A flow-sorting technique was developed to determine unperturbed
metabolic activities of phylogenetically characterized bacterioplankton groups with incorporation rates of [35S]methionine
tracer. According to fluorescence in situ hybridization with rRNA
targeted oligonucleotide probes, a clade of 
-proteobacteria, related
to Roseobacter spp., and a
Cytophaga-Flavobacterium cluster dominated the different
groups. Cytometric characterization revealed both these groups to have
high DNA (HNA) content, while the -proteobacteria exhibited high
light scatter (hs) and the Cytophaga-Flavobacterium cluster exhibited low light scatter (ls). A third abundant group with
low DNA (LNA) content contained cells from a SAR86 cluster of
-proteobacteria. Cellular specific activities of the HNA-hs group
were 4- and 1.7-fold higher than the activities in the HNA-ls and LNA
groups, respectively. However, the higher cellular protein synthesis by
the HNA-hs could simply be explained by their maintenance of a larger
cellular protein biomass. Similar biomass specific activities of the
different groups strongly support the main assumption that underlies
the determination of bacterial production: different bacteria in a
complex community incorporate amino acids at a rate proportional to
their protein synthesis. The fact that the highest growth-specific
rates were determined for the smallest cells of the LNA group can
explain the dominance of this group in nutrient-limited waters. The
metabolic activities of the three groups accounted for almost the total
bacterioplankton activity, indicating their key biogeochemical role in
the planktonic ecosystem of the Celtic Sea.
 |
INTRODUCTION |
A contemporary challenge in
microbial ecology is to understand the functional role of
phylogenetically defined bacterial populations in natural ecosystems.
Extensive biogeochemical studies have shown that bacteria constitute a
major component of carbon cycling in aquatic ecosystems as the main
consumers of dissolved organic matter (DOM) (see, for example,
references 2, 3, 12, and 13). Although the importance of DOM mineralization
is well recognized, the relative contributions of the major
phylogenetic groups of bacterioplankton to DOM consumption in the sea
are still under investigation.
Studies of heterogeneous natural microbial communities have been
significantly improved with the introduction of new techniques. One of
these uses a combination of microautoradiography and fluorescence in
situ hybridization (FISH) to identify individual bacterial cells that
have incorporated isotopically labeled tracers (24, 33). A second complementary technique is one that allows
quantification of tracer incorporation in flow cytometrically sorted
cells (6, 25, 34, 35). Several cellular optical properties
can be used to distinguish bacterial groups by flow cytometry, and the groups may be flow sorted to provide concentrated group-specific sample. The sorted cells can be subsequently used for molecular (4, 39) and radioactive tracer analyses.
The ease with which bacterioplankton can be stained for nucleic acid
content and analyzed cytometrically has allowed a distinction to be
made between a general group of bacteria with low DNA content (LNA) and
another with high DNA content (HNA) (8, 16, 26, 30, 31).
However, the biological and ecological nature of cytometrically defined
groups remains poorly understood. The potential of phylogenetic
affiliation of cells within discrete cytometric groups was demonstrated
for cyanobacteria (see, for example, references 9,
25, and 38). A recent study of
bacterioplankton in the North Sea showed that each cytometric group was
dominated by a different phylogenetic group of bacteria
(43). However, molecular analyses of sorted
nonphototrophic bacteria have demonstrated that cytometric groups could
also be comprised of different phylogenetic groups of bacteria
(14).
The aim of the present study was to extend these recent developments in
bacterial phylogeny by determining the in situ metabolic activities of
dominant groups of marine bacterioplankton. Our approach has been to
use a radioactively labeled amino acid tracer to quantify cellular
activity. We hypothesized that all bacterioplankton cells incorporate
the tracer, with incorporation rate indicating cellular metabolic
activities, and that these could be related to cellular biomass. To
test the hypothesis, we developed a sensitive tracer technique and
applied it to natural bacterioplankton communities of the Celtic Sea.
