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Systematic identification of allosteric protein-metabolite interactions that control enzyme activity in vivo

Nature Biotechnology volume 31, pages 357–361 (2013)Cite this article

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Abstract

Recent data suggest that the majority of proteins bind specific metabolites1,2 and that such interactions are relevant to metabolic and gene regulation3,4,5. However, there are no methods to systematically identify functional allosteric protein-metabolite interactions4,6. Here we present an experimental and computational approach for using dynamic metabolite data to discover allosteric regulation that is relevant in vivo. By switching the culture conditions of Escherichia coli every 30 s between medium containing either pyruvate or 13C-labeled fructose or glucose, we measured the reversal of flux through glycolysis pathways and observed rapid changes in metabolite concentration. We fit these data to a kinetic model of glycolysis and systematically tested the consequences of 126 putative allosteric interactions on metabolite dynamics. We identified allosteric interactions that govern the reversible switch between gluconeogenesis and glycolysis, including one by which pyruvate activates fructose-1,6-bisphosphatase. Thus, from large sets of putative allosteric interactions, our approach can identify the most likely ones and provide hypotheses about their function.

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Figure 1: Gluconeogenesis reverses upon hexose availability.
Figure 2: Metabolites regulate flux reversal during carbon source switches.
Figure 3: Validation of allosteric interactions identified in vivo.

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References

  1. Li, X., Gianoulis, T.A., Yip, K.Y., Gerstein, M. & Snyder, M. Extensive in vivo metabolite-protein interactions revealed by large-scale systematic analyses. Cell 143, 639–650 (2010).

    Article  CAS  Google Scholar 

  2. Gallego, O. et al. A systematic screen for protein-lipid interactions in Saccharomyces cerevisiae. Mol. Syst. Biol. 6, 430 (2010).

    Article  CAS  Google Scholar 

  3. Hilser, V.J. An ensemble view of allostery. Science 327, 653–654 (2010).

    Article  CAS  Google Scholar 

  4. Lindsley, J.E. & Rutter, J. Whence cometh the allosterome? Proc. Natl. Acad. Sci. USA 103, 10533–10535 (2006).

    Article  CAS  Google Scholar 

  5. Gerosa, L. & Sauer, U. Regulation and control of metabolic fluxes in microbes. Curr. Opin. Biotechnol. 22, 566–575 (2011).

    Article  CAS  Google Scholar 

  6. Heinemann, M. & Sauer, U. Systems biology of microbial metabolism. Curr. Opin. Microbiol. 13, 337–343 (2010).

    Article  CAS  Google Scholar 

  7. Petschnigg, J., Snider, J. & Stagljar, I. Interactive proteomics research technologies: recent applications and advances. Curr. Opin. Biotechnol. 22, 50–58 (2011).

    Article  CAS  Google Scholar 

  8. Stormo, G.D. & Zhao, Y. Determining the specificity of protein-DNA interactions. Nat. Rev. Genet. 11, 751–760 (2010).

    Article  CAS  Google Scholar 

  9. Scheer, M. et al. BRENDA, the enzyme information system in 2011. Nucleic Acids Res. 39, D670–D676 (2011).

    Article  CAS  Google Scholar 

  10. Orsak, T. et al. Revealing the allosterome: systematic identification of metabolite−protein interactions. Biochemistry 51, 225–232 (2012).

    Article  CAS  Google Scholar 

  11. Grimbs, S., Selbig, J., Bulik, S., Holzhütter, H.-G. & Steuer, R. The stability and robustness of metabolic states: identifying stabilizing sites in metabolic networks. Mol. Syst. Biol. 3, 146 (2007).

    Article  Google Scholar 

  12. Goyal, S., Yuan, J., Chen, T., Rabinowitz, J.D. & Wingreen, N.S. Achieving optimal growth through product feedback inhibition in metabolism. PLoS Comput. Biol. 6, e1000802 (2010).

    Article  Google Scholar 

  13. Kremling, A., Bettenbrock, K. & Gilles, E.D. A feed-forward loop guarantees robust behavior in Escherichia coli carbohydrate uptake. Bioinformatics 24, 704–710 (2008).

    Article  CAS  Google Scholar 

  14. Curien, G. et al. Understanding the regulation of aspartate metabolism using a model based on measured kinetic parameters. Mol. Syst. Biol. 5, 271 (2009).

    Article  Google Scholar 

  15. Buescher, J.M., Moco, S., Sauer, U. & Zamboni, N. Ultrahigh performance liquid chromatography-tandem mass spectrometry method for fast and robust quantification of anionic and aromatic metabolites. Anal. Chem. 82, 4403–4412 (2010).

