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The opportunistic pathogen Legionella is the leading cause of reported waterborne disease outbreaks in the United States. Legionella can thrive under the warm, stagnant, low-disinfectant conditions characteristic of premise (i.e., building) plumbing systems, making it challenging to identify effective interventions for its control. Copper (Cu) is a promising antimicrobial that can be dosed directly to water via copper-silver ionization systems or released naturally via corrosion of Cu pipes to help control growth of Legionella and other pathogens. However, prior research has shown that Cu does not always reliably control Legionella and sometimes seems to even stimulate its growth. A deeper understanding of the mechanistic effects of Cu on Legionella, at both pure-culture and real-world scales, is critical in order to inform effective controls for Legionella. The overarching objective of the research embodied by this dissertation was aimed at elucidating the chemical and microbial interactions in premise plumbing that govern efficacy of Cu for Legionella control through a series of complementary bench-, pilot-, and field-scale studies. A critical review and synthesis of the literature identified important knowledge gaps in relation to antimicrobial effects of Cu. In particular, changes in the pH, phosphate corrosion control, and rising levels of natural organic matter (NOM) in distributed water are predicted to be important controlling factors. The type of sacrificial anode rod material employed in water heaters was also identified as an underappreciated factor, which directly affects pH, evolution of hydrogen gas as a microbial nutrient, and release of metals (such as aluminum) that bind copper. Microbiological factors: including growth phase of Legionella (e.g., exponential or stationary), strain-specific Cu tolerance, background microbiome composition, and the possibility that viable but non-culturable (VBNC) Legionella might still cause human disease, were also identified as major confounding factors. These knowledge gaps are addressed from various dimensions across each chapter of the dissertation. The effects of pH, orthophosphate corrosion inhibitor concentration, and NOM were examined in bench-scale pure culture experiments over a range of conditions relevant to drinking water. Cupric ions and antimicrobial effects were drastically reduced at pH >7.5, especially in the presence of phosphate, which precipitates copper, or NOM, which complexes the Cu in a form that is less bioavailable. Chick-Watson disinfection models indicated that soluble Cu was the most robust correlate with observed Cu antimicrobial effects across a range of tested waters. This new knowledge suggests that measuring soluble rather than total Cu would be much more informative to guide practitioners in dosing. The research also demonstrated that changes in pH or orthophosphate that have been made to control corrosion over the last few decades, have significantly altered Cu chemistry in buildings, undermining antimicrobial capacity and increasing likelihood of Legionella growth. Pilot-scale experiments confirmed that soluble Cu is an effective indicator of Cu antimicrobial capacity, even in more complex environments represented by realistic hot water plumbing systems. In particular, dosing of orthophosphate, which is widely added by drinking water utilities to control corrosion, directly reduces soluble copper and overall antimicrobial capacity. In some cases, Cu added together with orthophosphate apparently promoted the growth of Legionella, providing an example of at least one circumstance where Cu addition can induce interactive effects that elevate Legionella compared to a control system with trace Cu. It was also demonstrated for the first time that different water heater sacrificial anode types are subject to different corrosion processes, which indirectly influence Cu antimicrobial capacity. Specifically, aluminum ions released from aluminum anode corrosion at 1 mg/L can form an Al(OH)3 gel, which can remove >80% of the soluble Cu from water and reduce Cu antimicrobial effects by >2-log at pH=7. Corrosion from magnesium anodes was found to dramatically increase the pH from 6.8 to >8, which correspondingly reduces Cu antimicrobial capacity. Cu deposition further increased the anode corrosion rate and promoted evolution of hydrogen gas, which is a potent electron donor that stimulates autotrophic microbial growth especially with a magnesium anode. Electric powered anodes did not release metals or alter pH and thus did not diminish Cu antimicrobial capacity. Still, across the pilot-scale experiments, even very high levels of Cu (>1.2 mg/L) at low pH (<7) failed to fully eradicate culturable Legionella. The much lower than expected antimicrobial efficacy of Cu in the pilot-scale hot water plumbing systems was found to