Variability in growth/no growth boundaries of 188 different Escherichia coli strains reveals that approximately 75 % have a higher growth probability under low pH conditions than E. coli O157:H7 strain ATCC 43888

Abstract

This study investigated the variation in growth/no growth boundaries of 188 Escherichia coli strains. Experiments were conducted in Luria-Bertani media under 36 combinations of lactic acid (LA) (0 and 25 mM), pH (3.8, 3.9, 4.0, 4.1, 4.2 and 4.3 for 0 mM LA and 4.3, 4.4, 4.5, 4.6, 4.7 and 4.8 for 25 mM LA) and temperature (20, 25 and 30 °C). After 3 days of incubation, growth was monitored through optical density measurements. For each strain, a so-called purposeful selection approach was used to fit a logistic regression model that adequately predicted the likelihood for growth. Further, to assess the growth/no growth variability for all the strains at once, a generalized linear mixed model was fitted to the data. Strain was fitted as a fixed factor and replicate as a random blocking factor. E. coli O157:H7 strain ATCC 43888 was used as reference strain allowing a comparison with the other strains. Out of the 188 strains tested, 140 strains (∼75%) presented a significantly higher probability of growth under low pH conditions than the O157:H7 strain ATCC 43888, whereas 20 strains (∼11%) showed a significantly lower probability of growth under high pH conditions.

Growth ofMycobacterium tuberculosisat acidic pH depends on lipid assimilation and is accompanied by reduced GAPDH activity

“…It was therefore surprising to us that M. tuberculosis fails to replicate in mildly acidic conditions in vitro. Other bacteria that also encounter and persist in the face of acidic pH during infection such as Salmonella typhimurium, Shigella flexneri, Listeria monocytogenes, and Escherichia coli can replicate in vitro at much lower pH (pH 4.0 to 4.2) than reported for M. tuberculosis (32)(33)(34)(35). This led us to investigate how M. tuberculosis might be able to replicate in acidic environments.…”

Validation of selective agars for detection and quantification of Escherichia coli resistant to critically important antimicrobials

“…The reason for growth variation between strains was not clear as it was not the principal feature being evaluated. Most data in the current literature focuses on growth rate of E. coli strains under specific environmental conditions but none have evaluated possible factors influencing growth rates between E. coli strains (22)(23)(24). Significant interactions between strain with agar or antimicrobial are one such factor affecting growth rate but given the uniformity in performance across all agars with and without incorporation of antimicrobials, it suggests that this influence towards growth was minimal and not enough to affect the performance outcome of each agar.…”

Towards transparent and consistent exchange of knowledge for improved microbiological food safety

“…For example, in the last years new predictive microbial models have been developed considering different factors like, the structural characteristics of the food matrix [16] and the cross-protection between different stresses [17]. In addition, the modelling approach followed in those studies that consider the inter-strain variability of the same microbial species has changed with respect to previous studies [18,19]. Latter developments in predictive modelling approaches have also resulted in new model equations [20], stochastic models [6,21,22] and models considering dynamic conditions [23].…”