Applied Science, Faculty of
Chemical and Biological Engineering, Department of
DSpace
UBCV
Nogueira Varela, Julia
Doctor of Philosophy - PhD
University of British Columbia
Malaria presents a severe economic and healthcare burden for the developing world. Recent efforts to reduce the incidents of malaria-associated deaths achieved some success, but drug resistance is increasing and the number of drugs available to treat the diseases is thinning. The current thesis seeks to advance two complementary strategies to develop platform solutions to treat and prevent malaria. First, we applied cheminformatics to assess the chemical space of anti-malarial drugs to identify promising scaffolds. Open-source tools were used to analyze the scaffolds of candidates and approved anti-malarial drugs. Our scaffold-centric analysis reveals that the anti-malarial chemical space is disjointed and segregated into few dominant structural groups with these structures being distributed according to Paretos’ principle. This structural convergence can potentially be exploited for future drug discovery by incorporating it into bioinformatics workflows. This could be used to predict new combination therapies and areas for the development of new molecules. Our second strategy seeks to develop a better tool for repellent discovery; repellent usage to prevent mosquito bites is a safe way to control these infections. The current methods for repellent screening are time consuming. The olfactory pathway involves odorant receptors that form a heterodimeric ion channel with an odorant receptor co-receptor (Orco) and a switching odorant receptor (OR). The heterologous expression of these proteins in Xenopus-oocytes and HEK293 cells, have suggested that the Orco-OR complex is functional. While these hosts have permitted significant discoveries, they have slow growth rates and extensive handling requirements, which make them unwieldy for high-throughput screens. We sought to develop high-throughput repellent screens by reconstructing this olfactory pathway into a simpler host (Pichia pastoris) with the Orco receptor. The Anopheles gambiae Orco protein was successfully expressed, being able to discriminate between compounds (VUAA1, citronella and oct-1-en-3-ol) and doses (0.125 mM to 2 mM for VUAA1) when coupled with a reporter signal. In the future this system could be used to screen chemical libraries. Moreover, the heterologous expression of Orco protein could lead to future structural and functional investigation of OR compounds as well as the development of newer repellents and behavior-modifying compounds.
NOVEL APPROACHES TO TREATING AND PREVENTING MALARIA by Julia Nogueira Varela B.Sc., State University of Campinas, 2011 M.Sc., State University of Campinas, 2013 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Chemical and Biological Engineering) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) June 2019 © Julia Nogueira Varela, 2019 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: NOVEL APPROACHES TO TREATING AND PREVENTING MALARIA submitted by Julia Nogueira Varela in partial fulfillment of the requirements the degree of Doctor of Philosophy In Chemical and Biological Engineering Examining Committee: Dr. Vikramaditya G. Yadav, Chemical and Biological Engineering Supervisor Dr. Charles A. Haynes, Chemical and Biological Engineering Supervisory Committee Member Dr. Filip VanPetegem, Biochemistry and Molecular Biology Supervisory Committee Member Dr. Karen C. Cheung, Electrical and Computer Engineering University Examiner Dr. Susan A. Baldwin, Chemical and Biological Engineering University Examiner Additional Supervisory Committee Members: Dr. John Smit, Microbiology and Immunology Supervisory Committee Member Dr. James Piret, Chemical and Biological Engineering Supervisory Committee Member iii Abstract Malaria presents a severe economic and healthcare burden for the developing world. Recent efforts to reduce the incidents of malaria-associated deaths achieved some success, but drug resistance is increasing and the number of drugs available to treat the diseases is thinning. The current thesis seeks to advance two complementary strategies to develop platform solutions to treat and prevent malaria. First, we applied cheminformatics to assess the chemical space of anti-malarial drugs to identify promising scaffolds. Open-source tools were used to analyze the scaffolds of candidates and approved anti-malarial drugs. Our scaffold-centric analysis reveals that the anti-malarial chemical space is disjointed and segregated into few dominant structural groups with these structures being distributed according to Paretos’ principle. This structural convergence can potentially be exploited for future drug discovery by incorporating it into bioinformatics workflows. This could be used to predict new combination therapies and areas for the development of new molecules. Our second strategy seeks to develop a better tool for repellent discovery; repellent usage to prevent mosquito bites is a safe way to control these infections. The current methods for repellent screening are time consuming. The olfactory pathway involves odorant receptors that form a heterodimeric ion channel with an odorant receptor co-receptor (Orco) and a switching odorant receptor (OR). The heterologous expression of these proteins in Xenopus-oocytes and HEK293 cells, have suggested that the Orco-OR complex is functional. While these hosts have permitted significant discoveries, they have slow growth rates and extensive handling requirements, which make them unwieldy for high-throughput screens. We sought to develop iv high-throughput repellent screens by reconstructing this olfactory pathway into a simpler host (Pichia pastoris) with the Orco receptor. The Anopheles gambiae Orco protein was successfully expressed, being able to discriminate between compounds (VUAA1, citronella and oct-1-en-3-ol) and doses (0.125 mM to 2 mM for VUAA1) when coupled with a reporter signal. In the future this system could be used to screen chemical libraries. Moreover, the heterologous expression of Orco protein could lead to future structural and functional investigation of OR compounds as well as the development of newer repellents and behavior-modifying compounds. v Lay Summary Malaria treatment and control have come a long way in the last 20 years. There have been breakthroughs with new drugs, private and public initiatives and the development of preventative technologies. However, there is significant room for improvement. Therefore, this thesis aims to expand the options available to fight malaria and other mosquito-borne diseases. We have comprehensively analyzed approved and in clinical development drugs to treat malaria, using a freely available software and a desktop computer to conduct this analysis. We used synthetic biology to reproduce the mosquito’s sense of smell into a microorganism and subsequently demonstrated a functioning sense of smell in the microbial host. This work is an important and timely contribution to the field and greatly expands the collection of resources to fight malaria. vi Preface A version of chapter 2 has been published. Varela, J.N., Lammoglia Cobo, MF, Pawar, SV, Yadav, VG (2017) “Cheminformatic Analysis of Anti-malarial Chemical Space Illuminates Therapeutic Mechanisms and Offers Strategies for Therapy Development”. J. Chem. Inf. Model., 2017, 57 (9), pp 2119–2131. I designed the research question and the experiments with the help of Dr. Yadav and Dr. Pawar. I selected and analyzed all the data for the analysis from the databases at UCSF. I wrote and reviewed the manuscript. Dr. Pawar assisted with the analysis of the Novartis and GSK dataset. Ms. Lammoglia Cobo assisted with the analysis and the statistical validation of the Tanimoto pharmacophore index. Dr. Yadav assisted me with the data visualization, writing and reviewing the manuscript. In chapter 4, I designed the research question and the experiments with the help of Dr. Yadav and input from committee members Dr. Filip VanPetegem, Dr. John Smit and Dr. Charles Haynes. I performed the experiments and did the data analysis. In chapter 5, I designed the research question and the experiments with the help of Dr. Yadav and input from committee members Dr. VanPetegem, Dr. Smit and Dr. Haynes. I performed the experiments and did the data analysis. A manuscript detailing the experiments and results chapters 4 and 5 is being finalized for submission. vii Table of Contents Abstract ......................................................................................................................................... iii Lay Summary .................................................................................................................................v Preface ........................................................................................................................................... vi Table of Contents ........................................................................................................................ vii List of Tables ............................................................................................................................... xii List of Figures ............................................................................................................................. xiv List of Symbols ........................................................................................................................... xix List of Abbreviations ...................................................................................................................xx Acknowledgements ................................................................................................................... xxii Dedication ................................................................................................................................. xxiii Chapter 1: Introduction - The current state of Malaria therapy and prevention ...................1 1.1 Vector-borne diseases and Malaria ................................................................................. 1 1.1.1 Malaria transmission and parasite life cycle ............................................................... 2 1.1.2 Malaria prevention ...................................................................................................... 5 1.1.2.1 Available and prospective vaccines ................................................................... 5 1.1.2.2 Vector control – IRS (Indoor residual spraying) and ITNs (insecticide treated bed nets) ............................................................................................................................. 5 1.1.2.3 Transgenic mosquitoes ........................................................................................ 6 1.1.2.4 Repellent efficienty and testing .......................................................................... 7 1.1.3 Malaria treatment ........................................................................................................ 9 viii 1.2 Rationale ....................................................................................................................... 14 Chapter 2: Cheminformatic analysis of antimalarial chemical space for identification of pharmacoactive scaffolds ............................................................................................................17 2.1 Introduction to cheminformatics ................................................................................... 17 2.2 Materials and Method ................................................................................................... 19 2.2.1 Compounds compilation and database search .......................................................... 19 2.2.2 Bermis-Murcko scaffolds, JChem analysis and Cytoscape connectivity diagram ... 20 2.3 Results ........................................................................................................................... 22 2.3.1 Anti-malarial chemical space .................................................................................... 22 2.3.2 Cancer drugs comparison to dataset ......................................................................... 31 2.4 Discussion ..................................................................................................................... 34 Chapter 3: Mosquito anatomy and olfaction systems ..............................................................37 3.1 Mosquito olfaction: appendages and molecular basis .................................................. 37 3.2 A. gambiae olfactory system at a molecular level ........................................................ 41 3.3 Heterologous systems available for mosquito odorant receptor protein expression ..... 42 3.4 Orco function as a single channel and VUAA1 changes in the olfaction research ...... 43 3.5 Orco structure ................................................................................................................ 44 Chapter 4: Development and testing of a yeast whole-cell odorant biosensor .......................47 4.1 Introduction ................................................................................................................... 47 4.2 Materials and Methods .................................................................................................. 50 4.2.1 E. coli plasmid construction and cloning for transformation in P. pastoris ............. 50 4.2.2 Preparation of electrocompetent P. pastoris cells and cloning ................................. 52 4.2.3 Colony PCR and extraction of P. pastoris plasmid DNA ........................................ 53 ix 4.2.4 Expression of Orco by P. pastoris ............................................................................ 54 4.2.5 Lysis of the harvested P. pastoris cells ..................................................................... 55 4.2.6 Western Blotting ....................................................................................................... 55 4.2.7 RT-qPCR................................................................................................................... 56 4.2.8 Confocal Microscopy ................................................................................................ 58 4.2.9 Activity assay ............................................................................................................ 58 4.3 Results ........................................................................................................................... 61 4.3.1 P. pastoris cloning and expression .......................................................................... 61 4.3.2 P. pastoris confocal microscopy & confirmation of dye permeation into cells ....... 62 4.3.3 Confirmation of Orco RNA expression using RT-qPCR ......................................... 64 4.3.4 Assessment of the activity of Orco in a 1 mM Ca2+ solution ................................... 65 4.3.5 Assessment of the activity of Orco in a 5 mM Ca2+ solution ................................... 72 4.3.6 Estimation of the EC50 of VUAA1 in media comprising 1 mM and 5 mM Ca2+ .... 83 4.3.7 Effect of clonal variation on selection of the biosensor ............................................ 84 4.3.8 The impact of DMSO and extracellular calcium on the activity of Orco ................. 85 4.3.9 Response of Orco 21.1 cultures to citronellal in 5mM Ca2+ ..................................... 87 4.3.10 Simultaneous exposure of Orco to citronellal and VUAA1 in 5 mM Ca2+ Hank’s buffer of Orco 21.1 cultures to citronellal in 5mM Ca2+ ...................................................... 91 4.3.11 Exposure of Orco to oct-1-en-3-ol in the presence of 5 mM Ca2+ ............................ 95 4.4 Discussion ..................................................................................................................... 96 Chapter 5: Development of a bacterial olfactory biosensor ...................................................100 5.1 Matherials and methods .............................................................................................. 101 5.1.1 Plasmid (pACYC_Duet1, pTRC, pBAD33) construction ...................................... 101 x 5.1.2 E. coli transformation and cloning confirmation .................................................... 103 5.1.3 Construction of pTRC_Orco + pBAD33_FtsH cell line ........................................ 104 5.1.4 Protein expression with pACYC_Duet1_Orco, pTRC_Orco and pTRC_Orco + pBAD33_FtsH constructs ................................................................................................... 105 5.1.5 E. coli cell lysis and sample preparation ................................................................. 106 5.1.6 SDS page with Comassie Blue staining and Western Blotting............................... 107 5.1.7 RT-qPCR................................................................................................................. 107 5.2 Results ......................................................................................................................... 109 5.2.1 pACYC_Orco protein expression ........................................................................... 109 5.2.2 RT-qPCR pACYC_Orco ........................................................................................ 112 5.2.3 pTRC_Orco + pBAD33_FTSH and pTRC_Orco protein expression .................... 114 5.2.4 RT-qPCR pTRC_Orco + pBAD33_FtsH and pTRC_Orco .................................... 117 5.2.5 RT-qPCR pTRC_Orco + pBAD33_FtsH vs pTRC_Orco vs pACYC_Duet1_Orco ………................................................................................................................................. 121 5.3 Discussion ................................................................................................................... 122 Chapter 6: Concluding words and perspectives on future work ...........................................125 6.1 Anti-malarial chemical space analyzed in unprecedented detail ................................ 125 6.2 Aiding the quest of better repellants and mosquito behavior modifying compounds 127 6.3 Recomentaions for future work .................................................................................. 128 6.4 Doctoral output from Pharmaceutical Sciences PhD program UBC .......................... 129 Bibliography ...............................................................................................................................130 Appendices ..................................................................................................................................148 Appendix A ............................................................................................................................. 148 xi A.1 Tables with BM loose framework for datasets analyzed ........................................ 148 Appendix B ............................................................................................................................. 158 B.1 P. pastoris biosensor raw data for fluorescence values .......................................... 158 Appendix C ............................................................................................................................ 161 C.1 Maps of plasmid constructs .................................................................................... 161 Appendix D ............................................................................................................................. 163 D.1 Average Ct values for RT-qPCR ............................................................................. 163 xii List of Tables Table1.1 Chemical categories and mechanisms of action of approved anti-malarial compounds and drug candidates presently undergoing clinical testing ........................................................... 11 Table 2.1 Selected endoperoxides, 4- and 8- aminoquinoline compounds ................................... 27 Table 2.2 Compounds with known antifolate activity .................................................................. 28 Table 2.3 Selected Amino Alcohols, 8-aminoquinolines, Aminopyridines, Pyrazoles ................ 29 Table 2.4 Selected Anticancer Molecules .................................................................................... 30 Table 4.1 Primers used for cloning and colony PCR in E. coli ................................................... 53 Table 4.2 Primers used for RT-qPCR in E. coli ........................................................................... 58 Table 5.1 Primers used for molecular cloning of Orco and colony PCR confirmation .............. 105 Table 5.2 Primers used for RT-qPCR ......................................................................................... 108 Table A.1 Endoperoxides, with structure and their BM loose framework ................................ 148 Table A.2 Anticancer compounds with their structure and their BM loose framework ............. 149 Table A.3 Anticancer compounds with their structure and their BM loose framework (contd.) 150 Table A.4 Harvard lab set compounds with their structure and their BM loose framework comparison – 1 ............................................................................................................................ 151 Table A.5 Harvard lab set compounds with their structure and their BM loose framework comparison –2 ............................................................................................................................. 151 Table A.6 DeRisi lab set compounds with their structure and BM loose framework comparison ..................................................................................................................................................... 152 Table A.7 GSK set compounds with their structure and BMI loose framework comparison .... 154 xiii Table A.8 Novartis set compounds with their structure and BMI loose framework comparison..................................................................................................................................................... 156 Table B.1 Values for ΔF/Fo for different [VUAA1] in 1mM of [Ca2+] for Orco 21.1 ............... 158 Table B.2 Values for ΔF/Fo for different [VUAA1] in 1mM of [Ca2+] for GS115 .................... 158 Table B.3 Values for ΔF/Fo for different [VUAA1] in 5mM of [Ca2+] for Orco 21.1 ............... 159 Table B.4 Values for ΔF/Fo for different [VUAA1] in 5mM of [Ca2+] for GS115 ................... 159 Table B.5 Values for ΔF/Fo for different [VUAA1] in in 1mM and 5mM of [Ca2+] ................. 159 Table B.6 Values for ΔF/Fo for different [Citronella] in 5mM of [Ca2+] ................................... 160 Table B.7 Values for ΔF/Fo for different [VUAA1] measurements in different [Citronella] in 15mM of [Ca2+] ........................................................................................................................... 160 Table B.8 Values for ΔF/Fo for different [1-octen-3-ol] in 5mM of [Ca2+] ................................ 160 Table D.1 Average Ct values for Orco 21.1 and GS115 with different methanol concentrations..................................................................................................................................................... 163 xiv List of Figures Figure 1.1 – The malaria parasite (Plasmodium ssp.) life cycle ..................................................... 