    Article  CAS  Google Scholar 

  16. Ruehl, M. et al. Collisional fragmentation of central carbon metabolites in LC-MS/MS increases precision of 13C metabolic flux analysis. Biotechnol. Bioeng. 109, 763–771 (2012).

    Article  CAS  Google Scholar 

  17. Kuepfer, L., Peter, M., Sauer, U. & Stelling, J. Ensemble modeling for analysis of cell signaling dynamics. Nat. Biotechnol. 25, 1001–1006 (2007).

    Article  CAS  Google Scholar 

  18. Yuan, J., Bennett, B.D. & Rabinowitz, J.D. Kinetic flux profiling for quantitation of cellular metabolic fluxes. Nat. Protoc. 3, 1328–1340 (2008).

    Article  CAS  Google Scholar 

  19. Rabinowitz, J.D. & Kimball, E. Acidic acetonitrile for cellular metabolome extraction from Escherichia coli. Anal. Chem. 79, 6167–6173 (2007).

    Article  CAS  Google Scholar 

  20. 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  Google Scholar 

  21. Kümmel, A., Panke, S. & Heinemann, M. Putative regulatory sites unraveled by network-embedded thermodynamic analysis of metabolome data. Mol. Syst. Biol. 2, 0034 (2006).

    Article  Google Scholar 

  22. Turkheimer, F.E., Hinz, R. & Cunningham, V.J. On the undecidability among kinetic models: from model selection to model averaging. J. Cereb. Blood Flow Metab. 23, 490–498 (2003).

    Article  Google Scholar 

  23. Johnson, J.L. & Reinhart, G.D. MgATP and fructose 6-phosphate interactions with phosphofructokinase from Escherichia coli. Biochemistry 31, 11510–11518 (1992).

    Article  CAS  Google Scholar 

  24. Blangy, D., Buc, H. & Monod, J. Kinetics of the allosteric interactions of phosphofructokinase from Escherichia coli. J. Mol. Biol. 31, 13–35 (1968).

    Article  CAS  Google Scholar 

  25. Hines, J.K., Fromm, H.J. & Honzatko, R.B. Novel allosteric activation site in Escherichia coli fructose-1,6-bisphosphatase. J. Biol. Chem. 281, 18386–18393 (2006).

    Article  CAS  Google Scholar 

  26. Valentini, G. et al. The allosteric regulation of pyruvate kinase. A site-directed mutagenesis study. J. Biol. Chem. 275, 18145–18152 (2000).

    Article  CAS  Google Scholar 

  27. Wohl, R.C. & Markus, G. Phosphoenolpyruvate carboxylase of Escherichia coli. Purification and some properties. J. Biol. Chem. 247, 5785–5792 (1972).

    CAS  PubMed  Google Scholar 

  28. Xu, Y.-F. et al. Ultrasensitive regulation of anapleurosis via allosteric activation of PEP carboxylase. Nat. Chem. Biol. 8, 562–568 (2012).

    Article  CAS  Google Scholar 

  29. Fischer, E. & Sauer, U. Metabolic flux profiling of Escherichia coli mutants in central carbon metabolism using GC-MS. Eur. J. Biochem. 270, 880–891 (2003).

    Article  CAS  Google Scholar 

  30. Baba, T. et al. Construction of Escherichia coli K-12 in-frame, single-gene knockout mutants: the Keio collection. Mol. Syst. Biol. 2, 0008 (2006).

    Article  Google Scholar 

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Acknowledgements

We thank E. Zamora-Sillero, L. Gerosa, M. Zampieri and R. Milo for discussions. This work was supported by Deutsche Forschungsgemeinschaft grant Li 1993/1-1.

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Authors and Affiliations

  1. Institute of Molecular System Biology, ETH Zurich, Zurich, Switzerland

    Hannes Link, Karl Kochanowski & Uwe Sauer

  2. Life Science Zurich PhD Program on Systems Biology, Zurich, Switzerland

    Karl Kochanowski

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  1. Karl Kochanowski
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  2. Uwe Sauer
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Contributions

H.L. co-wrote the manuscript, performed and conceived experiments and computational analysis, K.K. performed experiments and constructed strains and edited the final draft, U.S. co-wrote the manuscript and directed the project.

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Correspondence to Uwe Sauer.

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The authors declare no competing financial interests.

Supplementary information

Supplementary Text and Figures

Supplementary Tables 2–5, Supplementary Figures 1–8 and Supplementary Methods (PDF 3092 kb)

Supplementary Table 1

Metabolite data of the glucose switch, the fructose switch and the control sample without substrate switching. (XLSX 36 kb)

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Link, H., Kochanowski, K. & Sauer, U. Systematic identification of allosteric protein-metabolite interactions that control enzyme activity in vivo. Nat Biotechnol 31, 357–361 (2013). https://doi.org/10.1038/nbt.2489

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