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Applied and Environmental Microbiology, April 2000, p. 1706-1710, Vol. 66, No. 4
0099-2240/00/$04.00+0
Copyright © 2000, American Society for Microbiology. All rights reserved.
Significance of Inoculum Size in the Lag Time of
Listeria monocytogenes
Jean-Christophe
Augustin,1,*
Agnès
Brouillaud-Delattre,2
Laurent
Rosso,3 and
Vincent
Carlier1
Service d'Hygiène et Industrie des
Denrées Alimentaires d'Origine Animale, Ecole Nationale
Vétérinaire d'Alfort, F-94704 Maisons-Alfort
Cedex,1 Agence Française de
Sécurité Sanitaire des Aliments, F-94703 Maisons-Alfort
Cedex,2 and Pôle de
Compétence en Sécurité des Aliments, Groupe
Danone, F-19100 Brive-la-Gaillarde,3 France
Received 8 November 1999/Accepted 6 January 2000
 |
ABSTRACT |
The lag time of Listeria monocytogenes growing under
suboptimal conditions (low nutrient concentrations, pH 6, and 6.5°C) was extended when the inoculum was severely stressed by starvation and
the inoculum size was very small. Predictive microbiology should deal
with bacterial stress and stochastic approaches to improve its value
for the agro-food industry.
| |
TEXT |
In predictive microbiology, it is
commonly assumed that inoculum size has no effect on subsequent
microbial growth, and some studies (8, 9, 10) have confirmed
this for growth of Listeria monocytogenes. However, Gay et
al. (12) observed that inoculum size could have an effect on
the duration of the lag phase of L. monocytogenes under
particular conditions simulating soft cheese ripening. This observation
could challenge the validity of predictive models because growth
modelling is usually done with initial concentrations of bacteria
higher than 103 CFU · ml
1, even though
foods are usually contaminated with lower numbers of cells.
It has been shown with other bacteria, like Bacillus
(19), Achromobacter delmarvae, Micrococcus
luteus (see reference 16 for review), and
Salmonella (26), that the duration of the lag phase depends inversely on the size of the inoculum. This phenomenon was observed only under a restricted range of conditions, for example,
in poor but not in rich media, with starved cells (16), or
with heat-injured cells (26). The aim of this work was to study the effect of inoculum size on the lag phase of L. monocytogenes growing in a poor medium, i.e., broth, containing
only 1 g of Bacto Peptone (Difco Laboratories, Detroit, Mich.) per
liter plus 0.85% sodium chloride (Prolabo, Paris, France) (TS), under
suboptimal conditions: pH 6 and 6.5°C and with cells stressed by starvation.
L. monocytogenes Scott A maintained on tryptone-soya agar
(Oxoid, Unipath, Ltd., Basingstoke, Hampshire, England) at 4°C was subcultured onto tryptone-soya agar (Oxoid) plus 0.6% yeast extract (AES, Combourg, France) (TSYE) at 37°C for 24 h, and five
colonies were transferred into TSYE broth and incubated at 30°C for
24 h. This culture was used to inoculate TSYE broth to prepare
inocula at 30°C. The change in viable count of L. monocytogenes in TSYE broth at 30°C determined on nonselective
TSYE agar and selective Palcam (Oxoid) agar is shown in Fig.
1. The percentage of injured cells was
determined by differential counts on selective and nonselective media
(24). Inocula were prepared at 30°C for 14, 160, and
840 h to obtain cells in late log phase, early stationary phase
after a decrease in cell viability of 96.8% viable cells and 35%
injured cells and late stationary phase with a loss of viability of
99.9% and 21% injured cells (Fig. 1). Samples were prepared by
filtering 10 ml of culture through a 0.45-µm-pore-size sterile filter
(Schleicher & Schuell, Dassel, Federal Republic of Germany). Cells were
washed three times with 10 ml of sterile TS and removed from the
membrane with 10 ml of TS.
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FIG. 1.
Viable counts on TSYE agar ( ) and Palcam agar ( )
of L. monocytogenes incubated in TSYE broth at 30°C.
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TS agar (100 ml) with pH adjusted to 6.0 with 1 M HCl in 250-ml flasks
was inoculated with 1 ml of a 10- or 100-fold dilution of filtered
cells and incubated at 6.5°C. Initial bacterial concentrations for
cells in late log and early and late stationary phases ranged from
104 to 102, 102 to
102, and 104 to 100 CFU · ml1, respectively. The cultures were grown once, except
for cells in late log phase with initial concentrations of
101 and 102 CFU · ml1,
which were grown in triplicate. Flasks containing high bacterial concentrations (more than 30 CFU · ml1) were
enumerated by performing plate counts on TSYE agar incubated at 37°C
for 24 h. Flasks containing low bacterial concentrations (<30
CFU · ml1) were enumerated by the
most-probable-number technique used by Gay et al. (12).
