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Identification of bacteria in potential mutualism with toxic Alexandrium catenella in Chilean Patagonian fjords by in vitro and field monitoring

  • Kyoko Yarimizu,

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

    Affiliation Microbial Genomics and Ecology, The IDEC Institute, Hiroshima University, Higashi-Hiroshima City, Hiroshima, Japan

  • Jorge I. Mardones,

    Roles Formal analysis, Methodology, Resources, Writing – original draft

    Affiliations Centro de Estudios de Algas Nocivas (CREAN), Instituto de Fomento Pesquero (IFOP), Puerto Montt, Chile, Centro de Investigación en Recursos Naturales y Sustentabilidad (CIRENYS), Universidad Bernardo O’Higgins, Santiago, Chile

  • Javier Paredes-Mella,

    Roles Formal analysis, Investigation, Writing – original draft

    Affiliation Centro de Estudios de Algas Nocivas (CREAN), Instituto de Fomento Pesquero (IFOP), Puerto Montt, Chile

  • Ishara Uhanie Perera,

    Roles Data curation, Formal analysis, Methodology, Software, Visualization

    Affiliation Microbial Genomics and Ecology, The IDEC Institute, Hiroshima University, Higashi-Hiroshima City, Hiroshima, Japan

  • So Fujiyoshi,

    Roles Methodology, Software, Visualization, Writing – review & editing

    Affiliation Microbial Genomics and Ecology, The IDEC Institute, Hiroshima University, Higashi-Hiroshima City, Hiroshima, Japan

  • Gonzalo Fuenzalida,

    Roles Investigation, Resources

    Affiliation Centro de Estudios de Algas Nocivas (CREAN), Instituto de Fomento Pesquero (IFOP), Puerto Montt, Chile

  • Jacquelinne J. Acuña,

    Roles Project administration, Writing – review & editing

    Affiliation Laboratorio Ecología Microbiana Aplicada (EMALAB), Scientific and Biotechnological Bioresource Nucleus (BIOREN-UFRO), Universidad de La Frontera, Temuco, Chile

  • Tay Ruiz-Gil,

    Roles Investigation

    Affiliation Laboratorio Ecología Microbiana Aplicada (EMALAB), Scientific and Biotechnological Bioresource Nucleus (BIOREN-UFRO), Universidad de La Frontera, Temuco, Chile

  • Marco Campos,

    Roles Investigation

    Affiliations Laboratorio Ecología Microbiana Aplicada (EMALAB), Scientific and Biotechnological Bioresource Nucleus (BIOREN-UFRO), Universidad de La Frontera, Temuco, Chile, Laboratorio de Investigación en Salud de Precisión, Departamento de Procesos Diagnósticos y Evaluación, Facultad de Ciencias de la Salud, Universidad Católica de Temuco, Temuco, Chile

  • Joaquin-Ignacio Rilling,

    Roles Investigation

    Affiliation Laboratorio Ecología Microbiana Aplicada (EMALAB), Scientific and Biotechnological Bioresource Nucleus (BIOREN-UFRO), Universidad de La Frontera, Temuco, Chile

  • Pedro Calabrano Miranda,

    Roles Investigation

    Affiliation Centro de Estudios de Algas Nocivas (CREAN), Instituto de Fomento Pesquero (IFOP), Puerto Montt, Chile

  • Jonnathan Vilugrón,

    Roles Investigation

    Affiliation Centro de Estudios de Algas Nocivas (CREAN), Instituto de Fomento Pesquero (IFOP), Puerto Montt, Chile

  • Oscar Espinoza-González,

    Roles Project administration, Supervision, Writing – original draft, Writing – review & editing

    Affiliation Centro de Estudios de Algas Nocivas (CREAN), Instituto de Fomento Pesquero (IFOP), Puerto Montt, Chile

  • Leonardo Guzmán,

    Roles Project administration, Supervision, Writing – review & editing

    Affiliation Centro de Estudios de Algas Nocivas (CREAN), Instituto de Fomento Pesquero (IFOP), Puerto Montt, Chile

  • Satoshi Nagai,

    Roles Methodology, Software, Writing – review & editing

    Affiliation Japan Coastal and Inland Fisheries Ecosystems Division, Fisheries Technology Institute, Japan Fisheries Research and Education Agency, Yokohama, Kanagawa, Japan

  • Milko A. Jorquera,

    Roles Conceptualization, Project administration, Writing – review & editing

    Affiliation Laboratorio Ecología Microbiana Aplicada (EMALAB), Scientific and Biotechnological Bioresource Nucleus (BIOREN-UFRO), Universidad de La Frontera, Temuco, Chile

  •  [ ... ],
  • Fumito Maruyama

    Roles Funding acquisition, Project administration, Supervision

    fumito@hiroshima-u.ac.jp

    Affiliation Microbial Genomics and Ecology, The IDEC Institute, Hiroshima University, Higashi-Hiroshima City, Hiroshima, Japan

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Abstract

The dinoflagellate Alexandrium catenella is a well-known paralytic shellfish toxin producer that forms harmful algal blooms, repeatedly causing damage to Chilean coastal waters. The causes and behavior of algal blooms are complex and vary across different regions. As bacterial interactions with algal species are increasingly recognized as a key factor driving algal blooms, the present study identifies several bacterial candidates potentially associated with Chilean Alexandrium catenella. This research narrowed down the selection of bacteria from the Chilean A. catenella culture using antibiotic treatment and 16S rRNA metabarcoding analysis. Subsequently, seawater from two Chilean coastal stations, Isla Julia and Isla San Pedro, was monitored for two years to detect Alexandrium species and the selected bacteria, utilizing 16S and 18S rRNA gene metabarcoding analyses. The results suggested a potential association between Alexandrium species and Spongiibacteraceae at both stations. The proposed candidate bacteria within the Spongiibacteraceae family, potentially engaging in mutualistic relationships with Alexandrium species, included the genus of BD1-7 clade, Spongiibbacter, and Zhongshania.

