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AbstractPrevention of the emergence and spread of foodborne diseases is an important prerequisite for the improvement of public health. Source attribution models link sporadic human cases of a specific illness to food sources and animal reservoirs. With the next generation sequencing technology, it is possible to develop novel source attribution models. We investigated the potential of machine learning to predict the animal reservoir from which a bacterial strain isolated from a human salmonellosis case originated based on whole‐genome sequencing. Machine learning methods recognize patterns in large and complex data sets and use this knowledge to build models. The model learns patterns associated with genetic variations in bacteria isolated from the different animal reservoirs. We selected different machine learning algorithms to predict sources of human salmonellosis cases and trained the model with Danish Salmonella Typhimurium isolates sampled from broilers (n = 34), cattle (n = 2), ducks (n = 11), layers (n = 4), and pigs (n = 159). Using cgMLST as input features, the model yielded an average accuracy of 0.783 (95% CI: 0.77–0.80) in the source prediction for the random forest and 0.933 (95% CI: 0.92–0.94) for the logit boost algorithm. Logit boost algorithm was most accurate (valid accuracy: 92%, CI: 0.8706–0.9579) and predicted the origin of 81% of the domestic sporadic human salmonellosis cases. The most important source was Danish produced pigs (53%) followed by imported pigs (16%), imported broilers (6%), imported ducks (2%), Danish produced layers (2%), Danish produced cattle and imported cattle (<1%) while 18% was not predicted. Machine learning has potential for improving source attribution modeling based on sequence data. Results of such models can inform risk managers to identify and prioritize food safety interventions.

AbstractNext‐generation sequencing (NGS) data present an untapped potential to improve microbial risk assessment (MRA) through increased specificity and redefinition of the hazard. Most of the MRA models do not account for differences in survivability and virulence among strains. The potential of machine learning algorithms for predicting the risk/health burden at the population level while inputting large and complex NGS data was explored with Listeria monocytogenes as a case study. Listeria data consisted of a percentage similarity matrix from genome assemblies of 38 and 207 strains of clinical and food origin, respectively. Basic Local Alignment (BLAST) was used to align the assemblies against a database of 136 virulence and stress resistance genes. The outcome variable was frequency of illness, which is the percentage of reported cases associated with each strain. These frequency data were discretized into seven ordinal outcome categories and used for supervised machine learning and model selection from five ensemble algorithms. There was no significant difference in accuracy between the models, and support vector machine with linear kernel was chosen for further inference (accuracy of 89% [95% CI: 68%, 97%]). The virulence genes FAM002725, FAM002728, FAM002729, InlF, InlJ, Inlk, IisY, IisD, IisX, IisH, IisB, lmo2026, and FAM003296 were important predictors of higher frequency of illness. InlF was uniquely truncated in the sequence type 121 strains. Most important risk predictor genes occurred at highest prevalence among strains from ready‐to‐eat, dairy, and composite foods. We foresee that the findings and approaches described offer the potential for rethinking the current approaches in MRA.

Whole-genome sequencing (WGS) has become a significant tool in investigating foodborne disease outbreaks and some countries have incorporated WGS into national food control systems. However, WGS poses technical challenges that deter developing countries from incorporating it into their food safety management system. A rapid scoping review was conducted, followed by a focus group session, to understand the current situation regarding the use of WGS for foodborne disease surveillance and food monitoring at the global level and identify key limiting factors for developing countries in adopting WGS for their food control systems. The results showed that some developed nations routinely use WGS in their food surveillance systems resulting in more precise understanding of the causes of outbreaks. In developing nations, knowledge of WGS exists in the academic/research sectors; however, there is limited understanding at the government level regarding the usefulness of WGS for food safety regulatory activities. Thus, incorporation of WGS is extremely limited in most developing nations. While some countries lack the capacity to collect and analyze the data generated from WGS, the most significant technical gap in most developing countries is in data interpretation using bioinformatics. The gaps in knowledge and capacities between developed and developing nations regarding use of WGS likely introduce an inequality in international food trade, and thus, relevant international organizations, as well as the countries that are already proficient in the use of WGS, have significant roles in assisting developing nations to be able to fully benefit from the technology and its applications in food safety management.

Introduction: Salmonella enterica serovar Typhimurium ST313 is an invasive and phylogenetically distinct lineage present in sub-Saharan Africa. We report the presence of S. Typhimurium ST313 from patients in the Democratic Republic of Congo and Nigeria. Methodology: Eighteen S. Typhimurium ST313 isolates were characterized by antimicrobial susceptibility testing, pulsed-field gel electrophoresis (PFGE), and multilocus sequence typing (MLST). Additionally, six of the isolates were characterized by whole genome sequence typing (WGST). The presence of a putative virulence determinant was examined in 177 Salmonella isolates belonging to 57 different serovars. Results: All S. Typhimurium ST313 isolates harbored resistant genes encoded by blaTEM1b, catA1, strA/B, sul1, and dfrA1. Additionally, aac(6')1aa gene was detected. Phylogenetic analyses revealed close genetic relationships among Congolese and Nigerian isolates from both blood and stool. Comparative genomic analyses identified a putative virulence fragment (ST313-TD) unique to S. Typhimurium ST313 and S. Dublin. Conclusion: We showed in a limited number of isolates that S. Typhimurium ST313 is a prevalent sequence-type causing gastrointestinal diseases and septicemia in patients from Nigeria and DRC. We found three distinct phylogenetic clusters based on the origin of isolation suggesting some spatial evolution. Comparative genomics showed an interesting putative virulence fragment (ST313-TD) unique to S. Typhimurium ST313 and invasive S. Dublin. © 2013 Leekitcharoenphon et al. ; SCOPUS: ar.j ; info:eu-repo/semantics/published

This work was supported by the Center for Genomic Epidemiology at the Technical University of Denmark funded by grant 09-067103/DSF from the Danish Council for Strategic Research, as well as by grant 3304-FVFP-08 from the Danish Food Industry Agency.

A total of 502 Campylobacter jejuni isolates from poultry in 12 different European countries (10 of them the largest poultry production countries in Europe) were whole genome sequenced to examine the genomic diversity of fluoroquinolone resistant (FQ‐R) and susceptible (FQ‐S) C. jejuni across the poultry producing European countries and to determine whether the emergence of fluoroquinolone resistance among C. jejuni is related to the transmission through countries or to the selection through fluoroquinolone use in the individual countries. A high genomic diversity was observed. The isolates clustered in four main clusters. All trees revealed that the isolates were clustered according to the presence/absence of the gyrA mutations causing fluoroquinolone resistance and ST‐types. The cgMLST trees of only FQ‐R and FQ‐S isolates showed that isolates from the same country of origin were distributed into multiple clusters similarly to the trees combining FQ‐R and FQ‐S isolates. The different phylogenetic methods, ranging from single nucleotide polymorphisms analysis to gene‐by‐gene approaches such as rMLST, cgMLST, wgMLST and core genome tree, provided concordant results, but it is not known which is the most accurate method for identifying the country of origin of the isolates. Allele frequency analysis of isolates under this study and a selection of previously published C. jejuni genomes in ENA showed association of geographical origin of poultry C. jejuni populations between Romania‐Poland, Italy‐Germany‐England, Portugal‐The Netherlands and USA‐Luxemburg. Allele frequency and phylogenetic analysis indicated that the isolates from Finland were genetically different from C. jejuni populations from other European countries included in this study. Trade pattern and antimicrobial use in livestock were not significantly associated with allele frequency or populations of C. jejuni, but data available to investigate these associations were limited.