Rare bacteria in seawater are dominant in the bacterial assemblage associated with the Bloom-forming dinoflagellate Noctiluca scintillans

Journal Pre-proofs Rare Bacteria in Seawater are Dominant in the Bacterial Assemblage Associated with the Bloom-forming Dinoflagellate Noctiluca scintillans Xiaomin Xia, Sze Ki Leung, Shunyan Cheung, Shuwen Zhang, Hongbin Liu PII: DOI: Reference:

S0048-9697(19)35099-5 https://doi.org/10.1016/j.scitotenv.2019.135107 STOTEN 135107

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Science of the Total Environment

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28 July 2019 19 October 2019 20 October 2019

Please cite this article as: X. Xia, S. Ki Leung, S. Cheung, S. Zhang, H. Liu, Rare Bacteria in Seawater are Dominant in the Bacterial Assemblage Associated with the Bloom-forming Dinoflagellate Noctiluca scintillans, Science of the Total Environment (2019), doi: https://doi.org/10.1016/j.scitotenv.2019.135107

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Rare Bacteria in Seawater are Dominant in the Bacterial Assemblage Associated with the Bloom-forming Dinoflagellate Noctiluca scintillans

Xiaomin Xia1,2*, Sze Ki Leung3, Shunyan Cheung3, Shuwen Zhang4,5, Hongbin Liu3,6*

Running title: Associated bacteria of Dinoflagellate Noctiluca 1, Key Laboratory of Tropical Marine Bio-resources and Ecology, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, P.R. China 2, Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), P.R. China 3, Division of Life Science, The Hong Kong University of Science and Technology, Hong Kong, China 4, Guangzhou Key Laboratory of Subtropical Biodiversity and Biomonitoring, College of Life Science, South China Normal University, Guangzhou, P.R. China 5, Guangdong Provincial key Laboratory of Healthy and Safe Aquaculture, College of Life Science, South China Normal University, Guangzhou, P.R. China 6, Hong Kong Branch of Southern Marine Science & Engineering Guangdong Laboratory, The Hong Kong University of Science and Technology, Hong Kong, China *Corresponding to: Hongbin Liu, [email protected]; Xiaomin Xia, [email protected]

Rare Bacteria in Seawater are Dominant in the Bacterial Assemblage Associated with the Bloom-forming Dinoflagellate Noctiluca scintillans

Xiaomin Xia1,2*, Sze Ki Leung3, Shunyan Cheung3, Shuwen Zhang4,5, Hongbin Liu3,6*

Running title: Associated bacteria of Dinoflagellate Noctiluca 1, Key Laboratory of Tropical Marine Bio-resources and Ecology, South China Sea Institute of Oceanology, Chinese Academy of Sciences, Guangzhou, P.R. China 2, Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), P.R. China 3, Division of Life Science, The Hong Kong University of Science and Technology, Hong Kong, China 4, Guangzhou Key Laboratory of Subtropical Biodiversity and Biomonitoring, College of Life Science, South China Normal University, Guangzhou, P.R. China 5, Guangdong Provincial key Laboratory of Healthy and Safe Aquaculture, College of Life Science, South China Normal University, Guangzhou, P.R. China 6, Hong Kong Branch of Southern Marine Science & Engineering Guangdong Laboratory, The Hong Kong University of Science and Technology, Hong Kong, China *Corresponding to: Hongbin Liu, [email protected]; Xiaomin Xia, [email protected]

Abstract Noctiluca scintillans is a bloom-forming dinoflagellate, which is widely distributed in the global coastal seas. Associated bacteria have been proven to be essential for the survival and growth of zooplanktons. However, the diversity and function of bacteria associated with Noctiluca scintillans are under studied and largely unknown. Here, we examined the diversity and function of bacteria associated with field-acquired and laboratory-maintained Noctiluca cells. Our results showed that the bacterial communities associated with the laboratory-maintained Noctiluca were dominated by Rhodobacterales, whereas those associated with the field-acquired Noctiluca varied over time. In addition, major Noctiluca-associated bacteria had low relative abundance in the ambient environment. We also observed that when fieldacquired Noctiluca were cultivated with a mono-species food source, there was a shift in the associated bacterial communities. Metagenomic analysis showed that genes involved in DNA replication/repair and osmotic regulation were more abundant than other genes in the Noctiluca-associated bacterial community. Furthermore, the associated bacteria were able to degrade various complex carbohydrates and actively participate in the nitrogen cycle in their host cells. In addition, a draft genome of the Rickettsiaceae strain was recovered, and we showed that the genome did not contain genes encoding hexokinase and phosphoglucomutase, two key enzymes involved in glucose utilization. Instead, the primary energy sources of this bacteria were shown to be glutamate, glutamine and pyruvate, which might be obtained from the host. We

suggest that in return, the Rickettsiaceae strain is likely to provide cofactors and amino acids to the host. This study highlights the spatial and temporal complexity of bacterial communities associated with Noctiluca, and provides valuable insights into the interaction between a host and its associated bacteria. Key words: Noctiluca, dinoflagellate-associated bacteria, Rickettsiaceae, metagenome

