Technische Universität Dresden Fakultät Umweltwissenschaften

Reservoirs of Antibiotic Resistance and Pathogens in Bacterial Communities of Anthropogenically Driven Environments

zur Erlangung des akademischen Grades DOCTOR RERUM NATURALIUM (Dr. rer. nat.)

vorgelegt von Dipl. Serena Caucci geboren am 16.10.1981 Ascoli Piceno, Italien

Gutachter: Prof. Dr. Thomas U. Berendonk Prof. Dr. Peter Krebs Dr. Fiona Walsh

Dresden, 01. June 2017

Supervisors: Prof. Dr. Thomas U. Berendonk – Technische Universität Dresden - TUD Prof. Dr. Hauke Harms -Hemholtz Research Centre for the Environment- UFZ

“E quindi uscimmo a riveder le stelle.” Dante Alighieri

______Ad Alessia

TABLE OF CONTENS

Abstract

Forewords

Introduction Wastewater treatment plant (WWTP) ...... 3 Bacterial community and pathogens in municipal WWTPs ...... 7 Chemicals in wastewater and their effect on the aquatic environment ...... 8 Antibiotics and antibiotic resistance in wastewater ...... 8 Intrinsic and acquired Antibiotic Resistance ...... 11 Horizontal transfer of resistance genes ...... 12 Extended spectrum beta lactamases (ESBLs) bacteria and cephalosporin resistance ...... 14 Aims of the study and structure of the thesis ...... 16

Chapter 1 The problem of wastewater as point source of antibiotic resistance in freshwater. Methods and technologies to tackle it 19 Introduction ...... 21 The role of wastewater treatment and pollutants on the spread of antibiotic resistance .... 21 Novel WWTP technologies to reduce antibiotic resistance in surface water ...... 24 Modern and classical methods to study antibiotic resistance in wastewater ...... 25

Chapter 2 Microbial analysis of various household-scale wastewater treatment plants 29 Introduction ...... 31 Material and Methods ...... 32 Results ...... 37 Discussion ...... 42

Chapter 3 Co-occurrence of antibiotics resistance in gram- negative freshwater bacteria exposed to constant anthropogenic load 49 Introduction ...... 51 Materials and Methods ...... 52 Results ...... 55 Discussion...... 63

Chapter 4 Increased levels of multiresistant bacteria and resistance genes after wastewater treatment and their dissemination into Lake Geneva, Switzerland 67 Introduction ...... 69 Materials and Methods ...... 70 Results ...... 77 Discussion...... 87

Chapter 5 Seasonality of antibiotic prescriptions for outpatients and resistance genes in sewers and wastewater treatment plant outflow 93 Introduction ...... 95 Material and Methods ...... 96 Results and Discussion ...... 100 Conclusions ...... 109

Chapter 6 Characterisation and distribution of extended spectrum beta- lactamases in bacteria from untreated wastewater and wastewater-impacted freshwater in Nigeria 113 Introduction ...... 115 Materials and methods ...... 116 Results ...... 118 Discussion...... 122

Synthesis and Future Directions Conclusions ...... 141 Filling the scientific gaps to tackle antibiotic resistance in the environment ...... 143 Acknowledgements

References to own original publications used in this thesis

Bibliography

Appendix

Abbreviations

Erklärung des Promovenden

Erklärung zur Eröffnung des Promotionsverfahrens

Curriculum vitae

ABSTRACT

The increase of antibiotic resistance against antibiotics (AB) is an alarming phenomenon threatening the human well being and heading back to the pre-antibiotic era where many infectious diseases may become again untreatable with antibiotics. There is a strong correlation between AB use and occurrence of resistances which suggests anthropogenically- driven environments as reservoirs of antibiotic resistant bacteria (ARB). Among anthropogenically-driven environments, wastewater treatment plants (WWTPs), have been linked with the increased incidence of pathogens and ARB in freshwater ecosystems. Principal goal of the study was to evaluate the role of WWTPs as environmental reservoirs for antibiotic resistance and pathogenic bacteria. In this thesis, classical and molecular microbiology methods were applied to analyse resistance levels of bacterial communities from wastewater and wastewater-polluted environments at different geographical locations of developed (German and Switzerland) and developing (Nigeria) countries. Additionally, a novel approach which makes use of a combination of quantitative genomics, Next Generation Sequencing and drug-related health data was applied to quantitatively detect antibiotic resistance genes (ARG) in wastewater and assess their seasonal dynamics. The relative abundance of ARG was not reduced by the WWTPs in the treated effluent. Effluents were responsible for significantly high levels of ARB and ARG in the receiving environment because capable of introducing and/or selecting ARB carrying ARG on genetic mobile elements. The ARG levels differed between seasons independently from the water sanitation. High ARG levels were displayed in autumn and winter in coincidence with the higher uptake of antibiotics by the outpatients of the municipality.Contrary to the ARG, the abundance of bacteria was reduced by WWTPs processes in the effluent of developed countries. The WWTPs were also responsible for the changes of the microbial community from the wastewater to the effluent. Contrary to developed countries, the poor treatment of wastewater in Nigeria facilitated the spread of multi-drug resistant pathogens in the freshwater ecosystem. These bacteria were also carriers of clinically relevant ARG contributing to the accumulation of multiresistant pathogens in the environment.

All in all, this thesis shows that well established WWTP technologies are not capable to prevent or reduce the abundance of ARG in the freshwater ecosystem and poorly treated wastewater enriches the environmental reservoirs of ARG. Antibiotic resistance can spread across taxonomically distant bacteria and therefore explain the strong dispersal of ARG in wastewater effluents. These results show that antibiotic resistances are also ubiquitous in the freshwater environment and that anthropogenically-driven environments determine the incidence of antibiotic resistance. As for the clinic, tackling the problem of antibiotic resistance in the environment is fundamental. Without any action, the environmental reservoirs of resistance will increase and therapeutic failure in the clinic will irreversibly compromise the biggest progress in medicine of the 20th century.

Forewords

Many aspects of socio-economic development are often dependent on water and referred as the water-energy-food ‘nexus’. Economic activities determine how water is distributed, managed and used but at the same time these activities have an influence on the quantity and quality of water resources themselves (Figure 1). Natural ecosystems are fundamental for our life and well-being. Unfortunately, in the past little attention has been paid to the effect of anthropogenic actions on the water cycle and this has caused serious degradation of water resources which implied a reduction of fundamental benefits to society.

Figure 1 Percentage of water withdrawal in 1995 and predicted worldwide freshwater stress in 2025 (UNEP 2000)

Between 2009 and 2050, urban populations are projected to grow by 2.9 billion – from 3.4 to 6.3 billion (UNDESA 2009). Therefore, urban areas are expected to concentrate the majority of the world’s population. Moreover, despite the 87% of the worldwide population gets access to water from improved sources, there is no knowledge on the percentage of population receiving a quality service (i.e. quality disinfection, or water quality control). Risks to human and ecosystem health are strongly connected to poor water quality. Urban settlements are the main source of point-source pollution and urban wastewater is particularly threatening because a growing number of freshwater sources and aquifers located near urban areas are facing pollution from pharmaceuticals, pesticides, nitrates and heavy metals (Figure 1a). While the mitigation of anthropogenic chemical contamination is

Figure 1a Principal human health and environmental risk related to wastewater worldwide recognized as vital, major challenges remain for new contaminants of emerging concern like antibiotic resistance genes (ARG) and antibiotic resistant bacteria (ARB) (UNEP 2008, WHO 2003a, 2014) where no mitigation strategies have been adopted. Antibiotic resistance is a phenomenon posing into risks one of our major achievements in modern medicine. The threat of antibiotic resistance in clinical settings is nowadays indisputable. The avowal of ARG as environmental pollutants is instead young and only recently, the connected risks have been taken into consideration. The environment can contribute to the burden of resistance and indeed affect human health (Martinez 2009a). In the light of this statement, the reduction of ARG pollution seems of high priority. For that, knowledge gaps on the spread, selection and accumulation of ARG in non clinical settings have to be filled.

In this thesis, new evidences on the contribution of anthropocenically-driven systems (WWTPs) to the dissemination of ARG or ARB will be presented. Additionally, the here generated scientific knowledge will be a base for water professionals to demonstrate that traditional WWTPs need further technological implementation toward the reduction of ARG levels in water effluents. The new perils and challenges facing the water ecosystem call to think beyond the traditional sector compartments. A concerted cross-sectional action by public health institutes, policy makers and scientists is therefore needed. New strategies to limit the evolution and spread of antibiotic resistance in the environment have to be elaborated. No global strategy or public health actions can be elaborated if no reliable data are available. Water professional therefore should progressively make use of standardised environmental surveillance in order to provide new tools, a better understanding of the dynamics of antibiotic resistance evolution in the aquatic ecosystem and try to contain it.

Introduction

Introduction

Wastewater treatment plant (WWTP)

Centralised municipal WWTPs

For more than one century, wastewater treatment has been one of the world largest technologies aiming to protect the environment (Nielsen & van Loosdrecht 2010). Several treatment systems have been elaborated in this time to fulfil different tasks (i.e. treatment of municipal, industrial or household, slaughterhouse wastes). There are three principal processes implemented in most of the modern WWTPs independent from the actual treatment system. (i) Mechanical treatment where large particles, fats and waste are removed through coarse bar screens, grit and grit chamber (ii) chemical treatment of the wastewater where phosphorus is removed through precipitation via precipitation through addition of iron or aluminium, and (iii) biological treatment where microorganisms are used for the removal of organic compounds and nitrogen. WWTP using activated sludge treatment is one of the most established technologies and is applied worldwide. The treatment relies on the retaining of settled biomass (sludge) generated in the treatment process and its reuse for inoculation of fresh untreated waste (Henze 2008). Sludge contains naturally selected microbial populations (Nielsen 2010) which are capable to remove water contaminants. The combination of wastewater and biological mass is commonly known as mixed liquor in activated sludge plants. The mixed liquor is discharged into settling tanks to separate treated water from sludge via sedimentation. The treated supernatant might undergo additional treatment before being discharged. Within this process, the plant can be designed alternatively for the removal of carbon and nitrogen or carbon, nitrogen and phosphorus, either chemically or biologically. A vast part of the German and Swiss municipal treatment plants belongs to the latter group and a simplified scheme below (Figure 2) depicts the typical treatment plant with enhanced biotechnological phosphorus removal.

Decentralised WWTPs

Contrary to municipal wastewater treatment plants, decentralised treatment plants (d- WWTPs) are principally defined by the fact that raw wastewater is treated next to the source instead of being confined in the sewer system (Wilderer & Schreff 2000). Decentralisation of small treatment plants is recognized as a suitable manner to give access to clean water and reduce environmental pollution in rural areas which have a lack of centralized WWTPs (Bieker et al. 2010, Larsen et al. 2013). Due to the relatively small size and carbon footprints, d-WWTPs have a reduced environmental impact. d-WWTP can be tailor-made to suit local climatic conditions, aesthetic requirements, water quality and usage. The treatment ability

- 3 - Introduction classification at the European level is defined by the Directive 91/271/EEC and is mainly based on the treated organic load expressed as person equivalent. Thus, d-WWTPs are appropriate for low-density communities and are relevant for the management of water quality of rivers and lakes (Gikas & Tchobanoglous 2009a). d-WWTPs have acquired popularity across the world (IPPC 2003) and in some countries the total number of small plants can treat a bigger volume of wastewater than the existing centralised ones (Deininger &Wilderer 2000, Massoud et al. 2009). An overview of the most commonly used d-WWTP technologies is presented in the Table 1. Until recently the reduction of bacteriological pollution in wastewater has not been a priority in Europe. Thus, currently no European directives are legislated for bacteriological quality in treated water from small d-WWTPs (Fong et al. 2007, Kremer et al. 2011, Lienemann et al. 2011). Pathogenic microorganisms responsible for water borne diseases include various species of enteric bacteria, protozoa, cyanobacteria, helminthes, and viruses (Asano 2007). Characterising microbial communities and how these relate to the purification efficiency of complex wastewater is fundamental for designing a reliable biological waste treatment process and pathogens removal (Graham & Smith 2004, Wagner et al. 2002). Unfortunately, even for municipal WWTPs, the costs and difficulties related to the identification and enumeration of pathogens are limiting these investigations.

Treatment stability versus microbial functional stability

A correct management of wastewater treatment plants independently whether centralized or not, has shown that system performance is reproducible under constant operating conditions (Wittebolle et al. 2009, Wittebolle et al. 2008). Despite the stability of operating conditions, continuous changes in richness and diversity of microbial communities inhabiting wastewater are observed (Kaewpipat & Grady 2002). This demonstrates that community composition in biological wastewater treatment plants develop dynamically and not necessarily predictably, as a function of the operating conditions (Wagner et al. 2002, Wang et al. 2014). Moreover, elevated biodiversity does not always correlate to the optimal wastewater treatment performances (Fernandez et al. 2000, Wittebolle et al. 2008) or to high microbial functional diversity, a factor often associated to successful plant performances (Miura et al. 2007). Nevertheless the good functioning of a wastewater treatment plant (as an ecosystem) does not rely only on biodiversity and functional diversity of the system but also on the variety of environmental conditions which bacteria are exposed to (Gentile et al. 2007). The microbial community inhabiting the plant might therefore differ with the treatment technologies in use and the WWTPs could render into different performances. Despite the shown relevance of the microbial diversity for their functional stability in WWTPs, (Briones & Raskin 2003, Gentile et al. 2007, Short et al. 2013) comparative investigations on the effect of technological processes on the bacterial community and pathogens diversity of treated water effluents are still lacking.

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Figure 2 Schematic representation of the municipal WWTP of Dresden utilizing the activated sludge technology. (Courtesy of N. Lucke – Stadtentwässerung Dresden)

Introduction

Table 1 Advantages and disadvantages of the most common secondary treatment methods

Unit Advantages Main disadvantages______

Media filters Easy operation and maintenance Costs if the media is not available High quality effluent (BOD and TSS) Regular maintenance required Nitrogen transformed to nitrate Clogging is possible vd No chemicals required Land area is the limiting factor Lagoons Effective in solids BOD pathogens Not very effective metals removal and ammonia removal Effluent required characteristics Cost-effective are unreliable, odor problem Easy to operate and maintain

Anaerobic & Aerobic Lagoons Pathogens removal Heavy metals removal Produce methane Land area is the limiting factor Simple and cost effective Odor production

Aerobic treatment Require regular maintenance

Suspended Growth (SG) High degree of nitrification Slight odor and noise

Sequencing Batch Reactor (SBR) Nitrogen removal Costs Low organic and TSS Operational control-maintenance v Small land area requirements

Constructed Wetlands (CW) Low organic and SS Periodic available maintenance Inexpensive to operate and construct Surface area not available for other purposes Reduced odors Require a continuous loading Able to handle variable WW loads Affected by seasonal variations Reduces land area needed for Overloads of ammonia and wastewater treatment solids can destroy the system Provide wildlife habitat Nutrients removal: reduced benefits Pathogens removal for water reuse in agriculture BOD, biochemical Oxygen demand; SS suspended solids; TSS, total suspended solids; WW, wastewater a

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Bacterial community and pathogens in municipal WWTPs

Worldwide, municipal wastewaters have a broadly similar composition with regard to content of organic matter and nutrients, but not their microbiological characteristics. Due to the difference in health conditions of people living in industrialized and developing countries, the pathogen load is notably different (Jimenez 2009). Bacterial communities play a pivotal role in wastewater treatment because they are responsible for the reduction of organic matter from wastewater (Wagner et al. 2002). The diversity and abundance of microbial communities in municipal WWTPs are high and differs among treatment plants (Wagner & Loy 2002, Wang et al. 2012, Xia et al. 2010). While the diverse bacterial structures among plants seem influenced by the operational conditions of each specific plant, the diversity of microbial communities appears to remain stable within a WWTP (Siripong & Rittmann 2007). Proteobacteria is the most abundant phylum in the aerated plants and can represent up to 70% of the community (Wang et al. 2012). Abundant phyla are α- β- and γ- Proteobacteria with the β-Proteobacteria as the most frequent and with the highest diversity. Other phyla are Firmicutes Bacteroidetes Actinobacteria and Fusobacteria Chloroflexi and the Planctomycetes (Wang et al. 2012). Many of these bacteria do survive the treatment and contribute to the microflora of the wastewater effluent (Ottoson & Stenstrom 2003). In addition, significant numbers of the ammoniaoxidizing microorganisms are detected: Nitrosococcus mobilis, Alcaligenes, and Brachymonas denitrificans-related microorganisms have been investigated due to their relevance for wastewater processing and pollutants removal (Mielczarek et al. 2013, Wagner et al. 2002). Bacteria responsible for denitrification in WWTPs are still largely unknown; many of them have been isolated for their denitrifying ability (Alcaligenes spp., Pseudomonas spp., Methylobacterium spp., Bacillus spp., Paracoccus spp. and Hypohomicrobium spp.) but their proportions in the in situ population are not well known (Mielczarek et al. 2013). Human pathogens are considered a normal inhabitant in domestic sewage and their elimination is one of the fundamental reasons for wastewater treatment. The processing of the wastewater has been shown to reduce the number of pathogenic organisms that may be present (Blatchley et al. 2007, Sahlstrom et al. 2004). Current efforts have concentrated on the improvement of WWTP technologies to increase the efficiency of fecal bacteria removal in large scale wastewater treatment plants (Vaz-Moreira et al. 2014). The standard methods to determine the reduced microbiological water pollution by human related pathogens in the effluent are still relying on the enumeration of a few indicator organisms, e.g. E. coli, coliforms and Enterococcus spp. although, WWTPs comprise of a larger diversity of pathogens (Lee et al. 2006, Sahlstrom et al. 2004, Savichtcheva et al. 2007). This is especially the case of d-WWTPs where the technology can be simpler than in treatment plants with a larger scale and therefore pathogens might survive the wastewater treatment. With the culturing approach many pathogens relevant for public health are not detected and an underestimation of infectious microorganisms in the effluent is very likely (Ulrich et al.

2005).

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Chemicals in wastewater and their effect on the aquatic environment

The dominating role of anthropogenic activities in the release of chemicals in the environment is nowadays undisputable. Domestic wastewater contains a cocktail of contaminants such as nutrients (N and P) pharmaceuticals, personal care products (PPCPs) and endocrine disrupting chemicals (EDCs) of which only a proportion is oxidized by conventional and advanced treatment plants (Fatta-Kassinos et al. 2011, Oakley et al. 2010). The discharge of effluent from WWTPs therefore has a strong effect on the health of aquatic ecosystems. WWTP effluents can release large amounts of organic matter, nutrients and microorganisms into receiving freshwater systems. Sub-optimal wastewater treatment performances can increase the load of nutrients leading to eutrophication (Gucker et al. 2006) and release at the same time bacterial pathogens turning into a threat for the human health. Increased organic matter can alter the microbial community structure and their function, especially in catchments area where urbanization is placing an elevated pressure upon WWTPs (Singer & Battin 2007, Wakelin et al. 2008). Most importantly, the impacts of stress and disturbance upon microbial communities can have implications for the ecosystem of freshwater community structure and human health.

Antibiotics and antibiotic resistance in wastewater

Among all chemical classes, antibiotics have been produced and consumed in exponential amounts since the Second World War. Antibiotics are an important component of pharmaceutical contamination in the WWTPs. The consumption and the consequent release of antibiotics into the environment provide a consistent bioavailable reservoir for micro- and macro-fauna in all ecosystems (Kookana et al. 2014). Antibiotics are also produced by naturally occurring organisms, but this fraction can be considered very little in the overall environment (Baquero et al. 2008, Gottlieb 1976). The bulk of antibiotics found in the environment originate from the commercial production (Davies & Davies 2010). Additionally, the alternative use of antimicrobial agents as growth promoter in animal husbandry and aquaculture, prophylactic use in veterinary and therapeutic overuse in medicine plays an important role in the development and spread of antibiotic resistance (Allen et al. 2010, Bush et al. 2011, Canton 2009). Classes of antibiotics used in medicine to treat bacterial infections in humans include among others aminoglycosides, tetracyclines, sulphonamides, quinolones, beta-lactams, macrolides, glycopeptides and phenicols (Aminov 2010). A collection of commonly used classes of antibiotics, their mechanisms of action and typical resistance mechanisms are presented in Table 2. The consumed antibiotics are not always fully degraded in the patients and will reach the WWTP either as non-degraded or partially degraded compounds or as metabolites (Rizzo et al. 2013b). Gullberg et al. (2011) showed that even at sub-therapeutic concentrations, antibiotics can select for antibiotic resistance (Gullberg et al. 2011). The presence of antibiotics in wastewater treatment plants and in their effluents might therefore promote horizontal gene transfer of resistance genes and potentially select for ARB into the environment. In Chapter 1, detailed information on the role of WWTPs as point-source of antibiotic resistant bacteria and antibiotic resistance

- 8 - Introduction genes (ARG&B) in freshwater and a critical overview on the current methods used to tackle the problem of antibiotic resistance in the environment are presented. Water environments (lakes, rivers, and wastewater effluents) are nowadays recognized as important facilitators for the spread of ARG&B (Michael et al. 2013, Rizzo et al. 2013b). Unfortunately, contrary to clinical settings no data are available on the epidemiology of antibiotic resistances in different environments (Lupo et al. 2012) making it difficult to predict the risk prediction of emergence and spread of ARG in the natural environment. Global efforts are therefore required to characterize antibiotic resistance in the natural environment and determine how contaminated environments by anthropogenic activities affect the proliferation of antibiotic resistance (Berendonk et al. 2015). The interaction between human associated and environmental bacteria in WWTPs via transfer of ARG is a relevant feature to understand the emergence of novel resistant mechanisms in human pathogens (Baquero et al. 2008, Fajardo et al. 2008). In such a case, horizontally acquired resistances are of a special concern for human health because of the rapid dissemination of ARG from the original host into various members of a microbial community or from environmental to clinical relevant microorganisms (Forsberg et al. 2012, Poirel et al. 2005).

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Table 2 Modes of action and resistance mechanisms of commonly used antibiotics in human therapy.

Antibiotic class and Target Mode(s) of resistance Detection in mobile representative antibiotics elements

β-Lactams Peptidoglycan Hydrolysis, efflux, altered YES Penicillins (ampicillin) biosynthesis target (Rice et al. 2005) Cephalosporins (cephamycin) (Endimiani et al. 2010) (Boyd et al. 2004) (Poirel et al. 2005) Aminoglycosides Translation Phosphorylation, YES gentamicin acetylation, (Shakil et al. 2008) streptomycin Enzymatic drug, inactivation, efflux, mutation, methylation

Glycopeptides Peptidoglycan Reprogramming YES vancomycin biosynthesis peptidoglycan biosynthesis (Leclercq et al. 1988) teicoplanin Translation Altered target, efflux YES Tetracyclines Ribosomal protection, (Rice et al. 2005) oxytetracyclin enzymatic drug inactivation doxycycline tetracycline

Macrolides Translation Hydrolysis, glycosylation, YES clarythromycin phosphorylation, efflux, (Szczepanowski et al. 2007) erythromycin altered target, enzymatic azithromicin drug inactivation, drug efflux

Sulfonamides C1 metabolism Efflux, altered target YES sulfamethoxazole Synthesis (Sorum et al. 2003) sulfadiazine inhibition

Phenicols Translation Acetylation, efflux, altered YES Chloramphenicol target (Shakil et al. 2008)

Quinolones DNA replication Acetylation, efflux, altered YES ciprofloxacin target. (Strahilevitz et al. 2009) levofloxacin DNA gyrase protection, (Richter et al. 2010) mutations in DNA gyrase and topoisomerase IV

Pyrimidines C1 metabolism Efflux, altered target YES Trimethoprim (Blahna et al. 2006)

YES Lincosamides Translation Nucleotidylation, efflux, Clindamycin altered target (Leclercq &Courvalin 1991)

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Intrinsic and acquired Antibiotic Resistance

Intrinsic antibiotic resistance

Antibiotic resistance can be classified as intrinsic or acquired. The distinction between the two types of resistance is of significance when determining the mechanism of resistance in a bacterium. Resistance is defined as intrinsic when the trait that is encoded by chromosomal genes is independent of antibiotic selection and not gained by horizontal gene transfer (Cox & Wright 2013). Intrinsic resistance is therefore present because of the physiology of that specific bacterium. For example, this is the case for organisms producing antibiotics. They need to protect themselves from the self-produced antibiotic (Leclercq et al. 2013). Intrinsic resistance is also related to the presence of efflux pumps in the membrane of bacteria which makes many gram-negative bacteria capable of effectively reducing the concentration of many antibiotics in the cell (Cox & Wright 2013) and therefore generate multi-drug resistant phenotypes. For instance, erythromycin has no effect against Escherichia coli (Piddock 2006) because of efflux pumps present in the bacteria. Intrinsic resistance genes are species specific however, the disruptions of genes encoding efflux pump proteins of unrelated functions can also render certain bacteria more susceptible to antibiotics (Gomez & Neyfakh 2006). Bacterial susceptibility to antibiotics depends on many other factors besides efflux pumps including the composition of the cell wall or the presence of genes coding for inactivation enzymes having specific cellular functions and may unintentionally contribute to the antibiotic resistance (Kohanski et al. 2010). Therefore, inactivating the inhibitors may increase the efficacy of the currently used antibiotics in medicine. The mechanisms of intrinsic resistance are complex and cannot be confined to a single gene response (Davies & Davies 2010). The possibility that the same gene could provide either intrinsic resistance in one species and acquired resistance in another makes the phenomenon of intrinsic resistance more complex, difficult to define, and understand.

Acquired antibiotic resistance

Adaptation of microbes occurs frequently as a response to antibiotics by the acquisition of genetic information (Martinez 2009b), but spontaneous gene mutation and recombinatorial events may result in antibiotic resistance even without a selective pressure (D'Costa et al. 2006). Acquired antibiotic resistance is a type of resistance which is acquired by mutation of bacterial chromosomal genes or by horizontal gene transfer of resistance genes (Palmer & Kishony 2013). While a mutation is relevant only for the bacterium that harbours it, the presence of a mobile antibiotic resistance gene in a bacterium might be relevant for the dissemination of resistance in a population. Based on these assumptions Martinez et al. (2015) suggested a new ranking for antibiotic resistance genes in the environment which was based on the associated public health risks that the resistance genes might pose. In this case, the health risk is meant as a combination of gene acquisition mechanisms and conferred resistance to human pathogens. According to Martinez, chromosomal genes belong to the intrinsic resistome of that bacterium and should

- 11 - Introduction be considered in the environment only as gene markers for the presence of bacteria and not as an antibiotic resistance. Conversely, if the resistant determinant is present on mobile elements, it should be considered a potential risk for the spread of resistances in the environment. In this way, the categories which will be considered at the highest risk will be those that include genes conferring resistance to antibiotics that are currently in use and located on mobile genetic elements (MGE) in non-pathogens, human pathogens or both primary and opportunistic pathogens (Martinez et al. 2015). Unfortunately, tracking the origin of ARG in clinically relevant bacteria is still challenging because the newly arisen resistance genes were disseminated via MGE widely (Stokes & Gillings 2011). The origin of resistance has been identified only in few cases and surprisingly enough, these resistances ancestrally belonged to environmental bacteria. The qnrA, a gene conferring resistance to quinolones via mutation of the DNA gyrase and topoisomerase IV, is linked to the Shewanella algae (Poirel et al. 2005) while vanX, a gene coding for vancomycin resistance seems to originate from Amycolatopsis orientalis, a natural producer of vancomycin and most likely the ancestor of the van genes operons that make enterococci resistant to vancomycin (D'Costa et al. 2011). The further integration of the van operons into transposons and conjugative plasmids has facilitated their spread (Courvalin 2006). For some classes of blactx-m type β-lactamases, the progenitor originates from Kluyvera ascorbata, an opportunistic pathogen with low clinical significance (Canton et al. 2012). blactx-m genes are clustering into the worrisome resistance class of extended-spectrum β- lacatamses (ESBLs). ESBLs in fact are not only capable to hydrolyze expanded spectrum cephalosporins or monobactams but their spread is mediated by MGEs with a highly successful survival of bacterial clones (Canton & Bryan 2012). Within the blaoxa family of β- lactamases, the majority of resistances were D plasmid borne genes belonging to Eneterobacteraceae, Pseudomonas spp. and Acinetobacter spp. while chromosomal located blaoxa genes were originally present in Aeromonas, Legionella, Shewanella, and

Campylobacter species (Girlich et al. 2010). blashv β-lactamases are derived from the chromosomal β-lactamases of Klebsiella pneumoniae; AmpC enzymes nowadays diffuse in Klebsiella spp. and E. coli is a chromosomal escapee from Citrobacter freundii, Hafnia alvei, Morganella morganii, and Enterobacter cloacae (Livermore 2003). Bacteria have multiple mechanisms to become resistant to drugs and, once resistant these bacteria can quickly develop high numbers of resistant progeny. If the resistance is a result of modification of an antibacterial target, efflux, enzymatic inactivation or arises into susceptible organisms via mutation or DNA transfer, natural selection will always favour mechanisms conferring resistance at the lowest fitness cost. Antibiotic selection may also foster resistant strains with enhanced survival ability. Therefore, once antibiotics have been used for long periods, evolution hones the resistant strains.

Horizontal transfer of resistance genes

Antibiotic resistance genes can be transferred via MGEs along with other genes e.g., virulence genes and other metabolic genes (Toussaint & Merlin 2002). The transfer of ARG takes place via conjugation (conjugative plasmids and integrating conjugative elements), transduction

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(bacteriophages) and transformation (cell free DNA), see Figure 3. MGEs have a complex structure and are composed of modular units, including integrons, insertion sequences and genomic islands (Toussaint & Merlin 2002). Conjugative plasmids are non-chromosomal circular or linear double-stranded DNA molecules capable of independent replication (Binh et al. 2008). They consist of an essential backbone of genes encoding replicative functions and a variable amount of different accessory genes including genes for antibiotic resistance, virulence or metabolic functions. Plasmids are known to harbour antibiotic resistance genes and transfer them among related bacterial species. Plasmids are transferred via conjugation mechanism that consists on the mating pair of two cells connect trough a specialized pore (pilus) which allow the transfer of the DNA into the recipient cell (Schroder &Lanka 2005). In particular, conjugative plasmids belonging to the incompatibility (Inc) groups IncP, IncQ, IncW and IncN are known as antibiotic resistance carriers (Perry & Wright 2013). As an example, tetracycline resistance genes (tet) are often found in plasmids ranging in size from 7 to 180 kb and are frequently carriers of other resistance genes e.g. against sulfonamide, aminoglycosides and trimethoprim (Philippon et al. 2002). Bacterial viruses and bacteriophages are known as the most abundant and fast replicating life forms on earth (Clokie et al. 2011). Genomes of bacteriophages can be either single-or double-stranded RNA or DNA and their size can range between few to several 100 kb. Bacteriophages can be virulent, lysing the infected cells, or lysogenic integrating to host the genome until the lytic cycle is activated by external signal (Fortier & Sekulovic 2013). Typically in lysogenic phase, the phage genome integrates into the host genome and replicates with it as a so called prophage. This lysogenic conversion provides to the new host additional phenotypic properties like toxin production and therefore increases their plasticity. Alternatively, phages, in a process called transduction, do not lyse a bacterium, following infection, but instead carry new genes into that bacterium. The host cell DNA is in fact packaged into the phage virion and later transferred to a new host via infection (Hynes et al. 2013). Transduction relocates sections of microbial chromosomes and is also involved in dissemination of antibiotic resistance genes (Balcazar 2014). Transformation is the third mechanism involved in horizontal gene transfer and allows the transfer of DNA between distantly related bacteria. Extracellular DNA is taken into microbial competent cells and stabilized in the new host cell through self-replication or DNA integration into the host chromosome via homologous recombination (Hynes et al. 2013). The mosaic-like structure of the mobile genetic elements is also responsible for the presence of multiple antibiotic resistance determinants in single mobile elements (Garriss et al. 2009). Wastewater treatment plants are potential horizontal gene transfer promoting ecosystems (Berendonk et al. 2015) and the presence of antibiotics, biocides and other pollutants are believed to further increase the likelihood of gene transfer events (Baker-Austin et al. 2006). The microbial species acquiring resistance genes in wastewater could be either human pathogens or environmental microorganisms. However, even harmless bacteria can pose a risk for the spread of antibiotic resistance due to their ability to store, combine and disseminate several resistance determinants to opportunistic pathogens (Finley et al. 2013).

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Figure 3 Horizontal Gene transfer (HGT) mechanisms responsible for the spread of antibiotic resistance genes. (Modified from Michigan State University, Antibiotic resistance learning site 2011)

Extended spectrum beta lactamases (ESBLs) bacteria and cephalosporin resistance

Extended spectrum beta-lactamases (ESBLs) mediate resistance to β-lactams and are considered among the most relevant antibiotics in human medicine (Poole et al 2004) accounting for up to 50-70% of the total antibiotic use in several countries (Korzeniewska & Harnisz 2013, Poole 2004). ESBL genes are often mobile and responsible for the production of enzymes inactivating cephalosporines via hydrolysis of the β-lactam ring. The majority of the ESBLs carrying cephalosporin resistances are due to variants of blatem, blashv and blactx-m genes. Cephalosphorinases have a fast evolution and new gene variants are emerging frequently. First ESBLs were isolated in Germany from clinical isolates of Klebsiella pneumoniae (Giamarellou 2005) only two years after the introduction of cephalosporines into the market. Since then, the occurrence of cephalosphorinases ESBL-producing bacteria has been reported in almost all developed countries and not only from the clinical environment (Nuesch-Inderbinen et al. 2013). ESBLs carrying multi-resistant enteric bacteria, in particular Escherichia coli and Stenothrophomonas maltophilia have recently received attention in many developed countries (Brooke 2012) because of the capability to survive in the environment and responsibility in a series of food contaminations. In developing countries, the extent of ESBLs spread in clinical and environmental settings is still uncertain but it is certain that the unrestricted access to antimicrobials (Plachouras et al. 2010) and the poor sanitation facilities in these region of the World contribute to the severity of pollution in freshwater environments (Adelowo et al. 2012b).

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Recently ESBL-producing bacteria were isolated from lakes in Bangladesh and in chlorinated drinking water or freshwater samples. The responsible genes for the resistance were widely disseminated in several bacterial species and most of them were identified as human pathogens (Haque et al. 2014, Walsh et al. 2011). Consumption or handling of polluted water could therefore lead to a colonization of the gastrointestinal tract of humans or animals by ESBL producing bacteria and potentially result in a transfer of resistance genes among commensal gut bacteria (Coleman et al. 2012). Polluted coastal water might also be a potential exposure pathway for resistant bacteria via wastewater contamination. Shuval et al. (2003), estimated that around 120 million cases of gastrointestinal diseases originate from contact to recreation or raw fish consumption from coastal water (Shuval 2003). Although these findings are not a proof for the re-introduction of ARB from the environment into the clinical settings, the increasing number of thalassogenic diseases indicates the impact of polluted water on human health.

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Aims of the study and structure of the thesis

The overall current state of knowledge is insufficient to assess the distribution and abundance of ARB and ARG in the environment at different government levels. The lack of information therefore, makes the risk assessment for ARG transfer from environmental bacteria to human-associated bacteria a challenging task. Moreover, in strong contrast to the clinical environment, there are no epidemiological data available on the antibiotic resistance in the environment and not enough data from the anthropogenically driven environments. Long term monitoring of the antimicrobial resistance reservoirs in those ecosystems is fundamental to predict the emergence and spread of new resistances of clinical concern. The association of environmental monitoring and public health data could be a step forward in understanding the temporal ARG dynamics. The resulting information indeed could reveal the synergistic effects of antibiotics consumption, antibiotic residues in the environment, and environmental characteristics of such anthropogenically driven environments. Among the anthropogenically-driven environments, WWTPs are known to bridge the gap between the human and aquatic systems thus allowing traits of pathogenic bacteria to reach autochthonous aquatic microorganisms and spread among these populations. The combination of the physico chemical characteristics of wastewater, the favorable growth conditions and the presence of persistent antibiotics in WWTPs may favor the transfer of antibiotic resistances from faecal pathogens to aquatic microorganisms (Martinez 2009b, Schwartz et al. 2003, Sorensen et al. 2003).

.In the light of this knowledge, the PhD project presented here evaluated the impact of wastewater and WWTPs on the spread of pathogens and ARG or ARB in multiple aquatic environments by means of classical cultivation-dependent and molecular biological methods. A detailed analysis for the characterization and quantification of the microbial populations (including antibiotic resistant bacteria) in untreated water, WWTPs and wastewater effluents was conducted. Links between human activities and the natural ecosystem influencing the evolution and spread of ARG were investigated and potential stressors causing selective pressure on microbial communities identified. The thesis contains five data chapters (Chapters 2 - 6), preceded by an Introduction, and a brief review on antibiotic resistance in WWTPs (Chapter 1) followed by a chapter on Synthesis and Future Directions. Each data chapter consists of a sub-set of the overall research objectives, while specifically focusing on a different study case in the context of wastewater treatment or wastewater as anthropogenic stressor for the environment.

