Abstract
Foodborne diseases (FBDs) are infections of the gastrointestinal tract caused by foodborne pathogens (FBPs) such as bacteria [Salmonella, Listeria monocytogenes and Shiga toxin-producing E. coli (STEC)] and several viruses, but also parasites and some fungi. Artificial intelligence (AI) and its sub-discipline machine learning (ML) are re-emerging and gaining an ever increasing popularity in the scientific community and industry, and could lead to actionable knowledge in diverse ranges of sectors including epidemiological investigations of FBD outbreaks and antimicrobial resistance (AMR). As genotyping using whole-genome sequencing (WGS) is becoming more accessible and affordable, it is increasingly used as a routine tool for the detection of pathogens, and has the potential to differentiate between outbreak strains that are closely related, identify virulence/resistance genes and provide improved understanding of transmission events within hours to days. In most cases, the computational pipeline of WGS data analysis can be divided into four (though, not necessarily consecutive) major steps: de novo genome assembly, genome characterization, comparative genomics, and inference of phylogeny or phylogenomics. In each step, ML could be used to increase the speed and potentially the accuracy (provided increasing amounts of high-quality input data) of identification of the source of ongoing outbreaks, leading to more efficient treatment and prevention of additional cases. In this review, we explore whether ML or any other form of AI algorithms have already been proposed for the respective tasks and compare those with mechanistic model-based approaches.
Highlights
Foodborne diseases (FBDs) are infections of the gastrointestinal tract caused by foodborne pathogens (FBPs) such as bacteria and several viruses, and parasites and some fungi
We mainly focus on single-genome short-read (Illumina) bacterial whole-genome sequencing (WGS); in cases where, to the best of our knowledge, no machine learning (ML) algorithms have been reported for the respective task, we briefly touch upon ML algorithms dedicated to ultra-long read technologies, 16S metataxonomics and shotgun metagenomics, as these approaches may find future applications in FBD outbreaks
Several ML-based tools have been developed for different steps of bacterial WGS analysis
Summary
Foodborne diseases (FBDs) are infections of the gastrointestinal tract caused by foodborne pathogens (FBPs) such as bacteria and several viruses, and parasites and some fungi. ML strategies have already been used for microbial diagnostics in diverse contexts, including (i) taxonomic grouping of metagenomics data (Sedlar et al, 2017; Afify and Al-Masni, 2018); (ii) classification of L. monocytogenes persistence in retail delicatessen environments (Vangay et al, 2014); (iii) phenotype prediction of bacterial strains based on presence/absence of particular genes (i.e., gene-trait matching) (Dutilh et al, 2013; Alkema et al, 2016; Farrell et al, 2018); (iv) to identify strains that demonstrate a higher probability to cause severe diseases (Wheeler et al, 2018); (v) to predict the host range of pathogens (Lupolova et al, 2017), e.g., identifying their signatures of host adaptation (Wheeler et al, 2018); and (vi) to predict the antimicrobial resistance potential of different E. coli strains (Her and Wu, 2018) or from different sources (Li et al, 2018). Metagenomic approaches would allow one to capture the full spectrum of microbes in foods entirely without prior need for culturing and isolation, allowing the detection of “viable but not cultivable," as well as non-viable microbes (Bergholz et al, 2014)
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