Introduction Dinoflagellates are a large group of flagellate eukaryotes, which live ubiquitously in marine environments and play diverse roles in the marine food web (Jeong et al., 2010). Noctiluca scintillans is a large-sized dinoflagellate (of between 200-600 μm), which occurs in both red and green forms (Harrison et al., 2011). Green Noctiluca which contains large numbers of green symbiotic prasinophytes is mixotrophic and is mainly distributed in the tropical western the North Pacific and Indian Ocean (Harrison et al., 2011). In contrast, red Noctiluca is an obligate heterotroph, which feeds on a variety of food items with a broad size and trophic spectrum (Umani et al., 2004), and it is commonly found in coastal areas in a range of temperate to sub-tropical locations (Harrison et al., 2011). In recent years, massive blooms of Noctiluca have been widely recorded in the global oceans (Baliarsingh et al., 2016; do Rosário Gomes et al., 2014; Goes et al., 2018; Zhang et al., 2017). For example, Hong Kong is one area that has been seriously affected by red Noctiluca (Harrison et al., 2011). Indeed, studies have shown that Noctiluca is the most common red tide species in Hong Kong coastal waters. It accounts for > 30% of the algal blooms recorded from 1980 to 2014 (Zhang et al., 2017), and it usually blooms during the winter and early spring (Harrison et al., 2011). Although Noctiluca is a non-toxic dinoflagellate, its bloom is harmful to commercial fish farming and to benthic fauna due to oxygen depletion (Harrison et al., 2017; Huang and Qi, 1997), potential ammonium toxicity when the bloom declines (Montani et al., 1998; T. Okaichi, 1976), and it acts as a vector of phycotoxins (Escalera et al., 2007).

Bacteria are fundamental components of the marine ecosystem (Sherr and Sherr, 1988), as they are involved in almost all of the biogeochemical cycles in marine waters (Arrigo, 2004). The majority of bacteria are free-living, while some are associated with plankton, and interactions between these plankton-associated bacteria and their host are highly sophisticated (Worden et al., 2015). For example, these bacteria have been shown to be essential for the survival and growth of zooplankton by providing several essential vitamins (e.g., Vitamin B12) and promoting the assimilation of iron (Amin et al., 2009; Croft et al., 2005). In return, they get their source of carbon from the host (De Corte et al., 2018; Utami et al., 2018). A previous study has shown that the bacterial communities, which are associated with zooplankton are distinct from other communities in the ambient seawater, and they mediate specific biogeochemical processes, which are generally underrepresented in these waters (De Corte et al., 2018). For example, Firmicutes, which grow in anaerobic conditions, are commonly associated with copepods (Shoemaker and Moisander, 2015). Moreover, it has been suggested that zooplankton can be serve as a vector for some pathogenic bacteria, such as those that transmit coral disease (Certner et al., 2017). However, despite the ecological importance of Noctiluca, few studies have investigated the diversity and function of the bacteria, which are associated with (either inside or on the surface of) this dinoflagellate. Lucas (1982) was the first to report the existence of intracellular bacteria inhabiting Noctiluca cells; it was shown that Noctiluca cell surface was free from bacterial growth, and intracellular bacteria were distributed in the cell cytoplasm (Lucas, 1982).

Subsequently, denaturing gradient gel electrophoresis (DGGE) was used to demonstrate the diversity of endocytic bacteria in Noctiluca, and it was suggested that some specific bacteria (mainly Gammaproteobacteria), which are not usually found in the same environment as the dinoflagellate, actually develop inside Noctiluca cells (Seibold et al., 2001). In the same year, some potentially toxic bacteria, affiliated with Alphaproteobacteria and Gammaproteobacteria, were isolated from Noctiluca cells (Kirchner et al., 2001). Although these two studies provided some valuable insights into the diversity of Noctiluca-associated bacteria, because the methods used were low resolution and culture-dependent, any minor or uncultivated bacteria could not be identified. Thus, there is still not much known about: 1) the community composition of bacteria associated with Noctiluca scintillans; 2) what function or roles the associated bacteria might play in/on Noctiluca cells; 3) how increasing the levels of the associated bacteria during a Noctiluca bloom might affect the marine ecosystem and human health. Therefore, in this study we used samples of Noctiluca collected from the field as well as laboratory-cultured Noctiluca cells, and used pyrosequencing to reveal the diversity, composition and functional characteristics of the bacterial communities associated with them both. Methods Field-acquired and laboratory-maintained Noctiluca scintillans cells The experimental setup used, is shown in Fig. S1. Field-acquired Noctiluca scintillans cells (hereafter called ‘field Noctiluca’) were collected from a long-term

observation site, which is a pier off Port Shelter (22.20.453N, 114.17.703E) in eastern Hong Kong. Samples were collected five times (i.e., during May and December 2016, January and February 2017, and March 2018, which we have called sampling times 1 to 5, respectively), with a 150 μm mesh net (Fig. S1). More than 1,000 Noctiluca cells were collected from each net tow sample, and transferred to 100 mL sterilized seawater (bacteria-free seawater) using a plastic dropper. These Noctiluca cells were then washed twice in 100 mL bacteria-free seawater to remove untargeted zooplankton species and free-living bacteria. Approximately 400 washed Noctiluca cells were then filtered onto a 10 μm polycarbonate membrane (47 mm diameter, Millipore) for analysis of the Noctiluca-associated bacteria prior to starvation. To exclude any ingested bacteria, another 400 washed cells were kept in 500 mL bacteria-free seawater in a temperaturecontrolled chamber at ~23 ± 1°C with light at an intensity of 50 μmol photon m−2s−1 in a 14:10 h light:dark cycle (Zhang et al., 2015) for three days. After three days in these starvation conditions, Noctiluca-associated bacteria were washed with bacteria-free seawater and filtered onto a 10 μm polycarbonate membrane. All the membrane samples (i.e., ± starvation) were stored at -20°C until they were used for DNA extraction. In addition, at each sampling time, 100 mL seawater collected from the sampling station was pre-filtered through a 3 μm polycarbonate membrane (47 mm diameter, Millipore) and filtered onto a 0.2 μm polycarbonate membrane (47 mm diameter, Millipore) for analyzing the environmental free-living bacterial community. Noctiluca cells that had been cultivated in the laboratory since 2011 (hereafter

