Metagenomic analysis in Lake Onego (Russia) Synechococcus cyanobacteria

JGLR-01192; No. of pages: 12; 4C: Journal of Great Lakes Research xxx (2017) xxx–xxx

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Metagenomic analysis in Lake Onego (Russia) Synechococcus cyanobacteria Aleksandra Vasileva a, Maria Skopina b, Svetlana Averina b, Olga Gavrilova b, Natalia Ivanikova b, Alexander Pinevich b,⁎ a b

Peter the Great St. Petersburg Polytechnic University, RASA Research Center, Laboratory of Bioinformatics, Polytechnicheskaya str., 29, 195251 St. Petersburg, Russia St. Petersburg State University, Department of Microbiology, Vasilyevsky Island, Liniya 16-ya, 29, 199178 St. Petersburg, Russia

a r t i c l e

i n f o

Article history: Received 1 April 2016 Accepted 1 March 2017 Available online xxxx Keywords: Lake Onego (Russia) Picocyanobacteria Synechococcus 16S rRNA gene pyrosequencing Phylotype

a b s t r a c t Despite Synechococcus picocyanobacteria being important in lacustrine ecosystems, they have received less attention than their marine counterparts. We describe for the first time, using 454 pyrosequencing, the diversity of Lake Onego cyanobacteria. The majority (97.52%) of 16S rRNA gene sequences observed in Synechococcus collected from the epilimnion belonged to OTUs (operational taxonomic units) I-XVI. The obtained pyrotags were classified, together with specially created database of 770 reference sequences, via the M-pick algorithm and compared with traditional (cited in the literature) assemblages, which for reason of reliability were reappraised. The entire set of sequences were attributed to freshwater Synechococcus phylotypes (FSP) 1-45. Most of the Lake Onego Synechococcus sequences (47.47%) belonged to OTU VII within FSP 14, which comprised ubiquitous strains, whereas the low-abundance FSP 20 (5% of sequences) demonstrated a limited geographical distribution. The study of connection between Lake Onego phylotypes and those in other large lakes should be continued because no analysis of a similar scale has been undertaken as yet. Cluster analysis via weighted UniFrac revealed no correlation between the spatial distribution of the lake's Synechococcus community and limnological parameters. Only two Synechococcus strains (both phycoerythrin-lacking) were obtained in culture confirming that lacustrine picocyanobacteria are difficult to culture. © 2017 Published by Elsevier B.V. on behalf of International Association for Great Lakes Research.

Introduction The microbial communities of large lakes in Northwest Russia are poorly understood. This is particularly astonishing because Lake Ladoga (18,135 km2 surface area; volume, 908 km3) and Lake Onego (9720 km2 surface area; volume, 295 km3) are the first and second largest of the European freshwater lakes. Lake Onego is of Paleozoic glacial-tectonic origin, and finished forming 12 Mya during the Late Valdai glaciation. It is fed by the inlet rivers Vodla, Vytegra, and Shuya, and it in turn feeds Lake Ladoga via the outlet channel represented by the Svir River (Fig. 1). The lake has a maximum depth of 127 m, with the average being 30 m. In May–June, surface water temperature is 5–10 °C, while in August it can reach 20–25 °C. In mid-January, a solid ice field appears in the lake's central region, while the near-shore borders are frozen by November–December (Bouffard et al., 2016). Lake Onego is subdivided into regions with contrasting limnological parameters. Of prime importance is the difference between the lake's central region (Big Onego) and the inlets; the former represents an oligotrophic ecosystem, whereas the latter are anthropogenically enriched as a result of drainage from the small industrial cities Kondopoga and Petrozavodsk.

⁎ Corresponding author. E-mail address: [email protected] (A. Pinevich).

A metagenomic analysis of the genetic diversity of Lake Ladoga's bacterioplankton was recently carried out (Skopina et al., 2015), whereas that of Lake Onego has not been studied until now. Previous research of the lake's phytoplankton was fragmentary and relied on low-resolution light microscopy; therefore, only the relatively large cyanobacteria were described (Sjarki and Sharov, 2003). The hard to visualize 0.2–2.0 μm diameter picocyanobacteria from the genera Synechococcus and Prochlorococcus (Callieri, 2008; Partensky and Garczarek, 2010) are most abundant and significant in aquatic ecosystems. Although light microscopy is commonly used for the primary identification of cyanobacteria in water samples, similar spherical to rod shaped Synechococcus and Prochlorococcus are more reliably distinguished with molecular methods (Scanlan et al., 2009). Synechococcus represents the main component of cyanobacterial picoplankton in (ultra-) oligotrophic lakes (Weisse, 1993; Callieri, 2008). In contrast, Prochlorococcus is thought to be absent from freshwater basins although low-abundant pyrotags very similar to those of P. marinus have been detected in 16S rRNA amplicon libraries created from samples from the epilimnion of Lake Ladoga (Skopina et al., 2015). Synechococcus picocyanobacteria diversity can be described by screening environmental DNA via PCR with group-specific primers targeting either the 16S rRNA gene or the cpcBA-IGS part of the phycocyanin operon (Robertson et al., 2001; Crosbie et al., 2003; Ernst et al., 2003). However, despite freshwater picocyanobacteria being of

http://dx.doi.org/10.1016/j.jglr.2017.03.003 0380-1330/© 2017 Published by Elsevier B.V. on behalf of International Association for Great Lakes Research.

Please cite this article as: Vasileva, A., et al., Metagenomic analysis in Lake Onego (Russia) Synechococcus cyanobacteria, J. Great Lakes Res. (2017), http://dx.doi.org/10.1016/j.jglr.2017.03.003

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A. Vasileva et al. / Journal of Great Lakes Research xxx (2017) xxx–xxx

Fig. 1. Map of Lake Onego showing sampling sites. The inset is a map of the Baltic Sea region.

prime importance in lacustrine ecosystems, they have received less attention than marine taxa. Hence, the paucity of data on their phylotypes, abundance, and biogeography impedes us from better understanding the properties of lacustrine ecosystems and, in the case of bloom-forming, especially toxin-producing taxa, impedes us from monitoring the quality of water destined for human usage. Data on picocyanobacterial diversity in Europe is particularly rare (Sánchez-Baracaldo et al., 2008), and Lake Onego remains one of the main gaps in the record.

The aim of this research was to provide insight into Lake Onego's cyanobacterial picoplankton diversity using high-throughput 454 pyrosequencing of the 16S rRNA gene, which allows for a high-scale molecular sampling and the creation of large amplicon libraries (which can comprise millions of pyrotags) while avoiding the need for clone library construction and Sanger sequencing of individual clones (Caporaso et al., 2011). The ultimate goal of Lake Onego cyanobacteria metagenomic analysis was to compare our phylotype designations to those currently used in the literature and deposited in databases. A reappraisal of the

Please cite this article as: Vasileva, A., et al., Metagenomic analysis in Lake Onego (Russia) Synechococcus cyanobacteria, J. Great Lakes Res. (2017), http://dx.doi.org/10.1016/j.jglr.2017.03.003

A. Vasileva et al. / Journal of Great Lakes Research xxx (2017) xxx–xxx

freshwater Synechococcus phylotypes is needed, because some phylotypes were established based on less than 60 and sometimes less than 20 sequences. Our data demonstrate that some of the traditional phylotypes are no longer valid when additional 16S rRNA gene sequences are included in the analysis. A striking example is the MH 305 cluster (Ivanikova et al., 2007; Felföldi et al., 2011b): the MH 305 sequence, on which this phylotype has been established, was placed in a different position on the Synechococcus tree constructed in our research. Therefore, for a reliable interpretation of metagenomic data on Lake Onego, a comparison with Synechococcus phylotypes in other large lakes was required. As part of this initial study of Lake Onego cyanobacterial diversity, the culturability and preliminary phenotypic characterization of the lake’s picocyanobacteria were also assessed.

