Next Article in Journal
Root Transcriptomic Analysis Reveals Global Changes Induced by Systemic Infection of Solanum lycopersicum with Mild and Severe Variants of Potato Spindle Tuber Viroid
Previous Article in Journal
Comparative Virological and Pathogenic Characteristics of Avian Influenza H5N8 Viruses Detected in Wild Birds and Domestic Poultry in Egypt during the Winter of 2016/2017
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Viruses
Volume 11
Issue 11
10.3390/v11110991
viruses-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Metagenomic Analysis of Virioplankton from the Pelagic Zone of Lake Baikal

by
Sergey A. Potapov
1,*,
Irina V. Tikhonova
1,
Andrey Yu. Krasnopeev
1,
Marsel R. Kabilov
2,
Aleksey E. Tupikin
2,
Nadezhda S. Chebunina
1,
Natalia A. Zhuchenko
1 and
Olga I. Belykh
1
1
Limnological Institute Siberian Branch of the Russian Academy of Sciences, Irkutsk, Ulan-Batorskaya 3, 664033, Russia
2
Chemical Biology and Fundamental Medicine Siberian Branch of the Russian Academy of Sciences, Lavrentiev Avenue 8, Novosibirsk 630090, Russia
*
Author to whom correspondence should be addressed.
Viruses 2019, 11(11), 991; https://doi.org/10.3390/v11110991
Submission received: 24 August 2019 / Revised: 18 October 2019 / Accepted: 27 October 2019 / Published: 29 October 2019
(This article belongs to the Section Bacterial Viruses)
Browse Figures
Versions Notes

Abstract

:
This study describes two viral communities from the world’s oldest lake, Lake Baikal. For the analysis, we chose under-ice and late spring periods of the year as the most productive for Lake Baikal. These periods show the maximum seasonal biomass of phytoplankton and bacterioplankton, which are targets for viruses, including bacteriophages. At that time, the main group of viruses were tailed bacteriophages of the order Caudovirales that belong to the families Myoviridae, Siphoviridae and Podoviridae. Annotation of functional genes revealed that during the under-ice period, the “Phages, Prophages, Transposable Elements and Plasmids” (27.4%) category represented the bulk of the virome. In the late spring period, it comprised 9.6% of the virome. We assembled contigs by two methods: Separately assembled in each virome or cross-assembled. A comparative analysis of the Baikal viromes with other aquatic environments indicated a distribution pattern by soil, marine and freshwater groups. Viromes of lakes Baikal, Michigan, Erie and Ontario form the joint World’s Largest Lakes clade.

1. Introduction

Viral communities that inhabit the water column (virioplankton) represent the range of all viruses, including those that affect eukaryotes, as well as archaeal viruses and bacteriophages. Viruses can contain both RNA and DNA, and their numbers are comparable in aquatic environments [1]. Bacteriophages constitute a large part of viral communities [2] and are active participants in the microbial loop. They affect genetic diversity and control the number of heterotrophic bacteria and cyanobacteria, and this factor largely determines the content and structure of plankton [3].
The study of viral communities is difficult for several reasons. The primary reason is because only a small part of their hosts and viruses themselves can be cultivated. Moreover, because no one gene is common for all viral genomes, viral communities cannot be studied in the same way as bacteria and eukaryotes based on the analysis of 16S rRNA and 18S rRNA genes, respectively. Therefore, individual groups of viruses are studied by signature genes. For example, genes for capsid proteins, such as g20 and g23 [4,5,6], serve as targets for identification of T4-like myoviruses, the polA DNA polymerase gene [7] for podoviruses, the polB gene [8] for members of the family Phycodnaviridae and the RdRp gene [9,10] for RNA viruses.
Until recently, the focus of aquatic virologists was on the viromes of marine environments, whereas there were fewer studies of freshwater viromes. The first study of freshwater bodies investigated the viral communities in fish ponds [11]. Subsequently, studies characterised RNA viromes from Lake Needwood (Maryland, USA) [12] and the viral diversity from Lake Limnopolar (Antarctica) [13]. There were also metagenomic studies of viruses from two sites of an aquaculture facility [14], two lakes in France, Bourget and Pavin [15], four freshwater bodies located in the Sahara desert [16] and the Feitsui Reservoir in North Taiwan [17]. In 2013, metagenomic analysis of viral communities from East Lake (China) revealed high genetic diversity of viruses [18]. Recently, six arctic freshwater bodies in Spitsbergen (Svalbard, Norway) were studied and compared with an Antarctic lake. The authors revealed a taxonomic similarity in the investigated water bodies. However, the viromes differed at the fine-grain genetic level. These data indicate differences among dominant species of viruses. Single-stranded DNA (ssDNA) viruses predominated in Arctic viromes; most viruses remained unidentified [19]. The viromes of the Great Lakes, including Ontario and Erie, exhibited the dominance of bacteriophage sequences as well as a high content of plant and animal viruses; a comparative analysis indicated a similar composition of viruses in both lakes [20]. In the eutrophic Lake Matoaka (USA), sequences that belonged to tailed bacteriophages prevailed along with Podoviridae family members [21]. In the virome from Lake Michigan, like other similar viromes, most of the generated open reading frames (ORFs) were assigned to hypothetical proteins [22]. In Lake Lough Neagh (Northern Ireland), 85% of the virome did not have homologues in the extant sequence databases [23]. These data were consistent with most previous metagenomic analyses. Subsequent analysis of the annual dynamics in the content of the viral community indicated that 20% of the viruses were not detected during specific periods of the year [24].
To date, there have been no studies on sequencing viromes from the pelagic zone of Lake Baikal. Recent studies focused on the identification and genetic diversity of viruses of the Tevenvirinae subfamily (order Caudovirales, family Myoviridae) via the signature genes g20 [25] and g23 [26,27].
Lake Baikal is oligotrophic; it is the deepest lake in the world (1642 m) and has a large freshwater supply (2.36 × 104 km3). Geological, geographical and hydrological characteristics of the lake reflect its uniqueness and high endemism of aquatic organisms [28]. Although some studies characterised viruses of Lake Baikal [25,26,29,30], their genetic diversity remains insufficiently investigated.
Spring is an important period in Lake Baikal. According to long-term data, this period corresponds to the highest algal biomass and rates of primary production [31,32,33]. The studied periods are regarded as early spring (under-ice period: February, March and April) and late biological spring (May and June) according to Kozhov [34]. The aim of this study was to determine the taxonomic composition of viral communities in the pelagic zone of Lake Baikal during the under-ice and late spring (open-water) periods, perform functional annotation of genes and comparative analysis with viromes previously obtained from other lakes and assemble contigs and compare them to the RefSeq and GenBank databases.

