Meta-analysis of pulsed-field gel electrophoresis fingerprints based on a constructed Salmonella database

High Genetic Diversity in Flavobacterium psychrophilum Isolates from Healthy Rainbow Trout (Oncorhynchus mykiss) Farmed in the Same Watershed, Revealed by Two Typing Methods

Flavobacterium psychrophilum affects salmonid health worldwide and causes economic losses. The genetic diversity of the pathogen must be considered to develop control methods. However, previous studies have reported both high and low levels of genetic diversity. The present longitudinal study aimed at assessing the genetic diversity of F. psychrophilum at a small temporal and geographic scale. Four farms located on the same watershed in France were studied. Rainbow trout (Oncorhynchus mykiss) batches were monitored, and apparently healthy individuals were sampled over 1 year. A total of 288 isolates were recovered from fish organs (gills and spleen) and eggs. Pulsed field gel electrophoresis revealed high genetic diversity. Multilocus sequence typing performed on a selection of 31 isolates provided congruent results, as follows: 18 sequence types (STs) were found, of which 13 were novel. The mean gene diversity (H = 0.8413) was much higher than that previously reported for this host species, although the sampling was restricted to a single watershed and 1 year. Seven isolates out of 31 were assigned to clonal complex ST10 (CC-ST10), which is the predominant clonal complex in the main salmonid production areas. A split decomposition tree reflected a panmictic population. This finding is important for aquaculture veterinarians in their diagnostic procedure, as the choice of adequate antibiotic treatment is conditioned by the correct identification of the causative agent. Furthermore, this study expands our knowledge on genetic diversity required for the development of an effective vaccine against F. psychrophilum IMPORTANCE The bacterium Flavobacterium psychrophilum is a serious pathogen in many fish species, especially salmonids, that is responsible for considerable economic losses worldwide. In order to treat infections and to develop vaccines, the genetic diversity of this bacterium needs to be known. We assessed the genetic diversity of F. psychrophilum isolates from apparently healthy rainbow trout raised in several fish farms in the same watershed in France. Two different genotyping methods revealed high diversity. The majority of isolates were unrelated to clonal complex sequence type 10 (CC-ST10), the clonal complex that is predominant worldwide and associated with disease in rainbow trout. In addition, we found 13 novel sequence types. These results suggest that a diverse subpopulation of F. psychrophilum may be harbored by rainbow trout.

Epidemiology of streptomycin resistant Salmonella from humans and animals in Ethiopia: A systematic review and meta-analysis

BACKGROUND

Streptomycin is used as an epidemiological marker in monitoring programs for antimicrobial resistance in Salmonella serovars and indicates the presence of pentaresistance. However, comprehensive data on streptomycin resistant Salmonella among human, animal, and animal products is lacking in Ethiopia. In this review, we aimed to assess heterogeneity and pooled proportion of Salmonella serovars to streptomycin resistance among human, animal and animal products in Ethiopia.

METHODS

We conducted a systematic review and meta-analysis of published literature from Ethiopia. We used the MEDLINE/ PubMed, Embase, Cochrane Library, and Google Scholar databases to identify genetic and phenotypic data on Salmonella isolates. To determine the heterogeneity and pooled proportion, we used metaprop commands and the random-effects model. Relative and cumulative frequencies were calculated to describe the overall preponderance of streptomycin resistance isolates after arcsine-transformed data. Metan funnel and meta-bias using a begg test were performed to check for publication bias.

RESULTS

Overall, we included 1475 Salmonella serovars in this meta-analysis. The pooled proportion of streptomycin resistance was 47% (95% CI: 35-60%). Sub-group analysis by target population showed that the proportion of streptomycin resistance in Salmonella serovars was 54% (95% CI: 35-73%) in animal, 44% (95% Cl: 33-59%) in humans and 39% (95% CI: 24-55%) in animals products. The streptomycin resistant Salmonella serovars were statistically increasing from 0.35(95% CI: 0.12-0.58) in 2003 to 0.77(95% CI: 0.64-0.89) in 2018. The level of multidrug-resistant (MDR) Salmonella serovars was 50.1% in the meta-analysis.

CONCLUSION

We found a high level of streptomycin resistance, including multidrug, Salmonella serovars among human, animals, and animal products. This resistance was significantly increasing in the last three decades (1985-2018). The resistance to streptomycin among Salmonella serovars isolated from animals was higher than humans. This mandates the continuous monitoring of streptomycin use and practicing one health approach to preventing further development of resistance in Ethiopia.

