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Applied and Environmental Microbiology, April 1999, p. 1619-1626, Vol. 65, No. 4
0099-2240/99/$04.00+0
Copyright © 1999, American Society for Microbiology. All rights reserved.
Biphasic Extracellular Proteolytic Enzyme Activity
in Benthic Water and Sediment in the Northwestern
Mediterranean Sea
Olivier
Tholosan,1
François
Lamy,1
Jean
Garcin,1
Thalia
Polychronaki,2 and
Armand
Bianchi1,*
Microbiologie Marine, Centre National de la
Recherche Scientifique-Institut National des Sciences de l'Univers,
Université de la Méditerranée, F-13288 Marseille
Cedex 9, France,1 and Institute of
Marine Biology of Crete, GR71003, Iraklion, Crete,
Greece2
Received 4 November 1998/Accepted 26 January 1999
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ABSTRACT |
In this study, we used the fact that bacteria are able to cleave a
fluorogenic substrate analog
(L-leucine-7-amido-4-methylcoumarin) to determine the
maximal ectoproteolytic activities (Vm) and
affinities (Km) of natural benthic microbial
communities by the multiconcentration kinetic method. This
investigation was performed during the winter and summer of 1997 with a
set of 36 samples of near-bottom water and sediment collected from a
coastal area and an offshore area in the western part of the Gulf of
Lions. The existence of biphasic microbial ectoproteolysis was
statistically confirmed for both the near-bottom water and the
sediment, regardless of the spatial and seasonal conditions. Globally,
72.2% of the entire set of bacterial consortia collected at the
water-sediment boundary layer showed biphasic microbial kinetics. A
specific estimator of the biphasicity indicated that deep benthic
bacterial consortia responded better with episodic nutrient supplies
than shallower benthic bacterial consortia responded.
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INTRODUCTION |
In aquatic environments nutrients
occur in sharp concentration gradients (3). To be
competitive under these conditions, heterotrophic bacteria develop
multiphasic enzymatic processes for the uptake of low-molecular-weight
organic compounds (4, 21, 34, 42). Previous authors have
clearly shown that in addition to the expected
low-Km transport systems (in the nanomolar range), assemblages of marine bacteria express transport systems with
apparent Km values of up to 10
4 M.
It is generally recognized that in sedimentary environments the benthic
microbial remineralization response is directly related to the episodic
supply of organic matter reaching the seafloor due to seasonal and
regional pulses of ocean surface production (7, 22, 33, 52).
The bulk (>95%) of the organic matter in marine sediments is composed
of polymeric, high-molecular-weight compounds (HMWC) (8).
Before heterotrophic bacteria can use these compounds, the compounds
must be hydrolyzed to monomeric compounds (12). This
hydrolysis, performed by bacterial ectoenzymes, is the rate-limiting
step in the transformation of organic matter in both pelagic and
benthic ecosystems (26). Furthermore, benthic environments
are well-known for having microniches around HMWC-rich organic
particles. Therefore, benthic microbial populations should be adapted
to produce ectoenzymatic systems that are able to hydrolyze HMWC at a
wide range of substrate concentrations. Nevertheless, it is only
recently that such multiphasic kinetics has been described for the
bacterial hydrolysis of organic polymers in the upper layer of
Antarctic deep-sea sediment (50).
It is generally assumed that in natural ecosystems, enzymatic reactions
follow Michaelis-Menten kinetics (32, 43), even if the
Michaelis-Menten equation appears to be a tool with limited capacity
(30). According to this model, the reaction velocity (V) can be related to the substrate concentration
(S) as follows: V = Vm(S)/Km + S), where
Vm is the maximal velocity of the enzyme reaction and Km is the half-saturation constant
(Michaelis constant). We used a nonlinear least-squares method to
calculate Vm and Km because the commonly used reciprocal transformations, such as Lineweaver-Burk plots (1/V versus 1/S), lead to
erroneous estimates for kinetic parameters (13, 20, 27, 32, 41,
55).
Because of the considerations described above, we used only the linear
transformation of a Lineweaver-Burk plot to display our data. In this
way, we observed biphasic kinetics during ectoproteolysis in the
overlying waters and marine sediments characteristic of the
Mediterranean continental shelf. The aims of this study were to examine
the actual significance of the kinetic parameters determined in
different environments by using the most appropriate nonlinear algorithm from the Michaelis-Menten kinetic model and then to validate
the occurrence of biphasic ectoproteolysis kinetics in the benthic
boundary layer.
