Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

Biotechnological domestication of pseudomonads using synthetic biology

Nature Reviews Microbiology volume 12, pages 368–379 (2014)Cite this article

Subjects

Key Points

  • One of the pillars of contemporary synthetic biology is the use of reliable biological chassis into which users can plug-in and plug-out genetic circuits and new-to-nature properties at will.

  • The microorganisms that are easiest to genetically manipulate in the laboratory are frequently suboptimal for industrial applications owing to physicochemical stress and harsh operation conditions.

  • Hallmark features of pseudomonads as synthetic biology platforms include their pre-endowed metabolic, physiological and stress-endurance traits, which make them adequate for real-life biotechnological needs.

  • The range of molecular tools that are available for the rational manipulation of pseudomonads is continuously increasing in variety and scope. These assets include streamlined plasmid vectors for gene expression and genome editing, as well as a number of in silico tools for the modelling and prediction of metabolic and physiological behaviour of these bacteria.

  • String–weight biological engineering enables the assembly of complex biosystems by combining the physical connectivity of available parts and modules with evolutionary gravitation of input–output transfer functions towards functional optimality.

Abstract

Much of contemporary synthetic biology research relies on the use of bacterial chassis for plugging-in and plugging-out genetic circuits and new-to-nature functionalities. However, the microorganisms that are the easiest to manipulate in the laboratory are often suboptimal for downstream industrial applications, which can involve physicochemical stress and harsh operating conditions. In this Review, we advocate the use of environmental Pseudomonas strains as model organisms that are pre-endowed with the metabolic, physiological and stress-endurance traits that are demanded by current and future synthetic biology and biotechnological needs.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Figure 1: The quest for an optimal synthetic biology chassis.
Figure 2: Ecological habitats of Pseudomonas spp.
Figure 3: Catabolism of hexoses in Pseudomonas spp: one substrate, different strategies.
Figure 4: Genetic traps to identify biotransformations.
Figure 5: The metabolic heart of Pseudomonas putida KT2440.

Similar content being viewed by others

References

  1. McPherson, M. J. & Parish, J. H. Applications of recombinant DNA in biotechnology. Nat. Prod. Rep. 4, 205–224 (1987).

    CAS  PubMed  Google Scholar 

  2. Malik, V. S. Biotechnology — the golden age. Adv. Appl. Microbiol. 34, 263–306 (1989).

    CAS  PubMed  Google Scholar 

  3. Ramos, J. L. et al. The behavior of bacteria designed for biodegradation. Biotechnology 12, 1349–1356 (1994).

    CAS  PubMed  Google Scholar 

  4. Pieper, D. H. & Reineke, W. Engineering bacteria for bioremediation. Curr. Opin. Biotechnol. 11, 262–270 (2000).

    CAS  PubMed  Google Scholar 

  5. Janssen, D. B., Dinkla, I. J., Poelarends, G. J. & Terpstra, P. Bacterial degradation of xenobiotic compounds: evolution and distribution of novel enzyme activities. Environ. Microbiol. 7, 1868–1882 (2005).

    CAS  PubMed  Google Scholar 

  6. Ferrer, M., Beloqui, A., Timmis, K. N. & Golyshin, P. N. Metagenomics for mining new genetic resources of microbial communities. J. Mol. Microbiol. Biotechnol. 16, 109–123 (2009).

    CAS  PubMed  Google Scholar 

  7. Dietrich, J. A., McKee, A. E. & Keasling, J. D. High-throughput metabolic engineering: advances in small-molecule screening and selection. Annu. Rev. Biochem. 79, 563–590 (2010).

    CAS  PubMed  Google Scholar 

  8. Galvão, T. C., Mohn, W. W. & de Lorenzo, V. Exploring the microbial biodegradation and biotransformation gene pool. Trends Biotechnol. 23, 497–506 (2005).

    PubMed  Google Scholar 

  9. Tuffin, M., Anderson, D., Heath, C. & Cowan, D. A. Metagenomic gene discovery: how far have we moved into novel sequence space? Biotechnol. J. 4, 1671–1683 (2009).

    CAS  PubMed  Google Scholar 

  10. Purnick, P. E. & Weiss, R. The second wave of synthetic biology: from modules to systems. Nature Rev. Mol. Cell. Biol. 10, 410–422 (2009).

    CAS  Google Scholar 

  11. Keasling, J. D. Synthetic biology for synthetic chemistry. ACS Chem. Biol. 3, 64–76 (2008).

    CAS  PubMed  Google Scholar 

  12. Endy, D. Foundations for engineering biology. Nature 438, 449–453 (2005). This paper makes a seminal contribution to the synthetic biology field and sets the basis for many of the current tenets within the discipline.

    CAS  PubMed  Google Scholar 

  13. Danchin, A. Scaling up synthetic biology: do not forget the chassis. FEBS Lett. 586, 2129–2137 (2012). This paper provides an enlightening discussion on the neglected issue of choosing an appropriate chassis for synthetic biology applications.

