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Shaping bacterial gene expression by physiological and proteome allocation constraints

Nature Reviews Microbiology volume 21, pages 327–342 (2023)Cite this article

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Abstract

Networks of molecular regulators are often the primary objects of focus in the study of gene regulation, with the machinery of protein synthesis tacitly relegated to the background. Shifting focus to the constraints imposed by the allocation of protein synthesis flux reveals surprising ways in which the actions of molecular regulators are shaped by physiological demands. Using carbon catabolite repression as a case study, we describe how physiological constraints are sensed through metabolic fluxes and how flux-controlled regulation gives rise to simple empirical relations between protein levels and the rate of cell growth.

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Fig. 1: Physiological constraints on gene expression.
Fig. 2: Growth dependence of ribosomal and non-ribosomal proteins.
Fig. 3: Modulation of carbon and nitrogen flux reveals protein synthesis constraints on catabolic and anabolic proteins.
Fig. 4: Coordination of catabolic and anabolic flux via cAMP–Crp signalling.
Fig. 5: Flux-controlled regulation and proteome remodelling during growth transitions.

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References

  1. Neidhardt, F. C., Ingraham, J. L. & Schaechter, M. Physiology of the Bacterial Cell: A Molecular Approach (Sinauer Associates, 1990).

  2. Bervoets, I. & Charlier, D. Diversity, versatility and complexity of bacterial gene regulation mechanisms: opportunities and drawbacks for applications in synthetic biology. FEMS Microbiol. Rev. 43, 304–339 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  3. Dorman, C. J. Structure and Function of the Bacterial Genome (Wiley-Blackwell, 2020).

  4. Henkin, T. M. & Peters, J. E. Snyder & Champness Molecular Genetics of Bacteria. 5 edn (ASM Press, 2020).

  5. Phillips, R. The Molecular Switch: Signaling and Allostery (Princeton University Press, 2020).

  6. van den Berg, J., Boersma, A. J. & Poolman, B. Microorganisms maintain crowding homeostasis. Nat. Rev. Microbiol. 15, 309–318 (2017).

    Article  PubMed  Google Scholar 

  7. Zhang, G. et al. Global and local depletion of ternary complex limits translational elongation. Nucleic Acids Res. 38, 4778–4787 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Klumpp, S., Scott, M., Pedersen, S. & Hwa, T. Molecular crowding limits translation and cell growth. Proc. Natl Acad. Sci. USA 110, 16754–16759 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  9. Dai, X. et al. Slowdown of translational elongation in Escherichia coli under hyperosmotic stress. mBio https://doi.org/10.1128/mBio.02375-17 (2018).

    Article  PubMed  PubMed Central  Google Scholar 

  10. Woldringh, C. L., Binnerts, J. S. & Mans, A. Variation in Escherichia coli buoyant density measured in Percoll gradients. J. Bacteriol. 148, 58–63 (1981).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Kubitschek, H. E. Buoyant density variation during the cell cycle in microorganisms. CRC Crit. Rev. Microbiol. 14, 73–97 (1987).

    Article  CAS  Google Scholar 

  12. Basan, M. et al. Inflating bacterial cells by increased protein synthesis. Mol. Syst. Biol. 11, 836 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  13. Oldewurtel, E. R., Kitahara, Y. & van Teeffelen, S. Robust surface-to-mass coupling and turgor-dependent cell width determine bacterial dry-mass density. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.2021416118 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  14. Cayley, S., Lewis, B. A., Guttman, H. J. & Record, M. T. Characterization of the cytoplasm of Escherichia coli K-12 as a function of external osmolarity: implications for protein-DNA interactions in vivo. J. Mol. Biol. 222, 281–300 (1991).

    Article  CAS  PubMed  Google Scholar 

  15. Milo, R. What is the total number of protein molecules per cell volume? A call to rethink some published values. BioEssays 35, 1050–1055 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  16. Balakrishnan, R. et al. Principles of gene regulation quantitatively connect DNA to RNA and proteins in bacteria. bioRxiv https://doi.org/10.1101/2021.05.24.445329 (2021).

