(199g) Analysis of the Effects of Varying Carbon Dioxide Concentration in the Biomass Production and Metabolic Network of the Microalgae Chlamydomonas Reinhardtii

The increase of air emissions originated from the burning of fossil fuels and the expected decrease in fossil resources have been an issue of world impact and socio-economic importance. According to previous reports, the carbon emissions in Colombia increased from 66.4 to more than 120 Million tons per year in the period between 1996 and 2010. The accumulation of air emissions derived from the burning of fossil fuels and industrial activities may affect the biodiversity, geographical distribution of species, seasonal timing and overall climate change. It is then important to contribute in finding and optimizing the use of alternative energy sources with a lower net gas emission and to accelerate the development of environmentally friendly technologies. Algae-based technologies of bioremediation coupled to biomass production are alternative strategies for reducing levels of air contaminants and creating new sources of renewable biomass which can be used for energy production, e.g., biofuels, or for the accumulation of other by-products, e.g., pigments. However, there are still limitations in terms of the costs of biomass production and further conversion of this material into useful fuels; the increase in biomass per cell may help to reduce these production-associated drawbacks (Stephenson, 2011). A better understanding and improvement of the biomass production in algae may accelerate its use as alternative energy source in industrial scale. The process of biomass production in microalgae is influenced, among other factors, by the availability of carbon dioxide and cultivation conditions (Collins, 2006). Changes on these parameters have shown to result in more or less biomass yield (Kliphius,2011; Renberg, 2010). It seems feasible that the production of biomass can be modulated by key metabolic pathways through environmental changes (e.g. nutrient and CO₂ availability) or molecular approaches (e.g., gene overexpression or gene knock out). Nevertheless, how to optimize the CO₂ capture and increase the biomass and lipid production in algae in a cost-effective and sustainable way is a question that remains to be answered. Under conditions of air-level carbon dioxide (CO₂) concentration a Carbon Concentrating Mechanism (CCM) is induced to facilitate cellular carbon uptake. The CCM increases the availability of carbon dioxide at the site of cellular carbon fixation, increasing photosynthetic rates. However, how CCM is controlled at the transcriptional and metabolic level is not well understood. Furthermore, the effects of increased CO₂ concentration in the biomass accumulation have not been completely characterized either. In our present work, we analysed the effects of varying concentrations of carbon dioxide in the cellular responses of the photosynthetic unicellular green alga Chlamydomonas reinhardtii. Experimental data for the transcript abundance of selected enzymes were integrated into the previously reconstructed metabolic network of C. reinhardtii (Chang, 2011), together with data of the biomass composition. This approach revealed new target genes and metabolic pathways for further metabolic engineering of biomass accumulation process.

Material and methods

Cells and culturing conditions

The microalga Chlamydomonas reinhardtii CC-1690 was obtained from the culture collection at ChlamyCenter (http://www.chlamy.org/strains.html) (University of Minnesota). Cells were cultured in a Bioreactor Bioengineering (Bioengineering AG, Wald, Switzerland), which permits to cultivate the cells under controlled temperature (21°C) and photoautotrophic conditions (100 µE.m^-2.s^-1). Cells were cultivated under four different CO₂ concentrations, varying from 0.04% to 10% CO₂, and the experimental data obtained in triplicate.

Transcripts profiling

The abundance of mRNA transcripts of selected genes which code for enzymes of the primary metabolism was quantified using Reverse-Transcription real-time PCR (RT-qPCR) as previously described (Winck, 2011) for cells cultivated at 0.04% CO₂ and cultivated at CO₂ condition where the biomass was highest. Primers were designed and computationally validated using the tool QuantPrime (Arvidsson, 2008).

Biomass characterization

Biomass was determined as dry weight and its composition was further characterized. Protein, lipids, nucleic acids, starch, pigments and carbohydrate content were quantified as previously described (Kliphuis, 2011). Briefly, the determination of the biomass composition was conducted by colorimetric and fluorometric assays.

Metabolic modelling and simulation

The metabolic network of Chlamydomonas reinhardtii previously reconstructed, named iRC1080 (Chang, 2011), enables the phenotypic prediction under conditions fixed as in the experimental conditions cited above. We carried out a sensitivity analysis by perturbing the network under different CO₂ levelsusing the software Xpress IVE^®. This allowed to detect the role of specific metabolic flows and this information could be matched with the abundance of mRNA transcripts in order to understand the underpinnings behind the responses of the microalga.

Results

The biomass of microalgae cultivated at different CO₂ concentrations was characterized and the variations on its composition were identified. Cells cultivated at high CO₂ concentration showed higher growth rates. Furthermore, the expression profiles for the enzyme coding genes were identified revealing changes on the cellular responses under varying CO₂ conditions. The experimental data from gene expression and biomass values were used for the metabolic network simulations. Our objective was to attain the maximum use of CO₂ with the maximum biomass production. For this, we have performed a sensitivity analysis, varying the limits of reactions, whereby we identified the reactions which play an important role in the uptake of CO₂, keeping an optimal biomass production. Consequently, it was possible to establish the required conditions to hold cellular viability within an optimal level concurrently with a maximum concentration of CO₂. This simulation were carried out taking advantage of the available resource of the network by means of which we can take different light sources to simulate the input photons to the metabolism, obtaining results in agreement with those reported previously. Finally, we fixed as light source White Led, which is the same used in our experiments, given that its applications are broader than those of other artificial light sources, because of their availability and economy. Our results revealed target genes and metabolic pathways which can be further investigated for the optimization of biomass accumulation using metabolic engineering approaches.

Conclusion

The integration of experimental data into metabolic networks permitted to perform simulation analysis of biomass accumulation in the microalgae Chlamydomonas reinhardtii. This approach revealed the identity of important metabolic pathways which can now be investigated on future strategies for the enhancement of biomass accumulation and constitutive high photosynthetic rates in microalgae species.   

References

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