FEMS Microbiology Ecology, 91, 2015, 1–12
doi: 10.1093/femsec/fiv002
Advance Access Publication Date: 12 January 2015
Research Article
RESEARCH ARTICLE
Viral and grazer regulation of prokaryotic growth
efficiency in temperate freshwater pelagic
environments
A.S. Pradeep Ram1,∗ , Jonathan Colombet1 , Fanny Perriere1 ,
Antoine Thouvenot2 and Telesphore Sime-Ngando1
1
Laboratoire Microorganismes : Génome et Environnement, UMR CNRS 6023, Clermont Université, Université
Blaise Pascal, BP 80026, 63171 Aubière Cedex, France and 2 Athos Environnement, Université Blaise Pascal, BP
80026, 63171 Aubière Cedex, France
∗ Corresponding author: Laboratoire Microorganismes: Génome et Environnement, UMR CNRS 6023, Clermont Université, Université Blaise Pascal, BP
80026, 63171 Aubière Cedex, France. Tel: +33-4-73-40-74-63; Fax: +33-4-73-40-76-70; E-mail: Pradeep Ram.ANGIA SRIRAM@univ bpclermont.fr
One sentence summary: The present study highlights the regulation of prokaryotic metabolism by prokaryote mortality forces such as viral lysis and
protistan bacterivory, which is thought to be potentially controlled by substrate availability
Editor: Riks Laanbroek
ABSTRACT
In aquatic systems, limited data exists on the impact of mortality forces such as viral lysis and flagellate grazing when
seeking to explain factors regulating prokaryotic metabolism. We explored the relative influence of top-down factors (viral
lysis and heterotrophic nanoflagellate grazing) on prokaryotic mortality and their subsequent impact on their community
metabolism in the euphotic zone of 21 temperate freshwater lakes located in the French Massif Central. Prokaryotic growth
efficiency (PGE, index of prokaryotic community metabolism) determined from prokaryotic production and respiration
measurements varied from 5 to 74% across the lakes. Viral and potential grazer-induced mortality of prokaryotes had
contrasting impact on PGE. Potential flagellate grazing was found to enhance PGE whereas viral lysis had antagonistic
impacts on PGE. The average PGE value in the grazing and viral lysis dominated lake water samples was 35.4% (±15.2%) and
17.2% (±8.1%), respectively. Selective viral lysis or flagellate grazing on prokaryotes together with the nature of contrasted
substrates released through mortality processes can perhaps explain for the observed variation and differences in PGE
among the studied lakes. The influences of such specific top-down processes on PGE can have strong implications on the
carbon and nutrient fluxes in freshwater pelagic environments.
Key words: viruses; prokaryotes; prokaryotic growth efficiency; viral lysis; heterotrophic nanoflagellate grazing potential;
lakes of French Massif Central
INTRODUCTION
Heterotrophic prokaryotes are a prime biological constituent
and integral part of natural aquatic ecosystems, where they
play key roles in the transformation and mineralization of or-
ganic matter (Azam and Malfatti 2007). Planktonic heterotrophic
prokaryotes contribute to carbon cycling by the production of
new biomass (prokaryotic secondary production, PP) and by
the remineralization of organic carbon (prokaryotic respiration,
Received: 9 October 2014; Accepted: 31 December 2014
C FEMS 2015. All rights reserved. For permissions, please e-mail: [email protected]
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FEMS Microbiology Ecology, 2015, Vol. 91, No. 1
PR). Understanding this dual character of planktonic prokaryotes in aquatic systems is a central paradigm of contemporary
microbial ecology (del Giorgio and Cole 1998). It was believed
that prokaryotic production was thought to be transferred to
higher trophic levels through grazing by protists (McManus and
Fuhrman 1988). However, with the findings of high viral abundances in planktonic environments (Bergh et al., 1989) and subsequent studies carried out in the past two decades has revealed
that viruses contribute significantly to the bulk of prokaryote
mortality (Weinbauer 2004; Sime-Ngando 2014). As a result, viral lyses disrupts the flow of carbon to higher trophic levels
and influence the biogeochemical cycles by increasing the residence time of carbon and mineral nutrients in the water column
(Thingstad et al., 1993; Suttle 2005; Bonilla-Findji et al., 2008).
This has greatly changed our conceptual understanding of the
structural and functional organization of microbial food webs of
aquatic systems.
Prokaryotic growth efficiency (PGE) is a fundamental attribute of microbial metabolism which largely determines the
ecological and biogeochemical roles of prokaryotes in microbial food webs and in aquatic systems (del Giorgio and Cole
1998). PGE, which is defined as the index of organic carbon
passing through prokaryotes, is central to the functioning of
aquatic systems and strongly reactive to changes in the environment (Comte and del Giorgio 2011). Among the trophic factors, the nature and quality of dissolved organic matter are
known to exert significant influence on them (del Giorgio and
Newell 2012). Viruses as a regulator of prokaryotic community
metabolism are less well studied, compared to substrate availability (Pradeep Ram et al., 2013). The effects of viruses on PGE,
which is an important factor to understanding microbial carbon flow, is less well understood than the effect on the prokaryote community (Bouvier and del Giorgio 2007; Sandaa et al.,
2009). To date, very limited information exists on the interactions of prokaryote metabolism and viruses and contrasting conclusions have been reported (Bonilla-Findji et al., 2008; Motegi
et al., 2009; Xu et al., 2013). It is not known whether the influence of viruses on prokaryote metabolism is the same across
different systems. Studies conducted so far have suggested viral
lysis to either enhance prokaryotic respiration and production
(Fuhrman 1999; Pradeep Ram et al., 2010) or enhance prokaryotic
respiration only with reduced production and growth efficiency
(Middelboe, Jorgensen and Kroer 1996; Bonilla-Findji et al., 2008;
Xu et al., 2013).
