injury or harm which serves as an evolutionary advantage. Pain sensation that is inappropriately magnified or more prolonged than what is necessary to notify an individual of the innocuous stimuli becomes maladaptive and debilitating. The line between adaptive (acute) and maladaptive (chronic) is rather ill-defined, which is rightly so since pain is a subjective experience consisting of a multitude of components, e.g., biochemical, neurological, and emotional. Often the diagnosis is based on the time window for which the pain persists. According to the International Association for the Study of Pain (IASP), chronic pain lasts for longer than the normal time for healing, normally referring to a convenient cut-off point of three months. Back pain and arthritis are the two most commonly reported types of chronic pain (Elliott et al., 1999). Another important chronic pain category is neuropathic pain which develops after nerve injury. Chronic pain surveys conducted in 2006 revealed ~19-48% prevalence in the general population, with approximately 8% of predominantly neuropathic origin (Torrance et al., 2006, Breivik et al., 2006). Neuropathic pain is often associated with worse prognosis and is more refractory to conventional analgesics than non-neuropathic pain, hence usually necessitating a different approach for management (Cohen and Mao, 2014). High prevalence of chronic pain not only severely impacts the sufferers’ quality of life, but also imposes an astronomical cost on the economy (~£5-£10 billion per year through pain-related loss of productivity in the UK), and a substantial burden on the healthcare sector (Phillips, 2009). Generally chronic pain results from broad changes in nociceptor function in terms of modulation and modification (Voscopoulos and Lema, 2010). Modulation covers reversible events that affect the excitability of a group of neurons that are designated for pain processing, i.e., nociceptive neurons or nociceptors. Modification on the other hand represents longer-lasting and more rigidified alterations that are associated with neuronal structure and connectivity.

Relay of pain information from the periphery to the CNS requires a series of finely-coordinated cellular events including signal transduction, conduction, and synaptic transmission. Peripheral nociceptors may possess unimodal or polymodal activities, meaning that they either respond to one or more types of noxious stimuli, e.g., thermal, chemical, or mechanical. Specifically, myelinated and fast-conducting Aδ

30 fibers function as mechanothermal receptors, while C-fibers are small, non-myelinated and slow-conducting nerves that respond to all three stimulus classes (Dubin and Patapoutian, 2010). Large-diameter Aβ fibers are highly myelinated and exclusively convey non-noxious touch and proprioceptive information. The first step in pain pathway is signal transduction which occurs in response to inflammatory mediators that are secreted in abundance by damaged and immune cells. The inflammatory molecules include adenosine-5’-triphosphate (ATP), proton (H+), bradykinin, prostaglandin, and nerve growth factor (NGF). These molecules bind and activate the corresponding transducers, e.g., ligand-gated ion channels, in the free nerve endings. Transduction agents can trigger a plethora of events, e.g., phosphorylation, receptor trafficking, and transcriptional regulation, which alter ion channel activity and contribute to peripheral sensitisation (Ji et al., 2002, Schmidt et al., 2009). Voltage-gated ion channels play a crucial role in the generation and conduction of action potentials. Spontaneous and ectopic activity of Na+ channels is one of the key elements that lead to the manifestations of neuropathic and chronic pain states (Devor, 2009). When the action potential reaches the central nerve terminal, synaptic transmission occurs via neurotransmitter release as a result of co-ordinated events by several presynaptic

2+ components, including Ca channels, and even Nav1.7 and TRPV1 (Black et al., 2012, Bennett and Woods, 2014).

Generation and propagation of action potentials are dependent on Nav channels, and their critical role is reflected by Nav abundance in the nociceptive dorsal root ganglia (DRG) neurons. All Nav subtypes have been identified in adult DRG nociceptive neurons, with the exception of Nav1.2 and Nav1.4 (Wang et al., 2011b). Nav channel expression and function are subject to modulation under physiological and pathological conditions. For instance, GPCR-activated PKA and PKC can phosphorylate the Nav channel to regulate its function as illustrated by the voltage-clamp studies on tetrodotoxin-resistant Na+ current in isolated sensory neurons (Gold et al., 1998). Neurotrophins have been shown to ameliorate the axotomy-induced reduction of DRG neuron specific Nav both in vitro and in vivo (Cummins et al., 2000). Nav1.7, 1.8, and 1.9 subtypes are predominantly found in the peripheral sensory neurons. These three isoforms display distinct kinetics and partake in different facets of action potential generation and conduction. Nav1.7 behaves as a threshold channel, and amplifies small

31 subthreshold inputs by producing a prominent ramp current (Rush et al., 2007). In most small DRG neurons, Nav1.8 is accountable for 80% of an inward current that is essential for the action potential upstroke (Renganathan et al., 2001). TTX-R Nav1.9 displays very slow activation and inactivation and does not contribute to the ascending portion of action potential (Cummins et al., 1999). With regards to pain conditions, Nav1.7 and

Nav1.8 as well as Nav1.3 are upregulated in blind-ending axons in human painful neuromas and this channel upregulation coincides with activation of p38 and ERK1/2

MAPK (Black et al., 2008). Subsequent studies revealed that phosphorylation of Nav1.7 by ERK1/2 MAPK alters its gating properties, conversely Nav1.8 phosphorylation by p38 MAPK changes its current densities and has no effect on its gating properties (Hudmon et al., 2008, Stamboulian et al., 2010). Following motor fibre injury, Nav1.3, 1.8, and 1.9 are reportedly upregulated by inflammatory cytokines, e.g., TNFα (He et al., 2010).

Painful and painless channelopathies In humans, SCN9A mutations have been linked to a myriad of inherited and acquired pain syndromes (Dib-Hajj et al., 2013). The first report was published by Yang et al. (2004) which identified Nav1.7 mutations amongst patients suffering from inherited erythromelalgia (IEM). Since then, many gain-of-function mutations in the SCN9A gene have been demonstrated in other chronic pain conditions, including paroxysmal extreme pain disorder PEPD (Fertleman et al., 2006), and idiopathic small fibre neuropathy I-SFN (Faber et al., 2012). On the contrary, loss-of-function mutations are found in a rare condition of congenital insensitivity to pain CIP (Cox et al., 2006, Goldberg et al., 2007), and anosmia (Weiss et al., 2011). These mutations and additional mutations identified by subsequent genetic studies solidly establish a genotype- phenotype association in terms of pain-related Nav1.7 channelopathies (Drenth and

Waxman, 2007). Taken together, these findings bring Nav1.7 into the spotlight as an ideal candidate for the ‘one gene, one drug, one disease’ paradigm. Less extensive literature exists for Nav1.8 and Nav1.9, but they too are implicated in pain pathologies.

Similar to Nav1.7, Nav1.8 and Nav1.9 gain-of-function mutations have been reported in some of the I-SFN cases (Han et al., 2014, Huang et al., 2014). Further, gain-of-function mutations of Nav1.9 have been identified in families with episodic pain disorders (Zhang et al., 2013). It is worth noting that the relationship between increased channel expression and pain hypersensitivity may not be as straightforward as one might think.

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A gain-of-function mutation in SCN11A gene encoding for Nav1.9 has been linked to congenital insensitivity to pain (Leipold et al., 2013).

A number of animal studies have been conducted providing both supporting and contradicting pieces of evidence on the causal relationship between Nav1.7 and pain etiology. Mice with knockdown of Nav1.7 in primary afferents (Yeomans et al., 2005), and nociceptor-specific Nav1.7 knockout mice (Nassar et al., 2004) developed reduced inflammatory hyperalgesia. Nassar et al. (2005) further demonstrated that nociceptor- specific deletion of Nav1.7 had no effect on the development of neuropathic pain, and that neuropathic pain was still evident in the double knockouts of Nav1.7 and Nav1.8.

The group went on to generate different mouse lines to examine Nav1.7 function in

Nav1.8-positive sensory neurons, all sensory neurons, and sensory plus sympathetic neurons (Minett et al., 2012). They found that Nav1.7 in Nav1.8-positive DRG neurons was important for detecting noxious mechanical stimuli. Whereas in the absence of

Nav1.8, Nav1.7 participated in the processing of noxious thermal stimuli. Nav1.7 deletion in all sensory neurons did not significantly affect the development of neuropathic pain, confirming their earlier findings of Nav1.7 irrelevance in neuropathic pain. However, neuropathic pain was reportedly abrogated in the spinal nerve transection (SNT) model when Nav1.7 ablation was applied to both sensory and sympathetic neurons; suggesting that the sympathetic component is essential for neuropathic pain, and that Nav1.7 in different neuronal populations is associated with both overlapping and distinct pain phenotypes. This observed link between Nav1.7 and neuropathic pain was substantiated by later work on spared nerve injury (SNI) and spinal nerve ligation (SNL) models of neuropathic pain where Nav1.7 mRNA in mouse DRG was dramatically downregulated following peripheral nerve injury (Laedermann et al., 2014). In the past, a global Nav1.7- null mutant was found to be neonatally lethal (Nassar et al., 2004), yet a non-lethal global knockout of Nav1.7 has recently been reported (Gingras et al., 2014). The studies found the knockouts to possess pain phenotypes that are analogous to human congenital insensitivity to pain. Specifically, the mutant mice were insensitive to painful tactile, thermal, and chemical stimuli, and were anosmic. According to these animal studies, it is clear that acute pain and some types of inflammatory and neuropathic pain are Nav1.7-dependent.

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Nav1.7 channel as a drug target for pain relief

Pan-Nav blockers are effective therapeutics for a wide range of conditions and are used as local anaesthetics (lidocaine), anticonvulsants (carbamazepine, phenytoin, lamotrigine), anxiolytics and analgesics (amitriptyline) (Rogers et al., 2006). As with other non-specific inhibitors, there is the need to overcome the liabilities of currently available medication with better drugs that have greater specificity, improved efficacy, and reduced/obliterated side effects. Critically, demand for novel analgesics has intensified in the midst of growing concern for the escalating opioid epidemic in the US over the past decades, which has led to an unprecedented number of cases of drug misuse, overdose, and fatalities (Manchikanti et al., 2012). The crisis has instigated an immense public scrutiny and pressure on the pharmaceutical industry to step in and introduce novel painkillers with minimal addictive properties. One strategy that many pharmaceutical companies have adopted focuses on the development of Nav isoform- selective compounds, with Nav1.7 being the centre of attention, and Nav1.3, 1.8 and 1.9 to a lesser extent. Structurally, Nav1.7 is fairly similar to other neuronal Nav subtypes

(Nav1.1, 1.2 and 1.6), and the skeletal muscle Nav1.4 (Theile and Cummins, 2011).

Nonetheless, with its expression mainly limited to the periphery, Nav1.7 is in a good starting position as a therapeutic target. Nav1.8 and 1.9, on the other hand, are markedly different in their sequences in comparison to other sodium channels (Goldin et al., 2000), and are possibly primed to potentiate isoform-selective targeting.

Small molecules have largely been identified and comprise an expanding collection of Nav1.7-selective compounds. For example, Merck developed a series of benzazepinone-based Nav1.7 selective blockers that have been shown to work in vivo in a rat model of spinal nerve ligation, though some of them displayed less than ideal pharmacokinetics (Hoyt et al., 2007). A small molecule BZP was claimed to be Nav1.7 selective and exhibited anti-nociceptive effects in rat models of inflammatory and neuropathic pain with no observable symptoms of motor impairment which are common side effects of the clinical standards gabapentin and mexiletine (McGowan et al., 2009). With accumulating knowledge on the peptide toxins that behave as ‘gating modifiers’ (Catterall et al., 2007), a somewhat novel targeting strategy mimicking the voltage-sensor trapping mechanism of these toxins has been embraced by many drug discovery teams. The attempts led to a new surge in Nav1.7-selective compounds,

34 including aryl sulphonamide which is a Nav1.7-selective small molecule antagonist that stabilises a non-conductive channel configuration by engaging the voltage sensor in domain 4 (Ahuja et al., 2015). Several other small molecules that show Nav1.7 selectivity have been developed, and patented; many of which are currently undergoing clinical trials, e.g., CNV-1014802 or Raxatrigine (Convergence), PF-05089771 (Pfizer), and GDC- 0276/GDC-0310 (Xenon/Genentech) (Zuliani et al., 2015, Emery et al., 2016). To our knowledge, there is only one report that claimed to have identified a recombinant mAb that displayed Nav1.7 selectivity and functional efficacy in animal pain models (Lee et al., 2014b). These findings have not been replicated however (Emery et al., 2016), and it seems that, to date, a functionally active Nav1.7-selective mAb has yet to be discovered.

Non-canonical roles of Nav channel in non-excitable cells and carcinomas The long-held belief that sodium channels are exclusively involved in membrane excitability has been challenged by observations of their endogenous expression in non- excitable cells, including, red blood cells (Hoffman et al., 2004), macrophages (Craner et al., 2005), oligodendrocytes (Tong et al., 2009), dendritic cells (Zsiros et al., 2009), and fibroblasts (Chatelier et al., 2012). It is worth noting that sodium currents have been recorded in patch-clamp studies on a variety of these cells (Black and Waxman, 2013).

The presence of Nav1.7 in dendritic cell subpopulation maintains the resting membrane potential in a depolarised state (Kis-Toth et al., 2011), and sustained inward Nav1.5 current has been observed in breast cancer cells (Gillet et al., 2009). Expression of Nav channels in these cells are dynamic, and may alter depending on the developmental, physiological, and pathological state (Black et al., 2010, Paez et al., 2009). This non- static nature relates to distinct characteristics of the cell in a particular state, and serves to implement diverse effector functions, e.g., motility, endosomal acidification and phagocytosis (Brackenbury et al., 2008, Black et al., 2013).

In addition, Nav channels are anomalously expressed in a wide range of tumours, for example, lymphoma, breast cancer, melanoma, colon, and ovarian cancer (Roger et al., 2006). In some cases, the channels are not present in the corresponding healthy tissues, e.g., breast cancer (Fraser et al., 2005). Interestingly, most Nav1.5 channels in MDA-MB-231 human breast cancer cells were found to be neonatal splice variants, hence have implications in the development of tumour-specific treatment (Brackenbury et al., 2007). Indeed, Nav1.5 inhibition by ranolazine was shown to inhibit Nav1.5 35 currents and reduce breast cancer cell invasiveness in vitro, and markedly attenuated lung colonisation in vivo (Driffort et al., 2014). On the other hand, in certain cancers, such as glioma, and ovarian cancer, Nav is normally expressed in the healthy counterparts (Black et al., 2009, Gao et al., 2010). In these cancers, the degree of channel overexpression is related to the aggressiveness of cancer metastasis. Metastasis refers to a multi-step cascade that is set out to spread the disease as far and wide within the body of the host (Yamaguchi et al., 2005). The mechanisms by which

Nav exerts its metastasis-related effects are only beginning to be elucidated and thus far most details have been obtained from breast cancer studies (Roger et al., 2015). Sodium ions are clearly an important factor, yet how much of the observed response is due to the fluctuation in Na+ concentration, membrane potential, or transporter- assisted ion exchange, remains unclear (Litan and Langhans, 2015). The three events are intricately intertwined, e.g., Na+ influx leads to an increase in intracellular Na+ concentration, simultaneously depolarises the membrane potential, and drives the ion exchanger in the energetically favourable direction. It has been shown that the depolarised state can trigger the Na+/Ca2+ NCX exchanger to reverse the direction, resulting in an increase in cytosolic Ca2+ which in turn facilitates NG2 cell migration (Tong et al., 2009).

One school of thought believes the interaction between Nav and the partner proteins to be critical, based on accumulating evidence that demonstrates various scaffolding proteins to associate with the channel to form multiprotein complexes

(Besson et al., 2015). Being part of the complexes, Nav expression, localisation, and activity are subject to necessary modulation. Rat Nav1.2 channels have been shown to colocalise with ankyrin G, and that this interaction dictates the correct localisation of the channels in the axon initial segment which is critical for the initiation of action potential (Lemaillet et al., 2003). Calmodulin interacts with Nav1.4, 1.5, and 1.6 to mediate Ca2+ sensitivity of these channels, and consequently their functional properties (Herzog et al., 2003, Kim et al., 2004). Na+ current was also shown to be regulated by EGF receptor kinase (Liu et al., 2007). EGF administration to the guinea pig ventricular myocytes considerably enhanced the cardiac Na+ current, while selective EFGR kinase inhibitor inhibited the current. The notion of complex formation coincides with the fact that there are several other ion channels and transporters involved in cell migration (for

36 extensive review, see Schwab et al. (2012)). By bringing different proteins in close proximity, it has been suggested that Nav activity gets transduced into the effects on motility through a local hub of ion fluctuations (Levitan, 2006).

The segregation between normal and tumour tissues is evident at least physically, and electrophysiologically. However, not until the last decade, with the availability of fast and efficient genetic profiling tools such as microarray did the investigation start to unravel the underlying gene expression patterns that underpin the distinct phenotypes in these two tissue types. Ma et al. (2009) carried out a series of gene expression profiling of tumour epithelial cells and tumour-associated stroma extracted from breast cancer patients. They found several genetic markers that correspond to the observed phenotypes and aggressiveness of the tumour, for example, a concomitant upregulation of matrix metalloproteases alongside the transition from preinvasive to invasive growth. Interestingly, gene expression signatures of the tumour epithelium and the associated stroma appeared to be considerably matched. This observation demonstrates the underlying genetic drive that governs the tumour phenotypes and strengthens the knowledge that tumour microenvironment is an important determinant in tumour cell dissemination and disease progression. In relation to Nav1.7, it seems that Nav1.7 upregulation in certain tumours fittingly falls under the tumour-related transcriptomic changes and ties in with the aggressive phenotypes exhibited by the metastatic cells. It is tempting to think that by suppressing

Nav1.7 expression or activity to resemble the healthy tissue more closely, one might be able to improve cancer prognosis. In fact, it has been speculated that the use analgesics that engage Nav for cancer surgery may have implications in lessening tumour reoccurrence (Fraser et al., 2014a).

Bringing together the two concepts in cancer metastasis where (i) Nav is recruited as part of the signalling complexes and that (ii) there are genetic undertones to the phenotypic presentations of cancer, it is prudent to question the possibility of

Nav functioning at a transcriptional level to regulate gene expression. The idea of ion- dependent transcriptional coupling is prevalent for voltage-gated calcium channel in relation to Ca2+ as a gene regulator (Barbado et al., 2009), but less so considering the

Nav channel. Until recently, Nav has always been known to play a major role in the generation of action potentials, but not much else. We would expect the transcriptional 37 effects of sodium channel activation/inhibition to be indirect, i.e., downstream. The hypothesis is that there is something unique about the gene level changes upon sodium channel perturbation which might enable us to study the channel in a variety of cellular contexts. Strikingly, complete abrogation of Nav1.7 in all sensory neurons led to marked alterations in their transcriptome (Minett et al., 2015). The group reported Penk mRNA to be dramatically upregulated in Nav1.7-null mutant mice. In addition, this upregulation appeared isoform-specific since it was not observed in Nav1.8 nor 1.9-null mice. Penk encodes precursors for enkephalins, which are one of the endogenous opioid peptides that bind the opioid receptors in the body to mediate analgesic responses (Koneru et al., 2009). Blocking opioid receptors with naloxone was then shown to restore the ability to perceive pain, both in these mice and in human patients. From these findings, it was suggested that another player, i.e., opioid, might contribute to the recurring analgesic properties of Nav1.7 deletion/inhibition encountered time and again by independent researchers (Nassar et al., 2004, Flinspach et al., 2017).

One follow-up study by a team at Genentech made use of tamoxifen-inducible

Nav1.7 conditional knockout (cKO) mice in an attempt to verify the transcriptional aspect of Nav1.7 regulation, and the involvement of opioids (Deng et al., 2016). They found similar pattern of transcriptional changes to the published results by Minett et al. (2015). Specifically, a small 2-3 fold increase in Penk mRNA was observed. What was less clear was that inhibition of the opioid receptors with naloxone did not reverse the antinociceptive behaviours in Nav1.7-cKO mice, contradicting the previous report.

Another study by Isensee et al. (2017) lent further credence to the idea that Nav1.7 might be acting in tandem with other signalling partners to implement the downstream effects. Essentially, the group asked if any alterations ensued from Nav1.7 deletion other than increased level of endogenous opioids. They found that pronociceptive serotonergic signalling through 5-HT4 was downregulated specifically in Nav1.7 knockouts, and that this observation was absent in Nav1.8-KO mice. The downregulation of pronociceptive signalling occurred in parallel with an increase in opioid-dependent antinociceptive effects. Overall, the results suggest the crucial role played by Nav1.7 in maintaining the balance between pro-and anti-nociceptive gradients. By obliterating

Nav1.7, the balance becomes skewed and tips towards the anti-nociceptive end of the scale which manifests as recurring analgesia associated with Nav1.7 deficiency.

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B. Antibody Phage Display

Shark IgNAR antibody Immunoglobulin New Antigen Receptor, i.e., IgNAR, is a heavy-chain only antibody that is naturally found in cartilaginous fish including sharks, rays, skates, and chimeras (Flajnik and Rumfelt, 2000). IgNAR was first discovered in a serum of a nurse shark Ginglymostoma cirratum by Flajnik and colleagues (Greenberg et al., 1995). It is one of the three Ig types found in this taxon; two others are IgM and IgW. Structurally IgNAR is a homodimer of two heavy chains and exists in both transmembrane and secretory form. Each chain consists of a variable domain and 5 constant domains, and dimerisation occurs at C1 and C3 domain. It remains unclear whether the constant regions on the IgNAR exert an effector function like those of the Ig molecule (Barelle et al., 2009). Antigen binding is mediated through the variable domain, known as vNAR which is closely related to Vα of the T-cell receptor (Zielonka et al., 2015). vNAR is ~12- 13 kDa in size and is the smallest antigen recognition entity in the animal kingdom known to date (Stanfield et al., 2004). It belongs to the Ig superfamily and has a β- sandwich configuration with two canonical cysteine residues residing within framework (FW) 1 and 3 typical of immunoglobulin fold (Figure 5; frameworks as white boxes, counted from left to right skipping one between HV2 and HV4). One feature that distinguishes vNAR from the mammalian V domain is that it only has CDR1 and CDR3, instead of 3 CDR loops (Figure 5B). Two non-CDR regions with high rate of somatic mutations are termed hypervariable loops (HV2, and HV4). These loops are located at the CDR2 truncation site and the site that corresponds to HV4 in T-cell receptors.

39

A B

Figure 5. Four vNAR subtypes, and 3D structure of a vNAR (Type II).

(A) vNARs are categorised into 4 subtypes based on the position of non-canonical cysteine residues or lack thereof (open circles). Open triangles depict canonical cysteine residues. Complementarity Determining Region 1 and 3 (CDR1, 3) are important for antigen binding. Additionally, there are two hypervariable loops that are highly mutated, HV2, and HV4. (B) Crystal structure of a Type II vNAR. It consists of Ig-like β-sandwich folds. The upright configuration of CDR3 is prototypical of Type II vNAR due to a disulphide bridge that forms between two cysteine residues in CDR1 and CDR3. PDB accession 2coq (Streltsov et al., 2005). The presence of non-canonical residues and their locations allow classification of vNARs into 4 closely related subtypes. Type I and II are the predominant subtypes that are expressed in adult sharks, and both are homologous to the V domains of mammalian Igs (Barelle et al., 2009). Type I has additional cysteines in FW2 and FW4, and another 2 to 4 cysteines in CDR3. Disulphide bridges are formed between cysteine residues in the FW and CDR3, forcing the CDR3 loop to distend more laterally. To date, Type I vNARs have only been found in the nurse shark, Ginglymostoma cirratum (Streltsov et al., 2004). Type II contains a prototypical disulphide bridge that connects CDR1 and CDR3, keeping these loops in an upright position above the FW regions (Figure 5B). The protrusive configuration of CDR3 in the Type II vNAR predisposes it to probing into clefts or grooves on the protein surface, which is ideal for targeting sites that are too embedded or hard to reach (Zielonka et al., 2015). Type III is a neonatal form found in early development before the adaptive immune response is fully matured. Type III is very similar to type II, albeit more limited CDR3 diversity and the presence of a conserved tryptophan adjacent to the cysteine in CDR1 (Streltsov et al., 2005). Type IV vNAR only consists of two canonical cysteine residues and completely lacks additional disulphide bridges formed by non-canonical cysteines that exist in other

40 aforementioned subtypes, leading to greater structural flexibility overall (Kovalenko et al., 2013).

Diversity of primary vNAR repertoire is almost entirely CDR3-based (Diaz et al., 2002). These variations are however limited by the non-canonical cysteine residue in CDR3 in vNAR Type I, II and III which imposes structural constraints on its amino acid composition. Despite these constraints, there is high degree of variability in the long and structurally complex CDR3 as a result of four gene-cluster rearrangement events to allow for diverse antigen recognition (Streltsov et al., 2005). High rates of somatic hypermutations have also been reported following antigen exposure in CDR1 in Type II, HV2 in Type I, and HV4 loop which are implicated in enhanced antigen binding.(Stanfield et al., 2007). It is proposed that in the somatically mutated vNAR, CDR3 is stabilised and provides a relatively fixed and rigid antigen binding site, resembling a lock-and-key binding mode. On the other hand, CDR3 loop in the germline/unmutated counterpart is more flexible, making it conducive to an induced-fit paradigm of antibody-antigen interaction. This CDR3 conformational plasticity is thought to contribute to further structural diversification in the primary vNAR repertoire. vNAR possesses several attributes that are desirable in efficacious therapeutics including its relatively small size (~12 kDa), high stability, high solubility, manufacturability, and the characteristically high-specificity and high-affinity epitope recognition. The vNAR molecules are also amenable to reformatting to eliminate unfavourable pharmacokinetic properties, e.g., rapid renal clearance via glomerular filtration (Muller et al., 2012). In addition, extensive CDR3 loop can potentially gain access to more recessed epitopes that are intractable to classical mAbs.

Bacteriophage and phage display technology Filamentous bacteriophages (Ff), e.g., M13, fd, and f1 are characteristically long and filament-like in shape. They are categorised as non-lytic in that they do not induce lysis of the host cells upon assembly, instead the cells can continue to grow and divide. Other phages such as T4, T7, and λ phages induce host cell lysis after infection and are not widely used for phage display. There are five coat proteins that form the exterior of a phage particle, i.e., the major coat protein pVIII and minor coat proteins pIII, pVI, pVII, and pIX (Figure 6). Thousands of pVIII copies are organised in a helical array surrounding an ssDNA genome with a few copies of the minor coat proteins capping the two short 41 ends (Rakonjac et al., 2011). All coat proteins can be used for display, but pIII and pVIII are the most commonly employed coat proteins for this purpose (Sidhu, 2001). pVIII makes up the majority of the packaging material and contributes up to ~90% of the total virion mass (Huang et al., 2012). Despite the vast surface coverage that would be ideal for display, pVIII is only suitable for displaying small peptides or proteins due to packaging constraints. pIII on the other hand can tolerate larger proteins, though phage infectivity might be compromised.

The key principle of all phage display systems is the direct linkage between a phenotype and its underlying genotype. Peptides or proteins are displayed through its tethering to the coat proteins on a phage particle. The fusion proteins are directed to the periplasm or inner membrane of a bacterial cell by an N-terminal signal sequence, e.g., pelB leader sequence. The proteins are then incorporated into a nascent phage during assembly (Figure 6). The genetic code is also packaged during the phage assembly process and is carried as a single-stranded DNA (ssDNA). Fully-formed phage particle can go on to infect more E. coli hosts by attaching its pIII to the F-pilus on the bacteria. The pilus is spontaneously extended and retracted, and this retraction ushers pIII across the outer membrane into the periplasm (Clarke et al., 2008). The periplasmic domain of the host TolA protein facilitates entry of the phage ssDNA into the cytoplasm, and disassembly and integration of the coat proteins into the inner membrane for recycling (Click and Webster, 1997). The ssDNA enters the cytoplasm and gets converted to dsDNA replicative form (RF). The RF acts as a template for synthesis of a positive strand ssDNA (aka infective form, IF) via a rolling-circle mechanism. IF replication and transcription of the complementary/negative strand are assisted by the host enzymes (Rakonjac et al., 2011). When the concentration of phage proteins has increased, pV dimer complexes with the newly synthesised ssDNA to form a phage packaging precursor. Packaging signal on the precursor binds a membrane-associated complex of pVII and pIX which identifies the phage ssDNA to be packaged and initiates assembly (Russel and Model, 1989). Assembly takes place along the essential pI/pXI/pIV export complex (Feng et al., 1999). Specifically, pI and pXI form an inner membrane complex, whereas pIV forms a channel on the outer membrane through which the assembling phage ascends (Mai-Prochnow et al., 2015). The process starts with association of pVIII to the DNA which is then transferred across the two membrane layers. Complete

42 dissociation of ssDNA from pV triggers an incorporation of pIII and pVI at the distal cap which finalises the packaging step, and releases the virion from the cell (Marvin et al., 2014).

Figure 6. Life cycle of a filamentous bacteriophage.

The five coat proteins are coloured differently in the diagram, i.e., pIII, pVI, pVII, pVIII, and pVIX. Phage infectivity is mediated by pIII which can attach itself to the F-pilus on a bacterial host and allows transfer of the phage ssDNA into the host. TolA regulates disassembly of the coat proteins which are retained in the inner membrane for recycling. Replication and expression of dsDNA are carried out by the host enzymes. The ssDNA fusion precursors and newly synthesised phage proteins are directed by the signal sequence to the inner membrane/periplasm where assembly takes place. Modified from Huang et al. (2012) with permission. Phage display system can be divided into three main classes based on the type of vector used; genomic, hybrid, and phagemid. For pIII display, these configurations are also referred to as “3”, “33”, and “3+3” respectively (Smith and Petrenko, 1997). The number simply represents the coat protein that is recruited for display (Figure 7). Genomic vector is modified to carry a fusion gene which leads to exclusive production of fusion proteins for packaging, hence all progeny phages will display the protein in a fully oligovalent manner. Depending on whether the display protein is on pIII or pVIII, the virions often exhibit compromised packaging efficiency (8 system), or infectivity (3 system). This oligovalency is reduced in the hybrid system. The hybrid vector effectively

43 carries two types of genes, a wild-type pIII or pVIII gene, and a fusion gene in the genomic backbone. The presence of two distinct encoders results in the production of both wild-type and fusion proteins, both of which can be used during packaging, thus implementing a decrease in the number of fusion proteins per phage particle. Phagemid vector is a compact plasmid that contains phage-derived origin of replication (aka intergenic region), an inherent plasmid origin of replication, some antibiotic resistance, and a fusion gene with a weak promoter. The phagemid lacks the genetic codes for production of all but one type of the coat proteins that are required for full phage assembly. As a consequence, phage production can only be achieved when a helper phage is present in the process known as ‘phage rescue’ (Paschke, 2006).

Helper phage is generally a regular phage that essentially provides the genetic codes for plasmid replication, synthesis of capsid proteins, phage assembly and export. The conventional phagemid system will therefore yield two distinct types of progeny phage particles; those that carry the phagemid genome, and those carrying the helper phage genome. Only the phagemid-carrying helper phage is useful for phage display selection. For the 3+3 system the number of fusion proteins on each virion ranges between 0 to 5, since there are three to five copies of pIII per phage (Tikunova and Morozova, 2009). Most commonly, a monovalent display is achieved. In fact, as many as 90% wild type phages have been reported, leaving only 10% to carry even one copy of the fusion protein (Griffiths and Duncan, 1998). One special type of helper phage, aka hyperphage, was invented by Rondot et al. (2001) for the 3+3 system. The hyperphage carries a phage genome with deleted pIII gene. The absence of a functional pIII gene in the hyperphage genome means that the only source of pIII for phage assembly is the phagemid genome that codes for pIII fusion protein. This selection pressure favours incorporation of several peptide/protein fusions in a single phage particle during assembly, i.e., oligovalent display. The team claimed that by having oligovalent display the outcome of affinity selection can be dramatically improved, but it is doubtful that infectivity will not be compromised as a result of low ratio of wild-type pIII.

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Figure 7. Genomic, hybrid, and phagemid systems.

Phage genomic vectors can be modified to carry a fusion gene that links the display protein to one of the capsid proteins, e.g., pIII or pVIII, known as 3, and 8, respectively. This vector system results in oligovalent display with compromised packaging efficiency (8 system) or infectivity (3 system). Hybrid system contains both constructs of wild-type gene, and fusion gene in the genomic vector (33 or 88) and have a reduced display valency in comparison to the original genomic vector. Lastly, phagemid system, 3+3 or 8+8, utilises a phagemid which is a compact ssDNA that only carries the fusion gene, and lacks the genes that encode important proteins for phage replication and assembly. These additional genes are supplemented by a helper phage. Image from Tikunova and Morozova (2009). Phage display libraries are mostly used to identify polypeptides or proteins that bind to the target of interest. Standard phage display methods are often used against purified antigens, particularly globular proteins. Nonetheless, selections against whole cells have proven to be a useful option especially for transmembrane (TM) proteins (Azzazy and Highsmith, 2002). Biopanning against TM proteins that are endogenously present in the cell.has been reported in numerous studies using adherent cells (Zhang et al., 2001, Eisenhardt and Peter, 2010), non-adherent cells (Shukla and Krag, 2005) in conventional culture dishes, and even in microfluidic channels (Wang et al., 2011a). Alternatively, selection against transfected cells that express foreign TM proteins also led to successful isolation of scFvs that bind TM proteins including human CD83, canine CD117, and bat CD11b (Jones et al., 2016). Multi-span membrane proteins expressed by recombinant methods have been used fruitfully as a target for affinity selection. For instance, enriched expression of 4-transmembrane claudin-1 (CLDN1) proteins on baculovirus particles does not necessitate protein purification. By using non-purified

CLDN1, Fab fragments that bind the native CLDN1 were identified (Hötzel et al., 2011). More successful phage-display attempts include targeting of live pathogens (for

45 extensive review, see Huang et al. (2012)), and in vivo panning (Arap et al., 2002). It is also interesting that phage display can be employed in completely different disciplines, for example, its utility as a toolkit for in vitro synthesis of nanostructures (Mao et al., 2003).

OsX-3 and OsX-4 libraries For our phage-display selections, we used OsX-3 and OsX-4 phage-display vNAR semi-synthetic libraries that were generated by Dr Julien Häsler (Ossianix; Häsler and Rutkowski (2015), US Patent No. US20170198281A1). Essentially a collection of Type II vNAR sequences were generated from overlap PCRs that incorporate a mutated framework (including mutations in CDR1, HV2, and/or HV4), and a randomised CDR3 with a fixed or loose cysteine and selected point mutations on the edges. CDR3 lengths of 11-18 mer were chosen for the OsX-3 library. These cDNAs were ligated to a pOsD2 phagemid vector (in-house variant of pSEX81) and transfected into TG1 E. coli (Lucigen). The vector contains a C-terminal 6X His tag, 2 amino acid linkers, a FLAG tag (DYKDDDDK), an amber stop codon (TAG), and a full-length pIII coding gene. The amber stop codon is located between the tags and the pIII protein, enabling expression of either a tagged vNAR form, or a fusion protein of vNAR and pIII. The latter fusion protein for phage display is produced in an amber-suppressor E. coli strain, whereas the former tagged vNAR is produced in a normal strain for monomer expression and purification. Amber codon is one of the natural stop codons that are recognised by a tRNA. Two other stop codons are termed ochre (TAA), and opal (TGA). In certain E. coli species, i.e., amber suppressor strains, the amber codon is not recognised as a stop codon and the transcript is read through without being terminated (Wang and Schultz, 2005). Other elements in the vector include lac operator, SfiI restriction sites, lacZα, viral F1 ori, ampR, and bacterial ColE ori. Phagemid vectors only carry pIII coding genes, thus helper phages are required for complete coat protein production and subsequent phage packaging for secretion (Figure 8).

