EVALUATION OF MICROBIAL SOURCE TRACKING ALTERNATIVE INDICATORS IN COMPARISON WITH CONVENTIONAL INDICATORS IN HUMAN AND COW WASTES By Sangeetha Srinivasan A DISSERTATION Submitted to Michigan State University in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY Crop and Soil Sciences 2010 ABSTRACT EVALUATION OF MICROBIAL SOURCE TRACKING ALTERNATIVE INDICATORS IN COMPARISON WITH CONVENTIONAL INDICATORS IN HUMAN AND COW WASTES By Sangeetha Srinivasan Sewage or fecal input into waterbodies results is a serious concern. Direct monitoring of waters for the presence of these waterborne pathogens is expensive, laborious, and time consuming. Therefore, fecal indicators such as E. coli and enterococci are used as regulatory tools to monitor water as presence of these indicators indicate potential presence of enteric pathogen. But critical uncertainties such as proliferation of bacteria in places where there is no fecal contamination and strong evidence for their extended survival and re-growth in sediments have furthered water pollution science towards new approaches. Recently, members of Bacteroides genus are found to have the features that will enable them to be used as alternative Microbial Source Tracking (MST) fecal indicators. But before they are used for the routine monitoring for fecal contamination, they need to be evaluated and validated. The overall aim of this research was to investigate the prevalence and concentrations of recently identified human and cow specific fecal markers in comparison with E. coli and enterococci in both the human and cow waste environments and to understand the variation expected when cultivation methods are replaced with qPCR methods in these environmental samples. Monitoring of raw sewage, septage samples and their treated effluents for human specific MST marker, Bacteroides thetatiataomicron during human waste treatment processes in comparison with conventional indicators such as E. coli and enterococci showed that B. thetaiotaomicron qPCR equivalent cells were present in significantly higher concentrations than that of E. coli or enterococci in raw sewage and septage and fate of these target qPCR signals were similar to E. coli and enterococci DNA during the 2 3 treatment of these wastes. It was observed that around 10 to 10 qPCR equivalent cells/100mL of these targets were being discharged through the final effluents into surface waters. Another specific aim of this research was to study the distribution of different cow specific markers in cow manure environment and use these markers to characterize contamination of water bodies due to runoff from manure farms. Three different specific MST targets, a Bacteroides bovine cluster, an M2 cow specific marker, and an M3 cow specific marker were evaluated in comparison with E. coli and enterococci qPCR target signals using samples from manure pits just prior to the pumping of this waste for land application. It was found that these manure samples had 5 7 E. coli and enterococci at concentrations of 10 -10 CFUs or cells/100 mL and cow 7 9 specific markers in the concentrations of 10 -10 qPCR target copies per 100 mL. This study suggested that M3 cow specific marker could be used as MST tools to monitor water bodies for contamination with agricultural wastes than M2 marker. This study explored the application of qPCR tools that targeted E. coli O157 specific eae and rfbE genes and detected these genes in human and cow waste environments. However, these assays were further inspected to evaluate their cross-specificity with other serotypes of E. coli that arose after some speculation about the concentration detected in environmental samples. In conclusion, qPCR is a viable alternative and it is recommended that E. coli, enterococci, B. thetaiotaomicron, and M3 marker be used for studies in future. Dedicated to the Lotus Feet of Lord Krishna iv ACKNOWLEDGMENTS It has been a great pleasure and honor to receive guidance from my mentor, Dr. Joan B. Rose. Thank you for your constant encouragement, and advice. Your sincere interest in bringing science and community together has been a great inspiration to me. I am also grateful to my committee members, Dr. Syed Hashsham, Dr. Sasha Kravchenko, and Dr. Evangelyn Alocilja for their valuable comments and suggestions. I would like to extend my thanks to all my lab colleagues who have helped me in one way or the other. I would like to specially thank Rebecca Ives and Joanna Pope for their suggestions on some of my dissertation chapters, Mark Weir for his statistical guidance, and Kelvin Wong for all his scientific advice and help. I am also indebted to past members of the Rose lab. Stephanie Molloy for encouraging me to start my PhD, Theng Theng Fong and Arun Nayak for training me on qPCR, and many others who would stop and share something valuable that helped me to make decisions as I started this journey. My sincere gratitude goes to my parents for their love and constant faith in me, to my late mother-in-law for her blessings, my sister-in-law and her husband, my brothers, my friends and other family members for their prayers and emotional support. Finally, to my loving husband, Srini, and our beautiful daughter, Vidhula, who were there to listen to my research sagas, who lifted me up whenever I fell during this journey, who would patiently wait for me to finish, who trusted and allowed me to be as ambitious as I wanted…… Thank you so much! I couldn’t have done this without your support! v TABLE OF CONTENTS LIST OF TABLES………………………………………………………. ix LIST OF FIGURES……………………………………………………… xi ABBREVIATIONS……………………………………………………… xv CHAPTER 1: INTRODUCTION………………………………………… 1 1.1 An Overview of Waterborne Pathogens and Water Quality Regulations… 2 1.2 Sources of Fecal Contamination in a Watershed…………………………... 7 1.3 Fecal Indicator Organisms………………………………………… 9 1.3.1 Escherichia coli………………………………………………… 10 1.3.2 Enterococci……………………………………………… 11 1.4 Detection of Escherichia coli and Enterococci from Water……… 12 1.4.1 Cultivation Methods: Membrane Filtration……………………… 12 1.4.2 Molecular Methods: Polymerase Chain Reaction and Quantitative Polymerase Chain Reaction (qPCR)……………………………………… 15 1.5 Microbial Source Tracking………………………………………………… 20 1.5.1 Selected MST Approaches………………………………………… 21 1.5.2 Genotypic Library-Independent Source Tracking Methods……… 24 1.6 References………………………………………………………………… 31 CHAPTER 2: RESEARCH OBJECTIVES 2.1 Problem Statement…………………………………………………… 2.2 Research Goals, Hypothesis and Objectives………………………… 2.3 Thesis Outline……………………………………………………… 2.4 References…………………………………………………………… 42 43 44 46 CHAPTER 3: METHODS DEVELOPMENT, EVALUATION AND APPLICATION 3.1 Introduction…………………………………………………………. 47 3.2 Materials and Methods 3.2.1 Development of a qPCR Assay for E. coli uidA gene and Enterococci 23S rRNA gene……………………………………………………… 52 3.2.1.1 Bacterial Strains and DNA extraction…………… 52 3.2.1.2 Preparation of qPCR Standards………… 52 3.2.1.3 Assay Performance……………………………… 54 3.2.2 Comparison between Auto and Manual DNA Extraction Method using Environmental Samples…………………………………… 57 3.2.2.1 Sample Processing and DNA extraction………… 57 3.2.2.2 Reproducibility………………………… 59 3.2.2.3 qPCR Analyses………………………… 59 3.2.2.4 Statistical Analysis……………………………… 60 3.2.3 Application of Ethidium Monoazide-qPCR for Differentiating between Live and Dead cells in Environmental Samples…………………… 61 vi 3.3 3.4 3.5 3.2.4 Preliminary Evaluation of 16S rRNA Bacteroides Human HF183 Markers by conventional PCR Assay……………………… 63 3.2.4.1 Application of HF 183 PCR assay for Monitoring Water Quality………………………………………. 63 3.2.4.2 Detection Limit of HF183 PCR Assay…………… 65 Results 3.3.1 Standardization and Specificity of E. coli uidA qPCR Assay…… 66 3.3.2 Standardization of Enterococci 23S rRNA Gene qPCR Assay… 69 3.3.3 Comparison of Concentrations of E. coli and Enterococcus spp. DNA by Manual and Automated Extraction…………………………… 70 3.3.3.1 Performance of Extraction Procedure………………… 80 3.3.4 Differentiation between Live and Dead cells using Ethidium Monoazide-qPCR in Environmental Samples…………… 81 3.3.5 Occurrence of 16S rRNA Bacteroides HF183 Human Marker in Surface Water Samples…………………………………………… 85 3.3.6 Detection Limit of 16S rRNA Bacteroides HF183 PCR Assay Using Raw Sewage………………………………………………… 88 Discussion………………………………………………………… 90 References………………………………………………………… 99 CHAPTER 4: EVALUATION OF FATE OF BACTEROIDES THETAIOTAOMICRON IN COMPARISON WITH CONVENTIONAL INDICATORS DURING SEWAGE AND SEPTAGE TREATMENT PROCESSES 4.1 Introduction………………………………………………………… 106 4.2 Materials and Method 4.2.1 Sample Collection and Processing……………………… 110 4.2.2 Analyses………………………………………………… 114 4.2.2.1 Cultivation Methods……………………… 114 4.2.2.2 DNA Extraction and qPCR Analyses……… 114 4.2.3 Statistics…………………………………………………… 119 4.3 Results 4.3.1 Wastewater Treatment Samples………………………… 120 4.3.2 Septage Treatment Samples……………………………… 140 4.4 Discussion………………………………………………………… 149 4.5 References…………………………………………………………… 160 CHAPTER 5: DISTRIBUTION OF COW SPECIFIC MICROBIAL SOURCE TRACKING MARKERS IN CATTLE MANURE 5.1 Introduction……………………………………………………… 5.2 Materials and Method 5.2.1 Sample Collection and Processing………………… 5.2.2 Analyses 5.2.2.1 Cultivation Methods……………………… 5.2.2.2 DNA Extraction and qPCR Analyses……… 5.2.3 Statistics…………………………………………… 5.3 Results vii 165 171 173 173 179 5.4 5.5 5.3.1 E. coli and Enterococci Cultivation Versus qPCR………… 5.3.2 Detection of qPCR target copies of Fecal Indicator Bacteria and Cow specific markers in manure pits………………………… Discussion………………………………………………………… References……………………………………………………… 180 187 194 202 CHAPTER 6: QUANTITATIVE PCR FOR DETECTION OF E. COLI O157:H7SPECIFIC eae AND rfbE GENES 6.1 Introduction……………………………………………………… 207 6.2 Materials and Method 6.2.1 Environmental samples……………………………………… 212 6.2.2 Bacterial strains……………………………………………… 212 6.2.3 qPCR Assay standardization and analysis…………………… 214 6.3 Results ……………………………………………………………… 217 6.4 Discussion……………………………………………………………… 222 6.5 References…………………………………………………………… 225 CHAPTER 7: SUMMARY AND CONCLUSIONS……………………… viii 229 LIST OF TABLES Table 1.1: Waterborne pathogens of concern to human health and their diseases……………………………………………………………………… 3 Table 1.2: Threshold levels of fecal indicator bacteria based on USEPA criteria for recreational waters……………………………………… 6 Table 1.3: Microbial source tracking approaches……………………………… 22 Table 3.1: Primers and probes used for E. coli and Enterococci qPCR assay….. 56 Table 3.2: Evaluation of the E. coli uidA gene qPCR assay against a variety of bacteria……………………………………………………………………… 68 Table 3.3: t-test results for comparison of means between the qPCR equivalent concentrations of E. coli from DNA extracted by auto and manual methods…… 71 Table 3.4: t-test results for comparison of means between the concentrations of Enterococcus spp. from DNA extracted by auto and manual methods…………. 72 Table 3.5: Coefficient of variation (CV %) for DNA concentrations and cell equivalent concentrations of E. coli and Enterococcus spp. performed by two individuals for auto and manual extraction methods……………… 81 Table 3.6: Occurrence of 16S rRNA Bacteroides (HF183) human marker as detected by conventional PCR and their comparison with E. coli and Enterococcus spp. (CFU/100mL) in surface water samples………………… 86 Table 3.7: Detection limit of 16S rRNA Bacteroides (HF183) assay using water seeded with raw sewage……………………………………………………… 89 Table 4.1: The bacteria and gene targets used for qPCR assays in the monitoring of raw sewage, septage, and treated effluents………………………………… 117 Table 4.2: Primer/ probe sequences, concentrations and PCR programs used for bacterial qPCR assays……………………………………………………… 118 Table 4.3: Range of physical and chemical characteristics for raw sewage and final effluent samples for all sampling dates……………………………… 120 Table 4.4: Average concentrations for E. coli, enterococci & B. thetaiotaomicron throughout wastewater treatment by cultivation and qPCR………………… 122 Table 4.5: Pearman’s correlation between conventional indicators and B. thetaiotaomicron in treated, untreated sets of data and pooled data set ix by qPCR ……………………………………………………………………… 127 Table 4.6: Average concentrations of bacteria in raw septage and effluent by cultivation and qPCR methods……………………………………… 141 Table 4.7: Summary of all qPCR applications to monitor the wastewater and treated effluents in the literature………………………………………… 152 Table 5.1: Selected waterborne outbreaks where cattle manure was suspected as the source of the etiological agents………………………………………… 167 Table 5.2: Bacteria and gene targets used in the study of cow feces and manure……………………………………………………………………… 177 Table 5.3: Primer/ probe sequences, concentration and PCR programs used for each assay…………………………………………………………………… 178 Table 5.4: Average log10 concentrations of qPCR target copies/mL of fecal indicator bacteria and cow specific markers from manure pits…………… 189 Table 5.5: Occurrence of cow Bacteroides markers in raw sewage and raw septage……………………………………………………………… 193 Table 6.1: E. coli strains used for specificity testing……………………… 213 Table 6.2: Primers and Probes used in detection of E. coli O157………… 216 Table 6.3: Assay specificity with E. coli O157 and non-O157 serotypes……. 218 Table 7.1: Average concentrations of the indicator organisms in the samples monitored in this research………………………………………… 231 Table 7.2: Average concentrations of the human and cow specific MST markers in the samples monitored in this research………………………… 232 x LIST OF FIGURES Figure 3.1: Standard curve for 10-fold serial dilutions of E. coli uidA gene ……………………………………………………………………… 67 Figure 3.2: Standard curve for 10-fold serial dilutions of Enterococcus spp. 23SrDNA gene ………………………………………………………………… 69 Figure 3.3: Regression analysis for concentrations of E. coli from both types of DNA extraction in raw sewage and primary effluent………… 74 Figure 3.4: Regression analysis for concentrations of Enterococcus spp. from both types of DNA extraction in raw sewage and primary effluent…………… 75 Figure 3.5: Regression analysis for concentrations of E. coli from both types of DNA extraction in secondary and tertiary effluents………………………… 76 Figure 3.6: Regression analysis for concentrations of enterococci from both types of DNA extraction in secondary and tertiary effluents………………… 77 Figure 3.7: Regression analysis for concentrations of E. coli from both types of DNA extraction in cow feces……………………………… 78 Figure 3.8: Regression analysis for concentrations of Enterococcus spp. from both types of DNA extraction in cow feces……………………………… 79 Figure 3.9: Average log10 qPCR target copies/ reaction for pure cultures of E. coli and Enterococcus spp. in EMA treated and untreated live and heat-killed cells………………………………………………………………… 83 Fig 3.10: Average log 10 concentrations of E. coli and Enterococcus spp. in EMA treated and untreated raw sewage and treated effluent samples……… 84 Figure 4.1: Schematic representation of sampling locations in the wastewater treatment plant at East Lansing……………………………………………… 112 Figure 4.2: Average log10 reductions of E. coli, enterococci CFUs and cell equivalents and B. thetaiotaomicron cell equivalents by wastewater treatment…………………………………………………………………… 124 Figure 4.3: Average log10 concentrations of bacteria in secondary effluents before and after chlorination using cultivation and qPCR………………… 126 Figure 4.4: Correlation between E. coli and Bacteroides thetaiotaomicron by qPCR in pooled data from wastewater treatement samples……………… 128 xi Figure 4.5: Correlation between E. coli and B. thetaiotaomicron by qPCR in untreated and treated data sets of wastewater samples…………………… 129 Figure 4.6: Correlation between enterococci and Bacteroides thetaiotaomicron by qPCR in pooled data from wastewater treatement samples………………… 130 Figure 4.7: Correlation between enterococci and B. thetaiotaomicron by qPCR in untreated and treated data sets of wastewater samples……………… 131 Figure 4.8: Comparison between log10 transformed average concentrations of E. coli by cultivation based (CFU/100 mL) and qPCR based methods (cells/100 mL) in waste water treatment plant samples………………………… 133 Figure 4.9: Comparison between log10 transformed average concentrations of enterococci by cultivation based (CFU/100 mL) and qPCR based methods (cells/100 mL) in waste water treatment plant samples……………………… 134 Figure 4.10: Correlation between concentrations of E. coli by cultivation methods (CFU/100 mL) and qPCR (cells/100 mL) in pooled data from wastewater treatement samples……………………………………………… 136 Figure 4.11: Correlation between concentrations of E. coli by cultivation methods (CFU/100 mL) and qPCR (cells/100 mL) in untreated and treated data sets of wastewater samples……………………………………………… 137 Figure 4.12: Correlation between concentrations of enterococci by cultivation methods (CFU/100 mL) and qPCR (cells/100 mL) in pooled data from wastewater treatement samples………………………………… 138 Figure 4.13: Correlation between concentrations of enterococci by cultivation methods (CFU/100 mL) and qPCR (cells/100 mL) in untreated and treated data sets of wastewater samples………………………………… 139 Figure 4.14: Average log reduction of E. coli, enterococci by cultivation and qPCR Bacteroides thetaiotaomicron targets by qPCR during septage treatment process…………………………………………………………... 142 Figure 4.15: Correlation between E. coli and Bacteroides thetaiotaomicron by qPCR in septage treatment plant…………………………………… 143 Figure 4.16: Correlation between enterococci and Bacteroides thetaiotaomicron in septage treatment samples………………………………………… 144 Figure 4.17: Correlation between concentrations of E. coli by cultivation methods (CFU/100 mL) and qPCR (cells/100 mL) in pooled data from xii raw septage and effluents………………………………………… 145 Figure 4.18: Correlation between concentrations of E. coli by cultivation methods (CFU/100mL) and qPCR (cells/100mL) in two sets of data from raw septage and effluents…………………………………………………… 146 Figure 4.19: Correlation between concentrations of enterococci by cultivation methods (CFU/100 mL) and qPCR (cells/100 mL) in pooled data from septage treatment samples……………………………… 147 Figure 4.20: Correlation between concentrations of enterococci by cultivation methods (CFU/100mL) and qPCR (cells/100mL) in two sets of data from raw septage and effluents………………………………… 148 Figure 5.1: Fecal contamination of catchment water associated with the manure environment………………………………………………… 168 Figure 5.2: Diagram representing sampling locations of manure samples…… 172 Figure 5.3: Average E. coli concentrations in cow feces by cultivation (CFU/g) and qPCR (cells/g) methods……………………………………… 181 Figure 5.4: Correlation between concentrations by cultivation methods (CFU/g) and qPCR (cells/g) in cow feces for E. coli and enterococci……… 182 Figure 5.5: Average log10 transformed E. coli concentrations from manure pits by cultivation and qPCR methods……………………… 184 Figure 5.6: Average log10 transformed enterococci concentrations from manure pits by cultivation and qPCR methods………………………………………… 185 Figure 5.7: Correlation between concentrations by cultivation methods (CFU/mL) and qPCR (cells/mL) manure pit samples for E. coli and enterococci 186 Figure 5.8: Comparison of qPCR copies signals for each target assay among the four pits …………………………………………………………………… 190 Figure 5.9 Comparison of qPCR copies signals for each target assay between May and November…………………………………………………………… 191 Figure 6.1: Occurrence of E. coli O157:H7 specific eae and rfbE genes in manure samples………………………………………………………………… 219 Figure 6.2: Occurrence of E. coli O157:H7 specific eae and rfbE genes in sewage samples…………………………………………………………………… 220 xiii Figure 6.3: Occurrence of E. coli O157:H7 specific eae and rfbE genes in septage samples………………………………………………………………… xiv 221 ABBREVIATIONS 4-methylumbelliferyl-β-D-glucuronide -MUG Beaches Environmental Assessment and Coastal Health -BEACH Biological Oxygen Demand –BOD Clean Water Act -CWA Coefficient of variation -CV Colony forming unit -CFU Combined Sewer Overflow -CSO Crossing point –Cp Cycle threshold -Ct Deoxynucleotide triphosphates -dNTPs Deoxyribonucleic acid -DNA Enteroaggregative -EAEC Enterohemorrhagic Escherichia coli –EHEC Enteroinvasive Escherichia coli –EIEC Enteropathogenic –EPEC Enterotoxigenic Escherichia coli –ETEC Ethidium Mono-Azide -EMA Fecal Coliform to Fecal Streptococcus -FC:FS Federal Water Pollution Control Act -FWPCA Heat- killed -HK Internal Transcribed Spacer -ITS Light cycler -LC xv Locus of Enterocyte Effacement -LEE Membrane Filtration -MF Membrane-Thermotolerant Escherichia coli -mTEC National Center for Biotechnology Institute -NCBI National Pollutant Discharge Elimination System -NPDES Ortho-Nitrophenyl-β-galactoside -ONPG Phosphate Buffered Saline -PBS Phosphate Buffered Water -PBW Polyvinylpolypyrolidone -PVPP Primary effluent -PE Propidium Mono-Azide –PMA Publicly Owned Treatment Works -POTW qPCR -quantitative Polymerase Chain Reaction Raw sewage -RS Reverse Transcription real-time -RT rt Sanitary Sewer Overflow -SSO Secondary effluent -SE Tertiary effluent -TE U.S Environmental Protection Agency -USEPA United States Department of Agriculture -USDA Viable But Nonculturable - VBNC Waste water treatment plant -WWTP World Health Organization –WHO xvi CHAPTER 1 INTRODUCTION Among all the problems faced by the world today, access to safe water remains a major global challenge. Fecal contamination from various sources further limits the use of available water due to the threat of water-borne pathogens in such polluted waters. Apart from limiting water sources available for drinking, domestic and recreational purposes, fecal contamination of fresh or marine water bodies often leads to a heavy economic loss due to closure of beaches and implementation of other measures needed to bring the contamination under control (Henrickson et al. 2001; Rabinovici et al. 2004). In addition, in spite of implementing stringent regulations for providing a safe water supply, the total number of waterborne illnesses per year in the U.S alone is estimated to be 19.5 million, which includes 5.4 million from municipal groundwater, 1.1 million from noncommunity groundwater sources, and 13 million from surface water supplies (Reynolds et al. 2008). From a global perspective, the estimated numbers of waterborne illnesses are even more dramatic, due to the fact that many countries lack adequate infrastructure to treat their wastewater and water supplies. The World Health Organization (WHO) estimates 1.8 million deaths around the world associated with diarrheal diseases each year with most of these due to unsafe water and poor sanitation. There is uncertainty in the number of illnesses as many waterborne diseases are not reported properly to the officials in many countries. 1 1.1 An Overview of Waterborne Pathogens and Water Quality Regulations Millions die annually worldwide due to diseases like cholera, dysentery and shigellosis mainly targeting children, elderly and poor communities (Ivnitski et al. 1999; Venter 2000; Leonard et al. 2003). Lack of efficient and inexpensive monitoring tools for water exacerbate those illnesses that are waterborne especially in heavily populated developing countries as there is often no way to measure the level of risk. The prominent waterborne pathogens of concern to human health and their diseases are given in Table 1.1. 2 Table 1.1: Waterborne pathogens of concern to human health and their diseases (Arnone and Walling 2007). Organism Bacteria Source relevant to water exposure Aeromonas hydrophila Campylobacter E. coli O157:H7 Aquatic environments Bird feces Cattle feces Helicobacter pylori Legionella spp. Leptospira spp. Mycobacterium avium complex Human feces Aquatic environments Urine of dogs, wild animals Salmonella spp. Salmonella typhi Shigella Vibrio cholerae Yersinia entercolitica Bird feces Domestic and wild animal feces Domestic and wild animal feces Human feces Asymptomatic human feces Animal feces Disease Gastroenteritis Diarrhea Gastroenteritis Peptic ulcer or gastric cancer Legionnaires disease Leptospirosis Pulmonary disease Salmonellosis Typhoid fever Shigellosis Cholera Yersinosis Virus Adenovirus Astrovirus Calcivirus Human feces Human feces Human feces Enterovirus Hepatitis A Hepatitis E Reo viris Rota virus Human feces Human feces Human and pig feces Human feces Human feces Gastroenteritis, Respiratory disease Gastroenteritis Gastroenteritis Gastroenteritis, Meningitis Infectious hepatitis Infectious hepatitis Gastroenteritis Gastroenteritis Human, animal & bird feces Human feces Human feces Human, animal & bird feces Bird & aquatic mammal feces Cryptosporidiosis Cyclosporiasis Amebiasis Giardiasis Meningoencephalitis Protozoa Cryptosporidium Cyclospora Entamoeba histolytica Giardia lamblia Naegleria fowleri 3 In US, there are strict regulations that address waterborne disease by regulating the quality of water. The Federal Water Pollution Control Act (FWPCA) of 1948 laid the foundation for all the ambient water quality standards. There were amendments made to this Act in 1972 and U.S Environmental Protection Agency (USEPA) enforced the “Clean Water Act” (CWA) (USEPA 1972). The main objective of this Act was to restore and maintain the chemical, physical and biological integrity of nation’s water and various programs have been implemented to achieve this goal. National Pollutant Discharge Elimination System (NPDES) program regulates any pollutant discharge from point sources into navigable waters. Under this permit program, secondary treatment standards for sewage are established for publicly owned treatment works (POTWs) to monitor the performance efficiency of treatment process. Based on this, the geometric mean of fecal coliforms in these secondary effluent samples collected during any 30-day period shall not exceed 200 CFU per 100 mL for fecal coliforms and the geometric mean during any seven-day period shall not exceed 400 CFU per 100 mL (US Environmental Protection Agency 1988). Another addition to the CWA is the Beaches Environmental Assessment and Coastal Health Act (BEACH Act) that was signed into law on October 10, 2000, with primary goals to improve water quality and protect public health at coastal recreational waters. The EPA developed criteria for fecal bacterial indicators, under this Act, and coastal and Great Lakes states have to either adopt these water quality criteria into their state standards and/or design and implement water quality monitoring programs with the approval from USEPA. 4 Impaired water bodies are identified based on exceeding Water Quality Standards (WQS) and corrective actions are taken by developing a Total Maximum Daily Load (TMDL) that determines the level of pollutants that can be discharged into waters which would still be able to meet WQS. The water quality standards based on correlation between levels of E. coli and enterococci and acceptable risk of swimming related illnesses were set in 1986 by USEPA and Table 1.2 shows these threshold levels for fresh and marine water. In order for a water body to be safe for recreational purposes; for example; the enterococci level should be below 35 enterococci CFU per 100 mL, thus setting a goal for swimmers to have a 1.9 percent or less chance of becoming ill. 5 Table 1.2: Threshold levels of fecal indicator bacteria based on USEPA criteria for recreational waters (USEPA 1986). Fecal indicator bacteria Concentration based on geometric mean (CFU/100 mL) Single-sample concentration (CFU/100 mL) Designated beach areas Moderate full- Lightly used fullbody contact body contact Rare full-body contact E. coli 33 61 78 107 151 Enterococci 126 235 298 409 575 35 104 158 276 501 Fresh water* Sea water* Enterococci *Acceptable swimming-associated gastroenteritis rate is 8 per 1000 swimmers for E. coli and enterococci in fresh water and 19 enterococci in sea water. 6 Pollution from non-point sources are regulated under CWA by implementing grants for the states that propose best management plans for controlling pollution discharged from various sources into surface waters. The EPA has also set standards for the use of biosolids during land application with Class B containing pathogens and Class A which are treated (USEPA 2003). 1.2. Sources of Fecal Contamination in a Watershed Sewage contamination of water is an important source of many of the waterborne pathogens associated with fecal pollution. The most serious fecal pollution occurs in developing and undeveloped parts of the world where there is inadequate sewage treatment (Tibbetts 2000). For example, in India, a study conducted by the Central Pollution Control Board indicated that in the year 2005, 921 cities had treatment capacity developed for only 27% of the total wastewater generated (Kumar 2009). However in the US, pathogen sources include Combined Sewer Overflows (CSO), Sanitary Sewer Overflows (SSO), septage, biosolids and poorly treated sewage and animal wastes. Combined Sewer Overflows are sewers that collect and transport rainwater runoff, domestic sewage and pre-treated industrial effluent in the same pipe to wastewater treatment plant where it it may undergo treatment before discharging into a water body. In the past all CSOs discharged directly to rivers, currently these are being fixed. Sanitary Sewer Overflows are the occasional raw sewage discharge into a water body due to severe weather, system or power failure, etc resulting in discharge of untreated waste into rivers and streams. These are serious concerns in many parts of the developed world including the US (USEPA 2004). Sludge or biosolids are the solid residues obtained after the treatment of wastewater and if not adequately treated may contain enteric pathogens. 7 Many of these biosolids are applied to the land with little treatment (Class B biosolids) such as in Michigan (http://www.michigan.gov/deq). Even with Class A biosolids, there is still a concern that bacterial pathogens may regrow (Zaleski et al. 2005a and 2005b). Leaking septic tanks are also a cause of human fecal contamination of water sources and waterborne pathogens (Fong et al. 2007). Many households or small communities use individual septic tanks. According to U.S Census Data on Small Community Housing and Wastewater Disposal and Plumbing Practices, eight USEPA regions in US have more than 50 percent of the small community housing on (http://water.epa.gov/infrastructure/wastewater/septic/census_index.cfm). septic tanks Septic tanks collect waste water from the house including toilets, sinks and laundry. The liquid is treated by soil infiltration and solids, known as septage are pumped out and often land applied with or without treatment. Agricultural animal wastes (manure) are also associated with microbial pathogens (Sobsey et al. 2001). Manure is used to supplement the soils with nutrients for crops, though if not treated properly can be a major source of fecal contamination of soil and water (McFarland et al. 1998). Waterborne outbreaks have been reported due to cow manure application and runoff from farms to nearby water bodies after heavy rainfall (Kistemann et al. 2002; Hrudey et al. 2003; McQuigge 2000). An outbreak caused by Campylobacter and E. coli O157 occurred in the Walkerton, Ontario, Canada in 2000 resulted in 2,300 illnesses and 7 deaths from contamination of the community well with cattle manure after a heavy rainfall (Hrudey et al. 2003). Pathogens such as E. coli, Salmonella, Campylobacter, Giardia and Cryptosporidia are present in manure (Pell 1997). 8 1.3 Fecal Indicator Organisms Routine monitoring of water bodies in the past for the presence of waterborne pathogens has not been practical as detection tools used for these pathogens were not available. This was because cultivation was the primary detection method and many of these pathogens escaped routine cultivation as they changed into a viable but nonculturable state (VBNC) making their detection more expensive and laborious or, in the case of viruses and parasites, cultivation procedures were not available for water samples (Oliver et al. 1991, Oliver 2002; Colwell & Huq 1994; Stevens et al. 2001; Venter 2000; George et al. 2001). Additionally, use of cultivation methods takes longer periods of time (e.g. days) to obtain confirmed positive results for some pathogens (Sartory and Watkins 1999; Venter 2000; Leonard et al. 2003; Taguchi et al. 2005). To assess microbial water quality, water bodies are still monitored for fecal indicator bacteria whose presence may indicate possible fecal contamination. Microbial fecal indicators are meant to represent the potential risk associated with the possible presence of other pathogens that also originate from the intestines of animals or humans. The idea of using bacterial indicator organisms for monitoring water was introduced by Frankland in 1891, and then in 1892, Schardinger suggested using E. coli for this purpose (Leclerc et al. 2001). The desired criteria for an ideal indicator organism are as follows (Bonde 1966; Manafi 1998; Stevens et al. 2001; Pletschke et al. 2006):  Be a member of intestinal microflora of warm blooded animals  Be present whenever enteric pathogens are present and not be found when they are absent 9  Relate its concentration directly to the degree of fecal contamination  Have a longer survival time than enteric pathogens  Not proliferate in water  Be resistant to treatment processes and adverse environmental factors similar to pathogens  Have an easy and cost-effective detection method The sections below reviews briefly two of the bacteria that are classified and used as indicator organisms, and are used for regulatory purposes. 