The application of physical stress to microorganisms is the most widely used method to induce cell inactivation and promote food stability. To survive, microorganisms have evolved both physiological and genetic mechanisms to tolerate some extreme physical conditions. This is clearly of significance to the food industry in relation to survival of pathogens or spoilage organisms in food. In some microorganisms, the “cold shock response” has been observed in response to abrupt changes to lower temperatures. This results in the production of specific sets of proteins (cold shock proteins), the continued synthesis of proteins involved in transcription and translation, and the repression of heat shock proteins. The addition of weak acid preservatives (for example, sorbates, benzoates) also induces a specific pattern of gene expression (for example, ‘Acid Tolerance Response’), which is likely to be required for optimal adaptation of bacteria to weak acid preservatives and low pH. The primary mode of the antimicrobial action of low pH is to reduce the internal cell pH (pHi) below the normal physiological range tolerated by the cell, leading to growth inhibition. Survival mechanisms involve maintaining pH homeostasis, and this is achieved by a combination of passive and active mechanisms. Microorganisms adapt to osmotic stress by accumulating non-ionic or compatible solutes such as trehalose, glycerol, sucrose, and mannitol. These compatible solutes help balance the osmotic pressure and help preserve protein function inside the cells. By understanding and controlling such mechanisms of adaptation, it may be possible to prevent growth of key microorganisms in food products.

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Adaptation of Microorganisms to Cold Temperatures, Weak Acid Preservatives, Low pH, and Osmotic Stress: A Review

Predictive modelling of the growth and survival of Listeria in fishery products

Listeria monocytogenes is well known for its robust physiology, which permits growth at low temperatures under conditions of high osmolarity and low pH. Although studies have provided insight into the mechanisms used by L. monocytogenes to allay the physiological consequences of these adverse environments, little is known about how these responses are coordinated. In the studies presented here, we have cloned the sigB gene and several rsb genes from L. monocytogenes, encoding homologs of the alternative sigma factor sigmaB and the RsbUVWX proteins, which govern transcription of a general stress regulon in the related bacterium Bacillus subtilis. The L. monocytogenes and B. subtilis sigB and rsb genes are similar in sequence and physical organization; however, we observed that the activity of sigmaB in L. monocytogenes was uniquely responsive to osmotic upshifting, temperature downshifting, and the presence of EDTA in the growth medium. The magnitude of the response was greatest after an osmotic upshift, suggesting a role for sigmaB in coordinating osmotic responses in L. monocytogenes. A null mutation in the sigB gene led to substantial defects in the ability of L. monocytogenes to use betaine and carnitine as osmoprotectants. Subsequent measurements of betaine transport confirmed that the absence of sigmaB reduced the ability of the cells to accumulate betaine. Thus, sigmaB coordinates responses to a variety of physical and chemical signals, and its function facilitates the growth of L. monocytogenes under conditions of high osmotic strength.

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Identification of the Gene Encoding the Alternative Sigma Factor ςB from Listeria monocytogenes and Its Role in Osmotolerance