Helper phage M13K07 is a derivative of M13. It carries a plasmid with Met40Ile mutation in gII, and inserted within the M13 origin of replication are p15A ori, and kanamycin resistance gene from Tn903 (Vieira and Messing, 1987). gII gene encodes regulatory proteins known as gIIp that are important for DNA synthesis during dsDNA replication and ssDNA production (Mai-Prochnow et al., 2015). M13K07 bearing a 46 modified plasmid by itself is able to replicate independently of gIIp through p15A ori. Following infection into a phagemid-bearing E. coli, gIIp preferentially binds the wild- type M13 or f1 ori within the phagemid genome over the disrupted M13 ori in the helper phage plasmid. This leads to preferential replication and packaging of the phagemid ssDNA into a phage particle for release. To sum up, these requirements mean that the phagemid packaging E. coli cell has to be of an amber suppressor strain for fusion protein to be produced from the phagemid construct and carries an F-plasmid to allow for phage infection through the F pilus.

Figure 8. Phagemid construction for phage-display vNAR/antibody platform.

Phagemid vector contains a vNAR/antibody gene connected to the pIII coding sequence and is selected for by ampicillin resistance. A helper phage M13K07 is an M13 derivative. It carries a plasmid with Met40Ile mutation in gII (replication protein) and inserted within the M13 origin of replication are p15A ori, and kanamycin resistance gene from Tn903. Helper phage is required for synthesis of the rest of the coat proteins for phage packaging and secretion (Soltes et al., 2007).

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Selection, screening and vNAR expression Phage display methods allow affinity selection through iterative steps that potentiate enrichment of target-binding phages. Typical selection steps are shown in Figure 9. For our set up, we specified the ectodomains that are likely targetable and generated peptide mimetics of the target sequences on the protein of interest, i.e.,

Nav1.7. We used peptidomimetic strategy instead of making use of the full protein because transmembrane proteins are notoriously difficult to express and isolate from the membrane while retaining proper folding and functionality. In general, selection steps are conducted as follows. Firstly, phage-display antibody library is rescued in the appropriate E. coli strain. Antibody-display phages are then incubated with the immobilised peptide/protein target, and any unbound phages are washed off. Subsequently target-bound phages are eluted specifically with trypsin, or non- specifically with TEA buffer, and amplified in E. coli. The amplified phages are used in the next round of affinity selection. Typically, 2-4 rounds of selections are carried out. Often the exact number relies on the observed enrichment ratio that is calculated as the experiment is being conducted. Based on the degree of enrichment, output clones from the chosen round are screened for their ability to bind to the target using a direct enzyme-linked immunosorbent (ELISA) assay. A quick and straightforward screening may be carried out on the antibody-phage fusion proteins which are generated and stored at the end of each selection round. A more conventional screening is performed using the His-FLAG tagged antibodies that are not linked to pIII.

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Figure 9. Phage display affinity selection. vNAR-display phages are added to the immobilised Nav1.7 peptide mimetics. Unbound phages are removed by washing, and target-bound phages are eluted specifically with trypsin. The eluted phages are then amplified in E. coli and used for the next round of selection. Modified image from Yun et al. (2009). As previously mentioned, the His-FLAG tagged vNARs can be expressed in a standard E. coli strain. The phagemid vector was engineered to contain a protein-coding gene under the control of the lac operator. The lac operon is one of the most commonly used systems for on-demand production of recombinant proteins in E. coli, and expression is normally induced with isopropyl β-D-1-thiogalactopyranoside (IPTG). IPTG is a non-hydrolysable analogue of allolactose, a lactose metabolite that binds to a subunit of the tetrameric lac repressor and releases it from the lac operator. Without the repressor, the lac operon is no longer repressed, allowing transcription to proceed for expression of protein of interest. For vNAR expression, we opted for a different induction route using Autoinduction media (Studier, 2005). The method relies on a defined media composition which induces protein expression in E. coli when the cells reach saturation. Specifically, glucose is available as an early energy source for the bacteria. High glucose level suppresses lactose metabolism by the cells and the lac repressor stays bound to the operator, blocking expression of downstream genes 49

(Lewis, 2005). The cells switch to lactose when glucose is depleted, lactose binds the repressor and frees it from the operator (Figure 10). Absence of the repressor allows RNA polymerase to bind to the operator site and recruit cAMP receptor protein to the nearby catabolite activating protein CAP binding site, together they initiate transcription of vNAR-coding gene. With the pelB leader sequence in the construct, newly synthesised recombinant vNARs are directed to the periplasm. By breaking the outer bacterial membrane, periplasmic extraction containing a pool of His-FLAG tagged vNARs can be obtained and used for ELISA screening. Protein purification is also possible with immobilised metal-ion affinity chromatography (IMAC) since the vNARs contain polyhistidine tags. Histidine residue contains an imidazole ring which is a strong electron donor and readily interacts with transition metal ions, e.g., Co2+, Ni2+, Cu2+ and Zn2+ (Bornhorst and Falke, 2000). This electrostatic interaction enables His-tagged proteins to be retained in the IMAC column matrix. Proteins can then be eluted from the column by pH adjustment or addition of free imidazole.

Figure 10. The lac operon and induction of recombinant vNAR expression.

For screening, the output clones sometimes need to be present in a non-fusion form, i.e., not tethered to the virion protein pIII. Inducible expression of recombinant His-FLAG tagged vNAR monomers is under the control of the lac operator. No protein synthesis occurs under high glucose condition as the lac operator is blocked by a repressor protein. Bacteria switch to lactose metabolism when glucose level is low. Lactose displaces the lac repressor, freeing the operator site for RNA polymerase recruitment. cAMP receptor protein (CRP) binds to the CAP site and interacts with RNA polymerase to initiate transcription of the downstream vNAR gene (Schmitz and Galas, 1979).

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C. Objectives In this chapter, our investigation revolved around two separate aspects with regards to Nav1.7. Firstly, we attempted to identify vNARs that selectively bind the predefined target site on the Nav1.7 channel. Secondly, we investigated non-canonical roles of Nav1.7 in cellular migration, and transcriptional regulation. The detailed steps are laid out as follows;

I. Phage-display selection for the discovery of Nav1.7-selective vNARs

▪ Identify extracellular immunogenic sites on the Nav1.7 channel that are not well-

conserved amongst the Nav isoforms ▪ Generate peptide mimetics of the target sites for use in phage-display affinity selection ▪ Optimise selection parameters ▪ Screen selection outputs for peptide-selective clones

▪ Screen peptide-selective clones for binding to the hNav1.7 channel heterologously expressed in transfected cells

II. Investigating Nav1.7 in migration and transcriptional regulation

▪ Perform transcriptional profiling with microarray on H460, and SH-SY5Y cells

that have been treated with Nav/Nav1.7 modulators, i.e., TTX, ProTx-II, and veratridine, to assess if the channels exert any regulatory role at a transcriptional level and identify a bespoke signature that might be useful for future drug discovery efforts ▪ Identify the optimal extracellular matrix for use in a scratch migration assay

▪ Investigate if Nav1.7 inhibition by ProTx-II affects the basal and/or elevated rate of migration of the H460 NSCLC cell line using the scratch assay

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Materials and Methods

Design of Polypeptides From N- to C-terminus, the peptides are structured as follows; Biotin-Ahx-

[sequence]-NH2). All biotinylated peptides were manufactured to > 95% purity by Severn Biotech, UK. The lyophilised products were reconstituted according to manufacturer’s instructions. Reconstituted peptides were further diluted in nuclease- free water to a concentration of 2 mg/mL, aliquoted, and stored at -20°C.

Phage Display Selection 1. Library Rescue Semi-synthetic vNAR libraries, OsX-3 and OsX-4, were constructed by Dr Julien Häsler (Ossianix, UK). Library design was based on the protein sequences of naïve Type 2 vNARs that were collected from 2 adult nurse sharks (Ginglymostoma cirratum). Briefly, cDNAs were inserted into pOsD2 (a derivative of pSEX81 originally purchased from Progen, UK) phagemid vectors, and transformed into TG1 E. coli (Lucigen, UK). OsX-3 phage-display vNAR library contains 1.6 x 1010 cfu, with varying CDR3 lengths from 11 to 18 amino acids. OsX-4 phage library, on the other hand, contains 5 x 109 cfu and composes of two CDR3 lengths of 28 and 32 residues.

Prior to selection, both OsX-3 and OsX-4 phage libraries were amplified using the following procedures. TG1 E. coli libraries were grown in 2xTY medium (16 g/L tryptone,

10 g/L yeast extract, 5 g/L NaCl) from OD600 (optical density at 600 nm) of 0.1 at 37°C,

250 rpm. Once OD600 reached 0.5, bacteria were infected with M13KO7 helper phage at estimated MOI (multiplicity of infection) of 20 and incubated at 37°C for 30 min stationary, followed by 1 h at 150 rpm. Infected bacteria were centrifuged at 4000 rpm for 15 min. Pellets were re-suspended in 2xTY-amp100 µg/mL-kan50 µg/mL and grown overnight at 30°C, 250 rpm. Overnight cultures were centrifuged at 6000 x g for 15 min at 4°C. Supernatants were mixed 4:1 with PEG/NaCl (20% PEG 6000 in 2.5 M NaCl). After 30 min incubation on ice, the suspension was centrifuged at 6000 g for 15 min. Pellets were re-suspended in PBS and centrifuged at 8000 rpm for 10 min. Supernatants were collected, diluted 1:1 in PBS, and re-centrifuged. Supernatants were mixed 4:1 with PEG/NaCl, incubated on ice for another 30 min, and centrifuged at 8000 rpm for 15 min. White pellets of phage crystals were re-suspended in PBS, and centrifuged at

52

13,000 rpm for 5 min. The phage solution was mixed with glycerol to 20% v/v, and aliquoted to be stored at -80°C.

Phage titre was determined by serially diluting the final phage solution from 10- 2, 10-4… 10-12 in PBS. 10-8, 10-10, and 10-12 dilutions were mixed 1:9 with E. coli K12

ER2738 growing at mid-log phase (an equivalent of OD600 of 0.5). The phage-bacteria mixture was incubated at 37°C for 30 min, and 1 in 10 culture volume was plated on

TYE-gmp100 µg/mL-glucose2% w/v agar plate (for 1L; 10 g tryptone, 5 g yeast extract, 3 g NaCl, 15 g agar, 44.4 mL of 45% w/v glucose, and 1 mL of 100 mg/mL ampicillin). On the following day, isolated colonies were counted, and titres were calculated.

2. Affinity Selection/Biopanning All selections were carried out in 4 rounds. Selection criteria were made increasingly stringent with each successive round through decreasing antigen concentrations, and increasing washes, to potentiate the attainment of high affinity binders.

2.1 Selection Steps

2.1.1 OsX-3 vNAR phage library selection against immobilised peptide antigens

Selection I: Passive adsorption of peptides on microplates

4 wells on 96-well microplate (Greiner Bio-One, UK) were coated overnight with appropriate concentrations of D2C1 peptides (refer to Table 2). Coated surface was washed 3x with 0.1% v/v Tween 20/PBS (PBST) and blocked in 1% w/v bovine serum albumin (BSA; Sigma-Aldrich, UK)/PBST for 1 h. In parallel, input phages were blocked in 1% w/v BSA/PBST. Blocked peptides were then incubated with blocked phage for 1.5 hours with shaking. All wells were washed with PBS and PBST.

Selection II: Indirect coating of biotinylated peptides via biotin-streptavidin interactions using streptavidin-coated paramagnetic microbeads

Input phages were blocked in 1% w/v BSA/PBST, and streptavidin-binding population were pre-cleared by incubating with an excess of naked streptavidin microbeads (Invitrogen, UK) for 1 hour. Unbound phages were collected, and the beads were discarded. D2C1 peptides were immobilised on streptavidin beads using pre- determined volume of beads for each round of selection (refer to Table 2). For panning, 53 depleted phage were added to the coated streptavidin beads. After 1.5 h incubation, unbound phages were removed, and the phage-peptide-bead complexes were washed with PBS, and PBST.

2.1.2 OsX-3 vNAR phage library selection against peptide antigens in solution

Selection III: Low stringency selection

In this selection, stringency was lowered by not significantly reducing the peptide concentration in later rounds of panning (5 – 2.5 – 1.25 – 0.625 µM), and by using fewer washes (2x5 – 2x5 – 2x10 – 2x10).

Streptavidin-binding population were pre-cleared from the pool of input phages by 1 h incubation with an excess of naked streptavidin beads. Pre-cleared phages were then blocked in 1% w/v BSA/PBST and mixed with blocked peptides in solution. After 1.5 h incubation on rotation at room temperature, streptavidin beads were added to the phage-peptide mix and incubated for 30 min to pull down biotinylated peptide-phage complexes. Streptavidin beads were washed with PBS and PBST.

Selection IV: Alternating streptavidin and neutravidin microbeads

Similar to Selection III, panning in Selection IV was carried out in solution. However, for every alternate round of panning, microbeads used were either streptavidin- or neutravidin-coated (Thermo Scientific, UK). Further, the selection was performed with typical stringency criteria, i.e. decreasing peptide concentration (5 – 2.5 – 0.5 – 0.1µM) and increasing number of washes (2x5 – 2x10 – 2x15 – 2x20). The same procedure as used in Selection III was followed.

2.2 Elution and Infection

Target-bound phages were specifically eluted with 2 mg/mL trypsin (Sigma, UK). After 20 min incubation at 37°C, 20% v/v foetal bovine serum (FBS; Gibco, UK) was added to stop the enzymatic activity. Eluted phages were mixed with E. coli K12 ER2738 growing at OD600 of 0.5 and incubated for 30 min at 37⁰C. Infected bacteria were serially diluted from 10-1 to 10-8. 5 µL of each dilution was applied dropwise on TYE- amp100 µg/mL-glucose2% w/v agar plate and grown overnight at 30°C for colony count. Residual E. coli culture was centrifuged at 4000 rpm for 10 min. Pellet was re- suspended in 2xTY, evenly spread on TYE-amp100 µg/mL-glucose2% w/v bioassay dish and 54 grown overnight at 30°C. Bacteria were recovered by scraping the bioassay dish in 2xTY. The suspension was centrifuged at 4000 rpm for 10 min. The pellet was re-suspended in

2xTY-glucose5% w/v-glycerol20% w/v, and aliquoted to be stored at -80°C.

2.3 Phage Amplification and Precipitation

E. coli K12 ER2738 culture infected with output phages from each selection round was diluted in 2xTY-amp100 µg/mL-tet5 µg/mL-glucose2% w/v to OD600 of 0.1 and grown to 0.5. M13KO7 helper phages were added to the culture at MOI of 20 and incubated at 37⁰C for 30 min stationary, followed by 1 h at 150 rpm. Infected bacteria were centrifuged at 4000 rpm for 15 min. Pellets were re-suspended in 2xTY-amp100 µg/mL- kan50 µg/mL, and grown overnight at 30°C, 250 rpm. Overnight culture was centrifuged at 6000 x g for 15 min, at 4°C. Phage-rich supernatant was mixed 4:1 with ice-cold PEG/NaCl, incubated on ice for 30 min, and centrifuged at 6000 x g for 15 min. The pellet was re-suspended in PBS and centrifuged at 8000 rpm for 10 min. Supernatant collected was diluted 1:1 in PBS and re-centrifuged. The supernatant was again mixed 4:1 with PEG/NaCl, incubated on ice for 30 min, and centrifuged at 8000 rpm for 15 min. Phage pellet was re-suspended in PBS, and centrifuged at 13000 rpm for 5 min. Glycerol was added to a final concentration of 20% v/v. Aliquots were prepared and kept at -80°C.

Phage titre was determined by serially diluting the phage solution (1 in 10) from 10-2, 10-4 … to 10-12 in PBS. 10-8, 10-10, and 10-12 dilutions were mixed with E. Coli K12

ER2738 grown at OD600 = 0.5. Infected bacteria were incubated for 30 min at 37°C, stationary. 1 in 10 culture volume was plated on TYE-amp100 µg/ml-glucose2% w/v agar plate and grown overnight at 30°C. Output colonies were counted, and the phage titre was calculated.

3. Output Screening 3.1 Sample Preparation

3.1.1 Monoclonal Phage Preparation

Output cultures were grown in 96-well deep well plates (Greiner Bio-One, UK)

for 4 h at 37°C in 2xTY-glucose2% w/v-amp100 µg/ml. M13K07 helper phage were added at MOI = 20, and incubated for 1 hour at 37°C, 150 rpm. Infected bacteria were

55

centrifuged at 3200 rpm for 10 minutes. Pellets were resuspended in 2xTY-amp100 µg/mL-

kan50 µg/mL, and grown overnight at 30°C, 250 rpm. Culture plate was centrifuged at 3200 rpm for 10 minutes, and supernatants containing phage were collected.

3.1.2 Periplasmic Extraction

Output cultures were grown in 96-well deep well plates for about 20 h at 30°C, 200 rpm in Autoinduction TB medium (Novagen, UK) supplemented with 1% v/v glycerol and 100 µg/ml ampicillin. Cultures were centrifuged at 2000 x g for 10 min, and supernatants were removed. Bacterial cell pellets were resuspended in ice-cold TES buffer (50 mM Tris pH 8, 1 mM EDTA, 20% w/v sucrose), followed by diluted TES buffer (1:5) and incubated on ice for 30 min with shaking. Cell suspension was then centrifuged at 2500 x g for 10 min, and supernatants containing periplasmic extract were collected.

3.1.3 Purified monomeric vNAR preparation

Selected clones were cultured in 0.5 – 1l Autoinduction TB-glycerol1% v/v-amp100

µg/mL broth for 20 h at 30°C, 200 rpm. Liquid culture was centrifuged at 6000 x g for 15 min. Pellet was resuspended in ice-cold TES buffer, followed by diluted TES buffer and incubated on ice for 30 min. Cell suspension was centrifuged at 8000 rpm for 15 min. Supernatant was collected and passed through a 0.45 µm syringe filter (Sartorius, UK). Filtered supernatant was mixed with 500 mM NaCl, 1X PBS, 10 mM imidazole, and 1 ml Ni2+ Sepharose bead slurry (Qiagen, UK) and incubated for 2 h at 4°C on rotation. Bead solution was transferred to a 20 mL column, washed twice with wash buffer (1X PBS, 10 mM imidazole, 500 mM NaCl). Proteins that bound to Ni2+ Sepharose beads were collected in elution buffer (1X PBS, 500 mM imidazole, and 500 mM NaCl). 3 rounds of buffer exchange were carried out in PBS using VivaSpin6 tubes 5000 MWCO (Sartorius, UK) until imidazole concentration was ~0.5 mM. Protein (vNAR) solution was filtered through 0.22 µm syringe filer (Millipore, UK), and stored at 4°C. vNAR concentration was determined on NanoVue spectrophotometer (GE, UK).

To confirm the presence of vNAR, sample was taken from the final solution and mixed with 1X LDS Sample Buffer (Life Technologies, UK), 10% v/v DL-dithiothreitol (DTT) solution (Sigma-Aldrich, UK), followed by 10 min incubation at 95⁰C. Sample was loaded on 4-12% polyacrylamide gel (Invitrogen, UK) and separated via electrophoresis 56 at constant 200 V for 35-40 min. The gel was then briefly rinsed with distilled water and incubated in Coomassie Blue solution (Expedeon, UK) for 30 min at room temperature. Staining solution was removed, and the gel was left to destain in distilled water overnight. Images were captured on GeneSnap (Syngene, UK).

3.2 Enzyme-Linked Immunosorbent Assay (ELISA)

ELISAs were performed on coated peptides, peptides in solution followed by captured on streptavidin-coated microplates, or immortalised cells of human origin

(inducible hNav1.7-HEK293, or H460 lung carcinoma cells). 1% w/v BSA/PBST was used as a blocking reagent for peptide-based ELISA, and 5% v/v FBS/DMEM for cell ELISA. In all ELISAs, positive and negative controls were used.

3.2.1 Biotinylated peptides directly coated on Maxisorp plates

96-well Maxisorp plates (Thermo Scientific, UK) were coated overnight at 4°C with 100 ng/well of peptides or proteins in PBS. Coated plates were washed 3x with PBST and blocked in 1% w/v BSA/PBST for 1 hour. Blocking reagent was then replaced by blocked phage supernatant, periprep, or vNAR monomers and incubated for 1 hour. Plates were washed 3x with PBST, followed by 1 h incubation with appropriate primary antibody (anti-M13-HRP 1:2000, anti-FLAG M2-HRP 1:2000 (Sigma, UK)). Antibody was removed, and all wells were washed 3x with PBST, followed by addition of TMB peroxidase substrate (KPL, UK). After 5-10 minutes, 1N HCl was added. Light absorbance at 450 nm was measured on Safire II Multi-detection Microplate Reader (Tecan, Switzerland).

3.2.2 Biotinylated peptides in solution, captured by streptavidin coated plates

Blocked phage, periprep, or monomers were incubated with blocked peptide solution for 1 hour. The ligand-peptide complexes were quickly captured via biotin- streptavidin interaction onto streptavidin coated plates (Greiner Bio-One, UK). Appropriate antibody conjugated to peroxidase was added and incubated for 1 hour at room temperature on shaking. All wells were washed 3x with PBST and incubated for 5-

10 minutes in TMB peroxidase substrate. 1N HCl was then added, and A450 nm was measured.

3.2.3 Adherent cells inducibly expressing hNav1.7 channels

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4 Induced, non-induced hNav1.7-HEK293 or H460 cells were seeded at 3x10 cells/well onto sterile 96-well cell culture plate (Greiner Bio-One, UK) pre-coated with 0.01% poly-L-lysine (Sigma, UK). After 48 h, media were removed. Cells were then incubated in blocked phage supernatant, periprep, or monomers for 1 h, and fixed with 4% paraformaldehyde (PFA)/PBS. Cells were washed once with Dulbecco’s PBS (DPBS +

CaCl2 + MgCl2; Gibco, UK), and probed with appropriate antibody for 1 hour. Cells were re-washed twice with DPBS and TMB peroxidase substrate was added. The reaction was left to develop for 3-5 min before addition of 1N HCl. A450 nm was measured.

3.3 Determination of hit sequences

Plasmid DNAs of the selected output clones were extracted using QIAprep Spin Miniprep Kit (Qiagen, UK). Bacteria were grown overnight at 37°C in low salt Luria- Bertani (LB) medium (10 g/L tryptone, 5 g/L yeast extract, 5 g/L NaCl). Plasmid extraction was performed according to manufacturer’s recommendation. Overnight cultures were centrifuged at 13300 rpm for 5 min. Bacterial pellets were resuspended in lysis buffer, followed by neutralisation buffer and re-centrifuged at 13300 rpm for 10 min. Supernatants containing plasmid DNAs were collected, washed in QIAprep spin- filter columns with wash buffer, and eluted with elution buffer. Concentrations of plasmid DNAs in the final elution were measured on NanoVue spectrophotometer (GE, UK). Samples were mixed with DNA gel loading dye (Life Technologies, UK), and separated on 1% w/v agarose gel supplemented with SYBR Safe DNA gel stain (Invitrogen, UK) via electrophoresis. Gel was visualised under UV trans illumination on GeneSnap (Syngene, UK). Plasmid DNA samples were sent to GATC, Germany for sequencing.

58 hNav1.7-expressing Cell Lines

1. Cell Culture H460 non-small cell lung carcinoma (NSCLC) cell line was purchased from ATCC, USA and cultured in RPMI-1640 + GlutaMAX™-I medium (Gibco, UK) supplemented with 10% fetal bovine serum (FBS; Gibco, UK), 50 U/ml penicillin and 50 ug/ml streptomycin

(Gibco, UK). Inducible hNav1.7-HEK293 cells were generated by Sian Frost, Ossianix. Flp- In T-REx System (Invitrogen, UK) was used to establish the inducible cell line. In brief, hNav1.7 α-subunit cDNA was purchased from Origene, UK and subcloned into pcDNA5/FRT/TO expression vector. To prepare a Flp-In T-REx host cell line, HEK293 cells were sequentially transfected with pFRT/lacZeo vector and pcDNA6/TR vector. hNav1.7 pcDNA5/FRT/TO expression vector was co-transfected with pOG44 plasmid into the host cell line. Successful integration of the Nav1.7 gene via Flp-mediated recombinase into the host cell was verified on the basis of hygromycin resistance. Inducible hNav1.7- HEK293 cells were cultured in Dulbecco’s Modified Eagle’s Medium (DMEM) + GlutaMAX™-I (Gibco, UK) supplemented with Tet System Approved FBS (Clontech, UK), penicillin/streptomycin, 200 µg/mL hygromycin B (Invitrogen, UK), and 15 µg/mL blasticidin (Invitrogen, UK). To induce hNav1.7 expression, 1 µg/ml of tetracycline made up in complete medium and cells were seeded for at least 24 h prior to an experiment.

All cells were maintained at 37°C, 5% CO2 in T75 flasks, and were passaged 2-3 times per week. Both H460 and HEK293 are adherent and were treated with 0.25% trypsin/EDTA solution (Gibco, UK) for 2-5 minutes to detach. Cells were maintained until no further than passage 20 at which point they were discarded and a new batch was initiated from liquid nitrogen stock.

2. Preparation of Cell Membrane Fraction

Tetracycline was added to the growth medium to induce Nav1.7 expression in hNav1.7-HEK293 cells. Induced, non-induced hNav1.7-HEK293, and parental Flp-In

HEK293 cells (negative control) were grown in T75 flasks for 48 hours at 37°C, 5% CO2 to 80-100% confluence. Cells were rinsed with PBS and harvested in ice-cold lysis buffer

(20 mM HEPES pH 7.0, 2 mM DTT, 0.25 M sucrose, 10 mM NaF, 1 mM Na3VO4, and 1X protease inhibitor cocktail). The solution was homogenised in ~10 sec burst for 3-4 times using a Polytron PT1200E handheld homogeniser (Kinematica, Switzerland). The homogenised suspension was then centrifuged for 10 min at 700 x g. The supernatant 59 was retained for centrifugation at 53,000 rpm for 30 min using Optima TLX ultracentrifuge (Beckman Coulter) with a fixed angle rotor TLA-120.2. The supernatant was decanted, and the pellet was rinsed with ice-cold PBS following by resuspension by homogenisation in ice-cold PBS. The final suspension contains the membrane fraction which was stored at -80°C.

3. Western Immunoblotting Cells were seeded to full confluence, washed in PBS, and lysed on ice in lysis buffer (50mM Tris/HCl pH 7.5, 150mM NaCl, 10% Glycerol, 1% Triton X-100, 1mM EDTA) with cOmplete™ Mini Protease Inhibitor Cocktail (Roche, UK). The lysates were centrifuged at 13300 rpm for 5 minutes and the resulting supernatants were collected and stored at -20°C until use. Protein concentrations in the supernatants were determined by Bradford assay using BCA Protein Assay Kit (Thermo Scientific, UK). 12 dilutions of cell lysates were prepared in duplicates in 1:20 lysis buffer and were incubated in Coomassie reagent for 20 minutes with agitation. Light absorbance at 595nm of each dilution/Coomassie mixture was measured on Safire II Multi-detection Microplate Reader (Tecan, Switzerland) and compared to the known concentrations of BSA standard to determine the total protein concentration.

HEK293 and H460 supernatants were mixed with 1X LDS Sample Buffer (Life Technologies, UK), 10% v/v DTT (Sigma-Aldrich, UK), 1:20 lysis buffer, and heat shocked at 75°C for 10 minutes. Protein samples were loaded on 4-12% polyacrylamide gel (Invitrogen, UK) and separated via electrophoresis (35-45 minutes, constant 200V). Polyvinylidene difluoride (PVDF) membrane (Life Technologies, UK) was briefly soaked in methanol, and incubated in ice-cold 1X NuPAGE transfer buffer (Life Technologies, UK) with 10% v/v methanol (Fisher Scientific, UK) for 5 min. Samples were blotted onto the activated membrane by a semi-dry transfer at constant 13V for 60-90 minutes on Trans- blot SD Semi-Dry Transfer Cell (Bio-Rad, UK). Following transfer, membrane was blocked in 5% w/v milk/0.1% v/v Tween20 in PBS (PBST) for 1 h, then incubated with primary antibody (anti Nav1.7 diluted at 1:400 in blocking buffer; Millipore, UK) at 4°C overnight. After 3X 5 min washes in PBST, membrane was incubated for 1 hour at room temperature in secondary antibody (anti-mouse IgG conjugated to peroxidase 1:10000; Sigma-Aldrich, UK). Membrane was washed in 3X 5-min in PBST, and blots were developed in 2mL TMB liquid substrate system for membrane (Sigma-Aldrich, UK). 60

Images were acquired on GeneSnap (Syngene, UK). Loading controls of β-actin were performed for all blots.

4. Immunocytochemistry

Induced and non-induced hNav1.7-HEK293 cells were seeded at 30,000 cells/well, and H460 NSCLC cells at 20,000 cells/well onto 8-well Lab-Tek Chamber Slide (Thermo Scientific, UK) pre-coated with 0.01% w/v poly-L-lysine (Sigma-Aldrich, UK), and incubated in complete media for 48 hours at 37°C, 5% CO2. Monomeric vNAR, hIgG1 Fc-vNAR fusion, or standard growth medium were added to appropriate wells, and incubated for 1 hour at room temperature. Solutions were removed, and cells were fixed in 4% w/v paraformaldehyde/PBS on ice for 20 min. After 3x washes with PBS, cells were permeabilised in 0.1% saponin (Sigma, UK)/PBS for 10 minutes at RT (this step was omitted in experiment using non-permeabilised cells). Cells were then washed 3X with PBS and blocked for 30 min in 0.1% w/v saponin/5% v/v goat serum/PBS (Sigma-Aldrich, UK). This was followed by 1 h incubation with appropriate primary antibody (anti-FLAG

M2 1:200 (Sigma, UK), anti-Nav1.7 1:200 (Millipore, UK)), 3X washes with 0.1% saponin, and 1 h incubation with appropriate secondary antibody (anti-mouse IgG conjugated to Alexa Fluor 555 1:200, anti-human IgG conjugated to Alexa Fluor 555 1:200 (Life Technologies, UK)). Cells were re-washed 2X in 0.1% v/v Triton/PBS, 2X PBS, 1X distilled water, and mounted in ProLong Diamond Antifade Mountant with DAPI (Life Technologies, UK). Slides were kept in the dark at 4°C for at least 24 h for the mounting medium to set.

Fluorescence staining was visualised under LSM710 confocal microscope (Carl Zeiss, Germany) using 60X oil immersion objective lens and appropriate excitation and emission wavelength filters for Alexa 555 and DAPI. Images were captured and processed on Zen2010 Imaging Software. Preliminary images were made on Olympus BX51 fluorescence microscope (Olympus, Japan) with 10X objective, TXRED and DAPI filters. Non-induced HEK293 cells were used as negative control, and induced HEK293 as positive controls for anti-Nav1.7 antibody specificity. In addition, incubation conditions with only primary (anti-Nav1.7) or secondary (anti-mouse or human IgG – Alexa) antibody were performed with H460 and induced HEK293 to determine the non- specificity of both antibodies.

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Scratch Wound Assay For matrix selection, all ECMs were sourced from Sigma-Aldrich. Collagen IV was reconstituted in 0.25% v/v acetic acid at 1 mg/mL, and further diluted in HBSS.

Fibronectin was reconstituted in ddH2O at 1 mg/mL. Laminin was purchased as a 0.5 mg/mL solution in TBS pH 7.4. All stock solutions were stored at -20°C, and slowly thawed at 4°C before use. H460 cells were seeded in reduced serum RPMI-1640 media in 24-well ImageLock plates (Essen BioScience) that had been pre-coated for 2 h at 37°C with either collagen IV at 5 µg/cm2, fibronectin at 2.5 µg/cm2, or laminin at 1 µg/cm2.

The plates were put in the incubator overnight at 37°C, 5% CO2 to reach full confluence. A scratch path was made at the centre of each well using the 4-needle WoundMaker (Essen BioScience) with fitted p10 micropipette tips. Each well was rinsed 3X with HBSS to eliminate the cell debris, and fresh reduced-serum media was added. The plate was placed in the Incucyte (Essen BioScience) and incubated for 6 h. Images of the scratch wound were captured at t0 and t6h, and the distances moved by the two cell fronts were measured. Cell front velocity was calculated as a migration parameter for comparison;

푤표푢푛푑 푤𝑖푑푡ℎ 푎푡 푡0 − 푡6 (µ푚) 6 ℎ . We then performed a preliminary experiment to select the ( ) 2 푓푟표푛푡푠 growth factor that best elevates the rate of H460 migration. Recombinant human EGF, FGF-2 and TGF-β1 were purchased from Peprotech, UK. All growth factors were prepared in 1% BSA/PBS solution that had been sterile-filtered through a 0.2-µm membrane. Cells were treated for 18 h with 5 ng/mL EGF, 5 ng/mL FGF-2, or 1 ng/mL TGF-β1. The rates of migration were then calculated for these conditions.

To determine the effect of Nav1.7 block on cell migration, H460 cells were seeded on fibronectin-treated ImageLock plate. The scratch wounds were made as described previously. The debris were washed away by rinsing 3X with HBSS. The cells were then treated for 18 h with vehicle (reduced serum media), 5 nM ProTx-II (Smartox Biotechnology), 10 ng/mL EGF, 1 µM gefitinib (Sigma-Aldrich), EGF plus gefitinib, or EGF plus ProTx-II. The Incucyte captured the images of the wound at every 6 h interval from t0 to t18h. Cell front velocity was calculated and used for comparison.

62 mRNA Microarray H460 NSCLC and SH-SY5Y neuroblastoma cells were seeded at 100,000 cells per well in a 12-well cell culture plate in the corresponding growth media. The cultures were incubated at 37°C, 5% CO2 for 48 h to reach ~80% confluence. Cells were then treated for 6 h with 1 µM TTX (Sigma-Aldrich), 50 µM veratridine (Sigma-Aldrich), or 10 nM ProTx-II (SmarTox). Each treatment was applied to 4 replicate wells. Culture media were removed, and cells were lysed in Absolutely RNA Miniprep Kit lysis buffer and β- mercaptoethanol (Agilent Technologies, UK). RNA was then extracted, and quality- assessed (RNA Integrity Number (RIN) >= 8). RNA expression levels were measured on the Affymetrix Human Genome U133 plus 2.0 (GPL570) chip (Dr David Chambers). Pre-processing was carried out using Affymetrix MAS5.0 and linear models were applied using limma package in R. Selected top responders were submitted to Enrichr webtool for pathway enrichment analysis.

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Results I The first part of our investigation focused on discovering novel vNARs that bind and block the function of Nav1.7. In order to achieve this goal, we employed a peptidomimetic approach by identifying a target site on the ectodomain of Nav1.7 and synthesised peptide mimetic of the site for use as “bait” in phage display affinity selection.

1. Target site determination 1.1 Sequence conservation We identified the second extracellular loop (E2) between segment 3 and 4

(S3/S4) of individual Nav1.7 domain as a probable target site. In particular, E2 on domain 2 was previously reported to be successfully targeted by a Nav1.7-neutralising monoclonal antibody (Lee et al., 2014b). Nav1.7 topology is well established and consists of four homologous domains; each with six α-helical transmembrane segments.

Homology modelling was carried out on each hNav1.7 domain using Swiss-Model web server tool (Biasini et al., 2014) to determine the location of E2 within the domain.

Crystal structure of the bacterial sodium channel, NavAb, was used as a template (pdb accession code: 3rvy, Payandeh et al. (2011)). E2 loop possesses many attributes that are suitable as the target site. Firstly, it is an extracellular motif, which means that it is more easily accessible compared to the more embedded transmembrane segment, or the intracellular portion. Secondly, sequence alignment demonstrates a number of non- conserved residues among all hNav subtypes which may lend themselves to selectivity targeting (Figure 11A). Another characteristic is that the E2 sequences of domain I-III are identical in human and mouse, although DIV-E2 displays a few non-conserved residues between the two species (Figure 11B). Species cross-reactivity can be extremely useful later in the drug discovery workflow when animal models are used to assess compound toxicity and efficacy.

