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Borges F, Briandet R, Callon C, Champomier-Vergès MC, Christieans S, Chuzeville S, Denis C, Desmasures N, Desmonts MH, Feurer C, Leroi F, Leroy S, Mounier J, Passerini D, Pilet MF, Schlusselhuber M, Stahl V, Strub C, Talon R, Zagorec M. Contribution of omics to biopreservation: Toward food microbiome engineering. Front Microbiol 2022; 13:951182. [PMID: 35983334 PMCID: PMC9379315 DOI: 10.3389/fmicb.2022.951182] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 07/14/2022] [Indexed: 01/12/2023] Open
Abstract
Biopreservation is a sustainable approach to improve food safety and maintain or extend food shelf life by using beneficial microorganisms or their metabolites. Over the past 20 years, omics techniques have revolutionised food microbiology including biopreservation. A range of methods including genomics, transcriptomics, proteomics, metabolomics and meta-omics derivatives have highlighted the potential of biopreservation to improve the microbial safety of various foods. This review shows how these approaches have contributed to the selection of biopreservation agents, to a better understanding of the mechanisms of action and of their efficiency and impact within the food ecosystem. It also presents the potential of combining omics with complementary approaches to take into account better the complexity of food microbiomes at multiple scales, from the cell to the community levels, and their spatial, physicochemical and microbiological heterogeneity. The latest advances in biopreservation through omics have emphasised the importance of considering food as a complex and dynamic microbiome that requires integrated engineering strategies to increase the rate of innovation production in order to meet the safety, environmental and economic challenges of the agri-food sector.
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Affiliation(s)
| | - Romain Briandet
- Université Paris-Saclay, INRAE, AgroParisTech, Micalis Institute, Jouy-en-Josas, France
| | - Cécile Callon
- Université Clermont Auvergne, INRAE, VetAgro Sup, UMR 545 Fromage, Aurillac, France
| | | | | | - Sarah Chuzeville
- ACTALIA, Pôle d’Expertise Analytique, Unité Microbiologie Laitière, La Roche sur Foron, France
| | | | | | | | - Carole Feurer
- IFIP, Institut de la Filière Porcine, Le Rheu, France
| | | | - Sabine Leroy
- Université Clermont Auvergne, INRAE, MEDIS, Clermont-Ferrand, France
| | - Jérôme Mounier
- Univ Brest, Laboratoire Universitaire de Biodiversité et Ecologie Microbienne, Plouzané, France
| | | | | | | | | | - Caroline Strub
- Qualisud, Univ Montpellier, Avignon Université, CIRAD, Institut Agro, IRD, Université de La Réunion, Montpellier, France
| | - Régine Talon
- Université Clermont Auvergne, INRAE, MEDIS, Clermont-Ferrand, France
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2
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Recht R, Omhover-Fougy L, Stahl V, Hamon E. Potential of multiparametric characterization of foodstuffs by nuclear magnetic resonance to better predict microbial behavior. MAGNETIC RESONANCE IN CHEMISTRY : MRC 2022; 60:719-729. [PMID: 35246874 DOI: 10.1002/mrc.5263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Revised: 02/23/2022] [Accepted: 03/01/2022] [Indexed: 06/14/2023]
Abstract
Numerous predictive microbiology models have been proposed to describe bacterial population behaviors in foodstuffs. These models depict the growth kinetics of particular bacterial strains based on key physico-chemical parameters of food matrices and their storage temperature. In this context, there is a prominent issue to accurately characterize these parameters, notably pH, water activity (aw ), and NaCl and organic acid concentrations. Usually, all these product features are determined using one destructive analysis per parameter at macroscale (>5 g). Such approach prevents an overall view of these characteristics on a single sample. Besides, it does not take into account the intra-product microlocal variability of these parameters within foods. Nuclear magnetic resonance (NMR) is a versatile non-invasive spectroscopic technique. Experiments can be recorded successively on a same collected sample without damaging it. In this work, we designed a dedicated NMR approach to characterize the microenvironment of foods using 10-mg samples. The multiparametric mesoscopic-scale approach was validated on four food matrices: a smear soft cheese, cooked peeled shrimps, cold-smoked salmon, and smoked ham. Its implementation in situ on salmon fillets enabled to observe the intra-product heterogeneity and to highlight the impact of process on the spatial distribution of pH, NaCl, and organic acids. This analytical development and its successful application can help address the shortcomings of monoparametric methods traditionally used for predictive microbiology purposes.