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In Chapter 1 the problem of ARG as an environmental pollutant will be reviewed and the interconnection between WWTPs and freshwaters for the spread of ARG identified. Here, the ability of traditional and novel WWTP technologies to limit the spread of ARG in freshwater ecosystems will be described; moreover in Chapter 1, the status quo and major drawbacks of current laboratory techniques to detect antibiotic resistances in WWTPs will be included. Due to the interdisciplinarity of the research project, multiple case studies and a long-term environmental survey were undertaken to address the following research questions:

i) Are available WWTP technologies differently driving the release of microbial contaminants (pathogens) in the plant effluents? Chapter 2

ii) Are WWTPs hotspots for the release and spread of ARG and ARB in the freshwater ecosystems? Chapter 3, 4 and 5

iii) Does anthropogenic action select for multiple antibiotic resistance (MAR) and ARG in wastewater microbial communities? Chapter 4 and 5

iv) Are there any seasonal dynamics in the release of antibiotic resistances genes by municipal wastewater treatment plants? Chapter 5

v) Is poorly treated wastewater a carrier of multiple ARG and potentially responsible for the spread of clinically relevant ARG&B in the freshwater ecosystem? Chapter 6

In the thesis, traditional and fully functioning wastewater treatment plants of different sizes and technologies were investigated for the spread of pathogens, antibiotic resistance and antibiotic resistant bacteria in water effluents and receiving rivers or lakes. The studies have been performed in Germany and Switzerland while the Nigerian freshwater ecosystem has been taken as a field study to assess the presence and distribution of clinically relevant ARG&B caused by the introduction of poorly treated wastewater into the environment.

In Chapter 7, the overall results of the study will be discussed and a critical perspective of the problem of antibiotic resistance genes in the environment will be given.

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Chapter 1

The problem of wastewater as point source of antibiotic resistance in freshwater. Methods and technologies to tackle it

The problem of wastewater as point source of antibiotic resistance in freshwater. Methods and technologies to tackle it

Introduction

Antibiotic resistance is a serious threat to public health in Europe leading to a continued increase of healthcare costs, prolonged hospital stays, treatment failures, and deaths. In the late 2013, alarming data on the spread of antibiotic resistance genes (ARG) have been published by the European Centre for Disease Prevention and Control. As an example, over the four years (2009 to 2012) resistance to third-generation cephalosporins in K. pneumonia and E. coli increased significantly at the European level. Furthermore, combined resistance to third-generation cephalosporins and two other important antimicrobial classes like fluoroquinolones and aminoglycosides increased for K. pneumonia. The newly combined resistance in K. pneumonia left to the health care system only a few therapeutic options (e.g., carbapenems) for treatment of infected patients (EARSS 2013). To face this increasing problem, the study on antibiotics resistance development has focussed on bacterial pathogens in clinical settings. Nevertheless, environmental reservoirs for ARG and non- clinically associated ecosystems play a strong role on the ARG spread and evolution in bacterial pathogens

The role of wastewater treatment and pollutants on the spread of antibiotic resistance

Antibiotic resistance is known as a natural phenomenon and bacteria can acquire genes conferring resistance to antimicrobials. Those genes are not naturally intrinsic to the human pathogens and therefore pathogens have obviously acquired those (if not as a mutation) from the environmental microbiota. Environmental bacteria are a rich source of genes that might act as resistance genes when entering pathogenic organisms, supplying in that way to the hidden ‘resistome’. Water beside the clinical settings is one of the most common ways of dissemination for resistant organisms among humans, and at the same time a bridge for resistance genes to reach environmental bacteria. WWTPs but also the sewage systems provide perfect conditions for the selection of bacterial resistances. In anthropogenically driven environments, human pathogenic and resistant bacteria are often released with the treated wastewater into the freshwater ecosystem where environmental bacteria can acquire ARG via horizontal gene transfer (HGT). WWTPs serve not only as reservoirs, but also as

21

Chapter 1

platforms for resistance gene spread in the environment (Martinez 2009b). Moreover, the introduction of antimicrobial agents, disinfectants or heavy metals in the wastewater increases the chance for the evolution of resistant organisms in the water environment. Risk assessment protocols for antibiotics and antibiotic resistant bacteria have to be generated and become integrating part of risk assessment for water. The traditional goal of wastewater treatment is to reduce the concentration of dissolved organic carbon, nitrogen, phosphorous and eliminate viable pathogens from the liquid effluent (Zhang et al. 2010). Physical (e.g. filtration), chemical (e.g. disinfection) and microbial activities are used to effectively remediate wastewater entering the treatment plant. Even though technological processes would completely eliminate pathogens from the effluents, favourable growth conditions in WWTPs like elevated nutrient content, temperature and persistent antibiotics may favour the transfer of antibiotic resistance genes from resistant faecal and pathogenic bacteria to aquatic microorganisms (Martinez 2009b, Schwartz et al. 2003). Furthermore, the elevated bacterial diversity, the high concentration of free DNA and of magnesium or calcium ions promotes the horizontal gene transfer of chromosomal and plasmid genes. However, common WWTPs are not designed to remove these factors entirely (Karaolia et al. 2014). The mobility of resistance genes relies on genetic elements such as conjugative transposons, integrons, gene cassettes or conjugative plasmids. In particular, conjugative plasmids are known as antibiotic resistance carriers and capable of self-replication. As a consequence, antibiotic resistances can easily spread between microorganisms and be released in the environment through WWTP effluents. Portuguese researchers have indeed estimated that within the effluent around 109–1012 Colony Forming Units (CFU/day) are released per inhabitant equivalent (Novo & Manaia 2010); among these 107–1010 display acquired antibiotic resistances. Moreover, considering that only 1% of the microbial fraction is culturable, the real estimation of resistant bacteria thriving in wastewater and sewage appears heavily underestimated (Szczepanowski et al. 2009). It clearly appears therefore that novel technologies have to be designed to prevent the distribution of ARG in the water ecosystem. Engineers need to look for novel goals for the WWTPs but aiming at the reduction of ARG in the effluents, requires interdisciplinary competences and additional studies. Sewage pipes of municipalities carry within their wastewater high amounts of pharmaceuticals which are present either as untransformed compounds or metabolites. The heavy contamination of wastewater with pharmaceuticals and in particular with antibiotics let the scientific community debate on the selective effect that these compounds might have on the resistant bacteria inhabiting waterwater (Fatta-Kassinos et al. 2011). Pharmaceuticals are a unique category of pollutants, and their elimination and transformation mainly happens during the treatment of the wastewater. Partial degradation of antibiotics can also occur in the sewer system. Antibiotics are mostly hydrophilic compounds since they are designed to be biologically resistant and therefore they are expected to remain in the aqueous phase of the wastewater. Beside the ß-lactams which are fully hydrolyzed, the removal rate of the other chemical classes of antibiotics is minor and often depends on the technology of the WWTPs (Kummerer 2009). The interaction of bacterial populations and antibiotics is still

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unclear even in the clinical settings where antibiotics are present in relevant concentrations (Martinez 2009b), nevertheless first studies revealed that antibiotics at sub-inhibitory concentrations can have an impact on cell functions and change the expression virulence factors or the transfer of antibiotic resistance in the environment (Gullberg et al. 2011). Concentrations of antibiotics therefore could be a useful indicator for the detection of antibiotic resistance selection in the wastewater. Sewers of hospitals unit are often the place where elevated concentrations of antibiotics are found. If we would compare specialized sewage for hospitals with sewage of municipalities, higher concentration of antibiotics and resistant bacteria would be detected in the wastewater originated from hospitals. Nevertheless, the total volume of hospital sewage wastewater is significantly lower than the total volume of municipal wastewater and therefore the overall exposure of the surface water to the municipal effluent is greater. Moreover, the majority of the antibiotics consumption by the population takes place at home (during post-hospitalization or after medical prescriptions) and the release of resistant bacteria and environmental relevant concentration of pharmaceuticals happens via municipal sewage systems. In Germany, each year, approximately 2000 tons of antibiotics are used for human and veterinary medicine (Meyer et al. 2013) but the total consumption of antibiotics in hospitals does not exceed 25% of the total prescribed antibiotics. Other important factors found influencing the selection of resistant bacteria in wastewater is the massive use of biocides or the release of heavy metals (HM) from stormwater into WWTPs (Stepanauskas et al. 2006). Biocides are widely used also in homes, soaps and disinfectants. Triclosan, one of the most common biocides, has been shown in both clinical and environmental settings to select for antibiotic resistance in Escherichia coli (Middleton & Salierno 2013) and ciprofloxacin resistance in triclosan-sensitive P. aeruginosa (Chuanchuen 2011) were isolated from surface water near wastewater treatment plant effluents. The interaction between antibiotics and triclosan was generated either by cross resistance via target gene mutation (mar gene) or by increased expression of a multidrug efflux pump (AcrAB) in the cell wall of bacteria. Increased tolerance to antibiotics and selection of resistant bacteria was often recorded in wastewater and sludge in presence of HM. In the natural ecosystems, the toxicity of heavy metals depends on the environmental conditions influencing the valence of the metal ions and therefore their bioavailability (Seiler et al. 2012). To avoid cellular damage caused by metal ions, bacteria evolved mechanisms of metal tolerance. Only later this mechanism was discovered to be an indirect path of resistance to antibiotics. The antibiotic selection could be displayed either via physiological (cross- resistance) or genetic (co-resistance) paths. In the case of cross-resistance, the pollution caused by HM trigger increasing levels of antibiotic tolerance due to co-resistance gene regulation, in the case of cross-selection, the genes responsible for antibiotic and heavy metal resistances are carried on the same mobile element and in presence of one of the two pollutants bacteria may spread the resistances via horizontal gene transfer (HGT) (Stepanauskas et al. 2006). Mechanisms understanding the maintenance and spread of the pool of antibiotic-resistance determinants in the environment through heavy metal selection

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are still very fragmented and focused mainly on clinically relevant species. The role of these metals in wastewater is poorly studied and would need more thoughtful studies.

Novel WWTP technologies to reduce antibiotic resistance in surface water

The most up to date WWTPs technologies were born to improve water quality of effluents by reducing both microbial load and pharmaceuticals. Luczkiewicz et al. (2011) and Ibáñez et al. (2013) tested the ozonation as an additional step after the clarification of the wastewater in laboratory conditions. In both cases, the disinfection of the effluent was mostly effective for both coliforms and antibiotics although residues of coliforms were found in the effluent (<100 colonies/100ml) even after the application of O3 in high concentrations (4 mg O3 L-1). Alternative method under studies is the ultra-violet (UV) irradiation. In this case, the avoidance of chemicals is one of the main advantages of this physical method (EPA 1999) but the prevalence of resistant bacteria seems to increase within the surviving organisms (Ibanez et al. 2013, Luczkiewicz et al. 2011). Furthermore Rizzo et al. 2013 demonstrated that different bacterium genotypes, even of the same species, carrying different resistances cannot be treated with the same UV intensity (Rizzo et al. 2013a). For an effective reduction of resistant bacteria an irradiation of one hour would be necessary but economically unfeasible and very space consuming. McKinney & Pruden (2012) came to the same consideration although the inactivation of methicillin resistant Staphylococcus aureus, Van resistant Enterococcus faecium, and multi-resistant E. coli and P. aeruginosa, was effective. Additional studies were done from the same group on the damage of extracellular resistant genes. Unfortunately the radiations necessary were higher than 200 mJ cm-2, dose far upper the maximum UV recommended by law in the United States. Under the 200 mJ cm-2, only disinfection of the outflow was possible (McKinney & Pruden 2012). Beside the technical difficulties to reduce the amount of ARG released in the outflow of WWTPs, genomic and cell wall structure of bacteria pose additional challenges to scientist and engineers. Gram-positive bacteria are in general more resistant to treatment than Gram-negative bacteria. The reduced genome size of most Gram-positive bacteria compared to Gram-negative reduce the targets for UV damage and eventually, leads to a reduced susceptibility of the strain to the treatment. Furthermore the peptidoglycan layer present in Gram-positive microorganisms gives an extra protection against the UV radiation (McKinney & Pruden 2012). Also genes are eliminated selectively. The ampC gene, encoding the expression of beta-lactamases, is more difficult to eliminate than the vanA (in E. faecium) and the mecA (in S. aureus) due to the higher number of adjacent thymines in the gene sequence (Bush et al. 1995). The selective action of physicochemical techniques on the antibiotics and bacterial population inhabiting wastewater would suggest a more natural method to reduce water pollution. Constructed wetlands have been recently introduced as a valid technique to overcome the problem encountered with ozonation and UV radiation but also constructed wetlands displayed pros and cons. E. coli and enterococci seem to be reduced almost (Sidrach-Cardona & Becares 2013) specifically in planted wetlands but the antibiotics removal was not sufficient or

- 24 - Chapter 1

concerned only some antibiotics. The selective elimination of pharmaceuticals relies on the uptake behavior of xenobiotics of plants which differs for different species. Moreover microorganisms in the soil matrix and plant’s uptake capacity could play a role as well (Hijosa-Valsero et al. 2011). Becauce of their cost effective technology and their efficient reduction of drug-resistant bacteria, constructed wetland is an useful technique (depending on the scale) to adopt in wastewater treatment. However, as in all semi-natural ecosystems, constructed wetlands are influenced by abiotic factors like temperature and area. Low temperature seems to reduce the removal of coliforms or resistant microorganisms (Novo et al. 2013) and the small surface area of the wetland is only beneficial for small urban regions with a lower volume of wastewater. All in all these treatments are, for different reasons, not fully eliminating either drug- resistant bacteria or antibiotics. Consequently environmental concentrations of antibiotics can be sufficient to exert a selective pressure on the microorganisms, favoring therefore the evolution of bacterial (multi-)resistances.

Modern and classical methods to study antibiotic resistance in wastewater

First step on the tackling of antibiotic resistance in the environment is always the identification of hotspots through the detection and quantification of ARG for the whole microbial community or single targeted microorganism. This task could be achieved via culture-based or molecular methods. Each method exhibits specific advantages and drawbacks. The introduction of DNA extraction kits into commerce has made the bacterial DNA extraction from environmental samples a routine approach. The so isolated DNA can be used either for the amplification of targeted DNA sequences (i.e. functional or 16S ribosomal RNA genes) or for direct sequencing. Antibiotic resistance genes are either isolated from environmental samples by cloning from cultured bacteria or by PCR amplification. In particular, quantitative PCR (qPCR) can give a reliable quantification on the prevalence of known ARG in wastewater and this prevalence could be used as an estimation ARG contamination level. Nevertheless, the PCR strongly depends on primers that are based on known resistance genes and is not feasible for the identification of novel genes. Indeed, the knowledge of ARG is mainly based on information acquired in the past from the study of individual resistant clinical isolates. Microbial communities and antibiotic resistance genes are instead quite diverse in the environment. Therefore it is of importance that when designing primers, only the conserved area of the genes will be used as a target of PCR amplifications because otherwise only certain sequence types of the environmental pool DNA will be amplified (Tamminen 2010, Tamminen & Virta 2015). Lately, the molecular-based approaches have been extended to the detection of genetic structures involved in ARG capture, namely integrons, enhancing the understanding of resistant bacteria and ARG dynamics in complex anthropogenic environments (Gillings et al. 2015). Despite the recent progresses in this field, the proof of ARG transfer in WWTPs

- 25 - Chapter 1

remains a difficult task and can be only studied on a case-by-case basis. One promising approach toward a better understanding of the distribution and transport of ARG in the environment is the recent application of metagenomic tools (Handelsman, 2004) as well as high-throughput sequencing techniques in environmental samples. These techniques allow an in-depth description of resistance genes or resistant bacteria in different environments. The 454 pyrosequencing and more recently the Illumina MiSeq technology allow high parallel sequencing of single amplicons without a need for library construction and elevated number of reads. High parallel sequencing of single amplicons is commonly applied for the phylogenetic profiling of complex microbial communities using 16S ribosomal RNA genes (Cai & Zhang 2013, Vanwonterghem et al. 2014) while metagenomic studies involving direct sequencing are used for the co-investigation of ARG levels in environmental pooled DNA (Kristiansson et al. 2011, Port et al. 2014). Main drawback of the cultivation-independent methods compared to the traditional microbial cultivation methods is that amplified genetic targets lose their genetic linkage to the other genetic properties present in the DNA pool. With this technique, the association of ARG and the 16S ribosomal RNA gene identifying the microbe which was carrying that gene is not possible. Culture-based antibiotic susceptibility testing is one of the most widely used antibiotic resistance test in hospitals and allows the comparison of non-clinical data with clinical data. However, antibiotic breakpoints have been characterized only within the context of their medical functions (Walsh & Duffy 2013) as susceptible, intermediate or resistant to an antibiotic. The problem arises when these definitions of resistance should be applied to environmental bacteria, for which no breakpoints exist. Furthermore, given the absence of standardized methods to assess antibiotic resistance in environmental samples, the resistance prevalence data in different part of the Globe or bacterial groups cannot be compared with accuracy. Nevertheless, the antibiotic susceptibility testing still remains a relatively cheap and straightforward technique and allows the phenotypic detection of uncharacterized or multiple resistance mechanisms if the bacteria investigated are culturable and previously known. Another approach that has been used to evaluate the level of antibiotic resistance in the environment takes into account the so called “percentage of resistance”. This percentage is represented as the ratio between the number of bacteria that are able to grow on media under antibiotic selection at concentrations close to the minimal inhibitory concentration (MIC) and the total number of bacteria growing on antibiotic-free media. Despite the fact that the culturable fraction of bacteria in the environment is estimated as the 1% of the overall microbial diversity (Torsvik et al. 2002), environmental studies take for practical reasons cultivation of specific bacteria as golden standard for microbiological risk assessment. In particular for wastewater, the traditional monitoring approach to assess the water quality of effluents has been since decades, the enumeration of coliforms, faecal enterococci and Escherichia coli. These tests and their interpretation are well documented, and therefore considered reliable. Moreover, E. coli is a useful enteric bacterium for the study of waterborne pathogens and it could be considered a good candidate for the monitoring of

- 26 - Chapter 1

ARG evolution and spread since E. coli is a bacterium able to uptake or transfer genes to other organisms and often exposed to multiple antibiotic treatments. Moreover the flexibility of its genome allows numerous commensal E. coli to acclimatize and proliferate in secondary habitats including natural ecosystems while other pathogens might not have the same ability (Byappanahalli et al. 2006). Taken together the drawbacks of culturable and non-culturable methods, it clearly appears that to address more specific questions on the evolution and spread of ARG in the environment, a method is required, which would be able of linking microbial functions with their phylogeny without culturing the bacteria. Single cell approaches have shown promising results for environmental samples (Stepanauskas & Sieracki 2007) (Marcy et al. 2007). Unfortunately, beside a first successful attempt of Tamminen et al (2010) which simultaneously detected ARG and non culturable microorganisms carrying the resistance genes (Tamminen 2010, Tamminen & Virta 2015) implementation of this method is nowadays still lacking.

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Chapter 2

Microbial analysis of various household-scale wastewater treatment plants

Microbial analysis of various household-scale wastewater treatment plants

Introduction

In rural areas of many countries, wastewater treatment in large, centralized wastewater treatment plants (WWTPs) is presently complemented by small, decentralised WWTPs directly connected to individual households (Chung et al. 2008, Libralato et al. 2012). This trend has increased in Europe where, due to the EU Water Framework Directive, the water quality of many (small) rivers needs to be improved (EC/60 2000). Traditionally, decentralised (d-) treatment consisted of clarification of waste water in an underground septic tank and discharge to a soil sorption system. Nowadays, d-WWTPs employ a variety of treatment processes including aeration, filtration, clarification and disinfection (Gikas &Tchobanoglous 2009b, Otterpohl et al. 2003). While numerous studies are available for large scale WWTPs, scarce data are available on the microbial communities involved in and released by the d-WWTP technologies. This surprises because it is well known that the microbial diversity of WWTPs is of great importance for their functional stability (Briones &Raskin 2003, Gentile et al. 2007). Furthermore, comparative investigations in d-WWTPs on the relationship between microbial diversity, pathogen content and treatment plant technology are also rare (Briones &Raskin 2003, Rittmann et al. 2006, Rizzo et al. 2013a, Zumstein et al. 2000). Assessing microbial communities and how they relate to the purification efficiency of complex wastewater is fundamental for designing any reliable biological waste treatment (Graham &Smith 2004, Wagner et al. 2002). Originally, WWTPs have been designed to reduce the transfer of organic matter, nitrogen and phosphorus into freshwater systems (Vasquez &Fatta-Kassinos 2013). The reduction of bacteriological pollution in wastewater has not been a priority in Europe until now and currently no European directives were legislated for bacteriological quality of treated water although waterborne diseases are a major threat for public health (Fong et al. 2007, Kremer et al. 2011, Lienemann et al. 2011). So far, the potential impact of pathogens is taken into account only within the directives concerning the water quality of recreational waters (EC/7 2006, Garrido-Perez et al. 2008) although waterborne diseases are estimated to be responsible for 4.0% of all deaths and 5.7% of the total disease burden worldwide (Pruesse et al. 2007). Therefore studies are called for, which investigate monitoring strategies concerning the diversity of bacteria and the abundance of pathogens in the effluents of wastewater treatment

Chapter 2 plants. Current efforts concentrate on the efficiency of WWTPs to remove faecal bacteria (Vaz-Moreira et al. 2014) but the standardised detection methods have shown significant limitations (Rousselon et al. 2004). These limitations are partly due to the use of a few cultivable indicator organisms for pathogen content of WWTP effluent. With such an approach many pathogens relevant for the public health are not detected, leading to an underestimation of infectious microorganisms (Ulrich et al. 2005). Wastewater effluents are one of main sources of pathogen contamination of aquatic ecosystems and contribute significantly to the dissemination of pathogens into the water environment and therefore have a strong impact on the water quality (George et al. 2002). The development of molecular techniques provides the opportunity for a more comprehensive investigation of the microbial diversity in functionally stable wastewater treatment plants, while allowing the test for potential pathogens escaping classical cultivation methods. The aim of this work was to characterize the microbial diversity in five d-WWTPs differing in technology (rotating biological contactor, rotating floating bed, sequencing batch reactor, trickling filter and constructed wetland) which are operating under constant conditions. The work was conducted at the demonstration center for household-scale wastewater treatment technologies in Leipzig where small d-WWTPs receive identical inflows. Molecular and cultivation based methods were employed to test for a correlation of e the microbial diversity of the treatment plants to standard chemical parameters of the effluent and the release of potential pathogens into the receiving environment.

Material and Methods

Selection of decentralised WWTP technologies

Samples were collected at the BDZ e.V. (Bildungs- und Demonstrationszentrum für dezentrale Abwasserbehandlung e.V.) training and demonstration centre for household-scale wastewater treatment technologies, located in Leipzig, Germany. All treatment plants share a common inflow (http://www.bdz-abwasser.de/en/welcome-homepage-bdz-e-v). We selected five common technologies for municipal water treatment (Figure 4). The inflow and the five effluents were collected weekly for nine weeks (from March to May 2012) in 1-liter glass bottles (Schott) and transported to the laboratory at 4°C. Samples were directly processed and the remaining material stored at -80°C until required. Experimental and operational conditions of the d-WWTPs are listed in Table 3.

Water quality of Effluents

Inflow and effluents of the d-WWTPs (Table 3) were monitored routinely (weekly for 9 weeks) for standard water quality parameters according to German regulations. Methods listed in Table 4 were conducted following the standard protocols.

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Figure 4 Schematic representation of the five household-scale wastewater treatment plants under investigation at the BDZ eV. (Leipzig, Germany). See also Table 4.

Microbial community characterisation and pathogens detection

In order to account for possible microbial community variations, water samples were collected on the 9th and 16th of March 2012. Five ml of inflow and 10 ml of each effluent were filtered and the recovered cells resuspended in 1 ml PBS before conducting molecular investigations. The total cell numbers of all samples were determined in triplicates after staining with 4´,6-diamidino-2-phenylindole (DAPI) under a fluorescence microscope (Wagner et al. 1993). Furthermore, the microbial communities from the 9th of March effluents were enriched in triplicates in Luria Broth (Difco/Becton Dickinson, Heidelberg, Germany) on shaken microtiter plates at room temperature until steady state conditions where reached. Microbial growth was monitored spectrophotometrically over the time as turbidity at 600 nm in a microtiter plate reader Spectramax 250 (Molecular Devices, Sunnywale, CA). Microbial cells were harvested by centrifugation.

Molecular methods assessing microbial diversity of effluents

The diversity of the original and enriched microbial community of the d-WWTP effluents was assessed by terminal restriction fragment length polymorphism (T-RFLP) analysis based on 16S rRNA genes. DNA was extracted in triplicates with the NucleoSpin® Soil kit (Macherey- Nagel GmbH & Co. KG; Düren, Germany) following the manufacturer’s instructions. Pooled DNA from triplicate extractions were used as PCR templates. Bacterial 16S rRNA gene fragments were PCR-amplified with the primers 27F-FAM (labelled at the 5’-end with phosphoramidite fluorochrome-5-carboxyfluorescein) and 1492R (Lane 1991). PCR was

- 33 - Chapter 2 performed in 25 µL samples containing 3 µL of 1:100 diluted template DNA (equivalent to 1– 2.5 ng), 5 pmol of each primer and 12.5 µL Taq Master Mix (Qiagen, Hilden, Germany). PCR cycle conditions were as described previously (Kleinsteuber et al. 2006). PCR products were purified using the Wizards SV PCR Clean-Up System (Promega, Mannheim, Germany) and quantified after agarose gel electrophoresis and ethidium bromide staining using the GENETOOLS program (Syngene, Cambridge, UK). Purified PCR products were digested with the restriction endonuclease MspI (New England Biolabs, Fankfurt/Main, Germany) and analysed by capillary electrophoresis on an ABI PRISM 3100 Genetic Analyzer (Applied Biosyste). The lengths of the fluorescent terminal restriction fragments (T-RF) were determined using the GENEMAPPER V3.7 software (Applied Biosystems), and their relative peak areas were determined by dividing the individual T-RF area by the total area of peaks within the threshold of 50–500 bp. Statistical analysis on T-RFLP fragments was performed by R-script using the PAST software version 2.0.6 (http://folk.uio.no/ohammer/past) based on Bray-Curtis similarity index (R_Development_core_team 2009). Peaks representing less than 1% of the total peak area were automatically discarded.

Clone library and amplified ribosomal DNA restriction analysis (ARDRA) To generate five clone libraries of the enriched effluents, bacterial 16S rRNA gene fragments were amplified by PCR using the primers 27F and 1492R in a PTC-200 Thermal Cycler (MJ Research, St. Bruno, Canada). The PCR conditions were the same as in the T- RFLP analysis. The PCR products were purified with an E.Z.N.A. Cycle-Pure Kit (peqLab Biotechnologie GmbH, Erlangen, Germany) and cloned using a QIAGEN PCR Cloning Kit according to the instructions of the manufacturer. From the five libraries of the enriched effluents (Wetland, SBR: Sequencing batch reactor, Trickl: Trickling filter, WSB: rotating/floating bed, DISC: rotating biological contactor technology), a total of 312 clones were picked and screened for the appropriate insert size by PCR using the vector-specific primers M13uni (5'-TGT AAA ACG ACG GCC AGT-3') and M13rev (5'-CAG GAA ACA GCT ATG ACC-3'). DNA of clones was extracted by microwave heat treatment as described previously (Orsini & Romano-Spica, 2001). DNA quality and quantity were determined by agarose gel electrophoresis and spectrophotometrically (Nanodrop® ND-1000; Peqlab, Germany). Ten ng of purified 16S rRNA gene amplicons of positive clones were digested with 2.5 U MspI (New England Biolabs, Ipswich) at 37°C overnight. Resulting restriction fragment patterns were separated electrophoretically on 2% MetaPhor agarose (Cambrex; distributed by Biozym, Germany, BioRad chamber; BioRad Laboratories) with subsequent SYBR GOLD nucleic acid gel staining (Invitrogen, Paisley, UK). Identification of restriction patterns and grouping into operational taxonomic units (OTU, for further information see section below) was carried out with the software Phoretix™ 1D Advanced version 5.20 (Nonlinear Dynamics, Newcastle, UK) applying the single linkage method for clustering. Dendrograms were calculated without differentiation of the sample origin (effluents). Generally, one representative of each OTU was subjected to sequence

- 34 - Chapter 2 analysis. For larger clusters (>10 isolates), 2–4 representatives were sequenced to reveal the cluster identity.

Table 3 Operational principles of the decentralised wastewater treatment plants (d-WWTPs) on a local scale at the BDZ e V. Demonstration Centre, Leipzig, Germany. d-WWTP Short name Operational principle

Rotating biological DISC After the primary sedimentation process, the contactor wastewater flows through a series of rotating discs. Due to a horizontal flow that covers the discs for a 50% of their surface, the microorganisms receive series of discontinuous aerobic/anaerobic phases.

Trickling filter Trickl Three steps process: 1. Pre-treatment 2. Biodegradation, microorganisms settle on solid material with high surface. 3. Final sedimentation: the treated water is recycled into step 2 to enhance the hygienization efficiency.

Sequencing batch SBR Discontinuous process. The aeration and the reactor post-treatment process are only timely separated because the two processes occur in the same tank. To ensure high biodegradation efficiency, the time and the functionality are defined and controlled.

Rotating/floating bed WSB Multiple steps system: 1. Sedimentation tank 2. Floating bed containing micro-cylinders supporting the microbial biofilm growth, additional aeration 3. Precipitation tank.

Constructed wetland Wetland Gravel bed, horizontal flow, Phragmitis australis as plant culture.

Sequencing and phylogenetic analysis of effluents Partial DNA sequencing of cloned 16S rRNA gene amplicons was performed using a BigDye RR Terminator AmpliTaq FS Kit version 3.1 (Applied Biosystems, Germany) and the sequencing primers 27F and 519R (Lane 1991). Electrophoresis and data collection was carried out on an ABI PRISM 3100 Genetic Analyzer (Applied Biosystems, Germany). Data were analysed by the ABI PRISM DNA Sequencing Analysis software, and sequences of both complementary strands were assembled by the ABI PRISM Autoassembler software.

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Nucleotide sequences were corrected manually with the Sequencher 4.8 software (Gene Codes Corporation, Ann Arbor, MI), aligned and counterchecked with BioEdit software before uploading to the BLASTN program (http://www.ncbi.nlm.nih.gov/BLAST) and the RDP Ribosomal Database Project II (http://rdp.cme.msu.edu) The phylogenetic analysis was accomplished with the ARB software, version Linux Beta 030822 (http://arb- home.de), (Ludwig et al. 2004). The determined sequences were initially aligned to the SILVA SSURef database Release 90 (www.arb-silva.de), (Pruesse et al. 2007) by the ARB Positional Tree Server and added to the SILVA tree using the Quick Add Parsimony tool and applying a Bacteria-specific filter. The alignment was verified by comparison with the next relative sequences and corrected manually. The final position within the ARB tree and bootstrap values were calculated by the Parsimony Interactive tool. The identified 16S rRNA gene sequences were deposited in the GenBank database under accession number BankIt1717158 (KJ703017 - KJ703100). The cloning effort was evaluated using rarefaction curves based on the OTU-abundances detected with each effluent enrichment with PAST software version 2.0.6. Only sequences were considered where an unambiguous affiliation at least on the family level was possible (for 100% of the database hits). We are aware of the limitations of richness estimates obtained by ARDRA and our cloning effort. It should be noted, however, that the applied methods fulfilled our needs for community comparison and as a reference for pathogen cultivation.

Statistical analyses

The structure and distribution of the microbial diversity was represented as Pareto Lorenz evenness curves, based on the intensity and banding pattern of the T-RFLP profiles. Pareto–Lorenz distribution curves were constructed as described by Marzorati et al. (2008). Briefly, for each T-RFLP profile, the respective peaks were ranked from high to low, based on their relative abundance (Marzorati et al. 2008). The cumulative proportions of peaks were plotted on x-axis and the respective cumulative intensities of bands on the y- axis. To test for a statistical difference of the banding pattern of the T-RFLPs an analysis of variance and the Wilcoxon post hoc test were conducted. In order to test for differences of the chemical parameters of effluents the variance over time and the within-technology variance was analyzed by a random effects ANOVA (n=9). To evaluate a posteriori the relative importance of environmental variables on the variation of bacterial community structures, a multi- dimensional scaling (n-MDS of 16S rRNA gene) of the T-RFLP profiles was performed. Numerical treatment and analysis of the data were carried out with R (http://www.r- project.org/index.html). The following environmental variables were included in the analysis: pH, total carbon (TC), inorganic carbon (IC), total organic carbon (TOC), redox potential, conductivity, biological oxygen demand (BOD5), total cell count (cell/ml) chemical

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oxygen demand (COD), ammonium NH4–N, nitrate NO3-N, total nitrogen bound (TnB), residual phosphorus (RP), retention time (Rt) and particulate filtration (AFS). Furthermore, the encountered concentrations of triclosan, methyl-triclosan, mercury, nickel, cadmium and copper were considered as additional variables. All data were log (x+1) transformed except for pH. All chemical parameters were initially included into the model, and successively step- wise removed to avoid multi-collinearity of the screened variables. Environmental factors best describing the most influential gradients in community composition were identified by forward selection with 999 unrestricted Monte Carlo permutations by MDS plot. To reveal the influences (positive and negative correlation) that chemical parameters of the plants had on the appearance of a) potential bacterial pathogens or b) bacterial families in the effluents, the MANOVA test based on dissimilarities was used.

Table 4 Analytical methods used to determine the standard water quality of household-scale wastewater treatment plants.

Parameter Method

pH DIN 38404-C5

Electrical conductivity DIN EN 27888-C8

Redox potential DIN 38404-C6

COD Hach-Lange test kit (LCK114), DIN 38409-43, photometric measurement

BOD5 DIN EN 1899-1

TOC (Total organic carbon) TOC/TN-Analyzer, Shimadzu, DIN 38409-27

TNb (Total nitrogen bond) TOC/TN-Analyzer, Shimadzu, DIN 38409-27

NH4-N (Ammonia Nitrogen) Ammonium-Test by MERCK (1.14752), ISO 7150/1, photometric measurement

NO3-N (Nitrate Nitrogen) Hach-Lange test kit (LCK114), DIN EN ISO 13395, spectrometric detection

TSS (Total suspended solids) DIN EN 872

Results

Standard chemical parameters

Results of chemical analyses are presented in Figure 5. For all plants (Table 4) with the exception of the trickling filter plant the data showed a low variation over time (timeline not shown). The high variation of the chemical parameters in the trickling filter plant effluents indicates a possible instability of the treatment process within the plant.

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Microbial diversity of effluents

T-RFLP of the 16S rRNA gene was used to characterize microbial community profiles in the original inflow and effluent water of d-WWTPs on two sampling days. For the 9th of March sample, the number of peaks (i.e. Taxa in Table 5) for bacterial T-RFs within the range of the size marker (50-550 bp) ranged from 37 to 89 OTUs per sample corresponding to Shannon indices between 2.51 and 4. Both, T-RF richness (OTUs) and Shannon diversity were higher in the wetland effluent than in WSB, Trickl and SBR effluent (Table 5). The DISC effluent displayed the lowest number of T-RFs. The evenness of the microbial communities was high in all systems (Figure 6).

Table 3 Diversity and evenness index calculation based on T-RFLP profiles of the original wastewater communities (inflow and effluents) of d-WWTPs.

Index Inflow DISC Trickl SBR WSB Wetland

Taxa 37 51 46 64 64 89

Shannon 2,194 2,512 2,828 2,893 2,838 4

Evenness 0,2426 0,2418 0,3676 0,2819 0,2669 0,6135

Table 4 Distance matrices based on the T-RFLP analysis of the original (a) and the enriched (b) microbial communities (inflow and outflows) of each house-hold wastewater treatment plant system.

a) Distance matrices based on original microbial communities

DISC WSB Trickl Wetland SBR _ Inflow 0.17315 0.0139 0.142 <0.0001 0.1353 DISC - 0.382 0.8 <0.0001 0.152 WSB - 0.175 <0.0001 0.218 Trickl - <0.0001 0.006 Wetland - <0.001 SBR - _

b) Distance matrices based on enriched microbial communities DISC WSB Trickl Wetland SBR _ Inflow 0.2 0.0036 0.098 0.0042 0.129 DISC - 0.0128 0.037 <0.0001 0.228 WSB - <0.0001 0.1266 0.8 Trickl - <0.0001 0.0026 Wetland - 0.02813 SBR - _

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Figure 5 Chemical analyses of the effluents of the five d- wastewater treatment plants over a period of three months (March-June 2012). The box and whisker plots represent the averaged values of chemical analyses in the effluents for each wastewater treatment

Figure 6 Log transformed Pareto-Lorenz distribution curves of the original wastewater communities (inflow and outflows) of d-wastewater treatment inflow and effluents

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Interestingly, the bacterial community of the constructed wetland effluent was most even (Tuckey´s honest significant difference), followed by the SBR and WSB communities, while the evenness values of Trickl and DISC effluent were approximately as high as that of the influent. The Pair-wise Wilcoxon test revealed highly significant differences in bacterial communities between plant effluents, with wetland communities differing from those of all the other treatments (P<0.001), and substantial differences between SBR and Trickl communities (P<0.001, note that minor dissimilarities were found in nearly all other pair- wise comparisons (Table 6a). These results are consistent with the clustering of microbial communities observed in non-metric multi-dimensional scaling analysis n-MDS (Figure 7a). The T-RFLP analysis performed on 16th March samples confirmed the earlier effluent community data (Figure 7b) despite evident dissimilarity of the two inflows (Appendix 1 and 2). From the overall 272 clones ranked in 29 OTUs after ARDRA analysis (data not shown), 86 clones were sequenced (Table 7). Of the major bacterial lineages, α-, β-, γ-, ε-Proteobacteria, Bacteroidetes, were present in the enriched wastewater effluents. The phylogenetic composition and relationship of the microbial populations are shown in Figure 9 and Table 7. The dominant class comprising 45.4% of all clones and was the β – Proteobacteria, genera Comamonas and Alcaligenes. They were mainly found in the constructed wetland. The second-most abundant class with a proportion of 24.6% was the γ – Proteobacteria, the genera Aeromonas and Pseudomonas, which was dominant in the SBR effluent. From all clone libraries, the sequencing coverage was predicted by comparing the number of OTUs observed with the number of sequences sampled. The rarefaction curves of all clone libraries began to plateau, except for the SBR effluent where the cloning effort might have been insufficient (Figure 8).With the exception of the constructed wetland, all enriched effluents contained a significant amount of pathogens. In the SBR 47% of clones were identified as Aeromonas hydrophilia. In the WSB and DISC the amount of pathogen species increased but the overall amount of pathogen taxa decreased to 23% - 30% of the clones respectively. The pathogen abundance in Trickl (38.5%) ranged between those of the other treatments. Interestingly, all the plants developing biofilms (Trickl, DISC and WSB) released similar pathogen communities (Table 7), (p< 0.05).