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A

B

Figure 11. Aligned E2 sequences from 4 domains of all hNav isoforms and mNav1.7.

Primary sequences that make up the E2 loops (blue) and part of the flanking transmembrane segments (black) in the 4 domains of the pore-forming α-subunit. Highlighted in orange are the residues that are not well conserved in hNav1.7 as compared to other hNav isoforms. The position of the E2 loop is approximate. (A) Sequences of 9 hNav isoforms. (B) Human and mouse Nav1.7 peptide sequences. Sequences were retrieved from UniProt (Apweiler et al., 2004), and alignment was generated using BioEdit v7.2.5 (Hall, 1999).

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From the sequence alignment we bioengineered the peptides as biotin-Ahx-

[sequence]-NH2. Essentially, four features were included in the complete sequence for synthesis. These features are (i.) biotinylation – streptavidin and biotin interaction can facilitate phage-display selection and subsequent screening, (ii.) cyclisation – by introducing two cysteines flanking the peptide sequence, theoretically they should form a disulphide bridge which bends the peptide into a cyclised conformation to better approximate the loop structure that it adopts in the native state, (iii.) aminohexanoic acid (Ahx) group – an inserted spacer group to prevent steric hindrance from the biotin and to offer the peptide more freedom of movement, and (iv.) C-terminal amidation – this removes the negatively charged carboxyl group from the C-terminus to better mimic naturally occuring peptide. The exact sequences are shown in Table 3. E1 sequence of domain 2 was also synthesised to be used as a decoy bait. It can also serve the purpose of peptide blocking, i.e., in a native protein the peptide is ‘blocked’ by neighbouring amino acids.

Table 3. Sequences of predicted E2 (and E1) loops on domain 1-4 of hNav1.7.

E2 mimetics of all 4 domains were synthesised as linear and ‘cyclic’ peptides. The numbers of residues vary from 10 to 22 mer and MWs from ~1400 to 2900 Da. Domain – Abbreviation Peptide Number Molecular Extracellular (Biotin-Ahx-[sequence]-NH2) of weight loop residues 1 – S3/S4 D1L LTEFVNLGNVS 11 1531 D1C CLTEFVNLGNVSC 13 1735 2 – S3/S4 D2L1 VELFLADVEG 10 1430 D2C1 CELFLADVEGLC 12 1646 – S1/S2 D2L2 HHPMTEEFKN 10 1608 D2C2 CHHPMTEEFKNVLC 14 2024 3 – S3/S4 D3L VTLVANTLGYSDLGPIK 17 2100 D3C CVTLVANTLGYSDLGPIKC 19 2304 4 – S3/S4 D4L VGMFLADLIETYFVSPTLFR 20 2658 D4C CVGMFLADLIETYFVSPTLFRC 22 2863

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1.2 Hydrophobic properties Synthesised polypeptides were reconstituted first in 100% DMSO, and further diluted in distilled water to a final concentration of 2 mg/mL. Of the ten biotinylated peptides that we designed for synthesis, only five were successfully reconstituted in water for use in the phage display selection. All domain 1 and 4 peptides were not fully soluble in water, neither was D2L1. This non-solubility could not be predicted from the % hydrophobic residues in the sequence (Table 4).

Table 4. Solubility of peptide mimetics of hNav1.7 E2 loops.

Percentages hydrophobic residues as calculated using peptide hydrophobicity/hydrophilicity analysis (Peptide 2.0 online tool).

Name % Hydrophobic Solubility in Solubility when residues 100% DMSO further diluted in (10 mg/mL) H2O (2 mg/mL) D1L 46 ✓ ✗ D1C 39 ✓ ✗ D2L1 60 ✓ ✗ D2C1 50 N/A ✓ D2L2 30 ✓ ✓ D2C2 36 ✓ ✓ D3L 47 ✓ ✓ D3C 42 ✓ ✓ D4L 60 ✓ ✗ D4C 55 ✓ ✗

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2. Optimisation of phage-display selection for a peptide target

2.1 Solid phase selection – Selection I and II We used 2 mg/mL of reconstituted D2C1 peptides to coat the surface of the selection wells. D2C1 is a shorthand for ‘Domain 2 Cyclic E2 loop mimetic’, which is a region on the hNav1.7 channel that we aimed to specifically target as this loop has the potential to confer isoform selectivity and affect channel gating and functionality. Initially, the peptide-coated wells were used for standard solid-phase affinity selection steps. OsX-3 library was rescued for this first attempt. The library is 1.6 x 1010 cfu in size and contains Type 2-like vNARs with randomised 11-18 mer CDR3 lengths. We kept track of the number of vNAR-display phages that were added at the beginning of the biopanning, and the number that we retrieved at the end. Percentages phage recovery 푃ℎ푎𝑔푒푠 표푢푡 were calculated for all selection rounds using the formula; 푥 100. The 푃ℎ푎𝑔푒푠 𝑖푛 number is indicative of whether the round has successfully led to enrichment of a selected population of binders. We found that by panning on the passively adsorbed peptides, there was no observable phage enrichment in any of the selection rounds (Figure 12A). This absence of enrichment was also supported by the total lack of binders to different biomolecules, i.e., D2C1 cyclic target, D2C2 cyclic decoy, streptavidin, and human serum albumin (Shinoda et al.) in an ELISA screen of 96 output clones from round 4 (Figure 12B). 1B8, a confirmed HSA-selective vNAR was included as a positive control. We typically screened the outputs using direct ELISA assay. In brief, the ELISA plate was coated with the biomolecule targets, followed by incubation with the periplasmic extraction that contains the output clones and detection using anti-FLAG antibody that recognises the FLAG tag on individual vNAR clone.

We suspected that peptide presentation by passive adsorption might be the underlying reason for this futile selection attempt and proceeded to check if the peptides might not adsorb well to the plastic surface on a standard untreated polystyrene plate. In parallel, we coated increasing concentrations of peptides on a specially-treated Maxisorp plate with hydrophilic surface and enhanced binding to a wide range of molecules. By detecting the presence of the peptides through their biotin tags using HRP-conjugated streptavidin, we found that even at the highest peptide concentration of 100 µg/mL there was very little adsorption of peptides onto the untreated surface (Figure 12C). This observation is a stark contrast to the Maxisorp plate 68 whereby the saturated level was reached at ~2 µg/mL peptide. Untreated polystyrene plate with hydrophobic surface is clearly not a suitable matrix for passive adsorption. From this point onwards, we used the Maxisorp surface for affinity selections and output screenings when required.

A B

Selection I % Phage Recovery Round 1 0.0013 Round 2 0.0011 Round 3 0.0002 Round 4 0.0004

C

Figure 12. Summary of Selection I outcomes.

(A) Percentages of phages that were recovered after each round of panning. It is apparent that no enrichment had been achieved. (B) ELISA results of periplasmic extraction from 96 clones retrieved after round 4. HSA-binding clone 1B8 was included as a positive control. (C) Passive adsorption of peptides on two different plate types. Increasing concentrations of D2C1 biotinylated peptides were coated on the plates o/n. Biotin-tagged peptides were detected with streptavidin-HRP conjugate and A450 was measured. Error bars = SEMs, n = 2.

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To proceed, we decided to make use of the biotin tag that we had engineered into the peptide construct. Instead of passive adsorption which inevitably results in unpredictable and random peptide configuration, we used streptavidin-coated beads to immobilise the D2C1 peptides via their biotin tag. This method of immobilisation restricts the possible configurations of the peptides to be presented during biopanning. We could also be certain that a majority of the peptides would be immobilised given the extremely potent and specific streptavidin-biotin interaction. Streptavidin is a non- glycosylated analogue of avidin, both have four biotin binding sites per molecule (Diamandis and Christopoulos, 1991). Streptavidin is preferable to avidin because of its lower non-specific binding. With this immobilisation method, we carried out another selection attempt and proceeded to round 3 and round 4 because we did not observe enrichment after the first two selection rounds. Percentage phage recovery dramatically increased after round 3 and stayed that way in round 4 (Figure 13A). When the output clones from both rounds were screened, we found that the perceived enrichment was effectively due to the presence of streptavidin-binding clones. These streptavidin binders account for eight of the total 96 clones in the screen (Figure 13B). It is not uncommon to retrieve clones that bind to components in the selection media; the most common being polystyrene, streptavidin, and serum albumin (Vodnik et al., 2011). We had included a library depletion step, i.e., incubating the library phages with naked streptavidin-coated beads to reduce the chance of selecting binders of streptavidin, yet evidently did not completely obliterate them. Again, the HSA-binding clone, 1B8 was included as a positive control.

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A

B

Figure 13. Summary of Selection II outcomes.

(A) Percentages of phages that were recovered in Selection II. Enrichment of ~100-fold was present at the transition from round 2 to 3. (B) Direct ELISA screen of 96 output clones from round 4 panning against 4 different biomolecules (D2C1, D2C2, streptavidin, and HSA). 8 streptavidin-binding clones were identified. HSA-selective clone was used as internal positive control.

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2.2 Solution phase selection – Selection III One possible explanation for our inability to extract peptide-binding vNARs from the phage-display library is that albeit its 1010 size, the library does not contain vNAR candidates that bind to our E2 peptides. To address this possibility, we carried out two parallel selections with the original OsX-3 library that had previously been used for the first two selections plus an additional OsX-4 library. OsX-4 library is a 5 x 109 cfu library that consists of Type 2-like vNARs with extensive CDR3 loop of either 28 or 32 amino acids. We also opted for solution-phase selection which does not require the peptides to be immobilised on a matrix and therefore there are no imposed restrictions on the peptide configuration during panning. Instead of using only D2C1 cyclic mimetic of the E2 loop on DII, we included two other peptides that were designed as decoys, D2L2, and D2C2, linear and cyclic mimetics of the E1 loop on DII. Unlike E2, the E1 loop is not connected to the S4 voltage sensor and therefore not directly coupled to channel gating. The free peptides were mixed with the vNAR-display phages and only later pulled down by streptavidin beads for washes and elution. Since it had proven difficult to recover any binders at all, we deliberately adopted low stringent conditions by keeping the peptide concentrations moderately high, and the number of washes minimal.

We found that for both selection attempts, enrichment was evident in round 4 (Figure 14A). By screening the OsX-3 output clones from this round, two clones B03 and E08 appeared to selectively bind to D2C1 peptide (mimetic of DII-E2) with > 3X signal on this peptide than HSA and two decoy peptides that were also included as the selection targets, D2L2 and D2C2 (Figure 14B). D2C1 binding by B03 was more potent than E08. As before, we identified four clones that exhibited affinity for streptavidin. OsX-4 output clones from round 4 were also screened. We found 5 streptavidin binders but none of the clones displayed selective binding to the peptides (results not shown). Taking into account the results from the first screen, we decided to pursue further screening of OsX-3 output clones. Specifically, the initial screen was carried out using the direct ELISA format as previously described. There is a possibility that the low yield of binders might be due to how the screening was set up. We were hoping that by simulating the panning layout we might be able to identify some hits that would otherwise be missed. To this end, we performed a capture ELISA assay using streptavidin-treated plate.

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Essentially, the output clones were incubated with biotinylated peptides in solution. The peptides and vNAR-bound peptides were subsequently pulled down on the streptavidin plate before probing with anti-FLAG antibodies to detect the FLAG-tagged vNAR monomers. This capture method did not identify any additional hits from what we saw in the direct ELISA assay (Figure 14C). The assay did confirm B03 as being D2C1-selective, i.e., > 3X more selective for D2C1 over D2L2, D2C2, and irrelevant P4 peptide that was not included in the selection. Notably, E08 selectivity was not apparent in this solution- phase assay.

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A

B

C

Figure 14. Summary of Selection III outcomes.

(A) Recovered phages after 4 biopanning rounds in Selection III using OsX-3 and OsX-4 vNAR libraries. (B) Direct and (C) Capture ELISA screens of periplasmic extraction from 96 clones that were retrieved at the end OsX-III round 4. Different probe molecules included D2C1 (DII-E2 mimetic), D2L2, D2C2, streptavidin, HSA, and P4 peptide (irrelevant to our selection system). 74

2.3 Further characterisation of putative hits from Selection III To further characterise the putative hits, B03 and E08, we expressed the non- fusion proteins in E. coli using autoinduction method (Studier, 2005). We also expressed one other clone, HSA-selective 1B8. His-tagged vNARs in the periplasm were purified by immobilised Ni2+ ion affinity chromatography. At this point we did not yet have the sequence information, and the yield of each clone was estimated based on the well- expressed and well-characterised BAFF-selective vNAR with MW ~13.5 kDa and a molar extinction coefficient of 14440 L.mol-1.cm-1. From 500 mL of the starting cultures, different yields were obtained for the three clones, i.e., 110 µg B03, 195 µg E08, and 72 µg 1B8. The three clones were serially diluted to give 12 vNAR concentrations which were added to three immobilised target molecules, D2C1 (E2 mimetic), D2C2 (decoy E1 mimetic), and HSA. The ELISA results are shown in Figure 15. It is clear that B03 exhibited selective binding to D2C1 over D2C2 and HSA, with reduced selectivity at higher concentrations and an estimated EC50 value of ~10 nM for D2C1. As expected 1B8 showed selectivity for HSA. The picture is less clear for E08, though it did display some preferential binding to D2C1 but not to the same extent as B03.

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A N a v C b in d in g “D2C1”

1 .5 B 0 3 E 0 8 1 B 8

1 .0

0

5

4 A 0 .5

0 .0 -1 2 -1 0 -8 -6 -4 [V N A R ], M

B Ir r C b in d in g “D2C2”

1 .5 B 0 3 E 0 8 1 B 8

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1 .5 B 0 3 E 0 8 1 B 8

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Figure 15. Binding of purified B03, E08, and 1B8 clones to biomolecules.

ELISA result demonstrating B03, E08, and 1B8 binding to (A) D2C1, (B) D2C2, and (C) HSA. 12 concentrations of each vNAR clone were tested, with 1 µM starting concentration and 1 in 3 serial dilutions. Based on the concentration response plot, the EC50 estimate of B03 for D2C1 is ~10 nM. Error bars = SEMs, n = 3.

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2.4 Sequence analysis of putative hits from Selection III In addition to further characterising the two clones, B03 and E08, we also wanted to explore the variety of CDR3 sequences in our selection outputs in the hope that the information would shed some light onto the low selection yields that we had contended with thus far. To accomplish these aims, we chose the outputs from OsX-3 low stringent affinity selection. Plasmid DNAs of 96 output clones from round 4 were extracted and sent for sequencing. We found that three of the four streptavidin- selective clones have the same CDR3 sequence, while sequencing failed for one other clone. The shared protein sequence is WAHPQSGCSVGDV. Importantly, the HPQ motif has previously been linked to streptavidin binding (Giebel et al., 1995). We also noticed 10 of the output clones shared an identical CDR3 sequence, i.e., VBRTPWSSCGWFDV. W denotes a tryptophan which contains an aromatic indole side chain. It is well established that aromatic residues are crucial for hydrophobic interactions with the polystyrene surface (Menendez and Scott, 2005). A few consensus sequences for plastic binding motifs have been proposed with two W residues separated by 1-3 random amino acids, e.g., WXXW (Adey et al., 1995). Unfortunately, enrichment of plastic binders occurred irrespective of the BSA blocking step employed in our protocol.

B03 clone Translated protein sequence and structural model of B03 vNAR are shown in Figure 16. B03 contains four cysteine residues; two in the framework regions and two in the CDR loops. CDR3 is made up of 17 amino acids, i.e., QRFRPTYNCGNWVRQDV. A quick sequence analysis indicates that B03 is a typical vNAR with no superficial characteristics that raise immediate concern.

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A ARVD-(13)-LTINCVLRDSICALSSTHW-(42)- GTYRCKVQRFRPTYNCGNWVRQDVYGGGTVVTVNA

B

Figure 16. B03 sequence and predicted structure.

(A) Abridged protein sequence of B03 from N- to C-terminus. Framework regions (black) are not fully shown, in place are the numbers of missing amino acid residues. CDR1 is highlighted in red, and CDR3 of 17-mer length in green. Cysteine residues are underlined. (B) B03 vNAR model generated by YASARA. CDR3 and CDR1 loops are labelled. E08 clone E08 clone was not successfully sequenced and the actual sequence information halted immediately before CDR3. We suspected that the failed sequencing attempt might be due the clone being a mixed population. To test this, we initiated a new culture using the original E08, streaked on an agar plate, and re-picked four individual colonies, subclone 1-4, for storage and sequencing. These four subclones were re- screened. We found that subclone 1 and 2 did not bind D2C1, while subclone 3 and 4 exhibited a small degree of D2C1 binding - however they also bound streptavidin to almost the same extent (Figure 17A). The sequencing results confirmed our hypothesis of mixed population (Figure 17B). Of these four clones, subclone 1 and 2 were successfully sequenced, revealing the two clones to be identical. Subclone 3 and 4 displayed jumbled-up CDR3 sequences which prematurely terminated during sequencing.

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A

B

E08 subclone 1 ARVD-(13)-LTINCVLRDSNCALSSTYW-(42)-GTYRCNVVBRTPWSSCGWFDVYGGGTVVTVNA E08 subclone 2 ARVD-(13)-LTINCVLRDSNCALSSTYW-(42)-GTYRCNVVBRTPWSSCGWFDVYGGGTVVTVNA E08 subclone 3 ARVD-(13)-LTINCVLRDSNCALSSXXW-(42)-GTYRCNVXLBXXIGDYDE E08 subclone 4 ARVD-(13)-LTINCVLRDSICALSSTHW-(42)-GTYRCKVIRXFXXGXCD Figure 17. Screening result and protein sequences of E08 subclone 1-4.

(A) ELISA screen of periplasmic extraction from E08 subclone 1-4. 6 different biomolecules were used to determine binding selectivity, D2C1, D2L2, D2C2, P4, streptavidin, and HSA. (B) Abridged sequences of E08 clone 1-4. Framework regions are not fully shown, CDR1 regions are coloured in red, CDR3 in green, and cysteine residues are underlined. X = unknown, and B = aspartate or asparagine. Since E08 subclone 3 and 4 still showed some degree of D2C1 binding, we decided to detangle these clones further. Again, we initiated starting cultures of subclone 3 and 4 separately. 2 colonies were picked from each and named subclone 5- 8. Subsequently, the sequences of these subclones were determined (Figure 18). Like the original E08 clone, sequencing of subclone 5 and 6 halted immediately before CDR3. Subclone 8 contained a frameshift and was read out-of-frame from CDR3. Subclone 6 was successfully sequenced though the clone contains an unpaired cysteine residue which is often problematic. Having carried out two rounds of colony picking to resolve the original E08 mixed population, the subclones did not convincingly bind D2C1 with selectivity, and some of the clones were still mixed. Together with the original result that demonstrates E08 to have lower potency than B03, we decided to drop E08.

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E08 subclone 5 ARVD-(13)-LTINCVLRDSXCALSSXLW-(42)-GTYRCNVXX E08 subclone 6 ARVD-(13)-LTINCVLRDSNCALSSTLW-(42)-GTYRCNVLLBGBVGDYDEBDVYGDGTVAVTVNA E08 subclone 7 ARVD-(13)-LTINCVLRDSICALSSXLW-(42)-GTYRCKVXX E08 subclone 8 ARVD-(13)-LTINCVLRDSICALSSTHW-(42)-GTYRCKVSMVFPTGXSRXGXXGMXRRW Figure 18. Protein sequences of E08 subclone 5-8.

Abridged sequences of E08 subclone 5-8, frameworks shown in black, CDR1 in red, CDR3 in green, and cysteine residues underlined. X = unknown, B = Asp or Asn.

2.5 B03, a putative D2C1-selective vNAR Peptidomimetic approach was employed to simplify the steps involved in target expression and selection, but ultimately the hits would have to bind the native channel to proceed to optimisation. Nav1.7 channels are multi-pass membrane protein which requires lipid bilayer for proper assembly. Other than endogenous expression in a primary cell culture or immortalised cell line, the use of a transfected mammalian cell line is another option for expressing this type of proteins. Ossianix (Sian Frost) had generated a cell line with inducible hNav1.7 expression using the Flp-In T-Rex system (Ward et al., 2011). Simplistically, the system utilises the regulatory elements from the E. coli Tn10-encoded tetracycline resistance operon, and the gene of interest is integrated into the genome at the FRT site (Hillen and Berens, 1994, Ward et al., 2011). Tetracycline-regulated expression works on the basis of tetracycline binding to the Tet repressor, which then frees up the promoter site for transcription (Yao et al., 1998). One key advantage of the inducible expression system is that the cell line can simultaneously be used for its hNav1.7 expression as well as a negative control.

We measured hNav1.7 level in the whole-cell lysates that were generated from cells that had been induced with tetracycline for 24 h, 48 h, and 72 h. The expression was not detectable at 24 h, barely visible at 48 h, and most robust after 72 h induction

(Figure 19A). Expression of hNav1.7 after 72 h induction was also seen when we stained the Tet-on and Tet-off cells and visualised the fluorescence emission. We had to permeabilise the cells prior to staining as the anti-Nav1.7 monoclonal antibodies used recognise an intracellular motif on the protein (mouse anti-Nav1.7 clone N68/6, Millipore). Most staining was concentrated within the cytosolic compartment (Figure 19C). Surface expression was also visible but was less distinguished. This was likely due to the fact that the cell line was engineered to carry only the SCN9A sequence which encodes the α-subunit of hNav1.7. Better surface expression is supposedly aided by β

80 subunits. To check that the upregulated hNav1.7 was present within the membrane and not only as inclusion bodies in the cytosol, membrane fractions were prepared from 72h-induced cells, non-induced cells, and Flp-In parental cells (Ctrl). Western blot of the membrane fractions from these cells demonstrates induced hNav1.7 expression in the membrane partitioning (Figure 19B). This characteristic was crucial for our investigation as we wanted to test if the vNAR binds to hNav1.7 on the cell surface.

A B

C

Figure 19. Confirmed hNav1.7 expression in inducible hNav1.7-HEK293 cell line.

(A) Western blot of hNav1.7 channel expression in the induced cells under 3 induction windows; 24 h, 48 h, and 72 h. Whole cell lysates were used. Best expression was observed at 72 h timeframe. Protein size ~ 225 kDa, and β-actin as loading control. (B) Western blot of membrane fractions of control Flp-In HEK293 cells, 72h-induced, and non-induced hNav1.7-HEK293 cells. (C)

Images of non-induced and 72h-induced hNav1.7 cells. hNav1.7 expression was probed with anti-

Nav1.7 Ab and visualised via Alexa 555 fluorophore.

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Having confirmed the inducible hNav1.7 expression in the in-house hNav1.7-

HEK293 cell line, we used this cell line to investigate whether B03 binds the hNav1.7 channel expressed in a cell. We used two approaches to detect hNav1.7-specific binding of B03. Firstly, ELISA assay was performed to measure the amount of B03 vNARs that were retained in the cell-coated well of the assay plate after repeated washing. The binding signals were determined for decreasing concentrations of B03 in the hNav1.7- expressing and non-expressing cells, i.e., with versus without tetracycline to induce expression (Figure 20). No significant differences were observed for the binding of B03 under these two conditions (two-way ANOVA; F(1,56) = 1.709, p = 0.196). In addition, different vNAR concentrations did not significantly alter the degree of binding (F(6,56) = 2.143, p = 0.062). The lack of significance indicates that B03 probably does not bind the hNav1.7 channel on the cell surface. To substantiate this finding, we also tried to detect binding using an immunostaining technique. Cells with and without induced hNav1.7 expression were incubated with 1 µM B03 vNAR. The presence of B03 vNARs was probed with anti-FLAG antibodies and visualised via Alexa 555 fluorophore (Figure 20C). We did not observe substantial fluorescence signal when examining the slides that carry fixed hNav1.7-expressing cell, suggesting no detectable B03 binding. Similarly, we found no evidence of non-specific binding by B03 as demonstrated by minimal fluorescence in the hNav1.7 non-expressing cell.

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A

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C

Figure 20. Cell binding by purified B03 vNARs.

(A) ELISA result showing hNav1.7 expression in Tet-on and Tet-off cells. Significantly greater expression was observed in Tet-on cells as compared to Tet-off counterpart (1-tailed t-test p = 0.047, n = 3). (B) Detection of B03 binding to hNav1.7-expressing cell with cell-based ELISA. Statistical significance was determined by two-way ANOVA (F(1,56) = 1.709, p = 0.196). N= 3 experiments, error bars = SEMs. (C) Immunocytochemical staining of Tet-on and Tet-off cells incubated with 1 µM B03 vNAR. No observable fluorescence indicative of B03 binding to the hNav1.7 channel in the induced cell (right panel). Nor was there non-specific binding as illustrated by no detectable B03 in the non-induced cell (left panel). 83

These findings do not fully support B03 binding to the hNav1.7 channel in the cell membrane. We were interested to know if B03 might recognise the peptide if it was tethered to a protein scaffold that would present it as an exposed epitope. This would not only allow us to test if B03 binding to D2C1 was somehow conformation-specific, but also opens up a potential avenue to use the peptide-inserted scaffold as a new bait in future selection attempts. To explore this idea, we integrated D2C1 sequence in the CDR3 or HV4 position in a template vNAR, g0. vNARs that carry the D2C1 sequence in either region were referred to as grafted vNARs; g1 (CDR3) and g2 (HV4), respectively (Figure 21A). B03 selectively bound the loose D2C1 peptide (> 3X compared to other target molecules) but did not bind restraint D2C1 in the vNAR scaffold (Figure 21B). The results point towards the possibility that B03 might be specific to D2C1 peptide in restricted conformation and failure to adopt this conformation within a native protein structure might partly explain the lack of cell binding that we observed earlier.

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A

B

Figure 21. D2C1-grafted vNARs and B03 selectivity for D2C1.

(A) From left to right, g0, g1, g2. g0 is a template vNAR scaffold with no insertion. Highlighted in yellow are HV4 and CDR3 of g0, CDR3 with grafted D2C1 sequence (ELFLADVEGLS) in g1, and HV4 with grafted D2C1 sequence (FLADVEG) in g2. (B) Monoclonal phage ELISA to determine B03 selectivity for isolated D2C1 relative to D2C1 grafted on the vNAR scaffold (g1, g2), g0, D2C2 and plastic surface. 1B8, an HSA-selective vNAR was included as control.

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Up to this point, our investigation had revealed that B03 exhibited selective binding to the E2 mimetic D2C1, but unfortunately did not recognise the epitope in the native channel. Based on the graft result, B03 might also display conformation-specific selectivity. From the above results it can be concluded that a peptidomimetic approach is not without its challenges. Nonetheless, given the potential reward of obtaining a neutralising antibody and the proof-of-principle that (at least one) vNAR-display phage against the peptidomimetic can be selected, we believed further studies were warranted. There was a possibility that other vNARs exist that selectively bind the isolated peptides and also recognise the epitope in the native protein. To make sure that we did not rule out this possibility we attempted another phage-display affinity selection, still using the isolated peptides as the target (Selection IV).

2.6 Alternating support matrices - Selection IV In the previous selections we had retained a considerable number of streptavidin and plastic binders. In the hope to impede this enrichment of non-target binding clones, we decided to try the alternating support matrix approach. The protocol involved using streptavidin or neutrAvidin-coated beads as an immobilisation matrix in alternate rounds of biopanning. By reducing exposure to a single matrix type, we expected the enrichment of background-binding clones to be lessened. Similar alternating background principle proved to be effective in isolating antibodies that showed selective binding to E47 transcription factor from panning against peptide and protein target (Lu and Sloan, 1999). NeutrAvidin is a deglycosylated form of avidin that was developed commercially and has lower isoelectric point than streptavidin and avidin (Hiller et al., 1990). It was specifically designed to reduce the nonspecific binding characteristic that is inherent to other biotin-binding proteins. By performing a quick screen using monoclonal phage ELISA, we found two clones that displayed > 3X selectivity for D3C peptide. However, we could not replicate this result in another ELISA (data not shown) and concluded that there were no promising hits in the panel of output clones from this selection.

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To our disappointment, the repeated selection attempts (see

Table 5 for summary) have not led to the discovery of novel vNARs that selectively target Nav1.7. Several key points can be made regarding our results. Firstly, non-treated polystyrene surface is not suitable for passive adsorption of peptides.

Secondly, putative hNav1.7-E2 sequences are highly hydrophobic and, as a result, their peptidomimetics have poor water solubility. In terms of phage-display selection, biopanning can yield clones that bind to elements in the selection, although in our case the target of interest was not one of them. We inadvertently identified a handful of streptavidin and plastic binders, and that some of these binders contain the prototypical streptavidin- or polystyrene-binding motifs.

Table 5. Selection Criteria.

Overall, four selections were carried out. The first two selections were carried out against peptide antigens that were immobilised on a solid support via passive adsorption or streptavidin- biotin interaction. The latter two were performed in solution-phase to reduce conformational constraints. Distinct combinations of biotinylated peptides were used as antigens in different selections with decreasing concentrations as the selection round progressed. Trypsin was used to specifically elute target-binding clones in all selection attempts. Selection/ Mode of Target Round 1 – 2 – 3 – 4 Number of Mode Library panning peptide washes of (PBS, PBST) elution I Total peptide 2x5 – 2x5 – OsX-3 Immobilised Domain 2 concentration 2x10 – 2x15 antigens D2C1 (µg/mL) 50 – 25 – 5 – 1 Trypsin II Volume of beads (µL) 2x5 – 2x5 – OsX-3 400 – 200 – 100 – 20 2x10 – 2x15 III Domain 2 Total peptide 2x5 – 2x5 – OsX-3 & Peptides in D2C1, D2L2, concentration (µM) 2x10 – 2x10 OsX-4 solution D2C2 5 – 2.5 – 1.25 – 0.625 IV Domain 2, 3 Total peptide 2x5 – 2x10 – OsX-3 D2C1, D2C2, concentration (µM) 2x15 – 2x20 D3L, D3C 5 – 2.5 – 0.5 – 0.1

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Discussions I In this chapter, the aim of our investigation was to identify single-domain antibodies (vNARs) that block Nav1.7 and further develop them as novel analgesics. At the outset this could clearly be viewed as a “high-risk high-reward” project. Nav1.7 is a sizeable transmembrane protein which is notoriously difficult to express. Furthermore, when large proteins are used as immunogens or baits, most of the antibodies that bind do not actually block function. Indeed, in a parallel project at Ossianix that used the native protein approach, several vNARs were obtained that could bind to the P2X3 receptor but none inhibited function. To circumvent this limitation, we adopted a peptidomimetic approach by generating short peptide mimetics of the functionally important target sites on the Nav1.7 channel. We then used these mimetics as bait to isolate vNARs that selectively bind to them from the phage display libraries. Disappointingly, we only derived one ‘hit’ that appeared to selectively bind to one of the peptide mimetics, but the hit failed to recognise the native hNav1.7 channel expressed in the cell membrane. We will consider what could possibly be the reasons for the low selection yield and our inability to extract target binders. These considerations will be discussed under four separate headings; the feasibility of peptidomimetic approach, Nav1.7 druggability, technical limitations, and challenges associated with targeting membrane protein.

1. Feasibility of peptides as a target in phage-display antibody selection It is unlikely for isolated peptides to fully mimic the configuration that they would adopt when being part of a native protein, i.e., the naturally-folded epitope. Nonetheless, peptidomimetic strategy has been successfully employed by many researchers for the generation of epitope-specific antibodies.(Trier et al., 2012). Lee et al. (2014b) used this strategy to generate peptide baits and injected them into mice.

They claimed to have isolated a high-affinity neutralising mAb specific for Nav1.7. However, this claim has not been replicated by later studies (Emery et al., 2016). Along the same line, peptide vaccines have made significant progress as a highly efficient venue for cancer treatment (Slingluff, 2011). Taken together, these findings stipulate that limited epitope on the peptide antigen is sufficient to elicit a noticeable and antigen-specific response by the immune system. Efficient response relies on inherent characteristics of the immune system particularly one that pertains to affinity

88 maturation. This attribute is theoretically non-existent in the pre-defined phage-display antibody library and is absolutely critical in determining the selection outcome. Nonetheless, secondary libraries may be constructed based on the initial hits for another selection attempt to potentially improve the affinity of the hits. It is worth noting that, in ribosome display, each selection round includes a PCR amplification step which introduces spontaneous mutations (Bradbury et al., 2011). These mutations resemble the underlying mechanism of affinity maturation and have been reported to lead to the evolution of picomolar-affinity antibodies from a fully synthetic scFv library (Hanes et al., 2000).

Phage-display attempts that are made against peptide target are scarce. To our knowledge, only a handful of instances have been reported to date (Table 6; the list is not exhaustive). In particular, peptide MUC1 and p21Ras that were successfully used as the target in phage display selection are actually well-established epitopes that had already been validated as antibody-raising immunogens. For successful peptidomimetic strategy not only does the peptide have to be successfully used to bait peptide-selective binders, the binders ultimately have to recognise the same peptide sequence in an epitope on the native protein antigen. In our case, we have derived one peptide- selective binder. However, we did not succeed in obtaining novel binders that recognise the native hNav1.7 channel.

Table 6. Successful phage display selections against peptide targets

Peptide Sequence Length % Hydrophobic Reference residues MUC1 PDTRPAPGSTAPPAHGVTSA 20 50 Griffiths et al. (1993) p21Ras DTAGQEEYSAMRDQYMRTGE 20 20 Persic et al. (57-76) (1999) Ku86 VFEEGGDVDDLLDMI 15 47 Siegel et al. (2000) F1 NENLLRFFVAPFPEV 15 67 Duan and F2 FRQFYQLDAYPSGA 14 43 Siegumfeldt F3 PIGSENSEKTTMPLW 15 40 (2010)

It is obvious that the large size of the phage-display vNAR libraries (~1010) in no way guarantees that target binders exist. Further, even if the library contains several target binders, short-length peptide means that there is only limited interface available

89 for interaction (Molek et al., 2011). Since the library cannot affinity-maturate to yield antibodies that are specifically tailored for the small epitope, any weak antigen- antibody interaction will likely get eliminated during the selection process. Conversely for recombinant protein there possibly are several hot spots that can take part in the interaction, which may increase the stability as well as the likelihood of interaction. The downside is that the protein also contains functionally redundant residues that are present only to reinforce structural integrity (Elcock, 2001). If these residues are immunogenic, they can interfere with antibody generation during immune response and phage-display selection. It is also worth noting that segregation between functional and structural residues is not always clear-cut (Magyar et al., 2004).

2. Druggability of the E2 loops on Nav1.7 To be targeted successfully by a therapeutic agent, the target has to be both druggable and disease-modifying. We have not yet discussed the druggability of the E2 loop of Nav1.7. Nonetheless, the channel is indisputably a prime contributor to pain- related disorders and therefore validated as a therapeutic target with high probability of disease modifying outcomes. Druggability is a critical determinant of success in drug discovery. The term refers to the characteristics of a pocket on the protein surface to behave as a high-affinity, high-specificity binding site for drug-like molecule (Hajduk et al., 2005). Undoubtedly, the Nav channels are druggable as they are subject to inhibition by a number of well-known pan-Nav inhibitors such as tetrodotoxin (TTX), lidocaine, and carbamazepine. Pan-Nav activity however is not always desirable as it increases the risk of adverse effects upon administration, and dosing determination is critical for safe usage. Improvement is needed in terms of Nav specificity and this has led to numerous attempts to identify targetable sites on the Nav channel that offer selectivity, accessibility, and functional modulation (Bagal et al., 2015). Isoform-selective Nav modulators are certainly achievable (for recent patent review, see Zuliani et al. (2015)).

