The dynamics of blaTEM resistance genes in Salmonella Typhi.

Salmonella Typhi (S. Typhi) is an important pathogen causing typhoid fever worldwide. The emergence of antibiotic resistance, including that of blaTEM genes encoding to TEM [Formula: see text]-lactamases has been observed. This study aimed to investigate the dynamics of blaTEM genes in S. Typhi by analyzing the phylogeny and flanking region patterns and phylogenetic associating them with metadata (year, country) and genomic data (genotypes, antibiotic resistance genes (ARGs), plasmids). Genomic sequences of publicly available S. Typhi harboring blaTEM (n = 6079), spanning from 1983 to 2023, were downloaded and analyzed using CSIPhylogeny for phylogeny, Flankophile for identifying genetic contexts around blaTEM genes and GenoTyphi for determining genotypes, ARGs and plasmid replicons. We found that blaTEM-positive isolates occurred most commonly in specific location, especially in Asia and Africa and clustered among a limited number of genotypes. Flankophile identified 740 isolates (12.2%) with distinct flanking region patterns, which were categorized into 13 patterns. Notably, 7 patterns showed a predominantly phylogenetic association with genotypes. Additionally, these 7 patterns exhibited relation to the country, ARGs and plasmid replicons. Further examination of the flanking region patterns provided association with mobile genetic elements (MGEs). Taken together, this study suggests that blaTEM has been acquired by S. Typhi isolates a limited number of times and subsequently spread clonally with specific genotypes.

Predicting Listeria monocytogenes virulence potential using whole genome sequencing and machine learning.

Contamination with food-borne pathogens, such as Listeria monocytogenes, remains a big concern for food safety. Hence, rigorous and continuous microbial surveillance is a standard procedure. At this point, however, the food industry and authorities only focus on detection of Listeria monocytogenes without characterization of individual strains into groups of more or less concern. As whole genome sequencing (WGS) gains increasing interest in the industry, this methodology presents an opportunity to obtain finer resolution of microbial traits such as virulence. Within this study, we therefore aimed to explore the use of WGS in combination with Machine Learning (ML) to predict L. monocytogenes virulence potential on a sub-species level. The WGS datasets used in this study for ML model training consisted of i) national surveillance isolates (n = 169, covering 38 MLST types) and ii) publicly available isolates acquired through the GenomeTrakr network (n = 2880, spanning 80 MLST types). We used the clinical frequency, i.e., ratio of the number of clinical isolates to total amount of isolates, as estimate for virulence potential. The predictive performance of input features from three different genomic levels (i.e., virulence genes, pan-genome genes, and single nucleotide polymorphisms (SNPs)) and six machine learning algorithms (i.e., Support Vector Machine with a linear kernel, Support Vector Machine with a radial kernel, Random Forrest, Neural Networks, LogitBoost, and Majority Voting) were compared. Our machine learning models predicted sub-species virulence potential with nested cross-validation F1-scores up to 0.88 for the majority voting classifier trained on national surveillance data and using pan-genome genes as input features. The validation of the pre-trained ML models based on 101 previously in vivo studied isolates resulted in F1-scores up to 0.76. Furthermore, we found that the more rapid and less computationally intensive raw read alignment yields comparably accurate models as de novo assembly. The results of our study suggest that a majority voting classifier trained on pan-genome genes is the best and most robust choice for the prediction of clinical frequency. Our study contributes to more rapid and precise characterization of L. monocytogenes virulence and its variation on a sub-species level. We further demonstrated a possible application of WGS data in the context of microbial hazard characterization for food safety. In the future, predictive models may assist case-specific microbial risk management in the food industry. The python code, pre-trained models, and prediction pipeline are deposited at (https://github.com/agmei/LmonoVirulenceML).

Association of health, nutrition, and socioeconomic variables with global antimicrobial resistance: a modelling study.

