Transforming Shiga toxin-producing Escherichia coli surveillance through whole genome sequencing in food safety practices

Cross-sectoral genomic surveillance reveals a lack of insight in sources of human infections with Shiga toxin-producing Escherichia coli, the Netherlands, 2017 to 2023

Shiga toxin-producing Escherichia coli (STEC) is a zoonotic pathogen associated with illness ranging from mild diarrhoea to haemolytic uremic syndrome (HUS) or even death. Cross-sectoral data sharing provides an opportunity to gain insight in reservoirs and sources of human infections and starting points for pro-active measures. Nevertheless, phylogenetic clustering of STEC strains from animals, food and human cases is low in the Dutch surveillance system. This is partly due to the substantial contribution of international travel and person-to-person spread in the STEC epidemiology. Furthermore, some STEC strains causing disease in humans may have a human reservoir. Although the main reservoirs and sources are included in the Dutch monitoring programmes, some animals and food products may be under-recognised as potential sources of human infections. More effort in investigating the role of other reservoirs beyond the well-known can provide a better understanding on STEC ecology in general, improving surveillance and source attribution, and ultimately provide better guidance for monitoring and source finding. This also implies having good diagnostics in place and isolates available for typing. Therefore, on the human side of the surveillance, the decision has been made to start isolating STEC at national level.

Genomic Evaluation of Multidrug-Resistant Extended-Spectrum β-Lactamase (ESBL)-Producing Escherichia coli from Irrigation Water and Fresh Produce in South Africa: A Cross-Sectional Analysis

Escherichia coli, both commensal and pathogenic, can colonize plants and persist in various environments. It indicates fecal contamination in water and food and serves as a marker of antimicrobial resistance. In this context, 61 extended-spectrum β-lactamase (ESBL)-producing E. coli from irrigation water and fresh produce from previous studies were characterized using whole genome sequencing (Illumina MiSeq). The Center for Genomic Epidemiology and Galaxy platforms were used to determine antimicrobial resistance genes, virulence genes, plasmid typing, mobile genetic elements, multilocus sequence typing (MLST), and pathogenicity prediction. In total, 19 known MLST groups were detected among the 61 isolates. Phylogroup B1 (ST58) and Phylogroup E (ST9583) were the most common sequence types. The six ST10 (serotype O101:H9) isolates carried the most resistance genes, spanning eight antibiotic classes. Overall, 95.1% of the isolates carried resistance genes from three or more classes. The blaCTX-M-1, blaCTX-M-14, and blaCTX-M-15 ESBL genes were associated with mobile genetic elements, and all of the E. coli isolates showed a >90% predicted probability of being a human pathogen. This study provided novel genomic information on environmental multidrug-resistant ESBL-producing E. coli from fresh produce and irrigation water, highlighting the environment as a reservoir for multidrug-resistant strains and emphasizing the need for ongoing pathogen surveillance within a One Health context.

Closing the gap: Oxford Nanopore Technologies R10 sequencing allows comparable results to Illumina sequencing for SNP-based outbreak investigation of bacterial pathogens

Whole-genome sequencing has become the method of choice for bacterial outbreak investigation, with most clinical and public health laboratories currently routinely using short-read Illumina sequencing. Recently, long-read Oxford Nanopore Technologies (ONT) sequencing has gained prominence and may offer advantages over short-read sequencing, particularly with the recent introduction of the R10 chemistry, which promises much lower error rates than the R9 chemistry. However, limited information is available on its performance for bacterial single-nucleotide polymorphism (SNP)-based outbreak investigation. We present an open-source workflow, Prokaryotic Awesome variant Calling Utility (PACU) (https://github.com/BioinformaticsPlatformWIV-ISP/PACU), for constructing SNP phylogenies using Illumina and/or ONT R9/R10 sequencing data. The workflow was evaluated using outbreak data sets of Shiga toxin-producing Escherichia coli and Listeria monocytogenes by comparing ONT R9 and R10 with Illumina data. The performance of each sequencing technology was evaluated not only separately but also by integrating samples sequenced by different technologies/chemistries into the same phylogenomic analysis. Additionally, the minimum sequencing time required to obtain accurate phylogenetic results using nanopore sequencing was evaluated. PACU allowed accurate identification of outbreak clusters for both species using all technologies/chemistries, but ONT R9 results deviated slightly more from the Illumina results. ONT R10 results showed trends very similar to Illumina, and we found that integrating data sets sequenced by either Illumina or ONT R10 for different isolates into the same analysis produced stable and highly accurate phylogenomic results. The resulting phylogenies for these two outbreaks stabilized after ~20 hours of sequencing for ONT R9 and ~8 hours for ONT R10. This study provides a proof of concept for using ONT R10, either in isolation or in combination with Illumina, for rapid and accurate bacterial SNP-based outbreak investigation.