2. Materials and Methods

2.1. Sample Collection and Sequencing

For the concentration of the viral fraction and DNA extraction, ~25 L samples were taken on 22 March and 8 June 2018, 7 km and 3 km, respectively, from the Listvyanka settlement (southern basin of Lake Baikal). From each layer (0, 5, 10, 15, 20, 25 and 50 m), 3.5 L were sampled and mixed to obtain an integrated sample of the 0–50 m layer. Subsequently, the sample was filtered through polycarbonate filters with a pore size of 0.4 μm (Millipore, Burlington, MA, USA) to remove phyto- and zooplankton. The filtrate was concentrated using a VivaFlow 200 tangential flow ultrafiltration system (Sartorius, Göttingen, Germany) to the final volume of ~20 mL. It was passed through a nozzle with a pore size of 0.2 μm (Sartorius, Göttingen, Germany) to remove bacteria and concentrated using a Vivaspin Turbo 15 (50 kDa; Sartorius, Göttingen, Germany) to a volume of ~100 μL. The samples were processed for several hours; during filtration and concentration, the samples were refrigerated at 4 °C.
To obtain free virus particles, the sample was treated with DNase (1000 U/mL; Thermo Fisher Scientific, Waltham, MA, USA) at 37 °C for 30 min. DNase was inactivated by addition of 20 μL 50 mM ethylenediaminetetraacetic acid (EDTA) at 65 °C for 10 min [35]. The presence of bacterial DNA was examined by polymerase chain reaction (PCR) using universal bacterial primers 27L (5’-AGAGTTTGATCATGGCTCAG-3’) and 1542R (5’-AAGGAGGTGATCCAGCCS-3’) [36] to confirm the removal of external bacterial DNA. PCR showed the absence of bands.
The samples were processed with sodium dodecyl sulphate (SDS) and proteinase K. DNA was extracted using the standard phenol-chloroform method. The DNA concentration was measured using a Qubit 2.0 Fluorimeter (Invitrogen, Carlsbad, CA, USA), according to the manufacturer’s instructions (~50 ng of DNA was obtained). The extracted DNA was stored at −80 °C.
DNA was fragmented with a Covaris S2 (USA), and libraries were prepared using the NEBNext Ultra II DNA Library Prep Kit for Illumina (New England Biolabs, Ipswich, MA, USA). Whole genome amplification technology was not used. The obtained DNA libraries were sequenced on a MiSeq device (Illumina, San Diego, CA, USA) using the Kit v3 2 × 300 reagents (Illumina, San Diego, CA, USA) in the “Genomics Core Facility” (ICBFM SB RAS, Novosibirsk, Russia).
The samples were labelled as follows: 7 km from the Listvyanka settlement (March)–BVP1, and 3 km from the Listvyanka settlement (June)–BVP2; BVP1_2 indicated the cross-assembled sample.

2.2. Water Chemistry Analyses

The concentrations of total phosphorus, total nitrogen, total organic carbon, nitrate, nitrite and chlorophyll a were determined as previously described [33]. The concentration of oxygen was measured using the SBE 25 Sealogger CTD (Sea-Bird Electronics, Bellevue, Washington DC, USA).

2.3. Microbial Enumeration

Bacteria were quantified by epifluorescence microscopy using the fluorochrome 4’,6’-diamidino-2-phenylindole dihydrochloride (DAPI) [37]. Picoplanktonic cyanobacteria were quantified by phycobilin autofluorescence. The number of bacteria, cyanobacteria and virus particles was estimated by filtration using polycarbonate filters with a pore size of 0.2 μm (Sartorius, Göttingen, Germany). SYBR Green I fluorochrome [35] and 0.02 µm filters (Whatman, Maidstone, England) were used to detect virus particles. Bacteria and virus particles were counted on an Axio Imager M1 fluorescence microscope (Zeiss, Oberkochen, Germany) equipped with an HBO 100W mercury lamp and an AxioCam camera (Pixera Corp., Santa Clara, CA, USA) with a 100× magnification lens.
Samples (1 L volume) for qualitative analysis of phytoplankton were fixed with Lugol’s solution and then concentrated by sedimentation. Algae were counted twice in a 0.1 mL Nageotte chamber under a “Peraval” light microscope with 720× and 1200× magnification. Thin cells of Synedra acus subsp. radians (Kütz.) Skabitchevsky formed by the sexual process did not have silica valves, and thus 50 mL samples were filtered onto 1 μm pore-size “Millipore” polycarbonate filters and stained by DAPI for visualisation of nuclei and chloroplasts. Subsequently, the cells were counted directly on the filter using an Axio Imager M1 microscope with 200× magnification.

2.4. Bioinformatics Analysis

The virome was analysed with the online pipeline Meta Genome Rapid Annotation using Subsystem (MG-RAST); raw data were uploaded [38].
The assembly and analysis of contigs included the following stages. Quality control was performed using the Fast QC program (http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Then, the obtained data were processed with Trimmomatic v. 0.36 [39], using the parameter SLIDINGWINDOW:4:20. Sequences shorter than 50 nucleotides were excluded from the analysis; adapters were removed. The SPAdes 3.13.0 metagenomics assembler, metaSPAdes, with default parameters was used for the de novo assembly. All calculations were performed on the HPC-cluster “Akademik V.M. Matrosov” (“Irkutsk Supercomputer Centre of SB RAS, http://hpc.icc.ru”).
Contigs were assembled with SPAdes using the VirSorter (v. 1.0.3) tool. The contigs that belonged to viruses were sorted out (Virome database). Contigs that were shorter than 5 kilobase pairs (Kbp) were removed before processing. The length of contigs was chosen as the most optimal ratio of recall value to precision value. This value, according to the simulation, is also the minimum necessary for the correct identification of viral sequences [40]. Then, the closest homologues were determined using blastn (DB RefSeq 2019 and GenBank 2019), with an e-value parameter of 10−3. To predict genes, the MetaGeneMark tool [41] was used; subsequently, a search in the NR NCBI (2019) database was performed manually with blastp. The contig and genome architectures were drawn using EasyFig [42]. To check the coverage of the Baikal contigs with reads, the programmes BWA (v. 0.7.17; MEM algorithm, Li H., [43]) and SAMtools (v. 1.9) [44] were used. The result was visualised in the IGV genome browser (v. 2.4.14) [45].
A comparative analysis of viromes was performed using the online web server http://www.metagenassist.ca. The dendrogram was obtained from the comparison of the Baikal viromes with 23 viromes from different sources (bay, ocean [46], river [47], lakes [13,20,21,22,23], soil [48], hydrothermal fluid [49] and an aquaculture facility [11]; Supplementary Information Table S1). For input data, the operational taxonomic unit (OTU) tables with the assigned taxonomy were used. The parameters were as follows: Column-wise normalisation–Log (generalised log2 transformation), Analyse by taxonomy–genus, Distance Measure–Pearson, Clustering Algorithm–Average.
The original unprocessed reads were uploaded to the MG-RAST server (BVP1 ID mgm4814173.3, BVP2 ID mgm4816981.3) and NCBI (SRA project PRJNA547700).

3. Results

3.1. Environmental Characteristics

Table 1 shows temperature, Secchi disk transparency, concentrations of total phosphorus, organic carbonate and nitrogen and the content of oxygen, nutrients and chlorophyll a in the pelagic zone of Lake Baikal. Based on these data, the trophic state of Lake Baikal was assigned as oligotrophic with mesotrophic characteristics, according to the Vollenweider & Kerekes classification [50]. The diatom Fragilaria radians (Kützing) D.M. Williams and Round, also known as S. acus subsp. radians (Kützing) Skabitchevsky (Algae Base taxonomy [51]), dominated phytoplankton in March and June. The number of virus particles under the ice was slightly higher than during the open-water period. On the contrary, the total number of bacteria decreased in June compared to March (Table 1).

3.2. Overview of the Lake Baikal Virome

The obtained total “raw data” were as follows: BVP1, 3223426, and BVP2, 4,136,035 reads (2 × 300) of 301 nucleotides in length; GC-content was 43% and 44%, respectively (Table 2).

3.3. Taxonomic Composition

The bulk of sequences (84.1% for BVP1 and 57.3% for BVP2) did not show any similarity with sequences from the databases (Figure 1). This fact has been stated in all previously obtained viromes. We assigned 8.8% (BVP1) and 4.5% (BVP2) of all annotated sequences to the Viruses domain.
The identified viruses mainly belonged to tailed bacteriophages of the order Caudovirales, which includes the families Myoviridae, Siphoviridae and Podoviridae. Among them, the Myoviridae phages predominated; they represented 51.7% (BVP1) and 62.4% (BVP2). The share of Siphoviridae was 28.1% (BVP1) and 14.4% (BVP2), and the contribution of Podoviridae was 9.3% (BVP1) and 12.4% (BVP2).
Overall, we identified 18 families of viruses that affect bacteria, algae, birds, fish, insects, humans, etc. Myoviridae, Siphoviridae, Podoviridae, Phycodnaviridae and Poxviridae comprised 97% of all identified families (Table 3).
The share of single-stranded viruses was 0.02% for BVP1 and 0.003% for BVP2.

