Journal of Oceanology and Limnology   2022, Vol. 40 issue(2): 592-604     PDF       
http://dx.doi.org/10.1007/s00343-021-1005-1
Institute of Oceanology, Chinese Academy of Sciences
0

Article Information

Wang Ting, Chen Xi, Li Jialin, Qin Song
Distribution and phenogenetic diversity of Synechococcus in the Bohai Sea, China
Journal of Oceanology and Limnology, 40(2): 592-604
http://dx.doi.org/10.1007/s00343-021-1005-1

Article History

Received Jan. 5, 2021
accepted in principle Feb. 21, 2021
accepted for publication Apr. 13, 2021
Distribution and phenogenetic diversity of Synechococcus in the Bohai Sea, China
Ting Wang1,2, Xi Chen3, Jialin Li2,4, Song Qin2,4     
1 College of Environmental Science and Engineering, Ocean University of China, Qingdao 266100, China;
2 Key Lab of Coastal Biology and Biological Resource Utilization, Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China;
3 College of Marine Life Science, Ocean University of China, Qingdao 266005, China;
4 Center for Ocean Mega-Science, Chinese Academy of Sciences, Qingdao 266071, China
Abstract: Synechococcus is one of the most abundant picocyanobacteria in marine ecosystem,and the absence of Prochlorococcus would make it indispensable as a primary producer in the Bohai Sea,North China. However,the abundance distribution and genetic diversity of Synechococcus in this region have rarely been reported. In this study,the distribution pattern of Synechococcus abundance was investigated during four cruises in April,June,August,and November from 2018 to 2019, moreover,its phenogenetic diversity was studied based on high-throughput sequencing of the cpeBA operon. The results demonstrate that phycoerythrin-containing Synechococcus was most abundant in August when temperature was high and oxygen saturation was low. During this period,Synechococcus pigment type (PT) 2 was abundant in the Bohai Bay and Laizhou Bay under conditions of high nutrient concentration,temperature,and turbidity. In comparison,PT3,especially those clusters characterized with high or variable ratio of phycourobilin and phycoerythrobilin,was predominant in the Bohai Strait and Liaodong Bay under conditions of high salinity,pH,and oxygen saturation. Furthermore,co-occurrence correlations using network analysis revealed that Synechococcus PTs were related to 15.37%–43.48% of the prokaryotic genera. Synechococcus PT3c/PT3d and PT2 were the most important PTs in the network. The hierarchical clustering revealed that taxa co-occurred with Synechococcus PTs differed among samples. It could be attributed to the substance exchange and the environmental impact,which calls for more studies in the future.
Keywords: Synechococcus    phenogenetic diversity    co-occurrence network    coastal ecosystem    Bohai Sea    
1 INTRODUCTION

Picocyanobacteria are among the most abundant picoplankton and important primary producers in the marine environment, which mainly consist of Synechococcus and Prochlorococcus (Flombaum et al., 2013; Guidi et al., 2016). Prochlorococcus dominates in the euphotic zone of tropical and subtropical open oceans and rapidly declines at latitudes beyond 40°N and 40°S, as well as at depths below 150 m and temperatures below 15 ℃ (Johnson et al., 2006). In contrast, Synechococcus is much more widely distributed, existing in ecosystems from equatorial to polar oceans and from estuary to open ocean (Partensky et al., 1999). In particular, Synechococcus is more abundant in coastal (Li, 1998) and upwelling areas (Cuevas and Morales, 2006). The highest abundance so far has been reported in the strong upwelling areas of the Costa Rica Dome, reaching 106 cells/mL(Saito et al., 2005). By estimates of hourly carbon fixation rates, Flombaum et al. (2013) predicted that Synechococcus was responsible for 16.7% of ocean net primary production, which is nearly double of Prochlorococcus (8.5%). Recent studies showed that Prochlorococcus was undetected in sea areas including the north part of the Yellow Sea (Zhao et al., 2019) and Bohai Sea (Yan et al., 2020) in China. Synechococcus was the dominant picocyanobacteria in the studied area, which may play a more important role in building microbial food webs of the ecosystem.

