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Escherichia coli translation strategies differ across carbon, nitrogen and phosphorus limitation conditions

Nature Microbiologyvolume 3pages939947 (2018) | Download Citation


For cells to grow faster they must increase their protein production rate. Microorganisms have traditionally been thought to accomplish this increase by producing more ribosomes to enhance protein synthesis capacity, leading to the linear relationship between ribosome level and growth rate observed under most growth conditions previously examined. Past studies have suggested that this linear relationship represents an optimal resource allocation strategy for each growth rate, independent of any specific nutrient state. Here we investigate protein production strategies in continuous cultures limited for carbon, nitrogen and phosphorus, which differentially impact substrate supply for protein versus nucleic acid metabolism. Unexpectedly, we find that at slow growth rates, Escherichia coli achieves the same protein production rate using three different strategies under the three different nutrient limitations. Under phosphorus (P) limitation, translation is slow due to a particularly low abundance of ribosomes, which are RNA-rich and thus particularly costly for phosphorous-limited cells. Under nitrogen (N) limitation, translation elongation is slowed by processes including ribosome stalling at glutamine codons. Under carbon (C) limitation, translation is slowed by accumulation of inactive ribosomes not bound to messenger RNA. These extra ribosomes enable rapid growth acceleration during nutrient upshift. Thus, bacteria tune ribosome usage across different limiting nutrients to enable balanced nutrient-limited growth while also preparing for future nutrient upshifts.

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We thank the members of the Gitai, Wingreen and Rabinowitz laboratories for helpful discussions. We thank G.-W. Li for his support for the ribosome profiling experiments. We thank the Microarray Core Facility at the Lewis-Sigler Institute (D. Sanchez, J. M. Miller, J. Wiggins and W. Wang) for RNA-Seq sample processing and sequencing and the Princeton Proteomics Core Facility (H. Shwe and T. Srikumar) for ribosome profiling sample processing. L. Parsons provided helpful technical support for bioinformatics analysis of the sequencing data. We thank the Botstein lab, particularly S. Silverman, for chemostat operation support and all former members for discussions relating to microbial growth. This work was supported by grants from the National Institutes of Health (DP1AI124669 and R01GM082938) and NSF (PHY-1607612).

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Author notes

    • Junyoung O. Park

    Present address: Department of Chemical and Biomolecular Engineering, University of California, Los Angeles, CA, USA


  1. Department of Molecular Biology, Princeton University, Princeton, NJ, USA

    • Sophia Hsin-Jung Li
    • , Ned S. Wingreen
    •  & Zemer Gitai
  2. Princeton Center for Theoretical Science, Princeton University, Princeton, NJ, USA

    • Zhiyuan Li
  3. Lewis-Sigler Institute for Integrative Genomics, Princeton University, Princeton, NJ, USA

    • Junyoung O. Park
    • , Joshua D. Rabinowitz
    •  & Ned S. Wingreen
  4. Department of Chemical and Biological Engineering, Princeton University, Princeton, NJ, USA

    • Junyoung O. Park
  5. Department of Physics, Princeton University, Princeton, NJ, USA

    • Christopher G. King
  6. Department of Chemistry, Princeton University, Princeton, NJ, USA

    • Joshua D. Rabinowitz


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S.H.L., J.O.P., J.D.R., N.S.W. and Z.G. designed the experiments. S.H.L., J.O.P. and C.G.K. performed experiments. Z.L. and N.S.W. constructed the mathematical models. S.H.L. conducted computational analysis of sequencing data. S.H.L, Z.L., N.S.W. and Z.G. wrote the paper with assistance from J.D.R.

Competing interests

The authors declare no competing interests.

Corresponding authors

Correspondence to Ned S. Wingreen or Zemer Gitai.

Supplementary information

  1. Supplementary Information

    Supplementary Notes, Supplementary Figures 1–9, Supplementary Table 1, Supplementary References.

  2. Reporting Summary

  3. Supplementary Table 2

    All RNA-to-protein ratios quantified across nutrient limitations and genetic backgrounds.

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