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 growth is limited by the flux through photosystem I, terminal respiratory oxidases are predicted to be an important mechanism for removing excess reductant. Similarly, under photosystem II flux limitation, excess electron carriers must be removed via cyclic electron transport. Furthermore, in silico calculations were in good quantitative agreement with the measured growth rates whereas predictions of reaction usage were qualitatively consistent with protein and mRNA expression data, which we used to further improve the resolution of intracellular flux values.

Cyanobacteria have been promoted as platforms for biofuel production due to their useful physiological properties such as photosynthesis, relatively rapid growth rates, ability to accumulate high amounts of intracellular compounds and tolerance to extreme environments. However, development of a computational model is an important step to synthesize biochemical, physiological and regulatory understanding of photoautotrophic metabolism (either qualitatively or quantitatively) at a systems level, to make metabolic engineering of these organisms tractable. When integrated with other genome-scale data (e.g., expression data), numerical simulations can provide experimentally testable predictions of carbon fluxes and reductant partitioning to different biosynthetic pathways and macromolecular synthesis. This work is the first to computationally explore the interactions between components of photosynthetic and respiratory systems in detail. In silico predictions obtained from model analysis provided insights into the effects of light quantity and quality upon fluxes through electron transport pathways, alternative pathways for reductant consumption and carbon metabolism. The model will not only serve as a platform to develop genome-scale metabolic models for other cyanobacteria, but also as an engineering tool for manipulation of photosynthetic microorganisms to improve biofuel production.

Funding: The research was supported by the Genomic Science Program (GSP), Office of Biological and Environmental Research (OBER), U.S. Department of Energy, and is a contribution of the PNNL Biofuels and Foundational Scientific Focus Areas (SFAs). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Copyright: © 2012 Vu et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

In this work, we developed the first genome-scale metabolic model of Cyanothece 51142 and used a combination of computation and experimental approaches to investigate how photosynthetic and respiratory fluxes affect metabolism. Discrete representation of PS II and PS I and their integration with multiple respiratory pathways enabled modeling of photon fluxes and electron flux distributions under conditions of variable light quality and intensity. The predicted changes in growth rates of Cyanothece 51142 in response to changes in light input were experimentally tested using a photobioreactor with controlled sources of monochromatic 630 and 680 nm light. We also carried out computational and experimental analyses of light- and nitrogen-limited chemostat growth of Cyanothece 51142 and used mRNA and protein expression data to constrain model-predicted flux distributions. Both in silico and experimental data suggest that respiratory electron transfer plays a significant role in balancing the reductant (NADPH) and ATP pools in the cells during photoautotrophic growth. This study is a first step towards a systems-level analysis of cyanobacterial metabolism, as it integrates information into a genome-scale reconstruction to understand metabolism qualitatively and quantitatively through a constraint-based analysis [9] . We also discuss strategies for improving internal flux distributions through integration of in silico simulations and data.

Sequencing of the Cyanothece 51142 genome [6] has enabled application of high-throughput genomic approaches to study the unique physiological and morphological features of this organism. Transcriptomic and proteomic studies have been conducted to analyze global gene expression patterns under a variety of environmental conditions and infer regulatory pathways that govern the organism's diurnal growth [7] , [8] . The availability of genomic information also provides means to construct genome-scale constraint-based models of metabolism, which are powerful tools for systems-level analysis and prediction of biological systems response to environmental cues and genetic perturbations [9] , [10] . Such models have been developed for a variety of biological systems [9] but only in a few studies has this approach been applied to photosynthetic microorganisms, including Synechocystis sp. PCC 6803 [11] –, Rhodobacter sphaeroides [14] , and Chlamydomonas reinhardtii [15] , [16] . However, the modeling of metabolism in oxygenic photoautotrophs is an intriguing problem due to the complexity of photosynthetic and respiratory electron transport chains, and the potential effects of two distinct photosystems upon the generation and fate of reductant and energy that drives the remainder of metabolism.

