Seasonal Succession, Host Associations and Biochemical Roles of Aquatic Viruses in a Eutrophic Lake Plagued by Cyanobacterial Blooms

Seasonal Succession, Host Associations and Biochemical Roles of Aquatic Viruses in a Eutrophic Lake Plagued by Cyanobacterial Blooms | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Seasonal Succession, Host Associations and Biochemical Roles of Aquatic Viruses in a Eutrophic Lake Plagued by Cyanobacterial Blooms Ling Yuan, Pingfeng Yu, Xinyu Huang, Ze Zhao, Linxing Chen, Feng Ju This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3510205/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background. Viruses are important biogeochemical mediators and ecological drivers in freshwater ecosystems. Although the environmental implications of viruses in ecosystems have been preliminarily explored, the dynamics of viruses and host associations over the seasons and blooming periods in eutrophic freshwater ecosystems remain elusive. Results. Here, we recovered 41,997 unique viral clusters at approximately species level from planktonic microbiomes of Lake Taihu, a large and eutrophic lake that suffered from yearly Microcystis -dominated harmful algal blooms (HABs) in China. The viral clusters showed distinct seasonal succession driven by environmental factors (mainly nutrients and temperature) and microbial communities (mainly Cyanobacteria and Planctomycetes ). Host prediction highlighted the roles of the viruses in affecting the bacteria-driven nitrogen and phosphate cycling through infection. Further statistical analyses revealed that the HAB-induced environmental and microbial variations affected viral strategies including lifestyles, host range, and virus-encoded auxiliary metabolic genes (vAMGs) distributions. Viruses infecting Proteobacteria and Actinobacteria showed enhanced lysogenic lifestyle and condensed host ranges during HAB peak in summer, while viruses infecting Bacteroidota selected the opposite strategy. Notably, vAMGs were most abundant before HAB outbreak in spring, compensating for host bacterial metabolism including carbohydrates metabolism, photosynthesis, and phosphate regulation. Conclusion. This study elucidated relationship between viral community and bloom-associated environment, suggested the dynamic viral strategies and prominent biochemical roles in the eutrophic freshwater ecosystems. Viromics Host-virus relationship Virus-encoded auxiliary metabolic gene Harmful algal blooms Microcystis Microbiome Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Excessive nitrogen and phosphorus inputs to the freshwater ecosystems can lead to eutrophication [ 1 ]. Eutrophication and concomitant harmful algae blooms (HABs) generate detrimental effects on the water quality, social economy, and human health [ 2 – 4 ]. Cyanobacteria and other bacteria play crucial roles in biogeochemical cycling and further influence freshwater ecosystems during HABs [ 5 , 6 ]. Specifically, cyanobacteria are often the main microorganisms and primary producers in eutrophic lakes, while heterotrophic bacteria act as major drivers of organic matter consumption and energy flow [ 7 – 9 ]. Extracellular products released by cyanobacteria can affect the growth of other bacteria [ 10 ], while other bacteria can provide nutrients and growth factors for cyanobacteria [ 5 ]. However, our understanding of the viral community in eutrophic freshwater with cyanobacterial HABs remains elusive despite recent recognition of their broad implications on the microbiome structure, metabolic function, and microbial evolution in ecosystems [ 11 – 13 ]. Viruses outnumber their microbial hosts by order of magnitude and are known as the most abundant biological entities on Earth [ 14 ]. Viruses can affect the microbial community through viral lysis, and the lysed host cell biomass returns into the surroundings as dissolved organic matters, which not only redirects the nutrient and energy flow but also mediates biodiversity [ 15 , 16 ]. Viruses can enhance host metabolism by manipulating host-encoded metabolic networks, or by introducing virus-encoded auxiliary metabolic genes (vAMGs) [ 17 ]. Recent studies reveal that environmental stresses can shift virus-bacterium associations (e.g., viral lifestyle, bacterial antiphage systems, and host range) [ 18 , 19 ]. In particular, vAMGs encoding functions involved in central carbon metabolism [ 20 ], photosynthesis, [ 21 ] and nutrient cycling [ 22 , 23 ] have already been widely reported in the cyanophage genomes. The cyanophage infection and vAMGs involved in photosynthesis would affect the net primary productivity [ 24 ]. Therefore, in the eutrophic freshwater ecosystems suffering from yearly cyanobacterial bloom, viruses have the potential to impact the microbial community and the water environment through infection and own-encoding auxiliary metabolic genes. Lake Taihu, the third largest freshwater lake in China, is the source of irrigating and drinking water for over 40 million people [ 2 ]. This important lake has received much attention due to its eutrophication and cyanobacterial HABs [ 25 ]. The yearly cyanobacterial HAB in Taihu is dominated by Microcystis and usually occurs in late spring, reaches a peak in summer, and begins to decay in autumn [ 26 ]. The periodic cyanobacterial HABs provide an ideal scenario to investigate how cyanobacteria-bacteria-virus relationships as well as viral ecological roles respond to HAB formation and dissipation in freshwater ecosystems. Elucidating these cyanobacteria-bacteria-virus systems can bridge the knowledge gap in the microbial ecology and biochemistry underlying eutrophication and algal blooming in the freshwater ecosystems and may inspire ecological control of HABs. In this study, we aim to reveal the viral community succession, adaptive strategies, and ecological potentials in a eutrophic lake with periodic cyanobacterial HAB. To target viruses of both cyanobacteria and planktonic bacteria, we explored the viral diversity in the free-living biomass metagenomes of water microbiomes from Lake Taihu and tracked the spatiotemporal and seasonal succession patterns of viral community. Environmental conditions and host microbes were analyzed to determine the major drivers for viral community structure and succession. Computational approaches were adopted to reveal the virus-host relationships and variations of virus-related features (lifestyle and host range) in different seasons. Additionally, the metabolic types and distribution of vAMGs were characterized to assess the ecological roles of viruses in the eutrophic freshwater ecosystems. This study suggested that the viruses in the eutrophic freshwater ecosystems with cyanobacterial HABs can impact the host microbial community as well as the water environment, and the viral succession, strategies and ecological roles may change with the bloom-associated environment. Methods Sample collection and environmental parameters In total, 16 surface water samples were collected on 21 July 2020 (Summer, SU), 13 October 2020 (Autumn, AU), 16 January 2021 (Winter, WI), and 24 May 2021 (Spring, SP) in four sites in the Lake Taihu (Fig. 1 a) to represent four seasons and three lake regions (Gonghu Bay (Site 1), The Meiliang Bay (Site 2), and the open water (Site 3 and Site 4), respectively. Environmental parameters were measured as described previously [ 27 ]. Briefly, temperature (T), pH, dissolved oxygen (DO), and conductivity were determined in situ , total nitrogen (TN), total phosphorus (TP), dissolved total nitrogen (DTN), nitrate (NO 3 − -N), ammonium (NH 3 + -N), dissolved total phosphorus (DTP) and phosphate (PO 4 3− -P) were determined with the collected water samples. Phosphate was measured for water samples from summer and autumn (n = 8), other environment parameters were measured for water samples from spring, summer, and autumn (n = 12), while the environmental data for water samples from winter was not available. DNA extraction, library construction, and sequencing For each sample, 5-L surface water samples were filtered through a 48-µm sieve followed by 2.0-µm and 0.2-µm polycarbonate filters (Millipore, USA) to obtain three different size fractions which were named as free-living (0.2-2µm), particle-associated (2–48µm), and colony (> 48µm) according to the size of the bacterial hosts, respectively. All 16 free-living and 16 particle-associated samples from four seasons, and one colony sample during the summer peak of the cyanobacterial HAB (Fig. S1 ) were selected for DNA extraction and metagenomic sequencing. Then, DNA extraction was performed using the DNeasy PowerSoil Kit (Qiagen, Germeny) following the manufacture’s instruction. Shotgun metagenomic sequencing libraries were constructed by NEB Next® Ultra™ DNA Library Prep Kit for Illumina (NEB, USA), and paired-end sequencing (2 \(\times\) 150 bp) was conducted on Illumina HiSeq platform. Quality control of raw reads and , de novo assembly Raw metagenomic reads were quality trimmed using Trimmomatic (v0.39) [ 28 ] with parameters ‘SLIDINGWINDOW:15:25 LEADING:20 TRAILING:20 MINLEN:95’. Assembly was performed for each metagenome using metaSPAdes (v3.14.1) [ 29 ] with kmers ‘-k 21,33,55,77,99,127’. Metagenomic analysis of microbial community Read-based and assembly-based metagenomic approaches were both used in complementary to achieve taxonomic profiling of microbial communities and genome-level taxonomic and functional analyses of microbial populations [ 30 ]. First, clean metagenomic reads were analyzed to determine global taxonomic profile and community composition by mOTUs3 [ 31 ]. Furthermore, metagenome-assembled genomes (MAGs) were reconstructed using metaBAT2 [ 32 ] from all metagenomes individually. After MAG quality evaluation by CheckM [ 33 ], filtered high-quality MAGs (those with completeness – 5 × contamination ≥ 50) [ 34 ] were dereplicated by dRep [ 35 ] with default parameters, the dereplicated MAGs were retained for further analysis. Taxonomy classification of MAGs were determined by GTDB-tk [ 36 ]. Open reading frames (ORFs) of MAGs were predicted by Prodigal v 2.6.3 [ 37 ] with the “-p meta” option. Functional annotation of MAGs was performed by kofamscan [ 38 ]. Viral contigs identification The viral contigs were recovered from 16 free-living biomass metagenomes (0.2-2µm) for the following reasons. Metagenomes in the cellular fractions (0.2µm filtered) have become useful resources for recover and analyze viral sequences in recent years [ 39 , 40 ] although this fraction was generated without viral particle enrichment. The cellular proviruses, viruses undergoing the lytic cycle in the virocell, free virions retained on filters, and the potential microbial hosts were all included in the cellular fraction. Cyanobacteria Microcystis dominated the particle-associated and colony samples (e.g., 47% in SU1-PA, 41% in SP4-PA, and 87% in SU4-Colony) (measured by mOTUs3, Dataset S5), which made it more difficult to recover low-abundance viruses. Therefore, in order to recover more viruses (both lytic and lysogenic) as possible and retain host signals in the meanwhile, 0.2-2.0 µm fraction was selected for viral contigs identification (See below). Viral contigs were identified by running a combination of VirSorter2 [ 41 ], VirFinder [ 42 ] and CheckV [ 43 ] for all assembled contigs (from free-living biomass metagenomes) longer than 5 kbp. Briefly, contigs with i) VirSorter2 max score > 0.95 or VirFinder max score > 0.9, ii) CheckV completeness > 0 and the number of viral genes – 5 \(\times\) host genes ༞ 0, were defined as viral contigs. After removing host contamination (predicted by CheckV), the viral contigs were clustered based on a pairwise ANI (average nucleotide identity) method ( https://bitbucket.org/berkeleylab/checkv/src/master/scripts/ ) [ 43 ] using the recommended parameters, i.e., 95% ANI and 85% AF (alignment fraction), from MIUViG (Minimum Information about an Uncultivated Virus Genome) [ 44 ]. A total of 59,430 viral contigs and 41,997 unique viral clusters were recovered from the metagenomes. ORFs of viral contigs were predicted by Prodigal v 2.6.3 [ 37 ]. Taxonomy assignment of viral clusters Taxonomy assignment was established first using vConTACT v2.0 [ 45 ] with default parameters. The gene-sharing network constructed by vConTACT was then visualized in Cytoscape v3.8.1 [ 46 ]. For viral contigs that could not be clustered with a reference virus from the database (ProkaryoticViralRefSeq201), the taxonomic classification was performed based on a majority rule. If > 50% ORFs of a viral contig were assigned to the same family with a blastp bitscore > 50 against viral proteins from CheckV genome database [ 43 ], it was considered part of that viral family [ 47 ]. The taxonomy assignment of viral clusters was represented by the representative viral contig of the corresponding viral cluster. Functional annotation and phylogenetic analysis Functional annotation was performed by screening each viral ORF against pfam (v34.0) [ 48 ], KEGG [ 49 ], Uniprot90 database [ 50 ] using hmmsearch [ 51 ] (bit-score > 100), kofamscan [ 38 ], and usearch [ 52 ] (global identity > 50%), respectively. Then, vAMGs were obtained by manual curation of the functional annotations. Under the vAMGs classification scheme [ 53 ], metabolic genes directly involved in viral replication (e.g., nucleotide metabolism, replication and repair) were not included in the analysis [ 54 ]. Reference sequences similar to four photosynthetic vAMGs ( psb A, psb D, pet E, and pet F) were recruited from NCBI nr database (blastp, bit-score > 50, e-value < 0.00001). The reference sequences were then clustered by cd-hit [ 55 ] with 90% global sequence identity. vAMGs and representative reference sequences were aligned by MUSCLE [ 56 ]. The phylogenetic trees were computed from fasttree [ 57 ] and visualized in iTOL [ 58 ]. The annotation of lysogenic marker proteins was extracted from the functional annotation run by hmmsearch against pfam (E-value < 10 − 5 ) (v34.0) [ 48 ], and the accession list of lysogenic marker proteins (i.e., transposase, integrase, resolvase, excisionase and recombinase proteins) were provided in [ 19 ]. The viral genome with at least one lysogenic marker protein was regarded as possible lysogenic phage. Viral host prediction To efficiently track viral hosts, four commonly used bioinformatic approaches with different design principles were co-used to predict potential hosts for viruses: i) CRISPR spacer match, ii) tRNA match, iii) genome homology match and iv) kmer features. For CRISPR spacer match, CRISPR spacers were predicted for all > 5 kb contigs in the metagenomic assemblies with CRT [ 59 ]. The predicted CRISPR spacers were then matched to all viral contigs by BLASTn-short with 97% identity, 90% coverage and 1 mismatch allowed [ 60 ]. The retained hits were considered as candidate virus-host pairs. The taxonomic annotation of potential host contigs not belonging to MAGs was determined by CAT [ 61 ] with default parameters. For tRNA match, bacterial tRNA sequences were predicted in viral contigs using tRNAscan-SE(v1.23) with option ‘-B’ [ 62 ] and then searched against recovered MAGs using BLASTn. The hits with 95% global nucleotide identity were identified as possible hosts. For genome homology match, viral contigs were compared to recovered