The effect of transient temperatures on the growth of Salmonella typhimurium LT2. I: Cycling within the growth region

Delineating community outbreaks of Salmonella enterica serovar Typhimurium by use of whole-genome sequencing: insights into genomic variability within an outbreak

Whole-genome next-generation sequencing (NGS) was used to retrospectively examine 57 isolates from five epidemiologically confirmed community outbreaks (numbered 1 to 5) caused by Salmonella enterica serovar Typhimurium phage type DT170. Most of the human and environmental isolates confirmed epidemiologically to be involved in the outbreaks were either genomically identical or differed by one or two single nucleotide polymorphisms (SNPs), with the exception of those in outbreak 1. The isolates from outbreak 1 differed by up to 12 SNPs, which suggests that the food source of the outbreak was contaminated with more than one strain while each of the other four outbreaks was caused by a single strain. In addition, NGS analysis ruled in isolates that were initially not considered to be linked with the outbreak, which increased the total outbreak size by 107%. The mutation process was modeled by using known mutation rates to derive a cutoff value for the number of SNP difference to determine whether or not a case was part of an outbreak. For an outbreak with less than 1 month of ex vivo/in vivo evolution time, the maximum number of SNP differences between isolates is two or four using the lowest or highest mutation rate, respectively. NGS of S. Typhimurium significantly increases the resolution of investigations of community outbreaks. It can also inform a more targeted public health response by providing important supplementary evidence that cases of disease are or are not associated with food-borne outbreaks of S. Typhimurium.

Cold shock CspA and CspB protein production during periodic temperature cycling in Escherichia coli

Background

Temperature is an important environmental factor which can dramatically affect biochemical processes in bacteria. Temperatures above optimal cause heat shock, while low temperatures induce cold shock. Since the physiological response of the bacterium Escherichia coli to slow temperature fluctuation is not well known, we investigated the effect of periodic temperature cycling between 37° and 8°C with a period of 2 h on proteome profile, cold shock CspA and CspB protein and gene production.

Results

Several proteins (i.e. succinyl-CoA synthetase subunit alpha, periplasmic oligopeptide-binding protein, maltose-binding periplasmic protein, outer membrane porin protein, flavodoxin-1, phosphoserine aminotransferase) were up or down regulated during temperature cycling, in addition to CspA and CspB production. The results indicate that transcription of cspA and cspB increased during each temperature downshift and consistently decreased after each temperature upshift. In sharp contrast CspA-FLAG and CspB-FLAG protein concentrations in the cell increased during the first temperature down-shift and remained unresponsive to further temperature fluctuations. The proteins CspA-FLAG and CspB-FLAG were not significantly degraded during the temperature cycling.

Conclusion

The study demonstrated that slow periodic temperature cycling affected protein production compared to cells constantly incubated at 37°C or during classical cold shock. Bacterial cspA and cspB mRNA transcript levels fluctuated in synchrony with the temperature fluctuations. There was no corresponding pattern of CspA and CspB protein production during temperature cycling.

Conditioning of the membrane fatty acid profile of Escherichia coli during periodic temperature cycling

The membrane fatty acid composition of Escherichia coli becomes conditioned during periodic temperature cycling between 37 and 8 degrees C. After several cycles of temperature change, the bacteria become locked into a low-temperature physiology. Even after a prolonged incubation at high temperature the membrane fatty acid composition of conditioned cells was similar to that of cold-stressed cells.

Effect of refrigerating delayed shipments of raw ground beef on the detection of Salmonella Typhimurium

In eight separate trials, four groups of raw ground beef samples were inoculated with 0.04 to 0.3 CFU/g of Salmonella Typhimurium (DT 104). Each group consisted of four 25-g samples (three inoculated and one uninoculated). After inoculation, these samples were shipped by overnight courier in Shipping containers with ice packs from the U.S. Department of Agriculture (USDA), Eastern Regional Research Center, in Wyndmoor, Pa., to the U.S. Food Safety and Inspection Service (FSIS), Eastern Laboratory, in Athens, Ga. A total of 128 samples (32 in each of four groups) were shipped. A temperature data logger was placed inside each shipping container to record the temperature during shipping and storage. The first group of ground beef samples was analyzed within approximately 1 h of arrival. The second group of samples was left in the original containers, with a gel ice pack, for 24 h before processing. The third and fourth groups of samples were removed from the original shipping containers and stored at room temperature (21 +/- 2 degrees C) for 6 h and then in a refrigerator at 4 +/- 2 degrees C for 24 and 48 h, respectively, before analysis. The samples were analyzed for the presence of Salmonella according to the USDA/FSIS Microbiological Laboratory Guidebook, chapter 4.02. There was no significant difference in the presence and levels of Salmonella in ground beef among the four test groups. These data show that it is acceptable to process the late-arriving ground beef samples for the detection of Salmonella if they are kept in a refrigerator (4 +/- 2 degrees C) for 24 to 48 h or when the shipments arrive late (24 h in the container with ice pack).

