A data integration and visualization resource for the metabolic network of Synechocystis sp. PCC 6803.

Plant Physiology Preview. Published on January 13, 2014, as DOI:10.1104/pp.113.224394

Running head: Graphical Mapping of Synechocystis Metabolism Corresponding author: Bas Teusink Address: VU University, De Boelelaan 1085, 1081 HV, Amsterdam, The Netherlands Telephone number: +31 20 59 89435 E-mail address: [email protected] Research Areas: (i) Breakthrough Technologies (ii) Systems and Synthetic Biology

Downloaded from www.plantphysiol.org on July 8, 2014 - Published by www.plant.org Copyright © 2014 American Society of Plant Biologists. All rights reserved.

Copyright 2014 by the American Society of Plant Biologists

A Data Integration and Visualization Resource for the Metabolic Network of Synechocystis sp. PCC 6803 Timo R. Maarleveld1-3, Joost Boele2, Frank J. Bruggeman2,4, and Bas Teusink2,4

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Life Sciences, Centrum voor Wiskunde en Informatica (CWI), Science Park 123, 1098

XG Amsterdam, The Netherlands 2

Systems Bioinformatics, Amsterdam Institute for Molecules Medicines and Systems

(AIMMS), VU University Amsterdam, De Boelelaan 1085, 1081 HV, Amsterdam, The Netherlands 3

BioSolar Cells, P.O. Box 98, 6700 AB Wageningen, The Netherlands

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Kluyver Centre for Genomics of Industrial Fermentation / NCSB, P.O. Box 5057, 2600

GA Delft, The Netherlands

One-sentence summary: Data integration and visualization in systems biology is greatly facilitated by the development of an interactive graphical map of the metabolism, which we illustrate with the model cyanobacterium Synechocystis sp. PCC6803.


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TRM acknowledges funding from the BioSolar Cells project, co-financed by the Dutch Ministry of Economic Affairs. BT and JB acknowledges the Centre for Integrative Bioinformatics VU (IBIVU) and the Amsterdam Institute for Molecules, Medicines and Systems (AIMMS) for support. FJB acknowledges funding of NWO-VIDI Project 864.11.011. BT further acknowledges the Netherlands Genomics Initiative and ZonMW for Zenith grant 40-41009-9810038. Corresponding author: [email protected] The author(s) responsible for distribution of materials integral to the findings presented in this article in accordance with the Journal policy described in the Instructions for Authors (http://www.plantphysiol.org) is (are): B. Teusink ([email protected]). Downloaded from www.plantphysiol.org on July 8, 2014 - Published by www.plant.org Copyright © 2014 American Society of Plant Biologists. All rights reserved.

Abstract Data integration is a central activity in systems biology. The integration of genomic, transcript, protein, metabolite, flux, and computational data yields unprecedented information about the system level functioning of organisms. Often, data integration is done purely computationally, leaving the user with little insight besides statistical information. In this article, we present a visualization tool for the metabolic network of Synechocystis PCC6803, an important model cyanobacterium for sustainable biofuel production. We illustrate how this metabolic map can be used to integrate experimental and computational data for Synechocystis systems biology and metabolic engineering studies. Additionally, we discuss how this map, and the software infrastructure that we supply with it, can be used in the development of other organism-specific metabolic network visualizations. Besides a Python console package VoNDA (http://vonda.sf.net), we provide a working demonstration of the interactive metabolic map and the associated Synechocystis genome-scale stoichiometric model, as well as various ready-to-visualize microarray data sets, at http://f-a-m-e.org/synechocytis.


