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Applied and Environmental Microbiology, April 2009, p. 2132-2138, Vol. 75, No. 7
0099-2240/09/$08.00+0     doi:10.1128/AEM.01796-08
Copyright © 2009, American Society for Microbiology. All Rights Reserved.

Stress- and Growth Rate-Related Differences between Plate Count and Real-Time PCR Data during Growth of Listeria monocytogenes ^,

Franziska Reichert-Schwillinsky,¹ Carmen Pin,² Monika Dzieciol,¹ Martin Wagner,¹ and Ingeborg Hein^1*

Institute for Milk Hygiene, Milk Technology, and Food Safety, Department for Farm Animals and Veterinary Public Health, University of Veterinary Medicine, Veterinaerplatz 1, 1210 Vienna, Austria,¹ Institute of Food Research, Norwich Research Park, Colney, Norwich NR4 7UA, United Kingdom²

Received 4 August 2008/ Accepted 21 January 2009

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To assess the overestimation of bacterial cell counts in real-time PCR in relation to stress and growth phase, four different strains of L. monocytogenes were exposed to combinations of osmotic stress (0.5 to 8% [vol/vol] NaCl) and acid stress (pH 5 to 7) in a culture model at a growth temperature of 10°C or were grown under optimal conditions. Growth curves obtained from real-time PCR, optical density, and viable count data were compared. As expected, optical density data revealed entirely different growth curves. Good to moderate growth conditions yielded good correlation of real-time PCR data and plate count data (r² = 0.96 and 0.99) with similar cell counts. When growth conditions became worse, the numbers of CFU decreased during the stationary phase, whereas real-time-PCR-derived bacterial cell equivalents differed in this regard; the correlation worsened (r² = 0.84). However, fitted growth curves revealed that maximum growth rates calculated from real-time PCR data were not significantly different from those derived from plate count data. The overestimation of bacterial cell counts by real-time PCR observed in the stationary phase under higher-stress conditions might be explained by the accumulation of viable but nonculturable bacteria or dead bacteria and extracellular DNA. Considering these results, real-time PCR data collected from naturally contaminated samples should be viewed with caution.

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The ubiquitous gram-positive bacterial species Listeria monocytogenes is a major food-borne pathogen. Clinical symptoms of infection with this zoonotic pathogen include febrile gastroenteritis, which might be underdiagnosed, as well as meningoencephalitis, focal infections due to septicemia, abortion, or stillbirth. The average case-fatality rate reported is 20 to 30%, which is rather high for a bacterial food-borne disease (36). In 2006 the incidence in Austria was 0.1/100,000 inhabitants, with a mortality rate of 20%, which is typical for severe cases of this infection (4). Symptoms in animals are similar to those observed in humans. In addition, L. monocytogenes-induced mastitis in farm animals used for milk production is of special importance because it signifies an economic loss for the dairy industry (33).

Traditional microbiological techniques for detection and quantification of this pathogen are time-consuming. The horizontal method of detection in food and feed, including confirmation of suspected colonies, takes about 1 week (2, 3). Thus, faster alternative methods such as PCR or real-time PCR were developed to support or replace traditional techniques (16). In many cases, these DNA-based methods are combined with enrichment to increase the target concentration (30, 34). Recent efforts focused on skipping the enrichment step, thus enabling faster and direct detection as well as quantification of L. monocytogenes in a wide variety of sample types such as food, blood, or sewage sludge (7, 20, 35, 38). However, this approach bears the possibility of false-positive results and overestimation of the bacterial cell count due to the detection of viable but nonculturable (VBNC) cells (which might or might not be a benefit for risk assessment), dead target cells, or extracellular DNA from these cells by PCR, which would otherwise be diluted in the enrichment (18, 11, 24).

RNA is less stable than DNA and was used for PCR-based detection of viable L. monocytogenes (21, 29). However, 16S rRNA is too stable for viable/dead differentiation, and even the success of an mRNA-based approach depends on careful selection of the target sequence. Longer targets provided better estimates of viability than shorter targets (21, 29). Transfer mRNA was suggested as an alternative for detection of viable L. monocytogenes (30). Since gene expression varies according to the environment, RNA-based quantification is difficult (27).