    Article  Google Scholar 

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

    Article  Google Scholar 

  18. Schmidt, A. et al. The quantitative and condition-dependent Escherichia coli proteome. Nat. Biotechnol. 34, 104–110 (2016).

    Article  CAS  PubMed  Google Scholar 

  19. Mori, M. et al. From coarse to fine: the absolute Escherichia coli proteome under diverse growth conditions. Mol. Syst. Biol. 17, e9536 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  21. Jun, S., Si, F. W., Pugatch, R. & Scott, M. Fundamental principles in bacterial physiology-history, recent progress, and the future with focus on cell size control: a review. Rep. Prog. Phys. 81, 80 (2018).

    Article  Google Scholar 

  22. 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).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Maaloe, O. in Gene Expression Biological Regulation and Development (ed Goldberger, R. F.) 487–542 (Plenum Press, 1979).

  26. Klumpp, S., Zhang, Z. & Hwa, T. Growth rate-dependent global effects on gene expression in bacteria. Cell 139, 1366–1375 (2009).

    Article  PubMed  PubMed Central  Google Scholar 

  27. Hui, S. et al. Quantitative proteomic analysis reveals a simple strategy of global resource allocation in bacteria. Mol. Syst. Biol. 11, 784 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  28. Schaechter, M., Maaloe, O. & Kjeldgaard, N. O. Dependency on medium and temperature of cell size and chemical composition during balanced grown of Salmonella typhimurium. J. Gen. Microbiol. 19, 592–606 (1958).

    Article  CAS  PubMed  Google Scholar 

  29. Mairet, F., Gouze, J. L. & de Jong, H. Optimal proteome allocation and the temperature dependence of microbial growth laws. NPJ Syst. Biol. Appl. 7, 14 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  30. Kaspy, I. et al. HipA-mediated antibiotic persistence via phosphorylation of the glutamyl-tRNA-synthetase. Nat. Commun. 4, 3001 (2013).

    Article  PubMed  Google Scholar 

  31. Chubukov, V., Gerosa, L., Kochanowski, K. & Sauer, U. Coordination of microbial metabolism. Nat. Rev. Microbiol. 12, 327–340 (2014).

    Article  CAS  PubMed  Google Scholar 

  32. Magasanik, B. Catabolite repression. Cold Spring Harb. Symposia Quant. Biol. 26, 249–256 (1961).

    Article  CAS  PubMed  Google Scholar 

  33. Deutscher, J. The mechanisms of carbon catabolite repression in bacteria. Curr. Opin. Microbiol. 11, 87–93 (2008).

    Article  CAS  PubMed  Google Scholar 

  34. Gorke, B. & Stulke, J. Carbon catabolite repression in bacteria: many ways to make the most out of nutrients. Nat. Rev. Microbiol. 6, 613–624 (2008).

    Article  PubMed  Google Scholar 

  35. Epps, H. M. & Gale, E. F. The influence of the presence of glucose during growth on the enzymic activities of Escherichia coli: comparison of the effect with that produced by fermentation acids. Biochem. J. 36, 619–623 (1942).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Ullmann, A. & Monod, J. Cyclic AMP as an antagonist of catabolite repression in Escherichia coli. FEBS Lett. 2, 57–60 (1968).

    Article  CAS  PubMed  Google Scholar 

  37. Perlman, R. & Pastan, I. Cyclic 3’5-AMP: stimulation of beta-galactosidase and tryptophanase induction in E. coli. Biochem. Biophys. Res. Commun. 30, 656–664 (1968).

    Article  CAS  PubMed  Google Scholar 

  38. Zubay, G., Schwartz, D. & Beckwith, J. Mechanism of activation of catabolite-sensitive genes: a positive control system. Proc. Natl Acad. Sci. USA 66, 104–110 (1970).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  39. Saier, M. H. Jr, Feucht, B. U. & Hofstadter, L. J. Regulation of carbohydrate uptake and adenylate cyclase activity mediated by the enzymes II of the phosphoenolpyruvate: sugar phosphotransferase system in Escherichia coli. J. Biol. Chem. 251, 883–892 (1976).