3.4. Analysis of Sequences at the Genus Level

At the genus level, most annotated sequences belonged to T4-like viruses of the Myoviridae family. T4-like viruses are lytic bacteriophages; hence, this fact indicates a greater role of lytic phages in the plankton of Lake Baikal.
At the species level, in March (BVP1), Prochlorococcus phage P-SSM2 (3374) had the highest number of hits in the Myoviridae family, followed by Flavobacterium phage 11b (1719), part of the Siphoviridae family, and Synechococcus phage Syn5 (227), a member of the Podoviridae family. In June (BVP2), there were Prochlorococcus phage P-SSM2 (9716), Flavobacterium phage 11b (701) and Roseobacter phage SIO1 (655), part of the Podoviridae family.

3.5. Functional Analysis

MG-RAST uses several databases for the functional annotation of reads, including four databases that allow for hierarchical functional annotation: Kyoto Encyclopaedia of Genes and Genomes (KEGG) Orthology (KO), Clusters of Orthologous Groups (COG), eggNOG and SEED Subsystems. We applied a functional classification based on SEED Subsystems. The database searches against SEED in the MG-RAST subsystem resulted in 69,486 (BVP1) and 363,448 (BVP2) hits.
In the BVP1 sample, we identified 27.41% (19,044) of the classified reads as a part of the functional category “Phages, Prophages, Transposable elements, Plasmids” (Figure 2). These genes are associated with phage replication and packaging of virus particles (e.g., terminase, integrase, helicase and primase). “Phages, prophages” were the largest part of this group (97% of all classified reads in this group), whereas 1.3% of reads belonged to Gene Transfer Agents (GTA). In the functional category “Phages, Prophages, Transposable elements, Plasmids”, we assigned a small number of reads to the functional categories “Pathogenicity islands” (1.57%) and “Transposable elements and integrons” (0.16%). Notably, the subgroup with the most reads in the “Phages, prophages” category was “r1t-like streptococcal phages” (54.1%).
In the BVP2 sample, MG-RAST annotated 28 functional categories; each was subdivided into distinct subsystems. Some reads (9.6%, 34871) belonged to the functional category “Phages, Prophages, Transposable elements, Plasmids”. The largest part, 13.2% (47855), was the clustering-based subsystems category (e.g., biosynthesis of galactoglycans and related lipopolysaccharides; catabolism of an unclassified compound, etc. and other clusters identified as unclassified). The NULL subcategory included 29,684 sequences with the prevalence of Ribonucleotide reduction (3428) and Phosphate metabolism (3211) at level 3. The subgroup with the most reads in the “Phages, Prophages” category was “r1t-like streptococcal phages” (58.6%), similar to BVP1; phage protein and phage terminase annotations were most commonly identified.

3.6. Contig Analysis

The total number of obtained contigs was 255,462 for BVP1, 388,735 for BVP2 – 388,735 and 544,501 for BVP1_2 (cross-assembled). Further analysis included only contigs with a length of 5 Kb or more (Table 4).
After VirSorter processing, the number of contigs was 376 in BVP1, 776 in BVP2 and 1136 in cross-assembled BVP1_2. We annotated these contigs with blastn according to the Refseq 2019 and GenBank 2019 databases; the e-value was 10−3 (Table 5). At the first step, contigs were annotated using the Refseq database. Contigs that were not assigned to the closest relative at the first step were then annotated using the GenBank database. The remaining contigs were not assigned to the closest relative because they had very low similarity.
In the BVP1 sample, we annotated 242 contigs (156 RefSeq and 86 GenBank) of 376; 34 contigs had the closest cyanophage relative (Synechococcus or Prochlorococcus; 66.0–84.8% similarity); 13 contigs were similar to Yellowstone Lake virophage (66.9–86.7% similarity); 14 contigs belonged to Cellulophaga phage (70.2–88.4% similarity) and four contigs were Pelagibacter phages (73.0–86.4% similarity). Among the uncultivated viruses, the majority of hits belonged to Dishui Lake virophage 1, 11 contigs (67.6–79.7% similarity).
In the BVP2 sample, we annotated 527 contigs (377 RefSeq and 150 GenBank) of 776, among which 111 contigs had the highest similarity with cyanophages (66.4–87.1% similarity). Additionally, 20 contigs belonged to the Yellowstone Lake virophages (66.5–86.7% similarity), 18 contigs to the Pelagibacter phages (64.7–81.4% similarity) and 15 contigs to the Cellulophaga phage (72.7–88.4% similarity). Finally, 51 contig belonged to uncultivated Mediterranean phage (65.0–86.0% similarity).
In the cross-assembled BVP1_2 sample, we annotated 772 contigs (538 RefSeq and 234 GenBank) of 1136. A total of 151 contigs had the highest similarity with cyanophages (63.2–92.7% similarity), 36 contigs belonged to the Yellowstone Lake virophages (66.5–86.7% similarity), 20 contigs were part of Pelagibacter phages (65.4–90.4% similarity) and 39 contigs belonged to Cellulophaga phage (67.9–94.9% similarity). Finally, 81 contig belonged to uncultivated Mediterranean phage (63.2–84.7% similarity).
The number of annotated contigs with a length greater than 25 Kbp was 16 in BVP1, 31 in BVP2 and 54 in cross-assembled BVP1_2.
The BWA program was used to map back to the raw data. For BVP1, 14% of reads were mapped to VirSorter contigs, and 12.8% of reads for BVP2.
Table 5 shows the first 10 contigs for each assembly with a minimum e-value. Other annotated contigs are shown in Supplementary Table S2.
Figure 3 shows the genetic map of the BVP1_2_NODE_212 contig. Based on the analysis of the terminase large subunit, we classified this phage as closely related to the cultured marine Synechococcus phage Bellamy (MF351863) and uncultured Mediterranean phage (KT997817). According to the results of the MetaGeneMark analysis, the number of predicted ORFs was 32. We visualised the contig coverage with reads in the IGV genome browser (Supplementary Information Figure S1). Visualisation indicated no gaps and uniform coverage.

3.7. Comparative Analysis of Viromes

A dendrogram constructed with the agglomerative hierarchical clustering demonstrated a clear separation of viromes into groups: Soil, marine and freshwater (Figure 4). Viromes from Lake Baikal (BVP1 and BVP2) form a cluster with viromes of the world’s largest lakes (Michigan, Ontario and Erie). BVP1 has a separate branch in this cluster. BVP2 is located closer to viromes from Lakes Ontario and Erie. Viromes from Lakes Matoaka and Lough Neagh, as well as fish ponds, form a eutrophic cluster. Table 6 shows the main characteristics of the lakes.

