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Rapid Construction of Stoichiometrically Controlled Metabolic Pathways to Identify in vivo Rate-Limiting Steps Walter Thavarajah, Fernando Contreras, Roshni Ravindran, Vivienne Gunadhi, Tiffany Tran, Jenny Lee

Advisors: Phillip Kyriakakis, Bartlomiej Borek, Jahir Gutierrez, Todd Coleman

Stoichiometric control of enzyme levels allows for empirical testing of rate-limiting steps, circumventing some of the limitations of current investigative approaches. We tested this by expressing several stoichiometric permutations of the bacterial Lux system in Saccharomyces Cerevisiae to determine its rate-limiting steps by measuring the resulting light production. This identification of rate-limiting steps then allows us to optimize the pathway and construct an improved, experimentally validated mathematical model. By strategically altering biosynthetic gene expression, we gain a means to tailor the reporter/sensor to better suit our needs and a generalizable method to rapidly optimize metabolic pathways.

Three of the permutations involve the overproduction of a different Lux C, D, or E enzyme. A control sequence with no overproduced enzyme is used to establish a baseline level of luminescence.

Additional Plasmid Features: 1.  NADPH-Flavin Oxidoreductase

(frp): Regenerates FMNH2, a necessary cofactor for luciferase activity1

2.  E n h a n c e d L u x A B : E n s u r e s luciferase is not rate limiting, hiding the rate-limiting enzyme in the CDE complex2

3.  P 2 A L i n k e r s : E n s u r e s stoichiometric protein production by allowing translation from a single mRNA strand3

The experimental constructs will express the Lux genes in a 1:1:2 ratio.

[1] Cui B, Zhang L, Song Y, Wei J, Li C, et al. (2014) Engineering an Enhanced, Thermostable, Monomeric Bacterial Luciferase Gene As a Reporter in Plant Protoplasts. PLoS ONE 9(10): e107885. doi:10.1371/journal.pone.0107885 [2] Gupta, R. K., Patterson, S. S., Ripp, S., Simpson, M. L. and Sayler, G. S. (2003), Expression of the Photorhabdus luminescens lux genes (luxA, B, C, D, and E) in Saccharomyces cerevisiae. FEMS Yeast Research,4: 305–313. doi: 10.1016/S1567-1356(03)00174-0 [3] Szymczak-Workman AL, Vignali KM, Vignali DA. Design and construction of 2A peptide-linked multicistronic vectors. Cold Spring Harbor Protoc. 2012(2): 199–204. doi: 10.1101/pdb.ip067876 [4] Meighen EA & Dunlap PV (1993) Physiological, biochemical and genetic control of bacterial bioluminescence. Adv Microb Physiol 34: 1–67. [5] L. Wall, A. Rodriguez, and E. Meighen (1986) Intersubunit transfer of fatty acyl groups during fatty acid reduction.J. Biol. Chem. 261: 15981-15988. [6] A Rodriguez and E Meighen. (1985) Fatty acyl-AMP as an intermediate in fatty acid reduction to aldehyde in luminescent bacteria.J. Biol. Chem. 260: 771-774. [7] Byers, D.M. and Meighen, E.A. (1985). Acyl-Acyl carrier protein as a source of fatty acid for bacterial bioluminescence. Proc. Natl. Acad. Sci. USA 82, pp. 6085-6089. [8] Soly, R.R. and Meighen, E.A. (1991). Identification of the Acyl Transfer Site of Fatty Acyl-Protein Synthetase from Bioluminescent Bacteria. J. Mol. Biol. 219, 69-77. [9] Wall, L. , Rodriguez, A. , and Meighen E. (1986) Intersubunit Transfer of Fatty Acyl Groups during Fatty Acid Reduction. J. Biol. Chem. 261: 15981-15988. [10] J W Hastings and C Balny (1975)The oxygenated bacterial luciferase-flavin intermediate. Reaction products via the light and dark pathways.J. Biol. Chem. 250: 7288-7293.

We developed a mathematical model to understand the relationship between the enzymes in our genetic constructs and the cellular environment a priori. Using custom MATLAB analysis code and kinetic information from in vitro experiments found in literature, we simulated the bioluminescent reaction network as an isolated system under steady state conditions and acquired preliminary luminescence results.

ABSTRACT

ENZYME KINETICS CONSTRAINT-BASED MODELING

MODULE/DESIGN

REFERENCES

OVERVIEW

FUTURE DIRECTIONS

Bartlomiej Borek facilitated the development of the dynamic model. Jahir Gutierrez facilitated the development of the constraint-based model. Phillip Kyriakakis designed the project and facilitated the development of the wet lab component. Todd Coleman provided the lab space.

To study the relationship between growth and light production, in silico, we introduced the isolated Lux system into a pre-existing SBML model17, for Saccharomyces Cerevisiae (s288c). Using the COBRA toolbox in MATLAB and parameters from literature, we established flux boundaries in attempt to parallel in vivo and environmental conditions18. After simulating the flux trade-off between growth and light (Fig. 5), we inferred that the Lux system must draw key metabolites away from the growth reaction. To test our hypothesis, we constrained growth and found reactions strongly coupled to light. By manually tracing these reactions to their respective metabolic pathways, we mapped the relationship between growth and light production (Fig. 6)19,20. With experimental results, we can further explore this coupling and improve light production by upregulating targeted metabolic pathways.

University  of  California,  San  Diego,  La  Jolla,  CA  92093,  USA.

Following the work of Stekel15,16, a set of differential equations were developed to describe the flux of individual substrates in the reaction network (Fig. 3) and simulated using custom MATLAB analysis code .