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MATERIALS AND METHODS |
Study area and sample collection.
This study was part of the
MATER and PNOC programs. The general objective of these programs was to
estimate the energy and matter flow through the water column and the
sediment in different margin zones of the northwestern part of the
Mediterranean Sea. Samples were collected during the winter (February)
and summer (August) of 1997 during the BBLL 2 and BBLL 4 cruises,
respectively. During each of these cruises a shallow area (along the
coast of Banyuls-sur-mer) and a deeper area (Lacaze-Duthiers Canyon)
were investigated (Table 1).
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TABLE 1.
Locations and depths of the sampling stations in the
northwestern Mediterranean Sea during the BBLL2 cruise (4 to 13 February 1997) and the BBLL4 cruise (8 to 12 August 1997)
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In the coastal area we obtained samples at two sites, at depths of 35 and 86 m. The sediments in this area consisted of fine
mud that
was black up to the sediment surface, and the redox boundary
occurred
at depths between 0.3 and 2 cm. The organic matter contents
were 2.5 to
3.2 and 2.4 to 2.8% in the surface layers (0 to 2
cm) and subjacent
sediment layers (2 to 10 cm), respectively (
37).
In the
Lacaze-Duthiers Canyon area samples were collected along
the canyon
axis and from the adjacent open slope at depths of
920 and 800 m,
respectively. The sediments consisted of fine clays,
and the redox
boundary occurred at depths between 4 and 18 cm.
The organic matter
composition of the sediment (total and organic
carbon, total nitrogen,
amino acid, and sugar contents) and the
distribution of meiobenthic
organisms in the surface deposits
of the northwestern Mediterranean
margin have been described by
Buscail and Germain (
10) and
de Bovée et al. (
16), respectively.
Samples were
collected with a multiple corer (
5). Cores disturbed
during
retrieval were
discarded.
Samples were immediately processed on the research vessel. For each
core, the near-bottom water (NBW) was carefully and aseptically
collected by siphoning it into a polycarbonate container. The
last few
milliliters was discarded to prevent introduction of
mud particles into
the seawater sample. The core was then extruded
and sliced (0 to 2, 2 to 4, and 4 to 6 cm) with sterile cutters.
Each sediment slice was
transferred into a sterile 180-ml plastic
vial and suspended in its own
NBW (previously filtered with a
0.2-µm-pore-size filter) so that it
was diluted 1/4 (vol/vol)
and constituted a sediment
slurry.
Bacterial abundance. (i) Sediment.
Sediment slurries were
fixed with filtered (pore size, 0.2 µm) formaldehyde (final
concentration, 4%). Fixed samples were stored at 5°C until they were
processed in the laboratory on land. In order to avoid biased counts,
we varied the sample dilution depending on the sediment type and the
bacterial abundance, as described by Yamamoto and Lopez
(57). Therefore, a series of dilutions prepared with
filtered (pore size, 0.2 µm) seawater was used to obtain a final
dilution of 1/1,600. Sediment slurries were sonicated for two periods
consisting of 30 s of sonication separated by a 1-min cooling
period by using a Vibra Cell 600 sonic probe equipped with a 5-mm
microtip providing ~45 W cm2, and then they were
stained with 4',6-diamidino-2-phenylindole (DAPI) (final concentration,
5 µg ml1). Each preparation was stained for at least
3 h in the dark at 5°C, and then 0.5 to 1.0 ml was filtered onto
a black 0.2-µm-pore-size Nuclepore filter. For each filter, the
bacteria in 30 microscope fields were counted with an epifluorescence
microscope (45).
(ii) NBW samples.
NBW samples were immediately fixed by
adding filtered (pore size, 0.2 µm) formaldehyde (final
concentration, 4%) and were stored at 5°C until they were processed
in the laboratory on land. Subsamples (1 to 20 ml) were sonicated,
stained, and filtered as described above. Bacterial counts were
determined by epifluorescence microscopy by using an image analysis
system (53).
Extracellular proteolytic activity.