4. Discussion

Using high-throughput sequencing, we obtained the first data on the content and structure of viral communities from the pelagic zone of Lake Baikal, the world’s oldest and largest lake in terms of area and water volume.
The Lake Baikal pelagic zone is an oligotrophic water body, as stated previously [52] and confirmed by our data on the concentration of total phosphorus, nitrogen, chlorophyll a content and water transparency. In May and June, there is a steady stratification of the water column that corresponded to the spring period (inverse thermal stratification). At Lake Baikal, early June refers to the period of biological spring. Water chemistry characteristics in March and June 2018 were similar in the interannual aspect compared to the previous years of observations; no changes were found. Notably, under the ice, the development level of the diatom F. radians, which dominated the plankton of the lake, was slightly higher compared to June, as was the concentration of chlorophyll a. In general, the concentration of chlorophyll a corresponded to the characteristics we previously observed in March and June 2016 at the same station [33]. The total number of virus particles and bacteria was similar to the same periods of other years [33,53].
Although the viral sequences in the two viromes showed significant and distinct diversity, they were similar in taxonomic composition; bacteriophages of the order Caudovirales (89.3%) dominated. In other freshwater bodies, viruses from the order Caudovirales also predominated among the virus fraction. Lake Limnopolar, lakes of the Svalbard archipelago and Lakes Bourget and Pavin are the exception. Bacteriophages can be easily isolated from natural samples and sequenced, and thus their sequences predominate in databases. This fact explains their prevalence in viromes [22], and our data support this statement.
In virioplankton of Lake Baikal, the bulk of the order Caudovirales comprised phages that belong to the Myoviridae family. The members of this family were also the most numerous in Lakes Erie and Ontario [20], East Lake (Aug 2009, Dec 2009, and June 2010) [18], Lake Michigan [22], in tropical water bodies of Singapore [54] and freshwater bodies of the Sahara desert [16].
Sequences of the Baikal viromes annotated from databases were mostly bacterial (85.2% for BVP1 and 92.9% for BVP2). The bacterial part is probably overvalued due to the erroneous classification of metavirome sequences of prophages because, according to databases, they are of bacterial origin [55].
Of special interest is a small number of viruses with ssDNA in Lake Baikal; their content was 0.02% (BVP1) and 0.003% (BVP2). The same low content was noted in Lake Lough Neagh (0.5%). On the contrary, the share of ssDNA viruses in Lakes Pavin and Bourget was 80% and 85%, respectively [15]. In the spring sample, ssDNA viruses dominated viromes of Arctic lakes (74%); in the summer sample, they were 9% [19]. This distinction is due to the difference in the preparation of metagenomic libraries for sequencing [56,57]. In most studies, sampling for virome sequencing was performed from the surface water layers (0–5 m). In some cases, the shallowness of the water body limited the sampling depth.
Notably, sequences of the viral fraction were mostly dissimilar to the sequences from databases [13,20]. This so-called “viral dark matter” [58] represents content and structure that remain unknown in all studied samples [18,20,21,23].
After assembling the reads, there were no whole genome sequences that exhibited significant similarity to the reference sequences. Long contigs (over 25 Kbp) represented 5% (BVP1), 5% (BVP2) and 5.6% (BVP1_2) of the sequences that belonged to viruses after VirSorter processing. The identified pool of the Baikal contigs probably represents endemic Baikal viruses, or it is associated with a small number of reference genomes from freshwater bodies in the database. In general, the cross-assembled method yields longer contigs.
The BVP1_2_NODE_212 contig was the most similar to the reference sequences and had a great number of known genes. According to the RefSeq database, Prochlorococcus phage P-TIM68 was the closest relative of this contig. Although Prochlorococcus species are marine cyanobacteria, previous studies determined that Prochlorococcus phages may contain several genes that are similar to other T4-type viruses. They are common for all cyanophages, and in our case the same picture is likely [59]. For example, in Lake Michigan many annotated viral proteins belong to Prochlorococcus phage P-SSM2 [22].
Comparative analysis of the Baikal viromes in functional categories demonstrated that the “Phages, Prophages, Transposable elements, Plasmids” category prevailed during the under-ice period. The predominance of this category likely indicates the active replication (reproduction) of viruses under the ice. There are more bacteria during this period. In the late spring, the clustering-based subsystems category contained the most sequences, together with the NULL subcategory, which represented the bulk of sequences from this category. The NULL subcategory may contain some wrong assignments, and this possibility may indicate either the uniqueness or lack of sequences with known functions in the SEED database.
Agglomerative hierarchical clustering of viromes clearly separated viromes into freshwater, soil and marine groups. Studies by other authors confirm such clustering [15,21].
On the dendrogram, the Baikal viromes are most closely located near viromes from the world’s other largest lakes in terms of area, such as Ontario, Erie and Michigan, and form the joint World’s Largest Lakes (WLL) clade. Lakes Michigan, Erie and Ontario are a part of the Great Lakes system in North America; they are located in temperate climate zone, like Lake Baikal. Lake Michigan was previously mesotrophic; currently, its trophic state is defined as oligotrophic [60]. Lake Erie is mesotrophic, and Lake Ontario is oligo-mesotrophic. Obviously, combining these viromes into the WLL cluster reflects several characteristics, including morphometry, geographical location and trophic state, all of which determine the content and structure of viral communities. Centric diatoms, primarily, members of the genera Aulacoseira and Stephanodiscus, are the drivers of the spring planktonic communities in the Great Lakes. In Lake Baikal, the complex of endemic diatoms, mainly of Aulacoseira baicalensis and Stephanodiscus meyeri, dominated under the ice. However, since 2007 Baikal phytoplankton have shown a change in the diatom communities: A smaller and weakly silicified diatom F. radians (same as S. acus) has dominated the spring plankton of the lake [33].
Similar to Lake Baikal, most viruses in the viromes of Lake Michigan belong to the order Caudovirales, inside which the families Myoviridae, Siphoviridae and Podoviridae dominate (27%, 23% and 9%, respectively, of the total number of known virus sequences) [22]. In viromes of Lakes Erie and Ontario, the double-stranded DNA (dsDNA) phages of the order Caudovirales were also the most numerous; the Myoviridae family predominated (79.7%). Podoviridae (7.9%) and Siphoviridae (4.5%) were the second and third predominant families [20]. Concomitantly, viruses that affect algae (the Phycodnaviridae family; 4.3%) and insects (and animals; the Iridoviridae family; 2.6%) were the most representative. Notably, viromes studied in Lakes Ontario, Erie and Michigan were sampled from the surface in the coastal area. In our case, we obtained viromes from the pelagic layer of 0–50 m. Nevertheless, they formed a joint cluster. As mentioned above, a separate branch represents BVP1 (under-ice period) in this cluster. The seasonal taxonomic shift can explain the observed nature of the separation of the Baikal viromes and their position on the dendrogram because viromes from Lakes Michigan, Ontario, Erie, and BVP2 were sampled in the summer, namely June and July.
Two clusters comprised the neighbouring eutrophic clade: Viromes from fish ponds and viromes from eutrophic shallow lakes Lough Neagh (Ireland) and Matoaka (USA).
River viromes, which group with viromes from Lake Limnopolar, represent a separate branch of the freshwater clade. This clustering is likely due to the similar content of the dominant virus families. In Lake Limnopolar, ssDNA viruses of the families Circoviridae, Nanoviridae and Microviridae prevail in the spring period, whereas in the summer period, Phycodnaviridae and bacteriophages of the order Caudovirales prevail. The families Microviridae and Myoviridae dominate in samples from the Amazon River. Viruses from the families Circoviridae, Podoviridae, Phycodnaviridae and Siphoviridae were also numerous.
After a comparative analysis of the results, we concluded that methodological specifics of sample preparation for high-throughput sequencing, read depth and bioinformatics data processing are crucial. Standardisation of virome processing requires platforms with an installed pipeline, such as MG-RAST, to avoid problems with different settings of processing programmes, sequence annotation and evaluation of alpha and beta diversity. The presence of bacterial DNA, as well as ultramicrobacteria (less than 2 μm in size) that can pass through a 0.2 μm filter, significantly complicates the analysis of viromes. In this study, we used prefiltration through filters with a pore size of 0.2 μm. The separation of virus particles in a gradient of caesium chloride [35] or chemical flocculation [61] may be possible alternatives. Notably, there is a lack of data on the whole genomes of viruses. The isolation and cultivation of virus strains followed by the characterisation of their genomes are an important component for future studies.

