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Seasonal dynamics of tetracycline resistance genes and antibiotics in a British Columbia agricultural… Keen, Patricia Lynn 2009

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SEASONAL DYNAMICS OF TETRACYCLINE RESISTANCE GENES AND ANTIBIOTICS IN A BRITISH COLUMBIA AGRICULTURAL WATERSHED  by  PATRICIA LYNN KEEN B.Sc. University of British Columbia, 1986 M.A. University of British Columbia, 2002  A THESIS SUBMITTED IN PARTIAL FULFILMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE STUDIES (Resource Management and Environmental Studies) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) May 2009 © Patricia Lynn Keen, 2009  ABSTRACT The central hypothesis of this research program was that antibiotic resistance genes and/or antibiotics resulting from the veterinary use could be transported through ecosystems and are the most important agricultural cause promoting antibiotic resistance development in environmental bacteria. The underlying premise of this hypothesis was that exposure of bacteria to sub-inhibitory concentrations of antibiotics and/or mobile genetic elements that encode for antibiotic resistance may affect bacterial ecology in natural ecosystems. This, in turn, could threaten the efficacy of human and veterinary medicine through increasing the probability of environmental exposure of pathogens to resistant bacteria species. The examination of this hypothesis required testing of an alternate hypothesis that transport through the environment of antibiotics at significant concentrations could be responsible for de novo induction of resistance in environmental bacteria. Antibiotic residues were measured in environmental samples collected from a British Columbia agricultural watershed by electrospray ionization liquid chromatography tandem mass spectrometry (ESI LC MS/MS). Four tetracycline resistance genes, chosen from the ribosomal protection protein supergroup, were measured in the same samples by real time quantitative polymerase chain reaction analyses (qPCR). Chemical analyses by ESI LC MS/MS of water, soil and compost samples were highly variable and did not reliably confirm concentrations of antibiotics in the receiving environment under investigation indicating that these might be close to the detection limits. However, the four selected tetracycline resistance genes could be consistently measured by qPCR in the same samples and monitored over time.  Low relative  abundance of the four tetracycline resistance genes (compared to bacterial biomass as indicated by measurement of abundance of 16S rRNA genes) was observed in warm summer months. High relative abundance of the four tetracycline resistance genes was measured in wetter winter months. This seasonal trend recurred during the 1.5 year monitoring period. Positive statistical correlations (p < 0.05) between instantaneous and 48 h stream discharge and the total of the four tetracycline resistance genes demonstrated  ii  that precipitation and hydrologic conditions influence the transport of antibiotic resistance genes in natural ecosystems. Measured mass transport rates of tetracycline resistance genes along one stream increased with higher rainfall. In the soil of a field fertilized by poultry compost, the relative abundance of the total of four tetracycline resistance genes and the proportion of resistance genes within the bacteria biomass (as represented by normalization to abundance of 16S rRNA genes) remained relatively constant over time. There was no statistically significant difference between the mean gene abundance of the total of four selected tetracycline resistance genes in poultry compost or in soil fertilized with the same poultry compost over a period of five months. Using mesocosm experiments, exposure to light, water quality and presence of periphyton biofilms were among the factors demonstrated to influence the fate of antibiotic residues and tetracycline resistance genes in aquatic ecosystems.  iii  TABLE OF CONTENTS ABSTRACT ..................................................................................................................... ii TABLE OF CONTENTS ................................................................................................... iv LIST OF TABLES ............................................................................................................ vii LIST OF FIGURES ......................................................................................................... viii LIST OF ABBREVIATIONS ............................................................................................ xi ACKNOWLEDGEMENTS ............................................................................................. xiii STATEMENT OF CO-AUTHORSHIP .......................................................................... xiv CHAPTER 1 - RESEARCH SCOPE AND LITERATURE REVIEW .............................. 1 1.1 Antibiotic Resistance Genes as Environmental Contaminants ......................... 1 1.2 Scope of Research ............................................................................................. 2 1.3 Study Region ..................................................................................................... 5 1.4 Experimental Techniques.................................................................................. 7 1.5 Organization of the Thesis ................................................................................ 8 1.6 Literature Review............................................................................................ 10 1.6.1 Introduction ............................................................................................... 10 1.6.2 Antibiotic Resistance ................................................................................ 13 1.6.3 Co-selection of Antibiotic and Metal Resistance...................................... 15 1.6.4 Environmental Contamination .................................................................. 16 1.6.5 Antibiotic Resistance Genes as Environmental Contaminants ................. 21 1.6.6 Antibiotics and Animal Waste Treatment................................................. 23 1.6.7 Implication for Human Health .................................................................. 26 1.6.8 Analysis of Antibiotics ............................................................................. 29 1.6.9 Analysis of Resistance Genes ................................................................... 31 1.7 References ....................................................................................................... 34 CHAPTER 2 - MEASURING SOME COMMON VETERINARY ANTIBIOTICS AND TETRACYCLINE RESISTANCE GENES IN A BRITISH COLUMBIA AGRICULTURAL WATERSHED .......................................................... 58 2.1 Introduction ..................................................................................................... 59 2.2 Materials and Methods .................................................................................... 62 2.2.1 Field Collection ......................................................................................... 62 2.2.2 Sample Preparation ................................................................................... 63 2.2.3 Antibiotic Residue Analyses ..................................................................... 63 2.2.4 Chromatographic Conditions .................................................................... 65 2.2.5 Preparation of Standard Solutions ............................................................ 65 2.2.6 Tetracycline Resistance Gene Measurement ............................................ 66 2.2.7 Determination of Water Quality Parameters............................................. 68 2.2.8 Quality Assurance/Quality Control........................................................... 68 2.2.9 Statistical Analyses ................................................................................... 69 2.3 Results ............................................................................................................. 69 2.3.1 Determination of Antibiotic Residues in Stream Water ........................... 69 2.3.2 Determination of Tcr Genes by Real-time qPCR ...................................... 74 2.3.3 Determination of Water Quality Parameters............................................. 79 2.4 Discussion ....................................................................................................... 82 2.4.1 Determination of Antibiotic Residues ...................................................... 82  iv  2.4.2 Determination of Tetracycline Resistance Genes ..................................... 85 2.4.3 Determination of Water Quality Parameters and Relation to Land Use ... 86 2.5 Conclusions ..................................................................................................... 89 2.6 References ....................................................................................................... 90 CHAPTER 3 - SEASONAL DYNAMICS OF TETRACYCLINE RESISTANCE GENES IN A BRITISH COLUMBIA AGRICULTURAL WATERSHED ................................................................................................................... 99 3.1 Introduction ..................................................................................................... 99 3.2 Materials and Methods .................................................................................. 103 3.2.1 Sample Collection ................................................................................... 103 3.2.2 Monitoring Tetracycline Resistance Genes ............................................ 104 3.2.3 Water Quality Analyses .......................................................................... 106 3.2.4 Rainfall and Climate Data ....................................................................... 107 3.2.5 Assessment of Land Use ......................................................................... 107 3.2.6 Estimates of Mass Transport of Tetracycline Resistance Genes along the Sumas River ............................................................................................ 108 3.2.7 Data Analyses ......................................................................................... 108 3.3 Results ........................................................................................................... 109 3.3.1 Measurement of the Tetracycline Resistance Genes .............................. 109 3.3.2 Monitoring Water Quality....................................................................... 115 3.3.3 Establishing Precipitation Patterns and Stream Flow July 2004 – March 2006......................................................................................................... 122 3.3.4 Estimates of Mass Transport ................................................................... 123 3.3.5 Correlations ............................................................................................. 129 3.4 Discussion ..................................................................................................... 130 3.4.1 Seasonal Dynamics of Tetracycline Resistance Genes ........................... 130 3.4.2 Monitoring Water Quality....................................................................... 137 3.4.3 Influence of Land Use in the Sumas Watershed ..................................... 139 3.5 Conclusions ................................................................................................... 141 3.6 References ..................................................................................................... 142 CHAPTER 4 - TRACKING TETRACYCLINE RESISTANCE GENES THROUGH THE COMPOSTING PROCESS AND FIELD DISTRIBUTION OF POULTRY MANURE ............................................................................ 150 4.1 Introduction ................................................................................................... 151 4.2 Materials and Methods .................................................................................. 154 4.2.1 Sampling ................................................................................................. 154 4.2.2 Antibiotic Analyses ................................................................................. 155 4.2.3 DNA Extraction ...................................................................................... 156 4.2.4 Real-time PCR Assays ............................................................................ 157 4.2.5 Antibiotic Susceptibility Tests ................................................................ 157 4.2.6 Data Analyses ......................................................................................... 158 4.3 Results ........................................................................................................... 158 4.3.1 Chemical Analysis of Tetracycline Residues ......................................... 158 4.3.2 Microbiological Analyses ....................................................................... 159 4.3.3 Antimicrobial Susceptibility Tests .......................................................... 162 4.4 Discussion ..................................................................................................... 162  v  4.4.1 Chemical Analysis of Tetracycline Residues ......................................... 163 4.4.2 Microbiological Analyses ....................................................................... 166 4.5 Conclusions ................................................................................................... 171 4.6 References ..................................................................................................... 172 CHAPTER 5 - CONCLUSION ...................................................................................... 179 5.1 Discussion Relating Manuscripts to Each Other .......................................... 179 5.2 Summary of Conclusions .............................................................................. 181 5.3 Recommendations for Future Research ........................................................ 184 5.4 References ..................................................................................................... 188 Appendix 1: Primers and TaqMan Probes Used in Preparation of Amplicons for Tetracycline Resistance Gene Determination ......................................... 210 Appendix 2: Whisker Box Plots of Recovery of 200 μg/L Antibiotic QC Standards of Analytes in Stream Water Samples (see Table 3.1 for abbreviations). N = 36............................................................................................................. 211 Appendix 3: Extraction and Determination of Chlorophyll a in Periphyton .................. 212 Appendix 4: Whisker Box Plots of Water Quality Parameters Comparing Dry (May – September) and Wet (October – March) Seasons. .................................. 213 Appendix 5: Spearman Rank Correlations for Tetracycline Resistance Genes and 16S rRNA Normalized Genes and Water Quality Parameters for All Sites .. 220 Appendix 6: Supplemental Material – Fate of Oxytetracycline Resistance Genes in Aquatic Systems: Migration from the Water Column to Peripheral Biofilms................................................................................................... 229 Appendix 7: Supplemental Material – Oxytetracycline Degradation ............................. 241 Appendix 8: Supplemental Material – Field Microcosm Study ..................................... 247 Appendix 9: Water Quality and Microbiological Parameters Measured at Each Site July 2004 – March 2006 ................................................................................. 253  vi  LIST OF TABLES Table 1.1: Some common antibiotics and their physical chemical properties. ................. 12 Table 2.1: Manual tuning parameters of selected antibiotics using Ultima Quatro 11 LC ESI MS/MS ............................................................................................................... 64 Table 2.2: Chromatographic gradient conditions for solvent system used for ESI LC MS/MS analyses of water extracts for antibiotic compounds .................................. 65 Table 2.3: Confirmed land use activities within 200 m of sampling point at each site monitored in 2004 along the Sumas River stream network ...................................... 79 Table 2.4a: Water quality summary comparing Sumas River (average of 5 sites) to control stream............................................................................................................ 80 Standard deviations are in parentheses ............................................................................. 80 Table 2.4b: Water and microbiological quality summary comparing Sumas River (average of 5 sites) to control stream ........................................................................ 81 Standard deviations are in parentheses ............................................................................. 81 Table 2.5: Comparison between antibiotic concentration determined in this investigation and those reported in current literature for environmental water samples................ 84 Table 3.1: Confirmed land use activities within 200 m of sampling point at each site monitored in 2005 – 2006 along the Sumas River stream network ........................ 107 Table 3.2: Summary of water quality collected March 2005 – March 2006. Values presented are the averages (arithmetic means) for all 8 Sumas River sites with standard deviations in brackets. .............................................................................. 117 Table 3.2 (cont.): Summary of water quality and microbiological data collected March 2005 – March 2006. Values presented are the averages (arithmetic means) for all Sumas River sites (n = 8) with standard deviations in brackets. ............................ 118 Table 3.3: Calculated stream flows in m3/s compared to Environment Canada measured flows ........................................................................................................................ 124 Table 3.4: Total precipitation (mm) recorded at Abbotsford during Sumas watershed monitoring experiments. ......................................................................................... 132 Table 3.5: Comparison between tetracycline resistance genes measured in this investigation for environmental water samples and those reported in current literature. ................................................................................................................. 137 Table A6.1: Mesocosm configuration for June 2005 experiments (* indicates biofilm substrates placed in treatment tank) ........................................................................ 234 Table A6.2: Mean water quality parameters for biofilm experiment treatments (95% confidence intervals are in parentheses; n=20) – 2005 mesocosm experiment ...... 236 Table A6.3: Chlorophyll a concentration on the biofilm disks – 2005 mesocosm experiment (average of 3 samples/day) .................................................................. 237 Table A6.4: Average abundance of 16S rRNA genes – 2005 mesocosm experiment (average of 3 samples/day) ..................................................................................... 237 Table A7.1: Oxytetracycline dose in each mesocosm treatment tank - 2004 experiment ................................................................................................................................. 242  vii  LIST OF FIGURES Figure 1.1: Location of the Sumas Watershed .................................................................... 6 Figure 2.1: Location of sampling sites along the Sumas River ........................................ 62 Figure 2.2: Concentrations of antibiotic residues and total abundance of Tcr genes determined in stream water samples (arithmetic mean of 3 runs for residues; arithmetic mean of 3 separate extracted samples for Tcr genes). ............................. 71 Figure 2.2 (cont.): Concentrations of antibiotic residues and total abundance of Tcr genes determined in stream water samples (arithmetic mean of 3 runs for residues; arithmetic mean of 3 separate extracted samples for Tcr genes). ............................. 72 Figure 2.2 (cont.): Concentrations of antibiotic residues and total abundance of Tcr genes determined in stream water samples (arithmetic mean of 3 runs for residues; arithmetic mean of 3 separate extracted samples for Tcr genes). ............................. 73 Figure 2.3: Tetracycline resistance gene and 16S rRNA gene profiles at each sampling site: July – Dec 2004. Values are the arithmetic mean of 3 separately extracted samples taken from each site; error bars represent standard deviation. .................... 75 Figure 2.3 (cont.): Tetracycline resistance gene and 16S rRNA gene profiles at each sampling site: July – Dec 2004. Values are the arithmetic mean of 3 separately extracted samples taken from each site; error bars represent standard deviation. .... 76 Figure 2.3 (cont.): Tetracycline resistance gene and 16S rRNA gene profiles at each sampling site: July – Dec 2004. Values are the arithmetic mean of 3 separately extracted samples taken from each site; error bars represent standard deviation. .... 77 Figure 2.4: Average of 5 stream sites of tetracycline resistance genes normalized to 16S rRNA genes (abundance). Values are the average over 5 sites of arithmetic means of three replicates per site; error bars represent standard deviation.......................... 78 Figure 3.1: Location of sampling sites along the Sumas River. ..................................... 104 Figure 3.2: Tetracycline resistance gene abundance profiles: March 2005 – March 2006. All measurements are the average abundances of 3 replicates taken at each site; error bars represent standard deviation. ........................................................................... 110 Figure 3.2 (cont.): Tetracycline resistance gene abundance profiles: March 2005 – March 2006. All measurements are the average abundances of 3 replicates taken at each site; error bars represent standard deviation. .......................................................... 111 Figure 3.2 (cont.): Tetracycline resistance gene abundance profiles: March 2005 – March 2006. All measurements are the average abundances of 3 replicates taken at each site; error bars represent standard deviation. .......................................................... 112 Figure 3.2 (cont.): Tetracycline resistance gene abundance profiles: March 2005 – March 2006. All measurements are the average abundances of 3 replicates taken at each site; error bars represent standard deviation. .......................................................... 113 Figure 3.2 (cont.): Tetracycline resistance gene abundance profiles: March 2005 – March 2006. All measurements are the average abundances of 3 replicates taken at each site; error bars represent standard deviation. .......................................................... 114 Figure 3.3: Average of total tetracycline resistance genes at 8 stream sites normalized to average 16S rRNA genes for the period of March 2005 – March 2006. Values represent the arithmetic mean of 3 total Tcr gene measurements normalized to the arithmetic mean of 3 16S rRNA gene measurements; error bars represent standard deviation. ................................................................................................................. 115  viii  Figure 3.4a: Whisker box plots of turbidity during dry conditions (top) and wet conditions (bottom) between July 21, 2004 – March 16, 2006. Values of mild outliers (1.5 times the IQR) are denoted by ◦ and extreme outliers (3 times the IQR) are denoted by *. ..................................................................................................... 119 Figure 3.4b: Whisker box plots of NO3-N during dry conditions (top) and wet conditions (bottom) between July 21, 2004 – March 16, 2006. Values of mild outliers (1.5 times the IQR) are denoted by ◦ and extreme outliers (3 times the IQR) are denoted by *.......................................................................................................................... 120 Figure 3.4c: Whisker box plots of PO4-P during dry conditions (top) and wet conditions (bottom) between July 21, 2004 – March 16, 2006. Values of mild outliers (1.5 times the IQR) are denoted by ◦ and extreme outliers (3 times the IQR) are denoted by *.......................................................................................................................... 121 Figure 3.5: Total monthly precipitation and average stream discharge in Sumas watershed (Environment Canada, 2006). ................................................................................. 123 Figure 3.6 Calculated mass transport rates of tetracycline resistance genes at four upstream sites on the Sumas River (July – December 2005); error bars represent standard deviation. .................................................................................................. 125 Figure 3.6 (cont.): Calculated mass transport rates of tetracycline resistance genes at four upstream sites on the Sumas River (July – December 2005); error bars represent standard deviation. .................................................................................................. 126 Figure 3.7: Calculated mass transport rates of 16S rRNA genes at four upstream sites on the Sumas River (July – December 2005); error bars represent standard deviation. ................................................................................................................................. 127 Figure 3.7 (cont.): Calculated mass transport rates of 16S rRNA genes at four upstream sites on the Sumas River (July – December 2005); error bars represent standard deviation. ................................................................................................................. 128 Figure 3.8: Spearman rank correlation diagram for Sumas River sites 1, 2, 3, 4 & 7 (excluding control site 9) between total tetracycline resistance genes and total tetracycline resistance genes normalized to 16S rRNA genes and water quality parameters. .............................................................................................................. 130 Figure 4.1: Relative abundance of Tcr genes in poultry compost – January – September 2004 (average values; n = 3); error bars represent standard deviation. .................. 160 Figure 4.2: Relative abundance of Tcr genes in soil fertilized by poultry compost – September 2004 – January 2005 (average values; n = 3); error bars represent standard deviation. .................................................................................................. 161 Figure 4.3: Livestock in the Sumas Watershed (Statistics Canada, 1996, 2001, 2007) . 163 Figure 4.4: Total Tcr and 16S rRNA genes measured in compost (top) and soil samples (bottom). Values presented are the average of 3 measurements per sample extract; error bars represent standard deviation. .................................................................. 168 Figure A6.1: Mesocosms used in the 2005 experiments ................................................ 232 Figure A6.2: Biofilm substrate apparatus – 2005 experiment ........................................ 235 Figure A6.3: Absolute tetracycline resistance gene abundance in water column and biofilms in light only treatment over time (percentages refer to relative proportion of total tetracycline resistance gene abundance of biofilm compared to the whole tank). Values presented are averages for n = 3. ................................................................ 238  ix  Figure A7.1: Degradation curves for oxytetracycline on September 16, 2004. Values represent average of 3 measurements for each of 3 samples; error bars indicate standard deviation. .................................................................................................. 245 Figure A8.1: Field microcosms – schematic of March 6, 2006 experiment set-up ........ 248 Figure A8.2: Timeline of sampling program – March 6, 2006 experiment .................... 249 Figure A8.3: Tetracycline resistance gene profiles – field experiment in Sumas River March 6, 2006. Values presented are averages for n = 3. ...................................... 250  x  LIST OF ABBREVIATIONS 16S rRNA  16S subunit of ribosomal ribonucleic acid  A  area  APUA  Alliance for Prudent Use of Antibiotics  BOD  biochemical oxygen demand  BC MAFF  British Columbia Ministry of Agriculture, Food and Fisheries  DNA  deoxyribonucleic acid  DO  dissolved oxygen  DCC  demeclocycline  DYC  doxycycline  CTC  chlortetracycline  EDTA  ethylene diamine tetra-acetic acid  ESI LC MS/MS FVRD  electrospray ionization liquid chromatography tandem mass spectrometry Fraser Valley Regional District  GC/MS  gas chromatography mass spectrometry  GIS  geographic information system  HPLC  high performance liquid chromatography  IQR  interquartile range  LC50  lethal concentration for 50% mortality  LIN  lincomycin  LLC  limited liability company  MEC  measured environmental concentration  MIC  minimum inhibitory concentration  MRSA  methicillin-resistant Staphylococcus aureus  n/a  not analysed  n/d  not detected  OLM  oleandomycin  OMP  ormetroprim  OTC  oxytetracycline  xi  PAR  photosynthetically active radiation  ppb  parts per billion  ppm  parts per million  Q  flow  qPCR  real-time polymerase chain reaction analyses  RPP  ribosomal protection protein  SCP  sulfachloropyridazine  SMX  sulfamethoxazole  SMZ  sulfamethazine  SD  standard deviation  SPE  solid phase extraction  Tcr  tetracycline resistance genes  TLM  tilmicosin  TMP  trimethroprim  TSS  total suspended solids  TTC  tetracycline  TYL  tylosin  UBC  University of British Columbia  US EPA  United States Environmental Protection Agency  US FDA  United States Food and Drug Administration  v  velocity  VRE  vancomycin-resistant Enterococci  VSs  volatile solids  WHO  World Health Organization  xii  ACKNOWLEDGEMENTS I offer my heartfelt thanks to my supervisor, Dr. Ken Hall and my committee, Dr. Les Lavkulich, Dr. Bob Hancock, Dr. Pierre Bérubé, and Dr. David Graham for guiding me through this process. I am also deeply grateful to Dr. Charles Knapp for his help throughout my learning journey.  Dr. Hans Schreier has also helped me on many  occasions throughout these past few years and for this, I am truly grateful as well. I owe sincere thanks to Dr. Julian Davies, Dr. Vivian Miao, Dr. Karen Lu, Dr. Hai Xu and Waldan Kwong for teaching me so much in a very short time. This research would never have been possible without the contributions of Dr. Dayue Shang from the Western Region Organic Residues Laboratory of Health Canada and Dr. Merv Wetzstein from BC Ministry of Agriculture Food and Fisheries. I thank Dr. Nancy Dewith and Dr. Heather Hannah also from BC MAFF for their help with the poultry compost part of this research. I owe many thanks to Susan Harper and Paula Parkinson from UBC environmental engineering laboratory and to Angelo di Cicco and Helen Nikolidakis from Health Canada for their help always. Christie Engemann, Laura Adams and Mark Hanson have been tremendously helpful in all the studies conducted at the University of Kansas in Lawrence. I am forever grateful to Charlene Knapp for her assistance and friendship as well. I cherish the help and support of some very special friends: Derek Smith, Mark Montforts, Paolo Marcazzan, Annette Muttray, Heather Goble, Belidson Dias, Sandra Brown, David Brownstein, Glenys Webster, Paul Luchkow, Teresa Frolek, Lisa Waddell, Debbie Angel, Julio Reboucas, and my sisters May du Monceau and Katia Freire. I offer a special thank you to Raphaël Fugère for his help in many phases of my studies throughout the years. I am most grateful for the financial assistance for this research that has been supported by contributions from a Natural Science and Engineering Research Council (NSERC) grant to Dr. Ken Hall, from the Canadian Water Network Centres of Excellence and from a Health Canada Agricultural Policy Research grant. None of this would have been possible without Steve Clark. Thank you.  xiii  STATEMENT OF CO-AUTHORSHIP Results derived from the research of this thesis form the basis of co-written manuscripts contributed to three peer-reviewed journals.  The following chapters summarize the  contributions to these journals:   Chapter 2 - Between July 2004 and December 2004, Patricia Keen conducted the field monitoring program in the Sumas Watershed, performed some of the laboratory analyses including some real-time PCR analyses, analysed the data and prepared the manuscript. Patricia was involved in extraction and preparation of samples and standards for the chemical analyses while Derek Smith conducted the instrumental analyses between July 2004 and March 2005. Dayue Shang from Health Canada Western Regional Laboratory supervised the method development and analyses of water samples and contributed to financial support. Charles Knapp from the University of Kansas conducted much of the real-time PCR analyses of the stream samples and David Graham supervised the incorporation of microbiological monitoring methods into the program of study.  Ken Hall  supervised the overall program of study, assisted in field sampling and flow measurements and provided financial support. The results of this investigation are presented in: Measuring some common veterinary antibiotics and tetracycline resistance genes in a British Columbia agricultural watershed. 2008. Patricia L. Keen, Charles W. Knapp, Derek R. Smith, Kenneth J. Hall, Dayue Shang & David W. Graham. In preparation for submission for peer review.   Chapter 3 – In March 2005, Patricia Keen resumed the field monitoring of the Sumas River, performed some of the laboratory analyses including some real-time qPCR analyses, measured stream flows, analysed the data and prepared the manuscript.  Chemical analyses of antibiotic residues were discontinued and  monitoring efforts concentrated on measuring tetracycline resistance genes and water quality in the study system. Charles Knapp from the University of Kansas performed most of the real-time qPCR analyses the stream samples and David Graham supervised the incorporation of continued field monitoring into the  xiv  overall program of study. Ken Hall supervised the research, assisted in field sampling and flow measurements and provided financial support throughout. The results of these experiments are provided in: Keen, Patricia L. Charles W. Knapp, Kenneth J. Hall & David W. Graham. 2008. Seasonal dynamics of tetracycline resistance genes in a British Columbia agricultural watershed. In preparation for submission for peer review.   Chapter 4 – Between July 2004 and March 2006, Patricia participated in all aspects of the method development process for the LC ESI MS/MS analyses of compost and soil samples under the supervision of Dayue Shang. Merv Wetzstein from British Columbia Ministry of Agriculture Food and Fisheries (BC MAFF) provided the poultry compost samples from their one year monitoring project for analyses in this study. Samples were collected by Nancy Dewith and Heather Hanna. Angelo di Cicco extracted, prepared and analysed most of the compost and soil samples. The Health Canada Western Regional Laboratory contributed to funding of this research. Charles Knapp and David Graham supported the realtime qPCR analyses of the poultry compost samples. Patricia conducted some of the laboratory analyses, analysed the data and prepared the manuscript. Ken Hall supervised the overall program of research and provided financial support throughout. The results of this experiment are summarized in: Keen, Patricia L., Charles W. Knapp, Derek R. Smith, Dayue Shang, Kenneth J. Hall & David W. Graham. 2008. Tracking tetracycline resistance genes through the composting process and field distribution of poultry manure. In preparation for submission for peer review.  xv  CHAPTER 1 - RESEARCH SCOPE AND LITERATURE REVIEW 1.1 Antibiotic Resistance Genes as Environmental Contaminants Examinations of surface waters for the presence of contaminants have revealed widespread distribution of pharmaceuticals, including compounds that retain their biological activity after excretion into the receiving environment (Halling-Sørensen et al., 1998; Daughton and Ternes, 1999; Kolpin et al., 2002). Antibiotics, in particular, are notorious as environmental contaminants due to their intrinsic biological activity. This underlies the widely-held concern that exposure to antibiotic compounds or resistant pathogens via environmental transport pathways could potentially impact human health by compromising medical or veterinary therapies (Christensen, 1998). The Millennium Ecosystem Assessment report (United Nations Environmental Program, 2005) has identified the development of antibiotic resistant bacteria and the emergence/reemergence of infectious diseases as substantial contributors to ecosystem-mediated factors impacting on human health. The research presented in this thesis is the first exploration of the potential for transport of antibiotic resistance genes linked to the agricultural use of antibiotics as environmental contaminants in a watershed located in south western British Columbia, Canada. The presence and relative abundance of antibiotic resistance genes in surface waters may be indicative of the pathways of dissemination of bacteria that have been exposed to selective pressures for development of resistance traits prior to their release into the receiving environment. To date, there is little information regarding the baseline levels of antibiotic resistance genes in the soil and water in agricultural watersheds and the factors that govern their persistence in aquatic ecosystems are not well-understood. The central hypothesis of this doctoral thesis is that antibiotic resistance genes or antibiotics can be transported through ecosystems and are the most important agricultural cause promoting antibiotic resistance development in environmental bacteria.  The examination of this hypothesis required testing of an alternate  hypothesis that transport through the environment of antibiotics at significant concentrations could be responsible for de novo induction of resistance in environmental bacteria.  1  Antibiotics and antibiotic resistance genes are recognized as environmental contaminants capable of exacerbating the risk associated with development of antibiotic resistance in bacteria. Mechanisms for transfer of bacteria and resistance genes via the environment have been identified as potential contributors to the spread of antibiotic resistance from animals to humans and vice versa (Teale, 2002). Exchange of resistance genes from the environmental gene pool to various bacteria species and the horizontal transfer of resistance plasmids in microbial populations are assumed to occur in natural systems (Davies, 1997). There is a great deal of current interest in exploring the environmental transport of antibiotics and antibiotic resistance genes and the provision of opportunities to impose selective pressure to develop antibiotic resistance in bacteria. To this author’s knowledge, the research described in this dissertation describes the first investigation of this problem in a British Columbia agricultural watershed. 1.2 Scope of Research Levy (1992) warned of the paradox created by society’s growing dependency on antibiotics to treat infection in humans and animals and the concomitant risk of dynamic evolution of resistant strains of pathogenic bacteria that would counteract the effectiveness of treatment of infections.  