Cyanothece spp. are unicellular, diazotrophic cyanobacteria that temporally separate light-dependent oxygenic photosynthesis and glycogen accumulation from N 2 fixation at night [1] . When grown under nutrient excess, Cyanothece sp. strain ATCC 51142 (thereafter Cyanothece 51142) cells can accumulate significant amounts of storage polymers including glycogen, polyphosphates, and cyanophycin [2] . The inter-thylakoid glycogen granules are significantly larger in size than those found in other cyanobacteria, which points at an unusual branching pattern and packaging of this compound. From a biotechnological perspective, this presents an intriguing theoretical possibility to accumulate substantially higher amounts of polyglucose without any significant increase in the number of granules [3] . Cyanothece 51142 is also of interest for bioenergy applications due to its ability to evolve large quantities of H 2 . Remarkably, H 2 production in this organism can occur under light conditions in the presence of O 2 and is mediated by nitrogenase [4] , [5]

Results

Metabolic network reconstruction and initial model validation To build a constraint-based metabolic model of Cyanothece 51142, a genome-scale metabolic network was reconstructed using the genome annotation and data from NCBI [6], SEED [17], KEGG [18]–[20], and CyanoBase [21], [22]. The resulting iCce806 network contains 806 genes and 667 metabolic and transport reactions (see Dataset S1 and Tables S1, S2, S3 for network details). Most of the 42 reactions without genes associated with them were added to complete metabolic pathways needed for biomass production. The final reconstruction encompasses central metabolic pathways such as the Calvin-Benson cycle, the pentose phosphate pathway (PPP), reactions within the tricarboxylic acid (TCA) cycle, as well as, the complete set of anabolic pathways involved in biosynthesis of glycogen, cyanophycin, amino acids, lipids, nucleotides, vitamins, and cofactors. Pathways for glycolate synthesis (via ribulose-1,5-bisphosphate carboxylase/oxygenase, i.e., photorespiration), glycolate conversion to serine, and glycerol catabolism are also included. Photosynthetic electron transfer associated with the thylakoid membrane is represented as a set of four separate reactions, including light capture by photosystem II (PS II) and photosystem I (PS I), electron transfer between the two photosystems, and cyclic electron transfer around PS I. Similarly, respiratory electron transfer is represented by reactions catalyzed by terminal cytochrome c oxidase (COX), quinol oxidases (QOX, both bd- and bo-types), NADH dehydrogenases (NDH, type 1 and 2), and succinate dehydrogenase. In addition, two reactions (NADP+- and ferredoxin- requiring) for flavin-dependent reduction of O 2 (i.e., Mehler reactions) were included. A simplified scheme of the photosynthetic and respiratory electron transfer reactions in iCce806 is shown in Figure 1. PPT PowerPoint slide

