Waterborne pathogen presence caused by fecal pollution is a leading cause of morbidity and mortality worldwide. In developed countries, this problem can result in waterborne outbreaks. Research suggests that there is a need for better fecal indicators because current methods (total coliforms and E. coli) are insufficient. This study investigated Bacteroidales 16S rRNA markers as a microbial source tracking tool in an agricultural watershed. Correlations between pathogens and markers were also investigated. Water quality monitoring was conducted following assay validation of ruminant-, bovine-, human-specific, and universal Bacteroidales markers. Results revealed a positive relationship between E. coli and the universal marker. Ruminant- and bovine-specific marker detection was associated with increased runoff due to precipitation; however, the human associated marker was not detected. Furthermore, no correlations between Campylobacter, Salmonella, or E. coli O157:H7 could be made. Consequently, these techniques have potential to become a powerful tool; however, further research is needed

A DNA microarray capable of detecting 445 virulence factors (VFs) and antibiotic resistance genes was used to assess human and animal fecal E. coli isolates for pathogenic potential and host specificity. The only enteric pathotype detected was atypical EPEC, which was found in 3.7% of all isolates. 17% of human isolates were extraintestinal pathotypes, with the majority of these being uropathogenic. Isolates from humans and chickens were the most likely to have resistance to at least one class of antibiotic. VFs that were found almost exclusively in human isolates, when compared to one other group, included sat (10% of human isolates and no animal isolates), iucD and iut (24% of human, <1% mammal) and iha (16% human, <1% wild avian). Decision trees utilizing multiple probes to identify the source of an E. coli isolate were able to correctly identify the source of 79% of validation isolates in a human vs. animal comparison. / Environment Canada

The City of Georgetown, Kentucky relies on a vast karst spring network as a drinking water source. This karst feature has several inputs from sinkholes and streams in the Cane Run Watershed: a watershed associated with a variety of land uses in the recharge area. The recharge area encompasses the area from North Lexington to Georgetown and is composed of urban, suburban, agricultural and industrial usage. A serious water quality issue exists with respect to the impact of fecal contamination within the spring recharge area. Identification of fecal contamination is quantified by microbial indicators adapted from surface water applications: fecal load (E. coli), fecal source (two human-host specific Bacteroides DNA markers) and fecal age (AC/TC ratio). These three criteria are used in a categorical Microbial Source Tracking (MST) model to assign a Sanitary Category Value (SCV) between 0 and 3 for each sample location. Low SCVs (1.5) are associated with high values of fecal load, low fecal age and detectable concentration of human-specific markers. SCV measured during dry weather conditions are indicative of potentially leaking human sewers. Due to retention and conservation of fecal load (E. coli) and age (AC/TC) microbial indicators in the karstic environment, ambiguous SCV model results cannot pinpoint, with statistical confidence, fecal sources in a karstic environment. Human-host specific genetic markers (HF183 and HuBac) were also detected at all sample sites above limits of detection, indicating steady inflow of fecal material during all sample events. By adding a flow multiplier and expressing HF183 and HuBac values as a load, it was strongly indicated that a human fecal source was entering the groundwater conduit and impacting Royal Spring independent from other upstream fecal sources. Interpretation of these trends, while strongly indicated, cannot be supported with statistical evidence.

京都大学 / 0048 / 新制・課程博士 / 博士(工学) / 甲第22762号 / 工博第4761号 / 新制||工||1745(附属図書館) / 京都大学大学院工学研究科都市環境工学専攻 / (主査)教授 田中 宏明, 教授 米田 稔, 准教授 松田 知成 / 学位規則第4条第1項該当 / Doctor of Philosophy (Engineering) / Kyoto University / DFAM

