Mechanistic Insights Into Arsenite Oxidase and Implications for Its Use As a Biosensor

Mechanistic insights into arsenite oxidase and implications for its use as a biosensor

Cameron Misha Manson Watson

UCL

Submitted for the degree of Doctor of Philosophy

I, Cameron Misha Manson Watson, confirm that the work presented in this thesis is my own. Where information has been derived from other sources, I confirm that this has been indicated in the thesis.

Signed:

Dated:

Abstract

Arsenic is an environmental toxin which poses a threat to >140 million people worldwide. The respiratory enzyme arsenite oxidase (Aio) from various bacteria couples the oxidation of arsenite to the reduction of electron acceptors. The Aio from Rhizobium sp. str. NT-26 is in development as an arsenic biosensor. Aio consists of a large subunit (AioA), containing a molybdenum centre and a 3Fe-4S cluster, and a small subunit (AioB) containing a Rieske 2Fe-2S cluster.

The first objective was to identify the rate-limiting step of Aio catalysis to establish if the rate could be improved. The rate-limiting step was found to be electron transfer from the 2Fe-2S cluster to cytochrome c by using stopped-flow spectroscopy, steady- state kinetics and isothermal titration calorimetry. An AioB mutant (F108A) specifically reduced activity with cytochrome c by affecting electron transfer. The AioB subunit was expressed alone and was able to weakly associate with cytochrome c suggesting that the AioA subunit is important in the cytochrome c interaction. Unfortunately, the AioA subunit was unstable alone so its cytochrome c interaction was not characterised.

Most AioB possess a disulphide bridge proposed to be involved in electron acceptor selectivity. The NT-26 Aio does not possess a disulphide bridge while that of Alcaligenes faecalis does. Site-directed mutagenesis introduced and removed a disulphide bridge into the NT-26 and Alcaligenes faecalis Aio respectively. Presence of the disulphide bridge increased activity with azurin and decreased activity with cytochrome c.

The oxidation of antimonite by Aio was examined to determine how the presence of antimony might affect biosensor performance and to assess if Aio could be used as an antimonite biosensor. Antimonite was found to be a potent, competitive inhibitor of Aio because the product of antimonite oxidation dissociates slowly from the active site. The impact of this on the biosensor’s viability is discussed.

Impact Statement

The rate-limiting step of Aio catalysis has been shown to be electron transfer to the electron acceptor, cytochrome c, and this work has been published. Cytochrome c serves as a mediator between the Aio and the electrode of the arsenic biosensor. It was found that it is unlikely that this interaction can be improved and that only a 50% increase could be achieved before further engineering of the enzyme would be required meaning that the wild-type Aio should be used for the biosensor.

The role of the AioB disulphide bridge in electron acceptor specificity was explored. The AioB disulphide bridge has previously been suggested to be involved in electron acceptor specificity and the data presented in this thesis provides experimental evidence confirming it which is expected to be published. The expression system developed for AioB developed could also be useful for studies into Rieske function. The phylogeny of the Rieske proteins suggests that the disulphide bridge was substituted by a hydrophobic, aromatic residue in the Aio of the Alphaproteobacteria, possibly to facilitate electron transfer to cytochrome c. The

Rieske protein of the bc1 complex uses cytochrome c1 as an electron acceptor but possesses a disulphide bridge. The results of this study could be used to inform mutagenesis and engineering studies with the bc1 complex.

A heterologous expression system for the Pseudomonas aeruginosa azurin has been developed. Azurin has been shown to have potential clinical significance in the treatment of malaria, HIV and some cancers. Azurin is expensive to purchase and the expression system described in this thesis could facilitate further research into its clinical potential or marketisation by providing a cheaper source of the protein.

The NT-26 Aio has been shown to be able to oxidise antimonite and this process was characterised in this thesis. This is the first study into the interaction between a molybdoenzyme and antimony and is expected to be published. It was found that the previously reported kinetics values for antimonite oxidation were inaccurate and corrected values have been presented. Antimonite was found to act as a potent inhibitor of arsenite oxidation by the Aio and the mechanism for this was

4 characterised, demonstrating that the oxidised antimony product dissociates slowly from the active site, retarding arsenite oxidation. This finding demonstrates that 1) the NT-26 Aio is inappropriate as a biosensor for antimonite as the rate of catalysis is too slow; and 2) that the Aio may not be able to be used as a biosensor in waters with high antimony concentrations such as in industrial waste or fracking sites. A market analysis is presented and discusses the benefits of the Asian versus North American markets. Considering the findings of this thesis, the Asian market is considerably more desirable than the North American as antimony pollution appears to be considerably lower.

Table of Contents

Abstract 3

Impact Statement 4

Acknowledgements 20

Abbreviations 21

1.1 Arsenic and antimony 24

1.1.1 Arsenic 24

1.1.2 Antimony 28

1.1.3 Arsenic and antimony in biology 30

1.2 Molybdenum enzymes 34

1.2.1 Xanthine Oxidase family 35

1.2.2 Sulphite oxidase family 36

1.2.3 Mo/W-bis pterin guanosine dinucleotide family 37

1.2.4 Role of pyranopterins 38

1.3 Rieske proteins 39

1.3.1 Rieske families 40

1.3.2 Rieske Evolution 43

1.4 Arsenite oxidase 43

1.4.1 The Aio enzyme subunits and cofactors 45

1.4.2 Evolution of arsenite oxidase 47

1.4.3 The arsenite oxidase gene cluster 49

1.4.4 Electron acceptors to the arsenite oxidase 50

1.4.5 Catalytic mechanism of the arsenite oxidase 52

1.4.6 Kinetics of Aio 54

1.5 Biosensors 56

1.5.1 Arsenic Biosensors 58

1.5.2 Business case for Aio as an arsenic biosensor 61

1.6 Protein engineering 63

1.6.1 Rational Design 63

1.6.2 Directed Evolution 64

1.7 Aims of this study 65

2.1 Introduction 67

2.1.1 Electron transport in arsenite oxidase 67

2.1.2 Stopped-flow spectroscopy 69

2.1.3 Isothermal titration calorimetry 70

2.1.4 Aims 72

2.2 Methods 73

2.2.1 WT and F108A Aio expression systems 73

2.2.2 Conformation of presence of aioBA genes in recombinant plasmids by restriction digestion and sequencing 73

2.2.3 Cloning of the aioB gene for heterologous expression in E. coli 74

2.2.4 Aio expression and purification 74

2.2.5 Expression and purification of AioB 75

2.2.6 Protein concentration determination 76

2.2.7 UV-visible spectroscopy 76

2.2.8 Inductively coupled plasma mass spectrometry 76

2.2.9 Polyacrylamide gel electrophoresis 77

2.2.10 Steady-state kinetics 77

2.2.11 The oxidised and reduced spectra of horse heart cytochrome c 78

2.2.12 Stopped-flow spectroscopy of Aio with arsenite and cytochrome c 79

2.2.13 Stopped flow kinetics of AioB versus cytochrome c 80

2.2.14 Isothermal titration calorimetry of Aio versus cytochrome c 80

2.2.15 ITC of AioB versus cytochrome c 81

2.2.16 Statistical Analysis 82

2.3 Results and Discussion 83

2.3.1 Confirmation of the recombinant plasmids containing the aioBA genes 83

2.3.2 Purification and co-factor determination of the WT and F108A Aio 84

2.3.3 The visible absorbance spectra of WT Aio 85

2.3.4 Arsenite steady-state kinetics of WT Aio with DCPIP and cytochrome c 86

2.3.5 The reductive-half reaction of Aio 92

2.3.6 The oxidative-half reaction of Aio 95

2.3.7 The interaction of the Aio with cytochrome c 96

2.3.8 The mechanism of Aio catalysis 100

2.3.9 The effect of the AioB-F108A mutation on Aio Activity 103

2.3.10 The effect of the F108A mutation on electron transfer rates 106

2.3.11 The role of the AioB-F108 residue in electron transfer 109

2.3.12 Investigation into the interaction of AioB with horse heart cytochrome c 112

2.3.13 Cloning of the aioB gene 113

2.3.14 Expression and characterisation of heterologously expressed AioB 114

2.3.15 The interaction of AioB with cytochrome c 117

2.3.16 AioB heterologous expression system in E. coli 121

2.3.17 Engineering higher catalytic rates for Aio 122

2.3.18 Conclusions 123

3.1 Introduction 126

3.1.1 Structural diversity in arsenite oxidase Rieske proteins 127

3.1.2 Electron acceptor specificity of arsenite oxidases 128

3.1.3 Aims 129

3.2 Methods 131

3.2.1 Conformation of expression systems by restriction digest and sequencing 131

3.2.2 Azurin expression and purification 131

3.2.3 Aio expression and purification 132

3.2.4 Concentration determination 132

3.2.5 Characterisation of enzyme preparation purity, co-factor content and UV-visible spectra 133

3.2.6 Structural Alignment and electrostatic surface generation 133

3.2.7 Steady-state Kinetics 133

3.2.8 Statistical Analysis 135

3.2.9 Sequence alignment and phylogenetic tree reconstruction 135

3.3 Results 136

3.3.1 Expression and purification of azurin 136

3.3.2 Characterisation of heterologously expressed Pseudomonas aeruginosa azurin 137

3.3.3 Expression of WT and mutant arsenite oxidase from NT-26 and A. faecalis 139

3.3.4 Characterisation of WT and mutant arsenite oxidase from NT-26 and A. faecalis 141

3.3.5 Steady-state kinetics of WT and mutant NT-26 and A. faecalis arsenite oxidases 152

3.3.6 Sequence alignment and phylogenetic reconstruction of AioB, bc1 and

b6f Rieske proteins 158

3.4 Discussion 161

3.4.1 Investigation into the mechanism by which the disulphide bridge and phenylalanine/glycine effect electron acceptor specificity 161

3.4.2 Steady-state kinetics of arsenite oxidases 165

3.4.3 The effect of the disulphide bridge on enzyme stability 166

3.4.4 The evolution of the AioB-electron acceptor interaction 166

3.4.5 Azurin expression systems 169

3.4.6 Key findings, conclusions and next steps 170

4.1 Introduction 173

4.1.1 Demand for an antimony biosensor 173

4.1.2 Aims 174

4.2 Methods 175

4.2.1 Steady-state Kinetics 175

4.2.2 Reductive Titration 175

4.2.3 Extended X-ray absorbance fine structure of the antimonyl tartrate-Aio complex 176

4.2.4 Stopped-flow kinetics 177

4.3 Results 178

4.3.1 Re-analysis of Aio antimonyl tartrate steady-state kinetics 178

4.3.2 Reductive Titration of Aio by arsenite and antimonyl tartrate 179

4.3.3 Aio cytochrome c kinetics with antimonite as an electron donor 181

4.3.4 Comparison of the pH profiles of Aio turnover of arsenite and antimonyl tartrate with cytochrome c as an electron acceptor 182

4.3.5 Inhibition of arsenite oxidation by antimonyl tartrate 183

4.3.6 Extended X-ray absorption fine structure analysis of the interaction of antimonyl tartrate with the Aio active site 185

4.3.7 Stopped-flow kinetics of enzyme reduction by antimonyl tartrate 186

4.3.8 Stopped-flow kinetics of the reduction of cytochrome c by arsenite and antimonyl tartrate catalysed by multiple turnovers of Aio 188

4.3.9 The effect of salt on Aio oxidation of antimonyl tartrate 190

4.4 Discussion 195

4.4.1 The rate-limiting step of antimonyl tartrate oxidation by Aio 195

4.4.2 Biphasic kinetics of antimonyl tartrate oxidation by Aio 197

4.4.3 Mechanism of antimony inhibition 199

4.4.4 Antimonite as an electron donor and inhibitor of Aio – effect on the range of the arsenic biosensor 202

4.4.5 The effect of salt on Aio kinetics with antimonyl tartrate 204

4.4.6 Aio as a sensor for antimony 205

4.4.7 Key findings and conclusions 207

5.1 Summary of findings 210

5.2 The development of the arsenic biosensor 213

5.3 Development and demand for an antimony biosensor 214

5.4 Enzyme Engineering 216

5.4.1 Rational Design of Aio 216

5.4.2 Directed evolution 220

5.4.3 Engineering: risk versus reward 221

5.5 Future Directions 222 11

Appendix A - Arsenic biosensor market report 224

A.1 - Global Arsenic 224

A.2 - Introduction to the product 227

A.3 - Market Analysis 228

A.4 - Pricing strategies and distribution channels 242

A.5 - Potential Markets to expand into 245

A.6 - Conclusions 252

Appendix B – Determination of protein concentrations using Bradford assays 254

Appendix C – Calibration of size exclusion chromatograph columns 256

C.1 - 200 Superdex 256

C.2 - 75 Superdex 257

Appendix D - AioA Expression 258

D.1 - Methods 258

D.2 - Results 259

D.3 - Discussion 263

Appendix E – Accession numbers for sequence alignment 266

Appendix F – 16S rRNA Phylogeny 268

Appendix G – Additional Aio versus antimonyl tartrate or arsenite titration repeats 269

Appendix H – Arsenite steady-state kinetics of the AioA-S102C Aio mutant with cytochrome c as an electron acceptor 270

Watson C, Niks D, Hille R, Vieira M, Schoepp-Cothenet B, Marques AT, Romão MJ, Santos-Silva T & Santini JM (2017). Electron transfer through arsenite oxidase: Insights into Rieske interaction with cytochrome c. Biochim Biophys Acta - Bioenerg 12

1858, 865–872.

Badilla C, Osborne TH, Cole A, Watson C, Djordjevic S & Santini JM (2018). A new family of periplasmic-binding proteins that sense arsenic oxyanions. Sci Rep1–12.

List of figures

Figure 1.1: Structure of elemental arsenic. 25 Figure 1.2: Structures of compounds that the three oxidation states of arsenic is commonly found in. 25 Figure 1.3: Example of the colour reference chart for a standard Gutzeit method field arsenic test kit. 28 Figure 1.1: Incorporation of the Aio into an electron transport pathway. 32

Figure 1.5: Generic core of the xanthine oxidase family. 36 Figure 1.6: Generic core of the sulphite oxidase family. 37 Figure 1.7: Generic core of the Mo/W-bis pterin guanosine dinucleotide family. 38 Figure 1.8: Dihydro and tetrahydro states of the pyranopterin. 39 Figure 1.9: Representative structures of Rieske families. 41 Figure 1.10: Structures of high potential and AioB RIeske proteins. 42 Figure 1.11: Crystal structure of Rhizobium sp. str. NT-26 Aio. 45 Figure 1.12: Cofactors of Aio. A) Mo-cofactor showing a central Mo atom coordinated by two molybdenum guanine dinucleotide pterins. 47 Figure 1.13: Phylogenetic tree showing the evolutionary relationship between Aio, Arx, Arr, nitrate reductase (Nar) and polysulfide reductase (Psr). 48 Figure 1.14: Arsenite oxidation gene cluster. 50 Figure 1.15: Proposed reaction mechanism at the Aio active site in which the Mo- cofactor oxidises arsenite to arsenate. 53 Figure 1.16: Schematic representation of the sequence of reactions that occurs in mediated and unmediated electron transfer. 57 Figure 2.1: Proposed electron transport pathway for the NT-26 Aio. 68 Figure 2.2: Diagram of stopped-flow equipment. 70 Figure 2.3: Diagram of isothermal titration calorimetry equipment. 72 Figure 2.4: pPROEX-Htb - aioBA recombinnant plasmid. 73 Figure 2.5: Photos of restriction digest and gel electrophoresis of A) Aio WT and B) F108A confirming the presence of the aioBA insert in both expression systems. 84 Figure 2.6: A) Gel filtration chromatograph of the WT NT-26 Aio. B) Gel filtration chromatograph of the NT-26 F108A mutant. 85

Figure 2.7: A) The oxidised and reduced spectra of heterologously expressed NT-26 Aio in Tris HCl buffer pH 8. B) The difference spectrum of oxidised minus reduced for NT-26 Aio. 86 Figure 2.8: Steady-state kinetics of arsenite oxidation by the heterologously expressed NT-26 Aio with DCPIP as the artificial electron acceptor. 88 Figure 2.9: Steady-state kinetics of arsenite oxidation by the Aio with horse heart cytochrome c as the electron acceptor. 89 Figure 2.10: Oxidised and reduced spectra of horse heart cytochrome c determined in Tris-HCl pH 8 buffer. 91 Figure 2.11: Steady-state kinetics cytochrome c reduction by the Aio in the presence of 2500 μM arsenite. 92 Figure 2.12: Stopped-flow spectroscopy monitoring the reduction of the Aio by arsenite. 93 Figure 2.13: Stopped-flow spectroscopy monitoring the reduction of horse heart cytochrome c by the Aio and arsenite. 96 Figure 2.14: ITC of the Aio titrated against cytochrome c. 97 Figure 2.15: Double reciprocal plots of Aio steady state kinetics with arsenite and cytochrome c. 101 Figure 2.16: Catalytic cycle for the Aio with arsenite and cytochrome c. 103 Figure 2.17: The F108A mutant steady-state kinetics of arsenite with cytochrome c as an electron acceptor. 104 Figure 2.18: The F108A mutant steady-state kinetics of cytochrome c in the presence of 2500 μM arsenite. 105 Figure 2.19: The F108A mutant steady-state kinetics of arsenite with DCPIP as an electron acceptor. 106 Figure 2.20: Stopped-flow spectroscopy monitoring the reduction of the F108A mutant by arsenite. 107 Figure 2.21: Stopped-flow spectroscopy monitoring the reduction of horse heart cytochrome c by the F108A mutant and arsenite. 108 Figure 2.22: Structural alignment of WT Aio (green) and F108A (purple). 110 Figure 2.23: ITC of the F108A mutant titrated against cytochrome c. 111

Figure 2.24: Photos of restriction digest and gel electrophoresis confirming the presence of the aioB gene in the pProEX-Htb+ plasmid. 113 Figure 2.25: Purification and characterisation of the heterologously expressed AioB. A) Gel filtration chromatograph showing two peaks containing the AioB representing two oligomeric states. B) SDS polyacrylamide gel showing the results of the size exclusion chromatography illustrating that both peaks contain AioB. C) UV-visible spectra of AioB representing both peaks from size exclusion chromtaography normalised such that Abs280 = 1.0. 115 Figure 2.26: A) The oxidised and reduced spectra of heterologously expressed NT-26 AioB in Tris-HCl buffer pH 8. B) The difference spectrum of oxidised minus reduced for NT-26 AioB. 117 Figure 2.27: ITC of the AioB titrated against cytochrome c. 118 Figure 2.28: Stopped-flow spectroscopy monitoring the reduction of horse heart cytochrome c by reduced AioB. 119 Figure 2.29: Heterotetrameric structure of Aio. 121 Figure 3.1: Coordination of the Rieske cluster. 126 Figure 3.2: Structural alignment of Rhizobium NT-26 (green) and A. faecalis (red) Aio demonstrates high structural homology. 128 Figure 3.3: Agarose gel electrophoresis photo of pET22b+ with azu insert. 136 Figure 3.4: A) Gel filtration chromatograph of heterologously expressed P. aeruginosa azurin. B) SDS-polyacrylamide gel heterologously expressed P. aeruginosa azurin. 137 Figure 3.5: A) Visible spectra of oxidised and reduced heterologously expressed azurin. B) Difference spectrum of heterologously expressed azurin. 138 Figure 3.6: Agarose gel electrophoresis photo showing restriction digest of pProEx- Htb+ + aioBA. 139 Figure 3.7: A) Gel filtration chromatograph of the A. faecalis WT Aio. B) Gel filtration chromatograph of the A. faecalis C65F/C80G mutant. C) Photo of an SDS polyacrylamide gel of Aio enzymes. 140 Figure 3.8: Structural alignment of the Rieske cluster of NT-26 (green) and A. faecalis WT (red) Aio (green). 141

Figure 3.9: A) Structural alignment of the Rieske cluster of NT-26 WT Aio (green) and NT-26 F108C/G123C mutant (blue). B) Structural alignment of the Rieske Cluster of A. faecalis WT Aio (red) and NT-25 F108C/G123C mutant (blue). 142 Figure 3.10: A) Structural alignment of the Rieske cluster of A. faecalis WT Aio (red) and A. faecalis C65F/C80G mutant (beige). B) Structural alignment of the Rieske cluster of NT-26 WT Aio (green) and A. faecalis C65F/C80G mutant (beige). 143 Figure 3.11: Temperature profile of A) NT-26 WT and F108C/G123C activity with DCPIP and arsenite; B) A. faecalis WT and C65F/C80G activity with DCPIP and arsenite. 145 Figure 3.12: Molecular surface of P. aeruginosa azurin coloured based on electrostatic potential. 147 Figure 3.13: Molecular surface of horse heart cytochrome c coloured based on electrostatic potential. 148 Figure 3.14: Molecular surface of the NT-26 WT and F108C/G123C Aio coloured based on electrostatic potential. 150 Figure 3.15: Molecular surface of the A. faecalis WT and C65F/C80G Aio coloured based on electrostatic potential. 151 Figure 3.16: Michaelis-Menten fit of steady-state kinetics results with azurin as an electron acceptor and excess arsenite. 153 Figure 3.17: A) Michaelis-Menten fit with cytochrome c as the electron acceptor and excess arsenite. 155 Figure 3.18: Michaelis-Menten fit of the NT-26 WT and the F108C/G123C mutant with 20 μM cytochrome c and arsenite. 156 Figure 3.19: A) Michaelis-Menten fit of steady-state kinetics results with varying concentrations of arsenite and DCPIP as an electron acceptor. 157 Figure 3.20: Conservation of cysteine residues that form the disulphide bridge in Rieske proteins as shown by multiple alignment of protein sequences. 158

Figure 3.21: Maximum-likelihood phylogenetic tree of AioB, bc1 Rieske and b6f Rieske.

Rooted with archaeal bc1 from S. tokodaii str. 7. 160 Figure 3.22: Comparison of horse heart cytochrome c and A. aeolicus cytochrome c555. 168

Figure 4.1: Structures of A) antimonyl tartrate (adapted from Wijeratne et al., 2010) and B) arsenite ion . 176 Figure 4.2: Fits of antimony steady state rates. Single Michaelis-Menten (Blue) and Double Michaelis-Menten.(red). 179 Figure 4.3: Reductive titrations of Aio versus A) arsenite and B) antimonyl tartrate. 180 Figure 4.4: Steady-state kinetics of cytochrome c and antimonyl tartrate fit with a linear function. 182 Figure 4.5: pH profile of Aio activity with arsenite and antimonyl tartrate as substrates. 183 Figure 4.6: Michaelis-Menten fits of arsenite kinetics with Aio and cytochrome c in the presence of 0.1 μM Sb, 1 μM Sb and no Sb. 185 Figure 4.7: Mo K-edge EXAFS data of Aio saturated with antimonyl tartrate. 186 Figure 4.8: Stopped-flow spectroscopy of WT Aio reduced by antimonyl tartrate at 25oC. 188 Figure 4.9: A) Stopped flow spectroscopy of the reduction of cytochrome c by antimonyl tartrate catalysed by multiple turnovers of WT Aio. 189 Figure 4.10: Effect of 100 mM concentrations of different salts on the profile of the antimonyl tartrate/Aio activity assay. 191 Figure 4.11: A) Effect of varying concentrations of ammonium sulphate on the profile of the arsenite/Aio assay. 193 Figure 4.12: A) Effect of varying concentrations of ammonium sulphate on the profile of the antimonyl tartrate/Aio assay. 193 Figure 4.13: Crystal structure of the antimony and molybdenum cofactor of the Aio/antimony complex. 201

List of tables

Table 1.1: Electron acceptor specificity of different Aio. 51 Table 2.1: Primers used for the cloning of aioB in pProEX-Htb+. 74 Table 2.2: Summary of kinetic rate constant for the WT Aio. 96 Table 2.3: Rate constants of WT and F108A Aio. 108 Table 3.1: Metal content of WT and mutant Aio as determined by ICP-MS 144 Table 3.2: Reduction potentials of NT-26 and A. faecalis Aio and disulphide bridge mutants. 146 Table 4.1: Rate acceleration of Aio antimonyl tartrate assays with cytochrome c as an electron acceptor in the presence of 100 mM salt. 192

Acknowledgements

I would like to thank Joanne Santini for conceiving the project and supervision throughout the PhD; Chris Kay for secondary supervision; Katherine Thompson for acting as thesis chair, support and advice; Thomas Osborne for supervision, training, mentorship and advice; for Russ Hille and Dimitri Niks for their assistance, hospitality and advice regarding stopped-flow spectroscopy and molybdoenzyme chemistry in general while visiting University of California, Riverside; Graham George for advice and EXAFS data with simulations; Barbara Schoepp-Cothenet for advice and EPR data; Teresa Santos-Silva, Marta Viera and Maria Romão for advice and X-ray crystal structures; Tony Cass for biosensor and biochemical advice; Stanislav Strekopytov for ICP-MS experiments; Mohammad Talha Arooz and Tina Daviter for advice on ITC; Thomas Warelow for proofreading, training and advice. Many thanks to Sarah Jones and Simona Della Valle for help and encouragement.

I would like to acknowledge financial support from the Biotechnological and Biological Sciences Research Council, The Biochemical Society, The Microbiological Society, The Bogue Fellowship and the UCL Graduate School.

Abbreviations

Aio Arsenite oxidase Amp Ampicillin Arr Arsenate reductase Arx Anaerobic arsenite oxidase As Arsenic ATP Adenosine triphosphate bis-MGD bis-molybdopterin guanine dinucleotide bp base pairs BSA Bovine serum albumin CHES N-Cyclohexyl-2-aminoethanesulfonic acid DCPIP Dichlorophenolindolphenol DFT Density functional theory DMSO Dimethylsulfoxide DNA Deoxyribonucleic acid

Em Midpoint potential EPR Electron paramagnetic resonance EXAFS Extended X-ray Absorbance Fine Structure HEPES 4-(2-hydroxyethyl)-1-piperazineethanesulfonic acid ICP-MS Ionically coupled plasma mass spectrometry IPTG Isopropyl β-D-1-thiogalactopyranoside kDa kiloDalton LB Lysogeny broth LUCA Last universal common ancestor MCL Maximum contaminant level MES 2-(N-morpholino)ethanesulfonic acid Mo Molybdenum mV milliVolts NADH Nicotinamide adenine dinucleotide Nar Nitrate reductase O Oxygen PCR Polymerase chain reaction PGD Mo/W-bis pterin guanosine dinucleotide family

Psr Polysulfide reductase RMSD Root-mean-Square deviation RNA Ribonucleic acid Sb Antimony Sec General secretion SRCD Synchrotron radiation circular dichroism Tat Twin arginine translocation secretory system Tris Tris(hydroxymethyl)aminomethane UV Ultra violet W Tungsten WHO World Health Organisation WT Wild-type XAS X-ray absorption spectroscopy

Chapter 1

General Introduction

1.1 Arsenic and antimony

1.1.1 Arsenic

Arsenic is toxic. It has earnt a reputation as one of the most infamous elements in the periodic table. Arsenic has been used for millennia to settle old scores, “resolve” disputes and clear the way for personal advancement. Arsenic has gone by several names including “inheritance powder” and the “poison of kings” due to its use by impatient heirs. It was a popular method of murder from the time of the Roman Empire well into the Renaissance due to the fact that the symptoms of arsenic poisoning were poorly defined and arsenic is odourless, colourless and tasteless when mixed into food and drink. Arsenic has claimed many notable historical figures including Napoleon Bonaparte, George III of Great Britain and Francesco I de’Medici. It dropped out of favour with assassins in 1836 when a test for low levels of arsenic was developed (Nriagu, 2002).

Arsenic has been used for less nefarious means such as in agriculture to fend off insects, fungi and bacteria from damaging crops as well as a feed additive for pigs and chickens to increase weight gain and stave off illness (Nachman et al., 2005). Arsenic has found use as an anti-cancer therapy and a primitive treatment for syphilis (Gibaud & Jaouen, 2010). Gallium arsenide is an important semiconducting material used to make integrated circuits that are much faster than the conventional silicon ones (Bagshaw, 1995). In Victorian times arsenic was mixed with vinegar and chalk before being drunk by wealthy women. The pallid complexion it gave them told the world they did not have to toil in the fields and were therefore of high status – another example of arsenic being used to improve one’s station only this time without murder (Whorton, 2010).

Arsenic is a group 15 metalloid located just below phosphorous on the periodic table and constitutes 0.00001% of the Earth’s crust making it the 20th most abundant element. Pure arsenic can exist in three allotropes: yellow (Figure 1.1A), grey (Figure 1.1B) and black (though black arsenic has never been isolated) (Norman, 1997). It has

24 three other oxidation states that it is commonly found in: arsine (-3), arsenite (+3) and arsenate (+5) (Figure 1.2).

Figure 1.1: Structure of elemental arsenic. A) Yellow arsenic: exists as a As4 in a trigonal pyramidal structure. B) Grey arsenic: exists as sheets of hexagonal As lattices, the bulk solid is made of up multiple layers loosely held together by electrostatic forces.

Figure 1.2: Structures of compounds representing the three oxidation states that arsenic is commonly found in. A) Arsine (-3). B) Arsenite (+3). C) Arsenate (+5). The pKa values for arsenate are 2.19, 6.94 and 11.5. The pKa values for arsenate are 2.19,

6.94 and 11.5. The pKa value for arsenite is 9.2 (Greenwood & Earnshaw, 1997).

1.1.1.1 Arsenic in the environment Approximately 99% of the Earth’s arsenic is found in rocks and minerals the most common of which being arsenopyrite (FeAsS). The remaining 1% is in waters, sediment and the atmosphere (Nriagu, 2002). The weathering of arsenic minerals is a natural source of arsenic contamination however anthropogenic activities can also

25 release arsenic into aquatic environments. A particularly potent source of arsenic contamination is through mining activities as many valuable metals naturally occur in the form of arsenic minerals. The burning or smelting of these materials results in the release of arsenic into the environment (Smedley & Kinniburgh, 2002).

Arsenite and arsenate predominate in freshwater environments. Organoarsenic compounds, such as methylarsine, dimethylarsine, arsenobetaine and various arsenosugars are also prevalent in the environment, often being detected in ecosystem biota and excreta of farm animals (Oremland & Stolz, 2003) (Francesconi & Kuehnelt, 2002).

1.1.1.2 Soluble arsenic toxicity, mobility and health implications The speciation of soluble inorganic arsenic depends on various conditions such as pH, oxygen presence and reducing potential of the environment. In oxic waters, the arsenate form is more prevalent and behaves in a similar way to other trace oxyanions such as chromate or selenite in that it is soluble at neutral pH and is increasingly soluble as pH increases. Arsenate’s solubility makes it mobile in freshwater environments, however, it’s mobility is somewhat constrained by its tendency to adsorb minerals like alumina or ferrihydrite (Smedley & Kinniburgh, 2002). In anoxic waters, or reducing conditions, arsenite is more prevalent. Arsenite is soluble under reducing conditions at neutral pH (unlike other oxyanions like chromate and selenite, which precipitate). Arsenite also adsorbs less readily to alumina and ferrihydrite than arsenite and so tends to be more mobile. The exception is in acidic conditions when high concentrations of sulphide are present as the arsenite will precipitate as the mineral orpiment (As2S3). Arsenite and arsenate’s solubility and mobility make arsenic one of the most problematic environmental contaminants in the world (Smedley & Kinniburgh, 2002).

Arsenate is a structural analogue to phosphate. It enters the cell through the phosphate transport system and can be used by ATPase to arsenylate ADP the product of which spontaneously hydrolyses resulting in a futile cycle in the cell. Arsenite is more mobile and approximately one hundred times more toxic than arsenate. It enters cells through the glycerol transport system (Meng et al., 2004).

Arsenite binds to sulfhydryl groups of proteins and dithiols and so disrupts redox homeostasis (Ellis et al., 2001).

Arsenic polluted water currently poses a threat to over 150 million people worldwide (Shankar et al., 2014). The lethal doses of arsenite and arsenate are 3.5 mg/kg and 14 mg/kg body weight respectively (Yamauchi & Flower, 1994). Chronic exposure to sub-lethal levels of arsenic contaminated water has been linked to bladder, kidney, lung and skin cancers as well as skin lesions, hypertension, diabetes mellitus, neuropathy, pulmonary disease and peripheral vascular disease (Smith et al., 2000) (Flanagan et al., 2012). Due to the implications of chronic arsenic exposure the World Health Organisation (WHO) has set a maximum contaminant level for inorganic arsenic in drinking water at 10 μg/L (World Health Organisation, 2011). Arsenic exposure of human populations is predominant in India and Bangladesh where it affects 135 million people combined (Murcott, 2012). However, millions of people are affected in other countries including the USA, China and Mexico (Murcott, 2012).

1.1.1.3 Current methods for arsenic detection The detection of soluble arsenic is typically carried out by the highly sensitive laboratory methods atomic absorption spectroscopy, fluorescence spectroscopy (Anawar, 2012), inductively coupled plasma optical emission or mass spectrometry (Komorowicz & Baralkiewicz, 2011). Although these techniques are highly sensitive, typically being able to detect arsenic in the μg/L range, they require highly experienced personnel to operate and are expensive. A typical household in West Bengal may only have an annual income of $200 (Barnwal et al., 2017). These methods are therefore inappropriate for the vast majority of arsenic affected people worldwide due to their prohibitively high price and the expertise required to operate the equipment. A more thorough survey of these methodologies can be found in Appendix A.3.5 - Current Laboratory Methods.

Many field test kits for arsenic are available. The market is dominated by the Gutzeit method in which arsenic in the sample is reduced to arsine gas (a volatile, poisonous gas) by exposure to hydrochloric acid. Arsine is then reacted with mercuric bromide (also poisonous) on a test strip to produce an orange colour (Sanger, 1908). The

27 shade of orange is then compared to a colour sheet, darker colours correlate with higher concentrations of arsenic (an example is shown in Figure 1.3). These techniques are not easy to perform, they require multiple steps, measuring of specific volumes and handling of poisonous chemicals. Gutzeit method techniques are also inaccurate as they are only semi-quantitative and kits that can detect below the WHO limit of 10 μg/L are more expensive. Methods to improve accuracy use colorimetry devices to quantify the depth of colour, however, these instruments are very expensive for field kits. The Merck Spectroquant uses silver instead of mercury but still involves the production of arsine. The analytical device is also cumbersome and very expensive. A more thorough analysis of these methods is discussed in Appendix A.3.4 - Competitive products.

Arsenic (μg/L)

Figure 1.3: Example of the colour reference chart for a standard Gutzeit method field arsenic test kit.

1.1.2 Antimony

Antimony is also a group 15 element and is located below arsenic on the periodic table. Elemental antimony exists as a grey metalloid. But has two metastable forms: black, yellow and explosive (an allotrope formed through electrolysis that explodes when scratched). Like arsenic, antimony has four common oxidation states: stibine (- 3), antimony (0), antimonite (+3) and antimonate (+5). The average abundance of antimony in the Earth’s crust is estimated to be 0.2 to 0.5 ppm (Filella et al., 2002a). Antimony is the 9th most mined metal or metalloid (Roper et al., 2012). It is found in over 100 different mineral types and is sometimes found in its elemental form, however, stibnite (Sb2S3) is by far the most common mineral ore (Filella et al., 2002a). Antimony is primarily used industrially for products such as semiconductors, infrared detectors and diodes. Antimony is relatively inflexible compared to most metals and

28 so is often alloyed for further applications such as lead storage batteries, solder and sheet metal. Antimony oxide can be used as a flame retardant and in the production of rubbers, plastics and paints. Antimony sulphide is used in the production of explosives and pigments. Antimony compounds have also been used as medicines such as in the treatment of Leishmaniasis and as an emetic (Filella et al., 2007) (Sundar & Chakravarty, 2010).

1.1.2.1 Antimony in the environment, solubility and health implications Antimony is naturally solubilised from the lithosphere into waters and soils. However, aqueous concentrations remain low due to its low solubility. Natural antimony mineral erosion occurs mainly through processes such as volcanic eruptions and wind erosion. Anthropogenic sources of antimony include waste incinerators, metal processing works, mines, industrial coal burning, oil fuel burning and dusts from industry. Both antimonite and antimonate exist in aquatic environments. Concentrations of environmental aquatic antimony are typically less than 1 μg/L in non-polluted waters (Filella et al., 2002a) (Filella et al., 2002b). When the antimony present in oxic, neutral pH aquatic samples is speciated antimonate tends to be the dominant oxidation state though significant proportions of antimonite have been reported (Filella et al., 2002a). Likewise, antimonite tends to be the dominant oxidation state in anoxic, low pH samples though antimonate has been reported in these environments (Filella et al., 2002a). Antimony occurs at concentrations no greater than 1 μg/L in non-polluted waters but concentrations one hundred times greater than this have been documented in waters proximal to anthropogenic sources (Filella et al., 2002a).

Antimony is also toxic. Like arsenic, the trivalent form is considered more toxic (Filella et al., 2007). Exposure is associated with headaches, abdominal pain, constipation, colic, loss of appetite, small mouth ulcers, dizziness, weight loss, albuminuria and glycosuria (Cooper & Harrison, 2009). Chronic exposure can result in lung, heart and gastrointestinal diseases as well as reproductive disorders and cancers (Cooper & Harrison, 2009). The WHO has placed an upper limit of antimony concentrations in drinking water of 20 μg L-1 (World Health Organisation, 2003). Approximately 5-20% of ingested antimony is absorbed by the human digestive tract (Pierart et al., 2015). 29

1.1.3 Arsenic and antimony in biology

1.1.3.1 Biological arsenic transformations Many organisms can detoxify their cells of arsenic or even, despite its toxicity, use it as an energy source for growth. Cycling between arsenite and arsenate occurs naturally through abiotic and biotic processes. Abiotic cycling occurs slowly, so biotic processes are the main mechanism of cycling between the two oxidation states in the environment (Inskeep et al., 2007).

Organisms conserve energy for growth from oxidation-reduction reactions. A primary electron donor (e.g. sugars) is oxidised and the electrons passed through a series of an electron transport chain to a terminal electron acceptor (e.g. oxygen). An electron transport chain consists of a series of oxidoreductive electron carriers that cross a membrane. The energy gained from the oxidoreduction reactions drives proton translocation across the membrane to establish a proton gradient which is used to drive ATP production via ATP synthase. The reduction potential of a substance (it’s tendency to be oxidised or reduced) influences its potential as an electron acceptor or donor. The difference in reduction potential between the acceptor and donor is proportional to the energy available to the organism (Madigan, 2000).

Arsenate can be used as a terminal electron acceptor, in which free energy is available from its reduction when coupled to the oxidation of organic (e.g. acetate) or inorganic (e.g. H2S; Equation 1.1) compounds as part of an electron transport chain that terminates in the arsenate reductase (Arr) molybdoenzyme. Arsenate reduction has been observed in members of the Gammaproteobacteria, Deltaproteobacteria, Epsilonproteobacteria and Crenarchaeota (Oremland & Stolz, 2003) (Silver & Phung, 2005).

V - + III Equation 1.1: H2As O4 + H2S + H  H3As O3 +S + H2O

(ΔG = - 100.5 kJ/mol) (Rochette et al., 2000)

Microbial arsenite oxidation was first observed in 1918 in Bacterium arsenoxydans (Green, 1918). Other species capable of arsenite oxidation have been found in a 30 range of physiologically diverse organisms including the Alphaproteobacteria, Betaproteobacteria, Gammaproteobacteria and Deinococcus-Thermus lineage (van Lis et al., 2013). These have been isolated from a diverse range of environments including hot, moderate and cold temperatures as well as both anaerobic and aerobic conditions (Santini & vanden Hoven, 2004) (Osborne et al., 2013) (Gihring & Banfield, 2001). The oxidation of arsenite when coupled to the reduction of oxygen (Equation 1.2), nitrate or chlorate is an exergonic reaction. Arsenite oxidation can either occur heterotrophically (requiring organic matter for growth) or chemolithoautotrophically (where carbon dioxide is the sole carbon source) (Santini & vanden Hoven, 2004). The Rhizobium sp. str. NT-26, isolated from an arsenopyrite containing gold-mine in Northern Territory, Australia, is a chemolithoautotrophic arsenite oxidiser. It is able to respire aerobically with arsenite (meaning it uses oxygen as the terminal electron acceptor) using the molybdenum containing enzyme arsenite oxidase (Aio) (Santini

& vanden Hoven, 2004). A cytochrome c552 (in NT-26) acts as the electron donor to Aio and transfers electrons to cytochrome c oxidase which pumps protons into the periplasm. This provides the proton motive force to drive the ATP-synthase to generate ATP (Figure 1.4) (Santini & Ward, 2012).

III V 2- V - + Equation 1.2: 2H3As O3 + O2  HAs O4 + H2As O4 + 3H

(ΔG = -175.8 kJ/mol) (Santini et al., 2000)

Figure 1.4: Incorporation of the Aio into an electron transport pathway. Arsenite is oxidised by the Aio. Electrons are transferred to cytochrome c which and then to cytochrome c oxidase where they are used for the reduction of O2 to water and to pump protons into the periplasm. The protons then pass back through the ATP synthase to generate ATP from ADP and phosphate. Thick, straight arrows represent proton movement and thin, curly arrows represent the electron pathway.

1.1.3.2 Biological antimony transformations Antimony is a non-essential element in organisms which is perhaps not surprising given its toxicity. Many organisms have evolved to detoxify, remove or prevent the uptake of arsenic and it is speculated that some of these mechanisms are employed for antimony as well but knowledge of interactions of cells with antimony is scarce and much less understood than their interaction with arsenic (Filella et al., 2007).

Antimony uptake by cells was assumed to be via passive diffusion, however, studies in E. coli suggest that GlpF, a glycerol conducting channel which has also been shown to be involved in the uptake of arsenic, is involved in antimony uptake suggesting that antimony may gain access to cells via transmembranal uptake (Sanders 1997) (Meng et al., 2004).

Various families of transporter proteins appear to be involved in antimony efflux. ArsB, an arsenite removal system, has been observed to grant microorganisms tolerance to antimony (Mobley & Rosen, 1982) (Chen et al., 1986). Yeast cadmium

32 factor (which sequesters glutathione chelates of heavy metals in vacuoles) has been explicated in antimony tolerance by transporting glutathione conjugated antimony into the vacuole (Ghosh et al., 1999). Mutants of Acr3 (arsenic compound resistance) in yeast show increased sensitivity to antimony (Wysocki et al., 2001).

Despite it being thermodynamically unlikely, antimonite has been found in oxic water and antimonate has been found in anoxic water. Biological redox activity has been suggested as an explanation for this observation (Filella et al., 2002a).

There is growing experimental evidence to support the biological reduction of antimonate to antimonite. For example, the freshwater algae Chlorella vulgaris, isolated from an arsenic-polluted environment, was observed to excrete 40% antimonate and 60% antimonite when exposed to the former suggesting reduction as a detoxifying mechanism (Maeda & Ohki, 1998) (Maeda et al., 1998). Antimonate (in the form of various pentavalent organic antimonials) is the primary drug of choice for Leishmaniasis. It is generally accepted that the drug must be reduced to antimonate (Goodwin & Page, 1943) and that this occurs either in the macrophage, the parasite or both (Roberts & Rainey, 1993) (Sereno et al., 1998) (Shaked-Mishan et al., 2001) (Filella et al., 2007), however, mechanisms of reduction (enzymatic or non-enzymatic) remain controversial (Haldar et al., 2011). Indigenous microorganisms of anaerobic calcerous soil were observed to reduce antimonate to antimonite though the biochemical mechanisms are currently unknown (Hockmann et al., 2014). A chemolithoautotrophic Rhizobium species was observed to use H2 as the electron donor for the reduction of antimonate though, again, the specific biochemical mechanisms are poorly understood (Lai et al., 2016).

There is evidence for the biological oxidation of antimonite to antimonate. Agrobacterium tumefaciens str. 5A, which oxidises arsenite using Aio, also oxidises antimonite to antimonate. It appears to do this using multiple pathways including Aio as mutation of AioA (removing arsenite oxidation function) reduced antimonite oxidation by one third (Lehr et al., 2007). Most observations of biological antimonite oxidation report that it occurs during heterotrophic growth suggesting that it is used as a detoxification mechanism (Li et al., 2016). However, Stibiobacter senarmontii

33 was found to be able to couple antimonite oxidation to O2 reduction to support chemoautrophic growth (Lialikova, 1974). In vitro studies have also found that the NT-26 Aio could oxidise antimonyl tartrate (an antimonite molecule) although approximately 6500-fold slower than it does for arsenite (Wang et al., 2015).

Arsenite oxidase (Aio) can catalyse the oxidation of both arsenite and antimonite. Aio has a large catalytic Mo-containing subunit and a small Rieske protein subunit. Mo- containing enzymes and Rieske proteins will be discussed before further information about the Aio is presented.

1.2 Molybdenum enzymes

A variety of transition metals have been co-opted by biological systems to provide greater catalytic diversity. Molybdenum is one such transition metal which, despite its low prevalence in the Earth’s crust, is readily available for biological uptake due to the high solubility of its salts. The only organisms that do not require molybdenum use tungsten instead. Both molybdenum and tungsten are group 6 elements, with tungsten lying immediately below molybdenum in the periodic table, explaining their chemical similarity. They are both also respectively the only 4d and 5d transition metals used as cofactors in enzymes. Molybdenum and tungsten enzymes are an extremely diverse family of enzymes catalysing a multitude of redox reactions utilising the IV and VI oxidation states which are stable at physiological conditions. The V oxidation state is also accessible meaning that molybdenum and tungsten enzymes can act as transducers between two and one electron transfers. Molybdenum and tungsten enzymes are fundamental to physiology, playing important catalytic roles in sulphur, nitrogen and carbon metabolism (Hille, 2002).

Some transition metal cofactors are coordinated to the peptide via residues such as serine, cysteine or histidine while others are complexed by larger prosthetic groups such as heme – an iron bound by a porphyrin group. Molybdenum and tungsten cofactors fall in to the latter group as all consist of a central metal Mo or W, complexed by either one or two pyranopterin dithiolene ligands (see Section 1.2.4 for in-depth discussion on the structure and function of pyranopterins). The

34 variability in active site structures and subsequent chemistry divides molybdenum and tungsten enzymes into distinct families. Traditionally, there are three molybdoenzyme families: xanthine oxidase, sulphite oxidase and dimethylsulfoxide (DMSO) reductase; and two tungstoenzyme families: aldehyde:ferrodoxin reductases and formate dehydrogenases.. The xanthine oxidase family have only one pterin and are not covalently linked to the peptide while the sulphite oxidases also have one pterin but are covalently linked to the peptide. The DMSO reductases have two pterins and a covalent link to the peptide as do the formate dehydrogenases. Finally, the aldehyde reductases have two pterins and no covalent bond to the peptide. Owing to an increasing number of exceptions the two tungsten families and the DMSO reductase family have been consolidated into the Mo/W-bis pterin guanosine dinucleotide family as they all share Mo/W catalytic centres with two pterins (Grimaldi et al., 2013).

1.2.1 Xanthine Oxidase family

Members of the xanthine oxidase family of Mo-containing enzymes tend to catalyse the oxidative hydroxylation of a carbon centre of their substrates. The first member of this family isolated was the bovine xanthine oxidase (Greenlee & Handler, 1964). Other members of this family include xanthine dehydrogenase, carbon monoxide dehydrogenase from Oligotropha carboxidivorans (Dobbek et al., 1999) and aldehyde:ferredoxin oxidoreductase from Desulfovibrio gigas (Romao et al., 1995). The xanthine oxidase family is characterised by having one pyranopterin cofactor and a LMoVIOS(OH) core in the oxidised state (in which L represents the dithiolene ligand of the pyranopterin (Figure 1.5) (Romão, 2009) .

Figure 1.5: Generic core of the xanthine oxidase family which possess only one pyranopterin, an oxo ligand, a hydroxy ligand and a sulfide ligand.

1.2.2 Sulphite oxidase family

The sulphite oxidase family of Mo-containing enzymes typically catalyse the transfer of an oxygen atom either to or from a substrate. The family includes the eukaryotic sulphite oxidase which catalyses the oxidation of sulphite to sulphate as the final step of the oxidative degradation of S-containing amino acids which is a critical step in the detoxification of sulphite; bacterial sulphite dehydrogenase which couples sulphite oxidation to ferricytochrome c reduction; and the eukaryotic assimilatory nitrate reductases which catalyses the first step of nitrate assimilation in plants, algae and fungi (Romão, 2009) (Hille et al., 2014). The family is characterised by having one pyranopterin cofactor and a LMoVIO(OH)(S-Cys) oxidised core in which the cysteine ligand is provided by the peptide (Figure 1.6). While the xanthine oxidase and sulphite oxidase families appear to be very similar (both only possess one pyranopterin, have square pyramidal geometry, possess three sulphur bonds as well as an apical oxo ligand and an oxygen atom in the equatorial plane), overlaying the pyranopterin cofactors reveals that their apical oxo ligands are oriented in opposing directions (Hille, 2002) (Hille et al., 2014). This may reflect that the pyranopterins have different functions in the two families and is discussed in Section 1.2.4 (Rothery et al., 2012).

Figure 1.6: Generic core of the sulphite oxidase family which possess a single pyranpoterin, an oxo ligand, a sulfide ligand and are coordinated to the peptide via a cysteine residue.

1.2.3 Mo/W-bis pterin guanosine dinucleotide family

The Mo/W bis pterin guanosine dinucleotide family is the most diverse group of Mo- and W- containing enzymes. It was originally split into three distinct families: the Mo- containing DMSO reductase family, the W-containing aldehyde ferredoxin oxidoreductase family and the W-containing formate dehydrogenase family. This change in nomenclature was due to the fact that the DMSO reductase of Rhodobacter capsulatus (Schneider et al., 1996) (the first member of the family isolated) was considered a poor representative of the breadth of the family (Grimaldi et al., 2013). Enzymes of this family typically catalyse transfer of an oxygen atom or dehydrogenation of a substrate. Examples of members of the family include nitrate reductase, formate dehydrogenase and DMSO reductase which catalyse redox reactions that are key steps in carbon, nitrogen and sulphur cycles respectively (Grimaldi et al., 2013). The family is characterised by the possession of two

VI pyranopterins and a L2Mo (O)(Y) core where Y is a sixth ligand that can be a Ser, Cys, SeCys or Asp (from the peptide), an -OH or =O group (Figure 1.7). While many W- containing enzymes have no direct bond to the peptide, Aio is the only know Mo- containing member of this group not to possess a direct peptide linkage (Pushie & George, 2011).

Figure 1.7: Generic core of the Mo/W-bis pterin guanosine dinucleotide family. The central metal atoms can be either Mo or W. The cofactor possesses two pyranopterins, and at least one oxo ligand. The final ligand is either an oxo ligand, a hydroxy ligand, or is an amino acid of the peptide.

1.2.4 Role of pyranopterins

The pyranopterins of tungsten and molybdoenzymes seem to play a critical role in active site chemistry. It appears that the primary function of the other pyranopterin is in tuning the redox potential of the Mo/W. In general, a pyranopterin can exist in one of two redox states: 10, 10a dihydro and tetrahydro (Figure 1.8). The dihydro and tetrahydro states have significantly different conformations with the dihydro being more planar. Analyses of structures from members of all molybdoenzyme families have found that pyranopterins can exist on a continuum between these two states. The exact redox state and conformation a pyranopterin adopts appears to be determined by protein fold and interacting residues. A pyranopterin in the dihydro conformation is predicted to have more extensive pi-delocalisation facilitating electronic communication between the dithiolene chelate and the pyrimidine ring, decreasing the dithiolene’s electron donating ability and resulting in more positive reduction potentials for the Mo/W ion. Tungsten and molybdoenzymes therefore present an attractive prospect for enzyme engineering as the redox potential of the metal is, at least in theory, highly tuneable (Rothery et al., 2012).

Figure 1.8: Dihydro (A) and tetrahydro (B) states of the pyranopterin. Note the protonation of the pyrazine ring’s nitrogen and subsequent changes in bonding to change the pyranopterin from a dianionic dithiol chelate to a monoanionic thiol ditioloene chelate. The N and C atoms that are protonated and deprotonated in the two forms are shown in red.

Mo/W-bis pterin guanosine dinucleotide family members have two pyranopterins. These are classified based on their proximity to the neighbouring cofactor in electron transport (usually a 4Fe-4S or 3Fe-4S cluster). There appears to be selection to prevent flexibility of electron transfer pyranopterins suggesting pyranopterin’s have a dual role. The proximal pyranopterin is closer to the neighbouring cofactor and appears to generally be more distorted implying it is tetrahydro. The distal pyranopterin is further away and more planar, implying dihydro conformation. Curiously, the pyranopterin of sulphite oxidases resembles the distal and in xanthine oxidases it more closely resembles the proximal. It has been suggested that this is because electron transfer is plausible through xanthine oxidases’ pyranopterin and Mo/W-bis pterin guanosine dinucleotides’ proximal pyranopterin but not through DMSO reductases’ distal or sulphite oxidases’. It has been suggested that the xanthine oxidases’ and Mo/W-bis pterin guanosine dinucleotides’ proximal pyranopterins are for electron transfer while sulphite oxidases’ and Mo/W-bis pterin guanosine dinucleotides’ distal pyranopterins are for reduction potential tuning (Rothery et al., 2012).

1.3 Rieske proteins

The AioB small subunit of arsenite oxidases contains a Rieske type 2Fe2S cluster. Rieske proteins were first isolated in 1964 from cytochrome c reductase (Rieske et

39 al., 1964). They differ from standard 2Fe-2S clusters as the redox active iron is complexed by two histidine residues instead of two cysteine residues. Rieske cluster potentials can vary considerably from +350mV (for example in a ubiquinol oxidising cytochrome bc1 complex) to -150mV (for example in a Rieske type ferrodoxin) (Zu et al., 2003). The reduction potentials are also pH dependent depending on the protonation state of the histidine residues: at low pH the histidines are uncharged whilst at high pH they are deprotonated giving them negative charge. This is in contrast to standard 2Fe-2S clusters in which the binding cysteine residues are always negatively charged meaning their reduction potentials are stable with respect to pH (Zu et al., 2003).

