(410g) Development of a Constraint-Based Model for Photobiological Production of Hydrogen in Cyanothece Sp. ATCC 51142

Energy crisis is one of the world's major problems. For many years, energy production has heavily relied on the availability of fossil-fuels which are becoming increasingly limited and expensive. Research investments and efforts have been extensively focused on the development of clean and efficient processes for sustainable energy sources, of which hydrogen is considered as one of the promising future fuels (1). Among bio-based hydrogen-producing processes, oxygenic photosynthesis (where oxygen is produced as a by-product) receives great interest for it makes use of the abundance of sunlight and carbon dioxide, and thus produces hydrogen essentially at no substrate cost. However, oxygen produced in this process irreversibly inhibits nitrogenase and hydrogenase enzymes that are responsible for hydrogen production. Alternatively, indirect photosynthesis in which oxygen and hydrogen production are temporally or spatially separated can resolve the enzyme inhibition issue. This process however is poorly understood and the reported efficiency of hydrogen production is rather low (0.355mmol/L-hr in Anabaena variabilis) (2). Therefore, developing a model for this process is critical in not only understanding the physiology of the process but also identifying factors that impact hydrogen production rates. Constraint-based stoichiometric models (3) have been useful in biological discovery (4) and metabolic engineering to identify strains with significantly improved phenotypes (5-7).

Here we will present the reconstruction of a genome-scale metabolic network for Cyanothece sp. ATCC 51142, a cyanobacterium that can temporally separate the evolution of oxygen and hydrogen upon light-dark growth conditions. We used a RAST server (8) to generate, and integrate in the SEED database (9), an initial set of subsystems-based gene annotations for Cyanothece sp. that were further manually refined and improved while constructing the metabolic model. This model currently includes 798 genes, 682 proteins, 630 metabolites and 656 reactions accounting for common pathways such as central metabolism, nucleotide and amino acid biosynthesis, and those that are more unique to cyanobacteria such as photosynthesis, carbon fixation and cyanophycin production. The resulting model can produce hydrogen, and utilize ammonia, nitrate and molecular nitrogen as different nitrogen sources for in silico growth. Photosynthesis was modeled as sequential reactions that occur in each photosystem, I and II, in order to study the effect of different light wavelengths, and separate photosystem activities on cellular growth and hydrogen production rate.

Fixing metabolic gaps is an important step in reconstructing metabolic networks, especially if the gaps prevent the production of metabolites which are essential for cellular growth. For example, Cyanothece sp. ATCC 51142 lacks the gene encoding L-threonine deaminase, which catalyzes the conversion of L-threonine to 2-oxobutanoate, a precursor of isoleucine biosynthesis. Subsequently, our initial reconstruction was unable to synthesize isoleucine and this metabolic gap was fixed by including an alternative pathway for 2-oxobutanoate biosynthesis from pyruvate and acetyl-CoA, the pathway similar to that found in Geobacter sulfurreducens (10). We are currently using various bioinformatic and comparative genomic techniques, such as the analysis of conserved operons and regulons, to identify candidate genes filling-in other gaps in our metabolic reconstruction. In addition to gap-filling, such analysis of genome context often allows for the prediction of new metabolic genes and pathways contributing to the further refinement and expansion and of the metabolic model.

It has been shown that with the accumulation of glycogen in the light phase, there is a burst of glycogen degradation in the dark phase that results in an increasing level of nitrogen fixation and respiration (11). The reconstructed metabolic network and its corresponding constraint-based model can be used to study the correlation between glycogen accumulation and degradation with hydrogen production. The predictions from the constraint-based model will be useful for improving hydrogen production using metabolic engineering approaches.

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