BACKGROUND: Although antimicrobial use is a key selector for antimicrobial resistance, recent studies have suggested that the ecological context in which antimicrobials are used might provide important factors for the prediction of the emergence and spread of antimicrobial resistance. METHODS: We used 1547 variables from the World Bank dataset consisting of socioeconomic, developmental, health, and nutritional indicators; data from a global sewage-based study on antimicrobial resistance (abundance of antimicrobial resistance genes [ARGs]); and data on antimicrobial usage computed from the ECDC database and the IQVIA database. We characterised and built models predicting the global resistome at an antimicrobial class level. We used a generalised linear mixed-effects model to estimate the association between antimicrobial usage and ARG abundance in the sewage samples; a multivariate random forest model to build predictive models for each antimicrobial resistance class and to select the most important variables for ARG abundance; logistic regression models to test the association between the predicted country-level antimicrobial resistance abundance and the country-level proportion of clinical resistant bacterial isolates; finite mixture models to investigate geographical heterogeneities in the abundance of ARGs; and multivariate finite mixture models with covariates to investigate the effect of heterogeneity in the association between the most important variables and the observed ARG abundance across the different country subgroups. We compared our predictions with available clinical phenotypic data from the SENTRY Antimicrobial Surveillance Program from eight antimicrobial classes and 12 genera from 56 countries. FINDINGS: Using antimicrobial use data from between Jan 1, 2016, and Dec 31, 2019, we found that antimicrobial usage was not significantly associated with the global ARG abundance in sewage (p=0·72; incidence rate ratio 1·02 [95% CI 0·92-1·13]), whereas country-specific World Bank's variables explained a large amount of variation. The importance of the World Bank variables differed between antimicrobial classes and countries. Generally, the estimated global ARG abundance was positively associated with the prevalence of clinical phenotypic resistance, with a strong association for bacterial groups in the human gut. The associations between bacterial groups and ARG abundance were positive and significantly different from zero for the aminoglycosides (three of the four of the taxa tested), ß-lactam (all the six microbial groups), fluoroquinolones (seven of nine of the microbial groups), glycopeptide (one microbial group tested), folate pathway antagonists (four of five microbial groups), and tetracycline (two of nine microbial groups). INTERPRETATION: Metagenomic analysis of sewage is a robust approach for the surveillance of antimicrobial resistance in pathogens, especially for bacterial groups associated with the human gut. Additional studies on the associations between important socioeconomic, nutritional, and health factors and antimicrobial resistance should consider the variation in these associations between countries and antimicrobial classes. FUNDING: EU Horizon 2020 and Novo Nordisk Foundation.

An Overview of Antimicrobial Resistance Profiles of Publicly Available Salmonella Genomes with Sufficient Quality and Metadata.

Salmonella enterica (S. enterica) is a commensal organism or pathogen causing diseases in animals and humans, as well as widespread in the environment. Antimicrobial resistance (AMR) has increasingly affected both animal and human health and continues to raise public health concerns. A decade ago, it was estimated that the increased use of whole genome sequencing (WGS) combined with sharing of public data would drastically change and improve the surveillance and understanding of Salmonella epidemiology and AMR. This study aimed to evaluate the current usefulness of public WGS data for Salmonella surveillance and to investigate the associations between serovars, antibiotic resistance genes (ARGs), and metadata. Out of 191,306 Salmonella genomes deposited in European Nucleotide Archive and NCBI databases, 47,452 WGS with sufficient minimum metadata (country, year, and source) of S. enterica were retrieved from 116 countries and isolated between 1905 and 2020. For in silico analysis of the WGS data, KmerFinder, SISTR, and ResFinder were used for species, serovars, and AMR identification, respectively. The results showed that the five common isolation sources of S. enterica are human (29.10%), avian (22.50%), environment (11.89%), water (9.33%), and swine (6.62%). The most common ARG profiles for each class of antimicrobials are ß-lactam (blaTEM-1B; 6.78%), fluoroquinolone [(parC[T57S], qnrB19); 0.87%], folate pathway antagonist (sul2; 8.35%), macrolide [mph(A); 0.39%], phenicol (floR; 5.94%), polymyxin B (mcr-1.1; 0.09%), and tetracycline [tet(A); 12.95%]. Our study reports the first overview of ARG profiles in publicly available Salmonella genomes from online databases. All data sets from this study can be searched at Microreact.