1.3.1 Rieske families

The function of all Rieske proteins is electron transfer. However, the Rieske proteins are a large protein family containing functionally diverse groups all possessing the conserved sequence motifs for Fe-S cluster binding: [CxHxGC] and [CxCH(S/A/G)x(Y/F)] (cofactor binding residues are in bold). The family can be broadly split into two groups: high potential and low potential. This classification system refers specifically to the reduction potential of the 2Fe-2S co-factor (Schneider & Schmidt, 2005).

High potential Rieske proteins are present in many photosynthetic and respiratory electron transfer chains from all kingdoms of life and are typically vital components of larger protein complexes. For example, both the metabolic bc1 and photosynthetic b6f complexes contain Rieske subdomains which are responsible for electron transfer (Schneider & Schmidt, 2005). Electron donors for the Rieske proteins tend to be quinols while the electron acceptors are high potential redox proteins like cytochromes, blue copper proteins or high potential Fe-S proteins. The metabolic bc1 and photosynthetic b6f complexes use cytochromes c1 and f respectively. (Schmidt & Shaw, 2001) (Van Driessche et al., 2003). High potential Rieske proteins typically have reduction potentials in the range of +100 to +570 mV (Schneider & Schmidt, 2005). All high potential Rieske proteins share the same minimal structure which includes a disulphide bridge proximal to the 2Fe-2S cluster (Figure 1.9A).

Low potential Rieske proteins typically have reduction potentials in the range of -150 to +5 mV. Unlike the high potential Rieske proteins, it is not uncommon for members of the low potential family to occur as individual proteins, such as Rieske ferrodoxins, or as part of larger complexes, such as in the hydroxylases. The physiological electron donors are other Fe-S clusters and cytochromes while the electron acceptors are either the low-potential Rieske cluster of a hydroxylase or oxygenase, or oxygen bound to the active site of the enzyme itself. Low potential Rieske proteins are lacking the disulphide bridge proximal the 2Fe-2S cluster (Figure 1.9B) (Schneider & Schmidt, 2005).

Figure 1.9: Representative structures of Rieske families. A) Bovine heart bc1 Rieske (PDB ID: 1RIE) representing the high potential Rieske proteins (Iwata et al., 1996). B) Napthalene-2-dixygenase (PDB ID: 2QPZ) representing the low potential Rieske proteins. Note that the protein architecture around the 2Fe-2S cluster is noteably different with the low potential Rieske lacking a disulphide bridge (Brown et al., 2008).

It has been suggested that AioB (arsenite oxidase small subunit) Rieske proteins cannot be clearly classified into one of these two groups (Schneider & Schmidt, 2005). An initial phylogenetic analysis suggested that AioB clusters as a distinct group beside the bacterial and archaeal high potential Rieske proteins which is unsurprising given the current understanding that Aio occurred in the last universal common ancestor (LUCA) (Schmidt & Shaw, 2001). The reduction potential of AioB ranges from +60 to +225 mV (Lebrun et al., 2003) (Warelow et al., 2013) suggesting that it is closer to the high potential Rieske proteins though still somewhat intermediary (being on the lower end of their range) (Schneider & Schmidt, 2005). The structure of AioB is similar to the minimal structure of the high potential Rieske proteins with many possessing a confirmed (by X-ray crystallography) or putative disulphide bridge proximal to the 2Fe-2S cluster (Figure 1.10) (Ellis et al., 2001). Curiously, NT-26 AioB, an alphaproteobacterium, does not contain a disulphide bridge, instead possessing a phenylalanine and a glycine (Warelow et al., 2013). Preliminary investigations into the function of the disulphide bridge in AioB have suggested it is not critical in defining reduction potential but may play a role in electron acceptor specificity (Warelow et al., 2013) .

Figure 1.10: Structures of high potential and AioB RIeske proteins. A) Alcaligenes faecalis AioB (PDB ID 1G8K) (Ellis et al., 2001). B) Bovine heart bc1 Rieske (PDB ID: 1RIE) (Iwata et al., 1996). C) Thermosynechococcus elongatus BP-1 b6f (PDB ID: 3AZC) (Veit et al., 2012). Note that all possess a disulphide bridge proximal to the 2Fe-2s cluster. 42

1.3.2 Rieske Evolution

Rieske proteins are present in all three domains of life suggesting that Rieske proteins were present in LUCA. This suggests that the early evolution of the Rieske center occurred under the anoxic conditions of the early Earth. It has been suggested that Rieske proteins share a common ancestor with rubredoxins (proteins with single redox active iron centres that are involved in electron transfer) due to their similar binding motifs (Link, 1999). It has been postulated that this early Rieske protein was involved in some kind of redox chain and persisted in evolution due to that fact that Rieske proteins are able to possess much higher reduction potentials than four cysteine 2Fe-2S clusters (Schmidt & Shaw, 2001).

The duplication of the early Rieske protein gave rise to the origins of the two Rieske families. One family became the low potential Rieske family. Since these are structurally and functionally very simple it is probable that they more closely resemble the ancestral Rieske protein. The other Rieske family gained a membrane anchor and was incorporated into a primitive cytochrome-Rieske complex which would later go on to become the bc1 and, eventually, b6f complexes. This is the high- potential Rieske family (Schmidt & Shaw, 2001). Phylogenetic reconstruction of bc1,

AioB and b6f Rieske proteins demonstrates that AioB diverged from bc1 before the divergence of archaea and bacteria as well as long before the evolution of b6f and that the divergence of AioB and bc1 occurred before the existence of LUCA. However, owing to AioB’s structural and functional similarity to bc1 it is likely that these two diverged after the initial duplication that produced the fundamental two Rieske families (Lebrun et al., 2006).

1.4 Arsenite oxidase

Arsenite oxidase (Aio) is a member of the Mo/W-bis pterin guanosine dinucleotide (PGD) (formerly DMSO reductase) family of Mo-containing enzymes that oxidises arsenite and antimonite (Santini & vanden Hoven, 2004) (Wang et al., 2015). It has been purified and characterised from several prokaryotes including the alphaproteobacterium Rhizobium sp. str NT-26 (Santini & vanden Hoven, 2004), the

43 betaproteobacteria Ralstonia sp. str. 22 (Lieutaud et al., 2010), A. faecalis (Anderson et al., 1992), Polaromonas sp. str. GM1 (Osborne et al., 2013) and Hydrogenophaga sp. str. NT-14 (vanden Hoven & Santini, 2004), the actinobacterium, Arthrobacter sp. str. 15b (Prasad et al., 2009) and the heat-tolerant thermococcus Thermus thermophilus (Heath, 2012).

The Aio of NT-26 and A. faecalis are the most extensively studied, with structures resolved for both using X-ray crystallography (Warelow et al., 2013) (Ellis et al., 2001). Aio has been isolated from the periplasm or in association with the cytoplasmic membrane. It consists of two heterologous subunits: AioA is approximately 90 kDa and contains a redox active molybdenum centre at the active site and a 3Fe-4S cluster; AioB is approximately 15 kDa and contains a Rieske 2Fe-2S cluster. (Ellis et al., 2001) (Warelow et al., 2013). The crystal structure of NT-26 Aio is shown in Figure 1.11. Aio has been observed in various quaternary structures including heterodimeric, -tetrameric and -hexameric depending on the purification conditions and the organism from which the Aio is isolated (Ellis et al., 2001) (vanden Hoven & Santini, 2004) (Lieutaud et al., 2010) (Osborne et al., 2013) (Warelow et al., 2013).

Aio has been found to be directed to the periplasm by the twin-arginine translocation (Tat) secretory pathway. The Tat-secretory pathway exports folded proteins to the periplasm, as opposed to the general secretory pathway (Sec) which transports unfolded peptides. Utilisation of the Tat secretory pathway is common for enzymes containing metal prosthetic groups as it allows maturation of the holoenzyme in the cytoplasm (Berks et al., 2005). Only the small subunit, AioB, possesses a Tat signal suggesting that the entire Aio protein is folded in the cytoplasm before export to the periplasm (Santini & Ward, 2012). It was partially this feature which allowed for the successful heterologous expression of NT-26 Aio in the cytoplasm of Escherichia coli DH5α. Replacement of the Tat signal on the AioB subunit with a histidine-tag allowed expression of Aio in the cytoplasm and purification of the enzyme by nickel affinity chromatography (Warelow et al., 2013).

Figure 1.11: Crystal structure of Rhizobium sp. str. NT-26 Aio. The large subunit, AioA, is shown in green. The small subunit, AioB, is shown in blue. The molybdenum of the Mo-cofactor is shown as a metallic blue sphere while the molybdopterins are shown as sticks.The Fe-S clusters are shown as yellow and orange spheres. PDB ID: 4AAY.

1.4.1 The Aio enzyme subunits and cofactors

The structures of both the A. faecalis and NT-26 Aio have been solved by X-ray crystallography. The two structures have high overall similarity, however, the NT-26 Aio was crystallised as a heterotetramer while the Aio of A. faecalis was crystallised as a heterodimer. The large subunit, AioA, consists of four peptide domains in a pseudotetrahedral configuration. The small subunit, AioB consists of two peptide domains with an incomplete six-stranded, anti-parallel β-barrel at one end of the protein. The two subunits are associated via four backbone hydrogen bonds and

45 approximately 27 other direct hydrogen bonds (Ellis et al., 2001) (Warelow et al., 2013).

AioA, the large catalytic subunit of Aio, contains a bis-molybdopterin guanine dinucleotide (bis-MGD) cofactor at its active site. The bis-MGD is a single Mo atom complexed by two pyranopterins (Figure 1.12A) making Aio a member of the Mo/W- bis pterin guanosine dinucleotide family of enzymes. The bis-MGD is coordinated to the peptide by a network of hydrogen bonds and salt bridges and is located at the base of a highly solvated, flattened funnel-shaped cavity in the space between the four domains of AioA. Unlike all other Mo-containing members of the Mo/W-bis pterin guanosine dinucleotide family, Aio does not possess a covalent link to the peptide (Ellis et al., 2001)(Warelow et al., 2013) meaning it more closely resembles many W-containing enzymes such as the aldehyde:ferrodoxins (Hille, 2002). The crystal structures of Aio both observed the reduced state of the molybdenum atom, presumably due to photoreduction by the X-rays used in crystallography. The oxidised state of the molybdenum atom in Aio has been analysed by X-ray absorption and Raman spectroscopy which suggest the presence of one Mo=O bond and either a Mo-O, Mo-OH or Mo=O bond (Conrads et al., 2002). Protein film voltammetry and extended X-ray absorbance fine structure (EXAFS) observed two Mo=O bonds on the oxidised molybdenum atom (Hoke et al., 2004) (Warelow et al., 2017).

AioA also contains a high potential 3Fe-4S cluster which is coordinated to the peptide via three cysteine residues (one for each of the Fe atoms) (Figure 1.12B). A serine residue occupies the site that, in a 4Fe-4S cluster, would be the location of a fourth cysteine to complex a fourth Fe. This serine has been suggested to be important in electron transfer (Ellis et al., 2001).

The small subunit, AioB, contains a high potential Rieske 2Fe-2S cluster (Figure 1.12C). Rieske clusters differ from typical 2Fe-2S clusters in that one of the Fe atoms is coordinated by two histidine residues as opposed to two cysteines. A disulphide bridge is found proximal to the Rieske 2Fe-2S cluster in many Aio such as A. faecalis however it is notably absent in the Aio of alphaproteobacteria and some archaea (Duval et al., 2010). Rhizobium sp. str. NT-26 is one such example of an

46 alphaproteobacterium in which a phenylalanine and glycine are present in place of the disulphide bridge (Warelow et al., 2013). The role of the disulphide bridge is not entirely understood, though it is thought that it may influence electron acceptor selectivity. The 3Fe-4S and 2Fe-2S clusters have been shown to be fully matured in the cytoplasm (Duval et al., 2010) (van Lis et al., 2013) unlike other Rieske proteins

(such as prokaryotic cytochrome bc1 complex) that are thought to mature after translocation to the periplasm (Bachmann et al., 2006).

Figure 1.12: Cofactors of Aio. A) Mo-cofactor showing a central Mo atom coordinated by two molybdenum guanine dinucleotide pterins. B) 3Fe-4S cluster. C) Rieske 2Fe- 2S cluster.

1.4.2 Evolution of arsenite oxidase

Arsenite is the predominant soluble species of arsenic found in anoxic environments yet has been shown to be mainly oxidised by O2-respiration. The inverse is true for arsenate which is exclusively converted by anaerobic metabolisms but is predominately found in oxic environments. Arsenite oxidation has been shown to be

47 an ancient process by phylogenetic analysis and has been suggested to have been present in LUCA (Lebrun et al., 2003) (Duval et al., 2008). There are two known enzymes that carry out arsenite oxidation, Aio and an anaerobic arsenite oxidase (Arx) (a variant of the anaerobic arsenate reductase; Arr) (van Lis et al., 2012) (Zargar et al., 2012). LUCA is believed to have existed before the great oxygenation event (GOE) and so Aio must have been functionally anaerobic. Arx may have been the precursor to Aio (Kulp et al., 2008) however as Aio catalysed arsenite oxidation has been shown to be coupled to the reduction of nitrate, which was present in the atmosphere before the great oxygenation event, it is also possible that Aio evolved independently (Oremland et al., 2002). Phylogenetic analysis of Aio, Arx, Arr, polysulfide reductase (Psr) and nitrate reductase (Nar) suggest that Aio evolved independently to the other arsenic oxidoreductive enzymes (Figure 1.13) (van Lis et al., 2013).

Figure 1.13: Phylogenetic tree showing the evolutionary relationship between Aio, Arx, Arr, nitrate reductase (Nar) and polysulfide reductase (Psr). Archael branches are shown in yellow and purple while bacterial branches are shown in green and blue. The scale bar represents 0.1 changes per amino acid (van Lis et al., 2013) (presented with permission from Elsevier).

Aio is more phylogenetically widespread than Arx and homologues of the genes that code for the Aio, aioA and aioB, have been identified in the genomes of bacteria and archaea (Lebrun et al., 2003) (van Lis et al., 2013). Both AioB and AioA have homologs in functionally unrelated enzymes. AioB is homologous to the Rieske subunit of the cytochrome bc1 and b6f complexes which are found in all domains of life and are key components of electron transport chains (Schmidt & Shaw, 2001) (Lebrun et al., 2006). AioA is similar in structure to the Mo-containing enzymes assimilatory and dissimilatory nitrate reductases and the formate dehydrogenases. However, AioA possesses a 3Fe-4S instead of 4Fe-4S cluster as these enzymes do as the intrinsically higher reduction potential of the 3Fe-4S cluster is required for arsenite oxidation (Lebrun et al., 2003).

Another notable difference between Aio and other Mo-containing enzymes is the lack of a covalent bond from the peptide to the Mo atom of the active site. However, this feature is typical of the aldehyde:ferrodoxin family of tungsten-containing enzymes (Hille et al., 2014). It is thought that molybdenum and tungsten predominately existed as MoS2 and WS2 as opposed to their respective oxyanions prior to the GOE. WS2 is more soluble than MoS2 and would therefore have presumably been the more bioavailable species. W-containing enzymes are therefore thought to pre-date Mo-containing enzymes (L’vov et al., 2002). MoO4 is more soluble than WO4, so it is thought that as global oxygen levels increased molybdenum began to predominate in organisms over tungsten (supported by the fact that a number of enzymes have been shown to incorporate molybdenum and tungsten interchangeably (Schmitz et al., 1992). It has therefore been proposed that Aio, because it possesses a Mo atom in a typical W active site conformation, may represent an evolutionary intermediate between W-containing and Mo-containing enzymes (Hille et al., 2014).

1.4.3 The arsenite oxidase gene cluster

The core of the aio gene cluster (Figure 1.14) is formed of the aioA and aioB genes which encode AioA and AioB respectively. aioB is always upstream of aioA (van Lis et

49 al., 2013). The aio gene cluster is present in a phylogenetically diverse array of arsenite-organising microorganisms (van Lis et al., 2013).

The aio gene cluster contains genes encoding other proteins involved in arsenite oxidation both upstream and downstream of aioBA. The genes aioX, aioS and aioR are found upstream of aioBA and encode for a periplasm associated arsenite binding protein, a sensor histidine kinase and a response regulator respectively. These proteins regulate the expression of Aio in response to periplasmic arsenite concentrations. The genes for electron acceptors to Aio are often located downstream of aioBA such as cytC (which encodes a cytochrome-c552) in NT-26. Further downstream are located genes involved in molybdenum cofactor biosynthesis such as moeA (van Lis et al., 2013).

Figure 1.14: Arsenite oxidation gene cluster.

1.4.4 Electron acceptors to the arsenite oxidase

Cellular energy production is best described by the chemiosmotic theory in which electrons derived from reduced substrates, such as glucose or arsenite, are channelled towards terminal electron accepting substrates via membrane integral and/or associated enzymes known as an electron transport chain. The cascade of oxidoreduction reactions in the electron transport chain facilitates the production of a cross-membrane proton gradient which drives the production of adenosine triphosphate (ATP) by ATP synthase. Arsenite oxidising microorganisms have been shown to gain energy from the oxidation of arsenite and the reduction of terminal electron acceptors oxygen, nitrate or chlorate. The arsenite oxidation electron transport chain has not been characterised in full, though various stages of the process have been characterised with two major group variations: heterotrophic and chemiautotrophic arsenite oxidation. In heterotrophic arsenite oxidation the electrons are thought to be transported to a membrane bound cytochrome c oxidase complex which reduces oxygen to water, coupled to the movement of protons from the cytoplasm to the periplasm (vanden Hoven & Santini, 2004). In chemiautotrophic

50 growth, the electrons carried by cytochrome c can be redirected to a bc1 complex which reduces quinone. Reduced quinone can then transfer electrons to nicotinamide adenine dinucleotide (NADH) dehydrogenase to reduce NAD+ to NADH which drives the energetically unfavourable steps in the Calvin cycle to fix inorganic carbon (Berg et al., 2010). The terminal oxidases vary depending on terminal electron acceptor and as such have been observed to be denitirification enzymes, photosynthetic reaction centres or cytochrome oxidases (vanden Hoven & Santini, 2004) (van Lis et al., 2012).

The physiological electron acceptors to Aio have been shown to include c-type cytochromes and a cupredoxin-type copper protein, azurin (Anderson et al., 1992) (vanden Hoven & Santini, 2004) (Santini & vanden Hoven, 2004) (Branco et al., 2009) (Lieutaud et al., 2010). Selectivity for these different electron acceptors has been found to be dependent on the specific Aio strain. For example, NT-26 Aio can reduce cytochrome c but not azurin, while the inverse is true for A. faecalis (Santini et al., 2007) (Anderson et al., 1992). The processes behind this selectivity are not well understood, however, as all tested electron acceptors have reduction potentials greater than 240 mV it is assumed not to be due to reduction potentials (Santini & Ward, 2012). It is possibly due to the net charge of the acceptors as the isoelectric point of cytochrome c is 10.7 (Theorell & Akesson, 1941) while that of azurin is 5.4 (Augustin et al. 1983) A summary of different Aio selectivity for various electron acceptors is summarised in Table 1.1.

Table 1.1: Electron acceptor specificity of different Aio

Aio Bovine A.a NT-26 S22 A.f P.a DCPIP Heart c555 c552 c554 azurin Azurin c Rhizobium NT-26 + - + - NA - + H. arsenicoxydans - + - + NA + + A. faecalis - + - + + + + Ralstonia S22 - + - + NA + + Polaromonas GM1 - NA - NA NA + + *Bovine heart c = bovine heart cytochrome c; A.a c555 = Aquifex aeloicus c-type cytochrome; NT-26 c552 = Rhizobium NT-26 cytochrome c552; S22 c554 = Ralstonia sp. 22 c-type cytochrome; A.f. azurin = A. faecalis azurin; P.a. azurin = Pseudomonas aeruginosa azurin; DCPIP = dichlorophenolindolphenol. 51

1.4.5 Catalytic mechanism of the arsenite oxidase

The molybdenum active site of Aio oxidises arsenite to arsenate. A proposed reaction mechanism for Aio oxidation of arsenite is shown in Figure 1.15. The reaction is initiated by donation of the lone pair of electrons on arsenite to the π* antibonding orbital of one of the Mo=O bonds. Observations from EXAFS and density functional theory (DFT) simulations suggest that the oxidised form of the molybdenum centre is in a relatively high energy conformation due to the positioning of the pterins. Upon reaction with arsenite the pterins twist to accommodate a transition state in which arsenite and the Aio are bonded via the oxygen. This intermediate then breaks down to form the lower energy reduced molybdenum centre and arsenate (Warelow et al., 2017). The molybdenum is then re-oxidised by reducing the 3Fe-4S and Rieske 2Fe- 2S clusters in what is thought to be two strictly one-electron transfers and the oxo ligand is replaced by the reduction of water. The Rieske 2Fe-2S cluster then reduces a physiological electron acceptor such as cytochrome c or azurin as part of an electron transport chain, usually resulting in the reduction of molecular oxygen (Anderson et al., 1992) (Santini et al., 2007) (Lieutaud et al., 2010).

Figure 1.15: Proposed reaction mechanism at the Aio active site in which the Mo- cofactor oxidises arsenite to arsenate.

The reduction midpoint potentials (Em) of the three Aio cofactors have been determined using protein film voltammetry. The Em of the molybdenum centre of the A. faecalis and NT-26 Aio were determined to be 292 mV and 367 mV respectively. A. faecalis was found to have pH dependence of -59 mV per pH unit increase suggesting a coupled two electron, two proton reduction (Hoke et al., 2004). NT-26 was found to have dependence of -33 mV per pH unit increase suggesting a coupled two-electron, single proton reduction (Bernhardt & Santini, 2006). Electron paramagnetic resonance (EPR) spectroscopic redox titrations determined the 3Fe-4S cluster reduction potential to be 260 mV in A. faecalis and 270 mV in NT-26. EPR was also used to determine that the reduction potentials of the Rieske 2Fe-2S cluster were 155 mV in A. faecalis and 225 mV in NT-26 (Hoke et al., 2004) (Warelow et al., 2013).

The three redox-active co-factors of Aio are approximately equidistant from each other with the 3Fe-4S cluster being approximately 13 Å from the molybdenum atom and 14 Å from the Rieske cluster. An electron transfer pathway through the A. faecalis Aio has been proposed and involves a complex network of covalent and hydrogen bonding interactions between the pyrazine ring of the MGD cofactor and the two Fe-S clusters. In the shortest pathway, electrons will pass through AioA-Ser99 to AioA-His62 which lies at the interface between the large and small subunits. His81 (one of the Rieske complexing histidines) is thought to mediate electron transfer to the phyiological electron acceptor as it is the only ligand exposed to solvent and is positioned near the surface of the Rieske subunit (Ellis et al., 2001). However, neither the electron transport mechanism through Aio nor the rates of electron transfer have been determined.

1.4.6 Kinetics of Aio

Most chemical reactions carried out by enzymes are far too rapid to observe using simple methods of mixing and analysis (such as manual mixing and UV-visible spectrophotometry). Thus, steady-state conditions are used to measure kinetic parameters. Steady-state conditions are achieved by an enzyme when it is catalytically cycling at its maximum efficiency given certain substrate concentrations. There are various mechanisms that describe enzymatic catalysis but by far the most prevalent is the Michaelis-Menten model. This model assumes that the enzyme and substrate follow the scheme presented in Equation 1.3, in which E = enzyme, S = substrate and P = product. The Michaelis-Menten model assumes that the formation of the enzyme-substrate complex is reversible and that the formation of product is irreversible.

Equation 1.3: 퐸 + 푆 ↔ 퐸푆 → 퐸 + 푃

In steady-state kinetics, the rate of change, v, of the substrate is monitored (often by colour change though various analytical techniques can be used. Various concentrations of substrate are used, with lowest rates being observed at the lowest concentrations. By analysing a range of concentrations, the maximum velocity, Vmax, can be learnt which is the maximum rate achievable by the enzymatic system. The 54 concentration of substrate at which the rate is half of Vmax is called the Michaelis constant, KM, and is often used as a measure of the enzyme’s affinity for the substrate

(with lower KM’s signifying higher affinity). The relationship between these parameters is described in Equation 1.4 with S = substrate concentration (Michaelis & Menten, 1913).

푉 푆 Equation 1.4: 푣 = 푚푎푥 퐾푀+푆

By normalising the v and the Vmax by the enzyme concentration, it is possible to obtain the specific activity or the turnover number, kcat which are essentially the same, just with different units. The kcat is generally more useful than specific activity as it measures the moles of substrate turned over per second per mole of enzyme and is directly comparable to non-catalytic chemical rates.

Heterologously expressed NT-26 Aio has had its arsenite kinetics determined using dichlorophenolindolphenol (DCPIP) and horse heart cytochrome c as electron acceptors. The steady state kinetics of arsenite with DCPIP were reported to be: KM

-1 -1 -1 = 68 μM, Vmax = 4.9 μmol min mg , Kcat = 9.3 s (Warelow et al., 2013) (Warelow,

2014). With horse heart cytochrome c they were reported as KM = 9.3 μM, Vmax =

-1 -1 -1 120.4 μmol min mg and Kcat = 228 s (Wang et al., 2015).

Aio has recently demonstrated the ability to oxidise antimonite (in the form of antimonyl tartrate) in vitro with cytochrome c as an electron acceptor. However, the

Vmax is approximately 6,500 times lower that it is with arsenite. The steady-state

-1 -1 kinetics of the antimonite were: KM = 163 nM, Vmax = 18.4 nmol min mg and Kcat = 34.8 ms-1 (Wang et al., 2015).

Aio can rapidly and selectively oxidise arsenite which has made it an ideal candidate for the development of a biosensor to detect arsenic in drinking water. A general overview of biosensors and their applications in environmental arsenic monitoring will now be presented before discussing the Aio biosensor.

1.5 Biosensors

A biosensor is a self-contained device that can provide specific quantitative or semi- quantitative analytical information using a biological recognition element. Biological recognition elements include cofactors, enzymes, antibodies, organelles, tissues, microorganisms or cells of higher organisms that interact with the target analyte. A transducer is required to convert the biochemical signal produced by the recognition element to an electrical signal. Transducers can be electrochemical, mechanical, optical or thermal sensors. Biosensors offer several advantages over conventional analytical techniques such as the fact they are often possible to miniaturise which improves their portability and that they are able to measure an analyte in complex samples with minimal preparation (Rodriguez-Mozaz et al., 2006).

The first biosensor to reach widespread use was the glucose biosensor first proposed by Clark and Lyons (1962). This sensor involved the use of the enzyme glucose oxidase and an electrochemical transducer. The electrons gained from the oxidation of glucose were transferred to an amperemeter with the total amount of current measured being proportional to the concentration of glucose. Due to the glucose oxidase’s high specificity for glucose and ability to work in blood (a complex sample) the glucose oxidase biosensor became a standard tool in diabetes management (Wang, 2008).

Electrochemical biosensors are the most diverse and dynamic type of biosensor. They measure the production or loss of electrons (amperometric) or ions (potentiometric) of redox reactions. Amperometric biosensors rely on the production of a reduced redox-active species by a biological recognition element (typically an oxidoreductive enzyme). A general scheme for an amperometric biosensor is shown in Figure 1.16. Electrochemical oxidation or reduction of the product is then detected by an electrochemical transducer, typically either a metal or carbon based electrode (Davis, 1985) (Pohanka & Skládal, 2008). Amperometric biosensors can operate by direct electrochemical oxidation/reduction of the catalytic product, direct electron transfer from the enzyme to the transducer or indirect electron transfer via a mediator. Most amperometric biosensors use an enzyme immobilised on the transducer surface.

Potentiometric biosensors work under much the same principal but using an ion- selective or gas sensing electrode to monitor the production of ions. However, most potentiometric biosensors have non-linear responses and require strict pH monitoring meaning their practical applications are severely limited (Davis, 1985) (Chaubey & Malhotra, 2002) (Su et al., 2011).

Figure 1.16: Schematic representation of the sequence of reactions that occurs in mediated and unmediated electron transfer.

Many amperometric biosensors employ electron transfer mediators. These participate in the redox reaction with the biological component of the biosensor and facilitate rapid electron transfer from the recognition element to the transducer. Biosensors that incorporate mediators are known to be less sensitive to interfering substances because the use of a mediator enables the biosensor to function at a lower working potential. Mediators can also reduce the oxygen concentration and pH dependence of a system (Chaubey & Malhotra, 2002) (Dzyadevych et al., 2008). Common mediators include organic dyes (e.g. methylene blue), inorganic redox ions (e.g. ferricyanide) and ferrocene derivatives (ferrocene carboxylic acid). Less common mediators include organosulfur compounds and conducting salts (Cass et al., 1984) (Pandey et al., 1997) (Pandey et al., 1998) (Chaubey & Malhotra, 2002) (Dzyadevych et al., 2008) (Chaubey and Malhotra 2002, Dzyadevych 2008, Pandey

1998, Pandey 1997, Cass 1984). Natural mediators can also be used such as cytochromes, quinones and co-enzymes (Dzyadevych et al., 2008).

1.5.1 Arsenic Biosensors

As discussed in Section 1.1.1.1, over 200 million people worldwide are at risk of exposure to arsenic in their drinking water (Naujokas et al., 2013). The WHO guideline for arsenic levels in safe drinking water is 10 μg/L yet concentrations of 3000 μg/L have been found in wells in the USA (World Health Organisation, 2011) (Naujokas et al., 2013). The scale of the crisis is difficult to estimate as current monitoring and detection systems are inadequate and new contaminated sites are regularly discovered. Conventional methods of arsenic detection are often unavailable to the public or too complex or expensive meaning that regular arsenic monitoring is not possible. Current field test kits are difficult to use, inaccurate, unreliable and often do not detect at the WHO threshold (Naujokas et al., 2013). Biosensors have been proposed as potential means of providing inexpensive, user- friendly, reliable and accurate methods of arsenic detection. Several different systems have been proposed for arsenic detection but at time of writing none have been adopted.

1.5.1.1 Whole cell arsenic biosensors Whole cell biosensors use living cells as the recognition element. The cells are typically engineered to produce a quantifiable bioreporter (measurable signal) which correlates with analyte concentration. Microogranisms are generally preferred due to their well-established mutation techniques, simplicity of growth and general robustness. The most common bioreporters used involve expression of reporter genes, such as bacterial bioluminescence genes (i.e. luxCDABE from Vibrio harveyi), by promoters that are induced by the target analyte (Bousse, 1996).

Several whole cell arsenic biosensor systems have been reported which use the bacterial arsenic detoxification system encoded by the ars operon. The ars operon contains genes that encode ArsR, a repressor, ArsB, an arsenite specific efflux pump and ArsC, an arsenate reductase (Carlin et al., 1995). The ars promoter regulates expression of the ars operon which is itself regulated by ArsR. Upon binding of 58 arsenite or arsenate ArsR is released from the ars promoter inducing expression of the operon. As ArsR is also expressed by the ars operon, transcription is repressed once sufficient arsenic is removed from the cell, acting as a negative feedback loop (Daunert et al., 2000) (Santini & Ward, 2012). Whole-cell biosensors are produced by transforming microorganisms with a second copy of the ArsR operator-promoter sequence transcriptionally fused to a bioreporter gene (Diesel et al., 2009). When exposed to arsenic, the expression of both the ars operon and reporter genes will be unrepressed meaning that the bioreporter will express. The negative feedback loop caused by the expression of ArsR alongside the bioreporter means that the magnitude of the response is dependent on arsenic concentration. This system has been successfully implemented using the bioreporters bacterial luciferase and β- galactosidase and in the microorganisms E. coli, Bacillus subtilis, Staphylococcus aureus and Rhodopseudomonas palustric. The system has been shown to produce a response to arsenic concentrations below 10 μg/L in 30 minutes to 4 hours (Diesel et al., 2009).

An alternative whole cell biosensor system utilises the Sacchromyces cerevisiae promoter of the UFO1 protein which is induced in response to UV damage and arsenate. Similarly to the ars systems, the promoter is fused to a bioreporter gene such as those encoding luciferase. This system was too sensitive for field applications, only giving reliable data with arsenate concentrations between 7.5 x 10-5 and 7.5 x 10-2 μg/L. It is also only sensitive to arsenate and does not measure total arsenic without further processing of the sample (Bakhrat et al., 2011).

Several whole cell biosensor systems for arsenic detection are in commercial development. These have the advantage of being relatively cheap but also highly specific as at their core they utilise proteins that have evolved to respond specifically to arsenic. For example, the arsenic biosensor collaboration used genetically modified Bacillus subtilis to generate a pH change in the solution proportional to the concentration of arsenic. pH indicator is then used to create a visual reporting system (Ajioka, 2017). The ARSOlux system uses E. coli to produce a luminescent bioreporter to give a fluorescent readout which is then interpreted by a luminometer (ARSOlux, 2017). 59

Despite promising preliminary results with whole cell arsenic biosensors, there are currently no products at or close to market. The ars operon systems are limited by the fact that arsenite and arsenate have variable binding constants for ArsR resulting in varied expression of bioreporters. Specificity has also proven to be an issue as ArsR binds antimony and UFO1 responds to chromium, both of which can be present in environmental water samples (Bousse, 1996) (Bakhrat et al., 2011). Slow response times of all whole cell biosensors (0.5-48 hours) have also brought into question their utility as current kits generate results in under 30 minutes. The Arsenic Biosensor Collaboration and ARSOluxe systems’s progress have also been stymied by strict regulation on the environmental use and release of genetically modified organisms (de Mora et al., 2011).

1.5.1.2 Electrochemical arsenic biosensors Electrochemical biosensors for arsenic have not been explored in as many instances as whole cell biosensors. The native Aio from Rhozibium sp. str. NT-26 has been used as a biosensor for arsenite by depositing the enzyme onto the surface of a multiwalled carbon nanotube modified glassy carbon electrode. The system accurately reported arsenic concentrations in the range of 1-500 μg/L. Aio catalysed the oxidation of arsenite and directly transferred electrons to the electrode, requiring no mediator. Readings were obtained in 10 seconds. The electrodes were stable for several days when stored in 2-(N-morpolino)ethanesulfonic acid (MES) buffer pH 5.5 at 4 °C. The system was effective for samples in the pH range 6-8 and was found to be insensitive to elevated levels of elements found commonly in natural water sources (Al, Ba, Cd, Cr, Cu, Fe, Mn, Ni, Sr and Zn). The NT-26 Aio biosensor accurately detected 20 μg/L arsenite in spiked river, tap and commercial water samples. While electroactive species were found to interfere with the biosensor performance, this was circumvented using a dual-electrode system to reduce signal created by non-specific electroactive species. Unfortunately, the biosensor was only sensitive to arsenite and not arsenate (Male et al., 2007). NT-26 Aio continues to be in active development as a biosensor for arsenic by BioNano Consultuing with the key challenges being to determine optimal electrode conditions, long-term storage of the enzyme, upscaling of enzyme production and determination of total arsenic.

Horse heart cytochrome c is now used as a mediator between the Aio and the electrode.

Various alternative electrochemical biosensing approaches have been reported. Urease and acetylcholinesterase have been used as electrochemical biosensors which function through the inhibition of enzymatic activity by arsenic have been reported. Both were able to detect under 10 μg/L (Pal et al., 2007) (Sanllorente- Méndez et al., 2010). The oxidation of L-cysteine upon the electrochemical reduction of arsenate to arsenite using screen-printed carbon electrodes reported a 1.2-4.6 μg/L limit of detection (Sarkar et al., 2010). Damage to DNA caused by arsenite measured by oxidation with the aid of a rubidium or cobalt complex has also been suggested as it was able to detect arsenic contaminations lower than 50 μg/L (Labuda et al., 2005). However, all these approaches are limited due to lack of specificity for arsenic as any inhibitory substance will yield a positive result. For example, acetylcholinesterase is also inhibited by Cu2+, Cd2+, Hg2+ and Zn2+ (Frasco et al., 2005) and humic acid (organic acids commonly found in soil) can also damage DNA (Lu et al., 1998).

1.5.2 Business case for Aio as an arsenic biosensor

Arsenic contamination of groundwater is a global problem. Unacceptably high levels of arsenic occur on every continent (excluding Antarctica). The majority of the worst affected areas are in Asia with approximately 140 million (of approximately 200 million worldwide) cases being localised to Bangladesh and India (Murcott, 2012). The NT-26 Aio is in active development as a biosensor for arsenic contaminated water samples. Bangladesh is the primary market of interest for the biosensor though there are also aspirations to enter other markets such as China (8.2 million exposed) and the USA (9 million exposed). This section will give a brief overview of the business case for the Aio biosensor. A complete report can be found in Appendix Appendix A - Arsenic biosensor market report.

The Aio arsenic biosensor utilises sensor strips consisting of an electrode with Aio and horse heart cytochrome c (acting as a mediator) printed and dried on the surface in a stabilising buffer for long term storage. The sensor strip is placed into a portable 61 voltammeter and then exposed to the water sample which rehydrates the enzyme. Arsenite concentrations are measured by amperometry with the current being proportional to the arsenite concentration. Note that at time of writing the system can only detect arsenite but methods to reduce arsenate to arsenite are being investigated.

The Aio arsenic biosensor would be competitively positioned in the sector of arsenic monitoring. It is significantly more accurate and reliable than most products on the market (giving a quantitative read out). It can detect at much lower concentrations than the standard field tests. The biosensor is also much safer and easier to use than most other kits. The expected response time of three minutes is much faster than all current test kits and could dramatically increase the number of well tests per day it is possible to perform.

Drawbacks of the arsenic biosensor are that it requires an electronic device. This increases the overall cost and therefore price of the kit but could be circumvented with “Blade and Razor” pricing strategies to recoup losses made on the device with large margins for the test strips. The device requires batteries which is a disadvantage compared to most other kits which do not. However, for precise quantification, battery operated devices are essential, so this is less likely to be an issue.

In the broader context of water monitoring, the arsenite biosensor is at a disadvantage as it is not able to detect multiple analytes which is increasingly common in these products (some claiming to detect as many as eleven analytes). However, there is a shift in industrial drinking water monitoring towards easy-to-use, low maintenance, highly accurate, low cost, in situ devices (Frost and Sullivan, 2016a) (Frost and Sullivan, 2016b).

In Bangladesh, the target price is $0.70-1.00 to allow the biosensor to compete with existing field tests. However, developed markets, such as the USA’s, could probably sustain a much higher price per test. Laboratory testing of water samples currently costs around $40-400 so it seems that the market could sustain much higher prices. However, more research into how facilities perform water testing must be conducted

62 as it is possible that many companies send one water sample to one laboratory that tests for all possible contaminants.

Overall, there is a global demand for arsenic testing, particularly in Asia and North America. This is likely to increase over the next few decades as global population and demand for clean, safe drinking water increase. The global instrumentation market is growing at a modest rate in all sectors and geographic regions (Frost and Sullivan, 2016a). The general public is becoming increasingly aware of the problems associated with arsenic and with new industries such as fracking increasing the risk of arsenic contamination it is likely that the demand for arsenic testing will increase globally (Frost and Sullivan, 2016b).

1.6 Protein engineering

The NT-26 Aio is in development as a biosensor for arsenic. This thesis is broadly concerned with developing deeper insights into the mechanisms of arsenite and antimonite oxidation by Aio to assess if the Aio can be engineered to perform as a better biosensor for arsenic or as a sensor for other environmental pollutants, such as antimony. At time of writing, no attempts to engineer molybdoenzymes such as Aio for industrial applications have been reported.

Protein engineering is the process of using amino acid substitutions to alter the function of proteins to improve their usefulness and value. Protein engineering can be used for a variety of outcomes including improving protein stability, sensitivity, activity (in the case of enzymes), tethering capability and creating fusion proteins with reporter proteins (i.e. fluorescent proteins) (Lambrianou et al., 2008). There are two general strategies for protein engineering: rational design and directed evolution.

1.6.1 Rational Design

Rational design protein engineering approaches make use of detailed knowledge of the structure and function of a protein to make desired changes. Site-directed mutagenesis methods are well developed so rational design approaches have the

63 advantage of generally being inexpensive. However, the drawback of rational design is that detailed structural-functional information is often unavailable. Even when structural and functional information is available in abundance, it is typically incredibly difficult to predict the effects of various mutations as structural information usually only provides a static image of a protein structure and even small differences between the actual protein and a predicted model can have large effects on the protein folds (Richardson & Richardson, 1989) (Bornscheuer & Pohl, 2001).

Rational design has been used to develop various biosensors. The effect of the mutation depends on the desired function of the biosensor. For example, it might be necessary to alter the binding site of a protein to make it sensitive to a new substrate. For example, computational loop remodelling of five different periplasmic binding proteins from E. coli were engineered to be receptive to the non-biological chemical trinitrotoluene (TNT), an explosive and carcinogenic pollutant (Looger et al., 2003). It may be necessary to alter the function of the protein or add a bioreporter. For example, a fluorescence biosensor for β-lactam antibiotics was developed by designing a β-lacatamase mutant that had no hydrolytic activity (meaning it only bound β-lactam) and was fused to a fluorescence reporter protein to generate a dose-dependent fluorescent output (Chan et al., 2004).

1.6.2 Directed Evolution

Directed evolution is a method that that mimics the process of natural selection to engineer proteins towards a user-defined goal. It involves subjecting a gene to iterative rounds of mutagenesis, selection and amplification. The key advantage of directed evolution over rational design is that it does not require modelling of the impact of a given mutation (Lutz, 2010). The issue with directed evolution is that it is a costly and time-demanding methodology. Directed evolution methods would require:

• A method to produce mutants such as error prone PCR.

• The maintenance and storage of a library containing all generated mutants.

• A procedure to screen all mutants for the desired phenotype.

• This process to be repeated many times until an industrially acceptable

phenotype is achieved (Lutz, 2010).

Directed evolution has been successful in a variety of areas. For example, it has been used to improve protein stability with respect to temperature. Five generations of directed evolution converted Bacillus subtilis subtilisin E into a functional equivalent of its thermophilic homilog thermitase from Thermoactinomyces vulgaris, increasing the optimal temperature of the subtilisin by 17 °C (Zhao & Arnold, 1999). Directed evolution has also been used to alter substrate specificity. The substrate specificity of 2-keto-3-deoxy-6-phosphogluconate aldolase from Thermotaga maritima was altered from the three-carbon pyruvate to the four-carbon 2-keto-4- hydroxyoctonate (Cheriyan et al., 2011).

1.7 Aims of this study

The principal aim of this study was to develop a greater understanding of the mechanisms involved in arsenite oxidation by Aio. Mechanistic insights into Aio catalysis should prove invaluable in the development of the Aio arsenic biosensor.

This study consisted of three objectives:

1) Identify and characterise the rate limiting step of Aio catalysis and assess if

the catalytic rate of Aio can be improved.

2) Explore Aio electron acceptor specificity.

3) Characterise the mechanism of antimonite oxidation by Aio and assess how

this affects the performance of the Aio arsenite biosensor and if Aio can be

used as an antimonite biosensor.

Chapter 2

The rate-limiting step of arsenite oxidase catalysis

2.1 Introduction

This chapter describes the identification and investigation of the rate-limiting step of the NT-26 arsenite oxidase (Aio) oxidation of arsenite. Oxidoreductases can essentially be thought of as scaffolds that support co-factors which are the fundamental driving factors of their reactions. The protein positions co-factors and substrates in favourable distances and configurations so that electron transfer can occur between them. However, all these reactions occur at different rates. The slowest rate defines how rapidly an enzyme can function and is therefore known as the rate-limiting step. Once the rate-limiting step has been identified, it can theoretically be engineered using a rational design method to be faster, thereby increasing the overall rate of the enzyme.

2.1.1 Electron transport in arsenite oxidase

The Aio couples the oxidation of arsenite to arsenate with the reduction of physiological electron acceptors such as cytochrome c (Santini et al., 2007) or azurin (Anderson et al., 1992) in vivo. The Aio possesses three cofactors: a molybdenum cofactor, a 3Fe-4S cluser and a Rieske 2Fe-2S cluster. A general scheme for electron transfer through the enzyme was proposed by Ellis et al. (2001) for the Alcaligenes faecalis Aio. A similar pathway to the proposed one is shown for the NT-26 Aio in Figure 2.1. The pathway can be divided into four reactions: 1) arsenite is oxidised by the molybdenum centre at the active site; 2) the molybdenum centre transfers electrons one at a time to the 3Fe-4S cluster via one of the pyranopterins and either Thr242 and Cys27 or Asn722 and Arg104; 3) the 3Fe-4S cluster transfers electrons to the 2Fe-2S cluster via Ser102, Gln140 and His105; 4) the 2Fe-2S cluster reduces the electron acceptor via His124. Note that the distance from the proximal pyranopterin to the 2Fe-2S cluster is only 14.7 Å and so direct electron transfer to the 2Fe-2S cluster from the Mo-pterin is also possible (Figure 2.1). The rates of each of these reactions are currently unknown and must be determined to find the rate-limiting step. It is hypothesised that the reduction of the electron acceptor is rate-limiting in the Aio as the Rieske cluster is involved in the rate-limiting step in the bc1 and b6f complexes (Hong et al., 1999) (Soriano et al., 2002) and also because the catalytic

67 rate of the Aio varies depending on the electron acceptor used (Anderson et al., 1992) (Lieutaud et al., 2010) (Warelow et al., 2013) (Wang et al., 2015) .

Figure 2.1: Electron transport distances through the Aio. Distances shown are calculated in PyMOL and are in angstroms.

Rieske proteins of the Aio, the bc1 and the b6f complexes generally possess a disulphide bridge proximal to the 2Fe-2S cluster (Ellis et al., 2001) (Link & Iwata, 1996) (Lin et al., 2006). AioB from members of the Alphaproteobacteria and some Archaea are exceptions to this (Lebrun et al., 2003). The NT-26 Aio possesses a phenylalanine and a glycine in place of the two cysteines. Preliminary investigations of the NT-26 AioB-F108A mutant, in which the phenylalanine is substituted with an alanine, showed reduced activity with cytochrome c (Joanne Santini, UCL, pers. comm.). The F108A mutant therefore made an interesting subject of investigation into the rate-limiting step in comparison with the wild-type (WT) Aio.

The rate-limiting step of the Aio was identified and characterised using stopped-flow spectroscopy and isothermal titration calorimetry respectively. These techniques will now be briefly introduced.

2.1.2 Stopped-flow spectroscopy

The chemical reactions that take place within a single turnover of an enzyme are often far too rapid to be observed using simple analytical methods of mixing and observation. Stopped-flow spectroscopy is an analytical method that enables the observation of rapid reactions and determination of their rates in real time. Fundamentally, stopped flow is a rapid mixing technique. The equipment set-up is shown in Figure 2.2. Stopped-flow uses a pneumatic drive to rapidly mix chemicals and then follows their reaction, usually via UV-visible spectroscopy. When the pneumatic drive compresses the reactant containing syringes, it simultaneously expands the stop syringe which contains the spent reactants. Its expansion causes it to push on a sensor which triggers the analytical device to start taking readings. In the case of the stopped-flow experiments discussed in this chapter, a photodiode array was used to take UV-visible spectra every millisecond meaning that this stopped-flow set up was able to detect reactions that occurred in times greater than 1 ms.

Stopped-flow spectroscopy has been used to identify the reaction rates of a number of molybdoenzymes including chicken liver sulphite oxidase (Brody & Hille, 1999), Ralstonia eutropha formate dehydrogenase (Niks et al., 2016), Rhodobacter capsulatus DMSO reductase (Mcalpine & Bailey, 1997), bovine xanthine oxidase (Davis et al., 1982) and Oligotropha carboxydovorans CO dehydrogenase (Zhang et al., 2010). It was therefore thought to be an ideal technique to monitor the rates of electron transfer in the Aio and identify the rate-limiting step.

Figure 2.2: Diagram of stopped-flow equipment. The drive depresses the syringes containing solution A and solution B (which would be two reactants of interest). These are rapidly passed through the mixing chamber and into the observation chamber where either absorbance of fluorescence changes of the product formation/substrate consumption are observed. Data capture is triggered as the stop syringe expands and triggers a sensor.

2.1.3 Isothermal titration calorimetry

Understanding the way that proteins bind to substrates and other proteins is critical when discussing enzymatic reactions. Two species can be expected to bind when the interaction has a favourable change in Gibbs free energy (∆G). A negative ∆G means that the reaction is spontaneous and Equation 2.1 shows that this is highly dependent on two factors: change in enthalpy (∆H) and change in entropy (∆S) (with T = temperature).

Equation 2.1: ∆G = ∆H − T∆S 70

Negative values of ∆H can result in negative ∆G. These reactions release heat and are therefore known as exothermic reactions. In protein complexes, exothermic reactions are often seen when electrostatic forces (the attraction between positive and negative charges) are responsible for binding. The heat generated by the formation of electrostatic bonds drives the reaction.

Positive values of ∆S can also drive a reaction. Increases in entropy essentially mean that there has been an increase in the disorder of a system. This is typically seen in hydrophobic interactions because solvent molecules (usually water in biological systems) form cage-like structures around hydrophobic, non-polar residues as polar water is unable to interact with them. The interaction of hydrophobic surfaces squeezes these water molecules out of the binding sites, breaking the cage structures and increasing the entropy. As the cage-like structures are held together by hydrogen bonding, their breakdown requires heat input meaning these reactions are endothermic.

The ∆G then defines the position of the equilibrium between the unbound enzyme and substrate with the complex. This equilibrium is known as the association constant (ka) and is related to ∆G as shown in Equation 2.2 in which R = the ideal gas constant and T = temperature.

Equation 2.2: ∆퐺 = −푅푇푙푛푘푎

Isothermal titration calorimetry (ITC) is a technique that determines the thermodynamic parameters of a binding reaction (∆H, ∆S and ka). A diagram of an isothermal titration calorimeter is seen in Figure 2.3. The device consists of a sample cell, in which the protein of interest is loaded, a reference cell, which usually contains water, and a syringe, in which the substrate is loaded. The substrate is titrated into the sample cell in regular injections spaced by a constant time interval. Small fluctuations in temperature caused by binding are measured by the equipment and the amount of energy required to make the sample cell the same temperature as the reference cell is measured (this maintenance of isothermal conditions is what gives ITC its name). The heat change diminishes for subsequent injections as the sample becomes saturated with substrate. 71

Figure 2.3: Diagram of isothermal titration calorimetry equipment. The sample cell is typically filled with the protein as it requires a lower concentration than the syringe which typically contains the substrate. The reference sample is usually filled with water though buffers are occasionally used. The syringe injects substrate into the sample cell at periodic time intervals. The binding of substrate to protein causes small temperature fluctuations. The ITC records how much energy is required to return the sample cell to the temperature of the reference cell.

2.1.4 Aims

The aims of this chapter are broadly concerned with identifying and characterising the rate-limiting step of NT-26 Aio catalysis. This will be achieved by using steady- state kinetics, stopped-flow spectroscopy and isothermal titration calorimetry to identify:

1. The rate limiting step of Aio catalysis. 2. The mechanism of Aio catalysis. 3. The interaction of Aio with cytochrome c. 4. The effect of the AioB-F108A mutation on kinetic and thermodynamic parameters.

2.2 Methods

2.2.1 WT and F108A Aio expression systems

The strains containing recombinant plasmids for the NT-26 WT aioBA gene and AioB- F108A mutant aioBA gene were provided by Joanne Santini, UCL.

2.2.2 Conformation of presence of aioBA genes in recombinant plasmids by restriction digestion and sequencing

Presence of the pProEX-Htb+ plasmid (Invitrogen) and aioBA gene insert was confirmed for Aio WT and F108A strains by restriction digest and sequencing (a map of the recombinant plasmid is shown in Figure 2.4). Plasmids were isolated using a QIAprep Miniprep kit according to the manufacturer’s instructions (QIAgen). 500 µg (determined using a nanodrop spectrophotometer 280 nm absorbance) of each plasmid was digested using restriction endonucleases Pst1 and EcoR1 in a total volume of 20 µl overnight at 37 °C. The bands were then visualised in a 0.8 % agarose gel with a 1 kb + marker (ThermoFisher Scientific).

Figure 2.4: pPROEX-Htb - aioBA recombinnant plasmid; includes encoding region for an IPTG inducible trc-promoter (pink), histidine tag (blue), multiple cloning site (red), ampicillin resistance gene (orange) and the aioBA genes (dark and light green respectively).

Sequences of gene inserts were confirmed by EurofinsGenomics using 5 μL of 100- 120 ng/µl plasmid and 5 μL of 5 µM M13 reverse primer.

2.2.3 Cloning of the aioB gene for heterologous expression in E. coli aioB was amplified from the pProEX-Htb+-aioBA plasmid construct using the primers in Table 2.1 (restriction sites are underlined). The restriction enzyme EcoRI and PstI were used to digest the amplified fragments and pProEX-Htb+. T4 DNA ligase was used to ligate the fragments into the plasmid by incubation at 4 °C overnight. The forward primer was the same that was used to clone aioBA in Warelow et al. (2013).

Table 2.1: Primers used for the cloning of aioB in pProEX-Htb+

AioB Forward 5’-GCGAATTCAAGCTACCGCGGCGGCAGGGGTC-3’

AioB Reverse 5’-GCCTGCAGATAGAACGTTGGACAGACG-3’

The aioB construct was transformed into E. coli str. C43 that had been made competent by growing 10 mL cultures of cells in LB overnight, pelleting the cells by centrifugation at 4000 xg and resuspending in 4 °C distilled water. Pelleting and resuspension was repeated five times to make the cells competent.

The competent cells were transformed with the plasmid construct by shocking at 2.5 V and then incubating in Super Optimal broth with Catabolite repression (SOC) media

(2 % tryptone, 0.5 % yeast extract, 10 mM NaCl, 2.5 mM KCl, 10 mM MgCl2, 10 mM

MgSO4, 20 mM glucose) for 1 hour at 37 °C and 180 rpm shaking.

The successful incorporation of aioB into pProEX-Htb+ was confirmed by restriction digest and sequencing as described in Section 2.2.2

2.2.4 Aio expression and purification

Bacterial liquid cultures were always grown with shaking at 180 rpm in Lysogeny Broth (LB) (per L of water: 10 g tryptone, 5 g yeast extract, 10 g NaCl, pH 7) with 100 µg/ml ampicillin unless otherwise stated. For heterologous expression of the Aio, E. coli strain DH5α containing the plasmids with recombinant aioBA genes for the WT Aio and F108A mutant were first grown for 7 hours at 37 °C in a 3 ml culture. 1 % inoculum was transferred to 100 ml media in a 500 ml Schott bottle containing 1 mM

74 sodium molybdate and grown overnight at 28 °C. 5 % inoculum was transferred to a 2 l culture in a 5 l volumetric flask containing 40 µM Isopropyl β-D-1- thiogalactopyranoside (IPTG) and 1 mM sodium molybdate and grown for 24 hours at 21 °C.

