Toxic and non

Communications Earth & Environment volume 4, Article number: 263 (2023) Cite this article

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Dinoflagellates encompass highly abundant and diverse toxin-producing species among marine phytoplankton. Previous works suggested that some bacterial taxa may affect toxins production in dinoflagellates, however, little is known about impact of toxic dinoflagellates on their co-existing bacterial flora. Here we characterized the bacterial communities associated with 22 clonal cultures of dinoflagellates using DNA metabarcoding method, including 11 confirmed toxic species and 11 species that have not been reported to be toxic. Beta diversity analysis revealed that all the 11 species in toxic group were clustered together and distinctly separated from non-toxic group, strongly suggesting the bacterial community composition was largely influenced by toxic dinoflagellate hosts. The toxic group was found to include higher relative abundance of non-carbohydrate utilizers and xenobiotic-degrading taxa and showed inhibitory effects on algicidal bacteria. These findings suggested that toxic dinoflagellates host bacterial communities distinctive from non-toxic species in multiple ways in their phycospheres.

The ecological interactions between phytoplankton and bacteria are among the most important quantitative links in global carbon and nutrient cycles1,2. Phytoplankton excrete organic compounds that represent an important fraction of primary production, forming the base of the marine microbial food web3. Many direct interactions have been proposed to take place in the phycosphere, which is defined as a diffusive boundary layer immediately surrounding phytoplankton cells and is enriched in organic matter2,4. The associated bacterial communities that consume phytoplankton metabolites has cascading effects on food web and impacts the rates and efficiencies of marine organic matter transformation, with the latter a key factor in ocean–atmosphere CO2 balance1,5. The configuration of such interactions is likely to be extremely diverse, since examples of bacteria–phytoplankton relationships vary broadly from pathogenic to mutualistic, which impacts both biology of algal host and composition of bacterial communities simultaneously2,3,5,6.

Dinoflagellates are ubiquitous unicellular microalgae, occupying important niches in global marine and freshwater environments. Members in this lineage are not only vital primary producers and grazers, but also the major causative agents of harmful algal blooms (HABs). Merely about 2% of algal species have been reported as producing HABs, of which 75% are dinoflagellates7. This algal group also includes the highest number of toxin-producing species among marine phytoplankton8. Their toxins can be extremely lethal and many of them are effective at far lower dosages than conventional chemical agents9. Toxic dinoflagellate blooms draw great attention due to their widespread impacts on aquaculture, fishery, and coastal ecosystems9,10,11,12,13. Dinoflagellate toxins also can be accumulated and retained in marine zooplankton, causing fish kills and even leading to human poisoning via the consumption of contaminated seafood14,15. While the toxic dinoflagellate HABs have been extensively studied from the ecological perspective, the biological characteristics of dinoflagellate-derived toxic compounds and particularly their effects on the co-occurring microbes remain obscure15,16. Among all dinoflagellate toxins, only a limited number have been identified, such as the well-known shellfish poisoning toxins, including paralytic shellfish toxins (PSTs), amnesic shellfish toxins (ASTs), diarrhetic shellfish toxins (DSTs), neurotoxic shellfish toxins (NSTs), ciguatera finfish toxins (CTXs), azaspiracid toxins (AZTs), and karlotoxins10,11,12,13. Many ichthyotoxic (fish-killing) toxins are as yet unidentified (see reviews by refs. 1,17). Moreover, the toxic compounds produced by dinoflagellates exhibited a wide variety of biological activities and thus had distinct effects on the co-occurring microbial community (reviewed in ref. 16).