In addition to viruses, flagellate grazing can also structure
prokaryote communities (Longnecker et al., 2010). Since prokaryotic metabolism is strongly dependent on the prokaryote assemblage (Comte and del Giorgio 2009), we hypothesize that
selective lysis or grazing of prokaryote cells by viruses and flagellates (top-down forces) together with the nature of nutrients
released during mortality processes can influence patterns of
PGE. To test the above hypothesis, we studied the effects of
both top-down (viral lysis and heterotrophic nanoflagellate grazing) control processes on prokaryotic community metabolism
in 21 temperate lakes of French Massif Central. The type of
prokaryotic mortality in these study lakes has previously been
shown to vary depending on the nature and activity of individual top-down forces (Pradeep Ram et al., 2014). The present
investigation is one among the few carried out in freshwater
systems where both viruses and flagellates were simultaneously taken into account to investigate top-down control processes on prokaryote metabolism at the natural community
level.
MATERIALS AND METHODS
Study sites and sampling
The 21 lakes were distributed across a natural climatic gradient (350–1197 m above sea level) and spanned a distance
of 120 km in the Auvergne region of French Massif Central.
They included both natural and man-made water bodies with
considerable heterogeneity in terms of surface area and catchment characteristics (Table 1). Water samples were collected on
one occasion each in June, July and August 2011 using a Van Dorn
bottle at 0.5 m depth over the deepest part of the lake. Samples
were immediately pre-filtered through 150 μm pore size nylon
filter (to eliminate the predatory metazoan zooplankton) into insulated carboys that had been previously cleaned with 1.2N HCl
and rinsed three times with Milli Q and lake water. Since the
distance of each lake to the laboratory was different, the utmost
care was taken to transport the water samples in refrigerated
boxes (filled with ice packs and coolants) to arrive within a few
hours at the laboratory, where they were processed immediately.
Abundances of viruses and prokaryotes
Enumeration of viruses and prokaryotes in glutaraldehyde (0.5%
final concentration) preserved samples were performed within
an hour of fixation using a FACS Calibur flow cytometer (Becton
Dickinson, Franklin Lake, NJ, USA) equipped with an air-cooled
laser providing 15 mW at 488 nm with the standard filter set-up
as described by Marie et al. (1999), Brussaard (2004) and Duhamel
and Jacquet (2006). Briefly, extracted samples were diluted with
filtered TE buffer (10 mM Tris-HCL and 1 mM EDTA, pH 8) and
stained with SYBR Green I (10 000-fold dilution of commercial
stock, Molecular Probes, Oregon, USA). Mixture was incubated
for 5 min, heated for 10 min at 80◦ C in the dark and cooled
for 5 min prior to analysis. Populations of viruses and prokaryotes differing in fluorescence intensity were distinguished on
plots of side scatter versus green fluorescence (530 nm wavelength, fluorescence channel 1 of the instrument). Flow cytometry list modes were analysed using CellQuest Pro software (BD
Biosciences, version 4.0). A blank was routinely examined to
control for contamination of the equipment and reagents.
Prokaryotic community metabolism
We followed three distinct aspects of prokaryotic carbon
metabolism: PR, PP and PGE. We used the protocol presented by
Pradeep Ram et al. (2013) to determine PR and PGE. Briefly, lake
water samples were gently filtered through 1 μm polycarbonate
(47 mm diameter) filters (Whatman, England) held in a Millipore
filter holder using a peristaltic pump and silicone acid-washed
tubing. Depending on the lake samples, the filters were replaced
often to minimize the loss of prokaryotes due to clogging. This
size fractionation procedure separated bacterioplankton from
other planktonic components so that we could measure PR and
PP with minimal interference from other planktonic organisms.
The chosen pore size filters were effective and allowed over 85%
of the free-living prokaryotes to pass through (depending on the
lakes), which was eventually confirmed by flow cytometry analysis of prokaryote abundances before and after filtration of water
samples.
PR was determined from the decline in dissolved oxygen concentration over the course of 24-h incubation period in filtered
water samples. For PR, the samples were carefully siphoned into
acid-washed-calibrated borosilicate glass BOD bottles (150 mL
Pradeep Ram et al.
3
Table 1. Geographical coordinates and morphometric characteristics of the studied lakes.
Lake
Ambert
Anschald
Aubusson
Aydat
Banne d’Ordanche
Chabreloche
Cournon
Cunlhat
Fades-Besserves
Gour de Tanzenat
Lapeyrouse
Laqueille
Pavin
Ribeyre
Saint Antheme
Saint Eloy les Mines
Saint Remy
Servant
Super Besse
Tour d’Auvergne
Vernet la Varenne
Latitude
N
Longitude
E
Altitude
(m. a.s.l)
Max. depth
(m)
Lake area
(ha)
Watershed area
(km2 )
45◦ 32 53.05
45◦ 50 35.05
45◦ 44 51.68
45◦ 39 50.58
45◦ 36 11.77
45◦ 53 00.67
45◦ 44 31.45
45◦ 37 24.35
45◦ 54 47.05
45◦ 58 47.14
46◦ 13 24.56
45◦ 39 57.46
45◦ 49 47.22
45◦ 33 52.16
45◦ 31 15.06
46◦ 09 16.85
45◦ 53 41.21
46◦ 08 19.69
45◦ 30 27.43
45◦ 32 01.93
45◦ 28 30.00
3◦ 43 59.27
2◦ 50 25.80
3◦ 36 28.44
2◦ 59 04.09
2◦ 44 25.04
3◦ 41 47.29
3◦ 13 07.93
3◦ 33 34.42
2◦ 45 08.39
2◦ 59 31.74
2◦ 52 00.18
2◦ 43 30.29
2◦ 88 66.67
2◦ 56 39.41
3◦ 54 55.19
2◦ 50 02.26
3◦ 36 01.11
2◦ 55 38.43
2◦ 51 22.82
2◦ 41 04.27
3◦ 26 40.70
528
700
408
825
1100
350
352
497
497
630
490
1018
1197
830
925
502
640
592
1150
932
817
2.5
14.0
13
15.0
4.3
3.5
5.0
3.2
11.0
66.0
8.5
6
92
2.5
7.0
12.0
7.0
7.3
9.0
6
7.5
2.5
20
28
60
2
3
6
4
400
33
25
3
44
2
3
33
14
8
2.5
5
3
3.3
2.7
46.1
30
0.5
12.5
1.1
10.7
640
4.6
0.9
6.0
0.5
12.9
57.9
6.1
3.5
4.3
3.7
0.8
1.6
Latitudes and longitudes for the lakes are given as degree◦ minutes second .
