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Chemotaxis shapes the microscale organization of the ocean’s microbiome

Abstract

The capacity of planktonic marine microorganisms to actively seek out and exploit microscale chemical hotspots has been widely theorized to affect ocean-basin scale biogeochemistry1,2,3, but has never been examined comprehensively in situ among natural microbial communities. Here, using a field-based microfluidic platform to quantify the behavioural responses of marine bacteria and archaea, we observed significant levels of chemotaxis towards microscale hotspots of phytoplankton-derived dissolved organic matter (DOM) at a coastal field site across multiple deployments, spanning several months. Microscale metagenomics revealed that a wide diversity of marine prokaryotes, spanning 27 bacterial and 2 archaeal phyla, displayed chemotaxis towards microscale patches of DOM derived from ten globally distributed phytoplankton species. The distinct DOM composition of each phytoplankton species attracted phylogenetically and functionally discrete populations of bacteria and archaea, with 54% of chemotactic prokaryotes displaying highly specific responses to the DOM derived from only one or two phytoplankton species. Prokaryotes exhibiting chemotaxis towards phytoplankton-derived compounds were significantly enriched in the capacity to transport and metabolize specific phytoplankton-derived chemicals, and displayed enrichment in functions conducive to symbiotic relationships, including genes involved in the production of siderophores, B vitamins and growth-promoting hormones. Our findings demonstrate that the swimming behaviour of natural prokaryotic assemblages is governed by specific chemical cues, which dictate important biogeochemical transformation processes and the establishment of ecological interactions that structure the base of the marine food web.

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Fig. 1: Use of ISCA to probe for chemotaxis towards phytoplankton-derived DOM in the natural environment.
Fig. 2: ‘Generalist’ and ‘specialist’ prokaryotic taxa responding to phytoplankton–DOM.
Fig. 3: Enrichment of prokaryotic genes involved in phytoplankton–bacteria interactions.
Fig. 4: Specific associations between prokaryotes and phytoplankton-derived metabolites.

Data availability

The raw metabolome data files were deposited in MetaboLights under accession number MTBLS1980. The raw metagenome fastq files were deposited in the Sequence Read Archive (SRA) under accession number PRJNA639602. The raw amplicon fastq files were deposited in the SRA under accession number PRJNA707306. The 16S rRNA gene sequences of the three isolates were deposited in GenBank under accession numbers: MT826233, MT826234 and MZ373175Source data are provided with this paper.

Code availability

All custom analysis scripts are available on GitHub (https://github.com/JB-Raina-codes/ISCA-paper).

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Acknowledgements

The authors thank G. Gorick for his work on Fig. 1a, L. Bennar, G. Kholi, M. Fabris, N. Le Reun, M. Giardina and D. Hughes for laboratory assistance and E. Botté for her support throughout this project. This work was supported by the Gordon and Betty Moore Foundation through a grant to J.R.S., G.W.T., P.H. and R.S. (GBMF3801) and two Investigator Awards to R.S. (GBMF3783 and GBMF9197; https://doi.org/10.37807/GBMF9197), and through Australian Research Council grants DP110103091 to J.R.S., G.W.T. and R.S and DP180100838 to J.R.S., R.S. and J.-B.R. B.S.L. was supported by the Simons Foundation (Award 594111). G.W.T. was supported by an Australian Research Council Future Fellowship (FT170100070), P.H. was supported by Australian Research Council Laureate Fellowship (FL150100038). C.R. was supported by an Australian Research Council Future Fellowship (FT170100213). J.-B.R. was supported by an Australian Research Council Fellowship (DE160100636).

Author information

Authors and Affiliations

Authors

Contributions

J.-B.R., B.S.L., C.R., R.S., P.H., G.W.T. and J.R.S. designed all the experiments. J.-B.R., C.R., A.B. and N.S. performed all the experiments. J.-B.R., A.L. and H.M. generated and analysed the metabolomic data. J.-B.R., C.R., F.R., B.S.L. and D.H.P. generated and analysed the metagenomic data. J.-B.R., M.O., B.S.L., A.B., V.I.F. and B.S. generated and analysed the amplicon data and performed the network and correlative analyses. J.-B.R., B.S.L., R.S. and J.R.S. wrote the manuscript. All authors edited the manuscript before submission.

Corresponding authors

Correspondence to Jean-Baptiste Raina or Justin R. Seymour.

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The authors declare no competing interests.

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Nature thanks Hans-Peter Grossart, Karla Heidelberg and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data figures and tables

Extended Data Fig. 1 ISCA deployments through a two years period at Clovelly Beach (33.91°S, 151.26°E).