The published literature is now rich with  examples of scientific evidence indicating that exposure to antibiotic substances can promote the selection of resistant strains of bacteria. Antibiotics can retain their intrinsic biological activity following excretion from humans or animals to which they were administered (Fleming, 1929), a characteristic that has particularly important implications for their distribution in the environment as a result of waste disposal. Exposure to antibiotic compounds promotes selection for antibiotic resistance in bacteria at the genetic level (Levy, 1992; Halling-Sørensen et al., 1998). The tetracycline class of antibiotics is a representative example of drugs, commonly used in both human and veterinary medicine, for which the selection for resistance genes as a result of exposure is now well-characterized (Roberts, 1996; Roberts, 2005). Populations of bacteria may be affected i) within the gut of the treated animal, where orally-delivered antibiotic concentrations are likely to be highest, ii) in the waste disposal process where variable  2  populations of bacteria face highly varied concentrations of antibiotics (Golet et al., 2002; Hamscher et al., 2002) and iii) in the receiving environment where the presence of low concentrations of several antibiotics has been reported (Kolpin et al., 2002; Zuccato et al., 2000). The first key research question of this dissertation was therefore: Can antibiotic compounds (in sufficient levels that may promote resistance in environmental bacteria) and tetracycline resistance genes be routinely monitored in the Sumas watershed?  The analyses of antibiotic residues were limited to determination of  compounds commonly used in veterinary medicine. More than 50% of antibiotics produced are used in veterinary therapy (Health Canada, 2002). There is now considerable scientific evidence to support a correlation between the use of antimicrobials in the treatment of food-producing animals and the development of resistance among common pathogens (World Health Organization, 2000). The transport of resistant organisms through the food chain to humans has been suggested to be a threat to human health (Witte, 1998). Food is now considered to be one of the main vehicles for zoonotic transfer, between animals and humans, of antibiotic resistant bacteria and resistance genes (Perreten et al., 1997; Teuber, 1999; Threlfall, 2002). However, the role of surface water, as both a source of drinking water and a transport medium for antibiotic resistance genes has not been well-studied. Transport pathways are routes, courses, or ways by which biotic or abiotic matter can be dispersed throughout ecosystem compartments. In general, water flow plays a central role in this process. Precipitation patterns and hydrologic conditions in the receiving environment affect the transport routes, and thus the exposure of food animals to waterborne bacteria. Extrapolating from the published evidence, the second research question studied in this thesis was: Could seasonal patterns be identified in the abundance of tetracycline resistance genes measured in surface waters of the Sumas watershed? Disposal of animal manure on land is an important source of bioactive contaminants such as veterinary drugs (Boxall et al., 2006; Boxall et al., 2004; Montforts et al., 1999) for release into the environment.  As agricultural activity intensifies within a finite  geographic area, increasing amounts of waste from food animal production requires  3  disposal often through either application to the land as a fertilizer or by export from the watershed.  Contaminants associated with animal waste that can impose selective  pressure on development of bacterial antibiotic resistance warrant investigation particularly given the poor understanding of mechanisms that transfer mobile genetic elements between bacteria in the environment.  Davies (1994) pointed out that Nature is  a repository of a substantial pool of antibiotic resistance genes accessible through genetic exchange to other bacteria. Gene flux among bacteria appears to be relatively facile (Blattner, 2005) and readily responsive to environmental changes (Levy & Miller, 1989). Thus my third research question was aimed at exploring the likely sources of antibiotic resistance genes that are suspected to be present in surface water, namely: Could the fertilization of fields with manure derived from food animals serve as a source of dissemination of tetracycline resistance genes into the receiving water courses? Important knowledge gaps remain in the understanding of the abiotic or biotic processes that govern the fate of antibiotics or antibiotic resistant genes in the environment. Photolysis, hydrolysis, and sorption are among processes that, contingent on the physical and chemical properties of antibiotic compounds, can influence their persistence in environmental compartments (Oka & Patterson, 1995, Cardoza et al., 2005). Photolysis appears to be the primary process affecting the persistence of tetracycline compounds (Sanderson et al., 2005; Boreen et al., 2003; Oka et al. 1989) and the decay of tetracycline resistance genes has been reported to be influenced by light (Engemann et al., 2006). It is accepted that bacteria resident in biofilms are more resistant to the action of antibiotics than bacteria suspended in an aqueous medium (Allison et al., 2000) and it has been shown that fluid flow conditions can deform biofilm communities (Stoodley et al., 1999; Stoodley et al., 2002; Stoodley et al., 2005). My fourth research question was thus: Could the factors that appear to affect the abundance of tetracycline resistance genes observed through field monitoring be reproduced under more controlled conditions in model ecosystems and can mechanisms that affect fate be delineated? Induction of antimicrobial resistance by antibiotics is a global concern.  Misplaced  confidence that medical technology can counter the development of resistant bacteria has been undermined by disturbing trends suggesting a greater number of resistant infections, 4  an expanded need for more potent antimicrobials to treat them, the lack of discovery of new antibiotic classes, and the creation of further opportunities for their misuse (Levy, 1992). The environmental impacts of pollutants and altered weather patterns associated with climate change affect the incidence and distribution of infectious diseases spread by insects and other vectors. Combined with this, the increased incidence of AIDS and cancer in the human population causes a larger proportion of patients to be immunocompromised and thus, at greater risk of contracting infections. Population aging is also an important factor in assessing the health consequences resulting from the development of antimicrobial resistance, in that a growing proportion of the elderly population require hospitalization and faces greater exposure risk to the highly resistant strains of pathogens that are sometimes concentrated in hospitals. Over the past thirty years, the development of resistant strains of bacteria has been accompanied by the resurgence in the industrialized world of diseases which were previously thought to be controlled (World Health Organization, 2000).  Transmission of infectious diseases and resistant  microorganisms are no longer confined to local ecosystems, as they can be quickly and easily spread between continents given the unprecedented growth of global trade and travel (Richet et al., 2001). 1.3 Study Region The fieldwork for this project was conducted in the Canadian portion of the Sumas watershed (Figure 1.1). Representing nearly 10,000 hectares of trans-boundary land in the Sumas Prairie, the watershed is located 60 km east of Vancouver, British Columbia. It is one of the most economically important agricultural areas in Canada for the production of poultry, dairy, hogs, fruits, vegetable produce and nursery farms (Statistics Canada, 2006).  Although the municipalities of Abbotsford and Chilliwack, located  within the watershed, have undergone considerable suburban development, land use continues to be dominated by agriculture. Most of the Sumas Prairie has an average elevation of less than 6 m above sea level (Environment Canada, 1998). The local hydrologic conditions within the watershed are monitored on a daily basis by Environment Canada and the location of this station was chosen as one of the stream sampling sites for this investigation (Environment Canada Station ID: 0MH029: Sumas  5  River near Huntington 49°0'9" N; 122°13'50" W). The climate of the watershed is typical of the near-coastal regions of south-western British Columbia with temperatures that are rarely extreme and weather conditions that are favorable for growing a wide variety of vegetable and berry crops, mushrooms, lawn turf, and animal food crops throughout most of the year.  Figure 1.1: Location of the Sumas Watershed  6  Historically, the Sumas watershed was submerged as a lake joined to the sea during much of the Quaternary period. The soils that resulted from the eventual drainage of the lake in the Sumas watershed are mainly comprised of sands and loams (Luttmerding, 1980). The surficial geology of the area is dominated by post-glacial lacustrine deposits in the Sumas Prairie region and eolian deposits over glacial till on the mountains bounding the watershed (Luttmerding, 1980). A natural landslide in a headwater tributary of the Sumas River delivers serpentinic material (specifically asbestos) into the river which resulted in above-average concentrations of trace metals such as nickel, chromium, cobalt and magnesium observed in the stream water (Schreier & Taylor, 1981). Research conducted in the Sumas watershed has produced an extensive archive of scientific information describing the processes that occur in, and characteristics of the local receiving environment (Schreier & Taylor, 1981; Schreier et al., 2001).  This  existing knowledge has served as an excellent background for studying further the effects of the input of antibiotic resistance genes and/or antibiotic residues in a watershed dominated by intense agricultural production.  Soil and water quality have been  monitored frequently over that past 30 years, land use patterns have been mapped, census statistics have been collected for animal and human population estimates, and climate conditions are recorded on a daily basis. The history of the watershed is thus well-known and receiving environment sampling sites are easily accessible (Schreier et al., 2001). 1.4 Experimental Techniques Liquid chromatography electrospray ionization tandem mass spectrometry (LC ESI MS/MS) was used for all chemical analyses of antibiotic residues. Determination of antibiotics in all samples was conducted at the Western Regional Laboratory of Health Canada located in Burnaby, British Columbia while general chemistry of water samples (nitrate, phosphate, chloride, suspended and volatile solids) was analyzed in the environmental laboratory at the UBC Department of Civil Engineering.  All  measurements of the specific tetracycline resistance genes were performed using realtime quantitative polymerase chain reactions (qPCR) in the environmental laboratory at the Department of Civil, Environmental and Architectural Engineering of the University  7  of Kansas in Lawrence, Kansas. All of the large-scale mesocosm experiments were conducted at the Nelson Environmental Studies Area at Lawrence, Kansas. 1.5 Organization of the Thesis The thesis is comprised of six chapters, with chapters 2-5 consisting of four independent manuscripts that are in the process of submission, have been submitted or are published in peer-reviewed journals.  Each chapter is constructed as the basis of stand-alone  manuscripts and thus some repetition, in particular of, materials, methods and statistical treatment of data will be repeated from one chapter to the next. The first chapter included the introduction of the central hypothesis of this doctoral thesis, states the key research questions that were addressed and provides a literature review of the published evidence to support need for this investigation. Chapter 2 addresses the first research question and presents the results of the determination of tetracycline resistance genes and antibiotics in the Sumas watershed over the sampling period July-December 2004. The methodology used for the analyses is described in detail LC ESI MS/MS was used for the determination of 14 antibiotic compounds commonly used in veterinary medicine, while 4 common tetracycline resistance genes were determined by qPCR. Chemical analyses of antibiotic residues did not provide reliable evidence that these compounds could be monitored in the Sumas watershed on a routine basis.  Evaluation of the four tetracycline resistance genes,  however, revealed their abundance in stream water samples in statistically significant lower concentrations during periods of low precipitation (warm summer months). Their measured increase in concentration after October 2004 coincided with greater precipitation and frequent land application of animal manure. This chapter is based on the manuscript “Measuring some common veterinary antibiotics and tetracycline resistance genes in a British Columbia agricultural watershed” (Keen, et al., in preparation for submission). Chapter 3 responds to the second research question and reports the observations with respect to seasonal change of tetracycline resistance gene concentrations in water samples collected throughout the monitoring periods, June 2005 - March 2006. Seasonal patterns 8  are described relative to observations of the quality of the receiving water, the hydrologic conditions, and the changes in rainfall events.  The abundance pattern of the 4  tetracycline resistance genes observed in the fall-winter of 2004 was also observed in fall-winter of 2005. Statistical correlations were calculated between concentrations of tetracycline resistance genes and some important water quality parameters, stream discharge rates and precipitation levels recorded at stations along the Sumas River. Mass flux measurements of tetracycline resistance genes indicate that antibiotic resistance genes fluctuate along a finite continuous length of the Sumas River but no consistent trend could be established. This chapter is based on the manuscript “Seasonal dynamics of tetracycline resistance genes in a British Columbia agricultural watershed” (Keen et al., in preparation for submission). Chapter 4 addresses the third research question and specifically explores whether field application of poultry waste as fertilizer could be a source of antibiotic resistance genes in surface water. The chapter reports the results of the first effort to monitor tetracycline resistance genes following one broiler production cycle and subsequent field application of poultry compost in the Lower Fraser Valley of British Columbia. Based on the qPCR analyses of these samples, this work could not establish that surface run-off from fertilized fields makes a substantial contribution to the levels of selected tetracycline resistance (Tcr) genes measured in receiving water. The final chapter summarizes the findings of this research and presents concluding remarks with some recommendations for work. The overarching goal of this work is to contribute to the existing body of knowledge concerned with risks associated with exposure of bacteria to antibiotic resistance genes or antibiotic residues present as contaminants in natural ecosystems. Antibiotics are substances that are naturally produced by microorganisms or produced by synthetic routes to kill (bacteriocidal) or prevent the growth (bacteriostatic) of other microorganisms.  Antimicrobial resistance is the ability of some bacteria, viruses,  parasites, fungi and other microorganisms to resist chemical substances (including synthetic compounds) that can kill them or prevent their growth. The terms “antibiotic resistance” and “antimicrobial resistance” are not technically identical although they are 9  frequently used interchangably.  For the purpose of this thesis, the term “antibiotic  resistance” will be used throughout to mean resistance to those substances synthetically produced or produced by/derived from certain bacteria to kill or prevent the growth of other bacteria. 1.6 Literature Review 1.6.1  Introduction  Among the pharmaceuticals that are continuously released as complex mixtures in the environment, those compounds that retain their intended biological activity after metabolic excretion and disposal deserve special consideration.  This subset of  pharmaceuticals and personal care products is known to be widely distributed throughout aquatic and terrestrial ecosystems (Halling-Sørensen et al., 1998; Ternes, 2001; Kolpin et al., 2002; Boxall et al., 2003).  As “pseudopersistent” environmental contaminants  (Hernando et al., 2006), antibiotics are of particular concern in that exposure to these compounds is likely to introduce the selective pressure leading to an increase in the rate of resistance development in some bacteria (Barbosa & Levy, 2000).  Water transport of  antibiotics can thus create favourable conditions for the exertion of selective pressure on development of antibiotic resistance in environmental bacteria.  In addition, facile  waterborne dissemination of genetic material throughout the receiving environment can promote free exchange of the genetic determinants of resistance between bacterial species. This, in turn, amplifies the risk to human and ecosystem health particularly when pathogens could be exposed to elevated contaminant concentrations. The published literature provides abundant evidence supporting the need to investigate the transport of antibiotics or antibiotic resistance genes through ecosystems. It has been convincingly argued that selection for antibiotic resistant phenotypes of bacteria occurs in the natural environment (Davies, 1994; Alonso et al., 2001; Séveno et al., 2002). Microorganisms and antibiotic resistant genes can easily transfer between ecosystems: from humans to animals to soil and water (and vice versa). The impact of antibiotic resistance on soil microorganisms (many of which are yet uncharacterized and cannot be  10  cultured under laboratory conditions) is extremely important given the wide range of ecosystem services performed by these organisms in diverse ecological niches. Horizontal gene transfer in soil has been suggested to affect the evolution of new bacterial traits that may, in turn, affect natural microbial communities (Hill & Top, 1998; Nwosu, 2001). Environmental conditions that could promote the spread of antibiotic resistance in bacteria, through antibiotic exposure or exchange of genetic material, are likely to have impacts on ecosystems. Antibiotics belong to a broader family of molecules, including pharmaceutical drugs, disinfectants or other chemical substances, which have the ability to kill or inhibit the growth of microorganisms (Walsh, 2003). Antimicrobial activity has been demonstrated for thousands of molecules ranging from chemicals found in coffee (Almeida et al., 2006) to copper (Noyce et al., 2006).  Naturally-derived antibiotics, semi-synthetic and  synthetic antimicrobial drugs are commonly used in human and veterinary therapy. The development of antibiotics was initially driven by the need to combat both internal and external bacterial infections in humans. Over time, the efficacy of antibiotic treatment of infectious disease in humans led to extending the application of the same classes of antibiotics in animals and also plants. The recognition that antibiotics could accelerate the growth of food animals and poultry (Coates et al., 1955) has resulted in the widespread use of antibiotics in animal feed at sub-therapeutic concentrations. Moreover, the use of low levels of antibiotics as feed additives to enhance animal growth has been demonstrated to affect antibiotic resistance in bacteria hosted by humans (Levy et al., 1976). This has contributed to the controversy regarding the impacts on human health of antibiotic growth promoters routinely fed to food animals (Burg, 1986). Antibiotics can be grouped into specific classes based on their structures, chemical properties, or mechanisms of action (Walsh, 2003). Different classes of antibiotics act on distinct targets in bacterial cells and inhibit bacteria by interfering with essential cellular processes. Examples of some of the common classes of antibiotics include tetracyclines, β-lactams, macrolides, aminoglycocides, sulfonamides, quinolones and some compounds that act as ionophores.  With the exception of antibiotics that act as ionophores,  compounds belonging to these drug classes can be used in both human and veterinary 11  medicine (Callaway et al., 2003). In general, Gram-negative bacteria (those species with cell walls composed of peptidoglycan layers and intact outer membranes) and Grampositive bacteria (those species with peptidogylcan layers and usually lack intact outer membranes of their cell walls) respond to different mechanisms of action of antibiotic compounds (Scholar & Pratt, 2000). In many cases, the same classes of antibiotics are used to treat both human and animal diseases arising from bacterial infections. Table 1.1 provides some examples of common classes of antibiotics and some of their physical chemical properties. Table 1.1: Some common antibiotics and their physical chemical properties. (Streng et al., 1976 ; Escribano et al., 1997 ; Nowara et al., 1997 ; Rabølle & Spliid, 2000 ; Loke et al., 2002 ; Johnson et al., 2002 ; Jiménez-Lozano et al., 2002 ; Thiele-Bruhn, 2003; Dolliver & Gupta, 2008) Molar mass (g/mol)  Water solubility (mg/L)  Log Kow  pKa  β-Lactams (penicillins, cephalosporins)  334.4 - 470.3  22 - 10100  0.9-2.9  2.7 – 4.0  Macrolides (tylosin, erythromycin, oleandomycin)  687.9 - 916.1  0.45 - 15  1.6 – 3.1  7.7 – 8.9  Tetracyclines (chlortetracycline, tetracycline, oxytetracycline)  444.5 – 527.6  230 - 5200  -1.3 – 0.05  3.3/9.3/7.7  Aminoglycocides (neomycin, kanamycin, streptomycin)  332.4 – 615.6  10 mg/L – 500 g/L  -8.1 - -0.8  6.9 – 8.5  Quinolones (ciprofloxacin, enrofloxicn, sarafloxicin)  229.5 – 417.6  3.2 - 17790  -1.0 – 1.6  5.8 - 8.6  Sulfonamides (sulfadiazine, sulfamethoxine, sulfamethoxazole) Polypeptides (virginiamycin) Ionophores (monensin, salinomycin)  172.2 – 300.3  7.5 - 1500  -0.1 – 1.7  3.1/4.5/10.6  499.6 - 1038  incomplete  -1.0 – 3.2  -  670.9 – 751.0  2.2x10-6 – 3.1x10-  5.4 – 8.5  6.4 – 6.7  Glycopeptides (vancomycin)  1450.7  >1000  Not soluble in octanol  10  Antibiotic class (example compounds)  3  12  1.6.2  Antibiotic Resistance  Specific microorganisms, such as bacteria, can produce defensive chemicals that provide competitive advantage over other species for securing food resources (Hancock, 2005) and thereby ensuring species survival. Antibiotic resistance develops as the natural ecological response by bacteria exposed to the substances intended to be lethal (bacteriocidal) or to retard their growth (bacteriostatic). Prior to the widespread use of antibiotics, most populations of commensal and pathogenic bacteria were susceptible to antibiotic action. As intentional and unintentional exposure to antibiotics increased over time, more and more resistant strains of bacteria evolved thereby reducing the number of susceptible strains. Bacteria have adapted several qualities that make them particularly efficient in developing resistance. Their ability to rapidly multiply or their ability to be metabolically adaptable increases the probability of transfer of resistance genes between bacteria during replication. Bacteria can transfer their resistance genes to other related bacteria by passing genetic material from one organism to another by means of phages, plasmids or transposons (Levy, 2000).  A vast exchange of genes is continuously taking place  between individuals and the common environmental pool and thus resistance to compounds can readily spread through a bacterial population (Davies, 1994).  The  likelihood that bacteria will adapt and replicate rather than be controlled increases when antibiotics are used incorrectly – through incorrect prescription for the wrong illness, treatment duration that is too short (patient non-compliance), at doses that are too low or through use of drugs of inadequate potency or through uncontrolled release to the environment (Levy, 1992; Levy, 1997). Three critical factors that influence development of antimicrobial resistance in organisms are i) the exposure to antibiotic compounds ii) the selective propagation of resistance genes (Levy, 1994) and iii) the “fitness” cost of some resistance genes or plasmids. Bacteria can develop de novo resistance through genetic mutation within the organism itself or by acquisition of existing resistance genes transferred from another organism. 13  Acquisition of resistance genes is a continuous process in bacterial populations but it is generally the presence of a selective pressure, such as exposure to antibiotics that results in the emergence of a resistant strain. Five key mechanisms by which mobile genetic elements that contain coding for resistance can be transferred between bacteria are: 1. Conjugation of resistance plasmids (self-replicating DNA that is maintained separate from the chromosomes within a cell) at close junctions between donor and recipient cells that allows transfer of the resistant plasmid to a cell that previously did not contain the resistant plasmid, while retaining a copy of the resistant plasmid for the donor itself (Lujan et al., 2007). 2. Transposition where genetic elements, called transposons, insert into the DNA strand independent of the usual recombination process. Transposons are the key element in the formation of plasmids and this transposable genetic material can be identical despite derivation from various origins (Martin et al., 1990). This is particularly important in the development of multiple antimicrobial resistances (Villa & Carattoli, 2005). 3. Integrons are non-mobile DNA elements that frame a region into which a gene “cassette” (encoding for resistance or another function) can be inserted (Hall & Collis, 1995). 4. Transduction in which viruses transfer genes between bacteria (Arber, 1960). 5. Transformation via direct transfer of DNA. Antibiotic resistance varies considerably between host, bacterial genera, species, strains and even individual isolates. Several bacterial species are known to be examples of reservoirs of genes that easily encode for antimicrobial resistance (Leclercq & Courvalin, 1996; Kuhn et al., 2000). Cross resistance develops in bacteria when exposure to one class of antibiotics induces selection for resistance to other compounds in the group via a given biochemical mechanism.  Cross resistance can also occur between different  antibiotic classes as a result of two distinct biochemical mechanisms: overlapping of targets in bacterial ribosomes or active efflux of drugs (Courvalin, 2001).  The  continuous release of low levels of antibiotics to the environment results in a constant pressure on microorganisms to survive. In the receiving environment, bacteria can inherit 14  and freely exchange genetic material via plasmids and these can encode for resistance to multiple antibiotic agents (Davies, 1981). Promiscuity of susceptible and resistant strains of bacteria in ecosystem compartments compounds the risk of spreading antibiotic resistance to human and animal populations. 1.6.3  Co-selection of Antibiotic and Metal Resistance  Sufficient evidence exists to support the claim that there is a relationship between exposure to metal contamination and the development of antibiotic resistance in bacteria (Calomiris et al., 1984; Alonso et al., 2001; Baker-Austin et al., 2006; Matyar et al., 2008). Bacteria have developed an arsenal of strategies to reduce the toxicity of their immediate surroundings. Among these defensive mechanisms are: specific metal efflux systems, binding of metals by extracellular polysaccharides and transformation of metals to more volatile or less toxic ionic forms (Ford, 1994). In some instances, similar efflux mechanisms can extrude both antibiotics and heavy metals in certain bacterial species (Mata et al., 2000). Long-term exposure to metals represents an important selection pressure since metals are not subject to degradation (Stepanauskas et al., 2006). The persistence, widespread distribution and recalcitrance of metal contaminants in an ecosystem can result in a selection process for antibiotic resistance in some species of bacteria in the same system although our current understanding of this complex interrelationship is not extensive. The mechanisms of co-selection of antibiotic and metal resistance in bacteria include coresistance and cross resistance (Baker-Austin et al., 2006). Co-resistance can occur when a physical linkage exists between specific genes for different resistance traits located together on the same genetic element (i.e. plasmids, transposons or integrons) (Chapman, 2003). Cross-resistance results from two different antimicrobial agents attacking the same target via a similar pathway thereby leading to resistance to both agents (Chapman, 2003). Bacterial tolerance to metals and also antibiotics has been determined in bacteria populations found in metal contaminated and natural reference streams (Wright et al., 2006). Although not the focus of this dissertation, it is understood that metal  15  contamination, as well as other toxicants, in the environment may contribute to the presence and spread of antibiotic resistance traits in bacteria. 1.6.4  Environmental Contamination  In addition to the direct effects of antibiotics on human pathogens via medical prescription, there are a number of indirect environmental pathways of exposure (Halling-Sørensen et al., 1998).  Several published reviews discuss environmental  behaviour and analyses of antibiotics in environmental compartments (Linton, 1977; Halling-Sørensen et al., 1998; Hirsch et al., 1998; Jjemba, 2002; Diaz-Cruz et al., 2003, Thiele-Bruhn, 2003). Antibiotic resistant organisms can be transferred to bacterial hosts resident in humans via the food chain or by exposure through drinking water (Endtz et al., 1991; Shoemaker et al., 1992; Bates et al., 1994; Pathak & Gopal, 1994; Bager et al., 1997; Van den Bogaard & Stobberingh 2000; Huffling, 2006) Changes in transport patterns (and changes of hydrologic conditions) associated with seasonal weather variation have been shown to influence the risk of exposure of organisms to biologically active contaminants in the receiving environment (Vieno et al., 2005). Various investigators have measured concentrations of compounds from several antibiotic classes in environmental compartments.  Tylosin is a macrolide antibiotic  commonly used in agriculture (now banned in Scandinavian countries but not in the EU or North America) which has particular implications on the development of crossresistance in human therapy. It has been reported in surface water (about 20 ng/L) (Song et al., 2007) and demonstrated to degrade in sunlight (Ingerslev et al., 2001; Hu & Coates, 2007). Sulfonamides (primarily sulfamethazine and sulfamethoxazole) used in human and veterinary therapy have been measured in water samples in concentrations between 0.1 μg/L and 1.0 μg/L (Lindsey et al., 2001; Kolpin et al., 2002; Heberer, 2002; Grant et al., 2003; Batt et al., 2005). Analyses of various water samples for 18 antibiotics from the classes of macrolides, sulfonamides, tetracyclines, and penicillins found only sulfamethoazole, trimethoprim, chloramphenicol and roxithromycin in concentrations that were greater than 0.06 μg/L (Hirsch et al., 1999). Generally the levels of antibiotics detected in environmental samples fall well below those shown to select resistance under  16  laboratory conditions and thus there is some question as to whether these concentrations are sufficient to “select” for resistance in the environment. This does not, however, preclude the possibility that biological activity of certain antibiotics may follow a hormesis model for concentrations that may be well below detection limits of current analytical technology for environmental samples. Although antibiotics are required by law to undergo rigorous evaluation and clinical trials prior to approval for application in human medicine, our knowledge of the effects of antibiotic release to the environment remains fragmentary.  Some antibiotics have  demonstrated lethal and sub-lethal toxic effects in non-target environmental species. Acute and chronic toxicity bioassays of several antibiotics using Daphnia magna (Wollenberger et al., 2000), Selenastrum capricornutum (Lanzky & Halling-Sørensen, 1997) and Artemia (Migliore et al., 1997) determined that environmental exposure to low concentrations of the drugs can have toxic effects in some species. Sulfadimethoxine was found to have sub-lethal toxic effects on post-germinative growth and development of roots, hypocotyls and leaves of three plant species (Migliore et al., 1995). Toxicity studies of Liguoro et al. (2003) used Selenastrum capricornutum to establish the EC50 for oxytetracycline (4.18 mg/L) and tylosin (0.95 mg/L).  Toxic and genotoxic  evaluations of erythromycin, oxytetracyclin, sulfamethoxazole, ofloxacin, lincomycin and clarithromycin determined that EC50 values ranged between 10.2 – 64.5 mg/L for rotifers and crustaceans (Isidori et al., 2005). These investigators also examined the potential genotoxicity and mutagenesis of these same antibiotics and found ofloxacin was the only genotoxic compound while sulfamethoxazole, ofloxacin and lincomycin were mutagenic (Isidori et al., 2005). Blue-green algae (cyanobacteria) appear to be very sensitive to a number of antibiotics including tetracyclines, sarafloxicin, benzyl penicillin, amoxicillin, spiramycin with EC50 values for all of these compounds reported to be less than 100 μg/L (Boxall et al., 2003). Published evidence thus indicates the possibility that the presence and persistence of antibiotics as environmental contaminants may put ecosystem health at risk. The key sources of antibiotics as environmental contaminants are the result of excretion of human or animal waste following medical therapy.  Effluents from wastewater 17  treatment plants and land application of biosolids are sources of antibiotic residues in the environment (Giger et al., 2003; Batt et al., 2005; Joss et al., 2006; Kinney et al., 2006; Lapen et al., 2008). Concentrations of antibiotics found in wastewater treatment plants with influent from hospitals are different than those which do not receive sewage from hospitals (Kummerer, 2001; Golet et al., 2002a; Batt et al., 2007; Duong et al., 2008). Fertilization of crop fields with animal manure or compost represents a major agricultural contributor of veterinary antibiotic residues measured in the environment (Boxall et al., 2002; Sengeløv et al., 2003; Kay et al., 2005; Boxall et al., 2006; Kemper, 2008). Antibiotics are also released to the environment through processes associated with pharmaceutical production, apiculture (Peng et al., 1996), orchard fruit production (Schnabel & Jones, 1999) and aquaculture (Cabello, 2006). In aquaculture, antibiotics are commonly administered to fish contained in food, occasionally in baths or by injection (Sørum, 2006). Leaching of unconsumed food or fish faeces releases antibiotic contaminants to the marine environment. The presence of antibiotics, oxytetracycline in particular, in marine sediments has been implicated in the development of antibiotic resistance in bacteria residing in marine sediment (Kerry et al., 1996; Huys et al., 2000; Sørum, 2006).  Several investigators have reported  oxytetracycline measured in the marine environment (Samuelsen, 1989; Hektoen et al., 1995; Miranda & Zemelman, 2002). It was also measured in low concentrations (10.2 μg/g in wet weight tissue) in blue mussels (Mytilus edulis) collected near fish farms (Coyne et al., 1997). Determinations of antibiotic residues in marine sediments near other fish farms did not reveal the presence of oxytetracycline, sulfadimethoxine and ormetoprim above method detection limits (Capone et al., 1996). Italian researchers (Lalumera et al., 2004) determined oxytetracycline (246.3 μg/kg) in aquatic sediments near fish farms although in concentrations much less than those observed by other researchers (1000-4000 μg/kg OTC) in the sediments near salmon farms (Herwig et al., 1997). Fertilization of soils with animal manure has been suggested as an important agricultural source of antibiotics and/or antibiotic resistance genes in watersheds. In a study of a Colorado River watershed, five common tetracycline drugs used in agriculture were 18  determined (0.08 – 0.30 μg/L) in water and sediment from all five sites sampled along the investigated watercourse (Yang & Carlson, 2003). Sulfadimethoxine, was detected in animal wastes used for field fertilization in an intensive farming region (Migliore et al., 1995). Arikan et al. (2008) frequently detected chlortetracycline and sulfamethoxazole in run-off water from adjacent fields fertilized only with poultry litter in Maryland USA. Few studies have addressed persistence and degradation properties of sulfonamides (Ingerslev & Halling-Sørensen, 2000; Ingerslev et al., 2001, Wang et al., 2006) despite the frequent claim that the persistence of these compounds poses an environmental risk. Chlortetracycline has been determined in soil amended by poultry manure (Warman & Thomas, 1981).  Several chemical and physical factors are known to influence the  persistence and fate of antibiotics in the soil environment (Jones et al., 2005).   Photodegradation, biodegradation, adsorption to particulates and chemical complexation or chelation are among key processes that can alter antibiotic concentrations that are measured in environmental compartments. Direct photolysis is thought to be a key degradation process of several antibiotics in the aquatic environment.  Sulfathiazole is one of the most photoreactive compounds  (environmental half-life of 4 h at pH 7) and sulfamethoxazole photodegrades much more slowly (environmental half life of 36 h at pH 7) (Boreen et al., 2004). Recent reports by Werner et al. (2006) describe the influence of water hardness on the photochemical reaction kinetics of tetracycline.  Their experiments were designed to mimic  environmentally relevant solar conditions and showed that the pseudo-first order rate constant for tetracycline photolysis varied by up to one order of magnitude over a range of natural pH and water hardness values. In the marine environment, oxytetracycline was degraded by light in a process with a half-life ranging between 128-300 days depending on environmental conditions (Lunestad et al., 1995). Mesocosm-scale evaluations carried out by Cardoza et al. (2005) demonstrated that photodegradation dictates the fate of ciprofloxacin especially when levels of particulate organic carbon are low (t½ ~1.5 h based on laboratory experiments). Biodegradation is another important process that also influences the persistence of antibiotics in the receiving environment. Wu et al. (2009) found that members of the 19  tetracycline and sulfonamide classes of antibiotics present in biosolids degraded appreciably within 30 days in non-sterile conditions (with the exception of ciprofloxacin which did not degrade during the experimental period).  In manure composting  experiments conducted by Dolliver et al (2008), there were no significant differences between managed and control treatments for degradation of chlortetracycline (99% degraded), tylosin (76% degraded), monensin (54& degraded) and sulfamethazine (did not degrade) after 35 days. Crude lignin peroxidase prepared from a white rot fungus has been demonstrated to degrade tetracycline and oxytetracycline by about 95% within five minutes under specific temperature and pH conditions (Wen et al., 2009). Biodegradation of different antibiotic compounds is influenced by several important factors that include temperature, physical chemical properties and biological processes of the receiving water or soil communities. Tetracyclines (Figueroa et al., 2004) and sulfonamides (Gao & Pedersen, 2005) show a marked tendency to absorb onto clays over a broad range of pH values.  Studies  conducted by Cardoza et al. (2005) indicated that adsorption onto particulate matter in the receiving environment appeared as the dominant mechanism governing the disappearance of ciprofloxacin in field conditions.  