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Chemotaxis increases metabolic exchanges between marine picophytoplankton and heterotrophic bacteria

Nature Microbiology volume 8, pages 510–521 (2023)Cite this article

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Abstract

Behaviours such as chemotaxis can facilitate metabolic exchanges between phytoplankton and heterotrophic bacteria, which ultimately regulate oceanic productivity and biogeochemistry. However, numerically dominant picophytoplankton have been considered too small to be detected by chemotactic bacteria, implying that cell–cell interactions might not be possible between some of the most abundant organisms in the ocean. Here we examined how bacterial behaviour influences metabolic exchanges at the single-cell level between the ubiquitous picophytoplankton Synechococcus and the heterotrophic bacterium Marinobacter adhaerens, using bacterial mutants deficient in motility and chemotaxis. Stable-isotope tracking revealed that chemotaxis increased nitrogen and carbon uptake of both partners by up to 4.4-fold. A mathematical model following thousands of cells confirmed that short periods of exposure to small but nutrient-rich microenvironments surrounding Synechococcus cells provide a considerable competitive advantage to chemotactic bacteria. These findings reveal that transient interactions mediated by chemotaxis can underpin metabolic relationships among the ocean’s most abundant microorganisms.

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Fig. 1: Reciprocal exchanges between Synechococcus and M. adhaerens HP15.
Fig. 2: Reciprocal exchanges between Synechococcus and M. adhaerens HP15 are enhanced by chemotaxis.
Fig. 3: Numerical simulations reveal the dynamic interactions between Marinobacter and Synechococcus.
Fig. 4: DOM enhancement due to chemotaxis depends strongly on the size and concentration of phytoplankton cells.

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Data availability

All chemotaxis, growth, metabolomics and NanoSIMS data are available at Zenodo (https://zenodo.org/record/7509161#.Y7fUcRVBw2w; https://doi.org/10.5281/zenodo.7509161). Source data are provided with this paper.

Code availability

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

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Acknowledgements

The authors thank F. Carrara and A. Hein for useful discussions. This work was supported by an Australian Research Council grant (DP180100838) to J.R.S. and J.-B.R. J.-B.R. was supported by an Australian Research Council Fellowship (FT210100100). D.R.B. was supported by an Australian Research Council Fellowship (DE180100911). D.R.B. performed simulations using The University of Melbourne’s High-Performance Computer Spartan (https://doi.org/10.4225/49/58ead90dceaaa). R.S. acknowledges support from a grant by the Simons Foundation (542395) as part of the Principles of Microbial Ecosystems Collaborative (PriME), a Gordon and Betty Moore Symbiosis in Aquatic Ecosystems Initiative Investigator Award (GBMF9197; https://doi.org/10.37807/GBMF9197) and a grant from the Swiss National Science Foundation (315230_176189). We acknowledge use of the Microscopy Australia Ion Probe Facility at The University of Western Australia, a facility funded by the University, State and Commonwealth Governments. This project used NCRIS-enabled Metabolomics Australia infrastructure at the University of Melbourne, funded through BioPlatforms Australia.

Author information

Author notes

  1. These authors contributed equally: Jean-Baptiste Raina, Marco Giardina, Douglas R. Brumley.

Authors and Affiliations

  1. University of Technology Sydney, Climate Change Cluster, Faculty of Science, Ultimo, New South Wales, Australia

    Jean-Baptiste Raina, Marco Giardina, Mathieu Pernice & Justin R. Seymour

  2. School of Mathematics and Statistics, The University of Melbourne, Parkville, Victoria, Australia

    Douglas R. Brumley

  3. Centre for Microscopy Characterisation and Analysis, The University of Western Australia, Perth, Western Australia, Australia

    Peta L. Clode, Paul Guagliardo & Jeremy Bougoure

  4. UWA School of Biological Sciences, The University of Western Australia, Perth, Western Australia, Australia

    Peta L. Clode

  5. UWA Oceans Institute, The University of Western Australia, Perth, Western Australia, Australia

    Peta L. Clode

  6. Metabolomics Australia, Bio21 Institute, The University of Melbourne, Parkville, Victoria, Australia

    Himasha Mendis

  7. Department of Earth Atmospheric and Planetary Sciences, Massachusetts Institute of Technology, Cambridge, MA, USA

    Steven Smriga

  8. Department of Biotechnology and Biomedicine, Technical University of Denmark, Kgs, Lyngby, Denmark

    Eva C. Sonnenschein

  9. Department of Biosciences, Swansea University, Singleton Park, Swansea, UK

    Eva C. Sonnenschein

  10. Department of Life Sciences & Chemistry, Jacobs University Bremen, Bremen, Germany

    Matthias S. Ullrich

  11. Institute of Environmental Engineering, Department of Civil, Environmental and Geomatic Engineering, ETH Zurich, Zurich, Switzerland

    Roman Stocker

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Contributions

J.-B.R., D.R.B., S.S., R.S. and J.R.S. designed the experiments. M.G. and J.-B.R. conducted the experimental work. M.G., P.L.C., P.G. and J.B. conducted the NanoSIMS work. J.-B.R. and H.M. conducted the metabolomics. D.R.B. conducted the agent-based simulations. E.C.S. and M.S.U. provided the bacterial strains and mutants. J.-B.R., D.R.B., R.S. and J.R.S. wrote the manuscript, and all authors edited subsequent versions.

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

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Extended data

Extended Data Fig. 1 Chemotactic response of Marinobacter adhaerens HP15 to metabolites exuded by Synechococcus.

