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Dinoflagellates alter their carbon and nutrient metabolic strategies across environmental gradients in the central Pacific Ocean

Nature Microbiology volume 6, pages 173–186 (2021)Cite this article

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Abstract

Marine microeukaryotes play a fundamental role in biogeochemical cycling through the transfer of energy to higher trophic levels and vertical carbon transport. Despite their global importance, microeukaryote physiology, nutrient metabolism and contributions to carbon cycling across offshore ecosystems are poorly characterized. Here, we observed the prevalence of dinoflagellates along a 4,600-km meridional transect extending across the central Pacific Ocean, where oligotrophic gyres meet equatorial upwelling waters rich in macronutrients yet low in dissolved iron. A combined multi-omics and geochemical analysis provided a window into dinoflagellate metabolism across the transect, indicating a continuous taxonomic dinoflagellate community that shifted its functional transcriptome and proteome as it extended from the euphotic to the mesopelagic zone. In euphotic waters, multi-omics data suggested that a combination of trophic modes were utilized, while mesopelagic metabolism was marked by cytoskeletal investments and nutrient recycling. Rearrangement in nutrient metabolism was evident in response to variable nitrogen and iron regimes across the gradient, with no associated change in community assemblage. Total dinoflagellate proteins scaled with particulate carbon export, with both elevated in equatorial waters, suggesting a link between dinoflagellate abundance and total carbon flux. Dinoflagellates employ numerous metabolic strategies that enable broad occupation of central Pacific ecosystems and play a dual role in carbon transformation through both photosynthetic fixation in the euphotic zone and remineralization in the mesopelagic zone.

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Fig. 1: Protistan community composition across the METZYME transect.
Fig. 2: Differentially abundant dinoflagellate proteins between the euphotic and mesopelagic zones.
Fig. 3: Distinct dinoflagellate functional metabolism between the euphotic and mesopelagic zones of the central Pacific.
Fig. 4: Dinoflagellates restructure iron and nitrogen metabolism depending on external concentrations.
Fig. 5: Dinoflagellate protein abundance is linked to carbon export in the central Pacific Ocean.

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Data availability

The mass spectrometry global proteomics data and metatranscriptome-derived FASTA file has been deposited with the ProteomeXchange Consortium through the PRIDE112 repository under accession number PXD014230. Metaproteomic annotations and total spectral counts from this analysis are also available on the Ocean Protein Portal (proteinportal.whoi.edu/). Nutrients, dissolved cobalt, pigments and conductivity, temperature and depth physiochemical information is available through the NSF’s Biological and Chemical Oceanography Data Management Office (BCO-DMO) repository under project number 2236. Metatranscriptomic reads have been deposited with the NCBI under Bioproject no. PRJNA555787. The 16S rRNA raw reads are available on the NCBI under Biosample accession nos. SAMN12331629–SAMN12331670 and the 18S rRNA raw reads under Biosample accession nos. SAMN12332710–SAMN12332751. The 0.2–3-µm metagenomic assembly has been deposited with the NCBI under accession no. GCA_900411625.

Code availability

The R code used to create the heatmaps, ordinations and the 18S rRNA and WGCNA analyses are available on github (github.com/cnatalie/METZYME).

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Acknowledgements

We thank the captain, crew and scientific party of the October 2011 R/V Kilo Moana cruise. This research was funded through National Science Foundation (NSF) grant nos. OCE-1031271, 1924554, 1850719 and 1736599, Gordon and Betty Moore Foundation grant nos. 2724 and 3782, the Center for Microbial Oceanography Research and Education and the Woods Hole Oceanographic Institution Ocean Life Institute to M.A.S. N.R.C. was supported by grant no. 544236 from the Simons Foundation. A.E.A. acknowledges support from the NSF-OCE-1756884 grant and a Gordon and Betty Moore Foundation grant no. GBMF3828. G.R.D. acknowledges support from NSF grant no. OCE-1061876. N.A.H. was supported by NSF Graduate Research Fellowship no. 1122274, and J.K.S. was supported by a NASA Postdoctoral Program Fellowship. We acknowledge T. Geopfert (Arizona State University) and D. Wang (ExxonMobil) for sampling assistance, and thank J. Jennings (Oregon State University) for processing the macronutrient samples and S. Davies (Boston University) for providing WGCNA code. J. Bowman’s (Scripps Institution of Oceanography) pplacer blog provided invaluable support during phylogenetic tree construction. We thank M. Johnson (Woods Hole Oceanographic Institution (WHOI)), S. Hu (WHOI), A. Frank (WHOI), J.P. Balmonte (Uppsala), Lisa Nigro (University of Connecticut), C. Moreno (University of North Carolina-Chapel Hill (UNC)) and S. H. Jang (UNC) for helpful feedback and suggestions on the manuscript.

