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Photosynthetic functions of Synechococcus in the ocean microbiomes of diverse salinity and seasons

  • Yihwan Kim,

    Roles Data curation, Formal analysis, Visualization, Writing – original draft

    Affiliation Department of Computer Science and Engineering, Hanyang University, Seoul, Korea

  • Jehyun Jeon,

    Roles Data curation, Formal analysis, Writing – original draft

    Affiliation Department of Computer Science and Engineering, Hanyang University, Seoul, Korea

  • Min Seok Kwak,

    Roles Data curation, Resources

    Affiliation Department of Biology, Kongju National University, Kongju, Korea

  • Gwang Hoon Kim,

    Roles Conceptualization, Methodology, Supervision, Writing – original draft

    Affiliation Department of Biology, Kongju National University, Kongju, Korea

  • InSong Koh,

    Roles Conceptualization, Supervision, Writing – review & editing

    Affiliation Department of Biomedical Informatics, Hanyang University, Seoul, Korea

  • Mina Rho

    Roles Conceptualization, Formal analysis, Funding acquisition, Methodology, Project administration, Supervision, Writing – original draft, Writing – review & editing

    minarho@hanyang.ac.kr

    Affiliations Department of Computer Science and Engineering, Hanyang University, Seoul, Korea, Department of Biomedical Informatics, Hanyang University, Seoul, Korea

Photosynthetic functions of Synechococcus in the ocean microbiomes of diverse salinity and seasons

  • Yihwan Kim, 
  • Jehyun Jeon, 
  • Min Seok Kwak, 
  • Gwang Hoon Kim, 
  • InSong Koh, 
  • Mina Rho
PLOS
x

Abstract

Synechococcus is an important photosynthetic picoplankton in the temperate to tropical oceans. As a photosynthetic bacterium, Synechococcus has an efficient mechanism to adapt to the changes in salinity and light intensity. The analysis of the distributions and functions of such microorganisms in the ever changing river mouth environment, where freshwater and seawater mix, should help better understand their roles in the ecosystem. Toward this objective, we have collected and sequenced the ocean microbiome in the river mouth of Kwangyang Bay, Korea, as a function of salinity and temperature. In conjunction with comparative genomics approaches using the sequenced genomes of a wide phylogeny of Synechococcus, the ocean microbiome was analyzed in terms of their composition and clade-specific functions. The results showed significant differences in the compositions of Synechococcus sampled in different seasons. The photosynthetic functions in such enhanced Synechococcus strains were also observed in the microbiomes in summer, which is significantly different from those in other seasons.

Introduction

Microbes adapt to their environments to play crucial roles in the ecosystems. In the marine environment, cyanobacteria are major contributors for the carbon and nitrogen cycles, carrying out about 25% of the total carbon fixation in the ocean [1]. In addition, cyanobacteria are known as the only bacteria that perform oxygenic photosynthesis [2]. Such crucial roles would inevitably be affected by the global warming, and result in the changes of marine microbial communities. Due to their ecological importance and the benefit of small genome size, many genomes have been sequenced and studied in diverse genera in cyanobacteria [3].

Synechococcus belongs to Cyanobacteria, and is one of the most important components of photosynthetic picoplankton in the temperate to tropical oceans. It is widespread throughout the global oceanic surface and freshwater environment with different temperature, thermal amplitude, light, and nutrients [1, 48]. Compared with Prochlorococcus, which is another major constituent in cyanobacteria, Synechococcus shows a wider geographical distribution [1]. As a photosynthetic bacterium, Synechococcus also has an efficient mechanism to adapt to the changes in salinity and light intensity [9, 10]. Therefore, analysis of the distributions and functions of such microorganisms in the ever changing river mouth environment, where freshwater and seawater mix, should help better understand their roles in the ecosystem [11, 12].

With recent advances in the sequencing technology and computational methods, more comprehensive genomic approaches are applied to survey the compositions and functions of the entire microbial community. The pioneering ocean microbiome project, Global Ocean Sampling (GOS) Project, started in early 2000 [13]. Since then, many ocean microbiome projects have unveiled the global landscape of the bacterial distributions and their functions in the marine environments [1416]. Such studies suggest that the ocean microbiomes are significantly diverse in terms of depth, temperature, and nutrient. For example, the Tara Oceans Project showed a significant correlation among the species, functional richness, and water depth [17]. In particular, photosynthetic cyanobacteria such as Prochlorococcus and Synechococcus were observed in all mesopelagic samples, constituting about 1% of the composition. However, there are few metagenomic studies on a group of photosynthetic algae in the river mouth environment.

