Enteroaggregative Escherichia coli Have Evolved Independently as Distinct Complexes within the E-coli Population with Varying Ability to Cause Disease

Enteroaggregative E. coli (EAEC) is an established diarrhoeagenic pathotype. The association with virulence gene content and ability to cause disease has been studied but little is known about the population structure of EAEC and how this pathotype evolved. Analysis by Multi Locus Sequence Typing of 564 EAEC isolates from cases and controls in Bangladesh, Nigeria and the UK spanning the past 29 years, revealed multiple successful lineages of EAEC. The population structure of EAEC indicates some clusters are statistically associated with disease or carriage, further highlighting the heterogeneous nature of this group of organisms. Different clusters have evolved independently as a result of both mutational and recombination events; the EAEC phenotype is distributed throughout the population of E. coli

Epidemiological links and antimicrobial resistance of clinical Salmonella enterica ST198 isolates: a nationwide microbial population genomic study in Switzerland

Salmonella is a leading cause of foodborne outbreaks and systemic infections worldwide. Emerging multi-drug resistant Salmonella lineages such as a ciprofloxacin-resistant subclade (CIPR) within Salmonella enterica serovar Kentucky ST198 threaten the effective prevention and treatment of infections. To understand the genomic diversity and antimicrobial resistance gene content associated with S. Kentucky in Switzerland, we whole-genome sequenced 70 human clinical isolates obtained between 2010 and 2020. Most isolates belonged to ST198-CIPR. High- and low-level ciprofloxacin resistance among CIPR isolates was associated with variable mutations in ramR and acrB in combination with stable mutations in quinolone-resistance determining regions (QRDRs). Analysis of isolates from patients with prolonged ST198 colonization indicated subclonal adaptions with the ramR locus as a mutational hotspot. SNP analyses identified multiple clusters of near-identical isolates, which were often associated with travel but included spatiotemporally linked isolates from Switzerland. The largest SNP cluster was associated with travellers returning from Indonesia, and investigation of global data linked >60 additional ST198 salmonellosis isolates to this cluster. Our results emphasize the urgent need for implementing whole-genome sequencing as a routine tool for Salmonella surveillance and outbreak detection

Identification and characterisation of enteroaggregative Escherichia coli subtypes associated with human disease

Enteroaggregative E. coli (EAEC) are a major cause of diarrhoea worldwide. Due to their heterogeneity and carriage in healthy individuals, identification of diagnostic virulence markers for pathogenic strains has been difficult. In this study, we have determined phenotypic and genotypic differences between EAEC strains of sequence types (STs) epidemiologically associated with asymptomatic carriage (ST31) and diarrhoeal disease (ST40). ST40 strains demonstrated significantly enhanced intestinal adherence, biofilm formation, and pro-inflammatory interleukin-8 secretion compared with ST31 isolates. This was independent of whether strains were derived from diarrhoea patients or healthy controls. Whole genome sequencing revealed differences in putative virulence genes encoding aggregative adherence fimbriae, E. coli common pilus, flagellin and EAEC heat-stable enterotoxin 1. Our results indicate that ST40 strains have a higher intrinsic potential of human pathogenesis due to a specific combination of virulence-related factors which promote host cell colonization and inflammation. These findings may contribute to the development of genotypic and/or phenotypic markers for EAEC strains of high virulence

Salmonella nomenclature in the genomic era: a time for change

Salmonella enterica nomenclature has evolved over the past one hundred years into a highly sophisticated naming convention based on the recognition of antigens by specific antibodies. This serotyping scheme has led to the definition of over 2500 serovars which are well understood, have standing in nomenclature and, for the majority, biological relevance. Therefore, it is highly desirable for any change in naming convention to maintain backwards compatibility with the information linked to these serovars. The routine use of whole genome sequencing and the well-established link between sequence types and serovars presents an opportunity to update the scheme by incorporating the phylogenetically relevant sequence data whilst preserving the best of serotyping nomenclature. Advantages include: overcoming the variability in antibody preparations; removing the need to use laboratory animals and implementing a truly universal system. However, the issue of trying to reproduce the phenotyping gold standard needs to be relaxed if we are to fully embrace the genomic era. We have used whole genome sequence data from over 46,000 isolates of Salmonella enterica subspecies enterica to define clusters in two stages: Multi Locus Sequence Typing followed by antigen prediction. Sequence type—serotype discrepancies were resolved using core SNP clustering to determine the phylogenetic groups and this was confirmed by overlaying the antigenic prediction onto the core SNP clusters and testing the separation of clusters using cgMLST Hierarchical Clustering. This allowed us to define any major antigenic clusters within an ST—here called the MAC type and written as ST-serovar. Using this method, 99.96% of Salmonella isolates reported in the UK were assigned a MAC type and linked to a serovar name taken from the Kauffmann and White scheme. We propose a change for reporting of Salmonella enterica sub-types using the ST followed by serovar

Genomic surveillance detects Salmonella enterica serovar Paratyphi A harbouring blaCTX-M-15 from a traveller returning from Bangladesh

