Since the invention of high-throughput sequencing, the majority of experiments studying bacterial microbiomes have relied on the PCR amplification of all or part of the gene for the 16S rRNA subunit, which serves as a biomarker for identifying and quantifying the various taxa present in a microbiomic sample. Several computational methods exist for analyzing 16S amplicon based metagenomics, but the most commonly used bioinformatics tools are unable to produce quality genus-level or species-level taxonomic calls and may underestimate the degree to which such calls are possible. In this thesis, I have used 16S sequencing data from mock bacterial communities to evaluate the sensitivity and specificity of several bioinformatics pipelines and genomic reference libraries used for microbiome analyses, with a focus on measuring the accuracy of species-level taxonomic assignments of 16S amplicon reads. With the efficacy of these tools established, I then applied them in the analysis of data from two studies into human microbiomes. I evaluated the metagenomics analysis tools Qiime 2, Mothur, PathoScope 2, and Kraken 2, in conjunction with reference libraries from GreenGenes, Silva, Kraken, and RefSeq, using publicly available mock community data from several sources, comprising 137 samples with varied species richness and evenness, several different amplified regions within the 16S gene, and both DNA spike-ins and cDNA from collections of plated cells. PathoScope 2 and Kraken 2, both tools designed for whole genome metagenomics, outperformed Qiime 2 and Mothur, which are theoretically specialized in 16S analyses. I used PathoScope 2 to analyze longitudinal 16S data from infants in Zambia, exploring the maturation of nasopharyngeal microbiomes in healthy infants, establishing a range of typical healthy taxonomic profiles, and identifying dysbiotic patterns which are associated with the development of severe lower respiratory tract infections in early childhood. I used Qiime 2 to analyze 16S data from human subjects in a controlled dietary intervention study with a focus on dietary carbohydrate quality. I correlated alterations in the gut microbiome with various cardiometabolic risk factors, and identified increases in some butyrate-producing bacteria in response to complex carbohydrates. I also constructed a metatranscriptomics pipeline to analyze paired rRNA-depleted RNAseq data. My evaluation of 16S methods should improve 16S amplicon analyses by advocating for the modernization of computational tools; my analysis of infant nasopharyngeal microbiomes lays groundwork for future predictive models for childhood disease and longitudinal microbiomic studies; my analysis of gut microbes illuminates the mechanisms through which bacteria can mediate cardiovascular health. Taken together, the research I present here represents a significant contribution to 16S metagenomics and its application to epidemiology, clinical nutritional science.

Palaeogenetic research on human pathogenic and microbiomic bacteria has been largely restricted to bloodborne pathogens from skeletal tissue and, due to short lengths of degraded ancient DNA, small-scale single loci studies. My thesis has expanded the breadth and depth of palaeomicrobial knowledge via the study of novel specimen types with next-generation technologies. Presented in sandwich thesis format, I discuss genome-scale studies of three previously-unstudied historical pathogens: 19th century Vibrio cholerae (cholera) from an alcohol-preserved intestine from Philadelphia, and medieval Staphylococcus saprophyticus (urinary tract infections) and Gardnerella vaginalis (bacterial vaginosis) from calcified urogenital infections of a Trojan woman. Cholera persists as a dangerous modern disease that was also responsible for severe historic epidemics. My research confirms that 19th century pandemics were caused by an O1 classical strain that may have possessed genomic features that contributed increased virulence. S. saprophyticus and G. vaginalis are opportunistic pathogens of the urogenital microbiome, especially in reproductive-age females. Using very high endogenous DNA content of the calcified infections, I have reconstructed one of the most complete ancient bacterial genomes for S. saprophyticus and coding genome for G. vaginalis. Both ancient pathogens possess most of the virulence and urogenital adaptive genes of modern strains, indicating similar ecological roles for these species in past female health. Finally, I successfully use LLMDA microarray technology (never before utilized for ancient DNA research) to detect ancient pathogens. LLMDA provides an inexpensive and informative alternative to high-throughput sequencing for assessing the metagenomic content of ancient samples. Together, my findings provide a framework emphasizing the need to broadly study past microbiomes in conjunction with specific pathogens. Using molecular data, this work supports anthropological views of infectious disease ecology related to the first epidemiological transition and historical narratives. Taken together with the recent literature on ancient pathogen genomes, my findings indicate that palaeogenome sequences may not necessarily reveal any specific signatures of greater virulence, and interpretations of past diseases must necessarily take into account additional host, environmental, and cultural factors. / Thesis / Doctor of Philosophy (PhD)