We compared the in situ metabolic activities of cells flow sorted from
the dominant groups of bacterioplankton collected in water layers with
different biogeochemical regimens. The selected stations offered a
range of hydrological conditions suitable for revealing general trends
by using a pooled data set of bacterioplankton activity.
| |
MATERIALS AND METHODS |
Sampling site.
The study was undertaken on cruise D246 on
board the R.R.S. Discovery in shelf waters of the Celtic Sea between 22 and 28 May 2000. Three stations with different hydrographic regimens were chosen: one in the vicinity of the Irish coast (station G, 51o30'N, 7o20'W), one close
to the shelf break (station E, 50o11'N,
8o36'W), and one in the middle of the Celtic Sea
Deep (station F, 51o13'N,
6o23'W). The stations had a pronounced vertical
density gradient, i.e., a pycnocline, that separated the surface mixed
layer from deeper waters (Fig. 1).
Because it was affected by internal waves, the pycnocline spanned 25 to
30 m at station E, while in more coastal waters the pycnocline was
sharper at only 5 to 10 m thick. Each station was sampled two to
four times at 12-h intervals. Seawater was collected with a rosette of
Niskin bottles mounted on a Neil Brown Mk3C
conductivity-temperature-depth profiler (General Oceanics). Ten to
twelve depths were sampled from the top 65 m of 80- to 130-m water
columns with a depth resolution of 1 to 10 m. The pycnocline layer
was sampled with the highest depth resolution. Bacterial abundance was
determined at all depths sampled. Rates of bacterioplankton
incorporation of [3H]leucine and
[35S]methionine were measured in water samples
collected at six depths on each CTD cast. The samples used for rate
determinations were initially collected into acid-washed 1-liter
thermos flasks by using acid-soaked silicone tubing and then processed
within 1 h of sampling. In addition, 10 selected samples
representative of the three distinct water layers, namely, the surface
mixed layer, the pycnocline, and the deep-water layer at the three
stations, were used for more detailed analyses (diamonds, Fig. 1). The
analyses included flow sorting of three main groups of bacterioplankton for FISH and tracer studies.
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FIG. 1.
Vertical distribution of seawater density and total
bacterioplankton numbers at three studied stations in the Celtic Sea
(St. G, E, and F). Lines show the individual profiles of seawater
density index,  t, triangles show the bacterial numbers
at sampled depths, and diamonds indicate the samples chosen for
flow-sorting studies. Seawater density (in kilograms
decimeter 3) is equal to 1 + (t × 103).
|
|
Flow cytometry.
For flow cytometric analyses, replicated
2-ml samples were fixed with 1% paraformaldehyde (PFA) and incubated
at 2°C for 24 h. Subsequently, the samples were frozen and kept
at 
20°C. The abundance of heterotrophic bacteria was routinely
determined with a FACSort flow cytometer (Becton Dickinson, Oxford,
United Kingdom) after staining with SYBR Green I DNA stain as described
previously (30, 42). Bacterioplankton cellular protein
contents were determined with a FACStar Plus flow cytometer (Becton
Dickinson, Mountain View, Calif.) after simultaneous staining with
Hoechst 33342 DNA stain and SYPRO Red protein stain (41).
The SYPRO protein cell fluorescence was calibrated by using four
bacterial cultures. Yellow-green microspheres of 0.5 µm (Fluoresbrite
Microparticles; Polysciences, Warrington, Pa.) were used in all
analyses as an internal standard to normalize samples and to calculate
bacterial protein content and bacterial concentration. The absolute
concentration of beads in a standard stock suspension was determined by
flow cytometric counting of beads in volumes dispensed with an
automatic microinjector (KD Scientific). Bacterial biomass was
calculated by multiplying bacterial concentration with the mean protein
content of bacterial cells.
Identification of flow-sorted bacteria.
Cells, double
stained with Hoechst 33342 and SYPRO Red, were sorted by using the
FACStar flow cytometer. FISH with sorted cells harvested on 0.2-µm
(pore-size) polycarbonate filters was done according to the protocol of
Glöckner et al. (17) with some modifications
(14), including the use of helper probes (15). The sorted cells were hybridized with a set of
probes listed in Table 1. New probes were
developed with the ARB program package (O. Strunk, B. Gross, B. Reichel, M. May, S. N. Hermann, et al.