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3
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Verheyen D, Van Impe JFM. The Inclusion of the Food Microstructural Influence in Predictive Microbiology: State-of-the-Art. Foods 2021; 10:foods10092119. [PMID: 34574229 PMCID: PMC8468028 DOI: 10.3390/foods10092119] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/30/2021] [Revised: 08/27/2021] [Accepted: 08/30/2021] [Indexed: 12/15/2022] Open
Abstract
Predictive microbiology has steadily evolved into one of the most important tools to assess and control the microbiological safety of food products. Predictive models were traditionally developed based on experiments in liquid laboratory media, meaning that food microstructural effects were not represented in these models. Since food microstructure is known to exert a significant effect on microbial growth and inactivation dynamics, the applicability of predictive models is limited if food microstructure is not taken into account. Over the last 10-20 years, researchers, therefore, developed a variety of models that do include certain food microstructural influences. This review provides an overview of the most notable microstructure-including models which were developed over the years, both for microbial growth and inactivation.
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Affiliation(s)
- Davy Verheyen
- BioTeC+, Chemical and Biochemical Process Technology and Control, Department of Chemical Engineering, KU Leuven, Gebroeders de Smetstraat 1, 9000 Ghent, Belgium;
- OPTEC, Optimization in Engineering Center-of-Excellence, KU Leuven, 3000 Leuven, Belgium
- CPMF2, Flemish Cluster Predictive Microbiology in Foods—www.cpmf2.be, 9000 Ghent, Belgium
| | - Jan F. M. Van Impe
- BioTeC+, Chemical and Biochemical Process Technology and Control, Department of Chemical Engineering, KU Leuven, Gebroeders de Smetstraat 1, 9000 Ghent, Belgium;
- OPTEC, Optimization in Engineering Center-of-Excellence, KU Leuven, 3000 Leuven, Belgium
- CPMF2, Flemish Cluster Predictive Microbiology in Foods—www.cpmf2.be, 9000 Ghent, Belgium
- Correspondence:
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4
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Fritsch L, Baleswaran A, Bergis H, Lintz A, Hamon E, Stahl V, Augustin JC, Guillier L. A microscopy-based approach for determining growth probability and lag time of individual bacterial cells. Food Res Int 2021; 140:110052. [PMID: 33648277 DOI: 10.1016/j.foodres.2020.110052] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/29/2020] [Revised: 12/13/2020] [Accepted: 12/16/2020] [Indexed: 12/01/2022]
Abstract
The development of relevant predictive models for single-cell lag time and growth probability near growth limits is of critical importance for predicting pathogen behavior in foods. The classical methods for data acquisition in this field are based on turbidity measurements of culture media in microplate wells inoculated with approximately one bacterial cell per well. Yet, these methods are labour intensive and would benefit from higher throughput. In this study, we developed a quantitative experimental method using automated microscopy to determine the single-cell growth probability and lag time. The developed method consists of the use of direct cell observation with phase-contrast microscopy equipped with a 100× objective and a high-resolution device camera. The method is not a time-lapse method but is based on the observation of high numbers of colonies for a given time. Automation of image acquisition and image analysis was used to reach a high throughput. The single-cell growth probabilities and lag times of four strains of Listeria monocytogenes were determined at 4 °C. The microscopic method was shown to be a promising method for the determination of individual lag times and growth probability at the single-cell level.
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Affiliation(s)
- Lena Fritsch
- French Agency for Food, Environmental and Occupational Health & Safety (Anses), Laboratory for Food Safety, Université Paris-Est, Maisons-Alfort F-94701, France
| | - Abirami Baleswaran
- French Agency for Food, Environmental and Occupational Health & Safety (Anses), Laboratory for Food Safety, Université Paris-Est, Maisons-Alfort F-94701, France
| | - Hélène Bergis
- French Agency for Food, Environmental and Occupational Health & Safety (Anses), Laboratory for Food Safety, Université Paris-Est, Maisons-Alfort F-94701, France
| | - Adrienne Lintz
- Aérial, Technical Institute of Food Industry, Parc d'innovation, 250 rue Laurent Fries, 67400 Illkirch, France
| | - Erwann Hamon
- Aérial, Technical Institute of Food Industry, Parc d'innovation, 250 rue Laurent Fries, 67400 Illkirch, France
| | - Valérie Stahl
- Aérial, Technical Institute of Food Industry, Parc d'innovation, 250 rue Laurent Fries, 67400 Illkirch, France
| | - Jean-Christophe Augustin
- French Agency for Food, Environmental and Occupational Health & Safety (Anses), Laboratory for Food Safety, Université Paris-Est, Maisons-Alfort F-94701, France; Ecole Nationale Vétérinaire d'Alfort, 94700 Maisons-Alfort, France
| | - Laurent Guillier
- French Agency for Food, Environmental and Occupational Health & Safety (Anses), Laboratory for Food Safety, Université Paris-Est, Maisons-Alfort F-94701, France.