MAGs with BLASTn to indicate possible prophage integration or horizontal gene transfer events [ 13 ]. A host prediction was made when an MAG displayed a region similar to a viral contig ≥ 2.5 kb at ≥ 90% identity. For kmer features, two kmer-based tools, WIsH [ 63 ] (k = 8) and PHIST [ 64 ] (k = 25) were applied to predict host MAGs for viral contigs. The recovered MAGs were included in potential host dataset for WisH and PHIST. Complete viral genomes of human and vertebrates downloaded from NCBI ( https://www.ncbi.nlm.nih.gov/genome/ ) were used as negative dataset for WisH. Only the predictions by WiSH with P 10 kmers were retained. To further check Microcystis phages, the genes of viral cluster representatives linked to Cyanobacteria Microcystis were annotated by BLASTn against NCBI nr database (threshold of 50 for bit score and 10 − 5 for E-value). The viral clusters encoded at least one Microcystis virus-like or Microcystis -like gene were regarded as high-confidence Microcystis phages. The annotation of Microcystis phages predicted by host prediction was listed in Dataset S8. Viral and MAGs quantification To calculate relative abundance of viral contigs in each metagenome, clean reads were mapped to viral contigs by bowtie2 [ 65 ] with parameter ‘--very-sensitive’. The abundance was normalized to RPKM (reads per kilobase per million) with the in-house python scripts. Then, the relative abundance of each viral cluster was calculated based on the sum of relative abundance of its members (i.e., affiliated viral contigs). The occurrence of a viral contig in a given metagenome was determined if at least 80% of its full sequence could be mapped by the clean reads. The relative abundance of viral contigs was also used to represent the abundance of its encoding gene (e.g., vAMGs). Similarly, to calculate relative abundance of MAGs in each metagenome, clean reads were mapped to MAGs by bowtie2 with parameter ‘--very-sensitive’ and abundance was normalized to RPKM. The occurrence of an MAG in a given metagenome was determined if at least 80% of its full sequence could be mapped by the clean reads. The virus/host ratio (VHR) was defined as the viral cluster abundance divided by the host genome abundance and was calculated for each pair of virus-host associations. Statistical analysis and visualization Principal component analysis (PCA) was performed using ‘prcomp’ function in R. To identify the environmental parameters and microbial community structure driving the distribution of viral community in Taihu, redundancy discriminant analysis (RDA) was applied using ‘rda’ function in the vegan R package [ 66 ]. The abundance of microbial community was calculated at the phylum level by motus3 [ 31 ]. The input abundance tables of viral community and microbial community included those viral clusters or microbial phylum occurred at least in 50% of the samples, respectively. A correlation between viral abundance and an environmental parameter was considered robust correlated if the Pearson’s correlation coefficient was > 0.7 (or < − 0.7) and FDR-adjusted P < 0.05 [ 67 ], and the script used for correlation analysis is available at https://github.com/emblab-westlake/MbioAssy1.0/ . The statistical significance of virus-related variables in different seasons (i.e., proportion of lysogenic phages, abundance percentage of broad host phages, and encoding pattern of vAMGs) were determined by Kruskal − Wallis test ( P < 0.05) in python using the function “kruskal wallis” in the scipy package. The networks for viral clusters and environmental parameters, and viral-host associations were visualized in Cytoscape v3.8.1 [ 46 ]. Results and Discussion The viral community structure in Lake Taihu succeeded by season DNA viruses are increasingly recognized as major ecological drivers for their effects on microbial community diversity and composition, horizontal gene transfer and recycling of carbon and nutrients [ 24 , 68 ]. To investigate DNA viruses in a eutrophic freshwater ecosystem, the viral contigs were predicted from the 16 free-living (0.2–2.0 µm) biomass metagenomes of Lake Taihu water samples (four seasons and four sites, Fig. 1 a and Fig. S1 ). A total of 59,430 viral contigs were recovered from 344.1 GB metagenomes, and the viral contigs were further clustered into 41,997 viral clusters (95% ANI and 85% AF) (Dataset S1). These viral clusters exhibited a wide taxonomical and host range, including Caudovirales (15,139), NCLDV (1,044), and virophages (58), which were known of infecting prokaryotes, eukaryotes, and giant viruses, respectively (Table 1 ). Table 1 The taxonomical statistics of viral clusters recovered in free-living biomass metagenomes of Lake Taihu. Viral family Lineage/Group Reported hosts Number of viral clusters Percentage (%) Ackermannviridae Caudovirales Prokaryote; Gammaproteobacteria 12 0.03 Autographiviridae Caudovirales Prokaryote; Bacteria 18 0.04 Herelleviridae Caudovirales Prokaryote; Firmicutes 31 0.07 Iridoviridae NCLDV Eukaryote; Amphibians, fish, and invertebrates 14 0.03 Lavidaviridae Virophage Virus, NCLDV 58 0.14 Marseilleviridae NCLDV Eukaryote; Amoeba 3 0.007 Mimiviridae NCLDV Eukaryote; Protists 93 0.22 Myoviridae Caudovirales Prokaryote; Bacteria and archaea 7973 18.98 Phycodnaviridae NCLDV Eukaryote; Eukaryotic algae 927 2.21 Pithoviridae NCLDV Eukaryote; Amoeba 4 0.01 Podoviridae Caudovirales Prokaryote; Bacteria 1064 2.53 Poxviridae NCLDV Eukaryote; Vertebrates and arthropods 3 0.007 Siphoviridae Caudovirales Prokaryote; Bacteria and archaea 6041 14.38 Unclassified Unclassified Unclassified 25756 61.33 To examine the pattern of viral distribution, the viral clusters were characterized in terms of abundance rank and abundance contribution (Fig. 1 b). The abundance of viral clusters showed a sharp drop at the beginning of the ranking, and then tended to level off, about 10% of the viral clusters explained 50% of the total viral abundance (Fig. 1 b). This phenomenon implied that the viral distribution in Taihu may be consistent with the Bank model, that is, a small fraction of viruses in the community are of high abundance, while the rest majority of viruses are in low abundance [ 69 ]. PCA analysis based on viral abundance showed a distinct separation between seasons (Fig. 1 c). This is in accordance with the bacterial community patterns (Fig. S2 ), suggesting that the viral clusters in Taihu showed more significant variation over the temporal than spatial scale. Further, we constructed gene-sharing networks clearly displaying the viral taxonomy and specific succession patterns across the seasons (Fig. 2 ). For example, one Myoviridae supercluster was abundant in spring and stayed in low-abundance in other seasons (Fig. 2 , blue circle). One Siphoviridae supercluster was overall abundant across seasons but showed different distribution patterns of abundance of viral cluster members (Fig. 2 , yellow circle). Phycodnaviridae is a viral family that infected eukaryotic algae chlorella [ 70 ]. The supercluster of Phycodnaviridae was almost absent in summer and autumn but thrived in winter, and then declined in spring again (Fig. 2 , green circle). These variations of viral succession patterns may be due to the different optimal environmental conditions, host community structure, and lifestyles of viruses (e.g., lytic or lysogenic) [ 71 ]. Viral community succession co-driven by environmental factors and microbial hosts Viral community structure can be influenced by both environmental conditions and host microorganisms [ 47 ]. RDA analysis revealed that the predominant environmental factors driving the water viral community structure were DTN (72.34%, P = 0.003), temperature (60.77%, P = 0.030), and NO 3 -N (58.97%, P = 0.015) (Fig. 1 e and Dataset S2), which well separated the 12 viral communities of three seasons including spring, summer and autumn (Fig. 1 e and Dataset S2). Other environmental parameters (e.g., DO and pH) did not explain significant variation of viral communities ( P > 0.5), indicative of their limited associations with viral clusters. Moreover, significant (FDR-adjusted P 0.7) positive (n = 2,556) and negative (n = 1,477) pairwise correlations between environmental parameters and viral clusters were identified (Fig. S3a and 3b). The dominant factors showing positive correlations with viral clusters included TN (n = 1074, 42.0%), NO 3 -N (n = 1020, 39.9%) and temperature (T, n = 664, 26.0%), while the dominant factors showing negative correlations included NO 3 -N (n = 554, 37.5%), DTP (n = 337, 22.8%) and PO 4 -P (n = 213, 14.4%). The environmental parameters were important factors in changing and driving the viral community structure and succession. For example, the free viruses released from host cells are directly exposed to the environment, and the environmental conditions may change the infectivity and adsorption ability of the viruses, which plays a decisive role in the next round of infection [ 72 ]. Although not all variations could be explained by the measured environmental parameters (34.25% cumulative variation could not be explained in RDA analysis, Fig. 1 e), these results indicated nutrients (i.e., nitrogen and phosphorous) and temperature were among the key environmental drivers shaping the water viral community in Taihu. Our analyses indicated that the bacterial community distribution had a high influence on the distribution of viral community as well, with 41.72% and 22.42% of the cumulative variance explained by two RDA axes (Fig. 1 f). Particularly, Cyanobacteria pointed to summer samples and contributed the most to separating the 16 viral communities of four seasons (73.85%, P = 0.001), followed by Planctomycetes (63.52%, P = 0.001) (Dataset S2). In order to simultaneously examine the influence of environmental factors and microbial community on virus community structure, variance partitioning analysis was additionally performed based on samples from three seasons (spring, summer and autumn). The representative environmental parameters (TN, NO 3 -N, DTN, TP and temperature) explained 21% of the total variation in the viral community structure while the representatives of the microbial community ( Cyanobacteria , Planctomycetes , Proteobacteria , Actinobacteria , Bacteroidetes ) explained 15% variation. The environmental parameters interacted with microbial community together explained 22% of viral community structural variation (Fig. S3c). Environmental parameters can influence the planktonic microbial community distribution in Taihu, and the microbial community can then directly affect viral infection. Moreover, viral infection can, in turn, constrain host distribution through lytic infection, and sometimes viruses can also help their hosts to survive nutrient-limiting conditions [ 17 ]. Together, the water environment, microbial community, and viral community continuously and closely interact and influence each other. Viruses infecting planktonic bacteria involved in nitrogen and phosphate cycling are prevalent in Lake Taihu To predict specific virus-bacteria associations, a total of 197 unique and high-quality bacterial MAGs were first recovered from Taihu metagenomes (Dataset S3), including one cyanobacterial Microcystis genome (SU4-Colony_bin2, completeness = 87.5%, contamination = 0.84%). Through four bioinformatic approaches integrating CRISPR spacer match, tRNA match, genome similarity, and kmer features (as detailed in Methods), 5,615 (13.4%) viral clusters were linked with the recovered MAGs (or contigs), which could be affiliated to specific genera (Dataset S1 and Dataset S4). Predicted bacterial hosts spanned 11 phyla dominated with Proteobacteria (1,733), Bacteroidota (1,408), and Actinobacteriota (1,172) (Fig. 3 and Dataset S1). Nitrogen and phosphorus were found as significant environmental drivers of viral community structure in this study (see above), consistent with their predicted roles in microbial community structure in Taihu [ 73 ]. There were 165 MAGs (83.8%) potentially involved in phosphate regulon or (and) transport (Dataset S3), suggesting the prevalence of phosphate-cycling genes in Taihu lake water microbiomes. Correspondingly, among the 5,615 viral clusters with putative hosts, 5,211 (92.8%) viral clusters were predicted to infect hosts encoding genes of phosphate cycling. Therefore, changes in microbial infection and mortality by viruses can affect the transport and regulation of phosphate by the microbial community in Taihu. In addition, 2,140 (38.1%) viral clusters were linked with 38 MAGs (19.3%), which were identified to encode at least one gene involved in nitrogen metabolism (i.e., nitrification, denitrification, assimilatory nitrate reduction, dissimilatory nitrate reduction, nitrogen fixation and nitrate/nitrite transport) (Dataset S3). For example, two MAGs from Proteobacteria Burkholderiaceae (SU4_bin105 and WI1_bin13) encoded methane/ammonia monooxygenase subunits A, B and C ( pmo A/B/C- amo A/B/C), and were linked to 13 and 26 viral clusters, respectively. Five MAGs encoding nxr A and nxr B (and thus possibly involved in nitrite oxidation) were all linked to several viral clusters (Dataset S1). Although no MAG had a complete denitrification pathway, 11 MAGs encoded denitrification genes for partial denitrification. WI3_bin37 from Bacteroidota JAAFJM01 encoded nir K and nos Z, indicating this bacterium had the potential to produce nitric oxide, and WI3_bin37 was the MAG linked with the greatest number of viral clusters (702) (Fig. 3 ). One MAG (SU1-PA_bin7) of Cyanobacteria Dolichospermum linked to 324 viral clusters and was found to encode genes involved in assimilatory nitrate reduction, nitrate/nitrite transport and nitrogen fixation. Together, in Lake Taihu, the viruses infecting bacteria involved in nitrogen (i.e., nitrogen and phosphorous) cycling are prevalent and thus the virus may act as an essential player and ecological driver in this microbial system. Viruses shifted adaptive strategies with bloom-induced environmental changes The viruses shift lifestyles (lytic and lysogenic) and host ranges in response to biotic (e.g., host physiology) and abiotic changes (e.g., environmental conditions) [ 74 ]. Viral clusters encoding lysogenic marker proteins were considered as possible lysogenic phages (See Methods), and viral clusters predicted to have two or more host genera were considered as broad host range phages. The variations in the proportion of lysogenic phages and the abundance percentage of broad host range phages in different seasons and bacterial phyla were compared. Proteobacteria , Actinobacteria , and Bacteroidota were always the main bacterial taxa closely correlated with cyanobacterial blooms in freshwater ecosystems [ 75 , 76 ]. The viruses infecting Proteobacteria and Actinobacteria both had a highest proportion with lysogenic lifestyle in summer (11.38 ± 0.31% and 3.38 ± 0.16%, respectively) than in other seasons (Table 2 ), andthe lowest abundance percentage of broad host range phages in summer (9.76 ± 0.85% and 6.78 ± 1.20%) (Table 2 ). These results indicated that Proteobacteria and Actinobacteria might respond to the cyanobacterial HABs and associated environmental stress by enhancing lysogenicity and condensing host ranges. In contrast, only a relatively lower proportion of Bacteroidota phages conducted lysogenic lifestyle (2.19 ± 0.59%) and lowest in summer (1.62 ± 0.14%) (Table 2 ), which implied that viruses of Bacteroidota in Taihu were more likely to infect hosts in the lysis form especially when the bloom peaked. In terms of host range, the abundance percentage of host range phages infecting Bacteroidota were highest in summer. Therefore, host tracking suggested that, compared with Proteobacteria and Actinobacteria , the viruses infecting Bacteroidota adopted opposite viral strategies when exposed to cyanobacterial HABs. Table 2 The proportion of potential lysogenic phages (upper) and abundance percentage of broad host range phages (lower) in seasonal samples in Taihu. The proportion of potential lysogenic phages for each phylum was calculated as the number of lysogenic phages infecting this phylum divided by the total number of viral clusters infecting this phylum. The abundance percentage of broad host range phages for each phylum was calculated as the summed abundance of broad host range phages infecting this phylum divided by the total abundance of viral clusters infecting this phylum. Only phyla with more than two lysogenic phages (upper) or broad host range phages (lower) in the samples were shown (as average ± standard deviation). For each phylum, the proportion of potential lysogenic phages (upper) and abundance percentage of broad host range phages (lower) significantly different across seasons were marked with an asterisk (Kruskal − Wallis test, P < 0.05). Summer Autumn Winter Spring Lysogenic (%) Actinobacteriota 3.38 ± 0.16 2.82 ± 0.29 3.11 ± 0.72 2.84 ± 0.39 Bacteroidota * 1.62 ± 0.14 2.34 ± 0.34 2.00 ± 0.45 2.80 ± 0.53 Gemmatimonadota * 13.04 ± 1.44 9.02 ± 0.37 5.62 ± 1.85 8.70 ± 3.29 Planctomycetota 17.86 ± 0.91 16.32 ± 0.87 17.01 ± 1.66 17.71 ± 1.88 Proteobacteria * 11.38 ± 0.31 9.73 ± 0.53 9.02 ± 0.54 8.88 ± 0.88 Verrucomicrobiota * 8.48 ± 0.83 11.16 ± 1.08 13.49 ± 1.38 9.11 ± 2.59 Host range (%) Actinobacteriota * 6.78 ± 1.20 10.85 ± 1.24 8.43 ± 1.03 8.27 ± 1.31 Bacteroidota 4.23 ± 0.86 1.95 ± 0.50 3.04 ± 1.02 3.89 ± 1.21 Gemmatimonadota 20.88 ± 3.60 27.02 ± 5.08 19.53 ± 1.57 17.30 ± 6.83 Planctomycetota * 16.99 ± 3.43 18.71 ± 2.94 8.97 ± 2.54 28.25 ± 9.44 Proteobacteria 9.76 ± 0.85 14.50 ± 1.44 13.25 ± 2.73 11.73 ± 2.79 Verrucomicrobiota * 4.96 ± 0.67 11.89 ± 1.36 7.75 ± 1.71 3.77 ± 1.15 Additionally, Planctomycetota witnessed the highest proportion of lysogenic phages (17.22 ± 1.53%) among all phyla and the proportion were not significantly different between seasons, but the proportion of broad host range phages of Planctomycetota displayed a significant difference between seasons (Table 2 ). Verrucomicrobiota showed the highest proportion of lysogenic phages in winter, which may be selected by low temperatures and poor nutritional conditions in winter (Table 2 ). In Lake Taihu suffering from yearly cyanobacterial HABs, environmental conditions constantly changed between seasons. A more suitable lifestyle and host range for viruses may benefit for surviving. In turn, viral strategies also affect microbial communities, for example, viral lifestyle conversion and infection of different hosts would mediate more frequent horizontal gene transfer events. A consortium of Microcystis phages may affect the progression of cyanobacterial blooms Taihu experiences the yearly bloom of cyanobacteria dominated by Microcystis , which has caused great eco-environmental concerns [ 77 , 78 ]. The reads- and assembly-level quantification both verified Cyanobacteria Microcystis dominated the summer samples (Dataset S3 and Dataset S5). After summer, Microcystis largely declined in the autumn and winter samples and rose again in the spring samples, when the next cyanobacterial HAB may be imminent. Considering that Microcystis were the most abundant microorganisms during the bloom (Dataset S5), viruses that infect Microcystis may act as abundant and key ecosystem players in Taihu. After inspection of viral genes annotation, 19 viral clusters were identified as high-confidence Microcystis phages (Fig. S4). Microcystis phages exhibited similar succession patterns to the Microcystis host, i.e., the abundance of most Microcystis phages reached a peak during summer cyanobacterial HABs, declined in autumn, then were undetectable in winter, and reappeared in spring with their hosts (Fig. 4 ). The identified Microcystis phages in Taihu may be in different forms of infection. For example, some Microcystis phages exhibited features of lytic virus in some samples, e.g., AU1_16663, AU4_1689, AU4_505, SU4_8548, and WI2_3694 maintained VHR > 1 in the four-summer free-living biomass samples, and were still detectable in the autumn samples when the Microcystis MAG was almost undetectable (Fig. 4 and Dataset S1). The lytic Microcystis phages remained in the autumn samples may play roles on HAB dissipation through infection and host lysis. AU4_1689 and WI2_3694 exhibited significantly high abundances in the spring samples, which were higher than the host abundance in all particle-associated and free-living biomass samples in the same season (Fig. 4 ). These two Microcystis phages may slow down the outbreak of Microcystis blooms by virulent infection during that period. Although typical lysogenic marker genes were not detected in the identified Microcystis phage genomes, SU3_11320 was probably a prophage that infected the Microcystis MAG suggested by two features. First, a 2910 bp fragment on SU3_11320 had 99.3% similarity to the Microcystis host genome. Second, SU3_11320 kept VHR 48µm), which was likely due to its presence in the intracellular host genome. This lysogenic Microcystis phage coexisted stably with the host. Functionally, most of the annotatable genes of Microcystis phages were viral marker genes (e.g., phage terminase, phage capsid protein and phage head-tail attachment protein, Fig. S4). The phosphate starvation-inducible protein pho H in WI2_3694 was the only vAMG found in Microcystis phage genomes. In summary, the diversity, abundance succession, potential lifestyles and gene function of Microcystis phages were explored here, providing new insights into the viruses directly affect cyanobacterial HAB of Lake Taihu. Viruses in Lake Taihu encode auxiliary metabolic capabilities to compensate for host metabolism Viruses can impact biogeochemical cycling not only through infection and host lysis, but also by reprogramming metabolic networks of virocells (living-form of the infected cells) [ 79 ], or more pointedly, by expressing vAMGs [ 80 ]. A total of 2,771 potential vAMGs were identified and grouped into 15 metabolic categories and 114 unique gene types (Dataset S6). The vAMGs involved in carbohydrates metabolism were the most abundant metabolic category (580 genes), followed by those involved in metabolism of vitamins and cofactors (509 genes), amino acids (365 genes), phosphate regulation (361 genes), and queuosine synthesis (242 genes), and these vAMG groups also showed a relatively high abundance among all groups (Fig. 5 a). Among the 114 vAMGs gene types, the encoding percentage of 77 (67.0%) gene types significantly varied (Kruskal − Wallis test, P < 0.05) between seasons (Dataset S7), indicating that the bloom-induced variations in freshwater environment may affect the occurrence of vAMGs. The overall abundance of vAMGs were highest in spring. The vAMGs of carbohydrates metabolism, phosphate regulation, amino acids metabolism, vitamins and cofactors metabolism, photosynthesis, and glycans metabolism were highest in spring both in terms of abundance and frequency of occurrence (Fig. 5 a and Table S1 ). Taking advantage of these identified vAMGs, viruses may involve in various metabolic pathways through host metabolic networks (Supplementary Text). Functionally, the most representative vAMGs were those involved in photosynthesis, Calvin cycle repression, and pentose phosphate pathway. For some cyanophages, the host energy production is not sufficient to meet their demands for replication [ 17 ]. Controlling host central metabolism away from CO 2 fixation to viral DNA and protein synthesis, and carrying vAMGs of photosynthesis and pentose phosphate pathway were common strategies of viruses [ 17 ]. Four vAMGs involved in photosynthesis ( psb A, psb D, pet E, and pet F) were identified in our metagenomic datasets of Taihu lake water (Fig. 5 b), with 52 and 37 viral clusters carried psb A and psb D, respectively (Dataset S1). These genes are typical vAMGs of cyanophage in marine, which encode the core photosystem II reaction center D1 protein and D2 protein [ 81 ]. Virus-encoded psb A and psb D can potentially replenish the inhibited host homologous proteins during infection and continue to provide energy (ATP) for viral replication [ 82 ]. Two photosynthetic electron transport proteins plastocyanin ( pet E) and ferredoxin ( pet F) were identified in 6 and 48 viral clusters (Dataset S1). These photosynthetic vAMGs relied on cellular photosynthesis system to produce and obtain the additional energy, so the phages encoding photosynthetic genes were likely to infect phototrophic microorganisms (e.g., Cyanobacteria ). Phylogenetic analysis revealed that most of the reference genes related to viral photosynthetic vAMGs came from Cyanobacteria (Fig. S6). The previously discussed Microcystis phages were not detected to encode a photosynthetic gene. Based on host prediction, among the vial clusters encoding photosynthetic genes, 5 viral clusters, were predicted to infect Cyanobacteria Cyanobium , and one was predicted to infect Cyanobacteria Dolichospermum . Additionally, 4 viral clusters encoding photosynthetic genes were clustered with reference genomes of Cyanobacteria Synechococcus by vCONTACT2. This phenomenon may be caused by the different niches of the cyanobacterial hosts. Microcystis occupied a dominant ecological niche in the microbial community and was probably able to provide enough energy on its own, while cyanobacteria of other genera were in low abundance, and the associated virus-encoded photosynthetic genes could enhance the host photosynthesis to obtain extra energy and provide selective advantage for their hosts. A CP12 protein, which directs carbon flux from Calvin cycle to pentose phosphate pathway [ 83 ], which were identified in 47 viral clusters. Four gene types of vAMGs involved in pentose phosphate pathway were also found in the viral clusters, including transaldolase tal (n = 21), glucose 6-phosphate dehydrogenase zwf (n = 2), 6-phosphogluconate dehydrogenase gnd (n = 21), and phosphate pyrophosphokinase prs (n = 26) (Fig. 5 b and Dataset S6). These viruses-encoded CP12 genes in concert with pentose phosphate pathway genes may help the biosynthesis of ribose 5-phosphate and reducing power that increase dNTP synthesis for viral replication [ 16 ]. In summary, the vAMGs involved in photosynthesis supplied a clue that viruses can hijack host metabolism to produce energy. The identification of viral CP12 and pentose phosphate pathway genes indicated that at least some water viruses in lake Taihu tended to control host metabolism and use the host energy for dNTP synthesis rather than carbon fixation, echoing previous findings in the marine viruses [ 83 – 85 ]. Conclusion This study unraveled the double-strand DNA viruses in a typical eutrophic freshwater lake Taihu suffering from cyanobacterial HABs. The virus, host microbes, and the freshwater environment were tightly associated with each other (Fig. 6 ). The explored host associations and encoded vAMGs indicated the ecological roles of viruses in eutrophic freshwater. In detail, the predicted virus-host relationship suggested both cyanobacterial, bacterial and eukaryotic community can be affected by viruses through infection. The identified vAMGs revealed that viruses had the ability to alter host metabolism and participate in carbon flux and nutrients cycling in this aquatic environment. The subsequent finding of significant variation of these virus-related features across seasons including lysogenic phage proportion, broad host range phages abundance, and vAMGs abundance recommended that viral strategies were changing and succeeding with the bloom-associated environment and the structural shift in host microbes. While the sampling processing of free-living (0.2 -2.0 µm) biomass may lead to the loss of some free viruses, the analytic results of viruses in the planktonic biomass on a spatiotemporal scale should be effective and consistently affected (if any) due to the identical processing protocol for all samples. To conclude, the first catalog of viruses infecting planktonic microbes in Lake Taihu was established. Through assessing seasonal succession, host relationship and viral auxiliary metabolic functions, this study untangled the ecological links between viruses, host microbes and environmental factors in the eutrophic freshwater ecosystem with cyanobacterial HABs. Declarations Availability of data and materials The data that support the findings of this study including metagenomic sequencing data and viral sequences have been deposited into CNGB Sequence Archive (CNSA) of China National GeneBank DataBase (CNGBdb: www.cngb.org/cnsa) with accession number CNP0004588 (https://db.cngb.org/search/project/CNP0004588). Acknowledgements We thank Prof. Wei Zhu’s group members at Hohai University as well as Dr. Han Gao at Westlake University for the great supports in the field sampling and laboratory experiments. We thank Dr. Linxing Chen at University of California, Berkeley for his kind and useful suggestions. We thank Ms. Yisong Xu and Mr. Guoqing Zhang at Westlake University for their helpful technical supports. We thank the Westlake University HPC Center for computation support. Funding This work was supported by the HRHI program 202309010 of Westlake Laboratory of Life Sciences and Biomedicine, the National Science Foundation of China (Grant no. 22241603), and the Zhejiang Provincial Natural Science Foundation of China under (Grant No. LR22D010001). Ethics declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare no competing interests. References Liu W, Qiu R. Water eutrophication in China and the combating strategies. Journal of Chemical Technology & Biotechnology. 2007;82(9):781-6; doi: 10.1002/jctb.1755. Guo L. Doing battle with the green monster of Taihu Lake. 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PeerJ. 2021;9:e11447; doi: 10.7717/peerj.11447. Puxty RJ, Millard AD, Evans DJ, Scanlan DJ. Shedding new light on viral photosynthesis. Photosynth Res. 2015;126(1):71-97; doi: 10.1007/s11120-014-0057-x. Hurwitz BL, U'Ren JM. Viral metabolic reprogramming in marine ecosystems. Curr Opin Microbiol. 2016;31:161-8; doi: 10.1016/j.mib.2016.04.002. Thompson LR, Zeng Q, Kelly L, Huang KH, Singer AU, Stubbe J, et al. Phage auxiliary metabolic genes and the redirection of cyanobacterial host carbon metabolism. Proc Natl Acad Sci U S A. 2011;108(39):E757-64; doi: 10.1073/pnas.1102164108. Gao EB, Huang Y, Ning D. Metabolic Genes within Cyanophage Genomes: Implications for Diversity and Evolution. Genes (Basel). 2016;7(10); doi: 10.3390/genes7100080. Sullivan MB, Huang KH, Ignacio-Espinoza JC, Berlin AM, Kelly L, Weigele PR, et al. Genomic analysis of oceanic cyanobacterial myoviruses compared with T4-like myoviruses from diverse hosts and environments. Environ Microbiol. 