It is usually tolerable for a Nav modulator to have imperfect selectivity, yet one key consideration is that it must have low activity against the cardiac isoform, Nav1.5, unless required. Of course, requirement for blood-brain-barrier (BBB) penetration needs to be considered for the CNS isoforms and any discovery strategies should be tailored accordingly.

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For targetable site, we emulated the strategy used by Lee et al. (2014b) and singled out an E2 loop that connects to the voltage sensing segment S4. By targeting the loop, we hoped to functionally modulate the channel by indirectly engaging the S4 segment. Interestingly, there are a number of naturally-occurring toxins that bind specifically and with high affinity to hNav1.7 through interaction with E2 of domain II. At present, there is no available crystal structure of hNav1.7. Instead we utilised the known crystal structure of a related Nav channel from bacteria (NavAb) for homology modelling. The bacterial Nav channel consists of four identical domains, while hNav1.7 is a single protein that folds into four homologous domains. By using homology modelling, we obtained the predicted location of E2 on individual domain. Comparing our prediction to the information on Uniprot database, we found that the exact E2 locations do not fully coincide between predictions. In fact, according to the Uniprot database the putative E2 sequence of DII consists of only two residues (DII – EG; DI - NLGNVS, DIII - TLGYSDLG, DIV - LADLIETYFVSPT). Protein-protein interaction is pivotal in target engagement by therapeutic agents, including extended hydrophobic grooves on the target surface and binding-induced conformational changes (Arkin et al., 2014). If the E2 loop in isolation is not conducive for binding/interaction, it is prudent to question its druggability.

We had noted during our investigation that E2 sequences possess moderate to high percentages of hydrophobic residues and many of E2 mimetics could not be solubilised in water. In fact, primary sequence analysis revealed the hydrophobic nature of the peptide chain at the E2 position in all domains of hNav1.7, as shown in the Kyte- Doolittle hydropathy plots (Figure 22). The plots depict the regions of high to low hydropathic indication based on the properties of individual amino acids in the primary sequence (Kyte and Doolittle, 1982). Considering the hydrophobic profile, it is tempting to speculate that the E2 loops are probably somehow shielded from water molecules albeit supposedly being an extracellular motif. This paradoxical combination of being extracellular yet hydrophobic makes E2 a prime candidate for specificity targeting, but also poses extreme difficulty for the peptidomimetic approach given its hydrophobic nature. There are ways to improve solubility such as introducing hydrophilic residues to either end of the E2 mimetics. Several bioengineering approaches are also available to create synthetic peptides that better mimic the protein both in terms of structure and

91 function, including backbone modification, and stimuli responsiveness (see Groß et al. (2016) for comprehensive review). These tools would be useful for designing synthetic peptides for future use.

Figure 22. Kyte-Doolittle hydropathy plot of the 4 domains of hNav1.7.

Labelled as S1-S6 are the six TM segments, and E2 is an extracellular linkage between S3 and S4. The hydropathy indices range from 4.5 (most hydrophobic; Ile) to -4.5 (most hydrophilic; Arg) (Kyte and Doolittle, 1982). The plot was generated using ProtScale – an ExPASy online tool developed by Gasteiger et al. (2003).

Toxins are well-known Nav-selective blockers and have been intensively studied. Different toxins can have either pore-blocking or gating-modifying properties and may engage the channel at six potential binding sites (Catterall et al., 2007). It is interesting that both DII-E2 and DIV-E2 constitute a toxin binding site, known as site 4 and site 3 respectively. DI-E2 and DIII-E2 are less known as a critical determinant of subtype- specific binding. In particular, alternative splicing has been noted in DI-E2 linker (Raymond et al., 2004). Nonetheless, mutating a specific residue in DI-E2 of rNav1.2a has resulted in a significant reduction in binding affinity of ProTx-II for rNav1.2a by 2.7 to 13.5-fold (Bosmans et al., 2008). One notable difference between DI/DIII-E2 and DII/DIV-E2 is the length of the pore loop (~100 mer vs ~60 mer; Figure 22). It would be interesting to see whether this feature affects motif accessibility in the fully-formed channel. Subtype-specific toxins that interact with DII-E2 to trap the S4 voltage sensor in the resting state include ProTx-II (Smith et al., 2007), and huwentoxin-IV (Xiao et al., 92

2008). ProTx-II has been shown to exert its Nav1.7 selectivity through engaging with a phenylalanine residue in DII/E2 sequence, i.e., hNav1.7 Phe813 (Schmalhofer et al., 2008). This residue is essential but not sufficient to confer its high affinity however. Residue E818 of DII-E2 on the other hand appears to be the main determinant of huwentoxin-IV inhibitive effect on hNav1.7 activity (Xiao et al., 2010).

Most eukaryotic proteins undergo essential posttranslational modifications

(PTMs), and Nav1.7 channel proteins are no exception. A wide range of reversible and irreversible PTMs regulate the structure, stability and proper functioning of the protein, to name but a few are glycosylation, phosphorylation, acetylation, and methylation

(Mann and Jensen, 2003). Of particular relevance for structural targeting of Nav1.7 is glycosylation which refers to addition of oligosaccharide chain aka glycan to a protein to form a glycoprotein. Heterologous expression studies have demonstrated that Nav1.7 exist in various glycosylated states between the core state of ~250 kDa and the fully- glycosylated form of ~280 kDa (Laedermann et al., 2013). One alternative glycosylated form appears to be promoted by co-expression of Nav1.7 α-subunit with a β1-subunit. One N-linked glycosylation site is situated within the DI-E2 sequence, i.e., LTEFVNLGNVS (asparagine 209). Other sites are also N-linked, e.g., Asn residue at position 283 (DI-E3), 1352 (DIII-E3), 1366 (DIII-E3), 1375 (DIII-E3) (Apweiler et al., 2004). Structural arrangement of the four domains would bring DIII-E3 in close proximity to DII-E2, and DI-E3 to DIV-E2 (Figure 4B). In the presence of a bulky glycan chain, it is within reason to extrapolate how glycosylation, if present, might deter access to DI-E2, DII-E2, and DIV- E2. This notion is particularly interesting given the naturally-occurring toxins that can engage E2 segment on the Nav1.7 channel. For the most part, distinct configurations adopted by the channel in different gating modes may explain the differential accessibility of the extracellular motifs.

It is possible to discern any involvement of glycosylation in target binding by using deglycosylation reagent that removes both O-linked and N-linked glycans from the glycosylation sites, for example, trifluoromethanesulphonic acid (Edge, 2003). Interestingly, phage display has also been used to detect post-translational modification (Kehoe et al., 2006). Not only are PTMs involved in the normal functioning of the channel, they are heavily implicated in peripheral chronic pain syndromes (Laedermann et al., 2015). This dynamic nature of Nav1.7 regulation further complicates how the 93 channel can be targeted and adds to its questionable druggability which is evident in little progress made by the pharmaceutical industry despite the immense resources and manpower that have been allocated in pursuit of novel Nav1.7 inhibitors in the past decades.

3. Limitations and Future Directions Despite its usefulness, phage-display methodology harbours a number of limitations which inevitably influence the selection outcome. Certain clones that carry the vNAR insertion may be unstable, e.g., toxic or interfere with phage infectivity. These unstable clones may have problematic expression or defective propagation irrespective of their selectivity for the target and rapidly disappear from the phage pool (Derda et al., 2011). Another limitation is inherent to the bacterial protein secretion capability, which might result in incorrectly formed eukaryotic proteins (Doerner et al., 2014). Two prominent instances of selection biases are the target-unrelated enrichment of (i) selection-related protein-display phages, and (ii) propagation-related phages (Vodnik et al., 2011). We have in fact come across the first type; selection-related target-unrelated phages. The retention of these phages occurred despite our extra measure to exclude them via a negative selection step, i.e., subtractive panning in an equivalent setting without the target. The latter type of phages are not as easy to detect from ELISA screen alone. We carried out iterative selection rounds to ensure enrichment of high- affinity target-binding phages. However, by doing so we also risked losing the target binding phages through repeated competition with target-unrelated phages that have propagation advantage. One option to minimise this risk is to not repeat the panning round and use deep sequencing to identify positive phages from the pool derived from the first and only selection round (’t Hoen et al., 2012).

Report of the shutdown of the pain research unit within a major pharmaceutical company emphasises the extreme difficulties involved in Nav1.7 targeting (Garde, 2015). There are various discovery routes that are currently being explored, including the natural toxin repertoires (Yang et al., 2013, Klint et al., 2015), and targeting different extracellular moiety on the channel, e.g., E3 targeting has resulted in a non-functional

Nav1.7-selective antibody (Xu et al., 2005). Other than focusing on Nav1.7, one obvious alternative is to identify other pain targets which might be more druggable. As previously discussed Nav1.7 is extensively modulated by PTMs. This notion opens up 94 another venue for channel intervention. Instead of directly engaging the channel protein, it might be practical to look into targeting the key players in PTMs. Recently emerged evidence also highlighted the existence of Nav1.7-independent pain mechanisms (Minett et al., 2014). A patient who suffers from congenital insensitivity to pain, a Nav1.7 channelopathy, reported symptoms diagnostic of neuropathic pain

(Wheeler et al., 2014). This brings into question the Nav1.7 position in the ‘one gene, one drug, one disease’ paradigm. There is no doubt though that Nav1.7 is a key player in pain mechanisms, but maybe not to the all-encompassing extent that was originally thought to be. In fact, researchers have started tackling Nav1.7 simultaneously with other targets, e.g., the opioid system, and found that engaging multiple pain components prove to be effective in ameliorating symptoms of pain (Minett et al., 2015).

4. Challenges The most obvious route for targeting transmembrane (TM) proteins is arguably through the use of heterologous expression in a mammalian cell line. Biopanning in living cells has resulted in identification of target-specific clones for a vast array of TM proteins (Jones et al., 2016). Yet cell panning can be problematic due to the presence of countless irrelevant epitopes that may interfere with the selection against the epitope of interest. However, isolated TM proteins are notoriously difficult to express and purify. Adding the sheer size and hydrophobic content of a fully-formed multi-pass hNav1.7 channel can only mean that its functional reconstitution remains elusive. At present, attempts to generate its crystal structure have been severely hindered. The difficulty is due to a number of pre-requisite features for the proteins to be fully formed and functional, including but not exclusive to the hydrophobicity content, and PTMs. Numerous expression systems have been utilised for TM protein expression and display, including bacteria (Desvaux et al., 2006), yeast (Pepper et al., 2008), and phages (Vithayathil et al., 2011). Each system has its own advantages and drawbacks and have to be thoroughly reviewed when selecting an expression host (Bernaudat et al., 2011). Advances in cell-free expression methodology in the recent years offer an alternative route for overcoming the aforementioned challenges when it comes to in vitro synthesis of multi-span membrane proteins (Shinoda et al., 2016). Synthesis of membrane proteins can be induced in biomimetic-supported systems that make use of protein

95 translation machinery supplied by the prokaryotic or eukaryotic cell extracts (Sachse et al., 2014). The synthesised TM proteins are then incorporated into synthetic (liposomes, nanodiscs, bicelles) or biological (microsomes, inverted vesicles) membrane structures for functional reconstitution (Serebryany et al., 2012). It is highly doubtful that any progress will soon reach the stage that allows successful expression of the massive 260 kDa hNav1.7 protein, yet the concept appears attainable rather than unachievable in decades to come.

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Results II

Non-canonical roles of Nav1.7 In this section, we moved on from the phage display attempt to identify novel

Nav1.7-selective vNARs to investigating the non-canonical roles of voltage-gated sodium channels (for relevant intro, see page 34). We first asked if Nav/Nav1.7 regulates gene expression. To gain insight into the role of Nav/Nav1.7 in transcription, we analysed the transcriptional changes of the cell lines that had undergone treatment with pan-Nav and

Nav1.7 selective inhibitor; TTX and ProTx-II, respectively. Enrichment analysis revealed the top responders to be classified in motility-related pathways. We followed this up by determining if the Nav1.7 inhibition impedes cellular migration using a scratch assay.

1. Transcriptional regulation through Nav1.7

One school of thought for the non-electrogenic functions of Nav1.7 relates to the fact that many ion channels constitute a building block of the signalling protein complexes (Levitan, 2006). For instance, K+ channels have been found to associate with both cytosolic and cytoskeletal proteins, and can trigger intracellular signalling in the absence of ion flux through the pore (Kaczmarek, 2006). For Nav channels, the accessory β subunits not only modulate ion channel function, but also play an additional role as cell adhesion molecules which can interact with one another to induce signalling in neighbouring cells (Lee et al., 2014a). Notably, Minett et al. (2015) described altered gene expression, specifically, increased Penk mRNA in the sensory neurons of mouse

DRG in response to Nav1.7 deletion. To extend this finding, we asked if Nav1.7 is tied to transcriptional regulation in the cell line that is not electrically excited. And if so, could we harness this attribute for drug discovery purpose by constructing a well-defined panel of Nav1.7-responsive genes. The panel could be useful for assessing the efficacy of putative inhibitors in blocking Nav1.7-specific gene regulation.

To accomplish these aims, we first carried out full transcriptional profiling of

H460, a lung cancer cell line with confirmed Nav1.7 expression (Figure 25, and full characterisation by Roger et al. (2007)). Our strategy was to inhibit the channel activity by using Nav-specific inhibitors. In brief, we treated the cultured cells with basal media, or media supplemented with 1 µM TTX (to block all Nav), or ProTx-II (to block Nav1.7 selectively) for six hours. As the IC50 of ProTx-II for Nav1.7 is approximately 1 nM (Park et al., 2014), we decided to investigate two concentrations of ProTx-II; 1 nM and 10 nM. 97

We have also established the inhibitive effect of ProTx-II in an electrophysiology experiment (see Appendix; courtesy of Dr Ramin Raouf). Samples were collected and analysed for changes in transcript expression in each condition with Affymetrix 3’-IVT cDNA microarray and HGU133 plus 2.0 microchip (Dr David Chambers). Logic dictated that if gene expression is under the influence of sodium channels during normal growth conditions, then blocking channel function should reveal the network that is regulated. For data analysis, Affymetrix Microarray Suite 5 (MAS5.0) expression summary and detection call algorithm was used to pre-process the results, and the probesets were prefiltered by removing those that were labelled Absent in all array chips. Linear models (limma package) were applied to determine the significance of the observed transcriptional changes.

Considering the stringent criteria (absolute lfc > 2 with adjusted p value < 0.05), we found that MRPL4 was massively downregulated (lfc of -5) in the TTX condition (Figure 23, top panel). At adj. p < 0.1, we observed four additional transcripts with increased expression, namely, KLHL5, TMEM120A, IL3, and TUBE1. Following the treatment with 1 nM or 10 nM ProTx-II, there were no transcripts whose changes are associated with adj. p < 0.05 (Figure 23, middle and bottom panels). However, for 1 nM ProTx-II at adj. p < 0.1 five genes can be identified as being significantly upregulated. These 5 genes are KLHL5, IL3, ZNF385C, TMEM242, and TMEM120A. Despite all transcripts having adj. p > 0.1, the top 6 responders in the case of 10 nM ProTx-II treatment are CCNA1, KLHL5, TMEM120A, IL3, TUBE1, and ZNF385C. All in all, it seems that similar transcripts comprise the top responders in both TTX and ProTx-II conditions, i.e., KLHL5, IL3, TMEM120A, TUBE1, CCNA1, and ZNF385C (Table 7). Broadly speaking, KLHL5, and TUBE1 encode cytoskeletal and structural proteins (Dhanoa et al., 2013). IL3 encodes interleukin-3 - a cytokine important for immune response, cell growth, differentiation, and apoptosis (Reddy et al., 2000). CCNA1 is implicated in cell cycle regulation (Ji et al., 2004). Interestingly, MRPL4 appeared TTX-specific, and TMEM242’s significant downregulation was specific to 1 nM ProTx-II treatment. Their functions however are not well-characterised, and it is unclear what their expression changes might translate into. Additionally, PENK was not significantly altered in our investigation, despite an earlier report describing a significant change in Penk mRNA expression in the

DRG neuron of Nav1.7-KO mice (Minett et al., 2015).

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Figure 23. Transcriptional responses in the H460 cells treated with TTX or ProTx-II.

H460 transcriptional changes following 6 h treatment with (top) 1 µM TTX, (middle) 10 nM ProTx-II, or (bottom) 1 nM ProTx-II. Considering adj. p < 0.05 and lfc > 2 (blue) as the DE (differential expression) criteria, MRPL4 (red dot) was markedly downregulated following TTX treatment. No apparent DE genes were identified in the ProTx-II conditions. 99

Table 7. Common DE genes in the TTX- or ProTx-II-treated H460 cells.

The DE genes that were identified following Nav inhibition with either TTX or ProTx-II. Average expression (Ave Exprs) and fold changes (FC) are log2 values. P values shown in the table are the maximal values amongst the three conditions, as stated. Associated functions are also listed, based on the information on Genecards database. PTX1, PTX10 = 1 nM or 10 nM ProTx-II. Gene Symbol Gene Name Known Functions Ave Exprs, FC, Adj. p condition KLHL5 Kelch like family Actin binding 4.49, +4.42, 0.0515 member PTX1 IL3 Interleukin 3 Cytokine - cell growth, 4.80, +4.08, 0.0515 differentiation PTX1 CCNA1 Cyclin A1 Cell cycle regulation 4.82, +3.50, 0.1332 PTX10 TUBE1 Tubulin 1 Cytoskeletal protein 4.76, +4.43, 0.0688 TTX TMEM120A Transmembrane - 6.81, +3.54, 0.0554 protein 120A TTX ZNF385C Zinc finger - 4.70, +4.21, 0.0592 protein PTX1 MRPL4 (TTX Mitochondrial - 4.69, -5.11 0.0279 only) ribosomal protein L4 TMEM242 Transmembrane - 5.40, -4.51 0.0592 (only PTX1) protein 242

Identifying the top players is certainly useful, yet it is also interesting to build a bigger picture of how different genes are positioned systemically, and how the responses are reflected in a network of distinct and overlapping pathways. We considered a less stringent cut-off criterion of non-adj. p values < 0.01 and found the top responders in both TTX- and ProTx-II conditions to overlap considerably. Specifically, approximately 14-20% of the responders appeared in all three conditions, and 25-35% in two out of three conditions. This clearly suggests that these are bona fide genes regulated by sodium channels as changes in their expression are detected with disparate pharmacological tools. By relaxing the selection criteria, approximately 300 genes were submitted on Enrichr webtool for pathway enrichment analysis. Analysis of the TTX top responders showed that the genes encode elements in the regulatory pathways for MAPK signalling, cell cycle regulation, endocytosis, TGF-β signalling, apoptosis, and phagocytosis (Table 8). These pathways seem to fit the roles that we had reviewed earlier pertaining to Nav in a non-electrogenic setup. Reassuringly, non-small cell lung cancer is one of the top eight pathways, confirming the identity of the cell line 100 simply through enrichment analysis of the top ~300 transcripts. Furthermore, with regards to the ProTx-II treatment, gene ontology (GO) cellular component revealed that, by and large, the top genes encode structural proteins for the formation of microtubule skeleton and nuclear spindles. As such, it came as no surprise that GO biological process grouped the genes under endocytic recycling, transport, cell cycle and apoptosis, similar to the TTX-based gene ontology.

Table 8. Top eight KEGG 2016 pathways of the top responders from TTX treatment.

The top TTX responders were selected based on non-adj. p values < 0.01 and entered to the Enrichr webtool for pathway and gene ontology analyses. Top-8 KEGG2016 pathways (Kanehisa et al., 2006), associated p and z-score outputs are listed below.

Term P-value Z-score Proteoglycans in cancer_Homo sapiens_hsa05205 0.0006 -2.01 MAPK signaling pathway_Homo sapiens_hsa04010 0.0031 -1.95 Cell cycle_Homo sapiens_hsa04110 0.0033 -1.66 Endocytosis_Homo sapiens_hsa04144 0.0112 -1.86 TGF-beta signaling pathway_Homo sapiens_hsa04350 0.0168 -1.72 Apoptosis_Homo sapiens_hsa04210 0.0242 -1.78 Non-small cell lung cancer_Homo sapiens_hsa05223 0.0275 -1.77 Fc gamma R-mediated phagocytosis_Homo sapiens_hsa04666 0.0234 -1.69

By excluding the probesets that were labelled Absent in all chips only, we noticed that the average expression values of most of the top responders identified were on the side of low expression; with log2 average values ~ 4 while the full range is between ~2 to 17. To facilitate the detection of DE transcripts with moderate to high expression, we alternatively used a 75% fraction-Present (7 of 9 chips) to prefilter the dataset. Previous systematic evaluation has shown that the fraction-Present prefiltering may facilitate identification of the DE genes (McClintick and Edenberg, 2006). The cut- off threshold for the absolute change was set at being no less than 20% and non-adj. p < 0.05, from which we identified two transcripts that recurred in four conditions (TTX and three concentrations of ProTx-II) from two separate microarray experiments; MALAT1 and SDCCAG1. MALAT1 expression was reduced by -50 to -60%. SDCCAG1, on the other hand, was upregulated by 80 to 90%. Earlier studies confirmed these genes to be particularly relevant to the lung cancer cell lineage (Bi et al., 2005, Gutschner et al., 2013). MALAT1 is a shorthand for metastasis associated lung adenocarcinoma transcript 1, a long non-protein-coding RNA which can function as an oncogene (Ren et al., 2016). SDCCAG1 (serotologically defined colon cancer antigen 1), aka NEMF (nuclear exporter

101 mediator factor) encodes a component of the ribosome quality control complex, and its inactivation has been illustrated in several lung carcinoma cell lines (Bi et al., 2005).

We have demonstrated that the non-excitable H460 lung cancer cells responded to pan-Nav and selective Nav1.7 inhibition at a transcriptional level to some extent. To eliminate the possibility that the observed transcriptional changes were wholly lung cancer-specific, we turned to another cell line; SH-SY5Y neuroblastoma. Nav expression in the SH-SY5Y cell line was fully characterised by Vetter et al. (2012), and Nav1.7 was found to be the prominent subtype. We also confirmed the protein expression with western immunoblotting as shown in Figure 25. Transcriptional profile of the SH-SY5Y cell line was characterised using the same method as previously described. In addition to the three conditions that we investigated in the H460 cell line, i.e., vehicle, TTX, and ProTx-II, we also included an additional agonist treatment with veratridine to explore the transcriptional effects of channel activation (Figure 24). Using the stringent criteria (absolute lfc > 2 with adj. p value < 0.05), we found that STEAP4 was the only gene that responded significantly to both veratridine and TTX (Table 9). STEAP4 was upregulated in both conditions, despite the opposite effects that the two reagents supposedly had on the Nav channel. In the veratridine treatment, there was an additional DE gene that was downregulated by lfc of -5, i.e., BTN1A1. Unexpectedly, ProTx-II treatment did not significantly alter gene expression in the SH-SY5Y cell line.

Table 9. DE genes in the SH-SY5Y cells following veratridine or TTX treatment.

Listed are the gene symbols, names, functions (info from Genecards database), lfc and adj p values. VTD = veratridine, TTX = tetrodotoxin.

Gene Symbol Gene Name Known Functions Log2FC Adj. p (Treatment) STEAP4 Six-transmembrane Metalloreductase - +5.41 (VTD) 0.0114 epithelial antigen inflammatory and +5.24 (TTX) 0.0289 of prostate 4 metabolic responses BTN1A1 Butyrophilin Member of Ig -5.02 (VTD) 0.0009 subfamily 1 superfamily, cell member A1 surface receptor

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Figure 24. Volcano plots of SH-SY5Y transcriptional responses to veratridine, TTX, or ProTx-II.

Transcriptional changes in the SH-SY5Y neuroblastoma cells in response to 6 h treatment with three different Nav modulators; (top) veratridine, (middle) TTX, or (bottom) 10 nM ProTx-II. Red = probesets exhibiting significant responses with adj. p value < 0.05, blue = absolute lfc magnitude > 2, grey = the rest of the probesets. 103

STEAP4 stands for six-transmembrane epithelia antigen of prostate 4, which belongs to a family of metalloreductase-encoding genes with three other closely-related members; STEAP1-3 (Ohgami et al., 2006). There is ample evidence linking STEAP proteins to different cancer types (Gomes et al., 2012), however, to my knowledge, no studies have been done to investigate STEAP mRNA and proteins in neuroblastomas. Somewhat intriguing is that in most cancer cell lines reported, STEAP4 mRNA levels were non-detectable while considerable amounts could be detected in the healthy tissue counterparts. Indeed, we observed low STEAP4 expression in our vehicle-treated

SH-SY5Y cells. This low expression was immensely upregulated upon Nav modulation by either TTX or veratridine (log average expression ~ 3; Table 9). BTN1A1 is most known for its role in milk lipid secretion during lactation, although it has also been demonstrated to inhibit T-cell activation (Smith et al., 2010). Importantly, increased levels of BTN1A1 and related proteins appear to be relevant for intestinal inflammation, and colon cancer (Lebrero‐Fernández et al., 2016). We also noticed that even with prefiltering, both STEAP4 and BTN1A1 expression levels were marginally above background, which heeded some caution with regard to interpreting the apparent changes in their expression.

All in all, some transcriptional changes were elicited from Nav1.7 inhibition in the H460 cells, however the responses were relatively muted in SH-SY5Y. We then asked what could possibly explain the different magnitudes of transcriptional responses in the two cell lines. One possibility was that the levels of Nav1.7 expression are markedly dissimilar and prompted us to investigate the Nav1.7 presence in cell membrane fraction using western immunoblotting with anti-Nav1.7 mAbs. Qualitatively, greater intensity was observed in the H460 membrane fraction compared to the SH-SY5Y sample (Figure 25A). We also analysed SCN9A mRNA expression by calculating non- parametric ranks of the average expression values in the microarray dataset and subtracting from the global average rank gathered from various cell backgrounds. This value is bigger for H460 compared to SH-SY5Y (Figure 25B), which is indicative of higher expression and is in line with the blot result. From these observations, it seems that the extent of the transcriptional responses reflects the quantity of Nav1.7 protein, as well as the relative rank (i.e., expression) of SCN9A mRNA.

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A B

SCN9A Probeset H460 SH-SY5Y 206950_at 0.623 0.569 229199_at 0.580 0.458 Average 0.60 0.51

Figure 25. Western blot of Nav1.7 in cell membrane samples, and relative rank of SCN9A.

(A) Nav1.7 in membrane fractions of the H460, or SH-SY5Y cells. β-actin as a loading control. (B) Non-parametric rank differences of SCN9A expression in H460, and SH-SY5Y. Relative ranks were calculated for the two SCN9A probesets, and subtracted from the corresponding global ranks, giving the rank difference values. Positive rank difference signifies greater expression than the global average. The bigger the value, the further away it is from the average expression.

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2. Investigating the roles of Nav1.7 in cell migration using a scratch assay

Based on the GO enrichment analysis, it is likely that Nav/Nav1.7 inhibition affects the transcripts that regulate cell survival, motility, and endocytic/phagocytic processes. In fact, Nav1.7 is amongst the known Nav isoforms (e.g., Nav1.5, 1.6) that are the key players in the regulation of physiological motility, and pathological metastasis

(Black and Waxman, 2013). Specifically, Nav1.7 is expressed in a subset of monocyte- derived dendritic cells, and functions in the regulation of their chemokine-induced migration (Kis-Toth et al., 2011). Further, Nav1.7 upregulation has also been reported in breast (Fraser et al., 2005), prostate (Diss et al., 2005), and non-small cell lung cancers

(Roger et al., 2007). The presence of Nav1.7 in these cancer cells is reportedly implicated in driving aberrant migration, and invasion (Fulgenzi et al., 2006, Fraser et al., 2014b). These two attributes are not identical, though highly interconnected, and constitute the fundamental characteristics of tumour progression. Migration refers to the movement from one point (often the site where the primary tumour resides) to another through repetitive cycles of adhesion, detachment, and polymerisation steps (Friedl and Wolf, 2003). Invasion refers to the ability of a metastatic cell to penetrate the tissue barrier by breaking down the extracellular matrix components (Glentis et al.,

2014). The roles of Nav1.7 in cell migration is rather controversial. One study suggests that Nav1.7 may only be involved in cell invasion, not migration (Roger et al., 2007). We wanted to see for ourselves if cell migration is Nav1.7-dependent. To this end, we studied cell migration under the condition where Nav1.7 is functionally inhibited with

ProTx-II, a well-established Nav1.7-specific tarantula toxin that behaves as a functional antagonist (Schmalhofer et al., 2008).

To measure cell migration, we used a well-established scratch assay (Liang et al., 2007). Essentially, a scratch path was made through a confluent monolayer of H460 cells using a p10 micropipette tip. Within a specified period, the distances encroached by the two cell fronts were measured, i.e., gap closure. Preliminary experiments were carried out to select the extracellular matrix to be used for the migration assay. We investigated the rate of H460 migration on plastic, and three extracellular matrix (ECM) types, i.e., collagen IV, fibronectin, and laminin. To minimise cell proliferation, we used reduced serum (1% FBS) media as the basal media and deliberately restricted the time frame within which the cells were treated to < 18 h since a longer window (>24 h)

106 cannot distinguish cell proliferation from motility (Rodriguez et al., 2005). For an average cell, a total cycle time is ~ 24 h (Bernard and Herzel, 2006). Cell front velocity was calculated from the distance travelled by each front after 6 h incubation. One-way analysis of variance revealed that the matrix type had a significant effect on the rate of migration (F(3,20) = 14.76, p < 0.0001). Significantly faster migration was observed in all ECM conditions as compared to plastic (Figure 26, Dunnett’s p values < 0.01). On average, migration on fibronectin was the fastest, with > 2X the rate seen on plastic. According to this finding, fibronectin was used to pre-coat the cell culture plate in the subsequent scratch assay.

Figure 26. Preliminary experiments for ECM selection.

Cell migration was measured on 4 matrices. We determined statistical significance with single- factor ANOVA (F(3,20) = 14.76, p < 0.0001). Post hoc Dunnett’s tests were then applied to assess the significance of the differences between the migration rate on individual ECMs versus plastic. All three ECMs significantly increased the rate of migration (p < 0.01). Migration was the fastest on fibronectin; > 2X the basal rate on plastic. Error bars = SEMs, n = 6. The roles played EGF/EGFR interaction in cell migration are well-established (Mendelsohn, 2003, Harms et al., 2005). In particular, EGF has been demonstrated to upregulate Nav1.7 mRNA expression in the H460 NSCLC cell line, which in turn drives cell invasion (Campbell et al., 2013). We first tested if the basal migration rate could be increased by EGF treatment. By comparing EGF to FGF/TGF, we found that only EGF significantly increased the rate of H460 migration (Figure 27A, Dunnett’s p value < 0.05). To proceed, we investigated six treatment conditions; vehicle, EGF, gefitinib (EGFR 107 inhibitor), EGF plus gefitinib, ProTx-II, and EGF plus ProTx-II (Figure 27B). One-way ANOVA revealed a significant treatment effect (F(5,18) = 7.44, p = 0.0006). To start with, we asked if Nav1.7 block had any effect on the basal rate of H460 migration. There was no difference between the cell front velocity in the vehicle-treated and ProTx-II- treated condition (Tukey’s post hoc p value > 0.05), suggesting that Nav1.7 block did not affect the basal migration rate. Cell front velocity was significantly greater in the EGF- treated cells compared to the cells treated with vehicle (p < 0.01). To determine whether this increase was reversible, EGFRs were blocked by a specific inhibitor, gefitinib. We found that gefitinib by itself did not alter the rate of cell migration. However, when applied together with EGF, gefitinib significantly reduced the EGF- augmented rate to the level close to that of the control (EGF vs EGF plus gefitinib, p < 0.05). Along the same lines, H460 cells were treated with ProTx-II in combination with

EGF to characterise the effects of Nav1.7 block on the elevated cell migration. ProTx-II did not affect the increased velocity driven by EGF in the EGF plus ProTx-II condition (EGF vs EGF plus ProTx-II, p > 0.05).

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A

B

Figure 27. Migration rates of the H460 cells in different treatment conditions.

H460 NSCLC cells were grown to full confluence on fibronectin-coated cell culture wells. A scratch path was made, and the cells were treated with different reagents for 18 h. Time lapse images of the scratch wound were taken using the Incucyte. The distances moved by the cell fronts were measured, and the cell front velocity was calculated. Statistical significance was assessed using one-way ANOVA. (A) Migration rates were compared between EGF-, FGF-, and TGF-treated H460 cells. The presence of the growth factor had a significant effect on cell migration (F(3,8) = 4.75, p = 0.0347). Only EGF significantly increased the rate as compared to vehicle (Dunnett’s p value < 0.05). (B) A significant treatment effect was observed (F(5,18) = 7.44, p = 0.0006). ProTx-II did not alter the basal rate of cell migration as compared to the control group. EGF significantly increased the rate of migration (Tukey’s post hoc p value < 0.01). EGF-elevated migration could be reversed with gefitinib, an EGFR-specific small-molecule inhibitor (p < 0.05). On the contrary, the elevated rate was not altered when EGF was applied together with ProTx-II. N = 4 experiments - each with 4 replicate wells, error bars = SEMs. 109

Discussions II In the second half of the chapter, we primarily investigated the non-electrogenic functions associated with Nav1.7. These entailed investigations into the transcriptional responses elicited by pan-Nav, or Nav1.7 inhibition and the non-consensus involvement of Nav1.7 in cell migration.

1. Transcriptional profiling for the screening of novel Nav1.7 inhibitors Our initial objective was to establish a set of differentially expressed (DE) transcripts to utilise as a screening tool for potential Nav1.7 inhibitors. We started by determining the transcriptional profiles of the H460 cell line in response to Nav inhibition either by TTX (pan-Nav) or ProTx-II (Nav1.7). GO analysis of the top DE transcripts indicates enrichment in the pathways that are involved in cell cycle regulation, apoptosis, and phagocytosis, befitting to the known roles of Nav/Nav1.7 in the regulation of cell motility and survival (Meguro et al., 2009, Campbell et al., 2013). What is less clear is exactly how these pathways were altered, and what were the downstream consequences of such alterations. We then went on to test a different cell line, SH-SY5Y. H460 lung cancer cells transcriptionally responded more significantly to

Nav/Nav1.7 inhibition than the SH-SY5Y cell line. A parsimonious explanation for this observation is the relatively low level of SCN9A transcript in the SH-SY5Y neuroblastoma relative to the global average, with our studies also showing moderate levels of the receptor by western blotting. The number of DE transcripts in both cell lines is limited and the exact magnitude by which these transcripts respond to pan-Nav or Nav1.7 inhibition requires further investigation. We also identified transcripts that uniquely responded to ProTx-II, not TTX, and vice versa. We cannot exclude the possibility that some of our results will reflect false positives and it is for this reason that we have focussed on transcripts that change in a consistent fashion following treatment with differing agents that chiefly block the same channel. Possibly, the use of other Nav1.7- specific reagents in future experiments will confirm whether the observed transcriptional changes in our investigation are reproducible and may provide more detail on the putative transcriptional effects of Nav1.7 inhibition.

A study by Minett et al. (2015) gave credence to the putative connection between Nav1.7 and transcriptional control; describing transcriptome changes in the

DRGs of sensory neuron-specific Nav1.7-null mice. Specifically, PENK mRNA that 110 encodes enkephalin opioids was found to be markedly upregulated. The group postulated that the concomitant upregulation of opioid signalling was at least partially responsible for the antinociceptive effects of Nav1.7 silencing. What is interesting though is that some aspects of the findings have not been replicated by later study (Deng et al., 2016). More explicitly, similar transcriptional responses were observed in the tamoxifen-inducible adult-onset Nav1.7-cKO mice. However, inhibition of the opioid receptors with naloxone did not reverse the insensitivity-to-pain phenotypes in the

Nav1.7-cKO mice, bringing into question the importance of the opioid system in anti- nociception in the context of Nav1.7 deficiency. In our investigation, we did not observe a significant increase in the level of PENK mRNA, or discernible evidence of opioid upregulation in response to Nav inhibition in the two cell lines; H460, and SH-SY5Y. These observations could be due to the fact that we used reagents that supposedly block the Nav channel in the cancer cell lines, whereas Minett et al. (2015) completely knocked out Nav1.7 in the mouse DRG neurons. One would expect that full Nav1.7 deficiency would trigger system-wide responses, whereas pharmacological intervention with selective or non-selective inhibitors are more likely to induce shorter-term effects that are less dramatic.