Protease activity in
sediment slurries was measured by using the fluorogenic substrate
analog L-leucine-7-amido-4-methylcoumarin (Leu-MCA) (Sigma
Chemical Co.). Leu-MCA is commonly use for exoproteolytic activity
assays (14, 24, 26, 48, 50). This compound is not a natural
substrate, but justification for its use as an assay substrate has been
discussed previously (25). Indeed, hydrolysis of Leu-MCA
occurs at the same rate as uptake of labelled proteins occurs
(6), and Leu-MCA competes well with easily degradable
natural peptides (12), probably because exoproteases have a
low levels of specificity (11, 23). A stock solution of
Leu-MCA (10 mM) was prepared in methylcellosolve (ethylene glycol
monomethyl ether) and stored at 
20°C. Previous experiments have
shown that methylcellosolve at the concentration present in the samples
does not have a significant effect on enzymatic activity measurements
(49).
(i) Sediment samples.
Sediment slurries (1/4, vol/vol) were
distributed into sterile polycarbonate tubes (1 ml per tube). In order
to determine the kinetic parameters (Vm and
Km), we used the multiconcentration kinetic
method (56). Substrate was added at eight different final
concentrations to the slurry samples (10, 25, 50, 100, 200, 333, 500, and 1,000 µM). The samples were incubated in the dark at the in situ
temperature (usually 13 ± 1°C, but 17 ± 1°C for shallow
samples collected in the summer). Enzyme assays were performed in time
course experiments for 1 h. For each sample and for each substrate
concentration, three tubes containing sediment slurry were boiled for
30 min before the substrate was added in order to control abiotic
cleavage of Leu-MCA. At the end of the 1-h incubation period the tubes
were vortexed and then centrifuged (10 min, 2,680 × g).
Fifty microliters of the overlying water was diluted with 3 ml of
MilliQ water. Fluorescence was determined with a Hoefer model TKO 100 spectrofluorometer (emission wavelength, 445 nm). To quantify the
7-amido-4-methylcoumarin (MCA) liberated by bacterial attack on the
peptide bond of MCA-Leu, it was necessary to construct a calibration
curve under the same conditions in order to account for the adsorption
of MCA onto the sediment particles (36, 39, 40). The contact
between a substrate and an enzyme depends on the characteristics of the
sediment (i.e., the size of the particles and the density and nature of
the minerals) (29, 47). Therefore, a calibration curve was
obtained for each sediment slice by using a range of MCA concentrations
between 1 and 60 µM.
(ii) NBW samples.
When NBW samples were examined, seven
different concentrations of Leu-MCA (10, 20, 50, 100, 200, 500, and 800 µM) were used to determine Vm and
Km. The time course experiments lasted 6 and 24 h for the coastal and deep sampling stations, respectively. For
each incubation period, subsamples were poured into a quartz cuvette
and fluorescence was measured as described above. MilliQ water was used
as a blank. A standard curve was drawn by using a range of MCA concentrations.
Data management.
The basic hypothesis of this work was that
protease activity in natural environments follows Michaelis-Menten
kinetics. The least-squares method used to determine the optimal values
of Michaelis-Menten parameters (i.e., parameter values that gave the
best fit to the data) involved two steps. First, we defined a scalar
function of Vm and Km to
measure the distance between the data and the fitted function (that is,
the cost function). Then, we used the Levenberg-Marquardt algorithm
(28) to determine parameter values that minimized this cost
function. The cost function can be written as:
![Cost(V<SUB>m</SUB>, K<SUB>m</SUB>)=&Sgr;[(d−x)/w]<SUP>2</SUP>](/content/vol65/issue4/fulltext/1619/img001.gif)
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(1)
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where
d is the experimental value,
x is
the corresponding value of the fitted function (i.e., Michaelis-Menten
kinetics)
evaluated at
Vm and
Km, and
w is a weighting factor which
is generally
the data standard error (included to give more weight to
the more
precise data). Assuming that the standard error was the same
for
every
d value, we set
w values at 1. A
summation was performed
for all of the experimental data for one sample
(that is, the
free MCA concentrations measured during the entire set of
time
course experiments corresponding to the range of MCA-Leu
concentrations).
The increases in MCA concentrations were fitted to the
following
function:
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(2)
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where
S(
t0) is the substrate
concentration added at the beginning of a time course experiment. The
integrated form of equation
2 was used to calculate
x
values:
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(3)
|
where
x(
t0) is the autofluorescence
measured at the beginning of each time course experiment and
t is the time since the
addition of the substrate. A
logarithmic transformation was used
to constrain parameter values to be
positive. At the beginning
of the minimization scheme, the initial
values of
Vm and
Km were
estimated by assuming that each time course experiment followed
linear
kinetics and that the highest substrate concentration was
the
saturation
concentration.