1. Introduction

Alexandrium catenella is a toxin-producing phytoplankton species that has increasingly damaged coastal marine environments worldwide [13]. Since the first detection of this species in 1972 in the Magallanes region of Chile, its range has expanded slowly to the north, reaching the Aysén region in 1992, the southern Chiloé Island in the Los Lagos region in 2002, the Pacific coast of Chiloé Island in 2009, the coast of the Los Rios region in 2016, and recently the northern Bio-Bio region [411]. Alexandrium catenella produces paralytic shellfish toxins. The toxic cells first accumulate in bivalve tissues, leading to illness and death in higher trophic levels of organisms, including humans, upon digestion. These events bring severe implications to the local marine ecosystems and subsequently lead to coastal closure and restrictions on shellfish and salmon harvesting, which are the pillars of the Chilean economy [7, 8, 12, 13]. For example, the A. catenella bloom in the Aysén region in 2002 resulted in the loss of 1,800 metric tons of farmed salmon [14]. Similarly, a bloom in the Los Lagos region in 2006 impacted both the shellfish and salmon industries, causing losses equivalent to $9.2 million USD [7, 14, 15]. Two massive blooms of A. catenella and a dictyochophyceae of Pseudochattonella verruculosa in 2016 during the ‘El Niño Godzilla event’ affected 200 shellfish farms and 600 km of benthic artisanal fisheries and costed US$800 million in losses for the salmon industry [16, 17]. These impacts on the Chilean coastal waters by A. catenella blooms have increased over the past decade [10, 18].

The causes of harmful algal bloom (HABs) have been investigated mainly from the oceanographic factors such as water temperature, salinity, dissolved oxygen, phosphate, nitrogen, and silicate, namely physicochemical association as HAB factors. However, the potential role of bacteria in HAB formation, growth, and decline is also becoming a trending topic. Since Bell and Mitchell [19] reported in 1972 that bacterial communities inhabited around microalgal communities [20], studies have increasingly reported that algal–bacterial associations are particular and involve a complex exchange of nutrients and signaling molecules in their synergetic or antagonistic relationships [2124]. In Chile, HAB research from an algal-bacterial mutualism perspective has just begun, and very few prior studies are available for bacterial interactions with Chilean A. catenella. For instance, in 2002, Córdova et al. [25] reported that the tentatively identified bacteria from a Chilean A. catenella exhibited strain-specific commensalism with several organisms, including Aeromonas salmonicida, Flavobacterium breve, Pseudomonas diminuta, Pasteurella haemolytica, Proteus vulgaris, Pseudomonas putida, Pseudomonas versicularis, and Moraxella sp. Uribe and Espejo [26] demonstrated that saprophytic bacteria enhanced the Chilean A. catenella’s toxicity five times more than the axenic culture. Amaro et al. [27] reported that three bacteria in a Chilean A. catenella culture released algal-lytic compounds, aminopeptidase, lipase, glucosaminidase, and alkaline phosphatase. Nevertheless, precise mechanisms of how specific bacteria interact with A. catenella in the HAB dynamics are still under investigation.

The aim of this study was to nominate potential bacteria associated with Chilean A. catenella from culture-based experiments and field monitoring and to provide information to help A. catenella bloom prediction and countermeasures from the bacteria point of view. We treated a Chilen strain of A. catenella culture with antibiotics to narrow the selection of bacteria possibly related to the strain and identified the taxonomy with 16S rRNA metabarcoding analysis. Specifically, the bacteria attached to A. catenella cells or cell walls likely migrate into the culture solution and are essential for algal growth [2832]. Consequently, these bacteria were identified through the analysis. We then monitored the selected bacteria at two coastal stations, Isla San Pedro and Isla Julia, in the Chilean Patagonian fjords for two years.

2. Materials and methods

The materials and reagents used in this study are listed in Table 1.

2.1 Growth media

All glass containers were soaked in 3 N hydrochloric acid for at least 2 days, rinsed with Milli-Q water, dried in a laminar-flow air bench, and autoclaved at 121°C for 30 minutes. Alternatively, sterile containers (Nunc cell culture-treated flasks with filter caps) were used. Seawater (SW) from Metri (-41.597; -72.7056, Los Lagos, Chile) was filtered through a 0.22-μm pore-sized membrane, mixed with 0.005% hydrochloric acid, and autoclaved at 121°C for 30 minutes. The average salinity of SW was confirmed to be 33, and the pH of the autoclaved SW ranged between 8.0 and 8.2 at ambient temperature. Sterile L1 nutrient [33] was added to the sterile SW to make a growth media per the manufacturer’s instructions.

2.2 Algal maintenance

Alexandrium catenella strain CREAN AC11 was isolated from a cyst collected from Isla San Pedro in 2014 and used for the experiments herein. Cultures were maintained in SW+L1 in T25 cell culture flasks under 50 μmol photons m−2 s−1 on a 16:8 h light:dark cycle at 15±2°C (standard growth condition). The upper portion of a culture containing healthy cells was diluted to <500 cells mL−1 with fresh SW+L1 media every 3 weeks.

2.3 Bacteria screening from the A. catenella culture

All procedures were done in a laminar-flow hood with sterile devices. Fifty mL of A. catenella culture was filtered through a 0.22-μm membrane as a pre-antibiotic-treated sample (pre-treated sample), and the membrane was stored at −20°C for metabarcoding analysis.

A schematic procedure of antibiotic treatment on A. catenella is shown in Fig 1. Approximately 105 cells L−1 of A. catenella in SW+L1 were treated with 0.1% (v/v) antibiotics (penicillin 5 units mL−1, streptomycin 5 μg mL−1, and neomycin 10 μg mL−1) for 24 hours under the standard growth condition. The culture was transferred into a 15- mL sterile tube and centrifuged at 2,290 G-force for five seconds to collect cell pellets. After removing the supernatant, the pellet was washed three times and re-suspended with SW+L1. The antibiotic-treated culture was tested for free of culturable bacterial by spreading 10-μL of the culture on a marine broth agar plate (5 g L−1 peptone, 1 g L−1 yeast extract, 15 g L−1 agar in 75% SW) followed by incubation at 25°C for 2–3 days. 1 L was immediately.

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Fig 1. Schematic procedure of antibiotic treatment on A. catenella: Approximately 105 cells L−1 of A. catenella in SW+L1 were treated with 0.1% (v/v) antibiotics for 24 hours under the standard growth condition.

The culture was transferred into a 15-mL sterile tube and centrifuged at 2,290 G-force for five seconds to collect cell pellets. After removing the supernatant, the pellet was washed three times and re-suspended with SW+L1: Blue dot = free-living (FL) bacteria; Yellow circle = an A. catenella cell; Yellow circle with blue dots = particle-associated (PA) bacteria in an A. catenella cell.

https://doi.org/10.1371/journal.pone.0301343.g001

The control for the agar plate test was the media without A. catenella cells. The agar plate test was performed daily on the post-antibiotic-treated culture, and it should be noted that bacteria reappeared in the A. catenella culture after three days (Fig 2). The culture was left to grow with the reappeared bacteria for 3 weeks. The upper portion of a culture containing healthy cells was diluted to <500 cells mL−1 with SW+L1 in a new sterile container and treated again with 0.1% (v/v) antibiotics for 24 hours. The cells were washed three times to remove antibiotics, re-suspended with sterile SW+L1 in a new sterile container, and then left to grow for 3 weeks. This process was repeated for five consecutive subcultures. The fifth subculture was grown for 3 weeks, and the total volume of the fifth culture (50 mL) was filtered through a 0.22-μm pore-sized membrane, which was stored at −20°C for metabarcoding analysis (post-treated sample).

