RESUMEN
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.
Asunto(s)
Bacterias , Microbiología de Alimentos , Enfermedades Transmitidas por los Alimentos , Estudio de Asociación del Genoma Completo , Aprendizaje Automático , Humanos , Bacterias/genética , Enfermedades Transmitidas por los Alimentos/microbiología , Enfermedades Transmitidas por los Alimentos/genética , Variación Genética , Genoma Bacteriano , Estudio de Asociación del Genoma Completo/métodos , FenotipoRESUMEN
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.
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ADN Bacteriano , Microbiología de Alimentos , Leche , Técnicas de Amplificación de Ácido Nucleico , Reacción en Cadena de la Polimerasa , Salmonella , Técnicas de Amplificación de Ácido Nucleico/métodos , Microbiología de Alimentos/métodos , Animales , Leche/microbiología , Salmonella/genética , Salmonella/aislamiento & purificación , ADN Bacteriano/genética , ADN Bacteriano/aislamiento & purificación , Reacción en Cadena de la Polimerasa/métodos , Enfermedades Transmitidas por los Alimentos/microbiología , Escherichia coli/genética , Escherichia coli/aislamiento & purificación , Técnicas de Diagnóstico Molecular/métodos , PorcinosRESUMEN
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.
Asunto(s)
Microbiología de Alimentos , Enfermedades Transmitidas por los Alimentos , Listeria monocytogenes , Reacción en Cadena de la Polimerasa Multiplex , Salmonella , Listeria monocytogenes/genética , Listeria monocytogenes/aislamiento & purificación , Microbiología de Alimentos/métodos , Salmonella/genética , Salmonella/aislamiento & purificación , Reacción en Cadena de la Polimerasa Multiplex/métodos , Enfermedades Transmitidas por los Alimentos/microbiología , Enfermedades Transmitidas por los Alimentos/diagnóstico , Reacción en Cadena en Tiempo Real de la Polimerasa/métodos , Humanos , ADN Bacteriano/genética , ADN Bacteriano/análisisRESUMEN
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.
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Azidas , Microbiología de Alimentos , Viabilidad Microbiana , Propidio , Reacción en Cadena en Tiempo Real de la Polimerasa , Microbiología de Alimentos/métodos , Azidas/química , Propidio/análogos & derivados , Reacción en Cadena en Tiempo Real de la Polimerasa/métodos , Bacterias/genética , Bacterias/aislamiento & purificación , Enfermedades Transmitidas por los Alimentos/microbiología , Microscopía Fluorescente/métodos , HumanosRESUMEN
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.
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Microbiología de Alimentos , Enfermedades Transmitidas por los Alimentos , Secuenciación de Nucleótidos de Alto Rendimiento , Listeria monocytogenes , Microbiología de Alimentos/métodos , Secuenciación de Nucleótidos de Alto Rendimiento/métodos , Enfermedades Transmitidas por los Alimentos/microbiología , Enfermedades Transmitidas por los Alimentos/diagnóstico , Listeria monocytogenes/aislamiento & purificación , Listeria monocytogenes/genética , Escherichia coli O157/aislamiento & purificación , Escherichia coli O157/genética , Humanos , Serotipificación/métodos , ADN Bacteriano/genética , ADN Bacteriano/análisis , Salmonella typhimurium/aislamiento & purificación , Salmonella typhimurium/genéticaRESUMEN
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.
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Manipulación de Alimentos , Microbiología de Alimentos , Manipulación de Alimentos/métodos , Enfermedades Transmitidas por los Alimentos/microbiología , Viabilidad Microbiana , Bacterias/crecimiento & desarrollo , HumanosRESUMEN
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.
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Campylobacter , Farmacorresistencia Bacteriana , Aprendizaje Automático , Espectrometría de Masa por Láser de Matriz Asistida de Ionización Desorción , Espectrometría de Masa por Láser de Matriz Asistida de Ionización Desorción/métodos , Campylobacter/efectos de los fármacos , Antibacterianos/farmacología , Humanos , Microbiología de Alimentos/métodos , Pruebas de Sensibilidad Microbiana/métodos , Enfermedades Transmitidas por los Alimentos/microbiología , Bacterias/efectos de los fármacosRESUMEN
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.
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Biopelículas , Microscopía Confocal , Biopelículas/crecimiento & desarrollo , Microscopía Confocal/métodos , Microbiología de Alimentos/métodos , Imagenología Tridimensional/métodos , Enfermedades Transmitidas por los Alimentos/microbiología , Ensayos Analíticos de Alto Rendimiento/métodos , Procesamiento de Imagen Asistido por Computador/métodosRESUMEN
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.
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Biomarcadores , Microbiología de Alimentos , Cromatografía de Gases y Espectrometría de Masas , Listeria monocytogenes , Metabolómica , Metabolómica/métodos , Cromatografía de Gases y Espectrometría de Masas/métodos , Biomarcadores/análisis , Microbiología de Alimentos/métodos , Listeria monocytogenes/metabolismo , Listeria monocytogenes/aislamiento & purificación , Salmonella enterica/metabolismo , Escherichia coli O157/metabolismo , Escherichia coli O157/aislamiento & purificación , Enfermedades Transmitidas por los Alimentos/microbiología , MetabolomaRESUMEN
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.