4 Figure 2.1 – Anti-malarial chemical space ................................................................................... 23 Figure 2.2 – Nodal analysis of anti-malarial chemical space ....................................................... 32 Figure 2.3 – Anti-malarial chemical space is distributed according to Pareto’s principle ........... 33 Figure 3.1 – Mosquito olfactory appendages ................................................................................ 39 Figure 3.2 – Model of odorant receptor signalling ....................................................................... 40 Figure 3.3 – VUAA1 structure...................................................................................................... 44 Figure 3.4 – Orco topology and architecture ................................................................................ 45 Figure 4.1 – Proposed scheme for the biosensor’s operation in P. pastoris ................................. 49 Figure 4.2 – Western blot of P. pastoris membrane and whole cell fractions using an anti-c-Myc antibody......................................................................................................................................... 62 Figure 4.3 – Confocal micrographs P. pastoris stained with Fluo 4AM ...................................... 63 Figure 4.4 – Relative overexpression of Orco transcripts compared to TAF-1 RNA .................. 64 Figure 4.5 – Fluorescence emitted over time by cultures of Orco 21.1 in 1 mM Ca2+ Hank’s buffer following activation of the channel by 0.125 mM of VUAA1 .......................................... 66 Figure 4.6 – Fluorescence emitted over time by cultures of Orco 21.1 in 1 mM Ca2+ Hank’s buffer following activation of the channel by 0.25 mM of VUAA1 ............................................ 67 Figure 4.7 – Fluorescence emitted over time by cultures of Orco 21.1 in 1 mM Ca2+ Hank’s buffer following activation of the channel by 0.50 mM of VUAA1 ............................................ 67 Figure 4.8 – Fluorescence emitted over time by cultures of Orco 21.1 in 1 mM Ca2+ Hank’s buffer following activation of the channel by 1 mM of VUAA1 ................................................. 68 xv Figure 4.9 – Fluorescence emitted over time by cultures of Orco 21.1 in 1 mM Ca2+ Hank’s buffer following activation of the channel by 1.5 mM of VUAA1 .............................................. 68 Figure 4.10 – Fluorescence emitted over time by cultures of Orco 21.1 in 1 mM Ca2+ Hank’s buffer following activation of the channel by 2 mM of VUAA1 ................................................. 69 Figure 4.11 – Relationship between the first response of fluorescence emitted by cultures Orco 21.1 and GS115 and the concentration of VUAA1 in the extracellular media ............................. 70 Figure 4.12 – Detailed comparison between the sensitivities of cultures of Orco 21.1 and GS115 to VUAA1 ..................................................................................................................................... 71 Figure 4.13 – Fluorescence emitted over time by cultures of Orco 21.1 in 5 mM Ca2+ Hank’s buffer following activation of the channel by 0.125 mM of VUAA1 .......................................... 73 Figure 4.14 – Fluorescence emitted over time by cultures of Orco 21.1 in 5 mM Ca2+ Hank’s buffer following activation of the channel by 0.25 mM of VUAA1 ............................................ 74 Figure 4.15 – Fluorescence emitted over time by cultures of Orco 21.1 in 5 mM Ca2+ Hank’s buffer following activation of the channel by 0.50 mM of VUAA1 ............................................ 74 Figure 4.16 – Fluorescence emitted over time by cultures of Orco 21.1 in 5 mM Ca2+ Hank’s buffer following activation of the channel by 1 mM of VUAA1 ................................................. 75 Figure 4.17 – Fluorescence emitted over time by cultures of Orco 21.1 in 5 mM Ca2+ Hank’s buffer following activation of the channel by 1.5 mM of VUAA1 .............................................. 75 Figure 4.18 – Fluorescence emitted over time by cultures of Orco 21.1 in 5 mM Ca2+ Hank’s buffer following activation of the channel by 2 mM of VUAA1 ................................................. 76 Figure 4.19 – Relationship between the first response of fluorescence emitted by cultures Orco 21.1 and GS115 and the concentration of VUAA1 in the extracellular media for 5 mM Ca2+ concentration ................................................................................................................................. 77 xvi Figure 4.20 – Detailed comparison between the sensitivities of cultures of Orco 21.1 and GS115 to VUAA1 in the extracellular media for 5 mM Ca2+ concentration ............................................ 78 Figure 4.21 – Dependence of temporal responses of Orco 21.1 cultures to excitation by VUAA1 on extracellular calcium ................................................................................................................ 80 Figure 4.22 – Dependence of temporal responses of Orco 21.1 cultures to excitation by VUAA1 on extracellular calcium ................................................................................................................ 82 Figure 4.23 – EC50 for the interaction of VUAA1 and Orco in 1 mM of extracellular calcium . 83 Figure 4.24 – EC50 for the interaction of VUAA1 and Orco in 5 mM of extracellular calcium . 84 Figure 4.25 – Response of two Orco expressing P. pastoris clones to VUAA1, a known agonist of the protein ................................................................................................................................. 85 Figure 4.26 – Effect of DMSO in P. pastoris Orco system with 1mM of extracellular calcium . 86 Figure 4.27 – Effect of DMSO in P. pastoris Orco system with 5mM of extracellular calcium . 87 Figure 4.28 – Addition of citronella oil at a dilution of 1:15 in 5 mM Ca2+ Hank’s buffer and its effect on Orco expressed by the Pichia clones ............................................................................. 88 Figure 4.29 – Addition of citronella oil at a dilution of 1:7 in 5 mM Ca2+ Hank’s buffer and its effect on Orco expressed by the Pichia clones ............................................................................. 89 Figure 4.30 – Addition of citronella oil at a dilution of 1:3 in 5 mM Ca2+ Hank’s buffer and its effect on Orco expressed by the Pichia clones ............................................................................. 89 Figure 4.31 – Addition of citronella oil at a dilution of 1:1 in 5 mM Ca2+ Hank’s buffer and its effect on Orco expressed by the Pichia clones ............................................................................. 90 Figure 4.32 – Activity of Orco as a function of citronella dilutions ............................................. 91 Figure 4.33 – Increase of Orco activity following addition of 1 mM VUAA1 to cells previously exposed to citronellal in a 1:15 solution with 5 mM Ca2+ Hank’s buffer ..................................... 92 xvii Figure 4.34 – Increase of Orco activity following addition of 1 mM VUAA1 to cells previously exposed to citronellal in a 1:7 solution with 5 mM Ca2+ Hank’s buffer ....................................... 93 Figure 4.35 – Increase of Orco activity following addition of 1 mM VUAA1 to cells previously exposed to citronellal in a 1:3 solution with 5 mM Ca2+ Hank’s buffer ....................................... 94 Figure 4.36 – Increase of Orco activity following addition of 1 mM VUAA1 to cells previously exposed to citronellal in a 1:1 solution with 5 mM Ca2+ Hank’s buffer ....................................... 95 Figure 4.37 – Exposure of Orco to varying dilutions of oct-1-en-3-ol in a 5 mM Ca2+ Hank’s buffer ............................................................................................................................................. 96 Figure 5.1 – SDS page stained with comassie blue of pACYC_Duet constructs ....................... 110 Figure 5.2 – Western Blot with anti 6X His Tag of pACYC_Duet constructs ........................... 111 Figure 5.3 – Relative quantification of Orco transcripts relative to 16S RNA with pACYC_Duet1_Orco construct .................................................................................................. 113 Figure 5.4 – SDS page stained with coomassie blue of pTRC_Orco and pTRC_Orco + pBAD33_FtsH constructs ........................................................................................................... 115 Figure 5.5 – Western Blot with anti 6X His Tag of pTRC_Orco and pTRC_Orco + pBAD33_FtsH constructs ........................................................................................................... 116 Figure 5.6 – Relative quantification of Orco transcripts relative to 16S RNA with pTRC_Orco construct ...................................................................................................................................... 118 Figure 5.7 – Relative quantification of Orco transcripts relative to 16S RNA with pTRC_Orco + pBAD33_FtsH construct ............................................................................................................. 119 Figure 5.8 – Relative quantification of FtsH transcripts relative to 16S RNA with pBAD33_FtsH construct ...................................................................................................................................... 120 xviii Figure 5.9 – Relative quantification of Orco transcripts relative to 16S RNA with pTRC_Orco + pBAD33_FtsH, pTRC_Orco and pACYC_Duet1_Orco ............................................................ 121 Figure C.1 – Map of the pPICZA_Orco plasmid used in this study ........................................... 161 Figure C.2 – Plasmid maps for the E. coli constructs ................................................................. 162 xix List of Symbols oC – degrees Celsius µL – Microliter mL – Milliliter mM – mili Molar M – Molar mg – miligrams rpm – revolutions per minute rcf – relative centrifugal force kD – kilo Daltons xx List of Abbreviations ARA – Arabinose BM – Bermis-Murcko BMGY – Buffered Glycerol-complex Medium BMMY – Buffered Methanol-complex Medium BSA – Bovine Serum Albumin DDT – dichloro diphenyl trichloro-ethane DEET – Diethyltoluamide or N,N-Diethyl-meta-toluamide DHFR – dihydrofolate reductase DHPS – dihydropteroate synthetase DTT – Diotherol EC50 – Effective Concentration EDTA – Ethylenediamine tetraacetic acid IPTG – Isopropyl β-D-1-thiogalactopyranoside IR – Ionotropic receptors IRS – Indoor residual spraying ITN – Insecticide treated bed nets G6PD – glucose-6-phosphate dehydrogenase GPCR – G-protein coupled receptors GR – Gustatory receptors LB – Lysogeny broth MOA – Mechanism of action OBP – Odorant binding protein xxi OR – Odorant receptor Orco – Odorant receptor co-receptor ORN – Odorant receptor neurons PCR – Polymerase Chain Reaction PMSF – Phenylmethylsulfonyl Fluoride PYR – Pyrimethamine RT-qPCR – Reverse Transcriptase Quantitative Polymerase Chain Reaction TBS – Tris-buffered Saline TBST – Tris-buffered saline and Tween20 TRP – Transient receptor potential channel Tween 20 – Polysorbate 20 YPD – Yeast Peptone Dextrose YPDS – Yeast Peptone Dextrose Sorbitol xxii Acknowledgements I would like to acknowledge all the people that aided this work. My advisor, Dr. Vikramaditya G. Yadav for accepting me in his lab, for the support and mentorship during my PhD. My lab mates for sharing input and feedback in my work through lab presentations and conversations: Sonal, Carmen, Protiva, Sandip, Joe, Adhithi, Nina, Cinthia, Gaurav, Amir. I apologize if I forgot someone. The faculty and staff at UBC for helping with this process. I also would like to thank my Supervisory Committee members: Dr. Charles Haynes, Dr. John Smit, Dr. Filip Van Petegem and Dr. James Piret, for the scientific input and feedback that allowed this work to proceed. The Ecoscope program (NSERC-CREATE), and Dr. Steve Hallam, for the funding, training and mentorship. To my friends from Brazil and Vancouveer who kept me going despite the hurdles, Carolina, Valeria, Danilo, Marcelo, Marysol, Jose, Vongai, Rafaella, Catarina, Felipe, Juliana, Rene, Kyoko, Dahai, Jennifer. To my partner, Devinder, for keeping things seemingly in place during this tough period, I love you. To my aunt Dora and my grandmother Bira, who inspire me to be the best possible version of myself every day and for being the strongest people I’ve ever known. To my brother Heitor and Juliana, thanks for the laughs and for getting Hulk. To my parents, Marly and Paulino for the financial and emotional support that carried me through this process, this work and dream would not have been achieved without your support and love from day one. Muito obrigada por tudo, eu amo muito todos voces. xxiii Dedication To my parents, my grandmother and all my family here and there. 1 Chapter 1: Introduction – The current state of Malaria therapy and prevention 1.1 Vector-borne diseases and Malaria Vector-borne diseases represent a big economic burden for the developing world1. Diseases such as malaria, dengue fever, yellow fever and filariasis present the heaviest burden2. According to the World Health Organization, vector-borne diseases account for more than 17% of all infectious diseases in the world and lead to over 1 million deaths annually3. Over 2.5 million people are at risk of contracting dengue fever alone3. Moreover, the vector-borne disease profile is changing in Africa due to the increase in temperature and changes in the mosquito breeding areas, adding complexity to an already heavy burden4. Climate change and population migration has increased the spread of these diseases and widened the natural habitat of mosquitoes5,6. As a consequence vector-borne diseases have now reached parts of the developed world5,6. Malaria is a deadly parasite vector-borne disease7. It has been responsible for over 430 thousand deaths worldwide in 2015 and 445 thousand in 20168. It still represents a major health concern and infects over 210 million people around the world annually9. It is a big economic burden for the developing world, causing great losses in productivity10. Malaria affects populations in the tropical areas, such as Sub Saharan Africa, parts of Asia and South America1. Over the past decade there has been a steep decline in the cases of malaria all over the globe8. International organizations, foundations and agencies have worked together to significantly decrease the number of deaths3. Despite all the efforts, malaria is still the main cause of death of children under the age of 5 in Africa, claiming over 445 thousand lives every year in the 2 continent10–12. Therefore, there is an acute and urgent need to increase our drug repertoire and prevention tools against malaria as well as constant need of research and awareness to eradicate the disease. 1.1.1 Malaria transmission and parasite life cycle Malaria is transmitted by the female species of the mosquito Anopheles gambiae13. Global warming has changed the mosquito geographical location, making it more prone to regions where it had been previously eradicated14. The changes in the patterns of infections have made malaria more difficult to contain and eradicate4. Malaria is caused by the Plasmodium genus of parasites10. There are 6 recognized species of plasmodium that can infect humans; namely Plasmodium falciparum, Plasmodium vivax, Plasmodium malariae and sympatric species Plasmodium ovale curtisi, Plasmodium ovali walliker and Plasmodium cynomolgi8. The most prevailing and lethal amongst these is P. falciparum, which is predominantly found in Africa10. Its prevalent Asian counterpart is P. vivax15. While P. falciparum is responsible for the highest mortality rates, it does not cause re-infection; as opposed to P. vivax and P. ovale that can stay dormant in the liver as hypnozoites8,15. The diversity of parasite species and their complex life cycle makes diagnosis and treatment of malaria a complex and time consuming effort16. The Plasmodium ssp. has a complex life mainly because it has two main hosts – the female mosquito insect Anopheles gambie and humans17. In humans, the parasite has two major distinct life stages: the liver and the blood stages17. 3 The parasite enters the mosquito as gametocytes when the mosquito bites a contaminated human host13. In the mosquito’s gut the gametocytes reproduce sexually, forming an ookinete followed by an oocyst13. The oocyst bursts and liberates sporozoites, that migrate to the mosquito salivary glands and are injected into the human host when the mosquito needs to feed on blood13. Once a human host is infected by the sporozoites, they enter the blood stream and migrate to the liver13. The sporozoites invade the hepatocytes and multiply inside to become merozoites13. The hepatocytes burst with excessive parasites, liberating merozoites that invade red blood cells and start the asexual cycle13. Inside erythrocyte they evolve into trophozoites followed by schizont13. The multiplication of the parasite eventually ruptures red blood cells and releases schizonts in the blood stream13. This cell lysis causes the symptoms of malaria, and is part of the asexual cycle, that takes 48 h to conclude and, unless treatment is undertaken, can continue to happen13. The haploid parasite in this phase can also differentiate into gametocytes in the red blood cells13. The gametocytes keep the malaria transmission cycle ongoing, since the human host is susceptible to mosquito bites and the gametocytes can be ingested by the mosquitoes when they are feeding10,13. Figure 1.1 illustrates the parasite complex life cycle. 4 Figure 1.1 – The malaria parasite (Plasmodium ssp.) life cycle. (Reprinted with permission from (Phillips, 2017))18. This figure describes the life cycle of the parasite, as mentioned in the text. The parasite enters the human host as oocysts that evolve into sporozoites, which migrate into the liver and evolve to merozoites. Once they leave the liver they start to infect red blood cells, forming trophozoites that evolve into schizonts. Schizonts rupture the cells and start the re-infection cycle, causing the symptoms. In this phase the parasite can also be evolved into a gametocyte. Gametocytes are circulating in the human host blood stream and once a mosquito bites the host, the gametocites infect the mosquito and start the vector cycle; going from zygote to ookinete in the mosquito gut and being release as oocysts in the mosquito salivary glands. 5 1.1.2 Malaria prevention 1.1.2.1 Available and prospective vaccines The only available vaccine for malaria is RTS, S, (AS01 or AS02) developed by PATH Malaria Vaccine Initiative and GlaxoSmithKline Biologicals19. The vaccine targets the circumsporozoite protein of P. falciparum and has AS01 as an adjuvant19. It demonstrated an efficacy of approximately 50% against all clinical and severe malaria episodes in children from 5 to 17 months in a phase 3 clinical trial19. The vaccine was more efficient in the first 12 months of immunization19. Therefore the need for new vaccines that can be more efficient still exists. New vaccine targets such as the merozoite surface antigens are being researched to inhibit the invasion of red blood cells by the parasite20. Other novel vaccines are also being developed and are currently under phase II clinical investigation12. 1.1.2.2 Vector control – IRS (Indoor residual spraying) and ITNs (insecticide treated bed nets) Vector control is another widely used way to prevent the spread of malaria21. The first great attempt to control and eliminate A. gambiae was with the use of synthetic insecticide DDT (dichlorodiphenyltrichloroethane) in mid-20th century21,22. However, due to the high levels of toxicity for humans and environmental hazards, DDT use has decreased12,21,23. Today, the most used strategies to prevent the transmission of malaria through mosquito bites is the use of insecticide-embedded nets, insecticide spraying in house and repellent lotions24,25. Since A. 6 gambiae has a pattern of biting during the evening, the use of these strategies before sleeping reduces the number of bites and consequentially the chances of contracting malaria21. Studies done with pyrethroid-insecticide-treated mosquitos nets in Africa demonstrated reduction in mortality in children under the age of 5; with a 15% reduction in Burkina Faso and a 27% reduction in Kenya26. Moreover, indoor residual spraying has been shown to reduce the malaria incidence for the household27. It acts by repelling and/or eliminating mosquitoes from entering the houses27. Nevertheless mosquitoes are also acquiring an increasing resistance to the available insecticides, putting in jeopardy the work that has been done to prevent malaria spread across Africa and other continents13,28. 1.1.2.3 Transgenic mosquitoes Synthetic biology has provided new tools to engineer mosquitoes to prevent the spread of infectious diseases29. However, considering the low GDP of developing countries, the cost of this preventative technology is high29. Two strategies that have been proposed are: the construction of mosquitoes that produce infertile offspring and the use of Wolbachia bacteria that infects mosquitoes, preventing parasites to survive inside of them30. Studies with the A. gambiae Y chromosome have shown that the creation of infertile males to fecundate females might lead to a decrease in the mosquito population31. Field studies for the potential dispersion of engineered mosquitoes were conducted in Brazil, with results demonstrating that variables such as location, continuous release, and favorable habitat interferes with the viability of the mosquitoes32. The data showed that the predicted re-capture of the mosquitoes was lower than expected, suggesting their death in the open environment32. 7 Both strategies, the Sterile Insect Technique and Wolbachia-based Incompatible Insect Technique, were compared by Atyame et al. 2015. They found that both technologies had similar results in a controlled field study and that early release of the mosquitoes (after breeding) was equally more effective33. Although an effective short term solution, engineered mosquitoes are still expensive, time consuming and pose questions to the environment29. Researchers will still need to clear public health concerns and logistics before introducing engineered mosquitoes into the wild29. 1.1.2.4 Repellents efficiency and testing An insect repellent can be defined as a substance applied to exposed skin, fabric or objects to protect against mosquito bites2. This odorant substance (repellent) can act by masking human odors and “pushing” mosquitoes away34. Studies have also suggested that insect repellent molecules reduce the contact between mosquito and host by interacting with odorant receptors (ORs) and odorants, leading to changes in the olfactory-driven behavior35. On the other hand, attractants would be substances that “pull” mosquitos into traps, usually keeping them away from humans34. Repellents can be a viable preventative tool when their cost-effectiveness and protective efficiency is proved in the field36,37. Repellents work by confusing the mosquitoes’ sense of smell36,37. A study by Tsitoura, P., et al, demonstrated that some repellents, such as ethyl cinnamate, isopropyl cinnamate, cuminic alcohol and carvacol, act by inhibiting mosquitos’ olfactory receptors, acting as antagonists of these receptors38. However, DEET, the benchmark standard for repellent testing, did not present the same levels of inhibition in the tested receptors38,39. 