Phylogenetically, marine Synechococcus can be classified into three subclusters (Herdman et al., 2001), each of which can be further divided into diverse clades (Scanlan, 2012). The genotype of Prochlorococcus is clearly associated with specific environmental characteristics, while the biogeography of Synechococcus's genotype is ambiguous owing to the parallel evolution of Synechococcus clades and pigment types (PTs) (Kent et al., 2019). Compared to arbitrary clade delineation, Synechococcus PTs are more directly associated with microbial traits, which can be referred for better investigating the distribution pattern and niche division of Synechococcus (Martiny et al., 2013). In terms of phenogenetic diversity corresponding to PTs, Synechococcus can be divided into three main categories, PT1, PT2, and PT3, based on the differences in the compositions of phycobiliprotein (PBP) (Everroad and Wood, 2012). Synechococcus PT1 is known to consist of phycocyanin only (PC, encoded by the cpcBA operon), and the rods are composed of phycocyanobilin (PCB, Amax=620 nm) only. Synechococcus PT2 contains both PC and phycoerythrin-I (PE-I, encoded by cpeBA). Its PBP rods consist of both PCB and the phycoerythrobilin (PEB, Amax=550 nm). Synechococcus PT3 is more complex that possesses PC, PE-I, and phycoerythrin-II (PE-II, encoded by mpeBA) whose robs bind PCB, PEB, and phycourobilin (PUB, Amax=495 nm). Based on the ratio of PUB꞉PEB, PT3 has been categorized into at least four subtypes, PT3a (PUB꞉PEB < 0.6), PT3b (0.6 < PUB꞉PEB < 1.6), PT3c (PUB꞉PEB≥1.6), and PT3d (variable PUB꞉PEB). Generally, PT1 is confined to estuaries and low salinity surface waters dominated by red light, which is almost absent in the oceanic waters (Wood et al., 1998). PT2 appears in transition zones between brackish and oceanic areas or coastal shelf waters (Chung et al., 2015), while PT3 shows an increasing PUB꞉PEB ratio along the gradient of onshore mesotrophic waters to offshore oligotrophic waters (Larsson et al., 2014). Sequencing methods based on the PBP-encoding genes, such as cpcBA (Haverkamp et al., 2009) and cpeBA (Everroad and Wood, 2006), have been used to distinguish the PTs. The cpcBA operon can detect PT1, PT2, and PT3 simultaneously, but it almost cannot distinguish the subtypes of PT3. In comparison, the cpeBA operon cannot distinguish PT1 but can identify PT2 and more diverse PT3 subtypes, including 3a, 3c/3dB, and 3b/3dA. It is needed to roughly assess the variety of PTs according to geographical characteristics of the studied sea area, and cpeBA operon was selected to obtain the accurate category information in the Bohai Sea.

As one of the most ubiquitously distributed primary producers, Synechococcus plays important biogeochemical roles in the marine ecosystem (Flombaum et al., 2013; Dvořák et al., 2014). Photosynthesis is the basis of material exchange and energy flow between Synechococcus and the ecosystem (Guidi et al., 2016). Synechococcus is also a mixotroph directly incorporating organic resources including amino acids (Paerl, 1991), dimethylsulfoniopropionate (DSMP) (Ruiz-González et al., 2012), and sugars (del Carmen Muñoz-Marín et al., 2017). Via interactions with other microorganisms, Synechococcus can be indirectly involved in more metabolic activities, which amplify its ecological effects (Tai et al., 2009). For example, Synechococcus produces biomass and ultimately converts it into particulate or dissolved organic matter (DOM) (McCarren et al., 2010). Interactions between Synechococcus and heterotrophs targeting DOM supply carbon and energy to marine food webs (Christie-Oleza et al., 2017; Zheng et al., 2020). If these heterotrophic bacteria can metabolize certain substances, the interactions between them will inevitably contribute to the biogeochemical cycle of these substances. Therefore, the unveiling of the relationship between microbes and Synechococcus may help us better understand the role of Synechococcus in biogeochemical cycles.

