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Disease-associated changes to the gastrointestinal (GI) microbiome have been detected in a variety of chronic human illnesses. Currently, GI microorganisms are thought to play a major role in systemic health and homeostasis through interactions with the immune system of their host. In recent years, the available technology for capturing and characterizing the GI microbiome has expanded greatly, resulting in a rapid expansion of the field. In particular, culture-independent technologies allowing for direct analysis of microbial genes, transcripts, proteins, and other metabolites (collectively referred to as meta-omics) from stool samples show potential for personalized disease detection and monitoring through non-invasive screening. However, due to the high degree of variability inherent in microbiome profiles, establishing a consensus for microbial signatures or biomarkers of disease across studies is difficult. Differences in study methodology can further complicate comparisons, since the laboratory protocols for sequence-based data capture and analysis are varied, and there are several commercial kits and reagents available for the storage and isolation of microbial DNA and RNA from stool for downstream microbiome profiling. Research has shown that the choice of nucleic acid extraction kit and storage method can affect resulting nucleic acid quality. In the current methodological study, a single stool sample from a healthy donor is divided and processed using multiple commercially available methods for nucleic acid stabilization and isolation in order to evaluate the effect of processing on observed sequence-based microbiome profiles. The research herein demonstrates that the experimental methodology used to stabilize and isolate nucleic acids from human stool samples can significantly impact the ability to capture GI microbiome diversity from metagenomic and metatranscriptomic data. Notably, GI microbiome characteristics commonly used as health markers in disease

Helicobacter pylori is a Gram-negative, flagellate, microaerophilic member of the Epsilonproteobacteria, with a spiral or curved bacilli morphology (Marshall and Warren 1984). The bacterium resides in the human stomach, and infections can span decades, often going undetected due to minor pathogenesis (Kusters et al. 2006). Infections frequency is higher in the Global South, and is associated with low socioeconomic status, education, remoteness, and inaccessibility to healthcare (Hooi et al. 2017; Bruce and Maaroos 2008). This infection is a public health concern because H. pylori infection increases the risk of adenocarcinoma of the gastric mucosa and gastric B-cell mucosal-associated lymphoid tissue (MALT) lymphoma, as well as peptic ulcer disease (Haley and Gaddy 2015). Indigenous communities in Canada experience increased rates of H. pylori infection, which is associated with poverty, crowded living conditions, and inaccessible health care. Infection is a concern for Indigenous communities given the bacterium's implication in the development of associated cancers, which is a severe health burden that too disproportionately affects Indigenous peoples. This project builds upon ongoing community-driven research by the Canadian North H. pylori (CANHelp) Working Group investigating the impact of H. pylori infection. In this research project we present the sequence H. pylori genomes of 57 CANHelp isolates sampled from the Western Canadian Arctic communities of Aklavik, Inuvik, Old Crow, Ross River, Teslin, and Fort McPherson and a comparator cohort of 73 southern Manitoban isolates. I developed and implemented a contamination detection pipeline to ensure only H. pylori sequence reads were used in genome assembly. The genomes were analyzed for their presence of: known markers associated with antimicrobial resistance to clarithromycin, metronidazole, levofloxacin, and amoxicillin; virulence factors like cagA, the cagPI, vacA, outer membrane proteins such as babA, and the

Background: Over 296 million people worldwide are living with chronic hepatitis B (CHB) infection who require monitoring of viral activity and disease progression. Serum HBV RNA is a promising, although poorly characterized in its encapsidated form, new biomarker in CHB management. No standardized method for serum HBV RNA quantification has been established. Aims: The project aims to characterize the HBV serum transcriptome in order to better understand its composition and to develop and validate, both analytically and clinically, a 3′ RACE RT-qPCR assay for quantification of relevant transcripts within serum HBV RNA. Methods: Nanopore long read sequencing and Northern blotting were employed to characterize the HBV RNA in the serum of 13 patients in different phases of CHB. Sequencing data analysis was done using three isoform detection workflows. A 3′ RACE RT-qPCR method was developed using published primers for the most relevant RNA species determined by serum HBV RNA characterization. The analytical limit of detection and quantification, linearity, inter- and intra-assay repeatability, and clinical specificity and sensitivity were evaluated using synthetic pre-genomic RNA (pgRNA) and specimens from various patient populations. Intra- and inter-laboratory ring trials involving three laboratories were completed. Results: Long read sequencing found higher proportions of spliced variants in patients with HBV genotype B and C, and in HBeAg positive patients with ALT ≤ ULN (upper limit of normal). All chosen isoform detection workflows showed high agreement in most samples, and pgRNA was the most abundant isoform in most patients. Northern blotting was ineffective at detecting serum HBV RNA. The 3′ RACE RT-qPCR assay performed well during analytical and clinical validation and inter- and intra-laboratory analysis demonstrated moderate to high agreement among participants. Conclusions: Long read sequencing is a promising tool for the characterization of the serum HBV tr

