The global spread of antibiotic resistance warrants concerted surveillance in the clinic and in the environment. The widespread use of metagenomics for various studies has led to the generation of a large amount of sequencing data. Next-generation sequencing of microbial communities provides an opportunity for proactive detection of emerging antibiotic resistance genes (ARGs) from such data, but there are a limited number of pipelines that enable the identification of novel ARGs belonging to diverse antibiotic classes at present. Therefore, there is a need for the development of computational pipelines that can identify these putative novel ARGs. Such pipelines should be scalable, accessible and have good performance. To address this problem we develop a new method for predicting novel ARGs from genomic or metagenomic sequences, leveraging known ARGs of different resistance categories. Our method takes into account the physio-chemical properties that are intrinsic to different ARG families. Traditionally, new ARGs are predicted by making sequence alignment and calculating sequence similarity to existing ARG reference databases, which can be very time consuming. Here we introduce an alignment free and deep learning prediction method that incorporates both the primary protein sequences of ARGs and their physio-chemical properties. We compare our method with existing pipelines including hidden Markov model based Resfams and fARGene, sequence alignment and machine learning-based DeepARG-LS, and homology modelling based Pairwise Comparative Modelling. We also use our model to detect novel ARGs from various environments including human-gut, soil, activated sludge and the influent samples collected from a waste water treatment plant. Results show that our method achieves greater accuracy compared to existing models for the prediction of ARGs and enables the detection of putative novel ARGs, providing promising targets for experimental characterization to the scientific community. / Master of Science / Various bacteria contain genes that allow them to survive and grow even after the application of antibiotics. Such genes are called antibiotic resistance genes (ARGs). Each ARG has properties that make it resistant to a particular class of antibiotics. This class is called the resistance class/category of the gene. Antimicrobial resistance (AMR) is one of the biggest challenges to public health in recent times. It has been projected that a large number of deaths might occur due to AMR in the future. Therefore, there is a need for monitoring AMR in various environments. Currently, developed methods use the sequence's similarity with the existing database as a feature for ARG prediction. Some tools also use the 3D structure of proteins as a feature for ARG prediction. In this thesis, we develop a tool that incorporates both the sequence similarity and the structural information of proteins for ARG prediction. The structural information is encoded with physio-chemical properties (such as hydrophobicity, molecular weight etc.) of the amino acids. Our results show the efficacy of the pipeline in various environments. Results also show that our method achieves accuracy greater than existing models for the prediction of ARGs from metagenomic data. It also enables the detection of putative novel ARGs, providing promising targets for experimental characterization to the scientific community.

Antibiotic resistance is a growing human health threat, making infections more difficult to treat and increasing fatalities from and cost of treatment of associated diseases. The rise of multidrug resistant pathogens threatens a return to the pre-antibiotic era where even the most common infections may be impossible to treat. It is estimated that the majority of global antibiotic use, and use in the U.S., is dedicated towards livestock, where they are used to promote growth, treat, or prevent disease. Given that exposure to antibiotics selects for antibiotic resistant bacteria (ARBs) and can stimulate the horizontal transfer of their associated antibiotic resistance genes (ARGs), it is important to examine livestock operations as a reservoir of resistance. Correspondingly, there is growing interest in identifying how agricultural practices can limit the potential for spread of antibiotic resistance through the "farm to fork continuum," starting with antibiotic use practices, manure management and land application and ending with the spread of ARBs and ARGs present onto edible crops and serving as a route of exposure to consumers. This study focused specifically on the effect of composting on the prevalence of ARBs and ARGs in cattle manure. Three composting trials were performed: small-scale, heat-controlled, and large-scale. The small-scale composting trial compared dairy and beef manures, with or without antibiotic treatment (treated beef cattle received chlortetracycline, sulfamethazine, and tylosin while treated dairy cattle received cephapirin and pirlimycin), subject to either static or turned composting. The heat-controlled composting trial examined only dairy manure, with or without antibiotic treatment, subject to static composting, but using external heat tape applied to the composting tumblers to extend the duration of the thermophilic (>55°C) temperature