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Fig 2. A schematic idea of bacterial growth in the antibiotic-treated A. catenella culture: A post-treated A. catenella culture was placed under the standard growth conditions.

The culture was checked daily by gar plates. The culturable bacteria were observed in the 3-day-old algal solution, while a control solution (sterile media) remained bacteria-free.

https://doi.org/10.1371/journal.pone.0301343.g002

2.4 Metabarcoding analysis

The membranes of the pre-treated and post-treated samples were processed for DNA extraction using the Chelex-buffer method [34]. The extracted DNA was processed for high-throughput amplicon sequencing with the primer set (Table 2) as detailed in the visual protocols [35, 36]. The obtained sequences were analyzed with DADA2 v.1.14.1 [38, 39]. Taxonomic identification was done on the sequences assigned to amplicon sequence variants (ASVs) against SSU Ref tree of SILVA release 132 and SILVA release 138 for 18S rRNA and 16S rRNA gene amplicon sequences, respectively [40]. For 16S rRNA sequences, singletons, mitochondria, and chloroplasts were removed (S1-S3 Tables in S1 File). The data were eliminated from the analysis when the total sequence reads were considerably low (Goods coverage below 99%), which could lead to bias in algal–bacterial correlation analysis.

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Table 2. First PCR primers for metagenomic library preparation: The primers used for 16S rRNA sequencing are 16S-341F and 16S805R.

The primers used for 18S rRNA sequencing are SSU-F1289 and SSU-R1772.

https://doi.org/10.1371/journal.pone.0301343.t002

2.5 Monitoring of algae and bacteria in the Isla San Pedro and Isla Julia

Water was collected at 1.5 km off the coast of Isla Julia (-43.901; -73.704) (hereafter “Isla Julia”) and 1 km off the coast of Isla San Pedro (-43.313; -73.662) (hereafter “Isla San Pedro”) biweekly from March 2019 to March 2021 (Fig 3). Sampling could not be performed between Apr 2020 and Jun 2020 owing to the lockdown mandate for the COVID-19 pandemic. Water was collected at a 10-m depth with a deployed conductivity–temperature–depth (CTD) sampler from a survey ship and transferred into a triple-washed plastic container. Samples were processed per the protocol of Yarimizu et al. [35]: 1 L was immediately filtered through a 1-μm filter membrane followed by a 0.22-μm membrane to separately collect larger bacteria/attached bacteria and smaller and freely present bacteria (hereafter particle-associated (PA) and free-living (FL) bacteria). To analyze the 18S rRNA gene, another 1 L of the sample was filtered through a 0.22-μm membrane. The membranes were stored in a freezer on the ship, and the metabarcoding analysis was performed in a laboratory (Section 2.4). For microscopic phytoplankton identification, 200 mL of the water sample was concentrated ×20 using a filter set. The concentrated samples (10 mL) were fixed with Lugol’s iodine to a final concentration of 1%. The Lugol-fixed samples were stored in a refrigerator until microscopic analysis was performed to identify and quantify algal genus names. Chlorophyll a (chl a) was measured by CTD during the sampling.

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Fig 3. Map of sampling stations: The routine sampling was performed at the two stations, 1.5 km off the coast of Isla Julia (-43.901; -73.704, the lower red dot in the map) and 1 km off the coast of Isla San Pedro (-43.313; -73.662, the upper red dot in the map).

This map was created with Ocean Data View (ODV) [37] retrieved in 2022 from https://odv.awi.de.

https://doi.org/10.1371/journal.pone.0301343.g003

2.6 Statistical analysis

Pearson’s correlation was also used to determine the linear correlation between Alexandrium and bacteria. Furthermore, cross-correlations were used to elucidate any time-lagged relationships between Alexandrium and bacteria species that had a significant correlation by Pearson’s correlation. The statistical analyses were performed using R version 4.1.2 (R Core Team, 2021).

3. Results

3.1 Culture study

The A. catenella culture treated with antibiotics was initially free of culturable bacteria, but some culturable bacteria appeared in the algal solution within three days, while a control solution (sterile media) remained free of culturable bacteria (Fig 2). The bacteria in the pre-treated and post-treated cultures were compared by metabarcoding analysis (Table 3 and S1 Fig in S1 File). Proteobacteria displayed the dominant phyla in both pre- and post-treated cultures. Of the Proteobacteria, Gammaproteobacteria was the most abundant class, followed by Alphaproteobacteria and Deltaproteobacteria in both pre- and post-treated cultures. The relative abundance of Bacteroidetes and Deltaproteobacteria were increased in the post-treated culture. The notably increased (>5%) class of bacteria after treatment were Alteromonadaceae, Cyclobacteriaceae, Nannocystaceae, Spongiibacteraceae, and Thalassospiraceae.

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Table 3. Bacteria composition in A. catenella culture before and after antibiotic treatment: The relative abundance of bacteria in the A. catenella culture was compared before and after antibiotic treatment by 16S rRNA gene metabarcoding assay.

https://doi.org/10.1371/journal.pone.0301343.t003

3.2 Field study

3.2.1 Detection of Alexandrium spp.

Alexandrium spp. were monitored along the coasts of Isla San Pedro and Isla Julia for 2 years. No Alexandrium blooms occurred during this period. However, 18S rRNA gene metabarcoding analysis identified Alexandrium spp. in the Isla Julia water on 22 Oct 2020, comprising 0.9% of the total reads (Fig 4). Similarly, the metabarcoding analysis detected Alexandrium spp. in the Isla San Pedro water on the same day and at two additional time points (04 Jan 2020 and 12 Mar 2020). Yet, these relative abundances were approximately 0.1%, which is tenfold lower than that observed in Isla Julia (Fig 4). Microscopy at both stations could not detect Alexandrium cells, likely due to their very low absolute cell counts (S2 Fig in S1 File).

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Fig 4.

