A global resource for genomic predictions of antimicrobial resistance and surveillance ofSalmonellaTyphi at Pathogenwatch

Abstract

<jats:title>Abstract</jats:title><jats:sec><jats:title>Background</jats:title><jats:p>Microbial whole-genome sequencing (WGS) is now increasingly used to inform public health investigations of infectious disease. This approach has transformed our understanding of the global population structure of <jats:italic>Salmonella enterica</jats:italic> serovar Typhi (<jats:italic>S.</jats:italic> Typhi), the causative agent of typhoid fever. WGS has been particularly informative for understanding the global spread of multi-drug resistant (MDR) typhoid. As WGS capacity becomes more decentralised, there is a growing opportunity for collaboration and sharing of surveillance data within and between countries to inform disease control policies. This requires freely available, community driven tools that reduce the barriers to access genomic data for public health surveillance and that deliver genomic data on a global scale.</jats:p></jats:sec><jats:sec><jats:title>Methods</jats:title><jats:p>Here we present the Pathogenwatch (<jats:ext-link xmlns:xlink="http://www.w3.org/1999/xlink" ext-link-type="uri" xlink:href="https://pathogen.watch/styphi">https://pathogen.watch/styphi</jats:ext-link>) scheme for <jats:italic>S.</jats:italic> Typhi, a web application enabling the rapid identification of genomic markers of antimicrobial resistance (AMR) and contextualization with public genomic data to identify high-risk clones at a population level. Data are delivered in single genome reports or in collections of genomes combined with geographic and other data using trees, maps and tables.</jats:p></jats:sec><jats:sec><jats:title>Results</jats:title><jats:p>We show that the clustering of <jats:italic>S.</jats:italic> Typhi genomes in Pathogenwatch is comparable to established bioinformatics methods, and that genomic predictions of AMR are largely concordant with phenotypic drug susceptibility data. We demonstrate the public health utility of Pathogenwatch with examples selected from over 4,300 public genomes available in the application.</jats:p></jats:sec><jats:sec><jats:title>Conclusions</jats:title><jats:p>Pathogenwatch democratises genomic epidemiology of <jats:italic>S.</jats:italic> Typhi by providing an intuitive entry point for the analysis of WGS and linked epidemiological data, enabling international public health monitoring of the emergence and spread of high risk clones.</jats:p></jats:sec>

Glycoconjugate vaccines against Salmonella enterica serovars and Shigella species: existing and emerging methods for their analysis

“…Emerging advantageous genetic mutations for pathogen survival and ciprofloxacin resistance (recommended as first-line treatment) were recently reported by Pathogenwatch. With increasingly limited treatment options for S. Typhi, control strategies like vaccination and hygiene maintenance must be established (Argimón et al 2020).…”

Overcoming Data Bottlenecks In Genomic Pathogen Surveillance

“…For those wanting to run consistent pipelines, online platforms offer capacity without requiring more in-depth bioinformatics expertise. Pathogenwatch offers a web-based platform for AMR analysis and phylogeny generation of Campylobacter, Klebsiella, Neisseria gonorrhoeae, Staphylococcus aureus, and Salmonella Typhi [13,14]. The Center for Genomic Epidemiology (CGE) website offers services for phylogenetic tree building and AMR prediction [45].…”

“…The specialist knowledge required to process WGS data, and understand the databases underpinning interpretation and the principles required when working with big data, is not often a component of the education of many health care professionals. If the WGS analysis software has a familiar web-based interface, such as those found on the Galaxy, Centre for Genomic Epidemiology, and Pathogenwatch web applications, less training is required with a focus being on interpretation of the data rather than performing the analysis [11][12][13][14]. However, this limits users to the analyses available on the web applications and does not prepare those wanting to perform more in-depth bioinformatics analyses.…”

Genome-wide analysis provides a deeper understanding of the population structure of the Salmonella enterica serotype Paratyphi B complex in Bangladesh

“…We used the same approach to detect virulence factors, using the Virulence Factor Database (VFDB) [ 42 ]. The Salmonella In Silico Typing Resource (SISTR) [ 43 ], implemented in PathogenWatch [ 44 ], was used for in silico serotype prediction of the sequenced genomes. The mapping-based allele typer SRST2 [ 45 ] was used to assign sequence types (STs) to each genome according to the S. enterica MLST database.…”