Quantification of L. monocytogenes to gather data for predictive microbiology and to aid risk assessment is an important issue (26). DNA-based real-time PCR could be used as a fast substitute for culture techniques to gather quantitative information, but the problems mentioned above might limit its use for that purpose. Optical density methods are commonly used but require high concentrations of bacteria (12). Real-time PCR is more sensitive than optical density methods; thus, monitoring of lower concentrations of bacteria is possible. Comparative data sets for traditional and alternative methods are prerequisite for evaluation of the suitability of alternative methods for gathering data for risk assessment.

The aim of this study was to assess the suitability of real-time PCR for the quantification of L. monocytogenes under different stress conditions (low pH and high NaCl concentration) and no-stress conditions in a culture model. Growth curves of four different isolates were constructed from real-time PCR, plate count, optical density, and microscopic viable count data. Differences between the methods were assessed with regard to the stress applied and the growth phase. In addition, maximum growth rates were calculated and modeled in order to compare the different methods.

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Bacterial isolates.
L. monocytogenes 4019 (EGDwt), 535 (serotype 4b, a field isolate associated with a clinical outbreak in Switzerland between 1983 and 1987), and 3251 and 3301 (both serotype 4b and field isolates from cheese dairies) were used. All isolates were obtained from bacteria collected at the Institute of Milk Hygiene, Milk Technology and Food Science, Department of Veterinary Public Health, University of Veterinary Medicine, Vienna, and were stored at –80°C using the Microbank bacterial preservation system (Pro-Lab, Toronto, Canada).

Suboptimal growth conditions.
All isolates were cultured in 10 ml of brain heart infusion (BHI) broth (Difco, Becton Dickinson and Company, Sparks, MD) and incubated at 37°C for 8 h. The isolates were then subcultured in modified BHI broth supplemented with yeast extract (3 g liter^–1) (Merck, Darmstadt, Germany) and glucose (2 g liter^–1) (Merck) and buffered with a K₂HPO₄-KH₂PO₄ (0.1 mol liter^–1) solution at a 1:1 (vol/vol) ratio (Merck) to pH 7.0 (37) for 18 h at 20°C. The numbers of CFU in the subculture were determined on plate count agar (Merck) after incubation for 24 h at 37°C. Bacteria were adjusted to a starting concentration of approximately 10² CFU/ml and grown at 10°C for up to 58 days in modified BHI broth with the addition of different quantities of NaCl (0.5, 4, or 8% [vol/vol]) (Merck) at pH 5, 6, or 7 (37). The pH was adjusted with 12 M HCl (Merck) before autoclaving.

Samples were taken every 8 h during the first 3 days, every 12 h from day 4 to day 7, and every 24 h from day 8 to day 58. At each sampling time the optical density at 600 nm was immediately recorded using an HP 8452A diode array spectrophotometer (Hewlett Packard, Vienna, Austria). One-milliliter aliquots of each culture were stored at –80°C with 250 µl glycerol. Aliquots were brought slowly to room temperature, subjected to DNA isolation and real-time PCR, and plated onto plate count agar. Plates were incubated for 48 h at 37°C.

Optimal growth conditions (37°C, pH 7.2, and 0.5% NaCl).
Isolates were grown in tryptone soy broth with 6% (wt/vol) yeast extract (TSB-Y) (Oxoid Ltd., Basingstoke, Hampshire, United Kingdom) overnight at 37°C. Bacteria were adjusted to a starting concentration of approximately 10⁴ CFU/ml and grown under constant shaking (150 rpm) at 37°C in 50 ml TSB-Y for up to 17 h. The precise starting concentration was determined on tryptone soy agar with 6% (wt/vol) yeast extract (TSA-Y) (Merck) after incubation for 24 to 48 h at 37°C. Samples were taken hourly from 1 h to 9 h after starting the experiment. A separate set of experiments was performed taking hourly samples from 10 h to 17 h after starting the experiment. At each sampling time (i) the number of CFU was determined on TSA-Y after incubation for 24 to 48 h at 37°C, (ii) the numbers of bacterial cell equivalents (BCE) recovered by centrifugation and in the supernatant were determined by real-time PCR, and (iii) the numbers of viable and dead cells in the culture were determined by microscopic analysis. The Live/Dead BacLight bacterial viability kit (Molecular Probes, Willow Creek, OR) was selected for viable/dead differentiation of bacteria. Samples were processed as published recently (13).

