Antimicrobial Resistant Escherichia Coli in Alberta's Rural Well Water

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2017 Antimicrobial Resistant Escherichia coli in Alberta's Rural Well Water

Meyer, Kelsey

Meyer, K. (2017). Antimicrobial Resistant Escherichia coli in Alberta's Rural Well Water (Unpublished master's thesis). University of Calgary, Calgary, AB. doi:10.11575/PRISM/24938 http://hdl.handle.net/11023/3976 master thesis

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Antimicrobial Resistant Escherichia coli in Alberta’s Rural Well Water

Kelsey Meyer

A THESIS

SUBMITTED TO THE FACULTY OF GRADUATE STUDIES

IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE

DEGREE OF MASTER OF SCIENCE

GRADUATE PROGRAM IN MICROBIOLOGY AND INFECTIOUS DISEASES

CALGARY, ALBERTA

JULY, 2017

The consumption of rural well water (RWW) contaminated with antimicrobial resistant

(AMR) Escherichia coli has been linked to human carriage of resistance. Our objective was to determine whether AMR and extended spectrum beta lactamase (ESBL) producing E.coli are present in Alberta’s RWW. Resistant isolates were detected with an agar screen on (up to) 20 isolates from each sample, and AMR was measured with NARMS Sensititre™ panels. Disk diffusion assays detected ESBL-producing E.coli, and spatial clusters of AMR E.coli were assessed using ArcGIS (version 10.4.1) and SaTScan™ (version 9.4.4). Among 1129 samples,

22% contained AMR E.coli including four ESBL-producers. Resistance to three or more classes of antimicrobials was observed in 48% of AMR E.coli isolates, and a significant cluster of AMR

E.coli was detected between Calgary and Lethbridge (p<0.05). Our results suggest AMR and

ESBL-producing E.coli are present in Alberta’s rural well water, posing a risk to human and animal health.

ii Acknowledgements

This thesis would not have been possible without the incredible inspiration and support I received from so many individuals – thank you all for your contributions to my thesis and for being a part of this journey. I owe my deepest gratitude to my supervisor, Dr. Sylvia Checkley, for her unwavering support throughout my degree, for encouraging my academic growth, but also my career development and personal growth. Her continuous support, patience, and knowledge led me to where I am today, and I am so grateful to have had such a caring and encouraging supervisor.

I would also like to thank my committee members, Dr. Rebekah DeVinney and Dr. Karen

Liljebjelke, for their insight and encouragement throughout my studies. Without their knowledge and guidance, this thesis would not have been possible. Thank you to Dr. Marie Louie for her support, knowledge, and career guidance, and for opening my eyes to the world of clinical microbiology. I would also like to acknowledge the generous contributions of Dr. Betty-Ann

Henderson for her guidance with my healthcare epidemiology specialization.

Thank you to the entire team at the Provincial Laboratory for Public Health in Calgary and

Edmonton for their hard work in preparing the samples for this study. A special thank you to

Lorraine Ingham, Nancy Yuen and their team for archiving samples, as well as Dr. Norman

Neumann, Colin Reynolds and Candis Scott for preparing samples and providing insight and knowledge on the project. I would not be here today if it weren’t for Christina Ferrato, Joanne

Callfas and the bacteriology team who hired me as a summer student three years ago and helped me develop a passion for clinical microbiology, so thank you for believing in me. And a particular thank you to Bryanne Rempel, who was my mentor in microbiology and life in general.

iii I would especially like to thank my husband, Dylan Meyer, for his support and love while

I pursued my Master’s degree. He took care of me when I forgot to take care of myself, he listened, asked questions and encouraged me throughout the entire process and I cannot thank him enough for his continued support.

I would like to thank my mother and father, Jacquie and Bruce, and my brother, Nathan for always believing in me and teaching me how to believe in myself. You were all there for me when

I needed encouragement and you kept me sane when I felt overwhelmed. And a special thank you to my late grandmother, Marion, for convincing all of her friends and family I was a doctor, and having them ask me for medical advice. The weird sense of confidence this gave me helped me to believe in myself more than anything else.

I am incredibly thankful for my dear friends, particularly Rebecca Cooper and Kristin

Roeke, for keeping me grounded when my head was in a cloud of literature reviews, and for talking me down in moments of panic. Thank you to my microbiology colleagues, in particular Erik, Alya and Rai for cheering me on and sharing in my love of microbiology.

Finally, I would like to acknowledge the funders that made this project possible, in particular Alberta Agriculture and Forestry, as well as the Government of Alberta for the Queen

Elizabeth II Graduate Student Scholarship, and the University of Calgary for the Eyes High Travel

Award.

iv Dedication

I would like to dedicate this thesis to my husband, Dylan, for always encouraging and never doubting me. When I lost track of time and worked late into the evening he would have dinner ready for me, and when I was making a mountain out of a molehill he would talk me down and help me to rationalize the situation.

I would also like to dedicate this thesis to my parents and grandmother for teaching me how to believe in myself and for supporting me every step of the way.

v Table of Contents Abstract ...... ii Acknowledgements ...... iii Dedication ...... v List of Tables ...... x List of Figures and Illustrations ...... xi List of Symbols, Abbreviations and Nomenclature ...... xv Epigraph ...... xix

CHAPTER ONE: LITERATURE REVIEW, OBJECTIVES AND INTRODUCTION TO THE PROJECT ...... 1 1.1 Antimicrobial and antibiotics ...... 2 1.1.1 Antimicrobial stewardship in Canada ...... 3 1.2 Resistance to antimicrobials ...... 5 1.2.1 Measuring AMR ...... 5 1.2.2 Impact and spread of AMR ...... 6 1.2.3 Natural AMR ...... 8 1.2.4 Acquisition of resistance determinants ...... 9 1.2.5 Types of antimicrobials, and mechanisms of AMR ...... 12 1.2.5.1 Nucleic acid synthesis inhibitors ...... 14 1.2.5.2 Inhibition of cell wall synthesis ...... 18 1.2.5.3 Inhibition of protein synthesis ...... 21 1.3 Resistance reservoirs ...... 26 1.3.1 Resistance in humans ...... 26 1.3.2 Resistance in Animals ...... 29 1.3.2.1 Resistance in food animal agriculture ...... 29 1.3.3 Resistance in crop agriculture ...... 31 1.3.4 Resistance in aquaculture ...... 32 1.3.5 Resistance in the environment ...... 32 1.3.5.1 Resistance in water ...... 33 1.4 Groundwater and well water ...... 35 1.4.1 Groundwater contamination ...... 36 1.4.2 Testing groundwater contamination ...... 37 1.4.2.1 Escherichia coli ...... 38 1.5 One Health ...... 39 1.6 Resistance surveillance in Canada ...... 40 1.7 Summary ...... 40 1.8 Objectives and hypotheses ...... 42 1.9 Goals of this thesis ...... 43

CHAPTER TWO: POPULATIONS OF E.COLI IN ALBERTA’S RURAL WELL WATER ...... 46 2.1 Background ...... 46 2.2 Specific methods ...... 49 2.2.1 Sample collection and sample inclusion ...... 49 2.2.1.1 Sample preparation ...... 50 2.2.1.2 Growth and isolation of E.coli colonies ...... 50

vi 2.2.2 Screening presumptive E.coli isolates for AMR ...... 51 2.2.3 Biochemical testing for species identification ...... 52 2.2.4 Measuring minimum inhibitory concentrations ...... 52 2.2.5 Quality control ...... 54 2.2.5.1 Agar screen quality control ...... 54 2.2.5.2 Biochemical tests ...... 55 2.2.5.3 Minimum inhibitory concentrations ...... 56 2.2.6 Pulsed-field gel electrophesis (PFGE) ...... 56 2.2.6.1 Samples ...... 56 2.2.6.2 Cell suspension ...... 57 2.2.6.3 Preparation of agarose plugs ...... 57 2.2.6.4 Cell lysis ...... 58 2.2.6.5 Washing agarose plugs ...... 58 2.2.6.6 Digestion of DNA in agarose plugs ...... 58 2.2.6.7 Preparing the electrophoresis chamber ...... 59 2.2.6.8 Preparing agarose gels ...... 59 2.2.6.9 Running electrophoresis ...... 60 2.2.6.10 Staining and analyzing the PFGE agarose gel ...... 60 2.3 Results ...... 61 2.3.1 Biochemical profiles ...... 61 2.3.2 Banding patterns ...... 62 2.3.3 Antimicrobial resistance profiles ...... 62 2.3.4 Number of distinct isolates per sample ...... 63 2.4 Discussion ...... 64 2.5 Conclusions ...... 67

CHAPTER THREE: ANTIMICROBIAL RESISTANT E.COLI DETECTION ...... 69 3.1 Background ...... 69 3.1.1 Current Knowledge on AMR bacteria in Groundwater ...... 69 3.2 Specific methods ...... 71 3.2.1 Sample collection and sample inclusion ...... 71 3.2.2 Growth and isolation of E.coli colonies ...... 71 3.2.3 Screening presumptive E.coli isolates for AMR ...... 72 3.2.3.1 Selection and isolation of presumptively resistant E.coli isolates .....72 3.2.4 Biochemical testing for species identification ...... 73 3.2.5 Measuring MICs ...... 73 3.3 Quality control ...... 73 3.4 Results ...... 73 3.4.1 Mixed populations within samples ...... 73 3.4.2 Rural Well Water Samples ...... 73 3.4.3 Total Sample Numbers ...... 75 3.4.4 AMR E.coli Detected via Agar Screen Plate Method ...... 76 3.4.5 Results of API® Biochemical Tests ...... 76 3.4.6 Antimicrobial Susceptibility Testing via NARMS SensititreTM Panels ...... 77 3.4.6.1 AMR E.coli isolates with resistance to each of the 14 antimicrobials tested ...... 81 3.4.6.2 Antimicrobial Classes with Resistance ...... 82 vii 3.4.6.3 Multi-drug Resistance by Antimicrobial ...... 84 3.4.6.4 Multi-class Resistance by Class ...... 85 3.4.6.5 Common resistance profiles among AMR E.coli isolates ...... 86 3.4.6.6 Negative controls ...... 88 3.4.6.7 Isolates intermediate to antimicrobials ...... 89 3.5 Discussion ...... 89 3.6 Conclusions ...... 95

CHAPTER FOUR: RELEVANCE TO HUMAN AND ANIMAL HEALTH ...... 97 4.1 Background ...... 97 4.1.1 E.coli and human health ...... 97 4.1.2 Phylogenetic groups of E.coli ...... 98 4.1.3 ESBLs ...... 99 4.1.4 AmpC β-Lactamases ...... 101 4.1.5 Carbapenems ...... 102 4.1.6 ESBL Testing ...... 102 4.1.7 STEC ...... 103 4.2 Specific Methods ...... 105 4.2.1 ESBL Methods ...... 105 4.2.1.1 Inclusion criteria ...... 105 4.2.1.2 Determining phylogenetic grouping ...... 105 4.2.1.3 ESBL- and AmpC-producing E.coli detection via disk diffusion ...108 4.2.2 STEC Methods ...... 109 4.2.2.1 Inclusion criteria ...... 109 4.2.2.2 Quantitative polymerase chain reaction for stx1 and stx2 genes .....109 4.2.2.3 Controls for qPCR of stx1 and stx2 genes ...... 110 4.2.2.4 qPCR cycling conditions...... 110 4.2.2.5 Stx1/IAC Multiplex qPCR ...... 110 4.2.2.6 Stx2 Simplex qPCR ...... 111 4.3 Results ...... 112 4.3.1 Phylogenetic groups ...... 112 4.3.2 AmpC- and ESBL-producing E.coli ...... 113 4.3.3 STEC ...... 117 4.4 Discussion ...... 118 4.4.1 ESBL and AmpC Discussion ...... 118 4.4.2 STEC Discussion ...... 122 4.5 Conclusions ...... 123

CHAPTER 5: SPATIAL ANALYSIS OF AMR E.COLI IN ALBERTA ...... 124 5.1 Background ...... 124 5.1.1 Agriculture and population within Alberta ...... 124 5.1.2 Factors affecting groundwater contamination ...... 124 5.1.3 Spatial analysis ...... 126 5.1.4 Objectives ...... 126 5.2 Specific Methods ...... 127 5.2.1 Data Sources ...... 127 5.2.2 Geolocation ...... 127

viii 5.2.3 Spatial analysis methodology ...... 128 5.2.3.1 Spatial clustering ...... 128 5.3 Results ...... 129 5.3.1 Clusters and proportions of AMR positive samples ...... 129 5.3.2 Clusters and proportions of MCR positive samples ...... 132 5.3.3 Clusters and proportions of ESBL positive samples ...... 134 5.3.4 Clusters and proportions of samples with resistance to AMG ...... 136 5.3.5 Clusters and proportions of samples with resistance to CEPH ...... 138 5.3.6 Clusters and proportions of samples with resistance to CHL ...... 139 5.3.7 Clusters and proportions of samples with resistance to MAC ...... 141 5.3.8 Clusters and proportions of samples with resistance to PCN ...... 142 5.3.9 Clusters and proportions of samples with resistance to QNL ...... 143 5.3.10 Clusters and proportions of samples with resistance to SULF ...... 144 5.3.11 Clusters and proportions of samples with resistance to TET ...... 146 5.4 Discussion ...... 148 5.5 Conclusions ...... 151

CHAPTER SIX: FINAL REMARKS AND FUTURE DIRECTIONS ...... 152

REFERENCES ...... 154

APPENDIX A: CHAPTER TWO SUPPLEMENTARY DATA ...... 183

APPENDIX B: CHAPTER THREE SUPPLEMENTARY DATA ...... 186

APPENDIX C: CHAPTER FOUR SUPPLEMENTARY DATA ...... 191

APPENDIX D: CHAPTER FIVE SUPPLEMENTARY DATA ...... 192

ix List of Tables

Table 1.1. The predominant use of groundwater in Canada by Province (adapted from Government of Canada, 2013)...... 36

Table 2.1. Antimicrobial resistance profiles of control used for agar screens...... 55

Table 3.1 Diversity of Resistance Profiles among samples with multiple distinct AMR E.coli isolates...... 78

Table 3.2. Number of isolates with each antimicrobial resistance profile among 285 AMR E.coli isolates...... 87

Table 4.1: Primers used for PCR Amplification during Phylogenetic Testing ...... 106

Table 4.2. Primers and Probes used for the stx1/IAC Multiplex qPCR Reaction...... 111

Table 4.3. Primers and Probes used for the stx2 Simplex qPCR Reaction...... 112

Table 4.4. Resistance profiles and phylogenetic groups of ESBL-producing E.coli...... 114

Table 4.5. Resistance profiles and phylogenetic groups of AmpC-producing E.coli...... 114

Supplementary Table 2.1. Number of distinct biotypes, banding patterns, antibiograms and number of E.coli isolates picked per sample for nine rural well water samples...... 183

Supplementary Table 2.2. Range, MIC50 and MIC90 for up to 20 E.coli isolates from each of nine E.coli positive rural well water samples, as determined by NARMS™ Sensititre panels...... 184

Supplementary Table 3.1. Number and percentage of isolates with resistance to each of 14 antimicrobials tested by NARMS Sensititre™ panels...... 187

Supplementary Table 3.2. Number and percentage of isolates with resistance to each of eight classes of antimicrobials tested by NARMS Sensititre™ panels...... 188

Supplementary Table 3.3. Antimicrobial resistance profiles of all E.coli isolates with intermediate MIC values to any of the 14 antimicrobials tested on the NARMS Sensititre™ panel...... 189

Supplementary Table 4.1. Antimicrobial classes with resistance among 22 AmpC- and four ESBL-producing E.coli...... 191

Supplementary Table 5.1. Results of non-significant clusters of resistance as determined by SaTScan (version 9.4.4)...... 192

x List of Figures and Illustrations

Figure 1.1. Environmental reservoirs of AMR genes highlighting the associations between potential reservoirs of AMR bacteria, adapted from Wellington et al. (2013) [171]...... 28

Figure 1.2. Overview of experimental design and project aims...... 45

Figure 2.1. The number of distinct biochemical profiles among multiple E.coli isolates from each of nine rural well water samples...... 61

Figure 2.2. Number of distinct banding patterns among multiple E.coli isolates from each of nine rural well water samples...... 62

Figure 2.3. Number of distinct biotypes, banding patterns and antibiograms among multiple E.coli isolates from nine rural well water samples...... 64

Figure 3.1. Number of samples (a) submitted for testing at ProvLab and (c) tested for AMR per year during the study period. Differences in the number of samples (b) submitted for testing and (d) tested for AMR every two months from 2006 to 2016. Samples tested for AMR tested positive for E.coli upon submission to ProvLab...... 75

Figure 3.2. Non-E.coli species identified by API® 20E biochemical strips with growth on agar screen after selection from X-Gluc plates...... 77

Figure 3.3. Percentage of samples with one, two or three distinct resistance profiles among up to 20 E.coli isolates screened for resistance from 1129 E.coli positive rural well water samples...... 78

Figure 3.4. Number of AMR E.coli isolates with resistant and intermediate profiles for 14 antimicrobials tested by NARMS Sensititre™ panels...... 82

Figure 3.5. Number of E.coli isolates resistant (or intermediate) to each of eight classes of antimicrobials...... 84

Figure 3.6. Number of antimicrobials with resistance among 285 AMR E.coli isolates...... 85

Figure 3.7. Number of antimicrobial classes with resistance among 285 AMR E.coli isolates...... 86

Figure 3.8. Public Health poster outlining the levels of AMR E.coli observed in Alberta’s rural well water sources...... 96

Figure 4.1. Decision tree used to determine the phylogenetic group of each E.coli strain based on the results of PCR amplification of chuA and yjaA genes and the DNA fragment TSPE4.C2...... 108

Figure 4.2. Summary of phylogenetic group results for ESBL- and AmpC-producing E.coli...... 116

xi Figure 4.3. Number of ESBL- and AmpC-producing E.coli isolates with resistance to each of eight classes of antimicrobials...... 117

Figure 5.1. Proportion of AMR E.coli positive wells in Alberta (at the sample level aggregated to census divisions) out of E.coli positive rural well water samples submitted to ProvLab Calgary between 2006 and 2016 by census division...... 130

Figure 5.2. Clusters of high proportions of AMR E.coli positive samples out of E.coli positive rural well water samples submitted to ProvLab Calgary between 2006 and 2016...... 131

Figure 5.3. Antimicrobial resistance results for E.coli positive rural well water samples submitted to ProvLab Calgary between 2006 and 2016 tested for AMR E.coli...... 131

Figure 5.4. Proportion of multi-class resistant E.coli positive wells in Alberta (at the sample level aggregated to census divisions) out of E.coli positive rural well water samples submitted to ProvLab Calgary between 2006 and 2016 by census division...... 133

Figure 5.5. Clusters of both low proportions of MCR E.coli positive samples out of E.coli positive rural well water samples submitted to ProvLab Calgary between 2006 and 2016...... 133

Figure 5.6. Rural well water samples submitted to ProvLab Calgary between 2006 and 2016 positive for ESBL-producing E.coli...... 135

Figure 5.7. Cluster of rural well water samples positive for ESBL-producing E.coli submitted to ProvLab Calgary between 2006 and 2016. Cluster in red has a p-value > 0.05...... 135

Figure 5.8. Proportion of wells positive for E.coli resistant to aminoglycosides in Alberta (at the sample level aggregated to census divisions) out of E.coli positive rural well water samples submitted to ProvLab Calgary between 2006 and 2016 by census division. ...137

Figure 5.9. Proportion of wells positive for E.coli resistant to cephalosporins in Alberta (at the sample level aggregated to census divisions) out of E.coli positive rural well water samples submitted to ProvLab Calgary between 2006 and 2016 by census division. ...138

Figure 5.10. Proportion of wells positive for E.coli resistant to chloramphenicol in Alberta (at the sample level aggregated to census divisions) out of E.coli positive rural well water samples submitted to ProvLab Calgary between 2006 and 2016 by census division. ...140

Figure 5.11. Clusters of rural well water samples positive for E.coli resistant to chloramphenicol antimicrobials. Results displayed as a proportion of E.coli positive well water samples that were submitted to ProvLab Calgary between 2006 and 2016 and tested for AMR E.coli...... 140

Figure 5.12. Proportion of wells positive for E.coli resistant to macrolide antimicrobials in Alberta (at the sample level aggregated to census divisions) out of E.coli positive rural

xii well water samples submitted to ProvLab Calgary between 2006 and 2016 by census division...... 141

Figure 5.13. Proportion of wells positive for E.coli resistant to penicillin antimicrobials in Alberta (at the sample level aggregated to census divisions) out of E.coli positive rural well water samples submitted to ProvLab Calgary between 2006 and 2016 by census division...... 142

Figure 5.14. Proportion of wells positive for E.coli resistant to quinolone antimicrobials in Alberta (at the sample level aggregated to census divisions) out of E.coli positive rural well water samples submitted to ProvLab Calgary between 2006 and 2016 by census division...... 143

Figure 5.15. Proportion of wells positive for E.coli resistant to sulfonamide antimicrobials in Alberta (at the sample level aggregated to census divisions) out of E.coli positive rural well water samples submitted to ProvLab Calgary between 2006 and 2016 by census division...... 145

Figure 5.16. Proportion of wells positive for E.coli resistant to tetracycline antimicrobials in Alberta (at the sample level aggregated to census divisions) out of E.coli positive rural well water samples submitted to ProvLab Calgary between 2006 and 2016 by census division...... 147

Figure 5.17. Clusters of rural well water samples positive for E.coli resistant to tetracycline antimicrobials. Results are displayed as a proportion of E.coli positive well water samples that were submitted to ProvLab Calgary between 2006 and 2016 and tested for antimicrobial resistant E.coli...... 147

Supplementary Figure 2.1. Raw PFGE image of seven E.coli from a single sample (sample number 1614). Lane 1, XbaI-digested DNA of S.enterica serovar Braenderup H9812, used as size standard; lane 2, isolate 1; lane 3, isolate 6; lane 4, isolate 7; lane 5, isolate 11; lane 6, XbaI-digested DNA of S.enterica serovar Braenderup H9812; lane 7, isolate 13; lane 8, isolate 14; lane 9, isolate 15; lanes 10 and 11, XbaI-digested DNA of S.enterica serovar Braenderup H9812...... 185

Supplementary Figure 3.1. Proportion of submitted rural well water samples positive for E.coli every two months from August 1, 2006 to August 31, 2016...... 186

Supplementary Figure 5.1. Cluster of rural well water samples with low proportions of antimicrobial resistant E.coli. Results displayed as a proportion of E.coli positive well water samples that were submitted to ProvLab Calgary between 2006 and 2016 and tested for antimicrobial resistant E.coli. (Clusters in black have a p value > 0.05)...... 196

Supplementary Figure 5.2. High-proportion clusters of rural well water samples with multi- class resistant E.coli. Results displayed as a proportion of E.coli positive well water samples that were submitted to ProvLab Calgary between 2006 and 2016 and tested for antimicrobial resistant E.coli. (Clusters in black have a p value > 0.05)...... 196

xiii Supplementary Figure 5.3. Cluster of rural well water samples positive for E.coli resistant to aminoglycoside antimicrobials. Results displayed as a proportion of E.coli positive well water samples that were submitted to ProvLab Calgary between 2006 and 2016 and tested for antimicrobial resistant E.coli. (Clusters in black have a p value > 0.05)...... 197

Supplementary Figure 5.4. Clusters of rural well water samples positive for E.coli resistant to cephalosporin antimicrobials. Results displayed as a proportion of E.coli positive well water samples submitted to ProvLab Calgary between 2006 and 2016 and tested for antimicrobial resistant E.coli. (Clusters in black have a p value > 0.05)...... 197

Supplementary Figure 5.5. Clusters of rural well water samples positive for E.coli resistant to macrolide antimicrobials. Results displayed as a proportion of E.coli positive well water samples that were submitted to ProvLab Calgary between 2006 and 2016 and tested for antimicrobial resistant E.coli. (Clusters in black have a p value > 0.05)...... 198

Supplementary Figure 5.6. Clusters of rural well water samples positive for E.coli resistant to penicillin antimicrobials. Results displayed as a proportion of E.coli positive well water samples that were submitted to ProvLab Calgary between 2006 and 2016 and tested for antimicrobial resistant E.coli. (Clusters in black have a p value > 0.05)...... 198

Supplementary Figure 5.7. Clusters of rural well water samples positive for E.coli resistant to quinolone antimicrobials. Results displayed as a proportion of E.coli positive well water samples that were submitted to ProvLab Calgary between 2006 and 2016 and tested for antimicrobial resistant E.coli. (Clusters in black have a p value > 0.05)...... 199

Supplementary Figure 5.8. Point data and clusters of rural well water samples positive for E.coli resistant to quinolone antimicrobials. Data is shown at the sample level for rural well water samples submitted to ProvLab Calgary between 2006 and 2016 and tested for antimicrobial resistant E.coli. (Clusters in black have a p value > 0.05)...... 199

Supplementary Figure 5.9. Clusters of rural well water samples positive for E.coli resistant to sulfonamide antimicrobials. Results displayed as a proportion of E.coli positive well water samples that were submitted to ProvLab Calgary between 2006 and 2016 and tested for antimicrobial resistant E.coli. (Clusters in black have a p value > 0.05)...... 200

xiv List of Symbols, Abbreviations and Nomenclature

Symbol/Abbreviation Definition

AME Aminoglycoside-modifying enzyme

AMG Aminoglycoside

AMP Ampicillin

AMR Antimicrobial resistant or antimicrobial resistance

AUG2 Amoxicillin/clavulanic acid 2:1

AXO Ceftriaxone

AZI Azithromycin

BHI Brain Heart Infusion

BSA Bovine Serum Albumin

CAT Chloramphenicol acetyltransferase

CEPH Cephalosporin

CHL Chloramphenicol

CIPARS Canadian Integrated Program for Antimicrobial Resistance Surveillance

CLSI Clinical and Laboratory Standards Institute

CTX-M Cefotaxime-Munich

CVMA Canadian Veterinary Medical Association

DHFR Dihydrofolate reductase

DHPS Dihydropteroate synthase

xv EHEC Enterohemorrhagic E.coli

EPEC Enteropathogenic E.coli

ERIC-PCR Enterobacterial repetitive intergenic consensus polymerase chain reaction

ESBL Extended spectrum beta-lactamase

FIS Sulfisoxazole

FOX Cefoxitin

GEN Gentamicin

HUS Hemolytic Uremic Syndrome

MAC Macrolide

MCR Multi-class resistance

MDR Multi-drug resistance

MFS Major Facilitator Superfamily

MIC Minimum inhibitory concentration

MLST Multilocus sequence typing

MUG 4-methylumbelliferyl-beta-D-glucuronide

NAL Nalidixic acid

NARMS National Antimicrobial Resistance Monitoring System

NDM-1 New Delhi Metallo-beta-lactamase-1

ONPG Ortho-Nitrophenyl-Galactopyranoside

OR Odds ratio

xvi PABA Para-aminobenzoic acid

PCN Penicillin

PCR Polymerase Chain Reaction

PFGE Pulsed-Field Gel Electrophoresis

PHAC Public Health Agency of Canada

PBP Penicillin binding protein

PR Prevalence ratio

ProvLab Provincial Laboratory for Public Health

QNL Quinolone

CIP Ciprofloxacin qPCR Quantative polymerase chain reaction

REP-PCR Repetitive Extragenic Pallindromic Polymerase Chain Reaction

ST Sequence type

STEC Shiga toxin-producing E.coli

STR Streptomycin stx1 Shiga toxin 1 stx2 Shiga toxin 2

SULFA Sulfonamide

SXT Sulfamethoxazole/Trimethoprim

TET Tetracycline tRNA Transfer Ribonucleic Acid

xvii TSB Tryptic soy broth

WWTPs Wastewater treatment plants

XNL Ceftiofur

xviii Epigraph

“When antibiotics first came out, nobody could have imagined we’d have the resistance problem we face today. We didn’t give bacteria credit for being able to change and adapt so fast.”

– Bonnie Bassler

xix

CHAPTER ONE: LITERATURE REVIEW, OBJECTIVES AND INTRODUCTION TO

THE PROJECT

The use of antimicrobials has saved millions of lives in the past 70 years since their widespread use in human medicine began, causing a dramatic increase in life expectancy.

However, bacteria quickly developed mechanisms to resist these antimicrobials, creating global concern about the future effectiveness of currently available treatments. The global spread of antimicrobial resistant (AMR) bacteria and AMR genes are of concern, due to the increased mortality and length of hospital stay for patients infected with AMR bacteria [1].

Antimicrobial resistance is a growing problem globally, as bacteria causing infections and illnesses are becoming increasingly resistant. This is resulting in increased human illness and death, increased healthcare costs and length of treatment, and negative side effects from treatment with multiple and more powerful medications [2, 3]. Increased prevalence of AMR can be attributed to exposure to selective pressures, transmission of AMR bacteria/genes [4] and human- to-human transmission. Furthermore, transfer of AMR bacteria can occur through ingestion of contaminated food and water [5-7], posing a threat to human and animal populations due to potential transfer of AMR genes to resident microbes within the gut. Previous studies have found that risk factors for colonization with AMR bacteria include being hospitalized, attending a daycare, living with another individual colonized with AMR bacteria, and consuming water contaminated with resistant bacteria [8-10].

Antimicrobial resistance poses a threat to the current and future treatment of infectious diseases in human and animals, and is a major environmental concern worldwide due to the release of AMR bacteria and genes from anthropogenic sources and the overuse of antimicrobials in

human and animal medicine [1, 11-14]. Increased dissemination of AMR bacteria and genes in the environment is linked to horizontal gene transfer, genetic mutation and selective pressures caused by antimicrobials, biocides, chlorine, disinfectants and heavy metals [15-19]. Hotspots for AMR have been observed in environmental regions subjected to anthropogenic pressures such as municipal wastewater, pharmaceutical effluents, aquaculture facilities and animal husbandry facilities [20-22]. Environmental sites subjected to anthropogenic pressures often have higher bacterial loads and subtherapeutic concentrations of antimicrobials when compared with environments less affected by anthropogenic influences [20-22], providing a suitable environment for AMR bacteria to persist and transfer resistance genes.