called ‘lab Noctiluca’ (Zhang et al., 2015), were also collected in August and December 2016 (Fig. S1). These cells were washed and starved using the same procedures used for the field Noctiluca cells, as previously described (Fig. S1). These samples were also stored at -20 °C until DNA extraction. Cultivation of the field Noctiluca cells To test whether environmental changes might affect the community of bacteria associated with Noctiluca, approximately 1,000 Noctiluca cells collected in February 2017 were cultivated in two 1 L glass beakers at 23 ± 1°C with light at an intensity of 50 μmol photon m−2s−1 in a 14:10 h light:dark cycle for 18 days. The cells were fed with Thalassiosira weissflogii, which is a mono-specific food source. After cultivation, the cells were then washed and collected, as described above (Fig. S1). DNA extraction, PCR, pyrosequencing and metagenomic sequencing Genomic DNA was extracted from Noctiluca cells on the polycarbonate membranes following an enzyme/phenol-chloroform extraction protocol (Riemann et al., 2000), with slight modifications. In brief, Noctiluca cell-containing membrane was cut into 3 mm×3 mm pieces with sterile scissors and transferred into a 2 mL tube with 0.5 mL of solution I (Tris-HCl 50 mM, EDTA 50 mM, and sucrose 50 mM; pH 8.0). After three freeze (-80°C)/thaw (60°C) cycles, freshly made lysozyme (5 mg mL-1, final concentration) was then added to the tube and incubated for 1 h at 37°C. Proteinase K and sodium dodecyl sulfate at final concentrations of 2 mg mL-1 and 0.5%, respectively, were then added, and the tube was incubated at 60°C for 2 h. After incubation, DNA in

the solution was extracted once with 0.5 mL phenol-chloroform-isoamyl alcohol (at a 25:24:1 ratio), and then twice with 0.5 mL of chloroform-isoamyl alcohol (at a 24:1 ratio). The supernatant was then transferred to a new tube and 490 µL isopropyl alcohol (i.e., 70% volume of the supernatant) was added. After incubation at -20°C overnight, the tube was centrifuged at 12,000 g for 10 min. The precipitated DNA was then washed twice using 0.2 mL 70% ethanol, and resuspended in 0.1 mL TE buffer (10 mM TrisHCl, 1 mM EDTA, pH 8.0). For pyrosequencing, the V3 and V4 regions of the 16S rRNA gene were amplified from the extracted genomic DNA using the following barcoded primers: 341F (5’adaptor+barcode+CCTAYGGGRBGCASCAG-3’),

and

806R

(5’-

adaptor+GGACTACNNGGGTATCTAAT-3’) (Beman et al., 2007). The PCR reaction was performed in a 25 µL reaction volume containing 1.5 mM MgCl2, 1× PCR buffer, 0.5 µM of each primer, 0.2 mM of each dNTP, 1.0 unit of Platinum® Taq DNA polymerase (Invitrogen) and 1 µL genomic DNA. PCR was performed at the following conditions: 5 min initial denaturation at 95°C, followed by 30 cycles of 95°C for 30 sec, 55°C for 30 sec, and 72°C for 60 sec; and then a final extension at 72°C for 7 min before holding at 4 °C. All of the reactions were performed in triplicate. The amplicons were then gel-purified using the Invitrogen gel purification kit (Invitrogen) and sequenced using a GS Junior pyrosequencing system (Roche), according to the manufacturer’s instructions. For metagenomic sequencing, approximately 10,000 Noctiluca cells were

collected from the pier in March 2018 (sample collection #5), and DNA was extracted using the modified enzyme/phenol-chloroform protocol described above. The extracted DNA was then sequenced with the Illumina Miseq platform at Novogene Co. Ltd (Beijing, China). Data analysis The 16S rRNA gene sequences were analyzed using the microbial ecology community software, Mothur (Schloss et al., 2009). To start, tags and primers were removed. Then, sequences with an average quality score below 20 were removed whereas those with lengths ranging between 300 bp to 600 bp were kept for downstream analysis. Sequences were denoised using the shhh.seqs command, with sigma value 0.01, and chimeras were analyzed using the chimera.uchime command and removed. The remaining sequences were then identified using the Greengene database with cutoff value 60% (Desantis et al., 2006). Sequences identified as belonging to either chloroplasts or mitochondria, or which were unknown, were removed. Operational taxonomic units (OTUs) were calculated at a cutoff level of 3%. Singletons (i.e., OTUs with just one sequence), were removed using the remove.rare command. The sequence statistics are shown in Table S1. Coverage of most samples was greater than 96.5%, indicating that our sequencing efforts could assess the bacterial community compositions adequately. To estimate similarity among the samples, hierarchical cluster analysis was conducted based on Bray-Curtis dissimilarity and complete linkage cluster mode using

the Primer 5 software (PRIMER-E LTD, Plymouth, HK). Non-metric multidimensional scaling (NMDS) analysis and hierarchical cluster analysis (UPGMA) were also conducted based on the relative abundance of OTUs in each sample. Venn diagrams at 3% sequence dissimilarity were generated using Mothur to show both unique and shared OTUs in each water sample. A principal component analysis (PCA) was performed using Canoco V5 to obtain relative sequence abundance data at the family level. To determine significant differences between bacterial communities before and after laboratory cultivation, the Statistical Analysis of Metagenomic Profiles (STAMP) software package was used (Parks et al., 2014), and the table of the relative abundance of each family was used as input. P values were calculated using a Student's two sided t-test. To identify the major OTUs, the representative sequence of each OTU was extracted using the get.oturep command in Mothur, which is based on the distance method. Nearest relatives of the top 40 most abundant OTUs (relative abundance) were retrieved from the NCBI database (http://www.ncbi.nlm.nih.gov) as the reference sequences. A phylogenetic tree was constructed with MEGA 7 using the maximum likelihood method, with 1,000 bootstrap replicates based on the Kimura 2-parameter model (Kumar et al., 2016). The relative abundance of the top 40 OTUs were displayed with the phylogenetic tree using iTol (Letunic and Bork, 2016). Since an uneven sequencing depth might result in an inflated estimate of microbial diversity, the sub.sample command in Mothur was used to equalize the sampling efforts