Materials and methods Study sites and sampling Lake Onego epilimnionic plankton were collected in August 2011 during the cruise aboard the Ecolog research vessel. A survey of three field sites across the entire lake was conducted at Stations K3, K6, and C1 (see Fig. 1). Limnological parameters at the hydrographic stations (see Table 1) were obtained by means of conventional methods (Sabylina et al., 2010) and kindly provided by the employees of Institute of Northern Water Problems (Karelian Research Centre of Russian Academy of Sciences). Lake water samples (1–5 L) were collected from 2 m depth with 10 L polypropylene containers and instantly subjected to the gravity pre-filtration through a 2 cm layer of cotton wool into sterile 6 L polypropylene bottles. This procedure facilitated the subsequent sample filtration via membrane filters which otherwise became obstructed with zoo/phytoplankton and detritus. Plankton passing through the pre-filter were collected on 0.45 μm polycarbonate membranes (Sartorius, Germany) by vacuum filtration using a Millipore WP6110060 pump (USA) with the maximum water output of 37 L/min. In total, 2 filters were obtained for each field station. Filters used for direct DNA extraction were frozen and stored at −20 °C until processed. Duplicate filters for Synechococcus isolation were placed onto 1% Bacto-agar (Difco, USA) modified BG-11 medium slabs (see Skopina et al., 2015) and kept in darkness at room temperature until the end of the cruise.

DNA extraction Frozen filters were placed into 50 mL polypropylene vials, and the membrane-attached cell mass was treated with 3 mL CTAB buffer lysis cocktail (0.1 M Tris-HCl/20 mM EDTA-Na 2 [pH 8.0], 1.5 M NaCl, 8% cetyltrimethylammonium bromide, 2 μg/mL Proteinase K [Sigma, USA], 0.2% 2-mercaptoethanol) for 4 h at 56 °C with continuous stirring. Proteins were removed from the lysate by emulsification with an equal volume of chloroform/isoamyl alcohol (24:1 vol/vol) at − 20 °С for 30 min. Upon centrifugation at 14,000 ×g for 15 min, the supernatant was mixed with cold isopropyl alcohol (2:3 vol/vol). The mixture was kept at −20 °C overnight. The DNA pellet was then washed with 75% ethanol and dissolved in 50 μL double distilled water.

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PCR conditions The cyanobacterial 16S rRNA gene was amplified from the environmental DNA templates collected at sampling Stations K3, K6, and C1 with the group-specific primers CYA106F (5′-CGGACGGGT GAGTAACGCGTGA-3′) and CYA789R (5′-GACTACAWGGGGTATCTAA TCCCWTT-3′) (Nübel et al., 1997). The same primers were employed in fusion primer constructions for DNA pyrosequencing (see below). The PCR mixture contained 5 μL 10× commercial PCR buffer (100 mM KCl, 20 mM Tris-HCl/0.1 mM EDTA-Na2 [pH 8.0], 0.5% Tween-20, 50% glycerol), 1.5 mМ MgCl2, 10 ng of DNA, 0.2 mM each dNTP, 100 nM each primer, and 1.25 U Taq polymerase (Fermentas, Lithuania) in a 50 μL reaction mixture. PCR amplification was performed in a MyCycler Thermal Cycler (BioRad, USA) with an initial denaturation at 95 °C (5 min) and 34 subsequent cycles of denaturing at 94 °C for 30 s, annealing at 60 °C for 30 s, elongation at 72 °C for 2 min, and final synthesis at 74 °C for 4 min. Genomic DNA of phycoerythrin-containing Synechococcus sp. CALU-1097 previously isolated from Lake Ladoga was used as a control. The amplicons were separated in 1% agarose (Fermentas, Lithuania) containing TAE buffer (0.04 M Tris, 0.04 M acetic acid [pH 8.0], 2 mM EDTA-Na2). PCR amplicon library construction and pyrosequencing The amplified DNA fragments were used to construct 16S rRNA gene libraries. DNA-containing bands were cut from agarose gels and purified with QIAquick Gel Extraction Kit (Qiagen, Germany). DNA preparations were checked for concentration and quality by agarose gel electrophoresis. Pyrosequencing was performed using group-specific primers CYA106F and CYA789R (Nübel et al., 1997). The latter was used in fusion primer constructions which additionally contained standard service sequences (represented by sequencing adapter and DNA barcode) and sample-varying multiplex identifiers (MIDs). An example of the fusion forward primer was as follows: 5′-CGTATCGCCTCCCTCGCGCCA3′ - sequencing adapter/5′-TCAG-3′ - DNA barcode/5′-TCTCTATGCG-3′ Roche MID-11 (absent in the fusion reverse primer)/CYA106F template group-specific forward primer. The sequence of fusion reverse primer was as follows: 5′-CGTATCGCCTCCCTCGCGCCA-5′ - sequencing adapter/5′-TCAG-3′ - DNA barcode/CYA789R - template group-specific reverse primer. Sample-specific multiplex forward primers were employed whereas the fusion reverse primers were uniform for all samples. Analysis of amplicon libraries was performed with a GS Junior System (Roche, USA) according to the manufacturer’s protocol. Primary data treatment, primer/marker removal, chimeric sequences filtration, selection of sequences longer than 400 bp, and quality control of sequences were carried out in QIIME program (Caporaso et al., 2010b). Removal of chimeric sequences was completed with the help of DECIPHER's Find Chimeras web tool. URL: http://decipher.cee. wisc.edu/FindChimeras.html (accession date: 15.05.2014). Potentially biased singletons (see Kunin et al., 2010; Agogué et al., 2011) were excluded from the pyrotag data. OTUs picking operations and phylogenetic tree reconstruction Lake Onego reads were combined in a common dataset with Synechococcus reference sequences selected using three main criteria:

Table 1 Limnological parameters measured at Lake Onego sampling sites. Sampling dates were 2–8 August 2011. Mean values for three independent measurements are given. Sampling site

Lake maximal depth (m)

Sampling layer temperature (°C)

pH

O2 (mg/L)

Corg. (mg/L)

Chl a (mg/L)

Nammonium; nitrate; org. (mg/L)

Pinorg.; total (μg/L)

Kondopoga Bay, Station K3 (62°10′0.33″N, 34°16′47″E) Kondopoga Bay, Station K6 (62°4′53″N, 34°29′0.4″E) Big Onego, Station C1 (61°48′11″N, 35°27′53″E)