Relative abundance of target bacteria in Isla San Pedro and Isla Julia: The relative abundance of Alteromonadaceae, Cyclobacteriaceae, Nannocystaceae, Spongiibacteraceae, and Thalassospiraceae were monitored on the coast of A) Isla San Pedro and B) Isla Julia from March 2019 to March 2021 using 16S rRNA gene metabarcoding analysis (left Y-axis). Dinoflagellate Alexandrium species were monitored at the same stations using 18S rRNA gene metabarcoding analysis (right Y-axis).

https://doi.org/10.1371/journal.pone.0301343.g004

To understand the algal biomass at the two stations, the total algal cell numbers was counted using microscopy and chl a with a CTD sampler. Both values generally increased during the austral summer, spanning from December to March (Fig 5). Thus, the detection of Alexandrium spp. in October at both stations using 18S rRNA metabarcoding (Fig 4) sindicates that Alexandrium spp. can be present in Isla Julia and Isla San Pedro outside of the prime season when phytoplankton actively proliferate.

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Fig 5. Monitoring of total phytoplankton cell counts and chl a in Isla San Pedro and Isla Julia: The phytoplankton species were identified by microscopy and cell counts were recorded.

The figure shows total cell counts per mL of water sample in A) Isla Julia and B) Isla San Pedro from March 2019 to March 2021. Chl a was measured during each sampling using a CTD sampler.

https://doi.org/10.1371/journal.pone.0301343.g005

3.2.2 Detection of target bacteria.

The classes of bacteria that notably increased in the culture after antibiotic treatment—Alteromonadaceae, Cyclobacteriaceae, Nannocystaceae, Spongiibacteraceae, and Thalassospiraceae—were monitored at Isla San Pedro and Isla Julia using 16S rRNA gene metabarcoding analysis over a span of 2 years. Among these five bacterial classes, Alteromonadaceae and Cyclobacteriaceae were sporadically detected throughout the study period, while Nannocystaceae and Thalassospiraceae were rarely observed (Fig 4). The relative abundance of Spongiibacteraceae increased simultaneously with that of Alexandrium spp. at both stations on 22 Oct 2020 (Fig 4). Pearson’s correlation analysis was conducted to evaluate the relationship between Alexandrium spp. and these bacterial classes, revealing a significant correlation between Spongiibacteraceae (FL and PA) and Alexandrium spp., particularly at Isla Julia (Table 4). Subsequently, cross-correlation analysis was applied to the Isla Julia dataset to ascertain any time lag between Alexandrium spp. and Spongiibacteraceae. No time-lagged relationship between Spongiibacteraceae and Alexandrium spp. was identified, suggesting that they increased and decreased simultaneously (S3 Fig in S1 File).

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Table 4. Correlation between A. catenella and class of bacteria selected by culture screening: The class of bacteria selected by the culture study, Alteromonadaceae, Cyclobacteriaceae, Nannocystaceae, Spongiibacteraceae, and Thalassospiraceae were monitored at Isla Julia and Isla San Pedro for 2 years using 16S rRNA metabarcoding analysis.

Alexandrium spp. was also monitored simultaneously using 18S rRNA metabarcoding analysis. Pearson’s correlation was used to determine the linear correlation between the Alexandrium and these classes of bacteria obtained from the field monitoring.

https://doi.org/10.1371/journal.pone.0301343.t004

3.2.3 Candidates of bacteria genus potentially related to Alexandriu spp.

Because there was a significant correlation between Alexandrium spp. and Spongiibacteraceae at Isla Julia (Table 4), Spongiibacteraceae in Isla Julia were further investigated at the genus level. Four genus taxonomies were assigned under Spongiibacteraceae in the Isla Julia dataset: BD1-7 clade, Oceanicoccus, Spongiibacter, and Zhongshania. Subsequently, Pearson’s correlation analysis was performed to determine the correlation between Alexandrium spp. and these subdivisions of Spongiibacteraceae (Table 5): A strong correlation was observed between Alexandrium spp. with the BD1-7 clade (FL) and Zhongshania (FL and PA). Although the relative abundance of BD1-7 clade (FL) was very low on 22 Oct 2020 when Alexandrium spp. was detected, its pattern of increase and decrease clearly aligned with that of Alexandrium spp. (Fig 6). Zhongshania (FL and PA) also coexisted with Alexandrium spp. on the same day, with relative of 0.08% and 0.02%, respectively (Fig 6). In Isla San Pedro, a strong correlation was also observed between Alexandrium spp. and BD1-7 clade (PA). While the genus Spongiibacter was detected at both stations, its correlation with Alexandrium spp. was not confirmed by Pearson’s correlation coefficient. Meanwhile, the Spongiibacteraceae that survived the culture experiment predominantly belonged to the genus Spongiibacter, as identified by the DADA2/SILVA statistical tools. Thus, an association between Spongiibacter and A. catenella remains a possibility (S4 Table in S1 File).

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Fig 6. Time course relative abundance of BD1-7 clade and Zhongshania in Isla Julia: The relative abundance of BD1-7 clade and Zhongshania in Isla Julia were monitored from March 2019 to March 2021 using 16S rRNA gene metabarcoding analysis and plotted with that of Alexandrium spp.

https://doi.org/10.1371/journal.pone.0301343.g006

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Table 5. Correlation between A. catenella and bacteria genera belonging to Spongiibcateracea in the field: The four bacteria genera belonging to Spongiibacteraceae were detected in Isla Julia and Isla San Pedro Pedro during the 2 years of monitoring using 16S rRNA metabarcoding analysis: BD1-7 clade, Oceanicoccus, Spongiibacter, and Zhongshania.

Pearson’s correlation was used to determine the linear correlation between the Alexandrium and these bacteria genera.

https://doi.org/10.1371/journal.pone.0301343.t005

4. Discussion

The interaction between algae and bacteria is highly complex, and understanding its mechanism cannot be achieved through short-term investigations. One primary reason is that their interaction is very specific and particular, varying from one strain to another. Moreover, even if an interaction is observed in a laboratory setting, it may not necessarily manifest the same way in natural environments. Natural environmental conditions are continually changing, posing significant challenges to verifying algal-bacterial interactions in the field. Therefore, it is crucial to uncover existing knowledge about these interactions, document previously unrecognized phenomena, and diligently accumulate insights for future reference. To this end, any discoveries related to algal-bacterial interactions hold value, and long-term field monitoring spanning several decades is essential to validate such findings.