Microorganisms in the phycosphere play important roles on algal growth, population succession, algal biomass decay and nutrient cycling3,5,6. The influence of bacteria on toxic algae is of particular interest and has drawn increasing scientific attentions. Over the last few decades, substantial studies have examined the diversity and composition of bacterial consortia co-existing with toxin-producing dinoflagellate species18,19,20,21,22,23,24. Among them, some works focused on the influence of bacterial associates on growth and physiology of toxic dinoflagellates, while most majority of these work were anchored on well-known PSTs producers, Alexandrium sp.25,26,27,28 and Gymnodinium catenatum29,30,31,32, and CTXs producer Gambierdiscus sp.24,33. The associated bacterial microbiome has been suspected of either producing or modifying some dinoflagellate-derived toxins26,27,28,29,30,33,34,35,36. The bacterial source of PST production was first put forward by Silva et al.37, who described the presence of intracellular bacteria in PST-producing dinoflagellates. Several hypotheses have been proposed to account for bacterial influence PST production in laboratory cultures of dinoflagellate species: autonomous production of PST-like toxins35,38, modulating the toxicity of their host26,27,30, biotransformation of the PST derivatives39, or possible a combination of these factors. In addition, the CTXs production of Gambierdiscus spp. was reported to be regulated by quorum-sensing bacteria40,41, and particular bacterial groups were responsive to toxicity changes of G. balechii24. Although previous works have highlighted some common and/or important bacterial taxa might act as stimulatory or inhibitory factors to affect toxin production of dinoflagellates, the vast majority of the foregoing insights were derived from species producing well-identified toxin. Limited information is currently available about the interaction between “other” toxic dinoflagellates (i.e., species have been confirmed to be toxic but their toxins have not been fully identified) and their co-occurring bacterial consortia. Moreover, scarce work has addressed on the putative impact of dinoflagellate-derived toxins on the associated bacterial microbiome. All of these together call for more comprehensive studies on the interactions between toxic dinoflagellates and associated bacteria.

In this study, we examined the bacterial flora associated with 22 clonal cultures of dinoflagellates raised in the laboratory with different periods of time, including 11 well-confirmed toxic species (“TOX” group) and 11 species that have not been reported to be toxic at least up to date (“NONTOX” group), via high-throughput sequencing of 16S rRNA gene amplicons. Our aims were to characterize the species diversity and community composition of bacterial consortia living with the laboratory-cultured toxic dinoflagellates and primarily explore the putative effects of dinoflagellate-derived toxins on the whole bacterial microbiomes. Our findings enriched current knowledge about the relationship between toxic dinoflagellates and associated bacterial assemblage in the phycosphere.

The prokaryotic 16S rRNA gene metabarcoding sequencing generated 1,844,028 raw reads, corresponding to 0.90 Gb of raw data, with an average of 83,819 sequences per sample. A total of 1,643,013 clean reads were obtained, with effective sequences per sample varied from 64,027 to 83,788 (Supplementary Data 1). Good’s coverage values (an indicator of sample completeness) of all the 22 samples were 1.00 (Supplementary Data 2) and rarefaction curves tended to reach saturation with increasing sequencing reads (Supplementary Fig. 1), together indicating that sufficient sequences were harvested to uncover the vast majority of prokaryotic taxa in all the samples. Dereplication using DADA2 plugin within the QIIME2 tool yielded 1075 amplicon sequencing variants (ASVs, sequences clustered at a 100% sequence identity). All the 1075 effective ASVs were further annotated against the SILVA database, while ASVs annotated as “unclassified” kingdom were filtered out. The remaining 1054 ASVs belonging to bacteria kingdom were used for subsequent analyses.

Based on the assignments in SILVA database, the entire prokaryotic assemblage covered 16 phyla, 30 classes, 72 orders, 134 families, 270 genera, and 362 species. The number of recovered bacteria at the feature level per sample varied from 38 to 63 (mean = 50) (Supplementary Data 3). Generally, Proteobacteria was the absolutely predominant phylum, account for 92.88% of all the ASVs, followed by Bacteroidetes (5.01%) and Cyanobacteria (0.99%) (Fig. 1a). At class level, γ-proteobacteria (58.31%), α-proteobacteria (33.96%), and Bacteroidia (3.04%) exhibited relatively high abundances, ranking among the top 3 predominant groups (Supplementary Fig. 2). At genus level, Methylophaga (23.26%), Ponticoccus (17.65%), Alteromonas (13.80%), Marinobacter (13.75%), Thalassospira (8.55%), Alcanivorax (6.53%), Loktanella (2.23%), Balneola (1.97%), Labrenzia (1.44%), and UC (unclassified) Cryomorphaceae (1.02%) were the most dominant genera (relative abundance >1%), which together contributed up to 90.19% bacterial taxa for all samples analyzed (Fig. 1b).

a 15 phylum; b top 30 genera. The X-axis shows sample IDs. The abbreviations of sample IDs are the same as in Table 1. The Y-axis shows the relative abundance (%) in total effective ASVs. UC unclassified.