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Brotes de Enfermedades , Enfermedades Transmitidas por los Alimentos , China/epidemiología , Humanos , Enfermedades Transmitidas por los Alimentos/epidemiología , Enfermedades Transmitidas por los Alimentos/microbiología , Composición Familiar , Contaminación de Alimentos/análisis , Contaminación de Alimentos/estadística & datos numéricos , Vibrio parahaemolyticus/aislamiento & purificación , Salmonella/aislamiento & purificación , AnimalesRESUMEN
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.
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Brotes de Enfermedades , Intoxicación Alimentaria por Salmonella , Humanos , Vietnam/epidemiología , Masculino , Adulto , Femenino , Intoxicación Alimentaria por Salmonella/epidemiología , Intoxicación Alimentaria por Salmonella/microbiología , Persona de Mediana Edad , Preescolar , Niño , Adolescente , Lactante , Salmonella/aislamiento & purificación , Adulto Joven , Enfermedades Transmitidas por los Alimentos/epidemiología , Enfermedades Transmitidas por los Alimentos/microbiología , Microbiología de Alimentos , AncianoRESUMEN
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.
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Cambio Climático , Brotes de Enfermedades , Infecciones por Escherichia coli , Escherichia coli O157 , Microbiología de Alimentos , Enfermedades Transmitidas por los Alimentos , Lactuca , Lactuca/microbiología , Humanos , Infecciones por Escherichia coli/epidemiología , Infecciones por Escherichia coli/microbiología , Infecciones por Escherichia coli/transmisión , Reino Unido/epidemiología , Escherichia coli O157/aislamiento & purificación , Escherichia coli O157/genética , Enfermedades Transmitidas por los Alimentos/epidemiología , Enfermedades Transmitidas por los Alimentos/microbiología , Secuenciación Completa del Genoma , Escherichia coli Shiga-Toxigénica/aislamiento & purificación , Escherichia coli Shiga-Toxigénica/genética , Adulto , Persona de Mediana Edad , Femenino , Masculino , Contaminación de Alimentos/análisis , Anciano , Animales , Adolescente , NiñoRESUMEN
Studies on climate variables and food pathogens are either pathogen- or region-specific, necessitating a consolidated view on the subject. This study aims to systematically review all studies on the association of ambient temperature and precipitation on the incidence of gastroenteritis and bacteraemia from Salmonella, Shigella, Campylobacter, Vibrio, and Listeria species. PubMed, Ovid MEDLINE, Scopus, and Web of Science databases were searched up to 9 March 2023. We screened 3,204 articles for eligibility and included 83 studies in the review and three in the meta-analysis. Except for one study on Campylobacter, all showed a positive association between temperature and Salmonella, Shigella, Vibrio sp., and Campylobacter gastroenteritis. Similarly, most of the included studies showed that precipitation was positively associated with these conditions. These positive associations were found regardless of the effect measure chosen. The pooled incidence rate ratio (IRR) for the three studies that included bacteraemia from Campylobacter and Salmonella sp. was 1.05 (95 per cent confidence interval (95% CI): 1.03, 1.06) for extreme temperature and 1.09 (95% CI: 0.99, 1.19) for extreme precipitation. If current climate trends continue, our findings suggest these pathogens would increase patient morbidity, the need for hospitalization, and prolonged antibiotic courses.
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Enfermedades Transmitidas por los Alimentos , Temperatura , Humanos , Enfermedades Transmitidas por los Alimentos/epidemiología , Enfermedades Transmitidas por los Alimentos/microbiología , Incidencia , Lluvia , Gastroenteritis/epidemiología , Gastroenteritis/microbiología , Bacteriemia/epidemiología , Bacteriemia/microbiologíaRESUMEN
BACKGROUND: Foodborne diseases (FBDs) represent a significant risk to public health, with nearly one in ten people falling ill every year globally. The large incidence of foodborne diseases in African low- and middle-income countries (LMIC) shows the immediate need for action, but there is still far to a robust and efficient outbreak detection system. The detection of outbreak heavily relies on clinical diagnosis, which are often delayed or ignored due to resource limitations and inadequate surveillance systems. METHODS: In total, 68 samples of non-typhoidal Salmonella isolates from human, animal and environmental sources collected between November 2021 and January 2023 were analyzed using sequencing methods to infer phylogenetic relationships between the samples. A source attribution model using a machine-learning logit-boost that predicted the likely source of infection for 20 cases of human salmonellosis was also run and compared with the results of the cluster detection. RESULTS: Three clusters of samples with close relation (SNP difference < 30) were identified as non-typhoidal Salmonella in Harar town and Kersa district, Ethiopia. These three clusters were comprised of isolates from different sources, including at least two human isolates. The isolates within each cluster showed identical serovar and sequence type (ST), with few exceptions in cluster 3. The close proximity of the samples suggested the occurrence of three potential outbreaks of non-typhoidal Salmonella in the region. The results of the source attribution model found that human cases of salmonellosis could primarily be attributed to bovine meat, which the results of the phylogenetic analysis corroborated. CONCLUSIONS: The findings of this study suggested the occurrence of three possible outbreaks of non-typhoidal Salmonella in eastern Ethiopia, emphasizing the importance of targeted intervention of food safety protocols in LMICs. It also highlighted the potential of integrated surveillance for detecting outbreak and identifying the most probable source. Source attribution models in combination with other epidemiological methods is recommended as part of a more robust and integrated surveillance system for foodborne diseases.