We additionally pinpointed two recurring genes in the H460 cell line, MALAT1 and SDCCAG1, that have some surprising lung cancer-related implications. Although the changes in their expression are rather modest, i.e., less than 2-fold, or 100%, the direction of change is particularly interesting since MALAT1 is also known as an oncogene and has been shown to be a critical regulator of lung cancer progression (Gutschner et al., 2013). It has been suggested that inactivating MALAT1 might offer another therapeutic angle for cancer treatment (Ren et al., 2016). Along the same line, SDCCAG1, a human homologue of drosophila CALIBAN gene, was demonstrated to function as a tumour suppressor in human lung cancer cells both in vitro (Bi et al., 2005), and in vivo (Mortin et al., 2008). Activation of SDCCAG1 would prove beneficial for the prevention of tumour progression. It is probably highly speculative to assume

Nav inhibition would definitely be an effective measure for cancer treatment from these observations alone. Nevertheless, in light of our results whereby Nav inhibition affects various cancer-contributing genes simultaneously, the notion certainly warrants further investigation. Coupled with the idea of cancer as a channelopathy that has rapidly

111 gained a foothold in recent years (Litan and Langhans, 2015), targeting the Nav channel might indeed offer an alternative avenue. There have been suggestions that pan-Nav analgesics might even have some unexpectedly beneficial effects in terms of disease recurrence when used in cancer surgery (Fraser et al., 2014a).

2. Nav1.7-mediated regulation of cell migration

For the latter part of this section, we explored the effects of Nav1.7 inhibition on cell migration. To do this, we used ProTx-II, a Nav1.7-specific inhibitor in one of the experimental conditions to block the Nav1.7 channel. A scratch assay was employed to assess the difference in the migration rates of H460 cells in the control and ProTx-II condition. We found no significant difference between the rates in the two conditions. To follow up, we asked if the migration rate could be enhanced and, if so, whether the increase was subject to modulation through Nav1.7. The migration rate was elevated following EGF treatment, however this increased rate was not significantly altered when

ProTx-II was supplied. Under our assay conditions, it appears that Nav1.7 block by ProTx- II does not influence the basal and elevated migration rate of the H460 cells. Our findings are in line with Campbell et al. (2013) where Nav1.7 was inhibited with TTX or siRNA and no effect on migration was observed. In light of contradictory reports of

Nav1.7 involvement in cell migration in other cell types, for instance, aortic smooth muscle cells (Meguro et al., 2009) and monocyte-derived dendritic cells (Kis-Toth et al.,

2011), we cannot conclusively say whether migration is dependent on Nav1.7 as a rule. The cellular context, the channel activity at a physiological level, and the nature of the assay are likely accountable, at least partially, for the differences in Nav1.7-related behaviours.

3. Limitations and Future Directions Six-hour treatment was used in all microarray experiments, and the outcome was specific for this time window. It would be interesting to monitor gene expression at different time points, as well as to investigate any behavioural responses that occur downstream of the transcriptional responses. Our investigation was carried out exclusively on immortalised cell lines, which are relatively easy to handle and inexpensive compared to primary cultures, tissues or animals. Nevertheless, experiments on the latter might offer some insightful results to complement our findings. As with any other experimental techniques, technical limitations are applicable 112 to microarray methodologies and analyses. Microarray can detect expression data for thousands of transcripts, enabling identification of differentially expressed (DE) genes. Practically, microarray relies on multiple hybridisation steps, thus one non-optimal condition can lead to a domino effect that is unlikely to be detected at the final detection step. One key assumption in the experiments is that all experimental conditions were optimal; from probe design to hybridisation efficiency (Jaksik et al., 2015). Hence it is important to validate the expression outcome by using different quantitation techniques, e.g., real-time PCR to confirm if the responses are consistent.

Distinct pre-processing techniques for microarray dataset are known to generate different results (Millenaar et al., 2006). The well-known techniques include MAS5.0 (Affymetrix), RMA, and dChip, to name a few. The inconsistency associated with pre-processing is however not within the scope of our investigation. We consistently used MAS5.0 to pre-process all datasets. We also included a pre-filtering step since filtering of the transcripts according to the Present/Absent calls has been shown to reduce incidence of false positives (McClintick and Edenberg, 2006), and increase the number of differentially expressed genes detected (Hackstadt and Hess, 2009, Archer and Reese, 2010). A number of statistics-based and/or ranking-based DE assignment criteria with varying degrees of conservativeness have been developed and used by various research groups, for example, model-free approaches including nonparametric t-test, Wilcoxon or Mann-Whitney rank sum test, and a correlation-based heuristic method (Troyanskaya et al., 2002), or model-based approaches, e.g., empirical Bayes with moderated t-test (Ritchie et al., 2015). Yet ultimately, there is no consensus regarding how to best classify the transcripts as being differentially expressed, the so- called DE genes.

We hypothesised that Nav1.7 inhibition would lead to considerable transcriptional responses and that, ultimately, we would select potential DE genes to be validated and used for screening of novel Nav1.7 inhibitors. The idea of microarray- based class prediction and discovery is not novel. Several research groups have attempted to utilise the tool for disease profiling (Quackenbush 2006), and drug classification (Natsoulis et al., 2005). However, there are caveats and challenges that are difficult to avoid and overcome. Two inevitable restraints are the curse of dimensionality and of dataset sparsity (Somorjai et al., 2003). The former refers to the 113 number of transcripts that are simultaneously detected (too many features), whereas the latter denotes the limited number of samples (too few samples). Since it is not feasible to eliminate these two restraints, an emphasis has instead been placed on downstream processing, and analysis tools. It is important to keep in mind that albeit the availability of sophisticated processing tools, the crux of the matter is the quality of the actual dataset, described by the phrase ‘garbage in, garbage out’.

Several in vitro assays have been developed for migration studies. One prominent assay is the scratch or wound healing assay that we used to study the effects of Nav1.7 inhibition on cancer cell migration. The scratch protocol is rather simple and quick to set up and carry out. Tailor-made tools for the purpose also facilitate repeatability and consistency of the setup, namely, the 4-needle WoundMaker which allows greater precision and control over the wound width and position, and the ImageLock plate which enables image capture of the same fields of view throughout the entire experiment. There are inherent limitations and drawbacks to the scratch assay that should also be noted. It is unpredictable how much of the plastic surface or the ECM substrate gets scraped off, and mechanical damage to the cells might have occurred which can also affect cellular behaviour and introduce experimental artifacts (Kramer et al., 2013). Another common assay is a transwell migration or Boyden chamber assay, which is based on the two chambers separated by a porous membrane (Boyden, 1962). Each chamber contains some culture medium, and transmigration across the membrane is monitored. It would be interesting to attempt an experiment with the transwell technique to see if the cells behave similarly in a different assay setting. One feature that most in vitro migration assays have in common is that cells are grown in monolayer, meaning that the dimensionality is almost strictly 2D. This is in stark contrast to a 3D structure in vivo, which supports the notion that in vitro results might not fully reflect what happens in vivo. An extra dimension brings additional elements and interactions such as mechanical support, cushioning, and biomolecules, many of which are crucial in governing cell motility (Frantz et al., 2010).

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CHAPTER 2: NEUROTROPHIN/RECEPTOR INTERACTIONS AND SMALL MOLECULE MODULATORS

Introduction “Now equilibrium is the very opposite of disorder.” – Rudolf Arnheim

To rectify any error in a given system, it is often informative to understand how the system functions normally. This statement is especially relevant for drug development effort to counteract pathological disorders and diseases. This introduction is aimed to provide a rationale for why and how we attempted to target the TrkB receptor. The first part of the review will describe the interactions between neurotrophins and their receptors, and the importance of these interactions in maintaining the normal functioning of the developing and mature nervous system. In the second part we will examine some examples of dysregulation that lead to the manifestations of a plethora of disease conditions. Lastly, we will cover the different methods that scientists have employed in an attempt to treat the conditions effectively - mainly revolving around the central theme of bringing the system back towards the original state or equilibrium. The details in this review are specific to neurotrophins but the general concepts should be applicable to any interactions involving binding partners.

A. Neurotrophin/Receptor Interactions in the Developing and Mature Nervous System Neurotrophins are a small family of secreted proteins. The term originates from two stems; neuro- and -trophos (from Greek meaning nourishing), i.e., growth factor for neuron. Four related proteins are grouped under this family; nerve growth factor (NGF), brain-derived neurotrophic factor (BDNF.), neurotrophin-3 (NT-3), and neurotrophin-4 (NT-4). Neurotrophins are synthesised and released by the sympathetic and sensory target organs as well as the neurons themselves (Huang and Reichardt, 2001). The secreted neurotrophins bind to their cognate receptors in the cell membrane. This interaction plays a key role in the control of axonal and dendritic growth, neuronal differentiation, migration, synaptogenesis and synaptic pruning in the developing nervous system in vertebrates (Davies, 1994, Choo et al., 2017). Additionally, neurotrophins are important for neuronal survival, apoptosis, and synaptic plasticity in the mature nervous system (Kirstein and Farinas, 2002, Yamada and Nabeshima, 2003).

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Neurotrophin receptors are also expressed in non-neuronal cells including glial cells such as microglia and astrocytes (Roy and Pahan, 2013), cardiomyocytes (Feng et al., 2015), and immune cells (Vega et al., 2003). Further, growing evidence suggests the importance of neurotrophins in cell cycle regulation mainly through p75NTR under normal conditions, injury, and stress (Cragnolini et al., 2012, Zanin et al., 2016).

Two distinct families of neurotrophin receptors are the tropomyosin receptor kinase (Trk) of the receptor tyrosine kinase family and p75NTR of the tumour necrosis factor (TNF) receptor superfamily. There are three types of Trk receptors which exhibit high affinity binding to the cognate ligands (Kd ~ 0.01 nM). TrkA binds NGF and to a lesser extent NT-3, TrkB binds BDNF and NT-4, and TrkC binds NT-3. p75NTR on the other hand displays non-selectivity, showing similarly low affinity binding (Kd ~ 1 nM) to all neurotrophin types (Pattarawarapan and Burgess, 2003). Alternative splice variants of TrkB and TrkC do not have a kinase domain, and instead signal through a cytoplasmic domain via less well-defined mechanisms (Vega et al., 2003, Fulgenzi et al., 2015). Different receptor subtypes are found in varying proportions depending on the neuronal subpopulation. TrkA is the predominant neurotrophin receptor in sympathetic and sensory neurons of the peripheral nervous system (Majdan et al., 2001). TrkB and TrkC are primarily expressed in the CNS neurons such as those in the cerebral cortex, hippocampus and cerebellum, but also found throughout the nervous system (Dieni and Rees, 2002, Fukumitsu et al., 2006, Cheng and Mattson, 1994). Spatial localisation of the Trk receptor is apparently dynamic. Mouse trigeminal ganglion is one example of neuronal population that changes from BDNF-responsive to NGF-responsive during development, by altering receptor surface expression (Buchman and Davies, 1993, Pinon et al., 1996). The switch is partly explained by the fact that newly generated neurons possess different properties. Nevertheless, labelling studies have confirmed the switch in the old population of neurons that used to exhibit BDNF responsiveness and later became responsive to NGF (Enokido et al., 1999). This example illustrates the highly dynamic pattern of neurotrophin expression that is subject to sequential changes in accordance with the developmental stages.

Neurotrophins and Trk receptors are homodimeric proteins. The neurotrophin dimer is ~25 kDa in size, and each dimer consists of a highly conserved cysteine knot in the core that holds the monomers together (Chao, 2003). The Trk receptors sit in the 116 cell membrane and consist of three distinct domains; extracellular, transmembrane, and intracellular catalytic domain (Figure 28, left). TrkA, TrkB, and TrkC receptors display 50-55% sequence homology in the extracellular domain, and approximately 75% homology in the tyrosine kinase domain (Brodeur et al., 2009). The extracellular domain can be further divided into five subdomains; cysteine-rich domain 1 (CRD1), leucine-rich region 1-3 (LRR1-3), CRD2, immunoglobulin-like domain 1 (IgL-D1), and IgL-D2 (Marchetti et al., 2015). Domain swapping and mutation studies identified IgL-D2 as the ligand binding domain, and implicated IgL-D1 in the regulation of receptor dimerization and prevention of ligand-independent activation (Urfer et al., 1995, Arevalo et al., 2000). In particular, ligand-independent activation of TrkA and TrkB is speculated to be dampened down by interaction of p75NTR with IgL-D1 (Zaccaro et al., 2001). p75NTR arguably potentiates binding of the cognate ligand to the Trk receptor (Huang and Reichardt, 2003). Whether this potentiation occurs through p75NTR interaction with the ligand directly or through its effect on the conformation of the Trk receptor is not fully resolved. There is a suggestion that no direct interaction is present between p75NTR and TrkA, instead affinity potentiation occurs through downstream communication (Wehrman et al., 2007).

Figure 28. Structural configuration of Trk and p75NTR receptors.

The Trk receptors consist of three domains; extracellular, transmembrane, and intracellular tyrosine kinase domain (TKD). The extracellular domain can be further divided into 5 subdomains, i.e., 2 cysteine-rich domains, CRD1 and 2, flanking the leucine-rich regions LRR1-3. More proximal to the membrane are two Ig-like domains IgL-d1 and IgL-d2. p75NTR is structurally distinct from Trk, and contains an extracellular domain with 4 CRDs, a transmembrane segment, and intracellular chopper domain (CD) and death domain (DD). Modified image from Marchetti et al. (2015). 117

p75NTR is a ~75 kDa protein; approximately half the size of a full-length Trk protein (Zampieri et al., 2005). It was the first member of the TNF receptor superfamily to be identified, with a total of ~25 receptors in the family, including, TNFR1 and 2, Fas, CD30 and CD40 (Roux and Barker, 2002). Each p75NTR monomer consists of four cysteine-rich extracellular domains, a transmembrane domain, and a cytoplasmic death domain (Figure 28, right). Truncated p75NTR isoforms exist either as a result of alternative splicing or proteolysis, e.g., a splice variant lacking CRD2-CRD4 which does not bind neurotrophin (Dechant and Barde, 1997). The crystal structure of NGF in complex with the extracellular domain of p75NTR suggests an asymmetric 2:1 NGF:p75NTR stoichiometry, which in part explains the non-selectivity of p75NTR to distinct neurotrophins (He and Garcia, 2004). A later report of the NT-3/p75NTR complex on the contrary found a symmetric 2:2 NT-3:p75NTR formation (Gong et al., 2008). The group suggested that the observed 2:1 complexes that were reported previously were an experimental artefact that resulted from deglycosylated proteins. Regardless, it appears that p75NTR can adopt different oligomeric states, i.e., monomeric, dimeric, and trimeric (Yaar et al., 2002, Vilar et al., 2009). This is in line with the propensity of other TNFR members to trimerise (Chan, 2007), various p75NTR molecular masses (Yaar et al., 2002), and that some of the p75NTR binding partners are trimeric proteins, e.g., TRAF6, and RVG (Ye et al., 2002, Langevin et al., 2002). Monomers and trimers have been shown to coexist on the cell surface and their relative expression underlies the diverse and contradictory activities of p75NTR (Anastasia et al., 2015).

Like many bioactive polypeptides, neurotrophins are synthesised in the endoplasmic reticulum as a precursor, pre-pro-NT. Removal of the N-terminal signal peptide from the pre-pro-NT turns it into pro-NT. Enzymatic cleavage of the pro-domain from the dimerised pro-NT is carried out by furin or pro-convertase in the trans-golgi network or secretory vesicles before mature neurotrophins are released (Lessmann and Brigadski, 2009). Alternatively, the cleavage may occur extracellularly by serine protease plasmin or matrix metalloproteinase MMP-7 or MMP-9 (Spinnler et al., 2011, Lee et al., 2001). Pro-NT and mature neurotrophins differ in their binding properties. Mature neurotrophins preferentially bind to the Trk receptors to provide survival-supporting signal whereas pro-NT interacts with p75NTR to induce cell apoptosis (Lu et al., 2005).

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The presence of a co-receptor, sortilin or SorCS2, has proven to be essential for the preferential binding of pro-NT to p75NTR as it binds a conserved motif in the pro-NT pro-domain (Nykjaer et al., 2004, Teng et al., 2005). Interestingly, sortililin also associates with the Trk receptors to regulate anterograde trafficking of the receptors along the axon which is shown to be critical for signalling (Vaegter et al., 2011). Not only does the pro-domain contribute to difference in the binding properties of pro-NT and its mature counterpart leading to opposing outcomes, the pro-domain also provides a scaffold for oxidative folding of the mature region (Hauburger et al., 2007), and plays a crucial role in intracellular trafficking and secretion (Lu et al., 2005). Recent findings also suggest the ability of TrkA and TrkC but not TrkB to induce cell death (Nikoletopoulou et al., 2010). The lack of death-inducing properties of TrkB possibly plays a key role in poorer prognosis for TrkB neuroblastomas as compared to their TrkA counterpart (Schramm et al., 2005, Brodeur et al., 2009).

Upon binding, neurotrophins promote Trk homodimerisation which brings the cytoplasmic tail of each monomer in close proximity; favouring trans-phosphorylation of at least five tyrosine residues by intracellular kinases (Cunningham et al., 1997). The phosphorylated residues provide docking sites for adaptor proteins including SH2 containing proto-oncogene (Shc), growth factor-receptor bound protein-2(Grb2), and Gbr2-associated Binder-1 (GAB1) which couple the receptor to downstream signalling cascades (Huang and Reichardt, 2001). The long-held notion of ligand-induced receptor dimerisation has however been contested by studies that detected an inactive dimerised form of the Trk receptor in the absence of ligand (Shen and Maruyama, 2011). Three canonical pathways are the molecular switches associated with neurotrophin activity (Figure 29); Ras/mitogen-activated protein kinase (MAPK), phosphatidylinositol-3 kinase (PI3K)/Akt, and phospholipase C gamma (PLCγ). Generally, pro-survival signals are triggered downstream of NT binding to the Trk receptor. Trk- mediated pro-survival effects are well-established through studies using null knockout, conditional knockout and transgenic mice (see review by Holcmann et al. (2015)). Trk- deficient mouse models have been generated for individual isoforms, and they all display similar abnormalities in the CNS and PNS such as selective neuronal loss, aberrant morphologies; with TrkB deficit being the most severe (Klein et al., 1993, Klein et al., 1994, Smeyne et al., 1994, Tessarollo et al., 1997, Perez-Pinera et al., 2008).

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Neurotrophin administration has been shown to rescue abnormal phenotypes in the affected neurons as well as increase neuronal survival (Matsuzaki et al., 2004, Lee et al., 2013).

Figure 29. Three canonical pathways of Trk signalling.

Ligand binding either induces receptor dimerisation or simply stabilises the pre-existing receptor dimer. Trk dimerisation allows trans-phosphorylation of tyrosines (Tyr) in the intracellular TK domain to occur. The phosphorylated Tyr residues recruit intracellular effectors, which in turn activate PI3K/Akt, MAPK/ERK, or PLCγ/Ca2+ pathway. These signalling cascades are involved in different regulatory processes for, e.g., cell growth, survival, and synaptic plasticity. Image from Autry and Monteggia (2012). p75NTR does not possess an intrinsic kinase activity and like many other proteins, e.g., β-amyloid precursor protein (APP), and Notch, it undergoes a two-step regulated intramembrane proteolysis. The first proteolysis is catalysed by metalloproteases to release the extracellular fragment. Subsequent cleavage by γ- secretase yields a membrane-bound C-terminal fragment, and a soluble intracellular fragment (Kanning et al., 2003). The transmembrane domain is α-helical and thought to assist receptor oligomerisation, and while still attached to the intracellular domain after the first cleavage, may facilitate γ-secretase activity (Sykes et al., 2012). Once cleaved, the soluble intracellular fragment plays an integral role in downstream signalling (Kenchappa et al., 2006). Downstream effects appear to be induced through association of the intracellular death domain with cytoplasmic adaptor proteins, such as, ribosome

120 inactivating protein-2 (RIP-2), neurotrophin receptor interacting factor (NRIF), tumour necrosis factor receptor-associated factor-6 (TRAF-6), and Fas-associated phosphatase- 1 (FAP-1) (Verbeke et al., 2013, Qu et al., 2013). A diverse array of signalling pathways that are linked to p75NTR include Ras homologue gene family member A (RhoA), c-Jun N-terminal kinase (JNK), MAPK, and nuclear factor kappaB (NFκB) (Reichardt, 2006). Interestingly, the transmembrane fragment of p75NTR has been shown to stimulate TrkB phosphorylation (Saadipour et al., 2017), and promote cell survival by decreasing TRAIL-induced PARP and caspase-3 cleavages, i.e., apoptotic signals (Verbeke et al., 2013). Besides, activation of Trk has also been demonstrated to promote p75NTR cleavage (Urra et al., 2007). It is speculated that the proportion of pro-NT to mature NT is increased during neuronal degeneration and injury (Lu et al., 2005). This shift explains the elevated apoptosis signalling upon p75NTR activation by pro-NGF seen in various cell types under injury, including oligodendrocytes (Beattie et al., 2002), corticospinal neurons (Harrington et al., 2004), and smooth muscle cells (Lee et al., 2001). Evidence suggests that p75NTR induces cell death independently of TrkA and that the survival of sympathetic neurons during development is promoted via TrkA through silencing of the background apoptosis signals (Majdan et al., 2001). This report is in line with the relative abundance of pro-NGF and pro-BDNF, and extremely low levels of mature neurotrophins in the brain (Fahnestock et al., 2001, Mowla et al., 2001).

Despite the vast array of independent activities, interplays between receptors have been demonstrated in co-expression studies. Co-expressing TrkA with p75NTR potentiates NGF binding to TrkA (Hempstead et al., 1991). Conversely, reduced NGF signalling through TrkA can occur following p75NTR blockade (Barker and Shooter, 1994). Heterodimeric Trk/p75NTR proteins have also been reported with enhanced TrkA affinity for NGF, and reduced TrkA and TrkB affinity for NT-3 (Bothwell, 2016). At a molecular level, NGF-induced activation of the MAPK pathway via TrkA dampens down the Akt pathway that is activated in parallel via NGF binding to p75NTR (Lad et al., 2003). Cross-regulation of signals has also been observed between TrkC with p75NTR. Specifically, TrkC-mediated differentiation is facilitated by activation of p75NTR (Ivanisevic et al., 2003). Pan-inhibition of the Trk receptor by a kinase inhibitor prevented proteolytic cleavage of p75NTR, which in turn reduced proliferation- mediated Akt signalling in brain tumour-initiating cells (Forsyth et al., 2014). Crosstalk

121 between different members of receptor tyrosine kinase has also been observed, e.g., TrkB and Ret interact to regulate biochemical and morphological differentiation of neuroblastomas (Esposito et al., 2008).

B. Consequences of Dysregulated Neurotrophin/Receptor Interactions Considering neurotrophins are the key regulators in various facets of neuronal development and maintenance, it comes as no surprise that severe neuropathological conditions ensue from any fault in the precisely orchestrated web of neurotrophic interactions. Neurotrophins can influence the manifestations of diseases in many ways and data have only begun to accumulate on the underlying mechanisms of neurotrophins in diseases albeit their involvement being acknowledged and targeted in drug discovery attempts for several decades. We will cover pathologies that occur as a consequence of reduced, excessive, and impaired neurotrophic signalling.

In Parkinson’s disease, a reduction in mRNA and protein levels of BDNF and NGF has been observed in substantia nigra - an area in the brain where massive neuronal loss is evident (Mogi et al., 1999). BDNF depletion is also believed to contribute to striatal degeneration in Huntington’s disease (Strand et al., 2007). In major depressive disorders, reduced levels of BDNF and its cognate receptor TrkB were detected in the serum of patients and the hippocampus in post-mortem tissues of sufferers (Castren and Rantamaki, 2010, Ray et al., 2011). Similarly, decreases in BDNF and/or TrkB have been documented in bipolar disorders, anxiety, schizophrenia, neurodevelopmental, and eating disorders (see extensive review by Autry and Monteggia (2012)). Different approaches have been employed to augment the levels of neurotrophins in the conditions where they are lacking. With decreased NGF, it is within reason to postulate that neurons that do not receive the survival signal will eventually die off. Naturally occurring neuronal loss as a consequence of a limiting amount of neurotrophins is a normal feature of development to match neuronal numbers to target size (Kristiansen and Ham, 2014). In the adult brain, massive loss of neurons is often seen as a decrease in regional brain volume and can have deleterious effects when it is manifested as brain shrinkage with associated functional defects. NGF gene therapy has been shown to improve neuronal responses, including axonal sprouting, and cell hypertrophy in AD patients (Tuszynski et al., 2015). BDNF is believed to be indispensable for synaptic plasticity, and synaptic dysregulation is postulated to be an initial step in the cascades 122 that lead to eventual neuronal degeneration (Mariga et al., 2017). Antidepressants were found to increase BDNF-dependent TrkB phosphorylation in the prefrontal cortex, angulate cingulate cortex, and hippocampus (Saarelainen et al., 2003). This increased phosphorylation was absent in the transgenic mice that expressed a dominant negative form of TrkB, suggesting that inactive TrkB is accountable for depressive behaviours that are reversible by antidepressant-induced increases in TrkB signalling. It is worth noting that even though a majority of reports emphasise the importance of BDNF/TrkB in the etiology of neuropathologies, there is also evidence of reduced TrkA/NGF or TrkC/NT-3 in connection with neurological disorders, such as Alzheimer’s diseases (Counts et al., 2004, Budni et al., 2015), and schizophrenia (Weickert et al., 2005).

On the contrary, high levels of NGF and TrkA have been found in cancer cells of both neuronal and non-neuronal origin, e.g., breast, lung, prostate, glia, and pancreas (Demir et al., 2016). A large body of research carried out in breast and prostate cancer have confirmed the roles played by NGF signalling in TrkA-mediated proliferation and p75NTR-mediated survival (Molloy et al., 2011). Moreover, TrkB overexpression is associated with a more aggressive phenotype in a number of human cancers including lung, breast, and pancreatic cancer (Sclabas et al., 2005, Sinkevicius et al., 2014, Kim et al., 2015). Recent study using non-small-cell lung cancer cells established that autocrine signalling of BDNF regulates STAT3 activation, which in turn facilitates STAT3-mediated BDNF synthesis in a feedback loop. Higher BDNF level is associated with increased phosphorylated TrkB indicative of receptor activation, and subsequently Akt-mediated cell proliferation (Chen et al., 2016). Likewise, TrkC is found to be highly expressed in metastatic breast cancer and its activation can mediate tumorigenicity by inhibiting TGF-β tumour suppressor signalling (Kim et al., 2016). Upregulated neurotrophin activity is also apparent in inflammatory diseases as illustrated by conspicuous BDNF immunoreactivity in a wide range of immune cells including T cells and macrophages in actively demyelinating areas of multiple sclerosis lesions (Kerschensteiner et al., 2003). Pain symptoms associated with neuropathies are linked to inflammation-mediated NGF upregulation. High NGF levels promote nociceptor sensitisation through interplay between TrkA and TRPV1 – the two receptors frequently co-expressed in nociceptive neurons (Chao et al., 2006).

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Lastly, impaired signalling denotes a whole range of aberrant signalling events other than the overactive and reduced activity previously described. One example is atypical activation of the Trk receptors by ligands other than neurotrophins, e.g., glycyl- tRNA synthetase (GlyRS) mutants that are causative of Charcot-Marie-Tooth disease (CMT). The mutant protein exhibits aberrant activity against the Trk receptor that is not present in the wild-type GlyRS protein. Spurious signalling can scramble the coordinated events crucial for neuronal survival and fate determination and contributes to CMT manifestation (Sleigh et al., 2017). Another possibility is the mismatched signalling events which can occur through various mechanisms; one being the abnormal NTRK gene fusion (NTRK1, NTRK2, and NTRK3 encodes TrkA, TrkB, and TrkC, respectively). Specifically, the 5’ portion of a non-NTRK gene is fused to the 3’ portion of NTRK. Following transcription and translation, this construct gives rise to a chimeric protein with a non-native extracellular domain and native Trk transmembrane and tyrosine kinase domain (Amatu et al., 2016). In fact, the name Tropomyosin receptor kinase (Trk) was derived from the gene that was first discovered as an oncogenic fusion with the tropomyosin gene in colon carcinoma (Nakagawara, 2001). Trk fusion proteins are often constitutively active or display heightened kinase activity, thus behaving as an oncogene. The degree of rearrangement determines whether receptor activation induces oncogenic or pro-apoptotic outcomes (Demir et al., 2016). In fact, the distinctions between aberrant signalling and heightened/reduced signalling are not clear-cut. Given that early studies are merely morphological and histological examination or crude quantitation of gene expression, there are no available sequence data directly derived from those cases to explain the perceived reduction or increase in neurotrophin/receptor expression. It is likely that two possible mechanisms are contributing towards the alterations in expression or activity, either mediated by the expected variants of the receptors whose expression levels are abnormal, or by the unpredicted presence of oncogenic Trk fusion products from random chromosomal rearrangements. Recurrent Trk fusions have been reported in numerous cancers, notably, TPM3-NTRK1 in sarcoma and thyroid cancer, PAN3-NTRK2 in head and neck squamous cell carcinoma, AFAP1-NTRK2 in low-grade glioma, TRIM24-NTRK2 in lung adenocarcinoma, and ETV6-NTRK3 in secretory breast carcinoma (Tognon et al., 2002, Stransky et al., 2014). It has been suggested that Trk fusions are rare in common

124 cancers, but for some rare cancers the fusions are critical in determining the defining oncogenic characteristics (Creancier et al., 2015, Levitan, 2017).

C. Drug Discovery Perspectives For a two-player system such as ligand-receptor interaction, methods of intervention mostly rely on two disparate angles. Firstly, one may attempt to tackle ligand availability either by altering its presence in the system, e.g., interfering with synthesis and release, or simply administering extra ligands to the body. Alternatively, ligand availability can be disturbed through modulation of its binding ability; most commonly a result of the molecule that competes for the ligand’s binding site, i.e., competitive agonism/antagonism. Secondly, intervention attempts may be focused on receptor availability, possibly by altering receptor biosynthesis or surface expression. Another method is to introduce the molecule that interacts with the receptor in an allosteric manner to induce a conformational change to the binding pocket so that it no longer recognises the ligand, i.e., non-competitive agonism/antagonism.

Natural products, synthetic compounds, and proteins are all xenobiotics or foreign bodies that have to be introduced into the body. On the flip side, this means the compounds originate from outside the body which lend themselves to being created, manipulated, and perfected by scientists. Ex vivo gene therapy also falls under this general concept in a sense that genetic alterations of the cell occur ex vivo, before transplanting it back into the body. Conversely, a less explored route is to interfere with ligand/receptor synthesis and mobilisation. This route of interference requires the changes to originate from within the body which are commanded by genetic codes and signalling pathways. This requirement necessitates the ability to probe inside the cell at a genetic and molecular level which until recently has not been possible. Gene transfer has been around for some time but advances in in vivo gene therapy have opened up another treatment venue which has slowly begun to take off. In vivo gene therapy involves direct introduction of genetic materials into the body, in the hope that they will integrate themselves into the genome and start correcting the defective gene. The review will primarily focus on the traditional route that several other drug discovery teams and ours have adopted, i.e., developing xenobiotic agents that interfere with neurotrophin-receptor interactions. Gene therapy as a treatment option for neurotrophin-related disorders will be briefly touched upon where relevant. 125

I. Neurotrophin angle – increasing or decreasing functional levels of ligand An obvious starting point is probably to use the neurotrophin ligands for treatment when deficiency is detected. Since these ligands are naturally occurring and already have confirmed activities, there are no intermediate steps required for toxicity assessment or optimisation. Indeed, direct usage of the native neurotrophins has been employed in many studies involving animals as well as patients. Administration of NGF rescued cholinergic neurons in the transected fimbria (Hefti, 1986), and ameliorated atrophic phenotypes in the cholinergic neurons of aged rodents (Fischer et al., 1987). Treatment of hippocampal tissues extracted from the BDNF knockout mice with recombinant BDNF completely reversed the deficits in basal synaptic transmission and hippocampal long term potentiation (Patterson et al., 1996). BDNF treatment also rescued damaged synapses from NMDA receptor blockade in tadpoles (Hu et al., 2005). Besides, BDNF has been tested in a number of ALS clinical studies, however it did not exert a significant effect on the majority of the ALS patients treated (The BDNF Study Group (Phase III), 1999, Kalra et al., 2003). In monocular-deprived mice, NT-4 delivery via microspheres into the ferret cortex rescued the atrophic neurons in the lateral geniculate nucleus (Riddle et al., 1995), although a later study found evidence that the NT-4 effects might be rather short-lived. Axotomised retinal ganglian cells in rats were not rescued by prolonged treatment (14 days) with NT-4 (Clarke et al., 1998).

Therapeutic values of native neurotrophins are limited due to their short plasma half-life and poor BBB penetration (Poduslo and Curran, 1996). These limitations led to subsequent development of neurotrophin mimetics that exhibit better pharmacokinetics. Peptide mimetics of the active loops on NGF demonstrate good NGF agonistic properties in a low micromolar range, and one mimetic with the highest in vitro activity was reported to ameliorate neuropathic behaviours in a rat model of peripheral neuropathic pain (Colangelo et al., 2008). Small molecule BDNF mimetics identified through in silico screening reportedly bind TrkB with nanomolar affinities, and a prototype compound was found to prevent neuronal degeneration in in vitro models of neurodegenerative disease, and by activating downstream effectors the compound considerably improved performance of the rats suffering from brain trauma in motor learning tasks (Massa et al., 2010). Previous work by our lab members used a peptidomimetic approach and synthesised a cyclic peptide that mimics the interacting

126 motif (SRRGE) on NT-4 (Williams et al. (2005); Figure 30D). This peptide was shown to embody the activity of BDNF and NT-4 fully and promoted neurite outgrowth of the cerebellar granule cells.

Other than supplying the system with purified ligands, neurotrophin levels could be elevated via cellular delivery using fibroblasts that have been genetically modified to produce neurotrophins. Neurotrophin delivery via ex vivo gene therapy proved to be effective at enhancing both morphological and functional recovery after spinal cord or CNS injury in rodents, and the growth responses persisted for an extended time window (Blesch et al., 2002). BDNF-secreting cell grafts placed in the injured spinal cord following subcortical lesions significantly increased growth of the surrounding, but not the lesioned corticospinal neurons, in a mouse model of CNS injury (Lu et al., 2001). On- demand expression of NGF was demonstrated with tetracycline-regulated NGF- producing fibroblasts, and the controlled NGF delivery benefited neuronal survival, and axonal growth (Blesch et al., 2001). BDNF-producing bone marrow stromal cells grafted into the transected spinal cord have been shown to promote axonal regeneration (Koda et al., 2007). Constitutive neurotrophin secretion was detected in grafted neural stem cells which promoted extensive axonal growth after spinal cord injury (Lu et al., 2003). In vivo gene therapy is another treatment avenue that holds great promise. Basically, genetically modified DNA materials that have been integrated into a carrying vector are targeted to a specific cell type to correct the defective gene or enable the cell to constitutively express the proteins of interest. Adeno-associated virus (AAV)-based vector system is one of the most commonly used methods for gene transfer. Clinical trials have successfully implemented vector-based delivery and yielded remarkable results in liver-directed and retinal gene therapy for patients with haemophilia B, and type 2 Leber congenital amaurosis, respectively (Naldini, 2015). Critically, injection of adenoviral vectors containing NT-3 and BDNF gene into the scala media of guinea pigs with hearing loss was shown to promote neuronal survival in the cochleae at 7 and 11 weeks post injection, demonstrating that in vivo gene therapy is effective at conferring the neurotrophin-induced neuroprotective effects (Atkinson et al., 2012).