The chemotactic index, Ic denotes the concentration of cells within ISCA wells, normalized by the mean concentration of cells within wells containing no chemoattractants (filtered ESAW), after 30 min laboratory deployment. Wells containing Synechococcus exudates (1 mg ml−1) and 10% Marine Broth (MB) contained significantly more bacteria than the ESAW control (ANOVA, n = 5 biologically independent samples, p < 0.005; Supplementary Table 5). Error bars represent standard error of the mean.

Extended Data Fig. 2 Dissolved Organic Matter (DOM) exposure of model bacteria.

Mean DOM exposure for three bacterial motility strategies across three different Synechococcus concentrations (leakage rate L = 0.052 pmol hr−1). Chemotaxis conferred an enhancement in the DOM exposure by 2.1-, 1.3-, and 1.1-fold, for Synechococcus concentrations of 103, 104, and 105 cells ml−1 respectively, compared to non-chemotactic (ΔcheA) or non-motile (ΔfliC) mutants.

Extended Data Fig. 3 Residence time of model bacteria.

(a). The bacterial residence time depends on the radius of the analysis zone and motility strategy. For ∆cheA mutants, the residence time grows linearly with radius. However, WT cells exhibit a steep increase for small radii, reflecting their capacity to detect the phytoplankton exudates. (b) The rate at which the residence time increases with radius reveals the zone in which chemotactic bacteria exhibit the strongest behavioral response to the DOM gradient. From this the encounter radius of 35 μm can be extracted. Other model parameters include L = 0.052 pmol hr−1, \(\rho = 10^3\,{{{\mathrm{cells}}}}\,{{{\mathrm{ml}}}}^{ - 1}\).

Extended Data Fig. 4 DOM profile does not depend strongly on bacterial consumption.

In each plot, the steady state DOM profile emerges due to a balance between constant phytoplankton exudation and diffusion-limited uptake by bacteria. (a) DOM profile for four different bacterial concentrations. (b) Restricting bacteria to lie in the region R < R0 has a minor influence on the resultant DOM profile.

Extended Data Fig. 5 Growth of Synechococcus sp. CS-94 RRIMP N1 and Marinobacter adhaerens HP15.

(a) Growth curves of M. adhaerens HP15 wild type (WT), non-chemotactic mutant (ΔcheA), and non-motile mutant (ΔfliC), each separately co-cultured with Synechococcus at an initial concentration of 103 cells ml−1 for both partners. (b) Simultaneous growth curve of Synechococcus for the same three co-culture experiments. Note: to clearly visualise differences in cell numbers during early timepoints, Synechococcus cell numbers are plotted on a logarithmic scale. Asterisks indicate timepoints at which treatments are significantly different (simple main effect test, p < 0.05, Supplementary Table 9). Error bars represent standard error of the mean (n = 4 biologically independent samples). (c) Growth curves of Marinobacter adhaerens HP15 wild type (WT), non-chemotactic mutant (ΔcheA), and non-motile mutant (ΔfliC) in Marine Broth. Error bars represent standard error of the mean (n = 3 biologically independent samples). Asterisks indicate timepoints at which treatments are significantly different (simple main effect test, p < 0.05, Supplementary Table 10).

Extended Data Fig. 6 DOM concentration within a 2D cross-section of the full 3D profile.

Results correspond to a Synechococcus concentration of ρ = 103 cells ml−1. Other parameters as in Supplementary Table 8. The white scale bar represents 1 mm.

Extended Data Fig. 7 DOM exposure of model bacteria.

The mean DOM concentration experienced by (a) non-chemotactic (∆cheA) mutants and (b) chemotactic (WT) bacteria, as a function of phytoplankton concentration (cells ml−1) and DOM leakage rate L (pmol hr−1).

Extended Data Fig. 8 Phytoplankton exudation rate affects bacteria-phytoplankton distances and bacterial ‘trapping’.

(a) Bacteria-phytoplankton distance is strongly affected by phytoplankton exudation rate. These data show the distance to the nearest hotspot, averaged over time (3 h co-incubation) and bacterial population (500 cells), as a function of DOM leakage rate L (pmol hr−1). Results are shown for three different phytoplankton concentrations, 103 (dotted), 104 (dashed), 105 cells ml−1 (solid), and for three different bacterial mutants: chemotactic WT (blue), non-chemotactic ∆cheA (orange), non-motile ∆fliC (red). (b) Bacteria-phytoplankton trapping statistics. These data show the percentage of bacterial cells that are situated within 35 μm of a phytoplankton cell (phycosphere), as a function of DOM leakage rate L (pmol hr−1). For each datapoint, results have been averaged over time (3 h co-incubation) and bacterial population (500 cells). Results are shown for three different phytoplankton concentrations, 103 (dotted), 104 (dashed), 105 cells ml−1 (solid), and for three different bacterial mutants: chemotactic WT (blue), non-chemotactic ∆cheA (orange), non-motile ∆fliC (red).

Extended Data Fig. 9 Distribution of the single cell enrichment data reported in Figs. 1 and 2.

(a) 15N uptake of M. adhaerens (103: n = 166; 104: n = 286; 105: n = 172) and (b) 13C uptake of Synechococcus (103: n = 10; 104: n = 17; 105: n = 37).

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Supplementary Fig. 1 and Tables 1–10.

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Supplementary Tables 1–10

Full Supplementary Tables 1–10.

Source data

Source Data Fig. 1

Raw data for Fig. 1.

Source Data Fig. 2

Raw data for Fig. 2.

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Raina, JB., Giardina, M., Brumley, D.R. et al. Chemotaxis increases metabolic exchanges between marine picophytoplankton and heterotrophic bacteria. Nat Microbiol 8, 510–521 (2023). https://doi.org/10.1038/s41564-023-01327-9

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