capacity) to avoid air bubble formation. Time-zero samples (initial) were immediately fixed with Winkler’s reagents. Another
set of samples were incubated in darkness at in situ temperature
(±1◦ C) and fixed with Winkler’s reagent after 24-h incubation period (final). Dissolved oxygen concentration in the initial and final replicates was determined by the Winkler method based on
endpoint detection (Carignan, Blais and Vis 1998; Pradeep Ram
et al., 2010). Respiration rates were calculated as differences in
oxygen concentration between initial and final replicate bottles,
over the duration of the incubation period. We used a factor of
0.375 to convert from oxygen to carbon units assuming a respiratory quotient of 1.
The prokaryote growth rate (μ) was determined in another
set of batch culture carried out simultaneously by diluting the
1-μm-filtered water samples 4-fold with 0.02 μm ultrafiltrate
and incubated in the dark at in situ temperature (±1◦ C) over 24h period. Prokaryotic cell increase over incubation period was
measured and their growth rate was calculated by the formula:
μ = (lnN24h − lnN0h )/T, where N0h and N24h represent prokaryote abundance at 0 and 24 h, respectively. PP (cells l−1 d−1 ) was
estimated by multiplying μ by the initial prokaryote abundance.
In order to obtain PP in carbon equivalents, prokaryotic carbon
content of 20 fg C per cell (Lee and Fuhrman 1987) was used as
a constant conversion factor.
PGE was then calculated as PP/(PP + PR) and expressed in percentage (del Giorgio and Cole 1998).
Viral lytic infection
Prokaryotic cells contained in 8 mL of formalin-fixed water samples (final concentration 2% v/v) were collected on triplicate electron microscope grids (400-mesh, carbon-coated Formvar film)
by ultracentrifugation (Optima LE-80K, Beckman Coulter SW40
Ti Swing-Out-Rotor at 70 000 × g for 20 min at 4◦ C) according
to Pradeep Ram and Sime-Ngando (2008). Each grid was stained
at room temperature (ca. 20◦ C) for 30 s with uranyl acetate (2%,
pH = 4), rinsed twice with 0.02-μm-filtered distilled water to
remove excess stain and dried on filter paper. Grids were examined using a JEOL 1200EX transmission electron microscope
operated at 80 kV and a magnification of 20 000–60 000× to
distinguish between prokaryotic cells with and without intracellular viruses. A prokaryote was considered infected when at
least five viruses, identified by shape and size, were clearly visible inside the host cell. At least 400–600 prokaryotic cells were
inspected per grid to determine frequency of visibly infected
prokaryotic cells (FVIC). Because mature phages are visible only
late in the infection cycle, FVIC counts were converted to frequency of infected cells (FIC) using the equation FIC = 9.524
FVIC – 3.256 (Weinbauer, Winter and Höfle 2002). Assuming that
in steady state, infected and uninfected cells were grazed at
the same rate, and that the latent period equalled the prokaryotic generation time, FIC was converted to prokaryotic mortality
(VIBM, as percentage of prokaryotic production) using the equation: VIBM = (FIC + 0.6 FIC2 )/(1 − 1.2FIC) (Binder 1999).
Heterotrophic nanoflagellate abundance and grazing
potential
Samples for the measurement of heterotrophic nanoflagellate
(HNF) abundance were fixed with 1% glutaraldehyde (final concentration). Primulin-stained HNF collected on 0.8-μm polycarbonate black filters (25 mm diameter) were counted under UV
excitation under a LEICA epifluorescent microscope as described
by Caron (1983). A total of 200–400 HNFs from each slide was
counted along several transects (SD <10%). All solutions were
filter-sterilized and a blank was routinely examined to control for contamination of equipment and reagents. To estimate
the loss of prokaryotes to HNF grazing (i.e. potential rate of
bacterivory by HNF), we used an approach based on the average flagellate clearance rate obtained from published reports in
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FEMS Microbiology Ecology, 2015, Vol. 91, No. 1
Table 2. Means and in brackets standard deviation for physicochemical features and chlorophyll concentration of the 21 lakes sampled between
June and August 2011.