The conventional understanding is that algal blooms are controlled by physical and chemical oceanic parameters, regardless of whether the sources are natural or anthropogenic. However, recent evidence has substantiated the relationship between bacteria and the algal bloom mechanism. Gajardo et al. [44] pointed out that understanding algal-bacterial interactions may offer a novel approach to predicting algal blooms, considering their significant impact on ecosystem biodiversity and productivity throughout their coevolutionary trajectories. Accordingly, this study focused on the selection of bacteria that may interact with the dinoflagellate of A. catenella, which has caused increasing damage along the Chilean coast. This study used the A. catenella culture prepared from the target site to screen bacteria by applying antibiotics and genomic analysis techniques. It is possible that these bacteria that survived the treatments simply favored the culture condition and selectively grew over the series of subcultures. However, our culture experiment showed that the A. catenella culture grew healthy with the surviving bacteria, which leaves the possibility that these bacteria may have a relationship with Chilean A. catenella. The class of bacteria, Alteromonadaceae, Cyclobacteriaceae, Nannocystaceae, Spongiibacteraceae, and Thalassospiraceae, remarkably increased after the culture screening process. Thus, these bacteria were monitored for two years at Isla Julia and Isla San Pedro in the Gulf of Corcovado in southern Chile, where salmon and shellfish harvesting are famous. In particular, the field monitoring at Isla Julia observed that the relative abundance of Spongiibacteraceae simultaneously increased and decreased with that of Alexandrium spp., suggesting that Spongiibacteraceae may have a certain interaction with this Chilean A. catenella strain.

At the time of this report, the family Spongiibacteraceae comprises six recognized genera: Dasania, Marortus, Neomelitea, Sinobacterium, Spongiibacter, and Zhongshania, as listed on [https://lpsn.dsmz.de/family/spongiibacteraceae]. Additionally, two other genera, Oceanicoccus and clade BD1-7 have been reported. These bacterial genera have been isolated from marine waters, algae, and corals, yet their specific roles remain largely unknown [45]. Fu et al. noted that Spongiibacter was recently discovered in phycospheres associated with phytoplankton [46]. Yu et al. indicated that Spongiibacteraceae are commonly found in coral holobionts, playing a significant role in the global marine carbon cycle and energy metabolism [47]. Heins et al. identified a bacterial strain related to clade BD1-7 in bloom samples and observed frequent occurrences of Spongiibacteraceae on particles [48]. Moreover, Spongiibacteraceae has been recognized as potential oil-degrading bacteria within microbial communities during oil spill experiments [49]. Further investigation of Spongiibacteraceae in relation to A. catenella could shed light on the mechanisms behind A. catenella blooms from an algal-bacterial mutualistic perspective. Subsequent research is warranted to explore the interactions between A. catenella and these bacteria, as well as the contribution of algal-bacterial interactions to A. catenella blooms.

One way to confirm the effect of bacterial species belonging to Spongiibacteraceae on A. catenella is to compare algal-bacterial co-cultures with algal axenic cultures. Our next objective is to prepare an axenic culture of this A. catenella strain and to perform binary culture experiments. We have yet to initiate the binary culture study. As widely recognized, a primary yet formidable challenge in binary culture studies lies in establishing axenic algal cultures. Completely removing bacteria adhering to algal cells proves exceedingly difficult, a challenge that numerous studies have highlighted concerning the preparation and maintenance of axenic algal cultures [2832]. Alexandrium catenella is no exception to this difficulty [25, 41]. Furthermore, the potential for algae to be axenic varies by strain, complicating the process even further [32]. Maki and Imai [32] attempted to prepare axenic culture from five strains of Heterocapsa circularisquama, achieving success with only one strain. Some prior binary culture studies were conducted with so-called “axenic” cultures, noting the limitation of making a completely bacteria-free algal culture. However, to precisely understand the effects of bacteria on algae, having an axenic algal culture is fundamental. Thus, once we devise a method to render this A. catenella strain bacteria-free, our investigations will delve into algal-bacterial mutualisms. Specifically, we aim to identify signaling molecules and metabolites exchanged between A. catenella and specific Spongiibacteraceae species through metatranscriptome analyses. Furthermore, we hope to determine the mechanism behind how A. catenella and the bacteria benefit each other through the production and exchange of the molecules.

The difficulty of removing attached bacteria from algal cells implies that these attached bacteria might be essential for algal survival. This study observed that bacteria reappeared in the algal culture regardless of multiple antibiotic and washing processes, presumably migrating from the attached bacteria in the cell wall or inside the dinoflagellate [25, 26, 32]. Further investigation is required to understand the algal-bacterial relationship. In general, the roles of attached bacteria include stimulatory/inhibitory and synergetic/antagonistic effects. The stimulatory effect arises when bacteria provide nutrients to phytoplankton, particularly when the phytoplankton cannot photosynthesize due to limited light or nutrients. For example, bacteria were constantly observed in the cytoplasm and food vacuoles of H. circularisquama cells [32], and bacteria engulfed by food vacuoles of Uroglena americana reportedly provided essential phospholipids for the algal growth under nutrient-deficient conditions [50]. There are reports about bacterial contributions to vitamin B and iron uptakes of dinoflagellate Lingulodinium polyedrum [51, 52]. There is also a report about the synergetic interaction between Pseudonitzchia multiseries and Sulfitobacter by exchanging hormones, indole-3-acetic acid, ammonia, and organosulfur [22]. In contrast, the bacterial inhibitory role pertains to the decline and extinction of algal biomass resulting from the presence of algicidal bacteria and antagonistic interactions between bacteria and algae; such bacteria have also been reported [53, 54]. Mayali and Azam [55] stated that bacteria could swim to the surface of algal cells, use their hydrolases, and subsequently kill the metabolically coupled algal species. It is unclear how bacterial species of Spongiibacteraceae interact with the Chilean A. catenella strain at this point. However, we speculated that certain species of Spongiibacteraceae, potentially subdivisions of BD1-7 clade and Zhongshania, may exert a stimulatory or synergistic effect on A. catenella. This speculation arises because these bacteria were detected along with A. catenella in both Isla Julia and Isla San Pedro, and a positive correlation was demonstrated through statistical analysis. Similarly, there might be a stimulatory or synergistic effect on A. catenella from Spongiibacter, given their simultaneous presence at both stations and the persistence of a high relative abundance of Spongiibacter in the post-treated culture.

