Prediction of Listeria monocytogenes Clonal Complexes from Multilocus Variable Number Tandem Repeat Analysis Patterns Using a Machine Learning Approach.

Multilocus variable number tandem repeat analysis (MLVA) is a molecular subtyping technique that remains useful for those without the resources to access whole genome sequencing for the tracking and tracing of bacterial contaminants. Unlike techniques such as multilocus sequence typing (MLST) and pulsed-field gel electrophoresis, MLVA did not emerge as a standardized subtyping method for Listeria monocytogenes, and as a result, there is no reference database of virulent or food-associated MLVA subtypes as there is for MLST-based clonal complexes (CCs). Having previously shown the close congruence of a 5-loci MLVA scheme with MLST, a predictive model was created using the XGBoost machine learning (ML) technique, which enabled the prediction of CCs from MLVA patterns with ∼85% (±4%) accuracy. As well as validating the model on existing data, a straightforward update protocol was simulated for if and when previously unseen subtypes might arise. This article illustrates how ML techniques can be applied with elementary coding skills to add value to previous-generation molecular subtyping data in-built food processing environments.

Application of Whole Genome Sequencing to Aid in Deciphering the Persistence Potential of Listeria monocytogenes in Food Production Environments.

Listeria monocytogenes is the etiological agent of listeriosis, a foodborne illness associated with high hospitalizations and mortality rates. This bacterium can persist in food associated environments for years with isolates being increasingly linked to outbreaks. This review presents a discussion of genomes of Listeria monocytogenes which are commonly regarded as persisters within food production environments, as well as genes which are involved in mechanisms aiding this phenotype. Although criteria for the detection of persistence remain undefined, the advent of whole genome sequencing (WGS) and the development of bioinformatic tools have revolutionized the ability to find closely related strains. These advancements will facilitate the identification of mechanisms responsible for persistence among indistinguishable genomes. In turn, this will lead to improved assessments of the importance of biofilm formation, adaptation to stressful conditions and tolerance to sterilizers in relation to the persistence of this bacterium, all of which have been previously associated with this phenotype. Despite much research being published around the topic of persistence, more insights are required to further elucidate the nature of true persistence and its implications for public health.