Using GWAS and Machine Learning to Identify and Predict Genetic Variants Associated with Foodborne Bacteria Phenotypic Traits.

One of the main challenges in food microbiology is to prevent the risk of outbreaks by avoiding the distribution of food contaminated by bacteria. This requires constant monitoring of the circulating strains throughout the food production chain. Bacterial genomes contain signatures of natural evolution and adaptive markers that can be exploited to better understand the behavior of pathogen in the food industry. The monitoring of foodborne strains can therefore be facilitated by the use of these genomic markers capable of rapidly providing essential information on isolated strains, such as the source of contamination, risk of illness, potential for biofilm formation, and tolerance or resistance to biocides. The increasing availability of large genome datasets is enhancing the understanding of the genetic basis of complex traits such as host adaptation, virulence, and persistence. Genome-wide association studies have shown very promising results in the discovery of genomic markers that can be integrated into rapid detection tools. In addition, machine learning has successfully predicted phenotypes and classified important traits. Genome-wide association and machine learning tools have therefore the potential to support decision-making circuits intending at reducing the burden of foodborne diseases. The aim of this chapter review is to provide knowledge on the use of these two methods in food microbiology and to recommend their use in the field.

Direct or DNA Extraction-Free Amplification and Quantification of Foodborne Pathogens.

The use of direct nucleic acid amplification of pathogens from food matrices has the potential to reduce time to results over DNA extraction-based approaches as well as traditional culture-based approaches. Here we describe protocols for assay design and experiments for direct amplification of foodborne pathogens in food sample matrices using loop-mediated isothermal amplification (LAMP) and polymerase chain reaction (PCR). The examples provided include the detection of Escherichia coli in milk samples and Salmonella in pork meat samples. This protocol includes relevant reagents and methods including obtaining target sequences, assay design, sample processing, and amplification. These methods, though used for specific example matrices, could be applied to many other foodborne pathogens and sample types.

The Use of Multiplex Real-Time PCR for the Simultaneous Detection of Foodborne Bacterial Pathogens.

Foodborne pathogens continue to be a major health concern worldwide. Culture-dependent methodologies are still considered the gold standard to perform pathogen detection and quantification. These methods present several drawbacks, such as being time-consuming and labor intensive. The implementation of real-time PCR has allowed to overcome these limitations, and even reduce the cost associated with the analyses, due to the possibility of simultaneously and accurately detecting several pathogens in one single assay, with results comparable to those obtained by classical approaches. In this chapter, a protocol for the simultaneous detection of two of the most important foodborne pathogens, Salmonella spp. and Listeria monocytogenes, is described.

Viability Detection of Foodborne Bacterial Pathogens in Food Environment by PMA-qPCR and by Microscopy Observation.

Foodborne pathogens are responsible for foodborne diseases and food poisoning and thus pose a great threat to food safety. These microorganisms can adhere to surface and form a biofilm composed of an extracellular matrix. This matrix protects bacterial cells from industrial environmental stress factors such as cleaning and disinfection operations. Moreover, during these environmental stresses, many bacterial species can be entered in a viable but nonculturable (VBNC) state. VBNC cells are characterized by an active metabolism and a loss of cultivability on conventional bacteriological agar. This leads to an underestimation of total viable cells in environmental samples and thus may pose a risk for public health. In this chapter, we present a method to detect viable population of foodborne pathogens in industrial environmental samples using a molecular method combining propidium monoazide (PMA) and quantitative PCR (qPCR) and a fluorescence microscopic method associated with the LIVE/DEAD BacLight™ viability stain.

Application of MinION Long-Read Sequencer for Semi-targeted Detection of Foodborne Pathogens.

Foodborne pathogens remain a serious health issue in developed and developing countries. Safeness of food products has been assured for years with culture-based microbiological methods; however, these present several limitations such as turnaround time and extensive hands-on work, which have been typically address taking advantage of DNA-based methods such as real-time PCR (qPCR). These, and other similar techniques, are targeted assays, meaning that they are directed for the specific detection of one specific microbe. Even though reliable, this approach suffers from an important limitation that unless specific assays are design for every single pathogen potentially present, foods may be considered erroneously safe. To address this problem, next-generation sequencing (NGS) can be used as this is a nontargeted method; thus it has the capacity to detect every potential threat present. In this chapter, a protocol for the simultaneous detection and preliminary serotyping of Salmonella enterica serovar Enteritidis, Salmonella enterica serovar Typhimurium, Listeria monocytogenes, and Escherichia coli O157:H7 is described.