be partially explained by the properties of the strain that colonized the systems. Based on fitting the data to a Chick-Watson disinfection model, the outbreak-associated strain that was inoculated into the systems was estimated to be 7 times more tolerant to Cu compared to the common lab strain applied in the bench-scale tests. Further, exponential growth phase L. pneumophila were found to be 2.5 times more susceptible to Cu relative to early stationary phase cultures. It is important to also recognize that, in the pilot-scale systems, drinking water biofilms and the amoeba hosts that colonize them can further shield Legionella from the antimicrobial effects of Cu. Application of shotgun metagenomic sequencing offered the opportunity to more deeply examine the response of Legionella and other pathogens to Cu dosed to the pilot-scale hot water systems in the context of the broader microbiome. It was found that metagenomic analysis provided a sensitive indication of the bioavailability of Cu to the broader microbial community inhabiting the hot water systems, further confirming that the outbreak-associated strain of Legionella that colonized the rigs was relatively tolerant of Cu. Functional gene analysis provided further insight into the mechanistic action of Cu, suggesting multi-modal action of both membrane damage and interruption of nucleic acid replication. The metagenomic analysis further revealed that protozoan host numbers tended to increase in the pilot-scale systems with time, and this could also increase the potential for Legionella proliferation with time. Additional pure culture studies aiming to further assess the mechanistic action of Cu provided strong evidence that Cu can induce a VBNC state for Legionella. This is a concern, given that other studies have indicated that VBNC Legionella are still capable of causing legionellosis. However, VBNC cells are not detected by conventional culturing. Multiple lines of evidence supported the conclusion that Cu induced a VBNC state for Legionella, including membrane integrity, enzyme activity, ATP generation, and Amoebae resuscitation assays applied to two different strains of L. pneumophila. After exposure to Cu, up to a 5-log (99.999%) reduction in culturable Legionella was observed, whereas corresponding reductions in the various viability measures were only by <1-log (90%). In other words, conventional culturing may miss up to 99.99% of the Legionella that is still capable of causing disease. To our knowledge, this is the first study that has assessed the potential for Cu-induced VBNC Legionella. Additional research is needed to further quantify the contribution of VBNC status to challenges in effective Cu-based control of Legionella in premise plumbing. This research further examines, for the first time, the proteomic response of Legionella to Cu, comparing both presumably VBNC and culturable cells. Functional annotation of proteins that were differentially produced by the cells in response to Cu addition revealed that VBNC L. pneumophila modulated its proteome to favor cell membrane- and motility-related proteins, while reducing production of other proteins related to primary metabolism compared to culturable cells. These observations are consistent with the metagenomic-based observations and support the hypothesis that Cu inactivates cells by damaging the cell membrane. The findings also confirmed reduced general cell metabolism that is characteristic of a VBNC state. This dissertation highlights the important and complex effects of Cu on Legionella growth in potable water systems as modified by water chemistry, water heater anode type, characteristics of the surrounding microbiome, and Legionella strain characteristics and growth status. The findings raise important questions about how to measure disinfectant efficacy and provide fundamental new knowledge that can help to better optimize the application of Cu as an antimicrobial to drinking water systems and better protect public health. / Doctor of Philosophy / The opportunistic pathogen Legionella is the leading cause of reportable waterborne disease outbreaks in the United States. Legionella is capable of growing in drinking water plumbing systems in homes, hospitals, hotels, and other buildings. Legionella is spread by inhaling tiny droplets of water that are suspended in the air when using the water, for example when showering, resulting in a severe and deadly form of pneumonia called Legionnaires' Disease. Copper is a promising antimicrobial that can be dosed directly into a building's water system by installing a copper-silver ionization system. There is also interest in understanding whether copper released naturally from copper pipes could help control Legionella. However, prior research indicates that copper sometimes inhibits, sometimes has no effect, and sometimes even seems to stimulate Legionella growth. The purpose of