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| AccliPhot Progress Report Period covered: October 2013 - March 2014 Name: Kailash Adhikari Date: 9 April, 2014 Scientific progress: The aim of this study is to understand the photosynthetic behavior of Ara- bidopsis thaliana and Chlamydomonas reinhardtii. Genome-scale metabolic models (GSMs) of these organisms were constructed for this purpose and their responses under varying intensities of light and ATP demand were stud- ied, which are discussed below. An updated version of a previous genome scale metabolic model of Ara- bidopsis was constructed using the AraCyc database (version 11.5) with com- partmental information and intracellular transporters obtained from Cheung (2013). Iterative steps of curation and validation was performed using the established methodology of the Cell System Modelling (CSM) group. The model consists of 1548 reactions (including intra cellular transporters) and 1108 metabolites in 5 compartments (Cytosol, Mitochondria, Plastid, Peroxysome, Vacuole) according to Cheung (2013) and has been verified for energy and redox conservation and atomic balance of C, N, S and P. Biomass precursors (amino acids, nucleotides base, lipid, starch, cellulose and lignin) are being produced under both heterotrophic and phototrophic conditions using CO2 , NH4 / NO3 , SO4 and Pi as the source of nutrients. Light is the sole energy source under light conditions while oxidation of starch is the source of energy under dark conditions. Linear Programming (LP) based solutions were used to examine changes in reaction flux distribution in response to increasing photon flux with the objective of minimizing total reaction flux. The minimum photon flux for which a solution could be found was 0.31 light flux units corresponding to a maximally efficient quantum demand of 11.86 photons per carbon fixed, which is slightly lower then the value calculated for Rice model (13.4 per carbon fixed) Poolman et al, (2013). The light scan results show that at the lower light intensity, photosynthetic O2 is utilized by mitochondrial respira- tion and CO2 thus evolved is refixed in chloroplast while ATP is generated by the operation of mitochondrial electron transport chain, powered by re- ductants supplied by the mitochondrial malate oxaloacetate shunt. These 1 results are qualitatively comparable to the results obtained by Poolman et al, (2013) and supports to the view that mitochondrial respiration has a role in optimizing photosynthetic performance. With increasing light intensity, mitochondrial ATP production starts to de- cline while production of ATP by cyclic and non-cyclic light reactions in- creases. The net evolution of O2 from chloroplast is nearly constant while reactions unique to Calvin cycle and other biosynthetic pathway reactions maintained high positive fluxes. At very high light intensity ascorbate glu- tathione cycle along with xanthophyll cyle are turning on which means that non-photochemical quenching mechanism is dissipating the excess energy. However it is interesting to note that photorespiration which has been sug- gested as a protective mechanism in plants that acts by dissipating excess energy at high light intensities is not evident in these results. Under dark conditions, oxidation of starch serves as the source of energy to the plant. Pentose phosphate pathway specific reactions were upregulated while reactions unique to Calvin cycle carried no flux. However reactions common to OPPP and Calvin cycle were operational at both light and dark conditions. As this kind of behavior is believed to occur under the influence of thioredoxin system, it was interesting to observe such effect for this model. LP solutions were also used to determine potential reaction fluxes with the increasing demand of ATP. The overall response was qualitatively similar to the results in earlier study, Poolman et al, (2009). Interestingly, CO2 assim- ilating reaction, Rubisco carboxylase, was active at very low ATP demand, although its activity at slightly higher demand fell to zero and was overtaken by conventional TCA cycle reactions. A preliminary genome-scale metabolic model of Chlamydomonas model has been constructed using the ChlamyCyc database (version 3.5). Compartmen- tal information and transporter were used as for the Arabidopsis model and similar curation and validation steps were followed. The model is atomically balanced in terms of C, N, S and P and consists of 1769 reactions and 1704 metabolites. Model is able to produce all the amino acids, some nucleotide precursors (GMP, AMP, dAMP), cellulose and starch. The model is being assessed for its ability to produce other biomass components. 2 Future Work: • Composition of the amino acids, nucleotide, starch, cellulose, lignin, chlorophyll A and B and fatty acid in the Arabidopsis leaves and Chlamydomonas under the phototrophic conditions will be obtained from the experimental collaborators. These biomass data will then be used as constrains to respective models and their behavior under vary- ing input of light and ATP demand will be evaluated. Further study will be done to come up with new hypotheses and experimental design to test them. • Responses under nutrient limitation of nitrogen, sulphur and phospho- rous will be investigated for both of these models. • Intracellular transport plays major roles in maintenance of cellular ac- tivity by transporting metabolites between compartments but they also account for the major energy budget in the cell. The intracellular trans- porters used in this model are taken from Cheung (2013) and the pres- ence of most of these are questionable, so the flux solutions for such reactions will be further evaluated to decide upon their relevance in the model. Publications: Study of Photosynthetic Properties of in Genome Scale Metabolic Model of Arabidopsis thaliana - (Poster Presentation), Adhikari, Poolman, Fell and Cheung, SysBio2014, Innsbruck, 2-8 March 2014 Training and Personal Development: • Weekly CSM Group meetings, Oxford Brookes University, Sept 2013 – Mar 2014. • Weekly Biological and Medical Science seminar series, Oxford Brookes University, Sept 2013 - Mar 2014. • Health and Safety induction, Oxford Brookes University, June 2013. • Equal Opportunity and Diversity, online training, Oxford Brookes Uni- versity, July 2013. • Display Screen Equipment Training, Oxford Brookes University, July 2013. 