Introduction

As part of our goal to engineer the microalgae C. reinhardtii to sequester toxic arsenic from groundwater sources, we chose to introduce a phytochelatin synthase gene(PCS) into the organism’s nuclear genome. This gene is derived from Arabidopsis thaliana, a small flowering plant and long-standing model organism for laboratory work ^[1]. However, there are many steps involved in the synthesis of phytochelatins, and even though phytochelatin synthase is the rate-limiting enzyme in the production of phytochelatins, we wanted to get a deeper understanding of the kinetics of this pathway in order to better inform our approach towards optimizing C. reinhardtii’s heavy metal uptake abilities.

To further investigate the kinetics of C. reinhardtii’s phytochelatin synthesis pathway, we constructed a mathematical model that utilizes Michaelis-Menten kinetics to account for seven of the molecules and enzymes implicated in the production of phytochelatins. Details of the construction of the model and its findings are below.

Modeling Arsenic Uptake in C. reinhardtii

All heavy metal toxins are taken up by plants via phytochelatins, and one of the most heavily studied heavy metals is Cadmium, especially in its relationship to plants [2]. We used the Cd-PCS model detailed by Mendoza-Cózatl and Moreno-Sánchez (2005) as a template to build our own As-PCS model off of [3].

Figure 1. Schematic representations of GSH metabolism under various metabolic conditions ^[3].

According to Mendoza-Cózatl and Moreno-Sánchez, the main metabolites that needed to be included in the model were Glu (glutathione), Cys (cysteine), Gly (glycine), Xe (Xenobiotic), GS-Xe (glutathione synthetase-xenobiotic), GSH (reduced glutathione), and γ-ECS (γ-glutamylcysteine synthetase). These are precursor compounds that most directly impact phytochelatin synthesis. We used MATLAB’s SimBiology toolkit to establish many of the initial parameters of these species as defined by Mendoza-Cózatl and Moreno-Sánchez.

Findings and Implications

In our version of this model, the molar concentrations of each of the compounds involved in phytochelatin synthesis can be adjusted to simulate how phytochelatin synthesis is impacted by the abundance or scarcity of these molecules. We simulated the steady states to verify that levels of all of the molecules implicated in the PCS pathway do not induce harmful resource competition within the cell. Once our model verified these results, we began our transformations in the wet lab.

This model can also be expanded to provide more specific information regarding the rate of arsenic uptake if the concentration of environmental arsenic and its uptake rate are known. With this information, assumptions can be made as to how long it would take for C. reinhardtii to uptake a given amount of arsenic in a body of water, which can then be used to further inform the proposed water filter design.

Figure 2. Output from the model demonstrating relative concentrations of each of the compounds involved in phytochelatin synthesis under normal conditions.

View the model here.