[http://www.mikro.biologie.tu-muenchen.de.]). The specificity of
probes was ensured by altering the formamide concentration of the
hybridization buffer used for FISH with natural samples. The probes
were commercially synthesized and labeled with CY3 dye (Interactiva,
Ulm, Germany). Cells were viewed by using an Axioplan epifluorescence
microscope equipped with a 100' Plan Neofluar objective (Zeiss, Jena,
Germany), and at least 300 cells were counted per sorted sample.
Probe-positive cells were presented as fractions of cells stained with
the general nucleic acid dye DAPI (4',6'-diamidino-2-phenylindole).
Amino acid incorporation and bacterioplankton production.
Two amino acids, [3H]leucine and
[35S]methionine, were used as precursors. The
[3H]leucine incorporation rates estimated
production of bacterioplankton biomass (21). The
[35S]methionine was added at tracer
concentration to determine the in situ rates of amino acid
incorporation and therefore the protein synthesis of bacterioplankton.
The latter rates were used as an index of cellular metabolic activity.
Triplicate subsamples (1 ml) from samples collected at six depths were
inoculated with L-[4,5-3H]leucine
(63 Ci mmol1; ICN Pharmaceuticals, Ltd.) at a
20 nM final concentration or with
L-[35S]methionine (>1,000 Ci
mmol1; Amersham Pharmacia Biotech UK,
Ltd.) at a <0.3 nM final tracer concentration and then
incubated in the dark at the in situ temperature of the surface mixed
layer. The whole content of one of three tubes was fixed at 0.5, 1, or
1.5 h by mixing with an equal volume of 10% trichloroacetic acid
(TCA). The sample particulate material was harvested onto
0.2-µm (pore-size) nylon filters (Supor; Pall Corporation).
Radioactivity incorporated into TCA-insoluble material was counted with
a RackBeta 1219 liquid scintillation counter (LKB-Wallac, Turku,
Finland). The rate of precursor incorporation was calculated as the
slope of the linear regression of radioactivity against incubation time
(r2 > 0.99, P < 0.0001). Incorporation rate constants, i.e., the fractions of the added
precursor incorporated by bacterioplankton per hour, were used for
comparison. Leucine incorporation rates were converted into bacterial
biomass production by using a previously determined conversion factor
of 640 g of bacterial protein per mol of incorporated leucine
(43).
Flow sorting of bacterioplankton cells labeled with
[35S]methionine tracer
Methionine
incorporation by bacterioplankton was measured as described above with
minor modifications, i.e., subsamples were fixed with 1% PFA after a
3-h incubation. Subsamples were stored at 2°C before they were
stained with SYBR Green I, and groups were flow sorted by using a
FACSort flow cytometer set to single-cell sort mode. Sorted cells were
collected onto 0.2-µm (pore-size) nylon filters and washed with 10 ml
of ultrapure water (18.2 M
resistivity), and the filters were
subsequently radioassayed. Three proportional numbers (e.g., 2,000, 4,000, and 6,000) of the cells were sorted, and the cellular activity
was determined as the slope of the linear regression of radioactivity
against the number of sorted bacteria
(r2 > 0.96, P < 0.0001). A mean coefficient of variance for the measured activities of
sorted cells was 6%.
| |
RESULTS |
Intercalibration of sorting gates of the two flow
cytometers used.
Limitations in instrument design prevent the use
of a single flow cytometer for all analyses. Accurate cellular DNA and
protein measurements were not possible with a single laser FACSort
instrument, while flow sorting of radioactively labeled bacteria with
the drop sorter of a FACStar instrument was not safe. Therefore,
different flow cytometers were used for sorting bacterioplankton groups for FISH and tracer studies.
A cross calibration of flow cytometers was undertaken to ensure that
the same groups were recognized and sorted by the two instruments.