Method to Study the Survival Abilities of Foodborne Bacterial Pathogens Under Food Processing Conditions.

Properly using controllable atmospheric containers can facilitate investigations of the survival abilities and physiological states of key and emerging-foodborne pathogens under recreated applicable food processing environmental conditions. Notably, saturated salt solutions can efficiently control relative humidity in airtight containers. This chapter describes a practical experimental setup, with necessary prerequisites for exposing foodborne pathogens to simulated and relevant food processing environmental conditions. Subsequent analyses for studying cell physiology will also be suggested.

Integrating MALDI-TOF Mass Spectrometry with Machine Learning Techniques for Rapid Antimicrobial Resistance Screening of Foodborne Bacterial Pathogens.

Although MALDI-TOF mass spectrometry (MS) is considered as the gold standard for rapid and cost-effective identification of microorganisms in routine laboratory practices, its capability for antimicrobial resistance (AMR) detection has received limited focus. Nevertheless, recent studies explored the predictive performance of MALDI-TOF MS for detecting AMR in clinical pathogens when machine learning techniques are applied. This chapter describes a routine MALDI-TOF MS workflow for the rapid screening of AMR in foodborne pathogens, with Campylobacter spp. as a study model.

Characterization of Foodborne Pathogens in Biofilms: A Four-Dimensional Structural Dynamics Approach Using HCS-CLSM.

The functional properties of biofilms are intimately related to their spatial architecture. Structural data are therefore of prime importance to dissect the complex social and survival strategies of biofilms and ultimately to improve their control. Confocal laser scanning microscopy (CLSM) is the most widespread microscopic tool to decipher biofilm structure, enabling noninvasive three-dimensional investigation of their dynamics down to the single-cell scale. The emergence of fully automated high content screening (HCS) systems, associated with large-scale image analysis, has radically amplified the flow of available biofilm structural data. In this contribution, we present a HCS-CLSM protocol used to analyze biofilm four-dimensional structural dynamics at high throughput. Meta-analysis of the quantitative variables extracted from HCS-CLSM will contribute to a better biological understanding of biofilm traits.

Identifying Putative Biomarkers of Foodborne Pathogens Using a Metabolomic Approach.

Metabolomics is the study of low molecular weight biochemical molecules (typically <1500 Da) in a defined biological organism or system. In case of food systems, the term "food metabolomics" is often used. Food metabolomics has been widely explored and applied in various fields including food analysis, food intake, food traceability, and food safety. Food safety applications focusing on the identification of pathogen-specific biomarkers have been promising. This chapter describes a nontargeted metabolite profiling workflow using gas chromatography coupled with mass spectrometry (GC-MS) for characterizing three globally important foodborne pathogens, Escherichia coli O157:H7, Listeria monocytogenes, and Salmonella enterica, from selective enrichment liquid culture media. The workflow involves a detailed description of food spiking experiments followed by procedures for the extraction of polar metabolites from media, the analysis of the extracts using GC-MS, and finally chemometric data analysis using univariate and multivariate statistical tools to identify potential pathogen-specific biomarkers.

An outbreak of Shiga toxin-producing Escherichia coli (STEC) O157:H7 associated with contaminated lettuce and the cascading risks from climate change, the United Kingdom, August to September 2022.

Shiga-toxin producing Escherichia coli (STEC) O157 is a food-borne pathogen which causes gastrointestinal illness in humans. Ruminants are considered the main reservoir of infection, and STEC exceedance has been associated with heavy rainfall. In September 2022, a large outbreak of STEC O157:H7 was identified in the United Kingdom (UK). A national-level investigation was undertaken to identify the source of the outbreak and inform risk mitigation strategies. Whole genome sequencing (WGS) was used to identify outbreak cases. Overall, 259 cases with illness onset dates between 5 August and 12 October 2022, were confirmed across the UK. Epidemiological investigations supported a UK grown, nationally distributed, short shelf-life food item as the source of the outbreak. Analytical epidemiology and food chain analysis suggested lettuce as the likely vehicle of infection. Food supply chain tracing identified Grower X as the likely implicated producer. Independent of the food chain investigations, a novel geospatial analysis triangulating meteorological, flood risk, animal density and land use data was developed, also identifying Grower X as the likely source. Novel geospatial analysis and One Health approaches are potential tools for upstream data analysis to predict and prevent contamination events before they occur and to support evidence generation in outbreak investigations.