Cells were harvested by centrifugation at 6,500 xg for 15 minutes. Cells were suspended in binding buffer (20 mM potassium ortho-phosphate, 500 mM NaCl, 40 mM imidazole pH 7.3) and re-pelleted by centrifugation at 31,000 xg for 10 minutes. Cell pellets were weighed and re-suspended in binding buffer (10 ml buffer per g of wet weight of cells). Cells were broken using a cell disruptor at 14 Psi. Cell debris was removed from the lysate by centrifugation at 39,000 xg for 30 minutes.

The lysate was passed through a 1 ml His-Gravitrap Ni-affinity column (GE Healthcare) and washed with 10 x 10 ml of binding buffer. The enzyme was eluted using 20 mM potassium ortho-phosphate, 500 mM NaCl, 500 mM imidazole pH 7.3 buffer. Prior to size exclusion chromatography the buffer was exchanged to 50 mM Tris-HCl pH 8 buffer using a PD-10 desalting column (GE Healthcare). The sample was then concentrated using a 100,000 Da Vivaspin centrifuge concentrator (Sartorius, Stedium Biotech) before being loaded onto a Superdex 200 gel filtration column (GE Healthcare) for size exclusion chromatography. The flow rate was 0.5 mL/min.

2.2.5 Expression and purification of AioB

The protocol for heterologous expression of the AioB in E. coli was adapted from Schmidt et al. (1997)and Boxhammer et al. (2008) expression protocols for the

Thermus thermophilus bc1 Rieske protein. E. coli str. C43 transformed with the pProEX-Htb+-aioB plasmid were grown overnight at 37 °C and 250 rpm in 500 mL LB and 100 μg mL-1 ampicillin in 2 L Erlenmeyer flasks. An additional 500 mL of LB and 100 μg mL-1 ampicillin was added and the cells grown for a further 30 minutes under the same conditions. IPTG and FeSO4 were added such that the final concentrations were 400 μM and 1 mM, respectively and the cells were incubated under the same conditions for a further 4 hours.

Following induction cells were harvested and AioB purified using the same protocol as for Aio except that a Superdex 75 gel filtration column (GE Healthcare) was used instead of a Superdex 200. The flow rate was 0.8 mL/min.

2.2.6 Protein concentration determination

The protein concentration of the Aio was determined using a nanodrop to measure the 280 nm absorbance with a 340 nm correction. The extinction coefficients were predicted using the Edelhoch method (Edelhoch, 1967). For the WT and F108A Aio the extinction coefficient used was 142,100 M-1cm-1. The nanodrop method was verified to be accurate through comparison with the Bradford method (Appendix B).

The concentration of the AioB was determined using the 430 nm absorbance and an extinction coefficient of 11.0 mM-1 cm-1 which was determined experimentally by spectroscopy and ionically coupled plasma-mass spectrometry (ICP-MS) (Section 2.3.14).

2.2.7 UV-visible spectroscopy

UV-visible spectra of enzymes were recorded using 10-20 µM enzyme in 50 mM Tris- HCl pH 8 buffer in a 0.5 ml quartz cuvette using a CaryWin 200 Spectrophotometer. For the WT Aio and F108A Aio, 2.5 mM arsenite was used as a reductant to obtain the reduced spectra. For AioB, 1 mM sodium dithionite was used as a reductant. All spectra were recorded at 25 °C.

2.2.8 Inductively coupled plasma mass spectrometry

Inductively coupled plasma-mass spectrometry (ICP-MS) analysis was carried out to determine the Mo and Fe content of enzyme preparations by the Natural History Museum (London) using a Varian ICP-MS. ICP-MS was conducted by S. Strekopytov. 10 µg of enzyme in 20 µl of 20 mM Tris-HCl (pH 8) was hydrolysed with 143 µl 70% nitric acid (99.9% purity) overnight at 37 °C. 1837 µl of milliQ water was added to the samples before analysis.

2.2.9 Polyacrylamide gel electrophoresis

SDS-polyacrylamide gel electrophoresis was carried out using the PhastSystem (GE Healthcare) with pre-made 12.5 % polyacrylamide gels and buffer strips (GE Healthcare). Prior to loading on the gel, samples were boiled in a water bath for 2 minutes in loading buffer (final concentration 2.5 % SDS, 5 % 2-mercaptoethanol, 0.01 % bromophenol blue). Following electrophoresis, the gel was stained using Coomassie brilliant blue in the PhastSystem development unit (GE Healthcare) as per the manufacturer’s instructions. To determine the size of the protein bands a series of standards were used: phosphorylase b (97.0 kDa), albumin (66.0 kDa), ovalbumin (45.0 kDa), carbonic anhydrase (30.0 kDa), trypsin inhibitor (20.1 kDa) and α- lactalbumin (14.4 kDa) at concentrations of 1 µg/ml.

2.2.10 Steady-state kinetics

Steady-kinetics experiments were carried out using either dichlorophenolindophenol (DCPIP) or horse heart cytochrome c as electron acceptors at 25 °C.

2.2.10.1 DCPIP Assays DCPIP assays were performed in 1 ml glass cuvettes using 50 mM MES pH 5.5 buffer, 0.3 mM DCPIP, 40-200 µM Aio and 5-2500 µM arsenite. The reaction was followed at

-1 -1 600 nm. The εred-ox of DCPIP used to calculate specific activity was 8.2 mM cm . The rate recorded was the fastest rate observed during each assay.

2.2.10.2 Preparation of cytochrome c for steady-state kinetics and stopped-flow kinetics 1 mM horse heart cytochrome c (SigmaAldrich) was fully oxidised by the addition of 3 mM (final concentration) potassium ferricyanide until no further change was observed at 550 nm (signifying full oxidation of cytochrome c). Ferricyanide was then removed by desalting into 50 mM Tris-HCl pH 8. The concentration of cytochrome c was determined with the 550 nm absorbance and ɛ = 8.4 mM-1 cm-1. 150 μl aliquots of cytochrome c was snap frozen with liquid nitrogen and stored at -80 °C. Cytochrome c preparations were only thawed once.

2.2.10.3 Cytochrome c Assays Horse heart cytochrome c assays were performed in 1 ml quartz cuvettes using 50 mM Tris-HCl pH 8 buffer, 1-20 µM cytochrome c, 0.1-2 nM Aio and 5-2500 µM arsenite. Note that for conditions in which the cytochrome c concentration was altered arsenite concentration was fixed at 2500 µM and for conditions where arsenite concentration was altered cytochrome c concentration was fixed at 20 µM. The reaction was followed at either 550 nm (for 20 µM cytochrome c) or 416 nm (for

1 - 10 µM cytochrome c). The εred-ox used to calculate specific activities were 21.1 mM-1 cm-1 for 550 nm (Van Gelder & Slater, 1962) and 57.5 mM-1 cm-1 for 416 nm (see section 2.3.4).

2.2.10.4 Data processing and fitting of steady-state kinetics Specific activity (SA) was calculated using Equation 2.3, where v is the observed rate, ε is the reduced minus oxidised extinction coefficient and [E] is the concentration of the Aio in mg/mL.

푣 Equation 2.3: 푆퐴 = /[퐸] 휀

The steady-state kinetics data was fit with the Michaelis-Menten function (Equation 2.4) to determine kinetic parameters in which v now represents the specific activity,

KM is the Michaelis constant, Vmax is the maximum velocity and [S] is the substrate concentration.

푉 +[푆] Equation 2.4:푣 = 푚푎푥 퐾푀+[푆]

2.2.11 The oxidised and reduced spectra of horse heart cytochrome c

To determine an extinction coefficient for the Soret peak (416 nm) that would be consistent with the one used for 550 nm the spectrum of 5 µM oxidised cytochrome c was recorded using a CaryWin UV-visible spectrophotometer in 50 mM Tris-HCl pH 8. The cytochrome c was then reduced using excess dithionite and the spectra recorded again when all dithionite had oxidised (followed by observing the absorbance at 320 nm at which dithionite strongly absorbs). This was done so that

78 concentrations of cytochrome c lower than 5 μM could be monitored as the Soret peak absorbs more intensely than 550 nm.

2.2.12 Stopped-flow spectroscopy of Aio with arsenite and cytochrome c

The reductive half reaction of the WT Aio and the F108A mutant with arsenite was monitored by stopped-flow spectroscopy using an SX-20 stopped-flow spectrophotometer (Applied Photophysics, Inc.) equipped with a photodiode array and a photomultiplier tube detection and running ProData SX 2.2.5.6 acquisition software. Stopped-flow spectroscopy was conducted in collaboration with Russ Hille and Dimitri Niks (University of California Riverside). All experiments were conducted in 50 mM Tris-HCl (pH 8) at 5 °C. 1.2 mL 30 µM Aio was placed in a glass tonometer and made anaerobic by stirring in an O2 scrubbed Argon rich environment for 1 hour. The Aio was mixed by the stopped-flow apparatus in a 1:1 ratio with 500 µM arsenite. The reaction was followed using a photodiode array to observe the whole UV-visible spectrum. To observe the interaction with cytochrome c, 30 µM Aio was instead mixed with 60 µM cytochrome c and 30 µM arsenite (anaerobic) to catalyse a single turnover.

The spectral changes at a specific wavelength (specific wavelengths and rationale for use are explained in Results) were analysed and the rate determined using Equation

2.5 in which At = absorbance at time, t; A∞ = absorbance at infinite time (i.e. absorbance at the end of the reaction); n = number of kinetic phases observed; An = magnitude of the total absorbance change; and k = rate. The operator was added or subtracted depending on if the absorbance was decreasing or increasing respectively.

−푡/푘푛 Equation 2.5:퐴푡 = 퐴∞ ± ∑ 퐴푛푒

As experiments were carried out at 5 °C, the rates were adjusted using the Arrhenius equation (Equation 2.6) to make them comparable with steady-state experiments which were performed at 25 °C.

Equation 2.6: 푘 = 퐴푒−퐸푎/(푅푇)

2.2.13 Stopped flow kinetics of AioB versus cytochrome c

Stopped-flow experiments were carried out in same way as they were for Aio (Section 2.2.12) except AioB was prepared for stopped-flow analysis by first reducing it with sodium dithionite as, lacking AioA, it could not be reduced by arsenite. The sodium dithionite was removed by size exclusion chromatography. 20 μM of reduced AioB was reacted with 20 μM oxidised cytochrome c in the stopped-flow apparatus. The AioB was purified at UCL (London), snap frozen in liquid nitrogen and shipped on dry ice to University of California, Riverside where these experiments were carried out by D. Niks (University of California, Riverside).

2.2.14 Isothermal titration calorimetry of Aio versus cytochrome c

To eliminate the possibility of electron transfer between the Aio and cytochrome c, both were reduced by the addition of excess dithionite which was then removed by size exclusion chromatography using 200 and 75 Superdex size exclusion chromatograph columns respectively.

ITC was carried out using a MicroCal200 ITC (GE Healthcare). 300 µl of 75 µM reduced WT or F108A Aio was loaded into the cell in 50 mM Tris-HCl pH 8. 40 µl of 750 µM reduced cytochrome c in 50 mM Tris-HCl pH 8 Aio was loaded into the injection syringe. The cytochrome c was injected into the enzyme solution in 2 or 1.5 µl aliquots with 1000 rpm stirring at 25 °C.

The resultant peaks of the thermogram were integrated and a 1:1 binding model fit to the data. The binding model is based on the total heat content of the solution, Q, which is the internal energy of the cell at any given injection relative to the internal energy of the cell before any injections. Q is defined in Equation 2.7 where n = number of sites, Mt = bulk concentration of macromolecule in the cell, Xt = the bulk concentration of ligand in the cell, ΔH = change in enthalpy, ka = association constant and V0 = cell volume.

푛푀 ∆퐻푉 푋 1 푋 1 2 4푋 Equation 2.7: 푄 = 푡 0 [1 + 푡 + − √(1 + 푡 + ) − 푡 ] 2 푛푀푡 푛퐾푀푡 푛푀푡 푛푘푎푀푡 푛푀푡

The concentration of macromolecule and ligand in the cell decreases with every injection as small volumes of the contents of the cell are displaced (keeping the total cell volume constant). Therefore, the correct expression for the change in total energy content, ΔQ(i), after i injections is given in Equation 2.8. Where dVi is the volume of the ith injection.

푑푉 푄(푖)+푄(푖−1) Equation 2.8: ∆푄(푖) = 푄(푖) + 푖 [ ] − 푄(푖 − 1) 푉0 2

The analysis software in Origin (Origin, Inc.) uses these two functions to fit a binding model by first estimating a value of n, Ka and ΔH and calculating ΔQ(i). It then compares these values with the measured heat for each experimental injection and then iteratively improves the initial values for n, Ka and ΔH until theire is no improvement based on a chi-squared score. The ∆S is calculated by determining the

∆G from the ka using Equation 2.2 and then using the Gibbs free energy equation (Equation 2.1) and the measured value of ∆H to calculate ∆S.

The Kd was determined based on it’s relationship with ka (Equation 2.9).

1 Equation 2.9: 푘푑 = 푘푎

2.2.15 ITC of AioB versus cytochrome c

ITC experiments for low affinity systems can be studied by increasing the concentrations used. The equation for the c-value is shown in Equation 2.10, where

N = stoichiometry, ka = the association constant, P = the concentration of protein. c is ideally in the range of 20 to 80. However, this was not possible with AioB as preliminary investigations showed that a concentration of 4.2 mM AioB would be required to give a c-value of 20 (which would also require a concentration of 42 mM cytochrome c). The solution to this was to continue to use a low concentration of AioB but use a much higher concentration of cytochrome c. This causes the reaction to continue to saturation by increasing the final molar ratio. The binding model can then be fit, though the stoichiometry must be assumed and fixed (as 1:1 in this case) (Turnbull & Daranas, 2003). AioB ITC experiments were carried out using 86 – 168

μM AioB and 80 μl (two injection syringes worth) of 8.4 mM cytochrome c to ensure that the reaction proceeded to saturation.

Equation 2.10: 푐 = 푁푘푎푃

2.2.16 Statistical Analysis

The statistical significance between kinetic values determined for WT and mutant Aio was calculated using a Student’s T-test. At least three replicates were performed using separate enzyme preparations in all cases and published values were used when available (these are stated in the text).

2.3 Results and Discussion

In this study, the rate-limiting step of the NT-26 Aio arsenite oxidation was investigated using steady-state kinetics, stopped-flow spectroscopy and ITC. The rate-affecting mutant, F108A, and the NT-26 AioB were also investigated to further understand the processes involved.

2.3.1 Confirmation of the recombinant plasmids containing the aioBA genes

The plasmids bearing the WT and F108A aioBA genes were provided by Joanne Santini, UCL. Before attempting any investigations, it was important to first confirm that both plasmids contained the aioBA gene insert by restriction digest and gel electrophoresis as well as sequencing.

The results of the restriction digestion shown in Figure 2.5 shows one band of approximately 5000 bp which corresponds to the pProEX-Htb+ plasmid which has a size of 4717 bp and one band of approximately 3000 bp which corresponds to the aioBA insert which has a size of 3066 bp. The chromatograph of the nucleotide sequence was converted to FASTA format sequence data using ChromasLite (software.informer, 2016). The sequencing confirmed the presence of the aioBA genes in both expression systems. Sequencing also confirmed the substitution of the codon TTT to GCG in F108A.

Figure 2.5: Photos of restriction digest and gel electrophoresis of A) Aio WT and B) F108A confirming the presence of the aioBA insert in both expression systems. The marker was a 1kb+ Gene Ruler (ThermoFisher Scientific).

2.3.2 Purification and co-factor determination of the WT and F108A Aio

Both the WT Aio and the F108A mutant purified as heterotetramers of approximately 220 kDa in size according to size exclusion chromatography (Figure 2.6A and B) (Appendix C for calibration of SEC column). Enzyme purity and the presence of both the AioA and AioB subunit was confirmed by analysing the proteins on SDS polyacrylamide gels (Figure 2.6C). This showed that both WT and F108A have two bands, one of approximately 97 kDa and one approximately 20.1 kDa. These correspond to AioA (91.3 kDa) and AioB + His-Tag (18.2 kDa). The final yield of WT was approximately 1.2 mg L-1 of culture whilst F108A’s was approximately 0.9 mg L-1 of culture. These are similar to previously obtained values for the WT Aio which report 1.1 mg L-1 of culture (Warelow et al., 2013). ICP-MS was used to determine cofactor content. One sample of WT Aio had its cofactor content determined to be 72.8 % Mo saturation and 84.1 % Fe saturation. The Mo content was lower than previously reported (83%) though this was measured as total concentration of bis-MGD cofactor. It is possible the discrepancy is because not all Mo atoms were fully hydrolysed from the cofactor and so they were not detected by ICP-MS (Warelow et al., 2013). Two samples of F108A had their cofactor content determined. Mo Saturation was 66.8 % and 72.0%. Fe saturation was 76.7 % and 74.9%.

Figure 2.6: A) Gel filtration chromatograph of the WT NT-26 Aio. B) Gel filtration chromatograph of the NT-26 F108A mutant. SDS polyacrylamide gel of Aio WT and F108A Aio. Markers are phosphorylase b (97.0 kDa), albumin (66.0 kDa), ovalbumin (45.0 kDa), carbonic anhydrase (30.0 kDa), trypsin inhibitor (20.1 kDa), α- lactalbumin (14.4 kDa) (GE Healthcare).

2.3.3 The visible absorbance spectra of WT Aio

Visible absorption spectra of transition metal cofactor containing enzymes are useful as they can provide a way to directly monitor the oxidation state or presence of cofactors. For example, many transition metal containing proteins’ spectra are different in the oxidised and reduced forms. It was important to record the oxidised and reduced spectra of the NT-26 Aio to be able to follow the reduction of the enzyme by arsenite in stopped-flow kinetics. There are no published visible absorption spectra of the NT-26 Aio but there is one for the A. faecalis Aio in which there are two peaks at 450 nm and 680 nm which are believed to be mostly due to contributions by the Fe-S clusters and Mo respectively (Anderson et al., 1992).

The visible absorption spectra of the oxidised and reduced WT NT-26 Aio are shown

- in Figure 2.7A. The oxidised spectrum has one broad peak at 450 nm (ɛ450 = 14.1 mM

1 -1 -1 -1 cm ) and a shoulder at 650 nm (ɛ680 = 5.0 mM cm ). Reduction of the enzyme with either arsenite or dithionite achieved identical results with the disappearance of the shoulder at 650 nm and shifting of the 450 nm peak to a shoulder at 420 nm (ɛ450 =

-1 -1 -1 -1 12.5 mM cm and ɛ420 = 13.5 mM cm ). The difference spectrum (Figure 2.7B)

85 shows two prominent maxima at 475 nm and 700 nm which correspond to the 450 nm peak and 650 nm shoulder. These results are virtually identical to the A. faecalis Aio spectra in which the oxidised spectrum exhibited a peak at 450 nm and shoulder at 682 nm with extinction coefficients of 14.5 mM-1 cm-1 and 5.6 mM-1 cm-1 respectively while the reduced spectrum showed a shoulder at 420 nm with extinction coefficient of 11.9 mM-1 cm-1 (Anderson et al., 1992).

35 A Oxidised

30 Reduced )

-1 25

cm 20 -1

(

 10

0 B

2.0 )

-1 1.5

cm -1

1.0

(

 0.5

0.0 300 400 500 600 700 800 Wavelength (nm)

Figure 2.7: A) The oxidised and reduced spectra of heterologously expressed NT-26 Aio in Tris HCl buffer pH 8. B) The difference spectrum of oxidised minus reduced for NT-26 Aio.

2.3.4 Arsenite steady-state kinetics of WT Aio with DCPIP and cytochrome c

To investigate the kinetics of the WT and mutant Aio it was necessary to compare the results from this study to previously published data for the WT Aio using DCPIP and cytochrome c as electron acceptors (Warelow et al., 2013) (Wang et al., 2015).

DCPIP was previously used as an artificial electron acceptor in kinetic studies of both the A. faecalis and NT-26 WT Aio (Anderson et al., 1992) (Santini & vanden Hoven, 2004) (Warelow et al., 2013). DCPIP’s molar absorption coefficient is pH dependent and past studies have used the incorrect molar absorption coefficient of 23.0 mM-1 cm-1. The true value at pH 5.5 is actually 8.2 mM-1 cm-1 meaning activities were undervalued by 2.8-fold (Armstrong, 1964) (Warelow, 2014). Values presented in this thesis have been calculated using the correct extinction coefficient. For comparison, literature DCPIP activity values have been adjusted up 2.8-fold for experiments conducted at pH 5.5.

With DCPIP as the electron acceptor the heterologously expressed NT-26 WT Aio was

-1 -1 -1 found to have Vmax of 4.2 µmol min mg , KM of 72.2 µM and kcat of 7.9 s (Figure

-1 -1 2.8). This is similar to the published values of Vmax = 4.9 ± 0.03 µmol min mg

-1 -1 (adjusted from a value of 1.74 µmol min mg ) and KM = 68 ± 4.8 µM (Warelow et al., 2013).

4.5

4.0

) -1

3.5

mg -1

3.0 mol min mol

 2.5

2.0

1.5 V = 4.2 mol min-1 mg-1

Specific Activity ( Activity Specific max

1.0 KM = 72.2 M -1 Kcat = 7.9 s 0.5

0 500 1000 1500 2000 2500 Arsenite conc. (M)

Figure 2.8: Steady-state kinetics of arsenite oxidation by the heterologously expressed NT-26 Aio with DCPIP as the artificial electron acceptor. Arsenite concentrations were: 2500, 1000, 250, 75, 25 and 5 μM. DCPIP concentration was 300 μM. All readings were taken in duplicate with one enzyme preparation, where only one datapoint is seen for a concentration it is because the results were similar such that the data points overlap. Conducted at 25 °C.

The NT-26 Aio has been shown to be able to donate electrons to c-type cytochromes

(Santini et al., 2007). Cytochrome c552 is a physiological electron acceptor for the NT- 26 Aio. Horse heart cytochrome c has proved to be an effective substitute for the cytochrome c552 and so was used throughout this study as it can be purchased at high purity (>95%) (SigmaAldrich) (Santini et al., 2007) (Wang et al., 2015).

The kinetic properties of the WT Aio with cytochrome c as an electron acceptor were

-1 -1 -1 found to be Vmax = 112.4 µmol min mg , KM = 13.0 µM and kcat = 211.2 s (Figure

-1 -1 2.9). These are similar to the published values of Vmax = 120.1 ± 6.0 µmol min mg and KM = 9.3 ± 1.5 µM (Wang et al. 2014).

120

110 )

-1 100

mg -1 90

mol min mol 80 

50 Specific Activity ( Activity Specific -1 -1 Vmax = 112.4 mol min mg

40 KM = 13.0 M -1 Kcat = 211.2 s 30

0 500 1000 1500 2000 2500 Arsenite conc. (M)

Figure 2.9: Steady-state kinetics of arsenite oxidation by the Aio with horse heart cytochrome c as the electron acceptor. Arsenite concentrations were: 2500, 1000, 250, 75, 25 and 5 μM. Cytochrome c concentration was 20 μM. All readings were taken in duplicate with one enzyme preparation, where only one datapoint is seen for a concentration it is because the results were similar such that the data points overlap. Conducted at 25 °C.

It has been suggested that enzymes involved in arsenic metabolism have evolved to function at higher efficiencies than arsenic resistance enzymes (Stolz & Basu, 2018)

8 -1 -1 because the metabolic Aio of Ralstonia sp. 22 possesses a Kcat/KM = 3.8 x 10 M s

(with c554 cytochrome as an electron acceptor) (Lieutaud et al., 2010) (which approaches the diffusion limit of 109 M-1s-1 (Albery & Knowles, 1976)) while the A.

6 -1 -1 faecalis Aio (involved in resistance) possesses a Kcat/KM of 3.6 x 10 M s (Anderson et al., 1992). The steady-state kinetics of the metabolic arsenate reductase (Arr) have

-1 recently been determined and are exceptionally rapid with Kcat = 9820 s . It also

8 -1 -1 approaches the diffusion limit (Kcat/KM = 3.0 x 10 M s ) (Glasser et al., 2018). The

7 -1 -1 NT-26 Aio arsenite kinetics possesses a Kcat/KM of 2.4 x 10 M s which, while not close to the diffusion limit, is still higher than that of the A. faecalis Aio meaning that

89 it provides further evidence of higher efficiencies in the enzymes involved in arsenic metabolism over those involved in resistance.

As the kinetic values obtained in these experiments were consistent with the literature values it was concluded that the protocols used, and subsequent enzyme preparations were suitable for continued use in this study.

Cytochrome c steady state kinetics of WT Aio

The full catalytic mechanism of the Aio was investigated in this study, this includes both the oxidation of arsenite and the reduction of cytochrome c. While the arsenite kinetics have been determined (and verified above) previous attempts to perform steady state kinetics for cytochrome c with the NT-26 Aio have proven unsuccessful as it appears that the KM is below 5 µM at which the absorbance of 550 nm is too low to monitor (Warelow, 2014).

The Soret point of cytochrome c at 416 nm has a much higher extinction coefficient than 550 nm and so could be used for lower concentrations of cytochrome c (1-5 µM). However, the literature source for the extinction coefficients used for 550 nm does not contain information for any other wavelengths (Van Gelder & Slater, 1962). An extinction coefficient for the Soret point was obtained from Margoliash & Frohwirt (1956) (Δɛ = 40.3 mM-1 cm-1) however this overestimated specific activity by approximately 50%. Thus, visible absorption spectra of oxidised and reduced cytochrome c were recorded, and the extinction coefficients determined using Van Gelder’s values for 550 nm. This resulted in the spectra shown in Figure 2.10 which indicated a 416 nm Δɛ = 57.5 mM -1 cm -1.

140 Oxidised A Reduced

120

 )

-1 100

cm 80 -1

mM (

 40

20 B

 )

-1 0 cm

-1 -20 mM

( -40  -60

-80 350 400 450 500 550 600 Wavelength (nm)

Figure 2.10: A) Oxidised and reduced spectra of horse heart cytochrome c determined in Tris-HCl pH 8 buffer. The concentration and extinction coefficient were determined by using the 550 nm absorbance and values from van Gelder (1962). B) Difference spectrum (oxidised minus reduced) of horse heart cytochrome c.

Aio steady-state kinetics of cytochrome c were determined using 2.5 mM arsenite and 0.5 – 10 μM cytochrome c. The kinetic properties of cytochrome c and Aio with

-1 -1 excess arsenite were Vmax = 226 ± 17 µmol min mg , KM = 1.0 ± 0.1 μM and kcat = 427

-1 ± 32 s (Figure 2.11). Note that the Vmax and kcat are given for cytochrome c turnover and are therefore double the values for arsenite as the reaction ratio is 1:2 for arsenite: cytochrome c. Halving these values to 113.0 µmol min-1 mg-1 and 214 s-1 shows the Vmax and kcat obtained in this experiment are consistent with those obtained for arsenite. The KM is also 5-fold lower than 5 µM which explains why no significant changes in rate were seen at concentrations of 5 µM and greater as the system was approaching saturation.

240

220

200

) -1

mg 180 -1

160

mol min mol  ( 140

120

100 -1 -1 Spec. Activity Activity Spec. Vmax = 226 mol min mg

80 KM = 1.0 M -1 Kcat = 427 s 60

0 2 4 6 8 10 Cytochrome c conc. (M)

Figure 2.11: Steady-state kinetics cytochrome c reduction by the Aio in the presence of 2500 μM arsenite. Cytochrome c concentrations were 10, 5, 4, 3, 2, 1 and 0.5 μM. Data points represent the average of three experiments with three separate enzyme preparations. Error bars represent standard deviation. Conducted at 25 °C.

2.3.5 The reductive-half reaction of Aio

There are four electron transfer events in Aio: arsenite to the molybdenum centre, the molybdenum centre to the 3Fe-4S cluster, the 3Fe-4S cluster to the Rieske 2Fe- 2S cluster and finally from the 2Fe-2S cluster to the terminal electron acceptor (cytochrome c in this study) (Ellis et al., 2001). The molybdenum centre and the Fe-S clusters have distinct spectral features at 680 nm and 450 nm respectively meaning their reduction could be followed independently by stopped-flow UV-visible spectroscopy.

In a stopped-flow experiment monitoring the reduction of the Aio by arsenite (Figure 2.12), quenching of the 680 nm absorption band occurred in the mixing dead time of the instrument of (1 ms) implying that the molybdenum centre was reduced by arsenite at a rate of >4000 s-1. The 450 nm absorption band quenched at an arsenite independent rate of 592 ± 20 s-1 and was monophasic, suggesting that electron

92 transfer from the molybdenum centre to both Fe-S clusters are similar and indistinguishable via the exponential fitting procedure or that the measured rate represents electron transfer to the Rieske 2Fe-2S while electron transfer to the 3Fe- 4S is considerably faster.

0.6

0.30

0.5 0.29 450 0.28

0.4

Abs 0.27

0.26 0.00 0.02 0.04

Abs 0.3 time (s)

0.2

Oxidised 0.1 1 ms 9 ms End

0.0 300 400 500 600 700 Wavelength (nm)

Figure 2.12: Stopped-flow spectroscopy monitoring the reduction of the Aio by arsenite. Inset: Reduction at 450 nm fit with a single exponential decay function to determine the rate constants of the reaction.

Rate constants for the reduction of molybdenum centres of molybdoenzymes are quite variable and the rapid reduction of the molybdenum centre of Aio is at the high end. However, it is not a unique observation, the reductive half reaction of the DMSO reductase from Rhodobacter capsulatus also occurs within the mixing dead time at 10 °C (Mcalpine & Bailey, 1997). The molybdenum containing formate dehydrogenase from Ralstonia eutropha (another DMSOR family member), is also quite rapid with a rate of 140 s-1 at 10 °C (approximately 400 s-1 at 25 °C) (Niks et al., 2016). Chicken liver sulphite oxidase reacts slightly slower with sulphite (150-200 s-1 at 25 °C) (Brody & Hille, 1999). Some of the slower observed rates occurred in the xanthine oxidase family: bovine milk xanthine oxidase reacts with xanthine at 14 s-1

93 at 25 °C and Oligotropha carboxydovorans CO dehydrogenase reacts with CO at 11.4 s-1 at 4 °C (Davis et al., 1982) (Zhang et al., 2010). The Aio is unique among molybdoenzymes in that that it does not have a stable Mo(V) state (it is unobservable in the WT enzyme) (Hoke et al., 2004) (Duval et al., 2016). This may be important in rate acceleration at the molybdenum centre however, since the reduction of the molybdenum occurs in the mixing dead-time for both the Aio and the R. capsulatus DMSO reductase (which has a stable Mo(V) state (Cobb et al., 2005)), it is not possible to compare their rates to conclude that cooperative electron transfer is important in rapid reaction rates.

The reduction potentials of the molybdenum centre, 3Fe-4S cluster and the Rieske 2Fe-2S cluster are all similar at +240 mV, +270 mV and +225 mV respectively (Warelow et al., 2013) (Duval et al., 2016). It’s unlikely that differences between the cofactor reduction potentials play a significant role in defining the observed electron transfer rates. The rate of Fe-S reduction is most likely limited by dissociation of arsenate (product) from the active site. This is also consistent with the observation that the Fe-S reduction rate is arsenite independent.

The Aio Fe-S clusters appear to become reduced simultaneously, consistent with the apparent two-electron re-oxidation of the reduced Mo(IV) centre and failure to observe the Mo(V) state. The Mo(V) state is sufficiently stable in most molybdoenzymes to allow the molybdenum centre to act as a transducer between obligate one- and two-electron transfer centres (Hille, 2002). It appears that the hydrogen bonding network of the MGD cofactor in Aio has been highly optimised to destabilise the Mo(V) state (Duval et al., 2016) perhaps due to the instability of As(IV), which rapidly decays to As(V) and superoxide (Klaning et al., 1989). Production of toxic superoxide may have provided the evolutionary selection pressure to eliminate one-electron transfer between arsenite and the molybdenum centre. The presence of the two Fe-S clusters would have then been required to permit re-oxidation of the molybdenum centre, allowing the enzyme to act as a transducer and couple the oxidation of arsenite to the reduction of physiological one-electron acceptors.

2.3.6 The oxidative-half reaction of Aio

The final step in the Aio catalytic cycle is the oxidative-half reaction which involves electron transfer from the Rieske 2Fe-2S cluster to the terminal electron acceptor. In

NT-26, cytochrome c552 can serve as the electron acceptor for Aio. However, horse heart cytochrome c is used in this study as it has been shown to be an acceptable substitute (Santini et al., 2007).

For the reduction of cytochrome c, the spectral change at 551 nm was followed as this corresponds to reduced cytochrome c. Changes in Aio’s absorbance were not expected to interfere with that of cytochrome c. The absorbance change at 551 nm is much larger for cytochrome c than it is for Aio meaning that most of the change can be attributed to the former. Furthermore, reduction of Aio could be ruled out as this results in a reduction in absorbance as opposed to an increase which is seen for cytochrome c. The oxidation of Aio by cytochrome c would also have the same rate as cytochrome c’s reduction meaning the spectral change would be representative of one reaction.

The reduction of cytochrome c was biphasic (Figure 2.13). The first phase accounted for 97% of the absorbance change and had a rate of 389 ± 7 s-1. The second phase had a rate of 11 ± 6 s-1 and most likely corresponds to when equilibrium was achieved between the Aio and cytochrome c and is therefore not relevant to enzymatic turnover (Figure 2.13). The rate constants of the Aio are summarised in Table 2.2. The reduction of cytochrome c was the slowest process observed in stopped-flow experiments and was similar to the Kcat, which means that the reduction of cytochrome c is the rate-limiting step of Aio catalysis of arsenite.

0.6 0.48

0.44

0.5 551

0.40 Abs 0.36 0.4 0.32 0.0 0.2 0.4 0.6 0.8 1.0

Abs time (s) 0.3

0.2 1 ms 5 ms 10 ms 15 ms 0.1 End

450 500 550 600 650 700 Wavelength (nm)

Figure 2.13: Stopped-flow spectroscopy monitoring the reduction of horse heart cytochrome c by the Aio and arsenite using 60 μM cytochrome c, 30 μM arsenite and 15 μM Aio. Inset: Reduction at 551 nm fit with a double exponential decay function to determine the rate constants of the reaction.

Table 2.2: Summary of kinetic rate constant for the WT Aio

Electron transfer step Kinetic phase Rate (s-1)

Reduction of Mo-cofactor K680 >4000

Reduction of Fe-S clusters K450 592

Reduction of cytochrome c K551 389

Turnover number Kcat 427

2.3.7 The interaction of the Aio with cytochrome c

As reduction of cytochrome c was determined to be the rate-limiting step, the interaction of the Aio and cytochrome c was further studied by ITC. An endothermic

96 interaction was observed between the WT Aio and cytochrome c with a Kd = 2.3 ± 0.7 µM (Figure 2.14). The stoichiometry was 0.85 which is roughly consistent with the amount of Fe saturation in enzyme preparations indicating that cofactor incorporation is required for cytochrome c to bind.

Time (min) 0 10 20 30 40 50 60 70 80 0.8

0.6

µcal/sec 0.4

0.2

0.0 6.0

4.0

of injectant of -1

2.0 kcal mol kcal

0.0 0.0 0.5 1.0 1.5 2.0 Molar Ratio

Figure 2.14: ITC of the Aio titrated against cytochrome c. Top: Raw thermogram output of injections of cytochrome c into Aio. Bottom: The integrated heats of injections plotted against molar ratio of cytochrome c to Aio and fit with a single binding model to determine thermodynamic parameters of binding. The ITC experiment was repeated with three separate enzyme preparations.

The KM and Kd can be used to determine the specific mechanism that is rate-limiting in Aio reduction of cytochrome c. The reduction of cytochrome c can be split into two distinct events based on Michaelis-Menten kinetics: complex formation and product formation and dissociation. Stopped-flow spectroscopy is unable to differentiate between these two events as it was only able to detect spectral changes associated with the reduction of cytochrome c. Therefore, if complex formation were to happen 97 at a rate of 392 s-1 then reduction of cytochrome c could not happen any faster, no matter how rapid electron transfer was.

The KM and the Kd are defined in Equation 2.11 and Equation 2.12 respectively. The equations are defined in terms of rate constants: complex formation from reactants

(k+1), complex breakdown into reactants (k-1) and complex breakdown into product

(k2) (which is assumed to be irreversible). The Kd is simply a measure of complex formation, or affinity, while the KM is a measure of both complex formation as well as reaction to form product. Critically, either complex formation (k-1/k+1) or product formation (k2) can be rate-limiting.

푘−1+푘2 Equation 2.11:퐾푀 = 푘+1

푘−1 Equation 2.12: 퐾푑 = 푘+1

It can be seen from the equations that KM is equal to the sum of Kd and the ratio of k2 and k+1. When k2 is rate-limiting, it is smaller than k+1 and the ratio is smaller than

1. Therefore, in instances where k2 is rate limiting, the KM is roughly equal to Kd.

Conversely, when k2 is not rate-limiting, it is larger than k+1 and so the ratio is greater than 1 which results in the KM being much larger than the kd. The KM for the WT Aio and cytochrome c is 1.0 μm which is only 2.3-fold lower than the Kd of 2.3 µM. It is likely that this small difference between the Kd and KM is due to the oxidation state of cytochrome c as small structural changes on the surface of cytochromes c have been observed in crystal structures (Berghuis & Brayer, 1992) (Yoshikawa et al., 1998) and the two oxidation states of horse heart cytochrome c have been shown to have slightly different (2 to 10-fold) affinities for cytochrome c peroxidase (Erman &

Vitello, 2002). The similarity in kd and KM suggests that k2 of the reaction, and therefore electron transfer and production of reduced cytochrome c, is rate-limiting in the catalytic cycle of Aio which is not uncommon in enzymes that follow Michaelis-

Menten kinetics under saturating conditions. Conversely, the Kcat/KM ratio was 4 x 108 M-1 s-1 which would suggest that Aio is ‘catalytically perfect’, meaning that the cytochrome c reaction is diffusion limited (not electron transfer limited) (Albery & Knowles, 1976). While both these models are accurate for single-substrate enzymes,

98 the KM’s of substrates of multiple substrate enzymes are often altered by the rate of the other half-reaction (Cleland, 1963). Electron transfer is therefore considered to be the most likely rate-limiting step as ‘catalytic perfection’ is a very rare phenomenon.

It is perhaps unsurprising that the AioB subunit is implicated in the rate-limiting step. Rieske proteins are implicated in the rate-limiting steps of numerous enzymatic reactions. For example, in the bc1 complex of Rhodobacter sphaeroides, the rate- limiting step was shown to be the oxidation of ubihydroquinone by the Rieske 2Fe- 2S cluster as it presented the largest barrier for activation energy (Hong et al., 1999). Reduction of cytochrome f by the Rieske 2Fe-2S was determined to be the rate limiting step of the b6f complex in oxygenic photosynthesis in Chlamydomonas reinhardtii (Soriano et al., 2002).

Rieske proteins have highly tuneable reduction potentials as well as high structural plasticity suggesting that they may be involved in electron transfers where the reduction potential must be very specific or where high promiscuity is beneficial. For example, the Rieske protein of Thermus thermophilus experiences a decrease in reduction potential of 440 mV as the histidine residues are deprotonated at high pH (Lin et al., 2006) (this phenomenon is not seen in 2Fe-2S clusters which are ligated by four cysteine residues instead of two histidine and two cysteine (Zu et al., 2003)).

Point mutations that alter the hydrogen bonding network of the bc1 Rieske cluster have been shown to induce changes in the reduction potential as large as 130 mV (Sarewicz et al., 2015). With regards to structural plasticity, small redox state dependent structural changes in proline residues proximal to the 2Fe-2S cluster have been observed by NMR in the Rieske protein of T. thermophiles. and been shown to influence hydroubiquinone binding (Hsueh et al., 2010). Members of the Rieske dioxygenase family also tend to exhibit a high degree of substrate promiscuity (Parales et al., 2000) (Parales et al., 1998) (Jouanneau et al., 2006).

In NT-26 it appears that Aio is able to use at least two physiological electron acceptors as the cytochrome c552 knock-out mutant continues to oxidise arsenite (Santini et al., 2007). Furthermore, the arsenite oxidases from A. faecalis and Ralstonia sp. 22 have

99 been shown to use multiple electron acceptors such as azurin and c-type cytochromes (Anderson et al., 1992) (Lieutaud et al., 2010). It is possible that this promiscuity of the Aio Rieske cluster allows it to act as a variable interface between the enzyme and its substrates. There is increasing support for models of modular electron transport chains with high degrees of plasticity, in which different protein- protein complexes form to respond dynamically to changes in the environment (Lapuente-Brun et al., 2013) (Porras & Bai, 2015) (Acin-Perez & Enriquez, 2014). In fact, a review by Schneider & Schmidt (2005) specifically highlights Rieske proteins as important factors in allowing organisms to adapt their electron transfer chains to changing environmental conditions. It is therefore possible that the reduction of cytochrome c by the Rieske cluster in Aio is rate-limiting because it is the most varied aspect of the catalytic cycle and a compromise between rapid rates and plasticity has been reached.

2.3.8 The mechanism of Aio catalysis

The catalytic mechanism of the NT-26 Aio has been shown to be double displacement for arsenite oxidation coupled to DCPIP reduction (Warelow, 2014). However, this has not been shown for the reduction of cytochrome c.

The catalytic mechanism was investigated by using double-reciprocal plots of Aio activity at varied concentrations of both arsenite and cytochrome c (with all reactions followed at 416 nm). The double reciprocal plots are shown in Figure 2.15. Approximately parallel lines were produced which indicated a double displacement mechanism. A similar experiment was conducted by Anderson et al. (1992) using the A. faecalis Aio and azurin as an electron acceptor instead of cytochrome c which also showed a double displacement mechanism.

100

As(III) conc. 0.035 2500  30  20  10  0.030 5 

0.025

mg) -1

0.020

0.015 1/Spec. Activity (U Activity 1/Spec. 0.010

0.005

0.000 0.0 0.2 0.4 0.6 0.8 1.0 1/ cytochrome c conc. (-1)

Figure 2.15: Double reciprocal plots of Aio steady state kinetics with arsenite and cytochrome c. Concentrations of arsenite used were 2500, 30, 20, 10 and 5 μM. Concentrations of cytochrome c used were 10, 5, 3, 2 and 1 μM. All data points represent the average of three assays with one enzyme preparation. Error bars represent standard deviation. Conducted at 25 °C.

In double displacement mechanisms, the enzyme is converted to a new form by its reaction with one substrate and is then converted back to its original form by reaction with another substrate. This is simplest when considering electron transfer, substrate 1 reduces the enzyme, the enzyme then reduces substrate 2, re-oxidising the enzyme making it available to react with another molecule of substrate 1 etc. The reactive scheme for a double displacement mechanism is expressed in Equation 2.13 where S and P denote the first substrate and product; Q and R denote the second substrate and product; and E and F denote the two forms of the enzyme.

Equation 2.13: 퐸 + 푆 ↔ 퐸푆 → 퐹 + 푃 → 퐹 + 푄 ↔ 퐹푄 → 퐸 + 푅

101

The reactive scheme for the Aio is slightly complicated by the fact that arsenite is a two-electron donor, while cytochrome c is a one electron acceptor (meaning that two cytochromes c are required for every one arsenite). A model is proposed in Figure 2.16. This model starts with fully oxidised Aio. Arsenite rapidly reduces the Mo-centre at a rate greater than 4000 s-1. The electrons are then transferred to the Fe-S clusters simultaneously at a rate of 592 s-1 (this rate is probably limited by the dissociation of arsenate from the active site as the Mo cannot be re-oxidised until the arsenate has left). The Mo is then rapidly reduced by arsenite again. A single cytochrome c binds to Aio (as ITC has shown there is only one cytochrome c binding site) and accepts one electron. The single remaining electron probably exists on both Fe-S clusters given that they have very similar reduction potentials (3Fe-4S = +270 mV; 2Fe-2S = +225 mV ) (Hoke et al., 2004) (Warelow et al., 2013). A second cytochrome c then binds Aio once the site is exposed and accepts the second electron. It is only at this point that the Mo is able to transfer electrons to the Fe-S clusters because Aio Mo is only capable of two electron transfer as Mo(V) signals have never been observed in the WT enzyme (Duval et al., 2016) (Hoke et al., 2004).

102

Figure 2.16: Catalytic cycle for the Aio with arsenite and cytochrome c. The number of electrons on the Aio cofactors is shown in brackets. FeS represents both the 3Fe- 4S and the 2Fe-2S clusters as they are indistinguishable in stopped-flow experiments. Rates are based on stopped-flow data at 25 °C.

In the proposed mechanism, the Aio essentially has two forms: when the Mo is reduced (Mo(IV)) Aio reduces cytochrome c; when the Mo is oxidised (Mo(VI)) Aio oxidises arsenite. It is unlikely that fully oxidised Aio exists in the cycle given how much more rapid the reductive half-reaction is than the oxidative half-reaction.

2.3.9 The effect of the AioB-F108A mutation on Aio Activity

AioB lacks a disulphide bridge proximal to the 2Fe-2S cluster, instead containing a phenylalanine and a glycine at the positions otherwise occupied by cysteines in the

Aio of bacteria from the Betaproteobacteria as well as bc1 and b6f Rieske proteins. Mutation of AioB Phe 108 to Ala (F108A) was made to analyse what effect, if any,

103 removal of the large, hydrophobic and aromatic phenylalanine had on enzyme activity.

The kinetic properties of the F108A mutant with cytochrome c as an electron

-1 -1 acceptor were found to be Vmax = 3.2 ± 0.1 µmol min mg , KM = 2.0 ± 0.4 µM and

-1 kcat = 6.0 ± 0.3 s (Figure 2.17). The activity of F108A with cytochrome c is 97 % lower than the WT Aio. The kinetic properties of cytochrome c and F108A Aio with excess

-1 -1 arsenite were Vmax = 7.5 ± 0.1 µmol min mg , KM = 1.2 ± 0.2 μm and kcat = 14.2 ± 0.2

-1 -1 -1 -1 s (Figure 2.18). Again, halving the Vmax and kcat (3.8 µmol min mg and 7.1 s ) makes these results consistent with arsenite values. The activity of F108A was 30- fold lower than the WT Aio (t-test p = 0.0001) while the KM for cytochrome c was not significantly altered (t-test p = 0.2377).

3.4

3.2

) ) -1

mg 3.0 -1

mol min mol 2.8 

2.6

-1 -1 Vmax = 3.2 mol min mg

Specific Activity ( Activity Specific 2.4 KM = 2.0 M -1 Kcat = 6.09 s 2.2

0 500 1000 1500 2000 2500 Arsenite conc. (M)

Figure 2.17: The F108A mutant steady-state kinetics of arsenite. Arsenite concentrations were 2500, 1000, 250, 75, 25 and 5 μM. Cytochrome c concentration was 20 μM. Data points represent the average of three experiments with three separate enzyme preparations. Error bars represent standard deviation. Conducted at 25 °C.

104

) -1

mg 5 -1

mol min mol 4

 (

-1 -1 Spec. Activity Activity Spec. Vmax = 7.5 mol min mg

2 KM = 1.2 M -1 Kcat = 14.2 s

1 0 2 4 6 8 10

Cytochrome c conc. (M)

Figure 2.18: The F108A mutant steady-state kinetics of cytochrome c in the presence of 2500 μM arsenite. Cytochrome c concentrations were 10, 5, 4, 3, 2, 1 and 0.5 μM. Data points represent the average of three experiments with three separate enzyme preparations. Error bars represent standard deviation. Conducted at 25 °C.

In contrast to its activity with cytochrome c, the Aio F108A mutant was found to have higher activity with DCPIP as the electron acceptor. The kinetic parameters were Vmax

-1 -1 -1 = 5.8 ± 0.2 µmol min mg , KM = 94.6 ± 8.5 µM and kcat = 10.9 ± 0.4 s (Figure 2.19). The activity of F108A with DCPIP as an electron acceptor was significantly, 20-30% higher than WT (t-test p = 0.001).

105

) 5

-1

mg -1

mol min mol  3

Specific Activity ( Activity Specific -1 -1 Vmax = 5.8 mol min mg 1 KM = 94.6 M -1 Kcat = 10.9 s

0 0 500 1000 1500 2000 2500 Arsenite conc. (M)

Figure 2.19: The F108A mutant steady-state kinetics of arsenite with DCPIP as an electron acceptor. Arsenite concentrations were: 2500, 1000, 250, 75, 25 and 5 μM. DCPIP concentration was 300 μM. Data points represent the average of three experiments with three separate enzyme preparations. Error bars represent standard deviation. Conducted at 25 °C.

Steady-state kinetics showed that the F108A mutation had resulted in a substantial decrease in cytochrome c activity while not affecting cytochrome c affinity significantly and increasing DCPIP activity.

2.3.10 The effect of the F108A mutation on electron transfer rates

The F108A mutant made an excellent candidate for further research into the rate- limiting step of Aio catalysis (reduction of cytochrome c) as the mutation specifically reduced cytochrome c activity. Identical stopped-flow kinetics experiments to the ones performed with WT Aio were therefore performed with the F108A mutant.

The reduction of the F108A mutant by arsenite was followed by stopped-flow spectroscopy. The quenching of the 680 nm peak was within the mixing dead time of 1 ms meaning the reduction of molybdenum by arsenic had a rate >4000 s-1. The 450

106 nm peak quenched at a rate of 564 ± 46 s-1 (Figure 2.20). These results are not significantly different to WT Aio (t-test p = 0.3884) meaning that the F108A mutation has not affected the reductive half reaction of Aio.

0.6

0.21

0.5 450

0.20 Abs 0.4

0.19

0.3 0.00 0.02 0.04 Abs time (s)

0.2

Oxidised 0.1 1 ms 9 ms End

0.0 300 400 500 600 700 Wavelength (nm)

Figure 2.20: Stopped-flow spectroscopy monitoring the reduction of the F108A mutant by arsenite. Inset: Reduction at 450 nm fit with a single exponential decay function to determine the rate constants of the reaction.

For the reduction of cytochrome c, the reaction was triphasic (Figure 2.21). The first phase appears to be the reduction of Aio by arsenite given it was a reduction in absorbance as opposed to the increase seen with cytochrome c. It was also roughly similar to the value for 450 nm reduction seen in the previous experiments with a rate of 738 ± 44 s-1. The second phase accounted for 84% of the total absorbance change and had a rate of 9.2 ± 0.4 s-1 (the rate constants for the F108A mutant and

WT Aio are summarised in Table 2.3). The rate of cytochrome c reduction rate is similar to the Kcat for F108A and the slowest rate observed meaning it is therefore the rate-limiting step. The final phase had a rate of 1.6 ± 0.4 and is again most likely when equilibrium between F108A and cytochrome c is achieved making it irrelevant

107 enzymatically. The principal effect of the F108A mutation was a reduction in the rate of electron transfer to cytochrome c.

0.6 0.50

0.45 0.5

551 0.40 Abs 0.35 0.4

0.30

0.0 0.2 0.4 0.6 0.8 1.0 Abs 0.3 time (s)

0.2 1 ms 100 ms 500 ms 1000 ms 0.1 End

450 500 550 600 650 700 Wavelength (nm)

Figure 2.21: Stopped-flow spectroscopy monitoring the reduction of horse heart cytochrome c by the F108A mutant and arsenite. Inset: Reduction at 551 nm fit with a triple exponential decay function to determine the rate constants of the reaction.

Table 2.3: Rate constants of WT and F108A Aio.

Electron transfer step Kinetic phase WT Rate (s-1) F108A Rate (s-1)

Reduction of Mo-cofactor K680 >4000 >4000

Reduction of Fe-S clusters K450 592 564

Reduction of cytochrome c K551 389 9.2

Turnover number Kcat 427 14.2

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2.3.11 The role of the AioB-F108 residue in electron transfer

The F108A mutation has been shown to reduce cytochrome c activity without significantly affecting the KM. It does this by specifically reducing the rate of electron transfer from the Rieske 2Fe-2S cluster to cytochrome c (as shown by stopped-flow spectroscopy). The F108A mutations has also been shown to slightly but significantly increase activity with DCPIP. The next section of this chapter will seek to determine what the functional role of the F108 residue is considering these findings.

There are three potential roles of the F108 residue: as a critical residue in defining the reduction potential of the Rieske 2Fe-2S cluster, involved in the electron transport pathway or as an important residue in establishing the Aio-cytochrome c complex.

F108 does not play a critical role in defining the reduction potential of the Rieske 2Fe- 2S cluster. Redox titration and electron paramagnetic resonance (EPR) were used to demonstrate that the reduction potential of the F108A 2Fe-2s cluster is +205 ± 10 mV (Watson et al., 2017) which is not significantly different to the WT 2Fe-2S cluster’s reduction potential (+225 ± 10 mV) (t-test p-value = 0.0705) (Warelow et al., 2013). This result is consistent with the observed steady state activities as a reduction potential change that reduced activity with cytochrome c might also reduce activity with DCPIP as the electron acceptor as opposed to increasing it like the F108A mutation did.

Residue F108 must therefore be important in the electron transport pathway or in binding cytochrome c. The X-ray crystal structure of the F108A mutant was resolved by Teresa Santos-Silva (Universidadae de Lisboa) to a resolution of 2.2 Å (Watson et al., 2017). A structural alignment was performed with the WT Aio to visualise differences between the two structures and is shown in Figure 2.22. It can be observed that F108 (in WT) is positioned such that it partially covers the 2Fe-2S cluster, acting as something of a cap. F108 would be expected to prevent access of smaller molecules to the 2Fe-2S cluster. A108 (in the F108A mutant) is a much smaller residue than Phe and so serves as a much less obstructive cap, allowing greater access to the 2Fe-2S cluster by small molecules like DCPIP thus explaining the 109 observed increase in activity with DCPIP. However, this leaves the question as to why the loss of the cap resulted in such a dramatic decrease in cytochrome c activity.

Figure 2.22: Structural alignment of WT Aio (green) and F108A (purple). The residue at position 108 is marked by its number. The 2Fe-2S cluster is shown as spheres. PDB ID: 4AAY for the WT Aio and 5NQD for the F108A mutant.

