Sensitization of Escherichia coli to nisin by maltol and ethyl maltol

F. SCHVED, M.D. PIERSON AND B.J. JUVEN. 1996. When used separately, 20 mmol 1^-1 maltol or 1600 AU ml^-1 nisin resulted in a 0–0.6 log₁₀ reduction in viable counts of Escherichia coli in a buffer system. However, when added in combination they yielded a 1.8–5. 5–log-cycle reduction in viable counts of E. coli at pH 5.0 and 6.8 respectively. It is postulated that maltol (and ethyl maltol) destabilizes the cell outer membrane by chelation of Mg²⁺ and/or Ca²⁺, thus permeabilizing the E. coli cell to nisin.     

Control of urease formation in certain aerobic bacteria

Summary During nitrogen starvation, a 20- to 250-fold increase in specific urease activity was observed in extracts of P. aeruginosa, P. fluorescens, Hydrogenomonas, M. denitrificans, M. cerificans and B. megaterium. In contrast to these species, high levels of urease were observed in P. vulgaris strains and in S. ureae under all growth conditions. No urease was detectable in strains of E. coli, S. marcescens and B. polymyxa, regardless of growth conditions.Incubated in the absence of an exogenous nitrogen source, the specific urease activity increased during a period of 10 to 20 h in P. aeruginosa, Hydrogenomonas and M. denitrificans. Phosphate starvation did not significantly effect urease formation in these strains. The increase in specific urease activity was found to be repressed by exogenous nitrogen sources, including urea. Inhibition by chloramphenicol, other inhibitors, and by the lack of oxygen or fructose, indicated that a derepressive urease formation may occur in these strains. The involvement of traces of urea possibly released from endogenous sources during starvation is discussed.     

Pattern formation in prey-taxis systems

In this paper, we consider spatial predator-prey models with diffusion and prey-taxis. We investigate necessary conditions for pattern formation using a variety of non-linear functional responses, linear and non-linear predator death terms, linear and non-linear prey-taxis sensitivities, and logistic growth or growth with an Allee effect for the prey. We identify combinations of the above non-linearities that lead to spatial pattern formation and we give numerical examples. It turns out that prey-taxis stabilizes the system and for large prey-taxis sensitivity we do not observe pattern formation. We also study and find necessary conditions for global stability for a type I functional response, logistic growth for the prey, non-linear predator death terms, and non-linear prey-taxis sensitivity.     

Pattern formation in prey-taxis systems

In this paper, we consider spatial predator–prey models with diffusion and prey-taxis. We investigate necessary conditions for pattern formation using a variety of non-linear functional responses, linear and non-linear predator death terms, linear and non-linear prey-taxis sensitivities, and logistic growth or growth with an Allee effect for the prey. We identify combinations of the above non-linearities that lead to spatial pattern formation and we give numerical examples. It turns out that prey-taxis stabilizes the system and for large prey-taxis sensitivity we do not observe pattern formation. We also study and find necessary conditions for global stability for a type I functional response, logistic growth for the prey, non-linear predator death terms, and non-linear prey-taxis sensitivity.