8 In field studies natural repellents demonstrate up to 96% protection whereas DEET protection was 84%39. The results presented by Moore et al. (2002) demonstrated that natural products can have similar efficiency to established repellents39. Furthermore, the population in this research showed preference towards natural products instead of DEET39. Another field study by Moore et al. demonstrated that the use of essential oils increased the protection to 95% in 2.5 hours of application against Mansonia spp. when supplementing ITNs40. The way repellent screening is currently done is time consuming41,42. It relies on a volunteer inserting their forearm or leg into a box with mosquitoes and waiting to be bitten41,42. After a certain period of time, the number of bites on the volunteer’s skin is counted41,42. Based on the number of bites per amount of time, a repellent efficacy is estimated41,42. One problem with this method is that it demands pre-testing with toxicity, sensitization and dermatological profile of the substance before testing on humans, carrying uncertainty about the outcome42. Because of the inconveniences associated with this approach, new mosquito behavioral tests were developed42,43. One mosquito behavioral test consisted of a cylindrical device that traps the mosquitos on one side and the chemical compound is added on the other44. The agitation or lack of activity of mosquitoes towards or against the compound is measured to evaluate the activity of the repellent44. Another similarly developed device is a Y-tube olfactometer43. The Y-tube has different sections that can measure the mosquitoes’ preferences for certain odorants43. The use of artificial membranes and warm bodies have also been coupled to some devices to mimic human contact while the mosquitoes were isolated in a box42. With the sequencing of the A. gambiae genome, the understanding of the mosquito olfactory proteins started to evolve45–47. The molecular understanding of the olfactory receptors’ role in mosquito olfaction led to the development of a novel high-throughput screening platform 9 with the use of HEK293 cell lines48. The A. gambiae main olfaction cascade was expressed in HEK293 and a calcium imaging dye was used to measure the activity against selected chemical compounds48. The system enabled the screening of a molecular library and the start of a transformation in the way repellents were screened and designed48. 1.1.3 Malaria treatment Malaria has been treated with the same arsenal of drugs, until the discovery of artemisinin12. Artemisinin is the gold standard therapy for malaria treatment and is administered as a combination therapy, it has a broad spectrum of action, being active against the plasmodium in both the asexual and sexual blood stages12,49. Aminodiaquine is part of a combination therapy with artesunate, piperaquine is given in combination with dihydroartemisinin while ferroquine is also being given in combination with artesunate50,51. The most accepted hypothesis for its' mechanism of action is the reduction of the endoperoxide bond by the ferrous iron when the parasite is digesting hemoglobin, and the release of the reactive radicals lead to the parasites' death12. A problem with the artemisinin derivates is that their half life is short, which makes it difficult to maintain the minimum drug concentration in the blood to kill the parasite50,51. Most of the available drugs for malaria treatment focus on the blood stages, where the symptoms of the disease start to show, combination therapies also target the blood stage7. These combination therapies have more than one target inside the parasite and the inclusion of drugs that target the liver stage could improve the parasite clearance12. Other drugs used, such as KAE609 (Spiroindole), act on the blood stage of the parasite and inhibit the parasite outer membrane transporter – P-type ATPase4 (Pf Atp4) that is 10 responsible for sodium homeostasis, leading to an increase of intracellular calcium and the parasites’ death12. Pyronaridine and atovaquane both have activity in the liver and sexual stages of the parasite as well as the blood one52. Atovaquane targets the cytochrome b (Cytb), while pyrimethamine targets dihydrofolate reductase (DHFR) and sulfadoxine targets dihydropteroate synthetase (DHPS)53. Pyrimethamine (PYR) is a dihydrofolate reductase (DHFR) inhibitor preventing the folate synthesis for the parasite DNA synthesis54. Primaquine is a synthetic molecule designed based on chloroquine and used for P. vivax infections as well, its mechanism of action is still unknown55. This drug is effective in killing the parasite in the liver and blood stages, and cures the patient from the infection; however, it causes hemolytic anemia in patients that have a deficiency in glucose-6-phosphatase, which includes a great part of the African population17. Chloroquine acts by preventing the parasite from detoxifying the heme group from the digestive vacuole leading to its death by toxicity12. The mechanism of action for quinine, 4-aminoquinolines, and 4-methilaminoquinolines is also unknown, however, since they act in the blood phase it is believe their mechanism of action (MOA) is connected to the same parasite detoxification MOA as endoperoxides53. Quinidine, the most used drug in the past, has showed strong signs of resistance across the whole spectrum of parasites56. Like most infectious diseases the problem with fighting malaria is the increasing threat of drug resistance57. Drug resistance emerges with the low compliance of patients, who once seeing improvement in their condition, stop the treatment or forget to take the medication at the right time58. This leads to incomplete treatment and risk of re-infection, as we previously mentioned in the mosquito life-cycle18. Amongst the other difficulties is the emerging resistance to the current drug therapy that has been linked to several causes: Plasmodium ssp. genes’ polymorphism, poor drug 11 pharmacology58. Resistance to several drugs and classes, such as chloroquine, antifolates and sulfadoxine-pyrimethamine, has been attributed the mutation of drug transporter genes - pfcrt and pfmdr1, pfdhfr, pfdhps respectively- that leads to an increase in the efflux of the drug from the parasite50,52,58,59. The recently approved drug for the treatment of P. vivax, tafenoquine, can eliminate the recurring infection60. However, if a patient has a G6PD deficiency, this drug can cause severe anemia60. The ways to prevent the increase in drug resistance is to keep new drugs in the pipeline and to treat patients as soon as resistance is identified61. Currently anti-malarial molecules are screened using high-throughput whole-cell assays with the parasite itself, protein kinase activity assays or through functional genomics, the mechanisms of these compounds are also poorly understood51,62,63. For the reasons mentioned before, the treatment choice is a vital part of malaria eradication strategies. Other drugs for the treatment of malaria and their mechanism of action are explained in Table 1.1. Table 1.1 – Chemical categories and mechanisms of action of approved anti-malarial compounds and drug candidates presently undergoing clinical testing Compound category (letter code for Figure 2.1) Drug[Clinical phase, if not approved] (compound # in Table S1) Mechanism of Action (MoA) Endoperoxides (A) Artemisinin (1) Dihydroartemisinin (2) Artemether (3) Artesunate (4) Artemisone (5) 3-Artesanilide (6) Broad-spectrum anti-malarial drugs that target the asexual and sexual blood stages through heme-mediated decomposition of their endoperoxide bridges, which produces toxic radicals. 4-Aminoquinolines (B) Piperaquine (7) Selectively target the blood stage of 12 Amodiaquine (8) Chloroquine (9) Naphthoquine (10) Hydroxychloroquine (11) Pyronaridine (12) Tert-butyl isoquine (13) AQ-13 (14) malarial parasite. They inhibit the conversion of hematin to hemozoin, which subsequently disrupts the membrane function. 8-Aminoquinolines (C) Primaquine (15) Tafenoquine (16) Bulaquine (17) NPC-1161B[Pre-clinical] (47) Effective during the liver and blood stages. The mechanisms of action are poorly characterized for most molecules in this class. Amino alcohols (D) Quinine (18) Lumefantrine (19) Halofantrine (20) Mefloquine (21) Mefloquine interacts with phospholipids in the cell membrane. The MoAs for the other amino alcohols are unknown. Anti-folates (E) Dapsone (22) Pyrimethamine (23) Proguanil (24) Chlorproguanil (25) Cycloguanil (26) Ferroquine[Phase 2A] (58) Interfere with folate biosynthesis in the parasite. Ferroquine, on the other hand, inhibits hemozoin formation. Sulfonamides (F) Sulfadoxine (27) Sultamethoxazole (28) Sulfadiazine (29) Inhibit the dihydropteroate pathway that is essential for tetrahydrofolate biosynthesis during the asexual phase of the blood stage. Antibiotics (G) There are several distinct MoAs within this category. The letter codes for these MoAs have been listed after the compound number. Azithromycin (30, G1) Semi-synthetic azalide derivative of erythromycin that inhibits the 70S sub-unit of the Plasmodium spp. ribosome. Trimethoprim (31, G2) Closely related to pyrimethamine. It is a pyrimidine inhibitor of dihydrofolate reductase (DHFR). Tetracycline (32, G3) A slow-acting drug targets the apicoplast of the malarial parasite. Fosmidomycin (33, G4) Inhibits the reductoisomerase that acts on 1-deoxy-D-xylulose-5-phosphate, a key intermediate in the non-mevalonate pathway for isoprenoid synthesis. 13 Thiostrepton (34, G5) Targets the ribosome in the apicoplast. Doxycyclin (35, G6) Synthetic tetracycline that targets the apicoplast. Also used as a prophylactic agent with chloroquine. Mirincamycin (36, G7) Lincosamide antibiotic that acts on the apicoplast. Others (H) There are several distinct MoAs within this category. The letter codes for these MoAs have been listed after the compound number. Atovaquone (37, H1) A hydroxyl napthoquinone that acts on cytochrome BC1. Riboflavinn (38, H2) Inhibits hemozoin synthesis in the asexual phase of the blood stage. Pentamidine (39, H3) MoA is unknown but it is speculated that the drug inhibits hemozoin formation in the parasite’s food vacuole. Dehydroepiandrosterone or DHEA (40, H4) Prohormone synthesized from cholesterol. It has inhibits glucose-6-phosphate dehydrogenase (G6PDH) in the parasite. Cycloheximide (41, H5) Inhibits protein synthesis. Deferoxamine (43, H6) Iron-chelating agent Methylene Blue (44, H7) Interferes with hemoglobin and heme metabolism in the digestive organelles. Also selectively inhibits glutathione reductase and acts as a chloroquine sensitizer. Pyrazoles (I1) 21A02[Pre-clinical] (45) MoA is unknown. Aminopyridine (I2) MMV390048[Pre-clinical] (46) MoA is unknown. Quinolone-3-diarylether (I3) ELQ-300[Pre-clinical] (48) Inhibits mitochondrially expressed cytochrome BC1 Immucillin G (I4) BCX4945[Pre-clinical] (49) Inhibitor of purine nucleoside phosphorylase (PNP) 1,2,4,5-Tetraoxane (I5) RKA182[Pre-clinical] (50) Interferes with hemoglobin catabolism in the parasite. Diaminopyridine (I5) P218[Pre-clinical] (51) Inhibits DHFR and folate biosynthesis. Triazolopyrimidine (J) DSM265[Phase 1] (54) Inhibits dihydroorotate 14 dehydrogenase (DHOD). Imidazolopiperazine (L1) KAF156[Phase 2A] (55) Believed to target a transmembrane domain of the cyclic amine resistance locus protein (CarI), though the precise location is unknown. Spiroindolone (L2) NITD609[Phase 2A] (56) Targets P-type ATPase 4 (Atp4), which was initially identified as a calcium ion pump but is now reported as being important for maintaining sodium homeostasis in the parasite. Inhibition of this enzyme increases the intracellular sodium concentration, which causes the parasitic cell to swell and die. 1,2,4-Trioxolane (L3) OZ439[Phase 2A] (57) OZ277[Phase 3] (63) These compounds interfere with hemoglobin catabolism in the parasite. Natural products from Nauclea pobeguinil (M1) Strictosamide[Phase 2B/3] (59) MoA is unknown. Natural products from Argemone mexicana (M2) Berberine[Phase 2B/3] (60) Protopine[Phase 2B/3] (61) Allocryptopine[Phase 2B/3] (62) MoA is unknown. 1.2 Rationale for study design The constant evolution of the parasite and the global challenges have added layers of complexity to the treatment and prevention of malaria. However, with the constant addition of new platforms and information, we can continue to reduce human mortality as well as the number of people who are infected by malaria. 15 We designed this study based on two approaches: one to develop new platforms for new anti-malarial treatment options, and the second to screen for new chemicals that could act as A. gambiae repellents. In the treatment space, an in-silico approach was used to investigate the anti-malarial chemical space. We sought to determine how the anti-malarial chemical space was organized. Our approach was to observe if there were similarities between the approved drugs and drug candidate scaffolds, to gain insights into unknown mechanisms of actions. Scaffold similarities would also aid us in the design of newer compounds and combination therapies as potential new treatments for malaria. In the preventative approach we have worked on the development of a biosensor to screen for new repellents. A heterologous host would express the mosquito odorant receptors and be coupled to a signal. The expression of odorant receptors has been previously accomplished in Xenopus oocytes, HEK293 and empty Drosophila neurons. However, it has never been accomplished in bacteria or yeast. Simpler hosts would present a more economical alternative to screen for chemical libraries when compared to the ones currently used. In this work we first started with Escherichia coli as heterologous host and we were not able to express the main odorant receptor (Orco). Hence, we turned to Pichia pastoris in which we successfully expressed Orco. This enabled the construction of a biosensor that can be used as a starting point to develop a new screening platform, similar to how drug discovery assays are currently done. Our overall research question targeted two important aspects of the fight against malaria. For treatment 16 • What can we learn to propose new treatments for malaria, considering the anti-malarial drug space is saturated? • Where cheminformatics tools could be used to find new scaffolds for the malaria treatment? • How could this analysis open new perspectives in the mechanism of action of drugs in the pipeline that we currently don’t have? For prevention • How could we improve the way new repellents are currently being screened with the use of synthetic biology? • What type of biosensor would need to be constructed to provide us information on which chemicals would work well as repellents? • Could we construct this biosensor in a simple cell system and still obtain reliable data? 17 Chapter 2: Cheminformatic analysis of antimalarial chemical space for identification of pharmacoactive scaffolds 2.1 Introduction to cheminformatics Antimalarial drugs have been hitherto designed using traditional approaches64. While these methods can guide the development of new drugs, the availability of newer, improved computational tools and artificial intelligence (AI) could greatly accelerate drug discovery65. To substantiate these claims, we implemented open-source cheminformatics tools to identify promising scaffolds that could form the basis of new synthetic and semi-synthetic drugs. The results and conclusions of our analysis were recently corroborated by the approval by the Food and Drug Administration of the use of tafenoquine60. Our work establishes a strong foundation for the use of even more sophisticated computational tools to improve the translation rates of drug discovery programs. Cheminformatics refers to study of small molecules with the use of computational tools that includes the vast databases of chemicals with physical-chemical properties, structural information based on 2D and 3D representations and developed algorithms for the study of these molecules66,67. With the increasing amount of data being produced by high throughput assays and databases the use of cheminformatics tools has become more relevant to harvest information from the sources in a faster way68. The use of cheminformatics and computational tools to find new treatment options for diseases is extremely helpful, especially in the case where pathogens leading to infectious diseases evolve and acquire resistance to available therapies69. 18 The re-examination of the vast volumes of data generated by previous high-throughput screenings using cheminformatics tools can be used to improve R&D outcomes66,67,70. Such analyses require minimal computing infrastructure and are incredibly fast and easy to undertake68,69. Moreover, they often succeed in focusing drug discovery programs68,69. Chemical computational analysis can also be a useful tool to help predict the MOA of unknown substances by analyzing the structure activity functions in the molecules53. Similar compounds usually have a common target and similar structure activity53. Amongst the similarity indexes available in cheminformatics, the Tanimoto coefficient provides a robust and reliable measurement of differences between molecules71. When the Tanimoto coefficient is compared to other indexes for molecular fingerprinting calculations it has shown close values to the average for different sets of data, demonstrating consistency72. New scaffolds for chemokine receptors were also identified using the Tanimoto coefficient73. The Tanimoto equation can be written as71: 𝑓(𝐴,𝐵) = 𝐴.𝐵|𝐴2| + |𝐵2| − 𝐴.𝐵 A and B are expressed in terms of vector scalar product and magnitude for each structure, which in the bit vector map would have values of either 0 or 1. We assessed these claims in the present study by employing open-source cheminformatics tools installed on a personal computer to analyze the chemical space traversed by all approved anti-malarial drugs and promising compounds from chemical libraries maintained by pharmaceutical companies and academic research groups. We extracted the Bemis-Murcko scaffolds of every molecule and performed a similarity analysis on the simplified structures in order to generate a connectivity diagram. The compounds 19 that are linked together in this diagram can be inferred to have a common target and a similar MOA53. Our analysis illuminates MOAs that are unclear or unknown, provides recipes for new anti-malarial combination therapies and identifies promising structures for lead identification and optimization towards development of new, standalone anti-malarial drugs74. 2.2 Materials and Methods 2.2.1 Compounds compilation and database search For this work Excel 2017 was used with the addition of JChem base 17.7.0 for office plugin (ChemAxon- https://chemaxon.com/products/jchem-for-office). Six hundred and fourteen molecules were analyzed in this part of the study, in order to generate a connectivity diagram. The list of all the molecules was based on several different sources. 63 compounds were approved for clinical use or in phase 3 of clinical trials; they were sourced using: Pubmed, SciFinder, Reaxys and Google Scholar. These molecules act on either the liver and/or blood stage of the life cycle of the parasite with different mechanisms of action (Table 1.1). For the other compounds, 313 (out of 400 compounds in the library) were selected from a chemical library synthesized by research groups at the University of California, San Francisco (UCSF) and 58 (out of 5370 compounds in the library) were selected from Harvard University. The selected compounds comprise only a small fraction of the entire library. Only those molecules that inhibit growth of the parasite by 50% or greater at a dose of 5 μM were selected from the UCSF library, whereas only active molecules were picked from the Harvard library. The ChEMBL database is comprised of over 370 thousand compounds and for that reason only some structures were used 20 for the analysis, compounds from the Novartis and GSK chemical libraries that are available in this database were also selected. However, since the Novartis and GSK libraries together comprised of over 6000 compounds that weren’t all unique, only 51 unique compounds from each library were selected. The lack of computing power to analyze all the samples and the uncertainty about their activity outcomes in in-vitro assays also played a role in choosing the final number for the set. Our investigation set also comprised 78 approved anti-cancer drugs as negative controls for our analysis. 2.2.2 Bermis-Murcko scaffolds, JChem analysis and Cytoscape connectivity diagram All the 614 compound structures were transformed into a SMILES formula and were extracted from the SMILES specifications and imported into MarvinSketch version 17.7.(www.chemaxon.com/products/marvin/marvinsketch/) for identification of their loose Bermis-Murcko (BM) scaffolds. The resulting scaffold data was once again expressed in SMILES specification and exported to an Excel spreadsheet. Loose BM scaffolds for two molecules, N-acetyl-D-penicillamine and deferoxamine (compounds 42 and 43), could not be identified by the software. All analyses from there onwards were performed using the JChem plugin version 17.7.0 (www.chemaxon.com/products/jchem-for-office/) in excel 2017. The BM loose framework was chosen instead of the BM framework because it takes into account different atoms, instead of only carbon and hydrogen. The SMILES specifications for loose BM scaffolds of the 614 structures were used to generate a Tanimoto similarity matrix using an built-in similarity estimation function in the plugin. Our choice of the Tanimoto index was informed by previous studies that reported a high degree of reproducibility of the calculation 21 in analysis of numerous chemical databases71,72. JChem offers a choice between several Tanimoto similarity indices that differ in the manner in which the fingerprints of the molecule are screened. Of these, contrasting a molecule’s chemical-hashed or pharmacophore fingerprints are two of the most common approaches to evaluate Tanimoto similarity. We evaluated the mean similarity between any two molecules in a population comprising of 3960 bioactive molecules from the ChEMBL database not known to have anti-malarial activity using both fingerprinting approaches, and determined that the difference between the means is statistically significant (α = 0.05). The choice of a pharmacophore fingerprinting was based on its ability to better capture the properties of the biological binding events75. Since two molecules with similar pharmacophore fingerprints are more likely to bind to a common target in the parasite. The method’s conclusions are also likely to be more impactful on medicinal chemistry than conclusions drawn using chemical-hashed fingerprints. Consequently, we opted to use Tanimoto similarities evaluated through pharmacophore fingerprinting to generate the similarity matrix. A previous report established that similarities of 0.7 or higher translate to functional equivalence76. Accordingly, we defined molecules having a similarity coefficient of 0.7 or higher as being similar. Conversely, molecules with similarity coefficients below this threshold were considered to be dissimilar. As a consequence, we further processed the matrix using a customized script that ignored similarities below 0.7. This operation generated a sparse matrix, wherein each populated cell confirmed structural connectivity for the pair evaluated in that cell. The sparse matrix was then inputted to Cytoscape (www.cytoscape.org) to generate the connectivity diagram for the population (Figure 2.1). Each node in the diagram corresponds to a unique compound among the 614 molecules analyzed and a connection between any two nodes indicates 22 a minimum similarity of 0.7 between the two molecules. Since antimalarial compounds such as the amino alcohols, 4- and 8-aminoquinolines, and atovaquone, as well as combination therapies such as artemether-lumefantrine are administered orally. We also assessed each molecule’s compliance with Lipinskis’ rule of 5 using an in-built JChem function and rendered it in Cytoscape. Since the chemical structures of the compounds were being compared for use in a biological system and taken orally as previously mentioned. The Lipinskis’ rule of 5 was utilized to assure that all compounds had drug-like characteristics, making the pharmacophore similarity assessment valid. This rule generally states that an orally active drug must have the following characteristics: no more than 5 hydrogen bond donors, no more than 10 hydrogen bond acceptors, a molecular mass less than 500 daltons and log P up to 577. The drugs should meet at least 3 of the aforementioned criteria77. 