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5
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Possas A, Bonilla-Luque OM, Valero A. From Cheese-Making to Consumption: Exploring the Microbial Safety of Cheeses through Predictive Microbiology Models. Foods 2021; 10:foods10020355. [PMID: 33562291 PMCID: PMC7915996 DOI: 10.3390/foods10020355] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2021] [Revised: 02/01/2021] [Accepted: 02/03/2021] [Indexed: 12/12/2022] Open
Abstract
Cheeses are traditional products widely consumed throughout the world that have been frequently implicated in foodborne outbreaks. Predictive microbiology models are relevant tools to estimate microbial behavior in these products. The objective of this study was to conduct a review on the available modeling approaches developed in cheeses, and to identify the main microbial targets of concern and the factors affecting microbial behavior in these products. Listeria monocytogenes has been identified as the main hazard evaluated in modelling studies. The pH, aw, lactic acid concentration and temperature have been the main factors contemplated as independent variables in models. Other aspects such as the use of raw or pasteurized milk, starter cultures, and factors inherent to the contaminating pathogen have also been evaluated. In general, depending on the production process, storage conditions, and physicochemical characteristics, microorganisms can grow or die-off in cheeses. The classical two-step modeling has been the most common approach performed to develop predictive models. Other modeling approaches, including microbial interaction, growth boundary, response surface methodology, and neural networks, have also been performed. Validated models have been integrated into user-friendly software tools to be used to obtain estimates of microbial behavior in a quick and easy manner. Future studies should investigate the fate of other target bacterial pathogens, such as spore-forming bacteria, and the dynamic character of the production process of cheeses, among other aspects. The information compiled in this study helps to deepen the knowledge on the predictive microbiology field in the context of cheese production and storage.
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6
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Dzianach PA, Dykes GA, Strachan NJC, Forbes KJ, Pérez-Reche FJ. Challenges of biofilm control and utilization: lessons from mathematical modelling. J R Soc Interface 2019; 16:20190042. [PMID: 31185817 PMCID: PMC6597778 DOI: 10.1098/rsif.2019.0042] [Citation(s) in RCA: 26] [Impact Index Per Article: 5.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2019] [Accepted: 05/10/2019] [Indexed: 12/11/2022] Open
Abstract
This article reviews modern applications of mathematical descriptions of biofilm formation. The focus is on theoretically obtained results which have implications for areas including the medical sector, food industry and wastewater treatment. Examples are given as to how models have contributed to the overall knowledge on biofilms and how they are used to predict biofilm behaviour. We conclude that the use of mathematical models of biofilms has demonstrated over the years the ability to significantly contribute to the vast field of biofilm research. Among other things, they have been used to test various hypotheses on the nature of interspecies interactions, viability of biofilm treatment methods or forces behind observed biofilm pattern formations. Mathematical models can also play a key role in future biofilm research. Many models nowadays are analysed through computer simulations and continue to improve along with computational capabilities. We predict that models will keep on providing answers to important challenges involving biofilm formation. However, further strengthening of the ties between various disciplines is necessary to fully use the tools of collective knowledge in tackling the biofilm phenomenon.