range. The large-scale composting trial examined dairy manure, with or without antibiotic treatment, subject to static composting at a much larger scale that is more realistic to typical farm practices. Samples were analyzed to assess phenotypic resistance using the Kirby Bauer disk diffusion method and by diluting and plating onto antibiotic-supplemented agar. Genetic markers of resistance were also assessed using quantitative polymerase chain reaction (qPCR) to quantify sul1 and tet(W) ARGs; metagenomic DNA sequencing and analysis were also performed to assess and compare total ARG abundance and types across all samples. Results indicate that composting can enrich indicators of phenotypic and genetic resistance traits to certain antibiotics, but that most ARGs are successfully attenuated during composting, as evidenced by the metagenomic sequencing. Maintaining thermophilic composting temperatures for adequate time is necessary for the effective elimination of enteric bacteria. This study suggests that indicator bacteria that survive composting tend to be more resistant than those in the original raw manure; however, extending the thermophilic stage of composting, as was done in the heat-controlled trial, can reduce target indicator bacteria below detection limits. Of the two ARGs specifically quantified via qPCR, prior administration of antibiotics to cattle only had a significant impact on tet(W). There was not an obvious difference in the final antibiotic resistance profiles in the finished beef versus dairy manure composts according to metagenomics analysis. Based on these results, composting is promising as a method of attenuating ARGs, but further research is necessary to examine in depth all of the complex interactions that occur during the composting process to maximize performance. If not applied appropriately, e.g., if time and temperature guidelines are not enforced, then there is potential that composting could exacerbate the spread of certain types of antibiotic resistance. / Master of Science / Antibiotics are drugs that are used to treat bacterial infections by killing the bacteria that cause the infection. Bacterial infections now exist that are resistant to several antibiotics; which are extremely difficult and costly to treat. Many antibiotics are used in the agriculture industry where they are used to promote growth, treat, or prevent disease in livestock animals. The antibiotics may then cause an increase in antibiotic resistance in bacteria by encouraging changes to the DNA of the bacteria which allow them to survive in the presence of antibiotics that would normally kill them. These DNA segments are called antibiotic resistance genes. Once developed, bacteria can share resistance genes among themselves, allowing for single bacteria that can resist several types of antibiotics. For this reason, it is important to see if it is possible to prevent the spread of antibiotic resistance from animal agriculture to people. One way that people could be affected would be if produce were exposed to resistant bacteria when grown in soil that had been fertilized with manure or compost. This study looks at the impact of composting on the presence and amount of antibiotic resistance genes in composted cattle manure. Three composting trials were performed: small-scale, heat-controlled, and large-scale. The small-scale composting trial compared dairy and beef manures, with or without antibiotic treatment, with or without regular turning during composting. The heat-controlled composting trial examined only dairy manure, with or without antibiotic treatment, without regular turning during composting, but using external heat to maintain high temperatures. The large-scale composting trial examined dairy manure, with or without antibiotic treatment, without regular turning during composting, but at a larger scale that is more realistic to how composting is actually performed on farms. Antibiotic resistance of compost bacteria was tested by growing bacteria on nutrient-dense plates containing antibiotic disks and measuring how much each antibiotic prevented the growth of the bacteria, in terms of the diameter about each disk where bacteria did not grow. Individual target resistance genes were measured throughout the study by using a method called qPCR. Metagenomic analysis was performed to identify all of the genes, especially resistance genes, in each of the samples. Results v show that composting may increase antibiotic resistance in bacteria that survive the composting process, but that most resistance genes are themselves reduced. The key to successful composting is maintaining high temperatures for as long as possible; this is necessary to kill off infectious bacteria. Extending the high temperature (>55°C) phase of composting is a potential method for improving the effectiveness of composting in eliminating pathogens and destroying resistance genes. Results were not significantly affected by whether antibiotics were given to the cattle and were not different between dairy or beef cattle. Based on these results, composting is a promising method of reducing resistance genes in composted manure, but further research is necessary to maximize performance. If not performed correctly, composting could have the opposite effect and be detrimental.