1.3.1 Escherichia coli Bacterial fecal indicators belonging to the family Enterobacteriaceae which include a group known as coliforms (defined as rod-shaped, Gram-negative non-spore forming bacteria and include a sub-group known as fecal coliform bacteria which can grow at o 44.5 C. Coliforms are abundant in the intestines and feces of all warm blooded animals (Leclerc et al. 2001). Escherichia coli (E. coli), a specific member of the coliform group, has been used to assess microbial quality of recreational water especially freshwater bodies as well as drinking water (Leclerc et al. 2001). E. coli is a facultative anaerobic, motile bacterium and can utilize wide variety of substrates to support their growth. This specific genus and species are present in high concentrations in digestive tract of warm blooded animals and are distinguished from other coliform group by their biochemical characteristics such as lack of urease enzyme that hydrolyses urea to carbondioxide and ammonia. Based on previous epidemiological studies, E. coli was found to correlate well with swimming related illnesses in recreational fresh water bodies (Cabelli et al. 1982; Wade et al. 2003; Wiedenmann et al. 2006) even better than that of fecal coliforms. This 10 has led the USEPA to recommend that E. coli be used to judge safe recreational waters (USEPA 1986). However, E. coli, like other coliforms, have been found to proliferate in soil and plants (Hardina & Fujioka 1991) in pristine areas where there is no input from fecal contamination (Niemi et al. 1997) capable of extended survival and re-growth in soils, especially in tropical environments (Byappanahalli & Fujioka 1998). 1.3.2 Enterococci Enterococci, belonging to the family Enterococcaceae, are facultatively anaerobic, catalase negative, non-sporing bacteria which are able to tolerate 40% bile, a pH of 9.6 and 6.5% sodium chloride (Cai 1999). They are able to survive at a temperature range of o o 10-45 C, with an optimum growth temperature at 37 C. This group of bacteria was originally classified as Streptococcus species until Schleifer and Kilpper grouped them separately as Enterococcus in 1984, (Law-Brown and Meyers 2003; Müller et al. 2001) and were suggested to be indicators of fecal contamination earlier in the 1900s due to their abundance in the intestines and feces of all warm blooded animals Various species are found to be selectively predominant in different warm blooded animal intestines. For example, E. columbae is found only in pigeons (Baele et al. 2002) whereas E. canintestini and E. asini have been isolated from dogs and donkeys respectively (DeVaux et al. 1998; Naser et al. 2005). E. phoeniculicola was isolated from uropygial (preen) gland secretion of Redbilled Woodhoopoes (Law-Brown and Meyers 2003). Enterococcus faecalis occurs in wide range of hosts but some studies suggest that occurrence of this species in a fecal polluted area is more indicative of human fecal contamination (Bonilla et al. 2006; Kuntz et al. 2004; Wheeler et al. 2002). 11 Epidemiological studies conducted by USEPA found that high densities of enterococci correlated well with swimming related gastrointestinal illness in recreational waters, and USEPA set water quality criteria using these organisms as one of the indicators (U.S. Environmental Protection Agency 1986). It has been suggested that the enterococci are better well-correlated with fecal pathogens than E. coli and are more suitable as an indicator in monitoring marine recreational water quality (Kinzelman et al. 2003). However, enterococci are also isolated from other sources and environments such as food, plants, oysters (Fortina et al. 2004; Müller et al. 2001; Ott et al. 2001; Silva et al. 2004; Whitman et al. 2005); and in water bodies with no fecal pollution (Svec et al. 2001 and 2005). They are also found to survive and replicate in sediments, fresh water, forage plants and algae (Desmarais et al. 2002; Hartel et al. 2005; Müller et al. 2001; Ott et al. 2001;Whitman et al. 2003, 2005). This limits their capability as an indicator of fecal contamination of water. 1.4 Detection of Escherichia coli and Enterococci from Water 1.4.1 Cultivation Methods: Membrane Filtration Methods conventionally used to detect indicators from water sources include Membrane Filtration (MF) which is a cultivation based technique that requires the bacteria to be grown on a filter which is placed on specific growth media containing agar. A water sample is passed through a 47mm diameter membrane filter by applying negative pressure, with a porosity of 0.2 µm or 0.45 µm that is effective in retaining bacteria from the sample. The membrane is then placed on growth media specific for 12 each bacterium in petri plates and incubated under optimum conditions. After the incubation period, colonies are enumerated on the filter. The membrane filtration methods currently used to detect E. coli and enterococci from water were tested and implemented by USEPA as Method 1603 and Method 1600 respectively. The initial idea of the MF technique for detection of E. coli was proposed by Dufour et al. in 1981 as a two-step membrane filtration that combined cultivation technique using membrane-Thermotolerant Escherichia coli agar (mTEC agar) and subsequent confirmation by urease test which was later modified by EPA in 1998 as a single step cultivation based method (EPA Method 1603) using Modified mTEC Agar. The Modified m-TEC agar medium has a chromogen (5-bromo-6-chloro-3-indolyl-β-Dglucuronide) which is broken down to glucuronic acid by E. coli possessing β-Dglucuronidase enzyme and forms red or magenta-colored colonies, thus providing results within 24 hours (U.S. Environmental Protection Agency 2000). The Modified m-TEC agar has Sodium lauryl sulfate and Sodium desoxycholate to inhibit the growth of Grampositive bacteria. The false positive rate with this is medium is less than 1.0% and false negative rate is 4.0% for environmental water samples (USEPA, 2005a). A membrane filtration method for isolation of enterococci was introduced by Slanetz et al in 1957 using selective media that could differentiate the fecal enterococci as a subpopulation of the generic fecal strepotococci. In 1975, Levin et al. proposed a two step method that combined incubation on mE agar for 48 hours and further incubation on Esculin Iron agar that differentiated enterococci from other streptococci. USEPA, in 1997, modified this into a one step method (USEPA, 2002) by reducing the incubation time to 24 hours and adding a chromogen, indoxyl-β-D-glucoside that is utilized by 13 enterococci possessing β-D-glucosidase enzyme to an insoluble indigo blue complex to form a blue halo around the colony by diffusion. This mEI agar media, in which enterococci grows by hydrolyzing esculin, has ingredients that have inhibitory effect on fungi and Gram-negative bacteria. There is a false positive rate of 6.0% and false negative rate of 6.0% for environmental water samples with this medium (Messer and Dufour 1998). Enzymatic detection of E. coli based on defined substrate technology was proposed by Edberg and Edberg in 1988. This technology is based on vital nutrient growth substrates that are supplied specifically for target organisms and does not contain any substrates that support growth of other bacteria. Then during the process of substrate utilization, a chromogenic product is released from the substrate, thus indicating the presence of the target microorganism. Several commercial products are available based on this technology. Colilert (IDEXX Laboratories, Portland, ME, USA) is an example that makes use of this technology for simultaneous detection of coliforms and E. coli. Basically, ortho-Nitrophenyl-β-galactoside (ONPG) and 4-methylumbelliferyl-β-Dglucuronide (MUG) are carbon sources that are metabolized by β-galactosidase in coliforms that change the color of the medium to yellow and β-glucuronidase in E. coli to produce fluorescence. A parallel approach for detection of enterococci uses Enterolert (IDEXX Laboratories, Portland, ME, USA) where enterococci produce fluorescence when the nutrient source is metabolized due to the enzymes present in enterococci (Fricker and Fricker, 1994). The false positive rate and false negative rate associated with Enterolert was reported to be 5.1 and 0.4%, respectively, for recreational waters (Budnick et al. 1996). However, this method also lacks specificity. There is a lack of efficient tools 14 to identify the wide range of enterococci species in water; to date there are 47 known enterococcal species of clinical, animal and environmental origin (Brtkova 2010). 1.4.2 Molecular Methods: Polymerase Chain Reaction and Quantitative Polymerase Chain Reaction (qPCR) Invention of polymerase chain reaction (PCR), a technique that amplifies and makes millions of copies of specific target regions of deoxyribonucleic acid (DNA), in 1983 paved the way for gene specific amplification and identification (Mullis and Faloona 1987). This tool helped to overcome many problems associated with culture based methods. PCR was quickly adapted for medical diagnostics and finally for detection of specific microbes in environmental samples including food and water (Brunk et al. 2002). Polymerase Chain Reaction is initiated with the denaturation of double stranded DNA by o holding the temperature at around 95 C for 10-15 minutes. Repeated cycles, usually up to 40 cycles of these three processes as described below results in amplification of target DNA. o i) Denaturation at 95 C- The bond that holds two strands of DNA is broken, reducing double helix to a single strand. o ii) Annealing at 60 C or a primer-dependent temperature - Primers, which are short synthetic oligonucleotides and serve as initiation point for DNA synthesis, bind to “complementary sequence spots” due to the formation of hydrogen bonds. o iii) Extension at 72 C - Deoxynucleotide triphosphates (dNTPs) are added to the 3’ end of the primer by polymerase enzyme, thus completing strand synthesis. 15 Amplified product can be visualized by a method called Agarose Gel Electrophoresis which separates DNA fragments by their length. An electric field moves the negatively charged nucleic acid molecules through an agarose matrix. Shorter molecules move farther through gel pores by a phenomenon called “sieving”. The amplified product can be visualized in the the gel as bands by addition of stains such as ethidium bromide which intercalates between DNA bases and fluoresces when viewed using a UV transilluminator (Voytas 2000) Quantitative PCR (qPCR) also called as real-time PCR is a modification of conventional PCR that allows the quantification of target DNA in real-time without further analysis. Quantitative PCR, using Taqman probes, was first described by Heid et al (1996). The basic principle is the same as that of conventional PCR but in this case a fluorescent DNA specific probe made up of oligonucleotides, such as Taqman® Probe, or dye, such as SYBR Green® is added to the reaction mix. Primers and probes are designed using programs such as Beacon Designer (Premier Biosoft) Primer Express Applied Biosystems or Roche Primer Design (Roche Diagnostic Corportation), in such a way that the nucleotide sequences in these short oligonucleotide fragments can bind specifically to a target sequence of DNA present in the sample during the annealing step in PCR. In the absence of target sequence, there is no binding of these primers or probes and hence there is no amplification. While designing the primers and probe sequences, care should be given to avoid any significant cross homologies by performing a BLAST search of primers and probe sequences using GenBank nucleotide database at National Center for Biotechnology Institute (NCBI) (Dieffenbach et al. 1995). It is an important step in determining the specificity of the assay as any sequence similarity of target gene 16 with other genes could result in cross amplification and false positives (Gunson et al. 2006) The mechanism behind qPCR is in the design of the probe tagged with a dye termed a “reporter” at the 5’ end of the DNA and at the 3’ end another dye termed “quencher” is used. Both these dyes are at close proximity and when there is no amplification in progress, release of fluorescent signals by reporter dyes are suppressed by the quencher dyes. As the addition of nucleotides to the 3’ end of the primers by Taq Polymerase enzyme are in progress during amplification, it reaches the annealed probe and cleaves the probe due to 5’ exonuclease activity of polymerase enzyme (any base or obstacle downstream of the growing nucleotide chain is removed due to this activity). Cleavage of the probe at 5’end increases the distance between both the dyes and that increases fluorescent emissions of the reporter dye. All qPCR machines will have a detector system to capture the increase in reporter signals. The Crossing point (Cp) is the cycle at which PCR amplification curves begins and is the point that is most reliably proportional to the initial concentration of the DNA target. As the quantity of the amplified product increases, the fluorescent signals of reporter dye also increase, which is measured by the software to calculate the concentration of target DNA in the reaction mixture, thus providing rapid sensitive and quantitative results (Valasek and Repa 2005). Efficient qPCR protocols include removal of PCR inhibitors in the sample, good primers and probe design, optimization of reagent concentration and cycling conditions, and avoidance of any external contamination to ensure improved assay sensitivity and specificity (Hoorfar et al. 2004). Sensitivity refers to the assay’s capability to detect very low concentrations of a target gene. An efficient qPCR assay can detect, sometimes as 17 low as 2 gene copies per reaction (Nayak and Rose 2007). The success of qPCR is also in the ability of the assay to detect actual target without amplifying any other DNA present in the samples whenever the assay is run under the same reaction conditions and this is referred to as “specificity”. Fluorescent dyes, such as SYBR Green, intercalates with any double-stranded DNA (including non-specific PCR products or primer-dimers) and fluoresces during qPCR; thus reducing specificity of the assay. However, when a fluorescent probe that corresponds to part of sequence of target DNA is used in qPCR, it will specifically bind to the target thus providing more accurate quantification results (Hoorfar et al. 2004). Quantitative PCR is a promising tool especially for the detection of many important pathogens in water. Some strains of pathogens such as Vibrio cholera, Helicobacter pylori etc. enter into a physiological state called VBNC (Viable But Nonculturable) in which the cells are metabolically active but cannot be grown in culture media (Oliver 2005). Quantitative PCR is able to detect the cells in this state, thus reducing the false negatives which would otherwise arise by using cultivation based methods. The ability to provide data in a shorter period of time is another advantage of qPCR. While cultivation based methods require at least 24-48 hours before results can be obtained, qPCR can provide results in less than an hour (Klein 2002). This is critical for prompt decision making to prevent major adverse effects on human health or economic losses associated with pollution of drinking water and recreational water. However, there are some disadvantages that need to be considered before traditional cultivation methods are replaced by qPCR methods. Some of the concerns that need to be addressed while using qPCR for detection and quantification of target organisms include the inability to 18 distinguish between live and dead cells, variations in the DNA recovery, misinterpretation of cell numbers based on target gene copies that vary with different bacteria, and false interpretation of target concentrations due to qPCR inhibition and inefficiency (Wilson 1997). Finally qPCR tools are often more expensive as compared to cultivation methods. Application of qPCR for monitoring E. coli and enterococci in water was first published by Frahm and Obst (2003) using Taqman probe assays that targeted the uid A and 23S rRNA genes respectively. They used an overnight enrichment step to quantify the E. coli targets in drinking water samples as the quantification was negative with either no or short enrichment steps. This study used plasmid copies during standardization, and the detection limit of the assay was around 10-100 copies per reaction and final results were obtained within five hours following overnight incubation. Foulds et al (2002) applied a Taqman qPCR assay targeting lacZ gene to quantify E. coli in distilled water and river water samples seeded with E. coli and were able to detect three copies per reaction of target gene in each sample without the overnight enrichment steps reducing the detection time to two hours following DNA extraction. Another qPCR assay targeting Internal Transcribed Spacer (ITS) region of E. coli between 16S-23S rRNA subunit genes (Khan et al, 2007) had a detection limit of 70 copies /reaction in spiked agricultural water as determined from the standard curve. Another Taqman based qPCR targeting uidA gene for E. coli was developed by Silkie et al. (2008) to test protocols for reagent decontamination during qPCR assays. Recently, the protocol was also applied to measure concentrations of E. coli in raw sewage and 7 fresh animal feces and quantified levels of E. coli to be 10 cells per 100 mL of raw 19 6 8 sewage and 10 -10 cells in per gram of dry weight of animal feces. Host specificity and detection limit for this assay were not evaluated. A qPCR assay (based on Scorpion qPCR chemistry in which primers are covalently linked to the probe) was utilized to target the uidA gene and compare to cultivation based defined substrate (IDEXX Colilert18) and colilert in a study that monitored wastewater and surface water for E. coli to evaluate the capacity of the assay for future applications (Lavender and Kinzelman 2009). They found a positive correlation between the colilet and qPCR in both these samples. The qPCR assay for enterococci developed by Frahm and Obst (2003) targeted the 23S rRNA gene from a wide range of Enterococcus spp. Out of 47 known enterococcal species, the assay tested positive for the following enterococci species; E. faecalis, E. faecium, E. durans, E. hirae, E. gallinarum, and E. casseliflavus. These primer sets have been applied in epidemiological studies by USEPA (Wade et al. 2006) and the qPCR for the 23S rRNA is being considered by USEPA as a potential method to be included in new regulations for monitoring recreational beach water quality (Boehm et al. 2009). 1.5 Microbial Source Tracking Microbial source tracking (MST) is a field that has enabled differentiation of various sources of fecal contamination which in turn has assisted in addressing effective control of the pollution inputs (Stewart et al. 2003). MST generally involves examination of fecal indicator bacterial genetic markers which can then be associated with a specific host and it is associated with feces such as human (e.g. septage) or animal (e.g. cattle manure). Host specificity is assumed to be due to the confined occurrence of key types of fecal 20 indicators in animals due to nutrient selection as well as physiology of different intestines (Shanks et al. 2006). 1.5.1 Selected MST Approaches There are two fundamental approaches to MST; one is based on phenotypes and other based on genotypes. Phenotypic approaches are based on some morphological or biochemical traits whereas genotypic approaches are related to the organism’s nucleic acid sequences (Simpson et al. 2002). In MST using cultivation methods, target organisms from samples are grown in specific culture media and once the targets are verified; genotypic or phenotypic approach is applied. An alternative approach is to use cultivation-independent method where the nucleic acids, DNA or RNA are extracted directly from the samples and genotypic methods are applied for MST. This approach saves time and is also used to examine targets that are either present in low numbers in the sample or cannot be grown in culture media. Another classification of MST methods is library dependent and uses reference libraries (Simpson 2002), of a large number of bacterial isolates which are collected and characterized from humans, cows, swine etc for a particular genetic or phenotypic characteristic. Isolates from one source are analyzed, compared and contrasted against isolates in the libraries using mathematical approaches. Library-independent methods require no reference library but are based on detection of host specific target organisms in which specificity and sensitivity has been defined (USEPA 2005b). This will be described in more detail in the following section 21 Table 1.3: Microbial source tracking approaches (Scott et al. 2002; Simpson et al. 2002) Methods Principle Limitations Antibiotic Resistance Analysis (ARA) Different species of bacteria vary in their response to antibiotic treatment. -Adequate database of profiles is needed. -Time consuming. Carbon utilization Profile (Nutritional Analysis) Different bacteria use wide range of carbon and nitrogen sources for energy and growth -Nutrient requirements of bacteria may be influenced by environmental factors, so may not be right approach for field application. -Not many case studies done. Fecal Coliform/Fecal Streptococci (FC/FS) Ratio Human feces normally has a ratio of fecal coliform to fecal streptococci greater than or equal to 4.0, whereas ratios below 0.7 are associated with animal feces. -Fecal enterococci densities vary with individuals based on diet. -Coliform survival varies with environmental factors. Serogrouping Bacterial strains of the same species have different somatic antigenic determinants. - Adequate databank of anti-sera profiles is needed. Phenotypic Librarydependent Methods Phenotypic Libraryindependent Methods Genotypic Librarydependent Methods Ribotyping Genetic differences in the genomic sequences within or flanking the 16S and 23S rRNA genes are detected. 22 -Slow and inconclusive results Table 1.3 (cont’d) Randomly Amplified Polymorphic DNA (RAPD) analysis Repetitive element sequence-based PCR (rep-PCR) PFGE (Pulsed-Field Gel Electrophoresis) PCR conditions done using non-selective primers at high stringency produce a series of strain specific primer and template dependent PCR products. Specific fingerprints are generated using interspersed repetitive DNA sequences located in different parts of the target indicator genome. Variability in sequences in the genome result in DNAfragment banding pattern detecting genetic differences between strains -Lack of reproducibility. -Lab to lab variation. -cell culture required. -may require large database of isolates. -variability increases as database increases. -Time consuming and tedious. -Not suitable for rapid identification of large number of strains. Genotypic Libraryindependent Methods Molecular SpeciesSpecific (Host-Specific) Indicators Primers are designed to match specific genes of target organisms. 23 -Little is known about survival and distribution in water systems -Host specificity need to be tested extensively 1.5.2 Genotypic Library-Independent Source Tracking Methods In this section, library-independent cultivation-independent methods are reviewed as rest of the source tracking methods are beyond the scope of this research. This source tracking approach offers several advantages; saves time and cost of collecting and fingerprinting bacterial isolates from all possible fecal sources, utilizes molecular methods such as PCR or qPCR, therefore, results are obtained faster than culturedependent methods, target organisms that are difficult to grow in culture media can also be explored for source tracking, as the DNA extract can be used for multiple assays targeting multiple markers. Although generally culture independent, some protocols in this approach required that the target organism be grown in specific culture media (due to sensitivity) prior the DNA analysis. For example, a gene for an enterococcal surface protein (esp) found in Enterococcus faecium was found to be specific for human fecal contamination (Scott et al. 2005) and has been applied for MST in water using a cultivation-PCR protocol (Ahmed et al. 2008; Wong et al. 2008). MST assays for the detection of host-specific Bacteroides are currently one of the most frequently used methods (Santo Domingo and Sadowsky 2007). Bacteroides are obligately anerobic, Gram negative, rod shaped, and non-endospore forming bacteria and are normally commensals that constitute the most numerous members of the intestinal flora of all warm blooded animals (Wexler 2007) This group of bacteria has been suggested as alternative fecal indicators as these are found in much higher concentrations than E. coli in the warm-blooded humans and animals. One gram of feces may have nearly 10 11 Bacteroides cells (Finegold et al. 1983) and E. coli is found using culture 24 7 methods at about 10 CFU per gram of human feces (Fiksdal 1985; Wang et al. 1994, 1996). They do not survive well outside the host organism (Avelar et al. 1998; Kreader 1998) and are less likely to re-grow in the environment (Fiksdal et al. 1985). Bernhard and Field (2000) utilized molecular tools such as PCR, length heterogeneity PCR (LHPCR) and terminal restriction fragment length polymorphism (T-RFLP) to identify Bacteroides 16S rDNA fragments that had multiple sequences resulting in host specific gene clusters for which primers were designed for a PCR assay. It has been estimated that of the total fecal bacteria in the gut, 30% are Bacteroides, (Holdeman et al. 1976), and 10% represent of these have host-specific markers (Bernhard and Field 2000). Conventional PCR assay was designed targeting a human (HF183 marker) specific clone in the 16S rRNA of the Bacteroides –Prevotella group. The host specificity tested for this assay showed that out of 13 human samples tested, 11 tested positive and out of three sewage samples tested, all tested positive for the human HF183 16S rRNA marker (Bernhard and Field 2000). The host specificity for this marker was further evaluated and confirmed by Ahmed et al. (2008). Out of the 155 feces samples tested from various animals, none showed amplifications using the primers for HF183 marker. Quantification of the HF183 marker using a SYBR Green qPCR done by Seurinck et al. (2005). Even though this assay showed high specificity to human fecal contamination, it also amplified 25% and 14% of the dog and cat fecal samples tested, respectively. Human and cow-specific assays targeting Bacteroides 16S rRNA were developed by Layton et al. (2006). Even though the human specific assay amplified the human target in 90% of the human fecal samples, it also amplified the human target in 38% of cow, 13% of horse, 88% of dog, and 71% of cat fecal samples. Similarly, host specificity tested 25 using primer sets developed by Okabe et al. (2007) targeting another region of 16S rRNA fragment from humans of Bacteroides-Prevotella showed cross reactivity with clones from human, cow, and pig feces. A Bacteroides assay by Kildare et al. (2007) showed cross specificity with dog feces (one fecal sample out of eight fecal samples tested) though it was positive in 32 human fecal samples tested and negative in 33 animal feces that included feces from 8 cows, 8 horses, 7 cats and 10 seagulls . Several of these assays targeting human Bacteroides 16S rRNA genes are reported to show cross reactivity with fish feces (McLain et al. 2009). Assays that were believed to be host specific are being reported to react with other fecal sources (Layton et al., 2006; Kildare et al., 2007). This cross amplification is thought be due to the use of 16S rRNA genes (Shanks et al 2007). Continued research into defining the identification of hostspecificity and evaluating geographic distribution of 16S rRNA Bacteroides markers is required before a false positive or false negative rate can be determined. Other specific genes belonging to certain species of Bacteroides are being recommended as MST targets for human fecal pollution and are being evaluated. In this regard, Bacteroides thetaiotaomicron was suggested as a good candidate as MST marker not only for its occurrence in high concentrations in human feces but also due to its high specificity when tested with other animal feces (Carson et al. 2005). Testing was undertaken on 35 beef cattle, 26 dairy cattle, 24 chicken, 29 turkey, 35 horse, 44 swine, and 17 goose fecal samples and only five of 31 dog fecal samples showed a cross reaction (Carson et al. 2005). Markers that have the potential to track bovine fecal pollution have also been developed. A conventional PCR assay that targeted a CF123 gene cluster of what may be 26 a cow specific fecal clone for Bacteroides 16S rRNA gene was developed by Bernhard and Field (2000). This used a forward primer (CF128) combined with a reverse primer that targeted Bacteroides-Prevotella group. Detection limit of this assay was found to be 1 × 10 −12 grams of cow fecal DNA using 10 fecal samples in a composite (Lamendella et al. 2006). One study that tested specificity of the assay found cross amplification with chicken, deer, goat and sheep feces. Layton et al. (2006) later developed qPCR assays targeting another specific Bacteroides 16S rRNA set of genes. Clone libraries from human and cow feces were produced by using a conventional PCR that targeted general Bacteroides and sequences of these clones were aligned to identify regions that were specific for each host fecal clone library. Primers and Taqman based probes were designed targeting these specific regions and were further evaluated for host specificity. All 11 bovine fecal samples were amplified using this assay. Bovine associated assay (Bobac assay) did not show cross amplification with any other host fecal DNA tested except with one dog sample (of four), out of 6 human, 6 swine, and 7 equine fecal samples tested. Okabe et al. (2007) used the same approach to design cow-specific Bacteroides primers for qPCR assay. However, the specificity was reported only on the basis of sequence alignment and was not tested in fecal samples from multiple hosts. More recently, a metagenomic approach called Genome Fragment Enrichment (GFE) method using DNA hybridization was used to identify host-specific fragments that were not 16S rRNA targets (Shanks et al. 2006, 2008). In this approach, bovine-specific genome fragments were generated by hybridizing biotin labeled bovine DNA with DNA from another host fecal sample and screened further using bioinformatic tools for 27 confirmation. BLASTx analysis revealed potential relationship of these fragment sequences to Bacteriodes. Further assignment of these fragments into functional groups based on a previous B. thetaiotaomicron VPI-5482 genome annotation indicated that these fragments belonged to genes that encode bacterial surface and secreted proteins. Out of the three such bovine specific fragments selected after screening, qPCR assays were designed for two of these markers, termed as M2 and M3 (Shanks et al. 2006; 2008). Host specifcity of these assays were tested using 175 fecal DNA extracts from 24 different animal species and both the assays showed 100% specificity, thus no false positives were identified at a rate of 99%. M2 assay revealed 100% sensitivity whereas for M3 assay the sensitivity was 98% when tested using DNA from 247 individual bovine fecal samples representing 11 different populations, with a quantification limit of 25 copies per reaction. However, there were concerns that sensitivity and prevalence could vary for these assays with geographical distributions and cattle herd populations which should be evaluated before applying these assays in a particular location for MST purposes (Shanks et al. 2008). Other MST methods using genotypic library-independent approaches included STIb, STII and LTIIa heat stable enterotoxin gene targets in E. coli to track cattle (Khatib et al. 2002) and swine (Khatib et al. 2003) feces respectively, host specific virus assays such as human adenoviruses and bovine polyomaviruses (Bofill-Mas et al. 2006), porcine adenoviruses (Hundesa et al. 2009) etc. These approaches have greater false negative rates or are much more expensive than examining bacterial targets. There are limitations of any MST approach. Prevalence and concentration of these markers may vary with individuals of a species, or across different geographical 28 locations. There is a possibility for these markers to show up only in “pooled” samples such as raw sewage or manure lagoon samples and not be detected in individual fecal samples. Some markers could be present in such low concentrations that large volumes of sample are required to detect them. 