Table S1 Summary of number of phytoplankton taxa. Shown are the minimum, maximum and mean number of taxa sampled for the main phytoplankton
groups for each lake, with the observation period (month) given in brackets. Also shown for each phytoplankton group are: “sub-total”, the total number of
taxa observed throughout the year (by lake); “persisting taxa”, the percentage of taxa that were found in all samples taken throughout the year (by lake);
“species in common”, the number of taxa common to both lakes; and “total”, the total number of taxa found, both lakes combined.
Lake Bourget
Phytoplankton groups
Cyanobacteria
Chlorophyceae
Chrysophyceae
Dinophyceae
Diatomeae
Cryptophyceae
Zygophyceae
Lake Annecy
Annecy & Bourget
Min. (month)
Max. (month)
Mean
Sub total
Persisting
taxa
Min. (month)
Max. (month)
Mean
Sub total
Persisting
taxa
Species in
common
Total
1 (Mar, May)
2 (Mar)
1 (Jan)
0 (Jan, Feb, Nov)
2 (*)
1 (Jul)
0 (*)
9 (Sep)
11 (Jun, Oct)
13 (Aug, Oct)
4 (May, Sep)
9 (Jan)
4 (*)
2 (May)
4
5
7
2
4
3
<1
16
31
37
8
18
5
3
0%
3.2%
0%
0%
0%
20%
0%
0 (Feb)
3 (Jun)
7 (Jan)
0 (Nov)
1 (Oct)
2 (May to Jul)
0 (*)
3 (Jan)
7 (Nov)
19 (Oct)
3 (Mar)
10 (Aug)
4 (*)
2 (Aug)
2
7
12
2
6
3
<1
12
19
32
9
20
4
2
0%
10.5%
3.1%
0%
5%
25%
0%
7
12
16
7
8
4
1
21
38
53
10
30
5
4
**indicates
three months
months can
can be
beimplied.
implied.For
ForDiatomeae
Diatomeaein
inLake
LakeBourget:
Bourget:Apr,
Apr,May,
May,Jul
Juland
andSep.
Sep.For
ForCryptophyceae
CryptophyceaeininLake
LakeBourget:
Bourget:Feb,
Feb,
Mar,
Sep
and
Oct.
Zygophyceae
Lake
Bourget:
Jan,
Mar,
indicates that
that more
more than
than three
Mar,
Sep
and
Oct.
ForFor
Zygophyceae
in in
Lake
Bourget:
Jan,
Mar,
Apr,
Jul and
andSep
SeptotoNov.
Nov.
Cryptophyceae
in Lake
Annecy:
Feb Sep
andtoSep
to For
Nov.
For Zygophyceae
Lake Annecy:
JanOct
to and
Jun,Nov.
Oct and
Only one
species
(Erkenia subaequiciliata)
Apr, Jun,
Jun, Jul
ForFor
Cryptophyceae
in Lake
Annecy:
Feb and
Nov.
Zygophyceae
in LakeinAnnecy:
Jan to Jun,
OnlyNov.
one species
(Erkenia
subaequiciliata
) was reportedwas
for reported for
Prymnesiophyceae in both lakes.
Prymnesiophyceae in both lakes.
1
Table S2 Presence or absence of signature genes of phytoplankton viruses in virus-like-particles (VLPs)
sorted using flow cytometry (FCM). The tests were carried out using PCR to amplify genes from the
sorted VLP after concentration using 30KD-Amicon (Millipore) with primers described in Table 1.
Then the correct size of the amplicons was verified using electrophoresis in 1.5% agarose gel to
determine whether a specific viral group was present or not. The sample codes give the lake
(A=Annecy, B=Bourget) and the month of sampling (1-11). The “-2” designates the second sampling
time. So, for example, A6 and A6-2 are the first and second samples taken in June from Lake Annecy.
B6 is the first sample taken in June from Lake Bourget. The seawater samples were collected in the
summer from coastal waters of the VilleFranche and Roscoff Bays (France).
Samples
A6
A6-2
A10
A11
B6
Freshwater
B8-2
B9-2
B10
B11
B11-2
Villefranche/mer
Seawater
Roscoff
VLP2
VLP1
VLP2
VLP1
VLP2
VLP1
VLP2
VLP2
VLP1
VLP2
VLP1
VLP2
VLP1
VLP2
VLP1
VLP2
VLP1
VLP2
VLP1
VLP1
VLP2
mcp
-
2
polB
+
+
+
+
-
Viral gene markers
psbA
g23
+
+
+
-
g20
+
+
-
Table S3 Summary of Pearson's correlation analysis between the abundance of specific viral DGGE
bands and specific phytoplankton taxa. For each lake are reported the taxa being significantly related
to the largest number of DGGE bands (p<0.05) and the percentage of bands accounting for the whole
community.