Comparing TOX and NONTOX groups, no significant differences were found in alpha diversity indices (including Shannon diversity, Simpson evenness, and Observed species; ANOVA, p > 0.05; Supplementary Fig. 3a). Further PCoA plot was conducted to illustrate similarity and dissimilarity between groups in ASVs complexity. Although bacterial taxa in NONTOX group exhibited indiscernible affiliation with one another, all the 11 samples in TOX group clustered together and were distinctly separated from NONTOX samples (Fig. 2). At phylum level, while the two groups shared 10 phyla in common, TOX and NONTOX groups contained 1 and 2 unique phyla, respectively (Supplementary Fig. 3b). Among the 16 bacterial phyla identified from the two groups, significantly higher relative abundance of Proteobacteria was found in TOX group than that in NONTOX group (Supplementary Fig. 4a). At class level, the two groups shared 23 common classes, while TOX and NONTOX groups had 3 and 4 unique classes, respectively (Supplementary Fig. 3b). Prokaryotic classes of γ-proteobacteria and Clostridia exhibited higher abundance in TOX group, whereas α-proteobacteria was significantly higher in NONTOX group (Supplementary Fig. 4b). At genus level, 109 genera were shared by both groups, whereas 53 and 101 unique genera were present in TOX and NONTOX groups, respectively (Supplementary Fig. 3b). Compared with NONTOX group, TOX group were notably enriched with genera including Methylophaga, Alcanivorax, UC Cryomorphaceae, Ralstonia, Stenotrophomonas, but significantly lowered relative abundances of Ponticoccus, Thalassospira, Pseudomonas, Labrenzia, Loktanella, Sphingomonas, UC γ-proteobacteria were observed in this group (Fig. 3 and Supplementary Data 4).

Yellow circle: TOX group that includes 11 well-confirmed toxic dinoflagellate species; Blue triangle: NONTOX group that consists of 11 dinoflagellate species that have not been reported to be toxic as yet. The 11 species in TOX group clustered together based on the similarity of 95%. The analysis was performed using QIIME (version 2) plugin.

TOX: group including 11 well-confirmed toxic dinoflagellate species; NONTOX: group including 11 dinoflagellate species that have not been reported to be toxic up to date. The Y-axis shows the relative abundance of the 12 bacterial genera in the two groups. The analysis were performed at the genus level for 16S rRNA gene amplicons between the two groups via QIIME (version 2) plugin. Data were analyzed by the Wilcoxon rank-sum test (Mann–Whitney U-test). The bar plot was constructed based on the nonparametric Wilcoxon test (p < 0.05, q < 0.1) at the genus level. The box represents the median and interquartile range of the data set. The middle line of the box indicates the median. The top and bottom lines of the box note the upper and lower quartiles, respectively. The whiskers start at the edges of the box (the upper and lower quartiles) and extend to maximum and minimum values in the data. Individual dot denotes outlier.