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Appl Environ Microbiol 2:49-63. Zaleski, K.J., K.L. Josephson, C.P. Gerba, and I.L. Pepper. 2005b. Potential regrowth and recolonization of Salmonella and indicators in biosolids and biosolid amended soil. Appl. Environ. Microbiol 71:3701–3708. 41 CHAPTER 2 RESEARCH OBJECTIVES 2.1 Problem Statement Risks to human health are imposed by both human and non-human sources of fecal contamination of our waterways, although these risks may differ with different fecal sources. Indictor organisms play an important role in minimizing or avoiding these risks by implying the occurrence of fecal contamination in a water body. But very often, the sources of fecal inputs remain unknown and the pollution problems persist for longer periods of time or keep occurring intermittently. Alternative indicators, such as those belonging to genus Bacteroides, due to their source tracking capability are being considered for water quality evaluation and may be included in the new or revised water quality criteria for recreational waters by U.S. EPA (USEPA 2007). Even though some studies have been conducted to evaluate the host specificity, sensitivity, and geographical distribution of some of these host-specific markers (USEPA 2005), there are still significant data gaps that need to be addressed to obtain a better understanding of these markers and their applications. Among several research gaps that need to be addressed, one of the priorities is to determine relationships between these markers and current regulatory indicators such as E. coli and enterococci in various fecal sources of water pollution using standard and new detection methods. These gaps, if addressed, will help regulatory agencies provide recommendations for use of these markers in NPDES permits for wastewater treatment plants or Best Management Practices (BMP) for nonpoint sources of fecal contamination. 42 2.2 Research Goals, Hypothesis and Objectives The overall aims of this research were to investigate the prevalence and concentrations of recently identified human and cow specific Bacteroides markers in comparison with E. coli and enterococci in both the human and cow waste environments and to monitor these environments for occurrence of E. coli O157:H7. In this study, there was also an attempt made to understand the variation expected when cultivation methods are replaced with qPCR methods in different samples. The main hypotheses were: 1) Human specific Bacteroides thetaiotaomicron DNA target is present in similar levels as that of E. coli and enterococci and is a good substitute for conventional indicators in predicting human fecal contamination. 2) High concentrations of human specific Bacteroides thetaiotaomicron DNA markers are present throughout the wastewater treatment processes; 4) Prevalence and concentration of cow markers such as cow specific Bacteroides 16S rRNA markers, cow M2 and M3 markers differ in different cow manure samples. The specific objectives were: 1) to monitor concentrations of Bacteroides thetaiotaomicron, E. coli and enterococci in raw sewage and septage samples and evaluate fate of these organisms and/or their DNA during wastewater treatment processes; 2) to evaluate the prevalence and concentrations of different Bacteroides cow markers in cow manure samples; 3) to monitor cow manure, sewage and septage samples for occurrence of E. coli O157; and 4) to evaluate and compare cultivation methods with qPCR methods for monitoring different environments. 43 2.3 Thesis Outline After chapter 1 (included as a brief literature review of water quality) and chapter 2 (on objectives), chapter 3 addresses methods development. This was an evaluation of a Taqman qPCR assay for generic E. coli that was used in all monitoring studies, comparing and evaluating an automated DNA extraction method with manual extraction kits for environmental samples. An ethidium monoazide-qPCR method to distinguish live from dead cells in treated and untreated waste samples was explored. In addition, a preliminary evaluation of the 16S rRNA Bacteroides human marker by conventional PCR was undertaken. Chapter 4 details the goals, methods and results from samples collected from waste water and septage treatment facilities. This was focused on understanding the fate of human specific DNA markers and conventional indicators throughout treatment process and characterizing effluent quality using these DNA targets, as well as comparing qPCR to cultivation methods. Chapter 5 describes the methods and findings of the monitoring of cow manure lagoons using three different cow specific Bacteroides markers, and conventional indicators. Comparison between cultivation methods and qPCR methods were performed using two targets, E. coli and enterococci using individual cow feces and manure lagoon samples. Chapter 6 focuses on the monitoring of cow manure lagoons, sewage, and septage samples for E. coli O157. However, this chapter also discusses the issues with specificity of qPCR assays used for the detection of this pathogen. Chapter 7 summarizes the conclusions of this research and addresses future directions. 44 REFERENCES 45 2.4 References USEPA. 2005. Microbial Source Tracking Guide Document. EPA-600/R-05/064, Office of Research and Development, Washington D. C. USEPA. 2007. Report of the experts scientific workshop on critical research needs for the development of new or revised recreational water quality criteria, EPA-823-R-07007. United States Environmental Protection Agency, Washington, DC. 46 CHAPTER 3 METHODS DEVELOPMENT, EVALUATION AND APPLICATION 3.1 Introduction Recent advances in molecular biology such as polymerase chain reaction (PCR) have revolutionized microbiology and are beginning to be used to develop a tool box to address the microbial quality of recreational and drinking water environments (Noble et al. 2006). Quantitative PCR known as qPCR has many advantages such as producing results more rapidly (30 minutes to 2 hours), detection of non-cultivatable microbes compared to standard cultivation methods, and providing quantitative results with a wider 0 8 detection range (10 - 10 copies/reaction) (Mackay 2004). Escherichia coli (E. coli) and Enterococcus spp. have long been used as indicators of water pollution. Cultivation methods are used to enumerate these indicator organisms such as EPA Method 1600 for Enterococcus spp. and 1603 for E. coli (Messer and Dufour 1998; USEPA 2002; 2005). However, it takes at least 24 hours to obtain the results using these methods. Multiplex PCR targeting lacZ and lamB followed by detection of amplified genes using specific probes were the initial molecular approaches for detection of E. coli in water (Bej et al. 1991a). Other genes such as phoE genes and 16S rRNA genes have also been targeted for molecular detection of E. coli (Spierings et al. 1993; Tsai and Olson 1992; Tsen et al. 1998) by conventional PCR. One of the most widely studied targets is the beta-glucuronidase in E. coli which is coded for by the uidA gene (Bej et al. 1991a, b; Buckalew et al. 2006; Gracias and McKillip 2004; Tryland and Fiksdal 1998) and PCR methods targeting that gene have been developed and evaluated 47 to monitor water quality (Foulds et al. 2002, Frahm and Obst 2003). A new qPCR method targeting the uidA gene was developed and used in the research presented in this dissertation for quantification of E. coli in different samples. This assay was initially designed with an idea to develop a multiplex qPCR assay to detect generic E. coli and E. coli O157 simultaneously in one single run from different samples. But further evaluation during assay development did not give satisfactory results and the idea of multiplexing assays was abandoned. However, the qPCR assay for detection of generic E. coli was found to be satisfactory based on the specificity testing and therefore was further evaluated and applied throughout this research. The qPCR assay used in this study for Enterococcus spp., targeting 23S rRNA gene, was originally developed by Ludwig & Schleifer (2000) and had been further evaluated by Frahm & Obst (2003). They tested specificity of the assay and most strains of Enterococcus spp. were detected that are commonly found in the environment. Many studies have used this qPCR assay to detect Enterococcus spp. (He and Jiang 2005; Noble et al. 2006; Shivaganesan et al. 2008; Viau and Peccia 2009; Wade et al. 2006). Noble et al. (2006) included this assay in a multi-tiered approach to assess the level of contamination in Santa Monica Bay, California. Despite the fact that qPCR equivalent cells of Enterococcus spp. remained steady even when their cultivable levels were reduced due to inactivation by sunlight, the advantage of using Enterococcus spp. qPCR assay in the tool-box approach to provide quantitative and rapid water quality test results to beach quality managers was illustrated in their study. Wade et al. (2006) used this Enterococcus spp. qPCR assay and found a strong correlation between Enterococcus spp. qPCR daily average concentrations (cells/100mL) and swimming related gastrointestinal 48 illnesses in two of the Great Lakes beaches. This assay has also been applied for evaluating levels of Enterococcus spp. in biosolid samples (Viau and Peccia 2009), sewage and river water samples (He and Jiang 2005) and currently, U. S. EPA is validating this assay for monitoring marine and fresh ambient waters. The Enterococcus spp. qPCR assay protocol used by U. S. EPA is in the following link: http://www.epa.gov/waterscience/methods/method/biological/rapid1.pdf “Method A: Enterococcus spp. in Water by TaqMan Quantitative Polymerase Chain Reaction (qPCR) Assay”. This Enterococcus spp. qPCR targeting 23S rRNA gene was standardized and applied for all monitoring studies in this research. One of the most important steps in obtaining reliable data from qPCR methods is cell lysis and DNA extraction. It was noted that the lysis method used should ensure the disruption of thick Gram-positive cell walls (i.e., enterococci) but should not be so harsh that it causes shearing of released DNA from Gram-negative cells (i.e., E. coli) (Schneegurt et al. 2003). Early in methods development for extraction of DNA, Holben (1997) included separation of cells from soils by centrifugation and then extracted DNA from each cell fraction. This protocol included lysozyme for lysis of the cells, followed by caesium chloride-ethidium bromide density centrifugation and ethanol precipitation for DNA recovery. This method was tedious and included an overnight incubation to recover the DNA. Another popular approach was a direct lysis method (Ogram 2000) which included physically breaking the cells (bead beating) followed by a chemical lysis step (sodium dodecyl sulphate) and precipitation of DNA using polyethylene glycol and finally elution of DNA by phenol-chloroform extraction. This method was observed later to shear DNA with poor yields (Leff 1995). 49 Today most laboratories use commercial kits such as Qiagen Stool kit, MoBio Soil or Fecal extraction kits (Lebuhn et al. 2005; Rose et al. 2003) but these methods can still be laborious, time-consuming, with an increased risk of cross contamination, variability and errors. Automated DNA extraction methods have been evaluated for clinical specimens (Beuselinck et al. 2005; Wilson et al. 2004), to screen for viral pathogens in routine blood donations but have not been used often for environmental samples (Fafi-Kremer et al. 2004; Hourfar et al. 2005; Mengelle et al. 2003; Pichl et al. 2005). Rasmussen et al. (2009) compared different automated extraction instruments for viral nucleic acids in an interlaboratory study and found consistency between different instruments. They reported that automated DNA extraction was rapid, sensitive, reproducible and highly efficient from clinical samples. In this study, an automated DNA extractor was evaluated and compared to commercial extraction kits, QIAmp DNA mini kit (Qiagen, Valencia, CA, USA) and QIAamp DNA Stool Mini Kit (Qiagen, Valencia, CA, USA) using treated and untreated sewage and cow feces for the quantification of E. coli and Enterococcus spp. by qPCR. Bacteroides-Prevotella 16S rRNA HF 183 human marker has been reported to be an effective marker to track human fecal contamination (Bernhard and Field 2000b) and its geographical stability has been evaluated in different locations such as in the USA (Bernhard et al. 2003), France (Gourmelon et al. 2007), United Kingdom, Portugal, Ireland (Gawler et al. 2007), and Australia (Ahmed et al. 2008) where it was found to be suitable based on specificiy for tracking human fecal contamination in surface water bodies thus far in the US, Europe and Australia. 50 Although molecular methods such as qPCR are a promising tool for environmental monitoring, one disadvantage of this method is its inability to differentiate between live and dead cells. When the cells die in the environment, their DNA persists and can be detected by molecular methods (Young et al. 2007). Thereby suggesting the levels of fecal contamination is commensurate with live pathogens, and could be considered falsepositive results. One of the strategies used to overcome this issue has been to detect less stable RNA using Reverse-Transcription real-time PCR (RT-rtPCR) (Bustin 2000), which has been applied to detect pathogens from environmental samples but viability has not been verified (Gonzalez-Escalona et al. 2009; Sharma 2006). Lower recovery of RNA and reduced efficiency in amplification were the disadvantages with this approach (McKillip et al. 1999). Another strategy proposed to differentiate live and dead cells in qPCR was the use of Ethidium Mono-Azide (EMA) or Propidium Mono-Azide (PMA) dyes (Rudi et al. 2005). These dyes penetrate the damaged membranes of dead cells but cannot do so in the live cells whose membranes are intact. Once inside the cells, the dyes intercalate between the double strands of DNA upon photoactivation thus preventing amplification during PCR, in theory producing amplification of DNA from only live cells. EMA-qPCR has been applied in pure cultures and in other samples such as food (Wang and Levin 2005), soil and biofilms (Pisz et al. 2007) and water samples (Delgado et al. 2009; Gedalanga and Olson 2009). In this study, EMA-qPCR was evaluated using pure cultures of E. coli and Enterococcus spp., and raw sewage and final chlorine disinfected effluent samples from a wastewater treatment plant. The specific objectives of this chapter are summarized below; i) Standardisation of E. coli and Enterococcus spp. qPCR assays; 51 ii) Comparison of an automated DNA extraction method with manual methods using environmental samples; iii) Application and evaluation of Bacteroides-Prevotella 16S rRNA HF 183 human marker conventional PCR assay; and iv) Evaluation of EMA-qPCR using pure cultures and wastewater treatment samples. 3.2 Materials and Methods 3.2.1 Development of a qPCR Assay for E. coli uidA gene and Enterococcus spp. 23S rRNA gene 3.2.1.1 Bacterial Strains and DNA extraction E. coli ATCC strain 15597, Enterococcus faecalis ATCC strain 19433, Klebsiella pneumoniae ATCC strain 13883, Shigella flexneri ATCC 12022, Pseudomonas aeruginosa ATCC 10145, Enterobacter aerogenes ATCC 13048, Citrobacter freundii ATCC 8090 were used as part of the initial development and testing of the qPCR method. E. coli strains used for specificity testing were provided by Microbial Evolution Laboratory, National Food Safety and Toxicology Center, at MSU. All the bacterial o strains were grown in Trypticase Soy Broth at 37 C for 18-24 hours. DNA was extracted from these cultures using QIAmp DNA mini kit (Qiagen, Valencia, CA, USA) according to the manufacturer’s instructions. 3.2.1.2. Preparation of qPCR Standards Inorder to prepare the standards, the uidA gene of E. coli was amplified separately using a primer set that flanked the qPCR target amplicon sequences; the sequence for forward primer was 5’-GCAGTCTTACTTCCATGATTTCTTTA-3’ and for the reverse 52 primer, it was 5’-TAATGCGAGGTACGGTAGG-3’. These primers were developed using the Roche Light Cycler Primer Design Software. Polymerase chain reaction (PCR) was performed in a 25 μL total reaction mix which contained 14 μL Hotstart DNA Polymerase Mastermix, 0.4 μM of each primer, 3 μL of the template DNA and molecular graded water (QIAgen, Valencia, CA, USA) to make up a final volume of 25 μL. The o PCR was initiated with a denaturation step for 10min at 95 C followed by 35 cycles of 30 o o o o sec at 95 C; 30 sec at 57 C and 60 sec at 72 C and a final extension cycle at 72 C for 8 min. Negative controls (reaction mix with molecular graded water instead of DNA) were included in the PCR runs. The PCR product of 522 bp size was visualized in 1.2% agarose gel stained with Gel-red by means of UV-trans illumination and a 100-bp DNA ladder (Promega, Madison, USA) was used as a marker. Similarly, the 23SrDNA gene of Enterococcus spp. was amplified using the forward 5’ATCTACCCATGTCCAGGTTGAAG3’ primer and reverse primer 5’CCATCTCGGGTTACCGAATTCAG3’. These primers were developed using the Roche Light Cycler Primer Design Software. Polymerase chain reaction (PCR) was performed in a 25μL total reaction mix which contained 15 μL Hotstart DNA Polymerase Mastermix, 0.4 μM of each primer, 2 μL of the template DNA and molecular graded water (QIAgen, Valencia, CA, USA) for a final volume of 25 μL. The PCR was initiated o o with a denaturation step for 10min at 95 C followed by 35 cycles of 30 sec at 94 C; 30 o o o sec at 60 C and 30 sec at 72 C and a cycle of final extension at 72 C for 10 min. Negative controls (reaction mix with molecular graded water instead of DNA) were 53 included in the PCR runs. The product of size 223 bp was visualized in 1.2% agarose gel stained with Gel-red as described previously. The amplified PCR products for both E. coli and Enterococcus spp. denaturation step were cloned into TOPO PCR 2.1 and transformed with the TOPO10 F’ competent cells (Invitrogen Inc., Carlsbad, CA, USA), according to the protocol provided by the manufacturer and the transformants were carefully picked, and inoculated in Luria– Bertani broth plus ampicillin (50mg/ml). The transformed bacteria were grown for 12-16 o h in a shaker incubator set at 200rpm at 37 C. Plasmids were extracted using the QIAGEN spin kit (Valencia, CA, USA) and were sequenced at the Research Technology Support Facility (RTSF) at Michigan State University that confirmed the insertion of the target inside the vector. The concentration of the plasmids was measured on a nanospectrophotometer several times and the mean value was calculated. These were then serially diluted ten-fold to construct qPCR standard curves. 3.2.1.3 Assay Performance The quantification of uidA gene for E. coli was optimized by using the primers and probes described in Table 3.1 and were designed using the Roche Light Cycler Primer Design Software and these primer designs were further checked for presence of any secondary structures that could lead to qPCR inefficiency by an Oligonucleotide company (TIB Molbiol, Freehold, NJ, USA). Ten-fold dilutions of plasmids were 6 1 prepared ranging from 1.2x10 to 1.2x10 copies per reaction and were used as templates to prepare the standard curves. The reaction mix for amplification of uidA gene consisted of 2µL of the Fast Start Light Cycler Mastermix (Roche, Indianapolis, IN), 0.5 µM of each of forward and reverse primers, 0.2 µM of the probe A (Table 3.1), 3.2 µM MgCl2 54 and nuclease free water to a final volume of 20 µL. The assay was carried out in LightCycler 2.0 ® (Roche, Indianapolis, IN) through the following temperature profiles; o initial denaturation at 10 minutes at 95 C followed by 45 cycles of denaturation for 6 o o o seconds at 94 C; annealing for 8 seconds at 58 C and extension at 72 C for 8 seconds. Triplicate analysis was done for each dilution and negative controls. The primers and the probes used for the Enterococcus spp. targeting 23S rRNA gene were originally described by Ludwig & Schleifer (2000). The sequences for primers and probes used in this study are described in Table 3.1. A series of ten-fold dilutions of 6 0 corresponding plasmids were prepared ranging from 3x10 to 3.2x10 and was used as template to prepare the standard curve. The reaction mix for qPCR consisted of 4µL of Taqman Light Cycler Mastermix (Roche, Indianapolis, IN), 0.5 µM of each of forward and reverse primers, 0.4 µM of the probe A (Table 3.1), 5 µL of plasmid DNA and nuclease free water to a final volume of 20 µL. The assay was carried out in LightCycler ® 2.0 (Roche, Indianapolis, IN) through the following temperature profiles; initial o o denaturation at 10min at 95 C followed by 35 cycles of denaturation for 30 sec at 94 C; o o annealing for 30 sec at 60 C and extension at 72 C for 8 sec. Triplicate analysis was done for each dilution and all negative controls. For both assays, Cycle threshold (Ct) was measured during each amplification and was analyzed by absolute quantification method (Ginzinger 2002) Efficiency of amplification (E) was also estimated from standard curve by applying the formula, E= -1/slope (10 ) -1. 55 Table 3.1: Primers and probes used for E. coli and Enterococci qPCR assay Bacteria E. coli Target gene uidA Enterococci 23S rRNA Primer/probe sequence FP 5’-CAATGGTGATGTCAGCGTT-3’ RP 5’-ACACTCTGTCCGGCTTTTG-3’ Probe 6FAM-TTGCAACTGGACA AGGCACCAGC-BBQ 5’-AGAAATTCCAAACGAACTTG-3’ 5’-CAGTGCTCTACCTCCATCATT-3’ 6FAM-TGGTTCTCTCCGAAA TAGCTTTAGGGCTA-TAMRA 56 Amplicon size Reference 163bp This study 91bp Ludwig & Schleifer (2000) 3.2.2 Comparison between Auto and Manual DNA Extraction Methods using Environmental Samples 3.2.2.1 Sample Processing and DNA extraction a) Treated and untreated sewage samples were collected from a wastewater treatment plant (WWTP) in Michigan. The untreated sewage refers to raw sewage collected from a facility that serves around 90,000 people. Treated sewage included effluents from 1) primary treatment, after the solids have been settled, 2) secondary treatment, where primary clarified sewage has undergone activated sludge process and chlorination, and 3) tertiary treatment, effluent from secondary treatment post sodium bisulfate dechlorination and filtration through rapid sand filters. Raw sewage (n=9) and primary effluent samples (n=9) secondary and tertiary effluents (n=9 and 9, respectively) were used for the study, for a total of 36 samples from the wastewater environment. b) Fecal samples were also collected from individual cows from a cattle dairy farm at Michigan State University (n=9). Samples were stored and transported on ice and processed within 2 hrs after collection. Fifty milliliters of raw sewage sample were centrifuged at 8000xg for 20 minutes. Around 48 mL of the supernatant was discarded and remaining sample was mixed well by vortexing. The volume was recorded and from this, 400 μL of the pellet was used for manual DNA extraction with the QIAmp DNA mini kit (Valencia, CA, USA), another 400 µL of the aliquot was used for extraction by Roche MagNaPure LC instrument (Roche Applied Sciences, Indianapolis, Ind.) and remainder was stored at -80 o C. These volumes were included in the calculation when final concentrations of targets were calculated after qPCR. 57 For treated sewage, one liter of secondary and tertiary effluents was filtered using 90 mm, 0.45 µm pore size nitrocellulose membrane filters (Millipore, Billerica, Mass.) after which the filters were folded and immersed to the 45 mL mark with sterile phosphate buffered saline (PBS) in 50 mL centrifuge tubes. The tubes were then vortexed at high speed for 30-45 seconds to detach the cells from membrane. The filters were removed and the tubes were centrifuged at a speed of 8000xg for 15 minutes. Around 48 mL of the supernatant was discarded and remaining sample was mixed well by vortexing; from this 400 µL of the pellet aliquot was used for manual DNA extraction by using the QIAmp DNA mini kit (Valencia, CA, USA). Another 400 µL of the aliquot was used for extraction by Roche MagNaPure LC instrument (Roche Applied Sciences, Indianapolis, Ind.). For DNA extraction using Roche MagNaPure automated instrument, an additional lysis was performed by mixing the 400 µL of concentrated samples with 180 µL of the MagNaPure lysis buffer and 20 µL of the Proteinase K (20 mg/mL) which was incubated o at 65 C for 30 minutes. The mixture was then centrifuged at 500g for 30 seconds to settle down the particles and the supernatant was used for DNA extraction by the instrument. This autoextractor employs a magnetic-bead technology for extraction. The initial steps are cell lysis and protein digestion followed by nucleic acid binding to the surface of the magnetic glass particles. The nucleic acid-bead complex formation is further separated by magnetism. After the removal of cellular debris by multiple washing steps, there is detachment of nucleic acids from the beads and DNA is eluted out. Simultaneously, a manual DNA extraction was carried out on the same samples using the QIAmp DNA mini kit (Qiagen, Valencia, CA, USA). 58 Both extraction methods resulted in a final volume of 200 µL of DNA suspended in TE buffer. Negative controls (molecular grade water) were used to check for cross contamination in both extraction methods. The concentrations of extracted DNA were determined by using Nanodrop ND-1000 Spectrophotometer. Two hundred milligrams of cow feces were weighed and placed in 2 mL micro centrifuge tubes for DNA extraction using QIAamp DNA Stool Mini Kit (Qiagen, Valencia, CA, USA) and similarly for extraction by Roche MagNaPure LC (Roche Applied Sciences, Indianapolis, Ind.) instrument as described above. 3.2.2.2 Reproducibility In order to assess the intervariability in extraction, eight replicates of concentrated raw sewage samples were subjected to DNA extraction by both instrument and manual methods following the same procedures described above. For all the replicates, the same volume of lysis buffer (180 μL) and proteinase K (20 μL) were added to the tubes and subjected to auto extraction. DNA concentrations in all extracts were analyzed by qPCR methods for both E. coli and Enterococcus spp. The extractions were also carried by two different people to assess the variability in both instrument and manual methods. 3.2.2.3 qPCR Analyses DNA samples extracted from all wastewater samples and cow feces were analyzed by qPCR for E. coli and Enterococcus spp. One of the plasmid dilutions used for creating the standard curve was also included in the assay as a positive control. The qPCR analysis was performed using the same reaction mix components and cycling conditions as used for the plasmid standard curves. Five microlitres of the extracted sample were used as the template. The reaction was performed for duplicates for all samples and 59 negative controls were included. The copies of uidA gene of E. coli and 23S rRNA of Enterococcus spp. present in the sample were quantified from the standard curves obtained earlier. The copies of the corresponding genes were converted to cell equivalents; in the case of E. coli, only one copy of the uidA gene is present in a cell, thus one copy number corresponds to one cell. However in case of Enterococcus spp., there are four copies of 23S rRNA present in a cell, therefore four copies in Enterococcus spp. qPCR assay corresponds to one cell. All the final concentrations for qPCR analyses were reported as qPCR equivalent cells/100mL for all sewage samples and qPCR equivalent cells/gram of wet weight for fecal samples after conversion of copy numbers to cells. 3.2.2.4 Statistical Analysis All statistical analyses were performed using SAS software 9.2 (SAS Inc, 2002). The data were log-transformed to achieve normal distribution and meet the assumptions of a parametric test. Simple t-tests were used to compare the means of concentrations of the qPCR and the cultivation method results (p< 0.05). The coefficient of variation (CV %) was calculated to evaluate the intervariability in extraction procedures using the formula; CV%= (standard deviation/ mean) x 100. Linear regression analysis was performed using scatter plots of log10 cells/100 mL of E. coli and Enterococcus spp. for comparison between manual and instrument DNA extraction from the samples tested. 60 3.2.3 Application of Ethidium Monoazide-qPCR for Differentiating between Live and Dead cells in Environmental Samples E. coli ATCC strain 15597, Enterococcus faecalis ATCC strain 19433 were used to evaluate EMA-qPCR using pure cultures. Freezer stocks of these bacteria were revived o overnight in Trypticase Soy Broth (TSB) by incubating the tubes at 37 C in a shakerincubator (NewBrunswickScientific, New Jersey, USA) at 150rpm. From this, cultures were further inoculated into 25 mL of TSB and incubated again in the same conditions for 6 hours. Cultures were serially diluted in Phosphate Buffered Saline (PBS) and Colony Forming Units (CFU/mL) of E. coli and Enterococcus spp. were enumerated by plating 0.1ml of dilutions of cultures on Trypticase Soy Agar (TSA) plates and incubating o at 37 C for 18-24 h. Microcentrifuge tubes containing one mL aliquots of the culture were immersed in o water bath set at 80 C for 15 minutes for heat-killing. These conditions for heat-killing were used after testing different combinations of temperature and time. Loss of viability was tested by plating 100 µL from the heat-killed aliquote on TSA plates and incubating o at 37 C for 18-24 h. EMA (Biotium, Hayward, CA, USA) was dissolved in sterile distilled water to a stock concentration of 1 mg/mL and aliquoted into microcentrifuge tubes covered with aluminum foil for storage in the dark at −80°C. Further processing using EMA was done under minimal light conditions as EMA is sensitive to degradation by light. Fresh and heat-killed aliquots of E. coli and Enterococcus spp. were centrifuged at 13,000 x g for 5 min, the supernatants were discarded and the pellets were resuspended in 1mL of EMA at 61 a concentration of 2.5 µg/mL. The tubes were then incubated in the dark for 5 min after which they were placed at a distance of 20cm away from light source using 500 W, 120 V halogen T2.5 lamp. This light exposure for 2 minutes was done to photoactivate EMA to form covalent bonds between DNA strands. Cultures exposed to EMA were centrifuged at 13,000 x g for 3 minutes and washed with one mL sterile PBW to remove any unbound EMA and the tubes were kept at 4 o C in the dark until further processing. Another set of the aliquots of fresh and heat-killed cultures were stored at 4 o C until further processing to be used as controls without addition of EMA. Raw sewage (100 mL) and final chlorine disinfected effluent (2 L) samples were collected in triplicates from the wastewater treatment plant at East Lansing, MI. Viable counts of E. coli and Enterococcus spp. were enumerated by following U. S. EPA Method 1603 and 1600. Serial dilutions of the raw sewage (10 -1 -5 through 10 ) were made and one mL from these dilutions was filtered through 47 mm diameter, 0.45 µm pore size, membrane filters. For treated effluents, one hundred milliliters of samples were filtered. The filters were placed on mTEC agar and mEI agar plates. The mTEC agar plates were incubated 35 ± 0.5°C for 2 h, followed by incubation in a water bath at 44.5 ± 0.2°C for 22 h and mEI plates were incubated for 24 h at 41°C and colonies were counted. Fifty milliliters of raw sewage and effluent samples (1L) were processed as previously described in section 3.2.2.1. Both raw sewage and effluent pellet suspensions were vortexed and one mL from each sample was transferred into 5mL glass centrifuge tubes for EMA exposure for 5 minutes and another one mL was used as a control without 62 EMA addition. Sterile distilled water with EMA treatment and without EMA were used as negative controls. DNA was extracted from all the EMA exposed and unexposed pure culture and sewage samples using the QIAmp DNA mini kit (Valencia, CA, USA) following o manufacturer’s instructions. The DNA was stored at -20 C until analysis. Quantitative PCR was performed on the DNA extract to quantify E. coli uidA gene and Enterococcus spp. 23S rRNA gene by following the same protocol as described in section 3.2.1.3 using the primers and probes given in Table 3.1. 