Patchy promiscuity: machine learning applied to predict the host specificity of Salmonella enterica and Escherichia coli

Abstract

Salmonella enterica and Escherichia coli are bacterial species that colonize different animal hosts with sub-types that can cause life-threatening infections in humans. Source attribution of zoonoses is an important goal for infection control as is identification of isolates in reservoir hosts that represent a threat to human health. In this study, host specificity and zoonotic potential were predicted using machine learning in which Support Vector Machine (SVM) classifiers were built based on predicted proteins from whole genome sequences. Analysis of over 1000 S. enterica genomes allowed the correct prediction (67 –90 % accuracy) of the source host for S. Typhimurium isolates and the same classifier could then differentiate the source host for alternative serovars such as S. Dublin. A key finding from both phylogeny and SVM methods was that the majority of isolates were assigned to host-specific sub-clusters and had high host-specific SVM scores. Moreover, only a minor subset of isolates had high probability scores for multiple hosts, indicating generalists with genetic content that may facilitate transition between hosts. The same approach correctly identified human versus bovine E. coli isolates (83 % accuracy) and the potential of the classifier to predict a zoonotic threat was demonstrated using E. coli O157. This research indicates marked host restriction for both S. enterica and E. coli, with only limited isolate subsets exhibiting host promiscuity by gene content. Machine learning can be successfully applied to interrogate source attribution of bacterial isolates and has the capacity to predict zoonotic potential.

AB_SA: Tracing the source of bacterial strains based on accessory genes. Application toSalmonellaTyphimurium environmental strains

“…Consistent with previously published source attribution studies, the overall self-attribution accuracy of this study was 75-80% (17; 18; 12).…”

“…Machine learning approaches are gaining interest in identifying the underlying genetic features associated with traits of microbial pathogens (14) and their use is also discussed for tracing the origin of an outbreak (15). For source attribution, recent studies consider this approach for predicting the source of sporadic human cases (16; 17; 18). In particular, (17) applied a Random Forest classifier for genomic source prediction.…”

AB_SA: Accessory genes-Based Source Attribution – tracing the source of Salmonella enterica Typhimurium environmental strains

“…Supervised machine-learning approaches are gaining interest in the identification of the causal genetic features associated with the phenotypic traits of microbial pathogens [14], and their use has been discussed for tracing the origin of an outbreak [15] as well. Recent studies also considered such approaches for predicting the source of sporadic human cases [16][17][18]. In particular, Zhang et al [17] applied a random forest classifier for genomic source prediction.…”

Four European Salmonella Typhimurium datasets collected to develop WGS-based source attribution methods

“…The potential of applying sequence data for source attribution purposes based on a machine learning approach has recently been reported 3,4 and discussed 5 . These agree on the potential of the machine learning method to discriminate between different sources and applicability to trace foodborne outbreaks.…”