according to the sample with the lowest number of sequences. To increase the number of reads for the diversity analysis, the read number of the samples with far fewer reads than all the others (samples 1st_L_BS_Noc1, 1st_L_BS_Noc2 and 1st_L_AS_Noc2) were multiplied by two (Peura et al., 2012), and then normalized. Subsequently, Ace and Shannon estimators were generated in Mothur as proxies of the α diversity index. Good’s coverage was also measured, to evaluate the sampling depth. Metagenomic analysis Metagenomic sequences obtained from Illumina sequencing were trimmed using Trimmomatic (Bolger et al., 2014), and assembled using Spades with k-mer 33, 55 and 77 (Bankevich et al., 2012). Gene prediction, annotation, taxonomic identification and abundance calculations were conducted with SqueezeM, using the default setting (Tamames and Puentesanchez, 2018). Taxonomic classification was performed using DIAMOND against the NR protein database of National Center for Biotechnology Information (NCBI), with an e-value cutoff of 0.001. The KEGG database was used for the annotation of function. Genes encoding carbohydrate metabolizing enzymes were identified by using the predicted genes against the Carbohydrate-Active EnZymes database (CAZY) (Lombard et al., 2014). Binning was conducted using MYCC (Lin and Liao, 2016). The quality of microbial genomes obtained was assessed using CheckM (Parks et al., 2015). A bin containing contigs of Rickettsiaceae with 99.05% completeness was submitted to RAST for gene prediction and annotation (Aziz et al., 2008). The predicted open reading frames (ORFs) were further analysed using

eggNOG-mapper (http://eggnogdb.embl.de/#/app/emapper), and the pathway was searched using KEGG Mapper (https://www.genome.jp/kegg/tool/map_pathway1.html). All the sequences obtained in this study have been deposited in the NCBI Sequence Read Archive (SRA) under BioProject accession numbers: PRJNA501781 and PRJNA503518. Results The relationship between Noctiluca-associated and ambient seawater bacterial communities NMDS and UPGMA clustering were applied to analyze the relationship between the bacterial communities. Our results showed that the bacterial communities associated with Noctiluca and those in the ambient seawater were clustered into different groups (Fig. 1, Fig. S2), indicating that they had distinct compositions. The bacterial communities associated with the laboratory-cultivated Noctiluca samples (collected in Aug. 2016 and Dec. 2016) were grouped together, whereas for the samples collected in the field, the Noctiluca-associated bacterial communities clearly formed three distinct clusters (Fig. 1). These data might indicate that the composition of the bacterial communities associated with the laboratory-maintained Noctiluca was more stable than that associated with the field-acquired Noctiluca. We compared the diversity of the bacterial communities in the ambient water and in Noctiluca cells (Fig. S3). Somewhat surprisingly, we found that the diversity was not always higher in the ambient water. For example, in the field samples collected in

December 2016 (sampling time 2), the bacteria in the ambient water exhibited far lower diversity than did those associated with the Noctiluca cells. Similar results were also observed in the laboratory-maintained Noctiluca samples. Community compositions of the Noctiluca-associated bacteria In total, 29 bacterial phyla were detected associated with Noctiluca cells, although most of them had a relative abundance less than 1.0%. Proteobacteria were the most abundant bacterial phylum associated with Noctiluca; they contributed ~8.8%-96.0% of the associated bacterial communities. The majority of Proteobacteria were Alphaproteobacteria and Gammaproteobacteria (Fig. 2A). After Proteobacteria, the Bacteroidetes was the second most abundant bacterial phylum associated with Noctiluca cells. This had a relatively high abundance in the samples collected in February 2017 (sampling time 4) and in the laboratory-cultivated samples. In the samples collected in May 2016 (sampling time 1), the ZB3 phylum was the major Noctiluca-associated bacteria, exhibiting a relative abundance of up to 87%. In contrast, only 20 bacterial phyla of free-living bacterial communities were retrieved in the ambient water samples. In addition, some bacterial phyla, such as Gemmatimonadetes, Chlorobi and Nitrospirae, which were identified in the Noctiluca-associated samples, were not detected in the ambient water samples. At the family level, different bacterial families dominated in the Noctilucaassociated and free-living communities (Fig. 2B). In addition, in the Noctilucaassociated bacterial community, the dominant bacteria varied over time. In the

laboratory-maintained Noctiluca samples, the dominant associated bacteria were members of the Rhodobacteraceae family (Fig. 2B). In the field-acquired Noctiluca samples collected in May 2016 (sampling time 1), the unclassified BS119 affiliated with ZB3 was the dominant bacteria in the associated bacterial community (Fig. 2B). In addition, after 3 days of starvation, the relative abundance of BS119 increased from around 40.0% to greater than 85.0%. In the field samples collected on December 2016 and January 2017 (sampling times 2 and 3), the Vibrionaceae accounted for ~45.7- 87.9% of the associated bacterial communities, whereas in the field samples collected on February 2017 (sampling time 4), the dominant associated bacteria shifted to members of the Rhodobacterales, Cytophageles, Rickettsiales orders, as well as several unclassified Bacteroidetes. PCA at the family level also demonstrated temporal variation of the Noctiluca-associated bacteria (Fig. 3). As revealed by phylogenetic analysis, the dominant OTUs associated with Noctiluca cells were different from those in the ambient waters (Fig. 4). For example, OTU4 (clustered with uncultured Bacteroidetes BT172 from the shrimp pond sediment), OTU5 (clustered with Vibrio sp. strain JAM5 from seawater), and OTU11 (clustered with uncultured Rhodobacteria), were the most dominant OTUs in the water samples. In contrast, OTU1 (clustered with V. marisflavi strain WH134), OTU2 (clustered with unclassified Flavobacteriacea), and OTU3 (clustered with ZB3 strains), dominated in Noctiluca cells. With regards to the field samples collected in December 2016 (sampling time 2), although the bacterial communities in the ambient waters and those