5. Conclusions

For the first time, we characterised the content of viruses in the pelagic zone of Lake Baikal, the world’s oldest and largest lake. The data showed the presence of a significant number of bacteriophage taxa compared to eukaryotic and archaeal viruses. Members of the order Caudovirales dominated the bacteriophages, with the prevalence of the Myoviridae family; Siphoviridae and Podoviridae were the second and third dominant families, respectively. We assembled the contig with a length of 29,000 bp, which belongs to cyanophage. A comparative analysis of viromes indicated their separation into marine, freshwater and soil clades and allowed us to assign the WLL cluster, which includes viral communities of Lake Baikal and the Great Lakes of North America.

Supplementary Materials

The following are available online at https://www.mdpi.com/1999-4915/11/11/991/s1, Supplementary Information, Table S1: Viromes used in building dendrogram; Table S2: Annotated contigs, Figure S1: Coverage contig BVP1_2 _212 by reads.

Author Contributions

S.A.P.: Sample processing, bioinformatics analysis and preparation of manuscript; I.V.T.: Sampling, sample processing and preparation of manuscript; A.Y.K.: Bioinformatics analysis, M.R.K., A.E.T.: sequencing; N.S.C.: Water chemistry analysis; N.A.Z.: Sampling and water chemistry analysis; O.I.B.: Sample processing and preparation of manuscript.

Funding

This work was supported by Basic Research Project № 0345-2018-0003 (AAAA-A16-116122110061-6) and Russian Foundation for Basic Research grants № 18-34-00513 (AAAA-A18-118032190060-5) and №18-54-05005 (AAAA-A18-118030190015-1).

