한국미생물·생명공학회지 (Microbiology and Biotechnology Letters)

  • 제49권4호
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  • Pages.478-484
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  • 2021
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  • 1598-642X(pISSN)
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  • 2234-7305(eISSN)
한국미생물·생명공학회 (The Korean Society for Microbiology and Biotechnology)
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How Do Bacteria Maximize Their Cellular Assets?

  • Kim, Juhyun (School of Life Science, BK21 FOUR KNU Creative BioResearch Group, Kyungpook National University)
  • 투고 : 2021.10.27
  • 심사 : 2021.12.15
  • 발행 : 2021.12.28
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초록

Cellular resources including transcriptional and translational machineries in bacteria are limited, yet microorganisms depend upon them to maximize cellular fitness. Bacteria have evolved strategies for using resources economically. Regulatory networks for the gene expression system enable the cell to synthesize proteins only when necessary. At the same time, regulatory interactions enable the cell to limit losses when the system cannot make a cellular profit due to fake substrates. Also, the architecture of the gene expression flow can be advantageous for clustering functionally related products, thus resulting in effective interactions among molecules. In addition, cellular systems modulate the investment of proteomes, depending upon nutrient qualities, and fast-growing cells spend more resources on the synthesis of ribosomes, whereas nonribosomal proteins are synthesized in nutrient-limited conditions. A deeper understanding of cellular mechanisms underlying the optimal allocation of cellular resources can be used for biotechnological purposes, such as designing complex genetic circuits and constructing microbial cell factories.

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참고문헌

  1. Klumpp S, Scott M, Pedersen S, Hwa T. 2013. Molecular crowding limits translation and cell growth. Proc. Natl. Acad. Sci. USA 110: 16754-16759. https://doi.org/10.1073/pnas.1310377110
  2. Klumpp S, Zhang Z, Hwa T. 2009. Growth rate-dependent global effects on gene expression in bacteria. Cell 139: 1366-1375. https://doi.org/10.1016/j.cell.2009.12.001
  3. Scott M, Klumpp S, Mateescu EM, Hwa T. 2014. Emergence of robust growth laws from optimal regulation of ribosome synthesis. Mol. Syst. Biol. 10: 747. https://doi.org/10.15252/msb.20145379
  4. Korem Kohanim Y, Levi D, Jona G, Towbin BD, Bren A, Alon U. 2018. A bacterial growth law out of steady state. Cell Rep. 23: 2891-2900. https://doi.org/10.1016/j.celrep.2018.05.007
  5. Kim J, Darlington APS, Bates DG, Jimenez JI. 2021. The interplay between growth rate and nutrient quality defines gene expression capacity. bioRxiv. 2021.2004.2002.438188.
  6. Kim J, Darlington A, Salvador M, Utrilla J, Jimenez JI. 2019. Trade-offs between gene expression, growth and phenotypic diversity in microbial populations. Curr. Opin. Biotechnol. 62: 29-37. https://doi.org/10.1016/j.copbio.2019.08.004
  7. Sneppen K, Krishna S, Semsey S. 2010. Simplified models of biological networks. Annu. Rev. Biophys. 39: 43-59. https://doi.org/10.1146/annurev.biophys.093008.131241
  8. Lozada-Chavez I, Janga SC, Collado-Vides J. 2006. Bacterial regulatory networks are extremely flexible in evolution. Nucleic Acids Res. 34: 3434-3445. https://doi.org/10.1093/nar/gkl423
  9. Shis DL, Matthew R, Bennett, Igoshin OA. 2018. Dynamics of bacterial gene regulatory networks. Ann. Rev. Biophys. 47: 447-467. https://doi.org/10.1146/annurev-biophys-070317-032947
  10. Vind J, Sorensen MA, Rasmussen MD, Pedersen S. 1993. Synthesis of proteins in Escherichia coli is limited by the concentration of free ribosomes. Expression from reporter genes does not always reflect functional mRNA levels. J. Mol. Biol. 231: 678-688. https://doi.org/10.1006/jmbi.1993.1319
  11. Shachrai I, Zaslaver A, Alon U, Dekel E. 2010. Cost of unneeded proteins in E. coli is reduced after several generations in exponential growth. Mol. Cell 38: 758-767. https://doi.org/10.1016/j.molcel.2010.04.015
  12. O'Brien EJ, Utrilla J, Palsson BO. 2016. Quantification and classification of E. coli proteome utilization and unused protein costs across environments. PLoS Comput. Biol. 12: e1004998. https://doi.org/10.1371/journal.pcbi.1004998
  13. Mori M, Schink S, Erickson DW, Gerland U, Hwa T. 2017. Quantifying the benefit of a proteome reserve in fluctuating environments. Nat. Commun. 8: 1225. https://doi.org/10.1038/s41467-017-01242-8
  14. Kotte O, Volkmer B, Radzikowski JL, Heinemann M. 2014. Phenotypic bistability in Escherichia coli's central carbon metabolism. Mol. Syst. Biol. 10: 736. https://doi.org/10.15252/msb.20135022
  15. van Boxtel C, van Heerden JH, Nordholt N, Schmidt P, Bruggeman FJ. 2017. Taking chances and making mistakes: non-genetic phenotypic heterogeneity and its consequences for surviving in dynamic environments. J. R. Soc. Interface 14: 20170241.