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.

The Microbial Genetic Diversity and Succession Associated with Processing Waters at Different Broiler Processing Stages in an Abattoir in Australia.

The high organic content of abattoir-associated process water provides an alternative for low-cost and non-invasive sample collection. This study investigated the association of microbial diversity from an abattoir processing environment with that of chicken meat. Water samples from scalders, defeathering, evisceration, carcass-washer, chillers, and post-chill carcass rinsate were collected from a large-scale abattoir in Australia. DNA was extracted using the Wizard® Genomic DNA Purification Kit, and the 16S rRNA v3-v4 gene region was sequenced using Illumina MiSeq. The results revealed that the Firmicutes decreased from scalding to evisceration (72.55%) and increased with chilling (23.47%), with the Proteobacteria and Bacteroidota changing inversely. A diverse bacterial community with 24 phyla and 392 genera was recovered from the post-chill chicken, with Anoxybacillus (71.84%), Megamonas (4.18%), Gallibacterium (2.14%), Unclassified Lachnospiraceae (1.87%), and Lactobacillus (1.80%) being the abundant genera. The alpha diversity increased from scalding to chilling, while the beta diversity revealed a significant separation of clusters at different processing points (p = 0.01). The alpha- and beta-diversity revealed significant contamination during the defeathering, with a redistribution of the bacteria during the chilling. This study concluded that the genetic diversity during the defeathering is strongly associated with the extent of the post-chill contamination, and may be used to indicate the microbial quality of the chicken meat.

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.

Prevalence and epidemiological distribution of selected foodborne pathogens in human and different environmental samples in Ethiopia: a systematic review and meta-analysis.

Bacterial Foodborne Pathogens (FBP) are the commonest cause of foodborne illness or foodborne diseases (FBD) worldwide. They contaminate food at any stages in the entire food chain, from farm to dining-table. Among these, the Diarrheagenic Escherichia coli (DEC), Non typhoidal Salmonella (NTS), Shigella spp. and Campylobacter spp. are responsible for a large proportion of illnesses, deaths; and, particularly, as causes of acute diarrheal diseases. Though existing studies indicate the problem may be severe in developing countries like Ethiopia, the evidence is commonly based on fragmented data from individual studies. A review of published and unpublished manuscripts was conducted to obtain information on major FBP and identify the gaps in tracking their source attributions at the human, animal and environmental interface. A total of 1753 articles were initially retrieved after restricting the study period to between January 2000 and July 2020. After the second screening, only 51 articles on the humans and 43 on the environmental sample based studies were included in this review. In the absence of subgroups, overall as well as human stool and environmental sample based pooled prevalence estimate of FBP were analyzed. Since, substantial heterogeneity is expected, we also performed a subgroup analyses for principal study variables to estimate pooled prevalence of FBP at different epidemiological settings in both sample sources. The overall random pooled prevalence estimate of FBP (Salmonella, pathogenic Escherichia coli (E. coli), Shigella and Campylobacter spp.) was 8%; 95% CI: 6.5-8.7, with statistically higher (P <  0.01) estimates in environmental samples (11%) than in human stool (6%). The subgroup analysis depicted that Salmonella and pathogenic E. coli contributed to 5.7% (95% CI: 4.7-6.8) and 11.6% (95% CI: 8.8-15.1) respectively, of the overall pooled prevalence estimates of FBD in Ethiopia. The result of meta-regression showed, administrative regional state, geographic area of the study, source of sample and categorized sample size all significantly contributed to the heterogeneity of Salmonella and pathogenic E. coli estimates. Besides, the multivariate meta- regression indicated the actual study year between 2011 and 2015 was significantly associated with the environmental sample-based prevalence estimates of these FBP. This systematic review and meta-analysis depicted FBP are important in Ethiopia though majority of the studies were conducted separately either in human, animal or environmental samples employing routine culture based diagnostic method. Thus, further FBD study at the human, animal and environmental interface employing advanced diagnostic methods is needed to investigate source attributions of FBD in one health approach.