Lake
Ambert
Anschald
Aubusson
Aydat
Banne d’Ordanche
Chabreloche
Cournon
Cunlhat
Fades-Besserves
Gour de Tanzenat
Lapeyrouse
Laqueille
Pavin
Ribeyre
Saint Antheme
Saint Eloy les Mines
Saint Remy
Servant
Super Besse
Tour d’Auvergne
Vernet la Varenne
Temperature
(◦ C)
Dissolved oxygen
(mg l−1 )
TOC (mg l−1 )
TN (mg l−1 )
Chlorophyll a
(μg l−1 )
21.9 (±1.2)
18.3 (±1.3)
21.2 (±2.0)
18.5 (±1.7)
18.5 (±3.7)
18.4 (±1.5)
22.8 (±2.5)
20.4 (±1.7)
20.7 (±2.4)
20.0 (±1.5)
19.7 (±1.9)
17.5 (±2.5)
14.4 (±1.5)
19.8 (±2.2)
19.3 (±1.8)
21.1 (±1.2)
20.4 (±1.8)
19.3 (±1.8)
16.3 (±3.0)
19.6 (±2.5)
21.2 (±1.9)
10.3 (±0.6)
10.2 (±0.4)
10.2 (±0.4)
10.5 (±0.6)
10.8 (±0.5)
10.4 (±0.3)
10.6 (±0.3)
10.5 (±0.5)
9.8 (±0.4)
10.2 (±0.3)
9.6 (±0.3)
11.1 (±1.4)
9.7 (±0.5)
10.8 (±0.4)
10.3 (±0.3)
10.0 (±0.7)
10.6 (±0.3)
9.6 (±0.2)
10.7 (±0.3)
10.5 (±0.7)
10.1 (±0.2)
9.2 (±2.1)
8.1 (±1.4)
11.3 (±5.9)
4.2 (±0.8)
9.0 (±8.1)
8.5 (±4.9)
11.0 (±5.4)
9.7 (±6.6)
8.2 (±5.8)
6.1 (±3.2)
12.0 (±6.0)
5.0 (±0.8)
3.7 (±2.2)
8.7 (±5.9)
7.8 (±4.5)
6.6 (±2.5)
10.0 (±4.7)
7.3 (±1.7)
6.9 (±6.9)
10.8 (±10.6)
8.7 (±2.5)
0.4 (±0.1)
0.4 (±0.1)
0.8 (±0.2)
0.4 (±0.1)
0.5 (±0.3)
0.6 (±1.0)
0.5 (±0.2)
0.7 (±0.3)
0.8 (±0.1)
0.3 (±0.1)
0.7 (±0.1)
0.9 (±0.1)
0.2 (±0.1)
0.4 (±0.2)
0.4 (±0.2)
0.3 (±0.1)
0.9 (±0.3)
1.1 (±0.2)
0.4 (±0.2)
0.5 (±0.2)
0.5 (±0.1)
8.8 (±6.4)
11.2 (±6.1)
16.4 (±8.4)
9.3 (±2.3)
23.7 (±12.6)
3.7 (±1.9)
7.6 (±3.4)
19.9 (±2.1)
17.0 (±17.3)
2.0 (±0.5)
40.4 (±23.1)
10.9 (±5.7)
1.8 (±2.6)
7.4 (±5.2)
13.6 (±1.5)
3.8 (±3.4)
9.4 (±7.4)
7.2 (±4.5)
9.2 (±4.5)
13.2 (±2.2)
13.3 (±3.9)
freshwater lakes of French Massif Central (Carrias, Amblard
and Bourdier 1996; Thouvenot et al., 1999). Potential HNF grazing rates (FLG, cells mL−1 d−1 ) were calculated as follows:
in situ prokaryotic abundance × in situ HNF abundance ×
mean flagellate clearance rate (2.1 nl ind−1 h−1 ) (Pradeep Ram
et al., 2014).
Physico chemical parameters
Water temperature and dissolved oxygen profiles were determined in situ using a WTW-OXI 320 multiparameter probe. Total
organic carbon (TOC) was determined by high-temperature catalytic oxidation method (680◦ C) using a TOC analyser (Shimadzu
TOC-V CPN, Japan) (Lønborg and Søndergaard 2009) and total nitrogen (TN) using the same analyser with an attached measuring unit (Mahaffey et al., 2008). Concentrations of organic carbon
and nitrogen were obtained from 5 point potassium hydrogen
phthalate and potassium nitrate standard calibration curve, respectively. All reported values were corrected for the instrument
blank, and the CV was <5.0%. Chlorophyll a (Chl) concentrations
were determined spectrophotometrically from samples (500 mL)
collected on Whatman GF/F filters. Pigments were extracted in
90% acetone overnight in the dark at 4◦ C, and concentrations
were calculated from SCOR UNESCO (1966) equations. Nutrients
and chlorophyll concentrations were analysed in the triplicate
samples.
Statistical analysis
Differences in physicochemical and biological variables between
lakes were tested by one-way analysis of variance. Potential
relationships among variables were tested by linear pairwise
Pearson correlations. Data were log-transformed to satisfy the
requirements of normality and homogeneity of variance necessary for parametric statistics. All statistical analyses were per-
formed with Minitab software for Windows (Release 15, Minitab,
State College, PA, USA).
RESULTS
In situ conditions
The mean values of ambient physical and chemical conditions of the 21 lakes during the summer of 2011 are shown in
Table 2. Overall, lake water temperature varied between 13.4 and
25.4◦ C and oxygenic conditions (dissolved oxygen concentration >9 mg l−1 ) prevailed in all the sampled lake euphotic zones.
The investigated lakes differed distinctly in terms of organic carbon and chlorophyll concentration. The TOC varied from 3.7 to
12.0 mg l−1 and the values were an order higher than TN (0.3–1.1
mg l−1 ). Chlorophyll concentration showed strong and large variability (coefficient of variation = 83%) between the lakes. Chlorophyll values ranged between 1.7 and 61.4 μg l−1 , with maximum values observed in the Lake Lapeyrouse for all the sampled
months.
Standing stocks
The mean microbial characteristics of the investigated lakes are
summarized in Table 3. Viral (VA) and prokaryotic abundance
(PA) varied by an order of magnitude between the lakes. This
was reflected in the virus to prokaryote ratio (VPR) which ranged
from 2 to 42 (average = 14.0 ± 6.5) (Fig. 1). The maximum VA (18.9
× 107 mL−1 ) and PA (20.2 × 106 cells l−1 ) that were observed in the
same lake (i.e. Lapeyrouse) also recorded the average maximum
chlorophyll concentration (40.4 ± 23.1 μg l−1 ) (Fig. 1, Table 2). This
VA and PA maxima was 3- and 5-fold higher than the average
values observed for viruses (5.5 × 107 mL−1 ) and prokaryotes (4.5
× 106 cells l−1 ), respectively for the surveyed lakes. The distribution of abundance of viruses followed that of prokaryotes, which
resulted in a strong correlation (r = 0.71, P < 0.001) between
5
Pradeep Ram et al.