Real-time PCR.
Five hundred microliters of the culture was subjected to DNA isolation in duplicate using the NucleoSpin tissue kit and the support protocol for gram-positive bacteria (Machery-Nagel, Düren, Germany). Real-time PCR targeting a 274-bp region of the prfA gene of L. monocytogenes was performed as published recently using an Mx3000p real-time PCR cycler (Stratagene, La Jolla, CA) (34). The primers were purchased from MWG Biotech (Ebersberg, Germany) and the 5'-6-carboxyfluorescein- and 3'-minor groove binder-labeled TaqMan probe from Applied Biosystems (Foster City, CA). The 25-µl PCR mixture contained 20 mM Tris-HCl; 50 mM KCl; 3 mM MgCl₂; 500 nM of each primer; 250 nM of the probe; 200 µM (each) of dATP, dTTP, dGTP, and dCTP; 1.5 U of Platinum Taq DNA polymerase (Invitrogen, Lofer, Austria); and 5 µl template DNA. Amplification following initial denaturation at 94°C for 2 min was performed in 45 cycles of 94°C for 15 s and 64°C for 1 min.

The quantification standard for real-time PCR analysis matched the respective isolate used in the experiments. Each L. monocytogenes isolate was grown overnight in TSB-Y at 37°C. One milliliter was subjected to DNA isolation as described above. The DNA concentration was measured fluorimetrically using a Hoefer DyNA Quant 200 device (GE Healthcare Bio-Sciences AB, Uppsala, Sweden). The copy number of the prfA gene was determined by assuming that 1 ng of DNA equals 3.17 x 10⁵ copies of the entire genome (based on the molecular weight of the genome of L. monocytogenes) and that the prfA gene is a single-copy gene (34). The real-time PCR results were expressed as BCE. The number of BCE/ml was deduced from the volume of the culture subjected to DNA isolation, the volume of the DNA solution after isolation, and the volume of the DNA solution that was finally added to the PCR mixture.

Data analysis.
Comparison of data sets between different methods was performed using a sign test at a significance level of 0.05 (39). For the analysis of the data sets obtained under suboptimal conditions (10°C), growth curves obtained for all isolates from log-transformed data from the three different methods (real-time PCR, plate count method, and optical density measurement) at each combination of NaCl and pH were fitted to the model of Baranyi and Roberts (5). The maximum specific growth rate (µ_max) was estimated for each growth condition and measurement method. The dependence of the natural logarithm of µ_max on the NaCl concentration and on the pH was modeled by polynomial equations. An F test was used to decide whether the different measurements, optical density, viable count, and real-time PCR were significantly different. To do that, the residuals and degree of freedom of a model fitted to the joint data sets were compared with those of the models fitted separately to each data set by an F test as described by Brown and Rothery (9).

Comparison of the three growth curves measured by real-time PCR, plate counts, and microscopic viable counts under optimal conditions (37°C) was carried out using an F test as described above after the counts were fitted to the model of Baranyi and Roberts (5, 9).

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Comparison of growth curves obtained under suboptimal growth conditions.
The concentrations of bacteria at time zero were 1.3 x 10² CFU/ml, 1.2 x 10³ CFU/ml, 2.0 x 10² CFU/ml, and 1.8 x 10² CFU/ml for isolates EGDwt, 535, 3251, and 3301, respectively. Some samples had to be excluded from the analysis due to mold contamination in the culture flask (Table 1). No isolates were able to grow at pH 5 with 8% NaCl, and all grew very slowly at pH 5 with 4% NaCl. In these cases, a reliable quantification by real-time PCR was not possible due to the low number of bacteria (<1,000 CFU/ml) at many of the sampling points. Thus, these samples were not included in the real-time PCR analysis.

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TABLE 1. Categories of growth curves derived from real-time PCR data at different NaCl concentrations and pHs

When comparing the growth curves obtained with the different methods, the curves derived from the plate count method and real-time PCR looked rather similar, whereas curves constructed from optical density data were different. The main difference was due to the detection limit, so that a long period of time elapsed prior to detection of growth. This time to detection was longer than the lag time measured by real-time PCR and plate counts.

