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 C-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 CO. 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.

Pooled CRISPRi screening of the cyanobacterium Synechocystis sp PCC 6803 for enhanced industrial phenotypes

“…Here, data revealed instances of both rigidity and plasticity in Synechocystis central carbon metabolism. For example, the production of pyruvate in Synechocystis has been predicted to be mainly through decarboxylation of oxaloacetate by NADPdependent malic enzyme, not dephosphorylation of phosphoenolpyruvate by pyruvate kinase 18 . However, malic enzyme (me, slr0721) and pyruvate kinase (pyk2, sll1275) both have low fitness scores ( Supplementary Fig.…”

Genome-Scale Fluxome of Synechococcus elongatus UTEX 2973 Using Transient 13C-Labeling Data

“…Furthermore, reaction lumping restricted the metabolite-labeling dynamics afforded by a core model, leading to significant disagreements between experimentally measured labeling dynamics and those predicted using simplified models (Nöh and Wiechert, 2011). In order to mitigate the effect of these factors on the accuracy of flux estimation, the scope of the INST-MFA technique has been expanded to genome-scale models with the implementation of an efficient algorithm for the simulation of metabolitelabeling dynamics (Gopalakrishnan et al, 2018). Meta-analysis of the labeling dynamics observed under photoautotrophic conditions in Synechocystis sp.…”

“…Meta-analysis of the labeling dynamics observed under photoautotrophic conditions in Synechocystis sp. PCC 6803 (Synechocystis 6803) using genome-scale INST-MFA showed that the genome-scale mapping model (GSMM), namely imSyn617, fitted the experimental data better than the core model (Gopalakrishnan et al, 2018). Interestingly, the flux distribution obtained using imSyn617 was qualitatively different from the one obtained using the core model, as indicated by the reliance on transaldolase (TAL) for the synthesis of Fructose-6-Phosphate (f6p), a bifurcated photorespiratory C2 cycle, and off-gassing of fixed CO 2 by anabolic reactions outside core metabolism.…”

Challenges of cultivated meat production and applications of genome‐scale metabolic modeling

“…A recent review examines the current status and broad applications of GSMs 38 . In addition, the use of stable isotope labels (i.e., 13 C‐metabolic flux analysis [MFA], 13 C‐MFA) has extensively been used in conjunction with GSMs to ascertain internal metabolic fluxes in both microbial 52,53 and eukaryotic 54 systems. These require not only a metabolic model but also atom mapping models that describe the origin and destination of carbons from reactants to products, 55 as indicated in Figure 2b.…”