1.1 Antimicrobial and antibiotics

In the 19th century, microorganisms were discovered to be the root cause of human disease, and secondary metabolites produced by certain organisms were discovered as effective antimicrobial agents against bacteria [23]. In 1941, Selma Waksman used the term antibiotic to describe a small molecule made by a microbe that antagonizes the growth of another microbe. The term antimicrobial is more broad and refers to a substance which can be natural, synthetic or semisynthetic and is able to inhibit the growth of a microorganism while causing little or no damage to the host [24]. Throughout this text the term antimicrobial will be used in order to include a broader set of substances than the term antibiotic, although this text will focus on antimicrobials with antibacterial activity.

The development of penicillin (PCN), streptomycin (STR), chloramphenicol (CHL) and tetracycline (TET) between 1945 and 1955 marked the beginning of the “antibiotic age”. The discovery and development of antimicrobials, together with better hygiene and vaccination

programs, greatly reduced illness and death caused by infectious disease [25]. Since the introduction of antimicrobials, however, these compounds (and bacteria conferring resistance to them) have spread throughout nearly all ecosystems worldwide [26].

Antimicrobials can be secreted extracellularly by bacteria, exerting their effects on neighboring organisms [27], and in natural environments it is difficult to produce an antimicrobial yield high enough to inhibit the growth of neighboring microbes [28]. This suggests the primary role of antimicrobials in natural environments is not to inhibit the growth (bacteriostatic) or kill

(bactericidal) other microorganisms but, rather, to act as a means of bacterial communication [28,

29]. Antimicrobials can be released into natural environments through the release from anthropogenic or veterinary sources [20-22], and the presence of sub-therapeutic concentrations of antimicrobials can lead to the development of AMR [30-32].

Although many antimicrobials are based on natural antibiotics produced by bacteria, many others are of synthetic or semi-synthetic origin. To be a successful antimicrobial, the target of interest must be different between eukaryotic and prokaryotic cells to avoid harming the host while successfully inhibiting bacterial growth [33]. The main targets of antimicrobial agents include bacterial cell wall synthesis (b-lactams, glycopeptides, bacitracin), protein synthesis (TET, macrolides, phenicols, lincosamides, streptograms, AMG), nucleic acid synthesis or function

(nitroimidazoles, quinolones, rifamycins, nitrofurans), folic acid synthesis (SULF and trimethoprim), or cell membrane function (polymyxins).

1.1.1 Antimicrobial stewardship in Canada

The use of antimicrobials in human and veterinary medicine is crucial to the treatment of infectious diseases in the context of both public health and veterinary medicine. However, the

overuse of antimicrobials in both humans and animals has been indicated as a major driver of AMR

[3, 34, 35] and reducing the overuse of antimicrobials has been suggested as a measure to reduce

AMR development [3, 36].

The Canadian Veterinary Medical Association (CVMA) and the Public Health Agency of

Canada (PHAC) support the judicious use of antimicrobials by veterinarians and physicians to reduce the risk of AMR development in Canada [37, 38]. The CVMA supports a One Health approach to the use of antimicrobials in veterinary medicine, calling for collaborations between veterinary and human health sciences in relation to the prudent use of antimicrobials and control of AMR [37].

Many countries, including Canada and the U.S., have policies on extra-label drug use in food-producing animals aimed at reducing the risk of AMR bacteria in both human and veterinary medicine [37]. Although many antimicrobials outlined below may be used in human and animal medicine, it is important to note that guidelines for on-label and extra-label use of antimicrobials in Canada are outlined, requiring that the dose, frequency and duration stated on the federal label are adhered to. Any extra-label use of antimicrobials must have strong evidence-based support

[37]. The U.S. Food & Drug Administration has approved fluoroquinolones and cephalosporins

(CEPH) for extralabel use in food-producing animals only under specific treatment conditions [39] and in Canada, CHL has been banned for use in food-producing animals [40]. Together, using a

One Health framework aimed at reducing AMR development in humans and animals, PHAC and

CVMA promote judicious use of antimicrobials in Canada [37, 38].

1.2 Resistance to antimicrobials

Antimicrobial resistance is broadly defined as the “ability of a microorganism to withstand the effects of biocides or the agents that are intended to kill or control them” [41]. A microorganism is classified as resistant if the bacteria is not killed or inhibited by a concentration found in vivo concentration which the majority of organisms within that strain is susceptible to, and/or a concentration that is “acting upon the majority of cells in that culture” [41]. In a clinical setting,

AMR is recognized when a patient is not responding to treatment.

1.2.1 Measuring AMR

The two main methods of measuring AMR include genetic methods in search of genes conferring resistance to specific antimicrobials, and phenotypic methods measuring the ability of an organism to grow in the presence of specific concentrations of antimicrobials. Quantitative phenotypic tests for AMR include agar dilution and broth microdilutions [42], all of which produce a minimum inhibitory concentration (MIC) which is the lowest antimicrobial concentration that can inhibit visible growth of a bacteria [24]. Qualitative phenotypic testing includes disk diffusion or concentration gradient tests which measure the zone of inhibition for each antimicrobial [42].

In North America, results of AMR testing follow standardized methods outlined by the

Clinical and Laboratory Standards Institute (CLSI) in the CLSI guidelines [42, 43]. All susceptibility testing methods require species-specific breakpoints to interpret the results as susceptible, intermediate or resistant. Breakpoints, also known as interpretive criteria, are reported to clinicians and used in surveillance reports and have been defined in a variety of ways depending on the field of study. Within the scientific and medical community, various types of breakpoints have been defined. Microbiological or wild-type breakpoints are often used in surveillance and

refer to the MIC for a specific antimicrobial that is used to distinguish wild-type populations from those that have acquired resistance mechanisms [44]. Alternatively, clinical breakpoints refer to

MICs separating bacteria with a high level of treatment success in comparison to those where treatment is more likely to fail [44]. On a population level, epidemiological breakpoints are used to identify emerging resistance in specific microorganism populations [45]. When developing breakpoints, factors such as in vitro susceptibility, pharmacokinetics and pharmacodynamics are considered.

There are many genes conferring resistance to particular antimicrobials, and combinations of genes resulting in multi-drug resistance (MDR). However, our study will focus on phenotypic testing for AMR bacteria, as the presence of specific resistance genes does not necessarily correlate with a resistance phenotype. Phenotypic antimicrobial susceptibility testing can predict how a microorganism will respond to antimicrobial treatment as it would in a human or animal host.

1.2.2 Impact and spread of AMR

Antimicrobial resistance has become a major public health issue globally. When antimicrobials are used in human and animal medicine, bacteria can resist these unfavorable conditions and multiply at the expense of bacteria which are inhibited by the antimicrobials.

Furthermore, differing bacterial species can transfer resistance genes, contributing to the dissemination of AMR bacteria within different ecosystems. In polymicrobial environments, AMR bacteria can transfer genes between different species, including transferring genes between environmental and pathogenic bacteria.

Through natural and acquired resistance, many bacteria of importance to human and animal medicine have become resistant to nearly every class of antimicrobial used in clinical and

veterinary medicine. Antimicrobial resistance is a leading public health concern of the 21st century and threatens the current treatment of bacterial infections as well as the long-term value of antimicrobials. Pathogenic AMR bacteria are an increasing cause of morbidity and mortality, particularly in developing regions of the world [46]. Infections caused by AMR bacteria may lead to longer hospitalization, longer treatment time, failure of treatment, toxicity and an increased likelihood of death [1]. Patients may require multiple and more powerful medications, resulting in increased healthcare expenses. The cost of AMR in the US is an estimated $21-34 billion per year and more than eight million extra days spent in hospital [1].

The rise in AMR has led to concern of a ‘post-antibiotic era’ in which common infections and minor injuries that previously had been treatable, can once again cause serious complications and/or death.

Bacteria naturally evolve in response to selective pressures, allowing the AMR strains to survive and flourish, however the emergence and spread of AMR can be accelerated by anthropogenic pressures. For instance, the overuse of antimicrobials in human and animal medicine can provide a selective advantage for AMR organisms [47] in the gut, allowing for the overgrowth of AMR bacteria. The AMR organisms, in some cases may continue to flourish after the selective pressure is withdrawn. Furthermore, the use of disinfectants or biocides for water treatment, household cleaning, etc. can act as a selective pressure, facilitating the emergence of

AMR bacteria [48, 49].

Another important factor affecting the emergence of AMR bacteria is the decline of new antimicrobial development. The discovery of new antimicrobial classes has declined since the mid-

1980s, with very few new antimicrobials being discovered in the past 30 years [50, 51]. As our

armory of antimicrobials has declined and bacteria have been exposed to selective pressures favoring the development of AMR, bacteria have become increasingly resistant worldwide.

Resistance determinants are often discovered soon after an antimicrobial is introduced to clinical use, and the treatment of bacterial pathogens with antimicrobials directly affects the resistance frequency in these microorganisms [52-55].

After a resistance phenotype emerges within a previously susceptible species, how quickly and efficiently this organism spreads is impacted by the degree of resistance expressed, the ability of the organism to tolerate the resistance mechanism, and many other factors [56]. Therefore, the number of species affected and how quickly a new phenotype spreads is often unpredictable.

1.2.3 Natural AMR

Microorganisms have been present on earth for over 3.8 billion years, continually adapting and responding to environmental selective pressures [57]. AMR is a natural property of bacteria and occurs in environments with little to no human impacts, including wildlife and remote regions of the earth [12, 58-61]. For instance, DNA sequences from Alaskan permafrost date back to the

Late Pleistocene age and have shown resistance to many antimicrobials used in human medicine including TET, PCN, and vancomycin, among others [14, 62]. Although many AMR genes are ancient and predate the selective pressures of modern antimicrobial use, clinically relevant AMR genes known to be in pathogenic and opportunistic pathogens have increased in the past 70 years, mainly in areas with strong human influences [15, 63-65].

Natural microbial communities undergo selective pressures to resist antimicrobials produced by neighboring fungi and bacteria that naturally occur in the environment [57]. Most strains that produce antimicrobials also carry genes encoding resistance to those same

antimicrobials [66, 67], although retrospective studies indicate that AMR genes have been present in bacteria that did not produce antimicrobials before drugs were commonly used [68-70].

Alternative cellular and environmental roles of known resistance genes have been suggested. For example, mutations in ribosomal proteins resulting in resistance to STR, spectinomycin or macrolides (MAC) can result in altered phenotypes and bacteria with these mutations have had a selective advantage under certain laboratory conditions [12]. Thus, the widespread AMR observed in nature may have occurred due to unrelated selective pressures, with these mutations conferring a selective advantage in their environment.

AMR occurred, even on plasmids, before the extreme selection pressure of the antimicrobial era, and these bacteria were predisposed to exchange resistance elements. These abilities likely facilitated the rapid emergence of AMR in pathogenic bacteria.

1.2.4 Acquisition of resistance determinants

Antimicrobial resistance mechanisms can originate as protective mechanisms in environmental bacteria in response to environmental selection pressures, but AMR can also occur in the environment due to contamination from bacteria originating in humans and animals [12, 15,

60, 71, 72]. Bacteria may have “intrinsic” or “accidental” resistance in which proteins with other cellular functions also contribute to an AMR phenotype. For example, efflux pumps may be selected in environments with potentially threatening compounds, as they provide a mechanism of exporting toxins out of the cell [73, 74]. Intrinsic resistance mechanisms are a result of inherent functional or structural characteristics of the bacteria, allowing for the bacteria to tolerate a certain antimicrobial or class of antimicrobials [75]. Intrinsic resistance can be due to innate production of drug inactivating enzymes, an inability of the antimicrobial to enter the cell, extrusion of the

antimicrobial due to genetically encoded exporters, and/or a lack of affinity for the antimicrobial

[73-75].

Resistance in pathogenic bacteria is complicated by the ability of bacteria to acquire resistance through gene mutations or genetic recombination by horizontal gene transfer

(conjugation, transformation and transduction) [72, 76-78]. AMR genes can be obtained in the form of mobile elements, including plasmids, transposons and integrons [79], which allow for mobilization and transfer of resistance genes to the same or different bacterial species in any environment, including the gut microbiome [80, 81]. Of particular importance to our study is the ability of bacteria to transfer resistance determinants in environmental reservoirs with bacteria from human, animal and environmental sources.

Acquired AMR results from biochemical processes that are encoded by specific genes.

AMR may arise due to (1) mutation of cellular genes, (2) the acquisition of exogenous AMR genes and/or (3) mutation of acquired genes [56].

Antimicrobial targets which are integral to cellular growth and/or maintenance are proteins encoded by genes. Genes encoding these proteins may undergo mutation(s) that alter the antimicrobial-target interaction. For instance, STR resistance is often a ribosomal mutation [82], and fluoroquinolone resistance can occur due to mutations of cellular topoisomerase genes [83].

Once a mutation and subsequent AMR occurs, a mechanism may or may not persist, depending whether the mechanism is tolerable to the cell or not [56]. If fitness is reduced, the mutation will likely not be favored in an environment free of selective pressures. However, certain mutations conferring AMR can also increase bacterial fitness [84] and may persist in the absence of selective pressures. Furthermore, in situations where mutational resistance confers a fitness cost, compensatory mutations can often result in less of a fitness cost [56].

Genes conferring AMR can be acquired through many mechanisms. Natural transformation occurs when bacteria uptake naked DNA from the environment, recombining this

DNA into the bacterial chromosome resulting in a functional gene [56]. If the recombined gene product is less susceptible to inhibition than the native protein(s), AMR can result. For example, penicillin-binding proteins (PBP) genes in Streptococcus pneumoniae are often taken up as exogenous DNA [56, 85]. However, many bacteria are incapable of natural transformation and the most common mechanism for genetic exchange is the transfer of conjugative plasmids [56, 86].

Although certain plasmids have a narrow host range, others can transfer between a broad range of species. Resistance determinants can be transferred between plasmids (including between plasmids of narrow and broad host ranges), and can integrate into the chromosome of recipient strains [56]. In addition, bacteriophages can be utilized for genetic exchange of AMR genes. This may occur via specialized or generalized transduction in which small portions of chromosomal

DNA or a specific size of plasmid or chromosomal DNA are integrated into the host genome.

Transposons can encode conjugation functions, enabling DNA transfer between bacterial chromosomes [56]. Non-conjugative transposons can also transfer genes through integration into transferable plasmids, as is the case with specific transposons conferring resistance to erythromycin and vancomycin [56, 87, 88].

Acquired genes may also undergo mutations which result in AMR for certain antimicrobials. Ampicillin (AMP) was the first PCN developed with clinical activity against gram- negative bacilli including E.coli and within a few years, AMR was detected in many E.coli strains due to the production of a plasmid-mediated β-lactamase referred to as TEM. In the years following this discovery, many new β-lactamases were discovered with increased resistance to β- lactam antimicrobials. Many β-lactamases have been derived the TEM enzyme due to mutations

within the blaTEM gene [56], highlighting the ability of bacteria to develop resistance through mutations in previously acquired genes.

1.2.5 Types of antimicrobials, and mechanisms of AMR

Antimicrobial resistance can be a result of many diverse mechanisms and these often differ based on the species of bacteria in question. Specific mechanisms of AMR and modes of dissemination within bacterial species evolve over time. However, as this study focuses on AMR in E.coli, the mechanisms of resistance discussed will focus on E.coli and the antimicrobial classes included will focus on the classes tested in this study. E.coli can have numerous diverse mechanisms of resistance all occurring together, and is considered a model organism for studying resistance in fecal bacteria [89].

There are four general mechanisms of resistance that occur in bacteria, including (1) reduced cellular uptake preventing the antimicrobial from reaching its target, (2) general or specific efflux pumps resulting in efflux from the cell, (3) modification or degradation of the antimicrobial, rendering it inactive and (4) antimicrobial target modification by mutation or specialized enzymatic changes [41, 90].

For an antimicrobial to be effective, it must reach its target. However, resistance mechanisms can target barrier mechanisms, resulting in an antimicrobial having reduced access to its target [56]. In many gram-negative bacteria, a reduced number of porins have been linked to cefoxitin (FOX) and ceftazidime resistance [91, 92], although reduced porin activity must be in combination with β-lactamase resistance to confer high-level resistance [56]. Another barrier mechanism reducing the effectiveness of specific antimicrobials includes a barrier in the cytoplasmic membrane. For example, aminoglycosides (AMG) cannot be transported across the

cytoplasmic membrane in anaerobic environments, as their movement is an oxygen-dependent process [93]. Therefore, many bacteria demonstrate resistance to AMG in anaerobic environments.

Another common mechanism of resistance is the use of general or specific efflux pumps resulting in antimicrobial expulsion from the cell. Efflux pumps are often located in the cytoplasmic membrane, utilizing proton motive force to aid in antimicrobial efflux. There are three main types of drug efflux pumps. Firstly, major facilitator superfamily (MFS) efflux pumps are often found in gram-positive bacteria. Two well-known MFS pumps include the MdeA in S. aureus and EmrB in E.coli involved in multidrug efflux [56, 94, 95]. The second type of efflux pump are the resistance-nodulation-division family, which include the AcrAB-TolC multidrug efflux pump of E.coli [56, 96]. The third type of efflux pumps are the small multidrug resistance family which includes EmrR of E.coli [56, 97]. The combination of various efflux pumps may result in higher MICs to certain antimicrobials or combinations of antimicrobials than a single pump alone [98]. Furthermore, an efflux pump on its own may not be sufficient to confer AMR, however in combination with other resistance mechanisms clinically significant levels of AMR may be reached.

Another common resistance mechanism involves modifying or degrading the antimicrobial, rendering the agent inactive. Many antimicrobial-modifying enzymes have been discovered, including β-lactamases, aminoglycoside-modifying enzymes (AME) and CHL acetyltransferases. Certain bacteria are able to acquire modifying enzymes, whereas others may be intrinsic to the species. For example, chromosomal β-lactamases are intrinsic to most gram- negative bacilli, often expressed at very low levels conferring resistance to very susceptible β- lactams such as Klebsiella pneumoniae resistance to AMP via the SHV-1 chromosomal enzyme.

In E.coli, β-lactamases are expressed at such low levels that resistance is not apparent to any β-

lactams under normal circumstances [56]. Furthermore, AMEs can be intrinsic in certain species

[99, 100]. Modifying enzymes generally confer high levels of resistance to target antimicrobials, such as the TEM-1 β-lactamase in E.coli resulting in an increased AMP MIC from 8 to >10,000

µg/mL [56].

A fourth mechanism of resistance includes modification of the target molecule by mutation or specialized enzymatic changes. Because antimicrobial-target molecule interactions are usually very specific, a small change in the target molecule can have a great effect on antimicrobial binding affinity. For example, erythromycin ribosomal methylases confer resistance to MAC, lincosamides and streptogramin B [101], and PBP modifications can significantly alter affinities for β-lactam antimicrobials [85]. Modification of PBPs is a common resistance mechanism in gram-positive bacteria, whereas β-lactamase production is more common in gram-negative bacilli. Βeta- lactamases produced by gram-positive bacteria diffuse into the external environment, whereas those produced by gram-negative bacteria are contained within the periplasmic space, which may explain the success of β-lactamases in producing resistance in gram-negative bacilli [56].

Additional examples of altered target modification include altered cell wall precursors conferring resistance to glycopeptides, mutated DNA gyrase and topoisomerase IV conferring resistance to fluoroquinolones, and ribosomal protection conferring resistance to tetracyclines (TET). The level of resistance resulting from target modification is variable and depends on the ability of the target to perform its wild type function [56].

1.2.5.1 Nucleic acid synthesis inhibitors

Nucleic acid synthesis is required for bacterial survival, and the highly conserved mechanisms of synthesizing DNA and RNA allow for the development of antimicrobials with a

broad spectrum of activity against bacteria. Fluoroquinolones, sulfamethoxazole/trimethoprim

(SXT) and rifamycins target DNA/RNA synthesis, although this review will focus on fluoroquinolones and SXT as these antimicrobial classes will be investigated in our study.

1.2.5.1.1 Quinolones

1.2.5.1.1.1 Quinolones: Use and mechanism of action

Cell division, mRNA transcription and DNA synthesis all require topoisomerase-catalyzed

DNA strand breakage [102-104]. Quinolone (QNL) antimicrobials (ex. Ciprofloxacin, levofloxacin, nalidixic acid, etc.) exploit this cellular requirements for topoisomerases and were developed as derivatives of nalidixic acid (NAL) which was introduced in the 1960s for treatment of urinary tract infections [105]. Due to the poor absorption, moderate antibacterial activity and relatively high MICs of bacteria against NAL, its use has declined in human and animal medicine.

However, new QNL antimicrobials have increased antibacterial activity and enhanced pharmacokinetic properties when compared to NAL [106]. Many modifications to the QNL structure have produced compounds with varying pharmacokinetic and antimicrobial properties.

Ciprofloxacin (CIP) has enhanced gram-negative and positive activity, whereas other QNL modifications have increased activity against gram-positive bacteria.

Quinolone antimicrobials have a bactericidal effect, targeting DNA gyrase (topoisomerase

II) and topoisomerase IV to prevent DNA strands from rejoining to complete DNA synthesis [107-

109]. In the presence of QNL antimicrobials, double-stranded DNA breaks are formed and trapped by covalently linked topoisomerases that are functionally compromised [110-113]. The formation of these QNL-topoisomerase-DNA complexes results in DNA replication machinery arrest at blocked replication forks leading to the inhibition of DNA synthesis [108].

In general, the QNL antimicrobials are bactericidal, with activity against a wide range of gram-positive organisms and various gram-positive aerobes, and have additional activity against intracellular pathogens.

Ciprofloxacin is one example of a QNL antimicrobial used for a variety of diseases in human medicine including the treatment of anthrax exposure, bone and joint infections, bacterial enteric infections, gonococcal infections, infectious diarrhea, UTIs, skin infections, respiratory tract infections, and many others [114].

1.2.5.1.1.2 Quinolones: Mechanisms of AMR

Resistance to fluoroquinolones can be chromosomally or plasmid mediated [115] and cross-resistance is common among many closely related QNL antimicrobials. The primary mechanisms of resistance to QNL include mutations in DNA gyrase and topoisomerase IV, as well as active efflux [116]. In gram-negative bacteria, mutations in the GyrA subunit occur more often than GyrB, and in gram-positive bacteria the most common mutations occur in topoisomerase IV

[116]. Although a single mutation in gyrA of E.coli can sufficiently cause high-level resistance to

NAL [117-119], additional mutations in gyrA and/or topoisomerase IV genes are required for high- level resistance to other fluoroquinolones [117, 118, 120, 121]. High level resistance generally occurs due to second-step mutations including efflux pump regulator mutations which result in increased efflux and high-level MDR.

1.2.5.1.1.3 Sulfonamides: Use and mechanism of action

Folic acid is an essential nutrient required for the growth and development of animals, humans and bacteria. Although humans and animals can rely on dietary folic acid, bacteria produce

folic acid with a synthesis pathway that is absent in humans and animals, making this a very specific target for antimicrobials. Sulfonamides (SULF) are structural analogues of para- aminobenzoic acid (PABA) and act to inhibit dihydropterate synthetase, an enzyme that facilitates the production of folic acid from PABA. By blocking the production of folic acid and many enzymes required for biogenesis of purine bases required for RNA formation, many cellular processes are inhibited including protein synthesis, metabolic processes, growth and replication.

This results in an overall bacteriostatic effect, although high concentrations (including in urine) can be bactericidal [122].

Sulfonamides (ex. sulfamethoxazole, sulfisoxazole, etc.) are active against a broad spectrum of gram-positive organisms and many gram-negatives, as well as Plasmodium and

Toxoplasma spp. Due to widespread resistance, this class of antimicrobials are commonly used in combination with other drugs [123].

1.2.5.1.1.4 Sulfonamides and Trimethoprim: Mechanisms of AMR

Mutational resistance to trimethoprim involves promoter mutations resulting in the overproduction of dihydrofolate reductase (DHFR) enzymes in E.coli [56, 119]. Horizontal gene transfer can also occur, with the acquisition of dhfr genes which increase resistance to trimethoprim.

Resistance to SULF often occurs as a result of point mutations in chromosomal dihydropteroate synthase (DHPS) in the folic acid pathway [124]. Sulfonamide resistance can also occur due to horizontal gene transfer with dhps genes conferring resistance. Genes conferring resistance to SULF are often incorporated into MDR integrons which are frequently integrated into transferable plasmids [56].

1.2.5.2 Inhibition of cell wall synthesis

Bacterial cells maintain their structural integrity and survive unfavorable environmental conditions in part due to their encasement in peptidoglycan layers. Peptidoglycan layers are maintained using two enzymes, transglycosylases and PBPs [113]. Many antimicrobial classes target cell wall synthesis with a high degree of success, as this mechanism is integral to the survival of bacteria. Glycopeptide, bacitracin, cycloserine, fosfomycin and β-lactam (PCN, CEPH, carbapenem and monobactam) antimicrobials target bacterial cell wall synthesis. For the purposes of our study, this review will focus on CEPH and PCN, which are β-lactam antimicrobials with bactericidal properties

Beta-lactam antimicrobials including PCN, CEPH and carbapenems are characterized by a four-membered, nitrogen-containing β -lactam ring at the core of their structure. The β-lactam structure is analogous to the terminal D-alanyl-D-alanine dipeptide of peptidoglycan, allowing this antimicrobial to act as a substrate for PBPs. Upon binding, the PBPs are unable to perform their role in cell wall biosynthesis, thus inhibiting transpeptidation and peptidoglycan cross-linking

[113]. Beta-lactam antimicrobials exert bactericidal action only on cells undergoing cell wall synthesis, and in gram-negatives a loss of the peptidoglycan layer causes a weakened cell wall resulting in osmotic changes that may induce cell lysis [113, 125-127]. Furthermore, intracellular prevention of cross-linking can result in lipoteichoic acid release and the subsequent suicide response in exposed gram-positive bacteria [128].

1.2.5.2.1 Cephalosporins: Use and mechanism of action

Cephalosporins are classified based on their time of introduction and spectrum of activity in groups referred to as generations. First generation CEPHs (ex. cephalexin) were the first to be

produced and are highly effective against gram-positive cocci. They are generally used to treat skin and soft-tissue infections. Second generation CEPHs (ex. FOX) are slightly less active against gram-positive cocci than first generations. However, they are also active against certain gram- negative bacilli and are useful for polymicrobial infections and enteric bacterial infections. The third generation of CEPHs (ex. ceftriaxone, cefotaxime, etc.) have higher activity against gram- negative bacteria than both first and second generations, although their activity against gram- positives differs between the specific antimicrobials in this group. Third generation CEPHs are active against Enterobacteriaceae that do not produce ampC β-lactamases or extended-spectrum

β-lactamases (ESBL). Fourth generation CEPHs (ex. cefepime) have strong activity against both gram-positive and gram-negative bacteria, and enhanced activity against ESBL-producing

Klebsiella pneumoniae and Escherichia coli, as well as ampC β-lactamases-producing

Enterobacteriaceae [129, 130]. The newest class of CEPHs are fifth generation (ex. ceftaroline) and their activity against gram-positives and gram-negatives are similar to third- and fourth- generation CEPHs. Fifth generation CEPHs have enhanced activity for specific drug-resistant microbes including methicillin-resistant Staphylococcus aureus and Enterococcus faecalis [130].

Cephalosporins are often used as a last resort in clinical and veterinary settings, and their use is avoided if more narrow-spectrum antimicrobials are available. Their use in human medicine is for a broad variety of infections of the respiratory system, urinary tract, and skin [131].

1.2.5.2.2 Penicillins: Use and mechanism of action

Penicillin was discovered by Alexander Fleming in 1928 and this was the first class of antimicrobials to be used in medicine. Penicillins (ex. AMP, oxacillin, piperacillin, etc.) kill susceptible bacteria through the inhibition of transpeptidases that catalyze the final step of cell

wall biosynthesis, therefore inhibiting the final cross-linking of peptidoglycan synthesis. Penicillin antimicrobials are active against many gram-positive bacteria and some gram-negative cocci, although widespread resistance to PCN has reduced their efficacy in human and animal medicine.

Penicillins are often used to treat UTIs, meningococcal meningitis, respiratory infections, and many other bacterial infections [132].

1.2.5.2.3 β-lactams: Mechanisms of AMR

Resistance to β-lactam antimicrobials has become a worldwide health crisis, particularly with the rise in clinical cases of New Delhi metallo-beta-lactamase 1 (NDM-1) strains [133].

Production of β-lactamases are the most widespread and threatening mechanism of resistance to

β-lactam antimicrobials (PCN, CEPH, monobactams and carbapenems) [134], and functions by hydrolyzing the β-lactam ring of the antimicrobial [135]. The production of β-lactamases cleaving the β-lactam ring of the antimicrobial interferes with cell wall synthesis inhibition in the bacteria.

Furthermore, mutational changes to PBPs or acquisition of different PBPs can result in the inability of the antimicrobial to bind PBP and inhibit cell wall synthesis. It is believed that PBPs and β- lactamases diverged from a common ancestor, as PBPs possess the ability to catalyze β-lactam antimicrobials to a lesser extent.

Resistance mediated by PBPs for normally susceptible bacteria can be due to (1) overproduction of PBPs; (2) acquiring a foreign low affinity PBP; (3) recombination of a susceptible PBP with a resistant type; and (4) PBP point mutations causing lower affinity for the

β -lactam antimicrobial [56]. Furthermore, resistance to β-lactams depends on the number of β- lactam molecules relative to the number of targets.

Recombination with foreign DNA can result in resistance to β-lactam antimicrobials. This resistance phenomenon occurs when native, susceptible PBPs recombine with those from more resistant species, and is restricted to species capable of undergoing natural transformation or taking up environmental DNA [56]. Furthermore, point mutations in the pbp genes may result in lower affinity for specific β-lactam antimicrobials.

