Article | Published:

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

Nature Microbiologyvolume 3pages939947 (2018) | Download Citation

Abstract

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.

Access optionsAccess options

Rent or Buy article

Get time limited or full article access on ReadCube.

from$8.99

All prices are NET prices.

Additional information

Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

References

  1. 1.

    You, C. et al. Coordination of bacterial proteome with metabolism by cyclic AMP signalling. Nature 500, 301–306 (2013).

  2. 2.

    Li, G.-W., Burkhardt, D., Gross, C. & Weissman, J. S. Quantifying absolute protein synthesis rates reveals principles underlying allocation of cellular resources. Cell 157, 624–635 (2014).

  3. 3.

    Scott, M., Klumpp, S., Mateescu, E. M. & Hwa, T. Emergence of robust growth laws from optimal regulation of ribosome synthesis. Mol. Syst. Biol. 10, 747–747 (2014).

  4. 4.

    Giordano, N., Mairet, F., Gouzé, J.-L., Geiselmann, J. & de Jong, H. Dynamical allocation of cellular resources as an optimal control problem: novel insights into microbial growth strategies. PLoS Comput. Biol. 12, e1004802 (2016).

  5. 5.

    Dennis, P. P. & Bremer, H. Modulation of chemical composition and other parameters of the cell at different exponential growth rates. EcoSal Plus https://doi.org/10.1128/ecosal.5.2.3 (2008).

  6. 6.

    Neidhardt, F. C. & Magasanik, B. Studies on the role of ribonucleic acid in the growth of bacteria. Biochim. Biophys. Acta 42, 99–116 (1960).

  7. 7.

    Baracchini, E. & Bremer, H. Determination of synthesis rate and lifetime of bacterial mRNAs. Anal. Biochem. 167, 245–260 (1987).

  8. 8.

    Schaechter, M., Maaløe, O. & Kjeldgaard, N. O. Dependency on medium and temperature of cell size and chemical composition during balanced growth of Salmonella typhimurium. Microbiology 19, 592–606 (1958).

  9. 9.

    Scott, M., Gunderson, C. W., Mateescu, E. M., Zhang, Z. & Hwa, T. Interdependence of cell growth and gene expression: origins and consequences. Science 330, 1099–1102 (2010).

  10. 10.

    Yamamoto, T., Izumi, S. & Gekko, K. Mass spectrometry of hydrogen/deuterium exchange in 70S ribosomal proteins from E. coli. FEBS Lett. 580, 3638–3642 (2006).

  11. 11.

    Nomura, M. & Gourse, R. Regulation of the synthesis of ribosomes and ribosomal components. Annu. Rev. Biochem. 53, 75–117 (1984).

  12. 12.

    Maaloe, O. & Kjeldgaard, N. O. Control of Macromolecular Synthesis (WA Benjamin, New York, 1966).

  13. 13.

    Dai, X. et al. Reduction of translating ribosomes enables Escherichia coli to maintain elongation rates during slow growth. Nat. Microbiol. 2, 16231 (2016).

  14. 14.

    Lewin, B. Genes VIII (Pearson Education, Upper Saddle River, 2003).

  15. 15.

    Wanner, B. L. Gene regulation by phosphate in enteric bacteria. J. Cell. Biochem. 51, 47–54 (1993).

  16. 16.

    Ahn, K. & Kornberg, A. Polyphosphate kinase from Escherichia coli. Purification and demonstration of a phosphoenzyme intermediate. J. Biol. Chem. 265, 11734–11739 (1990).

  17. 17.

    Schleif, R., Hess, W., Finkelstein, S. & Ellis, D. Induction kinetics of the L-arabinose operon of Escherichia coli. J. Bacteriol. 115, 9–14 (1973).

  18. 18.

    Dalbow, D. G. & Young, R. Synthesis time of β-galactosidase in Escherichia coli B/r as a function of growth rate. Biochem. J. 150, 13–20 (1975).

  19. 19.

    Qin, D. & Fredrick, K. Analysis of polysomes from bacteria. Meth. Enzymol. 530, 159–172 (2013).

  20. 20.

    Beller, R. J. & Lubsen, N. H. Effect of polypeptide chain length on dissociation of ribosomal complexes. Biochemistry 11, 3271–3276 (1972).

