Laing, C. R. | bioRxiv Science

bioRxiv2024

Persistent cross-species transmission systems dominate Shiga toxin-producing Escherichia coli O157:H7 epidemiology in a high incidence region: a genomic epidemiology study

BackgroundSeveral areas of the world suffer notably high incidence of Shiga toxin-producing Escherichia coli, among them Alberta, Canada. We assessed the impact of persistent cross-species transmission systems on the epidemiology of E. coli O157:H7 in Alberta. MethodsWe sequenced and assembled 229 E. coli O157:H7 isolates originating from collocated cattle (n=108) and human (n=121) populations from 2007-2015 in Alberta. We constructed a timed phylogeny using BEAST2 using a structured coalescent model. We then extended the tree with human isolates through 2019 (n=430) to assess the long-term disease impact of locally persistent lineages. Shiga toxin gene (stx) profile was determined for all isolates. ResultsDuring 2007 to 2015, we estimated 108 (95% HPD 104, 112) human lineages arose from cattle lineages, and 14 (95% HPD 5, 23) from other human lineages; i.e., 88.5% of human lineages arose from cattle lineages. We identified 11 persistent lineages local to Alberta, which were associated with 38.0% (95% CI 29.3%, 47.3%) of human isolates. Of 117 isolates in locally persistent lineages, 6.0% carried only the Shiga toxin gene stx2a and the rest both stx1a and stx2a. During the later period, six locally persistent lineages continued to be associated with human illness, including 74.7% (95% CI 68.3%, 80.3%) of reported cases in 2018 and 2019. The stx profile of isolates in locally persistent lineages shifted from the earlier period, with 51.2% encoding only stx2a. ConclusionsOur study identified multiple locally evolving lineages transmitted between cattle and humans persistently associated with E. coli O157:H7 illnesses for up to 13 years. Of concern, there was a dramatic shift in locally persistent lineages toward strains with the more virulent stx2a-only profile. Locally persistent lineages may be a principal cause of the high incidence of E. coli O157:H7 in locations such as Alberta and offer opportunities for understanding the disease ecology supporting E. coli O157:H7 persistence, as well as for local prevention efforts.

microbiology↗

bioRxiv2020

Rapid and accurate SNP genotyping of clonal bacterial pathogens with BioHansel

BioHansel performs high-resolution genotyping of bacterial isolates by identifying phylogenetically informative single nucleotide polymorphisms (SNPs), also known as canonical SNPs, in whole genome sequencing (WGS) data. The application uses a fast k-mer matching algorithm to map pathogen WGS data to canonical SNPs contained in hierarchically structured schemas and assigns genotypes based on the detected SNP profile. Using modest computing resources, BioHansel efficiently types isolates from raw sequence reads or assembled contigs in a matter of seconds, making it attractive for use by public health, food safety, environmental, and agricultural authorities that wish to apply WGS methodologies for their surveillance, diagnostics, and research programs. BioHansel currently provides canonical SNP genotyping schemas for four prevalent Salmonella serovars--Typhi, Typhimurium, Enteritidis and Heidelberg--as well as a schema for Mycobacterium tuberculosis. Users can also supply their own schemas for genotyping other organisms. BioHansels quality assurance system assesses the validity of the genotyping results and can identify low quality data, contaminated datasets, and misidentified organisms. BioHansel is targeted to support surveillance, source attribution, risk assessment, diagnostics, and rapid screening for public health purposes, such as product recalls. BioHansel is an open source application with packages available for PyPI, Conda, and the Galaxy workflow manager. In summary, BioHansel performs efficient, rapid, accurate, and high-resolution classification of bacterial genomes from sequence reads or assembled contigs on standard computing hardware. BioHansel is suitable for use as a general research tool as well as in fully operationalized WGS workflows at the front lines of infectious disease surveillance, diagnostics, and outbreak investigation and response. Impact statementPublic health, food safety, environmental, and agricultural authorities are currently engaged in a global effort to incorporate whole genome sequencing technologies into their infectious disease research, surveillance, and outbreak investigation programs. Its widespread adoption, however, has been impeded by two major obstacles: the need for high performance computing to generate results and the expert knowledge required to interpret and communicate those results. BioHansel addresses these limitations by rapidly genotyping pathogens from whole genome sequence data in an accurate, simple, familiar, and easily sharable manner using standard computing resources. BioHansel provides a compact and readily interpretable genotype based on canonical SNP genotyping schemas. BioHansels genotyping nomenclature encodes the pathogens position in its population structure, which simplifies and facilitates its comparison with actively circulating strains and historical strains. The genotyping information provided by BioHansel can identify points of intervention to prevent the spread of pathogenic bacteria, screen for the presence of priority pathogens, and perform source attribution and risk assessment. Thus, BioHansel serves as a readily accessible and powerful WGS method, implementable on a laptop, for genotyping pathogens to detect, monitor, and control the emergence and spread of infectious disease through surveillance, screening, diagnostics, and outbreak investigation and response activities. Data summaryBioHansel is a Python 3 application available as PyPI, Conda Galaxy Tool Shed packages. It is an open source application distributed under the Apache License, Version 2.0. Source code is available at https://github.com/phac-nml/biohansel. The BioHansel user guide is available at https://bio-hansel.readthedocs.io/en/readthedocs/. Supplementary Materials are available at https://github.com/phac-nml/biohansel-manuscript-supplementary-data. The authors confirm all supporting data, code and protocols have been provided within the article or through supplementary data files.

bioinformatics↗