In this study, we have profiled the distribution of Synechococcus collected in the river mouth of Kwangyang Bay, Korea, as a function of salinity and temperature. Five collection sites were selected from the freshwater in Seomjingang river to the open sea of Kwangyang Bay. In an effort to identify the contributors of Synechococcus in microhabitats in detail, we found close Synechococcus strains and analyzed their clade-specific functions in higher resolution. In conjunction with comparative genomics approaches [1820] using the sequenced genomes in a wide phylogeny of Synechococcus, the Korean ocean microbiome data was analyzed in terms of the composition and functions. In addition, the major lineages of the Synechococcus strains in the microbiome and their phylogenetic characteristics were investigated as well.

Results

Synechococcus composition in the marine microbiome

The distribution of Synechococcus was investigated as a function of the temperature and salinity by using a total of 15 ocean samples that were collected from five different sites at three different time points. The salinity of the samples increases from site 1 to site 5. The temperature ranges from 7 to 8°C for the winter samples; from 16 to 17°C for the spring samples; from 24 to 26°C for the summer samples (Fig 1, Table 1). The seasonal changes in the bacterial composition of five collection sites were profiled using marker genes (S1 Fig). Interestingly, Synechococcus blooms were observed only in the samples collected in summer, not in winter or spring.

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Fig 1. Microbiome collection sites in Kwangyang bay, Korea.

https://doi.org/10.1371/journal.pone.0190266.g001

The distribution of Synechococcus strains was further investigated by using all Synechococcus genes in the 13 Synechococcus complete genomes (S1 Table). The entire set of genes were clustered together with those predicted from the five microbiomes of the summer. When the clustering threshold of 70% sequence similarity was applied, a total of 75,215 genes in the ocean microbiomes were clustered with most of the genes in six strains (Fig 2A). Different Synechococcus strains dominated in freshwater (RCC307 in site 1 and 2) and seawater (CC9902 and CC9605 in site 4 and 5) environments. When the clustering threshold was increased to 95%, this pattern became more evident (Fig 2B). For the strains other than RCC307, CC9902 and CC9605, the proportion of the genes that were clustered with each metagenome was mostly smaller than 10% of each strain, which might be the housekeeping genes. With more stringent clustering threshold of 95%, more exclusive clustering was observed (Fig 2B). This finding implies that the genes in the microbiome are highly homologous with six Synechococcus strains, not with the other seven strains (Fig 2B).

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Fig 2. Coverage of entire genes of the Synechococcus strains in each microbiome sample.

(A) Gene coverage of each strain by using CD-HIT clustering with 70% protein similarity. (B) Gene coverage of each strain with 95% protein similarity. The values in cells are the ratio of genes clustered (%) in each strain.

https://doi.org/10.1371/journal.pone.0190266.g002

Interestingly, two samples collected in river mouth (SKY1B1508) and water front (SKY2B1508) are enriched with RCC307, whereas the other two samples collected in open sea (SKY4B1508 and SKY5B1508) are enriched with CC9902 (Fig 2B). As freshwater and seawater mixes in river mouth (Site 1) and water front (Site 2), the salinity varied in the range of 2.5–29 ppt depending on sampling season and time (Table 1). In details, when the clustering threshold of 95% sequence similarity was applied, 64% and 69% of the genes in RCC307 were clustered with the genes in the SKY1B1508 and SKY2B1508, respectively, with the average similarity of 99.26%. Samples collected from sites in the open sea (Site 4 and 5, salinity >30 ppt) showed different enrichment patterns. About 65% and 68% of the genes in CC9902 were clustered with the genes in SKY4B1508 and SKY5B1508, respectively, with the average similarity of 98.34%. The sequence similarities against two strains, RCC307 and CC9902, are high enough to suggest that the two groups of samples with lower salinity and higher salinity are enriched with two different taxa close to Synechococcus RCC307 and CC9902, respectively. These results suggest that the populations of Synechococcus changed at the border of waterfront although there was no barrier but the salinity difference. A similar pattern was observed in the previous study, which showed that CC9902 is positively correlated with salinity in the surface layer of the ocean [21]. In addition, the strain CC9902 was also observed in the location of similar latitude as the samples in this study, considering the observation that Clades I and IV are enhanced at the latitudes above 30°N and below 30°S [8]. Notably, the strain CC9902 was observed in the summer sample (24°C for Site 4 and 5), but was not observed in the winter or spring samples (8.2 and 16°C, respectively).