To assess the metabolic inferences of whole bacterial communities in TOX and NONTOX dinoflagellate groups, the functional repertoire was predicted using the PICRUSt (Phylogenetic Investigation of Communities by Reconstruction of Unobserved States) algorithm. Generally, several differences in the metabolic potential of bacterial communities were predicted between the two groups, which covered metabolic processes of carbohydrates, amino acids, energy, nucleosides, nucleotides, and other compounds, such as vitamins, secondary metabolites. Three categories namely, cellular processes, genetic information processing, and unclassified, showed significantly higher abundances in TOX than in NONTOX group, whereas organismal systems, metabolism, and environmental information processing were significantly dominant in NONTOX group (KEGG level 1; Fig. 4a). For the secondary functional modules (KEGG level 2), 14 categories of functions (enzyme families, cell motility, xenobiotics biodegradation and metabolism, metabolism of cofactors and vitamins, glycan biosynthesis and metabolism, signal transduction, lipid metabolism, translation, genetic information processing, folding, sorting and degradation, replication and repair, environmental adaptation, cellular processes and signaling, and poorly characterized) were greatly enriched in TOX group. While NONTOX group had significantly higher abundances in metabolism of biosynthesis of other secondary metabolites, energy metabolism, transcription, transport and catabolism, signaling molecules and interaction, carbohydrate metabolism, amino acid metabolism, metabolism of other amino acids, nucleotide metabolism, membrane transport, and metabolism of terpenoids and polyketides (Fig. 4b). Differential function prediction at KO (KEGG Orthology) assignments also showed that entries annotated such as, pimeloyl-[acyl-carrier protein] methyl ester esterase and members pertaining to oxidative stress resistance, catalase-peroxidase, monothiol glutaredoxin, hydroxyacylglutathione hydrolase, glutathione synthase, glutamate-cysteine ligase, cytochrome bd ubiquinol oxidase subunit I, cytochrome bd ubiquinol oxidase subunit II and molecular chaperone Hsp33 etc., were predominantly higher in TOX group than those in NONTOX group (Supplementary Fig. 5).

a KEGG level 1; b KEGG level 2. TOX: toxic group, including 11 well-confirmed toxic dinoflagellate species; NONTOX: group including 11 dinoflagellate species that have not been reported to be toxic as yet. Gene functions were predicted from 16S rRNA gene-based microbial compositions at the ASV level using the PICRUSt algorithm to make inferences from KEGG annotated databases via QIIME (version 2) plugin. Relative signal intensity was normalized by the number of the genes for each indicated metabolic pathway. 95% confidence interval is where 95% of the samples fall within this range. The right longitudinal axis are the p values calculated by the t test method. Symbols *, **, and *** indicate the difference at significant levels with p < 0.05, p < 0.01, and p < 0.001, respectively. Mean proportions indicate the mean proportional of the functional term in the two group. Difference in mean proportions is shown as the distance from the dots to the dashed line, representing different values between the relative abundance of a certain functional term in a group and the mean proportions of the certain functional term. The dots of the two groups are on each side of the dashed line, in which group the relative abundance is higher, the dots will be shown as the color of the group.

In this study, all the 22 dinoflagellate cultures were maintained under routine laboratory conditions for months to more than 18 years, in which well-adapted microbial communities have been established and stabilized during long-term maintenance6,42. Compared with the generally less than 30 bacterial isolates (on species level) coupling with laboratory-cultured microalga–bacteria biofilms (ref. 43 and the references therein), our results showed relatively higher diversity of bacterial consortia harbored in these 22 laboratory-raised dinoflagellate cultures. No significant difference was detected in terms of alpha diversity indices between TOX and NONTOX groups (Supplementary Fig. 3a), indicating their bacterial associates did not show significant distinction in ASVs diversity. However, beta diversity analysis found that all the 11 species in TOX group clustered together and were distinctly separated from NONTOX group (Fig. 2), implying that bacterial community composition of TOX group exhibited structural conservation across the 11 toxic species and were significantly distinguished from those of NONTOX group. Our results suggested that the bacterial community composition was largely influenced by toxic dinoflagellate hosts. It is also plausible that the relatively stable and constant cultivation conditions (e.g., temperature, light, nutrients, antibiotics etc.) in our laboratory played important roles in establishing and maintenance of bacterial communities in the phycosphere amongst these dinoflagellate cultures. It would be very interesting to investigate the shift in microbial community associated with laboratory raised phycospheres under different cultivation conditions in further studies.