3.2.4 Preliminary Evaluation of 16S rRNA Bacteroides Human HF183 Markers by conventional PCR Assay 3.2.4.1 Application of HF 183 PCR Assay for Monitoring Water Quality A total of 48 surface water grab samples were collected at four different locations in Michigan in 2007 and 2008 ; five sampling sites at Silver Lake (SL), five sites at Saginaw Bay (SB), four sites at Coldwater Creek on six sampling dates (CCA, CCB, CCC, CCD), and two sites at Buck Creek on seven sampling dates (BC11 and BC12). These sampling sites were selected based on concerns from the local governments and citizens about the quality of the water. Water samples were collected in sterile containers o and were stored at 4 C until further processing which was always less than 24 hours. Different volumes of the water samples were filtered and analyzed for E.coli and Enterococcus spp. using U.S.EPA method 1603 and 1600 respectively. Water (1L-4L) was filtered through the 0.45-μmpore size, 90mm diameter membrane filter and filtered volume was included in the final calculation. New filters were used whenever the filters were clogged due to the suspended solids. These filters were then soaked in 45mL 63 Phosphate Buffered Saline (PBS) in 50mL disposable centrifuge tubes, vortexed for 2-3 minutes. The tubes were centrifuged for 2-3 minutes at 8,000xg for 20 minutes. The supernatant was gently removed to a volume of five mL of pellet. From this, one mL of the sample was transferred into a 2mL centrifuge tube and after centrifugation at 20,000 x g for 2 minutes, the supernatant was discarded. The process was repeated until a total volume of 3 mL was pelleted down for DNA extraction using the QIAamp DNA kit (QIAGEN) extraction kit following the manufacturer’s instructions. The remaining two o mL of the pellet was stored at -80 C for any future analysis. The eluted volume o containing the DNA was in 200 μL volumes and stored at -20 C.. The human Bacteroides 16S rRNA HF 183 marker was amplified with the forward primer 5′ ATCATGAGTTCACATGTCCG3′ and Bac708 reverse primer 5′ CAATCGGAGTTCTTCGTG 3′ (Bernhard and Field 2000b). The PCR reaction was carried out in a 25 μL reaction mixture containing 14 μL HotStar Taq Master Mix, 1 μL of each primer (10μM concentration), 6 μL of molecular grade water and 3 μL of template DNA. Amplification was carried out with the following cycling conditions: 15 o min at 95 C for initial denaturation and 35 cycles of amplification steps consisting of o o o denaturation at 94 C for 30 s, annealing at 58 C for 30 s and extension at 72 C for 60 s. o Following the amplification cycles, the final extension was done for 8 minutes at 72 C. DNA from sewage samples that was confirmed to be positive for human specific Bacteroides markers by sequencing the amplified products and BLAST analysis were used as the positive control and molecular grade water was used as the negative control. Amplified products were detected by visualizing the PCR product by electrophoresis 64 through 1.2% agarose gel and then exposing it to UV transilluminator. The samples were recorded as positive when the bands corresponded to the positive control at around 585bp. 3.2.4.2 Detection limit of HF183 PCR Assay Raw sewage sample was collected in 50mL sterile centrifuge tubes from wastewater treatment plant in East Lansing and was transported to laboratory on ice. The sample was mixed well by vortexing for two minutes and serial dilutions from 10 -1 -4 through 10 were made using sterile PBS. One milliliter from each dilution was suspended in one liter sterile distilled water (triplicates were made for each dilution). The mixture was stirred using a magnetic stirrer for 15- 20 minutes. One milliliter from each of the suspensions was directly used for DNA extraction. Also, different volumes (1mL and 1000mL) were filtered using 0.45 µm pore size nitrocellulose membrane filters (Millipore, Billerica, Mass.) and the filters were folded and immersed to the 45 mL mark with sterile phosphate buffered saline (PBS) in 50 mL centrifuge tubes. The tubes were then vortexed at high speed to detach the cells from membrane. The volumes were centrifuged at a speed of 8000xg for 20 minutes and the supernatant was gently removed to a one mL volume. From this pellet, 400 µL was used for DNA extraction using the QIAmp DNA mini kit (Valencia, CA, USA) following the manufacturer’s instructions. The total final volume extracted was 200 µL per sample. The extracted DNA samples were subjected to PCR for detecting the human Bacteroides marker HF 183 amplified as described previously. A sample consisted of three replicates and each was reported as positive or negative. 65 3.3 Results 3.3.1 Standardization and Specificity of E. coli uidA qPCR Assay The standard curve for the E. coli qPCR assay is shown in Figure 3.1. A linear relationship was observed between the log concentration (copies per reaction) and the Cycle threshold (Ct). The efficiency of amplification calculated for the E. coli assays based on the slopes of -3.32 was 104%. In qPCR standardization, with ten-fold serial dilution of plasmids, the Ct should be separated by approximately 3.3 cycles. With this slope value, amplification efficiency of a robust, reproducible qPCR assay will be 100%. Any efficient qPCR reaction should have amplification efficiency of 90-105%; efficiency below 90% or above 105% indicates poor primer design, suboptimal cycling conditions, pipetting errors, etc (Rasmussen 2001). E. coli qPCR assay specificity was also tested by performing qPCR on different organisms. The results are summarized in Table 3. 2. The E. coli qPCR assay was found to detect all six strains of E. coli tested with strong amplification signals and did not detect eight of the other bacteria including enterococci, Enterobacter, Klebsiella, Proteus, Pseudomonas and Salmonella, but did test positive with Shigella flexneri. 66 Figure 3.1: Standard curve for 10-fold serial dilutions of E. coli uidA gene. y = -3.2274x + 38.439 R2 = 0.9959 40 Cycle number 35 30 25 20 15 10 5 0 0 1 2 3 4 5 6 Log concentration (copies/reaction) 67 7 Table 3.2: Evaluation of the E. coli uidA gene qPCR assay against a variety of bacteria Organisms* Source +/- for qPCR E. coli C3000 E. coli O26:H11 E. coli O55.H7 E. coli O111:H8 E. coli O118:H16 E. coli O76:H19 Klebisiella pneumonia Citrobacter freundii Enterobacter aerogenes Salmonella paratyphi Proteus vulgaris Pseudomonas aeruginosa Shigella flexneri Enterococcus faecium Enterococcus faecalis ATCC Human Meat Human Human Human ATCC ATCC ATCC ATCC ATCC ATCC ATCC ATCC ATCC + + + + + + + - *All non-ATCC strains were obtained from Microbial Evolution Laboratory, National Food Safety and Toxicology Center, at MSU 68 3.3.2 Standardization of Enterococcus spp. 23S rRNA Gene qPCR Assay The amplification curve generated for Enterococcus spp. 23SrDNA gene is shown in Figure 3.2. A linear relationship was observed between the log concentration and the Cycle threshold. The amplification efficiency calculated for the 23SrDNA Enterococcus spp. assay based on the slopes of the -3.34 was 99%. Figure 3.2: Standard curve for 10-fold serial dilutions of Enterococcus spp. 23SrDNA gene. 40 y = -3.3472x + 39.574 R² = 0.9923 Cycle number 35 30 25 20 15 10 0 1 2 3 4 5 6 Log concentration(copies/reaction) 2 7 Linear regression analysis shows an R of 0.992, a slope of -3.34 and an intercept of 39.574. 69 3.3.3 Comparison of Concentrations of E. coli and Enterococcus spp. DNA by Manual and Automated Extraction The concentrations of E. coli and Enterococcus spp. were determined by qPCR and compared for both instrument and manual DNA extracts of treated and untreated sewage, and cow feces. Additional lysis step was used for all samples with the automated system. All results were log10 transformed before statistical analyses. In order to determine if there was a statistically significant difference between the methods, a simple t-test for comparing means was used. Statistical analyses showed that qPCR equivalent cells of E. coli/100 mL of raw sewage, primary effluent and secondary effluent were statistically higher for the autoextractor as compared to manual extraction (p<0.05) (Table 3.3). In tertiary effluent samples and cow feces both methods returned equivalent cells/100ml. For Enterococcus spp., there was no statistically significant difference found between extraction methods in any of the samples except for cow feces, where the manual extraction was found to show significantly higher (0.44 log10) concentrations of Enterococcus spp. (p<0.05) (Table 3.4). The mean difference of qPCR equivalent cell concentrations of E. coli and Enterococcus spp. between instrument and manual extracted DNA ranged from 0.31 to 0.61 and 0.09 to 0.26 log10 units respectively in various samples. 70 Table 3.3: t-test results for comparison of means between the qPCR equivalent concentrations of E. coli from DNA extracted by auto and manual methods. Average cells/100 mL Automated method Manual method Standard error for mean difference t-statistic (p) Raw sewage 4.62E+06 2.18E+06 0.31 0.07 4.09 (0.001) Primary effluent 6.79E+06 2.24E+06 0.55 0.16 3.51 (0.004) Secondary effluent 3.20E+03 1.22E+03 0.61 0.23 2.60 (0.02) Tertiary effluent 2.58E+03 1.50E+03 0.42 0.38 1.11 (0.28) Cow feces 7.97E+05 3.75E+05 0.34 0.38 0.62(0.54) Samples a Mean difference a n = 9 for each type of sample. 71 Table 3.4: t-test results for comparison of means between the concentrations of Enterococcus spp. from DNA extracted by auto and manual methods Average cells/100 mL Samples Raw sewage Automated method 2.23E+07 Manual method 2.10E+07 Standard error for mean difference 0.29 t-statistic (p) 0.57 (0.57) Primary effluent 1.99E+07 1.58E+07 0.24 0.23 1.02 (0.32) Secondary effluent 1.34E+05 7.16E+04 0.23 0.29 0.80 (0.43) Tertiary effluent 1.49E+05 7.96E+04 0.09 0.30 0.25 (0.80) Cow feces 1.79E+04 n = 9 for each kind of sample. 4.69E+04 -0.44* 0.14 -3.95 (0.001) a Mean difference 0.26 a * - sign indicates that the concentration of Enterococcus spp. was more in manual extracted DNA than the machine. 72 Correlation and regression analysis were also performed on the qPCR equivalent concentrations of E. coli and Enterococcus spp. from DNA by manual and automated extraction for E. coli in raw sewage and primary effluent (Fig 3.3), in secondary and tertiary effluent (Fig 3.5), and in cow feces (Fig 3.7), and for enterococci in raw sewage and primary effluent (Fig 3.4), in secondary and tertiary effluent (Fig 3.6), and in cow feces (Fig 3.8). A positive correlation was found between qPCR equivalent cells of E. coli and Enterococcus spp. in DNA from instrument and manual extraction from all samples except for Enterococcus spp. from cow feces. Strong correlations were found between both the extraction methods for E. coli cell equivalent concentrations in tertiary effluents (r=0.98, p<0.05), and cow feces (r= 0.97, p<0.05), and Enterococcus spp. cell equivalent concentrations in raw sewage (r=0.93, p<0.05) and primary effluent (r=0.97, p<0.05). 73 Figure 3.3: Regression analysis for concentrations of E. coli from both types of DNA log10 cells per 100 mL (machne) extraction in raw sewage and primary effluent* y = 0.3662x + 4.4985 (Primary effluent) R² = 0.4558 7.5 7 6.5 y = 0.8492x + 1.26 (Raw sewage) R² = 0.3171 6 5.5 5 5 5.5 6 6.5 7 7.5 log10 cells per 100 mL (manual) Raw sewage Primary effluent *number of raw sewage samples, n=9, number of primary effluent samples, n=9 74 Figure 3.4: Regression analysis for concentrations of Enterococcus spp. from both types log10 cells per 100 mL (machne) of DNA extraction in raw sewage and primary effluent* y = 1.6381x - 4.864 (Primary effluent) R² = 0.8817 7.5 7 6.5 6 y = 0.5899x + 3.0897 (Raw sewage) R² = 0.9534 5.5 5 5 5.5 6 6.5 7 7.5 log10 cells per 100 mL (manual ) Raw sewage Primary effluent *number of raw sewage samples, n=9, number of primary effluent samples, n=9 75 Figure 3.5: Regression analysis for concentrations of E. coli from both types of DNA extraction in secondary and tertiary effluents* log10 cells per 100 mL (machine) 5 y = 0.7041x + 1.2201 (Tertiary effluent) R² = 0.9837 4 3 2 1 y = 1.5732x - 2.5619 (Secondary effluent) R² = 0.7535 0 0 1 2 3 4 log10 cells per 100 mL (manual) Secondary effluent 5 Tertiary effluent *number of secondary effluents, n=9; number of tertiary effluents, n=9 76 Figure 3.6: Regression analysis for concentrations of enterococci from both types of log10 cells per 100 mL (machine) DNA extraction in secondary and tertiary effluents* y = 0.9496x + 0.4658 (Secondary effluent) R² = 0.7144 6 5 4 3 y = 0.705x + 1.4445 (Tertiary effluent) R² = 0.3841 2 2 3 4 5 6 log10 cells per 100 mL (manual) Secondary effluent Tertiary effluent *number of secondary effluents, n=9; number of tertiary effluents, n=9 77 Figure 3.7: Regression analysis for concentrations of E. coli from both types of DNA log10 cells per 100 mL (machine) extraction in cow feces* y = 0.9365x + 0.6619 R² = 0.9663 7 6 5 4 3 2 2 3 4 5 6 log10 cells per 100 mL (manual) *Number of cow fecal samples, n=9 78 7 Figure 3.8: Regression analysis for concentrations of Enterococcus spp. from both types of DNA extraction in cow feces log10 cells per 100 mL (machine) 7 6 y = -0.3969x + 5.9838 R² = 0.0902 5 4 3 2 2 3 4 5 6 log10 cells per 100 mL (manual) *Number of cow fecal samples, n=9 79 7 3.3.3.1 Performance of Extraction Procedure Variation between the auto extraction and the manual method was evaluated with replicates of raw sewage for qPCR equivalent cell concentrations of E. coli and Enterococcus spp. and for DNA concentration as measured by Nanodrop Spectrophotometer. It was found that nanograms of DNA per microliter extracted by the instrument had less variation with coefficient of variation (CV) of 4.50% as compared to manual DNA extract concentration with a CV of 13.29%. The qPCR equivalent cell concentration of E. coli per reaction for raw sewage showed a CV (%) of 1.31 for instrument and 1.67 for manual DNA extraction whereas cell concentration of Enterococcus spp. per reaction for raw sewage showed a CV(%) of 1.48 for instrument and 1.71 for manual DNA extraction. Intervariability in extraction by both auto and manual methods performed by two different individuals was also tested. DNA concentrations (nanograms of DNA per microliter) of samples extracted by instrument showed a CV of 5.07% and that of manual DNA extraction showed a CV (%) of 20.65. The qPCR equivalent cell concentration of E. coli per reaction for raw sewage showed a CV (%) of 0.86 for instrument and 1.88 for manual DNA extraction whereas cell concentration of Enterococcus spp. per reaction showed a CV (%) of 1.36 for instrument and 1.83 for manual DNA extraction by the second individual. 80 Table 3.5: Coefficient of variation (CV %) for DNA concentrations and cell equivalent concentrations of E. coli and Enterococcus spp. performed by two individuals for auto and manual extraction methods Individual 1 Instrument Manual CV% CV% Sample (raw sewage) Individual 2 Instrument Manual CV% CV% DNA concentration (ng/µL) 4.50 13.29 5.07 20.65 Enterococcus spp. concentration (cells/rxn) 1.48 1.71 1.36 1.83 E. coli concentration (cells/rxn) 1.31 1.67 0.86 1.88 * Raw sewage (n=9) from the same sample concentrate was used for the experiment. 3.3.4 Differentiation between Live and Dead cells using Ethidium Monoazide- qPCR in Environmental Samples Heat-killed and live cells of E. coli and Enterococcus spp. were treated with 2.5µg/mL of EMA, followed by exposure to light for 2 minutes for photolysis. As shown in Fig 3.9, this resulted in a 2-3 log reduction in qPCR target copies as compared to the controls that were not treated with EMA. There was no reduction in the concentrations of both the targets in cells that were not heat-killed but were exposed to EMA. Concentrations of viable E. coli and Enterococcus spp. (CFU/mL) enumerated in TSA 7 plates were around 10 CFU/mL and were comparable to the qPCR equivalent cells/mL in live and heat-killed cells that were not pre-treated with EMA. 81 Raw sewage and final effluent samples were treated with EMA and as shown in Fig 3.10, there was not much difference between qPCR target copies of E. coli and Enterococcus spp. in EMA treated samples and samples without EMA added. Viable counts of E. coli and Enterococcus spp. as enumerated by cultivation on mTEC and mEI 5 5 media were 1.09 x 10 CFU/mL and 2.47x10 CFU/mL respectively in raw sewage and 5 qPCR equivalent concentrations for these targets were 2.26 x 10 cells/mL and 3.27 x 5 10 cells/mL respectively. Cultivable counts of E. coli and Enterococcus spp. in final effluent were only 0.13 CFU/mL and 0.66 CFU/mL respectively whereas the qPCR 2 2 equivalent cells of these targets were 1.80 x 10 cells/mL and 2.43 x10 cells/mL respectively. Negative controls with and without EMA treatment did not show any amplification signals. 82 Fig 3.9: Average log10 qPCR target copies/ reaction for pure cultures of E. coli and Enterococcus spp. in EMA treated and untreated live and heat-killed (HK) cells* log10 qPCR target copies/reaction 6.00 5.00 4.00 3.00 2.00 1.00 0.00 E.coli LIVE EMA- HK EMA- enterococci LIVE EMA+ *Number of replicates for each treatment=3 83 HK EMA+ Figure 3.10: Average log10 concentrations of E. coli and Enterococcus spp. in EMA treated and untreated raw sewage and treated effluent samples. log10 qPCR target cells/mL 7.00 6.00 5.00 4.00 3.00 2.00 1.00 0.00 E. coli enterococci E. coli Raw sewage EMA- enterococci Final effluent EMA+ *Number of replicates for raw sewage=3 *Number of replicates for treated effluent=3 84 3.3.5 Occurrence of 16S rRNA Bacteroides (HF183) Human Marker in Surface Water Samples Forty eight surface water samples from different locations in Michigan were collected evaluated for E. coli and Enterococcus spp. concentration by cultivation methods and for the presence of 16S rRNA Bacteroides (HF183) Human Marker by conventional PCR 0 4 (Table 3.6). E. coli concentrations ranged from 5.7x10 to 1.51x10 CFU/100mL and Enterococcus spp. ranged from 9.70x10 1 to 2.20x10 3 CFU/100mL. Seven samples showed the presence of human specific Bacteroides marker; three of them in samples collected from Silver Lake, two from Saginaw Bay and one each from Cold Water Creek and Buck Creek. 85 Table 3.6: Occurrence of 16S rRNA Bacteroides (HF183) human marker as detected by conventional PCR and their comparison with E. coli and Enterococcus spp. (CFU/100mL) in surface water samples. Surface water Sample ID* SL1 SL2 SL3 SL4 SL5 SB1 SB2 SB3 SB4 SB5 CCA1 CCA2 CCA3 CCA4 CCA5 CCA6 CCB1 CCB2 CCB3 CCB4 CCB5 CCB6 CCC1 CCC2 CCC3 CCC4 CCC5 CCC6 CCD1 CCD2 CCD3 CCD4 CCD5 Bacteroides 16SrRNA E. coli Enterococci human marker +/- cfu/100mL cfu/100mL + + + + + - 4.03E+01 9.20E+01 1.35E+01 8.10E+00 5.70E+00 5.33E+02 2.42E+03 2.08E+03 1.56E+02 1.47E+02 7.40E+02 4.00E+02 4.70E+02 7.07E+02 5.90E+02 1.93E+02 1.80E+03 2.00E+02 2.45E+02 8.57E+02 7.80E+01 1.32E+02 4.40E+02 1.51E+04 2.80E+02 6.27E+02 1.12E+02 8.30E+01 1.45E+02 6.70E+01 1.30E+02 1.50E+01 8.00E+00 7.47E+01 9.10E+01 2.10E+01 4.40E+01 9.70E+01 3.60E+02 7.33E+02 6.67E+02 5.00E+01 8.50E+01 7.90E+03 7.90E+02 5.50E+02 9.93E+02 8.30E+02 5.73E+02 2.32E+03 4.70E+02 2.60E+02 1.74E+03 2.67E+02 1.63E+02 9.30E+02 2.20E+03 5.50E+02 1.36E+03 3.97E+02 1.80E+02 6.80E+02 9.80E+02 2.00E+03 4.10E+01 6.40E+01 86 Table 3.6 (cont’d) CCD6 BC11-1 BC11-2 BC11-3 BC11-4 BC11-5 BC11-6 BC11-7 BC12-1 BC12-2 BC12-3 BC12-4 BC12-5 BC12-6 BC12-7 5.10E+01 3.50E+02 2.80E+02 3.90E+02 2.50E+02 2.40E+02 3.00E+02 1.20E+02 1.40E+03 7.60E+02 2.10E+02 4.60E+02 4.20E+02 8.50E+02 1.40E+02 + + - 4.70E+01 3.30E+02 6.90E+03 4.60E+02 4.30E+02 5.40E+01 8.50E+02 1.10E+02 4.10E+02 1.50E+04 6.80E+02 1.00E+03 7.70E+02 3.90E+03 2.20E+02 Highlighted rows are the samples that showed positive for 16S rRNA Bacteroides (HF183) human marker *SL-Silver lake *SB=Saginaw Bay *CCA=Coldwater Creek at location A *CCB= Coldwater Creek at location B *CCC= Coldwater Creek at location C *CCD= Coldwater Creek at location D *BC-Buck Creek 87 3.3.6 Detection Limit of 16S rRNA Bacteroides (HF183) PCR Assay Using Raw Sewage The limit of detection was tested for the 16S rRNA Bacteroides HF183 assay using tenfold dilutions of raw sewage in sterile distilled water which also gave the efficiency of the method for detecting low levels of fecal contamination of water. Results are summarized in Table 3.7. When 1mL of sewage was suspended in 1L of water, the signal was present in all the volumes that were analyzed, whether 1 mL or 1000 mL was membrane filtered, or whether there was direct DNA extracted from 1 mL. Same results were observed with 100 µL of sewage seeded in 1L of water. With 10 µL of sewage seeded into 1 L water (10 -5 dilution), positive signals were observed with 1mL DNA extracted directly as well as 1000mL filtered but not with 1mL membrane filtered which suggests the possible loss of targets while processing the samples by membrane filtration. The signal was present in 1 µL sewage seeded into water but only with 1000 mL filtered and there were no markers detected in the 1 mL samples that were processed directly. There was no signal obtained with any of the volumes processed for 0.1 µL into 1 L of -7 water (10 dilution). 88 Table 3.7: Detection limit of 16S rRNA Bacteroides HF183 assay using water seeded with raw sewage Volume of sewage seeded into 1L sterile distilled water (dilutions) Number of positives/ number of replicates DNA extracted from 1mL directly 1mL MF 1000 mL MF -3 1000 µL (10 ) 3/3 -6 1 µL (10 ) -7 0.1 µL (10 ) 89 3/3 1/3 3/3 0/8 6/8 0/8 10 µL (10 ) 3/3 2/8 -5 3/3 3/3 100 µL (10 ) 3/3 3/3 -4 0/8 2/8 3.4 Discussion In this study, a qPCR assay that targeted the uidA gene of E. coli was developed and evaluated. This gene codes for the enzyme, β-glucuronidase (GUS) that catalyzes the hydrolysis of glucuronide compounds and phenotypic detection methods have utilized this enzyme activity (Bej et al. 1991a, b; Buckalew et al. 2006; Chao et al. 2004; Gracias and McKillip 2004; Servais et al. 2005; Tryland and Fiksdal 1998). The assay developed in this dissertation had a detection limit of 10 copies/reaction as determined from the standard curve. The sensitivity of the uidA E. coli qPCR assay developed in this study was better than the uidA qPCR developed by Frahm et al (2003) and ITS qPCR assay described above but did not match up with sensitivity of lacZ assay. The uidA qPCR developed by Frahm et al 2003 had a detection limit 10-100 copies per reaction. The improved detection limit of the assay developed in this study could be due to better primer/probe design or reaction conditions. The uidA qPCR assay used in this study has been tested for its specificity with E. coli and non-E. coli strains and was specific except with Shigella sp with which some amplification signals were detected. The uidA assay by Frahm et al (2003) was reported to be specific, however, the cross reactivity of the assay was tested only with Aeromonas sp, Enterobacter sp, Pantoea agglomerans, Klebsiella sp, and Serratia sp.; and Shigella spp. was not included in their study. Bej et al. (1991a) also reported the amplification of Shigella flexneri uidA gene with the primers developed for E. coli and negative for other members of coliform group. The ITS qPCR assay showed no cross reactivity with isolates of 22 different bacteria (Khan et al. 2007). Specificity was not evaluated in any other assays described here. Shigella is closely related to E. coli and shows 99% 90 homology for uidA gene sequence. Despite this cross-reation, the detection of Shigella in the assay developed in this dissertation for E. coli will not cause false positives in polluted waters due the high ratio of E. coli to Shigella. One of the key issues for qPCR assays for water samples is reproducibility, interferences and cross-contamination. An automated method for extracting DNA from environmental samples was evaluated in this study. Other studies have compared the extraction of DNA for real-time PCR detection of Clostridium difficile from human fecal samples using QiAamp DNA blood mini kit (after a pretreatment with polyvinylpolypyrolidone (PVPP)) compared to automated extraction with the Roche MagnaPure LC (pre-treatment using the Stool Transport and Recovery (STAR) buffer). That study reported no difference in Ct values between the two extraction methods (van den Berg et al. 2006) which indicated that there was no difference between the concentrations of Clostridium difficile DNA extracted by manual and machine. The same instrument has also been applied for extraction of DNA from drinking water while studying the occurrence and genetic diversity of Legionella spp. (Wullings & van der Kooij 2006). Since method evaluation was not their goal, the autoextraction method was not evaluated or compared with any other existing methods. The results in this study indicate that autoextractor showed significantly higher or equal efficiency compared to manual kits in extracting DNA from treated and untreated sewage, but with cow feces for enterococci, the autoextraction was not efficient. This could be due high fiber content of the cow feces and other factors. The fibrous nature of cow feces could facilitate attachment of cells, thus resulting in low recovery of DNA 91 during extraction specifically for Gram-positive cells perhaps due to the thick peptidoglycan biochemistry; however the Clostridium study cited above reported good extraction of this gram positive bacterium using the machine for human feces. Even though there are no reports that gives direct evidence for this assumption, there is evidence of that enteric bacteria attach to sediments and different soil fractions with varying organic content (Guber et al. 2007; Jeng et al. 2005). Efficient DNA extraction (concentration and level of purity) from environmental matrices is influenced by various parameters as inhibitors are concentrated along with the DNA which influences the PCR reaction (Lakay et al. 2007). The importance of the lysis step in recovering DNA from organisms possessing rigid cell walls has been reported before through studies which contrasted different DNA extraction methods for fungi such as Aspergillus sp (Fredericks et al. 2005) and Rhizopus oryzae (Francesconi et al. 2008). DNA recovery from cow fecal samples using the autoextractor improved when a pretreatment step using lysis buffer and Proteinase K was included. The lysis step included in the autoextraction process may not be sufficient to lyse the maximum numbers of cells in complex samples. In this investigation, optimal concentration of lysis buffer for pre-treatment of samples was found to be 180 µL. Without the lysis step or even with lesser volume of lysis buffer (60 µL), the qPCR concentrations of Enterococcus spp. in autoextracted DNA from cow fecal samples were reduced by at least one log. Increasing the volume of lysis buffer in the pretreatment did not further improve DNA recovery from E. coli or Enterococcus spp. cells, thus the volume used in our protocol was adequate. It has been shown that excess buffer in the lysis step during 92 extraction can result in shearing of DNA and may not give favorable results (Leuko et al. 2007). Advantages in using the autoextractor included improved consistency and decreased variability. With the pre-treatment step included, the time it takes for extracting DNA from eight samples was slightly less when using the automated method (1 hour) as compared to the manual kit (1.5 hours). Previous studies in clinical samples have also compared the cost of DNA extraction using commercially available manual kits and automated methods (Knepp et al. 2003). Roche MagNaPure LC extraction is currently more expensive than the manual QIAamp DNA extraction with QIAamp kit reported to cost $2.64 per specimen with the MagNaPure costing around $3.58 per specimen (the price excludes cost of plastics). Wastewater samples worked well with the automated protocol; however, application of any automatic extracting system for recovering DNA from other challenging matrices such as soil would need further evaluation (Liles et al. 2008). Scientific developments such as qPCR will likely yield changes in future regulations and monitoring for drinking and recreational waters. Therefore efficient and reliable data becomes an important issue for these emerging techniques. These preliminary studies have demonstrated that automation can be used to improve efficiency and reproducibility of DNA extraction and that qPCR can be used to describe bacterial concentrations in wastewater. New instrument configurations for automation which can handle more samples and greater diversity of matrices for environmental testing would be beneficial in analysis of wastewater and recreational waters. 93 Ethidium MonoAzide (EMA) was chosen for this study because of its lower costs as compared to Propidium MonoAzide. EMA has been found to penetrate into live cells (Nocker et al. 2006).but the treatment of samples with low concentrations of EMA has also been shown that the DNA from live cells can still be amplified (Lee and Levin 2006). EMA-qPCR was found to distinguish between live cells and heat-killed cells in this study and some other studies as well (Nocker et al. 2006; Varma et al. 2009; Gedalanga and Olson 2009). However, with raw sewage and chlorine treated wastewater effluents, there was no distinction found between live and dead cells. There are several possibilities for this. One reason could be due to the effect of the turbidity on the samples In this study large volume concentrated samples from sewage were used for the EMA-treatment and due to the high turbidity of these samples, light penetration may have diminished and reduced the photolysis of the dyes and binding with DNA. The effect of turbidity on the efficiency of EMA-qPCR method has been studied before (Gedalanga and Olson 2009) and at turbidities higher than 10 NTU, the method was not able to distinguish between live and dead. The high turbidity of the concentrated effluent samples could have interfered with the method in this dissertation study. Another possibility could be the presence of viable but nonculturable cells present in the disinfected effluents whose cell membranes are intact which was preventing penetration of EMA or PMA into the cells. A previous study has recommended UV irradiation as a wastewater disinfection method due to the observation that bacterial cells move into a non-cultivable state at chlorine levels used for wastewater disinfection and are recovered later (Blatchey et al. 2007). Previously, two groups have applied the 94 combination of EMA or PMA and qPCR method in wastewater treatment samples. One study spiked the secondary activated effluent with heat-killed E. coli O157 cells found a 1.5 log10 difference between qPCR equivalent cells in samples treated with EMA and those not treated with EMA. E. coli O157 concentrations by qPCR on samples treated with EMA were in the same levels as that of plate counts from these samples (Gedalanga and Olson 2009). Another study applied the method using PMA dye in wastewater effluents spiked with heat-killed enterococci cells and by using samples directly (Varma et al. 2009). They found that the even though PMA treatment on spiked samples showed a difference of 0.5 log10 units as compared to spiked PBS controls, when waste water and effluents were monitored directly, there was no difference between samples treated with dye and those untreated. A significant difference between colony counts (CFU) and qPCR equivalent cells in disinfected effluents was observed in both those studies and this study. But inspite of treatment of samples with EMA or PMA, there appeared little reduction in qPCR signals and was definitely not comparable to colony counts suggesting that there may still be some cells present in the disinfected effluent samples that are not penetrated by EMA or PMA. More work is warranted on chlorine-killed bacteria and the use of such dyes for measuring viability by qPCR. Surface water samples collected from different locations in Michigan showed that human fecal marker signals were absent even when concentrations E. coli and Enterococcus spp. were higher than the critical levels. This could be due to several reasons. The higher levels of E. coli and enterococci may be due to some other source of fecal input which was not detected by HF 183 assay, or could have been due to the 95 regrowth of these indicators in the sediments. Four of the sites (SL1, SL3, SL5 and CCD6) that showed positive for human specific HF183 marker had E. coli levels below the Michigan water quality standards for recreational waters based on a single sample maximum (300CFU/mL). Detection of HF183 marker in water when the levels of fecal indicator bacteria are below the standards have been reported before and they also found no direct relationship between indicator bacterial levels and detection of HF183 marker (Santoro and Boehm 2007). This lack of correlation could be due to the variation in the rate of inactivation of cultivable cells and DNA. Human specific Bacteroides 16S rRNA markers have been reported to persist for a longer time in waters as compared to cultivable cells of fecal indicator bacteria (Okabe et al. 2007). For the water samples tested in this study, 1000-4000 mL was filtered and the limit of -6 detection determined in this study was around 10 . However, the study was conducted using spiked distilled water and detection limit in surface water matrix may vary. Previous studies have also observed that the HF 183 marker was detected when raw -6 sewage was diluted up to 1.4 x10 serial dilutions but seeded into a large volume of 1000 mL of sterilized bay water (Bernhard and Field 2000a). Another study has detected the HF183 marker up to dilution 1x10 -7 in sewage samples serially diluted and 300 mL of the seeded water was tested by the PCR assay (Ahmed et al. 2008). These preliminary studies lead the way for addressing the objectives of this research. Based on the results, it was concluded that: 1) The new protocol, primers and probes for E. coli was a good method for characterizing human and cow waste environments. 