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Affiliation(s)
- Paulina A. Dzianach
- School of Natural and Computing Sciences, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, UK
- School of Public Health, Curtin University, Perth, Australia
| | - Gary A. Dykes
- School of Public Health, Curtin University, Perth, Australia
| | - Norval J. C. Strachan
- School of Natural and Computing Sciences, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, UK
| | - Ken J. Forbes
- School of Medicine, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, UK
| | - Francisco J. Pérez-Reche
- School of Natural and Computing Sciences, Medical Sciences and Nutrition, University of Aberdeen, Aberdeen, UK
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7
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Modelling the fate and serogroup variability of persistent Listeria monocytogenes strains on grated cheese at different storage temperatures. Int J Food Microbiol 2018; 286:48-54. [DOI: 10.1016/j.ijfoodmicro.2018.07.021] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/12/2018] [Revised: 07/06/2018] [Accepted: 07/16/2018] [Indexed: 01/19/2023]
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8
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Tack ILMM, Nimmegeers P, Akkermans S, Logist F, Van Impe JFM. A low-complexity metabolic network model for the respiratory and fermentative metabolism of Escherichia coli. PLoS One 2018; 13:e0202565. [PMID: 30157229 PMCID: PMC6114798 DOI: 10.1371/journal.pone.0202565] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/06/2018] [Accepted: 08/06/2018] [Indexed: 01/01/2023] Open
Abstract
Over the last decades, predictive microbiology has made significant advances in the mathematical description of microbial spoiler and pathogen dynamics in or on food products. Recently, the focus of predictive microbiology has shifted from a (semi-)empirical population-level approach towards mechanistic models including information about the intracellular metabolism in order to increase model accuracy and genericness. However, incorporation of this subpopulation-level information increases model complexity and, consequently, the required run time to simulate microbial cell and population dynamics. In this paper, results of metabolic flux balance analyses (FBA) with a genome-scale model are used to calibrate a low-complexity linear model describing the microbial growth and metabolite secretion rates of Escherichia coli as a function of the nutrient and oxygen uptake rate. Hence, the required information about the cellular metabolism (i.e., biomass growth and secretion of cell products) is selected and included in the linear model without incorporating the complete intracellular reaction network. However, the applied FBAs are only representative for microbial dynamics under specific extracellular conditions, viz., a neutral medium without weak acids at a temperature of 37℃. Deviations from these reference conditions lead to metabolic shifts and adjustments of the cellular nutrient uptake or maintenance requirements. This metabolic dependency on extracellular conditions has been taken into account in our low-complex metabolic model. In this way, a novel approach is developed to take the synergistic effects of temperature, pH, and undissociated acids on the cell metabolism into account. Consequently, the developed model is deployable as a tool to describe, predict and control E. coli dynamics in and on food products under various combinations of environmental conditions. To emphasize this point,three specific scenarios are elaborated: (i) aerobic respiration without production of weak acid extracellular metabolites, (ii) anaerobic fermentation with secretion of mixed acid fermentation products into the food environment, and (iii) respiro-fermentative metabolic regimes in between the behaviors at aerobic and anaerobic conditions.
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Affiliation(s)
| | | | - Simen Akkermans
- BioTeC+, Department of Chemical Engineering, KU Leuven, Ghent, Belgium
| | - Filip Logist
- BioTeC+, Department of Chemical Engineering, KU Leuven, Ghent, Belgium
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9
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Tack ILMM, Nimmegeers P, Akkermans S, Hashem I, Van Impe JFM. Simulation of Escherichia coli Dynamics in Biofilms and Submerged Colonies with an Individual-Based Model Including Metabolic Network Information. Front Microbiol 2017; 8:2509. [PMID: 29321772 PMCID: PMC5733555 DOI: 10.3389/fmicb.2017.02509] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2017] [Accepted: 12/01/2017] [Indexed: 11/13/2022] Open
Abstract
Clustered microbial communities are omnipresent in the food industry, e.g., as colonies of microbial pathogens in/on food media or as biofilms on food processing surfaces. These clustered communities are often characterized by metabolic differentiation among their constituting cells as a result of heterogeneous environmental conditions in the cellular surroundings. This paper focuses on the role of metabolic differentiation due to oxygen gradients in the development of Escherichia coli cell communities, whereby low local oxygen concentrations lead to cellular secretion of weak acid products. For this reason, a metabolic model has been developed for the facultative anaerobe E. coli covering the range of aerobic, microaerobic, and anaerobic environmental conditions. This metabolic model is expressed as a multiparametric programming problem, in which the influence of low extracellular pH values and the presence of undissociated acid cell products in the environment has been taken into account. Furthermore, the developed metabolic model is incorporated in MICRODIMS, an in-house developed individual-based modeling framework to simulate microbial colony and biofilm dynamics. Two case studies have been elaborated using the MICRODIMS simulator: (i) biofilm growth on a substratum surface and (ii) submerged colony growth in a semi-solid mixed food product. In the first case study, the acidification of the biofilm environment and the emergence of typical biofilm morphologies have been observed, such as the mushroom-shaped structure of mature biofilms and the formation of cellular chains at the exterior surface of the biofilm. The simulations show that these morphological phenomena are respectively dependent on the initial affinity of pioneer cells for the substratum surface and the cell detachment process at the outer surface of the biofilm. In the second case study, a no-growth zone emerges in the colony center due to a local decline of the environmental pH. As a result, cellular growth in the submerged colony is limited to the colony periphery, implying a linear increase of the colony radius over time. MICRODIMS has been successfully used to reproduce complex dynamics of clustered microbial communities.