Whole genome sequencing (WGS) has been used routinely by Public Health England (PHE) for identification, surveillance and monitoring of resistance determinants in referred Salmonella isolates since 2015. We report the first identified case of extended-spectrum-β-lactamase (ESBL) Salmonella enterica serovar Paratyphi A (S. Paratyphi A) isolated from a traveller returning to England from Bangladesh in November 2017. The isolate (440915) was resistant to ciprofloxacin and harboured both the mobile element ISEcp9 -blaCTX-M-15-hp-tnpA and blaTEM-191, associated with ESBL production. Phenotypic resistance was subsequently confirmed by Antimicrobial Susceptibility Testing (AST). S. Paratyphi A 440915 harboured an IncI1 plasmid previously reported to encode ESBL elements in Enterobacteriaceae and recently described in a S. Typhi isolate from Bangladesh. Results from this study indicate the importance of monitoring imported drug resistance for typhoidal salmonellae as ceftriaxone is the first line antibiotic treatment for complicated enteric fever in England. We conclude that WGS provides a rapid, accurate method for surveillance of drug resistance genes in Salmonella, leading to the first reported case of ESBL producing S. Paratyphi A and continues to inform the national treatment guidelines for management of enteric fever

Use of whole-genus genome sequence data to develop a multilocus sequence typing tool that accurately identifies Yersinia isolates to the species and subspecies levels

The genus Yersinia is a large and diverse bacterial genus consisting of human-pathogenic species, a fish-pathogenic species, and a large number of environmental species. Recently, the phylogenetic and population structure of the entire genus was elucidated through the genome sequence data of 241 strains encompassing every known species in the genus. Here we report the mining of this enormous data set to create a multilocus sequence typing-based scheme that can identify Yersinia strains to the species level to a level of resolution equal to that for whole-genome sequencing. Our assay is designed to be able to accurately subtype the important human-pathogenic species Yersinia enterocolitica to whole-genome resolution levels. We also report the validation of the scheme on 386 strains from reference laboratory collections across Europe. We propose that the scheme is an important molecular typing system to allow accurate and reproducible identification of Yersinia isolates to the species level, a process often inconsistent in nonspecialist laboratories. Additionally, our assay is the most phylogenetically informative typing scheme available for Y. enterocolitica

Whole Genome Sequencing for Public Health Surveillance of Shiga Toxin-Producing Escherichia coli Other than Serogroup O157

Shiga toxin-producing Escherichia coli (STEC) are considered to be a significant threat to public health due to the severity of gastrointestinal symptoms associated with human infection. In England STEC O157 is the most commonly detected STEC serogroup, however, the implementation of PCR at local hospital laboartories has resulted in an increase in the detection of STEC other than serogroup O157 (non-O157 STEC). The aim of this study was to evaluate the use of whole genome sequencing (WGS) for routine public health surveillance of non-O157 STEC by comparing this approach to phenotypic serotyping and PCR for subtyping the stx-encoding genes. Of the 102 isolates where phenotypic and genotypic serotyping could be compared, 98 gave fully concordant results. The most common non-O157 STEC serogroups detected were O146 (22) and O26 (18). All but one of the 38 isolates that could not be phenotypically serotyped (designated O unidentifiable or O rough) were serotyped using the WGS data. Of the 73 isolates where a flagella type was available by traditional phenotypic typing, all results matched the H-type derived from the WGS data. Of the 140 sequenced non-O157 isolates, 52 (37.1%) harboured stx1 only, 42 (30.0%) had stx2 only, 46 (32.9%) carried stx1 and stx2. Of these, stx subtyping PCR results were available for 131 isolates and 121 of these had concordant results with the stx subtype derived from the WGS data. Non-specific primer binding during PCR amplification, due to the similarity of the stx2 subtype gene sequences was the most likely cause. The results of this study showed WGS provided a reliable and robust one-step process for characterisation of STEC. Deriving the full serotype from WGS data in real time has enabled us to report a higher level of strain discrimination while stx subtyping provides data on the pathogenic potential of each isolate, enabling us to predict clinical outcome of each case and to monitor the emergence of hyper-virulent strains

Rapid geographical source attribution of Salmonella enterica serovar Enteritidis genomes using hierarchical machine learning.

Salmonella enterica serovar Enteritidis is one of the most frequent causes of Salmonellosis globally and is commonly transmitted from animals to humans by the consumption of contaminated foodstuffs. In the UK and many other countries in the Global North, a significant proportion of cases are caused by consumption of imported food products or contracted during foreign travel, therefore making the rapid identification of the geographical source of new infections a requirement for robust public health outbreak investigations. Herein, we detail the development and application of a hierarchical machine learning model to rapidly identify and trace the geographical source of S. Enteritidis infections from whole genome sequencing data. 2,313 S. Enteritidis genomes, collected by the UKHSA between 2014-2019, were used to train a 'local classifier per node' hierarchical classifier to attribute isolates to 4 continents, 11 sub-regions and 38 countries (53 classes). The highest classification accuracy was achieved at the continental level followed by the sub-regional and country levels (macro F1: 0.954, 0.718, 0.661 respectively). A number of countries commonly visited by UK travellers were predicted with high accuracy (hF1: >0.9). Longitudinal analysis and validation with publicly accessible international samples indicated that predictions were robust to prospective external datasets. The hierarchical machine learning framework provided granular geographical source prediction directly from sequencing reads in <4 minutes per sample, facilitating rapid outbreak resolution and real-time genomic epidemiology. The results suggest additional application to a broader range of pathogens and other geographically structured problems, such as antimicrobial resistance prediction, is warranted