2.3 Results 2.3.1 Anti-malarial chemical space The chemical space, or chemical network, composed by approved anti-malarial drugs and drug candidates is comprised of 391 connected nodes and 302 isolated nodes (Figure 2.1). The nodes had on average 1.629 neighbors that translate into a clustering coefficient of 0.219 and centralization factor of 0.033 for the entire network (Figure 2.2). The heterogeneity of the network, which is identified as the ratio of the standard deviation to the mean of the number of neighbor that a node has is estimated to be 1.90278. 23 24 Figure 2.1 – Anti-malarial chemical space. (Reprinted with permission from (Varela et al, 2017))79. The network diagram for the 614 molecules that were investigated in this part of the study reveals that the anti-malarial chemical space is disjoined and comprises several hub nodes. 25 Taking a closer look into the anti-malarial drug space, it can be seen that within the endoperoxide class, two drugs – artemisone and 3-artesanilide – do not bear any similarities to any of the other drugs in the class or to each other. Intra-class similarity is observed within the 4-aminoquinolines, amino alcohols, and sulfonamides. On the other hand, inter-class similarity is observed between 4- and 8-aminoquinoline compounds. Amodiaquine and tert-butyl isoquine, which belong to the former group (Table 2.1), are similar to tafenoquine from the latter group; and primaquine (Table 2.1), another 8-aminoquinoline, is similar to the 4-aminoquinolines, chloroquine hydroxychloroquine and AQ-13 (Table 2.1). Scaffold similarity between a well established antibiotic that inhibits DHFR – trimethoprim – and the UCSF drug candidates MMV665928, MMV665929 and MMV665949 (Table 2.2), whose MOAs are poorly understood, suggests that their MOAs could be similar to established drugs. Trimethoprim is also similar to dapsone (Table 2.2), an anti-folate and approved anti-malarial drug, and the anti-cancer drug anastrazole (Table 2.2), that inhibits aromatase or estrogen synthase. Structural similarity is also observed between amino alcohols with unknown MOAs such as halofantrine and methylene blue (Table 2.3), which targets the malarial parasite through as many as three distinct routes. Of the molecules that are currently in clinical testing, berberine and protopine (Table 2.3) are similar to one another, and these molecules are natural products in plant extracts of Argemone mexicana, an endemic Mexican type of poppy. When looking into pre-clinical candidates we can also see similarities between compounds from different libraries such as NPC1161B and MMV390048 (Table 2.3). The former is an 8-aminoquinoline and the latter is an aminopyridine with an unknown MOA. NPC1161B is also similar to the 4-aminoquinolines, amodiaquine, and tert-butyl isoquine (Table 26 2.1). These structural relationships can provide a window into the MOAs of 8-aminoquinolines, as well as aminopyridine drugs. MMV390048 is also similar to the quinolone-3-diarylether, ELQ-300 (Table 2.3), which is a pre-clinical drug candidate. Additionally, the triazolopyrimidine DSM265 (Table 2.3), a phase 1 candidate, is also similar to several 4- and 8-aminoquinolines. Triazolopyrimidines are strong inhibitors of dihydroorotate dehydrogenase (DHOD), a key enzyme in pyrimidine biosynthesis. The pyrazole-based preclinical candidate 21A02 (Table 2.3) forms a distinct sub-set with compounds from the Novartis and UCSF libraries. This result reveals a potentially underexploited role for pyrazole compounds in the treatment of malaria. Lastly, over half of the molecules in anti-malarial space bear no resemblance to other molecules in the investigated set. Further information on the molecules and their structure compared to their BM loose scaffold can be seen in the appendix from Table A.1 to Table A.8. 27 Table 2.1 – Selected endoperoxides, 4- and 8- aminoquinoline compounds 28 Table 2.2 – Compounds with Known Antifolate Activity 29 Table 2.3 – Selected Amino Alcohols, 8-aminoquinolines, Aminopyridines, Pyrazoles 30 Table 2.4 – Selected Anticancer Molecules 31 2.3.2 Cancer drugs comparison to dataset When it comes to the cancer drugs, their inclusion yield interesting observations, such as the prohormone dehydroepiandrosterone (DHEA), which is a potent anti-malarial agent, being similar to the anti-cancer drugs prednisone, exemestane, and megestrol (Table 2.4). DHEA inhibits glucose-6-phosphate dehydrogenase, which is chiefly responsible for controlling the pentose phosphate pathway that maintains the redox balance in cells, it has also been implicated in controlling apoptosis and angiogenesis, thus explaining its role as a target for anti-cancer therapies80. The target of biosynthesis intermediates that the parasites share with diseases such as cancer provides a platform for drug discovery. This observation is corroborated by other connections between the anti-malarial and anti-cancer space, such as anti-malarial drugs guanil and chlorproguanil that target folate biosynthesis that are similar to the anti-cancer drugs procarbazine, chlorambucil, and vorinostat (Table 2.4). Inhibition of folate biosynthesis is a proven strategy for treating cancer. Proguanil is also similar to several molecules in the Harvard library – HMS2093O22, HMS2093C12, and HMS1920P10 (Table 2.4). 32 Figure 2.2 – Nodal analysis of anti-malarial chemical space. (Reprinted with permission from (Varela et al, 2017)). The chemical network comprises 391 connected nodes and 302 isolated nodes. The nodes have, on average, 1.629 neighbors. The network has a clustering coefficient of 0.219. The clustering coefficient for a node is the ratio of the number of connections between that node and its neighbors and the maximum number of connections that could possibly exist between that node and its neighbors. The clustering coefficient for the network is the average of the clustering coefficient of all nodes that it comprises. The centralization factor of the network is estimated to be 0.033, which indicates that the network comprises a larger number of smaller, segregated groups. 33 Figure 2.3 – Anti-malarial chemical space is distributed according to Pareto’s principle. (Reprinted with permission from (Varela et al, 2017)). The emergence of a power law phenomenon suggests that the chemical network could be the product of thermodynamic convergence. This observation raises prospects of the use of principles from network thermodynamics to evolve a chemical library to eventually comprise molecules that are highly drug-like. 34 2.4 Discussion From the results obtained from the JChem analysis both clustering coefficients (0.219) and centralization factors (0.033) range between 0 and 1 (Figure 2.2), with the lower numbers indicating that the network comprises of a larger number of small, segregated groups. These factors and a high heterogeneity suggest that the anti-malarial chemical network contains several hub nodes (Figure 2.1), implying that the chemical space is disjoined and segregated into a few dominant structural groups such as the endoperoxides that exhibit high level of intra-class similarity but do not bear any resemblance with other classes of anti-malarial drugs. Even within the endoperoxide class we can see that there are two drugs that are not similar to any other or each other, this appears to be the product of medicinal chemistry activities aimed at improving the residence time of the drugs in the bloodstream. Scaffold similarity is a way to understand and infer the unknown MOAs of molecules. For instance, while the mechanism by which the 8-aminoquinolines inhibit P. falciparum is poorly understood, their similarity to the 4-aminoquinolines suggests that they too might act either by inhibiting the conversion of hematin to hemozoin, which subsequently disrupts the parasite’s plasma membrane during the blood stage of its life cycle, or by preventing the parasite from detoxifying the heme group50. Other structural similarity between trimethoprim (table 2.2) and anastrazole could be exploited to co-formulate the drugs into a combination therapy that minimizes the likelihood of emergence of drug resistance. In fact, this strategy has already been effectively employed to develop combination therapies comprising anti-folates and 4-aminoquinolines. Another example can be pyrimethamine (table 2.2), an anti-folate drug, similar to MMV020505 (table 2.2), 35 another molecule from the UCSF library. The latter is a 4-aminoquinoline derivative and is similar to a number of compounds in that family. Several 4-aminoquinolines have been investigated as combination therapies with pyrimethamine, and some have had a widespread adoption in regions where the malarial parasite has developed resistance to chloroquine (table 2.1)81. Within the natural plant extracts, the structural similarity between berberine and protopine has been shown, suggesting that they may be products of the same metabolic pathway in the A. mexicana plants. Diversity-oriented synthesis, wherein a common scaffold is diversely functionalized, is very common in plant secondary metabolic pathways82,83. ELQ-300 interferes with the activity of cytochrome b1 within the parasite, which suggests that MMV390048 might be acting on P. falciparum through multiple mechanisms. Similar insights can be drawn from the connections between the UCSF, Harvard, Novartis and GSK libraries to approved anti-malarial drugs. This observation clearly suggests that the pharmacobiology of anti-malarial drugs has not been entirely surveyed and paves the way for employing chemical genomics to investigate compound-target relationships in the parasite and develop a systems-level understanding of anti-malarial pharmacobiology84. The high number of isolated nodes suggests that the network is sparse. In fact, the network is distributed according to Pareto’s principle, the 80/20 rule (Figure 2.3). Pareto’s principle states that roughly 80% of effects originate from 20% of causes. In the case of anti-malarial chemical space, we observe that the scaffolds of approximately 80% of approved drugs and drug candidates comprise just 20% of all scaffolds in the space. Given the diverse origins of the molecules considered in this study, it is highly improbable that they are distributed in this 36 manner owing to deliberate design. It is likely that anti-malarial chemical space is the product of thermodynamic convergence85. 37 Chapter 3: Mosquito anatomy and olfaction systems 3.1 Mosquito olfaction: appendages and molecular basis of olfaction Mosquitoes and insects generally rely on olfactory sense and chemo sensitization for sugar feeding, reproduction and oviposition86,87. While olfaction plays a key role, however, other cues such as heat and light can provide spacial guidance87. Blood feeding mosquitoes have a highly specialized sense of smell that can differentiate between mammal species88,89. They have a preference for humans based on the type of odors that are emitted from sweat and skin micro flora88,89. For instance, A. gambiae is attracted by a mixture of lactic acid, acetone, oct-1-en-3-ol and CO2 that humans exhale, while Aedes aegypti is attracted by ammonia, L-lactic acid, fatty acids and CO290. There are 460 different types of mosquitoes in the Anopheles genus, but the most anthropophilic and capable of transmitting malaria is A. gambiae91. The A. gambiae female is the one responsible for the spread of malaria parasite species (e.g. P. falciparum and P. vivax)12. Insects rely on different families of receptor proteins: odorant receptors (ORs), ionotropic receptors (IRs) and carbon dioxide-sensing gustatory receptors (GRs) to navigate through surroundings92. The most well studied system is the ORs93. Mosquitoes find their targets by navigating their environment with the use of the following olfactory appendages: the antenna, the maxillary palp and the proboscis (Figure 3.1-a)86. Inside each one these appendages lies sensory hairs (olfactory sensillas)86. The sensillas are small perforated cuticular structures containing odorant receptor neurons (ORNs) (Figure3.1-b), that are the basis of the odorant chemical response86. The neurons have insect-specific odorant 38 receptors that are responsible for sensing odor, directly or through the aid of odorant binding proteins (OBPs) (Figure 3.2)94. OBPs are used to aid in the trafficking of hydrophobic odorants to the ORs95. Some OBPs have been considered to assist in the mediation of specific chemicals in the mosquito olfaction96. One examples is OBP1 that has been shown to bind to indole in A. gambiae96. In the mapping of the A. gambiae genome, 79 odorant receptors were identified and in further studies 75 were proved to be functional45,88. The transcriptome of the odorant receptors were studied in A. gambiae and confirmed the differences between males and females regarding OR expression97. Male and female mosquitoes express different ORs in the antennas88,97. This distinction confirms that a major part of the olfaction is due to chemoreceptors expressed in this organ, since male mosquitoes do not feed on human blood97. More studies of the A. gambiae female mosquito antenna transcriptome, taken right after blood meals, demonstrated different trends in odorant receptors shifting from feeding to oviposition98. The results enabled the identification of ORs and chemicals that are most relevant at specific feeding times98. 39 Figure 3.1 – Mosquito olfactory appendages. (Reprinted with permission from (Kaupp, 2011))99. On (a) the mosquito head showing the antenna, maxillary palp and proboscis location can be observed. On (b) we can see how the organs are located inside each one of the three appendages, visualizing how the organ interacts with the environment. The dendrites are located inside an aqueous environment with pores, for the entrance of chemicals. 40 Figure 3.2 – Model of odorant receptor signalling. Figure (A) shows how the odorant system works at a molecular level inside the sensilla of the insects. An odorant, that is normally a volatile hydrophobic chemical, binds to an odorant binding protein (OBP). When it reaches close to the odorant receptor system OBP releases the chemical and once in contact with the heteromeric OR/Orco ionotropic channel it binds to the channel. The channel opens up allowing cations in the cell which leads to a fast response from the mosquito. If the odorant is not 41 hydrophobic, the chemical enters the sensilla and contacts the OR/Orco channel directly. Figure (B) demonstrates how Orco can act alone as an ionotropic channel in models. The odorant is diluted in the media and once in contact with Orco, the chemical binds to the receptor. Similar to (A), cations also enter the cell and elicit a response in the system. 3.2 A. gambiae olfactory system at a molecular level When the first account of the A. gambiae genome results were published, studies suggested that the odorant receptors were similar to the mammalian ones, acting like GPCR proteins45. After more studies, results demonstrated that the A. gambiae odorant receptors were membrane proteins with 7 transmembrane domains like GPCRs38. However, the insect odorant receptors had a reverse topology, with a N-terminal in the cytoplasm and the C –terminal outside the cell38. Eventually it was reported that insect odorant receptors were indeed heteromeric ligand-gated non-selective cationic channels100. Further insect odorant receptor models reported that an odorant receptor co-receptor (Orco) was needed to be expressed with a secondary odorant receptor for the channel to be active100,101. Previously, insect odorant receptors when expressed alone had no effective activity100. Nevertheless when expressed together with an odorant receptor co-receptor (Orco) they have demonstrated to function as a non-selective cationic channel (Figure 3.2 – A)100,102. Orco is highly conserved amongst the insect OR families due to its ubiquitous role in the mosquito olfactory senses99. In addition Orco can form a receptor (channel) on its own (Figure 3.2 – B) allowing for new agonists and antagonists to be found95,103,104. 42 There has been some debate on the role or existence of a second messenger system in insect olfaction101,105. However, the most accepted theory is that insects rely heavily on ionotropic response, due to the fast response of the system to ionic concentration changes in the cell that is needed from insects to be able to act fast and navigate their environment101,105,106. The structure for Orco in A. gambiae is still undetermined. However, a cryo-EM study was able to provide the Orco structure for Aprocrypta bakeri species107. The protein structure showed a new channel-like architecture with 4 subunits symmetrically arranged around a central pore107. The finding was different from the previously proposed models but still had the proposed transmembrane domains107. 3.3 Heterologous systems available for mosquito odorant receptor protein expression The A. gambiae odorant proteins have been cloned most often in three different types of cells: HEK29348, Drosophila empty neurons108 and Xenopus oocytes94. In those systems, an odorant receptor was always co-expressed with Orco106. Orco alone can also function as a homomeric cationic channel103. It has been proposed that Orco would be an ideal target for repellent design since the presence of receptors is essential for the mosquito sense of smell to function106. Orco is highly conserved across insect phyla demonstrating its key role in invertebrate olfaction106. Studies done by Carey et al. (2010) with Drosophila empty neurons expressing odorant receptors from A. gambiae allowed for the de-orphanization of the receptor family108. These were followed by studies done by Wang et al. (2010) in Xenopus oocytes, that confirmed the presence of broadly and narrowly tuned receptors in the A. gambiae OR family94. The work with 43 Droshopila demonstrated that some receptors, such as AgOR8 and AgOR2 are narrowly tuned for oct-1-en-3-ol and indole, respectively108. While other receptors, such as AgOR50 respond to a broader number of chemicals108. Despite successful expression in these systems, the number of odorants that these heterologous hosts were able to screen was limited due to constraints of cell manipulation94,108. For that reason, a new system for high throughput screening was set up with the use of HEK293 cells48. This system was used to screen the small molecule chemical library of Vanderbilt University48. The aim was to identify molecules that could act as potential repellents that could interfere with OR activities48. From the high throughput screening, a new compound that was able to interact with odorant receptors was found: VUAA1 (2-(4-ethyl-5-(pyridin-3-yl)-4H-1,2,4-triazol-3- ylthio)-N-(4-ethylphenyl)acetamide)48. 3.4 Orco function as a single channel and change to VUAA1 in the olfaction research With the discovery of the Orco-specific allosteric agonist, VUAA1, new studies started to be done to further understand the receptors’ pharmacology48,103. Since then VUAA1 has been used in several studies as a control107,109. A study by Taylor, R.W, et al. demonstrated that the structure activity of different VUAA1 based compounds presented different results for in vitro and in vivo assays depending on the mosquito species tested; these findings lead to the preference of VUAA1 as a Orco agonist104. Despite VUAA1 specific interaction with the Orco receptor, it presented challenges to become a repellent: the main one was its lack of volatility48,104. Repellents need to mask the odor coming from the human skin and they do so by evaporating from the skin and confusing the mosquito104,110,111. Insect repellents can work as 44 agonist or antagonists to modulate the response that initiates at the ORs level112. The confusion arises from the different MOAs that have been proposed for insect repellency111,112. Insects can rely on more than one system to guide them through the environment101. This has also been demonstrated in a study by DeGennaro et al, 2013113. In this study when Orco was mutated in A. aegypti , the mosquitoes were no longer attracted to human odors in the absence of CO2113. However, when CO2 was re-introduced, the mosquitoes attraction was re-covered113,114. This demonstrated that even when Orco is not fully functional, it can still work in the presence of CO2, like previously suggested, and that the mosquito olfaction relies on other receptors besides ORs101. Figure 3.3 – VUAA1 structure. 3.5 Orco structure Orco is a seven transmembrane helixes protein with a reverse topology, with an N-terminal in and a C-terminal out of the cell, as previous computer models have suggested115. The protein structure for A. gambiae Orco has not been determined. However, the first Cryo-EM structure for Orco was elucidated for Apocrypta bakeri107. It was established that Orco formed a homomeric cationic channel by assembling as a pinwheel tetrameric unit107. This unit comprised of four transmembrane domains surrounding a central pathway that conducts the ions107. The inter-subunit connections are formed by a small intracellular anchor domain107. The majority of 45 the protein is localized in the membrane, with short loops projecting out107. The anchor domain is composed of a cytoplasmic helical bundle, containing the majority of the inter-subunit interactions107. Orco is highly conserved in the insect species and its main conserved regions are located in the pore and anchor domains. This allows for Orco of different species to combine with ORs in multiple ways107. Figure 3.4 from Butterwick et al. illustrates all the Orco characteristics mentioned before107. The precise stoichiometry of Orco to ORs has not been established yet102. In the study done by Pask et al, 2011, HEK293 cells expressing both Orco-ORs and Orco alone showed that cation permeability changes when Orco is solely expressed with the addition of VUAA1102. The expressed channels (Orco and Orco-ORs) are more responsive to monovalent cations (Rb+ and K+) than divalent ones (Ca2+ and Mg2+)102. The Orco structure shed some light on the problem; it was found that the interactions between subunits are within pore-forming helices107. The Orco pore is formed in the center of the tetramer where the S7 loop (Figure 3.3 f) is narrower near the extracellular part107. Due to the small opening, fewer hydrated ions can pass through107. 