Yes / Background. Bacterial bloodstream infection (bBSI) is one of the leading causes of death in critically ill patients and accurate diagnosis is therefore crucial. We here report a 16S metagenomics approach for diagnosing and understanding bBSI. Methodology/Principal Findings. The proof-of-concept was delivered in 75 children (median age 15 months) with severe febrile illness in Burkina Faso. Standard blood culture and malaria testing were conducted at the time of hospital admission. 16S metagenomics testing was done retrospectively and in duplicate on the blood of all patients. Total DNA was extracted from the blood and the V3–V4 regions of the bacterial 16S rRNA genes were amplified by PCR and deep sequenced on an Illumina MiSeq sequencer. Paired reads were curated, taxonomically labeled, and filtered. Blood culture diagnosed bBSI in 12 patients, but this number increased to 22 patients when combining blood culture and 16S metagenomics results. In addition to superior sensitivity compared to standard blood culture, 16S metagenomics revealed important novel insights into the nature of bBSI. Patients with acute malaria or recovering from malaria had a 7-fold higher risk of presenting polymicrobial bloodstream infections compared to patients with no recent malaria diagnosis (p-value = 0.046). Malaria is known to affect epithelial gut function and may thus facilitate bacterial translocation from the intestinal lumen to the blood. Importantly, patients with such polymicrobial blood infections showed a 9-fold higher risk factor for not surviving their febrile illness (p-value = 0.030). Conclusions/Significance. Our data demonstrate that 16S metagenomics is a powerful approach for the diagnosis and understanding of bBSI. This proof-of-concept study also showed that appropriate control samples are crucial to detect background signals due to environmental contamination. / This work was supported by the Flemish Ministry of Sciences (EWI, SOFI project IDIS). / This paper has been subject to a correction. Please see Correction file above.

Metagenomics is a culture-independent framework for deciphering the complexity of biological communities, often with a focus on microbial communities in a specific environment. The applicability of this approach is widespread due to the ubiquity and presence of unculturable microbes in many environments which can only be investigated using culture-independent methods. With advances in DNA sequencing and the introduction of high-throughput sequencing technologies, studying microbial life as communities has become more accessible. However, the breadth of data generated dictates that computational processing steps must be in place to analyze the data. Due to the large diversity in computational and bioinformatic steps possible for metagenomic data, differences in methods of analysis can lead to discordant interpretations of results. The performance of different metagenomics methods must therefore be assessed to establish the effect on the interpretation of results. Taxonomic classification is an integral step in metagenomic analysis and many tools exist for this purpose. To determine which tools are better suited for particular types of metagenomic data, a comparative analysis of performance was conducted for numerous tools. The findings suggest that hybrid programs may have the best performance and warrant further investigation. Programs such as CLARK, KRAKEN, and MEGAN also performed well and are suitable for metagenomic analysis. Utilizing these methods, investigation into the bacterial populations of four freshwater beaches was examined. Bacterial communities in beach waters and sands were more distinct in terms of taxonomic composition than communities in different lakes. Functional capacity was stable between beach habitats, although enrichment of anaerobic and stress related genes in the sand suggests that this is a relatively harsh environment. The detection of sequences belonging to pathogens in the sands of these beaches also has implications for public health and warrants changes in water quality monitoring procedures. / Thesis / Master of Science (MSc)

Residents in rural communities across Canada rely on groundwater as their main drinking water source, but the private maintenance of this source may increase the risk of fecal contamination caused by human or animal wastes. Wainfleet, a rural Ontario community, has been under an active boil water advisory for the past decade. The last study to assess groundwater quality, performed in 2007, determined that half of the 586 groundwater wells contained exceedances in total fecal coliform and E. coli counts. A critical examination of fecal contamination levels and its sources is not only necessary for maintaining public health in the township, but is also an opportunity to examine the robustness of culture-independent methods for quantifying and sourcing fecal contamination in groundwater environments across Canada. For this project, culture-based and culture-independent methods were utilized to quantify and source any fecal contaminants in Wainfleet’s groundwater. Culture counts of fecal indicator bacteria (FIB) suggested that some of the groundwater wells were receiving more fecal contamination than others, as expected based on a previous study that was conducted 10 years prior. The groundwater wells with higher E. coli counts also had higher read counts of microbes like Campylobacterales which could come from septic tanks and higher concentrations of oxidized nitrogen which can also indicate human-based fecal contamination. Finally, fecal contamination in groundwater wells with E. coli tested positive for the human Bacteroidales marker. Taken together, this study shows that fecal contamination pervades groundwater wells across the boil water advisory zone, much of which originates from leaking septic tanks and poorly-constructed groundwater wells. In this study, we have shown that a suite of protocols, from physiochemical quantification to targeted sequencing and qPCR, can be used to complement culture-based assays in quantifying and pinpointing fecal contamination in groundwater sources. / Thesis / Master of Science (MSc) / Boil water advisories are enacted when fecal contamination levels exceed provincial limits. Standard methods for quantifying fecal contamination use the culture-based detection of fecal indicator bacteria. Sequencing the 16S rRNA gene and amplifying Bacteroidales markers can also be used to identify novel fecal markers and quantify host-specific contaminants in source waters. Using culture and genetic-based methods determined that groundwater wells across Wainfleet, a Niagara township with the longest active boil water advisory in Canada, contain septic tank microbes and are primarily contaminated by leaking septic tanks. Genetic-based assays can complement culture-based detection of fecal bacteria in groundwater sources across Canada.