  16. Martins BM, Locke JC. 2015. Microbial individuality: how single-cell heterogeneity enables population level strategies. Curr. Opin. Microbiol. 24: 104-112. https://doi.org/10.1016/j.mib.2015.01.003
  17. Mackey MC, Santillan M, Yildirim N. 2004. Modeling operon dynamics: the tryptophan and lactose operons as paradigms. C R Biol. 327: 211-224. https://doi.org/10.1016/j.crvi.2003.11.009
  18. Lawrence JG. 2002. Shared strategies in gene organization among prokaryotes and eukaryotes. Cell 110: 407-413. https://doi.org/10.1016/S0092-8674(02)00900-5
  19. Osbourn AE, Field B. 2009. Operons. Cell. Mol. Life Sci : CMLS 66: 3755-3775. https://doi.org/10.1007/s00018-009-0114-3
  20. Merino E, Jensen RA, Yanofsky C. 2008. Evolution of bacterial trp operons and their regulation. Curr. Opin. Microbiol. 11: 78-86. https://doi.org/10.1016/j.mib.2008.02.005
  21. Assinder SJ, Williams PA. 1990. The TOL plasmids: determinants of the catabolism of toluene and the xylenes. Adv. Microb. Physiol. 31: 1-69. https://doi.org/10.1016/S0065-2911(08)60119-8
  22. Marques S, Manzanera M, Gonzalez-Perez MM, Ruiz R, Ramos JL. 1999. Biodegradation, plasmid-encoded catabolic pathways, host factors and cell metabolism. Environ. Microbiol. 1: 103-104. https://doi.org/10.1046/j.1462-2920.1999.00010.x
  23. Worsey MJ, Williams PA. 1975. Metabolism of toluene and xylenes by Pseudomonas (putida (arvilla) mt-2: evidence for a new function of the TOL plasmid. J. Bacteriol. 124: 7-13. https://doi.org/10.1128/jb.124.1.7-13.1975
  24. Bertoni G, Marques S, de Lorenzo V. 1998. Activation of the toluene-responsive regulator XylR causes a transcriptional switch between sigma54 and sigma70 promoters at the divergent Pr/Ps region of the TOL plasmid. Mol. Microbiol. 27: 651-659. https://doi.org/10.1046/j.1365-2958.1998.00715.x
  25. Marques S, Gallegos MT, Manzanera M, Holtel A, Timmis KN, Ramos JL. 1998. Activation and repression of transcription at the double tandem divergent promoters for the xylR and xylS genes of the TOL plasmid of Pseudomonas putida. J. Bacteriol. 180: 2889-2894. https://doi.org/10.1128/jb.180.11.2889-2894.1998
  26. Ramos JL, Marques S, Timmis KN. 1997. Transcriptional control of the Pseudomonas TOL plasmid catabolic operons is achieved through an interplay of host factors and plasmid-encoded regulators. Ann. Rev. Microbiol. 51: 341-373. https://doi.org/10.1146/annurev.micro.51.1.341
  27. Gallegos MT, Marques S, Ramos JL. 1996. Expression of the TOL plasmid xylS gene in Pseudomonas putida occurs from a alpha 70-dependent promoter or from alpha 70- and alpha 54-dependent tandem promoters according to the compound used for growth. J. Bacteriol. 178: 2356-2361. https://doi.org/10.1128/jb.178.8.2356-2361.1996
  28. Kim J, Silva-Rocha R, de Lorenzo V. 2021. Picking the right metaphors for addressing microbial systems: economic theory helps understanding biological complexity. Int. Microbiol. 24: 507-519. https://doi.org/10.1007/s10123-021-00194-w
  29. Kim J, Perez-Pantoja D, Silva-Rocha R, Oliveros JC, de Lorenzo V. 2016. High-resolution analysis of the m-xylene/toluene biodegradation subtranscriptome of Pseudomonas putida mt-2. Environ. Microbiol. 18: 3327-3341. https://doi.org/10.1111/1462-2920.13054