this dissertation was to better understand how the chemical properties of the drinking water, such as pH, presence of corrosion inhibitors that are commonly added to the water by utilities, and natural organic matter impact the ability of copper to kill Legionella. Impacts of the design of the drinking water system were also examined, for example, the material used in the anodes of water heaters to prevent corrosive damage to other system components was hypothesized to change the water chemistry in such a way that could also interfere with copper disinfection. Finally, the effect of the strain of Legionella, its growth phase (exponential or stationary), and culturability status (culturable versus viable but non-culturable) was also examined. Experiments were conducted over a wide range of conditions, from bench-scale pure culture experiments of a few days to full-scale plumbing systems over a period of 3.5 years. The complementary approaches maximize the strength of scientific conclusions about approaches that can more effectively control Legionella. Several discoveries were made as a result of this research that can help to improve the use of copper for controlling Legionella in drinking water systems. In particular, it was found that it is best to keep the pH less than 7.5, because above pH 7.5 copper reacts with orthophosphate corrosion inhibitor or natural organic matter in the water in a manner that makes it less potent to microbes. Through disinfection modeling it was found that soluble copper was the best predictor of the ability to kill Legionella. Therefore, it is recommended from this research that practitioners should monitor soluble copper instead of total copper for the purpose of assessing Legionella control. From the pilot-scale experiments, it was further found that the type of anode installed in the water heater can affect the ability of copper to kill Legionella. Magnesium anodes performed the worst, likely because they raised the pH above the recommended level of 7.5. Aluminum anodes were also a problem because aluminum ions released form an aluminum hydroxide gel that can remove more than 80% of the soluble copper from water. Electric powered anodes did not reduce copper antimicrobial effects by raising pH or forming a gel, but they are much less commonly used. A surprising finding throughout this study was that very high levels of copper (>1.2 mg/L) were required to measurably reduce Legionella in the pilot-scale systems. In the pure culture experiments, it was found that the outbreak-associated strain from Quincy, IL, that was inoculated into the system was highly copper tolerant. This demonstrated that the strain of Legionella that colonizes a particular drinking water system could be the reason why copper is sometimes less effective. Pure culture experiments also found that stationary phase Legionella are more difficult to kill than exponential phase Legionella, which could explain some discrepancies among lab studies reported in the literature. A particularly noteworthy discovery of this research was that copper can make it appear as if Legionella have been killed, because the traditional culture media indicate that there is no growth on the Petri dish; however, they are in fact still alive and capable of causing human disease. This is referred to as a "viable but non-culturable (VBNC)" state. The VBNC state of Legionella was confirmed using an array of techniques (membrane integrity, enzyme activity, ATP generation, and amoebae resuscitation) for two strains of L. pneumophila. We also examined how VBNC Legionella cellular functions were impacted by copper using whole cell proteome, i.e., analysis of all of the proteins extracted from Legionella. Copper induced VBNC Legionella modulated its proteome to favor cell membrane and motility related proteins, and reduced others related to primary metabolism compared with culturable cells. These results were consistent with those obtained via shotgun metagenomic analysis of the microbial community DNA in the pilot-scale water systems. Given the potential for VBNC organisms to prevail in systems disinfected with copper, it is recommended to supplement culture-based monitoring with molecular-based monitoring, e.g., with quantitative polymerase chain reaction. This dissertation highlights the important and complex effects of copper on Legionella growth in potable water systems. The findings help to inform guidance on how to improve the antimicrobial effect of copper, through adjusting the water chemistry, selecting appropriate water heater anodes, and optimizing the overall hot water system design. The dissertation also helps to inform improved strategies for monitoring the efficacy of copper for killing Legionella in real-world systems. Overall, the findings can help to improve policy and practice aimed at reducing the incidence of Legionnaires' Disease and protecting public health.