46 Figure 3.4 – Orco topology and architecture (Reprinted with permission from (Butterwich, 2018). (a) The topology of Orco can be seen with the anchor domain that allows for the interactions with other subunits. (b) A lateral view of the protein structure, as well as a top view of how the 4 units are interacting. (c) The hydrophilic amino acid interactions are shown. (d) 2 Orco units can be seen interacting laterally. (e-g) Top view of how the tetramers assemble. 47 Chapter 4: Development and testing of a yeast whole-cell odorant biosensor 4.1 Introduction The way new repellents are screened is expensive, time consuming and represents a challenge34,111. It relies on mosquito behavior assays that involve putting mosquitos in contact with chemicals in a box and observing their reaction to the chemicals presented42. Faster methods involving the use of HEK29348, Xenopus oocytes94 and Drosophila empty neurons108 have been developed but these are challenging to deploy. We sought to improve the speed and accuracy of mosquito repellant discovery through the development of a microbial whole-cell odorant biosensor. Mosquito odorant receptors work as cationic channels with the constantly expressed Orco and other ORs100. They form a heteromeric channel that interacts with chemicals, as explained in chapter 3100. Orco can also be expressed individually and can function as a cationic channel thereafter, as explained in chapter 3103. Ergo, we have selected Orco as an initial target for expression. The activity of the possible sensor will be measured by detecting fluorescence emitted by the calcium concentration shift in the cell by using a calcium binding dye that has been previously used in other systems48. In light of the inherent simplicity of the odorant biosensor, we opted to construct it in the yeast Pichia pastoris in order to validate our design and prove the concept. P. pastoris has been used to express a range of eukaryotic proteins and is a desirable host in its own right owing to ease of cloning, easier maintenance and deployment as a screening platform compared to mammalian cell lines116–118. Although S. cerevisiae is also a viable candidate for expression and 48 has been used previously to express mammalian odorant receptor proteins to detect explosives118–121, we did not consider it in the current study. The major difference between using S. cerevisiae and P. pastoris is that the former can maintain plasmids intracellularly whereas the latter integrates transformed genes into its chromosome122. Regardless of the choice of the host, the proposal to construct a functioning mosquito olfactory system in either organism for use in screening of behavior-modifying compounds such as repellents is novel and we were greatly enthused by previous studies that suggested that heterologous membrane proteins can be functionally expressed in yeast118–121. The literature suggests that repellents act by blocking or modifying normal responses in the Orco-OR combinations that would normally respond to attractants123. Repellents, could then either act by inhibiting the odorant response, acting as antagonists, or by over activating the response, acting as agonists38,48,123. The P. pastoris system presents some advantages when compared to other cell lines. Xenopus oocytes require micro-injections for the transient expression of odorant receptors and each oocyte presents a variation105. The generation of Drosophila empty neurons cell lines expressing the odorant receptors is time consuming, and there is a low likelihood of making the screening high throughput105. P. pastoris also has a short doubling time (110 min)124 when compared to HEK 293 cell lines (24 h)125 and Sf9 cells lines (30 h)126. Yeast can be dried and stored for further use for longer periods of time, and can tolerate harsh growing environments127. In addition, yeast cells are less prone to contamination, whereas HEK 293 cells have to be tested for the presence of mycoplasma128. Furthermore, there are numerous molecular tools available for genetic engineering in yeast, which allows the system to be used in a modular way, facilitating the cloning of different proteins116,129–131. 49 We used the calcium-binding dye, Fluo 4AM (Invitrogen, MA, USA) to measure the activity of the Orco cationic channel48. This dye has been previously used in other studies involving filamentous fungi and there is a preponderance of evidence that suggests that the dye permeates the cells and accumulates within yeast cells at appreciable concentrations132. We hypothesized that functional Orco should behave as an ionotropic channel following activation by an odorant or odorant-OBP complex, thereby catalyzing the influx of cations such as calcium into the cell; and the binding of calcium to intracellular Fluo 4AM should emit fluorescence48. A schematic of how the biosensor will function is shown in Figure 4.1. Figure 4.1 – Proposed scheme for the biosensor’s operation in P. pastoris. Functional expression of Orco in the membrane of P. pastoris will facilitate the influx of calcium into the cell following activation of Orco by an odorant. The subsequent binding of calcium and Fluo 4AM in the cytoplasm will emit fluorescence, which will be captured by a photodiode array detector. Emission of fluorescence and signal strength are correlated to agonism. The absence of 50 fluorescence after the receptor is stimulated by an agonist suggests that the first molecule is an antagonist. 4.2 Materials and Methods 4.2.1 E. coli plasmid construction and cloning for transformation in P. pastoris All restriction enzymes used in this study were of the high-fidelity form and were purchased from New England Biolabs, MA, USA. The amino acid sequence of Orco was retrieved from the UniProt database and reverse translated to acquire the cDNA sequence. The latter was then codon optimized for expression in P. pastoris and synthesized (GenScript, Nanjing, China). We used the P. pastoris GS115 as the base strain for all experiments in this study, and the pPICZA plasmid. Both, the strain and the plasmid were generously donated to us by Dr. Steven Hallam (UBC Microbiology & Immunology). All sub-cloning was performed in E. coli DH5α cells. E. coli DH5α strain was used for the initial plasmid construction. The cells were made chemically competent in order to incorporate the plasmids as previously described133. Briefly, a 1 L flask with 200 mL of LB was inoculated with the 200 µL starter E. coli DH5α culture and optical density (defined as the absorbance at 600 nm – OD600) was measured in a Lambda 650 UV/Vis Spectrophotometer (Perkin Elmer, MA, USA) until an optical density of 0.4. After reaching the optical density the cells were transferred to a cooled 50 mL falcon tube and were spun at 3,500 rpm at 4oC for 10 min in an Allegra general purpose centrifuge (Beckman Coulter, CA, USA). The media was discarded and the pellet was re-suspended in a solution of 100 mM 51 MgCl2 and 100 mM CaCl2. The cells were spun again for 10 min at 3,500 rpm at 4oC for 10 min. The same procedure was repeated once more. The cells were then re-suspended in a solution of 85 mM CaCl2 and 15% glycerol and spun again at 3,500 rpm at 4oC for 10 min. The cells were re-suspended one last time in 85 mM CaCl2 and 15% glycerol. They were aliquoted in sterile micro-centrifuge tubes, flash froze in liquid nitrogen and stored at -80oC until further use. The pPICZA plasmid and the PCR product of the Orco gene were digested with EcoRI and NotI for 15 min at 37oC, and the reaction mixtures were then heat-inactivated for 20 min at 65oC. The PCR product of Orco was generated with a PCR reaction using Phusion High-Fidelity Polymerase (Thermo Fisher Scientific, MA, USA), following the manufacturer’s instructions. Details about the primer sequences are available in Table 4.1. The linearized fragments were then ligated together using T4 DNA ligase (Thermo Fisher Scientific, MA, USA) at room temperature for 30 min and the ensuing pPICZA_Orco construct was transformed into chemically competent E. coli DH5α strain, as previously described133. Briefly, 50 µL of chemically competent cells were incubated with roughly 10 ng of DNA for 30 min on ice. The cells were then heat-shocked by exposure to 42oC for 60 sec, followed by a second incubation on ice for 5 min. LB media with low salt concentration was added to the cells at this point and the vials were transferred to a 37oC incubator for 90 min. Next, the cells were plated on LB agar plates that were supplemented with 45 µg/mL of zeocin (Invitrogen, MA, USA) and incubated overnight at 37oC. A sizable number of colonies are formed on the plate after incubation, and some of these colonies were picked and introduced into colony PCR reactions. The reaction was done using Mango Mix (Meridian Biosciences, OH, USA) according to the manufacturer’s instructions. The primers used in the colony PCR reactions have been listed in Table 4.1. The size of the products was verified using gel electrophoresis. 52 Colonies of the successful transformants were then re-picked and inoculated into 5 mL of LB medium supplemented with 45 µg/mL zeocin. The culture tubes were incubated at 37oC and agitated at 250 rpm for roughly 16 h. The cells in the culture tubes were harvested and the plasmid was extracted with the aid of a Gene Jet miniprep kit (Thermo Fischer Scientific, MA, USA) using the manufacturer’s recommendation. The plasmid samples were then sequenced to confirm identity (Figure C.2) and then transformed into P. pastoris GS115. Briefly, 5-10 μg of the plasmid was digested with SacI (New England Biolabs, MA, USA) at 37oC for 20 min, followed by heat inactivation at 65oC for 20 min. The linearized plasmid was then extracted using phenol-chloroform extraction method. 4.2.2 Preparation of electrocompetent P. pastoris cells and cloning P. pastoris GS115 was made electrocompetent as previously described129. Briefly, a starter culture of GS115 was transferred to a 100 ml of YPD in a 1 L baffled flask. The larger culture was propagated until it reached an optical density of 1.3-1.5, following which it was centrifuged at 2,000 rpm at 4oC for 5 min. The resulting cell pellet was re-suspended in 50 mL of ice-cold, sterile water, and the mixture was re-centrifuged under the same conditions; this process was repeated two more times. Then, the cells were re-suspended in ice-cold 1 M sorbitol followed by a final round of centrifugation. Lastly, the cells were re-suspended in 200 µL of ice-cold, 1 M sorbitol. These cells, which were now electrocompetent, were kept on ice and transformed on the same day. For transformation, 80 μL of the cells were mixed with 10 ng of the linearized pPICZA_Orco plasmid prepared earlier and later transferred to a 0.2 cm electroporation cuvette. 53 The transformation mixture was incubated on ice for 5 min and then electropulsed in an Eppendorf electroporator (Eppendorf, Germany). Following electroporation, 1 mL of ice-cold, 1M sorbitol was immediately added to the cuvette and the ensuing mixture was then transferred to a sterile 15 mL culture tube. The tube was incubated at 30°C for 3 h. Next, approximately 50-200 μL of the sessile mixture was spread onto YPDS plates supplemented with 100 µg/mL of zeocin and incubated in a darkened incubator at 30°C for 2-3 days, after which colonies became sufficiently large to observe with the naked eye. Table 4.1 – Primers used for molecular cloning of Orco and colony PCR confirmation Gene (plasmid) Primers (forward and reverse, 5’ to 3’) Orco (pPICZA) GGAATTCCATGCAGGTTCAGCCGA TGCTGGTTCAGTTAAAATAACTCGAGACGA Orco confirmatory PCR GCTTTTTCTAGAACAAAAACTC TGAAACTGAAACTTGAGAAATT AOX1 CGACTGGTTCCAATTGACAAGCT GGCAAATGGCATTCTGACATCCT 4.2.3 Colony PCR and extraction of P. pastoris DNA Successful transformation of P. pastoris was confirmed through colony PCR. Colonies were handpicked and suspended in a 100 µL solution of 0.2 M lithium acetate and 1% SDS in a microcentrifuge tube. This tube was subsequently incubated for 5 min at 70oC, and later diluted with 300 µL of 96% ethanol. The diluted mixture was vortexed for 5 sec and then centrifuged for 54 3 min at 12,000 rpm. The resulting supernatant was discarded and the pellet was washed with 70% ethanol and re-centrifuged for 3 min at the same speed. After the supernatant was discarded yet again, the pellet was finally suspended in 80 µL of nuclease-free water and centrifuged for a final time at 12,000 rpm for 15 sec. 1 µL of the supernatant was then used in PCR reaction using Phusion High-Fidelity Polymerase (Thermo Fisher Scientific, MA, USA), following the manufacturer’s instructions. The sizes of the PCR products were confirmed using DNA gel electrophoresis. Bands of the correct size were subsequently cleaned using the Gene Jet kit (Thermo Fisher Scientific, MA, USA) according to the manufacturer’s recommended protocol and the samples were then sequenced to confirm successful transformation 4.2.4 Expression of Orco by P. pastoris We arbitrarily selected two colonies bearing the correct sequence of Orco for further testing. These colonies were picked and transferred to 5 mL of YPD media and grown overnight at 30oC under constant agitation at 200 rpm. 0.1 mL of the overnight cultures were then transferred to 250 mL baffled flasks comprising 30 mL of BMGY media and grown for a further 24 h. The OD600 of the cultures were constantly monitored. The cultures were subsequently centrifuged at 2000 rpm for 3 min and suitably re-suspended in 30 mL of BMMY media containing either 0.1% or 0.01% (volume basis) methanol to attain a starting optical density of 1, and these cultures were propagated at 30oC for 48 h under constant agitation at 200 rpm. pPICZA plasmid has an AOX1 promoter that is methanol-induced134. In order to maintain the methanol concentration stable, the inducer was added every 18 h. Following culturing, the cells 55 were harvested via centrifugation at 3000 rpm and 4oC. The samples were stored at -80oC for further processing. 4.2.5 Lysis of the harvested P. pastoris cells The frozen pellets were re-suspended in 10 mL of breaking buffer (50 mM potassium phosphate, 100 mM NaCl, 1 mM EDTA, 5 vol. % glycerol, 100 mM PMSF) and centrifuged for 5 min at 4oC and 3500 rpm, as previously described129. Briefly, the resulting supernatant was discarded and additional breaking buffer was added. 700 µL of this suspension was transferred to a microcentrifuge tube. Glass beads (0.5 mm ø) were then added to the tube until a point when the liquid meniscus in the tube was approximately 2 mm above the submerged beads129. The suspension was then periodically vortexed for 3-5 min interspersed with 5 min of cooling on ice. This protocol was repeated 3 times. The supernatant was collected and was centrifuged for 15 min at 14,000 rpm and 4oC. A small volume of the supernatant was stored for further analysis and the remainder was centrifuged at 100,000 rcf and 4oC for 1 h to isolate the membrane fraction. The supernatant from this sample was decanted and the pellets were re-suspended in a membrane buffer solution comprising 50 mM Tris, 120 mM NaCl, 2 mM EDTA, 10 vol. % glycerol at pH 8 for subsequent assessment using SDS-Page and Western blotting. 4.2.6 Western Blotting The protein concentrations in the isolated membrane fractions were measured using the Bradford assay (Biorad, CA, USA) according to manufacturer’s instructions, and the samples 56 were then diluted using 2X Laemmli buffer (Biorad, CA, USA) to achieve a consistent concentration in each of them. The membrane fraction diluted samples were incubated at 37oC for 30 min while the whole cell fraction samples were incubated at 95oC for 5 min prior to loading onto pre-cast Mini PROTEAN TGX SDS-Page gels (Biorad, CA, USA). The SDS-Page gel was removed from the cast and moved to a Mini Trans-Blot cell for eventual transfer to an Immun-Blot® PVDF membrane. The blotting cell was filled with ice-cold transfer buffer (25 mM Tris, 1.92 M glycine, 20 vol.% methanol at pH 8.5) and the gel was gently agitated in the buffer for 1 h at 100 V. Following the transfer, the membrane was blocked using a 5% skim milk solution with TBST (137 mM NaCl, 19 mM Tri-Base and 1 vol. % Tween-20) for 1 h under constant shaking at room temperature and then washed for 10 min with TBST. The washing was repeated two more times and the membrane was finally incubated overnight with the primary antibody for the c-Myc tag (Invitrogen, MA,USA) at 4oC under constant shaking. Following this step, the membrane was washed three more times with TBST and later incubated at room temperature with the Goat Anti-Mouse IgG secondary antibody HRP re-suspended with 5% milk (Invitrogen,MA,USA) solution for 1 h. The membrane was washed with TBST and incubated for 5 min with with Clarity Western ECL Blotting Substrates (Biorad,CA,USA) for visualization with ChemiDoc™ MP Imaging Systems (Biorad, CA, USA). 4.2.7 RT-qPCR The expression of Orco by the differentially induced cell cultures of P. pastoris was assessed using RT-qPCR. RT-qPCR is used to measure the amount of RNA transcripts in the cell. The RNA is used as a starting material and with the use of a reverse transcriptase is 57 converted into cDNA. cDNA is used in the quantification step135. For that the cells were harvested and re-suspended in lysis buffer of the PureLink RNA mini kit (Invitrogen, MA, USA) in order to make a paste. This paste was ground in the presence of dry ice using a mortar and pestle in order to break the yeast cell wall. The grounded mass was then treated using PureLink RNA mini kit in accordance with the manufacturer’s instructions, to extract RNA, which was then quantified using a Nanodrop 2000 (Thermo Fischer Scientific, MA,USA). These samples were then used to prepare reverse transcriptase quantitative PCR mixtures and the same concentration of RNA was used in all samples. The PCR was performed in two steps. Initially, the RNA was reverse transcribed to cDNA using the iScript™ Superscript reverse transcriptase (Biorad, CA, USA), according to manufacturer’s instructions. The cDNA product was subsequently diluted twenty-fold with RNAse-free and DNAse-free water to the range of detection according to the manufacturer’s guidelines. SSO Adavanced™ Universal SYBR Green Super Mix (Biorad, CA, USA) was then added to the samples according to the manufacturer’s instructions and the PCR was run in a CFX Connect Real-Time PCR Detection System (Biorad, CA, USA). We prepared three technical and two biological replicates for each sample, and obtained the Ct values for each. The ΔΔCt method was used for relative quantification of the expression levels for the Orco and FtsH genes, and the TAF-1 gene was used as an internal control. Data analysis was performed using Microsoft Excel and graphs were prepared using one-way ANOVA with a Tukey post hoc test in Graphpad Prism (GraphPad, CA, USA). 58 Table 4.2 – Primers used for RT-qPCR Gene Primers (forward and reverse, 5’ to 3’) Orco GTTCAGCCGACAAAATACGTG ACCATAATCAGAGTCCATCTG TAF-1 CTGGAGTTCTTGATGGTAGTGG GGTGGCTTTCTTATGCTTGC 4.2.8 Confocal Microscopy We used confocal microscopy to assess the permeation of the Fluo 4AM dye (Invitrogen, MA, USA) into P. pastoris cells. Briefly, cultures of P. pastoris were grown in 50 mL of BMGY media in 250 mL baffled flasks BMGY for 24 h at 30oC under constant agitation at 200 rpm. The cultures were then centrifuged at 2000 rcf for 15 min at room temperature and the cells were re-suspended in Hank’s buffer (Invitrogen, MA,USA) loaded with 2.5 mM of Fluo 4AM. The cells were incubated in this solution for 45 min at 37oC and protected from exposure to light. The cells were then washed 3 times with PBS to remove all traces of extracellular dye and then re-suspended in Hanks Buffer for microscopic analysis using an Olympus FV-1000 microscope under 60X magnification. The microscopy work was conducted at the UBC Bioimaging Facility. 4.2.9 Activity assay The P. pastoris colonies that yielded a positive band in the c-Myc Western blots were selected for the activity assays. The cells were grown in BMGY for 24 h, after that the OD600 59 was measured. The cell volume was calculated to make the final optical density concentration of the cells in the new medium to 1.0, being centrifuged at 2000 rcf for 5min. After that the cells were re-suspended in BMMY medium with 0.1 vol.% methanol for 48 h. The cultures were then centrifuged at 5000 rcf for 5 min at room temperature and re-suspended in a 1:1 volumetric mixture of BMMY medium and Hank’s buffer dosed with 2.5 mM of Fluo 4AM. The cells were incubated in the dark at 37oC for 45 min, subsequently spun down and washed three times with PBS. The activity assays were conducted at two concentrations of calcium ions in the medium – 1 mM and 5 mM. For the 1 mM calcium assays, the washed cells were simply re-suspended in Hank’s buffer, whereas additional calcium chloride was added to the re-suspended cells to raise the concentration to 5mM of calcium chloride in the buffer. VUAA1, citronellal and 1-octen-3-ol (all purchased from Sigma Aldrich, MO, USA) were then added to the solutions at fixed times and/or doses. The activity assays were performed in 96-well plates, and three technical and two biological replicates were analyzed for each assay condition. The fluorescence emission was recorded over 7 min at 520 nm following excitation at 485 nm. VUAA1, citronellal and oct-1-en-3-ol (all purchased from Sigma Aldrich, MO, USA) were diluted in DMSO (Sigma Aldrich, MO, USA) and then added to the solutions at fixed times and/or doses. The fluorescence readings were processed by first estimating the difference between the fluorescence recorded at a particular time point and the initial fluorescence recording in the well. The same calculation was performed for the wells housing only the buffer. 𝐹𝑤𝑤𝑤𝑤,𝑓𝑓𝑓𝑓𝑤 − 𝐹𝑤𝑤𝑤𝑤,𝑓𝑓𝑓𝑖𝑓𝑓𝑤 = ∆𝐹 𝐹𝑏𝑏𝑓𝑓𝑤𝑏,𝑓𝑓𝑓𝑓𝑤 − 𝐹𝑏𝑏𝑓𝑓𝑤𝑏,𝑓𝑓𝑓𝑖𝑓𝑓𝑤 = ∆𝐹𝑏𝑏𝑓𝑓𝑤𝑏 60 The two differences were then normalized with respect to the initial fluorescence recordings. ∆𝐹𝐹𝑤𝑤𝑤𝑤,𝑓𝑓𝑓𝑖𝑓𝑓𝑤 = 𝐹𝑠ℎ𝑓𝑓𝑖,1 ∆𝐹𝑏𝑏𝑓𝑓𝑤𝑏𝐹𝑏𝑏𝑓𝑓𝑤𝑏,𝑓𝑓𝑓𝑖𝑓𝑓𝑤 = 𝐹𝑠ℎ𝑓𝑓𝑖,2 The processed fluorescence reading was estimated as the difference between the normalized fluorescence readings, and has been in all the data plots in this chapter. 𝐹𝑠ℎ𝑓𝑓𝑖,1 − 𝐹𝑠ℎ𝑓𝑓𝑖,2 = 𝐹 All calculations were performed using Microsoft Excel and the plots were prepared using Graphpad Prism (GraphPad, CA, USA). Statistical significance was assessed using the first recorded response after the addition of the compounds. The measured fluorescence value of each sample was processed according to the described equations and an unpaired t-test was done in values obtained for each group. Likewise, the EC50 of VUAA1 was determined by plotting the concentrations and the engendered fluorescence responses. 61 4.3 Results 4.3.1 P. pastoris cloning and expression The Western blot established successful expression and localization of the Orco protein in the membrane of P. pastoris (Figure 4.2). Orco has a molecular weight of 54 kDa, and the blot image in Figure 4.2 confirms the existence of a single protein of this size in the membrane fraction. Similarly, no c-Myc-tagged protein is detected in the P. pastoris GS115 strain which also affirms that heterologous expression of Orco by P. pastoris. The Orco 21.1 strain was named because it was the 21 generation of clones obtained until we were able to find a functional working cell. 21 plasmids were constructed in E. coli prior to the construction of the Orco 21.1 strain. Many of these plasmids had frame shifts, cloning errors and issues with the 6X his-tag, which was initially being used for detection of Orco in the system. The inability to obtain a western blot image with the 6X his-tag made us shift to the use of a c-myc tag for cloning confirmation. The number followed the 21 is the colony that was randomly picked for sequencing and testing. 