Potential Influence of Regulation of the Food Value Chain on Prevalence and Patterns of Antimicrobial Resistance: the Case of Tilapia (Oreochromis niloticus).

The current study was designed to evaluate the potential impact of the level of regulation on the prevalence and patterns of antimicrobial agent resistance in bacteria isolated from fish. The study sites included two large lakes and both semiregulated and unregulated fish value chains. A total of 328 bacterial isolates belonging to 11 genera were evaluated for antimicrobial susceptibility testing using the disk diffusion method. The bacterial species were tested against 12 different antibiotics (trimethoprim-sulfamethoxazole, tetracycline, ampicillin, cefotaxime, chloramphenicol, nalidixic acid, amoxicillin, meropenem, ciprofloxacin, nitrofurantoin, cefuroxime, and kanamycin). Data analysis was done to assess the heterogeneity in proportion of resistant bacterial species within and between the two value chains using a random-effects model proposed by DerSimonian and Laird (Control Clin Trials 7:177-188, 1986). Statistical heterogeneity within and between groups was estimated using the Cochran chi-square test and the Cochrane I2 index. The overall proportion of bacterial species resistant to antimicrobial agents in semiregulated and unregulated value chains ranged from 0.00 to 0.88 and 0.09 to 0.95, respectively. Shigella spp. had the highest proportion of bacteria that were resistant to most of the antimicrobial agents used. The bacterial species were highly resistant to ampicillin and amoxicillin, and the highest multidrug resistance capacity was observed in Shigella spp. (18.3%, n = 328), Vibrio spp. (18.3%), and Listeria monocytogenes (12.2%). We observed strong heterogeneity within and between the two value chains regarding proportion of resistant bacterial species. Sun-dried fish in both value chains had significantly high proportions of resistant bacterial species. Comparing the two value chains, the unregulated value chain had a significantly higher proportion of bacterial species that were resistant. In order to mitigate the risk of transmitting antimicrobial-resistant bacteria to consumers along the fish value chain, good manufacturing practices coupled with identification and management of possible sources of contamination are recommended for fish and potentially other foods distributed along the less regulated value chains. IMPORTANCE In order to mitigate the risk of transmitting antimicrobial-resistant bacteria to consumers along the fish value chain, good manufacturing practices coupled with identification and management of possible sources of contamination are recommended for fish and potentially other foods distributed along the less regulated value chains.

Quantitative Microbial Risk Assessment Based on Whole Genome Sequencing Data: Case of Listeria monocytogenes.

The application of high-throughput DNA sequencing technologies (WGS) data remain an increasingly discussed but vastly unexplored resource in the public health domain of quantitative microbial risk assessment (QMRA). This is due to challenges including high dimensionality of WGS data and heterogeneity of microbial growth phenotype data. This study provides an innovative approach for modeling the impact of population heterogeneity in microbial phenotypic stress response and integrates this into predictive models inputting a high-dimensional WGS data for increased precision exposure assessment using an example of Listeria monocytogenes. Finite mixture models were used to distinguish the number of sub-populations for each of the stress phenotypes, acid, cold, salt and desiccation. Machine learning predictive models were selected from six algorithms by inputting WGS data to predict the sub-population membership of new strains with unknown stress response data. An example QMRA was conducted for cultured milk products using the strains of unknown stress phenotype to illustrate the significance of the findings of this study. Increased resistance to stress conditions leads to increased growth, the likelihood of higher exposure and probability of illness. Neglecting within-species genetic and phenotypic heterogeneity in microbial stress response may over or underestimate microbial exposure and eventual risk during QMRA.

Application of Whole-Genome Sequences and Machine Learning in Source Attribution of Salmonella Typhimurium.

Prevention 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.

A High Resolution DNA Melting Curve Analysis for the Rapid and Efficient Molecular Diagnostics of Extended Spectrum ß-Lactamase Determinants from Foodborne Escherichia coli.