Introduction The recent advances in high-throughput experimental technologies and whole genome-sequencing resulted in an explosion of biological data availability. These genome-scale data sets are generally described as “omics” data sets of which transcriptomics, metabolics, and fluxomics data are examples (Gstaiger and Aebersold, 2009, Wang et al., 2009, Kahn, 2011, Grabherr et al., 2011). Analyses and integration of these data types can reveal important information about the system level functioning of organisms (Berger et al., 2013) and these methods are having a large impact on biology. To gain additional insight into cellular behavior, data sets are also used to develop computational models at a genome-scale level. Both the central role of metabolism for maintenance of cellular integrity and growth, and the availability of genome-scale data sets led to an increased interest in genome-scale reconstructions of metabolism. These reconstructions are based on the metabolic reactions that they can perform, given the content of their genome (Oberhardt et al., 2009, Thiele and Palsson, 2010). Essentially all metabolic reactions are catalyzed by enzymes, but for most reactions, the enzyme kinetics are unknown. Consequently, genome-scale reconstructions are often solely based on the reaction stoichiometry, and they are therefore generally referred to as genome-scale stoichiometric models (GSSMs) of metabolism. To leverage the biological information that is included in GSSMs and to compute physiological properties, various stoichiometric network analysis tools are available (Price et al., 2004, Maarleveld et al., 2013). These tools can be used to computationally study the growth characteristics of micro-organisms in detail. For instance, Flux Balance Analysis (FBA) and Flux Variability Analysis (FVA) have been used to predict the internal flux distribution and its variability while optimizing for cellular growth yield (Mahadevan and Schilling, 2003, Orth et al., 2010, McCloskey et al., 2013). Stoichiometric computational methods can also directly be of practical value. For example, OptKnock (Burgard et al., 2003) can be used to predict which gene knockout strategies would result in increased production of metabolites of interest, such as biofuels. The analysis of GSSMs results in large data sets that often contain thousands of reactions. These results come in the form of long lists of (reaction) identifiers and (flux) values. This makes the interpretation of these data—which often come in files comprising thousands of lines—a rather tedious process. Yet to be of any biological value, interpretation of e.g. FBA results in terms of biological functions, modules, and pathways is required. To achieve this, adequate visualization is paramount, and attempts to visualize genome-scale data have indeed been made. These approaches can be classified into supervised (human-driven) and non-supervised (fully automatic) ones. The latter approach, fully automated mapping of genome-scale models, results in (mathematical) graphs that are even more difficult to interpret than the raw data one set out to visualize on them, so we will not discuss them in detail here. Human-driven visualization methods, on the other hand, are considerably more popular, as hand-drawn maps are markedly easier to interpret. Tools like KEGG (Kanehisa and Goto, 2000), Cytoscape (Smoot et al., 2011), CellDesigner (Funahashi et al., 2003), and the COBRA Toolbox (Schellenberger et al., 2011) each offer their own particular take on modeling and visualization. Nevertheless, since none of them was originally designed for visualizing genome-scale models or genome-scale data, they are of limited use for this purpose. This becomes clear when one attempts to share, extend, or adapt an existing map—commonly, users who attempt this quickly find their progress obstructed by the need for special software, incompatible identifiers, or simply by not being able to access an editable version of the desired image. Understandably, these issues have thus far prevented the wide-spread communal use of visualization aids, as well as