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Authors and Affiliations

  1. Marine Chemistry and Geochemistry Department, Woods Hole Oceanographic Institution, Woods Hole, MA, USA

    Natalie R. Cohen, Matthew R. McIlvin, Dawn M. Moran, Noelle A. Held, Jaclyn K. Saunders & Mak A. Saito

  2. Department of Earth Science, University of Southern California, Los Angeles, CA, USA

    Nicholas J. Hawco

  3. Biology Department, Woods Hole Oceanographic Institution, Woods Hole, MA, USA

    Michael Brosnahan

  4. Department of Biology, College of Charleston, Charleston, SC, USA

    Giacomo R. DiTullio

  5. Ocean Sciences Department, University of California, Santa Cruz, Santa Cruz, CA, USA

    Carl Lamborg

  6. Microbial & Environmental Genomics, J. Craig Venter Institute, La Jolla, CA, USA

    John P. McCrow, Chris L. Dupont & Andrew E. Allen

  7. Scripps Institution of Oceanography, Integrative Oceanography Division, University of California, San Diego, La Jolla, CA, USA

    Andrew E. Allen

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  1. Natalie R. Cohen
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Contributions

N.R.C. analysed the data and wrote the first draft of the manuscript. D.M.M., M.R.M., N.A.H., M.A.S. and C.L. collected multi-omics samples. M.R.M. analysed the proteomic samples by mass spectrometry. D.M.M. processed, extracted and prepared the protein samples for the proteomic analysis. N.A.H. and J.K.S. contributed to the development of the metaproteomic pipeline. N.J.H. quantified the dissolved cobalt concentrations. M.B. contributed to dinoflagellate physiology interpretations. G.R.D. collected and generated the pigment data. C.L. deployed the sediment traps, calculated carbon flux measurements and organized the expedition as co-chief scientist. C.L.D. processed and annotated the metagenomics data. A.E.A. and J.P.M. contributed the 18S and 16S rRNA metabarcoding and metatranscriptomic data. M.A.S. designed the study, organized the expedition as co-chief scientist and guided the interpretations. All authors contributed to the final version of the manuscript.

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Correspondence to Natalie R. Cohen, Andrew E. Allen or Mak A. Saito.

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Extended data

Extended Data Fig. 1 Nitrate + nitrite, phosphate and silicate concentrations along the transect.

White dots represent CTD sampling depths where physiochemical data, pigments, macronutrients and trace metals were collected. Black dots indicate locations where filters were processed for metaproteomic, metatranscriptomic and 18s rRNA analyses. Two black triangles represent depths at which only metatranscriptomic and 18S rRNA information is available (St. 1, 600 m; St. 3, 80 m).

Extended Data Fig. 2 Dinoflagellate taxonomic annotations across the transect.

a, Dinoflagellate family-level and (b) genus-level relative community composition determined through 18S rRNA, metatranscriptomic and metaproteomic analyses from 3-51 µm filter fractions, highlighting the abundance of the Kareniaceae-like family and Karlodinium and Karenia-like genera in transcripts and proteins. Taxonomic annotations were assigned based on assembled metatranscriptome matches to the PhyloDB database, containing marine protistan21 and bacterial transcriptomes and genomes. 18S rRNA annotations were assigned using the PR2,22 database. ‘NA’ represents dinoflagellate OTUs without family or genera-level taxonomy available in the PR2 database. In the transcript pool, Karenia and Karlodinium genera together comprised an average 42 ± 0.02% of the total dinoflagellate reads in the euphotic zone (< 200m) and 42 ± 0.04% in the mesopelagic (≥ 200m). In the protein pool, these two genera comprised an average 45 ± 10% of the spectral counts in the euphotic zone and 71 ± 12% in the mesopelagic.

Extended Data Fig. 3 Pigment profiles along the METZYME transect determined by high performance liquid chromatography (HPLC). White dots represent sampling depths.

Graphed in Ocean Data View (ODV) using DIVA interpolation.

Extended Data Fig. 4 Log2 NSAF-normalized dinoflagellates spectral count heatmap displaying relative protein abundance at the PFam annotation level.

The top 50 PFam-annotated genes with highest deviations from the mean (variances) across samples are shown. The depth annotation bar highlights samples from the surface (<200 m, white), deep (>200 m, black) and 200 m (gray). Dendrogram shows similarity in spectral abundance among samples based on Euclidean distance and hierarchical clustering. Each row represents a PFam annotation, with spectral counts associated with identical PFams summed together. Multiple PFam annotations of the same contig are separated by an underscore.

Extended Data Fig. 5 Weighted correlation network analysis (WGCNA) eigengene modules using log2 TPM-normalized KEGG-annotated dinoflagellate transcripts.

