Kinetic metabolic modelling for the control of plant cells cytoplasmic phosphate

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Abstract

A previously developed kinetic metabolic model for plant metabolism was used in a context of identification and control of intracellular phosphate (Pi) dynamics. Experimental data from batch flask cultures of Eschscholtiza californica cells was used to calibrate the model parameters for the slow dynamics (growth, nutrition, anabolic pathways, etc.). Perturbation experiments were performed using a perfusion small-scale bioreactor monitored by in vivo 31P NMR. Parameter identification for Pi metabolism was done by measuring the cells dynamic response to different inputs for extracellular Pi (two pulse-response experiments and a step-response experiment). The calibrated model can describe Pi translocation between the cellular pools (vacuole and cytoplasm). The effect of intracellular Pi management on ATP/ADP and phosphomonoesters concentrations is also described by the model. The calibrated model is then used to develop a control strategy on the cytoplasmic Pi pool. From the identification of the systems dynamics, a proportional-integral controller was designed and tuned. The closed-loop control was implemented in the small-scale NMR bioreactor and experimental results were in accordance with model predictions. Thus, the calibrated model is able to predict cellular behaviour for phosphate metabolism and it was demonstrated that it is possible to control the intracellular level of cytoplasmic Pi in plant cells.

Introduction

Plant cell metabolism possesses unique properties that can hinder the technological and commercial success of a bioprocess. One of these features is the capacity to accumulate high levels of nutrients. As an example, five- to 10-fold variations in intracellular inorganic phosphate (Pi) concentration are observed over the time course of plant cells cultures (van Gulik et al., 1993; Lamboursain and Jolicoeur, 2005). This can affect the cells secondary metabolites production potential (Lamboursain and Jolicoeur, 2005) and growth kinetics (Cloutier et al., 2007b) […].

Understanding plant cell metabolic regulation is a challenging issue. As regards phosphate, there is regulation for Pi management under Pi limitation (Plaxton, 1998), which allows plant cells to optimize their use of Pi. Abel et al. (2000) observed that Pi-limited tomato cells exhibited a significant increase in phosphodiesterase secretion, which allows degrading extracellular nucleotides debris. Wu et al. (2003) observed that 29% of the genes in Arabidopsis thalina were up- or down-regulated in Pi-limiting conditions. Many of the down-regulated genes in these conditions were related to functional groups such as photosynthesis and nitrogen assimilation. Intracellular Pi also plays a central role in the regulation of enzymes activity through reversible phosphorylation processes. Many enzymes in plant glycolysis are regulated by this process, among which are pyruvate kinase, phosphoenolpyruvate carboxykinase and sucrose phosphate synthase (Huber et al., 1994). Intracellular Pi may also affect energetic shuttles concentrations and equilibrium (ATP/ADP) since it is directly involved in the conversion of ADP to ATP.

Therefore, plant cell metabolism and its regulation by Pi pools is a complex issue and has to be addressed properly, especially as its intracellular dynamics are important. Control of plant primary metabolism by metabolites concentrations (as opposed to the traditional view of gene and enzyme regulated metabolic pathways) is a subject that is gaining attention recently. Plant metabolomics for primary metabolism was reviewed by Fiehn (2006) and studies on plant metabolomics show that metabolites level can be the controlling element in plant metabolism. Borland et al. (1999) reported that the intracellular malate concentration can control the crassulacean acid metabolism in plants under certain conditions and Carrari et al. (2003) also reported that the regulation of TCA cycle by metabolic intermediates is significant in plants.