Abiotic factors, operating parameters and bacterial community structure of effluents

Factors potentially associated with the observed variations of the structure of the bacterial communities were assessed by multivariate analysis (n-MDS). Of the considered chemical parameters treatment technology (P<0.001), ammonium (NH4), (P<0.05), inorganic carbon (IC) (P<0.05) and retention time (tR), (P<0.05) as well as total carbon content (Tc) were found to be correlated with the bacterial community structure of treated wastewater (Figure 7a). In particular, IC positively correlated with the wetland community structure whereas + NH4 mainly correlated with two of the effluent communities of d-WWTPs (WSB and SBR). No correlations where shown for the inflow and SBR, Trickl effluent communities with none of the operational and chemical parameters object of this study.

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Overall in this study no correlation was shown between the considered chemical parameters and the pathogens detected in the effluents. Strong correlations were instead found between bacterial families and chemical data (Appendix 3). In detail, Bacterioidaceae were negatively correlated with BOD5, TOC, triclosan and nickel concentration but positively with the Rt. The family of Alcaligenaceae negatively correlated to TC, cadmium (Cd), and nickel (Ni) - concentration but was positively correlated with NO3 and RP. The abundance of + Comamonadaceae instead was inversely correlated with NH4 , IC and TnB and positively - correlated with NO3 and RP. Additional positive correlations were revealed between Aeromonadales, Pseudomonadales, Clostridiales and Rt whereas total bond carbon (TbC) and chemical oxygen demand (COD) seems to negatively correlate with these class order.

Figure 7 n-MDS plot of inflow and effluents based on T-RFLP analysis of (a) original wastewater communities from the 09.03.2012 sampling campaign and (b) original wastewater communities of both sampling campaigns 09.03 and 16.03.2012 of d-WWTPs. Treatments are indicated as triangles. Inflow, DISC: rotating biological contactor, Trickl: trickling filter, SBR: sequencing batch reactor, Wetland: constructed wetland, WSB: Rotating floating bed. Factors significantly determining the community structure of isolates (P < 0.05, P<0.01 P<0.1) are indicated by solid arrows (calculated with Monte Carlo permutation with 1000 permutations) NH4: Ammonium, IC: Inorganic Carbon, tR: Retention time, TnB: Total Nitrogen bond, Conduct: conductivity). Clone library diversity and pathogen identification

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Figure 8 Rarefaction analysis of five clone libraries based on 16S rRNA gene amplification of the enriched microbial community from d- WWTPs

Discussion

The objective of this study was to characterize and compare the microbial communities in the effluents of d-WWTPs differing in their water treatment technology. The effect of the treatment technology was correlated to the general efficiency of nutrient removal or to the release of pathogen diversity. The microbial diversity of the effluents as resolved by T-RFLP analysis, clearly differed between the studied wastewater treatment technologies though the efficiency of bacterial removal (bacterial abundance) did not differ significantly. The highest bacterial diversity in the effluent was observed for the constructed wetlands. To test the robustness of these data, a second sampling campaign and analysis was performed two weeks later. Interestingly, the microbial community shifts of the inflow and effluents taken on the 16th of March were as different from each other as they were from those in the samples taken on the 9th of March (Figure 7b). This indicates that the effects of d-WWTPs technology were at least as important as the original composition of the inflow. Furthermore, since there is no evidence to believe that the common inflow was unevenly distributed among the five d-WWTPs, differences in community composition of the effluents suggest that the treatment technology partially controlled the selection of unique microbial community compositions (Table 5). We could thus show that each treatment technology selectively fostered the survival and growth of distinct communities, which were only partially influenced by bacteria in the inflow. Subdominant and rare bacterial species disappeared during the passage. Our data are in concordance with data from previous studies on larger treatment plants, where this selective effect has been termed as population average effect (Osborn et al. 2000, Wery et al. 2008). This effect explains the composition of the effluent of a treatment plant as an outcome of processes within the plants and its influent bacterial composition. This indicates that all commensal bacteria present in the wastewater are the product of bacteria originating from sewage pipes, which mix with environmental bacteria within the treatment plants and consequently form a new average population.

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Table 5 Phylogenetic affiliation of 35 ARDRA-determined phylotypes (OTUs) of the enriched d-WWTP effluents according to RDP classifier; the closest (cultured) relatives according to RDP seqmatch tool are shown in the adjacent column (accession numbers are indicated). The abundances of the OTUs are given by the percentage of representatives (of the total analysed clones per single d- wastewater treatment plant) detected from the effluents enrichments. In bold, identified clones as potential pathogens.

______Effluents______

Phyla OTU affiliation Closest (cultured) relative Accession DISC Trickl SBR WSB Wetland affiliation (RDP classifier tool) (RDP seqmatch tool) number (n=49) (n=66) (n=47) (n=55) (n=55) ______%______Alpha- Caulobacterales Brevundimonas disminuta DQ857897 2 6 - 5.5 - Proteobacteria

Beta- Burkholderiales Alcaligens sp. DQ406733 12 4.5 2 49 32.8 Proteobacteria Bordetella avium AM167904 - 9 - - - -43- Bordetella sp. DQ406733 - 3 - - - Bordetella trematum EU727194 - 3 - - - Comamonas sp. DQ256357 26.5 21 6 5.5 32.3 Comamonas denitrificans 12 7.5 - - 3.6 Comamonas terrigena AM184229 8 - - 5.5 1.8 Comamonas testosteronii AY628412 - - - - 3.6 Kerstersia gyiorum HM117848 - - - - 1.8 Gamma- Aeromonadales Aeromonas hydrophilia X74677 6 1.5 47 Chapter - 21.8 Proteobacteria Enterobacteriales Klebsiella sp. EU545402 8 - - - 3.6 Enterobacter sp. AY376693 - 6 - 3.6 - Pseudomonas sp. AY954288 - - 8 - - Pseudomonas putida AB180734 2 13.5 6 3.6 - Pseudomonas gingerii AF320991 - - 2 1.8 - ____

Chapter 2

Continued Table 5 ______Effluents______Phyla OTU affiliation Closest (cultured) relative Accession DISC Trickl SBR WSB Wetland affiliation (RDP classifier tool) (RDP seqmatch tool) number (n=49) (n=66) (n=47) (n=55) (n=55) ______%______Epsilon- Campylobacterales Arcrobacter cryaeropholius U25805 - 15 - - - Proteobacteria Bacteroidetes Bacteroidales Bacteroides sp. AF139525 - 3 2 5.5 - Bacteroides graminisolvens EF398304 - - 2 - - Bacterioides thetaiotaomicron AY895195 4 - - - - Parabacteroides distasonis AY975576 2 3 - 3.6 - Parabacteroides sp. AF280841 2 1.5 - - 12.8 Flavobacteria Chryseobacterium sp. AJ495802 - - 6 - - Bacteroidales Disgonomonas mosii AJ318110 4 - - 5.5 - Firmicutes Bacilli Bacillus sp. DQ490402 - - - - - Bacillus cereus GU384225 - - - 5.5 - Lactobacillaceae Enterococcus faecalis AY376693 - - - 3.6 - Clostridia Clostridium sp. U85415 - 4.5 - - - Clostridium frigidicarnis AF069742 20 - - - 1.8 a Percentage of potential pathogens 30 38.5 47 23 7.2 %

Chapter 2

Given the above described results, we argue that the effluent microbial diversity reflects the microbial diversity within the treatment plant. To analyze this microbial diversity further we performed a Pareto-Lorenz analysis. The concept of this analysis originated in economics (Kleiber 2005) and has been applied frequently in microbial ecology and biotechnology. The Pareto-Lorenz function (Figure 8) illustrates that in the case of the DISC and Trickl treatments, the microbial community of the effluents is dominated by a few taxa, which likely might represent the catabolic potential of the wastewater treatment plants. The constructed wetland technology instead displayed a higher number of abundant microbial taxa accompanied by numerous other rare taxa. This may have implications for the process resilience of the treatment plants as often the diversity and functional redundancy of the microbial community determine the functional stability of d-WWTPs (Allison &Martiny 2008, Briones &Raskin 2003). This is explained by the studies, which demonstrated that wastewater microbial communities containing more taxa are more likely to have more functional traits (Cadotte et al. 2011, Hernandez-Raquet et al. 2013, Johnson et al. 2014) although stability of the functionality does not necessarily imply stability of community structures. A higher functional stability assures a quick recovery of the plant from a stress condition thanks to the functional redundancy of the microbial community (Fernandez et al. 2000). This may therefore imply a more stable process stability of the treatment plant, containing a higher microbial diversity and in our study this would be the constructed wetland. The advantage of the T-RFLP analysis employed in the present study is its relative speed and economy (Abdo et al. 2006). Unfortunately, the high richness (i.e. T-RF number) of environmental T-RFLP profiles or the presence of dominant peaks can lead to an underestimation or total omission of rare species because of their limited detection threshold and dynamic range (Bent &Forney 2008, Zhang et al. 2008). Furthermore, two or more phylogenetic distinct organisms might be assigned to single T-RFs (Schutte et al. 2008). These limitations which likewise affect the detection of pathogenic bacteria guided us to combine T-RFLP with enrichment cultivation. Enriched samples revealed that the constructed wetland releases the lowest diversity of potential pathogens. A possible explanation for this higher removal of pathogens is the semi-natural ecosystem characteristic of the constructed wetland. Several factors related to this characteristic have been described by other studies, such as the antimicrobial potential of roots exudates (Vacca et al. 2005), predation mechanisms and an intense activity of bacteria in biofilm-forming zones (Decamp &Warren 2000). The effluents of the other treatment technologies contained a higher diversity of pathogens. Intriguingly, similar treatment technologies seemed to foster the growth of similar pathogens (Table 7), which is also reflected in the overall similarity of the diversity of the corresponding effluents. Technologies such as SBR characterized by forced aeration and sludge formation selected for a higher amount of pathogens in the effluents mainly belonging to the family of Aeromonadaceae. Treatment plants based on biofilm- forming treatment technologies (Trickl, Discs and WSB) contained the highest diversity of pathogens in their effluents. Generally it is interesting to note that those effluents, which contain the lowest overall microbial diversity contained the highest diversity of pathogens.

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The combined approach of molecular and cultivation based analysis revealed that the overall microbial community diversity detected with the molecular method did not indicate the likelihood of pathogen release by the treatment plants under study. The results rather suggest that the cultivable bacterial fraction is a better indicator for the presence of pathogens. This is in concordance with previous studies, which demonstrated a clear correlation of cultivable bacteria and qPCR detected pathogens (Viau &Peccia 2009). It is important to note, that the same study also called for caution to use cultivable bacteria as indicator for pathogens as they estimated the concentration of total genomes between two and four orders of magnitude higher than the number of pathogen genomes (He &Jiang 2005, Monpoeho et al. 2004, Noble et al. 2006). Other studies also demonstrated that the number of cultivable bacteria cannot be used to indicate the abundance of pathogens like Legionella pneumophila or different species of Clostridium. Nevertheless, our study suggests that molecular methods such as T-RFLP applied directly on wastewater samples are suited to estimate microbial diversity and can be used as an indicator of the microbial functional stability of small WWTPs. However, it appeared not the most appropriate to indicate the risk of pathogen release, thus requiring cultivation or the application of more comprehensive methods such as metagenomic approaches. Comparative assessments of microbial diversity are labour and time intensive, especially if species identification is intended. Despite these constraints, we could show the effect of treatment processes and their consistency over the sampling period. Whether the observed differences in microbial community structure are consistent over a longer time periods would be the subject of an additional study. So far, technologies of WWTPs have been investigated mainly in order to improve the removal of organic substances and nutrients (Tanner et al. 2005). Despite modern risk assessments, the potential growth of pathogens in the natural environment has been only scarcely considered and restricted to specific group indicators (WHO 2003a). The need for more information on the release of microbial communities into the environment seems especially important for small-scale of decentralized wastewater treatment technologies. Mainly, because European legislation does not call for any microbiological tests of the effluent, detailed information on the microbial diversity of effluents is scarce. At the same time, d-WWTPs are increasingly employed to improve the water quality of rural areas. As in many rural areas the use of drinking water is often also realized with local water resources, the release of pathogens of small treatment plants into these local resources seems of special significance. Therefore, further studies (and potentially new regulations) are required in this context.

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Figure 9 Phylogenetic relationship of effluent clones retrieved from d-WWTPs DISC: Rotating biological contactor (yellow), Trickl: trickling filter (red), SBR: sequencing batch reactor (purple), Wetland: constructed wetland (green), WSB: rotating floating bed plant technology (blue). The determined partial 16S rRNA gene sequences were aligned to the SILVA SSURef database (release 90); the final positions within the tree and bootstrap values were calculated using the ARB Parsimony Interactive tool. Only bootstrap values above 50% are shown. Scale bar=10% nucleotide

Chapter 3

Co-occurrence of antibiotics resistance in gram- negative freshwater bacteria exposed to constant anthropogenic load

Co-occurrence of antibiotics resistance in gram-negative freshwater bacteria exposed to constant anthropogenic load

Introduction

Antibiotic resistance in bacteria is a phenomenon in rapid expansion and is evolving from resistance to single classes of antibiotics to multiple drugs resistance. Antibiotic resistance (AR) can be intrinsic, acquired by horizontal gene transfer or mediated by single proteins like efflux pumps which are able to transport several antibiotics suggesting that the elements involved in the resistance phenotype were originally involved in other bacterial physiological traits (Fajardo et al. 2008). The rise of multiple drug resistance (MDR), antibiotics and antibiotic resistances were mostly studied in terms of infection treatments and prevention of pathogenic bacteria (Pruden et al. 2006). Nevertheless, multiresistance is not a unique trait in clinically relevant pathogens and can also be detected in environmental microorganisms. However, it is not clear yet how widespread multiresistances are in natural ecosystems, or in which combinations resistances might co-occur. The transfer of specific antibiotic resistance elements from pathogenic microorganisms to the new hosts is known to confer better chances of survival and proliferation of multi-resistant bacteria in natural ecosystems (Aminov &Mackie 2007). The mobility of resistance genes relies on genetic elements such as conjugative transposons, integrons (Poirel et al. 2009), gene cassettes or conjugative plasmids (Binh et al. 2008). In particular, conjugative plasmids belonging to the incompatibility (Inc) groups IncP, IncQ, IncW and IncN are known as antibiotic resistance carriers (Perry &Wright 2013). These plasmids exhibit a broad host range for transfer and self-replication; they mostly occur in gram-negative bacteria, though some plasmids are transferable into gram-positive species as well, like RP4 (Musovic et al. 2006) or pJP4 (Kiesel et al. 2007). While spontaneous gene mutation and recombinatorial events may result in antibiotic resistance even without selective pressure, adaptation of microbes as a response to antibiotics frequently occurs by the acquisition of genetic information via mobile genetic elements (Martinez 2009b). Municipal wastewater often elevated amount of partially degraded or undegraded antibiotics. Moreover WWTPs bridge the gap between the human and the aquatic systems thus allowing genetic traits of potential multiple resistant bacteria to reach autochthonous aquatic microorganisms and to spread among these populations. In the current work we have combined cultivation-based methods with molecular approaches to link the identity of bacterial isolates from freshwater with antibiotic resistance profiles and

Chapter 3 to compare the intrinsic resistance profiles of freshwater bacteria and clinical bacteria from the same bacterial order. To this end we analyzed antibiotic and multiple antibiotic resistances in a population of cultivable gram negative bacteria from a river exposed to a continuous discharge of a WWTP effluent. We collected bacteria from the downstream and far upstream water of the receiving river and used ten common antibiotics to screen for antibiotic resistance and to also estimate multiple resistances in the isolated bacteria. Furthermore, to judge whether the identified resistances might be spread by horizontal gene transfer, presence and self-transmissibility of plasmids from isolates as well as the transfer of the AR to recipient microorganisms was exemplarily examined.

Materials and Methods

Study sites and sample collection

Samples were collected from (i) the downstream of the river Kleine Luppe just after receiving the discharge of a municipal wastewater treatment plant effluent (WW), (ii) 950 m downstream (DW) of the discharge point in the river and (iii) the spring (SW) of the river Gänsebach as a reference for a surface water system with limited exposure to anthropogenic activities such as agriculture and water sanitation facilities. The municipal wastewater treatment plant (WWTP) Rosental in Leipzig (Kommunale Wasserwerke Leipzig GmbH, Saxony, Germany) treats 125.000 m3 wastewater per day. Chemical data and nominal values of the WWTP outflow are given in Error! Reference source not found.. The WWTP outflow discharges in the river Kleine Luppe, which branches as a tributary of the river Weiße Elster in Leipzig (51° 20′ 1″ N, 12° 20′ 50″ O). Both rivers are beta-mesosaprobic to alpha- mesosaprobic (Freistaat Sachsen, 2003, Gewässergüte) and pass the urban area of Leipzig. The Gänsebach is an oligotrophic tributary of the Weiße Elster located in a water protection area in the Erzgebirge mountains (50° 24´ – 51° 00´ N, 12°20´ - 14°00´E) close to the village Lengefeld (Saxony). The area is surrounded by forests and receives only low input of phosphorous and nitrogen. The river is subject to episodic acidification caused by leaching of overburden of the Erzgebirge mining region. Sampling of the wastewater effluent was conducted at three time points, i.e. in July 2007, December 2007 and May 2008 to account for possible seasonal changes of bacterial cell numbers. Spring and downstream water were sampled in July 2007 and May 2008. All samples were collected in triplicates in sterile 1000-ml bottles, cooled during transportation and processed in the laboratory on the same day. Ten-ml aliquots were filtered through 0.2-µm pore-size membrane filters after appropriate dilution (IsoporeTM Membrane Filter GTTP14250, Millipore). Cells were fixed with 4% paraformaldehyde, washed with phosphate buffered saline (PBS) and frozen (-20°C) until total cell counting.

Isolation of bacteria and Antibiotic Resistance Test

Total cell counts were determined in duplicates after staining with 4´,6-diamidino-2- phenylindole (DAPI) (Wagner et al., 1993). Cultivable aerobic bacteria were grown on

- 52 - Chapter 3 duplicate R2A and LB agar plates supplemented with cycloheximid (0.5 mg/ml) to suppress fungal growth. Incubation was performed at room temperature and colony forming units (CFU) were counted after 24 h and one week. Single colonies were collected and at least ten morphologically different colonies were taken from each plate and streaked onto a new plate to ensure the purity of the isolates. Pure isolates were grown in liquid media containing the same components as the plates minus the agar. A Gram test was conducted with 3% KOH (Gregersen 1978) to select Gram-negative bacteria for further investigations. Resistances to antibiotics were determined for all Gram-negative isolates using the agar diffusion disc test (OXOID) on Mueller Hinton Agar according to (da Silva et al. 2006a). Ten antibiotics including natural, semi- and synthetic antimicrobials were selected as representatives of six chemical classes commonly used for domestic treatment of illnesses caused by Gram-negative bacteria : the -lactames ampicillin (AMP) and cephalotin (KF); the quinolones ciprofloxacin (CIP) and nalidixic acid (NA); the macrolides amikacin (AK), kanamycin (KA) and streptomycin (ST); the sulfonamide trimethoprim (W); as well as tetracycline (TE) and chloramphenicol (C) as representative antibiotics of the homonymous two classes. Inhibition zones were measured after overnight incubation at room temperature (D'Costa et al. 2006). Isolates were classified according to the EUCAST disc diffusion method (Matuschek et al. 2014). Bacterial phylogeny was linked to the antibiotic resistance profiles in order to compare the intrinsic resistance profiles of the isolates and clinical bacteria from the same bacterial order (Walsh &Duffy 2013) Multiple resistances where categorised as multiple drug resistance (MDR) when isolates possessed resistances against two or more chemical classes of antibiotics.

Table 6 Chemical data of the outflow of the Rosental municipal wastewater treatment plant.

Average Approved Concentrations on July 23rd 2007b concentrationa concentrationa (mg/liter) (mg/liter) (mg/liter)______

BSB5 4 25 not detected

CSB 26.8 75 31.3

Total Posphate 0.3 1 0.14

Total Nitrogen 9.3 25 8.58______

a Average (for 2008) and standard maximum concentrations (approved values) of the WWTP Rosenthal b Results of chemical analyses at the WWTP Rosental at the time of sampling

Microbial community Identification

Total genomic DNA was extracted from over 300 isolates with the Tissue Kit (Nucleospin® Tissue, Macherey–Nagel). Bacterial 16S rRNA gene fragments were PCR-amplified as described elsewhere (Kleinsteuber et al. 2006) using the bacterial universal primers 27F and

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1492R (Lane et al. 1991). Amplification products were screened by amplified ribosomal DNA restriction analysis (ARDRA) with the restriction enzymes AluI and HaeIII (New England Biolabs, Ltd.) and grouped according to their ARDRA patterns. Representative PCR products from each cluster were selected for partial sequencing. Generally, one representative of each OTU was subjected to sequence analysis; for larger clusters (>10 isolates), 2–3 representatives were sequenced. Following purification with the peqGOLD Cycle–Pure Kit (peQLab Biotechnologie GmbH), DNA sequencing was performed with an ABI PRISM 3100 Genetic Analyzer (Applied Biosystems) using the BigDye RR Terminator AmpliTaq FS Kit 1.1 (Applied Biosystems, Weiterstadt, Germany) and the primers 27F and 519R (Lane et al. 1991). Data were analysed and assembled by ABI PRISM DNA (Sequencing Analysis and ABI PRISM Autoassembler software). The BLASTN and Seqmatch tools were used for identification after uploading the sequences to the GenBank database and the Ribosomal Database Project Release 10 respectively. Sequences were submitted to Genbank under the accession number KF681362-KF681409.

Transfer of Antibiotic Resistance and Plasmid Replicon Typing

Plasmid DNA was extracted from representative isolates obtained from wastewater effluent (n=43) and the spring water (n=32). These two sampling sites were used to compare plasmid diversity, self-transmissibility and potential AR transfer in natural (SW) and anthropogenic (WW) ecosystems. Plasmid extractions were performed using the Plasmid DNA Purification kit AX100 (NucleoBond® PC100, Macherey & Nagel, Düren Germany) for isolates and transconjugants. PCR with eight sets of primers specific for plasmids of the incompatibility (Inc) groups IncQ, IncN, IncP (α-β-γ-δ) and IncP9 were performed as published by (Bahl et al. 2009, Gotz et al. 1996). The reference plasmids RP4 (IncPα), R751 (IncPβ), RSF1010 (IncQ), pWW0 (IncP9), and (IncN) were used as positive controls. Heat-denatured plasmid- free E.coli cells were used as negative controls. Positive amplicons were subsequently transferred onto a nylon membrane by using vacuum blotting equipment. Hybridisation was performed with positive amplifications products using the DIG Easy Hyb kit (Roche) and digoxigenin-labeled PCR products from the purified reference plasmids RP4, RSF1010 and R751 as hybridisation probes. To determine whether the plasmids were self-transmissible and able to transfer resistance genes, eight multi-resistant isolates (6 from downstream of the wastewater effluent and 2 from the spring water) were used as donors, while E. coli HB 101 (ATCC33694) holding chromosomal streptomycin resistance (500µg/ml) was used as recipient and for transconjugant counter selection. E. coli HB 101 was used for conjugation experiments because this strain could be considered as both environmental and potential pathogen and therefore represents a potential model bacterial recipient for AR in the aquatic environment.

Log phase grown donors and the recipient were adjusted to OD600 = 0.3; 1:1 conjugation mixtures and transferred on R2A agar for overnight incubation at 30°C. Serial dilutions of the conjugation mixtures were spread on LB agar plates (amended with 100 µg/ml streptomycin) and incubated at 30°C overnight. Fifty colonies from each conjugation experiment were re-inoculated on the same agar. Transconjugants were checked for

- 54 - Chapter 3 resistance patterns by conducting diffusion tests with all antibiotics object of this study. Plasmids restriction was performed with EcoRI or AluI enzyme (New England Biolabs, Ltd.) at 37°C for 14h (overnight). Gel electrophoresis was run at at 10 V / cm for 40 minutes on 1% agarose gels and the plasmid size determined.

Statistical analysis

One-way ANOVA followed by the Turkey test in the post-hoc analysis was performed with the software R version 386.2.15.1 (R_Development_core_team 2009) to determine whether the microbial community structure at the sites had statistical distinction, and only the P values below 0.05 were considered as significant. The number of isolates resistant to each antibiotic or antibiotic class was compared by means of a Spearman correlation test, which is a nonparametric test suitable for a small number of data points.

Results

Isolate identification and co-occurrence of antibiotics resistance A total of 312 microorganisms were isolated from the three sampling sites. The 180 Gram- negative isolates (100, 46 and 66% of the isolates from the spring water (SW), the water having received the wastewater effluent (WW) and water further downstream (DW), respectively) were used for further susceptibility analysis to 10 antibiotics (Table 7, Figure 10). More than 87% of the isolates were resistant to at least one of the tested antibiotics. Resistant strains for each of the ten antibiotics were found in isolates from WW, while up to 9 and 7 resistances to antibiotics were found in individual DW and SW isolates, respectively. Overall, the highest percentages of resistant isolates were found for cephalotin, trimethoprim, ampicillin and nalidixic acid. Resistance patterns of spring water isolates were distinct from those of the other two sites (p< 0.05); as for the other sites cephalotin and trimethoprim resistant isolates were dominating, whereas no chloramphenicol, streptomycin and tetracycline resistant isolates were found (Figure 11). Total cell numbers did not differ among the samples sites (Table 8). The corresponding cultivable fractions were 10 - 250 times lower with the highest cultivability observed in the WW samples. Phylogenetic profiling with ARDRA and partial 16S rRNA gene sequencing grouped the isolates into 16 genera and 12 families (Table 8) typically inhabiting semi-anthropic aquatic environments (Garrido et al. 2014). The majority of the sequences were highly similar (> 98%) to known species of the families Aeromonadaceae, Enterobacteriaceae, Pseudomonaceae, Flavobacteriaceae, and Comamonadaceae with different relative abundances in each site. Overall, samples taken near the WWTP discharge point (WW) contained a much higher number of different cultured species, while most of the microorganisms isolated from far downstream (DW) were also present in the WW samples (Table 6). Members of the Aeromonadaceae and the Flavobacteriaceae dominated the isolates from WW, while members of the Pseudomonaceae and isolates closely related to Yersinia intermedia were found only in spring water samples (SW). Seven isolates belonging to the genera Pseudoxanthomonas, Mycoplana and

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Pedobacter were excluded from the antibiotic resistance analysis either due to the lack of published data on antibiotic susceptibility patterns of the wild-type.

Figure 10 Prevalence of total resistance against antibiotics in isolates from spring water (SW), pooled wastewater effluent (WW) and downstream water (DW). KF cephalotin; AMP ampicillin; W trimethoprim; NA nalidixic acid C chloramphenicol; CIP ciprofloxacin; ST streptomycin; KA kanamycin; AK amikacin; TE tetracycline

Table 7 Absolute numbers of isolates, gram-negative, and resistant bacteria from the spring water of the river Gänsebach (SW), processed wastewater effluent (WW) and the receiving river downstream of the WWTP (DW).

______

SW WW pool WW1 WW2 WW3 DW Total i Isolates no. 50 212 48 50 114 50 312

Gram-negatives 50 98 23 23 51 33 180

Resistant bacteria 50 65 22 16 27 15 130

______

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Figure 11 Prevalence of non-intrinsic resistance in antibiotic resistant strains from spring water (SW, black bars), wastewater effluent (WW, dark grey bars) and downstream water (DW, light grey bars). Tot: Total strains showing at least one acquired resistance. KF cephalotin; AMP ampicillin; W trimethoprim; NA nalidixic acid C chloramphenicol; CIP ciprofloxacin; ST streptomycin; KA kanamycin; AK amikacin; TE tetracycline

Table 8 Total cell counts (after DAPI staining) and plate counts of cultivable aerobic heterotrophic bacteria (as CFU) in the processed wastewater effluent (WW), the receiving river downstream of the WWTP (DW) and upstream in the spring water of the river Gänsebach (SW).

______WW1___ WW2__ WW3_____ DW______SW______

Total cell counts (cells/ml) 3.1x105 4x105 5.1x106 6.6x105 4.7x105

Total plate counts of aerobic 3.1x104 5.1x104 2.04x104 3.6x103 4.05x104 ______WW1, WW2 and WW3: wastewater effluents from sample campaigns in July 2007, December 2007 and May 2008. SW and DW: water sampled in May 2008.

In order to distinguish between intrinsic resistance and potentially acquired resistance profiles we separated the resistance levels according to most frequently identified bacterial

- 57 - Chapter 3 orders and compared the intrinsic resistance profiles of the isolates and clinical bacteria from the same bacterial order (Appendix 4), (Walsh &Duffy 2013). Spring water isolates consisted of only isolated two genera, i.e. Pseudomonas and Yersinia. Pseudomonadales isolates showed a resistance pattern similar to the correspondent pathogen Pseudomonas aeruginosa. Only 26% of the Pseudomonadales bacteria displayed non-intrinsic resistances. The typical antibiotics used to treat Pseudomonas species infections include the fluoroquinolones, amikacin and gentamicin. The intrinsic resistance is not yet identified in clinical Pseudomonas therefore the observed levels of resistance was low (for our isolates) (Walsh et al. 2011). The Yersinia strains found in SW instead harboured non-intrinsic resistances to a single antibiotic, either to cephalotin or nalidixic acid. A majority of 84% of all bacteria isolated from WW displayed at least one non intrinsic- resistance either against trimetoprim (47% of the isolates), nalidixic acid (42%), cephalotin (29%), chloramphenicol (20%) or kanamycin (12%) (Fig.2). Non-intrinsic nalidixic acid resistance often co-occurred with chloramphenicol and/or cephalotin (p<0.05). Twenty per cent of the WW isolates showed multiple antibiotic resistances with 72.5% of the multiple resistant strains being Aeromonadales isolates. Clinical isolates of Burkholderia cepacia and Stenotrophomonas are intrinsically resistant to amikacin and gentamicin (Fajardo et al. 2008). All Burkholderiales isolates (Comamonadaceae) co-hosted non intrinsic trimetoprim and cephalotin resistances. The antibiotic resistance patterns of the Enterobacteriales isolates from WW instead corresponded to the intrinsic resistance pattern of the correspondent clinically relevant counterpart (Leclercq et al. 2013). Contrary to WW, the antibiotic resistances pattern of the DW isolates did not correspond to the intrinsic resistance of the respective pathogens or wild-type and were instead characterized by the presence of additional resistances: members of the Aeromonadales frequently displayed either trimethoprim (75%) or nalidixic acid (35%) resistance, while members of the family of Flavobacteriaceae mostly showed a double resistance to trimethoprim and cephalotin. The pathogenic bacteria Pseudomonas aeruginosa, Burkholderia cepacia, Acinetobacter species, Achromobacter xylosoxidans, Yersinia (with some exceptions) and Klebsiella species, Aeromonas species are intrinsically resistant to the β-lactam antibiotics (Fajardo et al. 2008) AMP and KF.

Multiple drug resistance (MDR) Isolates exhibiting multiple drug resistance (MDR), i.e. resistance to antibiotics from several classes were detected in all three sampling sites. Most of the isolates were resistant to representatives of at least 2 to 6 chemical classes of antibiotics (WW 94%, DW 83%, and SW 94%) (Figure 12a), but only 50% of them displayed co-occurrence of non-intrinsic resistances to different classes of antibiotics (Figure 12b). In WW, DW and SW the mean numbers of non-intrinsic resistances were 1.8 (range 0-6), 1.58 and 1.09 (ranges 0-4), respectively (p<0.05). Significant correlations using Spearman's rank correlation test were found only between antibiotics belonging to the same chemical class, like β-lactams (ampicillin, cephalotin) or aminoglycosides (streptomycin, amikacin)

- 58 - Chapter 3 with Spearman coefficients of 0.55 and 0.81, respectively. Non intrinsic trimethoprim and nalidixic acid resistance often co-occurred (Spearman coefficient 0.5).

Figure 12 Multidrug resistances in spring water (SW, black bars), wastewater effluents (WW, dark grey bars), and downstream water (DW, light grey bars). The y-axis represents the numbers of antibiotic classes to which an isolate either was resistant (a) or possessed acquired resistances (b). The x-axis represents the frequency (in percent) of the resistance profile within the respective bacterial community

Plasmid replicon typing and horizontal gene transfer of antibiotic resistances Nearly all extracted plasmids from the 75 WW (WW3 n=52) and SW (n=50) isolates generated amplicons of the predicted size using the specific Inc-primers; presence of Inc- groups were further confirmed by southern blot hybridization. WW isolates carried exclusively broad-host range IncP1-α and IncQ plasmids, which co-occurred in 74% of the isolates (Table 9). Assignment to an Inc group was not possible for 9% of the WW isolates. In contrast, SW isolates were characterized by a low prevalence of IncQ plasmids (9%); trfA2 specific amplification products showed strong hybridization signals to either IncP1-α or IncP1-β probes (Table 12).

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Table 9 Classification of bacterial isolates from the spring water of the river Gänsebach (SW), processed wastewater effluent (WW) and the receiving river downstream of the WWTP (DW). Shown are the closest cultured relative in Genbank and the absolute numbers.

______Species Family SW WWpool WW1 WW2 WW3 DW Aeromonas hydrophilia Aeromonadaceae 0 1 0 0 1 0 Aeromonas media 0 4 0 0 4 0 Aeromonas punctata 0 5 0 0 5 0 Aeromonas salmonicida 0 1 0 0 1 0 Aeromonas spp. 0 39 7 0 32 20 Klebsiella pneumoniae Enterobacteriaceae 0 2 2 0 0 0 Klebsiella oxytoca 0 4 0 0 4 0 Yersinia intermedia 4 0 0 0 0 0 Citrobacter spp. 0 2 0 0 2 0 Pseudomonas spp. Pseudomonadaceae 46 0 0 0 0 2 Chryseobacterium spp. Flavobacteriaceae 0 5 2 3 0 6 Chryseobacterium jl. 0 2 2 0 0 0 Flavobacterium johnsoniae 0 15 2 13 0 3 Acidovorax spp. Comamonadaceae 0 5 0 0 5 0 Acidovorax delafieldii 0 2 0 0 2 0 Acinetobacter spp. 0 2 2 0 0 0 Brevundimonas spp. Caulobacteraceae 0 1 1 0 0 0 Arcobacter suis Campylobacteraceae 0 1 0 0 1 0 Mycoplana Brucellaceae 0 1 0 0 1 0 Pedobacter deajeanensis Sphingobacteriaceae 0 2 0 1 1 0 Pedobacter koreensis 0 1 1 0 0 0 Pseudoxanthomonas mexicana Xanthomonadaceae 0 1 0 0 1 0 Pannonibacter Phragmitetus Rhodobacteriaceae 0 1 0 1 0 3

Total no. 50 98 19 18 60 34

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Table 10 Distribution of plasmids, incompatibility group membership and antibiotic resistance patterns of representative isolates in spring water SW (*) and processed wastewater effluent WW (#). Bold labelled strains were used for conjugations experiments.

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Continue Table 10

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We further used plasmid-carrying MDR bacteria for conjugation experiments. Only strains displaying the broadest resistance pattern and antibiotic resistances to multiple chemical classes were selected. Six multiresistant isolates from WW and two from SW, representing human-impacted and undisturbed ecosystems, were used as donors for conjugational transfer into E. coli HB101. Conjugation took place in all eight cases but only three isolates expressed antibiotic resistances in the transconjugants. The plasmids from the WW isolates (Table 11), 1 (Chryseobacterium spp.) and 46 (Aeromonas spp.) mediated the transfer of ampicillin, cephalotin, and nalidixic acid resistance while the SW isolate 28-2 (Pseudomonas spp.) conferred only 2 out of 4 resistances (i.e. nalidixic acid and cephalothin resistance) to the recipient. Restriction profiling indicated plasmid size ranges between 2.4 and 18 Kb, while Inc-Replicon PCR confirmed the affiliation of the plasmids to the IncP1α group.

Discussion

The increase of antibiotic resistance in pathogenic bacteria in clinical environments is alarming (Martinez 2008). Waters beside clinical settings are one of the preferential ways of dissemination for resistant organisms among humans and at the same time a bridge for resistance genes to reach environmental bacteria. Yet, the role of environmental bacteria for the emergence and spread of antibiotic resistance has received much less attention so far and is still a controversial topic (Czekalski et al. 2012). In particular the relation between bacterial community composition and intrinsic antibiotic resistance patterns in aquatic systems has been rarely addressed. In order to analyse the resistance profile of gram negative bacteria in freshwater exposed to continuous discharge of WWs, we decided to investigate freshwater communities close to human activities i.e. downstream of WWTP, and compared them to a pristine freshwater population. Although culture techniques frequently result in a biased description of the bacterial communities under study they allow a detailed description of resistant phenotypes including intrinsic and novel resistance mechanisms. Moreover they represent reliable standards for clinical testing of antibiotic resistance levels. In our study the resistance levels against many classes of antibiotics were extremely high. Multi-drug resistant bacteria of the downstream effluent water isolates made up more than 80% of the isolates and were resistant against up to 8-10 antibiotics. Intrinsic resistance has been well characterized in clinical settings for human pathogens to reduce the failure of antimicrobial therapy (Fajardo et al. 2008). In order to identify the role of the intrinsic resistance in the isolated freshwater bacteria, the resistance phenotypes characteristic for the bacterial order were compared to the intrinsic resistances associated to either the wild type or clinical species of the same order. Isolates of the pristine water (source water) mainly belonging to the Pseudomonadales and in minor part to the Enterobacteriales displayed the same intrinsic resistance patterns of the known clinically relevant species within these orders. These isolates served as an approximate reference for non-human impacted freshwater ecosystems, and in fact bacterial isolates harboured only few non- intrinsic resistances. Notable exceptions were the Yersinia amilowora isolates, for which trimethoprim resistance did not belong to the specie-specific antibiotic resistance pattern.