Several other noteworthy observations were obtained in this study. The dominant class of bacteria in the A. catenella was Gammaproteobacteria, which is often associated with many algal cultures, particularly the dinoflagellates [21, 22, 30, 56]. The most dominant bacterial genus in the post-treated A. catenella culture was the genus Paraglaciecola (S4 Table in S1 File). These are reportedly cold-adapted marine bacteria that have been isolated from marine algae, seagrasses, Arctic sea ice algae, and the diatom Thalassiosira rotula [5760]. Given that Isla Julia and Isla San Pedro, where this A. catenella strain was isolated, experience relatively cold temperatures throughout the year (averaging 10–12°C), it remains plausible that Paraglaciecola and A. catenella share a mutualistic relationship. However, while this association was observed in the culture study, it wasn’t confirmed in the field study. Alternatively, Paraglaciecola might simply exhibit high resistance or remain inaccessible to the antibiotics, making them appear more abundant in culture than they truly are in their natural state. Continued monitoring of Paraglaciecola as a potential mutualistic bacterium for A. catenella is essential for confirmation.

Our ultimate goal is to integrate bacterial information into existing bloom prediction models, allowing for the prediction of A. catenella blooms from a bacterial perspective. Over the two years of monitoring, there were no A. catenella blooms detected at Isla Julia and Isla San Pedro, nor were A. catenella cells identified through microscopy. The Alexandrium data presented in this study were identified using 18S rRNA metagenomic analysis. This method has proven invaluable, revealing an identification that microscopy was not able to detect. With that said, using such a sporadic A. catenella data, constructing a bloom prediction model remains challenging. Continued monitoring of Isla Julia and Isla San Pedro is essential to accumulate sufficient data for a robust A. catenella prediction model.

In the meantime, we plan to incorporate an orthogonal absolute detection method for species of Spongiibacteraceae and A. catenella, such as Real-time PCR, in addition to the current microscopic method. The metabarcoding analysis utilized in this study is a groundbreaking molecular technique that can identify hundreds of ASVs of bacteria and eukaryotes from a single seawater sample, even within a complex, low-abundance community that is not conducive to conventional microscopy [16]. However, its primary application remains centered on relative detection [61, 62]. Incorporating real-time PCR or digital PCR to quantify species of Spongiibacteraceae and A. catenella could offer deeper insights into algal-bacterial mutualism.

5. Conclusion

This study evidenced a potential association between Alexandrium species and Spongiibacteraceae in Isla Julia and Isla San Pedro in southern Chile, based on both culture-based laboratory studies and two years of field monitoring. The suggested candidate bacteria within Spongiibacteraceae, possibly mutualistic with Alexandrium species, include the genus of BD1-7 clade, Spongiibacter, and Zhongshania. Further investigations are needed to confirm this association. However, gaining more knowledge about these bacteria could provide new insights into A. catenella blooms from an algal–bacterial perspective.

Acknowledgments

In memory of Professor Carl J. Carrano, who guided KY on how to study and understand algal–bacterial mutualisms thoroughly. We thank the microscope laboratory group at IFOP for advising us on phytoplankton identification. We also thank to the Scientific and Technological Bioresource Nucleus from Universidad de La Frontera (BIOREN–UFRO; https://bioren.ufro.cl/) for the availability of the DNA sequencers Illumina MiSeq and 3500 Genetic Analyzer financed by FONDEQUIP program (code EQM150126 and EQM170171, respectively). We also thank Natalie Kim, PhD. from Edanz (https://jp.edanz.com/ac) for editing a draft of this manuscript.