Lake Bourget
Chlorophyceae
Genetic
markers
Chrysophyceae
The most significant taxa
% bands
related
polB
Chlamydomonas conica
mcp
Diatomeae
The most significant taxa
% bands
related
16%
Dinobryon divergens
Fotterella tetrachlorella
16%
g20
-
g23
-
Cryptophyceae
The most significant taxa
% bands
related
16%
Cyclotella costei
Dinobryon cylindricum,
Dinobryon divergens and
Kephyrion sp2
15%
-
-
-
-
Dinophyceae
The most significant taxa
% bands
related
17%
Rhodomonas minuta
Cyclotella delicatula
21%
-
-
-
-
Zygophyceae
The most significant taxa
% bands
related
The most significant taxa
% bands
related
11%
Cyste de Ceratium
hirundinella
11%
Cosmarium depressum
var. planctonicum
8%
Cryptomonas rostrata
17%
Peridinium willei
15%
Cosmarium tenue
8%
-
-
-
-
-
-
-
-
-
-
-
-
-
-
Th
Ap
Ap
Lake Annecy
Chlorophyceae
Genetic
markers
Chrysophyceae
Diatomeae
Cryptophyceae
Dinophyceae
Zygophyceae
% bands
related
The most significant taxa
% bands
related
The most significant taxa
% bands
related
The most significant taxa
% bands
related
The most significant taxa
% bands
related
The most significant taxa
% bands
related
polB
Choricystis minor, Flagellé
diam 5µm, Oocystis
solitaria, Stichococcus
bacillaris and Pyramimonas
micron
17%
Kephyrion sp1
25%
Cyclotella delicatula
33%
Cryptomonas marsonii
and Cryptomonas sp.
17%
Katodinium fungiforme
8%
Cosmarium tenue and
Xanthidium alpinum
4%
mcp
Choricystis minor and
Chlorophycées sp.
19%
Kephyrion sp1
23%
Cyclotella delicatula
35%
Cryptomonas marsonii,
Cryptomonas sp. and
Rhodomonas minuta
16%
Gymnodinium helveticum
and Katodinium
fungiforme
13%
Cosmarium tenue
10%
g20
-
-
-
-
-
-
-
-
-
-
-
-
S
g23
-
-
-
-
-
-
-
-
-
-
-
-
S
The most significant taxa
Supporting Information
PCR condition optimization
The many steps related to PCR amplification can cause biased results, including the particular
kit, enzymes or thermal cycler used, number of cycles, etc. Prior to present this study, we
made a considerable effort to optimize PCR conditions. In this goal, we changed the
parameters and observed how they could affect each community “richness”. For the PCR, we
mainly played on the annealing temperature of each primer set to optimize the reaction for our
field samples. We used pooled field samples obtained from both lakes for the test and ran it in
duplicates. Based on the same sample, we tested a gradient of annealing temperatures to
3
Th
define the optimal one for each primer set. Equal volumes of PCR products were firstly
loaded in 1.5% agarose gel to verify the produced DNA quantity using gel electrophoresis,
and DGGE were run to examine the “richness”. The annealing temperature providing the
densest bands in the agarose gel was chosen for further refinement. We then refined the
annealing temperature with several gradient PCRs until no significative changes in bands
density (DNA quantity) and number were observed in DGGE.
DGGE condition optimization
For the DGGE, we played on the DGGE gradient for each set of amplicons (for each primer
set). Firstly, we ran the pooled field samples to observe the band positions in gels. DGGE
bands were excised from the most top and bottom side of each gel and verified by PCR
(sometimes by cloning-sequencing) to know the edge of the fingerprints. Several runs were
conducted to refine the gradient to separate maximally the bands and stopped until the gel
included all bands. The optimization consisted thus in a balance between the inclusion of all
possible bands and their optimal separation.
Comparison of DNA extraction methods
Short et al. (2010) reported that non-extraction methods (i.e. using the treated viral
concentrate as the PCR template) could give better result for diversity investigation. Several
methods exist to treat viral particles, however, no study reported so far how these different
treatments affect the efficiency of viral DNA liberated from the capsid, thereby the influence
on the “diversity”. We tested six DNA extraction methods (five non-extraction methods and
one extraction method using a kit, Fig. S1) on the same field sample obtained from Lake
Annecy and Bourget. We did not detect changes in the number and composition of DGGE
bands for g20 and g23 (Fig. S1), suggesting the absence of influence of DNA extraction
methods on cyanomyovirus and T4-like myoviruses. However, a significant difference was
observed for the phycodnaviruses. The “cold/freeze+heat” treatments (e.g. VCC, VCF)
resulted in higher DGGE band numbers for mcp than other methods (the “only heat”
treatment and kit). On the other hand, the “VC” method produced bands (e.g. bands “1” and
“2” in Fig. S1) that the “VCF and VCC” did not. Hence, we used the DNA extraction method
4
“VC” +“VCF” for polB and mcp gene amplification. By contrast, for psbA, g20 and g23 gene,
we used the “VC” method.
Fig. S1 DGGE comparison of different DNA extraction for the cyanomyoviruses (A: g20 gene), the T4like myoviruses (B: g23 gene) and the phycodnaviruses (C: mcp gene). The PCR and DGGE were
conducted using a pooled sample and conditions were described in the Materials and Methods. (i) VC:
PCR directly from the viral concentrate; (ii) VCC: cold-heat treatment (Chen et al. 1996), the chilly
viral concentrate was put at 95°C for 3 min, and in the ice for 2 min; (iii) VCS: before PCR, the viral
concentrate was heated 15 min at 95°C. (iv) VCB: boil-method (Chen et al. 2009), the viral
concentrate was boiled at 100°C for 10min. (v) VCF: freeze-thaw method (Short and Short, 2008),
the viral concentrate was freezed at -20°C and then heated 3min at 95°C for 3 repetition; (vi)Kit: viral
DNA was extracted by QIAmp MiniElute Virus Spin Kit (Qiagen) from the viral concentrate.
5
6