2010;12(11):3035-56; doi: 10.1111/j.1462-2920.2010.02280.x. Additional Declarations No competing interests reported. Supplementary Files SupplementaryDatasets.xlsx SupplementaryInformation.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3510205","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":245027547,"identity":"ca38edae-5129-4007-abec-d104288fad10","order_by":0,"name":"Ling Yuan","email":"","orcid":"","institution":"Westlake University","correspondingAuthor":false,"prefix":"","firstName":"Ling","middleName":"","lastName":"Yuan","suffix":""},{"id":245027548,"identity":"09a1dc38-6f23-482d-aa2b-e0aa38654c84","order_by":1,"name":"Pingfeng Yu","email":"","orcid":"","institution":"Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"Pingfeng","middleName":"","lastName":"Yu","suffix":""},{"id":245027549,"identity":"b7d47ac7-160b-46f3-9a34-49e806d31603","order_by":2,"name":"Xinyu Huang","email":"","orcid":"","institution":"Westlake University","correspondingAuthor":false,"prefix":"","firstName":"Xinyu","middleName":"","lastName":"Huang","suffix":""},{"id":245027550,"identity":"3b15056c-6f77-4902-a183-36f275b616f0","order_by":3,"name":"Ze Zhao","email":"","orcid":"","institution":"Westlake University","correspondingAuthor":false,"prefix":"","firstName":"Ze","middleName":"","lastName":"Zhao","suffix":""},{"id":245027551,"identity":"483c9d58-c5ef-4e9a-9ee5-eeb07f60c0b2","order_by":4,"name":"Linxing Chen","email":"","orcid":"","institution":"University of California","correspondingAuthor":false,"prefix":"","firstName":"Linxing","middleName":"","lastName":"Chen","suffix":""},{"id":245027552,"identity":"62947541-eabc-4a66-9c4e-6484dad9da42","order_by":5,"name":"Feng Ju","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA/ElEQVRIie2OsYoCMRCGJwS0ybKth3K+Qg5b0Vc5GbByOd/gtjqbqO36FtrZXZaA1XLbBmwUwcpCuEa9BS9rZWPUTjAfZP4U/8cMgMPxgJCQLEzUT38JMs/3awrlJtrmUbhNMc1cUXcoNEKy6h7S1tBPpdwldfCLHQ77qeWwCGltNJi3RhFCLHQbXsSGk35iUz5mZU/Mg7E2h7GtAq47nJIv65binyd+gu9UQZxtj9C8QSlQtpPBGBAU0xJ46ZoiVrTshfgZaeSqkiArJetu3Lcobz0kvyxr1PxhvFxuZo1Xv4eTxd6mhPmqswLLh7wsAFRPM7NVHA6H4+n5By8LWSQFWjijAAAAAElFTkSuQmCC","orcid":"","institution":"Westlake University","correspondingAuthor":true,"prefix":"","firstName":"Feng","middleName":"","lastName":"Ju","suffix":""}],"badges":[],"createdAt":"2023-10-30 06:29:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3510205/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3510205/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":46015285,"identity":"bd405a0b-cffb-429b-bd8f-977be8867481","added_by":"auto","created_at":"2023-11-07 15:19:12","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":279896,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSeasonal succession and driving factors of viral community in Lake Taihu. \u003c/strong\u003ea. Sampling dates and sites in Lake Taihu. Four sampling dates represent four seasons: summer, autumn, winter, and spring. Four sampling sites represent three lake regions: Gonghu Bay (Site 1), The Meiliang Bay (Site 3), and the open water (Site 2 and Site 4). b. One line represents the ranked abundance (measured by log\u003csub\u003e10\u003c/sub\u003e(RPKM+1)) of viral clusters, axes corresponding to the bottom and left. The other line represents the abundance contribution (measured by abundance percentage) of ranked viral clusters, axes corresponding to the top and right. c. PCA analysis of viral community structure in the free-living biomass samples. d. Number and percentage of abundant viral clusters share and unshare between seasons (with a cutoff of 50% abundance contribution). \u0026nbsp;Environmental (e) and microbial (f) explanation of the variations in viral community in Taihu by RDA analysis. The samples of winter were not included in the RDA analysis because the environmental parameters in winter was not available. Results of permutation test (n=999) for each variable are listed in Dataset S2.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-3510205/v1/e65a48162344813aa285dec2.png"},{"id":46016140,"identity":"33eeca8c-2bb1-4112-82f4-afe5553cb51d","added_by":"auto","created_at":"2023-11-07 15:27:12","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":334186,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eViral taxonomy and seasonal succession shown by a gene-sharing network. \u003c/strong\u003eThe underlying network (a-e) was determined by vCONTACT2. Each node presents a viral cluster. Color intensity represents viral abundance (a, b, c, and d) or viral family (e). Circles are specific viral superclusters (colored by taxonomy) with distinct seasonal changes as discussed in the text\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-3510205/v1/48bbd7e436d48e76f2530b0b.png"},{"id":46015289,"identity":"a6b07690-1249-48c7-bcd6-9a1e8dbf952b","added_by":"auto","created_at":"2023-11-07 15:19:12","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":304381,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVirus-host associations in Lake Taihu. \u003c/strong\u003eEach grey node presents a viral cluster and the viral clusters colored in dark gray encoded lysogenic marker proteins. Each colored node presented a host genus (colored by affiliated phylum). The linkage between a viral cluster and a bacterial genus represents a specific virus-host association. The size of host node represents the degree (the number of linked viral clusters). Only cyanobacterial genera and the genus in each phylum with largest degree were labeled.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-3510205/v1/bde03a8fc1327a3810846bc8.png"},{"id":46016141,"identity":"32dbdc76-5703-47be-a1d4-33e3d52c8074","added_by":"auto","created_at":"2023-11-07 15:27:12","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":72742,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe abundance trend of nineteen \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eMicrocystis\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003e phages and \u003c/strong\u003e\u003cem\u003e\u003cstrong\u003eMicrocystis\u003c/strong\u003e\u003c/em\u003e\u003cstrong\u003ehost genome in Lake Taihu. \u003c/strong\u003eThe abundance (measured by RPKM) of \u003cem\u003eMicrocystis\u003c/em\u003e phages and \u003cem\u003eMicrocystis\u003c/em\u003eMAG (SU4-Colony_bin2). The numbers marked in the figure represent the abundance of the SU4-Colony_bin2 in the samples.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-3510205/v1/1eab8b5cbe1ef292ef61f084.png"},{"id":46015284,"identity":"5e7e5a0f-e95e-4bf8-b0fb-0a7361914531","added_by":"auto","created_at":"2023-11-07 15:19:12","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":96097,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003evAMGs of various metabolic categories in Lake Taihu. \u003c/strong\u003ea. The abundance of each group of vAMGs in the four seasons in Lake Taihu. The abundance of each vAMGs group is calculated based on the sum of relative abundance (measured by RPKM) of its affiliated vAMGs. \u0026nbsp;b. Metabolic function network and gene abundance of vAMGs involved in pentose phosphate pathway and photosynthesis. Abun.: summed abundance in each season (measured by RPKM).\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-3510205/v1/621f3e5617360fae39527dc6.png"},{"id":46015286,"identity":"4ff3eac3-78ac-45ba-b11e-591c4e7a8305","added_by":"auto","created_at":"2023-11-07 15:19:12","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":234299,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eThe ecological links between viruses, microbes and environmental factors in a eutrophic freshwater ecosystem.\u003c/strong\u003e In the eutrophic freshwater ecosystem, the community structure and genomic diversity of host microbes as well as water environment can be influenced through viral infection. Virus can impact carbon flux and nutrients cycling in the water environmental by lysis of host cell and own-encoding AMGs. Environmental factors also have effect on viral infection. Therefore, the viruses, microbes and environmental factors closely influenced each other. Since environmental factors and host microbial community were constantly changed across seasons, virus-related features including lifestyle, host range, and encoding vAMGs were also differed across seasons. To display the variation of viral features between seasons, the total abundance of vAMGs, the proportion of \u003cem\u003eProteobacteria\u003c/em\u003eprophage, and the abundance percentage of\u003cem\u003e Proteobacteria\u003c/em\u003e broad host range phages were illustrated in the top bars as examples. The length of the top bars scaled with the statistical data.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-3510205/v1/274e4a68691a44c3e48877fe.png"},{"id":46839871,"identity":"7c98bc11-5d99-4f26-aa43-65aa846b3b83","added_by":"auto","created_at":"2023-11-21 10:22:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1854892,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3510205/v1/b11bfc09-30bb-4b4a-a22c-b28831946fa7.pdf"},{"id":46015292,"identity":"01d3db9e-94db-4ed8-99b7-de663d492248","added_by":"auto","created_at":"2023-11-07 15:19:13","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":13962990,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryDatasets.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-3510205/v1/25ceddc9c7a9442d0de08baa.xlsx"},{"id":46015290,"identity":"f1ce4457-9149-45e7-8948-8566652b7fe2","added_by":"auto","created_at":"2023-11-07 15:19:12","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":4727293,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryInformation.docx","url":"https://assets-eu.researchsquare.com/files/rs-3510205/v1/4265efe22e349687ce45aadb.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Seasonal Succession, Host Associations and Biochemical Roles of Aquatic Viruses in a Eutrophic Lake Plagued by Cyanobacterial Blooms","fulltext":[{"header":"Introduction","content":"\u003cp\u003eExcessive nitrogen and phosphorus inputs to the freshwater ecosystems can lead to eutrophication [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Eutrophication and concomitant harmful algae blooms (HABs) generate detrimental effects on the water quality, social economy, and human health [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Cyanobacteria and other bacteria play crucial roles in biogeochemical cycling and further influence freshwater ecosystems during HABs [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Specifically, cyanobacteria are often the main microorganisms and primary producers in eutrophic lakes, while heterotrophic bacteria act as major drivers of organic matter consumption and energy flow [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Extracellular products released by cyanobacteria can affect the growth of other bacteria [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], while other bacteria can provide nutrients and growth factors for cyanobacteria [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. However, our understanding of the viral community in eutrophic freshwater with cyanobacterial HABs remains elusive despite recent recognition of their broad implications on the microbiome structure, metabolic function, and microbial evolution in ecosystems [\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eViruses outnumber their microbial hosts by order of magnitude and are known as the most abundant biological entities on Earth [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Viruses can affect the microbial community through viral lysis, and the lysed host cell biomass returns into the surroundings as dissolved organic matters, which not only redirects the nutrient and energy flow but also mediates biodiversity [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Viruses can enhance host metabolism by manipulating host-encoded metabolic networks, or by introducing virus-encoded auxiliary metabolic genes (vAMGs) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Recent studies reveal that environmental stresses can shift virus-bacterium associations (e.g., viral lifestyle, bacterial antiphage systems, and host range) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In particular, vAMGs encoding functions involved in central carbon metabolism [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], photosynthesis, [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] and nutrient cycling [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e] have already been widely reported in the cyanophage genomes. The cyanophage infection and vAMGs involved in photosynthesis would affect the net primary productivity [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Therefore, in the eutrophic freshwater ecosystems suffering from yearly cyanobacterial bloom, viruses have the potential to impact the microbial community and the water environment through infection and own-encoding auxiliary metabolic genes.\u003c/p\u003e \u003cp\u003eLake Taihu, the third largest freshwater lake in China, is the source of irrigating and drinking water for over 40\u0026nbsp;million people [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. This important lake has received much attention due to its eutrophication and cyanobacterial HABs [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. The yearly cyanobacterial HAB in Taihu is dominated by \u003cem\u003eMicrocystis\u003c/em\u003e and usually occurs in late spring, reaches a peak in summer, and begins to decay in autumn [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The periodic cyanobacterial HABs provide an ideal scenario to investigate how cyanobacteria-bacteria-virus relationships as well as viral ecological roles respond to HAB formation and dissipation in freshwater ecosystems. Elucidating these cyanobacteria-bacteria-virus systems can bridge the knowledge gap in the microbial ecology and biochemistry underlying eutrophication and algal blooming in the freshwater ecosystems and may inspire ecological control of HABs.\u003c/p\u003e \u003cp\u003eIn this study, we aim to reveal the viral community succession, adaptive strategies, and ecological potentials in a eutrophic lake with periodic cyanobacterial HAB. To target viruses of both cyanobacteria and planktonic bacteria, we explored the viral diversity in the free-living biomass metagenomes of water microbiomes from Lake Taihu and tracked the spatiotemporal and seasonal succession patterns of viral community. Environmental conditions and host microbes were analyzed to determine the major drivers for viral community structure and succession. Computational approaches were adopted to reveal the virus-host relationships and variations of virus-related features (lifestyle and host range) in different seasons. Additionally, the metabolic types and distribution of vAMGs were characterized to assess the ecological roles of viruses in the eutrophic freshwater ecosystems. This study suggested that the viruses in the eutrophic freshwater ecosystems with cyanobacterial HABs can impact the host microbial community as well as the water environment, and the viral succession, strategies and ecological roles may change with the bloom-associated environment.