Comparative analysis revealed that three genera Methylophaga, Alcanivorax, and UC Cryomorphaceae represented the most significantly enriched genus associated with TOX group (Fig. 3). All of the three members utilize non-carbohydrate sources of carbon for growth (see below). The genus Methylophaga belonging to the γ-subclass of Proteobacteria is a unique methylotrophic group, which consumes one-carbon (C1) compounds of a more reduced form than that of CO2 as a sole source of energy and carbon44. Members of this genus are aerobic, halophilic, non-methane-utilizing methylotrophs. They can grow on the C1 compounds such as methanol, methylamine, dimethylamine, trimethylamine, and dimethylsulfide (DMS) as sole carbon source45,46 and use the ribulose monophosphate (RuMP) pathway of C1 compound assimilation47. The γ-proteobacteria Alcanivorax is obligate hydrocarbonoclastic taxa, a unique group with exclusively hydrocarbons nutrition utilization as a sole carbon and energy source48. Although some case studies showed that more compounds (i.e., DMS45; pyrene49; benzene and toluene50,51; aliphatic polyesters52) could be metabolized by some Alcanivorax species, carbohydrates and amino acids are not used by currently isolated Alcanivorax strains53,54,55. Cryomorphaceae represents a family within the phylum Bacteroidetes and comprises many genera in diverse marine and terrestrial habitats (as summarized in ref. 56). Notably, this family is generally unreactive in most standard phenotypic tests, indicating its narrow nutritional range57. Phenotypic data suggested that cultivated Cryomorphaceae taxa relied on organic acids and amino acids, and required vitamins for growth56,57. They cannot utilize carbohydrates, but possess usual capacity to grow on proteinaceous substances and degrade compounds using esterases56,57,58,59,60,61. Furthermore, the differential function prediction at KO assignments showed that the entry annotated as pimeloyl-[acyl-carrier protein] methyl ester esterase (EC: 3.1.1.85) was predominantly higher in TOX group (Supplementary Fig. 5). Our findings of significantly higher relative abundance of non-carbohydrate utilizers in TOX group suggested that toxic dinoflagellate hosts influenced the nutritional requirements of the bacterial community to some extent.

Compared with NONTOX group, the other two genera exhibited markedly higher abundance in TOX group were Ralstonia and Stenotrophomonas (Fig. 3), both of which are xenobiotic compounds degrading taxa (see below). TOX group also exhibited enhanced function in the KEGG level 2 module of “xenobiotics biodegradation and metabolism” (Fig. 4b). The genus Ralstonia of β-proteobacteria has global distribution and unusually wide host range (ref. 62 and the references therein). Members in this genus could use a variety of compounds as energy and carbon sources and thus survive and prosper in oligotrophic environment62,63. They have also been demonstrated to utilize a diverse range of xenobiotic organics, making them well adaptation to toxic environments64,65,66,67,68,69,70. The γ-proteobacteria Stenotrophomonas have been isolated from a variety of natural sources, including rhizospheres, soil, clinical material, aquatic habitats, and marine environments71,72,73. They could resist against a broad range of antibiotics and effectively degrade many xenobiotic compounds72,73,74,75,76,77. Several genes encoding key enzymes dedicating to xenobiotic degradation have also been uncovered in Stenotrophomonas sp. genomes78,79. It is inferred that the two xenobiotics-degrading taxa (Ralstonia and Stenotrophomonas) might be able to breakdown or metabolism some of dinoflagellate-derived toxic substances and/or derivatives, and hence to gain more ecological adaptation and survival advantages in the phycospheres of toxic dinoflagellates.

A total of 7 bacterial genera, Ponticoccus, Thalassospira, Pseudomonas, Labrenzia, Loktanella, Sphingomonas, and UC γ-proteobacteria, exhibited significantly lower abundances in TOX group (Fig. 3). Except for UC γ-proteobacteria, all the other six bacteria have been reported to have algicidal potency against dinoflagellates and/or other microalgae, implying the inhibitory effects of toxic dinoflagellates on algicidal bacteria. The metabolites of Ponticoccus sp. isolated from blooming dinoflagellate Prorocentrum donghaiense showed growth inhibition effects not only on its host (P. donghaiense), but also on another dinoflagellate Alexandrium tamarense and Prymnesiophyta Phaeocystis globose80. Benzoic acid produced by Thalassospira sp. could induce cell lysis of the dinoflagellate Karenia mikimotoi, possibly by passing through the cell membrane and acidifying the algal cytoplasm81. Many species in the genus Pseudomonas are known to have a wide algicidal range and lyse algae mainly by releasing effective chemical substance, such as 2,3-indolinedione (isatin), 1-hydroxyphenazine, oxychlororaphine, antibiotics, biosurfactants, and other unrecognized substances (as reviewed in ref. 82). They were reported to secrete hydrolytic components capable of destroying the cell wall of dinoflagellates83. The Labrenzia sp. showed direct algicidal activity on diatom Phaeodactylum tricornutum, and its lytic activity was temperature- and pH-dependent, but light-independent84,85. The Loktanella spp. isolated from culture of dinoflagellate Gambierdiscus belizeanus showed strong algicidal activity against its host (G. belizeanus) and another toxic dinoflagellate Coolia malayensis86. Members of Sphingomonas were known as algae-lysing bacteria yielding anti-cyanobacterial compounds to inhibit growth of cyanobacteria87,88. Bacterial algicidal activity is highly density-dependent89. It was proposed that microalgae and their associated algicidal bacteria could be mutually restrictive, which was probably an important factor for maintaining the dynamic balance of phycosphere80,89. Our finding appeared to be consistent with this reasoning.