The ‘module eigengene’ represents the first principal coordinate of the module and summarizes the module gene expression profile. Signed network analysis was performed using the WCGNA package in R23 with at least 75 genes per eigengene module, and modules merged displaying similar eigengene values across samples (MEDissThres= 0.3). a, Color scale bar represents Pearson correlation coefficients between environmental metadata and eigengene modules; correlation coefficients and two-tailed Student test unadjusted (default) p values are shown in each box. b, Eigenene expression values plotted alongside log2 TPM gene expression in the white (surface; top) and black (deep; bottom) modules. c, KEGG pathway identity of white and black WCGNA modules. A KEGG enrichment analysis was performed using clusterProfiler’s enrich function, which calculates overrepresentation of KEGG pathways compared to the total genes identified in the data set using a two-tailed hypergeometric test (Benjamin-Hochberg adjusted p-value < 0.05)24. Significantly enriched pathways are denoted with an asterisk (*).

Extended Data Fig. 6 Stacked pie charts depicting dinoflagellate genus-level relative community composition for genes of interest shown to be responsive to shallow (< 200m), deep (≥ 200m), oligotrophic (St. 1, 3, 9, 12) and equatorial upwelling (St. 5, 6, 8) environments.

The inner rings show genera composition based on normalized transcript read counts, and the outer rings show normalized protein spectral counts. Transcript and protein were averaged across samples within each of the four environments. Only shallow depths (< 200m) were included for the oligotrophic and equatorial pie charts. Abbreviations used are shown for each gene of interest, along with their IDs from KEGG, KOG or PFam databases. ‘Other’ represents other dinoflagellate genera in minor relative abundance and not included here (see Extended Data Fig. 2 for dinoflagellate genus-level relative abundance across samples).

Extended Data Fig. 7 Relative transcript and protein abundance for dinoflagellate genes of interest between depths and across latitudes.

a, Relative gene expression is shown as log2 transcripts per million (TPM; top), relative protein abundance as log2 normalized spectral abundance factor (NSAF; bottom). Undetected transcripts and proteins are indicated in gray. The depth annotation bar indicates samples from the surface (<200 m, white), deep (>200 m, black) and 200 m (gray), and the site annotation bar shows whether samples were collected from the oligotrophic gyres (St. 1, 3, 9, 12) or the equatorial upwelling zone (St. 5, 6, 8). b, Comparison of average transcript and average protein abundance fold changes between euphotic and mesopelagic zones. Only shared KEGG genes (KOs) are shown that were detected in at least one metatranscriptome and one metaproteome, with proteorhodopin (PFam PF01036) and ISIP2a (KEGG gene pti:PHATDRAFT_54465) manually added. Values of zero were changed to a small value (0.1) to allow for fold changes estimates. The black line illustrates the linear relationship between protein and transcript fold changes. Genes in the top right (quadrant 1) represent transcripts/proteins abundant in euphotic waters, genes in the bottom left (quadrant 3) represent transcripts/proteins abundant in the mesopelagic (FBA = fructose biphosphate aldolase; GAP2 = glyceraldehyde-3-phosphate dehydrogenase (NAD(P)); ISIP2A = iron starvation induced protein 2 [phytotransferrin]; GAPDH = glyceraldehyde 3-phosphate dehydrogenase).

Extended Data Fig. 8 Cladogram of translated dinoflagellate tubulin contigs with reference MMETSP dinoflagellate proteins (left) shown alongside tubulin gene expression in the mesopelagic compared to euphotic zone (log2 average fold change) (middle) and average expression levels (TPM-normalized transcript abundance) in the mesopelagic (right).

Alpha-tubulin is shown in blue, Gamma-tubulin in green, and beta-tubulin in orange. Reference sequences were aligned using MUSCLE v3.8 and the maximum-likelihood tree was created using RAxML v8.2.11 with the PROTGAMMALG model and 100 bootstrap replicates. Contigs were placed on the reference tree using pplacer v1.1alpha19. The cladogram is visualized with the R package ggtree v1.16.0.

Extended Data Fig. 9 Heatmap displaying relative dinoflagellate TPM-normalized gene expression from each station < 100m, displayed as row Z-score [(log2 TPM – mean)/standard deviation].

The top 45 KEGG annotated genes with highest transcript deviations from the mean (variances) are displayed. Along with the KEGG entries, five annotations not included in KEGG were manually added. Dendrogram shows similarity in transcript abundances based on Euclidean distance and hierarchical clustering, created with pheatmap v1.0.12. Each row represents a unique KEGG-annotated gene. The site annotation bar indicates whether samples were collected from the oligotrophic gyres (St. 1, 3, 9, 12) or the equatorial upwelling zone (St. 5, 6, 8). Color gradients represents low (yellow) to high (blue) gene expression.

Extended Data Fig. 10 Positive relationship between dinoflagellate protein abundance and carbon flux.