The Bohai Sea is the northernmost offshore of China, which has a typical temperate monsoon climate with obvious seasonal changes. It is composed of the Bohai Bay (BHB), Laizhou Bay (LZB), Liaodong Bay (LDB), and the central area (CA) connected to the Yellow Sea via the Bohai Strait (BS). The three bays are more influenced by anthropogenic activity than CA and BS (Lü et al., 2015; Zhang et al., 2015). Variations in environmental conditions between geographical regions may result in the difference in the biological distribution of the Bohai Sea. LZB and CA had the maximum annual mean biomass and primary production, while BHB had the lowest values (Wei et al., 2004). Besides, the annual cycle distribution pattern of phytoplankton was changed possibly based on the deepening of human influence. In recent years, diatoms were gradually replaced by dinoflagellates in the Bohai Sea. The semi-enclosed geographical location, combined with anthropogenic pollution, might also affect the distribution of Synechococcus in the Bohai Sea. However, its assembly composition and distribution pattern based on molecular methods remain unclear. Furthermore, the presence of various highconcentration pollutants also makes it meaningful to study the co-occurrence relationships between Synechococcus and other microbes in this coastal ecosystem.

2 MATERIAL AND METHOD 2.1 Sampling site and strategy

Four cruises were conducted in the Bohai Sea (37°N–41°N, 117°E–121°E) from 2018 to 2019 (Supplementary Fig.S1 & Supplementary Table S1). In each cruise, 6-10 stations were selected in each cruise according to the cruise plan. At each station, seawater was collected in Niskin bottles carried by a CTD rosette sampler (Sea-Bird Electronics Inc., Bellevue, WA, USA).

2.2 Environmental variable

Physicochemical parameters, including salinity, temperature, turbidity, and depth were recorded in situ using sensors of the CTD sampler (Sea-Bird Electronics Inc., Bellevue, WA, USA). Oxygen saturation, pH, as well as chlorophyll-a content were measured on boat with a probe (Hydrolab MS5; HACH, Loveland, CO, USA). The nutrient concentrations, including ammonium (NH4+), nitrite (NO2-), nitrate (NO3-), silicate (SiO44-), and phosphate (PO43-) concentrations, were determined using standard colorimetric methods with an AA3 segmented flow analyzer (Seal Analytical GmbH, Germany) (Dafner, 2015). The method detection limits are 0.03 μmol/L for NH4+, 0.008 μmol/L for NO2-, 0.02 μmol/L for NO3-, 0.02 μmol/L for SiO44-, and 0.01 μmol/L for PO43-.

2.3 Flow cytometry for analyzing Synechococcus abundance

In four cruises, seawater samples were collected every 5 m from the surface (0 m) to near the bottom (B) depending on the water depth. Owing to the cruise plan and the limited number of the sampler, 171 samples were collected in quintuplicate (Fig. 1). Seawater (1.40 mL) was fixed with paraformaldehyde (final concentration, 0.5%) immediately after collection and then rapidly frozen in liquid nitrogen (Li et al., 2019a). The abundance of PEcontaining Synechococcus was counted using a BD FACSAriaTM flow cytometer under the flow rate of 6 for 180 s. Fluorescence signals from PE fluorescence, chlorophyll a (excited by 488 nm), and PC (excited by 635 nm), as well as side scatter signals were collected. Yellow-green fluorescent beads (2.0 μm, Polysciences, Warrington, PA, USA) were added to each sample as the instrument internal standard (Zhao et al., 2016). Synechococcus was differentiated from the eukaryotic picoplankton based on their red fluorescence signal at 488 nm. In addition, PEcontaining Synechococcus was distinguished from PC-rich Synechococcus and Prochlorococcus according to their orange fluorescence signal (Supplementary Fig.S2) (van den Engh et al., 2017).

Fig.1 Temporal and spatial distribution of Synechococcus abundance in the Bohai Sea during four cruises (a) and the Pearson correlations between Synechococcus abundance and environmental variables of four cruises (b) In each part of (a), dots in left maps represents the maximum abundance along the water column at each station and dots in right graphs exhibit abundance corresponding to each water depth at each station.
2.4 DNA extraction, PCR, and sequencing

