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Martin CS, Jubelin G, Darsonval M, Leroy S, Leneveu-Jenvrin C, Hmidene G, Omhover L, Stahl V, Guillier L, Briandet R, Desvaux M, Dubois-Brissonnet F. Genetic, physiological, and cellular heterogeneities of bacterial pathogens in food matrices: Consequences for food safety. Compr Rev Food Sci Food Saf 2022; 21:4294-4326. [PMID: 36018457 DOI: 10.1111/1541-4337.13020] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 07/12/2022] [Accepted: 07/14/2022] [Indexed: 01/28/2023]
Abstract
In complex food systems, bacteria live in heterogeneous microstructures, and the population displays phenotypic heterogeneities at the single-cell level. This review provides an overview of spatiotemporal drivers of phenotypic heterogeneity of bacterial pathogens in food matrices at three levels. The first level is the genotypic heterogeneity due to the possibility for various strains of a given species to contaminate food, each of them having specific genetic features. Then, physiological heterogeneities are induced within the same strain, due to specific microenvironments and heterogeneous adaptative responses to the food microstructure. The third level of phenotypic heterogeneity is related to cellular heterogeneity of the same strain in a specific microenvironment. Finally, we consider how these phenotypic heterogeneities at the single-cell level could be implemented in mathematical models to predict bacterial behavior and help ensure microbiological food safety.
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Affiliation(s)
- Cédric Saint Martin
- MICALIS Institute, Université Paris-Saclay, INRAE, AgroParisTech, Jouy-en-Josas, France.,Université Clermont Auvergne, INRAE, UMR454 MEDIS, Clermont-Ferrand, France
| | - Grégory Jubelin
- Université Clermont Auvergne, INRAE, UMR454 MEDIS, Clermont-Ferrand, France
| | - Maud Darsonval
- MICALIS Institute, Université Paris-Saclay, INRAE, AgroParisTech, Jouy-en-Josas, France
| | - Sabine Leroy
- Université Clermont Auvergne, INRAE, UMR454 MEDIS, Clermont-Ferrand, France
| | - Charlène Leneveu-Jenvrin
- MICALIS Institute, Université Paris-Saclay, INRAE, AgroParisTech, Jouy-en-Josas, France.,Association pour le Développement de l'Industrie de la Viande (ADIV), Clermont-Ferrand, France
| | - Ghaya Hmidene
- Risk Assessment Department, ANSES, Maisons-Alfort, France
| | - Lysiane Omhover
- Aerial, Technical Institute of Agro-Industry, Illkirch, France
| | - Valérie Stahl
- Aerial, Technical Institute of Agro-Industry, Illkirch, France
| | | | - Romain Briandet
- MICALIS Institute, Université Paris-Saclay, INRAE, AgroParisTech, Jouy-en-Josas, France
| | - Mickaël Desvaux
- Université Clermont Auvergne, INRAE, UMR454 MEDIS, Clermont-Ferrand, France
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2
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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] [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)
- Frédéric Borges
- Université de Lorraine, LIBio, Nancy, France
- *Correspondence: Frédéric Borges,
| | - 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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3
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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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4
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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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5
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Liu Y, Wang X, Liu B, Yuan S, Qin X, Dong Q. Microrisk Lab: An Online Freeware for Predictive Microbiology. Foodborne Pathog Dis 2021; 18:607-615. [PMID: 34191593 DOI: 10.1089/fpd.2020.2919] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/13/2022] Open
Abstract
Microrisk Lab is an R-based online modeling freeware designed to realize parameter estimation and model simulation in predictive microbiology. A total of 36 peer-reviewed models were integrated for parameter estimation (including primary models of bacterial growth/inactivation under static and nonisothermal conditions, secondary models of specific growth rate, and competition models of two-flora growth) and model simulation (including integrated models of deterministic or stochastic bacterial growth/inactivation under static and nonisothermal conditions) in Microrisk Lab. Each modeling section was designed to provide numerical and graphical results with comprehensive statistical indicators depending on the appropriate data set and/or parameter setting. In this study, six case studies were reproduced in Microrisk Lab and compared in parallel with DMFit, GInaFiT, IPMP 2013/GraphPad Prism, Bioinactivation FE, and @Risk, respectively. The estimated and simulated results demonstrated that the performance of Microrisk Lab was statistically equivalent to that of other existing modeling systems. Microrisk Lab allows for a friendly user experience when modeling microbial behaviors owing to its interactive interfaces, high integration, and interconnectivity. Users can freely access this application at https://microrisklab.shinyapps.io/english/ or https://microrisklab.shinyapps.io/chinese/.