The accurate identification of Extended-Spectrum ß-Lactamase (ESBL) genes in Gram-negative bacteria is necessary for surveillance and epidemiological studies of transmission through foods. We report a novel rapid, cheap, and accurate closed tube molecular diagnostic tool based on two multiplex HRM protocols for analysis of the predominant ESBL families encountered in foods. The first multiplex PCR assay targeted blaCTX-M including phylogenetic groups 1 (CTX-M-1-15, including CTX-M-1, CTX-M-3 and CTX-M-15), 2 (CTX-M-2), and 9 (CTX-M-9-14, including CTX-M-9 and CTX-M-14). The second assay involved blaTEM /bla CTX-M /blaSHV, including TEM variants (TEM-1 and TEM-2), SHV-1-56 (SHV-1, SHV-2 and SHV-56), and CTX-M-8-41 (CTX-M-8, CTX-M-25, CTX-M-26 and CTX-M-39 to CTX-M-41). The individual melting curves were differentiated by a temperature shift according to the type of ESBL gene. The specificity and sensitivity of the first assay were 100% and 98%, respectively. For the second assay, the specificity and sensitivity were 87% and 89%, respectively. The detection of ESBL variants or mutations in existing genes was also demonstrated by the subtyping of a variant of the CTXM-1-15. The HRM is a potential tool for the rapid detection of present ß-lactamase genes and their characterization in a highly sensitive, closed-tube, inexpensive method that is applicable in high throughput studies.

Meta-Analysis and Meta-Regression Indicate Dynamic Prevalence and Moderators of Foodborne Pathogens in African Indigenous Fermented Milk.

As more microbiological data for indigenous fermented milk (IFM) becomes available, concern about their microbial safety becomes eminent. Nonetheless, these data are highly fragmented, and a tool is required to integrate existing data and to provide a basis for data-driven decision making for IFM's safety. Therefore, meta-analysis and meta-regression were conducted to estimate the prevalence of foodborne pathogens in IFM and to determine factors influencing the estimated values. Using Africa as a case, searches were systematically made for published data and relevant grey literature. Data from 18 studies in 15 countries were analyzed. Staphylococcus aureus (37%), pathogenic Escherichia coli (16%), Listeria monocytogenes (6%), and Salmonella spp. (3%) were the most prevalent pathogens with a pooled prevalence estimate of 12%. Heterogeneity among prevalence estimates was attributed to sampling point and microbial group but could be moderated by publication year, country cluster, and methods for microbial confirmation. The pooled prevalence estimates increased over time as more studies became available, whereby the odds were higher in studies from 2010 onwards than studies before 2010. From the analyses, S. aureus presented the greatest safety concern in African IFM. Future microbiological studies should take into consideration different IFM sampling points and advanced analytical methods to identify pathogens.

Machine Learning Methods as a Tool for Predicting Risk of Illness Applying Next-Generation Sequencing Data.

Next-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.

Improving hazard characterization in microbial risk assessment using next generation sequencing data and machine learning: Predicting clinical outcomes in shigatoxigenic Escherichia coli.