To further elucidate the role of F108, the interaction of F108A (Figure 2.23) and cytochrome c was investigated by ITC using identical conditions to those used previously with the WT Aio. The Kd for cytochrome c was found to be 7.8 ± 1.1 μM. The ΔH was found to be 3021 ± 298 and the ΔS was 33.5 ± 1.1. While the difference in Kd between the F108A mutant and the WT Aio is significant (t-test p = 0.0067) it is only a 3-fold increase. Taken alongside the observation that the F108A mutant does not have a significantly different KM for cytochrome c to WT this would suggest that the F108A substitution has not dramatically altered cytochrome c binding meaning the mutation must exert its effect via other means.

110

Time (min) 0 10 20 30 40 50 60 70 80 0.4

0.3

µcal/sec 0.2

0.1

0.0

3.0

2.0

of injectant of -1

1.0 kcal mol kcal

0.0

0.0 0.5 1.0 1.5 2.0 Molar Ratio

Figure 2.23: ITC of the F108A mutant titrated against cytochrome c. Top: Raw thermogram output of injections of cytochrome c into Aio. Bottom: The integrated heats of injections plotted against molar ratio of cytochrome c to Aio and fit with a single binding model to determine thermodynamic parameters of binding. The ITC experiment was repeated with three separate enzyme preparations.

The F108A mutation caused the ∆H to drop by 50% while the ∆S decreased by only 27%. The propensity for endothermic reactions to occur increases as the ∆S becomes more positive and decreases as the ∆H becomes more positive. These results suggest that F108 has a relatively neutral function in complex formation as both the ΔH and ΔS of cytochrome c binding to F108A are smaller compared to the WT Aio. It appears that the AioB-F108 is not critical to cytochrome c association but instead is important for electron transfer to cytochrome c. This would suggest that the AioB-F108 residue plays only a small role in the binding process, but once the protein: protein complex forms it is important in bringing the two proteins into the correct orientation for

111 electron transfer and/or in providing an electron transfer pathway between the Rieske 2Fe-2S cluster and heme c of the cytochrome. The rate of electron transfer exponentially decreases as distance increases meaning a relatively minor change in the complex structure can result in dramatic reductions in rate (Page et al., 1999).

Arsenite oxidases appear to show selectivity towards different electron acceptors based on their phylogeny rather than the reduction potential of the electron acceptor (Santini et al., 2007) (Anderson et al., 1992) (Lieutaud et al., 2010) (Duval et al., 2010). NT-26 is a member of the Alphaproteobacteria and can use cytochrome c as the electron acceptor, whereas members of the Betaproteobacteria such as A. faecalis and Ralstonia sp. 22 can use azurin and some c-type cytochromes as the electron acceptor. The main difference between the Rieske subunits is the absence of a disulphide bridge proximal to the 2Fe-2S cluster in alphaproteobacterial AioB. The presence of the disulphide bridge in the AioB has been shown to have a much smaller effect on reduction potential than in the bc1 complex (35 mV and 54-139 mV respectively) (Warelow et al., 2013) (Merbitz-Zahradnik et al., 2003) (Leggate & Hirst, 2005) (Zu et al., 2002). It has been previously suggested that the presence of the disulphide bridge in Aio modulates the electron acceptor selectivity (Lieutaud et al., 2010) (Duval et al., 2010). The results of this chapter support this notion as AioB-F108 appears to be important for electron transfer to cytochrome c. The role of the disulphide bridge in modulating electron acceptor specificity is explored in much greater depth in Chapter 3.

2.3.12 Investigation into the interaction of AioB with horse heart cytochrome c

The AioB-F108A mutant demonstrated that, while residue F108 was important in electron transfer to cytochrome c, it was not critical in complex formation. It was therefore of interest to further explore the formation and interaction of the AioB with cytochrome c. It was thought that nuclear magnetic resonance (NMR) would be ideal for examining dynamic responses in the protein architecture of the AioB when cytochrome c binds. NMR requires very high concentrations of protein and is only effective for relatively small proteins meaning that the whole of Aio could not be used. Fortunately, the AioB + His-tag is only ~20 kDa meaning it could be used for

112

NMR provided it was expressed without the AioA. It was therefore necessary to develop a heterologous expression system to purify high yields of the AioB and then determine if the interaction of the AioB with cytochrome c when the AioA was not present was similar to the entire enzyme’s complex

2.3.13 Cloning of the aioB gene

To express the NT-26 AioB without the AioA, a heterologous expression system was developed in E. coli using the pProEX-Htb+ vector as this was successful in the expression of the NT-26 Aio.

Presence of the pProEX-Htb+ plasmid containing aioB was confirmed by restriction digest with PstI and EcoRI and gel electrophoresis (Figure 2.24). One fragment at approximately 5000 bp and another at approximately 400 bp can be visualised. These correspond to the vector (4717 bp) and the aioB insert (434 bp). Sequencing of this insert confirmed that the aioB sequence was correct.

Figure 2.24: Photos of restriction digest and gel electrophoresis confirming the presence of the aioB gene in the pProEX-Htb+ plasmid. The marker used was a 1kb+ GeneRuler (ThermoFisher Scientific).

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2.3.14 Expression and characterisation of heterologously expressed AioB

The heterologously expressed AioB was purified by nickel affinity chromatography and size exclusion chromatography. Size exclusion chromatography yielded two overlapping peaks: one at 8.75 mL and the other at 10.5 mL (Figure 2.25A). According to the calibration of the size exclusion chromatography column using standards (Appendix C) these correspond to approximately 70 kDa and 35 kDa which are likely to be tetrameric and dimeric forms of AioB respectively. The SDS-polyacrylamide gel (Figure 2.25B) demonstrates that this is the case, as both peaks only contain AioB+His-tag (18.4 kDa).

The UV-visible spectra of both peaks were recorded to estimate co-factor saturation by examining the 280:450 ratio (and so give an estimate of protein: iron content) (Figure 2.25C). The tetramer was found to have a much higher 280:450 ratio of 1:0.19 compared to the dimer which was only 1:0.05 suggesting that the tetramer has almost four-fold higher cofactor saturation. For this reason, the tetramer was used for further experiments.

114

Figure 2.25: Purification and characterisation of the heterologously expressed AioB. A) Gel filtration chromatograph showing two peaks containing the AioB representing two oligomeric states. B) SDS polyacrylamide gel showing the results of the size exclusion chromatography illustrating that both peaks contain AioB. C) UV-visible spectra of AioB representing both peaks from size exclusion chromatography normalised such that Abs280 = 1.0. Markers are phosphorylase b (97.0 kDa), albumin (66.0 kDa), ovalbumin (45.0 kDa), carbonic anhydrase (30.0 kDa), trypsin inhibitor (20.1 kDa), α- lactalbumin (14.4 kDa) (GE Healthcare).

An attempt was made to determine if the tetramer would dissociate into lower order oligomeric states, or the monomeric state, at lower concentrations of the AioB. Size

115 exclusion chromatography was performed with the tetrameric fraction at 1 mg/mL, 0.1 mg/mL and 0.01 mg mL-1. No change was observed in the oligomeric state.

Size exclusion chromatography was only attempted using 50 mM Tris-HCl pH 8. Different buffers and salt concentrations were not attempted as the intent of this investigation was to investigate the interaction of the AioB with cytochrome c in conditions identical to the work already done with the Aio (in which all experiments, including size exclusion chromatography, were conducted using 50 mM Tris-HCl pH 8).

Co-factor saturation is often not 100 % in heterologous expression systems meaning that protein concentrations often over-predict the concentration of correctly folded protein (i.e. protein that contains cofactor). As the AioB has only one Fe-S cluster, the 430 nm absorbance peak can be normalised to Fe content. This gives an extinction coefficient that can be used to estimate the concentration of co-factor containing AioB. ICP-MS results indicate the Fe saturation was 62.5 ± 4.6 %. Figure 2.26 shows the oxidised and reduced visible spectra of the AioB with extinction coefficients normalised to the Fe content. There is a peak in the oxidised spectrum at 430 nm with ɛ = 11.0 mM-1cm-1 and a shoulder at 570 nm with ɛ = 6.5 mM-1 cm-1. This is similar to the published Thermus thermophilus Rieske visible spectra which has a peak at 460

-1 -1 -1 -1 nm (ɛ460 = 5.7 mM cm ) and a shoulder at 560 nm (ɛ560 = 3.0 mM cm ) (Fee et al., 1984). Note that these are the first recorded AioB UV-visible spectra.

The determination of the extinction coefficient spectra is incredibly important for any future work with the AioB expression system as it allows absolute determination of the concentration of the AioB that possesses the Rieske 2Fe-2S cluster in the sample.

116

Figure 2.26: The oxidised and reduced spectra of heterologously expressed NT-26 AioB in Tris-HCl buffer pH 8. B) The difference spectrum of oxidised minus reduced for NT-26 AioB.

2.3.15 The interaction of AioB with cytochrome c

To evaluate if the AioB was suitable for further investigation to study the Aio- cytochrome c interaction it was necessary to establish if it could bind and reduce cytochrome c in a manner similar to the Aio.

To determine if the AioB was also able to bind cytochrome c, the affinity of the AioB for horse heart cytochrome c was measured by ITC under the same conditions that were used for the Aio. The AioB could bind cytochrome c, albeit much less effectively than the Aio (Figure 2.27). The Kd was 366.8 ± 53.8 μM which is 160-fold higher than it was for the Aio (2.3 μM). The ΔH was 3104 ± 530 cal mol-1 (half that of the Aio: 6007 cal mol-1) and the ΔS was 26.1 ± 1.8 cal mol-1 deg-1 (half that of the Aio: 46.1 cal mol-1 deg-1).

117

Figure 2.27: ITC of the AioB titrated against cytochrome c. Top: Raw thermogram output of injections of cytochrome c into the AioB. Bottom: The integrated heats of injections plotted against molar ratio of cytochrome c to Aio and fit with a single binding model to determine thermodynamic parameters of binding. The ITC experiment was repeated with three separate enzyme preparations.

The rate of reduction of cytochrome c by the AioB was investigated using stopped- flow kinetics to compare this to the Aio by D. Niks (University of California, Riverside). The AioB was able to reduce cytochrome c in a biphasic manner (Figure 2.28). The predominant kinetic phase was very slow, having a rate of 0.120 ± 0.003 s-1 and was over 95 % of the total amplitude change. The other kinetic phase was faster at 3.32 ± 0.30 s-1. These slow rates most likely reflect the poor binding of cytochrome c to the AioB which meant that the concentration of cytochrome c was not saturating so quasi-first order conditions were not achieved. This is unsurprising given the high Kd.

118

Figure 2.28: Stopped-flow spectroscopy monitoring the reduction of horse heart cytochrome c by reduced AioB. Inset: Reduction at 551 nm fit with a double exponential decay function to determine the rate constants of the reaction.

ITC and stopped-flow spectroscopy demonstrate that the binding of cytochrome c to the AioB is much weaker than it is for the Aio. In turn, this appears to have dramatically lowered the electron transfer rate to the haem of the cytochrome. There are two major explanations for these results.

1) The binding site for cytochrome c is found on both the AioA and AioB subunits, so in the absence of the AioA, the AioB alone is not able to strongly bind it. The ΔH of the AioB with cytochrome c is 3104 cal mol-1, the ΔH of the F108A mutant was 3021 cal mol-1 while the ΔH of the WT Aio was 6007 cal mol-1. In the absence of AioA, the ΔH is halved and in the absence of the F108 residue (previously discussed as critical for cytochrome c complex orientation and/or electron transfer) the ΔH is also halved. Perhaps this reflects that both subunits contribute an equal amount of enthalpic change to the binding of cytochrome c.

2) The tetrameric nature of the heterologously expressed AioB is disrupting the binding surface of cytochrome c. With the current available data this cannot be

119 refuted. Ideally, the oligomeric state could have been ruled out as an effector by either performing ITC with the AioA or by obtaining the monomeric AioB. Unfortunately, neither of these were possible as it was not possible to express AioA alone (Appendix D) and the oligomeric state of the AioB was not concentration dependent (higher salt concentrations and different buffer conditions could not be used as these would affect the interaction with cytochrome c and make the result non-comparable to the WT Aio). An attempt to explore this issue was made by examining the crystal structure of the NT-26 Aio (Figure 2.29). Unsurprisingly, in the absence of the AioA, the AioB is left with a large surface that would typically be used for protein/protein binding. In addition, the Aio is known to exist as a heterotetramer (Warelow et al., 2013) and as can be seen from the crystal structure, the AioB of one heterodimer associates with the AioA of the other heterodimer. There are therefore two binding sites on the AioB that, in the absence of the AioA, would be exposed to the solvent. These seem the most likely candidates for the binding sites of the AioB tetramer. As they are present in the Aio heterotetramer, which is known to bind cytochrome c, it seems unlikely that they would impair binding in the AioB tetramer. However, it must be stressed that this cannot be concluded with current evidence.

120

Figure 2.29: Heterotetrameric structure of Aio. Heterodimer 1 is shown in green (AioA) and blue (AioB) while heterodimer 2 is shown in purple (AioA) and yellow (AioB). PDB ID: 4AAY.

The most direct way to further explore the specific roles of the AioB and the AioA in cytochrome c binding is to obtain a crystal structure of the Aio with cytochrome c bound. The effect of the AioB tetramer could be further explored by obtaining a crystal structure and comparing it to the putative binding site of cytochrome c on the Aio.

2.3.16 AioB heterologous expression system in E. coli

In this chapter, a heterologous expression system for the AioB was developed. The heterologous expression system produces 5 mg of AioB per L of culture. Heterologously expressed AioB purified as a tetramer had 62.5 % co-factor saturation.

While there are a number of heterologous expression systems for Rieske proteins from the bc1 complex, the b6f complex and the Rieske dioxygenases the heterologous expression system described in this chapter is the first, and only, expression system

121 for any AioB developed (as far as a thorough examination of the literature can determine).

The novelty of the NT-26 AioB expression system is further compounded by the fact that it is the only expression system for a metabolic Rieske protein that is lacking the disulphide bridge proximal to the [2Fe-2S] cluster, a trait which is unique to AioB proteins from the Alphaproteobacteria and some Archaea. The role of the disulphide bridge is explored in greater depth in Chapter 3.

2.3.17 Engineering higher catalytic rates for Aio

One of the major aims of this study was to assess if the rate of Aio catalysis could be improved by rational design. This was an important avenue of investigation for development of the Aio arsenic biosensor as improving the rate of Aio catalysis would result in smaller amounts of enzyme needed per test strip. This in turn would result in lower production costs which would either translate to greater profit or a cheaper, and therefore more accessible, product.

The fact that the rate-limiting step of Aio catalysis appears to be electron transfer makes rational design a very difficult approach for protein engineering. Electron transfer rates are governed by two major factors, the difference in reduction potential between the two redox partners (cytochrome c and the Rieske 2Fe-2S) and the distance between them.

It would be difficult to engineer the reduction potential. Although Rieske clusters’ reduction potentials are highly tuneable and there are numerous examples of point mutations both increasing and decreasing the reduction potential of Rieske clusters there do not appear to be any examples where the rate has been improved, only worsened by the changes (Denke et al., 1998) (Guergova-Kuras et al., 2000) (Warelow et al., 2013). Furthermore, altering the reduction potential would also likely alter the rate of electron transfer to the Rieske cluster (from the 3Fe-4S cluster) which could reduce the overall rate of the enzyme if it were to become rate-limiting.

122

The engineering of complex formation, to try to bring the co-factors closer together, is also likely to be very difficult. The Kd is already very low at 2.3 µM in WT Aio and improving binding might lower this further which would probably have the unintended consequence of lowering the rate of product (reduced cytochrome c) dissociation which would, in turn, lower the rate.

Further development of the AioB expression system might provide insights into the Aio-cytochrome c interaction that may prove invaluable in any engineering attempts.

For example, the interaction and redox-dependent structural changes of bc1 Rieske with ubiquinol from T. thermophilus were analysed by NMR (Hsueh et al., 2010) and a similar experiment with AioB and cytochrome c could yield valuable insights into their interaction. However, if the AioB is unable to bind cytochrome c strongly on its own (which the data presented in this study suggests) then it may be difficult to obtain structural information of the AioB-cytochrome c complex.

Finally, the NT-26 Aio has a catalytic efficiency of ~4 x 108 M-1 s-1placing it near the

9 -1 -1 diffusion limit of 10 M s (Bar-Even et al., 2011). The Kcat can therefore only be improved more than two-fold if the KM is also increased (i.e. the affinity for cytochrome c is decreased). It will probably be very difficult to predict amino acid substitutions which would raise the Kcat and the KM.

In conclusion, rational design as a method to improve the catalytic rate of Aio is likely to be incredibly difficult. Directed evolution is more likely to yield positive results as it does not require complex predictions to be made.

2.3.18 Conclusions

• The reduction of cytochrome c is rate-limiting in Aio catalysis. • The Aio has a double displacement catalytic mechanism with arsenite and cytochrome c. • The F108A amino acid substitution lowers cytochrome c activity specifically by reducing the rates of electron transfer – most likely by affecting the position of the haem cofactor in the complex. • A heterologous expression system for the AioB was developed.

123

• The AioB could bind cytochrome c weakly but it could not be determined if the weakness of the interaction compared to the WT Aio was due to the oligomeric state of the protein. • It is unlikely that the activity of the Aio with cytochrome c can be improved.

124

Chapter 3

The role of the disulphide bridge in electron acceptor specificity of arsenite oxidase

125

3.1 Introduction

The small subunit of arsenite oxidase (AioB) is a Rieske protein. Rieske proteins are 2Fe-2S clusters in which one of the Fe atoms is coordinated by two histidine residues rather than two cysteines (Figure 3.1). Rieske proteins can broadly be split into two families: high potential and low potential. AioB is viewed as a potential evolutionary intermediary of the two families but more closely resembles the high potential proteins which include the bc1 complex (in which it catalyses electron transfer from ubiquinol to cytochrome c) and the photosynthetic b6f complex (in which it carries out a similar function to bc1 only with cytochrome f instead) (Link & Iwata, 1996).

Figure 3.1: Coordination of the Rieske cluster. One of the Fe atoms is coordinated by two histidine residues, the other by two cysteines. Figure generated in PyMOL from NT-26 arsenite oxidase (PDB ID: 4AAY; Warelow et al., 2013).

Rieske proteins are notable for their widely ranging and highly tuneable reduction potentials. The observed range within the family extends from -150 mV to +570 mV (Schneider & Schmidt, 2005)(Brown et al., 2008). This was originally thought to be due to protein backbone structure, however, the large number of solved Rieske

126 crystal structures has shown this not to be the case (Colbert et al., 2000). Colbert et al. (2000) proposed that differences in the electrostatic environment are in fact responsible for the Rieske protein’s wide range of reduction potentials. This has been demonstrated via mutagenesis (Denke et al., 1998) (Schröter & Hatzfeld, 1998) and pH studies (Link et al., 1992) (Link, 1994) (Link et al., 1996). The variability of the Rieske protein’s reduction potential is not seen in other 2Fe-2S clusters (in which all Fe atoms are coordinated by cysteine’s). The Rieske protein’s reduction potential is linked, perhaps unsurprisingly, to the two coordinating histidine residues’ protonation state (Zu et al., 2001) (Zu et al., 2003).

As shown in Chapter 2, electron transfer from AioB to the electron acceptor is involved in the rate-limiting step of arsenite oxidation (the step specifically being electron transfer to the electron acceptor cytochrome c), Most work on the Rieske protein has been conducted on the bc1 complex and there is relatively little investigation specifically into AioB. This chapter will seek to explore the nature of AioB in the broader context of the Rieske family.

3.1.1 Structural diversity in arsenite oxidase Rieske proteins

Arsenite oxidase, or at least the aioB and aioA genes, have been identified in a wide array of organisms including Alphaproteobacteria (Santini & vanden Hoven, 2004) (Walczak et al., 2018) (Rhine et al., 2007), Betaproteobacteria (Anderson et al., 1992) (Koechler et al., 2010) and Archaea (Kawarabayasi et al., 1999).

Only two microbial species have had the structure of their Aio enzyme resolved: Alcaligenes faecalis (a betaproteobacterium) (Ellis et al., 2001) and Rhizobium sp. str. NT-26 (an alphaproteobacterium) (Warelow et al., 2013). As can be seen in Figure 3.2, these two have very similar structures, however, the NT-26 AioB does not possess a disulphide bridge proximal to the 2Fe-2S cluster while the A. faecalis Aio does. Structures of other Rieske proteins from bc1 and b6f also contain a disulphide bridge proximal to the 2Fe-2S cluster (Link & Iwata, 1996) (Lin et al., 2006). Its removal has been shown to affect activity via substrate binding and by altering the reduction potential of the 2Fe-2S cluster (Merbitz-Zahradnik et al., 2003) .

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Figure 3.2: Structural alignment of Rhizobium NT-26 (green) and A. faecalis (red) Aio demonstrates high structural homology. A close-up of the Rieske cluster is shown to show the presence of a disulphide bridge in A. faecalis and its absence in NT-26. Figure generated in PyMOL using crystal structures with PDB ID: 1G8K and 4AAY (Ellis et al., 2001) (Warelow et al., 2013).

3.1.2 Electron acceptor specificity of arsenite oxidases

The physiological electron acceptors used by Aio are highly variable and dependent on the phylogeny of the organism. For example, A. faecalis has been shown to be able to use the copper protein, azurin (Anderson et al., 1992), while NT-26 uses a cytochrome c552 (Santini et al., 2007). Aio also appears not to be limited to a single physiological electron acceptor per organism: arsenite oxidation by NT-26 was still detected after the mutation of its cytochrome c552 gene suggesting that it is able to employ alternative electron acceptors (Santini et al., 2007); Ralstonia sp. 22 was shown to be able to use Pseudomonas aeruginosa azurin and a variety of c-type cytochromes from various organisms as electron acceptors (Lieutaud et al., 2010).

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Electron acceptor specificity in Aio has been previously suggested to be linked to the presence or absence of the disulphide bridge (Lieutaud et al., 2010) (Warelow et al., 2013) (Hoke et al., 2004) (Duval et al., 2010). Preliminary investigations into the role of the disulphide bridge in Aio have been attempted. The AioB-C106A mutant in the Ralstonia sp. 22 Aio was created to remove the disulphide bridge (van Lis et al., 2012) and AioB-F108C/G123C in the NT-26 Aio to introduce it (Warelow et al., 2013). While the reduction potential was found to be slightly (though significantly) altered in both cases, the kinetics of these mutants with the physiological electron acceptors were not investigated. Therefore, the hypothesis that the disulphide bridge is important in defining electron acceptor specificity has not yet been tested.

As discussed previously, only two Aio have had their crystal structures resolved: A. faecalis and NT-26. Fortunately, the former possesses a disulphide bridge while the latter does not. Additionally, the A. faecalis Aio is known to use azurin as an electron acceptor while the NT-26 Aio is known to use cytochrome c.

3.1.3 Aims

This chapter will test the hypothesis that the disulphide bridge is important in defining electron acceptor specificity by using site-directed mutagenesis to introduce a disulphide bridge into the NT-26 Aio and replace the disulphide bridge of the A. faecalis Aio with a phenylalanine and a glycine (as seen in NT-26). EPR and X-ray crystallography will also be used to examine what effects the mutations have on reduction potential and structure. The activity of the WT and mutant enzymes will then be tested with azurin, cytochrome c and the artificial electron acceptor DCPIP. Phylogenetic reconstruction and sequence alignment will be used to investigate the evolution of the AioB disulphide bridge to try to infer functional reasons for its presence or absence. The aims will be achieved via the following objectives:

• Determine the kinetic parameters of the NT-26 WT and F108C/G123C Aio, A. faecalis WT and C65F/C80G Aio with cytochrome c, azurin and DCPIP as electron acceptors.

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• Investigate the effect that the disulphide bridge has on activity and affinity by examining kinetic, structural and reduction potential data. • Explore the prevalence and evolution of the presence or absence of the disulphide bridge in AioB using multiple sequence alignment and phylogeny reconstruction.

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3.2 Methods

3.2.1 Confirmation of expression systems by restriction digest and sequencing

All expression systems were provided by Joanne Santini, UCL. Site directed mutagenesis to generate amino acid substitutions were performed by Joanne Santini, UCL.

Presence of the recombinant pProeX-Htb+ plasmid and aioBA gene insert was confirmed for NT-26 F108C/G123C Aio by restriction digest as described for NT-26 WT Aio in Chapter 2. The methodology was the same for the Alcaligenes faecalis Aio and azurin expression systems except for A. faecalis Aio the restriction enzymes used were NotI and XhoI. For azurin, the plasmid was pET 22b+ and the restriction enzymes were XhoI and NcoI.

All genes were sequenced as described in Section 2.2.2 using the primer M13 reverse for Aio genes and T7 terminal for azurin.

The chromatographs of the nucleotide sequences were converted to FASTA format using ChromasLite. The sequencing confirmed the presence of the azu gene and the aioBA gene in all expression systems as well as the relevant mutations to generate the two amino acid substitutions required in each mutant.

3.2.2 Azurin expression and purification

The expression system for azurin was the azurin gene from Pseudomonas aeruginosa in plasmid pET22b+ and provided by Joanne Santini. Escherichia coli str. BL-21 was used for expression.

The expression methodology was based on the one developed by Chloe Economou (Queen Mary, University of London). The media was inoculated with freshly grown colonies from LB with ampicillin plates grown overnight at 37 °C. A single colony was used to inoculate 20 mL LB with 100 μg/ml ampicillin culture which was grown overnight at 37 °C and 200 rpm shaking in a 50 mL falcon tube. All 20 mL of the culture was transferred to 2 L LB containing 100 μg/ml ampicillin and 0.2 mM CuCl2 in a 5 L

131 conical flask. The culture was incubated at 28 °C with shaking (200 rpm) for 3 hours or until the OD600 reached 0.4-0.6. IPTG was added so the final concentration was 250 μM and incubation continued under the same conditions for a further 6 hours at which point the cells were harvested by centrifugation for 20 minutes at 6500 xg. The cells were snap frozen in liquid nitrogen and stored at -80 °C overnight.

The generation of cell extract and protein purification were performed as described for NT-26 WT Aio in Section 2.2.4 except that a 5 mL HisTrap HP histidine-tagged protein purification column (GE healthcare) was used in fast protein liquid chromatography (FPLC) due to the large volumes of cell extract used (~300 mL from 6 L of culture). 10 mL 1 mM ferricyanide was loaded onto the column after all lysate had been loaded to oxidise the azurin. The flow rate was 3 mL min-1.

3.2.3 Aio expression and purification

Both Alcaligenes faecalis Aio enzymes were expressed and purified as described for the NT-26 WT Aio in Section 2.2.4. NT-26 F108C/G123C was expressed as described for the NT-26 WT Aio in Section 2.2.4 but an additional step was added in the purification: upon elution from the His-column, the buffer of the enzyme was changed to MES pH 5.5 using a PD-10 desalting column. A brown precipitate was formed which was removed by centrifugation. This step was recommended to improve purity in previous purifications of the mutant (Joanne Santini – pers. comm). The buffer of the eluent was then exchanged again to 50 mM Tris.HCl 100 mM NaCl pH 8 before size exclusion chromatography.

3.2.4 Concentration determination

The protein concentration of NT-26 F108C/G123C was determined using a nanodrop as described in Section 2.2.6 and using an extinction coefficient of 144000 M-1 cm-1 as determined by Bradford reagent (Appendix B) and a molar mass of 113,255 Da.

For the Alcaligenes faecalis Aio, masses of 115,001 Da and 115,003 Da were used, and an extinction coefficient of 223000 M-1 cm-1 and 239000 M-1 cm-1 for the C65F/C80G and WT enzymes respectively as determined by Bradford reagent (Appendix B). The concentration was also checked using the 430 nm and 650 nm 132 absorbances using extinction coefficients of 14.5 and 5.6 mM-1 cm-1 respectively as determined in Anderson et al. (1992).

The concentration of azurin was determined using 630 nm absorbance and an extinction coefficient of 3.75 mM-1 cm-1 which was experimentally determined for heterologously expressed azurin using ICP-MS and UV-Vis spectroscopy (see Section 3.3.2). It is in good agreement with the native absorbance of 3.5 mM-1cm-1 (Brill et al., 1968).

3.2.5 Characterisation of enzyme preparation purity, co-factor content and UV-visible spectra

UV-Vis spectra were recorded for azurin and Aio as described in Section 2.2.7.

ICP-MS was performed for Aio and azurin as described in Section 2.2.8.

Protein gel electrophoresis was performed as described in 2.2.9.

3.2.6 Structural Alignment and electrostatic surface generation

Structural alignments were generated in PyMOL (Schrodinger, 2015) using the NT-26 Aio WT crystal structure (4AAY) (Warelow et al., 2013), the Alcaligenes faecalis Aio WT structure (1G8K) (Ellis et al., 2001), the crystal structures of the mutant Aio were provided by Teresa Santos-Silva (Universidadae de Lisboa).

The electrostatic surface of each protein was also generated in PyMOL using the surface potential function. This was done for all Aio structures as well as Pseudomonas aeruginosa azurin (1AZU) (Adman & Jensen, 1981) and horse heart cytochrome c (1HRC) (Bushnell et al., 1990)

3.2.7 Steady-state Kinetics

3.2.7.1 DCPIP DCPIP kinetics were performed as described in Section 2.2.10.1.

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3.2.7.2 Cytochrome c NT-26 F108C/G123C Aio assays were performed as described in Section 2.2.10.3. For the Alcaligenes faecalis Aio assays, one 200 μl assay was performed in a 200 μl cuvette using 2 mM horse heart cytochrome c, 200 μg/mL Aio and 2.5 mM arsenite in Tris-HCl buffer pH 8 at 25 °C. A single assay was performed to determine the rate for multiple concentrations of cytochrome c by using the absorbance at specific time points to determine the concentration of cytochrome c.

3.2.7.3 Azurin All azurin assays were conducted in a 200 μL cuvette using the single assay methodology described above for cytochrome c. The Alcaligenes faecalis Aio assays consisted of 500 μM or 900 μM heterologously expressed P. aeurginosa azurin, 1-2 μg/mL Aio (for WT) or 5-10 μg/ml Aio (for C60F/C85G) and 2.5 mM arsenite. Assays were conducted with 500 μM and 900 μM azurin; concentrations above 500 μM appeared to result in product inhibition causing the KM to be exaggerated (this was not observed at 500 μM). Therefore the 900 μM assay was used to only determine the rate at 900 μM azurin. All other rates were determined from the 500 μM azurin assay.

For the NT-26 F108C/G123C Aio, azurin assays were performed in a 200 μL cuvette using 500 μM azurin, 400-500 μg/mL Aio and 2.5 mM arsenite.

All assays were conducted in 50 mM Tris-HCl pH 8 buffer at 25 °C.

3.2.7.4 Temperature profile The temperature profiles for all four Aio were determined by observing rates of DCPIP reduction in steady-state kinetics assays (using 2.5 mM arsenite). Activity was monitored at temperatures of 25 °C, 40 °C, 50 °C, 60 °C, 65 °C and 70 °C. All assays were conducted in quartz cuvettes. The temperatures were set using the Varian Cary dual cell Peltier accessory. Reactions were incubated at each temperature for 5 minutes and the temperature of the sample confirmed with a digital temperature probe prior to the addition of arsenite.

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3.2.8 Statistical Analysis

3.2.9 Sequence alignment and phylogenetic tree reconstruction

All sequences were retrieved from GenBank using Basic Local Alignment Search Tool (BLAST) and NT-26 sequences as queries to identify aioBA genes (Altschul et al., 1990). 16S rRNA gene sequences were obtained by searching for species already identified for aio phylogenetic analysis. Nucleotide and amino acid sequences were aligned using MUSCLE (Edgar, 2004) in MEGA version 7 (Tamura et al., 2013) using default setting. A list of species used and accession numbers is provided in Appendix E.

Phylogenetic analysis was performed using MEGA (Tamura et al., 2013). Trees were constructed and edited in MEGA. Phylogenetic trees were constructed using the maximum likelihood method. Bootstrap values were calculated from from 100 re- samples.

Sequence alignments of the 16S RNA were unaltered to construct the phylogeny however the Rieske protein alignment required modification via removal of β-sheet regions as described in Lebrun et al. (2006).

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3.3 Results

3.3.1 Expression and purification of azurin

It was necessary to confirm that the plasmid construct contained the P. aeruginosa azu gene before attempting expression of azurin.

The presence of the pET 22b+ plasmid containing the P. aeruginosa azu gene was confirmed by restriction digest and gel electrophoresis. Figure 3.3 shows a gel photo with a band of approximately 5000 bp which corresponds to the plasmid (5493 bp). The band at approximately 400 bp corresponds to the azu gene (434 bp). Sequencing confirmed the insert was the azu gene. There is a faint band at ~2000 bp which is most likely uncut, circularised plasmid.

Figure 3.3: Agarose gel electrophoresis photo of pET22b+ with azu insert. Plasmid was digested with XhoI and NcoI. The marker used was 1kb+ gene ruler (ThermoFisher Scientific).

Azurin was successfully expressed as confirmed in Figure 3.4 which shows a gel filtration chromatograph and a photo of an SDS polyacrylamide gel. The large peak at 12 mL contained blue fractions while the small peak at 8 mL was colourless. The

136 gel shows one band (taken from the 12 mL fraction) at just larger than 14.4 kDa meaning it corresponds to azurin + His-tag (16,254 Da). The final yield of azurin was approximately 2 mg L-1 of culture.

Figure 3.4: A) Gel filtration chromatograph of heterologously expressed P. aeruginosa azurin. B) SDS-polyacrylamide gel heterologously expressed P. aeruginosa azurin. A band is observed at approximately 14.4 kDa which corresponds to azurin + His-tag. 1 μL of approximately 1 mg/mL azurin was loaded onto the gel. Markers are phosphorylase b (97.0 kDa), albumin (66.0 kDa), ovalbumin (45.0 kDa), carbonic anhydrase (30.0 kDa), trypsin inhibitor (20.1 kDa), α- lactalbumin (14.4 kDa) (GE Healthcare).

3.3.2 Characterisation of heterologously expressed Pseudomonas aeruginosa azurin

Visible absorption spectra of three azurin preparations were recorded. The extinction coefficient was calculated based on the copper concentration as determined by ICP- MS. This was to enable the most accurate determination of azurin concentration as only Cu-containing azurin is active (as without the Cu there is no redox-active component).

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The Cu concentration of three separate azurin preparations was determined using ICP-MS. The Cu concentration was 62.6 ± 6.0 % that of total protein concentration (determined by the Bradford method) (Appendix B). This indicates that cofactor saturation of the heterologously expressed azurin in this study was 62.6% and so the 630 nm absorbance was used to accurately determine the concentration of active azurin in the sample.

The visible absorption spectra of oxidised and reduced azurin is shown in Figure 3.5, as well as the difference spectrum. Azurin exhibited one peak at 630 nm with ε = 3.75 ± 0.25 mM-1 cm-1 which is similar to the value determined for natively expressed azurin from P. aeruginosa of 3.5 mM-1 cm-1 (Brill et al., 1968). The peak is completely

-1 -1 quenched upon the addition of dithionite and the value of εox-red = 3.75 mM cm was used in the calculation of steady-state rates of reduction with heterologously expressed azurin.

Figure 3.5: A) Visible spectra of oxidised and reduced heterologously expressed azurin. The extinction coefficient was determined based on Cu content. B) Difference spectrum of heterologously expressed azurin.

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3.3.3 Expression of WT and mutant arsenite oxidase from NT-26 and A. faecalis

Presence of the recombinant plasmid pProEX-HTB+ plasmid carrying NT-26 and A. faecalis WT and mutant aioBA were confirmed by restriction digest and sequencing. Figure 3.6 shows two bands for each plasmid, the upper vector band at 4717 bp and the aioBA insert band at 3066 bp.

Figure 3.6: Agarose gel electrophoresis photo showing restriction digest of pProEx- Htb+ + aioBA. NT WT = NT-26 WT Aio, NT mut = NT-26 F108C/G123C Aio, A.f. WT = A. faecalis WT Aio, A.f. mut = A. faecalis C65F/C80G Aio. Marker is Gene Ruler 1Kb+ (ThermoFisher Scientific).

Alcaligenes faecalis WT and C65F/C80G Aio were successfully expressed in E. coli DH5α. This was confirmed for both by SDS-polyacrylamide gel electrophoresis. Figure 3.7 shows that, after size exclusion chromatography, both proteins were obtained with high purity.

NT-26 F108C/G123C was also expressed in E. coli DH5α however, gel filtration yielded two overlapping peaks (Figure 3.7C). The first peak at 11.5 mL had arsenite oxidising activity with DCPIP while the centre and last peaks at 12.5 and 15 mL did not. Based on NT-26 F108C/G123C’s lane in the gel photo (Figure 7D – NT mut) it is likely that the first peak contains Aio while the second peak is an oligomeric form of AioB. This

139 conclusion was reached based on the ratio of AioB to AioA in the F108C/G123C lane which indicates a significant AioB impurity.

Figure 3.7: A) Gel filtration chromatograph of the A. faecalis WT Aio. B) Gel filtration chromatograph of the A. faecalis C65F/C80G mutant. C) Gel filtration chromatograph of the NT-26 F108C/G123C mutant. Photo of an SDS polyacrylamide gel of Aio enzymes. NT WT = NT-26 WT Aio, NT mut = NT-26 F108C/G123C Aio, A.f. WT = A. faecalis WT Aio, A.f. mut = A. faecalis C65F/C80G Aio. Markers are phosphorylase b (97.0 kDa), albumin (66.0 kDa), ovalbumin (45.0 kDa), carbonic anhydrase (30.0 kDa), trypsin inhibitor (20.1 kDa), α- lactalbumin (14.4 kDa) (GE Healthcare).

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3.3.4 Characterisation of WT and mutant arsenite oxidase from NT-26 and A. faecalis

3.3.4.1 The structures of WT and mutant Aio To confirm that the amino acid substitutions had introduced a disulphide bridge into the NT-26 Aio and removed it from A. faecalis Aio, X-ray-crystallography was performed by Teresa Santos-Silva (Universidadae de Lisboa) to resolve the mutant structures. Structural alignments were performed with the WT enzymes to explore differences between the structures and determine what effect, if any, the mutations had on the structure of Aio. This analysis was also performed to see if the mutation made the protein closer in structure to its counterpart i.e. to see if NT-26 F108C/G123C was closer in structure to A. faecalis WT than NT-26 WT is.

A structural alignment of the A. faecalis and NT-26 WT Aio was constructed using PyMOL. The two structures are very similar, with root mean squared deviation (RMSD) = 2.24 Å (Figure 3.8). The main structural difference between these two proteins is the presence of the disulphide bridge in the A. faecalis Aio, which is replaced by a phenylalanine and a glycine in the NT-26 enzyme.

Figure 3.8: Structural alignment of the Rieske cluster of NT-26 (green) (PDB ID: 4AAY) and A. faecalis WT (red) Aio (green) (PDB ID: 1G8K).

The disulphide bridge was successfully incorporated into NT-26 F108C/G123C. Figure 3.9A shows a structural alignment of NT-26 WT Aio and the F108C/G123C mutant.

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The structures are incredibly similar with RMSD = 0.117 Å. For the most part, the structures perfectly overlap each other, however, at the site of the substitution the disulphide bridge has caused a small shift in a loop proximal to the 2Fe-2S cluster by approximately 0.3 Å. The beta strands either side of this loop appear to be affected by the mutation in the image but closer examination of the residues showed that there had been no shift in atom positions meaning this is a result of the way the structure has been annotated. Figure 3.9B shows the structural alignment of the NT- 26 F108C/G123C mutant with the A. faecalis WT Aio. The NT-26 mutant is no more similar to A. faecalis WT Aio than NT-26 WT was with RMSD = 2.25 Å (WT = 2.24 Å). However, it is notable that at the site of the substitution the two structures are now very similar. The disulphide bridge has formed and is in the same rotamer confirmation as the A. faecalis Aio.

A B

Figure 3.9: A) Structural alignment of the Rieske cluster of NT-26 WT Aio (green) (PDB ID: 4AAY) and NT-26 F108C/G123C mutant (blue). B) Structural alignment of the Rieske Cluster of A. faecalis WT Aio (red) (PDB ID 1G8K) and NT-25 F108C/G123C mutant (blue).

The disulphide bridge was successfully substituted by phenylalanine and glycine in the A. faecalis C65F/C80G mutant. Figure 3.10A shows a structural alignment of the A. faecalis WT Aio with the A. faecalis C65F/C80G mutant. The structures are virtually identical with RMSD = 0.22 Å. The loop regions of the disulphide bridge appear to have moved by approximately 0.2 Å. A structural alignment of the NT-26 WT Aio with

142 the A. faecalis C65F/C80G found that the mutant is no more similar to the NT-26 WT than the A. faecalis WT Aio as the RMSD = 2.25 Å (Figure 3.10B). Of note is that that phenylalanine adopts a slightly different rotamer conformation than the NT-26 WT but is otherwise in a similar position.

A B

Figure 3.10: A) Structural alignment of the Rieske cluster of A. faecalis WT Aio (red) (PDB ID: 1G8K) and A. faecalis C65F/C80G mutant (beige). B) Structural alignment of the Rieske cluster of NT-26 WT Aio (green) (PDB ID: 4AAY) and A. faecalis C65F/C80G mutant (beige).

3.3.4.2 Co-factor saturation of WT and mutant arsenite oxidase from NT-26 and A. faecalis The co-factor saturation of enzyme preparations was determined by ICP-MS. Three preparations were tested for each Aio and the results are summarised in Table 3.1. Both A. faecalis enzymes had Mo and Fe content of 88.7-99.8% that of protein content suggesting that cofactor saturation was near complete for both. The NT-26 F108C/G123C mutant had much lower metal content than the NT-26 WT Aio, Mo being approximately one quarter that of WT and Fe being under one half. This may reflect the AioB impurity observed in the protein gel and, taken with the gel result, may also be indicative that NT-26 F108C/G123C Aio is intrinsically less stable than the WT. To make the NT-26 WT and F108C/G123C Aio activity results comparable, the F108C/G123C mutant activity values were multiplied by a factor of 3.76 (i.e. the difference in Mo content between the two proteins).

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Table 3.1: Metal content of WT and mutant Aio as determined by ICP-MS

Aio Mo % Fe %

NT-26 WT 83.1 ± 5.2a 77.6 ± 1.3a

NT-26 F108C/G123C 22.1 ± 5.5 35.6 ± 2.0

A. Faecalis WT 95.5 ± 9.1 96.5 ± 9.4

A. Faecalis C65F/C80G 99.8 ± 0.8 88.7 ± 6.7

a(Warelow et al., 2013)

(Error represents the standard deviation of three preparations.)

3.3.4.3 The effect of the disulphide bridge on temperature optima The temperature optima of the WT and mutant Aio was determined by monitoring activity with DCPIP as an electron acceptor over a range of temperatures to determine if the presence of the disulphide bridge altered the optimum temperature of enzymatic activity.

There are three notable observations from Figure 3.11. The first is that both the NT- 26 WT and F108C/G123C Aio activity with DCPIP peak at 65°C suggesting that the addition of the disulphide bridge to AioB has not affected tolerance with respect to higher temperatures. The second is that the F108C/G123C mutant has lower relative activity at all other temperatures, however, the only one of these temperatures at which this is significant is 25 °C (t-test p-value = 0.0031). Similarly to the NT-26 enzymes, both mutant and WT A. faecalis Aio peak in activity at 65 °C. The A. faecalis mutant Aio has slightly lower activity at all temperatures, however again, this is only significant at 25 °C (t-test p-value = 0.0368).

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Figure 3.11: Temperature profile of A) NT-26 WT and F108C/G123C activity with DCPIP and arsenite; B) A. faecalis WT and C65F/C80G activity with DCPIP and arsenite. Values are the average of three preparations, error bars represent standard deviation.

3.3.4.4 The effect of the mutations on the Rieske 2Fe-2S reduction potential The reduction potentials of the Rieske 2Fe-2S clusters of the NT-26 WT Aio, A. faecalis WT Aio and NT-26 F108C/G123C mutant have all been reported in Warelow et al. (2013). The reduction potential of the A. faecalis C65F/C80G mutant was provided by Barbara Schoepp-Cothenet (Aix-Marseille Univ, CNRS, BIP). The results are summarised in Table 3.2. The introduction of a disulphide bridge in the NT-26 F108C/G123C mutant lowers the reduction potential by 35 mV while it’s removal in the A. faecalis C65F/C80G mutant increases it by 30 mV.

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Table 3.2: Reduction potentials of NT-26 and A. faecalis Aio and disulphide bridge mutants determined by EPR.

Aio [2Fe-2S] reduction potential/ mV

NT-26 WT +225 ± 10a

NT-26 F108C/G123C +190 ± 10a

A. Faecalis WT +160a

A. Faecalis C65F/C80G +190 ± 10b

aWarelow et al., 2013

bPersonal communication with Barbara Schoepp-Cothenet, Aix-Marseille Univ, CNRS, BIP.

3.3.4.5 Investigation into the electrostatic surface potential of Aio and electron acceptors NT-26 F108C/G123C and A. faecalis C65F/C80G both substitute residues that are believed to form part of the electron acceptor binding surface. It was therefore of interest to analyse the electrostatic surface potential of these surfaces to determine if the mutations have affected these and, in doing so, affected electron acceptor interactions.

Electrostatic surface potential of azurin and cytochrome c

Before investigating the electrostatic surface potential of Aio it was essential to first investigate that of the electron acceptors: P. aeruginosa azurin and horse heart cytochrome c, to determine if they were different. The rationale being that if they have very similar surface potentials then it is unlikely that changes to the electrostatic surface potential of Aio induced by the mutations would have an effect. The surface potentials of azurin and cytochrome c are displayed in Figure 3.12 and Figure 3.13 respectively. In both instances, the binding site is circled. The binding sites were predicted based on mutagenesis studies performed for each protein (Speck et al., 1981) (van de Kamp et al., 1990) (Nar et al., 1991). As can be seen from Figure 17, the binding site of azurin is a predominately hydrophobic surface while for cytochrome c the binding surface is predominately positively charged (consisting of

146 a lysine ring around the heme crevice). Cytochrome c and azurin have very different electrostatic surface potentials at their binding sites meaning that changes in the electrostatic surface is worth exploring to explain differences in electron acceptor specificity.

Figure 3.12: Molecular surface of P. aeruginosa azurin coloured based on electrostatic potential. Image generated using structure PDB ID: 1AZU. The hydrophobic patch which is the putative binding site is circled.

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Figure 3.13: Molecular surface of horse heart cytochrome c coloured based on electrostatic potential. Image generated using structure PDB ID: 1HRC. The binding site which consists of a lysine ring is circled.

The effect of the mutations on the Aio electrostatic surface potential

Having identified that the surface potential of the binding sites of the electron acceptors were different, it was of interest to compare the electrostatic surface potentials of the NT-26 and A. faecalis WT and mutant enzymes to determine if it had been altered by the loss or gain of the disulphide bridge.

The electrostatic surface potential of the NT-26 Aio is shown in Figure 3.14. Before entering discussion on the effect of the mutation, it is interesting to note that NT-26 Aio’s putative binding site for cytochrome c (circled) is negatively charged which complements the binding surface of cytochrome c (the lysine ring being positively charged). Similarly, in the A. faecalis Aio the putative binding site (circled) for azurin in the WT Aio complements that of azurin by being predominately hydrophobic (Figure 3.15).

There does not appear to have been any large-scale changes in the electrostatic surface of the NT-26 Aio with the introduction of the disulphide bridge, however, there does appear to be a slight shift from negative to hydrophobic/uncharged around the putative binding site (Figure 3.14). Note, however, that these changes are

148 very small. The substitution of the disulphide bridge for phenylalanine and glycine does not appear to have any large-scale effect on the electrostatic potential of the surface of the A. faecalis C65F/C80G mutant (Figure 3.15). The mutant’s binding site does appear to have lost a small patch of negative charge near the top of the circled area which, if anything, would result in lower affinity for cytochrome c (due to cytochrome c’s positively charged binding site).

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Figure 3.14: Molecular surface of the NT-26 WT (left) and F108C/G123C (right) Aio coloured based on electrostatic potential. The putative binding site of cytochrome c is circled and is based on proximity to the Rieske cluster and of the mutation. The two molecules had their electrostatic surface potentials calculated simultaneously to ensure the consistency of colouring.

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Figure 3.15: Molecular surface of the A. faecalis WT (left) and C65F/C80G (right) Aio coloured based on electrostatic potential. The putative binding site of azurin is circled and is based on proximity to the Rieske cluster and of the mutation. The two molecules had their electrostatic surface potentials calculated simultaneously to ensure the consistency of colouring.

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3.3.5 Steady-state kinetics of WT and mutant NT-26 and A. faecalis arsenite oxidases

In order to determine what effect the mutations had on electron acceptor specificity the steady-state kinetics of azurin and cytochrome c were determined for both WT and mutant Aio. To determine if the arsenite kinetics had been affected, arsenite steady-state kinetics were determined using DCPIP as an electron acceptor (as it is an effective electron acceptor for both NT-26 and A. faecalis). The arsenite kinetics with cytochrome c were also determined for F108C/G123C for the same reason, however, this was not done for the A. faecalis Aio as saturating concentrations of cytochrome c could not be achieved. This was also not done with azurin as it was not possible to obtain high enough yields to achieve saturating concentrations of azurin.

3.3.5.1 The effect of phenylalanine/glycine and the disulphide bridge on azurin steady- state kinetics The NT-26 WT Aio activity was not detectable with P. aeruginosa azurin. The NT-26 Aio F108C/G123C mutant did exhibit activity with azurin although it was highly variable. Eight preparations were tested for azurin activity. Six of these had stable baselines before the addition of arsenite while the other two reduced azurin with no arsenite which may indicate that they produced reductive species such as super oxide which is known to reduce azurin (Shleev et al., 2006). The specific activity with 500 μM azurin for these six preparations was 251.0 ± 153.5 nmol min-1 mg-1. Four of these preparations were unable to completely reduce azurin which may have been due to the extended incubation times (a single azurin assay could take up to 30 minutes due to the low activity with azurin) resulting in the enzyme denaturing. The remaining two preparations could completely reduce azurin, and Michaelis-Menten data was obtained for these. Both preparations’ results are plotted in Figure 3.16A. The kinetic

-1 -1 values were: Vmax = 1011 and 691 nmol min mg , KM =705.6 and 643.3 μM and kcat = 1.0 and 1.3 s-1. Introduction of the disulphide bridge into the NT-26 Aio allowed it to reduce azurin.

The effect of removing the disulphide bridge on the A. faecalis Aio kinetics with azurin as the electron acceptor was tested. The A. faecalis WT Aio was found to have kinetic

-1 - properties with azurin as an electron acceptor of Vmax = 148.6 ± 16.7 µmol min mg

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1 -1 , KM = 311.0 ± 26.2 µM and kcat = 280.3 ± 31.6 s (Figure 3.16B). A. faecalis C65F/C80G

Aio was found to have kinetic properties with azurin as an electron acceptor of Vmax

-1 -1 -1 = 79.7 ± 9.4 µmol min mg , KM = 1465.7 ± 154.8 µM and kcat = 150.1 ± 17.7 s (Figure 3.16B). The removal of the disulphide bridge using azurin as the electron acceptor caused the Vmax of A. faecalis Aio to significantly decrease 2-fold (t-test p-value =

0.0034) and the KM to significantly increase 5-fold (t-test p-value = 0.0002). The removal of the disulphide bridge decreases both the activity with and the affinity for azurin.

Figure 3.16: A) Michaelis-Menten fit of steady-state kinetics results for the NT-26 F108C/G123C mutant with azurin as an electron acceptor and excess arsenite. The values from two enzyme preparations are shown as these were the only two preparations that were able to completely reduce azurin. B) Michaelis-Menten fit of steady-state kinetics values of the A. faecalis WT Aio and the C65F/C80G mutant with azurin as an electron acceptor and excess arsenite. Points are the average of three replicates, error bars represent standard deviation.

3.3.5.2 The effect of phenylalanine/glycine and the disulphide bridge on cytochrome c kinetics The kinetic parameters of the NT-26 and A. faecalis WT and mutant enzymes with horse heart cytochrome c were determined to see if introduction or loss of the

153 disulphide bridge resulted in changes to cytochrome c activity and affinity (as it did with azurin).

The activity of the WT NT-26 Aio with arsenite and horse heart cytochrome c has

- been reported in the literature and previous chapters: Vmax = 240.8 ± 12.0 μmol min

1 -1 mg , cytochrome c KM = 1.0 ± 0.1 μM and arsenite KM = 9.3 ± 1.5 μM (Wang et al., 2015) (Section 2.3.4) (all experiments were conducted at 25 ° in Tris-HCl buffer pH 8 which was also used in this study).

The kinetic parameters for cytochrome c with the NT-26 F108C/G123C Aio were

-1 -1 determined: Vmax = 185.6 ± 26.1 μmol min mg , KM = 2.0 ± 0.4 μM and kcat = 346.2 ± 48.7 s-1 (Figure 3.17A). The activity of F108C/G123C is significantly lower than WT (t- test p-value = 0.0292). The KM of the mutant is twice that of WT and the difference is statistically significant (t-test p-value = 0.0137). The introduction of the disulphide bridge, and removal of phenylalanine and glycine, has significantly reduced the activity and affinity of NT-26 Aio for cytochrome c.

In contrast to previous reports (Anderson et al., 1992), A. faecalis WT Aio can reduce equine cytochrome c, albeit very slowly with a specific activity = 455.5 ± 46.3 nmol min-1 mg-1 for 1 mM cytochrome c. Lower concentrations of cytochrome c were tested and are shown in Figure 3.17B. The reduction in rate is linearly related to the reduction in cytochrome c concentration i.e. a straight line fits this data better than the Michaelis-Menten model. It can therefore be concluded that, at 1 mM cytochrome c, the system is not approaching saturation. Due to the viscosity of the resultant solution it was not feasible to use higher concentrations of cytochrome c which meant that the KM and Vmax could not be determined for the A. faecalis WT Aio. Similarly, it was not possible to saturate the A. faecalis C65F/C80G mutant with cytochrome c so the Vmax and KM could also not be determined (Figure 3.17B). The specific activity at 1 mM cytochrome c was determined to be 1374.4 ± 309.7 nmol min-1 mg-1 which is approximately three-fold higher than WT and is significant (t-test p-value = 0.0071). Removal of the disulphide bridge, and introduction of phenylalanine and glycine in its place has increased A. faecalis Aio’s activity with cytochrome c.

