Elucidation of photoautotrophic carbon flux topology in Synechocystis PCC 6803 using genome-scale carbon mapping models

Abstract

Completeness and accuracy of metabolic mapping models impacts the reliability of flux estimation in photoautotrophic systems. In this study, metabolic fluxes under photoautotrophic growth conditions in the widely-used cyanobacterium Synechocystis PCC 6803 are quantified by re-analyzing an existing dataset using genome-scale isotopic instationary 13C-Metabolic Flux Analysis (INST-MFA). The reconstructed carbon mapping model imSyn617 and implemented algorithmic updates afforded an approximately 48% reduction in computation time. The mapping model encompasses 18 novel carbon paths spanning Calvin-Benson-Bassham cycle, photorespiration, an expanded glyoxylate metabolism, and corrinoid biosynthetic pathways and 190 additional metabolites absent in core models currently used for MFA. Flux elucidation reveals that 98% of the fixed carbons is routed towards biomass production with small amounts diverted towards organic acids and glycogen storage. 12% of the fixed carbons are oxidized to CO2 in the TCA cycle and anabolic reactions in peripheral metabolism. Flux elucidation using instationary MFA reveals that these carbons are not re-fixed by RuBisCO and are instead off-gassed as CO2. A newly discovered modality is the bifurcated topology of glycine metabolism using parts of photorespiration and the phosphoserine pathways to avoid carbon losses associated with glycine oxidation. The TCA cycle is shown to be incomplete with a bifurcated topology. Inactivity of futile cycles and alternate routes results in pathway usage and (in)dispensability predictions consistent with experimental findings. The resolved flux map is consistent with the maximization of biomass yield from fixed carbons as the cellular objective function. Flux prediction departures from the ones obtained with the core model demonstrate the importance of constructing mapping models with global coverage to reliably glean new biological insights using labeled substrates.