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Trimethoprim is a synthetic antibiotic not occurring naturally (Chang et al. 2008). As described for other regions (Petersen et al. 2002), existing aquaculture in the Lengenfeld area might be a potential source for diffuse trimethoprim resistance spread into the river.While the antibiotic resistances of isolates from the source water corresponded to the intrinsic resistance pattern of the respective order, non-intrinsic resistances were more frequent in the isolates from water downstream the WWTP (WW and DW) (Figure 12). One explanation for the effects of WTTPs on the acquisition of antibiotic resistance might be the fact the antibiotics are usually not completely removed during water purification processes. Furthermore, very low environmental concentrations of single antibiotics can select for plasmids carrying multiple-antibiotic resistances which eventually can contribute to the emergence antibiotic-resistant bacterial pathogens (Gullberg et al. 2011). Additional spots for antibiotic resistance acquisition could be the the sewage system which provides perfect conditions for the selection and also spread of bacterial resistances into environmental strains due to the high availability of nutrients and close contact between cells which favor HGT. Finally, in anthropic environments human pathogenic and resistant bacteria are often released with the sanitized wastewater. Despite the hygienization processes in the WWTPs resistant bacteria reach the freshwater ecosystem where environmental bacteria could acquire ARG via horizontal gene transfer (HGT). Since the WWTP we studied is not specialized in treating antibiotics, it would be interesting to further address the selective pressure effect of low concentration of antibiotics contributing to the increase of non-intrinsic resistances. This is especially true for the Aeromonadales and the Flavobacteriaceae (Burkholderiales), which in the WW samples were characterized by a co-occurrence of resistance to antibiotics from different classes. Members of both families are known for living in broad habitat ranges including freshwater, soil, marine systems, hospital devices and humans (Tamames et al. 2010) and might have key roles in the spread and transfer of multiple antibiotic resistance traits into surface water. In accordance with a previous study (Figueira et al. 2012), members of the Enterobacteriales, which typically are of human or animal origin and are common antibiotic-resistant bacteria in municipal wastewater, were always relatively less resistant than Aeromonadales. Contrary to the spring water, in WW and DW anthropogenic pollution seems to foster not only the spread of resistance of multiple drug resistances (Figure 12b) but also to contribute to the increase of antibiotic resistances within the same chemical class (ß-lactams or aminoglycosides for WW and DW, respectively). This might be explained by co-selection phenomena, which imply a reduction of the microbial sensitivity to a certain chemical class of antibiotics and at the same time an increase of the variety of molecules that bring about a resistance phenomenon (Lee et al. 2010). Raw wastewater are characterised by an elevated number of bacteria resulting from faeces, households and agricultural products (Novo &Manaia 2010). The sanitation process in WWTP reduces the total bacterial loads but also selects for bacterial taxa in the effluents (Reinoso et al. 2008) and potentially for multi resistant strains too (Czekalski et al. 2012). In our study, despite the different microbial communities in WW and DW, similarity of resistant patterns for non-intrinsic resistances characterised both sites. Elevated percentages of non-

- 64 - Chapter 3 intrinsic resistance were shown for three antibiotics namely trimethoprim (W), nalidixic acid (NA) and kanamycin (KA) in DW. Specifically, resistance to trimethoprim in most DW isolates co-occurred either with nalidixic acid resistance (Aeromonadales) or kanamycin resistance (Burkholderiales). On the other hand, MDR is directly linked to mobile genetic elements such as plasmids or transposons, which are capable of carrying multiple resistance genes (Levy &Marshall 2004). Their spread can also occur at low antibiotic concentrations (Ohlsen &Lorenz 2007) or even without a selective pressure (Luo et al. 2005). Therefore the observed co-occurring non-intrinsic resistances to trimethoprim and nalidixic acid might be due to horizontal gene transfer where either both resistance genes are carried and transferred simultaneously by the same mobile element or the two resistances have been acquired from different donors (Bennett 2008). Although nearly all investigated isolates coded for multiple resistances and carried self- transferable plasmids (Table 11), not all of the tested conjugative plasmids transferred AR from the donor to the transconjugants (Krol et al. 2011). Several mechanisms, which we could not investigate in the present study are possibly governing the linking of non-intrinsic resistances like multidrug efflux pumps active for more than a compound (Moore et al. 2010) or insertion of transposable elements or plasmids into the chromosome carrying resistances (Chiu &Thomas 2004). Despite we decided to focus on the screening of plasmids, further studies should also address the role of efflux pumps on the intrinsic resistance of environmental bacteria. Both vertical and horizontal transfer mechanisms seem possible for the increase of resistant bacteria, in particular against the quinolones. Increasing resistance to this synthetic antibiotic class is rather alarming and considered highly undesired for both clinical habitats and environmental ecosystems (Davies &Davies 2010). Quinolones are broad spectrum antibiotics and specifically used against bacteria causing illnesses difficult to treat (Scheld 2003). While resistance against nalidixic acid, the first ever produced synthetic quinolone, was frequently observed, resistance against ciprofloxacin, a second generation quinolone, was rare in the isolates. More investigation would be also necessary to explain the role of IncQ plasmids. This plasmid group acts as a “plasmid helper” (Rawlings &Tietze 2001) supporting AR transfer, contributing thus to the “reservoir of antibiotic resistances” in natural ecosystems (Schluter et al. 2007). The frequent occurrence of IncP1 plasmids in isolates from the freshwater close to the wastewater discharge points is likely connected to the transfer of AR genes in commensal (WW) but also autochthonous freshwater gram negative bacteria (SW). The increasing pollution of freshwater ecosystems by resistances from anthropogenically impacted habitats and the contribution of plasmids to their dissemination and maintenance might have in addition consequences for the evolution of environmental microorganisms (Martinez 2009b). An uncontrolled release of antibiotics and non-commensal resistant bacteria from engineered ecosystems to natural ones contributes to the development of new resistance pathways, the change of the natural resistance background, the potential reintroduction of resistant microorganisms into the human body and indeed to further AR gene spread among human

- 65 - Chapter 3 commensal bacteria (Wellington et al. 2013). Gullberg and colleagues (2011) have shown that the presence of antibiotics at nearly environmental concentrations in aquatic systems exert an effect for the selection of resistant phenotypes (Gullberg et al. 2011). Future studies should thus aim to clarify potential effects and address the hypothesis that even at low concentrations antibiotics might foster processes of horizontal gene transfer and maintenance of MDR in natural ecosystems.

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Chapter 4

Increased levels of multiresistant bacteria and resistance genes after wastewater treatment and their dissemination into Lake Geneva, Switzerland

Increased levels of multiresistant bacteria and resistance genes after wastewater treatment and their dissemination into Lake Geneva, Switzerland

Introduction

Antibiotic resistance has been the focus of research primarily in clinical settings (human and veterinary medicine), whereas the impact of environmental microbes as reservoirs of resistance factors and the impact of releasing antibiotic resistant bacteria (ARB) into the environment was not considered until quite recently. ARB and antibiotic resistance genes (ARG) are ubiquitous in nature (Aminov 2009) and can occur in high concentrations in clinical, industrial, and communal wastewater (Kümmerer 2004, Schwartz et al. 2003, Volkmann et al. 2004). These environments frequently contain elevated levels of antibiotics and wastewater effluents have been described as major sources of ARB in surface water (Blasco et al. 2009, Martinez 2009b) or soils (Heuer et al. 2008, Sengelov et al. 2003). Wastewater treatment plants (WTP) are considered to select for antibiotic resistance and to be important hot spots for horizontal gene transfer (HGT) of resistance genes (Baquero et al. 2008). There are two key concerns related to the continuous introduction of ARB and their resistance genes into the environment. The possible persistence and further dissemination of ARB in natural aquatic environments may contribute to the increase in infections with resistant pathogens. Secondly the dissemination of ARB and ARG may lead to an increase of the ARG pool in environmental bacteria, thus facilitating transfer of resistance into current and emerging pathogens. Switzerland is a country with comparatively low antibiotic consumption (lowest defined daily dose per 1000 inhabitants among European countries; (Filippini et al. 2006). Monitoring programs for antibiotic resistance in clinical settings have been established only recently (NRP49 2007). Several studies on the release of antibiotics and other micropollutants and their occurrence in sewage and natural environments have been conducted in Switzerland (Alder et al. 2001, Escher et al. 2011, Giger et al. 2003). However, the exposure of Swiss lakes to ARB and ARG has so far not received much attention. Lake Geneva is the largest fresh water reservoir of Western Europe but at the same time receives wastewater from the surrounding cities. The largest wastewater treatment plant belongs to the city of Lausanne. Studies have demonstrated the sediment pollution in the bay with heavy metals (Loizeau et al. 2004), and fecal indicator bacteria (Haller et al. 2009b, Poté et al. 2009a). The discharge of sewage from this plant has led to heavy pollution of its receiving water, the Vidy Bay. To

Chapter 4

the best of our knowledge no previous studies have been conducted on the input of ARB and ARG into Vidy Bay/Lake Geneva. In the present study, we aimed to evaluate the occurrence of ARB and ARG in the wastewater stream of Lausanne and the role of Lausanne’s WTP. In particular, we wanted to evaluate whether the WTP acts as a barrier for MRB in the wastewater stream, or conversely, provides an environment for selection of MRB and horizontal transfer of resistance factors. Finally, the role of the lake as a potential reservoir of ARB and ARG is discussed. We combined application of culture-based and culture-independent methods to allow both: the identification of important key species that might carry and further disseminate antibiotic resistance into the aquatic environment, as well as a culture-independent description of the dynamics of ARG in the various compartments of the Lausanne wastewater system and Lake Geneva.

Materials and Methods

Sampling and study site

Lake Geneva is located in the South Western part of Switzerland (Figure 13) and has a volume of 89 km3, a surface area of 580 km2 and a maximum depth of 310 m. The Vidy Bay, which accounts for 0.3% of the lake’s total volume, is located at the northern shore of the lake, next to the city of Lausanne. Lausanne’s WTP treats sewage from 214000 inhabitants, including wastewater from several health care centers. The largest one is the Centre Hospitalier Universitaire Vaudois (CHUV). The most important building in terms of capacity and antibiotic consumption is the main building which accounts for 71% of the CHUV sewage output. On average 410 m3 day−1 of raw sewage are released from this building to the Lausanne municipal sewer system. There is no pharmaceutical industry located in Lausanne, and intensive animal production is not prevalent in the vicinity. Hence it is assumed that the healthcare facilities are likely the main source of ARB. The WTP receives on average 107734 m3 day−1 of wastewater and discharges 86631 m3 day−1 of treated sewage directly into Vidy Bay. The discharge point is situated 700 m offshore at 30 m depth. During heavy rain events the capacity of the WTP is exceeded and untreated wastewater are then discharged into the Bay. This has led to heavy pollution (Loizeau et al. 2004, Poté et al. 2008). Only 3.2 km southwest from the WTP discharge point, Lausanne pumps lake water for its drinking water supply (St. Sulpice). Situated 1 km offshore and at 50 m depth the pump withdraws 100000 m3 of fresh water per day and drinking water is prepared via sand filtration and chlorination. Three sampling campaigns were conducted between February and April 2010 in order to sample all of the following sites once. (a) Wastewater samples were taken from the sewage pipe exiting the main building of the CHUV (sample code: HOS) and (b) at the in- and outlet of the WTP of Lausanne (WTPin/WTPout). (c) Lake water and sediment cores were sampled at two points in the Vidy Bay: directly at the outlet of the wastewater discharge pipe (sample code: STEP, “Station d’épuration”) and close to the end of the pipe supplying the drinking water pump (DP) of St. Sulpice (Figure 13).

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Figure 13 Map of the study area and sampling sites. Effluents of Lausanne’s wastewater treatment plant (WTP) are discharged via pipe 700 m off shore at 30 m depth (STEP, Station d’épuration, Swiss coordinates: 534672/ 151540). 3.2 km to the southwest, lake water is withdrawn for Lausanne’s drinking water supply (DP, Swiss coordinates: 531748/150195). Water was sampled at all points; sediment was sampled at STEP and DP

All wastewater samples were filled into ethanol-washed canisters (1l) while lake water (5 l) and sediment were sampled from the R/V La Licorne (Institute Forel, Geneva), equipped with a crane to which either a sediment corer (Uwitec, Austria) or a rosette sampling device (1018 Rosette Sampling System, General Oceanics Inc., FL, USA). The rosette sampling device consisted of eleven 1.7-l Niskin bottles,coupled to a CTD device (OCEAN SEVEN 316 Plus CTD, IDRONAUT S.r.l., General Oceanics), which allowed for online monitoring of dissolved oxygen, temperature, pH, and conductivity. Lake water was sampled at 20 m depth (below the thermocline) subsequent to recording a CTD profile. The maximum water depth at STEP and DP is 30 and 56 m, respectively. Around 45 ml aliquots of sample water were transferred to 50-ml polypropylene centrifuge tubes (Becton, Dickinson and Company, Sparks, NJ, USA) containing 5 ml of a 20% paraformaldehyde solution (final concentration 2%) to preserve cells for flow-cytometric counts of total bacteria (Troussellier et al. 1995). Samples were transported to the laboratory and stored at 4°C in the dark. Plating and filtration of water samples was carried out within 3–6 h of sampling. Sediment cores were stored for up to 1 week at 4°C in the dark before processing. Fixed samples for flow-cytometric counts were stored at −80°C and flow- cytometric analysis was carried out within 2–7 days.

Isolation and Quantification of Multiple Antibiotic Resistant Bacteria

For total and resistant viable cell counts (colony forming units – CFU), samples were processed as follows: (a) Lake water close to the DP: 4 l were filtered through sterile 0.2-μm- pore-sized polycarbonate Isopore™ membrane filters (147 mm in diameter, Millipore, Billerica, MA, USA). Filters were cut into small pieces and placed into 5–10 ml of sterile 0.05

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M pyrophosphate (pp) solution (pH 8.5). Cells were suspended in pp by vigorous vortexing and saline solution was added to give a final volume of 40 ml and a 100-fold concentrated microbial suspension relative to the raw sample. (b) Sediment cores: In order to detach bacteria from particles of the sediment surface layer (0–3 cm depth) 3 g sediment were added to 30 ml pp and vortexed for 2 h (Amalfitano &Fazi 2008). Particles were then allowed to settle for 18 min. The concentrated DP microbial suspension from procedure (a) was applied directly as well as diluted 1–10. The suspensions of procedure (b) as well as raw HOS, WTPin, WTPout, and STEP samples were diluted from 1:10 up to 1:105 in sterile 0.9% saline solution. Hundred microliters of each dilution were plated in triplicate on the following agar media cast in 9 cm diameter Petri dishes: nutrient agar (NAg) for copiotrophic bacterial counts, R2A for heterotrophs, phenylethyl alcohol (PEA) agar to select for Gram-positive bacteria, Pseudomonas isolation (PIA) agar to select for pseudomonads, and eosin methylene blue (EMB) agar to select for Gram-negative enterobacteria. All media (Difco™, Becton, Dickinson and Company, Sparks, NJ, USA) were supplemented with three combinations of antibiotics (Sigma-Aldrich, MO, USA) at inhibitory concentrations: (a) “old” Sul/Trm/Str: sulfamethoxazole/trimethoprim (Sul/Trm, 64 μg ml−1, 8:1) and streptomycin (Str, 32 μg ml−1), (b) “new” Nor/Cef: norfloxacin (Nor, 2 μg ml−1)/ceftazidime (Cef, 32 μg ml−1), and (c) “mixed” Cla/Tet: clarithromycin (Cla, 4 μg ml−1)/tetracycline (Tet, 8 μg ml−1). Combination (c) was not tested with EMB and PIA media that enrich for Gram-negative organisms, as clarithromycin mainly selects against Gram-positive bacteria. The concentration of each substance was applied according to DIN norms German Institute for Standardization (DIN) which develops official standards for susceptibility testing of antimicrobials in Germany and Europe in close collaboration with European Committee on Antimicrobial Susceptibility Testing (EUCAST) similar to CLSI guidelines in the US; Deutsches Institut für Normung e. V. (DIN Deutsches Institut für Normung e. V. 2004). The antibiotics incorporated in our approach were chosen in order to include representatives of different modes of action and different time spans of clinical deployment. Further criteria were the global but also local clinical relevance of the substances as well as their recent detection as micropollutants at our study sites (Blanc 2010).Preference was given to broad-spectrum antibiotics. The three antibiotic combinations applied in the present study represent “old” (sulfamethoxazole, trimethoprim, and streptomycin), “mixed-old and new” (clarithromycin, tetracycline), and “new” (norfloxacin, ceftazidime) classes. Furthermore, sulfamethoxazole and trimethoprim are mostly applied together in clinics, which led us to apply them in combination also in our study. Three plates of each medium without antibiotics served as positive controls and for determining total viable counts and one plate of each medium not receiving sample was incubated as a sterile control. In order to favor growth of bacteria which are adapted to lower temperatures, NAg and R2A plates were incubated at 25°C and evaluated for formation of colonies after 24, 48, and 72 h. PEA, PIA, and EMB plates were incubated at 37°C and evaluated after 24 and 48 h. All plates were incubated under aerobic conditions.

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Total Bacterial Counts from Lake and Wastewater Samples

Total bacterial counts of all water samples were determined once per sample by flow- cytometry (FC) as follows: 1 ml of the fixed sample was diluted 100–1000 times (to give 1000–10000 events per second) and stained with 10 μl of diluted SYBR® Green I solution (Invitrogen, Basel, Switzerland, 1:100 dilution in anhydrous dimethyl sulfoxide). After 15 min of incubation at 20°C in the dark, samples were analyzed on a PARTEC-PAS-III CyFlow Space flow cytometer (Partec GmbH, Münster, Germany).

Identification of Multiple Resistant Bacterial Isolates from Wastewater

From R2A and EMB plates supplemented with Sul/Trm/Str approximately 25 colonies were picked from each of the following environments: HOS, WTPin, and WTPout. Antibiotic R2A and EMB plates inoculated with lake samples contained less than 25 colonies. For this reason, colonies from all media supplemented with Sul/Trm/Str were picked. For identification, we performed colony PCR amplification of bacterial 16S rDNA gene fragments. The final reaction volume of 100 μl contained 20 μl of 5× Colorless GoTaq® Flexi buffer

(Promega, Madison, WI, USA), 3 mM MgCl2, 0.2 μM of each of the general bacterial primers 27f and 1492r(Lane 1991), 0.2 μM dNTPs, 1 mg ml−1 bovine serum albumin, 0.025 u μl−1 of GoTaq® Flexi DNA polymerase (Promega) and material from a single colony picked with sterile inoculation needles. 10 ng of DNA from E. coli served as a positive control and 1 μl of nuclease-free water (Qiagen, Germany) served as negative control in all PCRs carried out in this study. PCR reactions were run on a Touchgene Gradient Thermal Cycler (Techne, Cambridge, UK) with the following temperature program: initial denaturation for 5 min at 94°C followed by 35 cycles consisting of 30 s at 94°C, 1 min at 55°C and 1.5 min at 72°C, final extension for 5 min at 72°C.PCR products were digested overnight at 37°C with HhaI (Promega) for analysis of restriction fragment length polymorphism (RFLP) typing. The final reaction volume of 20 μl contained 2 μl of 10× multicore buffer, 0.2 μl of enzyme (2 u), 7.8 μl water and 10 μl of PCR product. Lambda phage DNA (Promega) served as positive control. RFLP digests were visualized by gel electrophoresis on 3% NuSieve Agarose gels (Cambrex Bio Science Rockland, Inc., ME, USA) and stained with GelRed™ (Biotium, Inc., Hayward, CA, USA). Isolates were grouped into ribo-types according to identical restriction patterns and one representative of each group was selected for sequencing. The respective PCR products were cleaned using the Wizard® SV Gel and PCR Clean-Up System (Promega) and quantified using a NanoDrop 1000 Spectrophotometer (Thermo Fisher Scientific Inc., Wilmington, USA). Unidirectional sequencing was carried out by Microsynth AG (Balgach, Switzerland) using the 27f primer. Sequences were analyzed via the BLASTn program (Altschul et al. 1990) and the Ribosomal Database Project (release 10) Classifier tool (Wang et al. 2007). All identified bacterial isolates from the wastewater compartments underwent analysis of minimal inhibition concentrations (MICs; provided subculturing succeeded).

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Determination of Minimal Inhibition Concentrations

Identified wastewater bacteria (HOS, WTPin, WTPout) isolated on R2A and EMB plates in the presence of Sul/Trm/Str were investigated for the MICs of various antibiotics using the Sensititre® broth dilution technique. As for the lake samples less than 25 colonies had formed on antibiotic supplemented plates, these were included neither in MIC testing nor in plasmid and resistance gene detection (see below). Strains were grown on R2A and EMB agar plates. However, for several bacterial isolates subculturing on these media resulted in poor or even no growth for which reason also other media, including NAg, EMB, Columbia Agar with 5% sheep blood (LaboLife Sàrl, Pully, Switzerland) or Tryptic Soy Agar (Difco™) plates were used, according to the growth requirements of the different identified genera. All agar media were supplemented with Sul/Trm/Str as described above and strains were allowed to grow for 72 h at the temperatures which were used during isolation (25 or 37°C), and at 30°C for identified pseudomonads. One to four colonies per strain were picked (dependent on quantity of biomass formed) and transferred to 1 ml of sterile millipore water. After vortexing, 10 μl of the suspension were transferred to 11 ml of a liquid medium based on the agar medium used for growth [R2A broth, Mueller Hinton broth (MHB), nutrient broth (NB), brain heart infusion broth (BHB), tryptic soy broth (TSB); replacing solid media listed above in this order]. Subsequently, Sensititre® 96 well susceptibility plates (TREK Diagnostic Systems, West Sussex, UK; plate formats applied in this study: EUMVS for Gram-negatives and NLM4 for Gram-positives) were inoculated with 50 μl culture medium per slot. A negative control was inoculated with sterile medium and a slot without antibiotic served as positive control. Plates were incubated at the appropriate temperature for each strain and evaluated for bacterial growth after 24 and 48 h. Plates were checked for biomass formation in each slot by measuring absorption at 570 nm using an Synergy™ HT Multi Detection Microplate Reader (BioTek, Winooski, VT, USA) and by eye. Strains were classified as resistant to the tested substances based on the observed minimum inhibitory concentration according to Deutsches Institut für Normung e. V. (DIN Deutsches Institut für Normung e. V. 2004) and European Committee on Antimicrobial Susceptibility Testing (EUCAST 2011). As for florfenicol no resistance breakpoints are available, strains were classified as resistant according to breakpoints for the closely related chloramphenicol (CHL).

Extraction, Replicon Typing of Plasmids and sul resistance genes detection from Isolated Strains

All Gram-negative wastewater bacteria referred to in the previous paragraphs were grown in 5 ml R2A broth, nutrient bouillon, LB, TSB, or BHB supplemented with 64 μg ml−1 Sul/Trm (8:1) and 32 μg ml−1 Str. The liquid cultures were shaken in 15-ml polypropylene tubes at 180 rpm and 25, 30, or 37°C for 24–48 h, depending on growth-preferences and growth rates of strains. Cells were harvested by centrifugation at 5000 rpm for 10 min at room temperature using an Eppendorf 5804R centrifuge with a swinging bucket rotor for 15-ml centrifuge tubes (Eppendorf, Hamburg, Germany). The supernatant was discarded and the pellets frozen at −20°C until plasmid extraction. Plasmid DNA was purified from frozen cell pellets using the

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PureYield™ Plasmid Miniprep System (Promega) for Gram-negative bacteria according to the manufacturers’ protocol. As positive controls we used E. coli J53 harbouring the 33-kb- sized plasmid R388, which inherits the sul1 gene for sulfonamide resistance. Liquid cultures of this strain were prepared in LB with 20 μg ml−1 trimethoprim. For Gram-positive strains we applied the PureYield™ Plasmid Midiprep System (Promega) adapted with a few modifications: in brief, cells were grown in 50 ml BHB for 20 h at 37°C and 180 rpm. Bacillus subtilis ssp. subtilis BD170 (DSMZ, Germany) carrying the 4.5-kb-sized plasmid pUB110 served as a positive control during extraction. Cell pellets were resuspended in 3 ml of the cell suspension solution supplied with the kit and incubated for 10 min at 37°C with 1 mg ml−1 lysozyme added (B. subtilis cells), or for 30 min at 37°C and 10 mg ml−1 of lysozyme and 1 ml of a 75% (w/v) saccharose solution (environmental isolates). Subsequently, cell lysis and neutralization was carried out according to the manufacturers guidelines, taking into account the recommended time to allow the formation of white flocculent before centrifugation of the lysate (Wegmüller &Schmid 2009). Plasmid DNA was further purified following the Promega protocol and finally eluted in 400 μl of nuclease-free water. Visualization of plasmid DNA was done by gel electrophoresis in 0.8% agarose gels at 50 V. Plasmid extracts showing several sharp bands were screened for the following Inc groups: IncP1α,β, IncP7, IncN, IncW, and IncQ, using the primers and PCR conditions as described previously (Carattoli et al. 2005, Gotz et al. 1996, Heuer et al. 2008, Levchuk et al. 2006, Suzuki et al. 2000). Plasmid extracts were evaluated as positive for the tested Inc groups if the PCR product exhibited a band of the correct size in the gel after electrophoresis. All plasmid extracts were screened for the presence of the three sulfonamide resistance genes sul1, sul2, and sul3 using a multiplex PCR assay as described previously (Heuer &Smalla 2007). The presence of sul genes was determined from detection of a PCR amplicon band of the expected size following agarose gel electrophoresis. A post-PCR identification of which sul type was present was not performed. As positive controls for sul1, sul2, and sul3 served plasmids R388 (E. coli J53), RSF1010 (E. coli DH5α), and pVP440 (E. coli K12), respectively. The formation of PCR products of the correct size was evaluated via gel electrophoresis in 2% agarose gels.

DNA Extraction from Water and Sediments

Water samples were filtered using 5- and 0.2-μm pore-size polycarbonate membrane filters (Isopore™, Ø 147-mm, Millipore). Filters were cut into quarters, folded, packed into clean self-seal bags, and shock-frozen in liquid nitrogen for subsequent storage at −80°C until DNA extraction (3–5 months). One quarter of the filter from each sampled location was extracted as follows: lysis of bacterial cells was carried out by a two-step procedure: (a) bead-beating in GOS buffer for 40 s at 4 ms−1 with a FastPrep®-24 Instrument (Biomedicals Europe, Illkirch, France). For this step we used 0.2 g of a 1:1 mixture of 106 μm and 150–212 μm glass beads (Sigma-Aldrich). (b) Freezing in liquid nitrogen for 1 min and thawing at room temperature (rt). Cell debris was removed by centrifugation for 30 min at maximum speed (13000 rpm, Eppendorf Centrifuge 5415R) at rt. The supernatant was treated for 30 min at 37°C with 10 mg ml−1 RNase A. DNA was extracted with 1/2 volume of phenol (pH 8, Sigma-Aldrich) and

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1/2 volume of chloroform/isoamyl alcohol (24:1, CIA, both Sigma-Aldrich) followed by a second extraction with 1 volume of CIA. DNA was precipitated with 1 volume of isopropanol. After centrifugation at 4°C and maximum speed for 30 min the DNA pellet was rinsed with 70% cold ethanol and dissolved in 20–30 μl of sterile Tris–EDTA buffer. Out of each sediment core, 0.5 g of surface layer material were transferred to sterile 2 ml screw-cap tubes (BRAND GmbH + Co KG, Wertheim, Germany) and stored at −80°C in 1.5 ml GOS buffer until DNA extraction. For cell lysis, a bead mixture as described above was added and after vortexing two times for 40 s each at 3000 rpm (maximum vortex velocity, Vortex Genie 2, Scientific Industries, Inc., NY, USA) the supernatant was extracted with 500 μl phenol (pH 8, Sigma-Aldrich), followed by extraction with 200 μl CIA and finally with 400 μl chloroform. Precipitation of DNA and subsequent steps were as described for the DNA extraction from filters. For both water filters and sediments one blank extract was prepared, containing only beads and chemicals, respectively one quarter of a clean filter.

Quantification of sul Resistance Genes and Bacterial 16S rRNA Gene Fragments in Environmental DNA Extracts

The sulfonamide resistance genes sul1 and sul2 and bacterial 16S rRNA gene fragments were quantified from environmental DNA extracts using quantitative real-time Taqman®-PCR. Reactions were run in volumes of 20 μl (sul1 and sul2) and 30 μl (16S rRNA), containing 1× TaqMan® Fast Universal PCR-Master Mix (Applied Biosystems, Foster City, CA, USA) and 5 μl of DNA (dilutions of quantification standards or diluted (1:10 to 1:105) DNA extracts of samples). Extraction blanks (see above) as well as PCR blanks containing 5 μl of nuclease- free water were run as negative controls. To check for the presence of PCR inhibitors, standard PCR amplification of bacterial 16S rRNA gene fragments was carried out as described above, using 1 μl of template at various dilutions. Dilutions revealing successful amplification according to agarose gel electrophoresis were then used for real-time PCR. Primers and probes for sul1 and sul2 genes, as well as their corresponding concentrations for PCR were as previously described (Binh et al. 2008, Heuer &Smalla 2007) PCR conditions for sul1 and sul2 were as follows: 2 min activation at 50°C, initial denaturation for 10 min at 94°C and 40 cycles of denaturation at 95°C for 15 s and annealing at 60°C for 3 min. For quantification of 16S rRNA genes we used universal primers and probes (Suzuki et al. 2000). Primer and probe concentrations as well as PCR conditions were applied according to (Newcombe et al. 2005). All samples and standards were run in triplicate in 96 well plates on a 7500 Fast Real-Time PCR System (Applied Biosystems) and analyzed using the automated settings of the 7500 Fast System SDS Software (Applied Biosystems). Standard curves were prepared from serial dilutions of the plasmids serving as positive controls for sul1 (R388) and sul2 (RSF1010) and a pGEM®-T Easy plasmid containing the target fragment of the 16S rRNA gene of E. coli. (position 1369–1492 of the E. coli reference genome, 123 bp). Dilution series were prepared as recommended by the Applied Biosystems Tutorial “Creating Standard Curves with Genomic DNA or Plasmid DNA Templates for Use in Quantitative PCR.” Efficiency (E) of each qPCR run was calculated from the slope of the standard curves according to the formula E = 10(−1/slope) − 1. The limit of quantification (LOQ) was determined

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by the most diluted standard in the standard curve that exhibited a standard deviation of Ct values of the triplicates smaller than 0.5 (Life Technologies Corporation, 2012).

Statistical Analysis

In order to determine significant differences of measurements in excess of the error of measurement, we performed pairwise t-tests in R, based on the triplicate measurements of either qPCR or plate counts. Correlation analyses for comparison of the outcome of different methods were performed using the data analysis tool in Microsoft Excel.

Results

Total Bacterial Load along the Wastewater Flow Line We determined the total bacterial load in samples along the wastewater flow of Lausanne into the Vidy Bay, Lake Geneva using a culture-based (plate counts) and two culture-independent methods (total cell counts by FC and direct quantification of 16s rRNA gene fragments using quantitative real-time PCR). For 16S rRNA genes the PCR efficiency was calculated to be 109.94% and the LOQ was determined at 300 copies of the target per 5 μl. All three methods show a decrease in total bacteria along the wastewater flow path from the urban catchment to the lake (Figure 14 and Figure 15A). The two culture-independent methods, agreed very well

in all analyzed water samples (correlation coefficient r2 = 0.94, Figure 15A). )

1 Water samples Sediment - 1,E+09

or g or 1,E+08 1 - 1,E+07 1,E+06 1,E+05

1,E+04 1,E+03 *

1,E+02 * n.d. 1,E+01

CHUV WTPout DP aq. STEP sd. DP sd. Tot viable Totcountsviable (CFUml R2A NAg PEA

Figure 14 Total viable counts, normalized to sample volume or mass, on agar media without antibiotics. An asterisk (*) indicates less than 10 CFU ml−1. n.d., no colonies detected. Error bars indicate standard deviation of triplicate platings of the same sample

On all of the five tested media, and for all tested samples, viable bacterial numbers determined by plate counts were at least two orders of magnitude lower than the bacterial numbers determined with the culture-independent approaches. The highest numbers were

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detected on the two general media R2A and NAg. The highest bacterial load was determined in the HOS sample based on FC and 16S rRNA gene copy numbers (p ≤ 0.01). Plate counts on the three selective media PIA (p ≤ 0.001), PEA (p ≤ 0.01), and EMB also revealed highest viable bacterial numbers in the HOS sample, although for EMB the difference was within the error of measurement. Both the general media R2A and NAg revealed slightly higher viable bacterial concentrations in the WTPin compared to the HOS sample but the difference was not significant (p ≥ 0.05) The discrepancy between untreated and treated wastewater samples (WTPin and WTPout, respectively) was about 0.5–1 orders of magnitude for all three methods but for 16S rRNA gene copy numbers the difference was not significant. According to this data wastewater treatment reduced the total bacterial load is by 73% based on total viable counts (averaged over all five media), 78% according to FC and 42% according to 16S rRNA gene copy numbers, respectively.Total viable counts in STEP lake water ranged from 101 to 104 CFU ml−1 but they were considerably lower (by 1–3 orders of magnitude) at the DP site (Figure 14). The trend of CFU-Total and FC counts along the flow path was mostly coherent, irrespective of the fact that the latter were generally 2–3 orders of magnitude higher. However, we observed an increasing divergence of the two methods in the lake samples: in contrast to the drop of bacterial plate counts from STEP to DP (p ≤ 0.05 for all media, except for PEA), FC counts revealed a similar bacterial load in both lake water samples of 106 events ml−1 (compare Figure 14 and Figure 15A). 16S rRNA gene copy numbers were even 1 order of magnitude lower for the STEP water sample compared to the DP sample by p ≤ 0.01. In sediment samples taken close to STEP total viable counts were approximately 10 times higher compared to DP sediments (Figure 14). Note that sediment viable counts per gram dry weight were at least 10 times higher compared to plate counts per milliliter of the overlying water columns at both lake sites. No qPCR quantification results for 16S rRNA genes are presented for sediments as we observed considerable inhibition for the method with these samples.

Multiresistant Bacteria and Resistance Genes along the Wastewater Flow Line As for the total bacterial load we also detected a decreasing trend for MRB determined by plate counts along the wastewater flow path from the urban catchment to the lake (HOS > WTPin > WTPout > STEP > DP, Figure 16 A-C. Plate counts for “new” antibiotics (Nor/Cef) were comparatively low along the whole wastewater flow line on four out of five of the tested media. Solely on PEA plates which select for Gram-positive bacteria, Nor/Cef resistance occurred in the same range as the numbers determined for the remaining antibiotics at the respective sampling sites Figure 16C. The highest concentration of viable potentially MRB along the flow path was found in the HOS sample, regardless of the antibiotic combination or growth medium Figure 16 A-C. However, in case of Nag plates supplemented with Sul/Trm/Str and Cla/Tet counts on PEA plates the differences between HOS and WTPin samples were within the error of measurement (p = 0.08 for both, respectively).

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Relative abundances of potentially MRB (i.e., MRB plate counts normalized to the total viable bacterial counts on the same medium) were mostly, but not exclusively, observed to be highest in HOS as well, ranging from 0.01 to 66% (Figure 16 D-F). Besides the high values in HOS we also observed high relative abundance of Cla/Tet resistance on PEA plates in the WTPin sample (40%) and NAg plates showed high relative abundances for this antibiotic combination in the WTPout sample (21%). Both values were in the same range as the relative abundances determined for the HOS sample (36 and 21% for PEA and Nag, respectively (Figure 16E).For Nor/Cef resistance slightly elevated relative abundances were observed in WTPin and WTPout, but only for Gram-positives (PEA medium, 2.8 and 3.1%, respectively) whereas their abundance was low for the remaining media (less than 1%). Abundances of Sul/Trm/Str MRB were quite homogenous for all media and ranged from 0.01 to 5.6%.

A

1,E+09

1,E+08

) 1 - 1,E+07 1,E+06 1,E+05 1,E+04 1,E+03 1,E+02 1,E+01

(copies or events mlor (copiesevents CHUV WTPout DP aq. Microbial cells or genecopies cells or Microbial FC 16s rRNA sul1 sul2

B 10%

8%

6%

copies) copies) 4% gene abundance abundance gene 2% sul 0,02% 0,02% (copies / 16S rRNA gene rRNA 16S / (copies 0% CHUV WTPin WTPout STEP DP aq. sul1 % sul2 % aq.