In addition, we found predominant enrichment of KOs (KEGG Orthology) functions pertaining to antioxidant response in TOX group. All these predicted KOs, including catalase-peroxidase, monothiol glutaredoxin, hydroxyacylglutathione hydrolase, glutathione synthase, glutamate-cysteine ligase, cytochrome bd ubiquinol oxidase subunit I, cytochrome bd ubiquinol oxidase subunit II and molecular chaperone Hsp33 are components in scavenging systems against reactive oxygen species (ROS). The catalase-peroxidase is a member of plant peroxidase superfamily Class I with bifunctional activities of catalase and peroxidase, both of which are well-known oxidoreductases involved in defenses against ROS90. Exposure to severe ROS causes activation of chaperone Hsp33 which are highly efficient chaperone holdases of preventing proteins irreversible aggregation under oxidative stress91. Cytochrome bd is a ubiquinol oxygen oxidoreductase of prokaryotic respiratory chains which is unique in its coupling the reduction of molecular oxygen (as reviewed in ref. 92). The glutathione synthase and glutamate-cysteine ligase are two key determinants involved in synthesis of glutathione (GSH), which is the essential metabolite required for resistance to oxidative stress93. The hydroxyacylglutathione hydrolase catalyzes the final step in the conversion of methylglyoxal to lactic acid and reduced GSH94. Glutaredoxin protect against oxidative stress by catalyzing reduction of protein mixed disulfides with GSH95. These results suggested that dinoflagellate-derived toxic substances could induce antioxidant enzymes activities and trigger oxidative stress response of the associated bacterial microbiomes. Further work combing physiological assessment with integrative exploration of transcriptome and metabolome are required to gain more insights into the interaction between toxic dinoflagellates and their bacterial associates.

In nature, dinoflagellate HABs are hotspots of primary production and produce large amounts of organic matter, causing significant shifts in microbial community structure and interactions96,97. Previous field case studies established that microbial communities and intracellular metabolite pools found in natural phycosphere could be very different based on both bacterial characteristics and bloom dynamics, suggesting a complex process for blooms in reshaping microbial associates98,99. In this study, the differential taxonomic and functional composition of bacterial community associated with laboratory-raised toxic and non-toxic dinoflagellates implied that the two group hosts could impact the associated prokaryotic microbiome in different ways under certain stable cultivation conditions. We speculate that, in natural environment, HABs formed by toxic and non-toxic dinoflagellates might also attract, harbor and prompt different and/or unique prokaryotic microbiomes, which in turn, are highly likely to influence the dynamics of the respective HABs.

