Molecular characterization of Cryptosporidium isolates from humans in Ontario, Canada.

BACKGROUND: Cryptosporidiosis is a gastrointestinal disease with global distribution. It has been a reportable disease in Canada since 2000; however, routine molecular surveillance is not conducted. Therefore, sources of contamination are unknown. The aim of this project was to identify species and subtypes of Cryptosporidium in clinical cases from Ontario, the largest province in Canada, representing one third of the Canadian population, in order to understand transmission patterns. METHODS: A total of 169 frozen, banked, unpreserved stool specimens that were microscopy positive for Cryptosporidium over the period 2008-2017 were characterized using molecular tools. A subset of the 169 specimens were replicate samples from individual cases. DNA was extracted directly from the stool and nested PCR followed by Sanger sequencing was conducted targeting the small subunit ribosomal RNA (SSU) and glycoprotein 60 (gp60) genes. RESULTS: Molecular typing data and limited demographic data were obtained for 129 cases of cryptosporidiosis. Of these cases, 91 (70.5 %) were due to Cryptosporidium parvum and 24 (18.6%) were due to Cryptosporidium hominis. Mixed infections of C. parvum and C. hominis occurred in four (3.1%) cases. Five other species observed were Cryptosporidium ubiquitum (n = 5), Cryptosporidium felis (n = 2), Cryptosporidium meleagridis (n = 1), Cryptosporidium cuniculus (n = 1) and Cryptosporidium muris (n = 1). Subtyping the gp60 gene revealed 5 allelic families and 17 subtypes of C. hominis and 3 allelic families and 17 subtypes of C. parvum. The most frequent subtype of C. hominis was IbA10G2 (22.3%) and of C. parvum was IIaA15G2R1 (62.4%). CONCLUSIONS: The majority of isolates in this study were C. parvum, supporting the notion that zoonotic transmission is the main route of cryptosporidiosis transmission in Ontario. Nonetheless, the observation of C. hominis in about a quarter of cases suggests that anthroponotic transmission is also an important contributor to cryptosporidiosis pathogenesis in Ontario.

Rapid and accurate SNP genotyping of clonal bacterial pathogens with BioHansel.

Hierarchical genotyping approaches can provide insights into the source, geography and temporal distribution of bacterial pathogens. Multiple hierarchical SNP genotyping schemes have previously been developed so that new isolates can rapidly be placed within pre-computed population structures, without the need to rebuild phylogenetic trees for the entire dataset. This classification approach has, however, seen limited uptake in routine public health settings due to analytical complexity and the lack of standardized tools that provide clear and easy ways to interpret results. The BioHansel tool was developed to provide an organism-agnostic tool for hierarchical SNP-based genotyping. The tool identifies split k-mers that distinguish predefined lineages in whole genome sequencing (WGS) data using SNP-based genotyping schemes. BioHansel uses the Aho-Corasick algorithm to type isolates from assembled genomes or raw read sequence data in a matter of seconds, with limited computational resources. This makes BioHansel ideal for use by public health agencies that rely on WGS methods for surveillance of bacterial pathogens. Genotyping results are evaluated using a quality assurance module which identifies problematic samples, such as low-quality or contaminated datasets. Using existing hierarchical SNP schemes for Mycobacterium tuberculosis and Salmonella Typhi, we compare the genotyping results obtained with the k-mer-based tools BioHansel and SKA, with those of the organism-specific tools TBProfiler and genotyphi, which use gold-standard reference-mapping approaches. We show that the genotyping results are fully concordant across these different methods, and that the k-mer-based tools are significantly faster. We also test the ability of the BioHansel quality assurance module to detect intra-lineage contamination and demonstrate that it is effective, even in populations with low genetic diversity. We demonstrate the scalability of the tool using a dataset of ~8100 S. Typhi public genomes and provide the aggregated results of geographical distributions as part of the tool's output. BioHansel is an open source Python 3 application available on PyPI and Conda repositories and as a Galaxy tool from the public Galaxy Toolshed. In a public health context, BioHansel enables rapid and high-resolution classification of bacterial pathogens with low genetic diversity.

Targeting discriminatory SNPs in Salmonella enterica serovar Heidelberg genomes using RNase H2-dependent PCR.

We report a novel RNase H2-dependent PCR (rhPCR) genotyping assay for a small number of discriminatory single-nucleotide polymorphisms (SNPs) that identify lineages and sub-lineages of the highly clonal pathogen Salmonella Heidelberg (SH). Standard PCR primers targeting numerous SNP locations were initially designed in silico, modified to be RNase H2-compatible, and then optimized by laboratory testing. Optimization often required repeated cycling through variations in primer design, assay conditions, reagent concentrations and selection of alternative SNP targets. The final rhPCR assay uses 28 independent rhPCR reactions to target 14 DNA bases that can distinguish 15 possible lineages and sub-lineages of SH. On evaluation, the assay correctly identified the 12 lineages and sub-lineages represented in a panel of 75 diverse SH strains. Non-specific amplicons were observed in 160 (15.2%) of the 1050 reactions, but due to their low intensity did not compromise assay performance. Furthermore, in silico analysis of 500 closed genomes from 103 Salmonella serovars and laboratory rhPCR testing of five prevalent Salmonella serovars including SH indicated the assay can identify Salmonella isolates as SH, since only SH isolates generated amplicons from all 14 target SNPs. The genotyping results can be fully correlated with whole genome sequencing (WGS) data in silico. This fast and economical assay, which can identify SH isolates and classify them into related or unrelated lineages and sub-lineages, has potential applications in outbreak identification, source attribution and microbial source tracking.