Figure 15 (A) Total gene copy numbers and flow-cytometric cell counts, normalized to sample volume. (B) Relative abundance of sulfonamide resistance gene copy numbers normalized to 16S rRNA gene copies. Error bars indicate standard deviation from triplicate qPCR

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To some extent we noticed variations in removal efficiency among the different media and antibiotics. For instance, Cla/Tet resistant Gram-positives (PEA medium) seem to be more drastically reduced during wastewater treatment compared to other Cla/Tet resistant bacteria growing on R2A and NAg (Figure 16B). Nor/Cef resistant Gram-positives (PEA medium) were found at 10-fold higher number compared to viable bacteria on the remaining media, and appeared to retain this elevated number even after passing the WTP (Figure 16C). Although relative abundances of potentially MRB usually decreased from WTPin to WTPout, we also observed similar proportions in both samples, e.g., for Nor/Cef resistant Gram- positives (2.8 and 3.1%) and even increasing numbers, e.g., from 7.5 to 22% on NAg plates supplemented with Cla/Tet, respectively (Figure 16E). The lake water generally exhibited low amounts of resistant bacteria. From STEP lake water samples we were able to isolate bacteria in the presence of Sul/Trm/Str (100 to 102 CFU ml−1) and Cla/Tet (101 to 102 CFU ml−1) but not in the presence of Nor/Cef. At the DP site, hardly any bacteria were cultured from lake water in the presence of antibiotics, despite the additional sample concentration step (Figure 16 A-C). In sediment samples Nor/Cef resistant colonies were formed solely on PEA plates from STEP, while none were obtained from DP sediments. Generally, STEP sediments contained higher numbers of MRB than DP sediments (Figure 16C). Compared to the overlying water column, the concentration of MRB in sediment were 1–3 orders of magnitude higher. Relative abundances of MRB in both sediment and lake samples were mostly below 1% except for Sul/Trm/Str plates which, on different selected media, reached up to 5% of the respective total viable counts (Figure 16 D). Real-time PCR quantification of sulfonamide resistance genes sul1 and sul2 in wastewater and lake water DNA-extracts revealed the highest load of both traits in the HOS sample and a general decrease along the flow path (Figure 15 A). This trend is in good agreement with the outcome of the culture-based approach. sul1 gene copy numbers correlated with Sul/Trm/Str plate counts on R2A and NAg plates (r² = 0.91, for both media), and the correlation was even closer for sul2 (r² = 0.99, for both media). However, copy numbers per milliliter of water or gram sediment (dwt) of both ARG were higher by 1–4 orders of magnitude compared to the respective viable counts in all analyzed samples (compare Figure 15 and Figure 16 A). In contrast to the culture-based results, qPCR results revealed a twofold increased level of sul1 (p ≤ 0.001) and only a 15% lower level of sul2 (p = 0.3) in the WTPout sample compared to the WTPin sample. Normalized to the 16S rRNA gene, the sul genes occurred at abundances of 0.28 to 7.7 and 0.02 to 2.1% for sul1 and sul2, respectively. The highest sul1 abundance was observed in the WTPout sample followed by HOS (Figure 15 B). The abundance of sul1 was found to be higher than sul2 in all samples with increasing discrepancy from wastewater to the lake samples. In lake water, the relative abundance of the two resistance genes was considerably lower (<1%) than in wastewater samples. qPCR efficiencies derived by the slopes of the standard curves for both sul genes were 93% for sul1 and 102% for sul2. The LOQ for both standards was at 300 copies per 5 μl. All diluted samples amplified within the range of the standard curve, above the LOQ.

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Important Genera Identified in Wastewater and Lake Samples In total, 163 isolates from HOS, WTPin, and WTPout obtained on Sul/Trm/Str supplemented R2A and EMB plates were identified to the genus level by RFLP typing and sequencing of the rRNA gene. Sequences that passed quality control and the chimera check using Bellerophon, version 3 (DeSantis et al. 2006) were submitted to GenBank and are available under accession numbers: JQ595461–JQ595555. We identified 27 different genera (Table 13), 16 in HOS, 14 in WTPin, and 13 in WTPout. The majority were Gram-negative bacteria, but we also identified a few Gram-positives in HOS (n = 6) and WTPin (n = 5), mostly Enterococci. Pseudomonads were found to be most abundant among isolates from all three sites. Enterococci and Brevundimonas were quite common in HOS, but the latter was also detected in WTPout. Escherichia/Shigella and Acidovorax species dominated the WTPin isolates. Escherichia/Shigella was also isolated frequently from WTPout, as were Sphingobacteria (accounting for ∼25% of the WTPout isolates). Furthermore, 17 genera were identified among the lake water bacteria (both STEP and DP) grown in the presence of Sul/Trm/Str (Table 13). In sediment from STEP and DP, Bacillus and Solibacillus, respectively, were the most frequently isolated strains. A further dominant genus in STEP sediment was again Brevundimonas.

Prevalence of Highly and Extremely Multiresistant Bacteria along the Wastewater Flow Path Resistance of identified multiresistant wastewater isolates obtained from Sul/Trm/Str plates was confirmed by determining the MICs of 14 antibiotics. The results of this experiment are summarized in Figure 17. Resistance toward the antibiotics applied in the isolation plates (Sul/Trm/Str) was confirmed. This resistance pattern usually coincided with a high tolerance or resistance to ampicillin, which was present in 76 and 77% of HOS and WTPin isolates (n = 42 and 22, respectively) and 87% of WTPout isolates (n = 45). Resistance patterns toward the 11 remaining substances tested varied among the different wastewater environments: the number of antibiotics to which more than 50% of the tested strains were resistant was 8 for the HOS sample. Among WTPin isolates only resistances against CHL and nalidixic acid (NAL) were detected frequently (in 55 and 67% of the isolates, respectively). When comparing the proportions of bacteria resistant against specific antibiotics in untreated (WTPin) and treated (WTPout) wastewater, similar or higher proportions were found in WTPout for 9 out of the 11 antibiotics not used in isolation. In the cases of gentamicin (GEN) and colistin (COL) the ratio was more than twice as high for WTPout. Only the resistances to fluoroquinolones (NAL and ciprofloxacin, CIP) occurred more frequently in WTPin isolates. We have furthermore evaluated the number of resistances per strain. Strains with resistance against more than six or eight antibiotics were labeled as highly and extremely multiresistant, respectively. When comparing the occurrence of highly and extremely MRB at the three sites (Figure 18), highest proportions were again detected in the HOS sample (86 and 77%, respectively) and an increased level in WTPout (82 and 60%, respectively) compared to the WTPin sample (71 and 33%, respectively). Thus, we observed an 11 and 27% higher abundance of highly and extremely multiresistant strains, respectively, in the WTPout

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sample compared to WTPin. The most frequently isolated genera of highly multiresistant species were Pseudomonas and Escherichia/Shigella which were present at all three locations. Enterococcus and Brevundimonas were present in HOS. While the former was otherwise only detected in WTPin, the latter was only found in WTPout. Acinetobacter was prevalent in both WTPin and WTPout and a very dominant and highly multiresistant genus in the WTPout sample was Sphingobacterium.

Prevalence of Plasmids and of Sul Genes in Selected Wastewater Isolates The extraction of plasmid DNA from 129 of the 169 wastewater isolates showed that 58, 77, and 46% of the strains isolated from HOS, WTPin, and WTPout haroured plasmids. For many of the tested strains agarose gel electrophoresis of the plasmid DNA extract revealed multiple bands in the low kilobit size range, indicating multiple plasmids per strain. Replicon typing of 78 plasmid positive extracts revealed only a low number of specific amplifications for IncP1α,β (n = 3), IncP7 (n = 8), IncQ (n = 17), IncW (n = 2), and no amplicons were detected for IncN. Though the yield of positives for the tested Inc groups was quite low, the results nevertheless gave further indications for the presence of multiple plasmids (of replicon types IncQ and IncP7) in three of the tested strains. All plasmid extracts, independent of their band pattern, were screened for presence or absence of sulfonamide resistance genes sul1, sul2, and sul3. Sul genes were amplified in 76 out of 129 plasmid extracts, accounting for 77, 40, and 52% of the investigated bacterial isolates from HOS, WTPin, and WTPout, respectively.

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Figure 16 (A–C) Total viable counts, normalized to sample volume or mass, in the presence of (A) Sul/Trm/Str, (B) Cla/Tet, and (C) Nor/Cef. n.d., no colonies detected. (*) less than 10 CFU ml−1. Error bars indicate standard deviation of triplicate platings of the same sample

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Continued Figure 16 (D–F) Abundance, relative to total viable counts with no antibiotics, or with (D) Sul/Trm/Str, (E) Cla/Tet, and (F) Nor/Cef. The insert in (F) shows abundance of selected samples at an adjusted scale. (*) indicates less than 1% relative abundance in (D–F). n.d., no colonies detected. Error bars indicate standard deviation of triplicate platings of the same sample

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100% 90% 80% 70% 60% 50% 40%

30% % resistantstrains% 20% 10% 0% Sul Trm Str Amp Fot Taz Gen Kan Nal Cip Tet Col Chl Ffn

HOS n=22 WTPin n=42 WTPout n=45

Figure 17 Percentage of bacterial isolates from HOS, WTPin, and WTPout that were classified as resistant to the listed antibiotics

100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% HOS WTPin WTPout

Resistance against ≥6 ≥8 antibiotics

Figure 18 Comparison of percentages of bacteria inheriting more than six (“highly multiresistant”) and more than eight resistances (“extremely multiresistant”) at the three sampling sites HOS, WTPin, and WTPout

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Table 11 Genera of Sul/Trm/Str resistant bacteria isolated from HOS, WTPin, and WTPout according to partial 16S rRNA gene sequences and their numerical distribution in the different wastewater compartments.

STEP STEP DP DP Genus HOS WTPin WTPout Total water sed water sed Achromobacter 2 2

Acidovorax 8 1 9

Acinetobacter 5 4 1 10

Aeromonas 1 1 1 6 2 11

Arthrobacter 1 1

Azospira 1 1

Bacillus 14 7 21

Bosea 3 3

Brevundimonas 8 4 9 21

Brochothrix 2 2

Chryseobacterium 2 2

Comamonas 2 2 4

Delftia 2 2

Enterococcus 5 3 1 9

Escherichia/Shigella 1 7 12 20

Flavobacterium 1 2 3

Lysinibacillus 1 1

Microbacterium 1 1

Phyllobacterium 3 3

Pigmentiphaga 2 2

Pseudomonas 15 11 7 33

Pseudorhodoferax 1 1

Rheinheimera 3 3

Rhizobium 3 1 4

Solibacillus 3 5 15 23

Sphingobacterium 1 14 15

Sphingobium 1 1

Sphingomonas 1 1

Sporosarcina 1 2 3

Stenotrophomonas 4 4 1 9

Tatumella 1 1

Thermohalobacter 1 1

Thermomonas 1 1

Tolumonas 1 1 2

Uruburella 1 1 2

Variovorax 5 5

Xanthobacter 3 1 1 5

Unidentified 5 2 4 5 10 1 9 36 n 52 54 57 15 45 14 37 274

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Discussion

Despite Switzerland is a country with comparatively low antibiotic consumption (Filippini et al. 2006) with this study we demonstrated that compared to the other tested water sources hospital effluents are strongly contaminated with MRB and ARG. These findings are in agreement with the assumption that MRB are mainly selected in clinical environments (Kümmerer 2009). Nevertheless, the overall impact of discharging untreated hospital effluents into the sewer system is still under debate. It has been demonstrated that significantly higher proportions of resistant bacteria were isolated from influents of WTPs receiving hospital wastewater than from influents only treating municipal wastewater (Garcia et al. 2007). However, Guardabassi et al. (2002) could not detect significantly elevated levels of resistant Acinetobacter species in wastewater downstream of a hospital (Guardabassi et al. 2002). Taking into account that typically, hospital effluent accounts for less than 1% of the total sewage arriving at the WTP, its impact on the receiving environment remains open. In Lausanne, hospital wastewater contributes to approximately 0.4% of the total sewage volume processed at the WTP (calculated from release data of the main building, which accounts for 71% of the “CHUV” (Blanc 2010). The reduced level of resistance at the WTPin site compared to HOS, is therefore expected. Nevertheless, the high levels of MRB and ARG observed in the WTP inflow in this study were still high, indicating that municipal wastewater is probably the bigger net source of resistances entering the WTP. Nonetheless, a separate treatment of hospital sewage with the goal of removing MRB (as well as the pharmaceutical load, see below) to prevent their mixing with communal effluents and the environment is desirable. Our observations indicate that the WTP of Lausanne strongly reduces the total number of MRB as an effect of bacterial load reduction in the water by WTP. In fact, we did not universally observe a strong reduction of the relative abundance of MRB according to the plate count analysis. In one case (Cla/Tet) we even observed an increase. The culture- independent approach also indicated a less efficient removal, respectively increase, of sul resistance genes during passage of the WTP. This data would suggest that there may be some selection for resistance factors during passage of the WTP. Finally, our results showed increased proportions of highly and extremely MRB among the isolated Sul/Trm/Str resistant strains in the sample of treated wastewater compared to the WTP inlet sample. A poor removal of MRB was observed since several bacterial genera of the raw sewage were also detected (pseudomonads and E. coli), as well as a positive selection on multiresistance in other taxa, e.g., Brevundimonas (only detected in hospital sewage and WTPout). As Sphingobacteria were quite abundant among the isolated highly MRB in the WTP it would be interesting to further investigate their potential as key players in the accumulation and subsequent transfer of highly multiresistant genotypes into the lake. Sphingobacteria are members of the Cytophaga/Flavobacteria/Bacteroidetes (CFB) group which are well known as regular members of freshwater aquatic microbial communities (Zwart et al. 2002). Close relatives of Sphingobacteria have been found in freshwater lakes (Grossart et al. 2008). The high degree of resistance to antimicrobial agents of the group is well known (Holmes 2006). Another important genus we identified that carried multiple resistance to the lake is Brevundimonas which is infrequently isolated from clinical settings (Han &Andrade 2005)

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but is typically established in fresh water communities and drinking water (WHO 2003a), which would indicate a potential of these MRB to become established in the receiving water. These organisms might thus play a role in preserving and disseminating clinical multiresistant determinants in lakes and even promote their return to clinical settings via drinking water. The ambivalent role of WTPs has been discussed in several reviews (Jury 2010, Kim &Aga 2007) and previous studies. The evidence presented so far is however in the majority based on using culture-dependent approaches. Some of these studies observed a general decrease in the prevalence of resistance against antimicrobial agents (Garcia et al. 2007, Iwane et al. 2001, Vilanova et al. 2002) but still stated that the removal efficiency was unsatisfactory (Duong et al. 2008, Prado et al. 2008)which is in line with our own findings. Auerbach et al. (2007) quantified various tetracycline resistance genes in influent and effluent of several WTPs and observed a general decrease in tet gene prevalence after sewage treatment. However, others observed a significant increase in antibiotic resistance prevalence, and concluded a selective effect for resistance genes during wastewater treatment (da Silva et al. 2006a, Da Silva et al. 2006b, Zhang et al. 2009). In summary, the picture that emerges is that at least some WTPs select for certain resistance genes and certain multiresistant species, whereas for other species, or under different conditions, a significant removal may be possible. The factors governing the efficiency of removal remain to be determined. An important factor fostering the selective effect of WTPs is the occurrence of HGT, which can be mediated via mobile genetic elements such as plasmids (conjugation), bacteriophages (transduction), or by uptake of free DNA from the surrounding environment (transformation). In the extreme case these processes might transfer resistance factors from multiresistant pathogens to bacteria which are further disseminated into the receiving aquatic environment. If these organisms subsequently become established in this environment, they will increase the resistance pool in the environment in the long term, which may allow other pathogens to recruit new resistances more easily. It has often been suggested that WTPs might be hot spots for HGT due to the favorable conditions which prevail in this environment, such as high bacterial densities, high oxygen, and high nutrient concentrations (Kim &Aga 2007). We found a high prevalence of plasmids, partly identified as broad-host range plasmids such as IncP, IncQ, and IncW, in the studied multiresistant wastewater isolates. Detection of sul genes in the plasmid fraction indicated that plasmids carried transferrable antibiotic resistance determinants. This provides some evidence that the occurrence of HGT events and associated spread of ARG is likely in the WTP environment. Resistance to important new antibiotic classes such as fluoroquinolones and new generation cephalosporins were in general less prevalent at all our test sites compared to resistances against older antibiotics, such as sulfonamides. A similar outcome of a screening we conducted in 2009 (data not shown) supports this conclusion. Only Gram-positive bacteria showed some degree of tolerance against these substances. However, it remains unclear whether this is due to intrinsic resistance or whether Gram-positives have developed resistance to fluoroquinolones and cephalosporins more successfully. Moreover, whereas for

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most of the tested substances we found an increased tolerance among selected bacterial isolates at the outlet of the WTP, we could not confirm this pattern for fluoroquinolones. Fluoroquinolone resistance can be mediated by chromosomal mutations that are thought to be transferred vertically and the plasmid-borne qnr genes, which have been described to occur in Gram-negative bacteria (Cattoir et al. 2008, Cattoir et al. 2007, Martínez-Martínez et al. 1998). We assume that plasmid-mediated resistance is still a less prevalent mechanism at our site compared to chromosomal mutations. Hence, resistance to fluoroquinolones might not be as easily transferable to other bacteria, even in presence of high CIP concentrations in the effluents (12). For instance, among the extremely multiresistant and highly prevalent strains of Sphingobactaria isolated at the outlet, none showed elevated tolerance to fluoroquinolones. It will be interesting to observe whether these resistances become more prevalent in wastewater and environmental communities over the next decades. The role of lakes as potential reservoirs of antibiotic resistance has been infrequently addressed (Auerbach et al. 2007, Jones et al. 1986). Most comparable studies have focused on rivers (Castiglioni et al. 2008, Cummings et al. 2010). One goal of our study was to gain first insights on the impact of discharged wastewater on the prevalence of MRB and ARG in the receiving water body, the Vidy Bay of Lake Geneva. Our data indicate pollution of water and sediments with ARB and resistance genes close to the outlet of the WTP compared to the remote site. These two area have been analyzed in preliminary experiments in 2009 (data not shown) which revealed a similar outcome as for the data presented here; however, the sediments exhibited higher numbers of MRB, than the overlying water column. In the extreme case no MRB were isolated from the water column but isolates were always obtained from the sediments, as, e.g., for Nor/Cef resistant bacteria. As expected, the total bacterial counts were also higher in sediments. Thus, lake sediments appeared as sites where resistance traits persisted and accumulated. The potential of the heavily polluted (e.g., with heavy metals) sediments in Vidy Bay to preserve fecal indicator bacteria has been demonstrated in previous studies (Haller et al. 2009b, Poté et al. 2009b) and the impact of the WTP on contamination of the Vidy Bay and its bacterial community is a well-established fact (Bravo et al. 2011, Haller et al. 2009a, Poté et al. 2009a). It is likely that selection and persistence of elevated levels of ARB and resistance genes in close proximity to the WTP outlet are favored, e.g., due to co-selection of antibiotic and heavy metal resistance (Stepanauskas &Sieracki 2007). The spatial distribution of resistances in the sediment is studied in more detail in ongoing research. The question remains open whether the level of ARG at the point close to the drinking water pump is also influenced by anthropogenic pollution (discharge of wastewater) or represents typical natural levels of these genes in fresh water environments. Compared to rivers (Li et al. 2009b, Vilanova et al. 2002), addressing this question in lakes is not an easy task as transport of contaminated water masses and progress of dilution depends on constantly changing wind and temperature regimes, as well as bathymetric characteristics that determine currents and mixing. Tracer experiments releasing bacteriophages from the WTP

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outlet revealed their transport to within 1.5 km of the drinking water pump within 5–6 h during holomixing in winter time (Goldscheider et al. 2007).

Table 12 Concentrations of ciprofloxacin and sulfamethoxazole in raw hospital sewage of the CHUV building (HOS), at sampling points: WTPin; WTPout of Lausanne’s WTP; lake water above the outlet of the WTP discharge pipe (STEP) and a reference point located southwest of STEP, 1.5 km from the DP.

HOS (ng/l) WTPin (ng/l) WTPout (ng/l) STEP (ng/l) Ref point (ng/l)

Sulfamethoxazole 1116 248 61 4 0.4

Ciprofloxacin 18281 3259 807 6 0.9

A similar tracer study carried out in 1997 even revealed bacteriophages released from the STEP in lake water in the immediate vicinity of the drinking water pump (Wildi &Rossi 1997) within 48 h. Studies on the transport and fate of antibiotics and other pharmaceuticals in Vidy Bay, which can be ascribed to the WTP discharge with some certainty, were also detected at 1.5 km distance from the DP (12 reference point) in direction of the drinking water pump at concentrations in the low nanogram per liter range (Bonvin et al. 2011). Thus, it cannot be ruled out that MRB and ARG released from the WTP might reach the DP and that the detected resistance levels in the present study might result or at least partly result from the impact of anthropogenic pollution and/or selection of resistance due to the presence of low antibiotic concentrations (Gullberg et al. 2011). In addition to direct contamination there is the question whether the chronic release of elevated levels of resistant bacteria and transfer of resistance vectors into natural microbial populations can alter the natural resistance background in the long term. Studies in soil have indicated that such long-term trends may exist (Knapp et al. 2009). Currently however, we lack data on the natural resistance background and an understanding of the impact of elevated antibiotic resistance in natural environments, which leaves us unable to assess the risks associated with the levels observed here. This is clearly an important goal for research in the future. In the present study we have analyzed a sample set of different aquatic compartments along a wastewater flow using different methodologies, which can be mainly differentiated into culture-based and culture-independent tools. The culture-based approach nicely allowed us to link antibiotic resistance to active (viable) bacterial cells present in the different environments and identify the bacterial taxa carrying resistances. Further, we were able to characterize detected strains with respect to multiple resistances and mobile genetic elements, such as plasmids and the genetic base for the observed resistance. The great limitation of this method is that only a small proportion of the natural bacterial community is accessible to culturing. It is well known that natural aquatic environments contain large proportions of non-culturable but viable bacteria (Oliver 2005). Less than 1% of environmental bacteria are culturable, and large parts of the bacterial diversity have so far

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not been cultured at all (Amann et al. 1995). Even lab strains fail to be cultured when living under harsh conditions such as in fresh water environments (Arana et al. 1997). Large differences (2–4 log units) between numbers obtained from heterotrophic plate counts and FC have been described, e.g., for raw water (Hoefel et al. 2003). The advantages of culture-independent approaches are evident particularly when dealing with oligotrophic environments such as fresh water and other natural ecosystems. Clearly, comparing plate counts to bacterial 16S rRNA gene copy numbers and flow-cytometric cell counts, results obtained from plate counts alone would have underestimated the actual prevalence of antibiotic resistance in the wastewater and especially at the two investigated sites in Lake Geneva. Of course biases for these methods exist as well, e.g., with respect to the method used for DNA extraction, PCR efficiencies, presence of PCR inhibitors or the gating parameters in the flow cytometer. Application of the culture-independent methods, FC, and qPCR, alone would have given no information on biological activity of the quantified bacterial traits (though for FC there exist procedures for-live-dead staining of cells) and no linkage of resistance genes and bacterial taxonomic identity. Determination of multiresistances and detection of the presence of mobile genetic elements within intact cells would likewise not have been possible. As such it has to be taken into account that for the high numbers (compared to the plate counts) of resistance genes and 16S RNA gene copy numbers or flow- cytometric counts, a certain fraction might result from disrupted or dead cells. The presence of multicopy plasmids carrying resistance genes, or the presence of abundant but non- culturable environmental strains may further contribute to the observed disparity between the two approaches. Taking the advantages and short-comings of the discussed methods into account it is perhaps surprising that, studies applying both culture-based and molecular tools in parallel are still limited in number although they can provide substantial information on the actual situation and related processes of antibiotic resistance in different compartments. Most previous studies discussing, e.g., the role of hospital effluents on potential contamination of nearby water sheds, focused on culture-based approaches (Manaia et al. 2010, Novais et al. 2005, Schwaber et al. 2006). Volkmann et al. (2004) could, e.g., confirm the contamination of different wastewater samples with genes conferring resistance to β- lactam antibiotics and vancomycin, whereas the staphylococci-specific methicillin resistance gene was found in significantly lower amounts. The result supported previous culture-based approaches in which methicillin resistant staphylococci were only isolated from clinical wastewater (Schwartz et al. 2003). Due to logistical constraints, particularly the high workload associated with culturing, it was not feasible to replicate the sampling, nor to sample all water sources at the same time, which of course implies that one should be careful in generalizing from the results presented here. Similar limitations are frequently encountered in comparable studies (Schwartz et al. 2006, Szczepanowski et al. 2009) Care was taken to sample under comparable circumstances (i.e., avoiding rainfall) and time of day, particularly in the actual sewage system, to obtain comparable samples. Temporal variation in total bacterial load and resistance counts might of course influence the general trends observed in our study. However, in two comparable studies where temporal variations were taken into account this seemed not to be the case. Da

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Silva et al. (2006) sampled a WTP in Chile during four separate sampling campaigns, representing spring, summer, autumn, and winter. They found higher fractions of resistant organisms in the warmer periods compared to the winter months, but the numbers usually varied within the same order of magnitude for all sampling events. The effect of treatment was consistent over time, i.e., treated effluents always exhibited higher resistance fractions compared to raw sewage. In contrast, Zhang et al. (2009) reported higher levels of resistance among Acinetobacter isolates from a WTP and its receiving river in the US during a cold and low flow sampling event compared to a warm and high flow sampling event. Still the trend of increased resistance from influent to effluent was observed during both campaigns. Finally, another study by Li et al. (2009) determined total cell counts (DAPI staining) and culturable bacteria (on TSA) of 3.3 ± 1.9 × 107 cells ml−1 and of 4.5 ± 2.1 × 104 CFU ml−1, respectively (averaged over three sampling campaigns carried out in December, April, and August) in the effluent of a WTP treating wastewater from an oxytetracycline production facility in China, indicating a seasonal variability of around 50%. We have carried out a similar campaign to that compared in the present study in 2009 which resulted in comparable trends, further supporting the validity of the data presented here (Nadine Czekalski, unpublished data). The molecular methods proved valuable and generally the trends observed were in good agreement with the cultivation-based data. Therefore, a more thorough time-resolved and replicated study based on this methodology would be a good approach to overcome the noted limitations. For future studies aiming to provide deeper insights into resistance background of natural aquatic environments as well as their pollution with resistant bacteria from anthropogenic sources, the use of advanced culture-independent tools, such as metagenomics and high throughput sequencing will be of great value. With respect to linking activity, mobility, and functional traits to bacterial taxa single cell sequencing approaches might probably become a helpful tool too, potentially replacing and even improving on current culture-based approaches.

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Chapter 5

Seasonality of antibiotic prescriptions for outpatients and resistance genes in sewers and wastewater treatment plant outflow

Seasonality of antibiotic prescriptions for outpatients and resistance genes in sewers and wastewater treatment plant outflow

Introduction

Antibiotic resistance is a growing global problem (Levy & Marshall, 2004, McKenna, 2013) that scientists are still trying to fully understand. What is certain is that antimicrobial drug consumption is the major driver of antibiotic resistance in human-pathogens. The correlation between the antibiotic use and the levels of resistance has been demonstrated worldwide (Achermann, et al., 2011, Hicks, et al., 2011, Vernaz, et al., 2011, Hutka & Bernard, 2014, Suda, et al., 2014) and used as an efficient proxy to estimate the threat of new resistant infections (Niederman, 2001, Davies & Davies, 2010). Within clinical studies, antibiotic resistance levels highly correlated with the corresponding prescription patterns (Lopez- Lozano, et al., 2000, Lepper, et al., 2002). Correlations between antibiotic prescriptions for outpatients and resistance profiles of hospital isolates have also been described (Hay, et al., 2005, Gottesman, et al., 2009). These findings lead researchers to the conclusion that strong seasonal variations in antibiotic use by outpatients lead to seasonal changes in antibiotic resistance levels (Sun, et al., 2012). Selective pressure from antibiotics takes primarily place in human gut and after antibiotic administration the excreted resistant bacteria, together with non-metabolized antibiotics (Sun, et al., 2012, Marx, et al., 2015), reach the wastewater treatment plant (WWTP). Sewers and WWTPs are the principal collectors of household and hospital wastes where mixtures of commensal and pathogenic bacteria together with the presence of selective agents create the optimal conditions for the selection of antibiotic resistance and spread of ARG, e.g. through horizontal gene transfer (Zhang, et al., 2011). Moreover, WWTPs bridge the gap between anthropogenic and natural environments, thus allowing commensal and pathogenic bacteria, resistant and non-resistant, to reach the freshwater ecosystem. Because of the seasonality in antibiotic prescriptions and consumption, the concentration of antibiotic residues found in wastewater differ substantially within a year (Coutu, et al., 2013, Marx, et al., 2015) and can potentially influence the abundance of antibiotic resistant bacteria and genes (ARB&G) present in the wastewater. Therefore, WWTPs represent a critical node to study the spread of antibiotic resistance, especially if treated wastewater is used as reclaimed water (Pruden, et al., 2013). The role of antibiotics as a selective agent in a municipal WWTP has been shown before (Gao, et al.,

Chapter 5

2012, McKinney & Pruden, 2012) but to date, it is not known whether the level of resistance genes in wastewater has a similar seasonal pattern as does the prescription and consumption of antibiotics. Wastewater treatment technology is known to reduce the number of bacteria in the wastewater (Schwartz, et al., 2006, Laht, et al., 2014, Alexander, et al., 2015) but if and how the composition of microbial communities change during treatment has not been extensively studied, especially in the context of antibiotic resistance. In this study, samples of wastewater and treated wastewater from Dresden municipality were taken seasonally over a two year period and the levels of 11 resistance genes were measured via qPCR. In addition, we used Illumina Miseq sequencing to reveal the microbial community structure of the wastewater in sewers and the treated wastewater in the effluent of the WWTP. We also determined the community changes in relation to wastewater treatment and seasons. We tested the hypothesis that resistance gene level will undergo seasonal changes correlated with the consumption of antibiotics among outpatients. Therefore, higher antibiotic prescription rate should result in higher levels of resistance genes. We also hypothesize that the environmental seasonal changes will influence the microbial community composition of both the wastewater and the treated wastewater and therefore influence the antibiotic resistance gene copy numbers carried by the bacteria in the treated wastewater. In general, we expect that the treatment processes would reduce bacterial and antibiotic resistance gene numbers, absolute and relative.

Material and Methods

Sewage and wastewater sampling The Dresden Kaditz WWTP processes wastewater for a loading capacity of 650,000 population equivalents, of which around 80% are living in Dresden. The catchment area has no known industries producing or applying significant amounts of antibiotics (e.g. industrial production or animal husbandry). The sewer system consists of about 800 km of combined sewers, around 450 km of a separate sewer system and 350 km for stormwater runoff. In Dresden, the River Elbe divides the municipality into two; the sewer system of the city is therefore built accordingly. The sewage of the two areas reaches the Kaditz WWTP through separate channels (SEWER1, SEWER2). They join into one common sewer, which is the inflow (MIXED INFLOW) of the wastewater treatment. Wastewater is processed via activated sludge technology and the treated wastewater (OUTFLOW) is released into the River Elbe. The monitoring campaign started in April 2012 and ended in December 2013 (Supplementary material Supplementary figure S0). During this time, samples of wastewater (SEWER1, SEWER2, MIXED INFLOW) and treated wastewater (OUTFLOW) were taken at least once per season. In 2013 the sampling was intensified and more samples were collected in the seasons with higher temperature variability (spring and summer). Technical challenges encountered during the sampling and the flooding of the River Elbe in 2013 led to an uneven sampling over the two years. 1L of composite water samples were taken and stored at +4 °C pending filtration within a few hours. All samples were taken in triplicates.

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Ambulatory and stationary antibiotic consumption

The ambulatory prescription data were provided by the AOK PLUS, the largest statutory health insurance company in the State of Saxony, Germany. About 41% of the population in Dresden is insured with the AOK PLUS while the remaining part is being held by other statutory and private health insurance companies. Ambulant data for 2012 and 2013 were provided weekly. Data from three in house hospital pharmacies were also available and estimated to cover about 65% of the hospital beds in the catchment area of the Dresden- Kaditz WWTP. Previous comparisons of the prescriptions for in- and out-patients showed a predominance of antibiotics prescribed in the ambulant (out-patient) sector with the exception of some antibiotics which were prescribed in both sectors (e.g. cephalosporins like cefuroxime and fluoroquinolones, like ciprofloxacin and levo-/ofloxacin) or predominantly prescribed in hospitals (e.g. cephalosporines, like cefotaxim and penicillins, like penicillin V). The data provided by the AOK PLUS health insurance refers to the principal thirteen substances (antibiotics) prescribed in Dresden and covering 80% of the total ambulatory prescriptions of the city. The pseudonymized data provided by the AOK PLUS include information about prescriptions and socio-demographic parameters (year of birth, place of residence). Information was available for the defined daily dose (DDD) indicating the assumed average maintenance dose per day for a drug used for its main indication in adults (Merlo, et al., 1996). The total number of DDD was obtained by multiplying the prescribed amount of each antibiotic (active substance in grams) with the DDD. Special attention was paid to the exact quantification of mono and fixed dose combinations. An analysis of the pharmaceutical registration number in combination with the total number of sold packages was used to validate the data (Timpel, et al., 2015).

Sample filtering and community DNA extraction Water samples were stored at +4 °C pending filtration (within a couple of hours). 20 mL of wastewater (SEWER1, SEWER2, and MIXED INFLOW) and 50 mL of treated wastewater (OUTFLOW) were filtered through polycarbonate filters (pore size 0.22µm, diameter 47mm, GE Water & Process Technologies). DNA was extracted using MoBio PowerWater® DNA isolation kit (MoBio Laboratories, Inc., CA, USA). Total DNA was recovered in 50 μL of elution buffer. The concentration of extracted DNA was measured using a NanoDrop Spectrophotometer ND-1000 (absorption readings at 260 nm). The extracted DNA was stored at -20 °C pending further analysis.

Detection and quantification of ARG copy numbers

Eleven resistance genes were surveyed: sul1, sul2, tetM, qnrB, blashv-34, blactx-m-32, blaoxa-58, vanA, blakpc-3, MecA, and dfrA1. The qPCR was performed using a Dynamo Flash SYBR Green qPCR kit (Thermo Scientific) using an EPPENDORF realplex machine. The thermal cycling conditions were as follows: 95 °C for 7 min, 40 cycles at 95 °C for 10 s and Tm for 30 s. A

- 97 - Chapter 5 melting curve was obtained to confirm amplification specificity. Reactions were conducted in 10 µL volumes on 96-well plates containing 1× Dynamo Flash SYBR Green master mix, 0.3 µM of each primer and 1 × ROX passive reference dye. Template DNA was used in qPCR reactions in the range of 2-12 ng; a fixed dilution of raw DNA extract was used. In parallel with the ARGs, the 16S rRNA gene copy numbers were quantified. Primers and annealing temperature, Tm (ºC) are shown in Appendix 5. The number of technical replicates in the qPCR assay was two.

Standards used for quantification A plasmid vector and fragments of ARG were used as standards for quantifying the raw qPCR results (Laht et al. 2014, Muziasari et al.; 2014 and this study). The 16S rRNA gene quantification standard was a genomic DNA from E. coli K12 (genome size 4.6 Mbp with seven copies of the rRNA operon). Standard curves (Ct per log copy number) for quantification of 16S rRNA gene and ARGs were obtained for each run using the plasmid constructs (or genomic E. coli DNA for 16S rRNA in ten-fold serial dilutions). The gene copy number in the standard was determined via the plasmid/genomic DNA concentration (measured using VICTOR™ X3 Multilabel Plate Readers Perkin-Elmer; (Muziasari, et al., 2014).

Selection, quantification and normalisation of ARG All investigated genes were selected according to their clinical relevance and previous detection in preliminary screenings from the WWTPs in the region (data not shown). Furthermore, ambulatory prescriptions for antibiotics and the likelihood of the genes to be encountered on mobile elements were taken into account. Attention was given to genes conferring resistance to vancomycin (vanA) and high-levels of methicillin (MecA) as an indicator of contamination of hospital related microorganisms or ARG in aquatic environmental bacteria. In addition, Berendonk et al. (2015) have suggested many of these genes as possible indicators to assess the antibiotic resistance status in environmental settings. The ARG levels in the sample were calculated using the standard curve equation and measured Ct value. The quality control of raw Ct values for standard curve and unknown samples was done before further analysis. The limit of quantification (LOQ) was defined as the lowest point on the linear part of the standard curve: for ARG 10 and for 16S rRNA 100 gene copies per reaction. As negative control, the reaction mix with nuclease-free water as the template was used. Three biological and two technical replicates were incorporated in the statistical analysis. Total copy numbers of all target genes were normalized to ng of DNA extracted from the samples (ngDNA-1). Additionally, ARG copy numbers were normalized to 16S rRNA gene copy numbers and to volume of water (gene copies mL−1). From here on, we refer to the

- 98 - Chapter 5 concentrations of the normalized data (16S rRNA) as ‘relative ARG levels’ and to the copy number in a 1ml water as ‘absolute ARG levels’. Effect of seasons on ARG levels and gene variability To determine the relationship between relative ARG levels and seasons, years and plant treatment, the relative ARG levels were standardized, square root transformed, and assembled into a matrix. A non-metric multidimensional scaling analysis (NMDS) based on Bray-Curtis dissimilarity was calculated using R (version 3.0.1) and the vegan package (Oksanen, et al., 2015). Furthermore, linear mixed models (LMM) were fitted to the ARG data using the lme4 package in R (Bates, et al., 2014). We predicted the relative level of ARG using the genes, sampling location (SEWER1, SEWER2, MIXED INFLOW, OUTFLOW), season, sampling position (INFLOW, OUTFLOW), and sampling dates as predictors. Each predictor was used as fixed or as random effect depending on the applied model for the best prediction of the respective fixed effect. Detailed information on the LMM is contained in Table 14. The model selection was performed using the maximum likelihood estimator (ML). Parameter estimation was performed using the restricted maximum likelihood estimator (REML). Model quality was assesed by model comparison using likelihood ratio tests and the Akaike Information Criterion (AIC). Values below LOQ were set to a standardized gene amount of 5e-8, which is an order of magnitude below LOQ.