Table 3. Means and in brackets standard deviation for microbial characteristics of the 21 lakes sampled between June and August 2011.
Lake
VA
(107 mL−1 )
Ambert
Anschald
Aubusson
Aydat
Banne d’Ordanche
Chabreloche
Cournon
Cunlhat
Fades-Besserves
Gour de Tanzenat
Lapeyrouse
Laqueille
Pavin
Ribeyre
Saint Antheme
Saint Eloy les Mines
Saint Remy
Servant
Super Besse
Tour d’Auvergne
Vernet la Varenne
8.1
10.3
3.4
5.6
2.9
1.5
5.1
10.7
6.1
1.2
11.9
1.9
2.6
10.0
1.1
2.2
3.8
7.3
2.8
6.4
10.4
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
2.9
2.9
2.8
1.2
0.5
1.0
2.0
2.1
2.6
0.3
6.1
0.8
0.3
3.6
0.2
0.7
1.5
3.3
0.8
0.9
3.7
PA
(106 cells mL−1 )
3.4
8.5
3.4
2.3
2.9
2.2
5.7
6.4
4.7
1.8
12.3
2.3
1.8
3.9
3.1
2.5
1.9
5.8
2.9
5.2
10.5
± 1.0
± 5.6
± 0.6
± 0.4
± 0.4
± 0.4
± 1.4
± 3.1
± 1.2
± 0.3
± 6.9
± 0.7
± 1.0
± 0.7
± 1.5
± 1.3
± 0.9
± 2.5
± 0.3
± 3.3
± 8.5
VPR
24.2
14.2
11.2
25.3
10.2
6.5
8.7
19.7
12.7
6.6
10.0
8.0
17.5
25.3
4.1
10.8
24.3
16.4
9.5
16.4
12.3
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
5.8
5.4
9.6
9.5
0.4
2.9
1.8
9.2
2.5
2.7
1.2
1.5
9.7
7.4
2.3
5.8
16.4
14.1
2.5
10.9
4.4
PP
(μg C l−1 d−1 )
16.8
27.0
12.2
12.0
11.0
7.2
29.0
19.1
17.8
13.7
41.3
12.9
4.2
18.3
8.2
19.3
12.4
33.1
10.8
17.6
29.0
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
2.1
11.3
5.1
4.5
6.3
2.0
4.6
7.2
6.3
8.9
15.1
3.0
2.6
8.4
1.6
9.9
4.6
4.5
4.2
16.0
9.6
PR
(μg C l−1 d−1 )
51.4
78.1
12.0
42.8
28.0
43.7
81.0
31.2
58.8
30.4
46.4
72.2
30.6
73.3
35.8
39.0
54.8
43.4
41.5
52.5
78.0
± 31.5
± 15.4
± 7.6
± 24.1
± 14.1
± 40.1
± 30.1
± 17.4
± 47.1
± 30.2
± 27.9
± 35.8
± 16.1
± 49.3
± 20.9
± 19.1
± 25.6
± 37.6
± 30.6
± 26.6
± 55.7
PGE
(%)
30.0
25.3
51.3
24.0
28.7
21.6
28.2
39.2
28.9
39.4
49.0
16.6
11.7
25.8
24.0
33.9
18.8
50.1
24.6
23.3
36.4
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
16.4
9.1
20.8
9.5
6.4
14.8
11.8
5.4
20.1
24.1
21.0
5.2
1.1
16.2
18.6
18.1
2.5
20.3
9.2
10.3
31.2
HNF
(103 cells mL−1 )
1.8
2.1
1.3
2.0
1.9
2.0
2.0
1.8
2.1
2.2
2.4
1.4
2.0
1.4
1.4
3.2
2.3
2.3
1.7
2.0
1.5
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
0.7
0.5
0.3
0.6
0.3
0.4
0.7
0.3
0.9
0.5
0.6
0.6
0.4
0.4
0.2
1.3
0.6
1.1
0.6
0.5
0.4
FIC
(%)
20.0
14.9
7.7
18.5
9.2
10.2
20.6
13.9
22.8
12.6
11.6
21.7
13.1
18.3
15.5
11.2
15.6
10.9
13.7
16.2
17.6
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
±
8.6
6.0
0.5
3.7
4.1
4.5
5.3
2.1
3.1
7.7
1.6
14.4
4.2
11.6
8.0
3.8
14.5
2.1
6.0
12.0
7.7
VA: viral abundance, PA: prokaryotic abundance, VPR: virus to prokaryote ratio, PP: prokaryotic production, PR: prokaryotic respiration, PGE: prokaryotic growth efficiency, HNF: heterotrophic nanoflagellate abundance, FIC: frequency of infected cells.
the two variables. Both VA and PA were significantly correlated
with water temperature (P < 0.05) and chlorophyll concentration
(P < 0.001). Among the standing stocks, the HNF abundance varied less between the lakes (Table 3). Their overall abundance
ranged from 0.9 to 4.6 × 103 cells l−1 (average = 2.0 ± 0.5 × 103
cells l−1 ) during the study period and were significantly correlated to chlorophyll (P < 0.001) and VA (P < 0.001).