1.2.5.2.3.1 Extended-spectrum β-lactamases

Over time, β-lactamases with an expanded spectrum have developed. These enzymes, referred to as ESBLs, are able to resist newly developed β-lactam antimicrobials, all sharing the ability to hydrolyze aztreonam and third generation CEPH, but are inhibited by clavulanic acid.

This group of enzymes are plasmid-mediated, diverse and complex in their properties, and are rapidly evolving. These enzymes will be discussed in further detail in chapter 4.

1.2.5.3 Inhibition of protein synthesis

The process of mRNA translation is comprised of three phases including initiation, elongation and termination, with the ribosome being the main structural unit responsible for all three phases. The ribosome consists of two ribonucleoprotein subunits, 50S and 30S, and protein synthesis inhibitors generally target one of these two subunits. The 30S subunit binds messenger

RNA and begins initiation, whereas the 50S subunit binds transfer RNA (tRNA) and controls elongation. Tetracyclines and AMG target the 30S ribosomal subunit, whereas MAC, streptogramins and phenicols target the 50S ribosomal subunit. This review will focus on TET,

AMG, MAC and phenicols [136].

1.2.5.3.1 Tetracyclines: Use and mechanism of action

Tetracyclines (ex. doxycycline, minocycline, TET) were the first major group of antimicrobials to be classified as ‘broad spectrum’ due to their wide range of activity against gram- positive and negative organisms including obligate anaerobes [137, 138].

This class of antimicrobials was quickly adopted as a method of treatment for chlamydia, rickettsiae, mycoplasma and protozoa infections in humans [138, 139]. The widespread use of TET in humans, animals and some plants and insects impacted the emergence of resistance among many bacterial species including E.coli [140].

Tetracycline antimicrobials inhibit the 30S ribosomal subunit by preventing the aminoacyl tRNA from binding the RNA-ribosome complex [137, 141, 142], inhibiting protein synthesis within the cells.

1.2.5.3.2 Tetracyclines: Mechanisms of AMR

The primary mechanisms of TET resistance in E.coli include: (1) target protection via ribosomal protection proteins, (2) active efflux, and (3) acquisition via horizontal gene transfer

[56, 143]. In Escherichia spp., tet(A), tet(B), tet(C), tet(D) and tet(E) are involved in active efflux processes [142]. Limited knowledge exists about tet(I) and tet(Y) but they are believed to be involved in efflux as well. One gene in particular, tet(E), differs from other TET resistance genes in E.coli as it is associated with large non-mobile, non-conjugative plasmids [144, 145] and has also been found in the chromosome [146]. This particular gene is limited in its distribution and is predominantly found in aquatic environments and polluted marine sediment [142, 147].

Furthermore, the tet(M) gene is shared between marine bacteria and human pathogens. It is widely distributed among numerous bacterial species derived from diverse human, animal and environmental sources including the aquatic environment [142, 148]. In addition to these

resistance genes, certain multidrug efflux pumps have demonstrated clinical resistance to TET and other compounds [149].

1.2.5.3.3 Aminoglycosides: Use and mechanism of action

Aminoglycosides can be narrow-spectrum (ex. STR) which are mainly active against aerobic, gram-negative bacteria, or expanded-spectrum (ex. gentamicin) which are active against aerobic bacteria as well as many gram-positive organisms [150]. This class of antimicrobials is often used to treat Enterobacteriaceae infections, as they are particularly active against aerobic gram-negative bacilli. Clinically, AMG are often used in combination with glycopeptides or β- lactams to treat serious infections caused by gram-positive cocci [151].

Aminoglycosides (ex. amikacin, gentamicin, STR, etc.) alter translation at various steps including initiation, elongation and termination, and are able to bind the 30S ribosomal subunit at the tRNA acceptor A-site. Binding to this A-site inhibits ribosomal translocation through tRNA mismatching which can result in protein mistranslation [113, 152, 153].

1.2.5.3.4 Aminoglycosides: Mechanisms of AMR

Resistance to AMG can emerge through four main mechanisms: (1) prevent ribosomal binding by altering the target site (ribosome), (2) reduced cell permeability, (3) efflux pumps and

(4) AMEs causing enzymatic inactivation. The most common mechanism of resistance in terms of frequency and level of resistance are the AMEs [154], which can be transferred between bacteria via transposons, plasmids and integrons [155]. On rare occasions it is suggested that certain AMEs may also confer resistance to fluoroquinolones [156]. These enzymes can be phosphotransferases, nucleotidyltransferases or adenyltransferases and acetyltransferases. Their function is to

covalently modify specific amino or hydroxyl groups resulting in reduced aminoglycoside- ribosome binding.

1.2.5.3.5 Macrolides and Phenicols: Use and mechanism of action

Within the ribosome, the 50S subunit is comprised of 5S, 23S RNA and 30 ribosomal proteins. Antimicrobials with the ability to block the 50S ribosomal subunit include CHL, MAC and clindamycin. Macrolides and CHL are typically bacteriostatic with a few species- or treatment- specific exceptions that are bactericidal [113].

Macrolide antimicrobials (ex. azithromycin, erythromycin, etc.) are characterized by a large lactone ring and are effective against aerobic gram-positive and negative bacteria in human and animal medicine [157]. They bind to the 23S rRNA reversibly, blocking elongation and therefore inhibiting protein synthesis.

Phenicols (ex. CHL) work in a similar manner to inhibit ribosomal peptidyl transferase activity, competing with mRNA for ribosomal binding sites required to form peptide bonds. By reversibly binding the 50S ribosomal subunit, phenicol antimicrobials prevent aminoacyl-tRNA from binding and therefore inhibit peptidyl transferase.

Phenicols are broad spectrum antimicrobials highly effective against gram-positive and - negative bacteria, anaerobes, spirochetes, chlamydia, rickettsia and mycoplasma [158]. However, its use in food-producing animals has been banned in the USA and Canada as it has been shown to cause blood dyscrasias in people [158, 159].

1.2.5.3.6 Macrolides: Mechanisms of AMR

Resistance to one MAC generally results in cross-resistance between other MAC antimicrobials, and can be plasmid-mediated or intrinsic. Furthermore, resistance can be inducible or constitutive. The majority of gram-negative bacteria are intrinsically resistant to low levels

MAC. However, for E.coli to resist clinical levels of MAC, further AMR mechanisms occur through acquisition or mutation [160, 161]. In gram-positive bacteria, the most common resistance mechanisms include (1) altered ribosome structure (including methylation) and (2) efflux pumps

[56].

Efflux pumps may confer resistance to MAC, most commonly Mef in gram-positive and

Acr-AB-TolC in E.coli and other gram-negative bacteria [162]. The Acr-AB-TolC efflux pump confers MDR to various broad spectrum antimicrobials including MAC [163].

The most common mechanism of altered ribosome structure conferring resistance to MAC antimicrobials is the methylation of ribosomes. Methylated ribosomes prevent erythromycin binding [101] and confer resistance to MAC, related lincosamides and streptogramin B, and is often accomplished by various erythromycin ribosomal methylase genes [157].

1.2.5.3.7 Chloramphenicols: Mechanisms of AMR

The primary mechanisms conferring resistance to CHL are: (1) decreased accumulation and (2) acetyltransferases [56, 158].

Decreased accumulation of CHL in bacterial cells occurs due to active efflux from MDR efflux pumps as well as CHL-specific efflux pumps. Many MDR efflux pumps that exist in both gram-positive and gram-negative bacteria. Alternatively, CHL-specific efflux pumps are able to remove this antimicrobial from bacterial cells with no cross-reaction with other antimicrobials [74,

164].

Chloramphenicol acetyltransferases (CATs) can be produced by bacteria to inactivate CHL antimicrobials, conferring extremely high levels of resistance in organisms expressing these enzymes. Although CATs may differ between species, structural similarities exist between different CAT variants [165].

1.3 Resistance reservoirs

There are many areas contributing to the propagation and dissemination of AMR genes in the global resistome including food animals, companion animals, ethanol fuel producers, aquaculture, vegetation, seed crops, fruit, industrial and household antibacterial chemicals and humans (particularly hospitals, urban and rural communities and extended care facilities). The two main sources of both AMR bacteria and AMR genes to the global resistome are human sewage and manure/litter from animals, although lesser contributors exist including storm water, wild animals, birds, pets and aquatic life [166]. Factors within specific environments may influence the propagation of AMR bacteria/genes such as the presence of antimicrobials or heavy metals [167,

168].

1.3.1 Resistance in humans

Antimicrobial resistance in humans may occur through many routes. Firstly, the consumption of antimicrobials can provide selective pressures for the survival of AMR bacteria within the gut microbiome. Human antimicrobial use has increased globally, with a 36% increase from 2000 to 2010 [169], which may affect the amount of AMR bacteria residing in human populations. However, one study in an isolated Bolivian population found high levels of AMR

E.coli despite the lack of access to modern health care and limited contact with outside

communities. Resistance genes isolated from humans within this group closely matched those from antimicrobial-exposed environments [170]. The AMR within this community arose in the absence of antimicrobials as a selective pressure, suggesting that AMR came from outside the community.

In human populations, AMR bacteria are often acquired as nosocomial infections in hospitals and intensive care units. This has been attributed to the large number of patients in the same general area, and the frequent and prolonged use of antimicrobial therapies. As the majority of people will receive care in a medical setting within their lifetime, there is a potential risk of anyone to acquire AMR bacteria. Furthermore, patients in medical settings are commonly more susceptible to infection due to underlying medical conditions. However, AMR infections outside of the hospital setting in urban and rural populations have increased in recent years, suggesting the acquisition of AMR bacteria from non-medical related sources [3].

AMR bacteria/genes can also be acquired through the consumption of food or water.

Humans may consume animal products that have accumulated AMR bacteria through the food chain, crops that have been exposed to contaminated manure or sludge, or fish that have been exposed to pharmaceuticals intentionally (eg. aquaculture) or unintentionally. They may acquire

AMR bacteria/genes from recreational waters including coastal water or beaches, or from consuming groundwater or surface water as drinking water sources [171]. After consumption from any of these sources, bacteria may pass through the system without causing illness. Alternatively, bacteria may take up residence in the gut microbiome, or may transfer resistance genes with other bacteria residing in the host [171].

Humans impact the global resistome through many routes including poor sanitation, indiscriminate antimicrobial use, untreated human and animal waste, and the contamination of land and water. Human feces are a common source of AMR bacteria/genes that contaminate the

environment through direct means or through the release of treated sewage water or sludge into various environmental sources. Antimicrobials as well as AMR bacteria and genes can enter the sewage system after human treatment [172] and become a part of sewage sludge, or released into rivers as treated sewage discharge. Although wastewater treatment removes a portion of the AMR genes, many persist following the treatment process [173, 174]. Antimicrobials and AMR bacteria/genes from humans can also directly contaminate the environment through irrigation with wastewaters and surface waters [175] which can subsequently transmit to surface water or groundwater [176, 177].

Figure 1.1. Environmental reservoirs of AMR genes highlighting the associations between potential reservoirs of AMR bacteria, adapted from Wellington et al. (2013) [171].

1.3.2 Resistance in Animals

1.3.2.1 Resistance in food animal agriculture

Antimicrobial use in animals can contribute to the emergence of AMR bacteria that may potentially be transmitted to humans or the environment, contributing to global resistome.

Increased exposure to antimicrobials and inappropriate dosing schedules are considered significant risk factors for increased AMR in veterinary medicine [178, 179].

In the late 1940s, low level antimicrobial use in animal feed was associated with growth promotion when chickens fed fermentation waste from TET production demonstrated significantly increased growth in comparison to controls [180]. This discovery contributed to the widespread use of antimicrobials in livestock production for disease prevention and growth promotion [181].

Although the use of antimicrobials has since been tightly regulated in Canada and many other countries worldwide, certain antimicrobials can still be used for prophylaxis, growth promotion and disease therapy. In some cases, farm-wide administration of prophylactic antimicrobials occurs in feed or water. Beef calves in feedlots may receive therapeutic doses of antimicrobials for anticipated outbreaks of respiratory disease, or other diseases, and swine may receive antimicrobials to prevent disease or promote growth. The use of subtherapeutic doses of antimicrobials for growth promotion is believed to enhance the selection of resistant bacteria to a greater extent than the therapeutic use of antimicrobials for the treatment of disease [182].

As is the case in human medicine, the use of antimicrobials in companion animals and food animals is essential to ensure their health and survival. However, a major concern exists with the transfer of AMR bacteria/genes between animals and humans through various mechanisms

including direct contact, consumption of animals or their products, and environmental contamination.

Antimicrobials and AMR bacteria/genes from food animals can be released into the environment directly through manure, or indirectly during the application of manure or slurry from livestock facilities to the land [183]. Concentrated animal feeding operations produce massive amounts of manure and can alter the microbial ecosystem and promote the transfer of resistance genes between humans, animals and the environment although composting and other pathogen reduction does occur [184].

AMR E.coli have been isolated from Canadian cattle and commonly demonstrate resistance to TET, sulfamethoxazole, AMP, CHL and streptomycin (STR) [185-187]. Canadian chicken and turkey flocks have recovered AMR E.coli with common resistance to TET, STR and sulfisoxazole (FIS) [188, 189].

Due to surveillance efforts focusing on AMR bacteria from food animals, a greater knowledge base is now present in Alberta for AMR E.coli. In 2013, 81% of the E.coli isolates isolated from chickens in Canada were resistant to at least one antimicrobial. The percentages of isolates resistant to at least one antimicrobial in turkeys were 68%, pigs/pork was 65% and cattle/beef was 25% [190]. Since 2002 in Canada, 50 E.coli isolates have been detected in food animals that are resistant to six or seven antimicrobial classes, which has remained low each year with nine isolates in 2013. However, MDR E.coli has been isolated from all food animal species tested, including retail beef, chickens, turkeys and pork [190].

1.3.3 Resistance in crop agriculture

Antimicrobials are often used to prevent and control infections in crop agriculture, which may select for AMR bacteria/genes and act as a possible source of AMR bacteria being transmitted to humans, animals and the environment. Antimicrobials are applied to suppress pathogen growth on flowers and leaf surfaces, and are used to avoid bacterial infections in many food crops, particularly pears, apples and peaches [191]. Fruit trees often undergo prophylactic antimicrobial treatment to control bacterial infections [192] through spraying, which can result in aerial drift of the antimicrobials.

To ensure sufficient crop yields and prevent moisture stress, crop agriculture usually requires irrigating fields. In Canada in 2010, the majority of irrigating farms were located in British

Columbia (40%) and Alberta (30%). The majority (3,260) of these farms used water from on-farm lakes and rivers, while many others (1,555) drew some or all of their water from a well source

[193].

When irrigation water sources are contaminated, the risk of crop contamination increases.

AMR E.coli have been detected in irrigation water, soil and lettuce [194], putting consumers at a risk of exposure to AMR bacteria. Various other food products have tested positive for AMR E.coli including vegetables, poultry, red meat and shrimp worldwide [195-198].

AMR E.coli can develop in crop agriculture as a result of prophylactic antimicrobial use, or these bacteria can contaminate crops through other sources such as animals, water and soil.

Once contaminated, these crops can be consumed by humans and animals, further perpetuating the cycle of contamination between humans, animals and the environment.

1.3.4 Resistance in aquaculture

Ocean fishery depletion has facilitated an increase in aquaculture practices worldwide, with fish farms typically consisting of large anchored pens or cages releasing waste into coastal waters.

Antimicrobials are primarily used in aquaculture to prevent or treat disease, and these antimicrobials can diffuse into the water surrounding their pens. The prophylactic use of antimicrobials in fish farms has led to increased numbers of resistant bacteria, and these bacteria can transfer AMR genes to human pathogens [199]. Pathogenic bacteria of fish share many of the same AMR genes as human pathogens including E.coli and these bacteria can transmit from aquatic to terrestrial environments [200].

1.3.5 Resistance in the environment

Antimicrobial resistance mechanisms can originate in environmental bacteria as natural protective mechanisms, or can be transmitted from bacteria and humans that contaminate the environment [12, 14, 15, 60, 71]. The use of antimicrobials in crop agriculture, aquaculture, animals and humans introduce selective pressures to the environment that may contribute to the development and persistence of AMR bacteria and AMR genes. Bacteria from environmental, anthropogenic and animal origin reside together in the environment, providing ideal conditions for new resistant strains to arise [171]. For this reason, nutrient-rich environments such as soil and water can act as hotspots for horizontal gene transfer [201].

Environmental bacteria can acquire resistance through various mechanisms including the uptake of resistance genes from the environment (eg. mobile genetic elements such as plasmids, integrons, gene cassettes or transposons). New evidence suggests AMR development in the environment is influenced by a diverse set of stressors including nutrient scarcity, reactive oxygen

species and cellular damage [202]. The potential for horizontal gene transfer of AMR genes between environmental and pathogenic bacteria is demonstrated in studies finding 100% sequence similarity of AMR genes from clinical pathogens and common soil bacteria [203].

Once in the environment, AMR bacteria and/or AMR genes can accumulate and spread across different biomes. Wildlife, wind and watershed each play a major role in transmitting AMR bacteria between diverse environments worldwide.

1.3.5.1 Resistance in water

Water is considered a significant reservoir of AMR bacteria and genes, and can act as a vehicle for resistance gene dissemination [15, 72]. This environmental reservoir can act as an amplifier and/or reservoir of genes already acquired by human pathogens, or a “bioreactor”, facilitating the exchange of resistance genes between pathogenic and non-pathogenic bacteria [15,

18, 204-207]. These AMR bacteria and AMR genes can then be released as pollutants into the environment, transferring genes to other bacteria, increasing resistance genes within the environmental gene pool.

Many bacteria are indigenous to water, whereas others are transiently and occasionally present due to shedding from animals, vegetation and soil surfaces. Enteric bacteria can be introduced to the environment via feces of humans and animals, and this may result in human transmission through recreation in contaminated waters and/or the consumption of contaminated drinking water, produce or fish. Of the bacterial species found originating in seawater from one study, over 90% were resistant to more than one antimicrobial and 20% were resistant to five or more [15, 208].

Antimicrobial residues have been found in water from pharmaceutical manufacturers

[209], ponds with runoff from animal wastes [210], aquaculture waters [211] and sewage outfalls

[18, 212]. Antimicrobials can also be discharged into water from treated sewage, along with bacteria from humans and animals [207, 213]. With the high diversity of bacteria in water from human, animal and environmental sources, and the selective pressures of antimicrobials present,

AMR bacteria and genes can develop in surface water, groundwater and drinking water sources

[214].

AMR bacteria have been detected in diverse water sources worldwide. In many countries, including Canada, surface water sources have been contaminated with bacteria carrying resistance genes [204, 215-221].

The emergence of AMR bacteria in drinking water sources [222-225] has highlighted the importance of studying this reservoir as a potential mechanism of resistance transfer to humans and animals. Resistance genes have been found to accumulate in native bacteria within drinking water sources [226], and diverse AMR patterns as well as a high occurrence of MDR bacteria have been observed among various genera and species within mineral and spring bottled waters [227-

232]. The presence of AMR bacteria within natural spring waters suggests that many of these resistance phenotypes are intrinsic and from anthropogenic sources.

Water acts as a major reservoir of AMR bacteria and genes both intrinsic to soil/water environments and as contaminants from human and animal sources. It provides the ideal conditions for horizontal gene transfer and exchange of resistance genes between environmental and pathogenic bacteria. Water then works as a passive transporter of microorganisms and AMR genes to humans, animals and other environments [227, 233, 234].

1.4 Groundwater and well water

Groundwater is an essential resource for one quarter of all Canadian residents who rely on this source of water for washing, drinking, farming and manufacturing [235]. Groundwater is contained within sand grains, rock crevices and in solution openings usually within 100 meters of the Earth’s surface, and the water contained in these areas contains much of the Earth’s fresh water

[235]. Groundwater flows from water-bearing formations underground called aquifers at varying rates. Water from precipitation and streams moves through an “unsaturated zone” in which water and air fill the gaps between soil, sand and sediments, to the “water table”. Below the water table is the “saturated zone” in which all water is referred to as “groundwater”. Groundwater can flow into underground aquifers and discharge into the sea or feed back into streams. Although groundwater exists everywhere within the saturated zone, aquifers contain high volumes of water within permeable rock or loose material and can be tapped into by a well [235].

Aquifers within Canada can be large or small and the source water varies based on regions within the country. Groundwater circulates as part of the hydrologic cycle, with precipitation and surface water sources recharging the groundwater which drains steadily towards its point of discharge [235].

Groundwater uses in Canada vary by province (Table 1.1), with approximately two thirds of users residing in rural areas. This water source can be used in many industries including agriculture, municipalities and for rural domestic use including as a drinking water source.

Table 1.1. The predominant use of groundwater in Canada by Province (adapted from Government of Canada, 2013).

Predominant Use of Groundwater (Data from Province Government of Canada, 2013)

Ontario, Prince Edward Island*, New Municipalities Brunswick, the Yukon

Alberta, Saskatchewan, Manitoba Agricultural industry for livestock watering

British Columbia, Quebec, the Industry Northwest Territories

Newfoundland, Nova Scotia Rural domestic Use *Prince Edward Island is dependent on groundwater for almost 100% of their domestic needs

1.4.1 Groundwater contamination

Although extensive studies have investigated the contamination of surface waters with

AMR bacteria, very few studies have investigated groundwater as a potential source of AMR bacteria in Canada, even though this resource supplies drinking water to almost nine million

Canadians [214, 235, 236]. Attention to the possible microbial contaminants within Canadian groundwater was brought forth in May 2000, when a heavy rainstorm contributed to the contamination of groundwater with manure containing E.coli O157:H7 and Campylobacter jejuni

[237]. In addition to the risks groundwater poses as a drinking water source, further risks are posed to the environment as groundwater can transport contaminants and pollutants from the land into lakes and rivers [235].

Microbial contamination of groundwater is often the result of human and animal practices or faulty infrastructure. Point source contaminants of groundwater include septic tanks, municipal landfills, livestock wastes, leaky sewer lines, and land spreading of sewage or sewage sludge, and

non-point source contaminants may come from rain and snow. In Canada, common contamination sources include underground tanks, septic tanks and livestock wastes [238]. Septic systems are designed to expel some waste to be degraded and absorbed by surrounding sand and subsoil, which can result in environmental contamination with human-associated bacteria [238].

Many factors affect the microbial contamination of groundwater including precipitation patterns, well slope, the type of soil around the well, uses of land surrounding a well, characteristics of the well and the type of aquifer. Furthermore, certain water sources may be more prone to contamination than others, for instance deep, confined aquifers may be less susceptible to contamination. Once groundwater is contaminated, substances can reside for between two weeks and 10,000 years, therefore when an aquifer is contaminated, it may be unsuitable for use for decades [238].

Studies suggest 20-40% of rural well water systems in Canada fail current water quality parameters [239]. Homeowners are able to submit rural well water samples for free testing at the

Provincial Laboratory for Public Health (ProvLab). However, testing of non-municipal rural well water is largely on a voluntary basis and is not regulated.

Predictions suggest within upcoming decades more contaminated aquifers will be found, new contamination sources will be identified and more contaminated groundwater will be discharged into streams, lakes and wetlands within Canada [238].

1.4.2 Testing groundwater contamination

The Canadian Drinking Water Guidelines were established by the Federal-Provincial-

Territorial Committee on Drinking Water in Canada to outline the guidelines for detecting microbiological, chemical and radiological contaminants in various drinking water sources. For

the purposes of our study, we will focus on the microbiological contaminants in groundwater.

Microbiological parameters for drinking water quality include two indicators: total coliforms and a fecal coliform, E.coli. Total coliforms refer to a genera of bacteria of fecal and non-fecal origin.

When present in drinking water, total coliforms indicate water quality changes, providing an indication of how well the drinking water treatment system is operating. In the case of groundwater, total coliforms may indicate vulnerability to contamination or the potential for bacterial regrowth [240]. The second indicator is E.coli which is a fecal coliform bacteria indicating recent fecal contamination and the potential presence of enteric pathogens capable of causing gastrointestinal illness [240]. Municipal drinking water and rural well water both involve testing for the same two indicators, however municipal drinking water testing is mandatory at various stages of treatment, whereas testing and treatment are both voluntary and not mandatory for rural well water.

In Alberta, rural well water testing is performed by ProvLab when samples are voluntarily submitted. Samples are tested for the presence of total coliforms and E.coli using an enzyme substrate Colilert test.

1.4.2.1 Escherichia coli

E.coli is a gram-negative bacteria of the Enterobacteriaceae family common to all warm- blooded animals. E.coli typically colonizes a human gastrointestinal tract within the first few hours after birth, and is able to coexist with mutual benefit to the host and bacteria. Commensal E.coli strains generally do not cause disease with the exception of immunocompromised hosts [241].

However, as a major component of the normal intestinal flora, common carrier of resistance genes, and species capable of horizontal gene transfer, these commensal E.coli strains do pose a risk of

transferring resistance genes to human pathogens. Commensal E.coli strains generally reside within the mucous layer of the mammalian colon and are the most abundant facultative anaerobic bacteria within the human intestinal microflora [241].

However, certain E.coli clones have acquired virulence attributes, allowing them to survive in different host environments and cause a broad spectrum of disease. There are six well-described categories of intestinal E.coli pathogens including enteropathogenic E.coli (EPEC), enterohemorrhagic E.coli (EHEC), enterotoxigenic E.coli, enteroaggregative E.coli, enteroinvasive E.coli, and diffusely adherent E.coli [241]. Among these E.coli pathogens, a broad spectrum of diseases can be caused in humans and animals. Uropathogenic E.coli are the leading cause of urinary tract infections, and are able to cause neonatal meningitis and nosocomial bacteremia. Enteric bacteria can cause enteritis in children, traveler’s diarrhea and foodborne illness [4].

As a major component of the normal intestinal flora of humans which commonly carries resistance genes and is able to initiate horizontal gene transfer with many different species, these bacteria are highly studied in relation to AMR transmission and carriage. E.coli with resistance to more than two classes of antimicrobial agents is a common finding in both human and veterinary medicine [4].

1.5 One Health

The concept of One Health acknowledges that the health of humans, animals and the environment are inter-connected and takes an integrated approach to understanding important biological issues such as AMR. The development and spread of AMR bacteria poses a threat to human, animal and environmental health on a global level. However, environmental AMR is often

not included in surveillance initiatives. Although the Canadian Integrated Program for

Antimicrobial Resistance Surveillance (CIPARS) includes AMR testing in both humans and animals, surveillance does not include environmental isolates such as soil, agricultural lands, groundwater or surface water.

1.6 Resistance surveillance in Canada

PHAC has numerous surveillance programs that monitor antimicrobial use and AMR.

CIPARS is a collaborative effort among numerous federal agencies, including PHAC, and has been modeled after the United States National Antimicrobial Resistance Monitoring System

(NARMS). CIPARS was designed to track AMR in enteric bacteria isolated from various livestock commodities both in humans and animals along the food-producing continuum, investigating the prevalence and trends in AMR found in animals and humans in the agri-food sector. This includes surveillance at farm, abattoir, and retail levels of food processing and includes both human and animal clinical isolates. Many human-specific surveillance programs exist as well, which are not specific to AMR but include many AMR pathogens. These include the Canadian Nosocomial

Infection Surveillance Program and the Enhanced Surveillance of Antimicrobial-Resistant

Gonorrhea Program. However, current surveillance initiatives do not gather data on AMR in any environmental reservoirs within Canada.

1.7 Summary

All infectious agents, including bacteria, viruses, fungi and parasites have strains that are able to resist multiple antimicrobials. Both AMR and MDR are major healthcare concerns worldwide, as they are associated with high rates of mortality and morbidity, as well as increased

medical costs due to prolonged infection times and extended courses of treatment. Specific strains of MDR E.coli are now globally disseminated and have been associated with the global spread of

ESBLs [242, 243]. Although MDR E.coli has been detected in food animals and humans during routine surveillance, the prevalence of MDR E.coli in Canadian groundwater remains to be elucidated.

Water acts as a reservoir for bacterial transfer between animals, humans, soil and aquatic life, and contributes to the widespread dissemination of AMR bacteria and genes within human, animal and environmental reservoirs.

Despite worldwide interest in AMR bacteria as a possible route of dissemination of resistance between various human, animal and environmental sources, very few studies have investigated AMR bacteria within groundwater sources, particularly in Canada.

Studies in other regions of the world have observed human, animal and environmental sources of AMR bacteria in groundwater. Various studies have suggested groundwater can become contaminated with AMR bacteria from nearby swine production facilities [244-247] or animal feeding operations [248]. However, one study also found that AMR genes in groundwater near swine production facilities are similar to AMR genes in environmental bacteria [249]. Human sources have also been linked to groundwater contamination with AMR bacteria. For instance,

Gallert et al. (2005) found many fecal coliforms in groundwater near a leaky sewer were often resistant to three or four antimicrobials, and the number of fecal coliforms decreased with greater distance from the leak [250]. A recent study investigated the presence of AMR in groundwater within Ireland, finding resistance to both human and veterinary antimicrobials, and a significant relationship between MDR to human antimicrobials and both septic tank density and the presence

of children under five years old. Resistance to veterinary antimicrobials suggested a significant relationship with livestock density and the presence of MDR E.coli [251].

The presence of AMR E.coli in rural well water has many implications, as this water can be immediately consumed by humans and animals without treatment, and testing of this water is not mandatory in Alberta. Furthermore, the consumption of water contaminated with AMR E.coli has been associated with human carriage of AMR E.coli, which can transfer resistance genes to resident microbes in the gut as well as other potentially pathogenic organisms.

1.8 Objectives and hypotheses

The overall objective of the project is to assess AMR E.coli, multi-class resistance (MCR) and ESBL-producing E.coli populations in rural well water and determine their geospatial patterns in southern Alberta. In order to obtain these results, four aims have been established (Figure 1.2).