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10
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Salazar JK, Sahu SN, Hildebrandt IM, Zhang L, Qi Y, Liggans G, Datta AR, Tortorello ML. Growth Kinetics of Listeria monocytogenes in Cut Produce. J Food Prot 2017; 80:1328-1336. [PMID: 28708030 DOI: 10.4315/0362-028x.jfp-16-516] [Citation(s) in RCA: 39] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022]
Abstract
Cut produce continues to constitute a significant portion of the fresh fruit and vegetables sold directly to consumers. As such, the safety of these items during storage, handling, and display remains a concern. Cut tomatoes, cut leafy greens, and cut melons, which have been studied in relation to their ability to support pathogen growth, have been specifically identified as needing temperature control for safety. Data are needed on the growth behavior of foodborne pathogens in other types of cut produce items that are commonly offered for retail purchase and are potentially held without temperature control. This study assessed the survival and growth of Listeria monocytogenes in cut produce items that are commonly offered for retail purchase, specifically broccoli, green and red bell peppers, yellow onions, canned green and black olives, fresh green olives, cantaloupe flesh and rind, avocado pulp, cucumbers, and button mushrooms. The survival of L. monocytogenes strains representing serotypes 1/2a, 1/2b, and 4b was determined on the cut produce items for each strain individually at 5, 10, and 25°C for up to 720 h. The modified Baranyi model was used to determine the growth kinetics (the maximum growth rates and maximum population increases) in the L. monocytogenes populations. The products that supported the most rapid growth of L. monocytogenes, considering the fastest growth and resulting population levels, were cantaloupe flesh and avocado pulp. When stored at 25°C, the maximum growth rates for these products were 0.093 to 0.138 log CFU/g/h and 0.130 to 0.193 log CFU/g/h, respectively, depending on the strain. Green olives and broccoli did not support growth at any temperature. These results can be used to inform discussions surrounding whether specific time and temperature storage conditions should be recommended for additional cut produce items.
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Affiliation(s)
- Joelle K Salazar
- 1 U.S. Food and Drug Administration, Division of Food Processing Science and Technology, Office of Food Safety, 6502 South Archer Road, Bedford Park, Illinois 60501
| | - Surasri N Sahu
- 3 Illinois Institute of Technology, Institute for Food Safety and Health, 6502 South Archer Road, Bedford Park, Illinois 60501; and
| | - Ian M Hildebrandt
- 1 U.S. Food and Drug Administration, Division of Food Processing Science and Technology, Office of Food Safety, 6502 South Archer Road, Bedford Park, Illinois 60501
| | - Lijie Zhang
- 2 U.S. Food and Drug Administration, Division of Virulence Assessment, Office of Applied Research and Safety Assessment, 8301 Muirkirk Road, Laurel, Maryland 20708
| | - Yan Qi
- 2 U.S. Food and Drug Administration, Division of Virulence Assessment, Office of Applied Research and Safety Assessment, 8301 Muirkirk Road, Laurel, Maryland 20708
| | - Girvin Liggans
- 4 U.S. Food and Drug Administration, Retail Food Protection Staff, Office of Food Safety, 5001 Campus Drive, College Park, Maryland 20740, USA
| | - Atin R Datta
- 3 Illinois Institute of Technology, Institute for Food Safety and Health, 6502 South Archer Road, Bedford Park, Illinois 60501; and
| | - Mary Lou Tortorello
- 1 U.S. Food and Drug Administration, Division of Food Processing Science and Technology, Office of Food Safety, 6502 South Archer Road, Bedford Park, Illinois 60501
