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. 2024 Apr 16;9(4):e0094923.
doi: 10.1128/msystems.00949-23. Epub 2024 Mar 5.

Taxonomic difference in marine bloom-forming phytoplanktonic species affects the dynamics of both bloom-responding prokaryotes and prokaryotic viruses

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Taxonomic difference in marine bloom-forming phytoplanktonic species affects the dynamics of both bloom-responding prokaryotes and prokaryotic viruses

Hiroaki Takebe et al. mSystems. .

Abstract

The production of dissolved organic matter during phytoplankton blooms and consumption by heterotrophic prokaryotes promote marine carbon biogeochemical cycling. Although prokaryotic viruses presumably affect this process, their dynamics during blooms are not fully understood. Here, we investigated the effects of taxonomic difference in bloom-forming phytoplankton on prokaryotes and their viruses. We analyzed the dynamics of coastal prokaryotic communities and viruses under the addition of dissolved intracellular fractions from taxonomically distinct phytoplankton, the diatom Chaetoceros sp. (CIF) and the raphidophycean alga Heterosigma akashiwo (HIF), using microcosm experiments. Ribosomal RNA gene amplicon and viral metagenomic analyses revealed that particular prokaryotes and prokaryotic viruses specifically increased in either CIF or HIF, indicating that taxonomic difference in bloom-forming phytoplankton promotes distinct dynamics of not only the prokaryotic community but also prokaryotic viruses. Furthermore, combining our microcosm experiments with publicly available environmental data mining, we identified both known and novel possible host-virus pairs. In particular, the growth of prokaryotes associating with phytoplanktonic organic matter, such as Bacteroidetes (Polaribacter and NS9 marine group), Vibrio spp., and Rhodobacteriales (Nereida and Planktomarina), was accompanied by an increase in viruses predicted to infect Bacteroidetes, Vibrio, and Rhodobacteriales, respectively. Collectively, our findings suggest that changes in bloom-forming species can be followed by an increase in a specific group of prokaryotes and their viruses and that elucidating these tripartite relationships among specific phytoplankton, prokaryotes, and prokaryotic viruses improves our understanding of coastal biogeochemical cycling in blooms.IMPORTANCEThe primary production during marine phytoplankton bloom and the consumption of the produced organic matter by heterotrophic prokaryotes significantly contribute to coastal biogeochemical cycles. While the activities of those heterotrophic prokaryotes are presumably affected by viral infection, the dynamics of their viruses during blooms are not fully understood. In this study, we experimentally demonstrated that intracellular fractions of taxonomically distinct bloom-forming phytoplankton species, the diatom Chaetoceros sp. and the raphidophycean alga Heterosigma akashiwo, promoted the growth of taxonomically different prokaryotes and prokaryotic viruses. Based on their dynamics and predicted hosts of those viruses, we succeeded in detecting already-known and novel possible host-virus pairs associating with either phytoplankton species. Altogether, we propose that the succession of bloom-forming phytoplankton would change the composition of the abundant prokaryotes, resulting in an increase in their viruses. These changes in viral composition, depending on bloom-forming species, would alter the dynamics and metabolism of prokaryotes, affecting biogeochemical cycling in blooms.

Keywords: Chaetoceros sp.; Heterosigma akashiwo; microcosm; phytoplankton bloom; prokaryotes; prokaryotic viruses.

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Conflict of interest statement

The authors declare no conflict of interest.

Figures

Fig 1
Fig 1
Shifts in the abundance of (A) prokaryotic cells and (B) viral particles during the microcosm experiment. Cell and viral counts were obtained using flow cytometry. The average cell and viral numbers in the triplicate flasks are shown. Error bars indicate standard deviation. Control: samples cultured without any additional organic matter. CIF treatment: samples cultured with CIF. HIF treatment: samples cultured with HIF.
Fig 2
Fig 2
Comparison of ASV compositions among control, CIF, and HIF treatments. The number of sequences of each sample was rarified into 9,450 reads prior to the analyses. Bray–Curtis dissimilarity among all samples was illustrated by PCoA. Samples are distinguished by colors based on the treatments and culture periods. Enlarged figures of areas (A)–(D) are shown separately.
Fig 3
Fig 3
Dynamics of ASVs which were found to be abundant in (A) CIF and (B) HIF treatments. Averages of the approximate cell number in the triplicate flasks are shown as plots in the log scale. The plot colors show the order-level taxonomy of each ASV. Dendrograms represent the similarity of dynamics among ASVs. ASVs abundant only in the CIF or HIF treatments are highlighted in red, and if they are treatment-specific ASVs, they are indicated by asterisks.
Fig 4
Fig 4
Dynamics of vOTUs that significantly increased in (A) CIF and (B) HIF treatments. Averages of approximate particle numbers in the triplicate flasks are shown by plots in the log scale. The plot colors show the taxonomy of the putative host. Dendrograms represent the similarity of dynamics among vOTUs. Treatment-specific vOTUs are highlighted in red.
Fig 5
Fig 5
Co-occurrence dynamics of the marine prokaryote viruses and prokaryotes in the Osaka Bay natural seawater samples. (A) ASV_3133 (Polaribacter) and Bacteroidetes vOTUs. (B) ASV_2987 (Planktomarina) and a Rhodobacteriales vOTU. (C) ASV_1069 (Vibrio) and a Vibrio vOTU. The environmental data sets collected monthly between May 2015 and November 2016 (Tominaga et al., in press) (25) were used. Relative abundance of each ASV was calculated by mapping quality-controlled reads of 16S rRNA genes to the ASV sequence with 100% identity using VSEARCH. Fragments per kilobase per mapped million reads (FPKM) value in each vOTU was calculated by mapping quality-controlled viral metagenomic reads to the sequence of the vOTU with 95% identity using Bowtie2. Co-occurrence dynamics are shown only if a significantly positive correlation is detected (Spearman correlation; r > 0.6, P < 0.01, and q < 0.05).

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