Three dominant groups were visualized on a flow cytometric signature of
bacterioplankton double stained for DNA and protein. Sorting gates were
drawn around the core of each group, and target cells were sorted by
using the FACStar instrument (Fig. 2a and b). Subsequently, the sorted cells were reanalyzed with the FACSort instrument. The sorted cells were seen as tight clusters by the second
flow cytometer (Fig. 2c to e). The positions of these clusters matched
the positions of the three main groups of bacterioplankton in a natural
sample analyzed on the second instrument (Fig. 2f). There was good
agreement between the sorting gates on the two instruments, with 95% ± 3% (n = 8) of cells sorted from gate 1 of the
FACStar recorded in gate 1 of the FACSort. The level of agreement
between the other two gates was slightly lower, with 75% ± 15%
(n = 8) and 80% ± 10% (n = 10) of
cells sorted on the FACStar instrument recorded on the FACSort
instrument in gates 2 and 3, respectively. Thus, we could conclude that
the same groups of bacterioplankton were targeted by the two flow
cytometers.
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FIG. 2.
(a and b) Flow-cytometric signatures of natural
bacterioplankton stained for nucleic acids and proteins, with
superimposed gates used for sorting three distinct groups by the
FACStar instrument. (c to f) Flow-cytometric signatures of sorted
HNA-hs, HNA-ls, and LNA (1, 2, and 3, respectively) groups (c, d, and
e, respectively) reanalyzed by the FACSort instrument and compared with
the flow-cytometric signature of the same unsorted bacterioplankton
sample (f), with the gates and regions used for sorting on the second
instrument indicated.
|
|
Phylogenetic affiliation of sorted cells.
Bacterial cells
sorted from the groups were identified by FISH. Because the composition
of cells sorted from the bacterioplankton samples at the three stations
was similar, the FISH results were pooled (Fig.
3). For the same reason, the FISH results
for the pycnocline and deep layer were also combined. In fact, no major differences in bacterioplankton community structure were detected between the surface mixed layer and deeper waters. However, a higher
percentage of cells hybridized with oligonucleotide probes in the mixed
layer compared to deeper waters.
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FIG. 3.
FISH with cells flow sorted from three groups (HNA-hs
[a], HNA-ls [b], and LNA [c]) of bacterioplankton, collected in
the surface mixed layer (Mixed) and the combined deep and pycnocline
layers (Deep & Pycno.). Hybridization results are presented as
percentages of probe-positive cells stained with DAPI. Probes: EUB338
for Bacteria, ALF968 for -proteobacteria, RSB67 for
the Roseobacter clade, GAM42a for -proteobacteria,
SAR86/GMP1245 for the SAR86 cluster of -proteobacteria, CF319a for
the Cytophaga-Flavobacterium cluster (see Table 1 for
details).
|
|
Between 79 and 97% of sorted cells belonged to the domain
Bacteria (Table 1, Fig. 3). Proteobacteria of the subdivision numerically dominated the cells sorted from the HNA-high
light scatter (hs) group, i.e., 60 and 40% of cells in the mixed layer and the deeper waters, respectively (Fig. 3a). Furthermore, one particular clade related to Roseobacter spp.
(43) represented most of these -proteobacteria.
Proteobacteria of the subdivision were another relatively abundant
group, although they represented 
10% of all sorted cells. The other
phylogenetic groups of bacteria tested (Table 1), i.e., the SAR86
cluster, the Cytophaga-Flavobacterium cluster, and the
Colwellia clade, represented even smaller percentages of the
total cells. The Cytophaga-Flavobacterium cluster
dominated cells sorted from the HNA-low light scatter (ls) group,
comprising 80 and 30% of the bacteria in the mixed layer and the
deeper waters, respectively (Fig. 3b). The -proteobacteria comprised
15 to 20% of all sorted cells from the HNA-ls group. The
-proteobacteria detected in this group could possibly be a result of
sorting impurities (Fig. 2d). Tiny bacteria from the LNA group were
more difficult to identify by FISH. The only probe that gave
reproducible hybridization with these cells was the SAR86 probe, and
even then only 10 to 20% of cells could be detected and these showed a
weak signal (Fig. 3c).