In order to find the homology of contigs that were assembled from the ocean microbiome, BLAST search [22] was applied against six complete genomes of Synechococcus as a reference. With 95% sequence similarity, RCC307 was covered in the sites 1, 2 and 3, whereas CC9902 was covered in the sites 4 and 5 (Fig 3). A total of 680, 693, and 581 contigs that cover 52.59% of genomes with higher than 95% of average similarity were assembled from the sites 1, 2, and 3. A total of 582 and 630 contigs that cover 46.47% of genomes with higher than 95% of average similarity were assembled from the sites 4 and 5.

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Fig 3. Homology of contigs assembled from the microbiome against the Synechococcus strains.

Each dot in scatter plot indicates the contig at the specific genome position (x-axis) with the sequence similarity against the reference genome (y-axis).

https://doi.org/10.1371/journal.pone.0190266.g003

Phylogenetic analysis of Synechococcus strains

A phylogenetic analysis was conducted on 13 complete Synechococcus genomes. In conjunction with comparative genomics approaches, all genes were clustered to find the orthologous genes in the Synechococcus strains. The genes are classified into three different types of genes: core genes that are shared by all strains; dispensable genes that are shared by some of the strains; strain-specific genes that are unique to a strain.

The phylogenetic tree and clustering were conducted by using two different approaches: multilocus sequence tags (MLST) using core genes; Jaccard index using dispensable genes. Two distinctive groups were observed in both approaches: one group of strains that are enriched in the samples, and the other group of strains that are not enriched in the samples. Seven Synechococcus strains (PCC7502, PCC6312, PCC7002, PCC 7942, PCC6301, JA-2, and Ja-3) were clustered together, whereas the other six Synechococcus strains (CC9605, CC9902, WH8102, WH7803, CC9311, and RCC307) were clustered together (Fig 4).

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Fig 4. Phylogenetic tree of thirteen Synechococcus strains.

(A) Heatamp of Jaccard index using dispensable genes. (B) Phylogeny using concatenated alignments of core genes.

https://doi.org/10.1371/journal.pone.0190266.g004

This observation is also consistent with recent studies that suggested the division of two separate lineages, Synechococcus and Parasynechococcus [23]. The new genus Parasynechococcus contains the strains CC9605, CC9902, WH8102, WH7803, CC9311, and RCC307, which show high homology with the genes in the ocean microbiome. Among Parasynechococcus strains, RCC307 and CC9902 build two separate lineages in the phylogenetic tree with the bootstrapping value of 100 (Fig 4B). In particular, two lineages were observed exclusively in the environments of different salinity: RCC307 enriched in lower salinity; CC9902 enriched in higher salinity (Fig 4). For the third lineage that consists of WH7803 and CC9311, a limited number of genes show sequence homology with the genes in the samples.

Phycobilisomes in Synechococcus

Given the important role of Synechococcus as a photosynthetic organism in the cyanobacterium, a comparative analysis was performed for the genes that are associated with the three components in phycobilisomes. Phycobilisomes are the major light-harvesting antennas, which are anchored to the thylakoid membranes. Phycobilisomes consist of three components, allophycocyanin (AP), phycocyanin (PC), and phycoerythrin (PE), and have different structures depending on the pigment composition. Parasynechococcus strains have phycoerythrobilin (PEB) and phycourobilin (PUB), which are more effective in light absorption and adaptation to the changes in light quantity [24, 25]. The results of this study revealed significantly differential gene sets that exist only in Parasynechococcus, but not in Synechococcus.

The phycobilisome-associated genes were screened from the 13 Synechococcus strains. A total of 26 KO genes that are related with 6 allophycocyanin, 7 phycocyanin, 11 phycoerythrin, and 2 phycoerythrobilin were screened to find the existence of such specific functions. All six allophycocyanin genes and most of the seven phycocyanin genes, except cpcD and cpcF, were observed in all 13 strains (Fig 5). Notably, cpcD does not exist in any of the Parasynechococcus strains. Since allophycocyanin and phycocyanin belong to the phycobiliprotein family, they play crucial roles as accessory pigments to chlorophyll in these photosynthetic Synechococcus.

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Fig 5. Phycobilisome gene sets in Synechococcus.

(A) Phycobilisome complex. (B) Heatmap of gene sets involved in forming phycobilisome complex.

https://doi.org/10.1371/journal.pone.0190266.g005

Interestingly, phycoerythrin genes showed a significantly different distribution between two groups of Synechococcus strains. Among 13 phycoerythrin genes, 11 genes exist only in the Parasynechococcus strains, not in the Synechococcus strains. Only two genes of cpeS and cpeT exist in all 13 strains. This observation could explain the fact that the Parasynechococcus strains in summer samples are enriched with the effective functions for light absorption and adaptation to changes in the light intensity.