    Article  CAS  PubMed  Google Scholar 

  40. Kolb, A., Busby, S., Buc, H., Garges, S. & Adhya, S. Transcriptional regulation by cAMP and its receptor protein. Annu. Rev. Biochem. 62, 749–795 (1993).

    Article  CAS  PubMed  Google Scholar 

  41. Postma, P. W., Lengeler, J. W. & Jacobson, G. R. Phosphoenolpyruvate:carbohydrate phosphotransferase systems of bacteria. Microbiol. Rev. 57, 543–594 (1993).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  42. Saier, M. H. Jr. Regulatory interactions involving the proteins of the phosphotransferase system in enteric bacteria. J. Cell. Biochem. 51, 62–68 (1993).

    Article  CAS  PubMed  Google Scholar 

  43. Deutscher, J., Francke, C. & Postma, P. W. How phosphotransferase system-related protein phosphorylation regulates carbohydrate metabolism in bacteria. Microbiol. Mol. Biol. Rev. 70, 939–1031 (2006).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  44. Epstein, W., Rothman-Denes, L. B. & Hesse, J. Adenosine 3’:5’-cyclic monophosphate as mediator of catabolite repression in Escherichia coli. Proc. Natl Acad. Sci. USA 72, 2300–2304 (1975).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  45. Hogema, B. M. et al. Catabolite repression by glucose 6-phosphate, gluconate and lactose in Escherichia coli. Mol. Microbiol. 24, 857–867 (1997).

    Article  CAS  PubMed  Google Scholar 

  46. Bettenbrock, K. et al. Correlation between growth rates, EIIACrr phosphorylation, and intracellular cyclic AMP levels in Escherichia coli K-12. J. Bacteriol. 189, 6891–6900 (2007).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  47. McFall, E. & Magasanik, B. Effects of thymine and of phosphate deprivation on enzyme synthesis in Escherichia coli. Biochim. Biophys. Acta 55, 900–908 (1962).

    Article  CAS  Google Scholar 

  48. Clark, D. J. & Marr, A. G. Studies on the repression of beta-galactosidase in Escherichia coli. Biochim. Biophys. Acta 92, 85–94 (1964).

    CAS  PubMed  Google Scholar 

  49. Mandelstam, J. The repression of constitutive beta-galactosidase in Escherichia coli by glucose and other carbon sources. Biochem. J. 82, 489–493 (1962).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  50. Magasanik, B. & Neidhardt, F. C. Inhibitory effect of glucose on enzyme formation. Nature 178, 801–802 (1956).

    Article  CAS  PubMed  Google Scholar 

  51. Ullmann, A. Catabolite repression: a story without end. Res. Microbiol. 147, 455–458 (1996).

    Article  CAS  PubMed  Google Scholar 

  52. Wanner, B. L., Kodaira, R. & Neidhardt, F. C. Regulation of lac operon expression: reappraisal of the theory of catabolite repression. J. Bacteriol. 136, 947–954 (1978).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  53. Magasanik, B. & Neidhardt, F. C. The effect of glucose on the induced biosynthesis of bacterial enzymes in the presence and absence of inducing agents. Biochim. Biophys. Acta 21, 324–334 (1956).

    Article  CAS  PubMed  Google Scholar 

  54. Dourado, H., Mori, M., Hwa, T. & Lercher, M. J. On the optimality of the enzyme-substrate relationship in bacteria. PLoS Biol. 19, e3001416 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  55. Mori, M., Hwa, T., Martin, O. C., De Martino, A. & Marinari, E. Constrained allocation flux balance analysis. PLoS Comput. Biol. 12, e1004913 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  56. 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 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  57. 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. Microbiol. 13, 298–309 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  59. Wu, C. et al. Cellular perception of growth rate and the mechanistic origin of bacterial growth laws. Proc. Natl Acad. Sci. USA 119, e2201585119 (2022).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Umbarger, H. E. Amino acid biosynthesis and its regulation. Annu. Rev. Biochem. 47, 532–606 (1978).