11 80 60

20.2 21.0 16.5

7.03 7.03 7.46

6.34 8.20 9.68

6.1 6.6 6.4

2.2 2.3 0.4

0.13; 0.06; 0.43 0.01; 0.09; 0.52 0.01; 0.17; 0.62

45; 101 1; 13 0; 9

Please cite this article as: Vasileva, A., et al., Metagenomic analysis in Lake Onego (Russia) Synechococcus cyanobacteria, J. Great Lakes Res. (2017), http://dx.doi.org/10.1016/j.jglr.2017.03.003

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i) freshwater origin; ii) length sufficient for reliable alignment; iii) ≥97% similarity with Lake Onego's. A collection of 770 Synechococcus references (Electronic Supplementary Material (ESM) Table S1) obtained via BLAST searches against the NCBI nucleotide database (nt) (http:// www.ncbi.nlm.nih.gov/BLAST/) represented the picocyanobacterial freshwater clade (Budinoff and Hollibaugh, 2007; Cai and Kong, 2013; Callieri et al., 2013; Castiglioni et al., 2004; Clingenpeel et al., 2011; Crosbie et al., 2003; Crump et al., 2007; Ernst et al., 2003; Felföldi et al., 2011a, 2011b; Fuller et al., 2003; Furtado et al., 2009; Honda et al., 1999; Ivanikova et al., 2007; Jasser et al., 2011; Jones et al., 2009; Katano et al., 2001; Kojima et al., 2012; Kolmonen et al., 2004; Leão et al., 2013; Liu et al., 2009; Lopes et al., 2012; Lyra et al., 2001; Mugnai et al., 2008; Mwirichia et al., 2011; Ohki et al., 2012; Ouellette et al., 2006; Rajaniemi-Wacklin et al., 2008; Robertson et al., 2001; Sánchez-Baracaldo et al., 2008; Schwartz et al., 2008; Shaw et al., 2008; Skopina et al., 2015; Somogyi et al., 2010; Sugita et al., 2007; Tang et al., 2009; Taton et al., 2006; Turner et al., 1999; Urbach et al., 1998; Van der Gucht et al., 2005; Vincent et al., 2000; West et al., 2001; Xing et al., 2009; Zhang et al., 2013; Zwart et al., 1998). The OTUs (operational taxonomic units) picking operations were performed in the QIIME program (Caporaso et al., 2010b). At the first stage, an initial filtration was employed to lower the number of analyzed sequences. To achieve that, the tags were aligned using the PyNAST algorithm (Caporaso et al., 2010a). As alignment template, the specially designed set of sequences “Greengenes corset” was used (URL: http://greengenes.lbl.gov/Download/Sequence_Data/Fasta_ data_files; date of accession: 15.05.2014). Then, aligned sequences were used for the initial 99.9%-similarity grouping of Lake Onego sequences together with reference sequences. For this purpose, the Mothur algorithm (Schloss et al., 2009) was used in the assembly of most similar sequences. At the second stage, one representative sequence was selected per each preliminary group based on the most abundant algorithm, and at the third stage chloroplast 16S rRNA gene sequences were removed based on the rdp method. At the fourth stage, filtered representatives of preliminary groups were used for the assignment of sequences to OTUs. Clustering methods can be subdivided into hierarchical and heuristic ones (Chen et al., 2013). Hierarchical methods are exemplified by the Mothur algorithm (Schloss et al., 2009), which distributes the bins among OTUs according to a calculated distance between sequence pairs. These methods are indisputably advantageous, because they compare all sequences between themselves, with a closest-to-reality result. However, hierarchical methods are extremely time consuming and, thus, they do not universally suit large-scale datasets. To avoid this drawback, heuristic methods implemented in the UCLUST algorithm can be used (Edgar, 2010). In particular, the heuristic M-pick algorithm yields OTUs via the % similarity-less, graph-based clustering (Wang et al., 2013). In this case, the sequences are initially filtered, so that some of them are considered “seeds”, i.e. the conditional focuses of future OTUs. The remaining sequences are then compared to the seeds. Given that certain sequences are definitely similar to a seed, the former are ascribed to an OTU. Because there is a preliminary stage, the heuristic clustering methods act faster, and are, therefore, more applicable to large datasets, although the reliability of results is lower compared with the hierarchical clustering methods. The interpretation of OTUs as phylotypes represents the next task; separate datasets require their own similarity thresholds that impede phylotyping even within an analysis, especially if the aligned sequences contain hyper-variable regions (Schloss and Westcott, 2011; Sun et al., 2012). This task can be also solved via the model-based clustering methods in the M-pick algorithm, in which only two parameters (ε and δ) should be set. In our case, the choice of ε = 0.025 and δ = 0.1 aimed at a maximum match between the obtained OTUs and the clusters which have been repeatedly mentioned in the literature (M-pick inventors recommended ε = 0.04 and δ = 0.1 for all taxons, although confined themselves to a species level only). Upon the

M-pick OTUs assignment, one representative sequence was selected per each OTU based on the most abundant algorithm. At the fifth stage, the alignment of OTUs representative sequences was performed with the MUSCLE algorithm (Edgar, 2004), and the phylogenetic tree was reconstructed via the Maximum Likelihood method (Nei and Kumar, 2000) in MEGA 6.0 (Tamura et al., 2013). The best-fit nucleotide substitution model was selected using the Modeltest program version 3.8 (Posada and Crandall, 1998). Evolutionary distances (in substitutions per 100 nt) were calculated using the 2-parameter distance model (Kimura, 1980). Bootstrap analysis based on 1000 repeats at nodes was evaluated by the appropriate function of the MEGA 6.0 program.

Diversity estimation and statistical analysis Calculations were carried out with the QIIME program (Caporaso et al., 2010b). To compare picocyanobacterial diversity in separate hydrographic stations, the Shannon index was calculated. To calculate differences between cyanobacterial communities in separate stations, the UniFrac method was used (Lozupone and Knight, 2005). Cluster statistical significance was determined via a bootstrap algorithm. To verify the influence of limnological parameters on cyanobacterial community composition, a correlation analysis using the Mantel test for 1000 permutations was carried out. All analytical procedures were conducted with normalized samples for the number of pyrotags across samples. As many as 2180 sequences were randomly chosen from each amplicon library which was equal to the lowest output for Station K3. The results were averaged for 10 random samples. Standard errors for all statistical analysis results were calculated in STATISTICA 7.0 (StatSoft Inc., Tulsa, OK, USA) at p b 0.001.

Strain isolation and culture methods Upon the cruise end, sampling filters were transferred to Petri dishes containing fresh 1% Bacto-agar (Difco, USA) modified BG-11 medium (Skopina et al., 2015) supplied with 250 μg/mL 80S-ribosome translation inhibitor cycloheximide (Serva, Germany). Sampling filters stored on agar plates at 20−22 °C were subjected to continuous illumination with 10 μmol photons/m2/s cool-white luminescent light (the same temperature and light culturing regimes were applied throughout). The emerging cyanobacterial colonies were transferred to 50 mL Erlenmeyer flasks containing modified BG-11 medium supplied with 250 μg/mL cycloheximide (Serva, Germany). Liquid cultures were kept unstirred for approximately two weeks. Cyanobacteria were separated from the accompanying eukaryotic microorganisms by repeated batch inoculations. Synechococcus strains were purified by means of dilution-to-extinction spreads onto 1% Bacto-agar/modified BG-11 medium, and the resulting growth was examined by light microscopy. Cultures were examined for purity on plates of modified BG-11 medium supplemented with glucose and Casamino acids (0.2 and 0.02%, respectively) as recommended by Rippka et al. (2000).