PowerPoint slide PNG larger image

larger image TIFF original image Download: Figure 1. Schematic representation of the electron transport and reductant partitioning pathways in Cyanothece 51142. Linear photosynthetic electron transfer: electrons from photosystem II (PS II) to photosystem I (PS I) are transferred through plastoquinone (Pq), cytochrome b 6 f complex (Cyt b6f), plastocyanin (Pc) and cytochrome c 6 (Cyt c6). From PS I electrons can be transferred to ferredoxin (Fd) via ferredoxin:NADP+ reductase (FNR) and subsequently to generate reductant in the form of NADPH. Cyclic photosynthetic electron transport: electrons can flow from Fd to Pq (FdPq reaction). Respiratory electron transfer: includes two cytochrome oxidases (COX), two cytochrome-quinol oxidases (QOX), and two types of NADH dehydrogenases (NDH-1 and NDH-2). Alternative sinks for reductant beyond CO 2 fixation: reduced Fd can be used by the nitrogenase (Nif) and by Mehler reactions to reduce O 2 . Bidirectional hydrogenase (Hox) can reversibly produce H 2 using NAD(P)H as an electron donor, while the uptake hydrogenase (Hup) consumes H 2 using Fd as an electron acceptor. Protons transferred across the thylakoid membrane are used by the ATPase to drive ATP synthesis. https://doi.org/10.1371/journal.pcbi.1002460.g001 For initial testing, we examined the ability of the constraint-based model of iCce806 to predict growth under photoautotrophic (using light and fixing CO 2 ), heterotrophic (using glycerol in the dark), and photoheterotrophic (using glycerol and light) conditions with different nitrogen sources. In silico calculated biomass yields, which simulated carbon or light- limited growth (Figure S1), qualitatively agreed with previously reported growth data for Cyanothece 51142 [1], [2], [23]. Other non-growth conditions that were simulated with the model, included nitrogen fixation as occurs during the dark phase of Cyanothece's ciracadian cycle [1]. In this case, the oxidation of glycogen provides reductant and ATP for nitrogenase, and we examined the model's ability to quantitatively predict the amount of nitrogen (N 2 ) that could be fixed and stored in the dark, by maximizing cyanophycin production from glycogen. Although H 2 is an obligate co-product of the nitrogenase reaction, no H 2 was produced in the initial simulations under dark N 2 -fixing conditions, contradicting experimental observations. Model examination revealed that all of the nitrogenase-generated H 2 was utilized by hydrogenases to reduce NAD(P) and ferredoxin, which ultimately increased cyanophycin production. When the three hydrogenase reactions (HDH_1, HDH_2, and UPHYDR) were eliminated from the model, the predicted ratio of fixed N 2 to consumed glycogen depended on the non-growth associated ATP requirement (NGAR), and was estimated to be 0.3 (NGAR = 2.8) or 0.67 (NGAR = 0) mole N 2 /mole glycogen, which was in accordance with an experimentally measured value of 0.51 [2]. Under this condition, the model predicted that H 2 production would have same yields as fixed N 2 (0.3 to 0.67 mole H 2 /mole glycogen) due to the stoichiometry of the nitrogenase reaction. We also evaluated how fluxes through electron transfer reactions are affected by the nitrogenase flux under N 2 -fixing dark conditions. With glycogen being the sole source of reductant for both ATP-generating oxidative phosphorylation and N 2 reduction, a balance between fluxes through respiratory pathways and nitrogenase reaction is needed. In the absence of the hydrogenase reactions, the model predicted that O 2 reduction via COX, QOX, or Mehler reactions are required to consume NADH resulting from glycogen catabolism (Figure S2). The model predicts that the COX reaction is required to achieve the maximum N 2 fixation rate since it generates more ATP than the QOX or Mehler pathways (∼9 O 2 are needed per N 2 fixed). This is consistent with the results from recent proteomic studies showing the CoxB1 (cce_1977) subunit of COX is more predominant during the dark [24], [25]. These results suggest terminal oxidases are important under dark N 2 -fixing conditions not only to generate an intracellular anaerobic environment for nitrogenase, but also to provide ATP for nitrogenase activity. As photosynthesis and respiratory electron transport chains are interconnected in cyanobacteria [26], these pathways were allowed to interact in the iCce806 model. To perform model robustness analysis, we computationally explored the impact of key photosynthetic and respiratory pathways on growth rate and intracellular flux distributions under varying photon uptake flux for PS I, while the photon uptake flux for PS II was fixed at 20 mmol·g−1 AFDW·h−1 (Figure 2). First, the model was evaluated assuming only linear photosynthetic electron transfer. In this case, all alternative reductant sinks including the proton and O 2 reduction as well as cyclic photosynthetic reactions around PS I were eliminated from the model (Figure 2A). Under this condition, growth only occurred at one value of photon uptake flux for PS I and extracellular organic products (ethanol, lactate and/or alanine with trace amounts of formate) would have to be secreted in order to generate enough ATP to support biomass production. Second, when cyclic photosynthetic reactions were added back, the photon uptake flux for PS I could vary with a fixed photon uptake flux for PS II, but significant amounts of extracellular products were still formed until the photon uptake flux for PS I exceeded ∼85 mmol·g−1 AFDW·h−1 (Figure 2B). No growth occurred unless PS I photon uptake flux was greater than or equal to the photon uptake flux for PS II. Only when the model was allowed to use both cyclic photosynthesis and O 2 reduction reactions were no extracellular products predicted and the photon uptake flux for PS I could be less than that for PS II (Figure 2C). Since experimental data does not indicate that any by-products including H 2 or organic acids are produced by Cyanothece 51142 at a detectable level during photoautotrophic growth with excess ammonium, a plausible mechanism for balancing growth through the generation of additional ATP may involve activity of the cytochrome oxidases. PPT PowerPoint slide