The primary objectives of this research were to demonstrate that: 1.) antibiotic resistant bacteria can promote the survival of antibiotic sensitive organisms when grown simultaneously as biofilms in antibiotics, 2.) community-level multiple antibiotic resistance of polymicrobial consortia can lead to biofilm formation despite the presence of multiple antibiotics, and 3.) biofilms may benefit plasmid retention and heterologous protein production in the absence of selective pressure. Quantitative analyses of confocal data showed that ampicillin resistant organisms supported populations of ampicillin sensitive organisms in steady state ampicillin concentrations 13 times greater than that which would inhibit sensitive cells inoculated alone. The rate of reaction of the resistance mechanism influenced the degree of protection. Spectinomycin resistant organisms did not support their sensitive counterparts, although flow cytometry indicated that GFP production by the sensitive strain was improved. When both organisms were grown in both antibiotics, larger numbers of substratum-attached pairs at 2 hours resulted in greater biofilm formation at 48 hours. For biofilms grown in both antibiotics, a benefit to spectinomycin resistant organism’s population size was detectable, but the only benefit to ampicillin resistant organisms was in terms of GFP production. Additionally, an initial attachment ratio of 5 spectinomycin resistant organisms to 1 ampicillin resistant organism resulted in optimal biofilm formation at 48 hours. Biofilms also enhanced the stability of high-copy number plasmids and heterologous protein production. In the absence of antibiotic selective pressure, plasmid DNA was not detected after 48 hours in chemostats, where the faster growth rate of plasmid-free cells contributed to the washout of plasmid retaining cells. The plasmid copy number per cell in biofilms grown without antibiotic selective pressure steadily increased over a six day period. Flow cytometric monitoring of bacteria grown in biofilms indicated that 95 percent of the population was producing GFP at 48 hours. This research supports the idea that ecological interactions between bacteria contribute to biofilm development in the presence of antibiotics, and demonstrates that community-level multiple antibiotic resistance is a factor in biofilm recalcitrance against antibiotics. Additionally, biofilms may provide an additional tool for stabilizing high copy number plasmids used for heterologous protein production.

To combat the ever-growing clinical burden imposed by antibiotic-resistant pathogens, multiple-antibiotic treatments are increasingly being considered as promising treatment options. The impact of multiple-antibiotic treatments on the evolution of resistance is not well understood however, and debate is ongoing about the effectiveness of various multiple-antibiotic treatments. In this thesis, I investigate how aspects of multiple-antibiotic treatments impact the rate of evolution of antibiotic resistance in the opportunistic human pathogen Pseudomonas aeruginosa. In particular, I look at the impact of interactions between antibiotics in combination on the evolution of resistance, and how creating heterogeneity in the antibiotic environment by rotating the antibiotics used may change the rate of evolution of resistance. I characterise the interactions present in 120 combinations of antibiotics and find that the type of interaction can be predicted by the mechanism of action of the antibiotics involved. I investigate the effect of a subset of these combinations on the evolution of antibiotic resistance. My results refute the influential but poorly-evidenced hypothesis that synergistic combinations accelerate the evolution of resistance, even when synergistic combinations have the same inhibitory effect on sensitive bacteria as additive or antagonistic antibiotic combinations. I focus on a combination of the antibiotics ceftriaxone and sulfamethoxazole and test whether it is more effective in preventing the evolution of resistance than predicted by the inhibitory effect of the combination on sensitive bacteria. I do not find the combination to be more effective than predicted. Finally, I create heterogeneous antibiotic environments by rotating the antibiotic present at different rates. For the first time in a laboratory setting, I test how varying the rate of fluctuation in the antibiotics present in a heterogeneous antibiotic environment impacts the rate of evolution of resistance. Unexpectedly, I find the rate of evolution of resistance increases with increasing levels of antibiotic heterogeneity.

Polymyxins are potent antibiotics that bind to the outer membrane of Gram-negative bacteria, entering the cell and disrupting the inner membrane, resulting in cell death. They were traditionally used as antibiotics of last resort, but the recent surge of multidrug resistant pathogens has renewed interest in these antibiotics. The emergence of polymyxin resistance determinants such as the recently discovered plasmid-mediated phosphoethanolamine transferase MCR-1 may put a strain on the future effectiveness of this antibiotic. One method to combat the rise in antibiotic resistant bacteria is through the identification and development of antibiotic adjuvants. These are small molecules that are able to inhibit the resistance mechanism, allowing previously ineffective antibiotics to once again become effective at treating bacterial infections. In this work, a high throughput cell-based screen was conducted using an in-house library of Actinomycete-derived crude cell extracts in order to search for a natural product inhibitor of an E. coli strain expressing mcr-1. In addition, the development of a new enzyme assay was attempted using purified MCR-1 C-terminal catalytic domain and a chromogenic substrate to test enzymatic activity in vitro, in hopes of establishing a simple means of studying inhibition of MCR-1. The structure-function relationship of MCR-1 was also explored by generating amino acid substitutions and studying their effect on the ability of the enzyme to confer resistance to colistin, as well as the generation of MCR-1 transmembrane truncation mutants to determine if it was possible to generate a shorter variant of MCR-1 that retained its enzymatic activity. This work furthers our understanding of the biochemistry and enzymology of MCR-1, and outlines attempts to identify inhibitors of MCR-1 in order to re-sensitize resistant bacteria to polymyxins. / Thesis / Master of Science (MSc) / Polymyxins are potent antibiotics that are threatened by the spread of multi-drug resistant bacteria. Resistance to these antibiotics is relatively rare, although the recent discovery of a mobile polymyxin resistance enzyme, MCR-1, threatens the future use of this antibiotic for treating infections, as it can readily transfer to other bacteria. The goal of this work was to search for a natural product inhibitor of MCR-1 in order to reverse its ability to confer resistance to polymyxins. A color-changing assay was conducted with MCR-1 in hopes of establishing a method to study the inhibition of MCR-1 in vitro. Additionally, amino acid substitutions were generated in MCR-1 to better understand how key amino acids affect enzyme function, as well as transmembrane domain truncations to determine if it was possible to create a shorter functioning variant of MCR-1.