REGISTRATION

We conducted a systematic review and meta-analysis after registration of the protocol in PROSPERO (CRD42019135116) following the MOOSE (Meta-Analysis of Observational Studies in Epidemiology).

Pulsed Field Gel Electrophoresis: Past, present, and future

Pulsed Field Gel Electrophoresis (PFGE) has been considered for many years the 'gold-standard' for characterizing many pathogenic organisms as well as for subtyping bacterial species causing infection outbreaks. This article reviews the basic principles of PFGE and it includes the main advantages and limitations of the different electrode configurations that have been used in PFGE equipment and their influence on the DNA electrophoretic separation. Remarkably, we summarize here the most relevant theoretical and practical aspects that we have learned for more than 20 years developing and using the miniaturized PFGE systems. We also discussed the theoretical aspects related to DNA migration in PFGE agarose gels. It served as the basis for simulating the DNA electrophoretic patterns in CHEF mini gels and mini-chambers during experimental design and optimization. A critical comparison between standard and miniaturized PFGE systems, as well as the enzymatic and non-enzymatic methods for intact immobilized DNA preparation, is provided throughout the review. The PFGE current applications, advantages, limitations and future challenges of the methodology are also discussed.

Prevalence, Serotyping, Molecular Typing, and Antimicrobial Resistance of Salmonella Isolated From Conventional and Organic Retail Ground Poultry

Ground poultry is marketed as a healthier alternative to ground beef despite the fact that poultry is a major source of foodborne Salmonella. The objectives of this study were to determine the prevalence of Salmonella in Oklahoma retail ground poultry and to characterize representative isolates by serotyping, antimicrobial resistance, PFGE patterns, and large plasmid profiling. A total of 199 retail ground poultry samples (150 ground turkey and 49 ground chicken) were investigated. The overall prevalence of Salmonella in ground poultry was 41% (82/199), and the incidence in conventional samples (47%, 66/141) was higher than in organic samples (27%, 16/58). The prevalence of Salmonella in organic ground chicken and organic ground turkey was 33% (3/9) and 26% (13/49), respectively. Twenty six Salmonella isolates (19 conventional and 7 organic) were chosen for further characterization. The following six serotypes and number of isolates per serotype were identified as follows: Tennessee, 8; Saintpaul, 4; Senftenberg, 4; Anatum, 4 (one was Anatum_var._15+); Ouakam, 3; and Enteritidis, 3. Resistance to 16 tested antimicrobials was as follows: gentamycin, 100% (26/26); ceftiofur, 100% (26/26); amoxicillin/clavulanic acid, 96% (25/26); streptomycin, 92% (24/26); kanamycin, 88% (23/26); ampicillin, 85% (22/26); cephalothin, 81% (21/26); tetracycline, 35% (9/26); sulfisoxazole, 27% (7/26); nalidixic acid, 15% (4/26); and cefoxitin, 15% (4/26). All isolates were susceptible to amikacin, chloramphenicol, ceftriaxone, and trimethoprim/sulfamethoxazole. All screened isolates were multidrug resistant (MDR) and showed resistance to 4-10 antimicrobials; isolates from organic sources showed resistance to 5-7 antimicrobials. PFGE was successful in clustering the Salmonella isolates into distinct clusters that each represented one serotype. PFGE was also used to investigate the presence of large plasmids using S1 nuclease digestion. A total of 8/26 (31%) Salmonella isolates contained a ∼100 Kb plasmid that was present in all Anatum and Ouakam isolates. In conclusion, the presence of multidrug resistant Salmonella with various serotypes, PFGE profiles, and large plasmids in ground poultry stresses the importance of seeking novel interventions to reduce the risk of this foodborne pathogen. Multidrug resistance (MDR) is considered a high additional risk and continued surveillance at the retail level could minimize the risk for the consumer.

