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Niche dimensions of a marine bacterium are identified using invasion studies in coastal seawater

Abstract

Niche theory is a foundational ecological concept that explains the distribution of species in natural environments. Identifying the dimensions of any organism’s niche is challenging because numerous environmental factors can affect organism viability. We used serial invasion experiments to introduce Ruegeria pomeroyi DSS-3, a heterotrophic marine bacterium, into a coastal phytoplankton bloom on 14 dates. RNA-sequencing analysis of R. pomeroyi was conducted after 90 min to assess its niche dimensions in this dynamic ecosystem. We identified ~100 external conditions eliciting transcriptional responses, which included substrates, nutrients, metals and biotic interactions such as antagonism, resistance and cofactor synthesis. The peak bloom was characterized by favourable states for most of the substrate dimensions, but low inferred growth rates of R. pomeroyi at this stage indicated that its niche was narrowed by factors other than substrate availability, most probably negative biotic interactions with the bloom dinoflagellate. Our findings indicate chemical and biological features of the ocean environment that can constrain where heterotrophic bacteria survive.

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Fig. 1: Conditions and methods for invasion studies.
Fig. 2: R. pomeroyi gene expression patterns in Monterey Bay invasion studies.
Fig. 3: Time course of relative gene expression indicating R. pomeroyi‘s responses to niche dimensions.
Fig. 4: Characterization of R. pomeroyi niche boundaries.

Data availability

Data that support the findings of the present study have been deposited in the National Center for Biotechnology Information‘s Sequence Read Archive with BioProject nos. PRJNA641119 (RNA-seq) and PRJNA511156PRJNA511331 (16S and 18S rRNA data), and the Biological and Chemical Oceanography Data Management Office under https://doi.org/10.1575/1912/bco-dmo.756413.2 at https://www.bco-dmo.org/dataset/756413/data (environmental data). Source data are provided with this paper.

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Acknowledgements

We thank C. Preston, J. Birch, C. Sholin and the MBARI ESP team for providing field sampling infrastructure and expertise; S. Sharma for providing bioinformatic assistance; C. Smith, C. Thomas and K. Esson for assisting with field and laboratory techniques; R. Michisaki for providing expertise on microbial biomass estimates; and the University of Georgia Genomics and Bioinformatics Core for supplying sequencing services. This work was supported by the Simons Foundation (grant no. 542391 to M.A.M.) within the Principles of Microbial Ecosystems (PriME) Collaborative and by NSF (IOS-1656311). The rRNA amplicon sequencing was provided through the DOE Joint Genome Institute Community Sequencing Program.

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Contributions

B.N. and M.A.M. conceived of the study. B.N. collected the data. B.N. and M.A.M. analysed the data and wrote the paper.

Corresponding author

Correspondence to Mary Ann Moran.

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The authors declare no competing interests.

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Peer review information Nature Microbiology thanks Virginia Armbrust and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

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Extended data

Extended Data Fig. 1 Protist and bacterial community composition during the 2016 Monterey Bay autumn bloom based on rRNA gene sequencing.

Each bar represents 1 replicate sample. a) 18S rRNA, order level. b) 18S rRNA, genus level. c) 16S rRNA, family level.

Source data

Extended Data Fig. 2

Salinity and temperature of seawater sampled by CTD at Monterey Bay Station MO in Fall, 2016.

Source data

Extended Data Fig. 3 Correlations of Z-score normalized R. pomeroyi gene expression module eigengenes with environmental data.

Expression was measured by RNAseq after incubation in seawater collected on 14 dates at Monterey Bay Station M0 in Fall, 2016. Dinoflagellate parasites are members of the Syndinales clade. Cells are colored by Pearson’s R parameter. Two stars indicate correlations at p < 0.01; one star indicates correlations at p < 0.05. d.f. = 12.

Source data

Extended Data Fig. 4 Correlations of representative R. pomeroyi genes for transport of ammonium (amtB) and urea (urtB) with Akashiwo biomass.

The extreme value of Akashiwo biomass is removed in the inset. **, significant Pearson’s R correlation, with p < 0.001 for amtB and urtB in the main figure, d.f. = 12; and p = 0.010 for amtB and p = 0.331 for umtB in the inset figure, d.f. = 11.

Source data

Supplementary information

Supplementary Information

Supplementary Tables 1 and 2.

Reporting Summary

Source data

Source Data Fig. 1

Microbial abundance and Chl a, gene expression data for glucose catabolism genes.

Source Data Fig. 2

Gene expression data for PCA, 18S rRNA amplicon relative abundance and mean z-scores for gene modules.

Source Data Fig. 3

Gene expression data by date and replicate.

Source Data Fig. 4

Gene expression data for ribosomal proteins, rpoD and dmdA, genome accession nos. and DMSP concentrations.

Source Data Extended Data Fig. 1

The 18S rRNA ASV abundance at the order level, 18S rRNA ASV abundance at the genus level and 16S rRNA ASV abundance at the family level.

Source Data Extended Data Fig. 2

Temperature and salinity data.

Source Data Extended Data Fig. 3

Environmental data and module eigengenes.

Source Data Extended Data Fig. 4

Nitrogen gene expression data and A. sanguinea biomass data.

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Nowinski, B., Moran, M.A. Niche dimensions of a marine bacterium are identified using invasion studies in coastal seawater. Nat Microbiol 6, 524–532 (2021). https://doi.org/10.1038/s41564-020-00851-2

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