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Affiliation(s)
- Yangtai Liu
- University of Shanghai for Science and Technology, Shanghai, China
| | - Xiang Wang
- University of Shanghai for Science and Technology, Shanghai, China
| | - Baolin Liu
- University of Shanghai for Science and Technology, Shanghai, China
| | - Sanling Yuan
- University of Shanghai for Science and Technology, Shanghai, China
| | - Xiaojie Qin
- University of Shanghai for Science and Technology, Shanghai, China
| | - Qingli Dong
- University of Shanghai for Science and Technology, Shanghai, China
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6
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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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7
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García MR, Cabo ML. Optimization of E. coli Inactivation by Benzalkonium Chloride Reveals the Importance of Quantifying the Inoculum Effect on Chemical Disinfection. Front Microbiol 2018; 9:1259. [PMID: 29997577 PMCID: PMC6028699 DOI: 10.3389/fmicb.2018.01259] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2018] [Accepted: 05/24/2018] [Indexed: 01/25/2023] Open
Abstract
Optimal disinfection protocols are fundamental to minimize bacterial resistance to the compound applied, or cross-resistance to other antimicrobials such as antibiotics. The objective is twofold: guarantee safe levels of pathogens and minimize the excess of disinfectant after a treatment. In this work, the disinfectant dose is optimized based on a mathematical model. The model explains and predicts the interplay between disinfectant and pathogen at different initial microbial densities (inocula) and dose concentrations. The study focuses on the disinfection of Escherichia coli with benzalkonium chloride, the most common quaternary ammonium compound. Interestingly, the specific benzalkonium chloride uptake (mean uptake per cell) decreases exponentially when the inoculum concentration increases. As a consequence, the optimal disinfectant dose increases exponentially with the initial bacterial concentration.
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Affiliation(s)
- Míriam R García
- Bioprocess Engineering Group, IIM-CSIC Spanish National Research Council, Vigo, Spain
| | - Marta L Cabo
- Microbiology Group, IIM-CSIC Spanish National Research Council, Vigo, Spain
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8
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García MR, Vázquez JA, Teixeira IG, Alonso AA. Stochastic Individual-Based Modeling of Bacterial Growth and Division Using Flow Cytometry. Front Microbiol 2018; 8:2626. [PMID: 29354110 PMCID: PMC5760514 DOI: 10.3389/fmicb.2017.02626] [Citation(s) in RCA: 22] [Impact Index Per Article: 3.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2017] [Accepted: 12/15/2017] [Indexed: 11/30/2022] Open
Abstract
A realistic description of the variability in bacterial growth and division is critical to produce reliable predictions of safety risks along the food chain. Individual-based modeling of bacteria provides the theoretical framework to deal with this variability, but it requires information about the individual behavior of bacteria inside populations. In this work, we overcome this problem by estimating the individual behavior of bacteria from population statistics obtained with flow cytometry. For this objective, a stochastic individual-based modeling framework is defined based on standard assumptions during division and exponential growth. The unknown single-cell parameters required for running the individual-based modeling simulations, such as cell size growth rate, are estimated from the flow cytometry data. Instead of using directly the individual-based model, we make use of a modified Fokker-Plank equation. This only equation simulates the population statistics in function of the unknown single-cell parameters. We test the validity of the approach by modeling the growth and division of Pediococcus acidilactici within the exponential phase. Estimations reveal the statistics of cell growth and division using only data from flow cytometry at a given time. From the relationship between the mother and daughter volumes, we also predict that P. acidilactici divide into two successive parallel planes.