96 2) Autoextractor protocol was a good approach to extract DNA from wastewater samples; however for cow waste samples, manual DNA extraction was found to be better. 3) Distinction between viable and non-viable cells using EMA proved to be efficient for pure cultures; however with samples such as raw sewage and treated effluent samples, the method did not produce satisfactory results. Therefore, this method was not used in this study. 4) The human marker developed by Bernhard and Field (2000b) had good detection limits but was not quantitative and not found in more than 14.5% of Michigan surface water tested where sewage was detected, therefore alterative markers were identified. 97 REFERENCES 98 3.5 References: Ahmed, W., J. Stewart, D. Powell, and T. Gardner. 2008. 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Wastewater is reported to have many pathogens belonging to different groups such as bacteria, viruses and protozoa, that are discharged to waste stream by infected humans (Nayak and Rose 2007; Lee et al. 2006; Kamel et al. 2010; Robertson et al. 2006). These pathogens can then enter into water bodies when inadequately treated waste is discharged, facilitating widespread transmission of waterborne diseases. Changing climatic patterns and urbanization are forcing communities to resort to alternative water supplies dominated by wastewater or directly to reclaimed wastewater which highlights the importance of proper treatment as a key barrier to transmission. Though strong regulations exist in many places such as the US and the European Union to regulate wastewater discharges; many countries lack such regulations and proper waste disposal systems. According to the 2006 United Nations Human Development Report, 2.6 billion people lack access to basic sanitation, and lack adequately treated wastewater. On-site wastewater disposal such as septic tanks has also been an issue regarding pathogen entry into and transmission through water. Leaking septic tanks is another cause of human fecal contamination of water sources (Fong et al. 2007), especially groundwater 106 sources. Many households or small communities use individual septic tanks. Septic tanks collect waste water from the toilets, showers, sinks, and laundry. The liquid is treated by soil infiltration and solids are pumped out and often land applied, with or without treatment. Monitoring the sewage environment and validating waste treatment by testing for all pathogens is not practical. Many pathogen methods are laborious, time consuming and expensive. Therefore microbial indicators of fecal contamination have been used instead to examine the performance of these waste treatment systems and the quality of effluents discharged or reused. Fecal coliform bacteria are generally the standard for addressing wastewater effluents and prevention of deterioration of water quality (Elmund et al. 1999). The standard for Michigan as stated in Rule 62 is “Discharges containing treated or untreated human sewage shall not contain more than 200 fecal coliform bacteria per 100 milliliters, based on the geometric mean of all of 5 or more samples taken over a 30-day period, nor more than 400 fecal coliform bacteria per 100 milliliters, based on the geometric mean of all of 3 or more samples taken during any period of discharge not to exceed 7 days” (Michigan Department of Environmental Quality). Traditionally, Escherichia coli (E. coli) and enterococci have been used to help regulate microbial water quality, and research related to these indicator bacteria has developed new criteria or standards around the globe on the acceptable levels of indicator bacteria in recreational or drinking water limiting possible health risks (Michigan Natural Resources and Environmental Protection Act 1994; USEPA 1986; WHO 2008). However, when these indicator bacteria are found, it is not possible with routine methods 107 to discriminate between various fecal sources as they are present in intestines of all warm-blooded animals (Field et al, 2003). Microbial source tracking (MST) is a field that has enabled the identification of sources of fecal contamination (Scott et al. 2002; Simpson et al. 2002; US EPA 2005) via generally genetic methods for E. coli and enterococci. Currently, enteric bacteria in the order of Bacteriodales have been proposed as an alternate indicator of fecal contamination along with identifiable genetic host specificity (Bernhard & Field 2000). Some of the members are bacteria in the genus Bacteroides which are obligately anaerobic, Gram negative, rod shaped, and non-endospore forming including Bacteroides fragilis and closely related species namely, B. caccae, B. distasonis, B. eggerthii, B. merdae, B. ovatus, B. stercoris, B. thetaiotaomicron, B. uniformis,and B. vulgates are included (Shah and Collins 1990). Bacteroides sp are normally commensals and make up more than one-quarter of the total gut bacteria in humans and many warm blooded animals (Daley and Shirazi-Beechey 2003; Franks et al. 1998; Harmsen et al. 2002, Holdman 1976, Wood et al. 1998). As Bacteroides are strict anaerobes, they do not survive long or reproduce outside the host organism in aerobic environments. This characteristic prevents these bacteria from proliferating in waters and sediments, unlike E. coli and enterococci (Avelar et al. 1998, Kreader 1998). However, it is more difficult to cultivate Bacteroides, thus molecular methods such as Polymerase Chain Reaction (PCR) and quantitative PCR (qPCR) targeting specific DNA from both live and dead cells have been relied upon for their detection. There are many specific target assays within Bacteroides available based on multiple sequences forming host specific 16SrRNA gene clusters. Recent studies have shown that there is cross reactivity 108 associated with these highly conserved regions of 16S rRNA gene clusters when it comes to distinguishing host specific targets (Sadowsky 2007). Thus, targeting other more specific genes as microbial source tracking markers involved directly in host microbial interaction has been suggested as an alternative approach (Shanks et al. 2007). Bacteroides thetaiotaomicron is a species which has been proposed to be examined for genes that may be more specific to a human host (Yampara-Iquise et al. 2008). This organism is ranked third highest in relative frequency (8.9%) of isolation from human feces in a study that looked at fecal flora of three men over 5 months with a total of 1442 isolates and their concentration was around 2.29x10 10 per gram of dry fecal matter (Holdeman et al. 1986). This organism is a symbiotic organism and its role in the intestine is to break down complex polysaccharides (Xu et al. 2003). Host specificity of these markers has been tested by Yampara-Iquise et al. (2008) by targeting putative mannanase 1-6 genes of these bacteria. Sequence analysis has suggested they are reliable human fecal specific markers that could be used to track human fecal contamination in water (Yampara-Iquise et al. 2008). In its report of the “Experts Scientific Workshop on Critical Research Needs for the Development of New or Revised Recreational Water Quality Criteria”, the US EPA (2007) stated as high/medium priorities to “conduct microbial fate and transport studies to determine relationships between traditional and new fecal indicators, index pathogens, and priority pathogens in treated effluents and in downstream recreational waters to compare and validate their applicability for specific criteria uses”. The specific objectives of this portion of the research were to: 109 1) Determine the concentrations of Bacteroides thetaiotaomicron α-mannanase 1-6 gene in raw sewage and septage and to study the fate of the molecular signal during treatment, and 2) Compare this target with conventional indicators such as E. coli and enterococci. This research hypothesized that a correlation exists between the Bacteroides thetaiotaomicron human marker and E. coli or enterococci as measured by cultivation or qPCR. 4.2 Materials and Methods 4.2.1 Sample Collection and Processing a) Wastewater treatment facility: Samples were collected from a municipal wastewater treatment plant, located in East Lansing, Michigan that serves around 90,000 people. The plant receives, on an average basis, a little less than 13.40 MGD wastewater inflow. Samples were collected during 18 sampling events that lasted from January 2009 to January 2010. Samples collected from this facility included: i) Raw sewage (RS) ii) Primary effluent (PE), after the solids have settled iii) Secondary effluent (SE), after activated sludge process and chlorination, and iv) Tertiary effluent (TE), effluent from secondary treatment post sodium bi-sulfite dechlorination and filtration through rapid sand filters. Apart from four types of samples described above, secondary treated samples in triplicates prior to the chlorination step were also collected during six sampling events in January 2010 (included in the 18 sampling events). The sampling locations are 110 schematically presented in Figure 4.1. During each sampling event, one hundred milliliters of raw sewage and primary effluent, 500 milliters of pre-chlorinated secondary effluents and two liters of secondary and tertiary effluents were collected in triplicates, transported to the laboratory on ice and processed immediately. Chlorinated effluents were collected in bottles added with Sodium Thiosulphate (1mL of 10% solution) to neutralize any residual chlorine present in the effluents. The residual chlorine on all sampling dates was found to be 0.01 mg/L in the final effluent. Data for physicochemical characteristics of raw sewage and final effluent samples were collected on all sampling dates. 111 Figure 4.1: Schematic representation of sampling locations in the waste water treatment plant at East Lansing. Influent Preliminary treatment Primary treatment Primary Clarification Wet Well Grinding Pumping Aerated Grit Removal Flow Equalization Raw sewage n=54 Primary effluent n=54 Waste Sludge Solid separation Dewatering Storage Landfill Secondary effluent (Pre-chlorination) n=18 Secondary effluent (Post chlorination) n=54 Secondary Treatment Return of activated sludge Secondary clarification Aeration Disinfection (Sodium hypochlorite) Tertiary Treatment Filtration Dechlorination (Sodium thiosulphate) Aeration Tertiary effluent n=54 n=54 112 Red Cedar River b) Septage treatment plant: Samples were collected from a septage treatment plant located in Charveloix, Michigan that utilizes an aerobic biological treatment system to treat septage wastes and discharges the treated effluent to the municipal sewer system. Briefly, septage is pumped from the trucks into screens and a de-grit chamber, which then flows into an equalization tank. The waste then goes through lime treatment process, after which it is pumped through a flocculation tank and a rotary screen thickener for biosolid production. Solids o produced are processed in a screw press that heats up to a minimum of 100 C for a minimum of 20 minutes; where the combination of pH during lime stabilization and high temperature treatment reduces microorganisms in the solids. Water extracted during solid production is then discharged into series of aerobic treatment tanks. These large tanks have microbial generators that provide a source of microorganisms. The organic wastes are reduced from the wastewater by these organisms in combination with naturally occurring microorganisms. Water then enters into settling tanks, the solid collected goes through lime treatment and a screw press processes. The clarified water is aerated further after which it is discharged as effluent into the municipal sewer system. The treatment also produces Class A biosolids after the dewatering stage. Samples were collected during eight sampling events that lasted from January 2009 until November 2009. During each event, triplicates of 50 mL raw septage and 500 mL of effluent were collected from the plant, placed on ice and shipped to Water Quality and Health Laboratory at Michigan State University, East Lansing, MI. 113 4.2.2 Analyses All samples were analyzed by cultivation methods for E. coli and enterococci and by qPCR methods for E. coli, enterococci and Bacteroides thetaiotaomicron. 4.2.2.1 Cultivation Methods U.S.EPA Membrane Filtration methods 1603 and 1600 were used for enumerating E. coli and Enterococci respectively (USEPA 2002; 2005). Serial dilutions of the raw sewage and primary effluents (10 -1 -5 through 10 ) were made and one mL from these dilutions was filtered through 47 mm diameter, 0.45 µm pore size, membrane filters. For pre-chlorinated secondary effluents, one mL of the sample was filtered directly and for secondary and tertiary treated effluents, one hundred milliliters of samples were filtered. For samples from the septage plant, one mL of raw septage samples was serially diluted and these dilutions were used for further bacterial indicator analysis. For effluent samples, volumes of 0.1 mL, 1 mL and 10 mL were used for filtration. The filters were placed on mTEC agar and mEI agar plates. The mTEC agar plates were incubated 35 ± 0.5°C for 2 h, followed by incubation in a water bath at 44.5 ± 0.2°C for 22 h and mEI plates were incubated for 24 h at 41°C and colonies were counted. The concentrations of E. coli and enterococci from all tested samples were reported as colony forming units (CFU) per 100 mL. 4.2.2.2 DNA Extraction and qPCR Analyses Waste water treatment plant samples: Fifty milliliters of raw sewage sample were centrifuged at 8000xg for 20 minutes. Around 48 mL of the supernatant was discarded and remaining sample was mixed well by vortexing. The volume was recorded and from this, 400 μL of the pellet was used for extraction using Roche MagNaPure LC instrument 114 o (Roche Applied Sciences, Indianapolis, Ind.) and remainder was stored at -80 C. These volumes were included in the calculation when final concentrations of targets were calculated after qPCR. For treated sewage, 200 mL of the pre-chlorinated secondary effluent sample and one liter of secondary and tertiary effluents was filtered using 90 mm, 0.45 µm pore size nitrocellulose membrane filters (Millipore, Billerica, Mass.) and the filters were folded and immersed to the 45 mL mark with sterile phosphate buffered saline (PBS) in 50 mL centrifuge tubes. The tubes were then vortexed at high speed to detach the cells from membrane for two minutes. The filters were removed and the tubes were centrifuged at a speed of 8000xg for 15 minutes and around 48 mL of the supernatant was discarded. The remaining sample was mixed well by vortexing; 400 µL of the pellet was used for extraction. DNA extraction using Roche MagNaPure automated machine included an external lysis and was performed by mixing 400 µL of the concentrated samples with 180 µL of o the MagNaPure lysis buffer and 20 µL of the Proteinase K (20 mg/mL) incubated at 65 C for 30 minutes. The mixture was then centrifuged at 500g for 30 seconds to settle down the particles in a microcentrifuge and the supernatant was used for DNA extraction in the machine. The eluted volume containing the DNA from all the samples was in 200 μL o volumes and stored at -20 C. Septage treatment plant samples: Raw septage samples were mixed thoroughly by vortexing for one minute and 600µL was taken from this for DNA extraction. For effluent samples, 50 mL of the sample was centrifuged at 8000g for 20 minutes. The supernatant was discarded and 1 mL of the pellet was left behind. From this, 600 µL was 115 used for DNA extraction. DNA extraction was carried out using Roche MagNaPure automated machine following the protocol described above. The bacteria genes, amplicons used given in Table 4.1 and the primers/probes and corresponding PCR programs are described in Table 4.2. DNA extracted from all samples was analyzed by qPCR using Roche LightCycler® 2.0 Instrument (Roche Applied Sciences, Indianapolis, IN). The reaction mixture for amplification of E. coli uidA gene consisted of 2µL of Roche Fast Start Light Cycler Mastermix, forward and reverse primers, probe, 3.2 mM MgCl2 and nuclease free water to make up the final volume to 15 µL. Cycle threshold temperature (Ct) was measured during each amplification and target gene concentration was analyzed automatically by absolute quantification method by the LightCycler® Software 4.0. The enterococci 23S rRNA and B. thetaiotaomicron αmannanase 1-6 qPCR assays were carried out using 10 µL of Light Cycler 480 Probes Mastermix (Roche, Indianapolis, IN), forward and reverse primers, probe, and nuclease free water to make up the final volume to 15 µL. One of the plasmid dilutions used for creating the standard curve for the targets was included in the assay as a positive control. Five microliters of the extracted sample were used as the template. The reaction was performed in duplicate for samples and negative controls were included. The copies of uidA gene of E. coli, 23S rRNA of enterococci gene and α-mannanase 1-6 gene of Bacteroides thetaiotaomicron present in the sample were quantified from the standard curves obtained earlier. The copies of the corresponding genes were converted to cell equivalents; in the case of E. coli and B. thetaiotaomicron, only one copy of the target gene is present in a cell, thus one copy number corresponds to one cell. However in case of enterococci, there are four copies of 116 23S rRNA present in a cell; therefore when final copies to cell conversions are done, four copies of enterococci qPCR targets corresponds to one cell. All final concentrations for qPCR analyses were reported as qPCR equivalent cells/100 mL for all samples. Table 4.1: The bacteria and gene targets used for qPCR assays in the monitoring of raw sewage, septage, and treated effluents. Bacteria Gene Amplicon size E. coli uidA 163 bp Developed in this study 91 bp Frahm & Obst (2003) 63 bp Yampara-Iquise et al. (2008) Enterococci B. thetaiotaomicron 23S RNA α-1-6 mannanase 117 Reference Table 4.2: Primer/ probe sequences, concentrations and PCR programs used for bacterial qPCR assays (References in Table 4.1). Bacteria Primer/probe sequence Primer/probe concentrations PCR programs ab o 5’-CAATGGTGATGTCAGCGTT-3’ b 0.2 µM 8 s at 72 C 0.5 µM 15 s at 95 C 0.5 µM 30 s at 60 C 6FAM-TGGTTCTCTCCGAAATAGCTTTAGGGCTA-TAMRA a 8 s at 58 C 5’-AGAAATTCCAAACGAACTTG-3’ b 0.5 µM 5’-CAGTGCTCTACCTCCATCATT-3’ B. thetaiotaomicron 6 s at 95 C 6FAM-TTGCAACTGGACAAGGCACCAGC-BBQ Enterococci 0.5 µM 5’-ACACTCTGTCCGGCTTTTG-3’ E. coli 0.4 µM 15 s at 72 C 5’-CATCGTTCGTCAGCAGTAACA 5’-CCAAGAAAAAGGGACAGTGG 6FAM-ACCTGCTG-NFQ 0.2 µM 15 s at 94°C 60 s at 60°C 5 s for 72°C o Repeated for 40 cycles, after an initial cycle of 10 min at 95 C (For E. coli and enterococci) o Repeated for 45 cycles, after an initial cycle of 15 min at 95 C (For B. thetaiotaomicron) 118 0.2 µM 0.1 µM o o o o o 4.2.3 Statistics Statistical analyses were performed using SAS software 9.2 (SAS Inc, 2002) and significance level was set at α=0.05. In order to fulfill the normality assumptions of tests used in the analysis, data were log10 transformed before conducting the tests. Analysis of variance (ANOVA) was performed to determine the differences in mean concentrations of target organisms in different treatment groups. If ANOVA was significant, multiple pairwise comparisons were carried out using Fisher’s Least Square Difference (LSD) test. Covariate analysis was performed to decide if correlation needed to be adjusted due to the effect of untreated (raw sewage and primary effluent) and treated (secondary effluent and tertiary effluent) groups from the waste water treatment plant. If there was an effect due to these variables, correlation was performed separately for both treated and untreated set of samples. Pearson’s correlation coefficient was used to test the relationship between conventional indicators and B. thetaiotaomicron. Linear regression analysis was used to estimate the coefficients of the linear equation for conventional indicators that best predicted the concentrations of B. thetaiotaomicron after various waste water treatment processes. 119 4.3 Results 4.3.1 Wastewater treatment samples The physicochemical characteristics for raw sewage and final effluent were recorded on all sampling dates and the ranges for these characteristics are summarized in Table 4.3. The pH was found consistent ranging from 6.6 to 7.2. Biological Oxygen Demand (BOD), suspended solids, phosphorus, and ammonia levels were all decreased by the activated sludge treatment as expected. Table 4.3: Range of physical and chemical characteristics for raw sewage and final effluent samples for all sampling dates. Samples BOD5 mg/L pH Raw sewage 6.6-7.0 Tertiary effluent 6.8-7.2 Suspended solids Total-Phosphorus mg/L mg/L Ammonia mg/L 12-223 20-274 1.5-6.7 3.1-36.2 1.0-6.0 1.0-7.0 0.5-2.1 0.1-2.4 Table 4.4 shows the average log10 transformed concentrations with the standard deviations for the 216 samples (54 after each treatment location) collected from the wastewater treatment plant. The qPCR equivalent concentrations of E. coli in raw sewage 5 7 5 ranged from 1.47x10 to 1.48x10 cells/100 mL, enterococci ranged from 7.08x10 to 5.75x10 7 cells/100 mL and for the B. thetaiotaomicron ranged from 7.76x10 120 6 to 7 5.68x10 cells/100 mL (Table 4.4). The final effluent, after the tertiary treatment had a 1 4 0 5 range of concentration of 2.16x10 to 4.39x10 of E. coli, 6.35x10 to 3.81x10 of enterococci and 9.79x10 2 to 1.59x10 5 of B. thetaiotaomicron qPCR equivalent cells/100mL (Table 4.4). The data for fecal coliform counts for final effluents were obtained for these 0 3 sampling dates from the treatment facility and ranged from 3.0x10 to 2.3x10 CFU/100 mL and the mean concentration was 1.91x10 2 CFU/100 mL. Concentrations of cultivatible E. coli in the same effluent samples ranged from below detection limit to 2 3.34x10 CFU/100 mL and that of enterococci were below detection limit to 2.31x10 1 2 1 CFU/100 mL. The mean concentrations of both were 4.64x10 and 3.03x10 CFU/100 mL respectively in final tertiary effluent. There were statistically significant differences in concentrations of all targets either by cultivation or qPCR methods between samples collected prior to disinfection and after disinfection (p<0.05). Ficher’s LSD showed that concentrations of B. thetaiotaomicron were significantly higher than E. coli or enterococci in all samples except in secondary and tertiary effluents where their concentrations were not significantly different from that of enterococci (p<0.05). Concentrations of E. coli were significantly higher than enterococci in all treated and untreated sewage samples by cultivation methods; however by qPCR method enterococci concentrations were found to be significantly higher than E. coli in secondary and tertiary effluents (p<0.05). 121 Table 4.4: Average log10 concentrations for E. coli, enterococci by cultivation and qPCR and B. thetaiotaomicron by qPCR throughout wastewater treatment. Samples RS PE SE TE a b b c d e a E. coli Enterococci E. coli Enterococci B. thetaiotaomicron (log10 CFU/100 mL) (log10 CFU/100 mL) (log10 cells/100 mL) (log10 cells/100 mL) (log10 cells/100 mL) Mean Std dev 6.21 0.26 5.72 0.42 6.46 0.59 6.63 0.51 7.26 0.24 Mean Std dev 6.17 0.26 5.58 0.27 6.48 0.72 6.75 0.40 7.31 0.41 Mean Std dev 1.01 0.84 0.64 0.91 3.05 0.95 4.13 0.84 4.19 0.42 Mean Std dev 1.04 0.93 0.75 1.01 2.82 1.19 3.59 1.12 3.67 0.60 n=54 for each treatment location c d e RS- Raw sewage, PE- Primary effluent, SE-secondary effluent, TE-Tertiary effluent 122 The overall average log10 reductions through the waste water treatment process and log10 reduction of each target cell during each step of treatment by cultivation and qPCR methods are shown in Figure 4.2. E. coli concentrations as evaluated by membrane filtration were found to show the highest overall log10 reduction of 5.17, followed by enterococci CFU with an average log10 reduction of 4.97. By qPCR, the overall log10 reductions were 3.64 for E. coli, 3.59 for enterococci and 3.03 for B. thetaiotaomicron. Primary treatment process of raw sewage did not significantly change the concentrations of any target organismss. During secondary treatment process, there was a significant reduction in all bacteria. There was an average log10 removal of 5.16 and 4.94 for E. coli and enterococci CFU concentrations, respectively by the secondary treatment process. The log10 reductions in qPCR equivalent cell concentrations were 3.43, 2.62 and 3.12 for E. coli, enterococci and B. thetaiotaomicron, respectively during this treatment step. After this step, there were no significant reductions in any of the targets during tertiary treatment. 123 Figure 4.2: Average log10 reductions of E. coli, enterococci CFUs and cell equivalents and B. thetaiotaomicron cell equivalents by waste water treatment. Raw sewage Primary Trt Secondary Trt Overall Log Removal Primary Trt Secondary Trt a Tertiary Trt b E. coli (CFU/100 mL) 0.04 5.16 -0.03 5.17 Enterococci (CFU / 100 mL) 0.14 4.94 0.11 4.97 E. coli (cells/100 mL) -0.01 3.43 0.23 3.64 Enterococci (cells/100 mL) -0.13 2.62 0.54 3.59 B. thetaiotaomicron (cells/100 mL) -0.05 3.12 0.52 3.03 a b includes sedimentation and disnfection includes filtration 124 Of the 18 sampling events, 6 events included collection of samples after the secondary clarification prior to chlorination step, along with the other locations throughout the treatment plant by cultivation and qPCR methods in order to examine specifically the disinfection step (Figure 4.3). During secondary activated sludge treatment prior to the chlorination step, there was a significant reduction in all target organisms. There was an average log removal of 2.75 and 3.11 for E. coli concentrations by cultivation method and qPCR respectively. For enterococci, the log reduction was 2.42 and 1.88 by cultivation method and qPCR respectively. The highest log removal in this stage was found to be for B. thetaiotaomicron, at a value of 3.31, as measured by qPCR. When chlorine was added to the effluent, there was a further significant reduction in the average concentrations; 2.42 for E. coli and 2.63 for enterococci as measured by the cultivation method. But as expected by qPCR methods, this reduction was only 0.31 and 0.40 for E. coli and enterococci respectively, and only 0.12 for B. thetaiotaomicron cells, none of which were found to be significant. 125 Figure 4.3: Average log10 concentrations of bacteria in secondary effluents before and after chlorination using cultivation and qPCR. Log10 average concentration 6.00 5.00 4.00 3.00 2.00 1.00 SE (Pre-chlorination) a B. thetaiotaomicron (cells/100ml) Enterococci (cells/100ml) E. coli (cells/100ml) Enterococci (CFU/100ml) E.coli (CFU/100ml) 0.00 SE (Post-chlorination) *number of samples, n=18 for each treatment location a secondary treatment pre-chlorination involves activated sludge process and secondary clarification. 126 Covariate analysis indicated that to determine the correlation between B. thetaiotaomicron and each conventional indicator, analysis had to be done on the untreated (raw sewage and primary effluent) group and treated (secondary effluent and tertiary effluent) group of samples separately. However, correlation between B. thetaiotaomicron and conventional indicators was also determined using pooled data from 216 samples from waste water treatment plant at all four locations to see the trend in the overall process. Significant correlations between each of the conventional indicators and B. thetaiotaomicron were observed using the pooled data. Table 4.5 presents Pearson’s correlation coefficients for both the conventional indicators and B. thetaiotaomicron in treated and untreated groups as well as pooled data. Correlation coefficient (r) between E. coli and enterococci was 0.9, correlation coefficient between E. coli and B. thetaiotaomicron was found to be 0.94 and between enterococci and B. thetaiotaomicron was 0.88 and these correlations were found to be statistically significant (p<0.05). Table 4.5: Pearman’s correlation between conventional indicators and B. thetaiotaomicron in treated, untreated groups of data and pooled data by qPCR Pearson’s correlation coefficient (R) Indicator bacteria Untreated a Treated b Pooled E. coli b c 0.66 0.93 Enterococci a 0.33 0.37 0.53 0.88 raw sewage and primary effluent data combined (n=108 samples) secondary and tertiary effluents data combined (n=108 samples) treated and untreated data combined (n=216 samples) 127 c Regression analysis was used to test the strength of association between conventional indicators and B. thetaiotaomicron. The equation for each regression is displayed on each chart. Figure 4.4 shows scatter-plot for E. coli and B. thetaiotaomicron concentrations from 2 pooled data from four treatments which displayed strong correlations with R =0.87 and figure 4.5 shows scatter-plots for E. coli and B. thetaiotaomicron concentrations for data 2 from untreated and treated sets. For untreated data set, R was only 0.11 and for treated data 2 set, it was 0.44. R ranges from 0 to 1 and values close to 1 denote strong association between variables. Figure 4.4: Correlation between E. coli and Bacteroides thetaiotaomicron by qPCR in pooled data from wastewater treatment samples. Log10 B. thetaiotaomicron concentration, (cells/100 mL) 10.00 8.00 y = 0.8523x + 1.5687 R² = 0.8766 6.00 4.00 2.00 0.00 0.00 2.00 4.00 6.00 8.00 Log10 E.coli concentration (cells/100 mL) Raw sewage Primary effluent Secondary effluent Treated and untreated data pooled, n=216 128 Tertiary effluent Figure 4.5: Correlation between E. coli and B. thetaiotaomicron by qPCR in untreated and Log10 B. thetaiotaomicron concentration, (cells/100 mL) treated groups of wastewater samples y = 0.1791x + 6.1151 R² = 0.1161 8.00 6.00 4.00 y = 0.4454x + 2.564 R² = 0.4429 2.00 0.00 0.00 2.00 4.00 6.00 Log10 E.coli concentration (cells/100 mL) Untreated sewage Treated sewage Linear (Untreated sewage) Linear (Treated sewage) n=108 samples (raw sewage and primary effluent data combined) n=108 samples (secondary and tertiary effluents data combined) 129 8.00 The scatter-plot for enterococci and B. thetaiotaomicron from pooled data from four 2 treatments which displayed strong correlation with R =0.79 is displayed in figure 4.6 and figure 4.7 shows scatter-plots for enterococci and Bacteroides concentrations for data from 2 2 untreated and treated groups of samples. For untreated data set, R was only 0.14 and R for treated group was 0.28. Figure 4.6: Correlation between enterococci and Bacteroides thetaiotaomicron by qPCR in Log10 B. thetaiotaomicron concentration, (cells/100 mL) pooled data from wastewater treatement samples. y = 0.9739x + 0.4525 R² = 0.7916 10.00 8.00 6.00 4.00 2.00 0.00 0.00 2.00 4.00 6.00 8.00 10.00 Log10 enterococci concentration, (cells/100 mL) Raw sewage Primary effluent Treated and untreated data pooled, n=216 130 Seondary effluent Tertiary effluent Figure 4.7: Correlation between enterococci and B. thetaiotaomicron by qPCR in untreated Log10 B. thetaiotaomicron concentration, (cells/100 mL) and treated data sets of wastewater samples y = -0.2777x + 9.1274 R² = 0.14 8.00 6.00 4.00 y = 0.3564x + 2.5134 R² = 0.2888 2.00 0.00 0.00 2.00 4.00 6.00 8.00 Log10 Enterococci concentration (cells/100 mL) Untreated sewage Treated sewage Linear (Untreated sewage) Linear (Treated sewage) n=108 samples (raw sewage and primary effluent data combined) n=108 samples (secondary and tertiary effluents data combined) 131 10.00 Figures, 4.8 and 4.9 contrast the average concentrations of E. coli and enterococci respectively, by CFU and qPCR throughout the treatment process. The mean log10 difference between concentrations of E. coli by cultivation methods and by qPCR prior to disinfection ranged between 0.25 and 0.31 whereas this difference in samples from postdisinfection step was around 1.78 to 2.04. The mean log10 difference between concentrations of enterococci by cultivation methods and that by qPCR prior to disinfection ranged between 0.81 and 1.17 whereas this difference in samples from post-disinfection step ranged from 2.84 to 3.49. There was a statistically significant difference in concentrations of E. coli between both cultivation and qPCR methods in all samples from wastewater treatment except raw sewage (p<0.05) and concentrations of enterococci differed significantly by both methods from all wastewater treatment samples (p<0.05). 