Our first aim is to determine how many isolates can be used to represent the different

E.coli populations in an individual rural well water sample. This will allow us to perform our antimicrobial susceptibility testing on a population of E.coli that accurately represents the diversity of E.coli in a typical rural well water sample in further aims. E.coli in groundwater sources can originate from various human, animal and environmental sources, which we hypothesize will contribute to a high diversity of E.coli in a single water sample.

Our second aim is to characterize the AMR prevalence, patterns, distribution and MCR within the population of water samples. This aim includes two steps, first an agar screen and confirmation of species ID, followed by antimicrobial susceptibility testing to determine the resistance profiles of these E.coli isolates. To detect potential AMR E.coli populations, an agar screen using MacConkey agar plates supplemented with seven antimicrobials common to human

medicine with concentrations has been set at two dilutions below the human breakpoint to ensure accurate detection of all intermediate and resistant E.coli. Biochemical tests confirm which of the presumptively resistant isolates were E.coli, and antimicrobial susceptibility testing is used to measure MICs for 14 antimicrobials. Due to the various human, animal and environmental sources affecting groundwater contamination, and the use of E.coli as an indicator of recent fecal contamination, we expect to see a high proportion of resistant E.coli isolates from rural well water.

Our third aim is to understand the relevance of these resistant isolates to human and animal medicine, where we are specifically investigating the presence of ESBLs and STEC within the AMR E.coli populations collected in aim two. We hypothesize that ESBL-producing E.coli will be present in isolates resistant to CEPH, and STEC will be present in a small proportion of the AMR E.coli due to the use of E.coli as an indicator of recent fecal contamination and the presence of STEC as a pathogen in humans and resident of the gut microbiome in many animal species.

Our final aim is to assess the spatial distribution of AMR and MDR E.coli within the province, specifically investigating whether clusters for AMR E.coli, MCR E.coli, and resistance to certain classes of antimicrobials exist within the province. We hypothesize that AMR and MCR

E.coli will have higher proportions in the province in areas with higher human and/or animal influences and contamination sources.

1.9 Goals of this thesis

There are many gaps in our understanding of how groundwater acts a vehicle for transmitting AMR E.coli between humans, animals and the environment. Although groundwater can become contaminated with E.coli from diverse anthropogenic, veterinary and environmental

sources, the diversity of E.coli in groundwater supplying wells in Alberta remains unknown. With the widespread detection of AMR E.coli species within humans, animals and many environments, and selective pressures for the development of AMR through human and animal excretion of antimicrobials, spraying of antimicrobials in crop agriculture, and the use of antimicrobials in aquaculture, it seems likely that the environment (including water) is driving the evolution of AMR in bacteria. Although preliminary studies suggest AMR E.coli are present in a geographically isolated area of Alberta’s rural well water, and the consumption of water contaminated with AMR

E.coli has been linked to human carriage of AMR E.coli, the prevalence of AMR E.coli within

Alberta remains unknown. With this study, we aim to determine the number of rural well water samples positive for AMR and MDR E.coli within Alberta’s rural well water. Furthermore, as

ESBL-producing E.coli and STEC have been detected in humans, animals and surface water, we aim to determine whether ESBL-producing E.coli and STEC are present in Alberta’s rural well water. With data on the AMR and MDR E.coli as well as STEC and ESBL-producing E.coli, questions arise in relation to whether spatial clusters of AMR and MDR E.coli exist within the province. This data can be used to guide policy changes and provide areas for further investigation into the potential risk factors for AMR contamination in Alberta.

Archived E.coli positive water samples (n = 1129)

Enrich in tryptic soy broth (TSB)

Plate to X-Gluc to detect E.coli

Aim 1: determine the Pick colonies from X-Gluc diversity of E.coli - Up to 20 colonies picked (as determined in within a single water Aim 1) sample

Agar Screen: for each colony picked, use a replicator method to plate each isolate to agar screen plates - 1 unsupplemented - 7 supplemented with antibiotics

If growth on 1 or more antibiotic plates, perform Aim 2: identification using API® 20E biochemical test strips detect AMR E.coli populations in private well water samples

Determine minimum inhibitory concentrations (MIC) to 14 antibiotics using NARMS Sensititre panels

If sample positive for: (1) stx1 and/or If resistant to Use spatial statistics to stx2 as determined by quantitative ceftriaxone (3rd PCR (qPCR) and (2) AMR E.coli as identify the location of each generation determined by NARMS Sensititre well contaminated with cephalosporin): panels AMR E.coli and spatially map the contamination Disk diffusion sites, and use spatial scan assay to detect qPCR AMR E.coli isolates ESBL- and/or statistics to identify for stx1 and stx2 genes from AmpC-producing temporal and/or spatial stx+ and AMR+ samples E.coli clusters.

Aim 4: Aim 3: determine the relevance to human and animal Detect hotspots for health (detect ESBL-producing E.coli and determine AMR E.coli positive whether AMR E.coli are STEC) wells within the province

Figure 1.2. Overview of experimental design and project aims.

CHAPTER TWO: POPULATIONS OF E.COLI IN ALBERTA’S RURAL WELL

WATER

2.1 Background

The species of E.coli is vastly diverse, with an estimated 42,500 gene families [252]. This incredible diversity may allow for increased adaptability and resistance to environmental stressors which, in turn, may provide reason for the survival of E.coli in diverse environments [253]. Over one million E.coli cells are present in one gram of feces and are released in the environment, which is considered a secondary habitat for E.coli [254]. However, environmental E.coli isolates are not always present as a result of human contamination. Studies of soil and animal feces suggest that

E.coli strains isolated from soils differ from the bacteria present within animals in the same regions

[255, 256], suggesting many environmental E.coli originate from sources other than animal feces.

Evidence suggests that up to 50% of the entire E.coli population globally is present in secondary habitats including soil and water environments [254]. Until recently, it was believed that E.coli was unable to grow in these environments [257], but evidence suggests there are naturalized E.coli populations that are able to survive and proliferate in secondary habitats [256,

258-261]. E.coli can survive for extended periods of time in the environment and may even replicate in certain secondary habitats such as water, algae, and soils from tropical [255, 258, 262-

265], subtropical [266], and temperate [255, 256, 258, 267-270] environments. Evidence suggests that warm, nutrient-rich conditions in tropical and subtropical regions may allow for sustained

E.coli survival and growth outside of warm-blooded hosts [257, 264]. In temperate environments, however, the addition of nutrients such as manure may influence the ability of E.coli to grow in colder, nutrient-poor environments [270].

E.coli is an incredibly diverse species of bacteria, both genetically and phenotypically. This diversity may be due to the ability of E.coli to mutate and acquire new genes via plasmid- and phage-mediated horizontal gene transfer. Over 700 serotypes of E.coli have been identified [271], and the various serotypes are distinguished based on “O” and “H” antigens as well as their flagella.

Furthermore, E.coli serotypes may differ in their carbon utilizing patterns, AMR profiles, and the ability to form biofilms or cause disease [244, 272-275].

The diversity of E.coli has been investigated in detail using various serotyping techniques.

Traditionally, pathogenic E.coli are classified by serotyping and/or multilocus sequence typing

(MLST) techniques, however pulse field gel electrophoresis (PFGE) has often been used in outbreak detection due to its high discriminatory power. To measure the diversity of E.coli in water environments, PFGE as well as repetitive extragenomic palindromic elements polymerase chain reaction (rep-PCR), and enterobacterial repetitive intergenic sequences (ERIC-PCR) have commonly been used, however PFGE has a higher discriminatory power than rep-PCR, ERIC-

PCR and MLST [276, 277]. For this reason, our study used PFGE to determine how many isolates of E.coli should be used to represent the different E.coli populations in an individual rural well water sample.

E.coli diversity has been assessed in various water environments, including beach waters, sewage, surface waters, groundwater and, recently, well water. The overall genotypic diversity in beach waters is high, with one study finding 130 unique sequence types (ST) with 79% of STs having been recovered only once [261]. Furthermore, beach waters have shown temporal and spatial clusters, suggesting natural selection within water environments favors certain genotypes [221, 261]. Although the diversity of E.coli in human and animal sources is higher than what is found in natural surface waters and beach sites, the diversity within surface waters supplied

by lakes, rivers and streams can reach 30% (ie. unique strain types comprise 30% of the isolate data sets) [278]. Evidence also suggests temporal and spatial domination by a few E.coli genotypes, with 90% of isolates having shared genotypes with fecal isolates. Many of these waterborne E.coli isolates do not share genotypes with fecal isolates, suggesting the presence of waterborne naturalized E.coli populations [279]. Although Li et al. (2013) suggest substantially lower concentrations of E.coli in groundwater than surface water [280], studies performed by

Uysal et al. (2013) revealed a high diversity of E.coli in groundwater-sourced well water using

PFGE [281]. Furthermore, previous studies in Alberta showed 14 to 14.6% of wells supplied by groundwater exceeded the limits for total coliforms and one and a half to six percent tested positive for the fecal indicator E.coli [282, 283].

E.coli populations in drinking water exhibit temporal diversity, with strains constituting 82% of the bacterial population in 2009 and only two percent of the population three years later [284].

These populations are expected to be spatially diverse as well, as studies suggest the diversity of

E.coli populations in soil and water are affected by various factors including pH, moisture, and the presence of organic matter or other bacteria in soil and water micro-environments [285, 286].

Previous studies observed genetically heterogeneous populations of E.coli between different well water samples using PFGE [281]. However, the diversity of E.coli isolates in a single water sample remains unknown, and the various human, animal and environmental factors affecting groundwater is expected to result in a highly diverse population of E.coli in a single well water sample. The aim of this chapter is to determine how many isolates can be used to represent the different E.coli populations in an individual rural well water sample.

2.2 Specific methods

To determine the number of E.coli isolates that could be used in our AMR aim to represent the different E.coli populations in an individual rural well water sample, the biochemical profiles,

AMR profiles and genetic differences of multiple isolates from each water sample were screened.

These samples have been isolated from groundwater and may contain mixed populations of bacteria including many non-E.coli isolates. Therefore, our selection criteria included up to 20 presumptive E.coli colonies, although in mixed populations lower colony numbers were often chosen due to the large proportion of atypical colonies in many samples. We decided to select 20 presumptive E.coli colonies from each sample as this is an attainable number of colonies that can be isolated from a single agar plate after streaking to isolation.

2.2.1 Sample collection and sample inclusion

Users of private well water sources in Alberta submit their rural well water samples to

ProvLab for microbial testing on a voluntary basis. At ProvLab, samples are tested using the defined substrate method for the presence of total coliforms and E.coli. The enzyme substrate procedure utilizes a Colilert® Test Kit (Colilert®, IDEXX Laboratories, USA) which contains two nutrient-indicators, ortho-Nitrophenyl-β-D-galactosidase (ONPG) and 4-Methylumbelliferyl-β-D- glucuronide (MUG), which can be metabolized by the coliform enzyme β-galactosidase and the

E.coli enzyme β-glucuronidase, respectively. In the presence of total coliform bacteria results in

β-galactosidase metabolizes ONPG, resulting in a change from colorless to yellow. The presence of E.coli results in β-glucuronidase metabolizing MUG, creating fluorescence.

Any samples submitted to ProvLab Calgary from southern and central Alberta from 2006 to 2016 positive for E.coli by Colilert® testing were archived for use in the AMR aim of this study.

As this aim was a pilot study for our resistance testing in chapter three, ten E.coli positive samples were used to determine how many isolates could be used to represent the different E.coli populations in an individual rural well water sample.

2.2.1.1 Sample preparation

For E.coli positive samples, 1mL was archived in skim milk at -80°C. When preparing for this study, archived samples received at ProvLab between 2006 and March 2016 were prepared at

ProvLab Edmonton and sent to Calgary in batches. For each batch, 1mL of the archived E.coli positive water sample was enriched in 9mL of tryptic soy broth (TSB) for 16-18 hours and 1mL was sent to Calgary on ice. Within one to two days of receipt in Calgary, 25µL of glycerol was added to the sample to produce a 20% glycerol solution stored at -80°C until subsequent workup within one year.

2.2.1.2 Growth and isolation of E.coli colonies

The 10 samples for use in this study were enriched in TSB at 35±2°C for 18-24 hours.

After incubation, samples were vortexed and two drops (approximately 50µL) of the enriched sample were inoculated onto X-Gluc agar plates (Dalynn, Canada) and streaked to isolation using

10µL loops before incubation at 35±2°C for 18-24 hours. This selective agar contains the X-Gluc substrate which is metabolized by the β-glucuronidase enzyme of E.coli, resulting in the production of a blue precipitate. Colonies lacking the β-glucuronidase enzyme appear colorless on the X-gluc agar.

Up to 20 presumptive E.coli isolates (blue colonies) were picked using 1µL inoculating loops and isolated separately, first by streaking to isolation on MacConkey agar and then on

Sheep’s Blood agar plates, each time picking a single colony for subsequent isolation and incubating at 35±2°C for 18-24 hours each time. The number of colonies picked from each sample depended upon the number of blue colonies visible on the plate. Many samples were overgrown with white colonies, resulting in fewer blue colonies being selected for workup. Similarly, many samples had very few or very small colonies that were difficult to pick when they were part of a mixed sample, in which case the selection of presumptive E.coli colonies was challenging. Colony morphologies on X-Gluc, MacConkey and Sheep’s Blood agar plates were recorded and compared while choosing isolates for the PFGE portion of this study. Each isolate was archived in brain- heart infusion (BHI) broth and 20% glycerol at -80°C for further workup.

After enrichment, one sample did not produce blue colonies on the X-Gluc agar and has been considered E.coli negative and removed from subsequent tests.

2.2.2 Screening presumptive E.coli isolates for AMR

Up to 20 presumptive E.coli isolates from each of nine water samples were individually screened for presumptive AMR via an agar screen plate method.

Each isolate was grown on Sheep’s Blood agar plates from the -80°C stock solutions before inoculating into a 96-well plate containing 200µL of TSB in each well. A 48-prong replicator with

3mm prongs was then sterilized in 70% ethanol, flamed and again sterilized in 70% ethanol. The replicator was used to transfer approximately 2µL of each E.coli isolate and eight control strains onto the agar screen plates.

The agar screen plates included one plain MacConkey agar plate and seven MacConkey agar plates supplemented with antimicrobials (gentamicin 8mg/mL, STR 32mg/mL, AMP

8mg/mL, NAL 4mg/mL, sulfamethoxazole 128mg/mL, FOX 32 mg/mL, TET 4mg/mL) prepared

by ProvLab in Edmonton, AB. Agar plates were incubated at 35±2°C for 18-24 hours and isolates with growth on one or more antimicrobial-supplemented agar plates were considered presumptively resistant.

2.2.3 Biochemical testing for species identification

Each presumptive E.coli isolate from all of the nine samples underwent biochemical tests to determine whether the isolate was E.coli. For species identification, API® 20E biochemical test strips (Biomerieux, St Laurent, QC, Canada) were used. Each presumptive E.coli isolate was isolated on a MacConkey or Sheep’s Blood agar plate from the archived isolate stored at -80°C and three to five isolated colonies were inoculated in a sterile 0.85% NaCl solution using a thin- tipped disposable pipette. The suspension was then pipetted up and down vigorously to emulsify the solution and achieve a homogeneous bacterial suspension. The API® 20E biochemical test strip was inoculated according to the package insert and incubated at 35±2°C for 18-24 hours.

Based on positive and negative results of each test, a seven-digit identification code was generated for each isolate and interpreted using the apiwebTM identification software. Isolates were considered to be E.coli if they met the inclusion criteria of being ≥90% E.coli by API® 20E test strips, however isolates found to be <90% likely to be E.coli or non-E.coli species did not undergo further testing.

2.2.4 Measuring minimum inhibitory concentrations

Resistance profiles for every isolate from each of the nine samples were measured in order to determine the MIC values and diversity of antibiograms within isolates from an individual water sample.

Each isolate confirmed as E.coli via API® biochemical tests was tested for MICs to 14 antimicrobials using broth microdilution antimicrobial susceptibility testing methods. Using the

CMV3AGNF Gram-negative NARMS Sensititre™ test panel (Thermo Fisher Scientific,

Burlington, ON, Canada), presumptively resistant E.coli isolates were tested for resistance profiles and MIC values for 14 antimicrobials spanning eight classes. Isolates were inoculated into a pre- aliquoted tube of Sensititre™ Demineralized Water (Thermo Fisher Scientific, Burlington, ON,

Canada) and adjusted to a 0.5 McFarland standard. The solution was vortexed and 10µL was transferred to Sensititre™ Mueller-Hinton broth (Thermo Fisher Scientific, Burlington, ON,

Canada). This solution was vortexed and subsequently inverted 10 times before inoculating each isolate into a separate NARMS Sensititre™ Gram-negative test panel using the Sensititre™

AutoInoculator to dispense 100µL of solution into each well. Plates were incubated for 18 or 24 hours at 36±2°C according to the package insert, and results were read automatically by the

AutoReader® and interpreted as per the CLSI guidelines by the Sensititre™ SWIN® software system (Thermo Fisher Scientific, Burlington, ON, Canada). In cases where the software could not interpret the MIC values, Sheryl Gow at the PHAC was consulted for an expert opinion on how

Canadian surveillance systems interpret their MIC values. For azithromycin (AZI) and FIS, MIC values were interpreted based on these suggestions [287].

When multiple E.coli isolates were detected within a single water sample, isolates were considered distinct from each other if resistance to each antimicrobial was different by two or more dilutions. In the case of isolates with one dilution difference, isolates were not considered to be distinct from one another.

The NARMS Sensititre® test panel antimicrobials, with MICs defined as resistant, include:

FOX 32 µg/mL, AZI 16 µg/mL, CHL 32 µg/mL, TET 32 µg/mL, ceftriaxone (AXO) 64 µg/mL,

amoxicillin/clavulanic acid 2:1 (AUG2) 32/16 µg/mL, CIP 4 µg/mL, gentamicin (GEN) 16 µg/mL,

NAL 32 µg/mL, ceftiofur (XNL) 8µg/mL, FIS 256 µg/mL, SXT 4/76 µg/mL, AMP 32 µg/mL, and

STR 64 µg/mL.

2.2.5 Quality control

2.2.5.1 Agar screen quality control

Various quality control measures were taken with each set of agar screen plates set up.

Negative controls included TSB with no inoculum to ensure no bacterial contamination was present in the enrichment broth, and a susceptible ATCC 25922 E.coli strain, which is a standard strain with known MIC values. Positive controls included two non-E.coli and four E.coli strains with known MIC values for each of the antimicrobials on the agar screen and NARMS panel. The two non-E.coli strains were chosen to ensure the inclusion of one non-lactose fermenting strain and a non-E.coli lactose-fermenting strain with known MIC values. Antimicrobial resistant laboratory strains Pseudomonas aeruginosa URH5057 and Klebsiella pneumoniae IS-625 were used as positive controls expected to grow on all antimicrobial plates. The E.coli positive controls used for this study were previously isolated from rural well water samples in an AMR study in

2006 [10, 223]. These control strains represented AMR E.coli isolated from well water and included four strains that were chosen to ensure the growth of both resistant and intermediate isolates for each antimicrobial whenever possible. Due to the lack of intermediate values for many antimicrobials in the CLSI guidelines, and the limited repository of AMR E.coli isolates from this study, intermediate isolates were only available for certain antimicrobials. The E.coli isolates used for this portion of the study are outlined in the Table 2.1.

Furthermore, one isolate from every 20 samples with no growth on the agar screen was tested for resistance via NARMS to ensure accuracy of the screening process and to ensure we were not missing resistant isolates.

Table 2.1. Antimicrobial resistance profiles of control used for agar screens. Antimicrobial QC Strain AMP FOX GEN NAL SMX STR TET

E.coli ATCC 25922 S S S S S S S

P. aeruginosa URH 5057 R R R R R R R

K. pneumoniae IS 625 R R R R R R R

E.coli AFN control R I S R S S S

E.coli AF control I I S S S S S

E.coli GTSS control S S I S R R R

E.coli TSS control S S S S R R I

2.2.5.2 Biochemical tests

To ensure accuracy of biochemical testing, a QC strain of E.coli (ATCC 25955) was tested with each new batch of API® 20E biochemical test strips, as per recommendations outlined in the

API® 20E package insert.

2.2.5.3 Minimum inhibitory concentrations

With each batch of NARMS SensititreTM panels tested, a susceptible laboratory strain

(E.coli ATCC 25922) was measured as a quality control strain to ensure accuracy of plate reading and interpretation.

2.2.6 Pulsed-field gel electrophesis (PFGE)

The results of PFGE provide information about the clonality of E.coli isolates in a single water sample. This information will guide our understanding about number of E.coli species present in a single water sample after our enrichment techniques that can be used to represent the entire population of E.coli in a single water sample.

2.2.6.1 Samples

Financial constraints limited the number of isolates that could be tested by PFGE to determine genetic diversity. Following confirmation of isolates as E.coli, five out of the (up to) 20 isolates from each sample were chosen for PFGE based on distinct biochemical profiles (as determined by API®) and morphologies on Sheep’s Blood, MacConkey or X-Gluc agar plates. In most cases, five isolates were chosen from each sample, as morphological and biochemical properties of these isolates suggested this number accurately represented the breadth of diversity in these eight samples. However, seven isolates were chosen from one sample as it had a higher diversity of biochemical differences, with five distinct biochemical profiles within the seven isolates.

For each gel, a control strain of Salmonella enterica serovar Braenderup ATCC H9812 was used to act as a size standard on the gel, and was suspended, washed and digested with the sample isolates.

2.2.6.2 Cell suspension

Archived isolates were removed from the stock solution stored at -80°C using a sterile inoculating loop (5uL) to scrape a small portion from the top of the frozen BHI and glycerol solution. These scrapings were streaked to isolation on Sheep’s Blood agar plates for 18 to 24 hours at 35±2°C. Cell suspension buffer (1M Tris-HCl, pH 7.2, 0.5M EDTA, pH 8.5-9.0) was distributed in 2mL aliquots into 5mL transparent glass tubes. Using a sterile cotton swab, a sweep of the agar plate was inoculated into the cell suspension buffer. The OD600 of cell suspensions were measured and adjusted to an absorbance of 0.46-0.48. Equipment was zeroed using a blank tube containing 2mL of sterile cell suspension buffer.

2.2.6.3 Preparation of agarose plugs

Plastic disposable plug molds were used to cast plugs with a piece of masking tape used to label each well. To prepare multiple agarose plugs from one bacterial suspension, 400µL of the cell suspension was added to multiple 1.5mL microcentrifuge tubes and 20µL of Proteinase K

(20mg/mL) was added to each suspension with gentle pipetting to mix the suspension.

Fresh melted 1% Seakem Gold agarose was prepared in 1X sterile TE buffer (pH 8.0) and placed in a 54°C water bath until use. After aliquoting the cell suspension, 400µL of melted agarose was added to each bacterial suspension and gently pipetted (two to three times), after which the

mixture was dispensed into two to three wells of the plastic disposable plug mold. The plugs were left to solidify at room temperature for approximately 10 minutes.

2.2.6.4 Cell lysis

Lysis buffer (1M Tris-HCl pH 7.2, 0.5M EDTA pH 8.0, 1% N-laurosylcarcosine, 0.5mg/ml

Proteinase K, adjust to pH 8.0, Proteinase K was added immediately before use) was distributed in 1.5mL aliquots into 1.5mL microcentrifuge tubes. Using a metal spatula, two plugs were pushed through the plug molds into the lysis buffer. Microcentrifuge tubes containing plugs in lysis buffer were incubated in a shaking incubator for 1.5 hours at 54°C.

2.2.6.5 Washing agarose plugs

Plug slices were washed four times for 10-15 minute incubation intervals in a 54°C shaking incubator. The first wash was with sterile distilled water, followed by three washes with pre- warmed 1X TE buffer. Following each wash step, the liquid was removed using a sterile disposable pipette with a fine tip, and approximately 1mL of fresh solution was added to the tubes and returned to the shaking incubator. Following the final wash, the 1X TE buffer was again removed, and fresh

1X TE buffer was added to the tubes for storage. Plugs were stored in 1X TE buffer at 4±2°C for up to one month.

2.2.6.6 Digestion of DNA in agarose plugs

One plug per sample was removed from the lysis buffer onto parafilm and cut widthwise into two to three slices with a sterile razor with each slice approximately 0.5mm thick. The slices were transferred into a 1.5mL microcentrifuge tube containing 1X CutSmart Buffer (New England

Biolabs, Canada). The remainder of the plug was transferred back into the microcentrifuge tube containing lysis buffer and was stored at 4°C for subsequent use.

Sample slices were equilibrated in 1X CutSmart Buffer at room temperature for five to ten minutes, after which the buffer was removed using a fine-tipped pipette and 300µL of a 1:10 XbaI

(New England Biolabs, Canada) enzyme cocktail was added to the plugs. Enzyme digestions occurred in a floating microcentrifuge tube rack in a circulating water bath for two hours at

37±2°C.

2.2.6.7 Preparing the electrophoresis chamber

For electrophoresis, a Bio-Rad CHEF mapper (Bio-Rad, Mississauga, ON, Canada) was used. Before running electrophoresis, the chamber was leveled and 2.2L of 0.5X TBE buffer was added to the chamber. The CHEF mapper was turned on to circulate buffer through the system and the cooling module was set at 15°C and allowed to cool for approximately 30 minutes prior to electrophoresis.

2.2.6.8 Preparing agarose gels

A 1% Seakam Gold agarose gel was prepared with 0.5X TBE buffer and stored in a 54±2°C water bath prior to use. Restriction-digested plug slices were taken from the 37±2°C water bath and the enzyme cocktail was removed with a fine-tipped pipette, adding back 0.5X TBE to each tube and incubating at room temperature for at least five to ten minutes. Plug slices for standard

(S. enterica serovar Braenderup) and test samples were loaded onto a comb with three test samples to every one standard, and the 1% agarose gel was poured over the comb containing plug slices.

Gels were covered and left to solidify at room temperature for 20-30 minutes.

2.2.6.9 Running electrophoresis

The Bio-Rad CHEF mapper (Bio-Rad, Mississauga, ON, Canada) was run on an Auto

Algorithm with default values except low MW (30kb), high MW (600kb) and run time (20 hours).

Default values for the initial and final switch times were 2.16s and 52.17s, respectively. Initial amperages were acceptable if within the range of 130-180 mAmp and the gel was set to run overnight for 20 hours.

2.2.6.10 Staining and analyzing the PFGE agarose gel

The agarose gel was stained in a 10X SYBR™ Safe DNA gel stain (Invitrogen™,

Burlington, ON, Canada) solution at room temperature for at least 30 minutes in the dark with gentle agitation. After staining, the gel was washed with sterile distilled water 2X each for 30 minutes to de-stain.

Gels were imaged with a Bio-Rad ChemiDoc™ MP Imaging System (Bio-Rad,

Mississauga, ON, Canada), pictures were exported as tagged image file format images and analyzed with BioNumerics (version 5.1, Applied Maths, NV, USA). DNA banding patterns were compared with the XbaI-digested S. enterica serovar Braenderup standard, and dendograms were created to determine the percentage similarity between banding patterns of various isolates from the same sample, and banding patterns with <80% similarity were considered as the same pattern.

As an example, Supplementary Figure 2.1 shows three distinct banding patterns: (1) lanes two, three, seven and nine, (2) lane four, and (3) lanes five and eight.

2.3 Results

2.3.1 Biochemical profiles

The E.coli positive samples included in this pilot study were tested by API® 20E test strips to determine the biochemical profiles of multiple isolates from each sample. By running 20 biochemical tests on API® 20E test strips, we were able to determine differences in the ability of each isolate to utilize various biochemical substrates, referred to as their biotypes.

Of the 11 samples included in the study, nine samples were positive for E.coli based on the inclusion criteria set for E.coli, and two samples did not meet the inclusion criteria.

E.coli were identified from nine samples, and seven samples had one common biotype among all the isolates in that sample. Two samples contained multiple biotypes among isolates within a single sample, in one case two distinct biotypes and another case five distinct biotypes

(Figure 2.1).

Figure 2.1. The number of distinct biochemical profiles among multiple E.coli isolates from each of nine rural well water samples.

2.3.2 Banding patterns The E.coli positive samples included in this pilot study were tested by PFGE to determine the number of distinct genetic profiles among multiple E.coli isolates from each rural well water sample. For each sample, five isolates were tested by PFGE based on differences in their biotypes and morphologies whenever possible. For sample 1614, seven isolates were tested by PFGE, as five distinct biotypes were detected by API®.

Six out of the nine rural well water samples tested had one distinct PFGE banding pattern among multiple isolates from each sample. One sample had three distinct banding patterns and two samples had two distinct banding patterns among multiple E.coli isolates from the individual water samples (Figure 2.2).

Figure 2.2. Number of distinct banding patterns among multiple E.coli isolates from each of nine rural well water samples.

2.3.3 Antimicrobial resistance profiles

To determine the number of distinct resistance profiles (antibiograms) for various E.coli isolates within individual water samples, isolates were tested for their MIC values to 14 antimicrobials using a NARMS Sensititre™ panel.

Among the nine samples positive for E.coli as determined by API, no differences were observed in the antibiograms of isolates from the same sample based on inclusion criteria of greater than or equal to two dilutions difference in MIC values. Sample inclusion criteria was set as greater than one dilution to ensure isolates were not considered distinct from others based on a one dilution difference that can be attributed to inoculating, reading, etc. For sample 1620, 10 isolates had an

MIC of eight for CHL, one isolate had an MIC of 16 and one isolate had an MIC of four, however this was not considered to meet the inclusion criteria as this can be attributed to individual sample variations and all other profiles were within one dilution difference. All samples within the pilot study were found to be susceptible to all antimicrobials tested on the NARMS Sensititre™ panel

(Supplementary Table 2.2).

