Team:TorontoMaRSDiscovery/Project

From 2009.igem.org

Revision as of 20:47, 19 October 2009 by Shung (Talk | contribs)
Home The Team The Project Parts Submitted to the Registry Modeling Bioinformatics Notebook


The Project Engineering bacterial micro-compartments to investigate metabolic channeling and its potential uses in biotechnological applications

Contents

Background

Figure 1: A three-component design for the controlled expression of encapsulin and targeted eCFP.

It is well established that genes and their products do not operate in isolation but rather form parts of integrated biochemical pathways. There is increasing evidence that in many pathways, individual components exhibit varying degrees of spatial organization ranging from sub-cellular compartmentalization to the formation of discrete complexes.

For metabolic processes, the co-localization of enzymatic components has been shown to promote the transfer of substrates between consecutive reactions in a process termed “channeling”1. Metabolic channeling is defined as the process in which the intermediate produced by one enzyme is transferred to the next enzyme without complete mixing in the bulk phase12. Channeling results in the efficient translocation of substrates between enzymes and has been proposed to result in the following benefits: 1) Optimization of catalytic efficiency by decreasing transit time for intermediates; 2) Relief of the effects of product inhibition; 3) Protection from the creation of potentially toxic or unstable intermediates; and 4) Regulation of substrate flux through mediating pathway cross-talk 1. Potential applications range from the production of valuable compounds such as therapeutic molecules and biofuels2 to the degradation of toxic wastes3.

We propose a novel interdisciplinary project to systematically investigate how nature implements metabolic channeling and how this knowledge may be exploited for biotechnological applications.


Experimental Approach

Objectives

  1. Design, construct and characterize a micro-compartment expression system in E. coli.
  2. Demonstrate in vivo assembly of the expressed micro-compartments.
  3. Target a fluorescent marker (eCFP) to the micro-compartment.
  4. Identify and prioritize candidate enzyme pairs for channeling.
  5. Apply channeling to selected enzyme pairs.

Phase-One

Using standard BioBrick parts25 we have designed and are in the process of constructing an expression system capable of producing functional, T. maritima derived, encapsulin micro-compartments in E. coli (Figure 1). To establish that these compartments are functional, we will test our ability to target peptides to this compartment using a recombinant, fluorescent marker protein (eCFP) with a c-terminal extension corresponding to the conserved targeting sequence described by Sutter et al 21 followed by fluorescence microscopy. We hypothesize that encapsulation of eCFP will prolong its half-life and that by varying the amounts of the de-repressors aTc and IPTG in the expression system it will be possible to obtain a state where sufficient encapsulin is produced to enclose detectable amounts of eCFP and where background signal is minimized. By measuring the difference in fluorescence between bacteria expressing encapsulin + eCFP versus those expressing only eCFP at a given optical density we hope to obtain an estimate of the amount of eCFP being protected from degradation and therefore localized to the micro-compartments.

Concurrently, we are evaluating enzymes for use in our system which are likely to be amenable to channeling as predicted by C. SanfordModeling and Computational Prediction of Metabolic Channeling27. Our bioinformatics team, which consists of undergraduate students as well as graduate student advisors who have modeling and metabolomics expertise, will attempt to identify associated pathways containing substrates and products that are 1) easily assayed (e.g. using colorimetric or spectrophotometric tests) and 2) potentially commercially relevant (i.e. produces a desirable product or breaks down an undesirable compound). Short-listed pathways will be modeled using SimBiology28 and Cell++29 (the latter was developed in our host laboratory) to predict the effect of channeling on pathway intermediates and products as an aid to selecting a Phase-Two target application.

Phase-Two

Using pairs of enzymes from pathways modeled in our earlier screens we will construct recombinants fused to the targeting sequence. These enzyme pairs will be co-expressed in the presence and absence of encapsulin and the effect on pathway intermediates and/or products will be assayed to determine the effects of channeling and to compare them with our modeled prediction. The nature of the assays will depend on the chosen system. As a further experiment, if possible, we would like to apply channeling to a branching pathway to evaluate the potential role of channeling in pathway switching.

Comments

  1. Size of compartments
    It is possible that the extremely small size (230-240 angstroms diameter with a pore size of approximately 5 angstroms) of the encapsulin micro-compartment could lead to problems in accommodating some enzymes as well as the passage of some metabolites.
  2. Control of expression
    In our system, each micro-compartment consists of sixty encapsulin monomers. The targeting sequence we used corresponds to the T. maritima ferritin-like protein (flp) extension in which the encapsulated enzymes are thought to form a pentamer of dimers21. This implies a particular optimum ratio of enzymes (or eCFP) to encapsulin which thereby must be achieved via matching of protein expression.
  3. Search for an assayable application
    It is possible that few commercially relevant pathways have products that are easily assayed by colorimetric or spectrophotometric means. If this turns out to be the case, we propose to test an alternative, readily assayable system regardless of commercial relevance, to first meet our scientific objectives. We note that there are several such pathways related to glycolysis that would meet this objective, including one previously modeled by C Sanford27.