their use in combination with computational results. However, the increased availability of high-throughput data makes the lack of adequate graphical interpretation tools more dearly felt than ever. Here, we address this problem by presenting the first comprehensive data visualization tool, namely, a graphical map of metabolism that is capable of displaying any type of data that is associated with reaction identifiers, gene identifiers, or metabolite identifiers. Based on the increased interest in photosynthetic micro-organisms and its importance as a model-organism for metabolic engineering studies, we decided to base our graphical metabolic map on the metabolic reconstruction of Synechocystis PCC 6803 (hereafter Synechocystis) (Shastri and Morgan, 2005, Knoop et al., 2010, Nogales et al., 2012, Saha et al., 2012), the best characterized and most extensively studied cyanobacterium. The direct conversion of carbon dioxide (CO2) into carbon compounds makes these micro-organisms of great interest for the development of production techniques for third-generation biofuels (Chisti, 2007, Ducat et al., 2011, Angermayr et al., 2012). We illustrate the use of the map of this organism’s metabolism and discuss how similar maps can be made for other organisms using the Synechocystis map as a template. Our map is also directly useful for other (micro-)organisms, since the metabolic core (e.g. the carbohydrate, energy, amino acid, and nucleotide metabolism) is highly conserved between different micro-organisms (Peregrin-Alvarez et al., 2009). Therefore, the map can easily be extended to cover additional pathways or organisms. This process will become progressively easier as more reactions are added to the map, as any added reactions can in turn be used in further mapping efforts. To demonstrate the usefulness of our graphical map, we have integrated it into FAME, the Flux Analysis and Modeling Environment (Boele et al., 2012) at http://f-a-m-e.org/synechocystis. Here, our map can be readily used for the analysis and visualization of the genome-scale model of Synechocystis, which also comes pre-loaded. The map and a Python console application of our visualization software VoNDA, Visualization of Network DAta, can also be downloaded separately from http://vonda.sf.net. The console application can be used to visualize the output of popular constraint-based modeling software such as the COBRA Toolbox. It can also be used to take advantage of the recent development of constraint-based modeling software in Python. Using VoNDA in combination with PySCeS-CBM (Olivier et al., 2005) or COBRApy (Ebrahim et al., 2013) allows for (interactive) modeling and visualization in Python.

Results and Discussion Synechocystis’ metabolic map We have developed a comprehensive metabolic map of Synechocystis (Figure 1A). It is based on the metabolic reconstruction of Nogales et al. (2012), which we extended with reactions from Knoop et al. (2010) and other sources (documented in Supplemental File S1). The extended model contains 904 reactions and 816 metabolites. The associated map provides an overview of the reactions in the model and the resulting metabolic pathways. Together, the model and the map act as a readily extensible framework for human-friendly visualization of simulation results or experimental data (Figure 1B-C). Throughout this project, we aimed at offering an environment that can easily be extended with additional data and analysis types. To ensure its applicability to a maximum number of data and analysis types, as well as to maximize its potential for re-use and adaptation by the cyanobacterium community or other communities, a number of considerations were made during the development of the graphical map of Synechocystis’ metabolism. An essential feature is that simulation results, such as Flux Balance Analysis (FBA) or Flux Variability Analysis (FVA), can be displayed within the tool that generated them (Figure 1A-B). Moreover, experimental data, such as various flavors of


omics’ data sets, can be displayed (Figure 1C). This was achieved by ensuring that the identifiers of the reactions, metabolites, and genes in the underlying model match those on the map. Such an annotation link is essential, and as long as experimental data sets use the same identifier classification, they can be visualized. In our map, the underlying identifiers can be easily detected by a mouseover of the element of interest in a web-browser. We used the Scalable Vector Graphics (SVG) format to represent the map, as this allowed us to make it interactive. Specifically, we included hyperlinks that can direct the user towards sources of additional information (e.g. KEGG) or, when FAME is used to generate simulation results and display them on the map, to details about the run that generated the displayed results. Had we utilized one of the budding visualization standards like the Systems Biology Graphical Notation (SBGN) (Novere et al., 2009) or SBML layout (Gauges et al., 2006), this functionality would require the user to install additional third-party software simply to view the image. Using SVG directly allows the user full graphical control over the image and allows direct embedding of dynamic, interactive elements that point to metabolite and reaction resources. The SVG format is commonly used on the web and popular image manipulation software (such as InkScape or Adobe Illustrator) can import SVG files. Therefore, these files can be used for figures in scientific publications. Using SVG also makes sure our map is readily extensible with new reactions or visualization applications. Moreover, the format is programmatically accessible, which allows for extraction and visualization of subnetworks of the entire metabolic map, or development of a new metabolic map for another organism, using this map as a blueprint. To demonstrate that our tool greatly simplifies the analysis of genome-scale data sets, we will provide two example use cases. One example concerns the physiological adaptation of cyanobacteria to changing environmental conditions. The other illustrates the expansion of the map with new reactions for biofuel production. Given GSSM constraints such as light availability, FBA is generally an accurate method to predict the optimal product or biomass yield. However, since these GSSMs are typically large, the associated metabolic flux distributions are large, which makes them difficult to analyze in detail. To exemplify this, FVA done on our GSSM predicts that, in an optimized GSSM, about 600-700 reactions can carry a flux during light and dark conditions. Our metabolic map allows for visualization of these FBA and FVA predictions, which allows for direct insight into, for instance, unexpected and interesting behavior.