Chemotactic index Ic, denoting the concentration of cells within ISCA wells, normalized by the mean concentration of cells within wells containing filtered seawater (FSW), after 60 min field deployment. Solid bars are significantly different from wells containing FSW (ANOVA (one-sided), p < 0.05, all p-values are reported in Supplementary Table 3). Each treatment was replicated across four different ISCAs (n = 4), except between April and August 2016 (n = 3). Data are presented as mean values ± SEM. FSW: filtered seawater, Syne: Synechococcus, Proch: Prochlorococcus, Duna: Dunaliella, Rhodo; Rhodomonas, Phaeo: Phaeocystis, Ehux: Emiliania, Prym: Prymnesium, Chae: Chaetoceros, Dityl: Ditylum, Phae: Phaeodactylum, Thala: Thalassiosira, Durus: Durusdinium, Alex: Alexandrium, Amphi: Amphidinium.

Extended Data Fig. 2 Environmental variables influencing the strength of chemotaxis.

(a) Average chemotactic index (Ic) elicited by the phytoplankton-derived DOM for each of the 12 ISCA deployments described in this study at Clovelly Beach (33.91°S, 151.26°E). Error bars: standard errors. (b) Correlogram of the metadata measured during each deployment (the size and colour of each bubble is proportional to the strength of the correlation). Only statistically significant correlations are not crossed (Pearson’s correlation (two-sided), p < 0.01). (c) Significant correlation between chemotactic index and temperature (Pearson’s correlation (two-sided), p < 0.01).

Extended Data Fig. 3 Differences in chemical composition between the phytoplankton-derived DOM.

(a) Heatmap of the 111 compounds identified between the different phytoplankton species. Data were log-transformed and mean centred. An interactive version of this figure is available (Fig. S2). (b) Principal component analysis (PCA) of chemical composition of the phytoplankton-derived DOM: displaying the top three components (explaining 64.7% of the variance).

Extended Data Fig. 4 Relative abundance of the prokaryotic families present in the bulk seawater, the FSW controls, and in the different phytoplankton-derived DOM.

Only taxa representing more than 2% of the communities are displayed in colours, those representing less than 2% and grouped as “Other”. ND: taxonomy not determined at the family level.

Extended Data Fig. 5 Prokaryotic taxa significantly enriched the phytoplankton-derived DOM treatments.

(a) Number of prokaryotic taxa enriched in each phytoplankton-DOM treatment (compared to filtered seawater controls). The full list of taxa significantly enriched in phytoplankton-derived DOM treatments can be found in Supplementary Table 6. (b) Network analysis showing the differentiation between “generalist” and “specialist” families at the taxonomic level. This network has the same topology than the Figure 2b. Chemotactic prokaryotic taxa (small circles; nodes) are linked to the treatments they responded to (large circles) by lines with colours corresponding to each treatment. Each node is colour coded based on its taxonomy. (c) Number of prokaryote taxa significantly enriched in one or more phytoplankton-derived DOM treatments (compared to filtered seawater controls). Another graphical representation of this data can be found in Figure 2b.

Extended Data Fig. 6 Genes involved in motility, chemotaxis and surface-attachment were significantly enriched in the ISCA treatments compared to the bulk seawater.

Data were log-transformed and mean-centred (n = 4) for each ISCA treatment. Asterisks highlight significant enrichment compared to the bulk seawater (F-tests (one-sided), p < 0.05).

Extended Data Fig. 7 Genes involved in the uptake and degradation of phytoplankton-derived molecules (selected from the literature)39,67,68,69,70, as well as in the transport of a range of labile substrates, were significantly enriched in the prokaryotic communities responding to phytoplankton-derived DOM.

Data were log-transformed and mean-centred (n = 4) for each ISCA treatment. Asterisks highlight significant enrichment compared to the FSW treatment (F-tests (one-sided), p < 0.05, all p-values are reported in Supplementary Table 8). DMSP: dimethylsulfoniopropionate; DHPS: 2,3-dihydroxypropane-1-sulfonate; GBT: Glycine betaine.

Extended Data Fig. 8 Assay testing the ability of the bacterial isolates to catabolize the validated chemoattractants (Figure 4b).

Each chemoattractant was inoculated at a concentration of 1 mM (n = 4) in an artificial seawater medium supplemented with 0.2% of casamino acids. After 48 h, the optical density (OD600) of each culture was compared to controls only containing casamino acids. DGDG: Digalactosyldiacylglycerol; 3-aminopip: 3-aminopiperidin-2-one. Solid bars are significantly different from wells containing FSW (ANOVA (one-sided), p < 0.05, all p-values are reported in Supplementary Table 9). Data are presented as mean values ± SEM.