References

  1. 1. Penna A, Garcés E, Vila M, Giacobbe MG, Fraga S, Lugliè A, et al. Alexandrium catenella (Dinophyceae), a toxic ribotype expanding in the NW Mediterranean Sea. Marine Biology. 2005 148(1):13–23.
  2. 2. Persich GR, Kulis DM, Lilly EL, Anderson DM, Garcia VMT. Probable origin and toxin profile of Alexandrium tamarense (Lebour) Balech from southern Brazil. Harmful Algae. 2006 5(1):36–44.
  3. 3. Anderson DM, Cembella AD, Hallegraeff GM. Progress in Understanding Harmful Algal Blooms: Paradigm Shifts and New Technologies for Research, Monitoring, and Management. Annu. Rev. Mar. Sci. 2012 4:143–176. pmid:22457972
  4. 4. Guzmán L, Campodónico GI, Antunovic M. Estudios sobre un florecimiento tóxico causado por Gonyaulax catenella en Magallanes. III. Fitoplancton asociado. Anales del Instituto de la Patagonia. 1975 V1(N.1-2):209–223.
  5. 5. Guzmán L, Pacheco H, Pizarro G, Alarcón C. Alexandrium catenella y veneno paralizante de los mariscos en Chile. (Eds. Sar E. A., Ferrario, M. E. & Reguera, B.) Floraciones Algales Nocivas en el Cono Sur Americano (1st edition) 235–256 (Madrid, Spain: Instituto Español de Oceanografía, 2002).
  6. 6. Molinet C, Lafon A, Lembeye G, Moreno C. Spatial and temporal distribution patterns of blooms of Alexandrium catenella (Whedon & Kofoid) Balech 1985, on inland seas of northwest Patagonia, Chile. Revista Chilena de Historia Natural. 2003 76:681–698.
  7. 7. Mardones J, Clement A, Rojas X, Aparicio C. Alexandrium catenella during 2009 in Chilean waters, and recent expansion to coastal ocean. Harmful Algae News. 2010 41:8–9.
  8. 8. Varela D, Paredes J, Alves-de-Souza C, Seguel M, Sfeir A, Frangópulos M. Intraregional variation among Alexandrium catenella (Dinophyceae) strains from southern Chile: Morphological, toxicological and genetic diversity. Harmful Algae. 2012 15:8–18.
  9. 9. Buschmann A, Farías L, Tapia F, Varela D, Vásquez M. Informe Final: Comisión Marea Roja (CMR). Alphen aan den Rijn: Wolters Kluwer. 2016.
  10. 10. Paredes J, Varela D, Martínez C, Zúñiga A, Correa K, Villarroel A, Olivares B. Population genetic structure at the northern edge of the distribution of Alexandrium catenella in the Patagonian fjords and its expansion along the open Pacific Ocean coast. Frontiers in Marine Science. 2019 5(532).
  11. 11. Paredes-Mella J, Mardones JI, Norambuena L, Fuenzalida G, Labra G, Espinoza-González O, et al. Toxic Alexandrium catenella expanding northward along the Chilean coast: New risk of paralytic shellfish poisoning off the Bío-Bío region (36° S). Mar Pollut Bull 2021 172:112783. pmid:34365161
  12. 12. Molinet C, Niklitschek E, Seguel M, Díaz P. Trends of natural accumulation and detoxification of paralytic shellfish poison in two bivalves from the Norwest Patagonian inland sea. Revista de Biologia Marina Y Oceanografia. 2010 45:195–204.
  13. 13. Díaz P, Alvarez G, Varela D, Santos I, Diaz M, Molinet C, et al. Impacts of harmful algal blooms on the aquaculture industry: Chile as a case study. Perspectives in Phycology. 2019 6(1–2): 39–50.
  14. 14. Fuentes-Grünewald C, Clement A, Aguilera BA. Alexandrium catenella bloom and the impact on fish farming in the XI region, Chile. Paper presented at the Books of proceedings of 12th International Conference on Harmful Algae, Copenhagen. 2008 183–186.
  15. 15. Mardones JI, Dorantes-Aranda JJ, Nichols PD, Hallegraeff GM. Fish gill damage by the dinoflagellate Alexandrium catenella from Chilean fjords: Synergistic action of ROS and PUFA. Harmful Algae. 2015 49:40–49. https://doi.org/10.1016/j.hal.2015.09.001
  16. 16. Mardones J, Krock B, Marcus L, Alves-de-Souza C, Nagai S, Yarimizu K, et al. From molecules to ecosystem functioning: insight into new approaches to taxonomy to monitor harmful algae diversity in Chile. (eds. In Clementson L. A., Eriksen R. S. & Willis A.). Advances in Phytoplankton Ecology (1st ed) 119–154 (Elsevier, 2021).
  17. 17. Trainer VL, Moore SK, Hallegraeff G, Kudela RM, Clement A, Mardones JI, et al. Pelagic harmful algal blooms and climate change: Lessons from nature’s experiments with extremes. Harmful Algae. 2020 91:101591. pmid:32057339
  18. 18. Paredes-Mella J, Varela D, Fernández P, Espinoza-González O. Growth performance of Alexandrium catenella from the Chilean fjords under different environmental drivers: plasticity as a response to a highly variable environment. J. Plankton Res. 2020 42(2):119–134.
  19. 19. Bell W, Mitchell R. Chemotactic and Growth Responses of Marine Bacteria to Algal Extracellular Products. Biological Bulletin. 1972 143(2):265–277.
  20. 20. Bell WH, Lang JM, Mitchell R. Selective stimulation of marine bacteria by algal extracellular products. Limnology and Oceanography. 1974 19(5):833–839.
  21. 21. Bertrand EM, McCrow JP, Moustafa A, Zheng H, McQuaid JB, Delmont TO, et al. Phytoplankton-bacterial interactions mediate micronutrient colimitation at the coastal Antarctic sea ice edge. PNAS. 2015 112(32):9938–9943. pmid:26221022
  22. 22. Amin SA, Hmelo LR, van Tol HM, Durham BP, Carlson LT, Heal KR, et al. Interaction and signaling between a cosmopolitan phytoplankton and associated bacteria. Nature. 2015 522:98–101. pmid:26017307
  23. 23. Ramanan R, Kim BH, Cho DH, Oh HM, Kim HS. Algae–bacteria interactions: Evolution, ecology and emerging applications. Biotechnol. Adv. 2016 34(1):14–29. pmid:26657897
  24. 24. Seymour JR, Amin SA, Raina JB, Stocker R. Zooming in on the phycosphere: the ecological interface for phytoplankton-bacteria relationships. Nat. Microbiol. 2017 2:17065. pmid:28555622
  25. 25. Córdova JL, Cárdenas L, Cárdenas L, Yudelevich A. Multiple bacterial infection of Alexandrium catenella (Dinophyceae). J. Plankton Res. 2002 24(1):1–8.
  26. 26. Uribe P, Espejo RT. Effect of Associated Bacteria on the Growth and Toxicity of Alexandrium catenella. Appl. Environ. Microbiol. 2003 69(1):659–662. pmid:12514056
  27. 27. Amaro AM, Fuentes MS, Ogalde SR, Venegas JA, Suarez-Isla BA. Identification and Characterization of Potentially Algal-lytic Marine Bacteria Strongly Associated with the Toxic Dinoflagellate Alexandrium catenella. J. Eukaryotic Microbiol. 2005 52(3):191–200. pmid:15926994
  28. 28. Green DH, Llewellyn LE, Negri AP, Blackburn SI, Bolch CJ. Phylogenetic and functional diversity of the cultivable bacterial community associated with the paralytic shellfish poisoning dinoflagellate Gymnodinium catenatum. FEMS Microbiol. Ecol. 2004 47(3):345–357. pmid:19712323
  29. 29. Jauzein C, Evans AN, Erdner DL. The impact of associated bacteria on morphology and physiology of the dinoflagellate Alexandrium tamarense. Harmful Algae. 2015 50:65–75.
  30. 30. Lupette J, Lami R, Krasovec M, Grimsley N, Moreau H, Piganeau G, et al. Marinobacter Dominates the Bacterial Community of the Ostreococcus tauri Phycosphere in Culture. Frontiers in Microbiology. 2016 7:1414. pmid:27656176