  21. 21.

    Nathans, D. Puromycin inhibition of protein synthesis: incorporation of puromycin into peptide chains. Proc. Natl Acad. Sci. USA 51, 585–592 (1964).

  22. 22.

    Steitz, J. A. Polypeptide chain initiation: nucleotide sequences of the three ribosomal binding sites in bacteriophage R17. RNA 224, 957–964 (1969).

  23. 23.

    Ingolia, N. T., Ghaemmaghami, S., Newman, J. R. S. & Weissman, J. S. Genome-wide analysis in vivo of translation with nucleotide resolution using ribosome profiling. Science 324, 218–223 (2009).

  24. 24.

    Doucette, C. D., Schwab, D. J., Wingreen, N. S. & Rabinowitz, J. D. α-Ketoglutarate coordinates carbon and nitrogen utilization via enzyme I inhibition. Nat. Chem. Biol. 7, 894–901 (2011).

  25. 25.

    Yuan, J. et al. Metabolomics‐driven quantitative analysis of ammonia assimilation in E. coli. Mol. Syst. Biol. 5, 302 (2009).

  26. 26.

    Ikeda, T. P., Shauger, A. E. & Kustu, S. Salmonella typhimurium apparently perceives external nitrogen limitation as internal glutamine limitation. J. Mol. Biol. 259, 589–607 (1996).

  27. 27.

    Subramaniam, A. R., Pan, T. & Cluzel, P. Environmental perturbations lift the degeneracy of the genetic code to regulate protein levels in bacteria. Proc. Natl Acad. Sci. USA 110, 2419–2424 (2013).

  28. 28.

    Sørensen, M. A. et al. Over expression of a tRNALeu isoacceptor changes charging pattern of leucine tRNAs and reveals new codon reading. J. Mol. Biol. 354, 16–24 (2005).

  29. 29.

    Hauryliuk, V., Atkinson, G. C., Murakami, K. S., Tenson, T. & Gerdes, K. Recent functional insights into the role of (p)ppGpp in bacterial physiology. Nat. Rev. Micro 13, 298–309 (2015).

  30. 30.

    Potrykus, K. & Cashel, M. (p)ppGpp: Still Magical?. Annu. Rev. Microbiol. 62, 35–51 (2008).

  31. 31.

    Mitkevich, V. A. et al. Thermodynamic characterization of ppGpp binding to EF-G or IF2 and of initiator tRNA binding to free IF2 in the presence of GDP, GTP, or ppGpp. J. Mol. Biol. 402, 838–846 (2010).

  32. 32.

    Milon, P. et al. The nucleotide-binding site of bacterial translation initiation factor 2 (IF2) as a metabolic sensor. Proc. Natl Acad. Sci. USA 103, 13962–13967 (2006).

  33. 33.

    Jiang, M., Sullivan, S. M., Wout, P. K. & Maddock, J. R. G-protein control of the ribosome-associated stress response protein SpoT. J. Bacteriol. 189, 6140–6147 (2007).

  34. 34.

    Gentry, D. R. & Cashel, M. Mutational analysis of the Escherichia coli spoT gene identifies distinct but overlapping regions involved in ppGpp synthesis and degradation. Mol. Microbiol. 19, 1373–1384 (1996).

  35. 35.

    Pavlov, M. Y. & Ehrenberg, M. Optimal control of gene expression for fast proteome adaptation to environmental change. Proc. Natl Acad. Sci. USA 110, 20527–20532 (2013).

  36. 36.

    Kjeldgaard, N. O., Maaløe, O. & Schaechter, M. The transition between different physiological states during balanced growth of Salmonella typhimurium. Microbiology 19, 607–616 (1958).

  37. 37.

    Brauer, M. J. et al. Coordination of growth rate, cell cycle, stress response, and metabolic activity in yeast. Mol. Biol. Cell 19, 352–367 (2008).

  38. 38.

    Paul, B. J., Ross, W., Gaal, T. & Gourse, R. L. rRNA transcription in Escherichia coli . Annu. Rev. Genet. 38, 749–770 (2004).

  39. 39.

