Environmental Microbiology (2010) 12(3), 628–641
doi:10.1111/j.1462-2920.2009.02103.x
Seasonal variations of phage life strategies and
bacterial physiological states in three northern
temperate lakes
emi_2103
628..641
C. F. Maurice,1 T. Bouvier,1* J. Comte,2
F. Guillemette2 and P. A. del Giorgio2
1
Université de Montpellier 2, CNRS-UMR5119.
Laboratoire Ecosystèmes Lagunaires, case 093. Place
Eugène Bataillon, 34095 Montpellier cedex 5, France.
2
Groupe Interuniversitaire de Recherche en Limnologie
(GRIL), Dépt des sciences biologiques, Université du
Québec à Montréal, CP 888, Succ. Centre-Ville,
Montréal, Québec Canada H3C 3P8.
Summary
The current consensus concerning the prevalence
of lytic and lysogenic phage life cycles in aquatic
systems is that the host physiological state may influence viral strategies, lysogeny being favoured when
hosts have reduced metabolic rates. We explored this
hypothesis, by following phage cycle dynamics, host
physiological state and metabolic activity over an
annual cycle in three lakes subjected to strong seasonal fluctuations, including 4–5 months of ice cover.
We observed marked seasonal dynamics of viral and
bacterial communities, with low bulk and cell-specific
bacterial metabolism in winter, and a dramatic
increase in injured bacteria under the ice cover in all
lakes. This period was accompanied by contrasting
patterns in the proportion of lysogenic cells. In the
eutrophic lake, times of low bacterial metabolic rates
and high proportion of damaged cells corresponded
to highest levels of lysogeny, supporting the notion
that hosts are a ‘refuge’ for viruses. In the two unproductive lakes, peaks of injured cells corresponded to
a minimum of lysogeny, suggesting an ‘abandon the
sinking ship’ response, where the prophage replicates before the loss of genome. We suggest that
these diverging responses to the host physiological
state are not contradictory, but rather that there may
be thresholds of cell stress and metabolic activity
leading to one or the other response.
Received 10 March, 2009; accepted 5 October, 2009. *For correspondence. E-mail [email protected]; Tel. (+33) 4 67 14 41 88;
Fax (+33) 4 67 14 37 19.
© 2009 Society for Applied Microbiology and Blackwell Publishing Ltd
Introduction
The lytic and lysogenic cycles are the two viral life strategies considered to be of major ecological importance.
These two viral strategies influence viral abundance
dynamics, as well as their interactions with their hosts,
their role in the regulation of microbial processes, biogeochemical cycles, gene transfer and prokaryotic diversity (Wilcox and Fuhrman, 1994; Wommack and Colwell,
2000; Weinbauer, 2004). The factors leading to the different phage life cycles in aquatic systems are poorly understood, and yet this information is key to our understanding
of the regulation of natural bacterioplankton communities
(Stewart and Levin, 1984; Weinbauer, 2004).
In the lytic cycle, a virulent phage infects a bacterium,
multiplies inside the host, and new viral particles are
released upon the cell’s lysis. In the case of lysogeny, a
temperate phage infects a bacterium and remains as a
prophage inside the host until an inducing event triggers
the lytic cycle and the production of new viral particles
(Wommack and Colwell, 2000; Weinbauer, 2004). Experimental studies using cultured phage-host systems, and
mathematical simulations converge to suggest that the
phage life cycle ‘decision’ depends on at least two interrelated factors: the relative abundance of the host, and its
growth and physiological state. For example, the lytic
cycle of the l phage prevails in fast-growing Escherichia
coli cells, whereas the lysogenic cycle is favoured in starving cells (Echols, 1972); lysogeny in Synechococcus is
more important in times of low host and phage abundance
(McDaniel and Paul, 2005); and in Listonella pelagia,
lysogeny prevails during periods of low salinity and
anoxia, two factors that negatively affect host physiology
(Williamson and Paul, 2006). It has thus been proposed
that the lytic cycle is favoured when the abundance and
growth of the host are sufficiently important to ensure
significant viral production, and that lysogeny, on the contrary, represents a ‘refuge’ strategy for viral populations
when bacterial cells are scarce, with low metabolic and
growth rates (Lenski, 1988; Wilcox and Fuhrman, 1994;
Weinbauer, 2004).
This question has been much more difficult to explore in
complex microbial assemblages found in aquatic ecosystems, and the regulation of these two life cycles across
Phage life strategies and bacterial physiological states 629
natural systems, or their temporal dynamics, is not well
understood. Weinbauer and colleagues (2003) compiled
published values of the frequency of lytic-stage infected
cells (FIC), and the frequency of lysogenic cells (FLC),
from point studies of natural marine systems, and showed
that high FLC values correlate with low FIC values; these
authors hypothesized that there may be a switch from one
cycle to another along a trophic gradient of marine
systems. This shift has also been suggested in seasonal
studies where the observed dynamics of FLC appeared to
be linked to changes in water temperature, primary and
bacterial production (BP) (Cochran and Paul, 1998; Williamson et al., 2002). Changes in nutrient availability and
in the trophic status of the system were thought to trigger
this shift, probably by affecting the host physiology
(Cochran and Paul, 1998; Williamson et al., 2002). Therefore, similarly to results obtained from phage-host
systems, host physiological state in natural communities
is thought to influence viral life cycle decisions, but to
date, no direct evidence of this link has been obtained in
natural systems, and all proposed links are based on
correlations between the number of lysogenic bacteria
and bulk measurements of bacterial metabolism, such as
production or growth (Williamson et al., 2002; Weinbauer,
2004). However, bulk measurements reflect changes in
the whole bacterial community metabolism, and can mask
internal variations of physiological states of individual
cells within the assemblage (Smith and del Giorgio,
2003). Few studies to date have attempted to link the host
physiological state at the single-cell level to changes in
viral cycles, either across systems or within a given
system in time. In particular, there have been no seasonal
studies that have explored simultaneously the variations
in both viral strategies (lysogeny versus lytic cycles), and
the bacterial host physiological state within the same
ecosystem.