Measles virus (MeV) is a highly infectious virus and the causative agent of measles disease. Despite the availability of a safe and cost-effective vaccine, measles is one of the world-leading causes of death in young children. Endemic circulation of the Measles virus in Canada has been interrupted since 1998 and all cases result from importation from endemic areas. The ongoing global elimination efforts have contributed to the decreasing diversity of MeV genome, creating difficulty in distinguishing independent importation of MeV from local transmission when relying only on the WHO recommended genotyping targets, the 450nt region of the C-terminus of the N gene, and the additional 1854nt H gene. Traceability of measles cases in outbreaks is essential to document MeV elimination status. A higher-resolution genotyping method or MeV whole genome sequencing (WGS) has been advocated by the World Health Organization (WHO) as the approach needed to overcome the current difficulty in resolving the decreasing diversity of MeV isolates. I applied WGS by Sanger and Illumina next generation sequencing (NGS) and extended genotyping to several Canadian and Brazilian outbreaks from 2011 to 2015, with the purposes of developing a WGS method that could be applied directly and efficiently to clinical specimens, sequencing the entire genome of MeV isolates from recent Canadian and international outbreaks, to determine the rate of variation of MeV genomes during transmission, identifying hypervariable genome regions and their ability to serve as a surrogate of WGS for distinguishing MeV isolates, establishing the minimum number of substitutions that could distinguish direct transmission from independent importations of similar viruses, and calculating the clock rate of MeV during transmission by using the dates of rash onset of the outbreak isolates and developing a strategy with Bayesian Evolutionary Analysis of Sampling Trees (BEAST) to distinguish chains of transmissions from endemi

Haemophilus influenzae is a human pathogen that can cause disease in young children and the elderly. While there are several typing methods used for H. influenzae, serotyping and multi locus sequence typing (MLST) are the two most commonly used methods. The antigenic properties of the polysaccharide capsule surrounding some H. influenzae are used to classify the encapsulated strains into six serotypes (a-f), whereas non-encapsulated strains are considered non-typeable (NTHi). Historically, H. influenzae serotype b (Hib) has been the leading cause of morbidity and mortality worldwide. The introduction of a Hib conjugate vaccine in the 1990s drastically reduced the incidence of Hib disease. In the following years however, serotype f (Hif) has emerged as the most dominant serotype in the general population while serotype a (Hia) has emerged in the indigenous populations of North America. Since the Hib vaccine does not protect against non-Hib strains, the rising rates of disease warrants investigation into the development of vaccines for other H. influenzae serotypes. Developing an effective vaccine for serotypeable strains requires an understanding of its population structure; however, the population structure of H. influenzae is currently unclear. Although the 7-gene MLST is commonly used in laboratories worldwide, advances in genome sequencing can be used to provide a vastly more detailed understanding of the population structure of H. influenzae. This study investigates the utility of a core genome MLST scheme (cgMLST) as a potential extended MLST scheme for H. influenzae typing. A total of 314 genomes were used to design a cgMLST schema. Minimum spanning trees were generated based on the 7-gene MLST, the ribosomal protein MLST (rMLST) and cgMLST schemas, and all three schemas were evaluated for concordance using Simpson’s index of diversity, the adjusted Rand coefficient and the adjusted Wallace coefficient. A single nucleotide variant (SNV) analysis was performed

Foodborne disease surveillance in Canada is a multifaceted endeavor and human clinical disease is captured by two major laboratory-based surveillance systems, which monitor bacterial enteric pathogens, including Salmonella. For years, these programs have relied on multiple methodologies for the identification and subtyping of these bacterial pathogens. Recently, public health officials have been looking towards whole genome sequencing (WGS) to provide a large dataset from which all the relevant and predictable information about an isolate can be mined, thereby replacing traditional laboratory-based surveillance methodologies. Rigorous validation and careful implementation plans are required before WGS-based analyses can replace traditional subtyping tests. The objectives of this research were to assess the ability of WGS-based analysis methods to replace traditional subtyping tests used in laboratory-based surveillance systems and to develop the considerations that can guide future implementation of WGS technology. Salmonella serotyping remains the gold-standard tool for the classification of Salmonella isolates and the Salmonella in silico Typing Resource (SISTR) was validated using a set of 492 clinical isolates to uncover gaps in prediction algorithms and improve tool development. Following further refinement, traditional serotyping was compared to SISTR along with another in silico serotyping tool, SeqSero, and 7-gene multilocus sequence typing (MLST) for serovar prediction, which can be adapted for in silico analysis. Successful results were obtained for 94.8%, 88.2%, and 88.3% of the 813 clinical and laboratory isolates tested using SISTR, SeqSero, and 7-gene MLST, respectively. While the three methods vary in their algorithms, all would be suitable for maintaining the historical records, surveillance systems, and communication structures currently in place. Additionally, two WGS-based phylogenetic analysis methods, the Single Nucleotide Variant Phylogenomics