Prokaryotic community metabolism
PR and PP were assessed to determine variations in PGE which
we used as an index of prokaryotic metabolism at the community level. PP ranged from 2.3 to 52.1 μg C l−1 d−1 (Fig. 2) and was
significantly correlated to microbial abundances (Table 4). On an
average irrespective of the study lakes, PR was 3-fold higher than
PP and ranged from 12 to 112 μg C l−1 d−1 (Fig. 2) with an overall mean value of 49.0 μg C l−1 d−1 . PR was positively correlated
to the measured environmental parameters such as water temperature and TOC and to abundances of prokaryotes and flagellates (Table 4). There was a significant positive (P < 0.05) relationship between PR and PP which followed a concave quadratic
regression with a PR maximum for a PP corresponding to
30.4 μg C l−1 d−1 (Fig. 3). Over the range of the reported data,
two types of relationship can be characterized between PR and
PP based on PP. If PP < 30 μg C l−1 d−1 , there were a positive relationship but the observed ‘slope’ became less and less positive
as PP increased. For higher values (i.e. PP > 30 μg C l−1 d−1 , the
relationship (slope) was negative. Because of the variability in
this relationship there was up to an order of magnitude in PP
for a given level of PR. The low strength of the relationship (r2
= 0.11) suggests that PR did not change proportionately to PP in
these systems, and indeed, the range of variation in PR (9-fold)
was narrower than in PP (23-fold). The variability in the relationship between PR and PP is expressed as a wide range of PGE in
these systems, from 5 to 74% (Fig. 2). The large degree of variability in the relationship between PR and PP was not random
but stemmed from the different responses to the environmental parameters.
Lytic-stage infections
Among the viral life styles, lytic viral infection, as determined
from visibly infected prokaryotic cells by transmission electron
microscopy was detected in all the lakes and sampled occasions.
These results were compared across the lakes and lytic infection varied by a factor of 10. Overall, FIC varied from 6.6 to 35.8%
with an average value of 15.1 ± 7.4%. Among the study lakes,
FIC maxima did not coincide with abundances of prokaryotes
or viruses and no significant correlation was observed between
them. High percentage of FIC coincided in the same lake which
also exhibited high PR, and both variables were significantly correlated (P < 0.001).
Prokaryotic mortality and the effects of top-down
factors on prokaryotic metabolism
Viral-mediated prokaryotic mortality (VIBM) ranged between 7.4
and 75.0% (mean = 22.1 ± 15.3%) in the investigated lakes. In
comparison to the above, potential flagellate grazing rates (FLG)
ranged from 0.7 to 19.3 × 108 cells l−1 d−1 (mean = 3.9 ± 3.4 ×
108 cells l−1 d−1 ) which corresponded to 8.5–88% (mean = 45.8 ±
20.5%) of total prokaryotic mortality (Fig. 4). Viruses and flagellates were responsible for bulk of prokaryotic mortality in majority of the lakes which ranged from 43 to 92% (mean = 70%).
Viruses and flagellates as factor of prokaryotic mortality varied
between lakes (Table 3) and the average ratio of viral lysis to potential flagellate grazing (VIBM: FLG) ranged from 0.2 to 4.1. In
lake samples dominated by viral mortality (VIBM: FLG > 1), PGE
6
FEMS Microbiology Ecology, 2015, Vol. 91, No. 1
109
June
Viruses
Prokaryotes
Virus:Prokaryote ratio
50
45
40
35
108
30
25
20
107
15
10
5
106
109
0
July
50
40
35
108
30
25
20
107
15
10
5
106
109
0
August
108
107
106
Figure 1. The average of VA, PA and VPR in the 21 lakes. Error bars represent SE (n = 3).
50
45
40
35
30
25
20
15
10
5
0
Virus : prokaryote ratio
Viruses, prokaryotes
45
Pradeep Ram et al.
June
120
PR
PP
PGE
80
70
100
60
80
50
60
40
30
40
20
20
10
0
0
120
July
70
100
60
80
50
60
40
30
40
20
20
10
0
120
0
August
100
80
70
60
80
60
40
50
40
30
20
20
0
Figure 2. The average values of PR, PP and PGE in the 21 lakes. Error bars represent SE (n = 3).
10
0
PGE (%)
PP, PR (µg C l-1 d-1)
80
7
8
FEMS Microbiology Ecology, 2015, Vol. 91, No. 1
Table 4. Pearson’s correlations (r) between variables in the pelagic zones of the lakes (n = 63).
Chl
PA
VA
HNF
FIC
FLG
PR
PP
PGE
Temp
TOC
Chl
PA
VA
NS
0.26∗
0.27∗
NS
NS
NS
0.43∗∗∗
NS
NS
0.37∗∗
NS
NS
NS
NS
NS
0.34∗∗
0.26∗
0.48∗∗∗
0.46∗∗∗
0.41∗∗∗
NS
NS
0.56∗∗∗
NS
0.33∗∗
0.32∗∗
0.71∗∗∗
NS
NS
0.85∗∗∗
0.33∗∗
0.71∗∗
NS
NS
NS
0.63∗∗∗
NS
0.54∗∗∗
NS
HNF
− 0.37∗∗
0.29∗
− 0.27∗
0.25∗
0.27∗
FIC
NS
0.61∗∗∗
NS
−0.64∗∗∗
FLG
NS
0.80∗∗∗
0.59∗∗∗
Prokaryotic respiration (µg C l-1 d-1)
Levels of significance: ∗ P < 0.05, ∗∗ P < 0.01, ∗∗∗ P < 0.001, NS: not significant.
Temp: temperature, TOC: total organic carbon, Chl: chlorophyll, PA: prokaryotic abundance, VA: viral abundance, HNF: heterotrophic nanoflagellate abundance, FIC:
frequency of infected cells, FLG: flagellate grazing potential, PR, prokaryotic respiration, PP: prokaryotic production, PGE: prokaryotic growth efficiency.