Morphology and pigment analysis Cell morphology was examined under a Leica 2500 microscope equipped with a DFC 500 camera (Leica Microsystems, Germany) under differential interference contrast. Сell size was measured under a Leica TCS SP5 confocal microscope (Leica Microsystems, Germany). Standard errors for the morphometric parameters in Synechococcus cultured strains were calculated in STATISTICA 7.0 (StatSoft Inc., Tulsa, OK, USA). Phycobiliprotein content was inferred from the fluorescence emission-specific peaks at 580 nm and 640–660 nm (fluorescence excitation wavelength, 545 nm) under a Leica TCS SP5 confocal microscope.

Please cite this article as: Vasileva, A., et al., Metagenomic analysis in Lake Onego (Russia) Synechococcus cyanobacteria, J. Great Lakes Res. (2017), http://dx.doi.org/10.1016/j.jglr.2017.03.003

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DNA extraction from cultured cyanobacteria We employed the DNA extraction method for unicellular cyanobacteria (Zinchenko et al., 1999), and used a variant of procedure applied to filter-attached cells (see above). The biomass obtained from a two-week old culture grown in liquid modified BG-11 medium was collected by centrifugation at 8000 ×g for 10 min at room temperature. The cell pellet was resuspended in 50 mM Tris-HCl/50 mM EDTA-Na2 (pH 8.0), centrifuged again, and subjected to DNA isolation. In contrast to the method used for filter-stored cells, cyanobacterial cells were incubated in the CTAB buffer/2-mercaptoethanol/Proteinase mixture for 1.5 h.

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oligotrophic (Carlson, 1977). The limnological parameters recorded at the sampling stations were close to previously reported values (Sabylina et al., 2010). 16S rRNA gene pyrosequencing data

Results and discussion

Pyrosequencing of the 16S rRNA gene libraries obtained via PCR with standard cyanobacterial primers yielded 42,142 reads. Reads of N 400 bp were used for the alignment. These reads were assigned to separate phylotypes via the M-pick algorithm (Wang et al., 2013). The analysis of high-throughput sequencing data involves a demarcation of OTUs which represent groups of sequences assembled under a defined similarity criterion. In most cases, 97% similarity is considered the consensus cutoff value between species (Stackebrandt and Ebers, 2006). However, according to the results obtained in a large-scale comparison of bacterial genomes, a more stringent value of 98.65% should be used instead of 97% criterion (Kim et al., 2014). In our case, we dealt with genetic diversity irrespective of taxonomy. Our choice of the M-pick algorithm for the assignment of nucleotide sequences to OTUs (see the Materials and methods) was of particular importance because the resulting image of the microbial community strongly depended on this procedure (White et al., 2010; Schloss, 2013). The bins were preliminarily distributed among groups with 99.9% inner homology using Mothur. This enabled us to limit the dataset volume without a distortion of the Synechococcus diversity pattern. Finally, representative sequences from these groups served for usage in M-pick. Upon quality control, a collection of 6875 sequences was obtained, 2187 sequences (17 OTUs) from Station K3, 2287 sequences (16 OTUs) from Station K6, and 2389 sequences (18 OTUs) from Station C1. In processing the combined data from all the stations, 12 OTUs represented by a sole sequence were obtained, and these singletons were omitted from further analysis.

Limnological characterization of sampling stations

Lake Onego cyanobacterial diversity

Our choice of sampling stations aimed to cover two regions (Fig. 1): Big Onego, oligotrophic (Station C), and Kondopoga Bay, anthropogenically enriched (Stations K3 and K6). Throughout the sampling period of 2nd–8th August 2011, water temperatures at all stations were typical for the phytoplankton vegetative season (Table 1). Additionally, the pH values were similar at all of the stations, although Station C1 exhibited an alkalinity shift possibly because of phytoplankton summertime activity. The dissolved oxygen content decreased from Station C1-K6-K3, which can be explained by anthropogenic input from Kondopoga City. Both total and organic nitrogen contents generally corresponded to the lake's mean values (Sabylina et al., 2010); ammonium nitrogen at Station K3 was 2.5 times the average value because of the proximity of Kondopoga City; nitrate nitrogen at the Kondopoga Bay stations was lower than the lake's mean value (Sabylina et al., 2010) possibly because of the intensive phytoplankton development. As with Big Onego, total phosphorus was extremely low at Stations C1 and K6, whereas at Station K3 it was relatively high and similar to that of other eutrophic water basins (Carlson, 1977). This was also likely the result of anthropogenic input from Kondopoga City. The N:P ratio is a principal nutrient parameter in aquatic ecosystems. A shift in values, in either direction, causes nutritional shortages in one or the other element (Rhee, 1972; Wehr, 1989), which suppresses large-size plankton growth while promoting that of picoplankton. Redfield N:P ratio of 16:1 is optimum, whereas an N:P b 16 indicates nitrogen limitation, and an N:P N 16 indicates phosphorus limitation (Geider and La Roche, 2002). The N:P ratios for Stations K3, K6, and C1 were 6.1, 47.7, and 88.9, respectively, indicating relatively low nitrogen in the first case, and relatively low phosphorus in the other two cases. The low chlorophyll a (see Table 1) indicated that the Kondopoga Bay stations were oligotrophic, whereas Station C1 was ultra-

The majority of 16S rRNA gene sequences (6693 pyrotags; 97.52%) belonged to the genus Synechococcus, and these sequences dominated in all stations (Fig. 2). Most of the remaining sequences corresponded to the filamentous cyanobacterium Anabaena (152 sequences; 2.22%), and they were only detected in the Station C1 samples (Table 2). 454-Pyrosequencing of DNA templates sampled at Station K3 yielded a small number of filamentous cyanobacteria from the genus Leptolyngbya (seven sequences; 0.1%). Finally, the samples collected at Stations K6 and C1 contained template DNA from the unicellular cyanobacterium Woronichinia (eight sequences; 0.12%), and DNA templates from Station K6 belonged to the unicellular cyanobacterium Snowella (three sequences; 0.04%) (Tables 2 and 3). Metagenomic analysis of Lake Onego cyanobacterial plankton revealed two unicellular genera (Woronichinia and Snowella) and two filamentous (the heterocyst forming Anabaena and the heterocyst nonforming Leptolyngbya) genera, in addition to Synechococcus. Cyanobacteria from the genera Woronichinia and Snowella often occur in freshwater plankton; they usually make up ~ 10% of cyanobacterial biomass although sometimes they amount to 93% of cells in blooms (Komárek and Komárková-Legnerová, 1992; Rajaniemi-Wacklin et al., 2005). Woronichinia and Snowella 16S rRNA gene sequences were distributed between OTU 1′ and OTU 2′ (≤ 5 sequences per sample). These OTUs may be considered as rare, or low abundance ones. Bacteria are often considered rare if they comprise up to 1% of the cells in a microbial sample, but others have used a cut-off of 0.1% or b0.01% (Fuhrman, 2009; Galand et al., 2009). Using percentages strongly depends on the total number of sequences, which has never been standardized. Hence, the percentage-based definition of rare OTUs is less useful than absolute values. The threshold of ≤5 of individual sequences was empirically determined for a soil microbiome (Elshahed et al.,

