Pathogenic potential assessment of the Shiga toxin-producing Escherichia coli by a source attribution-considered machine learning model

Foodborne bacterial pathogens: genome-based approaches for enduring and emerging threats in a complex and changing world

Foodborne illnesses pose a substantial health and economic burden, presenting challenges in prevention due to the diverse microbial hazards that can enter and spread within food systems. Various factors, including natural, political and commercial drivers, influence food production and distribution. The risks of foodborne illness will continue to evolve in step with these drivers and with changes to food systems. For example, climate impacts on water availability for agriculture, changes in food sustainability targets and evolving customer preferences can all have an impact on the ecology of foodborne pathogens and the agrifood niches that can carry microorganisms. Whole-genome and metagenome sequencing, combined with microbial surveillance schemes and insights from the food system, can provide authorities and businesses with transformative information to address risks and implement new food safety interventions across the food chain. In this Review, we describe how genome-based approaches have advanced our understanding of the evolution and spread of enduring bacterial foodborne hazards as well as their role in identifying emerging foodborne hazards. Furthermore, foodborne hazards exist in complex microbial communities across the entire food chain, and consideration of these co-existing organisms is essential to understanding the entire ecology supporting pathogen persistence and transmission in an evolving food system.

Recent advances in understanding the fitness and survival mechanisms of Vibrio parahaemolyticus

The presence of Vibrio parahaemolyticus (Vp) in different production stages of seafood has generated negative impacts on both public health and the sustainability of the industry. To further better investigate the fitness of Vp at the phenotypical level, a great number of studies have been conducted in recent years using plate counting methods. In the meantime, with the increasing accessibility of the next generation sequencing and the advances in analytical chemistry techniques, omics-oriented biotechnologies have further advanced our knowledge in the survival and virulence mechanisms of Vp at various molecular levels. These observations provide insights to guide the development of novel prevention and control strategies and benefit the monitoring and mitigation of food safety risks associated with Vp contamination. To timely capture these recent advances, this review firstly summarizes the most recent phenotypical level studies and provide insights about the survival of Vp under important in vitro stresses and on aquatic products. After that, molecular survival mechanisms of Vp at transcriptomic and proteomic levels are summarized and discussed. Looking forward, other newer omics-biotechnology such as metabolomics and secretomics show great potential to be used for confirming the cellular responses of Vp. Powerful data mining tools from the field of machine learning and artificial intelligence, that can better utilize the omics data and solve complex problems in the processing, analysis, and interpretation of omics data, will further improve our mechanistic understanding of Vp.

Harmonization of supervised machine learning practices for efficient source attribution of Listeria monocytogenes based on genomic data

BACKGROUND

Genomic data-based machine learning tools are promising for real-time surveillance activities performing source attribution of foodborne bacteria such as Listeria monocytogenes. Given the heterogeneity of machine learning practices, our aim was to identify those influencing the source prediction performance of the usual holdout method combined with the repeated k-fold cross-validation method.

METHODS

A large collection of 1 100 L. monocytogenes genomes with known sources was built according to several genomic metrics to ensure authenticity and completeness of genomic profiles. Based on these genomic profiles (i.e. 7-locus alleles, core alleles, accessory genes, core SNPs and pan kmers), we developed a versatile workflow assessing prediction performance of different combinations of training dataset splitting (i.e. 50, 60, 70, 80 and 90%), data preprocessing (i.e. with or without near-zero variance removal), and learning models (i.e. BLR, ERT, RF, SGB, SVM and XGB). The performance metrics included accuracy, Cohen's kappa, F1-score, area under the curves from receiver operating characteristic curve, precision recall curve or precision recall gain curve, and execution time.

RESULTS

The testing average accuracies from accessory genes and pan kmers were significantly higher than accuracies from core alleles or SNPs. While the accuracies from 70 and 80% of training dataset splitting were not significantly different, those from 80% were significantly higher than the other tested proportions. The near-zero variance removal did not allow to produce results for 7-locus alleles, did not impact significantly the accuracy for core alleles, accessory genes and pan kmers, and decreased significantly accuracy for core SNPs. The SVM and XGB models did not present significant differences in accuracy between each other and reached significantly higher accuracies than BLR, SGB, ERT and RF, in this order of magnitude. However, the SVM model required more computing power than the XGB model, especially for high amount of descriptors such like core SNPs and pan kmers.

CONCLUSIONS

In addition to recommendations about machine learning practices for L. monocytogenes source attribution based on genomic data, the present study also provides a freely available workflow to solve other balanced or unbalanced multiclass phenotypes from binary and categorical genomic profiles of other microorganisms without source code modifications.

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.

Genomics and pathotypes of the many faces of Escherichia coli

Escherichia coli is the most researched microbial organism in the world. Its varied impact on human health, consisting of commensalism, gastrointestinal disease, or extraintestinal pathologies, has generated a separation of the species into at least eleven pathotypes (also known as pathovars). These are broadly split into two groups, intestinal pathogenic E. coli (InPEC) and extraintestinal pathogenic E. coli (ExPEC). However, components of E. coli's infinite open accessory genome are horizontally transferred with substantial frequency, creating pathogenic hybrid strains that defy a clear pathotype designation. Here, we take a birds-eye view of the E. coli species, characterizing it from historical, clinical, and genetic perspectives. We examine the wide spectrum of human disease caused by E. coli, the genome content of the bacterium, and its propensity to acquire, exchange, and maintain antibiotic resistance genes and virulence traits. Our portrayal of the species also discusses elements that have shaped its overall population structure and summarizes the current state of vaccine development targeted at the most frequent E. coli pathovars. In our conclusions, we advocate streamlining efforts for clinical reporting of ExPEC, and emphasize the pathogenic potential that exists throughout the entire species.

Impact and prospect of the fourth industrial revolution in food safety: Mini-review

The fourth industrial revolution represented by big data and artificial intelligence (AI), already had a significant impact on the food industry. In this review, the impacts and prospects of the 4th industrial revolution in food safety were discussed. First, the general process and characteristics of AI application from data collection to visualization are covered. Additionally, various data collection and analysis methods are discussed, with emphasis on the collection of high variety, volume, and velocity data and visualization. Available literature presents examples of machine learning applications in food samples that are mostly associated with the classification of agricultural food items through convolutional neural networks. Based on these examples, the prospects of the 4th industrial revolution in food safety are categorized as follows: prediction of food safety risk, detection of foodborne pathogens, and food safety management. This mini-review will help understand the relationship between the 4th industrial revolution and food safety.