Considering the most abundance of Synechococcus in August (Yan et al., 2020), analyses of phenogenetic diversity were designed from 29 samples collected during the August 2018 cruise. Seawater was filtered through 48-μm nylon mesh and 0.22-μm polycarbonate membrane successively (Millipore Co., Bedford, MA, USA). The acquired membranes were stored in liquid nitrogen. After returning to the laboratory, a Fast DNA spin kit (MP BIO, USA) was used to extract DNA from the membrane (Li et al., 2019b). All collected samples were amplified via PCR (95 ℃ for 5 min, followed by 35 cycles at 95 ℃ for 30 s, 55 ℃ for 30 s, and 72 ℃ for 45 s and a final extension at 72 ℃ for 10 min) using primers peBF (5'-barcodeGACCTACATCGCWCTGGGYG-3') and peAR (5'-CCMACAACCARGCAGTAGTT-3') targeting Synechococcus PT2 and PT3, where the barcode is an eight-base sequence unique to each sample (Xia et al., 2018). Purified PCR products were quantified using Qubit®3.0 (Life Invitrogen) and every 24 amplicons with varying barcodes were mixed equally. The pooled DNA product was used to construct the Illumina pair-end library following Illumina's genomic DNA library preparation procedure. Then the amplicon library was paired-end sequenced (2×250 bp) by the Biozeron Biological Technology Co., Ltd. (Shanghai, China) using the PE250 Illumina Hiseq sequencing platform (Zhang et al., 2019). Fourteen samples from the surface and bottom layers were selected to conduct the microbial co-occurrence network by amplifying the 16S rRNA gene using primers 515F and 806R (Walters et al., 2016). The purified products of the 16S rRNA gene were sequenced at the Majorbio Co., Ltd. (Shanghai, China) using the PE300 Illumina MiSeq sequencing platform.

2.5 Processing of sequencing data

The standard procedure, including quality control (Brown et al., 2017), filtering of chimeras (Edgar et al., 2011), and elimination of redundancy (Bokulich et al., 2013), was used for processing the raw data of high-throughput sequencing. Only the front-end sequence of cpeBA operon was selected to analysis. Raw FASTQ files were first demultiplexed using inhouse Perl scripts according to the barcode sequence information for each sample with the following criteria: firstly, the 250-bp reads were truncated at any site receiving an average quality score < 20 over a 10-bp sliding window, discarding the truncated reads that were shorter than 50 bp. Then, exact barcode matching, two nucleotide mismatch in primer matching, and reads containing ambiguous characters were removed. Sequences were grouped into operational taxonomic units (OTUs) at a dissimilarity of 0.03. For cpeBA sequences, the representative sequences of 94 OTUs occupying more than 0.1% of all sequences (covered 88% of total reads on average) were selected and aligned with the reference sequences (Supplementary Table S2). The maximum likelihood genetic tree was constructed using Mega 7.014 with the model GTR+G+I and 200 bootstraps (Kumar et al., 1994). The online tool iTOL was used to draw the genetic tree (Letunic and Bork, 2019). For 16S rRNA sequences, the Greengenes v.135/16S database was used to obtain the taxonomic information. The function of the 16S rRNA sequencing data was annotated using the FAPROTAX database in Python v.2.7 (Louca et al., 2016).

2.6 Statistical analysis and visualization

The geographic information and the abundance of Synechococcus in each station were visualized using the software ODV (Ocean Data View) v5.1.7 (Schlitzer, 2002). Statistical analyses were conducted using the SPSS (Statistical Product and Service Solutions) software v17.0 and R language v3.6.1 (Ihaka and Gentleman, 1996). All variables were standardized and scaled using R. Analysis of variance (ANOVA) with a post-hoc test (LSD Test) was performed to evaluate the temporal and spatial variation in the abundance and environmental parameters. Pearson correlation matrixes among Synechococcus abundance and environmental parameters were calculated (one-tailed test). Package car in R was used to calculate variance inflation factor (VIF) of environmental factors (Fox and Weisberg, 2019). Package vegan in R was used to calculate α diversity (represented by Shannon diversity), principal co-ordinates analysis (PCoA), redundancy analysis (RDA), and Pearson correlation matrixes among prokaryotic genera and Synechococcus PTs (Oksanen et al., 2019). Gephi v0.9.2 (Bastian et al., 2009) was used to sketch the co-occurrence network. The hubba (key) score in each network was calculated using a plugin, CytoHubba, in the Betweenness method in software Cytoscape v3.8.0 (Chin et al., 2014).

3 RESULT 3.1 Seawater environmental parameters

The highest mean salinity, turbidity, oxygen saturation, and NO3- and PO43- contents were recorded in April 2018 (hereafter APR) (Table 1). The highest mean temperature, NO2- content, and SiO44- content appeared in August 2018 (hereafter AUG). The mean pH and NH4+ content were highest in June 2019 (hereafter JUN). The chlorophyll-a content did not vary significantly between the four cruises.

