Exploring Long-Read Metagenomics for Full Characterization of Shiga Toxin-Producing Escherichia coli in Presence of Commensal E. coli.

The characterization of Shiga toxin-producing Escherichia coli (STEC) is necessary to assess their pathogenic potential, but isolation of the strain from complex matrices such as milk remains challenging. In previous work, we have shown the potential of long-read metagenomics to characterize eae-positive STEC from artificially contaminated raw milk without isolating the strain. The presence of multiple E. coli strains in the sample was shown to potentially hinder the correct characterization of the STEC strain. Here, we aimed at determining the STEC:commensal ratio that would prevent the characterization of the STEC. We artificially contaminated pasteurized milk with different ratios of an eae-positive STEC and a commensal E. coli and applied the method previously developed. Results showed that the STEC strain growth was better than the commensal E. coli after enrichment in acriflavine-supplemented BPW. The STEC was successfully characterized in all samples with at least 10 times more STEC post-enrichment compared to the commensal E. coli. However, the presence of equivalent proportions of STEC and commensal E. coli prevented the full characterization of the STEC strain. This study confirms the potential of long-read metagenomics for STEC characterization in an isolation-free manner while refining its limit regarding the presence of background E. coli strains.

Combination of whole genome sequencing and supervised machine learning provides unambiguous identification of eae-positive Shiga toxin-producing Escherichia coli.

Introduction: The objective of this study was to develop, using a genome wide machine learning approach, an unambiguous model to predict the presence of highly pathogenic STEC in E. coli reads assemblies derived from complex samples containing potentially multiple E. coli strains. Our approach has taken into account the high genomic plasticity of E. coli and utilized the stratification of STEC and E. coli pathogroups classification based on the serotype and virulence factors to identify specific combinations of biomarkers for improved characterization of eae-positive STEC (also named EHEC for enterohemorrhagic E.coli) which are associated with bloody diarrhea and hemolytic uremic syndrome (HUS) in human. Methods: The Machine Learning (ML) approach was used in this study on a large curated dataset composed of 1,493 E. coli genome sequences and 1,178 Coding Sequences (CDS). Feature selection has been performed using eight classification algorithms, resulting in a reduction of the number of CDS to six. From this reduced dataset, the eight ML models were trained with hyper-parameter tuning and cross-validation steps. Results and discussion: It is remarkable that only using these six genes, EHEC can be clearly identified from E. coli read assemblies obtained from in silico mixtures and complex samples such as milk metagenomes. These various combinations of discriminative biomarkers can be implemented as novel marker genes for the unambiguous EHEC characterization from different E. coli strains mixtures as well as from raw milk metagenomes.

Hybrid Assembly from 75 E. coli Genomes Isolated from French Bovine Food Products between 1995 and 2016.

Here, we report the complete (or near-complete) genome sequences of 75 Escherichia coli isolates, including 71 stx-positive E. coli isolates, isolated in France between 1995 and 2016 from food of bovine origin. Genomes were assembled using a combination of long- and short-read sequencing.

Evaluation of high molecular weight DNA extraction methods for long-read sequencing of Shiga toxin-producing Escherichia coli.

Next generation sequencing has become essential for pathogen characterization and typing. The most popular second generation sequencing technique produces data of high quality with very low error rates and high depths. One major drawback of this technique is the short reads. Indeed, short-read sequencing data of Shiga toxin-producing Escherichia coli (STEC) are difficult to assemble because of the presence of numerous mobile genetic elements (MGEs), which contain repeated elements. The resulting draft assemblies are often highly fragmented, which results in a loss of information, especially concerning MGEs or large structural variations. The use of long-read sequencing can circumvent these problems and produce complete or nearly complete genomes. The ONT MinION, for its small size and minimal investment requirements, is particularly popular. The ultra-long reads generated with the MinION can easily span prophages and repeat regions. In order to take full advantage of this technology it requires High Molecular Weight (HMW) DNA of high quality in high quantity. In this study, we have tested three different extraction methods: bead-based, solid-phase and salting-out, and evaluated their impact on STEC DNA yield, quality and integrity as well as performance in MinION long-read sequencing. Both the bead-based and salting-out methods allowed the recovery of large quantities of HMW STEC DNA suitable for MinION library preparation. The DNA extracted using the salting-out method consistently produced longer reads in the subsequent MinION runs, compared with the bead-based methods. While both methods performed similarly in subsequent STEC genome assembly, DNA extraction based on salting-out appeared to be the overall best method to produce high quantity of pure HMW STEC DNA for MinION sequencing.

A step forward for Shiga toxin-producing Escherichia coli identification and characterization in raw milk using long-read metagenomics.

Shiga toxin-producing Escherichia coli (STEC) are a cause of severe human illness and are frequently associated with haemolytic uraemic syndrome (HUS) in children. It remains difficult to identify virulence factors for STEC that absolutely predict the potential to cause human disease. In addition to the Shiga-toxin (stx genes), many additional factors have been reported, such as intimin (eae gene), which is clearly an aggravating factor for developing HUS. Current STEC detection methods classically rely on real-time PCR (qPCR) to detect the presence of the key virulence markers (stx and eae). Although qPCR gives an insight into the presence of these virulence markers, it is not appropriate for confirming their presence in the same strain. Therefore, isolation steps are necessary to confirm STEC viability and characterize STEC genomes. While STEC isolation is laborious and time-consuming, metagenomics has the potential to accelerate the STEC characterization process in an isolation-free manner. Recently, short-read sequencing metagenomics have been applied for this purpose, but assembly quality and contiguity suffer from the high proportion of mobile genetic elements occurring in STEC strains. To circumvent this problem, we used long-read sequencing metagenomics for identifying eae-positive STEC strains using raw cow's milk as a causative matrix for STEC food-borne outbreaks. By comparing enrichment conditions, optimizing library preparation for MinION sequencing and generating an easy-to-use STEC characterization pipeline, the direct identification of an eae-positive STEC strain was successful after enrichment of artificially contaminated raw cow's milk samples at a contamination level as low as 5 c.f.u. ml-1. Our newly developed method combines optimized enrichment conditions of STEC in raw milk in combination with a complete STEC analysis pipeline from long-read sequencing metagenomics data. This study shows the potential of the innovative methodology for characterizing STEC strains from complex matrices. Further developments will nonetheless be necessary for this method to be applied in STEC surveillance.