    CAS  PubMed  Google Scholar 

  14. de Lorenzo, V. Beware of metaphors: chasses and orthogonality in synthetic biology. Bioeng. Bugs 2, 3–7 (2011).

    PubMed  Google Scholar 

  15. Wheeler, M. B. & Campion, D. R. Animal production — a longstanding biotechnological success. Am. J. Clin. Nutr. 58, (Suppl. 2) 276S–281S (1993).

    CAS  PubMed  Google Scholar 

  16. Sauer, M. & Mattanovich, D. Construction of microbial cell factories for industrial bioprocesses. J. Chem. Technol. Biotechnol. 87, 445–450 (2012).

    CAS  Google Scholar 

  17. Schmid, A. et al. Industrial biocatalysis today and tomorrow. Nature 409, 258–268 (2001).

    CAS  PubMed  Google Scholar 

  18. Foley, P. L. & Shuler, M. L. Considerations for the design and construction of a synthetic platform cell for biotechnological applications. Biotechnol. Bioeng. 105, 26–36 (2010).

    CAS  PubMed  Google Scholar 

  19. Dürre, P. New insights and novel developments in clostridial acetone/butanol/isopropanol fermentation. Appl. Microbiol. Biotechnol. 49, 639–648 (1998).

    Google Scholar 

  20. Karlsson, E. N., Johansson, L., Holst, O. & Lidén, G. in The Metabolic Pathway Engineering Handbook: Fundamentals (ed. Smolke, C.D) 21.21–21.23 (CRC Press, 2010).

    Google Scholar 

  21. Copley, S. D. Evolution of efficient pathways for degradation of anthropogenic chemicals. Nature Chem. Biol. 5, 559–566 (2009).

    CAS  Google Scholar 

  22. de Lorenzo, V. Systems biology approaches to bioremediation. Curr. Opin. Biotechnol. 19, 579–589 (2008).

    CAS  PubMed  Google Scholar 

  23. Kivisaar, M. Mechanisms of stationary-phase mutagenesis in bacteria: mutational processes in pseudomonads. FEMS Microbiol. Lett. 312, 1–14 (2010).

    CAS  PubMed  Google Scholar 

  24. Pérez-Pantoja, D., Nikel, P. I., Chavarría, M. & de Lorenzo, V. Endogenous stress caused by faulty oxidation reactions fosters evolution of 2,4-dinitrotoluene-degrading bacteria. PLoS Genet. 9, e1003764 (2013). This article reveals the role of oxidative stress caused by the biological oxidation of aromatic compounds as the driving force for the evolution of biodegradation pathways.

    PubMed  PubMed Central  Google Scholar 

  25. Nikel, P. I., Pérez-Pantoja, D. & de Lorenzo, V. Why are chlorinated pollutants so difficult to degrade aerobically? Redox stress limits 1,3-dichloroprop-1-ene metabolism by Pseudomonas pavonaceae. Phil. Trans. R. Soc. B 368, 20120377 (2013).

    PubMed  Google Scholar 

  26. Imlay, J. A. in EcoSal — Escherichia coli and Salmonella: Cellular and Molecular Biology (ed. Böck, A. et al.) (ASM Press, 2009).

    Google Scholar 

  27. Euzéby, J. P. List of bacterial names with standing in nomenclature: a folder available on the internet. Internatl J. Syst. Evol. Microbiol. 47, 590–592 (1997).

    Google Scholar 

  28. Palleroni, N. J. in Bergey's Manual of Systematic Bacteriology (ed. Kreig, N. R.) 141–199 (Williams & Wilkins, 1984).

    Google Scholar 

  29. Palleroni, N. J. The Pseudomonas story. Environ. Microbiol. 12, 1377–1383 (2010).

    CAS  PubMed  Google Scholar 

  30. Peix, A., Ramírez-Bahena, M. H. & Velázquez, E. Historical evolution and current status of the taxonomy of genus Pseudomonas. Infect. Genet. Evol. 9, 1132–1147 (2009).

    PubMed  Google Scholar 

  31. Gurney, R. & Thomas, C. M. Mupirocin: biosynthesis, special features and applications of an antibiotic from a Gram-negative bacterium. Appl. Microbiol. Biotechnol. 90, 11–21 (2011).

    CAS  PubMed  Google Scholar 

  32. Loper, J. E. et al. Comparative genomics of plant-associated Pseudomonas spp.: insights into diversity and inheritance of traits involved in multitrophic interactions. PLoS Genet. 8, e1002784 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  33. Balasubramanian, D., Schneper, L., Kumari, H. & Mathee, K. A dynamic and intricate regulatory network determines Pseudomonas aeruginosa virulence. Nucleic Acids Res. 41, 1–20 (2013).