Microbial community sequencing and data analysis The analysis of microbial community of the sewage and wastewater samples was performed on the previously extracted DNA and sequenced by MiSeq Illumina technology (300 bp paired end) on the variable region V1-V3 of 16S rRNA geneat the GATC Biotech AG (www.gatc-biotech.com). In detail, 43 DNA samples were used as a template in PCR reactions. This resulted in amplicons the size of 471 bp, the used primers were: 27F- AGAGTTTGATCCTGGCTCAG and 534R- ATTACCGCGGCTGCTGG (Methe, et al., 2012). The paired end sequences were merged based on overlapping bases using FLASh (Magoc & Salzberg, 2011) with maximum mismatch density of 0.25. Sequences were quality trimmed using the MOTHUR software package (Schloss, et al., 2009). To minimize the effect of random sequencing errors, both the low quality fragments and the sequences shorter than 280 bp were removed. The PCR chimeras were checked and filtered out by UCHIME (Edgar, et al., 2011). Each sample was randomly re-sampled and normalized at 25,230 sequences. The taxonomic assignment of OTUs was performed by RDP classifier at 80% threshold in MOTHUR (Wang, et al., 2007). The sequences have been deposited in the NCBI Short Read Archive under accession number SRP067506. Qualified sequences were assigned to operational taxonomic units (OTUs) based on a 97% sequence similarity. Taxonomic classification was based on NCBI Taxonomy - http://www.ncbi.nlm.nih.gov/taxonomy. The Shannon diversity index (H’) was calculated to estimate the diversity in the different samples. The Bray-Curtis dissimilarity index, which takes into account diversity and taxa differences between locations, was used to compare beta diversity among samples.

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The parsimony test, as implemented by MOTHUR, was used to assess whether two or more communities have the same composition. A Bonferroni correction was applied to adjust the significance level for multiple pairwise comparisons (p ≤ 0.05). Permutational multivariate analysis of variance (Adonis), and analysis of similarity (ANOSIM), were performed with Bray-Curtis dissimilarity index using the R environment (version 3.1.0; http://www.r- project.org/). Genera abundances were standardized, square root transformed, and assembled into a matrix to generate a multidimensional scaling (NMDS) plot to assess similarities among samples in a two-dimensional space and determine the relationship between genera according to seasons, years or plant treatment. Because of the community similarity between the wastewater (SEWER1, SEWER2 and MIXED INFLOW), the differentially most abundant genera between untreated and treated wastewater (OUTFLOW) were calculated only between INFLOW MIX and OUTFLOW samples. Calculations have been performed for all samples of the MIXED INFLOW+OUTFLOW dataset. The differentially most abundant taxa on 7 different taxonomic levels (OTU, species, genus, family, order, class, and phylum) were instead calculated in R version 3.2.1.with the phyloseq DESeq2 package (Anders & Huber, 2010). The difference in features between MIXED INFLOW and OUTFLOW were calculated taking the time in to consideration [~Area+Time (season+year)]. Canonical correspondence analysis (CCA) was calculated with R (package vegan) using the taxonomic data as well as the ARG to identify correlations between species, genes, and the respective sampling area (MIXED INFLOW, OUTFLOW). The CCA was calculated using the Taxonomic data as the species matrix and the absolute ARG levels as environmental matrix.

Results and Discussion

Selection of ARG and aim of the study The central goal of our study was to assess the presence and levels of multiple ARGs in the Dresden sewer system and municipal WWTP inflow and outflow during a two-year monitoring campaign (2012-2013). For this, samples were taken at least once per season. In 2013 the sampling was intensified and more samples were collected in the seasons with higher temperature variability (spring and summer), Appendix 5. The samples were filtered and the DNA extracted for a microbial community analysis using MiSeq and for the quantification of ARGs with qPCR (more detailed description in Materials and Methods). To correctly assess the ARG variability of a complex environment such as the urban wastewater system, we considered: 1) the seasonal differences of ARG levels, 2) the spatial variations analyzed as the relationships between ARG levels in the wastewater and treated wastewater 3) the composition of the microbial community in the water in time and space.

Eight out of eleven genes (blactx-m-32 blaoxa-58, blashv-34, dfrA1, sul1, sul2, tetM, vanA), (Figure

19) were detected in all sampling locations. Blakpc-3, qnrB, and mecA were not quantified because they were either not present or their level was under the detection limit. Sul1, sul2, and tetM were more abundant both in wastewater and treated wastewater, with the relative

- 100 - Chapter 5 level of sul1 being always the highest (Appendix 6 and 7). The higher relative ARG levels and absolute amount of sul1, sul2, and tetM can be explained with a longer history of usage of sulphonamide and tetracycline antibiotics (Pruden, et al., 2006, Heuer, et al., 2011) while plasmid-mediated quinolone resistance encoded by qnrB was first reported only in 2006 (Jacoby, et al., 2006). The bacterial numbers fluctuated between seasons (Table 13 ), The highest level of 16S rRNA copies was found in spring; for wastewater 6.93E+07 copies ng-1 and for treated wastewater 6.38E+06 copies ng-1. The lowest level of 16S rRNA copies were found in autumn with 6.90E+05 copies ng-1 for wastewater and 1.51E+04 copies ng-1 for treated wastewater (Table 13).

Antibiotic prescription analysis The antibiotic prescription levels were similar for the two investigated years (data not shown). As expected, the total antibiotic prescriptions followed the known seasonal trend (Sun, et al., 2012, Hutka & Bernard, 2014). The amount of all prescribed antibiotics by the city´s outpatients was always higher in the cold months compared to the warm ones (Figure 21) and the total consumption of antibiotics extrapolated by the correspondent antibiotic prescriptions ranged between 26 kg (August 2012) and 47 kg (January 2013) per month. While some antibiotic substances confirmed the general seasonal pattern (macrolides), others were either evenly prescribed over the year (quinolones, ß-lactams) or had no specific pattern, like for cephalosporins, penicillin, sulfamethoxazole- trimethoprim and licosamides. Examples of antibiotics showing different prescription patterns are presented in Figure 21).

Figure 19 Antibiotic resistance genes (ARG) copy numbers normalized to 16S rRNA gene copy numbers in wastewater (SEWER1, SEWER2, MIXED INFLOW) and OUTFLOW of Dresden WWTP. The line in each box marks the median and boxes: 25th and 75th percentiles; whiskers: 5th and 95th percentiles and outliers ±1.5 (q). * indicates that the concentration of the genes was below the detection limit of the quantitative PCR. Sampling points in spring 2012 and autumn 2012 are missing because of logistical problems

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Seasonal trends of ARG abundance The relative ARG levels were significantly different between seasons (Figure 20; analysis of variance function adonis: R2 = 15% p < 0.0001, Appendix 8, Figure 24, panel 1) and the seasonal differences for the overall relative ARG levels were independent from the location and year (p < 0.0001). Therefore, seasonality strongly influenced the relative level of ARG in wastewater. Seasonality is also a robust phenomenon in the antibiotic prescriptions for outpatients (Suda, et al., 2014). A higher number of antibiotics are prescribed by physicians in the cold months of the year (Sun, et al., 2012) when low temperatures contribute to the increase of infections from disease causing bacteria. It is known that selection of resistant bacteria can occur during or after antimicrobial treatment and the quantity of antibiotic consumption contributes to the release of ARB into sewage water by individual excretions (Rizzo, et al., 2013). In our study, the relative ARG levels encountered in the Dresden wastewater showed a corresponding pattern with the temporal analysis of the antibiotic prescriptions for outpatients (Figure 21 and Figure 19). ARG levels were indeed higher in autumn and winter (Figure 19), in correspondence with the yearly peak of antibiotic uptake. Similarly, the low level of antibiotic prescriptions in spring compared to autumn and winter corresponded to low levels of ARG in the wastewater in the same season. Partial disagreements were shown in the summer where the low rate of antibiotic prescriptions corresponded to variable ARG levels.

The partial discordance between the rate of prescription and the ARG levels in the wastewater could have an explanation in the different seasonal course of prescriptions of individual antibiotic substances (Suda, et al., 2014). Indeed in Dresden, antibiotics like cephalosporins, penicillin, sulfamethoxazole–trimethoprim, and lincosamides have no marked patterns of prescription (data not shown) and therefore their contribution to the levels of certain ARG (e.g. blashv34, sul1) in the wastewater may vary according to the year. The overall amount of prescribed antibiotics influences the amount of antibiotics and antibiotic residues in the wastewater thus may contribute to the overall selective pressure on the resistant bacterial fraction over the year. The seasonal proportions of prescribed antibiotics were mostly comparable to the ones analysed by Hutka & Bernard (2014) for the years 2005 -2011, thus revealing a similar uptake of antibiotic classes by the outpatients over the years. Marx et al. (2015) performed at the same WWTP a measurement of a variety of antibiotics in wastewater and detected them in therapeutic and sub-therapeutic concentrations (Marx, et al., 2015). This suggests that the selective pressure by antibiotics and antibiotic residues could lead to the selection and co-selection of ARGs (Michael, et al., 2013) in the wastewater. As a consequence, ARG levels might not always correspond, in proportion, to the outpatient antibiotic prescription rates. Therefore, even if ARG levels in wastewater are significantly different between seasons, the variation of ARG might not only be limited by the practitioner’s prescriptions but it is likely that other processes are involved (e.g. ecological processes) in the sewers or within the WWTP (Berendonk, et al., 2015). In fact, season-dependent environmental factors, like

- 102 - Chapter 5 temperature or precipitation of suspended solids in the sewers have been shown to interfere with the proliferation or survival of microbial taxa that mainly harbor ARGs or promote the growth of microorganisms which do not contain any of the tested resistance genes (Biggs, et al., 2011, VandeWalle, et al., 2012, LaPara, et al., 2015). It should be acknowledged that ARGs constitute only part of the bacterial genome. It is conceivable that a small environmental alteration affecting relevant taxa in a microbial community (carrying ARGs) may lead to differences in ARG abundances. Generally, the data support the notion that the prescription rate of single classes of antibiotics in Dresden for seasonal selections or persistence of ARG in municipal wastewater warrants further study.

Spatial variations between ARG levels in wastewater and treated water

Copy numbers of 16S rRNA in SEWER1, SEWER2, and MIXED INFLOW were of one or two orders of magnitude higher than in the respective OUTFLOW (Table 13). Differences between the wastewater and treated wastewater were statistically significant in all seasons (p < 0.01). No significant difference in the overall relative ARG levels were detected between SEWER1, SEWER2, and MIXED INFLOW (adonis 9999 permutations p = 0.7). Previous studies have shown that sewers can act as a reservoir for resident organisms (Chen, et al., 2003, Leung, et al., 2005) and that the in situ diversity of biofilm forming bacteria does not differ between different sewers even if geographically distant (McLellan, et al., 2010). In light of this knowledge, part of the ARGs present in the wastewater could be restrained by sewers through the resident microorganisms and hence potentially influence their abundance in MIXED INFLOW reaching the WWTP. The comparison of relative ARG levels between the different parts of the wastewater (SEWER1, SEWER2 and MIXED INFLOW) revealed no significant differences (p < 0.01). Because of this, the spatial variation analysis of ARGs was carried out only between MIXED INFLOW, as representative of wastewater, and the OUTFLOW. No significant differences between the overall relative ARG levels of MIXED INFLOW and OUTFLOW (adonis, 9999 permutations) were found for the two investigated years. With the notable exception of vanA which was constantly enriched in the OUTFLOW (Fig.19, p < 0.001), the relative copy numbers of ARG did not decrease significantly during treatment (Fig.19, p < 0.0001 adonis, 9999 permutations). While previous studies have already demonstrated that the relative number of resistance genes were not reduced during the treatment processes in WWTP (Laht, et al., 2014, Alexander, et al., 2015), to our knowledge, only one study has previously demonstrated an enrichment of vanA in the treated wastewater (Alexander, et al., 2015).

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Figure 20 Spatiotemporal changes in relative antibiotic resistance gene (ARG) levels before and after wastewater treatment. Non-metric multidimensional scaling (NMDS) plot based on Bray- Curtis index of ARG in wastewater (SEWER1, SEWER2, MIXED INFLOW) and treated water (OUTFLOW) of Dresden municipality. Symbols indicate samples of wastewater and treated water for the year 2012 and 2013

Figure 21 Sum of monthly prescribed antibiotics (in Kg) by the AOK health insurance for the year 2012 and 2013 in Dresden, Germany. Blue bars: monthly antibiotic prescriptions; Yellow line: monthly clindamycin prescription (lincosamide with no marked pattern); Red line: monthly amoxicillin prescription (ß-lactam with a constant prescription over the year). Clindamycin and amoxicillin are included in the overall monthly prescription of antibiotics. Numbers 1-12 in the x - axis refer to the calendar months January – December

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ARG seasonal variations The relative gene level of single genes varied within the samples (Figure 24, panel 2) and their variability was explained by the gene type and the season. The highest variation of relative gene abundance was shown for sul1 while genes blactx-m32, blaoxa58 and vanA did not show strong gene level variation between seasons. For all the genes, higher relative level was always encountered in the colder months, either in autumn or in winter (Figure 24, panel 3). The analysis of a complex linear mix model (Table 14 , AIC: -960.6) indicated an effect of the sampling location (MIXED INFLOW, OUTFLOW) on a single ARG for most of the genes (with the exception of vanA). No stable difference between the relative ARG levels (ΔARG) of the treated wastewater and wastewater could be shown to support either a constant reduction or increase of the relative ARG levels in the OUTFLOW over the year (Figure 24, panel 4). Therefore, we conclude that although the wastewater treatment processes might have an effect on a single ARG, the effect is always gene specific and season dependent.

Seasonality effect on microbial community composition The analysis of the qPCR results suggested that the difference in ARG levels was attributed to season and not to wastewater treatment processes, prompting us to explore the effect of seasonality on the bacterial community composition of wastewater and treated wastewater. The total number of raw reads for the 43 investigated samples was 11238980. After trimming and joining the read pairs, the number of reads was 5929221 and sequence clustering yielded a total of 5590 operational taxonomic units (OTUs), each containing sequences that shared at least 97% identity. Contrary to the qPCR results on ARGs, wastewater (SEWER1, SEWER2 and MIXED INFLOW) and treated wastewater (OUTFLOW) resulted in two distinct microbial communities (Fig.22; adonis R2: 13%, p < 0.001). The year and season effect was not statistically significant for discriminating microbial communities from the same sampling location (p = 0.8) and water samples of the same season formed no temporally distinct clusters. 85-90% of the highly diverse bacterial community in all samples was represented by the Phyla Actinobacteria, Bacteroidetes, Firmicutes, and Proteobacteria (Appendix 9). Although the overall microbial community did not change significantly within the samples of the same location over the seasons, minor changes can be noticed. This is especially evident for SEWER2, MIXED INFLOW and OUTFLOW in spring-summer 2013. During this time, the weather was characterised by heavy raining events, which have also led to a flooding of the city by the Elbe River. The elevated amounts of raining water supplied into the sewers (and therefore into the MIXED INLOW) are the likely reasons for a higher dilution factor (MIXED INFLOW) or for the detachment of parts of the biofilm coating the sewers (SEWER2). We think that this might have caused the differences in microbial communities compared to other seasons in the wastewater (Appendix 8). Because of the strong rain events, the WWTP was not capable to fully process the wastewater and the outflow resulted in poorly sanitized water; in addition, a safety system of the WWTP (combined sewer overflow) which allows the wastewater to bypass the plant, contributed to the similarity in microbial community composition between the “treated wastewater” (OUTFLOW) and the wastewater

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(Fig.22).Compared to wastewater, the treated wastewater displayed a higher phylogenetic diversity (p < 0.001) and higher variation among temporal replicate samples (more OTUs in winter and summer). This might be an artefact caused by a higher number of redundant sequences (abundant taxa) in the wastewater compared to treated wastewater. The richness of genera among the wastewater samples did not differ significantly (Appendix 9). In general, the microbial diversity in the treated wastewater was higher than in the wastewater in all seasons except for spring where the diversity was lower. In this case, the lower diversity was not due to the reduced bacterial abundance because among all the treated wastewater samples, the treated wastewater (OUTFLOW) in spring displayed the highest microbial abundance (Table 13).

Table 13 Gene copy numbers of 16S rRNA normalized to DNA content (per ng of DNA) for wastewater (SEWER1, SEWER2, MIXED INFLOW [M. INFLOW]) and treated water (OUTFLOW). The here represented concentrations are averaged over biological triplicates and duplicate measurements.

2012 2013

Area Season 16S rRNA copies nr ng-1

SEWER1 SPRING 1.28E+07 6.93E+07 SEWER2 SPRING 1.24E+07 1.77E+07

M.INFLOW SPRING - 5.64E+07 OUTFLOW SPRING 2.98E+06 6.38E+06

SEWER1 SUMMER 9.60E+06 1.27E+ 07

SEWER2 SUMMER 2.13E+05 1.73E+07 M.INFLOW SUMMER 5.43E+05 1.38E+07 OUTFLOW SUMMER 1.89E+04 1.27E+06

SEWER1 AUTUMN - 6.37E+05

SEWER2 AUTUMN - 7.96E+05 M.INFLOW AUTUMN 6.90E+05 1.06E+06 OUTFLOW AUTUMN 1.51E+04 5.44E+04

SEWER1 WINTER 8,01E +05 5,59E+06

SEWER2 WINTER 4,22E+06 7,75E+06 M.INFLOW WINTER 5,32E+05 3,51E+06 OUTFLOW WINTER 3,75E+04 5,24E+05

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Figure 22 Spatiotemporal changes in wastewater community structures before and after treatment. Non metric multidimensional scaling (NMDS) plot based on Bray-Curtis index of microbial community inhabiting the waste- and treated water of Dresden municipality Symbols indicate

samples of wastewater and treated water for the year 2012 and 2013

Relationship between microbial community composition and ARG Canonical Component Analysis (CCA) showed that despite the distinct community differences between treated wastewater and wastewater, fitting of absolute ARG levels to the wastewater bacterial community composition (MIXED INFLOW,OUTFLOW) showed a weak correlation for most of the investigated ARGs. This means that the variation of the ARG levels cannot be explained by the shift of the microbial community from wastewater to treated wastewater (Figure 23). Indeed, as for the microbial community inhabiting the wastewater and treated wastewater, genera characterizing the OUTFLOW (differently abundant genera, Appendix 10) did not correlate with the majority of the ARGs, indicating that the ARGs are also widely distributed among these genera. However, vectors fitted for vanA and sul1 indicated a partially different distribution of the microbial community in the OUTFLOW. The responsible process for the non-reduction of ARG in the OUTFLOW is still unclear for most of the investigated genes but in the case of vanA, an explanation is possible. The analysis of the 30 dominant genera in OUTFLOW and MIXED INFLOW (Appendix 11) revealed a presence of the Enterococcus genus which is an opportunistic pathogen known to carry the clinically relevant vanA (Gilmore, et al., 2013). The WWTP processes high abundances of the genus Enterococcus in wastewater, which suggests that mechanisms of selection rather than HGT are mainly involved in the enrichment of vanA in the treated wastewater. This result is worrying, because the persistence and the enrichment of vanA in the treated wastewater could lead to a permanent

- 107 - Chapter 5 contamination of the receiving river sediments and therefore potentially spread the resistance for one of the last resort antibiotics (Narciso-da-Rocha, et al., 2014).

Figure 23 Canonical Correspondence Analysis of MiSeq community profiles from waste- and treated water with passively fitted relative ARG abundance. Arrows indicate the magnitude of measurable variables associated with community structures. Red crosses: the bacterial genera present in treated and untreated water; green triangles: differently abundant genera between treated water and wastewater (Appendix 10); open circles: INFLOW MIX; filled circles: OUTFLOW. vanA and sul1 accounted for 30% and 24% of the variation of microbial community and correlated positively with the OUTFLOW

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Table 14 Influence of season, type of gene, sampling location, and their interactions on the relative ARG levels for wastewater and treated water by a linear mixed model (Akaike Information Criterion (AIC)).

Nr. Modelformula AIC nullmodel 0 normalized Abundance ~ 1 - 711.3 single fixed effects normalized Abundance ~ Season + (1|Sampling date) + (1|Genes) + 1-1 +(1|Position) -834.7 normalized Abundance ~ Genes + (1|Sampling date) + 1-2 +(1|Season) + (1|Position) -848.3 normalized Abundance ~ Position + (1|Sampling date) + (1|Season) + 1-3 +(1|Genes) -831.1 multiple fixed effects normalized Abundance ~ Season:Genes + (1|Sampling date) 2-1 +(1|Position) -902.7 normalized Abundance ~ Season:Position + (1|Sampling date) + 2-2 +(1|Genes) -835.1 normalized Abundance ~ Genes:Position + (1|Sampling date) + 2-3 +(1|Season) -853.1 normalized Abundance ~ Season:Genes:Position + 3 +(1|Sampling date) -960.6

Effect of WWTP processes on absolute ARG levels

The wastewater contained substantial quantities of each of the investigated ARGs (Figure 25). As in the case of relative ARG levels, absolute gene level of sul1, tetM and sul2 dominated in the microbial community in both years with a concentration of approximately 7 8 −1 10 - 10 gene copies mL . In contrast, the concentration of blactx32, dfrA1, blaoxa58, blashv34 was approximately 106 gene copies mL−1, and the concentration of vanA was the lowest with approximately 104 gene copies mL−1. In 2012, the absolute levels of ARG in the treated wastewater decreased two to three orders of magnitude with the exception of vanA which decreased in only one order of magnitude (Figure 25). The WWTP was capable to reduce the absolute quantities of ARG from the wastewater. This reduction related directly to the bacterial removal rate which is commonly observed in conventional WWTPs. In 2013, although a similar reduction was observed, the wastewater treatment processes did not reduce the blashv34 and tetM as much as in the previous year. While for blashv34 the non- reduction was constant over the year, for tetM the reduction was not observed in some seasons. These results show that the influences of the wastewater treatment on the reduction

- 109 - Chapter 5 of the ARG levels (absolute and relative) are strongly dependent on the season and type of ARG.

Conclusions

Seasonality of ARGs in wastewater has often been part of a scientific debate but the necessary data for sound conclusions is still missing. We present evidence for a seasonal pattern of ARGs in the wastewater of Dresden, which was independent from the treatment processes and microbial composition of the wastewater, but related to the seasonality of the prescription level of antibiotics in the city. Neither the transport trough the sewers nor the treatment of wastewater resulted in a reduction of the relative level of ARGs. Contrasting to this showed the analysis of the microbial community clear differences in the dominant taxa between wastewater and treated wastewater. Therefore, the analysis of ARGs and the community suggests that the bacteria carrying the resistance genes are either not part of the dominant microbial community or that processes such as HGT disconnected the community and ARG profiles of the treated wastewater. Investigations focusing on ARGs carrying bacteria in the WWTP certainly require more attention and deeper investigation, but for that a different experimental approach would be needed and a broader spectrum of ARGs investigated. Advanced methods as the recently published EPIC-PCR (Spencer, et al., 2015) would allow linking ARGs to the species carrying the resistance gene and could be used to investigate directly the dynamics of ARGs and the corresponding resistant bacteria.

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111

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Figure 24 Influence of season, type of gene, sampling location and their interactions on the relative ARG levels for wastewater and treated water by a linear mixed model. Effect of season (Panel 1), gene type (Panel 2), gene type – season interaction (Panel 3). Effect of the wastewater treatment plant processes (location), type of gene and season on the ARG levels (Panel 4). Variation in relative ARG level is here express as the difference between relative ARG level of the OUTFLOW and MIXED INFLOW (delta (Δ) ARG level). Values above the dashed line (Δ POSITIVE) represent an increase of resistance genes in the treated water. Error bars represent the standard deviation

Chapter 5

Figure 25 Antibiotic resistance gene (ARG) levels (copy numbers ml-1) in wastewater (filled symbols) and treated water (open symbols). ARG levels were plotted separately for the year 2012 and 2013. Data are displayed chronologically. Error bar: standard deviation. In 2013 the levels for shv34 and tetM overlap and therefore the symbols overlap as well

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Chapter 6

Characterisation and distribution of extended spectrum beta- lactamases in bacteria from untreated wastewater and wastewater-impacted freshwater in Nigeria

Chapter 6

Characterisation and distribution of extended spectrum beta lactamases in bacteria from untreated wastewater and wastewater-impacted freshwater in Nigeria

Introduction

Among clinical bacteria, one of the threatening resistances reducing modern antibiotics effectiveness is the extended-spectrum β-lactamases (ESBLs). ESBL genes are often mobile and responsible for the production of enzymes inactivating cephalosporins via hydrolisation of the β-lactam ring (Poole 2004). β-lactams are a class of antibiotics (Poole 2004) accounting for up to 50-70% of the total antibiotic use in several countries (Korzeniewska &Harnisz 2013) including Nigeria (Ogbolu et al. 2011). Worldwide, ESBLs resistance was reported not only in bacterial strains isolated from hospitals, patients, healthy animals and food products but recently these resistances have also been detected in the environment (Lupo et al. 2012, Zurfluh et al. 2013). Water represents one of the important environmental links between clinically relevant resistances and anthropogenic pollution (Tacao et al. 2012). The presence of ESBLs resistant bacteria in the aquatic ecosystem is considered a serious public health threat (Laxminarayan et al. 2013, Okeke et al. 2007), especially in countries like Nigeria where majority of the population do not have access to public water supply and hence depend on alternative sources such as rivers lakes and shallow hand-dug wells (Adelowo et al. 2012a, Adelowo &Fagade 2009) which are often polluted by wastewater. The most common sources of water pollution in Nigeria are households and hospitals which directly release, among other contaminants, a cocktail of antibiotics and potentially resistant pathogens with the wastewater into the aquatic ecosystem. The unrestricted access to antimicrobials by the population (Plachouras et al. 2010) and the poor sanitation facilities of Nigeria contribute to the severity of antibiotic resistance pollution in many national freshwater (WHO 2003b, 2009). Consequently the water ecosystem increases the risk of infections caused by the reintroduction of resistant pathogens into humans. In Nigeria, very little information exists on the occurrence and spread of ESBLs in the aquatic ecosystems. Available studies on ESBLs were mainly conducted on clinical isolates (Ogbolu et al. 2011, Soge et al. 2006). The aim of this study was therefore to investigate on the occurrence of ESBL genes in gram negative bacteria isolated from untreated wastewater and wastewater contaminated aquatic ecosystems in order to identify the role of the environment as a reservoir of multiple antibiotic resistant bacteria and the spread of ESBLs in the aquatic ecosystem.

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Materials and methods

Samples and Sampling Locations

A total of 34 polluted water sources widely distributed in the southern and north central part of Nigerian were investigated represented Figure 26. Five hundred milliliters of water samples were collected twice between August 2012 and May 2013 as follows: a) Hospital wastewater and groundwater. Samples from hospital sources were collected from seven hospitals located in two states in Nigeria, Ogun State (n=5) in the South-west and Benue State (n=2) in the North-central region. None of the hospitals had wastewater treatment facility at the time of sample collection and the untreated wastewater originating from the different hospital divisions are released either into open sewers (hospitals in Benue State) or open drains (hospitals in Ogun State). Additionally, water samples from three 3 hand-dug and three boreholes located either near the wastewater discharge or the wastewater flow channel of the hospitals were collected to investigate the potential contamination of drinking water by ESBLs producing bacteria. b) Fish farm ponds. Water were collected from three fish farms located in Ibadan (IBD 1 and IBD 2; no. of ponds =7 and 2 respectively) and Ilesha (ILE1 no. of ponds =6) in the South-western Nigeria in March 2013. Up till the time of sample collection, oxytetracycline and neocloxin (mixture of oxytetracycline, chloramphenicol and neomycin) were commonly used in the IBD 1 and IBD 2 ponds to treat fish pathogen infections while no previous history of antibiotic use is known at the ILE 1) Domestic wastewater and rivers. Wastewater were collected from the treatment pond at the University of Ibadan, Oyo State, Nigeria while freshwater from 3 polluted rivers receiving wastewater in the South-west of Nigeria: Zik River flowing through the Campus of the University of Ibadan in Oyo State and Ikpoba River and River Cross respectively in Edo and Cross River States in the South-South region of Nigeria.

Isolation of resistant bacteria

Water samples (25 mL) were inoculated into tryptic soy broth amended with ceftazidime (6 µg/mL) incubated overnight at 35°C and spiked onto triplicates plates of Müller Hinton (MHA) and Eosin Methylene Blue (EMB) agar plates for 24 hours at 35°C. Single colonies were picked and streaked to obtain pure cultures.

Antimicrobial susceptibility testing and ESBL production

Bacterial isolates were tested for phenotypic resistance to four antibiotics (OxoidTM): sulphamethoxazole/trimethoprim (25 µg), tetracycline (30 µg), ciprofloxacin (5 µg) and ceftazidime (30 µg) by agar disc diffusion and inhibition zones measured and interpreted as described by the Clinical and Laboratory Standards Institute (CLSI 2011). Isolates showing resistance or intermediate resistance to ceftazidime were selected for Double Disc Synergy Test (DDST) to confirm ESBL production. DDST was carried out as described by CLSI.

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DNA extraction and Identification of bacteria Suspected E. coli were identified by streaking on chromogenic selective BrillianceTM E. coli/coliform agar while the other isolates (including ambiguous E. coli colonies) were identified by 16S rDNA sequencing as described elsewhere (Lane 1991). Genomic DNA for 16S rDNA amplification was extracted with the microwave based method (Orsini &Romano- Spica 2001).

Figure 26 Geopolitical map of Nigeria and sampling Regions. South-West Region: A (Oyo State; Zik River, Wasterwater oxidation ponds,), B (Osun; Ilesha fish farm ponds, Ilesha River), C (Ogun; wastewater, groundwater, hospitals). South-South Region: D (Edo; Ikpoba River) E (Cross River States). North-Central: F (Benue State; hospitals)

Characterization of β-lactamases, integrons and plasmids

Bacteria suspected of producing ESBL were screened by standard PCR for the presence of bla-TEM, bla-SHV, bla-CTX-M, ampC gene bla-FOX and integrons classes 1, 2 and 3 (Table 15). Plasmids of ESBL producing isolates were extracted with Nucloespin plasmid purification kit (Qiagen) according to the manufacturer’s instructions and degenerate primers targeting ten relaxase MOB families MOB F11, F12, P11, P12, P13, P14, P3, P4, H121 and Q11 were used to amplify the conjugative relaxase genes as described by Alvarado et al. (2012). The selected primers used to target relaxases of the plasmid Incompatibility groups IncN, IncW, IncP9, IncF complex, Inc9, IncP1 complex, Incl1 complex, IncK, IncB/O, IncL/M, IncQ1, IncQ2, IncP6, IncX, IncA/C and IncU are described in Table 18 (Alvarado et al. 2012). Correct size amplicons for ESBL genes, integrons and relaxases PCR were sequenced to confirm their identity.

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Results

ESBLs producing bacteria identification and antibiotic susceptibility testing A total of one hundred and forty three (143) isolates with reduced susceptibility to ceftazidime were isolated from the different water samples. DDST screening revealed the production of ESBLs in 114 isolates (79.7%) where 40.4% (n=46) originated from hospitals, 11.4% (n=13) isolates from rivers/domestic wastewater and 48.2% (n=55) isolates from fish farm ponds. ESBL producing isolates of hospital and river/domestic wastewater origin were identified as mostly members of the Enterobacteriaceae family while non-lactose fermenting gram negative bacteria predominated in fish farm ponds (Table 17). In hospital wastewater majority of isolates were identified as E. coli and Proteus mirabilis, River/domestic wastewater instead displayed higher bacteria diversity but E. coli was unrepresented in these samples. In fish farm ponds Stenotrophomonas maltophilia dominated among ESBLs bacteria. Interestingly, in the fish farm ILE1 where no history of antibiotics use was known, the isolation of ceftazidime resistant bacteria was significantly low (Data not shown). All 114 isolates displayed resistance to ceftazidime while 93% were resistant to tetracycline, 64.0% to ciprofloxacin and 50.9% to sulphamethoxazole/Trimethoprim (Figure 27). Almost all isolates (95.5%) from hospitals wastewater displayed resistance to more than two of the tested antibiotics while in the other sampling sites, the multiple resistance levels in the isolates was lower; 61.5% for river/domestic wastewater and 8.2% for fish farm water (Table 17). However, in contrast to the recent global emergence of resistance to sulphamethoxazole/Trimethoprim in species of Stenotrophomonas, isolates of Stenotrophomonas from the fish farms in the present study showed low resistance levels to sulphamethoxazole/Trimethoprim (SXT) (Table 17,Figure 27).

Characterization of ESBL determinants and relationship to the water habitats Ninety six of 114 (84.2%) isolates with reduced susceptibility to ceftazidime and positive to

DDST carried at least one of the ESBL genes bla-SHV, bla-TEM, bla-CTX-M. None of the isolates carried bla-FOX gene (Table 17). bla genes were detected in 44 of 46 (95.6%) isolates from the hospital, 43 of 55 (78.2%) isolates from fish farm ponds and 9 of 13 (69.2%) river/domestic wastewater isolates. Overall, bla-SHV was detected in 54 (56.3%) isolates, bla-TEM in 43

(44.8%) and bla-CTX-M in 18 (18.8%). Majority of the bla-CTX-M-positive isolates were detected in hospital water; specifically: in Shigella flexnerii from the North-central region hospitals and in E. coli, Shigella sonnei and E. hermannii from the South-west region hospitals (Table 17).

None of the isolates from fish farm ponds carried bla-CTX-M but the gene was present in two isolates (Escherichia fergusonii and K. pneumonia subsp. Pneumonia) from river water.

Interestingly, two ESBLs producing bacteria carrying bla-CTX-M (Shigella sonnei and the E. coli) were also isolated from the bore-hole and shallow wells near the hospital wastewater runoff highlighting the potential public health risk associated with the use of these water for domestic purposes.

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Table 15 Primers used for Int and ARG PCR assays.

Primer Target gene Sequence (5’-3’) Ta (°C) Amplicon size (bp)

TEMf blaTEM ATAAAATTCTTGAAGACGAAA 50 1079 TEMr GACAGTTACCAATGCTTAATCA

SHVf blaSHV CGCCGGGTTATTCTTATTTGTCGC 60 776 SHVr TCTTTCCGATGCCGCCGCCAGTCA

CTX-Mf blaCTX-M ATGTGCAGYACCAGTAARGTKATGGC 58 593 CTX-Mr TGGGTRAARTARGTSACCAGAAYCAGCGG

FOXf blaFOX AACATGGGGTATCAGGGAGATG 64 190

-

119 119 FOXr CAAAGCGCGTAACCGGATTGG

-

intI1F intI1 CCT CCC GCA CGA TGA TC 55 280 intI1R TCC ACG CAT CGT CAG GC

intI2F intI2 TTA TTG CTG GGA TTA GGC 50 233 intI2R ACG GCT ACC CTC TGT TAT C

intI3F intI3 AGT GGG TGG CGA ATG AGT G 50 600 intI3R TGT TCT TGT ATC GGC AGG TG______

Chapter 6

Table 16 Degenerate Primers for MOB Typing.

Primer Inc Group Primer sequence 5’-3’ Cycles Ta (°C) Size (bp) MOB F MOBF11-f IncN, IncW, IncP9 GCA GCG TAT TAC TTC TCT GCT GCC GAY GAY TAY TA 25 cycles 53°C 234 MOBF1-r ACT TTT GGG CGC GGA RAA BTG SAG RTC MOBF12-f IncF Complex, Inc9 AGC GAC GGC AAT TAT TAC ACC GAC AAG GAY AAY TAY TA 25 cycles 55°C 234 MOB P MOBP11-f IncP1 Complex CGT GCG AAG GGC GAC AAR ACB TAY CA 25 cycles 60°C 180 MOBP1-r AGC GAT GTG GAT GTG AAG GTT RTC NGT RTC MOBP12-f IncI1 Complex, IncK, IncB/Q GCA CAC TAT GCA AAA GAT GAT ACT GAY CCY GTT TT 30 cycles 53.8°C 189 MOBP131-f IncL/M AAC CCA CGC TGC AAR GAY CCV GT 30 cycles 59°C 180 MOBP14-f IncQ2, IncP6 CGC AGC AAG GAC ACC ATC AAY CAY TAY RT 25 cycles 50°C 174 MOBP3-f IncX, CC GTG AGC CAA ATC ACA CAG AAT ATK RTB TT 25 cycles 50°C 177 MOBP3-r CG AAA GCC AAC ATG AAC ATG HGG ATK HTC

-

120 MOBP4-f IncU GCG TTC AGG ATG GTC YTB TCS ATG CC 25 cycles 64°C 163

- MOBP4-r C GGT TTT GAC CGT CAG ATG SVM ATG CGG

MOBQ MOBQ11-f IncQ1 CAA TCG TCC AAG GCG AAR GCN GAY TA 30 cycles 50°C 331 MOBQ11-r CG CTC GGA GAT CAT CAY YTG YCA YTG MOBH H121-f IncA/C, ICEs G CCA GCT TCC GAA TCA CAY CAY CAY CG 25 cycles 59°C 313 H121-r R391-like Elements G TCG CTT GTC GCG CCA CCG DAT RAA RTA ______

Chapter 6

blaSHV occurred most frequently among isolates from rivers/domestic wastewater (88.9%) and fish farms ponds (69.1%) but less frequently in hospital wastewater (17.4 %). Sequencing of representative amplicons confirmed the identity of the detected ß-lactam genes. All sequences were deposited in GENBANK data base. Sequencing revealed all bla-CTX-M and bla-

TEM showed sequence identities (99-100%) with blaCTX-M-15 (accession no: KJ451410.1) and bla-TEM 1b (accession no: KF 976462.2) respectively. In contrast, sequencing showed that representative bla-SHV bearing isolates carried bla-SHV-1, bla-SHV-11, bla-SHV-12 and bla-SHV-121 (GenBank accession nr. KC699840.1, GU083599.1, DQ219473.1, KF585135.1, KJ083256.1, GU428198.1)

Multiple ß-lactam determinants occurrence in ESBLs producing bacteria The occurrence of multiple ß-lactam resistance genes was detected in 16.7% of the ESBLs positive isolates (Table 17). bla-TEM and bla-SHV co-occurred in isolates from hospital wastewater (E. coli n=5 ; Proteus mirabilis n=5) and river/domestic wastewater isolates (C. freundii n=1; K. pneumonia subsp. Pneumonia n=1). Co-occurrence of bla-TEM and bla-CTX-M was mainly found in hospitals wastewater isolates in the South-west region of Nigeria (E. coli n=5) and neighboring hand-dug well (E. coli n=1). bla-CTX-M and bla-SHV were displayed together in ESBL positive E. coli (n=1) isolated from borehole water. Co-occurrence of the three bla genes was found in three isolates from hospital (E. coli and E. hermannii) and river water (Klebsiella pneumoniae subsp. Pneumoniae). None of the ESBLs isolates from fish farms displayed multiple ß-lactam resistance determinants.