associated with Noctiluca cells were both dominated by the Vibrionaceae, phylogenetic analysis showed that in the former, they were mainly contributed by OTU5 (clustered with Vibrio sp. strain JAM5), whereas in the latter, they were mainly OTU1 (clustered with V. marisflavi strain WH134). Temporal variation in the dominant OTUs of the Noctiluca-associated bacterial community was also observed among the different field samples (Fig. 4). We compared the composition of the Noctiluca-associated bacterial communities before and after three days of starvation. After starvation, the dominant OTUs in the associated bacterial communities did not change, although in most cases the OTU number decreased (Fig. 5). The number of core OTUs that were shared by both the freeliving and associated bacterial communities ranged from 22-28. However, the composition of the core OTUs was distinct in the free-living and associated bacterial communities. In addition, the OTUs that dominated the associated bacterial communities had low relative abundances in the environment, indicating the development of distinct bacteria in Noctiluca cells. We also observed that the composition of core OTUs shifted with the time of sampling. Associated bacterial community composition before and after cultivation for 18 days In order to examine whether environmental changes might affect the composition of the Noctiluca-associated bacteria, we cultivated Noctiluca collected in the field with a mono-species food source (i.e., Thalassiosira weissflogii) in the laboratory for 18 days.

Subsequent NMDS analysis showed that after cultivation, the composition of the bacterial community of Noctiluca cells changed (Figs 1and 6). For example, the relative abundance of the Vibrionaceae increased significantly, whereas that of Rickettsiaceae and unclassified Bacteroidetes decreased significantly (Fig. 6). Indeed, after cultivation, the Vibrionaceae became the dominant bacterial family of the associated bacterial community. Metagenomic analysis of the Noctiluca-associated bacteria The metagenomic data obtained from the Noctiluca field sample collected in March 2018 (sampling time 5) and before starvation, were used to characterize the functions of the bacterial community associated with Noctiluca. Unfortunately, the DNA from the other samples was insufficient for metagenomic sequencing. In the March 2018 sample (sampling time 5), the bacterial community was dominated by the Alphaproteobacteria (Fig. S4). The 50 most abundant identified genes were cataloged into seven KEGG pathways (Fig. 7). The most abundant gene, hupB, codes for histonelike DNA-binding protein. This protein is capable of stabilizing DNA and thus prevents its denaturation even in extreme environmental conditions. Among the genes related to signaling and cellular processes, mazF, prop and ABCB-BAC genes, which are all involved in membrane transport, were more abundant than other genes (Fig. 7, Supplemental Dataset 1). In the MazE-MazF system, MazF cleaves RNA at the 5′-end of ACA sequences; in this way it inhibits protein synthesis and thus controls cell growth. The prop gene encodes for the proton symporter protein, which senses osmotic shifts

and responds by importing osmolytes. The ABCB-BAC gene is related to the ATPbinding cassette pump; this utilizes energy released by ATP hydrolysis to move substrates across cell membranes. In addition to the mazF, prop and ABCB-BAC genes, genes related to peptide, polar amino acid and sugar transport were detected. For example, VirB4 and VirB6 which encode subunits of type IV secretion systems, were the most abundant genes in the membrane transport pathway. It has previously been shown that type IV secretion systems of bacteria are important for genetic exchange and the delivery of effector molecules to eukaryotic target cells (Cascales and Christie, 2003). Noctiluca-associated bacteria might be able to utilize various complex molecules, which are released by Noctiluca, as their source of carbon. We investigated the abundance of genes related to carbohydrate metabolism using the CAZy database (Fig. 8, Supplemental Dataset 2). In general, genes of the polysaccharide lyase (PL) and auxiliary activities (AA) families were not abundant. We only detected five PL families in our dataset. Of these five, PL0, which encodes pectate lyase, was more abundant than the others. Regarding the AA families, the most abundant family was AA3, which includes cellobiose dehydrogenase, glucose 1-oxidase, aryl alcohol oxidase, alcohol oxidase, and pyranose oxidase. We also identified ten carbohydrate esterase (CE) families, the members of which are mainly carbohydrate-active esterase enzymes. Among these, CE11 (UDP-3-0-acyl N-acetylglucosamine deacetylase), was the most abundant, and the majority of genes encoding this enzyme were derived from the

Rickettsiales (Supplemental Dataset 2). Genes that encode glycoside hydrolases (GH) family enzymes were also abundant in the dataset. Indeed, we identified 68 GH families, of which GH3 and GH23 were predominant. GH3 was mainly from the Rickettsiales, whereas GH23 was mainly derived from unclassified Alphaproteobacteria. The genome of the Noctiluca-associated Rickettsiaceae strain Members of the Rickettsiaceae family were commonly found to be associated with Noctiluca cells even after 3 days of starvation (Fig. 4). A draft genome of the Rickettsiaceae strain with 99.05% completeness was assembled from the metagenomic data. The genome size was 1.3 Mb and its GC content was 33.1% (Table 1). The 16S rRNA gene sequence of this strain exhibited 100% similarity with OTU8, which was widely detected in the field-collected Noctiluca cells (Fig. 4). In total, 1,387 genes were found (Table 1), of which 1,015 genes could be assigned into 19 clusters of orthologous groups (COGs) functional classes (Fig. S5). Although