2.3.4 Number of distinct isolates per sample

Within the nine samples tested, six had only one distinct biotype, banding pattern, and antibiogram, suggesting only one distinct isolate of E.coli was detected in these samples. Biotype data suggests one sample had three distinct biochemical profiles and one sample had two distinct biochemical profiles among the isolates from those individual samples. Furthermore, results of

PFGE suggest one sample had three distinct banding patterns and two samples had two distinct banding patterns (Supplementary Table 2.1; Figure 2.3).

Figure 2.3. Number of distinct biotypes, banding patterns and antibiograms among multiple E.coli isolates from nine rural well water samples.

2.4 Discussion

Among the nine samples positive for E.coli, six samples suggest only one distinct isolate exists or is found within the population of E.coli recovered from the sample. Based on our API® results, the highest number of biochemically distinct phenotypes observed from a single water sample was five from a sample with 19 colony picks. Results of PFGE on seven isolates from this sample demonstrate that these five biochemically distinct phenotypes correspond to only three different PFGE patterns. Multiple biotypes were associated with three distinct banding patterns by

PFGE, possibly indicating the ability of bacteria to utilize different resources under slightly different conditions. Therefore, within nine water samples (n=47 isolates, five from each sample and one sample with seven isolates) the highest number of distinct E.coli isolates observed within 64

a single water sample was three. In addition, two biochemically identical E.coli isolates appear to have different banding patterns, confirming that our morphological and biochemical similarities as determined on X-Gluc, MacConkey agar, Blood agar and API® results cannot be used to identify differences between multiple E.coli isolates from the same rural well water sample.

Furthermore, two biochemically distinct E.coli isolates from one sample had the same banding pattern by PFGE, further confirming that biochemical results may be a reliable indicator of genetic differences between E.coli isolates.

Antibiogram data as determined by NARMS Sensititre® panels suggested that the highest number of distinct isolates of E.coli within a single water sample was one. The MIC50, MIC90 and range for each antimicrobial were less than or equal to two dilutions per sample, demonstrating a low variability in AMR profiles within and between the rural well water samples tested after enriching and plating to selective media. It is possible that a low variation in resistance profiles in our study may have been due to low sample numbers as we did not find any clinically significant resistance among our isolates. However, the panel of antimicrobials tested in our study included four to nine different concentrations of each antimicrobial, many of which were in the intermediate and resistant zone based on clinical definitions. Therefore, for susceptible E.coli, AMR profiles may not be the most accurate way of measuring the differences between these isolates given the low sample numbers.

We hypothesized that many distinct populations of E.coli would be detected in an individual rural well water sample due to the diverse set of human, animal and environmental sources of contamination for groundwater sources. Our results indicated that one third of the rural well water samples had more than one isolate of E.coli detected after enrichment and selection of up to 20 presumptive E.coli colonies from each sample. Therefore, we did observe multiple distinct

E.coli isolates in our rural well water samples, although AMR profiles were not different between isolates (Supplementary Table 2.1; Figure 2.3). To further understand the differences in AMR profiles among E.coli from the same water sample, results of our AMR study in chapter three will be compared with the results of our pilot study.

This pilot study was set up to determine whether a diverse set of E.coli isolates could be detected by picking multiple colonies from an X-Gluc plate, and to determine the number of colony picks required to observe a diverse population of E.coli from these samples. The previous study investigating AMR E.coli in Alberta’s rural well water picked only a single E.coli isolate from each presumptively resistant sample and may have missed resistant isolates that were present in the samples assessed during the study. Our results suggest a diverse set of E.coli can be present in a single rural well water sample, including a variety of E.coli and non-E.coli strains, as indicated by the overgrowth of non-E.coli colonies on many X-gluc plates, suggesting that if a single colony was picked from each sample, a variety of E.coli isolates may not be included for resistance testing in our study. With up to three distinct isolates of E.coli in a single water sample, 20 colony picks will be continued into the resistance study in order to maximize the probability of isolating up to three distinct isolates from an individual water sample.

Although previous studies have investigated the genetic diversity of E.coli and many other bacteria in surface waters and recreational waters, only one previous study has investigated the diversity of E.coli in groundwater, to our knowledge. Uysal et al. (2013) found a high genetic diversity among E.coli isolated from groundwater-sourced well water, and detected five resistance profiles among 42 AMR E.coli isolates, with 85.7% of isolates being the same profile [281]. This high heterogeneity of bacteria in water environments has been observed in many studies within water environments [276, 288], however our study is the first to investigate the distinct populations

of E.coli within individual rural well water samples. The diversity between sampling has been well described, and the previous study investigating AMR E.coli in well water was comparing diversity between multiple groundwater-sourced well water samples, whereas our results are comparing the number of distinct populations of E.coli within an individual rural well water sample.

However, several limitations are present, as the isolates have been frozen and enriched, then plated to selective media. These three variables may result in changes to bacteria within the sample and may give a selective advantage or disadvantage to certain E.coli populations from the original sample. Therefore, the results we have observed may not be representative of the original diversity of E.coli strains within the water sample upon submission to ProvLab. Furthermore,

“relatedness” as determined by PFGE cannot be considered a true measure of phylogenetic relationships as it does not differentiate to the degree of single nucleotides. Further studies seeking to determine the diversity of E.coli within individual rural well water samples should investigate

E.coli populations after sampling (before enrichment) with whole genome sequencing to determine single basepair changes that may result in differences in biotypes and antibiograms.

2.5 Conclusions

Although many studies have investigated the heterogeneity of E.coli between water samples over time and in various locations, ours is the first to investigate distinct populations of E.coli populations within individual rural well water samples, comparing biochemical, genetic and AMR data. Our results indicate a single rural well water sample in Alberta consists of up to three distinct

E.coli isolates with distinct banding patterns and, in some cases, biochemical profiles, however our AMR data suggests no differences in AMR profiles among these isolates. This question will be addressed further in the next chapter. The results of our pilot study will be used to guide the

number of isolates we pick from each individual sample when testing for AMR within our samples to ensure we are able to detect up to three distinct isolates of E.coli in an individual water sample.

CHAPTER THREE: ANTIMICROBIAL RESISTANT E.COLI DETECTION

3.1 Background

3.1.1 Current Knowledge on AMR bacteria in Groundwater

Studying AMR bacteria in groundwater sources may provide an understanding for the extent to which human actions have altered water ecosystems and the indigenous organisms present in them. Previous studies have linked AMR bacteria to environmental sources [249], food- animals [244-248] and humans [250] worldwide. E.coli isolates from rural, untreated groundwater supplies have also been shown to transfer AMR to other E.coli and Salmonella Typhimurium isolates in vitro in a nutrient-rich environment [289]. However, despite the growing concern about

AMR bacteria in groundwater, very few studies have investigated this issue in Canada.

With recent interest in AMR bacteria in groundwater, a few studies have investigated AMR bacteria in groundwater worldwide and the potential sources of contamination. A recent study monitoring AMR genes within E.coli isolated from groundwater (private wells) in Ireland found moderate resistance to human antimicrobials and extremely high resistance to veterinary antimicrobials. A significant (p = 0.002-0.011) association was found between resistance to antimicrobials used in humans and both (a) septic tank density and (b) the presence of vulnerable sub-populations (children under five years old). Furthermore, a significant (p<0.001) relationship was observed between livestock density and the prevalence of MDR E.coli [251]. Additionally,

AMR bacteria have been detected in groundwater underlying dairy operations [290], swine confinement operations [245], and several studies have reported E.coli surviving in groundwater after excretion from animals [291, 292]. Among the AMR bacteria detected near swine confinement operations, TET resistance genes were detected repeatedly over a three year period

both from groundwater underlying the swine confinements and in wells located up-gradient of those wells [245]. These findings suggest that AMR bacteria in groundwater are influenced by swine and dairy operations, livestock density, septic tank density and the presence of vulnerable sub-populations, but that AMR bacteria are also present in groundwater distant from these influences. This outlines the potential for other environmental or unknown sources of AMR bacteria within groundwater and a gap in our knowledge about the presence of AMR bacteria in groundwater-sourced private wells within Canada.

Previous collaborations with our laboratory investigated factors associated with AMR in rural well water from Alberta and Ontario. Coleman et al. (2013) assessed risk factors associated with increased AMR in E.coli isolated from rural well water samples [223]. Shore wells were at an increased risk of having AMR E.coli in comparison to drilled wells (Odds Ratio [OR] of 2.8) and farms housing chickens or turkeys were also at an increased risk of contamination with AMR

E.coli (OR 3.0). Furthermore, various risk factors were associated with contamination with multi- class resistant E.coli (three or more classes of antimicrobials) including housing swine (OR 5.5) or cattle (OR 2.2), and wells located in gravel (OR 2.4) or clay (OR 2.1) compared to loam [223].

As outlined above, many human (septic tank density), environmental (shore wells, gravel or clay), and animal influences (livestock density, presence of dairy or swine operations, presence of chickens or turkeys) can result in groundwater contamination with AMR bacteria [245, 251,

290]. Because groundwater sources can be immediately consumed as drinking water, this poses a potential risk to the health of the consumer. In collaboration with our laboratory, previous studies investigated risk factors associated with human carriage of AMR E.coli. International travel

(prevalence ratio [10] of 1.33), having a child in diapers (PR 1.33), being male (PR 1.33) and frequently handling raw meats (PR 1.10) were associated with human carriage of AMR E.coli.

Most important to our study was the finding that the consumption of rural well water contaminated with AMR E.coli was associated with human carriage of AMR E.coli [10].

The present study will detect AMR E.coli among diverse E.coli populations from archived rural well water samples submitted on a voluntary basis to ProvLab in Calgary, Alberta.

3.2 Specific methods

3.2.1 Sample collection and sample inclusion

The rural well water samples used for this study were previously described in chapter 2.

The most recently archived 1129 samples have been included in this study, ranging from August

28, 2006 to August 31, 2016.

Among 1129 samples, 1123 had accurage dates linked to the samples and were included in our analysis of samples submitted over time.

Culture conditions, enrichment, isolation of E.coli, resistance screening, species identification and resistance testing methods are consistent with the methods outlined in chapter two.

3.2.2 Growth and isolation of E.coli colonies

Culture conditions for E.coli isolation from rural well water samples were consistent with chapter 2. Twenty blue colonies from X-Gluc agar were selected for further work-up based on the results obtained from our pilot study. Morphologically distinct colonies were selected whenever possible.

To increase productivity when picking 20 colonies from each sample, instead of streaking to isolation twice before screening our isolates for resistance, each presumptive E.coli isolate

picked from an X-Gluc agar plate was isolated into a 96-well plates containing 200µL of TSB in each well. Plates were stored at 4°C for up to 24 hours before screening for resistance. This method was chosen to save time and money as it was decided that streaking up to 20 colonies from each of 1129 samples before screening for resistance would be labor-intensive and it would be more efficient to only isolate presumptively resistant colonies.

3.2.3 Screening presumptive E.coli isolates for AMR

Screening methods were consistent with the methods outlined in chapter 2, however the way our isolates were isolated after the screen was different (see next section), as the isolates had not been isolated before the screen in this resistance study.

3.2.3.1 Selection and isolation of presumptively resistant E.coli isolates

A diverse set of bacteria from many different human, animal and environmental sources can potentially contaminate groundwater. Therefore, there is the potential to have multiple AMR

E.coli populations within an individual sample. For this reason, when multiple resistance profiles were observed on antimicrobial-supplemented media, one isolate of each resistance profile was selected for further workup. For instance, if isolates one and six grew on TET- and AMP- supplemented plates and isolate 20 grew on only TET-supplemented plates, either isolates one or six would be picked and isolate 20 would be picked for further workup. Isolates were streaked to isolation on MacConkey agar plates and incubated at 35±2°C for 18-24 hours. One colony was picked from each plate and further streaked onto a Blood agar plate to ensure no mixed populations during the next step. Blood agar plates were incubated at 35±2°C for 18-24 hours after which isolates were frozen at -80°C in BHI and 20% glycerol for further workup.

3.2.4 Biochemical testing for species identification

Biochemical testing methods were consistent with the methods described in chapter 2.

3.2.5 Measuring MICs

Methods for measuring the MIC values of the presumptively resistant E.coli isolates were performed as described in chapter 2.

3.3 Quality control

The quality control measures outlined in chapter two for our agar screen, biochemical testing and resistance testing techniques were carried out as described in chapter 2.

3.4 Results

3.4.1 Mixed populations within samples

Before deciding to plate our enriched samples to X-Gluc, a trial was performed by plating the enriched samples directly to agar screen plates. However, the bacterial diversity on these agar screen plates was incredibly high and we were not able to detect E.coli due to the overgrowth of multiple other species in many of our samples. This also prompted us to begin another study on the AMR profiles among non-E.coli isolates in these rural well water samples.

3.4.2 Rural Well Water Samples

The rural well water samples tested in this study are submitted on a voluntary basis and are from private or semi-private water sources that have been submitted to ProvLab on a voluntary basis, but do not include municipal water supplies. Examples of semi-private and private water

supplies in Alberta are campgrounds, private farms, garages, community halls, etc. All of the samples were brought to ProvLab Calgary, therefore the majority of samples are from southern and central Alberta, as the nearest testing facility for well owners south of Red Deer is Calgary, whereas owners north of Red Deer test their water supplies at ProvLab Edmonton.

The number of samples submitted to ProvLab varied each year over the study period

(Figure 3.1), with the highest number of sample submissions occurring in 2007 and a general decrease in the number of samples archived each year after 2007. The number of sample submissions also varied by month and season (Figure 3.1), with a general increase from May to

October.

However, these values do not represent the total number of samples that were tested in our study. The samples described above were all private drinking water samples submitted to ProvLab.

These samples were then tested for E.coli, and the number of E.coli positive samples also varied by year and month (Supplementary Figure 3.1).

It should be noted that E.coli positive samples were saved at ProvLab only when time permitted, and the total E.coli positive samples archived for use in our study does not represent the total number of E.coli positive samples submitted to ProvLab during each time period. Among the

67,339 rural well water samples tested at ProvLab during our study period, 1328 samples were positive for E.coli, among which 1129 samples were archived and tested for AMR in our study.

Individuals may test their well multiple times over the ten year period, therefore there may be multiple samples from each individual well. However, we know from previous studies in

Alberta that the frequency of rural well water submissions in Alberta is low [293], so many well owners may test only once within the ten years.

A B 10000 10000 9000 2006 9000 8000 8000 2007 7000 7000 2008 6000 6000 2009 5000 5000 2010 4000 4000 2011 3000 3000 2000 2000 2012 Number of Sample Submissions 1000 Number of Sample Submissions1000 2013 0 0 2014 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Jan-Feb Mar-Apr May-Jun Jul-Aug Sept-Oct Nov-Dec Year Month 2015

C 200 D 200 180 2006 180 160 160 2007 140 140 2008 120 120 2009 100 100 2010 80 80 2011 of Samples Tested for AMR 60 60 40 40 2012 20 20

Number 2013 0 Number of Samples Tested for AMR0 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 Jan-Feb Mar-Apr May-Jun Jul-Aug Sept-Oct Nov-Dec 2014 Year Month 2015

3.4.3 Total Sample Numbers

From 2006-2016, 1129 E.coli positive rural well water samples were enriched in TSB broth and streaked onto selective X-Gluc agar plates to screen for E.coli isolates. Among these samples,

20,902 presumptive E.coli colonies were screened for AMR. Fifteen samples had no visible blue colonies typical of E.coli and therefore no colonies were selected for further workup. One sample was not found in the repository of archived samples from ProvLab. Among all samples where

E.coli was not found, X-Gluc plates indicated the growth of non-E.coli species and the lack of

E.coli may have been indicative of the faster doubling times of these organisms in comparison to

E.coli.

3.4.4 AMR E.coli Detected via Agar Screen Plate Method

Among the 1129 samples screened, 835 had 20 blue colonies picked from the X-Gluc agar plate. Fifteen samples had no E.coli on X-Gluc plates but showed growth of colorless non-E.coli isolates. The overgrowth of colorless, non-E.coli colonies resulted in less than ten colony picks for

62 samples.

A total of 1129 E.coli positive water samples were screened for resistance and 374 samples

(33%) had at least one presumptive E.coli isolate with growth on the agar screen. When multiple

AMR profiles were observed, a single isolate with each individual profile was chosen for further workup.

3.4.5 Results of API® Biochemical Tests

API® 20E biochemical tests were used for species identification of presumptively resistant isolates. Among the isolates determined to be non-E.coli, many species were identified including

Citrobacter (48 isolates), Klebsiella (16), Enterobacter (11), Kluyvera (10), Salmonella (6),

Raoultella (5), Pantoea (4), Moellerella (2), Erwingella (1), Rahnella (1), and Shigella (1) species

(Figure 3.2). Furthermore, 13 isolates were determined to be low discrimination E.coli that did not meet the inclusion criteria. These isolates ranged from 45.5 to 87.7% E.coli based on API® 20E biochemical tests. In addition, two isolates were identified as 89.5% E.coli and were rounded up to the nearest whole number to meet the inclusion criteria for E.coli identification.

Shigella Salmonella Raoultella Rahnella Pantoea Moellerella Kluyvera Klebsiella Erwingella Enterobacter Citrobacter

0 10 20 30 40 50 60

Figure 3.2. Non-E.coli species identified by API® 20E biochemical strips with growth on agar screen after selection from X-Gluc plates.

3.4.6 Antimicrobial Susceptibility Testing via NARMS SensititreTM Panels

Among the 1129 E.coli positive rural well water samples tested, 248 samples (22%) were positive for at least one AMR E.coli isolate based on NARMS Sensititre™ panel results. Within these 248 samples, 285 E.coli isolates were resistant, and an additional nine E.coli isolates were included as intermediate.

Within the 248 samples positive for AMR E.coli, 208 samples had only one resistance profile. These samples may have had multiple E.coli isolates detected on the X-Gluc plates, however the agar screen and NARMS resistance profiles show only one distinct resistance profile among the (up to) 20 isolates screened from the sample.

Many samples had more than one distinct resistance profile among the AMR E.coli isolates from that sample. Thirty-six samples (15% of AMR E.coli positive samples) had two distinct

resistance profiles and five samples (2%) had three distinct resistance profiles. Samples with more than one distinct AMR E.coli isolate have been outlined in Figure 3.3.

1 Profile 2 Profiles 3 Profiles

2% 15%

83%

Figure 3.3. Percentage of samples with one, two or three distinct resistance profiles among up to 20 E.coli isolates screened for resistance from 1129 E.coli positive rural well water samples.

Table 3.1 Diversity of Resistance Profiles among samples with multiple distinct AMR E.coli isolates. NARMS Sample NARMS Resistance Profile Intermediate Number Results 979 AMP, GEN, STR, FIS, TET, SXT STR, FIS, TET, SXT 1010 GEN, FIS, TET AMP, STR, FIS, TET, SXT AMP, STR, TET 1061 CHL, STR, FIS, TET AMP, CHL, STR, FIS

1084 STR, FIS, SXT STR TET 1087 STR, FIS, SXT AMP, FIS, TET, SXT FIS, SXT 1119 AUG2, AMP, FOX FIS, TET, SXT CHL, FIS, STR, TET 1147 AUG2, AMP, FOX FIS, TET 1167 FIS, TET STR, FIS, TET 1173 FIS TET 1223 AMP, TET TET 1260 AMP, NAL, TET NAL, TET 1269 TET, STR AMP, TET 1271 TET, STR AMP, FIS, TET, SXT 1331 TET AMP, STR 1340 AMP, STR, TET AMP, FIS, STR 1419 FIS CHL, STR, FIS, TET, SXT 1496 AMP, CHL, STR, FIS, TET STR, FIS, TET 1534 FIS, TET AMP, STR, CHL, FIS, TET, SXT 1566 AMP, STR, FIS, TET STR 1573 AMP, CIP, NAL, STR, FIS, TET TET 1577 TET CHL, STR, FIS, TET, SXT GEN AZI, CHL, FIS, TET, SXT 1627 AUG2, AMP, FOX, XNL, AXO, STR, TET AMP, CHL, STR, FIS, TET, SXT 1630 AMP, CHL, FIS, TET TET

1659 AUG2, AMP, FOX, XNL, AXO, CHL, CIP, GEN, NAL, STR, FIS, TET, SXT AMP, CHL, CIP, NAL, STR, FIS, TET, SXT 1712 AMP, CIP, NAL, FIS, TET, SXT AUG2 STR, FIS, TET 1715 TET AUG2, AMP FOX 1733 AMP, STR, FIS, TET AMP, CHL, NAL, FIS, TET, SXT 1744 CIP, NAL, TET AMP, AZI, XNL, AXO, CIP, GEN, NAL, STR, FIS, SXT AMP, NAL, TET 1774 AMP, AZI, XNL, AXO, CIP, GEN, NAL, STR, FIS, SXT AMP, NAL, TET 1785 FIS, STR, TET CHL FIS, TET 1791 STR, FIS, TET AMP, AZI, NAL, STR, FIS, TET, SXT 1807 AMP, CIP, NAL NAL 1809 AMP, GEN TET 1829 AMP, CHL, STR, FIS, TET, SXT AUG2, AMP 1850 TET STR, TET 1907 STR, TET AUG2, AMP, FOX, XNL, AXO, TET 1976 AZI, NAL, TET AZI, NAL 1988 AUG2, AMP, FOX, XNL, AXO, STR, FIS, TET, SXT STR, TET 2003 FIS, SXT TET ER781058 TET AUG2, AMP, FOX, XNL, AXO, CHL, STR, FIS, TET

3.4.6.1 AMR E.coli isolates with resistance to each of the 14 antimicrobials tested

To calculate the number of AMR E.coli isolates positive for resistance to each antimicrobial tested, we divided the total number of AMR E.coli isolates resistant to that particular antimicrobial by the total number of AMR E.coli isolates. Seventy seven percent of the AMR

E.coli isolates were resistant to TET. The next most common antimicrobial we observed resistance to was FIS, with 52% of AMR E.coli isolates resistant to this antimicrobial, however when sulfamethoxazole and trimethoprim were given in combination, resistance was only detected in

24% of the AMR E.coli isolates. Resistance to STR was observed in 47%, and resistance to AMP was observed in 38% of resistant isolates. Less than 20% of AMR E.coli were resistant to CHL,

NAL and AUG2. Furthermore, less than 10% of AMR E.coli isolates were resistant to AZI, FOX,

XNL, AXO, GEN and CIP (Supplementary Table 3.1; Figure 3.4).

To calculate the total number of of E.coli positive samples with resistance to each antimicrobial, the number of samples with at least one E.coli isolate resistant to the antimicrobial were divided by the total number of samples tested. The percentage of E.coli positive samples with resistance to TET, FIS, STR and AMP were 17, 12, 11 and 8.9% (respectively). In comparison to

FIS (12%), SXT resistance was observed in 5.7% of E.coli positive rural well water samples.

Resistance to CHL, NAL, AUG2 and AZI was observed in 4.3, 3.0, 2.7 and 2.4% of samples

(respectively). Lastly, the lowest levels of resistance were found for FOX (2.2%), XNL (2.1%),

AXO (2.1%), GEN (2.0%) and CIP (1.4%) (Supplementary Table 3.1).

Tetracycline

Sulfisoxazole

Streptomycin

Ampicillin

Trimethoprim/Sulphamethoxazole

Chloramphenicol

Nalidixic Acid

Amoxicillin/Clavulanic Acid Intermediate Resistant Azithromycin

Cefoxitin

Ceftriaxone

Ceftiofur

Gentamicin

Ciprofloxacin

0 50 100 150 200 250 Number of AMR E.coli Isolates

Figure 3.4. Number of AMR E.coli isolates with resistant and intermediate profiles for 14 antimicrobials tested by NARMS Sensititre™ panels.

3.4.6.2 Antimicrobial Classes with Resistance

As many AMR mechanisms confer resistance to multiple antimicrobials from the same class, it is important to observe the levels of resistance to each class of antimicrobials. Among the

14 antimicrobials tested on the NARMS Sensititre™ panel, one MAC (AZI), one CHL (CHL), two QNL (CIP, NAL), three CEPH (XNL, FOX, AXO), two PCN (AUG2, AMP), two AMG

(GEN, STR), one TET (TET) and two SULF (FIS, SXT) were included.

The percentage of AMR E.coli isolates resistant to TET, SULF and AMG was 78, 52 and

48%, respectively. The next most common resistance was to PCN, with 38% of AMR E.coli isolates demonstrating resistance to this class of antimicrobials. Resistance to CHL and the QNL antimicrobials was observed in 18 and 14% of AMR E.coli isolates, respectively. Lastly, the lowest resistance we observed was to the CEPH and MAC, with 9.8 and 8.8% of AMR E.coli isolates exhibiting resistance to these antimicrobial classes (Figure 3.5).

Again, it is important to observe the total amount of resistance to each class of antimicrobials as a proportion of the total number of E.coli positive rural well water samples tested.

Among the 1129 E.coli positive rural well water samples tested, 17% had at least one AMR E.coli isolate resistant to TET, 12% resistant to SULF and 11% resistant to AMG. Furthermore, 8.9% of samples had one or more AMR E.coli isolates resistant to PCN compared to 4.3% for CHL antimicrobials. The lowest resistance observed was to the QNL, CEPH and MAC antimicrobial classes, with 3.0, 2.5 and 2.1% of samples having at least one E.coli isolate resistant to these antimicrobials (Supplementary Table 3.2).

Tetracycline

Sulfonamide

Aminoglycoside

Penicillin

Intermediate Chloramphenicol Resistant

Quinolone

Cephalosporin

Macrolide

0 50 100 150 200 250 Number of AMR E.coli Isolates

Figure 3.5. Number of E.coli isolates resistant (or intermediate) to each of eight classes of antimicrobials.

3.4.6.3 Multi-drug Resistance by Antimicrobial

Resistance to three or more antimicrobials, often referred to as MDR, was common among the AMR E.coli isolates detected in our study. Multi-drug resistance, as defined by resistance to three or more antimicrobials, was observed in 53% of AMR E.coli isolates. Six isolates (2% of

AMR E.coli isolates) were resistant to every antimicrobial tested (Figure 3.6).

60 Isolates

50 E.coli

Number of AMR 20

0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Number of Antimicrobials

Figure 3.6. Number of antimicrobials with resistance among 285 AMR E.coli isolates.

3.4.6.4 Multi-class Resistance by Class

Among the 285 AMR E.coli isolates detected in this study, 31% were resistant to only one class of antimicrobials, with the majority of isolates being resistant to two or more classes.

Resistance to three or more classes of antimicrobials, termed MCR, was observed in 48% of the

AMR E.coli isolates detected. Six AMR E.coli isolates were resistant to every class of antimicrobial tested, and a greater number were resistant to three antimicrobial classes than were resistant to two classes in part because the second most common AMR profile conferred resistance to AMG, TET and SULF antimicrobials (Figure 3.7).

Twelve percent of E.coli positive rural well water samples tested positive for MCR E.coli.

100

70 Isolates 60 E.coli 50

30 Number of AMR 20

0 1 2 3 4 5 6 7 8 Number of Classes of Antimicrobials

Figure 3.7. Number of antimicrobial classes with resistance among 285 AMR E.coli isolates.

3.4.6.5 Common resistance profiles among AMR E.coli isolates

The high levels of resistance to TET, SULF, AMG and PCN antimicrobials is apparent within the most common resistance profiles among the 285 AMR E.coli isolates. Various combinations of TET, SULF and AMG antimicrobials make up the top four most common resistance profiles, with PCN, CHL and CEPH antimicrobials within many of the other resistance profiles and MAC antimicrobials being the least common among resistance profiles. In total, 55 distinct resistance profiles were present when grouped by antimicrobial class.

The most common resistance profile, observed within 60 of 285 AMR E.coli isolates

(21%), was resistance to TET alone. The next most common resistance phenotype, which is the most common MCR profile, conferred resistance to AMG/TET/SULFA and occurred in 11% of

AMR E.coli isolates. The next three most common resistance profiles within the AMR E.coli isolates collected were TET/SULFA (7%), AMG/TET (5%) and CHL/AMG/TET/SULFA (4%)

(Table 3.2).

Table 3.2. Number of isolates with each antimicrobial resistance profile among 285 AMR E.coli isolates. Number of AMR E.coli Antimicrobial resistance profiles isolates with resistance profile TET 60 AMG, TET, SULFA 32 TET, SULFA 21 AMG, TET 15 CHL, AMG, TET, SULFA 12 PCN, AMG, TET 9 CHL, PCN, AMG, TET, SULFA 9 PCN, TET 8 SULFA 8 PCN 7 AMG 7 PCN, AMG, TET, SULFA 6 PCN, AMG, SULFA 6 MAC, CHL, QNL, CEPH, PCN, AMG, TET, 6 SULFA PCN, TET, SULFA 5 CEPH, PCN 5 QNL 5 PCN, AMG 3 QNL, PCN, TET 3 AMG, SULFA 3 QNL, PCN, TET, SULFA 3 CHL, QNL, PCN, AMG, TET, SULFA 3 CHL, CEPH, PCN, AMG, TET, SULFA 3

QNL, PCN, AMG, TET, SULFA 2 CHL, PCN, TET, SULFA 2 CHL, TET, SULFA 2 CEPH, PCN, AMG, TET 2 MAC, CHL, PCN, AMG, TET, SULFA 2 CHL, PCN, AMG, SULFA 2 CEPH, PCN, TET 2 MAC, CHL, TET, SULFA 2 QNL, TET 2 CEPH, PCN, AMG, TET, SULFA 2 CHL, QNL, CEPH, PCN, AMG, TET, SULFA 2 MAC, QNL, PCN, AMG, TET, SULFA 2 QNL, PCN 2 MAC, PCN, AMG, TET, SULFA 2 MAC, QNL, TET 1 MAC, QNL, CEPH, PCN, AMG, SULFA 1 CHL, CEPH, PCN, TET, SULFA 1 MAC, CHL, SULFA 1 MAC, QNL, PCN, AMG, SULFA 1 CHL, QNL, PCN, TET, SULFA 1 CHL, PCN, AMG, TET 1 PCN, SULFA 1 MAC, QNL, AMG, SULFA 1 MAC, PCN, AMG, SULFA 1 MAC, QNL, CEPH, PCN, AMG, TET, SULFA 1 CHL, AMG, SULFA 1 MAC 1 MAC, QNL 1 MAC, CEPH, PCN 1 QNL, AMG, TET, SULFA 1 QNL, CEPH, PCN 1 QNL, CEPH, PCN, AMG, SULFA 1

3.4.6.6 Negative controls

A presumptive E.coli isolate from every 20 samples with growth typical of E.coli on the no-antimicrobial control plate, and no growth on the antimicrobial-supplemented screen plates, was used as a negative control to determine whether all of the AMR E.coli were being picked up

with the agar screen. A total of 40 negative controls were determined to be E.coli by API® biochemical tests, one sample did not grow and two samples did not fit the inclusion criteria for

E.coli identification. Of these two isolates, one was a low discrimination E.coli (<90% match) and the other was Kluyvera sp. Every E.coli isolate was susceptible to all 14 antimicrobials except for one isolate that was intermediate to CHL, which is not an antimicrobial used on our agar screen.