Partition coefficients for antibiotic compounds  (including tetracyclines) adsorbed onto dissolved organic matter vary considerably (between 100 – 50,000 L/kg) suggesting that mechanisms other than hydrophobicity play an important role in the sorption of these compounds (Tolls, 2001). Rabølle & Spliid (2000) found that oxytetracycline and tylosin were strongly adsorbed to agricultural soils with no detectable concentrations of either antibiotic detected in aqueous leachate from various soil types. These observations imply that multiple processes govern the mobility of these antibiotic compounds through the soil environment. The physical chemical properties of several antibiotic compounds, most notably tetracyclines and most ionophores make them ideal candidates for reactions with metal ions. When the molecular structure of the antibiotic offers favourable conditions for sequestering metal cations, there are potentially both positive and negative consequences of these reactions. Hydrous oxides of aluminum and of iron are important mineral constituents of soils. It has been shown that ciprofloxacin forms strong complexes with 20  hydrous oxides of both aluminum and iron over a broad range of pH values which suggests a tendency to adsorb onto clay minerals, although the presence of other chelating agents, dissolved humic substances, and competing cations such as Ca2+ and Mg2+ must be considered in determining the environmental fate of flouroquinolones (Gu & Karthikeyan, 2005). Wang et al. (2008) reported that copper (II) present in agricultural soils increased the adsorption of tetracyclines to soil minerals over a wide range of pH values thereby reducing the mobility of tetracyclines in aqueous leachate. Receiving environmental conditions and the cumulative effects of other contaminants, such as metals, will affect persistence and bioavailability of antibiotics in aquatic systems. In the agricultural watersheds in Canada, as in many countries, antibiotics from several classes are known to be routinely prescribed by veterinarians to ensure animal health. To date, information regarding the presence and fate of various classes of antibiotics in environmental compartments of Canadian watersheds is limited. 1.6.5  Antibiotic Resistance Genes as Environmental Contaminants  There are many organisms whose physiology or biochemistry causes them to be intrinsically insensitive to certain agents. However, changes in their susceptibility caused either by mutation or incorporation of genetic information encoding for resistance to specific stressors such as antibiotic drugs, can change the characteristics of the organism. In receiving environments where bacteria provide critical ecological services, changes to their innate patterns of resistance can have particularly important environmental consequences (Davison, 1999).  The spread of antibiotic-resistant bacteria in the  environment is dependent on the presence and transfer of resistance genes among microorganisms, on the resulting mutations, and on the selection pressure to retain these genes within the population. Since only about 5-10% of environmental bacteria can be cultivated, prediction of the transfer of already-resistant strains of bacteria is nearly impossible and thus, monitoring antibiotic resistance as environmental contaminants is particularly problematic (Kümmerer, 2004).  The prevalence of antibiotic resistance in  environmental bacteria strains can be influenced by the use of antibiotics in several ways, notably:  21    the spread of resistant strains or their genes from human and agricultural systems;    the evolution and selection of new resistant strains;    the amplification of pre-existing resistant strains in the environment (Kümmerer, 2004).  For some time there has been concern that genes that code for antibiotic resistance could transfer genetic material among bacteria in natural environments (Lorenz & Wackernagel, 1994; Yin & Stotzky, 1997; Davison, 1999; Andersen et al., 2001) and the potential for exchange of genetic material within dense, diverse microbial communities is widely recognized. Scott (2002) determined that identical resistance genes were present in diverse species of bacteria isolated from different hosts suggesting that antibiotic resistance genes and novel conjugative transposons can freely exchange between commensal and pathogenic bacteria. The relative ease of transfer of antibiotic resistance between commensal bacteria and pathogens is of particular concern when these organisms have the potential to enter the human food chain (Kachatourians, 1998; Mølbak, 2004). The extensive use of the tetracycline family of antibiotics in human and veterinary medicine has resulted in widespread selection of tetracycline resistant organisms (Alekshun, 2005). More than thirty-eight tetracycline resistance genes have now been sequenced and their mechanisms of resistance in bacterial hosts are known (Roberts, 2005). The tetracycline resistance genes are grouped depending on their mechanism of action and individually named by capital letters or numbers (eg. tet (A), tet(W) & tet (35)).  Twenty-three genes, commonly called efflux proteins, that code for energy-  dependent efflux membrane-associated proteins that export tetracycline out of cells are known (Roberts, 2008a). Genes that code for interruption of the binding of tetracycline to cellular ribosomes are referred to as ribosomal protection proteins (RPP). Of the eleven RPP tetracycline resistance genes that are known, tet (O), tet (M), tet (Q) and tet (W), have been found in over 90% of identified resistant species and are among the most common tetracycline resistance in current databases (Roberts, 2008a).  Because  tetracycline resistance genes are well-characterized and can be reliably detected in  22  bacteria from diverse sources, they are being increasingly used as markers for quantifying antibiotic resistance in the environment. Other antibiotic resistance genes are characterized in several bacteria species although they are not yet monitored as frequently in the receiving environment as some tetracycline resistance genes.  Genes that code for resistance to fluoroquinolones  (Kehrenberg et al., 2005; Garnier et al., 2006), macrolides (Jensen et al., 2002), sulfonamides (Antunes et al., 2005; Schmitt et al., 2006) and trimethoprim (Sköld, 2001) have been isolated from bacteria strains that can occur in the environment.  There are  now 66 genes identified that confer resistance to macrolide, lincosamide, streptogramin, ketolide and oxazolidinone antibiotics (Roberts, 2008). Of this group of genes, 16 of them have been associated with other antibiotic or heavy metal resistance genes, class 1 and 2 integrons and transposons (Roberts, 2008b). Environmental transport of mobile genetic elements such as these is likely to facilitate further spread of antibiotic resistance among bacteria, including human pathogens. Antibiotic resistance genes are now recognized as important environmental contaminants (Pruden et al., 2006). It remains unclear whether transport of antibiotic resistance genes (or the antibiotics) released into aquatic systems can exert sufficient selection pressure on bacteria to develop de novo resistance traits. 1.6.6  Antibiotics and Animal Waste Treatment  Antibiotics are administered to animals in medicated feed, via injection or by topical application. Depending on the particular drug and the animal species, these compounds are excreted as the parent compounds, as conjugates or as oxidation or hydrolysis products of the parent compounds (Tolls, 2001). Thus urine, feces or manure may contain antibiotic compounds in concentrations sufficient to contaminate the aquatic and terrestrial environment (Montforts et al., 1999). Therapeutic treatments are intended for animals that are diseased but it is often more efficient to treat entire herds by medicating the feed or water (Merck, 1998). Mass-medication practices, called metaphylaxis, are the only practical treatment strategy for some animals, such as poultry or fish where the intent is to treat the sick animals while preventing the spread of the disease to other 23  members of the group. This process usually involves therapeutic doses of the antibiotic drug administered over short periods of time.    Animal waste disposal practices can allow antibiotic resistant genes to spread through soil bacterial communities (Boxall et al., 2002; Thiele-Bruhn, 2003; Schmitt et al., 2004). Composting, or the biological decomposition of organic matter by micro-organisms, is a common process to treat animal waste (Rynk et al., 1992). Manure is frequently used as agricultural fertilizer and very little is known about the fate and concentrations of antibiotics following the composting process.  Despite rapid degradation in water,  detectable concentrations of oxytetracycline and tylosin can be determined in soil more than one month after manure application (Liguoro et al., 2003). The study showed that tylosin was no longer detected in the soil after 45 days although oxytetracycline (t ½ (in soil) = 30 days) could still be detected (820 μg/kg) in the fertilized soil 5 months after manure application. Land application of manure may elevate the abundance of antibiotic resistance genes in the receiving environment although, to date, there is little information to describe differences from baseline conditions in uncontaminated systems. Researchers have demonstrated that several classes of antibiotics including tetracyclines and sulfonamides can be found in hog waste lagoons (Meyer et al., 1999) in concentrations up to 0.70 mg/L, which are closer to concentrations relevant to resistance selection. Similar antibiotics (including tetracyclines) have been determined in wells located near hog waste lagoons, suggesting that the compounds can transport across the clay liners of the lagoons and/or that they persist through the lagooning process and are subsequently released upon land application (Meyer et al., 2000). A recent study by Kim and Carlson (2007) explored the temporal and spatial trends of some tetracycline, sulfonamide and macrolide antibiotics. Concentrations were found to be highest during the winter months, with those predominantly human-used antibiotics being detected downstream of the wastewater treatment plant while those of antibiotics used more frequently in animals being found in the region with significant agricultural activity. The presence of antibiotic residues in manure has been demonstrated to affect anaerobic digesters (Poels et al., 1984; Lallai et al., 2002) and nitrifying systems (Campos et al.,  24  2001) in animal waste treatment processes. In a study examining the fate and effects of oxytetracycline during anaerobic digestion of manure from therapeutically treated calves, the half life for OTC was determined to be 56 days in manure slurry (Arikan et al., 2006). The concentrations of three important metabolites of OTC did not vary significantly but the cumulative biogas production declined 27% in digesters containing manure from medicated calves, compared to that obtained from digesters with un-medicated manure. Aminov et al. (2001) used PCR techniques to determine the presence of several tetracycline resistance genes in groundwater sampled 250 m downstream of a swine waste lagoon. These researchers were able to construct a fingerprint for genes which encoded for cellular efflux mechanisms for tetracycline.  The fingerprints differed  between the resistance genes found in the animal feed and the resistance genes found in swine waste. Real-time PCR was used to measure three groups of tetracycline resistance genes (tet) (primarily efflux genes and ribosomal protection genes) in waste obtained from swine and cattle feedlot operations (Yu et al., 2005). Fewer copies of all three groups of tet resistance genes were found in cattle manure than in swine manure. Significantly reduced gene copy numbers of tet resistance genes were measured in swine compost while lagoon storage and upflow biofilters appeared to have little influence on gene abundance.  An investigation of the impact of cattle feedlots operations on the  abundance of six tet resistance genes in downstream surface waters also used qPCR to monitor resistance genes (Peak et al., 2006). This study measured tet (M), tet (O), tet (Q), tet (W), tet (B), and tet (L) and reported that tet resistance gene abundance was correlated to the on-site antibiotic use practises. Feedlots designated as ‘high-use’ of antibiotics were associated with the highest abundance of tet resistance genes measured downstream of the operation; while ‘low-use’ or ‘mixed-use’ feedlots had lower relative abundance of tet resistance genes in downstream watercourses. This study also suggested the seasonal nature of tet resistance gene profiles with 10-100 times greater abundance in the fall versus the summer.  25  1.6.7  Implication for Human Health  There are two key ways in which antibiotics can directly affect the health of humans. First, direct exposure to the drug, or its metabolized product, occurs during the intended consumption of medical prescription drugs to treat the symptoms of an illness. Second, though the dietary pathway whereby consumption of food animals subjected to antibiotic treatment during production opens the possibility of zoonotic (animal to human) transfer of strains of bacteria that are resistant to the antibiotics intended to treat disease. Following treatment with antibiotics, withdrawal times specific to each drug must be observed between the treatment and harvesting of food animals in order to prevent harmful drug residues in meat, milk and eggs. Any meat or food product found to contain concentrations of antimicrobial residues that exceed a threshold level at the end of the withdrawal period may be banned from human consumption. The direct dietary pathway also includes the consumption of water containing antibiotic residues, resistant strains of water-borne pathogens or of crops that have accumulated contaminants from manure or slurry used in soil fertilization. Sulfamethoxazole and erythromycin have been measured in US chlorinated drinking water above the 10 ng/L detection limit (28 and 49 ng/L, respectively) (Ye & Weinberg, 2007) however these concentrations are considerably lower than those implicated in selection for antibiotic resistance in bacteria. Untreated groundwater used for drinking has been identified as a potential source for the spread and transfer of antibiotic resistant bacterial strains (Sørum & L’Abée-Lund, 2002) and antibiotic resistance has been reported in heterotrophic bacteria of mineral water sources (Messi et al., 2005). Fate and effects of antibiotics present in the environment influence the transport of residues and/or antibiotic resistant genes through the trophic hierarchy leading ultimately to human consumption. Use of antibiotics as growth promoters or as prophylactic veterinary treatment in food animal production has resulted in the selective pressure for commensal and pathogenic bacterial species residing in animal guts to acquire antibiotic resistance. In the mid 1950s, it was found that low concentrations of penicillin supplied continuously in chicken feed promoted growth in ways that were distinct from the therapeutic effects of the same  26  drugs administered at higher doses against specific diseases (Coates et al., 1955). There are several mechanisms by which antibiotics can promote animal growth, most of which result from improved feed conversion efficiency in the intestines.  Antibiotics can  accelerate growth rates in animals by altering the microflora community structure resident in the gastrointestinal tract (Visek, 1978). The action of antibiotics can increase the populations of beneficial organisms that improve the digestion process and absorption of nutrients through intestinal walls while simultaneously depressing the populations of deleterious bacteria (including those that produce toxins).  In addition, there is  histological evidence that exposure to antibiotics reduces the thickness of gastrointestinal walls (Strauch, 1987). When antibiotics inhibit bacteria involved in the fermentation process of ruminants, there are accompanying benefits of increased energy conversion and reduced biogas formation (Sanz et al., 1996).  Sub-inhibitory concentrations of  antibiotics are suspected to play an important role as signalling molecules between cells, which in turn, could lead to physiological changes in bacteria (Davies et al., 2006; Yim et al., 2007; Fajardo & Martinez, 2008). Continuous administration of antibiotics in animal feed has consequences on human and ecosystem health that are different than those resulting from exposure to antibiotics used in the treatment of disease. The past few decades have witnessed a global trend of decreasing animal herd numbers in the production of food animals in developed countries, particularly of poultry and swine, while an increasing one in individual herd size. Accompanying this intensification of animal production is the need for strict sanitary health schemes.  Animal housing  conditions have been demonstrated to influence the airborne Gram-negative bacterial flora, especially from the families Enterobacteriaceae, Pseudomonadaceae and Neisseriaceae (Zucker et al., 2000). In general, disease will be more prevalent in large groups of intensively managed animals than in individual animals kept in more extensive conditions. Indirect exposure of humans to environmental sources of antibiotics can also occur through unintended bioaccumulation in non-target species also consumed by humans. Human exposure to antibiotic residues or antibiotic resistant pathogens themselves can also result from occupational exposure in intensively reared livestock facilities 27  (Hamscher et al., 2003; Gibbs et al., 2004).  Indirect environmental exposure of  pathogens to very low concentrations of antibiotics may promote resistance development by genetic change in pathogens that could find their way into the human food chain. Several studies have concluded that antibiotic residues may be taken up by plants (Migliore et al., 1995; Migliore et al., 1996; Migliore et al., 1998; Migliore et al., 2003, Kumar et al., 2005).  Substantial amounts of antibiotics (up to 90% for some  compounds) are excreted in urine and feces as the bioactive parent compounds (Phillips et al., 2004). Exposure experiments using carrots and lettuce carried out by Boxall et al. (2006) have shown that after routine application to soil of veterinary medicines in manure at environmentally realistic concentrations, antibiotics including oxytetracycline, tylosin, enrofloxacin and trimethoprim can be detected in the plants. Plants are also known to be important sources of plant-derived products that are either antibacterial in their own right or that act as modulators of resistance for certain bacterial strains (Gibbons, 2005) The World Health Organization has identified the development of antimicrobial resistance in the human population as one of the three most serious health risks of the 21st century (World Health Organization, 2000).  Veterinary use of antibiotics has been  demonstrated to affect zoonotic transfer of resistant pathogens between animals and humans (Threlfall et al., 1986; Devriese et al., 1996; Piddock, 1996; DANMAP, 1999; Stöhr & Wegener, 2000).  The Swann Report (1969) outlined a series of guiding  principles for reducing the risk of antimicrobial resistance development in the human population resulting from current practices in veterinary medicine in the production of food animals. This report continues to be the benchmark for health policy decisions in Britain and several other nations. As a result of the Swann Report, the British Veterinary Products Committee was set up in 1970 and recent recommendations (1997) include phasing out the use of growth promoting antibiotics as feed additives, optimizing dosing rates, identifying new antimicrobial products, and collecting data on the trends of resistance in bacteria from animals and food of animal origin. Antimicrobial resistance issues form only part of a larger problem of food-borne infections (Prescott, 1999).  The majority of antimicrobial resistant organisms or  28  antimicrobial resistance genes originating from animals reach humans through food (Prescott, 1999).  The public perception that exposure to antibiotics or antibiotic  resistance genes via food or water can compromise human health is best addressed through the results of sound science and risk assessment (Hurd, 2006). There is a need for better understanding of factors that lead to spread of antibiotic resistance in environmental bacteria given the greater attention of the media and the public towards health consequences of antibiotic resistance in pathogens. 1.6.8  Analysis of Antibiotics  Sensitive methods of analyses are required for routine determinations of antibiotic residues that include monitoring drug doses for therapeutic purposes, ensuring food safety, maintaining quality control of pharmaceutical formulation and determining presence as environmental contaminants (Stead, 2000). A number of qualitative methods including X-ray crystallography, nuclear magnetic resonance spectroscopy, and mass spectrometry are commonly used to detect antibiotic compounds, while frequently used quantitative techniques include microbiological assays, radioimmunoassays, gas chromatography, thin layer chromatography and a number of spectroscopic methods (Richardson, 2003; Knecht et al., 2004).  Liquid chromatography tandem mass  spectrometry (LC MS/MS) methods are now considered the most sensitive and selective for determination of trace concentrations of antibiotics in a variety of matrices ranging from food (Cinquina et al., 2003; Horie et al., 2003; Hall et al., 2004; Huang et al., 2006; Hamscher et al., 2006) to soil samples (Niessen, 1998; Schlüsener et al., 2003; Sczesny et al., 2003). Samples from the aquatic environment (primarily water samples) have been analyzed for analytes representing one to three classes of the antibiotic drugs (Hirsch, 1998; Lissemore et al., 2006). There have been no reports of multi-residue analyses of environmental samples for compounds of more than three antibiotic classes. Analyses of sulfamethoxazole, trimethroprim, and triclosan in water samples have been performed following solid phase extraction (SPE) with methyl tributyl ether (MTBE) and methanol (Vanderford et al., 2003).  Electrospray ionization liquid chromatography  tandem mass spectrometry (ESI LC/MS/MS) was used in positive ion mode to determine  29  the three antibiotics. A fully automated SPE method was demonstrated by Stoob et al. (2005) to efficiently determine several sulfonamide compounds in natural water in Switzerland. Loffler & Ternes (2003) determined the aminoglycocide, gentamicin, in hospital wastewater effluent (0.4 – 7.6 μg/L) using SPE with methanol:acetic acid (10:1 v/v) and ESI LC MS/MS. Tetracycline compounds were determined in water and waste lagoon samples using SPE with methanol and ESI LC MS/MS by Zhu et al. (2001). These researchers used oxalic acid, 0.5 M EDTA and 0.05 M citric acid (pH 2.5) to overcome the problematic tendency of tetracycline compounds to readily form complexes with residual silanol groups or metal cations.  Tetracycline compounds are not stable in acidic conditions below pH 2  (Oka & Patterson, 1995).  Tetracycline, oxytetracycline and chlortetracycline were  analyzed in water by acetonitrile extraction buffered with 0.05 M phosphoric acid, 0.05 M citric acid and 0.05 M EDTA (pH~2.8) SPE, and LC ESI MS/MS (Zhu et al., 2001). Reverté et al. (2003) determined three tetracyclines, enrofloxacin and ciprofloxacin in water and sewage treatment plant effluent by methanol extraction (no reported EDTA or buffer added), SPE and LC ESI MS. Some antibiotics, such as novobiocin and roxithromycin, have been determined in surface water and wastewater effluents using positive and negative voltage switching microbore LC MS/MS (Miao & Metcalfe, 2003).  Analysis of groundwater and surface water  samples from the U.S. using solid phase extraction (SPE) and LC/ESI-MS determined six sulfonamides and five tetracycline compounds in concentrations that ranged between 0.06 and 1.34 μg/L (Lindsey et al., 2001). Fluoroquinolones were determined in urban wastewater using mixed-phase cation-exchange cartridges with fluorescence detection coupled with liquid chromatography tandem mass spectrometry (Golet et al., 2001). Samples were eluted with aqueous ammonia and methanol. Among the fluoroquinolones determined in the analyses, the two most common human-use members of this antibiotic class were found in concentrations of 249-405 ng/L for ciprofloxacin and 45-120 ng/L for norfloxicin in both primary and tertiary wastewater effluents. In another study, Golet et al., (2002b) reported improved quantitative determination of fluoroquinolones by using an accelerated solvent extraction process with 50 mM aqueous phosphoric 30  acid/acetonitrile (1:1) at an extraction temperature of 100oC and pressure of 100 bar prior to SPE. They found ciprofloxacin and norfloxacin in sewage sludge and sludge treated soil in concentrations ranging from 1.40 to 2.42 mg/kg of dry matter. A multi-residue method of analysis for tetracyclines and their 4-epimers was developed for use in pork tissues using extraction with sodium succinate solution (pH 4.0) and trichloracetic acid, SPE and LC ESI MS/MS (Cherlet et al., 2003). Sczesny et al. (2003) reported their method for residue analysis of a similar set of tetracycline analytes in eggs, animal slurry and fertilized soil using multiple liquid extraction with acetonitrile, citrate buffer (pH 4.7) and ethyl acetate (Hamscher et al., 2002) and detection by microbiological assay combined with LC ESI MS/MS. In their review of LC MS/MS analyses of emerging pollutants in the aquatic environment, Lopez de Alda et al. (2003) pointed out the extreme importance of appropriate pH conditions for extraction of tetracycline compounds given their innate tendency to chelate. 1.6.9  Analysis of Resistance Genes  Real-time polymerase chain reaction (qPCR) analysis has become an established valuable tool for quantifying tetracycline resistance genes in environmental samples (Smith et al., 2004). PCR is a sensitive and accurate analytical technique that amplifies the number of copies of DNA fragments (Whelan et al., 2003). The exact nucleotide sequence of the four components of DNA (adenine, thymidine, cytosine and guanine abbreviated A, T, C and G) on either side of the region of interest must be already known. Short pieces of DNA, called primers, are synthesized to exactly match the order of appearance of the nucleotides on the DNA segments that flank the region of interest. The analyte DNA mixture is heated (~95oC) and a large excess of primer relative to the amount of DNA to be amplified, is added to ensure that the two strands of the DNA helix always bind to primers and not to other strands. The reaction mixture is then cooled (~40oC) to allow the primers to anneal to the template DNA strands and the presence of the enzyme DNA polymerase at moderate temperature (~72oC) produces copies of a selected region of the DNA helix. These copies of both strands of the selected region of interest in the DNA helix, as well as the original DNA strands, then serve as templates for further rounds of  31  amplification.  The DNA polymerase enzyme continues to synthesize new DNA  representing only the specific region over several cycles until enough DNA is produced for quantification by spectroscopic or electrophoretic means. The positive amplification of DNA by PCR enables the precise identification of the source of the amplified material. Real-time quantitative PCR offers a broad dynamic range for detecting gene sequences with improved sensitivity and precision over traditional PCR (Heid et al., 1996; Gibson et al., 1996; Lockey et al., 1998). The traditional method of PCR using fluorescence detection of ethidium bromide on agarose gels is also applied for molecular analyses of environmental samples although qPCR provides distinct advantages of method automation, higher resolution and wider dynamic range of detection. The key difference between real time qPCR and traditional PCR is that gene sequences are detected while the amplification reaction is occurring rather that at the end point of the reaction of traditional PCR.  Molecular analyses of environmental microbiota samples often apply  traditional PCR methods for qualitative determination of target genes and qPCR for quantitation after presence of specific genes has been confirmed. Comparison of gene sequences of their 16S rRNA subunit has become one of the most popular genetic techniques for identification of bacterial isolates (Clarridge, 2004; Mignard & Flandrois, 2006). The permanent structural part of a ribosome is called ribosomal ribonucleic acid (rRNA). Regions of the DNA molecule that encode for ribosomal RNA are referred to as rDNA. The 16S rRNA subunit has been accepted as a reliable bacteria genetic marker since it occurs in most species of bacteria and its function has not appeared to change over time (Janda & Abbott, 2007). Bacterial populations derived from similar niches can be described by measuring their small subunit, 16S, ribosomal DNA (Macrae, 2000). 16S rDNA can be measured as indicative of 16S rRNA gene abundance which, in turn, can be used as an indicator of bacterial biomass (Muyzer et al., 1993; Nakatzu et al., 2000). It is important to note, however, that the measurement of resistance gene abundance relative to 16S rRNA genes cannot distinguish between high levels of resistance in one species, lower levels of resistance in many different species or occurrence of resistance genes in dead bacteria.  32  Villedieu et al. (2003) used PCR techniques to characterize the abundance and diversity of tet resistance genes encoding for ribosomal protection in the oral microflora of healthy adults. They found that tet (M) genes (79%) dominated the composition of 105 isolates tested with tet (W) being the second most common gene determined in 21% of all isolates. In the same study, the ribosomal protection genes tet (O) and tet (Q) were also found with much lower frequency in human oral microflora. Real-time qPCR was used to measure seven RPP tet resistance genes in swine waste lagoons and the underlying groundwater (Chee-Sanford et al., 2001). Evidence from this study suggested that the elevated abundance of tet (M) in bacteria collected from groundwater beneath two separate swine production facilities was the result of contamination from faecal bacteria common in pigs. Automated methods for measuring the spread of resistance genes among bacterial strains, using both phenotypic tests and genotypic tests, can now be used for monitoring and surveillance (Billy, 2003). The body of published literature provides evidence that environmental exposure of bacteria to antibiotics or antibiotic resistance genes has been clearly identified as a potential risk to ecosystem health. There is a paucity of information regarding the presence, persistence and fate of both antibiotic residues and antibiotic resistance genes in the receiving environment of agricultural watersheds. The following chapter describes the measurement of tetracycline resistance genes and some antibiotics commonly used in veterinary medicine in samples collected from a stream network in the Sumas watershed.  33  1.7 References 1.  Alekshun, M.N. 2005. Tetracycline and resistance. In Frontiers in Antimicrobial Resistance – A Tribute to Stuart B. Levy. White, D.G., M.N. Alekshun & P.F. McDermott, Editors. American Society for Microbiology Press. Washington DC, USA.  2.  Allison, D.G., A.J. McBain & P. Gilbert. 2000. Microbial biofilms: Problems of control. In: Community Structure and Cooperation in Biofilms. D.G. Allison, P. Gilbert, H. Lappin-Scott & M. Wilson., editors. Reading: Society for General Microbiology. Pp. 309-327.  3.  Almeida, A.A.P., A. Farah, D.A.M. Silva, E.A. Nunan & M.B.A. Glória. 2006. Antibacterial activity of coffee extracts and selected coffee chemical compounds against Enterobacteria. J. Agric. Food Chem. 54(23): 8738-8744.  4.  Alonso, A., P. Sanchez & J.L. Martinez. 2001. Environmental selection of resistance genes. Environ. Microbiol. 3(1): 1-9.  5.  Aminov, R.I., N. Garrigues-Jeanjean & R.I. Mackie. 2001. Molecular ecology of tetracycline resistance: development and validation of primers for detection of tetracycline resistance genes encoding ribosomal protection proteins.  Appl.  Environ. Microbiol. 67(1): 22-32. 6.  Andersen, J.T., T. 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Airborne Gram-negative bacterial flora in animal houses. J. Vet Med. B. 47: 37-46.  57  CHAPTER 2 - MEASURING SOME COMMON VETERINARY ANTIBIOTICS AND TETRACYCLINE RESISTANCE GENES IN A BRITISH COLUMBIA AGRICULTURAL WATERSHED1 An essential first step in assessing potential risk of exposure to antibiotics or antibiotic resistance genes as environmental contaminants is validation of reliable techniques for their measurement. The first research question of this investigation is explored in this chapter: Can antibiotic compounds (in sufficient levels that may promote resistance in environmental bacteria) and tetracycline resistance genes be routinely monitored in the Sumas watershed? There is a commonly held belief that veterinary antibiotics may be present, as environmental contaminants, in sufficient concentrations to affect bacteria in agricultural watersheds. This chapter describes the methodology and results of chemical analyses of antibiotic residues and the use of molecular methods to measure selected antibiotic resistance genes in environmental water samples collected from a British Columbia watershed characterized by high intensity of agricultural activity. It was hypothesized that the concentrations of the target antibiotic residues and the abundance of selected antibiotic resistance genes could be reliably detected in samples from a receiving environment where antibiotics are known to be prescribed by veterinarians attending animal herds and poultry flocks within the watershed.  The  analyses of residues were limited to determination of antibiotics commonly used in veterinary medicine with the key objective of the chemical analyses being development of a precise and sensitive multi-residue method suitable for routine monitoring of antibiotics in environmental water samples.  Application of molecular methods to  analyses of environmental samples is relatively new and thus the goal of this investigation was also to explore the practicality of using the method for measurement of abundance of specific antibiotic resistance genes in environmental samples that are likely to contain potentially interfering substances. As discussed in the present chapter, there is evidence that the selected antibiotics may be present in low concentrations (although at or near method detection limits) but could not be detected with acceptable precision and  1  A version of this chapter will be submitted for publication. Keen, P.L., Knapp, C.W., Bérubé, P., Smith, D.R., Hall, K.J., Shang, D. and Graham, D.W. Measuring Some Common Veterinary Antibiotics and TetracyclineRresistance Genes in a British Columbia Agricultural Watershed.  58  accuracy for routine monitoring and that molecular methods (qPCR) could more reliably determine differences in abundance of 4 tetracycline resistance genes over time. 2.1 Introduction The potential development of antimicrobial resistance in pathogens where there is a possibility of transfer to the human population is recognized as an important risk to human and ecosystem health. Central to this debate is the growing body of scientific evidence to suggest that involuntary exposure to certain classes of antibiotic drugs used for veterinary therapy or growth promotion of animals can promote resistance among bacterial strains that can be transferred to humans (Levy, 1994; Piddock, 1996; Witte, 1998).  Presence of antibiotic compounds as environmental contaminants has been  demonstrated by a number of research groups (Halling-Sørensen et al., 1998; Daughton & Ternes, 1999; Hirsch et al., 1998; Jjemba, 2002; Kolpin et al., 2002 Diaz-Cruz et al., 2003; Golet et al., 2003).  This has led to concern that environmental exposure to  antibiotic residues may increase the potential for indigenous bacteria, including pathogens, to develop antimicrobial resistance. Antibiotics represent only one member group of a much larger family of pollutants that ranges from prescription drugs to human personal care products for which understanding of risk consequences of environmental exposure is just beginning. Maintaining optimal health of large populations of livestock often involves the therapeutic use of antibiotics to treat bacterial-related illness and to prevent large-scale outbreaks of infectious disease. Bacteria, including pathogens, present among the gut flora of animals, can develop antibiotic resistance by adapting resistance-encoding genes when exposed to the antibiotics used to ensure animal health. Relatively high doses of antibiotics are prescribed in veterinary pharmacotherapy, thereby enabling potentially active residues and antibiotic resistant bacteria from the gut of animals to be released to the environment via excretion. Other researchers have demonstrated that antibioticresistance genes can accumulate and transfer among other ecosystem micro-organisms (Dröge et al., 1999; Chee-Sanford et al., 2001; Aminov et al., 2002; Schmitt et al., 2006; Peak et al., 2006). Management of agricultural waste in which antibiotic residues or  59  antibiotic resistant genes are present is particularly important where there is possibility to compromise water quality by transmission or de novo development of antibiotic resistance among other bacteria species. The non-therapeutic use of antibiotics as growth promoters in the agricultural production of poultry, cattle and swine remains a contentious issue when causal links between effects of antibiotics as environmental contaminants and development of antimicrobial resistance among bacterial species accessible to the human population are yet to be determined. Paralleling the increased awareness of bioactive compounds (not only antibiotics) as potential environmental contaminants has been growing interest in the development of analytical methods of measuring a vast array of pharmaceuticals and personal care products in a variety of sample matrices. Several papers have described analyses of antibiotics in water (Hirsch et al., 1998; Zhu et al., 2001; Reverté et al., 2003; Blackwell et al., 2004; Yang & Carlson, 2003) wastewater (Ternes et al., 2001; Miao et al., 2004; Metcalfe et al., 2003; Renew & Huang, 2004), sediment (Löffler & Ternes, 2003) soil (Golet et al., 2003; Loke et al., 2003; Jacobsen et al., 2004) and food products (Moates, 2000; Bruno et al., 2001; Riediker & Stadler, 2001; Cherlet et al., 2002; Verzegnassi et al., 2002; Cherlet et al., 2003; Cinquina et al., 2003, Horie et al., 2003; Wang, 2003; Gikas et al., 2004). To date, most analytical methods focus on one or a few classes of antibiotic compounds at a time in the various matrices.  The use of liquid  chromatography tandem mass spectrometry for analyses of antibiotics has become very popular due to the sensitivity and selectivity of the method and its applicability to a broad range of sample matrices (Niessen, 1998). Application of molecular methods to the assessment of effects in receiving environments is relatively new. The tetracycline family of antibiotics are among the most commonly prescribed veterinary drugs worldwide (Levy, 1992) and now over thirty tetracycline resistance genes are known (Roberts, 1996, 2000, 2005).  Analyses using real-time  polymerase chain reactions (PCR) have been demonstrated to reliably measure some of these genes in various samples (Smith et al., 2004; Peak et al., 2006). Although antibiotic residues may be present in very low concentrations in ecosystem compartments, they may not be readily detected by analytical chemistry methods.  