Table 1 Mean, maximum, and minimum values of environmental variables in the Bohai Sea of four cruises

Spatially, samples from bay stations had high levels of NO2- and PO43- and turbidity owing to the exogenous input. In contrast, samples from BS1 and BS2, located in the junction of the Bohai Sea and Yellow Sea, had higher pH and salinity and lower nutrient concentration owing to good exchange of water. Vertically, the chlorophyll-a content increased significantly with water depth from 0-m layers to > 30-m layers.

3.2 Temporal and spatial distribution of Synechococcus abundance

The average abundance of Synechococcus in the Bohai Sea was highest in AUG (9.4×103 cells/mL), followed by that in JUN (4.3×103 cells/mL), November 2019 (hereafter NOV) (1.9×103 cells/mL), and APR (2.6×102 cells/mL) (Fig. 1a).

In the APR cruise, Synechococcus abundance was 102 cells/mL on an average, with the exception of higher fluctuations in the BHB, where the abundance ranged from several cells/mL (BHB5-5m, LDB1-5m, and LDB1-10m) to 103 cells/mL (BHB1-15m, BHB4- 15m). In AUG, the average Synechococcus abundance was more than 30 times of that in APR. Higher abundances (> 104 cells/mL) were observed in BHB1, BHB2, CA2, and BS2. In particular, the abundance was highest in CA2-10, reaching 8.4×104 cells/mL. Higher abundance was always found in the lower and bottom water layers in the water column. In JUN, the highest abundance was detected in LZB2, ranging from 1.5×104 cells/mL to 1.6×104 cells/mL. Unlike the other Bohai Sea areas, the abundance in LDB was maximum in JUN. In the NOV cruise, in BS2, the abundance was more than 103 cells/mLin 5 m to 15 m water layers, while it was only 102 cells/mL in other remaining layers.

Based on the calculation of Pearson correlation, PO43- and SiO44- contents, temperature, oxygen saturation, and salinity were found correlated with Synechococcus abundance (Fig. 1b). In particular, temperature (R=0.36, P < 0.01) and oxygen saturation (R=-0.36, P < 0.01) showed the most correlation.

3.3 Phenogenetic diversity of Synechococcus in AUG

The high-throughput sequencing of cpeBA generated 1 286 538 qualified sequences from 29 samples. The Good's coverage of each sample was > 99.9%, indicating adequate assessment in each sample. The OTU number and community diversity (Shannon index) of Synechococcus PTs always showed strong variation with depth. The minimum detected OTUs and Shannon index appeared in BS1-B. The most diverse Synechococcus PTs were found at station BHB3 where the mean content of NO2- and turbidity were also highest (Fig. 2a). The PCoA with Permanova test demonstrated that along the PC1, samples from LZB and BHB were separated from the others (Fig. 2b). Along with the PC2, samples from BS were varied significantly from those of CA. However, they were not always separated with their geographical patterns; for example, BHB2 located near BHB3 was clustered with CA1 and LDB1.

Fig.2 Alpha diversity at OTU level (representative by Shannon index) (a) and the PCoA with Permanova Test at OTU level (b) In (b), the number after "-" represents the sampling depth (m) of this sample; B represents the bottom layer; lowercase letters in top and right subgraphs indicate significant difference among geographical regions along PC1 and PC2, respectively (LSD test, P < 0.05).

The maximum-likelihood (ML) tree constructed using cpeBA sequences showed that Synechococcus PT2 and PT3a were well classified, while Synechococcus PT3c and 3d formed one clade (Supplementary Fig.S3). In samples of BHB and LZB, PT2 was the majority, with the proportion more than 50%, meanwhile, the percentages of PT3a and PT3c/PT3d were relatively lower (Fig. 3). In stations BHB3 and LZB1, PT3c/PT3d rarely occurred with certain unclassified sequences. PT3a was most abundant in station BS1, while PT3c/PT3d was most abundant in stations BS2 and LDB1. Besides, a strong vertical variation in the composition of Synechococcus PTs was detected in station BS1. The percentages of PT2 and PT3a decreased with water depth, and the lowest value of PT2 percentage of this cruise was observed in BS1-B (1.01%). On the contrary, the percentage of PT3c/PT3d increased with depth, and the highest PT3c/PT3d percentage of this cruise was reached in BS1-B (77.19%).

