Literature DB >> 16488043

Effect of environmental parameters (temperature, pH and a(w)) on the individual cell lag phase and generation time of Listeria monocytogenes.

K Francois1, F Devlieghere, A R Standaert, A H Geeraerd, J F Van Impe, J Debevere.   

Abstract

The effect of the individual environmental factors temperature (2-30 degrees C), pH (4.4-7.4) and a(w) (0.947-0.995) as well as the combinations of these factors on the individual cell lag phase and the generation time of Listeria monocytogenes was investigated. Individual cells were isolated using a serial dilution protocol in microtiter plates, and subsequent growth was investigated by optical density (OD) measurements at 600 nm. About 100 replicates were made for each set of environmental conditions. Part of the data were previously published in Francois et al. (Francois, K., Devlieghere, F., Smet, K., Standaert, A.R., Geeraerd, A.H., Van Impe, J.F., Debevere, J., 2005a. Modelling the individual cell lag phase: effect of temperature and pH on the individual cell lag distribution of Listeria monocytogenes. Int. J. Food Microbiol. 100, 41-53.), but were recalculated here using the calibration curves for transformation of optical density to colony forming units/ml from Francois et al. (Francois, K., Devlieghere, F., Standaert, A.R., Geeraerd, A.H., Cools, I., Van Impe, J.F., Debevere, J., 2005b. Environmental factors influencing the relationship between optical density and cell count for Listeria monocytogenes. J. Appl. Microbiol. 99, 1503-1515), as this calibration curve appeared to be dependent on the environmental parameters. The previous dataset was also extended with a factor a(w), observed individually and combinations with the above mentioned environmental factors. Individual cell lag phases and subsequent growth rates were calculated assuming an exponential growth model. The results are discussed as mean values to determine the general trends and in addition, histograms are made and statistical distributions are fitted to the different data sets. When stress levels increased, the mean values and the variability observed for the individual cell lag phases increased, resulting in broader histograms and distributions that were shifting to the right. Also the gravity point of the distributions was shifting from a skewed left type to a more symmetrical type. The best description of the data is obtained with an exponential distribution for low stress levels, a gamma distribution for intermediate stress and a Weibull distribution for severe stress levels. When only low stress levels were applied, a significant percentage of the cells showed no lag phase. In those cases, a new approach was used to obtain better fits: cells with a lag phase and those without a lag phase were separated using a binomial distribution while in a second step, a gamma or a Weibull distribution is fitted to the fraction of cells showing a lag phase. A normal distribution is used to describe the variability of the generation times. These distributions can be applied to refine the exposure assessment part of the risk assessment concerning L. monocytogenes by incorporating intercellular variability.

Entities:  

Mesh:

Substances:

Year:  2006        PMID: 16488043     DOI: 10.1016/j.ijfoodmicro.2005.11.017

Source DB:  PubMed          Journal:  Int J Food Microbiol        ISSN: 0168-1605            Impact factor:   5.277


  9 in total

1.  The effects of combinatorial treatments with stress inducing molecules on growth of E. coli colonies.

Authors:  Steven L Middler; Salvador Gomez; Christapher D Parker; Peter M Palenchar
Journal:  Curr Microbiol       Date:  2011-10-04       Impact factor: 2.188

2.  Estimating single-cell lag times via a Bayesian scheme.

Authors:  P K Malakar; G C Barker
Journal:  Appl Environ Microbiol       Date:  2008-09-19       Impact factor: 4.792

3.  Influence of environmental stress on distributions of times to first division in Escherichia coli populations, as determined by digital-image analysis of individual cells.

Authors:  Gordon W Niven; Jennifer S Morton; Tamara Fuks; Bernard M Mackey
Journal:  Appl Environ Microbiol       Date:  2008-04-18       Impact factor: 4.792

4.  The role of the pH conditions of growth on the bioadhesion of individual and lawns of pathogenic Listeria monocytogenes cells.

Authors:  Bong-Jae Park; Nehal I Abu-Lail
Journal:  J Colloid Interface Sci       Date:  2011-03-12       Impact factor: 8.128

5.  A Diffusion Model to Quantify Membrane Repair Process in Listeria monocytogenes Exposed to High Pressure Processing Based on Fluorescence Microscopy Data.

Authors:  Bahareh Nikparvar; Alicia Subires; Marta Capellas; Manuela Hernandez-Herrero; Peter Crauwels; Christian U Riedel; Nadav Bar
Journal:  Front Microbiol       Date:  2021-05-13       Impact factor: 5.640

6.  Short-term genome evolution of Listeria monocytogenes in a non-controlled environment.

Authors:  Renato H Orsi; Mark L Borowsky; Peter Lauer; Sarah K Young; Chad Nusbaum; James E Galagan; Bruce W Birren; Reid A Ivy; Qi Sun; Lewis M Graves; Bala Swaminathan; Martin Wiedmann
Journal:  BMC Genomics       Date:  2008-11-13       Impact factor: 3.969

Review 7.  Colonial vs. planktonic type of growth: mathematical modeling of microbial dynamics on surfaces and in liquid, semi-liquid and solid foods.

Authors:  Panagiotis N Skandamis; Sophie Jeanson
Journal:  Front Microbiol       Date:  2015-10-29       Impact factor: 5.640

8.  Modeling Stochastic Variability in the Numbers of Surviving Salmonella enterica, Enterohemorrhagic Escherichia coli, and Listeria monocytogenes Cells at the Single-Cell Level in a Desiccated Environment.

Authors:  Kento Koyama; Hidekazu Hokunan; Mayumi Hasegawa; Shuso Kawamura; Shigenobu Koseki
Journal:  Appl Environ Microbiol       Date:  2017-02-01       Impact factor: 4.792

9.  Comparison of the Effects of Environmental Parameters on the Growth Variability of Vibrio parahaemolyticus Coupled with Strain Sources and Genotypes Analyses.

Authors:  Bingxuan Liu; Haiquan Liu; Yingjie Pan; Jing Xie; Yong Zhao
Journal:  Front Microbiol       Date:  2016-06-23       Impact factor: 5.640

  9 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.