Concepts and tools for predictive modeling of microbial dynamics

Description of microbial cell (population) behavior as influenced by dynamically changing environmental conditions intrinsically needs dynamic mathematical models. In the past, major effort has been put into the modeling of microbial growth and inactivation within a constant environment (static models). In the early 1990s, differential equation models (dynamic models) were introduced in the field of predictive microbiology. Here, we present a general dynamic model-building concept describing microbial evolution under dynamic conditions. Starting from an elementary model building block, the model structure can be gradually complexified to incorporate increasing numbers of influencing factors. Based on two case studies, the fundamentals of both macroscopic (population) and microscopic (individual) modeling approaches are revisited. These illustrations deal with the modeling of (i) microbial lag under variable temperature conditions and (ii) interspecies microbial interactions mediated by lactic acid production (product inhibition). Current and future research trends should address the need for (i) more specific measurements at the cell and/or population level, (ii) measurements under dynamic conditions, and (iii) more comprehensive (mechanistically inspired) model structures. In the context of quantitative microbial risk assessment, complexity of the mathematical model must be kept under control. An important challenge for the future is determination of a satisfactory trade-off between predictive power and manageability of predictive microbiology models.

Measurements and predictions of growth for Listeria monocytogenes and Salmonella during fluctuating temperature II. Rapidly changing temperatures

Growth of Listeria monocytogenes and Salmonella was examined during various rates of increase and decrease in temperature from and to the minimum for growth. Growth was little affected by even the most rapid changes and injury or lag was not observed. Subsequent investigations of growth during periods of rapid variation in temperature from and to temperatures below the growth minimum again had little effect and growth was satisfactorily predicted using the dynamic model of Baranyi and Roberts [Int. J. Food Microbiol. 23 (1994) 277] in conjunction with the data of Food Micromodel.

Quantitative microbiology: a basis for food safety

Because microorganisms are easily dispersed, display physiologic diversity, and tolerate extreme conditions, they are ubiquitous and may contaminate and grow in many food products. The behavior of microbial populations in foods (growth, survival, or death) is determined by the properties of the food (e.g., water activity and pH) and the storage conditions (e.g., temperature, relative humidity, and atmosphere). The effect of these properties can be predicted by mathematical models derived from quantitative studies on microbial populations. Temperature abuse is a major factor contributing to foodborne disease; monitoring temperature history during food processing, distribution, and storage is a simple, effective means to reduce the incidence of food poisoning. Interpretation of temperature profiles by computer programs based on predictive models allows informed decisions on the shelf life and safety of foods. In- or on-package temperature indicators require further development to accurately predict microbial behavior. We suggest a basis for a "universal" temperature indicator. This article emphasizes the need to combine kinetic and probability approaches to modeling and suggests a method to define the bacterial growth/no growth interface. Advances in controlling foodborne pathogens depend on understanding the pathogens' physiologic responses to growth constraints, including constraints conferring increased survival capacity.

A Combined Model for Growth and Subsequent Thermal Inactivation of Brochothrix thermosphacta

A mathematical technique for integrating growth and thermal inactivation models of microorganisms into a smooth combined model that can be applied to circumstances under which the temperature gradually rises from growth to inactivation regions is described. For the death part of the model, a correction term is introduced to allow for additional resistance of the cells gained during slow heating. The model was validated with Brochothrix thermosphacta heated in broth at rising temperatures.

The effect of transient temperatures on the growth of Salmonella typhimurium LT2. II: Excursions outside the growth region

The effect of fluctuating temperatures on microbial growth is important in the passage of foods from production to consumption. Suspensions of Salmonella typhimurium have been subjected to sinusoidally time-varying temperatures of periods from 60 to 240 min between 4 degrees and 22 degrees C, that is within and below the growth temperature range. The suspensions were prepared with two concentrations of sodium chloride and adjusted to two different values of pH. The change in the numbers of viable bacteria was measured with time and the experimental growth curves and average generation times compared with predictions based on isothermal growth data. Generally, the experimental average generation times exceeded the predictions by not more than 10%. In enumerating viable bacteria in the suspensions containing 3.5% (w/v) sodium chloride it was necessary to use sodium chloride in the diluent and recovery medium in order to recover the bacteria quantitatively.