Extended Data Fig. 9 Control for bacterial growth during the ISCA deployment time.

(a) Comparison of prokaryotic cell counts before, and then 1 h and 5 h after post incubation with phytoplankton-derived DOM (1 mg mL−1). The number of prokaryotic cells were not statistically different between pre-incubation and one hour of incubation (ANOVA (one-sided), n = 3, p = 0.8026). Data are presented as mean values ± SEM. (b) Principal component analysis (PCA) of bacterial community composition resulting from incubations (explaining 80.4% of the variance), revealing the overlap between bacterial community compositions pre-incubation and those after 1 h of incubation. An analysis of similarities confirmed that community compositions were not significantly different pre-incubation and after one hour of incubation (ANOSIM; 99,999 permutations; n = 33; R = 0.108; p = 0.2), but significant differences were observed after five hours (R = 0.602; p = 0.001).

Extended Data Fig. 10 Control for shifts in bacterial composition during the ISCA deployment time.

Relative abundance of the bacterial communities (at the ASV level) before, 1 h and 5 h of incubation with phytoplankton-derived DOM (1 mg mL−1). The legend only shows the 30 most abundant ASVs.

Supplementary information

Supplementary Information

This file contains Supplementary Fig. 1; legend for Supplementary Fig. 2; Supplementary Tables 2, 10, 11; legends for Supplementary Tables 1, 3–9, 12; and Supplementary References.

Reporting Summary

Supplementary Figure 2

See Supplementary Information file for Supplementary Fig. 2 legend.

Supplementary Table 1

Occurrence of the eukaryotic phytoplankton genera used for our in situ chemotaxis assay (raw counts) at three coastal sites near Sydney, Australia, over a two years period (Cobblers Beach, Salmon Haul, Taren Point). This table is too large to be displayed in this document and is available as a separate file.

Supplementary Table 3

Sums of squares (SS), mean squares (MS) and significance levels for the ANOVAs of the chemotactic responses reported in Fig. 1b and Extended Data Fig. 1, with simple main effect test (diff: differences in mean; lower and upper: confidence intervals). All data were log transformed to meet the test assumptions. This table is too large to be displayed in this document and is available as a separate file.

Supplementary Table 4

Summary of PERMANOVA comparing the chemical composition of the ten phytoplankton-derived DOM treatments. Bray–Curtis similarity, 999 unrestricted permutations, main effect and pairwise tests. Abbreviation: Alex: Alexandrium; Amphi: Amphidinium; Chae: Chaetoceros; Dityl: Ditylum; Duna: Dunaliella; Ehux: Emiliania; Phae: Phaeodactylum; Prym: Prymnesium; Syne: Synechococcus; Thala: Thalassiosira. This table is too large to be displayed in this document and is available as a separate file.

Supplementary Table 5

Summary of PERMANOVA comparing the taxonomic composition of the prokaryotic communities between the different treatments. Bray–Curtis similarity, 999 unrestricted permutations, main effect and pairwise tests. This table is too large to be displayed in this document and is available as a separate file.

Supplementary Table 6

Summary of the taxa-specific enrichments between filtered seawater (control) and phytoplankton-derived DOM treatments using the R package metagenomeSeq1. This table is too large to be displayed in this document and is available as a separate file.

Supplementary Table 7

Summary of PERMANOVA comparing the functional capabilities of the prokaryotic communities between the different treatments. Bray–Curtis similarity, 999 unrestricted permutations, main effect and pairwise tests. This table is too large to be displayed in this document and is available as a separate file.

Supplementary Table 8

Summary of the functional gene enrichment within phytoplankton-derived DOM treatments relative to the filtered seawater control using the R package metagenomeSeq1. This table is too large to be displayed in this document and is available as a separate file.

Supplementary Table 9

Sums of squares (SS), mean squares (MS) and significance levels for the ANOVAs of the chemotactic responses and growth reported in Fig. 4b, with simple main effect test (diff: differences in mean; lower and upper: confidence intervals). This table is too large to be displayed in this document and is available as a separate file.

Supplementary Table 12

Quality check results of the ISCA metagenomes and all additional controls (mock communities: “mock_ACE”; DNA extraction controls: “Neg_Feb”; Library prep controls: “Neg_ACE”; and undeployed ISCA treatments: “Control_undeployed”). This table is too large to be displayed in this document and is available as a separate file.

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Raina, JB., Lambert, B.S., Parks, D.H. et al. Chemotaxis shapes the microscale organization of the ocean’s microbiome. Nature 605, 132–138 (2022). https://doi.org/10.1038/s41586-022-04614-3

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