Measurement of  60  tetracycline resistance genes may be an indicator that some bacterial species resident in the water column may have experienced selective pressure to develop resistance following exposure to tetracycline antibiotics either within an animal or via environmental contamination. Diversity of physical chemical properties among various antibiotic drugs and complexity of environmental sample matrices contribute to the problematic nature of simultaneous analyses of complex mixtures of these classes of veterinary drugs. For this reason, measuring tetracycline resistance genes is a useful indication of the resultant effect of bacterial exposure to the tetracycline antibiotics which may or may not occur in detectable concentrations in environmental samples. Described within this chapter, are the results of a study conducted between July and December 2004 that simultaneously measured concentrations of some common antibiotic compounds primarily used in veterinary medicine, some common water quality parameters and monitored four selected Tcr genes in the receiving water of a British Columbia agricultural watershed. Of the 38 acquired Tcr genes known to date (Roberts, 2005), tet(O), tet(M), tet(Q), tet(W) can be commonly found in several bacteria species present in the gut flora of poultry, cattle and swine. They were specifically chosen in the current study (as in the case of previous studies (Chee-Sanford et al., 2001; Peak et al., 2006)) as potential biomarker genes indicative of environmental contaminants linked to animal waste. Agricultural livestock production within the Sumas watershed, located in the Fraser Valley of British Columbia, Canada, has risen dramatically over the past ten years and with this, the disposal of greater amounts of agricultural waste is a key concern of regulators. The Sumas River stream network provided a good study location to determine antibiotic residues and antibiotic resistance genes in the receiving water of a watershed in which veterinary use of antibiotics is known to occur. Antibiotic residue analyses was conducted by liquid chromatography electro-spray ionization tandem mass spectrometry (LC ESI MS/MS) and the range of analytes included representatives of tetracycline, macrolide, sulfonamides, diaminopyrimidine, and glycopeptide classes of antibiotic drugs.  Molecular methods to measure using real-time quantitative PCR  (qPCR) of four tetracycline resistance genes (tet(O), tet(M), tet(Q), tet(W)) and 16S  61  rDNA in water column bacteria was conducted in concert with the chemical analyses of the water sample collected from stations over the same monitoring period. 2.2 Materials and Methods 2.2.1  Field Collection  The Sumas watershed represents nearly 5700 hectares of land in the Sumas Prairie, located 60 km east of Vancouver. It is one of the leading economically important agricultural areas in Canada for production of poultry, dairy, hogs, fruit and vegetable produce and nursery farms (Statistics Canada, 2006). Five sample sites located along an approximately 23 km segment of the Sumas River were selected for collection of water samples (Figure 2.1). This stretch of the Sumas River is a second order stream which flows northward towards the Fraser River from the Canada – US border. Several dairies, poultry and swine farms are located adjacent to the river system. In addition to these sample sites, one negative control site (Station 9), situated at high elevation on a first order stream flowing from a forested headwater on the eastern boundary of the watershed, was designated as a reference site where chemical analyses revealed negligible possibility of urban or agricultural influence on the water quality.  Figure 2.1: Location of sampling sites along the Sumas River  62  Samples were collected monthly with the exception of the period near mid-November 2004. Monitoring efforts intensified after this date (the deadline beyond which spreading of compost materials or liquid manure on agricultural land is forbidden in the province of British Columbia) to coincide with changes in distribution of compost by farmers on local fields and weather conditions. Water samples for residue analyses were collected in 1 L amber glass bottles without additional preservation and kept chilled at 4°C until analyses and four replicates from each sample site were collected for microbial analyses in acid washed, autoclaved 250 mL amber bottles. Field measurements of temperature and conductivity (reported as specific conductivity) were determined at each sampling station using a Yellow Springs Instrument (YSI) Model #30M/50 meter. Dissolved oxygen was measured in situ with an YSI Model #58 portable meter and turbidity was measured using a Hach model 2100P portable turbidimeter. 2.2.2  Sample Preparation  Calibration standards, quality assurance samples and blanks were subjected to the same extraction procedure. Internal standards (500 μL oleandomycin or sulfamethazine-phenyl 13  C6) were added to each sample. Ten mL aliquots of samples were combined with  extraction solvent (30% methanol made with 0.05 M EDTA and 0.1 M citric acid), agitated by 30 seconds of vortex mixing and 20 minutes of sonication. Samples were then centrifuged at high speed for 6 minutes prior to evaporation in the dark to less than 1 mL but not dryness using an EZ 2 personal evaporator. Final volumes were made up to 2.0 mL using 30% acetonitrile and filtered before being dispensed to amber glass vials for LC ESI MS/MS analyses. 2.2.3  Antibiotic Residue Analyses  Determination of 14 antibiotic analytes was conducted by liquid chromatography electrospray ionization tandem mass spectrometry (LC ESI MS/MS). The chromatographic system used was an Agilent Technologies 1100 Series HPLC connected to an ESI interface and equipped with automatic injectors, degasser, a quaternary pump and column oven. The mass spectrometer used was a Quatro Ultima (MicroMass). The collision gas was argon used at pressures between 2.3-2.5 x 10-3 bar. Optimum parameters of the ESI  63  interface for each individual compound were determined through operation in positive ion mode by separate flow injection analyses in full scan. Two daughter ions were selected for quantification in the multiple reaction monitoring mode (MRM) for each analyte and the optimization parameters for the various antibiotic analytes are given in Table 2.1. Table 2.1: Manual tuning parameters of selected antibiotics using Ultima Quatro 11 LC ESI MS/MS Antibiotic  Abbreviation  Parent Ion  Daughter 1  Daughter 2  Cone  Collision  (mass units)  (mass units)  (mass units)  Voltage  Energy  (eV)  (eV)  Tylosin  TYL  916.4  174.1  318  60  33  Tilmicosin  TLM  869.6  696.6  435.3  39  28  Virginiamycin  VGM  526  352  335.2  17  30  Chlortetracycline  CTC  479.2  444.3  462.2  19  17  Tetracycline  TTC  445.2  426.0  410.2  17  15  Demeclocyline  DCC  965.2  448.3  431.4  17  15  Doxycycline  DYC  445.3  426.0  410.2  15  18  Oxytetracycline  OTC  461.2  426.1  443.1  18  11  Sulfamethoxazole  SMX  254.0  156.0  107.9  17  14  Sulfachloropyridazine  SCP  284.7  156.0  107.9  20  12  Sulfamethazine  SMZ  279.0  156.0  107.9  20  18  Trimethoprim  TMP  290.3  230.0  123.2  35  26  Ormetroprim  OMP  275.3  259.1  123.2  37  26  Lincomycin  LIN  407.4  126.0  359.2  24  19  Oleandomycin  OLM  688.2  544.3  -  24  21  The detection limits of the LC ESI MS/MS analyses of samples in these experiments represent the detection limits which were based on existing standard operating procedures for the analyses of antibiotic residues at the Health Canada Western Regional Laboratory and met QA/QC criteria specified by the laboratory. For water samples, the detection limit for antibiotics analysed by LC ESI MS/MS was 2 μg/L. Concentrations of OTC were determined using Quanlynx 3.5 software with all calculations including correction for recoveries. 64  2.2.4  Chromatographic Conditions  The HPLC columns used for analyte separation prior to introduction into the tandem mass spectrometer were a Zorbax RX-C8 5 μm of dimensions 2.1 x 150 mm combined with a C8 high purity guard column. Injection volume was 20 μL. A binary gradient separation system was used with solvent A being Milli-Q (Millipore, Bedford, MA, USA) water with 0.1% formic acid, 0.01 M ammonium formate and 100 M EDTA and solvent B was acetonitrile with 0.1% formic acid, 0.01 M ammonium formate and 100 M EDTA added. Analysis run time for each sample was 22 minutes at constant flow rate of 0.24 mL/min. The gradient conditions are summarized in Table 2.2. Table 2.2: Chromatographic gradient conditions for solvent system used for ESI LC MS/MS analyses of water extracts for antibiotic compounds Time (min) 0 2.0 3.0 15.0 16.0 22.0  Solvent B (%) 95 50 95 95 5 5  Since the suite of antibiotic analytes included compounds of diverse chemical properties and structures, isocratic conditions for the separation by liquid chromatography were unsuitable.  Oxytetracycline compounds, in particular, have very similar chemical  structures and elute through the column within a narrow time window near 12 min. Increasing solvent B concentration of the mobile phase permits analyte compounds (particularly the oxytetracyclines and antibiotics that may co-elute) to elute sooner than using isocratic conditions while decreasing the proportion of solvent B at 12 min helps to improve resolution of analyte peaks. 2.2.5  Preparation of Standard Solutions  Standards of the target antibiotic analytes were purchased from Sigma-Aldrich. The isotope labeled internal standard sulfamethazine-phenyl  13  C6 was purchased from 65  Cambridge Isotope Labs (Andover, MA, USA). All solvents were HPLC grade supplied by BDH chemicals. All stock standard solutions of the 14 antibiotics were prepared by dissolving 10 mg in 10 ml of 100% methanol and kept at 4°C in dark test tubes. The standard working solutions were prepared new for each analysis by diluting the 1000 μg/L stock solutions with 30% methanol. Ultra pure water prepared with a Milli-Q water purification system (Millipore, Bedford, MA, USA) was used throughout for solution preparation. Oleandomycin was used as the internal standard for all chemical analyses of antibiotics. 2.2.6  Tetracycline Resistance Gene Measurement  Samples for measurement of tetracycline resistance genes were transported on ice following collection. They were filtered within two hours from the time of sample collection with the filters immediately frozen on dry ice until extraction at the Department of Civil, Architectural and Environmental Engineering at the University of Kansas, Lawrence, Kansas. Every effort was made to minimize further biodegradation of microbiological samples from time of collection. One hundred mL volumes of samples were filtered through pre-sterilized 0.22 μm Nalgene disposable filter funnels (NNI, Rochester NY). Filters were extracted for real-time PCR analysis of DNA for four selected tetracycline resistance genes using MoBio UltraClean Soil DNA kits (Solona Beach, CA) with minor method modifications (MoBio Laboratories, 2004).  Filters,  beads and extraction buffers were combined, homogenized for 30 seconds (speed 5.5) using a FastPrep (Qbiogene, Irvine, CA) cell disruptor and then incubated at 70°C for 10 minutes to enhance lysis of Gram-positive bacteria. Following incubation, samples were re-agitated for 30 seconds (speed 4.5) and subjected to the further purification steps of the kit manufacturer’s protocol without modification. All resulting 50 μL samples were stored at -20°C prior to PCR analysis. Four tetracycline resistant genes, tet (M), tet (O), tet (Q), and tet (W), and 16S rDNA were selected for quantification by real-time PCR analyses. The Taqman probe/primer sets (all sequences denoted 5’ to 3’) and the plasmid standards used in the analyses have been described previously for tet (M) (Peak et al., 2006), tet (O), tet (Q) and tet (W)  66  (Smith et al., 2004) and are summarized in Appendix 1.  The sequences for the  probes/primers used for the 16S rDNA analyses were developed from Harms et al., (2003). Sample aliquots of 2 μL were mixed with iQ Supermix PCR reagents (BioRad, Hercules, CA) for the DNA templates. A BioRad iCycler equipped with an iCycler iQ fluorescence detector using BioRad software version 2.3 was used for performing the reactions. Standard curves were constructed from quantification of copy numbers for each gene prepared by serial dilution of the appropriate plasmid DNA that ranged from 1.0 to 1 x 107 copies per reaction. Relative abundance of quantified resistance genes are expressed as copies/mL of receiving water filtered. Molecular analyses by PCR involve reactions of enzymes. As such, the method is subject to some important inhibitors such as chlorophyll, polyaromatic hydrocarbons, metals, polyphenols and humic substances (Alvarez et al., 1995). Throughout the analytical procedure, steps were taken to minimize the effect of possible inhibitors on the determination of the Tcr genes. Samples were collected carefully to ensure all replicates were similar with minimal presence of macrophytes.  After each filtration and  centrifugation, samples were washed with a phosphate buffer solution to reduce possible inhibition by polyphenol substances. During the DNA extraction procedure, chemical treatments included washing with an aqueous tris-EDTA solution (to minimize metal interference) and rinsing with ethanol/salt solutions (to minimize the interference by humic substances). Dilutions to compensate for excessive concentrations of RNA or DNA were made by trial and error but confirmed by gel electrophoresis to ensure optimal concentrations for UV spectrophotmetric detection using SYBR green dye. After method optimization, all stream water samples collected during the monitoring period were treated the same and thus every effort was made to ensure possible interference was consistent from one sample to the next. The 16S rDNA subunits are sequences that encode for ribosomal RNA (rRNA) in genes. These 16S rDNA subunits are ubiquitous, although in differing copy numbers, and have sections that are highly conserved despite evolutionary stress that may lead to gene mutation. The 16S rRNA gene is the segment of DNA in bacteria that is now commonly used for taxonomic purposes (Palys et al., 1997; Kolbert & Persing, 1999; Drancourt et 67  al., 2000). The trend in current literature is to report results as abundance of 16S rRNA genes although 16S rDNA is often formally measured (Harmsen & Karch, 2004). “The 16S rRNA gene is also designated 16S rDNA, and the terms have been used interchangeably: current ASM policy is that "16S rRNA gene" be used” (Clarridge, 2004).  Real-time PCR analyses in these experiments measured 16S rDNA as an  indicator of microbial biomass (16S rRNA measurement is complicated by the instability of the analyte). Results of all qPCR analyses of tetracycline resistance genes in this research are reported as abundance of 16S rRNA genes. 2.2.7  Determination of Water Quality Parameters  In addition to field measurements of temperature, specific conductivity, turbidity and dissolved oxygen, some common water quality parameters were monitored on each sampling day.  Laboratory analyses of water quality parameters were conducted in  accordance with Standard Methods for the Examination of Water and Wastewater (APHA, 1995). Total solids (for determining dry weight sample equivalents where necessary) were measured using method 2540 B (dried at 105°C), suspended solids were measured by method 2540 C (dried at 105°C) and fixed and volatile solids were also measured by method 2540 E (ignited at 550°C). Samples collected for nitrate/nitrite and phosphate were preserved in the field immediately with 0.1 g/100 mL phenylmercuric acetate in 20% acetone. Cadmium reduction flow injection analysis (Lachat Instruments, QuikChem 8000) was used to measure nitrate/nitrite using standard method 4500-NO3- I, mercuric thiocyanate flow injection analysis was used to measure chloride by standard method 4500-Cl- G and phosphate was measured by flow injection analysis for ortho phosphate using method 4500-P G. 2.2.8  Quality Assurance/Quality Control  All analyses in this research were conducted in accordance with standard operating procedures where one existed; analytical chemical method development was based on modifications to existing standard operating procedures of the Health Canada Western Regional Laboratory. Quality control practices for all of the chemical determinations included recovery of known additions, analyses of externally supplied standards, analyses  68  of reagent blanks, calibration with certified standards, analyses of duplicates and maintenance of control charts. For antibiotic residue analyses, internal standards were added to all samples, 200 μg/L QC standards were simultaneously run in triplicate with every analyses, three reagent blanks were analysed with every analysis, field blanks were analysed at the beginning of the project and approximately every tenth sample was run in duplicate. For molecular methods of resistance gene determination, sterile conditions were maintained throughout where practically possible. Four replicates of all samples were collected with three replicates run for each analyses and one replicate saved for archival purposes (stored at -80oC). All standards were run in duplicate. The qPCR analyses were repeated whenever regression coefficients of standard curves failed to meet acceptable criteria. 2.2.9  Statistical Analyses  Statistical analyses of the differences between total gene numbers were performed using SPSS (v 11.01, Chicago, IL) data analysis software.  Arithmetic means and 95%  confidence intervals were used as the statistical descriptors for resistance gene copy numbers. The difference between the means of the total of four Tcr genes was assessed by paired Mann-Whitney U tests. Data were not normally distributed and thus parametric statistical tests were not used. Data were log transformed before statistical analysis. Differences were considered significant at p < 0.05. 2.3 Results 2.3.1  Determination of Antibiotic Residues in Stream Water  Residues of 14 selected antibiotics were monitored in the Sumas watershed for each sampling date at each site between July and December 2004. All 14 antibiotic analytes were measured. However, only the antibiotic residues which were detected at >2.0 μg/L are reported in Figure 2.2.  Only members of the tetracycline class of antibiotics,  sulfamethazine, tylosin and virginiamycin were detected in the Sumas watershed  69  receiving waters at concentrations greater than 2 μg/L during the 2004 monitoring period. Recoveries of analytes in stream water samples were highly variable (ranging between 61.2 – 116.8 % for oxytetracycline). A summary of the relative recoveries of 200 μg/L QC standards in the various sample matrices are provided in Appendix 2.  70  Site 1 50.0  Oxytetracycline (OTC)  35.0  Doxycycline (DXC) Demeclocycline (DMC)  30.0 25.0  40000  Chlortetracycline (CTC)  20.0  Tetracycline (TTC) Sulfamethazine (SMZ)  15.0  Tylosin (TYL)  10.0  50000  30000 20000  Virginiamycin (VGM) 10000  r  Total genes (copies/ml)  40.0  Total Tc genes (copies/mL)  Concentration (ug/L)  45.0  60000  5.0 0.0 Jul-04  Aug-04  Sep-04  Oct-04  Nov-04  Dec-04  0 Jan-05  Sampling Date  Site 3 50.0  35.0 30.0 25.0 20.0 15.0 10.0  Total genes (copies/ml)  50000  Oxytetracycline (OTC) Doxycycline (DXC) Demeclocycline (DMC)  40000  Chlortetracycline (CTC) Tetracycline (TTC) Sulfamethazine (SMZ)  30000 20000  Tylosin (TYL) Virginiamycin (VGM)  10000  r  Concentration (ug/L)  40.0  Total Tc genes (copies/mL)  45.0  60000  5.0 0.0 Jul-04  Aug-04  Sep-04  Oct-04  Nov-04  Dec-04  0 Jan-05  Sampling Date  Figure 2.2: Concentrations of antibiotic residues and total abundance of Tcr genes determined in stream water samples (arithmetic mean of 3 runs for residues; arithmetic mean of 3 separate extracted samples for Tcr genes).  71  Site 5 50.0  35.0 30.0 25.0 20.0 15.0 10.0  Total genes (copies/ml) Oxytetracycline (OTC) Doxycycline (DXC) Demeclocycline (DMC) Chlortetracycline (CTC) Tetracycline (TTC) Sulfamethazine (SMZ) Tylosin (TYL) Virginiamycin (VGM)  50000 40000 30000 20000 10000  r  Concentration (ug/L)  40.0  Total Tc genes (copies/mL)  45.0  60000  5.0 0.0 Jul-04  Aug-04  Sep-04  Oct-04  Nov-04  Dec-04  0 Jan-05  Sampling Date  Site 7  Concentration (ug/L)  40.0  Total genes (copies/ml)  35.0  Doxycycline (DXC)  30.0  Demeclocycline (DMC) Chlortetracycline (CTC)  25.0 20.0 15.0 10.0  50000  Oxytetracycline (OTC) 40000 30000  Tetracycline (TTC) Sulfamethazine (SMZ)  20000  Tylosin (TYL) Virginiamycin (VGM)  10000  r  45.0  Total Tc genes (copies/mL)  60000  50.0  5.0 0.0 Jul-04  Aug-04  Sep-04  Oct-04  Nov-04  Dec-04  0 Jan-05  Sampling Date  Figure 2.2 (cont.): Concentrations of antibiotic residues and total abundance of Tcr genes determined in stream water samples (arithmetic mean of 3 runs for residues; arithmetic mean of 3 separate extracted samples for Tcr genes).  72  Site 8  Concentration (ug/L)  40.0 35.0 30.0 25.0 20.0 15.0 10.0  Total genes (copies/ml) Oxytetracycline (OTC)  50000  Doxycycline (DXC) Demeclocycline (DMC)  40000  Chlortetracycline (CTC) Tetracycline (TTC)  30000  Sulfamethazine (SMZ) Tylosin (TYL) Virginiamycin (VGM)  20000 10000  r  45.0  Total Tc genes (copies/mL)  60000  50.0  5.0 0.0 Jul-04  Aug-04  Sep-04  Oct-04  Nov-04  Dec-04  0 Jan-05  Sampling Date  Site 9 (Control) 50.0  Oxytetracycline (OTC)  35.0  Doxycycline (DXC) Demeclocycline (DMC)  30.0 25.0  50000 40000  Chlortetracycline (CTC)  30000  Tetracycline (TTC)  20.0 20000  15.0 10.0  10000  r  Total genes (copies/ml)  40.0  Total Tc genes (copies/mL)  Concentration (ug/L)  45.0  60000  5.0 0.0 Jul-04  Aug-04  Sep-04  Oct-04  Nov-04  Dec-04  0 Jan-05  Sampling Date  Figure 2.2 (cont.): Concentrations of antibiotic residues and total abundance of Tcr genes determined in stream water samples (arithmetic mean of 3 runs for residues; arithmetic mean of 3 separate extracted samples for Tcr genes).  73  2.3.2  Determination of Tcr Genes by Real-time qPCR  All four tetracycline resistance genes, tet (O), tet (M), tet (Q) and tet (W), were detected at the various sampling sites within the Sumas watershed (Figure 2.1). The abundance of these genes were not measured above the method detection limit (>10 copies/mL) at the negative control site. The average (5 stream sites) of the abundance of four Tcr genes was statistically significantly higher in the segment of the Sumas River than in the reference control site (p < 0.05) from Oct 6, 2004 to Dec 13, 2004. In general, abundance of tet (O) was comparatively low with tet (Q) and tet (W) dominating the abundance profiles of the selected genes measured in wet winter months. Tetracycline resistance gene and 16S rRNA gene profiles for each of 5 stream sites and the negative control site (Site 9) are presented in Figure 2.3  74  Site 1  r  6.00E+07 TetM  50000  TetQ  5.00E+07  TetO 40000  TetW  4.00E+07  16S rRNA 30000  3.00E+07  20000  2.00E+07  10000  1.00E+07  0  16S rRNA gene abundance (copies/mL)  Tc gene abundance (copies/mL)  60000  0.00E+00 Jul 22, Aug 19, Oct 6, Oct 28. Nov 8, Nov 26, Dec 2, Dec 13, 2004 2004 2004 2004 2004 2004 2004 2004 Date  Site 3 6.00E+07  r  TetM TetQ  50000  TetO TetW 16S rRNA  40000  5.00E+07 4.00E+07 3.00E+07  30000 2.00E+07 20000  1.00E+07  10000  16S rRNA gene abundance (copies/mL)  Tc gene abundance (copies/mL)  60000  0.00E+00  0  -1.00E+07 Jul 22, Aug 19, 2004 2004  Oct 6, 2004  Oct 28. 2004  Nov 8, 2004  Nov 26, Dec 2, Dec 13, 2004 2004 2004  Date  Figure 2.3: Tetracycline resistance gene and 16S rRNA gene profiles at each sampling site: July – Dec 2004. Values are the arithmetic mean of 3 separately extracted samples taken from each site; error bars represent standard deviation.  75  Site 5 6.00E+07 TetM  50000  TetQ TetO TetW  40000  5.00E+07 4.00E+07  16S rRNA 30000  3.00E+07  20000  2.00E+07  10000  1.00E+07  0  16S rRNA gene abundance (copies/mL)  r  Tc gene abundance (copies/mL)  60000  0.00E+00 Jul 22, Aug 19, 2004 2004  Oct 6, 2004  Oct 28. 2004  Nov 8, 2004  Nov 26, Dec 2, Dec 13, 2004 2004 2004  Date  Site 7  r  6.00E+07 TetM TetQ TetO TetW 16S rRNA  50000 40000  5.00E+07 4.00E+07 3.00E+07  30000 2.00E+07 20000  1.00E+07  10000  16S rRNA gene abundance (copies/mL)  Tc gene abundance (copies/mL)  60000  0.00E+00  0  -1.00E+07 Jul 22, Aug 19, 2004 2004  Oct 6, 2004  Oct 28. 2004  Nov 8, 2004  Nov 26, 2004  Dec 2, Dec 13, 2004 2004  Date  Figure 2.3 (cont.): Tetracycline resistance gene and 16S rRNA gene profiles at each sampling site: July – Dec 2004. Values are the arithmetic mean of 3 separately extracted samples taken from each site; error bars represent standard deviation.  76  Site 8  r  6.00E+07 TetM  50000  5.00E+07  TetQ TetO TetW  40000  4.00E+07  16S rRNA 30000  3.00E+07  20000  2.00E+07  10000  1.00E+07  0  0.00E+00 Jul 22, 2004  Aug 19, 2004  Oct 6, 2004  Oct 28. 2004  Nov 8, 2004  Nov 26, 2004  Dec 2, 2004  16S rRNA gene abundance (copies/mL)  Tc gene abundance (copies/mL)  60000  Dec 13, 2004  Date  Site 9 (Control) 60000  6.00E+07  r  TetQ TetO  40000  TetW  5.00E+07 4.00E+07  16S rRNA  3.00E+07  30000 2.00E+07 20000  1.00E+07  10000  16S rRNA gene abundance (copies/mL)  Tc gene abundance (copies/mL)  TetM 50000  0.00E+00  0  -1.00E+07 Jul 22, 2004  Aug 19, 2004  Oct 6, 2004  Oct 28. 2004  Nov 8, 2004  Nov 26, Dec 2, 2004 2004  Dec 13, 2004  Date  Figure 2.3 (cont.): Tetracycline resistance gene and 16S rRNA gene profiles at each sampling site: July – Dec 2004. Values are the arithmetic mean of 3 separately extracted samples taken from each site; error bars represent standard deviation.  77  For most of the sites, microbial biomass concentrations, as indicated by measurements of higher abundance of 16S rRNA genes, remained within a similar relative magnitude throughout the 2004 monitoring period. However, lowest abundance of total tetracycline resistance genes was measured in summer and early fall months (July until the beginning of October). Highest abundance of total tetracycline resistance genes was measured in winter months of November and December 2004. The average from the 5 stream sites of all four measured tetracycline resistance genes were normalized to 16S rRNA genes to indicate the relative proportion of total microbial biomass that contained genetic tetracycline resistance elements. Figure 2.4 illustrates the average of the tetracycline resistance genes normalized to 16S rRNA genes. Significantly higher abundance (p = 0.018) of genetic tetracycline resistance elements was present in the Sumas River in November and December 2004 than in July and August 2004. r  0.006 0.005 0.004  rRNA)  r  Normalized genes (Tc /16S  0.007  Average Tc genes normalized to average 16S rRNA genes (5 sites)  0.003 0.002 0.001 0 Jul-04  Jul-04  Aug-04  Sep-04  Oct-04  Nov-04  Dec-04  Date  Figure 2.4: Average of 5 stream sites of tetracycline resistance genes normalized to 16S rRNA genes (abundance). Values are the average over 5 sites of arithmetic means of three replicates per site; error bars represent standard deviation.  78  2.3.3  Determination of Water Quality Parameters  All five sites are situated within a segment of the Sumas River of about 23 km which flows down stream (north towards discharge in the Fraser River) from Site No. 1 through flat agricultural land (Figure 2.1). Table 2.3 summarizes the predominant land use activities within a 200 m radius of the sampling point at each site along the Sumas River and its associated stream network. There is a similar pattern of land use along this stretch of the Sumas River which is dominated by livestock facilities (poultry, dairy and swine) and fields of food crops (including berries, corn and hay). Results of water quality analyses and the total of four tetracycline resistance genes of stream water samples from the Sumas River are provided in Table 2.4. There were no statistically significant differences among the water quality parameters of the 5 stream sites on each sampling day and thus the average (standard deviation in parentheses) of these values was statistically compared to the negative control site. Site No.1 was the most upstream site and Site No. 8 was located at the furthest downstream point of the Sumas River segment under investigation. Table 2.3: Confirmed land use activities within 200 m of sampling point at each site monitored in 2004 along the Sumas River stream network  Site number  Predominant land use within 200 m of sampling point  1  3 dairy farms; 1 poultry farm; 4 corn or hay fields  3  1 swine farm; 1 dairy farm; 2 hay fields  5  1 lawn turf farm; 4 corn or hay fields  7  1 poultry farm; 2 hay fields  8  1 beef cattle farm; 1 hay field  9 (negative control)  Forested slope; moderately dense underbrush  79  Table 2.4a: Water quality summary comparing Sumas River (average of 5 sites) to control stream          22-Jul-04 19-Aug-04 6-Oct-04 29-Oct-04 8-Nov-04 21-Nov-04 2-Dec-04 13-Dec-04  Stream Control Stream Control Stream Control Stream Control Stream Control Stream Control Stream Control Stream Control  Temperature (C) 22.1 19.8 21.2 19.6  (2.8)  13.1 12.2 9.1 8.3 10.1 9.9 6.5 5.2 6.9 5.1 5.7 6.7  Dissolved Oxygen (mg/L)  Specific Conductivity (μS/cm) 309.8 (21.8) 140 299.8 (26.0) 137  5.6 2 8.4 2  (1.0)  Turbidity (NTU)  Total Suspended Solids (mg/L) 3.1 (0.5) 3.6 10 (8.5) 1.8  Volatile Suspended Solids (mg/L) 1.9 (0.3) 1.2 3.7 (2.8) 0  7.6 9.3 7.5 9.1  (2.3)  (0.8)  5.2 10.2  (2.4)  306.4 121  (23.3)  7.6 1  (4.5)  9 2.4  (6.6)  3.6 0  (2.4)  (0.7)  8.2 12.8 5.5 10.8 7.1 13.1 7.6 12.9 9.1 11.8  (3.1)  295.6 100 176 53.4 262.8 88.8 277.8 62.6 238.8 85  (18.2)  7.7 5.8 24.1 1.03 11 1.4 21 1.3 52 15.9  (3.9)  6.8 36.2 17.8 3.6 7.1 1 16.8 10.2 28.2 27.4  (6.6)  2.2 6.4 4 0.6 1.9 0.6 4 0.8 4.8 2.8  (0.8)  (2.2)  (0.7) (0.4) (0.7) (0.5)  (3.4)  (2.7) (3.2) (3.4) (1.3)  (13.1) (30.9) (37.4) (35.4)  (6.1)  (6.2) (5.1) (14.4) (39.0)  (4.5) (3.1) (8.5) (10.8)  (1.2) (0.9) (2.4) (3.0)  Standard deviations are in parentheses  80  Table 2.4b: Water and microbiological quality summary comparing Sumas River (average of 5 sites) to control stream         22-Jul-04  19-Aug-04 6-Oct-04 29-Oct-04 8-Nov-04 21-Nov-04 2-Dec-04 13-Dec-04  Stream Control Stream Control Stream Control Stream Control Stream Control Stream Control Stream Control Stream Control  NOx-N (mg/L)  0.98 0.11  (0.98)  1.22 0.29 1.56 0.29 4.6 0.47 3.74 0.34 2.62 0.39 2.75 0.42  PO4-P (mg/L)  0.03 0.01  (0.02)  (0.84)  0.07 n/d  (0.89)  0.04 n/d 0.14 n/d 0.07 0.01 0.1 0.01 0.19 0  (1.07) (1.45) (0.98) (0.46)  Cl- (mg/L)  Total Tcr genes (copies/mL) 19.2 0.8 13.2 0.8  (9.2)  15.74 0.78  (3.05)  (0.02)  15.56 0.69  (1.95)  495.7 0.4  (508.2)  (0.01)  14.66 0.94 11.23 0.59 10.95 1.42 12.43 1.12 9.68 1.5  (2.42)  488.8 0.5 1286 3.7 3515 1.3 1852 2.2 19365 0.3  (299.4)  (0.04) (0.05) (0.03) (0.07)  (2.39) (6.18) (5.25) (3.27)  (18.4)  (341.0) (5490.0) (861.0) (19145.0)  16S rRNA (copies/mL) 3.29x106 2.01x105 1.29x107 2.80x105 7.76x106 9.29x105 2.26x106 1.92x105 1.02x107 2.24x106 1.98x106 4.84x104 3.60x106 2.04x106 4.03x106 9.17x105  (1.03x106) (9.42x106) (4.76x106) (1.61x106) (4.78x106) (7.84x105) (9.49x105) (2.43x106)  Standard deviations are in parentheses  81  2.4 Discussion Previous studies have demonstrated increasing nitrate concentrations in the Sumas River system over the past fifteen years are most likely due to higher numbers of animals resident in the watershed (Berka, 1996; Smith, 2004; Schreier et al., 2001). Nitrate, phosphate and suspended solid concentrations measured between August and December 2004 were higher in Sumas River samples than in control samples.  Nutrient  measurements (specifically nitrate and phosphate) are reasonable indicators of influence of agricultural activity on the receiving environment (Parris, 1998). Measurements of turbidity and suspended solid material concentrations are indicators of soil erosion into receiving water systems. In the province of British Columbia, nitrate, phosphate and suspended solid material are among the water quality parameters that must not exceed permit threshold concentrations before discharge from point sources into the environment.  Measurements of elevated concentrations of nitrate, phosphate and  suspended solid material in the Sumas River stream network are indicative of the presence of contaminants with likely contributions from agricultural sources (including antibiotics and tetracycline resistance genes). 2.4.1  Determination of Antibiotic Residues  In the present study, the LC ESI MS/MS method used was not capable of simultaneously detecting all 14 different antibiotic compounds of interest in complex environmental water matrices. The concentrations of the antibiotics in samples that are reported are those that were greater than the detection limit of the method. The target antibiotics determined in stream water were not detected in any of the samples at levels above 46 μg/L (OTC Oct 29, 2004). Several months were invested in the method development of existing protocols to optimize extraction procedures, clean-up strategies, instrument parameters and other important analytical techniques prior to their application in the experimental monitoring program (July-December 2004).  Unfortunately, chemical  analyses of water samples collected from the Sumas River provided insufficient evidence to routinely measure antibiotics present as contaminants in concentrations that may have potential to exert selective pressure in environmental bacteria to develop antibiotic resistance. The data collected from this investigation demonstrated that the presence and 82  concentrations of antibiotic compounds could not be routinely measured in samples from the Sumas watershed with consistent precision and accuracy required of a scientific method. Although the analytical method could be used to simultaneously determine the 14 target antibiotic analytes in clean laboratory prepared samples, its use could not be extended for monitoring antibiotic residues in these environmental water samples. Multi-residue methods of antibiotic determination in water using liquid chromatography mass spectrometry have been reported (Brown et al., 2006; Calamari et al., 2003; Kolpin et al., 2002; Lindsey et al., 2001).  Extraction techniques, clean-up procedures and  analyte recoveries are highly variable. To date, there are surprisingly few reports of antibiotic concentrations in environmental water samples in the published literature. Table 2.5 compares the antibiotic concentrations determined in the present investigation with those reported by other researchers.  83  Table 2.5: Comparison between antibiotic concentration determined in this investigation and those reported in current literature for environmental water samples. Maximum Measured Concentration (μg/L)  Detection limit (μg/L)  Recovery (%)  Oxytetracycline Chlortetracycline Oxytetracycline Chlortetracycline Doxycycline Oxytetracycline  48.8 34.4 0.34 0.69 N/D N/D  2.0  31 – 117  Keen, 2008  0.1  97.5 – 99  Kolpin et al., 2002  0.01  20 – 180  Oxytetracycline Chlortetracycline Tetracycline Sulfamethazine Sulfamethoxazole Sulfamethazine Sulfamethoxazole Sulfamethazine Sulfamethoxazole Sulfamethazine Sulfamethoxazole Tylosin Tylosin  1.34 0.15 0.11 30.7 51.3 0.22 1.9 0.22 1.02 N/D 0.3 34.6 0.28  0.1 2.0  100 + 14 89 + 13 198 + 13 73 – 95  Brown et al., 2006 Lindsey et al., 2001  0.05  97.5 – 99  0.1 0.01  130 + 17 91 + 13 20 – 180  2.0 0.05  91 – 98 97.5 – 99  Virginiamycin Virginiamycin  38.8 N/D  2.0 0.1  73 – 89 97.5 – 99  Antibiotic  Reference  Keen, 2008 Kolpin et al., 2002 Lindsey et al., 2001 Brown et al., 2006 Keen, 2008 Kolpin et al., 2002 Keen, 2008 Kolpin et al., 2002  N/D = not detected above reported detection limit Several factors contribute to the complexity in using ESI LC MS/MS to simultaneously determine antibiotics in environmental water samples. Several compounds, such as the tetracyclines and tylosin, are known to readily degrade in light (Boreen et al., 2004; Lunestad et al., 1995; Samuelsen, 1989). Temperature and receiving water pH also play important roles in stability of some antibiotics (Doi & Stoskopf, 2000). It is generally accepted that there are three pKa values for oxytetracycline (3.3, 7.3 and 9.1) that yield different ionic species present under different pH conditions (Kan & Petz, 2000; Qiang & Adams, 2004).  The pH of the receiving environment determines the predominant  84  oxytetracycline ionic species which, in turn, influences its bioavailability and its ability to be detected in ESI LC MS/MS analyses of stream water samples. Tetracycline antibiotics are noted to effectively chelate metals (Grenier et al., 2000; Ekanem & Adeniran, 2003; Rigos et al., 2006), particularly divalent metal cations (Clive, 1968; Loke et al., 2002). Sorption of antibiotics to particulate material is influenced by factors such as soil composition (eg. clay or sandy), pH, ionic strength, type of exchangeable cation and ratio of sorbate to sorbent (Boxall et al., 2002; Figueroa et al., 2004; Figueroa & MacKay, 2005; Gao & Pedersen, 2005; Gu & Karthikeyan, 2005). A recent review by O’Connor and Aga (2007) of determination of tetracycline residues in environmental samples has suggested that the “one extraction fits all” method may not be appropriate when pH, percent clay and percent organic matter vary between samples. Given the diversity of chemical properties of the 14 antibiotic analytes selected for determination in these experiments, the present research also suggests that one single multi-residue method using ESI LC MS/MS may not be suitable when environmental conditions vary considerably for each sampling time. 2.4.2  Determination of Tetracycline Resistance Genes  Antibiotic resistance genes are recognized as emerging environmental contaminants (Pruden et al., 2006) that can be directly connected to agriculture (Chee-Sanford et al., 2001). Although multi-residue chemical analyses could not reliably detect antibiotics at concentrations above the detection limit (2.0 μg/L) during the 2004 monitoring period in the Sumas River system, application of molecular methods appeared to reliably indicate the fluctuation over time of four selected tetracycline resistance genes measured in stream water samples of this investigation. These results demonstrate that the more abundant tetracycline resistance genes measured in bacteria collected from stream water were tet (W) and tet (Q). The tet (O) gene was observed to be the least abundant of the measured resistance genes in the same samples. Other researchers have reported similar trends in the frequency of detecting tet (W) and tet (Q) genes in an agricultural watershed (Koike et al., 2007). These ribosomal protection protein (RPP) genes have been reported to be the most common in animal fecal samples (Patterson et al., 2007) and agricultural input is a likely contributor to their abundance in environmental samples. Regular monitoring  85  between July and December 2004 of the abundance of the four selected tetracycline resistance genes and 16S rRNA genes indicated that they are transported in receiving water of the Sumas watershed. Competing processes influence the persistence of tetracycline resistance genes, as well as other contaminants, in receiving water samples. Decay rates of tetracycline resistance genes have been demonstrated to be lower in dark conditions than for systems with higher light exposure (Engemann et al., 2006; Engemann et al., 2008) and biological responses were thought to play an important role in this observation. In field conditions, ecological pressure could be a contributing factor to the lower relative abundance of the four tetracycline resistance genes measured in the summer than those measured in winter months. Bacteria possessing tetracycline resistance genes found in animal intestines or concentrated animal waste would likely be acclimated to high-nutrient, low oxygen environments. When these bacteria are exposed to conditions of nutrient dilute, highly oxygenated receiving environments (common during summer season), their ability to persist is likely to decrease. It is possible that these bacteria would be out-competed by other aquatic organisms or they may be the victims of protozoan predation. Summer sunlight is likely to fuel population growth of aquatic organisms (via primary production) leading to higher abundance of competitors and increased predation pressure. Warmer temperatures and longer periods of daylight in summer also enable degradation of other photo-sensitive compounds including antibiotic residues and other organic contaminants. 