    Article  CAS  PubMed  Google Scholar 

  61. Reitzer, L. Nitrogen assimilation and global regulation in Escherichia coli. Annu. Rev. Microbiol. 57, 155–176 (2003).

    Article  CAS  PubMed  Google Scholar 

  62. Huergo, L. F. & Dixon, R. The emergence of 2-oxoglutarate as a master regulator metabolite. Microbiol. Mol. Biol. Rev. 79, 419–435 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  63. Kochanowski, K. et al. Global coordination of metabolic pathways in Escherichia coli by active and passive regulation. Mol. Syst. Biol. 17, e10064 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  64. Varma, A. & Palsson, B. O. Stoichiometric flux balance models quantitatively predict growth and metabolic by-product secretion in wild-type Escherichia coli W3110. Appl. Environ. Microbiol. 60, 3724–3731 (1994).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  65. Kochanowski, K. et al. Functioning of a metabolic flux sensor in Escherichia coli. Proc. Natl Acad. Sci. USA 110, 1130–1135 (2013).

    Article  CAS  PubMed  Google Scholar 

  66. Bennett, B. D. et al. Absolute metabolite concentrations and implied enzyme active site occupancy in Escherichia coli. Nat. Chem. Biol. 5, 593–599 (2009).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  67. Hu, X. P., Dourado, H., Schubert, P. & Lercher, M. J. The protein translation machinery is expressed for maximal efficiency in Escherichia coli. Nat. Commun. 11, 5260 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  68. Marr, A. G. Growth rate of Escherichia coli. Microbiol. Rev. 55, 316–333 (1991).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  69. Li, S. H. et al. Escherichia coli translation strategies differ across carbon, nitrogen and phosphorus limitation conditions. Nat. Microbiol. 3, 939–947 (2018).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Prossliner, T., Gerdes, K., Sorensen, M. A. & Winther, K. S. Hibernation factors directly block ribonucleases from entering the ribosome in response to starvation. Nucleic Acids Res. 49, 2226–2239 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  71. Monod, J. in Selected Papers in Molecular Biology by Jacques Monod (eds Lwoff, A. & Ullmann, A.) (Academic Press, 1978).

  72. Hermsen, R., Okano, H., You, C., Werner, N. & Hwa, T. A growth-rate composition formula for the growth of E.coli on co-utilized carbon substrates. Mol. Syst. Biol. 11, 801 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  73. Okano, H., Hermsen, R., Kochanowski, K. & Hwa, T. Regulation of hierarchical and simultaneous carbon-substrate utilization by flux sensors in Esherichia coli. Nat. Microbiol. 5, 206–215 (2020).

    Article  CAS  PubMed  Google Scholar 

  74. Wang, X., Xia, K., Yang, X. & Tang, C. Growth strategy of microbes on mixed carbon sources. Nat. Commun. 10, 1279 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  75. de Groot, D. H., Hulshof, J., Teusink, B., Bruggeman, F. J. & Planque, R. Elementary Growth Modes provide a molecular description of cellular self-fabrication. PLoS Comput. Biol. 16, e1007559 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  76. Okano, H., Hermsen, R. & Hwa, T. Hierarchical and simultaneous utilization of carbon substrates: mechanistic insights, physiological roles, and ecological consequences. Curr. Opin. Microbiol. 63, 172–178 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  77. Hwa, T. in The Physics of Living Matter: Space, Time and Information (eds Gross, D., Sevrin, A. & Shraiman, B.) 87–98 (World Scientific Publishing Co., 2020).

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

    Article  PubMed  PubMed Central  Google Scholar 

  79. Yuan, J., Fowler, W. U., Kimball, E., Lu, W. & Rabinowitz, J. D. Kinetic flux profiling of nitrogen assimilation in Escherichia coli. Nat. Chem. Biol. 2, 529–530 (2006).