Large-scale salmonella outbreak associated with banh mi, Viet Nam, 2024.

Objective: To investigate the cause of a foodborne outbreak that occurred in Dong Nai province, Viet Nam, in 2024, and implement control measures. Methods: An initial investigation was conducted to confirm the outbreak, which was followed by epidemiological and environmental investigations to find the plausible causative food item. Clinical specimens and food samples were tested to identify the pathogen. Results: A total of 547 symptomatic cases were recorded, of whom two were in severe condition requiring extracorporeal membrane oxygenation and ventilation, one of whom died. Among 99 interviewed cases, the mean incubation time was 9 hours (range 2-24 hours), with the main symptoms being fever, abdominal pain, diarrhoea and vomiting. All patients had eaten banh mi from a local bakery. Salmonella spp. were identified in food samples and clinical specimens. The bakery halted production, and the outbreak ended after 1 week. Discussion: All the patients were exposed to only one food in common, which facilitated the investigation process. This outbreak is a reminder to small retailers and take-away shops of the importance of food safety management in preventing similar future outbreaks. All food handlers must comply with food hygiene principles, especially in hot temperatures, which boosts bacterial growth.

Surveillance of household foodborne disease outbreaks in Zhejiang Province, China, 2010-2022.

BACKGROUND: Foodborne diseases are a growing public health concern worldwide and households are a common setting. This study aimed to explore the epidemiological characteristics of household foodborne disease outbreaks in Zhejiang Province and propose targeted prevention and control measures. METHODS: Descriptive statistical methods were used to analyze household foodborne disease outbreak data collected from the Foodborne Disease Outbreaks Surveillance System in Zhejiang Province from 2010 to 2022. RESULTS: Household foodborne disease outbreaks showed an upward trend during the study period (Cox-Staurt trend test, p = 0.01563 < 0.05). These outbreaks mainly occurred from June to September, with 62.08% (352/567) of all reported outbreaks. The number of reported outbreaks varied in 11 prefectures, with a maximum of 100 and a minimum of only 7. Household foodborne disease outbreaks had a wide spectrum of etiologic factors. Mushroom toxins accounted for the largest proportion of all etiologies (43.39 %) and caused the highest proportion of hospitalization (54.18%) and death (78.26%). Such outbreaks are caused by accidently eating wild poisonous mushrooms. Bacterial infection (16.23%) was the second most common etiology, with Salmonella spp. and Vibrio parahaemolyticus being the primary pathogens. These outbreaks were caused by improper storage, improper processing or a combination of factors, and the foods involved were mainly aquatic animals, eggs and cooked meat. Other identified etiologies included plant toxins (9.52%), chemicals (7.23%), animal toxins (3.70%), and viruses (1.76%). Among the above-mentioned etiologies, mushroom toxins, bacteria, and animal toxins had seasonal characteristics. Analysis of regions and etiologies revealed that the proportion of various etiologies was different in 11 prefectures. Wild mushrooms (43.39%), aquatic animals (9.88%), and toxic plants (8.47%) were the top three foods involved in these outbreaks. The most common factors contributing to household foodborne disease outbreaks were inedibility and misuse (59.08%), followed by multiple factors (7.58%), improper storage (7.41%), and improper processing (7.41%). CONCLUSIONS: Household foodborne disease outbreaks were closely related to the lack of knowledge regarding foodborne disease prevention. Therefore, public health agencies should strengthen residents' surveillance and health education to improve food safety awareness and effectively reduce foodborne diseases in households. In addition, timely publicity and early warning by relevant government departments, the introduction of standards to control the contamination of pathogenic bacteria in raw materials, and strengthened supervision of the sale of substances that may cause health hazards, such as poisonous mushrooms and nitrites, will also help reduce such outbreaks.

Campylobacteriosis: A rising threat in foodborne illnesses.

