ODE model for transporter reducing the impact of intracellular metabolite cytotoxicity in yeast Saccharomyces cerevisiae
Introduction--Background
The accumulation of certain terpenes, including sesquiterpenes and triterpenes which host members we are intended to produce in yeast, within yeast cells could potentially be toxic to them, leading to reduction in growth and yield of compounds of interest (Chen, 2023). Most terpenes have high cytotoxic potential shown in various model organisms, and the effect is primarily caused by plasma membrane disruption, lipid peroxidation, and mitochondrial impairment (Agus, 2021).
It is expected that transporting those products to the culture media outside of the cell could lower the impact of intracellular metabolite toxicity on cellular growth. Hence, we aim to explore if engineering terpene exporting transporters could improve cell growth and production of santalol and ambrein, two terpenoids we are interested in, through in silico modeling and simulation.
Through literature mining, we found that the AaPDR3 transporter is capable of transporting sesquiterpenoid β-caryophyllene in yeast cells (Fu et al., 2017) and CsMATE1 transporter is capable of transporting triterpenoid cucurbitacin C, in yeast microsomal vesicles (Ma et al., 2023). Thus, in our models, the assumption that AaPDR3 and CsMATE expressed in yeast cells are able to export santalol and ambrein, respectively, at the same rate as β-Caryophyllene and cucurbitacin C was made.
Modeling Process
Firstly, yeast biomass growth over time (dP/dt) follows simple logistic growth (equation [1]):
Then, the basic rate of metabolite production in respect to time (dM/dt) is modelled using a simple law of mass action equation (equation [2]):
Next, intracellular toxicity of metabolites produced is taken into account when modelling the biomass dynamics. Equation [1] is modified into equation [3] to make actual growth rate inversely correlated to the amount of intracellular metabolite:
Engineering terpene transporters is expected to export metabolites of interest and relieve the effect of intracellular metabolite cytotoxicity. The amount of metabolite exported per unit time (written as dE/dt)is modelled using the Michaelis-Menten equation (equation [4]):
Taking export into account, intracellular metabolite dynamic could be modified to equation [5]:
Results
Engineering AaPDR3 transporter to export santalol
We used the ODE solver “deSolve” in Rstudio to simulate the model we wrote and obtained the results below：
Engineering CsMATE1 transporter to export ambrein
Although we have not managed to produce ambrein in yeast cells successfully, engineering of transporter could still be potentially useful in boosting ambrein yield once it could be produced in yeast cells. Using similar means, the effect of CsMATE1 transporter on yeast growth and ambrein production is assessed.
Discussion
As seen in the results, our model shows that with a suitable transporter, we could reduce the effect of cytotoxicity to a decent extent. By reducing intracellular metabolite accumulation, yeasts display a higher rate of growth and more efficient production of compounds of interest.
However, there are still certain flaws in our model:
Compared to actual yield, metabolite yield predicted by the model looks unrealistically high, since factors like metabolite stability, nutrient availability, homeostasis inside the cell and production rate of metabolites (which in our model is only a theoretical maximum value obtained from flux balance analysis), all of which could reduce production yield, were not taken into consideration. Also, the process of extraction could cause the loss of metabolites.
Meanwhile, the parameters we have used are not proven to be the exactly suitable transporter for santalol or ambrein. As a result, it is possible that the transporter would not export the metabolites at the rate we expected, but rather, it would have a lower rate, or wouldn’t export at all.
Despite the drawbacks we have identified from the model, the design is still capable of supporting the concept that transporter engineering could lead to reduction in metabolite toxicity impact and enhancement in yield.
This is why we still need to improve and add more details to our model. Further experimental investigations should be done also to reduce the error between the real metabolite production results and the modeled one.
Looking into the future
Experiment
We are planning to express various of sesquiterpenes and triterpenoid transporters which we consider as suitable for the export of santalol and ambrein in our engineered yeast. The expected outcome would be having higher metabolite production rate and yield, which will lower our cost for the fermentation process and save time.