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Figure 3.17: A) Michaelis-Menten fit of the NT-26 WT Aio and the F108C/G123C mutant with cytochrome c as the electron acceptor and excess arsenite. B) Linear fit of the A. faecalis WT Aio and the C65F/C80G mutant with cytochrome c and excess arsenite. Values are the average of three separate enzyme preparations, error bars represent standard deviation.

The steady state kinetics of arsenite oxidation with 20 μM cytochrome c as an electron acceptor were also determined for the NT-26 mutant and WT Aio (Figure

3.18). The Vmax is half that determined for cytochrome c meaning that the two values are consistent and demonstrates that this is the activity of the system when it is fully

-1 -1 saturated (Vmax = 91.2 ± 11.8 μmol min mg ). The KM for arsenite was 10.1 ± 1.4 μM which is not significantly different to the WT Aio (t-test p-value = 0.5365). The F108C/G123C substitution therefore has no effect on arsenite affinity but does affect the rate of the enzyme and its affinity for cytochrome c.

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Figure 3.18: Michaelis-Menten fit of the NT-26 WT and the F108C/G123C mutant with 20 μM cytochrome c and arsenite. F108C/G123C Aio values are the average of three separate enzyme preparations, error bars represent standard deviation. The WT values are the averages of two repeats with one enzyme preparation. Only the lower concentrations are displayed so that the curve can be visualised.

3.3.5.3 The effect of phenylalanine/glycine and the disulphide bridge on arsenite steady-state kinetics with DCPIP as an electron acceptor The arsenite steady-state kinetics with DCPIP as an electron acceptor were determined for all WT and mutant Aio because both the NT-26 and A. faecalis Aio are known to use DCPIP as an electron acceptor (Anderson et al., 1992) (Warelow et al., 2013). Conducting all experiments under the same conditions (MES buffer pH 5.5, 25 °C) meant that it would be possible to determine if the mutations had effected arsenite kinetics.

The kinetic properties of arsenite oxidation by the NT-26 WT Aio with DCPIP as an

-1 -1 electron acceptor were Vmax = 4.8 ± 0.03 µmol min mg , KM = 68.0 ± 4.8 µM and kcat

-1 = 9.1 ± 0.1 s (Warelow et al., 2013). For the NT-26 F108C/G123C Aio these were Vmax

-1 -1 -1 = 8.4 ± 0.4 µmol min mg , KM = 50.0 ± 5.6 µM and kcat = 15.6 ± 0.7 s (Figure 3.19A). 156

The substitution has resulted in an almost 2-fold significant increase in activity (p- value = 0.0001) and a slight, yet significant, decrease in arsenite affinity (p-value = 0.0134).

The kinetic properties of arsenite oxidation by A. faecalis WT Aio with DCPIP as an

-1 -1 electron acceptor were Vmax = 9.1 ± 0.7 µmol min mg , KM = 62.5 ± 5.1 µM and kcat

-1 = 17.2 ± 1.4 s . For the A. faecalis C65F/C80G mutant these were Vmax = 4.9 ± 0.6

-1 -1 -1 µmol min mg , KM = 75.2 ± 6.8 µM and kcat = 9.3 ± 1.1 s (Figure 3.19B). The mutation has resulted in a significant, 2-fold decrease in activity (p-value = 0.0014) but the affinity for arsenite showed no significant change (p-value = 0.0608). The substitution of the disulphide bridge for phenylalanine and glycine causes a reduction in DCPIP activity but no change in arsenite affinity in the A. faecalis Aio.

Figure 3.19: A) Michaelis-Menten fit of steady-state kinetics results of the NT-26 WT Aio and the F108C/G123C mutant with varying concentrations of arsenite and DCPIP as an electron acceptor. F108C/G123C shows the average and standard deviation of three preparations. WT Aio was from one preparation and was consistent with published values (Warelow et al., 2013). B) Michaelis-Menten fit of steady-state kinetics results of the A. faecalis WT Aio and the C65F/C80G mutant with varying concentrations of arsenite and DCPIP as an electron acceptor. All values are the average of three preparations, error bars represent the standard deviation. 157

3.3.6 Sequence alignment and phylogenetic reconstruction of AioB, bc1 and b6f Rieske proteins

A sequence alignment was constructed to assess the prevalence of the disulphide bridge proximal to the Rieske 2Fe-2S cluster. Figure 3.20 shows a sequence alignment of representatives of AioB, bc1 and b6f Rieske proteins. As can be seen from the alignment, the cysteine residues that would make the disulphide bridge are present in all representatives of Rieske proteins except for arsenite oxidases in members of the Alphaproteobacteria and the mesophilic Archaea (H. tebenquichense is a halophile and is the only sampled Archaeon to possess a putative disulphide bridge). In the Alphaproteobacteria, the cysteine residues are replaced by a conserved glycine residue and a partially conserved, hydrophobic residue such as phenylalanine or tryptophan. There does not seem to be any obvious trend in the three representative arsenite oxidases of mesophilic archaea as to what residues have replaced the disulphide bridge.

Figure 3.20: Conservation of cysteine residues that form the disulphide bridge in Rieske proteins as shown by multiple alignment of protein sequences. Disulphide bridge residues are highlighted in yellow.

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The sequence alignment was used to build a phylogenetic tree (Figure 3.21) of the displayed Rieske protein sequences using the methods discussed in Lebrun et al. (2006) to explore the evolutionary relationships of AioB and high potential Rieske proteins. The phylogeny demonstrates two key insights into the evolution of the disulphide bridge:

1) The first is that the Aio Rieske and bc1 Rieske proteins appear to have diverged at LUCA which has been suggested previously (Lebrun et al., 2003).

This is supported as both bc1 Rieske and AioB diverged before Bacteria and

Archaea did. All bc1 representatives and all AioB representatives bar the Alphaproteobacteria and mesophilic Archaea have disulphide bridges meaning that the ancestral protein either possessed a disulphide bridge and the alphaproteobacterial and archaeal AioB independently lost theirs or the

ancestral protein did not possess a disulphide bridge and the bc1 Rieske and AioB acquired theirs independently. The former seems the most likely given

that there are no known examples of bc1 without a disulphide bridge (Schmidt & Shaw, 2001). 2) The two AioB clades (and, in fact, the only two clades in the Rieske phylogeny) that do not have disulphide bridges are the mesophilic Archaea and the Alphaproteobacteria. Unsurprisingly, the Archaea cluster away from the rest of the AioB tree (following the 16S phylogeny; Appendix F) and it seems unlikely that the absence of a disulphide bridge in both clades is connected. However, the Alphaproteobacteria are not clustered away from the rest of the AioB proteins suggesting that the substitution of the disulphide bridge to the partially conserved feature of glycine and hydrophobic residues likely had functional consequences.

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Figure 3.21: Maximum-likelihood phylogenetic tree of AioB, bc1 Rieske and b6f Rieske.

Rooted with archaeal bc1 from S. tokodaii str. 7. The tree with the highest log likelihood (-5123.34) is shown. The percentage of trees in which the associated taxa clustered together is shown next to the branches. Initial tree(s) for the heuristic search were obtained automatically by applying Neighbor-Join and BioNJ algorithms to a matrix of pairwise distances estimated using a JTT model, and then selecting the topology with superior log likelihood value. The tree is drawn to scale, with branch lengths measured in the number of substitutions per site. The analysis involved 28 amino acid sequences. There were a total of 209 positions in the final dataset.

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3.4 Discussion

In this study, the role of the AioB disulphide bridge in defining electron acceptor specificity was explored by introducing and removing the disulphide bridge in NT-26 and A. faecalis Aio respectively. The effect of the disulphide bridge on reduction potential, thermostability, electrostatic surface potential, structure and steady-state kinetics was explored. Finally, the evolution of the disulphide bridge in AioB, the bc1 and b6f complexes was explored.

3.4.1 Investigation into the mechanism by which the disulphide bridge and phenylalanine/glycine effect electron acceptor specificity

Steady-state kinetics experiments have yielded complementary results, the NT-26 F108C/G123C mutant gained a disulphide bridge, DCPIP activity increased, it gained azurin activity and cytochrome c activity and affinity decreased. The A. faecalis C65F/C80G mutant exhibited the opposite, the disulphide bridge was replaced by phenylalanine and glycine, DCPIP activity decreased, azurin activity and affinity decreased and cytochrome c activity increased. The effect of the disulphide bridge therefore appears to increase activity with DCPIP and azurin, while the effect of phenylalnine/glycine is to increase activity with cytochrome c.

There are three potential explanations for the roll of the disulphide bridge to explain these observations:

1. They alter the reduction potential of the 2Fe-2S Rieske cluster meaning that electron transfer rates to the electron acceptors are altered. 2. They alter the electrostatic potential of the surface of the protein meaning that electron acceptors interact with them differently. 3. The pathway and/or distance from 2Fe-2S cluster to the electron acceptor cofactor is altered by structural changes, either in the backbone or specifically due to the amino acid substitution.

The presence of the disulphide bridge appears to decrease the reduction potential of the Rieske 2Fe-2S cluster by 30-35 mV in NT-26 and A. faecalis Aio. The reduction

161 potential of horse heart cytochrome c is +250 mV (Pande & Myer, 1978) and the reduction potential of P. aeruginosa azurin is +341 mV (Garner et al., 2006). Both electron acceptors have reduction potentials considerably higher than the 2Fe-2S cluster in all instances. It therefore seems unlikely that the relatively small shifts attributed to the status of the disulphide bridge affect electron acceptor specificity.

The mutations do not appear to have resulted in any large-scale changes in electrostatic surface potentials of the NT-26 or A. faecalis Aio. Some subtle changes have been observed but on closer examination of structural alignments, there does not appear to have been any change in the position of residues in these areas. Electrostatic surface potential calculations are typically low resolution and so it is difficult to draw conclusions on such subtle changes in surface charge, however, it would not be especially surprising that these mutations would not cause significant changes to the electrostatic surface potential. All residues involved (phenylalanine, glycine and cysteine) are hydrophobic (or at the very least uncharged). The effects of the mutations on activity and affinity are dramatic so it would be expected that if the cause of the observed phenotype was electrostatic it would be easily observed in the surface potential. For example, in cytochrome c peroxidase, charge reversal mutants decreased affinity for cytochrome c 1.6 to 73-fold (Erman et al., 2015). Unfortunately while it is widely acknowledged that the hydrophobic patch is involved in complex formation by azurin, there does not appear to have been any studies to date investigating its affinity with enzymes using site-directed mutagenesis (Xu et al., 2010). In conclusion, while it is not possible to rule out that there have been some slight changes to the surface potential induced by the mutations it seems unlikely that they would be solely responsible for the observed kinetics changes as no large- scale changes have been observed and the substitutions themselves are not charge reversing.

Neither the introduction of a disulphide bridge nor its removal appears to have induced significant structural changes to the backbone of the Aio. As reduction potential and electrostatic potential have also been discredited as option this leaves the substitutions themselves as the most likely route cause of the kinetic results.

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There were two kinetic effects observed: a disulphide bridge resulted in increased azurin and DCPIP activity while a phenylalanine/glycine pair caused an increase in cytochrome c activity. It is possible that these phenomena are largely due to the influence of the phenylalanine. In Section 2.3.11, it was discussed that the phenylalanine acted as a sort of cap for the 2Fe-2S cluster that limited access for DCPIP. The truncation of this cap in the AioB-F108A mutant allowed slightly more access to DCPIP so increased activity. In the NT-26 F108C/G123C mutant, the residue that would ordinarily be this cap has a different rotamer conformation as it is involved in formation of the disulphide bridge meaning it is now incapable of capping the 2Fe-2S cluster. This explains why the Aio with the disulphide bridges both have two-fold higher activity with DCPIP than the Aio with the phenylalanine. It also explains why the F108C/G123C mutant has 2-fold higher activity with DCPIP than the F108A mutant; the cap is completely removed in the former and partially removed in the latter.

The introduction of the phenylalanine cap is also likely the reason for reduced azurin activity in the A. faecalis C65F/C80G mutant and its removal responsible for the NT- 26 F108C/G123C mutant’s gain in azurin activity. Azurin probably binds to Aio via a hydrophobic interaction of flat surfaces on both proteins. The introduction of phenylalanine to the Aio surface may result in two things: 1) it makes the surface less flat affecting shape complementarity resulting in decreased affinity and 2) it creates a greater distance between the 2Fe-2S cluster and the Cu atom of azurin via introduction of a cap affecting the electron transfer rate and decreasing activity. For the NT-26 F108C/G123C mutant, the removal of the phenylalanine would allow the azurin greater access to the Rieske 2Fe-2S cluster which allows low, but detectable, activity. Note that it was not possible to measure the KM of azurin for the F108C/G123C mutant reliably, however, for two preparations it was, on average, 674.4 μM. This is only two-fold higher than the A. faecalis WT Aio (311.0 μM) and notably much lower than the A. faecalis C65F/C80G mutant (1465.7 μM). This may reflect the deleterious effect of the phenylalanine on azurin binding, but it is difficult to comprehensively draw this conclusion as azurin affinity for the NT-26 WT Aio is

163 unknown because there was no detectable activity and so a KM could not be determined.

A different explanation is necessary for the effect of the introduction or loss of the phenylalanine on the cytochrome c interaction. As presented in Section 2.3.9, F108A had a reduction in activity with cytochrome c as the electron acceptor by 97% and had no significant or relatively small effects on affinity (depending on the technique used). This was thought to be due to a lower electron transfer rate from the Rieske cluster to the heme either by affecting the distance between Rieske and heme in the final complex or by disruption of an electron transport pathway. The NT-26 F108C/G123C mutant exhibited only a 23% reduction in activity with cytochrome c and a 2-fold reduction in affinity so it is important to explain why gain of the disulphide bridge was not as effective at reducing cytochrome c compared with the F108A substitution. The disulphide bridge must therefore either be better than alanine for: 1) Aio-cytochrome c complex formation or 2) an electron transfer pathway. The former seems unlikely as the KM of F108C/G123C is lower for cytochrome c than WT or F108A. Electron transfer seems to be the more likely explanation as the disulphide bridge has much higher electron density than alanine. A similar suggestion has been made for cytochrome c oxidase in which the W143A mutant reduced activity with cytochrome c by 95 % (Yuejun Zhen, Curtis W. Hoganson & Ferguson-Miller, 1999). Ideally, ITC would have been used to determine the Kd of the F108C/G123C Aio-cytochrome c interaction, however, ITC was not feasible as the instability of the NT-26 F108C/G123C mutant meant that sufficient yields were not possible to obtain and because the enzyme would likely degrade over the long experimental times of ITC (1-2 hours).

In A. faecalis, the addition of the phenylalanine increased activity with cytochrome c 3-fold. It was not possible to determine if affinity was affected as neither WT Aio nor the C65F/C80G mutant was saturable up to ~1 mM. While not conclusive, this result could lend itself to the theory that phenylalanine is critical in an electron transport pathway specifically to cytochrome c but without affinities it is impossible to draw this conclusion from A. faecalis.

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In conclusion, it seems the major affecter in AioB electron acceptor specificity is the phenylalanine rather than the disulphide bridge. It may serve as a cap for azurin and DCPIP, inhibiting their access to the Rieske cluster and it appears to serve as a pathway for electron transfer from the cluster to the heme of cytochrome c. However, this cannot be conclusively stated from the data presented here. Structures of each Aio with azurin and cytochrome c bound would enable a much deeper understanding of their interactions and would test the ideas presented here by allowing distances and conformations to be measured and visualised.

3.4.2 Steady-state kinetics of arsenite oxidases

The Vmax of NT-26 F108C/G123C with DCPIP is reported in this study to be 8.4 μmol min-1 mg-1 which differs to the published value of 1.5 μmol min-1 mg-1 (Warelow et al., 2013). There are a number of reasons for this: the first is that the incorrect extinction coefficient for DCPIP was used in Warelow et al. (as discussed in Section 2.2.10.1), the second is that the activity has not been adjusted with respect to metal content (as it was not determined in Warelow et al.). However, these adjustments still do not make the values similar. It is possible there had been a switch with the S126T mutant in the original Warelow et al. study as it exhibits activity that is highly similar to what was observed here for F108C/G123C. Note that the F108C/G123C mutant has been sequenced in this study and confirmed to have the correct sequence.

WT A. faecalis DCPIP is also not consistent with the results obtained with the native enzyme (heterologous: 9.1 μmol min-1 mg-1; native: 3.0 μmol min-1 mg-1). Anderson et al. (1992) used the incorrect extinction coefficient for DCPIP of 23 mM-1 cm-1. Using

-1 -1 the correct value of 8.2 results in a Vmax of 8.4 μmol min mg which is similar to the heterologous system.

-1 WT A. faecalis was reported to have Kcat = 280 s and KM = 311 μM with the native A.

-1 faecalis Aio and azurin of Kcat = 27 s and KM = 70 μM (Anderson et al., 1992). There are various reasons why this may be the case: this study used P. aeruginosa azurin instead of A. faecalis, the buffer used in Anderson et al. was MES pH 6 and some

165 velocity readings were unreliable in Anderson et al. because of curvature in the rate data (Anderson et al., 1992).

The arsenite kinetics have been determined for Aio from the Betaproteobacteria Polaromonas GM1, Ralstonia sp. 22 and Thiomonas delicata with DCPIP as an

-1 - electron acceptor and exhibit a broad range of Vmax from 4.0 to 12.6 μmol min mg 1, unfortunately no mutagenesis studies have been undertaken to determine if loss of the disulphide bridge or gain of a phenylalanine reduce DCPIP activity in these enzymes (Lieutaud et al., 2010) (Osborne et al., 2013) (Teoh et al., 2017). At time of writing, the kinetics of electron acceptors with Aio do not appear to have been determined for any species with the exception of azurin with the native A. faecalis Aio. Ralstonia sp. 22 has had its arsenite kinetics determined with various c-type cytochromes but the KM of these has not been determined and so comparisons cannot be made to this study (Lieutaud et al., 2010).

3.4.3 The effect of the disulphide bridge on temperature optima

All enzymes exhibited peak activity at 65 °C which indicates that the temperature optima of the NT-26 and A. faecalis Aio were not changed by their respective mutations. This is perhaps unsurprising as both the thermotoletant and psychrotolerant Aio, which have higher and lower temperature optima respectively, possess a disulphide bridge like the A. faecalis Aio (Heath, 2013) (Osborne et al. 2012). It is possible that these findings indirectly lend themselves to the hypothesis that the disulphide bridge is important in defining electron acceptor specificity as they demonstrate that the disulphide bridge is not important in establishing temperature optima, at least in these enzymes.

3.4.4 The evolution of the AioB-electron acceptor interaction

A sequence alignment and phylogeny of AioB, bc1 and b6f from a diverse array of phyla were constructed to analyse the prevalence of the Rieske disulphide bridge and its evolution. It was found that all Rieske proteins except AioB from Alphaproteobacteria and Archaea conserved the cysteine’s that form the disulphide bridge. The phylogeny closely resembled the 16S RNA phylogeny and showed that

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AioB and bc1 were present in LUCA which is consistent with other phylogenetic studies (Duval et al., 2010).

All alphaproteobacterial Aio sampled are missing the disulphide bridge and, in its place, they possess a bulky, aromatic, hydrophobic amino acid and a glycine. Perhaps in Alphaproteobacteria, AioB evolved after the divergence of other proteobacteria to be optimised to interact with proteins with high homology with horse heart cytochrome c. This can be seen from the putative binding site which is a complementary charge to the binding site of cytochrome c. The substitution of the disulphide bridge for phenylalanine or other aromatics may have been a critical step in this optimisation as it allowed faster electron transfer to the heme (which is known to be the rate-limiting step – Section 2.3.6). By speeding up the oxidation of arsenite, the mutation would allow individuals possessing it to metabolise or detoxify (arsenate is 100 times less toxic than arsenite) arsenic faster and in doing so gain an evolutionary advantage.

Arsenite oxidisers that are members of the Betaproteobacteria have been observed to use c-type cytochromes as well as azurin as electron acceptors but do not use cytochrome c (with the inverse being true for Alphaproteobacteria) (Lieutaud et al., 2010). Genomic analysis suggests that a cytochrome c or c-type cytochrome gene is often clustered with aio genes (van Lis et al., 2013). Phylogenies of these cytochromes suggests a clear distinction of two clades which mirror the phylogeny of AioB with one being the Alphaproteobacteria and the other the Betaproteobacteria (Schoepp-Cothenet, pers. comm.). Proteins with high homology to horse heart cytochrome c are included in the former. A representative of the latter that has had its structure resolved is Aquifex aeolicus cytochrome c555. A. aeolicus cytochrome c555 has a similar basic structure to horse heart cytochrome c (RMSD = 4.59) (Figure 3.22) but the putative binding site (based on the position of the heme crevice) is predominately hydrophobic which is electrostatically unlike horse heart cytochrome c which is positively charged (Obuchi et al., 2009). This suggests that there has perhaps been coevolution between c-type cytochromes and arsenite oxidases in the Proteobacteria. Arsenite oxidiser members of the Alphaproteobacteria have evolved an electrostatic interaction which is aided by the 167 presence of a hydrophobic, aromatic reside while those of the Betaproteobacteria have instead evolved a hydrophobic interaction which benefits from the presence of the disulphide bridge.

Figure 3.22: Structural alignment of horse heart cytochrome c (pink) (PDB ID: 1HRC) and A. aeolicus cytochrome c555 (blue) (PDB ID: 2ZXY).

If the substitution of the disulphide bridge for an aromatic amino acid proved successful for Aio in improving its interaction with cytochrome c, this leaves the question as to why the same has not occurred for the bc1 complex. Mutants that remove the disulphide bridge in bc1 have been found to damage the ubiquinol binding site (Merbitz-Zahradnik et al., 2003). Electron transfer from ubiquinol to the

Rieske is rate-limiting in bc1 (unlike Aio in which it is Rieske to cytochrome c) (Covian & Trumpower, 2009). There is therefore selection pressure acting to preserve the disulphide bridge to aid in ubiquinol binding and no selection pressure acting for its substitution by an aromatic amino acid to aid in electron transfer to cytochrome c.

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3.4.5 Azurin expression systems

In this study, azurin has been heterologously expressed in E. coli and purified by affinity chromatography. The yield is approximately 2 mg L-1 of culture. The cofactor saturation is 63 %. This is not the first recorded instance of azurin expression in E. coli. It was first achieved by Karlsson et al. (1989) who trialled a variety of plasmid vectors and were able to achieve yields as high as 29 mg L-1 of culture. However, this protocol was based on acid precipitation of the azurin protein and subsequent

2+ reincorporation of the Cu ion by soaking in a 0.5 M CuSO4 solution.

Many studies have continued to use precipitation and Cu2+ reincorporation techniques (Chang et al., 1991) (Harris et al., 2006). The apparent best yields of azurin in the literature are 25-75 mg L-1 of culture which were obtained by Berry et al. (2010) who used BL21 DE3 E. coli (as was done in this study), the original plasmid from Karlsson et al. (1989) and a protein precipitation and ion reincorporation methodology.

The problem with precipitation methodologies is that they fundamentally risk the integrity of the protein because the protein must come out of solution to be obtained. This results in large scale losses of the Cu2+ ion and hence necessitates an additional step to reincorporate it. This in turn can influence scalability and production times if the protein is to ever be commercially produced. Two other heterologous expression systems for azurin in E. coli have been produced. At least one has been reported to use his-tag affinity chromatography (Alessandrini et al., 2008), another has used a GST-tag for high affinity chromatography (Choi et al., 2011). These methodologies have either reported disappointing yields (i.e. >1 mg L- 1 culture) or have simply not reported them. This study has achieved a yield of 2 mg L-1 of culture using a simple one-step affinity chromatography procedure which, from analysis of the literature, is a promising yield for an affinity chromatography system.

Several studies have suggested that azurin may have potential medical applications. It has potential as an anti-cancer agent: Yamada et al. (2002) observed that bacterial azurin preferentially entered human melanoma cells (over non-cancerous cells) and induced apoptosis. It was also found to bind to the EphB2-Fc receptor, which is 169 involved in cancer progression, with high affinity thus interfering with cell signalling and inhibiting continued tumour growth (Chaudhari et al., 2007). Azurin has also been found to prevent the proliferation of the malarial parasite Plasmodium falciparum and also prevent HIV-1 growth in mononuclear red blood cells (Chaudhari et al., 2006). Azurin is clearly an interesting molecule that requires further research, however, the current price for 1 mg of azurin is £440 (Sigma-Aldrich, 2018) meaning that high-yield, high purity, easy-to-use expression systems are required. This is not to mention that if any medical applications are discovered a high yield expression system would be required to bring the product to market. Future work on the azurin expression should try and identify why Karlsson et al.’s system achieved such high yields (most likely the plasmid, as the work presented here used the same strain of E. coli and very similar expression conditions) and combine this with affinity chromatography to eliminate the precipitation step and permit scale-up.

3.4.6 Key findings, conclusions and next steps

• Incorporation of a disulphide bridge into NT-26 Aio increased activity with DCPIP and azurin but lowered activity with cytochrome c. • Substitution of the disulphide bridge in A. faecalis Aio with a phenylalanine and glycine increased activity with cytochrome c but decreased activity with DCPIP and azurin. • Both mutants had lower stability with respect to low temperature than their respective wild-types. • The effects on activity were not due to changes in the 2Fe-2S reduction potential, changes to the electrostatic surface potential of the protein or from changes to the backbone structure of the protein. • The disulphide bridge creates a flat surface for azurin binding and exposes the 2Fe-2S cluster to facilitate electron transfer. It has relatively high electron density and so does not inhibit electron transfer to cytochrome c as much as mutations like F108A.

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• The phenylalanine acts as a pathway for electron transport to cytochrome c however it also caps the 2Fe-2S cluster and makes the binding surface less flat meaning it reduces activity with azurin and DCPIP. • The disulphide bridge proximal to the 2Fe-2S cluster is highly conserved in AioB but it is notably absent in Alphaproteobacteria and Archaea. The Alphaproteobacteria appear to possess a large, aromatic hydrophobe and a glycine in its place. • Alphaproteobacterial and archaeal AioB probably lost their disulphide bridges independently as the residues that are in its place are not conserved between the clades. • A heterologous expression system for Pseudomonas aeruginosa azurin has been developed. • Future work should focus on resolving crystal structures of the Alcaligenes faecalis Aio with azurin and NT-26 Aio with cytochrome c to better understand how these proteins interact.

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The mechanism of antimonite oxidation by arsenite oxidase

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4.1 Introduction

Antimony is a group 15 element that shares several properties with arsenic: it is naturally solubilised from the lithosphere into waters and soils; it primarily exists as stibine (-3), antimony (0), antimonite (+3) and antimonate (+5); it is toxic. The WHO has set an upper limit on antimony concentrations in drinking water at 20 μg/L (WHO, 2003). As discussed in Section 1.1.3.2, it is increasingly apparent that microorganisms play a significant role in the speciation, mobility and bioavailability of antimony in nature. Due to antimony’s toxicity, it is important to further elucidate biological Sb interactions to develop strategies for its remediation (Li et al., 2016).

4.1.1 Demand for an antimony biosensor

The WHO has set a maximum contaminant level for antimony concentrations in drinking water of 20 μg/L (World Health Organisation, 2003). Antimony can be taken up by plants and photosynthetic biofilms, entering the food chains of contaminated environments (Telford et al., 2009) (Dovick et al., 2015). Antimony has also been observed to leach from the polyethylene terephthalate that most plastic bottles are made of into the water they contain (Westerhoff et al., 2008).

Determination of antimony concentrations in environmental water samples is currently carried out by laboratory techniques such as spectrophotometric, voltammetry, atomic fluorescence and mass spectrometry. These techniques have detection limits as low as 0.02 μg/L and so are suitable for monitoring environmental samples as they are well below the WHO limit of 20 μg/L. However, these techniques are limited by the fact they require extensive training, require expensive equipment to use and cannot be used in situ (Niedzielski & Siepak, 2003).

As discussion in Section 1.1.3.2, antimony is cycled through its oxidation states by various microbial processes. The biochemical processes responsible for antimony redox are not well understood. Aio can oxidise antimonite, albeit 6,500-fold slower than it oxidises arsenite. Aio is currently in development as a biosensor for arsenite and an easy to use, in situ, cheap sensor for antimony would be useful in assessing the scale of environmental contamination which is important in developing

173 remediation strategies. It was therefore of interest to assess if Aio could be used as a biosensor for antimony which required further biochemical characterisation of how the enzyme oxidises antimonite.

4.1.2 Aims

1. Further characterise the oxidation of antimonite by Aio by studying the antimony and cytochrome c steady-state kinetics (as preliminary experiments have shown no antimonite oxidation with DCPIP as an electron acceptor), whether antimony inhibits Aio arsenite oxidation and the effect of pH and various salts on catalytic rate. 2. Analyse the interaction between Aio and antimony using stopped-flow kinetics and extended X-ray absorbance fine structure (EXAFS). 3. Assess the impacts of these findings on a potential antimony biosensor and on the arsenite biosensor that is currently in development.

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4.2 Methods

Most of the techniques implemented are described in previous chapters. All experiments used heterologously expressed Aio. The antimonite source used was potassium antimonyl tartrate C8H10K2O15Sb2 (SigmaAldrich) as this was used in previous work (Wang et al., 2015).

4.2.1 Steady-state Kinetics

Steady-state kinetics were performed as described for arsenite with cytochrome c as described previously (Section 2.2.10.3) except that the final concentration of Aio was 200 nM and antimonyl tartrate was used as the electron donor instead of arsenite

2M solutions of ammonium sulphate, calcium chloride, sodium chloride, ammonium chloride, sodium sulphate and tartaric acid were prepared in 50 mM Tris-HCl pH 8. This ensured that high volumes of the salts could be added to assays without affecting buffer composition.

A mixed buffer was prepared in order to investigate Aio activity across a pH range without changing the contents of the assay (only the pH) as has been done previously for Aio investigations (Duval et al., 2016). The buffer consisted of 12.5 mM each of MES, Tris, HEPES and CHES buffers. A stock solution was made and adjusted to pH’s 6, 7, 8, 9 and 10 before being diluted to the final concentration. These buffers were used in steady-state assays with cytochrome c, Aio and arsenite or antimonyl tartrate.

The inhibitory effects of antimonyl tartrate on Aio arsenite oxidation were investigated by incorporating 0.05 μM and 0.5 μM antimonyl tartrate into arsenite steady-state kinetics assays (see Section 2.2.10.3 for assay conditions). 2500, 1000, 250, 75 and 5 μM concentrations of arsenite were used.

4.2.2 Reductive Titration

Aio was prepared for titrations by oxidising it with ferricyanide which was then removed by size exclusion chromatography using a Superdex 200 gel filtration

175 column as described previously (Section 2.2.4). 500 μl of ~15 μM Aio was titrated against 1 μl aliquots of 1 mM arsenite or 0.5 mM antimonyl tartrate. Spectra were recorded at 25 °C in Tris-HCl buffer pH 8 after the addition of each aliquot and mixing. The concentration of enzyme in the sample was determined by nanodrop and visible spectra and the concentration of available sites adjusted according to 80% co-factor saturation as described in Warelow et al. (2013). The equivalent electrons were calculated based on arsenite being able to donate two electrons and antimonyl tartrate being able to donate four (Figure 4.1).

A B

Figure 4.1: Structures of A) antimonyl tartrate (adapted from Wijeratne et al., 2010) and B) arsenite ion .

4.2.3 Extended X-ray absorbance fine structure of the antimonyl tartrate-Aio complex

EXAFS Aio samples were prepared to concentrations of approximately 75 mg/ml. Aio samples were oxidised by the addition of 500 mM potassium ferricyanide which was then removed by buffer exchange with a PD-10 desalting column as per the manufacturer’s instructions. Samples were sent to the Stanford Synchrotron Radiation Light Source on dry ice.

The recording of XAS measurements was performed by G.N. George (University of Saskatchewan, Canada) at the Stanford Synchrotron Radiation Light Source (SSRL). X- ray data was collected using the structural molecular biology XAS beamline 7-3. For each sample the K-edge of molybdenum was scanned (8 to 12 scans accumulated, each of approximately 40 min in duration), between excitation energies of 19980 to 20100 eV and averaged. Energy calibrations were performed with reference to the

176 absorption of a molybdenum foil measured simultaneously with each scan, assuming a lowest energy inflection point of 20003.9 eV.

4.2.4 Stopped-flow kinetics

Stopped-flow spectroscopy experiments were carried out in collaboration with R. Hille at University of California, Riverside.

All experiments were conducted in Tris-HCl buffer pH 8.

The reduction of Aio by antimonyl tartrate was conducted as described for arsenite in Section 2.2.12 only replacing arsenite with 400 and 40 μM of antimonyl tartrate (different concentrations were used to determine the effect of concentration on rate) and at 25 °C. Reduction of Fe-S clusters and the molybdenum centres were followed at 450 nm and 680 nm respectively.

Multiple turnovers of Aio were monitored by mixing 10 μM Aio with 20 μM arsenite or 10 μM antimonyl tartrate and 60 μM cytochrome c. This would catalyse approximately three turnovers of Aio. The reduction of cytochrome c was followed by monitoring absorbance change at 551 nm at 5 °C.

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4.3 Results

In this chapter, the interaction of Aio with antimony was investigated. A previous study showed that Aio can oxidise antimonite (from antimonyl tartrate) to antimonate and the overall objective of this chapter was to develop a deeper understanding of the mechanisms involved in Aio antimonite oxidation to assess the impact of this on the Aio biosensor and if it could be adapted to develop an antimonite biosensor.

4.3.1 Re-analysis of Aio antimonyl tartrate steady-state kinetics

The NT-26 Aio catalyses antimonite oxidation (Wang et al., 2015). The non-linear regression presented in the Wang et al. study doesn’t accurately fit the data. A double Michaelis-Menten model (Equation 4.1) seems to be more representative of the data suggesting that the reaction is biphasic (Figure 4.2). This result has been verified in this study with a single experiment freshly prepared Aio and new antimony dilutions. The new kinetic values for the Wang et al. data are: K1 = 30.9 ± 7.1 μM, K2

-1 -1 -1 -1 = 94.5 ± 6.0 nM, V1 = 5.2 ± 0.1 nmol min mg , V2 = 15.9 ± 1.0 nmol min mg (error represents standard deviation of three separate experiments). The values of this

-1 study were K1 = 71.9 ± 20.3 μM, K2 = 132.7 ± 12.0 nM, V1 = 4.3 ± 0.0002 nmol min

-1 -1 -1 mg , V2 = 13.2 ± 0.0002 nmol min mg (error represents standard error from the Michaelis-Menten model regression). Both datasets are clearly better represented by the double Michaelis-Menten model, however, the KM values are notably higher in this study. The standard error of the KM values were typically >50% for individual fits of the Wang et al. data which might explain the discrepancy. It is also possible that the antimonite concentration is half what it should be in Wang et al. (suggesting that the antimonyl tartrate concentration and not the antimonite concentration was used meaning that all KM values must be doubled which would bring them into closer accordance to this study).

푉 ([푆]) 푉 ([푆]) Equation 4.1: 푣 = 1 + 2 퐾1+[푆] 퐾2+[푆]

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Figure 4.2: Fits of antimony steady state rates. Single Michaelis-Menten (Blue) and Double Michaelis-Menten (red). A) Data from Wang et al. (2015). B) Additional repeat using fresh enzyme prep. and new antimonyl tartrate dilutions.

4.3.2 Reductive Titration of Aio by arsenite and antimonyl tartrate

Antimonyl tartrate is not an oxyanion like arsenite. It is a complex salt consisting of potassium ions, antimonite ions and deprotonated tartaric acid (refer to Figure 4.1 in Section 4.2.2). The degree of dissociation of the antimonyl tartrate complex into its constituents (Sb and tartaric acid) appears to be pH dependent (Sun et al., 2000). It was not known to what degree the antimonyl tartrate dissociated in the assay conditions (50 mM Tris-HCl pH 8). Titrations of Aio versus arsenite or antimonyl tartrate were performed to establish if both Sb atoms of the antimonyl tartrate were accessible to Aio. As Aio has the capacity to hold four electrons (two on the Mo- centre and one for each of the two Fe-S clusters), the rationale was that if both Sb atoms were accessible then the number of electron equivalents to fully reduce Aio would be approximately four, however, if only one Sb was accessible then the number of electron equivalents would be eight.

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Reduction was followed at the two largest changes in absorbance (detailed in Section 2.3.3): 700 nm and 475 nm, corresponding to the molybdenum centre and the Fe-S clusters respectively. The experiment was repeated with three separate enzyme preparations and representative results are shown in Figure 4.3 (repeats are shown in Appendix G). The 475 nm absorbance was quenched by 2.2 ± 0.1 electron equivalents of arsenic and 2.2 ± 0.5 electron equivalents of antimony. The 700 nm absorbance was quenched by 3.3 ± 0.9 electron equivalents of arsenic and 3.2 ± 0.2 electron equivalents of antimony. While the 475 nm quenching in 2 electron equivalents was expected, 700 nm in 3.2 and 3.3 was lower than the expected 4 electron equivalents. This is probably because the enzyme degraded towards the end of the experiment which was what happened when an arsenite titration was attempted with A. faecalis Aio (Anderson et al., 1992). However, there is no significant difference in the electrons required to quench each wavelength for arsenic and antimony (475 nm t-test p-value = 1; 700 nm t-test p-value = 1). This confirms that both Sb atoms are accessible to Aio.

Figure 4.3: Reductive titrations of Aio versus A) arsenite and B) antimonyl tartrate. Figures are from a single preparation though the experiment was repeated with three separate preparations. Conducted at 25 °C.

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4.3.3 Aio cytochrome c kinetics with antimonite as an electron donor

Steady state kinetics of Aio with antimonyl tartrate appear to be biphasic (Section 4.3.1). Steady state kinetics were attempted with 1-10 μM of cytochrome c to see if steady state kinetics of the reduction of cytochrome c were also biphasic. The change in rate over this concentration range was linear and ranged only from 35 to 41 nmol min-1 mg-1 (Figure 4.4) which suggests that the system was close to saturation over the whole concentration range and that the KM is much lower than what was observed for arsenite (1.0 μM; Section 2.3.4). This is not surprising as Aio employs a double-displacement mechanism and in enzymes that exhibit double displacement kinetics the KM of one substrate is influenced by the turnover rate of the other substrate. This is expressed in Equation 4.2 in which k-1 and k1 are the dissociation and association of oxidised cytochrome c into the active site respectively, k2 is the dissociation of reduced cytochrome c and k4 is the dissociation of the antimony product (Cleland, 1963). As can be seen, for k2>k4, the KM will be reduced by a degree

-1 proportional to the ratio of k2: k4 which, in this case, is very large as k2 ~ 400 s and

-1 k4 ~ 0.08 s . This places the KM for cytochrome c with antimony at approximately 50,000 times lower than with arsenite and therefore well out of observable ranges.

푘−1+ 푘2 푘4 Equation 4.2: 퐾푀 = ( )( ) 푘1 푘2+ 푘4

181

0.050 )

-1 0.045

mg -1

0.040

mol min mol 

0.035

Spec. Activity ( Activity Spec. 0.030

0.025

0 2 4 6 8 10 Cytochrome c conc. (M)

Figure 4.4: Steady-state kinetics of cytochrome c and antimonyl tartrate fit with a linear function. Cytochrome c concentrations used were 10, 5, 4, 3, 2 and 1 μM. Each point represents one individual assay’s rate from a single enzyme preparation. Conducted at 25 °C.

4.3.4 Comparison of the pH profiles of Aio turnover of arsenite and antimonyl tartrate with cytochrome c as an electron acceptor

As discussed previously, the Sb atoms in antimonyl tartrate are more labile at higher pHs (Sun et al., 2000) and so it was necessary to determine if the pH profile changed.

The activity of Aio with arsenite and antimonite was recorded from pH 6 to 10 using a mixed buffer (meaning that all assays were identical in their chemical constituents apart from changes in pH). Results are summarised in Figure 4.5. Arsenite has a peak in activity between pH 8 and 9 which is consistent with previous pH profiles (in which activity has different peaks between pH 8-9 depending on buffer used) (Warelow, 2014). However, over this pH range antimony did not exhibit any maxima with the highest rate observed being at the upper limit of the pH range – pH 10.

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100 Arsenite Antimony

40 % of max. observed activity observed max. of %

0 6 7 8 9 10 pH

Figure 4.5: pH profile of Aio activity with arsenite and antimonyl tartrate as substrates. Percentages were calculated based on the single highest observed activity value. Error bars represent standard deviation of three experiments each using separate enzyme preparations. Conducted at 25 °C.

4.3.5 Inhibition of arsenite oxidation by antimonyl tartrate

It was important to determine whether antimonite inhibited Aio activity as this could affect biosensor performance and range. Aio is able to slowly oxidise antimonite from antimonyl tartrate and Wang et al. (2015) reported that assays which contained 500 μM arsenite and antimonyl tartrate showed 10-fold reduced activity compared to assays with just arsenite (though improved activity compared to just antimonyl tartrate). The findings reported strongly suggest that antimonyl tartrate inhibits the reaction of Aio with arsenite though no additional inhibition study was attempted.

Presented in Figure 4.6 is an inhibition study comparing the Michaelis-Menten kinetics of Aio with arsenite and cytochrome c under three conditions: No antimonite, 0.1 μM antimonite and 1 μM antimonite. No antimonite had a KM for

-1 -1 arsenite of 11.1 μM and Vmax of 112.0 μmol min mg which was similar to the published values of 9.3 μM and 120.4 μmol min-1 mg-1 respectively (Wang et al. 2015).

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In the presence of 0.1 μM antimonite, the KM increased to 124.0 ± 13.2 μM and the

-1 -1 Vmax was unchanged at 116.8 ± 11.5 μmol min mg . Similarly, at 1 μM antimonite,

-1 -1 the KM increased to 653.2 ± 73.6 μM and the Vmax was 119.8 ± 1.1 μmol min mg . Note that the concentration of Aio used in these assays (1-2 nM) is such that antimonyl tartrate activity would be so low as to be imperceptible meaning that the

KM’s are accurate representations of arsenite. The results are strongly indicative of competitive inhibition because the KM increased with increasing inhibitor, but the

Vmax remained unchanged. This is because as the ratio of substrate to inhibitor increases, the substrate is much more likely to occupy the active site than the inhibitor so at high relative concentrations of substrate the Vmax is eventually obtained. However, at lower relative concentrations of substrate the inhibitor is likely to occupy the active site and so rate is reduced, therefore lowering the rate at lower concentrations and increasing the KM. Based on the finding that antimony is a competitive inhibitor the ki was calculated to be 7.9 ± 0.1 nM using Equation 4.3 where KM(app) = the observed KM, KM = the KM when no inhibitor is present, [I] = the concentration of inhibitor and Ki = the inhibitory constant.

[퐼] Equation 4.3: 퐾푀(푎푝푝) = 퐾푀(1 + ) 퐾푖

184

120

) 100

-1

mg -1

80 mol min mol

 60

Specific Activity ( Activity Specific 20 0.1 M Sb 1 M Sb 0 No Sb

0 500 1000 1500 2000 2500 As (III) conc. (M)

Figure 4.6: Michaelis-Menten fits of arsenite kinetics with Aio and cytochrome c in the presence of 0.1 μM Sb, 1 μM Sb and no Sb. Error bars represent the standard deviation of three experiments using three separate enzyme preparations. Conducted at 25 °C.

4.3.6 Extended X-ray absorption fine structure analysis of the interaction of antimonyl tartrate with the Aio active site

Extended X-ray absorption fine structure (EXAFS) is an element specific technique that examines the geometry, bond lengths and electronic structure of atoms. The technique is based on the absorption of X-ray photons by core electrons of a target element which excites them to higher energy states. When X-ray radiation is scanned through the core binding energy of an electron shell, absorption increases radically and gives rise to an absorption edge. The energies of absorbed radiation at these edges correspond to the binding energies of electrons. EXAFS is a technique that observes energies higher than the absorption edge. Electrons are ejected from the target atoms due to the substantial amounts of radiation provided by the absorbed X-ray photon. The leaving electron’s wave is backscattered by the neighbouring atoms, giving rise to detectable variations. These variations are highly specific to the 185 bonding environment of the target atoms and so give highly detailed insights into the local structure. The objective was to determine if antimonyl tartrate interacted with the Aio active site in a manner similar to arsenite.

In collaboration with Prof. Graham George (University of Saskatchewan), EXAFS analysis on Aio with antimonyl tartrate was performed (Figure 4.7). The results are identical to those with arsenite (Warelow et al., 2017). Three peaks were observed which correspond to: 1) the oxo groups on the molybdenum, 2) the Mo-S bonds for the pterins and 3) to a long Mo-Sb bond, most likely through an oxygen atom. This suggests that the reaction mechanism for antimonyl tartrate is the same as it is for arsenic and confirms that Sb dissociates from the tartrate complex and enters the active site in a manner similar to arsenite.

Figure 4.7: Mo K-edge EXAFS data of Aio saturated with antimonyl tartrate. Provided by Prof. Graham George, University of Saskatchewan.

4.3.7 Stopped-flow kinetics of enzyme reduction by antimonyl tartrate

As the rate of antimonyl tartrate oxidation by Aio is so 6,500-fold than for arsenite, it was of interest to analyse the rates of electron transfer in the system. As was done for arsenite, stopped flow kinetics was used to observe the rates of electron transfer when antimonyl tartrate was used as the electron donor to Aio.

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At 25 °C, Aio was fully reduced by antimonyl tartrate in two phases (Figure 4.8). The first phase occurred within the mixing dead time and caused partial quenching of both the 450 nm peak and the 680 nm peak. Furthermore, the 300-750 nm spectrum of Aio was distorted, flattening out the peaks (seen in Figure 8 at 60 ms). This loss of definition in spectra is commonly symptomatic of scattering caused by precipitation in the observation chamber. However, the second phase of the reaction proceeded to the spectrum of reduced Aio with no scattering (Figure 8 – 60 s) (it is identical to arsenite reduced Aio seen in Section 2.3.3) meaning precipitation was unlikely as this would cause scattering throughout the experiment. Therefore, the result of the first phase seems to represent an intermediate state that is specific to antimonyl tartrate’s reduction of Aio.

The second phase of the reduction proceeded at a rate of 0.046 ± 0.0048 s-1 at 450 nm and 0.046 ± 0.0046 s-1 at 680 nm. These rates were concentration independent (being the same for 20 µM and 200 µM antimonyl tartrate) implicating them in product (antimonate) dissociation. The rates are ~5000-fold lower than the kcat of Aio with arsenite and cytochrome c which is consistent with the steady state results of ~6,500-fold lower activity (Wang et al., 2015). This phase may therefore represent the breakdown of an Aio-antimony intermediate as well as the rate-limiting step.

When this experiment was repeated at 5 °C, the first phase still occurred in the mixing dead time for 200 µM antimonyl tartrate and produced the same distorted intermediate spectra. When using 20 µM antimonyl tartrate, a small part of the first phase was observed with a rate of 404.2 ± 40.6 s-1 at 450 nm and rate of 422.9 ± 22.9 s-1 at 680 nm meaning the first phase is concentration dependent. No second phase was observed most likely because it was progressing too slowly to be seen with stopped-flow spectroscopy (stopped-flow experiments are generally not performed for times greater than one minute as this can result in data capture failure and therefore misleading results).

187

Figure 4.8: Stopped-flow spectroscopy of WT Aio reduced by antimonyl tartrate at 25oC. The oxidised spectra before addition of antimonyl tartrate is shown as well as spectra taken at times 60 ms, 5 s, 20 s and 60 s (fully reduced) after the addition of antimonyl tartrate. B) Single exponential fit of spectral change at 450nm with k = 0.046-1. C) Single exponential fit at of spectral change at 680 nm with k = 0.046 s-1.

4.3.8 Stopped-flow kinetics of the reduction of cytochrome c by arsenite and antimonyl tartrate catalysed by multiple turnovers of Aio

The spectral change at 551 nm was used to monitor the reduction of cytochrome c by Aio as described previously (Section 2.2.12) however, in this instance, multiple turnovers of Aio were observed because it was of interest to determine if the biphasicity observed in the reductive reaction of Aio with antimonyl tartrate was mirrored in the cytochrome c.

The rate of cytochrome c’s reduction with arsenite was 99.5 ± 1.9 s-1 and was biphasic and similar to the single turnover experiments (with only 2% of total amplitude change being attributable to the rate of equilibration) (Section 2.3.6). With antimonyl tartrate, the reduction of cytochrome c was biphasic (Figure 4.9). The first phase proceeded at a rate of 285.1 ± 50.3 s-1 and was approximately one third of the

188 spectral change observed (amplitude = 0.08). The second phase had a rate of 0.012 ±0.002 s-1 and was approximately the remaining two thirds of the spectral change observed (amplitude = 0.13). Assuming an Arrhenius reaction (Section 2.2.12; Equation 2.6) then the rate of the second phase is similar to the rate observed in the reduction of Aio by antimonyl tartrate at 25 oC (0.48 s-1 and 0.46 s-1 respectively). Furthermore, the second phase with antimonyl tartrate is approximately 8000-fold slower than with arsenite which is consistent with steady state results. It is important to note that the reaction was not allowed to progress until all cytochrome c had been reduced (again, due to long experimental times) meaning that while a third of the observed spectral change occurred in the fast phase, this only represents approximately 12% reduction of the cytochrome c.

Figure 4.9: A) Stopped flow spectroscopy of the reduction of cytochrome c by antimonyl tartrate catalysed by multiple turnovers of WT Aio. Times depicted are 1 ms, 3 ms, 20 ms, 5 s and 20 s. B) Double exponential fit of spectral change at 550 nm

-1 -1 with k1 = 285 s and k2 = 0.012 s .

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4.3.9 The effect of salt on Aio oxidation of antimonyl tartrate

A crystal structure of Aio with antimonite (from antimonyl tartrate) in the active site has been obtained by Teresa Santos-Silva (Universidade de Lisboa; pers. comm.). Curiously, it was only possible to obtain this structure with ammonium sulphate present in the crystallisation buffer. When crystals were obtained without ammonium sulphate, no antimony was found in the active site. This raised the question of what, if any, effect ammonium sulphate (and other salts) might have on the activity of Aio.

3.4.6.1 Effects of different salts on Aio activity with cytochrome c and antimonyl tartrate The effect of ammonium sulphate on Aio activity with antimonyl tartrate was tested because it was only possible to crystallise Aio with antimonite in the presence of 2 M ammonium sulphate. The first experiment attempted was an antimonite activity assay (Section 4.2.1) with Aio and cytochrome c except with 100 mM ammonium sulphate present. The activity increased as the reaction progressed, reaching peak activity just before full reduction of cytochrome c (Figure 4.10).

To determine if the rate acceleration of antimonite oxidation by Aio in the presence of ammonium sulphate was an effect specific to ammonium sulphate or a more general influence of high salt concentrations the experiment was repeated using 100 mM concentrations of various salts (Figure 4.10 shows representative results): ammonium chloride to test if the effect was due to ammonium, sodium sulphate to test if the effect was due to sulphate, sodium chloride to test if it was a generic salt effect, calcium chloride to test if the effect was due to the presence of a divalent cation. While both sodium chloride and sodium sulphate produced a slight recreation of ammonium sulphate’s result, these were nowhere near as dramatic. Ammonium chloride was the most similar to ammonium sulphate, exhibiting approximately half the effect, this was found to be due to ammonium chloride having half the concentration of ammonium than ammonium sulphate. Doubling ammonium chloride’s concentration to 200 mM reproduced the effect. Calcium chloride did not

190 reproduce the effect. These experiments were replicated for three separate enzyme preparations (Table 4.1).

0.6

0.5

Abs 0.4

Sodium Sulfate 0.3 Sodium Chloride Calcium Chloride Ammonium Sulfate Ammonium Chloride

0.2 2 4 6 8 10 12 time (mins)

Figure 4.10: Effect of 100 mM concentrations of different salts on the profile of the antimonyl tartrate/Aio activity assay. These results have all been repeated with three different enzyme preparations and rate acceleration percentages for each are summarised in Table 4.1. Reactions were followed at 550 nm.

191

Table 4.1: Rate acceleration of Aio antimonyl tartrate assays with cytochrome c as an electron acceptor in the presence of 100 mM salt.

Fastest rate observed as a percentage Salt of initial rate

Ammonium chloride 241 ± 53

Ammonium sulphate 406 ± 90

Calcium chloride 182 ± 35

Sodium chloride 215 ± 59

Sodium Sulphate 295 ± 85

3.4.6.2 Effect of the concentration of ammonium sulphate on Aio activity with arsenite and antimonite as electron donors To establish how rate acceleration was affected by the concentration of ammonium sulphate in the assay, a range of concentrations were used and their influence on antimonite oxidation rate compared to their effect on arsenite oxidation rate. Results are shown in Figure 4.11 for arsenite and Figure 4.12 for antimonyl tartrate. The effect of ammonium sulphate on As oxidation is consistent with previous work which showed that as salt concentration increases, the rate decreases – this is most likely due to the salt interfering with the electrostatic interaction of cytochrome c and Aio (Warelow, 2014). However, for antimonite oxidation, increasing concentration of ammonium sulphate caused the fastest observed reaction rate to increase through the course of the experiment. This trend continued until 500 mM ammonium sulphate at which point it appeared that the salt concentration is high enough to slow the cytochrome c reduction rate such that it has now become rate-limiting.

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Figure 4.11: A) Effect of varying concentrations of ammonium sulphate on the profile of the arsenite/Aio assay. B) Fastest (initial) rate observed in the reaction profile. Error bars are the standard deviation of three separate experiments each with a different enzyme preparation. Where error bars cannot be seen they are occluded by the data point. Conducted at 25 °C.

Figure 4.12: A) Effect of varying concentrations of ammonium sulphate on the profile of the antimonyl tartrate/Aio assay. B) Fastest (black) and initial (red) rate observed in the reaction profiles. Error bars are the standard deviation of three separate

193 experiments each with a different enzyme preparation. Where error bars cannot be seen they are occluded by the data point. Conducted at 25 °C.