16S rRNA gene sequencing in cultured strains 16S rRNA gene fragments were amplified using the primers CYA106F and CYA789R. DNA was eluted from the gel slabs and purified as above. 16S rRNA gene amplicons were sequenced on a CEQTM8000 8-channel capillary sequencer according to the manufacturer's protocol (Beckman Coulter Inc., Fullerton, CA, USA). The reaction mixture contained 8 μL Dye Terminator Cycle Sequencing Quick Start Master Mix (Beckman Coulter Inc.), 2 μL 10× sequencing buffer, and 14 μL deionized water. The aliquots (8 × 3 μL) were mixed with 1.6 pmol of group-specific primers (see above) and 10–25 fmol of template DNA. PCR was performed with 30 cycles of denaturing at 96 °C (20 s), annealing at 50 °C (20 s), and synthesis at 60 °C (4 min). The PCR products were precipitated from cold 96% ethanol by centrifugation at 5000 ×g for 15 min, then washed and dried at room temperature. Prior to sequencing, the pellets were dissolved in Sample Loading Solution (Beckman Coulter Inc.).

Please cite this article as: Vasileva, A., et al., Metagenomic analysis in Lake Onego (Russia) Synechococcus cyanobacteria, J. Great Lakes Res. (2017), http://dx.doi.org/10.1016/j.jglr.2017.03.003

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A. Vasileva et al. / Journal of Great Lakes Research xxx (2017) xxx–xxx

Fig. 2. Distribution of PCR-detected Lake Onego cyanobacteria among genera (%). Stations K3, K6 and C1 represent sampling sites. The 0–90% interval is occupied with Synechococcus pyrotags.

2008), and was later extended to other microbiomes, e.g., the human oral cavity and an anaerobic sulfide- and sulfur-rich spring sediment (Zaura et al., 2009; Youssef et al., 2010). The same threshold has been applied in our study. Importantly, we chose to exclude singletons, and since our sequencing depth was fairly low we may have missed additional rare cyanobacterial taxa. The rarity of Woronichinia and Snowella was possibly related to (or even resulted from) seasonal phytoplankton dynamics (Pedrós-Alió, 2012). These cyanobacteria may have decreased in abundance under a change in environmental conditions while other genera transiently predominated. Affiliation of Lake Onego Synechococcus: an updating appraisal of freshwater Synechococcus traditional assemblages To affiliate the Synechococcus OTUs, we compared them with traditional assemblages using new sequence data from Lake Onega. As a whole, cyanobacteria of the genus Synechococcus are known to comprise a separate clade on dendrograms constructed with the application of various phylogeny markers, marine strains standing apart from their freshwater counterparts (Urbach et al., 1998). Freshwater Synechococcus strains are assembled into separate phylotypes consisting of either 16S rRNA gene sequences or cpcBA-IGS sequences of the phycocyanin operon. Synechococcus strains are subdivided into PE- and PC-rich, and these pigments were long considered a reliable discriminatory marker. However, strains of both kinds were later found to coexist in some phylotypes. Notably, no other diagnostic characters that strictly correlate with Synechococcus phylogeny - except for 16S rRNA gene sequences - have ever been reported (Ernst et al., 2003). Until

now, as many as 28 assemblages have been distinguished based on partial 16S rRNA gene sequences (see ESM Table S2); in this work, their names are according to those used in most of publications. Unfortunately, the preferred elaborate clusterization using 16S rRNA gene sequence data (which we also used in our analysis) was insufficiently accurate: the same sets of sequences often appeared under different names, and assemblages were arbitrarily termed “clusters”, “clades” or “groups”. For more clarity, we indicated all known names and synonyms in 28 traditional assemblages (ESM Tables S2; S3). These assemblages initially embraced sequences of distinct geographical origins, as well as cultured strains showing similar phycobiliprotein compositions (see ESM Table S1); other unifying characters were considered later. Interestingly, Cluster LS I and Cluster LS II (see ESM Table S2) stood out with respect to their geographic origin. These clusters have been established based on clonal sequences detected in Lake Superior only (Ivanikova et al., 2007), although Cluster LS II was later shown to also include sequences from European and Asian samples. The discovery of these clusters led to a suggestion that the lacustrine Synechococcus ecosystems may contain geographically limited phylotypes. This hypothesis was corroborated by a recent finding that Lake Ladoga Group LL picoplankton (see ESM Table S2) consists of nearly endemic phylotypes, i.e. those showing a very limited geographical distribution (Skopina et al., 2015). Importantly, some of the above-mentioned 28 assemblages were established based on less than 60, or in some cases, less than 20 sequences and thus obtained insufficient statistical support. Initially, in our tree reconstructions we tested various bin combinations, with special attention paid to the validity of previously established assemblages. Our result showed that most of traditional assemblages were weakly

Table 2 Distribution of main non-Synechococcus OTUs between Lake Onego limnological stations. The values reported are % of sequences total; zero, no sequences found. Phylotype

Station K3

Station K6

Station C1

All stations

Closest match; GenBank accession; similarity

OTU 1 OTU 2

0.32 0

0 0

0 6.36

0.10 2.22

Uncultured Leptolyngbya sp. clone SV 1077; JQ859357; 96% Anabaena variabilis CIBNOR 23; AY274616; 98%

Please cite this article as: Vasileva, A., et al., Metagenomic analysis in Lake Onego (Russia) Synechococcus cyanobacteria, J. Great Lakes Res. (2017), http://dx.doi.org/10.1016/j.jglr.2017.03.003

A. Vasileva et al. / Journal of Great Lakes Research xxx (2017) xxx–xxx

7

Table 3 Distribution of low-abundance non-Synechococcus OTUs between Lake Onego limnological stations. The values reported are numbers of sequences; zero, no sequences found. Phylotype