Antibiotic resistance is a global threat that can severely imperil public health. To curb the spread of antibiotic resistance, it is imperative that efforts commensurate with a “One Health” approach are undertaken. Given that interconnectivities among ecosystems can serve as conduits for the proliferation and dissemination of antibiotic resistance, it is increasingly being recognized that a robust global environmental surveillance framework is required to promote One Health. The ideal aim would be to develop approaches that inform global distribution of antibiotic resistance, help prioritize monitoring targets, present robust data analysis frameworks to profile resistance, and ultimately help build strategies to curb the dissemination of antibiotic resistance. The work described in this dissertation was aimed at evaluating and developing different data analysis paradigms and their applications in investigating and characterizing antibiotic resistance across different resistomes. The applications presented in Chapter 2 illustrate challenges associated with various environmental data types (especially metagenomics data) and present a path to advance incorporation of data analytics approaches in Environmental Science and Engineering research and applications. Chapter 3 presents a novel approach, ExtrARG, that identifies discriminatory ARGs among resistomes based on factors of interest. The results in Chapter 4 provide insight into the global distribution of ARGs across human fecal and sewage resistomes across different socioeconomics. Chapter 5 demonstrates a data analysis paradigm using machine learning algorithms that helps bridge the gap between information obtained via culturing and metagenomic sequencing. Lastly, the results of Chapter 6 illustrates the contribution of phages to antibiotic resistance. Overall, the findings provide guidance and approaches for profiling antibiotic resistance using metagenomics and machine learning. The results reported further expand the knowledge on the distribution of antibiotic resistance across different resistomes. / Antibiotic resistance is a global threat that can severely imperil public health. To curb the spread of antibiotic resistance, it is imperative that efforts commensurate with a "One Health" approach are undertaken. Given that interconnectivities among ecosystems can serve as conduits for the proliferation and dissemination of antibiotic resistance, it is increasingly being recognized that a robust global environmental surveillance framework is required to promote One Health. The ideal aim would be to develop approaches that inform global distribution of antibiotic resistance, help prioritize monitoring targets, present robust data analysis frameworks to profile resistance, and ultimately help build strategies to curb the dissemination of antibiotic resistance. The work described in this dissertation was aimed at evaluating and developing different data analysis paradigms and their applications in investigating and characterizing antibiotic resistance across different resistomes. The applications presented in Chapter 2 illustrate challenges associated with various environmental data types (especially metagenomics data) and present a path to advance incorporation of data analytics approaches in Environmental Science and Engineering research and applications. Chapter 3 presents a novel approach, ExtrARG, that identifies discriminatory ARGs among resistomes based on factors of interest. The results in Chapter 4 provide insight into the global distribution of ARGs across human fecal and sewage resistomes across different socioeconomics. Chapter 5 demonstrates a data analysis paradigm using machine learning algorithms that helps bridge the gap between information obtained via culturing and metagenomic sequencing. Lastly, the results of Chapter 6 illustrates the contribution of phages to antibiotic resistance. Overall, the findings provide guidance and approaches for profiling antibiotic resistance using metagenomics and machine learning. The results reported further expand the knowledge on the distribution of antibiotic resistance across different resistomes. / Doctor of Philosophy / Antibiotic resistance is ability of bacteria to withstand an antibiotic to which they were once sensitive. Antibiotic resistance is a global threat that can pose a serious threat to public health. In order to curb the spread of antibiotic resistance, it is imperative that efforts commensurate with the "One Health" approach. Since ecosystem networks can act as channels for the spread and spread of antibiotic resistance, there is growing recognition that a robust global environmental monitoring framework is required to promote a true one-health approach. The ideal goal would be to develop approaches that can inform the global spread of antibiotic resistance, help prioritize monitoring objectives and present robust data analysis frameworks for resistance profiling, and ultimately help develop strategies to contain the spread of antibiotic resistance. The objective of the work described in this thesis was to evaluate and develop different data analysis paradigms and their applications in the study and characterization of antibiotic resistance in different resistomes. The applications presented in Chapter 2 illustrate challenges associated with various environmental data types (especially metagenomics data) and present a path to advance incorporation of data analytics approaches in Environmental Science and Engineering research and applications. The Chapter 3 presents a novel approach, ExtrARG, that identifies discriminatory ARGs among resistomes based on factors of interest. The chapter 5 demonstrates a data analysis paradigm using machine learning algorithms that helps bridge the gap between information obtained via culturing and metagenomic sequencing. The results of Chapters 4 provide insight into the global distribution of ARGs across human fecal and sewage resistomes across different socioeconomics. Lastly, the results of Chapter 6 illustrates the contribution of phages to antibiotic resistance. Overall, the findings provide guidance and approaches for profiling antibiotic resistance using metagenomics and machine learning. The results reported further expand the knowledge on the distribution of antibiotic resistance across different resistomes.