Phylogroup and virulence gene association with clinical characteristics of Escherichia coli urinary tract infections from dogs and cats

Escherichia coli isolates from infections outside the gastrointestinal tract are termed extra-intestinal pathogenic E. coli (ExPEC) and can be divided into different subpathotypes; one of these is uropathogenic E. coli (UPEC). The frequency with which UPEC strains cause urinary tract infections in dogs and cats is not well documented. We used an oligonucleotide microarray to characterize 60 E. coli isolates associated with the urinary tract of dogs ( n = 45) and cats ( n = 15), collected from 2004 to 2007, into ExPEC and UPEC and to correlate results with patient clinical characteristics. Microarray analysis was performed, and phylogroup was determined by a quadruplex PCR assay. Isolates that were missing 1 or 2 of the gene determinants representative of a function (capsule, iron uptake related genes, or specific adhesins) were designated as "non-classifiable" by microarray. Phylogroup B2 was positively associated with the UPEC subpathotype ( p < 0.0005) and negatively associated with "non-classifiable" isolates ( p < 0.0005). Phylogroup D was positively associated with ExPEC pathotype ( p = 0.025) and negatively associated with UPEC subpathotype ( p = 0.014). The ExPEC pathotype was positively associated with hospitalization for one or more days ( p = 0.031). The UPEC subpathotype was negatively associated with previous antimicrobial therapy ( p = 0.045) and previous hospitalization within the 3 mo prior to the positive culture ( p = 0.041). The UPEC subpathotype was positively associated with prostatitis ( p = 0.073) and negatively associated with current immunosuppressive therapy ( p = 0.090). Our results indicate that the case history observations may be critically important during the interpretation of laboratory results to encourage judicious use of antimicrobials.

A Salmonella Enterica Subsp. Enterica Serovar Enteritidis Foodborne Outbreak after Consumption of Homemade Lasagne

In the latest year, and also in 2013, Salmonella was the most frequently detected causative agent in foodborne outbreaks (FBOs) reported in Europe. As indicated in EFSA report (2015) the serotypes mostly associated to FBOs are S. Typhimurium and Enteritidis; while Salmonella Typhimurium is generally associated with the consumption of contaminated pork and beef, FBOs due to Salmonella Enteritidis are linked to eggs and poultry meat. In this study it is described the investigation of a domestic FBO involving four adults and linked to homemade lasagne. Investigations were performed to determine the relatedness of Salmonella strains, identify the sources of infection, and trace the routes of Salmonella contamination in this FBO. Salmonella strains were isolated in 3 out of 4 patient stool samples and from lasagne and all of them were serotyped as S. Enteritidis. Pulsed-field gel electrophoresis (PFGE) analysis revealed the genotypical similarity of all the strains. Although serotyping and PFGE analysis identified the common food source of infection in this FBO, it was not possible to determine how or at what point during food preparation the lasagne became contaminated with Salmonella.

Usefulness of pulsed-field gel electrophoresis profiles for the determination of Salmonella serovars

We created a database consisting of a large number of Salmonella pulsed-field gel electrophoresis (PFGE) profiles covering a wide range of different serovars. This database was used for the prediction of the serovars based on the PFGE profiles for isolates from Taiwan and Denmark. The PFGE profiles proved very useful in the determination of a serovar although serovar prediction was more efficient for local isolates than those from a distant geographic area. To use a highly stringent band matching tolerance in the BioNumerics software is also important for the grouping of serovars.

Pulsed-field gel electrophoresis of Salmonella enterica

Pulsed-field gel electrophoresis (PFGE) is considered a "gold standard" for the molecular characterization of a number of bacterial strains. Its strength relies on its high discriminatory power, together with its high reproducibility. For many years, an international network, PulseNet International, allows the rapid comparison of PFGE data obtained all over the world, and it provides a valuable tool to promptly recognize the epidemiological dynamics of many pathogens, including Salmonella enterica. Here we describe the laboratory procedure to perform the standardized protocol for the PFGE typing of S. enterica strains.

A composite model for subgroup identification and prediction via bicluster analysis

Background

A major challenges in the analysis of large and complex biomedical data is to develop an approach for 1) identifying distinct subgroups in the sampled populations, 2) characterizing their relationships among subgroups, and 3) developing a prediction model to classify subgroup memberships of new samples by finding a set of predictors. Each subgroup can represent different pathogen serotypes of microorganisms, different tumor subtypes in cancer patients, or different genetic makeups of patients related to treatment response.

Methods

This paper proposes a composite model for subgroup identification and prediction using biclusters. A biclustering technique is first used to identify a set of biclusters from the sampled data. For each bicluster, a subgroup-specific binary classifier is built to determine if a particular sample is either inside or outside the bicluster. A composite model, which consists of all binary classifiers, is constructed to classify samples into several disjoint subgroups. The proposed composite model neither depends on any specific biclustering algorithm or patterns of biclusters, nor on any classification algorithms.