Particulate export estimates through average mass flux (a) and particulate carbon flux (b) from the oligotrophic (St. 1, red) to equatorial upwelling region (St. 5, blue), as visualized by latitude in Fig. 5 (n = 3 tubes per depth from the same sediment trap array; error bars represent standard deviation.). c, Vertical profiles of absolute dinoflagellate exclusive protein spectral counts derived from the 3-51 µm size fraction and (d) absolute Prochlorococcus exclusive spectral counts from the 0.2-3 µm fraction along the surface gradient. Absolute spectral counts were not NSAF-normalized as performed in the functional analysis. Depth-integrated spectral counts from (e) 50-200m and (f) 200-400m highlight changes to protein inventory across the biogeochemical gradient and between the euphotic and mesopelagic zones, with the coefficient of carbon flux attenuation (b value) shown in brown (three depths per station were used to calculate the slope (b) via non-linear flux curve fitting to the Martin power law). Depth-integrated values were obtained by calculating the area under the profile for spectral counts versus depth. Depth-integrated dinoflagellate spectral counts show a positive relationship with carbon flux to 150 m (g) in contrast to Prochlorococcus which demonstrates a negative relationship.

Supplementary information

Supplementary Information

Supplementary Discussion, Figs. 1–12 and descriptions of Tables 1–11.

Reporting Summary

Supplementary Table 1

Raw transcript read counts, and taxonomic and functional annotations. Rows correspond to assembled ORFs and columns contain mapped raw reads. Samples are labelled first by station followed by depth and McLane pump number (for example, 1_050_06 = St. 1, 50 m, pump no. 6).

Supplementary Table 2

TPM-normalized transcript counts for each dinoflagellate ORF along with KEGG, KOG and PFam functional annotations.

Supplementary Table 3

Raw exclusive spectral counts and annotations for each ORF. Rows correspond to assembled ORFs and columns contain spectral counts per sample.

Supplementary Table 4

NSAF-normalized and exclusive spectral counts associated with dinoflagellates, subset to the PFAM annotation level. Counts associated with PFAM domains corresponding to the same annotation were summed together. Contigs containing multiple PFAM domains are separated by an underscore. P-value results from a two-tailed permutation test performed to assess differential abundance between euphotic (<200 m) and mesopelagic (≥200 m) samples are included under column ‘permutation p’. P values were corrected for multiple hypothesis testing using the max-T method. Adjusted P values are included under ‘maxT adj p’.

Supplementary Table 5

18S, 16S, 16S-plastid rRNA OTU tables and taxonomic annotations. 16S rRNA OTUs were classified using the SILVA rRNA database27, and 18S rRNA OTUs using the Protist Ribosomal Reference (PR2) database22 containing curated dinoflagellate 18S rRNA sequences28. Eukaryotic 16S rRNA plastid sequences were separated from the prokaryotic 16S rRNA fraction by searching against phytoREF5. Rows correspond to OTUs and columns contain reads mapped from each sample. Samples are labelled first by station followed by depth and McLane pump number (for example, 1_050_06 = St. 1, 50 m, pump no. 6).

Supplementary Table 6

Normalized transcript (T) and protein (P) abundances and average fold changes between euphotic and mesopelagic zones used in Extended Data Fig. 7. Only shared KEGG genes (KOs) are shown that were detected in at least one metatranscriptome and one metaproteome, with genes of interest proteorhodopin (PFam PF01036) and ISIP2a (KEGG gene pti:PHATDRAFT_54465) manually added. Values of zero were changed to a small value (0.1) to allow for fold change estimates.

Supplementary Table 7

KEGG genes associated with WGCNA color modules depicted in Extended data Fig. 5. The Pearson correlation coefficient between each gene and temperature is shown along with the associated P value. As temperature is higher in surface waters, genes positively correlated with temperature indicate higher expression shallower in the water column. Module membership for each gene is also shown along with P values, and indicates Pearson correlation between gene expression and module eigengene. The default WGCNA module colours were manually changed from turquoise and blue to white and black, respectively, for visual consistency with other figures.

Supplementary Table 8

Dissolved Fe concentrations (20–500 m).

Supplementary Table 9

Carbon export and protein biomass parameters shown in Extended Data Fig. 10: carbon flux, mass flux, b values and depth-integrated protein counts for dinoflagellates and Prochlorococcus.

Supplementary Table 10

HPLC two-dimensional active modulation gradient.

Supplementary Table 11

Peptide-spectrum matches (PSMs, total spectral counts), number of mass spectra identified (MS2) and ratios (PSM/MS2) for metaproteomic samples.

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Cohen, N.R., McIlvin, M.R., Moran, D.M. et al. Dinoflagellates alter their carbon and nutrient metabolic strategies across environmental gradients in the central Pacific Ocean. Nat Microbiol 6, 173–186 (2021). https://doi.org/10.1038/s41564-020-00814-7

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