RecBCD operon in Synechococcus

RecBCD genes encode an enzyme complex that initiates homologous recombination processes that repair double-strand breaks in eubacteria [2628]. In our KEGG analysis, these recBCD genes are only encoded in Parasynechococcus. Especially, KEGG orthologs from homologous recombination pathway (K03581, K03582 and K03583) were observed only in Parasynechococcus strains, whereas some Synechococcus have KEGG ortholog K01144 related to the recBCD operon (Fig 6).

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Fig 6. RecBCD operon in Synechococcus.

(A) RecBC pathway of KEGG. (B) Heatmap of KEGG orthologs involved in the pathway.

https://doi.org/10.1371/journal.pone.0190266.g006

Distribution of the most proteins involved in the subsequent processes of homologous recombination did not show the difference between Parasynechococcus and Synechococcus. A previous study on the comparative analysis of homologous recombination systems found that Synechococcus WH8102 and Prochlorococcus marinus strains are the only genomes having the recBCD gene set [29]. Our observation is consistent with this fact. Our results also suggest that the living conditions of Parasynechococcus, in which the DNA is prone to photochemical damage [30], might have forced Parasynechococcus to have the recBCD gene set.

The heatmap showed that there are five genes (DPO3A2, rnhA-dnaQ, DPO3T, DPO3C, and DPO3P) that do not exist in any of the Synechococcus strains (Fig 6B). Such genes are involved in constructing the DNA polymerase III complex. In addition, there are also three missing orthologs: priB, priC, and dnaT. Lastly, there is an ortholog showing some differences, except for recBCD operon, between the two clades. The DPO3E, which is a DNA polymerase III subunit epsilon, is encoded only in S. elongatus, Synechococcus PCC6312, and all Parasynechococcus strains known.

Phycobilisome pathway in the marine microbiome

We observed that Synechococcus is one of the major constituents in the summer samples, and exists differentially among the samples of different temperatures. In particular, three strains, CC9605, CC9902 and RCC307, in different lineages of marine Synechococcus were inferred as very close strains in the summer samples (Fig 2). From this observation, we hypothesize that the photosynthesis functions are significantly enhanced in the summer samples. Indeed, our metagenome analysis fully confirmed this postulation. In order to investigate the light-harvesting functions in different marine environments, the phycobilisome pathway was screened from the marine microbiome data at different salinity and temperature.

The homology search against 489 genes that belong to 26 phycobilisome-related KOs revealed that light-harvesting functions are significantly enhanced in the summer samples at different salinity (Fig 7). Notably, cpcD (phycocyanin linker protein, K02287) and cpeU (billin biosynthesis protein, K05384) were not observed in any of the summer samples. A similar result was observed when the phycobilisome-related genes were screened from 13 Synechococcus strains; CpcD gene was not found in any of the Synechococcus strains. Notably, previous study also reported that cpcD does not exist in many Synechococcus strains including RCC307 and CC9902 [24]. Overall, phycobilisome-related genes are widely distributed in the summer samples, but not in most of the winter or spring samples. Most of the phycobilisome-related genes are more enriched in site 4 and 5 of higher salinity. In particular, phycoerythrin genes are more enriched in site 4 and 5 (Fig 7).

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Fig 7. Phycobilisome gene sets in the ocean microbiomes.

Heatmap of gene sets involved in forming phycobilisome complex. SKY is abbreviation of Sea of Kwangyang bay; 1B, 2B, 3B, 4B, and 5B represent the sampling sites in Fig 1; 1502, 1505, and 1508 indicate the sampling date (YYMM).

https://doi.org/10.1371/journal.pone.0190266.g007

Notably, no phycobilisome-related functions were found in five winter samples of different salinity. In the spring samples, most of the phycobilisome-related functions were not found; apcA genes (allophycocyanin alpha subunit, K02092) were observed in the site 2, 3, and 5 of the spring samples; all apc (allophycocyanin alpha subunit, K02092, K02093, K02094, K02095, K02096, and K02097), cpcA (phycoerythrin class chain, K05376), and cpeB (phycoerythrin class beta chain, K05377) genes were found in the site 5 of the spring sample. This might be due to the homology of such genes in other strains that are close to Synechococcus strains.