Cellular and biomass specific activities of sorted groups.
The
three cytometric groups were also sorted in order to estimate
group-specific metabolic activity (Fig. 2f). In fact, two different
regions of the HNA-hs group (Fig. 2f, 1a and
1b) were sorted on seven of ten occasions. On
average, the cellular activity of bacteria sorted from region
1b was 2.7 ± 0.45 times higher than the
cellular activity of bacteria sorted from region
1a. The -proteobacteria comprised 55% ± 13%
and 64% ± 26%, Roseobacter spp. comprised 51% ± 17%
and 58% ± 15%, and -proteobacteria comprised 7% ± 2.5% and
15% ± 4.5% of cells sorted from regions 1a and
1b, respectively. Because the phylogenetic
compositions of bacteria within regions 1a and
1b were similar, the activities of cells for
regions 1a and 1b were
pooled together to determine the mean activities of bacteria in the
HNA-hs group.
Using the determined activities and proportions of the total number of
cells in each of the three groups, we calculated the activity of an
average bacterioplankton cell in each analyzed sample and compared them
with the estimated mean activities of all bacterioplankton cells. The
latter values were calculated by dividing the measured rates of
[35S]methionine tracer incorporation per
milliliter by the total number of bacterioplankton cells per
milliliter. The sorted cells accounted for 93% of the total
bacterioplankton activity. The different fixation procedures used for
bulk measurements (5% TCA) and bacterioplankton sorting (1% PFA) did
not affect the determined rates. This is in close agreement with
earlier studies, wherein oceanic bacterioplankton, labeled with either
[3H]thymidine or
[14C]leucine and harvested unfixed and
fixed (5% TCA) at the end of incubations, had very similar
radioactivity independent of fixation (40).
The rates of [35S]methionine tracer
incorporation by sorted cells from the HNA-hs group were approximately
twice as high in the surface mixed layer as in either the pycnocline or
the deep-water layers (Fig. 4a). Also the
activities of cells from the HNA-hs group were considerably higher than
the activities of cells from the HNA-ls and LNA groups. The activities
of cells from the HNA-ls group were less variable between water layers.
Activities of cells from the LNA group reached high values in the
surface mixed layer and were comparable with activities of cells in the
HNA-hs group. This was somewhat surprising because the mean protein
content of cells from the LNA group was only ca. 20% of the protein
content of cells from the HNA-hs group.
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FIG. 4.
Comparison of cellular specific activities (a),
corresponding biomass specific activities (b), and specific growth
rates (c) of cells sorted from the bacterioplankton groups (HNA-hs,
HNA-ls, and LNA) from the surface mixed layers (mixed), pycnocline
(pycno), and deep-water layers (deep) sampled. Error bars indicate a
single standard deviation of specific activities determined at
three studied stations.
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Cellular protein contents, averaged for all stations, were
significantly higher (t test, = 5%) in the surface
mixed layer than in deeper waters: 31 ± 4 versus 22 ± 8 fg
of protein cell1, 7.4 ± 1.3 versus
5.7 ± 1.3 fg of protein cell1, and
6.3 ± 0.9 versus 4.7 ± 0.7 fg of protein
cell1 for the HNA-hs, HNA-ls, and LNA groups,
respectively. Consequently, in the surface mixed layer the LNA group
had 2.5 times higher biomass specific activity than the HNA groups
(Fig. 4b). The biomass specific activity of the HNA-ls group remained
constant throughout the water column, while the activities of the other
two groups decreased in deeper waters. The biomass specific activities
of the LNA and HNA-hs groups were remarkably similar in the pycnocline and deep layers despite a nearly fivefold difference in protein content.
Contributions of groups to total bacterioplankton biomass and
activity.