Campylobacteriosis is a foodborne illness that is contracted by eating contaminated food, particularly animal products like meat from diseased animals or corpses tainted with harmful germs. The epidemiology of campylobacteriosis varies significantly between low-, middle-, and high-income countries. Campylobacter has a complicated and poorly known survival strategy for getting past host barriers and causing sickness in humans. The adaptability of Campylobacter to unfavorable environments and the host's immune system seems to be one of the most crucial elements of intestinal colonization. A Campylobacter infection may result in fever, nausea, vomiting, and mild to severe bloody diarrhea in humans. Effective and rapid diagnosis of Campylobacter species infections in animal hosts is essential for both individual treatment and disease management at the farm level. According to the most recent meta-analysis research, the main risk factor for campylobacteriosis is travel, which is followed by eating undercooked chicken, being exposed to the environment, and coming into close contact with livestock. Campylobacter jejuni, and occasionally Campylobacter coli, are the primary causes of Campylobacter gastroenteritis, the most significant Campylobacter infection in humans for public health. The best antibiotic medications for eradicating and decreasing Campylobacter in feces are erythromycin, clarithromycin, or azithromycin. The best strategy to reduce the number of human infections caused by Campylobacter is to restrict the amount of contamination of the poultry flock and its products, even if the majority of infections are contracted through handling or ingestion of chicken.

[Epidemiological characteristics and spatiotemporal clustering analysis on foodborne infection of Vibrio parahaemolyticus in Ningbo, 2014-2022].

Objective: To explore the epidemiological characteristics and spatiotemporal clustering of foodborne infection of Vibrio (V.) parahaemolyticus in Ningbo, Zhejiang Province, from 2014 to 2022, and provide reference and evidence for the prevention and control of related diseases. Methods: The incidence data on of foodborne infection of V. parahaemolyticus in Ningbo from 2014 to 2022 were collected from Ningbo Foodborne Disease Surveillance System, and the case counts and the positive rates in different districts (counties, cities) were calculated. Spatial autocorrelation analysis and spatiotemporal scanning analysis were conducted to analyze the spatiotemporal clustering of the diseases. Results: A total of 1 822 cases of foodborne infection of V. parahaemolyticus were reported in Ningbo from 2014 to 2022, with an overall positive rate of 3.78%. Spatial autocorrelation analysis showed that the positive rate of foodborne infection of V. parahaemolyticus in Ningbo was unevenly distributed from 2014 to 2022, Ninghai was a high-high clustering area, while Zhenhai was a high-low clustering area, and Jiangbei was a low-low clustering area. The annual incidence was high during July-September. Spatiotemporal scanning analysis found one class Ⅰ spatiotemporal clustering area and three class Ⅱ spatiotemporal clustering areas, with the class Ⅰ spatiotemporal clustering area being observed in Jiangbei and Zhenhai from 2019 to 2022. Conclusions: Spatiotemporal clustering of foodborne infection of V. parahaemolyticus existed in Ningbo from 2014 to 2022, with an annual high incidence period from July to September. The key areas for the prevention and control of foodborne infection of V. parahaemolyticus are coastal districts (counties, cities) in Ningbo.

Managing the microbiological safety of tilapia from farm to consumer.

Tilapia stands out as one of the most extensively farmed and consumed fish species globally, valued for its ease of preparation and relative affordability. Although tilapia is a valuable protein source, it can also function as a vector for foodborne pathogens. This literature review reveals that tilapia could carry a variety of contamination with various foodborne pathogens, including Plesiomonas shigelloides, diarrheagenic Escherichia coli, Vibrio parahaemolyticus, Salmonella Weltevreden, Salmonella enterica, Shigella, Staphylococcus aureus, Campylobacter jejuni, Clostridium botulinum, and Listeria monocytogenes. Although guidelines from entities, such as the Global Seafood Alliance, Aquaculture Stewardship Council, and International Organization for Standardization, have been established to ensure the microbiological safety of tilapia, the unique challenges posed by pathogens in tilapia farming call for a more nuanced and targeted approach. Recognizing that contaminants could emerge at various stages of the tilapia supply chain, there is a crucial need for enhanced detection and monitoring of pathogens associated with this fish and its culturing environment. Additionally, it is essential to acknowledge the potential impact of climate change on the safety of tilapia, which may elevate the prevalence and contamination levels of pathogens in this fish. Proactive measures are essential to understand and mitigate the effects of climate change on tilapia production, ensuring the sustainability and safety of this seafood product for both present and future generations.

Carbon nanomaterial-based aptasensors for rapid detection of foodborne pathogenic bacteria.