18 colonies were in average screened through each constructed transformation, leading us to a final random strain Orco 21.1 as the working strain. 62 Figure 4.2 – Western blot of P. pastoris membrane and whole cell fractions using an anti-c-Myc antibody. The lanes have been ordered from left to right and denote the membrane (1) and whole cell fractions (2) of Orco 21.1 and the whole cell fraction of GS115 (3). Biological replicates of these experiments are shown as lanes 4, 5 and 6, respectively. The first lane (not numbered) corresponds to the protein ladder. 4.3.2 P. pastoris confocal microscopy & confirmation of dye permeation into cells The permeation of Fluo 4AM into the cells of P. pastoris is arguably the most crucial step in the implementation of the assay. We verified that Fluo 4AM permeates into the cells and that permeation is strongly correlated with incubation time (Figure 4.3)132. Although permeation is non-uniform within a given population of cells, it is still representative and able to generate a 63 detectable signal in the assays. Permeation is non-uniform because of the yeast system thick cell wall. Figure 4.3 – Confocal micrographs P. pastoris stained with Fluo 4AM. The micrographs were captured using an Olympus FV-1000 microscope at 60X magnification. The size bar corresponds to 5 µm. Images were obtained at the UBC Bioimaging facility. The images were taken from different samples that were incubated for the same period of time. 64 4.3.3 Confirmation of Orco RNA expression using RT-qPCR In addition to confirmation of expression through Western blotting, we also quantified transcription of the Orco gene and assessed its dependence on inducer concentration using RTq-PCR. The cultures responded to both concentrations of methanol that were tested in the experiments, and it appears that controlling the concentration of methanol can modulate the transcription and eventual expression levels of Orco. The latter is a significant result since it offers us a much-needed extra degree of freedom to mitigate potential metabolic stresses were they to occur during functional testing of the expressed proteins. Nevertheless, we used 0.1% methanol in all subsequent experiments. The average Ct values used for this experiment can be found in Appendix D.1 (Table D.1). 65 Figure 4.4 – Relative overexpression of Orco transcripts compared to TAF-1 RNA. Both methanol concentrations resulted in overexpression of Orco RNA transcripts compared to TAF-1. The relative overexpression was quantified using the ΔΔCt method. Also, as expected, the GS115 strains do not produce any Orco transcripts. Statistical significance was determined using the one-way ANOVA unpaired test at α = 0.05. 4.3.4 Assessment of the activity of Orco in a 1 mM Ca2+ solution We tested the activity of Orco and its suitability as an odorant biosensor by exposing cultures of Orco 21.1 to defined concentrations of VUAA1, a known agonist of Orco. Incidentally, VUAA1 was the very first agonist discovered by researchers at Vanderbilt University and the name of the compound is an abbreviation of Vanderbilt University Anopheles Agonist (VUAA)48. When we tested the cultures 24 h after they were induced with 0.1% methanol, we did not observe appreciable fluorescence (data not shown). However, we observed significant fluorescence after 48 h of culturing. These results reveal that expression and successful localization of Orco within the membranes of P. pastoris cells takes between 24-48 h, and once these processes are complete, cells expressing Orco respond to VUAA 1 (Figures 4.5-4.10). While the fluorescence emitted by the cultures exhibits a steady, linear increase over the 7-minute run at VUAA1 concentrations of 0.125 mM and 0.25 mM (Figures 4.5 and 4.6), the responses for VUAA1 concentrations of 0.5 mM and higher exhibit maxima within the first 2 min of the run before steadily converging to a steady state in the second half of the run (Figures 4.7-4.9). It must be noted that VUAA1 was added to the cultures 1 min into the run to allow the baseline to equilibrate. As a consequence, all runs appear to be 6 min in duration. Also, we 66 observed that the difference between the relative fluorescence emitted by cultures of Orco 21.1 and GS115 that were dosed with 0.125 mM and 0.25 mM VUAA1 was not statistically significant. However, the differences grew to become statistically significant at VUAA1 doses of 1 mM (p < 0.01), 1.5 mM (p < 0.001) and 2 mM (p < 0.01). Figure 4.5 – Fluorescence emitted over time by cultures of Orco 21.1 in 1 mM Ca2+ Hank’s buffer following activation of the channel by 0.125 mM of VUAA1. The fluorescence recording for the GS115 strain are shown in Figure 4.11. VUAA1 is added to the cultures 1 min into a 7 min run, which results in data collection for 6 min. 67 Figure 4.6 – Fluorescence emitted over time by cultures of Orco 21.1 in 1 mM Ca2+ Hank’s buffer following activation of the channel by 0.25 mM of VUAA1. Figure 4.7 – Fluorescence emitted over time by cultures of Orco 21.1 in 1 mM Ca2+ Hank’s buffer following activation of the channel by 0.50 mM of VUAA1. 68 Figure 4.8 – Fluorescence emitted over time by cultures of Orco 21.1 in 1 mM Ca2+ Hank’s buffer following activation of the channel by 1 mM of VUAA1. Figure 4.9 – Fluorescence emitted over time by cultures of Orco 21.1 in 1 mM Ca2+ Hank’s buffer following activation of the channel by 1.5 mM of VUAA1. 69 Figure 4.10 – Fluorescence emitted over time by cultures of Orco 21.1 in 1 mM Ca2+ Hank’s buffer following activation of the channel by 2 mM of VUAA1. We subsequently plotted the first recorded fluorescence to VUAA1 (t = 1min) for each run and compared it with GS115 cultures dosed with the same concentrations of VUAA1 (Figures 4.11 and 4.12). The differences between the relative fluorescence emitted by cultures of Orco 21.1 and GS115 over their respective baselines was significant at VUAA1 concentrations that exceeded 0.5 mM. 70 Figure 4.11 – Relationship between the first response of fluorescence emitted by cultures Orco 21.1 and GS115 and the concentration of VUAA1 in the extracellular media. The difference between the relative fluorescence emitted by the two cultures is not statistically significant for VUAA1 doses that are equal to or below 0.125 mM. However, the differences grew to become statistically significant at VUAA1 doses of 1 mM (p < 0.01), 1.5 mM (p < 0.001) and 2 mM (p < 0.01). 71 72 Figure 4.12 – Detailed comparison between the sensitivities of cultures of Orco 21.1 and GS115 to VUAA1. The difference between the relative fluorescence emitted by the two cultures is not statistically significant for VUAA1 doses that are equal to or below 0.5 mM. However, the differences grew to become statistically significant at VUAA1 doses of 1 mM (p < 0.01, **), 1.5 mM (p < 0.001, ***) and 2 mM (p < 0.01, **). The analyses were performed using an unpaired t test. 4.3.5 Assessment of the activity of Orco in a 5 mM Ca2+ solution Increasing the concentration of extracellular calcium to 5 mM resulted in increased sensitivity of the Orco biosensor to VUAA1. The signal-to-noise ratio for all cultures of Orco 21.1 had increased, and the fluorescence emitted after VUAA1 excitation at even 0.125 mM exhibited a maximum within the first 2 min and later reached a slightly lower steady state level in the second half of the run (Figure 4.13-4.18). 73 Figure 4.13 – Fluorescence emitted over time by cultures of Orco 21.1 in 5 mM Ca2+ Hank’s buffer following activation of the channel by 0.125 mM of VUAA1. The fluorescence recording for the GS115 strain are shown in Figure 4.19. VUAA1 is added to the cultures 1 min into a 7 min run, which results in data collection for 6 min. 74 Figure 4.14 – Fluorescence emitted over time by cultures of Orco 21.1 in 5 mM Ca2+ Hank’s buffer following activation of the channel by 0.25 mM of VUAA1. Figure 4.15 – Fluorescence emitted over time by cultures of Orco 21.1 in 5 mM Ca2+ Hank’s buffer following activation of the channel by 0.50 mM of VUAA1. 75 Figure 4.16 – Fluorescence emitted over time by cultures of Orco 21.1 in 5 mM Ca2+ Hank’s buffer following activation of the channel by 1 mM of VUAA1. Figure 4.17 – Fluorescence emitted over time by cultures of Orco 21.1 in 5 mM Ca2+ Hank’s buffer following activation of the channel by 1.5 mM of VUAA1. 76 Figure 4.18 – Fluorescence emitted over time by cultures of Orco 21.1 in 5 mM Ca2+ Hank’s buffer following activation of the channel by 2 mM of VUAA1. Moreover, at this concentration of calcium, statistically significant differences between the performance of the Orco 21.1 clone and GS115 began to emerge at VUAA1 concentrations as low as 0.125 mM. The p-value for the difference in the relative fluorescence of the two cultures exposed to 0.125 mM VUAA1 was now < 0.01 and < 0.001 for all other concentrations (Figures 4.19 and 4.20). 77 Figure 4.19 – Relationship between the first response of fluorescence emitted by cultures Orco 21.1 and GS115 and the concentration of VUAA1 in the extracellular media for 5 mM Ca2+ concentration. The difference between the relative fluorescence emitted by the two cultures is statistically significant for all VUAA1 doses. The p-value is < 0.01 for 0.125 mM VUAA1 and p < 0.001 for all other concentrations. 78 79 Figure 4.20 – Detailed comparison between the sensitivities of cultures of Orco 21.1 and GS115 to VUAA1 in the extracellular media for 5 mM Ca2+ concentration. The difference between the relative fluorescence emitted by the two cultures is statistically significant for all VUAA1 doses. The p-value is < 0.01 (**) for 0.125 mM VUAA1 and p < 0.001 (***) for all other concentrations when an unpaired t test was done. 80 81 Figure 4.21 – Dependence of temporal responses of Orco 21.1 cultures to excitation by VUAA1 on extracellular calcium. The agonist concentrations ranged from 0.125 mM to 0.5 mM. 82 Figure 4.22 – Dependence of temporal responses of Orco 21.1 cultures to excitation by VUAA1 on extracellular calcium. The agonist concentrations ranged from 1 mM to 2 mM. 83 These results suggest that the linear range of detection for VUAA1 in Hank’s buffer comprising 5 mM of Ca2+ spans 0.125-2 mM. This range corresponds closely with that of a similar biosensor constructed in HEK293 cells48, thereby confirming that the yeast-based biosensor is able to replicate the behavior of the mammalian cells. 4.3.6 Estimation of the EC50 of VUAA1 in media comprising 1 mM and 5mM Ca2+ The data in figures 4.11 and 4.19 was used to estimate the EC50 for the interaction of VUAA1 with Orco. The EC50 is the concentration of chemical needed to activate 50% of a receptors response. The EC50 of VUAA1 in 1 mM and 5 mM of extracellular calcium was estimated to be 0.69 mM and 0.38 mM respectively, which confirms the increased sensitivity that was observed previously. Figure 4.23 – EC50 for the interaction of VUAA1 and Orco in 1 mM of extracellular calcium. The EC50 is estimated to be 0.69 mM. 84 Figure 4.24 – EC50 for the interaction of VUAA1 and Orco in 5 mM of extracellular calcium. The EC50 is estimated to be 0.38 mM. 4.3.7 Effect of clonal variation on selection of the biosensor We selected two P. pastoris clones, Orco 21.1 and Orco 21.3, for this study. Both clones were generated in the same transformation step and their use herein highlights the influence of clonal variation on the successful design and selection of whole-cell odorant biosensors. The level of activity of Orco by the 21.3 clone was markedly lower than the 21.1 clone. Although the fluorescence emitted by both Orco clones was significantly higher than the GS115 wild-type strain, the fluorescence emitted by cultures of Orco 21.1 was much greater than cultures of Orco 21.3 with a similar optical density (Figure 4.25). 85 Figure 4.25 – Response of two Orco expressing P. pastoris clones to VUAA1, a known agonist of the protein. Orco 21.1 and 21.3 are transformants derived from the same experiment and the difference in their responses to VUAA1 reveals the influence of clonal variation on the design and selection of whole-cell biosensors. The fluorescence was recorded in Hank’s buffer comprising 1 mM Ca2+ and the first response to the addition of VUAA1 was recorded. 4.3.8 The impact of DMSO and extracellular calcium on the activity of Orco Since stock solutions of all the ligands investigated in this thesis – VUAA1, citronellal and oct-1-en-3-ol – were diluted in DMSO, and since DMSO is known to impact cells in myriad ways, which implies that it could interfere with the assay, we also performed the assay using DMSO alone. As we had done earlier, we pipetted 1 µL of pure DMSO into cultures of Orco 21.1 and GS115 cells in Hank’s buffer comprising 1 mM Ca2+ and recorded the fluorescence emitted over a 7-minute interval (Figure 4.26). The volume of DMSO investigated is the same as the volume of the VUAA1 solution that is added to the cultures. Although the fluorescence 86 emitted by Orco 21.1 cells is slightly higher, the first reading after the addition of VUAA1 is still markedly lower than the fluorescence recorded upon exposure to 0.25 mM and higher concentrations of VUAA1. We repeated the assay with 5 mM of Ca2+ in the extracellular buffer and noticed a similar trend (Figure 4.27), which confirms that the biosensor is not negatively impacted by the use of DMSO and that the Orco continues to function as a cationic channel in the manner it was intended to. Figure 4.26 – Effect of DMSO in P. pastoris Orco system with 1mM of extracellular calcium. Addition of 1 µL of DMSO to the cultures of Orco 21.1 and GS115 in Hank’s buffer comprising 1 mM Ca2+ shows no significant changes in fluorescence. 87 Figure 4.27 – Effect of DMSO in P. pastoris Orco system with 5mM of extracellular calcium. Addition of 1 µL of DMSO to the cultures of Orco 21.1 and GS115 in Hank’s buffer comprising 1 mM Ca2+ shows no significant changes in fluorescence. 4.3.9 Response of Orco 21.1 cultures to citronellal in 5mM Ca2+ Citronellal is a major component of citronella oil and is widely used as an insect repellent36. However, while it has been shown to be an agonist of Orco in D. melanogaster, its effect on A. gambiae Orco is unclear136. We employed the P. pastoris whole-cell biosensor to systematically probe this effect. Citronellal is a natural compound that occurs in oil extracts of lemon grass36. Since it is difficult to separate from other components in the oil, it is often applied as a mixture that is referred to as citronella oil36. We used citronella oil containing 93% citronellal for our investigations. The cells of Orco 21.1 were tested in Hank’s buffer comprising 5 mM of Ca2+. Since citronella oil is highly volatile and largely insoluble in aqueous solutions, 88 which precludes accurate molarity measurements, we pipetted defined volumes of the oil into the media and correlated the fluorescence readings to fold dilutions. We tested 1:1 (or a 50% or 0.5 dilution), 1:3 (0.25), 1:7 (0.125) and 1:15 (0.0625) mixtures of citronella oil in the buffer and, as we had done earlier, recorded the fluorescence emitted by the cells over a 7 min run. The citronella oil was pipetted into the medium 1 min into the run. The fluorescence recordings in each of the conditions tested (Figures 4.28 – 4.31) were similar of those samples pipetted with DMSO (Figure 4.27). Moreover, the fluorescence seems to plateau after 7 min, the fluorescence trend reaches numbers higher than DMSO in the concentration of 1:15 of citronellal added to the buffer. These observations, in conjunction, suggest that citronellal might be activating Orco in lower concentrations after time, not as rapidly as VUAA1. Figure 4.28 – Addition of citronella oil at a dilution of 1:15 in 5 mM Ca2+ Hank’s buffer and its effect on Orco expressed by the Pichia clones. 89 Figure 4.29 – Addition of citronella oil at a dilution of 1:7 in 5 mM Ca2+ Hank’s buffer and its effect on Orco expressed by the Pichia clones. Figure 4.30 – Addition of citronella oil at a dilution of 1:3 in 5 mM Ca2+ Hank’s buffer and its effect on Orco expressed by the Pichia clones. 90 Figure 4.31 – Addition of citronella oil at a dilution of 1:1 in 5 mM Ca2+ Hank’s buffer and its effect on Orco expressed by the Pichia clones. We re-plotted the first recorded fluorescence reading after the addition of Citronellal for each run as a function of fold dilution of citronellal (Figure 4.32), like previously done for VUAA1 concentrations (Figure 4.11, 4.21). 91 Figure 4.32 – Activity of Orco as a function of citronella dilutions. The measurements were made in a 5 mM concentration of Ca2+ in Hank’s buffer and represent the lowest readings for each run, right after citronellal is added. 4.3.10 Simultaneous exposure of Orco to citronellal and VUAA1 in 5mM Ca2+ Hank’s buffer We also assessed the ability of Orco to recover its cation channeling activity following exposure to citronellal to evaluate if citronellal was acting as an antagonist, agonist or non-active by sequentially exposing it to VUAA1. We exposed Orco 21.1 cells to the same levels of citronellal that were used in section 4.3.9 and then added VUAA1 at a concentration of 1 mM roughly midway through the run. 1 mM of VUAA1 has been shown to elicit fluorescence emission that is at least 50% higher than the control. We recorded three fluorescence readings 92 after exposure to citronellal and another three readings following the addition of VUAA1 (Figures 4.33-4.36). Figure 4.33 – Increase of Orco activity following addition of 1 mM VUAA1 to cells previously exposed to citronellal in a 1:15 solution with 5 mM Ca2+ Hank’s buffer. The arrow denotes the very first reading following addition of VUAA1 to the solution at the 5 min reading. 93 Figure 4.34 – Increase of Orco activity following addition of 1 mM VUAA1 to cells previously exposed to citronellal in a 1:7 solution with 5 mM Ca2+ Hank’s buffer. The arrow denotes the very first reading following addition of VUAA1 to the solution at the 5 min reading. 94 Figure 4.35 – Increase of Orco activity following addition of 1 mM VUAA1 to cells previously exposed to citronellal in a 1:3 solution with 5 mM Ca2+ Hank’s buffer. The arrow denotes the very first reading following addition of VUAA1 to the solution at the 5 min reading. 95 Figure 4.36 – Increase of Orco activity following addition of 1 mM VUAA1 to cells previously exposed to citronellal in a 1:1 solution with 5 mM Ca2+ Hank’s buffer. The arrow denotes the very first reading following addition of VUAA1 to the solution at the 5 min reading. It is noticeable that the first measured fluorescence shift in each of the previous progressively declined as the amount of citronellal in the medium rose. This suggests that there could be allosteric agonism or a non-activity of the compound at play. This observation merits further consideration in future work. 4.3.11 Exposure of Orco to oct-1-en-3-ol in the presence of 5mM Ca2+ Oct-1-en-3-ol is the principal attractant of female A. gambiae mosquitoes in human sweat137. Previous studies have revealed that the molecule activates other olfactory receptors in female A. gambiae mosquitoes. Therefore, we concluded our assessment of the biosensor by 96 evaluating the interaction between Orco and oct-1-en-3-ol as well. We noticed a similar trend in the fluorescence as the data for citronellal and DMSO. However, the readings were of comparable magnitude to those of DMSO, which confirms that oct-1-en-3-ol does not or interacts weakly with Orco. Figure 4.37 – Exposure of Orco to varying dilutions of oct-1-en-3-ol in a 5 mM Ca2+ Hank’s buffer. We evaluated similar dilution levels as those for citronellal. 4.4 Discussion Orco interference is a common strategy in repellent design since the protein is an integral part of the olfactory cascade in mosquitoes123,138. Ergo, we focused on expression of Orco in a microbial host and its subsequent use as a screening platform for repellant design. Additionally, since most olfactory receptors and co-receptors share similar structures and topologies within the 97 membrane, not only does this study serve as a proof-of-concept demonstration, but it also establishes an easy-to-construct and versatile platform that can be easily expanded to investigate a wide assortment of olfactory receptors. We have conclusively demonstrated that P. pastoris can functionally express Orco. We also attempted to express Orco in specialized strains of E. coli but were unsuccessful despite adopting a wide variety of approaches, as described in chapter 5. We later investigated several parameters that putatively control expression and function of Orco, including inducer concentrations, time of culturing and assay conditions such as extracellular calcium concentrations in the reaction buffer. Saliently, we observed that the cells do not express protein at levels that would be informative for an assay 24 h after induction but that one has to wait for 48 h before using the cells. The assay was also performed at 24 h and no activity was seen. The clone that we eventually selected for further characterization demonstrated a higher level of responsiveness to small-molecule agonists when compared to the other tested strain (Orco 21.3), as well as molecules that do not or interact non-specifically with Orco. For example, Pichia cells expressing Orco responded predictably to VUAA1, which has been identified previously as a potent agonist of Orco103, and raising the concentration of extracellular calcium in the media improves the sensitivity and widens the linear range of detection of the compound. Incidentally, VUAA1 allosteric agonism of Orco triggers the olfactory cascade that actually confuses the mosquito into inaction, thereby making it an excellent repellent104,139. However, VUAA1 has harnessed limited consideration by repellent manufacturers since it is not readily volatilized34. The development of an accurate, high-throughput platform such as ours creates opportunities to perform medicinal chemistry to probe structure-activity relationships more effectively and raises prospects for the discovery of effective, volatile repellents based on the structure of VUAA1103. 