Differences between the growth curves derived from BCE and plate counts were observed mainly in the stationary growth phase. According to these differences, the growth curves were grouped into three categories. Category 1 included growth curves with a relatively long stationary phase during which no reduction in the number of CFU was registered. Curves obtained by real-time PCR and plate counts were similar in shape, and the correlation between CFU and BCE counts was high (0.96) (Fig. 1A). Category 2 growth curves had a relatively long stationary phase during which a decline in CFU counts was registered. This decrease in counts was not detected by real-time PCR (Fig. 1B). The overall correlation between CFU and BCE counts was 0.87, whereas the correlation for log-phase data was 0.95 and that for stationary-phase data was 0.29. Category 3 included growth curves marked by slow growth during the entire observation period (Fig. 1C). Therefore, the observation of the stationary phase was not sufficient to detect whether the plate counts decreased. In most cases the difference between CFU and BCE counts was small, but the difference increased during the observation period; BCE counts became higher. Thus, the overall correlation was 0.94 (Fig. 2). When relating the categories to incubation conditions, it was evident that category 1 (no decline in CFU counts during the stationary phase) was observed under low-stress conditions, whereas categories 2 and 3 were observed under gradually more stressful conditions (Table 1).

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FIG. 1. Examples of category 1 (isolate 535, pH 7, 4% NaCl) (A), category 2 (isolate 535, pH 6, 4% NaCl) (B), and category 3 (isolate 535, pH 7, 8% NaCl) (C) growth curves derived from real-time PCR data. Growth curves constructed from log10 BCE/ml (), log10 CFU/ml (), and log10 optical density (OD) values () are shown.

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FIG. 2. Difference between log10-transformed real-time PCR data (BCE/ml) and plate count data (CFU/ml) for category 1, 2, and 3 growth curves of L. monocytogenes.

Figure 2 shows the differences between real-time PCR and plate count data. The main difference was found in the stationary phases of curves belonging to category 2. For log-phase data of category 2, the differences were not significant (P = 1.00), whereas the difference between the data sets was significant for stationary-phase data and category 3 data (P < 0.001 and P = 0.02). Category 1 data gathered under suboptimal growth conditions were analyzed together with category 1 data gathered under optimal growth conditions.

The population density at the end of the observation period ranged from 8.26 to 9.23 log CFU/ml for category 1 growth curves, from 5.23 to 7.35 log CFU/ml for category 2 growth curves, and from 7.00 to 9.3 log CFU/ml for category 3 growth curves.

Modeling growth rates obtained under suboptimal growth conditions.
µ_maxs were fitted to the growth curves of each strain obtained from real-time PCR, plate count, and optical density data under every condition tested (Fig. 3; see Table S1 in the supplemental material). Polynomial models were fitted to describe the dependence of growth rates on pH and NaCl concentrations and were used to compare the rates obtained by the three methods, employing an F test. Figure 3 shows the P values associated with the F tests. For all the isolates the growth rates fitted to real-time PCR counts were not significantly different from those obtained by plate counts, while the rates fitted to optical density data were significantly lower. Generally, similar data were obtained for all isolates tested, but the real-time PCR and plate count data suggest that the EGDwt isolate grew faster (Fig. 3).

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FIG. 3. Comparison between growth rates measured by real-time PCR (green), the plate count method (blue), and optical density (OD) (red). Filled circles show the measured rates, while surfaces show the fitted models.

Comparing growth curves obtained under optimal growth conditions.
In addition to determination of BCE/ml and CFU/ml, microscopic counts of viable and dead cells were carried out using a commercially available live/dead stain. Applying the classification of growth curves used for data obtained under suboptimal growth conditions, all curves measured under optimal conditions belonged to category 1.

The data in Table 2 show the growth parameters for each isolate as estimated from curves of plate count, real-time PCR, and microscopic viable count data. The growth curves obtained by the three methods were not significantly different, and the median differences between log-transformed real time-PCR counts, plate counts, and microscopic viable counts were close to zero, i.e., –0.39 (standard deviation [SD], 0.32; plate counts versus real-time PCR), 0.21 (SD, 0.29; viable counts versus real-time PCR), and 0.34 (SD, 0.31; viable counts versus plate counts). The difference between the log-transformed microscopic dead cell counts and the log-transformed BCE/ml measured in the supernatant was close to zero, although the variability was greater than that when comparing counts of live cells by either method, i.e., 0.18 (SD, 0.66; dead cells versus BCE in supernatant). When these category 1 data were combined with those gathered under suboptimal growth conditions, there was no significant difference between real-time PCR and the plate count method (P = 0.85).