    CAS  PubMed  Google Scholar 

  34. Rodríguez-Rojas, A., Oliver, A. & Blázquez, J. Intrinsic and environmental mutagenesis drive diversification and persistence of Pseudomonas aeruginosa in chronic lung infections. J. Infect. Dis. 205, 121–127 (2012).

    PubMed  Google Scholar 

  35. Fuhrer, T., Fischer, E. & Sauer, U. Experimental identification and quantification of glucose metabolism in seven bacterial species. J. Bacteriol. 187, 1581–1590 (2005).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. May, T. B. & Chakrabarty, A. M. Pseudomonas aeruginosa: genes and enzymes of alginate synthesis. Trends Microbiol. 2, 151–157 (1994).

    CAS  PubMed  Google Scholar 

  37. de Lorenzo, V. From the selfish gene to selfish metabolism: revisiting the central dogma. BioEssays. 36, 226–235 (2014).

    CAS  PubMed  Google Scholar 

  38. Oberhardt, M. A., Puchałka, J., Fryer, K. E., Martins dos Santos, V. A. P. & Papin, J. A. Genome-scale metabolic network analysis of the opportunistic pathogen Pseudomonas aeruginosa PAO1. J. Bacteriol. 190, 2790–2803 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. Blank, L. M., Ionidis, G., Ebert, B. E., Bühler, B. & Schmid, A. Metabolic response of Pseudomonas putida during redox biocatalysis in the presence of a second octanol phase. FEBS J. 275, 5173–5190 (2008).

    CAS  PubMed  Google Scholar 

  40. Aires, J. R., Köhler, T., Nikaido, H. & Plésiat, P. Involvement of an active efflux system in the natural resistance of Pseudomonas aeruginosa to aminoglycosides. Antimicrob. Agents Chemother. 43, 2624–2628 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  41. Vettoretti, L. et al. Efflux unbalance in Pseudomonas aeruginosa isolates from cystic fibrosis patients. Antimicrob. Agents Chemother. 53, 1987–1997 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  42. Udaondo, Z. et al. Analysis of solvent tolerance in Pseudomonas putida DOT-T1E based on its genome sequence and a collection of mutants. FEBS Lett. 586, 2932–2938 (2012).

    CAS  PubMed  Google Scholar 

  43. Timmis, K. N. Pseudomonas putida: a cosmopolitan opportunist par excellence. Environ. Microbiol. 4, 779–781 (2002).

    PubMed  Google Scholar 

  44. Poblete-Castro, I., Becker, J., Dohnt, K., Martins dos Santos, V. A. P. & Wittmann, C. Industrial biotechnology of Pseudomonas putida and related species. Appl. Microbiol. Biotechnol. 93, 2279–2290 (2012).

    CAS  PubMed  Google Scholar 

  45. Kevles, D. J. Ananda Chakrabarty wins a patent: biotechnology, law, and society. Hist. Stud. Phys. Biol. Sci. 25, 111–135 (1994).

    CAS  PubMed  Google Scholar 

  46. Wu, X. et al. Comparative genomics and functional analysis of niche-specific adaptation in Pseudomonas putida. FEMS Microbiol. Rev. 35, 299–323 (2011).

    CAS  PubMed  Google Scholar 

  47. Matilla, M. A. et al. Complete genome of the plant growth-promoting rhizobacterium Pseudomonas putida BIRD-1. J. Bacteriol. 193, 1290 (2011).

    CAS  PubMed  Google Scholar 

  48. Roca, A. et al. Analysis of the plant growth-promoting properties encoded by the genome of the rhizobacterium Pseudomonas putida BIRD-1. Environ. Microbiol. 15, 780–794 (2013).

    CAS  PubMed  Google Scholar 

  49. Silby, M. W., Winstanley, C., Godfrey, S. A., Levy, S. B. & Jackson, R. W. Pseudomonas genomes: diverse and adaptable. FEMS Microbiol. Rev. 35, 652–680 (2011).

    CAS  PubMed  Google Scholar 

  50. Jiménez, J. I., Miñambres, B., García, J. L. & Díaz, E. Genomic analysis of the aromatic catabolic pathways from Pseudomonas putida KT2440. Environ. Microbiol. 4, 824–841 (2002). A comprehensive survey of the many biochemical mechanisms that enable P. putida to grow on diverse carbon substrates.

    PubMed  Google Scholar 

  51. Mohn, W. W., Garmendia, J., Galvão, T. C. & de Lorenzo, V. Surveying biotransformations with à la carte genetic traps: translating dehydrochlorination of lindane (γ-hexachlorocyclohexane) into lacZ-based phenotypes. Environ. Microbiol. 8, 546–555 (2006).

    CAS  PubMed  Google Scholar 

  52. Orphan, V. J. Methods for unveiling cryptic microbial partnerships in nature. Curr. Opin. Microbiol. 12, 231–237 (2009).

    CAS  PubMed  Google Scholar 

  53. Watrous, J. et al. Mass spectral molecular networking of living microbial colonies. Proc. Natl Acad. Sci. USA 109, E1743–E1752 (2012).

    CAS  PubMed  Google Scholar 

  54. Bacchus, W. & Fussenegger, M. Engineering of synthetic intercellular communication systems. Metab. Eng. 16, 33–41 (2013).

    CAS  PubMed  Google Scholar 

  55. Ishige, T., Honda, K. & Shimizu, S. Whole organism biocatalysis. Curr. Opin. Chem. Biol. 9, 174–180 (2005).

    CAS  PubMed  Google Scholar 

  56. Inoue, A. & Horikoshi, K. A. Pseudomonas thrives in high concentrations of toluene. Nature 227, 264–265 (1989). This is the very first report of the isolation of a Pseudomonas species that is highly tolerant to solvents.

    Google Scholar 

  57. Ramos, J. L., Duque, E., Huertas, M. J. & Haidour, A. Isolation and expansion of the catabolic potential of a Pseudomonas putida strain able to grow in the presence of high concentrations of aromatic hydrocarbons. J. Bacteriol. 177, 3911–3916 (1995).

    CAS  PubMed  PubMed Central  Google Scholar 

  58. Weber, F. J., Ooijkaas, L. P., Schemen, R. M., Hartmans, S. & de Bont, J. A. Adaptation of Pseudomonas putida S12 to high concentrations of styrene and other organic solvents. Appl. Environ. Microbiol. 59, 3502–3504 (1993).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. Ramos, J. L. et al. Mechanisms of solvent tolerance in Gram-negative bacteria. Annu. Rev. Microbiol. 56, 743–768 (2002).

    CAS  PubMed  Google Scholar 

  60. Domínguez-Cuevas, P., González-Pastor, J. E., Marqués, S., Ramos, J. L. & de Lorenzo, V. Transcriptional tradeoff between metabolic and stress-response programs in Pseudomonas putida KT2440 cells exposed to toluene. J. Biol. Chem. 281, 11981–11991 (2006).

    PubMed  Google Scholar 

  61. Rühl, J., Schmid, A. & Blank, L. M. Selected Pseudomonas putida strains able to grow in the presence of high butanol concentrations. Appl. Environ. Microbiol. 75, 4653–4656 (2009).

    PubMed  PubMed Central  Google Scholar 

  62. Nielsen, D. R. et al. Engineering alternative butanol production platforms in heterologous bacteria. Metab. Eng. 11, 262–273 (2009).

    CAS  PubMed  Google Scholar 

  63. Luengo, J. M., García, B., Sandoval, A., Naharro, G. & Olivera, E. R. Bioplastics from microorganisms. Curr. Opin. Microbiol. 6, 251–260 (2003).

    CAS  PubMed  Google Scholar 

  64. Anderson, A. J. & Dawes, E. A. Occurrence, metabolism, metabolic role, and industrial uses of bacterial polyhydroxyalkanoates. Microbiol. Rev. 54, 450–472 (1990).

    CAS  PubMed  PubMed Central  Google Scholar 

  65. Keshavarz, T. & Roy, I. Polyhydroxyalkanoates: bioplastics with a green agenda. Curr. Opin. Microbiol. 13, 321–326 (2010).

    CAS  PubMed  Google Scholar 

  66. Nikel, P. I., de Almeida, A., Melillo, E. C., Galvagno, M. A. & Pettinari, M. J. New recombinant Escherichia coli strain tailored for the production of poly(3-hydroxybutyrate) from agroindustrial by-products. Appl. Environ. Microbiol. 72, 3949–3954 (2006).

    CAS  PubMed  PubMed Central  Google Scholar 

  67. Gao, X., Chen, J. C., Wu, Q. & Chen, G. Q. Polyhydroxyalkanoates as a source of chemicals, polymers, and biofuels. Curr. Opin. Biotechnol. 22, 768–774 (2011).

    CAS  PubMed  Google Scholar 

  68. Kim, Y., Kim, H. W., Chung, M. G. & Rhee, Y. H. Biosynthesis, modification, and biodegradation of bacterial medium-chain-length polyhydroxyalkanoates. J. Microbiol. 45, 87–97 (2007).

    PubMed  Google Scholar 

  69. Arias, S., Bassas-Galia, M., Molinari, G. & Timmis, K. N. Tight coupling of polymerization and depolymerization of polyhydroxyalkanoates ensures efficient management of carbon resources in Pseudomonas putida. Microb. Biotechnol. 6, 551–563 (2013).

    PubMed  PubMed Central  Google Scholar 

  70. Tripathi, L. et al. Pseudomonas putida KT2442 as a platform for the biosynthesis of polyhydroxyalkanoates with adjustable monomer contents and compositions. Bioresour. Technol. 142, 225–231 (2013).

    CAS  PubMed  Google Scholar 

  71. Escapa, I. F. et al. Disruption of β-oxidation pathway in Pseudomonas putida KT2442 to produce new functionalized PHAs with thioester groups. Appl. Microbiol. Biotechnol. 89, 1583–1598 (2011).

    CAS  PubMed  Google Scholar 

  72. Regenhardt, D. et al. Pedigree and taxonomic credentials of Pseudomonas putida strain KT2440. Environ. Microbiol. 4, 912–915 (2002).

    CAS  PubMed  Google Scholar 

  73. Nakazawa, T. Travels of a Pseudomonas, from Japan around the world. Environ. Microbiol. 4, 782–786 (2002).

    CAS  PubMed  Google Scholar 

  74. Feist, C. F. & Hegeman, G. D. Regulation of the meta cleavage pathway for benzoate oxidation by Pseudomonas putida. J. Bacteriol. 100, 1121–1123 (1969).

    CAS  PubMed  PubMed Central  Google Scholar 

  75. Williams, P. A. & Murray, K. Metabolism of benzoate and the methylbenzoates by Pseudomonas putida (arvilla) mt-2: evidence for the existence of a TOL plasmid. J. Bacteriol. 120, 416–423 (1974).

    CAS  PubMed  PubMed Central  Google Scholar 

  76. Federal Register. Appendix E, Certified host-vector systems. 47, 17197 (1982).

  77. Nelson, K. E. et al. Complete genome sequence and comparative analysis of the metabolically versatile Pseudomonas putida KT2440. Environ. Microbiol. 4, 799–808 (2002). This paper reports the completion of the genome sequencing that enabled the understanding and rational exploration of the metabolic diversity and physiological properties of strain KT2440.

    CAS  PubMed  Google Scholar 

  78. Martins dos Santos, V. A. P., Heim, S., Moore, E. R., Strätz, M. & Timmis, K. N. Insights into the genomic basis of niche specificity of Pseudomonas putida KT2440. Environ. Microbiol. 6, 1264–1286 (2004).

    Google Scholar 

  79. Chavarría, M., Nikel, P. I., Pérez-Pantoja, D. & de Lorenzo, V. The Entner–Doudoroff pathway empowers Pseudomonas putida KT2440 with a high tolerance to oxidative stress. Environ. Microbiol. 15, 1772–1785 (2013).

    PubMed  Google Scholar 

  80. Ebert, B. E., Kurth, F., Grund, M., Blank, L. M. & Schmid, A. Response of Pseudomonas putida KT2440 to increased NADH and ATP demand. Appl. Environ. Microbiol. 77, 6597–6605 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  81. Nogales, J., Palsson, B. Ø. & Thiele, I. A genome-scale metabolic reconstruction of Pseudomonas putida KT2440: iJN746 as a cell factory. BMC Syst. Biol. 2, 79 (2008). This paper reports on the development of the first genome-wide metabolic model of strain KT2440, which has enabled the identification of this bacterium as a useful microbial cell factory.

    PubMed  PubMed Central  Google Scholar 

  82. Puchałka, J. et al. Genome-scale reconstruction and analysis of the Pseudomonas putida KT2440 metabolic network facilitates applications in biotechnology. PLoS Comput. Biol. 4, e1000210 (2008).

    PubMed  PubMed Central  Google Scholar 

  83. Sohn, S. B., Kim, T. Y., Park, J. M. & Lee, S. Y. In silico genome-scale metabolic analysis of Pseudomonas putida KT2440 for polyhydroxyalkanoate synthesis, degradation of aromatics and anaerobic survival. Biotechnol. J. 5, 739–750 (2010).

    CAS  PubMed  Google Scholar 

  84. van Duuren, J. B. et al. Reconciling in vivo and in silico key biological parameters of Pseudomonas putida KT2440 during growth on glucose under carbon-limited condition. BMC Biotechnol. 13, 93 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  85. Nikel, P. I., Kim, J. & de Lorenzo, V. Metabolic and regulatory rearrangements underlying glycerol metabolism in Pseudomonas putida KT2440. Environ. Microbiol. 16, 239–254 (2014).

    CAS  PubMed  Google Scholar 

  86. Kim, J., Oliveros, J. C., Nikel, P. I., de Lorenzo, V. & Silva-Rocha, R. Transcriptomic fingerprinting of Pseudomonas putida under alternative physiological regimes. Environ. Microbiol. Rep. 5, 883–891 (2013).

    CAS  PubMed  Google Scholar 

  87. Park, S. J. et al. PutidaNET: interactome database service and network analysis of Pseudomonas putida KT2440. BMC Genomics 10, S18 (2009).

    PubMed  PubMed Central  Google Scholar 

  88. Winsor, G. L. et al. Pseudomonas Genome Database: improved comparative analysis and population genomics capability for Pseudomonas genomes. Nucleic Acids Res. 39, D596–D600 (2011).

    CAS  PubMed  Google Scholar 

  89. Schweizer, H. P. Vectors to express foreign genes and techniques to monitor gene expression in Pseudomonads. Curr. Opin. Biotechnol. 12, 439–445 (2001).

    CAS  PubMed  Google Scholar 

  90. Tavita, K. et al. Homologous recombination is facilitated in starving populations of Pseudomonas putida by phenol stress and affected by chromosomal location of the recombination target. Mutat. Res. 737, 12–24 (2012).

    CAS  PubMed  Google Scholar 

  91. Kolisnychenko, V. et al. Engineering a reduced Escherichia coli genome. Genome Res. 12, 640–647 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  92. Martínez-García, E. & de Lorenzo, V. Engineering multiple genomic deletions in Gram-negative bacteria: analysis of the multi-resistant antibiotic profile of Pseudomonas putida KT2440. Environ. Microbiol. 13, 2702–2716 (2011). This paper presents a method for the seamless deletion of genetic elements in Pseudomonas spp. and other Gram-negative bacteria.

    PubMed  Google Scholar 

  93. Martínez-García, E. & de Lorenzo, V. Transposon-based and plasmid-based genetic tools for editing genomes of Gram-negative bacteria. Methods Mol. Biol. 813, 267–283 (2012).

    PubMed  Google Scholar 

  94. Martínez-García, E., Nikel, P. I., Chavarría, M. & de Lorenzo, V. The metabolic cost of flagellar motion in Pseudomonas putida KT2440. Environ. Microbiol. 16, 291–303 (2014).

    PubMed  Google Scholar 

  95. LePrince, A., de Lorenzo, V., Voller, P., van Passel, M. W. & Martins dos Santos, V. A. P. Random and cyclical deletion of large DNA segments in the genome of Pseudomonas putida. Environ. Microbiol. 14, 1444–1453 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  96. Bagdasarian, M. et al. Specific purpose plasmid cloning vectors. II. Broad host range, high copy number, RSF1010-derived vectors, and a host-vector system for gene cloning in Pseudomonas. Gene 16, 237–247 (1981).

    CAS  PubMed  Google Scholar 

  97. Nikel, P. I. & de Lorenzo, V. Engineering an anaerobic metabolic regime in Pseudomonas putida KT2440 for the anoxic biodegradation of 1,3-dichloroprop-1-ene. Metab. Eng. 15, 98–112 (2013). This paper is a practical example of how synthetic biology tools can be used to change the entire lifestyle of a bacterium, in this case, moving the metabolism of P. putida towards anaerobiosis.

    CAS  PubMed  Google Scholar 

  98. de Lorenzo, V. & Timmis, K. N. Analysis and construction of stable phenotypes in Gram-negative bacteria with Tn5- and Tn10-derived minitransposons. Methods Enzymol. 235, 386–405 (1994).

    CAS  PubMed  Google Scholar 

  99. Martínez-García, E., Calles, B., Arévalo-Rodríguez, M. & de Lorenzo, V. pBAM1: an all-synthetic genetic tool for analysis and construction of complex bacterial phenotypes. BMC Microbiol. 11, 38 (2011).

    PubMed  PubMed Central  Google Scholar 

  100. Nikel, P. I. & de Lorenzo, V. Implantation of unmarked regulatory and metabolic modules in Gram-negative bacteria with specialised mini-transposon delivery vectors. J. Biotechnol. 163, 143–154 (2013).

    CAS  PubMed  Google Scholar 

  101. Vora, T., Hottes, A. K. & Tavazoie, S. Protein occupancy landscape of a bacterial genome. Mol. Cell 35, 247–253 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  102. Sousa, C., de Lorenzo, V. & Cebolla, A. Modulation of gene expression through chromosomal positioning in Escherichia coli. Microbiology 143, 2071–2078 (1997).

    CAS  PubMed  Google Scholar 

  103. Choi, K. H. & Schweizer, H. P. Mini-Tn7 insertion in bacteria with single attTn7 sites: example Pseudomonas aeruginosa. Nature Protocols 1, 153–161 (2006).

    CAS  PubMed  Google Scholar 

  104. Lambertsen, L., Sternberg, C. & Molin, S. Mini-Tn7 transposons for site-specific tagging of bacteria with fluorescent proteins. Environ. Microbiol. 6, 726–732 (2004).

    CAS  PubMed  Google Scholar 

  105. de las Heras, A., Carreño, C. A. & de Lorenzo, V. Stable implantation of orthogonal sensor circuits in Gram-negative bacteria for environmental release. Environ. Microbiol. 10, 3305–3316 (2008).

    CAS  PubMed  Google Scholar 

  106. Miyazaki, R. & van der Meer, J. R. A new large-DNA-fragment delivery system based on integrase activity from an integrative and conjugative element. Appl. Environ. Microbiol. 79, 4440–4447 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  107. Cases, I. & de Lorenzo, V. Promoters in the environment: transcriptional regulation in its natural context. Nature Rev. Microbiol. 3, 105–118 (2005).

    CAS  Google Scholar 

  108. Blatny, J. M., Brautaset, T., Winther-Larsen, H. C., Haugan, K. & Valla, S. Construction and use of a versatile set of broad-host-range cloning and expression vectors based on the RK2 replicon. Appl. Environ. Microbiol. 63, 370–379 (1997).

    CAS  PubMed  PubMed Central  Google Scholar 

  109. de Lorenzo, V., Fernández, S., Herrero, M., Jakubzik, U. & Timmis, K. N. Engineering of alkyl- and haloaromatic-responsive gene expression with mini-transposons containing regulated promoters of biodegradative pathways of Pseudomonas. Gene 130, 41–46 (1993).

    CAS  PubMed  Google Scholar 

  110. Panke, S., de Lorenzo, V., Kaiser, A., Witholt, B. & Wubbolts, M. G. Engineering of a stable whole-cell biocatalyst capable of (S)-styrene oxide formation for continuous two-liquid-phase applications. Appl. Environ. Microbiol. 65, 5619–5623 (1999).

    CAS  PubMed  PubMed Central  Google Scholar 

  111. Silva-Rocha, R., de Jong, H., Tamames, J. & de Lorenzo, V. The logic layout of the TOL network of Pseudomonas putida pWW0 plasmid stems from a metabolic amplifier motif (MAM) that optimizes biodegradation of m-xylene. BMC Syst. Biol. 5, 191 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  112. Billerbeck, S., Calles, B., Müller, C. L., de Lorenzo, V. & Panke, S. Towards functional orthogonalization of protein complexes: individualization of GroEL monomers leads to distinct quasi-homogeneous single rings. ChemBiochem 14, 2310–2321 (2013).

    CAS  PubMed  Google Scholar 

  113. Calles, B. & de Lorenzo, V. Expanding the Boolean logic of the prokaryotic transcription factor XylR by functionalization of permissive sites with a protease-target sequence. ACS Synth. Biol. 2, 594–603 (2013).

    CAS  PubMed  Google Scholar 

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

    CAS  PubMed  Google Scholar 

  115. Garmendia, J., de las Heras, A., Galvão, T. C. & de Lorenzo, V. Tracing explosives in soil with transcriptional regulators of Pseudomonas putida evolved for responding to nitrotoluenes. Microb. Biotechnol. 1, 236–246 (2008).

    CAS  PubMed  PubMed Central  Google Scholar 

  116. Zhang, C., Wei, Z. H. & Ye, B. C. Quantitative monitoring of 2-oxoglutarate in Escherichia coli cells by a fluorescence resonance energy transfer-based biosensor. Appl. Microbiol. Biotechnol. 97, 8307–8316 (2013).

    CAS  PubMed  Google Scholar 

  117. de las Heras, A., Carreño, C. A., Martínez-García, E. & de Lorenzo, V. Engineering input/output nodes in prokaryotic regulatory circuits. FEMS Microbiol. Rev. 34, 842–865 (2010).

    CAS  PubMed  Google Scholar 

  118. Basu, S., Gerchman, Y., Collins, C. H., Arnold, F. H. & Weiss, R. A synthetic multicellular system for programmed pattern formation. Nature 434, 1130–1134 (2005).

    CAS  PubMed  Google Scholar 

  119. Silva-Rocha, R. & de Lorenzo, V. Engineering multicellular logic in bacteria with metabolic wires. ACS Synth. Biol. http://dx.doi.org/10.1021/sb400064y (2014).

  120. Gross, R., Buehler, K. & Schmid, A. Engineered catalytic biofilms for continuous large scale production of n-octanol and (S)-styrene oxide. Biotechnol. Bioeng. 110, 424–436 (2013).

    CAS  PubMed  Google Scholar 

  121. Pósfai, G. et al. Emergent properties of reduced-genome Escherichia coli. Science 312, 1044–1046 (2006).

    PubMed  Google Scholar 

  122. Ma, S., Tang, N. & Tian, J. DNA synthesis, assembly and applications in synthetic biology. Curr. Opin. Chem. Biol. 16, 260–267 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  123. Huerta, S. Structural design in the work of Gaudí. Archit. Sci. Rev. 49, 324–339 (2006).

    Google Scholar 

  124. Ben Yehezkel, T., Biezuner, T., Linshiz, G., Mazor, Y. & Shapiro, E. Programmable in vivo selection of arbitrary DNA sequences. PLoS ONE 7, e47795 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  125. Oliver, A., Cantón, R., Campo, P., Baquero, F. & Blázquez, J. High frequency of hypermutable Pseudomonas aeruginosa in cystic fibrosis lung infection. Science 288, 1251–1254 (2000).