Widespread use of antibiotics in livestock production can result in the dissemination of bacteria carrying antibiotic resistance genes (ARGs) to the broader environment. Within agroecosystems, ARGs can pose a risk to livestock handlers, farmers, and ultimately consumers. The overall goals of this dissertation are to examine the presence of resistance (antibiotic, metal) in agricultural soils and evaluate the most critical potential points of best management control of antibiotic resistance spread along the agricultural production chain. The relative impacts of agricultural practices, manure management, native soil microbiota, and type of crop grown and harvested on the agricultural resistome are multi-dimensional and cannot be captured via a single analytical technique or by focusing on one specific point in the agricultural process. Culture-, molecular "indicator"-, and next-generation sequencing- based methods were employed to characterize antibiotic resistance via taxonomic and functional profiles on the broader manure, soil, and vegetable surface microbial communities through 16S rRNA amplicon sequencing and shotgun metagenomics. Although antibiotic concentrations dissipated in the soil after 28 days after amendment application, antibiotic resistance presence was recoverable throughout the entire 120d growing season in the compost and manure amendments, the amended soil, and on vegetable surfaces. The addition of organic fertilizers increased antibiotic resistance presence compared to background levels. Further, metals and metal resistance were also measured in the amended soils and were found to be at greater levels in the inorganically fertilized soils compared to the manures and compost amended soils. Analysis of the widespread agroecosystem microbial community composition and broader metagenome has characterized varying genera profiles in the soil and on the vegetable surfaces and specific ARG and mobile genetic element (plasmid) co-occurrences. These co-occurrences highlight which ARGs may be most critical for future antibiotic resistance dissemination research. It is imperative to employ multiple methods when measuring agricultural resistance, as one method alone may miss significant patterns and lead to different best management recommendations. Linking the livestock manure, soil, and vegetable surface-associated ARBs, ARGs, resistomes, and microbiomes will help identify critical control points for mitigation of agricultural dissemination of antibiotic resistance to the environment and food production. / Doctor of Philosophy / By 2050, it is estimated that antibiotic resistant infections will be the leading cause of death worldwide. It is important to consider human, animal, and environmental health when researching antibiotic resistance, which is known as a "One Health" approach. In this dissertation work, I focus on the environmental side of antibiotic resistance in our agricultural systems. Agriculture is a known source of antibiotic resistance due to its use of antibiotics in livestock as a treatment for illness, and in some instances, as a growth promoter. Over one growing season, I measured antibiotic resistance in an agricultural setting using many techniques. First, I analyzed the effects of inorganic (chemical) versus organic (manure and compost) fertilization on antibiotic resistance in the soil. I measured antibiotic resistance by growing antibiotic resistant bacteria, quantifying specific antibiotic resistant genes (ARGs) using DNA amplification, and quantifying all the ARGs in the soil using a next-generation sequencing (NGS) technique called shotgun metagenomics. I found that adding manure to the soil increases ARGs compared to background soil levels, and that composting in an effective management strategy in decreasing ARGs in the soil over time. Second, I analyzed the same effects of fertilization on metal resistance in the soil. I was able to use the same NGS dataset to measure metal resistance genes (MRGs). I found that adding inorganic chemical fertilizer increases MRGs in the agricultural soils compared to the organic (manure or compost) fertilizer. Additionally, I studied the microbes that live in the agricultural soils using another kind of NGS data specific for microbial identification. I found that although there were small differences between the microbial populations in the soil when fertilizers were added, they returned to similar composition over the growing season. Lastly, I measured antibiotic resistance and microbes throughout the entire agricultural system. I picked the point of fertilization (manure management), soil, and the lettuce surface to evaluate if antibiotic resistance spreads from the farm to the vegetable that ends up on a consumer's plate. I found that at each point antibiotic resistance is present, but at different levels. Composting reduces ARGs compared to raw manure. Agricultural soils may act as a natural buffer to antibiotic resistance. Lettuce plants grown in compost fertilized soils have less ARGs than lettuce plants grown in manure. There are many agricultural management practices that effectively reduce antibiotic resistance and using all of them plus many measurement methods will ultimately help farmers and consumers reduce antibiotic resistance in our agricultural systems.