glucose

6-phosphate

can

be

imported

into

cells

via

the

glucose‐6‐phosphate receptor (UhpC), the Rickettsiaceae strain might not utilize this as an energy source due to the lack of key genes required for converting glyceraldehyde3-phosphate to pyruvate (Fig. 9). Instead, glucose 6-phosphate might be used to synthesize 5-phospho-alpha-D-ribose 1-diphosphate (PRPP), a precursor of the purine and pyrimidine biosynthetic pathway (Fig. 9). In addition, genes that encode key enzymes required for glucose utilization, such as hexokinase and glucose 1dehydrogenase, were not found. The primary energy sources of this bacteria might

therefore be glutamate, glutamine and pyruvate. Glutamate can be either imported directly from the host, transformed from proline (from the host), or synthesized from ammonia via glnA and glnU genes. Pyruvate is transformed from serine and glycine, and is an alternative source of energy for the tricarboxylic acid (TCA) cycle. Moreover, a gene encoding ATP/ADP translocase was detected, which suggests that this bacteria might acquire some of its ATP from the host cell. The Rickettsiaceae genome also included genes associated with the biosynthesis of cofactors and vitamins, such as those involved in biotin and folate synthesis (Supplemental Dataset 2, Fig. 9). In addition, this Rickettsiaceae strain was unlikely to be motile due to the absence of genes related to motility and chemotaxis (Supplemental Dataset 3). Discussion Rare bacteria in seawater are dominant in the bacterial assemblage associated with Noctiluca It has been estimated that Noctiluca cells can harbor up to 106 bacteria (Kirchner, 1999), and they are mainly affiliated with Gammaproteobacteria, such as Vibrio and Marinobacter (Seibold et al., 2001). In the present study, 29 bacterial phyla were detected associated with Noctiluca cells, indicating that the bacteria associated with Noctiluca are far more diverse than previously reported. In addition, we observed bacteria that were rare in the ambient seawater, dominated the Noctiluca-associated bacterial assemblages (Figs 4 and 5). This observation is consistent with previous studies on sponge (Erwin et al., 2012) and crustacean zooplankton (De Corte et al.,

2018), suggesting that host-microbe interactions benefit certain microbial populations, such as those of the rare biosphere (Troussellier et al., 2017). In addition, most recently it has been suggested that copepods attract and support the growth of specific bacteria groups, such as Vibrionaceae, via chemical and physical stimuli, and that they can even effectively farm Flavabacteriaceae and Pseudoaltermonadaceae in or on their body (Shoemaker et al., 2019). Our observation that rare species in ambient seawater developed in/on Noctiluca cells suggests that dinoflagellates Noctiluca might use similar mechanisms for selecting and farming certain bacteria from the ambient seawater. This is evidenced by the results of our (and other reports of) metagenomic analysis, which indicate that: 1) the low pH in Noctiluca cells (i.e., the cytoplasm has a pH of 3.5(Nawata and Sibaoka, 1976)), select for bacteria with genes encoding proton symporter proteins (Fig. 7); and 2) genes involved in utilizing various complex molecules released by the host cells are present in the associated bacterial community ((De Corte et al., 2018); Fig. 8). On the other hand, as Noctiluca-associated bacterial community and free-living community are dominated by different bacterial species, which have distinct substrate utilization patterns (Bickel and Tang, 2014), the presence of Noctiluca-associated bacteria may expand the functionality of aquatic microbial communities. A previous study in the York River has shown that anaerobic bacteria in zooplankton-associated microhabitats increase the total number of substrates used by 50 % over what is used by aerobic free-living bacteria (Bickel and Tang, 2014). Hence, increasing in abundance of the associated bacteria during a Noctiluca bloom might play

a significant role in shaping the composition of the overall marine carbon pool. The composition of bacterial assemblage associated with Noctiluca cells varied over time The composition of bacterial assemblages associated with Noctiluca was not constant but varied with the time of sampling. This result is consistent with previous studies on the dinoflagellate, Alexandrium catenella (Zhou et al., 2018) and the copepod, Calanus finmarchicus (Datta et al., 2018). The combined results acquired in these different species support the conclusion that the endocytic bacteria of zooplankton display temporal variations (Heidelberg et al., 2002). However, other reports indicate a relative stable bacterial assemblage composition, such as that associated with the Hawaiian bobtail squid (Kerwin and Nyholm, 2017) and various sponges (Erwin et al., 2015). The temporal variability of bacteria associated with Noctiluca might be due to the relatively unstable microenvironment of the latter because of their small size. Indeed, it has been previously reported that associated bacteria of meso/micro-zooplankton are more easily influenced by the microbial community in the ambient water (Gong et al., 2016; Grossart et al., 2010), variations in temperature (Zhou et al., 2018) and other environmental factors, than bacteria that inhabit other marine metazoan species. In this new study, we also observed that the associated bacterial assemblage composition dramatically changed (dissimilarity=89.3%, SIMPER analysis) when we cultivated the field-collected Noctiluca with a mono-species diet for 18 days. This suggests that food source is also an important factor that influences the associated bacterial community.

Dominant species of the Noctiluca-associated bacterial community Although many bacterial species coexisted in the Noctiluca-associated bacterial community, a small number of species were predominant (Fig. 4). For example, we showed that the dominant associated bacteria varied among ZB3, Rhodobacteria, Rickettsiaceae and Vibrionaceae. It is interesting that in the Noctiluca-associated bacterial community, the relative abundance of ZB3 bacteria was as high as 87.2% (in the sample collected in May 2016; sampling time 1), although similar trends for ZB3 have been detected previously in the marine sponge (Kennedy et al., 2014) and in the protists, Cercozoa and Chrysophyta (Martinezgarcia et al., 2012). It is known that ZB3 can adapt to low oxygen conditions as it has been found in the marine oxygen minimal zone (Wright et al., 2012) and in sediment (Carnevali et al., 2019). In addition, the genome of a ZB3 bacteria that specifically attaches to the ectosymbiotic spirochetes of protists in the gut of termites, was recently reported (Utami et al., 2018). It was suggested that this bacteria hydrolyses and ferments cellulose/cellobiose to H2, CO2, acetate and ethanol (Utami et al., 2018). In the termite gut, the H2 and CO2 produced can be utilized by the ectosymbiotic spirochetes, which possess key genes for reductive acetogenesis from these two substrates. This decreases the high H2 partial pressure inside their protist hosts. As ZB3 is an anerobic bacteria (Utami et al., 2018), it probably occurs in the cytoplasm of Noctiluca cells. If it also ferments cellulose and produces H2 in Noctiluca as it does in the termite gut, then it would be interesting to find out how the former can deal with the high partial pressure of H2. Unfortunately, we did not