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSample collection and environmental parameters\u003c/h2\u003e \u003cp\u003eIn total, 16 surface water samples were collected on 21 July 2020 (Summer, SU), 13 October 2020 (Autumn, AU), 16 January 2021 (Winter, WI), and 24 May 2021 (Spring, SP) in four sites in the Lake Taihu (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea) to represent four seasons and three lake regions (Gonghu Bay (Site 1), The Meiliang Bay (Site 2), and the open water (Site 3 and Site 4), respectively. Environmental parameters were measured as described previously [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Briefly, temperature (T), pH, dissolved oxygen (DO), and conductivity were determined \u003cem\u003ein situ\u003c/em\u003e, total nitrogen (TN), total phosphorus (TP), dissolved total nitrogen (DTN), nitrate (NO\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e\u0026minus;\u003c/sup\u003e-N), ammonium (NH\u003csub\u003e3\u003c/sub\u003e\u003csup\u003e+\u003c/sup\u003e-N), dissolved total phosphorus (DTP) and phosphate (PO\u003csub\u003e4\u003c/sub\u003e\u003csup\u003e3\u0026minus;\u003c/sup\u003e-P) were determined with the collected water samples. Phosphate was measured for water samples from summer and autumn (n\u0026thinsp;=\u0026thinsp;8), other environment parameters were measured for water samples from spring, summer, and autumn (n\u0026thinsp;=\u0026thinsp;12), while the environmental data for water samples from winter was not available.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDNA extraction, library construction, and sequencing\u003c/h2\u003e \u003cp\u003eFor each sample, 5-L surface water samples were filtered through a 48-\u0026micro;m sieve followed by 2.0-\u0026micro;m and 0.2-\u0026micro;m polycarbonate filters (Millipore, USA) to obtain three different size fractions which were named as free-living (0.2-2\u0026micro;m), particle-associated (2\u0026ndash;48\u0026micro;m), and colony (\u0026gt;\u0026thinsp;48\u0026micro;m) according to the size of the bacterial hosts, respectively. All 16 free-living and 16 particle-associated samples from four seasons, and one colony sample during the summer peak of the cyanobacterial HAB (Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) were selected for DNA extraction and metagenomic sequencing. Then, DNA extraction was performed using the DNeasy PowerSoil Kit (Qiagen, Germeny) following the manufacture\u0026rsquo;s instruction. Shotgun metagenomic sequencing libraries were constructed by NEB Next\u0026reg; Ultra\u0026trade; DNA Library Prep Kit for Illumina (NEB, USA), and paired-end sequencing (2 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\times\\)\u003c/span\u003e\u003c/span\u003e 150 bp) was conducted on Illumina HiSeq platform.\u003c/p\u003e \u003cp\u003e \u003cb\u003eQuality control of raw reads and\u003c/b\u003e, \u003cb\u003ede novo\u003c/b\u003e \u003cb\u003eassembly\u003c/b\u003e\u003c/p\u003e \u003cp\u003eRaw metagenomic reads were quality trimmed using Trimmomatic (v0.39) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] with parameters \u0026lsquo;SLIDINGWINDOW:15:25 LEADING:20 TRAILING:20 MINLEN:95\u0026rsquo;. Assembly was performed for each metagenome using metaSPAdes (v3.14.1) [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] with kmers \u0026lsquo;-k 21,33,55,77,99,127\u0026rsquo;.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eMetagenomic analysis of microbial community\u003c/h2\u003e \u003cp\u003eRead-based and assembly-based metagenomic approaches were both used in complementary to achieve taxonomic profiling of microbial communities and genome-level taxonomic and functional analyses of microbial populations [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. First, clean metagenomic reads were analyzed to determine global taxonomic profile and community composition by mOTUs3 [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Furthermore, metagenome-assembled genomes (MAGs) were reconstructed using metaBAT2 [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] from all metagenomes individually. After MAG quality evaluation by CheckM [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], filtered high-quality MAGs (those with completeness \u0026ndash; 5 \u0026times; contamination\u0026thinsp;\u0026ge;\u0026thinsp;50) [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e] were dereplicated by dRep [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] with default parameters, the dereplicated MAGs were retained for further analysis. Taxonomy classification of MAGs were determined by GTDB-tk [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Open reading frames (ORFs) of MAGs were predicted by Prodigal \u003cem\u003ev\u003c/em\u003e2.6.3 [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e] with the \u0026ldquo;-p meta\u0026rdquo; option. Functional annotation of MAGs was performed by kofamscan [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eViral contigs identification\u003c/h2\u003e \u003cp\u003eThe viral contigs were recovered from 16 free-living biomass metagenomes (0.2-2\u0026micro;m) for the following reasons. Metagenomes in the cellular fractions (0.2\u0026micro;m filtered) have become useful resources for recover and analyze viral sequences in recent years [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] although this fraction was generated without viral particle enrichment. The cellular proviruses, viruses undergoing the lytic cycle in the virocell, free virions retained on filters, and the potential microbial hosts were all included in the cellular fraction. \u003cem\u003eCyanobacteria Microcystis\u003c/em\u003e dominated the particle-associated and colony samples (e.g., 47% in SU1-PA, 41% in SP4-PA, and 87% in SU4-Colony) (measured by mOTUs3, Dataset S5), which made it more difficult to recover low-abundance viruses. Therefore, in order to recover more viruses (both lytic and lysogenic) as possible and retain host signals in the meanwhile, 0.2-2.0 \u0026micro;m fraction was selected for viral contigs identification (See below).\u003c/p\u003e \u003cp\u003eViral contigs were identified by running a combination of VirSorter2 [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], VirFinder [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] and CheckV [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] for all assembled contigs (from free-living biomass metagenomes) longer than 5 kbp. Briefly, contigs with i) VirSorter2 max score\u0026thinsp;\u0026gt;\u0026thinsp;0.95 or VirFinder max score\u0026thinsp;\u0026gt;\u0026thinsp;0.9, ii) CheckV completeness\u0026thinsp;\u0026gt;\u0026thinsp;0 and the number of viral genes \u0026ndash; 5 \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\times\\)\u003c/span\u003e\u003c/span\u003e host genes ༞ 0, were defined as viral contigs. After removing host contamination (predicted by CheckV), the viral contigs were clustered based on a pairwise ANI (average nucleotide identity) method (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bitbucket.org/berkeleylab/checkv/src/master/scripts/\u003c/span\u003e\u003cspan address=\"https://bitbucket.org/berkeleylab/checkv/src/master/scripts/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] using the recommended parameters, i.e., 95% ANI and 85% AF (alignment fraction), from MIUViG (Minimum Information about an Uncultivated Virus Genome) [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. A total of 59,430 viral contigs and 41,997 unique viral clusters were recovered from the metagenomes. ORFs of viral contigs were predicted by Prodigal \u003cem\u003ev\u003c/em\u003e2.6.3 [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eTaxonomy assignment of viral clusters\u003c/h2\u003e \u003cp\u003eTaxonomy assignment was established first using vConTACT v2.0 [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e] with default parameters. The gene-sharing network constructed by vConTACT was then visualized in Cytoscape v3.8.1 [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. For viral contigs that could not be clustered with a reference virus from the database (ProkaryoticViralRefSeq201), the taxonomic classification was performed based on a majority rule. If\u0026thinsp;\u0026gt;\u0026thinsp;50% ORFs of a viral contig were assigned to the same family with a blastp bitscore\u0026thinsp;\u0026gt;\u0026thinsp;50 against viral proteins from CheckV genome database [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e], it was considered part of that viral family [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. The taxonomy assignment of viral clusters was represented by the representative viral contig of the corresponding viral cluster.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eFunctional annotation and phylogenetic analysis\u003c/h2\u003e \u003cp\u003eFunctional annotation was performed by screening each viral ORF against pfam (v34.0) [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], KEGG [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e], Uniprot90 database [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e] using hmmsearch [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e] (bit-score\u0026thinsp;\u0026gt;\u0026thinsp;100), kofamscan [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e], and usearch [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e] (global identity\u0026thinsp;\u0026gt;\u0026thinsp;50%), respectively. Then, vAMGs were obtained by manual curation of the functional annotations. Under the vAMGs classification scheme [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e], metabolic genes directly involved in viral replication (e.g., nucleotide metabolism, replication and repair) were not included in the analysis [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eReference sequences similar to four photosynthetic vAMGs (\u003cem\u003epsb\u003c/em\u003eA, \u003cem\u003epsb\u003c/em\u003eD, \u003cem\u003epet\u003c/em\u003eE, and \u003cem\u003epet\u003c/em\u003eF) were recruited from NCBI nr database (blastp, bit-score\u0026thinsp;\u0026gt;\u0026thinsp;50, e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.00001). The reference sequences were then clustered by cd-hit [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e] with 90% global sequence identity. vAMGs and representative reference sequences were aligned by MUSCLE [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. The phylogenetic trees were computed from fasttree [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e] and visualized in iTOL [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe annotation of lysogenic marker proteins was extracted from the functional annotation run by hmmsearch against pfam (E-value\u0026thinsp;\u0026lt;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e) (v34.0) [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], and the accession list of lysogenic marker proteins (i.e., transposase, integrase, resolvase, excisionase and recombinase proteins) were provided in [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The viral genome with at least one lysogenic marker protein was regarded as possible lysogenic phage.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eViral host prediction\u003c/h2\u003e \u003cp\u003eTo efficiently track viral hosts, four commonly used bioinformatic approaches with different design principles were co-used to predict potential hosts for viruses: i) CRISPR spacer match, ii) tRNA match, iii) genome homology match and iv) kmer features.\u003c/p\u003e \u003cp\u003eFor CRISPR spacer match, CRISPR spacers were predicted for all \u0026gt;\u0026thinsp;5 kb contigs in the metagenomic assemblies with CRT [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. The predicted CRISPR spacers were then matched to all viral contigs by BLASTn-short with 97% identity, 90% coverage and 1 mismatch allowed [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. The retained hits were considered as candidate virus-host pairs. The taxonomic annotation of potential host contigs not belonging to MAGs was determined by CAT [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e] with default parameters.\u003c/p\u003e \u003cp\u003eFor tRNA match, bacterial tRNA sequences were predicted in viral contigs using tRNAscan-SE(v1.23) with option \u0026lsquo;-B\u0026rsquo; [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e] and then searched against recovered MAGs using BLASTn. The hits with 95% global nucleotide identity were identified as possible hosts.\u003c/p\u003e \u003cp\u003eFor genome homology match, viral contigs were compared to recovered MAGs with BLASTn to indicate possible prophage integration or horizontal gene transfer events [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. A host prediction was made when an MAG displayed a region similar to a viral contig\u0026thinsp;\u0026ge;\u0026thinsp;2.5 kb at \u0026ge;\u0026thinsp;90% identity.\u003c/p\u003e \u003cp\u003eFor kmer features, two kmer-based tools, WIsH [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e] (k\u0026thinsp;=\u0026thinsp;8) and PHIST [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e] (k\u0026thinsp;=\u0026thinsp;25) were applied to predict host MAGs for viral contigs. The recovered MAGs were included in potential host dataset for WisH and PHIST. Complete viral genomes of human and vertebrates downloaded from NCBI (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/genome/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/genome/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) were used as negative dataset for WisH. Only the predictions by WiSH with \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e and the predictions by PHIST based on \u0026gt;\u0026thinsp;10 kmers were retained.\u003c/p\u003e \u003cp\u003eTo further check \u003cem\u003eMicrocystis\u003c/em\u003e phages, the genes of viral cluster representatives linked to \u003cem\u003eCyanobacteria Microcystis\u003c/em\u003e were annotated by BLASTn against NCBI nr database (threshold of 50 for bit score and 10\u003csup\u003e\u0026minus;\u0026thinsp;5\u003c/sup\u003e for E-value). The viral clusters encoded at least one \u003cem\u003eMicrocystis\u003c/em\u003e virus-like or \u003cem\u003eMicrocystis\u003c/em\u003e-like gene were regarded as high-confidence \u003cem\u003eMicrocystis\u003c/em\u003e phages. The annotation of \u003cem\u003eMicrocystis\u003c/em\u003e phages predicted by host prediction was listed in Dataset S8.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eViral and MAGs quantification\u003c/h2\u003e \u003cp\u003eTo calculate relative abundance of viral contigs in each metagenome, clean reads were mapped to viral contigs by bowtie2 [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e] with parameter \u0026lsquo;--very-sensitive\u0026rsquo;. The abundance was normalized to RPKM (reads per kilobase per million) with the in-house python scripts. Then, the relative abundance of each viral cluster was calculated based on the sum of relative abundance of its members (i.e., affiliated viral contigs). The occurrence of a viral contig in a given metagenome was determined if at least 80% of its full sequence could be mapped by the clean reads. The relative abundance of viral contigs was also used to represent the abundance of its encoding gene (e.g., vAMGs).