The optimized parameters corresponding to the minimization solution
were designated
Vmopt and
Kmopt. The
systematic indetermination
(
Ssys) was written as follows:
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(4)
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where
ndf is the number of degrees of freedom of the
regression (that is, the number of data points minus the number of
parameters)
and
Ssys measures the distance between the data
and the fitting
function.
Ssys is an indicator of the
goodness of fit. In our
study (i.e., when
w was 1 in
equation 1),
Ssys also provided an
estimate of the standard
deviation when the Michaelis-Menten kinetics
(depending on the sample
and the monophasic or biphasic kinetics)
correctly described the
nonrandom processes contained in the
data.
The correlation factor r:cfr (0 < cfr < 1) and parameter
indetermination values were obtained from the variance-covariance
matrix. These indetermination values were multiplied by the
Ssys value to obtain linear approximations of the standard
errors of
the parameters (
49).
To determine whether there was actually a biphasic mechanism, we
performed three minimizations for each sample corresponding
to the
following three types of Michaelis-Menten kinetics: global
kinetics, in
which all substrate concentrations were used, and
low and high
kinetics, in which the low and high substrate concentrations
defined by
the Lineweaver-Burk plots were used. The objective
was to determine
whether two-phase kinetics gave a better fit
for the entire data set
than single-phase kinetics gave. To ascertain
whether two additional
parameters improved the fit significantly,
we calculated the variance
(
Var):
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(5)
|
where the cost function values correspond to the three
minimization solutions and the denominator represents the number of
additional parameters. We compared this variance to the variance
of the
biphasic kinetics, which was used as a reference:
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(6)
|
where
n is the number of data in the entire data set
and 4 is the number of parameters of the biphasic kinetics
(
Vmlow,
Kmlow,
Vmhigh,
Kmhigh). Following this,
we used the
F test of Fisher-Snedecor:
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(7)
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RESULTS AND DISCUSSION |
Statistical proof of the existence of a biphasic mode.
Using
linear transformation of data in Lineweaver-Burk plots (1/V
versus 1/S), we observed biphasic kinetics in
ectoproteolysis in the NBW and sediment samples collected from both
coastal and pelagic areas during two cruises (Fig.
1 and 2).
Globally, the first kinetic phase (Fig. 1 and 2, lines A1 and B1)
corresponded to low substrate concentrations (ranges, 10 to 100 and 25 to 150 µM for the NBW and sediment samples, respectively), while the second phase (lines A and B) corresponded to high substrate
concentrations (ranges, 200 to 800 and 200 to 1,000 µM for the NBW
and sediment samples, respectively).
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FIG. 1.
Lineweaver-Burk plot for NBW samples collected during
the winter. Lines A and B are the regression lines that represent two
levels of high extracellular enzyme reactions for sample K3 and for
pooled data for samples K2 and K4, respectively, while lines A1 and B1
are the corresponding regression lines for low extracellular enzyme
reactions.
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FIG. 2.
Lineweaver-Burk plot for surface sediment samples
(depth, 0 to 2 cm) collected during the winter. Lines A and B are the
regression lines that represent two levels of high extracellular enzyme
reactions for sample K4 and for pooled data for samples K1, K2, and K3,
respectively, while lines A1 and B1 are the corresponding regression
lines for low extracellular enzyme reactions.
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We calculated the variances (equations 5 and 6) by using these low and
high substrate concentration ranges. When the
F test
(equation 7) showed that the variances were significant at a
probability
of 0.1, we assumed that the biphasic mode was meaningful
enough
to explain the kinetics of the entire data set (Tables
2 and
3).
When the
F test showed that there was not a significant difference
at
a probability of 0.1, we assumed that there was no statistical
proof
for the existence of a biphasic mode and that the low and
high kinetics
corresponded to the same Michaelis-Menten kinetics.
In the latter case,
the parameter values for the global kinetics
were considered the best
estimates of the actual kinetic parameter
values. Finally, to assess
the validity of the statistical conclusions,
the data point positions
were examined with respect to the response
of the model (Fig.
3). Indeed, these conclusions were based
on
the hypothesis that the data were consistent with the
Michaelis-Menten
kinetics.
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TABLE 2.
Microbiological and statistical parameters for NBW
samples collected in the winter and the summer at four sampling
stations in the northwestern Mediterranean Sea
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TABLE 3.