  30. Silva-Rocha Randde Lorenzo V. 2012. Stochasticity of TOL plasmid catabolic promoters sets a bimodal expression regime in Pseudomonas putida mt-2 exposed to m-xylene. Mol. Microbiol. 86: 199-211. https://doi.org/10.1111/j.1365-2958.2012.08184.x
  31. Vitale E, Milani A, Renzi F, Galli E, Rescalli E, de Lorenzo V, et al. 2008. Transcriptional wiring of the TOL plasmid regulatory network to its host involves the submission of the sigma54-promoter Pu to the response regulator PprA. Mol. Microbiol. 69: 698-713. https://doi.org/10.1111/j.1365-2958.2008.06321.x
  32. Zimmerman SB, Trach SO. 1991. Estimation of macromolecule concentrations and excluded volume effects for the cytoplasm of Escherichia coli. J. Mol. Biol. 222: 599-620. https://doi.org/10.1016/0022-2836(91)90499-V
  33. Kim J, Goni-Moreno A, de Lorenzo V. 2021. Subcellular architecture of the xyl gene expression flow of the TOL catabolic plasmid of Pseudomonas putida mt-2. mBio. 12: e03685-20.
  34. Kim J, Goni-Moreno A, Calles B, de Lorenzo V. 2019. Spatial organization of the gene expression hardware in Pseudomonas putida. Environ. Microbiol. 21: 1645-1658. https://doi.org/10.1111/1462-2920.14544
  35. Castellana M, Wilson MZ, Xu Y, Joshi P, Cristea IM, Rabinowitz JD, et al. 2014. Enzyme clustering accelerates processing of intermediates through metabolic channeling. Nat. Biotechnol. 32: 1011-1018. https://doi.org/10.1038/nbt.3018
  36. dos Santos VT, Bisson-Filho AW, Gueiros-Filho FJ. 2012. DivIVA-mediated polar localization of ComN, a posttranscriptional regulator of Bacillus subtilis. J. Bacteriol. 194: 3661-3669. https://doi.org/10.1128/JB.05879-11
  37. Dugar G, Svensson SL, Bischler T, Waldchen S, Reinhardt R, Sauer M, et al. 2016. The CsrA-FliW network controls polar localization of the dual-function flagellin mRNA in Campylobacter jejuni. Nat. Commun. 7: 11667. https://doi.org/10.1038/ncomms11667
  38. Moffitt JR, Pandey S, Boettiger AN, Wang S, Zhuang X. 2016. Spatial organization shapes the turnover of a bacterial transcriptome. ELife 5: e13065. https://doi.org/10.7554/elife.13065
  39. Kannaiah S, Livny J, Amster-Choder O. 2019. Spatiotemporal organization of the E. coli transcriptome: Translation independence and engagement in regulation. Mol. Cell 76: 574-589 e577. https://doi.org/10.1016/j.molcel.2019.08.013
  40. Fazal FM, Chang HY. 2019. Subcellular spatial transcriptomes: Emerging frontier for understanding gene regulation. Cold Spring Harb Symp. Quant. Biol. 84: 31-45. https://doi.org/10.1101/sqb.2019.84.040352
  41. Nevo-Dinur K, Nussbaum-Shochat A, Ben-Yehuda S, AmsterChoder O. 2011. Translation-independent localization of mRNA in E. coli. Science 331: 1081-1084. https://doi.org/10.1126/science.1195691
  42. Gyorgy A, Jimenez JI, Yazbek J, Huang HH, Chung H, Weiss R, et al. 2015. Isocost lines describe the cellular economy of genetic circuits. Biophys. J. 109: 639-646. https://doi.org/10.1016/j.bpj.2015.06.034
  43. Carbonell-Ballestero M, Garcia-Ramallo E, Montanez R, RodriguezCaso C, Macia J. 2016. Dealing with the genetic load in bacterial synthetic biology circuits: convergences with the Ohm's law. Nucleic Acids Res. 44: 496-507. https://doi.org/10.1093/nar/gkv1280
  44. Shopera T, He L, Oyetunde T, Tang YJ, Moon TS. 2017. Decoupling resource-coupled gene expression in living cells. ACS Synth. Biol. 6: 1596-1604. https://doi.org/10.1021/acssynbio.7b00119
