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

Abstract

The partitioning of pathogenic strains isolated in environmental or human cases to their sources is challenging. The pathogens usually colonize multiple animal hosts, including livestock, which contaminate the food-production chain and the environment (e.g. soil and water), posing an additional public-health burden and major challenges in the identification of the source. Genomic data opens up new opportunities for the development of statistical models aiming to indicate the likely source of pathogen contamination. Here, we propose a computationally fast and efficient multinomial logistic regression source-attribution classifier to predict the animal source of bacterial isolates based on ‘source-enriched’ loci extracted from the accessory-genome profiles of a pangenomic dataset. Depending on the accuracy of the model’s self-attribution step, the modeller selects the number of candidate accessory genes that best fit the model for calculating the likelihood of (source) category membership. The Accessory genes-Based Source Attribution (AB_SA) method was applied to a dataset of strains of Salmonella enterica Typhimurium and its monophasic variant ( S . enterica 1,4,[5],12:i:-). The model was trained on 69 strains with known animal-source categories (i.e. poultry, ruminant and pig). The AB_SA method helped to identify 8 genes as predictors among the 2802 accessory genes. The self-attribution accuracy was 80 %. The AB_SA model was then able to classify 25 of the 29 S . enterica Typhimurium and S . enterica 1,4,[5],12:i:- isolates collected from the environment (considered to be of unknown source) into a specific category (i.e. animal source), with more than 85 % of probability. The AB_SA method herein described provides a user-friendly and valuable tool for performing source-attribution studies in only a few steps. AB_SA is written in R and freely available at https://github.com/lguillier/AB_SA.

WGS-Based Lineage and Antimicrobial Resistance Pattern of Salmonella Typhimurium Isolated during 2000–2017 in Peru

“…The population contained an open pan-genome with many repertoire genes for global and Peruvian isolates comparable to previous reports [ 19 , 63 , 64 ]. However, the core gene content was smaller than previously reported (3672 genes) [ 19 ] Fu et al (3846 genes) [ 64 ].…”

Risk factors for sporadic salmonellosis: a systematic review and meta-analysis

“…The evidenced strong effects of non-food exposures in this metaanalysis suggest that the risk of acquiring salmonellosis is multifactorial and that other sources than the foodborne ones, such as contact with animals (Hoelzer et al, 2011;Lambertini et al, 2016) and environmental waters (Guillier et al 2020), are worth investigating too.…”

Bayesian Source Attribution of Salmonella Typhimurium Isolates From Human Patients and Farm Animals in England and Wales

“…The disadvantage of the approach is that it can only infer source attribution for the animal subtypes where there is a corresponding human case of that subtype. Alternative approaches to source attribution which attempt to allocate a source for all the human isolates have recently been developed based on machine learning ( Lupolova et al, 2017 ; Munck et al, 2020a,b ) or on multinomial logistic regression ( Guillier et al, 2020 ). These methods attempt to determine host specific features of the core genome or accessory genes that can then be used to infer the source of each human isolate.…”