obtain any information about the genome of ZB3 associated with Noctiluca, and so we still do not know the metabolic properties of this bacteria. Rickettsiaceae and Vibrionaceae are often intracellular parasites or pathogens of different hosts, including human (Cottingham et al., 2003; Maeda et al., 2010). They are also commonly detected as endophytic bacteria in marine protist (Anderson, 2014; Ferrantini et al., 2009; Martinezgarcia et al., 2012). It has been suggested that hitchhiking on migrating zooplankton can be an important mechanism for rapidly relocating microorganisms, including pathogens (Grossart et al., 2010). Thus, the widespread distribution of Noctiluca in the global ocean might help potential pathogenic bacteria such as Vibrio to gain access to various marine locations. In our study, the Vibrio associated with Noctiluca was mainly OTU1, and its 16S rRNA gene sequence was identical to that of V. marisflavi (Wang et al., 2011). V. marisflavi, together with V. aestivus and V. stylophorae, consists the Marisflavi clade (Lucena et al., 2012), which have previously been isolated from seawater and corals (Lucena et al., 2012; Sheu et al., 2011; Wang et al., 2011). However, in our study, V. marisflavi was not abundant in the ambient waters; instead, OTU5, which is clustered with the Vibrio sp. strain, JAM5, was sometimes abundant in these waters. Our study suggests that among Vibrio spp., only some members are selected by Noctiluca cells. In the phylogenetic tree (Fig. S6), the Rickettsiaceae-like OTU was grouped with Rickettsiaceae endosymbionts of marine species such as Volvox carteri and Carteria cerasiformis (green alga), and it was genetically distant from Rickettsia species

associated with terrestrial organisms. This suggests that Rickettsiaceae associated with marine organisms and those associated with terrestrial organisms might have different evolutionary routes and physiological characteristics. By analyzing the genome sequence of the Rickettsiaceae endosymbiont of Noctiluca, we found that glucose 6phosphate can be imported into the cell via use of the UhpC homolog. This is similar to what happens for the symbiotic bacteria of protists (Hongoh et al., 2008). However, the Rickettsiaceae endosymbiont of Noctiluca is unable to utilize glucose 6-phosphate as an energy source due to a lack of several key enzymes (Fig. 9). This is in contrast to the findings of previous studies, which indicate that glucose 6-phosphate is the major carbon and energy source of protist symbiotic bacteria (De Corte et al., 2018; Hongoh et al., 2008). Instead, the primary sources of energy of the Rickettsiaceae endosymbiont of Noctiluca are glutamate, glutamine and pyruvate. These have previously been shown to be the main sources of energy of R. prowazekii (Yu, 2015). In addition, another previous study has reported that Rickettsia strains have a membrane-bound ATP/ADP translocase, which mediates the exchange of extracellular ATP and intracellular ADP, indicating that ATP from the host cells might be required by Rickettsia strains (Yu, 2015). Indeed, in our study, a copy of the ATP/ADP translocase gene (TLCC) was detected in the genome of the Rickettsiaceae endosymbiont of Noctiluca (Table S2). All these characteristics suggest that the survival of the Rickettsiaceae endosymbiont heavily depends on its Noctiluca host (Fig. S6). This might be a reason why this bacteria was not previously isolated from Noctiluca cells using the culture-dependent method

(Seibold et al., 2001), and why it is not abundant in seawater. From the point of view of the host, we suggest that the Rickettsiaceae endosymbiont might provide cofactors (e.g., biotin and folate), and amino acids (Fig. S7), which are essential for the growth of Noctiluca. Noctiluca is an important agent of nutrient regeneration in the marine ecosystem (Montani et al., 1998; Zhang et al., 2017), which as it accumulates and regenerates large amounts of dissolved inorganic nutrients, such as ammonia. It has been shown, for example, that in Sagami Bay, Japan, during April to July when Noctiluca is abundant during April to July in Sagami Bay, Japan, its intracellular dissolved nutrient contents can account for an average of 49.2–63.7% of the total ammonia stocks in the euphotic zone (Ara, 2013). In addition, our previous study also showed that the level of intracellular ammonia could reach ~184.81 ± 3.71 pmol per Noctiluca cell (Zhang et al., 2017). Although Noctiluca itself does not produce any toxins, the high concentration of ammonia that accumulates might be toxic to marine organisms (Montani et al., 1998; T. Okaichi, 1976). Indeed, the genome of the Rickettsiaceae endosymbiont of Noctiluca suggests that ammonia might be generated via the degradation of serine, glycine and glutamate. Thus, increasing the number of Rickettsiaceae in Noctiluca cells might lead to an accumulation of ammonia. On the other hand, the NtrC-NtrB two-component system, which controls the expression of the nitrogen-regulated (ntr) genes in response to nitrogen limitation, was also found in the genome of the Rickettsiaceae endosymbiont of Noctiluca (Fig. 9). It has previously been shown that under nitrogen