Discussion

Synechococcus is an important photosynthetic picoplankton in the temperate to tropical oceans. As a photosynthetic bacterium, Synechococcus is known to have an efficient mechanism to adapt to the changes in salinity and light intensity. In this study, we investigated ocean microbiomes of Kwangyang Bay where freshwater and seawater mix. A total of fifteen ocean microbiomes were obtained from five sites of different salinity in three different seasons. The analysis revealed significant differences in the composition of Synechococcus strains and their photosynthetic functions in the freshwater to open sea of Kwangyang Bay in different seasons.

In order to determine the specific lineages of Synechococcus strains in the microbiome, we applied comparative genomics to the analysis of microbiome. Even with the limited number of sequenced genomes available, we could successfully determine the very close lineages to the Synechococcus strains in the ocean microbiome. Two distinctive lineages are associated with two groups of samples in different salinity.

Marine picocyanobacteria Synechococcus species are the most abundant and widespread primary producers in the ocean. It has been reported that Synechococcus partition the ocean into four distinct regimes that are distinguished by the temperature, macronutrients (nitrate and phosphate), and iron availability [8]. The results from our metagenome analysis showed that the salinity difference works as a barrier between terrestrial and oceanic species of Synechococcus. Although many Synechococcus can survive wide range of salinity, the enriched population was clearly different across the waterfront where freshwater mixes with seawater, suggesting that salinity should be considered as an environmental factor that partitions Synechococcus distribution.

Temperature is another important environment factor that could affect the occurrence of Synechococcus strains. In particular, Clades I and IV are enhanced at cold temperature (latitudes above 30°N and below 30°S), compared with the Clades II and III (latitudes between 30°N and 30°S) [8]. In our study, the strain CC9902 of Clades IV was observed in the summer samples (24°C), not in winter (8.2°C) or spring samples (16°C). The strain CC9902 is enriched in the environment of slightly higher temperature (24°C), compared with the previous study conducted at 10–20°C [8].

Synechococcus plays an important role as a photosynthetic organism in cyanobacteria. As shown in the comparative analysis of sequenced genomes, phycobilisome-pathway was observed only in marine Synechococcus strains such as RCC307, CC9902 and CC960, but not in other lineages. The photosynthetic functions of such enhanced Synechococcus strains are observed in the microbiomes in summer, which is significantly different from those in other seasons. The analysis of the microbiome in different seasons also showed such distinctions of the phycobilisome pathway in different seasons. It implies that salinity and temperature are important environmental factors for the composition and function of Synechococcus in the ocean microbiome.

Salinity affects the growth, photosynthetic parameters, and nitrogenase activity of planktonic cyanobacteria. In addition, salinity is a main controlling factor for the blooms of N2-fixing cyanobacteria in estuaries [31]. Although many cyanobacteria can survive in a wide range of salinity, their growth and photosynthetic efficiency are significantly affected by the salinity [32]. When cyanobacteria are exposed to high saline conditions, the amount of phycobiliprotein eventually changes during acclimation [31]. Due to the current limitations of metagenome sequencing, it was difficult to sequence and assemble the entire set of genes involved in phycobilisome pathway in this study. It is yet to be addressed whether the absence of cpcD and cpeU genes in summer samples is related to their function.

Materials and methods

Sample collection and preparation

A total of 15 samples were collected from five different sites representing freshwater and seawater environment in Seomjingang river and Kwangyang Bay (Fig 1). 20 L of surface water was collected in each site and brought to the laboratory for filtering to isolate metagenomics sample. The waters were successively passed through membrane filters of 20 μm, 8 μm, 3 μm, 0.45 μm, and 0.22 μm pore size (Millipore, USA). The membranes except for 20 μm and 8 μm pore size were homogenized in liquid nitrogen, and DNA was isolated using Meta-G-Nome DNA isolation Kit (Epigentre, USA). The seasonal salinity and temperature of each collection site are summarized in Table 1. Site 1 and 2 are located in the Seomjingang River. Site 3 is the river front where fresh water and seawater mix during sub- and ebbtide. Site 4 and 5 correspond to open sea environment. All raw sequencing data described in this study is available at European Nucleotide Archive (ENA) with the accession number ERP024429.