    Laursen, B. S., Sørensen, H. P., Mortensen, K. K. & Sperling-Petersen, H. U. Initiation of protein synthesis in bacteria. Microbiol. Mol. Biol. Rev. 69, 101–123 (2005).

  40. 40.

    Xiao, H. et al. Residual guanosine 3’,5’-bispyrophosphate synthetic activity of relA null mutants can be eliminated by spoT null mutations. J. Biol. Chem. 266, 5980–5990 (1991).

  41. 41.

    Rojas, A.-M., Ehrenberg, M. N., Andersson, S. G. E. & Kurland, C. G. ppGpp inhibition of elongation factors Tu, G and Ts during polypeptide synthesis. Mol. Gen. Genet. 197, 36–45 (1984).

  42. 42.

    Zarrinpar, A., Chaix, A., Yooseph, S. & Panda, S. Diet and feeding pattern affect the diurnal dynamics of the gut microbiome. Cell Metab. 20, 1006–1017 (2014).

  43. 43.

    Korem Kohanim, Y., Levi, D., Jona, G., Bren, A. & Alon, U. A bacterial growth law out of steady-state. 23, 2891–2900 (2018).

  44. 44.

    Erickson, D. W. et al. A global resource allocation strategy governs growth transition kinetics of Escherichia coli. Nature 551, 119–123 (2017).

  45. 45.

    Mori, M., Schink, S., Erickson, D. W., Gerland, U. & Hwa, T. Quantifying the benefit of a proteome reserve in fluctuating environments. Nat. Commun. 8, 1225 (2017).

  46. 46.

    Towbin, B. D. et al. Optimality and sub-optimality in a bacterial growth law. Nat. Commun. 8, 14123 (2017).

  47. 47.

    Peters, J. M. et al. A comprehensive, CRISPR-based functional analysis of essential genes in bacteria. Cell 165, 1493–1506 (2016).

  48. 48.

    Kafri, M., Metzl-Raz, E., Jona, G. & Barkai, N. The cost of protein production. Cell Rep. 14, 22–31 (2016).

  49. 49.

    Metzl-Raz, E. et al. Principles of cellular resource allocation revealed by condition-dependent proteome profiling. eLife 6, e03528 (2017).

  50. 50.

    Baba, T. et al. Construction of Escherichia coli K-12 in-frame, single-gene knockout mutants: the Keio collection. Mol. Syst. Biol. 2, 2006.0008 (2006).

  51. 51.

    Ares, M. Bacterial RNA isolation. Cold Spring Harb. Protoc. 2012, 1024–1027 (2012).

  52. 52.

    Zhu, M., Dai, X. & Wang, Y.-P. Real time determination of bacterial in vivoribosome translation elongation speed based on LacZα complementation system. Nucleic Acids Res. 44, e155 (2016).

  53. 53.

    Afgan, E. et al. The Galaxy platform for accessible, reproducible and collaborative biomedical analyses: 2016 update. Nucleic Acids Res. 44, 3–10 (2016).

  54. 54.

    Li, H. & Durbin, R. Fast and accurate long-read alignment with Burrows-Wheeler transform. Bioinformatics 26, 589–595 (2010).

  55. 55.

    Dunn, J. G. & Weissman, J. S. Plastid: nucleotide-resolution analysis of next-generation sequencing and genomics data. BMC Genom. 17, 958 (2016).

  56. 56.

    Woolstenhulme, C. J., Guydosh, N. R., Green, R. & Buskirk, A. R. High-precision analysis of translational pausing by ribosome profiling in bacteria lacking EFP. Cell Rep. 11, 13–21 (2015).

Download references

Acknowledgements

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).

Author information

Author notes

    • Junyoung O. Park

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

Affiliations

  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

Authors

  1. Search for Sophia Hsin-Jung Li in:

  2. Search for Zhiyuan Li in:

  3. Search for Junyoung O. Park in:

  4. Search for Christopher G. King in:

  5. Search for Joshua D. Rabinowitz in:

  6. Search for Ned S. Wingreen in:

  7. Search for Zemer Gitai in:

Contributions

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.

About this article

Publication history

Received

Accepted

Published

DOI

https://doi.org/10.1038/s41564-018-0199-2

Further reading