The main aim of the study was to analyse the impact of physico-chemical parameters on drinking water quality, biofilm formation and antibiotic resistant bacteria in the drinking water distribution system in Mafikeng, North West Province, South Africa. Another objective was to isolate and characterise Pseudomonas and Aeromonas species from drinking water distribution system and detect the virulence gene determinants in the isolates by PCR analysis. The physico-chemical data obtained were subjected to statistical analysis using Excel 2007 (Microsoft) and SPSS (version 14.0) programmes. Pearson’s correlation product of the moment was used to determine the correlation between EC, TDS, pH and temperature. The two tailed test of significance (p<0.05) was used in order to determine the significance of the result. Antibiotic susceptibility tests were performed using Kirby-Bauer disk diffusion method. Cluster analysis based on the antibiotic inhibition zone diameter data of different organisms isolated from different sites was determined and was expressed as dendograms using Wards algorithm and Euclidean distance of Statistica version 7. Specific PCR was used to determine the identities of presumptive Pseudomonas and Aeromonas species through amplification of the gyrB, toxA and the ecfX gene fragments. Virulence gene determinants for the confirmed Pseudomonas and Aeromonas species were detected by amplifying the exoA, exoS and exoT genes and the aerA and hylH gene fragments, respectively. A Gene Genius Bio imaging system (Syngene, Synoptics; UK) was used to capture the image using GeneSnap (version 3.07.01) software (Syngene, Synoptics; UK) to determine the relative size of amplicons. Physico-chemical parameters were monitored from three drinking water sources three times a week and bacteriological quality was monitored weekly for four months from raw and treated drinking water. Water samples were analysed for pH, temperature, total dissolved solids (TDS) and electric conductivity (EC). Bacterial consortia from drinking water samples were isolated using selective media and enumerated. The results revealed a good chemical quality of water. However, the microbial quality of the water is not acceptable for human consumption due to the presence of Pseudomonas, Aeromonas, faecal coliforms (FC), total coliforms (TC) and Heterotrophic bacteria. The results showed that the drinking water is slightly alkaline with pH value ranging between7.7 to 8.32. What is of concern was the microbial quality of the water. Pseudomonas sp., faecal coliforms (FC), total coliforms (TC) and heterotrophic bacteria were present in some of the treated water samples. The most significant finding of this study is that all drinking water samples were positive for Pseudomonas sp.(>100/100ml), but also that when one considers the TDS it demonstrates that water from the Modimola Dam has an impact on the quality of the mixed water. The prevalence and antibiotic resistance profiles of planktonic and biofilm bacteria isolated from drinking water were determined. The susceptibility of these isolates was tested against 11 antibiotics of clinical interest and the multiple antibiotic resistance (MAR) patterns were compiled. The most prevalent antibiotic resistance phenotype observed was KF-AP-C-E-OT-K-TM-A. All isolates from all samples were susceptible to ciprofloxacin. However, all faecal coliforms and Pseudomonas spp. were susceptible to neomycin and streptomycin. On the contrary all organisms tested were resistant to erythromycin (100%) trimethoprim and amoxycillin. Cluster analysis based on inhibition zone diameter data could not differentiate the various isolated into sample types. The highest prevalence of antibiotic resistant isolates was observed in Modimola Dam and Molopo eye. Biofilms were investigated in both raw water and treated drinking water sources for the presence of faecal coliforms, total coliforms, Pseudomonas spp., Aeromonas spp. and heterotrophic bacteria based on conventional microbiology and molecular methods. Drinking water biofilms were grown twice and the biofilm developing device containing copper and galvanized steel coupons were utilized. The Mini Tap filter, a home water treatment device which can be used at a single faucet, under constant flow was used during the second collection of treated water samples from cold water taps. Scanning electron micrograph revealed the existence of biofilms in all the sites investigated and the highest density was obtained on galvanized steel coupons. Isolates were tested against the antibiotics ampicillin (10μg), cephalothin (5μg), streptomycin (10μg), erythromycin (15μg), chloramphenicol (30μg), neomycin (30 μg), amoxycillin (10 μg), ciprofloxacin (5 μg), trimethoprim (25μg), kanamycin (30μg), and oxytetracycline (30μg). The multiple antibiotic resistance profiles and the presence of virulence related genes were determined. Various types of drug resistance and presence of virulence genes were observed. The most prevalent resistance