Utilizing the Microbiota and Machine Learning Algorithms To Assess Risk of Salmonella Contamination in Poultry Rinsate

A data-driven approach for prioritising microbial and chemical hazards associated with dairy products using open-source databases

This study presents a data-driven approach for classifying food safety alerts related to chemical and microbial contaminants in dairy products using the Rapid Alert System for Food and Feed (RASFF) and the World Health Organization (WHO)'s Global Environmental Monitoring System (GEMS) food contaminants databases. This research aimed to prioritise microbial and chemical hazards based on their presence and severity through exploratory data analysis and to classify the severity of chemical hazards using machine learning (ML) approaches. It identified Listeria monocytogenes, Escherichia coli, Salmonella, Pseudomonas spp., Staphylococcus spp., Bacillus cereus, Clostridium spp., and Cronobacter sakazakii as the microbial hazards of priority in dairy products. The study also prioritised the top ten chemical hazards based on their presence and severity. These hazards include nitrate, nitrite, ergocornine, 3-MCPD ester, lead, arsenic, ochratoxin A, cadmium, mercury, and aflatoxin (G1, B1, G2, B2, G5 and M1). Using ML techniques, the accuracy rate of classifying food safety alerts as either 'serious' or 'non-serious' was up to 98 %. Additionally, the study identified Reference dose (RfD), substance amount, notification type, product, and substance as the most important features affecting the ML models' performance. These ML models (decision trees, random forests, k-nearest neighbors, linear discriminant analysis, and support vector machines) were also validated on an external dataset of RASFF alerts related to chemical contaminants in dairy products. They achieved an accuracy of up to 95.1 %. The study's findings demonstrate the models' robustness and ability to classify food safety alerts related to chemical contaminants in dairy products, even on new data. These results can enhance the development of more effective machine-learning models for classifying food safety alerts related to chemical contaminants in dairy products, highlighting the importance of developing accurate and efficient classification models for timely intervention.

Next-Generation Sequencing for the Detection of Microbial Agents in Avian Clinical Samples

Direct-targeted next-generation sequencing (tNGS), with its undoubtedly superior diagnostic capacity over real-time PCR (RT-PCR), and direct-non-targeted NGS (ntNGS), with its higher capacity to identify and characterize multiple agents, are both likely to become diagnostic methods of choice in the future. tNGS is a rapid and sensitive method for precise characterization of suspected agents. ntNGS, also known as agnostic diagnosis, does not require a hypothesis and has been used to identify unsuspected infections in clinical samples. Implemented in the form of multiplexed total DNA metagenomics or as total RNA sequencing, the approach produces comprehensive and actionable reports that allow semi-quantitative identification of most of the agents present in respiratory, cloacal, and tissue samples. The diagnostic benefits of the use of direct tNGS and ntNGS are high specificity, compatibility with different types of clinical samples (fresh, frozen, FTA cards, and paraffin-embedded), production of nearly complete infection profiles (viruses, bacteria, fungus, and parasites), production of "semi-quantitative" information, direct agent genotyping, and infectious agent mutational information. The achievements of NGS in terms of diagnosing poultry problems are described here, along with future applications. Multiplexing, development of standard operating procedures, robotics, sequencing kits, automated bioinformatics, cloud computing, and artificial intelligence (AI) are disciplines converging toward the use of this technology for active surveillance in poultry farms. Other advances in human and veterinary NGS sequencing are likely to be adaptable to avian species in the future.

Relationship of the Poultry Microbiome to Pathogen Colonization, Farm Management, Poultry Production, and Foodborne Illness Risk Assessment

Despite the continuous progress in food science and technology, the global burden of foodborne illnesses remains substantial, with pathogens in food causing millions of infections each year. Traditional microbiological culture methods are inadequate in detecting the full spectrum of these microorganisms, highlighting the need for more comprehensive detection strategies. This review paper aims to elucidate the relationship between foodborne pathogen colonization and the composition of the poultry microbiome, and how this knowledge can be used for improved food safety. Our review highlights that the relationship between pathogen colonization varies across different sections of the poultry microbiome. Further, our review suggests that the microbiome profile of poultry litter, farm soil, and farm dust may serve as potential indicators of the farm environment's food safety issues. We also agree that the microbiome of processed chicken samples may reveal potential pathogen contamination and food quality issues. In addition, utilizing predictive modeling techniques on the collected microbiome data, we suggest establishing correlations between particular taxonomic groups and the colonization of pathogens, thus providing insights into food safety, and offering a comprehensive overview of the microbial community. In conclusion, this review underscores the potential of microbiome analysis as a powerful tool in food safety, pathogen detection, and risk assessment.