Despite the sharp pycnocline, the vertical distribution
of bacterioplankton at station G was uniform at ca. 0.6 × 109 cells liter1
throughout the water column, while the surface mixed layer was enriched
with bacterioplankton at the other two stations (Fig. 1). The
concentrations of total bacterioplankton decreased from ca. 0.9 × 109 cells liter1 in the
surface mixed layer to 0.45 × 109 cells
liter1 in the deep layer at stations E and F. Cells from the HNA-ls group were the most numerous in the surface
layer, constituting ca. 45% of the bacterioplankton. Their
contribution to the total numbers was lower at depth, representing ca.
30%. The percentage of cells from the HNA-hs group gradually decreased
with depth from ca. 40 to 15%, while the percentage of cells from the
LNA group increased with depth from 10 to 60%. The latter numerically dominated the deep-water layer. The total biomass of bacterioplankton, 10 to 15 µg of protein liter1, was
significantly higher in the surface mixed layer than in the deep layer,
i.e., 2 to 4 µg of protein liter1. The HNA-hs
group dominated the bacterioplankton biomass in the water column,
particularly in the surface layer, where it accounted for almost 80%
of biomass (Fig. 5). The HNA-ls group
contributed ca. 20% to biomass in all layers, and the contribution of
the LNA group rose with depth from <10 to 25%.
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FIG. 5.
Comparison of sorted group (HNA-hs, HNA-ls, and LNA)
contribution to total bacterioplankton biomass, with corresponding
group contributions to total metabolic activities. A solid line shows
statistically significant linear regression;
r2 is the corresponding regression
coefficient.
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Methionine was rapidly incorporated by bacteria at a rate of 4 to 7%
h1 (1 to 1.7 times
day1) and 0.5 to 2% h1
(0.1 to 0.5 times day1) in the mixed layer and
deeper layers, respectively. It was apparent that the HNA-hs group was
the major contributor (70 to 80%) to bacterioplankton incorporation of
methionine in the surface mixed layer and pycnocline layer, but the
contributions of all three groups was comparable in the deep layer
(Fig. 5). There was a significant positive relationship between the
contribution to bacterioplankton biomass and to bacterioplankton
metabolic activity by each group (Fig. 5). Additionally, the bacterial
production determined as the rate of leucine incorporation was strongly
correlated with the abundance of the HNA-hs group
(r2 = 0.84, n = 43, P < 0.0001) but was not significantly related to the
abundance of either of the other two groups
(r2 0.3) or to total
bacterioplankton abundance (r2 = 0.5).
Correlation between incorporation rates of methionine and
bacterioplankton production.
Bacterial production was determined
by adding 20 nM [3H]leucine that deliberately
elevated ambient amino acid concentration. Rates of
[35S]methionine incorporation (fraction
hour1) strongly correlated with the
corresponding estimates of bacterial production (micrograms of protein
liter1 day1) (slope,
62.3 ± 1.8; r2 = 0.88;
n = 48; P < 0.0001). The strong
correlation between [35S]methionine (<0.3 nM)
and [3H]leucine (20 nM) incorporation rates by
bacterioplankton in the Celtic Sea agrees with the observations of
limnic bacterioplankton in summer (22). The strong
relationship between the two parameters allowed the estimation of
group-specific growth rates (Fig. 4c). The methionine incorporation
rate constants of the groups were converted into leucine incorporation
rates by using the regression calculation described above.
Group-specific growth rates were calculated from the values of
group-specific production and group-specific protein biomass of each
bacterioplankton group. The group-specific growth rates were within the
range of values reported for oceanic bacterioplankton (see, for
example, reference 42). The LNA group had a growth rate of
0.4 day1 in the surface mixed layer two and
four times higher than the growth rates of HNA-hs and HNA-ls groups,
respectively. In the pycnocline and deeper waters, all groups had
relatively similar growth rates of ca. 0.12 day1 (Fig. 4c).
| |
DISCUSSION |
Determination of in situ metabolic activity of flow-sorted
bacterioplankton.