phenotype observed was KF-AP-C-E-OT-TM-A. In conclusion, the results indicated the occurrence of faecal indicator bacteria in the drinking water destined for human consumption. Faecal indicator bacteria are the major contributors of poor drinking water quality and may harbour opportunistic pathogens. This highlighted survival of organisms to treatment procedures and the possible regrowth as biofilms in plumbing materials. The detection of large proportion of MAR Aeromonas and Pseudomonas species which possessed virulent genes was a cause of concern as these could pose health risks to humans. The data obtained herein may be useful in assessing the health risks associated with the consumption of contaminated water. / PhD (Microbiology), North-West University, Potchefstroom Campus, 2014

Water is considered the most vital resource on earth and its quality is deteriorating. Not all residents living in South Africa‘s rural areas have access to treated drinking water, and use water from rivers, dams, and wells. The quality of these resources is unknown, as well as the effects of the bacteria in the water on human health. The heterotrophic plate count (HPC) method is a globally used test to evaluate microbial water quality. According to South African water quality guidelines, water of good quality may not contain more than a 1 000 coliforming units (CFU)/mℓ. There is mounting evidence that HPC bacteria may be hazardous to humans with compromised, underdeveloped, and weakened immune systems. In this study the pathogenic potential of HPC bacteria was investigated. Samples were collected from boreholes in the North West Province and HPCs were enumerated with a culture-based method. Standard physico-chemical parameters were measured for the water. Different HPC bacteria were isolated and purified and tested for α- or β-haemolysis, as well as the production of extracellular enzymes such as DNase, proteinase, lecithinase, chondroitinase, hyaluronidase and lipase, as these are pathogenic characteristics. The isolates were identified with 16S rRNA gene sequencing. The model for the human intestine, Hutu-80 cells, were exposed to the potentially pathogenic HPC isolates to determine their effects on the viability of the human cells. The isolates were also exposed to different dilutions of simulated gastric fluid (SGF) to evaluate its effect on the viability of bacteria. Antibiotic resistant potential of each isolate was determined by the Kirby-Bauer disk diffusion method. Three borehole samples did not comply with the physico-chemical guidelines. Half of the samples exceeded the microbial water quality guideline and the greatest CFU was 292 350 CFU/mℓ. 27% of the isolate HPC bacteria were α- or β- haemolytic. Subsequent analysis revealed the production of: DNase in 72%, proteinase in 40%, lipase and lecithinase in 29%, hyaluronidase in 25% and least produced was chondroitinase in 25%. The HPC isolates identified included: Alcaligenes faecalis, Aeromonas hydrophila and A. taiwanesis, Bacillus sp., Bacillus thuringiensis, Bacillus subtilis, Bacillus pumilus, Brevibacillus sp., Bacillus cereus and Pseudomonas sp. All the isolates, except Alcaligenes faecalis, were toxic to the human intestinal cells to varying degrees. Seven isolates survived exposure to the most diluted SGF and of these, four isolates also survived the intermediate dilution but, only one survived the highest SGF concentration. Some isolates were resistant to selected antibiotics, but none to neomycin and vancomycin. Amoxillin and oxytetracycline were the least effective of the antibiotics tested. A pathogen score was calculated for each isolate based on the results of this study. Bacillus cereus had the highest pathogen index with declining pathogenicity as follows: Alcaligenes faecalis > B. thuringiensis > Bacillus pumilus > Pseudomonas sp. > Brevibacillus > Aeromonas taiwanesis > Aeromonas hydrophila > Bacillus subtilis > Bacillus sp. The results of this study prove that standard water quality tests such as the physico-chemical and the HPC methods are insufficient to provide protection against the effects of certain pathogenic HPC bacteria. / MSc (Environmental Sciences), North-West University, Potchefstroom Campus, 2014

The main aim of the study was to analyse the impact of physico-chemical parameters on drinking water quality, biofilm formation and antibiotic resistant bacteria in the drinking water distribution system in Mafikeng, North West Province, South Africa. Another objective was to isolate and characterise Pseudomonas and Aeromonas species from drinking water distribution system and detect the virulence gene determinants in the isolates by PCR analysis. The physico-chemical data obtained were subjected to statistical analysis using Excel 2007 (Microsoft) and SPSS (version 14.0) programmes. Pearson’s correlation product of the moment was used to determine the correlation between EC, TDS, pH and temperature. The two tailed test of significance (p<0.05) was used in order to determine the