Microbiological and statistical parameters for sediment
samples collected in the winter and the summer at four sampling
stations in the northwestern Mediterranean Sea
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FIG. 3.
Data resulting from the model responses for two surface
sediment samples (depth 0 to 2 cm) collected in coastal (A) and
pelagic (B) areas during the winter (BBLL2 cruise). The dashed and
solid lines are the model responses for high and low substrate
concentrations, respectively.
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This method of comparison was chosen (
18) because the
parameter values for the high and low kinetics were highly correlated,
and thus the Student
t test appeared to be inappropriate.
Furthermore,
parameter indeterminations could not be used as standard
deviations
of a Gaussian distribution because the Michaelis-Menten
model
is
nonlinear.
Ectoproteolytic measurements in the sediment.
When both the
low and high kinetic data were considered, the average Ssys
value was 155 times higher for the sediment samples (Table 3) than for
the NBW samples (Table 2) with respect to the Michaelis-Menten kinetics
(equation 4); this discrepancy may reflect specific artifacts due to
sediment microbial populations (51). It is generally
accepted that the significance of enzymatic activity measurements in
sediment samples is less obvious than the significance of enzymatic
activity measurements in water samples. This is mainly due to the poor
diffusion and rapid adsorption onto sediment particles of the labelled
substrate added (44, 47). However, equally important are the
drastic modifications of the natural environment that occur during
sample processing, such as disruption of sediment particles and
bacterial aggregates through dilution, vibration, and sonication. When
the method involves the addition of a fluorogenic substrate,
calibration with the fluorescent part of this compound permits
subtraction of the error due to adsorption processes. However,
adsorption also interferes with the actual availability of the
introduced compound to bacteria (17, 47), and unfortunately
it seems to be impossible to correct data for this bias. The
multiconcentration method involves adding different concentrations of
substrate to a series of subsamples; due to adsorption processes,
however, the actual availability of the substrate to bacteria does not
increase linearly as the substrate concentration increases. As
expected, at low substrate concentrations adsorption onto particles is
rapid, which leads to a drastic decrease in the actual availability of
the substrate to bacteria. At high substrate concentrations, however,
when all adsorption sites on the particles are saturated, substrate
availability suddenly increases. With Leu-MCA, this nonlinear
relationship between the actual concentrations available and the
concentrations added (used to determine enzymatic kinetics) may alter
the classic shape of Michaelis-Menten kinetics.
Relevance of the substrate concentration range.
The range of
substrate concentrations is the key factor in determining enzymatic
kinetics. Usually, handling difficulties limit the number of substrate
concentrations used. Furthermore, the lowest and highest concentrations
used are often arbitrarily defined since the natural concentrations and
the main characteristics of the microbial consortia frequently are not
known before a new series of samples is processed. If the range of
concentrations is ill-adapted, the kinetic parameters cannot be
determined. Thus, parameter indeterminations and the correlation factor
cfr are statistical tools which allow researchers to evaluate the
adequacy of the range of substrate concentrations used (Tables 2 and
3). If the cfr value is greater than 0.98, then all of the substrate concentrations are in the linear part of Michaelis-Menten kinetics. In
this case only the Vm/Km ratio
(i.e., the initial slope of the kinetics) can be determined, while each
parameter value is meaningless and both parameter indeterminations are
high. If parameter indeterminations show that Vm
can be determined but Km cannot be determined
(the cfr value is acceptable), then all substrate concentrations are in
the saturation part of Michaelis-Menten kinetics.
The mean cfr values for global kinetics for sediment and NBW samples
were 0.92 and 0.71, respectively. Higher cfr values were
obtained for
sediment samples, in which the proteolytic activities
were nearly
1,000-fold higher than the proteolytic activities
in NBW samples due to
higher levels of bacterial abundance (Tables
2 and
3). As the
concentrations of Leu-MCA added were in the
same micromolar range for
the water and sediment samples, it was
more likely that this range
covered the saturation part of Michaelis-Menten
kinetics in the NBW
samples than in the sediment samples. The
concentrations of Leu-MCA
used (10 to 1,000 µM) were chosen in
order to include the natural
combined leucine concentrations,
which were expected to vary between 50 and 300 µM in surficial
sediments (
9), and therefore, we
could obtain measurements
under natural substrate concentration
conditions. This raised
the problem of choosing between the following
two strategies for
measuring enzymatic activity: (i) increase the
number of measurements
in order to include all enzymatic kinetics and
ensure accurate
determinations of both kinetic parameters and (ii)
limit measurements
to in situ substrate concentrations in order to
focus on estimating
in situ enzymatic activity rates while allowing
some kinetic parameters
to be
undetermined.