Visualizing the physiological adaptation to light and dark conditions We will provide an example of unexpected behavior that can be found by modeling cyanobacteria in different environmental conditions in this section. Cyanobacteria such as Synechocystis are subject to a diurnal light-dark cycle and adjust their metabolism via circadian control. In the GSSM, we can simulate these conditions by changing the availability of light (photon influx) and adjusting the model constraints to indicate whether glycogen is to be produced as part of biomass (see Supplemental File S2 for details). In Figure 2, we illustrate FBA predictions of the metabolic activity under light and dark conditions. We limit ourselves to the visualization of the metabolic surroundings of ribulose-1,5-bisphosphate carboxylase oxygenase (RuBisCO). RuBisCO is a well-known key enzyme for carbon fixation during photo-autotrophic growth, which carboxylates or oxygenates D-ribulose 1,5-bisphosphate. The carboxylation process (carbon fixation) yields two molecules of 3phosphoglycerate (3PG), while the oxygenation process (photorespiration) results in one molecule each of 3PG and 2phosphoglycolate (2PG). 2PG is toxic to Synechocystis and inhibits proper functioning of the Calvin-Benson cycle (Bauwe et al., 2012) and, as a result, reduces the conversion of solar energy into chemical energy. Nevertheless, both carbon fixation Downloaded from www.plantphysiol.org on July 8, 2014 - Published by www.plant.org Copyright © 2014 American Society of Plant Biologists. All rights reserved.

and photorespiration are essential for cyanobacteria in photo-autotrophic growth conditions (Eisenhut et al., 2008). We predicted steady-state flux values through this segment of metabolism by using FBA to optimize the biomass synthesis rate. Note that this rate optimization is carried out relative to other fixed nutrient uptake reaction rates and that we are effectively optimizing flux ratios (yields) rather than rates. Now, instead of working one’s way through a list of about 1000 predicted flux values for unexpected behavior, our map directly shows that both FBA and FVA predict that, besides carbon fixation, photorespiration is essential for obtaining an optimal growth yield in light and dark conditions (Figure 2A– B). The reason is that, in the metabolic model, photorespiration is the only way to produce glyoxylate, which is necessary for the production of the amino acids cysteine, glycine, and serine. As “dark photorespiration” is not generally considered plausible, an alternative was proposed by Knoop and coworkers (2010). They added the conversion of L-proline to hydroxyproline (identified by a BLAST search (Altschul et al., 1997)) to provide a second, perhaps more plausible mechanism for glyoxylate synthesis in dark conditions. After inclusion of this reaction, our computational analysis showed that in light conditions no activity was predicted for the conversion of Lproline to hydroxyproline due to a lower carbon efficiency. According to FBA simulations, glyoxylate synthesis via Lproline breakdown would be more efficient in dark conditions (Figure 2C). Besides directing fixated carbon towards the production of precursors for (immediate) growth (e.g. nucleic acids or amino acids), Synechocystis also synthesizes the storage compound glycogen (Figure 2A). During photo-autotrophic growth, this results in a sub-optimal growth strategy as a lower growth rate will be obtained. In the absence of light, Synechocystis consumes the stored glycogen via either fermentation or respiration, depending on the availability of O2. In the absence of light and presence of O2, FBA predicts the breakdown of previously stored glycogen and energy generation via respiration. This enables the organism to maintain its integrity and grow under dark conditions. If the Synechocystis’ maintenance requirements in the absence of light were known, modeling could help determine the minimal glycogen amount that should be stored during the light phase. This could represent an optimal strategy for this organism—optimize glycogen storage during the day to grow as fast as it can and then survive the night on the stored glycogen.