No description available.

Cyanobacterial algal blooms have been increasing in frequency and severity over the past few years in Ontario. Depending on the presence of toxigenic Cyanobacteria, these blooms have the potential to release toxins into the water, posing a public and environmental risk to humans and animals. Although traditional methods of studying Cyanobacteria provide important information regarding the microbial community, metagenomic sequencing allows for a more comprehensive examination of microbial diversity and functional capacities as limitations in cultivating organisms is circumvented. Therefore, to gain insight into the community composition of freshwater blooms and to compare them to non-bloom sites within Ontario, we collaborated with the Ministry of the Environment and Climate Change (MOECC) to undergo a high throughput DNA sequencing approach for a comparative metagenomic analysis. In 2015, 108 bloom and non-bloom samples were collected and sent for 16S rRNA sequencing and a subset of these were chosen for shotgun metagenomic sequencing. Our study focuses on comparing community structure and functional differences that may exist between bloom and non-bloom sites as well as analyzing differences in cyanobacterial communities across bloom sites. Our findings reveal differences in the microbial communities between these two environments. At the functional level, large-scale functionalities were conserved across the two groups but differences in specialized functions were revealed. Overall, our results show that metagenomics is a powerful tool for delineating functional and taxonomic analysis of bloom and non-bloom sites across Ontario. The second part of this work studied the utilization of the molecular marker, Conserved Signature Proteins (CSPs), as a valid method for identifying Cyanobacteria to facilitate the problem of cyanobacterial taxonomic classification. It was found that CSPs proved to be reliable in identifying Cyanobacteria within environmental samples when compared to amplicon and shotgun metagenomic sequencing approaches. / Thesis / Master of Science (MSc)

The mangrove ecosystem is a marginal and complex ecosystem. Mangrove trees can tolerate heat, desiccation, high salinity, radiation, and anoxic conditions. The physiological features of mangroves help them tolerate these stressors, but their relationship with prokaryotic communities also plays a role in a productive mangrove ecosystem, mainly in nutrient cycling and biogeochemical transformation. In Saudi Arabia, a few studies were conducted to understand the microbial communities residing in the mangrove ecosystem. Most of the studies were focused on the sediments or rhizosphere of the most dominant species in the kingdom, Avicennia marina. In this study, the bacterial composition of two mangrove species (Avicennia marina and Rhizophora mucronata) and the relationship between them was explored using next generation amplicon sequencing of the V3-V4 region of the 16S rRNA. In both species, samples from four compartments were collected (sediments, rhizosphere, roots, and leaves). Both species had a similar microbial composition, with Proteobacteria and Chloroflexi being the most dominant phyla in all compartments. The lack of difference in alpha diversity measures (number of ASVs and Shannon-diversity index) between species highlights the symbiotic relationship between the trees. Previous studies have reported that A. marina has a more diverse microbial community than R. mucronata, however this difference was not significant in our samples. The multivariate analysis showed us that the microbial composition of the leaf and root samples was grouped separately from the microbial composition of sediment and rhizosphere samples, highlighting the specific microbial composition of each compartment. In addition, the enriched strains in each cluster were explored and related to the surrounding environment of the mangrove ecosystem, followed by the exploration of unique strains in each compartment using SIMPER analysis. In conclusion, this study provides the first information on the Red Sea Northern mangrove (Al-Wajh region) tree microbiomes, encompassing roots, leaves, rhizosphere, and sediments. Furthermore, by showing that some bacteria can colonize different plant compartments we contribute to disentangling their propagation channels within plants.

