Disentangling the contributions of initial heterogeneities and dynamic stress adaptation to nonlinearities in bacterial survival curves

Leonidas Georgalis, Kah Yen Claire Yeak, Christina Tsimpou, Pablo S. Fernandez, Marjon Wells-Bennik, Alberto Garre*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

2 Citations (Scopus)

Abstract

The deviations from log-linearity that are often observed in bacterial survivor curves can be explained using different arguments, both biological and experimental. In this study, we used Bacillus subtilis as a model organism to demonstrate that the generally accepted vitalistic arguments (initial heterogeneities in the stress resistance of the cells in the population) may fail to describe microbial inactivation in some situations. In this sense, we showed how dynamic stress acclimation during an isothermal treatment provides an alternative explanation for survivor curves with an upwards curvature. We also provided an innovative experimental approach based on preadaptation experiments to evaluate which hypothesis is more suitable for the bacterial response. Furthermore, we used our experimental results to define bounds for the possible stress acclimation that may take place during dynamic treatments, concluding that the magnitude of stress acclimation may be larger for dynamic treatments than for isothermal experiments. We also evaluated the contribution of the SigB general stress response system to heat resistance by comparing the heat survival of wt and the ΔsigB mutant. Both strains survived better in 51, 52.5 and 55 °C when cells were pre-adapted at 48 °C than non-pre-adapted cells. However, ΔsigB was less resistant to heat than wt due to the missing SigB general stress system. Although these conclusions were based on B. subtilis as a model organism, this study can be the first step towards the development of a novel methodology able to estimate dynamic effects using only isothermal experiments. This would improve the models developed within the predictive microbiology community, improving our ability to predict microbial inactivation during industrial treatments, which are most often dynamic.

Original languageEnglish
Article number113385
JournalFood Research International
Volume173
Issue numberPart 2
DOIs
Publication statusPublished - Nov 2023

Keywords

  • Bacillus subtilis
  • Microbial inactivation
  • Pasteurisation
  • Predictive microbiology
  • SigB
  • Variability

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