Plasmid-integron diversity and relationship to genetic content Integrons and plasmids typing

Overall, integrons were detected in 61.5% of the isolates. Int1 was the most prevalent class in hospital and domestic/river water and was associated with the presence of at least one ESBL genes (Table 17). Integrons were seldom detected in fish farm isolates. Int2 was detected only in two isolates from hospital wastewater which were also positive for Int1. In all samples, plasmids were associated with a significant number of ESBLs-carrying strains (Table 1). Overall, plasmids were detected in 60 (62.5%) of the ESBLs-producing bacteria, more frequently in hospitals and rivers/domestic wastewater isolates (Table 17). 23 of the detected plasmids are not typable by Degenerate Primer MOB Typing (DPMT). Relaxases of the MOB F12 family were most prevalent and was found in (83.8%) of the 37 typable plasmids while MOB F11 and P11 were less represented among the isolates being found in 22.9% and 20% respectively (Table 18). All isolates of Stenotrophomonas positive for relaxase genes were form a fish farm (IBD1) (Table 17). Multiple relaxases were detected in 7 (20.0%) of the plasmids all of which showed multiple plasmid detection bands in gel electrophoresis. Representative amplicons of the DPMT sequenced showed 100% identity with conjugative transfer relaxases TraI (Accession no KF732966.1).

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100 80 60 40

20

Prevalence of of Prevalence Resistance (%) Resistance 0 CAZ CIP SXT TET Antibiotics

Domestic WWs and WW polluted rivers Hospital WWs and WW polluted water Fish farm ponds

Figure 27 Phenotypic pattern of antibiotic resistance among ESBLs-positive isolates in the different water environment. CAZ: Ceftazidime, CIP: Ciprofloxacin, TET: tetracycline, SXT: Sulphamethoxazole/Trimethoprim

Discussion

Direct discharge of untreated wastewater from households and hospitals into surface water is common in Nigeria. This practice makes of the aquatic ecosystem a breeding ground for potentially pathogenic bacteria. We demonstrated that the discharge of untreated wastewater not only has a significant impact on the release of organisms of clinical importance but also fosters the spread of ESBLs-producing bacteria in the natural water ecosystem. Moreover, most of the isolated ESBL-producers bacteria were of clinical relevance (Stenotrophomonas, Aeromonas, Acinetobacter, Providencia, E. coli, Klebsiella, Enterobacter, Shigella, Citrobacter and Proteus) and resistant to other antimicrobial classes else than the ß-lactams. In Nigeria it was estimated that 29% of the national burden of disease is linked to the environment (Hawkey &Jones 2009). Consequently, the potential risk of pathogen reintroduction into humans is likely and because of the broad arrangement of antimicrobial determinants into the ESBLs-bacteria, these bacteria might contribute to the reduction of antimicrobials treatment effectiveness. The cause of ESBLs resistance mainly resulted from the dissemination of blaSHV, blaCTX-M and blaTEM among the isolated bacteria. To our knowledge, this is the first report on blaCTX-M pollution in Nigerian rivers and drinking water. Moreover, we first report on the isolation of ESBLs Stenotrophomonas maltophilia carrying blaSHV in fish farm ponds. The wide presence of ESBLs genes in the aquatic ecosystems is a clear indication of freshwater contamination by anthropogenic sources. The spread of pathogenic ESBLs producing-bacteria via wastewater thus contributes to the accumulation of highly mobile ESBL genes in the aquatic ecosystem and broadens the environmental reservoirs of ARG of clinical importance. Our broad geographical investigation revealed a relationship between Nigerian regions and the prevalence of ESBLs bacterial species in wastewater hospitals. The hospital wastewater of

- 122 - Chapter 6 the North-central region were mainly characterized by the presence of resistant Proteus mirabilis isolates while in the South-west regions, E. coli resistant isolates dominated. A possible explanation for this finding is on the epidemiology of ESBL genes which is continuously changing and it is known to shows marked geographic differences in distribution of host and genotypes of β-lactamases. In any case, the current detection of ESBL producing E. coli and P. mirabilis in the aquatic ecosystem is likely connected to the infection of these bacteria in the Nigerian population (Endimiani et al. 2005, Oteo et al. 2010). Contrary to the ESBLs-producing bacteria, the detected genes responsible for the ESBLs resistance are independent of the geographical location and are an outcome of the microbial community reaching the freshwater by untreated wastewater (Tacao et al. 2012). Rivers and fish farm ponds receiving domestic wastewater were often carrying SHV genes while hospital wastewater polluted environments displayed mostly blaCTX-M and blaTEM genes. Remarkably, at the time of the investigation the detection of blaSHV in fish farm ponds Stenotrophomonas maltophilia coincided with the first identification of bla-SHV in pathogenic S. maltophilia of Nigerian hospitals (Brooke 2012, Ogbolu et al. 2013) . This evidence demonstrates once more the role of wastewater as ARG spread facilitator between clinical settings and the environment (Martinez 2009b). Additionally, the high isolation rate of ESBLs producing bacteria in fish farm ponds with a known history of antibiotic use adds evidence to the knowledge that a continued use of antibiotics in aquaculture increases levels of antibiotic resistance either in the fish microbiome or in bacterial communities of fish related environments (Cabello 2006, Ozaktas et al. 2012). Majority of the isolates displaying ESBL genes are also carrying Integrons (Int1). In this study, the co-localization of multiple antibiotic resistance, Integrons and plasmids was frequent (37.5%) in ESBLs producing bacteria suggesting that Int1 may to play a role on the prevalence of bla genes in polluted water ecosystems via mechanisms of horizontal gene transfer. Int1 is in fact known to possess gene cassettes containing ARG and is often carried on mobile elements (Stalder et al. 2012, Tada et al. 2014). To our knowledge, this study was the first attempt to use DPMT to investigate plasmid diversity in a polluted aquatic ecosystem. However, conjugative relaxases could only be typed in isolated members of the Enterobacteriaceae family. Design of more specific primers for relaxases characterization among environmental microbial community is therefore needed before adopting this method in environmental and public health surveillance. In conclusion we demonstrated the significant introduction and presence of clinically relevant ESBL-producing bacteria by wastewater into a variety of water ecosystems across multiple regions of Nigeria. The elevated detection of blaSHV among ESBL producers in our study is in contrast to other similar investigations in the environment (Chouchani et al. 2013, Korzeniewska &Harnisz 2013, Korzeniewska et al. 2013, Lu et al. 2010, Zurfluh et al. 2013) reporting blaCTX-M as the most prevalent ESBL resistance gene. Our findings might be an indication for a peculiar epidemiology of ESBLs within the Nigeria environment which differs from other areas of the world. The widespread occurrence of ESBL producers across multiple bacterial hosts in Nigerian water though worrisome was however expected.

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Weak policies regulating the availability and use of antibiotics and the lack of wastewater treatment facilities ensure the proliferation of antibiotic resistance in the environment.

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Table 17 Phenotypic resistance pattern and prevalence of ß-lactamases among gram negative ESBLs producing bacteria.

______Sampling site Species Phenotypic Integrons ARG #Plasmid ______traits ______

CAZ CIP SXT TET Domestic wastewater /WW polluted rivers

Zik river, University of Ibadan Citrobacter freundii OZ9 R R R R Int1 blaTEM, blaSHV +

“ Escherichia fergusonii OZ24 R R R R Int1 blaCTX-M + “ Aeromonas punctata OZ51 I S I R Int1 ND -

“ Klebsiella pneumoniae I S S R ND blaTEM, blaSHV, blaCTX-M +

subsp.Pneumoniae OZ63 Wastewater oxidation Pond Acinetobacter baumannii OZ43 I R R R ND ND +

-

125 125 “ Aeromonas punctata OZ53 R I R R int1 ND +

- “ Stenotrophomonas maltophilia OZ85 R R R R ND blaSHV +

Wastewater flow channel Klebsiella pneumoniae subsp. R S S S int1 blaSHV + Rhinoscleromatis OZ49

Ikpoba River Klebsiella varicola IK16 R R R R ND blaSHV +

“ Stenotrophomonas maltophilia IK37 R I S R ND blaSHV -

“ Enterobacter asburiae IK42 R R R R ND blaSHV + Cross River Providencia rettgeri CR230 R I S S Int1 ND +

“ Providencia rettgeri CR4 R S R R int1 blaSHV + ND: Not detected, R: Resistant, I: Intermediate resistance, S: Sensitive, CAZ: Ceftazidime, CIP: Ciprofloxacin, SXT: sulphamethoxazole/Trimethoprim, TET: Tetracycline, ARG: Antibiotic resistance genes. #: + or – indicate the presence or absence of plasmid(s) while isolates with multiple plasmids and multiple relaxases are indicated by an asterisk

Chapter 6

Continue Table 17 ______Sampling site Species Phenotypic Integrons ARG #Plasmid traits ______

CAZ CIP SXT TET ______Hospital wastewater and WW polluted water

Wastewater, Benue State Proteus mirabilis MK220 I R R R int1 blaTEM, blaSHV -

“ Proteus mirabilis MK221 R R R S int1 blaTEM, blaSHV +

“ Proteus mirabilis MK222 R S S R int1 blaTEM, blaSHV -

“ Proteus mirabilis MK223 R S R R int1 blaTEM, blaSHV -

“ Proteus mirabilis MK224 R R S R int1 blaTEM, blaSHV, -

“ Proteus mirabilis MK226 R R R S int1, int2 blaTEM -

“ Proteus mirabilis MK227 R S R S int1, int2 blaTEM -

-

“ Shigella flexneri MK228 R R S R int1 bla +

126 CTX-M Wastewater, Ijebu-Ode

-

Ogun State Escherichia coli EOd1 R R R R int1 blaTEM +

“ Escherichia coli EOd3 R R R R int1 blaTEM +

“ Escherichia coli EOd5 R R R R ND blaTEM +

“ Escherichia coli EOd8 R R R R int1 blaTEM, blaCTX-M +

“ Escherichia coli EOd9 R R R R int1 blaTEM +

“ Escherichia coli EOd10 R R R R int1 blaTEM +

“ Escherichia coli EOd11 R R R R int1 blaTEM, blaCTX-M +

“ Escherichia coli EOd12 R R R R int1 blaTEM, blaCTX-M .+

“ Escherichia coli EOd13 R R R R int1 blaTEM + ND: Not detected, R: Resistant, I: Intermediate resistance, S: Sensitive, CAZ: Ceftazidime, CIP: Ciprofloxacin, SXT: sulphamethoxazole/Trimethoprim, TET: Tetracycline, ARG: Antibiotic resistance genes.#: + or – indicate the presence or absence of plasmid(s) while isolates with multiple plasmids and multiple relaxases are indicated by an asterisk

Chapter 6

Continue Table 17

______Sampling site Species Phenotypic Integrons ARG #Plasmid traits ______

CAZ CIP SXT TET Hospital wastewater and WW polluted water Wastewater, Ijebu-Ode

Ogun State Escherichia coli EOd14 R R R R int1 blaTEM +

“ Escherichia coli EOd17 R R R R int1 blaTEM +*

“ Escherichia coli EOd19 R R R R int1 blaCTX-M +

“ Escherichia coli EOd20 R R R R int1 blaCTX-M +

“ Escherichia coli EOd21 R R R R int1 blaCTX-M +

Wastewater, Ijebu-Igbo

Ogun State Escherichia coli EId3 R R R R int1 blaTEM, blaCTX-M +

-

“ Escherichia coli EId9 R R R R int1 blaTEM -

127 “ Escherichia coli EId10 R R R R int1 blaTEM, blaCTX-M -

-

“ Raoultella ornithinolyticaEId13 R S R R int1 blaTEM +* Hand-dug well, Ijebu-Igbo

Ogun State Escherichia coli EIw2 R R R R int1 blaTEM +*

“ Enterobacter amnigenus EIw4 R R R R int1 blaTEM +

“ Escherichia coli EIw10 R S R R int1 blaTEM, blaCTX-M - Wastewater, Ago-Iwoye

Ogun State Escherichia coli EAd2 R R R R int1 blaTEM +

“ Escherichia coli EAd9 R R R R int1 blaCTX-M, -

“ Escherichia coli EAd10 R R R R int1 blaCTX-M, +

“ Escherichia coli EAd15 R R R R int1 blaTEM, blaSHV, blaCTX-M, +

“ Escherichia coli EAd16 R R R R int1 blaTEM +* “ Escherichia coli EAd19 R R R R ND ND +

Chapter 6

Continue Table 17

______Sampling site Species Phenotypic Integrons ARG #Plasmid traits______CAZ CIP SXT TET_ Hospital wastewater and WW polluted water

“ Escherichia coli EAd14 R R R R int1 blaTEM - “ Escherichia coli EAw1 R R R R ND ND -

“ Escherichia hermanniiEAw2 R R R R int1 blaTEM, blaSHV, blaCTX-M, - Hand-dug well, Ago-Iwoye

Ogun State Shigella Sonnei EAw8 R R R R int1 blaCTX-M +

Wastewater, Ibiade

Ogun State Escherichia coli EWd1 R R R R int1 blaTEM +

-

128 “ Escherichia coli EWd5 R R R R int1 blaTEM +

“ Escherichia coli EWd8 R R R R int1 blaTEM +

-

“ Escherichia coli EWd9 R R R R int1 blaTEM +

“ Escherichia coli EWd13 R R R R int1 blaTEM + Borehole water, Ibiade

Ogun State Escherichia coli EWb2 R R R R int1 blaTEM +*

“ Escherichia coli EWb3 R R R R ND blaSHV, blaCTX-M +

ND: Not detected, R: Resistant, I: Intermediate resistance, S: Sensitive, CAZ: Ceftazidime, CIP: Ciprofloxacin, SXT: sulphamethoxazole/TrimethoprimTET: Tetracycline, ARG: Antibiotic resistance genes. #: + or – indicate the presence or absence of plasmid(s) while isolates with multiple plasmids and multiple relaxases are indicated by an asterisk

Chapter 6

Continue Table 17

______Sampling site Species Phenotypic Integrons ARG #Plasmid traits______

CAZ CIP SXT TET Fish Farm ponds IBD 1, Ibadan

Pond 4 Chryseobacterium jejuense IBD1-47 R S S R ND blaTEM - Pond 4 Stenotrophomonas maltophilia IBD1-54 R S S R int1 ND + Pond 3 Stenotrophomonas maltophilia IBD1-55 R I S R int1 ND + Pond 4 Stenotrophomonas maltophilia IBD1-56 R S S R int1 ND +

Pond 1 Stenotrophomonas maltophilia IBD1-58 R S S R int1 ND +

Pond 4 Stenotrophomonas maltophilia IBD1-74 R I I R int1 blaTEM -

-

Pond 2 Stenotrophomonas maltophilia IBD1-75 R R S R ND blaTEM +

129 Pond 5 Pseudomonas hibiscicola IBD1-85 R S S I int1 ND -

-

Pond B Stenotrophomonas maltophilia IBD1-84 R I S R ND blaSHV -

Pond 4 Stenotrophomonas maltophilia IBD1-90 R I S R ND blaSHV -

Pond 1 Stenotrophomonas maltophilia IBD1-104 R I S I ND blaSHV -

Pond A Stenotrophomonas maltophilia IBD1-108 R I S R ND blaSHV -

Pond 3 Stenotrophomonas maltophilia IBD1-113 R R I R ND blaSHV -

Pond 5 Stenotrophomonas maltophilia IBD1-120 R I S R ND blaSHV +

Pond 5 Stenotrophomonas maltophilia IBD1-125 R S S R ND blaSHV -

Pond B Stenotrophomonas maltophilia IBD1-132 R I S R ND blaSHV -

Pond 5 Stenotrophomonas maltophilia IBD1-137 R I S I ND blaSHV +

ND: Not detected, R: Resistant, I: Intermediate resistance, S: Sensitive, CAZ: Ceftazidime, CIP: Ciprofloxacin, SXT: sulphameth/Trimethoprim, TET: Tetracycline, ARG: Antibiotic resistance genes. #: + or – indicate the presence or absence of plasmid while isolates with multiple plasmids and relaxases are indicated by an asterisk

Chapter 6

Continue Table 17

______Sampling site Species Phenotypic Integrons ARG #Plasmid traits ______

CAZ CIP SXT TET Fish Farm ponds IBD 1, Ibadan

Pond 2 Stenotrophomonas maltophilia IBD1-138 R I S R ND blaSHV -

Pond 1 Stenotrophomonas maltophilia IBD1-139 R S S R ND blaSHV -

Pond A Stenotrophomonas maltophilia IBD1-141 R R R R ND blaSHV +

Pond 5 Stenotrophomonas maltophilia IBD1-142 R S S R ND blaSHV -

Pond A Stenotrophomonas maltophilia IBD1-151 R I R R ND blaSHV -

Pond 5 Stenotrophomonas maltophilia IBD1-159 R I S R ND blaSHV +

Pond 2 Stenotrophomonas maltophilia IBD1-168 R S S R ND blaSHV +*

-

130 130 Pond 5 Stenotrophomonas maltophilia IBD1-169 R I S R ND blaTEM +*

Pond 1 Stenotrophomonas maltophilia IBD1-170 R S S R ND blaSHV -

-

Pond 5 Shigella dysenteriae IBD1-175 R S S R int1 blaTEM +

Pond 5 Stenotrophomonas maltophilia IBD1-182 R R S I ND blaSHV -

Pond A Stenotrophomonas maltophilia IBD1-185 R I S R ND blaSHV -

Pond 3 Stenotrophomonas maltophilia IBD1-193 R I S R ND blaSHV -

Pond 5 Stenotrophomonas maltophilia IBD1-195 R I S R ND blaSHV -

Pond 3 Stenotrophomonas maltophilia IBD1-196 R S S S ND blaSHV +

Pond 2 Stenotrophomonas maltophilia IBD1-198 R S S I ND blaSHV -

Pond A Stenotrophomonas maltophilia IBD1-200 R S S R ND blaSHV -

Pond 5 Stenotrophomonas maltophilia IBD1-218 R R R R ND blaSHV - ND: Not detected, R: Resistant, I: Intermediate resistance, S: Sensitive, CAZ: Ceftazidime, CIP: Ciprofloxacin, SXT: sulphameth./Trimethoprim, TET: Tetracycline, ARG: Antibiotic resistance genes. #: + or – indicate the presence or absence of plasmids. Isolates with multiple plasmids and relaxases are indicated by an asterisk.

Chapter 6

Continue Table 17

______Sampling site Species Phenotypic Integrons ARG #Plasmid trait ______

CAZ CIP SXT TET Fish Farm ponds IBD 1, Ibadan

Pond 1 Stenotrophomonas maltophilia IBD1-235 R I S S ND blaSHV -

Pond 5 Stenotrophomonas maltophilia IBD1-240 R S S R ND blaSHV -

Pond 5 Stenotrophomonas maltophilia IBD1-241 R I S R ND blaSHV -

Pond 5 Stenotrophomonas maltophilia IBD1-242 R I S I ND blaSHV -

Stream Stenotrophomonas maltophilia IBD1-127 R S S R ND blaSHV -

Stream Stenotrophomonas maltophilia IBD1-204 R S S S ND blaSHV -

Stream Stenotrophomonas spp IBD1-239 R S S R int1 blaSHV -

-

131 131 Pond 1 Stenotrophomonas maltophilia IBD1-57 R I S R ND ND - Pond 3 Stenotrophomonas maltophilia IBD1-59 R R S R ND ND +

- Pond 4 Stenotrophomonas maltophilia IBD1-67 R S S R ND ND + Pond 3 Stenotrophomonas maltophilia IBD1-77 R I S R ND ND - Pond 3 Pseudomonas geniculata IBD1-80 R S S R ND ND - IBD 2, Ibadan

Pond A Stenotrophomonas maltophilia IBD2-98 R S S I ND blaSHV -

Pond A Stenotrophomonas maltophilia IBD2-99 R S S R ND blaSHV -

Pond A Stenotrophomonas maltophilia IBD2-186 R S I R ND blaSHV -

Pond A Stenotrophomonas spp. IBD2-234 R S S R ND blaSHV -

Pond A Stenotrophomonas spp. IBD2-236 R I S R ND blaSHV - ILE1, Ilesha

Pond F Stenotrophomonas maltophilia ILE1-164 R R S I ND blaSHV - Stream Stenotrophomonas maltophilia ILE1-70 R S S I int1 ND - Stream Stenotrophomonas maltophilia ILE1-79 R S S R ND ND

Chapter 6

Table 18 Conjugative relaxases detected in isolates of wastewater and wastewater polluted water in Nigeria.

Relaxase group Isolates a Source______MOB F11 K. varicola IK16 Ikpoba River S. maltophilia IBD1-168 a Pond 2 Farm1, Ibadan S. maltophilia IBD1-75 Pond 2 Farm1, Ibadan E. coli EOd17 Wastewater, Ijebu-Ode, Ogun State E. coli EIw2 Well water, Ijebu-Igbo, Ogun State E. coli EAd16 Wastewater; Ago-Iwoye, Ogun State E. coli EAd19 Wastewater, Ago-Iwoye, Ogun State E. coli EWb2a Borehole water, Ibiade, Ogun State ______MOBF12 P. rettgeri CR230 Cross River E. amnigenus EIw4 Well water, Ijebu-Igbo, Ogun State E. coli EIW10 Well water, Ijebu-Igbo, Ogun State S. sonnei EAw8 Well water, Ago-Iwoye, Ogun State E. coli EWb2a Borehole water, Ibiade, Ogun State S. maltophilia IBD1-169 a Pond 5 Farm1, Ibadan

R. ornithinolytica EId13 a Wastewater, Ijebu-Igbo, Ogun State E. coli EOd1 Wastewater, Ijebu-Ode, Ogun State E. coli EOd3 Wastewater, Ijebu-Ode, Ogun State E. coli EOd5 Wastewater, Ijebu-Ode, Ogun State E. coli EOd8 Wastewater, Ijebu-Ode, Ogun State E. coli EOd9 Wastewater, Ijebu-Ode, Ogun State E. coli EOd10 Wastewater, Ijebu-Ode, Ogun State E. coli EOd11 Wastewater, Ijebu-Ode, Ogun State E. coli EOd12 Wastewater, Ijebu-Ode, Ogun State E. coli EOd13 Wastewater, Ijebu-Ode, Ogun State E. coli EOd14 Wastewater, Ijebu-Ode, Ogun State E. coli EOd17 Wastewater, Ijebu-Ode, Ogun State E. coli EOd19 Wastewater, Ijebu-Ode, Ogun State E. coli EOd20 Wastewater, Ijebu-Ode, Ogun State E. coli EOd21 Wastewater, Ijebu-Ode, Ogun State E. coli EId3 Wastewater, Ijebu-Igbo, Ogun State E. coli EAd2 Wastewater, Ago-Iwoye, Ogun State E. coli EAd10 Wastewater, Ago-Iwoye, Ogun State E. coli EAd15 Wastewater, Ago-Iwoye, Ogun State E. coli EAd16 a Wastewater, Ago-Iwoye, Ogun State E. coli EWd1 Wastewater, Ibiade, Ogun State E. coli EWd5 Wastewater, Ibiade, Ogun State E. coli EWd8 Wastewater, Ibiade, Ogun State a: Isolates carrying multiple relaxases

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Continue Table 18

______

Relaxase group Isolates a Source______E. coli EWd9 Wastewater, Ibiade, Ogun State E. coli EWd13 Wastewater, Ibiade, Ogun State ______MOBP11 S. maltophilia IBD1-168 Pond 2 Farm1, Ibadan S. maltophilia IBD1-169 Pond 5 Farm1, Ibadan S. dysenteriae IBD1-175 Pond 5 Farm1, Ibadan R. ornithinolytica EId13a Wastewater, Ijebu-Igbo, Ogun State E. coli EOd17 a Wastewater, Ijebu-Ode, Ogun State E. coli EAd16a Wastewater, Ago-Iwoye, Ogun State E. coli EIw2 a Well water, Ijebu-Igbo, Ogun State ______a: Isolates carrying multiple relaxases

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Synthesis and Future Directions

Synthesis and Future Directions

Currently, sustainable and safe urban water cycles have a high priority on the policy agenda of many EU countries and elsewhere (Pruden et al. 2013). One of the most important concerns regarding urban wastewater effluents that are treated by ‘conventional’ technologies is the release of micro-pollutants into the environment which could threaten the biological diversity and affect the ecosystem services at different levels. Chemical pollutants are not the only threat associated to wastewater. Pathogens, antibiotic resistant bacteria (ARB) and corresponding antibiotic resistance genes (ARG) are also part of the effluent composition of WWTPs. Sewage and treatment systems are the most critical points for microbial pollution in urban areas, because they are continuously subject to high loads of so called ‘microbial pollutants’. Despite this knowledge, there are more questions than solutions in controlling pathogens and ARB in water. The limited knowledge thus, does not allow designing the most efficient and rational ways of management and use of resources that could significantly reduce this environmental and public health threat. A good functioning of a wastewater treatment plant does not rely only on biodiversity and functional diversity of the system but also on a variety of environmental conditions which bacteria are exposed to (Gentile et al. 2007). The study on decentralized WWTPs (d-WWTPs) described in Chapter 2 supports this hypothesis. The stable plant conditions of the different technologies translate into similar plants performances but do garantee for comparable microbial or pathogens diversity between d-WWTP effluents. Therefore both, the technological process and the inflow community composition are influencing the structure of the effluents only in part (question i). These interpretations should however, be treated with caution as pathogen identification was based on methods that do not allow the identification of rare species in highly diverse environments. This might lead to a potential underestimation of the pathogen diversity in the effluents (Bent &Forney 2008, Zhang et al. 2008). Application of more comprehensive methods e.g. metagenomics could overcome such obstacles. Nevertheless, microbial communities with high functional traits (Johnson et al. 2014) provide a buffer for a quick recovery of the plant from a stress condition (Fernandez et al. 2000). As a consequence this may thus result in a more stable process of the plant which, in the case of constructed wetlands investigated in Chapter 2, turns into a more effective treatment of the wastewater. Still unknown are the performances of the d-WWTPs over a long time periods; nevertheless, the study described in thesis could be a good starting material for further research.

Synthesis and Future Directions

While the spread of pathogens can be limited by ad-hoc optimization of plant technologies, conventional WWTPs cannot avoid the spread of ARB and ARG. Indeed, the effective reduction of the total bacterial load in the effluent by conventional WWTPs demonstrated in Chapter 3 and 4 does not assure the reduction of ARB in the water (Chapters 3, 4 and 5). WWTPs instead foster the spread of multiple antibiotic resistances (Chapters 3 and 4) against either different chemical classes or within a class of antibiotics. The taxon diversity of resistant bacteria in the effluents reveals that both, poor removal of multiresistant strains and positive selection mechanisms on resistance in other taxa are likely. At the same time, the high prevalence of broad-host range plasmids in the multiresistant wastewater isolates and the detection of sul genes in the plasmid DNA indicate that these mobile elements are capable of carrying transferrable antibiotic resistance determinants. In support of these findings, performed conjugative experiments show the self-replication of the R plasmid and partial transfer of antibiotic resistances into new hosts (Chapter 3). Thus, genetic recombination among bacteria via HGT events co-exists with the selection mechanisms in WWTPs. WWTPs were therefore demonstrated as hot-spots for the spread of ARG&B in the freshwater ecosystem (question ii), (LaPara et al. 2011, Martinez 2009b, Szczepanowski et al. 2009). The study on the Swiss and German WWTPs and their sourrounding environment demonstrate that the severity of water and sediments pollution with ARB and ARG is inversely proportional to the distance of the point source of pollution (Chapters 3 and 4). Sites close to the effluent of the WWTPs are always more polluted than remote sites. However, sediments always exhibit higher numbers of multiple resistant bacteria (MRB) than the overlying water column. Thus, sediments are sites where resistance traits persist and accumulate (Chapter 4). Contrary to rivers, the proofing that the antibiotic resistance levels are influenced by WWTPs pollution is challenging.was a straight forwa, in lakes this proof is more challenging to obtain because the transport of polluted water masses depends on changing wind, temperature regimes and water mixing (Iwane et al. 2001, Li et al. 2009a), (question iii). When assessing the level of AR pollution, additional difficulties may come from the lack of data on the natural resistance background. In this optic, the identification of intrinsic resistance might not be possible and therefore the estimation of the real impact of antibiotic resistance in natural environments may become a challenge. Despite the evident difficulties, in this thesis and in other pioneering studies, first attempts towards a proper assessment of antibiotic resistance in freshwater or soil have begun (Chapter 3), (Knapp et al. 2008, Walsh &Duffy 2013). However, further studies are of fundamental relevance to assess whether chronic release of ARG&B or transfer of resistance vectors into natural microbial populations can alter the natural resistance background in the long term. The constant dissemination of household microbial pollution into WWTPs contributes to the constant release of ARG in the water effluents (Rizzo et al. 2013b). Nevertheless, the persistence and abundance of multiple ARG in the wastewater strongly vary with the season (Chapter 5, question iv). The presence of antibiotics and antibiotic residues in wastewater is known to exert a selective pressure on the resistant bacterial community (Marx et al. 2015) but is not the first selection that bacteria are subject to. Indeed, first selection pressure from antibiotics takes place in the guts of humans (Davies &Davies 2010) and only after antibiotic administration, the excreted resistant bacteria reach the WWTPs.

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In this thesis and for the first time, it has been demonstrated that ARG levels in sewers do also reflect the use of antibiotics by resident outpatients. This evidence proofs that, most likely, the ARG levels in wastewater are the result of the antibiotic selection on ARB begun in the human body and maintained in wastewater by secondary antibiotic pressure. The two- year monitoring of multiple ARG in wastewater described in Chapter 5 reveals high ARG levels in autumn and winter which correspond to the yearly peak of antibiotic uptake by outpatients; similarly, low level of antibiotics uptake in spring corresponds to low levels of ARG in the wastewater. The reason behind this correspondence is likely the seasonal epidemics of bacterial infections which drive the antibiotics consumption within the calendar year (Hay et al. 2005). In fact, the higher probability for bacterial infections in the colder months translates into high levels of antibiotics prescription and consequently in a high selective pressure on the gut microbiota of outpatients. Therefore, seasonal variations in antibiotic use influence the seasonal levels of multiple ARG encountered in wastewater (Chapter 5, question iii). Plausible reasons for the partial disagreement on the expected ARG levels and the antibiotic consumption in the summer season could be either the permanent course of prescriptions for specific antibiotic substances (Sun et al. 2012) or the presence of unrelated antibiotic residues in the wastewater selecting for ARB (Pruden et al. 2013). Thus, the variation of ARG is not only limited by practitioner’s prescriptions but may involve other processes within the WWTP. The identification of such processes is matter of ongoing research. Due to the novelty of the approach used in this thesis to explain the seasonality of ARG in the environment, more data have to be produced as well as more hypotheses has to be tested. Additional studies investigating the relationship between single class, combination of antibiotics and related ARG could determine whether the relationship antibiotic prescription-ARG in wastewater would still hold. Moreover, it would be of social and public health interest to see how these results may vary over heterogeneous municipality subpopulations. Nevertheless, it is already clear from this study that the implementation of policies and public campaigns to lower antibiotic use could be an effective measure to control of ARG&B in the freshwater ecosystem and in general to maintain the effectiveness of existing drugs. Contrary to what shown for the levels of ARG in wastewater, environmental seasonality seems not to influence the microbial community structure of the wastewater over the years. The effluent microbial community composition is mainly a function of the wastewater treatment process (Chapter 5). In fact the WWTP process generates a stable and comparable effect on the effluents and neither the microbial community structure nor the magnitude of microbial reduction varies between the seasons. Unfortunately, as encountered in the studies included in Chapter 3 and 4 and in other studies (Alexander et al. 2015, Laht et al. 2014), the WWTP processing does not assure the reduction of relative ARG levels in the effluent. In fact, the differences in microbial community structure and the comparable relative abundance of ARG between non-treated and effluent demonstrate that dissemination processes of multiple ARG are likely (questions ii and iii) among bacteria in WWTPs. Moreover, the persistence and the enrichment of the clinically relevant vanA in the effluent could conceivably lead to a permanent contamination of the

- 139 - Synthesis and Future Directions receiving river sediments and therefore represent a sink of resistance for one of the last resort antibiotics (question ii). Suspected mechanisms driving the ARG dissemination could be either the horizontal gene transfer between WWTP resident bacteria or the selection of resistant taxa by the WWTP processes. Which of the two processes is the principal responsible for the spread of ARG in the environment was not possible to determine in this thesis beside for vanA where the positive selction of Enterococcus fostered the enrichment of the vancomycin resistance gene. As a consequence of any of the two mechanisms, the ARG are not only carried by the dominant microbial community which is mostly impacted by the wastewater treatment process but also by other non-dominant taxa. This allows the movement of ARG from the wastewater into the environment after wastewater treatment. The problem of antimicrobial resistance knows no boundaries (Figure 28). In both industrialised and developing countries, ARB are cause of costly and deadly infections in hospitals. In rich countries however, infection control systems and good hygiene practices slow the spread of resistance. At the same time, while most of the industrialized countries have been capable to contain infections through modern water supply and advanced sanitation techniques, the lack of facilities makes people living in the developing countries struggling with these infections (Figure 29); (WHO 2014). In chapter 6, the evident pollution of Nigerian water bodies by poorly treated wastewater from households and hospitals reveal the wide spread of ESBL resistance genes in multiple species of clinical relevance. None of the Nigerian rivers, fish farming ponds, open wards and hand-dug well taken into consideration by this thesis were found non-contaminated by ESBLs producing bacteria (question v, Chapter 6). The epidemiology of ESBL genes is continuously changing due to the presence of these genes in mobile elements (Overdevest et al. 2011). Findings in Chapter 6 demonstrate that as for ESBLs carried by bacterial pathogens in humans, ESBL genes encountered in bacteria isolated in Nigerian freshwater have marked differences in geographical distribution of host and genotypes of β-lactamases. The isolation of ESBLs producing bacteria from wastewater and wastewater polluted environments confirms the presence of different dominant ESBLs-producing species (E.coli, Stenotrophomonas, Proteus mirabilis) in different Nigerian regions which were geographically distant (question v). Independently from the region of isolation, resistant bacteria carry integrons Int1 often co-presence with plasmids likely contributing to the prevalence and spread of ESBLs (blactx-m and blashv) genes in polluted water ecosystems via mechanisms of horizontal gene transfer. In Nigeria, as the majority of the population does not have access to public water supply, hence water provisioning depends on alternative sources like rivers, shallow wells and lakes. Consequently, it seems that the Nigerian freshwater ecosystems are one of the responsible for secondary-infections via reintroduction into humans of resistant pathogens while wastewater contributes to the accumulation of highly mobile ESBL genes in the aquatic ecosystem.

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Figure 28 Prediction of deaths for antibiotic resistance by 2050

Another possibility for resistant pathogens to be re-introduced in humans is via food consumption. The investigated Nigerian fish farm ponds are characterized by a high water contamination by Stenotrophomonas maltophilia carrying blashv. The source of this resistant bacterium may be found either in the feaces of farmed fish fed with antibiotics or in the discharge of polluted wastewater into the ponds. Evidences provided in the Chapter 6 might lead to the first option. Bacterial isolation performed in a pond with no history of antibiotics showed (compared to the other fish farm ponds) a consistent reduction in numbers of ESBLs producing bacteria. However, due to the exiguity of the isolation effort more studies should be performed in Nigerian fish farms in order to confirm these findings. Whether ESBLs producing bacteria can be actually detected in the fish gut and in other anatomic parts of the fish may be worth additional investigations.

Conclusions

In this work the abundance, persistence and spread of ARG in the environment was studied using molecular and classic microbiological methods. Trough multiple case studies and a long-term survey it was shown that antibiotic ARG&B are abundant in anthropogenically- driven environments and that ARG can reach the freshwater ecosystem via WWTPs effluent with ease. Although WWTPs are effective in reducing microbial pollution, ARG and ARB persist in the wastewater after treatment, both under presence and absence of selection pressure. The release of wastewater effluent in water bodies also impacts the ARG&B levels of sediments near the discharge sites. The impact of WWTPs on the presence of multiple resistant bacteria seems to be stronger for sediments than for the water.