Models describing the limits of growth of pathogens under multiple constraints will aid management of the safety of foods which are sporadically contaminated with pathogens and for which subsequent growth of the pathogen would significantly increase the risk of food-borne illness. We modeled the effects of temperature, water activity, pH, and lactic acid levels on the growth of two strains of Listeria monocytogenes in tryptone soya yeast extract broth. The results could be divided unambiguously into “growth is possible” or “growth is not possible” classes. We observed minor differences in growth characteristics of the two L. monocytogenes strains. The data follow a binomial probability distribution and may be modeled using logistic regression. The model used is derived from a growth rate model in a manner similar to that described in a previously published work (K. A. Presser, T. Ross, and D. A. Ratkowsky, Appl. Environ. Microbiol. 64:1773‐1779, 1998). We used “nonlinear logistic regression” to estimate the model parameters and developed a relatively simple model that describes our experimental data well. The fitted equations also described well the growth limits of all strains of L. monocytogenes reported in the literature, except at temperatures beyond the limits of the experimental data used to develop the model (3 to 35°C). The models developed will improve the rigor of microbial food safety risk assessment and provide quantitative data in a concise form for the development of safer food products and processes. Predictive microbiology combines mathematical modeling with experimental data on combinations of factors that influence the growth of food spoilage and/or food-borne pathogenic microorganisms. The models developed are intended to predict the fate of microorganisms in foods. Since the experimental data are usually derived from studies using laboratory media, the models must be validated with data collected under conditions under which food products are customarily stored. Predictive microbiology models can be divided into kinetic models and probability models. With the former type, one calculates the microbiological life of food products, i.e., the period of time during which the number of microorganisms in the food is less than a specified value. With the latter type, one determines whether a microorganism can grow and identifies storage conditions with a low or nil probability of growth. Kinetic and probability models may be closely related, because the probability of detectable growth within a specified time period depends on germination, lag, and generation times, i.e., on kinetic parameters. In some cases, a probability model may be derived from a kinetic model by some simple mathematical transformations. For example, in references 33, 35 and 41, a kinetic model was transformed into a probability model by taking the natural logarithm of both sides of the original equation and then replacing one side with the “logit” of a probability, i.e., ln [P/(1 2 P)], where P is the probability that growth occurs. Low levels of Listeria monocytogenes, i.e.,

https://www.researchgate.net/profile/David_Ratkowsky/publication/12270665_Growth_Limits_of_Listeria_monocytogenesas_a_Function_of_Temperature_pH_NaCl_and_Lactic_Acid/links/02e7e52b356292bc1a000000.pdf

Growth limits of Listeria monocytogenes as a function of temperature, pH, NaCl, and lactic acid.

Minimum growth temperatures and those of decreased growth were determined for 100 strains of listerias. The ability of 78 strains of Listeria monocytogenes isolated from animals and 22 non-haemolytic strains to grow at low temperatures was studied, using a flooding technique, in a plate-type continuous temperature gradient incubator at temperatures between -1.6 and 14.5 degrees C. The mean minimum temperature for L. monocytogenes was +1.7 +/- 0.5 degrees C. The growth of non-haemolytic listerias was unobservable at +1.7 +/- 0.5 degrees C. The L. monocytogenes strains grew at about 0.6 degrees C lower than the non-pathogenic strains. No differences in growth temperatures were observed among L. monocytogenes strains isolated from different sources. The serovars with the OI antigen grew at lower temperatures (+1.0 +/- 0.3 degrees C) than the other common serovar 4b (+1.3 +/- 0.4 degrees C). The results indicate that L. monocytogenes grows better than non-haemolytic strains under cold conditions. The possible role of haemolysins as growth factors is also discussed.

Minimum growth temperatures of Listeria monocytogenes and non‐haemolytic listeria

Listeria monocytogenes grew between 3 and 45C. Growth at 10C was at a rate characteristic of psychrophilic bacteria. The increase in the duration of the lag phase of growth with decreasing temperatures was expressed with a hyperbolic curve which indicated an infinite lag time at 3.3C. If this relationship between lag time and temperature is a general phenomenon, it would provide a relatively rapid method for estimating minimal temperatures of growth. Initial rates for oxygen consumption during glucose metabolism and for uptake of the nonmetabolized sugar, 2-deoxyglucose, had temperature coefficients (10–30C) of about 1.5. Growth at 10C increased the rate and the capacity of 2-deoxyglucose uptake by exponential-phase cells. Uptake and incorporation of L-leucine was relatively rapid at 10C but the temperature coefficients were greater than 2. High temperature coefficients (greater than 2.5) for nicotinamide adenine dinucleotide oxidase and lactic dehydrogenase activity in cell-free preparations indicated th...

Renée Bourgeois

Papers

Psychrotrophic properties of Listeria monocytogenes