  31. 31. Liu CL, Place AR, Jagus R. Use of Antibiotics for Maintenance of Axenic Cultures of Amphidinium carterae for the Analysis of Translation. Mar. Drugs. 2017 15(8):242. pmid:28763019
  32. 32. Maki T, Imai I. Relationships between intracellular bacteria and the bivalve killer dinoflagellate Heterocapsa circularisquama (Dinophyceae). Fisheries Science. 2001 67(5): 794–803.
  33. 33. Guillard RRL, Hargraves PE. Stichochrysis immobilis is a diatom, not a chrysophyte. Phycologia. 1993 32(3):234–236.
  34. 34. Nagai S, Yamamoto K, Hata N, Itakura S. Study of DNA extraction methods for use in loop-mediated isothermal amplification detection of single resting cysts in the toxic dinoflagellates Alexandrium tamarense and A. catenella. Mar. Genomics. 2012 7:51–56. pmid:22897963
  35. 35. Yarimizu K, Fujiyoshi S, Kawai M, Norambuena-Subiabre L, Cascales EK, Rilling JI, et al. Protocols for Monitoring Harmful Algal Blooms for Sustainable Aquaculture and Coastal Fisheries in Chile. Int. J. Environ. Res. Public Health. 2020 17(20). pmid:33092111
  36. 36. Yarimizu K, Fujiyoshi S, Kawai M, Acuña JJ, Rilling JI, Campos M, et al. A Standardized Procedure for Monitoring Harmful Algal Blooms in Chile by Metabarcoding Analysis. JoVE. 2021 174:e62967. pmid:34515692
  37. 37. Schlitzer R. (2022) Ocean data view. Available at: http://odv.awi.de.
  38. 38. Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJ, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods. 2016 13(7):581–583. pmid:27214047
  39. 39. R Core Team, 2020. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.
  40. 40. Yilmaz P, Parfrey LW, Yarza P, Gerken Jan, Pruesse E, Quast C, et al. The SILVA and “All-species Living Tree Project (LTP)” taxonomic frameworks. Nucleic Acids Res., 42(Database issue). 2014 D643-D648:(214). pmid:24293649
  41. 41. Klindworth A, Pruesse E, Schweer T, Peplies J, Quast C, Horn M, et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 2013 41(1):e1. pmid:22933715
  42. 42. Tanabe A, Nagai S, Hida K, Yasuike M, Fujiwara A, Nakamura Y, et al. Comparative study of the validity of three regions of the 18S-rRNA gene for massively parallel sequencing-based monitoring of the planktonic eukaryote community. Molecular Ecology Resources. 2016 16:402–414. pmid:26309223
  43. 43. Nishitani G, Satoshi N, Shiho H, Yuki K, Kiyonari S, Takashi K, et al. Multiple plastids collected by the Dinoflagellate Dinophysis mitra through Kleptoplastidy. Appl. Environ. Microbiol. 2012 78(3):813–821. pmid:22101051
  44. 44. Gajardo G, Morón-López J, Vergara K, Ueki S, Guzmán L, Espinoza-González O, et al. The holobiome of marine harmful algal blooms (HABs): A novel ecosystem-based approach for implementing predictive capabilities and managing decisions. Environmental Science & Policy. 2023 143:44–54. https://doi.org/10.1016/j.envsci.2023.02.012
  45. 45. Yoon J. Spongiibacter pelagi sp. nov., a marine gammaproteobacterium isolated from coastal seawater. Antonie van Leeuwenhoek. 2022 115(4):487–495. pmid:35129702
  46. 46. Fu H, Smith CB, Sharma S, Moran MA. Genome Sequences and Metagenome-Assembled Genome Sequences of Microbial Communities Enriched on Phytoplankton Exometabolites. Microbiol. Resour. Announce.2020 9(30):e00724–00720. pmid:32703840
  47. 47. Yu X, Yu K, Liao Z, Chen B, Deng C, Yu J, et al. Seasonal fluctuations in symbiotic bacteria and their role in environmental adaptation of the scleractinian coral Acropora pruinosa in high-latitude coral reef area of the South China Sea. Science of The Total Environment. 2021 792:148438. pmid:34153755
  48. 48. Heins A, Amann RI, Harder J. Cultivation of particle-associated heterotrophic bacteria during a spring phytoplankton bloom in the North Sea. Systematic and Applied Microbiology. 2021 44(5) 126232. pmid:34399113
  49. 49. Shai Y, Rubin-Blum M, Angel D, Guy SV, Zurel D, Astrahan P, et al. Response of oligotrophic coastal microbial populations in the SE Mediterranean Sea to crude oil pollution; lessons from mesocosm studies. Estuarine Coastal and Shelf Science. 2020 249: 107102.
  50. 50. Kimura B, Ishida Y. Photophagotrophy in Uroglena americana, Chrysophyceae. Jpn. J. Limnol. (Rikusuigaku Zasshi) 1985 46:315–318. https://doi.org/10.3739/rikusui.46.315.
  51. 51. Cruz-López R, Maske H, Yarimizu K, Holland NA. The B-vitamin mutualism between the dinoflagellate Lingulodinium polyedrum and the bacterium Dinoroseobacter shibae. Frontiers in Marine Science. 2018 5(274).
  52. 52. Yarimizu K, Cruz-Lopez R, Carrano CJ. Iron and Harmful Algae Blooms: Potential Algal-Bacterial Mutualism between Lingulodinium polyedrum and Marinobacter algicola. Frontiers in Marine Science. 2018 5(180).
  53. 53. Meyer N, Bigalke A, Kaulfuß A, Pohnert G. Strategies and ecological roles of algicidal bacteria. FEMS Microbiol. Rev. 2017 41:880–899. pmid:28961821
  54. 54. Coyne KJ, Wang Y, Johnson G. Algicidal Bacteria: A Review of Current Knowledge and Applications to Control Harmful Algal Blooms. Frontiers in Microbiology. 2022 13. pmid:35464927
  55. 55. Mayali X, Azam F. Algicidal bacteria in the sea and their impact on algal blooms. J Eukaryot Microbiol. 2004 51(2):139–144. pmid:15134248
  56. 56. Hu L, Peng X, Zhou J, Zhang Y, Xu S, Mao X, et al. Characterizing the Interactions Among a Dinoflagellate, Flagellate and Bacteria in the Phycosphere of Alexandrium tamarense (Dinophyta). Frontiers in Mar. Sci. 2015 2(100).
  57. 57. Bech PK, Schultz-Johansen M, Glaring MA, Barbeyron T, Czjzek M, Stougaard P. Paraglaciecola hydrolytica sp. nov., a bacterium with hydrolytic activity against multiple seaweed-derived polysaccharides. Int. J. Syst. Evol. Microbiol. 2017 67(7):2242–2247. pmid:28671532
  58. 58. Rapp JZ, Fernández-Méndez M, Bienhold C, Boetius A. Effects of Ice-Algal Aggregate Export on the Connectivity of Bacterial Communities in the Central Arctic Ocean. Frontiers in Microbiology. 2018 9:1035–1035. pmid:29875749
  59. 59. Wang Y, Zhang Y, Liu T, Zhu X, Ma J, Su X, et al. Paraglaciecola marina sp. nov., isolated from marine alga (Sargassum natans (L.) Gaillon). Int. J. Syst. Evol. Microbiol. 2020 70(8):4451–4457. pmid:32687464
  60. 60. Mönnich J, Tebben J, Bergemann J, Case R, Wohlrab S, Harder T. Niche-based assembly of bacterial consortia on the diatom Thalassiosira rotula is stable and reproducible. ISME J. 2020 14(6):1614–1625. pmid:32203123
  61. 61. van der Loos L, Nijland R. Biases in bulk: DNA metabarcoding of marine communities and the methodology involved Running title: methodology of marine DNA metabarcoding. Mol Ecol. 2020 30(13):3270–3288. pmid:32779312
  62. 62. Ershova E, Wangensteen O, Descoteaux R, Barth-Jensen C, Præbel K. Metabarcoding as a quantitative tool for estimating biodiversity and relative biomass of marine zooplankton. ICES J. Mar. Sci. 2021 78(9):3342–3355.