Fungi play important roles in various environments. Some of them infect plants and cause economically important diseases. However, many fungal pathogens cause similar symptoms or are even spread asymptomatically, making it difficult to identify them morphologically. Therefore, culture-independent, sequence-based diagnostic methods that can detect and identify fungi independently of the symptoms that they cause are desirable. Whole genome metagenomic sequencing has the potential to enable rapid diagnosis of plant diseases without culturing pathogens and designing pathogen-specific probes. In my study, the MinION nanopore sequencer, a portable single‐molecule sequencing platform developed by Oxford Nanopore Technologies, was employed to detect the fungus Calonectria pseudonaviculata (Cps), the causal agent of the devastating boxwood blight disease of the popular ornamental boxwood (Buxus spp.). Various DNA extraction methods and computational tools were compared. Detection was sensitive with an extremely low false positive rate for most methods. Therefore, metagenomic sequencing is a promising technology that could be implemented in routine diagnostics of fungal diseases. Other fungi may play important roles in the atmosphere because of their ice nucleation activity (INA). INA is the capacity of some particles to induce ice formation above the temperature that pure water freezes (-38°C). Importantly, INPs affect the ratio of ice crystals to liquid droplets in clouds, which in turn affects Earth's radiation balance and the intensity and frequency of precipitation. A few fungal species can produce ice nucleating particles (INPs) that cause ice formation at temperatures ≥ –10°C and they may be present in clouds. Two such fungal genera are Fusarium and Mortierella but little is known about their INPs and the genetic basis of their INA. In my study, F. avenaceum and M. alpina were examined in detail. INPs of both species were characterized and it was found that strains within both species varied in regards to the strength of INA. Whole genome sequencing and comparative genomic studies were then performed to identify putative INA genes. Differential expression analyses at different growth temperatures were also performed. INP properties of the two species shared similarities, both appearing to consist of secreted aggregates larger than 30 kDa. Low temperatures induced INA in both species. Lists of candidate INA genes were identified based on their presence in the strains with the strongest INA and/or induction of their expression at low temperatures and because they either encode secreted proteins or enzymes that produce other molecules known to have INA in other organisms. These genes can now be characterized further to help identify the fungal INA genes in both species. This can be expected to help increase our understanding of the role of fungal INA in the atmosphere. / Doctor of Philosophy / Fungi are important to life on Earth and play roles in the environments that surround us. On the one hand, fungi can make plants sick and some plant diseases may even cause economic losses to farmers. If the cause of a disease can be identified accurately in an early stage before symptoms develop, disease transmission may be prevented and plants may be protected from disease. However, it is a challenge to find out which fungus causes which disease since symptoms of different fungal diseases look very similar. Typically, we have to wait for plants to become very sick or we have to isolate the fungus that causes a disease to identity it, which may be time-consuming and not lead to precise identification. DNA sequencing technologies have the potential to lead to more sensitive, faster, and more accurate disease diagnosis and, therefore, may help prevent disease outbreaks. In my study, the MinION nanopore sequencer, a small portable device, was used to detect the fungus causing boxwood blight on boxwood. By loading the DNA of unhealthy boxwood on the device, the boxwood blight pathogen was identified within a very short time. Thus, this method is a promising diagnostic method that may be applied to detect other plant fungal diseases as well. On the other hand, fungi may affect Earth's climate by affecting how many water droplets in clouds are frozen, which in turn affects Earth's temperature and how often and how much it rains and snows. Fungi may affect the freezing of water droplets in clouds since some of them have ice nucleation activity (INA), which is the capacity to catalyze ice formation at a higher temperature than the temperature at which pure water freezes (-38°C), and they may be present in clouds. So far, INA has only been found in a few fungi, including the species Fusarium avenaceum and Mortierella alpina, but the mechanism of their INA is poorly understood. In my study, multiple F. avenaceum and M. alpina strains were examined in detail. Two approaches were used. First, strains in each species were compared with each other to find out how strong their INA is. Once it was found that they differed in their strength of INA, their genomes were sequenced and compared to find genes present in the most active strains and missing from the least active strains since it is these genes that may contribute to INA. It was also found that both fungal species had stronger INA when they were grown at lower temperatures. Therefore, the expression of their genes between higher and lower temperatures was compared to find the genes that were more highly expressed at lower temperatures since it is these genes that may cause INA. Based on previous studies, fungal INPs may either consist of secreted proteins or be the products of biosynthetic gene clusters. Therefore, the list of potential genes was reduced by looking for genes encoding either secreted proteins or biosynthetic gene clusters. The list of these potential INA genes will make it easier to identify the INA genes in F. avenaceum and M. alpina and determine the role of fungi in affecting the weather and climate on Earth.