3 • AccliPhot annual meeting and Thermodynamic workshop ,Corpus Christi College, September 8- 14, 2013. • Metabolic Pathway Analysis Conference, Corpus Christi College, Septem- ber 16-20, 2013. • Welcome To Brookes, Oxford Brookes University, November 2013. • MSc module in Computing - ’Formal Software’ Engineering, Oxford Brookes University, Oct-Dec 2013 ( Passed with Distinction). • Research Student Symposium, Faculty of Health and Life Sciences, Oxford Brookes University, January 2014. • Statistical Methods for data Analysis I workshop. Oxford Brookes University, 20-21 Jan 2014. • Advanced lecture Course on System Biology - SysBio2014, Innsbruck, 2-8 March 2014. • AccliPhot webinar, 20 March 2014. • Oxford Brookes University, Graduate College Training Workshops: Induction for research students, October 2013. Research career pathway event, March 2014. References: Poolman, M.G., Miguet L., Sweetlove L.J. and Fell D.A. (2009) ’A genome- scale metabolic model of Arabidopsis and some of its properties’, Plant Phys- iol, vol. 151, p. 1570 – 1581. Poolman, M.G., Kundu S., Shaw R., Fell D.A. (2013) ’Responses to light intensity in genome-scale model of rice metabolism’, Plant Physiol, vol. 162, p. 1060 – 1072. Cheung, C.Y.M., Williams, T.C.R, Poolman M.G, Fell D.A., Ratcliffe R.G, Sweetlove L.J. (2013), ’A method for accounting for maintenance cost in flux balance analysis improves the prediction of plant cell metabolic phenotypes under stress conditions’, The Plant Journal vol. 75, p. 1050 - 1061. Cheung, C.Y.M.(2013), ’Genome scale metabolic models of plant tissues’. PhD Thesis. |
Genome-scale metabolic model of Arabidopsis thaliana and Chlamydomonas reinhartii. Human life is dependent on different plant products and with growing population, demand for food and energy is increasing but resources to meet them remains limited on earth, so there is a need to find the ways to balance thischain. Green plants and algae can synthesize useful nutrients using carbon dioxide, water and sunlight form the environment by a process called photosynthesis and it is possible to engineer their metabolic process to increase the yield and also use them for production of biofuels. This project is focused on use of computational modelling techniques to study and analyze the metabolic behavior of Arabidopsis thaliana - a multicellular flowering plant and Chlamydomonas reinhardtii - an unicellular green algae in response to light and other environmental conditions. In order to understand their metabolism and engineer them, genome-scale metabolic models (GSM) of both will be assessed and analyzed. A GSM represents the entire metabolic capabilities of an organism and is built from data extracted typically from annotated genome databases. Linear Programming (LP) will then be used to explore distribution of reaction rates i.e. the effect of each reactions over the metabolic network under variety of assumed environmental conditions. LP is a mathematical method to compute an optimal solution under given parameters such as maximizing biomass production in our case. The experimental part of the project involves examination of biomass composition, signaling pathways etc and will be carried out in collaboration with other partners in the consortium. Both modelling and experimental results will be analyzed to identify potential cause of damage to the plants form environmental stress conditions and formulate strategies to mitigate such effects to obtained desired outputs form their metabolic activity. New hypotheses will be proposed based on these observations about operating characteristics of metabolic networks of Arabidopsis and Chlamydomonas and will be tested in collaboration with experimental and industrial partners. |
Lay Summary of the Project.
Genome-scale metabolic model of Arabidopsis thaliana and Chlamydomonas reinhartii.
Human life is dependent on different plant products and with growing population, demand for food and energy is increasing but resources to meet them remains limited on earth, so there is a need to find the ways to balance thischain. Green plants and algae can synthesize useful nutrients using carbon dioxide, water and sunlight form the environment by a process called photosynthesis and it is possible to engineer their metabolic process to increase the yield and also use them for production of biofuels.
This project is focused on use of computational modelling techniques to study and analyze the metabolic behavior of Arabidopsis thaliana - a multicellular flowering plant and Chlamydomonas reinhardtii - an unicellular green algae in response to light and other environmental conditions. In order to understand their metabolism and engineer them, genome-scale metabolic models (GSM) of both will be assessed and analyzed. A GSM represents the entire metabolic capabilities of an organism and is built from data extracted typically from annotated genome databases. Linear Programming (LP) will then be used to explore distribution of reaction rates i.e. the effect of each reactions over the metabolic network under variety of assumed environmental conditions. LP is a mathematical method to compute an optimal solution under given parameters such as maximizing biomass production in our case. The experimental part of the project involves examination of biomass composition, signaling pathways etc and will be carried out in collaboration with other partners in the consortium.
Both modelling and experimental results will be analyzed to identify potential cause of damage to the plants form environmental stress conditions and formulate strategies to mitigate such effects to obtained desired outputs form their metabolic activity. New hypotheses will be proposed based on these observations about operating characteristics of metabolic networks of Arabidopsis and Chlamydomonas and will be tested in collaboration with experimental and industrial partners.