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TABLE 2. Estimation of initial bacterial cell concentration, lag phase, µmax, and final bacterial cell concentration from averaged real-time PCR-, plate count-, and microscopic viable cell count-derived data sets under optimal growth conditions

Regarding the accumulation of dead cells and free DNA during growth, 0.47% (mean; SD, 0.37) of all cells were dead during the stationary phase, with no major changes in the course of that phase; 0.73% (mean; SD, 0.9) of BCE counted in real-time PCR were recovered from the supernatant. A slight increase of BCE in the supernatant was registered at the last two monitoring points (0.95% and 2.16%, respectively).

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The main objective of this study was to assess the suitability of real-time PCR for the quantification of L. monocytogenes in relation to stress (low pH and high NaCl concentration) and growth phase in a culture model.

With respect to plate count data collected under similar conditions, which were extracted from the Combase database (www.combase.cc), the L. monocytogenes isolates investigated in this study showed comparable growth behavior (data not shown). No isolate was able to grow at pH 5/8% NaCl, which confirmed previous results obtained under identical growth conditions (37). Thus, the selected isolates seem to be representative of the species.

When comparing growth curves derived by real-time PCR, plate count, and optical density data under stress conditions, the optical density data clearly yielded entirely different curves. Even under relatively good growth conditions, a long period of time elapsed prior to growth detection by that method, which was due to the high detection limit for optical density measurement. Once the detection threshold had been reached, the optical density- and plate count-derived growth curves were not parallel. In general, lower growth rates were estimated from the optical density curves. This is because this technique identifies only relatively high concentrations of bacteria (>5 x 10⁷ cells/ml), which are achieved at the end of the exponential phase when growth declines prior to the cells entering the stationary phase. In many cases a slight decrease in optical density data was observed during the stationary phase, which was frequently not seen in the plate count data. Optical density is related to the number of cells and also to the size of cells and the quantity of debris in the sample. Cell stress may influence cell morphology, which might have been the reason for the observed decrease in the stationary phase (17). A reduction in cell length and an increase in cell width upon starvation were described for L. monocytogenes (19). It is generally agreed that the relationship between optical density data and plate count data is very complex (15).

Comparison of real-time PCR data with plate count data yielded good correlation for relatively good to moderate growth conditions in the log phase (category 1 and 2 curves) and for relatively good growth conditions in the stationary phase as well (category 1 curves). In addition, the cell counts derived from both methods were similar. However, when growth conditions grew worse, the difference between the two methods increased, with an increase in real-time PCR-derived BCE counts, and the correlation also turned worse (category 3 curves). On the other hand, models of growth rates derived from all real-time PCR and plate count data were not significantly different. This might be explained by the facts that µ_max is calculated in the exponential phase and that most of the growth curves belonged to categories 1 and 2. Bacterial cultures are a collection of viable, VBNC, and dead cells and may also contain cell debris and extracellular DNA. The plate count method and real-time PCR detect different parts of this population (32). It may be safely assumed that as stress increases, part of the bacteria were transformed into the VBNC status or died, which explains the overquantification observed in real-time PCR.

Regarding growth of L. monocytogenes under optimal growth conditions, plate count and real-time PCR data were highly similar in the exponential and stationary phases, yielding similar growth models as well. Viable counts were slightly higher but also similar, confirming the presence of mainly viable and culturable cells and the absence of VBNC cells under these conditions. Negligible accumulation of dead cells and extracellular DNA was observed, but not all extracellular DNA might have been recovered in the supernatant. Part of it might have been associated with cell debris collected by centrifugation.

The presence of more than one genome per bacterial cell, especially in fast-growing cells, might be another reason for overquantification by real-time PCR (1). The lack of difference between the real-time PCR and plate count data at optimal and less stressful growth conditions suggests that under these conditions, the majority of the bacterial population had only one genome per cell. On the other hand, overquantification under stress conditions does not seem likely to be due to the presence of more than one genome per cell. Completion of replication cycles (chromosome runout) leading to the presence of multiple genomes per cell upon entering starvation has been described (10). Depending on the species, complete cell division cycles might or might not be completed afterwards (23). However, conditions might not have been limited enough to induce such a response, and there are no data indicating whether this response occurs in L. monocytogenes. Growth phase-related differences between plate count data and real-time PCR data were reported for Escherichia coli also and were attributed to changes in the DNA content of the cells, differences in resistance to lysis, and the accessibility of target DNA (25).