Fig.3 Composition of Synechococcus PTs The number after "-" represents the sampling depth (m) of this sample; B represents the bottom layer.

The analysis of VIF filtered out three environmental variables, pH, temperature, and oxygen saturation. The remaining ten factors, including Synechococcus abundance, totally constrained 71% community variation of Synechococcus PTs (P < 0.01), according to the RDA with the permutation test (Table 2). Contents of NO2- and chlorophyll a, as well as Synechococcus abundance were factors that correlated significantly with the composition of Synechococcus PTs.

Table 2 RDA with the permutation test between Synechococcus PTs and environmental variables

Pearson correlation between environmental variables and PTs of Synechococcus demonstrated that each Synechococcus PT was associated with different environmental parameters (Fig. 4). Synechococcus PT2 significantly correlated with nine environmental parameters, covering six positive relationships and three negative relationships. Synechococcus PT3a significantly correlated with five environmental parameters, including contents of NO2-, NO3-, PO43-, SiO44-, and chlorophyll a (all were negatively correlated). Synechococcus PT3c/PT3d also significantly correlated with nine environmental parameters, while the correlation tendency was generally opposite to that of PT2. In addition, compared with PT2, Synechococcus PT3c/PT3d correlated to depth, while did not correlate to the content of chlorophyll a. Other unclassified Synechococcus PT correlated significantly only to the NO3- content.

Fig.4 Pearson correlation between environmental variables and PTs of Synechococcus *: P < 0.05; **: P < 0.01.
3.4 Microbial co-occurrence with Synechococcus PTs

The 16S rRNA gene generated 61 317 quality sequences per sample with the Good's coverage of more than 99.9%. The Shannon index of the prokaryotic community ranged from 2.75 (0-m layer of CA1 (CA1- 0)) to 5.03 (bottom layer of BS1 (BS1-B)) (Supplementary Fig.S4). In total, 12 phyla were detected with a relative abundance of more than 1% in all samples (Supplementary Fig.S5). Taxa belonging to the phyla Proteobacteria, Actinobacteria, and Cyanobacteria were the most dominant. Cyanobacteria mainly contained the genus Synechococcus accounted for 2.06% (LZB1-B) to 59.18% (CA1-0) of the prokaryotic community. Prochlorococcus was not detected. Noticeably, the relative abundance of Synechococcus can be heavily underestimated because of the multiple copies of 16S rRNA genes in the genomes of heterotrophic bacteria (Zheng et al., 2020).

In the co-occurrence network combining the prokaryotic community and Synechococcus PTs, genetic correlation linked 829 nodes via 39 648 connections, corresponding to 39 377 positive and 271 negative correlations (Fig. 5). Each node represented a prokaryotic genus or Synechococcus PT. In total, Synechococcus PTs were directly connected to 243 genera, which could be classified into 22 phyla. These taxa occupied 15.37%-43.48% of the whole prokaryotic community. The most abundant phyla related to Synechococcus PTs were Proteobacteria (10.70% on average), Actinobacteria (7.76%), Firmicutes (3.89%), Planctomycetes (2.25%), Bacteroidetes (1.61%), and Verrucomicrobia (1.12%). PT3c/PT3d correlated with most prokaryotic genera (110), followed by PT2 (76) and PT3a (64). Other unclassified PTs only correlated with 49 genera. The cytoHubba analysis revealed that PT3c/PT3d (hubba score was 7 304 and rank was 9 of 829 nodes) and PT2 (hubba score was 6 031 and rank was 13 of 829 nodes) were the most important PTs in the network (Supplementary Table S3). In addition, hierarchical clustering of prokaryotic phyla related to Synechococcus PTs demonstrated that BS1 was farthest from other samples.

