Genome-scale modeling of light-driven reductant partitioning and carbon fluxes in diazotrophic unicellular cyanobacterium Cyanothece sp. ATCC 51142

Comprehensive classification and perspective for modelling photorespiratory metabolism

Biological processes involved in photorespiratory and photosynthetic metabolism operate concurrently and affect the interplay between carbon and nitrogen assimilation reflected in plant growth. Experimental evidence has indicated that photorespiratory metabolism has a wide-ranging influence not only on other principal metabolic pathways but also on a multitude of signalling cascades. Therefore, accurate quantitative models of photorespiration can provide a means for predicting and in silico probing of plant behaviour at various levels of the system. We first present a comprehensive classification of current models of photorespiratory metabolism developed within the existing carbon-centric modelling paradigm. We then offer a perspective for modelling photorespiratory metabolism by considering the coupling of carbon and nitrogen metabolism in the context of compartmentalised, genome-scale metabolic models of C3 plants. In addition, we outline the challenges stemming from the need to consider plant metabolic and signalling pathways in assessing the still controversial role of photorespiration and to confront the devised models with the ever-increasing amounts of high-throughput data.

Flux balance analysis of cyanobacterial metabolism: the metabolic network of Synechocystis sp. PCC 6803

Cyanobacteria are versatile unicellular phototrophic microorganisms that are highly abundant in many environments. Owing to their capability to utilize solar energy and atmospheric carbon dioxide for growth, cyanobacteria are increasingly recognized as a prolific resource for the synthesis of valuable chemicals and various biofuels. To fully harness the metabolic capabilities of cyanobacteria necessitates an in-depth understanding of the metabolic interconversions taking place during phototrophic growth, as provided by genome-scale reconstructions of microbial organisms. Here we present an extended reconstruction and analysis of the metabolic network of the unicellular cyanobacterium Synechocystis sp. PCC 6803. Building upon several recent reconstructions of cyanobacterial metabolism, unclear reaction steps are experimentally validated and the functional consequences of unknown or dissenting pathway topologies are discussed. The updated model integrates novel results with respect to the cyanobacterial TCA cycle, an alleged glyoxylate shunt, and the role of photorespiration in cellular growth. Going beyond conventional flux-balance analysis, we extend the computational analysis to diurnal light/dark cycles of cyanobacterial metabolism.

Computational evaluation of Synechococcus sp. PCC 7002 metabolism for chemical production

Cyanobacteria are ideal metabolic engineering platforms for carbon-neutral biotechnology because they directly convert CO2 to a range of valuable products. In this study, we present a computational assessment of biochemical production in Synechococcus sp. PCC 7002 (Synechococcus 7002), a fast growing cyanobacterium whose genome has been sequenced, and for which genetic modification methods have been developed. We evaluated the maximum theoretical yields (mol product per mol CO2 or mol photon) of producing various chemicals under photoautotrophic and dark conditions using a genome-scale metabolic model of Synechococcus 7002. We found that the yields were lower under dark conditions, compared to photoautotrophic conditions, due to the limited amount of energy and reductant generated from glycogen. We also examined the effects of photon and CO2 limitations on chemical production under photoautotrophic conditions. In addition, using various computational methods such as minimization of metabolic adjustment (MOMA), relative metabolic change (RELATCH), and OptORF, we identified gene-knockout mutants that are predicted to improve chemical production under photoautotrophic and/or dark anoxic conditions. These computational results are useful for metabolic engineering of cyanobacteria to synthesize value-added products.

Feedback-controlled LED photobioreactor for photophysiological studies of cyanobacteria

A custom photobioreactor was designed to enable automatic light adjustments using computerized feedback control. The system consisted of a 7.5-L cylindrical vessel and an aluminum enclosure housing quantum sensors and light-emitting diode arrays, which provide 630 or 680 nm light to preferentially excite the major cyanobacterial pigments, phycocyanin and/or chlorophyll a, respectively. Custom-developed software rapidly measures light transmission and subsequently adjusts the irradiance to maintain a defined light profile to compensate for culture dynamics, biomass accumulation, and pigment adaptations during physiological transitions, thus ensuring appropriate illumination across batch and continuous growth modes. In addition to chemostat cultivation, the photobioreactor may also operate as a turbidostat, continuously adjusting the media dilution to achieve maximal growth at a fixed culture density. The cultivation system doubles as an analytical device, using real-time monitoring to avoid sampling bias (e.g., in-situ light-saturation response), determine conditions for optimal growth, and observe perturbation responses at high time-resolution.