3.4.6.7 Isolates intermediate to antimicrobials

Nine E.coli isolates were found to be intermediate to one or more antimicrobials and not resistant based on CLSI guidelines for the human breakpoints of the 14 antimicrobials on our

NARMS Sensititre™ panels. Eight E.coli isolates were detected that were intermediate to TET and one isolate that was intermediate to both AUG2 and FOX.

Additionally, many of the AMR E.coli isolates were also intermediate to various antimicrobials including combinations of the CEPH class of antimicrobials including AUG2 (6

E.coli isolates), AMP (3), FOX (3), and AXO (2). Additionally, a few AMR E.coli isolates were intermediate to TET (3), GEN (3) or CHL (3) (Supplementary Table 3.2).

3.5 Discussion

Antimicrobial resistance is a major risk to the health of human and animal populations worldwide, and it has been estimated that 10,000,000 people will die of AMR infections per year by 2050 if the current trend of rising resistance continues [294]. Although the spread of AMR bacteria through animals and humans has been the focus of many studies, very little work has investigated the role of drinking water in the spread of AMR bacteria. We hypothesized in this study that due to the potential for groundwater contamination from a wide variety of human, animal

and environmental sources, we expected to find a high number of AMR E.coli isolates in Alberta’s rural well water samples.

In Alberta, approximately 3% of rural well water samples test positive for E.coli when submitted for voluntary testing [293]. In our study, 1129 E.coli positive well water samples from

2006-2016 sampled from southern and central Alberta were screened for AMR E.coli. We detected at least one AMR E.coli isolate from 22% of 1129 E.coli positive water samples. This suggests that over one fifth of the water samples that fail the current water quality parameters for fecal coliforms will contain AMR E.coli. The consumption of well water contaminated with AMR E.coli has previously been linked to human carriage of resistant bacteria [10], and poses a significant risk to populations relying on these water sources for their daily household needs.

The majority of samples contained one distinct AMR E.coli isolate, however, many samples contained multiple AMR E.coli isolates with distinct resistance phenotypes, contrary to the low variation of resistance profiles detected in chapter 2. Although our pilot study had a large number of isolates, there were only nine samples included in the study. With 16% of the samples positive for AMR E.coli (3.5% of rural well water samples tested) having more than one resistance profile, it is unlikely that this would have been detected with the low sample numbers in our pilot study. However, this data does suggest a variety of distinct resistant profiles among E.coli from the same water sample, although genotypic tests are required to determine whether these E.coli isolates are distinct strains. The diversity of resistance profiles could be a result of multiple sources of contamination, and/or could be a result of the transfer of AMR genes between bacteria living in the same community, which has previously been described in water [295].

The present study found that 22% of E.coli positive rural well water samples had at least one AMR E.coli isolate, compared to 11.4% of E.coli positive samples tested in 2006 in a study

looking at AMR contamination in two regions of Alberta and Ontario [296]. These results suggest that levels of resistance among E.coli populations in Alberta’s groundwater may have doubled over time. However, this may be attributed to differences in the areas sampled between the two studies, as the previous study sampled only a small geographic region in central Alberta and had lower sample numbers.

E.coli isolates in this study were screened against 14 antimicrobials from eight distinct classes, and among the 285 AMR E.coli isolates detected, 55 distinct resistance profiles were present. The highest resistance was to TET, SULF, AMG and PCN, which is consistent with findings from other studies in groundwater [248] as well as humans and food animals [297-300].

Economides et al. (2012) found that among E.coli from groundwater samples collected at confined animal feeding operations, the highest levels of resistance were to sulfamethoxazole, STR and

TET, with an equal proportion of resistance to each antimicrobial [248]. However, other studies found high levels of resistance to AMP and cephalothin [289] or AMG [251]. Although the levels of resistance to various antimicrobials among E.coli isolates from groundwater were not previously studied, E.coli from human and food animal sources are often resistant to TET, AMP and SULF

[297-299]. Ampicillin resistance has even been observed in E.coli from feces of animals living on organic farms when the use of antimicrobials is restricted [301], potentially highlighting the acquisition of this resistance from another source, or co-selection of this resistance from another selective pressure. The high levels of resistance to TET, AMP and SULF has been increasing over time among E.coli isolates from humans and food animals, and the most frequent co-resistant phenotypes observed among these AMR isolates are AMP+STR and TET+SULFA [300].

Seventy-eight percent of AMR E.coli isolates in our study were resistant to TET, which is common among many animals and humans due to the rampant resistance to this antimicrobial

throughout the world. Specific TET resistance genes are common in aquatic systems, namely tet(E)

[146] and tet(M) [142, 148], as well as many multidrug efflux pumps [149], and future studies should investigate the genetic basis for resistance to TET and the antimicrobials with high levels of resistance in our study.

The most common resistance profile (TET) and most common MCR profile

(AMG/TET/SULFA) were conserved from the AMR study in Alberta approximately 10 years ago and were approximately the same proportion of the total number of AMR E.coli isolates [296].

Resistance to beta-lactams in our study was common, and this mechanism of resistance is not intrinsic to E.coli but, rather, due to the acquisition of resistance genes or chromosomal mutations. The high level of resistance to AMP was common among groundwater isolates from our study as well as many studies in humans and food animals [297-300].

Although it appears that the majority of E.coli isolates from human and food animal sources are resistant to the same antimicrobial classes as observed in our study, we saw a greater number of AMR E.coli isolates resistant to AMG than was reported in these studies. However, one study measuring resistance among E.coli from groundwater found that 93% of E.coli isolates were resistant to AMG [251], highlighting the possibility that AMG resistance is higher in groundwater than in food animals and humans.

A retrospective study from 1950 to 2002 found that the proportion of E.coli with MCR among 1729 E.coli isolates from various food and animal sources increased from 7.5% in the 1950s to 63.6% in the 2000s [300]. Increasing MCR may be observed in groundwater as well, as 48% of

AMR E.coli isolates in our study was an increase since the study in Alberta and Ontario from 2004 to 2006 which found 42% of AMR E.coli isolates from rural well water samples had MCR [296].

Furthermore, McKeon et al. (1995) found that 14% of E.coli isolates from untreated groundwater

supplies had MCR in 1995, and the E.coli isolates collected from untreated groundwater were able to transfer and receive AMP resistance from other coliform and non-coliform bacteria from the same source [289]. The ability of E.coli to transfer resistance between coliform and non-coliform bacteria may have led to an increase in MDR over time, and may have contributed to the additional resistance genes obtained by E.coli isolates from the same sample that have resistance profiles differing by one or two antimicrobials. Furthermore, MDR has been linked to AMP and TET resistance [298], which is consistent with our findings of high levels of MCR and resistance to both AMP and TET.

Among the AMR E.coli isolates detected in our study, resistance to FIS was higher than resistance to the combination of sulfamethoxazole and trimethoprim, suggesting that giving antimicrobials in combination may be an important measure in combatting AMR in clinical isolates.

To assess the ability of our agar screen to detect AMR E.coli we isolated negative control presumptive E.coli isolates from every 20 isolates appearing susceptible on the agar screen. All negative control isolates were susceptible except one isolate that was intermediate to CHL.

Because the agar screen did not include antimicrobials from the MAC or CHL class, we decided that the screen was sufficiently picking up resistance to the antimicrobials on the screen but may miss resistance to other classes of antimicrobials not represented in our screen. This is a limitation within our results, as the resistance detected among AMR E.coli isolates may be biased toward the antimicrobials included in the screen and the low levels of resistance to CHL and MAC may be partially attributed to not including these antimicrobials in our agar screen.

We also detected many samples with no E.coli present, despite these samples testing positive for E.coli by Colilert® at ProvLab. This may be attributed to the freezing and enrichment

process, as a large diversity of non-E.coli isolates were detected in many samples and may have quicker doubling times, allowing for a greater population expansion during the enrichment period.

This enrichment process remains a limitation in our study, as the original proportions of each population of bacteria within the mixed population of bacteria may not be depicted after enrichment due to the differences in doubling times of various strains of bacteria.

Several limitations exist in our study with respect to the samples we tested and our extrapolation of data to the larger population of rural well owners in Alberta. The voluntary nature of sample submissions to ProvLab introduces a bias as to the types of samples we have tested, as many factors affect the decision of a well owner to test their water for microbial contaminants. A systematic review on the factors influencing perceptions of private water quality in North America

(submitted for publication in 2017) suggests that many factors influence the perceptions of well owners including, but not limited to, perceived risks of the water, infrastructure of well water, past experiences, external information, demographics as well as values, attitudes and beliefs of the well owners themselves [302]. Furthermore, many extraneous factors may affect the individual well owner’s decision to test their water, such as proximity to testing facilities [303]. Therefore, the characteristics of rural well water samples submitted may not be representative of all rural wells in the province.

Sample collection is not controlled, as well owners collect their own rural well water samples and submit them to ProvLab. Furthermore, repeat testing can occur, resulting in multiple submissions from the same well within one year. Because the land locations provided with each sample are based on quarter sections, our results cannot indicate whether multiple wells were tested on the same section of land, or whether repeat samples were taken from the same well over time.

Because of this lack of resolution with respect to the location of our data, we cannot distinguish between sporadic contamination events or constant problem areas within the data [283].

3.6 Conclusions

Within southern Alberta’s rural well water sources, 22% of E.coli positive samples tested in our study contained AMR E.coli. The highest levels of resistance was to TET, SULFA, AMG and PCN antimicrobials, which is consistent with previous findings. However, overall levels of resistance increased by 10%, and the percentage of AMR E.coli isolates resistant to three or more classes of antimicrobials (48%) increased when compared with a study conducted on rural well water samples from Alberta and Ontario in 2008 [296]. The levels of AMR E.coli in our study suggest a potential public health concern and should be used to guide policy decisions and should be a part of public health campaigns for individuals utilizing Alberta’s rural well water sources

(Figure 3.8).

Antimicrobial Resistance in your Rural Well Water?

Have you tested your rural well water? Our study detected ANTIBIOTIC RESISTANT E.coli in rural well water samples submitted for routine testing in southern Alberta from 2006 to 2016 that tested positive for E.coli. Antibiotic resistant E.coli were detected in 22% of rural well water samples positive for E.coli based on Provincial Laboratory testing in Calgary.

Half of the resistant bacteria had the ability to resist three or more classes of antibiotics!

Without proper testing and treatment, these bacteria may go unnoticed and remain in your water!

For more information on how to test your well water: www.ahs.ca/eph

Figure 3.8. Public Health poster outlining the levels of AMR E.coli observed in Alberta’s rural well water sources.

CHAPTER FOUR: RELEVANCE TO HUMAN AND ANIMAL HEALTH

4.1 Background

4.1.1 E.coli and human health

E.coli is a common commensal bacteria within the gastrointestinal tract of humans and animals, however certain strains are responsible for intestinal and extra-intestinal infections in humans and animals [241]. Two particularly important bacteria that cause a concern for public health and veterinary medicine are Shiga toxin-producing E.coli (STEC) and ESBL-producing

E.coli.

Urinary tract infections [304], meningitis [305], bacteremia [306], and diarrheal disease

[307] are common clinical symptoms caused primarily by a few pathovars of E.coli. One type of pathogenic E.coli in particular, STEC, can reside in the guts of ruminants including cattle, goats, deer, sheep and elk [308-310]. Humans often acquire STEC from many sources in the food chain

(vegetables, sprouts, milk, juices, fruit and meat) or waters used for drinking, bathing and recreation [311]. Human illnesses caused by STEC are broad, but include bloody diarrhea and a condition associated with high mortality rates called hemolytic uremic syndrome (HUS) [312].

E.coli is used as a model organism to measure the development of resistance after exposure to antimicrobials due to the widespread nature of this bacteria within human and animal gastrointestinal tracts. Resistance is common and generally develops through mutations or acquisition of mobile genetic elements. Resistance to third-generation CEPH is often mediated by

ESBLs and is transmissible to other bacteria via inter-and intra-species transmission. ESBL- producing E.coli are of high importance, as the only available treatment option for severe infections is the use of carbapenems.

4.1.2 Phylogenetic groups of E.coli

Although E.coli are a single species of bacteria, they are genetically diverse and can have complex interactions with human hosts, both as commensals and pathogens. E.coli can be divided into four main phylogenetic groups (A, B1, B2 and D) based on three genetic markers, chuA, yjaA and the DNA fragment TspE4.C2 [313-315], and further divided into seven subgroups (A0, A1, B1,

B22, B23, D1, D2) [316]. Many characteristics differ between the phylogenetic groups such as their sugar utilization, AMR profiles and growth rates [316, 317] as a result of genetic differences between the groups.

Ecological niches also differ between the phylogenetic groups [318]. The majority of environmental strains are within the B1 subgroup [261], whereas strains from groups B2 and D contain more virulence factors than groups A and B1 [319]. Extraintestinal strains of critical importance to human medicine are usually within these two groups (B2 and D) [320, 321], whereas commensal strains are often from groups A and B1 [322]. Furthermore, intestinal pathogenic strains are often detected within phylogenetic groups A, B1 and D [323].

Phylogenetic analyses within E.coli also give clues as to the source of the strain. For instance, Escobar-Páramo et al. (2006) found that groups B1 and D were more prominent in birds, whereas groups A and B1 were more common in non-human animals, and groups A and B2 were the most common groups found in humans [324]. Further studies suggest that the B1 strain was found to be most prevalent in cow, goat and sheep fecal samples, and similarity indexes indicated the E.coli population structures were similar between humans and pigs, as well as between cows, sheep and goats [316].

Within our study, phylogenetic groups will be determined among AMR E.coli of critical importance to human medicine (potential ESBL-producing E.coli) to determine the relevance of these bacteria as potential pathogens.

4.1.3 ESBLs

The most common mechanism of resistance to β-lactam antimicrobials in E.coli and many other gram-negative bacteria is through the production of β-lactamases [4, 325]. Among the various types of β-lactamases are broad spectrum, extended-spectrum, AmpC β-lactamases and carbapenemases. The production of ESBLs by Enterobacteriaceae are of public health concern globally, as they have been implicated in MDR infections within hospitals and the community.

ESBL-producing E.coli have been an important cause of urinary tract infections since the late

1990s [325] and limited therapeutic options are available for the treatment of infections caused by

ESBL-producing organisms [326]. A recent study of the AMR disease threats in Canada concluded that ESBL-producing Enterbacteriaceae are the pathogen of highest threat to Canadians [327].

The Centers for Disease Control and Prevention in the United States of America estimates the burden of illness caused by ESBL-producing Enterobacteriaceae to be 26,000 drug resistant infections resulting in 1,700 deaths each year and a total of $40,000 in excess medical costs for each infection [3].

The first ESBL-producing bacteria was reported in 1983 in Germany and 200 variants of this enzyme have since been identified worldwide [325]. ESBL production commonly occurs due to point mutations in blaTEM, blaSVH, or blaCTX genes, altering the primary amino acid sequence of the enzyme [328].

These enzymes are typically encoded by mobile genes and are commonly associated with

MDR [329, 330]. Plasmids encoding ESBLs often contain genes for resistance to SULF, AMG and occasionally fluoroquinolones [331-333], and are frequently associated with transposons and integrons [334]. The associations with plasmids and integrons increase the potential for MDR development, limit the treatment options available for ESBL-producing pathogens and facilitate inter- and intra-species dissemination of ESBLs [332, 335].

ESBLs are typically inhibitor-susceptible β-lactamases with the ability to hydrolyze PCN,

CEPH and aztreonam. These bacteria are a serious public health concern, as the number of ESBL- producing bacteria have been increasingly reported worldwide and many isolates have serious epidemic potential [332, 335, 336]. The emergence of CTX-M enzymes in E.coli and the subsequent dissemination in many environments has been a global public health issue, particularly in nursing homes and the community [330]. Community isolates are commonly CTX-M-producing

E.coli [336] and these isolates are often resistant to many other classes of antimicrobials including

GEN, CIP, tobramycin, TET and SXT [337].

With recent interest in the potential transmission of ESBL-producing E.coli between humans and animals, many studies have investigated the presence of ESBL-producing E.coli in humans and animals within Alberta [326, 331, 337-340], suggesting transmission of ESBL- producing bacteria between human and animal populations. One study in particular found that from 2007-2010, increased bloodstream infections attributed to ESBL-producing E.coli were observed [339]. In addition, another study during this time detected ESBL-producing E.coli in cattle with high sequence similarity of their ESBL-bearing plasmids to those found in human clinical isolates [340].

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Mulvey et al. (2009) highlighted the potential link between ESBL-producing E.coli in food producing animals and human infections in Canadian hospitals [340]. With the use of β-lactam antimicrobials in animal production and human medicine, particularly in hospital settings, ESBL genes have rapidly emerged in E.coli populations worldwide [341]. In retail chicken, many ESBL genes have been detected [342], with high sequence similarity to human isolates [343], posing a risk to human health [344]. Furthermore, ESBL-producing E.coli with high sequence similarity within the blaCTX-M-15 gene have been detected between those found in domestic animals and humans, and those found in wildlife [345, 346].

However, very few studies have investigated the potential role of the environment, particularly water, as a reservoir of ESBL-producing E.coli allowing for transmission between humans and animals. One study detected ESBL-producing E.coli in beach and private drinking water in Canada, with 45.8% of ESBL-producing E.coli from beach and private drinking water samples being MDR [347]. Gao et al. (2014) found that ESBL-producing E.coli from chickens were linked to downstream ESBL-producing E.coli in surface waters [348]. However, the potential of groundwater being a reservoir and potential contamination source for human and animal infections with ESBL-producing E.coli in Canada has not been investigated.

4.1.4 AmpC β-Lactamases

Wild-type E.coli produce low levels of chromosomal AmpC β-lactamases that do not contribute to a clinically relevant level of resistance to β-lactams [349, 350]. Resistance to β- lactams has been reported as a result of β-lactamase overproduction through gene amplification within the chromosome, or promoter region mutations resulting in overexpression [349-352]. Cells overexpressing AmpC β-lactamases have a similar phenotype to ESBLs, however they are not

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commonly inhibited by cephamycins or β-lactam-β-lactamase inhibitor combinations [353]. Like

ESBLs, these enzymes are typically associated with MDR and have been detected in E.coli [354].

Although phenotypic tests do not differentiate between chromosomal and plasmid-mediated

AmpC β-lactamases in E.coli, a lack of MDR suggests chromosomal AmpC, whereas the presence of MDR is consistent with either plasmid-mediated or chromosomal AmpC production [355].

4.1.5 Carbapenems

Carbapenem-resistant microorganisms are diverse in their ability to hydrolyze carbapenems, and their ability to hydrolyze other β-lactams varies between organisms. These micoorganisms are often associated with extensive and total AMR, and have been reported worldwide [355]. Carbapenemases can be transmissible or chromosomally-encoded, and the transmissible enzymes can be acquired unpredictably by pathogens of critical importance to human and animal health [355].

4.1.6 ESBL Testing

Clinical ESBL testing in Canada follows the CLSI guidelines, which recommend a phenotypic confirmatory combined-disk test to detect ESBL production in Enterobacteriaceae.

With this method, growth-inhibitory zones around antimicrobials including cefotaxime and ceftazidime with or without clavulanate are measured and results are interpreted to indicate the presence of absence of ESBL-producing Enterobacteriaceae [161].

Results of ESBL tests can be masked by other enzymes that hydrolyze the same substrates but are resistant to clavulanate. This may be due to AmpC cephalosporinases, either plasmid- mediated or overproduction of the chromosomally encoded AmpC enzyme [355-357]. The

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production of metallo-β-lactamases may also interfere with ESBLs, as these enzymes hydrolyze most β-lactams, including carbapenems, and are resistant to clavulanate.

Due to AmpC β-lactamases and carbapenemases interfering with the detection of ESBL testing, newer methods are emerging to detect underlying ESBLs among AmpC-producing

Enterobacteriaceae [335]. Of the many tests developed to improve ESBL detection, recent methods include inhibitors of AmpC to increase the detection of ESBL-producers among AmpC- producing bacteria. The Mast® D68C test has demonstrated great efficacy with detecting ESBL- and AmpC-production [357, 358].

Nourrisson et al. (2014) found the sensitivity of this test to be lower (73.1%) than the CLSI method (87.5%) when tested on a diverse set of ESBL producers, as false-negative results can occur with strains producing enzymes such as complex mutant enzymes or carbapenemases resistant to both clavulanate and cloxacillin. However, this was due to the inclusion of strains for which additional investigations were recommended, and the inclusion of complex mutant strains.

When these strains were excluded, the sensitivity of this kit was 92.1%. To ensure our sensitivity is as high as possible, all strains for which supplementary investigations are recommended will be followed up with the suggested tests.

4.1.7 STEC

Shiga toxin-producing E.coli was discovered in 1977 and named based on the ability to produce one or more of the cytotoxins, Shiga toxin 1 (stx1) and Shiga toxin 2 (stx2) [359]. In 1982, an association was made between STEC and both HUS and E.coli O157:H7 [360-362].

This pathogen is associated with outbreaks of diarrhea (or enteric disease) in individuals of all ages and is an important cause of hemorrhagic colitis and HUS in people worldwide. This

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pathogen is of particular concern in children, as toxin-associated diarrhea leads to HUS in 5-8% of children [363], which is characterized by progressive renal failure associated with hemolytic anemia and thrombocytopenia. Diarrhea-associated HUS is the most common cause of acute renal failure in children [362, 364], outlining the importance of this pathogen as a public health concern.

Shiga toxin-producing E.coli comprise over 400 serotypes varying in both their potential to cause human disease and their physiological characteristics [365, 366]. Although many serogroups of E.coli are able to produce Shiga toxins, the major serotypes of concern for human disease are E.coli O157:H7, O26, O103, O111, O121, O45, O91 and O145 [367, 368]. The most common cause of STEC infections in North America is E.coli O157:H7, causing both sporadic infections and outbreaks of disease [362, 369].

Ruminants are a natural reservoir of STEC [370], and transmission of STEC to humans often occurs through the consumption of food or water sources contaminated with cattle feces.

Transmission of STEC to humans can occur through the consumption of contaminated food, in particular through consumption of undercooked ground beef and many foods that are eaten raw, such as lettuce, sprouts or spinach. Furthermore, water, or unpasteurized milk or juices can become contaminated with STEC from cattle feces and act as a reservoir of infection [371]. Recently E.coli

O121, a non-O157 STEC strain, was implicated in a national outbreak in Canada associated with various flour and flour products [372].

STEC have been detected in many water sources [373, 374] and implicated in outbreaks associated with water [375] and well water [376]. Until recently, STEC had not been isolated from

Canadian groundwater, and with Alberta being the largest cattle-producing province in the country, the potential for environmental contamination by (or with) STEC is of particular importance in this province. A recent study in Alberta found STEC in the same E.coli positive

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rural well water samples as in our studies for this thesis [377], however the resistance profiles of these isolates remain unknown.

4.2 Specific Methods

4.2.1 ESBL Methods

4.2.1.1 Inclusion criteria

The NARMS Sensititre™ panels were interpreted via the CLSI guidelines (as described in chapter two) and the SWIN® software system (Thermo Fisher Scientific, Burlington, ON,

Canada), which flagged 27 possible ESBL-producing isolates due to their resistance (or intermediate resistance) to a 3rd generation CEPH (AXO). If flagged as a possible ESBL-producer, the E.coli isolate was chosen for further testing to conclude whether the isolate was an ESBL- producer and the phenotypic group it belonged to. Two isolates from sample 1687 were both tested for ESBLs despite their NARMS resistance profile differing by only one antimicrobial that was one dilution apart. Based on our inclusion criteria set forth in previous chapters, we considered these isolates to be the same, however disk diffusion and biotyping was performed to further justify this decision or inform whether this was an appropriate inclusion criteria.

4.2.1.2 Determining phylogenetic grouping

To determine the phylogenetic group of each potential ESBL-producing E.coli isolate, PCR for genes chuA, yjaA and the DNA fragment TSPE4.C2 was interpreted based on Figure 4.1 to distinguish between subtypes A, B1, B2 and D.

4.2.1.2.1 Culture conditions

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Potential ESBL-producing E.coli isolates were stored as stock solutions in BHI with 20% glycerol after isolation from the agar screen described in chapter two. Isolates were taken from stock solutions and streaked to isolation on Blood agar or MacConkey plates and incubated at

35±2°C for 18-24 hours.

4.2.1.2.2 PCR amplification

PCR was performed as previously described as a method do determine phylogenetic groups of E.coli [378]. Each reaction was performed using a 20µL mixture consisting of 2µL of 20X buffer supplied with the Taq polymerase, 20pmol of each primer (Table 4.1), 2.5U of Taq polymerase, 200ng of genomic DNA and 2µM of each deoxynucleoside triphosphate.

Denaturation occurred for 5 min at 94°C followed by 30 cycles of 30 seconds at 94°C, 30s at 55°C and 30 seconds at 72°C with a final extension step of 7 minutes at 72°C.

Table 4.1: Primers used for PCR Amplification during Phylogenetic Testing Sequence Primer

ChuA.1 Primer 5’-GACGAACCAACGGTCAGGAT-3’

ChuA.2 Primer 5’-TGCCGCCAGTACCAAAGACA-3’

YjaA.1 Primer 5’-TGAAGTGTCAGGAGACGCTG-3’

YjaA.2 Primer 5’-ATGGAGAATGCGTTCCTCAAC-3’

TspE4C2.1 Primer 5’-GAGTAATGTCGGGGCATTCA-3’

TspE4C2.2 Primer 5’-CGCGCCAACAAAGTATTACG-3’

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4.2.1.2.3 Gel Electrophoresis

During the final PCR cycle, 0.4g of GelPilot® LE Agarose (Qiagen, Toronto, ON, Canada) was added to 50mL of 50X TAE buffer (1L 50X TAE stock, 242g Tris base, 57.1mL acetic acid,

100mL 0.5M EDTA), microwaving and intermittently swirling the solution until all particles were dissolved. The agarose solution was poured into a casting stand and 3µL of RedSafe™ stain

(iNtRON Biotechnology, Lynnwood, WA, USA) per 50mL of agarose was added to the solution before cooling. The gel was set at room temperature for 30 minutes and wells were loaded with

7µL of sample with 1µL of GelPilot® Loading Dye (Qiagen, Toronto, ON, Canada) and 3µL of

GelPilot® 1kb Plus Ladder (Qiagen, Toronto, ON, Canada). The agarose gel was run at 110V for

50 minutes and imaged using the Bio-Rad ChemiDoc™ MP imaging system (Bio-Rad,

Mississauga, ON, Canada) to look for the presence of chuA and yjaA genes, and the DNA fragment

TSPE4.C2.

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Figure 4.1. Decision tree used to determine the phylogenetic group of each E.coli strain based on the results of PCR amplification of chuA and yjaA genes and the DNA fragment TSPE4.C2.

4.2.1.3 ESBL- and AmpC-producing E.coli detection via disk diffusion

The Mast® D68C ESBL and AmpC detection kit is based on a combination disk method using both clavulanate and cloxacillin. Disk A contains 10µg of cefpodoxime as the screening agent, disk B contains 10µg of cefpodoxime and clavulanate as the ESBL inhibitor, disk C contains

10µg of cefpodoxime and cloxacillin as the AmpC inhibitor and disk D contains 10µg of cefpodoxime in combination with both clavulanate and cloxacillin. Mueller Hinton agar was inoculated according to the manufacturer’s recommendations using a 0.5 McFarland standard cellular suspension density using a sterile swab, and one of each type of disk was placed in one of four quadrants on the plate. Plates were then incubated at 37°C for 18-24 hours. The results were interpreted by comparing disks A, B, C and D inhibition zone diameters around the disks using the

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interpretation spreadsheet provided by Mast® to determine whether isolates were AmpC- and/or

ESBL-producers.

4.2.2 STEC Methods

4.2.2.1 Inclusion criteria

Rural well water samples included in chapters two and three for resistance testing were split before use in this study into two separate vials at ProvLab. One vial was used for a population- wide screen for stx1 and stx2 genes by Colin Reynolds at the University of Alberta/ProvLab in

Edmonton, AB. The second vial was used in this study to screen for resistant E.coli populations, as described in chapters two and three of this thesis.

Samples positive for stx1 and/or stx2 genes and for one or more AMR E.coli isolate were included in this portion of our study. All AMR E.coli isolate(s) from samples positive for stx1 and/or stx2 genes at the population level were sent to Colin Reynolds for further testing to determine whether the AMR E.coli isolates in these samples were the potential source of stx1 and/or stx2 genes in these samples.