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11
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Pérez‐Rodríguez F, Carrasco E, Bover‐Cid S, Jofré A, Valero A. Closing gaps for performing a risk assessment on Listeria monocytogenes in ready‐to‐eat (RTE) foods: activity 2, a quantitative risk characterization on L. monocytogenes in RTE foods; starting from the retail stage. ACTA ACUST UNITED AC 2017. [DOI: 10.2903/sp.efsa.2017.en-1252] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/19/2022]
Affiliation(s)
| | | | - Sara Bover‐Cid
- Institut de Recerca i Tecnologia Agroalimentàries (IRTA) Food Safety Programme Spain
| | - Anna Jofré
- Institut de Recerca i Tecnologia Agroalimentàries (IRTA) Food Safety Programme Spain
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12
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Ding T, Liao XY, Dong QL, Xuan XT, Chen SG, Ye XQ, Liu DH. Predictive modeling of microbial single cells: A review. Crit Rev Food Sci Nutr 2017; 58:711-725. [DOI: 10.1080/10408398.2016.1217193] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/21/2022]
Affiliation(s)
- Tian Ding
- Department of Food Science and Nutrition, Zhejiang Key Laboratory for Agro-Food Processing, Zhejiang University, Hangzhou, Zhejiang, China
| | - Xin-Yu Liao
- Department of Food Science and Nutrition, Zhejiang Key Laboratory for Agro-Food Processing, Zhejiang University, Hangzhou, Zhejiang, China
| | - Qing-Li Dong
- Institute of Food Quality and Safety, University of Shanghai for Science and Technology, Shanghai, China
| | - Xiao-Ting Xuan
- Department of Food Science and Nutrition, Zhejiang Key Laboratory for Agro-Food Processing, Zhejiang University, Hangzhou, Zhejiang, China
| | - Shi-Guo Chen
- Department of Food Science and Nutrition, Zhejiang Key Laboratory for Agro-Food Processing, Zhejiang University, Hangzhou, Zhejiang, China
| | - Xing-Qian Ye
- Department of Food Science and Nutrition, Zhejiang Key Laboratory for Agro-Food Processing, Zhejiang University, Hangzhou, Zhejiang, China
| | - Dong-Hong Liu
- Department of Food Science and Nutrition, Zhejiang Key Laboratory for Agro-Food Processing, Zhejiang University, Hangzhou, Zhejiang, China
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13
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A Computational Study of Amensalistic Control of Listeria monocytogenes by Lactococcus lactis under Nutrient Rich Conditions in a Chemostat Setting. Foods 2016; 5:foods5030061. [PMID: 28231156 PMCID: PMC5302389 DOI: 10.3390/foods5030061] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2016] [Revised: 08/24/2016] [Accepted: 08/29/2016] [Indexed: 11/22/2022] Open
Abstract
We study a previously introduced mathematical model of amensalistic control of the foodborne pathogen Listeria monocytogenes by the generally regarded as safe lactic acid bacteria Lactococcus lactis in a chemostat setting under nutrient rich growth conditions. The control agent produces lactic acids and thus affects pH in the environment such that it becomes detrimental to the pathogen while it is much more tolerant to these self-inflicted environmental changes itself. The mathematical model consists of five nonlinear ordinary differential equations for both bacterial species, the concentration of lactic acids, the pH and malate. The model is algebraically too involved to allow a comprehensive, rigorous qualitative analysis. Therefore, we conduct a computational study. Our results imply that depending on the growth characteristics of the medium in which the bacteria are cultured, the pathogen can survive in an intermediate flow regime but will be eradicated for slower flow rates and washed out for higher flow rates.