Environmental conditions determine the amounts of VBNC, dead cells, and extracellular DNA in a naturally contaminated sample. In the culture model presented, differences between plate count and real-time PCR data were observed for stationary-phase growth under stress conditions. This suggests that the additional stress of entering the stationary phase induced cell death sufficient to lead to overestimation of the number of bacteria present in the samples by real-time PCR. It is not to be expected that L. monocytogenes might grow into stationary phase in food, but stress conditions imposed on the bacteria during food processing and storage might be enough to induce substantial cell death. However, the persistence of dead cells and extracellular DNA depends on the condition of bacterial cells before death, the chemical and physical composition of the environment (bound DNA might be protected from degradation), and microbial activity (6, 24, 28). Others also reported overquantification of different bacteria by real-time PCR in relation to stress and composition of the samples: overquantification of Bifidobacterium longum but not Bifidobacterium lactis by real-time PCR during fermentation of oat drink was observed, overquantification of E. coli in activated sludge was more pronounced at stages with lower microbial activity, and numbers of Pseudomonas fluorescens EPS62e determined with real-time PCR were similar to plate count data for apple blossoms but not for apple leaves because stresses were higher on the latter (22, 24, 32). Thus, the complete history and composition of the sample have to be taken into account when assessing the possibility for overestimating the number of a specific bacterial species by real-time PCR (24). In addition, extracellular DNA might be lost during DNA isolation, but VBNC and dead cells will be recovered. There might be a benefit for risk assessment from detection of VBNC L. monocytogenes in real-time PCR but whether VBNC L. monocytogenes poses a threat to human health is controversial (11). These cells might represent injured cells needing time and optimal conditions for repair and recovery (14). Insights into the contributions of VBNC and dead cells to the overquantification of L. monocytogenes by real-time PCR under the stress conditions applied in this study could be gained by characterizing the metabolic activity and the membrane integrity of the bacterial population (8).

In conclusion, for low-stress conditions the plate count and real-time PCR data were similar, thus enabling assessment of growth kinetics by the alternative method. Differences between the two data sets were related to the stress imposed on L. monocytogenes and changed during growth. Thus, real-time PCR data collected for naturally contaminated samples should be regarded with caution. Efforts to include fluorescent dyes such as ethidium monoazide or propidium monoazide for viable/dead differentiation in real-time PCR based on blocking the signal from extracellular DNA and DNA from dead cells are most appropriate and should be pursued further (13, 31). However, the performance of these dyes is species dependent, and they might have a mutagenic effect due to their association with DNA; thus, the results obtained in this study might aid in a more selective approach to using these dyes. Similar comparative experiments could be performed to evaluate the suitability of RNA targets for quantification of L. monocytogenes. A further important implication of the results is that real-time PCR quantification standards related to numbers of CFU determined in the culture used for DNA isolation might add a bias to the analysis, depending on the physiological state of the bacterial population present in the culture.

 
This work was funded by the Vienna Science and Technology Fund, Austria (grant no. LS231); the Christian Doppler Laboratory for Molecular Food Analysis, Vienna, Austria; and the European Union-funded Integrated Project BIOTRACER (contract 036272) under the 6th RTD Framework.

 
* Corresponding author. Mailing address: Institute for Milk Hygiene, Milk Technology, and Food Safety, Department for Farm Animals and Veterinary Public Health, University of Veterinary Medicine, Veterinaerplatz 1, 1210 Vienna, Austria. Phone: 43 1 25077 3507. Fax: 43 1 25077 3590. E-mail: ingeborg.hein{at}vu-wien.ac.at

Published ahead of print on 30 January 2009.

Supplemental material for this article may be found at http://aem.asm.org/.

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Applied and Environmental Microbiology, April 2009, p. 2132-2138, Vol. 75, No. 7
0099-2240/09/$08.00+0     doi:10.1128/AEM.01796-08
Copyright © 2009, American Society for Microbiology. All Rights Reserved.

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