Station K3

Station K6

Station C1

All stations

Closest match; GenBank accession; similarity

OTU 1′ OTU 2′

0 0

5 3

3 0

8 3

Uncultured Woronichinia sp. clone K3-09; GU784981; 99% Snowella litoralis OTU37S04; AJ781040; 99%

supported; ESM Fig. S1 gives an example of a tree with low bootstrap support. Moreover, nodes lost support when supplemented with the formerly unavailable or ignored 16S rRNA gene sequences. An example was the MH 305 cluster (Ivanikova et al., 2007; Felföldi et al., 2011b); sequence MH 305, on which this phylotype has been established, stood apart in the recalculated trees. Therefore, we attempted an extended phylogenetic analysis on a specially compiled database of 770 Synechococcus picocyanobacteria reference sequences (see ESM Table S1). We used sequence data from GenBank because curated databases (e.g. SILVA) often have incomplete (e.g. missing taxonomic or georeferencing data) or inaccurate records. Therefore, we combined 770 published sequences describing Synechococcus with our pyrosequencing data to assess the diversity of these cyanobacteria from Lake Onego. Using the M-pick algorithm we identified 45 freshwater Synechococcus phylotypes. Some of these phylotypes represented traditional assemblages enriched with extra 16S rRNA gene sequences. For instance, Group F corresponds to FSP 2. Another example was the “Marine Synechococcus clade”, which corresponds to FSP 45. In contrast, some traditional assemblages were mixtures of sequences that were in neighboring positions on dendrograms published elsewhere. Thus, the sequences previously attributed to Clusters LS I and LS II clustered with the sequences from Lake Biwa, Lake Mondsee, Eastern Tibetan Plateau lakes, and small lakes in the USA and UK. Therefore, the suggested endemism of Clusters LS I and LS II (Ivanikova et al., 2007) should be questioned. A summary of relationship between FSP 1–45 and freshwater Synechococcus traditional assemblages is given in Table 4. Lake Onego Synechococcus genetic diversity Metagenomic analysis of Lake Onego cyanobacteria produced 16 OTUs containing partial Synechococcus 16S rRNA gene sequences (Fig. 3). The largest portion of these sequences (47.47%) belonged to OTU VII which is in FSP 14. This phylotype contained sequences from various habitats and lakes of different trophic levels; it incorporated the traditional Clade III, Clade II (except two sequences), MH 305 cluster (except the MH 305 sequence itself), some sequences from the Halotolerants Athalassohaline crater-lakes cluster, Novel clade (Callieri et al., 2013), and Group A = Cyanobium gracile cluster. The OTU VII sequences seemed to be homogeneously distributed in Lake Onego, whereas the abundance of smaller phylotypes varied between sampling sites. Thus, in the near-shore Station K3, OTU X (27.61% of local sequences) and OTU XV (11.61%) were observed which belong to FSPs 15 and 1, respectively. FSP 15 included the traditional Group I and Subalpine cluster II, some sequences from the L. Nahuel Huapi Argentina cluster and Cluster PD II. Group I, which contained sequences from various water reservoirs, athalassohaline lakes in particular, incorporated two database sequences previously ascribed to Group LL (Skopina et al., 2015), a sequence from Group A = Cyanobium gracile cluster, and several formerly obtained outgroup sequences. In the near-shore Station K6, OTU III (21.41% of local sequences), OTU V (13.65%), and OTU VI (9.96%) were observed which belong to FSPs 7, 8, and 13, respectively. FSP 7 includes some sequences from the Group A = Cyanobium gracile cluster, Lake Biwa strains group E, and Lake Biwa strains group LBB3. Although the strains in this phylotype were isolated in different continents from water reservoirs of various types, their preferred niche was possibly oligotrophic or mesooligotrophic. Minor FSP 8 was mainly represented by sequences from

the Lake Biwa strains group E, which inhabited ultra-oligotrophic, mesotrophic, and eutrophic water reservoirs. FSP 13 incorporated the Bornholm Sea cluster, Group Cz, some sequences from Cluster G, Lake Biwa strains group E, Lake Biwa strains group LBB3, and a sequence from cluster L4. This phylotype was found in various aquatic ecosystems; as many as half of the strains were isolated from oligotrophic, mesotrophic, and eutrophic lakes, whereas the remaining strains originated from marine estuaries, saline ponds, and brackish marshlands. In the offshore Station C1, OTU XIII (10.25% of local sequences), OTU XV (9.98%), and OTU X (9.31%) were observed which belong to FSPs 20, 1, and 15, respectively. FSP 20 contains only two strains formerly ascribed to the Halotolerants Athalassohaline crater-lakes cluster and Novel clade (Callieri et al., 2013). The first strain was isolated from the athalassohaline Lake Aljojuca, Mexico, whereas the other strain originated from the oligotrophic Lake Lago Maggiore, Italy. Noteworthy, these strains are rare, and they inhabit extreme environments. Because this is the first large-scale metagenomic survey of freshwater Synechococcus, the study of correspondence between Lake Onego phylotypes and those in other large lakes should be continued. A substantial basis for comparison could be sought in the data on Lake Superior. Here, as many as 296 partial Synechococcus 16S rRNA gene sequences were obtained via clone library construction (Ivanikova et al., 2007). Most of them (71.6%) came from FSP 12, which also included sequences from oligotrophic lakes in the Eastern Tibetan Plateau (also obtained via clone libraries) and sequences from Lake Mondsee (obtained via cultured isolates). Although this phylotype might be characteristic of oligotrophic lakes, it represented only 0.27% of the Lake Onego 16S rRNA gene sequences, and it was completely absent from Station K6. The remaining Lake Onego Synechococcus from FSP 9 (2.47% of all sequences), FSP 16 (1.7%), FSP 17 (1.24%), FSP 5 (1.02%), FSP 6 (0.57%) and FSP 4 (0.3%) represented cosmopolitan strains inhabiting freshwater basins of different ecological statuses. Also, FSPs 34 and 35 were observed in Lake Onego; one pyrotag (in the first case) clustered with a sequence from Lake Superior, while the rest clustered with Lake Balaton sequences. However, in Lake Onego, these pyrotags only accounted for 0.63% and 0.27% of the total. A similar comparison can be made with Lake Ladoga (Skopina et al., 2015), where a metagenomic analysis yielded ~ 150 Synechococcus pyrotags, most of which belonged to MH 305 cluster and Lake Biwa groups (which partially corresponded to FSPs 13 and 14; see Table 4). We should point out that the majority of freshwater Synechococcus phylotypes contained relatively few reference sequences (see Table 4). In contrast, FSPs 4, 5, and 12 included most of reference sequences. Besides, none of the phylotypes were solely composed of Lake Onego sequences. Finally, no Synechococcus OTUs were particularly low in abundance (≤ 5 of individual sequences). In conclusion, most Lake Onego Synechococcus tags belonged to phylotypes that are ubiquitous in freshwater systems. Furthermore, 10.25% of the sequences from Station C1 constituted the low-abundance FSP 20 and demonstrated a limited geographical distribution; in other words, these cyanobacteria may be nearly endemic.