2.4.3  Determination of Water Quality Parameters and Relation to Land Use  Land use in the immediate surrounding area affects the transport of water-borne contaminants to receiving waters. The region under investigation is flat with several hectares under cultivation as hay or corn fields (See table 2.3). There are poultry, swine, dairy, and beef cattle farms located (often within sight of the sampling points) along the segment of the Sumas River examined in this study. Fields are fertilized by tractor distribution of liquid manure slurries or non-liquid compost prior to the growing season (at the same time as intensity and frequency of rainfall events generally declines). Liquid manure slurries are usually composed of cattle or swine waste whereas the low moisture  86  content of poultry manure makes it unsuitable for distribution as liquid fertilizer (British Columbia Ministry of Agriculture, Food & Fisheries, 1992). Fertilization using liquid manure slurries on hay, corn or berry fields may increase the rate of transfer of waterborne substances into soils, particularly during periods of limited rainfall. During the growing season, transpiration by plants also plays a role in the hydrologic cycle that, in turn, has an effect on stream flows (Weisman, 1977). The climate of south western British Columbia is conducive to growing multiple crops of hay (usually 2 cuts per year) whereas corn and berry fields are generally harvested once per year. Hay fields are often fertilized immediately post-harvest and therefore receive more frequent application of manure-based fertilizer. In the immediate vicinity of the sampling sites along Sumas River system examined in this investigation, in 2004 there were at least 10 hay fields greater than 0.2 hectare and therefore contaminant contributions (including antibiotic residues, antibiotic resistance genes, and nutrients) could originate from these fields. Patterns of precipitation in the Sumas watershed are key elements in the distribution of water-borne contaminants. In the summer, less rainfall, warmer temperatures (facilitating water evaporation) and water removal for field irrigation purposes result in smaller water flows that contribute aqueous contaminants to the receiving stream network. In late fall and winter, greater intensity and frequency of precipitation increases stream base flows and allows transport of aqueous contaminants via percolation into the soil until saturation conditions are reached. In addition, after field crop harvest and after some vegetation dies, there are greater areas of exposed soil that can encourage soil erosion and allow delivery of water-borne contaminants to receiving waters via overland flow (Udelhoven et al. 1998). More frequent and intense periods of precipitation can lead to higher stream water levels, greater opportunities for re-suspension of sediment and larger contributions from groundwater (Droppo & Stone, 1994) thus causing higher concentrations of particulate matter and particle-associated contaminants in receiving water. In 2004, the monthly precipitation increased gradually during autumn months to reach a maximum cumulative total of 281.9 mm in November however, stream discharge increased sharply between October (2.66 m3/s) and November (9.85 m3/s) and remained above 10 m3/s until after December 2004 (See Figure 3.6).  87  Hydrologic factors influence the presence and transport of contaminants including antibiotic residues, antibiotic resistance genes, nutrients and other water quality parameters. As soils become saturated during periods of high rainfall, overland flow of contaminants can make greater contributions to surface water and groundwater (Wörman, 1995). Local hydrology can provide different conditions that can increase or decrease bioavailable concentrations of contaminants, including antibiotics or abundance of tetracycline resistance genes, depending on soil quality and hydraulic conductivity. Soils composed of clay-like or small particulate size material and/or lower hydraulic conductivity offer greater opportunities for surface interactions and thus, concentrations of antibiotics detected in associated water samples would be lower. Higher surface area of soils also provides substrates for biofilm growth. Biofilms, in turn, can have an important influence on suspended solid material and particle-associated contaminants in streams by altering the density, buoyancy and flocculation properties of particles and aggregates (Ten Brinke, 1994). There is a constant flux of non-point source inputs of all contaminants that is partially governed by the dynamic interaction of physical, chemical and ecological forces. It is likely that the combination of land use activities, patterns of precipitation and hydrologic conditions affects the water quality of the stream network of the Sumas watershed. These factors, among many others, also influence the water quality measured at the negative control reference site although in a different way than in the stream network of the agricultural watershed.  In the present investigation, higher nitrate,  phosphate and suspended solid concentrations measured at the control stream site in winter months may be the result of less nutrient uptake by vegetation in the understory of the proximal forest and greater contributions from soil erosion as forest land surfaces become more exposed to rainfall.  Tetracycline resistance genes were not found at  statistically significantly higher concentrations in the negative control stream in the winter than in summer months. A direct causal relationship between agricultural sources and the concentrations of contaminants measured in stream water samples cannot be verified based on the data provided herein.  It is, however, likely that such an  interconnection exists.  88  2.5 Conclusions The results of these experiments demonstrate that:   The four selected tetracycline resistance genes are present and transported in receiving water of the watershed under investigation.    In this case study, molecular methods of measuring selected tetracycline resistance genes appeared to be a reasonable indicator of a seasonal pattern of distribution in water samples collected from an agricultural watershed stream.    Multi-residue analyses by LC ESI MS/MS did not regularly detect 14 selected antibiotics in environmental water samples as environmental contaminants in the Sumas watershed at the detection limits of the method developed.    Higher microbial biomass (as indicated by abundance of 16S rRNA genes) was observed in warmer summer months although the relative abundance of the four tetracycline resistance genes was lower during the same period.    Higher relative abundance of the tetracycline resistance genes in water-borne bacteria of the Sumas River was observed in wetter winter months (November and December 2004).    Nitrate, phosphate and suspended solid material measurements are also higher during wetter months and are thus reasonable indicators of agricultural influence on stream water quality in the Sumas River.  Some antibiotic residues could be detected in water samples from the Sumas River, measured nitrate concentrations in the same water are higher than in control samples (for which agricultural influence is unlikely) and abundance of tetracycline genes is also higher in bacteria from the same stream water than in the control samples. Reproducible scientific evidence is required before thoughtful risk management strategies can be recommended.  The following chapter describes the results of repeating the field  monitoring experiment with focus on tetracycline resistance genes as an optimal indicator of transport of contaminants that could affect resistance traits in strains of bacteria resident in an agricultural watershed. Correlations will also be made with water quality and hydrologic conditions.  89  2.6 References 1. Alvarez, A.J., M.P. Buttner & L.D. Stetzenbach. 1995. PCR for bioaerosol monitoring: Sensitivity and environmental interference.  Appl. Environ.  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Evolution of antibiotic occurrence in a river through pristine, urban and agricultural landscapes. Wat. Res. 37: 4645-4656. 87. Zhu, J., D.D. Snow, D.A. Cassada, S.J. Monson & R.F. Spalding. 2001. Analysis of oxytetracycline, tetracycline and chlortetracycline in water using solid-phase extraction and liquid chromatography-tandem mass spectrometry. J. Chromatog. A. 928: 177-186.  98  CHAPTER 3 - SEASONAL DYNAMICS OF TETRACYCLINE RESISTANCE GENES IN A BRITISH COLUMBIA AGRICULTURAL WATERSHED2 Four common tetracycline resistance genes could be measured regularly in water samples collected from the Sumas River while 8 veterinary antibiotics could only be periodically detected in the same samples. This chapter responds to the second research question: Can seasonal patterns be identified in the abundance levels of tetracycline resistance genes measured in surface waters of the Sumas watershed?  It reports the  observations of the seasonal fluctuations of tetracycline resistance genes and common water quality parameters in stream water samples collected between March 2005 and March 2006. Monthly chemical analyses of surface water samples for antibiotic residues are not reported here as measurements were spurious and inconsistent for all water samples analyzed between March 2005 and March 2006.  It was hypothesized that  regular monitoring of 4 common Tcr genes using qPCR could serve as a molecular biomarker of contributions to environmental contamination from animal waste sources. It was further postulated that abundance of these Tcr genes may be correlated to seasonal trends of water quality parameters that are relatively easy to measure. General seasonal patterns of tetracycline resistance gene abundance emerged and are described relative to observations of receiving water quality, hydrologic conditions and changes in rainfall events. The increase in abundance of the 4 Tcr genes observed in the fall-winter of 2004 was also observed in fall-winter of 2005. Results indicate that statistical correlations were observed between some important water quality parameters and also between stream discharge rates and rainfall measurements at sites along the Sumas River. Measurements of tetracycline resistance gene mass flux demonstrated that antibiotic resistance genes are gained and lost along a finite continuous section of the Sumas River although no consistent trend was established. 3.1 Introduction World wide, there is growing public and scientific scrutiny of the relationship between exposure to antibiotics and increased prevalence of antibiotic resistance in pathogens. 2  A version of this chapter will be submitted for publication. Keen, P.L., Knapp, C.W., Hancock, R.E.W., Hall, K.J. and Graham, D.W. Seasonal Dynamics of Tetracycline Resistance Genes in a British Columbia Agricultural Watershed.  99  Greater production of food animals required to serve human dietary needs has been accompanied by the increased veterinary use of antibiotics to both protect animals from pathogen-related disease and to promote animal growth. Environmental exposure to contaminants that exert selection pressure on bacterial pathogens to develop resistance traits has been recognized as a risk to human and ecosystem health for some time (Levy, 1992; Davies, 1994; Levy, 1997, Witte, 2000). Although direct causality is impossible to prove, it is likely that extending the opportunities for bacteria cells in the environment to contact antibiotics or genetic material that confers antibiotic resistance will affect biochemical processes of various microbial hosts. Our knowledge of bacterial diversity, ecology and the role of antibiotic resistance in nature are, at best, fragmentary (Davies, 1997). In order to limit the spread of antibiotic resistance in bacteria strains, including pathogens, epidemiological and laboratory evidence suggests that it is wise to control the opportunities for environmental exposure wherever possible. The assessment of risk associated with antibiotics and antibiotic resistance genes as environmental contaminants is fundamentally different than that of other bioactive pollutants that result from human activities. Many clinically relevant antibiotics are derived from soil-dwelling organisms (D’Costa et al., 2006; Josephson, 2006). Bacteria species have survived in nature for thousands of years with substantial expansion of their exposure to human or veterinary antibiotics occurring only since the last century. Susceptible bacteria die whereas those species capable of developing resistance to stressors that threaten their ability to live and reproduce can survive. Bacteria and complex organisms share common cycles of evolutionary change although time scales are very different.  Anthropogenic contributions to the reservoir of resistance  determinants found in nature are likely to interfere with natural processes of evolution in bacteria which, in turn, affect interdependent human and natural ecosystems. Linked human and natural systems are ever-changing. The response to system perturbations, or ecological resilience (Holling, 1973), is highly variable, unpredictable and often difficult to measure. Introduction of antibiotics or antibiotic resistant genes to the receiving environment are likely to affect the growth, change, evolution and resilience of diverse species of bacteria in ecosystems. Antibiotic residues have been detected in 100  receiving waters (Hirsch et al., 1998; Daughton & Ternes, 1999; Zuccato et al., 2000; Kolpin et al., 2002; Diaz-Cruz et al., 2003; Boxall et al., 2004) and have been found to persist in soils (Galvalchin & Katz, 1994; Tolls, J., 2001; Hamscher et al., 2002; De Liguoro et al., 2003) in concentrations for which the effects on development of antibiotic resistance in resident bacteria are largely unknown.  Cogent evidence suggests that  release of antibiotic resistance genes from distribution of human and animal waste contributes to further development of antibiotic resistance in soil bacteria (Alonso et al., 2001; Poté et al., 2003; Onan & LaPara, 2003; Kümmerer, 2004, Patterson et al., 2007). Because bacteria occupy critical niches in overlapping human and natural systems, disturbing equilibrium through environmental contamination by antibiotics or antibiotic genes could trigger a cascade of influences that have profound consequences on human and ecosystem health. Facile contaminant traffic throughout ecosystems enhances opportunities for exposure of organisms to stressors.  Multiple transport pathways for widespread distribution of  antibiotics or antibiotic resistance genes from various sources in ecosystem compartments have been identified (Jørgensen & Halling-Sørensen, 2000; Heberer, 2002; Rooklidge, 2004). The concentrations of antibiotics measured in aquatic systems are well below the minimum inhibitory concentration for many relevant bacteria species (Witte, 2000). However, the influence of low concentrations of antibiotics on natural microbial populations is largely unknown. Antibiotic resistant pathogens have been demonstrated to transport via stream networks (Kelch & Lee, 1978; French et al., 1987; Ash et al., 2002). Resistance plasmid transfer and conjugal transfer of antibiotic resistance traits among bacteria in aquatic ecosystems has also been documented (Witte, 2000). Microcosm and field studies have determined that horizontal gene transfer among bacteria via conjugation, transformation or transduction occurs in terrestrial and aquatic habitats (van Elsas, 1992; Basquero & Blázquez, 1997; Dröge et al., 1999; Kümmerer, 2004). Small mobile DNA elements, including gene cassettes or integrons, that transfer antibiotic resistance among bacteria can move freely throughout the natural environment (Séveno et al., 2002; Rowe-Magnus & Mazel, 2002; Michael et al., 2004).  101  Knowledge of the baseline levels of antibiotics or antibiotic resistance genes in environmental bacteria is limited. Until recent years, the chasm between the routine application of molecular tools of analyses and regular field monitoring of environmental quality was relatively wide.  Chee-Sanford et al., (2001) used PCR techniques to  demonstrate that tetracycline resistance genes occur in the environment as a direct result of agricultural activities within a watershed. Environmental distribution of tetracycline resistance among isolates from natural (nonclinical), non-selected populations of E. Coli from 12 animal sources and humans has been documented (Bryan et al., 2004). Results of that study suggested that human activity (specifically, the use of tetracycline antibiotics) provide environments that select for resistant strains and encourage the transfer of genetic information from unrelated bacterial species. PCR measurement of bacterial tetracycline resistance genes have been used as distinct genotypic markers to indicate the dissemination and mobility of antibiotic resistance genes as groundwater contaminants that originated from swine lagoons (Mackie et al., 2006; Koike et al., 2007). Recent studies (Kobashi et al., 2007) used PCR analyses of 350 isolates from bacteria collected from livestock feces, farmyard manure and fertilized soil (representing 28 different genera) to measure 15 Tcr genes. Of these isolates, tet (M), tet (O), tet (Q), and tet (W) were among the more common Tcr genes found in commensal bacteria from swine and poultry manure.  Molecular methods have become accepted as reliable  monitoring tools for examining antibiotic resistance in the environment. There is a growing body of evidence that strengthens the link between antibiotic resistance genes as environmental contaminants and agricultural sources. This chapter describes the results of the first effort to assess the seasonal dynamics of antibiotic resistance gene abundance in receiving waters of a British Columbia agricultural watershed. Four common tetracycline resistance genes and some important water quality parameters were monitored during March 2005 – March 2006. Nutrients (nitrate/nitrite and phosphate) and chloride concentration were among the water quality measurements conducted for each sampling date. Earlier investigation (described in Chapter 2) revealed that the abundances of 4 selected tetracycline resistance genes in water samples collected from the Sumas River were low in dry summer months and  102  increased considerably as seasons shifted towards autumn-winter conditions despite relatively consistent abundance of microbial biomass. Because the chemical analyses did not reliably detect antibiotic residues during the 2004 monitoring period, this was discontinued and the focus was directed towards monitoring of tetracycline resistance genes. Stream flow rates were measured for part of the sampling period in order to assess the mass flux of tetracycline resistance genes along one segment of the Sumas River. The seasonal dynamics of the system were explored by correlating the abundance of the 4 tetracycline resistance genes with precipitation and stream discharge data collected during the monitoring time. 3.2 Materials and Methods 3.2.1  Sample Collection  The 2005 program of sampling expanded on the results of monitoring 4 tetracycline resistance genes and antibiotic residues between July – December 2004. Water samples were collected from a total of eight sampling sites (Figure 3.1) located along the same stretch of the Sumas River that was studied in 2004. Three sampling sites (Sites 2, 4 & 6) were added in the region of high intensity of dairy and poultry production downstream of Site No. 1. This particular segment of the Sumas River is a second order stream which flows northward towards the Fraser River from the Canada – US border (located 60 m immediately south of Site 1). Several dairies, poultry and swine farms are located adjacent to the river system. The same control site from the previous year’s investigation (Site 9), situated at high elevation on a first order stream flowing from a forested headwater on the eastern boundary of the watershed, was used as a reference site.  103  Figure 3.1: Location of sampling sites along the Sumas River. Samples for microbial analyses (4 replicates) were collected in acid washed, autoclaved 250 mL amber glass bottles.  Field measurements of temperature and conductivity  (reported as specific conductivity) were determined at each sampling station using a Yellow Springs Instrument (YSI) Model #30M/50 meter.  Dissolved oxygen was  measured in situ with an YSI Model #58 portable meter and turbidity was measured using a Hach model 2100P portable turbidimeter. 3.2.2  Monitoring Tetracycline Resistance Genes  Samples for microbial analyses (transported on ice) were filtered within two hours of sample collection with the filters immediately frozen on dry ice and kept frozen at -20oC until extraction at the Department of Civil, Architectural and Environmental Engineering at the University of Kansas, Lawrence, Kansas. One hundred mL volumes of samples were filtered through pre-sterilized 0.22 μm Nalgene disposable filter funnels (NNI, Rochester NY). Filters from all four replicates were extracted for real-time quantitative PCR analysis of DNA for the four selected tetracycline resistance genes using MoBio UltraClean Soil DNA kits (Solona Beach, CA) with minor method modifications (MoBio Laboratories, 2004). Filters, beads and extraction buffers were combined, homogenized 104  for 30 seconds (speed 5.5) using a FastPrep (Qbiogene, Irvine, CA) cell disruptor and then incubated at 70oC for 10 minutes to enhance lysis of Gram-positive bacteria. Following incubation, samples were re-agitated for 30 seconds (speed 4.5) and subjected to the further purification steps of the kit manufacturer’s protocol without modification. All resulting 50 μL samples were stored at -20oC prior to analysis. Three replicates were analysed by qPCR and one sample replicate was saved at -80oC for archival purposes. Four tetracycline resistant genes, tet (M), tet (O), tet (Q), and tet (W), and 16S rDNA were selected for quantification by real-time quantitative PCR analyses. The Taqman probe/primer sets (all sequences denoted 5'-3') and the plasmid standards used in the analyses have been described previously for tet (M) (Peak et al., 2006), tet (O), tet (Q) and tet (W) (Smith et al., 2004) and are summarized in Appendix 1. The sequences for the probes/primers used for the 16S rDNA analyses were developed from Harms et al., (2003). Sample aliquots of 2 μL were mixed with iQ Supermix PCR reagents (BioRad, Hercules, CA) for the DNA templates. A BioRad iCycler equipped with an iCycler iQ fluorescence detector using BioRad software version 2.3 was used for performing the reactions. Standard curves were constructed from quantification of copy numbers for each gene prepared by serial dilution of the appropriate plasmid DNA that ranged from 1.0 to 1 x 107 copies per reaction. Concentration of quantified resistance genes are expressed as copies/mL of receiving water filtered. Molecular analyses by PCR involve reactions of enzymes. As such, the method is subject to some important inhibitors such as chlorophyll, polyaromatic hydrocarbons, metals, polyphenols and humic substances (Alvarez et al., 1995). Throughout the analytical procedure, steps were taken to minimize the effect of possible inhibitors on the determination of the Tcr genes. Samples were collected carefully to ensure all replicates were similar with minimal presence of macrophytes.  After each filtration and  centrifugation, samples were washed with a phosphate buffer solution to reduce possible inhibition by polyphenol substances. During the DNA extraction procedure, chemical treatments included washing with an aqueous tris-EDTA solution (to minimize metal interference) and rinsing with ethanol/salt solutions (to minimize the interference by humic substances). Dilutions to compensate for excessive concentrations of RNA or 105  DNA were made by trial and error but confirmed by gel electrophoresis to ensure optimal concentrations for UV spectrophotmetric detection using SYBR green dye. After method optimization, all stream water samples collected during the monitoring period were treated the same and thus every effort was made to ensure possible interference was consistent from one sample to the next. The 16S rDNA subunits are sequences that encode for ribosomal RNA (rRNA) in genes. These 16S rDNA subunits are ubiquitous, although in differing copy numbers, and have sections that are highly conserved despite evolutionary stress that may lead to gene mutation. The 16S rRNA gene is the segment of DNA in bacteria that is now commonly used for taxonomic purposes (Palys et al., 1997; Kolbert & Persing, 1999; Drancourt et al., 2000). The trend in current literature is to report results as 16S rRNA although 16S rDNA was formally measured (Harmsen & Karch, 2004). Real-time PCR analyses in these experiments measured 16S rDNA as an indicator of microbial biomass (16S rRNA measurement is complicated by the instability of the analyte). Results of all rt-qPCR analyses of tetracycline resistance genes in this research are reported as abundance of 16S rRNA genes. 3.2.3  Water Quality Analyses  Laboratory analyses of water quality parameters were conducted in accordance with Standard Methods for the Examination of Water and Wastewater (APHA, 1995). Total solids (for determining dry weight sample equivalents where necessary) were measured using method 2540 B (dried at 105°C), suspended solids were measured by method 2540 C (dried at 105°C) and fixed and volatile solids were also measured by method 2540 E (ignited at 550°C). Samples collected for nitrate/nitrite and phosphate were preserved in the field immediately with 0.1 g/100 mL phenylmercuric acetate in 20% acetone. Cadmium reduction flow injection analysis (Lachat Instruments, QuikChem 8000) was used to measure nitrate/nitrite using standard method 4500-NO3- I, mercuric thiocyanate flow injection analysis was used to measure chloride by standard method 4500-Cl- G and phosphate was measured by flow injection analysis for ortho-phosphate using method 4500-P G.  106  3.2.4  Rainfall and Climate Data  Rainfall and other climate data were retrieved from Environment Canada archived data of precipitation measured at Station 1100030 (Abbotsford Airport) for the period of this investigation. Mean monthly discharge data was retrieved from the Environment Canada archived data measured at Environment Canada Station ID: 0MH029: Sumas River near Huntington (49°0'9" N; 122°13'50" W) (Environment Canada, 2006). Site No.1 and the Environment Canada Station ID: 0MH029 are located at the same point on the Sumas River. 3.2.5  Assessment of Land Use  Location, number of farms and land use within a radius of 200 m from each sampling site (Table 3.1) was confirmed by field visit. There are 5 poultry farms, 5 dairy farms, 2 hog farms, 2 beef cattle farms, 2 raspberry farms, 1 lawn turf farm, and 1 golf course located within this defined region. Table 3.1: Confirmed land use activities within 200 m of sampling point at each site monitored in 2005 – 2006 along the Sumas River stream network Site number  Predominant land use within 200 m of sampling point  1  3 dairy farms; 1 poultry farm; 4 corn or hay fields  2  1 dairy farm; 3 hay or corn fields  3  1 swine farm; 1 dairy farm; 2 hay fields  4  1 poultry farm; 1 swine farm; 1 berry farm; 2 hay fields  5  1 lawn turf farm; 1 berry farm; 4 corn or hay fields  6  1 poultry farm; 2 hay or corn fields; 1 golf course  7  1 poultry farm; 2 hay fields  8  1 beef cattle farm; 1 hay field  9 (negative control)  Forested slope; moderately dense underbrush  107  3.2.6  Estimates of Mass Transport of Tetracycline Resistance Genes along the Sumas River  In order to examine the potential mass flux of the four tetracycline resistance genes along a specified ~12.6 km segment of the watercourse network within the Sumas watershed, stream velocities were measured monthly at four stations between July and December 2005.  A Swoffer Model 3000 flow meter was used to measure the velocities at 3-5  points across the cross-section of the stream (either by wading across the width of the stream or using an inflatable canoe when stream depth increased).  Flow (Q) was  calculated (using Equation 1) by assuming uniform velocity (v) at 0.3 times depth and that depth was uniform across estimated cross-sectional area (A) (Chanson, 2003). Q (m3/sec) = vA 3.2.7  (Equation 1)  Data Analyses  Statistical analyses of the differences between total gene numbers were performed using SPSS (v 13.01, Chicago, IL) data analysis software.  Arithmetic means and 95%  confidence intervals were used as the statistical descriptors for resistance gene copy numbers. The difference between the means for the abundance of the total of four tetracycline resistance genes was assessed by paired Mann-Whitney U tests. Data were log transformed before statistical analysis. Differences were considered significant at p < 0.05. Spearman’s Rank correlations (2-tailed) were calculated using SPSS statistical software Version 13.01 to compare water quality parameters with tetracycline resistance gene abundances over the period of July 2004 (2004 data presented in Chapter 3) to March 2006.  The null hypothesis was that no relationship exists between water quality  parameters and measured tetracycline resistance genes. This non-parametric test was used for statistical analyses since data were not normally distributed. Perfect correlation at p < 0.01 is indicated by Spearman’s rho correlation coefficient (2-tailed) of 1.000. Calculated values of Spearman’s rho correlation coefficient which were less than -0.500 indicated negative correlation between parameters at p < 0.05 while calculated values of Spearman’s rho correlation coefficient below -0.630 were significant at p < 0.01 using 108  Microsoft Excel statistical software. Spearman’s rho correlation coefficient which were greater than 0.500 indicated positive correlation between parameters at p < 0.05 and calculated values of Spearman’s rho correlation coefficient above 0.610 (based on 18 observations) were significant at p < 0.01. Box with “whisker” plots were constructed using SPSS Version 13.01 software. The area of the box represents the inter-quartile range (IQR) of 50% of the data values for each parameter. The location of the median line in each box indicates the skewness of the data. The “whisker” line of the plot represents those data points with values between the IQR and 1.5 times the IQR. Outlier points are distinguished as values that are between 1.5 and 3 times the IQR (represented by a circle on each graph) and extreme outliers are marked by an asterisk. 3.3 Results 3.3.1  Measurement of the Tetracycline Resistance Genes  All four of the selected tetracycline resistance genes, tet (O), tet (M), tet (Q) and tet (W) were detected at the various sampling locations along the segment of the Sumas River under investigation. Some of these genes were measured with very low abundance at the reference control site.  The general trend of seasonal variation of water column  abundance of tetracycline resistance genes that was observed in 2004 recurred during the 2005 – 2006 monitoring period. Figure 3.2 illustrates the change in relative abundance profile of the selected Tcr gene profiles and the abundance of 16S rRNA genes monitored at each site (site 1 was the furthest upstream; site 8 was the furthest downstream) between March 2005 and March 2006.  109  Site1 6.00E+07  40000 TetM  5.00E+07  30000  TetW  4.00E+07  16S rRNA  3.00E+07 20000 2.00E+07 1.00E+07  10000  16S rRNA gene abundance (copies/mL)  TetO  r  Tc gene abundance (copies/mL)  TetQ  0.00E+00 0  -1.00E+07 Mar- Apr- May- Jun- Jul- Aug- Sep- Oct- Nov- Dec- Jan- Feb- Mar05 05 05 05 05 05 05 05 05 05 06 06 06 Date  Site 2 6.00E+07  40000  5.00E+07  TetW  4.00E+07  16S rRNA 3.00E+07 20000 2.00E+07 1.00E+07  10000  16S rRNA gene abundance (copies/mL)  30000  TetQ TetO  r  Tc gene abundance (copies/mL)  TetM  0.00E+00 0  -1.00E+07 Mar- Apr- May- Jun- Jul- Aug- Sep- Oct- Nov- Dec- Jan- Feb- Mar05 05 05 05 05 05 05 05 05 05 06 06 06 Date  Figure 3.2: Tetracycline resistance gene abundance profiles: March 2005 – March 2006. All measurements are the average abundances of 3 replicates taken at each site; error bars represent standard deviation. 110  Site 3 6.00E+07  40000  4.00E+07  16S rRNA 3.00E+07 20000 2.00E+07 1.00E+07  10000  16S rRNA gene abundance (copies/mL)  30000  5.00E+07  TetQ TetO TetW  r  Tc gene abundance (copies/mL)  TetM  0.00E+00 0  -1.00E+07 Mar- Apr- May- Jun- Jul- Aug- Sep- Oct- Nov- Dec- Jan- Feb- Mar05 05 05 05 05 05 05 05 05 05 06 06 06 Date  Site 4 6.00E+07 5.00E+07 4.00E+07  16S rRNA 3.00E+07 20000 2.00E+07 1.00E+07  10000  16S rRNA gene abundance (copies/mL)  30000  TetM TetQ TetO TetW  r  Tc gene abundance (copies/mL)  40000  0.00E+00 0  -1.00E+07 Mar- Apr- May- Jun- Jul- Aug- Sep- Oct- Nov- Dec- Jan- Feb- Mar05 05 05 05 05 05 05 05 05 05 06 06 06 Date  Figure 3.2 (cont.): Tetracycline resistance gene abundance profiles: March 2005 – March 2006. All measurements are the average abundances of 3 replicates taken at each site; error bars represent standard deviation. 111  Site 5 6.00E+07  40000  5.00E+07  TetO TetW  4.00E+07  16S rRNA 3.00E+07 20000 2.00E+07 1.00E+07  10000  16S rRNA gene abundance (copies/mL)  30000  TetQ  r  Tc gene abundance (copies/mL)  TetM  0.00E+00 0  -1.00E+07 Mar- Apr- May- Jun- Jul- Aug- Sep- Oct- Nov- Dec- Jan- Feb- Mar05 05 05 05 05 05 05 05 05 05 06 06 06 Date  Site 6 40000  6.00E+07 5.00E+07  TetW 16S rRNA  4.00E+07 3.00E+07  20000 2.00E+07 1.00E+07  10000  16S rRNA gene abundance (copies/mL)  30000  TetQ TetO  r  Tc gene abundance (copies/mL)  TetM  0.00E+00 0  -1.00E+07 Mar- Apr- May- Jun- Jul- Aug- Sep- Oct- Nov- Dec- Jan- Feb- Mar05 05 05 05 05 05 05 05 05 05 06 06 06 Date  Figure 3.2 (cont.): Tetracycline resistance gene abundance profiles: March 2005 – March 2006. All measurements are the average abundances of 3 replicates taken at each site; error bars represent standard deviation. 112  Site 7 40000  6.00E+07  4.00E+07 3.00E+07  20000  2.00E+07 1.00E+07  10000  0.00E+00  16S rRNA gene abundance (copies/mL)  30000  5.00E+07  TetQ TetO TetW 16S rRNA  r  Tc gene abundance (copies/mL)  TetM  -1.00E+07 0  -2.00E+07 Mar- Apr- May- Jun- Jul- Aug- Sep- Oct- Nov- Dec- Jan- Feb- Mar05 05 05 05 05 05 05 05 05 05 06 06 06 Date  Site 8 40000  6.00E+07 TetM 5.00E+07  TetO 4.00E+07  TetW 16S rRNA  3.00E+07 20000 2.00E+07 1.00E+07  10000  16S rRNA gene abundance (copies/mL)  30000  r  Tc gene abundance (copies/mL)  TetQ  0.00E+00 0  -1.00E+07 Mar- Apr- May- Jun- Jul- Aug- Sep- Oct- Nov- Dec- Jan- Feb- Mar05 05 05 05 05 05 05 05 05 05 06 06 06 Date  Figure 3.2 (cont.): Tetracycline resistance gene abundance profiles: March 2005 – March 2006. All measurements are the average abundances of 3 replicates taken at each site; error bars represent standard deviation. 113  Site 9 40000  6.00E+07 5.00E+07  TetO TetW  4.00E+07  16S rRNA 3.00E+07 20000 2.00E+07 1.00E+07  10000  16S rRNA gene abundance (copies/mL)  30000  TetQ  r  Tc gene abundance (copies/mL)  TetM  0.00E+00 0  -1.00E+07 Mar- Apr- May- Jun- Jul- Aug- Sep- Oct- Nov- Dec- Jan- Feb- Mar05 05 05 05 05 05 05 05 05 05 06 06 06 Date  Figure 3.2 (cont.): Tetracycline resistance gene abundance profiles: March 2005 – March 2006. All measurements are the average abundances of 3 replicates taken at each site; error bars represent standard deviation. Microbial biomass, as indicated by measurement of abundance of 16S rRNA genes, was of similar magnitude among all stream sites during the monitoring period and higher (p < 0.05) than that measured at the control site.  Lowest abundance of total tetracycline  resistance genes, however, was measured in summer months (July until the beginning of October) of both 2004 and 2005. Highest abundance of total tetracycline resistance genes was measured in winter months of November and December (both 2004 and 2005) and in January to March 2006. Tet (M) appeared to be more prevalent in 2004 while tet (W) was more prevalent in the winter of 2005. In both years, the abundance of tet (O) was low. The tetracycline resistance genes, tet (W) and tet (Q) were the most abundant in wet winter months of December 2004 and November 2005 to February 2006. The total of the four measured tetracycline resistance genes were normalized to 16S rRNA genes to indicate the relative proportion of total microbial biomass that demonstrated genetic tetracycline resistance elements (Figure 3.3). The averages of Tcr  114  and of 16S rRNA gene abundances from all eight sites along the Sumas River were calculated prior to normalization. The relative abundance of total tetracycline resistance genes (predominantly tet (W), tet (Q) and tet (M)) normalized to 16S rRNA genes indicate that higher proportions of genetic tetracycline resistance elements occurred in October 2005 and between January and March 2006.  r  Average Tc genes normalized to average 16S rRNA genes (8 Sites) 0.0035  0.0025 0.002  Normalized genes (Tc  r  /16S rRNA)  0.003  0.0015 0.001 0.0005 0 Mar- Apr- May- Jun05 05 05 05  Jul05  Aug- Sep- Oct- Nov- Nov- Dec- Jan- Feb05 05 05 05 05 05 06 06 Date  Figure 3.3: Average of total tetracycline resistance genes at 8 stream sites normalized to average 16S rRNA genes for the period of March 2005 – March 2006. Values represent the arithmetic mean of 3 total Tcr gene measurements normalized to the arithmetic mean of 3 16S rRNA gene measurements; error bars represent standard deviation. 3.3.2  Monitoring Water Quality  Box plots of the water quality comparison between wet and dry conditions suggest a seasonal trend that turbidity (Figure 3.4a), nitrate (Figure 3.4b) and orthophosphate (Figure 3.4c) are higher during the wet season than in the dry season. Chloride and specific conductivity are lower during the same period. Turbidity levels were similar among most stream sites in the dry season with more variability and higher 115  measurements reported at sites likely to adopt different stream flow conditions. Along the stream segment between Site 1 and Site 4, water quality conditions were similar (no statistically significant difference) while further downstream higher variability of water quality parameters may have reflected difference in stream flow conditions. This higher variability observed between Site 5 and Site 8 is likely the result of return water from field irrigation or differences in tributary contributions. Table 3.2 summarizes the water quality data collected between March 2005 and March 2006 and compares the average of measurements from eight sites along the Sumas River with measurements from the control reference site. Whisker box plots comparing wet and dry season measurements of all of the water quality parameters monitored are provided in Appendix 4 and a summary of all water quality data is provided in Appendix 9.  116  Table 3.2: Summary of water quality collected March 2005 – March 2006. Values presented are the averages (arithmetic means) for all 8 Sumas River sites with standard deviations in brackets. Temperature (C) 23-Mar-05 28-Apr-05 31-May-05 21-Jul-05 15-Aug-05 26-Sep-05 1-Nov-05 6-Dec-05 11-Jan-06 10-Feb-06 16-Mar-06  Stream Control Stream Control Stream Control Stream Control Stream Control Stream Control Stream Control Stream Control Stream Control Stream Control Stream Control  7.9 4.4 13.7 10 15.6 12.5 20.2 17.9 20.6 17.5 13 11.3 9.6 7.9 5.4 3.3 6.7 6.6 5.6 4 6.5 4.8  (0.17) (0.70) (0.19) (0.96) (1.19) (0.57) (0.23) (0.54) (0.10) (0.10) (0.14)  Dissolved Oxygen (mg/L) 10.6 (0.14) 13.5 9.6 (0.93) 11.1 8.7 (3.52) 14.1 11.8 (0.90) 12.8 9.6 (3.15) 10.6 8.4 (3.10) 11.6 6.8 (0.45) 12.4 9.3 (1.24) 13.9 9 (0.20) 12.6 10 (0.42) 11.2 9.5 (1.39) 13  Specific Conductivity (μS/cm) 246.6 (24.43) 94.3 286.8 (29.45) 237 283.6 (17.28) 100 314.4 (25.78) 133 332.3 (0.88) 142 296.8 (10.00) 134 199.4 (45.48) 66 249.5 (22.81) 80 117.1 (16.12) 57 288.9 (27.57) 13.8 303 (25.90) 117.5  Turbidity (NTU) 14 1.3 12.8 1.8 13 3.9 5.9 1.5 2.9 0.83 5.1 0.78 15.2 25.4 9.6 3.8 65 17.3 18.2 2 13.3 1.8  (1.79) (2.95) (3.49) (1.58) (0.77) (2.21) (4.57) (4.91) (17.15) (12.84) (3.23)  Total Suspended Solids (mg/L) 15.5 (3.37) 1.4 12.8 (3.05) 4.8 9.2 (1.67) 2.4 3.1 (0.77) 2.2 5.5 (1.99) 1.4 6.1 (3.31) 1.4 20.6 (7.54) 37.4 12.5 (18.75) 5 51.4 (27.14) 24 14.8 (14.62) 10.6 11.9 (2.92) 1.6  Volatile Suspended Solids (mg/L) 3.6 (0.53) 0.8 3.7 (0.93) 2.6 2.6 (0.52) 1.8 1.3 (0.38) 0.6 2.6 (0.87) 0.8 2.1 (1.41) 1.2 4 (0.61) 9.8 2.9 (3.67) 1.4 7.7 (4.04) 3.6 2.4 (2.25) 0.8 5 (0.20) 1.2  117  Table 3.2 (cont.): Summary of water quality and microbiological data collected March 2005 – March 2006. Values presented are the averages (arithmetic means) for all Sumas River sites (n = 8) with standard deviations in brackets. NOx-N (mg/L) 23-Mar-05 28-Apr-05 31-May-05 21-Jul-05 15-Aug-05 26-Sep-05 1-Nov-05 6-Dec-05 11-Jan-06 10-Feb-06 16-Mar-06  Stream Control Stream Control Stream Control Stream Control Stream Control Stream Control Stream Control Stream Control Stream Control Stream Control Stream Control  2.76 0.315 1.76 0.421 1.93 0.319 1.86 0.321 2.51 0.575 1.97 0.279 2.6 0.635 2.15 0.39 2.02 0.578 2.3 0.441 2.56 0.378  (0.62) (0.57) (0.94) (1.01) (1.26) (1.27) (0.21) (0.57) (0.24) (0.45) (0.89)  PO4-P (mg/L) 0.095 0.0052 0.098 0.0018 0.075 n/d 0.026 0.0058 0.059 0.0078 0.04 n/d 0.167 n/d 0.037 0.0008 0.306 0.0113 0.102 0.0039 0.088 0.0038  (0.01) (0.02) (0.02) (0.00) (0.04) (0.01) (0.05) (0.01) (0.47) (0.01) (0.02)  Cl- (mg/L) 10.5 0.435 13.14 0.458 13.84 0.604 14.33 0.43 15.4 0.779 14.13 0.788 9.85 1.19 13.71 0.339 3.85 0.608 7.71 0.647 9.03 0.664  (1.21) (1.94) (2.03) (1.45) (1.17) (1.76) (2.41) (1.60) (0.97) (0.87) (0.79)  Total Tcr genes (copies/mL) 912.6 1 71.42 4.7 40.26 0.5 10.42 < 0.5 13.86 < 0.5 22.08 1.1 14051.2 6.9 207.18 < 0.5 1539.74 1.5 5041.84 5.6 2060.66 0.6  (250.0) (33.6) (41.9) (9.6) (12.0) (21.8) (7720.0) (160.0) (6000.0) (11500.0) (3420.0)  16S rRNA (copies/mL) 7.69x106 1.23x105 8.60x106 7.78x104 3.16x106 1.31x105 5.71x106 8.27x103 1.08x107 5.03x103 3.92x106 2.39x104 5.47x106 6.04x104 3.11x106 6.60x104 1.10x106 5.12x104 4.51x106 4.45x106 7.34x106 9.97x104  (9.77x106) (8.30x106) (5.58x106) (4.74x106) (4.09x106) (2.08x106) (2.33x106) (1.88x106) (6.25x105)  (5.08x106)  118  Figure 3.4a: Whisker box plots of turbidity during dry conditions (top) and wet conditions (bottom) between July 21, 2004 – March 16, 2006. Values of mild outliers (1.5 times the IQR) are denoted by ◦ and extreme outliers (3 times the IQR) are denoted by *.  