Preparation of shotgun metagenomic sequences

Metagenomic sequencing was carried out by Macrogen (Seoul, Korea) using HiSeq 2000 platform (Illumina, San Diego, USA). Samples were prepared according to the Illumina protocols. The quantity and quality of DNA were measured by PicoGreen and Nanodrop, respectively. For a 350 bp insert size, one microgram of genomic DNA was fragmented by using Covaris. The fragmented DNAs were blunt-ended and phosphorylated. Following end repair, the appropriate library size was selected using different ratios of the sample purification beads. A single ‘A’ was ligated to the 3′ end, and Illumina adapters were ligated to the fragments. The final ligated product was quantified by using qPCR according to the qPCR Quantification Protocol Guide, and qualified by using the Agilent Technologies 2100 Bioanalyzer (Agilent technologies, Palo Alto CA, USA). All samples were sequenced to generate 151 bp paired-end reads. The average data size is 27Gbps per sample. Low-quality reads were filtered out by using Sickle [33] (Phred quality score > = 20).

Estimation of bacterial composition

In order to analyze the relative abundance of bacterial composition in the marine microbiome, MetaPhlAn2 was applied with the default parameter options only for the bacterial composition (i.e.—ignore_archaea,—ignore-eukaryotes, and—ignore-viruses) [34]. To investigate the conservation against putative Synechococcus strains in the microbiome, a homology search was conducted by using BLAST [22] against 6 Parasynechococcus complete genomes (CC9902, CC9311, CC9605, RCC307, WH7803, and WH8102) from the NCBI repository. In each sample, the assembled contigs over 1 kbp were searched as a query with the stringent threshold (E-value < = 1E-10 and sequence identity > = 40%), and the best hits were retained. Scatter plots were generated by using Plotly [35].

Clustering the genes in the ocean microbiome with Synechococcus and Parasynechococcus genes

The shotgun metagenome reads were filtered and assembled by using MEGAHIT [36] with default k-mer options. Subsequently, all contigs over 1,000 bp were used to identify the proteins in the microbiome by using FragGeneScan [37]. The predicted proteins in each sample were clustered with the proteins in 13 complete reference Synechococcus. In order to analyze the various aspects, CD-HIT [38] clustering was conducted with two different options of protein sequence similarity and shorter sequence coverage options: 70 over 70 (i.e. 70% sequence similarity over 70% of the shorter sequence); 90 over 90; 95 over 95. Subsequently, the proportion of clustered proteins of each Synechococcus strain was calculated and presented in the heatmap.

Construction of phylogenetic tree on Synechococcus and Parasynechococcus

All 13 complete genomes of Synechococcus strains were obtained from the NCBI ftp repository (2015 April). A detailed genomic information on the Synechococcus strains is summarized in S1 Table. A total of 35,769 protein sequences in Synechococcus were clustered using CD-HIT (with 40% of protein similarity and 40% of shorter sequence coverage) [38] to find the orthologs genes in each cluster. As a result, 566 clusters were identified as core genes, 5,206 clusters as dispensable genes, and 4,030 clusters as strain-specific genes.

Jaccard Index were calculated using 5,206 dispensable proteins of 13 Synechococcus strains. Bi-clustering was conducted with average linkage and Euclidean distances. The phylogeny of Synechococcus was constructed using all identified 566 core proteins. Multiple sequence alignment was conducted separately on proteins in each core cluster by using MUSCLE [39]; all alignments in each strain were concatenated in the same order. Subsequently, neighbor-joining trees were constructed using MEGA [40] (ver. 7.0.14) with several options (100 for bootstrapping, Poisson model for substitution model, uniform rates, and pairwise deletion for gap/missing treat).

Functional analysis of Synechococcus proteins and ocean microbiomes

Functional analyses were conducted on Synechococcus by using KEGG database v54 [41]. Proteins in Synechococcus were aligned against the protein sequences in KEGG database by using BLASTp [22] (E-value < 1.0E-10). The best hit of the query was assigned based on the E-value. For multiple best hits with the same E-value, alignment with the best sequence similarity was selected. Most of the KEGG genes can be mapped to KEGG ortholog, but there are a few cases that cannot be mapped to KEGG ortholog. In this case, we choose the second alignment based on the E-value and sequence similarity.

The same KEGG orthologs of the phycobilisome complex were used both for comparative genomics of Synechococcus strains and for the analysis of the functions in microbiomes. BLASTP of microbiome reads were performed against proteins in KEGG database (E-value < 1E-10; sequence identity > 90%).

Supporting information

S1 Fig. Bacterial composition of 15 microbiomes.

https://doi.org/10.1371/journal.pone.0190266.s001

(TIF)

Acknowledgments

We thank Dr. Mi Young Lim for helpful discussion.