Synechococcus diversity indexes Diversity estimates revealed differences between the hydrographic stations. The Shannon diversity values calculated for the near-shore Stations K3 and K6 and the offshore Station C1 were 2.30 ± 0.05, 2.53 ± 0.07, and 2.28 ± 0.05, respectively. Therefore, diversity differed

Please cite this article as: Vasileva, A., et al., Metagenomic analysis in Lake Onego (Russia) Synechococcus cyanobacteria, J. Great Lakes Res. (2017), http://dx.doi.org/10.1016/j.jglr.2017.03.003

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A. Vasileva et al. / Journal of Great Lakes Research xxx (2017) xxx–xxx

Table 4 Relationship between FSPs and published Synechococcus phylotypes for the reference sequences and the pyrosequencing data collected from Lake Onego. Phylotype Number of reference sequences

Traditional assemblage

FSP 1 FSP 2 FSP 3 FSP 4 FSP 5 FSP 6 FSP 7

12 6 3 86 80 24 20

FSP 8 FSP 9 FSP 10 FSP 11 FSP 12

8 19 16 11 281

FSP 13

58

FSP 14

51

FSP 15

32

FSP 16

13

FSP 17

7

FSP 18 FSP 19 FSP 20

6 3 2

FSP 21 FSP 22 FSP 23 FSP 24 FSP 25

2 2 1 1 1

FSP 26 FSP 27 FSP 28 FSP 29 FSP 30 FSP 31 FSP 32 FSP 33 FSP 34 FSP 35 FSP 36 FSP 37 FSP 38 FSP 39 FSP 40 FSP 41 FSP 42 FSP 43 FSP 44 FSP 45

1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 6

Group LL (Skopina et al., 2015) Group F (Felföldi et al., 2011b) Antarctic strains (Vincent et al., 2000) Group A = Cyanobium gracile cluster (Ernst et al., 2003) Group B = Subalpine cluster I (Ernst et al., 2003) Group M (Jasser et al., 2011) Lake Biwa strains group LBB3 (Budinoff and Hollibaugh, 2007) Lake Biwa strains group E (Crosbie et al., 2003) MH 301 cluster (Ivanikova et al., 2007) Cluster PD I (Ivanikova et al., 2007) Cluster G (Budinoff and Hollibaugh, 2007) Lake Biwa strains group H (Crosbie et al., 2003), Cluster LS I (Ivanikova et al., 2007), Cluster LS II (Ivanikova et al., 2007). Bornholm Sea cluster (Ernst et al., 2003), Lake Biwa strains group E (Crosbie et al., 2003), Lake Biwa strains group LBB3 (Budinoff and Hollibaugh, 2007), Cluster G (Budinoff and Hollibaugh, 2007), Group Cz (Jasser et al., 2011), L4 (Lopes et al., 2012). Group A = Cyanobium gracile cluster (Ernst et al., 2003), Clade II (Budinoff and Hollibaugh, 2007), MH 305 cluster (Ivanikova et al., 2007), Cluster PD I (Ivanikova et al., 2007), Clade III (Sánchez-Baracaldo et al., 2008), Halotolerants Athalassohaline crater-lakes cluster (Callieri et al., 2013), L. Atexcac Mexico cluster (Callieri et al., 2013), Novel clade (Callieri et al., 2013). Group I (Crosbie et al., 2003), Subalpine cluster II (Ernst et al., 2003), Cluster PD II (Ivanikova et al., 2007), L. Nahuel Huapi Argentina cluster (Callieri et al., 2013). Lake Biwa strains group E (Crosbie et al., 2003), Group B = Subalpine cluster I (Ernst et al., 2003), LB03 cluster (Felföldi et al., 2011b). Lake Biwa strains group LBB3 (Budinoff and Hollibaugh, 2007), Halotolerants Athalassohaline crater-lakes cluster (Callieri et al., 2013). Ungrouped Ungrouped Halotolerants Athalassohaline crater-lakes cluster (Callieri et al., 2013), Novel clade (Callieri et al., 2013). Group A = Cyanobium gracile cluster (Ernst et al., 2003) Novel clade (Callieri et al., 2013) Clade II (Budinoff and Hollibaugh, 2007) Group B = Subalpine cluster I (Ernst et al., 2003) Halotolerants Athalassohaline crater-lakes cluster (Callieri et al., 2013) L. Atexcac Mexico cluster (Callieri et al., 2013) L. Atexcac Mexico cluster (Callieri et al., 2013) L. Atexcac Mexico cluster (Callieri et al., 2013) MH 305 cluster (Ivanikova et al., 2007) Novel clade (Callieri et al., 2013) Novel clade (Callieri et al., 2013) Novel clade (Callieri et al., 2013) Novel clade (Callieri et al., 2013) Cluster PD II (Ivanikova et al., 2007) Ungrouped Ungrouped Ungrouped Ungrouped Ungrouped Ungrouped Ungrouped Ungrouped Ungrouped Ungrouped Marine Synechococcus clade (Ernst et al., 2003)

significantly between Stations K3 and K6 and between Stations K6 and C1 (р b 0.001), but not between Stations K3 and C1 (p N 0.05). The relatively low Shannon diversity values observed at all stations were unusual for microbial ecosystems. This is perhaps because the

entire Synechococcus community shifted toward a single dominant representative, presumably FSP 14, which comprised ~ 50% of the sequences obtained (see above). Because Station K6 had the highest Shannon index value, this sampling site encompassed a broader diversity of Synechococcus compared with the other two locations. Comparative analysis of Synechococcus communities from different sampling stations The application of cluster analysis by weighted UniFrac revealed a difference between the Synechococcus communities that was most pronounced between Stations K6 and C1 (Table 5). These habitats have different ecological statuses, and so the distinction between local Synechococcus communities was not a surprise. However, the limnological parameters at Stations K6 and C1 contrasted with those at Station K3 (see Table 1). Therefore, it could be hypothesized that none of these parameters influence the Synechococcus community. Stations K3 and C1 and Stations K3 and K6 are close to each other. The fact that Station K6 stood apart from other stations (although it is in close proximity to Station K3, and has similar limnological parameters to Station C1) has no simple explanation; in this case, some unaccounted environmental factors may be operating. The structure of aquatic microbial communities is affected by a variety of environmental factors: water temperature, pH, O2/N/P/Corg., and light intensity/quality among others (Methé and Zehr, 1999; Yannarell and Triplett, 2004; Lindström et al., 2005; Yannarell and Triplett, 2005; Shade et al., 2008). The Mantel test results revealed no significant correlation between Synechococcus community composition and any of the above limnological parameters (p N 0.05) (Table 6). The absence of a correlation between spatial distribution of the lake's Synechococcus community and measured limnological parameters measured is surprising. The first reason may be that Synechococcus diversity has been classified according to the 16S rRNA genes' sequences rather than the housekeeping genes' sequences. Hence, resultant phylotypes should not necessarily distribute themselves among local niches with different sets of not investigated limnological parameters. Another reason may be found in the Baas Becking-Beijerinck anti-endemism principle “Everything is everywhere” (De Wit and Bouvier, 2006) if one applies this principle to a distinct territory. Finally, one cannot exclude amplification biases which may misrepresent the picture of Synechococcus distribution. Cultured Lake Onego Synechococcus In addition to the metagenomic analysis, we isolated two cultured Synechococcus strains, which, once rendered axenic, were deposited in the St. Petersburg State University culture collection (CALU). They belonged to the picocyanobacteria because their mean size was ~1 × 2 μm (ESM Table S4), and they were elongated in form (ESM Fig. S2). The difference in dimensions was not statistically significant (p N 0.05) and thus size could not be used as a method to distinguish among these strains. According to the autofluorescence maximum at 630–650 nm, both cultured strains possessed phycocyanin but lacked phycoerythrin (575–580 nm maximum absent). Marine Synechococcus are mostly PE-rich cyanobacteria; however, their freshwater counterparts are either PE- or PC-rich (Callieri et al., 2013). Water transparency can determine which phycobiliprotein is present; red-colored strains preferentially inhabit clear waters, whereas in turbid waters blue-green strains predominate. Because Lake Onego is highly transparent, especially in offshore regions, the majority of Synechococcus strains are likely PE-rich, although further investigation with flow cytometry is required to confirm this. Vertical distribution of marine picocyanobacteria depends on both light intensity and light quality. The first aspect is exemplified by high light (HL) and low light (LL) Prochlorococcus, respectively (Huang et