    Article  CAS  PubMed  Google Scholar 

  80. Basan, M. et al. A universal trade-off between growth and lag in fluctuating environments. Nature https://doi.org/10.1038/s41586-020-2505-4 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  81. Balakrishnan, R., de Silva, R. T., Hwa, T. & Cremer, J. Suboptimal resource allocation in changing environments constrains response and growth in bacteria. Mol. Syst. Biol. 17, e10597 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  82. Lengeler, J. W. in Regulation of Gene Expression in Escherichia coli (eds Lin, E. C. C. & Lynch, A. S.) Ch. 11, 231–254 (Chapman and Hall, 1996).

  83. Magasanik, B. in The Lactose Operon (eds Beckwith, J. & Zipser, D.) 189–219 (Cold Spring Harbor Laboratory, 1970).

  84. 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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  85. Riley, M., Pardee, A. B., Jacob, F. & Monod, J. On the expression of a structural gene. J. Mol. Biol. 2, 216–225 (1960).

    Article  CAS  Google Scholar 

  86. Bren, A. et al. Glucose becomes one of the worst carbon sources for E.coli on poor nitrogen sources due to suboptimal levels of cAMP. Sci. Rep. 6, 24834 (2016).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  88. Muller, S., Regensburger, G. & Steuer, R. Enzyme allocation problems in kinetic metabolic networks: optimal solutions are elementary flux modes. J. Theor. Biol. 347, 182–190 (2014).

    Article  CAS  PubMed  Google Scholar 

  89. Bruggeman, F. J., Planque, R., Molenaar, D. & Teusink, B. Searching for principles of microbial physiology. FEMS Microbiol. Rev. https://doi.org/10.1093/femsre/fuaa034 (2020).

    Article  PubMed  PubMed Central  Google Scholar 

  90. Dourado, H. & Lercher, M. J. An analytical theory of balanced cellular growth. Nat. Commun. 11, 1226 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  91. Potrykus, K., Murphy, H., Philippe, N. & Cashel, M. ppGpp is the major source of growth rate control in E. coli. Environ. Microbiol. 13, 563–575 (2011).

    Article  CAS  PubMed  Google Scholar 

  92. Sanchez-Vazquez, P., Dewey, C. N., Kitten, N., Ross, W. & Gourse, R. L. Genome-wide effects on Escherichia coli transcription from ppGpp binding to its two sites on RNA polymerase. Proc. Natl Acad. Sci. USA 116, 8310–8319 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  93. Hengge-Aronis, R. Recent insights into the general stress response regulatory network in Escherichia coli. J. Mol. Microbiol. Biotechnol. 4, 341–346 (2002).

    CAS  PubMed  Google Scholar 

  94. Richter, K., Haslbeck, M. & Buchner, J. The heat shock response: life on the verge of death. Mol. Cell 40, 253–266 (2010).

    Article  CAS  PubMed  Google Scholar 

  95. Imlay, J. A. The molecular mechanisms and physiological consequences of oxidative stress: lessons from a model bacterium. Nat. Rev. Microbiol. 11, 443–454 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  96. Si, F. et al. Mechanistic origin of cell-size control and homeostasis in bacteria. Curr. Biol. 29, 1760–1770.e7 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  97. Zheng, H. et al. General quantitative relations linking cell growth and the cell cycle in Escherichia coli. Nat. Microbiol. 5, 995–1001 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  98. Colin, A., Micali, G., Faure, L., Cosentino Lagomarsino, M. & van Teeffelen, S. Two different cell-cycle processes determine the timing of cell division in Escherichia coli. eLife https://doi.org/10.7554/eLife.67495 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  99. Cooper, S. On the fiftieth anniversary of the Schaechter, Maaloe, Kjeldgaard experiments: implications for cell-cycle and cell-growth control. Bioessays 30, 1019–1024 (2008).

    Article  CAS  PubMed  Google Scholar 

  100. Gefen, O., Fridman, O., Ronin, I. & Balaban, N. Q. Direct observation of single stationary-phase bacteria reveals a surprisingly long period of constant protein production activity. Proc. Natl Acad. Sci. USA 111, 556–561 (2014).