Systems that undergo strong seasonal successions
that profoundly influence bacterial activity and physiological state are interesting to assess the potential ‘hostinduced lysogenic switch’. For example, in southern
Québec, there are tens of thousands of lakes spanning a
wide range in trophic status and productivity, but in addition, these lakes undergo strong seasonal changes,
including ice cover for 4–5 months. During these periods,
temperatures remain uniformly low (< 4°C), and there is
little or no primary production, little external organic matter
input and greatly reduced microbial activity. During the
ice-free season, however, these same lakes express a
wide range in trophic status and productivity. Thus, the
combination of extreme seasonality and enrichment gradients within these lakes results in a wide range of forcing
factors and environmental stresses, both shaping microbial metabolism and physiology. These lakes in southern
Québec therefore provide an interesting study site to
explore how variations of the bacterial physiological state,
in response to varying environmental fluctuations, can
influence viral life cycles.
The main goal of this study was to examine in detail the
seasonal dynamics of viral life cycles, and of their host
metabolism and physiological states over an annual cycle
in three lakes in southern Québec, which are located in
the same general catchment area, are covered by ice and
snow from mid-December to April, but differ greatly in their
trophic status. Our first objective was to determine if there
is a clear seasonal pattern in the proportion of lysogenic
cells and of lytic-stage infected cells, and whether these
seasonal dynamics are similar along the trophic gradient.
The second objective was to explore if the seasonal
dynamics in viral life cycles correlate with both bulk and
single-cell measures of bacterial metabolism and physiological state of the host.
Results
Physico-chemical conditions
The ambient physical and chemical conditions in the three
lakes are summarized in Table 1. The ice cover was
present during three sampling dates, from February to the
end of March, and the thickness of the ice varied from 30
to 60 cm. Water temperature ranged from 0°C to 3°C in
winter and from 22°C to 24.5°C in summer. Water temperature and oxygen concentration were not significantly
different between lakes (P > 0.05). Bowker had the lowest
dissolved organic carbon (DOC) concentrations, ranging
from 1.9 to 2.9 mg l-1, whereas both Fraser and Bran de
Table 1. Physicochemical conditions in lakes Bowker, Fraser and Bran de Scie during the study.
Lake
Temperature (°C)
Ice (cm)
Chl a (mg l-1)
DOC (mg l-1)
TP (mg l-1)
TN (mg l-1)
Oxygen (mg l-1)
Bowker
9.9
(2.0–23.3)
40.0
(30.0–45.0)
1
(0–1.8)
2.3
(2.0–2.9)
5
(3.3–8.0)
0.2
(0.1–0.3)
11.4
(8.4–15.0)
Fraser
10.0
(1.5–24.5)
54.3
(45.0–60.0)
1.8
(0–4.2)
5.7
(4.7–6.8)
11.5
(9.0–16.1)
0.4
(0.3–0.5)
11.3
(8.3–15.2)
Bran de Scie
8.9
(0.1–24.1)
50.7
(42.0–60.0)
1.6
(0–6.0)
5.7
(4.8–6.7)
16.2
(7.5–61.7)
0.4
(0.2–0.5)
11.0
(7.9–14.8)
Average values and range.
© 2009 Society for Applied Microbiology and Blackwell Publishing Ltd, Environmental Microbiology, 12, 628–641
630 C. F. Maurice et al.
Table 2. Bacterial and viral abundances, bacterial metabolic activity and relative proportions of HNA and LNA cells and damaged cells (PI+) for
the three lakes.
Bacterial abundance
(106 cells ml-1)
Viral abundance
(107 virus ml-1)
BGE (%)
% HNA cells
% LNA cells
% PI+ cells
Bowker
1.8 ⫾ 1.0
(0.5–3.5)
4.8 ⫾ 1.5
(2.9–7.1)
12.1
(0.2–28.6)
48.2 ⫾ 17.2
(25.5–85.7)
51.7 ⫾ 17.2
(14.2–74.5)
31.4 ⫾ 10.9
(14.4–49.8)
Fraser
3.4 ⫾ 2.8
(0.5–8.4)
7.1 ⫾ 1.6
(4.0–9.3)
18.0
(0.7–44.0)
62.5 ⫾ 15.3
(35.3–83)
37.5 ⫾ 15.3
(17–64.7)
32.1 ⫾ 11.5
(14.9–54.4)
Bran de Scie
4.1 ⫾ 3.7
(0.5–13.3)
7.8 ⫾ 2.4
(3.6–12)
24.3
(0.4–57.7)
68.1 ⫾ 9.9
(54.4–85.5)
31.9 ⫾ 9.9
(14.5–45.6)
34.1 ⫾ 11.2
(15.4–50.8)
Lake
Average values ⫾ SD and range.
Scie had on average threefold higher DOC (5–6.7 mg l-1
respectively). There was a trend of declining DOC concentration from winter to spring and summer, and then
increasing once again towards fall in all three lakes. Both
total phosphorus (TP) and nitrogen (TN) were also lowest
in Bowker (3.3–8 mg l-1 and 0.1–0.3 mg l-1 respectively),
intermediate in Fraser (9–16.1 mg l-1 and 0.3–0.5 mg l-1)
and highest in Bran de Scie (7.5–61.7 mg l-1 and
0.2–0.5 mg l-1). Concentrations of chlorophyll a (Chl a)
showed strong seasonal variations in the three lakes,
ranging from non-detectable under the ice cover to over
6 mg Chl a l-1 in Bran de Scie, the most productive lake.
Overall, these results indicate that the trophic gradient
found in these lakes remains throughout the experiment,
with roughly similar seasonal patterns of variation of environmental factors.
Bacterial and viral abundance
The range and average values of bacterial and viral abundance are summarized in Table 2. The most productive
lake, Bran de Scie, had the highest average bacterial
abundance. There were clear seasonal patterns in bacterial abundance in all three lakes, with winter abundances
significantly lower than in summer (Fig. 1). Average viral
abundance was significantly lower in Bowker (P < 0.05),
and bacterial and viral abundances tended to covary in
the three lakes (Fig. 1). The virus-to-bacteria ratio (VBR)
varied from 8 to 121 in Bowker (average 46.3), from 6.2 to
177.5 in Fraser (average 47.9) and from 9 to 189 in Bran
de Scie (average 46.6). This ratio also showed clear seasonality, with a peak occurring in all lakes during the ice
cover period (Fig. 2), coinciding with the lowest bacterial
abundances (< 20% of summer values); in contrast, viral
abundance remained at roughly similar levels as those
found throughout the year (overall winter average abundance > 90% of overall summer average abundance).
Upon the loss of the ice cover, the VBR declined steeply
from an overall average of 94.3 to 14.7, and then
increased again in fall (Fig. 2).
Bacterial single-cell characteristics
Flow cytometry enabled the discrimination of bacterial
cells with high (HNA) and low (LNA) nucleic acids contents, and cells with damaged membranes (PI+), and the
ranges in the proportions of these different fractions are
given in Table 2. The proportion of PI+ cells averaged
32.5% overall, and this average was remarkably similar
between lakes. There was a clear seasonal trend, with a
dramatic increase and a peak reaching nearly 55% of total
bacterial cells during the ice-covered period, as well as a
sharp decline in the proportion of injured cells in spring
and early summer (Fig. 3A). This pattern was consistent
in all three lakes. Later in the summer, Bowker and Fraser
showed very similar oscillating patterns of variation in
%PI+ cells, whereas Bran de Scie showed a clear departure from this pattern. Comparison of data from the three
lakes also suggests a gradient in the proportions of HNA
and LNA cells, with oligotrophic lake Bowker having
significantly lower proportions of HNA cells than the
eutrophic lake Bran de Scie during the ice-covered period,
as well as important seasonal dynamics (data not shown).
It is thus clear from these single-cell dynamics that
bacterial communities undergo major changes in their
physiological state during winter, particularly under the
ice cover.
Bacterial community metabolism
Bacterial respiration (BR) and BP were assessed to determine variations in bacterial growth efficiency (BGE), which
we used as an index of bacterial physiological and energetic status at the community level. BGE varied widely,
ranging from 0.2% to 57.7%, and was on average lower in
lake Bowker (average 12.1%), intermediate in lake Fraser
(average 18.0%) and highest in lake Bran de Scie
(average 24.3%). There was a strong seasonality of BR
and BP in the three lakes, which led to a seasonality of
BGE. In Fraser and Brande Scie, the highest values were
found in June, whereas in Bowker, it occurred in April
(Fig. 3B).
© 2009 Society for Applied Microbiology and Blackwell Publishing Ltd, Environmental Microbiology, 12, 628–641
Phage life strategies and bacterial physiological states 631
Bran de Scie: 1.6%), with similar seasonal patterns in all
three lakes (Fig. 4). The absolute number of visibly
infected cells also remained low with limited (Cv < 2) seasonal variations (data not shown). Burst size (BS) was
generally low (overall average 17.6), and no significant
difference was observed between the three lakes,
although there was a consistent trend of lake Bowker
having lower values (mean = 11.7), relative to Fraser
(mean = 19.7) and Bran de Scie (mean = 21.4). The BS
was lowest in winter and early spring, and increased
towards summer and fall (Fig. 5), leading to an overall
positive correlation between BS and temperature (not
shown).
Lysogeny
To quantify the fraction of lysogenic cells, we monitored
bacterial and viral abundances in our incubations every
12 h, and were thus able to determine the time-course of
induction. Induction, in terms of a significant decline in
bacterial abundance and concomitant viral production,
was detected in 23 experiments out of 30 samples.
Maximum induction generally took place at 36 h, although
several summer samples had peak induction at 24 h. The
FLC varied between 5.7% and 72.7%, with an overall
average of 28.9% (average for Bowker: 28.9%, Fraser:
24% and Bran de Scie: 33.4%). Two distinct seasonal
patterns of FLC occurred in these lakes during the survey
(Fig. 4). In eutrophic lake Bran de Scie, FLC values were
significantly higher in winter (P < 0.05), quickly declining
with the loss of the ice-cover, to reach minimum values in
Fig. 1. Seasonal variation of bacterial (¥106 ml-1) and viral
(¥107 ml-1) abundances in the three lakes. Bars represent standard
deviations for triplicates and duplicates respectively.
Lytic-stage infections and burst size
The FIC was comparable across lakes (P > 0.05), and
generally low (average Bowker: 1.9%, Fraser: 1.6% and
Fig. 2. Changes in VBR during the studied period in the three
lakes.
© 2009 Society for Applied Microbiology and Blackwell Publishing Ltd, Environmental Microbiology, 12, 628–641
632 C. F. Maurice et al.
Fig. 3. Variations of the host physiological state (damaged PI+
cells), and BGE in the three lakes during the studied period. Bars
represent the standard deviation for triplicates. (A) Proportion of
damaged PI+ cells; (B) BGE.
spring (Fig. 4). Lakes Bowker and Fraser, on the other
hand, both showed a very different seasonal pattern, with
lowest values of FLC in winter under the ice, increasing
dramatically in spring (Fig. 4). As we describe below, Bran
de Scie had other distinct patterns relative to the other two
lakes, suggesting that seasonal viral–bacterial dynamics
may be fundamentally different in lakes of different trophic
status.
Fig. 4. Variations of the FIC and FLC in the three lakes during the
study period.
Relations between viral life strategies and
environmental factors
There were no clear trends between the FIC or FLC and
individual environmental factors, bacterial and viral
© 2009 Society for Applied Microbiology and Blackwell Publishing Ltd, Environmental Microbiology, 12, 628–641
Phage life strategies and bacterial physiological states 633
abundances or bacterial physiological states when we
considered the pooled data from the three lakes.
However, a significant negative correlation was obtained
between FIC [and frequency of visibly lytic-stage
infected cells (FVIC), data not shown] and BGE (Fig. 6),
suggesting a link between the physiological state of
the community as a whole and the incidence of lytic
Fig. 5. Seasonal variation of the BS in the three lakes during the
study period. Bars represent the standard deviation for duplicates.
Fig. 7. Relationships between the FLC and the proportion of
damaged cells (PI+) in lakes (A) Bran de Scie and (B) Bowker and
Fraser.
Fig. 6. Relationship between BGE and FIC. All data:
y = 0.72 + 3.9e(-0.15x), r2 = 0.56, P < 0.001. Bowker:
y = 0.62 + 4.5e(-0.3x), r2 = 0.73, P < 0.001. Bran de Scie:
y = 0.76 + 2.9e(-0.09x), r2 = 0.44, P < 0.01. Fraser: y = 0.49 + 4.6e(-0.2x),
r2 = 0.94, P < 0.0001.
infection. The BS was positively correlated with total
bacterial abundance, Chl a and TP concentrations (data
not shown), suggesting increased viral yield under conditions of higher overall system productivity. Yet under
these higher productivity conditions, increased BSs and
virion production did not lead to detectable increases
of lytic-stage infected cells, and FIC values always
remained below 7%, a paradoxical situation we discuss
below. On the other hand, we found a link between the
%PI+ cells and FLC, but the patterns were very different
between lakes: the correlation was positive in lake Bran
de Scie (Fig. 7A) and negative in lakes Bowker and
Fraser (Fig. 7B).
© 2009 Society for Applied Microbiology and Blackwell Publishing Ltd, Environmental Microbiology, 12, 628–641
634 C. F. Maurice et al.
debate. There is evidence that, over very large gradients
and across ecosystem types, there may be systematic
patterns in the relative importance of the different viral life
cycles (Weinbauer et al., 2003). Yet the question still
remains as to whether within a given ecosystem there
may be a consistent seasonal succession in terms of viral
strategies, and whether this succession might be driven,
at least in part, by shifts in the physiological state of the
bacterial hosts. There are only a handful of studies that
have followed the relative abundance of lysogenic bacteria along an annual cycle (Cochran and Paul, 1998; Säwström et al., 2007), but to our knowledge none have
included measurements of the viral lytic infection or
aspects of bacterial physiology, so the link between lysogeny, viral lytic infection and host physiology remains
unclear. In this regard, strong seasonality coupled to wide
environmental gradients that characterize northern lakes,
provide an interesting scenario to explore the temporal
coupling between viral replication strategies and host
physiological state within a single type of ecosystem,
because this seasonality generates extreme variations in
bacterial dynamics, in terms of abundance and physiological state, and overall community metabolism.
Seasonal bacterial and viral dynamics
Fig. 8. Relationships between the FIC and FLC in lakes (A) Bran
de Scie and (B) Bowker and Fraser.
Relations between viral life strategies
Overall, there was no correlation between FIC and FLC
with the pooled lake data; however, there were two significant, yet contrasting relations when the data from the
different lakes were analysed separately (Fig. 8): FIC and
FLC were positively correlated in Bran de Scie, and negatively in Bowker and Fraser. These distinct patterns
support the hypothesis that viral–bacterial dynamics may
be fundamentally different between these systems.
Discussion
The factors regulating the prevalence of the major viral life
strategies in natural aquatic ecosystems are still under
Winter is a prominent feature of the seasonal succession
of northern lakes. In addition to low temperatures, the
lakes are subjected to 3–5 months of ice cover. During
this time, lakes are isolated from the atmosphere and do
not exchange gases; light penetration ranges from
extremely low to zero, and connections with the surrounding drainage basin in terms of nutrient and carbon inputs
are at their weakest. It may be precisely due to these
conditions that winter has traditionally been viewed as a
period of relative ‘calm’ from an ecological and biogeochemical point of view in these northern landscapes,
and thus seldom considered in seasonal studies. Yet
there is evidence from a few studies carried out either in
high-latitude lakes (Madan et al., 2005; Pearce et al.,
2007) or marine sites (Wells and Deming, 2006) that there
can be complex and active microbial communities under
the ice, and our results agree with these studies: we
observed extremely dynamic bacterial and viral communities under the ice cover, with sharp, and often asynchronous, oscillations of both components throughout the
winter months (Fig. 1).
Although the annual pattern of variations in bacterial
and viral abundance differed between lakes, the resulting
seasonal pattern of variation in the VBR was strikingly
similar among systems: during ice-free periods, the VBR
remained relatively constant and similar across lakes, and
within the range reported for other systems (Maranger
and Bird, 1995; Wommack and Colwell, 2000). During the
© 2009 Society for Applied Microbiology and Blackwell Publishing Ltd, Environmental Microbiology, 12, 628–641
Phage life strategies and bacterial physiological states 635
ice cover however, there was a dramatic and synchronous
increase in VBR in all three lakes, well above most previously reported values (Fig. 2). In all three cases, this
peak was the result of a decline in bacterial abundance
and a sharp increase in viral abundance.
These results are in contrast with previous studies that
have suggested that VBR tends to be higher in environments characterized by fast bacterial growth and production (Maranger and Bird, 1995; Wommack and Colwell,
2000), because the highest VBR did not occur at the times
of highest cell-specific BP and of BGE (Figs 2 and 3). The
mechanisms underlying this dramatic increase in the VBR
are unclear, but our calculations show that the decline in
bacterial abundance in the middle of winter under the ice,
combined with our measured BS, can explain on average
only 35% of the increase in viral abundance observed in
the same period (Fig. 1). These results would suggest
other sources of water column viruses during the winter
period, such as pico- and nanoplankton infection and
release from sediment. A complementary explanation
would be a reduced decay rate of viruses due to the
absence of the virucidal UV radiations under the ice,
which could allow an accumulation of viruses in the
system and thus amplify the observed viral and VBR
peaks. Both hypothesises have been invoked in previous
studies from marine and brackish systems where high
virus-to-prokaryote ratios (> 50) were also observed
(Weinbauer et al., 2003; Vanucci et al., 2005; Parada
et al., 2007).
Seasonal changes in bacterial physiological and
metabolic state
In natural systems, cells composing the bacterial community are distributed along a continuum that extends from
death to very high activity (Smith and del Giorgio, 2003).
The use of two fluorescent probes (Syto13 and propidium
iodide) allowed us to assess the temporal variability of
cells with high and low nucleic acid contents, and with
potentially damaged membranes. These categories are in
no way absolute, and depend on the detection method
used, but nevertheless offer valuable insight into the
metabolic and physiological changes that occur within
these bacterial communities. In this regard, there is now
growing literature in aquatic microbial ecology showing
the usefulness of these approaches to define the physiological structure of bacterioplankton communities (see
the thorough review of the topic in del Giorgio and Gasol,
2008). The average observed values of the proportions of
HNA, LNA and PI+ cells are in the range of previously
obtained values (Gasol et al., 1999; Bouvier et al., 2007).
The most striking feature of the seasonal variability in
single-cell characteristics was a synchronous increase in
the proportion of PI+ cells in the three lakes during the ice
cover period, with an overall average exceeding 50% of
total cells (Fig. 3). These results indicate that natural bacterial communities undergo major shifts in their physiological state during winter ice cover, and that there is a major
source of physiological stress affecting these bacterial
communities in winter. The peak in damaged cells was
followed by a generalized increase in HNA cells right after
the loss of the ice cover (April–May), suggesting the
release from this winter stress and the resumption of more
active growth.
The nature of the stress that results in this massive
mid-winter peak of damaged bacteria remains unclear.
Others have shown significant temperature effects on cell
structure (Beney and Gervais, 2001), and nutrient deprivation related to water column stability under ice cover
conditions (Malm, 1999; Malits et al., 2004). It is interesting to note that in our study lakes the peak of damaged
cells occurred at times of relatively low BGE (Fig. 3),
which could suggest a link between cell damage and/or
death and low availability of C and energy in the system,
the latter reflected in low BGE. Regardless of its underlying causes, our results suggest that mid to late winter is a
period not only of relatively slow growth and low overall
bacterial activity, but also of significant physiological
stress, all of which could affect the dynamics of viral life
cycles, as we discuss below.
Seasonal dynamics of FIC
The proportion of lytic-stage infected cells FIC are in the
low range of reported values for lakes (from 0% to 45.7%;
Weinbauer, 2004), as well as the observed BSs (Parada
et al., 2006), with a lower average value of 17, compared
to previous studies in northern lakes (average BS: 37,
Parada et al., 2006). The FIC showed consistent patterns
in all three lakes (Fig. 4), with increasing values during the
ice cover period, then declining with the loss of the ice
cover, remaining low during spring and summer, and
increasing synchronously in the fall in all lakes. The seasonality of the BS and the dynamics of viral and bacterial
abundances under the ice indicate that although FIC
values remain low, significant new viral production takes
place throughout the year (Figs 1, 4 and 5).
In early spring and summer, there was the somewhat
paradoxical situation where peaks of viral abundance
occurred at times of low FIC values in all three lakes.
These large variations in viral abundance suggest very
active viral production, which appears incompatible with
the low values of FIC, even when the larger BSs observed
during this period are factored in. In addition, our results
show that there are only small changes in the absolute
number of visibly infected cells between winter and
summer, suggesting that the dramatic shifts in FIC are
mostly due to changes in total bacterial abundance,
© 2009 Society for Applied Microbiology and Blackwell Publishing Ltd, Environmental Microbiology, 12, 628–641
636 C. F. Maurice et al.
whereas the number of infected cells remains relatively
constant. These low FIC values in early spring and
summer also occurred at times of high virus–bacteria
encounter rates (estimated encounter rates based on
bacterial and viral abundances of 38, 60 and 60 viruses
bacteria-1 day-1 in Bowker, Fraser and Bran de Scie
respectively), which should in fact lead to high rates of
lytic infection and FIC values (Murray and Jackson, 1992).
The period of low FIC values also coincided with times
when BGE and BP values were highest resulting in a
negative correlation between BGE and FIC (Fig. 6) and
BP (data not shown). One possible explanation for the
drop of FIC in this period may be related to an increase in
the average growth rates and a decline in the phage
maturation period (period where intracellular virions are
visible) in these more active cells, which could lead to a
lower probability of detection of visibly infected cells by
transmission electron microscopy (TEM), and an apparent
decline in FIC. In this regard, there are uncertainties associated with the estimates of FIC from FVIC in natural
populations (Binder, 1999; Weinbauer et al., 2002),
involving conversion factors that are at least in part
dependent upon the maturation period. For this reason,
we repeated the analysis using the FVIC values, which
may be more appropriate as they are independent of
these potential biases. The FVIC showed the same type
of pattern described above for FIC, with a negative relationship with BGE and BP (data not shown). This is evidence that the time to complete a lytic cycle may vary with
the level of activity of the community, and suggests that for
a given rate of infection, infection of highly active cells with
short phage maturation periods could yield lower apparent proportion of infected cells. If this were the case, the
observed decline in infected cells would not reflect an
actual decline in infection rates, but rather a shift in the
length of the lytic cycle (and more specifically of the phage
maturation period) that prevents the accumulation of
visibly infected cells. The phage maturation period of
infected E. coli in cultures tends to decline with increasing
growth rates (Hadas et al., 1997; You et al., 2002), but this
pattern has yet to be demonstrated for natural aquatic
bacterial communities.
An alternative explanation to the negative relationship
between BGE and FIC could be that viral infection preferentially targets highly active bacteria, which may be
characterized by high growth and BGE, and this selective
removal would lead to declines in overall community BGE
with increasing rates of viral infection. Bonilla-Findji and
colleagues (2008) and Motegi and colleagues (2009)
have shown that viruses can depress community BGE in
experimental manipulations, which is in agreement with
this last hypothesis. The two scenarios that we propose
above are both consistent with reported patterns in bacteria and viruses in sediments, where highly abundant
and dynamic viral communities have been observed, but
where there are typically few visibly infected cells and
thus low apparent FIC (Filippini et al., 2006; Danovaro
et al., 2008). Regardless of the underlying mechanisms,
our results would suggest that within a given system,
seasonal variations in FIC should not be automatically
taken as a proxy of variations in actual infection rates or of
viral-induced bacterial mortality.
Seasonal dynamics of lysogeny
The proportion of lysogens varied seasonally within each
lake (Fig. 4), and although our average FLC values are
high (Bowker: 31%, Bran de Scie: 33.4% and Fraser:
24%), they remain in the range of reported values for
open-water and ice-covered lakes (between 0.1% and
89.5%; Lisle and Priscu, 2004; Säwström et al., 2007).
We obtained two different seasonal patterns of FLC within
these systems: one in lake Bran de Scie and the other
shared by lakes Bowker and Fraser (Fig. 4). In Bran de
Scie, the peak of FLC occurred during the ice cover period
and decreased with time until the loss of the ice cover. In
contrast, lower FLC values were obtained during the ice
cover period in lakes Bowker and Fraser, with a peak
occurring after the loss of the ice. Previous studies have
indicated that lysogeny should predominate at periods of
low bacterial and viral abundances and resulting low
virus–host encounter rates (Wilcox and Fuhrman, 1994;
Weinbauer, 2004). However, the absence of correlation
between FLC and bacterial or virus abundances, or with
the virus–host encounter rates in this study, would
suggest that in these lakes, FLC is not a function of the
bacterial and viral abundances, and that other factors are
involved.
The physiological state of the host has been suggested
as a potential controlling factor of viral life strategies (Williamson et al., 2002). In our study, the lysogenic cycle was
not linked to bacterial carbon metabolism at the community level, but appeared to be linked to the physiological
state at the single-cell level, particularly in relation to
damaged (PI+) cells. Our results show a consistent pattern
where there is a large increase in the apparent proportion
of damaged cells in winter, coinciding with shifts in FLC in
all three lakes (Fig. 7).
While this link should be further explored in other
systems, our experimental evidence strongly supports the
hypothesis that lysogeny may be linked to host physiology, at least at the bulk, community level.
In this regard, it is clear that viral/bacterial interactions
occur not at the community level, but within constrained
host/viral systems, and that the bulk dynamics that we
observe in situ represents the sum of all these individual
interactions in the coexisting viral/bacterial couples within
a given community. It is possible that each viral/bacterial
© 2009 Society for Applied Microbiology and Blackwell Publishing Ltd, Environmental Microbiology, 12, 628–641
Phage life strategies and bacterial physiological states 637
system has its own patterns related to host physiology
and metabolism, with different thresholds and outcomes,
but current technology does not allow to distinguish and
follow all these individual interactions. Therefore, we do
not know how the physiological status of cells belonging
to specific bacterial populations may influence the interactions with its corresponding bacteriophage. Our results
would suggest, however, that there may be specific
events during the year, or key environmental factors that
influence the ecophysiology of a wide range of phylogenetic groups in a similar manner, and that this is in turn
reflected in the bulk viral dynamics along the seasonal
succession. In the case of our study, the long under-ice
winter period, and its associated temperature, nutrient
and organic C stress, seem to have a strong influence on
the collective physiological state of bacterioplankton, and
our results would suggest that this effect may spill over at
the viral level.
Because correlations were positive in lake Bran de Scie
(Fig. 7A) and negative in lakes Bowker and Fraser
(Fig. 7B), we suggest that the mechanisms behind this
relationship may be different. We propose two scenarios
where the virus will react specifically to the physiological
state of the host: In lake Bran de Scie, where there was a
positive relationship between damaged cells and FLC
(Fig. 7A), lysogeny may constitute a refuge for viral communities, allowing phages to persist until more favourable
conditions for virion production appear; this scenario is in
accordance with the current consensus that lysogeny is
more important when bacteria show slow growth rates
and metabolic activity (Schrader et al., 1997; Williamson
and Paul, 2006). This scenario is further supported by the
negative correlation found in this lake between the % HNA
cells and FLC (data not shown), which would suggest that
bacterial communities harbouring a higher fraction of cells
with a high growth potential do not favour the lysogenic
cycle (Williamson and Paul, 2006).
In lakes Bowker and Fraser, on the other hand, where
there was a negative relationship between damaged
cells and FLC values (Fig. 7B), a bacterial community
composed of a high fraction of damaged or relatively
inactive hosts may not favour the lysogenic cycle. It has
been shown that alterations of bacterial DNA and its replication can induce the lytic cycle of phage l (Echols,
1972; Gottesman and Oppenheim, 1994), and when
lysogenic bacteria undergo drastic environmental stress,
they may be induced (Clarke, 1998), in what has been
termed the ‘abandoning the sinking ship’ concept (Weinbauer, 2004). We propose that under challenging environmental conditions that profoundly alter the bacterial
physiological state, prophages may be induced to
‘abandon the sinking ship’ before the loss of the viral
genome, thus ensuring the production, though modest,
of new progeny.
The two scenarios that we describe above are not necessarily contradictory, in that there may be threshold
levels of bacterial activity and cell damage that determine
one or the other response. In this regard, it is possible that
while all three lakes showed significant cell damage
during ice cover, at least in part due to stress induced by
starvation and energy deprivation, as we discussed
above, this stress may be lessened in the eutrophic lake
that typically has higher nutrient and organic matter available to bacteria.
Other variables that were not assessed in this study,
such as community composition and diversity, may also
have been involved in regulating the observed bacterial–
viral interactions. If certain bacterioplankton species were
specifically only infected through the lysogenic or the lytic
pathway, temporal changes in their abundances would
result in shifts in the measured proportions of both life
cycles. The difficulty in addressing this question is that
changes in the observed life cycles might be associated
to: (i) ‘natural senescence’ (no induction) of lysogens and
(ii) naturally occurring inducing events (such as UV, pollutants, physiological states). This hypothesis would also
imply that virus–host interactions are specific. These fundamental issues have not been clearly resolved in natural
bacterial and viral communities (Sano et al., 2004;
Hewson and Fuhrman, 2007) and will pose a challenge
for viral ecologists in the years to come.
Links between viral life strategies
Weinbauer and colleagues (2003) reported a strong negative relationship between FIC and FLC in two contrasting
aquatic systems, and suggested that there might be a
shift in the relative importance of one cycle over the other
along large environmental gradients, for example oligotrophic to more productive sites. Other studies have
shown seasonal variability in either FIC or FLC (Cochran
and Paul, 1998; Williamson et al., 2002; Säwström et al.,
2007), but no study to date has followed both cycles within
a given ecosystem in time, so it is unclear to what extent
the cross-system pattern of shifting dominance of FIC and
FLC described by Weinbauer and colleagues (2003) is
also expressed within a lake as a pattern of seasonal
alternation. Our results show contrasting patterns in this
regard: in lakes Bowker and Fraser there was a weak, but
significant negative relationship between FIC and FLC
(Fig. 8B), similar to the pattern proposed by Weinbauer
and colleagues (2003). However, it is interesting to note
that in these two lakes, there is no replacement or exclusion dynamics of these cycles: FLC values remain high
and we observed a low but relatively constant fraction of
lytic-stage infected cells throughout the annual cycle.