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Affiliation(s)
- Míriam R García
- Bioprocess Engineering Group, Marine Research Institute-Spanish National Research Council (IIM-CSIC), Vigo, Spain
| | - José A Vázquez
- Group of Recycling and Valorisation of Waste Materials, Marine Research Institute-Spanish National Research Council (IIM-CSIC), Vigo, Spain
| | - Isabel G Teixeira
- Oceanology, Marine Research Institute-Spanish National Research Council (IIM-CSIC), Vigo, Spain
| | - Antonio A Alonso
- Bioprocess Engineering Group, Marine Research Institute-Spanish National Research Council (IIM-CSIC), Vigo, Spain
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9
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Ricci A, Allende A, Bolton D, Chemaly M, Davies R, Fernández Escámez PS, Girones R, Herman L, Koutsoumanis K, Nørrung B, Robertson L, Ru G, Sanaa M, Simmons M, Skandamis P, Snary E, Speybroeck N, Ter Kuile B, Threlfall J, Wahlström H, Takkinen J, Wagner M, Arcella D, Da Silva Felicio MT, Georgiadis M, Messens W, Lindqvist R. Listeria monocytogenes contamination of ready-to-eat foods and the risk for human health in the EU. EFSA J 2018; 16:e05134. [PMID: 32760461 PMCID: PMC7391409 DOI: 10.2903/j.efsa.2018.5134] [Citation(s) in RCA: 152] [Impact Index Per Article: 25.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/11/2022] Open
Abstract
Food safety criteria for Listeria monocytogenes in ready-to-eat (RTE) foods have been applied from 2006 onwards (Commission Regulation (EC) 2073/2005). Still, human invasive listeriosis was reported to increase over the period 2009-2013 in the European Union and European Economic Area (EU/EEA). Time series analysis for the 2008-2015 period in the EU/EEA indicated an increasing trend of the monthly notified incidence rate of confirmed human invasive listeriosis of the over 75 age groups and female age group between 25 and 44 years old (probably related to pregnancies). A conceptual model was used to identify factors in the food chain as potential drivers for L. monocytogenes contamination of RTE foods and listeriosis. Factors were related to the host (i. population size of the elderly and/or susceptible people; ii. underlying condition rate), the food (iii. L. monocytogenes prevalence in RTE food at retail; iv. L. monocytogenes concentration in RTE food at retail; v. storage conditions after retail; vi. consumption), the national surveillance systems (vii. improved surveillance), and/or the bacterium (viii. virulence). Factors considered likely to be responsible for the increasing trend in cases are the increased population size of the elderly and susceptible population except for the 25-44 female age group. For the increased incidence rates and cases, the likely factor is the increased proportion of susceptible persons in the age groups over 45 years old for both genders. Quantitative modelling suggests that more than 90% of invasive listeriosis is caused by ingestion of RTE food containing > 2,000 colony forming units (CFU)/g, and that one-third of cases are due to growth in the consumer phase. Awareness should be increased among stakeholders, especially in relation to susceptible risk groups. Innovative methodologies including whole genome sequencing (WGS) for strain identification and monitoring of trends are recommended.
