Genome-Scale Metabolic Model Reconstruction and Investigation into the Fluxome of the Fast-Growing Cyanobacterium Synechococcus sp. PCC 11901.

Abstract

The ability to convert atmospheric CO2 and light into biomass and value-added chemicals makes cyanobacteria a promising resource microbial host for biotechnological applications. A newly discovered fastest-growing cyanobacterial strain, Synechococcus sp. PCC 11901, has been reported to have the highest biomass accumulation rate, making it a preferred target host for producing renewable fuels, value-added biochemicals, and natural products. System-level knowledge of an organism is imperative to understand the metabolic potential of the strain, which can be attained by developing genome-scale metabolic models (GEMs). We present the first genome-scale metabolic model of Synechococcus sp. PCC 11901 (iRS840), which contains 840 genes, 1001 reactions, and 944 metabolites. The model has been optimized and validated under different trophic modes, i.e., autotrophic and mixotrophic, by conducting an in vivo growth experiment. The robustness of the metabolic network was evaluated by changing the biomass coefficient of the model, which showed a higher sensitivity toward pigments under the photoautotrophic condition, whereas under the heterotrophic condition, amino acids were found to be more influential. Furthermore, it was discovered that PCC 11901 synthesizes succinyl-CoA via succinic semialdehyde due to its imperfect TCA cycle. Subsequent flux balance analysis (FBA) revealed a quantum yield of 0.16 in silico, which is higher compared to that of PCC 6803. Under mixotrophic conditions (with glycerol and carbon dioxide), the flux through the Calvin cycle increased compared to autotrophic conditions. This model will be useful for gaining insights into the metabolic potential of PCC 11901 and developing effective metabolic engineering strategies for product development.