98 Cells expressing Orco cationic channels respond to VUAA1 agonism in a manner that is similar to TRP channels, including TRPA1 channels expressed in D. melanogaster and A. gambiae136,140,141. The signal peaks within 2 min after the addition of VUAA1 and later subsides to a lower steady-state value, and the peak fluorescence reading varies linearly with VUAA1 concentration over a fairly wide range. For 1 mM of extracellular calcium, statistically significant differences over the control are observed at concentrations of 1.0 mM and higher, whereas statistically significant differences emerge at VUAA1 concentrations as low as 0.125 mM when the extracellular Ca2+ concentration is 5 mM. We believe that we can increase the sensitivity and signal-to-noise ratio of the sensor further by using a higher concentration of calcium, because differently from other systems, P. pastoris can survive in higher concentrations of Calcium142. However, we limited our testing to 5 mM since most of the studies in the literature had their experimentation done with calcium concentrations ranging from 1 mM to 5 mM103,107,139,143,144. P. pastoris and other yeasts have been shown to grow normally at extracellular calcium concentrations as high as 100 mM142. We are also confident that the fluorescence emitted by the cells is the product of channel activation since the normal Ca2+ concentration in yeast cells is typically between 50 and 200 nM142,145. Moreover, the Pichia Orco biosensor behaves comparably to more complicated systems involving mammalian cells such as HEK293 and Xenopus oocytes, especially at higher extracellular calcium concentrations107,146. The EC50 of VUAA1 in HEK293 and Xenopus assays were determined to be 0.1 mM and 0.19 mM, respectively, whereas the corresponding values for Pichia in 1 mM and 5 mM Ca2+ buffer were 0.69 mM and 0.38 mM, respectively107,146. We also studied the effect of citronellal on Orco. Although citronellal is a known agonist of Orco and TRPA1 receptors in D. melanogaster, its effect on A. gambiae Orco (albeit expressed 99 heterologously in P. pastoris) has never been studied previously136. The volatility of citronellal presented unique challenges to its testing, and we were not able to measure a signal on Orco. We could see a tendency of increasing fluorescence with time in the lower concentrations of citronellal, possibly demonstrating that there is relative longer time response to the odorant. The re-addition of VUAA1 to the media recovered the signal (Figures 4.33 to 4.36), showing that VUAA1 would still act as an agonist in the presence of compounds that did not completely activate Orco demonstrates that citronellal was not acting as an antagonist38. Finally, oct-1-en-3-ol, which is a known agonist of A. gambiae OR8 but not Orco exhibited expected behavior in our assay108. In closing, our platform offers numerous advantages over alternative platforms such as HEK29348, Sf9147,148 and Xenopus149 oocyte whole-cell biosensors, and Drosophila empty neurons108. These cell lines require extensive effort and resources to maintain, and require the use of patch clamping to assess receptor activation or deactivation, which hinders the screening process48,105,149. However, a Xenopus oocyte system was made differently than the high throughput activity ones, the oocyte was attached to microfluidic chambers and was more robust 149. In contrast, our approach is modular and can be rapidly altered to probe other olfactory receptors, and none of the previous approaches can compete with the ease of culturing, scalability, low maintenance and cost, and rapid turnover of P. pastoris150. The yeast-based sensor also does not require the use of antibiotics, which is a significant advantage over other screening platforms48,103,105. Beyond repellent (and attractant) screening, the P. pastoris odorant sensor could also be used as an investigative tool in other fields such as agriculture and the study of pollination, as well as aromachology. 100 Chapter 5: Development of a bacterial olfactory biosensor As was described previously in Chapter 4, the current methodology for screening repellents is ineffective, cumbersome and expensive41,42,111. We sought to greatly improve the speed and accuracy of mosquito repellant discovery through the development of a microbial whole-cell odorant biosensor. When this thesis work was first initiated, we chose E. coli as the first system due to its ease of cloning, low cost of operations, fast duplication time and past proficiency in the system151–153. E. coli has also been used as a biosensor in our research group previously to identify specific metabolites byproducts in the degradation of lignin154. Although E. coli has been showed to successfully express membrane proteins, expression of odorant receptors is challenging155–157. In order to express the eukaryotic membrane protein we first sourced PubMed and synthesized codon optimized versions of the sequences. Optimization has been shown to increase the probability of the translation of proteins and sub sequential expression158,159. We used the E. coli C43 strain that was first developed by Miroux and Walker for membrane protein production160. This strain has demonstrated the most success in expressing membrane proteins compared to BL21(DE3)160. A study by Wagner et al. 2008, identified that mutations in the lacUV5 promoter that govern the expression of T7 RNA polymerase are responsible for improving the likelihood of membrane protein expression in these strains161. The choice of C43 instead of C41 was based on the demonstration that the C43 strain could hold plasmids for toxic membrane proteins in a more stable way than C41 when both strains were used in a membrane protein expression experimentation162. 101 We first cloned the gene into the pACYC_Duet1 plasmid with the intent of allowing the system to express a second membrane protein, in this case another OR, in the second cloning site of the plasmid. Since the combination of Orco-OR is also used in the literature to understand the action of odorants in mosquitoes48. pACYC_Duet1 also has a T7 promoter, previously used in the expression of eukaryotic membrane proteins in E. coli163,164. The second plasmid constructed also had the same T7 promoter. In this study we cloned a single copy of Orco alone, for the reasons mentioned before152. This first attempted Orco-based system construction in E. coli was unsuccessful. We encountered formidable challenges to functionally express Orco, a transmembrane protein, in E. coli and all of our attempts to troubleshoot the problem, including use of specialized Walker strains, co-expression of a protease protein and different expression conditions (temperature and inducer) were unsuccessful. 5.1 Materials and Methods 5.1.1 Plasmids (pACYC_Duet1, pTRC, pBAD33) construction All restriction enzymes used in this study were of the high-fidelity form and were purchased from New England Biolabs, MA, USA. The amino acid sequence of Orco was retrieved from the UniProt database as previously mentioned in chapter 4. The latter was then codon optimized for expression in E. coli and synthesized (GenScript, Nanjing, China). The FtsH – an AAA protease – sequence (NCBI 947690) was amplified from E. coli DNA with the primers in Table 5.1. We used the E. coli C43 strain as the base strain for expression of 102 membrane protein in this work. The strain was generously donated to us by Dr. Filip Van Petegem (UBC-Biochemistry). All sub-cloning was performed in E. coli DH5α cells. The Orco sequence was amplified with the primers for different enzymatic endings for cloning into pACYC_Duet1 (MilliporeSigma, MA, USA) and pTRC plasmid (Figure C.2) and the FtsH sequence was amplified with different enzymatic endings for cloning in pBAD33. The Phusion High-Fidelity DNA polymerase (Thermo Fisher Scientific, MA, USA) was used for the PCR reactions following the manufacturers’ instruction. For all the cloning 1 µg of Orco/FtsH PCR products and 1 µg of the working plasmid were used. The pACYC-Duet-1 plasmid and Orco were double digested with BamHI and HindIII for 15 min at 37oC and inactivated with the addition of 6 µL of 6X gel loading dye (NEB, MA, USA). The samples were loaded in a 10% agarose gel with a 1 kb-plus ladder (Invitrogen, MA, USA) and run in TAE buffer (40 mM Tris- Acetate and 1 mM EDTA) in an electrophoresis chamber at constant voltage (100 V) for 1 h. The corresponding bands were cut from the gel and extracted with the Gene Jet Gel extraction and DNA clean up micro kit (Thermo Fisher Scientific, MA, USA) according to the manufacturer’s instruction. The pTRC plasmid and Orco were double digested with XhoI and NcoI for 15 min at 37oC and heat inactivated for 10 min at 80oC. The pBAD33 plasmid and FtsH PCR product were double digested with SacI and HindIII for 15 min at 37oC and inactivated for 10 min at 80oC. The enzymatic reactions were cleaned up with Gene Jet Gel extraction and DNA clean up micro kit (Thermo Fisher Scientific, MA, USA) according to the manufacturer’s instruction. The enzymatic digestion products were ligated together with T4 ligase (Thermo Fisher Scientific, MA, USA). The 1:3 ratio of plasmid to vector was used for all the three constructs: 103 pACYC_Duet1, pTRC and pBAD33. The plasmids and the genes were ligated for 30 min at room temperature. 5.1.2 E. coli transformation and cloning confirmation E. coli DH5α strain was used for the initial plasmid construction. The cells were made chemically competent in order to incorporate the plasmids as previously described in Chapter 4. E. coli DH5α chemically competent cells were used to transform the following constructs: pACYC_Duet-1_Orco, pTRC_Orco and pBAD33_FtsH. The same transformation method was used as previously described in Chapter 4. The colonies obtained from the plasmid transformations were picked the next morning and re-suspended in DNAse-free water. This suspension was used to screen for colonies that had the Orco and the FtsH gene of interest with the use of colony PCR. The reaction was done with Mango Mix (Meridian Biosciences, OH, USA) according to the manufacturer’s instructions. The primers used are in table 5.1. The PCR products were confirmed by electrophoresis. The colonies with the correct band size were grown overnight in LB broth with the appropriate concentration of antibiotic at 37oC and under constant agitation at 250 rpm. The plasmids were extracted the next day with Gene Jet miniprep kit (Thermo Fisher Scientific, MA, USA) according to the manufacturer’s instructions and were sent for sequencing to Genewiz services at UBC (Genewiz, NJ, USA). The obtained sequences were compared to the in silico constructs in Snapgene® (GSL Biotech, IL, USA). The confirmed plasmids were used for cloning in E. coli C43 strain. The same procedure previously described for E. coli DH5α cloning was used for sub cloning in the E. coli C43 strain. 104 5.1.3 Construction of pTRC_Orco + pBAD33_FtsH cell line To construct the last cell line (pTRC_Orco + pBAD33_FtsH) the pTRC_Orco construct in E. coli C43 was made electrocompetent as previously described133. Briefly, 50 µL of the starter culture of pTRC_Orco was transferred to 50 mL of LB broth with 50 µg/mL of ampicillin and grown until optical density reached 0.6. The cells were transferred to a cold conical tube and pelleted by centrifugation for 10 min at 3,500 rpm at 4oC. The supernatant was discarded and the pellet was washed with the same volume of cold sterile MiliQ Water and centrifuged for 10 min at 3,500 rpm at 4oC twice. The cells were lastly washed with cold 10% sterile glycerol solution and centrifuged for 10 min at 3,500 rpm at 4oC. The cells were re-suspended 10% glycerol, fresh frozen in liquid nitrogen and stored in -80oC until further use. The resulting electrocompetent strain was used for transformation with the pBAD33_FtsH plasmid as previously described133. The cells and the plasmid were incubated on ice for 5 min in an electroporation cuvette. The mixture was electroporated in an Eppendorf electroporator (Eppendorf, Germany). Afterwards LB broth was added to the cuvette and the cells were transferred to a microcentrifuge tube. They were incubated at 37oC for 90 min and plated in LB agar with 35 µg/mL of chloramphenicol and 50 µg/mL of ampicillin. The cells were grown overnight at 37oC and in the following morning colonies were picked for confirmation of cloning, as previously described. 105 Table 5.1 – Primers used for cloning and colony PCR Gene (plasmid) Primers (forward and reverse, 5’ to 3’) Orco (pACYC_Duet1) CCACAGCCAGGATCCCATGCAGGTTCAGCC ATTCGGGTTGCCATTGCGACCATTTT Orco (pTRC) GACGATGACGACAAGATGCAAGTCCAACCGACCA GTGGTGGTGGTGGTGGTGGCTGCACCAACACCATG FtsH (pBAD33) ATGGCGAAAAACCTAATACTCTGGCT AAGCTTTTACTTGTCGCCTAACTGC Orco (pACYC_Duet1 and pTRC) colony PCR GCTTTTTCTAGAACAAAAACTC TGAAACTGAAACTTGAGAAATT 5.1.4 Protein expression with pACYC_Duet1_Orco, pTRC_Orco and pTRC_Orco + pBAD33_FtsH constructs The starter cultures for each plasmid were added in a 1:100 dilution to the flaks with 50 mL of fresh LB broth and the selected antibiotic. The cultures were grown in three different conditions: 37oC, 28oC and 12oC, for the initial screening with pACYC_Duet1_Orco. For the screening with pTRC_Orco and pTRC_Orco + pBAD33_FtsH, two different conditions were used: 28oC and 12oC. Different temperatures followed the same protocol for initial growth. For 37oC, after the cultures reached an optical density of 0.5 the cells were induced with 0.05 mM and 0.1 mM of IPTG for pACYC_Duet-1_Orco. For the 28oC the cells were grown to an optical density of 0.5 and 0.05 mM and 0.1 mM of IPTG and 10 mM of arabinose were added. This was followed by incubation at 28oC with 250 rpm for 16 h. For the expression at 12oC the cells were 106 grown at 37oC for an optical density of 0.5 and they were transferred to a 12oC temperature with 250 rpm for 2 h before being induced, to accommodate to the temperature. After that period, 0.05 mM and 0.1 mM of IPTG and 10 mM of arabinose were added. The cultures were incubated for 24 h at 250 rpm under these conditions. After each one of these expression conditions the cells were harvested by centrifugation for 10 min at 4oC and 3,500 rpm. The cell pellet was stored at -80oC for further analysis. 5.1.5 E. coli cells lysis and sample preparation The pelleted E. coli cells with the plasmids were re-suspended with a lysis buffer (75 mM Tris-HCl, 300 mM NaCl, 200 mM EDTA, 10 µM BHT, pH=7.7 adjusted with HCl and NaOH, 1.1 mM PMSF, powered lysozyme 10 mM and 3 U of DNAse/RNAse and 0.1% triton-X). After re-suspension cells were incubated at room temperature for 30 min under constant shaking. After incubation, the cells were sonicated for 30 sec with 70% potency and were left on ice for 30 sec. The cells were then centrifuged for 20 min at 4oC at 12,000 rpm. The supernatant was collected in a new micro centrifuge tube. The protein concentration of each sample was measured using Bradford assay, as previously described in chapter 4. The samples were then prepared using 2X Laemmli buffer (Biorad, CA, USA) and transferred to a 95oC heat block for 5 min. These were used for the SDS page and western blotting. 107 5.1.6 SDS-Page with Coomassie Blue staining and Western Blotting Mini PROTEAN TGX SDS PAGE gels (Biorad, CA, USA) were used to run the samples, as previously described in Chapter 4. After the run, the gels were removed from the cast and pre-fixed with 50% Methanol, 10% Acetic Acid and 40% deionized water for 30 min under constant shake. 0.25% of Coomassie Blue R-250 Dye (Thermo Fisher Scientific, MA, USA) was added to the solution for 2 h until the gel was uniformly blue. The solution was removed and the gel was de-stained for 4 h with 5% methanol, 7.5% Acetic Acid and 87.5% deionized water solution. The gel was then put in a plastic cover and scanned. For western blot, the same procedure as previously described in Chapter 4 was used. The primary antibody used for the constructs was a 6X His Tag Monoclonal Antibody (Invitrogen, MA, USA). 5.1.7 RT-qPCR The RNA transcription of Orco and FtsH by the differentially induced cell cultures of E. coli was assessed using RT-qPCR. The cells were harvested and re-suspended in lysis buffer of the PureLink RNA mini kit; only strains grown at 28oC were used for this part. Total RNA was extracted using PureLink RNA mini kit (Invitrogen, MA, USA) according to the manufacturer’s instructions. The obtained RNA was measured using a nanodrop 2000 (Thermo Fisher Scientific, MA, USA) and stored at -80oC. The same concentrations of RNA were used for all the samples for running the RT-qPCR. RT-qPCR was run in 2 steps, as previously described in Chapter 4. The RNA was reverse transcribed to cDNA using iScript™ Advanced cDNA synthesis (Biorad, CA, USA) according to 108 manufacturer’s instructions. The cDNA product was used for the second step, with SSO Advanced™ Universal SYBR Green Supermix (Biorad, CA, USA). The primers used for this part are found in Table 4.2. The manufacturer’s instructions were used for amplification. The reaction was run in a CFX Connect Real-Time PCR Detection System (Biorad, CA, USA). The Ct values were obtained from the samples ran in triplicate (technical) and in duplicate (biological). The ΔΔCt method was used for relative quantification of the expression levels for the Orco and FstH genes with 16S gene as an internal control. Microsoft excel was used for the calculations of the relative quantification. The graphs were made using GraphPad Prism (GraphPad, CA, USA) with one way ANOVA statistical test un-paired test to verify statistical differences. Table 5.2 – Primers used for RT-qPCR Gene Primers (forward and reverse, 5’ to 3’) Orco GTTCAGCCGACAAAATACGTG ACCATAATCAGAGTCCATCTG 16S AAGTCGAACGGTAACAGGAAG AGGCAGTTTCCCAGACATTAC FtsH GGCTTCTATCTTCATCTCCTGG CAAACGACATGGCACCTTTG 109 5.2 Results 5.2.1 pACYC_Orco protein expression For the expression of the A. gambiae olfactory receptor protein, Orco, the gene sequence was codon optimized using the Genscript service (Jinsite Science and Technology, Nanjing Co). The pACYC_Duet1_Orco and pTRC_Orco vectors were built with a 6X His Tag for purification and identification of the heterologous expressed proteins (Figure C.2). After the confirmation of the cloning of the Orco gene in both plasmids, the cloned strains were used for expression of the membrane protein. The cell lysates were used for a protein expression confirmation in SDS-page and Western Blot. We observed that there were no differences in the SDS-page coomassie stained gels when the bacterial cells with the Orco gene plasmid were compared to the normal E. coli C43 strain (Figure 5.1). Orco has a molecular weight of 54kD. The overexpression of Orco could not be confirmed by SDS page. The first construct pACYC_Duet1_Orco was also tested with different expression conditions. It was initially only ran at 37oC overnight either 0.05 mM or 0.1 mM IPTG as inducer. Since no different bands were seen (Figure 5.1), different conditions for Orco expression were tested. Temperature and expression times were changed to 28oC for 16 h and 12oC for 24 h. IPTG concentrations were kept the same 0.05 mM and 0.1 mM. Although all these changes were incorporated, no difference was observed in the SDS page gel (Figure 5.1). In order to understand if the issues were the detection limits of the coomassie dye, a Western blot with the cell lysates was ran. Figure 5.2 shows the western blot image of the constructs. We can 110 observe that there were no bands present, demonstrating that the protein was not able to be expressed in the pACYC_Duet1_Orco plasmid construct in E. coli C43. Figure 5.1 – SDS page stained with comassie blue of pACYC_Duet constructs. Samples loaded from left to right are in the following order: 1 – pACYA_Duet1_Orco 37oC (0.05 mM IPTG), 2 – pACYA_Duet1_Orco 28oC (0.05 mM IPTG), 3 – pACYA_Duet1_Orco 12oC (0.05 mM IPTG), 4 – pACYA_Duet1_Orco 37oC (0.1 mM IPTG), 5 – pACYA_Duet1_Orco 28oC (0.1 mM IPTG), 6 –pACYA_Duet1_Orco 12oC (0.1 mM IPTG), 7 – E. coli C43 37oC 8 – E. coli C43 strain 28oC. We observed no difference between constructs when columns are compared to 7 and 8 corresponding to the untransformed E. coli strain. 111 Figure 5.2 – Western Blot with anti 6X His Tag of pACYC_Duet constructs. Samples loaded from left to right are in the following order: 1 – pACYA_Duet1_Orco 37oC (0.05 mM IPTG), 2 – pACYA_Duet1_Orco 28oC (0.05 mM IPTG), 3 – pACYA_Duet1_Orco 12oC (0.05 mM IPTG), 4 – pACYA_Duet1_Orco 37oC (0.1 mM IPTG), 5 – pACYA_Duet1_Orco 28oC (0.1 mM IPTG), 6 –pACYA_Duet1_Orco 12oC (0.1 mM IPTG), 7 – E. coli C43 37oC 8 – E. coli C43 strain 28oC. 112 5.2.2 RT-qPCR pACYC_Orco Since no expression bands for Orco were present in the gels and Western blots, RT-qPCR from the RNA of the transcripts was run. The ΔΔCt method was used and the 16S gene was chosen as an internal control to calculate relative quantification of the Orco transcripts. The Orco gene is being transcribed when we look at the relative expression of Orco when compared to the untransformed E. coli strains (Figure 5.3). This relative expression was also seen for different IPTG concentrations (0.05 mM and 0.1 mM). The greater amount of IPTG (0.1 mM) had a 4 fold increase when compared to the lower concentration. Showing a p<0.05 for the 0.1 mM IPTG concentration when compared to the others. Since the results observed for the expression of Orco in pACYC_Duet1_Orco were not successful, new constructs were designed to try to express the receptor based on successful literature membrane protein expression strategies156. 113 Figure 5.3 – Relative quantification of Orco transcripts relative to 16S RNA with pACYC_Duet1_Orco construct. We can observe the increase in Orco transcript when 0.1 mM of IPTG is used for expression. The 0.1 mM IPTG has a p<0.05 when compared to all other groups. 114 5.2.3 pTRC_Orco + pBAD33_FtsH and pTRC_Orco protein expression Another set of plasmids were designed to try to express Orco with a different strategy in E. coli. This new strategies followed the literature recommendations for membrane protein expression in E.coli156. The AAA proteinase FtsH, which is found in E. coli, was cloned in the pBAD_33 vector to be co-expressed with Orco. The same expression conditions previously mentioned were used. For the pBAD33 vector the arabinose inducer was used at a 10 mM concentration based on previous laboratory data of protein expression. We observed that the results for the SDS page were again inconclusive. No differences were seen between the C43 normal E. coli strain and the other constructs with Orco and FtsH (Figure 5.4). For that reason, a Western blot with 6X His Tag antibody was run under the same conditions as before. We did not observe any bands in this new Western blot (Figure 5.5), even with the new constructs. Therefore, in order to understand where the problem was located, the RNA transcripts were investigated by using RT-qPCR as mentioned before. 115 Figure 5.4 – SDS page stained with coomassie blue of pTRC_Orco and pTRC_Orco + pBAD33_FtsH constructs. Samples loaded from left to right are in the following order: 1 – pTRC_Orco 28oC (0.05 mM IPTG), 2 – pTRC_Orco 12oC (0.05 mM IPTG), 3 – pTRC_Orco 28oC (0.1 mM IPTG), 4 – pTRC_Orco 12oC (0.1 mM IPTG), 5 – pTRC_Orco +pBAD33_FtsH 28oC (0.05mM IPTG + 10mM Arabinose), 6 – pTRC_Orco + pBAD33_FtsH 12oC (0.05 mM IPTG + 10 mM Arabinose), 7 – pTRC_Orco + pBAD33_FtsH 28oC (0.1 mM IPTG + 10 mM Arabinose), 8 – pTRC_Orco + pBAD33_FtsH 12oC (0.1 mM IPTG + 10 mM Arabinose), 9 – E. coli C43 strain 28oC. We observed no difference between constructs when columns are compared to column 9 that corresponds to the untransformed E. coli strain. 116 Figure 5.5 – Western Blot with anti 6X His Tag of pTRC_Orco and pTRC_Orco + pBAD33_FtsH constructs. Samples loaded from left to right are in the following order: 1 – pTRC_Orco 28oC (0.05 mM IPTG), 2 – pTRC_Orco 12oC (0.05 mM IPTG), 3 – pTRC_Orco 28oC (0.1 mM IPTG), 4 – pTRC_Orco 12oC (0.1 mM IPTG), 5 – pTRC_Orco +pBAD33_FtsH 28oC (0.05 mM IPTG + 10 mM Arabinose), 6 – pTRC_Orco + pBAD33_FtsH 12oC (0.05 mM IPTG + 10 mM Arabinose), 7 – pTRC_Orco + pBAD33_FtsH 28oC (0.1 mM IPTG + 10 mM Arabinose), 8 – pTRC_Orco + pBAD33_FtsH 12oC (0.1 mM IPTG + 10 mM Arabinose), 9 – E. coli C43 strain 28oC. We observed no bands for the detection of a 6X His tag tagged protein. 