PowerPoint slide PNG larger image

larger image TIFF original image Download: Figure 2. Impact of electron transport pathways on growth and metabolism of Cyanothece 51142. (A) Effects of removing cyclic photosynthesis (via NDH-1, NDH-2, FdPq, G3PD_PQ, and SUCD_PQ) and alternative reductant sinks (H 2 production, COX, QOX, and Mehler reactions). (B) Effect of removing alternative reductant sinks but including all routes for cyclic photosynthesis. Shaded regions indicate that multiple flux values can achieve maximal growth rate. (C) All photosynthetic and respiratory electron flow routes operate, except H 2 production. https://doi.org/10.1371/journal.pcbi.1002460.g002

Effect of light quality on cellular growth and pathway utilization The discrete representation of PS II- and PS I-mediated reactions and their interactions with multiple respiratory reactions in iCce806 enabled further in silico analysis of growth and electron flux distributions under photoautotrophic conditions of variable light quality and intensity. In this case, the complete model was used to explore which reactions would be used to support maximal photoautotrophic growth rates for different levels of PS II and PS I photon uptake fluxes. To predict the corresponding growth rates under light-limited conditions, we constrained the photon uptake fluxes (ranging from 0 to 60 mmol·g−1 AFDW·h−1) through each photosystem. The resulting phenotypic phase plane (PhPP) contained three distinct regions (Figure 3A): in two regions growth was limited only by fluxes through PS II (region 1) or PS I (region 3), while in region 2 growth was limited by both PS II and PS I photon uptake fluxes (i.e., increases in either flux would improve growth rate). By adding artificial ATP or NADPH generating reactions (ADP+HPO 4 +H→ATP+H 2 O and NADP+H→NADPH) to the model and analyzing changes in predicted maximal growth rates, we were able to identify that in regions 1 and 3 growth was NADPH/reductant-limited, while in region 2 it was limited by energy supply (Figure 3A). PPT PowerPoint slide