References

  1. 1. Flombaum P, Gallegos JL, Gordillo RA, Rincon J, Zabala LL, Jiao N, et al. Present and future global distributions of the marine Cyanobacteria Prochlorococcus and Synechococcus. Proc Natl Acad Sci U S A. 2013;110(24):9824–9. pmid:23703908
  2. 2. Hernandez-Prieto MA, Semeniuk TA, Futschik ME. Toward a systems-level understanding of gene regulatory, protein interaction, and metabolic networks in cyanobacteria. Front Genet. 2014;5:191. pmid:25071821
  3. 3. Komárek Jiří K J, Mareš Jan, Johansen Jeffrey R.. Taxonomic classification of cyanoprokaryotes (cyanobacterial genera) 2014, using a polyphasic approach. Preslia. 2014;86:295–335.
  4. 4. Partensky F, Blanchot J, Vaulot D. Differential distribution and ecology of Prochlorococcus and Synechococcus in oceanic waters: a review. BULLETIN-INSTITUT OCEANOGRAPHIQUE MONACO-NUMERO SPECIAL-. 1999:457–76.
  5. 5. Richardson TL, Jackson GA. Small phytoplankton and carbon export from the surface ocean. Science. 2007;315(5813):838–40. pmid:17289995
  6. 6. Tai V, Palenik B. Temporal variation of Synechococcus clades at a coastal Pacific Ocean monitoring site. ISME J. 2009;3(8):903–15. pmid:19360028
  7. 7. Pittera J, Humily F, Thorel M, Grulois D, Garczarek L, Six C. Connecting thermal physiology and latitudinal niche partitioning in marine Synechococcus. ISME J. 2014;8(6):1221–36. pmid:24401861
  8. 8. Sohm JA, Ahlgren NA, Thomson ZJ, Williams C, Moffett JW, Saito MA, et al. Co-occurring Synechococcus ecotypes occupy four major oceanic regimes defined by temperature, macronutrients and iron. ISME J. 2016;10(2):333–45. pmid:26208139
  9. 9. Blumwald E, Tel-Or E. Salt adaptation of the cyanobacterium synechococcus 6311 growing in a continuous culture (turbidostat). Plant Physiol. 1984;74(1):183–5. pmid:16663376
  10. 10. Kulkarni RD, Golden SS. Adaptation to high light intensity in Synechococcus sp. strain PCC 7942: regulation of three psbA genes and two forms of the D1 protein. J Bacteriol. 1994;176(4):959–65. pmid:8106338
  11. 11. Mackey KR, Paytan A, Caldeira K, Grossman AR, Moran D, McIlvin M, et al. Effect of temperature on photosynthesis and growth in marine Synechococcus spp. Plant Physiol. 2013;163(2):815–29. pmid:23950220
  12. 12. Cassier-Chauvat C, Veaudor T, Chauvat F. Comparative Genomics of DNA Recombination and Repair in Cyanobacteria: Biotechnological Implications. Front Microbiol. 2016;7:1809. pmid:27881980
  13. 13. Rusch DB, Halpern AL, Sutton G, Heidelberg KB, Williamson S, Yooseph S, et al. The Sorcerer II Global Ocean Sampling expedition: northwest Atlantic through eastern tropical Pacific. PLoS Biol. 2007;5(3):e77. pmid:17355176
  14. 14. Tringe SG, von Mering C, Kobayashi A, Salamov AA, Chen K, Chang HW, et al. Comparative metagenomics of microbial communities. Science. 2005;308(5721):554–7. pmid:15845853
  15. 15. Palenik B, Ren Q, Dupont CL, Myers GS, Heidelberg JF, Badger JH, et al. Genome sequence of Synechococcus CC9311: Insights into adaptation to a coastal environment. Proc Natl Acad Sci U S A. 2006;103(36):13555–9. pmid:16938853
  16. 16. Fortunato CS, Crump BC. Microbial Gene Abundance and Expression Patterns across a River to Ocean Salinity Gradient. PLoS One. 2015;10(11):e0140578. pmid:26536246
  17. 17. Sunagawa S, Coelho LP, Chaffron S, Kultima JR, Labadie K, Salazar G, et al. Ocean plankton. Structure and function of the global ocean microbiome. Science. 2015;348(6237):1261359. pmid:25999513
  18. 18. Medini D, Donati C, Tettelin H, Masignani V, Rappuoli R. The microbial pan-genome. Curr Opin Genet Dev. 2005;15(6):589–94. pmid:16185861
  19. 19. Vernikos G, Medini D, Riley DR, Tettelin H. Ten years of pan-genome analyses. Curr Opin Microbiol. 2015;23:148–54. pmid:25483351