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Source: UNEP-GPA 2004

Figure 29 The ratio of treated wastewater reaching water bodies for 10 Regions 2009.

The integrated analysis of quantitative (qPCR), NGS and health-drug related data provides, for the first time, the proof and the explanation for recurrent seasonal pattern of selected antibiotic resistance genes in the wastewater. ARG patterns seem to be function of the the antibiotic uptake by outpatients but also of the microbial community inhabiting the wastewater. The investigation towards larger sets of ARG is recommended. With this regard, the use high-throughput qPCR assays instead of traditional qPCR could be a solution. This implementation to the approach could lead not only at the discovery of new ARG dynamics in wastewater systems but also at the identification of selected ARG markers for routine monitoring in wastewater and receiving environments. Poorly treated wastewater reaching the freshwater was proven as a major driver for the spread of ARB and emerging ARG like ESBLs. Poorly treated water is also potentially responsible for secondary infections in humans via the reintroduction of resistant pathogens via drinking water or food consumption. In this work only part of the questions related to the spread of ABR in the environment were addressed. Many answers are still remaining open. The avowal of ARG as severe environmental pollutant is still young and not many information are available. Although it is now clear that wastewater contributes to the accumulation of ARB and ARG in the aquatic ecosystem, ARG&B should be continuously monitored and the quantification of the risk assessed.

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Filling the scientific gaps to tackle antibiotic resistance in the environment

Lessons learned in one country can help others. Unfortunately, mainly because of methodological biases, knowledge on the worldwide levels of ARG&B in water ecosystems and the real influence of WWTPs on the spread of resistance is still fragmentary. While in the clinical sector, standardized methods for the detection and quantification of ARG&B have been implemented and internationally harmonized, scientists working from the environmental perspective on antibiotic resistance have never developed such methods. The disparity of surveillance strategies and the multitude of available protocols have thus generated a large amount of data that are hardly comparable and therefore difficult to use in the frame of a global assessment for antibiotic resistance in the water ecosystem. Moreover, the missing epidemiological data for environmental strains does not allow any comparison of resistance prevalence in different geographical regions to assess potential relationships between antibiotic resistance and antibiotic consumption. On top, the lack of long term monitoring of the environmental antibiotic resistance reservoirs (i.e. WWTPs) is hampering the prediction of the emergence and spread of new resistance determinants of clinical concern. In my personal opinion, before starting any kind of risk assessment on a global scale, it is necessary to have a clear idea of what is the magnitude of the antibiotic resistance problem in the water ecosystem (in this case) and in general, in all the anthropically-impacted ecosystems. In order to achieve this goal, routine monitoring programs of antibiotic resistance reservoirs in combination with harmonized protocols of selected resistance gene markers and ARB have to be established. This would provide background data to set first mitigation activities based on the presence of ARG, abundance of antibiotics and the presence of resistant bacteria of clinical relevance in the environment. Furthermore, an establishment of bio-banks and database for selected ARG&B would allow tracking ARG evolution in anthropogenically-driven environments along a continuum of space and time. No global strategies to contain resistance in the environment can be elaborated if no reliable data are available. New perils and challenges facing the water ecosystem call for thinking beyond traditional sector compartments. Water professionals supported by the scientific community should progressively make use of standardised environmental surveillance in order to provide better understanding of the dynamics of ARG evolution in the aquatic ecosystem. The generation of comparable data and the evaluation of temporal trends for antibiotic resistance is not only a problem of industrialised countries (Figure 30). Resource- limited countries which have access to antimicrobials cannot afford for basic tests to manage the development of antibiotic resistance already at the clinical level. Thinking at the use of these tests at the environmental level is, for these nations, simply not possible unless technical and financial support is secured. Concerted actions of industrialised countries aiming at the development of affordable strategies and in situ training of workers could be already of help.

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Figure 30 Worldwide map for the spread of multiple antibiotic resistant bacteria (WHO, 2014)

Furthermore, increasing funds (public and privates) supporting bilateral scientific collaboration between non- and industrialised countries could favor early detection of new antibiotic resistance determinants. This would translate in the partial containment of ARG spread and slow the process for their worldwide diffusion. Avoiding the recurrence of cases like the New Delhi Metallo-beta-lactamase-1 (NDM-1), firstly reported in travelers to India and now spreading throughout the world, is fundamental. The truth is we cannot turn a blind eye to our social ethical responsibility in the context of antimicrobial resistance. Tackling the problem of antibiotic resistance requires a willingness, on the part of governments, health care professionals, scientists and society to pay attention to the development of new resistances, containment of the already existing one and to treat antibiotics as a non- renewable resource protected in their own, and everyone else’s interest.

« Il est bien difficile, en géographie comme en morale, de connaître le monde sans sortir de chez soi » (Voltaire) [In geography, as in morals, it is very difficult to know the world without going from home. Voltaire]

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ACKNOWLEDGEMENTS

Without the help, wisdom and time of many people I have met on my way in these years, the completion of this thesis would have never been possible. First of all I wish to express my gratitude to my thesis supervisors Prof. Dr. Thomas U. Berendonk and Prof. Dr. Hauke Harms. You gave me the chance to begin my scientific career and have had trust on me and my skills despite my language barriers when I first arrived at the UFZ. Thanks for all the scientific freedom you gave me and the possibility to always follow my interests. Thanks Prof. Harms for your effort in guiding me through the difficulties of my PhD, you showed me what passion for science is about. Thanks Prof. Berendonk for having me introduced to many international scientific networks (COST DARE and NEREUS COST action, NORMAN network). Together with your guidance, my research horizon has opened- up and I have discovered the fun in writing projects. Together with my official supervisors I wish to deeply thank my “only -non-on-paper-supervisors” Dr. Antonis Chatzinotas and Dr. Bärbel Kiesel. Thanks for your continuous guidance, encouragements and support for the past several years. Thanks for the endless time you spent in improving my texts and the invaluable discussions at UFZ. With you I have always got an intellectually stimulating yet friendly environment for research. I would like to acknowledge Prof. Dr. Marko Virta for always welcoming me to work in his research group, the MMX group for the fruitful discussions and friendship and Dr. Helmut Bürgmann, Dr. Nadine Czekalski Prof. Dr. Michael Schröder and Norhan Mahfouz, Dr. Adelowo Olawale Olufemi (Wale) for the successful collaborations. I greatly acknowledge the members of the anti-RESIST for their help and cheerful attitude through the entire project. Thanks to my condition as “wandering PhD student” I had the time, luck and honor of being part of more than one research group which have become later on, my house. I will always remember with joy former and present members of the Environmental Microbiology group at UFZ. To you guys: THANK YOU. Tom, Julia, Karin, Ingo, Anett, Ute, Shoko, Makeba, Muhammad, Bärbel and Verena. Without you none of the extenuating tours de force in the lab wouldn´t have had success. Thanks for the wonderful coffee time and fun we always had together. You are special to me. Thanks also to Frau Riemenschneider for her help with the UFZ burocracy. UFZ for me has never been just a working place. It has also been a platform for the best friendships I ever had after my childhood: Shoko, Martina, Makeba, Dan, Cristobal, Vasi, Marie, Ida, Sara, Paula, Jan, Matteo, in you I have found my second family. I can proudly say “You made me what I am today”. My gratitude goes also to all the UFZ people which wanted to share good moments and whom has contributed to my professional growth: Dr. Hermann J. Heipieper, Dr. Uwe Kappelmeyer, Dr. Lorenz Adrian, Steven, Satoshi, Nori, Carlo. Thanks. A special acknowledgement goes to Inma and Marie my Biblio-power girls. Without you, I wouldn’t have been able to spend full days at the library working at the same time on my thesis and on the manuscripts draft. Thanks for your loyalty and support.

Indelible memories are also coming from my time at the Institute of Hydrobiology. With the informal environment which characterize our department, I could enjoy intellectual discussion, bbq, icecreams, fun and why not, the wild “Klausur” at the 19ein ecological station. At the institute, in 2012 I have met Damiano, at that time a smart master student and nowadays, an unreplaceable friend and colleague. Thank you for all the time you have spent listening to my bad luck, tears, joy, scientific doubts and request for a last-minute-help in the lab. Thanks my excel-geek. Every single day with you at the office has been joyful and it will always be. Thanks also to Veiko and Ana for reading and polishing my thesis. Thanks to Angela and her assistance with all the administrative issues. Thanks also to Thomas P. and Marcus for teaching me most of the statistics I know. In the past three years, I have also spent quite some time at the Helsinki University. There, I discovered that behind silent colleagues (“almost” all silent, right Windi?!) there are awesome people (Antti, Avinash, Christina, Jose, Kata, Manu, Windi) which I now have a great esteem on. Science and not only science has always been fun with you and I am already looking forward to next time together. Now, to my “Leipziger” family. Thanks for having been part and taken care of the social aspects of my life. Without you I would have been a pure “cave woman”. Gonzalo, Yarua, Kike, Ana, Enno, Jimmy, Ailette, Aida, Ernest, Monica, Edu, Anna, Edoardo, Ben, Thomas, Riccardo, Myriel, Carolina, Maite, Alvaro, Curzio, Muanuel you made sure I had enough time to release me from the stress and pressure on my free time. Priceless. Last but not least, thanks to my parents. My roots. Thank you for having been a constant sunny presence in my life. Thanks for let me experience life how I felt was right doing it. Thanks also for being my parachute when I was blindly jumping down to difficulties. I will always be your “Cimabue”. Love you. Gratitude goes also to the 2013-2014-2015 group, zia Elisabetta and zio Vittorio and to my best friend Andrea. Here, I do not need many words. You know you mean all to me.

“Se vuoi viaggiare veloce, viaggia da solo. Ma se vuoi andare lontano, viaggia in compagnia"

(If you want to run fast, run alone; if you want to run far, run together)

REFERENCES TO OWN ORIGINAL PUBLICATIONS USED IN THIS THESIS

Chapter 1 Caucci S. and Berendonk T.U. (2014). Environmental and public health implication of antibiotic resistance genes in municipal wastewater. Prävention und Gesundheitsförderung 9:175-179 DOI 10.1007/s11553-014-0445-2.

The chapter 1 differs from the original publication in the following respects: Title was changed. The abstract was removed from the main text of the article. The manuscript was extended with the description of cutting edge techniques to investigate ARG and ARB in the environment.

Chapter 2 Caucci S., Harms H Fetzer I., Chatzinotas A., Kiesel B. and Berendonk T.U. Microbial analysis of various household-scale wastewater treatment plants (Submitted).

Chapter 3 Caucci S., Fetzer I, Endes C., Kiesel B., Berendonk T.U., Harms H. and Chatzinotas, A. Co-occurrence of antibiotics resistance in gram-negative freshwater bacteria exposed to constant anthropogenic load (Submitted).

Chapter 4 Czekalski N., Berthold, T., Caucci S., Egli A., & Bürgmann H. (2012). Increased levels of multiresistant bacteria and resistance genes after wastewater treatment and their dissemination into Lake Geneva, Switzerland. Frontiers in Microbiology, 3, 106. doi:10.3389/fmicb.2012.00106. The original publication was modified with minor changes due to the structure of the Thesis. The abstract was removed and Material and method shortened. The numbering of figures and table was adapted to the structure of the thesis.

Chapter 5 Caucci S., Karkman A., Cacace D., Rybiki M., Timpel P., Voolaid V., Gurke R., Virta M. and Berendonk T.U. Seasonality of antibiotic prescriptions for outpatients and resistance genes in sewers and wastewater treatment plant outflow. FEMS Microbiology Ecology May 2016, 92 (5) fiw060; DOI: 10.1093/femsec/fiw060

Chapter 6 Adelowo O. O.*, Caucci S.*, Banjo O. A., Nnanna O.C., Awotipe E.O., Peters F.A., Ezekiel O.F. and Berendonk T.U. Characterisation and distribution of extended spectrum beta-lactamases in bacteria from untreated wastewater and wastewater-impacted freshwater in Nigeria (submitted). *Shared first authorship

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APPENDIX

Appendix 1 n-MDS plot on the original microbial communities (inflow and effluents) of the decentralised wastewater treatment plants (09.03.2011). Inflow, DISC: Rotating biological contactor, Trickl: trickling filter, SBR: sequencing batch reactor, Wetland: constructed wetland, WSB: rotating floating bed.

Appendix 2 n-MDS analysis on the enriched microbial communities (inflow and effluents) of the decentralised wastewater treatment plants (16.03.2011). Inflow, DISC: Rotating biological contactor, Trickl: trickling filter, SBR: sequencing batch reactor, Wetland: constructed wetland, WSB: rotating floating bed.

Appendix 3 Correlation between chemical data and identified bacterial families inhabiting dWWTPs effluents (a) and between chemical data (b).

Appendix 4 Intrinsic resistance of clinical bacteria and non-intrinsic resistance pattern of freshwater bacteria from the same order.

Appendix 5 Primers and melting temperature, (Tm) used for amplifying the target genes in this study.

Appendix 6 Relative abundance of antibiotic resistance genes (ARG copy numbers normalized to 16S rRNA gene copy numbers) in wastewater (SEWER1, SEWER2, MIXED INFLOW) and treated water (OUTFLOW) for the year 2012 and 2013.

Appendix 7 Relative abundance of antibiotic resistance genes (ARG copy numbers normalized to 16S rRNA gene copy numbers). Error bars indicate standard deviation of biological triplicates and technical duplicates of the samples (SEWER1, SEWER2, MIXED INFLOW and OUTFLOW) for the year 2012 and 2013 sampling campaign.

Appendix 8 Composition of wastewater (SEWER1: Sewer1, SEWER2: Sewer2, MIXED INFLOW: MIX Inflow) and treated water (OUTFLOW: OUT) bacterial communities at the phylum level. Histograms were based on the relative abundance of the phyla based on Illunina MiSeq sequencing.

Appendix 9 Richness of the microbial community based on the Shannon diversity index at the Genus level in the sewers and WWTP of Dresden. Samples are labeled according to the origin of the investigated microbial community SEWER1 (alt) SEWER2 (neu) and MIXED INFLOW (mix) and OUTFLOW (out), the relative sampling season (SPRING= spr, SUMMER= sum, AUTUMN=au, WINTER=win) and year (2012=12, 2013=13).

Appendix 10 Differently abundant genera present in the OUTFLOW in comparison to the MIXED INFLOW. The histogram represents the differently abundant genera. The values were calculated as differences in the proportions of reads assigned to the genera of treated water in relation to the wastewater. In detail, values were expressed in log-fold change and calculated for each genus and for each OUTFLOW sample as the difference in reads between OUTFLOW and MIXED INFLOW. When annotation did not allow the assignment of the reads at the genus level (--), a higher hierarchical level of annotation (family) was used.

Appendix 11 Table of the thirty most abundant genera in the a) MIXED INFLOW and b) OUTFLOW for the year 2012-2013. RDP classifier was used to assign the sequences to different taxonomic levels at 80% threshold, and the abundances were displayed as percentage of the total sequences in each group

Appendix 1

Appendix 2

Appendix 3

(a)

pH Cells Cd Cu Ni Triclosan Hg COD BOD NH4+ NO3- TnB TC IC TOC RP tR Bacterial Family cells/ml µg/L µg/L µg/L µg/L ng/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L mg/L days Bacteroidaceae ------+++ ------++

Alcaligenaceae ------+++ ------+++ Comamonadaceae - - - ++ - - - ++ ------+++ Enterobacteriaceae - - ++ ------++ Pseudomonadaceae - - ++ ------Pathogen presence Bacterial Class/ order Bacteroidales ------Aeromonadales +++ Burkholderiales - - - - - Clostridiales - - - ++ - - - ++ ------+++ Campylobacterales Enterobacteriales - - - - ++ ------+++ Caulobacterales ------Pseudomonadales - - - ++ -- - - - +++ The sign + and – represents positive and negative correlations, respectively. The significance of correlation is represented as: + / - correlation value from 0,5 to 0,6 (weak correlation); ++ / - - correlation value from 0,61 to 0,80 (correlation); +++/ - - - correlation value > 0,80 (strong correlation)

(b) Continue Appendix 3

Chemical Cd Cu Ni Zn Triclosan Hg COD BOD NH4+ NO3- TnB TC IC TOC pH RP parameter Cells nr/ml +++ +++ +++ +++ +++ +++

Cd (µg/L) ++ +++ ++ - +++ +++ +++ ++ - - -

Cu (µg/L) - - -

Ni (µg/L) ++ ++ +++ ++

Zn (µg/L) ++ - - - ++ ++

Triclosan (µg/L) - +++ +++ ++ +++

Hg (ng/L)

COD (mg/L) +++ +++

BOD (mg/L) ++ +++

NH4+ (mg/L) - ++ ++ - - -

NO3- (mg/L) ------

TnB (mg/L) ++ ++ - - -

TC (ng/L) ++ - - -

IC (mg/L) ++ - - -

The sign + and – represents positive and negative correlations, respectively. The significance of correlation is represented as: + / - correlation value from 0,5 to 0,6 (weak correlation); ++ / - - correlation value from 0,61 to 0,80 (correlation); +++/ - - -

correlation value > 0,80 (strong correlation)

Appendix 4

Bacterial order Intrinsic resistance (clinical strains) Non-intrinsic resistance (freshwater strains) Aeromonadales ampicillin, cephalotin,(all betalactams) trimethoprim, nalidixic acid, tetracycline ciprofloxacin chloramphenicol Burkholderiales ampicillin ,amikacin, kanamycin, chloramphenicol trimethoprim, cephalotin Pseudomonadales ampicillin, trimethoprim, tetracycline, chloramphenicol nalidixic acid, kanamycin amikacin Enterobacteriales ampicillin amikacin chloramphenicol cephalotin, nalidixic acid, trimethoprim Flavobacteriales ampicillin, tetracycline, chloramphenicol and macrolides trimethoprim, cephalotin nalidixic acid

Appendix 5

Gene Primers Sequence 5`-3` Tm (°C) Length (bp) References TetM fw GCAATTCTACTGATTTCTGC tetM 60 186 Tamminen et al. 2010 TetM r CTGTTTGATTACAATTTCCGC pA AGAGTTTGATCCTGGCTCAG 16S 60 350 Edwards et al. 1989 358r CTGCTGCCTCCCGTAGG ctxm32 fw CGTCACGCTGTTGTTAGGAA blactxm32 64 156 Szczepanowski et al. 2009 ctxm32 r CGCTCATCAGCACGATAAAG MecA fw AAAAAGATGGCAAAGATATTCAA mecA 63 185 Szczepanowski et al. 2009 MecA r TTCTTCGTTACTCATGCCATACA VanA fw TCTGCAATAGAGATAGCCGC vanA 60 376 Klein et al. 1998 VanA r GGAGTAGCTATCCCAGCATT Oxa58 fw GCAATTGCCTTTTAAACCTGA blaoxa58 63 152 Szczepanowski et al. 2009 Oxa58 r CTGCCTTTTCAACAAAACCC Sul1 fw GACGAGATTGTGCGGTTCTT sul1 64 185 Szczepanowski et al. 2009 Sul1 r GAGACCAATAGCGGAAGCC Sul2 fw GACGAGTTATCAACCCGCGAC sul2 64 147 Szczepanowski et al. 2009 Sul2 r CAGTTATCAACCCGCGAC SHV 34 fw GCGTTATTTTCGCCTGTGTA blashv34 60 200 Szczepanowski et al. 2009 SHV 34 r AGGTGCTCATCATGGGAAAG dnrfA fw TTCAGGTGGTGGGGAGATATAC dnrfA 60 150 Muziasari et al., 2014 dnrfA r TTAGAGGCGAAGTCTTGGGTAA kpc-3 fw CAGCTCATTCAAGGGCTTTC kpc 3 64 196 Szczepanowski et al. 2009 kpc-3 r GGCGGCGTTATCACTGTATT qnrB fw CGCGCTAACCTGAAAGATGC qnrB 67 257 This study qnrB r CCCATCCAACGGTTTTCCC

Appendix 6

Gene SEWER1 SEWER2 MIXED INFLOW OUTFLOW 2012

blactx-m32 4.40E-03 2.01E-03 2.57E-03 4.62E-03 blaoxa58 2.50E-03 3.49E-03 4.10E-03 1.61E-03 blashv34 3.12E-03 2.98E-03 2.22E-03 4.64E-03 drnfA 3.80E-02 1.46E-02 1.01E-02 2.39E-02 sul1 2.73E-01 3.74E-01 2.71E-01 6.31E-01 sul2 3.18E-03 5.26E-03 2.49E-03 3.30E-02 tetM 5.29E-02 5.60E-02 1.87E-02 1.24E-02 vanA 4.53E-04 4.86E-04 3.05E-04 1.09E-02

Gene SEWER1 SEWER2 MIXED INFLOW OUTFLOW 2013

blactx-m32 9.63E-04 4.63E-04 9.43E-04 1.58E-03 blaoxa58 4.94E-03 2.50E-03 3.48E-03 8.58E-04 blashv34 1.00E-02 6.57E-03 2.73E-03 2.87E-01 drnfA 3.30E-03 8.20E-04 7.36E-03 5.53E-04 sul1 9.80E-01 9.60E-01 9.90E-01 9.80E-01 sul2 2.99E-02 1.72E-02 1.72E-02 2.43E-02 tetM 7.91E-03 4.81E-03 4.81E-03 2.88E-01 vanA 5.65E-04 7.52E-04 1.22E-03 1.00E-03

Appendix 7

1,00E+00

1,00E-01

1 -

1,00E-02

1,00E-03

ARG gene copy nr 16SrRNA nr copy gene ARG 1,00E-04

1,00E-05

blactx-m32 blaoxa58 blashv34 drnfA sul1 sul2 tetM vanA

Appendix 8

Appendix 9

Appendix 10

Phylum Family Genus mix_au mix_au mix_spr mix_spr mix_sum mix_sum mix_sum mix_win mix_win 09_12 10_13 03_13 04_13 06_13 07_13 08_13_bis 02_12 12_13 Proteobacteria Campylobacterac Arcobacter 1061 15848 19562 12923 26474 27015 27802 11469 49125 eae Proteobacteria Comamonadaceae Acidovorax 95 5985 4759 3718 10979 10469 9894 3208 14924 Bacteroidetes NA NA 40 1 2 4 4 8 0 1 9 Bacteroidetes Flavobacteriaceae Flavobacterium 680 7285 17046 8066 7874 10215 4961 7134 5008 Firmicutes Carnobacteriaceae Trichococcus 2807 1073 11565 9661 1011 3100 833 5374 6979 Actinobacteria Intrasporangiacea Phycicoccus 63977 540 1147 768 570 1330 745 456 1226 e Bacteroidetes NA NA 5 31 10111 104 4 3 1 133 15 Bacteroidetes f__ g__ 0 16 35 19 26 34 6 44 9 Bacteroidetes Flavobacteriaceae NA 460 2512 1342 2793 4309 5377 5921 2112 1304 Bacteroidetes Bacteroidaceae Bacteroides 1186 2322 2667 7858 3217 2870 2601 1440 1590 Proteobacteria Oxalobacteraceae Janthinobacterium 688 880 5429 3476 1277 4182 1502 1965 3137 Fusobacteria Leptotrichiaceae g__ 1341 1819 1287 1179 3545 4328 3800 679 1244 Proteobacteria Rhodobacteraceae Rhodobacter 96 487 4587 4016 632 3007 1369 2047 2886 Bacteroidetes Flavobacteriaceae Flavobacterium 1084 739 1287 681 851 728 776 845 2612 Proteobacteria Comamonadaceae NA 4906 1549 670 1058 1497 3495 2009 572 2588 Bacteroidetes Flavobacteriaceae Flavobacterium 91 195 613 311 119 192 146 654 374 Proteobacteria Neisseriaceae NA 8 532 248 73 672 445 454 127 213 Nitrospirae Nitrospiraceae Nitrospira 93 772 20 169 407 212 241 125 200 Proteobacteria f__ g__ 1716 358 1670 1762 489 1343 690 1038 1115 TM7 f__ g__ 177 548 0 65 68 112 87 139 319 Firmicutes Enterococcaceae Enterococcus 497 622 462 247 698 242 806 544 1782 Firmicutes Ruminococcaceae NA 15 260 39 75 477 564 308 75 106 Bacteroidetes Flavobacteriaceae Flavobacterium 571 441 542 281 461 260 477 434 1213 Bacteroidetes Sphingobacteriace g__ 6 225 206 138 201 269 151 339 501 ae Proteobacteria Procabacteriaceae g__ 466 297 630 257 371 299 375 386 1030 Proteobacteria Acetobacteraceae g__ 956 376 33 1111 110 202 496 710 3261 Actinobacteria Gordoniaceae Gordonia 0 399 0 3 17 20 6 4 49 Proteobacteria Campylobacterac Arcobacter 33 224 2120 733 277 510 208 1514 739 eae Bacteroidetes Flavobacteriaceae Flavobacterium 823 613 145 942 2030 560 1657 186 227 Proteobacteria Sphingomonadace Novosphingobium 53 986 702 1047 688 1865 736 1215 698 ae Appendix 11 (a)

out_a out_a out_sp out_sp out_sp out_su out_su out_su out_sum out_wi out_wi out_wi

Family Genus u u r r r m m m 08_13_bi n n n Phylum 09_12 10_13 03_13 04_12 04_13 07_12 07_13 08_13 s 01_12 02_12 12_13 Proteobacteria Campylobacterac Arcobacter 11041 16398 2 29338 40 30909 4942 4698 6129 2976 3130 1750 eae Proteobacteria Comamonadacea Acidovorax 4615 6326 0 7392 1 14233 1994 2074 2486 831 859 589 e Bacteroidetes NA NA 33141 11 123 2 7560 901 8931 31631 11574 3044 324 2707 Bacteroidetes Flavobacteriaceae Flavobacterium 454 531 411 7759 358 716 1186 3711 1124 1205 591 227 Firmicutes Carnobacteriacea Trichococcus 180 219 69 18743 194 5979 111 129 141 314 240 317 e Actinobacteria Intrasporangiacea Phycicoccus 82 126 185 1188 1953 485 45 19 52 13 18 29 e Bacteroidetes NA NA 0 10 48327 449 5 6 0 0 11 21 18 8 Bacteroidetes f__ g__ 0 0 58594 2 1 1 1 0 7 4 9 1 Bacteroidetes Flavobacteriaceae NA 322 640 54 21 284 2342 294 539 473 69 293 81 Bacteroidetes Bacteroidaceae Bacteroides 460 557 88 1400 102 833 248 771 304 126 259 95 Proteobacteria Oxalobacteraceae Janthinobacterium 74 159 24 752 53 852 188 129 167 120 124 87 Fusobacteria Leptotrichiaceae g__ 438 679 26 3564 135 693 348 1457 317 74 401 78 Proteobacteria Rhodobacteracea Rhodobacter 138 82 15 1940 1 1979 33 41 57 32 119 106 e Bacteroidetes Flavobacteriaceae Flavobacterium 819 4952 15 704 441 1675 1579 1873 1785 795 1126 231 Proteobacteria Comamonadacea NA 285 343 0 459 21 4370 117 194 96 54 167 43 e Bacteroidetes Flavobacteriaceae Flavobacterium 47 64 28 14048 79 191 132 103 92 104 92 23 Proteobacteria Neisseriaceae NA 27 1367 203 0 1109 4 1421 4289 1743 1714 724 1406 Nitrospirae Nitrospiraceae Nitrospira 176 3064 3 362 233 96 146 5375 261 354 700 1387 Proteobacteria f__ g__ 18 60 67 1277 131 287 61 29 31 53 34 30 TM7 f__ g__ 114 1847 6 1 222 68 503 2217 710 324 156 4495 Firmicutes Enterococcaceae Enterococcus 222 1813 24 128 270 556 405 574 369 406 497 131 Firmicutes Ruminococcacea NA 45 599 38 0 702 4 1787 1921 2780 131 377 626 e Bacteroidetes Flavobacteriaceae Flavobacterium 294 1756 15 152 180 1013 463 587 473 447 385 85 Bacteroidetes Sphingobacteriac g__ 256 551 339 9 1663 102 900 1112 908 823 1050 752 eae Proteobacteria Procabacteriaceae g__ 361 1683 15 547 207 750 488 687 611 296 505 99 Proteobacteria Acetobacteraceae g__ 288 277 9 109 21 921 82 253 175 260 139 174 Actinobacteria Gordoniaceae Gordonia 227 2789 9 0 96 9 853 1193 1152 261 64 3455 Proteobacteria Campylobacterac Arcobacter 22 68 70 1193 148 124 131 102 30 376 170 36 eae Bacteroidetes Flavobacteriaceae Flavobacterium 199 57 2 192 13 651 375 529 411 9 94 13 Proteobacteria Sphingomonadac Novosphingobium 9 15 5 256 3 83 9 557 45 15 84 7 eae Appendix 11 (b)

ABBREVIATIONS

WWTPs Wastewater treatment plants HGT Horizontal gene transfer PCR Polimerase chain reaction NGS Next generation sequencing d-WWTPs Decentralised wastewater treatment plants CW Constructed Wetlands AG Attached Growth SBR Sequencing Batch Reactor SG Suspended growth

BOD5 Biological Oxygen Demand TSS Total Suspended Solid COD Chemical Oxygen Demand HM Heavy Metals Abs Antibiotics AR Antibiotic resistance MDR Multi Drug Resistance MAR multiple antibiotic resistances MGE Mobile genetic Elements MRB Multiresistant bacteria ARG Antibiotic Resistant Genes ARB Antibiotic resistant bacteria PPCPs Personal care products EDCs Endocrine disrupting chemicals ESBLs Extended-spectrum β-lacatamses WHO World Health Organization DNA Deoxyribonucleic acid CFU Colony Forming Units UV light Ultraviolet light MRSA Multi resistant Staphylococcus aureus T-RFLP Terminal Restriction Fragment Length Polymorphism LB Luria Broth ANOVA Analysis of variance MANOVA Multivariate analysis of variance OTUs Operational Taxonomic Units Inc-group Incompatibility group WW Wastewater CLSI Clinical and Laboratory Standards Institute MOB Mobilase EUCAST European Committee on Antimicrobial Susceptibility Testing DPMT Degenerate primers mobilase typing

ARDRA Amplified Ribosomal DNA Restriction Analysis

ERKLÄRUNG DES PROMOVENDEN

Die Übereinstimmung dieses Exemplars mit dem Original der Dissertation zum Thema: „“Reservoirs of Antibiotic Resistance and Pathogens in bacterial communities of Anthropogenically-driven Environments“ wird hiermit bestätigt.

……………………………………………… Ort, Datum

……………………………………………… Unterschrift (Vorname, Name)

ERKLÄRUNG ZUR ERÖFFNUNG DES PROMOTIONSVERFAHRENS

1. Hiermit versichere ich, dass ich die vorliegende Arbeit ohne unzulässige Hilfe Dritter und ohne Benutzung anderer als der angegebenen Hilfsmittel angefertigt habe; die aus fremden Quellen direkt oder indirekt übernommenen Gedanken sind als solche kenntlich gemacht. 2. Bei der Auswahl und Auswertung des Materials sowie bei der Herstellung des Manuskripts habe ich Unterstützungsleistungen von folgenden Personen erhalten: Thomas Berendonk Hauke Harms 3. Weitere Personen waren an der geistigen Herstellung der vorliegenden Arbeit nicht beteiligt. Insbesondere habe ich nicht die Hilfe eines kommerziellen Promotionsberaters in Anspruch genommen. Dritte haben von mir weder unmittelbar noch mittelbar geldwerte Leistungen für Arbeiten erhalten, die im Zusammenhang mit dem Inhalt der vorgelegten Dissertation stehen. 4. Die Arbeit wurde bisher weder im Inland noch im Ausland in gleicher oder ähnlicher Form einer anderen Prüfungsbehörde vorgelegt und ist – sofern es sich nicht um eine kumulative Dissertation handelt – auch noch nicht veröffentlicht worden. 5. Sofern es sich um eine kumulative Dissertation gemäß § 10 Abs. 2 handelt, versichere ich die Einhaltung der dort genannten Bedingungen. 6. Ich bestätige, dass ich die Promotionsordnung der Fakultät Umweltwissenschaften der Technischen Universität Dresden anerkenne.

Ort, Datum

Unterschrift des Doktoranden ______

CURRICULUM VITAE

EDUCATION 03/2011 - 09/2015 Technical University of Dresden – Institute of Hydrobiology PhD studies – Environmental Science 11/2014 Climate KIC – Valencia Innovator catalyst: Green skills for boosting transitions in water Management 09/2011 UmweltBundesAmt Dessau, Germany Regulatory Ecotoxicology Pesticide and biocides risk assessment 09/2000 to 06/2006 University of Florence – Italy MSc in Environmental Science and Forestry (Magna cum Laude) 09/1995 - 07/2000 High school graduation “Celsio Ulpiani” Ascoli Piceno – Italy

______WORK EXPERIENCE Since 02/2016 United Nation University UNU-FLORES (Waste Management Unit) 06/2011 - 06/2015 Technical University of Dresden, Germany - Researcher (Project: anti-RESIST)

10/2013 - 12/2013 University of Helsinki, Finland - Visiting Researcher 10/2012 - 12/2012 University of Helsinki, Finland - Visiting Researcher 06/2009 - present Terre Alte Picene s.r.l., Italy - Business to business marketing - renewable energy 03/2008 - 05/2011 Helmholtz Centre for Environmental Research (UFZ) Leipzig, Germany - Researcher 11/2007 - 02/2008 High School “ITGC Carducci” Fermo, Italy - Teacher 10/2005 - 10/2007 University of Florence, Italy - Research assistant 08/2006 - 09/2006 Agronomist office Agostino Agostini s.r.l., A.P., Italy Environmental consultant 06/2000 - 09/2000 Farmers association of Ascoli Piceno-Fermo, Italy Assistant Officer - CAP Policy MEMBERSHIP 2008-2015 German Association for General and Applied Microbiology (VAAM) 2015-2019 COST Action ES1403 NEREUS 2010-2013 COST Action TD0803 DARE 2013-2016 NORMAN project - 6th Framework Programme – Priority 6.3- N° 018486 GRANTS and AWARDS (2015) Scholarship of the Graduate school, Technische Universität Dresden (2015) Grant for Innovator catalyst: Green skills for boosting transitions in Water Management (2014) Conference Grant Travel Award Scholarship of the Graduate school Technische Universität Dresden

(2014-2016) PPP - Bilateral Collaboration Germany-Finland (DAAD-BMBF)

(2013) Grant Award for Short Term Scientific Mission 2013 Detection of antibiotic resistance gene in sewage pipes microbial community-2 (University of Helsinki, Prof. Virta Marko) (2012) Grant Award for Short Term Scientific Mission 2013 Detection of antibiotic resistance gene in sewage pipes microbial community-1 (University of Helsinki, Prof. Virta Marko) CONFERENCE PROCEEDINGS AND ORAL PRESENTATIONS Caucci S., Cacace D., Karkman A., Virta M., and Berendonk T.U. (2015) Persistence of resistance genes in sewage canalization networks of metropolitan areas: relationship between antibiotic selective pressure and antibiotic resistance genes. EDAR-3 International Symposium on the Environmental Dimension of Antibiotic Resistance. Wernigerode, Germany. Oral Presentation Caucci S. and Berendonk T.U. (2015) Monitoring of antibiotic resistances in influent and effluent of Dresden wastewater treatment plant. Untersuchung zu Einträgen von Antibiotika und der Bildung von Antibiotikaresistenz im urbanen Abwasser am Beispiel Dresden. Kadiz, Dresden. Oral Presentation Caucci S. and Berendonk T.U. (2014) Saisonalität der Multiresistenzen im Kläranlagenabwasser. Workshop „Antibiotikaresistenzen und Risikokommunikation“ ANTI-Resist Dresden-Kaditz Dresden. Oral Presentation Caucci S., Karkman A., Virta M. and Berendonk T.U. (2014) Persistence of resistance genes in sewage canalization network of metropolitan area(s). Relationship between antibiotic resistance genes and microbial community inhabiting wastewater. ISME15 Seoul, South Korea. Poster Presentation Caucci S., Kiesel B., Chatzinotas A., Berendonk and Hauke H. (2013) The spread of antibiotic resistances into the environment through waste water treatment plants. Antibiotic resistance correlates with metals and technological hygienization processes. FEMS 2013 Leipzig, Germany. Poster Presentation Caucci S., Cacace D. and Berendonk T.U. (2013) Escherichia coli: a model for monitoring antibiotic resistance into the environment and quantification of resistance genes in wastewater treatment plant. BAGECO, Ljubljana. Oral Presentation. Caucci S., Chatzinotas A., Kiesel B., Berendonk T.U. and Harms H. (2010) Influence of antibiotic and heavy metal resistances in water environment. CITE chemical in the environment Conference (23 March 2010 UFZ, Kubus Leipzig). Oral presentation Caucci S., Chatzinotas A., Kiesel B., Berendonk T.U. and Harms H. (2009) Mutual influence of antibiotic and heavy metal resistances in the environment. BAGECO Conference (Uppsala, Sweden 15-19 June 2009). Oral presentation Caucci S., Chatzinotas A., Kiesel B., Berendonk T.U. and Harms H. (2009) Wastewater treatment plants as hotspots for horizontal gene transfer: Community analysis combined with plasmid replicon based fingerprinting. VAAM Conference. (March 2009 Bochum, Germany). Poster presentation Caucci S. (2008) The pellets from residual biomass: A project for land use management and green energy generation Conference. Energy from biomass: A challenge for the Agriculture- EMAS organization (Monte San Vito, Italy 2008). Oral presentation