\u003c/p\u003e \u003cp\u003eSimilarly, to calculate relative abundance of MAGs in each metagenome, clean reads were mapped to MAGs by bowtie2 with parameter \u0026lsquo;--very-sensitive\u0026rsquo; and abundance was normalized to RPKM. The occurrence of an MAG in a given metagenome was determined if at least 80% of its full sequence could be mapped by the clean reads.\u003c/p\u003e \u003cp\u003eThe virus/host ratio (VHR) was defined as the viral cluster abundance divided by the host genome abundance and was calculated for each pair of virus-host associations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis and visualization\u003c/h2\u003e \u003cp\u003ePrincipal component analysis (PCA) was performed using \u0026lsquo;prcomp\u0026rsquo; function in R. To identify the environmental parameters and microbial community structure driving the distribution of viral community in Taihu, redundancy discriminant analysis (RDA) was applied using \u0026lsquo;rda\u0026rsquo; function in the vegan R package [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. The abundance of microbial community was calculated at the phylum level by motus3 [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The input abundance tables of viral community and microbial community included those viral clusters or microbial phylum occurred at least in 50% of the samples, respectively. A correlation between viral abundance and an environmental parameter was considered robust correlated if the Pearson\u0026rsquo;s correlation coefficient was \u0026gt;\u0026thinsp;0.7 (or \u0026lt;\u0026thinsp;\u0026minus;\u0026thinsp;0.7) and FDR-adjusted \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e], and the script used for correlation analysis is available at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/emblab-westlake/MbioAssy1.0/\u003c/span\u003e\u003cspan address=\"https://github.com/emblab-westlake/MbioAssy1.0/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. The statistical significance of virus-related variables in different seasons (i.e., proportion of lysogenic phages, abundance percentage of broad host phages, and encoding pattern of vAMGs) were determined by Kruskal\u0026thinsp;\u0026minus;\u0026thinsp;Wallis test (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in python using the function \u0026ldquo;kruskal wallis\u0026rdquo; in the scipy package. The networks for viral clusters and environmental parameters, and viral-host associations were visualized in Cytoscape v3.8.1 [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Results and Discussion","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eThe viral community structure in Lake Taihu succeeded by season\u003c/h2\u003e \u003cp\u003eDNA viruses are increasingly recognized as major ecological drivers for their effects on microbial community diversity and composition, horizontal gene transfer and recycling of carbon and nutrients [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. To investigate DNA viruses in a eutrophic freshwater ecosystem, the viral contigs were predicted from the 16 free-living (0.2\u0026ndash;2.0 \u0026micro;m) biomass metagenomes of Lake Taihu water samples (four seasons and four sites, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea and Fig. \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). A total of 59,430 viral contigs were recovered from 344.1 GB metagenomes, and the viral contigs were further clustered into 41,997 viral clusters (95% ANI and 85% AF) (Dataset S1). These viral clusters exhibited a wide taxonomical and host range, including \u003cem\u003eCaudovirales\u003c/em\u003e (15,139), NCLDV (1,044), and virophages (58), which were known of infecting prokaryotes, eukaryotes, and giant viruses, respectively (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe taxonomical statistics of viral clusters recovered in free-living biomass metagenomes of Lake Taihu.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eViral family\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLineage/Group\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReported hosts\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumber of viral clusters\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAckermannviridae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCaudovirales\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProkaryote; \u003cem\u003eGammaproteobacteria\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eAutographiviridae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCaudovirales\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProkaryote; Bacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.04\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHerelleviridae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCaudovirales\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProkaryote; \u003cem\u003eFirmicutes\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eIridoviridae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNCLDV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEukaryote; Amphibians, fish, and invertebrates\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLavidaviridae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVirophage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVirus, NCLDV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMarseilleviridae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNCLDV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEukaryote; Amoeba\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMimiviridae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNCLDV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEukaryote; Protists\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMyoviridae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCaudovirales\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProkaryote; Bacteria and archaea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePhycodnaviridae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNCLDV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEukaryote; Eukaryotic algae\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e927\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePithoviridae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNCLDV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEukaryote; Amoeba\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePodoviridae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCaudovirales\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProkaryote; Bacteria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePoxviridae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNCLDV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEukaryote; Vertebrates and arthropods\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eSiphoviridae\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eCaudovirales\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProkaryote; Bacteria and archaea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14.38\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnclassified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUnclassified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUnclassified\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25756\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e61.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTo examine the pattern of viral distribution, the viral clusters were characterized in terms of abundance rank and abundance contribution (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). The abundance of viral clusters showed a sharp drop at the beginning of the ranking, and then tended to level off, about 10% of the viral clusters explained 50% of the total viral abundance (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). This phenomenon implied that the viral distribution in Taihu may be consistent with the Bank model, that is, a small fraction of viruses in the community are of high abundance, while the rest majority of viruses are in low abundance [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. PCA analysis based on viral abundance showed a distinct separation between seasons (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ec). This is in accordance with the bacterial community patterns (Fig. \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e), suggesting that the viral clusters in Taihu showed more significant variation over the temporal than spatial scale.\u003c/p\u003e \u003cp\u003eFurther, we constructed gene-sharing networks clearly displaying the viral taxonomy and specific succession patterns across the seasons (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). For example, one \u003cem\u003eMyoviridae\u003c/em\u003e supercluster was abundant in spring and stayed in low-abundance in other seasons (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, blue circle). One \u003cem\u003eSiphoviridae\u003c/em\u003e supercluster was overall abundant across seasons but showed different distribution patterns of abundance of viral cluster members (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, yellow circle). \u003cem\u003ePhycodnaviridae\u003c/em\u003e is a viral family that infected eukaryotic algae chlorella [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. The supercluster of \u003cem\u003ePhycodnaviridae\u003c/em\u003e was almost absent in summer and autumn but thrived in winter, and then declined in spring again (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, green circle). These variations of viral succession patterns may be due to the different optimal environmental conditions, host community structure, and lifestyles of viruses (e.g., lytic or lysogenic) [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eViral community succession co-driven by environmental factors and microbial hosts\u003c/h2\u003e \u003cp\u003eViral community structure can be influenced by both environmental conditions and host microorganisms [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. RDA analysis revealed that the predominant environmental factors driving the water viral community structure were DTN (72.34%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003), temperature (60.77%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.030), and NO\u003csub\u003e3\u003c/sub\u003e-N (58.97%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee and Dataset S2), which well separated the 12 viral communities of three seasons including spring, summer and autumn (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee and Dataset S2). Other environmental parameters (e.g., DO and pH) did not explain significant variation of viral communities (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.5), indicative of their limited associations with viral clusters. Moreover, significant (FDR-adjusted \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and strong (Spearman r\u0026thinsp;\u0026gt;\u0026thinsp;0.7) positive (n\u0026thinsp;=\u0026thinsp;2,556) and negative (n\u0026thinsp;=\u0026thinsp;1,477) pairwise correlations between environmental parameters and viral clusters were identified (Fig. S3a and 3b). The dominant factors showing positive correlations with viral clusters included TN (n\u0026thinsp;=\u0026thinsp;1074, 42.0%), NO\u003csub\u003e3\u003c/sub\u003e-N (n\u0026thinsp;=\u0026thinsp;1020, 39.9%) and temperature (T, n\u0026thinsp;=\u0026thinsp;664, 26.0%), while the dominant factors showing negative correlations included NO\u003csub\u003e3\u003c/sub\u003e-N (n\u0026thinsp;=\u0026thinsp;554, 37.5%), DTP (n\u0026thinsp;=\u0026thinsp;337, 22.8%) and PO\u003csub\u003e4\u003c/sub\u003e-P (n\u0026thinsp;=\u0026thinsp;213, 14.4%). The environmental parameters were important factors in changing and driving the viral community structure and succession. For example, the free viruses released from host cells are directly exposed to the environment, and the environmental conditions may change the infectivity and adsorption ability of the viruses, which plays a decisive role in the next round of infection [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e]. Although not all variations could be explained by the measured environmental parameters (34.25% cumulative variation could not be explained in RDA analysis, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ee), these results indicated nutrients (i.e., nitrogen and phosphorous) and temperature were among the key environmental drivers shaping the water viral community in Taihu.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eOur analyses indicated that the bacterial community distribution had a high influence on the distribution of viral community as well, with 41.72% and 22.42% of the cumulative variance explained by two RDA axes (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ef). Particularly, \u003cem\u003eCyanobacteria\u003c/em\u003e pointed to summer samples and contributed the most to separating the 16 viral communities of four seasons (73.85%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001), followed by \u003cem\u003ePlanctomycetes\u003c/em\u003e (63.52%, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) (Dataset S2). In order to simultaneously examine the influence of environmental factors and microbial community on virus community structure, variance partitioning analysis was additionally performed based on samples from three seasons (spring, summer and autumn). The representative environmental parameters (TN, NO\u003csub\u003e3\u003c/sub\u003e-N, DTN, TP and temperature) explained 21% of the total variation in the viral community structure while the representatives of the microbial community (\u003cem\u003eCyanobacteria\u003c/em\u003e, \u003cem\u003ePlanctomycetes\u003c/em\u003e, \u003cem\u003eProteobacteria\u003c/em\u003e, \u003cem\u003eActinobacteria\u003c/em\u003e, \u003cem\u003eBacteroidetes\u003c/em\u003e) explained 15% variation. The environmental parameters interacted with microbial community together explained 22% of viral community structural variation (Fig. S3c). Environmental parameters can influence the planktonic microbial community distribution in Taihu, and the microbial community can then directly affect viral infection. Moreover, viral infection can, in turn, constrain host distribution through lytic infection, and sometimes viruses can also help their hosts to survive nutrient-limiting conditions [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Together, the water environment, microbial community, and viral community continuously and closely interact and influence each other.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eViruses infecting planktonic bacteria involved in nitrogen and phosphate cycling are prevalent in Lake Taihu\u003c/h2\u003e \u003cp\u003eTo predict specific virus-bacteria associations, a total of 197 unique and high-quality bacterial MAGs were first recovered from Taihu metagenomes (Dataset S3), including one cyanobacterial \u003cem\u003eMicrocystis\u003c/em\u003e genome (SU4-Colony_bin2, completeness\u0026thinsp;=\u0026thinsp;87.5%, contamination\u0026thinsp;=\u0026thinsp;0.84%). Through four bioinformatic approaches integrating CRISPR spacer match, tRNA match, genome similarity, and kmer features (as detailed in Methods), 5,615 (13.4%) viral clusters were linked with the recovered MAGs (or contigs), which could be affiliated to specific genera (Dataset S1 and Dataset S4). Predicted bacterial hosts spanned 11 phyla dominated with \u003cem\u003eProteobacteria\u003c/em\u003e (1,733), \u003cem\u003eBacteroidota\u003c/em\u003e (1,408), and \u003cem\u003eActinobacteriota\u003c/em\u003e (1,172) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Dataset S1).