119  Figure 3.4b: Whisker box plots of NO3-N during dry conditions (top) and wet conditions (bottom) between July 21, 2004 – March 16, 2006. Values of mild outliers (1.5 times the IQR) are denoted by ◦ and extreme outliers (3 times the IQR) are denoted by *. 120  Figure 3.4c: Whisker box plots of PO4-P during dry conditions (top) and wet conditions (bottom) between July 21, 2004 – March 16, 2006. Values of mild outliers (1.5 times the IQR) are denoted by ◦ and extreme outliers (3 times the IQR) are denoted by *.  121  3.3.3  Establishing Precipitation Patterns and Stream Flow July 2004 – March 2006  The total monthly precipitation and the average monthly stream flow for the duration of this investigation (data collected and reported by Environment Canada) is summarized in Figure 3.5. In 2004, average stream flow in the Sumas River increased in the fall as the total monthly rainfall gradually increased.  The highest total rainfall for 2004 was  observed in November which corresponded to stream flows that exceeded 10 m3/s in November, December and January 2005. Gradual increase in rainfall enables gradual saturation of the vadose zone. Ground saturation, in turn, leads to higher contributions to streams via overland flow and thus higher stream discharge rates. In contrast, total monthly precipitation for November and December 2005 was lower than in 2004 and stream flows in the Sumas River were about 4 m3/s. The total precipitation for the month of January 2006 (438.1 mm) was more than double that recorded for December 2005. This rainfall pattern resulted in a spike in mean monthly discharge (6.5 m3/s) for January 2006 which sharply contrasted the period of sustained high stream flow (9.85 – 10.5 m3/s) observed in November 2004 – January 2005. Saturation conditions of the local receiving ground were different than the previous year and thus observed stream flows were lower. Mean monthly discharge and mean daily discharges at site no.1 (on the sampling days) were used for comparison with the measured stream flows.  122  500 Stream Flow  10  Monthly Precipitation (mm)  400 350  8  300 6  250 200  4  150 100  2  Mean Monthly Discharge (m3/s)  450  12 Precipitation  50  ar  b  M  Fe  Ja  n  0  Ju ly Au g Se pt O ct No v De c Ja n Fe b M ar Ap ril M ay Ju ne Ju ly Au g Se pt O ct No v De c  0  Month: July 2004- March 2006  Figure 3.5: Total monthly precipitation and average stream discharge in Sumas watershed (Environment Canada, 2006).  3.3.4  Estimates of Mass Transport  The calculated stream flows (Table 3.3) based on measured stream velocities at four stations of the most southern segment (upstream) of the water course under investigation were within reasonable agreement with the data measured by Environment Canada at Site 1. Higher flows for stream discharge coincided with greater rainfall contributions in November and December 2005 (Figure 3.5). Variations in calculated stream flows may reflect unidentified contributions to or drains from the stream between stations and appreciable deviation from a rectangular stream bottom (as was assumed for the calculation of stream flows based on visual inspection at the sampling sites).  123  Table 3.3: Calculated stream flows in m3/s compared to Environment Canada measured flows July  Aug  Sept  Nov  Dec  21/2005  15/2005  27/2005  1/2005  6/2005  1  1.03  2.00  0.82  4.08  2.58  2  1.07  0.61  1.10  5.38  2.89  3  1.40  0.62  1.11  4.56  4.46  4  1.17  0.98  0.89  4.00  2.34  Average  1.17  1.05  0.98  4.51  3.07  STD dev  0.61  0.66  0.15  0.63  0.96  Site  EC* Monthly Average (m3/s) EC* Daily Measured (m3/s)  1.31  0.97  0.97  3.34  3.31  1.08  0.88  0.76  5.26  1.68  * = Environment Canada recorded stream discharge at Huntington Station (Site 1) The calculated mass transport rates of total tetracycline resistance genes (Figure 3.6) were considerably lower during the warmer late summer months between July and September 2005. A dramatic increase in mass transport rate of Tcr genes was observed in November 2005 which coincided with higher rainfall reported for the month of October by Environment Canada Weather office. The calculated mass transport rates of 16S rRNA genes were consistent between July and December 2005 (Figure 3.8).  124  r  Site 1- Mass Flux of Tc genes 1.40E+12  7 Discharge  6  Mass Flux  1.00E+12  5  8.00E+11  4  6.00E+11  3  4.00E+11  2  2.00E+11  1  0.00E+00  Discharge (m 3/s)  Mass Fux (copies/s)  1.20E+12  0 21-Jul-05  15-Aug-05  26-Sep-05  1-Nov-05  6-Dec-05  Date  r  Site 2 - Mass Flux of Tc genes 1.40E+12 1.20E+12  7 Discharge  6 5  8.00E+11  4  6.00E+11  3  4.00E+11  2  2.00E+11  1  0.00E+00  Discharge (m 3/s)  Mass Flux (copies/s)  Mass Flux 1.00E+12  0 21-Jul-05  15-Aug-05  26-Sep-05  1-Nov-05  6-Dec-05  Date  Figure 3.6 Calculated mass transport rates of tetracycline resistance genes at four upstream sites on the Sumas River (July – December 2005); error bars represent standard deviation.  125  r  Site 3 - Mass Flux of Tc genes 1.40E+12 1.20E+12  7 Discharge  6  1.00E+12  5  8.00E+11  4  6.00E+11  3  4.00E+11  2  2.00E+11  1  0.00E+00  0 21-Jul-05  15-Aug-05  26-Sep-05  1-Nov-05  Discharge (m 3/s)  Mass Flux (copies/s)  Mass Flux  6-Dec-05  Date  r  Site 4 - Mass Flux of Tc genes 1.40E+12 Discharge  6  Mass Flux  1.00E+12  5  8.00E+11  4  6.00E+11  3  4.00E+11  2  2.00E+11  1  0.00E+00  0 21-Jul-05  15-Aug-05  26-Sep-05  1-Nov-05  Discharge (m 3/s)  Mass Flux (copies/s)  1.20E+12  7  6-Dec-05  Date  Figure 3.6 (cont.): Calculated mass transport rates of tetracycline resistance genes at four upstream sites on the Sumas River (July – December 2005); error bars represent standard deviation.  126  Site 1 - Mass Flux of 16S rRNA genes 7  4.00E+13  Discharge  3.50E+13  Mass Flux  6 5  3.00E+13 2.50E+13  4  2.00E+13  3  1.50E+13  2  Discharge (m 3/s)  Mass Flux (copies/s)  4.50E+13  1.00E+13 1  5.00E+12 0.00E+00  0 21-Jul-05  15-Aug-05  26-Sep-05  1-Nov-05  6-Dec-05  Date  Site 2 - Mass Flux of 16S rRNA genes 4.50E+13  Mass Flux (copies/s)  3.50E+13  6  Discharge Mass Flux  5  3.00E+13 2.50E+13  4  2.00E+13  3  1.50E+13  2  Discharge (m 3/s)  4.00E+13  7  1.00E+13 1  5.00E+12 0.00E+00  0 21-Jul-05  15-Aug-05  26-Sep-05  1-Nov-05  6-Dec-05  Date  Figure 3.7: Calculated mass transport rates of 16S rRNA genes at four upstream sites on the Sumas River (July – December 2005); error bars represent standard deviation.  127  Site 3 - Mass Flux of 16S rRNA genes 7  4.00E+13  Discharge  3.50E+13  Mass Flux  6 5  3.00E+13 2.50E+13  4  2.00E+13  3  1.50E+13  2  Discharge (m 3/s)  Mass Flux (copies/s)  4.50E+13  1.00E+13 1  5.00E+12 0.00E+00  0 21-Jul-05  15-Aug-05  26-Sep-05  1-Nov-05  6-Dec-05  Date  Site 4 - Mass Flux of 16S rRNA genes 4.50E+13  Mass Flux (copies/s)  3.50E+13  6  Discharge Mass Flux  5  3.00E+13 2.50E+13  4  2.00E+13  3  1.50E+13  2  Discharge (m 3/s)  4.00E+13  7  1.00E+13 1  5.00E+12 0.00E+00  0 21-Jul-05  15-Aug-05  26-Sep-05  1-Nov-05  6-Dec-05  Date  Figure 3.7 (cont.): Calculated mass transport rates of 16S rRNA genes at four upstream sites on the Sumas River (July – December 2005); error bars represent standard deviation.  128  Observations of higher mass transport of total tetracycline resistance genes in November 2005 are related to the higher measurements of total tetracycline resistance genes sampled along the whole stream segment under investigation observed in both November-December 2004 and November-December 2005. 3.3.5  Correlations  Figure 3.8 illustrates the Spearman rank correlations between Tcr genes and 16S rRNA normalized genes and selected water quality parameters, stream discharge and precipitation for sites located along the main stream channel of the Sumas River. Statistical significance is assigned if p value is greater than 0.5 (positive correlation designated by +) or p value is less than -0.5 (negative correlation designated by -). Spearman rank correlations at p < 0.05 demonstrated positive correlations between both Tcr genes and 16S rRNA normalized genes and instantaneous discharge and 48 h discharge for all sampling dates. Along the four upstream sites of the Sumas River, there were significant positive correlations between Tcr genes (and 16S rRNA normalized genes) and instantaneous discharge, 48 h discharge and turbidity. For the four sites further downstream (Sites 5, 6, 7 & 8) there were significant correlations between the Tcr genes (and 16S rRNA normalized genes) and nitrate concentrations. Significant negative correlations were established for both Tcr and 16S rRNA normalized genes and chloride concentration and temperature.  There were no statistically significant correlations  between the average of 16S rRNA genes and any of the measured parameters (Spearman’s rho was between -0.408 and 0.395). A summary of the Spearman’s Rank correlations with correlation values for total tetracycline resistant genes, 16S rRNA normalized genes and individual tet resistance genes for all sites on the Sumas River can be found in Appendix 5.  129  S pecific C onductivity  T otal of the four T c r genes  ‐  +  +  ‐ T emperature C hloride  Ins tantaneous  D is cha rge 48 hour D is charge T urbidity  ‐ ‐  +  T otal T c r genes  normaliz ed  to 16S  rR NA genes  +  72 hour R ainfall  Figure 3.8: Spearman rank correlation diagram for Sumas River sites 1, 2, 3, 4 & 7 (excluding control site 9) between total tetracycline resistance genes and total tetracycline resistance genes normalized to 16S rRNA genes and water quality parameters. 3.4 Discussion 3.4.1  Seasonal Dynamics of Tetracycline Resistance Genes  This study represents the first examination of the seasonal dynamics of antibiotic resistance genes in British Columbia stream water over an extended monitoring period. Seasonal trends are well documented for contaminants such as nutrients (Berka, 1996), trace metals (Smith, 2004) and fecal coliform (Ross, 2006) in agricultural watersheds but not for the selected four tetracycline resistance genes. Over the course of the monitoring period, seasonal and spatial trends can be observed. Measurement of the small sub-unit 16S rRNA genes was used as an indicator of total bacterial biomass as previously described by other researhers (Palys et al., 1997; Kolbert & Persing, 1999; Drancourt et al., 2000).  Results are presented as both relative  abundance of total number of gene copies (Figure 3.2) and as well, normalized to 16S rRNA genes (Figure 3.3) as an approximation of the proportion of the bacterial biomass that displayed tetracycline resistance.  130  During the warm summer months, the abundance of the four Tcr genes was low while the total measured bacterial biomass, represented by abundance of 16S rRNA genes, indicated consistent population of bacteria (usually between 105 and 107 copies/mL) at each of the sites on the Sumas River throughout the year. Furthermore, significantly higher abundance (p < 0.05) of tetracycline resistance genes was measured in the Sumas River during November, December and January of 2004 – 2005 and November 2005 – March 2006 than the abundance of Tcr genes measured during July. It is possible that the higher relative abundance of Tcr genes during the winter includes naturally occurring trends of genes derived from compounds naturally produced by soil-dwelling organisms. However, the abundance of Tcr genes was statistically lower abundance of Tcr genes was measured in the control stream throughout the year (Figure 3.2), which strongly indicates that the observed seasonal pattern cannot be caused solely by internal sources. The most abundant tetracycline resistance genes observed in the field were tet (W) and tet(Q); tet (O) was observed to be the least abundant tetracycline resistance gene measured in the field collected samples. This observation supports previously observed abundance profiles by other researchers and reflects the prevalence of ribosomal protection type genes as being the most common tetracycline resistance gene in animal fecal samples (Patterson et al., 2007). Geophysical, hydrological and meteorological conditions in a given region govern the transport of contaminants in surface waters. Comparison of the seasonal profile of monthly precipitation and stream discharge (Figure 3.5) and the measured concentrations of the four Tcr genes in the Sumas River suggest that periods of higher rainfall during the winter increase the potential for transport of manure related contaminants including the tetracycline resistance genes via overland flow into receiving watercourses. At the same time, periods of sustained rainfall and higher stream flows increase the potential for dilution of contaminants in stream waters. Table 3.4 compares the total precipitation recorded for three winter months during the Sumas watershed monitoring experiments in 2004 and 2005.  The significantly lower relative abundance of tetracycline resistance  genes (p = 0.021) during the summer, occurred during periods of decreased stream flow and optimal temperatures for in situ bacteria reproduction during summer months. In 131  contrast, the significantly higher relative abundance of Tcr genes occurred during highflow conditions. Manure distribution rates on agricultural fields in the Sumas watershed are highest during the spring and fall. As greater numbers of Tcr genes are transported throughout ecosystem compartments, opportunities for horizontal gene transfer of genetic material that confers resistance between bacterial species are likely to increase. Table 3.4: Total precipitation (mm) recorded at Abbotsford during Sumas watershed monitoring experiments. Year  November  December  January  2004 - 2005  281.9  252.7  253.2  2005 - 2006  169.2  166.3  438.1  Measuring hydrologic flow at sampling sites allows contaminant loads to be estimated while considering the effects of variable stream discharge. Mass flux of tetracycline resistance genes during the fall of 2005 was highest in November. This coincided with a period when stream flow was high and the frequency of rainfall events was within seasonal average. A number of drainage ditches (small third order streams) discharge into the Sumas River. Farm sizes and topography along the portion of the Sumas River under investigation are similar although field drainage patterns and tributary contributions may differ slightly as the stream flows downstream.  During the crop  growing season, water is drawn from the stream for irrigation purposes but also flows back into receiving waters after spraying. Irrigation using stream water generally ends after October in the Sumas watershed and thus the highest mass transport observed in the 4 upstream stations may indicate greater contributions to the Tcr gene abundance in stream water from contaminants associated with overland flow. Several factors may contribute to monthly variations in the mass transport rates of the Tcr genes among which are unidentified groundwater inflow, stream water drawn for irrigation, return irrigation water and/or contributions from smaller drainage ditches or field tiles. Positive Spearman’s Rank correlations measured between tetracycline resistance genes and 16S rRNA normalized genes and turbidity, instantaneous discharge and 48 h discharge and in these experiments (Figure 3.9) for most sites located on the Sumas  132  River. These postive correlations reinforce the connection between the effects of rainfall events on stream flow and the concentrations of Tcr genes, bacterial biomass and particulate matter in receiving water.  Negative Spearman’s Rank correlations were  r  determined between the total Tc genes and specific conductivity and chloride concentrations at most sites along the Sumas River. Chloride concentration and specific conductivity can be used as indcators of the concentration of dissolved ions in water courses (Wang & Yin, 1997). Precipitation in the catchment contributes to stream flow and leads to reduction of the concentration of dissolved ions by dilution in the receiving water. The negative correlation between Tcr genes and chloride and specific conductivity may reflect the accompanying concentration changes as precipitation increases stream discharge. As soils become saturated during periods of high rainfall, over-land flow of contaminants can make greater contributions to surface water. Local hydrology provides different conditions that, in turn, increase or decrease concentrations of antibiotics or tetracycline resistance genes delivered to receiving waters depending on soil composition and hydraulic conductivity. If the soil is clay-like or small particulate size and hydraulic conductivity is lower, concentrations of antibiotics in water could decrease when there are greater surface interactions (Sithole & Guy, 1987) and tetracycline resistance genes could decrease as opportunities for resistance traits to be transferred to biofilms increases. (Results of experiments to explore the potential for transfer of Tcr genes between water column bacteria and biofilms are provided in the supplementary material of Appendix 6.) Concentrations of antibiotics and tetracycline resistance genes in groundwater or surface water could increase if hydraulic conductivity of soil is higher and soils have lower surface areas acting as biofilm substrates. This could be a concern if humans and animals may be drinking well-water supplied by ground water or surface water sources. The dynamic behaviour of stream channel conditions varies with seasonal contributions of rainfall and influence the fluid transport of contaminants in aquatic ecosystems. In general stream channel width and flow rates in the Sumas River increase in the downstream direction towards discharge into the Fraser River.  Changes in flow  conditions between channels and flood plains are also important. These are linked to 133  roughness characteristics described by different values of the roughness coefficient, Manning’s n (Arcement & Schneider, 1989; Freeman et al., 1998) of channel beds versus flood plains (Barnes, 1967) under conditions of unusually high stream discharge. On natural surfaces, likely to act as substrates for biofilms, water flow characteristics will be different for channel beds and flooded regions covered by vegetation. Thus changes in water flow conditions will affect the potential transport of contaminants in stream channels as they extend over banks into flood plains during periods of high discharge. In November 2005 during periods of high rainfall and high stream discharge, the stream channel at Site 1 extended approximately 20 m beyond the banks. Similar conditions were observed on downstream sites (Site 2, 3, and 4). In January 2006, during the period of highest recorded rainfall and stream discharge for 2006 (Environment Canada, 2006), the stream depth, as measured by the Environment Canada depth gauge located on the bridge above the Site 1 sampling site, was approximately 9.8 m and stream flow immediately upstream of the bridge extended approximately 40-50 m beyond the banks on adjacent fields. The stream width at the control site remained relatively constant during wet and dry seasons in both years although stream channel flow was higher during periods of higher precipitation. High measurements of the four tetracycline resistance genes were recorded in November 2004. During periods of higher rainfall and stream discharge, there may be greater contributions to the concentrations of Tcr genes from stream flow over flooded land beyond the stream banks or greater contributions from higher flow in third order streams and ditches.  Turbulent flow is associated with higher stream discharge and more  turbulent flow conditions would promote greater mixing of contaminants throughout the water column while relatively longer periods of more quiescent flow conditions over flooded fields may allow exchange of tetracycline resistance genes between biofilms and water columns. Altered stream flow conditions during periods of high rainfall (and flooding of adjacent fields) will encourage transport of manure-related environmental contaminants such as tetracycline resistance genes, microorganisms, nutrients or trace metals into main water courses via overland flow.  134  Temporal and spatial influences govern the potential transport pathways for tetracycline resistance genes.  Reports by Pruden et al. (2006) demonstrated that tetracycline  resistance genes were measured in highest concentrations near a dairy lagoon, followed by irrigation ditch water and then urban/agricultural impacted river sediment.  It is  important to recognize that antibiotic resistance genes occur in natural conditions in soil and water-borne bacteria (Séveno et al., 2002) and that environmental selective pressure is not always a consequence of antibiotic exposure. Moreover, selection responses due to exposure to other stressors such as heavy metals may contribute to the successful development of antibiotic resistance traits without direct exposure to antibiotics (Alonso et al., 2001). This factor is important to consider in the context of the Sumas watershed given that non-point sources of pollution include serious contributions of nutrients, metals, and other contaminants that may persist or bioaccumulate in the receiving environment and may exacerbate conditions for imparting selective pressure on bacteria to acquire antibiotic resistance (Berka, 1996; Smith, 2004). In addition to hydrologic influences on the measured abundance of tetracycline resistance genes in the Sumas River, seasonal changes in daylight periods may also affect both persistence of antibiotic residues and the decay rates of tetracycline resistance genes. Experiments by Engemann et al. (2006) demonstrated that decay rates for tetracycline resistance genes are different under light and under dark conditions. The decay rates for Tcr genes have been demonstrated to be lower in dark conditions than for systems with higher light exposure (possibly due to lower photolytic degradation activity, changes in ecological conditions or by altered photosynthesis). In agricultural watersheds, manure management practices vary regionally (Beaulieu, 2004). Ideally, manure is applied as soil fertilizer in early spring prior to the growing season in order to optimize crop yields. Many livestock farms, however, distribute manure in late fall and winter in order to decrease manure storage demands over the period when soil amendment is prohibited (Beaulieu, 2004). The timing of manure application to agricultural fields affects the transport of contaminants into nearby water courses. In this study, higher relative abundance of tetracycline genes were measured in the late fall, at times that coincide with frequent manure distribution on local fields and  135  more frequent rainfall events of higher intensities. Based on the above reasoning, the observed patterns indicate a likely connection between elevated abundance of Tcr genes measured in stream water and greater possibility of run-off from manure fertilized fields. The maximum abundance of Tcr genes in this investigation of the Sumas River appear to be comparable to the few quantitative measurements of Tcr genes in environmental samples reported in current literature. Table 3.5 summarizes these observations. Among the factors that may contribute to the abundance of Tcr genes measured in the Sumas watershed are:   increased application of manure on land in the immediate vicinity of the stream network;    hydrologic conditions that alter surface run-off and stream flow;    changes in degradation rates in connection with daylight exposure, temperature and ecological pressures;    changes in water quality conditions.  136  Table 3.5: Comparison between tetracycline resistance genes measured in this investigation for environmental water samples and those reported in current literature. Maximum abundance of the selected genes (normalized to 16S rRNA) (copies/mL)  Matrix  tet (Q) tet (Q) tet (Q) tet (Q) tet (O) tet (O) tet (O) tet (O)  1.18 x 10-2 1.21 x 10-1 8.60 x 10-2 ~7 x 10-2 1.06 x 10-4 4.55 x 10-1 9.08 x 10-4 ~8 x 10-4  tet (O) tet (M) tet (M) tet (M) tet (M) tet (W) tet (W) tet (W) tet (W)  ~8 x 10-2 1.49 x 10-2 3.32 x 10-1 8.54 x 10-2 ~2.5 x 10-1 1.7 x 10-2 4.70 x 10-1 4.47 x 10-2 ~1 x 10-4  tet (W)  ~1 x 10-2  stream water swine feedlot lagoons groundwater cattle feedlot lagoons stream water swine feedlot lagoons groundwater composted cattle manure cattle feedlot lagoons stream water swine feedlot lagoons groundwater cattle feedlot lagoons stream water swine feedlot lagoons groundwater composted cattle manure cattle feedlot lagoons  Tetracycline resistance genes measured  3.4.2  Reference Keen, 2008 Koike et al., 2007 Koike et al., 2007 Peak et al., 2006 Keen, 2008 Koike et al., 2007 Koike et al., 2007 Storteboom et al., 2007 Peak et al., 2006 Keen, 2008 Koike et al., 2007 Koike et al., 2007 Peak et al., 2006 Keen, 2008 Koike et al., 2007 Koike et al., 2007 Storteboom et al., 2007 Peak et al., 2006  Monitoring Water Quality  Distinct patterns were observed in the wet season and the dry season with some identifiable differences between parameters measured at sites located with different flow conditions, stream morphology and groundwater conditions (illustrated in the whisker box plots of Figure 3.5 a,b & c). Measurements of water quality parameters including concentrations of nutrients (nitrates and phosphates) were not significantly different among the sites along the section of the Sumas River under study. As an important indicator of agricultural pollution (Berka et al., 2001; Schreier et al., 2001; MacDonald, 2005), measurements of nitrate concentrations in the Sumas watershed monitored in this investigation suggest that the trend towards substantial contributions from animal manure is continuing. Previous studies have described contamination of the  137  Abbotsford Aquifer, increasing nitrate concentrations in the Sumas River and nitrate excess in the Sumas watershed due to higher numbers of animals (Berka, 1996; Smith, 2004; Schreier et al., 2001). Water quality parameters measured at sites along the Sumas River on January 11, 2006 coincided with the highest recorded rainfall of the entire monitoring time. For these sites, turbidity ranged between 56.3 NTU (Site 4) and 99.1 NTU (Site 1) with total suspended solids measured between 72 mg/L (Site 4) and 94.3 mg/L (Site 1).  Turbidity and  suspended solid concentrations are indicative of transport of particulate matter into water courses (Grippel, 1995) and consistent with the visual observation of flooding at lower elevation stations. Turbidity and suspended solid concentrations were lower during the dry season. The highest reported suspended solid concentration was 200 mg/L measured at Site 8 on October 29, 2004 which coincided with another period of higher stream discharge and higher rainfall for the month of October. Measurement of nitrate levels in this study supported the previously reported observation that elevated nitrate concentrations in the Sumas River occurred during the wet winter months while lower nitrate values were recorded in summer low flow conditions (Berka et al., 2001; Schreier et al., 2001; Solano, 2006). Phosphate concentrations (measured as ortho-phosphate) measured in all sites throughout the monitoring period were generally low. Concentrations throughout the stream network were comparatively lower during the dry season than in the wet season probably due to lower run-off conditions or uptake by plants. Chloride concentration and specific conductivity were found to be negatively correlated to the abundance of tetracycline resistance genes.  Measurements of chloride  concentration and specific conductivity are good indicators of contributions of dissolved ions from groundwater into receiving waters. In the summer, when groundwater makes substantial contributions to stream flows, Tcr were observed to be lower than in winter months.  138  3.4.3  Influence of Land Use in the Sumas Watershed  Previous research (Hooda et al., 1997; Lapp et al., 1998; Magner & Alexander, 2002) has demonstrated that land use in an agricultural watershed compromises receiving water quality and impacts of land use on water quality specifically in the Lower Fraser Valley are well-documented (Cook, 1994; Berka, 1996; Schreier et al., 2001; Addah, 2002; Smith, 2004; MacDonald, 2005; Ross, 2006; Solano, 2006). Nutrients, primarily nitrate nitrogen and orthophosphate, originating from distribution of animal manure have been identified as key indicators of non-point sources of agriculture-related pollution of water courses in the Sumas watershed (Berka, 1996; Brisbin, 1995). Influence of agricultural activities on contamination of rivers (specifically in river sediments) by antibiotic resistance genes has been demonstrated (Pei et al., 2006). Excessive land application of animal manure increases the opportunity for nutrients, antibiotics and/or bacteria carrying antibiotic resistance genes contained within the manure to be transported in water. In the current investigation, estimates of land use revealed high diversity of farming operations located immediately next to the Sumas River and two associated tributaries in the Canadian portion of the watershed. Livestock are known to be reservoirs for a wide range of zoonotic pathogens. Agricultural activities dominate the land use within the Sumas watershed (including the US portion) and manure is seasonally distributed on a relatively unchanged land base. Of the individual land parcels observed within 500 m of the stream network under study, 14 of these farming operations were confirmed to produce poultry, cattle or hogs. There are several hay or corn fields greater than 1 hectare through which the Sumas River flows (Table 3.1). These large, flat fields are likely to receive land application of manure at least two times per year. In 1997, more than 90% of the land along the Sumas River was used for production of fruits, vegetables, poultry, hogs and dairy (Stevens & Eriksson, 1997) and this has remained relatively unchanged for the past decade.  Given the number of animal production farms located in  the Sumas watershed and their proximity to the Sumas River, it is highly likely that the measured abundance of the four tetracycline resistance genes in the stream network originate from agricultural sources.  139  Using nitrate as an indicator of non-point source contributions of pollutants from agriculture, reports of Berka (1996) and Addah (2002) confirmed that the upstream contributing area along receiving water courses influenced the measured nitrate concentrations downstream and were linked to agricultural sources located upstream. Nitrate concentration in receiving water was found to be positively correlated (Spearman Rank correlation p > 0.05) with poorly drained organic soils, clay and silt while negative correlations were reported for sand (Berka, 1996). In the current investigation, nitrate concentrations measured at sites 5, 6 and 8 (all tributary ditches to the Sumas River) were positively correlated to the total of the four measured tetracycline resistance genes (Spearman’s rho 0.759, 0.709 and 0.664 respectively). This spatial trend may reflect the cumulative effect of upstream contributions of both nitrate and tetracycline resistance genes from agricultural sources into the larger downstream stream channels. To this author’s knowledge, there are no reports of correlations between abundance of selected tetracycline resistance genes and water quality parameters in the existing literature. Optimal conditions for growing berries (raspberries) are well-drained with sandy soil. Annual fertilization of berry crops usually entails spraying with liquid fertilizer whereas compost or fertilizer in solid form is often spread on fields for vegetables, field corn, hay fields and lawn turf at the start of the growing season. Application of synthetic liquid fertilizers to raspberry fields during periods of generally high precipitation in the early spring have been shown to contribute to nitrate leaching into the Abbotsford – Sumas aquifer (Chesnaux et al., 2007). In the current investigation, there are two confirmed berry farms located within 500 m of the segment of the Sumas River for which water quality was monitored located between site 1 and site 4. These particular soil quality conditions could affect the transport of soluble nutruients, antibiotic residues and antibiotic resistance genes. Sandy soil would allow continuous drainage of aqueous contaminants at low levels into stream water. Soil comprised of clay, silt or high organic fractions would be less easily drained and permit longer times for surface interactions (e.g. adsorption) to occur.  140  3.5 Conclusions The following conclusions have been drawn from results of this research:   The presence of the four selected tetracycline resistance genes monitored between July – Dec 2004 was confirmed in the Sumas River during the monitoring period of 2005 – 2006.    The general trend of the seasonal dynamic pattern that emerged during the 2004 monitoring period was repeated in 2005.    Abundance of the four selected tetracycline resistance genes was negatively correlated with specific conductivity and chloride concentration at most sites along the Sumas River. 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Technol. 40 (23): 7445-7450. 64. Rooklidge, S.J.  2004.  Environmental antimicrobial contamination from  terraccumulation and diffuse pollution pathways. Sci. Total Environ. 325: 1-13. 65. Ross, D.J. 2006. Influence of Climate and Agricultural Land Use on Nutrient and Bacterial Cycling in Surface Waters of the Lower Fraser Valley, British Columbia. PhD thesis in Resource Management and Environmental Studies. University of British Columbia. 246 pp. 66. Rowe-Magnus, D.A. & D. Mazel. 2002. The role of integrons in antibiotic resistance gene capture. J. Med. Microbiol. 292: 115-125. 67. Schreier, H., R. Bestbier, S. Brown, K. Hall, S. von Westarp & L. Elliot. 2001. Agricultural Watershed Management. Resources and Environment.  Multimedia CD ROM.  University of British Columbia.  Institute for Vancouver,  Canada. 68. Séveno, N.A., D. Kallifidas, K. Smalla, J.D. van Elsas, J.–M. Collard, A.D. Karagouni & E.M.H. Wellington. 2002. 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Stevens, V. & A. Eriksson. 1997. Current trends along the Lower Fraser: Our rivers are talking…are we listening? Westwater Research Centre, University of British Columbia, Department of Fisheries and Oceans & Environment Canada. Vancouver, BC. 74. Storteboom, H.N., S.C. Kim, K.C. Doesken, K.H. Carlson, J.G. Davis & A. Pruden. 2007. Response of antibiotics and resistance genes to high-intensity and low-intensity manure management. J. Environ. Qual. 36: 1695-1703. 75. Tolls, J.  2001.  Sorption of veterinary pharmaceuticals in soils: A review.  Environ. Sci. Technol. 35(17): 3397-3405. 76. Wang, X. & Z-Y. Yin. 1997. Using GIS to assess the relationship between land use and water quality at a watershed level. Environ. Int. 23: 103-114. 77. Witte, W. 2000. Ecological impact of antibiotic use in animals on different complex microflora: Environment. Int. J. Antimicrob. Agents. 14: 321-325. 78. Zuccato, E., D. Calamari, M. Natangelo, & R. Fanelli.  2000.  Presence of  therapeutic drugs in the environment. The Lancet. 355: 1789-1790.  149  CHAPTER 4 - TRACKING TETRACYCLINE RESISTANCE GENES THROUGH THE COMPOSTING PROCESS AND FIELD DISTRIBUTION OF POULTRY MANURE3 It is a widely held belief that distribution of animal manure on land can spread antibiotic resistance genes and may extend opportunities for de novo development of resistance traits in environmental bacteria. This chapter presents evidence that aims to address the third research question of the thesis: Can fertilization of fields with animal manure serve as a tributary source of tetracycline resistance genes into the receiving water courses? It specifically describes the first effort to measure tetracycline resistance genes following field application of poultry waste as fertilizer in the Lower Fraser Valley. The seasonal changes in total abundance of Tcr genes, turbidity, suspended solids, nitrate and phosphate concentrations reported in the previous chapter led to the hypothesis that field fertilization with animal waste was a likely contributor to the contaminant load measured in the Sumas River.  Data was collected to investigate the hypothesis that field  application of poultry compost could increase the abundance of the four target Tcr genes measured in stream water of the Sumas watershed. The abundance of the four selected Tcr genes was relatively higher in 3 compost samples (July, August & September) prior to distribution on the field but not statistically significantly different from the abundance of Tcr genes found in the soil prior to spraying with poultry manure. The total abundance of Tcr genes and the abundance of 16S rRNA genes did not vary significantly over the six months after field application of the poultry compost. Based on the qPCR analyses of these samples, it was not possible to validate that surface run-off from fertilized fields made substantial contributions to abundance of Tcr genes in bacteria found in receiving water of the stream network. Chemical analyses of soil and poultry compost using ESI LC MS/MS provided unreliable and insufficient evidence to track the antibiotic residues through the composting process. The extraction procedure may have been subject to interference from higher concentrations of lipids in the compost samples.  3  A version of this chapter will be submitted for publication. Keen, P.L., Knapp, C.W., Shang, D., Wetzstein, M., Hall, K.J. and Graham, D.W. Tracking Tetracycline Resistance Genes Through the Composting Process and Field Distribution of Poultry Manure.  150  4.1 Introduction The health of food-producing animals is intrinsically linked to human health and for some time, the putative consequence that antimicrobial resistance may be transmitted via the human food chain has been central to this debate. Antibiotics are used to treat infections in human and animal populations and in addition, it has long been recognized that long-term low dose application of antibiotics for growth promotion in food animals plays a role in the development of antimicrobial resistance (Mølbak, 2004). Antibiotics are prescribed in doses that are frequently only partially metabolized leaving a considerable fraction of the drug excreted while retaining its antimicrobial activity (Elmund et al., 1971). The concomitant risk is that unintended exposure of bacteria to these compounds may encourage the de novo development of resistance in non-target species of microorganisms.  