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10
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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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11
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Aguirre JS, Koutsoumanis KP. Towards lag phase of microbial populations at growth-limiting conditions: The role of the variability in the growth limits of individual cells. Int J Food Microbiol 2016; 224:1-6. [PMID: 26900994 DOI: 10.1016/j.ijfoodmicro.2016.01.021] [Citation(s) in RCA: 20] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2015] [Revised: 10/29/2015] [Accepted: 01/31/2016] [Indexed: 02/08/2023]
Abstract
The water activity (aw) growth limits of unheated and heat stressed Listeria monocytogenes individual cells were studied. The aw limits varied from 0.940 to 0.997 and 0.951 to 0.997 for unheated and heat stressed cells, respectively. Due to the above variability a decrease in aw results in the presence of a non-growing fraction in the population leading to an additional pseudo-lag in population growth. In this case the total apparent lag of the population is the sum of the physiological lag of the growing cells (time required to adjust to the new environment) and the pseudo-lag. To investigate the effect of aw on the above lag components, the growth kinetics of L. monocytogenes on tryptone soy agar with aw adjusted to values ranging from 0.997 to 0.940 was monitored. The model of B&R was fitted to the data for the estimation of the apparent lag. In order to estimate the physiological lag of the growing fraction of the inoculum, the model was refitted to the growth data using as initial population level the number of cells that were able to grow (estimated from the number of colonies formed on the agar at the end of storage) and excluding the rest data during the lag. The results showed that for the unheated cells the apparent lag was almost identical to the physiological lag for aw values ranging from 0.997 to 0.970, as the majority of the cells in the initial population was able to grow in these conditions. As the aw decreased from 0.970 to 0.940 however, the number of cells in the population which were able to grow, decreased resulting to an increase in the pseudo-lag. The maximum value of pseudo-lag was 13.1h and it was observed at aw=0.940 where 10% of the total inoculated cells were able to grow. For heat stressed populations a pseudo-lag started to increase at higher aw conditions (0.982) compared to unheated cells. In contrast to the apparent lag, a linear relation between physiological lag and aw was observed for both unheated and heat stressed cells.
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Affiliation(s)
- Juan S Aguirre
- Laboratory of Food Microbiology and Hygiene, Department of Food Science and Technology, School of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece; Depto. Nutrición, Bromatología y Tecnología de los Alimentos, Facultad de Veterinaria, Universidad Complutense, Ciudad Universitaria, Madrid 28040, Spain
| | - Konstantinos P Koutsoumanis
- Laboratory of Food Microbiology and Hygiene, Department of Food Science and Technology, School of Agriculture, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece.
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12
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Bridier A, Hammes F, Canette A, Bouchez T, Briandet R. Fluorescence-based tools for single-cell approaches in food microbiology. Int J Food Microbiol 2015; 213:2-16. [PMID: 26163933 DOI: 10.1016/j.ijfoodmicro.2015.07.003] [Citation(s) in RCA: 23] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2015] [Revised: 06/26/2015] [Accepted: 07/03/2015] [Indexed: 12/31/2022]
Abstract
The better understanding of the functioning of microbial communities is a challenging and crucial issue in the field of food microbiology, as it constitutes a prerequisite to the optimization of positive and technological microbial population functioning, as well as for the better control of pathogen contamination of food. Heterogeneity appears now as an intrinsic and multi-origin feature of microbial populations and is a major determinant of their beneficial or detrimental functional properties. The understanding of the molecular and cellular mechanisms behind the behavior of bacteria in microbial communities requires therefore observations at the single-cell level in order to overcome "averaging" effects inherent to traditional global approaches. Recent advances in the development of fluorescence-based approaches dedicated to single-cell analysis provide the opportunity to study microbial communities with an unprecedented level of resolution and to obtain detailed insights on the cell structure, metabolism activity, multicellular behavior and bacterial interactions in complex communities. These methods are now increasingly applied in the field of food microbiology in different areas ranging from research laboratories to industry. In this perspective, we reviewed the main fluorescence-based tools used for single-cell approaches and their concrete applications with specific focus on food microbiology.
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Affiliation(s)
| | - F Hammes
- Eawag, Swiss Federal Institute of Aquatic Science and Technology, Dübendorf, Switzerland
| | - A Canette
- INRA, UMR1319 Micalis, Jouy-en-Josas, France; AgroParisTech, UMR Micalis, Jouy-en-Josas, France
| | | | - R Briandet
- INRA, UMR1319 Micalis, Jouy-en-Josas, France; AgroParisTech, UMR Micalis, Jouy-en-Josas, France.
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13
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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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