    Article  CAS  PubMed  Google Scholar 

  101. Kaplan, Y. et al. Observation of universal ageing dynamics in antibiotic persistence. Nature 600, 290–294 (2021).

    Article  CAS  PubMed  Google Scholar 

  102. Biselli, E., Schink, S. J. & Gerland, U. Slower growth of Escherichia coli leads to longer survival in carbon starvation due to a decrease in the maintenance rate. Mol. Syst. Biol. 16, e9478 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  103. Krasny, L. & Gourse, R. L. An alternative strategy for bacterial ribosome synthesis: Bacillus subtilis rRNA transcription regulation. EMBO J. 23, 4473–4483 (2004).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  104. Muller, A. L. et al. An alternative resource allocation strategy in the chemolithoautotrophic archaeon Methanococcus maripaludis. Proc. Natl Acad. Sci. USA https://doi.org/10.1073/pnas.2025854118 (2021).

    Article  PubMed  PubMed Central  Google Scholar 

  105. Atkinson, G. C., Tenson, T. & Hauryliuk, V. The RelA/SpoT homolog (RSH) superfamily: distribution and functional evolution of ppGpp synthetases and hydrolases across the tree of life. PLoS One 6, e23479 (2011).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  106. Boutte, C. C. & Crosson, S. Bacterial lifestyle shapes stringent response activation. Trends Microbiol. 21, 174–180 (2013).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  107. Zavrel, T. et al. Quantitative insights into the cyanobacterial cell economy. eLife https://doi.org/10.7554/eLife.42508 (2019).

    Article  PubMed  PubMed Central  Google Scholar 

  108. Costello, A. & Badran, A. H. Synthetic biological circuits within an orthogonal central dogma. Trends Biotechnol. 39, 59–71 (2021).

    Article  CAS  PubMed  Google Scholar 

  109. Kim, J., Darlington, A., Salvador, M., Utrilla, J. & Jimenez, J. I. Trade-offs between gene expression, growth and phenotypic diversity in microbial populations. Curr. Opin. Biotechnol. 62, 29–37 (2020).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  110. Cardinale, S. & Arkin, A. P. Contextualizing context for synthetic biology–identifying causes of failure of synthetic biological systems. Biotechnol. J. 7, 856–866 (2012).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  111. Brophy, J. A. & Voigt, C. A. Principles of genetic circuit design. Nat. Methods 11, 508–520 (2014).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  112. Qian, Y., Huang, H. H., Jimenez, J. I. & Del Vecchio, D. Resource competition shapes the response of genetic circuits. ACS Synth. Biol. 6, 1263–1272 (2017).

    Article  CAS  PubMed  Google Scholar 

  113. Weisse, A. Y., Oyarzun, D. A., Danos, V. & Swain, P. S. Mechanistic links between cellular trade-offs, gene expression, and growth. Proc. Natl Acad. Sci. USA 112, E1038–E1047 (2015).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  114. Braniff, N., Scott, M. & Ingalls, B. Component characterization in a growth-dependent physiological context: optimal experimental design. Processes 7, 23 (2019).

    Article  Google Scholar 

  115. Ronne, H. Glucose repression in fungi. Trends Genet. 11, 12–17 (1995).

    Article  CAS  PubMed  Google Scholar 

  116. Compagno, C., Dashko, S. & Piskur, J. in Molecular Mechanisms in Yeast Carbon Metabolism (eds Compagno, C. & Piskur, J.) 1–21 (Springer, 2014).

  117. Kafri, M., Metzl-Raz, E., Jonas, F. & Barkai, N. Rethinking cell growth models. FEMS Yeast Res. https://doi.org/10.1093/femsyr/fow081 (2016).

    Article  PubMed  Google Scholar 

  118. Boer, V. M., Crutchfield, C. A., Bradley, P. H., Botstein, D. & Rabinowitz, J. D. Growth-limiting intracellular metabolites in yeast growing under diverse nutrient limitations. Mol. Biol. Cell 21, 198–211 (2010).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  119. Metzl-Raz, E. et al. Principles of cellular resource allocation revealed by condition-dependent proteome profiling. eLife https://doi.org/10.7554/eLife.28034 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  120. Hackett, S. R. et al. Systems-level analysis of mechanisms regulating yeast metabolic flux. Science https://doi.org/10.1126/science.aaf2786 (2016).