Environmental exposure to waste may contribute to  ecosystem-mediated transport of antibiotic residues, resistant pathogens and/or resistance genes which, in turn, add to the reservoir of mobile genetic elements that confer resistance. An important pathway for the transfer of antibiotic residues or antibiotic resistance genes is the consumption of food crops grown on land fertilized with composted animal manure. Several investigators have demonstrated that plants species, including food crops common in human and animal diets, can take up antibiotics from manure-amended soil (Jjemba, 2002; Migliore et al., 2003; Kumar et al., 2005; Boxall et al., 2006; Kong et al., 2007; Dolliver et al., 2007). Uptake of antibiotics by food crops may increase the potential for human exposure to low concentrations of antibiotic residues or antibiotic resistance genes although the health consequences of such remains largely unexplored (Dolliver et al., 2007). There is growing interest in exploring the persistence and potential transport pathways of manure-borne antibiotic residues or antibiotic resistance genes following land application of animal waste (Baguer et al., 2000; Loke et al., 2003; Boxall et al., 2004; Burkholder et al., 2007). The trend in agricultural production of food animals to increase herd sizes while reducing the number of individual operations is accompanied with the need for  151  disposal of larger amounts of manure. Animal waste compost serves as a valuable resource for improving drainage, structure and nutrient capacity in agricultural soil. Composting practices are required to comply with waste management regulations which are intended to protect the overall health of the local receiving environment. Studies of composted swine manure have reported significant reduction of tetracycline resistance genes, especially ribosomal protection protein genes, over the course of the composting process (Yu et al., 2005).  Duration of composting and compost conditions have  important consequences on the degradation of antibiotic residues and the potential spread of antibiotic resistance genes through agricultural environments. It is important that management practices for disposal of wastes containing antibiotic residues or antibiotic resistant genes harmonize with the existing knowledge of natural receiving conditions and be sensitive to natural ecosystem responses to environmental change. Bacteria form one of the most important groups in soil communities by performing crucial ecosystem services, including closing the nutrient and geochemical cycles. Bacteria present in soil can act as reservoirs for antibiotic resistance genes with vast genetic diversity among species (Riesenfeld et al., 2004). Metal composition of soils can affect bioavailability of introduced antibiotic contaminants (Wang et al., 2008) and can exert selective pressures on indigenous bacteria for long periods of time (Silver & Phung, 1996). Bacteria isolated from soils with higher metal concentrations have been shown to contain more antibiotic resistant plasmids (Rasmussen & Sorensen, 1998) and it has been suggested that interacting processes in soils between metal resistant and antibiotic resistant plasmids influence the mobility of antibiotic resistance genes between populations of indigenous bacteria present in the environment (Alonso et al., 2001). The cumulative effect of distribution of agricultural pollutants including metals, stressors that confer resistance and excess nutrients throughout the environment is likely to impact microbial communities. In a pivotal study conducted in 1976, Levy et al. demonstrated that tetracycline resistance genes present in E. coli strains could be transferred between chickens and from chickens to humans. Members of the tetracycline family of antibiotics are among the most widely prescribed antibiotics in veterinary medicine (Levy, 1998). The modes of action of the 152  drug are well known and the mechanisms by which bacterial genes encode for resistance are equally well-documented (Roberts, 1996). Analytical methods have been described for determining concentrations of the tetracycline family of antibiotics in water (Zhu et al., 2001), wastewater (Yang et al., 2005), food (Coyne et al., 1997; Cinquina et al., 2003; Wan et al., 2005), soil (Rabølle & Spliid, 2000; Jacobsen et al., 2004) and animal manure (Loke et al., 2002). O’Connor & Aga (2007) reviewed the analyses of tetracyclines in soil matrices and discussed several important factors that compromise quantification. Oxytetracycline and chlortetracycline are frequently prescribed for therapeutic use in poultry although neither compound is commonly used for growth promotion in egg laying chickens (Singer & Hofacre, 2006). Recent studies of a major agricultural centre in  the  state  of  Maryland  USA  frequently  detected  chlortetracycline  (and  sulfamethoxazole) above the detection limit of 0.001 μg/L in the surface waters adjacent to fields fertilized only with poultry litter (Arikan et al., 2008). Soil column experiments have demonstrated that amplification and attenuation of tetracycline resistance in soil bacteria is influenced by exposure to low concentrations of tetracycline (Rysz & Alvarez, 2004). Poté et al. (2003) supported these observations and found that DNA degradation was proportional to residence time of plasmids in the experimental saturated soil column. Their results suggest that there is potential for resistance genes to be transported over long distances in water-saturated soil or groundwater. Transport of tetracycline resistance genes is an important consideration given that the potential for effects on soil bacteria communities and the potential for interference in essential soil functions has already been demonstrated (Schmitt et al., 2006). The main objective of this experiment was to assess the likelihood that transport of contaminants originating from composted poultry waste could increase the possible opportunities for de novo development of resistance in non-target environmental bacteria. Samples were taken from a broiler production farm located in the key agricultural watershed in southwestern British Columbia, Canada over the course of one year. Complementary methods of microbiological and chemical analyses were used to detect the possible effects of both tetracycline residues and tetracycline resistance genes on 153  environmental bacteria. The investigation measured the abundance of four tetracycline resistance genes chosen from the ribosomal protection protein (RPP) super-group (tet M, tet O, tet Q and tet W) using real-time quantitative polymerase chain reactions (qPCR). Antimicrobial susceptibility to oxytetracycline was measured by standard disk diffusion methods. The investigation attempted to track the presence of antibiotic residues (tetracycline, oxytetracycline, chlortetracycline, doxycycline and demeclocycline) through the composting process and field distribution of composted poultry waste by analyses using electro-spray ionization liquid chromatography tandem mass spectrometry (ESI LC MS/MS). Reported here are the results of analyses of the poultry waste compost and soil samples taken between January 2004 and January 2005. 4.2 Materials and Methods 4.2.1  Sampling  This study monitored the poultry waste compost from one broiler production cycle beginning in April 2004. Chickens were housed in an open floor barn for 38 days and compost was stored in a dedicated concrete floor containment area until it was spread on a near-by field beginning in mid-May 2004. Litter, compost and soil samples were collected in plastic 500 ml containers from the medium-sized broiler production facility located in the Sumas watershed between January 24, 2004 and January 25, 2005. All samples were collected by a veterinarian with strict compliance to biohazard protocols since the Fraser Valley region (which included the poultry farm involved in this study) was placed under quarantine on February 19, 2004 due to a severe epidemic of Avian flu. Representative sub-samples of compost and soil were collected for analyses using molecular methods in small 1 mL centrifuge tubes.  All samples for chemical  determination were kept frozen in the dark at -20°C prior to analyses. One sample of barn floor litter was collected before introduction of the birds and was also kept frozen in the dark at -20°C prior to analyses. Following dispatch of the birds to slaughter on March 4, 2004 until April 14, 2004, the compost pile was sampled weekly at the surface and inside the pile at a depth of approximately one meter. The compost was distributed on a field also located at the same poultry farm and both remaining compost and field soil were sampled once per month until January 25, 2005. The field under investigation was  154  historically cultivated and fertilized (usually each year) with poultry compost.  For  comparative purposes, soil was collected in January from the bank of the control site selected for research in the Sumas watershed. It was located on a forested mountain slope 30 m south of the control stream. 4.2.2  Antibiotic Analyses  Determination of five tetracycline compounds (oxytetracycline, chlortetracycline, demeclocycline, doxycycline and tetracycline) was conducted by liquid chromatography electro-spray ionization tandem mass spectrometry (LC ESI MS/MS).  The  chromatographic system used was an Agilent Technologies 1100 Series HPLC connected to an ESI interface and equipped with automatic injectors, degasser, a quaternary pump and column oven. The mass spectrometer used was a Quatro Ultima (MicroMass). The collision gas was argon used at pressures between 2.3-2.5 x 10-3 bar.  Optimum  parameters of the ESI interface for each individual tetracycline compound were determined through operation in positive ion mode by separate flow injection analyses in full scan. Two daughter ions were selected for quantification and confirmation in the multiple reaction monitoring mode (MRM) for each analyte and the optimization parameters for the various tetracycline analytes are given in Table 2.1 (Chapter 2). The HPLC columns used for analyte separation prior to introduction into the tandem mass spectrometer were a Zorbax RX-C8 5 μm of dimensions 2.1 x 150 mm combined with a C8 high purity guard column. The injection volume was 20 μL. A binary gradient separation system was used with solvent A being Milli-Q (Millipore, Bedford, MA, USA) water with 0.1% formic acid, 0.01 M ammonium formate and 100 M EDTA and solvent B was acetonitrile with 0.1% formic acid, 0.01 M ammonium formate and 100 M EDTA added. Analysis run time for each sample was 22 minutes at a flow rate of 0.24 mL/min. Concentrations of OTC were determined using Quanlynx 3.5 software and calculations of degradation curves were performed using Microsoft Excel software. Standards of the target tetracycline analytes were purchased from Sigma-Aldrich. All solvents were HPLC grade supplied by BDH chemicals. All stock standard solutions of the 5 tetracycline antibiotics were prepared by dissolving 10 mg in 10 mL of 100% 155  methanol and kept at 4°C in dark test tubes. The standard working solutions were prepared new for each analysis by diluting the 1000 mg/L stock solutions with 30% methanol. Ultra pure water prepared with a Milli-Q water purification system (Millipore, Bedford, MA, USA) was used throughout for solution preparation. Calibration standards, poultry waste compost and soil samples, quality assurance samples and blanks were subjected to the same extraction procedure. Internal standards (500 μL oleandomycin) were added to each sample. Five grams (dry weight basis) of samples were combined with extraction solvent (30% acetonitrile, 30% isopropyl alcohol made to volume with 0.05 M EDTA), 0.1 M citric acid and ethyl acetate, agitated by 30 seconds of vortex mixing and 20 minutes of sonication. Samples were then centrifuged at high speed for 6 minutes and the extraction sequence was repeated. Both extracts were combined with 1.0 mL of diethylene glycol prior to evaporation in the dark to near dryness using an EZ 2 personal evaporator. Final volumes were made up to 2.0 mL using 30% acetonitrile and filtered through 0.22 μm Nalgene disposable filters (NNI, Rochester NY) before being dispensed to amber glass vials for HPLC ESI MS/MS analyses. Three quality control samples (200 μg/L of each analyte) were taken through the analyses and whenever possible every tenth sample was analysed in duplicate. 4.2.3  DNA Extraction  Samples for microbial analyses were kept frozen at -80°C until extraction at the Department of Civil, Architectural and Environmental Engineering at the University of Kansas, Lawrence, Kansas. Samples of 0.25 g of compost or soil (dry weight basis) were extracted for qPCR analysis of DNA using MoBio UltraClean Soil DNA kits (Solona Beach, CA) with minor method modifications (MoBio Laboratories, 2004). Samples, beads and extraction buffers were combined, homogenized for 30 seconds (speed 5.5) using a FastPrep (Qbiogene, Irvine, CA) cell disruptor and then incubated at 70°C for 10 minutes to enhance lysis of Gram-positive bacteria. Following incubation, samples were re-agitated for 30 seconds (speed 4.5) and subjected to the further purification steps of the kit manufacturer’s protocol without modification. All resulting 50 μL DNA extracts were stored at -20°C prior to analysis.  156  4.2.4  Real-time PCR Assays  Four RPP tetracycline resistance genes ((tet (M), tet (O), tet (Q), and tet (W)) and 16SrRNA genes were selected for quantification by qPCR analyses.  The TaqMan  probe/primer sets and the plasmid standards used in these analyses have been described previously for tet (M) (Peak et al., 2007), tet (O), tet (Q) and tet (W) (Smith et al., 2004) and for 16S-rDNA (Harms et al., 2003) and are presented in Appendix 1. Sample aliquots of 2 μL DNA templates were mixed with iQ Supermix PCR reagents (BioRad, Hercules, CA). A BioRad iCycler equipped with an iCycler iQ fluorescence detector using BioRad software version 2.3 was used for performing the reactions. Standard curves were constructed from quantification of copy numbers for each gene prepared by serial dilution of the appropriate plasmid DNA that ranged from 1.0 to 1 x 107 copies per reaction. All samples were analysed in triplicate and thus reported abundances represent the arithmetic mean of three measurements. Concentration of quantified resistance genes are expressed as copies/mg of soil or compost sample extracted (dry weight basis). 4.2.5  Antibiotic Susceptibility Tests  The Kirby-Bauer or disk diffusion test following Avian and National Antimicrobial Resistance Monitoring System (NARMS) protocol (US Department of Agriculture, 2000) was used to determine if three species of pathogenic bacteria isolated from compost samples were susceptible or resistant to oxytetracycline.  Tests were performed in  accordance with the International Committee on Laboratory Standards. Nutrient agar plates were poured to a depth of 4 mm and after solidification; plates were streaked for confluent growth.  Broth cultures were diluted with sterile saline to match a 0.5  McFarland turbidity standard. Small paper disks infused with a given concentration of oxytetracycline were placed on the plates prior to incubation. Triplicate plates were prepared with E. coli, Salmonella spp. and Enterococci spp.  Following the 24 h  incubation period, bacteria-free circles of various sizes indicated the zones of inhibition to the given antibiotic for that particular bacteria species. Smaller zones of inhibition imply great resistance to the given antibiotic.  157  4.2.6  Data Analyses  Statistical analyses of the differences between total gene numbers were performed using SPSS (v 11.01, Chicago, IL) data analysis software.  Tetracycline resistance gene  abundances were compared before and after application of compost to field soil. Arithmetic means and 95% confidence intervals were used as the statistical descriptors for resistance gene copy numbers. The difference between the gene abundance means in poultry compost and fertilized soil was assessed by paired Mann-Whitney U tests. Data were non-normally distributed and parametric tests could not be used. Data were log transformed before statistical analysis. Differences were considered significant at p < 0.05. 4.3 Results 4.3.1  Chemical Analysis of Tetracycline Residues  Method development using ESI LC MS/MS over the period of eighteen months failed to yield a sensitive and reliable procedure for determining tetracycline residues in the poultry waste compost and soil samples. The preparation of matrix-matched standards was a particular challenge given the heterogeneity of the samples. Initially, a sample of organic poultry compost was selected for use in preparation of compost standards. This material was found to contain concentrations of oxytetracycline above 1000 μg/L before spiking with analyte compounds and thus could not be used for matching of standard matrices. Standards were prepared using material (soil collected from a forest control site) that was most similar to the compost and the soil samples. No tetracycline residues were detected in the chosen standard matrix in concentrations above detection limits before spiking. Over the course of the investigation, refining a suitable extraction method that offered detection limits that were comparable to literature values and instrumental response that was consistent for all compost and soil samples proved to be problematic. A variety of solvent mixtures were tested for the liquid/liquid extraction in combination with several buffers including tetraflouroacetic acid, formic acid, citrate buffer (at multiple pH values below 4), ammonium acetate buffer, phosphate buffer and triethylamine.  Although  158  samples were consistently ‘dirty’, solid phase extraction (SPE) did not offer any improvement in separation of the tetracycline analytes in these samples and thus the adjusted extraction method did not include SPE in sample preparation. In this investigation, highly variable concentrations of five antibiotic analytes from the tetracycline family of antibiotics could be detected in compost samples.  However,  analyte recoveries were inconsistent, concentration measurements in compost or fertilized soil samples could not be reasonably reproduced and thus, no quantitative data for antibiotic residues are presented here. For the samples evaluated in this study, moisture contents varied considerable, ranging between 4-52 % moisture among representative samples measured. The lowest moisture contents were observed in the sub-surface compost samples and the highest moisture occurred in soil samples collected in fall or winter months. The quality of compost samples was also highly variable. High lipid fractions were observed (mostly in surface samples) and thus separation of an extract that contained only the tetracycline analytes of interest was difficult.  Following the extraction procedure, several compost samples  (mostly compost surface samples) appeared as brown viscous liquids which were unsuitable for analyses by ESI LC MS/MS. Using a modified procedure of hexane extractable lipid separation determination (extraction of 5 g dry weight compost samples with 10 ml hexane, filtration, evaporation and weighing of residue), approximate lipid fractions of some compost surface samples were determined to be within the range of 1.6-3.9 % w/w. 4.3.2  Microbiological Analyses  Results of the qPCR determination of tetracycline resistance genes tet (M), tet (O), tet (Q),  and  tet  (W)  in  poultry  compost  are  shown  in  Figure  4.1.  r  The relative abundance profile of the same four Tc genes measured in soil fertilized by poultry compost over six months is presented in Figure 4.2. Results of the qPCR for the same four tetracycline resistance genes in the soil control samples were different from those measured in the compost and soil collected from the farm field. The soil from samples collected from a forested mountain slope located near several large trees 159  appeared to be more enriched than the field soil with the abundance of 16S rRNA genes measured to be 8.0 x 1010 copies/mg (average of 3 replicates). The relative abundance of the tetracycline resistance genes in the control soil was tet (O) (5.5 x 106 copies/mg), tet (Q) (7.0 x 106 copies/mg) and tet (W) (6.2 x 106 copies/mg) and tet (M) (5.2 x 107 copies/mg).  Total tet Resistance Genes - Poultry Compost 1.00E+09  9.38E+08  Abundance (copies/mg)  tet M tet Q 8.00E+08  tet O tet W  6.00E+08  4.00E+08  2.00E+08 9.47E+07 0.00E+00  9.59E+04  1.35E+04  Jan 25/04 before birds  June 2/04  8  .8  0 E  + 0 8  7  .8  0 E  + 0 8  6  .8  0 E  + 0 8  5  .8  0 E  + 0 8  4  .8  0 E  + 0 8  3  .8  0 E  + 0 8  2  .8  0 E  + 0 8  1  .8  0 E  + 0 8  8  .0 0 E  + 0 7  -2  .0 0 E  + 0 7  2.74E+07 July 23/04  Aug 20/04  Sept 6/04  Sample date  Figure 4.1: Relative abundance of Tcr genes in poultry compost – January – September 2004 (average values; n = 3); error bars represent standard deviation.  160  Total tet Resistance Genes - Poultry Soils 1.00E+07 8  .6  5  E  + 0 6  7  .6  5  E  + 0 6  6  .6  5  E  + 0 6  5  .6  5  E  + 0 6  4  .6  5  E  + 0 6  3  .6  5  E  + 0 6  2  .6  5  E  + 0 6  1  .6  5  E  + 0 6  6  .5  0 E  + 0 5  -3  .5  0 E  + 0 5  tet Q tet O tet W  5.00E+06  3.03E+04  Sample date  Nov 4/04  Oct 29/04  Oct 20/04  Oct 14/04  Oct 8/04  Sept 15/04  Sept 8/04  Sept PRE spray  0.00E+00  1  .6  6  E  + 0 4  4.64E+03  Jan 25/05  3  06  Dec 10/04  6E  E+ . 34  Dec 3/04  3. 0  + 06  Sept 24/04  Abundance (copies/mg)  tet M  Figure 4.2: Relative abundance of Tcr genes in soil fertilized by poultry compost – September 2004 – January 2005 (average values; n = 3); error bars represent standard deviation. The relative abundance of Tcr genes was higher (although not statistically significant) in the forested control soil than in the agricultural field soil. For the purposes of this investigation, the experiment was considered as a time-course evaluation using the tetracycline resistance gene abundance from the pre-sprayed soil from September 2004 as the control. Samples of compost and soil after distribution of compost on the field under study were specifically selected to follow persistence of these genes under normal weathering conditions. There was no statistically significant difference between the mean gene abundance of the total of four Tcr genes in poultry compost or in soil fertilized with the same poultry compost over a period of six months (Sept 2004 – Jan 2005).  161  4.3.3  Antimicrobial Susceptibility Tests  Samples of litter and compost (prior to distribution on field soil) were collected every two weeks for antimicrobial susceptibility (AMS) tests beginning in January 2004. The last day that Salmonella spp. could successfully be cultured from the compost pile was March 8, 2004.  E. coli could not be cultured from samples after March 16, 2004 and  Enterococcus spp. was successfully cultured only until March 23, 2004. Collection of the bacteria samples for AMS testing was suspended in April 2004 as cultures of the three target bacterial species were not successfully cultured in samples collected after March 23, 2004. Breakpoints used for the assessment of antimicrobial susceptibility of the three selected bacterial species to tetracyclines were defined by NARMS criteria (susceptible if < 4 μg/mL; resistant if > 16 μg/mL) (US Department of Agriculture, 2000). Antimicrobial susceptibility tests revealed that every isolate from every culture from all three bacterial species was resistant to both oxytetracycline and tetracycline. 4.4 Discussion Within the Sumas watershed in British Columbia over the past decade, the growth of the poultry (as well as cattle and swine) population within the region is a striking example of intensified agricultural production. Since 1996, data from the Canadian agricultural census reveals that the total population of chickens on census day (broilers, breeding stock and laying hens) has grown from 872,075 to 8,431,946 (Statistics Canada, 1996; Statistics Canada, 2001; Statistics Canada, 2007). The 2006 poultry population was based on a total of 244 farms reporting. Figure 4.3 illustrates the relative increase in poultry (chickens only excluding turkeys and specialty bird species), swine and cattle within the Sumas watershed between 1996 and 2001. The increase in poultry population within the boundaries of an agricultural watershed is accompanied by considerable increase in the volume of manure requiring disposal. The average weekly production of chicken manure using an open floor housing system ranges between 0.27 kg/week (for layer pullets) to 1.0 kg/week (for broiler breeder) per chicken (BC Ministry of Agriculture Food and Fisheries, 1992).  While land application of  poultry manure is a valuable soil conditioner, the amount of composted manure that can  162  be distributed on crop land as fertilizer is limited.  Agronomic and environmental  conditions dictate the acceptable times of the year during which manure may be spread on cropland (usually until mid-October in the British Columbia south coast) and thus, compost may be required to remain in storage until appropriate seasonal conditions allow land application of manure. As manure can be a major pollution source and contributor to surface and groundwater contamination, land application of composted manure must be managed such that crop production can be optimized without any accompanying hazard to the receiving environment.  Total livestock population on census day 9,000,000 Poultry  7,000,000  Pigs  70,000 60,000  Cattle  50,000  5,000,000  40,000  4,000,000  30,000  3,000,000  20,000  2,000,000  10,000  1,000,000 0  0 1996  2001  2006  Year  Figure 4.3: Livestock in the Sumas Watershed (Statistics Canada, 1996, 2001, 2007) 4.4.1  Chemical Analysis of Tetracycline Residues  Variation in compost quality, most notably in presence of lipids, between surface and sub-surface samples was observed in most of the samples collected between March 4 and April 14, 2004.  In the province of British Columbia, composting of dead birds and  broken eggs is an acceptable practice provided that certain guidelines are observed (BC Ministry of Agriculture Food and Fisheries, 1992).  These guidelines provide basic  163  Pigs & Cattle  8,000,000  6,000,000 Poultry  80,000  requirements for carcass disposal including temperature and duration of composting, structure and location of composting facility and approximate capacity (to accommodate normal mortality rate of 1.5 head/1000 market aged birds weighing approximately 2.0 kg). Addition of fully composted birds (when there are no signs of bones or feathers) to manure for eventual land spreading is permitted (BC Ministry of Agriculture Food and Fisheries, 1992). It is possible that the variability of lipid content in the compost samples reflected presence of poultry carcasses or broken eggs primarily near the surface of the compost pile.  In this study, the presence of variable lipid fractions in compost samples  compromised the extraction process and affected the chemical determination by ESI LC MS/MS. The recent review by O’Connor and Aga (2007) summarized several of the complications that make chemical analyses of tetracyclines in manure and soil samples particularly challenging. These authors highlighted the wide variability of conditions which affect tetracycline measurement. These range from field conditions (soil type, drainage and the amount of mixing that occurs after manure application to soil) to physical chemical processes of extraction (interference of co-extracted organic matter, effects of surface tension and viscosity). The analytical recoveries for tetracycline residues from soil or manure determined by several mass spectrometry techniques appear to range from about 30% to over 100% (O’Connor & Aga, 2007). An earlier review (Anderson et al., 2005) of analytical techniques for measuring tetracycline in various (mostly food-related) sample matrices described many of the aforementioned analytical complication. The results of the present study have demonstrated some of the challenges reported by several other research teams. The poultry production facility where these studies were conducted was not affected by the avian flu virus although quarantine conditions were imposed on all poultry farms in the region after February 19, 2004. Approximately 19 million birds within the Fraser Valley were destroyed during the outbreak of avian influenza A (H5N1) virus. Canadian Food Inspection Agency lifted the ban on movement of poultry on April 8, 2004 after no further cases of avian flu were observed. Normal mortality rates at the poultry farm involved in this study were reported and during the period of evaluation, birds did not 164  require medical treatment that was different from other years. Usually, oxytetracycline is prescribed by a veterinarian when birds display symptoms of illness and the treatment dose is administered to the flock via their water system. Birds produced during the broiler cycle studied in this investigation did not require therapeutic administration of oxytetracycline. The resulting measured lipid fraction of this poultry waste compost did not reflect atypically high numbers of poultry carcasses or broken eggs. The tetracycline family of antibiotics is well known to be sensitive to both heat (Loftin et al., 2008) and light thus temperature within a compost pile and exposure to daylight will affect the persistence of these compounds. When executed properly, the composting process undergoes three phases: mesophilic phase (temperatures between 20-40°C), thermophilic phase of intense microbial activity (usually above 40oC but below 60-80°C) and the cooler temperature curing phase during which slower processes of decomposition take place (Tiquia & Tam, 2000; Cooke et al., 2001; Wang et al., 2007; Petric & Selimbasic, 2008). Arikan et al. (2007) determined the half life of OTC in composted cattle manure/straw/woodchips to be approximately 3.2 days. Other researchers have demonstrated that concentrations of OTC in manure-amended surface soil declined to below 50% of the initial concentration (270 μg/kg) applied to experimental land plots after 3 weeks (Aga et al., 2005). Dolliver and Gupta (2008a) demonstrated the effect of seasonal precipitation and land tillage on concentrations of antibiotic residues in run-off from fertilized fields.  These investigators concluded that antibiotic concentrations  (chlortetracycline, monensin and tylosin) were higher in run-off from land which was not tilled during non-growing periods of higher precipitation. Stoob et al. (2007) reported that sorption of antibiotics (sulfonamides in this example) and surface flow as a result of weather conditions influenced the dissipation and transport of antibiotic residues. Recent studies of Dolliver and Gupta (2008b) also found that antibiotics (specifically chlortetracycline, monensin and tylosin) measured in run-off leachate from compost piles were positively correlated to the initial concentration of antibiotics in the manure. Competing physical and chemical processes influence the concentrations of antibiotic residues found in soil and run-off samples.  165  Recent reports of other researchers (published since 2007) as outlined above have clearly demonstrated that antibiotic residues are present in compost and soil. Conditions of the poultry compost pile under investigation in the present study were favourable for the degradation of tetracycline residues by heat and light. Sub-surface temperatures of the compost pile were consistently about 40°C (40.6°C + 3.2) and its location allowed exposure to normal daylight cycles. Results of chemical analyses of these compost and soil samples, alone, cannot determine whether tetracycline residues contribute to contamination of run-off from fertilized fields. These findings reinforce the need for further research in chemical analyses of tetracycline residues in complex organic matrices in order to better understand the degradation and transport mechanisms of these compounds in the terrestrial and aquatic environments. 4.4.2  Microbiological Analyses  Analyses of four specific tetracycline resistance genes in compost by qPCR determined relative abundances ranging between 1.35 x 104 copies/mg (June 2/04) and 9.38 x 108 copies/mg (Aug 20/04). The relative abundance of tet (M), tet (O), tet (Q), and tet (W) in soil varied among samples but appeared to remain constant over time after application of compost onto the field. The abundance of the Tcr genes neither declined nor increased over the monitoring period. The total abundance of the Tcr genes for the barn litter before introduction of the birds on January 25, 2004 was 4 orders of magnitude lower than that observed for the compost or soil samples after the broiler production cycle (Figure 4.2). Of the total Tcr gene abundance for the litter sample, comparatively higher abundance of tet (M) was observed than for any of the other samples measured. In contrast with results reported by Patterson et al. (2007) in which no ribosomal protection protein genes in agricultural soils from several European countries could be detected by microarrays using PCR amplicons, RPP Tcr genes were observed in soil fertilized by poultry compost in this investigation. The abundance of the total of four tetracycline resistance gene and 16S rRNA genes as an indication of the total bacterial biomass is shown in Figure 4.4. During the period of July – Sept, when temperature conditions would favour periods of intense microbial activity,  166  the highest copy numbers of tet resistance genes were observed in the compost samples but 16S rRNA gene measurements indicate that higher numbers of total bacterial species were also present in the compost. This finding is important in that a greater proportion of the microbial biomasss contained Tcr genes in the compost during the same seasonal conditions when lower relative abundance of Tcr genes was observed in water-borne bacteria samples collected from receiving water (see Chapter 3). These results and the results of the antimicrobial susceptibility tests suggest that the higher relative proportion of tetracycline resistant organisms occur in the bacteria of poultry manure.  167  7.00E+09 Total tet resistance genes  Gene abundance (copies/mg)  6.00E+09  16S rRNA genes  5.00E+09  4.00E+09  3.00E+09  2.00E+09  1.00E+09  0.00E+00 Jan 25/04 before birds  June 2/04  July 23/04  Aug 20/04  Sept 6/04  Sample Date  8.00E+08 Total tet resistance genes  Gene abundance (copies/mg)  7.00E+08  16S rRNA genes  6.00E+08 5.00E+08 4.00E+08 3.00E+08 2.00E+08 1.00E+08  Jan 25/05  Dec 10/04  Dec 3/04  Nov 4/04  Oct 29/04  Oct 20/04  Oct 14/04  Oct 8/04  Sept 24/04  Sept 15/04  Sept 8/04  Sept PRE spray  0.00E+00  Sample Date  Figure 4.4: Total Tcr and 16S rRNA genes measured in compost (top) and soil samples (bottom). Values presented are the average of 3 measurements per sample extract; error bars represent standard deviation.  168  The relative abundance of tetracycline resistance genes in compost or soil is dependent on the resident bacteria communities and the composition of gut microflora of the animals from which the compost manure is derived. Both the bacterial species present and the local ecology of these bacterial communities are highly variable. Chee-Sanford et al. (2001) confirmed the presence of eight classes of RPP tetracycline resistance genes in lagoons and groundwater under swine production facilities.  Smith et al. (2004)  observed significantly higher concentrations of tet (W) and tet (O) genes downstream of feedlot lagoons. Recent studies of sediment from the Mekong River have detected tet (W), tet (S) and tet (O) in most of the samples analysed (Kobayashi et al., 2007). Exposure to light has been implicated as affecting the decay rate of tetracycline resistance genes (Engemann et al., 2006).  In the current experiment, the measured relative  abundance of the four common tetracycline resistance genes in soil after application of the poultry compost may reflect conditions achieved after input, transport in surface runoff and degradation processes reach equilibrium. Selecting an appropriate control site to compare relative abundance of tetracycline resistance genes in soil bacteria is difficult given the vast diversity in community structures among various locations. Recently, Kobashi et al. (2007) reported that a forest soil reference site was reasonable for use in their evaluation of tetracycline resistance genes in agricultural soil and various animal manures since no RPP Tcr genes were found in 6 isolates from the forest control soil. Bacteria communities are likely to be different in forested soils near tree roots and in soils collected from flat agricultural fields. Analyses of the control soil used in the present investigation confirmed this and revealed a rich bacterial population with a relative abundance of the four measured tetracycline resistance genes dominated by tet (M). In contrast to the findings of Kobashi et al. (2007), soil from the forested site of this investigation could not be used as an appropriate control from which to compare the background Tcr gene abundance for a soil that has not been fertilized by poultry compost. Antimicrobial susceptibility testing of isolates of the three species of pathogenic bacteria revealed that all of the samples that were successfully cultured from the compost were resistant to tetracyclines (as defined by NARMS criteria for susceptibility/resistance). 169  This method is particularly valuable for assessing antimicrobial susceptibility/resistance of pathogens which can be cultured in the laboratory. Other researchers have used the same AMS method to establish positive correlation between the incidence of tetracycline resistance in Salmonella spp., Campylobacter spp. and E. coli isolated from dairy calves and feeding of oxytetracycline medicated milk replacer (Kaneene et al., 2008). Bunner et al. (2007) conducted a study to compare the antimicrobial susceptibility of E. coli isolated from fecal samples from pigs raised in conventional farms and those reared in antibiotic-free conditions. They reported that tetracycline was among the antibiotics found in the three most frequent patterns of multiple resistances in the E. coli from pig fecal samples (streptomycin-tetracycline, sulfamethoxazole-tetracycline, and kanamycinstreptomycin-sulfamethoxazole-tetracycline). Results of the present study confirm that tetracycline resistance is often found in some species of pathogenic bacteria found in poultry, cattle and swine waste. Although the selected bacteria species appeared to die off in the composting process, the abundance of four tetracycline resistance genes that were measured in compost and the abundance measured in field soil prior to fertilization were similar. It is important to consider that oxytetracycline is a naturally occurring antibiotic produced by Streptomyces rimosus (Chopra & Roberts, 2001) and thus it is difficult to determine the background profile of tetracycline resistance genes in soil bacteria from diverse locations. This investigation suggests that the bacteria represented by abundance of 16S rRNA genes and four selected tetracycline resistance genes measured on soil fertilized by poultry compost remains relatively constant throughout the fall – winter season. The seasonal trend that was observed in water-borne bacteria collected from the Sumas River (Chapter 3 and 4) was not observed in bacteria collected from the terrestrial receiving environment in this experiment. Measurements of turbidity in the Sumas River increased with more frequent rainfall events in the fall – winter season indicating that soil erosion is likely to be occurring as soil becomes saturated and overland run-off contributes to stream flow. For this reason, contribution of tetracycline resistance genes associated with soil bacteria should be considered among the possible contaminants in stream waters and groundwater although specific source tracking is extremely difficult.  170  4.5 Conclusions   The relative abundance of the four tetracycline resistance genes and the proportion of resistance genes within the bacteria biomass remained relatively constant over time although tet (W) genes were detected in greater abundance in compost samples and both tet (O) and tet (W) genes were detected in greater abundance later in the season after field distribution.    