A total of 22 representative marine dinoflagellate species were investigated in the present study (see Table 1 for details). Among them, 11 species are well confirmed to be toxic and were categorized as “TOX” group, including three PST-producer (Alexandrium pacificum, A. andersonii, Gymnodinium catenatum), a karlotoxin-producer (Karlodinium veneficum), a brevetoxins-producer (Karenia brevis), an probable AZT-producer (Azadinium poporum), and other five species that have been confirmed to be toxic but their toxins have not been fully identified (Karlodinium austral, Karenia mikimotoi, Margalefidinium polykrikoides, A. leei, and Pheopolykrikos hartmannii). The other 11 species (Kryptoperidinium foliaceum, Biecheleriopsis adriatica, Biecheleria brevisulcata, Heterocapsa triquetra, Symbiodinium sp., Pseliodinium pirum, Scrippsiella plana, Pelagodinium beii, Gymnodinium microreticulatum, Prorocentrum donghaiense, and Wangodinium sinense) that are not yet reported to be toxic were labeled as “NONTOX” group (Table 1). In our laboratory, cultures were routinely maintained in f/2-Si medium100 prepared with autoclaved (121 °C, 30 min) and pre-filtered (0.22 μm membrane filter; Millipore, Billerica, MA, USA) natural seawater (salinity 32–33). All the cultures were kept at 20 ± 1 °C in an incubator with a 12:12 h light:dark cycle, illuminated by a bank of cool white fluorescent lights providing a photon flux of ~100 μmol photons m−2 s−1. All the 22 strains of dinoflagellate cultures used in this study were regularly maintained in the laboratory at Institute of Oceanology, Chinese Academy of Sciences.

Cultures at exponential growth stage were inoculated into six-well disposable sterilized culture plates (Corning, Glendale, AZ, USA) containing 10 mL of the seawater-based f/2-Si medium and then incubated for 15 days under the same temperature and illumination as the routine culture conditions. Samples were then collected when all the cultures were at their stationary growth stage as pre-determined. All the cells in each sample (approximately 104–105 cells) were harvested by centrifugation, pelleted in a 1.5 mL centrifuge tube and immediately used for the genomic DNA isolation. DNA extraction and purification was performed using a Plant DNA Extraction Kit (Tiangen, Beijing, China) and eluted with 50 μL TE buffer. Nuclear-free water processed through DNA extraction served as the negative control. The DNA quality and purity were estimated using a NanoDropTM 1000 spectrophotometer (Thermo Fisher Scientific, Waltham, MA, USA), and then stored at −80 °C for further use.

From each DNA extract, the V3-V4 hypervariable region of the bacterial 16S rRNA gene was amplified with the primers of 341F (5’-CCTACGGGNGGCWGCAG-3’) and 805R (5’-GACTACHVGGGTATCTAATCC-3’)101. Nuclease-free water was used as the sample blank. PCR was carried out as the following condition: 98 °C for 30 s, followed by 35 cycles of denature at 98 °C for 10 s, annealing at 54 °C for 30 s, and extension at 72 °C for 45 s, with a final extension at 72 °C for 10 min. All PCR reactions were conducted with Phusion Hot Start Flex Master Mix (New England Biolabs, Beverly, MA, USA; catalog no. M0536L) as described previously102 and amplicons were checked on a 2% agarose gel electrophoresis. The retrieved DNA was purified with Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA), uniquely barcoded for each sample, and pooled for sequencing on the NovaSeq PE250 platform (LC-Bio Technology Company, Hangzhou, Zhejiang, China).

The raw sequencing data were deposited in the NCBI Short Read Archive (SRA) database and are accessible with the project number PRJNA771505. Quality trimming and length filtering were performed on the raw reads in Fqtrim (version 0.9.7, available online: https://ccb.jhu.edu/software/fqtrim/index.shtml). Generally, raw paired-end reads with 10 bp of minimal overlapping, the length less than 200 bp, average quality score less than 20, and 20% of maximum mismatch rate were trimmed. The chimeric reads were further filtered using Vsearch program (version 2.3.4)103. Paired-end reads was sorted into samples based on their unique barcode, then trimmed of barcodes and primer sequences, and merged using FLASH tool (version 1.2.6)104. The amplicon sequence variants (ASVs, sequences clustered at 100% sequence similarity) were obtained with DADA2 package (version 3.6.1)105. All the ASVs were annotated by conducting BLAST search against the SILVA database106 and denominated at the domain, phylum, class, order, family, genus, and species levels. Relative abundance of each ASV was estimated based on its read counts normalized to the total number of good quality reads. Rarefaction curves were generated for each sample using custom Perl scripts. Alpha diversity indices (Shannon diversity, Simpson evenness, Chao1 richness, Observed species richness, and Goods coverage) and beta diversity of PCoA (Principal coordinated analysis) based on the weighted-uniFrac distance were calculated via QIIME (version 2) plugin107. Both alpha and beta diversity analyses were performed at the ASVs level. Venn diagrams showing the shared and unique features were plotted with BioVenn (http://www.biovenn.nl/index.php). The Wilcoxon rank-sum test was used to compare bacterial abundance and diversity. The significance of variance between or among samples was tested with one-way ANOVA or t test (for comparison between two groups) using the software SPSS (version 22.0) (SAS Institute Inc., Cary, NC, USA). The significance level in all statistical analyses was set at 0.05 unless otherwise stated.