The molar overproduction of both LuxE and LuxC (LuxEC) lead to a higher steady state production of RCHO, which resulted in an increase in light production. Interestingly, the change in RCOOH concentration for both the control (LuxCDE) and the overproduced LuxEC construct was similar. Additionally, the overproduction of LuxD, lead to a faster RCOOH yield, as expected; however, the lowered RCHO steady state concentration was unexpected. In summary, these results (Fig. 4) suggest that high LuxD concentrations saturate the LuxEC complex, which effectively reduces the production of RCHO and therefore light emission (4B).

Due to the equal molar production of enzyme provided by our genetic strategy, we can assume that the overproduction of a particular enzyme would approximately double its corresponding reaction rate. Furthermore, since the LuxC and LuxE subunits cannot perform effectively when isolated4, the LuxC and LuxE reaction rate equations were collapsed to one16. Additionally, we assumed that the overexpression of LuxC and LuxE would produce the same emission, at steady state, as our control (LuxCDE).

ACKNOWLEDGEMENTS

Figure 1: Bioluminescent reaction network.

In the aldehyde synthesis pathway, controlled by LuxCDE, the transferase (LuxD) diverts RCOOH from the fatty acid biosynthesis pathway by cleaving to different acyl groups4. RCOOH is then activated by the synthetase subunit (LuxE) and transferred to the reductase subunit (LuxC) before being reduced with NADPH to RCHO5,6,7,8,9. Afterwards, RCHO enters the light production pathway10,11,12,13,14, which is controlled by a luciferase complex (LuxA+LuxB) and its immediate cellular environment, and stimulates light emission in the presence of cofactors: molecular oxygen and reduced flavin (FMNH2).

The microbial Lux system can be interpreted as a two-component module, the aldehyde synthesis pathway and the light production pathway, coupled by an aldehyde (RCHO) and fatty acid (RCOOH).

Background

[11] James E. Becvar, Shiao-Chun Tu, and J. W. Hastings (1978) Activity and stability of the luciferase-flavin intermediate Biochemistry 17 (9), 1807-1812 DOI: 10.1021/bi00602a036. [12] Husam Abu-Soud, Leisha S. Mullins, Thomas O. Baldwin, and Frank M. Raushe. (1992) “Stopped-flow kinetic analysis of the bacterial luciferase reaction”. Biochemistry 31 (15), 3807-3813. [13] Frank M. Raushel, Husman M. Abu-Soud, Leisha S. Mullins, Wilson A. Francisco, Thomas O. Baldwin. “Kinetic and Mechanistic Investigation of the Bacterial Luciferase Reaction” [14] H. Watanabe, T. Nagoshi, H. Inaba (1993).Luminescence of a bacterial luciferase intermediate by reaction with H2O2: the evolutionary origin of luciferase and source of endogenous light emission Biochim. Biophys. Acta, 1141, pp. 297–302. [15] Welham P, Stekel D (2009) Mathematical model of the lux luminescence system in the terrestrial bacterium Photorhabdus luminescens. Mol Biosyst 5(1):68–76. [16] Iqbal M. , Stekel D. (2015). An extended mathematical model of Lux bioluminescence in bacteria. [unpublished] [17] Herrgard, M et al. “A consensus yeast metabolic network reconstruction obtained from a community approach to systems biology.” Nat Biotechnol 26 (2008) : 1155-1160. [18] Dikicioglu, Duygu, Betul Kırdar, and Stephen G. Oliver. "Biomass Composition: The “elephant in the Room” of Metabolic Modelling."Metabolomics (2015): n. pag. Web. [19] Kanehisa, M., Goto, S., Sato, Y., Kawashima, M., Furumichi, M., and Tanabe, M.; Data, information, knowledge and principle: back to metabolism in KEGG. Nucleic Acids Res. 42, D199–D205 (2014). [20] Kanehisa, M. and Goto, S.; KEGG: Kyoto Encyclopedia of Genes and Genomes. Nucleic Acids Res. 28, 27-30 (2000).

Experimental data can further validate and refine predictive capabilities of the model. A measured growth curve, as well as values for nutrient uptake and secretion, can be used to constrain the flux of the respective reaction. Additionally, we expect to improve the isolated model’s predictive power by deriving steady state equations for the in vivo light reaction mechanism.

Figure 4: Change in substrate and product concentration and light yield for the different genetic constructs. (A) Change in RCHO concentration. (B) Change in light production at steady state levels of RCHO. (C) Change in RCOOH concentration. (D) Summary plot of light production at steady state.

Figure 2: Reactions detailing the bioluminescent network in Figure 1. Our system was modeled as the exchange of reactants and products amongst the enzymes and enzyme complexes.

Figure 3: (A) Enzyme reaction rates. (B) Substrate and product differential equations.

Figure 6: Metabolic pathway depicting the flow of metabolites into the Lux system. Upregulation at any step of this process can result in an increase in light production.

D-Glucose (e)

Alpha-D-

Glucose-6P

PRPP

3-Amino-

Isobutanoate

Pyruvate

Acetyl-CoA

Glycolosis

Pentose Phosphate Pathway (leads to growth)

Pyrimidine Metabolism

Valine, Leucine, Isoleucine Biosynthesis

Pyruvate Metabolism

TCA Cycle

Myristoyl-ACP

Fatty Acid Biosynthesis

Light

Lux System

Glycolosis

Pentose Phosphate Pathway (leads to growth)

Pyrimidine Metabolism

Valine, Leucine, Isoleucine Biosynthesis

Pyruvate Metabolism

TCA Cycle

Fatty Acid Biosynthesis

Lux System

Figure 5: Simulation results show an inverse relationship between light production and growth, thus implying that both networks are coupled.

Complete plasmid with no overexpressed Lux genes.