Growth curves were fitted using the logistic equation with delay, i.e.,
with a breakpoint at the transition between the lag and the exponential
phase (3, 18, 24):
where
x(
t) is the bacterial
concentration (CFU per milliliter) at time
t (in hours),
x0 is the initial bacterial concentration,
xmax is the maximum bacterial concentration,
lag is the duration
of the lag phase (in hours), and
µmax is the maximum specific
growth rate (per
hour).
Fits were performed by nonlinear regression using the
weighted-least-squares criterion (5) by minimizing the
sum of the weighted squared residuals (SWSR). SWSR is defined as
follows:
where
n is the number of datum points and
wi is the reciprocal of the variance of
log(
xi) observed. These variances were
estimated
from replicates when they were available or were theoretically
calculated from the variances of the numbers of microorganisms
per
sample (
11) and from dilution and plating errors
(
15).
The minimum SWSR values were computed with the NLINFIT
subroutine
of MATLAB 5.2 software (The Math Works Inc., Natick, Mass.),
modified
for weighted method of least
squares.
Maximum specific growth rates and lag times obtained for growth of
L. monocytogenes in TS agar at 6.5°C are shown in Table 1. Confidence regions for parameter
estimations were defined as parameter values checking Beale's
inequality (4, 20):
where
is the vector of parameter values,
is the
vector of weighted-least-squares-estimated parameters,
n is
the number
of datum points,
p is the number of parameters,
and
Fp, n-p
is the
quantile
for Fisher's
F distribution with
p and
n -
p degrees of freedom. Random sampling in the parameter space of
points whose SWSR value checked the inequality was made with MATLAB
software. Confidence limits for parameter values were determined
by
projecting these points in each of the parameter planes (Fig.
2). No significant effect of inoculum
size or duration of storage
at 30°C in TSYE broth was observed on
maximum specific growth
rates (Table
1) or on lag time with storage of
14 h, and an average
lag time of 40.6 ± 13.4 h
(standard deviation [SD]) was obtained.
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TABLE 1.
Growth parameters of L. monocytogenes in TS,
pH 6, at 6.5°C after different durations of storage
at 30°Ca
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FIG. 2.
Growth curve of L. monocytogenes
grown in TS, pH 6, at 6.5°C after storage at 30°C for 160 h.
The solid line is the best fit of the growth model to the data, and the
dotted lines define the 95% confidence region for the growth curve.
Vertical bars indicate 1 SD of the bacterial cell count. At the bottom
are the 95% confidence regions of estimated growth parameters.
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Increasing lag time was observed with storage for 160 h when the
inoculum size was decreased from 102 to 100
CFU · ml1 (Table 1). Because large 95% confidence
regions were obtained for these estimated lag times, this increase was
not significant. The growth curve with an initial bacterial
concentration of 102 CFU · ml1 was
used to derive growth parameters with their confidence regions (Fig. 2)
and to predict confidence regions of growth curves for smaller inoculum
sizes (Fig. 3). Because the growth model
is a monotonous function for each parameter, confidence regions
( = 0.05) for the predicted growth curves were
constructed by determining the minimum and the maximum of the function
when the parameter values belong to the confidence regions
(13). Longer lag times were observed for inoculum sizes of
101 to 101 CFU · ml1 but
not with the inoculum size of 102 CFU · ml1. The effect of inoculum size on lag time was
considered not significant given the available datum sets. The average
lag time for the cells stored for 160 h at 30°C was 85.1 ± 16.2 h (SD).
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FIG. 3.
Predicted growth curves and observed bacterial
cell counts of L. monocytogenes in TS, pH 6, at 6.5°C
after storage for 160 h at 30°C for suspensions with initial
bacterial concentrations of 101 (a), 100 (b),
101 (c), and 102 (d) CFU · ml1. The solid lines define the 95% confidence regions
of the predicted growth curves from the parameters estimated with the
initial bacterial concentration of 102 CFU · ml1. Vertical bars indicate 1 SD.