    Article  PubMed  PubMed Central  Google Scholar 

  121. Brown, C. M. & Rose, A. H. Effects of temperature on composition and cell volume of Candida utilis. J. Bacteriol. 97, 261–270 (1969).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  122. Alberghina, F. A., Sturani, E. & Gohlke, J. R. Levels and rates of synthesis of ribosomal ribonucleic acid, transfer ribonucleic acid, and protein in Neurospora crassa in different steady states of growth. J. Biol. Chem. 250, 4381–4388 (1975).

    Article  CAS  PubMed  Google Scholar 

  123. Kochanowski, K. et al. Systematic alteration of in vitro metabolic environments reveals empirical growth relationships in cancer cell phenotypes. Cell Rep. 34, 108647 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  124. Hecht, K. A., O’Donnell, A. F. & Brodsky, J. L. The proteolytic landscape of the yeast vacuole. Cell Logist. 4, e28023 (2014).

    Article  PubMed  PubMed Central  Google Scholar 

  125. Tyo, K. E., Liu, Z., Magnusson, Y., Petranovic, D. & Nielsen, J. Impact of protein uptake and degradation on recombinant protein secretion in yeast. Appl. Microbiol. Biotechnol. 98, 7149–7159 (2014).

    Article  CAS  PubMed  Google Scholar 

  126. Armstrong, J. Yeast vacuoles: more than a model lysosome. Trends Cell Biol. 20, 580–585 (2010).

    Article  CAS  PubMed  Google Scholar 

  127. Hays, S. G., Yan, L. L. W., Silver, P. A. & Ducat, D. C. Synthetic photosynthetic consortia define interactions leading to robustness and photoproduction. J. Biol. Eng. 11, 4 (2017).

    Article  PubMed  PubMed Central  Google Scholar 

  128. Chuang, J. S., Frentz, Z. & Leibler, S. Homeorhesis and ecological succession quantified in synthetic microbial ecosystems. Proc. Natl Acad. Sci. USA 116, 14852–14861 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  129. Amarnath, K. et al. Stress-induced cross-feeding of internal metabolites provides a dynamic mechanism of microbial cooperation. bioRxiv https://doi.org/10.1101/2021.06.24.449802 (2021).

    Article  Google Scholar 

  130. Pinheiro, F., Warsi, O., Andersson, D. I. & Lassig, M. Metabolic fitness landscapes predict the evolution of antibiotic resistance. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-021-01397-0 (2021).

    Article  PubMed  Google Scholar 

  131. Reitzer, L. Biosynthesis of glutamate, aspartate, asparagine, L-alanine, and D-alanine. EcoSal https://doi.org/10.1128/ecosalplus.3.6.1.3 (2004).

    Article  Google Scholar 

  132. Goldman, E. & Jakubowski, H. Uncharged tRNA, protein synthesis, and the bacterial stringent response. Mol. Microbiol. 4, 2035–2040 (1990).

    Article  CAS  PubMed  Google Scholar 

  133. Kotte, O., Zaugg, J. B. & Heinemann, M. Bacterial adaptation through distributed sensing of metabolic fluxes. Mol. Syst. Biol. 6, 355 (2010).

    Article  PubMed  PubMed Central  Google Scholar 

  134. Winkler, M. E. & Ramos-Montanez, S. Biosynthesis of histidine. EcoSal https://doi.org/10.1128/ecosalplus.3.6.1.9 (2009).

    Article  Google Scholar 

  135. Irving, S. E., Choudhury, N. R. & Corrigan, R. M. The stringent response and physiological roles of (pp)pGpp in bacteria. Nat. Rev. Microbiol. 19, 256–271 (2021).