Orally delivered Bacillus subtilis expressing chicken NK-2 peptide stabilizes gut microbiota and enhances intestinal health and local immunity in coccidiosis-infected broiler chickens

We recently reported a stable Bacillus subtilis-carrying chicken NK-lysin peptide (B. subtilis-cNK-2) as an effective oral delivery system of an antimicrobial peptide to the gut with therapeutic effect against Eimeria parasites in broiler chickens. To further investigate the effects of a higher dose of an oral B. subtilis-cNK-2 treatment on coccidiosis, intestinal health, and gut microbiota composition, 100 (14-day-old) broiler chickens were allocated into 4 treatment groups in a randomized design: 1) uninfected control (CON), 2) infected control without B. subtilis (NC), 3) B. subtilis with empty vector (EV), and 4) B. subtilis with cNK-2 (NK). All chickens, except the CON group, were infected with 5,000 sporulated Eimeria acervulina (E. acervulina) oocysts on d 15. Chickens given B. subtilis (EV and NK) were orally gavaged (1 × 10¹² cfu/mL) daily from d 14 to 18. Growth performances were measured on d 6, 9, and 13 postinfection (dpi). Spleen and duodenal samples were collected on 6 dpi to assess the gut microbiota, and gene expressions of gut integrity and local inflammation makers. Fecal samples were collected from 6 to 9 dpi to enumerate oocyst shedding. Blood samples were collected on 13 dpi to measure the serum 3-1E antibody levels. Chickens in the NK group showed significantly improved (P < 0.05) growth performance, gut integrity, reduced fecal oocyst shedding and mucosal immunity compared to NC. Interestingly, there was a distinct shift in the gut microbiota profile in the NK group compared to that of NC and EV chickens. Upon challenge with E. acervulina, the percentage of Firmicutes was reduced and that of Cyanobacteria increased. In NK chickens, however, the ratio between Firmicutes and Cyanobacteria was not affected and was similar to that of CON chickens. Taken together, NK treatment restored dysbiosis incurred by E. acervulina infection and showed the general protective effects of orally delivered B. subtilis-cNK-2 on coccidiosis infection. This includes reduction of fecal oocyst shedding, enhancement of local protective immunity, and maintenance of gut microbiota homeostasis in broiler chickens.

Machine learning-based typing of Salmonella enterica O-serogroups by the Fourier-Transform Infrared (FTIR) Spectroscopy-based IR Biotyper system

BACKGROUND

Salmonella enterica is among the major burdens for public health at global level. Typing of salmonellae below the species level is fundamental for different purposes, but traditional methods are expensive, technically demanding, and time-consuming, and therefore limited to reference centers. Fourier transform infrared (FTIR) spectroscopy is an alternative method for bacterial typing, successfully applied for classification at different infra-species levels.

AIM

This study aimed to address the challenge of subtyping Salmonella enterica at O-serogroup level by using FTIR spectroscopy. We applied machine learning to develop a novel approach for S. enterica typing, using the FTIR-based IR Biotyper® system (IRBT; Bruker Daltonics GmbH & Co. KG, Germany). We investigated a multicentric collection of isolates, and we compared the novel approach with classical serotyping-based and molecular methods.

METHODS

A total of 958 well characterized Salmonella isolates (25 serogroups, 138 serovars), collected in 11 different centers (in Europe and Japan), from clinical, environmental and food samples were included in this study and analyzed by IRBT. Infrared absorption spectra were acquired from water-ethanol bacterial suspensions, from culture isolates grown on seven different agar media. In the first part of the study, the discriminatory potential of the IRBT system was evaluated by comparison with reference typing method/s. In the second part of the study, the artificial intelligence capabilities of the IRBT software were applied to develop a classifier for Salmonella isolates at serogroup level. Different machine learning algorithms were investigated (artificial neural networks and support vector machine). A subset of 88 pre-characterized isolates (corresponding to 25 serogroups and 53 serovars) were included in the training set. The remaining 870 samples were used as validation set. The classifiers were evaluated in terms of accuracy, error rate and failed classification rate.

RESULTS

The classifier that provided the highest accuracy in the cross-validation was selected to be tested with four external testing sets. Considering all the testing sites, accuracy ranged from 97.0% to 99.2% for non-selective media, and from 94.7% to 96.4% for selective media.

CONCLUSIONS

The IRBT system proved to be a very promising, user-friendly, and cost-effective tool for Salmonella typing at serogroup level. The application of machine learning algorithms proved to enable a novel approach for typing, which relies on automated analysis and result interpretation, and it is therefore free of potential human biases. The system demonstrated a high robustness and adaptability to routine workflows, without the need of highly trained personnel, and proving to be suitable to be applied with isolates grown on different agar media, both selective and unselective. Further tests with currently circulating clinical, food and environmental isolates would be necessary before implementing it as a potentially stand-alone standard method for routine use.