Results

The composite model was shown to have an overall accuracy of 97.4% for a synthetic dataset consisting of four subgroups. The model was applied to two datasets where the sample’s subgroup memberships were known. The procedure showed 83.7% accuracy in discriminating lung cancer adenocarcinoma and squamous carcinoma subtypes, and was able to identify 5 serotypes and several subtypes with about 94% accuracy in a pathogen dataset.

Conclusion

The composite model presents a novel approach to developing a biclustering-based classification model from unlabeled sampled data. The proposed approach combines unsupervised biclustering and supervised classification techniques to classify samples into disjoint subgroups based on their associated attributes, such as genotypic factors, phenotypic outcomes, efficacy/safety measures, or responses to treatments. The procedure is useful for identification of unknown species or new biomarkers for targeted therapy.

Topic modeling for cluster analysis of large biological and medical datasets

BACKGROUND

The big data moniker is nowhere better deserved than to describe the ever-increasing prodigiousness and complexity of biological and medical datasets. New methods are needed to generate and test hypotheses, foster biological interpretation, and build validated predictors. Although multivariate techniques such as cluster analysis may allow researchers to identify groups, or clusters, of related variables, the accuracies and effectiveness of traditional clustering methods diminish for large and hyper dimensional datasets. Topic modeling is an active research field in machine learning and has been mainly used as an analytical tool to structure large textual corpora for data mining. Its ability to reduce high dimensionality to a small number of latent variables makes it suitable as a means for clustering or overcoming clustering difficulties in large biological and medical datasets.

RESULTS

In this study, three topic model-derived clustering methods, highest probable topic assignment, feature selection and feature extraction, are proposed and tested on the cluster analysis of three large datasets: Salmonella pulsed-field gel electrophoresis (PFGE) dataset, lung cancer dataset, and breast cancer dataset, which represent various types of large biological or medical datasets. All three various methods are shown to improve the efficacy/effectiveness of clustering results on the three datasets in comparison to traditional methods. A preferable cluster analysis method emerged for each of the three datasets on the basis of replicating known biological truths.

CONCLUSION

Topic modeling could be advantageously applied to the large datasets of biological or medical research. The three proposed topic model-derived clustering methods, highest probable topic assignment, feature selection and feature extraction, yield clustering improvements for the three different data types. Clusters more efficaciously represent truthful groupings and subgroupings in the data than traditional methods, suggesting that topic model-based methods could provide an analytic advancement in the analysis of large biological or medical datasets.

Phylogenetic diversity of the enteric pathogen Salmonella enterica subsp. enterica inferred from genome-wide reference-free SNP characters

The enteric pathogen Salmonella enterica is one of the leading causes of foodborne illness in the world. The species is extremely diverse, containing more than 2,500 named serovars that are designated for their unique antigen characters and pathogenicity profiles—some are known to be virulent pathogens, while others are not. Questions regarding the evolution of pathogenicity, significance of antigen characters, diversity of clustered regularly interspaced short palindromic repeat (CRISPR) loci, among others, will remain elusive until a strong evolutionary framework is established. We present the first large-scale S. enterica subsp. enterica phylogeny inferred from a new reference-free k-mer approach of gathering single nucleotide polymorphisms (SNPs) from whole genomes. The phylogeny of 156 isolates representing 78 serovars (102 were newly sequenced) reveals two major lineages, each with many strongly supported sublineages. One of these lineages is the S. Typhi group; well nested within the phylogeny. Lineage-through-time analyses suggest there have been two instances of accelerated rates of diversification within the subspecies. We also found that antigen characters and CRISPR loci reveal different evolutionary patterns than that of the phylogeny, suggesting that a horizontal gene transfer or possibly a shared environmental acquisition might have influenced the present character distribution. Our study also shows the ability to extract reference-free SNPs from a large set of genomes and then to use these SNPs for phylogenetic reconstruction. This automated, annotation-free approach is an important step forward for bacterial disease tracking and in efficiently elucidating the evolutionary history of highly clonal organisms.