There was no statistically significant difference between the mean gene abundance of the total of four selected tetracycline resistance genes in poultry compost or in soil fertilized with the same poultry compost over six months. The abundance of the Tcr genes neither declined nor increased over the monitoring period and therefore with respect to the initial hypothesis of this investigation, there is insufficient evidence to confirm that land fertilization with poultry compost increases abundance of selected Tcr genes in stream water bacteria.    The relative abundance of the four tetracycline resistance genes were different in soil collected from a forested control site and soil collected from a flat cultivated agricultural field.  Selection of an appropriate control site for comparing  tetracycline resistance genes in soil bacteria is difficult.   Chemical analyses by LC ESI MS/MS of soil and poultry waste compost samples did not provide satisfactory sensitivity and precision to quantify selected antibiotics suggesting that these are near or below the limits of detection.  171  4.6 References 1. Aga, D.S., S. O'Connor, S. Ensley, J.O. Payero, D. Snow & D. Tarkalson. 2005. 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A. 928: 177-186.  178  CHAPTER 5 - CONCLUSION The central hypothesis of this doctoral thesis was that antibiotic resistance genes or antibiotics can be transported through aquatic ecosystems and could be the most important  agricultural  cause  promoting  antibiotic  resistance  development  in  environmental bacteria. 5.1 Discussion Relating Manuscripts to Each Other Results of the literature review verified the need to investigate antibiotics and antibiotic resistance genes as environmental contaminants in agricultural watersheds. Chapter 2 responded to the first research question to investigate whether the presence and concentrations of antibiotic compounds and tetracycline resistance genes be reliably determined and routinely monitored in the Sumas watershed.  Concentrations of  antibiotic residues in stream water samples were measured close to or below the detection limits of the analyses.  For this reason, chemical analyses alone could not provide  sufficient evidence that levels of antibiotics exist in the environment at concentration that could exert selective pressure to promote resistance in environmental bacteria. Measurement of relative abundance of four selected tetracycline resistance genes provided the first evidence that contamination of stream networks with antibiotic resistance genes or antibiotic residues in the Sumas watershed changes considerably at different times of the year. Building upon results presented in Chapter 2, a reproducible seasonal pattern was identified in the abundance of tetracycline resistance genes measured in surface waters of the Sumas watershed and discussed in detail in Chapter 3.  In the Sumas River,  tetracycline resistance gene abundance appeared to follow a seasonal cycle of low relative abundance in water column bacteria in summer months and high relative abundance in wetter winter months. Some of the factors postulated to contribute to this effect observed in summer were influence of warmer temperatures, sunlight and ecological pressure on gene disappearance, optimal conditions for biofilm growth and  179  reduced opportunities for introduction of new bacterial hosts due to lower precipitation. In wetter winter months, increase of relative abundance of tetracycline resistance genes was suspected to be influenced by changes to hydrologic conditions that mobilize soil bacteria as rainfall increases, higher stream flow effects on biofilm surfaces and lower gene decay rates during shorter periods of daylight.  Positive Spearman’s Rank  correlations between stream discharge rates (instantaneous and 48 h) and turbidity and the tetracycline resistance genes monitored in the Sumas River suggests that bacteria suspended in the water column can move freely by way of overland flow of water from precipitation delivered into small streams and can be transported via stream networks. Parenthetically, distribution of animal waste on land in the Sumas watershed considerably increases during the late fall period that coincides with more frequent rainfall events. This enhances the opportunities to mobilize animal waste related bacteria in receiving water. Other researchers have identified fertilization of fields with animal manure as a source of dissemination of tetracycline resistance genes into the receiving water courses. The objective of experiments summarized in Chapter 4 was to examine whether the relative abundance of four selected tetracycline resistance genes changed during the manure composting process and following the distribution of poultry compost as field fertilizer. The relative abundance of Tcr genes in soil samples remained generally consistent over several months following application on one field although the relative abundance of Tcr genes measured in the Sumas River increased as rainfall and stream flow increased. In contrast, despite higher relative abundance of Tcr genes measured in the soil of a forested control site, statistically lower relative abundance of Tcr genes was measured in water column bacteria at the control site throughout the year.  Results of this particular  experiment cannot determine whether field fertilization with poultry compost contributes the relative abundance of tetracycline resistance genes measured in the water column. However, positive Spearman’s Rank correlation between turbidity and Tcr genes and the seasonal increase in turbidity as rainfall events become more frequent and intense during the year suggests that Tcr genes associated with particulates can be transported in receiving waters.  180  Results of the mesocosm experiments verified that exposure to sunlight affects decay rates of tetracycline resistance genes in water-borne bacteria (details in Appendix 7). In mesocosms under warm sunny conditions, oxytetracycline readily degraded. Nutrient enrichment and introduction of bacteria associated with animal waste also affect the relative abundance of tetracycline resistance genes. Changes in tetracycline resistance gene abundance in biofilms grown on artificial substrates can be measured in mesocosm experiments (details in Appendix 6). Tetracycline resistance genes may transfer between mobile planktonic bacteria and sessile bacteria secured in biofilm. The experimental design of the mesocosm exposure study could not allow for explicit differentiation between tetracycline resistance genes in the periphyton grown on artificial substrates and those in the biofilm growing on the periphyton. However, measurement of tetracycline resistance genes in the experimental system shows that the total bacterial communities that would be built in biofilms under natural conditions are likely to contain tetracycline resistance genes in them.  As  temperature increases, populations of some species of planktonic bacteria are likely to increase. If ecological conditions favour bacterial species found in biofilm to have greater abundance of tetracycline resistance genes, the likelihood that exchange of genetic material between sessile bacteria and suspended planktonic bacteria may also increase. 5.2 Summary of Conclusions The following conclusions have been drawn based on results of experiments performed within this program of research:   The four selected tetracycline resistance genes are present and transported in receiving water of the watershed under investigation.    In this case study, qPCR measurement of selected tetracycline resistance genes appeared to be a stronger candidate than chemical analyses of antibiotics to indictate the seasonal patterns of the distribution of agriculture-linked contaminants in water samples collected from the Sumas River.  181    Multi-residue analyses of 14 antibiotics in environmental water samples by LC ESI MS/MS did not yield sufficient evidence to reliably monitor their presence as environmental contaminants in the Sumas watershed at the detection limits achieved.    Higher microbial biomass (as indicated by abundance of 16S rRNA genes) was observed in warmer summer months although relative abundance of the four tetracycline resistance genes was lower during the same period.    The relative abundance of the four tetracycline resistance genes measured in the stream varies with season – higher relative abundance of the tetracycline resistance genes in water-borne bacteria of the Sumas River was observed in wetter winter months (November and December 2004).    Nitrate, phosphate and suspended solid material measurements are also higher during wetter months and are reasonable indictors of agricultural influence on stream quality in the Sumas River.    The general trend of the seasonal dynamic pattern that emerged during the 2004 monitoring period repeated in 2005.    Abundance of the total of four selected tetracycline resistance genes was negatively correlated to specific conductivity and chloride concentration at most sites along the Sumas River. The abundance of the total of these same four genes was positively correlated to turbidity at all sites except one site and positively correlated to nitrate concentration at 3 of the 4 smaller tributary stream sites.    Positive Spearman’s Rank correlations were determined between abundance of the total of four tetracycline resistance genes and instantaneous discharge and 48 h discharge at all sites on the Sumas River.    In poultry compost, the relative abundance of the four tetracycline resistance genes and the proportion of resistance genes within the bacteria biomass (as represented by normalization to 16S rRNA genes) remained relatively constant over time. Tet (W) genes were detected in greater abundance in samples collected from the compost pile and tet (O) genes were detected in greater abundance later in the season after field distribution.  182    There was no statistically significant difference between the mean gene abundance of the total of four selected tetracycline resistance genes in poultry compost or in soil fertilized with the same poultry compost over a period of six months.    The relative abundance of the four tetracycline resistance genes were different in soil collected from a forested control site and soil collected from a flat cultivated agricultural field.  Selection of an appropriate control site for comparing  tetracycline resistance genes in soil bacteria is difficult.   Chemical analyses by LC ESI MS/MS of soil and poultry waste compost samples did not provide satisfactory sensitivity and precision to quantify selected antibiotics.    Oxytetracycline degraded rapidly in sunlight in mesocosm experiments conducted in Lawrence, Kansas with the half-life of the compound determined to be 3 h 46 min.    The first order decay coefficient for total abundance of six selected tetracycline resistance genes was always higher in sunlight than in dark conditions.    In static treatments of mesocosms located in Lawrence, Kansas, the four tetracycline resistance genes (tet (M), tet (O), tet (Q) and tet (W)) appeared to be gained and lost in biofilms over 14 days suggesting that it is possible these genes could migrate between water column bacteria and those in periphyton biofilms.    Beaker-scale microcosm experiments conducted in situ on the edge of the Sumas River could not verify the trend in tetracycline resistance gene decay observed in the mesocosm study. Significant differences between abundance of tetracycline resistance genes in static systems and in dynamic field systems with semicontinuous waste input could not be established using this experimental design.    Mesocosm experiments provided evidence that the lower relative tetracycline resistance gene abundance observed in the Sumas River system during summer months may be the result of longer periods of exposure to sunlight and/or degradation due to warmer summer temperatures.  183  The examination of central hypothesis required testing of an alternate hypothesis that transport through the environment of antibiotics at significant concentrations could be responsible for de novo induction of resistance in environmental bacteria.  Facile  transport through ecosystem compartments optimizes conditions for exposure of bacteria to tetracycline resistance genes which, in turn, enables potential for exchange of genetic material that confers resistance between bacteria species.  This can have direct  consequences on resident species (including livestock), overall ecosystem health and public health. Limiting the distribution of environmental contaminants that allow cell to cell contact with stressors is necessary to ensure decreased opportunities of horizontal gene transfer among bacteria that could promote antibiotic resistance in clinically relevant species. 5.3 Recommendations for Future Research Every program of research generates a wide array of new research questions to be addressed by future scholars. Methods of chemical analyses for antibiotic residues by liquid chromatography electro-spray ionization tandem mass spectrometry (LC ESI MS/MS) of environmental samples have improved since commencement of this research. Although some new methods for analyses of antibiotics have been reported (Gros et al., 2009), there remains a need for analytical method development and improved extraction techniques, especially in complex heterogeneous matrices. Future studies could further explore potential pathways for antibiotics and antibiotic resistance genes by using experimental systems that are sized between model mesocosms and the actual agricultural watershed. Smaller land plot areas within the watershed could be monitored over time to examine environmental effects of tetracycline resistance genes linked to given manure application rates and magnitude. As hydraulic conductivity of receiving soils governs the flow of water and accompanying contaminants in receiving environments and intrinsic permeability of soils varies with soil type, soil quality and particle size, experiments to evaluate the movement of tetracycline resistance genes through natural soils of different types and hydraulic conductivities would yield valuable information.  184  Although it is possible to culture only a small portion of soil bacteria populations, it would be useful to attempt to characterize some of the bacteria species given the broad genetic diversity of organisms in collected environmental samples. Further research is required to ascertain whether antibiotic resistance genes migrate between bacteria in biofilms and planktonic strains of bacteria. Genetic markers such as green fluorescent proteins could be used to track specific resistance genes in a culturable species of biofilm bacteria and in the same strain of bacteria in the water column. Genetic methods offer promising tools for tracking the epidemiology of antibiotic resistance in zoonotic pathogens found in the gut flora of food animals. Techniques such as multilocus sequence typing (MLST) systems (Miller et al., 2006) in food-borne pathogen strains could provide valuable evidence to link dissemination of antibiotic resistance genes to food animal sources. Commensal bacteria in poultry manure appear to be a reservoir for antibiotic resistance and virulence genes in chicken production facilities.  Multiple antibiotic-resistant  commensal E. coli and Salmonella strains carrying virulence genes have been found on commercial broiler chicken farms (Diarrassouba et al., 2007; Diarra et al., 2007). Cultivable commensal bacteria (Enterococcus spp., Escherichia coli, and Campylobacter spp.) isolated from the cecal droppings of broiler chickens have been found to harbour a variety of tet resistance determinants regardless of the tetracycline exposure history of the birds (Fairchild et al., 2005). Despite the growing consumer interest in purchasing organic chicken products, there does not seem to be a difference between patterns of tetracycline resistance in organic and conventionally reared chickens (Proietti et al., 2007). Further research is needed to examine antibiotic resistance in the gut flora of chickens, cattle and swine. Resistance to fluoroquinolone antibiotics (e.g. ciprofloxacin, enrofloxicin and oxolinic acid) in zoonotic pathogens is an extremely important health risk. Other important antibiotic genes such as quinolone resistance genes should be investigated as environmental contaminants in bacteria present in agricultural watersheds.  185  Future monitoring efforts for examining antibiotic resistance genes in aquatic ecosystems should also include measurement of trace metals as both analytical determination and resistance traits of bacteria are affected by metals. It is also important to establish the relative contributions of both domestic wastewaters and agricultural run-off to the abundance of antibiotic resistance genes measured in the receiving environment. Another emerging issue among the possible contributors to development of antimicrobial resistance is the ubiquitous presence of triclosan in wastewater and surface run-off as a result of household and commercial disinfectant use. Among the recommendations for future study based on this research would be inclusion of triclosan among the analytes for chemical analyses and, with the development of a suitable probe for measuring genes displaying resistance to triclosan, using microbiological analyses to measure potential for transport of triclosan in receiving environments. This study has contributed to some important knowledge gaps previously identified by other scholars. The results of this program of research provide evidence that the seasonal dynamics of the receiving watershed could influence the transport of antibiotic resistance genes throughout ecosystem compartments.  Confounding environmental factors add  layers of complexity to interpretation of experimental findings that can, in many instances, be controlled in laboratory investigation.  It is the blend of laboratory  experimentation and field-based observation that provides the strongest evidence for evaluating the ecosystem and human health risk associated with antibiotics and antibiotic resistance genes as environmental contaminants. The overarching aim of this doctoral thesis was to provide evidence that transport of antibiotics or antibiotic resistance genes in the environment may facilitate the exposure of bacteria to agents that exert selection pressure for development of antimicrobial resistance traits.  Data provided herein demonstrates that four selected tetracycline  resistance genes could be used as indicators that antibiotic resistance genes are present in stream waters of an agricultural watershed and are subject to seasonal changes in environmental conditions. Although there is a popularly held belief that distribution of compost on agricultural land as field fertilizer increases the abundance of antibiotic  186  resistance genes in soil over time, this doctoral thesis does not provide quantitative evidence that such an increase occurred over six months.  However, the empirical  evidence supported by this research, in situ observations and well-documented changes in water quality parameters over several years in the watershed under investigation, seem to indicate a likely connection between agricultural activities and the sources of antibiotic resistance genes in the environment. The results of this research suggest that minimizing the human and ecosystem health risks associated with exposure to antibiotic resistance in bacteria can be accomplished through pollution prevention, careful management of agricultural waste and communication of research results. 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B. 47: 37-46.  209  Appendix 1: Primers and TaqMan Probes Used in Preparation of Amplicons for Tetracycline Resistance Gene Determination Target 16S rDNA Tet(M) 6 Tet(O) 7 Tet(Q) 7 Tet(W) 7  Sense primer 1  TaqMan Probe 2  Anti-sense primer  ATGGCTGTCGTCAGCT 3  CCAAAGTACCACCATACGCAG 4  ACGGGCGGTGTGTAC 5  GGTTTCTCTTGGATACTTAAATCAATCR AAGAAAACAGGAGATTCCAAAACG AGGTGCTGAACCTTGTTTGATTC GCAGAGCGTGGTTCAGTCT  ATGCAGTTATGGARGGGATACGCTATGGY ACGTTATTTCCCGTTTATCACGG TCGCATCAGCATCCCGCTC TTCGGGATAAGCTCTCCGCCGA  CCAACCATWCAATCCTTGTTSAC CGAGTCCCCAGATTGTTTTTAGC GGCCGGACGGAGGATTT GACACCGTCTGCTTGATGATAAT       1  – Sequences denoted 5'-3'. – Sequences modifications added: 5'-FAM (6-carboxyfluorescein; fluorophore); 3'-TAMRA (carboxytetramethylrhodamine; quencher). 3 –(Ferris & Muyzer, 1996) 4 –(Harms et al. 2003) 5 –(Lane, 1991) 6 – (Peak et al.,2006) 7 –(Smith et al. 2004). 2  210  Appendix 2: Whisker Box Plots of Recovery of 200 μg/L Antibiotic QC Standards of Analytes in Stream Water Samples (see Table 3.1 for abbreviations). N = 36.  211  Appendix 3: Extraction and Determination of Chlorophyll a in Periphyton Samples of periphyton were collected on Days 0, 1 and 7 of the summer 2005 mesocosm experiment and immediately frozen. All samples were stored at -20C in the dark until extraction. Analytical Procedure:   Periphyton samples were extracted November 28, 2005.    Acetone extraction solution was prepared using 90% HPLC grade acetone combined with 10% milliQ water.    Each 1 cm2 250 grit sandpaper disk was placed in a 15-mL centrifuge tube containing 10 mL of aqueous acetone extraction solution.    Sample tubes were placed in a test tube rack covered entirely with aluminum foil and held for 24 hr at 4°C for extraction of pigments    All samples were equilibrated to room temperature prior to being centrifuged at ~1500 rpm for 5 minutes.    Chlorophyll a was determined by measuring fluorescence with a 10-AU fluorometer (Turner Designs) at the UBC Zoology laboratory.    Fluorescence was measured before and after acidification with 3 drops of 50% HCl.    Chlorophyll a was calculated as follows:  [Chlorophyll a] = 1.79 x 1.128 [Fo – Fa] x v/a Where: Fo = fluorescence before acidification Fa = fluorescence after acidification 1.79 = acid ratio correction factor 1.128 = conversion factor for specific sensitivity v = volume extracted (0.01 L) a = substratum area (cm2)  212  Appendix 4: Whisker Box Plots of Water Quality Parameters Comparing Dry (May – September) and Wet (October – March) Seasons.  213  214  215  216  217  218  219  Appendix 5: Spearman Rank Correlations for Tetracycline Resistance Genes and 16S rRNA Normalized Genes and Water Quality Parameters for All Sites Site 1 - Correlations (Spearman Rank correlation coefficients in parentheses) Parameter Tet (W)  Correlation (+ or -) + + -  With Parameters Instantaneous discharge(0.726) Discharge 48 h (0.647) Temperature (-0.744) Specific conductivity (-0.523) Turbidity (-0.542) Chloride (-0.825)  Tet (O)  + +  Instantaneous discharge(0.538) Rainfall 72 h (0.522)  Tet (Q)  + + + + + + + + + + + -  Instantaneous discharge(0.682) Discharge 48 h (0.574) Rainfall 72 h (0.541) Specific conductivity (-0.568) Chloride (-0.546) Instantaneous discharge(0.744) Discharge 48 h (0.647) Chloride (-0.715) Instantaneous discharge(0.762) Discharge 48 h (0.653) Temperature (-0.670) Turbidity (0.514) Chloride (-0.814) Instantaneous discharge(0.609) Discharge 48 h (0.600) Rainfall 72 h (0.513) Temperature (-0.740) Turbidity (-0.522) Chloride (-0.788)  Tet (M) Total Tcr Genes  Genes Normalized to 16S rRNA  220  Site 2 - Correlations (Spearman Rank correlation coefficients in parentheses) Parameter Tet (W)  Tet (O)  Tet (Q) Tet (M)  Total Tcr Genes  Genes Normalized to 16S rRNA  Correlation (+ or -) + + + + + + + + + + + + + + + + + + +  With Parameters Instantaneous discharge(0.833) Discharge 48 h (0.738) Rainfall 72 h (0.624) Temperature (-0.745) Specific conductivity (-0.627) Turbidity (0.809) Chloride (-0.752) Instantaneous discharge(0.826) Discharge 48 h (0.755) Rainfall 72 h (0.589) Turbidity (0.550) Rainfall 72 h (0.624) Turbidity (0.582) Instantaneous discharge(0.874) Discharge 48 h (0.790) Turbidity (0.770) Chloride (-0.758) Instantaneous discharge(0.810) Discharge 48 h (0.667) Temperature (-0.682) Turbidity (0.800) Chloride (-0.793) Instantaneous discharge(0.771) Discharge 48 h (0.714) Temperature (-0.533) Turbidity (0.717)  221  Site 3 - Correlations (Spearman Rank correlation coefficients in parentheses) Parameter Tet (W)  Tet (O)  Tet (Q)  Tet (M) Total Tcr Genes  Genes Normalized to 16S rRNA  Correlation (+ or -) + + + + + + + + + + + + + + + + + + + + -  With Parameters Instantaneous discharge(0.794) Discharge 48 h (0.750) Rainfall 72 h (0.596) Temperature (-0.693) Specific conductivity (-0.709) Turbidity (0.699) Chloride (-0.737) Instantaneous discharge(0.796) Discharge 48 h (0.690) Rainfall 72 h (0.507) Specific conductivity (-0.520) Turbidity (0.514) Chloride (-0.561) Instantaneous discharge(0.726) Discharge 48 h (0.609) Temperature (-0.542) Turbidity (0.554) Chloride (-0.805) Instantaneous discharge(0.853) Discharge 48 h (0.759) Instantaneous discharge(0.815) Discharge 48 h (0.738) Temperature (-0.728) Specific conductivity (-0.512) Turbidity (0.672) Chloride (-0.840) Instantaneous discharge(0.763) Discharge 48 h (0.745) Rainfall 72 h (0.519) Temperature (-0.730) Specific conductivity (-0.500) Turbidity (0.715) Chloride (-0.836)  222  Site 4 - Correlations (Spearman Rank correlation coefficients in parentheses) Parameter Tet (W)  Tet (O)  Tet (Q)  Tet (M)  Total Tcr Genes  Genes Normalized to 16S rRNA  Correlation (+ or -) + + + + + + + + + + + + + + + + + + + + +  With Parameters Instantaneous discharge(0.833) Discharge 48 h (0.857) Rainfall 72 h (0.624) Specific conductivity (-0.700) Turbidity (0.700) NOX (-0.518) Chloride (-0.624) Instantaneous discharge(0.922) Discharge 48 h (0.874) Rainfall 72 h (0.741) Specific conductivity (-0.679) Turbidity (0.578) Instantaneous discharge(0.643) Discharge 48 h (0.595) Rainfall 72 h (0.549) Temperature (-0.551) Specific conductivity (-0.636) Chloride (-0.551) Instantaneous discharge(0.738) Discharge 48 h (0.810) Temperature (-0.579) Turbidity (0.627) Chloride (-0.774) Instantaneous discharge(0.810) Discharge 48 h (0.810) Rainfall 72 h (0.561) Temperature (-0.524) Specific Conductivity (-0.545) Turbidity (0.764) Chloride (-0.788) Instantaneous discharge(0.771) Discharge 48 h (0.886) Temperature (-0.577) Specific conductivity (-0.600) Turbidity (0.800)  223  Site 5 - Correlations (Spearman Rank correlation coefficients in parentheses) Parameter Tet (W)  Tet (O)  Tet (Q)  Tet (M)  Total Tcr Genes  Genes Normalized to 16S rRNA  Correlation (+ or -) + + + + + + + + + + + + + + + + + + + + + + + + + + + + -  With Parameters Instantaneous discharge(0.844) Discharge 48 h (0.859) Rainfall 72 h (0.606) Temperature (-0.756) Specific conductivity (-0.842) Turbidity (0.613) NOX (0.789) Chloride (-0.724) Instantaneous discharge(0.755) Discharge 48 h (0.706) Rainfall 72 h (0.628) Specific conductivity (-0.643) NOX (0.741) Instantaneous discharge(0.564) Discharge 48 h (0.504) Rainfall 72 h (0.565) Specific conductivity (-0.535) NOX (0.594) Chloride (-0.519) Instantaneous discharge(0.674) Discharge 48 h (0.662) Rainfall 72 h (0.526) Temperature (-0.670) Turbidity (0.506) NOX (0.624) Chloride (-0.767) Instantaneous discharge(0.791) Discharge 48 h (0.785) Rainfall 72 h (0.521) Temperature (-0.802) Specific Conductivity (-0.688) Turbidity (0.577) NOX (0.759) Chloride (-0.805) Instantaneous discharge(0.714) Discharge 48 h (0.741) Rainfall 72 h (0.576) Temperature (-0.848) Specific Conductivity (-0.637) Turbidity (0.527) NOX (0.694) Chloride (-0.756)  224  Site 6 - Correlations (Spearman Rank correlation coefficients in parentheses) Parameter Tet (W)  Tet (O)  Tet (Q)  Tet (M)  Total Tcr Genes  Genes Normalized to 16S rRNA  Correlation (+ or -) + + + + + + + + + + + + + + + + + + + + + -  With Parameters Instantaneous discharge (0.667) Discharge 48 h (0.690) Rainfall 72 h (0.601) Specific conductivity (-0.636) NOX (0.645) Chloride (-0.834) Instantaneous discharge(0.810) Discharge 48 h (0.810) Rainfall 72 h (0.549) Specific conductivity (-0.645) NOX (0.627) Chloride (-0.797) Instantaneous discharge(0.619) Discharge 48 h (0.548) Temperature (-0.709) Specific conductivity (-0.518) NOX (0.627) Chloride (-0.706) Instantaneous discharge(0.786) Discharge 48 h (0.762) Rainfall 72 h (0.601) Specific conductivity (-0.655) NOX (0.645) Chloride (-0.806) Instantaneous discharge(0.810) Discharge 48 h (0.810) Temperature (-0.591) Specific Conductivity (-0.664) NOX (0.709) Chloride (-0.870) Instantaneous discharge(0.600) Discharge 48 h (0.771) Specific Conductivity (-0.550) NOX (0.600) Chloride (-0.770)  225  Site 7 - Correlations (Spearman Rank correlation coefficients in parentheses) Parameter Tet (W)  Tet (O)  Tet (Q)  Tet (M)  Total Tcr Genes  Genes Normalized to 16S rRNA  Correlation (+ or -) + + + + + + + + + + + + + + + + + + + + + + -  With Parameters Instantaneous discharge (0.750) Discharge 48 h (0.718) Rainfall 72 h (0.619) Temperature (-0.837) Specific conductivity (-0.877) Turbidity (0.632) Chloride (-0.847) Instantaneous discharge(0.738) Discharge 48 h (0.712) Rainfall 72 h (0.775) Specific conductivity (-0.716) Chloride (-0.555) Instantaneous discharge(0.765) Discharge 48 h (0.732) Temperature (-809) Specific conductivity (-0.742) Turbidity (0.519) Chloride (-0.812) Instantaneous discharge(0.847) Discharge 48 h (0.799) Rainfall 72 h (0.581) Temperature (-0.784) Specific conductivity (-0.781) Turbidity (0.561) Chloride (-0.857) Instantaneous discharge(0.785) Discharge 48 h (0.747) Rainfall 72 h (0.520) Temperature (-0.885) Specific Conductivity (-0.832) Turbidity (0.616) Chloride (-0.843) Instantaneous discharge(0.745) Discharge 48 h (0.749) Rainfall 72 h (0.599) Temperature (-0.762) Specific Conductivity (-0.785) Turbidity (0.654) Chloride (-0.809)  226  Site 8 - Correlations (Spearman Rank correlation coefficients in parentheses) Parameter Tet (W)  Tet (O) Tet (Q) Tet (M) Total Tcr Genes  Genes Normalized to 16S rRNA  Correlation (+ or -) + + + + + +  With Parameters Instantaneous discharge(0.568) Discharge 48 h (0.511) Rainfall 72 h (0.508) Temperature (-0.536) Turbidity (0.651) NOX (0.689) Turbidity (0.533)  + + + + + + + + + + + + + +  Instantaneous discharge(0.546) Rainfall 72 h (0.533) Turbidity (0.690) Instantaneous discharge(0.561) Turbidity (0.754) NOX (0.520) Instantaneous discharge(0.625) Discharge 48 h (0.507) Rainfall 72 h (0.502) Temperature (-0.559) Turbidity (0.785) NOX (0.664) Instantaneous discharge(0.533) Temperature (-0.699) Turbidity (0.671) NOX (0.728)  227  Site 9 (control) - Correlations (Spearman Rank correlation coefficients in parentheses) Parameter Tet (W)  Correlation (+ or -)  With Parameters  Tet (Q)  + +  Instantaneous discharge(0.522) NOX (0.512)  Tet (M)  + +  Instantaneous discharge(0.525) Discharge 48 h (0.537)  Tet (O)  Total Tcr Genes Genes Normalized to 16S rRNA  228  Appendix 6: Supplemental Material – Fate of Oxytetracycline Resistance Genes in Aquatic Systems: Migration from the Water Column to Peripheral Biofilms Environmental conditions influence the mobility of antibiotics and antibiotic resistance genes through water courses and soils.  The physical properties of the receiving  environment, water quality, local ecology of biota, hydrology, climatic conditions and cumulative impacts of other contaminants play important roles in determining the fate and persistence of antibiotics and antibiotic resistance genes. Several antibiotics are known to readily degrade in the presence of light (Hidalgo et al., 1993; Doi & Stoskopf, 2000; Halling-Sørensen et al., 2003; Andreozzi et al., 2005; Castillo et al., 2007).  The  environmental fate of antibiotics, although related to the fate of antibiotic resistance genes, should be considered independently since the factors that govern bioavailability of chemical compounds are different from the factors that influence the transfer of genetic material between bacterial species. While the refractory chemical behaviour of some antibiotics released to the environment may limit their bioavailability, the persistence of antibiotic resistance genes may have different environmental consequences on bacterial species that are either mobile in the water column or fixed as stationary communities in biofilm. Biofilms are structured communities of sessile bacteria embedded in a biopolymer matrix that can form on almost any surface. They typically coat any suitably conditioned surface of static or biological material and remain attached to the substrate unless hydrodynamic flow over the biofilm re-suspends dislodged bacterial cells in the surrounding fluid (Hunt & Parry, 1998).  Bacterial communities in biofilm and  populations of mobile bacteria in the fluid redistribute between each other in a dynamic process that is considerably influenced by environmental factors (O’Toole et al., 2000; Purevdorj et al., 2002). Availability of nutrients and freedom from grazing pressures on bacteria by protozoans and nematodes are important to the formation of biofilms (Hassink et al., 1993).  Biofilm formation is not limited to aqueous environments.  Optimal conditions for building functional consortia of bacteria into unique ecological niches depend on the local chemical and biological conditions of soils and sediments. Bacteria in biofilms generally display wide phenotypic variety that enables the  229  community as a whole to resist various stresses including the action of antibiotic compounds. Bacteria resident in biofilms are more resistant (sometimes by two or three orders of magnitude) to the action of antibiotics than bacteria suspended in an aqueous environment (Allison et al., 2000).  Several factors may be responsible for this  phenomenon. Biofilms are composed of multiple layers of cells and it is possible that surface cells may respond differently to antibiotic stressors than more deeply embedded cells. The exopolymeric coating that is typically observed on biofilms may prevent diffusion of antibiotics to reach bacterial communities within the biofilm and may act as a protective barrier from other environmental stressors.  The overall growth rate of  bacterial cells in biofilms, although composed of a heterogenous population of species with variable individual growth rates, is usually slower with accompanying reduced metabolic activity and potentially reduced antibiotic susceptibility (Pace et al., 2006). Synergism between bacteria communities within biofilms may also play a key role in the development of resistance to stressors that include antibiotics, metal toxicity, predation, fluctuations in dissolved oxygen concentration, and reductions in exposure to light. Mesocosms are large-scale systems that allow gathering of information on the environmental consequences of stressors at greater scales of complexity than those offered by laboratory experiments. Treatments in mesocosms can be replicated and standardized such that integrated effects at the population or community level can be observed without compromising the receiving ecosystem integrity. Previous experiments have demonstrated that these systems effectively enable some experimental conditions to be manipulated or held constant, while other conditions mimic those of natural ecological processes (Graham et al. 1999; Knapp et al., 2003; Ensz et al., 2003; Brain et al., 2004; Knapp et al., 2005). Mesocosms are especially useful in collecting information about environmental fate processes in that these experiments introduce a level of natural variability in ecosystem response that cannot be attained through use of single species bioassays.  230  To date, relatively few studies have investigated the persistence and fate of antibiotic compounds and associated antibiotic resistance genes in the receiving environment. In the experiments presented here, some environmental factors which may attenuate the presence of oxytetracycline residues and four key tetracycline resistance genes in mesocosm and field conditions were explored. The specific aims of the experiment presented here were to establish whether exposure to oxytetracycline under daylight cycles for two weeks would affect the abundance of tetracycline resistance genes associated with biofilms present in the mesocosms. A significant amount of information about the mechanism of tetracycline resistance at the cellular level has been accrued (Sapunaric et al., 2005) and several tetracycline resistance genes representing efflux, ribosomal protection protein and mutational mechanisms are well characterized (Roberts, 1996; 2000; 2005). Assessment of the potential for mobility of tetracycline resistance genes is important given that their continuous introduction into the receiving environment may foster a cycle of exchange of genetic material between bacterial communities in biofilms and more mobile counterparts. Both mesocosm and field studies have demonstrated that horizontal gene transfer among bacteria species occurs in nature (Dröge et al., 1999). Several examples of three key gene transfer mechanisms have been reported to occur in the environment: conjugation (Bale et al., 1987), transformation (Williams et al., 1996) and transduction (Saye et al., 1990). A small-scale microcosm field experiment was also undertaken to examine whether the observations regarding the possible transfer of tetracycline resistance genes between waterborne bacteria and biofilms recorded in the mesocosm studies may also occur in a natural system that receives semi-continuous inputs of contaminants (Appendix 8). Static mesocosm experiments were conducted June 23 – August 7, 2005 at the University of Kansas facilities located at the Nelson Environmental Studies Area near Lawrence, Kansas (Figure A6.1).  Cylindrical fibreglass flat-bottomed tank mesocosms  3  (approximately 11.3 m ) were arranged in rows within shallow host ponds filled with local receiving water (much like large-scale outdoor water baths). Individual tanks were filled to a depth of about 1.4 m with approximately 11,000 L of pristine water supplied by a local protected reservoir (Figure A6.1). Water does not flow through the individual 231  static mescosm tanks. Three 39 cm x 53 cm plastic trays containing sediments were placed on the bottom of each tank to provide microbial inocula to each model habitat. Previous mesocosm studies (Graham et al., 1999, Knapp et al., 2003) conducted at the same facilities had established that three week was sufficient for the treatmemt systems to reach steady state and thus the biological communities in the mesocosms were allowed to acclimate for three weeks prior to experimental manipulation. Based on the decay pattern previously determined (experimental method provided in Appendix 7), OTC was added in the appropriate concentrations to each of the treatment tanks at night (~18:00) every two days throughout the experiment.  Figure A6.1: Mesocosms used in the 2005 experiments Water samples were collected every two days during the experiment using treatmentdedicated 1.2-m length, 25-mm diameter PVC samplers. The sample collection tube was pre-rinsed with tank water prior to collecting water samples. The samples were stored in  232  sterile 500-mL acid-washed, amber glass bottles equipped with Teflon-lined caps, and temporarily placed on ice. Water samples (250 mL ) were collected every two days during the experiment for OTC ELISA analyses at the University of Kansas environmental engineering laboratory. Mesocosms were used to collect the first quantitative field data that examined the migration and attenuation of tetracycline resistance genes in receiving waters. The fate of some selected tetracycline resistance genes in bacterial hosts released from cattle feedlot wastewater was assessed under different receiving conditions. The key hypotheses of the investigation proposed that light supply, microbial supplements (via periodic river water additions) and exposure to oxytetracycline affected the relative abundance of tetracycline resistance genes in the waterborne bacteria. These experiments represent the first attempt to incorporate consideration of migration of tetracycline resistance genes into peripheral compartments into models for predicting their fate in aquatic systems. Synthetic substrata were immersed in duplicate treatment tanks, one exposed to light only and the other to light with 250 μg/L oxytetracycline added.  Selected tetracycline  resistance genes (tet (M), tet (O), tet (Q), and tet (W)) and