These reports on intracellular dynamics of plant metabolism partly explain why little success is achieved when applying the classical tools of metabolic engineering to improve plant-based bioprocesses. Metabolic flux analysis (MFA) and metabolic control analysis (MCA) can be used to improve the catalytic properties of cells through a better understanding of their metabolic features. Even though these approaches are now applied to plant cells (Rontein et al., 2002; Hughes et al., 2004; Sriram et al., 2004; Ratcliffe and Shachar-Hill, 2006) many challenges are still encountered because of the limited quantitative data on the specific dynamics and regulation of plant cell metabolism. Moreover, the common approaches (MFA, MCA, isotopomer balancing, etc.) to study metabolic systems are mostly based on steady-state analysis, which is obviously not representative of a majority of plant cells bioprocesses.

Since plant cells intracellular nutrients levels affect pant metabolism and complicate the analysis of the system, the modulation or control of these nutrients levels will be a fundamental advance in our capacity to understand and improve the metabolic properties of plant cells. In this study, the predictive capacity of a kinetic metabolic model will be used as a basis to develop a closed-loop control strategy for plant cells cytoplasmic Pi concentration in a small-scale bioreactor monitored by 31P NMR. This is, to the best of the authors’ knowledge, the first time that closed-loop control is achieved on intracellular concentration […]. This study will thus provide powerful analytical and experimental tools for future works on the regulation and control of metabolic systems.

Section snippets

Dynamic modelling for control of plant cell metabolism

The model used in this study is based on previous works (Leduc et al., 2006; Cloutier et al., 2007a) on hairy roots metabolism. Here the model will be applied to shake flask cultures of Eschscholtiza californica. The model is also used to analyse Pi compartmentation, regulation and metabolism of the same E. californica cell line cultivated in a perfusion small-scale bioreactor monitored by 31P NMR. The 31P NMR experiments yield valuable insight on phosphate and energy metabolism by rapid and

Model description

The model for E. californica suspension (Fig. 1) is based on the model presented for hairy roots in Cloutier et al. (2007a). This fully dynamic model includes 46 reactions and 41 metabolic species. A complete description of the model, stoichiometric equations, fluxes regulation and kinetic parameters is presented in Appendix A. The hypotheses for model development were discussed in Leduc et al. (2006) and Cloutier et al. (2007a) with extensive description of pathways and regulations mechanisms.

Development of a controller for cytoplasmic phosphate

Since in vivo NMR allows for a fast and on-line measurement of the Pi pools in the cells, it is possible to implement a control strategy on Pi metabolism using the extracellular Pi (EPi) concentration as the manipulated variable. As was observed by Mauch et al. (1997) the intracellular dynamics of a metabolic system can be affected by an adequate perturbation in extracellular concentrations. The manipulated variable used in this study (EPi) is easily modulated since the NMR small-scale

Plant cells cultures

E. californica cultures were established as described in Lamboursain and Jolicoeur (2005). Cultures were performed in 125 mL shake flasks on an orbital shaker at 120 RPM using a B5 medium (Gamborg et al., 1968) supplemented with 3% glucose, 0.1 mg L−1 kinetin and 0.2 mg L−1 2-4-D. The flasks, containing 40 mL of sterile medium, were inoculated with 20 g of a 10 days old suspension. Sampling was performed every day with whole flasks taken as samples (n=3). Biomass concentration (gDW L−1 and gFW L−1) was

The model describes the dynamics of plant cells batch cultures

Fig. 3 presents the comparison between simulated metabolites profiles and experimental data points for a shake flask culture. The overall performance of the model at describing available data in batch cultures is similar to what was obtained for C. roseus hairy roots cultivated in Petri dishes (Cloutier et al., 2007b). This observation suggests that the proposed modelling basis for metabolism can be easily adapted to different plant species, as was observed in Cloutier et al. (2007b) for

Conclusions

A dynamic metabolic model for plant cells was used to analyse and control Pi metabolism. The model was calibrated using data from batch cultures in shake flask. In vivo 31P NMR spectroscopy was performed to obtain online data on phosphate related compounds and to precisely identify parameters of phosphate and energetic metabolism. The performance of the model at describing available experimental data was verified successfully. Using the calibrated model, a proportional-integral controller was

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