Plasmid profile and pulsed-field gel electrophoresis analysis of Salmonella enterica isolates from humans in Turkey

This study was conducted for typing Salmonella enterica subspecies enterica strains in Turkey using pulsed–field gel electrophoresis (PFGE) and plasmid DNA profile analysis. Fourty-two strains were isolated from clinical samples obtained from unrelated patients with acute diarrhea. The samples were collected from state hospitals and public health laboratories located at seven provinces in different regions of Turkey at different times between 2004 and 2010. The strains were determined to belong to 4 different serovars. The Salmonella enterica strains belonged to the serovars Salmonella Enteritidis (n = 23), Salmonella Infantis (n = 14), Salmonella Munchen (n = 2), and Salmonella Typhi (n = 3). Forty-two Salmonella enterica strains were typed with PFGE methods using XbaI restriction enzyme and plasmid analysis. At the end of typing, 11 different PFGE band profiles were obtained. Four different PFGE profiles (type 1, 4, 9, and 10) were found among serotype S. Enteritidis species, 3 different PFGE profiles (type 3, 5, 6) were found among S. Infantis species, 2 different PFGE profiles were found among S. Typhi species (type 2 and 11), and 2 different PFGE profiles were found among S. Munchen species (type 7, 8). The UPGMA dendrogram was built on the PFGE profiles. In this study, it was determined that 4 strains of 42 Salmonella enterica strains possess no plasmid, while the isolates have 1–3 plasmids ranging from 5.0 to 150 kb and making 12 different plasmid profiles (P1–P12). In this study, we have applied the analysis of the PFGE patterns and used bioinformatics methods to identify both inter and intra serotype relationships of 4 frequently encountered serotypes for the first time in Turkey.

Genetic diversity of food-isolated Salmonella strains through Pulsed Field Gel Electrophoresis (PFGE) and Enterobacterial Repetitive Intergenic Consensus (ERIC-PCR)

All over the world, the incidence of Salmonella spp contamination on different food sources like broilers, clams and cow milk has increased rapidly in recent years. The multifaceted properties of Salomnella serovars allow the microorganism to grow and multiply in various food matrices, even under adverse conditions. Therefore, methods are needed to detect and trace this pathogen along the entire food supply network. In the present work, PFGE and ERIC-PCR were used to subtype 45 Salmonella isolates belonging to different serovars and derived from different food origins. Among these isolates, S. Enteritidis and S. Kentucky were found to be the most predominant serovars. The Discrimination Index obtained by ERIC-PCR (0.85) was slightly below the acceptable confidence value. The best discriminatory ability was observed when PFGE typing method was used alone (DI = 0.94) or combined with ERIC-PCR (DI = 0.93). A wide variety of profiles was observed between the different serovars using PFGE or/and ERIC-PCR. This diversity is particularly important when the sample origins are varied and even within the same sampling origin.

Data mining tools for Salmonella characterization: application to gel-based fingerprinting analysis

Background

Pulsed field gel electrophoresis (PFGE) is currently the most widely and routinely used method by the Centers for Disease Control and Prevention (CDC) and state health labs in the United States for Salmonella surveillance and outbreak tracking. Major drawbacks of commercially available PFGE analysis programs have been their difficulty in dealing with large datasets and the limited availability of analysis tools. There exists a need to develop new analytical tools for PFGE data mining in order to make full use of valuable data in large surveillance databases.

Results

In this study, a software package was developed consisting of five types of bioinformatics approaches exploring and implementing for the analysis and visualization of PFGE fingerprinting. The approaches include PFGE band standardization, Salmonella serotype prediction, hierarchical cluster analysis, distance matrix analysis and two-way hierarchical cluster analysis. PFGE band standardization makes it possible for cross-group large dataset analysis. The Salmonella serotype prediction approach allows users to predict serotypes of Salmonella isolates based on their PFGE patterns. The hierarchical cluster analysis approach could be used to clarify subtypes and phylogenetic relationships among groups of PFGE patterns. The distance matrix and two-way hierarchical cluster analysis tools allow users to directly visualize the similarities/dissimilarities of any two individual patterns and the inter- and intra-serotype relationships of two or more serotypes, and provide a summary of the overall relationships between user-selected serotypes as well as the distinguishable band markers of these serotypes. The functionalities of these tools were illustrated on PFGE fingerprinting data from PulseNet of CDC.

Conclusions

The bioinformatics approaches included in the software package developed in this study were integrated with the PFGE database to enhance the data mining of PFGE fingerprints. Fast and accurate prediction makes it possible to elucidate Salmonella serotype information before conventional serological methods are pursued. The development of bioinformatics tools to distinguish the PFGE markers and serotype specific patterns will enhance PFGE data retrieval, interpretation and serotype identification and will likely accelerate source tracking to identify the Salmonella isolates implicated in foodborne diseases.