Fig.5 Microbial community co-occurrence network associated with Synechococcus PTs (a) and the heat map showing the scaled relative abundance of predominant phyla associated with Synechococcus PTs using color legend (b) In (a) the network of microbial community relationships encompassing all prokaryotic genera and Synechococcus PTs is constructed from the correlation matrix using a force-directed layout algorithm; red nodes marked with words represent four PTs of Synechococcus; gray nodes represent the genera with P > 0.05 with Synechococcus PTs; and orange nodes represent the genera with P < 0.05 with Synechococcus PTs. In (b), the number after "-" represents the sampling depth (m) of this sample; B represents the bottom layer.
4 DISCUSSION

During the four cruises, the abundance of Synechococcus exhibited seasonal variations of AUG > JUN > NOV > APR (Fig. 1), which is in accordance with the biomass of autotrophic picoplankton in the Bohai Sea in 2015 (Yan et al., 2020). By comparison, the abundance in August of Synechococcus detected in 2015 was two orders of magnitude higher than the result in this study. In addition to the inter-annual changes, the absence of some high-abundance stations might be responsible for the lower abundance. Previous studies have observed that Synechococcus abundance in southeastern CA was as high as 106 cells/mL, which was close to the highest known Synechococcus abundance in the world (Saito et al., 2002). This area was not sampled due to the cruise plan. In terms of abundance range, Synechococcus in Bohai was more similar to that in the Yellow Sea and East China Sea. The average Synechococcus abundance ranged from 104 cells/mL from August to October in the Yellow Sea (Li et al., 2006), which decreased to 3.5×102 cells/mL and 7.49×103 cells/mL during the 2007 spring bloom (Zhao et al., 2013). From May to August, the abundance in the East China Sea was between 2.5×102 cells/mL to 2.9×104 cells/ mL and 2.6×102 cells/mL to 1.4×105 cells/mL for the non-bloom and bloom forms, respectively (Zhao et al., 2016). Spatial variations in Synechococcus abundance were followed the trend CA (7.7×103 cells/ mL) > BHB (4.9×103 cells/mL) > LZB (4.0×103 cells/ mL) > BS (2.6×103 cells/mL) > LDB (1.1×103 cells/ mL). This demonstrated that there were regional characteristics in the abundance distribution of Synechococcus in the Bohai Sea.

The common assumption of the marine geographic distribution of Synechococcus PTs was that various PTs often co-occur but with one type of dominants (Haverkamp et al., 2009). Synechococcus phenogenetic diversity was markedly different along two turbidity gradients in the South and East China Seas: Synechococcus PT2 dominated in the coastal waters of the South China Sea with high turbidity; Synechococcus PT3c/PT3d were predominant in oceanic waters of the South China Sea, while PT3a was the major pigment type throughout the transect of the East China Sea (Xia et al., 2018). Consistent with the assumption, a more complex distribution pattern was demonstrated in the Bohai Sea. PT2 was abundant in the BHB and LZB but less in the BS, which could be severely affected by the input of exogenous substances. PT3 showed an opposite distribution trend, which was predominant in the BS, CA, and LDB and rare in the BHB and LZB. In particular, PT3a (green-light specialists) with low PUB꞉PEB (< 0.6) was the most abundant in the BS1, while the others (blue-light specialists or chromatic acclimation) were dominant in the LDB and BS2. It should be noted that the cpeBA gene cannot identify Synechococcus PT1 with PC-only. However, from the Tara Oceans database, PT1 was almost absent in oceanic waters (Grébert et al., 2018). The distribution pattern of Synechococcus PTs is determined by multiple factors, including geographical region and environmental parameters. The correlation analysis between PTs and environmental variables further demonstrated that PT2 occupied regions with high nutrient concentration, temperature, and turbidity; PT3, especially those that possessed high or variable PUB꞉PEB, acclimated in seawaters of high salinity, pH, and oxygen saturation (Fig. 4). Over the past few decades, Synechococcus PTs distribution and their constraints were investigated worldwide, including the Chesapeake Bay (Chen et al., 2004), the Martha's Vineyard coastal observatory (Hunter-Cevera et al., 2016), the Baltic Sea (Larsson et al., 2014), the oceanic water of the Atlantic (Olson et al., 1988), and the Black Sea (Wood et al., 1998). The coincided conclusion is that PT2 dominant in coastal shelf waters or the transition zones with intermediate optical properties, while PT3 is abundant over onshore mesotrophic waters (green-light dominance) to offshore oligotrophic waters (blue light penetrates the deepest). As the second-most abundant phytoplankton group in the global marine ecosystems, the contribution of Synechococcus to global primary production and carbon cycling is a matter of interest for scientists worldwide (Guidi et al., 2016). Providing photosynthetic organic carbon as output materials, Synechococcus can coexist via extensive interactions with various heterotrophic bacteria, such as Flavobacteria, Bacteroidetes, Phycisphaerae, Gammaproteobacteria, and Alphaproteobacteria (Christie-Oleza et al., 2015; Zheng et al., 2018, 2020).Based on the co-occurrence network, the relationship was constructed between Synechococcus and numerous prokaryotic genera in situ (Fig. 5). These genera can be divided into 22 phyla, which occupied 15.37%-43.48% of the whole prokaryotic community, highlighting the important role of Synechococcus in the ecosystem. In addition, the result of cytoHubba analysis demonstrated that among Synechococcus PTs, PT3c/PT3d and PT2 contributed more to the microbial community than others (Supplementary Table S3). Although Synechococcus can directly incorporate and release some substances (Moore et al., 2002; Bertilsson et al., 2003), labile cellular products released via viral-mediated cell lysis are the main materials that are interchanged with heterotrophic bacteria (Middelboe et al., 2003). In addition to cycle back into the atmosphere in the form of CO2, the carbon derived from cell lysates can be transported via the dissolved organic carbon (DOC)-bacteriaDOC loop (Talmy et al., 2019). Nitrogen, phosphorus, and other nutrients are regenerated as inorganic compounds during the degradation of cell lysates, which can also support localized microbial survival (Haaber and Middelboe, 2009; Zheng et al., 2021). Functional microbiota participating in nitrogen cycle activities identified using the FAPROTAX database, which is a powerful database relating species taxonomy to functional annotation based on 16S rRNA high-throughput sequencing (Louca et al., 2016). The result showed that the five genera participating in the nitrogen cycle and co-occurred with Synechococcus PTs were Kordia, Aeromonas, Stenotrophomonas, SUP05 cluster, and Thauera (Supplementary Fig.S6). It is well known that Synechococcus is photosynthetic oxygen-producing plankton, while the role of Synechococcus in nitrogen cycles is ambiguous; hence, investigating how they are associated with bacteria in nitrogen reduction activities is worthwhile. The co-occurrence between them might be attributed to the degradation of nitrogen compounds from DOM of Synechococcus by these microbes, but it needs to be verified using co-culture systems and more genomic data in further work.