  45. Borkowski O, Bricio C, Murgiano M, Rothschild-Mancinelli B, Stan GB, Ellis T. 2018. Cell-free prediction of protein expression costs for growing cells. Nat. Commun. 9: 1457. https://doi.org/10.1038/s41467-018-03970-x
  46. Ceroni F, Algar R, Stan GB, Ellis T. 2015. Quantifying cellular capacity identifies gene expression designs with reduced burden. Nat. Methods 12: 415-418. https://doi.org/10.1038/nmeth.3339
  47. Ceroni F, Boo A, Furini S, Gorochowski TE, Borkowski O, Ladak YN, et al. 2018. Burden-driven feedback control of gene expression. Nat. Methods 15: 387-393. https://doi.org/10.1038/nmeth.4635
  48. Darlington APS, Kim J, Jimenez JI, Bates DG. 2018. Engineering translational resource allocation controllers: Mechanistic models, design guidelines, and potential biological implementations. ACS Synth. Biol. 7: 2485-2496. https://doi.org/10.1021/acssynbio.8b00029
  49. Scott M, Gunderson CW, Mateescu EM, Zhang Z, Hwa T. 2010. Interdependence of cell growth and gene expression: origins and consequences. Science 330: 1099-1102. https://doi.org/10.1126/science.1192588
  50. Scott M, Hwa T. 2011. Bacterial growth laws and their applications. Curr. Opin. Biotechnol. 22: 559-565. https://doi.org/10.1016/j.copbio.2011.04.014
  51. Baracchini E, Bremer H. 1991. Control of rRNA synthesis in Escherichia coli at increased rrn gene dosage. Role of guanosine tetraphosphate and ribosome feedback. J. Biol. Chem. 266: 11753-11760. https://doi.org/10.1016/S0021-9258(18)99021-6
  52. Stevenson BS, Schmidt TM. 2004. Life history implications of rRNA gene copy number in Escherichia coli. Appl. Environ. Microbiol. 70: 6670-6677. https://doi.org/10.1128/AEM.70.11.6670-6677.2004
  53. Gyorfy Z, Draskovits G, Vernyik V, Blattner FF, Gaal T, Posfai G. 2015. Engineered ribosomal RNA operon copy-number variants of E. coli reveal the evolutionary trade-offs shaping rRNA operon number. Nucleic Acids Res. 43: 1783-1794. https://doi.org/10.1093/nar/gkv040
  54. Corrigan RM, Bellows LE, Wood A, Grundling A. 2016. ppGpp negatively impacts ribosome assembly affecting growth and antimicrobial tolerance in Gram-positive bacteria. Proc. Natl. Acad. Sci. USA 113: E1710-1719.
  55. Lemke JJ, Sanchez-Vazquez P, Burgos HL, Hedberg G, Ross W, Gourse RL. 2011. Direct regulation of Escherichia coli ribosomal protein promoters by the transcription factors ppGpp and DksA. Proc. Natl. Acad. Sci. USA 108: 5712-5717. https://doi.org/10.1073/pnas.1019383108
  56. Magnusson LU, Farewell A, Nystrom T. 2005. ppGpp: a global regulator in Escherichia coli. Trends Microbiol. 13: 236-242. https://doi.org/10.1016/j.tim.2005.03.008
  57. Sun D, Lee G, Lee JH, Kim HY, Rhee HW, Park SY, et al. 2010. A metazoan ortholog of SpoT hydrolyzes ppGpp and functions in starvation responses. Nat. Struc. Mol. Biol. 17: 1188-1194. https://doi.org/10.1038/nsmb.1906
  58. Zhu M, Dai X. 2019. Growth suppression by altered (p)ppGpp levels results from non-optimal resource allocation in Escherichia coli. Nucleic Acids Res. 47: 4684-4693. https://doi.org/10.1093/nar/gkz211
  59. Ross W, Sanchez-Vazquez P, Chen AY, Lee JH, Burgos HL, Gourse RL. 2016. ppGpp binding to a site at the RNAP-DksA interface accounts for its dramatic effects on transcription initiation during the stringent response. Mol. Cell 62: 811-823. https://doi.org/10.1016/j.molcel.2016.04.029