Each year, millions of people suffer from foodborne illness due to the consumption of food contaminated with pathogenic bacteria, which severely challenges global health. Therefore, it is essential to recognize foodborne pathogens swiftly and correctly. However, conventional detection techniques for bacterial pathogens are labor-intensive, low selectivity, and time-consuming, highlighting a notable knowledge gap. A novel approach, aptamer-based biosensors (aptasensors) linked to carbon nanomaterials (CNs), has shown the potential to overcome these limitations and provide a more reliable method for detecting bacterial pathogens. Aptamers, short single-stranded DNA (ssDNA)/RNA molecules, serve as bio-recognition elements (BRE) due to their exceptionally high affinity and specificity in identifying foodborne pathogens such as Salmonella spp., Escherichia coli (E. coli), Listeria monocytogenes, Campylobacter jejuni, and other relevant pathogens commonly associated with foodborne illnesses. Carbon nanomaterials' high surface area-to-volume ratio contributes unique characteristics crucial for bacterial sensing, as it improves the binding capacity and signal amplification in the design of aptasensors. Furthermore, aptamers can bind to CNs and create aptasensors with improved signal specificity and sensitivity. Hence, this review intends to critically review the current literature on developing aptamer functionalized CN-based biosensors by transducer optical and electrochemical for detecting foodborne pathogens and explore the advantages and challenges associated with these biosensors. Aptasensors conjugated with CNs offers an efficient tool for identifying foodborne pathogenic bacteria that is both precise and sensitive to potentially replacing complex current techniques that are time-consuming.

Machine learning surveillance of foodborne infectious diseases using wastewater microbiome, crowdsourced, and environmental data.

Clostridium perfringens (CP) is a common cause of foodborne infection, leading to significant human health risks and a high economic burden. Thus, effective CP disease surveillance is essential for preventive and therapeutic interventions; however, conventional practices often entail complex, resource-intensive, and costly procedures. This study introduced a data-driven machine learning (ML) modeling framework for CP-related disease surveillance. It leveraged an integrated dataset of municipal wastewater microbiome (e.g., CP abundance), crowdsourced (CP-related web search keywords), and environmental data. Various optimization strategies, including data integration, data normalization, model selection, and hyperparameter tuning, were implemented to improve the ML modeling performance, leading to enhanced predictions of CP cases over time. Explainable artificial intelligence methods identified CP abundance as the most reliable predictor of CP disease cases. Multi-omics subsequently revealed the presence of CP and its genotypes/toxinotypes in wastewater, validating the utility of microbiome-data-enabled ML surveillance for foodborne diseases. This ML-based framework thus exhibits significant potential for complementing and reinforcing existing disease surveillance systems.

Pre-packaged cold-chain ready-to-eat food as a source of sporadic listeriosis in Beijing, China.

OBJECTIVES: Using a sporadic case of listeriosis suspected to have been caused by consuming a pre-packaged cold-chain ready-to-eat (RTE) food in Beijing, China in 2021 as an exemplar, this study demonstrated the importance of thoroughly investigating the source of listeriosis up to the production point for mitigating infection risk during routine monitoring of Listeria in food facilities and national surveillance program using whole-genome sequencing (WGS). METHODS: Epidemiological, laboratory, traceback, and plant investigations were used to identify the source of infection. RESULTS: WGS showed the isolate from the patient was genetically indistinguishable from that of the implicated food. During a plant investigation, L. monocytogenes was detected in 26% (9/35) of the environmental samples and one of two raw material samples, confirming the source. CONCLUSION: To our knowledge, this is the first investigation in China linking a case of L. monocytogenes infection to a suspected food and its production environment. This report highlights the risk of L. monocytogenes contamination of RTE food and demonstrates the role of food safety risk monitoring in identifying potential sources of infection. Reinforcing control programs in RTE processing plants, intensified surveillance of microorganisms in food products and targeted health education is required to mitigate the infection risk.

Machine learning-enabled colorimetric sensors for foodborne pathogen detection.

In the past decade, there have been various advancements to colorimetric sensors to improve their potential applications in food and agriculture. One application of growing interest is sensing foodborne pathogens. There are unique considerations for sensing in the food industry, including food sample destruction, specificity amidst a complex food matrix, and high sensitivity requirements. Incorporating novel technology, such as nanotechnology, microfluidics, and smartphone app development, into colorimetric sensing methodology can enhance sensor performance. Nonetheless, there remain challenges to integrating sensors with existing food safety infrastructure. Recently, increasingly advanced machine learning techniques have been employed to facilitate nondestructive, multiplex detection for feasible assimilation of sensors into the food industry. With its ability to analyze and make predictions from highly complex data, machine learning holds potential for advanced yet practical colorimetric sensing of foodborne pathogens. This article summarizes recent developments and hurdles of machine learning-enabled colorimetric foodborne pathogen sensing. These advancements underscore the potential of interdisciplinary, cutting-edge technology in providing safer and more efficient food systems.