Exploring the properties of Synechocystis photosynthesis Besides photorespiration, photosynthesis is another key part of Synechocystis’ metabolism that is essential for growth. We studied the flexibility of this metabolic subnetwork by maximizing the specific growth rate as a function of the bicarbonate (HCO3-) and photon uptake rates (Figure 3A). The specific growth rates we predict are in agreement with experimentally measured rates (Shastri and Morgan, 2005). We subsequently selected three combinations of bicarbonate and photon uptake rates for further exploration (Figure 3A). Briefly, these are: i. a state in which carbon and light are both limiting for growth (carbon and light limiting state, CLLS), ii. a state in which light, but not carbon limits growth (light limiting state, LLS), and iii. a state in which carbon, but not light limits growth (carbon limiting state, CLS). We give a detailed overview of the characteristics of these conditions in Supplemental File S2, and of the FBA predictions under each condition in Table I. In this section, we will discuss some analysis results that illustrate the practical application of our tool. Visualization of FBA predictions on our metabolic map immediately shows that the model predicts different photosynthesis strategies under different conditions, indicating the flexibility of this system. The predictions in the CLLS (Figure 3B) and LLS (Figure 3C) are conceptually identical, although the predicted absolute fluxes are different. However, the two conditions can be distinguished on the basis of carbon secretion: The CO2 that cannot be fixed in the LLS is released by the cell. Downloaded from www.plantphysiol.org on July 8, 2014 - Published by www.plant.org Copyright © 2014 American Society of Plant Biologists. All rights reserved.

By comparing the visualization of photosynthetic fluxes, it is readily apparent that a different strategy is exploited in the CLS (Figure 3D). Excess photons are used to transfer protons from the cytosol to the thylakoid, a process which is carried out by quinol oxidase (Cytochrome BD). These protons are then used to generate ATP. This explains the relatively low flux through photosystem I compared to photosystem II (JPSI/JPSII) and relatively high ATP formation flux compared to the NADPH formation flux (JATP/JNADPH); more details can be found in Table I). The resulting ATP is subsequently used in metabolism. FVA results (not shown) suggest that quinol oxidase is not essential for obtaining a maximal biomass yield. For example, the Mehler reaction can be used to oxidize the excess NADPH to NADP+ (Figure S-1A); in that case, JPSI/JPSII=1 and JATP/JNADPH=1.28 indicating linear electron flow. Besides the photosynthetic linear electron flow, different alternative electron flow (AEF) pathways exist which allow for different ATP/NADPH ratios (Kramer and Evans, 2011). From Figures 3B-D we can immediately conclude that these AEF pathways are active during (these instances of) conditions of optimal growth yield. Indeed, when the AEF pathways are removed from the model, the predicted growth yield is lower, indicating that AEF pathways are essential if maximum yield is to be attained under these conditions (Figure S-1B). Apart from plotting model predictions on the map, we can use the tool for visualization of many more data types. In Figure 4 we use microarray data under conditions of carbon limitation (similar to the conditions modeled above). Visualizing this type of data allows for convenient identification of genes that are over- or underexpressed compared to wild-type, in a pathway-specific manner. For instance, compared to wild-type, our visualization shows that many genes associated to photosynthesis and respiration are initially underexpressed (Figure 4A) upon CO2 limitation. The transcriptomics data are from a time-series, whereas predictions from FBA assume steady state; therefore, these data sets are not directly comparable. After 12 hours of CO2 limitation, the situation most likely represents a steady-state condition and it can therefore be compared with FBA predictions. When this comparison is made, 73% of the photosynthetic gene expression data agree with predicted fluxes, while 27% do not match the flux predictions. For instance, genes associated with linear electron flow were underexpressed (Figure 4B). Indeed, FBA results visualized in Figures 3B and 3D show that lower fluxes are predicted in most reactions associated to the linear electron flow. In contrast, a decrease in ATPase gene expression was found under CLS conditions, whereas FBA/FVA predicted a higher flux through this enzyme. The gene expression data also indicate increased expression of NDH-1 and NDH-1 type 3. While FBA does not predict a flux through these reactions in CLS conditions, FVA results indicate that a flux through NDH-1 related pathways would decrease the growth yield in LLS, but not in CLS conditions. Thus, the NDH-1 gene expression data, too, are consistent with the predicted solution space for Synechocystis’ photosynthesis fluxes.