5 CONCLUSION

The abundance of Synechococcus was investigated in the Bohai Sea during the four cruises using flow cytometry. The result revealed regional and temporal variations in Synechococcus abundance among the five regions of the Bohai Sea. The phenomenon may be attributed to the change of environmental factors, as temperature and oxygen saturation. Considering AUG as a representative, the genus composition of Synechococcus PTs was studied based on highthroughput sequencing of the cpeBA operon, demonstrating that PT2 abundant in regions with high nutrient concentration, temperature, and turbidity; PT3 predominant in waters of high salinity, pH, and oxygen saturation in the Bohai Sea. The results showed the phenogenetic diversity of Synechococcus in the Bohai Sea for the first time, which is prospective to explore the effects of human activities or environmental changes on the distribution characteristics and community composition of pico-plankton in the coastal sea. Besides, the correlationship between Synechococcus PTs and numerous prokaryotic genera was analyzed, which showed divergent composition between samples. The co-occurrence might be attributed to the interchange of substances and the impacts of environmental variables. The result suggested that co-occurred microbes may involve in nitrogen metabolism, which is a clue for the future research of the ecological function of each Synechococcus PT in biogeochemical cycling.

6 DATA AVAILABILITY STATEMENT

The sequence data of this study were deposited in the sequence read archive of NCBI (US National Center for Biotechnology Information; https://www.ncbi.nlm.nih.gov/). A bio-project associated with this study was applied for and processed in NCBI with the accession number PRJNA 688318. All raw sequencing data were stored under this accession number.

7 ACKNOWLEDGMENT

The samples were collected by R/V Chuangxin I. We acknowledge the assistance from the Engineering and Technical Service, Institute of Oceanology, Chinese Academy of Sciences, for organizing research voyages and sharing open data. We extend our gratitude to the journal reviewers for their comments and suggestions, which helped in significantly improving the manuscript.

Electronic supplementary material

Supplementary material (Supplementary Figs.S1–S6 and Tables S1–S3) is available in the online version of this article at https://doi.org/10.1007/s00343-021-1005-1.

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