The ever decreasing cost and increase in throughput of next generation sequencing (NGS) techniques have resulted in a rapid increase in availability of NGS data. Such data have the potential for rapid, reproducible and highly discriminative characterization of pathogens. This provides an opportunity in microbial risk assessment to account for variations in survivability and virulence among strains. A major challenge towards such attempts remains the highly dimensional nature of genomic data versus the number of isolates. Machine learning-based (ML) predictive risk modelling provides a solution to this "curse of dimensionality" while accounting for individual effects that are dependent on interactions with other genetic and environmental factors. This pilot study explores the potential of ML in the prediction of health endpoints resulting from shigatoxigenic E. coli (STEC) infection. Accessory genes in amino acid sequences were used as model input to predict and differentiate health outcomes in STEC infections including diarrhea, bloody diarrhea, hemolytic uremic syndrome and their combinations. Outcomes severity was also distinguished by hospitalization. A matrix of percent similarity between accessory genes and the E. coli genomes was generated and subsequently used as input for ML. The performances of ML algorithms random forest, support vector machine (radial and linear kernel), gradient boosting, and logit boost were compared. Logit boost was the best model showing an outcome prediction accuracy of 0.75 (95% CI: 0.60, 0.86), an excellent or substantial performance (Kappa = 0.72). Important genetic predictors of riskier STEC clinical outcomes included proteins involved in initial attachment to the host cell, persistence of plasmids or genomic islands, conjugative plasmid transfer and formation of sex pili, regulation of locus of enterocyte effacement expression, post-translational acetylation of proteins, facilitation of the rearrangement or deletion of sections within the pathogenic islands and transport macromolecules across the cell envelope. We propose further studies are proposed on the proteins with undefined or unclear functionality. One protein family in particular predicted HUS outcome. Toxin-antitoxin systems are potential stress adaptation markers which may mediate environmental persistence of strains in diverse sources. We foresee the application of ML approach to the set-up of real-time online analysis of whole genome sequence data to estimate the human health risk at the population or strain level. The ML approach is envisaged to support the prediction of more specific STEC clinical endpoints type by inputting isolate sequence data.

Microbiological Safety of Fresh Tilapia ( Oreochromis niloticus) from Kenyan Fresh Water Fish Value Chains.

This study evaluated the microbiological safety of fresh Nile tilapia ( Oreochromis niloticus) from Kenyan fresh water fish value chains. One hundred seventy-six fish samples were analyzed. The microbial counts of hygiene indicators, total viable aerobic count (TVC), total coliforms, and fecal coliforms isolated by using culture techniques were enumerated, and microbial pathogens present in the fish samples were identified and characterized by using molecular methods. The diversity of bacterial isolates was determined by using the Shannon-Weaver diversity index. The mean of TVC in the samples was 4.44 log CFU/g. A comparison with the European Commission and International Commission on Microbiological Specifications for Foods standards showed two fish samples had counts above the 5.00 log CFU/g limit for TVC, and all the fish samples had total coliform and fecal coliform counts above 2.00 and 1.00 log CFU/g, respectively. Pathogenic strains, including Shiga toxin-producing and enteropathogenic Escherichia coli, Listeria monocytogenes, Yersinia enterocolitica, Klebsiella pneumoniae, and Salmonella enterica, were identified in the fish samples. The diversity of 1,608 bacterial isolates was higher in semiregulated chains than unregulated chains. The diversity was also high at the retail stage of the fish value chain. In conclusion, fresh Nile tilapia samples were above some of the set food safety standards and may be a source of foodborne pathogens. Further microbial risk assessment for detected pathogens is recommended to further support public health protection, taking into account growth, inactivation through cooking, processing, survival, and consumption.

Species Distribution and Antimicrobial Profiles of Enterococcus spp. Isolates from Kenyan Small and Medium Enterprise Slaughterhouses.

The present study aimed at identifying and assessing antimicrobial resistance of Enterococcus spp. isolated from small and medium enterprise slaughterhouses in Kenya. In total, 67 isolates were recovered from 48 of 195 samples examined from beef carcasses, personnel, and cutting equipment in five small and medium enterprise slaughterhouses. The isolates were identified by using matrix-assisted laser desorption-ionization time of flight mass spectrometry and screened thereafter for their resistance against 12 antibiotics by using a disk diffusion assay. The isolates ( n = 67) included Enterococcus faecalis (41.8%), Enterococcus mundtii (17.9%), Enterococcus thailandicus (13.4%), Enterococcus faecium (9.0%), Enterococcus hirae (7.5%), Enterococcus casseliflavus (6.0%), and Enterococcus devriesei (4.5%). None of the isolates were resistant to ciprofloxacin, penicillin, ampicillin, vancomycin, nitrofurantoin, teicoplanin, linezolid, and levofloxacin. Resistance to rifampin (46.3%), erythromycin (23.9%), tetracycline (20.9%), and chloramphenicol (7.5%) was distributed among six of the seven species. All E. thailandicus were resistant to rifampin, erythromycin, and tetracycline. E. faecalis was resistant to rifampin (60.7%), tetracycline (17.9%), erythromycin (14.3%), and chloramphenicol (10.7%). Resistance to two or three antibiotics was observed in 26.9% of the enterococci isolates. The isolation of enterococci that are resistant to clinically relevant antibiotics, such as erythromycin, is of a serious concern given the role enterococci play in the transfer of antibiotic resistance genes.