The human gut is colonized by a vast bacterial community that is currently rather well characterized at the species level. Yet, each of these species harbor a tremendous amount of individual genetic variations. Our understanding of the human gut microbiome, its dynamics, composition and impact on host health requires a deeper characterization of its bacteria. The amount of publicly available shotgun sequencing data as well as development of computational tools allowed to reach strain-level resolution in metagenomic analysis. In this thesis, I present systematic approaches to study the strain-level variation using complementary phylogenetic and pangenomic methods aiming to address fundamental questions about microbiome transmission in early life as well as impact of functions encoded by microbiome strains on host health. Across two different cohorts, I used a recently-developed strain-tracking method to assess the impact of delivery conditions on the initial seeding of the infant gut microbiome. While mode of delivery (vaginal or C-section) had a great impact on the amount of mother strains transmitted to the infant, place of delivery (home or hospital) and breastfeeding duration also had an impact on the ongoing development, strain replacement or persistence over the first year of life. In comparison, the father appeared as a stable source of strains independent of the delivery mode. This initial mother seeding, despite being reduced in C-section delivery, can be compensated by Fecal Microbiota Transfer, demonstrating the need of fecal microbiota exposure in seeding during vaginal delivery. In addition, strain dynamics was shown partially explained by differences in the carbohydrates degrading capacities, especially the ability to feed on Human Milk Oligosaccharides. These differences in metabolism between strains were also observed by their respective empirical growth rate that was seen associated with transmission and persistence in the infant gut. To further systematically assess the differences of metabolic capacities between strains and the impact on hosts, I developed a new method to identify gene groups (PanPhlAn Genomic Islands, PGIs) co-present across conspecific strains in metagenomic samples. By applying this method on a large collection of over 10,000 samples, I was able to build a set of 5,315 PGIs. Deeper characterization of these PGIs revealed horizontal gene transfer across species, high variation in carbohydrate metabolism capacities and association with the host lifestyle and health status. Together, these analyses demonstrated the complementary aspects of strain variation andstressed out the need to encompass both strain phylogeny and gene content to fully understand the microbiome at the strain-level.

Antibiotic resistance is currently a global threat spanning clinical, environmental, and geopolitical research domains. The environment is increasingly recognized as a key node in the spread of antibiotic resistance genes (ARGs), which confer antibiotic resistance to bacteria. Detecting ARGs in the environment is the first step in monitoring and controlling antibiotic resistance. In recent years, next-generation sequencing of environmental samples (metagenomic sequencing data) has become a prolific tool for the field of surveillance. Metagenomic data are nucleic acid sequences, or nucleotides, of environmental samples. Metagenomic sequencing data has been used over the years to detect and analyze ARGs. An intriguing instance of ARGs is the multi-functional ARG, where one ARG encodes two or more different antibiotic resistance functions. Multi-functional ARGs provide resistance to two or more antibiotics, thus should have evolutionary advantage over ARGs with resistance to single antibiotic. However, there is no tool readily available to detect these multi-functional ARGs in metagenomic data. In this study, we develop a computational pipeline to detect multi-functional ARGs in metagenomic data. The pipeline takes raw metagenomic data as the input and generates a list of potential multi-functional ARGs. A plot for each potential multi-functional ARG is also created, showing the location of the multi-functionalities in the sequence and the sequencing coverage level. We collected samples from three different sources: influent samples of a wastewater treatment plant, hospital wastewater samples, and reclaimed water samples, ran the pipeline, and identified 19, 57, and 8 potentially bi-functional ARGs in each source, respectively. Manual inspection of the results identified three most likely bi-functional ARGs. Interestingly, one bi-functional ARG, encoding both aminoglycoside and tetracycline resistance, appeared in all three data sets, indicating its prevalence in different environments. As the amount of antibiotics keeps increasing in the environment, multi-functional ARGs might become more and more common. The pipeline will be a useful computational tool for initial screening and identification of multi-functional ARGs in metagenomic data. / Master of Science / Antibiotics are the drug to fight against the infection of bacteria. Since the first antibiotic was discovered in 1928, many antibiotic drugs have been developed. At the same time, scientists discovered many genes responsible for the resistance of antibiotic drugs. Nowadays, antibiotic resistance is a global threat. Detecting antibiotic resistance genes in the environment is the first step toward monitoring and controlling antibiotic resistance. In recent years, next-generation sequencing has been widely used to get the DNA sequence from the environment. Metagenomics analysis has been used over the years to detect and analyze ARGs. In the literature, it has been reported that a single gene could carry two parts of sequences corresponding to two different ARGs, thus conferring resistance to two different antibiotics. This fusion might have some evolutionary advantages. In this study, we developed a novel computational tool to detect multi-functional ARGs. We collected data from three sources: the treatment plant water, the hospital wastewater, and the reclaimed water, and identified 19, 57, and 8 potential bi-functional ARGs in each source, respectively. After we manually inspected the result, we found three most likely bi-functional ARGs. We also found one bi-functional ARG that appears in all three datasets. The gene is responsible for aminoglycoside and tetracycline resistance. The tool will serve as the initial screening step to detect multi-functional ARGs.