120.0
100.0
80.0
60.0
40.0
20.0
0.0
0.0
10.0
20.0
30.0
40.0
Prokaryotic production (µg C l-1 d-1)
50.0
60.0
Figure 3. Relationship between PR and PP (PR = −0.05PP2 + 3.04PP + 18.27, r2 =
0.11, P < 0.05) in the lakes.
ranged from 11 to 31% (mean = 17 ± 8%) and these values were
significantly lower (P < 0.001) when compared to the values observed in grazer control-dominated (VIBM: FLG > 1) lake samples which ranged from 11 to 74% (mean = 35 ± 15%). The ratio of VIBM: FLG had contrasting impact on PGE. Overall across
the lakes, FLG was found to enhance PGE (log PGE = −0.065 ×
log(FLG)2 + 1.58 × log(FLG) − 7.35, r = 0.59, P < 0.001), whereas
viral infection rates had adverse impact on PGE (log PGE = −1.60
× log(FIC)2 + 3.08 × log(FIC) + 0.07, r = −0.64, P < 0.001). The negative relationship between PGE and FIC was stronger and more
significant when the FIC was greater than 10%. Differential relationship of FLG and viral infection with PGE could be best described by a concave quadratic regression model (Fig. 5).
DISCUSSION
The present investigation is one of the few which discusses the
issue of top-down regulation (viral lysis and HNF grazing) of
prokaryotic community metabolism (referred to as PGE) in the
pelagic realm of temperate freshwater lakes. Our findings indicate that both factors through their mortality processes had a
substantial impact on the overall growth efficiency of prokaryotic community in different fashions, which consequently can
have significant implication in the flow of nutrients in freshwater food webs.
Methodological and general considerations
Simultaneous measurements of PR and PP are crucial for realistic estimation of PGE in aquatic systems. Therefore, for this
present study we adopted size fractionation combined with
short-term incubation (< 24 hr) approach of water samples
(Kritzberg et al., 2005; Pradeep Ram et al., 2010, 2013) to obtain
a more ecological relevant data especially in freshwater systems, as there can be a rapid turnover of organic matter and
also to avoid the possibility of overutilization of recalcitrant matter. Size fractionation approach by filtration procedure obviously
alters prey–predator interactions and can lead to release of inorganic and organic substrates if cells are broken during filtration (Nagata and Kirchman 1990; Pomeroy, Sheldon and Sheldon 1994). Therefore, this technique is not without its share of
potential problems associated with enclosure effects. Although
filtration method overcomes the limitations such as specific substrate biases, i.e. nutrient depletion and the growth of populations that are not representative of the in situ taxonomic groups,
some inherent limitations do exists. Confining microbial population in containers for relatively long incubation periods (> 24
hr) may induce changes in prokaryotic community composition
(Gattuso et al., 2002) and/or exhaust trophic resources (Massana
et al., 2001). Therefore, in our study short-term incubation procedure was found sufficient to detect linear decrease in dissolved
oxygen concentration with concomitant increase in prokaryotic
abundance for the determination of PR and PP, respectively. This
procedure essentially minimized the bias involved in PGE estimation. Given the nature and type of filter used (polycarbonate instead of coarse glass fibre which can rupture the fragile
cells), size fractionation artefacts in our study were considered
not substantial.
It should be noted that there are several assumptions underlying the calculation of viral-mediated prokaryotic mortality
from FVIC estimates using published models. As the parameters
involved in the models can vary depending on environmental
conditions, the applicability of these parameters in each of our
studied lakes has yet to be validated; therefore, the extent and
cause of this variability remains unclear. For the determination
of FLG in our study, prokaryote mortality due to flagellates was
calculated using an average flagellate clearance rate obtained
from previous published reports from the lakes of French Massif
Central. As we can expect a certain degree of variability in clearance rate between the lakes, the use of an average value can perhaps have its influence on the flagellate grazing rates. However,
the clearance rate chosen in our study was more realistic and
close to the range of published reports from temperate freshwater lakes (Sanders et al., 1989; Lymer, Lindstrom and Vrede 2008).
Spatial variability in PGE
In recent years, there has been increased interest regarding the
magnitude and regulation of PGE in aquatic systems. In our
Pradeep Ram et al.
100
June
80
60
40
20
Viral lysis, Flagellate grazing (% of PP)
0
100
July
80
60
40
20
0
100
August
80
60
40
20
0
Figure 4. Viral and potential grazer-induced mortality of prokaryotes in the 21 lakes.
Viral lysis
Flagellate grazing
9
log prokaryotic growth effiiency
10
FEMS Microbiology Ecology, 2015, Vol. 91, No. 1
trients and organic carbon) to be the potential factors in controlling PGE (Apple, del Giorgio and Kemp 2006; del Giorgio and
Newell 2012). In addition to the above factors, top-down factors
such as viral infection and protistan bacteriovory can influence
PGE at the community level by their selective lysis or grazing of
prokaryotic cells and also by the release of substrates through
their mortality processes which can at times substantially contribute to the overall nutrient pool (Thomas et al., 2011).
2.0
1.8
1.6
1.4
1.2
1.0
Top-down control of PGE
0.8
7.8
8.0
8.2
8.4
8.6
8.8
9.0
log potential flagellate grazing
9.2
9.4
log prokaryotic growth efficiency
2.0
1.8
1.6
1.4
1.2
1.0
0.8
0.9
1.0
1.1
1.2
1.3
1.4
1.5
log frequency of viral infected prokaryotic cells
1.6
Figure 5. Relationship of PGE with FLG (log PGE = −0.065 × log(FLG)2 + 1.58 ×
log(FLG) − 7.35, r = 0.59, P < 0.001) and viral-infected prokaryotic cells (log PGE
= −1.60 × log(FIC)2 + 3.08 × log(FIC) + 0.07, r = −0.64, P < 0.001) in the lakes.
study, the substantial variability in PGE (range from 5 to 74%)
between the lakes is considered to be common feature of natural prokaryotic assemblages as the reported values were within
the expected range based on the average level of productivity of
freshwater systems (del Giorgio and Cole 1998; Smith and Prairie
2004; Kritzberg et al., 2005; Pradeep Ram et al., 2013). Among the
prokaryotic metabolic parameters, PR explained only 11% of the
variability in PP. Quadratic regression analysis suggested that
two types of relationship could be characterized between PP and
PR based on PP values. With a PP threshold level of 30 μg C l−1 d−1 ,
the nature of slope between PP and PR was found to vary between the investigated lakes which can result in contrasting PGE
values. Although PP and PR are essentially linked to heterotrophy, they are not always coupled which can lead to large variation in PGE values (del Giorgio, Pace and Fischer 2006; Pradeep
Ram et al., 2010). In our study, PP and PR varied in response to
changes in environmental parameters. Uncoupling between respiration and production measurements is considered to be advantageous to natural bacterioplankton as it provides metabolic
flexibility necessary to cope with ever-changing nutrient conditions that are more often encountered in lake ecosystems (del
Giorgio and Cole 1998). In our study, PP explained most of the
variation in PGE which agrees with the previous findings from
freshwater environments (Kritzberg et al., 2005; del Giorgio, Pace
and Fischer 2006; Pradeep Ram et al., 2013). This above aspect
is useful as one can derive appropriate values of PGE from PP
measurements alone (r2 = 0.45) and in fact driven by an inherent autocorrelation in that PP is part of the PGE calculation (data
not shown).
Studies conducted elsewhere have often showed that temperature and the nature of substrate availability (inorganic nu-
In this study, both viruses and protists were simultaneously
taken into account to investigate top-down control of prokaryotic community metabolism. Across the lakes, both viruses and
flagellates exerted their influence on PGE. Viruses through their
lytic infection showed antagonistic influence on PGE. High viral
lysis was accompanied by low PGE values in the studied lakes.
Quadratic regression analysis indicated that the negative impact of viruses on PGE was observed when the percentage of
viral-infected prokaryotic cells (FIC) was greater than 10%. In our
study, 12% of the data which fell in the category FIC < 10% were
not found to have a substantial impact on PGE. From our findings, it could be inferred that a minimum threshold viral infection level of 10% is required for viruses to exert its impact on
community PGE. Therefore, the viral impact on PGE may not be
the same and could differ among the investigated lakes. Overall
decline in community PGE in lakes dominated by viral lysis over
flagellate grazing was due to preferential lysis of active members
of prokaryotic community which tend to exhibit high growth
rates and activity (Pradeep Ram et al., 2014). Our conclusion finds
support from other previous studies from marine systems where
viruses have shown to depress PGE at community level (BonillaFindji et al., 2008; Motegi et al., 2009). Viral lytic infection transforms microbial biomass into dissolved and particulate organic
matter. While high labile cellular components are rapidly assimilated by non-infected prokaryote cells, the less labile particulate forms such as cell debris (colloids and cell fragments) are
incorporated in to prokaryote biomass by the action of its exoenzymes which are associated with high respiratory losses, resulting in less flow of carbon to higher trophic level (Middelboe,
Jorgensen and Kroer 1996; Fuhrman 1999). Additionally, higher
respiration of prokaryotic community in the presence of viruses
in our study is also suggestive of the role of prokaryotes as oxidizers of organic matter, hence as CO2 producers, and remineralizers of N, P or Fe (Middelboe and Lyck 2002; Bonilla-Findji et al.,
2008).
In contrast to viral lysis, FLG was found to enhance PGE. High
grazing activity that was accompanied by low levels of viral infections coincided with high PP and PGE values. In contrast to
viral lysis, flagellate grazing results in more effective release
of inorganic nutrients (labile nitrogen and phosphorus), which
can be incorporated into prokaryote biomass at higher efficiency
(Zweifel et al., 1993). Our previous freshwater microcosm studies conducted during the nutrient-limited period have already
shown flagellate grazing activity to enhance PGE through nutrient regeneration processes (Pradeep Ram and Sime-Ngando
2008). Our investigation shows that top-down factors can regulate PGE of an ecosystem, as the contrasted nature of substrates (i.e. labile versus complex) released during the mortality processes can have strong impact on overall prokaryotic
metabolism. Therefore, top-down control can have indirect influence on prokaryote metabolism and be closely linked to substrate supply.
Pradeep Ram et al.
CONCLUSIONS
In spite of limitations and pitfalls associated with the methodological aspects, we were able to show contrasting patterns
of PGE in relation to top-down factors. Our findings suggest that top-down factors should be considered as important
constituents when seeking to explain factors controlling PGE in
aquatic systems. The relative roles of top-down and bottomup control may vary with ecosystem, although published data
are equivocal on this issue. In our study, both viral and grazing
activity had indirect influence on the prokaryotic metabolism
possibly through the substrate supply (i.e. nature of nutrients)
that could be utilized by non-infected prokaryotes, to promote
growth and production of certain prokaryotic groups. Insufficient information exists on the regulation of top-down control on prokaryotic metabolism and their subsequent impact on
community composition in aquatic systems. The above aspect
need to be sufficiently addressed in future studies. The outcome of our field investigation has to be considered with caution, as experimental verification is required to suffice the above
findings. Microcosm experiments using water samples from different lakes that are representative of different trophic status
are being carried out using reconstitution approaches to separately determine the impact of top-down factors on their growth
efficiency with concomitant change in prokaryotic community
composition.
ACKNOWLEDGEMENTS
We thank the personnel of ATHOS Environnement (Clermont
Ferrand, France) for technical support and participation in the
project. We appreciate two anonymous reviewers for their time,
effort and valuable contributions to this manuscript.
Conflict of interest statement. None declared.
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