References

  1. Atsumi, S. & Liao, J.C. Metabolic engineering for advanced biofuels production from Escherichia coli. Curr Opin Biotechnol (2008).
  2. Villas-Boas, S.G. & Bruheim, P. The potential of metabolomics tools in bioremediation studies. Omics 11, 305-13 (2007).
  3. Wu, S. & Chappell, J. Metabolic engineering of natural products in plants; tools of the trade and challenges for the future. Curr Opin Biotechnol 19, 145-52 (2008).
  4. Sonderegger, M., Schumperli, M. & Sauer, U. Metabolic engineering of a phosphoketolase pathway for pentose catabolism in Saccharomyces cerevisiae. Appl Environ Microbiol 70, 2892-7 (2004).
  5. Dejong, J.M. et al. Genetic engineering of taxol biosynthetic genes in Saccharomyces cerevisiae. Biotechnol Bioeng 93, 212-24 (2006).
  6. Ro, D.K. et al. Production of the antimalarial drug precursor artemisinic acid in engineered yeast. Nature 440, 940-3 (2006).
  7. Bloom, J.D. et al. Evolving strategies for enzyme engineering. Curr Opin Struct Biol 15, 447-52 (2005).
  8. Alvizo, O., Allen, B.D. & Mayo, S.L. Computational protein design promises to revolutionize protein engineering. Biotechniques 42, 31, 33, 35 passim (2007).
  9. Hjersted, J.L., Henson, M.A. & Mahadevan, R. Genome-scale analysis of Saccharomyces cerevisiae metabolism and ethanol production in fed-batch culture. Biotechnol Bioeng 97, 1190-204 (2007).
  10. Conrado, R.J., Varner, J.D. & Delisa, M.P. Engineering the spatial organization of metabolic enzymes: mimicking nature's synergy. Curr Opin Biotechnol (2008).
  11. Spivey, H.O. & Ovadi, J. Substrate channeling. Methods 19, 306-21 (1999).
  12. Ovadi, J. & Saks, V. On the origin of intracellular compartmentation and organized metabolic systems. Mol Cell Biochem 256-257, 5-12 (2004).
  13. Takahashi, K., Arjunan, S.N. & Tomita, M. Space in systems biology of signaling pathways--towards intracellular molecular crowding in silico. FEBS Lett 579, 1783-8 (2005).
  14. Dix, J.A. & Verkman, A.S. Crowding effects on diffusion in solutions and cells. Annu Rev Biophys 37, 247-63 (2008).
  15. Hyde, C.C., Ahmed, S.A., Padlan, E.A., Miles, E.W. & Davies, D.R. Three-dimensional structure of the tryptophan synthase alpha 2 beta 2 multienzyme complex from Salmonella typhimurium. J Biol Chem 263, 17857-71 (1988).
  16. Sampson, E.M. & Bobik, T.A. Microcompartments for B12-dependent 1,2-propanediol degradation provide protection from DNA and cellular damage by a reactive metabolic intermediate. J Bacteriol 190, 2966-71 (2008).
  17. Kourtz, L. et al. A novel thiolase-reductase gene fusion promotes the production of polyhydroxybutyrate in Arabidopsis. Plant Biotechnol J 3, 435-47 (2005).
  18. Meynial Salles, I., Forchhammer, N., Croux, C., Girbal, L. & Soucaille, P. Evolution of a Saccharomyces cerevisiae metabolic pathway in Escherichia coli. Metab Eng 9, 152-9 (2007).
  19. Bobik, T.A. Polyhedral organelles compartmenting bacterial metabolic processes. Appl Microbiol Biotechnol 70, 517-25 (2006).
  20. Sutter, M. et al. Structural basis of enzyme encapsulation into a bacterial nanocompartment. Nat Struct Mol Biol (2008).
  21. Yeates, T.O., Kerfeld, C.A., Heinhorst, S., Cannon, G.C. & Shively, J.M. Protein-based organelles in bacteria: carboxysomes and related microcompartments. Nat Rev Microbiol (2008).
  22. Yeates, T.O., Tsai, Y., Tanaka, S., Sawaya, M.R. & Kerfeld, C.A. Self-assembly in the carboxysome: a viral capsid-like protein shell in bacterial cells. Biochem Soc Trans 35, 508-11 (2007).
  23. English, R.S., Jin, S. & Shively, J.M. Use of Electroporation To Generate a Thiobacillus neapolitanus Carboxysome Mutant. Appl Environ Microbiol 61, 3256-3260 (1995).
  24. Canton, B., Labno, A. & Endy, D. Refinement and standardization of synthetic biological parts and devices. Nat Biotechnol 26, 787-93 (2008).
  25. Ohi, M., Li, Y., Cheng, Y. & Walz, T. Negative Staining and Image Classification - Powerful Tools in Modern Electron Microscopy. Biol Proced Online 6, 23-34 (2004).
  26. Sanford, C. M.Sc. Thesis: Modeling and Computational Prediction of Metabolic Channeling (2009).
  27. SimBiology 3.0 - Model, simulate, and analyze biological systems. Vol. 2009 (The MathWorks).
  28. Sanford, C., Yip, M.L., White, C. & Parkinson, J. Cell++--simulating biochemical pathways. Bioinformatics 22, 2918-25 (2006).
  29. Davidson, A. Personal communication. (2009).
  30. Che, A. Personal communication. (2009).
Retrieved from "http://2009.igem.org/Team:TorontoMaRSDiscovery/Project"