Network Approach to Source Attribution of Salmonella enterica Serovar Typhimurium and Its Monophasic Variant

Bayesian evaluation of meat juice ELISA for detecting Salmonella in slaughtered pigs without specifying a cut-off

BACKGROUND

Consumption of pork and pork products is a major source of human infection with Salmonella. Salmonella is typically subclinical in pigs, making it difficult to identify infected pigs. Therefore, effective surveillance of Salmonella in pigs critically relies on good knowledge on how well the diagnostic tests used perform. A test that has been used in several countries for Salmonella monitoring is serological testing of meat juice using an ELISA (MJ ELISA) to detect antibodies against Salmonella. This MJ ELISA data could be used to estimate infection prevalence and trends. However, as the MJ ELISA output is a sample-to-positive (S/P) ratio, which is a continuous outcome rather than a binary (positive/negative) result, the interpretation of this data depends upon a chosen cut-off.

AIM

To apply Bayesian latent class models (BLCMs) to estimate diagnostic accuracy of the MJ ELISA test values in the absence of a gold standard without needing to apply a cut-off.

METHODS AND RESULTS

BLCMs were fitted to data from a UK abattoir survey carried out in 2006 in order to estimate the diagnostic accuracy of MJ ELISA with respect to the prevalence of active Salmonella infection. This survey consisted of a MJ ELISA applied in parallel with the bacteriological testing of caecal contents, carcass swabs and lymph nodes (n = 625). A BLCM was also fitted to the same data but with dichotomisation of the MJ ELISA results, in order to compare with the model using continuous outcomes. Estimates were obtained for sensitivity and specificity of the ELISA over a range of S/P values and for the bacteriological tests and were found to be similar between the models using continuous and dichotomous ELISA outcomes.

CONCLUSION

The Bayesian method without specifying a cut-off does allow prevalence to be inferred without specifying a cut-off for the ELISA. The study results will be useful for estimating infection prevalence from serological surveillance data.

A Review on Microbiological Source Attribution Methods of Human Salmonellosis: From Subtyping to Whole-Genome Sequencing

Salmonella is one of the main causes of human foodborne illness. It is endemic worldwide, with different animals and animal-based food products as reservoirs and vehicles of infection. Identifying animal reservoirs and potential transmission pathways of Salmonella is essential for prevention and control. There are many approaches for source attribution, each using different statistical models and data streams. Some aim to identify the animal reservoir, while others aim to determine the point at which exposure occurred. With the advance of whole-genome sequencing (WGS) technologies, new source attribution models will greatly benefit from the discriminating power gained with WGS. This review discusses some key source attribution methods and their mathematical and statistical tools. We also highlight recent studies utilizing WGS for source attribution and discuss open questions and challenges in developing new WGS methods. We aim to provide a better understanding of the current state of these methodologies with application to Salmonella and other foodborne pathogens that are common sources of illness in the poultry and human sectors.

Comparison of Source Attribution Methodologies for Human Campylobacteriosis

Campylobacter spp. are the most common cause of bacterial gastrointestinal infection in humans both in Denmark and worldwide. Studies have found microbial subtyping to be a powerful tool for source attribution, but comparisons of different methodologies are limited. In this study, we compare three source attribution approaches (Machine Learning, Network Analysis, and Bayesian modeling) using three types of whole genome sequences (WGS) data inputs (cgMLST, 5-Mers and 7-Mers). We predicted and compared the sources of human campylobacteriosis cases in Denmark. Using 7mer as an input feature provided the best model performance. The network analysis algorithm had a CSC value of 78.99% and an F1-score value of 67%, while the machine-learning algorithm showed the highest accuracy (98%). The models attributed between 965 and all of the 1224 human cases to a source (network applying 5mer and machine learning applying 7mer, respectively). Chicken from Denmark was the primary source of human campylobacteriosis with an average percentage probability of attribution of 45.8% to 65.4%, representing Bayesian with 7mer and machine learning with cgMLST, respectively. Our results indicate that the different source attribution methodologies based on WGS have great potential for the surveillance and source tracking of Campylobacter. The results of such models may support decision makers to prioritize and target interventions.