Heightened levels of neurotrophin ligands are not always beneficial. Intravenous and subcutaneous administration of recombinant NGF in healthy volunteers induced muscle pain and skin hyperalgesia that persisted for weeks (Hirose et al., 2016). In some 127 instances, increased neurotrophin activity needs to be attenuated; most commonly achieved by so-called blockers of either the ligands or the receptors. One approach for ligand interference is to block or distort the Trk-interacting interface on the neurotrophin. A successful drug that was developed on the basis of this rationale is tanezumab, a humanised IgG2 monoclonal antibody that binds with high affinity to an NGF monomer (Abdiche et al., 2008). This NGF-neutralising antibody interferes with the ability of NGF to activate TrkA and has been shown to alleviate symptomatic pain in patients with osteoarthritis and chronic low back pain (Lane et al., 2010, Katz et al., 2011). Other anti-NGF compounds have also been shown to diminish tactile, thermal, and mechanical allodynia in rodent models of chronic and neuropathic pain (Ro et al., 1999, Wild et al., 2007, Cheng et al., 2009). Early treatment of bone cancer with anti- NGF reduced tumour-induced sprouting of CGPR positive fibres (Jimenez-Andrade et al., 2011). Behaviours associated with bone cancer pain (e.g., flinching) and limited use of tumour-bearing limb were also ameliorated (McCaffrey et al., 2014).

Given ample evidence in support of the promise of anti-NGF, a great deal of manpower and resources have been allocated in R & D. Regrettably, all anti-NGF trials were put on hold by the FDA in 2010 after reports of serious joint-related adverse events in clinical trials (Hochberg, 2015). Osteoarthritis patients who were administered anti-NGF therapy of either tanezumab, tanezumab plus NSAID, or fulranumab sustained extensive bone damage and joint destruction. The ban was eventually lifted in 2013 on most of the osteoarthritis trials on the basis of non-clinical data reviews, and suggested measures to mitigate the risks with better screening for adverse effects (Chang et al., 2016). Several anti-NGF therapeutics that were stalled have been revived, including the notable tanezumab (Schnitzer et al., 2015), and fulranumab (Mayorga et al., 2016) in phase III clinical trials. These trials have so far yielded promising results, in particular, a noticeable decrease in patients’ experience of pain compared to placebo. Depending on the tanezumab dose, as much as 20 to 50% reduction in pain scores have been replicated in a number of trials (Kelleher et al., 2017). Nonetheless, it remains to be seen if the safety concern for anti-NGF will be eradicated for it to be licensed for market release. Anti-BDNF agents have not been as widely researched as anti-NGF possibly due to the critical roles of BDNF/TrkB in the CNS, but they certainly have potentials for cancer treatment. As previously described, elevated expression of BDNF/TrkB in cancer

128 has been characterised in both neural and non-neurogenic tumours, and is associated with poorer disease outcomes (Roesler et al., 2011).

II. Targeting the Trk receptor – direct inhibition of enzymatic activity An alternative to ligand intervention is to interfere with the receptor to counteract the dysregulation within the neurotrophin system. This route has in fact been the mainstream approach in the development of neurotrophin-specific intervention. The focus has logically been placed on the Trk receptors as they exhibit high affinity binding to the cognate neurotrophins and the isoforms exhibit spatial segregation within the nervous system to a large extent. First-generation Trk inhibitors are predominantly inhibitors of the kinase domain. This is partly due to a substantial body of knowledge that was available at the time about the kinase structure and functions, and that kinase inhibitor screens are relatively easy to configure. Kinases themselves are ubiquitous in biology, constituting the third most populous protein family (Endicott et al., 2012). Typical kinases phosphorylate serine/threonine residues or tyrosine residues, which act as one of the key molecular switches that turn the protein ‘on’ or ‘off’. It is logical to target the kinase domain as its catalytic activity constitutes the signalling component of the receptor. In fact, drugs that function as enzyme inhibitors make up a significant portion of the present-day therapeutic market (Copeland et al., 2007).

Given the varying degrees of homology between the family members, relatively specific kinase inhibitors exist (Bain et al., 2007). The first X-ray crystallographic data on the kinase structure is of Protein Kinase A, a Ser/Thr kinase (Knighton et al., 1991). This came out almost a decade before the first crystal structure of NGF in complex with the binding domain of TrkA (IgL2-d2) was published (Wiesmann et al., 1999). Structure of the tyrosine kinase was uncovered by Hubbard et al. (1994); disclosing the kinase domain as a bilobed structure with a deep cleft positioned between the β-stranded N- and α-helical C-terminal lobes bridged together with a hinge linker. Conserved Asp-Phe- Gly (DFG) acts as an activation loop to regulate the enzyme activity (Huse and Kuriyan, 2002). In an unbound state, i.e., apo-form, the DFG segment is buried within an ATP- binding site. This keeps the kinase in its ‘off’ state, which is also known as DFG-out conformation. When activated, protein conformational changes rotate the DFG residues away from the ATP-binding site, allowing ATP to bind, i.e., DFG-in conformation. 129

Different kinase inhibitors display different binding mechanisms and are filed under three distinct types I-III (Bailey et al., 2017). Type I inhibitors engage the ATP-binding site and sterically block an ATP molecule from fitting in the pocket. Type II inhibitors stabilise the ‘off’ state of the kinase by engaging both the ATP-binding site and an adjacent hydrophobic pocket. Type III inhibitors do not compete with ATP binding and bind allosterically to an extended hydrophobic patch on the kinase (Garuti et al., 2010).

One of the known Trk inhibitors that compete with ATP binding to the kinase domain is K252a (Ruggeri et al., 1999). It was originally discovered as a Ser/Thr kinase inhibitor, but later evaluated to be an inhibitor of the Trk receptors (Tapley et al., 1992), and a potent inhibitor of MLK3 activity (Roux et al., 2002). K252a has been shown to impede metastasis in in vitro models of Trk-driven malignancies (Morotti et al., 2002), and inhibit growth of human lung adenocarcinomas (Perez-Pinera et al., 2007). K252a however did not show activity in vivo (Akinaga et al., 1992), and many K252a derivatives have been developed since which exhibit better in vivo efficacy, e.g., CEP751 (Evans et al., 1999). Another Tyr kinase inhibitor that has recently been tested in clinical trials is entrectinib, a selective pan-Trk, ALK and ROS inhibitor (RXDX-101; Ignyta, Inc.). Entrectinib was shown to significantly inhibit the growth of neuroblastomas both in vitro and in vivo (Iyer et al., 2016). In two phase-I entrectinib trials, there were no reports of major adverse events (Drilon et al., 2017), although, some common adverse events with grade 1 or 2 severity were observed including fatigue and nausea, all of which were reversible with dose adjustment. The drug was shown to be well-tolerated and demonstrated robust anti-tumour activity in patients with metastatic solid tumours who harboured either NTRK, ROS1, or ALK gene rearrangements.

Having established the therapeutic values of pan-Trk inhibitors, it would be interesting to know if an isoform-specific Trk inhibitor can offer better safety profile and more controllable/defined activity. It is widely believed that developing an isoform- specific kinase inhibitor of the Trk receptor is non-feasible due to high sequence homology between the Trk kinase domains. Of the 40 residues in the kinase domain that participate in ligand interaction, only two residues are different in TrkA compared to TrkB, and all are identical between TrkB and TrkC (Bertrand et al., 2012). In spite of these observations, Mathieu and colleagues argued that targeting the Trk kinase domain does not necessarily equate poor isoform selectivity. The group presented the 130 first high-resolution crystal structures of human apo-TrkA and apo-TrkB kinase domains. The crystal structure of human TrkC kinase domain in complex with an inhibitor has also been elucidated (Albaugh et al., 2012). According to the report, TrkB kinase domain consists of the common kinase features which include largely β-stranded N-lobe, α- helical C-lobe, and a hinge linker connecting the two lobes. In an inactivated state, the domain displays a DFG-out conformation with unphosphorylated tyrosine residues in the fully-ordered activation segment. The general structure of apo-TrkA is rather similar to that of apo-TrkB, bar the major differences in the hinge region situated between the kinase insert domain (KID) and the C-terminus. Residues of the hinge loop are not well- conserved between the three Trk isoforms. Structurally, KID in TrkA is positioned closer to the hinge region and lies above the C-terminus. On the contrary, the C-terminal end in TrkB is closer to the hinge region, with the KID located below the hinge. In addition, a network of local interactions within the hinge region is present exclusively in TrkA, suggesting that TrkA hinge conformation might be more constrained than that of TrkB or TrkC. Comparison between TrkC and apo-TrkA/apo-TrkB revealed structural differences in the N-lobe, the positioning of the C-lobe α-helices, and a disordered KID region. From these observations, the authors proposed that the hinge region might allow differentiation between TrkA and the other isoforms, i.e., selectivity targeting.

III. Targeting the extracellular domain of Trk(B) The kinase domain only constitutes an intracellular tail of the Trk receptor. The extracellular portion of the receptor is formed by five subdomains, with the juxta- membrane subdomain 5, IgL-d2, providing the binding pockets for neurotrophins. Structures of IgL-D2 of all human Trk isoforms have been resolved using crystallographic techniques (Ultsch et al., 1999). Crystal structure of the complex of TrkA-IgL-D2 with NGF uncovers two hydrophobic patches on the domain which provide key interfaces for ligand binding (Wiesmann et al., 1999). Importantly, no significant conformational changes of IgL-D2 are observed in the bound state. The first patch on the receptor spans the AB, C’D, EF loops as well as the C-terminus with approximately 50% homology among the interacting residues both on the neurotrophins and the corresponding members of the Trk receptors. These observations led to speculation that this patch constitutes a conserved docking site in all members of the Trk family. Residues making up the second patch, on the other hand, display little sequence conservation (~20% of

131 the total interacting residues on both binding partners). Interestingly, there appears to be prominent conformational changes in the N-terminal residues 2-9 of NGF which make up the selective binding interface on the ligand side. These residues sit on the ABED β-sheet of TrkA-IgL-D2 which forms the so-called specificity patch. Mutation experiments provide supporting evidence for the roles of these N-terminal residues of the neurotrophins in determining binding affinity (Urfer et al., 1998). Subsequent elucidation of the structure of TrkB-IgL-D2 in complex with NT-4 established similar findings and further suggested that α-helical structure of the N-terminal surface of the ligand is only adopted upon interaction with the specificity patch on the receptor (Figure 30A; Banfield et al. (2001)).

Figure 30.Crystal structure of TrkB-IgL-d2 in complex with NT-4.

(A) NT-4 dimer in complex with TrkB IgL-d2 (D5). Conformational changes occur in the ligand upon docking on the receptor. Two key regions for interaction are present; the conserved patch, and the specificity patch. PDB accession 1hcf (Banfield et al., 2001). (B) The “druggable” cavities were defined based on water-like probe clusters on the surface of TrkB IgL-d2. A space filled cavity at this site is shown in (C) and in combination with the 9SRRGELA14 part of the neurotrophin in (D). Courtesy of Dr Gareth Williams. 132

Conventional wisdom suggests that targeting neurotrophin binding to the Trk receptor is likely to have a low probability of success due to the relatively large NT-Trk interacting interface and the inherent difficulty of inhibiting this with a small molecule. Nevertheless, pioneering work, largely from the James Well lab, has popularised the notion of hot spots at protein-protein interfaces that confer most of the binding energy (Clackson and Wells, 1995). These hot spots tend to cover a small conformationally- adaptable hydrophobic area which might be more amenable to the binding of small molecules, and thereby interfere with ligand binding. A number of years ago, our group showed that small peptide mimetics of the N-terminal sequence of BDNF could inhibit TrkB-dependent neurite outgrowth of cultured cerebellar granule cells provided they were constrained in a loop structure by a disulphide bridge (Williams et al., 2005). Based on the crystal structure, the peptide is predicted to interact with the relatively shallow specificity patch on the receptor (Figure 30A). Although the small N-terminal loop from the neurotrophin makes contact with the cis-dimer (top red loop), this is a relatively minor interaction and is quite discrete from the major ligand binding face of the receptor. Interestingly, the equivalent peptides from TrkA and TrkC binding neurotrophins were also found to inhibit the BDNF/TrkB neurite-outgrowth response, albeit only at 10-20 fold higher concentrations. This is likely due to the fact that all of the peptides contain a common RGE sequence that is conserved in the neurotrophin N- termini. Importantly, this conserved motif interacts with the specificity patch (Figure 30D).

Given our peptide results, it can be postulated that small drug-like molecules might antagonise neurotrophins based on their ability to interact with the specificity patch. To this end, the lab embarked on a virtual screening program to identify small molecules that might inhibit TrkB function on the predication that they can dock into the pocket at this site (Figure 30B-C). In parallel studies another group used essentially the same strategy to discover a small molecule that was named ANA-12. ANA-12 is claimed to be a non-competitive TrkB antagonist and its TrkB selectivity was illustrated in a neurite outgrowth assay. At a low micromolar concentration ANA-12 fully inhibited BDNF-stimulated neurite outgrowth of PC12 cells that have been transfected to express high levels of the human TrkB receptor, whilst having no effect on NGF and NT-3 responses when used at up to 100 μM on sister cell lines expressing the human TrkA or

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TrkC receptor (Cazorla et al., 2011). It was reported to act via a two-site mode of action by binding to high- and low-affinity sites on the TrkB IgL-d2 discrete from the main BDNF binding site as discussed above. Our group adopted the specificity-patch approach and made use of a Chembridge library that contains over 480,000 small molecules. At present, there are no crystallographic data on the BDNF/TrkB complex, and the scaffold used for in-silico screening by our lab member, Dr Gareth Williams, was that of the NT-4/TrkB complex (PDB accession 1hcf, Banfield et al. (2001)). The screen identified a series of potential hits, which were purchased and assayed for activity in reporter cell lines that heterogeneously express the individual human Trk receptors. We discovered a small molecule, A3, that functions as a partially selective non-competitive TrkB antagonist in the reporter assay. ANA-12 is now entering the mainstream and is in common use as a specific and selective TrkB inhibitor. Since its discovery in 2011, ANA- 12 has been used in at least 62 published studies both in vitro and in vivo (based on a pubmed search in Feb 2018). On this basis it is of considerable importance to determine if it is truly a specific and selective TrkB antagonist.

Given that similar strategies were used to identify ANA-12 and A3, a detailed comparison of their activity and selectivity would also cast light on the druggability of the specificity patch on the Trk receptor. Furthermore, with the non-competitive nature of their antagonism, detailed studies are required to determine if they are full antagonists or biased antagonists. We reasoned that transcriptional profiling of their activity in the presence and absence of BDNF might be valuable for assessing their efficacy and specificity. The transcriptional work is described in detail in the latter part of this chapter.

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D. Small Molecule Similarity Search In the last part of this chapter, I briefly described the results from a preliminary work using a machine learning algorithm (i.e., random forests) to predict the likelihood of Trk-binding in silico. The prediction model is fundamentally a variation of similarity assessment which is most often employed for the search of a chemical space. Typical assessment subjects pre-computed topological 2D fingerprints to similarity search which can be performed by algorithms that determine feature overlap, e.g., TOPOSIM (topological similarity), LaSSI (latent semantic structural indexing), TV (trend vector), and Daylight (Kearsley et al., 1996, Hull et al., 2001, Sheridan et al., 1994). Daylight is widely adopted and considered a de facto standard for topological similarity search (Daylight Chemical Information Systems, www.daylight.com). 3D-based searches can be implemented with algorithms such as SQ, and FP (Sheridan and Kearsley, 2002, Miller et al., 1999). One distinct advantage of performing 2D or 3D similarity search using, e.g., a distance-based index like Tanimoto coefficient, is that the search can be carried out with as few as one hit structure being available. When large numbers of active and inactive molecules are known, however, classification techniques are arguably more suitable. Classification techniques include basic methods, e.g., clustering and partitioning, and machine learning algorithms, e.g., support vector machine (SVM), decision trees (DT), k-nearest neighbours (kNN), naïve Bayes, and artificial neural network (ANN) (Lavecchia, 2015).

In the simplest case of classification, two labelled categories are known, e.g., active versus inactive, and fingerprints of all the compounds are available. Appropriate algorithm is applied to the fingerprint data (i.e., feature vector) to learn the underlying substructures that contribute to the observed activity in the form of mathematical functions (i.e., mapping the feature vectors to the property of interest). These functions are later applied to novel datasets to predict the likelihood of unknown compounds belonging to the active or inactive group (Mitchell, 2014). Ensemble methods are built upon individual algorithms, and combine them to improve overall prediction accuracy (Dietterich, 2000). Moreover, the ensemble techniques make the best of limited sample size by generating multiple models to minimise the potential of overfitting. The most commonly used ensemble methods are bagging, boosting, and random forests (Yang et al., 2010). In particular, random forests (RF)-based classification was adopted in our

135 preliminary testing to determine if machine learning is a viable option to explore for future search of novel Trk inhibitors. The technique was developed by Breiman (2001) and essentially builds many decision trees during training. Each tree is derived based on the training set which is selected through random sampling with replacement from the original dataset. About one third of the data are kept out of training, aka out-of-bag cases, to be used as a test set. Performance of an RF classifier on the test set is evaluated as out-of-bag error rates. Cross-validation can also be used to combine measures of fit (prediction error) of the selected RF classifier to provide a more accurate estimate of model prediction performance.

Similarity assessment is also useful for quantitative SAR (QSAR) predictions of lead-likeness and drug-likeness which pertain to potency, selectivity, and ADMET (absorption, distribution, metabolism, excretion, and toxicity). The ability to predict these properties allows the development team to make an informed decision regarding the route by which the chemical modifications are taking and forms the basis of what is known as ‘property-based drug design’. Property-based rational approach is usually employed near the end of the hit-to-lead phase after potency and selectivity have been satisfactorily enhanced. Highly influential drug-likeness criteria were proposed by Lipinski in 1997 and are collectively known as Lipinski’s rule of five (Ro5) (Lipinski et al., 1997). The rule was formulated on the basis of good solubility and permeability, the properties which rely on the molecule having <5 H-bond donors, <10 H-bond acceptors, MW <500 Da, and cLogP <5. Several variations of this rule have been proposed. One well-known example is the rule of three (Ro3) which provides a framework for lead- likeness: cLogP <3, MW < 300 Da, <3 H-bond donors and acceptors, and <3 rotatable bonds (Congreve et al., 2003). These stipulations essentially provide some leeway for further development of the compound towards being drug-like.

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E. Objectives In this chapter, we initially investigated the properties of ANA-12 based on the published claim that it is a selective TrkB antagonist. Transcriptional profiling was then utilised for detailed characterisation of the activity of ANA-12 and A3 as both compounds were identified through the same targeting strategy. Additionally, we hoped to clarify whether these non-competitive antagonists exhibit full or biased antagonism. The steps in our investigation are listed below;

▪ Investigate TrkB selectivity of ANA-12, a proclaimed TrkB-selective antagonist, using a beta-lactamase reporter assay ▪ Identify BDNF-responsive and invariant transcripts using microarray and bioinformatics ▪ Transcriptional characterisation of TrkB modulation by BDNF, ANA-12, and A3 using the multiplex assay ▪ Preliminary investigation to assess the viability of random forest classifier as an in silico predictive tool to identify novel Trk binders from a compound library

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Materials and Methods

TrkB-NFAT-bla CHO-K1 Assay TrkB-NFAT-bla CHO-K1 reporter cell line was purchased from Invitrogen, UK. Cells were passaged twice a week, and grown in DMEM-GlutaMAX medium (Gibco, UK) supplemented with 10% dialysed FBS, 100 U/mL penicillin, 100 µg/mL streptomycin, 5 µg/mL blasticidin, 200 µg/mL zeocin, 0.1 mM non-essential amino acid solution (NEAA), and 25 mM HEPES buffer. All additional reagents for tissue culture were purchased from Sigma, UK. For an experiment, 2x104 cells were seeded in each well of a black-wall clear-bottom 96-well plate (Corning, USA). The assay medium used was DMEM- GlutaMax medium supplemented with 0.5% dialysed FBS, pen/strep, 0.1 mM NEAA, and

25 mM HEPES. The cells were incubated overnight at 37⁰C, 5% CO2. The following day, appropriate concentration of each treatment reagent was made up in assay medium and added to each assay well to achieve the final concentration of 25 ng/mL BDNF (Peprotech, UK), 10 µM A3 (Chembridge, UK), 20 µM ANA12 (MedChem Express, USA), 1 µM ionomycin (Cayman Chemical), 0.5 µM thapsigargin (Sigma, UK), unless otherwise stated. BDNF stock was made up in distilled water. A3, ANA12, ionomycin, and thapsigargin were reconstituted in DMSO at 10mM, aliquoted and stored at -20⁰C. The manufacturer’s recommended protocol was followed for the assay. In brief, cells were treated for 4 h, after which the membrane-permeant fluorescent substrate, coumarin cephalosporin fluorescein-acetoxymethyl ester (CCF4-AM and CCF2-AM; ThermoFisher, UK) was added. The plate was left at room temperature for 1 h in the dark, and read using the FLEX Station (Molecular Devices, USA). Β-lactamase cleaves the CCF2 substrate, causing a shift in FRET emission. The excitation filter was set at 405 nm, and the emission filters at 460 and 530 nm on the plate reader. Ratio of the two emission wavelengths (λ1/λ2) was calculated as a measure of β-lactamase activity, which in turn is an indicator of receptor activation.

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Figure 31. CellSensor Trk-NFAT-bla CHO-K1 reporter assay.

(A) CHO-K1 cell line is engineered to carry the NFAT-bla construct and overexpress human TrkB receptors. Binding of the ligand to TrkB receptor activates phospholipase C (PLC). PLC catalyses the hydrolysis of phosphatidylinositol 4,5-biphosphate (PIP2) to inositol 1,4,5-triphosphate (IP3) 2+ and diacyl glycerol (DAG). IP3 can mobilise Ca ions from an intracellular store, the endoplasmic reticulum, and produce Ca2+ influx across the plasma membrane (Putney and Tomita, 2012). Ca2+ ions activate calcineurin (CN), a serine/threonine phosphatase, which catalyses the dephosphorylation of NFAT (Crabtree, 2001). Dephosphorylated NFAT translocates into the nucleus and binds to the NFAT promoter region on the NFAT-bla construct to regulate transcription of the β-lactamase gene. (B) CCF2-AM substrate is converted to CCF2 by esterases when it enters the cytoplasm. CCF2 is a substrate of β-lactamase and gets cleaved into two constituent parts. The cleaved product gives off shorter λ emission when excited. This change in FRET is measurable on a fluorescence plate reader and the relative amount of the two substrate products are indicative of the enzyme activity, and thus of the binding and activation of the receptor (Jones and Padilla-Parra, 2016).

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Tango-CB1 β-arrestin Assay Tango-CB1 U2OS cell line was purchased from Invitrogen, UK. The cell line was generated by transfecting a Tango-enabling cell line with a fusion construct of human CB1 receptor and the GAL4-VP16 chimeric protein. The Tango-enabling cell line contains another expression construct of β-arrestin2:TEV protease fusion protein, and UAS-bla reporter gene (Doucette et al., 2009). When ligand binds the CB1 receptor, G protein gets phosphorylated and recruits β-arrestin2:TEV protease fusion. Cleavage of the receptor by TEV protease releases GAL4-VP16 transcription factor which translocates into the nucleus and induces transcription of β-lactamase gene. Similar to Trk-NFAT-bla assay, the binding events are quantitated by FRET-based measurement.

Tango-CB1 U2OS cells were maintained in complete growth medium (McCoy’s 5A medium with 10% dialysed FBS, 0.1 mM NEAA, 100 U/mL penicillin, 100 mg/mL streptomycin, 200 µg/mL zeocin, 50 µg/mL hygromycin, and 100 µg/mL geneticin; Gibco). Assay medium was made up of McCoy’s 5A with 0.5% dialysed FBS, pen/strep, 0.1 mM NEAA, and 25 mM HEPES. Cells were harvested in assay medium and seeded at 20,000 cells/well in a black-wall clear-bottom 96-well cell culture plate overnight at

37⁰C, 5% CO2. Reagents were diluted in assay medium and added to the assay wells with the final concentration of 5 µM for ACEA (Tocris Bioscience, UK), and 10 µM for ANA-12, unless otherwise stated. After 4 h incubation, CCF4-AM/CCF2-AM substrate was added, and the plate was left at RT for 1 h. Measurement parameters were set as follows; excitation λ at 409 nm, two emission λs at 460 and 530 nm. The ratios of 460nm/530nm emission were calculated and used for analysis.

140 mRNA Microarray SH-SY5Y neuroblastoma cell line was kindly provided by Dr Paul Francis’ group (Wolfson CARD, KCL UK), and maintained in DMEM-high glucose medium (Sigma, UK). NCI-H460 non-small cell lung cancer cell line was purchased from ATCC and maintained in RPMI 1640-GlutaMAX medium (Gibco, UK). Complete growth media for both cell lines were supplemented with 10% FBS (PAA Laboratories, UK), 100-unit penicillin, and 100 μg/mL streptomycin (Sigma, UK). Cells were seeded at 105 cells per well in the corresponding complete growth medium in a 12-well cell culture plate, and incubated at 37⁰C, 5% CO2 until ~70-80% confluence was reached (approximately 48 h). Cells were treated with vehicle, 25 ng/mL BDNF (Peprotech, UK) for 18 h at 37⁰C, 5% CO2. Each treatment was applied to four replicate wells. Culture media were removed, and cells were lysed in Absolutely RNA Miniprep Kit lysis buffer and β-mercaptoethanol (Agilent Technologies, UK). RNA was then extracted, and quality-assessed (RNA Integrity Number (RIN) >= 8). RNA expression levels were measured on the Affymetrix Human Genome U133 plus 2.0 (GPL570) chip (courtesy of Dr David Chambers).

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Multiplex Assay

1. RNA Extraction NFAT-TrkB cells were seeded in assay media overnight at ~70% confluent. RNA was extracted and purified using RNeasy Mini Kit (Qiagen, UK). Briefly, cells were harvested in 600 µL Buffer RLT with 6 µL β-mercaptoethanol. 600 µL of 70% ethanol was added to the lysate and transferred to a spin column. The mix was centrifuged for 3 min at 13,000 rpm, and the flow-through decanted. The filter on the spin column was washed once with 400 µL Buffer RW1, twice with 200 µL Buffer RPE, and RNA was eluted in 30 µL RNase-free water. RNA concentration of the eluate was determined using NanoDrop ND1000 spectrophotometer (ThermoFisher, UK).

2. Treatment Conditions and Cell Lysate Preparation NCI-H460 and SH-SY5Y cells were maintained in full growth (10% FBS, pen/strep) media, RPMI 1640-GlutaMAX, and DMEM-high glucose, respectively. TrkB-NFAT-bla CHO-K1 cells were grown in DMEM-GlutaMax complete growth medium. TrkB-SHSY5Y cell line was purchased from Kerafast USA and maintained in complete growth medium which consists of RPMI 1640-GlutaMAX, 10% FBS, pen/strep, and 0.3 mg/mL geneticin (ThermoFisher, UK). Cells were harvested in appropriate reduced serum (0.5% FBS) media and seeded at 1.6x105 cells/well in a 24-well sterile tissue culture plate

(ThermoFisher, UK). Cells were left overnight at 37⁰C, 5% CO2, followed by 4 h or 18 h incubation in 25 ng/mL BDNF, 10 µM A3, 10 µM ANA-12, or 100 nM K252a (Sigma, UK) or specified combinations. Following the treatment, cells were lysed in 200 µL diluted working lysis mixture (QuantiGene, UK). For one 24-well plate, mix 17 µL proteinase K with 1.7 mL lysis buffer and 3.4 mL PBS. The plate was incubated in lysis mixture for 30 min at 50⁰C, 200 rpm. Lysates were immediately used for hybridisation protocol or stored at -80⁰C for later use.

3. Hybridisation and Signal Amplification For one well of a 96-well plate, working bead solution was made up by mixing 9.2 µL nuclease-free water, 6.6 µL lysis buffer, 1 µL blocking reagent, 0.2 µL proteinase K, 0.5 µL capture beads, and 2.5 µL probe set, totalling 20 µL. All reagents were supplied as part of the Plex Assay Kit (QuantiGene, UK). 80 µL of cell lysate and 20 µL working bead solution were mixed and added to each well. Three blanks and a positive control were included in every run. The assay plate was incubated for 18 h at 54⁰C, 600 rpm. 142

After overnight hybridisation, the plate was secured on a magnetic hand-held platform, and the assay wells were washed 3X in 100 µ wash buffer. 100 µL of pre-amplifier solution was added, and incubated for 1 h at 50⁰C, 600 rpm. The incubation-wash step was repeated with amplifier, and label probe solution. After 3X washes for label probe solution, 100 µL SAPE working reagent (3 µL SAPE per 1 mL SAPE diluent) was added to each assay well and incubated for 30 min at RT, 600 rpm. The wells were washed 3X with SAPE wash buffer. Lastly, 130 µL of SAPE wash buffer was added, and mixed well by agitating at 800 rpm for 3 min. The plate was read immediately with the MAGPIX platform and xPONENT software (Luminex, USA). Relative expression values were calculated relative to the GEO mean of FOXJ2 and HLCS median fluorescence intensities (MFIs). Calibration and verification of the MAGPIX system were performed at least once a week to ensure optimal performance.

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Figure 32. Multiplex Assay Workflow.

(A) Biological samples are lysed in lysis buffer. The lysates contain target RNAs which are then mixed with other assay components including paramagnetic capture beads, capture extenders, label extenders, and blocking probes. After overnight hybridisation at 54⁰C, the beads are washed to get rid of any unbound components. Signal amplification is carried out in sequential steps with pre-amplifier, amplifier, and biotinylated label probe. For detection, streptavidin phycoerythrin substrate is added. The signals are read with Luminex MAGPIX. (B) In the MAGPIX chamber, captured beads are excited by green and red LED beams. The two emissions are interpreted to identify the bead label and quantify the expression level of the corresponding transcript. Images from Quantigene.

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Analysis and Predictive Modelling R software (R Core Team, 2017) and packages were used for microarray analysis. For each condition, triplicate data were generated. Signal intensities were corrected for background and normalised using Affymetrix MAS5.0 to obtain the final probeset expression level. MAS5.0-processed dataset was then pre-screened based on Present/Marginal/Absent calls. Probesets labelled as absent in all replicates for every treatment conditions were excluded in subsequent analysis. Probeset IDs were then annotated and signal intensities within each treatment group were averaged. Fold change was calculated by dividing expression mean of the treatment group by that of the control group. Linear models were fitted to the data, and moderated F- and t- statistics were computed using limma package (Ritchie et al., 2015) for statistical inference. For database analysis, p ≤ 0.05 was used as an arbitrary threshold in conjunction with ≥ 20% fold change in gene expression. The lists of differentially expressed genes were compared between treatment conditions, and different cell lines.

Scikit-learn module (Pedregosa et al., 2011) was used for our preliminary work on predictive modelling with random forest (Python 3.5). First, we collected SMILES strings of a total of 82 small molecules that had been tested for Trk binding in the NFAT- bla assay. All SMILES were converted to Morgan/Circular Fingerprints with radius 2. Prediction errors were measured in terms of out-of-bag estimates to determine the optimal parameters for n_estimators (15 to 175 trees) and max_features (sqrt, log2, none). Random forest classifier with 70 trees was applied to the fingerprint-containing array to learn an objective function. Prediction accuracy was estimated with 5-fold cross validation.

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Results I

Investigating ANA-12 selectivity using a beta-lactamase reporter assay In this section we intended to determine the selectivity of ANA-12, a reportedly TrkB-selective small-molecule antagonist. The key assay used for this purpose is a TrkB NFAT-bla cell reporter assay (Invitrogen). The cells were engineered to carry a full length human TrkB, and a β-lactamase reporter gene (bla) downstream of nuclear factor of activated T-cells (NFAT) (Wang et al., 2008a). Receptor activation leads to translocation of NFAT into the nucleus, which in turn activates bla transcription. β- lactamase enzyme cleaves an added substrate, giving rise to a product with different FRET emission. Difference between the emission wavelengths of the substrate and the product allows quantitation of the β-lactamase activity, which reflects the extent of receptor activation.

1. ANA-12 inhibition in the presence of BDNF – an endogenous TrkB agonist Preliminary work in our lab found that ANA-12 can inhibit BDNF responses in the TrkB NFAT-bla assay. However, when used at 10 µM, ANA-12 inhibits NGF-induced response in the TrkA cells by ~30%, BDNF-induced responses by 55%, and NT3-induced TrkC responses by 65% (personal communications; unpublished observations). This could reflect the direct activity of ANA-12 on all three Trk receptors, or it might reflect an effect at a site downstream from the Trk receptor in the signalling pathway. To investigate this, I first confirmed that the TrkB cell line responded to BDNF and established a full concentration-response curve with a half maximal response at 2.7 ng/mL and a maximal response at 25 ng/mL of BDNF (Figure 33A). We confirmed that ANA-12 could inhibit the BDNF response (25 ng/mL) in a concentration-dependent manner with an IC50 of 1.6 µM and a maximal inhibition of 75-80% at 25 µM (Figure 33B). These results are consistent with ANA-12 interacting with TrkB to inhibit the BDNF-induced β-lactamase activity in the TrkB-NFAT-bla cells, but they do not exclude the possibility of an effect at a downstream step in the signalling pathway.

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Figure 33. BDNF and ANA-12 concentration responses.

(A) Increasing concentrations of BDNF (5 steps from 0.04 to 125 ng/mL) were applied to the TrkB-NFAT-bla cell to determine the concentration that maximally evoke the β-lactamase activity. 2.7 ng/mL BDNF induces a half-maximal response. The response reaches the threshold and levels off at 25 ng/mL. N = 4 independent experiments each with 5-7 replicates, error bars = SEMs (mean of means). (B) Percentage BDNF-response inhibition by 6 concentrations of ANA-12. ANA-12 dose-dependently inhibits the BDNF response with an estimated IC50 of 1.6 µM. Maximal inhibition is 75-80%, achieved at 25 µM of ANA-12. [BDNF] = 25 ng/mL, N = 3, error bars = SEMs.

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2. ANA-12 inhibition in the presence of ionomycin – a Ca2+ ionophore In order to investigate the possibility of an effect at the downstream site, we next looked at Ca2+ mobilisation since an increase in intracellular calcium is a pivotal step in the second messenger pathway coupling TrkB activation to the NFAT pathway (Wang et al., 2008a). We used ionomycin, a selective Ca2+ ionophore derived from Streptomyces conglobatus. Ionomycin extracts cations, primarily Ca2+, from an aqueous phase into an inorganic phase, therefore favours Ca2+ influx across the cell membrane Ionomycin raises cytosolic [Ca2+] either by (i) increasing Ca2+ transport through the native channels on the membrane due to its ionophoric properties, i.e., extracellular

2+ 2+ Ca source, or (ii) through PLC/IP3-dependent Ca mobilisation from an intracellular store (Morgan and Jacob, 1994, Dedkova et al., 2000). As discussed in the original methods paper, if ANA-12 inhibition is TrkB-specific it should not have an effect on the Ca2+-dependent increase in β-lactamase activity by ionomycin. A full concentration response curve shows that ionomycin elicits a half-maximal response at 0.4 µM, and a maximal β-lactamase activation at 1 µM of ionomycin (Figure 34A). One noteworthy difference is that the maximal ionomycin response is about 2.5 times the size of the maximal BDNF response (when normalised against the same control). Similar to its effect on the BDNF-induced response, ANA-12 inhibited the 1 µM ionomycin response in a concentration-dependent manner with an IC50 of 1.5 µM and a maximal inhibition of ~80% at 25 µM (Figure 34B). These results demonstrate that in the absence of TrkB- specific activation, ANA-12 can somehow suppress Ca2+-mediated increase in bla transcription which might be indicative of a non-Trk-directed activity.