limitation, the NtrC-NtrB two-component system stimulates the expression of glnA, and subsequently increases the level of ammonia assimilation (Bolay et al., 2018). These results suggest that host-associated bacteria actively participate in the nitrogen cycle in their host cells (Yu et al., 2015). In conclusion, our study provides new insights into the diversity and functional characteristics of the bacterial communities associated with the bloom-forming heterotrophic dinoflagellate, Noctiluca scintillans. We have shown that the Noctiluca‐associated bacterial community, which is dominated by rare bacteria in seawater, is highly specialized and able to utilize various complex molecules. Furthermore, dominant Noctiluca-associated bacteria are not stable and varied among ZB3, Rhodobacteria, Rickettsiaceae and Vibrionaceae. Thus, we conclude that Noctiluca might serve as a microbial “seed bank” of marine rare species and its associated bacterial community can be easily affected by changes in the environment. In addition, wide-spread of Noctiluca might act as a vector help to disperse marine bacteria, including pathogens, across a broad geographical range. The increase in associated bacteria that accompanies a Noctiluca bloom might lead to a more comprehensive substrate usage. Moreover, genome analysis of the Rickettsiaceae endosymbionts of Noctiluca has shown that this bacteria is specific adaptations for symbiotic life and actively participate in the nitrogen cycle, suggesting its important role in controlling the concentration of ammonia and the regeneration of nutrients in Noctiluca cells. The relationship between the associated bacterial community and

ammonium toxicity during Noctiluca bloom is worthy of further study. Overall, given the wide distribution and frequent bloom of Noctiluca, the microbial processes occurring in/on Noctiluca cells by specific bacteria are likely to be globally important.

Acknowledgement This work was supported by the Key Special Project for Introduced Talents Team of Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou) （ GML2019ZD0405), National Natural Science Foundation of China (31971501, 41906117), CAS Pioneer Hundred Talents Program and the South China Sea Institute of Oceanography, CAS (Y8SL031001, Y9YB021001), and Guangdong MEPP Fund [NO. GDOE (2019) A23]. This study was also supported by grants from the Research Grants Council of the Hong Kong Special Administrative Region, China (T21/602/16, 16128416, 16101318), and a Seed Collaborative Research Fund (SKLMP/SCRF/0016) provided by the State Key Laboratory of Marine Pollution (SKLMP).

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Figure 1. NMDS analysis showing the relationship between Noctiluca-associated

and ambient seawater bacterial communities. Bacterial communities in ambient seawater were shown in stars, and the communities associated with Noctiluca were shown in circles. Different colors indicate samples from different sampling times. Noc: bacterial associated with Noctiluca cells (labelled in red), water: bacteria in ambient water (labelled in blue), F: field Noctiluca, L: lab maintained Noctiluca, inL: field Noctiluca after 18 days in lab cultivation, number (1 or 2) following the “water” or “Noc”: sample replicate, AS: after 3 days of starvation. BS: before 3 days of starvation.

Figure 2. Bacterial community composition associated with Noctiluca and ambient seawater. A, phylum level. B, family level. Number in the sample names indicate different sampling times. Noc: bacterial associated with Noctiluca cells, water: bacteria in ambient water, inL: field Noctiluca after 18 days in lab cultivation (green line marked), number (1 or 2) following the “water”, “Noc” and “inL”: sample replicate, AS: after 3 days starvation. BS: before 3 days starvation.

Figure 3. PCA analysis showing the relationships between free-living bacterial communities and Noctiluca-associated bacterial communities.

Figure 4. The ML phylogenetic tree of the top 40 most abundant OTUs. Right panel shows the relative abundance of OTUs in each sample. Bootstrap values higher than 50% were shown as black dots.

Figure 5. Venn diagrams showing bacterial communities associated with Noctiluca and bacterial communities in the ambient seawaters. Bar charts showing the compositions of the core OTUs (labelled in Red in the Venn diagrams) in each sampling time. A, B, First field samples. C,D, Second field samples. E,F, Third field samples. G,H, Fourth field samples. Numbers in red/blue indicate the relative abundance of shared OTUs in each sample.

Figure 6. The STAMP (Statistical Analysis of Metagenomic Profiles) analysis of the relative abundance of the heterotrophic bacterioplankton families enriched or depleted after 18 days cultivation. Corrected p values were calculated using t tests.

Figure 7. The 50 most abundant (classified) genes and their function in the metagenomic dataset.

Figure 8. Abundance of hydrolytic enzymes in the metagenomic dataset.

Figure 9. Overview of basic metabolic pathways of Rickettsiaceae strain associated

with

Noctiluca.

Yellow

circles

are

ABC

transporters.

*HB:

Hydroxybutanoyl. **Transporter involved in amino acid transport: YxeN/YxeO, LysX/LysY, YecS/YecC. ?: key genes involved in transferring glyceraldehyde-3P to

phosphoenolpyruvate were not detected.

Table 1 The characteristics of genome of Rickettsiaceae strain associated with Noctiluca Features

Value

Assembly size (Mb)

1.346

Completeness

99.05%

Contigs

32

Longest contig (bp)

329448

G+C content

33.5%

No. of protein-coding sequences

1387

No. of coding sequences in the RAST subsystem

588

No. of predicted RNAs

38

No. of predicted transporters

18

Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:

We reported 3 key findings in this manuscript: 1, We found that bacteria associated with Noctiluca displayed temporal variations in community composition. The dominant species of bacteria that were associated with Noctiluca, such as ZB3 and Rickettsiales, were found with low relative abundance in the ambient environment.

2, Cultivating the field Noctiluca cells with mono-species food source shifted the

associated bacterial communities.

3, A genome of Rickettsiaceae strain that was widely detected associated with Noctiluca was assembled from the metagenomic sequences. Key enzymes involved in glucose utilization, such as hexokinase and phosphoglucomutase, were not found in the genome. The primary energy source of this bacteria may be oxidation of glutamate that is transformed from proline and glutamine or imported from the host. The associated bacteria was highly dependent on their host.