Microbial activity is often measured as the rate
of incorporation of isotopically labeled compounds into microbial
biomass. Flow cytometric sorting has been employed successfully for
determining ultraphytoplankton group-specific assimilation of
CO2 (25, 34) and inorganic nitrogen
(27). Specific activities of bacterial groups have also
been determined by using 3H-labeled amino acids
(mixed) (6, 7) or [3H]leucine
(35). In these experiments, labeled amino acids were added
at concentrations up to 80 nM and, as a result, elevated the ambient
concentrations of amino acids in seawater (see, for example, reference
20). In such amino acid enrichment experiments, the
activities of some groups of bacteria could be overestimated because of
their ability to use the added compound as an alternative nutrient. It
was also recently shown that -proteobacteria could differ in their
preferences for amino acids compared to bacteria from the
Cytophaga-Flavobacterium cluster (10).
By using nonperturbing, tracer concentrations of
[35S]methionine (<0.3 nM), we could monitor
natural levels of protein synthesis in cells sorted from targeted
groups. In addition to the advantages of a tracer study, the high
specific activity of [35S]methionine allowed us
to sort fewer cells (500 to 20,000) from selected groups for reliable
determination of their activity. In previous studies, experiments were
not replicated presumably because of the long time required for sorting
a sufficient number (50,000 to 500,000) of labeled cells (5,
23).
The disadvantage of flow cytometry is that it is limited to separating
cells based on DNA, protein contents, and light scatter, and a
cytometric group can be phylogenetically diverse. The advantage of
using FISH microautoradiography is in enumerating cells in a specific
phylogenetic group that are incorporating a particular compound.
However, the flow-sorting technique provides more biogeochemically relevant information, such as what fraction of total incorporation can
be attributed to a cytometric group, which in certain cases can be
dominated by one phylogenetic group or even a single clade of bacteria
(Fig. 3a and b). Accurate measurements of cellular activity can be done
by using the latter technique, and the measured activity is a mean
value for a number of sorted cells. The sorting technique also allows
analysis of variability within groups, e.g., in the same HNA-hs group
larger cells that were richer in protein were more metabolically active
than smaller cells.
The other noteworthy result of the present study was that the
activities of the sorted groups accounted for 93% of the total bacterioplankton activity. This implies that the studied groups were
ecologically very important, and the contribution of bacteria growing
on suspended detrital particles or algae was insignificant. However, it
is important to note that we measured total activity in relatively
small volumes of seawater, and it is possible that large detrital
particles, which can be hot spots of bacterial activity
(2), could be underrepresented.
Phylogenetic composition of cytometric groups of marine
bacterioplankton.
We found that the percentage of cells detected
by FISH with specific probes was higher in the surface layer than in
deeper waters, presumably because of lower cellular activities of
bacteria in deeper waters (Fig. 4a). Although a range of side scatter
and DNA obviously occurs within each group, phylogeny seems to be constant, e.g., groups 1a and
1b. Two cytometric groups were dominated by
particular phylogenetic groups of bacteria (Fig. 3). The HNA-hs group
was dominated by the Roseobacter-related clade of
-proteobacteria. Interestingly, this clade seems to be widespread
since it also dominated the bacterioplankton in the North Sea
(43). The HNA-ls group was dominated by the
Cytophaga-Flavobacterium cluster. About 80% of the smallest
cells sorted from the LNA group hybridized with general bacterial probe
Eub338, but only 10 to 20% of cells could be identified by one of the
probes tested, the one specific to the SAR86 cluster (32).
The difference may possibly be explained by additional diversity not
covered by the set of probes used (Table 1). The complete
characterization of the phylogenetic diversity of the LNA group is
beyond the scope of this study and will be addressed in forthcoming work.
Although the Cytophaga-Flavobacterium cluster often
numerically dominates marine bacterioplankton (see, for example,
references 11, 18, 37, and
43), these bacteria had very low growth rates (<0.1
day1) in the surface mixed layer of the Celtic
Sea (Fig. 4c). It was the clade of -proteobacteria that seemed to
dominate bacterioplankton biomass and production during the early
summer (see, for example, reference 43 and the present
study). Possibly, the efficient utilization of seasonally abundant
dissolved organic compound or compounds, produced by proliferating
phytoplankton, e.g., dimethylsulfoniopropionate, can give these
-proteobacteria an edge in competition with other bacterioplankton
(43). However, these bacteria showed similar or lower
biomass specific activities than other groups (Fig. 4b), indicating
that the HNA-hs group is vulnerable to low DOM in the deeper waters,
where its contribution to bacterioplankton activity is considerably
decreased (Fig. 5).
