Literature DB >> 19735318

Quantifying the heterogeneous heat response of Escherichia coli under dynamic temperatures.

E Van Derlinden1, I Lule, K Bernaerts, J F Van Impe.   

Abstract

AIMS: Non-sigmoid growth curves of Escherichia coli obtained at constant temperatures near the maximum growth temperature (T(max)) were previously explained by the coexistence of two subpopulations, i.e. a stress-sensitive and a stress-resistant subpopulation. Mathematical simulations with a heterogeneous model support this hypothesis for static experiments at 45 degrees C. In this article, the behaviour of E. coli, when subjected to a linearly increasing temperature crossing T(max), is studied. METHODS AND
RESULTS: Subpopulation dynamics are studied by culturing E. coli K12 MG1655 in brain heart infusion broth in a bioreactor. The slowly increasing temperature (degrees C h(-1)) starting from 42 degrees C results in growth up to 60 degrees C, a temperature significantly higher than the known T(max). Given some additional presumptions, mathematical simulations with the heterogeneous model can describe the dynamic experiments rather well.
CONCLUSIONS: This study further confirms the existence of a stress-resistant subpopulation and reveals the unexpected growth of E. coli at temperatures significantly higher than T(max). SIGNIFICANCE AND IMPACT OF THE STUDY: The growth of the small stress-resistant subpopulation at unexpectedly high temperatures asks for a revision of currently applied models in food safety and food quality strategies.

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Year:  2009        PMID: 19735318     DOI: 10.1111/j.1365-2672.2009.04512.x

Source DB:  PubMed          Journal:  J Appl Microbiol        ISSN: 1364-5072            Impact factor:   3.772


  2 in total

1.  High Heating Rates Affect Greatly the Inactivation Rate of Escherichia coli.

Authors:  Juan-Pablo Huertas; Arantxa Aznar; Arturo Esnoz; Pablo S Fernández; Asunción Iguaz; Paula M Periago; Alfredo Palop
Journal:  Front Microbiol       Date:  2016-08-11       Impact factor: 5.640

2.  A Bayesian non-parametric mixed-effects model of microbial growth curves.

Authors:  Peter D Tonner; Cynthia L Darnell; Francesca M L Bushell; Peter A Lund; Amy K Schmid; Scott C Schmidler
Journal:  PLoS Comput Biol       Date:  2020-10-26       Impact factor: 4.475

  2 in total

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