132 Figure 4.8: Comparison between log10 transformed average concentrations of E. coli by cultivation based (CFU/100 mL) and qPCR based methods (cells/100 mL) in waste water treatment plant samples. Log10 concentrations per 100 mL 8.00 6.00 4.00 2.00 0.00 RS E.coli (cfu/100mL) PE SE E.coli(cells/100mL) RS- Raw sewage PE- Primary effluent SE- Secondary effluent TE- Tertiary effluent Total number of samples, N=216 (n=54 for each treatment) 133 TE Figure 4.9: Comparison between log10 transformed average concentrations of enterococci by cultivation based (CFU/100 mL) and qPCR based methods (cells/100 mL) in waste water treatment plant samples. Log10 concentrations per 100 mL 8.00 6.00 4.00 2.00 0.00 RS enterococci(cfu/100mL) PE SE TE enterococci(cells/100mL) RS- Raw sewage PE- Primary effluent SE- Secondary effluent TE- Tertiary effluent Total number of samples, N=216 (n=54 for each treatment) Correlation between concentrations by cultivation and qPCR methods for E. coli and enterococci in wastewater treatment plant samples were determined. There was a strong correlation between two methods when pooled data from all treatments was used for analysis for E. coli and enterococci, with R=0.89 and 0.87, respectively. Regression analysis was used to test the strength of this association. Figure 4.10 shows the scatter-plot for E. coli CFU/100mL by cultivation and E. coli cells/100mL by qPCR from pooled data from four 2 treatments which displayed significant coefficient of determination, R =0.80 (p<0.05) and 134 figure 4.12 shows the scatter-plot for enterococci concentrations by both methods with a 2 lower but yet significant R of 0.75 (p<0.05). This significance was due to the similar trends associated with reduction through the treatment process. However, there was no significant correlation found between both methods for concentrations of E. coli with data from untreated and treated sets (Figure 4.11). For untreated data set, correlation coefficient, R was only 0.01 and for treated data set, it was 0.24. Value of R close to +1 denotes positive correlation, close to -1 denotes negative correlation and that of zero denotes no correlation. A similar trend was observed for enterococci also with no significant correlation found between both methods with data from untreated, where R was only 0.01 and treated sets, where R was equal to 0.21 (Figure 4.13). 135 Figure 4.10: Correlation between concentrations of E. coli by cultivation methods Log10 E. coli concentration (cells/100mL) (CFU/100 mL) and qPCR (cells/100 mL) in pooled data from wastewater treatment samples. 8.00 y = 0.6763x + 2.2264 R² = 0.8017 6.00 4.00 2.00 0.00 0.00 2.00 4.00 6.00 8.00 Log10 E.coli concentration (CFU/100 mL) Raw sewage Primary effluent Secondary effluent Total number of samples, n=216 (54 for each treatment) 136 Tertiary effluent Figure 4.11: Correlation between concentrations of E. coli by cultivation methods (CFU/100 mL) and qPCR (cells/100 mL) in untreated and treated data sets of wastewater Log10 E. coli concentration (cells/100 mL) samples 8.00 y = -0.1007x + 7.0839 R² = 0.0015 6.00 4.00 y = 0.3317x + 2.57 R² = 0.0639 2.00 0.00 0.00 2.00 4.00 6.00 8.00 10.00 Log10 E.coli concentration (CFU/100 mL) Untreated sewage Treated Sewage Linear (Untreated sewage) Linear (Treated Sewage) n=108 samples (raw sewage and primary effluent data combined) n=108 samples (secondary and tertiary effluents data combined) 137 Figure 4.12: Correlation between concentrations of enterococci by cultivation methods (CFU/100 mL) and qPCR (cells/100 mL) in pooled data from wastewater treatment samples. Log10 enterococci concentration (cells/100mL) 10.00 y = 0.5604x + 3.4677 R² = 0.7593 8.00 6.00 4.00 2.00 0.00 0.00 2.00 4.00 6.00 8.00 Log10 enterococci concentration (CFU/100 mL) Raw sewage Primary effluent Secondary effluent Total number of samples, N=216 (n=54 for each treatment) 138 Tertiary effluent Figure 4.13: Correlation between concentrations of enterococci by cultivation methods (CFU/100 mL) and qPCR (cells/100 mL) in untreated and treated data sets of wastewater Log10 enterococci concentration (cells/100 mL) samples 10.00 y = 0.016x + 6.5897 R² = 0.0002 8.00 6.00 4.00 y = 0.2604x + 3.6339 R² = 0.0441 2.00 0.00 0.00 2.00 4.00 6.00 8.00 Log10 enterococci concentration (CFU/100 mL) Untreated sewage Treated Linear (Untreated sewage) Linear (Treated) n=108 samples (raw sewage and primary effluent data combined) n=108 samples (secondary and tertiary effluents data combined) 139 10.00 4.3.2 Septage samples The average log10 transformed concentrations of E. coli and enterococci by cultivation methods and qPCR, and B. thetaiotaomicron by qPCR for a total of 48 samples (24 for each treatment) from the septage treatment plant are shown in Table 4.6. 6 The qPCR equivalent concentrations of E. coli in raw septage ranged from 6.80x10 to 8 6 7 6.23x10 cells/100mL, enterococci ranged from 3.72x10 to 6.235x10 cells/100mL and B. 7 8 thetaiotaomicron were in the range of 1.19x10 to 1.17x10 cells/100mL. The final effluent, 2 4 after the treatment had a range of concentration of 9.09x10 to 5.92x10 qPCR equivalent 4 5 3 cells/100mL of E. coli, 4.23x10 to 4.57x10 cells/100mL of enterococci and 3.83x10 to 3.67x10 5 cells/100mL of B. thetaiotaomicron (Table 4.7). There was no significant difference between B. thetaiotaomicron and conventional indicators as measured by qPCR 7 in raw septage and they were present in concentrations of >10 cells/100mL. E. coli and enterococci when measured by cultivation method were at least one log10 lower as compared to qPCR equivalent concentrations. 140 Table 4.6: Average concentrations of bacteria in raw septage and effluent by cultivation and qPCR methods. Samples* E.coli enterococci E.coli enterococci B. thetaiotaomicron (log10 CFU/100mL) (log10 CFU/100mL) (log10 cells/100mL) (log10 cells/100mL) (log10 cells/100mL) Raw septage Mean Std dev 6.47 0.45 6.36 0.82 7.33 0.68 7.31 0.36 7.55 0.34 Effluent Mean Std dev 3.96 0.86 4.07 0.96 3.51 0.67 5.32 0.28 4.42 0.55 *Total number of samples, n=48 (24 samples for each treatment) 141 Following the treatment, there was a significant average log10 reduction in all target organisms, shown in Figure 4.14. The highest log10 reduction during treatment was found for E. coli qPCR equivalent cells with a log10 difference of 3.82 whereas the difference in log10 concentrations for E. coli as measured by cultivation methods was 2.52. However, this difference in log10 removals between both methods is due to the higher initial qPCR equivalent concentrations in raw septage. The log10 reduction for enterococci by cultivation methods was 2.29 whereas this difference was only 1.99 by qPCR. The log10 reduction for B. thetaiotaomicron was found to be 3.13 Figure 4.14: Average log reduction of E. coli, enterococci by cultivation and qPCR and B. thetaiotaomicron targets by qPCR during septage treatment process. E.coli (cfu/100mL) 2.52 enterococci (cfu/100mL) 2.29 E.coli (cells/100mL) 3.82 B. thetaiotaomicron (cells/100mL) 3.13 enterococci (cells/100mL) 1.99 Number of samples, n=48 (24 samples from each treatment location) 142 Regression analysis was used to test the strength of association between conventional indicators and B. thetaiotaomicron (Figures 4.15 and 4.16). The equation for each regression is displayed on each chart. E. coli and Bacteroides concentrations displayed 2 strong correlations with R =0.91 and between enterococci and Bacteroides showed 2 correlation with R =0.92; with only little scatter at the different sites. Figure 4.15: Correlation between E. coli and Bacteroides thetaiotaomicron by qPCR in septage treatment plant. y = 0.8166x + 1.389 R² = 0.9199 Log10 B. thetaiotaomicron concentration, (cells/100mL) 10.00 8.00 6.00 4.00 2.00 0.00 0.00 2.00 4.00 6.00 8.00 10.00 Log10 E.coli concentration, (cells/100mL) Raw septage Effluent Total number of samples, n=48 (24 samples from raw septage and 24 treated effluents) 143 Figure 4.16: Correlation between enterococci and Bacteroides thetaiotaomicron in Log10 B. thetaiotaomicron concentration, (cells/100 mL) septage treatment samples. 10.00 y = 1.5x - 3.4896 R² = 0.9237 8.00 6.00 4.00 2.00 0.00 0.00 2.00 4.00 6.00 8.00 10.00 Log10 enterococci concentration, (cells/100 mL) Raw septage Effluent Total number of samples, n=48 (24 samples from raw septage and 24 treated effluents) Correlation between concentrations of E. coli and enterococci present in pooled data from septage treatment plant samples were determined. Both the correlations were found 2 2 to be significant with R =0.67 for E. coli and R =0.60 for enterococci. Regression analysis was used to test the strength of association between methods for E. coli and enterococci, as shown in Figures 4.17 and 4.19, respectively. This association was due to the similar trends associated with reduction through the treatment process which changes when raw septage and effluent samples are analyzed as two separate data sets as shown in 144 Figures, 4.18 and 4.20, there was a weak correlation between cultivation and qPCR methods for concentrations of E. coli with correlation coefficient, R=0.26 in raw septage and a significant correlation with R=0.66 (p<0.05) in effluent samples. However, for enterococci, there was no correlation between both methods in raw septage and effluent samples. Figure 4.17: Correlation between concentrations of E. coli by cultivation methods Log10 E. coli concentration (cells/100mL) (CFU/100 mL) and qPCR (cells/100 mL) in pooled data from raw septage and effluents. y = 1.111x - 0.1833 R² = 0.6723 10.00 8.00 6.00 4.00 2.00 0.00 0.00 2.00 4.00 6.00 8.00 Log10 E.coli concentration (CFU/100mL) Raw septage Effluent Total number of combined data from two sets, n=48 (24 samples from raw septage and 24 treated effluents) 145 Figure 4.18: Correlation between concentrations of E. coli by cultivation methods Log10 E. coli concentration (cells/100 mL) (CFU/100mL) and qPCR (cells/100mL) in two data sets from raw septage and effluents. 10.00 y = 0.3137x + 5.4315 R² = 0.0732 8.00 6.00 4.00 y = -0.4086x + 5.3714 R² = 0.4489 2.00 0.00 0.00 2.00 4.00 6.00 8.00 Log10 E.coli concentration (CFU/100 mL) Raw septage Effluent Linear (Raw septage) Linear (Effluent) Number of samples, n=24 for each data series 146 10.00 Figure 4.19: Correlation between concentrations of enterococci by cultivation methods Log10 enterococci concentration (cells/100 mL) (CFU/100 mL) and qPCR (cells/100 mL) in pooled data from septage treatment samples. y = 0.5459x + 3.5724 R² = 0.6011 10.00 8.00 6.00 4.00 2.00 0.00 0.00 2.00 4.00 6.00 8.00 Log10 enterococci concentration (CFU/100 mL) Raw septage Effluent Total number of combined data from two sets, n=48 (24 samples from raw septage and 24 treated effluents) 147 Figure 4.20: Correlation between concentrations of enterococci by cultivation methods (CFU/100mL) and qPCR (cells/100mL) in two sets of data from raw septage and effluents. y = -0.1855x + 8.6929 R² = 0.1317 Log10 enterococci concentration (cells/100 mL) 10.00 8.00 6.00 4.00 y = 0.0028x + 5.2979 R² = 0.0001 2.00 0.00 0.00 2.00 4.00 6.00 8.00 Log10 enterococci concentration (CFU/100 mL) Raw septage Effluent Linear (Raw septage) Linear (Effluent) Number of samples, n=24 for each data series 148 10.00 4.4 Discussion This study represents a characterization of human waste treatment using a novel MST target, B. thetaiotaomicron in comparison with the conventional indicators E. coli and enterococci. This study is the first to contrast raw sewage and septage and to study the fate of all of these bacterial molecular signals during waste treatment. In this study, occurrence of unique human fecal specific marker, B. thetaiotaomicron in raw sewage and septage and its fate during their treatment process has been quantified. It was found that B. thetaiotaomicron qPCR equivalent cells were present in significantly higher concentrations than that of E. coli or enterococci in raw sewage and septage. No studies have quantified B. thetaiotaomicron levels in both these samples. Yampara-Iquise 2 9 (2008) found a wide range of B. thetaiotaomicron from 6.88x10 to 1.07x10 cells in one gram of human feces, when stool samples from 10 human subjects were analyzed by qPCR targeting the α-mannanase 1-6 gene. This variation was attributed to the variation in DNA extraction efficiency. Other human fecal MST markers such as the host-specific Bacteroides-Prevotella 16S rRNA gene markers have been detected in raw sewage and septage at concentrations 8 9 of 10 -10 cells per 100ml (Seurinck et al 2005, Silkie & Nelson 2009; Sercu et al 2009) 5 11 whereas these markers were found in a wide range of concentrations from 10 – 10 cells per gram of wet human feces (Seurinck et al, 2005; Okabe et al, 2007; Sercu et al, 2009). There is still some speculation about cross reactivity of these human specific 16S rRNA genetic markers with cat and dog feces (Kildare et al, 2007). Conversion of 16S rRNA gene copies into cell counts is not accurate as copy number of Bacteroides- 149 Prevotella 16S rRNA varies in different species of the genera. Therefore, comparison between Bacteroides 16S rRNA human marker and B. thetaiotaomicron α-mannanase 1-6 gene, where one copy of gene represents one cell, may not be the best approach for determining the sensitivity of these markers. 6 E. coli was found to occur at concentrations of 10 target gene copies per 100ml of raw sewage in this study which is almost three logs lower than the concentrations 9 reported by another study who found 10 copies of lacZ gene targets for E. coli by qPCR in the same volume of raw sewage (Wery et al. 2008). But in five other studies, qPCR cell concentrations of E. coli or enterococci in raw sewage agree with previous reports on the concentrations of both these indicator organisms in raw sewage by qPCR assays targeting uidA (Table 4.7). One possibility for such high numbers of E. coli found by Wery et al. could be due to the cross amplification of the E. coli lacZ qPCR assay with other bacterial strains that harbor this gene. The specificity of the assay was tested with only four bacteria during the assay development of this PCR assay (Foulds et al. 2002). Enterococci qPCR assays published before have mostly targeted 23S rRNA gene using the same primer sets as used in this study and have found similar concentrations of target gene copies in raw sewage. Log10 removal of enterococci qPCR signals across the wastewater treatment found in this study is almost similar to the study by Varma et al. (2009) (Table 4.7). Strong correlation between conventional indicators and B. thetaiotaomicron markers in both sewage and septage samples was observed. This relationship started to diminish after qPCR signals were reduced following secondary treatment. Strong correlations 150 between Bacteroides 16S rRNA human markers and indicators such as total and fecal coliforms or E. coli have been observed before with untreated sewage samples (Savichtcheva et al. 2007; Dick and Field. 2004). This lack of correlation between facultatively anaerobic E. coli or enterococci and obligately anaerobic Bacteroides sp in treated effluent could be due to the difference in their removals by activated sludge, but more importantly a background signal of DNA is found in effluents. Summary of all studies that have monitored raw sewage using qPCR for indicators and MST is given in the Table below. 151 Table 4.7: Summary of all qPCR applications to monitor the wastewater and treated effluents in the literature Treatment Sampling Samples, n= RS PE SE (Pre-cl) SE (Post-cl) TE Septage STE Grab Grab Grab Grab Grab Grab Grab 54 54 18 54 54 24 24 RS PE SE (Pre-cl) SE (Post-cl) Grab 9 9 9 9 RS Grab 12 RS Septage Grab Grab 3 3 RS PE TE (UV disinfected) Grab Grab Grab 5 5 5 Bacteroides Human x E. coli Enterococci Marker Target gene* Target gene* Target gene* a alpha-mannanase* uidA* 23S rRNA* 2.88E+06 1.71E+07 1.82E+07 3.02E+06 2.25E+07 2.04E+07 1.01E+03 1.68E+05 1.84E+04 1.12E+03 5.40E+04 1.55E+04 6.61E+02 1.56E+04 4.68E+03 2.14E+06 8.17E+07 3.55E+07 3.24E+03 8.36E+05 2.63E+04 b 16S rRNA x x 2.76E+07 3.71E+07 4.12E+05 1.58E+05 - uidA* 2.00E+07 23S rRNA* 1.00E+07 uidA* c 2.85 1.75E+09 1.59E+09 3.47E+07 3.17E+07 b 16S rRNA 7.94E+08 b 16S rRNA References This study Varma et al. 2009 Silkie and Nelson 2009 - 7.8E+08 3.9E+08 Sercu et al. 2009 - - Lavender and Kinzelman 2009 2.59 152 c Table 4.7 (cont’d) RS PE SE (Pre-cl) composite 6 6 6 lacZ* 1.34E+09 1.05E+09 2.95E+05 Wery et al.2008 - b 16S rRNA RS PE Grab Grab 12 12 - - 2.10E+05 2.90E+05 b 16S rRNA Savichtcheva et al. 2007 RS Grab 4 - 1.72E+10 Seurinck et al.2006 RS TE (UV disinfected) Grab Grab 3 3 uidA* 1.66E+07 6.12E+02 - Lee et al. 2006 x qPCR target gene copies/100mL and a B. thetaiotaomicron b Order Bacteriodales c Log10 difference between raw sewage and Tertiary effluent (Initial raw sewage or final effluent concentrations not reported) RS-Raw sewage PE=Primary effluent after grit removal SE(Pre-cl)=Secondary effluent from secondary clarifier prior to chlorination SE(Post-cl)=Secondary effluent after disinfection TE= Teritiary effluent after filtration or UV disinfection STE=septage treated effluent 153 During primary treatment in wastewater treatment process, there was no significant change in the concentrations of any of the targets. Primary treatment of sewage primarily allows heavy solids to settle down when oil, grease float to the surface which is then skimmed off and settled particles are also removed. Another study that monitored treatment process for different indicators found that enterococci concentrations were reduced by 0.4 log10 units during primary sedimentation step (Lucena et al, 2004). A recent case study in Netherland that monitored the changes in effluent when raw sewage was directly bypassed to further processes without primary settling observed that there was no improvement in the quality of effluent nor did it improve removal efficienciesrates and microbiology populations when the influent was loaded with particulate matter (Puig et al, 2010). Significant reduction in all targets (both cultivable and qPCR equivalent cells) were observed during secondary treatment process comprising of biological process (activated sludge), sedimentation and disinfection (chlorination). Most of the molecular signals were lost during secondary treatment before disinfection due to biological and sedimentation process. Reduction of bacteria during activated sludge process is due to several factors such as attachment to solids, sedimentation and subsequent removal in sludge, predation by protozoa, alterations in parameters such as pH, temperature, and competition with naturally occurring microbes. In many places, sludge is recycled by land application but this also imposes serious health risks to humans and animals due to the presence of many pathogenic microorganisms (Gerba and Smith, 2005). Based on 4 evaluation of treatment steps in this study, it is estimated that approximately 10 CFU/ml of E. coli or enterococci could be ending up in sludge. This is a rough estimate as other 154 mechanisms such as predation, inactivation etc also play a role in reduction of cells. Previous sludge monitoring studies show varying concentrations of E. coli in sludge; 10 5 4 6 CFU/g wet weight (Payment et al, 2001) 10 -10 cu/g dry weight (Pourcher et al, 2005). Results in this study suggest that qPCR equivalent cells may be found in higher concentrations in sludge as compared to cultivable cells. So, typically when qPCR is applied to monitor sludge it should detect higher concentrations of bacterial targets than cultivation methods. A previous study that monitored liquid sludge detected 3.7 log higher concentrations of E. coli by qPCR method than by the cultivation based method (Wery et al, 2008). The findings in this study support the previous recommendation of proper treatment of sludge for the inactivation of pathogens before land application. It was also observed that filtration did not additionally remove any of the targets tested. The treatment plant uses rapid sand filters; slow sand filters are found to be more efficient in removing pathogens than rapid sand filters (Pundsnack, 2001). The efficiency of rapid sand filtration in physically removing microorganisms and suspended particles during waste water treatment process is enhanced when coagulants are added. One study found a positive relationship between removal of pathogens such as Cryptosporidium and enhanced coagulation conditions (Dugan et al. 2001). In the absence of coagulants, particle size remains small due to lack of chemical modifications, thus increasing the probability of penetration through the pores in the filter bed. As there is no floc formed in the filter due to the absence of coagulants, microorganisms are also not retained efficiently in the filters thus increasing their concentrations in final effluents (Koivunen et al. 2003). 155 Under the Clean Water Act of 1972, wastewater treatment plant discharges into streams have to be monitored to meet the permitted levels of pollutants specified in National Pollutant Discharge Elimination System (NPDES). Wastewater treatment plants use fecal coliforms to monitor the treatment efficacy. Even though E. coli, a fecal coliform is reduced during the treatment process, other coliforms such as Klebsiella, Enterobacter, and Citrobacter have been found to proliferate during wastewater treatment processes and in effluents which can lead to large numbers of numbers of fecal coliforms in wastewater effluents. Wastewater effluents alone may not be responsible for high fecal coliform counts in surface water and any other fecal sources located upstream of the plant could also be contributing the bacteria in to the streams. Fecal coliforms were considered for monitoring recreational water quality after epidemiological studies conducted by U.S. Public Health Service (PHS) who found that any increase in the levels of fecal coliforms could possibly be used as a “warning signal” (Stevenson, 1953) and National Technical Advisory Committee commissioned by Federal Water Pollution Control Administration in 1968 recommended fecal coliforms to be used for monitoring recreational water quality (USEPA 1976). Later, U. S. EPA adapted E. coli and enterococci as new water quality criteria based on other epidemiological studies conducted in marine and fresh water beaches to find the relationship between these indicator levels and swimming related illnesses (USEPA 1986). The Beaches Environmental Assessment and Coastal Health (BEACH) Act of 2000 are currently exploring new and rapid technologies such as qPCR to monitor water quality at recreation beaches (Environmental Protection Agency, 2000). 156 Thus, recreational water quality regulations are improvised based on epidemiological studies or with development in technologies. However, other programs under CWA such as NPDES permits still use fecal coliforms as criteria with levels not to exceed 200 CFU/mL based on the geometric mean of five or more samples from wastewater discharges collected over a 30-day period. As USEPA is in the process of implementing new and revise water quality criteria for recreational water, changes are necessary in NPDES indicator bacteria permits for discharges from wastewater plants and other point sources. Wade et al (2006) found a strong correlation between enterococci qPCR daily average concentrations and swimming related gastrointestinal illnesses in two of the Great Lakes beaches they studied by applying the 23S rRNA qPCR assay for enterococci. According to their study, a log10 increase in the daily average of qPCR cell equivalents of enterococci was associated with 1.30 (95% CI, 1.08–1.57) increase in the odds of gastrointestinal illness for any contact with water. The types of enterococci concentrations that were found at the beaches ranged from 1.90 to 2.04 log10 qPCR cell equivalents per 100ml. Wastewater effluents were suspected as impacting these beaches. The discharged effluents from wastewater treatment plant in this dissertation study had a log10 qPCR cell concentration of 3.59 per 100ml of enterococci. The B. thetaiotaomicron qPCR equivalent cell levels (log10 average of 3.67 cells/100mL) were almost in the same levels as that of enterococci in 100mL of treated final effluents. These numbers would be expected to be reduced by dilution of sewage effluent into rivers and could even be undectable levels by the time the surface water at the discharge site flows to the 157 recreational waters depending on the distance and the dilution between recreational beaches and wastewater treatment locations. Other factors such sunlight degradation of DNA. However, following a CSO or SSO event the levels of these signals would be very high.. High numbers of qPCR target signals detected in the effluents in this study and correlations found in the epidemiological study by Wade et al. suggest that viability may not be an issue in applying qPCR tools in monitoring water environment for both enterococci and Bacteroides to protect public health risk. 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Use of a Bacteroides thetaiotaomicron-specific alpha-1-6, mannanase quantitative PCR to detect human faecal pollution in water. Journal of Applied Microbiology 105:16861693. 164 CHAPTER 5 EVALUATION OF COW SPECIFIC MICROBIAL SOURCE TRACKING MARKERS IN CATTLE MANURE 5.1 Introduction Contamination of surface and ground water by animal wastes is a severe problem that can lead to illness and death in humans due to transmission of pathogens. There have been many waterborne disease outbreaks where animal waste has been the suspected source of the etiological agent (Table 5.1). Unlike human waste, animal waste is not treated adequately due to the cost required to treat the large amount of waste produced. United States Department of Agriculture (USDA) estimates that US produces 335 million tons of “dry matter” waste annually in farms (USDA 2006) and animal feeding operations produce 100 times more manure than sludge from human waste treatment per year (Gerba and Smith 2005). Therefore, manure is applied to fields prior to each growing season as an efficient disposal alternative due to the high costs involved in the treatment of these wastes and to utilize the high nutrient content. Surface water or ground water can be fecally contaminated by animal waste through many routes (Figure 5.1). Fecal contamination of surface water bodies occurs through by runoff often after a storm event following manure application (Culley and Philips, 1982). Significantly high levels of bacteria in overland flow samples have been demonstrated by simulated rainfall events on a steep grazed hillside (Collins et al. 2005). Direct contamination of water by animals is another problem encountered by regions where cattle are not confined to a smaller grazing area. Elevated levels of fecal coliforms in 165 streams following grazing by livestock animals have been reported (Gary et al., 1983, Doran et al., 1981; Howell et al., 1995). The impacts of water quality by grazing cattle studied in New Zealand showed that E. coli counts increased up to 100 times background levels in a stream after a dairy herd had crossed over. It was estimated that around 10 11 E. coli were deposited into the river during each crossing event (Davies-Colley et al. 2004). Fecal contamination of ground water by manure also occurs during which microbes are transported through pores of soil and this is influenced by factors such as soil type, bacterial shape, size, vegetative state of bacteria etc (Pachepsky et al. 2006). The outbreak in Walkerton which is listed below in Table 5.1 was suspected to be due to contamination of groundwater with manure (McQuigge 2000; Hrudey et al. 2003). 166 Table 5.1: Selected waterborne outbreaks where cattle manure was suspected as the source of the etiological agents Location Year Pathogens Morbidity Mortality Walkerton, Canada Ontario, Canada 1995 E. coli O157:H7 Campylobacter spp. E. coli O157:H7 Africa 1992 E. coli O157:H7 Milwaukee, WI New York 1993 1999 Cryptosporidium parvum E. coli O157:H7 Campylobacter spp. UK 1989 Cryptosporidium parvum 516 cases - Ayrshire, UK 1988 Cryptosporidium parvum 27+ - Bradford, UK 1992 Cryptosporidium parvum 125 cases - Sakai City, Japan a 2000 1995 E. coli O157:H7 12 680 cases 3 deaths Cabool, MO 1990 Exact number not reported E. coli O157:H7 243 cases 4 deaths 167 References 1346 cases 6 deaths Health Canada, 2000 1 case 1000s of cases 400 000 cases 116 cases - Jackson et al., 1998 some deaths 87 deaths 2 deaths a Isaacson et al., 1993 Mac Kenzie et al., 1994 CDC, 1999 Richardson et al., 1991 Smith et al., 1989 Atherton, 1995 Hideshi et al, 1999 Swerdlow et al; 1992 Figure 5.1: Fecal contamination of catchment water associated with the manure environment (Adapted from Hooda et al. 2000) Manure waste (eg; slurry, agricultural field ) Grazing livestock in field Indoor livestock barns Leaching, Runoff Rodents, birds as vectors and reservoirs Contaminated feces Ditches, field drains Animals entering into streams and ditches Direct fecal contamination Catchment water 168 Farmyard drains and ditches Fecal indicator bacteria such as E. coli and enterococci are very common in bovine manure and are used to monitor manure contaminated water. Even though high numbers of these bacteria in water bodies denote input of feces from warm blooded animals, it does not differentiate between the sources. Public health risks due to agriculture runoff can be addressed primarily by using Microbial Source Tracking (MST) tools that enable the differentiation of fecal inputs due to cattle from those of other animals. This will aid in development, prevention, and control strategies. One of the popular microbiological MST tools used to differentiate different animal fecal contamination from human in 1950's and 60's was the Fecal Coliform to Fecal Streptococcus (FC:FS) Ratio. Humans 7 6 have excretion levels of 1.30x10 CFU/g for fecal colifoms and 3.00x10 CFU/g for fecal streptococci thus giving a FC:FS ratio of 4.3 whereas cows supposedly have an 5 6 excretion levels of 2.30x10 CFU/g for fecal colifoms and 1.30x10 CFU/g for fecal streptococci thus giving a FC:FS ratio of 0.17 (Gerba 2000). However, this method was discontinued as MST tool as it was shown not to be scientifically valid. These reasons include alteration of ratio due to different die-off rates of fecal streptococci, varied bacterial concentration outside of pH range of 4.0-9.0, and unreliable ratio with pollution from several sources, among others (Meays et al. 2004). One approach for molecular based MST for bovine specific fecal contamination has been conventional PCR, targeting Bacteroides-Prevotella 16S rRNA (Bernhard and Field 2000). Another method has been a quantitative approach using qPCR assays targeting 16S rRNA genes of Bacteroides-Prevotella group (Kildare et al. 2007; Layton et al. 2006; Okabe et al. 2007), M2 and M3 genetic markers with Bacteroidales-like sequences obtained by genomic fragment enrichment method (Shanks et al. 2006). Real-time 169 reverse transcription assays have also been used for bovine fecal source tracking, for + instance, targeting F -specific RNA coliphages (Kirs and Smith 2007) and bovine enterovirus RNA (Jiménez-Clavero 2005). Almost all the bacterial bovine specific MST assays target 16S rRNA genes from the order Bacteriodales. Successful development and use of these assays depend on the hostspecificity of the assay because of the prevalence of this bacterial group in different animals such as horse, pigs, human, elk, gulls, dogs, and cats. Different factors such as age, diet, and climatic conditions influence the bacterial flora in rumen intestines (Gerard-Champod et al. 2009) and therefore, prevalence of different bovine specific markers was shown to vary with herds with different diet and geographical location (Shanks et al. 2006). Thus, selection of most prevalent markers is necessary to characterize bovine fecal pollution in a particular water-shed. This study focused on evaluating and characterizing the concentrations of the three different cow specific markers by qPCR, namely, cow specific Bacteroides 16S rRNA (Bobac qPCR), M2 marker (M2 qPCR) and M3 marker (M3 qPCR) in manure pit samples collected just prior to field application. A specific goal of this study was to compare concentrations of fecal indicator bacteria such as E. coli and enterococci in manure pit samples and fresh cow feces by applying agar based cultivation and qPCR methods. In order to evaluate the restricted occurrence of these cow specific markers in human waste environment, qPCR assays were also applied to raw sewage and septage samples. 