Growth strategy of microbes on mixed carbon sources

Abstract

A classic problem in microbiology is that bacteria display two types of growth behavior when cultured on a mixture of two carbon sources: the two sources are sequentially consumed one after another (diauxie) or they are simultaneously consumed (co-utilization). The search for the molecular mechanism of diauxie led to the discovery of the lac operon. However, questions remain as why microbes would bother to have different strategies of taking up nutrients. Here we show that diauxie versus co-utilization can be understood from the topological features of the metabolic network. A model of optimal allocation of protein resources quantitatively explains why and how the cell makes the choice. In case of co-utilization, the model predicts the percentage of each carbon source in supplying the amino acid pools, which is quantitatively verified by experiments. Our work solves a long-standing puzzle and provides a quantitative framework for the carbon source utilization of microbes.

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References 51

1. 1.

   Duclaux E. Traité de Microbiologie. 1899.

   Not in our library

2. 2.

   Monod, J. Recherches sur la croissance des cultures bactériennes. Thesis, Hermann et Cie, Paris (1942).

   Not in our library

3. 3.

   Monod J. The phenomenon of enzymatic adaptation and its bearings on problems of genetics and cellular differentiation. Growth. 1947;11:223-289.

   Not in our library

4. 4.

   Görke B, Stülke J. Carbon catabolite repression in bacteria: many ways to make the most out of nutrients. Nat. Rev. Microbiol. 2008;6:613-624.

   Not in our library10.1038/nrmicro1932

5. 5.

   Gancedo JM. Yeast carbon catabolite repression. Microbiol. Mol. Biol. Rev. 1998;62:334-361.

   Not in our library10.1128/mmbr.62.2.334-361.1998

6. 6.

   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. 2015;11:801.

   Not in our library10.15252/msb.20145537

7. 7.

   Kleijn RJ, et al. Metabolic fluxes during strong carbon catabolite repression by malate in Bacillus subtilis. J. Biol. Chem. 2010;285:1587-1596.

   Not in our library10.1074/jbc.m109.061747

8. 8.

   Narang A, Pilyugin SS. Bacterial gene regulation in diauxic and non-diauxic growth. J. Theor. Biol. 2007;244:326-348.

   Not in our library10.1016/j.jtbi.2006.08.007

9. 9.

   Magasanik B. Catabolite repression. Cold Spring Harb Sym. Quant. Biol. 1961;26:249-256.

   Not in our library10.1101/sqb.1961.026.01.031

10. 10.

    Deutscher J, Francke C, Postma PW. How phosphotransferase system-related protein phosphorylation regulates carbohydrate metabolism in bacteria. Microbiol. Mol. Biol. Rev. 2006;70:939-1031.

    Not in our library10.1128/mmbr.00024-06

11. 11.

    Neidhardt FC, Magasanik B. Inhibitory effect of glucose on enzyme formation. Nature. 1956;178:801-802.

    Not in our library10.1038/178801b0

12. 12.

    Deutscher J. The mechanisms of carbon catabolite repression in bacteria. Curr. Opin. Microbiol. 2008;11:87-93.

    Not in our library10.1016/j.mib.2008.02.007

13. 13.
    Hierarchy of non-glucose sugars in Escherichia coli · 2014

    No notice
14. 14.

    Kolb A, Busby S, Buc H, Garges S, Adhya S. Transcriptional regulation by cAMP and its receptor protein. Annu. Rev. Biochem. 1993;62:749-795.

    Not in our library10.1146/annurev.bi.62.070193.003533

15. 15.

    Busby S, Ebright RH. Transcription activation by catabolite activator protein (CAP). J. Mol. Biol. 1999;293:199-213.

    Not in our library10.1006/jmbi.1999.3161

16. 16.

    Müller-Hill B. The lac Operon. 1996.

    Not in our library

17. 17.

    Jacob F, Monod J. Genetic regulatory mechanisms in the synthesis of proteins. J. Mol. Biol. 1961;3:318-356.

    Not in our library10.1016/s0022-2836(61)80072-7

18. 18.

    You C, et al. Coordination of bacterial proteome with metabolism by cyclic AMP signalling. Nature. 2013;500:301-306.

    Not in our library10.1038/nature12446

19. 19.
    Emergence of robust growth laws from optimal regulation of ribosome synthesis · 2014

    No notice
20. 20.