4.2.2.2 Quantitative polymerase chain reaction for stx1 and stx2 genes

AMR E.coli isolates in 1.5mL vials with 1mL of BHI with 20% glycerol were sent to

Edmonton on dry ice and were stored at -80°C for up to one week before running quantitative polymerase chain reaction (qPCR)1. Individual isolates were thawed and transferred to a specified well in a 96-well Greiner plate, which were stored at -20°C until qPCR analysis. Thawed plates

1 This portion of the study was performed by Colin Reynolds at the University of Alberta under the supervision of Dr. Norman Neumann. 109

were boiled at 95°C for 10 minutes (Eppendorf Mastercycler Thermal Cycler) for cell lysis, after which the plates were centrifuged at 12000 rpm for 2 minutes. Five microliters of each sample was then transferred to the corresponding well on an ABI Fast 96-well Real-Time PCR plate containing

15µl of the Stx1/IAC or Stx2 Master Mix solution. On each PCR plate, five 1:10 dilutions

(50,000/5000/500/50/5 copies per reaction) of plasmid DNA containing the Stx gene under investigation (pCR2.1-Stx1/ pCR2.1-Stx2) were included in order to construct the standard curve needed for quantification.

4.2.2.3 Controls for qPCR of stx1 and stx2 genes

Three no template control wells containing only Stx Master Mix solution and PCR water served as negative molecular controls for each PCR plate. Furthermore, both positive and negative culture controls were also included in each PCR plate. The positive control strain used was a strain of E.coli (ATCC 35150) which encodes both stx1 and stx2 genes; the negative control strain used was a strain of Klebsiella pneumoniae ssp. pneumoniae (ATCC 29518) and does not have either the stx1 or stx2 genes.

4.2.2.4 qPCR cycling conditions

The qPCR cycling conditions included a primary holding stage at 50°C for 2 minutes, a secondary holding stage at 95°C for 30 seconds, followed by 40 cycles of 95°C each for 3 seconds, and 60°C for 30 seconds. Reactions were run using an ABI 7500 Fast Real-Time PCR System.

4.2.2.5 Stx1/IAC Multiplex qPCR

For the qPCR reaction, 15µl of 1.25x Master Mix solution, 5µl of pIDTsmart-IAC (20 copies/µl), and 5µl of sample were mixed for a total volume of 25µl per sample. The Master Mix 110

solution for each reaction included 1µl of PCR water, 12.5µl 2x TaqMan Fast Advanced Master

Mix, 0.5µl Bovine Serum Albumin (BSA) (10mg/mL), and 1µl 25x Primer/Probe mix. The

Primer/Probe (25x) mix included 0.450µM of Stx1-F primer, 0.450µM of Stx1-R primer, 0.125µM of Stx1 Probe, 0.450µM of IAC-F primer, 0.450µM of IAC-R primer and 0.125µM of IAC Probe.

Table 4.2. Primers and Probes used for the stx1/IAC Multiplex qPCR Reaction. Sequence Primer

Stx1 Forward 5’-CATCGCGAGTTGCCAGAAT-3’

Primer

Stx1 Reverse 5’-GCGTAATCCCACGGACTCTTC-3’

Primer

Stx2 Probe 5’-[FAM]-CTGCCGGACACATAGAAGGAAACTCATCA-[TAMRA]-3’

IAC Forward 5’ -CTAACCTTCGTGATGAGCAATCG-3’

Primer

IAC Reverse 5’-GATCAGCTACGTGAGGTCCTAC-3’

Primer

IAC Probe 5’-[VIC]-AGCTAGTCGATGCACTCCAGTCCTCCT-[MGBNFQ]-3’

4.2.2.6 Stx2 Simplex qPCR

The qPCR reaction for stx2 simplex qPCR included 15µL 1.33x Master Mix solution and

5µL sample for a total volume of 20µL. The Master Mix solution for each reaction included 3.6µL

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of PCR water, 10µL of 2x TaqMan Fast Advanced Master Mix, 0.4µL BSA (10mg/mL) and 1.0µL

20x Primer/Probe mix. The 20x Primer/Probe mix included 0.450 µM of Stx2-F primer, 0.450µM

Stx2-R primer and 0.125µM of the Stx2 probe.

Table 4.3. Primers and Probes used for the stx2 Simplex qPCR Reaction. Sequence Primer

Stx2 Forward Primer 5’-CCGGAATGCAAATCAGTC-3’

Stx2 Reverse Primer 5’-CAGTGACAAAACGCAGAACT-3’

Stx2 Probe 5’-[FAM]-ACTGAACTCCATTAACGCCAGATATGA-[TAMRA]-3’

4.3 Results

4.3.1 Phylogenetic groups

Among the 27 possible ESBL-producing E.coli isolates, 11 were from the phylogenetic group A, six from group B1, two from B2 and eight from group D. All isolates from the phylogenetic group A were negative for chuA and TspE4.C2, while five were positive for YjaA2 and six were negative for this DNA fragment. Isolates from B2 were also all negative for chuA, but were positive for TspE4.C2 and all were negative for yjaA. The two isolates from subgroup B2 were positive for chuA, yjaA and TspE4.C2. Isolates from subgroup D were all positive for chuA and negative for yjaA2, and the majority (six of eight) of isolates were also positive for TspE4.C2.

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4.3.2 AmpC- and ESBL-producing E.coli

The two isolates from sample 1687 that differed by one antimicrobial which was one dilution different between isolates were both from phylogenetic group D and both were AmpC- producing E.coli, suggesting these E.coli may be the same strain and inclusion criteria were correct to consider these isolates as one clone.

Twenty-two isolates were positive for AmpC production, one isolate was negative for

AmpC and ESBL production, and four isolates were positive for ESBL production.

Among the four ESBL-producing E.coli ioslates detected, two were from the B2 phylogenetic group, suggesting they are possible extraintestinal virulent strains. The other two

ESBL-producing E.coli isolates were from the phylogenetic group A, suggesting they are possible commensal strains.

Although three ESBL-producing E.coli isolates were resistant to eight antimicrobials, one from the B2 group and two from the A group, the fourth ESBL-producer (from the B2 subgroup) was resistant to only four antimicrobials and was intermediate to AXO (Table 4.4).

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Table 4.4. Resistance profiles and phylogenetic groups of ESBL-producing E.coli. NARMS Resistance Profile NARMS Phylogenetic Sample Intermediate Group Number Profile

1843 AMP, XNL, AXO, CIP, NAL, STR, TET, SXT A

1744 AMP, XNL, AXO, CIP, GEN, NAL, STR, SXT B2

1004 AMP, TET, SXT AXO B2

E507264 AMP, XNL, AXO, GEN, STR, FIS, TET, SXT A

Every AmpC-producing E.coli isolate detected in our study was from the three

phylogenetic groups with fewer virulence genes than group B2. Among the 22 AmpC-producing

E.coli isolates detected in our study, eight were from the phylogenetic group A, six were from the

phylogenetic group B1 and eight were from the phylogenetic group D. No AmpC-producing E.coli

isolates were from group B2 (Table 4.5; Figure 4.2).

Table 4.5. Resistance profiles and phylogenetic groups of AmpC-producing E.coli. NARMS Resistance Profile NARMS Phylogenetic Sample Intermediate Group Number Profile

1270 AUG2, AMP, XNL, FOX, AXO, CHL, NAL, STR, A TET 1941 AUG2, AMP, FOX, XNL, AXO, CHL, STR, TET, A SXT 1988 AUG2, AMP, FOX, XNL, AXO, STR, TET, SXT A 1859 AUG2, AMP, FOX, XNL, AXO, CIP, NAL, STR A

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1627 AUG2, AMP, FOX, XNL, AXO, STR, TET A 1797 AUG2, AMP, FOX, XNL, AXO A ER781058 AUG2, AMP, FOX, XNL, AXO, CHL, STR, FIS, TET A 1563 AUG2, AMP, FOX AXO A 1907 AUG2, AMP, FOX, XNL, AXO, TET B1 1938 AUG2, AMP, FOX, XNL, AXO B1 1509 AUG2, AMP, FOX, XNL, AXO,NAL B1 1354146 AUG2, AMP, FOX, XNL, AXO, CHL, TET, SXT B1 124878 AUG2, AMP, FOX, XNL, AXO, CHL, GEN, STR, B1 TET 1021 STR, TET AXO, AMP B1 1687 AUG2, AMP, XNL, FOX, AXO, TET (NOTE: STR D MIC = 32) 1687 AUG2, AMP, XNL FOX, AXO, STR, TET (NOTE: D STR MIC = 64) 1805 AUG2, AMP, FOX, XNL, AXO, CHL, CIP, GEN, D NAL, STR, TET, SXT 1568 AUG2, AMP, FOX, XNL, AXO, CHL, CIP, GEN, D NAL, STR, TET, SXT 1578 AUG2, AMP, FOX, XNL, AXO, CHL, CIP, GEN, D NAL, STR, TET, SXT 1423 AUG2, AMP, FOX, XNL, AXO, CHL, CIP, GEN, D NAL, STR, TET, SXT 1051 AUG2, AMP, FOX, XNL, AXO, CHL, CIP, GEN, D NAL, STR, TET, SXT 1095 AUG2, AMP, FOX, XNL, AXO, CHL, CIP, GEN, D NAL, STR, TET, SXT

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6 ESBL-producers

AmpC-producers

Number of Isolates 4

0 A B1 B2 D Phylogenetic Group

Figure 4.2. Summary of phylogenetic group results for ESBL- and AmpC-producing E.coli.

The majority (23 out of 26) of the AmpC- and ESBL-producing E.coli isolates were resistant to eight or more antimicrobials, while two isolates were resistant to three antimicrobials and one was resistant to two antimicrobials. However, the isolates resistant to only two or three antimicrobials were intermediate to one to two antimicrobials as well.

Out of 26 E.coli isolates positive for either AmpC or ESBL-production, high levels of resistance were observed to PCN (25 isolates), CEPH (23), TET (20) and AMG (18), with lower levels of resistance to SULF (14), QNL (11) and CHL (11). Although a high proportion of both

ESBL- and AmpC-producing E.coli isolates were resistant to PCN, CEPH and AMG, and approximately half of the isolates were resistant to QNL, a difference existed between AmpC- producers and ESBL-producers with respect to CHL and SULF resistance. All of the ESBL-

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producing E.coli isolates were resistant to SULF, compared to approximately half of AmpC- producers. Chloramphenicol resistance was also observed in approximately half of AmpC- producers, but was not observed in any ESBL-producers (Supplementary Table 4.1; Figure 4.3).

Chloramphenicol

Quinolone

Sulfonamide

Aminoglycoside

Tetracycline

Cephalosporin

Penicillin

0 5 10 15 20 25 30 Number of Resistant Isolates

ESBL-producers AmpC-producers

Figure 4.3. Number of ESBL- and AmpC-producing E.coli isolates with resistance to each of eight classes of antimicrobials.

4.3.3 STEC

Twenty seven samples were positive for at least one AMR E.coli isolate and positive for stx1 and/or stx2 based on a population-wide screen performed at the University of Alberta/ProvLab

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in Edmonton, AB. Almost all positive samples (25 out of 27) contained stx1 in the population- wide screen, while 13 samples were positive for stx1 and stx2. Furthermore, 12 samples were positive for only stx1, while two samples were positive for stx2 only.

Although the source of stx1 and/or stx2 in these samples was unknown, any samples with

AMR E.coli isolates that were also positive for stx1 and/or stx2 were included in this portion of the study. Thirty-six AMR E.coli isolates from 27 samples were sent for qPCR for stx1 and/or stx2 genes within each of the samples. The qPCR results on AMR E.coli isolates from these samples showed that none of the AMR E.coli isolates were positive for stx1 or stx2 genes. Therefore, the source of stx1 and stx2 genes within each of the samples was not from the AMR E.coli isolates detected in chapter three of this study.

4.4 Discussion

Resistance to β-lactam antimicrobials has several public health implications, as very few successful treatment options exist for these infections. Resistance to β-lactams have been reported due to the production of ESBLs and AmpC β-lactamase overproduction [349-352]. Cells overexpressing AmpC β-lactamases have a similar phenotype to ESBL, and both are typically associated with MDR [353, 354].

4.4.1 ESBL and AmpC Discussion

Among 27 isolates tested, four ESBL-producing E.coli and 22 AmpC-producing E.coli were detected. All isolates were resistant or intermediate to at least four antimicrobials, suggesting a high level of MDR among these isolates. Plasmid-mediated transfer of resistance genes is a major source of ESBL transmission, and transferrable elements conferring resistance to other

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antimicrobials are carried alongside b-lactam resistance genes [379], resulting in high levels of

MDR among ESBL-producers. ESBL-producing Enterobacteriaceae cause many infections in humans, of particular importance are bloodstream infections. When dealing with these bacterial infections in a clinical setting, resistance to multiple classes of antimicrobials significantly limits the therapeutic options available [380].

Co-resistance occurs due to the accumulation of resistance mechanisms to different classes of antimicrobials in a single bacterial strain, resulting from mutations and/or acquiring resistance genes by horizontal gene transfer. Among the AmpC- and ESBL-producing E.coli isolates detected in our rural well water samples, resistance to PCN, CEPH, TET and AMG was common, whereas resistance to SULF, QNL and CHL was less common, and resistance to MAC was not detected in any of the isolates. Resistance to AMG, TET, CHL, trimethoprim and SULF can co-transfer with resistance to beta-lactams, as detected in ESBL-producing clinical isolates worldwide [381, 382].

Co-resistance to two or more classes of antimicrobials has also been observed in fecal samples from healthy humans and food-producing animals [383] and aquatic systems [384]. ESBL- producers in aquatic systems have shown co-resistance to TET, QNL and AMG, and can also harbor class one integrons, enabling the transfer of multiple resistance genes in a single transfer event [384]. Approximately half of the ESBL-producing isolates in the present study were resistant to QNL, and approximately 75% were resistant to TET and AMG, consistent with the co-resistance observed in previous studies [381, 382, 384].

Although proportions of resistance to PCN, CEPH, TET, AMG and QNL were similar between ESBL- and AmpC-producing isolates, differences were noted in resistance to SULF and

CHL. All of the ESBL-producers conferred resistance to SULF compared to approximately half of AmpC-producers. Additionally, none of the ESBL-producers conferred resistance to CHL in

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comparison to half of the AmpC-producers conferring this resistance to CHL. Our results suggest differences in the ability of AmpC- and ESBL-producing E.coli to develop or acquire resistance to CHL and SULF, although the differences may be due to the low number of ESBL-positive isolates detected.

Phylogenetic analysis of AmpC- and ESBL-producers resulted in the detection of two isolates from subgroup B2, both of which were ESBL-producers, and eight isolates from subgroup

D, which were all AmpC-producers. Isolates from subgroups B2 and D generally have more virulence factors than other subgroups [319], and consist of many extraintestinal virulent strains which cause severe disease [320, 321]. The presence of potential extraintestinal virulent strains from subgroups B2 and D that are resistant to multiple classes of antimicrobials including third generation CEPH suggests these strains can cause severe human disease and pose a significant challenge for clinical treatment options.

It has been estimated that strains of E.coli from group B2 account for approximately two thirds of infections caused by extraintestinal E.coli infections, including bacteremia, meningitis,

UTIs, etc [320, 321, 385]. However, a previous study looking at clinical ESBL-producing E.coli strains found that 36.4% of the isolates were from the phylogenetic group B2, 27.9% were from group A and also found isolates from group D (25.5%) and group B1 (10%). These results indicate a potential difference between the phylogenetic distribution of ESBL-producers toward non-B2 phylogenetic groups in clinical isolates [385]. Among our isolates, we observed ESBL-producing

E.coli isolates from the B2 and A subgroups, but no isolates from groups D or B1. The commensal strains from these groups may also be of importance to public health, as the community can act as a reservoir for ESBL-producing bacteria, and fecal carriage of commensal ESBL-producing E.coli has been linked to food-borne outbreaks [385]. Differences in the proportion of isolates from each

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phylogenetic group in our study when compared to results of clinical isolates may be due to the low sample number for ESBL-producing E.coli within our study, or may be indicative of different sources of ESBL-producing E.coli contamination in groundwater as the results are not consistent with clinical ESBL-producing E.coli data [385].

Among the AmpC-producers, the majority of isolates were from phylogenetic group A, D or B1, with no isolates from group B2, suggesting these isolates may be commensal, extraintestinal or intestinal virulent strains, with fewer pathogenic strains than the ESBL-producers. Previous studies have indicated that strains from phylogenetic groups other than B2 often express fewer virulence factors, but they have a higher prevalence of resistance to antimicrobials and are able to invade compromised hosts more often [385, 386]. The majority of environmental E.coli strains fall into group B1, and these isolates had resistance to fewer antimicrobials than strains from the other phylogenetic groups, suggesting the environmental strains of E.coli may have fewer selective pressures to develop resistance than those from phylogenetic groups A, B2 and D that are more often isolated from humans.

Although it remains unknown whether the production of AmpC by these AmpC-producers is chromosomal or plasmid-mediated, the MDR observed within each of the isolates is consistent with plasmid-mediated AmpC production [355]. Further tests could determine whether these isolates have chromosomal or plasmid-mediated AmpC production.

Several limitations exist in our ESBL- and AmpC-producing E.coli data, including the generalizability of these results due to voluntary sampling techniques. Furthermore, this study did not use the highest discriminatory power available to determine the phylogenetic group of these isolates, and for more specific phylogenetic grouping future studies should investigate phylogenetic groups to a higher resolution.

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4.4.2 STEC Discussion

Because STEC is a zoonotic pathogen commonly isolated from the gut of ruminants, it has been suggested that monitoring AMR among STEC isolates can provide insight into the transmission of AMR bacteria from ruminants to humans [387, 388]. Due to the large agriculture industry in Alberta, and previous findings of STEC in human, animal and environmental sources conferring resistance to a multitude of antimicrobials [389, 390], and the presence of STEC within

Alberta’s rural well water [377], we expected to find AMR expression among these STEC populations. Although many STEC isolates are susceptible to a variety of antimicrobials, recent studies suggest an increase of AMR in STEC [391-396]. Plasmids have been implicated as a source of AMR genes in STEC isolates [391, 395], and ESBL plasmids have been implicated in cases of

STEC in humans [396, 397]. However, resistance was not observed among STEC isolates in our study, which was the first to test AMR E.coli from groundwater for the presence of stx1 and/or stx2 genes, to our knowledge. Among the 27 samples positive for stx1 and/or stx2 genes based on a population-wide screen that also contained AMR E.coli detected in our study, none contained

AMR E.coli that were positive for either genes responsible for the production of Shiga toxin.

Although the AMR E.coli in these samples were not positive for stx1 and/or stx2, the samples were positive for these genes on a population-wide screen, suggesting either (a) multiple contamination sources, or (b) individual contamination sources that contain both AMR E.coli and

STEC. These results have potential public health implications, as our results indicate multiple strains of pathogenic E.coli exist within individual rural well water samples. Clinically, this has several implications, as consumption of rural well water with both AMR E.coli and STEC may result in mixed colonization/infection. Antimicrobial resistant bacterial infections often require

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treatment with multiple antimicrobials, and there are limited antimicrobials with efficacy against

MCR bacteria. When treating STEC infections, certain antimicrobials can increase the chance of developing HUS. Therefore, in a mixed infection with AMR E.coli and STEC it may be difficult to find a treatment option that is successful in treating both infections and does not worsen the symptoms of either.

4.5 Conclusions

Evidence suggests E.coli strains are increasingly becoming resistant and producing broad spectrum beta-lactamases [379], and a reservoir of enzymes may be in the commensal microbiota of health individuals and those taking antimicrobials [398]. Our results are the first to detect ESBL- and AmpC-producing E.coli in Alberta’s rural well water sources. Among the ESBL-producing

E.coli identified in this study, many are potential extraintestinal virulent strains and pose a significant public health risk, and all of the ESBL- and AmpC-producing E.coli identified in our study pose a significant challenge for future disease treatment.

Both the STEC and AMR E.coli results described here suggest multiple strains of pathogenic E.coli exist within an individual rural well water sample in Alberta, suggesting one potential contamination source with multiple organisms of public health importance, or multiple contamination sources in each rural well water sample. Mixed infections with STEC and AMR bacteria may have extremely limited treatment options, as many antimicrobials may worsen complications associated with STEC or further the burden of resistance.

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CHAPTER 5: SPATIAL ANALYSIS OF AMR E.COLI IN ALBERTA

5.1 Background

5.1.1 Agriculture and population within Alberta

The rural well water samples for this study have been collected from the southern portion of Alberta, which is a province located in Western Canada. The population of Alberta in 2016 was

4,252,900, accounting for 12% of the Canadian population. Population growth for this province was second highest in Canada during our study period from 2006-2016, with approximately

830,000 new residents to Alberta during this time [399]. The southern half of Alberta is where

90% of the population resides [400].

Alberta is an important agricultural hub in Canada, and Statistics Canada estimated that approximately 21% of Canadian farms were in the province of Alberta in 2011 [401]. The presence and practices on these farms may influence drinking water quality, as groundwater can be untreated when consumed as rural well water. Therefore, individuals consuming rural well water that has not been regularly tested or treated may be at risk of acquiring waterborne diseases.

5.1.2 Factors affecting groundwater contamination

E.coli can enter groundwater from a variety of sources and can have seasonal, temporal and spatial clusters [283, 402, 403] due to the many factors influencing microbial contamination of groundwater discussed in previous chapters. Contamination of Alberta’s groundwater with

E.coli can vary over time due to runoff, weather and climate patterns including major flooding events [283]. Furthermore, resistance genes in surface water/streams have shown seasonal variations in relation to flow changes that influence the transport of bulk materials. Evidence

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suggests that sediments being mobilized during high flow events are primary sources of AMR genes within aquatic systems [12, 72, 404, 405].

AMR E.coli contamination of rural well water has been linked to areas with human and animal fecal influences such as septic tank density and the presence of vulnerable sub-populations

[251], and to both dairy [290] and swine operations [245]. Seasonal increases in human cases of

E.coli O157:H7 [406] correlate with increases in cattle rectal samples [407] and groundwater

E.coli contamination [283].

Furthermore, MCR E.coli in groundwater has been linked to the density of livestock on nearby farms. With the large agricultural and food animal industry in Alberta, it is possible that spatial clusters of AMR and/or MCR E.coli will be found.

Because Alberta is a large agricultural hub and has a large human population, the majority of which reside in the southern half of the province, we hypothesized that clusters of AMR would exist in areas with higher selective pressures such as urban centers and large agricultural operations.

Information on the geospatial patterns of AMR and MCR E.coli within Alberta’s rural well water may aid in providing well owners and policymakers with information to assess risks of AMR

E.coli contamination within rural well water in southern Alberta. This may help to identify locations where possible interventions are needed, and guide policy decisions on rural well water testing and treatment requirements within Canada. Understanding the spatial distributions of AMR

E.coli may allow for linkages to clinical and surveillance data in humans and animals, and be used to interpret potential outbreak data in the future.

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5.1.3 Spatial analysis

Spatial analysis in epidemiology has been recorded for centuries, with one of the first major studies occurring in 1854 when John Snow traced a cholera outbreak in London to a contaminated water source by creating a map of the location of infected individuals [408]. Spatial mapping has since become a commonly used and valuable tool for tracking disease clusters and determining the source of infection by tracking locations with higher risk of infection or disease [408].

Spatial analysis has been applied to the study of water contamination worldwide. Notably, a Canadian study used SatScanTM to identify spatial clusters of private wells positive for E.coli contamination [402, 403]. Using this technology, clusters of E.coli O157 have been located within

Alberta [409] and spatial differences between outbreak and sporadic cases of E.coli O157 have been analyzed [410].

5.1.4 Objectives

Although seasonal, temporal and spatial variations in E.coli among rural well water samples in Alberta have been elucidated, and seasonal/temporal and spatial variations in AMR genes in surface waters have been studied worldwide, what remains unknown is whether AMR and/or MCR E.coli have seasonal, temporal or spatial patterns or clusters within Alberta. The objective of this chapter is to describe the geospatial patterns of AMR and MCR E.coli contamination of rural well water sources within southern Alberta. Due to time constraints, the temporal and seasonal clusters of AMR will not be outlined in this thesis but will be a part of future studies.

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5.2 Specific Methods

5.2.1 Data Sources

The data used for spatial analysis included all E.coli positive rural well water samples tested for AMR within our study, as described in previous chapters. All water samples were collected by the well overseer, and the inclusion of locational information and date of collection were hand written by the submitter [283]. Data provided for rural well water samples included quarter, section, township, range and meridian, as well as the date of collection. Some samples also included latitudinal and longitudinal coordinates.

Samples were not included if the location or year of collection could not be identified or were inaccurate. Furthermore, samples were only included if they were collected between August

28, 2006 and August 31, 2016, and if they were located in southern Alberta. Binary values were used for all positive and negative data values including the presence of AMR, MCR or resistance to specific classes of antimicrobials outlined in previous chapters.

5.2.2 Geolocation

Coordinates of the submission data, including quarter, section, township, range and closest meridian on its eastern side, were derived from the Alberta Township Survey System (ATS) used to locate parcels of land in Alberta [411]. The resolution of this data is approximately one quarter section (~800x800m) [283, 411]. In many cases, multiple samples were geolocated to the same quarter section, and due to the resolution of our data, we cannot distinguish between multiple wells or multiple samples from the same well within the quarter section.

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5.2.3 Spatial analysis methodology

Data values for quarter, section, township and range were converted to coordinates of latitude and longitude. The number of samples positive for AMR, MCR and resistance to each specific class of antimicrobial was included as binary data, and spatial patterns were analyzed in

ArcGIS (version 10.4.1, Esri Inc. 2015). Furthermore, spatial clusters were identified using

SaTScan™ (version 9.4.4, SaTScan, 2016).

Data was visualized with a geographic coordinate system of North American Datum of 1983 and projected to the North American Datum 1983 10 TM AEP Forest coordinate system.

Maps were created using the number of AMR positive wells as a proportion of the total number of wells tested. Similar maps were created for the proportion of wells positive for (1) resistance to each of the eight classes of antimicrobials tested, (2) MCR E.coli and (3) ESBL- producing E.coli using the same denominator. The first type of map created included positive wells that were treated as binary point data and displayed as individual well samples positive or negative for AMR E.coli. The second set of maps were chloropleth maps created with sample level data aggregated to a map of the Canadian Census Regions from 2006 to display the proportion of wells positive for various factors including AMR, MCR, and resistance to AMG, PCN, TET, SULF,

CEPH, CHL, QNL, and the presence of ESBLs.

5.2.3.1 Spatial clustering

SaTScan™ (version 9.4.4, SaTScan 2016) was used to identify spatial clusters for high and low levels of AMR and MCR E.coli, as well as high levels of resistance to each of the eight classes of antimicrobials tested and for ESBLs. To determine whether spatial clusters existed, we used a purely spatial analysis using a Bernoulli probability model as our input data was binary point data

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[412]. All other settings remained as default, and a separate scan was performed for high and low rate clusters to determine whether AMR and MCR E.coli clusters existed. Furthermore, separate scans were performed for high rate clusters of resistance to AMG, PCN, TET, SULF, CEPH, CHL,

QNL and ESBLs.

Cluster results were exported as shapefiles and added to existing chloropleth maps or point data maps where appropriate. Clusters with a p-value < 0.05 were considered to be statistically significant.

5.3 Results

5.3.1 Clusters and proportions of AMR positive samples

The proportion of E.coli positive rural well water samples that were positive for AMR

E.coli was highest in the central portion of southern Alberta around Drumheller, Calgary,

Lethbridge, and the area between these three cities (Figure 5.1; Figure 5.3). Two additional areas with an elevated proportion of AMR E.coli were found west of Red Deer and along the edge of the province west of Lethbridge (Figure 5.1). However, these regions had very few samples tested and the high proportion of positives may have been a result of these low sample numbers.

SaTScan™ was used to determine whether any clusters of high or low numbers of AMR

E.coli positive wells are present. Three clusters with high AMR E.coli positives were detected between Calgary and Lethbridge, close to Medicine Hat, and in an area between Calgary and

Drumheller, however only one cluster was statistically significant (Figure 5.2). This cluster was located between Lethbridge and Calgary, spanning 64 km with a total of 49 positive wells out of

126 wells tested in this region (Figure 5.2). The relative risk of AMR E.coli contamination within these wells was 2.07 with a likelihood ratio of 10.81 (p-value = 0.008) (Supplementary Table 5.1).

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Additionally, three non-significant clusters of low AMR positive wells were detected between

Lethbridge and Medicine Hat, southwest of Lethbridge, and between Calgary and Red Deer

(Supplementary Table 5.1; Supplementary Figure 5.1).

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Figure 5.2. Clusters of high proportions of AMR E.coli Figure 5.3. Antimicrobial resistance results for E.coli positive samples out of E.coli positive rural well water positive rural well water samples submitted to ProvLab samples submitted to ProvLab Calgary between 2006 and Calgary between 2006 and 2016 tested for AMR E.coli. 2016.

5.3.2 Clusters and proportions of MCR positive samples

Increased proportions of rural wells positive for MCR E.coli were observed in many regions of southern Alberta including the area surrounding Calgary, between Calgary and

Drumheller, west of Red Deer, a large region from Calgary to the southern corner of the province west of Lethbridge, as well as a region southeast of Lethbridge along the east edge of the province

(Figure 4). The highest proportion in southern Alberta is in a region west of Red Deer which may be due to only one well having been tested in this region (Figure 5.4).

Eight clusters of increased MCR E.coli positive samples were found, as well as three clusters of low proportions of MCR E.coli. Among the high proportion clusters, two were centered around Calgary, two were between Calgary and Lethbridge, two were slightly north of Lethbridge, one was located in the southwest corner of the province and the final cluster was centered around

Medicine Hat (Supplementary Figure 5.2). Among the low proportion clusters, one was centered slightly west of Calgary, another was centered in the southwest corner of the province and a third spanned a large region north of Taber and Medicine Hat (Figure 5.5). This last cluster was statistically significant (p-value = 0.037) with a log likelihood ratio of 8.79 and a relative risk of

0.081 (Supplementary Table 5.1). Among 93 rural well water samples tested in this area, only one sample was positive for MCR E.coli based on our testing.