117 5.2.4 RT-qPCR pTRC_Orco + pBAD33_FtsH and pTRC_Orco Based on the previous results obtained, we ran a RT-qPCR for both constructs: pTRC_Orco alone and pTRC_Orco + pBAD33_FtsH, to troubleshoot the system. The same ΔΔCt method with 16S as internal control was used to measure the relative quantification of Orco and FtsH for the E. coli cells. The results show that both transcripts for Orco and FtsH were increased when compared to the internal control (Figures 5.6, 5.7 and 5.8). The FtsH levels of untransformed E. coli C43 strain were lower when compared to the strain that had been transformed with an extra copy of the gene and induced (Figure 5.8). The levels of relative transcription of Orco for pTRC_Orco and pTRC_Orco + pBAD33_FtsH were similar. When we analyzed the results with a one-way ANOVA test, statistical significant differences were present when 0.1 mM IPTG was compared to other groups with no inducer or 0.05 mM of IPTG. 118 Figure 5.6 – Relative quantification of Orco transcripts relative to 16S RNA with pTRC_Orco construct. Comparison between the Orco transcripts to different concentrations of IPTG inducer (0.05 mM, 0.1 mM and no inducer), demonstrated that the 0.1 mM of IPTG has the highest amount of transcripts. A p<0.05 value was calculated when the 0.1 mM concentration of IPTG was compared to all other groups. 119 Figure 5.7 – Relative quantification of Orco transcripts relative to 16S RNA with pTRC_Orco + pBAD33_FtsH construct. Comparison between the Orco transcripts to different concentrations of IPTG inducer (0.05 mM, 0.1 mM and no inducer), demonstrated that the 0.1 mM IPTG has the highest relative quantification. The addition of the pBAD33_FtsH plasmid increased the expression of Orco in a lower concentration of IPTG when compared to the same concentration of IPTG on Figure 5.6 for the same plasmid. A p<0.05 value was calculated when the 0.1 mM concentration of IPTG was compared to all other groups. 120 Figure 5.8 – Relative quantification of FtsH transcripts relative to 16S RNA with pBAD33_FtsH construct. Comparison between the FtsH transcripts strains with arabinose (ARA), without arabinose and the E. coli strain with no plasmid. It was observed that the 10 mM of ARA has the highest amount of transcripts when compared to the strains with no inducers and the normal E. coli strain. A p<0.05 value was calculated for the 10 mM of ARA when compared to the other strains. 121 5.2.5 RT-qPCR pTRC_Orco + pBAD33_FtsH vs pTRC_Orco vs pACYC_Duet1_Orco We compared all the different constructs to understand if any differences between the RNA expression levels of Orco were present when different plasmids were used (Figure 5.9). We observed that the pACYC_Orco plasmid with the 0.1 mM concentration of IPTG (ORCO 0.1 IPTG) had the highest RNA relative quantification when compared to the others. Figure 5.9 – Relative quantification of Orco transcripts relative to 16S RNA with pTRC_Orco + pBAD33_FtsH, pTRC_Orco and pACYC_Duet1_Orco. The Orco symbol is used for the pTRC_Orco plasmid in the figure, the FtsH corresponds to the pBAD33_FtsH 122 plasmid in the figure. The ORCO symbol corresponds to the pACYC_Duet1_Orco in the figure. The 0.05 IPTG corresponds to the 0.05 mM concentration of IPTG, similar for the 0.1 IPTG that corresponds to a 0.1 mM of IPTG. The 10 ARA corresponds to 10 mM of arabinose. A p<0.001 value was calculated for the pACYC_Duet1_Orco 0.1 mM of IPTG when compared to all other group of constructs. 5.3 Discussion In this study we aimed to clone and express an odorant receptor protein from A. gambiae in E. coli. Orco was individually expressed due to its function as a homomeric ligand gated cationic channel165. The olfactory gene was able to be cloned in the pACYC_Duet1 and pTRC plasmids and transformed into the E. coli C43 strain. The C43 strain has previously been used to try to express olfactory membrane proteins155. We used cloning and expression strategies that were previously shown to be successful for membrane protein expression for this work162. The Walker strains were chosen because of the higher rate of success in the expression of membrane proteins when compared to BL21(DE3)160. Furthermore the genes were codon optimized to increase the probability of expression158. The first proposed cloning system tested in this work, the pACYC_Duet1_Orco, showed no differences in the SDS-Page when compared to the E. coli C43 strains (Figure 5.1). A Western Blot for the Orco 6X His Tag was performed to understand if the detection limit was an issue for the SDS-Page assay. No bands were seen in the Western Blot (Figure 5.2) and we concluded that Orco was not able to be expressed with the pACYC_Duet1 plasmid. In order to understand where the issue was occurring (transcription or translation part) to troubleshoot the 123 system, a RT-qPCR was performed. This method allowed us to access if RNA transcripts were being produced for the inserted Orco gene166. We observed that for pACYC_Duet1_Orco, the amount of RNA transcripts for Orco were present when compared to the untransformed C43 E. coli strain (Figure 5.3). The change in IPTG concentration, from 0.05 mM to 0.1 mM increased the levels of expression of Orco more than two times. We concluded that the transcription part for the pACYC_Duet1_Orco vector was functional. Therefore, we developed a new cloning system to express Orco based on the literature156. Link et al., 2008 expressed G protein-coupled receptors (GPCRs) in E. coli with the use of accessory proteins sucessfully156. Furthermore, from all the tested accessory proteins the FtsH demonstrated the highest yield of protein and activity being the one chosen for our next steps156. FtsH is an AAA+ protease, it is an essential protease that is responsible for the quality control of integral membrane proteins in E. coli167,168. It acts by degrading the proteins that haven’t been successfully folded and integrated in the membrane167,168. Extra copies of FtsH that have been linked to the increase in expression of GPCR in E. coli, were used in the system156. The extra gene for FtsH was cloned in a pBAD33_FtsH plasmid. The protein expression experiments for these new constructs were done under similar temperatures and conditions tested before. The results for the SDS-page gel dyed with Coomassie Blue were also similar to those obtained for pACYC_Duet1_Orco. Since there were no differences seen in the SDS-Page between the E. coli C43 strain and the transformed ones, a Western blot for the 6X His Tag was run. We observed the same results that were previously obtained with pACYC_Duet1_Orco. No bands were seen at the Western Blot at the expected 54kD weight of the Orco. 124 These results were followed by a RT-qPCR, to verify the presence of the genes transcripts of the new constructs. It was observed that the relative quantification of Orco RNA transcripts were present in both strains, the one with pTRC_orco and the one with pTRC_orcro + pBAD33_FtsH system. Both strains had similar results even when FtsH was added to the bacterial system. When comparing all of the RT-qPCR results, we observed that the strain with the highest number of transcripts was ORCO 0.1 IPTG (pACYC_Duet1_Orco with 0.1 mM of IPTG). However, those numbers did not translate into successful protein expression of Orco. All the different constructs, expression temperatures and inducer concentrations, were not effective to produce a working system. No expression bands were seen in the SDS-page gel or in the Western blot (Figures 5.1, 5.2, 5.4 and 5.5). These findings are corroborated by previous studies which demonstrated that the expression of insect odorant receptors in E. coli was not achieved155. The results obtained with the RT-qPCR demonstrated that the problem with the protein expression most likely localized in the translational machine. Even though some successes have been described in the literature, this study could not replicate those results153,156,160. A few reasons for this could be the lack of a post-translational machine in E. coli122. Another issue is the bacterial membrane composition that differs from eukaryotic ones in the lipid composition122. Carraher et al. (2013) had previously showed that the expression of mosquito odorant receptors in E. coli could not be achieved using a similar condition and codon optimization of the genes155. We came to a similar conclusion as Carraher et al. (2013). Therefore, we conclude that E. coli was not the system to express this mosquito olfactory receptor. 125 Chapter 6: Concluding words and perspectives on future work The global aim of this study was to develop new technological platforms that enable the discovery and development of new antimalarial drugs and mosquito behavior-modifying compounds. Both pursuits are important in decelerating the spread of malaria around the globe. The author of the thesis is originally from Brazil and that nation has suffered immensely from the ravages of mosquito-borne diseases such as malaria and, more recently, Zika. To this end, the work has made significant strides in both aspects and has laid strong foundations for future research on these topics. 6.1 Antimalarial chemical space analyzed in unprecedented detail The past decade has witnessed frenetic activity by the pharmaceutical industry and academic community to discover new anti-malarial drugs. Unfortunately, scientific and technical challenges have greatly constricted the discovery pipeline. The necessity for new and improved anti-malarial drugs is now particularly acute owing to the emergence of drug resistant strains of P. falciparum in Southeast Asia. In the first part of the thesis, we used open-source cheminformatics tools deployed on a personal computer to analyze the chemical space traversed by all approved anti-malarial drugs and promising compounds from chemical libraries maintained by pharmaceutical companies and academic research groups. The Bemis-Murcko scaffolds of every molecule were extracted and subsequently a network diagram was generated to assess scaffold similarity between the molecules. The analysis revealed some valuable insights about anti-malarial chemical space that are also corroborated by experimental evidence collected 126 by other research groups. Furthermore, it was observed that the anti-malarial chemical space is disjointed and segregated into a few dominant structural groups, and the chemical network is distributed according to Pareto’s principle. The emergence of a power law phenomenon within the network raises prospects for the use of principles of network thermodynamics to evolve the network to ultimately comprise a population of highly drug-like molecules. Additionally, scaffold similarity can be successfully employed to determine unknown mechanisms of action of drugs and promising candidates; and either formulate compatible combination therapies that exhibit desired pharmacokinetics and minimize the likelihood of emergence of drug resistance or synthesize scaffolds that co-localize two or more distinct structural moieties for polypharmacological activity169. It was also observed that over half of the molecules in anti-malarial space bear no resemblance to other molecules in the collection, which suggests that the pharmacobiology of anti-malarial drugs has not been entirely surveyed. The insights that were unearthed regarding scaffold similarity and the preponderance of a small number of scaffolds in anti-malarial chemical space, the potential use of chemical genomics to completely chart the pharmacobiology of anti-malarial drugs, and the identification of a clear opportunity to develop a vector support machine employing network thermodynamics provides a template for future activities in anti-malarial drug discovery. Lastly, the volume of insights that cheminformatics yields, when contrasted with how relatively straightforward it is to use, lays the groundwork for the use of similar techniques to assess a much larger pharmacoactive space to potentially improve research outcomes in drug discovery in general. 127 6.2 Aiding the quest of better repellents and mosquito behavior-modifying compounds Mosquito-borne diseases kill millions each year, and the death toll is particularly severe in the developing world3. These figures are only going to get worse as the ravages of climate change take hold. Repellents are the first and arguably best line of defense in the fight against mosquito-borne diseases36. They are cheap, scale easily to serve the needs of communities and do not necessitate drastic changes to people’s routines39,40. However, very few repellents have made their way to the market over the past few decades, largely owing to the immense technical challenges associated with screening repellents34. We re-imagined how repellent discovery should occur in the new age of synthetic biology and attempted to construct the mosquito olfactory cascade, including a read-out, in microbial hosts. We initially considered E. coli since it is one of the easiest hosts to manipulate but could not successfully express Orco in the bacterium despite using specialized Walker strains and even co-expressing of FtsH, an AAA protease, that had been previously shown to facilitate expression of G protein-coupled receptors (GPCRs) in E. coli156,160. We identified translation as the problematic step and duly moved to P. pastoris as a potential host. Not only were we able to engineer P. pastoris to successfully express Orco, but the protein exhibited complete functionality, including expected responses to known agonists and antagonists, in specially designed assays that are amenable for translation to high-throughput testing. We also investigated several parameters that putatively control expression and function, including inducer concentrations, time of culturing and assay conditions such as extracellular calcium concentrations in the reaction buffer. The Pichia Orco biosensor behaves comparably to more complicated systems involving mammalian cells such as HEK293 and Xenopus oocytes48,146. However, and arguably of greater significance, the Pichia sensor is modular, easy 128 to deploy, can be rapidly altered to probe other olfactory receptors, is easy to culture, scales seamlessly and is cheaper and faster compared to competing platforms in mammalian and insect cells. The sensor opens new frontiers for repellent discovery that is aligned with the infrastructure and competencies of the pharmaceutical industry, as well as other applications in aromachology. 6.3 Recommendations for future work The platforms developed during the course of this thesis could serve as strong foundational technologies for the development of more refined tools to aid in the fight against malaria and mosquito-borne diseases. The use of cheminformatics tools allowed us to investigate the antimalarial chemical space, which could aid in the investigation of new molecular scaffolds. Our work has already established the identities of the most promising chemical scaffolds for future drug discovery projects. One drug that we identified to be a promising scaffold for future drug discovery, tafenoquine, recently yielded FDA approval for malaria P. vivax treatment60. Among other benefits, researchers could use the information to guide medicinal chemistry efforts, develop schemes for syntheses and semi-syntheses, repurpose drugs, as well as develop better combination therapies. The prospects of combining chemical synthesis with synthetic biology is particularly promising. The next step in the development and refinement of the Pichia biosensor would be to test it against a larger library of molecules with known activity against mosquito olfactory proteins. Another recommended experiment would be to test the sensor in buffer containing higher concentrations of calcium, perhaps as high as 100 mM. We would also like to diversify the 129 catalogue of olfactory biosensors by expressing as many of the olfactory receptors of A. gambiae and A. aegypti in P. pastoris. This library of biosensors could become a transformative research tool for the research community, and facilitate endeavors such as structural biology and medicinal chemistry of mosquito olfaction. We would also like to take steps toward establishing an automated process using the high-throughput screening infrastructure of pharmaceutical R&D operations and possibly refine the assay to be compatible with this workflow. In closing, this thesis has made telling intellectual contributions to the development of microbial-based odorant biosensors and understanding antimalarial chemical space. It is hoped that these tools will be widely used by the research community to tackle the scourge of malaria and other mosquito-borne diseases. 6.4 Doctoral output from Pharmaceutical Sciences PhD program UBC Publications from work in Pharmaceutical Sciences: • Partial deletion of ROCK2 protects mice from high-fat diet-induced cardiac insulin resistance and contractile dysfunction. Soliman H, Nyamandi V, Garcia-Patino M, Varela JN, Bankar G, Lin G, Jia Z, MacLeod KM. Am J Physiol Heart Circ Physiol. 2015 Jul 1;309(1):H70-81. doi: 10.1152/ajpheart.00664.2014. • Attenuation of obesity-induced insulin resistance in mice with heterozygous deletion of ROCK2. Soliman H, Varela JN, Nyamandi V, Garcia-Patino M, Lin G, Bankar GR, Jia Z, MacLeod KM. Int J Obes (Lond). 2016 Sep;40(9):1435-43. 130 Bibliography 1. Enayati, A. & Hemingway, J. Malaria Management: Past, Present, and Future. Annu. Rev. Entomol. 55, 569–591 (2010). 2. A global brief on vector-borne diseases. WHO (2014). 3. 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Expert Rev Clin Pharmacol. 6, 41–47 (2013). 148 Appendices Appendix A A.1 Tables with BM loose framework for datasets analyzed Table A.1 – Endoperoxides, with structure and their BM loose framework Endoperoxides Structure BMS Loose framework Artemisinin CH3H3COOOCH3OO OOOOO Dihydroartemisinin CH3H3CHOOOCH3OO OOOO Artemether H3COOOCH3CH3H3COO OOOO Artesunate CH3H3COOHOOOOCH3OO OOOO 149 Artemisone CH3H3COOCH3OONSOO OSONOOOO 3-Artesanilide H3CSNHOOOCH3CH3H3COO NHOOOO Table A.2 – Anticancer compounds with their structure and their BM loose framework Compound Structure BM loose framework Procarbazine H3C NHHNO NHCH3H3C Vorinostat HONHOOHN Chlorambucil OHONCl Cl 150 Proguanil H3CCH3HNNHHNNHHNCl Chlorproguanil H3CCH3HNNHHNNHHNClCl Table A.3 – Anticancer compounds with their structure and their BM loose framework (contd.) Compound Structure BM loose framework Dehydroepiandrosterone (DHEA) CH3OHOCH3 O Prednisone HOH OOHCH3OHHOCH3 OO Exemestane HOCH3HHH2COCH3 OH2CO Megestrol HOH CH3OCH3HHOCH3 O 151 Table A.4 – Harvard lab set compounds with their structure and their BM loose framework comparison – 1 Compound Structure BM loose framework Proguanil H3CCH3HNNHHNNHHNCl HMS2093O22 H3COOOOOOOOOOH HMS2093C12 ClH3CCH3HNNHHNNHHNCl HMS1920P10 OHNOOAsOHHO O Table A.5 – Harvard lab set compounds with their structure and their BM loose framework comparison – 2 Compound Structure BM loose framework Atovaquone OOHOCl OO 152 HMS2089M14 HOClOO OO Table A.6 – DeRisi lab set compounds with their structure and BM loose framework comparison Compound Structure BM loose framework NPC-1161B H3CNHNCH3NH2OCH3OClClOOHOOH NO Amodiaquine H3C NH3CNHN ClHO NHN Tafenoquine H3CNNHCH3NH2OCH3OFFFOH3C NO Tert-butyl isoquine H3CH3CCH 3NHNHN ClOHHClHCl NHN 153 MMV000570 CH3HNCH3OHN HN N MMV665874 CH3HNCH3ClNNN NFFF HNNNN N DSM265 H3CFFNNN NNHSFFFFF NNN NNH MMV008212 H3C ON CH3NHHO NNH MMV000326 H3CN CH3HNFF F NHN 154 MMV009108 CH3N CH3SO OSNHH3CH3C N SNHN Table A.7 – GSK set compounds with their structure and BMI loose framework comparison Compound Structure BM loose framework 524875 (624875 (CHEMBL)) H3CNCH3HNOClF O AQ-13 CH3OHNCH3NHCH3OON N Primaquine H3CONHONCH2 N Dapsone H3C CH3NNH2NH2NNCl S 155 Pyrimethamine H3CNNH2N NH2Cl N N 541968 (TCMDC-125525, 641968 (CHEMBL), COMMERCIAL) BrNNH2NNH2HCl CH 4NN 543059 (TCMDC-137714, 643059 (CHEMBL)) H3CNNNH3CH3CNH2NH2HCl CH 4NNN Cycloguanil NH2SOOHNN N NNN 210852 - 310852 (CHEMBL) (Tebuquine) CH3CH3H3CNHHOClNHN Cl NHN ELQ-300 H3CFFNNN NNHSFFFFF OHNO 156 Table A.8 – Novartis set compounds with their structure and BMI loose framework comparison Compound Structure BM loose framework GNF-Pf-163 H3CNCH3NHCH3NCH3 N Amodiaqunie H3C NH3CNHN ClHO NHN Tert-butyl isoquine H3CH3CCH3NHNHN ClOHHClHCl NHN GNF-Pf-2277 H3C ON CH3NHNCH3H3C NHN Cycloguanil H3CNNNClH2N NH2CH3 NNN 157 GNF-Pf-806 H3CON NH2NNH2NH3CH3C NNN DSM265 H3CFFNNN NNHSFFFFF NNN NNH GNF-Pf-4215 CH3NNNNHNClCl NHNNNN 158 Appendix B B.1 P.pastoris biosensor raw data for fluorescence values Table B.1 Values for ΔF/Fo for different [VUAA1] in 1mM of [Ca2+] for Orco 21.1 Strain [VUAA1] [Ca2+] buffer n ± SEM Orco 21.1 0.125mM 1mM 0.06 ± 0.02 Orco 21.1 0.250mM 1mM 0.21 ± 0.02 Orco 21.1 0.500mM 1mM 0.70 ± 0.16 Orco 21.1 1.000mM 1mM 1.40 ± 0.10 Orco 21.1 1.500mM 1mM 2.00 ± 0.07 Orco 21.1 2.000mM 1mM 2.10 ± 0.15 Table B.2 – Values for ΔF/Fo for different [VUAA1] in 1mM of [Ca2+] for GS115 Strain [VUAA1] [Ca2+] buffer n ± SEM GS115 0.125mM 1mM 0.07 ± 0.01 GS115 0.250mM 1mM 0.11 ± 0.02 GS115 0.500mM 1mM 0.18 ± 0.04 GS115 1.000mM 1mM 0.21 ± 0.02 GS115 1.500mM 1mM 0.20 ± 0.06 GS115 2.000mM 1mM 0.30 ± 0.06 159 Table B.3 – Values for ΔF/Fo for different [VUAA1] in 5mM of [Ca2+] for Orco 21.1 Strain [VUAA1] [Ca2+] buffer n ± SEM Orco 21.1 0.125mM 5mM 0.32 ± 0.06 Orco 21.1 0.250mM 5mM 0.70 ± 0.13 Orco 21.1 0.500mM 5mM 1.88 ± 0.25 Orco 21.1 1.000mM 5mM 2.64 ± 0.32 Orco 21.1 1.500mM 5mM 2.81 ± 0.41 Orco 21.1 2.000mM 5mM 2.88 ± 0.35 Table B.4 – Values for ΔF/Fo for different [VUAA1] in 5mM of [Ca2+] for GS115 Strain [VUAA1] [Ca2+] buffer n ± SEM GS115 0.125mM 5mM 0.10 ± 0.03 GS115 0.250mM 5mM 0.07 ± 0.03 GS115 0.500mM 5mM 0.15 ± 0.03 GS115 1.000mM 5mM 0.21 ± 0.08 GS115 1.500mM 5mM 0.21 ± 0.09 GS115 2.000mM 5mM 0.28 ± 0.11 Table B.5 – Values for ΔF/Fo for different [VUAA1] in in 1mM and 5mM of [Ca2+] Strain Ligand [Ca2+] buffer Log EC50 Orco 21.1 VUAA1 1mM 0.69 mM Orco 21.1 VUAA1 5mM 0.38 mM 160 Table B.6 – Values for ΔF/Fo for different [Citronella] in 5mM of [Ca2+] Strain [Citronella] V:V [Ca2+] buffer n ± SEM Orco 21.1 0.0625 (1:15) 5mM 0.26 ± 0.01 Orco 21.1 0.125 (1:7) 5mM 0.18 ± 0.03 Orco 21.1 0.250 (1:3) 5mM 0.34 ± 0.02 Orco 21.1 0.500 (1:1) 5mM 0.22 ± 0.04 Table B.7 – Values for ΔF/Fo for different [VUAA1] measurements in different [Citronella] in 5mM of [Ca2+] Strain [Citronella] V:V [Ca2+] buffer n ± SEM Orco 21.1 0.0625 (1:15) 5mM 1.31 ± 0.13 Orco 21.1 0.125 (1:7) 5mM 1.28 ± 0.22 Orco 21.1 0.250 (1:3) 5mM 1.15 ± 0.10 Orco 21.1 0.500 (1:1) 5mM 1.10 ± 0.13 Table B.8 – Values for ΔF/Fo for different [1-octen-3-ol] in 5mM of [Ca2+] Strain [1-octen-3-ol] V:V [Ca2+] buffer n ± SEM Orco 21.1 0.125 (1:8) 5mM 0.30± 0.07 Orco 21.1 0.250 (1:4) 5mM 0.31± 0.03 Orco 21.1 0.500 (1:2) 5mM 0.17± 0.06 161 Appendix C C.1 – Maps of plasmid constructs Figure C.1 – Map of the pPICZA_Orco plasmid used in this study. P. pastoris GS115 transformants bearing this plasmid are referred to as Orco21.1 in this thesis. 162 Figure C.2 – Plasmid maps for the E. coli constructs. From left to right A (pACYC_Duet1_Orco), B (pTRC_orco) and C (pBAD33+FTSH). 163 Appendix D D.1 – Average Ct values for RT-qPCR Table D.1 – Average Ct values for Orco 21.1 and GS115 with different methanol concentrations (first run) Strain Ct Value TAF-1 (Average) Ct Value Orco (Average) Orco 21.1 0.01% MeOH 22.41 21.62 GS115 0.01% MeOH 23.32 31.38 Orco 21.1 0.1% MeOH 21.65 19.89 GS115 0.1% MeOH 22.96 30.44
Thesis/Dissertation
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Chemical and Biological Engineering
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
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Novel approaches to treating and preventing malaria
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