significance of the result. Antibiotic susceptibility tests were performed using Kirby-Bauer disk diffusion method. Cluster analysis based on the antibiotic inhibition zone diameter data of different organisms isolated from different sites was determined and was expressed as dendograms using Wards algorithm and Euclidean distance of Statistica version 7. Specific PCR was used to determine the identities of presumptive Pseudomonas and Aeromonas species through amplification of the gyrB, toxA and the ecfX gene fragments. Virulence gene determinants for the confirmed Pseudomonas and Aeromonas species were detected by amplifying the exoA, exoS and exoT genes and the aerA and hylH gene fragments, respectively. A Gene Genius Bio imaging system (Syngene, Synoptics; UK) was used to capture the image using GeneSnap (version 3.07.01) software (Syngene, Synoptics; UK) to determine the relative size of amplicons. Physico-chemical parameters were monitored from three drinking water sources three times a week and bacteriological quality was monitored weekly for four months from raw and treated drinking water. Water samples were analysed for pH, temperature, total dissolved solids (TDS) and electric conductivity (EC). Bacterial consortia from drinking water samples were isolated using selective media and enumerated. The results revealed a good chemical quality of water. However, the microbial quality of the water is not acceptable for human consumption due to the presence of Pseudomonas, Aeromonas, faecal coliforms (FC), total coliforms (TC) and Heterotrophic bacteria. The results showed that the drinking water is slightly alkaline with pH value ranging between7.7 to 8.32. What is of concern was the microbial quality of the water. Pseudomonas sp., faecal coliforms (FC), total coliforms (TC) and heterotrophic bacteria were present in some of the treated water samples. The most significant finding of this study is that all drinking water samples were positive for Pseudomonas sp.(>100/100ml), but also that when one considers the TDS it demonstrates that water from the Modimola Dam has an impact on the quality of the mixed water. The prevalence and antibiotic resistance profiles of planktonic and biofilm bacteria isolated from drinking water were determined. The susceptibility of these isolates was tested against 11 antibiotics of clinical interest and the multiple antibiotic resistance (MAR) patterns were compiled. The most prevalent antibiotic resistance phenotype observed was KF-AP-C-E-OT-K-TM-A. All isolates from all samples were susceptible to ciprofloxacin. However, all faecal coliforms and Pseudomonas spp. were susceptible to neomycin and streptomycin. On the contrary all organisms tested were resistant to erythromycin (100%) trimethoprim and amoxycillin. Cluster analysis based on inhibition zone diameter data could not differentiate the various isolated into sample types. The highest prevalence of antibiotic resistant isolates was observed in Modimola Dam and Molopo eye. Biofilms were investigated in both raw water and treated drinking water sources for the presence of faecal coliforms, total coliforms, Pseudomonas spp., Aeromonas spp. and heterotrophic bacteria based on conventional microbiology and molecular methods. Drinking water biofilms were grown twice and the biofilm developing device containing copper and galvanized steel coupons were utilized. The Mini Tap filter, a home water treatment device which can be used at a single faucet, under constant flow was used during the second collection of treated water samples from cold water taps. Scanning electron micrograph revealed the existence of biofilms in all the sites investigated and the highest density was obtained on galvanized steel coupons. Isolates were tested against the antibiotics ampicillin (10μg), cephalothin (5μg), streptomycin (10μg), erythromycin (15μg), chloramphenicol (30μg), neomycin (30 μg), amoxycillin (10 μg), ciprofloxacin (5 μg), trimethoprim (25μg), kanamycin (30μg), and oxytetracycline (30μg). The multiple antibiotic resistance profiles and the presence of virulence related genes were determined. Various types of drug resistance and presence of virulence genes were observed. The most prevalent resistance phenotype observed was KF-AP-C-E-OT-TM-A. In conclusion, the results indicated the occurrence of faecal indicator bacteria in the drinking water destined for human consumption. Faecal indicator bacteria are the major contributors of poor drinking water quality and may harbour opportunistic pathogens. This highlighted survival of organisms to treatment procedures and the possible regrowth as biofilms in plumbing materials. The detection of large proportion of MAR Aeromonas and Pseudomonas species which possessed virulent genes was a cause of concern as these could pose health risks to humans. The data obtained herein may be useful in assessing the health risks associated with the consumption of contaminated water. / PhD (Microbiology), North-West University, Potchefstroom Campus, 2014