The fact that the assay increases in rate as the reaction proceeds suggests that something changes through the course of the reaction that acts as an agonist. The most obvious candidates for species that change through the course of the reaction is the antimonyl tartrate itself as it is known that the antimony must leave the tartrate complex and that the antimony must be oxidised. There are therefore two candidates to test: tartaric acid and antimonate.

Addition of 10 mM tartaric acid to the reaction had no effect on rate with or without the presence of 100 mM Ammonium sulphate. Similarly, there was no difference with 100 mM tartaric acid. The contributions to the phenomenon would rely on <1 mM free tartaric acid concentrations so it seems that tartaric acid can be ruled out as an effector.

Unfortunately, at time of writing it has not been possible to obtain any antimonate due to a supplier shortage.

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4.4 Discussion

The focus of this chapter was to extend the understanding of NT-26’s interaction with antimonyl tartrate. The experiments conducted in this study have shed new light on the mechanism of Aio oxidation of antimonyl tartrate. This study has also identified that antimonyl tartrate is a potent competitive inhibitor of arsenite oxidation by Aio. The findings of this study are imperative in assessing whether Aio could be used as an antimony biosensor and if environmental antimony could affect the operation of the arsenite biosensor.

4.4.1 The rate-limiting step of antimonyl tartrate oxidation by Aio

In Chapter 2, stopped-flow kinetics was used to analyse electron transfer rates in Aio with arsenite as the electron donor and cytochrome c as the electron acceptor. At 25 °C, the reduction of Mo by arsenite occurred in the 1 ms dead time of the stopped- flow apparatus implying a rate constant >4000 s-1, the reduction of the Fe-S clusters was 592 s-1 and the reduction of cytochrome c was 389 s-1.

When the stopped-flow experiments were performed with antimonyl tartrate instead of arsenite the visible spectrum was partially quenched and distorted in the mixing dead time, with the shoulders at 450 nm and 680 nm becoming less defined. Performing this experiment at 5°C found that this rapid phase (which resulted in the distorted spectrum) was observable with 20 μM but not 200 μM antimonyl tartrate meaning the kinetics of this phase were concentration dependent and therefore likely represents formation of the antimonite-Aio complex. It is unusual that the visible spectra are distorted as this was not observed with arsenite (Section 2.3.5). This may represent the Mo(V) intermediate which is not typically observed in Aio/arsenite reactions (Hoke et al., 2004) (Duval et al., 2016). This seems unlikely as the entire spectrum is distorted, not just the Mo portion (680 nm shoulder). Perhaps a more likely explanation is that the distorted spectrum is that of the intermediate antimonite-Aio complex with the electrostatic environment of the Aio co-factors having been altered by the presence of the antimonite ion. The connection between reduction potential and visible absorbance spectra is best typified by the heme units

195 of cytochromes in which mutations around the heme altered the reduction potential and visible spectrum of the cytochrome (Aono et al., 2010). The explanation was that the mutations induced changes to the amount of electron density the heme donated to the Fe atom, altering the reduction potential. The visible absorption spectrum is based on the number and magnitude of electron transitions the metal can undergo which, like the reduction potential, is influenced by the electronic structure and electron environment of the atom (Olson et al., 2013). It is therefore a reasonable conclusion that the antimonite-Aio complex could have caused the distorted spectrum.

The second phase of the reductive reaction with antimonite resulted in the reaction terminating at the visible spectrum of fully reduced Aio at a rate of 0.046 s-1. This rate

-1 is similar to the kcat of antimonite oxidation (0.081 s ) and therefore likely represents the rate-limiting step. It was found to be concentration independent and therefore likely corresponds to the dissociation of the antimony product.

The rate of cytochrome c reduction by Aio at 25 °C with antimony as the electron donor was 0.048 s-1 suggesting that the rate of cytochrome c reduction was limited by the dissociation of the antimony product. A rapid phase was observed at the beginning of the cytochrome c reduction stopped-flow experiment. It was initially thought that this might suggest that the first turnover of Aio with antimonyl tartrate was rapid and all subsequent ones were slow (due to dissociation of product). However, the absorbance change is too low for this to be the case. One turnover would have increased the absorbance at 551 nm by ~ 0.21 absorbance units instead of only 0.08. Furthermore, the rate of this phase is 3-fold faster than arsenite-induced reduction of cytochrome c by Aio. It was then thought that the fast phase could be representative of the establishment of the Aio-antimony complex. However, because 200 μM antimonyl tartrate was used in the experiment, the formation of the antimony/Aio complex would have occurred in the mixing dead-time (as shown in the reductive experiments) meaning that the order of events must be complex formation, partial and rapid cytochrome c reduction, product dissociation, slow turnover of cytochrome c limited by antimony product dissociation. It appears that a more complex explanation of the rapid phase is required. 196

The cofactors of xanthine oxidase (another molybdoenzyme) have been shown to be in rapid equilibrium with one another with the distribution of electrons solely determined by the reduction potentials (Hille et al., 1981). A similar phenomenon was observed in succinate dehydrogenase (Bonomi et al., 1983). Perhaps the formation of the antimonite-Aio complex results in the two electrons from antimonite being distributed across all Aio cofactors (as opposed to with arsenite where they appear to reside on the Mo, then move to the Fe-S clusters). This allows partial reduction of cytochrome c as a proportion of the electron density is on the Rieske cluster. The distorted visible spectrum of the Aio-antimony complex suggests that the electron environment of Aio is different in the complex and Rieske cluster reduction potentials are highly sensitive to their environment (Liu et al., 2008). This could explain why the first cytochrome c reduction phase is so rapid, assuming the Rieske cluster’s reduction potential is lower when Aio is in complex with antimony, meaning the difference between it and cytochrome c’s reduction potentials would be greater and so electron transfer rate is increased (Page et al., 1999).

4.4.2 Biphasic kinetics of antimonyl tartrate oxidation by Aio

For comparison with previously published data, the steady state kinetics of Aio with antimonyl tartrate and cytochrome c were investigated. The published kinetics in Wang et al. (2015) have been fit with the standard Michaelis-Menten model. When the kinetics were repeated in this study, it was found that the Michaelis-Menten model did not fit the data well. Upon further investigation it was found that this was also true for the data in Wang et al. A double Michaelis-Menten model fit the data much better than the single model for the experimental data from this study and that from Wang et al. It is not surprising that the Double Michaelis-Menten model produced a better fit as it has more variables in it meaning that the model will generally be more flexible and result in better R2-values. It is therefore important to evaluate why antimonyl tartrate has resulted in double Michaelis-Menten kinetics.

Double Michaelis-Menten or biphasic kinetics occurs when there are two saturable substrate uptake mechanisms. For example, consider an enzyme with two active sites for the same substrate but one active site has higher affinity than the other. The

197 activity will increase linearly with increasing substrate as the high affinity active site is saturated. However, rather than activity plateauing when the high affinity activity site is saturated, the low affinity active site will start to take up the substrate meaning that activity will continue to increase until both active sites have been saturated. The question is, therefore, how something like this occurs in Aio and why with antimonyl tartrate but not arsenite.

Initially, it was thought that it might be the case that cytochrome c was causing the kinetics to be biphasic as this has been observed in other enzymes (Dodia et al., 2014). The Aio kinetics of cytochrome c with antimonyl tartrate were measured and the KM was found to be < 1 μM meaning that cytochrome c was saturated. The rate of antimony oxidation is so slow it results in a decrease in the KM for cytochrome c. While this means cytochrome c cannot be excluded as the effector of antimonyl tartrate’s biphasic kinetics it seems unlikely as cytochrome c does not have biphasic kinetics with arsenite.

Another possible reason for biphasic kinetics is that the two antimonite ions of each antimonyl tartrate molecule are not equivalent. Perhaps one antimonite leaves the complex easily while the other doesn’t. However, there is no indication from what little research has been done on antimonyl tartrate that this is the case (Sun et al., 2000). The titrations with antimonyl tartrate performed in this study established that both antimonite ions were accessible to Aio but it would be very difficult to track them individually.

Stopped-flow kinetics with antimonyl tartrate and Aio showed that the rate-limiting step of Aio is different with antimonyl tartrate than it is with arsenite. The rate- limiting step in the former is now the dissociation of the antimony product. This step is typically concentration independent however steady-state assays with different salts showed that this step is sensitive to changes in the buffer constituents. A possible explanation for the observed biphasic kinetics is that the high affinity phase is when the association of antimonite is rate-limiting while the low affinity phase is when the product dissociation is rate-limiting. However, higher concentrations of

198 antimonite increase the product dissociation rate making this also a saturable system which is not typically observed in Michaelis-Menten enzymes.

4.4.3 Mechanism of antimony inhibition

Wang et al. (2015) observed that steady-state kinetics assays that contained 500 μM antimonite and arsenite proceeded 10-fold slower than arsenite assays (but faster than antimonyl tartrate assays). Though not explicitly stated, this indicates that antimonyl tartrate inhibits arsenite turnover in Aio. To date, no further inhibition study has been attempted. It was important to understand this because any inhibitors of Aio could affect the function of the biosensor and therefore affect marketability.

In the present study, the inhibition of Aio by antimonyl tartrate was investigated. It was found that antimonyl tartrate serves as a competitive inhibitor of Aio meaning that it prevents arsenite oxidation by occupying the active site. The specific mechanism by which antimonyl tartrate exerts its effect can be deduced from the titration, EXAFS and stopped-flow data. The titration demonstrated that all antimonite ions of antimonyl tartrate are accessible to Aio meaning they must leave the tartrate complex. The antimonite then enters the active site and EXAFS show that they interact with the Mo active site in a manner identical to arsenite (Warelow et al., 2017). Once in the active site the antimonite forms a transition state complex with Aio as evidenced by the distorted spectra observed in stopped-flow. The breakdown of this complex to release the antimonate product is incredibly slow and has shown to be rate-limiting in antimonite-Aio kinetics. Aio with antimonite in the active site therefore becomes arrested and cannot participate in arsenite oxidation until the antimonate is released.

The chemistry of antimonite inhibition of Aio arsenite oxidation is curious for a variety of reasons:

First, antimonyl tartrate is a complex salt and the lability of the antimonite ions is dependent on pH – Sun et al. (2000) found that at pH 4, Sb3+ will not leave the tartrate but at pH 8 it will freely associate with glutathione. Aio oxidation of antimonyl

199 tartrate with cytochrome c as the electron acceptor has been shown in this study to have a different pH profile to arsenite and this may reflect antimonyl tartrate’s pH sensitivity. This is an important observation for the Aio biosensor in development as not only might antimonite interfere with arsenite readings, but the pH of the water may also affect the degree to which it interferes.

Second, the form that the antimonite is in when it enters the active site is currently unknown. As far as basic chemistry, discussions of antimonyl tartrate suggests the complex should dissociate into Sb3+ and tartaric acid. However, Sb3+ is not structurally

3- analogous to arsenite (AsO3 ) which raises the question of why it would associate with the active site so strongly. It is possible that the Sb3+ forms an antimonite

3- intermediary that resembles arsenite, most likely SbO3 (the ion of antimonous acid,

SbOH3) which then associates with the active site in a manner identical to arsenite as demonstrated with EXAFS. Crystal structures of the Aio-antimony complex strengthen this hypothesis as they indicate that the Sb3+ ion is associated with three oxygen atoms and one of the Mo oxo ligands in a pesudooctohedral geometry (Figure

4.13) (Santos-Silva, Universidadae de Lisboa; pers. comm.). Sb(OH)3 is the most common form of antimonite found environmentally (Wilson et al., 2010) meaning that if antimonite is present it is likely to be able to serve as an inhibitor to Aio and disrupt biosensor performance.

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Figure 4.13: Crystal structure of the antimony and molybdenum cofactor of the Aio- antimony complex. Antimony is shown in purple. Mo is shown in metallic blue. (Crystal structure provided by Santos-Silva, Universidade de Lisboa; pers. comm.)

Third, is the reason for why antimony would become arrested in the molybdenum active site. Arsenate was shown to dissociate from the active site at a rate of 592 s-1 (Section 2.3.5). Antimonate dissociates at a rate of 0.046 s-1. The two should have similar structures and XAS has shown the interactions of arsenite and antimonite with the Aio active site to be virtually identical. This leaves the explanation to be in the specific chemistry of antimony and how it differs from arsenic. Antimony is only slightly more electronegative than arsenic (1.9 and 2.0 respectively) so it is unlikely that this is the reason for the difference in product dissociation. Likewise, both arsenic and antimony compounds are relatively soft acids meaning this is also unlikely to provide an explanation. One chemical difference is that antimonate is a much weaker Lewis acid (able to accept an electron pair) than arsenate or phosphate (Filella et al., 2002a), however in theory, this would make the antimonate more stable than arsenate and therefore more likely to dissociate from the Mo.

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A potential explanation for why antimony becomes arrested in the Aio active site is that the equilibrium between antimonate free in solution and antimonate in Aio is only slightly in favour of the antimonate leaving, perhaps due to its poor solubility. The equilibrium with arsenate is strongly in favour of arsenate leaving, therefore arsenate is not an inhibitor (Warelow, 2014). However, for antimonate, the equilibrium is much less in favour of antimonate free in solution, so it is less likely to dissociate. When it does dissociate it is quickly replaced by antimonite. This is consistent with the observation that dissociation rate is sensitive to the solution as evidenced by biphasic kinetics and the effects that salt seem to have on rate. It is very important to investigate this because, if true, it could mean that antimonate is also an inhibitor of Aio. Antimonate is a much more common form of environmental antimony, particularly at neutral pH’s and in oxic waters (Filella et al., 2002a). Unfortunately, this could not be tested because of a supplier shortage of antimony pentoxide, the only commercially available form of antimonate.

4.4.4 Antimonite as an electron donor and inhibitor of Aio – effect on the range of the arsenic biosensor

In this chapter, antimonyl tartrate has been shown to be a competitive inhibitor of

Aio with Ki = 7.9 nM. The mechanism of inhibition has already been discussed but as Aio is in development as a biosensor it is important to discuss if this will affect biosensor performance and range. If arsenic frequently co-occurs with antimony, then this could result in false negatives (the biosensor will indicate much lower arsenic concentrations than are true) due to inhibition by antimony. Antimonite is also able to donate electrons to Aio meaning that if it is present then it could exaggerate arsenic concentrations, potentially yielding a false positive. It is critical to assess if it is still appropriate as an arsenic biosensor in the context of these new results.

As discussed in the market report of the Aio arsenic biosensor , there are broadly two markets to consider (Appendix A.3). The first is Bangladesh and West Bengal, India where combined there are over 135 million people consuming arsenic-contaminated water (Murcott, 2012). In this market the demand is for an in situ, easy-to-use,

202 reliable and cheap biosensor for arsenic. The other market discussed was North America which has approximately 9 million people affected by arsenic poisoning (Murcott, 2012), however, 45 million people have their water provided by private wells which require regular testing for arsenic (Cone, 2011). Waste from industrial activities such as mining and fracking also require testing for environmental contaminants like arsenic (Murcott, 2012) (Meyer, 2013).

In 2005, 245 tube wells in Bangladesh were tested for both antimony and arsenic contamination. While arsenic concentrations were highly variable with some exceeding 600 nM, antimony concentrations were consistently below the detection limit of 9 nM (McCarty et al., 2004). It is therefore unlikely that even the highest naturally occurring concentrations of antimony would affect biosensor performance. The biosensor functions by calculating total voltage generated from a sample meaning the only effect of the highest possible concentration of antimony (9 nM) is that the sensor will take slightly longer to produce a result which can be easily remedied by using more Aio per test. Unfortunately, there have not been any other studies analysing antimony concentrations in Bangladeshi or Indian tube wells, however, the available data indicates Aio is suitable as an arsenic biosensor for tube wells in India and Bangladesh.

Data for heavy metal contamination for private wells in North America is relatively scarce because these wells tend to be poorly monitored and there is no obligation for any data to be made publicly available. The Environmental Protection Agency does warn, however, that private wells may contain both arsenic and antimony (United States Environmental Protection Agency, 2017). One study in Pennsylvania (in which 3 million people rely on private well water) found that wells on average had approximately 50 nM antimony and 150 nM arsenic (Alawattegama et al., 2015). These findings complicate the use of Aio as an arsenic biosensor in USA private wells because 1) as antimony is also an electron donor, the total concentration of arsenic would be exaggerated from 150 nM to 200 nM and 2) the antimony present in the water sample might slow the overall rate of the biosensor meaning it would take longer to yield a result and possibly requires larger amounts of Aio on the test strip.

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Another aspect of the North American market is water waste from activities like mining and fracking. Arsenic and antimony concentrations were determined in three fresh water sources around Giant Mine, Canada. Arsenic ranged from 0.013 to 360 μM and antimony ranged from 1.3 to 13.2 μM (Fawcett et al., 2015). A similar study in Kantishna Hills mining district, Alaska found antimony concentrations in various surrounding water sources to exist at concentrations up to 1.9 μM (Ritchie et al., 2013). At three different fracking sites in USA, antimony concentrations were found to be 4 to 81 μM (Maguire-Boyle & Barron, 2014). The arsenite biosensor cannot distinguish between arsenite and antimonite and would give a result for the combination of the two. Furthermore, in all these instances, antimony concentration is 100 to 1000-fold greater than the Ki meaning that Aio activity is likely to be prohibitively slow. Provided these trends hold true for other mining and fracking activities, Aio is not suitable for use in monitoring these environments.

4.4.5 The effect of salt on Aio kinetics with antimonyl tartrate

The crystal structure of Aio with antimonite in the active site has been obtained and was only possible when the enzyme was crystallised in the presence of 2 M ammonium sulphate (crystallisation attempts without ammonium sulphate yielded Aio without antimony in the active site) (Santos-Silva, Universidade de Lisboa; pers. comm). It was therefore of interest to establish what effect, if any, ammonium sulphate had on antimonyl tartrate oxidation by Aio.

Addition of 100 mM ammonium sulphate to the antimonyl tartrate steady-state kinetics assay resulted in an unfamiliar phenomenon in which the rate was increasingly rapid as the reaction proceeded. This effect was reproduceable across multiple enzyme preparations and varied with the concentration of ammonium sulphate. A similar, though not as dramatic, effect was observed with other salts. Ammonium chloride produced an identical result to ammonium sulphate once the molarity of ammonium in the solution was made equal (accounting for the fact that ammonium sulphate has twice the ammonium of the chloride). The effect was not observed in arsenite assays, with salt instead inhibiting activity.

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The observations made with arsenite are not surprising. The rate-limiting step of the arsenite reaction is electron transfer to cytochrome c and increasing the ionic potential of the solution lowers cytochrome c’s affinity for Aio (Warelow, 2014).

It is not easy to explain the observations for the antimonyl tartrate assays containing ammonium sulphate. There are four major observations that can be used to explain the phenomenon: 1) The rate-limiting step of the antimonyl tartrate reaction is antimony product release; 2) antimony product release rate is suspected to be sensitive to the buffer environment as discussed as an explanation for biphasic steady-state kinetics (Section 4.4.2); 3) the rate of cytochrome c reduction increases as the reaction progresses and the most likely explanation is that one of the products of the reaction is improving rate, it has been demonstrated that this is not tartaric acid; 4) the salt effect is common to all salts but is most pronounced with ammonium salts.

Combining these factors, a potential explanation for rate acceleration of antimonite oxidation by Aio in the presence of ammonium sulphate is that one of the products of the reaction (antimonate) in the presence of salt increases the rate at which the antimony product dissociates. Perhaps the effect is most pronounced for ammonium containing salts due to ammonium’s similar structure to antimonate (both are tetrahedral – Santos-Silva, Universidadae de Lisboa; pers. comm). Therefore, as more product is produced, the reaction gets faster.

4.4.6 Aio as a sensor for antimony

Aio is currently being developed as an electrochemical biosensor for arsenite. This chapter and previous work have shown that Aio is also able to oxidise antimonite. One of the aims of this chapter was to assess if Aio could be used to develop an antimonite biosensor. There are two major scientific factors to consider when assessing the viability of Aio as an antimonite biosensor, these are: the biochemical feasibility of Aio to perform as a biosensor and the co-occurrence of arsenite and antimonite. The market viability of an antimony biosensor must also be discussed and is presented in Section 5.3.

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Speed and reliability are two major factors to consider when assessing if an enzyme is suitable for a biosensor. The kinetics of Aio with antimonite are highly reproducible between assays and separate enzyme preparations meaning that if it were to be used as a biosensor it can be expected to produce reliable data. However, Aio catalyses antimonite oxidation very slowly, approximately 6500-fold slower than it does arsenite. This means that the current arsenite test strips, which in general can yield a result in under 5 minutes, would take theoretically 540 hours to produce a result for antimonite which is wildly inappropriate. Using more Aio on the test strip would remedy this issue but would also increase the price of the test strip which may be permissible based on demand for an antimonite sensor. Alternatively, it has been shown in this chapter that the rate of antimonite oxidation can be improved in the presence of some salts such as ammonium sulphate. If the cause of this effect could be identified and implemented it would reduce the amount of Aio required per test strip. Note, however, that only a 3-fold improvement in rate has been achieved and that high concentrations of salt reduce cytochrome c activity, so it seems unlikely that the antimonite reaction will ever be similar in rate to arsenite. Another option is to attempt to engineer Aio to have faster rates for antimonite oxidation (Section 5.4).

Any biosensor using Aio will be sensitive to arsenite. For it to be used as a sensor for any other analyte there must either be no arsenite present in the sample or the sensor itself would have to somehow be rendered arsenite insensitive. The latter is unlikely to be achievable as EXAFS presented in this chapter have shown the interaction of antimonite and arsenite with Aio to be virtually identical. It is therefore important to assess the co-occurrence of antimony and arsenic as if there are few environments where antimony occurs without arsenic then it can be concluded that Aio is inappropriate as an antimony sensor.

Antimony tends to co-occur with arsenic in mine draining and potable water which is not surprising given their similar chemistry (Filella et al., 2002a) (Flakova et al., 2017) (Andra et al., 2014) (Seyfferth et al., 2010). The inverse is not true (arsenic occurs without significant presence of antimony) which again is not surprising as arsenic is more abundant and more soluble (McCarty et al., 2004). An antimony biosensor using Aio would therefore have limited range and reliability as any result would have 206 to have the total amount of arsenic quantified. The readings would be further complicated by the fact that antimonite is an inhibitor of arsenite oxidation by Aio.

It may be possible to engineer Aio to be more specific for antimonite (and less so for arsenite). For this to be done by rational design a greater understanding of the Aio- arsenite and antimonite interactions is required - a structure of the arsenite-Aio complex would be invaluable. For directed evolution, a high-throughput screening method must be devised. These are discussed in greater depth in Section 5.4.

Finally, antimony tends to occur in the environment at neutral pH as antimonate (+5) instead of antimonite (+3) meaning that a method to reduce all antimonate to antimonite would be required for the sensor to function (Filella et al., 2002a).

4.4.7 Key findings and conclusions

• The rate limiting step of Aio oxidation of antimonyl tartrate is dissociation of the oxidised antimony product. • The Aio-antimony complex appears to have a distorted visible spectrum – it has been speculated that this might be because the presence of antimony alters the electron environment of Aio cofactors. • Aio catalysed reduction of cytochrome c by antimonyl tartrate is biphasic. It is initially 3-fold faster than it is for arsenite but the second phase is limited at the rate of antimonate release. It has been speculated that this phenomenon is caused by the distortion to the electron environment of Aio cofactors in the Aio-antimony complex. • Stead-state kinetics of antimonyl tartrate with Aio are biphasic, possibly because the rate-limiting step (dissociation of antimonate) is sensitive to the buffer environment. • Sb3+ leaves the tartrate complex and enters the Aio active site. It interacts with the active site in a manner identical to arsenite.

• Antimonite is a potent inhibitor of Aio arsenite oxidation with Ki = 7.9 nM. This does not affect the ability of Aio to function as a biosensor for arsenite

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in Bangladesh and India but does prevent it from being used in industrial waste monitoring and in USA private water wells. • The antimony-Aio interaction is sensitive to salt. The rate of antimonyl tartrate oxidation by Aio has been improved 3-fold in the presence of 100 mM ammonium sulphate but only at the end of the reaction. • Aio could be considered for further development as a biosensor for antimonite but would require engineering to improve rate and selectivity as it is too slow, and antimony tends to occur where arsenic is.

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Chapter 5

General Discussion

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This study involved three principal aims: 1) to identify the rate-limiting step of Aio catalysis and assess if this could be improved for biosensor development; 2) to explore the role of the disulphide bridge proximal to the AioB Rieske cluster’s role in defining electron acceptor specificity; 3) to characterise the ability of Aio to oxidise antimonite and assess its suitability as an antimony biosensor.

5.1 Summary of findings

Arsenite is oxidised by the Aio at the molybdenum active site in a two-electron transfer process. From the Mo centre, the electrons are transferred one at a time to the 3Fe-4S and Rieske 2Fe-2S clusters before reducing an electron acceptor such as horse heart cytochrome c (Ellis et al., 2001). The rate limiting step of Aio catalysis was found to be reduction of the cytochrome c by the Rieske 2Fe-2S cluster which proceeded at a rate of 389 s-1 as measured by stopped-flow spectroscopy. The reduction of the Mo-centre by arsenite was too rapid to observe in the mixing dead time of 1 ms and the Fe-S clusters were reduced simultaneously at an arsenite independent rate of 592 s-1 suggesting that this rate was limited by the release of arsenate from the active site. ITC and steady-state kinetics were used to examine the affinity of Aio for cytochrome c. The interaction was found to be endothermic with a

Kd of 2.3 ± 0.7 μM and KM of 1.0 ± 0.1 μM. The similarity in these values suggested that electron transfer to cytochrome c (as opposed to formation of cytochrome c-Aio complex) was rate-limiting. It was concluded that it is unlikely that Aio can be engineered to be faster as it already appears to be highly optimised for both arsenite and cytochrome c.

The arsenite oxidases from the Alphaproteobacteria and some Archaea lack a disulphide bridge proximal to the Rieske cluster which appears to be present in many other AioB, the bc1 cluster and the b6f cluster (Section 3.3.6). The NT-26 AioB instead possesses a phenylalanine (F108) and a glycine (G123). The AioB-F108A mutant was found to increase activity with DCPIP by 20-30% but reduce activity with cytochrome c by 97%. Stopped-flow kinetics demonstrated that electron transfer rates were unaffected in the F108A Aio except for in the reduction of cytochrome c. Steady-state kinetics and ITC were again used to investigate the cytochrome c interaction with Aio

210 and found that there was no significant change in the KM and that the Kd had increased 3-fold compared to the WT Aio. These results suggested that the F108 residue was not important in cytochrome c association but was critical in electron transfer, either by acting as an electron transport pathway or by positioning the two redox centres to aid in electron transfer.

An attempt was made to further elucidate the AioB-cytochrome c interaction by expressing AioB alone. Stopped-flow kinetics and ITC demonstrated that AioB could bind and reduce cytochrome c, albeit very weakly and slowly (when compared to Aio). It could not be concluded if this was because AioA is also required for cytochrome c binding or because AioB was purified as a tetramer and the oligomeric state interfered with binding. An attempt was made to test the AioA-cytochrome c binding but AioA did not appear to be stable alone.

The role of the AioB disulphide bridge found in all AioB other than arsenite oxidisers of the Alphaproteobacteria and some Archaea has been suggested to be important in defining electron acceptor specificity (Hoke et al., 2004) (Lieutaud et al., 2010) (Duval et al., 2010) (Warelow et al., 2013). This was shown to be the case by comparing the interaction of the NT-26 WT and A. faecalis WT arsenite oxidases with their respective electron acceptors, cytochrome c and azurin, with mutants that added and removed the disulphide bridge respectively: NT-26 AioB-F108C/G123C and A. faecalis AioB-C65F/C80G. The NT-26 WT Aio reduced cytochrome c at a rate of 426 ± 23 s-1 and was not able to reduce azurin while the F108C/G123C Aio mutant reduced cytochrome c at a slower rate of 346 ± 49 s-1 and was able to reduce azurin at a rate of 0.5 ± 0.3 s-1 (for non-saturating concentrations of azurin at 500 μM). The

KM of cytochrome c was doubled by the mutation from 1.0 ± 0.1 μM in the WT Aio to 2.0 ± 0.2 μM in the F108C/G123C Aio mutant. The activity with DCPIP as an electron acceptor was also doubled by the mutation from 9.1 ± 0.1 s-1 to 15.6 ± 0.7 s-1. These results were mirrored in the A. faecalis Aio. A. faecalis WT reduced azurin at a rate of 280 ± 32 s-1 and cytochrome c at a rate of 0.9 ± 0.1 s-1 (unsaturated at 1 mM). The A. faecalis C65F/C80G reduced azurin at a slower rate 150 ± 18 s-1 and cytochrome c

-1 at a higher rate of 2.6 ± 0.6 s (unsaturated at 1 mM). The KM of azurin was increased

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5-fold by the A. faecalis mutation; 311.0 ± 26 μM to 1465.7 ± 154 μM. The activity with DCPIP was halved; 17.2 ± 1.4 s-1 to 9.3 ± 1.1 s-1.

Kinetics results had shown that the presence of the disulphide bridge increased activity with azurin and DCPIP but reduced it with cytochrome c. It was of interest to determine how the disulphide bridge defines electron acceptor specificity. The disulphide bridge could do this either by altering the reduction potential of the Rieske 2Fe-2S cluster, altering the electrostatic surface of the binding site or by altering the structure of the binding site. The reduction potentials of the 2Fe-2S clusters (determined by EPR), electrostatic surface potentials (calculated in PyMOL) and X-ray crystal structures were compared to try to identify how the disulphide bridge exerted its effect. It was found that the mutations had not significantly altered the reduction potentials and the electrostatic surface appeared unaltered. There was also no observable change in the peptide backbone meaning the effector must be the specific residues. It was suggested that the disulphide bridge creates a flatter surface which facilitates binding of azurin and access of small molecules like DCPIP. The F108 residue acts as a cap over the Rieske 2Fe-2S cluster, limiting azurin and DCPIP access but potentially serving as an electron transport pathway for reduction of cytochrome c.

The NT-26 Aio has been shown to be able to oxidise antimonite (in the form of antimonyl tartrate), albeit 6,500-fold slower than it reduces arsenite (Wang et al., 2015). It was of interest to investigate this interaction to assess if Aio could be used as a sensor for antimony as well as arsenic. It was important to verify the published results. While the specific activities were similar, it was found that the published steady-state kinetics of antimonite had used an inappropriate model and that double Michaelis-Menten kinetics better represented the data. Reductive titrations, EXAFS, X-ray crystallography (Santos-Silva, Universidadae de Lisboa, pers. comm.) and stopped-flow spectroscopy were used to understand the reaction mechanism at the active site. The reductive titration demonstrated that both Sb3+ ions of antimonyl tartrate were available to the Aio for catalysis. The X-ray crystal structure showed that the interacting species of antimonite was an oxyanion, most likely Sb(OH)3, and EXAFS showed that it interacted with the Mo-centre in an identical manner to 212 arsenite. Stopped-flow spectroscopy found that the oxidised antimony product dissociated from the active site at a rate of 0.05 ± 0.005 s-1 which was similar to the

-1 Kcat of 0.08 ± 0.0008 s . Antimonite acted as a potent competitive inhibitor of Aio activity with arsenite, with a Ki of 7.9 ± 0.1 nM as the oxidised antimony product arrests Aio catalysis as it dissociates from the active site very slowly. Ammonium sulphate (and some other salts although to a lesser degree) were found to improve the rate of antimonite oxidation although only with a 3-fold increase. Based on this study it appears that the WT Aio is probably unsuitable as an antimony biosensor as the rate of catalysis is too slow. The fact that antimonite also acted as a competitive inhibitor for arsenite oxidation by Aio may make it unsuitable as an arsenic sensor for environments with high concentrations of antimony contamination such as in fracking sites or mine drainage (Section 4.4.4).

5.2 The development of the arsenic biosensor

The NT-26 Aio is in development as a recognition element for a biosensor for arsenic. One of the objectives of this project was to further characterise the mechanisms of Aio catalysis to evaluate if there were any specific engineering targets that could improve rate or alter the substrate specificity of the biosensor.

It is desirable for the Aio to catalyse the oxidation of arsenite as rapidly as possible as this means that lower amounts of the enzyme can be used per test strip which, in turn, lowers the cost of each test. The rate-limiting step of Aio catalysis was found to be electron transfer to cytochrome c from the Rieske 2Fe-2S cluster. Cytochrome c is used as a mediator between the Aio and the electrode in the biosensor. It was concluded that, as the rate of Aio catalysis is so rapid and because the Kcat/KM ratio (4 x 108 mM-1 s-1) is close to the maximum attainable (109 mM-1 s-1), that it is unlikely that Aio could be engineered to be faster (Albery & Knowles, 1976). Furthermore, as arsenate dissociation is only ~50% faster than the rate-limiting step, an increase in rate of only 50% would be attainable before further engineering (this time at the active site) would be required. What is promising is that NT-26 Aio is highly optimised for arsenite oxidation and is therefore suitable for use as a biosensor.

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The interaction of NT-26 and A. faecalis Aio with cytochrome c and azurin has been investigated. While the specificity for different electron acceptors was altered by site directed mutagenesis this is unlikely to have practical applications with regards to the Aio biosensor. The Aio biosensor currently uses equine cytochrome c as the mediator for electron transfer to the electrode. If alternative electron acceptors must be considered (for example in the case that cytochrome c does not have a stable shelf-life or if common contaminants in water samples interfere with its Aio interaction) then this work might provide a strong basis for future research.

Perhaps one of the most important finding of this thesis with respect to the Aio biosensor is that antimonite is a competitive inhibitor of Aio arsenite oxidation. As discussed in Section 4.4.4 this means that the biosensor cannot be used in environments where there are high antimony concentrations (such as many industrial contamination sites). Critically, it is still viable in monitoring tube well arsenic concentrations in Bangladesh and India (Section 4.4.4). The business case in Appendix A.3 compares the North American market with the Bangladeshi and India markets. The North American market heavily relies on industrial applications to generate profit. The two markets must therefore be reanalysed as, considering these new findings, the Bangladeshi and Indian markets are dramatically more attractive despite being less profitable because the Aio biosensor has a much greater chance of actually working in these markets and therefore being bought, used and generating profit. The Aio biosensor business plan should focus solely on producing a product and market entry strategy for Bangladesh and India.

5.3 Development and demand for an antimony biosensor

Chapter 4 found that while Aio is able to oxidise antimonite, for it to be developed as a biosensor, substantial improvements must be made to activity and specificity for it to become a viable product. As will be discussed below, protein engineering of the active site of Aio, while not necessarily impossible, is likely to be a time and money intensive undertaking. It is therefore important to evaluate if there is demand for an antimony biosensor and if Aio could meet it.

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Human gastric bioaccessibility to antimony has been shown to be between 14 and 43 % (Pierart et al., 2015) and the WHO has set a limit on antimony concentration in drinking water at 20 μg/L (WHO, 2003). Antimony exists as soluble species (both antimonite and antimonate) in natural waters (Filella et al., 2002b). It is liberated from rock by anthropogenic activities such as mining but also potentially through biological activities (Filella et al., 2002a). There is evidence of some demand for an antimony sensor, for example, to monitor environmental contamination from mining, burning of fossil fuels, smelting and the incineration of municipal waste (Osako et al., 1997) (Nriagu & Pacyna, 1988) (Maeda, 1994). A report by the EU in 2008 stated that the levels and management of antimony trioxide were acceptable in all environments but that the antimony trioxide exposure of people who work with it required better monitoring and management (European Union, 2008).

As discussed in the market analysis for water testing (Appendix A3.3), sensors for specific analytes are unlikely to achieve high market uptake in industrial and developed markets as demand has shifted strongly in favour of multianalyte sensors (Frost and Sullivan, 2012). While this does not necessarily mean that an antimony sensor would not have value, it does mean that it would face stiff competition from existing laboratory techniques such as ICP-MS which would detect antimony and most other trace elements for ~$40. Any antimony sensor would have to consider this when determining price and target market i.e. it would have to be much cheaper than laboratory methods or offer some additional value. It is possible that the Aio biosensor could offer additional value as an in situ monitoring device, however, anodic stripping voltammetry methods have been portable for at least 20 years (Ashley et al., 1998) and have been shown to detect environmental contaminants simultaneously including arsenic, mercury, copper, selenium, zinc, lead (Locatelli & Torsi, 2001) (Desmond et al., 1998) and, most importantly, antimony (with a lower detection limit of 108 nM) (Lukáčová-Chomisteková et al., 2018).

In conclusion, while there is certainly some demand for devices that enable environmental antimony monitoring it seems unlikely that the WT Aio biosensor would be able to meet these (let alone compete with existing technologies) as it is not able to detect multiple analytes and its other features are not especially unique. 215

5.4 Enzyme Engineering

Protein engineering has been mentioned several times in this thesis, specifically in reference to improving catalytic rate and altering substrate specificity (with cytochrome c and antimonite respectively). Aio has three major cofactors that would present engineering targets: the Mo-centre and active site, the 3Fe-4S cluster and the AioB subunit. There are two approaches to enzyme engineering which will be discussed: rational design and directed evolution.

5.4.1 Rational Design of Aio

Rational design engineering attempts to use the body of knowledge surrounding an enzyme to identify specific targets for site-directed mutagenesis to engineer the enzyme for a specific function. In this discussion section, the feasibility and knowledge required to rationally design Aio will be discussed.

5.4.1.1 The molybdenum cofactor The rate at the Mo-centre is exceptionally fast (Section 2.3.5) meaning the aim of engineering projects at this site would be to expand or alter the substrate specificity of Aio. There are two factors to consider when engineering the Aio active site to have broader substrate specificity or faster activity with antimonite: the reduction potential of the Mo-centre and the substrate binding residues. Both of these must be in accordance with the substrate of interest for Aio to react with it and turn it over.

With regards to engineering the reduction potential, the optimal target of engineering is most likely the H-bonding network surrounding the Mo-cofactor. The roles of the pyranopterins are only just beginning to be understood and it appears they play a dual role in both defining the reduction potential of the Mo-centre and in electron transfer (Rothery et al., 2012). The H-bonding network surrounding the pyranopterins therefore makes an interesting target for engineering studies as altering this could alter the conformation of the pyranopterin and, in doing so, alter the properties of the Mo-centre resulting in unpredictable effects on the enzyme’s function. This has been demonstrated in the NT-26 Aio in which the AioA-Q726G

216 mutant (Q726 is a residue positioned just under the Mo-centre and H-bonds to both pyranopterins) stabilised the Mo(V) state (Duval et al., 2016). One thing to consider, however, is that the role of the pyranopterins in Aio is thought to be to elevate the Mo-centre to a high energy state which facilitates reaction with arsenite in what has been named the ‘pterin-twist’ (Warelow et al., 2017). Manipulation of the pyranopterin H-bonding environment would likely disrupt the ‘pterin-twist’ meaning the Mo-centre would no longer be in a high energy state possibly resulting in poor activity, regardless of the substrate.

The Mo-cofactor of Aio is at the base of a large, highly solvated, funnel-like cavity. The funnel is approximately 6 Å in diameter (Warelow et al., 2013) at its narrowest which means this precludes any potential substrates larger than this. Substrates that can access the base of the funnel must then be bound and orientated such that reaction with the Mo-centre is possible requiring the binding residues of arsenite to be identified and then their positions altered to bind alternative substrates. Small organoarsenicals might pose interesting substrates to investigate. For example, trimethylarsine is 4 Å in diameter and structurally analogous to arsenite. Organoarsenicals are prevalent in the environment and often detected in ecosystem biota and excreta of farm animals meaning that there may be demand for biosensors to monitor them (Oremland & Stolz, 2003).

There are limited studies that have attempted to alter substrate selectivity in molybdoenzymes and these appear to exploit native promiscuity. For example, in the DMSO reductase of E. coli which is able to catalyse reactions with 22 different substrates (SimalaGrant & Weiner, 1996). Using the crystal structure of R. capsulatus

DMSO reductase to identify candidates for mutagenesis, the KM for DMSO and pyridine N-oxide were altered 35-fold (Simala-Grant & Weiner, 1998). A similar study was performed on xanthine oxidase which has a broad range of substrates including purines, aldehydes and pteridines (Greenlee & Handler, 1964). Arg-310 was found to be critical in substrate selectivity and rate acceleration as it’s substitution by methionine caused the range of reaction rates with various substrates to decrease from 4000-fold to 4-fold (Pauff et al., 2007). In engineering the substrate specificity of the Aio, it would be imperative to identify mutagenesis targets, ideally with 217 structural data of arsenite bound Aio. Computational modelling could be used to try to identify mutagenesis targets. For example, a program has been developed that performs loop remodelling of an active site around a new substrate and successfully altered the specificity of human guanine deaminase to be 100-fold more specific for ammelide and 25000-fold less specific for guanine (Murphy et al., 2009).

Increasing the specificity of Aio for antimonite while also decreasing it for arsenite is likely to present a significant challenge due to the similarities in their structures (based on the assumption that they will interact with Aio through the same residues). Structural data of arsenite bound Aio to complement the existing antimonite bound structure would be critical in examining if there are any differences in the interactions of the two oxyanions. An additional avenue of research to further explore this issue is the periplasmic-binding proteins AioX and ArrX (AioX was briefly discussed as a sensor for arsenite which triggers the expression of Aio in Section 1.4.3; ArrX performs the same function for arsenate reductase (Badilla et al., 2018)) which show high specificity for arsenite and arsenate respectively but are insensitive to antimonite (they showed no binding in ITC experiments) and appear to use the same binding residues (based on sequence alignment) (Badilla et al., 2018). The arsenite bound structure of AioX has already been resolved (Badilla et al., 2018). The crystal structure of ArrX with arsenate would provide valuable insights into how these two proteins are able to select for very similar oxyanions. Similarly, the crystal structure of arsenate reductase (Arr) has recently been resolved with arsenate in the active site. A complimentary structure of Aio with arsenite in the active site would also provide insights into oxyanion selection (though Arr appears to also be able to bind arsenite so it may not be as selective as ArrX and AioX) (Glasser et al., 2018).

5.4.1.2 The 3Fe-4S cluster It appears that most work on 3Fe-4S cluster engineering involves interconverting between it and the similar 4Fe-4S cluster in enzymes such as DMSO reductase (Trieber et al., 1996) (Cheng et al., 2005), fumarate reductase (Manodori et al., 1992), hydrogenase (Rousset et al., 1998) (Bingemann & Klein, 2000) and an iron-sulphur flavoprotein (Leartsakulpanich et al., 2000) . These typically lower the protein stability which is unsurprising given the relative size of adding or removing an Fe 218 atom to the protein (Trieber et al., 1996) (Manodori et al., 1992) (Cheng et al., 2005) (Leartsakulpanich et al., 2000) (Bingemann & Klein, 2000) (Rousset et al., 1998). Conversion of the 3Fe-4S cluster to a 4Fe-4S cluster has, in fact, already been achieved in the Aio with the AioA-S102C mutant which had 3 % WT activity with arsenite and cytochrome c (Appendix G). X-ray crystallography confirmed the presence of the 4Fe-4S cluster (Santos-Silva, Universidade de Lisboa, pers. comm.). As the 3Fe-4S cluster is in the middle of the electron transport pathway it does not present an attractive engineering target as it does not directly interact with any substrates nor is it involved in the rate-limiting step.

5.4.1.3 The Rieske 2Fe-2S cluster Rieske clusters are highly sensitive to their micro-environment and the protonation state of the complexing histidine residues has been shown to partially control the reduction potential of the cluster (Zu et al., 2003). Mutations to the surrounding H- bond network or to the disulphide bridge (when it is present) have also been shown to alter the reduction potential and/or substrate specificity depending on the type of Rieske protein being investigated and the specific mutation (Zu et al., 2003). Notably, these have all had the effect of lowering activity with the native substrate but alternative substrates have not been trialled to see if substrate specificity is altered (Zu et al., 2003). The work presented in Chapter 3 demonstrates that it is possible to engineer the substrate specificity of AioB with site directed mutagenesis but the benefits of these particular mutations to the biosensor are limited (because the highest activity observed with cytochrome c – which is used as a mediator in the biosensor - was the NT-26 WT Aio and the highest activity with DCPIP and azurin was observed with the A. faecalis WT Aio) meaning that further study would need to be conducted to identify useful mutations.

Review of the literature shows that the only work regarding the engineering of catalytic rates and substrate specificity on Rieske proteins has been conducted on the Rieske non-heme iron dioxygenases (Parales, 2003) (Mohammadi et al., 2011) (Gally et al., 2015). While these proteins contain Rieske clusters, their active site is a non-heme iron meaning they are inappropriate models for AioB engineering (Ferraro et al., 2005). Engineering efforts should therefore start by examining the effects of 219 site directed mutations in the high potential Rieske proteins such as the bc1 complex and AioB itself. Most, if not all, mutations to the bc1 Rieske proteins appear to reduce rate (if any phenotype is observed at all) due to alterations in the midpoint potential of the 2Fe-2S cluster (Denke et al., 1998) (Schröter & Hatzfeld, 1998) (Guergova- Kuras et al., 2000) (Zu et al., 2003). The NT-26 F108A and F108C/G123C Aio mutants as well as the A. faecalis C65F/C80G Aio mutant demonstrated that mutation of residues at the binding site of the physiological electron acceptors to Aio can alter activity. Structures of the electron acceptor-Aio complexes would identify the electron acceptor binding sites and provide the most direct route to determine which residues are engineering targets.

5.4.2 Directed evolution

Directed evolution is a method that that mimics the process of natural selection to engineer proteins towards a user-defined goal involving iterative rounds of mutagenesis, selection and amplification.

While the Rieske cluster of Aio presents an attractive target for rational design engineering (because much work has been conducted on the impact of Rieske mutations) directed evolution might present a more attractive route to pursue engineering as it does not require modelling of the effects of mutations. With regards to engineering the substrate specificity of the Aio, the complexity of the Mo-cofactor means that directed evolution is a far more attractive methodology. While there are limited examples of molybdoenzyme substrate specificity being altered in a laboratory environment (Simala-Grant & Weiner, 1998) (Pauff et al., 2007), Aio shares a common ancestor with molybdoenzymes that catalyse reactions with polysulfide, nitrate and arsenate (van Lis et al., 2013) demonstrating that alteration of substrate specificity to different oxyanions via evolutionary processes is at least theoretically possible.

The fundamental issue with directed evolution is that it is a demanding methodology in terms of time and cost. To use the example of improving Aio activity with antimonite, the project would require:

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• A method to produce mutants such as error prone PCR. • The maintenance and storage of a library containing all generated mutants. As Aio has been heterologously expressed in E. coli these could be stored as frozen glycerol stocks. • A procedure to screen all mutants for the desired phenotype. In this instance, this would likely involve steady-state assays with cytochrome c and antimonyl tartrate or arsenite. • This process to be repeated many times until an industrially acceptable phenotype is achieved.

Development of a high throughput screening method is critical to the feasibility of a directed evolution project as libraries tend to consist of 103-106 transformants (Reetz et al., 2008). In the case of improving arsenite and antimonite oxidation rates the screening method would involve the determination of the steady-state rate. This can be achieved in a microwell plate. It is typical for variants to be grown in a 96-well plate, lysed and then the activity of the cell lysate monitored for all variants in a separate 96-well plate by monitoring the change in absorbance (Leemhuis et al., 2009).

5.4.3 Engineering: risk versus reward

Both rational design and directed evolution are methodologies that have the potential to engineer Aio but are likely to be considerable undertakings. It is imperative to evaluate the risk versus reward of any engineering project. There are broadly three projects to consider:

Improving rate with the electron acceptor: NT-26 Aio already appears to be highly

8 -1 -1 optimised to interact with cytochrome c based on its Kcat/KM ratio of 4 x 10 M s . It seems unlikely that improvements greater than two- to three-fold can be achieved

9 -1 -1 given that the Kcat/KM ratio is capped at 1 x 10 M s (Albery & Knowles, 1976).

Altering substrate specificity: There does not appear to be large demand for an antimonite biosensor and it appears that an Aio antimonite sensor would be readily outcompeted by methods such as anodic stripping voltammetry as the latter can act

221 as a multi-analyte sensor for which there is greater demand (Frost and Sullivan, 2016a) (Appendix A3.3). Engineering Aio to catalyse reactions with other substrates could be explored. However, previous studies of this ilk in molybdoenzymes exploit high native promiscuity which Aio does not appear to have (such as the previously discussed studies with DMSO reductase and xanthine oxidase). However, small organoarsenicals, such as methylarsine, are small enough to enter the Aio active site and might present interesting targets for engineering of the Aio substrate specificity.

Improving stability: Improving the stability of the Aio could increase the shelf-life of the biosensor test strips. Synchrotron radiation circular dichroism was used to follow the thermal unfolding of the Aio. It was found that the Aio begins to denature at 70 °C meaning the enzyme is quite stable with respect to temperature and so it is unlikely to need to require stability engineering (Warelow, 2014). No examples of mutations that improve stability in molybdoenzymes can be found in the literature at time of writing.

While there are various routes to pursue engineering Aio it seems that the potential benefits are limited.

5.5 Future Directions

The further research and development of the work presented in this thesis could proceed in a variety of directions. Firstly, the rate-limiting step of Aio from other species could be investigated. If it is a common theme that electron transfer to the electron acceptor is rate-limiting, then this would add weight to the notion that the AioB subunit has been optimised to function with multiple substrates. Crystal structures of NT-26 Aio in complex with cytochrome c and A. faecalis Aio with azurin would permit much deeper investigations into the electron acceptor selectivity of Aio. These crystal structures would also identify mutagenesis targets to explore the nature of the interactions and provide valuable insights to aid in the rational design of rate accelerating mutants if desired.

Independent expression of AioA and AioB was not successful in this study as the AioA proved to be unstable on its own and AioB purified as a tetramer casting the findings

222 regarding its interaction with cytochrome c into doubt. Successful expression of both subunits, ideally as monomers, would be necessary in exploring the properties of each. Such investigations could involve arsenite oxidising activity of AioA, thermal unfolding of both subunits alone to better understand Aio stability and potentially improve shelf-life by protein engineering and using NT-26 AioB as a model of a bc1- like Rieske protein that lacks a disulphide bridge (a property unique to Alphaproteobacteria and some Archaea). Independent expression might be achieved by exploring different purification conditions for AioA and different salt and pH buffer conditions for AioB.

The antimony work should be primarily continued by determining if antimonate is an inhibitor of Aio arsenite activity and if it is responsible for the rate acceleration observed in steady-state kinetics with ammonium sulphate. Additional research with antimony could focus on engineering attempts to improve catalytic rates with antimonite. Owing to the complexity of the Aio active site, a directed evolution methodology is most likely to yield positive results. However, if this project is conducted with the aim of producing an antimonite biosensor, a thorough cost/benefit analysis must be conducted to evaluate if this project is worthwhile.

It appears that Aio can oxidise antimonite partially because antimonite is isostructural to arsenite. An investigation into Aio activity with other substrates that are similar in structure to arsenite (such as methylarsine) would help to identify potential analytes for engineering the substrate specificity of the Aio.

In conclusion, there are a variety of avenues for further study that would not only provide greater insights into substrate selectivity of Aio’s electron donors and acceptors, the evolution of Rieske proteins in Aio and the properties of the individual subunits but also offer opportunities to attempt to engineer Aio for improved or alternative biosensor performance if desired.

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Appendices

Appendix A - Arsenic biosensor market report

The following is an analysis of the global arsenic monitoring market and the Aio biosensor’s position in it that I produced for BioNano Consulting as part of an industrial placement. The findings presented are the result of my own independent research using secondary and primary sources.

A.1 - Global Arsenic

According to the World Health Organisation, over 150 million people are affected by arsenic every year (World Health Organisation, 2011). Arsenic is a group 15 element that is commonly found in four different oxidation states: -III, 0, +III, +V. Of these, +III (arsenite) and +V (arsenate) are the most pertinent to environmental contamination being highly soluble in water. Arsenate replaces phosphate in ATP, catalysing a futile cycle in mitochondria. Arsenite is approximately one hundred times more toxic; it’s mode of toxicity being to arsenylate sulfhydryl groups of proteins (Murcott, 2012).

There are several sources of arsenic contamination (Murcott, 2012):

• Anthropogenic-related – 54 countries affected: Due to the use of arsenic in a wide variety of industrial applications, such as wood preservatives, agricultural chemicals, chemicals and chemical munitions. • Coal-related – 28 countries affected: Arsenic in coal released into the environment through weathering processes. • Geogenic-related arsenic – 68 countries affected: Naturally occurring arsenic that is most commonly released from iron oxide. Accumulates in aquifers under reducing conditions where it sorbs to iron (III) materials. It can also be cycled and solubilised by various microorganisms. • Mining-related (exclusive of coals) – 74 countries affected: All arsenic continaing minerals, especially sulphides, which are commonly associated

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with gold ore are a source of arsenic. Mining and smelting these materials creates arsenic rich slag and runoff which can leach into ground water. • Petroleum-related – 17 countries affected: Microorganisms can couple the reduction of iron (III) oxide with the oxidation of anthropogenic carbon such as petroleum which releases arsenic into groundwater. • Volcanogenic-related – 35 countries affected: Arsenic is a ubiquitous component of active and fossil geothermal systems and can sometimes release arsenic into the environment. Further, this could be anthropogenically manipulated by activities such as using geothermal hot springs as energy sources which could exacerbate the issue.

Arsenic contamination of groundwater is a global problem. Unacceptably high levels of naturally occurring arsenic contamination (>10 ppb as designated by the WHO) are present on every continent (Amini et al., 2008). A list of the top 20 countries for number of people affected by arsenic contamination in drinking water is provided in Table A1.1. As can be seen from the table (which has been coloured by geographic region), the majority of the worst affected areas are in the Asia and Pacific area. Of particular note are Bangladesh and India which together account for most of the arsenic contamination cases in the world (Murcott, 2012).

Note that Table A.1 (Murcott, 2012) conveys only the arsenic exposed population but not the number of cases of arsenicosis (advanced arsenic poisoning) in each country. Both India and Bangladesh are well known to have high incidences of arsenicosis (Ghosh et al., 2008) and it is possible that attention is only just turning to African countries. Arsenicosis has been documented in rural parts of China (Sun, 2004). Whilst 9 million people have been exposed to arsenic in the USA, and arsenic was found in the urine of individuals aged 6 and above in a survey, there are few if any recorded instances of arsenicosis (Centers for Disease Control and Prevention, 2016). There is recent evidence that arsenic in drinking water may have contributed to increased bladder cancer incidence in the USA (Ayotte et al., 2006) (Nuckols et al., 2011).