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14
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Skandamis PN, Jeanson S. Colonial vs. planktonic type of growth: mathematical modeling of microbial dynamics on surfaces and in liquid, semi-liquid and solid foods. Front Microbiol 2015; 6:1178. [PMID: 26579087 PMCID: PMC4625091 DOI: 10.3389/fmicb.2015.01178] [Citation(s) in RCA: 29] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2015] [Accepted: 10/12/2015] [Indexed: 01/09/2023] Open
Abstract
Predictive models are mathematical expressions that describe the growth, survival, inactivation, or biochemical processes of foodborne bacteria. During processing of contaminated raw materials and food preparation, bacteria are entrapped into the food residues, potentially transferred to the equipment surfaces (abiotic or inert surfaces) or cross-contaminate other foods (biotic surfaces). Growth of bacterial cells can either occur planktonically in liquid or immobilized as colonies. Colonies are on the surface or confined in the interior (submerged colonies) of structured foods. For low initial levels of bacterial population leading to large colonies, the immobilized growth differs from planktonic growth due to physical constrains and to diffusion limitations within the structured foods. Indeed, cells in colonies experience substrate starvation and/or stresses from the accumulation of toxic metabolites such as lactic acid. Furthermore, the micro-architecture of foods also influences the rate and extent of growth. The micro-architecture is determined by (i) the non-aqueous phase with the distribution and size of oil particles and the pore size of the network when proteins or gelling agent are solidified, and by (ii) the available aqueous phase within which bacteria may swarm or swim. As a consequence, the micro-environment of bacterial cells when they grow in colonies might greatly differs from that when they grow planktonically. The broth-based data used for modeling (lag time and generation time, the growth rate, and population level) are poorly transferable to solid foods. It may lead to an over-estimation or under-estimation of the predicted population compared to the observed population in food. If the growth prediction concerns pathogen bacteria, it is a major importance for the safety of foods to improve the knowledge on immobilized growth. In this review, the different types of models are presented taking into account the stochastic behavior of single cells in the growth of a bacterial population. Finally, the recent advances in the rules controlling different modes of growth, as well as the methodological approaches for monitoring and modeling such growth are detailed.
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Affiliation(s)
- Panagiotis N Skandamis
- Laboratory of Food Quality Control and Hygiene, Department of Food Science and Human Nutrition, University of Athens Athens, Greece
| | - Sophie Jeanson
- Institut National de la Recherche Agronomique, UMR1253 Science and Technology of Milk and Eggs Rennes, France ; AGROCAMPUS OUEST, UMR1253 Science and Technology of Milk and Eggs Rennes, France
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15
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El Haddad L, Roy JP, Khalil GE, St-Gelais D, Champagne CP, Labrie S, Moineau S. Efficacy of two Staphylococcus aureus phage cocktails in cheese production. Int J Food Microbiol 2015; 217:7-13. [PMID: 26476571 DOI: 10.1016/j.ijfoodmicro.2015.10.001] [Citation(s) in RCA: 22] [Impact Index Per Article: 2.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/30/2015] [Revised: 09/19/2015] [Accepted: 10/01/2015] [Indexed: 12/18/2022]
Abstract
Staphylococcus aureus is one of the most prevalent pathogenic bacteria contaminating dairy products. In an effort to reduce food safety risks, virulent phages are investigated as antibacterial agents to control foodborne pathogens. The aim of this study was to compare sets of virulent phages, design phage cocktails, and use them in a cocktail to control pathogenic staphylococci in cheese. Six selected phages belonging to the three Caudovirales families (Myoviridae, Siphoviridae, Podoviridae) were strictly lytic, had a broad host range, and did not carry genes coding for virulence traits in their genomes. However, they were sensitive to pasteurization. At MOI levels of 15, 45, and 150, two anti-S. aureus phage cocktails, each containing three phages, one from each of the three phage families, eradicated a 10(6)CFU/g S. aureus population after 14 days of Cheddar cheese curd ripening at 4°C. The use of these phages did not trigger over-production of S. aureus enterotoxin C. The use of phage cocktails and their rotation may prevent the emergence of phage resistant bacterial strains.
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Affiliation(s)
- Lynn El Haddad
- Département de biochimie, de microbiologie, et de bio-informatique, Faculté des sciences et de génie, Groupe de recherche en écologie buccale, Faculté de médecine dentaire, Félix d'Hérelle Reference Center for Bacterial Viruses, Université Laval, Québec G1V 0A6, Canada
| | - Jean-Pierre Roy
- Techniques de santé animale, Cégep de Sherbrooke, 475 rue du Cégep, Sherbrooke, Québec J1E 4K1, Canada
| | - Georges E Khalil
- Department of Behavioral Science, University of Texas MD Anderson Cancer Center, Houston, TX 77030, United States
| | - Daniel St-Gelais
- Food Research and Development Centre, Agriculture and Agri-Food, 3600 Casavant Blvd West, Saint-Hyacinthe, Québec J2S 8E3, Canada; Institut sur la Nutrition et les Aliments Fonctionnels (INAF), Université Laval, Québec G1V 0A6, Canada
| | - Claude P Champagne
- Food Research and Development Centre, Agriculture and Agri-Food, 3600 Casavant Blvd West, Saint-Hyacinthe, Québec J2S 8E3, Canada; Institut sur la Nutrition et les Aliments Fonctionnels (INAF), Université Laval, Québec G1V 0A6, Canada
| | - Steve Labrie
- Département des sciences des aliments, Faculté des sciences de l'agriculture et de l'alimentation, STELA, Université Laval, Québec G1V 0A6, Canada; Institut sur la Nutrition et les Aliments Fonctionnels (INAF), Université Laval, Québec G1V 0A6, Canada
| | - Sylvain Moineau
- Département de biochimie, de microbiologie, et de bio-informatique, Faculté des sciences et de génie, Groupe de recherche en écologie buccale, Faculté de médecine dentaire, Félix d'Hérelle Reference Center for Bacterial Viruses, Université Laval, Québec G1V 0A6, Canada.