Future Modeling
We intend to characterize the kinetics of transporters of interest in exporting our metabolites of interest and characterize the toxicity of the metabolites to acquire more accurate parameters. We are also planning to add fluctuations in our modeling according to our experiment results and experiment conditions.
How can the future team use our model?
Our model provides a method for other IGEM team to optimize their production of terpenes or other products through expressing transporters to export toxic substances from the cell.
Parameter table
References
Chen, L., Xiao, W., Yao, M., Wang, Y., & Yang, Y. (2023). Compartmentalization engineering of yeasts to overcome precursor limitations and cytotoxicity in terpenoid production. Frontiers in Bioengineering and Biotechnology, 11. https://doi.org/10.3389/fbioe.2023.1132244 Agus, H. H. (2021). Terpene toxicity and oxidative stress. Toxicology, 33–42. https://doi.org/10.1016/b978-0-12-819092-0.00004-2
Fu, X., Pu, S., He, Q., Shen, Q., Tang, Y., Pan, Q., Yan, T., Chen, M., Hao, X., Pin, L., Li, L., Wang, Y., & Sun, X. (2017). AaPDR3, a PDR Transporter 3, Is Involved in Sesquiterpene β-Caryophyllene Transport in Artemisia annua. Frontiers in Plant Science, 8. https://doi.org/10.3389/fpls.2017.00723
Ma, Y., Li, D., Zhong, Y., Wang, X., Li, L., Osbourn, A., Lucas, W. J., Huang, S., & Shang, Y. (2023). Vacuolar MATE/DTX protein‐mediated cucurbitacin C transport is co‐regulated with bitterness biosynthesis in cucumber. New Phytologist, 238(3), 995–1003. https://doi.org/10.1111/nph.18786
Improving santalol and ambrein production by reducing the effect of competing pathways
Introduction(Background)
Chemical reactions within a cell connect with one another to form a network known as the metabolic network. Many metabolites may participate in more than one reaction or biological process. When one metabolite is involved in two different reactions, a competition in metabolite flux between them will be resulted in.
When santalol and ambrein biosynthesis pathway are engineered in yeast, they compete for precursors with native pathways. Namely, santalol biosynthesis competes with squalene biosynthesis for farnesyl diphosphate (FPP) (Zha et al., 2020), while ambrein biosynthesis competes with ergosterol biosynthesis for squalene (Yota et al., 2020) . In yeast, squalene biosynthesis is catalysed by enzyme ERG9 and squalene epoxidation, the first committed step in ergosterol biosynthesis, is catalysed by enzyme ERG1
FBA is method to calculates the fluxes within the metabolic network model of a cell (Orth et al., 2010). Steady state, in which metabolic fluxes are independent of environmental perturbation, is assumed in FBA, and fluxes constrained by reaction stoichiometry and flux bonds. Linear programming is employed to optimise the flux towards certain metabolite(s) of interest. Hence, by changing the constraint of native reaction, the effect of reducing native gene expression, hence reaction flux, on the flux of engineered pathway can be estimated.
Results
Enhancing santalol biosynthesis by reducing FPP to squalene flux (ERG9 expression) Yeast culture takes place in YPD medium, where yeast cells receive 10 mmol/gdw/h of glucose influx, 2 mmol/gdw/h of oxygen influx and 0.5 mmol/gdw/h of 20 amino acids, thiamine, riboflavin, nicotinate, pyridorin, pantothenate, aminobenzoate and myoinositol influx (5% of glucose influx) (Suthers et al., 2020). We obtained the maximum growth rate of yeast by in YPD medium by setting the objective coefficient of growth to 1 and running growth optimisation:
The maximum growth rate of yeast is 0.34746 hr^-1, during which no santalol is produced. The optimal santalol flux was then calculated by constraining growth rate to 90% of maximal growth rate (the assumption that producing santalol will only result in 10% of reduction in growth rate is made) and setting the objective coefficient of santalol biosynthesis to 1: Maximum santalol production flux is 0.14732 mmol/gdw/h. Meanwhile, GPP to FPP flux equals to 0.14869 mmol/gdw/h and FPP to squalene flux equals to 0.00069 mmol/gdw/h. In this case, 0.11440 mmol/gdw/h represents the theoretical maximum santalol flux, when ERG9 expression level is the lowest. We then added constraint by setting lower bound of FPP to squalene flux to 10 different values, including theoretical minimum of 0.00069 mmol/gdw/h and theoretical maximum of 0.08 mmol/gdw/h, to simulate different levels of ERG9 expression. Santalol flux against squalene flux is plotted:
Although successful ambrein production in yeast is yet to be achieved, we hypothesise that reducing the expression of ERG1, which competes with BmeTC for squalene, could enhance the yield of ambrein.