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Figure 34. Ionomycin and ANA-12 concentration responses.

(A) Five concentrations of ionomycin were tested to determine the concentration that maximally induces the response in the TrkB-NFAT-bla cell. A half-maximal response is achieved at 0.4 µM of ionomycin. At 1 µM, the response reaches the peak after which it starts to level off. N = 4, error bars = SEMs. (B) ANA-12 inhibits the ionomycin response (1 µM) in a concentration-dependent manner (from 1.6 to 25 µM). IC50 of ANA-12 for the ionomycin inhibition is approximately 1.5 µM. [ANA-12] that maximally inhibits the response is 25 µM, with ~80% inhibition. N = 5, error bars = SEMs.

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3. ANA-12 inhibition in the presence of thapsigargin – an inhibitor of Ca2+ transporter We then went on to use thapsigargin which, like ionomycin, raises the amount of calcium ions in the cytosol. Thapsigargin is a potent non-competitive inhibitor of sarco-endoplasmic reticulum Ca2+ ATPases (SERCAs), an active Ca2+ transporter (Treiman et al., 1998). Inhibition of SERCAs by thapsigargin stops Ca2+ uptake by an intracellular store, leading to an increase in cytosolic free Ca2+. By constructing a full concentration response curve, we found that thapsigargin triggers a half-maximal response at 0.03 µM, and a maximal response at 0.2 µM (Figure 35A). The maximal thapsigargin response is on par (1.4 vs 1.6) with the equivalent BDNF response. ANA-12 appeared to have a concentration-dependent inhibitive effect on the thapsigargin- induced response, similar to what was seen previously with BDNF and ionomycin.

Specifically, ANA-12 inhibited the 0.5 µM thapsigargin response with an IC50 of 1.2 µM and a maximal ~80% inhibition at 25 µM. (Figure 35B).

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Figure 35. Thapsigargin and ANA-12 concentration responses.

(A) The TrkB reporter cells were treated with increasing concentrations of thapsigargin from 0.0016 to 5 µM (N = 4). A considerably variable response was elicited by thapsigargin, with an estimated EC50 of 0.03 µM and a maximal response at 0.2 µM. (B) 0.5 µM thapsigargin was used to induce the maximal β-lactamase activity. The line graph illustrates progressive inhibition of the thapsigargin response by increasing concentrations of ANA-12. ANA-12 displays a half- maximal inhibition at 1.2 µM and a maximal inhibition of ~80% at 25 µM. N = 5, error bars = SEMs.

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4. ANA-12 inhibition in an irrelevant (non-Trk) β-lactamase system ANA-12 might be acting at a step between TrkB activation and NFAT-dependent stimulation of β-lactamase expression, or alternatively it might directly interfere with the enzyme component. To test this hypothesis, we utilised a completely different reporter assay that shares β-lactamase activity as the endpoint readout. In brief, the Tango-CB1 bla cell line reports on CB1 receptor activation based on the same β- lactamase principle. Specifically, CB1R/GPCR activation recruits β-arrestin fusion protein to the receptor; causing a release of GAL4-VP16 transcription factor. The transcription factor translocates to the nucleus and binds to the UAS response element upstream of the bla gene to activate bla transcription. With one shared element being the β- lactamase, it makes a suitable system for testing if ANA-12 directly modulates the β- lactamase activity. ACEA, a selective cannabinoid CB1 receptor agonist, half-maximally triggered the response at 0.06 µM (Figure 36A). A maximal β-lactamase activity was observed at 5 µM of ACEA, and this response can be fully inhibited by a CB1 antagonist (Personal communication, Leanne Lu). As shown in Figure 36B, increasing concentrations of ANA-12 significantly increased the response ratio (a non-zero slope as determined by linear regression; F(1,4) = 116.2, p < 0.001) but had no effect on the 5 µM ACEA response (F(1,4) = 0.244, p = 0.648). These observations suggest that the previously noted non-Trk activity of ANA12 could not simply be a result of its direct activity against the β-lactamase - a reporter element in the assay.

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Figure 36. ANA-12 Inhibition of the ACEA-driven response in a CB1-Tango β-arrestin assay.

(A) Six concentrations of ACEA were assayed to identify the optimal concentration for CB1 receptor activation in the Tango reporter assay. A concentration response curve of ACEA demonstrates an estimated EC50 of 0.06 µM and a maximal response at 5 µM. (B) Increasing concentrations of ANA-12 were used to determine its inhibitive effect on the ACEA-induced response. Beyond 3 µM ANA-12, there is above-background β-lactamase activity which might suggest the non-specific effects of ANA-12 in the upper µM range (significantly non-zero slope; linear regression, F(1,4) = 116.2, p < 0.001). Increases in the β-lactamase activity as a consequence of 5 µM ACEA-activated CB1 receptor are not attenuated in the presence of µM ANA-12 (F(1,4) = 0.244, p = 0.648). N = 3 independent experiments, error bars = SEMs.

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To recap, in the TrkB-NFAT-bla cell line, BDNF can activate the TrkB receptor as reflected by an increase in the β-lactamase activity. ANA-12 inhibits the BDNF-induced response with a maximal 80% inhibition at 25 µM, and an estimated IC50 of 1.6 µM. Likewise, this partial inhibition by ANA-12 was seen with two other compounds that raise intracellular calcium; ionomycin and thapsigargin. According to the original methods paper (Wang et al., 2008a), increases in intracellular Ca2+ stimulate the β- lactamase expression by acting downstream from the TrkB receptor. A similar IC50 was observed for ANA-12 inhibition of ionomycin and thapsigargin responses (1.5, and 1.2 µM respectively). Moreover, the responses evoked by both Ca2+ mobilisers were maximally inhibited by ~80%. We also showed that this inhibition was not due to ANA- 12 exerting its effect directly on β-lactamase, a reporter element in the assay, as it failed to inhibit the ACEA-evoked response in an irrelevant bla reporter cell line.

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Discussions I In the first half of this chapter, we set out to investigate the selectivity of ANA- 12, a small-molecule antagonist by utilising a cell-based bla reporter assay. ANA-12 has been reported to be a selective TrkB antagonist that was identified and characterised by Cazorla et al. (2011). It was discovered through the rational virtual docking approach that has been successfully employed in an earlier attempt to identify small-molecule mimetics of BDNF (Massa et al., 2010). Essentially, the specificity patch on the TrkB-IgL- D2 subdomain was used as the target site. Compounds in Bioinfo-DB database with lead-like properties were docked into the specificity patch. Virtual hits were then clustered into different classes, and representatives of each class were synthesised for in vitro screening. Based on KIRA (Kinase Receptor Activation)-ELISA assay which detects receptor activation by measuring the level of phospho-TrkB, ANA-12 was reported to reduce the TrkB activity in TrkB-inducible cells, cortical neuron cultures, whole brain and specific brain regions extracted from ANA-12-treated mice. ANA-12 potency was reported in a submicromolar range, and its TrkB selectivity was demonstrated in a neurite outgrowth assay using PC12 cells that have been transfected to express high levels of the human Trk receptors. We first showed that the cell line responded significantly to BDNF, a cognate TrkB ligand. Following which we established the inhibitive effect of ANA-12 on the BDNF-induced response. We then proceeded to stimulate the cell line with two different Ca2+ mobilisers (ionomycin and thapsigargin) and assessed the ANA-12 activity against these responses.

1. ANA-12 Selectivity We have demonstrated that ANA-12 can partially inhibit all the responses either elicited by BDNF, ionomycin or thapsigargin. At first glance, this apparently non- selective inhibition might suggest that ANA-12 has some off-target activities. In fact, we have observed that ANA-12 inhibits the TrkC activity by a greater margin than TrkB (65% vs 55% inhibition by 10 μM ANA-12); contesting the claim that it is a selective TrkB antagonist (Cazorla et al., 2011). Intriguingly, the magnitudes of inhibition by the same ANA-12 concentrations were almost identical (~80%, 65%, and 50%) given the maximal response generated by different compounds; 25 ng/mL BDNF, 1 µM ionomycin, 0.5 µM thapsigargin. This observation is consistent with the similar IC50 values (1.6, 1.5, 1.2 µM; rough estimates from non-linear regression). Specifically, the original publication claims

155 that ANA-12 fully inhibits TrkB activation in recombinant cells and cortical neurons (at ~mM concentration) and impedes neurite outgrowth of the TrkB-expressing PC12 cells in a concentration-dependent fashion (e.g., 50% inhibition at 1 µM, and ~100% inhibition at 100 µM). At 100 µM ANA-12, neurite outgrowths of the TrkA and TrkC- expressing cells are reportedly unaffected. The group also docked ANA-12 into the TrkB specificity patch, illustrating that ANA-12 is surrounded by 4 contact residues that are TrkB-specific. Considering the fact that a short RGE sequence is the common motif for neurotrophin binding at the specificity site, it is doubtful that absolute selectivity can be achieved at this pocket and likely reflects why we found ANA-12 to be active against all three Trk isoforms.

To my knowledge, no other studies have attempted to thoroughly verify the original findings. Most follow-up studies have made use of ANA-12 on the presumption that it is a selective TrkB agent in in vivo models. For instance, ANA-12 injection into the nucleus accumbens (NAc) obliterated stress-induced BDNF-mediated social avoidance in mice with optically stimulated VTA-DA neurons (ventral tegmental area – dopaminergic; Walsh et al. (2013)). This effect was contrary to an increase in stress susceptibility on social interaction behaviour as a result of BDNF microinjection into the NAc. Similar ANA-12 effects on the molecular basis of social behaviours were also reported by Shirayama et al. (2015); showing NAc-specific decreases in phospho-TrkB in a mouse stress model of learned helplessness. Interestingly, these decreases were not observed in other brain regions associated with reduced BDNF levels, e.g., mPFC, CA3, and dentate gyrus of the hippocampus. Behavioural effects of ANA-12 inhibition at the NAc include increased sociability in the social interaction test, enhanced fear retention in the avoidance learning test, and no effect on social preference (Azogu and Plamondon, 2017). One in vitro study applied 10, 30, and 50 µM ANA-12 to four different TrkB-expressing lung carcinomas and found that ANA-12 concentration- dependently decreased cell migration in a transwell assay (Sinkevicius et al., 2014). ANA-12 associated reduction in migration was also observed in the shTrkB intervention and the cell line with non-functional TrkB. The group did not show a combination effect of BDNF plus ANA-12 however, as they were not trying to assess ANA-12 selectivity per se.

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Notably, BDNF expression is mediated by Ca2+ (Zheng et al., 2012). On this basis, it is tempting to speculate that by increasing the Ca2+ levels with ionomycin or thapsigargin, BDNF transcription and production might be indirectly upregulated and ANA-12 inhibition of the ionomycin/thapsigargin-evoked responses would be expected. In the original methods paper that describes the development and characterisation of the Trk-receptor cell lines, the authors demonstrated that K252a, a potent Trk kinase inhibitor, can fully inhibit the BDNF activity at concentrations that have no effect on the responses elicited by thapsigargin (Wang et al., 2008a). Bearing in mind that K252a is not specific to Trk, it cannot be viewed as a gold standard for assessing Trk specificity. Even at low nM concentrations, it has similarly high potency against Trk, MST2, NUAK1, and ML3K (IC50 ~ 1-4 nM; Martin et al. (2011)). What is clear from the K252a results is that there is some discrepancy between the compound effects on the downstream responses that are triggered by either neurotrophins or Ca2+ mobilisers such as thapsigargin. Besides, two of the RNAi oligos that were designed and verified to selectively target TrkB or TrkC have approximately 50% inhibitory effects on the thapsigargin responses in all three Trk cell lines, compared to full inhibition of the responses elicited by the cognate ligands in corresponding Trk cell lines (four other oligos do not inhibit the thapsigargin effect however). In particular, the authors postulated that any inhibitory effects of putative Trk modulators on the thapsigargin activity may suggest an effect on other targets that contribute to thapsigargin-induced signalling. Yet the fact that thapsigargin response in the Trk-carrying cell line may not necessarily be Trk independent suggests that apparent modulation of this response alone cannot reveal the extent of off-target activities (non-specificity) of the Trk inhibitors. This eventually boils down to the lack of a potent and specific tool compound. One way to test the relative contribution of the neurotrophin-Ca2+ feedback loop and assess Trk specificity is to compare the above results with respective effects in the parental NFAT cell line that does not carry a human Trk construct.

Our failure to reproduce the findings with regards to TrkB selectivity of ANA-12 is another case that adds to the reproducibility crisis in science (Baker, 2016). Further, the extent of ANA-12 non-specificity will require further investigation. In fact, it is quite common for researchers to submit the initial hits to a specially-designed cheminformatics software that can provide a predictive activity score based on

157 collections of known compound-activity relationship. One such tool (Molinspiration bioactivity score; www.molinspiration.com) integrates structures of representative ligands that are active on six major families of drug targets, and identify substructure features critical for each family using Bayesian statistics. Molinspiration predicts ANA-12 to belong to a class of protease inhibitors with a score of 0.33 (i.e., moderate activity; Figure 37). The tool correctly predicts K252a to exhibit high activity against a kinase and enzyme target (score of 1.27, and 0.70 respectively). A3, our in-house TrkB inhibitor, is assigned low activity scores against all six common targets. This is merely a prediction based on limited categories; the protease family alone constitutes 1 – 2% of the human genome and it is important not to view it as a single entity but as a touchstone of closely related protein networks (Greenbaum et al., 2002). More experiments are definitely necessary before any claims or contests are made with regards to whether ANA-12 binds a target other than the Trk receptor.

Figure 37. Predicted bioactivity score of ANA-12 by Molinspiration.

Predicted scores are calculated against 6 common drug targets; GPCRs, ion channels, kinases, nuclear receptors, proteases, and enzymes. The scores range from -5.00 (negligible activity), +0.20 to +0.49 (moderate activity), +0.50 to +2.00 (high activity).

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2. Limitations and Future Directions β-lactamase reporter assay is a straightforward assay that allows indirect measurement of receptor activation. By using different bla cell lines that express distinct receptor isoforms, the assay also allows us to determine isoform selectivity. Despite its usefulness, it is a single end-point assay meaning that the measurement is carried out at a single pre-specified timepoint. Other pharmacokinetics parameters cannot be discerned through the use of this assay system. A combination of both real- time and endpoint assays have been shown to be more effective at evaluating the parameters of interest, for example, drug toxicity in monolayer cell cultures (Single et al., 2015). Additionally, the assay can only reveal Ca2+/NFAT-based activity which is only one of the main signalling cascades associated with Trk activation. These limitations prompted us to explore the utility of transcriptional profiling that allows investigation into multiple transcripts that belong to various pathways for a more cohesive assessment of the activity of Trk compounds (see next section).

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Results II

Transcriptional characterisation of novel TrkB inhibitors For Trk-mediated signalling, there are three well-established canonical second

2+ messenger pathways by which a ligand exerts its regulatory effects; PLC/IP3/Ca , Ras/MAPK, and PI3K/Akt. The presence of these alternative pathways raises questions as to whether a Trk antagonist such as ANA-12 would exhibit biased antagonism. Several possible mechanisms have been proposed that might govern the presence of biased signalling, including multiple conformational states that a receptor can adopt, and different thresholds for recruitment of intracellular messengers (Smith et al., 2018). Indeed, biased effects on downstream pathways have been observed in Trk modulators, for instance, peptidomimetics of TrkC can behave as biased agonists (Chen et al., 2009). We wanted to design an assay that allows investigation into the effect of ANA-12 on a broad set of TrkB-dependent transcripts to clarify its mechanism of action. In addition, the assay should be applicable for the screening of other potential drug candidates that are directed at the neurotrophin/receptor interaction.

1. Building a neurotrophin-specific transcript set 1.1 Broad analysis of mRNA microarray The aim for this section was to identify the key regulators in the neurotrophin pathways to be used for transcriptional characterisation of novel TrkB modulators. We measured the transcriptional changes in cultured SH-SY5Y cells in response to neurotrophins, specifically BDNF, by using a microarray technology which allows expression levels of thousands of genes to be measured in relatively quick and straightforward steps. Effectively, we treated the neuroblastoma cells with vehicle, and 25 ng/mL BDNF separately for 18 h. We justified the use of the SH-SY5Y neuroblastoma cell line for our BDNF investigation by the fact that this neuronal cell line has been shown to respond positively to BDNF for cell differentiation and maintenance (Encinas et al., 2000). Our lab had also previously used a different neuroblastoma line SMS-KCN, which relies on TrkB autocrine signalling for survival. The BDNF concentration of 25 ng/mL was used as it elicits a maximal response by the TrkB receptor in the β- lactamase assay previously described.

Following BDNF treatment, total mRNAs were extracted and purified from the samples in each assay condition and added to the microarray chip for expression 160 profiling. R packages were used for analysis of the expression data. To start with, the raw expression data were processed using Microarray Analysis Suite 5 (MAS5; Affymetrix) which include background correction, and Perfect Match/Mismatch (PM/MM) correction to give the normalised expression values. Detection calls, i.e., Present/Marginal/Absent (P/M/A) were then used to pre-filter the results. Probesets that were labelled absent in all samples were removed from analysis. Linear model was fitted for every probeset using limma package with the aim to account for systemic variations and estimate the actual variability in the dataset. Limma applies moderated t-statistics to the defined contrast groups, i.e., control versus BDNF-treated. The results show 1037 genes with absolute log2 fold-change (lfc) of ≥ 2, i.e., 4-fold (Figure 38). Of these, 126 genes display the changes with associated p ≤ 0.01.

Figure 38. Volcano plot of BDNF-elicited transcriptional changes in SH-SY5Y.

Average fold changes of expression values were calculated between the BDNF-treated samples and the control samples. Each fold change is represented in a logarithmic base 2 format and plotted against log base 10 of the associated p value to obtain a volcano plot of the transcriptional response. 126 genes with absolute lfc ≥ 2 and p ≤ 0.01 are highlighted in red. In blue are 911 probesets with lfc ≥ 2 and p > 0.01. The rest of the probesets are coloured in grey. Data analysis was performed using R limma package. The plot was also generated in R. However, when multiple comparisons were corrected for using the Benjamin- Hochberg protocol, none of the observed changes were labelled as significant according to BH-adjusted p-values < 0.05. The top-ten genes display absolute lfc > 3, and adjusted p < 0.2. These genes are listed in Table 10. The list was submitted to an enrichment analysis platform, EnrichR (http://amp.pharm.mssm.edu/Enrichr/; Kuleshov et al.

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(2016)) for pathway analysis. None of the genes belong to neurotrophin-related pathways and even when the top transcripts with non-adj. p < 0.01 (n = 71) were selected and submitted to pathway analysis, the set did not return any neurotrophin pathways.

Table 10. Top-10 transcripts with absolute lfc > 2 as ranked by adjusted p-value.

Probeset ID Symbol Name logFC AveExpr t P.Value adj.P.Val 205486_at TESK2 testis-specific kinase 2 4.38 5.228 13.86 9.81E-06 0.095 236843_at NOX4 NADPH oxidase 4 -4.49 4.689 -13.25 1.27E-05 0.095 229493_at HOXD-AS2 HOXD cluster antisense RNA 2 3.66 5.606 13.05 1.38E-05 0.095 1564777_at HYALP1 hyaluronoglucosaminidase pseudogene 1 4.06 4.841 12.81 1.54E-05 0.095 229770_at GLT1D1 glycosyltransferase 1 domain containing 1 -5.25 4.730 -11.54 2.8E-05 0.118 232415_at PCDHB13 protocadherin beta 13 -4.87 5.106 -11.49 2.87E-05 0.118 1561432_at LOC105374419 uncharacterized LOC105374419 4.95 4.873 10.15 5.78E-05 0.180 238655_at ACAD10 acyl-CoA dehydrogenase family, member 10 -3.19 5.308 -10.05 6.14E-05 0.180 217040_x_at SOX15 SRY (sex determining region Y)-box 15 -3.45 5.838 -9.59 7.99E-05 0.180 235865_at CELF1 CUGBP, Elav-like family member 1 3.57 4.684 9.43 8.78E-05 0.180 The perceived lack of statistical significance is likely due to a combination of (i) the fold-changes not being substantial enough given the amount of BDNF, the limited window of treatment, or the low level of TrkB, and (ii) the small sample size (n = 3). Post-array analysis revealed that the muted response to BDNF by the SH-SY5Y cell line was possibly due to a relatively low expression of its cognate receptor, TrkB, which is encoded by NTRK2. Our SH-SY5Y cell line is apparently the TrkA-expressing type which is associated with more favourable prognosis (Schramm et al., 2005). The analysis was based on a non-parametric classification of individual probesets on the array chip (conducted by Dr Gareth Williams). Firstly, expression values of the genes in each experiment set are ranked from highest to lowest, and a relative rank is calculated as 1/nth. Average of this rank value is also calculated for each probeset across the deposited data for our particular array platform (GPL570). With our query set, the relative rank was generated and the difference of this rank between our query set and the database average was calculated for each probeset, i.e., rank difference. The rank difference is a non-parametric variable which can range from -1 to +1. The difference indicates whether the gene is more (positive) or less (negative) expressed in our sample of interest compared to its average expression across various cell populations.

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Table 11. Rank differences of neurotrophin-related genes in the SH-SY5Y cell line.

According to the analysis, the NTRK2 gene in our Probe ID Gene Symbol SH-SY5Y 208605_s_at NTRK1 0.314 parental SH-SY5Y cell line is expressed at an 207152_at NTRK2 -0.034 average level, as suggested by the near-zero 214680_at NTRK2 0.035 221795_at NTRK2 -0.079 differences in the rank difference values for all 221796_at NTRK2 -0.169 probes but one in the NTRK2 set (Table 11). BDNF 229463_at NTRK2 -0.016 236095_at NTRK2 0.020 gene expression is markedly above average by 1557795_s_at NTRK3 0.007 0.2 – 0.35 in rank. Also, NGF expression rank has 206462_s_at NTRK3 -0.178 213960_at NTRK3 -0.089 moved up by 0.3, implying that it is more 215025_at NTRK3 -0.040 expressed in this cell line than average. NGFR 215115_x_at NTRK3 -0.109 215311_at NTRK3 0.044 gene that codes for p75NTR is close to average 217033_x_at NTRK3 -0.267 in its expression rank, whereas NTRK3 and NGF 217377_x_at NTRK3 -0.378 228849_at NTRK3 -0.179 expression ranks appear lower than average. 231469_at NTRK3-AS1 -0.176 205858_at NGFR 0.056 This analysis suggests that the SH-SY5Y cells 206814_at NGF -0.118 express TrkA, and BDNF at possibly higher levels 206382_s_at BDNF 0.210 239367_at BDNF 0.348 than average, meanwhile exhibit average to low 1567359_at BDNF-AS 0.091 expression of TrkB and other neurotrophin 1567361_at BDNF-AS 0.021 related players.

1.2 Identification of the transcripts that display moderate changes consistently across independent experiments With the small sample size, it was difficult to conclude if the transcriptional changes in our microarray results were (i) genuinely BDNF-induced which may be contested by the average to subpar NTRK2 expression previously discussed, or (ii) significant or simply random given the BH-adj p values ≥ 0.1. It would seem logical to follow up the results from the non-parametric rank analysis and test our SH-SY5Y cell line with NGF, the high-affinity ligand for TrkA receptor. Nevertheless, we went on to explore the bioinformatics route and browsed the NCBI GEO database for independent experiments to identify the genes that moderately or extensively respond to neurotrophins in other biological systems.

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Table 12. NCBI-GEO neurotrophin experiments included in our analysis.

GEO Accession Neurotrophin Species, Sample Type Reference GSE18016 NGF, 1 h Rat, PC12 cell line Chung et al. (2010) GSE33639 NGF, 2/4 h Rat, PC12 cell line Mullenbrock et al. (2011) GSE9169 BDNF, 6/24/48/72 h Human, SH-SY5Y cell line Nishida et al. (2008) GSE11322 BDNF, 24/48 h Mouse, telencephalon Hayashi et al. primary cultures (2008) GSE14499 BDNF APP transgenic mouse, Nagahara et entorhinal cortex (EC) al. (2009) and hippocampus (HIP) GSE14505 BDNF Rat, EC and HIP Nagahara et al. (2009) GSE22525 BDNF, 2/4/8 h Human, SH-SY5Y cell line Chadwick et al. (2010)

Having identified the experiments to include in our analysis (Table 12), we characterised the magnitude of neurotrophin-induced response in each experimental condition by calculating the fold change of gene expression contrasting the treatment to the control. An arbitrary cut-off for the fold change magnitude was set at 20%, and p ≤ 0.05. The lists of differentially expressed (DE) genes that display changes above these thresholds from all NGF/BDNF experiments were compared. We wanted to identify a set of 20 transcripts that are robustly altered in expression in response to BDNF (and NGF) across a range of cell types as these would then serve as a bespoke transcriptional signature that could be measured at relatively low cost using the xMAP (Multiple Analyte Profiling) technology (Luminex MAGPIX; see review by Leng et al. (2008)). Probe design for the detection of mRNA transcripts in the sample is based on complementary nucleotide sequence. The standard length for an oligo probe is ~20 mer. The technology utilises microsphere beads that are colour-coded according to the specific transcript that the beads have been designed to detect. Unlike microarray, hybridisation occurs in solution phase. Additionally, the beads are impregnated with iron-containing magnetite particles which facilitates the removal of beads from the reaction suspension during repeated washes. By combining the differently labelled beads whereby each label corresponds to one particular transcript, we can simultaneously detect multiple transcripts in one assay setting, aka, multiplex assay.

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Figure 39. Selected genes and their grouping in the neurotrophin signalling pathways.

(A) 20 transcripts that consistently responded to BDNF/NGF were selected; these are shown in the table. Consistency score denotes the count of experimental conditions where the transcript was found to be upregulated (sum+) or downregulated (sum-) in the presence of either BDNF or NGF. (B) Enrichment analysis based on KEGG 2016 pathway annotation clusters the genes under canonical neurotrophin-related pathways (in blue square), which include MAPK, Ras, and PI3K signalling cascades (source: Enrichr). Simplistically, EIF4E, DDIT4, MTOR, and PPP2R5B are grouped under the PI3K/Akt pathway. RRAS, FGFR1, MAP2K5, JUN, MAP3K13, DUSP6, GAB1, TIAM1 belong to Ras/MAPK. CAMK2B falls under PLCγ/Ca2+. A B GENE sum+ sum- FGFR1 9 0 JUN 10 2 HMGCR 9 1 CAMK2B 10 0 EIF4E 8 0 DUSP6 12 3 EGR1 12 0 TRIB2 12 1 TIAM1 7 3 TRO 7 0 IL6ST 3 7 DDIT4 2 7 PPP2R5B 2 8 RRAS 0 7 MTOR 0 6 MAP3K13 0 8 MAP2K5 1 7 ID3 4 10 BAD 1 6 GAB1 0 9

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The selected 20 transcripts appeared differentially expressed in at least seven experimental conditions, 10 of which were consistently downregulated by NGF/BDNF, and 10 were upregulated. 14 genes were mapped to NT-related pathways according to KEGG2016-based pathway analysis on Enrichr, as illustrated in Figure 39. According to NCI-Nature Pathway Interaction Database (PID), EGR1 belongs to PI3K and PLCγ pathways. Four other genes include, TRO and IL6ST which are present in the Neurotrophin SuperArray series (GPL1180), and TRIB2, and ID3 which were selected primarily on the basis of their consistent responses across experiments. Most genes fall under either MAPK or PI3K, implying that our multiplex set might only be able to identify biased antagonism if the effects were biased between Ras/MAPK versus PI3K/Akt, but not PLCγ. Probe sequences for the transcripts in our multiplex set are listed in Appendix.

1.3 Selection of invariant genes In comparison to microarray, multiplex assay only detects a relatively small panel of transcripts (1 to 80) and the literature behind this technology is not as extensive. An abundance of microarray data enables sophisticated statistical analysis to be performed, e.g., empirical Bayes, which is not the case for the multiplex data. Like quantitative PCR, analysis of the expression reads from a multiplex experiment requires inclusion of housekeeping genes in the assay for normalisation. Our previous results from another bead-transcript panel showed the standard housekeeping genes, e.g., TXN2, RPLP0, and GAPDH to be affected by different treatment conditions. We hoped to identify novel genes with better housekeeping characteristics, i.e., exhibiting small expression variation in the face of diverse experimental conditions. To this end, we accessed over 100,000 experiments on the NCBI-GEO database and calculated the standard deviation of the expression values for individual probesets as a measure of their variability (courtesy of Dr Gareth Williams). We also calculated a relative rank of each probeset; where the more highly expressed probeset has the rank value closer to 0, and the probeset with lower expression will be closer to 1 in terms of its relative rank. The relative rank values range from 0.02 to 0.93, and the standard deviations from 0.09 to 0.41. The aim of this search was to select three genes that possess minimal variation in their expression. These three genes should also span the relative rank from 0.02 to 0.5 as this is an expression range of the DE genes in our 20-plex neurotrophin panel. To

166 accomplish this aim, a cut-off threshold was set at the relative rank < 0.1 and sd < 0.115, yielding 96 probesets on the more highly expressed end of the spectrum. For an intermediate range, the threshold was set at the rank between 0.1 and 0.3, and sd < 0.115. This moderately-expressed range vetted out 401 genes. Lastly, the threshold was set at 0.3 < rank < 0.5 with sd < 0.12 giving 234 genes. From a collective list of 731 genes, those associated with minimal number of probesets, i.e., 1 to 3, that display agreeable expression values and low sd were compared to the microarray results that we had on the SH-SY5Y cell line to confirm invariance. The final three genes that were selected to be included in the multiplex transcript set were FTL, FOXJ2, and HLCS (Table 13).

Gene No of probes Probe ID lfc MeanExprs RelRank sd FTL 2 212788_x_at -0.067 13.8 0.03 0.114 FOXJ2 2 203734_at -0.031 9.6 0.23 0.110 HLCS 2 209399_at 0.071 7.5 0.45 0.118 Table 13. Three invariant genes; FTL, FOXJ2 and HLCS.

FTL, FOXJ2 and HLCS were chosen based on their comparatively low variation across > 100k experiments on the NCBI-GEO database (sd < 1.2). Additionally, these genes are expressed at a different level ranging from the mean values of 7 to 14, equivalent to the relative rank values of 0.02 - 0.5. Expression of these genes were also confirmed to not have been altered by BDNF treatment in the SH-SY5Y cell line.

2. Validation of the neurotrophin-specific multiplex panel 2.1 Sensitivity of the multiplex assay to detect non-purified RNA samples in a whole-cell lysate Per manufacturer’s recommendation, no RNA purification is needed for the multiplex assay and, with cultured cells, the use of whole-cell lysate would give a reliable measurement of the RNA content. To test this claim, we performed four separate experiments using the SH-SY5Y and H460 cell line. mRNA and whole-cell lysate samples were generated from these cell lines. Expression levels of 50 transcripts in the two sample types were detected and the median expression values were ranked from highest to lowest. Relative rank was calculated as 1/nth. For each cell type, relative ranks of all 50 genes for the purified mRNA sample were plotted against the ranks calculated from the lysate (Figure 40). We wanted to investigate if the use of whole-cell lysate as a specimen would yield a similar expression pattern to that of purified RNA. The resulting

167 plots show a significant concordance (R2 = 0.944, and 0.972), indicating that the expression patterns obtained from the two sample types are very similar. This finding established that in comparison to purified RNA, RNA content in the lysate could be equivalently detected by the bead assay.

A

B

Figure 40. Relative rank of expression of 50 genes measured in two sample types.

Purified RNA and whole-cell lysates from (A) SH-SY5Y and (B) H460 cells were used for quantitation of gene expression. We found that given the same biological background, the two sample types yield similar relative expression values for the 50 genes in our test set (R2 = 0.944 for SH-SY5Y, and 0.972 for H460). This observation confirms that the multiplex assay is sensitive enough to reliably detect mRNA content in the non-purified lysate sample.

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It was logical to check whether the bead platform could differentiate the expression patterns of different cell types. We found that the relative rank of gene expression can markedly vary depending on the cell lineage. The plot of the relative ranks of 50 genes in SH-SY5Y versus H460 illustrates distinctive patterns of gene expression in these two cell lines (R2 = 0.262, Figure 41). In other words, the relative expression pattern that is characteristic of each cell type can be detected by the multiplex assay.

Figure 41. Distinguishable expression patterns between cell lines of different origins.

Expression patterns of 50 genes in the test set were distinctive between the two cell lines (R2 = 0.262; SH-SY5Y and H460 lysates). This observation demonstrates that the multiplex bead assay is sensitive enough to detect the characteristic patterns of distinct biological backgrounds.

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2.2 Minimal variability of the invariant genes Of the three invariant genes that were selected, i.e., FTL, HLCS, and FOXJ2, FTL had to be excluded from the analysis as its expression in our test specimens is consistently above 20,000 which is an upper limit of the linear dynamic range (log10 = 3- 4). Three treatment conditions were included in a preliminary experiment (vehicle, BDNF, ANA-12) to investigate the variability of HLCS and FOXJ2. Median fluorescence intensities of HLCS and FOXJ2 were found to mirror the lysate (RNA) quantities in each sample replicate (Figure 42). The charts suggest that these two transcripts accurately reflect the amount of RNA content in the sample, and that at least in the three conditions that were examined their expression stayed considerably invariant.

Figure 42. Confirmed invariance for the two putatively invariant genes.

This confirmation was based on the assumption that the amount of whole-cell lysate in the assay well directly reflects the total mRNA content that is being detected. Lysate concentrations were determined with BCA Protein Assay (ThermoFisher, UK). Similar expression patterns (left y-axis, blue-filled bars) were observed for the two invariant genes that were chosen from database analysis; HLCS and FOXJ2. Qualitatively these patterns also appear to reflect the amount of sample loading, as shown by lysate concentrations (same loading volume for each well; right y- axis, grey-filled bar).

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2.3 BDNF response of the 20-plex genes We had noted earlier that our SH-SY5Y cell line expresses low level of the TrkB- encoding gene. This low expression was confirmed in a multiplex assay with NTRK2 included as one of the probes in the plex set. Based on the fluorescence intensity, NTRK2 transcript was not detected above the background value, suggesting minimal to no expression. Similar findings of negligible TrkB expression in SH-SY5Y have been reported by others (Hecht et al., 2005). NTRK1 expression, on the contrary, appeared to be considerably above background (~10 vs 70 mfi, for equivalent amounts of sample), which is consistent with our analysis of expression rank difference. This minimal TrkB expression was substantiated by the minimal BDNF responses exhibited by the cell line (Figure 43).

Figure 43. BDNF non-response in the low TrkB SH-SY5Y cell line.

Cells were seeded at 1.6x105 cells/well o/n in a 24-well sterile tissue culture plate. The cultures were then treated with the control media (0.5% FBS DMEM) or media supplemented with 25 ng/mL BDNF for 18 h, and harvested in working lysis mixture (Quantigene, UK) according to manufacturer’s guideline. For each well of the 96-well assay plate, 80 µL cell lysate was mixed with 20 µL working bead solution that contains capture beads, probe sets and blocking reagent. After o/n cooperative hybridisation, signal detection and amplification were carried out and the plate was read using the Luminex MAGPIX platform. A full panel of transcripts are shown in this figure including 19 neurotrophin bespoke transcripts, 2 NTRK transcripts, and 2 housekeeping genes (FOXJ2, and HLCS); with the exception of EIF4E (non-detected), FTL and (above threshold). Relative expression values were calculated relative to the GEO mean of FOXJ2 and HLCS median fluorescence intensities (mfi). N = 2 independent experiments, each with 4 replicates.