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A uniform increase in lag time with decreased inoculum size was
observed for storage for 840 h, and the difference between lag
times obtained for initial concentrations of 104 and
100 CFU · ml1 was significant, as 95%
confidence regions did not overlap (Table 1). By using the growth curve
with an initial bacterial concentration of 104 CFU · ml1 to predict 95% confidence regions for growth curves
with smaller inoculum sizes (Fig. 4),
discrepancies between predicted growth regions and observed growth
curves were found for initial bacterial concentrations of
102 and 100 CFU · ml1.
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FIG. 4.
Growth curve of L. monocytogenes in TS, pH 6, at 6.5°C after storage for 840 h at 30°C for the suspension
with initial bacterial count of 104 CFU · ml1 (a) and predicted growth curves and observed
bacterial cell counts for suspensions with initial bacterial
concentrations of 102 (b) and 100 (c) CFU
· ml1. In panel a, the solid line is the best fit of
the growth model to the data and the dotted lines define the 95%
confidence region for the growth curve. In panels b and c, the solid
lines define the 95% confidence regions of the predicted growth
curves. Vertical bars indicate 1 SD of the bacterial cell count.
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The change from an inoculum of approximately 102 CFU
· ml1 maintained at 30°C for 14 h to an inoculum
of about 1 CFU · ml1 starved for 840 h at
30°C led to an increase in lag time from 32.6 to 66.6 h. The
extension of lag phase with physical injury of cells has been
frequently reported (14, 17, 21, 22), and models describing
the effect of heat injury on subsequent lag period have been published
for L. monocytogenes (6, 7, 23). Albertson et al.
(1) also observed a uniform increase in lag time with
increasing starvation time for a Vibrio sp., but no models
have been published.
The inoculum size effect, observed only with cells severely stressed by
starvation, could be explained by an increase in the variation of
individual cells' lag time when cells are stressed (2, 26).
Predictive microbiology should deal with stochastic models for the
prediction of the growth of the small populations typically encountered
in foods, especially when microbial cells have been subject to injury.
| |
ACKNOWLEDGMENTS |
This work was supported by the Association Vétérinaire
d'Hygiène Alimentaire.
We thank Cécile Lahellec and Olivier Cerf, who initiated this
work, and Tobin Robinson for the helpful and critical reading of the manuscript.
| |
FOOTNOTES |
*
Corresponding author. Mailing address: Service
d'Hygiène et Industrie des Denrées Alimentaires d'Origine
Animale, Ecole Nationale Vétérinarie d'Alfort, 7 avenue de
Général de Gaulle, F-94704 Maisons-Alfort Cedex, France.
Phone: (33) (0) 1.43.96.70.43. Fax: (33) (0) 1.43.96.71.21. E-mail:
augustin{at}vet-alfort.fr.
| |
REFERENCES |
| 1.
|
Albertson, N. H.,
T. Nyström, and S. Kjelleberg.
1990.
Macromolecular synthesis during recovery of the marine Vibrio sp. S14 from starvation.
J. Gen. Microbiol.
136:2201-2207.
|
| 2.
|
Baranyi, J.
1998.
Comparison of stochastic and deterministic concepts of bacterial lag.
J. Theor. Biol.
192:403-408[CrossRef][Medline].
|
| 3.
|
Baranyi, J.,
T. A. Roberts, and P. McClure.
1993.
A non-autonomous differential equation to model bacterial growth.
Food Microbiol.
10:43-59[CrossRef].
|
| 4.
|
Bates, D. M., and D. G. Watts.
1988.
Nonlinear regression analysis and its applications, p. 200-231.
Wiley, New York, N.Y.
|
| 5.
|
Box, G. E. P.,
W. G. Hunter, and J. S. Hunter.
1978.
Statistics for experimenters: an introduction to design, data analysis and model building, p. 505-509.
Wiley, New York, N.Y.
|
| 6.
|
Bréand, S.,
G. Fardel,
J. P. Flandrois,
L. Rosso, and R. Tomassone.
1997.
A model describing the relationship between lag time and mild temperature increase duration.
Int. J. Food Microbiol.
38:157-167[CrossRef][Medline].
|
| 7.
|
Bréand, S.,
G. Fardel,
J. P. Flandrois,
L. Rosso, and R. Tomassone.
1999.
A model describing the relationship between regrowth lag time and mild temperature increase for Listeria monocytogenes.
Int. J. Food Microbiol.
46:251-261[CrossRef][Medline].
|
| 8.
|
Buchanan, R. L., and J. G. Phillips.
1990.
Response surface model for predicting the effects of temperature, pH, sodium chloride content, sodium nitrite concentration and atmosphere on the growth of Listeria monocytogenes.
J. Food Prot.
53:370-376.
|
| 9.
|
Denis, F., and J.-P. Ramet.