Serotype Screening of Salmonella enterica Subspecies I by Intergenic Sequence Ribotyping (ISR): Critical Updates

(1) Background: Foodborne illness from Salmonella enterica subspecies I is most associated with approximately 32 out of 1600 serotypes. While whole genome sequencing and other nucleic acid-based methods are preferred for serotyping, they require expertise in bioinformatics and often submission to an external agency. Intergenic Sequence Ribotyping (ISR) assigns serotype to Salmonella in coordination with information freely available at the National Center for Biotechnology Information. ISR requires updating because it was developed from 26 genomes while there are now currently 1804 genomes and 1685 plasmids. (2) Methods: Serotypes available for sequencing were analyzed by ISR to confirm primer efficacy and to identify any issues in application. Differences between the 2012 and 2022 ISR database were tabulated, nomenclature edited, and instances of multiple serotypes aligning to a single ISR were examined. (3) Results: The 2022 ISR database has 268 sequences and 40 of these were assigned new NCBI accession numbers that were not previously available. Extending boundaries of sequences resolved hdfR cross-alignment and reduced multiplicity of alignment for 37 ISRs. Comparison of gene cyaA sequences and some cell surface epitopes provided evidence that homologous recombination was potentially impacting results for this subset. There were 99 sequences that still had no match with an NCBI submission. (4) The 2022 ISR database is available for use as a serotype screening method for Salmonella enterica subspecies I. Finding that 36.9% of the sequences in the ISR database still have no match within the NCBI Salmonella enterica database suggests that there is more genomic heterogeneity yet to characterize.

Source Attribution of Human Campylobacteriosis Using Whole-Genome Sequencing Data and Network Analysis

Campylobacter spp. are a leading and increasing cause of gastrointestinal infections worldwide. Source attribution, which apportions human infection cases to different animal species and food reservoirs, has been instrumental in control- and evidence-based intervention efforts. The rapid increase in whole-genome sequencing data provides an opportunity for higher-resolution source attribution models. Important challenges, including the high dimension and complex structure of WGS data, have inspired concerted research efforts to develop new models. We propose network analysis models as an accurate, high-resolution source attribution approach for the sources of human campylobacteriosis. A weighted network analysis approach was used in this study for source attribution comparing different WGS data inputs. The compared model inputs consisted of cgMLST and wgMLST distance matrices from 717 human and 717 animal isolates from cattle, chickens, dogs, ducks, pigs and turkeys. SNP distance matrices from 720 human and 720 animal isolates were also used. The data were collected from 2015 to 2017 in Denmark, with the animal sources consisting of domestic and imports from 7 European countries. Clusters consisted of network nodes representing respective genomes and links representing distances between genomes. Based on the results, animal sources were the main driving factor for cluster formation, followed by type of species and sampling year. The coherence source clustering (CSC) values based on animal sources were 78%, 81% and 78% for cgMLST, wgMLST and SNP, respectively. The CSC values based on Campylobacter species were 78%, 79% and 69% for cgMLST, wgMLST and SNP, respectively. Including human isolates in the network resulted in 88%, 77% and 88% of the total human isolates being clustered with the different animal sources for cgMLST, wgMLST and SNP, respectively. Between 12% and 23% of human isolates were not attributed to any animal source. Most of the human genomes were attributed to chickens from Denmark, with an average attribution percentage of 52.8%, 52.2% and 51.2% for cgMLST, wgMLST and SNP distance matrices respectively, while ducks from Denmark showed the least attribution of 0% for all three distance matrices. The best-performing model was the one using wgMLST distance matrix as input data, which had a CSC value of 81%. Results from our study show that the weighted network-based approach for source attribution is reliable and can be used as an alternative method for source attribution considering the high performance of the model. The model is also robust across the different Campylobacter species, animal sources and WGS data types used as input.