Microbial Source Tracking (MST) is a field of study that attempts to identify the source of fecal contamination in waterways in order to assist with development of remediation strategies. Biologists at Cal Poly Center for Applications in Biotechnology (CAB) are developing a new MST method using microbes from the genus Bacteroides. Bacteroides species are host-specific microorganisms that can theoretically be used to trace back to a single host species. After fecal samples are collected, biologists use Next-Generation Sequencing (NGS) techniques to obtain only the genetic sequences of microorganisms belonging to the phylum Bacteroidetes. Investigators hypothesize that similar sequences belong to the same phlyogenetic group (i.e., the same genus) and can therefore be computationally clustered. Each cluster of related sequences, typically 97% similar, is called an Operational Taxonomic Unit (OTU). Theoretically, an OTU acts as a molecular signature that can be traced back to a specific host genus. This thesis presents LOTUS, the Library of OTUs, a web-based computational tool for the preliminary investigation of the use of the Bacteroides OTU library as an MST method. This work discusses the four contributions of LOTUS: a database design which accurately models OTUs and the underlying relationships necessary for source tracking, a pipeline to create OTUs from raw sequencing reads, a method of assigning taxonomy to OTUs, and a web-based user interface. In preliminary testing for a reference library of twelve samples, LOTUS produced 1,431 OTUs, of which 891 were single-source (OTUs derived from sequences from a single host species). Using these OTUs, LOTUS was able to accurately taxonomically match four of five unknown test samples, showing promise for using OTUs as an MST method.

Prince William County, located in the rapidly developing Northern Virginia region, contains watersheds of mixed rural and urban/suburban uses. As part of Virginia regulations, recreational waters must be tested and remain under a certain standard for levels of fecal indicator bacteria (FIB). The sources of fecal pollution in neighboring watersheds within the county were determined over the 12 months previous to this project by performing Antibiotic Resistance Analysis (ARA, a microbial source tracking protocol) on Enterococcus and Escherichia coli (E. coli). This study indicated that multiple sources of pollution were present at all sampling locations and that the dominant sources of contamination were related to the land-use patterns and human activities that were adjacent to each location. The goal of the current project was to monitor and identify the sources of fecal pollution in eight streams in the Occoquan Basin (OQB) that have been classified as impaired waters due to high E. coli concentrations. Project objectives were i) employ microbial source tracking technology to identify the categories of sources that were responsible for the bacterial impairments; ii) develop and analyze appropriate Known Source Libraries (KSL's) to determine the best design for identifying the sources of water-sample isolates; and iii) evaluate the use of optical brighteners in freshwater by fluorometry as an indicator for human-origin pollution. One site on each of six streams and two sites on the remaining two (ten total) were selected for E. coli and Enterococcus monitoring and microbial source tracking. Repeated sampling of the ten locations for thirteen months assessed the concentrations of the bacteria over time, while comparison of monthly bacterial concentrations to the U.S. standards was used to verify the impaired water designation. Three thousand, four hundred and eighty-eight Enterococcus and 969 E. coli water-sample isolates were collected and evaluated to determine their sources. These isolates were compared to several known source libraries (KSL's) comprised of host-origin isolates collected from the Northern Virginia region. Linear discriminant analysis (LDA) using a KSL of unique isolates determined wildlife were the dominant source of fecal pollution. Results based on ARA were cross-validated through fluorometry of the water samples (to detect optical brighteners in detergents as human-derived pollution) and pulsed-field gel electrophoresis (PFGE, a DNA fingerprinting technique) of select E. coli isolates. In order to determine the best method to classify the water-sample isolates, variation in antibiotic resistance data representation, known source isolate inclusion, and LDA processing were compared. The KSL that used the most antibiotic resistance datapoints, contained no conflicting data, and performed most of the parameters associated with standard LDA, classified water-sample isolates the most successfully. This project involves the first thorough testing of fluorometry for the detection of human signatures in freshwaters. Monitoring results showed consistent Enterococcus and E. coli contamination in all eight streams, demonstrating that each had been correctly placed on Virginia's impaired waters list by state regulatory agencies. Counts between Enterococcus and E. coli did not correlate well, although concentrations of both indicator organisms were higher during dry months. Source tracking results determined a dominant wildlife signature at all sites. Few Enterococcus water-source isolates were classified as human and fluorescence at all sites was consistently low. KSL's with antibiotic resistance data represented as binary values classified isolates the best. Removal of conflicting isolates improved the KSL's rate of correct classification (RCC). Creation of an unknown category, clustering of the KSL, and only accepting results above a threshold did not appreciably improve the RCC. The KSL with the binary representation was not used to classify isolates because it violates the normal distribution assumption of LDA. Differences in the results of Enterococcus and E. coli source classifications indicated that contributing sources vary in frequency. Human fecal matter was shown to be of little concern because both Enterococcus ARA and fluorometry indicated low presence. The positive predictive value (PPV) statistic was found to be preferable to the minimum detectable percentage (MDP) because it does not depend on KSL size. Establishing confidence intervals to determine completeness of KSL allows one to determine whether particular methods to refine the KSL will be helpful. This project was successfully completed and the monitored streams were correctly identified by state authorities as impaired waters. Source tracking results often conflicted, although wildlife and pets were indicated as the major sources of impairment by ARA. More local source samples need to be taken to verify this result. The best ARA library design used only unique isolates, all pattern data points, and removed conflicting isolates. Continuing examination of the representation of library data as binary is necessary to determine whether the statistical assumptions in LDA prevent meaningful results. Evaluation of fluorometry was partially successful as the absence of "hotspots" of high fluorescent brighteners agreed with ARA results that indicated little contamination form human sources. The fluorometer continues to have potential as a metric of waste in freshwater although more work needs to be done to fully prove its utility. / Master of Science