    Scott M, Gunderson CW, Mateescu EM, Zhang Z, Hwa T. Interdependence of cell growth and gene expression: origins and consequences. Science. 2010;330:1099-1102.

    Not in our library10.1126/science.1192588

21. 21.
    Quantitative proteomic analysis reveals a simple strategy of global resource allocation in bacteria · 2015

    No notice
22. 22.

    Thattai M, Shraiman BI. Metabolic switching in the sugar phosphotransferase system of Escherichia coli. Biophys. J. 2003;85:744-754.

    Not in our library10.1016/s0006-3495(03)74517-2

23. 23.

    Edwards JS, Ibarra RU, Palsson BO. In silico predictions of Escherichia coli metabolic capabilities are consistent with experimental data. Nat. Biotechnol. 2001;19:125-130.

    Not in our library10.1038/84379

24. 24.

    Dekel E, Alon U. Optimality and evolutionary tuning of the expression level of a protein. Nature. 2005;436:588-592.

    Not in our library10.1038/nature03842

25. 25.

    Wortel MT, Peters H, Hulshof J, Teusink B, Bruggeman FJ. Metabolic states with maximal specific rate carry flux through an elementary flux mode. FEBS J. 2014;281:1547-1555.

    Not in our library10.1111/febs.12722

26. 26.

    Müller S, Regensburger G, Steuer R. Enzyme allocation problems in kinetic metabolic networks: optimal solutions are elementary flux modes. J. Theor. Biol. 2014;347:182-190.

    Not in our library10.1016/j.jtbi.2013.11.015

27. 27.

    Li SHJ, et al. Escherichia coli translation strategies differ across carbon, nitrogen and phosphorus limitation conditions. Nat. Microbiol. 2018;3:939-947.

    Not in our library10.1038/s41564-018-0199-2

28. 28.

    Schuster S, Hilgetag C. On elementary flux modes in biochemical reaction systems at steady state. J. Biol. Syst. 1994;2:165-182.

    Not in our library10.1142/s0218339094000131

29. 29.

    Neidhardt FC, Ingraham JL, Schaechter M. Physiology of the Bacterial Cell: a Molecular Approach. 1990.

    Not in our library

30. 30.

    Schönheit P, Buckel W, Martin WF. On the origin of heterotrophy. Trends Microbiol. 2016;24:12-25.

    Not in our library10.1016/j.tim.2015.10.003

31. 31.

    Lehninger, A. L., Nelson, D. L. & Cox, M. M. Lehninger Principles of Biochemistry 5th edn. (W.H. Freeman, New York, 2008).

    Not in our library

32. 32.

    Peyraud R, Kiefer P, Christen P, Portais JC, Vorholt JA. Co-consumption of methanol and succinate by Methylobacterium extorquens AM1. PLoS One. 2012;7:e48271.

    Not in our library10.1371/journal.pone.0048271

33. 33.

    Escalante-Chong R, et al. Galactose metabolic genes in yeast respond to a ratio of galactose and glucose. Proc. Natl Acad. Sci. USA. 2015;112:1636-1641.

    Not in our library10.1073/pnas.1418058112

34. 34.

    Monod J. The growth of bacterial cultures. Annu. Rev. Microbiol. 1949;3:371-394.

    Not in our library10.1146/annurev.mi.03.100149.002103

35. 35.

    Park JO, et al. Metabolite concentrations, fluxes and free energies imply efficient enzyme usage. Nat. Chem. Biol. 2016;12:482.

    Not in our library10.1038/nchembio.2077

36. 36.

    Bennett BD, et al. Absolute metabolite concentrations and implied enzyme active site occupancy in Escherichia coli. Nat. Chem. Biol. 2009;5:593.

    Not in our library10.1038/nchembio.186

37. 37.

    Phillips R, Milo R. A feeling for the numbers in biology. Proc. Natl Acad. Sci. USA. 2009;106:21465-21471.

    Not in our library10.1073/pnas.0907732106

38. 38.
    Optimality and sub-optimality in a bacterial growth law · 2017

    No notice
39. 39.

    Kussell E, Leibler S. Phenotypic diversity, population growth, and information in fluctuating environments. Science. 2005;309:2075-2078.

    Not in our library10.1126/science.1114383

40. 40.

    de Jong IG, Haccou P, Kuipers OP. Bet hedging or not? A guide to proper classification of microbial survival strategies. BioEssays. 2011;33:215-223.

    Not in our library10.1002/bies.201000127

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