170 5.2 Materials and Method 5.2.1 Sample Collection and Processing Fresh fecal samples were collected from individual cows during defecation at a dairy cattle farm at Michigan State University (n=10). Samples were also collected from four manure pits at Michigan State University dairy farms. Manure in these pits are emptied for land application three times during the year; Spring (around April-May), Summer (around July-August) and in Fall (around October-November). Sample collection for this study was done during the months of May and November in 2009 during the mixing of manure in these pits by agitation just prior to the field application. The four pits from which samples were collected are located in two different facilities; the Dairy Cattle Teaching and Research Center and the Beef Cattle Teaching and Research Center, are both located at MSU farms. Pit 1, Pit 2, and Pit 3 are located in the dairy cattle facility (Figure 5.2). Pit 4 is located in the beef cattle facility (not shown in the figure as the facility is a separate unit). During the month of May, samples were collected from all four pits. In the month of November, samples were collected from only three pits at the dairy cattle farm as there was no land application from the beef farm pit during that time. Five samples were collected from each pit in both seasons in specimen containers that holds approximately 125 mL of sample; thus a total of 35 samples (20 samples in May and 15 samples in November) were used for the study. Samples were transferred to laboratory on ice immediately after collection and were used for analysis immediately. Collection of raw sewage and septage samples from the respective treatment plants is described in Chapter 4 under section 4.2.1 in Materials and Methods. 171 Figure 5.2: Diagram representing sampling locations of manure samples. Dairy Cattle Teaching and Research Center at MSU North Slotted Barn Cows Pit 3 South Slotted Barn Cows Pit2 South Heifer Barn Calves Pit 1 172 5.2.2 Analyses 5.2.2.1 Cultivation Methods EPA Membrane Filtration methods 1603 and 1600 were used for enumerating E. coli and Enterococci respectively (USEPA 2002; 2005). For cow fecal samples, one gram (wet weight) of fresh feces was weighed and for manure, one mL of the sample was measured. These were serially diluted (10 -1 -5 through 10 ) with sterile Phosphate Buffered Saline (PBS). After mixing each dilution thoroughly by vortexing, membrane filtration was performed on these dilutions. One ml from these dilutions was filtered through 47 mm diameter, 0.45 µm pore size, membrane filters. E. coli colony forming units were enumerated after incubating filters on mTEC agar plates (Beckton Dickinson, MD USA) at 35 ± 0.5°C for 2 h, followed by incubation in a water bath at 44.5 ± 0.2°C for 22 h. Similarly, enterococci CFU were enumerated after incubating filters on mEI agar plates (Beckton Dickinson, MD USA) for 24 h at 41°C. The concentrations of E. coli and Enterococci from all tested fecal samples were reported as colony forming units (CFU) per gram and CFU per mL for manure samples. 5.2.2.2 DNA Extraction and qPCR Analyses Two hundred milligrams (wet weight) of cow feces were weighed and placed in 2 mL microcentrifuge tubes for DNA extraction. Four hundred microliters of manure samples were transferred into 2 mL microcentrifuge tubes. DNA was extracted from both sample types with the QIAamp DNA Stool Mini Kit (Qiagen). Briefly, stool samples were mixed with lysis buffer after which the impurities such as DNA-damaging substances and PCR inhibitors were adsorbed to InhibitEX matrix. Further, DNA was purified on QIAamp 173 Mini spin columns (Qiagen) and extracted by adding 200 µL elution buffer, after which o the DNA was stored at -20 C. Sample processing and DNA extraction from raw sewage and septage samples are described in Chapter 4 under section 4.2.2.2. The bacterial genes and amplicons used in the study for qPCR analyses are described in Table 5.2. The corresponding qPCR programs for each assay are described in Table 5.3. E. coli uidA and enterococci 23S qPCR assays were done using Roche LightCycler® 2.0 Instrument (Roche Applied Sciences, Indianapolis, IN) and all the three cow specific qPCR assays were performed using Roche LightCycler® 480 Instrument (Roche Applied Sciences, Indianapolis, IN). Standard curves were generated for each of these qPCR machines using the ten-fold dilutions of plasmids cloned with the DNA fragment carrying the qPCR target amplicon. DNA extracted from all fecal or manure samples were analyzed by qPCR for all targets. The reaction mixture for amplification of E. coli uidA gene consisted of 2 µL of the Roche FastStart LightCycler Mastermix, forward and reverse primers, Taqman probe, 3.2 mM MgCl2 and nuclease free water to make up the final volume to 15 µL. The enterococci and cow specific (BoBac, M2, and M3) assays were carried out separately using 10 µL of Roche LightCycler480 Probes® Mastermix, forward and reverse primers, probe, and nuclease free water to make up the final volume to 15 µL. Five microlitres of the extracted sample were used as the template. The reaction was performed in duplicate for samples and negative controls were included. Threshold cycle temperature (Ct) was measured during each amplification and target gene concentration was analyzed automatically by absolute quantification method by the LightCycler® Software 4.0. 174 For each qPCR assay, one of the plasmid dilutions used for creating the standard curve was also included in the assay as a positive control. The copies of all target genes present in the sample were quantified from the standard curves obtained earlier. The copies of the corresponding genes for E. coli and enterococci were converted to cell equivalents and final concentrations of qPCR analyses were reported as either qPCR equivalent cells/mL or qPCR target copies/mL. In the case of three cow specific MST qPCR assays, all the final concentrations qPCR analyses were reported as copies/mL of the manure samples. These cannot be converted to cells counts because either the 16S rRNA copy number of Bacteroides-Prevotella group varies with different species (for Bobac assay) or these assays were designed by metagenomic approach and no cultivable cells have been identified carrying the specific gene fragment to know the copy number (for M2 and M3 assays) (Layton et al 2006; Shanks 2008). Conventional PCR targeting CF123 cluster of Bacteroides-Prevotella Bovine CF123 cluster was performed in a Thermocycler using a PCR reaction mixture containing 14 μL HotStar Taq Master Mix (Qiagen; Valencia, CA), forward and reverse primers and molecular grade water to get final volume of 22 μL to which 3 μL of template DNA was added. Amplified products were detected by visualizing the PCR product by electrophoresis through 1.2% agarose gel and then exposing it to UV transilluminator. DNA from cow fecal samples that was confirmed to be positive for bovine CF123 cluster by sequencing the amplified products and BLAST analysis were used as the positive control. This was used as positive control as no pure isolates carrying this maker are available and therefore, the options were to either commercially generate plasmids carrying artificially synthesized CF123 cluster gene fragment or to use cow fecal DNA 175 that was positive for the same assay with the sequences verified. Molecular grade water was used as the negative control. The samples were recorded as positive when the bands corresponded to the positive control at around 525bp. 176 Table 5.2: Bacteria and gene targets used in the study of cow feces and manure Targets Assay gene Amplicon size E. coli E. coli uidA uidA 163bp Developed in this study Enterococcus sp Enterococci 23S 23S rRNA 91bp Frahm & Obst (2003) Bacteroides bovine cluster Bobac Cow specific 16S rRNA fragment 100bp Layton et al. (2006) HDIG domain protein 92bp Shanks et al. (2006) M3 cow specific marker M3 Sialic acid-specific 9-O-acetylesterase 122bp Shanks et al. (2006) Bacteroides-Prevotella Bovine CF123 cluster Cow specific 16S rRNA CF123 cluster 525bp Bernhard and Field. (2000) M2 cow specific marker a a Reference a a M2 a CF123* a quantitative PCR assay *conventional PCR assay 177 Table 5.3: Primer/ probe sequences, concentration and PCR programs used for each assay Assay E. coli uidA 5’-ACACTCTGTCCGGCTTTTG-3’ Primer/probe PCR programs o 0.5 µM 6 s at 95 C o 0.5 µM 8 s at 58 C 6FAM-TTGCAACTGGACAAGGCACCAGC-BBQ 0.2 µM 8 s at 72 C Enterococci 23S 5’-AGAAATTCCAAACGAACTTG-3’ 0.5 µM 15 s at 95 C 5’-CAGTGCTCTACCTCCATCATT-3’ 0.5 µM 30 s at 60 C 6FAM-TGGTTCTCTCCGAAATAGCTTTAGGGCTA-TAMRA 0.4 µM 15 s at 72 C GAAGRCTGAACCAGCCAAGTA 0.5 µM 10 s at 95 C GCTTATTCATACGGTACATACAAG 0.5 µM 30 s at 57 C 6-FAM-TGAAGGATGAAGGTTCTATGGATTGTAAACTT-TAMRA 0.2 µM 6 s at 72 C CGGCCAAATACTCCTGATCGT 0.5 µM 15 s at 95 C GCTTGTTGCGTTCCTTGAGATAAT 0.5 µM 60 s at 61 C 6FAMAGGCACCTATGTCCTTTACCTCATCAACTACAGACATAMRA 0.2 µM 10 s at 72 C CCTCTAATGGAAAATGGATGGTATCT 0.5 µM 10 s at 95 C CCATACTTCGCCTGCTAATACCTT 0.5 µM 60 s at 55 C 6-FAM-TTATGCATTGAGCATCGAGGCC-TAMRA 0.2 µM 1 s at 72 C CCAACYTTCCCGWTACTC CAATCGGAGTTCTTCGTG 0.4 µM 0.4 µM 30 s at 94 C Bobac M2 M3 *CF123 Primer/probe 5’-CAATGGTGATGTCAGCGTT-3’ o o o o o o o o o o o o o o o 30 s at 58 C o 60 s at 72 C *Conventional PCR assay 178 5.2.3 Statistics All the final concentrations in feces (n=10) and manure samples (n=35) were log10 transformed for normalization. In order to determine the significant differences between the concentrations of cultivable cells and qPCR equivalent cells for E. coli and enterococci in feces and manure samples, simple t-tests were performed at the 0.05 level of significance. Levene’s test for homogeneity of variances was performed using SAS software 9.2 (SAS Inc, 2002) to determine if t-test assuming equal or unequal variances should be used for comparison, with the hypothesis of equal variance being rejected at p<0.05. Pearson’s correlation coefficient was used to test the relationship between concentrations by cultivation and qPCR methods for E. coli and enterococci. With data for manure samples from pits collected in two months, a two-way ANOVA was performed to investigate two specific questions: 1) was there any significant difference in qPCR signals for a particular target between the four pits? 2) was there any significant differences in qPCR signals for a particular target between May and November? To answer the first question, data from the four pits collected in May alone were used as data for the month of November for Pit 4 was not available. Analysis of the data for the second question was done by pooling data from Pit 1, 2 & 3 collected in May and corresponding data collected in the month of November. Pit 4 was excluded from the analysis for the same reason described above. 179 5.3. Results 5.3.1 E. coli and Enterococci Cultivation Versus qPCR Fresh cow fecal samples and manure samples from pits were collected from MSU dairy farm. The mean concentration of cultivable E. coli in fresh fecal samples was found 5 to be 8.41x10 CFU/gram and the mean qPCR equivalent cell concentration in these 5 samples was 7.97x10 cells/gram. The mean concentration of Enterococcus spp. by 5 cultivation method in cow feces was found to be 4.80x10 CFU/gram and the mean 4 qPCR equivalent cell concentration in these samples was 4.69x10 cells/gram (Figure 5.3). There was no statistically significant difference between log10 transformed concentrations of E.coli by cultivation and qPCR in cow feces whereas the difference between concentrations of enterococci by both the methods was found to be significant (p<0.05). A significant (p<0.05) positive correlation was found between E. coli cultivable counts and qPCR equivalent cells with correlation coefficient, R = 0.87 and coefficient of determination, R 2 = 0.76. There was no correlation between concentrations of enterococci by cultivation and qPCR methods with R=0.007 (Figure 5.4). 180 Figure 5.3: Average E. coli concentrations in cow feces by cultivation (CFU/g) and qPCR (cells/g) methods 7.00 Log10 concentration 6.00 5.00 4.00 3.00 2.00 1.00 0.00 E. coli CFU/g enterococci cells/g Number of cow fecal samples, n=10 181 Figure 5.4: Correlation between concentrations by cultivation methods (CFU/g) and qPCR (cells/g) in cow feces for E. coli and enterococci Log10 concentration (cells/g) 7 R² = 0.7673 (E. coli) 6 5 4 R² = 6E-05 (enterococci) 3 2 2 3 4 5 Log10 concentration (CFU/g) E. coli Enterococci 182 6 7 2 For manure samples, the concentrations of E. coli ranged from 3.67x10 CFU/mL 4 to 2.40x10 CFU/mL by cultivation methods whereas by qPCR, the range was from 3 5 6.68x10 to 5.92x10 qPCR equivalent cells/mL. Concentations of enterococci in these 2 5 4 samples ranged from 3.33x10 to 1.40x10 CFU/mL by cultivation and 2.37x10 to 6 3.07x10 qPCR equivalent cells by qPCR method. The qPCR equivalent concentrations of E. coli were significantly higher than cultivable cells in all manure samples collected in months of May and November except in dairy pit 3 samples collected in November. For enterococci, qPCR method showed significantly higher concentrations than those by cultivation methods in samples from all pits (p<0.05). The difference between CFU and qPCR equivalent cells was higher in May than in those samples collected in November. (Figures 5.5 and 5.6). There was no correlation found between cultivable counts and qPCR equivalent cells for both E. coli and enterococci in manure samples (Figure 5.7). 183 Figure 5.5: Average log10 transformed E. coli concentrations from manure pits by Log10 concentration cultivation and qPCR methods 7 6 5 4 3 2 1 0 May Nov May Dairy1 E. coli CFU/mL Nov Dairy 2 May Nov Dairy 3 E. coli cells/mL Total number of samples, n=35 184 May Beef1 Figure 5.6: Average log10 transformed enterococci concentrations from manure pits by Log10 concentration cultivation and qPCR methods 7 6 5 4 3 2 1 0 May Nov May Dairy1 Nov Dairy 2 enterococci CFU/mL May Nov Dairy 3 enterococci cells/mL Total number of samples, n=35 185 May Beef1 Figure 5.7: Correlation between concentrations by cultivation methods (CFU/mL) and Log10 concentration (cells/mL) qPCR (cells/mL) manure pit samples for E. coli and enterococci 7.00 R² = 0.0347 (enterococci) 6.00 5.00 4.00 3.00 R² = 0.008 (E. coli) 2.00 1.00 1.00 E. coli 2.00 3.00 4.00 5.00 Log10 concentration (CFU/mL) Enterococci *Total number of samples, n=35 *Data from three dairy pits and one beef pit 186 6.00 7.00 5.3.2 Detection of qPCR target copies of fecal indicator bacteria and Cow specific markers in manure pits Manure pit samples were tested for two fecal indicators E. coli uidA, Enterococci 23S rRNA and three cow specific markers Bobac, M2, and M3 using qPCR assays, respectively. Final concentrations for these targets are all reported as copies/mL.Average log10 concentrations of indicators and cow-specific markers in manure samples from each pit collected in May and November are shown in Table 5.4. Concentrations of E. coli qPCR target copies ranged from 6.68x10 4 3 to 5.92x10 5 7 copies/mL while Enterococcus sp ranged from 9.48x10 to 1.23x10 copies/mL. Bobac 5 7 6 assay targets ranged from 9.60x10 to 4.03x10 copies/mL, M3 ranged from 2.60x10 to 7 4 6 1.84x10 copies/mL, and M2 ranged from 3.42x10 to 1.34x10 copies/mL. The concentrations in manure from four pits were compared only in May. There was no specific pattern observed in the occurrence of qPCR signals among the four pits for all the targets. Means of each target, expressed as copies/mL, were compared in all four pits as in Figure 5.8. E. coli qPCR target copies/mL were found to be significantly higher in Beef Pit1 than compared to other pits located in dairy cattle farm. Highest concentrations of enterococci were found to be in Dairy Pit 3 among all the four pits (p<0.05). Bobac genes were detected at significantly higher target copies than rest of the qPCR targets in all four pits and were found to be significantly consistent in all the pits (p<0.05). Among all the cow-specific markers, M2 was found to be occurring at significantly lower concentrations in all the pits (p<0.05). 187 Comparison of means of qPCR target copies using pooled data from dairy pits 1, 2 and 3 between May and November was conducted and as shown in Figure 5.9, there were significant differences between the signals in both the months for all the targets. The average qPCR target copies/mL were significantly higher in May than November for all the targets tested (p<0.05). 188 Table 5.4: Average log10 concentrations of qPCR target copies/mL of fecal indicator bacteria and cow specific markers from manure pits Dairy Pit 1 Dairy Pit 2 Dairy Pit 3 Beef Pit 1 Assays May Nov May Nov May Nov May Nov 4.51 (0.08) 3.96 (0.10) 4.82 (0.18) 4.49 (0.05) 4.74 (0.05) 4.53 (0.24) 5.58 (0.21) ND E. coli uidA 5.12 (0.09) 6.41 (0.04) 6.04 (0.04) 6.97 (0.08) 5.96 (0.05) 6.38 (0.29) ND Enterococci 23S 5.78 (0.07) 7.41 (0.04) 6.09 (0.13) 7.56 (0.08) 5.99 (0.09) 7.60 (0.06) 5.98 (0.06) 7.44 (0.25) ND Bobac 5.93 (0.10) 5.83 (0.42) 5.76 (0.07) 5.11 (0.11) 6.12 (0.07) 5.47 (0.11) 4.44 (0.37) ND M2 6.83 (0.03) 6.50 (0.17) 7.03 (0.44) 6.41 (0.06) 6.89 (0.06) 6.56 (0.02) 6.57 (0.23) ND M3 ND- Not determined Nov- November Numbers in parenthesis represents standard deviation for 5 replicates *Dairy Pit 1= Calves from dairy farm *Dairy Pit 2= Cows from dairy farm *Dairy Pit 3= Cows from dairy farm *Beef Pit 1= Cows from beef farm 189 Figure 5.8: Comparison of qPCR copies signals for each target assay among the four Log10 concentration, copies/mL pits 8 7 6 5 4 3 2 1 0 E. coliuidA Dairy 1 Enterococci23S Dairy 2 Assays E. coliuidA Enterococci23S Bobac M2 M3 Bobac Dairy 3 Pit 1* 4.51a 5.78 7.42d 5.93ef 6.83gh M2 M3 Beef 1 Pit 2* 4.82b 6.42c 7.36d 5.76e 7.03g Pit 3* 4.74ab 6.97 7.6d 6.13f 6.88g Pit 4* 5.58 6.38c 7.44d 4.44 6.57h *Means followed by the same letters across the rows are not significantly different from each other. *Pit 1= Calves from dairy farm *Pit 2= Cows from dairy farm *Pit 3= Cows from dairy farm *Pit 4= Cows from beef farm 190 Figure 5.9: Comparison of qPCR copies signals for each target assay between May and Log10 concentration, copies/mL November 8 7 6 5 4 3 2 1 0 May November Targets E. coliuidA Enterococci23S Bobac M2 M3 May* 4.7 6.4 7.46 5.94 6.91 November* 4.33 5.71 6.02 5.47 6.48 *Means followed by the same alphabet represents no significant difference between them 191 In order to evaluate the occurrence of these markers in human fecal environment, cow specific qPCR assays and CF 123 cluster conventional PCR assay were applied in raw sewage samples (n=18) and septage samples (n=8) collected for a previous study. There was no M2, M3 and CF 123 gene targets detected in any raw sewage and septage samples tested. However, Bobac gene targets were in concentrations ranging from 2 6 7.40x10 to 2.52x10 copies/mL (Table 5.5). 192 Table 5.5: Occurrence of cow specific markers in raw sewage and raw septage Samples M3 assay Raw sewage 1 Raw sewage 2 Raw sewage 3 Raw sewage 4 Raw sewage 5 Raw sewage 6 Raw sewage 7 Raw sewage 8 Raw sewage 9 Raw sewage 10 Raw sewage 11 Raw sewage 12 Raw sewage 13 Raw sewage 14 Raw sewage 15 Raw sewage 16 Raw sewage 17 Raw sewage 18 septage 1 septage 2 septage 3 septage 4 septage 5 septage 6 septage 7 septage 8 *conventional PCR assay - M2 assay - Bobac CF123 cluster* assay(copies/mL) 3.33E+05 3.39E+05 4.08E+05 1.36E+06 3.74E+05 2.42E+04 9.84E+05 5.60E+05 5.32E+05 4.92E+05 3.07E+05 1.85E+06 1.37E+06 3.40E+05 1.23E+06 6.72E+05 1.43E+06 1.12E+06 1.31E+05 7.40E+02 2.52E+06 - 193 - 5.4 Discussion Land application of animal manure for nutrient recycling is a common fertilization practice in many parts of the world. In order to reduce the high numbers of bacteria that are present in fresh feces, manure is stored for long periods in pits or lagoons before it is applied in land. It has been suggested that most of the microorganisms will be killed during storage due to the changes in the conditions once outside the host gut (Michino et al. 2005). However, bacterial concentrations may not be reduced sufficiently and may change to a different physiological state such as viable but non-culturable (VBNC) where many pathogenic bacteria will not be culturable in routine growth media, but will retain their virulence (Lleo et al. 2000; Baffone et al. 2003). Bacteria in the land applied manure may also survive for longer periods as they attach to the dry matter or soil particles which enhance protection from dessication (Oliver et al. 2006). This increases the risks of contaminating surface and groundwater resources, thus making it a significant source of non-point pollution. Due to the advantages of the field of Microbial Source Tracking (MST), new bovine markers have been developed as potential candidates to study impacts of animal manure on water quality (Bernhard and Field, 2000; Layton et al. 2006). Bacteroides bovine cluster 16S rRNA markers (Bobac), M2 (M2) and M3 markers (M3) were selected as cow specific markers in this study due to their reported host specificity. These markers were found to have 100% specificity when assessed during development of these assays (Shanks et al. 2008; Layton et al; 2006). Previous studies used these markers to monitor fresh feces from different cow herd populations or to assess watersheds for any bovine fecal contamination; however this is the first study to address the quantification of these 194 markers in manure pits. Conventional indicators E. coli and enterococci were also included in the study due to their current regulatory status in water quality monitoring. These fecal indicator bacteria have also been used to study the fate and transport of manure borne pathogens in the environment (Thurston et al. 2005; Thiagarajan et al. 2007). Methodologically, cultivation methods and qPCR were both used and compared for detection of E. coli and enterococci. Application of qPCR tools in such complex matrix needs evaluation due to the coextraction of PCR inhibitory substances during DNA extraction (Uwatoka et al.1996). Comparable qPCR equivalent cells and CFU observed in this study for E. coli in fresh feces is in agreement with a recent study that has compared qPCR with cultivation based methods for E. coli and enterococci for fresh feces (Klein et al. 2010). Although the qPCR assay for their study also targeted uidA gene, Brilliance E. coli/Coliform Selective Agar was used for plate counts. Even though there was a significant correlation between both methods in this study, Klein et al found greater correlation with a correlation coefficient (R) of 0.95. Concentrations of enterococci in fresh feces detected by qPCR were lower than CFUs cultivated on mEI agar media. This could be due to several reasons. Loss of DNA during extraction using manual kits is one possibility. Variability in total DNA yield with different DNA extraction methods has been reported previously (Yu and Morrison, 2004). An automated DNA extraction method was evaluated in this research to see the possibility of improving the concentrations of enterococci from cow feces but concentrations of enterococci were significantly higher in manual extraction kit (results described in Chapter 3). Another possibility is the variation in the copy number of 195 enterococci 23S rRNA gene between two and six copies among the enterococcal species (Oana et al. 2002) that could result in bias in the conversion of gene copy numbers to cell eauivalents. Standardization of enterococci qPCR targeting 23S rRNA gene was done using E. fecalis in several studies including this research, therefore copy number to cell conversions were done based on the four copies of the gene present in the species (Haughland et al. 2005; Wade et al. 2005). However, depending on the species abundance in a particular sample the accuracy of quantification could be affected and this quantification bias is being development for evaluated by USEPA as part of qPCR method monitoring recreational water quality (http://www.epa.gov/waterscience/methods/method/biological/rapid1.pdf) Another possibility in the lower qPCR signals as compared to CFU could be due to variation in the sequences of 23S rRNA gene between the different enterococci species. A highly conserved central loop of domain V of 23S RNA has been found to have sequences varying in different enteroccocal species that included isolates of E. faecium, E. faecalis, E. durans, E. hirae, E. gallinarum, E. casseliflavus, E. avium and percentage of homology based on these sequences varied between 91.4 to 100%. It is possible that in bovine intestine, the heterogeneity in sequences of enterococcal species is contributing to the low signals by qPCR as compared to CFU (Tsiodras et al. 2000). These results suggest that there is still a gap in the knowledge about ecology of enterococci in bovine intestines. Even though specificity has been tested for 23S rRNA assay for enterococci previously (Frahm and Obst 2003), there is no assurance that this assay is detecting all species of enterococci present in cow feces. 196 A 6% high false positive associated with mEI media could also result in high cultivable levels of enterococci in mEI than compared to qPCR levels. There is no literature for evaluation of this medium for growing enterococci from cow fecal samples and with the diversity of the bacteria as well as complexity of the fecal matrix, detection of false positives in mEI cannot be ruled out. In manure pits, where manure is stored for months before land application, die-off of bacteria is a natural phenomenon and it is not surprising to see higher concentrations of qPCR equivalent cells than cultivable counts for both E. coli and enterococci. However, there is also a possibility that some of these cells are transformed into viable but non cultivable state in manure and there is not much evidence in the literarature regarding this. It has been demonstrated that bacteria such as E. coli O157:H7 may become viable but nonculturable during conditions such as changes in temperature, available nutrients, or pH (Juhna et al. 2007; Kolling et al. 2001. Mizunoe et al. 1999). But the results indicate that there are some cultivable cells still present in the manure that are being applied on the fields, suggesting that pathogens that survive aging process of manure in pits can also be transported to the soil during land manure application. Based on the scheduled removal of manure from the pits sampled in this study, samples obtained in November had been aged in the pits only for approximately two to three months, whereas samples collected during May had been in the pit for five to six months. Higher concentrations (atleast one log10 higher) of cultivable cells in samples from November compared to May indicate that longer storage time in pits may be required to reduce the risks caused by pathogens in land applied manure. Log reduction of cultivable bacteria 197 during storage of manure pits have been found to be enhanced by addition of urea and ammonia (Ottoson et al. 2008). Any individual Bacteroides bovine cluster isolates carrying cow specific 16S rRNA fragments, targeted in CF123 conventional PCR or Bobac assay, have not been cultivated yet and M2 and M3 cow specific markers have been originally designed by a metagenomic approach; therefore there is not much known about factors that influence prevalence of these markers in different bovine populations. A recent study that has quantified these three cow-specific markers found that Bacteroides bovine cluster markers were more abundant in the feces collected from 11 different bovine populations as compared to M2 and M3 cow specific markers (Shanks et al. 2010). This is consistent with the results in all manure pits in this study with Bobac occurring in higher concentrations than M2 or M3 markers in May and in November. Variation in the concentrations of these markers could be attributed to either the variation in the densities of these targets in the cow intestines or it may be due to the difference in copy number of these target genes in the cells. Higher concentrations of Bobac markers could be due to the abundance of 16S rRNA copies which has been shown to vary between different bacteria and some bacterial genome may have upto 15 copies of this gene (Klappenbach et al. 2001). For M2 and M3 markers, there is no knowledge about the exact copy number of target genes (HDIG domain protein, Sialic acid-specific 9-O-acetylesterase) present in a cell. Functional annotation of these target genes indicate the involvement of these genes in energy metabolism and electron transport (Shanks et al. 2006) and based on the assumption that most functional genes 198 may have one copy of gene in a cell, M2 and M3 target genes may be one copy per cell; thus being detected in lower concentrations as compared to the Bobac markers. Discharge of cattle farm waste into municipal sewers is possible; therefore raw sewage samples sometimes test positive for cow specific MST markers (Wolfe et al. 2010). Raw sewage and septage samples collected for a previous study were therefore monitored for these cow specific markers as well as for Bacteroides-Prevotella bovine CF123 cluster marker. High concentrations of Bobac markers were detected in all 18 sewage samples and three out of 8 septage samples, when the rest of the cow specific markers were absent in these. This indicates the cross specificity of Bobac assay with other host feces. This cross specificity was further confirmed by performing the assay on four human fecal samples which all tested positive for the assay (results not shown). Conventional PCR assay for Bacteroides-Prevotella bovine CF123 cluster showed positive amplification in all pit samples tested (data not shown). The conventional PCR assay for CF 123 cluster has previously been used for feces from ruminants but was not found in raw sewage (Bernhard and Field 2000) which agrees with the findings in the current study. Shanks et al. (2010) reported 85% prevalence of Bacteroides-Prevotella bovine CF 123 cluster marker in 247 fecal samples collected from 11 herd populations. However, they also found this marker to be positive in feces from dog, chicken, duck and pig. In conclusion, it was found in this study that qPCR equivalent cells were either same or less than agar based cultivation based methods for E. coli and enterococci in cow feces but qPCR equivalent cells were higher in manure samples for both of these indicators. Thus, there is die-off during storage that is resulting in the detection of qPCR signals than 199 cultivable cells. M2 and M3 cow specific markers were found consistently in manure samples tested that were collected just prior to the field application. These markers could be used as MST tools to monitor agricultural pollution in water bodies. Among these two markers, M3 marker is recommended for MST application as it was found to occur in in significantly higher concentrations than M2 markers (p<0.05) in all the manure pits and this may need further evaluation in other geographical setting or with other cow populations. This study does not recommend using Bobac assay for MST due to its lack of specificity and results obtained using this assay could be misleading the source of fecal contamination. Overall, this study has demonstrated that cow specific markers such as M2 and M3 can be used as a tracer to study the transport and fate of molecular signals from manure applied land without getting any amplification from naturally occurring organisms present in soil. This study also recommends the need to address the issues such as inefficiency in DNA extraction, inability to distinguish dead and live cells etc associated with application of qPCR tools in complex environmental samples. 