Expansion of the map with a biofuel production pathway To illustrate how the metabolic map can be extended or modified to other micro-organisms, we added a biofuel production pathway to the map and model. Recently, the development of a Synechococcus elongatus PCC 7942 strain that produces the bulk chemical 2,3-butanediol (23BD) was reported (Oliver et al., 2013). The production of 23BD using cyanobacteria offers several benefits compared to other higher alcohols. For instance, 23BD has a low host toxicity, which allows for a higher biofuel product concentration. Moreover, the common metabolite pyruvate is used as a substrate for 23BD production, and NADPH can be utilized as a reducing agent; no conversion of NADH to NADPH is necessary. Finally, catabolic enzymes from O2-tolerant organisms can be used for the production of 23BD, which is important because O2 is produced by Downloaded from www.plantphysiol.org on July 8, 2014 - Published by www.plant.org Copyright © 2014 American Society of Plant Biologists. All rights reserved.

Synechocystis’ photosynthetic machinery. We therefore extended our map with a pathway that produces 23BD (see Materials and Methods and Figure S-2). We performed FBA on the model after adding the 23BD synthesis pathway, under the same conditions as above (i.e. CLLS, LLS, and CLS) (Figure 5). Ideally, cyanobacteria can be used as cell factories (Angermayr et al., 2012), i.e., a nongrowing population that catalyzes the conversion of CO2 into products of interest. To account for biomass replenishment and for the fact that a little growth will always occur, we fixed the growth rate to a basal growth level of 10% of its maximum specific growth rate. We then optimized for 23BD production (Figure 5A). Given these conditions, our GSSM predicted 23BD production rates up to 0.90 mmol gDW-1 h-1. Interestingly, the ratio of RuBisCo carboxylase activity over the photon uptake flux was about 1.5 times higher when we optimized the flux to 23BD compared to optimizing the specific growth rate (Table 1). Our metabolic map allowed for a straightforward identification of different factors (Figure S-3) that contributed to this difference. Firstly, HCO3- is not entirely converted into CO2 when biomass is synthesized. Instead, a substantial part (7%) of HCO3- is used for the synthesis of malonyl-CoA, an essential precursor for fatty acid biosynthesis. Secondly, the two reactions converting pyruvate to R-acetoin (the precursor of 23BD) release CO2, which is subsequently fixed in the Calvin cycle. Lastly, it takes fewer photons to optimize the flux to 23BD (compared to optimizing for biomass production). The production pathway of 23BD we added to the model requires NADPH, but not ATP (see Figure S-2B). Therefore, we expected a lower JATP/JNADPH ratio compared to the optimization of specific growth rate. Our simulations show that, when optimizing for 23BD production, the percentage of JPSI devoted to AEF pathways is much lower for both the CLLS and LLS (Figures 3B-C vs. Figures 5B-C). As a result, indeed, optimization of 23BD production required a lower JATP/JNADPH ratio. This ratio is relatively close to the ratio that is obtained via a linear electron flow. Therefore, one may expect that the AEF knock-out yields a similar flux towards 23BD. However, surprisingly, an AEF knock-out in the CLLS and LLS resulted in a decrease of 12% in 23BD production production rate. As it turns out, the relatively small fraction of flux through the AEFs is eventually used for HCO3- uptake. Without the AEF pathways, less HCO3- can be imported into the cell, which results in a lower production of 23BD. In contrast, in the CLS, where light is available in excess, many metabolic routes will lead to an optimal FBA solution, even though the objective that is optimized for may differ—in the case of our simulations, the objective was either growth (Figure 3D) or biofuel production (Figure 5D). Consequently, the characteristics of the system in a carbon limiting state are quite similar between these two objectives (cf. Figures 3D and 5D).