Plant pathogens are one of the biggest threats to plant health and food security worldwide. To effectively contain plant disease outbreaks, classification and precise identification of pathogens is crucial to determine treatment and preventive measurements. Conventional methods of detection such as PCR may not be sufficient when the pathogen in question is unknown. Advances in sequencing technology have made it possible to sequence entire genomes and metagenomes in real-time and at a relatively low cost, opening an opportunity for the development of alternative methods for detection of novel and unknown plant pathogens. Within this dissertation, an integrated approach is used to reclassify a high-impact group of plant pathogens. Additionally, the application of metagenomics and nanopore sequencing using the Oxford Nanopore Technologies (ONT) MinION for fungal and bacterial plant pathogen detection and precise identification are demonstrated. To improve the classification of the strains belonging to the Ralstonia solanacearum species complex (RSSC), we performed a meta-analysis using a comparative genomics and a reverse ecology approach to accurately portray and refine the understanding of the diversity and evolution of the RSSC. The groups identified by these approaches were circumscribed and made publicly available through the LINbase web server so future isolates can be properly classified. To develop a culture-free detection method of plant pathogens, we used metagenomes of various plants and long-read nanopore sequencing to precisely identify plant pathogens to the strain-level and performed phylogenetic analysis with SNP resolution. In the first paper, we used tomato plants to demonstrate the detection power of bacterial plant pathogens. We compared bioinformatics tools for detection at the strain-level using reads and assemblies. In the second paper, we used a read-based approach to test the feasibility of the methodology to precisely detect the fungal pathogen causing boxwood blight. Lastly, with the improvement in nanopore sequencing, we used grapevine petioles to investigate whether we can go beyond detection and identification and do a phylogenetic analysis. We assembled a metagenome-assembled genome (MAG) of almost the same quality as the genomes obtained from cultured isolates and did a phylogenetic analysis with SNP resolution. Finally, for the cases where there may be no related genome in the database like the pathogen in question, we used machine learning and metagenomics to develop a reference-free approach to detection of plant diseases. We trained eight different machine learning models with reads from healthy and infected plant metagenomes and compared the classification accuracy of reads as belonging to a healthy or infected plant. From the comparison, random forest was the best model in terms of computational resources needed while maintaining a high accuracy (> 0.90). / Doctor of Philosophy / Microbes are present in every environment on the planet and have been on Earth for billions of years. While some microbes are beneficial, others can cause diseases. To differentiate the ones causing diseases from those who do not, looking into the evolutionary forces making them different is crucial to classify and identify them correctly. Although microorganisms cause diseases in humans and animals, the ones causing diseases in plants are one of the biggest threats to plant health and food security worldwide. In a perfect world, plant diseases would be diagnosed by eye or simple procedures. However, when a plant disease is present, it is not always obvious which organism, if any, is causing the disease making it hard for outbreaks to be detected and contained promptly. With technological advances, it is now possible to obtain all the genetic information of not only one organism but all the organisms living in an environment at a time. This genetic information can then be used to precisely identify what organism is causing a disease in a plant for faster disease diagnosis and, consequently, more efficient disease prevention and control. In this dissertation, we used the bacterial group, called Ralstonia solanacearum species complex, which can cause different diseases in more than 200 crops, to investigate and understand the evolution and diversity of the members of this group. We also used newly developed technologies to obtain the genetic material of all the organisms living in multiple important plants including tomato, grapevine, and the ornamental bush, boxwood. Using this genetic material, we developed a methodology for the detection of bacteria and a fungus causing plant diseases. While this works well when the suspected organism or a similar one is available for comparison, the detection of plant diseases in cases where this information is not available is challenging. Machine learning models, where computers can learn complex patterns from data, have the potential to detect pathogens without the need to compare the sequences to sequences of other pathogens. Here we also used the genetic material to train and compare different machine learning models to classify plants as either being infected or healthy.