(149f) Metabolic Reconstruction of Central Carbon Metabolism and Antioxidant Production in Microalgae: Modelling and Optimization

An active interest in the pharmaceutical and food industry to produce high yields of antioxidants with high economic sustainability is driving research towards antioxidant rich microalgal cultures. Antioxidants among other bioactive compounds suppress the effect of free radicals within the human body by inhibiting cellular damage or obstructing the chain reaction that results in oxidation [1]. Synthetic antioxidants are discredited as carcinogenic promoters, which fuels the demand for antioxidant extraction from natural sources such as plants. Specifically, microalgae have a faster growth rate than traditional crops under high stress conditions and do not compete with conventional food crop for land space [2]. The influence that glucose, glycerol and bi-substrate feed have on the carotenoid profile was studied, with the ultimate goal of maximizing the yield of Lutein and Zeaxanthin which are found in high concentrations in the microalgal strain A. Protothecoides. Lutein and Zeaxanthin are xanthophyll carotenoids, which show high potential in preventing age-related vision impairment or blindness [3].

Current research is limited by non-specific knowledge on the antioxidant profile and growth rate of individual microalgal strains. Since distinct nutrients promote accumulation of specific products, a mathematical model was used to simulate and predict microalgal response to varying cultivation conditions. For a reliable model, the biochemical reactions within the microalgae were reconstructed and calibrated to predict the performance results for varying nutrient conditions. The mathematical model enables scale-up and control of process conditions for adjusting product composition based on market needs within a microalgal process system.

Metabolic reconstruction of central carbon metabolism and antioxidant pathways:

Heterotrophic microalgae have the ability to replace their photosynthetic functionality with the simultaneous assimilation of different carbon sources present in the culture media through ‘dark metabolism’, which allows them to grow in the absence of light [4]. The consumption of glucose is limited by the transport kinetics across the cellular membrane after which glucose undergoes the glycolysis reactions for energy production including an initial stage of oxidative phosphorylation and parallel oxidation through the Pentose Phosphate Pathway (PPP). Alternatively, glucose metabolism also occurs through the Embden-Meyerhof-Parnas (EMP) pathway, part of glycolysis, in the presence of light and aerobic conditions [5].

Although higher growth rate was observed in microalgal growth using glucose as the sole carbon source, the addition of glycerol was found to effectively modulate both overall substrate to biomass yield and antioxidant profile. The results indicate that biomass and antioxidant productivity can be enhanced by the use of glycerol, a typical waste product in the biodiesel industry, which further enhances the economic viability of the microalgal production process. There are few studies on the metabolic pathways that control glycerol assimilation for microalgal cells. However, we know from plant cells that an enzyme, glycerol kinase, converts glycerol into intermediates for the EMP pathway to form pyruvate, which later enters the Tricarboxylic acid cycle (TCA) for aerobic respiration and results in the formation of energy through the production of ATP molecules [6]

The metabolites formed from carbon assimilation in the glycolysis reactions trickle down into the terpenoid backbone biosynthesis. The biosynthesis pathways involved in the terpenoid backbone biosynthesis generate building blocks such as geranyl diphosphate (GPP), farsenyl diphosphate (FPP), and geranylgeranyl diphosphate (GGPP) which are important precursors of sterols and carotenoids [7]. Specifically, FPP and GGPP lead to the biosynthesis of xanthophyll carotenoids such as Lutein and Zeaxanthin, found in the lipid phase of the thylakoid membrane of chloroplasts. They function as photo protective receptors within the chloroplast to provide protection against the damaging effect of high light intensity [8]

Consequently, the reactions for the metabolic model account for the metabolic assimilation of glucose and glycerol in the intracellular organelles, among other nutrients, for the formation of antioxidants through the terpenoid backbone and carotenoid biosynthesis pathways. The robust pathways are lumped together to reduce the number of kinetic parameters that require optimization. The assembly of kinetic parameters and stoichiometry of the biochemical reactions for the synthesis of biomass, macromolecules, nucleic acid, intermediate metabolites, and energy was carried out from current literature on enzymatic activity.

Kinetic-based approach, parameter fitting and validation:

All the biochemical reactions within the cell are considered as enzyme led reactions, which can be represented through the Michaelis-Menten model. The metabolic model assumes the cellular structure as a unique compartment for which the transport kinetics have a negligible effect on biosynthesis flux rates. The model also presumes all cells grow with a singular objective, which means biosynthesis reactions must follow a specified metabolic pathway. The mathematical model uses the Michaelis-Menten equation to evaluate the flux of enzyme catalyzed reactions. The assembly of biochemical reactions in this framework forms a set of non-linear ordinary differential equations (ODE), which are evaluated using an ODE solver in Matlab.

The biochemical processes within the microalgae are a network of complex enzymatic reactions with several hundred unknown parameters. Current information on microalgae contains non-specific kinetic parameters, which require calibration using experimental data through a dynamic model. Subsequently, an optimization algorithm determines optimal solutions for kinetic parameters based on experimental datasets. The optimization problem approximates the specific enzyme activity for a given cell through a linear objective function under specified constraints. A series of linear objectives, such as high biomass yield, increased ATP usage and parameter fitting through experimental data, were investigated and enforced on the programming system to determine an optimum solution for the kinetic parameters.

Prior researchers have developed unstructured kinetic models using empirical datasets; however, they lack a biological basis, which limits their reliability. A metabolic model based on biochemical reactions within the microalgal cell improves understanding of internal pathways and can enable regulation of product composition by enforcing external conditions that promote formation of select products. Hence, metabolic modelling of biosynthesis pathways in the microalgae can facilitate the regulation of product yields to enhance process economy.

References:

[1] B. L. Tan, M. E. Norhaizan, W.-P.-P. Liew, and H. Sulaiman Rahman, “Antioxidant and Oxidative Stress: A Mutual Interplay in Age-Related Diseases,” Front. Pharmacol., vol. 9, p. 1162, Oct. 2018.

[2] G. Randrianarison and M. A. Ashraf, “Microalgae: a potential plant for energy production,” Geol. Ecol. Landscapes, vol. 1, no. 2, pp. 104–120, Apr. 2017.

[3] E.-S. M. Abdel-Aal, H. Akhtar, K. Zaheer, and R. Ali, “Dietary sources of lutein and zeaxanthin carotenoids and their role in eye health.,” Nutrients, vol. 5, no. 4, pp. 1169–1185, Apr. 2013.

[4] M. Khan, R. Karmakar, B. Das, F. Diba, and M. Razu, “Heterotrophic Growth of Micro Algae,” 2016, pp. 1–19.

[5] A. Klingner et al., “Large-Scale 13C Flux Profiling Reveals Conservation of the Entner-Doudoroff Pathway as a Glycolytic Strategy among Marine Bacteria That Use Glucose,” Appl. Environ. Microbiol., vol. 81, no. 7, pp. 2408–2422, 2015.

[6] M. Walsh et al., “Insights into an alternative pathway for glycerol metabolism in a glycerol kinase deficient Pseudomonas putida KT2440,” bioRxiv, 2019.

[7] I. Mendoza-Poudereux, E. Kutzner, C. Huber, J. Segura, I. Arrillaga, and W. Eisenreich, “Dynamics of Monoterpene Formation in Spike Lavender Plants,” Metabolites, vol. 7, no. 4, 2017.

[8] E. A. del Rio-Chanona, F. Fiorelli, D. Zhang, N. R. Ahmed, K. Jing, and N. Shah, “An efficient model construction strategy to simulate microalgal lutein photo-production dynamic process,” Biotechnol. Bioeng., vol. 114, no. 11, pp. 2518–2527, 2017.