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Table A.1: Top 20 countries affected by arsenic. Yellow = Asia, Green = Africa, Red = Europe, Blue = Americas (Murcott, 2012).

Percentage of Number of people affected Country countries (millions) population (%)

India 78.7 6

Bangladesh 57 35

Nigeria 11 6

Zimbabwe 9.3 58

USA 9 3

China 8.2 0.6

Vietnam 7 8

Myanmar 3.4 6

Niger 3.4 16

Saudi Arabia 3 9

Nicaragua 3 49

Portugal 2.8 27

Jordan 2.1 22

Argentina 2 5

Mexico 2 2

Serbia and Montenegro 2 18

Ukraine 1.6 4

Hungary 1.5 15

Bolivia 1.3 12

Turkey 1 1

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A.2 - Introduction to the product

The arsenic biosensor has two components: a reusable meter and a disposable test strip.

The meter is a basic handheld electronic device, similar to the one used for common glucose home tests. However, due to its use in external environments in developing countries it will be considerably more durable and hard wearing.

The test strip is an electrode with the arsenite oxidase, buffer mix and mediator depositied onto it. The test strip is loaded into the meter and disposed of after a reading is taken.

The product will also come with a water treatment column that should remove trace iron and reduce arsenate present to arsenite

The product functions as such:

1. The test strip is dipped into sample water. 2. Arsenite in the water transfers electrons to the enzyme, which is impregnated in the test strip. 3. Arsenite oxidase transfers electrons to a mediator. 4. The test strip had two electrodes – a test and reference electrode. The mediator transfers electrons to the test electrode. 5. The current generated from the electron transfer to the test electrode is measured by the meter which relates to arsenite concentration. To eliminate background noise the reading from the reference meter is subtracted from the test reading. 6. A precise quantification of arsenite concentration is displayed on the meter’s screen.

The whole process should take no more than 3 minutes.

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Figure A1.1: The electronic meter and test strip

A.3 - Market Analysis

A.3.1 - Overview of global analytical instrumentation market The global analytical instrumentation market had a revenue of $17.6 billion in 2016 and is expected to grow globally at a rate of 6.1% per annum. The market share is roughly equal between North American, Asian and Pacific and European Markets. Highest growth is expected in Asian and Pacific markets. Overall growth is being driven by advances and increasing demand for testing in the food testing, biopharma, life sciences and clinical markets (Frost and Sullivan, 2016c).

Figure A.2: Revenue share of analytical instrumentation market by geographic location.

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Figure A.3: CAGR of analytical instrumentation market by geographic region.

The market is highly fragmented with regards to the types of instrument. Liquid analysers (the class that the arsenic biosensor falls into) take up a significant share of 10.1%. 41% of annual revenue for the liquid analyser segment is generated by the North American market. Europe and APAC generate less with 22% and 27% respectively (Frost and Sullivan, 2016c).

Figure A.4: Market fragmentation of global analytical instrumentation market by type of instrument.

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Emerging regions such as Latin Americas, India, China, Singapore, Malaysia and South Korea are expected to witness tremendous industrial activity in the next few years which drives the need for analytical instruments. Many of these countries will also seek to export more products which will also generate higher demand for analytical instrumentation so that products can be ensured to be of the quality required by importing countries. It is suggested that manufacturers collaborate with regulatory bodies to produce products that meet specific demands of the market (Frost and Sullivan, 2016c).

A.3.2 - Overview of the global water testing device market In 2015 the global water analysis instrumentation market generated $810 million. It is projected to grow at 5.3% per annum. The market is considered to be mature. Similar to the general analytical instrument market, there is roughly equal market share between North American, European and Asian and Pacific markets. The APAC and RoW markets are projected to have slightly faster growth than North American and European markets, most likely being driven by the rapid growth and industrialisation seen in many countries of these regions (Frost and Sullivan, 2016a).

Figure A.5: Projected revenue growth of water testing market until 2022. Base year 2015.

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Figure A.6: Market fragmentation of global water testing market by geographic region.

In the last 50 years, global water consumption has quadrupled. It is expected that 25% of the world’s population will face severe water shortages in 2050. The demand for clean drinking water is intimately tied to the demand for water testing instrumentation. Many emerging economies such as China, India, Latin America and South Africa as well as developed economies such as USA, Germany, Japan and Australia have a huge demand for safe drinking water (Frost and Sullivan, 2016a).

The key market drivers are:

• Rising need for safe water • Increasingly stringent water quality regulations • Shale gas extraction requires rigorous testing of water and waste water as the process can result in contaminations (including arsenic – source?) in the water • A general drive to maximise efficiency in process industries is resulting in higher investment in analytical instrumentation (Frost and Sullivan, 2016a) (Frost and Sullivan, 2016b).

The key market restrains are:

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• Unstable economic conditions have resulted in many regulatory bodies being hesitant to invest in new instruments and technologies • Lack of skilled labour to operate many of these instruments (Frost and Sullivan, 2016a) (Frost and Sullivan, 2016b)

Figure A.7: CAGR of water instrument market by geographic location.

The market is dominated by continuous instruments which offer real time monitoring of water quality with integrated systems that enable instant reporting and remote data collection. They are preferred due to their simplicity and reliability as well as their tendency to be automated. There is a general preference by consumers for user friendly and automated systems that require little to no maintenance. There is also a strong drive towards multianalyte instruments, again this is most likely due to simplicity – by having one instrument for as many analytes as possible the customer has fewer technologies to train in and keep track of. Instrument manufacturing companies are also increasingly providing consulting services to assist with the technology as well as offering individual solutions to clients (Frost and Sullivan, 2017a) (Frost and Sullivan, 2016a).

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Figure A.8: Market share of water testing instrumentation market by type of instrument.

The market is highly fragmented by company. Hach company has the largest market share and operates in all three types of instrumentation. Xylem Inc., Yokogawa, Emerson and Endress + Hauser all specialise in continuous online instruments. Endress + Hauser is a leader in portable instruments (Frost and Sullivan, 2016a).

Competing factors are:

• Low total cost of ownership – a key purchasing factor for customers as these instruments are often costly to maintain and operate. • Customised solutions – companies are increasingly offering bespoke solutions and consultancy to solve customers’ specific problems, offering to provide solutions instead of just technologies. • Service and support – end users often lack the necessary skills to fully operate or manage the technology. This may have resulted in a shift in the channels used to reach end users, 40% of instruments are sold directly by the manufacturers with the remaining 60% sold by intermediaries. • Reliability – instrument downtime is very costly, the less maintenance required (even better if maintenance is automated) and the lower the risk of failure the better (Frost and Sullivan, 2016a).

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Figure A.9: Market fragmentation of water testing instrumentation market by company.

Overall, the water testing instrumentation market is expected to grow at a steady rate until 2022 being driven by increasing demand for clean drinking water, increasingly stringent water quality regulations across the globe and technology advancements making the instruments more user-friendly and easier to maintain (Frost and Sullivan, 2016a).

A.3.3 - Overview of the portable testing device market The portable testing equipment market was 2.3 billion USD in 2014. It is expected to grow by a CAGR of 5.6% until 2019. The market is mature and highly competitive with over 200 and rising companies operating in this segment. Growth is driven by growth in countries like China, Brazil and India. Growth is also being driven by improvements in battery technology and the fact that handheld equipment is starting to permeate the laboratory environment. Of increasing importance is instrument modularity meaning one instrument can test a number of analytes with optional add-ons (sometimes available at extra cost). Product differentiation and brand recognition are very important factors for vendor success in this segment (Frost and Sullivan, 2015).

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The market can be broadly divided into environmental, electrical and general purpose applications. Environmental is the smallest segment with 12% of the market share (Frost and Sullivan, 2015).

Figure A.10: Projected revenue growth of portable instrument market. Base year 2015.

Figure A.11: Market fragmentation of portable instrument market by type of instrument.

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The revenue of the portable environment testing market was $274 million in 2014 and is expected to grow with a CAGR of 4.5% until 2019, which is slightly slower than the broader portable testing equipment market. The market is highly fragmented and competitive with the vast majority of market share being held by small companies (Frost and Sullivan, 2015).

Figure A.12: Projected revenue growth of environmental portable instrument market until 2019. Base year 2019.

Figure A.13: Fragmentation of environmental portable equipment by company.

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A.3.4 - Competitive products Table A.2 shows current existing arsenic field testing products. The vast majority rely on the Gutzeit method and precise quantification requires expensive equipment and battery power.

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Table A.2: Current portable arsenic detection kits

Name of test Price/kit Price/test Upper Lower Time Ease Accura By- Batter ($) ($) limit limit to use of use cy product ies (ppb) (ppb) s

Merck Arsenic 69.60 0.696 3000 10 20-30 0.3 Semi- Arsine No testing kit mins quant gas, (mquant) mercury

Merck >4000 1.1 100 1 2 0.1 ± 4 Arsine Yes spectroquant hours ppb gas, silver product s

Arsenic low 106 1.06 500 10 20-30 0.3 Semi Arsine No range testing mins quant gas, kit (Hach) mercury

EZ arsenic 72 0.72 4000 10 20-30 0.3 Semi Arsine No test kit (Hach) mins quant gas, mercury

Visual Arsenic 170 0.85 500 10 20-30 0.6 Semi Arsine, No detection kit mins quant mercury (palintest/Wa gtech)

Digital 2000+ 0.85 100 2 20-30 0.6 ± 20% Arsine, Yes arsenator mins mercury (palintest/Wa gtech)

Quantofix 182 1.82 500 0.5 10-15 0.6 Semi- Arsine No most mins quant gas, sensitive mercury

Quantofix 79 0.79 3000 50 20 0.6 Semi- Arsine No least sensitive mins quant gas, mercury

Quantofix 83 0.83 500 10 10-15 0.6 Semi Arsein No mid sensitive mins quant gas, mercury

THM-01 95.94 19.19 ? 1 1 hour ? Semi Not No Comprehensi quant listed ve Heavy Metals Detector

Aquasol 50 0.5 3000 50 15 0.6 Semi Arsine, No mins quant mercury

LaMotte 153 3.06 400 4 15 0.6 Semi- Arsine No mins quant and mercury

Econoquick 186 1.86 1000 10 15 0.6 Semi- Arsine No industrial test mins quant and systems mercury

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A.3.5 - Current Laboratory Methods Table A1.3 lists the laboratory techniques which are generally used to determine arsenic concentrations in water samples in laboratories. Notably, most of these techniques are very expensive per test and this is mostly due to the expertise required as well as the maintenance of the machines, which are also very expensive. These techniques also generally take six months to generate results simply due to the lag time associated with a centralised processing laboratory. They are hardly appropriate for individuals seeking rapid results.

It appears that the handheld instrumentation market is starting to increase its market share of laboratory end users. Due to the expense and expertise required in running these machines, then an easy-to-use, portable device could potentially supplement, if not replace, these techniques in laboratories.

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Table A.3: Current laboratory tests for quantification of arsenic

Name of test Price of Price/test Lower Accuracy instrument ($) ($) limit (ppb)

Electro-thermal Atomic >12000 201-400 1 10.8% Absorption Spectrometric Method

Manual Hydride 4-12000 201-400 1 5% Generation/ Atomic Absorption Spectrometric Method

Silver >1000 <50 1 10% Diethyldithiocarbamat e Method

Inductively Coupled 25-75000 201-400 8 19% Plasma (ICP) Method

Inductively Coupled 25-75000 201-400 0.02 4.46% Plasma/Mass Spectrometry (ICP/MS) Method

A.3.6 - Potential new entrants There are a number of potential new entrants to the arsenic monitoring sector. There are innumerable examples in the literature of arsenic sensing systems, however, many of these have not progressed any further. Described here are some products that have been in development:

• The arsenic biosensor collaboration: this was an attempt to use a cartridge of genetically modified bacteria that would sense arsenic and respond by generating a pH change which could then be easily detected on a strip of paper. Due to the problems associated with using GMOs it appears this

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project has now shifted focus to using the cytoplasm of lysed cells to achieve a similar read out (Ajioka, n.d.). • The Oxford glassy electrode test: this is a simple electrochemical test. Electrochemical tests that lack a biochemical component are often non- specific. The creators are currently working to patent this technology (Bowen, 2017). • ARSOlux: freeze-dried bacteria genetically modified to produce green fluorescent protein when exposed to arsenic. Arsenic concentration is then determined with a fluorimeter. It is likely that this project, if it continues, will run into similar problems to the arsenic biosensor collaboration. The incubation time is in the order of several hours which is too long for arsenic testing. It is unclear from their description whether the technology can detect total arsenic or just arsenite (ARSOlux, 2017).

A.3.7 - Multi-analyte testing kits There are several multi-analyte testing kits that detect a wide array of water pollutants. These are typically sold for around $20-30 off-the-shelf or available for delivery by ordering on various websites. The target end-user appears to be owners of private wells, and other water reservoirs such as swimming pools. They typically are able to sense lead, E. coli, nitrates, nitrites, pH, copper, pesticides, iron, and chlorine. These tests are usually single use. None appear to be able to detect arsenic yet (Simplex, 2017a)(Amazon.co.uk, 2017)(DiscoverTesting.com, 2017). At least one of these multianalyte test producing companies also sells an arsenic test (that is only sensitive to 50 ppb) for $25 (Simplex, 2017b).

A.3.8 - Arsenic Biosensor Position in the Market The arsenic biosensor would be very competitively positioned in the sector of arsenic monitoring. It is significantly more accurate and reliable (giving a quantitative read out). It can detect at much lower concentrations than the standard field tests. The biosensor is also much safer and easier to use than most other kits. The expected 241

response time of three minutes is much faster than all current test kits and could dramatically increase the number of well tests per day it is possible to perform.

Drawbacks of the arsenic biosensor are that it requires a device. This increases the overall cost and price of the kit but could be circumvented with “Blade and Razor” pricing strategies to recoup losses made on the device with large margins for the test strips. The device requires batteries which is a disadvantage compared to most other kits which do not. However, for precise quantification, battery operated devices are essential so this is less likely to be an issue.

In the broader context of water monitoring, the arsenite biosensor is at a disadvantage as it is not able to detect multiple analytes which is increasingly common in these products (some claiming to detect as many as eleven analytes. However, there is a shift in industrial drinking water monitoring towards easy-to-use, low maintenance, highly accurate, low cost, in situ devices.

A.3.9 - General observations about the market Overall, there is a global demand for arsenic testing, particularly in Asia and North America. This is likely to increase over the next few decades as global population and demand for clean, safe drinking water increase. The global instrumentation market is growing at a modest rate in all sectors and geographic regions. The general public is becoming increasingly aware of the problems associated with arsenic and with new industries such as fracking increasing the risk of arsenic contamination it is likely that the demand for arsenic testing will increase globally.

A.4 - Pricing strategies and distribution channels

A.4.1 - Price per test In Bangladesh, the target price is $0.70-1.00 to allow the biosensor to compete in with existing field tests. However, developed markets, such as the USA’s, could probably sustain a much higher price per test. Laboratory testing of water samples

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currently costs around $200-400 so it seems that the market could sustain much higher prices of perhaps $20. However, more research into how facilities perform water testing must be conducted as it is possible that many companies send one water sample to one laboratory that tests for all possible contaminants.

A.4.2 - Device Pricing Models More established economies’ markets can be divided into private and governmental/corporate buyers. This is based on the assumption that government/industrial customers are more likely to need to perform thousands of tests and will therefore purchase a device that can be used for regular testing whilst private customers are less likely to perform regular testing, perhaps only performing a test once every few years.

Government/corporate segment – blade and razor model: The target price of the device was said to be in the range of $50-100. Whilst this price is higher than most of the standard testing kits, it would be dramatically cheaper than the two quantitative options on the market meaning this price is likely to be acceptable. The blade and razor model can be used to lower the price of the device by covering losses here with large margins on the test strips.

The distribution channel for this segment is likely to be through analytical equipment providers. These would act as a trusted source of quality equipment for the consumer and would help to increase the exposure and penetration of the product. 87.1% of sales in the handheld instrumentation sector are indirect because these instruments generally require less technical expertise than laboratory or continuous instruments (Frost and Sullivan, 2015) (Frost and Sullivan, 2013).

Private user segment – single use tests: Many of the multianalyte tests sold to private customers are single use suggesting that the blade and razor model for the arsenite biosensor may not work in this segment. If a single use, disposable test could be produced and sold at approximately $15-20 then it would be more competitive 243

and ensure a profit is made on every sale as opposed to the blade and razor model. In the USA, single-use multianalyte tests can be purchased directly from the manufacturer’s websites or from indirect sources like Amazon and Walmart.

Either user segment – rental model: An alternative model could involve renting the devices to water testing companies who then perform the tests for private individuals as a service. In a report on arsenic testing in the USA in 2002, it was found that 73% of consumers would want to consult with water treatment specialists when dealing with arsenic (Winter, 2002). Furthermore, Frost and Sullivan state that rental services for analytical instrumentation are currently at only 5% penetration in the global markets (staying in single digits for North American and European) generating $500 million a year. If this can be increased by even a small amount, the segment could yield exceptionally high returns (Frost and Sullivan, 2016d).

Either user segment – remote testing: Despite the sensor being a field test, it could also be used as a laboratory test. Customers could send BNC water samples that BNC test themselves and then send a report of the arsenic concentration via e-mail back to the customer. It appears that most facilities that have to perform water testing currently do this with labs that use large, expensive equipment. Due to the cheapness and simplicity of the biosensor, BNC could attempt to dramatically undercut these services for arsenic testing which appear to be in the order of $200-400 per test. BNC could attempt to rapidly gain market share by offering testing services for as low as $20 per test. This service might allow BNC to start generating revenue faster and perhaps give it more time and capital to invest in R&D of the biosensor – such as in- flow monitoring or single-use tests.

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A.5 - Potential Markets to expand into

A.5.1 - North America The North American market has approximately 11 million people exposed to arsenic, with 9 million of these people being in the USA. The USA, in general, is an attractive market to enter as it is stable and strong. North America has the biggest share of annual revenue in analytical instrumentation, water testing instrumentation and portable instrumentation markets, and these are all projected to grow in the short to mid-term at a CAGR of 4-5%. Water testing instruments are sold at a variety of prices.

There are, however, several issues with the US market:

• Demand – there is no evidence that anyone is suffering from environmental arsenic poisoning in the USA. There are no confirmed cases of arsenicosis and research into the relationship between drinking water arsenic concentrations and cancer is in its infancy. Most water testing is conducted in centralised testing facilities meaning there may be fewer end-users than the 9 million people exposed would make out. • Competition – water monitoring instrumentation are moving to multi-analyte testing. Basic, off-the-shelf water testing kits measure multiple analytes and a consumer are likely to opt for these as opposed to the similarly priced arsenic biosensor. • FDA approval – this typically takes 6 to 12 months to obtain and can be difficult for non-US companies.

These issues are not to say that the arsenic biosensor should not attempt to expand into the USA/North American region. It is likely that demand for an arsenic biosensor will increase as evidence is generated linking arsenic in drinking water to cancer (if not arsenicosis). Most arsenic testing is currently done in central laboratories though many industries now prefer to perform in situ monitoring. The arsenic biosensor could capitalise on this by providing a relatively cheap, easy to use, low maintenance 245

and in situ device. There also appears to be an increasing number of people in Central and South America exposed to arsenite and a presence in the USA may facilitate expansion into these areas.

There are broadly three market entry strategies for the arsenic testing market in the USA:

• Starting from scratch – this would be high risk in the USA. Obtaining FDA approval is a costly process that typically takes several months to a year (Administration, 2017). The fact that currently the arsenic biosensor only has one analyte may also limit its market penetration meaning that there may be little to no pay off even if FDA approval is obtained. • Partnership – partnership with an existing US company making biosensors would reduce the risks and some of the difficulties associated with obtaining FDA approval. It is also possible that the arsenic biosensor could be integrated into a multiple analyte testing kit which would allow it to compete more successfully. • Licensing – licensing the biosensor carries similar benefits to partnership but is lower risk at the cost of relinquishing more control and possibly profits of the biosensor to the partner.

As stated earlier, the end users of more established economies’ can be divided into two groups – high (governmental/corporate) and low (private) volume testers.

Additional considerations on entering the private well testing market – There are approximately 5000 private wells that require routine arsenic monitoring in the USA which serve 15 million households and approximately 60 million people (Cone, 2011) (Winter, 2002). The CDC recommends these wells are tested for contaminants at least once a year (Centers for Disease Control and Prevention, 2010). If BNC were to focus on this segment then it is imperative that household are the point of testing, not the wells. If the wells are tested then the market size (assuming 100% compliance with 246

CDC standards) is only 5000 tests a year, which is hardly enough to generate profit. If testing is conducted in households, then the market size increases to 2-15 million tests a year (depending on how many of these household occur in arsenic affected areas and assuming 100% compliance).

Due to the need for regular, household testing, entry into the private well testing US market would be helped by a strong marketing and educational campaign. Furthermore, people can be exposed to arsenic through a variety of means. Drinking water is the most obvious, but ingestible products made with water could also contain arsenic such as beer or wine. Rice and rice products are a famous example of a foodstuff that can have high levels of arsenic. A marketing campaign in the US that explains the dangers and sources of arsenic exposure would likely help to increase demand considerably. A strong marketing and education campaign might also allow entry into the US market sooner.

If a cheaper, single use arsenite test could be developed with the existing technology (these are typically sold for $20-30) or if an arsenic testing service/ device rental scheme were established it might facilitate entry into the US private market.

So-called “Light” arsenic removal devices are also being developed for private wells in the USA. These are arsenic remediation devices installed in private wells. It is likely that an easy to use, portable, cheap arsenic test would be highly valued by both the providers and the users of these devices (Sinclair, 2015).

Additional considerations on entering the governmental/industrial testing market – Public water providers are required to test water for common contaminants including arsenic. There are approximately 150,000 public water facilities in the US, all test for arsenic once a year (United States Environmental Protection Agency, 2008) (Centre for Disease Control andPrevention, 2017) (United States Environmental Protection Agency, 2007).

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Many industries are required to monitor their effluent (wastewaters) at least once a year to conform with EPA policy. A list of the top ten arsenic producing industries is provided in Table A.4. There are approximately 270,000 facilities in the USA that are required to report these figures to the EPA (United States Environmental Protection Agency, 2016).

An industry that does not yet feature in Table A1.4, but is likely to become a major source of arsenic contamination is the fracking industry. This industry uses exceptionally high volumes of pressurised water (up to one million gallons per well head to complete the process) to fracture rock to liberate pockets of natural gas (Easton, 2013). Approximately 60% of this water resurfaces, now containing various contaminants such as heavy metals, arsenic, radioactive materials, as waste and is stored in troughs around the drilling platform (Easton, 2013). Groundwater near fracking wells has been shown to have elevated arsenic levels suggesting a need for monitoring, management and remediation (Meyer, 2013). It is therefore no surprise that the fracking wastewater treatment industry is projected to grow at a CAGR of 30.3% and reach $3.8 billion by 2025 (Hardcastle, 2016). There are currently 1.7 million fracking wells in the USA and it is possible that all (if not the surrounding areas) require regular arsenic monitoring (along monitoring for other pollutants) (Kelso, 2015). If this is the case, fracking will dominate the market share of end-users.

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Table A.4: Top 10 US industries producing arsenic waste (United States Environmental Protection Agency, 2016)

Industry Avg. Arsenic conc. (ppb) Total produced (lbs/yr)

Sewerage Systems 20.1 226,276

Electrical Services 201 95,692

Hotels and Motels 540 3,764

Petroleum Refining 10.4 3,728

Wood Preserving 120 3,549

Water Supply 5.5 1,590

Smelting of nonferrous 180 1,496 metals

Gold Ores 5.3 1,319

Copper Ores 4.8 1,116

Refuse Systems 4.9 889

It appears that these types of industries are increasingly interested in easy-to-use, low maintenance, multi-analyte devices that monitor arsenic continuously to replace laboratory instrumentation. The arsenic biosensor meets two of these criteria, which, coupled with its competitive price (especially compared to laboratory analytical equipment) means that it may encounter few barriers in entering this segment. If a continuous measurement device (perhaps with replaceable cartridges) could be developed it will be even more competitively positions in this segment. Of great importance for entering this market is the identification of trusted vendors to sell the biosensor to the relevant industries. It is also important to consider how much these vendors will cut into the product’s profit margins.

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A.5.2 - Asia Pacific and China The vast majority of people exposed to arsenic globally are in Bangladesh and India. Six of the top ten countries for arsenic exposure are in Asia – India, Bangladesh, China, Myanmar, Vietnam and Saudi Arabia. In Bangladesh, India and China this is due to geogenic contamination of tube wells.

The APAC analytical instrumentation market, while slightly smaller than in North America, is growing more rapidly at a CAGR of 7-8%. The demand for reliable water sensing is increasing globally, being driven by rising population figures and industrialisation increasing public demand for clean, safe drinking water. This effect is more pronounced in Asia than, for example, the USA, as many Asian countries such as India and China are currently experiencing extremely rapid industrial growth.

Two of the major issues with entering the US market were lack of demand and competition from multianalyte sensors. China and India both have clear evidence of civilians with arsenicosis meaning there is clear demand for arsenic monitoring. Furthermore, most arsenicosis cases occur in poor, remote, rural areas where water is extracted from tube wells meaning that portable testing kits like the arsenic biosensor are ideal (as opposed to centralised testing). The arsenic biosensor is also less likely to be outcompeted by multianalyte sensors because these are used for screening water sources for common contaminants. In the case of Indian and Chinese tube wells, arsenic is known to be the problem, so the biosensor can be marketed as a specific solution. It has been found that low income households will spend 1.1% of their total earnings on accessing clean water (Das et al., 2016), assuming that there are five people per household, that annual income is $200 and that 20% of the money allocated to water quality is spent on arsenic testing then the market size is approximately $7 million per year. However, in India is it has also been found that civilians will generally opt for the cheapest test and often struggle to afford them meaning profits may be severely limited if not impossible in this specific market (as in Bangladesh) (Barnwal et al., 2017). 250

Another driver in the analytical instrumentation market is the increasingly high standards required for food export. Quality testing at the exporting countries side have shown to improve companies’ revenue (Frost and Sullivan, 2017b). Arsenic is a common contaminant of rice which is a major export of many Asian countries. Western countries (major importers of rice) are becoming increasingly aware and concerned of arsenic levels in rice. There is a potential opportunity for the arsenic biosensor in the quality control of food exports (such as rice) from Asia.

Regarding the three entry strategies discussed previously, starting from scratch seems to be less of a risk in APAC markets due to the higher demand for arsenic testing. There is still the risk of obtaining any regulatory approval necessary which will be different depending on the country being entered.

It is possible for BNC to establish a foothold in the APAC market by piloting the product in Bangladesh (which has been discussed as a fundamental goal of the project). It is unlikely that Bangladesh will generate significant profit for BNC (see biosensor Bangladesh business plan) but it could be used to establish the brand and efficacy of the arsenic biosensor. A potential strategy could be to work with NGOs like BRAC in Bangladesh to launch the arsenic biosensor. By selling the biosensor at cost in Bangladesh (as part of a humanitarian project in collaboration with NGOs) it could outcompete existing kits and dominate the market share. In addition, this would also significantly increase the profile of the biosensor which would assist in its penetration of other markets.

A possible long-term strategy could be to launch the biosensor in Bangladesh as a humanitarian project using this to establish the product and increase its profile. If the biosensor proves successful in Bangladesh, India would be the next logical market. India is a rapidly growing economy and is more likely to accept a higher cost for the biosensor. From here, the biosensor could continue to expand in APAC, into countries like China and Vietnam, or could consider other markets such as the USA.

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A.6 - Conclusions

• The analytical instrumentation, water testing and portable instrumentation markets are growing globally at CAGR of 4-7% due to a general increase in demand for easy to use, cheap sensors, a growing population and increasing demand for clean drinking water. • Most arsenic field test kits have a price/test of $0.70-1, produce toxic side products, take a long time to produce results and are complicated to use – the arsenic biosensor is likely to be a strong competitor as it is non-toxic, quick and easy to use. • Industries that must test for arsenic typically use laboratories but if the arsenic biosensor can be shown to be just as accurate (or simply accurate enough) then it may be able to penetrate these areas. • Most handheld devices/field test kits are sold indirectly as they are easy to use and the reputation of the vendor helps in market penetration. • The USA market shows promise but there must be separate strategies for industrial/governmental markets and private markets: o Governmental/industrial markets – the biosensor must compete with lab instrumentation by being cheaper, easier to use and lower maintenance; anticipate the rapid growth in the fracking market; develop a continuous detection system using the sensor; find trusted venders to sell the product. o Private well markets – generate demand with marketing and educational campaign; get private users to test water by household and not by well; develop a single-use product or testing service; diversify applications (water, food, drinks etc.). o General considerations – FDA approval; consider partnership to create multi-analyte test kits.

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• The APAC market could be considered, India is a rapidly developing country and has the highest number of arsenic affected people in the world. Profits here may be limited by how much consumers can pay.

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Appendix B – Determination of protein concentrations using Bradford assays

The Bradford method (Bradford, 1976) was used to determine the protein concentration and relate this to the 280 nm absorbance to determine extinction coefficients that could be used to estimate protein concentration.

Figure B.1: Bradford standard curve for determination of protein concentration of Aio and azurin

Table B.1: Results of Bradford assay and relation to nanodrop absorbance to determine extinction coefficient values.

Protein Conc. Determined by Absorbance at 280 Extinction Bradford Assay (μg/ml) nm (Nanodrop) coefficient (M-1 cm- 1) NT-26 WT Aio 937 ± 2 1.18 142100 A. Faecalis WT Aio 824 ± 57 1.71 223000 A. faecalis 675 ± 76 1.31 239000 C65F/C80G NT-26 1251 ± 168 1.59 144000 F108C/G123C

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Table B.2: ICP-MS determination of co-factor concentration and Bradford determination of protein concentration used to calculate extinction coefficients for UV-visible spectra

Protein ICP-MS determined Bradford determined cofactor content (μM) protein concentration (μM)

AioB Fe = 212.5, 103.8, 179.8 163.6, 107.5, 90.7

Azurin Cu = 71.0, 88.9 130.1, 144.2

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Appendix C – Calibration of size exclusion chromatograph columns

The 200 and 75 size exclusion chromatography columns used in this study were calibrated using the GE Healthcare calibration kits according to the manufacturer’s instructions.

C.1 - 200 Superdex

Table C.1: Elution volumes of size standard on the 200 Superdex SEC column

Elution Vol. (mL) Kav MW (Da) log(MW) Dextran 7.5 2000 Thyroglobulin 8.0 0.029062 669000 5.825426 Ferritin 9.5 0.116071 440000 5.643453 Aldolase 11.7 0.241594 158000 5.198657 Conalbumin 13.0 0.317192 75000 4.875061 Ovalbumin 14.1 0.379953 44000 4.643453 NT-26 Aio 12.4 0.282959 113252 5.054046

Figure C.1: Relation of Keq (as determined from elution volume) for all size standards (see Table A3.1) in relation to molecular weight on the Superdex 200 size exclusion

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chromatography column (GE Healthcare). The relationship between Keq(y) and molecular weight (x) is shown as an equation.

C.2 - 75 Superdex

Table C.2: Elution volumes of size standard on the 200 Superdex SEC column

Elution Vol. (mL) Kav MW (Da) log(MW) Dextran 7.8 2000 Conalbumin 9.2 0.096091 75000 1.875061 Ovalbumin 10.06 0.145291 44000 1.643453 Carbonic 11.29 0.215659 1.462398 Anhydrase 29000 Ribonuclease A 12.86 0.305478 13700 1.136721 Aprotinin 14.61 0.405594 6500 0.812913

Figure C.2: Relation of Keq (as determined from elution volume) for all size standards (see Table A3.1) in relation to molecular weight on the Superdex 75 size exclusion chromatography column (GE Healthcare). The relationship between Keq(y) and molecular weight (x) is shown as an equation.

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Appendix D - AioA Expression

It was of interest to express AioA on its own to determine what role, if any, AioA plays in electron acceptor interaction as it has been suggested that it facilitates binding of cytochrome c in NT-26 (Nitschke, Wolfgang pers. comm). In addition to this, like AioB, soul expression of AioA would also permit investigation into its properties (such as thermostability).

Expression of AioA also has some commercial potential. Aio is currently in development as a biosensor for arsenite. If it is possible to express AioA on its own, and it is active and stable, then it is important to assess if it is still able to couple the oxidation of arsenite to the reduction of electron acceptors. If this were the case then AioB could be removed completely from the system which would make the biosensor system simpler and potentially lower the overall cost.

D.1 - Methods

D.1.1Cloning of AioA and AioB aioA was amplified from the aioBA + pProEX-Htb+ plasmid construct using the primers in Table D.1. The restriction enzyme EcoRI and PstI were used to digest the amplified fragments and pProEX-Htb+. T4 DNA ligase was used to ligate the fragments into the plasmid by incubation at 4 °C overnight.

Table D.1: Primers used for cloning of aioA

AioA Forward 5’-GCGAATTCAAGCCTTCAAACGTCACATCGACCG-3’ AioA Reverse 5’-GCCTGCAGTCAAGCCGACTGGTATTCTTTCGA-3’

The aioA construct was transformed into E. coli str. DH5α that had been made competent by growing 10 mL cultures of cells in LB overnight, pelleting by the cells by centrifugation and resuspending in 4 °C distilled water. Pelleting and resuspension was repeated five times to make the cells competent. 258

The competent cells were transformed with the plasmid construct by shocking at 2.5 V and then incubating in Super Optimal broth with Catabolite repression (SOC) media

(2 % tryptone, 0.5 % yeast extract, 10 mM NaCl, 2.5 mM KCl, 10 mM MgCl2, 10 mM

MgSO4, 20 mM glucose) for 1 hour at 37 °C and 180 rpm shaking.

D.1.2 - Expression of AioA AioA was expressed as described for Aio except that the binding buffer contained 20 mM imidazole instead of 40 mM because it was not known if the His-tag would have similar affinity to Aio’s. This was because Aio’s His-tag is on AioB.

D.1.3 - Recording of UV-visible spectra As described in Section 2.2.7

D.1.4 - Steady-state kinetics Steady-state kinetics assays were performed with DCPIP and cytochrome c as electron acceptors using 2500 μM arsenite using the same methodology described in Section 2.2.10.

D.2 - Results

D.2.1 - Cloning of aioA An attempt was made to clone the aioA gene into the pProEX-Htb+ plasmid so that AioA could be heterologously expressed as a fusion protein with a His-tag in E. coli. Not all transformants sampled for the aioA plasmid were successful, however, three had bands at approximately 2500 bp which corresponds to the aioA insert (2402 bp) as well as a band at approximately 5000 bp corresponding to the PProEX-Htb+ plasmid (4717 bp) (Figure D.1).

Successful transformants were selected and the insert sequenced which confirmed that aioA had been successfully incorporated.

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Figure D.1: Photos of restriction digest and gel electrophoresis of aioA – pProEX-Htb+ constructs confirming the presence of the aioA gene insert.

D.2.2 - Expression and purification of AioA AioA was expressed using the standard protocol for Aio except with 20 mM instead of 40 mM imidazole in the binding buffer (to account for the possibility that the His- Tag might have lower affinity when fused to AioA instead of AioB). The rationale was primarily that what proved successful for Aio should also work for AioA. It is thought that one of the main limiting factors in Aio expression is the synthesis of the molybdenum co-factor and that long induction times allow the E. coli ample time to express the necessary proteins to produce it. It would stand to reason that the same would apply to AioA as it also has a molybdenum co-factor (unlike AioB).

No brown colouration was observed on the his-column upon application of the cell lysate. Brown colouration was expected as this signifies that iron is present in the protein (AioA has a 3Fe-4S cluster).

Gel filtration yielded two peaks and two shoulders (Figure D.2A). The first peak was at 7 mL which is the void volume of the column meaning this peak contains proteins with size greater than 700 kDa. The first shoulder was at 10.5 mL which corresponds to a size of ~200 kDa. The second peak was at 12.5 mL which corresponds to a mass of ~100 kDa. Finally, there was a shoulder at 14.5 mL corresponding to a size of ~45

260

kDa (Appendix C). All four of these features were analysed by SDS-Page polyacrylamide gel (Figure D.2B). The gel demonstrated that the first peak contained a protein of approximately 100 kDa in size which is likely to be AioA. As it eluted in the void volume is likely to have been in a high order oligomeric state (greater than a heptamer). The first shoulder also contained AioA, given its elution volume it is likely to be a dimeric form of AioA (~200 kDa). The second peak appears to be monomeric AioA (~100 kDa) with a slight impurity of 45 kDa. The second shoulder had the inverse of the second peak – a strong band at 45 kDa and weaker at 100 kDa. This suggests that the second peak is monomeric AioA while the shoulder is an impurity. It is not surprising that impurities would appear in this preparation as lower imidazole concentrations were used to ensure that no AioA was lost. No fractions from any peak or shoulder were coloured suggesting that no iron was present in any of the samples.

Figure D.2: Purification of AioA. A) Gel filtration chromatograph showing that AioA is purified multiple oligomeric states. B) SDS-Page polyacrylamide gel to determine the size of the proteins found in each peak and shoulder of the gel filtration chromatograph. Note that “shldr.” Is short hand for shoulder.

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D.2.3 - Visible spectrum of AioA and implications on cofactor content The visible spectrum of the monomeric AioA fraction was recorded to assess if Fe-S or Mo were present. This was possible in theory as Aio and AioB spectra have been studied. Fe-S clusters absorb at ~430 nm and the molybdenum cofactor absorbs at ~680 nm. It stood to reason therefore that AioA would absorb at both 430 nm and 680 nm.

The visible spectrum of AioA is shown in Figure D.3. As can be seen there are no significant absorbance features above 400 nm aside from a very small peak at 410 nm. For the sake of comparison of what might be expected, a theoretical spectrum of AioA is also shown. This was calculated by subtracting the spectrum of AioB from Aio. It features two peaks – one at 430 nm and one at 680 nm, corresponding to the 3Fe-4S cluster and the molybdenum cofactor respectively. It is important to note that the 430 nm contribution of AioA being relatively small to the overall 430 nm absorbance of Aio is consistent with previous work with A. faecalis Aio that found that the 3Fe-4S cluster only contributed approximately one quarter of the 430 nm absorbance (with the Rieske 2Fe-2S cluster accounting for the rest) (Cobb, 2005). The theoretical spectrum is therefore a fair comparator to demonstrate the AioA preparation’s poor cofactor content.

From this data, it appears that AioA does not contain significant co-factor saturation which is unsurprising as no fractions were brown which would signify the presence of Fe.

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Figure D.3: Visible absorbance spectrum of heterologously expressed NT-26 AioA (black) and the theoretical visible absorbance spectrum of AioA calculated by subtracting the empirically determined spectrum of AioB from Aio.

D.2.4 - Activity of AioA The activity of AioA was measured using DCPIP and cytochrome c as electron acceptors and arsenite as an electron donor. No activity was observed for either.

D.3 - Discussion

D.3.1 - AioA was not stable alone An expression system for AioA was made, however, the protein appeared to be unstable. The majority of AioA purified in a high order oligomeric state (greater than a heptamer). No fractions were coloured and visible spectroscopy demonstrated that the monomeric protein purified lacked significant absorbance bands in the regions expected based on Aio and AioB spectra suggesting the purified AioA was lacking significant Mo and Fe-S cofactor content. 263

In a previous study, circular dichroism determined the melting temperature of Aio and it was observed that the alpha helices of Aio were more thermolabile than the beta sheets. AioA has relatively more alpha helices in its structure than AioB which would suggest that it is the less thermostable. However, both subunits were stable in the circular dichroism experiment up to 77 °C (Warelow, 2014). This may therefore indicate that while AioA is technically more thermolabile, the presence of AioB stabilises it meaning that the enzyme does not denature until AioB does so. Ideally, this hypothesis would have been tested by purifying both subunits independently and performing CD melt experiments with each, however, as AioA does not appear to be stable on its own this is not possible.

It has been suggested that AioB is required for AioA to be stable (Hille, pers. comm.). This would not be particularly surprising as the aioB gene is always situated upstream of aioA and is transcribed first meaning that, natively, AioA would never be expressed without AioB present (van Lis et al., 2013). The crustal structure of Aio also demonstrates that AioA has predominately hydrophobic cleft for binding AioB (which is globular, not having any clefts). Exposure of this hydrophobic cleft could have led to substantial reductions in stability.

D.3.2 - Future It is unfortunate that it was not possible to express AioA on its own in the present study as there are a number of avenues of investigation it would prove beneficial in. These are:

• Characterising the relative thermostability of the two subunits which could be useful in biosensor development by helping to identify the less stable of the two, which would present a target for engineering and ultimately improve the shelf life of the arsenic biosensor.

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• Determining if AioA is involved in cytochrome c binding, however, this could be resolved much more directly by obtaining a crystal structure of the Aio/cytochrome c complex. • Use of AioA as a biosensor for arsenite. If AioA is able to couple the oxidation of arsenite to the reduction of an electrode then it could be used as a biosensor. The removal of AioB (and potentially a mediator) could simplify the system and improve performance and/or reduce costs.

Based on the results of this study, it appears that AioA has not folded correctly during expression because it has lost or been unable to support cofactors and has aggregated into high order oligomeric states. A potential solution to this is to trial different fusion protein partners that improve folding and stabilise folded proteins. For example, small ubiquitin binding protein (SUMO) has been shown to improve the folding of an array of proteins of different sizes, mass and function (Butt et al., 2005). Unfortunately, at time of writing, there have not been any fusion protein partners reported that are noted to specifically stabilise molybdenum containing enzymes. One study reports the use of both a his-tag and a glutathione-S-transferase as fusion partners for the heterologous expression of nitrate reductase but no difference in the final protein’s quality or stability was observed (or reported) (Pollock et al., 2002). A future study could attempt different expression conditions and fusion partners to try to stabilise AioA.

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Appendix E – Accession numbers for sequence alignment

Table E.1: Table of species used for Rieske sequence alignment and phylogeny reconstruction and Genbank accession numbers

Bc1 or b6f Species AioB accession no. 16s accession no accession no. Rhizobium sp. str. Pdb4AAY AF159453.1 NT-26 Roseovarius KX268605.1 conluentis Roseovarius sp. EAQ26063 217 Ochrobactrum ACK38266 AY429607.1 tritici Bosea sp. WAO KUL93389.1 DQ986321.1 Agrobactrum EHJ95437.1 KF460525.1 tumefaciens Alcaligenes faecalis WP 00380473.1 WP 003805394 KF500593.1 Herminiimonas WP 011870034.1 NR_125502.1 arsenicoxydans Achromobacter sp. ABP63659.1 EU073119.1 SY8 Ralstonia sp. 22 ACX69822.1 EU304284.1 Thiomonas NR_115341.1 arsenivorans Thiomonas APY18928.1 delicata Marinobacter EMP56894.1 NR_044509.1 santoriniensis Pseudomonas ACB05942.1 EU073110.1 stutzeri Sulfurospirillum AFL69680.1 NR_102929.1 barnesii Chlorobium ACD89476.1 NR_074355.1 limicola Chloroflexus NR_074226.1 aggregans Chloroflexus YP 001634828.1 aurantiacus

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Thermus BAD71924.1 NR_037066.1 thermophilus HB8 Thermus sp. HR13 ABB17183.1 AF384168.1 Sulfolobus tokadii BAB67501.1 BAK54637.1 AB022438.1 Aeropyrum pernix BAA81580.1 D83259.1 Pyrobaculum NC_008701.1 islandicum Pyrobaculum ABO08791.1 calidifontis Halorubrum WP 082238286 AJ276887.1 tenebquichense Spinacea oleracea Pdb1rfs b6f Mastigocladus Pdb4pv1 Laminosus b6f Bos tauros bc1 Pdb1rie Rhodobacter Pdb5kli sphaeroides bc1 Pseudomonas KDN97796 donghuensis bc1

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Appendix F – 16S rRNA Phylogeny

Figure F.1: Maximum likelihood phylogenetic reconstruction of the 16S phylogeny of species used in the AioB phylogenetic analysis. The evolutionary history was inferred by using the Maximum Likelihood method. The tree with the highest log likelihood (- 17589.36) is shown. The percentage of trees in which the associated taxa clustered together is shown next to the branches. The tree is drawn to scale, with branch lengths measured in the number of substitutions per site. The analysis involved 21 nucleotide sequences.

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Appendix G – Additional Aio versus antimonyl tartrate or arsenite titration repeats

Figure G.1: Additional repeats of Aio versus arsenite (A and B) and antimonyl tartrate (C and D) titrations. The point at which the titration reached 100% Abs change was taken as the end point of the titration and used to determine the number of electrons equivalents required to reduce the Aio.

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Appendix H – Arsenite steady-state kinetics of the AioA-S102C Aio mutant with cytochrome c as an electron acceptor

4 )

-1 3

-1 mol min mol

 2

1 -1 -1

Spec. Activity ( Activity Spec. Vmax = 3.9 mol min mg

KM = 33.9 M -1 Kcat = 7.4 s 0

0 500 1000 1500 2000 2500 As(III) conc. (M)

Figure H.1: Steady-state kinetics of arsenite oxidation by the Aio with horse heart cytochrome c as the electron acceptor. Arsenite concentrations were: 2500, 1000, 250, 75, 25 and 5 μM. Cytochrome c concentration was 20 μM. All readings were taken in duplicate with one enzyme preparation, where only one datapoint is seen for a concentration it is because the results were similar such that the data points overlap. Conducted at 25 °C.

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References

Acin-Perez R & Enriquez JA (2014). The function of the respiratory supercomplexes: The plasticity model. Biochim Biophys Acta - Bioenerg 1837, 444–450.

Adman ET & Jensen LH (1981). Structural Features of Azurin at 2.7 Angstroms Resolution. Isr J Chem.

Administration USF and D (2017). Frequently asked questions about the FDA drug approval process. Available at: https://www.fda.gov/drugs/resourcesforyou/specialfeatures/ucm279676.htm #4 [Accessed September 20, 2017].

Ajioka JW (2017). Arsenic Biosensor Collaboration. Available at: http://arsenicbiosensor.org/index-2.html [Accessed September 20, 2017].

Alawattegama SK, Kondratyuk T, Krynock R, Bricker M, Rutter JK, Bain DJ & Stolz JF (2015). Well water contamination in a rural community in southwestern Pennsylvania near unconventional shale gas extraction. J Environ Sci Heal - Part A Toxic/Hazardous Subst Environ Eng 50, 516–528.

Albery WJ & Knowles JR (1976). Evolution of enzyme function and the development of catalytic efficiency. Biochemistry 15, 5631–5640.

Alessandrini A, Bortolotti CA, Bertoni G, Vezzoli A & Facci P (2008). Ultraflat nickel substrates for scanning probe microscopy of polyhistidine-tagged proteins. J Phys Chem C 112, 3747–3750.

Altschul SF, Gish W, Miller W, Myers EW & Lipman DJ (1990). Basic local alignment search tool. J Mol Biol 215, 403–410.

271

Amazon.co.uk (2017). Watersafe Drinking Water Test Kit. Available at: https://www.amazon.co.uk/Watersafe-Drinking-Water-Test- Kit/dp/B00005AUHX [Accessed September 20, 2017].

Amini M, Abbaspour KC, Berg M, Winkel L, Hug SJ, Hoehn E, Yang H & Johnson CA (2008). Statistical modelling of global geogenic arsenic contamination in groundwater. Environ Sci Technol 42, 3369–3675.

Anawar HM (2012). Arsenic speciation in environmental samples by hydride generation and electrothermal atomic absorption spectrometry. Talanta 88, 30– 42.

Anderson GL, Williams J & Hille R (1992). The purification and characterization of arsenite oxidase from Alcaligenes faecalis, a molybdenum-containing hydroxylase. J Biol Chem 267, 23674–23682.

Andra SS, Makris KC, Charisiadis P & Costa CN (2014). Co-occurrence profiles of trace elements in potable water systems: a case study. Environ Monit Assess 186, 7307–7320.

Aono T, Sakamoto Y, Miura M, Takeuchi F, Hori H & Tsubaki M (2010). Direct

electrochemical analyses of human cytochromes b5 with a mutated heme pocket showed a good correlation between their midpoint and half wave potentials. J Biomed Sci 17, 90.

Armstrong JM (1964). The molar extinction coefficient of 2,6-dichlorophenol indophenol. Biochim Biophys Acta 86, 194–197.

ARSOlux (2017). ARSOlux. Helmholtz - Cent Environ Res. Available at: http://www.ufz.de/arsolux/index.php?de=20675 [Accessed September 20, 2017].

Ashley K, Mapp KJ & Millson M (1998). Ultrasonic extraction and field-portable anodic 272

stripping voltammetry for the determination of lead in workplace air samples. Am Ind Hyg Assoc J 59, 671–679.

Augustin MA, Chapman SK, Davies DM, Sykes AG, Speck SH, Margoliash E (1983). Interaction of cytochrome c with the blue copper proteins, plastocyanin and azurin. The Journal of Biological Chemistry 259, 6405 - 6409.

Ayotte J, Baris D, Cantor K, Colt J, Robinson G, Lubin J, Karagas M, Hoover R, Fraumeni J & Silverman D (2006). Bladder cancer mortality and private well use in New England: an ecological study. JECH ONLINE 60, 168–172.

Bachmann J, Bauer B, Zwicker K, Ludwig B & Anderka O (2006). The Rieske protein from Paracoccus denitrificans is inserted into the cytoplasmic membrane by the twin-arginine translocase. FEBS J 273, 4817–4830.

Badilla C, Osborne TH, Cole A, Watson C, Djordjevic S & Santini JM (2018). A new family of periplasmic-binding proteins that sense arsenic oxyanions. Sci Rep1– 12.

Bagshaw NE (1995). Lead alloys: past, present and future. J Power Sources 53, 25–30.

Bakhrat A, Eltzov E, Finkelstein Y, Marks RS & Raveh D (2011). UV and arsenate toxicity: a specific and sensitive yeast bioluminescence assay. Cell Biol Toxicol 27, 227–236.

Bar-Even A, Noor E, Savir Y, Liebermeister W, Davidi D, Taw DS & Milo R (2011). The Moderately Efficient Enzyme : Evolutionary and Physicochemical. Biochemistry 50, 4402–4410.

Barnwal P, van Geen A, van der Goltz J & Singh C (2017). Demand for environmental quality information and households’ response. Int Growth Cent.

Berg IA, Kockelkorn D, Ramos-Vera WH, Say RF, Zarzycki J, Hugler M, Alber BE & Fuchs

273

G (2010). Autotrophic carbon fixation in archaea. Nat Rev Microbiol 8, 447–460.

Berghuis AM & Brayer GD (1992). Oxidation State-dependent Conformational in Cytochrome c. J Mol Biol 223, 959–976.

Berks BC, Palmer T & Sargent F (2005). Protein targeting by the bacterial twin-arginine translocation (Tat) pathway. Curr Opin Microbiol 8, 174–181.

Bernhardt P & Santini J (2006). Protein film voltammetry of arsenite oxidase from the chemolithoautotrophic arsenite-oxidizing bacterium NT-26. Biochemistry 45, 2804–2809.

Berry SM, Baker MH & Reardon NJ (2010). Reduction potential variations in azurin through secondary coordination sphere phenylalanine incorporations. J Inorg Biochem 104, 1071–1078.

Bingemann R & Klein A (2000). Conversion of the central cluster into a cluster leads to reduced hydrogen‐uptake activity of the F420‐reducing hydrogenase of Methanococcus. Eur J Biochem 6618, 6612–6618.

Bonomi F, Pagani S, Cerletti P & Giori C (1983). Modification of the Thermodynamic Properties of the Electron‐Transferring Groups in Mitochondrial Succinate Dehydrogenase upon Binding of Succinate. Eur J Biochem 134, 439–445.

Bornscheuer UT & Pohl M (2001). Improved biocatalysts by directed evolution and rational protein design. Curr Opin Chem Biol 5, 137–143.

Bousse L (1996). Whole cell biosensors. Sensors Actuators B Chem 34, 270–275.

Bowen A (2017). Real world arsenic detection. Oxford Univ. Available at: https://innovation.ox.ac.uk/licence-details/real-world-arsenic-detection/ [Accessed September 20, 2017].

274

Boxhammer S, Glaser S, Kuhl A, Wagner A & Schmidt C (2008). Characterization of the recombinant Rieske [2Fe-2S] proteins HcaC and YeaW from E. coli. Biometals 21, 459–467.

Bradford MM (1976). A rapid and sensitive method for the quantitation of microgram quantities of protein utilizing the principle of protein-dye binding. Anal Biochem 72, 248–254.

Branco R, Francisco R, Chung AP & Morais PV (2009). Identification of an aox system that requires cytochrome c in the highly arsenic-resistant bacterium Ochrobactrum tritici SCII24. Appl Environ Microbiol 75, 5141–5147.

Brill AS, Bryce GF & Maria HJ (1968). Optical and Magnetic Properties of Pseudomonas Azurins. Biochim Biophys Acta 154, 342–351.

Brody MS & Hille R (1999). The Kinetic Behavior of Chicken Liver Sulfite Oxidase †. Biochemistry 38, 6668–6677.

Brown EN, Friemann R, Karlsson A, Parales J V., Couture MMJ, Eltis LD & Ramaswamy S (2008). Determining Rieske cluster reduction potentials. J Biol Inorg Chem 13, 1301–1313.

Bushnell GW, Louie G V & Brayer GD (1990). High-resolution Three-dimensional Structure of Horse Heart Cytochrome c. J Mol Biol 214, 585–595.

Butt TR, Edavettal SC, Hall JP & Mattern MR (2005). SUMO fusion technology for difficult-to-express proteins. Protein Expr Purif 43, 1–9.

Carlin A, Shi W, Dey S & Rosen BP (1995). The ars operon of Escherichia coli confers arsenical and antimonial resistance. J Bacteriol 177, 981–986.

Cass AEG, Davis G, Francis GD, Hill HAO, Aston WJ, Higgins IJ, Plotkin E V, Scott LDL & Turner APF (1984). Ferrocene-mediated enzyme electrode for amperometric

275

determination of glucose. Anal Chem 56, 667–671.