Acknowledgments

All calculations were performed on an HPC-cluster ‘Academician V.M. Matrosov’, Irkutsk Supercomputer Centre SB RAS, http://hpc.icc.ru.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Steward, G.F.; Culley, A.I.; Mueller, J.A.; Wood-Charlson, E.M.; Belcaid, M.; Poisson, G. Are we missing half of the viruses in the ocean? ISME J. 2013, 7, 672–679. [Google Scholar] [CrossRef] [PubMed]
  2. Ackermann, H.-W. Bacteriophages: Tailed. In Encyclopedia of Life Sciences; John Wiley & Sons, Ltd.: Chichester, UK, 2007. [Google Scholar]
  3. Schwalbach, M.; Hewson, I.; Fuhrman, J. Viral effects on bacterial community composition in marine plankton microcosms. Aquat. Microb. Ecol. 2004, 34, 117–127. [Google Scholar] [CrossRef] [Green Version]
  4. Fuller, N.J.; Wilson, W.H.; Joint, I.R.; Mann, N.H. Occurrence of a sequence in marine cyanophages similar to that of T4 g20 and its application to PCR-based detection and quantification techniques. Appl. Environ. Microbiol. 1998, 64, 2051–2060. [Google Scholar] [PubMed]
  5. Zhong, Y.; Chen, F.; Wilhelm, S.W.; Poorvin, L.; Hodson, R.E. phylogenetic diversity of marine cyanophage isolates and natural virus communities as revealed by sequences of viral capsid assembly protein gene g20. Appl. Environ. Microbiol. 2002, 68, 1576–1584. [Google Scholar] [CrossRef] [PubMed]
  6. Filee, J.; Tetart, F.; Suttle, C.A.; Krisch, H.M. Marine T4-type bacteriophages, a ubiquitous component of the dark matter of the biosphere. Proc. Natl. Acad. Sci. USA 2005, 102, 12471–12476. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  7. Breitbart, M.; Miyake, J.H.; Rohwer, F. Global distribution of nearly identical phage-encoded DNA sequences. FEMS Microbiol. Lett. 2004, 236, 249–256. [Google Scholar] [CrossRef]
  8. Chen, F.; Suttle, C.A. Amplification of DNA polymerase gene fragments from viruses infecting microalgae. Appl. Environ. Microbiol. 1995, 61, 1274–1278. [Google Scholar] [Green Version]
  9. Culley, A.I.; Lang, A.S.; Suttle, C.A. High diversity of unknown picorna-like viruses in the sea. Nature 2003, 424, 1054–1057. [Google Scholar] [CrossRef]
  10. Culley, A.I.; Mueller, J.A.; Belcaid, M.; Wood-Charlson, E.M.; Poisson, G.; Steward, G.F. The characterization of rna viruses in tropical seawater using targeted PCR and metagenomics. mBio 2014, 5, e01210–e01214. [Google Scholar] [CrossRef]
  11. Dinsdale, E.A.; Edwards, R.A.; Hall, D.; Angly, F.; Breitbart, M.; Brulc, J.M.; Furlan, M.; Desnues, C.; Haynes, M.; Li, L.; et al. Functional metagenomic profiling of nine biomes. Nature 2008, 452, 629–632. [Google Scholar] [CrossRef]
  12. Djikeng, A.; Kuzmickas, R.; Anderson, N.G.; Spiro, D.J. Metagenomic analysis of RNA viruses in a fresh water lake. PLoS ONE 2009, 4, 1–14. [Google Scholar] [CrossRef] [PubMed]
  13. López-Bueno, A.; Tamames, J.; Velázquez, D.; Moya, A.; Quesada, A.; Alcamí, A. High diversity of the viral community from an Antarctic lake. Science 2009, 326, 858–861. [Google Scholar] [CrossRef] [PubMed]
  14. Rodriguez-Brito, B.; Li, L.; Wegley, L.; Furlan, M.; Angly, F.; Breitbart, M.; Buchanan, J.; Desnues, C.; Dinsdale, E.; Edwards, R.; et al. Viral and microbial community dynamics in four aquatic environments. ISME J. 2010, 4, 739–751. [Google Scholar] [CrossRef] [PubMed]
  15. Roux, S.; Enault, F.; Robin, A.; Ravet, V.; Personnic, S.; Theil, S.; Colombet, J.; Sime-Ngando, T.; Debroas, D. Assessing the diversity and specificity of two freshwater viral communities through metagenomics. PLoS ONE 2012, 7, e33641. [Google Scholar] [CrossRef]
  16. Fancello, L.; Trape, S.; Robert, C.; Boyer, M.; Popgeorgiev, N.; Raoult, D.; Desnues, C. Viruses in the desert: A metagenomic survey of viral communities in four perennial ponds of the Mauritanian Sahara. ISME J. 2012, 7, 359–369. [Google Scholar] [CrossRef]
  17. Tseng, C.-H.; Chiang, P.-W.; Shiah, F.-K.; Chen, Y.-L.; Liou, J.-R.; Hsu, T.-C.; Maheswararajah, S.; Saeed, I.; Halgamuge, S.; Tang, S.-L. Microbial and viral metagenomes of a subtropical freshwater reservoir subject to climatic disturbances. ISME J. 2013, 7, 2374–2386. [Google Scholar] [CrossRef] [Green Version]
  18. Ge, X.; Wu, Y.; Wang, M.; Wang, J.; Wu, L.; Yang, X.; Zhang, Y.; Shi, Z. Viral metagenomics analysis of planktonic viruses in East Lake, Wuhan, China. Virol. Sin. 2013, 28, 280–290. [Google Scholar] [CrossRef]
  19. Aguirre de Cárcer, D.; López-Bueno, A.; Pearce, D.A.; Alcamí, A. Biodiversity and distribution of polar freshwater DNA viruses. Sci. Adv. 2015, 1, e1400127. [Google Scholar] [CrossRef] [Green Version]
  20. Mohiuddin, M.; Schellhorn, H.E. Spatial and temporal dynamics of virus occurrence in two freshwater lakes captured through metagenomic analysis. Front. Microbiol. 2015, 6, 960. [Google Scholar] [CrossRef] [Green Version]
  21. Green, J.C.; Rahman, F.; Saxton, M.A.; Williamson, K.E. Metagenomic assessment of viral diversity in lake matoaka, a temperate, eutrophic freshwater lake in southeastern Virginia, USA. Aquat. Microb. Ecol. 2015, 75, 117–128. [Google Scholar] [CrossRef]
  22. Watkins, S.C.; Kuehnle, N.; Ruggeri, C.A.; Malki, K.; Bruder, K.; Elayyan, J.; Damisch, K.; Vahora, N.; O’Malley, P.; Ruggles-Sage, B.; et al. Assessment of a metaviromic dataset generated from nearshore Lake Michigan. Mar. Freshw. Res. 2016, 67, 1700. [Google Scholar] [CrossRef]
  23. Skvortsov, T.; De Leeuwe, C.; Quinn, J.P.; McGrath, J.W.; Allen, C.C.R.; McElarney, Y.; Watson, C.; Arkhipova, K.; Lavigne, R.; Kulakov, L.A. Metagenomic characterisation of the viral community of lough neagh, the largest freshwater lake in Ireland. PLoS ONE 2016, 11, 1–19. [Google Scholar] [CrossRef] [PubMed]
  24. Arkhipova, K.; Skvortsov, T.; Quinn, J.P.; McGrath, J.W.; Allen, C.C.R.; Dutilh, B.E.; McElarney, Y.; Kulakov, L.A. Temporal dynamics of uncultured viruses: A new dimension in viral diversity. ISME J. 2018, 12, 199–211. [Google Scholar] [CrossRef] [PubMed]
  25. Butina, T.V.; Potapov, S.A.; Belykh, O.I.; Damdinsuren, N.; Choidash, B. Genetic diversity of the family Myoviridae cyanophages in Lake Baikal. Seriya Biologiya. Ekol. Izvestiya Irkutsk. Gos. Univ. 2012, 5, 17–22. [Google Scholar]
  26. Potapov, S.; Belykh, O.; Krasnopeev, A.; Gladkikh, A.; Kabilov, M.; Tupikin, A.; Butina, T. Assessing the diversity of the g23 gene of T4-like bacteriophages from Lake Baikal with high-throughput sequencing. FEMS Microbiol. Lett. 2018, 365, fnx264. [Google Scholar] [CrossRef] [PubMed]
  27. Butina, T.V.; Belykh, O.I.; Maksimenko, S.Y.; Belikov, S.I. Phylogenetic diversity of T4-like bacteriophages in Lake Baikal, East Siberia. FEMS Microbiol. Lett. 2010, 309, 122–129. [Google Scholar] [CrossRef] [Green Version]
  28. Kozhova, O.M.; Izmest’eva, L.R. Lake Baikal: Evolution and Biodiversity, 2nd ed.; Backhuys Publishers: Leiden, Germany, 1998. [Google Scholar]
  29. Potapov, S.A.; Krasnopeev, A.Y.; Tikhonova, I.V.; Galachyants, A.D.; Podlesnaya, G.V.; Khanaev, I.V.; Belykh, O.I. Characterization of the genetic diversity of T4-like bacteriophages in benthic biofilms of Lake Baikal. Bull. Irkutsk State Univ. Ser. Biol. Ecol. 2018, 25, 15–31. [Google Scholar] [CrossRef]
  30. Butina, T.V.; Belykh, O.I.; Belikov, S.I. Molecular-genetic identification of T4 bacteriophages in Lake Baikal. Dokl. Biochem. Biophys. 2010, 433, 175–178. [Google Scholar] [CrossRef]
  31. Straškrábová, V.; Izmest’yeva, L.R.; Maksimova, E.A.; Fietz, S.; Nedoma, J.; Borovec, J.; Kobanova, G.I.; Shchetinina, E.V.; Pislegina, E.V. Primary production and microbial activity in the euphotic zone of Lake Baikal (Southern Basin) during late winter. Glob. Planet. Chang. 2005, 46, 57–73. [Google Scholar] [CrossRef]
  32. Popovskaya, G. Ecological monitoring of phytoplankton in Lake Baikal. Aquat. Ecosyst. Heal. Manag. 2000, 3, 215–225. [Google Scholar] [CrossRef]
  33. Bondarenko, N.A.; Ozersky, T.; Obolkina, L.A.; Tikhonova, I.V.; Sorokovikova, E.G.; Sakirko, M.V.; Potapov, S.A.; Blinov, V.V.; Zhdanov, A.A.; Belykh, O.I. Recent changes in the spring microplankton of Lake Baikal, Russia. Limnologica 2019, 75, 19–29. [Google Scholar] [CrossRef]
  34. Kozhov, M.M. Biologiya ozera Baikal (Biology of Lake Baikal); Akad. Nauk SSSR: Moscow, Russia, 1962. [Google Scholar]
  35. Thurber, R.V.; Haynes, M.; Breitbart, M.; Wegley, L.; Rohwer, F. Laboratory procedures to generate viral metagenomes. Nat. Protoc. 2009, 4, 470–483. [Google Scholar] [CrossRef] [PubMed]
  36. Brosius, J.; Dull, T.J.; Sleeter, D.D.; Noller, H.F. Gene organization and primary structure of a ribosomal RNA operon from Escherichia coli. J. Mol. Biol. 1981, 148, 107–127. [Google Scholar] [CrossRef]
  37. Porter, K.G.; Feig, Y.S. The use of DAPI for identifying and counting aquatic microflora1. Limnol. Oceanogr. 1980, 25, 943–948. [Google Scholar] [CrossRef]
  38. Meyer, F.; Paarmann, D.; D’Souza, M.; Olson, R.; Glass, E.; Kubal, M.; Paczian, T.; Rodriguez, A.; Stevens, R.; Wilke, A.; et al. The metagenomics RAST server–a public resource for the automatic phylogenetic and functional analysis of metagenomes. BMC Bioinform. 2008, 9, 386. [Google Scholar] [CrossRef]
  39. Bolger, A.M.; Lohse, M.; Usadel, B. Trimmomatic: A flexible trimmer for Illumina sequence data. Bioinformatics 2014, 30, 2114–2120. [Google Scholar] [CrossRef]
  40. Roux, S.; Enault, F.; Hurwitz, B.L.; Sullivan, M.B. VirSorter: Mining viral signal from microbial genomic data. PeerJ 2015, 3, e985. [Google Scholar] [CrossRef]
  41. Zhu, W.; Lomsadze, A.; Borodovsky, M. Ab initio gene identification in metagenomic sequences. Nucleic Acids Res. 2010, 38, e132. [Google Scholar] [CrossRef]
  42. Sullivan, M.J.; Petty, N.K.; Beatson, S.A. Easyfig: A genome comparison visualizer. Bioinformatics 2011, 27, 1009–1010. [Google Scholar] [CrossRef]
  43. Li, H. Unpublished Work, The BWA-MEM Algorithm is Based on an Algorithm Finding Super-Maximal Exact Matches (SMEMs), Which was First Published with the Fermi Assembler Paper in 2012. Available online: http://bio-bwa.sourceforge.net/bwa.shtml (accessed on 28 October 2019).
  44. Li, H.; Handsaker, B.; Wysoker, A.; Fennell, T.; Ruan, J.; Homer, N.; Marth, G.; Abecasis, G.; Durbin, R. The Sequence Alignment/Map format and SAMtools. Bioinformatics 2009, 25, 2078–2079. [Google Scholar] [CrossRef] [Green Version]
  45. Robinson, J.T.; Thorvaldsdóttir, H.; Winckler, W.; Guttman, M.; Lander, E.S.; Getz, G.; Mesirov, J.P. Integrative genomics viewer. Nat. Biotechnol. 2011, 29, 24–26. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  46. Mcminn, A.; Gong, Z.; Liang, Y.; Wang, M.; Jiang, Y.; Yang, Q.; Xia, J. Viral diversity and its relationship with environmental factors at the surface and deep sea of Prydz Bay, Antarctica. Front. Microbiol. 2018, 9, 1–17. [Google Scholar] [CrossRef]