Prince William County, located in the rapidly developing Northern Virginia region, contains watersheds of mixed rural and urban/suburban uses. The project goal was to monitor and evaluate 21 stream locations, over 13 months, in the Occoquan Basin identified as impaired due to high E. coli densities. One site on each of eight streams, two sites on each of five streams, and three sites on the remaining stream were chosen for E. coli monitoring and microbial source tracking (MST). MST was performed using antibiotic resistance analysis (ARA) and fluorometric analysis. Escherichia coli was chosen as the indicator bacterium for purposes of comparison with previous project data and because a large body of evidence supports its use in freshwater systems. This study involved the only known MST project to incorporate data from five or more consecutive years. A total of 2854 environmental isolates were collected for analysis with ARA. These isolates were classified using a known source library (KSL) that consisted of 1003 unique resistance patterns. The resistance patterns of the KSL came from known fecal sources (human, pets, livestock, wildlife) in Prince William County. The KSL included isolates from previous years but was also updated with fresh isolates. The accuracy of the KSL was assessed through the use of a challenge set. The challenge set was classified against the KSL using discriminant analysis, verified by logistic regression. The average rate of correct classification was 93% for discriminant analysis and 96% for logistic regression. Results indicated that multiple sources of contamination were present at all sampling locations and that the major source(s) (human, pets, livestock, wildlife) of contamination were generally related to the land-use patterns and human activities at each location. Although no major or minor human signatures were found, all but two locations had either pet or livestock as the major signature, suggesting that human-related activities are playing a key role in contamination of the streams. Pets were the single most frequent major signature and wildlife was the most common minor signature. Fluorometric analysis was used to corroborate human-derived contamination. Fluorometric analysis has the ability to detect the presence of optical brighteners, synthetic compounds added to such household items as laundry detergent, dishwashing detergent and other washing agents. Despite having an undesirably high rate of false negatives (negative fluorometry readings not supported by ARA), fluorometric analysis maintained a low rate of false positives (positive fluorometry readings not supported by ARA) and continued to demonstrate its potential for source tracking. This project represented one of the first attempts at applying a full suite of performance criteria now recommended by the source tracking community for all MST projects. Such concepts as experimental design, toolbox approach, minimum detectable percentage, quantification, accuracy, specificity, robustness, range of applicability, and practicality were successfully incorporated. These performance criteria have in effect set a new standard to which all subsequent MST projects should adhere. / Master of Science

E. coli and Enterococcus were used to determine the fate and survival of fecal indicator bacteria (FIB) in sand and sediments. The microbial source tracking (MST) methods antibiotic resistance analysis (ARA), Bacteroides human-specific primer test, and fluorometry were compared against the FIBs to determine how reliable each method was in detecting the presence of human fecal contamination. Two phases (Summer 2009 and 2010) were evaluated based on the type of contamination event. A combined sewage overflow (CSO) event was simulated in Phase I, where large amounts of influent were added to sand and bay water columns over 1 to 4 days. In 2010, a low volume sewage leak was simulated in which smaller doses of influent were added to sand and bay water columns over a period of 5 to 15 days. Within each of the phases, both non- and re-circulated columns were also evaluated. Evaluation of FIB survival indicated that Enterococcus was able to stabilize and re-grow in the water and at the sediment/water interface within the Phase I non-circulated columns. E. coli was unable to re-grow and/or stabilize within any environment. Comparisons between the ARA and the FIBs revealed a large majority of isolates identified as coming from either bird or wildlife sources. Human sources were identified but at much lower concentrations than expected. Bacteroides results indicated strong relationships between the increase of FIB concentrations and the presence of the human-specific Bacteroides. Fluorometry results did not indicate any relationship with the FIBs. Unexpectedly, fluorometry readings increased as time progressed indicating that another compound was present that fluoresced at the same wavelength as optical brighteners (OBs). This project was one of the first to study the differences related to two different pollution events (CSO vs. sewage leak) while also evaluating what happens to pollution as it settles into the sediment. It was also unique because it compared bacterial (ARA), molecular (Bacteroides), and chemical (fluorometry) MST methods. / Master of Science

The microbial source tracking (MST) methods of antibiotic resistance analysis (ARA) and fluorometry (to detect optical brighteners in detergents) were used in the summers of 2004 and 2005 to determine the origins of fecal pollution at beaches with a past history of, or the potential for, high enterococci counts and posted advisories. At Hilton and Anderson beaches, ARA and fluorometry in the summer of 2004 detected substantial human-origin pollution in locations producing consistently high counts of Enterococcus spp. Investigations by municipal officials led to the fluorometric detection and subsequent repair of sewage infrastructure problems at both beaches. The success of these mitigation efforts was confirmed during the summer of 2005 using ARA and fluorometry, with the results cross-validated by pulsed-field gel electrophoresis (PFGE). Results at other beaches indicated that birds and/or wildlife were largely responsible for elevated enterococci levels during 2004 and 2005. The application of fluorometry proved difficult in opens waters due to high levels of dilution, but showed potential for use in storm drains. An additional study developed and tested a new library-based MST approach based on the pattern of DNA band lengths produced by the amplification of the 16S-23S rDNA intergenic spacer region, and subsequent digestion using the restriction endonuclease MboI. Initial results from small known-source libraries yielded high average rates of correct classification (ARCC). However, an increase in the library size was accompanied by a reduction in the ARCC of the library and the method was deemed unsuccessful, and unsuitable for field application. A final study focused on the potential for classification bias with disproportionate source category sizes using discriminant analysis (DA), logistic regression (LR), and k-nearest neighbor (K-NN) statistical classification algorithms. Findings indicated that DA was the most robust algorithm for use with source category imbalance when measuring both correct and incorrect classification rates. Conversely k-NN was identified as the most sensitive algorithm to imbalances with the greatest levels of distortion obtained from the highest k values. Conclusions of this project include: 1) application of a validation set, as well as a minimum detectable percentage to known-source libraries aids in accurately assessing the classification power of the library and reducing the false positive identification of contributing fecal sources; 2) the validation of MST results using multiple methods is recommended for field applications; 3) fluorometry displayed potential for detecting optical brighteners as indicators of sewage leaks in storm drains; 4) the digestion of the 16S-23S rDNA intergenic spacer region of Enterococcus spp. using MboI does not provided suitable discriminatory power for use as an MST method; and 5) DA was the least, and k-NN the most, sensitive algorithm to imbalances in the size of source categories in a known-source library. / Ph. D.

With the advent of computers and the information age, vast amounts of data generated in a great deal of science and industry fields require the statisticians to explore further. In particular, statistical and computational problems in biology and medicine have created a new field of bioinformatics, which is attracting more and more statisticians, computer scientists, and biologists. Several procedures have been developed for tracing the source of fecal pollution in water resources based on certain characteristics of certain microorganisms. Use of this collection of techniques has been termed microbial source tracking (MST). Most of the current methods for MST are based on patterns of either phenotypic or genotypic variation in indicator organisms. Studies also suggested that patterns of genotypic variation might be more reliable due to their less association with environmental factors than those of phenotypic variation. Among the genotypic methods for source tracking, fingerprinting via rep-PCR is most common. Thus, identifying the specific pollution sources in contaminated waters based on rep-PCR fingerprinting techniques, viewed as a classification problem, has become an increasingly popular research topic in bioinformatics. In the project, several statistical methods for classification were studied, including linear discriminant analysis, quadratic discriminant analysis, logistic regression, and $k$-nearest-neighbor rules, neural networks and support vector machine. This project report summaries each of these methods and relevant statistical theory. In addition, an application of these methods to a particular set of MST data is presented and comparisons are made.