Phylogeographic Clustering Suggests that Distinct Clades of Salmonella enterica Serovar Mississippi Are Endemic in Australia, the United Kingdom, and the United States

Salmonella enterica serovar Mississippi is the 2nd and 14th leading cause of human clinical salmonellosis in the Australian island state of Tasmania and the United States, respectively. Despite its public health relevance, relatively little is known about this serovar. Comparison of whole-genome sequence (WGS) data of S. Mississippi isolates with WGS data for 317 additional S. enterica serovars placed one clade of S. Mississippi within S. enterica clade B (“clade B Mississippi”) and the other within section Typhi in S. enterica clade A (“clade A Mississippi”), suggesting that these clades evolved from different ancestors. Phylogenetic analysis of 364 S. Mississippi isolates from Australia, the United Kingdom, and the United States suggested that the isolates cluster geographically, with U.S. and Australian isolates representing different subclades (Ai and Aii, respectively) within clade A Mississippi and clade B isolates representing the predominant S. Mississippi isolates in the United Kingdom. Intraclade comparisons suggested that different mobile elements, some of which encode virulence factors, are responsible for the observed differences in gene content among isolates within these clades. Specifically, genetic differences among clade A isolates reflect differences in prophage contents, while differences among clade B isolates are due to the acquisition of a 47.1-kb integrative conjugative element (ICE). Phylogenies inferred from antigenic components (fliC, fljB, and O-antigen-processing genes) support that clade A and B Mississippi isolates acquired these loci from different ancestral serovars. Overall, these data support that different S. Mississippi phylogenetic clades are endemic in Australia, the United Kingdom, and the United States.

IMPORTANCE The number of known so-called “polyphyletic” serovars (i.e., phylogenetically distinct clades with the same O and H antigenic formulas) continues to increase as additional Salmonella isolates are sequenced. While serotyping remains a valuable tool for reporting and monitoring Salmonella, more discriminatory analyses for classifying polyphyletic serovars may improve surveillance efforts for these serovars, as we found that for S. Mississippi, distinct genotypes predominate at different geographic locations. Our results suggest that the acquisition of genes encoding O and H antigens from different ancestors led to the emergence of two Mississippi clades. Furthermore, our results suggest that different mobile elements contribute to the microevolution and diversification of isolates within these two clades, which has implications for the acquisition of novel adaptations, such as virulence factors.

Recent Advances in Our Understanding of the Diversity and Roles of Chaperone-Usher Fimbriae in Facilitating Salmonella Host and Tissue Tropism

Salmonella enterica is one of the most diverse and successful pathogens, representing a species with >2,600 serovars with a variety of adaptations that enable colonization and infection of a wide range of hosts. Fimbriae, thin hair-like projections that cover the surface of Salmonella, are thought to be the primary organelles that mediate Salmonella's interaction with, and adherence to, the host intestinal epithelium, representing an important step in the infection process. The recent expansion in genome sequencing efforts has enabled the discovery of novel fimbriae, thereby providing new perspectives on fimbrial diversity and distribution among a broad number of serovars. In this review, we provide an updated overview of the evolutionary events that shaped the Salmonella chaperone-usher fimbriome in light of recent phylogenetic studies describing the population structure of Salmonella enterica. Furthermore, we discuss the complexities of the chaperone-usher fimbriae-mediated host-pathogen interactions and the apparent redundant roles of chaperone-usher fimbriae in host and tissue tropism.

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

The purpose of the study was to apply a Bayesian source attribution model to England and Wales based data on Salmonella Typhimurium (ST) and monophasic variants (MST), using different subtyping approaches based on sequence data. The data consisted of laboratory confirmed human cases and mainly livestock samples collected from surveillance or monitoring schemes. Three different subtyping methods were used, 7-loci Multi-Locus Sequence Typing (MLST), Core-genome MLST, and Single Nucleotide Polymorphism distance, with the impact of varying the genetic distance over which isolates would be grouped together being varied for the latter two approaches. A Bayesian frequency matching method, known as the modified Hald method, was applied to the data from each of the subtyping approaches. Pigs were found to be the main contributor to human infection for ST/MST, with approximately 60% of human cases attributed to them, followed by other mammals (mostly horses) and cattle. It was found that the use of different clustering methods based on sequence data had minimal impact on the estimates of source attribution. However, there was an impact of genetic distance over which isolates were grouped: grouping isolates which were relatively closely related increased uncertainty but tended to have a better model fit.