Reconstruction and comparison of the metabolic potential of cyanobacteria Cyanothece sp. ATCC 51142 and Synechocystis sp. PCC 6803

Cyanobacteria are an important group of photoautotrophic organisms that can synthesize valuable bio-products by harnessing solar energy. They are endowed with high photosynthetic efficiencies and diverse metabolic capabilities that confer the ability to convert solar energy into a variety of biofuels and their precursors. However, less well studied are the similarities and differences in metabolism of different species of cyanobacteria as they pertain to their suitability as microbial production chassis. Here we assemble, update and compare genome-scale models (iCyt773 and iSyn731) for two phylogenetically related cyanobacterial species, namely Cyanothece sp. ATCC 51142 and Synechocystis sp. PCC 6803. All reactions are elementally and charge balanced and localized into four different intracellular compartments (i.e., periplasm, cytosol, carboxysome and thylakoid lumen) and biomass descriptions are derived based on experimental measurements. Newly added reactions absent in earlier models (266 and 322, respectively) span most metabolic pathways with an emphasis on lipid biosynthesis. All thermodynamically infeasible loops are identified and eliminated from both models. Comparisons of model predictions against gene essentiality data reveal a specificity of 0.94 (94/100) and a sensitivity of 1 (19/19) for the Synechocystis iSyn731 model. The diurnal rhythm of Cyanothece 51142 metabolism is modeled by constructing separate (light/dark) biomass equations and introducing regulatory restrictions over light and dark phases. Specific metabolic pathway differences between the two cyanobacteria alluding to different bio-production potentials are reflected in both models.

Sustained H(2) production driven by photosynthetic water splitting in a unicellular cyanobacterium

The relationship between dinitrogenase-driven H(2) production and oxygenic photosynthesis was investigated in a unicellular cyanobacterium, Cyanothece sp. ATCC 51142, using a novel custom-built photobioreactor equipped with advanced process control. Continuously illuminated nitrogen-deprived cells evolved H(2) at rates up to 400 µmol ⋅ mg Chl(-1) ⋅ h(-1) in parallel with uninterrupted photosynthetic O(2) production. Notably, sustained coproduction of H(2) and O(2) occurred over 100 h in the presence of CO(2), with both gases displaying inverse oscillations which eventually dampened toward stable rates of 125 and 90 µmol ⋅ mg Chl(-1) ⋅ h(-1), respectively. Oscillations were not observed when CO(2) was omitted, and instead H(2) and O(2) evolution rates were positively correlated. The sustainability of the process was further supported by stable chlorophyll content, maintenance of baseline protein and carbohydrate levels, and an enhanced capacity for linear electron transport as measured by chlorophyll fluorescence throughout the experiment. In situ light saturation analyses of H(2) production displayed a strong dose dependence and lack of O(2) inhibition. Inactivation of photosystem II had substantial long-term effects but did not affect short-term H(2) production, indicating that the process is also supported by photosystem I activity and oxidation of endogenous glycogen. However, mass balance calculations suggest that carbohydrate consumption in the light may, at best, account for no more than 50% of the reductant required for the corresponding H(2) production over that period. Collectively, our results demonstrate that uninterrupted H(2) production in unicellular cyanobacteria can be fueled by water photolysis without the detrimental effects of O(2) and have important implications for sustainable production of biofuels.

IMPORTANCE

The study provides an important insight into the photophysiology of light-driven H(2) production by the nitrogen-fixing cyanobacterium Cyanothece sp. strain ATCC 51142. This work is also of significance for biotechnology, supporting the feasibility of "direct biophotolysis." The sustainability of the process, highlighted by prolonged gas evolution with no clear sign of significant decay or apparent photodamage, provides a foundation for the future development of an effective, renewable, and economically efficient bio-H(2) production process.

Identification of functional differences in metabolic networks using comparative genomics and constraint-based models

Genome-scale network reconstructions are useful tools for understanding cellular metabolism, and comparisons of such reconstructions can provide insight into metabolic differences between organisms. Recent efforts toward comparing genome-scale models have focused primarily on aligning metabolic networks at the reaction level and then looking at differences and similarities in reaction and gene content. However, these reaction comparison approaches are time-consuming and do not identify the effect network differences have on the functional states of the network. We have developed a bilevel mixed-integer programming approach, CONGA, to identify functional differences between metabolic networks by comparing network reconstructions aligned at the gene level. We first identify orthologous genes across two reconstructions and then use CONGA to identify conditions under which differences in gene content give rise to differences in metabolic capabilities. By seeking genes whose deletion in one or both models disproportionately changes flux through a selected reaction (e.g., growth or by-product secretion) in one model over another, we are able to identify structural metabolic network differences enabling unique metabolic capabilities. Using CONGA, we explore functional differences between two metabolic reconstructions of Escherichia coli and identify a set of reactions responsible for chemical production differences between the two models. We also use this approach to aid in the development of a genome-scale model of Synechococcus sp. PCC 7002. Finally, we propose potential antimicrobial targets in Mycobacterium tuberculosis and Staphylococcus aureus based on differences in their metabolic capabilities. Through these examples, we demonstrate that a gene-centric approach to comparing metabolic networks allows for a rapid comparison of metabolic models at a functional level. Using CONGA, we can identify differences in reaction and gene content which give rise to different functional predictions. Because CONGA provides a general framework, it can be applied to find functional differences across models and biological systems beyond those presented here.