\u003c/p\u003e \u003cp\u003eNitrogen and phosphorus were found as significant environmental drivers of viral community structure in this study (see above), consistent with their predicted roles in microbial community structure in Taihu [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. There were 165 MAGs (83.8%) potentially involved in phosphate regulon or (and) transport (Dataset S3), suggesting the prevalence of phosphate-cycling genes in Taihu lake water microbiomes. Correspondingly, among the 5,615 viral clusters with putative hosts, 5,211 (92.8%) viral clusters were predicted to infect hosts encoding genes of phosphate cycling. Therefore, changes in microbial infection and mortality by viruses can affect the transport and regulation of phosphate by the microbial community in Taihu. In addition, 2,140 (38.1%) viral clusters were linked with 38 MAGs (19.3%), which were identified to encode at least one gene involved in nitrogen metabolism (i.e., nitrification, denitrification, assimilatory nitrate reduction, dissimilatory nitrate reduction, nitrogen fixation and nitrate/nitrite transport) (Dataset S3). For example, two MAGs from \u003cem\u003eProteobacteria Burkholderiaceae\u003c/em\u003e (SU4_bin105 and WI1_bin13) encoded methane/ammonia monooxygenase subunits A, B and C (\u003cem\u003epmo\u003c/em\u003eA/B/C-\u003cem\u003eamo\u003c/em\u003eA/B/C), and were linked to 13 and 26 viral clusters, respectively. Five MAGs encoding \u003cem\u003enxr\u003c/em\u003eA and \u003cem\u003enxr\u003c/em\u003eB (and thus possibly involved in nitrite oxidation) were all linked to several viral clusters (Dataset S1). Although no MAG had a complete denitrification pathway, 11 MAGs encoded denitrification genes for partial denitrification. WI3_bin37 from \u003cem\u003eBacteroidota JAAFJM01\u003c/em\u003e encoded \u003cem\u003enir\u003c/em\u003eK and \u003cem\u003enos\u003c/em\u003eZ, indicating this bacterium had the potential to produce nitric oxide, and WI3_bin37 was the MAG linked with the greatest number of viral clusters (702) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). One MAG (SU1-PA_bin7) of \u003cem\u003eCyanobacteria Dolichospermum\u003c/em\u003e linked to 324 viral clusters and was found to encode genes involved in assimilatory nitrate reduction, nitrate/nitrite transport and nitrogen fixation. Together, in Lake Taihu, the viruses infecting bacteria involved in nitrogen (i.e., nitrogen and phosphorous) cycling are prevalent and thus the virus may act as an essential player and ecological driver in this microbial system.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eViruses shifted adaptive strategies with bloom-induced environmental changes\u003c/h2\u003e \u003cp\u003eThe viruses shift lifestyles (lytic and lysogenic) and host ranges in response to biotic (e.g., host physiology) and abiotic changes (e.g., environmental conditions) [\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. Viral clusters encoding lysogenic marker proteins were considered as possible lysogenic phages (See Methods), and viral clusters predicted to have two or more host genera were considered as broad host range phages. The variations in the proportion of lysogenic phages and the abundance percentage of broad host range phages in different seasons and bacterial phyla were compared. \u003cem\u003eProteobacteria\u003c/em\u003e, \u003cem\u003eActinobacteria\u003c/em\u003e, and \u003cem\u003eBacteroidota\u003c/em\u003e were always the main bacterial taxa closely correlated with cyanobacterial blooms in freshwater ecosystems [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. The viruses infecting \u003cem\u003eProteobacteria\u003c/em\u003e and \u003cem\u003eActinobacteria\u003c/em\u003e both had a highest proportion with lysogenic lifestyle in summer (11.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31% and 3.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16%, respectively) than in other seasons (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), andthe lowest abundance percentage of broad host range phages in summer (9.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85% and 6.78\u0026thinsp;\u0026plusmn;\u0026thinsp;1.20%) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). These results indicated that \u003cem\u003eProteobacteria\u003c/em\u003e and \u003cem\u003eActinobacteria\u003c/em\u003e might respond to the cyanobacterial HABs and associated environmental stress by enhancing lysogenicity and condensing host ranges. In contrast, only a relatively lower proportion of \u003cem\u003eBacteroidota\u003c/em\u003e phages conducted lysogenic lifestyle (2.19\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59%) and lowest in summer (1.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14%) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e), which implied that viruses of \u003cem\u003eBacteroidota\u003c/em\u003e in Taihu were more likely to infect hosts in the lysis form especially when the bloom peaked. In terms of host range, the abundance percentage of host range phages infecting \u003cem\u003eBacteroidota\u003c/em\u003e were highest in summer. Therefore, host tracking suggested that, compared with \u003cem\u003eProteobacteria\u003c/em\u003e and \u003cem\u003eActinobacteria\u003c/em\u003e, the viruses infecting \u003cem\u003eBacteroidota\u003c/em\u003e adopted opposite viral strategies when exposed to cyanobacterial HABs.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eThe proportion of potential lysogenic phages (upper) and abundance percentage of broad host range phages (lower) in seasonal samples in Taihu.\u003c/b\u003e The proportion of potential lysogenic phages for each phylum was calculated as the number of lysogenic phages infecting this phylum divided by the total number of viral clusters infecting this phylum. The abundance percentage of broad host range phages for each phylum was calculated as the summed abundance of broad host range phages infecting this phylum divided by the total abundance of viral clusters infecting this phylum. Only phyla with more than two lysogenic phages (upper) or broad host range phages (lower) in the samples were shown (as average\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation). For each phylum, the proportion of potential lysogenic phages (upper) and abundance percentage of broad host range phages (lower) significantly different across seasons were marked with an asterisk (Kruskal\u0026thinsp;\u0026minus;\u0026thinsp;Wallis test, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSummer\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAutumn\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWinter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSpring\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLysogenic (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eActinobacteriota\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e3.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.82\u0026thinsp;\u0026plusmn;\u0026thinsp;0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e3.11\u0026thinsp;\u0026plusmn;\u0026thinsp;0.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e2.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBacteroidota\u003c/em\u003e *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e1.62\u0026thinsp;\u0026plusmn;\u0026thinsp;0.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e2.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e2.00\u0026thinsp;\u0026plusmn;\u0026thinsp;0.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e2.80\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGemmatimonadota\u003c/em\u003e *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e13.04\u0026thinsp;\u0026plusmn;\u0026thinsp;1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e9.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e5.62\u0026thinsp;\u0026plusmn;\u0026thinsp;1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e8.70\u0026thinsp;\u0026plusmn;\u0026thinsp;3.29\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePlanctomycetota\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e17.86\u0026thinsp;\u0026plusmn;\u0026thinsp;0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e16.32\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e17.01\u0026thinsp;\u0026plusmn;\u0026thinsp;1.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e17.71\u0026thinsp;\u0026plusmn;\u0026thinsp;1.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eProteobacteria\u003c/em\u003e *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e11.38\u0026thinsp;\u0026plusmn;\u0026thinsp;0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e9.73\u0026thinsp;\u0026plusmn;\u0026thinsp;0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e9.02\u0026thinsp;\u0026plusmn;\u0026thinsp;0.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e8.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eVerrucomicrobiota\u003c/em\u003e *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e8.48\u0026thinsp;\u0026plusmn;\u0026thinsp;0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e11.16\u0026thinsp;\u0026plusmn;\u0026thinsp;1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e13.49\u0026thinsp;\u0026plusmn;\u0026thinsp;1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e9.11\u0026thinsp;\u0026plusmn;\u0026thinsp;2.59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHost range (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eActinobacteriota\u003c/em\u003e *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e6.78\u0026thinsp;\u0026plusmn;\u0026thinsp;1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e10.85\u0026thinsp;\u0026plusmn;\u0026thinsp;1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e8.43\u0026thinsp;\u0026plusmn;\u0026thinsp;1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e8.27\u0026thinsp;\u0026plusmn;\u0026thinsp;1.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eBacteroidota\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e4.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e1.95\u0026thinsp;\u0026plusmn;\u0026thinsp;0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e3.04\u0026thinsp;\u0026plusmn;\u0026thinsp;1.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e3.89\u0026thinsp;\u0026plusmn;\u0026thinsp;1.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eGemmatimonadota\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e20.88\u0026thinsp;\u0026plusmn;\u0026thinsp;3.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e27.02\u0026thinsp;\u0026plusmn;\u0026thinsp;5.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e19.53\u0026thinsp;\u0026plusmn;\u0026thinsp;1.57\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e17.30\u0026thinsp;\u0026plusmn;\u0026thinsp;6.83\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003ePlanctomycetota\u003c/em\u003e *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e16.99\u0026thinsp;\u0026plusmn;\u0026thinsp;3.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e18.71\u0026thinsp;\u0026plusmn;\u0026thinsp;2.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e8.97\u0026thinsp;\u0026plusmn;\u0026thinsp;2.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e28.25\u0026thinsp;\u0026plusmn;\u0026thinsp;9.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eProteobacteria\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e9.76\u0026thinsp;\u0026plusmn;\u0026thinsp;0.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e14.50\u0026thinsp;\u0026plusmn;\u0026thinsp;1.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e13.25\u0026thinsp;\u0026plusmn;\u0026thinsp;2.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e11.73\u0026thinsp;\u0026plusmn;\u0026thinsp;2.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eVerrucomicrobiota\u003c/em\u003e *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e4.96\u0026thinsp;\u0026plusmn;\u0026thinsp;0.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e11.89\u0026thinsp;\u0026plusmn;\u0026thinsp;1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e7.75\u0026thinsp;\u0026plusmn;\u0026thinsp;1.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e3.77\u0026thinsp;\u0026plusmn;\u0026thinsp;1.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAdditionally, \u003cem\u003ePlanctomycetota\u003c/em\u003e witnessed the highest proportion of lysogenic phages (17.22\u0026thinsp;\u0026plusmn;\u0026thinsp;1.53%) among all phyla and the proportion were not significantly different between seasons, but the proportion of broad host range phages of \u003cem\u003ePlanctomycetota\u003c/em\u003e displayed a significant difference between seasons (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). \u003cem\u003eVerrucomicrobiota\u003c/em\u003e showed the highest proportion of lysogenic phages in winter, which may be selected by low temperatures and poor nutritional conditions in winter (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In Lake Taihu suffering from yearly cyanobacterial HABs, environmental conditions constantly changed between seasons. A more suitable lifestyle and host range for viruses may benefit for surviving. In turn, viral strategies also affect microbial communities, for example, viral lifestyle conversion and infection of different hosts would mediate more frequent horizontal gene transfer events.