Given a set of 16S sequences from a sample, it is possible to identify the most closely related organisms with associated sequenced genomes, and propose that their associated functions are also present in the sampled microbiome108,109,110. Phylogenetic Investigation of Communities by Reconstruction of Unobserved States (PICRUSt) is an algorithm and software package that performs these predictions for 16S sequences, using a reference phylogeny to weight the relative functional contributions of closely related sequence genomes108,110. In this study, the functional prediction of bacterial microbiome was explored using the PICRUSt algorithm (version 2.3.0-b) to make inferences from KEGG database via QIIME (version 2) plugin107. The inputs to PICRUSt follow standard formats which produced from the16S rRNA gene-based microbial species compositions analysis done in QIIME scripts. The differentially abundant gene families and pathways were assessed using the software STAMP (version 2.1.3)111 subjected to t test, the threshold of p < 0.05 was used to display functions with statistically significant difference (at confidence interval 95%).

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Sequences of all 22 samples are available in the Sequence Read Archive (SRA), National Center for Biotechnology Information under the accession number PRJNA771505.

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This research was financially supported by the National Science Foundation of China (Grant No. 42176207), the Key Deployment Project of Centre for Ocean Mega-Research of Science, Chinese Academy of Sciences (Grant No. COMS2019Q09), and the Natural Science Foundation of Shenzhen, China (Grant No. JCYJ20210324094013037). We sincerely thank Dr. Fengting Li for her help with part of the experiments.

These authors contributed equally: Yunyan Deng, Kui Wang.

CAS Key Laboratory of Marine Ecology and Environmental Sciences, Institute of Oceanology, Chinese Academy of Sciences, 266071, Qingdao, China

Yunyan Deng, Zhangxi Hu & Ying Zhong Tang

Laboratory of Marine Ecology and Environmental Science, Qingdao National Laboratory for Marine Science and Technology, 266237, Qingdao, China

Yunyan Deng & Ying Zhong Tang

Center for Ocean Mega-Science, Chinese Academy of Sciences, 266071, Qingdao, China

Yunyan Deng & Ying Zhong Tang

Institute for Advanced Study, Shenzhen University, 518060, Shenzhen, China

Kui Wang

College of Fisheries, Guangdong Ocean University, 524088, Zhanjiang, China

Zhangxi Hu

Faculty of Synthetic Biology, CAS Key Laboratory of Quantitative Engineering Biology, Shenzhen Institute of Synthetic Biology, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences, 518055, Shenzhen, China

Qiang Hu

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Y.D. and K.W. performed the experiments, provided feedback on the experiments and results, and wrote the article with contributions of all authors; Z.H. maintained the algal cultures and prepared the samples; Q.H. designed the research; Y.Z.T. designed the research and edited the manuscript. All authors read and approved the final manuscript.

Correspondence to Ying Zhong Tang.

The authors declare no competing interests.

Communications Earth & Environment thanks the anonymous reviewers for their contribution to the peer review of this work. Primary handling editors: Erin Bertrand, Clare Davis. A peer review file is available.

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Deng, Y., Wang, K., Hu, Z. et al. Toxic and non-toxic dinoflagellates host distinct bacterial communities in their phycospheres. Commun Earth Environ 4, 263 (2023). https://doi.org/10.1038/s43247-023-00925-z

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Received: 10 February 2023

Accepted: 07 July 2023

Published: 18 July 2023

DOI: https://doi.org/10.1038/s43247-023-00925-z

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