    Article  CAS  PubMed  Google Scholar 

  136. Magnusson, L. U., Farewell, A. & Nystrom, T. ppGpp: a global regulator in Escherichia coli. Trends Microbiol. 13, 236–242 (2005).

    Article  CAS  PubMed  Google Scholar 

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Acknowledgements

This Review was shaped by extended discussion with numerous colleagues and collaborators over the years. It grew from early discussions with Eduard Mateescu and Stefan Klumpp, and with Hans Bremer, Lazlo Csonka, Antoine Danchin, Patrick Dennis, Peter Geiduschek, Sydney Kustu, Bill Loomis, Elio Schaechter, and Dalai Yan. Many insightful ideas came from colleagues whose proteomic data underlies the pie charts shown in the figures: Ruedi Aebersold, Gene-wei Li, Christina Ludwig, and especially Vadim Patsalo, Josh Silverman and Jamie Williamson. Our current understanding of proteome allocation constraints would not have been possible without the input of Rosalind Allen, Frank Bruggeman, Mans Ehrenberg, Suckjoon Jun, Meriem el Karoui, Karl Kochanowski, Martin Lercher, Fernanda Pinheiro, Uwe Sauer, Bas Teusink, Yiping Wang, and current and former members of the Hwa laboratory, especially Rohan Balakrishnan, Markus Basan, David Erickson, Tony Hui, Matteo Mori, Hiroyuki Okano, Severin Schink, Chenhao Wu, Conghui You and Zhongge Zhang. Support for the Hwa laboratory has been provided by the NIH (R01GM095903, R01GM109069), the NSF (PHY105873, MCB 1818384) and the Simons Foundation (330378). M.S. was supported by NSERC (2016-03658).

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Authors and Affiliations

  1. Department of Applied Mathematics, University of Waterloo, Waterloo, ON, Canada

    Matthew Scott

  2. Department of Physics, University of California at San Diego, La Jolla, CA, USA

    Terence Hwa

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Glossary

Anabolic enzymes

Enzymes responsible for biosynthesis, including amino acid and nucleotide synthesis.

Carbon catabolic proteins

Proteins responsible for the transport and breakdown of extracellular carbon sources. Operationally, these are genes regulated by cAMP–Crp.

C-line

The negative correlation between catabolic enzyme expression and growth rate in minimal media when growth rate is modulated by carbon source.

Diauxic growth

Multiple stages of exponential growth as carbon sources are preferentially utilized. The time to switch between carbon sources can take several hours.

Nutrient quality

The exponential growth rate can be modulated by changing the carbon source or nitrogen source, or enriching the medium with amino acids and nucleotides.

Per-total protein-mass abundance

The concentration constraint (biomass/cell water volume) allows a direct conversion between the concentration ((amount of a particular molecule)/(cell water volume)) and its abundance relative to total protein mass ((amount of a particular molecule)/(total protein mass)) assuming that the biomass contains a fixed fraction of protein.

Protein density

The buoyant density ((biomass and water mass)/cell volume) is independent of the growth rate under isotonic conditions. A constant density constraint (biomass/cell volume) therefore implies a constant concentration constraint (biomass/cell water volume). The cellular volume has strong growth-rate dependence; consequently, we do not use protein-number-per-cell as an abundance measure in this Review.

Protein mass fraction

The total number of a particular protein is proportional to its protein mass. Using the per-total protein-mass abundance defined above, the protein mass fraction (protein mass of a particular protein/total protein mass) is therefore a direct measure of concentration.

Proteome

The set of all expressed proteins in a given growth condition.

Proteome sectors

Subsets of the proteome that exhibit similar growth-rate dependence under various growth conditions.

R-line

The positive correlation between the abundance of protein-synthetic machinery (chiefly ribosomes) and growth rate when growth rate is modulated by nutrient quality.

Unregulated protein

Protein expression not subject to any transcriptional or post-transcriptional regulation.

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Scott, M., Hwa, T. Shaping bacterial gene expression by physiological and proteome allocation constraints. Nat Rev Microbiol 21, 327–342 (2023). https://doi.org/10.1038/s41579-022-00818-6

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