Genome-Scale Modeling of Light-Driven Reductant Partitioning and Carbon Fluxes in Diazotrophic Unicellular Cyanobacterium Cyanothece sp. ATCC 51142
Genome-scale metabolic models have proven useful for answering fundamental questions about metabolic capabilities of a variety of microorganisms, as well as informing their metabolic engineering. However, only a few models are available for oxygenic photosynthetic microorganisms, particularly in cyanobacteria in which photosynthetic and respiratory electron transport chains (ETC) share components. We addressed the complexity of cyanobacterial ETC by developing a genome-scale model for the diazotrophic cyanobacterium, Cyanothece sp. ATCC 51142. The resulting metabolic reconstruction, iCce806, consists of 806 genes associated with 667 metabolic reactions and includes a detailed representation of the ETC and a biomass equation based on experimental measurements. Both computational and experimental approaches were used to investigate light-driven metabolism in Cyanothece sp. ATCC 51142, with a particular focus on reductant production and partitioning within the ETC. The simulation results suggest that growth and metabolic flux distributions are substantially impacted by the relative amounts of light going into the individual photosystems. When photosystem II flux is high, terminal oxidases of respiratory electron transport are predicted to be an important mechanism for removing excess electrons. When photosystem I flux is high cyclic electron transport becomes important. Model predictions of growth rates were in good quantitative agreement with measured growth rates, and predictions of reaction usage were ualitatively consistent with protein and mRNA expression data, when these latter datasets were used to constrain the model.
- Research Organization:
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Sponsoring Organization:
- USDOE
- DOE Contract Number:
- AC05-76RL01830
- OSTI ID:
- 1115851
- Report Number(s):
- PNNL-SA-83135; KP1601010
- Journal Information:
- PLoS Computational Biology, 8(4):E1002460, Journal Name: PLoS Computational Biology, 8(4):E1002460
- Country of Publication:
- United States
- Language:
- English
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