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Figure 5.4. Proportion of multi-class resistant E.coli positive Figure 5.5. Clusters of both low proportions of MCR E.coli wells in Alberta (at the sample level aggregated to census positive samples out of E.coli positive rural well water divisions) out of E.coli positive rural well water samples samples submitted to ProvLab Calgary between 2006 and submitted to ProvLab Calgary between 2006 and 2016 by 2016. census division.

5.3.3 Clusters and proportions of ESBL positive samples

Four samples were positive for ESBL-producing E.coli within our testing period, three of which were located south of Calgary and North of Lethbridge, and the fourth sample was located southwest of Lethbridge (Figure 5.6). A non-significant cluster was found spanning a region on the southwest portion of Alberta between Calgary and Lethbridge (p-value = 0.60) (Figure 5.7).

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Figure 5.6. Rural well water samples submitted to ProvLab Figure 5.7. Cluster of rural well water samples positive for Calgary between 2006 and 2016 positive for ESBL- ESBL-producing E.coli submitted to ProvLab Calgary producing E.coli. between 2006 and 2016. Cluster in red has a p-value > 0.05.

5.3.4 Clusters and proportions of samples with resistance to AMG

Higher proportions of rural well water samples positive for resistance to AMG antimicrobials were observed within a central area between Calgary/Drumheller and Lethbridge, and along the east edge of southern Alberta (Figure 5.8). An increased proportion of AMG resistance west of Red Deer was also found, and may be attributed to the low sample numbers in this region.

No significant clusters of high AMG proportions were found in southern Alberta, although six non-significant clusters were identified (Supplementary Figure 5.3). Non-significant clusters existed slightly north of Calgary, central in the province between Calgary and Medicine Hat, northwest and northeast of Lethbridge, and in the southwest corner of the province.

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5.3.5 Clusters and proportions of samples with resistance to CEPH

An increased proportion of wells with E.coli resistant to CEPH antimicrobials was observed around Calgary and between Calgary and Drumheller, and along the edge of the province directly east of Drumheller (Figure 5.9).

Two non-significant clusters were identified between Calgary and Drumheller, and between

Taber and Lethbridge (Supplementary Figure 5.4).

5.3.6 Clusters and proportions of samples with resistance to CHL

An increased proportion of wells with E.coli resistant to CHL antimicrobials were identified in regions west and northwest of Lethbridge, east of Calgary and south of Drumheller, and east of Drumheller on the far edge of the province (Figure 5.10).

Seven clusters were identified in areas with an increased proportion of resistance to CHL antimicrobials. Two clusters were identified north of Calgary and one was identified southwest of the city. Two more clusters were identified north of Lethbridge with an additional cluster southeast of Medicine Hat (Figure 5.11). A significant (p-value = 0.026) cluster was identified southwest of

Drumheller and northeast of Calgary, with all three samples tested in this region being positive for

CHL resistance. This cluster spanned 9.2 km and had a relative risk of 24.7 and log likelihood ratio of 9.5 (Supplementary Table 5.1).

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Figure 5.10. Proportion of wells positive for E.coli resistant Figure 5.11. Clusters of rural well water samples positive for to chloramphenicol in Alberta (at the sample level E.coli resistant to chloramphenicol antimicrobials. Results aggregated to census divisions) out of E.coli positive rural displayed as a proportion of E.coli positive well water well water samples submitted to ProvLab Calgary between samples that were submitted to ProvLab Calgary between 2006 and 2016 by census division. 2006 and 2016 and tested for AMR E.coli.

5.3.7 Clusters and proportions of samples with resistance to MAC

Two regions in the province exhibited an increased proportion of resistance to MAC antimicrobials, an area west of Red Deer and an area east of Drumheller on the far east side of the province (Figure 5.12). However, the area west of Red Deer had only one sample tested from that region, so no proportion was possible.

Two non-significant clusters of MAC resistance were observed in an area northeast of

Lethbridge and an area east of Drumheller on the east edge of the province (Supplementary Figure

5.5; Supplementary Table 5.1).

Figure 5.12. Proportion of wells positive for E.coli resistant to macrolide antimicrobials in Alberta (at the sample level aggregated to census divisions) out of E.coli positive rural well water samples submitted to ProvLab Calgary between 2006 and 2016 by census division. 141

5.3.7 Clusters and proportions of samples with resistance to PCN

Resistance to PCN antimicrobials within E.coli populations from rural well water samples in southern Alberta was observed commonly around three urban centers: Calgary, Drumheller and

Lethbridge, with an additional cluster on the east edge of the province east of Drumheller (Figure

5.13).

Seven non-significant clusters were identified in regions between Calgary and Drumheller, south of Calgary, north and south of Taber, and southwest of Lethbridge (Supplementary Figure

5.6; Supplementary Table 5.1).

5.3.8 Clusters and proportions of samples with resistance to QNL

Two major regions within the province had higher proportions of resistance to QNL antimicrobials centered west of Red Deer and on the edge of the province east of Drumheller

(Figure 5.14).

Five non-significant clusters were identified, three of which were south of Calgary, one south of Lethbridge and Taber, and one northwest of Taber (Supplementary Figure 5.8).

5.3.9 Clusters and proportions of samples with resistance to SULF

The proportion of wells positive for E.coli resistant to SULF antimicrobials was highest along the east edge of southern Alberta, and the south edge leading up through Lethbridge and

Calgary to a region west of Red Deer. A particularly high proportion of resistance to SULF was observed in two regions, (1) south of Drumheller and east of Calgary, and (2) west of Red Deer

(Figure 5.15).

Nine non-significant clusters were identified for higher proportions of resistance to SULF antimicrobials in southern Alberta (Supplementary Figure 5.9). These regions were spread throughout the province, with multiple clusters centered around Calgary, Drumheller, Lethbridge and Medicine Hat, and additional clusters in the southwest corner of the province (Supplementary

Figure 5.9).

144

145

5.3.10 Clusters and proportions of samples with resistance to TET

The highest proportion of resistance to TET antimicrobials was observed around Drumheller,

Calgary and Lethbridge, as well as regions northwest of Lethbridge and a small region on the edge of the province west of Lethbridge (Figure 5.16). Elevated proportions slightly less than those described above were observed along the east edge of the province, a region southwest of

Lethbridge and from Lethbridge to Calgary, Drumheller and Red Deer in the central portion of southern Alberta (Figure 5.16).

Three clusters were identified for elevated proportions of resistance to TET antimicrobials.

One region was located between Calgary and Drumheller with a second region centered around

Medicine Hat (Figure 5.17). A third cluster spanning 65 km between Lethbridge and Calgary statistically significant (p-value = 0.0016) with 50 samples positive for TET resistance among 150 samples tested (Figure 5.17; Supplementary Table 5.1). The relative risk in this region was 2.31 with a log likelihood ratio of 12.4 (Supplementary Table 5.1).

146

147

Figure 5.16. Proportion of wells positive for E.coli resistant Figure 5.17. Clusters of rural well water samples positive for to tetracycline antimicrobials in Alberta (at the sample level E.coli resistant to tetracycline antimicrobials. Results are aggregated to census divisions) out of E.coli positive rural displayed as a proportion of E.coli positive well water well water samples submitted to ProvLab Calgary between samples that were submitted to ProvLab Calgary between 2006 and 2016 by census division. 2006 and 2016 and tested for antimicrobial resistant E.coli.

5.4 Discussion

Due to the large agricultural industry in Alberta and the higher population density in the southern half of the province, there are many selective pressures from humans and animals that may influence the development of AMR within regions in southern Alberta. These factors influenced our hypothesis that clusters of resistance would exist for AMR and MCR E.coli within this region.

In general, AMR was observed throughout the southern half of the province and, in agreement with our hypothesis, we found significant clusters with high proportions of AMR and resistance to certain antimicrobials. We also observed a significant cluster with a low proportion of MCR E.coli positive well water samples. Non-significant clusters were included in our results to indicate areas of interest for future studies.

Significant clusters of samples positive for AMR E.coli and E.coli resistant to TET were detected between Calgary and Lethbridge, as well as three of the four wells positive for ESBL- producing E.coli. This is a region of the province with a high density of livestock, a large human population, irrigation districts [413] and wastewater output [414] spanning several watersheds.

Because 78% of the water samples with AMR E.coli had resistance to TET, the TET resistance cluster may be due to the clustering of AMR E.coli in this region. Alternatively, the opposite may be true, that the cluster of AMR E.coli is a product of a statistically significant cluster of wells positive for TET resistance. The widespread use of TET in humans, animals and some plants has impacted the emergence of resistance worldwide [140], therefore it is plausible that the

148

use of tetracycline in this region may have contributed to a significant cluster of rural well water samples positive for resistance to TET antimicrobials.

A region between Calgary and Drumheller also had high proportions of AMR and MCR

E.coli, as well as a significant cluster of resistance to CHL antimicrobials. This region is at severe risk of erosion, and water erosion may reduce the quality of soil such that nearby water quality may be at risk [415]. However, this would likely result in a generalized increase in microbial contamination not specific to AMR bacteria. The increase in AMR, MCR and CHL resistant bacteria in this region is likely a result of many factors, one of which may involve human factors due to the proximity to Calgary, which is the largest urban center in the province with a population of 1,469,300 [416]. The cluster of resistance to CHL involved three positive wells in close proximity (cluster radius of 9.19km) and may indicate a localized problem requiring further investigation to determine the risk factors involved in these wells. Future studies should assess temporal and spatial temporal clustering and investigate potential contamination sources in the region.

A cluster was detected for low proportions of MCR in a region north of Taber and Medicine

Hat in the east-central part of southern Alberta. This region has three provincial parks and a lower population of humans and animals than many other regions in southern Alberta, and it spans multiple irrigation districts [413].

Our results suggest certain areas within Alberta have higher proportions of AMR E.coli,

CHL resistant E.coli, TET resistant E.coli, and one area has a lower proportion of MCR E.coli.

The sources of contamination in each case are likely multi-factorial and future studies should investigate exposures and associations in these regions.

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Previous studies have found many human and animal influences that affect AMR E.coli contamination of rural well water such as septic tank density, the presence of vulnerable sub- populations [251], and the presence of dairy [290, 334] and swine operations [245].

Alberta has strict requirements for catch basins, the use of irrigation water and the presence of wells near confined feeding operations [417]. Furthermore, although areas in our study with significant clusters are in regions with varying groundwater quality ranging from low to very high

[418], groundwater vulnerability is taken into account as part of a risk-based compliance program for confined feeding operations in Alberta. However, the potential risk to groundwater contamination from human and animal influences has been outlined in previous studies. Potential contamination sources for groundwater observed in the literature include septic tank density [251], and the presence of livestock [290].

There are multiple ways livestock density and wastewater output can influence environmental AMR bacteria and genes through (1) introducing AMR bacteria/genes to the environment through manure and manure spreading and (2) introducing antimicrobials, heavy metals and disinfectants/biocides to the environment, providing selective pressures for the development/survival of AMR bacteria. Many parts of southern Alberta have high levels of manure production [419], which may be influencing the environmental burden of AMR bacteria in soil and surrounding surface water and groundwater [184]. The regions implicated in clusters within our study have been associated with varying risk of surface water [420] and groundwater contamination [418], suggesting certain areas may be more vulnerable to microbial contamination of water within the province.

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A major factor implicated in releasing AMR bacteria into the environment is wastewater treatment plants (WWTPs), which release treated wastewater into surface water reservoirs, and previous studies and reviews have outlined the possibility of this release as a route for the spread of MCR bacteria [174, 207, 421]. Increased levels of multidrug efflux pumps and MDR genes have been observed in bacteria from WWTPs [422], as well as higher levels of integrons, which have been observed in E.coli isolates from WWTPs and downstream of WWTP discharge [421].

Several limitations exist within our study, including our difficulty with aggregated data using chloropleth maps. On such maps, certain areas such as the region west of Red Deer have only one or two wells tested in that region and this can result in a high proportion due to low sample numbers. Furthermore, the inability to geolocate all of our samples is a limitation in our spatial assessment with potential for bias in either direction as many samples had the incorrect or missing coordinate data.

5.5 Conclusions

Our results suggest certain areas within southern Alberta have a higher proportion of AMR

E.coli among E.coli positive wells tested in these regions, as well as higher proportions of resistance to CHL and TET, and lower proportions of MCR E.coli. Future studies should investigate the associations and exposures that may lead to these areas having higher proportions of resistant bacteria.

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CHAPTER SIX: FINAL REMARKS AND FUTURE DIRECTIONS

Antimicrobial resistance is one of the greatest medical challenges of the 21st century, with very limited treatment options for AMR and MCR bacteria. Our study aimed to investigate AMR

E.coli in groundwater within Alberta, and was the first to detect ESBL-producing E.coli in

Canadian rural well water sources.

Our results indicate that among rural wells that fail the current water quality guidelines, approximately one in five will contain AMR E.coli, and one in 10 will contain MCR E.coli.

Furthermore, we hypothesize that clusters of resistance in the province may be linked to many human, animal and environmental factors, and future studies should investigate associations and exposures in the regions with high- and low-proportion clusters of AMR and MCR E.coli. Direct studies should investigate the associations between AMR E.coli clusters and many factors including individual well characteristics, groundwater recharge rates through the unsaturated zone, and manure or septic contamination.

The AMR E.coli isolates detected in our study should be further investigated for virulence factors to determine the risk of these pathogens to human and animal health. Furthermore, although it would be a difficult task to determine the source of contamination for each of the wells positive for AMR E.coli, these samples are a part of a greater study and are being investigated for viruses,

Enterococcus sp. and Bacteroides sp. Future studies should combine this data to gain insight into potential contamination sources for these rural well water samples.

Future studies should also investigate temporal clustering within the province to determine whether seasonal or temporal factors affect contamination of rural well water sources with AMR

E.coli. Further investigation into the three CHL positive wells should determine whether there is 152

a common contamination source and whether this cluster was at one time or has been a recurring issue over the ten year period. Additionally, future studies should aim to determine factors influencing contamination with AMR E.coli the area with a significant cluster of AMR E.coli positive wells.

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182

APPENDIX A: CHAPTER TWO SUPPLEMENTARY DATA

Supplementary Table 2.1. Number of distinct biotypes, banding patterns, antibiograms and number of E.coli isolates picked per sample for nine rural well water samples.

Sample ID Number of Number of Number of Number of Picks Biotypes distinct Banding Antibiograms per Sample (API®) Patterns (PFGE) (NARMS SensititreTM Panels)

1613 1 1 1 6

1614 5 3 1 19

1616 1 2 1 16

1618 1 1 1 11

1619 1 1 1 15

1620 2 2 1 12

1621 1 1 1 14

1622 1 1 1 20

1623 1 1 1 12

183

Supplementary Table 2.2. Range, MIC50 and MIC90 for up to 20 E.coli isolates from each of nine E.coli positive rural well water samples, as determined by NARMS™ Sensititre panels.

Trimethoprim/

Amoxicillin/Clav Sulphamethoxaz

ulanic Acid Ampicillin Azithromycin Cefoxitin Ceftiofur Ceftriaxone Chloramphenicol Ciprofloxacin Gentamicin Nalidixic Acid Streptomycin Sulfisoxazole Tetracycline ole Range Range Range Range Range Range Range Range Range Range Range Range Range Range MIC50 MIC90 MIC50 MIC90 MIC50 MIC90 MIC50 MIC90 MIC50 MIC90 MIC50 MIC90 MIC50 MIC90 MIC50 MIC90 MIC50 MIC90 MIC50 MIC90 MIC50 MIC90 MIC50 MIC90 MIC50 MIC90 MIC50 MIC90 Sample

0.25 £0.01 £0.01 £0.01 £16

1613 2 2 2 2 to 4 2 2 2 2 2 4 4 4 to 0.5 0.5 0.5 £0.25 £0.25 £0.25 4 4 4 5 5 5 0.5 0.5 0.5 2 2 2 4 4 4 to 32 £16 32 £4 £4 £4 £0.12 £0.12 £0.12

£ 1 to £1 to £ £ £ £ 2 to £0.01 £0.01 £0.01 £0.25

1614 4 2 4 2 <=1 2 2 to 4 4 4 2 to 4 2 4 0.25 0.25 0.25 0.25 0.25 0.25 8 0.5 0.5 5 5 5 to 0.5 0.5 0.5 2 2 2 4 to 8 4 4 £16 £16 £16 £4 £4 £4 £0.12 £0.12 £0.12 184 8 to £0.01 £0.01 £0.01 £16

1616 2 to 4 4 4 2 to 4 2 2 4 to 8 4 4 4 4 4 0.25 0.25 0.25 £0.25 £0.25 £0.25 16 8 8 5 5 5 0.5 0.5 0.5 1 to 2 1 2 4 to 8 4 4 to 32 £16 32 £4 £4 £4 £0.12 £0.12 £0.12

0.25 £0.01 £0.01 £0.01 £16

1618 2 to 4 2 4 2 2 2 1 to 2 1 2 2 to 4 4 4 to 0.5 0.35 0.5 £0.25 £0.25 £0.25 £ 2 £ 2 £ 2 5 5 5 0.5 0.5 0.5 2 2 2 4 to 8 4 8 to 32 £16 32 £4 £4 £4 £0.12 £0.12 £0.12

£0.01 £0.01 £0.01 £16

1619 2 2 2 2 2 2 4 4 4 2 to 4 4 4 0.5 0.5 0.5 £0.25 £0.25 £0.25 8 8 8 5 5 5 0.5 0.5 0.5 2 2 2 4 4 4 to 32 £16 32 £4 £4 £4 £0.12 £0.12 £0.12

0.25 4 to £0.01 £0.01 £0.01 £0.25 £2 to £16

1620 4 4 4 2 to 4 4 4 1 to 8 4 8 4 4 4 to 0.5 0.5 0.5 £0.25 £0.25 £0.25 16 8 16 5 5 5 to 0.5 0.5 0.5 1 to 4 4 4 8 4 8 to 32 32 32 £4 £4 £4 £0.12 £0.12 £0.12

£2 to £0.01 £0.01 £0.01

1621 4 4 4 2 2 2 2 2 2 4 4 4 0.25 0.25 0.25 £0.25 £0.25 £0.25 4 £2 4 5 5 5 0.5 0.5 0.5 2 2 2 4 to 8 4 8 £16 £16 £16 £4 £4 £4 £0.12 £0.12 £0.12

£0.01 £0.01 £0.01 0.5 to

1622 4 4 4 2 to 4 4 4 4 to 8 4 8 4 to 8 4 8 0.5 0.5 0.5 £0.25 £0.25 £0.25 8 8 8 5 5 5 1 0.5 1 2 2 2 8 8 8 £16 £16 £16 £4 £4 £4 £0.12 £0.12 £0.12

£0.01 £0.01 £0.01 1623 4 4 4 4 4 4 2 2 2 4 4 4 0.5 0.5 0.5 £0.25 £0.25 £0.25 4 to 8 4 8 5 5 5 0.5 0.5 0.5 2 2 2 4 4 4 £16 £16 £16 £4 £4 £4 £0.12 £0.12 £0.12

185

APPENDIX B: CHAPTER THREE SUPPLEMENTARY DATA

0.07

0.06

0.05

0.04

0.03

0.02

0.01

0 Jan-Feb Mar-Apr May-Jun Jul-Aug Sept-Oct Nov-Dec

2006 2007 2008 2009 2010 2011

2012 2013 2014 2015 2016

Supplementary Figure 3.1. Proportion of submitted rural well water samples positive for E.coli every two months from August 1, 2006 to August 31, 2016.

186

Supplementary Table 3.1. Number and percentage of isolates with resistance to each of 14 antimicrobials tested by NARMS Sensititre™ panels.

Antimicrobial Number of Percent of Number of Percent of Isolates with AMR E.coli Samples E.coli Resistance Isolates with Positive with Resistance Samples Resistance Tested with Resistance

Tetracycline 223 77% 196 17%

Sulfisoxazole 149 52% 132 12%

Streptomycin 135 47% 124 11%

Ampicillin 108 38% 100 8.9%

Trimethoprim/Sulfamethoxazole 69 24% 64 5.7%

Chloramphenicol 52 18% 49 4.3%

Nalidixic Acid 41 14% 34 3.0%

Amoxicillin/Clavulanic Acid 30 11% 30 2.7%

Azithromycin 28 9.8% 27 2.4%

Cefoxitin 25 8.8% 25 2.2%

Ceftiofur 24 8.4% 24 2.1%

Ceftriaxone 24 8.4% 24 2.1%

Gentamicin 23 8.1% 23 2.0%

Ciprofloxacin 18 6.3% 16 1.4%

187

Supplementary Table 3.2. Number and percentage of isolates with resistance to each of eight classes of antimicrobials tested by NARMS Sensititre™ panels.

Antimicrobial Number of Percent of Number of Percent of Isolates with AMR E.coli Samples E.coli Positive Resistance Isolates with with Samples Resistance Resistance Tested with Resistance

Tetracycline 225 79% 196 17%

Sulfonamide 149 52% 132 12%

Aminoglycoside 139 48% 125 11%

Penicillin 109 38% 100 8.9%

Chloramphenicol 52 18% 49 4.3%

Quinolone 40 14% 34 3.0%

Cephalosporin 28 9.8% 28 2.5%

Macrolide 25 8.8% 24 2.1%

188

Supplementary Table 3.3. Antimicrobial resistance profiles of all E.coli isolates with intermediate MIC values to any of the 14 antimicrobials tested on the NARMS Sensititre™ panel.

Isolate NARMS Resistance Profile NARMS Intermediate Results

995-TET TET

1004-AMP AMP, FIS, TET, SXT AXO

1021-STR STR, FIS, TET AUG2, AMP

1084-STR STR TET

1110-TET FIS, TET AMP

1137-1SMX AZI, CHL, FIS, SXT TET

1170-AMP AUG2, FOX

1563-FOX AUG2, AMP, FOX AXO

1577-STR CHL, STR, FIS, TET, SXT GEN

1579-SMX CHL, STR, FIS, TET, SXT GEN

1603-TET TET

1611-NAL AMP, NAL AUG2

1626-STR CHL, STR, FIS TET

1628-FOX AUG2, AZI, FOX AMP

1646-AMP AMP, CHL, NAL, STR, FIS, TET, SXT AUG2

1712-NAL AMP, CIP, NAL, FIS, TET, SXT AUG2

1714-NAL AMP, CIP, NAL, FIS, TET, SXT AUG2 189

1715-FOX AUG2, AMP FOX

1785-STR FIS, STR, TET CHL

1830-SMX AMP, AZI, CHL, STR, FIS, TET, SXT AUG2

1831-TET TET

1867-TET TET

1904-GM CHL, STR, FIS, TET GEN

1912-TET TET

1931-TET TET

1958-STR AMP, FIS, SXT CHL

19874968-TET TET

E544936-TET TET

E447685-AMP AZI FOX, CHL

190

APPENDIX C: CHAPTER FOUR SUPPLEMENTARY DATA

Supplementary Table 4.1. Antimicrobial classes with resistance among 22 AmpC- and four ESBL-producing E.coli.

Antimicrobial Number ESBL-producers AmpC-producers Class Isolates (n=4) (n=22) Resistant

Penicillin 25 4 21

Cephalosporin 23 3 20

Tetracycline 20 3 17

Aminoglycoside 18 3 15

Sulfonamide 14 4 10

Quinolone 11 2 9

Chloramphenicol 11 0 11

191

APPENDIX D: CHAPTER FIVE SUPPLEMENTARY DATA

Supplementary Table 5.1. Results of non-significant clusters of resistance as determined by SaTScan (version 9.4.4).

Test Cluster Radius Number Number Relative Log P- Number of Wells of Risk Likelihood value Tested Positive Ratio Wells

Aminoglycoside 1 16.19 km 11 6 4.61 5.70 0.71 s 2 53.44 km 75 19 2.32 5.24 0.73

3 0 km 2 2 8.15 4.17 0.99

4 4.04 km 2 2 8.15 4.17 0.99

5 5.86 km 2 2 8.15 4.17 0.99

6 0.81 km 2 2 8.15 4.17 0.99

Cephalosporins 1 5.86 km 2 2 41.75 7.35 0.088

2 9.19 km 2 2 41.75 7.35 0.249

Chloramphenic 1 9.19 km 3 3 24.70 9.47 0.026 ol 2 4.04 km 2 2 23.86 6.28 0.559

3 2.56 km 2 2 23.86 6.28 0.559

4 8.60 km 14 4 7.21 4.72 0.774

5 5.15 km 3 2 15.88 4.41 0.922

6 4.37 km 4 2 11.89 3.59 0.985

7 3.46 km 5 2 9.50 3.04 0.999

192

Macrolides 1 52.84 km 9 2 11.30 3.10 0.94

2 18.10 km 25 3 6.45 2.95 0.94

Penicillins 1 8.63 km 4 3 8.47 5.05 0.63

2 55.65 km 32 9 3.39 4.99 0.78

3 0 km 2 2 11.13 4.79 0.97

4 0 km 2 2 11.13 4.79 0.97

5 0 km 2 2 11.13 4.79 0.97

6 5.86 km 2 2 11.13 4.79 0.97

7 0.81 km 2 2 11.13 4.79 0.97

Quinolones 1 0 km 2 2 41.75 7.35 0.088

2 5.86 km 2 2 41.75 7.35 0.249

3 4.93 km 3 2 27.79 5.46 0.577

4 40.60 km 22 3 5.89 2.78 0.989

5 23.85 km 10 2 8.25 2.54 0.998

Sulfonamides 1 11.60 km 12 7 4.92 7.19 0.202

2 2.32 km 3 3 8.13 6.24 0.581

3 9.19 km 3 3 8.13 6.24 0.581

4 7.53 km 14 7 4.21 5.89 0.647

5 0 km 2 2 8.05 4.15 0.992

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6 4.04 km 2 2 8.05 4.15 0.992

7 1.14 km 2 2 8.05 4.15 0.992

8 5.86 km 2 2 8.05 4.15 0.992

9 0.81 km 2 2 8.05 4.15 0.992

Tetracyclines 1 65.30 km 150 50 2.31 12.40 0.001 6

2 9.19 km 3 3 5.47 5.07 0.930

3 16.19 km 11 6 3.02 3.61 0.994

Multi-class 1 11.60 km 12 7 5.41 7.76 0.112 resistance (high rates) 2 2.67 km 3 3 8.89 6.50 0.481

3 9.19 km 3 3 8.89 6.50 0.481

4 0 km 2 2 8.79 4.32 0.993

5 0 km 2 2 8.79 4.32 0.993

6 4.04 km 2 2 8.79 4.32 0.993

7 5.86 km 2 2 8.79 4.32 0.993

8 0.81 km 2 2 8.79 4.32 0.993

Multi-class 1 78.02 km 93 1 0.081 8.79 0.037 resistance (low rates) 2 37.44 km 37 0 0 4.72 0.750

3 13.36 km 31 0 0 3.93 0.951

1 64.21 km 126 49 2.07 10.81 0.007 9

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Antimicrobial 2 9.19 km 3 3 4.51 4.49 0.985 resistance (high rates) 3 11.60 km 12 7 2.67 3.64 0.999

Antimicrobial 1 71.60 km 88 8 0.37 6.22 0.424 resistance (low rates) 2 13.90 km 22 0 0 5.73 0.576

3 21.59 km 16 0 0 4.14 0.992

Extended- 1 88.21 km 274 4 infinity 3.60 0.60 spectrum beta- lactamase producing E.coli

195

196

Supplementary Figure 5.1. Cluster of rural well water Supplementary Figure 5.2. High-proportion clusters of rural samples with low proportions of antimicrobial resistant well water samples with multi-class resistant E.coli. Results E.coli. Results displayed as a proportion of E.coli positive displayed as a proportion of E.coli positive well water well water samples that were submitted to ProvLab Calgary samples that were submitted to ProvLab Calgary between between 2006 and 2016 and tested for antimicrobial resistant 2006 and 2016 and tested for antimicrobial resistant E.coli. E.coli. (Clusters in black have a p value > 0.05). (Clusters in black have a p value > 0.05).

197

Supplementary Figure 5.3. Cluster of rural well water Supplementary Figure 5.4. Clusters of rural well water samples positive for E.coli resistant to aminoglycoside samples positive for E.coli resistant to cephalosporin antimicrobials. Results displayed as a proportion of E.coli antimicrobials. Results displayed as a proportion of E.coli positive well water samples that were submitted to ProvLab positive well water samples submitted to ProvLab Calgary Calgary between 2006 and 2016 and tested for antimicrobial between 2006 and 2016 and tested for antimicrobial resistant resistant E.coli. (Clusters in black have a p value > 0.05). E.coli. (Clusters in black have a p value > 0.05).

198

Supplementary Figure 5.5. Clusters of rural well water Supplementary Figure 5.6. Clusters of rural well water samples positive for E.coli resistant to macrolide samples positive for E.coli resistant to penicillin antimicrobials. Results displayed as a proportion of E.coli antimicrobials. Results displayed as a proportion of E.coli positive well water samples that were submitted to ProvLab positive well water samples that were submitted to ProvLab Calgary between 2006 and 2016 and tested for antimicrobial Calgary between 2006 and 2016 and tested for antimicrobial resistant E.coli. (Clusters in black have a p value > 0.05). resistant E.coli. (Clusters in black have a p value > 0.05).

199

Supplementary Figure 5.7. Clusters of rural well water Supplementary Figure 5.8. Point data and clusters of rural samples positive for E.coli resistant to quinolone well water samples positive for E.coli resistant to quinolone antimicrobials. Results displayed as a proportion of E.coli antimicrobials. Data is shown at the sample level for rural positive well water samples that were submitted to ProvLab well water samples submitted to ProvLab Calgary between Calgary between 2006 and 2016 and tested for antimicrobial 2006 and 2016 and tested for antimicrobial resistant E.coli. resistant E.coli. (Clusters in black have a p value > 0.05). (Clusters in black have a p value > 0.05).

200