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Given the low level of endogenous expression and the non-response observed, to verify the BDNF response of our neurotrophin 20-plex set, we purchased a TrkB- overexpressing SH-SY5Y cell line from Kerafast. NTRK2 expression was indeed much augmented in this cell line compared to the regular SH-SY5Y cell line (~20,000 vs 10 mfi, for equivalent amounts of sample). BDNF treatment of these cells led to significant transcriptional changes following 18 h treatment period. Of the 20 genes in the plex set, EI4FE expression was equal to the background measurement and was excluded from further analysis. Expression levels of 10 genes in the set were significantly altered by BDNF treatment (Figure 44). GAB1, MAP2K5, and CAMK2B were downregulated by 12%, 21%, and 29%, respectively (two-sample t-test; p = 0.03, 0.01, and 0.01). On the contrary, BAD, DUSP6, RRAS, HMGCR, EGR1, ID3, and TRIB2 were upregulated by 47%, 87%, 103%, 18%, 325%, 216%, and 440% (p values < 0.01). To see if the effects of BDNF on the transcripts were any different in a shorter timeframe, we also treated the TrkB- overexpressing SH-SY5Y cells for 6 h. We found a similar set of transcripts to be significantly altered by BDNF at 6 h as compared to 18 h (Figure 44), with the exception of BAD, RRAS, and ID3. Additionally, transcripts that were differentially expressed at 6 h but not at 18 h, are MAP3K13, JUN, and DDIT4. The changes were particularly prominent for EGR1 and TRIB2. EGR1 was upregulated by ~300% and 1400%, whereas TRIB2 expression was increased by 400% and 200% at 18 h and 6 h time point respectively. A summary of all percentage changes and associated p values is shown in Table 14. Importantly, the direction of transcriptional changes appeared to be mostly consistent with the deposited data (Figure 39A). Approximately ~70% (9 of 13) of the genes responded to BDNF in the expected direction, bar BAD, RRAS, ID3, and DDIT4.

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A B

Figure 44. TrkB-SHSY5Y transcripts with significant BDNF-induced changes.

Cells were seeded at 1.6x105 cells/well o/n in a 24-well sterile tissue culture plate. Following BDNF treatment, the cultures were harvested in working lysis mixture (Quantigene, UK) according to manufacturer’s guideline. For each well of the 96-well assay plate, 80 µL cell lysate was mixed with 20 µL working bead solution that contains capture beads, probe sets and blocking reagent. After o/n cooperative hybridisation, signal detection and amplification were carried out and the plate was read using the MAGPIX platform. (A) Relative expression values of the differentially expressed (DE) genes at 18 h BDNF treatment. (B) DE genes at 6 h BDNF treatment. At both 18 h and 6 h BDNF treatment windows, GAB1 and MAP2K5 were significantly downregulated while DUSP6, HMGCR, EGR1, and TRIB2 were upregulated. For the 6 h treatment only, downregulated MAP3K13, and upregulated JUN and DDIT4 were observed. Conversely, BAD and RRAS only appeared to be upregulated after 18 h treatment with BDNF. White diamond = average, black dot = each replicate, no. of replicates per condition = 4. Statistics in Table 14.

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Table 14. Transcripts in the multiplex set that show significant BDNF response.

Transcripts that consistently displayed a significant response at both 18 h and 6 h BDNF treatment windows are GAB1 and MAP2K5 (downregulation in red), and DUSP6, HMGCR, EGR1, and TRIB2 (upregulation in blue). At 6 h condition only, downregulated MAP3K13, and upregulated JUN and DDIT4 were observed. Conversely, BAD and RRAS expression only appeared to be augmented at 18 h. Most responses are robust with ≥ 30% changes. However, some changes are < 20% but are shown if associated p ≤ 0.05. Statistical significance was determined with Student’s t-test (ctrl vs 18 h BDNF, or ctrl vs 6 h BDNF; n = 4)

Gene 18h 6h p_18h p_6h GAB1 -12 -52 0.030 0.000 MAP2K5 -21 -41 0.010 0.003 MAP3K13 -28 0.003 BAD 47 0.004 DUSP6 87 85 0.009 0.010 RRAS 103 0.001 HMGCR 18 113 0.006 0.000 EGR1 325 1421 0.006 0.000 ID3 216 0.002 TRIB2 440 221 0.001 0.002 JUN 103 0.019 DDIT4 90 0.002 CAMK2B -29 244 0.010 0.016

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3. Transcriptional modulation by putative TrkB antagonists

3.1 Transcriptional pattern of ANA-12 inhibition Having established that 13 genes in the plex set significantly responded to BDNF, we went on to compare the BDNF response to the effects of selective TrkB antagonists, first ANA-12, then A3. All three conditions (control, BDNF, and BDNF+ANA12) were conducted alongside and expression analysis was performed using the same control set. Of the 10 genes that showed significant BDNF response at 18 h, ANA-12 significantly reversed BDNF-induced upregulation of four of these genes; DUSP6, BAD, RRAS, and ID3 by 28%, 14%, 54%, and 30% respectively (p < 0.05; MAPK, PI3K; Figure 45A). More descriptively, with the TRIB2 transcript, BDNF significantly increased expression in the absence and presence of ANA-12 by 440% and 518% (Tukey’s multiple comparisons test; p < 0.001). For the MAP3K13 transcript, no significant changes were observed. BDNF significantly increased DUSP6 expression by 87% (p < 0.001), and in the presence of ANA-12 the level was brought down by 28% (BDNF vs BDNF+ANA12; p < 0.001). For the HMGCR transcript, BDNF treatment induced a significant 20% upregulation (p < 0.001), and the presence of ANA-12 the observed increase was even bigger (50% upregulation compared to control, p < 0.001). GAB1 was not significantly downregulated by BDNF in the presence and absence of ANA-12. Similarly, no significant responses were observed for the JUN transcript at 18 h treatment period. For the DDIT4 transcript, BDNF did not significantly downregulate the expression, however in the presence of ANA-12 the transcript level was reduced by 25% (p < 0.001). Interestingly, the BAD transcript was increased by 47% (p < 0.001) as a result of BDNF treatment; this upregulation was reduced in the presence of ANA-12 by a non- significant 14% but did not quite reach the control level. A significant 103% upregulation of RRAS by BDNF (p = 0.003) was obliterated in the presence of ANA-12 (BDNF vs BDNF+ANA12; p = 0.002). For the MAP2K5, CAMK2B and ID3 transcript, the expression level was not significantly modulated by the BDNF treatment with and without ANA-12. In the case of EGR1, BDNF significantly upregulated the expression by 325% (p = 0.039) and 520% (p < 0.001) in the absence and presence of ANA-12. No significant difference was seen for ID3, PPP2R5B, TRO, and IL6ST, and FGFR1. For the MTOR transcript, BDNF+ANA12 reduced the expression by 23% (p = 0.002). Lastly, BDNF treatment with ANA-12 significantly reduced the levels of TIAM1 transcript by 45% (p = 0.028).

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At 6 h timepoint, significant ANA-12 reversal of the BDNF responses was seen only in the DUSP6 and HMGCR transcript (Figure 45B). With the DUSP6 transcript, 85% upregulation was seen in the BDNF condition (Tukey’s multiple comparisons test; p < 0.001), however in the presence of ANA-12 the increase in expression was ~51% (BDNF vs BDNF+ANA12; p = 0.015). BDNF significantly upregulated HMGCR expression by 113% (p < 0.001). This upregulation was no longer observed when ANA-12 was present. BDNF effects in the absence or presence of ANA-12 were quite similar in all transcripts but the aforementioned two. Specifically, the same magnitudes of expression changes in both conditions were observed in TRIB2 (~250%, p < 0.001), GAB1 (~55%, p < 0.05), DDIT4 (~70%, p < 0.005), MAP2K5 (~45%, p < 0.01), EGR1 (~1800%, p < 0.001), and TRO (~20%, p < 0.001). On the other end, with the JUN transcript, BDNF-induced increase in the expression level was significant (103%, p < 0.001) in the BDNF treatment, and the observed upregulation was bigger in magnitude in the presence of ANA-12 (190%, p < 0.001). MAP3K13, BAD, RRAS, CAMK2B, ID3, PPP2R5B, FGFR1, and TIAM1 expression in the BDNF conditions with and without ANA-12 were no different from control (p > 0.05). In the presence of ANA-12, MTOR expression was significantly downregulated by 23% (p = 0.032). IL6ST expression was significantly upregulated by 61% in the BDNF plus ANA-12 condition (p < 0.001).

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A

B

Figure 45. Expression values of 19 transcripts in 3 conditions (Ctrl, BDNF, ANA12+BDNF).

Relative expression values of 19 transcripts in the multiplex set after (A) 18 h or (B) 6 h treatment with control, BDNF, or BDNF+ANA-12. At 18 h, ANA-12 significantly reversed BDNF upregulation of DUSP6, BAD, RRAS, and ID3. For the 6 h condition, the BDNF-reversing effects of ANA-12 were only seen in the HMGCR and RRAS transcript. White diamond = average, black dot = individual replicate, no. of replicates per condition = 4.

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It was possible that ANA-12 might exhibit an inhibitive effect on the basal tone (i.e., ANA-12 as an inverse agonist). Therefore, we proceeded to treat the TrkB- overexpressing cells for 18 h or 6 h with ANA-12 on its own, instead of supplementing recombinant BDNF to additionally stimulate the TrkB receptors. If ANA-12 acted on the TrkB receptors, we would expect the transcriptional effects evoked by ANA-12 to oppose that of an agonist of the receptor, BDNF in this case. In fact, 9 of 13 transcripts in the BDNF response set were significantly downregulated by ANA-12 at 18 h (Figure 46). Critically, the ANA-12 effects were more muted in a shorter timeframe of 6 h. Only half of the 18-h transcripts were significantly altered, namely, TRIB2, MAP3K13, HMGCR, JUN, DDIT4, and ID3. More descriptively, the TRIB2 transcript was significantly downregulated by 19% and 34% at 18 h and 6 h treatment period (Student’s t-test; p = 0.002, 0.012). MAP3K13 expression showed 37% and 25% decreases (p = 0.002, 0.031). DUSP6 was only downregulated at 18 h by 26% (p = 0.002). For the HMGCR transcript, ANA-12 significantly reduced the expression by 32% and 37% (p = 0.000, and 0.014). GAB1 was downregulated by 11% at 18 h (p = 0.034). With the JUN transcript, 18 h treatment with ANA-12 reduced the expression by 34% (p = 0.005) whereas at 6 h treatment the transcript was upregulated by 32% (p = 0.045). DDIT4 was only downregulated by 6 h ANA-12 treatment (46%, p = 0.001). Following 18 h treatment, RRAS, CAMK2B, EGR1, PPP2R5B, MTOR and FGFR1 expression showed a 20%, 23%, 61%, 7%, 10%, and 23% reduction, respectively (p < 0.05). The levels of ID3 transcript on the other hand were augmented by 6 h ANA-12 treatment by 27% (p = 0.037). Expression of BAD, MAP2K5, TRO, TIAM1, and IL6ST were not significantly altered by ANA-12.

Interestingly, the majority of the observed changes are downregulation; the opposite of most upregulation induced by BDNF. By analysing the 18-h percentage transcriptional changes of all 19 transcripts in the plex set regardless of statistical significance, we also found that ANA-12 and BDNF-induced transcriptional changes are moderately anti-correlated (r = -0.440). One notable characteristic of ANA-12-induced changes is that they are generally much smaller in magnitude than the BDNF counterparts (Figure 44 and Table 14). The results also suggest a non-biased nature of ANA-12 antagonism as transcripts belonging to the three major neurotrophin signalling pathways are all affected.

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ANA-12 Gene 18h p_18h 6h p_6h TRIB2 -19 0.002 -34 0.012 MAP3K13 -37 0.002 -25 0.031 DUSP6 -26 0.002 HMGCR -32 0.000 -37 0.014 GAB1 -11 0.034 JUN -34 0.005 32 0.045 DDIT4 -46 0.001 BAD RRAS -20 0.031 MAP2K5 CAMK2B -23 0.007 EGR1 -61 0.000 ID3 27 0.037 PPP2R5B -7 0.015 MTOR -10 0.002 TRO FGFR1 -23 0.000 TIAM1 IL6ST Figure 46. ANA-12 transcriptional effects in the TrkB SH-SY5Y cell line.

(A) ANA-12 effects on 13 transcripts at 18 h treatment. (B) ANA-12 effects at 6 h treatment. (C) Percentage transcriptional changes in response to ANA-12 at 18 h, and 6 h. ANA-12 effects on gene expression are more widespread in the longer treatment. Percentage changes were calculated against the control expression, and statistical significance was determined using Student’s t-test (n = 4). The apparent transcriptional effect of ANA-12 by itself could be Trk- independent, even though the changes appeared antagonistic to those induced by BDNF. One way to eliminate this possibility was to test ANA-12 in the cell line that does not express the TrkB receptor. We considered generating a TrkB knock-out cell line, but our chance discovery of minimal NTRK2 expression in the regular SH-SY5Y led us to utilise this non-transfected cell line for our purpose (Figure 47). We found DUSP6, RRAS, and CAMK2B expression to be modestly downregulated by 18 h ANA-12 treatment (24%, 27%, 34%; p ≤ 0.002).

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Figure 47. ANA-12 effects in the low TrkB SH-SY5Y cell line.

Non-transfected SH-SY5Y cells were seeded and incubated o/n prior to 18 h treatment with 10 µM ANA-12. Gene expression was detected with Luminex MAGPIX. Relative expression levels of all transcripts in the bespoke panel in the control and ANA-12 conditions are shown. DUSP6, RRAS, and CAMK2B were significantly modulated by ANA-12. Error bars = SEMs from 2 independent experiments (each with n = 4). In conclusion, we have verified that 13 transcripts in the 19-plex set exhibited substantial responses to BDNF. In the TrkB-overexpressing SH-SY5Y cell line, ANA-12 by itself significantly altered 9 of these transcripts, plus FGFR1 in a longer treatment window of 18 h. The effect was not as extensive at 6 h, with less than 50% of the 18 h transcripts significantly affected. In the regular SH-SY5Y cell line with minimal NTRK2 expression, a limited number of transcripts showed significant responses to ANA-12, i.e., CAMK2B, RRAS, and DUSP6. Together these observations suggest that ANA-12 transcriptional modulation might be predominantly TrkB-dependent but likely constitutes some non-Trk element, as illustrated by its effect in the absence of the TrkB receptors. Additionally, within the scope of our response set, ANA-12 antagonism is non-biased.

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3.2 Transcriptional pattern of A3 inhibition We proceeded to investigate if A3, our in-house putative TrkB antagonist, exerted any modulatory effects on the key BDNF transcripts. Using the TrkB-SHSY5Y cell line, A3 was applied to the cells alongside BDNF and incubated for 18 h. In most transcripts, the presence of A3 did not significantly dampen down the BDNF-induced responses (Figure 49). Nonetheless, for the DUSP6, and HMGCR transcript, the reversal was significant (BDNF vs BDNF+A3; p < 0.01). DUSP6 expression displayed an 87% increase following BDNF treatment (p < 0.001). The magnitude of increase was smaller in the presence of A3 (63%, p < 0.001). BDNF augmented the levels of HMGCR transcript by 18% (p < 0.001), and in the presence of A3 the upregulation was 8% (p < 0.001). Other transcripts that responded similarly in both conditions include TRIB2 (~440%, p < 0.001), DDIT4 (16%, p < 0.05), BAD (~55%, p < 0.01), RRAS (~105%, p < 0.001), and EGR1 (~315%, p < 0.05). For the ID3 transcript, BDNF induced a significant upregulation by 216% (p = 0.021). TIAM1 was significantly downregulated by BDNF+A3 (p = 0.007). MAP3K13, GAB1, JUN, MAP2K5, CAMK2B, FGFR1, IL6ST did not show a significant response.

Figure 48. Transcriptional responses in the TrkB SH-SY5Y cell line from BDNF treatment w/wo A3.

Relative expression values of 19 transcripts following 18 h treatment with vehicle, BDNF, and BDNF+A3. Black dot = individual replicate, white diamond = average, n = 4.

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We then investigated the effects of A3 by itself, and found HMGCR, DDIT4, CAMK2B, MTOR, FGFR1, and TIAM1 to be significantly altered by –25%, +35%, -41%, - 15%, -26%, and -17% (p < 0.05; Figure 49A). When contrasted with the BDNF-induced changes, there is minimal anti-correlation (r = -0.074). To assess if these transcriptional effects were TrkB-dependent, A3 was applied to a regular SH-SY5Y cell line which displays minimal NTRK2 expression. Only CAMK2B expression was significantly downregulated by 26% (p = 0.025) by A3 in this cell line (Figure 49B). These results suggest that the extent of transcriptional modulation by A3 is less widespread compared to ANA-12.

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Figure 49. A3-induced transcriptional changes in the high- and low-TrkB cell lines.

Relative expression values of 19 transcripts in the (A) TrkB-overexpressing cell line following 18 h treatment (control vs A3-supplemented media). Black dot = individual replicate, white diamond = average, n = 4. (B) Gene expression in the low-TrkB SH-SY5Y cell line in the two treatment conditions (control, and A3-treated). Error bars = SEMs from 2 independent experiments (n = 4).

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We would expect compounds of the same class to display transcriptional fingerprints that are predominantly consistent and distinctive in their general patterns. To test this idea on ANA-12 and A3 as both are putative TrkB antagonists, we performed an analysis of the expression changes for all 19 transcripts in the multiplex set. The analysis revealed a moderate correlation between the transcriptional responses of ANA- 12 and A3 (r = 0.337; Figure 50A). More positively, when the expression values of all transcripts were plotted for all treatment conditions (BDNF, Ctrl, A3, ANA-12) as a heatmap, individual replicates were clustered into treatment-based groups as expected (Figure 50B). In particular, ANA-12 and A3 exhibit a very similar BDNF-opposing pattern albeit the arguably more drastic effects by ANA-12.

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Figure 50. A3 and ANA-12 response signatures.

(A) Percentage expression changes of 19 transcripts in the plex set following A3 and ANA-12 treatment. There appears to be a moderate correlation (r = 0.337) between the global transcriptional effect of the two compounds supposedly from the same class (i.e., TrkB antagonist). (B) Heatmap of the z-scores of relative expression values from the 4 treatment conditions; Ctrl, BDNF, ANA-12, and A3. Overall colour contrast illustrates the contradicting effects of the TrkB agonist, and antagonists (ANA12 and A3). The heatmap plot was generated with gplots package in R.

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4. Preliminary work on random forests to predict class membership of small molecules For the discovery of A3, our lab had mainly utilised the structure-based approach, i.e., through repeated in silico docking, cell-based reporter assay, and similarity assessment. After two iterative runs of virtual docking had been executed, similarity was assessed in terms of Tanimoto coefficient. Specifically, the similarity scoring step made use of the best compound chosen from the cellular assay as a key target in SAR-based similarity search. We asked if there was an alternative method for similarity assessment that could simultaneously utilise all the positive hits that we had gathered, rather than having to select only the best hit. Fortunately, this matter could be solved with machine learning algorithms that can make predictions based on statistical learning of the available features in a known dataset. We decided to test out a random forest classifier, one of the assemble methods, to determine if machine learning could lend itself to efficient in silico predictions of TrkB-selective hits. In our record, 82 small molecules were purchased and screened for Trk isoform selectivity using a Trk NFAT-bla reporter assay. Of these 82 molecules, 38 activate the Trk receptor while 44 do not. Chemical fingerprints of these molecules were generated and used to train a random forest classifier with bootstrap aggregating, i.e., bagging. Bagging generates subsets of data samples which are randomly rotated either as a training or test set during learning. Out-of-bag error rates were measured for random forest classifiers with 15 to 175 trees to identify the number of trees that yields the minimal error rate, i.e., n = 70 trees. Five-fold cross validation estimated the prediction accuracy to be 0.78 ± 0.23. Examples of correct and incorrect prediction are shown in Figure 51. Both molecules bind to the Trk receptor, yet only the molecule on the left was correctly labelled as Trk-binding with a probability score of 0.743. This preliminary run demonstrated that machine learning might be useful for future attempts to expand the hit space and predict probable compounds that might exhibit Trk activity. It would also be interesting to test out other machine learning methods, however the small number of samples in our dataset can be limiting.

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Figure 51. Instances of correct versus incorrect prediction.

Representative model-based similarity maps of the two small molecules that were subject to class prediction. Both molecules exhibited Trk binding in the reporter assay. L (left) was classified as Trk-binding (p = 0.743), whereas R (right) was incorrectly predicted to belong to the non- binding group (p = 0.843). The lines denote the decision boundary. Compared to the prediction model, green = high similarity, magenta = low similarity. Random forest classifier and the similarity maps were generated using scikit-learn and rdkit.

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Discussions II In the latter section of this chapter, we employed bioinformatics approaches to identify a small set of BDNF-responsive transcripts. Probes for these transcripts were synthesised in a made-to-order multiplex set for simultaneous and rapid quantitation of multiple transcripts. The aim of generating the BDNF-specific multiplex panel was to characterise the transcriptional pattern of a distinct class of compounds, i.e., TrkB antagonist (ANA-12 and A3).

1. Invariant Genes Our attempt to identify the housekeeping genes (HKGs) stemmed from the requirement for normalisation standards to be used in the multiplex assay. It is universally accepted that a defined subpopulation of the genome can be classified as HKGs, generally referring to the genes that are constitutively and invariantly expressed across backgrounds and conditions (Eisenberg and Levanon, 2013). Initially, we made use of the common HKGs, e.g., RPLP0, and GAPDH. Significant variations in their expression were present across conditions. Our observation is only one of the many instances of high variations reported in the common HKGs (Barber et al., 2005). Hence, we decided to search for invariant genes that would at least function well in our experimental setup. By using a non-parametric rank analysis, we identified two invariant genes, FOXJ2, and HLCS, and included them in our 20-plex BDNF expression set. They displayed consistent expression levels across conditions and were used for normalisation accordingly. Identifying the two invariant genes was sufficient for the purpose of our study, however it is known that hundreds of genes have been labelled as HKGs in a few large-scale profiling studies (Warrington et al., 2000, Eisenberg and Levanon, 2003), although the exact number and identity of the HKGs are contentious due to low overlap between the results (Zhu et al., 2008).

2. Utility of Transcriptional Screen We hoped to build a neurotrophin-specific transcriptional panel that could be used to transcriptionally characterise a potential Trk modulator. In a way, this would narrow the gap between target-based and phenotypic screen. While the transcript panel is target-specific, the inclusion of multiple transcripts that belong to diverse signalling pathways enables a more conceivable inference for phenotypic outcomes. Our working hypothesis was that a Trk agonist would have a transcriptional fingerprint 189 similar to that of an endogenous Trk ligand, be it NGF, BDNF or NT-3, whereas antagonists of the receptors would evoke transcriptional responses that are opposite to what is elicited by the cognate ligand. We focused on the TrkB receptor to start with but if this approach proved to work in TrkB, it should then be generalizable to other Trk receptors. With this objective, we identified 20 transcripts that exhibit consistent responses to NGF and BDNF using our in-house data on mouse cerebellar granule cells, and independent experiments that were published on the NCBI GEO database.

We expected the TrkB receptor to be activated by recombinant human BDNF, and that an antagonist would reverse the BDNF-induced transcriptional changes. In fact, we found that with the TrkB-overexpressing SH-SY5Y cell line, 13 transcripts in the 20- plex BDNF panel (65%) are significantly modulated by BDNF. Additionally, 4 of these DE genes (DDIT4, RRAS, ID3, and BAD) exhibit an increase in expression which is opposing to the consistent reduction earlier identified with database analysis. This discrepancy, particularly with BAD which encodes a pro-apoptotic regulator, is worth exploring in future research. Descriptively, ANA-12 only reverses the changes in four of these BDNF- responsive transcripts (DUSP6, BAD, RRAS, and ID3), while A3 alters the BDNF-induced changes in two transcripts; DUSP6 and HMGCR. On the other hand, ANA-12 or A3 by itself triggers significant transcriptional responses that appear to be antagonistic of the BDNF responses. ANA-12 elicits ~10-60% changes in 12 transcripts. A3 induces ~15-40% changes in 6 transcripts. These apparent responses however are not solely due to the compound having unexpected transcriptional effects, as they are almost fully absent (bar the 3 transcripts with 24-34% changes by ANA-12, and A3-modulated 26% change in CAMK2B) in the cell line with minimal NTRK2 expression. These observations suggest that ANA-12 (10 µM) might largely act on Trk, but likely have a smaller non-Trk effect, and so does A3 to a lesser extent. In summary, we have confirmed the transcripts that respond to BDNF, and that the pattern of these responses appears to be opposing that of the TrkB antagonists. The transcript set however is rather small, with only 13 BDNF- responsive genes. Importantly, Ca2+, MAPK, and PI3K-related transcripts are affected by both ANA-12 and A3, indicating they are likely non-biased antagonists. Panel modification by adding more PLCγ-related genes and BDNF-downregulated transcripts would provide a better reference frame for the relative extent of biased antagonism and balance out the upregulation-heavy set the original panel turned out to be.

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When exogenous BDNF is absent, transcriptional modulation by the small molecules by themselves can either be explained by non-specific activities, expected activities on the already existing ligand-independent/constitutive signalling, or a combination of both. It has been suggested that ligand-independent/constitutive activation of the Trk receptor is suppressed through IgL-D1 association with p75NTR (Zaccaro et al., 2001). Constitutive signalling might possibly be inferred from the use of a full Trk antagonist to determine the extent of antagonism against the baseline. Alternatively, we might want to know how much BDNF and p75NTR are already in the system. One simple modification to enable this would be to include the transcript probes that detect BDNF and NGFR (p75NTR) in the multiplex set. In addition, monitoring the activation of the channel might prove informative, since the channel may be present but not activated. One simple assay that can be used for this purpose is a tyrosine phosphorylation assay. Full characterisation of the key neurotrophin players in terms of transcriptional as well as protein expression in the cell line may also give a more integrated picture of the ongoing signalling activities.

3. Limitations and Future Directions The majority of the work was deliberately carried out at a transcriptome level. This was to explore how much information could be extracted from a specific omic data type. It is evident that some of the bioinformatics analyses in this chapter could easily be supported by benchwork experiments. These include the detection of protein expression with western immunoblotting or immunocytochemistry. Additional information will confirm the transition from transcription to translation since the presence of transcripts does not always translate to protein expression. However, most techniques for protein detection rely on the use of target-specific antibodies; the quality of which is sometimes questionable. An instance where protein detection could be implemented in this study is to verify the TrkB expression in both SH-SY5Y cell lines. It would also be informative to compare the multiplex results from the regular SH-SY5Y cell line with minimal TrkB expression to a TrkB-knockout or knockdown cell line with confirmed receptor non-functionality. Nonetheless, by employing bioinformatics we have proved the existence of a wealth of hidden information in the transcriptome database.

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Even with our careful deliberation during the selection of 20 neurotrophin- specific transcripts, only 65% of these transcripts responded significantly to BDNF in the TrkB-overexpressing cell line. Moreover, the transcriptional changes were often relatively small with the magnitude of < 30-50%, bar a few exceptions, i.e., EGR1 and TRIB2. Coupled with the small sample size, proving statistical significance can be doubtful and evasive. A simple solution is to update the transcript set, and if possible, the sample size should be increased. A robust and well-defined response set will enhance the sensitivity of the assay for the classification of distinct compound classes, and the utility of the transcript set to discern the presence or absence of biased agonism/antagonism. It would be interesting to test our multiplex panel on the samples collected from primary neuron cultures or brain homogenates and compare the results with the findings obtained from the immortalised cell specimens. In addition, one common issue between ANA-12 and A3 is that both small molecules are active in a submicromolar range in vitro which may be accountable for some degree of non- selectivity and compromised water solubility. Derivatives of the compounds with better efficacy and pharmacokinetics would be more ideal.

We have also carried out a preliminary investigation into the utility of machine learning (ML) in predicting novel compounds that might exhibit Trk binding characteristic. The outcome appeared quite promising, however we had a limited number of known compounds as the training set, which imposed restraints on the chemical space from which useful information could be extracted. Moreover, the learning process relies on big data to establish sufficient stability and reliability. One inherent issue with machine learning and other related learning-based algorithms, e.g., deep learning, and artificial intelligence, is that they behave like a black box, essentially lacking the transparency. Deciphering how the outcomes have been derived is often not feasible. To get the best of both worlds, combining ML with knowledge discovery algorithms (KDD) which allow knowledge extraction from large collections of data might be the best way forward (Gardiner and Gillet, 2015). Further, similarity assessment is strictly based on an SAR assumption. An instance where this assumption is violated is the similarity paradox where small structural changes drastically alter the biological activity of a molecule (Bajorath, 2002). A well-known example is the apparent lack of a clear structure-odor relationship in the olfactory system (Turin and Yoshii, 2003).

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Technically, careful consideration must be taken during algorithm selection, feature selection, and parameter optimisation as prediction accuracy is largely determined by these specifications (Varnek and Baskin, 2012). It is also important to be aware of all model assumptions as, often when the model supposedly fails, it is either due to the inherent characteristics of the dataset, or more likely a result of extreme violations of the key assumptions through user ignorance, or incorrect/insufficient tuning and diagnostics (Tarca et al., 2007).

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GENERAL DISCUSSIONS

One of our main objectives was to assess the utility of transcriptional characterisation as a screening tool for novel antagonist candidates to in part bridge the gap between phenotypic and target-based approach, and to enable elucidation of the antagonistic mechanism at play. To accomplish this, we have carried out transcriptional profiling of two different classes of compounds, a Nav1.7-specific inhibitor (ProTx-II), and putative antagonists of the TrkB receptor (ANA-12 and A3). The effects of these compounds in the cell lines have been condensed down to the bespoke transcripts that exhibited a significant response. We noticed that in response to ProTx-II treatment the top differentially-expressed transcripts are mostly clustered in pathways that are involved in cell growth and motility, consistent with the supposed Nav1.7 role in cancer metastasis. Unfortunately, the number of ProTx-II responsive transcripts were rather small, and we were not able to gather a full transcript panel for subsequent validation. Nonetheless, this initial outcome suggests that the idea of bespoke transcripts for a specific response is valid, somewhat in line with the ‘landmark gene’ concept adopted by the LINCS L1000 project (Duan et al., 2014). We further tested this idea by carrying out a database analysis to identify neurotrophin-responsive genes with consistent and detectable transcriptional changes in independent experiments and sample types. In tandem, 20 of the robust transcripts were nominated for probe synthesis in a multiplex assay.

We found that BDNF could induce transcriptional changes in most of the chosen transcripts, and that these transcripts also responded similarly to ANA-12 and A3, suggesting a shared mechanism of action. Moreover, it appears that BDNF generates a response pattern that is opposed to that evoked by the putative TrkB antagonists. This observation essentially supports the notion that different compound classes can be distinguished by their distinctive transcriptional fingerprints. Notably, we found that at best ANA-12 and A3 are modest antagonists as they partially blocked the transcriptional responses elicited by the activated TrkB receptor. Further, ANA-12 appears to be TrkB non-selective and non-specific at the micromolar working concentrations. Now that we have established the foundation of the transcriptional screen, with some updates to the original BDNF-responsive panel, other known or novel Trk antagonists could be screened and their profiles assembled and compared for any similarities or differences. 194

We also expect the multiplex screen to lend itself nicely to later-stage screening where ADMET properties are reviewed. This could be done simply by upgrading the transcript panel to include genes that are representatives of stress, apoptosis, or inflammatory responses.

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APPENDIX

A. Voltage clamp recordings of ProTx-II activity ProTx-II activity was determined by measuring inward currents in the 24 h- induced Nav1.7-HEK293 cells using the voltage clamp technique. All electrophysiological recordings were carried out by Dr Ramin Raouf (Wolfson CARD, KCL). Inward currents were recorded over 20-consecutive 5 mV steps from -60 to 40 mV; with the maximal absolute value of ~ 800 pA (Figure 52A). In the presence of 50 nM ProTx-II (Smartox), there was a reduction in the inward current in all three cells recorded. Specifically, the magnitude of the reduction grew over time. Figure 52B shows three representative traces of the inward current in one of the cells before and after ProTx-II application. At 0 mV, the current was reduced by ~15% and ~75% after 2 and 5 min treatment, respectively. The time trace illustrates the kinetics of ProTx-II block (Figure 52C). At -20 mV holding voltage, the inward current exhibited a gradual reduction over time. 20% reduction was observed at 120 sec (trace 1), 50% at 280 sec (trace 2), and 60% at 360 sec (trace 3). ~20-25% residual current remained as the decrease tapered off after ~400 sec. The recordings effectively confirm ProTx-II activity against Nav1.7 channels expressed in our Tet-inducible hNav1.7-HEK293 cell line.

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A

B

C

Figure 52. Voltage-clamp recordings of net inward current in induced hNav1.7-HEK293 cell.

(A) Nav1.7 current after 24 h tetracycline treatment. Left; Representative traces of net inward current obtained from voltage-clamp recordings of a 24 h hNav1.7-induced cell. Right; Illustration of 20 consecutive steps from -60 to 40 mV. (B) Nav1.7 I-V relationship. Inward currents were measured in the hNav1.7-expressing cell before and after incubation with 50 nM ProTx-II using 5 mV-stepping voltages from -60 to 40 mV. The traces illustrate the currents after 2 and 5 min ProTx-II treatment. ProTx-II was applied to 3 cells. All of them exhibited a reduction in current. (C) Kinetics of ProTx-II block at -20 mV. The currents progressively decreased and tapered off at around 20% residual after 450 sec. Courtesy of Dr Ramin Raouf.

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B. Probe set information of neurotrophin multiplex set The probe sets were designed by Quantigene specialists per request.

Accession No. Symbol Sequence Probe set region Specificity Length NM_006244 PPP2R5B 2766 894-1265 Human PPP2R5B NM_002529 NTRK1 2663 1040-1444 Human NTRK1, all three variants NM_000411 HLCS 6019 759-1330 All transcripts of human HLCS NM_021643 TRIB2 4408 2915-3499 Human TRIB2, both transcript variants NM_004958 MTOR 8733 3390-3905 Human MTOR NM_004721 MAP3K13 9945 1509-2000 All transcripts of human MAP3K13 NM_016157 TRO 2321 1787-2293 All transcripts of human TRO NM_022652 DUSP6 2404 91-643 Human DUSP6 variant 1 and 2 NM_000859 HMGCR 4589 1917-2483 Human HMGCR, both transcript variants NM_207123 GAB1 7926 752-1287 Human GAB1 transcript variant 1 and 2 NM_002228 JUN 3338 737-1158 Human JUN NM_019058 DDIT4 1752 68-484 Human DDIT4 NM_004322 BAD 1240 33-470 Human BAD NM_018416 FOXJ2 5499 1285-1709 Human FOXJ2 NM_023110 FGFR1 5917 5424-5864 Human FGFR1, all nine variants NM_006180 NTRK2 5608 1146-1637 Human NTRK2, all six variants NM_003253 TIAM1 7218 474-897 Human TIAM1 NM_006270 RRAS 1013 417-864 Human RRAS NM_002757 MAP2K5 2355 886-1529 All transcripts of human MAP2K5 NM_001220 CAMK2B 4586 3862-4221 Human CAMK2B, all ten variants NM_001968 EIF4E 4749 17-515 Human EIF4E NM_001964 EGR1 3136 558-1034 Human EGR1 NM_002167 ID3 1252 226-757 Human ID3 NM_000146 FTL 889 134-529 Human FTL NM_002184 IL6ST 9071 104-785 Human IL6ST

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