\u003c/p\u003e \u003cp\u003e \u003cb\u003eA consortium of\u003c/b\u003e \u003cb\u003eMicrocystis\u003c/b\u003e \u003cb\u003ephages may affect the progression of cyanobacterial blooms\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTaihu experiences the yearly bloom of cyanobacteria dominated by \u003cem\u003eMicrocystis\u003c/em\u003e, which has caused great eco-environmental concerns [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. The reads- and assembly-level quantification both verified \u003cem\u003eCyanobacteria Microcystis\u003c/em\u003e dominated the summer samples (Dataset S3 and Dataset S5). After summer, \u003cem\u003eMicrocystis\u003c/em\u003e largely declined in the autumn and winter samples and rose again in the spring samples, when the next cyanobacterial HAB may be imminent. Considering that \u003cem\u003eMicrocystis\u003c/em\u003e were the most abundant microorganisms during the bloom (Dataset S5), viruses that infect \u003cem\u003eMicrocystis\u003c/em\u003e may act as abundant and key ecosystem players in Taihu. After inspection of viral genes annotation, 19 viral clusters were identified as high-confidence \u003cem\u003eMicrocystis\u003c/em\u003e phages (Fig. S4). \u003cem\u003eMicrocystis\u003c/em\u003e phages exhibited similar succession patterns to the \u003cem\u003eMicrocystis\u003c/em\u003e host, i.e., the abundance of most \u003cem\u003eMicrocystis\u003c/em\u003e phages reached a peak during summer cyanobacterial HABs, declined in autumn, then were undetectable in winter, and reappeared in spring with their hosts (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe identified \u003cem\u003eMicrocystis\u003c/em\u003e phages in Taihu may be in different forms of infection. For example, some \u003cem\u003eMicrocystis\u003c/em\u003e phages exhibited features of lytic virus in some samples, e.g., AU1_16663, AU4_1689, AU4_505, SU4_8548, and WI2_3694 maintained VHR\u0026thinsp;\u0026gt;\u0026thinsp;1 in the four-summer free-living biomass samples, and were still detectable in the autumn samples when the \u003cem\u003eMicrocystis\u003c/em\u003e MAG was almost undetectable (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Dataset S1). The lytic \u003cem\u003eMicrocystis\u003c/em\u003e phages remained in the autumn samples may play roles on HAB dissipation through infection and host lysis. AU4_1689 and WI2_3694 exhibited significantly high abundances in the spring samples, which were higher than the host abundance in all particle-associated and free-living biomass samples in the same season (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). These two \u003cem\u003eMicrocystis\u003c/em\u003e phages may slow down the outbreak of \u003cem\u003eMicrocystis\u003c/em\u003e blooms by virulent infection during that period. Although typical lysogenic marker genes were not detected in the identified \u003cem\u003eMicrocystis\u003c/em\u003e phage genomes, SU3_11320 was probably a prophage that infected the \u003cem\u003eMicrocystis\u003c/em\u003e MAG suggested by two features. First, a 2910 bp fragment on SU3_11320 had 99.3% similarity to the \u003cem\u003eMicrocystis\u003c/em\u003e host genome. Second, SU3_11320 kept VHR\u0026thinsp;\u0026lt;\u0026thinsp;1 in all samples. Despite the small size of the virus, SU3_11320 displayed a high abundance (RPKM\u0026thinsp;=\u0026thinsp;88.7) in the colony sample (\u0026gt;\u0026thinsp;48\u0026micro;m), which was likely due to its presence in the intracellular host genome. This lysogenic \u003cem\u003eMicrocystis\u003c/em\u003e phage coexisted stably with the host. Functionally, most of the annotatable genes of \u003cem\u003eMicrocystis\u003c/em\u003e phages were viral marker genes (e.g., phage terminase, phage capsid protein and phage head-tail attachment protein, Fig. S4). The phosphate starvation-inducible protein \u003cem\u003epho\u003c/em\u003eH in WI2_3694 was the only vAMG found in \u003cem\u003eMicrocystis\u003c/em\u003e phage genomes. In summary, the diversity, abundance succession, potential lifestyles and gene function of \u003cem\u003eMicrocystis\u003c/em\u003e phages were explored here, providing new insights into the viruses directly affect cyanobacterial HAB of Lake Taihu.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eViruses in Lake Taihu encode auxiliary metabolic capabilities to compensate for host metabolism\u003c/h2\u003e \u003cp\u003eViruses can impact biogeochemical cycling not only through infection and host lysis, but also by reprogramming metabolic networks of virocells (living-form of the infected cells) [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e], or more pointedly, by expressing vAMGs [\u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. A total of 2,771 potential vAMGs were identified and grouped into 15 metabolic categories and 114 unique gene types (Dataset S6). The vAMGs involved in carbohydrates metabolism were the most abundant metabolic category (580 genes), followed by those involved in metabolism of vitamins and cofactors (509 genes), amino acids (365 genes), phosphate regulation (361 genes), and queuosine synthesis (242 genes), and these vAMG groups also showed a relatively high abundance among all groups (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea). Among the 114 vAMGs gene types, the encoding percentage of 77 (67.0%) gene types significantly varied (Kruskal\u0026thinsp;\u0026minus;\u0026thinsp;Wallis test, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) between seasons (Dataset S7), indicating that the bloom-induced variations in freshwater environment may affect the occurrence of vAMGs. The overall abundance of vAMGs were highest in spring. The vAMGs of carbohydrates metabolism, phosphate regulation, amino acids metabolism, vitamins and cofactors metabolism, photosynthesis, and glycans metabolism were highest in spring both in terms of abundance and frequency of occurrence (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea and Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). Taking advantage of these identified vAMGs, viruses may involve in various metabolic pathways through host metabolic networks (Supplementary Text).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFunctionally, the most representative vAMGs were those involved in photosynthesis, Calvin cycle repression, and pentose phosphate pathway. For some cyanophages, the host energy production is not sufficient to meet their demands for replication [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Controlling host central metabolism away from CO\u003csub\u003e2\u003c/sub\u003e fixation to viral DNA and protein synthesis, and carrying vAMGs of photosynthesis and pentose phosphate pathway were common strategies of viruses [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Four vAMGs involved in photosynthesis (\u003cem\u003epsb\u003c/em\u003eA, \u003cem\u003epsb\u003c/em\u003eD, \u003cem\u003epet\u003c/em\u003eE, and \u003cem\u003epet\u003c/em\u003eF) were identified in our metagenomic datasets of Taihu lake water (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb), with 52 and 37 viral clusters carried \u003cem\u003epsb\u003c/em\u003eA and \u003cem\u003epsb\u003c/em\u003eD, respectively (Dataset S1). These genes are typical vAMGs of cyanophage in marine, which encode the core photosystem II reaction center D1 protein and D2 protein [\u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e]. Virus-encoded \u003cem\u003epsb\u003c/em\u003eA and \u003cem\u003epsb\u003c/em\u003eD can potentially replenish the inhibited host homologous proteins during infection and continue to provide energy (ATP) for viral replication [\u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e]. Two photosynthetic electron transport proteins plastocyanin (\u003cem\u003epet\u003c/em\u003eE) and ferredoxin (\u003cem\u003epet\u003c/em\u003eF) were identified in 6 and 48 viral clusters (Dataset S1). These photosynthetic vAMGs relied on cellular photosynthesis system to produce and obtain the additional energy, so the phages encoding photosynthetic genes were likely to infect phototrophic microorganisms (e.g., \u003cem\u003eCyanobacteria\u003c/em\u003e). Phylogenetic analysis revealed that most of the reference genes related to viral photosynthetic vAMGs came from \u003cem\u003eCyanobacteria\u003c/em\u003e (Fig. S6). The previously discussed \u003cem\u003eMicrocystis\u003c/em\u003e phages were not detected to encode a photosynthetic gene. Based on host prediction, among the vial clusters encoding photosynthetic genes, 5 viral clusters, were predicted to infect \u003cem\u003eCyanobacteria Cyanobium\u003c/em\u003e, and one was predicted to infect \u003cem\u003eCyanobacteria Dolichospermum\u003c/em\u003e. Additionally, 4 viral clusters encoding photosynthetic genes were clustered with reference genomes of \u003cem\u003eCyanobacteria Synechococcus\u003c/em\u003e by vCONTACT2. This phenomenon may be caused by the different niches of the cyanobacterial hosts. \u003cem\u003eMicrocystis\u003c/em\u003e occupied a dominant ecological niche in the microbial community and was probably able to provide enough energy on its own, while cyanobacteria of other genera were in low abundance, and the associated virus-encoded photosynthetic genes could enhance the host photosynthesis to obtain extra energy and provide selective advantage for their hosts.\u003c/p\u003e \u003cp\u003eA CP12 protein, which directs carbon flux from Calvin cycle to pentose phosphate pathway [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e], which were identified in 47 viral clusters. Four gene types of vAMGs involved in pentose phosphate pathway were also found in the viral clusters, including transaldolase \u003cem\u003etal\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;21), glucose 6-phosphate dehydrogenase \u003cem\u003ezwf\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;2), 6-phosphogluconate dehydrogenase gnd (n\u0026thinsp;=\u0026thinsp;21), and phosphate pyrophosphokinase \u003cem\u003eprs\u003c/em\u003e (n\u0026thinsp;=\u0026thinsp;26) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb and Dataset S6). These viruses-encoded CP12 genes in concert with pentose phosphate pathway genes may help the biosynthesis of ribose 5-phosphate and reducing power that increase dNTP synthesis for viral replication [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn summary, the vAMGs involved in photosynthesis supplied a clue that viruses can hijack host metabolism to produce energy. The identification of viral CP12 and pentose phosphate pathway genes indicated that at least some water viruses in lake Taihu tended to control host metabolism and use the host energy for dNTP synthesis rather than carbon fixation, echoing previous findings in the marine viruses [\u003cspan additionalcitationids=\"CR84\" citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study unraveled the double-strand DNA viruses in a typical eutrophic freshwater lake Taihu suffering from cyanobacterial HABs. The virus, host microbes, and the freshwater environment were tightly associated with each other (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). The explored host associations and encoded vAMGs indicated the ecological roles of viruses in eutrophic freshwater. In detail, the predicted virus-host relationship suggested both cyanobacterial, bacterial and eukaryotic community can be affected by viruses through infection. The identified vAMGs revealed that viruses had the ability to alter host metabolism and participate in carbon flux and nutrients cycling in this aquatic environment. The subsequent finding of significant variation of these virus-related features across seasons including lysogenic phage proportion, broad host range phages abundance, and vAMGs abundance recommended that viral strategies were changing and succeeding with the bloom-associated environment and the structural shift in host microbes. While the sampling processing of free-living (0.2 -2.0 \u0026micro;m) biomass may lead to the loss of some free viruses, the analytic results of viruses in the planktonic biomass on a spatiotemporal scale should be effective and consistently affected (if any) due to the identical processing protocol for all samples. To conclude, the first catalog of viruses infecting planktonic microbes in Lake Taihu was established. Through assessing seasonal succession, host relationship and viral auxiliary metabolic functions, this study untangled the ecological links between viruses, host microbes and environmental factors in the eutrophic freshwater ecosystem with cyanobacterial HABs.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study including metagenomic sequencing data and viral sequences have been deposited into CNGB Sequence Archive (CNSA) of China National GeneBank DataBase (CNGBdb: www.cngb.org/cnsa) with accession number CNP0004588 (https://db.cngb.org/search/project/CNP0004588).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe thank Prof. Wei Zhu\u0026rsquo;s group members at Hohai University as well as Dr. Han Gao at\u0026nbsp;Westlake University for the great supports in the field sampling and laboratory experiments. We thank Dr. Linxing Chen at University of California, Berkeley for his kind and useful suggestions. We thank Ms. Yisong Xu and Mr. Guoqing Zhang\u0026nbsp;at Westlake University for their helpful technical supports. We thank the Westlake University HPC Center for computation support.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the HRHI program 202309010 of Westlake Laboratory of Life Sciences and Biomedicine, the National Science Foundation of China (Grant no. 22241603), and the Zhejiang Provincial Natural Science Foundation of China under (Grant No. LR22D010001).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eLiu W, Qiu R. 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Environ Microbiol. 2010;12(11):3035-56; doi: 10.1111/j.1462-2920.2010.02280.x.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Viromics, Host-virus relationship, Virus-encoded auxiliary metabolic gene, Harmful algal blooms, Microcystis, Microbiome","lastPublishedDoi":"10.21203/rs.3.rs-3510205/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3510205/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground.\u003c/h2\u003e \u003cp\u003eViruses are important biogeochemical mediators and ecological drivers in freshwater ecosystems. Although the environmental implications of viruses in ecosystems have been preliminarily explored, the dynamics of viruses and host associations over the seasons and blooming periods in eutrophic freshwater ecosystems remain elusive.\u003c/p\u003e\u003ch2\u003eResults.\u003c/h2\u003e \u003cp\u003eHere, we recovered 41,997 unique viral clusters at approximately species level from planktonic microbiomes of Lake Taihu, a large and eutrophic lake that suffered from yearly \u003cem\u003eMicrocystis\u003c/em\u003e-dominated harmful algal blooms (HABs) in China. The viral clusters showed distinct seasonal succession driven by environmental factors (mainly nutrients and temperature) and microbial communities (mainly \u003cem\u003eCyanobacteria\u003c/em\u003e and \u003cem\u003ePlanctomycetes\u003c/em\u003e). Host prediction highlighted the roles of the viruses in affecting the bacteria-driven nitrogen and phosphate cycling through infection. Further statistical analyses revealed that the HAB-induced environmental and microbial variations affected viral strategies including lifestyles, host range, and virus-encoded auxiliary metabolic genes (vAMGs) distributions. Viruses infecting \u003cem\u003eProteobacteria\u003c/em\u003e and \u003cem\u003eActinobacteria\u003c/em\u003e showed enhanced lysogenic lifestyle and condensed host ranges during HAB peak in summer, while viruses infecting \u003cem\u003eBacteroidota\u003c/em\u003e selected the opposite strategy. Notably, vAMGs were most abundant before HAB outbreak in spring, compensating for host bacterial metabolism including carbohydrates metabolism, photosynthesis, and phosphate regulation.\u003c/p\u003e\u003ch2\u003eConclusion.\u003c/h2\u003e \u003cp\u003eThis study elucidated relationship between viral community and bloom-associated environment, suggested the dynamic viral strategies and prominent biochemical roles in the eutrophic freshwater ecosystems.\u003c/p\u003e","manuscriptTitle":"Seasonal Succession, Host Associations and Biochemical Roles of Aquatic Viruses in a Eutrophic Lake Plagued by Cyanobacterial Blooms","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-11-07 15:19:07","doi":"10.21203/rs.3.rs-3510205/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1de7d9f1-693f-42d3-a265-d3e4cd229235","owner":[],"postedDate":"November 7th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-11-21T10:14:31+00:00","versionOfRecord":[],"versionCreatedAt":"2023-11-07 15:19:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3510205","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3510205","identity":"rs-3510205","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}