Under the ‘abandon the sinking ship’ scenario that we
proposed above for these two lakes, an increase in the
© 2009 Society for Applied Microbiology and Blackwell Publishing Ltd, Environmental Microbiology, 12, 628–641
638 C. F. Maurice et al.
proportion of damaged cells could be an induction signal
for the lysogens, with a corresponding decline in FLC. If
these induction events occur at specific points of the
seasonal cycle and are uncoupled from peaks in lytic
infection, this would generate the negative relationship
between FLC and FIC that we have observed.
In contrast, in Bran de Scie we found a positive relationship between FLC and FIC (Fig. 8A), indicating that an
increase of lysogens occurs with an increase of lytic-stage
infected cells within the system. Bettarel and colleagues
(2008) also reported this pattern and have hypothesized
that high and continuous induction of lysogenic cells could
result in this positive relationship between FLC and FIC if
lysogenic cells are also continuously generated. Our
results suggest that in this lake a significant portion of
cells continuously enter the lysogenic cycle under the
bacterial physiological refuge scenario. In this case, one
may consider the same type of scenario as Bettarel and
colleagues (2008), where there is a significant portion of
cells entering the lysogenic pathway coupled to an important induction rate taking place within these two lakes,
although we have no data on induction rates.
Regardless of which scenario predominates, our results
further point out the difficulty of exploring the relative
importance of viral life cycles in natural aquatic systems.
Indeed, the adequacy of the relationship between FIC and
FLC to assess the relative importance of lytic versus
temperate viral infection is essentially dependent on the
robustness of FIC as an indicator of virulent viral infection.
The latter, however, is questionable. On the one hand, we
have argued above that estimates of FIC based on TEM
counts may not always reflect the in situ rates of viral
infection and thus the corresponding lytic cycle, especially
under conditions of high turnover rates of bacterial infection (i.e. very short virion production and maturation
period). In addition, the FIC values obtained by direct TEM
observations do not allow the discrimination of cells
infected by virulent phages from induced lysogenic cells.
Although it is generally assumed that prophage induction
does not contribute significantly to visibly infected cell
counts and to total viral production in natural aquatic
systems (Wilcox and Fuhrman, 1994; Weinbauer and
Suttle, 1996), this assumption has rarely been tested and
there are known instances of high contribution of lysogens
(i.e. Cochran et al., 1998). In this context, a positive or
negative relationship between FIC and FLC cannot in itself
be taken as evidence of a particular pattern of replacement
or of relative importance of the two major viral strategies.
What our study does show is that there is considerable
seasonal variability in viral strategies and in the coupling
between bacteria and viruses, and that this coupling is
probably mediated by ecosystem-specific factors such that
it may not be possible to apply one unique model of viral life
cycle dynamics to different aquatic systems.
Experimental procedures
Sampling
The three studied lakes, Bowker (oligotrophic), Fraser
(mesotrophic) and Bran de Scie (eutrophic), are located
within in the same catchment area (Eastern townships,
Québec) and were monitored during 11 months, from December 2005 to November 2006, with samples taken every
3–4 weeks. During the ice cover season, a hole was drilled
through the ice and the water was sampled using an acidwashed Van-Dorn bottle at a 1 m depth. During the ice-free
period, the same sites were sampled at the same depth.
Samples were kept cool and in the dark, and stored for no
more than 3 h before processing in the lab. Temperature and
oxygen profiles were taken in situ with an YSI probe.
Bacterial community metabolism
We followed three distinct aspects of bacterial carbon
metabolism: BR, BP and BGE. BR was obtained by the
measurement of the consumption of oxygen in dark incubation of samples that were prefiltered through GF-C filters
(approximate pore size 1.5 mm) to remove larger planktonic
organisms. Incubations were carried out in flow-through
systems that could be sampled repeatedly over a 24–36 h
period, following del Giorgio and Bouvier (2002). Oxygen
concentration at each time point was analysed using a dualinlet mass-spectrometer (Kana et al., 1994; del Giorgio and
Bouvier, 2002); the respiration rate was calculated from the
regression between oxygen concentration and time, and the
rate converted to C units assuming a respiratory quotient of 1.
BP was determined using H3-leucine uptake method (Azam
et al., 1992), assuming a conversion factor of 3.1 kg C mol
leu-1 (Kirchman, 1993). We carried out BP measurements on
both the ambient unfiltered water, and on the samples taken
at each time point of the respiration incubations. The latter
were averaged to determine the BP for the filtered samples,
and used to calculate experimental BGE, as BGE =
[BP/(BP + BR)] ¥ 100. The BR and BP measurements were
carried out at ambient room temperature, which was always
around 20°C. This temperature was within 3°C of late spring,
summer and early fall, but more than 10°C from winter and
early spring temperatures, and thus the resulting patterns in
BGE could be biased by temperature effects (Apple et al.,
2006). We corrected all our experimental BGE values for
temperature using the empirical Q10 of BGE provided by
Apple and colleagues (2006). This correction influenced most
the winter and early spring measurements, and resulted in
values that were on average 20% higher than the uncorrected measurements, whereas the remainder of the values
were not greatly altered.
Bacterial enumeration and single-cell characteristics
Bacterial enumeration and analyses of single-cell characteristics were carried out using flow cytometry. All the cytometric
analyses of the in situ bacterial communities were performed
on fresh, unfixed samples using a FACScalibur FlowCytometer (FCM, BectonDickinson) equipped with an air-cooled
argon laser providing 15 mW at 488 nm and with standard
© 2009 Society for Applied Microbiology and Blackwell Publishing Ltd, Environmental Microbiology, 12, 628–641
Phage life strategies and bacterial physiological states 639
filter set-up. For total counts, and for the enumeration of cells
with HNA or LNA content, samples were stained with the
nucleic acid dye SYTO13 (Molecular Probes) at a final concentration of 2.5 mM for 5 min (del Giorgio et al., 1996).
Stained bacterial cells excited at 488 nm were detected
based on their 90° light scatter (SSC) and green fluorescence
(FL1). Manual gating was used to discriminate both HNA and
LNA fractions.