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Brotes de Enfermedades , Enfermedades Transmitidas por los Alimentos , Filogenia , Infecciones por Salmonella , Salmonella , Humanos , Etiopía/epidemiología , Salmonella/genética , Salmonella/aislamiento & purificación , Salmonella/clasificación , Enfermedades Transmitidas por los Alimentos/microbiología , Enfermedades Transmitidas por los Alimentos/epidemiología , Animales , Infecciones por Salmonella/epidemiología , Infecciones por Salmonella/microbiologíaRESUMEN
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.
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Enfermedades Transmitidas por los Alimentos , Aprendizaje Automático , Microbiota , Aguas Residuales , Aguas Residuales/microbiología , Enfermedades Transmitidas por los Alimentos/microbiología , Humanos , Colaboración de las Masas , Clostridium perfringens/aislamiento & purificaciónRESUMEN
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.
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Aptámeros de Nucleótidos , Técnicas Biosensibles , Microbiología de Alimentos , Nanoestructuras , Aptámeros de Nucleótidos/química , Técnicas Biosensibles/métodos , Nanoestructuras/química , Microbiología de Alimentos/métodos , Enfermedades Transmitidas por los Alimentos/microbiología , Bacterias/aislamiento & purificación , Carbono/química , HumanosRESUMEN
In pathogenic Bacillota, spores can form an infectious particle and can take up a central role in the environmental persistence and dissemination of disease. A poorly understood aspect of spore-mediated infection is the fibrous structures or 'endospore appendages' (ENAs) that have been seen to decorate the spores of pathogenic Bacilli and Clostridia. Current methodological approaches are opening a window on these long enigmatic structures. Using cryoID, Alphafold modelling and genetic approaches we identify a sub-class of robust ENAs in a Bacillus paranthracis foodborne outbreak strain. We demonstrate that L-ENA are encoded by a rare three-gene cluster (ena3) that contains all components for the self-assembly of ladder-like protein nanofibers of stacked heptameric rings, their anchoring to the exosporium, and their termination in a trimeric 'ruffle' made of a complement C1Q-like BclA paralogue. The role of ENA fibers in spore-spore interaction and the distribution of L-ENA operon as mobile genetic elements in B. cereus s.l. strains suggest that L-ENA fibers may increase the survival, spread and virulence of these strains.
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Bacillus , Proteínas Bacterianas , Esporas Bacterianas , Esporas Bacterianas/ultraestructura , Bacillus/genética , Proteínas Bacterianas/genética , Proteínas Bacterianas/metabolismo , Enfermedades Transmitidas por los Alimentos/microbiología , Enfermedades Transmitidas por los Alimentos/epidemiología , Familia de Multigenes , Brotes de Enfermedades , Microscopía por Crioelectrón , Operón/genéticaRESUMEN
In recent years, foodborne diseases have posed a serious threat to human health, and rapid detection of foodborne pathogens is particularly crucial for the prevention and control of such diseases. This article offers a detailed overview of the development of detection techniques for foodborne pathogens, transitioning from traditional microbiological culture methods to the current array of techniques, including immunological, molecular biological, and biosensor-based methods. It summarizes the technical principles, advantages, disadvantages, and research progress of these diverse methods. Furthermore, the article demonstrates that the combination of different methods enhances the efficiency and accuracy of pathogens detection. Specifically, the article focuses on the application and advantages of combining CRISPR/Cas systems with other detection methods in the detection of foodborne pathogens. CRISPR/Cas systems, with their high specificity, sensitivity, and ease of operation, show great potential in the field of foodborne pathogens detection. When integrated with other detection techniques such as immunological detection techniques, molecular biology detection techniques, and biosensors, the accuracy and efficiency of detection can be further improved. By fully utilizing these tools, early detection and control of foodborne diseases can be achieved, enhancing public health and preventing disease outbreaks. This article serves as a valuable reference for exploring more convenient, accurate, and sensitive field detection methods for foodborne pathogens, promoting the application of rapid detection techniques, and ensuring food safety and human health.
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Técnicas Biosensibles , Microbiología de Alimentos , Inocuidad de los Alimentos , Enfermedades Transmitidas por los Alimentos , Enfermedades Transmitidas por los Alimentos/microbiología , Enfermedades Transmitidas por los Alimentos/prevención & control , Microbiología de Alimentos/métodos , Inocuidad de los Alimentos/métodos , Humanos , Técnicas Biosensibles/métodos , Sistemas CRISPR-Cas , Contaminación de Alimentos/análisisRESUMEN
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.