1989.
Antibacterial activity of the lactoperoxidase system on Listeria monocytogenes in trypticase soy broth, UHT milk and French soft cheese.
J. Food Prot.
52:706-711.
|
| 10.
|
Duffy, G.,
J. J. Sheridan,
R. L. Buchanan,
D. A. McDowell, and I. S. Blair.
1994.
The effect of aeration, initial inoculum and meat microflora on the growth kinetics of Listeria monocytogenes in selective enrichment broths.
Food Microbiol.
11:429-438[CrossRef].
|
| 11.
|
Finney, D. J.
1978.
Statistical method in biological assay, p. 418-439.
Griffin, London, England.
|
| 12.
|
Gay, M.,
O. Cerf, and K. R. Davey.
1996.
Significance of pre-incubation temperature and inoculum concentration on subsequent growth of Listeria monocytogenes at 14°C.
J. Appl. Bacteriol.
81:433-438[Medline].
|
| 13.
|
Huet, S.,
E. Jolivet, and A. Messéan.
1992.
La régression non-linéaire: méthodes et applications en biologie, p. 201-213.
Institut National de la Recherche Agronomique, Paris, France.
|
| 14.
|
Jackson, H., and M. Woodbine.
1963.
The effect of sublethal heat treatment on the growth of Staphylococcus aureus.
J. Appl. Bacteriol.
26:152-158.
|
| 15.
|
Jarvis, B.
1989.
Errors associated with colony count procedures.
Prog. Ind. Microbiol.
21:95-116.
|
| 16.
|
Kaprelyants, A. S., and D. B. Kell.
1996.
Do bacteria need to communicate with each other for growth?
Trends Microbiol.
4:237-242[CrossRef][Medline].
|
| 17.
|
Kaufman, O. W.,
L. G. Harmon,
O. C. Pailthorp, and I. J. Pflug.
1959.
Effect of heat treatment on the growth of surviving cells.
J. Bacteriol.
78:834-838[Free Full Text].
|
| 18.
|
Kono, T.
1968.
Kinetics of microbial cell growth.
Biotechnol. Bioeng.
10:105-131[CrossRef].
|
| 19.
|
Lankford, C. E.,
J. R. Walker,
J. B. Reeves,
N. H. Nabbut,
B. R. Byers, and R. J. Jones.
1966.
Inoculum-dependent division lag of Bacillus cultures and its relation to an endogenous factor(s) ("schizokinen").
J. Bacteriol.
91:1070-1079[Abstract/Free Full Text].
|
| 20.
|
Lobry, J. R.,
L. Rosso, and J. P. Flandrois.
1991.
A FORTRAN subroutine for the determination of parameter confidence limits in non-linear models.
Binary
3:86-93.
|
| 21.
|
Mackey, B. M., and C. M. Derrick.
1982.
The effect of sublethal injury by heating, freezing, drying and gamma-radiation on the duration of the lag phase of Salmonella typhimurium.
J. Appl. Bacteriol.
53:243-251[Medline].
|
| 22.
|
Mackey, B. M., and C. M. Derrick.
1984.
Conductance measurements of the lag phase of injured Salmonella typhimurium.
J. Appl. Bacteriol.
57:299-308[Medline].
|
| 23.
|
McKellar, R. C.,
G. Butler, and K. Stanich.
1997.
Modelling the influence of temperature on the recovery of Listeria monocytogenes from heat injury.
Food Microbiol.
14:617-625[CrossRef].
|
| 24.
|
Meyer, D. H., and C. W. Donnelly.
1992.
Effect of incubation temperature on repair of heat-injured Listeria in milk.
J. Food Prot.
55:579-582.
|
| 25.
|
Rosso, L.,
S. Bajard,
J. P. Flandrois,
C. Lahellec,
J. Fournaud, and P. Veit.
1996.
Differential growth of Listeria monocytogenes at 4 and 8°C: consequences for the shelf life of chilled products.
J. Food Prot.
59:944-949.
|
| 26.
|
Stephens, P. J.,
J. A. Joynson,
K. W. Davies,
R. Holbrook,
H. M. Lappin-Scott, and T. J. Humphrey.
1997.
The use of an automated growth analyser to measure recovery times of single heat-injured Salmonella cells.
J. Appl. Microbiol.
83:445-455[CrossRef][Medline].
|
Applied and Environmental Microbiology, April 2000, p. 1706-1710, Vol. 66, No. 4
0099-2240/00/$04.00+0
Copyright © 2000, American Society for Microbiology. All rights reserved.
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