  47. Silva, B.S.; Coutinho, F.H.; Gregoracci, G.B.; Leomil, L.; Oliveira, L.S.; Froés, A.; Tschoeke, D.A.; Soares, A.C.; Cabral, A.S.; Ward, N.D.; et al. Virioplankton assemblage structure in the Lower River and ocean continuum of the Amazon. mSphere 2017, 2, e00366–e00417. [Google Scholar] [CrossRef] [PubMed]
  48. Yu, D.; Han, L. Diversity and distribution characteristics of viruses in soils of a marine-terrestrial ecotone in East China. Microb. Ecol. 2018, 375–386. [Google Scholar] [CrossRef]
  49. Nakai, R.; Abe, T.; Takeyama, H.; Naganuma, T. Metagenomic analysis of 0.2-μm-passable microorganisms in deep-sea hydrothermal fluid. Mar. Biotechnol. 2011, 13, 900–908. [Google Scholar] [CrossRef]
  50. Vollenweider, R.A.; Kerekes, J. Eutrophication of waters. Monitoring Assessment and Control; OECD Coope.: Paris, France, 1982. [Google Scholar]
  51. Guiry, M.D.; Guiry, G.M. AlgaeBase. World-Wide Electronic Publication, National University of Ireland, Galway. Available online: http://www.algaebase.org (accessed on 20 July 2018).
  52. Khodzher, T.V.; Domysheva, V.M.; Sorokovikova, L.M.; Sakirko, M.V.; Tomberg, I.V. Current chemical composition of Lake Baikal water. Inl. Waters 2017, 7, 250–258. [Google Scholar] [CrossRef]
  53. Potapov, S.A.; Butina, T.V.; Belykh, O.I. Mezhgodovaya dinamika chislennosti i vertikal’noye raspredeleniye virusnykh chastits v planktone oz. Baykal (The interannual dynamics and vertical distribution of virus-like particles of Lake Baikal). Monit. Syst. Environ. 2016, 6, 120–124. [Google Scholar]
  54. Gu, X.; Xiang, Q.; Tay, M.; Harn, S.; Saeidi, N.; Giek, S.; Kushmaro, A.; Thompson, J.R.; Gin, K.Y. Geospatial distribution of viromes in tropical freshwater ecosystems. Water Res. 2018, 137, 220–232. [Google Scholar] [CrossRef]
  55. Bruder, K.; Malki, K.; Cooper, A.; Sible, E.; Shapiro, J.W.; Watkins, S.C.; Putonti, C. Freshwater metaviromics and bacteriophages: A current assessment of the state of the art in relation to Bioinformatic challenges. Evol. Bioinform. 2016, 12, 25–33. [Google Scholar] [CrossRef]
  56. Kim, K.H.; Bae, J.W. Amplification methods bias metagenomic libraries of uncultured single-stranded and double-stranded DNA viruses. Appl. Environ. Microbiol. 2011, 77, 7663–7668. [Google Scholar] [CrossRef]
  57. Roux, S.; Solonenko, N.E.; Dang, V.T.; Poulos, B.T.; Schwenck, S.M.; Goldsmith, D.B.; Coleman, M.L.; Breitbart, M.; Sullivan, M.B. Towards quantitative viromics for both double-stranded and single-stranded DNA viruses. PeerJ 2016, 4, e2777. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  58. Reyes, A.; Semenkovich, N.P.; Whiteson, K.; Rohwer, F.; Gordon, J.I. Going viral: Next-generation sequencing applied to phage populations in the human gut. Nat. Rev. Microbiol. 2012, 10, 607–617. [Google Scholar] [CrossRef] [PubMed]
  59. Sullivan, M.B.; Coleman, M.L.; Weigele, P.; Rohwer, F.; Chisholm, S.W. Three Prochlorococcus cyanophage genomes: Signature features and ecological interpretations. PLoS Biol. 2005, 3, e144. [Google Scholar] [CrossRef] [PubMed]
  60. Mida, J.L.; Scavia, D.; Fahnenstiel, G.L.; Pothoven, S.A.; Vanderploeg, H.A.; Dolan, D.M. Long-term and recent changes in southern Lake Michigan water quality with implications for present trophic status. J. Great Lakes Res. 2010, 36, 42–49. [Google Scholar] [CrossRef]
  61. John, S.G.; Mendez, C.B.; Deng, L.; Poulos, B.; Kauffman, A.K.M.; Kern, S.; Brum, J.; Polz, M.F.; Boyle, E.A.; Sullivan, M.B. A simple and efficient method for concentration of ocean viruses by chemical flocculation. Environ. Microbiol. Rep. 2011, 3, 195–202. [Google Scholar] [CrossRef] [PubMed]
Viruses 11 00991 g001 550
Figure 1. Content and structure of two viromes from Lake Baikal according to the RefSeq (MG-RAST) database (e-value 10−5). (A) The percentage of “known” virome sequences compared to the RefSeq database. (B) Breakdown of the “known” sequences into viruses, bacteria, archaea or eukarya using similarity results against RefSeq. (C) Taxonomic composition at the viral family level. The “Other” category pools families that represented less than 0.5% of the full virome sequences.
Figure 1. Content and structure of two viromes from Lake Baikal according to the RefSeq (MG-RAST) database (e-value 10−5). (A) The percentage of “known” virome sequences compared to the RefSeq database. (B) Breakdown of the “known” sequences into viruses, bacteria, archaea or eukarya using similarity results against RefSeq. (C) Taxonomic composition at the viral family level. The “Other” category pools families that represented less than 0.5% of the full virome sequences.
Viruses 11 00991 g001
Viruses 11 00991 g002 550
Figure 2. Functional annotations of the BVP1 and BVP2 viromes by SEED Subsystems (MG-RAST).
Figure 2. Functional annotations of the BVP1 and BVP2 viromes by SEED Subsystems (MG-RAST).
Viruses 11 00991 g002
Viruses 11 00991 g003 550
Figure 3. Comparison of BVP1_2_ NODE_212 contig and Prochlorococcus phage P-TIM68.
Figure 3. Comparison of BVP1_2_ NODE_212 contig and Prochlorococcus phage P-TIM68.
Viruses 11 00991 g003
Viruses 11 00991 g004 550
Figure 4. Agglomerative hierarchical clustering tree for the comparative analysis of viromes.
Figure 4. Agglomerative hierarchical clustering tree for the comparative analysis of viromes.
Viruses 11 00991 g004
Table
Table 1. Characteristics during the sampling periods (mean values for 0–50 m layer, except for transparency).
Table 1. Characteristics during the sampling periods (mean values for 0–50 m layer, except for transparency).
Water PropertyBVP1BVP2
Water temperature (°C)0.4–1.3 (0.75*)2.7–2.8 (2.76)
pH7.92–7.98 (7.95)7.75–7.82 (7.79)
Ntotal (mg/L)0.17–0.31 (0.23)0.20–0.34 (0.29)
Ptotal (µg/L)11–15 (13)10–12 (11)
TOC (mg/L)0.7–1.3 (1.07)1.7–1.9 (1.8)
NO2− (mg/L)0.001 (the entire layer)0.001–0.003 (0.002)
NO3− (mg/L)0.34–0.45 (0.39)0.37–0.40 (0.39)
O2 (mg/L)13.5–14.8 (14.3)12.6–12.8 (12.7)
PO43− (µg/L)24–40 (30)22–26 (24)
Chla (µg/L)0.65–3.42 (1.83)1.31–1.59 (1.40)
Viruses (VLPs mL−1)2 (±1) × 1061.9 (±0.8) ×106
Bacteria (cell mL−1)1.5 (±0.7) × 1060.19 (±0.3) × 106
Transparency, m1116
* arithmetical mean.
Table
Table 2. Sequencing summary statistics for each virome (the number of reads is indicated).
Table 2. Sequencing summary statistics for each virome (the number of reads is indicated).
SampleRaw DataUploaded to MG-RAST Annotated, RefSeqSequences Containing Ribosomal RNA Genes
BVP132234261474135233310929
BVP2413603519562958353502675
Table
Table 3. Virus families in the BVP1 and BVP2 viromes.
Table 3. Virus families in the BVP1 and BVP2 viromes.
Virus FamilyPrimary HostRelative Abundance (% of Viral Sequences)
BVP1BVP2
MyoviridaeBacteria51.762.4
SiphoviridaeBacteria28.114.4
PodoviridaeBacteria9.312.4
PhycodnaviridaeAlgae6.16.9
PoxviridaeBirds, mammals, humans20.5
Unclassified viruses-1.72.2
IridoviridaeInsects, amphibians, fish, invertebrates0.50.8
Unclassified (Caudovirales)Bacteria0.20.2
BaculoviridaeInsects0.20.1
MarseilleviridaeAmoeba0.080.09
MicroviridaeBacteria0.020.003
NimaviridaeCrustaceans0.020.01
HerpesviridaeAnimals, including humans0.020.04
PolydnaviridaeInsects0.02-
AscoviridaeInvertebrates0.010.003
AsfarviridaeInsects, pigs0.010.01
LipothrixviridaeArchaea0.010.003
CircoviridaeBirds, mammals0.005-
ParvoviridaeWarm-blooded animals, humans0.005-
AlloherpesviridaeFish, amphibians0.0050.008
Table
Table 4. Data on the obtained contigs.
Table 4. Data on the obtained contigs.
SampleNumber of Contigs AssembledMax Length (bp) MedianNumber of Contigs ≥ 5 Kbp
BVP125,5462127,4983261383
BVP2388,7351,129,7554253041
BVP1_2544,5011,129,0003674438
Table
Table 5. Blast analysis of contigs identified in the Lake Baikal viromes.
Table 5. Blast analysis of contigs identified in the Lake Baikal viromes.
Contig Length (bp) Number of Identified Open Reading Frames (ORFs)Best BLAST Hit AffiliationAccession Number% IdentityQuery Cover (%)
BVP1_NODE_54491908Yellowstone Lake virophage 7NC_02825775.9234
BVP1_NODE_72477529Pelagibacter phage HTVC010PNC_02048173.0588
BVP1_NODE_93765658Synechococcus phage S-SM2NC_01527970.2089
BVP1_NODE_104160826Synechococcus phage S-RIP2NC_02083871.1869
BVP1_NODE_111058018Synechococcus phage S-SM2NC_01527972.9384
BVP1_NODE_667810718Synechococcus phage S-CBS4NC_01676667.7865
BVP1_NODE_697796712Flavobacterium phage 11bNC_00635671.8548
BVP1_NODE_1160562612Staphylococcus phage G1NC_00706699.7799
BVP1_NODE_116256217Synechococcus phage S-SM2NC_01527972.2382
BVP1_NODE_135250819Staphylococcus phage Sb-1NC_02300999.98100
BVP2_NODE_1582738510Synechococcus phage S-SM2NC_01527974.5290
BVP2_NODE_172270598Synechococcus phage S-SM2NC_01527970.4988
BVP2_NODE_1766697210Synechococcus phage S-CBS4NC_01676672.3655
BVP2_NODE_227559914Prochlorococcus phage P-SSM2NC_00688374.5973
BVP2_NODE_279552687Synechococcus phage S-SM2NC_01527971.9974
BVP2_NODE_281652447Prochlorococcus phage P-SSM2NC_00688373.9181
BVP2_NODE_303650047Pelagibacter phage HTVC010PNC_02048180.0089
BVP2_NODE_3441733110Synechococcus phage S-SM2NC_01527972.7555
BVP2_NODE_7211156620Synechococcus phage S-CAM9NC_03192267.1840
BVP2_NODE_96996929Synechococcus phage S-SKS1NC_02085170.8482
BVP1_2_NODE_8311369117Synechococcus phage S-SM2NC_01527974.4085
BVP1_2_NODE_1425101879Synechococcus phage S-SM2NC_01527970.5578
BVP1_2_NODE_182088949Synechococcus phage S-RIP2NC_02083871.1855
BVP1_2_NODE_3506586311Synechococcus phage S-CBS4NC_01676667.8375
BVP1_2_NODE_381255578Prochlorococcus phage P-SSM2NC_00688373.8683
BVP1_2_NODE_2122942732Prochlorococcus phage P-TIM68NC_02895568.5864
BVP1_2_NODE_4961828120Synechococcus phage S-SKS1NC_02085165.9327
BVP1_2_NODE_721149525Synechococcus phage S-SM2NC_01527972.4658
BVP1_2_NODE_8561353212Synechococcus phage S-SKS1NC_02085170.8281
BVP1_2_NODE_9961234717Pelagibacter phage HTVC010PNC_02048182.5377
Table
Table 6. Viromes from lakes used for agglomerative hierarchical clustering (includes the data source).
Table 6. Viromes from lakes used for agglomerative hierarchical clustering (includes the data source).
NameTrophic LevelAverage Depth (m)Area (km2)CountrySampling Data
Lake Michiganoligotrophic279
281 (max)		58,030USA06.13
07.13
Lake Baikaloligotrophic with mesotrophic characteristics744.7
1681 (max)		31,722RussiaThis study
Lake Eriemesotrophic19
64 (max)		25,700USA, Canada07.13
Lake Ontariooligo-mesotrophic86
244 (max)		19,500USA, Canada06.13
Lough Neagheutrophic9
31 (max)		392Northern Ireland04.14
Lake Matoakaeutrophic2.5
4.75 (max)		0.16USA03.13
Lake Limnopolarultra-oligotrophic5.5 (max)0.02Antarctica11.06
01.07