For almost all of the samples, the parameter standard errors and the
cfr values were higher for both the low and high kinetics
than for the
corresponding global kinetics (Tables
2 and
3).
This resulted from
using truncated ranges of Leu-MCA concentrations
to determine the
Vm and
Km values of each
phase of the biphasic
kinetics. However, as shown in Tables
2 and
3,
70% of the kinetic
parameters were correctly determined (cfr < 0.98), which confirmed
the relative suitability of the range of
concentrations
used.
Evaluation of the biphasic mode.
For the entire set of samples
which we studied, biphasic kinetics appeared to be the most common
kinetic mode for ectoproteolysis, mainly at the water-sediment
interface. The highest percentages of occurrence of microbial biphasic
ectoproteolysis were found in the NBW and superficial layer of
sediment, where 77.8 and 66.7%, respectively, of the samples studied
were characterized by a biphasic mode. It is possible to assume that
detection of a biphasic mode in sediment samples is an artifact
resulting from adsorption processes, as mentioned above. However,
detection of biphasic kinetics in NBW samples as well as in sediment
samples suggests that the biphasic mode cannot be an experimental
artifact resulting from adsorption processes. Conversely, real biphasic
kinetics may remain undiscovered due to missing points at high
concentrations. This may occur in sediment samples in which the ranges
of Leu-MCA concentrations used may be inadequate considering the high
proteolytic activities present (compared to NBW samples). Note that
detection of the biphasic mode was not altered when the kinetics was
underdetermined. Indeed, the difference in concentration between the
low and high kinetic data implies that only some of the kinetics was detected.
Even though the variance test (
F test) can result in
validation of the actual existence of biphasic kinetics, this test is
relatively tedious, and it is not appropriate for comparing the
importance of the biphasic mode in different samples. For this
reason,
we defined the biphasic indicator as follows:
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(8)
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where
Km/
Vm values correspond
to the slopes of the lines for high and low kinetics in the
Lineweaver-Burk representation
(Fig.
1) and
Ib is a measure
of the angle between the two lines.
It is interesting that globally the
highest
Ib values were obtained
for samples exhibiting
microbial biphasic kinetics, as revealed
by the
F test at
P < 0.1. This result validates the statistical
conclusion that a biphasic mode actually exists in microbial
populations.
Ib can also be interpreted as the ratio between
the initial slopes
(i.e.,
Vm/
Km) of
high and low Michaelis-Menten kinetics. Therefore,
Ib
measures the importance of the biphasic mode only in the linear
part of
Michaelis-Menten kinetics, not in the saturation part.
Fortunately,
most of our data allowed us to correctly determine
both low and high
Vm/
Km ratios. For some NBW
samples (samples
K2, K8, and K9), the standard error for
Km was at least 100 times
greater than the
standard error for
Vm (Table
2). For these
samples,
only
Vmhigh was constrained and
Kmhigh was not
constrained; therefore,
Ib was not calculated as not
significant.
Origin of the observed biphasic kinetics.
Several authors have
described the coupling between bacterial uptake of low-molecular-weight
compounds or bacterial biomass production and the seasonal fluctuations
in organic matter input in benthic systems (2, 7, 15, 19, 22, 33,
38, 46, 52). Recently, in a study of deep sediments of the
subantarctic Indian Ocean sector, Talbot and Bianchi (50)
suggested that biphasic kinetics could be an adaptation of marine
bacteria to the episodic supply of organic material reaching the
seafloor and that the two types of kinetics play a role in a
"hydrolysis relay" for the degradation of polymeric compounds (one
type of kinetics for low concentrations and the other for high
concentrations). The observation that the biphasic kinetics of
ectoenzymatic processes occurred in both the NBW and sediment layers of
the coastal and pelagic areas during the winter and summer sampling
periods could support this hypothesis.
Probably because of the lack of sufficient data, the
Ib
values (equation 8) for the sediment layers did not reveal significant
differences between the winter and summer periods (Fig.
4A). However,
the major differences in
the
Ib values were the differences between
the values for
the coastal and pelagic surficial sediments (Fig.
4B). Furthermore,
biphasicity in the sediment layers appeared
to be more pronounced in
pelagic sediments than in coastal sediments.
As shown in Fig.
5, this more pronounced biphasicity in
the deep
sea was mainly due to higher
Vmlow/
Kmlow
values
for coastal bacterial communities. It has been demonstrated that
in the open ocean only about 10% of the primary production descends
down to 150 m (
1) and only 1% of the
photosynthetically produced
material reaches the sea bottom at depths
of 4 to 5 km (
54).
Considering such poor organic material
input, it is possible to
assume that a high
Vmlow/
Kmlow
ratio is a fundamental
adaptation of a bacterial enzymatic system which
allows optimal
survival in the deep sea.
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FIG. 4.
Evolution of the kinetic mode through the
sediment (indicator of biphasicity Ib) during the summer
(n = 5) and the winter (n = 4) (A) and
in the pelagic area (n = 4) and the coastal area
(n = 5) (B).
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FIG. 5.
Comparison of the
Vm/Km ratios for low and high
kinetics in the surficial sediment layer (depth, 0 to 2 cm) for the
pelagic area (n = 4) and the coastal area (n = 5). The errors bars indicate standard deviations.
|
|
Even if our observations suggest that biphasic kinetics could reflect
adaptation of bacterial communities to episodic food
supplies, a
hypothesis that could satisfy the ecological point
of view, we should
consider other mechanisms that may account
for multiphasic kinetics
measurements. Primarily, experimental
data do not indicate the
enzymatic activities of natural bacterial
consortia. In multispecies
consortia, each bacterial species may
produce its own enzymatic system
characterized by specific kinetic
properties. Biphasic kinetics cannot
be considered adaptive mechanisms
but more likely are selective
mechanisms which result in optimal
efficiency in polymer degradation by
the bacterial community.
Furthermore, in natural bacterial communities,
each bacterial
species may produce several kinds of proteases which
have broad
specificity (
11). Indeed, using a collection of
44 bacterial
strains, Martinez et al. (
35) demonstrated that
cell-specific
proteolytic activities varied over a very broad range. In
fact,
experimental data clearly show that benthic bacterial communities
are able to efficiently degrade protein compounds at a wide range
of
substrate concentrations, expected to cover the natural variability
in
nutrient concentrations, but at this time we are not able to
precisely
determine which mechanism actually manages the multiphasic
kinetics
observed.
Conclusion.
The accuracy of kinetic parameter values depends
on the range of Leu-MCA concentrations used. This study shows that with
sediment samples, a lack of preliminary information concerning the
natural concentrations of organic biopolymers makes it difficult to
determine the ideal range of concentrations for the substrate added.
This range should be wide enough to avoid indetermination of some
kinetic parameters. In this study most kinetic parameters were
correctly determined, which confirmed the suitability of the range of
concentrations used. Our results, therefore, provide further evidence
that benthic bacterial populations use multiphasic ectoenzymatic
processes to hydrolyze biopolymers. The highest percentages of biphasic kinetics versus monophasic kinetics occur in the NBW and in the topmost
sediment pellicle, precisely where inputs of organic polymers fluctuate
greatly depending on the seasonal flux of particles from the surface.
Due to this microbial enzymatic process, it is essential to determine
the maximal ectoproteolytic velocities by a multiconcentration method.
This condition is enhanced in sediment environments in which microbial
populations are known to be more diverse.
| |
ACKNOWLEDGMENTS |
This work was supported by the European Commission's Marine
Science and Technology (MAST) Programme MATER under contract
MAS3-CT96-0051 and by the Institut National des Sciences de l'Univers,
Programme National d'Océanographie Côtière.
We thank R. Buscail and L. Medernac for the chemical analysis data. We
are grateful to F. Van Wambeke and two anonymous reviewers for helpful
comments on the manuscript. We especially thank the captains and crew
members of RVs Tethys II and L'Europe for their assistance during the cruises.
| |
FOOTNOTES |
*
Corresponding author. Mailing address: Microbiologie
Marine, Centre National de la Recherche Scientifique-Institut National des Sciences de l'Univers-EP 2032, Université de la
Méditerranée, F-13288 Marseille Cedex 9, France. Phone: 33 4 91 82 90 49. Fax: 33 4 91 82 90 51. E-mail:
a-bianchi{at}luminy.univ-mrs.fr.
| |
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Abstract
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