Keeping in mind the limitation of the FISH methodology, we can
cautiously conclude that the structure of the bacterioplankton community was generally similar in the water column at the three studied stations in the Celtic Sea and was comparable to the
bacterioplankton community structure in the North Sea in early summer
(43).
Specific activities of discriminated groups.
The fact that
biomass specific activities of different groups were relatively
uniform, except for high activity of LNA in the mixed layer (Fig. 4b),
has several interesting and important ramifications. First, higher
cellular protein synthesis by HNA-hs could be explained by maintaining
a larger cellular protein biomass. Second, the relatively similar
biomass specific activities of different groups strongly supports the
validity of the main assumptions that underlie bacterioplankton
production determination. Various bacteria in a complex planktonic
community incorporate amino acids at a rate proportional to their
protein synthesis (21, 36), and consequently a single
conversion factor can be employed to translate the former rate into the
latter. Third, the fact that the highest biomass specific activity and
growth-specific rates (Fig. 4c) were determined for the small cells of
the LNA group can explain dominance of this group in oligotrophic
oceanic waters (M. V. Zubkov, unpublished data).
Discrimination between bacterial groups with high and low DNA content
has been extensively discussed in the literature (see, for example,
references 8, 16, 26,
30, and 31). The HNA-hs and HNA-ls groups
pooled together were equivalent to the previously identified single
group of HNA cells. In the present study this pooled HNA group
accounted for 70 to 90% of the total bacterioplankton activity (Fig.
5). More interestingly, according to established views, LNA cells are
either inactive or even dead cells or cell fragments (see, for example,
references 16, 19, and 23). The
present measurements disagree with this view, because cells in the LNA
group were at least as active as other members of the community (Fig.
4). Comparable cellular activities for HNA and LNA bacteria were also
reported for freshwater bacterioplankton (7). However, the
contribution of LNA bacteria to the total bacterioplankton activity in
the surface mixed layer did not exceed 15% (Fig. 5) and was even less
in more-productive waters (e.g., see reference 23). In
deeper waters, the contribution by LNA was more significant,
representing up to 30% of total bacterial activity.
In the present study and in the North Sea (43), a
significant correlation between bacterial production and concentration of the HNA-hs group was found. Similar correlations between the pooled
HNA group and total bacterioplankton activity were found by other
researchers (23). The number of HNA bacteria has been suggested as an index for estimating bacterioplankton productivity (16). However, since the HNA-hs do not numerically
dominate the HNA, the idea may be misleading, and we caution against
relying on numerical dominance as an indicator of group importance in the community. The HNA-ls group dominated by the
Cytophaga-Flavobacterium cluster was the most abundant in
the surface mixed layer, although its growth-specific activity was the lowest.
Thus, the flow-sorting technique is a promising tool for studying
competition for dissolved organic and inorganic compounds between
phylogenetically distinct groups within microbial communities. Quantification of these competitive interactions will help to explain
mechanisms of microbial control of biogeochemical cycles in aquatic ecosystems.
M.V.Z. thanks Stephen Archer for many stimulating discussions. We
thank the anonymous reviewers for helpful critical comments.
This study is a part of a multidisciplinary program, Production and
Physical Interactions in the Euphotic Zone (PROPHEZE), of the Plymouth
Marine Laboratory and was supported by the U.K. Natural Environment
Research Council (NERC) and the Max-Planck Society. This study is part
of the NERC Marine and Freshwater Microbial Biodiversity (M&FMB)
thematic programme (NER/T/S/2000/00635). The research of M.V.Z. was
supported by an NERC postdoctoral research fellowship (GT5/98/16/MSTB).
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Abstract
Introduction