PowerPoint slide PNG larger image

larger image TIFF original image Download: Figure 3. Predicted effects of varying photon uptake rates on growth and electron transport pathways. (A) 2-D phenotypic phase plane (PhPP) displaying maximum growth rates for varying photon uptake rates. The PhPP has 3 distinct regions – in regions 1 and 3, flux through a single photosystem limit growth rates, whereas in region 2 flux increases through either photosystem will increase growth rate. (B) Pathway maps of electron transfer reactions in different PhPP regions. PhPP flux variability analysis was performed to see which flux is always required (red arrows), optional (green arrows), and blocked (blue arrows) across each of the three PhPP regions. https://doi.org/10.1371/journal.pcbi.1002460.g003 To analyze the effect of photon uptake rates on electron flux distributions, we calculated the flux ranges using flux variance analysis (FVA) for all photosynthetic and respiration reactions within each PhPP region (Figure 3B). In this instance, PhPP FVA was run with constraints that restrict the model to a given region and to the maximum growth for each point in the region (in contrast, standard FVA is used at a single point in a region). Using PhPP FVA, we identified active (both minimum and maximum flux values are positive or negative), inactive/blocked (minimum and maximum fluxes are both zero), and optional (which could have at least one zero and one non-zero flux value somewhere in the region) reactions leading to optimal solutions in each PhPP region. This new analysis technique allowed classification of reaction usage across entire regions of the PhPP and is not restricted to fixed points within a region. While linear photosynthesis was active and Mehler reactions were blocked across the entire PhPP, there were differences in the usage of photosynthetic and respiratory reactions observed within all three regions (Figure 3B). Surprisingly, while generation of NADPH from reduced ferredoxin via linear photosynthesis is the key source of reductant, ferredoxin-NADP+ oxidoreductase (FNR) was predicted to be active in region 2, but optional in regions 1 and 3. Closer examination of in silico calculated electron flux distributions revealed that, in addition to FNR, the model utilized a cycle involving glutamine synthetase, glutamate synthase and transhydrogenase, resulting in ATP-driven NADPH production. In regions 1 and 3, the model predicts there is excess ATP, and so this cycle can be used instead of FNR to move electrons from ferredoxin to NADPH. However, this cycle is unlikely to be of any physiological relevance since there has been no experimental data supporting this route for making NADPH, and FNR is essential for photoautotrophic growth in unicellular cyanobacteria such as Synechococcus 7002 [27]. Differences in the predicted usage of respiratory reactions were also found. In region 1, where growth is limited by the flux through PS I, at least one of the COX and QOX reactions must be active to oxidize excess electron carriers (Pc, Cyt c 6 , or Pq) generated from PS II. Similarly, in region 3 under PS II flux limitation, excess electron carriers (Pq, Fd) must be reduced via NDH-1 or –2 or ferredoxin-dependent cyclic electron transfer (FdPq). Conversely, due to ATP limitation in region 2, the model favored reactions with higher proton pumping capacities and so both the QOX and FdPq reactions were inactive. The usage of COX was optional in region 2 and depended on photon uptake rates (e.g., COX reaction was inactive at the boundary between regions 2 and 3). The model predictions (Figure 3A) were compared to batch growth experiments in the LED-photobioreactor which allowed instantaneous measurements of initial growth and photon uptake rates by Cyanothece 51142 cells exposed to different intensities and ratios of 630 and 680 nm light (Table 1). When Cyanothece 51142 cultures were illuminated with both 630 nm and 680 nm light, initial growth rates generally correlated with the total photon flux through PS II and PS I, with higher growth rates observed at 80 mmol·g−1 AFDW·h−1 total photon flux and 630 nm∶680 nm light ratio of 2∶1. When cultures were exposed to only a single wavelength of light (batch experiments 6–10), i.e., either 630 or 680 nm, Cyanothece 51142 cells displayed a similar trend with higher growth rates observed at higher photon flux intensities. The predicted growth rates were within 7% of the experimentally measured values, except for the two cases where single 630 nm wavelength irradiances were used (Table 1). The reasons for this are unclear but may be due to other physiological and/or biochemical phenomena such as state transitions that are not contained within the model but are operating in vivo. PPT PowerPoint slide

PowerPoint slide PNG larger image

larger image TIFF original image Download: Table 1. Comparison of growth rates predicted by simulation model to those experimentally measured in batch cultures. https://doi.org/10.1371/journal.pcbi.1002460.t001 Data from these batch experiments (batch experiments 1–5, Table 1) were also used to estimate the growth (GAR) and non-growth (NGAR) associated ATP requirements. NGAR is the amount of energy spent to maintain the cell (i.e., maintenance energy). GAR is defined as energy expenditures used on protein and mRNA turnover or repair, proton leakage, and maintenance of membrane integrity; it does not include ATP spent on polymerization reactions, which are already accounted for in the macromolecular synthesis pathways of the network. The time-averaged growth and photon uptake rates were used to constrain the model and the maximal amount of ATP hydrolysis was calculated (Figure S3) for each batch experiment. A plot of growth rate versus maximum ATP hydrolysis flux was generated and a linear fit used to estimate the GAR and NGAR values [28]. Specifically, the slope of the fitted line is the GAR (544 mmol·g−1 AFDW·h−1), and the y-intercept is NGAR (2.8 mmol·g−1 AFDW·h−1). The estimated GAR value is significantly higher than those reported from other bacteria [29]; however, these model estimates assume that all absorbed photons lead to photosynthetic fluxes (100% quantum efficiency) and that the overall efficiency of ATP production via all electron transfer reactions (photosynthetic and respiratory) are accurate. Depending on the growth condition the quantum yields can change, and for Cyanothece 51142 this value was reported to be between ∼70–100% for photoautotrophic growth [23]. Upon further analysis, we found the estimated Cyanothece ATP requirements were most sensitive to reductions in quantum efficiency and the amount of ATP generated by photosynthesis and respiration (Table S4). Since neither quantum efficiency nor combined photosynthetic and respiratory ATP production were experimentally measured for Cyanothece 51142, the original estimates, GAR = 544 and NGAR = 2.8 were used in all growth simulations.