Centers for Disease Control and Prevention (2010). Well Testing. Available at: https://www.cdc.gov/healthywater/drinking/private/wells/testing.html [Accessed September 20, 2017].

Centers for Disease Control and Prevention (2016). National Biomonitoring Program - Arsenic. Available at: https://www.cdc.gov/biomonitoring/Arsenic_FactSheet.html [Accessed September 20, 2017].

Centre for Disease Control and Prevention (2017). Drinking Water FAQ. Available at: https://www.cdc.gov/healthywater/drinking/drinking-water-faq.html [Accessed September 21, 2017].

Chan P-H, Liu H-B, Chen YW, Chan K-C, Tsang C-W, Leung Y-C & Wong K-Y (2004). Rational Design of a Novel Fluorescent Biosensor for β-Lactam Antibiotics from a Class A β-Lactamase. J Am Chem Soc 126, 4074–4075.

Chang TK, Iverson S a, Rodrigues CG, Kiser CN, Lew a Y, Germanas JP & Richards JH (1991). Gene synthesis, expression, and mutagenesis of the blue copper proteins azurin and plastocyanin. Proc Natl Acad Sci U S A 88, 1325–1329.

Chaubey A & Malhotra BD (2002). Mediated biosensors. Biosens Bioelectron 17, 441– 456.

Chaudhari A, Fialho AM, Ratner D, Gupta P, Hong CS, Kahali S, Yamada T, Haldar K, Murphy S, Cho W, Chauhan VS, Das Gupta TK & Chakrabarty AM (2006). Azurin, Plasmodium falciparum malaria and HIV/AIDS: Inhibition of parasitic and viral growth by azurin. Cell Cycle 5, 1642–1648.

Chaudhari A, Mahfouz M, Fialho AM, Yamada T, Granja AT, Zhu Y, Hashimoto W, Schlarb-Ridley B, Cho W, Das Gupta TK & Chakrabarty AM (2007). Cupredoxin- 276

cancer interrelationship: Azurin binding with EphB2, interference in EphB2 tyrosine phosphorylation, and inhibition of cancer growth. Biochemistry 46, 1799–1810.

Chen CM, Misra TK, Silver S & Rosen BP (1986). Nucleotide sequence of the structural genes for an anion pump. The plasmid-encoded arsenical resistance operon. J Biol Chem 261, 15030–15038.

Cheng VWT, Rothery RA, Bertero MG, Strynadka NCJ & Weiner JH (2005). Investigation of the environment surrounding iron-sulfur cluster 4 of Escherichia coli dimethylsulfoxide reductase. Biochemistry 44, 8068–8077.

Cheriyan M, Walters MJ, Kang BD, Anzaldi LL, Toone EJ & Fierke CA (2011). Directed evolution of a pyruvate aldolase to recognize a long chain acyl substrate. Bioorg Med Chem 19, 6447–6453.

Choi J-H, Lee M-H, Cho Y-J, Park B-S, Kim S & Kim G-C (2011). The Bacterial Protein Azurin Enhances Sensitivity of Oral Squamous Carcinoma Cells to Anticancer Drugs. Yonsei Med J 52, 773–778.

Clark LCJ & Lyons C (1962). Electrode systems for continuous monitoring in cardiovascular surgery. Ann N Y Acad Sci 102, 29–45.

Cleland W (1963). The kinetics of enzyme-catalyzed reactions with two or more substrates or products. Biochim Biophys Acta 67, 104–137.

Cobb N, Conrads T & Hille R (2005). Mechanistic Studies of Rhodobacter sphaeroides

Me2SO Reductase. J Biol Chem 280, 11007–11017.

Cobb NJ (2005). Spectroscopic and Kinetic Studies of Mononuclear Molybdenum Enzymes of the DMSO Reductase Family.

Colbert CL, Couture MMJ, Eltis LD & Bolin JT (2000). A cluster exposed: Structure of

277

the rieske ferredoxin from biphenyl dioxygenase and the redox properties of Rieske Fe-S proteins. Structure 8, 1267–1278.

Cone M (2011). Many U.S. Drinking Water Wells Contaminated with Arsenic, Other Elements. Sci Am.

Conrads T, Hemann C, George GN, Pickering IJ, Prince RC & Hille R (2002). The active site of arsenite oxidase from Alcaligenes faecalis. J Am Chem Soc 124, 11276– 11277.

Cooper R & Harrison A (2009). The exposure and health effects of antimony. Indian J Occup Env Med 13, 3–10.

Covian R & Trumpower BL (2009). The rate-limiting step in the cytochrome bc1 complex (ubiquinol-cytochrome c oxidoreductase) is not changed by inhibition of cytochrome b-dependent deprotonation: Implications for the mechanism of

ubiquinol oxidation at center P of the bc1 complex. J Biol Chem 284, 14359– 14367.

Das A, Roy J & Chakrabarti A (2016). Socio-Economic Analysis of Arsenic Contamination of Groundwater in West Bengal.

Daunert S, Barrett G, Feliciano JS, Shetty RS, Shrestha S & Smith-Spencer W (2000). Genetically engineered whole-cell sensing systems: coupling biological recognition with reporter genes. Chem Rev 100, 2705–2738.

Davis G (1985). Electrochemical techniques for the development of amperometric biosensors. Biosensors 1, 161–178.

Davis MD, Olson JS & Palmer G (1982). Charge transfer complexes between pteridine substrates and the active center molybdenum of xanthine oxidase. J Biol Chem 257, 14730–14737.

278

Denke E, Merbitz-Zahradnik T, Hatzfeld OM, Snyder CH, Link T a. & Trumpower BL (1998). Alteration of the Midpoint Potential and Catalytic Activity of the Rieske Iron-Sulfur Protein by Changes of Amino Acids Forming Hydrogen Bonds to the Iron-Sulfur Cluster. J Biol Chem 273, 9085–9093.

Desmond D, Lane B, Alderman J, Hill M, Arrigan DWM & Glennon JD (1998). An environmental monitoring system for trace metals using stripping voltammetry. Sensors Actuators B Chem 48, 409–414.

Diesel E, Schreiber M & van der Meer JR (2009). Development of bacteria-based bioassays for arsenic detection in natural waters. Anal Bioanal Chem 394, 687– 693.

DiscoverTesting.com (2017). Well Water Test Kit. Available at: https://www.discovertesting.com/products/display_products.sd?iid=3270&cat id=7&loc=show&headTitle=Well Water Test Kit [Accessed September 20, 2017].

Dobbek H, Gremer L, Meyer O & Huber R (1999). Crystal structure and mechanism of CO dehydrogenase, a molybdo iron-sulfur flavoprotein containing S- selanylcysteine. Proc Natl Acad Sci U S A 96, 8884–8889.

Dodia R, Meunier B, Kay CWM & Rich PR (2014). Comparisons of subunit 5A and 5B isoenzymes of yeast cytochrome c oxidase. Biochem J 464, 335–342.

Dovick MA, Kulp TR, Arkle R & Pilliod DS (2015). Bioaccumulation trends of arsenic and antimony in a freshwater ecosystem affected by mine drainage. Environ Chem 13, 149–159.

Van Driessche G, Vandenberghe I, Devreese B, Samyn B, Meyer TE, Leigh R, Cusanovich MA, Bartsch RG, Fischer U & Van Beeumen JJ (2003). Amino acid sequences and distribution of high-potential iron-sulfur proteins that donate electrons to the photosynthetic reaction center in phototropic proteobacteria. J

279

Mol Evol 57, 181–199.

Duval S, Nitschke W & Schoepp-Cothenet B (2008). S13.40 Characterization of the redox centres in arsenite oxidase. Biochim Biophys Acta - Bioenerg 1777, S97– S98.

Duval S, Santini JM, Lemaire D, Chaspoul F, Russell MJ, Grimaldi S, Nitschke W & Schoepp-cothenet B (2016). The H-bond network surrounding the pyranopterins modulates redox cooperativity in the molybdenum- bis PGD cofactor in arsenite oxidase. BBA - Bioenerg 1857, 1353–1362.

Duval S, Santini JM, Nitschke W, Hille R & Schoepp-Cothenet B (2010). The small subunit AroB of arsenite oxidase: lessons on the [2Fe-2S] Rieske protein superfamily. J Biol Chem 285, 20442–20451.

Dzyadevych S V, Arkhypova VN, Soldatkin AP, El’skaya A V, Martelet C & Jaffrezic- Renault N (2008). Amperometric enzyme biosensors: Past, present and future. IRBM 29, 171–180.

Easton J (2013). Fracking wastewater management. Water Wastewater Int. Available at: http://www.waterworld.com/articles/wwi/print/volume-28/issue- 5/regional-spotlight-us-caribbean/fracking-wastewater-management.html [Accessed September 21, 2017].

Edelhoch H (1967). Spectroscopic Determination of Tryptophan and Tyrosine in Proteins. Biochemistry 6, 1948–1954.

Edgar RC (2004). MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res 32, 1792–1797.

Ellis PJ, Conrads T, Hille R & Kuhn P (2001). Crystal structure of the 100 kDa arsenite oxidase from Alcaligenes faecalis in two crystal forms at 1.64 A and 2.03 A. Structure 9, 125–132. 280

Erman JE & Vitello LB (2002). Yeast cytochrome c peroxidase : mechanistic studies via protein engineering. Biochim Biophys Acta 1597, 193–220.

Erman JE, Vitello LB, Pearl NM, Jacobson T, Francis M, Alberts E, Kou A & Bujarska K (2015). Binding of Yeast Cytochrome c to Forty-Four Charge-Reversal Mutants of Yeast Cytochrome c Peroxidase: Isothermal Titration Calorimetry. Biochemistry 54, 4845–4854.

European Union (2008). Diantimony Trioxide Risk Assessment.

Fawcett SE, Jamieson HE, Nordstrom DK & McCleskey RB (2015). Arsenic and antimony geochemistry of mine wastes, associated waters and sediments at the Giant Mine, Yellowknife, Northwest Territories, Canada. Appl Geochemistry 62, 3–17.

Fee JA, Findling KL, Yoshida T, Hille R, Tarr E, Hearshen D, Dunham WR, Days EP, Kent TA & B EM (1984). Purification and Characterization of the Rieske Iron-Sulfur Protein from Thermus thermophilus. J Biol Chem 259, 124–133.

Ferraro DJ, Gakhar L & Ramaswamy S (2005). Rieske business: Structure-function of Rieske non-heme oxygenases. Biochem Biophys Res Commun 338, 175–190.

Filella M, Belzile N & Chen Y (2002a). Antimony in the environment: a review focused on natural waters: I. Occurrence. Earth-Science Rev 57, 125–176.

Filella M, Belzile N & Chen Y (2002b). Antimony in the environment: a review focused on natural waters: II. Relevant solution chemistry. Earth-Science Rev 59, 265– 285.

Filella M, Belzile N & Lett M-C (2007). Antimony in the environment: A review focused on natural waters. III. Microbiota relevant interactions. Earth-Science Rev 80, 195–217.

281

Flakova R, Krcmar D & Sracek O (2017). Occurrence of antimony and arsenic at mining sites in Slovakia: Implications for their mobility. Carpathian J Earth Environ Sci 12, 41–48.

Flanagan S V, Johnston RB & Zheng Y (2012). Arsenic in tube well water in Bangladesh: health and economic impacts and implications for arsenic mitigation. Bull World Health Organ 90, 839–846.

Francesconi K & Kuehnelt D (2002). Arsenic compounds in the environment. In Environmental chemistry of arsenic, ed. Frankenburger WT, pp. 51–94. Marcel Dekker, New York.

Frasco MF, Fournier D, Carvalho F & Guilhermino L (2005). Do metals inhibit acetylcholinesterase (AChE)? Implementation of assay conditions for the use of AChE activity as a biomarker of metal toxicity. Biomarkers Biochem Indic Expo response, susceptibility to Chem 10, 360–375.

Frost and Sullivan (2012). Sensors for water quality monitoring (technical insights).

Frost and Sullivan (2013). In-depth analysis of the global portable/handheld test equipment market.

Frost and Sullivan (2015). In-depth analysis of the global portable/handheld test equipment market.

Frost and Sullivan (2016a). Global Water Analysis Instrumentation Market, Forecast to 2022.

Frost and Sullivan (2016b). Water Analysis Instrumentation Market - A positive outlook.

Frost and Sullivan (2016c). Outlook of the global analytical instrumentation industry, 2017 and 2018.

282

Frost and Sullivan (2016d). Test and Measurement Market: High Penetration Potential for Rental and Leasing Services.

Frost and Sullivan (2017a). Future of analytical instrumentation market.

Frost and Sullivan (2017b). Growth of Analytical Instrumentation Market in BRICS.

Gally C, Nestl BM & Hauer B (2015). Engineering Rieske Non-Heme Iron Oxygenases for the Asymmetric Dihydroxylation of Alkenes. Angew Chemie - Int Ed 54, 12952–12956.

Garner DK, Vaughan MD, Hwang HJ, Savelieff MG, Berry SM, Honek JF & Lu Y (2006). Reduction potential tuning of the blue copper center in Pseudomonas aeruginosa azurin by the axial methionine as probed by unnatural amino acids. J Am Chem Soc 128, 15608–15617.

Van Gelder B & Slater E (1962). The extinction coefficient of cytochrome c. Biochim Biophys Acta - Bioenerg 58, 593–595.

Ghosh M, Shen J, Rosen BP & Kaback HR (1999). Pathways of As(III) detoxification in Saccharomyces cerevisiae. Cell Biol 96, 5001–5006.

Ghosh P, Roy C, Das NK & Sengupta SR (2008). Epidemiology and prevention of chronic arsenicosis: an Indian perspective. Indian J Dermatol Venereol Leprol 74, 582–593.

Gibaud S & Jaouen G (2010). Arsenic-Based Drugs: From Fowler’s Solution to Modern Anticancer Chemotherapy. In Medicinal Organometallic Chemistry SE - 1, ed. Jaouen G & Metzler-Nolte N, Topics in Organometallic Chemistry, pp. 1–20. Springer Berlin Heidelberg.

Gihring TM & Banfield JF (2001). Arsenite oxidation and arsenate respiration by a new Thermus isolate. FEMS Microbiol Lett 204, 335–340.

283

Glasser NR, Oyala PH, Osborne TH, Santini JM & Newman DK (2018). Structural and mechanistic analysis of the arsenate respiratory reductase provides insight into environmental arsenic transformations. Proc Natl Acad Sci.

Goodwin LG & Page JE (1943). A study of the excretion of organic antimonials using a polarographic procedure. Biochem J 37, 198–209.

Green HH (1918). Description of a bacterium which oxidizes arsenite to arsenate, and of one which reduces arsenate to arsenite, isolated from a cattle-dipping tank. S Afr J Sci 14, 465–467.

Greenlee L & Handler P (1964). Xanthine Oxidase. J Biol Chem 239, 1090–1095.

Greenwood, Norman N.; Earnshaw, Alan (1997). Chemistry of the Elements (2nd ed.). Butterworth-Heinemann.

Grimaldi S, Schoepp-Cothenet B, Ceccaldi P, Guigliarelli B & Magalon A (2013). The prokaryotic Mo/W-bisPGD enzymes family: a catalytic workhorse in bioenergetic. Biochim Biophys Acta 1827, 1048–1085.

Guergova-Kuras M, Kuras R & Ugulava N (2000). Mutagenesis of the Rieske iron-sulfur protein in Rhodobacter sphaeroides shows that both the thermodynamic gradient and the pK of the oxidized form determine the rate of quinol oxidation

by the bc1 complex. Biochemistry 39, 7436–7444.

Haldar AK, Sen P & Roy S (2011). Use of Antimony in the Treatment of Leishmaniasis: Current Status and Future Directions. Mol Biol Int 2011, 1–23.

Hardcastle J (2016). Fracking wastewater treatment technologies: which will emerge as the most popular? environmentalLEADER. Available at: https://www.environmentalleader.com/2016/04/fracking-wastewater- treatment-technologies-which-will-emerge-as-the-most-popular/ [Accessed September 21, 2017]. 284

Harris RL, Eady RR, Hasnain SS & Sawers RG (2006). Coordinate synthesis of azurin I and copper nitrite reductase in Alcaligenes xylosoxidans during denitrification. 241–249.

Heath MD (2012). Investigations of the arsenite oxidase from a model thermophile and characterisation of the enzyme from its mesophilic counterparts (thesis).

Hille R (2002). Molybdenum and tungsten in biology. Trends Biochem Sci 27, 360– 367.

Hille R, Fee JA & Massey V (1981). Equilibrium properties of xanthine oxidase containing FAD analogs of varying oxidation-reduction potential. J Biol Chem 256, 8933–8940.

Hille R, Hall J & Basu P (2014). The mononuclear molybdenum enzymes. Chem Rev 114, 3963–4038.

Hockmann K, Lenz M, Tandy S, Nachtegaal M, Janousch M & Schulin R (2014). Release of antimony from contaminated soil induced by redox changes. J Hazard Mater 275, 215–221.

Hoke KR, Cobb N, Armstrong F a & Hille R (2004). Electrochemical studies of arsenite oxidase: an unusual example of a highly cooperative two-electron molybdenum center. Biochemistry 43, 1667–1674.

Hong S, Ugulava N, Guergova-kuras M & Crofts AR (1999). The Energy Landscape for

Ubihydroquinone Oxidation at the Q o Site of the bc1 Complex in Rhodobacter sphaeroides. J Biol Chem 274, 33931–33944. vanden Hoven RN & Santini JM (2004). Arsenite oxidation by the heterotroph Hydrogenophaga sp. str. NT-14: the arsenite oxidase and its physiological electron acceptor. Biochim Biophys Acta 1656, 148–155.

285

Hsueh K, Westler W & Markley J (2010). NMR investigations of the Rieske protein from Thermus thermophilus support a coupled proton and electron transfer mechanism. J Am Ceram Soc 132, 7908–7918.

Inskeep WP, Macur RE, Hamamura N, Warelow TP, Ward SA & Santini JM (2007). Detection, diversity and expression of aerobic bacterial arsenite oxidase genes. Environ Microbiol 9, 934–943.

Jouanneau Y, Meyer C, Jakoncic J, Stojanoff V & Gaillard J (2006). Characterization of a naphthalene dioxygenase endowed with an exceptionally broad substrate specificity toward polycyclic aromatic hydrocarbons. Biochemistry 45, 12380– 12391. van de Kamp M, Silvestrini MC, Brunori M, van Beeumen J, Hali FC & Canters GW (1990). Involvement of the hydrophobic patch of azurin in the electron‐transfer

reactions with cytochrome c551 and nitrite reductase. Eur J Biochem 194, 109– 118.

Karlsson BG, Pascher T, Nordling M, Arvidsson RHA & Lundberg LG (1989). Expression of the blue copper protein azurin from Pseudomonas aeruginosa in Escherichia coli. FEBS Letters. 211–217.

Kawarabayasi Y et al. (1999). Complete genome sequence of an aerobic hyperthermophilic crenarchaeon. Aeropyrum pernix K1 DNA Res 6, 83–101.

Kelso M (2015). 1.7 million wells in the U.S. - A 2015 Update. FractTracker. Available at: https://www.fractracker.org/2015/08/1-7-million-wells/ [Accessed September 21, 2017].

Klaning U, Bielski B & Sehested K (1989). Arsenic(IV). A Pulse-Radiolysis Study. Am Chem Soc 28, 2717–2724.

Koechler S, Cleiss-Arnold J, Proux C, Sismeiro O, Dillies MA, Goulhen-Chollet F, 286

Hommais F, Lièvremont D, Arsène-Ploetze F, Coppée JY & Bertin PN (2010). Multiple controls affect arsenite oxidase gene expression in Herminiimonas arsenicoxydans. BMC Microbiol; DOI: 10.1186/1471-2180-10-53.

Komorowicz I & Baralkiewicz D (2011). Arsenic and its speciation in water samples by high performance liquid chromatography inductively coupled plasma mass spectrometry--last decade review. Talanta 84, 247–261.

Kulp TR, Hoeft SE, Asao M, Madigan MT, Hollibaugh JT, Fisher JC, Stolz JF, Culbertson CW, Miller LG & Oremland RS (2008). Arsenic(III) fuels anoxygenic photosynthesis in hot spring biofilms from Mono Lake, California. Science 321, 967–970.

L’vov NP, Nosikov AN & Antipov AN (2002). Tungsten-Containing Enzymes. Biochem 67, 196–200.

Labuda J, Bubnicova K, Kovalova L, Vanickova M, Mattusch J & Wennrich R (2005). Voltammetric Detection of Damage to DNA by Arsenic Compounds at a DNA Biosensor. Sensors (Basel) 5, 411–423.

Lai C-Y, Wen L-L, Zhang Y, Luo S-S, Wang Q-Y, Luo Y-H, Chen R, Yang X, Rittmann BE & Zhao H-P (2016). Autotrophic antimonate bio-reduction using hydrogen as the electron donor. Water Res 88, 467–474.

Lambrianou A, Demin S & Hall E a H (2008). Protein engineering and electrochemical biosensors. Adv Biochem Eng Biotechnol 109, 65–96.

Lapuente-Brun E, Moreno-Loshuertos R, Acín-Pérez R, Latorre-Pellicer A, Colás C, Balsa E, Perales-Clemente E, Quirós PM, Calvo E, Rodríguez-Hernández M a, Navas P, Cruz R, Carracedo Á, López-Otín C, Pérez-Martos A, Fernández-Silva P, Fernández-Vizarra E & Enríquez JA (2013). Supercomplex assembly determines electron flux in the mitochondrial electron transport chain. Science 340, 1567–

287

1570.

Leartsakulpanich U, Antonkine ML & Ferry JG (2000). Site-specific mutational analysis of a novel cysteine motif proposed to ligate the 4Fe-4s cluster in the iron-sulfur flavoprotein of the thermophilic methanoarchaeon methanosarcina thermophila. J Bacteriol 182, 5309–5316.

Lebrun E, Brugna M, Baymann F, Muller D, Lièvremont D, Lett M-C & Nitschke W (2003). Arsenite oxidase, an ancient bioenergetic enzyme. Mol Biol Evol 20, 686– 693.

Lebrun E, Santini JM, Brugna M, Ducluzeau AL, Ouchane S, Schoepp-Cothenet B, Baymann F & Nitschke W (2006). The Rieske protein: A case study on the pitfalls of multiple sequence alignments and phylogenetic reconstruction. Mol Biol Evol 23, 1180–1191.

Leemhuis H, Kelly RM & Dijkhuizen L (2009). Directed evolution of enzymes: Library screening strategies. IUBMB Life 61, 222–228.

Leggate EJ & Hirst J (2005). Roles of the disulfide bond and adjacent residues in determining the reduction potentials and stabilities of respiratory-type rieske clusters. Biochemistry 44, 7048–7058.

Lehr CR, Kashyap DR & McDermott TR (2007). New insights into microbial oxidation of antimony and arsenic. Appl Environ Microbiol 73, 2386–2389.

Li J, Wang Q, Oremland RS, Kulp TR, Rensing C & Wang G (2016). Microbial antimony biogeochemistry: Enzymes, regulation, and related metabolic pathways. Appl Environ Microbiol 82, 5482–5495.

Lialikova NN (1974). Stibiobacter senarmontii--a new microorganism oxidizing antimony. Mikrobiologiia 43, 941–948.

288

Lieutaud A, van Lis R, Duval S, Capowiez L, Muller D, Lebrun R, Lignon S, Fardeau M- L, Lett M-C, Nitschke W & Schoepp-Cothenet B (2010). Arsenite oxidase from Ralstonia sp. 22: characterization of the enzyme and its interaction with soluble cytochromes. J Biol Chem 285, 20433–20441.

Lin I, Chen Y, Fee JA, Song J, Westler WM & Markley JL (2006). Rieske Protein from Thermus thermophilus : 15 N NMR Titration Study Demonstrates the Role of Iron-Ligated Histidines in the pH Dependence of the Reduction Potential. 10672–10673.

Link TA (1994). Two pK values of the oxidised ‘Rieske’ [2Fe-2S] cluster observed by CD spectroscopy. Biochim Biophys Acta - Bioenerg 1185, 81–84.

Link TA (1999). The Structures of Rieske and Rieske-Type Proteins ed. Sykes AGBT-A in IC. Academic Press.

Link TA, Hagen WR, Pierik AJ, Assmann C & von Jagow G (1992). Determination of the

redox properties of the Rieske cluster of bovine heart bc1 complex by direct electrochemistry of a water- soluble fragment. EurJBiochem 208, 685–691.

Link TA, Hatzfeld OM, Unalkat P, Shergill JK, Cammack R & Mason JR (1996).

Comparison of the “Rieske” [2Fe-2S] Center in the bc1 Complex and in Bacterial Dioxygenases by Circular Dichroism Spectroscopy and Cyclic Voltammetry. Biochemistry 35, 7546–7552.

Link TA & Iwata S (1996). Functional implications of the structure of the “Rieske” iron-

sulfur protein of bovine heart mitochondrial cytochrome bc1 complex. Biochim Biophys Acta 1275, 54–60. van Lis R, Nitschke W, Duval S & Schoepp-Cothenet B (2013). Arsenics as bioenergetic substrates. Biochim Biophys Acta 1827, 176–188. van Lis R, Nitschke W, Warelow TP, Capowiez L, Santini JM & Schoepp-Cothenet B 289

(2012). Heterologously expressed arsenite oxidase: a system to study biogenesis and structure/function relationships of the enzyme family. Biochim Biophys Acta 1817, 1701–1708.

Liu W, Rogge CE, da Silva GFZ, Shinkarev VP, Tsai AL, Kamensky Y, Palmer G & Kulmacz RJ (2008). His92 and His110 selectively affect different heme centers of adrenal

cytochrome b561. Biochim Biophys Acta - Bioenerg 1777, 1218–1228.

Locatelli C & Torsi G (2001). A New Voltammetric Method for the Simultaneous Monitoring of Heavy Metals in Sea Water , Sediments , Ecosystem. Environ Monit Assess 75, 281–292.

Looger L, Dwyer M, Smith J & Hellinga H (2003). Computational design of receptor and sensor proteins with novel functions. Nature 423, 185–190.

Lu H, Tsai C & Lu F (1998). Oxidative stress induced by humic acid solvent extraction fraction in cultured rabbit articular chrondrocytes. J Toxicol Environ Heal Part A 54, 477–489.

Lukáčová-Chomisteková Z, Culková E, Bellová R, Melicherčíková D, Durdiak J, Beinrohr E, Rievaj M & Tomčík P (2018). Voltammetric detection of antimony in natural water on cathodically pretreated microcrystalline boron doped diamond electrode: A possibility how to eliminate interference of arsenic without surface modification. Talanta 178, 943–948.

Lutz S (2010). Beyond directed evolution - semi-rational protein engineering and design. Curr Opin Biotechnol 21, 734–743.

Madigan M (2000). Brock biology of microorganism. Prentice-Hall, New Jersey.

Maeda S (1994). Safety and environmental effects. In The Chemistry of Organic Arsenic, Antimony and Bismuth Compounds., ed. Wiley NY, p. 725–759.

290

Maeda S, Hideto F, Emi Y, Takayoshi K, Akira O & Kensuke N (1998). Bioaccumulation of Antimony by Chlorella vulgaris and the Association Mode of Antimony in the Cell. Appl Organomet Chem 11, 393–396.

Maeda S & Ohki A (1998). Bioaccumulation and Biotransformation of Arsenic, Antimony, and Bismuth Compounds by Freshwater Algae. Wastewater Treat with Algae Biotechnol Intell Unit.

Maguire-Boyle SJ & Barron AR (2014). Organic compounds in produced waters from shale gas wells. Environ Sci Process Impacts 16, 2237–2248.

Male K, Hrapovic S, Santini J & Luong J (2007). Biosensor for arsenite using arsenite oxidase and multiwalled carbon nanotube modified electrodes. Anal Chem 79, 7831–7837.

Manodori A, Cecchini G, Schroder I, Gunsalus RP, Werth MT & Johnson MK (1992). [3Fe-4S] to [4Fe-4S] cluster conversion in Escherichia coli fumarate reductase by site-directed mutagenesis. Biochemistry 31, 2703–2712.

Margoliash E & Frohwirt N (1956). Spectrum of Horse-Heart Cytochrome c. Biochem J 71, 570–572.

Mcalpine AS & Bailey SUE (1997). Stopped-flow studies on dimethylsulphoxide reductase from Rhodobacter capsulatus: kinetic competence of the dimethylsulphide-reduced intermediate. Biochem Soc Trans 26, S211.

McCarty KM, Senn DB, Kile ML, Quamruzzaman Q, Rahman M, Mahiuddin G & Christiani DC (2004). Antimony: An unlikely confounder in the relationship between well water arsenic and health outcomes in Bangladesh. Environ Health Perspect 112, 809–811.

Meng YL, Liu Z & Rosen BP (2004). As(III) and Sb(III) Uptake by GlpF and Efflux by ArsB in Escherichia coli. J Biol Chem 279, 18334–18341. 291

Merbitz-Zahradnik T, Zwicker K, Nett JH, Link TA & Trumpower BL (2003). Elimination of the Disulfide Bridge in the Rieske Iron-Sulfur Protein Allows Assembly of the [2Fe-2S] Cluster into the Rieske Protein but Damages the Ubiquinol Oxidation

Site in the Cytochrome bc1 Complex. Biochemistry 42, 13637–13645.

Meyer T (2013). High Levels of Arsenic Found ini Groundwater Near Fracking Sites. Sci Am.

Michaelis L & Menten ML (1913). Die kinetic der invertinwirkung. Biochem Z 49, 333– 369.

Mobley HL & Rosen BP (1982). Energetics of plasmid-mediated arsenate resistance in Escherichia coli. Proc Natl Acad Sci U S A 79, 6119–6122.

Mohammadi M, Kumar P, Barriault D, Bolin JT & Sylvestre M (2011). Retuning Rieske- type Oxygenases to Expand Substrate. J Biol Chem 286, 27612–27621. de Mora K, Joshi N, Balint BL, Ward FB, Elfick A & French CE (2011). A pH-based biosensor for detection of arsenic in drinking water. Anal Bioanal Chem 400, 1031–1039.

Murcott S (2012). Arsenic contamination in the world.

Murphy PM, Bolduc JM, Gallaher JL, Stoddard BL & Baker D (2009). Alteration of enzyme specificity by computational loop remodeling and design. Proc Natl Acad Sci U S A 106, 9215–9220.

Nachman KE, Graham JP, Price LB & Silbergeld EK (2005). Arsenic: A Roadblock to Potential Animal Waste Management Solutions. Environ Health Perspect 113, 1123–1124.

Nar H, Messerschmidt A, Huber R, van de Kamp M & Canters GW (1991). Crystal structure analysis of oxidized Pseudomonas aeruginosa azurin at pH 5.5 and pH

292

9.0. A pH-induced conformational transition involves a peptide bond flip. J Mol Biol 221, 765–772.

Naujokas MF, Anderson B, Ahsan H, Aposhian HV, Graziano JH, Thompson C & Suk WA (2013). The broad scope of health effects from chronic arsenic exposure: update on a worldwide public health problem. Environ Health Perspect 121, 295–302.

Niedzielski P & Siepak M (2003). Analytical methods for determining arsenic, antimony and selenium in environmental samples. Polish J Environ Stud 12, 653– 667.

Niks D, Duvvuru J, Escalona M & Hille R (2016). Spectroscopic and kinetic properties of the molybdenum-containing, NAD+-dependent formate dehydrogenase from Ralstonia eutropha. J Biol Chem 291, 1162–1174.

Norman NC (1997). Chemistry of Arsenic, Antimony and Bismuth. Springer Netherlands.

Nriagu J (2002). Arsenic poisoning throughout the ages.ed. (ed) FW. Marcel Dekker, New York.

Nriagu JO & Pacyna JM (1988). Quantitative assessment of worldwide contamination of air, water and soils by trace metals. Nature 333, 134–139.

Nuckols JR, Freeman LEB, Lubin JH, Airola MS, Baris D, Ayotte JD, Taylor A, Paulu C, Karagas MR, Colt J, Ward MH, Huang A-T, Bress W, Cherala S, Silverman DT & Cantor KP (2011). Estimating Water Supply Arsenic Levels in the New England Bladder Cancer Study. Environ Health Perspect 119, 1279–1285.

Obuchi M, Kawahara K, Motooka D, Nakamura S, Yamanaka M, Takeda T, Uchiyama S, Kobayashi Y, Ohkubo T & Sambongi Y (2009). Hyperstability and crystal

structure of cytochrome c555 from hyperthermophilic Aquifex aeolicus. Acta 293

Crystallogr Sect D Biol Crystallogr 65, 804–813.

Olson TL, Williams JC & Allen JP (2013). Influence of protein interactions on oxidation/reduction midpoint potentials of cofactors in natural and de novo metalloproteins. Biochim Biophys Acta - Bioenerg 1827, 914–922.

Oremland RS, Hoeft SE, Santini JM, Bano N, Hollibaugh R a & Hollibaugh JT (2002). Anaerobic Oxidation of Arsenite in Mono Lake Water and by a facultative, Arsenite-Oxidising Chemoautotroph Strain MLHE-1. Society 68, 4795–4802.

Oremland RS & Stolz JF (2003). The Ecology of Arsenic. Science (80- ) 300, 939 LP-944.

Osako M, Machida N & Tanaka M (1997). Risk management measure against antimony in residue after incineration of municipal waste. Waste Manag 16, 519–526.

Osborne TH, Heath MD, Martin AC, Pankowski J, Hudson-Edwards KA & Santini JM (2013). Cold-adapted arsenite oxidase from a psychrotolerant Polaromonas species. Metallomics 5, 318–324.

Page C, Moser C, Chen X & Dutton P (1999). Natural engineering principles of electron tunnelling in biological oxidation–reduction. Nature 13, 47–52.

Pal K, Chaudhury PK & Sarkar S (2007). Structure of the Michaelis complex and function of the catalytic center in the reductive half-reaction of computational and synthetic models of sulfite oxidase. Chem Asian J 2, 956–964.

Pande A & Myer YP (1978). The redox potential of horse heart cytochrome c. Biochem Biophys Res Commun 85, 7–13.

Pandey PC, Upadhyay S, Pathak HC & Pandey CMD (1998). Sensitivity, Selectivity and Reproducibility of Some Mediated Electrochemical Biosensors/sensors. Anal Lett 31, 2327–2348.

294

Pandey PC, Upadhyay S & Upadhyay B (1997). Peroxide biosensors and mediated electrochemical regeneration of redox enzymes. Anal Biochem 252, 136–142.

Parales J V., Parales RE, Resnick SM & Gibson DT (1998). Enzyme specificity of 2- nitrotoluene 2,3-dioxygenase from Pseudomonas sp. strain JS42 is determined by the C-terminal region of the alpha subunit of the oxygenase component. J Bacteriol 180, 1194–1199.

Parales RE (2003). The role of active-site residues in naphthalene dioxygenase. J Ind Microbiol Biotechnol 30, 271–278.

Parales RE, Lee K, Resnick SM, Lessner DJ & Gibson DT (2000). Substrate Specificity of Naphthalene Dioxygenase : Effect of Specific Amino Acids at the Active Site of the Enzyme Substrate Specificity of Naphthalene Dioxygenase : Effect of Specific Amino Acids at the Active Site of the Enzyme. 182, 1641–1649.

Pauff JM, Hemann CF, Jünemann N, Leimkühler S & Hille R (2007). The role of arginine 310 in catalysis and substrate specificity in xanthine dehydrogenase from Rhodobacter capsulatus. J Biol Chem 282, 12785–12790.

Pierart A, Shahid M, Séjalon-Delmas N & Dumat C (2015). Antimony bioavailability: Knowledge and research perspectives for sustainable agricultures. J Hazard Mater 289, 219–234.

Pohanka M & Skládal P (2008). Electrochemical Biosensors-Principles and Applications.

Pollock V V., Conover RC, Johnson MK & Barber MJ (2002). Bacterial expression of the molybdenum domain of assimilatory nitrate reductase: Production of both the functional molybdenum-containing domain and the nonfunctional tungsten analog. Arch Biochem Biophys 403, 237–248.

Porras CA-M & Bai Y (2015). Respiratory supercomplexes: plasticity and implications. 295

Front Biosci 20, 621–634.

Prasad KS, Subramanian V & Paul J (2009). Purification and characterization of arsenite oxidase from Arthrobacter sp. Biometals 22, 711–721.

Pushie MJ & George GN (2011). Spectroscopic studies of molybdenum and tungsten enzymes. Coord Chem Rev 255, 1055–1084.

Reetz MT, Kahakeaw D & Lohmer R (2008). Addressing the numbers problem in directed evolution. ChemBioChem 9, 1797–1804.

Rhine ED, Ní Chadhain SM, Zylstra GJ & Young LY (2007). The arsenite oxidase genes (aroAB) in novel chemoautotrophic arsenite oxidizers. Biochem Biophys Res Commun 354, 662–667.

Richardson JS & Richardson DC (1989). The de novo design of protein structures. Trends Biochem Sci 14, 304–309.

Rieske JS, MacLennan DH & Coleman R (1964). Isolation and properties of an iron- protein from the (reduced coenzyme Q)-cytochrome c reductase complex of the respiratory chain. Biochem Biophys Res Commun 15, 338–344.

Ritchie VJ, Ilgen AG, Mueller SH, Trainor TP & Goldfarb RJ (2013). Mobility and chemical fate of antimony and arsenic in historic mining environments of the Kantishna Hills district, Denali National Park and Preserve, Alaska. Chem Geol 335, 172–188.

Roberts WL & Rainey PM (1993). Antimony quantification in Leishmania by electrothermal atomic absorption spectroscopy. Anal Biochem 211, 1–6.

Rochette EA, Bostick BC, Li G & Fendorf S (2000). Kinetics of Arsenate Reduction by Dissolved Sulfide. Environ Sci Technol 34, 4714–4720.

296

Rodriguez-Mozaz S, Lopez de Alda MJ & Barcelo D (2006). Biosensors as useful tools for environmental analysis and monitoring. Anal Bioanal Chem 386, 1025–1041.

Romão MJ (2009). Molybdenum and tungsten enzymes: a crystallographic and mechanistic overview. Dalton Trans4053–4068.

Romão MJ, Archer M, Moura I, Moura JJ, LeGall J, Engh R, Schneider M, Hof P & Huber R (1995). Crystal structure of the xanthine oxidase-related aldehyde oxidoreductase from D. gigas. Science 270, 1170–1176.

Roper A, A. Williams P & Filella M (2012). Secondary antimony minerals: Phases that control the dispersion of antimony in the supergene zone.

Rothery R a, Stein B, Solomonson M, Kirk ML & Weiner JH (2012). Pyranopterin conformation defines the function of molybdenum and tungsten enzymes. Proc Natl Acad Sci U S A 109, 14773–14778.

Rousset M, Montet Y, Guigliarelli B, Forget N, Asso M, Bertrand P, Fontecilla-Camps JC & Hatchikian EC (1998). [3Fe-4S] to [4Fe-4S] cluster conversion in Desulfovibrio fructosovorans [NiFe] hydrogenase by site-directed mutagenesis. Proc Natl Acad Sci U S A 95, 11625–11630.

Sanger CR (1908). The Quantitative Determination of Arsenic by the Gutzeit Method. J Am Chem Soc 30, 1041–1042.

Sanllorente-Méndez S, Domínguez-Renedo O & Julia Arcos-Martínez M (2010). Immobilization of acetylcholinesterase on screen-printed electrodes. Application to the determination of arsenic(III). Sensors 10, 2119–2128.

Santini J & vanden Hoven R (2004). Molybdenum-containing arsenite oxidase of the chemolithoautotrophic arsenite oxidizer NT-26. J Bacteriol 186, 1614–1619.

Santini J & Ward S (2012). The metabolism of arsenite.

297

Santini JM, Kappler U, Ward SA, Honeychurch MJ, Rachel N & Bernhardt P V (2007).

The NT-26 cytochrome c552 and its role in arsenite oxidation. 1767, 189–196.

Santini JM, Sly LI, Schnagl RD & Macy JM (2000). A New Chemolithoautotrophic Arsenite-Oxidizing Bacterium Isolated from a Gold Mine : Phylogenetic, Physiological, and Preliminary Biochemical Studies. Appl Environ Microbiol 66, 92–97.

Sarewicz M, Dutka M, Pietras R, Borek A & Osyczka A (2015). Effect of H bond removal and changes in the position of the iron–sulphur head domain on the spin–lattice

relaxation properties of the [2Fe–2S]2+ Rieske cluster in cytochrome bc1. Phys Chem Chem Phys 17, 25297–25308.

Sarkar P, Banerjee S, Bhattacharyay D & Turner APF (2010). Electrochemical sensing systems for arsenate estimation by oxidation of l-cysteine. Ecotoxicol Environ Saf 73, 1495–1501.

Schmidt CL, Hatzfeld OM, Petersen A & Link TA (1997). Expression of the Solfolobus acidocaldarius Rieske Iron Sulfur Protein II ( SOXF ) with the Correctly Inserted [2Fe–2S] Cluster in Escherichia coli. Biochem Biphysical Res Commun 287, 283– 287.

Schmidt CL & Shaw L (2001). A comprehensive phylogenetic analysis of rieske and rieske-type iron-sulfur proteins. J Bioenerg Biomembr 33, 9–26.

Schmitz RA, Albracht SP & Thauer RK (1992). A molybdenum and a tungsten isoenzyme of formylmethanofuran dehydrogenase in the thermophilic archaeon Methanobacterium wolfei. Eur J Biochem 209, 1013–1018.

Schneider D & Schmidt CL (2005). Multiple Rieske proteins in prokaryotes: Where and why? Biochim Biophys Acta - Bioenerg 1710, 1–12.

Schneider F, Lowe J, Huber R, Schindelin H, Kisker C & Knablein J (1996). Crystal 298

structure of dimethyl sulfoxide reductase from Rhodobacter capsulatus at 1.88 A resolution. J Mol Biol 263, 53–69.

Schrodinger (2015). The PyMOL Molecular Graphics System, Version 1.8.

Schröter T & Hatzfeld O (1998). Mutational analysis of residues forming hydrogen

bonds in the Rieske cluster of the cytochrome bc1 complex in Paracoccus denitrificans. Eur J … 255, 100–106.

Sereno D, Cavaleyra M, Zemzoumi K, Maquaire S, Ouaissi A & Lemesre JL (1998). Axenically grown amastigotes of Leishmania infantum used as an in vitro model to investigate the pentavalent antimony mode of action. Antimicrob Agents Chemother 42, 3097–3102.

Seyfferth A, Webb SM, Andrews JC, Fendorf S & Seyfferth AL (2010). Arsenic Localization , Speciation , and Co- Occurrence with Iron on Rice (Oryza sativa L.) Roots Having Variable Fe Coatings Arsenic Localization , Speciation , and Co- Occurrence with Iron on Rice ( Oryza sativa L .) Roots Having Variable Fe Coatings. Environ Sci Technol 44, 8108–8113.

Shaked-Mishan P, Ulrich N, Ephros M & Zilberstein D (2001). Novel Intracellular Sb(V) reducing activity correlates with antimony susceptibility in Leishmania donovani. J Biol Chem 276, 3971–3976.

Shankar S, Shanker U & Shikha (2014). Arsenic Contamination of Groundwater : A Review of Sources, Prevalence, Health Risks, and Strategies for Mitigation. Sci World J 2014, 1–18.

Shleev S, Wettero J, Magnusson K-E & Ruzgas T (2006). Electrochemical characterization and application of azurin-modified gold electrodes for detection of superoxide. Biosens Bioelectron 22, 213–219.

Sigma-Aldrich (2018). Azurin from Pseudomonas aeruginosa. Available at: 299

https://www.sigmaaldrich.com/catalog/product/sigma/a3672?lang=en®ion =GB [Accessed April 12, 2018].

Silver S & Phung LT (2005). Genes and enzymes involved in bacterial oxidation and reduction of inorganic arsenic. Appl Environ Microbiol 71, 599–608.

Simala-Grant JL & Weiner JH (1998). Modulation of the substrate specificity of Escherichia coli dimethylsulfoxide reductase. Eur J Biochem 251, 510–515.

SimalaGrant JL & Weiner JH (1996). Kinetic analysis and substrate specificity of Escherichia coli dimethyl sulfoxide reductase. Microbiol - UK 142, Part 11, 3231– 3239.

Simplex (2017a). Simplex Health Complete Water Quality Test Kit 13-in-One. Available at: https://www.simplexhealth.co.uk/simplex-health-complete- water-quality-test-kit-13inone-p-322.html [Accessed September 20, 2017].

Simplex (2017b). Quick Test Kit for Arsenic 0-1 ppm. Available at: https://www.simplexhealth.co.uk/quick-test-kit-for-arsenic-01ppm-as-p- 376.html [Accessed September 20, 2017].

Sinclair R (2015). Build Profits with Light Commercial Arsenic Removal. Water Technol. Available at: https://www.watertechonline.com/build-profits-with- light-commercial-arsenic-removal/ [Accessed September 20, 2017].

Smedley P. & Kinniburgh D. (2002). A review of the source, behaviour and distribution of arsenic in natural waters. Appl Geochemistry 17, 517–568.

Smith A, Lingas E & Rahman M (2000). Contamination of drinking-water by arsenic in Bangladesh: a public health emergency. Bull World Heal Organ 78, 1093–1103. software.informer (2016). Chromas Lite 2.1. Available at: http://chromaslite.software.informer.com/2.1/.

300

Soriano GM, Guo L-W, De Vitry C, Kallas T & Cramer W a (2002). Electron transfer from the Rieske iron-sulfur protein (ISP) to cytochrome f in vitro. Is a guided trajectory of the ISP necessary for competent docking? J Biol Chem 277, 41865– 41871.

Speck SH, Koppenol W, Dethmers J, Osheroff N & Margoliash E (1981). Definition of cytochrome c binding domains by chemical modification . J Biol Chem 256, 7394– 7400.

Stolz JF & Basu P (2018). Unraveling the inner workings of respiratory arsenate reductase. Proc Natl Acad Sci201812841.

Su L, Jia W, Hou C & Lei Y (2011). Microbial biosensors: A review. Biosens Bioelectron 26, 1788–1799.

Sun G (2004). Arsenic contamination and arsenicosis in China. Toxicol Appl Pharmacol 198, 268–271.

Sun H, Yan SC & Cheng WS (2000). Interaction of antimony tartrate with the tripeptide glutathione. Eur J Biochem 267, 5450–5457.

Sundar S & Chakravarty J (2010). Antimony toxicity. Int J Environ Res Public Health 7, 4267–4277.

Tamura K, Stecher G, Peterson D, Filipski A & Kumar S (2013). MEGA6: Molecular Evolutionary Genetics Analysis Version 6.0. Mol Biol Evol 30, 2725–2729.

Telford K, Maher W, Krikowa F, Foster S, Ellwood MJ, Ashley PM, Lockwood P V & Wilson SC (2009). Bioaccumulation of antimony and arsenic in a highly contaminated stream adjacent to the Hillgrove Mine, NSW, Australia. Environ Chem 6, 133–143.

Teoh WK, Salleh FM & Shahir S (2017). Characterization of Thiomonas delicata

301

arsenite oxidase expressed in Escherichia coli. 3 Biotech 7, 1–7.

Theoreall H, Akesson A. (1941) Studies on cytochrome c. III. Titration Curves. J. Am. Chem. Soc., 1941, 63 (7), pp 1818–1820.

Trieber C, Rothery R & Weiner J (1996). Engineering a novel iron-sulfur cluster into the catalytic subunit of Escherichia coli dimethyl-sulfoxide reductase. J Biol Chem 271, 4620–4626.

Turnbull WB & Daranas AH (2003). On the Value of c: Can low affinity systems be studied by isothermal titration calorimetry? JACS 125, 14859–14866.

United States Environmental Protection Agency (2007). Arsenic in your drinking water.

United States Environmental Protection Agency (2008). FACTOIDS: Drinking Water and Ground Water Statistics for 2007.

United States Environmental Protection Agency (2016). Discharge Monitoring Report (DMR) Pollutant Loading Tool. Available at: https://cfpub.epa.gov/dmr/index.cfm [Accessed September 21, 2017].

United States Environmental Protection Agency (2017). Potential Well Water Contaminants and Their Impacts. Available at: https://www.epa.gov/privatewells/potential-well-water-contaminants-and- their-impacts [Accessed May 11, 2018].

Veit S, Takeda K, Tsunoyama Y, Rexroth D, Rogner M & Miki K (2012). Structure of a

thermophilic cyanobacterial b6f-type Rieske protein. Acta Crystallogr D Biol Crystallogr 68, 1400–1408.

Walczak AB, Yee N & Young LY (2018). Draft genome sequence of Bosea sp . WAO an arsenite and sulfide oxidizer isolated from a pyrite rock outcrop in New Jersey.

302

1–12.

Wang J (2008). Electrochemical Glucose Biosensors. Chem Rev 108, 814–825.

Wang Q, Warelow TP, Kang Y-S, Romano C, Osborne TH, Lehr CR, Bothner B, McDermott TR, Santini JM & Wang G (2015). Arsenite Oxidase Also Functions as an Antimonite Oxidase. Appl Environ Microbiol 81, 1959–1965.

Warelow TP (2014). Arsenite Oxidase as a Novel Biosensor for Arsenite (thesis).

Warelow TP, Oke M, Schoepp-Cothenet B, Dahl JU, Bruselat N, Sivalingam GN, Leimkühler S, Thalassinos K, Kappler U, Naismith JH & Santini JM (2013). The respiratory arsenite oxidase: structure and the role of residues surrounding the Rieske cluster. PLoS One 8, e72535.

Warelow TP, Pushie MJ, Cotelesage JJH, Santini JM & George GN (2017). The active site structure and catalytic mechanism of arsenite oxidase. Sci Rep 7, 1757.

Westerhoff P, Prapaipong P, Shock E & Hillaireau A (2008). Antimony leaching from polyethylene terephthalate (PET) plastic used for bottled drinking water. Water Res 42, 551–556.

WHO (2003). Antimony in Drinking-water.

Whorton JC (2010). The Arsenic Century: How Victorian Britain was Poisoned at Home, Work, and Play .ed. Oxford: Oxford University Press 2010. Cambridge Journals Online.

303

Wijeratne AB, Gracia J, Yang SH, Kroll P, Armstrong DW & Schug K a. (2010). New structural insight for antimony(III)-tartrate. Inorg Chem Commun 13, 1504– 1508.

Wilson SC, Lockwood P V., Ashley PM & Tighe M (2010). The chemistry and behaviour of antimony in the soil environment with comparisons to arsenic: A critical review. Environ Pollut 158, 1169–1181.

Winter C (2002). Boos POE sales by leveraging the new arsenic standard. WQP. Available at: https://www.wqpmag.com/boost-poe-sales-leveraging-new- arsenic-standard [Accessed September 20, 2017].

World Health Organisation (2003). Guidelines for Drinking-Water Quality.

World Health Organisation (2011). Arsenic in drinking water. Available at: http://www.who.int/water_sanitation_health/dwq/chemicals/arsenic.pdf [Accessed February 17, 2015].

Wysocki R, Chery CC, Wawrzycka D, Van Hulle M, Cornelis R, Thevelein JM & Tamas MJ (2001). The glycerol channel Fps1p mediates the uptake of arsenite and antimonite in Saccharomyces cerevisiae. Mol Microbiol 40, 1391–1401.

Xu C, Yin JJ & Zhao BL (2010). Structural characteristics of the hydrophobic patch of azurin and its interaction with p53: A site-directed spin labeling study. Sci China Life Sci 53, 1181–1188.

Yamada T, Goto M, Punj V, Zaborina O, Kimbara K, Gupta TK Das & Chakrabarty AM (2002). The Bacterial Redox Protein Azurin Induces Apoptosis in J774 Macrophages through Complex Formation and Stabilization of the Tumor Suppressor Protein p53. Society 70, 7054–7062.

Yamauchi H & Flower B (1994). Toxicity and metabolism of inorganic and methylated arsenicals. In Arsenic in the environment, part II: human health and ecosystem 304

effects., ed. Nriagu J, pp. 44–45. JohnWiley and Sons, New York.

Yoshikawa S, Shinzawa-itoh K, Nakashima R, Yaono R, Yamashita E, Inoue N, Yao M, Fei MJ, Libeu CP, Mizushima T, Yamaguchi H, Tomizaki T & Tsukihara T (1998). Redox-Coupled Crystal Structural Changes in Bovine Heart Cytochrome c Oxidase. Science (80- ) 280, 1723–1729.

Yuejun Zhen, Curtis W. Hoganson GTB & Ferguson-Miller S (1999). Definition of the interaction domain for cytochrome c on cytochrome c oxidase. J Biol Chem 274, 38032–38041.

Zargar K, Conrad A, Bernick DL, Lowe TM, Stolc V, Hoeft S, Oremland RS, Stolz J & Saltikov CW (2012). ArxA, a new clade of arsenite oxidase within the DMSO reductase family of molybdenum oxidoreductases. Environ Microbiol 14, 1635– 1645.

Zavodszky M, Chen C-W, Huang J-K, Zolkiewski M, Wen L & Krishnamoorthi R (2001). Disulfide bond effects on protein stability: Designed variants of Cucurbita maxima trypsin inhibitor-V. Protein Sci 10, 149–160.

Zhang B, Hemann CF & Hille R (2010). Kinetic and Spectroscopic Studies of the Molybdenum-Copper CO Dehydrogenase from Oligotropha carboxidovorans *. 285, 12571–12578.

Zhao H & Arnold FH (1999). Directed evolution converts subtilisin E into a functional equivalent of thermitase. Protein Eng Des Sel 12, 47–53.

Zu Y, Couture M, Kolling D & Crofts A (2003). Reduction potentials of Rieske clusters: importance of the coupling between oxidation state and histidine protonation state. Biochemistry 42, 12400–12408.

Zu Y, Fee JA & Hirst J (2001). Complete Thermodynamic Characterization of Reduction

and Protonation of the bc1-type Rieske [2Fe-2S] Center of Thermus 305

thermophilus. J Am Chem Soc 123, 9906–9907.

Zu Y, Fee JA & Hirst J (2002). Breaking and re-forming the disulfide bond at the high- potential, respiratory-type rieske [2Fe-2S] center of Thermus thermophilus: Characterization of the sulfhydryl state by protein-film voltammetry. Biochemistry 41, 14054–14065.

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