200 REFERENCES 201 5.5 References Atherton, F., C. P. S. Newman, and D. P. Casemore. 1995. An outbreak of waterborne cryptosporidiosis associated with a public water-supply in the uK. Epidemiology and Infection 115:123-131. Baffone, W., B. Citterio, E. Vittoria, A. Casaroli, R. Campana, L. Falzano, and G. Donelli. 2003. 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Rapid and efficient method to eliminate substances inhibitory to the polymerase chain reaction from animal fecal samples. Veterinary Microbiology 52:73-79. Wolf, S., J. Hewitt, and G. E. Greening. 2010. Viral Multiplex Quantitative PCR Assays for Tracking Sources of Fecal Contamination. Applied and Environmental Microbiology 76:1388-1394. Yu, Z. T., and M. Morrison. 2004. Improved extraction of PCR-quality community DNA from digesta and fecal samples. Biotechniques 36:808-+. 206 CHAPTER 6 QUANTITATIVE PCR FOR DETECTION OF E. COLI O157:H7 SPECIFIC eae AND rfbE GENES IN MANURE AND SEWAGE 6.1 Introduction E. coli, one of the most genetically diverse groups of bacteria, is a common inhabitant in the intestines of warm blooded animals. However, there are some strains that are pathogenic and can cause illnesses. Based on the mechanism of pathogenicity, there are five classes of diarrheagenic E. coli which are as follows: 1) enterotoxigenic or ETEC, 2) enteroinvasive or EIEC, 3) Shiga toxin-producing E. coli, which has a subgroup called enterohemorrhagic or EHEC, 4) enteropathogenic or EPEC, 5) enteroaggregative or EAEC. Each group carries virulence factors that are responsible for their respective pathogenicity. ETEC have genes that code for heat-labile (LT) or heat-stable (HT) toxins. EIEC invade and multiply within the epithelium of colon and have plasmid encoded genes responsible for virulence factors. STEC carry genes that code for Shiga-like toxins, which are subdivided into two major classes, stx1 and stx2. A subgroup of shiga toxin carrying E. coli, known as EHEC, additionally has a Locus of Enterocyte Effacement or LEE. LEE, a pathogenicity island that confers to EHEC additional virulence genes such as eae which codes for intimin, and Tir which regulates the translocated intimin receptor. EPEC harbors LEE but do not have stx genes (genes that attach and efface (A/E) causing lesions in the intestinal epithelium. EAEC have virulence factors that enable them to cause auto agglutination on the surface of intestinal mucosa enhancing mucosa production. 207 E. coli O157, one of the most important members of the EHEC group, is transmitted through human and animal fecal sources (Muniesa et al, 2006). The O157: H7 refers to the specific markers found on the surface of the bacterium. The pathogenicicty of this strain is due to the large quantities of powerful verotoxins or Shiga-like toxins (stx1 and stx 2). They cause hemorrhagic colitis (inflammation of the intestinal wall) and the toxins cause damage to endothelial cells in the kidneys, thus inhibiting the organs’ ability to function (Ludwig et al. 2002). Young children and the elderly can develop Hemolytic Uremic Syndrome (HUS) as a result of exposure to E. coli O157:H7, a condition that can lead to serious kidney damage and even death (Fitzpatrick, 1999). Though this bacterium was discovered in 1977, no infection by E. coli O157 was reported in humans until 1982 (Su and Brandt, 1995). E. coli O157 was first isolated from two separate outbreaks of gastrointestinal illness in 1982, one in Oregon and one in Michigan, characterized by severe crampy abdominal pain, starting with a watery diarrhea that is followed by grossly bloody diarrhea, and little or no fever. Both the outbreaks was traced back to eating sandwiches with contaminated beef at restaurants belonging to the same fast-food chain (Riley et al; 1983). Food and waterborne transmission are common. There were 31 waterborne outbreaks of E. coli O157 reported from the years 19822000 in United States and 21 were associated with recreational water and 10 with drinking water through contaminated local well water systems, municipal water supply systems, and spring water, residential faucet water, and ice that may have been cross contaminated (Rangel et al. 2005). 208 Cattle feces are a major reservoir of E. coli O157 (Al-Saigh et al, 2004; Bach et al 2004; Chapman et al, 1993; LeJeune et al, 2001; Meng et al. 2001; Shere et al. 2002) where they inhabit the intestine along with other microorganisms without actually causing any disease. The bacteria have also been isolated from sheep, dogs, goats, deer, horses and seagulls (Karch et al. 1999, Kudva et al, 1996). E. coli O157 outbreaks with ground beef as a transmission route were reported to be high in summer months; 71% occurred from May to August and this could be correlated with seasonal influence on prevalence rate in cattle which is high in summer and early fall (Hancock et al, 2001). This pathogen is able to survive for extended periods of time in manure (Franz et al, 2005; Kudva et al 1998; Wang et al 1996). Several studies have found them to survive well in low pH and low temperature for up to 56 days (Miller and Kaspar 1994; Conner and Kotrola 1995; Benjamin and Datta 1995). One mesocosm survival study found that O157 strain was reduced by 90% in wastewater from lagoons within 10 days and no cultivable colonies were observed after four weeks of the study (Ravva et al. 2006). Differentiation of E. coli O157 from other members of the family Enterobacteriaceae in cultivation based methods are based on their specific biochemical characteristics; lack of β-glucuronidase activity and negative D-sorbitol fermentation within 24 h at 37°C (Vernozy-Rozand 1999). Detection of E. coli O157 has been performed most often by using a differential media, sorbitol MacConkey agar where E. coli O157 colonies appear as white colonies due to their inability to ferment sorbitol, while other members of the family Enterobacteriaceae appear as pink colonies. Another cultivation media used for detection of this pathogen is Rainbow AgarO157, where β-glucuronidase negative strains such as E. coli O157 appear as black or gray colonies and other strains of E. coli that are 209 positive for this enzyme appear as purple, violet or blue colonies in this media (Radu et al. 2000). As with many other cultivation based methods, these methods take at least 24 hours to obtain the results. Since Polymerase Chain Reaction (PCR) is able to circumvent this issue by enabling detection of specific bacteria within a few hours, variants of PCR such as multiplex PCR, qPCR have been developed to detect and quantify E. coli O157 from various matrices. All of the qPCR assays in the literature for E. coli O157 are multiplexed, that is more than one gene is targeted in the same assay. This has proven to be a very useful method to discriminate between multiple closely related E. coli types. Since probes are labeled with different dyes that are detected at different wavelengths, multiplex assay targeting different genes of same organism or different organisms can be performed with in one tube. This method offers specificity and is cheaper as compared to singleplex PCR as reagent use will be reduced when multiple reactions are run in the same tube (Henegariu et al.1997). However, standardization of the assay may be more complex than for the singleplex PCR. Multiplex qPCR assays for detection of E. coli O157 have also been developed (Ibekwe et al. 2002; Sharma and Dean-Nystrom 2003; Grant, 2008 ; Oberst et al. 2003) One qPCR protocol targeted stx1 and stx 2 simultaneously after a 24 h enrichment in fresh produce spiked with two strains of E. coli O157:H7 (Grant, 2008). Another multiplex PCR assay targeted three genes in the same assay, stx1, stx 2 and eae genes to quantify E. coli O157:H7 in soil, manure cow fecal samples, and dairy wastewater. Specificity testing used 9 different strains of E. coli and E. coli O157:H7 and other strains such as O78:H16, O55: NM, O101, O116:H27, O91:H21. All non-O157:H7 strains had 210 either one or the other genes out of these three genes tested, while E. coli O157:H7 were positive for all the three genes (Ibekwe et al. 2002). Another used target gene for detection of E. coli O157:H7 included rfbE gene that encodes O antigen for O157 (Sharma 2006). Sequences specific to E. coli O157:H7 have been utilized to develop realtime PCR assay in combination with testing of viablility by reverse transcription of mRNA from O157:H7 cells (Sharma 2006). Multiplex real-time PCR assays targeting rfbE and eae genes specific for E. coli O157 were evaluated and to test for any amplification produced from non-E. coli O157 strains due to the presence of one or the other genes, these assays were tested for their specificity as a singleplex real-time PCR also (Sharma 2006).. Out of the 38 E. coli isolates tested, eae O157 assay showed positive amplification for three isolates of O157 strains, two isolates of O55:H7 and also showed a weak amplification signal one of the isolates of O111:H2. The rfbE specific primer sets amplified all three O157:H7 isolates but showed positive signals O149: NM and O157:H43. Several studies have used several combinations of stx1, stx2, eae, rfbE genes have been used to quantify E. coli O157 in food and water samples (Oberst et al. 2003; Childs et al.2006; Mull and Hill 2010; Bai et al.2010). In this study, environmental samples were monitored using qPCR assays for detection of E. coli O157. But one of the problems is that there is no consistent or consensus on how to measure E. coli O157 in environmental samples. Therefore, the goal of this study was to examine the best method and gene targets with some modifications to characterize the manure and human waste environments using primer sets that targeted O157 specific sequences from rfbE and eae genes. Since the primary goal of this study was specific detection of E. coli O157 and since various combinations of stx1 and stx2 genes may be 211 present in other Shiga toxin producing E. coli serotypes (Nataro and Kaper 1998), assays targeting these two genes were not included in this study as quantification of toxin genes were not the goal of the study. 6.2 Materials and Method 6.2.1 Environmental samples Manure from beef and dairy cattle manure pits (n=35), raw sewage collected from wastewater treatment facility at East Lansing, Michigan (n=18), and raw septage (n=8) collected from a septage treatment plant at Charveloix, Michigan were used in this study. Collection of samples, sample processing and DNA extraction are described in Sections 5.2.1 and 5.2.2, respectively. Sample processing and DNA extraction from raw sewage and septage samples are described in Chapter 4 under section 4.2.2.2. 6.2.2 Bacterial strains ATCC strain of E. coli O157:H7 (ATCC 43895) was used for preparing standard curves. All the non-ATCC E. coli strains used in this study and hosts from where they were isolated are given in Table 6.1. These strains were provided by Microbial Evolution Laboratory, National Food Safety and Toxicology Center, at Michigan State University. o All the bacterial strains were grown in Trypticase Soy Broth at 37 C for 18-24 hours. DNA was extracted from these cultures using QIAmp DNA mini kit (Qiagen, Valencia, CA, USA) according to the manufacturer’s instructions. 212 Table 6.1: E. coli strains used for specificity testing of E. coli O157 qPCR assays E. coli seroptypes Host O26:H11 O55:H7 O111:H8 O76:H19 O103:H2 O45:H2 O113:H21 O104:H21 O91:H7 O146:H21 O103:H6 O15:H27 O125:HNM O156:H21 O5:HN OX03:HNM O149:HNM O128:H2 O157:H7 O157:H7 O157:H7 O157:H7 O157:H7 O157:H7 O157:H7 human meat human human human human human human human human human human human cow cow cow cow human hamburger human human human human human human *All isolates were obtained from Microbial Evolution Laboratory, National Food Safety and Toxicology Center, at Michigan State University 213 6.2.3 qPCR Assay standardization and analysis This study addressed two genes. The standardization of eae and rfbE gene for E. coli was optimized by using the primers and probes described in Table 6.2. A series of tenfold 6 dilutions of DNA extracted from ATCC strain of E. coli O157:H7 ranging from 10 to 1 10 were prepared and used as templates to prepare the standard curves. The reaction mix for amplification of eae gene consisted of 10 µL of the LightCycler 480 Probes Mastermix (Roche, Indianapolis, IN), 0.5 µM of each of forward and reverse primers, 0.2 µM of the probe, 1.6 µM MgCl2 and nuclease free water to make up the final volume to 20 µL. The reaction mix for amplification of rfbE gene consisted of 10 µL of the LightCycler 480 Probes Mastermix (Roche, Indianapolis, IN), 0.5 µM of each of forward and reverse primers, 0.4 µM of the probe, 1.6 µM MgCl2 and nuclease free water to make up the final volume to 20 µL. ® The assays were carried out in LightCycler 480 (Roche, Indianapolis, IN) through o the following temperature profiles; initial denaturation at 10 minutes at 95 C followed by o o 50 cycles of denaturation for 20 seconds at 95 C; annealing for 60 seconds at 60 C and o extension at 72 C for 15 seconds. Triplicate analysis was done for each dilution and negative controls. The crossing point (Cp) of each PCR reaction was automatically determined by the LightCycler® Software 4.0 and final concentrations of eae and rfbE genes were analyzed by absolute quantification method. qPCR analysis for DNA extracts from E. coli isolates, manure, raw sewage and septage samples were carried out using the 214 same reaction mix and run conditions as described above for the standards. PCR-grade water was used as negative control and one of the dilutions were included as a positive control in each run. 215 Table 6.2: Primers and Probes used in detection of E. coli O157 Target genes Amplicon size Sequences FP 5'GTAAGTTACACTATAAAAGCACCGTCG3' eae0157 RP5'TCTGTGTGGATGGTAATAAATTTTTG3' Probe 5'HEX-AAATGGACATAGCATCAGCATAATAGGCTTGCT-BHQ1a5HEX Sharma et al. 106 FP 5'TCAAAAGGAAACTATATTCAGAAGTTTGA3' rfbEO157 RP5'CGATATACCTAACGCTAACAAAGCTAA3' Probe 5'Cy5-AATAAATTTGCGGAACAAAACCATGTGCAA-BHQ2a-Cy5 216 Reference (2006) Sharma et al. 129 (2006) 6.3 Results A qPCR was performed on DNA extracts for all E. coli O157:H7 and non-O157 serotype pure cultures. As shown in Table 6.3, there were 7 E. coli O157: H7 and 20 non-O157 serotypes tested. Twenty five out of 27 serotypes tested positive for one gene or the other. All seven E. coli O157 serotypes showed amplification at a very early Ct (typically around 11.00) for both the genes. Nine out of 20 non-O157 serotypes amplified at Ct of above 34 for both the genes. Serotypes O146:H21 and O128:H2 were negative for both the genes. Eight out of 20 serotypes were positive for just eae, whereas only one serotype, O118:H16 was positive for just rfbE gene. Among the 20 non-O157 strains, O55:H7 showed amplification at Ct of 17.51 for eae gene and O149:HNM showed amplification at a Ct of 17.66 for rfbE gene. 217 Table 6.3: Assay specificity with E. coli O157 and non-O157 serotypes E. coli seroptypes OX03:HNM* O91:H7 O103:H6 O146:H21 O104:H21 O113:H21 O149:HNM* O128:H2* O25:Hneg O15:H27 O125:HNM O156:H21* O5:HN* O76:H19 O111:H8 O118:H16 O55:H7 O45:H2 O103:H2 O26:H11 O157:H7 O157:H7 O157:H7 O157:H7 O157:H7 O157:H7 O157:H7 Cycle threshold eae rfbE 39.03 29.24 35.24 35.9 37.43 37.74 39.44 38.04 34.39 34.75 35.77 37.69 35.85 39.1 39.69 17.66 39.42 38.07 38.51 37.03 17.51 40.07 38.36 38.53 11.43 11.23 12.66 11.86 11.38 11.5 11.26 39.26 38.31 36.84 10.82 10.76 12.72 11.56 11.74 12.05 12.43 All serotypes are pathogenic except those marked with * *No data available about pathogenicity 218 Occurrence of E. coli O157:H7 specific eae and rfbE genes were quantified in manure, raw sewage and raw septage samples. In manure samples, rfbE and eae genes were detected only in the pit from the beef cattle farm and in none of the samples from pits in dairy cattle farm showed amplification. Average concentration of O157 specific eae gene detected in five samples 3 collected from beef cattle farm were 3.25x10 copies/mL and that of rfbE genes were 4.50x10 5 copies/mL (Figure 6.1). Cycles of threshhold for corresponding copies of reaction for these eae positive signals was around 38.80 and for rfbE positive signals, it was 33.70 Figure 6.1: Occurrence of E. coli O157:H7 specific eae and rfbE genes in manure samples log10 target copies/mL 7.00 6.00 5.00 4.00 3.00 2.00 1.00 - - - - - - 0.00 May Nov dairy pit 1 eae0157 May Nov dairy pit2 rfbE0157 219 May Nov dairy pit3 May beef pit1 Out of 18 raw sewage samples tested, rfbE gene was detected in 11 sewage samples ranging 5 5 from 1.35x10 to 3.28x10 copies/100 mL. The eae gene was detected in 10 of the 11 sewage 4 5 samples positive for rfbE gene ranging from 2.81x10 to 6.56x10 copies/100mL. Figure 6.2: Occurrence of E. coli O157:H7 specific eae and rfbE genes in sewage samples Log10 concentration, target copies/100mL 5 4 3 2 1 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 rfbE gene eae gene Raw sewage samples 220 Out of 8 septage samples tested only one sample showed amplification for the rfbE gene and four samples showed amplification for eae gene. The rfbE gene was present in the concentration 6 of 3.10x10 copies/100 mL in the one sample whereas the eae genes were detected in the 5 7 concentrations of 5.39x10 to 1.26x10 copies/100 mL. Figure 6.3: Occurrence of E. coli O157:H7 specific eae and rfbE genes in septage samples Log10 concentration target copies/100mL 8.00 6.00 4.00 2.00 0.00 1 2 3 4 5 Septage samples rfbE gene eae gene 221 6 7 8 6.4 Discussion In this study, qPCR assays for E. coli O157 were applied to detect any E.coli O157 specific eae and rfbE genes present in manure, raw sewage and septage samples. These genes were selected due to their reported specificity (Sharma 2006). E. coli O157 has been reported to be detected in cow manure environment (Omisakin et al. 2003; LeJeune et al. 2004; Kobayashi et al. 2003). The O157 specific target genes were detected only in the manure samples from beef cattle pit in this study and were not detected in any of the manure samples from pits in dairy cattle farm. One hypothesis for this finding is that difference in the diet supplied for both cattle groups may influence the colonization of cattle intestine with O157 (Callaway et al. 2009). Manure samples from beef and cattle dairy pits, had similar levels of generic E .coli using 5 qPCR for the uidA gene, present in concentrations of 10 cells/100ml. The same concentration of copies of rfbE genes were detected in the same samples, thus suggesting that the detection of rfbE genes lack specificity. In this study, high numbers of O157 specific eae and rfbE genes were detected in some of the raw sewage samples tested. In the past, E. coli O157 has been detected in raw sewage using immunomagnetic absorbent assay combined with cultivation method (Sahlström et al. 2004) or qualitatively by conventional PCR (Grant et al. 1996). But what was surprising in this study was that the concentrations of E. coli O157 specific genes were only one log lower than the concentrations of generic E. coli detected in the same volume of raw sewage using the qPCR assay that targeted uidA gene. In septage samples, the concentrations of generic E. coli present 7 8 5 were around 10 -10 cells/100 mL and O157 specific eae genes were at concentrations of 10 - 222 7 6 10 copies or 10 copies of rfbE genes in the same volume of sewage as with the manure this suggests that there could be some cross amplification in the samples using these assays. Specificity of the assay performed in this study showed weak amplification for many nonO157 strains tested, questioning the reliability of these genes for specific detection of E. coli O157. The cross reaction with E. coli O55:H7 by O157 specific eae qPCR was found during the development of assay (Sharma et al. 2006) and was also found in this study. E.coli O55: H7 has been suggested to be genetically related and part of an initial lineage for E. coli O157 and therefore many assays that target O157 may also amplify O55: H7 (Zhou et al. 2010). There is also a speculation of cross reactivity of any O157 specific assays with other serotypes that possess homologs of the particular gene targets (Sharma et al. 2006). The qPCR assay development and evaluation using multiple gene targets was carried out using seeded E. coli O157 or using isolates of non O157 serotypes for specificity testing (Ibekwe et al. 2003; Jothikumar and Griffiths 2002; Liu et al. 2010). When applied in environmental samples, these assays may express cross specificity with hundreds of serotypes of E. coli or other bacteria that have homologs of genes. Thus quantification of E. coli O157 with multiple qPCR assays targeting these genes may not be best approach if specific detection and quantification of E. coli O157 is the goal. The results of this study suggest that any specificity of reported E. coli O157 assays need further evaluation against many non-O157 serotypes before it is applied for detection of O157 in environmental samples. 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Li, B. Liu, L. Beutin, J. G. Xu, Y. Ren, L. Feng, R. T. Lan, P. R. Reeves, and L. Wang. 2010. Derivation of Escherichia coli O157:H7 from Its O55:H7 Precursor. Plos One 5: e8700. 228 CHAPTER 7 SUMMARY AND CONCLUSIONS Recent advances in MST field have detected targets that can be used for detection of fecal contamination as well as to determine the host of origin (human versus bovine) and source (sewage versus manure). The primary goals of this research were to investigate the prevalence and concentrations of human and cow specific MST markers in comparison with E. coli and enterococci in both the human and cow waste environments and their fate during wastewater and septage treatments. This study explored human and cattle waste environment for the following targets: E. coli Colony Forming Units (CFU), enterococci CFUs, E. coli qPCR equivalent cells and enterococci qPCR equivalent cells; and MST markers human specific α- mannannase B.thetaiotaomicron, Bobac 16S rRNA bovine cluster markers, M2 cow specific and M3 cow specific markers and pathogenic E. coli O157 specific gene targets. This study is the first to evaluate the MST markers in comparison with two regulatory conventional indicators, E. coli and enterococci using both conventional and qPCR tools. Septage, which is a non-point source of human fecal contamination, has not been monitored before for the levels of cultivable cells and qPCR signals of indicators. This study has made the first attempt to understand the fate of these signals during septage treatment. Evaluation of cow specific markers and indicators in manure used for land application has also not been published before. The major conclusions from this work are:  Evaluation of methods found that 1) quantitative results are preferable for measuring fecal contamination in waterbodies as there is background DNA signal with quantitative measures 229 one can follow the change in the signal, 2) an automated method can be used for DNA extraction from human raw waste and treated effluent samples after an external lysis step but the instrument used in this study is not suitable for recovery from cow fecal samples, 3) the EMA-qPCR method applied to distinguish live and dead cells in chlorinated effluents is not valid.  In characterizing human sewage environment, it was found that human marker, B. thetaiotaomicron qPCR equivalent cells were present in significantly higher concentrations than that of E. coli or enterococci in raw sewage and septage. However, the fate of this target qPCR signal was similar to E. coli and enterococci DNA during wastewater and septage treatment. A significant correlation was found between this MST marker and each of the conventional indicators throughout the waste treatment process for both raw sewage 2 3 and septage. It was observed that around 10 to 10 qPCR equivalent cells/100mL of these targets are being discharged through the final effluents into surface waters.  Non-point sources such as manure applied to land had cultivable E. coli and enterococci 5 cells in the concentrations of 10 CFU/100 mL. Cow specific markers, M3 were found at 8 7 concentrations of 10 copies/100 mL and M2 at levels of 10 copies/100 mL in the manure applied in the land. Another cow marker, Bacteroides bovine cluster was detected in human sewage environment and should not be used for source tracking cow fecal contamination.  Comparison of qPCR with cultivation methods performed with all the samples tested in this research suggested that qPCR could be used in the environmental monitoring to obtain quicker results. 230 The concentrations of indicators and MST markers quantified in the samples collected in this study are summarized in Table 7.1 and Table 7.2, respectively. Table 7.1: Average concentrations of the indicator organisms in the samples monitored in this research Samples Raw sewage Primary effluent Secondary effluent (Prechlorination) Secondary effluent (Post-chlorination) Tertiary effluent Raw septage Treated septage effluent Cow manure Cow feces* n 54 54 18 E. coli CFU/100mL 1.62E+06 1.48E+06 3.47E+03 Enterococci E. coli qPCR CFU//100mL cells/100mL 5.25E+05 2.88E+06 3.80E+05 3.02E+06 2.29E+03 1.00E+03 54 1.02E+01 4.37E+00 1.12E+03 1.35E+04 54 24 1.10E+01 2.95E+06 5.62E+00 2.29E+06 6.61E+02 2.14E+07 3.89E+03 2.04E+07 24 35 10 9.12E+03 6.14E+05 8.41E+05 1.17E+04 1.94E+06 4.80E+05 3.24E+03 4.61E+06 7.97E+05 2.09E+05 3.14E+07 4.69E+04 *Units for CFU= CFU/g *Units for qPCR= cells/g 231 EnterococciqPCR cells/100mL 4.27E+06 5.62E+06 4.19E+04 Table 7.2: Average concentrations of the human and cow specific MST markers in the samples monitored in this research Raw sewage Primary effluent Secondary effluent (Prechlorination) Secondary effluent (Post-chlorination) Tertiary effluent Raw septage Treated septage effluent Cow manure Cow feces n 54 54 18 B.thetaiotaomicrona Copies/100mL 1.82E+07 2.04E+07 1.82E+04 Bobac* Copies/100mL 2.65E+06 ND ND M2* Copies/100mL Neg ND ND M3* Copies/100mL Neg ND ND 54 1.55E+04 ND ND ND 54 24 4.68E+03 3.55E+07 ND 6.82E+07 ND Neg ND Neg 24 35 10 2.63E+04 ND ND ND 2.15E+09 ND ND 6.19E+07 ND ND 6.65E+08 ND a Human specific marker *Cow specific marker ND-Not Determined Neg-Negative Method development included development of a new qPCR for E. coli targeting uidA gene. Compared to the cultivation methods for the detection of E. coli such as cultivation on mTEC, enzyme based colilert assay, qPCR was rapid, and produced sensitive results. An automated method was used for extracting DNA from environmental samples due to its reproducibility, efficiency in reducing the technical variability, and lower risks of cross contamination. Significantly higher (p<0.05) DNA can be recovered using autoextraction compared to manual extraction kits from raw sewage and effluents from wastewater treatment process when tested for E. coli qPCR concentrations. The machine was not found to be satisfactory with DNA extraction from cow feces. New automation is needed for environmental samples as molecular methods are being applied widely in this field and DNA extraction plays a key role in any molecular investigation. 232 One major disadvantage of qPCR method is the inability to differentiate live and dead cells. This study attempted to address the viability issue by treatment of samples with Ethidium monoazide dye prior to DNA extraction. The method did not work with chlorine disinfected samples even though it was found to distinguish heat-killed cells. To understand the mechanism of chlorine disinfection on cultivable cells or DNA during wastewater treatment, chlorine dosage studies using pre-chlorinated secondary effluent seeded with viable bacterial cells in combination with EMA or other efficient dyes are recommended. Modifications in the sample treatment with dye may also improve the efficiency of the method but requires detailed evaluation. A new human marker B. thetaiotaomicron, α- mannanase gene qPCR assay was found in sewage 100% of the time the samples were tested and was reduced by secondary treatment process to 3 log10 units. This marker was found to be abundant in septage also with 7 4 concentrations around 10 cells/100mL which got reduced to 10 cells following the treatment by lime stabilization process. These markers were found to correlate significantly (p<0.05) with each of the conventional indicators, E. coli and enterococci throughout the wastewater treatment process. Two new cow markers M2 and M3 were found in all manure samples tested and should be explored further for MST applications. This study recommends M3 marker due to its occurrence in higher concentrations in manure samples tested. However, the prevalence of these two markers may vary in different cow herds and in different cow populations and should be tested further in other locations. The manure samples that were mixed and applied in the agricultural 4 7 lands had cow specific markers occurring at concentrations of 10 -10 qPCR target copies per 3 7 mL and conventional indicators occurring in the concentrations of 10 to 10 qPCR target copies 233 per mL. Occurrence of higher levels of cultivable cells of E. coli and enterococci in samples collected after a longer storage period of around six months than in samples after a storage period of two-three months suggested the importance of longer storage to reduce the pathogen levels in manure before the land application. One of the specific goals of this research was to monitor all these waste environments for the waterborne pathogen, E. coli O157 using published qPCR assays reported to be specific for this serotype of E. coli. E. coli O157 specific eae and rfbE genes were detected in beef manure pits, and also in human sewage and septage environments. But they were found at the same levels as that of generic E. coli target gene that were evaluated in the same samples. There are issues about cross specificity associated with the E. coli O157 specific eae and rfbE qPCR assays when they are applied in the environment due to the genetic variability of E. coli serotypes. Further evaluation and specificity testing for these E. coli O157 assays will be done in future to address the concerns found in this research. In conclusion, qPCR assay targeting α-mannanase 1-6 gene of B. thetaiotaomicron was found to be present in similar levels as that of E. coli and enterococci in human waste environment and appears to be a reliable source tracking indicator for predicting human fecal contamination. This study suggests that this marker be included in a tool-box approach to monitor water bodies and to quantify human fecal contamination in the water bodies. These human specific qPCR signals can be used to track the fate of bacterial signals once discharged into the surface waters through effluents and thus avoid interferences from any non-human fecal contamination. M3 cow specific marker is suggested to be a reliable marker as compared to M2 marker based on this study and recommended for source tracking of agricultural pollution in 234 water bodies. Occurrence of these markers in high concentrations in the manure applied in the field can be utilized in designing run-off studies from manure applied land. 235