Using similar method as santalol, we obtained the maximum growth rate of yeast under YPD medium as 0.34746 hr^-1 and theoretical maximum level of ambrein flux as 0.08727 mmol/gdw/h when growth rate is at 90% of maximum growth rate.
We then added constraint by setting lower bound of squalene to squalene epoxide flux to 10 different values, including theoretical minimum of 0.00069 mmol/gdw/h and theoretical maximum of 0.08 mmol/gdw/h, to simulate different levels of ERG1 expression. Ambrein flux against squalene flux is plotted:
Discussion
Through FBA modelling, we found out that reducing the flux of native pathways that use FPP and squalene could effectively enhance the yield of engineered sesquiterpenoid and triterpenoid products, which compete for the same substrate. We plan to perform promoter substitution experiments to prove our concepts here.
There are several drawbacks in doing FBA. Many assumptions, including steady state and engineered yeasts growing at 90% of maximum growth rate of native yeasts, were made, which can rarely be achieved in real life. The assumption that intracellular metabolites produced are not toxic to yeasts is also made, which we will address in the other section of modelling. Moreover, FBA only captures flux distribution in a steady state.
To improve the model, in the future, we plan to measure some key parameters such as growth rate and substrate uptake flux experimentally to make the constraints more accurate. We also plan to take changes in substrate uptake flux and growth rate with time into account through performing dynamic flux balance analysis.
Sidenote
Supplementing amino acids in YPD medium could not efficiently boost santalol production
We attempted to explore if supplementing amino acids favored by yeasts (Ser, Thr, Asp, Asn, Glu and Gln) could boost the production of santalol in engineered yeasts. The uptake fluxes of those amino acids were changed, with the assumption that the uptake of those amino acids in YPD medium is not saturated, and the production rate of santalol was examined. However, efficient improvement in santalol production was not observed when those amino acids are
supplemented. More details are presented in the supplementary material in our github page.
References Zha, W., An, T., Li, T., Zhu, J., Gao, K., Sun, Z., Xu, W., Lin, P., & Zi, J. (2020). Reconstruction of the Biosynthetic Pathway of Santalols under Control of the GAL Regulatory System in Yeast. ACS Synthetic Biology, 9(2), 449–456. https://doi.org/10.1021/acssynbio.9b00479
Yamabe, Y., Kawagoe, Y., Okuno, K. et al. Construction of an artificial system for ambrein biosynthesis and investigation of some biological activities of ambrein. Sci Rep 10, 19643 (2020). https://doi.org/10.1038/s41598-020-76624-y
Orth, J. D., Thiele, I., & Palsson, B. Ø. (2010). What is flux balance analysis? Nature Biotechnology, 28(3), 245–248. https://doi.org/10.1038/nbt.1614
Suthers, P. F., Dinh, H. V., Fatma, Z., Shen, Y., Chan, S. H. J., Rabinowitz, J. D., Zhao, H., & Maranas, C. D. (2020). Genome-scale metabolic reconstruction of the non-model yeast Issatchenkia orientalis SD108 and its application to organic acids production. Metabolic Engineering Communications, 11, e00148. https://doi.org/10.1016/j.mec.2020.e00148
All raw codes can be found on our GitHub page