Share and Cite

MDPI and ACS Style

Potapov, S.A.; Tikhonova, I.V.; Krasnopeev, A.Y.; Kabilov, M.R.; Tupikin, A.E.; Chebunina, N.S.; Zhuchenko, N.A.; Belykh, O.I. Metagenomic Analysis of Virioplankton from the Pelagic Zone of Lake Baikal. Viruses 2019, 11, 991. https://doi.org/10.3390/v11110991

AMA Style

Potapov SA, Tikhonova IV, Krasnopeev AY, Kabilov MR, Tupikin AE, Chebunina NS, Zhuchenko NA, Belykh OI. Metagenomic Analysis of Virioplankton from the Pelagic Zone of Lake Baikal. Viruses. 2019; 11(11):991. https://doi.org/10.3390/v11110991

Chicago/Turabian Style

Potapov, Sergey A., Irina V. Tikhonova, Andrey Yu. Krasnopeev, Marsel R. Kabilov, Aleksey E. Tupikin, Nadezhda S. Chebunina, Natalia A. Zhuchenko, and Olga I. Belykh. 2019. "Metagenomic Analysis of Virioplankton from the Pelagic Zone of Lake Baikal" Viruses 11, no. 11: 991. https://doi.org/10.3390/v11110991

APA Style

Potapov, S. A., Tikhonova, I. V., Krasnopeev, A. Y., Kabilov, M. R., Tupikin, A. E., Chebunina, N. S., Zhuchenko, N. A., & Belykh, O. I. (2019). Metagenomic Analysis of Virioplankton from the Pelagic Zone of Lake Baikal. Viruses, 11(11), 991. https://doi.org/10.3390/v11110991

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop