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Molecular crowding shapes gene expression in synthetic cellular nanosystems

Abstract

The integration of synthetic and cell-free biology has made tremendous strides towards creating artificial cellular nanosystems using concepts from solution-based chemistry, where only the concentrations of reacting species modulate gene expression rates. However, it is known that macromolecular crowding, a key feature in natural cells, can dramatically influence biochemical kinetics via volume exclusion effects, which reduce diffusion rates and enhance binding rates of macromolecules. Here, we demonstrate that macromolecular crowding can increase the robustness of gene expression by integrating synthetic cellular components of biological circuits and artificial cellular nanosystems. Furthermore, we reveal how ubiquitous cellular modules, including genetic components, a negative feedback loop and the size of the crowding molecules can fine-tune gene circuit response to molecular crowding. By bridging a key gap between artificial and living cells, our work has implications for efficient and robust control of both synthetic and natural cellular circuits.

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Figure 1: Shaping gene expression in artificial cellular systems by molecular crowding.
Figure 2: Molecular crowding modulates dynamics of gene expression.
Figure 3: Molecular crowding increases robustness of gene expression.
Figure 4: Volume-dependent impact of molecular crowding in artificial cells.

References

  1. Gibson, D. G. et al. Complete chemical synthesis, assembly, and cloning of a Mycoplasma genitalium genome. Science 319, 1215–1220 (2008).

    Article  CAS  Google Scholar 

  2. Pinheiro, V. B. et al. Synthetic genetic polymers capable of heredity and evolution. Science 336, 341–344 (2012).

    Article  CAS  Google Scholar 

  3. Nawroth, J. C. et al. A tissue-engineered jellyfish with biomimetic propulsion. Nature Biotechnol. 30, 792–797 (2012).

    Article  CAS  Google Scholar 

  4. Kim, J. & Winfree, E. Synthetic in vitro transcriptional oscillators. Mol. Syst. Biol. 7, 465 (2011).

    Article  Google Scholar 

  5. Fernandes, R., Roy, V., Wu, H. C. & Bentley, W. E. Engineered biological nanofactories trigger quorum sensing response in targeted bacteria. Nature Nanotech. 5, 213–217 (2010).

    Article  CAS  Google Scholar 

  6. Murtas, G., Kuruma, Y., Bianchini, P., Diaspro, A. & Luisi, P. L. Protein synthesis in liposomes with a minimal set of enzymes. Biochem. Biophys. Res. Commun. 363, 12–17 (2007).

    Article  CAS  Google Scholar 

  7. Schroeder, A. et al. Remotely activated protein-producing nanoparticles. Nano Lett. 12, 2685–2689 (2012).

    Article  CAS  Google Scholar 

  8. Martino, C. et al. Protein expression, aggregation, and triggered release from polymersomes as artificial cell-like structures. Angew. Chem. Int. Ed. 51, 6416–6420 (2012).

    Article  CAS  Google Scholar 

  9. Ishikawa, K., Sato, K., Shima, Y., Urabe, I. & Yomo, T. Expression of a cascading genetic network within liposomes. FEBS Lett. 576, 387–390 (2004).

    Article  CAS  Google Scholar 

  10. Leduc, P. R. et al. Towards an in vivo biologically inspired nanofactory. Nature Nanotech. 2, 3–7 (2007).

    Article  CAS  Google Scholar 

  11. Gardner, P. M., Winzer, K. & Davis, B. G. Sugar synthesis in a protocellular model leads to a cell signalling response in bacteria. Nature Chem. 1, 377–383 (2009).

    Article  CAS  Google Scholar 

  12. Mansy, S. S. et al. Template-directed synthesis of a genetic polymer in a model protocell. Nature 454, 122–125 (2008).

    Article  CAS  Google Scholar 

  13. Noireaux, V., Maeda, Y. T. & Libchaber, A. Development of an artificial cell, from self-organization to computation and self-reproduction. Proc. Natl Acad. Sci. USA 108, 3473–3480 (2011).

    Article  CAS  Google Scholar 

  14. Chang, T. M. Therapeutic applications of polymeric artificial cells. Nature Rev. Drug Discov. 4, 221–235 (2005).

    Article  CAS  Google Scholar 

  15. Jewett, M. C., Calhoun, K. A., Voloshin, A., Wuu, J. J. & Swartz, J. R. An integrated cell-free metabolic platform for protein production and synthetic biology. Mol. Syst. Biol. 4, 220 (2008).

    Article  Google Scholar 

  16. Ellis, R. J. Macromolecular crowding: obvious but underappreciated. Trends Biochem. Sci. 26, 597–604 (2001).

    Article  CAS  Google Scholar 

  17. Morelli, M. J., Allen, R. J. & Wolde, P. R. Effects of macromolecular crowding on genetic networks. Biophys. J. 101, 2882–2891 (2011).

    Article  CAS  Google Scholar 

  18. Zimmerman, S. B. & Trach, S. O. Estimation of macromolecule concentrations and excluded volume effects for the cytoplasm of Escherichia coli. J. Mol. Biol. 222, 599–620 (1991).

    Article  CAS  Google Scholar 

  19. Minton, A. P. The influence of macromolecular crowding and macromolecular confinement on biochemical reactions in physiological media. J. Biol. Chem. 276, 10577–10580 (2001).

    Article  CAS  Google Scholar 

  20. Minton, A. P. The effect of volume occupancy upon the thermodynamic activity of proteins – some biochemical consequences. Mol. Cell. Biochem. 55, 119–140 (1983).

    Article  CAS  Google Scholar 

  21. Li, G-W., Berg, O. G. & Elf, J. Effects of macromolecular crowding and DNA looping on gene regulation kinetics. Nature Phys. 5, 294–297 (2009).

    Article  CAS  Google Scholar 

  22. Beg, Q. K. et al. Intracellular crowding defines the mode and sequence of substrate uptake by Escherichia coli and constrains its metabolic activity. Proc. Natl Acad. Sci. USA 104, 12663–12668 (2007).

    Article  CAS  Google Scholar 

  23. Richter, K., Nessling, M. & Lichter, P. Experimental evidence for the influence of molecular crowding on nuclear architecture. J. Cell Sci. 120, 1673–1680 (2007).

    Article  CAS  Google Scholar 

  24. Elcock, A. H. Models of macromolecular crowding effects and the need for quantitative comparisons with experiment. Curr. Opin. Struct. Biol. 20, 196–206 (2010).

    Article  CAS  Google Scholar 

  25. Schoen, I., Krammer, H. & Braun, D. Hybridization kinetics is different inside cells. Proc. Natl Acad. Sci. USA 106, 21649–21654 (2009).

    Article  CAS  Google Scholar 

  26. Phillip, Y., Sherman, E., Haran, G. & Schreiber, G. Common crowding agents have only a small effect on protein–protein interactions. Biophys. J. 97, 875–885 (2009).

    Article  CAS  Google Scholar 

  27. Mika, J. T. & Poolman, B. Macromolecule diffusion and confinement in prokaryotic cells. Curr. Opin. Biotechnol. 22, 117–126 (2011).

    Article  CAS  Google Scholar 

  28. Laurent, T. C. The interaction between polysaccharides and other macromolecules. 5. The solubility of proteins in the presence of dextran. Biochem. J. 89, 253–257 (1963).

    Article  CAS  Google Scholar 

  29. Feder, T. J., Brust-Mascher, I., Slattery, J. P., Baird, B. & Webb, W. W. Constrained diffusion or immobile fraction on cell surfaces: a new interpretation. Biophys. J. 70, 2767–2773 (1996).

    Article  CAS  Google Scholar 

  30. Friedman, L. J. & Gelles, J. Mechanism of transcription initiation at an activator-dependent promoter defined by single-molecule observation. Cell 148, 679–689 (2012).

    Article  CAS  Google Scholar 

  31. Wang, Y., Guo, L., Golding, I., Cox, E. C. & Ong, N. P. Quantitative transcription factor binding kinetics at the single-molecule level. Biophys. J. 96, 609–620 (2009).

    Article  CAS  Google Scholar 

  32. Bancaud, A. et al. Molecular crowding affects diffusion and binding of nuclear proteins in heterochromatin and reveals the fractal organization of chromatin. EMBO J. 28, 3785–3798 (2009).

    Article  CAS  Google Scholar 

  33. Burg, M. B., Kwon, E. D. & Kultz, D. Osmotic regulation of gene expression. FASEB J. 10, 1598–1606 (1996).

    Article  CAS  Google Scholar 

  34. Martin, C. T. & Coleman, J. E. Kinetic analysis of T7 RNA polymerase–promoter interactions with small synthetic promoters. Biochemistry 26, 2690–2696 (1987).

    Article  CAS  Google Scholar 

  35. Gardner, T. S., Cantor, C. R. & Collins, J. J. Construction of a genetic toggle switch in Escherichia coli. Nature 403, 339–342 (2000).

    Article  CAS  Google Scholar 

  36. Spitzer, J. & Poolman, B. The role of biomacromolecular crowding, ionic strength, and physicochemical gradients in the complexities of life's emergence. Microbiol. Mol. Biol. Rev. 73, 371–388 (2009).

    Article  CAS  Google Scholar 

  37. Chen, H. Z. & Zubay, G. Prokaryotic coupled transcription-translation. Methods Enzymol. 101, 674–690 (1983).

    Article  CAS  Google Scholar 

  38. Milo, R. et al. Network motifs: simple building blocks of complex networks. Science 298, 824–827 (2002).

    Article  CAS  Google Scholar 

  39. Sunami, T. et al. Femtoliter compartment in liposomes for in vitro selection of proteins. Anal. Biochem. 357, 128–136 (2006).

    Article  CAS  Google Scholar 

  40. Harris, D. C. & Jewett, M. C. Cell-free biology: exploiting the interface between synthetic biology and synthetic chemistry. Curr. Opin. Biotechnol. 23, 672–678 (2012).

    Article  CAS  Google Scholar 

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Acknowledgements

The authors thank members of the LeDuc and Schwartz laboratories, the group of the Center for Mechanical Technology and Automation at University of Aveiro in Portugal, Dr. Shuqiang Huang, Dr. Gang Bao, and Dr. Lingchong You for discussions and comments, and R. Murphy, A. Mitchell, B. Armitage, T. Lee, F. Lanni and the Molecular Biosensor and Imaging Center for providing access to equipment. This work was partially supported by a Lane Postdoctoral Fellowship (C.T.), a Society in Science – Branco Weiss Fellowship (C.T.), NIH 1R01GM086237 (M.B. & S.S.), NIH 8U54GM103529 (M.B. & S.S.), NIH 1R01AI076318 (R.S), NIH 1R01CA140214 (R.S), NSF CMMI-1100430 (P.L.), NSF CMMI-0856187 (P.L.), NSF CMMI-1160840 (P.L.), ONR N000140910215 (P.L.), and NSF CPS-1135850 (P.L.).

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Contributions

C.T., S.S., R.S. and P.L. conceived the research and designed the experiments. C.T. and S.S. performed the experiments. C.T. carried out the modelling analysis. C.T., S.S., M.B. and P.L. provided materials and reagents. C.T., R.S. and P.L. interpreted the results and wrote the manuscript, with critical input from S.S. and M.B. All authors approved the manuscript.

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Correspondence to Russell Schwartz or Philip LeDuc.

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The authors declare no competing financial interests.

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Tan, C., Saurabh, S., Bruchez, M. et al. Molecular crowding shapes gene expression in synthetic cellular nanosystems. Nature Nanotech 8, 602–608 (2013). https://doi.org/10.1038/nnano.2013.132

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