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Exploiting rRNA operon copy number to investigate bacterial reproductive strategies

Nature Microbiology volume 1, Article number: 16160 (2016) Cite this article

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Abstract

The potential for rapid reproduction is a hallmark of microbial life, but microbes in nature must also survive and compete when growth is constrained by resource availability. Successful reproduction requires different strategies when resources are scarce and when they are abundant1,2, but a systematic framework for predicting these reproductive strategies in bacteria has not been available. Here, we show that the number of ribosomal RNA operons (rrn) in bacterial genomes predicts two important components of reproduction—growth rate and growth efficiency—which are favoured under contrasting regimes of resource availability3,4. We find that the maximum reproductive rate of bacteria doubles with a doubling of rrn copy number, and the efficiency of carbon use is inversely related to maximal growth rate and rrn copy number. We also identify a feasible explanation for these patterns: the rate and yield of protein synthesis mirror the overall pattern in maximum growth rate and growth efficiency. Furthermore, comparative analysis of genomes from 1,167 bacterial species reveals that rrn copy number predicts traits associated with resource availability, including chemotaxis and genome streamlining. Genome-wide patterns of orthologous gene content covary with rrn copy number, suggesting convergent evolution in response to resource availability. Our findings imply that basic cellular processes adapt in contrasting ways to long-term differences in resource availability. They also establish a basis for predicting changes in bacterial community composition in response to resource perturbations using rrn copy number measurements5 or inferences6,7.

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Figure 1: Maximum growth rate, carbon use efficiency and rrn copy number.
Figure 2: Protein synthesis phenotypes and rrn copy number.
Figure 3: Phylogenetic principal component analysis (pPCA) of genome content for 1,167 unique bacterial species.

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Acknowledgements

Support for this work was provided by the Department of Energy Office of Science Graduate Fellowship Program (DOE SCGF), made possible in part by the American Recovery and Reinvestment Act of 2009, administered by ORISE-ORAU under contract no. DE-AC05-06OR23100. Support was also provided by the National Science Foundation's Long-Term Ecological Research Program (grant no. DEB 1027253) and the National Institutes of Health (GM0099549). The authors thank A. Schmidt, C. Waldron, A. Venkataraman, B. Smith and M. Hoostal for their comments on the manuscript.

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Author notes

  1. Benjamin R. K. Roller

    Present address: † Present address: Center for Adaptation to a Changing Environment, ETH Zürich, 8092 Zürich, Switzerland.,

Authors and Affiliations

  1. Department of Internal Medicine, University of Michigan, Ann Arbor, 48109, Michigan, USA

    Benjamin R. K. Roller, Steven F. Stoddard & Thomas M. Schmidt

  2. Department of Microbiology and Molecular Genetics, Michigan State University, East Lansing, 48824, Michigan, USA

    Benjamin R. K. Roller

  3. Department of Ecology and Evolutionary Biology, University of Michigan, Ann Arbor, 48109, Michigan, USA

    Thomas M. Schmidt

  4. Department of Microbiology and Immunology, University of Michigan, Ann Arbor, 48109, Michigan, USA

    Thomas M. Schmidt

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Contributions

B.R.K.R. and T.M.S. conceived the study, interpreted the results and wrote the paper. B.R.K.R. performed the experiments, phylogenetic inferences and statistical analyses. S.F.S. provided custom relational databases, queries and Perl scripting to integrate the rrnDB, KEGG and NCBI taxonomy for genomic analyses.

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Correspondence to Thomas M. Schmidt.

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The authors declare no competing financial interests.

Supplementary information

Supplementary information

Supplementary Tables 1, 2, and 5, legends for Supplementary Tables 3 and 4, Supplementary Figures 1–5, Supplementary References (PDF 922 kb)

Supplementary Table 3

The 100 largest loadings on the first six pPCA axes in the separate analyses of modules and orthologues datasets. (XLSX 75 kb)

Supplementary Table 4

Bacteria included in this study. (XLSX 104 kb)

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Roller, B., Stoddard, S. & Schmidt, T. Exploiting rRNA operon copy number to investigate bacterial reproductive strategies. Nat Microbiol 1, 16160 (2016). https://doi.org/10.1038/nmicrobiol.2016.160

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