    CAS  PubMed  Google Scholar 

  126. van Ham, R. C. et al. Reductive genome evolution in Buchnera aphidicola. Proc. Natl Acad. Sci. USA 100, 581–586 (2003).

    CAS  PubMed  Google Scholar 

  127. Chou, H. H. & Keasling, J. D. Programming adaptive control to evolve increased metabolite production. Nature Comm. 4, 2595 (2013).

    Google Scholar 

  128. Shetty, R. P., Endy, D. & Knight, T. F. Engineering BioBrick vectors from BioBrick parts. J. Biol. Eng. 2, 5 (2008).

    PubMed  PubMed Central  Google Scholar 

  129. Anderson, J. C. et al. BglBricks: a flexible standard for biological part assembly. J. Biol. Eng. 4, 1 (2010).

    PubMed  PubMed Central  Google Scholar 

  130. Silva-Rocha, R. et al. The Standard European Vector Architecture (SEVA): a coherent platform for the analysis and deployment of complex prokaryotic phenotypes. Nucleic Acids Res. 41, D666–D675 (2013). Building on the standardization premise of synthetic biology, the authors of this paper propose the adoption of a universal framework for plasmid vectors.

    CAS  PubMed  Google Scholar 

  131. Pfleger, B. F., Pitera, D. J., Smolke, C. D. & Keasling, J. D. Combinatorial engineering of intergenic regions in operons tunes expression of multiple genes. Nature Biotech. 24, 1027–1032 (2006).

    CAS  Google Scholar 

  132. Santos, C. N. S. & Stephanopoulos, G. Combinatorial engineering of microbes for optimizing cellular phenotype. Curr. Opin. Chem. Biol. 12, 168–176 (2008).

    CAS  PubMed  Google Scholar 

  133. Wang, H. H. et al. Programming cells by multiplex genome engineering and accelerated evolution. Nature 460, 894–898 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  134. Agapakis, C. M. & Silver, P. A. Synthetic biology: exploring and exploiting genetic modularity through the design of novel biological networks. Mol. BioSyst. 5, 704–713 (2009).

    CAS  PubMed  Google Scholar 

  135. Jayanthi, S., Nilgiriwala, K. S. & Del Vecchio, D. Retroactivity controls the temporal dynamics of gene transcription. ACS Synth. Biol. 2, 431–441 (2013).

    CAS  PubMed  Google Scholar 

  136. Chavarría, M., Kleijn, R. J., Sauer, U., Pflüger-Grau, K. & de Lorenzo, V. Regulatory tasks of the phosphoenolpyruvate-phosphotransferase system of Pseudomonas putida in central carbon metabolism. mBio 3, e00028-12 (2012).

    PubMed  PubMed Central  Google Scholar 

Download references

Acknowledgements

This study was supported by the BIO and FEDER CONSOLIDER-INGENIO Program of the Spanish Ministry of Science and Innovation, the ST-FLOW, ARYSIS and EVOPROG Contracts of the EU, the ERANET-IB Program, and the PROMT Project of the CAM. PIN is a researcher from the Consejo Nacional de Investigaciones Científicas y Técnicas (Argentina) and holds a Marie Curie Actions Programme grant obtained from the EU (ALLEGRO, UE-FP7-PEOPLE-2011-IIF-300508).

Author information

Authors and Affiliations

  1. Systems and Synthetic Biology Program, Centro Nacional de Biotecnología, Consejo Superior de Investigaciones Científicas (CNB-CSIC), Madrid, 28049, Spain

    Pablo I. Nikel, Esteban Martínez-García & Víctor de Lorenzo

Authors
  1. Pablo I. Nikel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Esteban Martínez-García
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Víctor de Lorenzo
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Víctor de Lorenzo.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Related links

PowerPoint slides

Glossary

Forward-engineering

The rational design of complex objects with properties that can be quantitatively predicted from the attributes of the components.

Biological chassis

An autonomous genetic and/or biochemical scaffold that functions as an dynamic platform for implanting forward-designed biological devices.

Logic gates

Devices that execute binary, Boolean operations in which (typically) one or two inputs with values 1 or 0 are processed into one fixed, digital output (0 or 1 as well). The output of one gate can become the input of another gate, thereby enabling composability and scalability.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Nikel, P., Martínez-García, E. & de Lorenzo, V. Biotechnological domestication of pseudomonads using synthetic biology. Nat Rev Microbiol 12, 368–379 (2014). https://doi.org/10.1038/nrmicro3253

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nrmicro3253

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Translational Research

Sign up for the Nature Briefing: Translational Research newsletter — top stories in biotechnology, drug discovery and pharma.

Get what matters in translational research, free to your inbox weekly. Sign up for Nature Briefing: Translational Research