Enumeration of cells with a compromised membrane,
defined here as injured or damaged cells, was carried out
using the ‘LIVE/DEAD BacLight’ commercial kit (Molecular
Probes). This kit contains a mixture of a red cell-impermeant
nucleic acid stain (propidium iodide) that only penetrates cells
with damaged membranes and a green cell-permeant nucleic
acid stain (SYTO-9) that acts as a counter-stain for all cells.
The probes concentration and the incubation time have been
adjusted to the communities studied: 1.5 ml of the mixture of
the two stains was added to 0.5 ml of sample, and incubated
for 10 min (del Giorgio and Bouvier, 2002). The distinction of
cells with a damaged or compromised membrane was based
on their red (FL3) and green (FL1) fluorescence (Gasol et al.,
1999; Falcioni et al., 2008). To define the cytometric characteristics of the damaged cells in the bacterioplankton assemblages, we routinely ran controls that consisted of the same
natural water sample incubated with the ionophore Gramicidin S. This resulted in a rapid loss of cell membrane integrity,
and these samples were consequently used to characterize
the cytometric signature of damaged cells. Damaged cells
show higher FL3 (red) and lower FL1 (green), due to the
penetration of the red propidium iodide (PI), whereas intact
cells have higher FL1 than FL3 fluorescence. Fluorescent
latex beads (0.94 mm diameter, Polysciences) were added to
every sample as internal standard for cell counts, and to
normalize cell fluorescence and light scatter measurements.
The bead stock solution concentration was determined using
Trucount beads (Polysciences) prior to each FCM analysis.
All samples were run in and data were analysed using the
CellQuest Pro software.
Suttle, 1996), a 10-fold dilution using virus-free water from
the same site was carried out. Virus-free water was obtained
for each sample by tangential ultra-filtration on a 300 kDa
membrane (Pellicon system, Millipore). The quality of the
filtration was estimated for every sample and averaged 99%
of viruses removed (n = 33, data not shown). Incubations
were carried out in duplicate, and subsamples of each of
these (in triplicate) were taken every 12 h, and preserved in
formaldehyde (2% final concentration) for later enumeration
of total bacteria and viral abundances. Induction of lysogenic
bacteria was determined from increases in viral abundance
and decreases in bacterial abundance in the presence of
mitomycin C relative to controls (Cochran and Paul, 1998;
Tapper and Hicks, 1998).
FIC and BS
The FIC and BS were determined by TEM using a FEI
TECNAI microscope. Briefly, 500 ml of fixed sample (formaldehyde, 2% final concentration) was centrifuged at 30 000 g
for 10 min at 20°C (AirFuge Beckman) directly on duplicate
400-mesh copper formvar, carbon-coated electron microscope grids (3 mm diameter, Delta Microscopies). Samples
were then stained with uranyl acetate (final concentration
0.5%) for 20 s and rinsed twice in distilled water before observation. A bacterium was considered visibly infected when at
least five virus-like particles could be seen in its cytoplasm
(Weinbauer, 2004). At least 1000 bacteria per grid were
observed at 40 000¥ magnification, and at an acceleration
voltage of 120 kV. The FIC was obtained using published
conversion factors and the model of Binder (1999) with the
following equation: FIC = 7.1 FVIC - 22.5 FVIC2 (Weinbauer
et al., 2002). This model assumes a steady state, equal
grazing between infected and non-infected cells and that the
latent period is equivalent to the generation time of uninfected
cells (Binder, 1999).
Chemical variables
Viral enumeration
Viral abundance was determined by epifluorescence microscopy, following Chen and colleagues (2001). Briefly, fixed
samples (formaldehyde, 1% final concentration) were filtered
through 0.02-mm-pore-size Anodisc membrane filters
(Whatman) and stained with the nucleic acid-dye Sybr-Gold
(Molecular Probe) at 2.5¥ final concentration for 15 min in the
dark at room temperature. For each sample, duplicate filters
were made and > 300 viruses per filter were counted using an
Olympus AX70 epifluorescence microscope under blue light
excitation (488 nm).
The nutrient concentrations (TP, TN, Chl a and DOC) of each
lake were followed as well as the ice layer thickness. Phosphorus concentration was obtained by the molybdenum-blue
method, after persulfate digestion (Cattaneo and Prairie,
1995), and nitrogen concentration was measured as nitrates
after digestion with alkaline persulfate (Cattaneo and Prairie,
1995). All samples were processed on a Flow Solution IV
autoanalyser (O.I Analytical) using a 5-point calibration curve.
The DOC was measured on a TIC TOC analyser (O.I Analytical) using a high-temperature persulfate oxidation method.
Finally, Chl a concentration was determined from ethanol
extracts using a UV/Vis UltroSpec 2100 spectrophotometer
(Biochrom).
Enumeration of lysogenic cells
The FLC was obtained using 36 h incubations of water
samples with the inducing agent mitomycin C (1 mg ml-1 final
concentration) and untreated samples (control incubations) in
the dark at in situ temperature (Weinbauer and Suttle, 1996).
To reduce the possible effects of lytic infections during the
incubations, and to facilitate viral counts (Weinbauer and
Statistical analysis
Statistical analyses were performed with the JMP 5.01 software. Data and log-transformed data were checked for normality. To determine whether induction took place, a one-way
ANOVA between control and mitomycin C abundances was
done for each time point. The remaining fraction of data that
© 2009 Society for Applied Microbiology and Blackwell Publishing Ltd, Environmental Microbiology, 12, 628–641
640 C. F. Maurice et al.
was not normal was analysed with the Kruskal–Wallis comparison. The relationships between all the different variables
were explored using least-squares linear regression analysis
as well as multivariate regression analysis, and the different
lakes were compared with each other using the Tukey
Kramer test. A level of 0.05 was considered significant.
Acknowledgements
We are grateful to C. Beauchemin and L. Fauteux for help
during sampling and sample analysis. Thanks also to D. Bird
for access to equipment and advice concerning TEM, as well
as for interesting discussions. We wish to thank Y. Prairie and
R. de Wit for stimulating discussions and critical comments.
This work was supported by The National Science and
Engineering Research Council of Canada (P.D.G.), by ANR
AQUAPHAGE #07BDIV015-02 (to T.B.), and by PICS-CNRS
France-Québec (to T.B. and P.D.G.).
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