  20. 20. Dufresne A, Ostrowski M, Scanlan DJ, Garczarek L, Mazard S, Palenik BP, et al. Unraveling the genomic mosaic of a ubiquitous genus of marine cyanobacteria. Genome Biol. 2008;9(5):R90. pmid:18507822
  21. 21. Šilović Tina, Balagué Vanessa, Orlić Sandi, Pedrós-Alió C. Picoplankton seasonal variation and community structure in the northeast Adriatic coastal zone. FEMS microbiology ecology. 2012;82(3):678–91. pmid:22748097
  22. 22. Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. Journal of Molecular Biology. 1990;215:403–10. pmid:2231712
  23. 23. Coutinho F, Tschoeke DA, Thompson F, Thompson C. Comparative genomics of Synechococcus and proposal of the new genus Parasynechococcus. PeerJ. 2016;4:e1522. pmid:26839740
  24. 24. Six C, Thomas JC, Garczarek L, Ostrowski M, Dufresne A, Blot N, et al. Diversity and evolution of phycobilisomes in marine Synechococcus spp.: a comparative genomics study. Genome Biol. 2007;8(12):R259. pmid:18062815
  25. 25. Ting CS, Rocap G, King J, Chisholm SW. Cyanobacterial photosynthesis in the oceans: the origins and significance of divergent light-harvesting strategies. Trends Microbiol. 2002;10(3):134–42. pmid:11864823
  26. 26. Singleton MR, Dillingham MS, Gaudier M, Kowalczykowski SC, Wigley DB. Crystal structure of RecBCD enzyme reveals a machine for processing DNA breaks. Nature. 2004;432(7014):187–93. pmid:15538360
  27. 27. Li X, Heyer WD. Homologous recombination in DNA repair and DNA damage tolerance. Cell Res. 2008;18(1):99–113. pmid:18166982
  28. 28. Wigley DB. Bacterial DNA repair: recent insights into the mechanism of RecBCD, AddAB and AdnAB. Nat Rev Microbiol. 2013;11(1):9–13. pmid:23202527
  29. 29. Rocha EP, Cornet E, Michel B. Comparative and evolutionary analysis of the bacterial homologous recombination systems. PLoS Genet. 2005;1(2):e15. pmid:16132081
  30. 30. Bhandari R, Sharma PK. High-light-induced changes on photosynthesis, pigments, sugars, lipids and antioxidant enzymes in freshwater (Nostoc spongiaeforme) and marine (Phormidium corium) cyanobacteria. Photochem Photobiol. 2006;82(3):702–10. pmid:16464127
  31. 31. Moisander PH, McClinton E, Paerl HW. Salinity effects on growth, photosynthetic parameters, and nitrogenase activity in estuarine planktonic cyanobacteria. Microbial Ecol. 2002;43(4):432–42. pmid:12043002
  32. 32. Sudhir P, Murthy SDS. Effects of salt stress on basic processes of photosynthesis. Photosynthetica. 2004;42(4):481–6.
  33. 33. NA J, JN F. Sickle: A sliding-window, adaptive, quality-based trimming tool for FastQ files (Version 1.33) [Software]. https://github.com/najoshi/sickle. 2011.
  34. 34. Truong DT, Franzosa EA, Tickle TL, Scholz M, Weingart G, Pasolli E, et al. MetaPhlAn2 for enhanced metagenomic taxonomic profiling. Nat Methods. 2015;12(10):902–3. pmid:26418763
  35. 35. Plotly Technologies Inc. Collaborative data science. https://plot.ly/. 2015.
  36. 36. Li D, Liu CM, Luo R, Sadakane K, Lam TW. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics. 2015;31(10):1674–6. pmid:25609793
  37. 37. Rho M, Tang H, Ye Y. FragGeneScan: predicting genes in short and error-prone reads. Nucleic Acids Res. 2010;38(20):e191. pmid:20805240
  38. 38. Fu L, Niu B, Zhu Z, Wu S, Li W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics. 2012;28(23):3150–2. pmid:23060610
  39. 39. Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32(5):1792–7. pmid:15034147
  40. 40. Kumar S, Stecher G, Tamura K. MEGA7: Molecular Evolutionary Genetics Analysis Version 7.0 for Bigger Datasets. Mol Biol Evol. 2016;33(7):1870–4. pmid:27004904
  41. 41. Kanehisa M, Goto S. KEGG: kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 2000;28(1):27–30. pmid:10592173