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16
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Modeling microbial growth and dynamics. Appl Microbiol Biotechnol 2015; 99:8831-46. [DOI: 10.1007/s00253-015-6877-6] [Citation(s) in RCA: 40] [Impact Index Per Article: 4.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/08/2015] [Revised: 07/13/2015] [Accepted: 07/16/2015] [Indexed: 12/11/2022]
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17
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Østergaard NB, Christiansen LE, Dalgaard P. Stochastic modelling of Listeria monocytogenes single cell growth in cottage cheese with mesophilic lactic acid bacteria from aroma producing cultures. Int J Food Microbiol 2015; 204:55-65. [DOI: 10.1016/j.ijfoodmicro.2015.03.022] [Citation(s) in RCA: 13] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2014] [Revised: 01/14/2015] [Accepted: 03/21/2015] [Indexed: 11/27/2022]
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19
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Comparison of individual-based modeling and population approaches for prediction of foodborne pathogens growth. Food Microbiol 2015; 45:205-15. [DOI: 10.1016/j.fm.2014.04.006] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2014] [Revised: 04/17/2014] [Accepted: 04/17/2014] [Indexed: 11/21/2022]
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Bridier A, Sanchez-Vizuete P, Guilbaud M, Piard JC, Naïtali M, Briandet R. Biofilm-associated persistence of food-borne pathogens. Food Microbiol 2014; 45:167-78. [PMID: 25500382 DOI: 10.1016/j.fm.2014.04.015] [Citation(s) in RCA: 300] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2014] [Revised: 04/15/2014] [Accepted: 04/27/2014] [Indexed: 12/19/2022]
Abstract
Microbial life abounds on surfaces in both natural and industrial environments, one of which is the food industry. A solid substrate, water and some nutrients are sufficient to allow the construction of a microbial fortress, a so-called biofilm. Survival strategies developed by these surface-associated ecosystems are beginning to be deciphered in the context of rudimentary laboratory biofilms. Gelatinous organic matrices consisting of complex mixtures of self-produced biopolymers ensure the cohesion of these biological structures and contribute to their resistance and persistence. Moreover, far from being just simple three-dimensional assemblies of identical cells, biofilms are composed of heterogeneous sub-populations with distinctive behaviours that contribute to their global ecological success. In the clinical field, biofilm-associated infections (BAI) are known to trigger chronic infections that require dedicated therapies. A similar belief emerging in the food industry, where biofilm tolerance to environmental stresses, including cleaning and disinfection/sanitation, can result in the persistence of bacterial pathogens and the recurrent cross-contamination of food products. The present review focuses on the principal mechanisms involved in the formation of biofilms of food-borne pathogens, where biofilm behaviour is driven by its three-dimensional heterogeneity and by species interactions within these biostructures, and we look at some emergent control strategies.
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Affiliation(s)
| | - P Sanchez-Vizuete
- Inra, UMR 1319 Micalis, Jouy-en-Josas, France; AgroParisTech, UMR Micalis, Massy, France
| | - M Guilbaud
- Inra, UMR 1319 Micalis, Jouy-en-Josas, France; AgroParisTech, UMR Micalis, Massy, France
| | - J-C Piard
- Inra, UMR 1319 Micalis, Jouy-en-Josas, France; AgroParisTech, UMR Micalis, Massy, France
| | - M Naïtali
- Inra, UMR 1319 Micalis, Jouy-en-Josas, France; AgroParisTech, UMR Micalis, Massy, France
| | - R Briandet
- Inra, UMR 1319 Micalis, Jouy-en-Josas, France; AgroParisTech, UMR Micalis, Massy, France.
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