Please cite this article as: Vasileva, A., et al., Metagenomic analysis in Lake Onego (Russia) Synechococcus cyanobacteria, J. Great Lakes Res. (2017), http://dx.doi.org/10.1016/j.jglr.2017.03.003

A. Vasileva et al. / Journal of Great Lakes Research xxx (2017) xxx–xxx

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Fig. 3. Synechococcus sequence distribution among OTUs, phylotypes, and hydrographic stations. Table (right) shows % of sequences for a separate station (rows 1–3) or for all stations (row 4). The dendrogram (left) was obtained via Maximum Likelihood method (Nei and Kumar, 2000). Statistical significance values of branching were determined via a bootstrap analysis of 1000 alternative trees. FSP - freshwater Synechococcus phylotype. Asterisks indicate cultured strains. - No sequences obtained.

al., 2012). Deep-sea Synechococcus strains are PE-rich due to a lower penetration of long wavelength light into the euphotic zone (Scanlan et al., 2009). Taking into account that Lake Onego's samples were obtained from the near-surface water layer, preferential isolation of PElacking strains is not surprising. The absence of PE-rich representatives among our strains may be also due to enrichment and isolation conditions; different cyanobacteria variously adapt themselves to light, temperature, and nutrients regimes which are principally unlike environmental ones. In other words, the strains obtained may result from the laboratory selection. Finally, it may be hypothesized that, as more strains are cultured, PE-rich ones will be also obtained.

Table 5 Distances between Lake Onego cyanobacterial communities calculated via weighted UniFrac method (unitless values).

Station K3 Station K6 Station C1

Station K3

Station K6

Station C1

0 0.01895 0.01889

0.01895 0 0.02501

0.01889 0.02501 0

The CALU-1741 and CALU-1745 strains clustered with FSPs 20 and 4, respectively (see Fig. 3). Although FSP 20 was highly abundant at Station C1 (second highest number of tags), strain CALU-1741 was isolated from the material sampled at Station K6 in Kondopoga Bay but not in Big Onego (FSP 20 abundance was low at Station K6; 1.97% of local sequences). FSP 4, which was most often represented by cultivated Table 6 Correlation analysis data obtained for Lake Onego limnological parameters calculated via Mantel test. Limnological parameter

Correlation factor (r)

Significance value (p)

Water temperature pH O2 Corg. Chlorophyll a Ntotal Norg. Nammonium Nnitrate Ptotal Pinorg.

0.69 0.52 0.64 0.77 0.56 0.52 0.39 1.00 0.17 1.00 1.00

0.51 0.65 0.68 0.52 0.50 0.69 1.00 0.32 0.83 0.35 0.31

Please cite this article as: Vasileva, A., et al., Metagenomic analysis in Lake Onego (Russia) Synechococcus cyanobacteria, J. Great Lakes Res. (2017), http://dx.doi.org/10.1016/j.jglr.2017.03.003

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A. Vasileva et al. / Journal of Great Lakes Research xxx (2017) xxx–xxx

isolates rather than clonal sequences, was observed at low frequencies at all stations. Therefore, it could be assumed that picocyanobacteria of these phylotypes belonged to easily cultured Synechococcus strains. Actually, culture conditions may further the development of lowabundance environmental bacteria (Pedrós-Alió, 2012). However, Synechococcus picocyanobacteria are, as a rule, difficult to culture, likely as a result of ineffective adaptation to laboratory stresses (Ernst et al., 2003, 2005). Conclusions We provided the first description of the diversity of Lake Onego cyanobacteria using 454 pyrosequencing. The cyanobacterial picoplankton shared common features with that of other large lakes. Thus, most of the 16S rRNA gene sequences (97.52%) recovered from the lake's epilimnion belonged to Synechococcus OTUs, whereas Woronichinia and Snowella (≤ 5 sequences per library) which are, respectively, the members of Gomphosphaerioideae and Merismopedioideae families, represented a minority of cyanobacterial abundance. We used 770 published Synechococcus 16S rRNA gene sequences and 6875 newly collected sequences from Lake Onego. We identified 17 OTUs that did not always cluster consistently with existing groups. Therefore, we re-organized the data set into 45 FSPs that we used to examine diversity of the Synechococcus cyanobacteria from Lake Onego. The majority of the Lake Onego Synechococcus sequences (47.47%) belonged to OTU VII within FSP 14 which comprised strains from various habitats; in contrast, the low-abundance FSP 20 (5% of sequences) proved nearly endemic. The study of connection between Lake Onego phylotypes and those in other large lakes should be continued because no analysis with similarly large number of freshwater Synechococcus sequences has been undertaken as yet. Cluster analysis via weighted UniFrac did not reveal any correlation between limnological parameters and the structure of Synechococcus communities, indicating that some untested environmental factors are responsible. Finally, only two strains were obtained in culture, confirming the low culturability of lacustrine picocyanobacteria (which in the case of boreal Lake Onego were subject to low temperature selection). To summarize, the obtained results contribute to a better understanding of Synechococcus diversity, abundance and biogeography. Amplicon library accession and the availability of nucleotide sequences in cultured strains The GenBank accession numbers for the 16S rRNA gene amplicon libraries of environmental cyanobacteria recovered from the hydrographic stations K3, K6, and C1 are SRR1291088, SRR1291090, and SRR1291093, respectively. Those for the partial Synechococcus 16S rRNA gene sequences from the cultured strains CALU-1741 and CALU1745 are KJ865414 and KJ865418, respectively. Authors' contributions A.V. and M.S. constructed amplicon libraries and processed most of the bioinformatics data obtained in this study. S.A. isolated Synechococcus cultured strains. O.G. obtained lake water samples, did the morphology and pigment analyses. N.I. prepared DNA templates and performed primary analysis of sequencing results. A.P. conceived the study, evaluated the results and wrote the paper. Acknowledgements We are grateful to the crew of the Ecolog research vessel for help with sampling. We thank A.V. Sabylina from the Institute of Northern Water Problems (Karelian Research Centre of Russian Academy of Sciences, Petrozavodsk, Russia) for providing the Lake Onego limnological parameters and J.L. Slastina from the same institute for technical help

with sampling. We are especially grateful to the team of Genomic Technologies and Cell Biology Center (Agricultural Microbiology Institute, Russian Academy of Sciences, St. Petersburg) for 16S rRNA gene pyrosequencing. We thank the staff of St. Petersburg University Resource Center for Development of Molecular and Cell Technologies who helped with 16S rRNA gene Sanger sequencing. We also gratefully acknowledge technical help of St. Petersburg University Resource Center “Chromas”. This research was financed in part by the Russian Foundation for Fundamental Research (grant no. 16-04-00174). We would like to express our gratitude to the anonymous reviewers, who helped to improve the manuscript especially from viewpoints of the cyanobacterial genetic diversity, ecology and spatial distribution. Appendix A. Supplementary data Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.jglr.2017.03.003.

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