Draft Genome Sequences of Enterococcus mundtii Strains Isolated from Beef Slaughterhouses in Kenya.

We present here draft genome sequences of Enterococcus mundtii strains K7-EM, P2-EM, C11-EM, and H18-EM, which were isolated from slaughterhouse equipment, carcasses, and personnel of small- and medium-sized beef slaughterhouses in Kenya.

Microbial Contamination Level Profiles Attributed to Contamination of Beef Carcasses, Personnel, and Equipment: Case of Small and Medium Enterprise Slaughterhouses.

The microbial contamination level profiles (MCLPs) attributed to contamination of beef carcasses, personnel, and equipment in five Kenyan small and medium enterprise slaughterhouses were determined. Aerobic plate counts, Enterobacteriaceae, Staphylococcus, and Salmonella were used to determine contamination at four different slaughter stages, namely, dehiding, evisceration, splitting, and dispatch. Microbiological criteria of the four microorganisms were used to score contamination levels (CLs) as poor (0), poor to average (1), average (2), or good (3). MCLPs were further assigned to carcasses, personnel, and equipment at each stage by summing up the CL scores. The CL score attributed to aerobic plate count contamination was 2 or 3 for carcasses but 0 for personnel and equipment in almost all slaughterhouses. A score of 0 on carcasses was mostly attributed to Enterobacteriaceae at evisceration and to Salmonella at dehiding and evisceration. In addition, a score of 0 was mostly attributed to Staphylococcus contamination of personnel at dehiding. A score of 3 was attributed mostly to Enterobacteriaceae on hands at splitting, whereas a score of 2 was mostly attributed to the clothes at dehiding and evisceration. A CL score of 3 was mostly attributed to Enterobacteriaceae and Salmonella contamination of equipment at dehiding and splitting, respectively. Although CLs attributed to contamination of carcasses, personnel, and equipment ranged from 0 to 3, the maximum MCLP score of 9 was only attained in carcasses from two slaughterhouses at dehiding and from one slaughterhouse at dispatch. There is, therefore, a lot of room for small and medium enterprise slaughterhouses to improve their food safety objectives by improving food safety management systems at the points characterized by low CL scores.

Microbial Performance of Food Safety Control and Assurance Activities in a Fresh Produce Processing Sector Measured Using a Microbial Assessment Scheme and Statistical Modeling.

Current approaches such as inspections, audits, and end product testing cannot detect the distribution and dynamics of microbial contamination. Despite the implementation of current food safety management systems, foodborne outbreaks linked to fresh produce continue to be reported. A microbial assessment scheme and statistical modeling were used to systematically assess the microbial performance of core control and assurance activities in five Kenyan fresh produce processing and export companies. Generalized linear mixed models and correlated random-effects joint models for multivariate clustered data followed by empirical Bayes estimates enabled the analysis of the probability of contamination across critical sampling locations (CSLs) and factories as a random effect. Salmonella spp. and Listeria monocytogenes were not detected in the final products. However, none of the processors attained the maximum safety level for environmental samples. Escherichia coli was detected in five of the six CSLs, including the final product. Among the processing-environment samples, the hand or glove swabs of personnel revealed a higher level of predicted contamination with E. coli , and 80% of the factories were E. coli positive at this CSL. End products showed higher predicted probabilities of having the lowest level of food safety compared with raw materials. The final products were E. coli positive despite the raw materials being E. coli negative for 60% of the processors. There was a higher probability of contamination with coliforms in water at the inlet than in the final rinse water. Four (80%) of the five assessed processors had poor to unacceptable counts of Enterobacteriaceae on processing surfaces. Personnel-, equipment-, and product-related hygiene measures to improve the performance of preventive and intervention measures are recommended.