Implementation With the development of the first interactive map of cyanobacterial metabolism, we provide the community with a graphical means of interpreting the large amounts of data generated by high-throughput experimental or computational methods. Given the absence of commonly available visualization options for results of analyses of genome-scale models, our main aim was the creation of an immediately useful map of Synechocystis metabolism. Visualization of heterogeneous data on this map is enabled by the standalone VoNDA package or the integration into FAME. Besides providing a pathway-oriented, human-friendly overview of the contents of the genome-scale metabolic model of Synechocystis, we included a number of features in our graphical map that will facilitate interpretation despite not being


common practice in current-day visualization efforts. An important one is the choice to include multiple instances of common metabolites, such as ATP, ADP, NAD(P)(H), and other co-factors. These metabolites are included on the map in various locations, which greatly reduces the clutter that would arise if only one instance of each were drawn (graph based visualization solutions tend to do this). Other helpful features are hidden when they are not in use, but deserve a brief explanation here. Space has been reserved on the map to display flux values for reactions and the direction in which the reaction takes place. An example are reactionassociated squares, which can be used for the visualization of gene expression data (Figure 1C and Figure 4). Each metabolic reaction has three such squares associated with it; if a reaction has more than three genes associated with it, we visualize the three genes with the highest differential expression value. Furthermore, when displaying run results generated in FAME, metabolite names link to a page with a list of fluxes that produce and consume the respective metabolite. Finally, when displaying run results, the lines that represent reactions are color-coded to represent the magnitude of the flux through them (or the variability of this flux, in the case of FVA results), and an explanatory color bar is displayed at the top of the image.

Exploiting Synechocystis’ metabolic map for other micro-organisms To alleviate the effects of our design decisions on users that would have favored another approach, we created the entire map in SVG, an open source image format. Our GSSM uses the BiGG nomenclature (Schellenberger et al., 2010), and therefore, our map is of direct value for all GSSMs that utilize this commonly used nomenclature. In addition, all identifiers on the map can be easily migrated to cognate identifiers in another nomenclature system (e.g. (Lang et al., 2011, Bernard et al., 2012, Kumar et al., 2012)). More importantly, the map can be modified to suit different species than Synechocystis. The addition of reactions and metabolites to the map can be done using either text or image manipulation software; when using an image editor, one must make sure that each new element is somehow assigned the correct identifier. We provide an online SVG-editor in FAME, which allows for easy manipulation of the metabolic map, including any results superimposed on it. While the quality of any customized map will be strongly dependent on the knowledge and skill of whoever modified it, and of the quality of the model it describes, this work presents the community with a solid basis from which to start this process. We provide an example of how one can go about this in Materials and Methods and in Figure S-2. Our demonstration of the expansion of the map and model with a biofuel production pathway not only illustrates the extensibility of our work, but also how adequate visualization can lead to novel biological insights. Regardless of any discussion about its implementation, the presentation of the first map of metabolism that is both computer-friendly and human-friendly will surely help advance our understanding of Synechocystis, but also of other organisms to which the map can be adapted. By offering this resource not only as a flat file, but also as an integrated part of an interactive online analysis environment, we hope it will be of even greater use to the community.

Conclusion The increased pace at which experimental data can be generated has fueled genome-scale research efforts, which presently call for genome-scale interpretation efforts. For the case of Synechocystis, an important model organism for biotechnological applications, we now enable the interpretation of these data from a biological (pathway) perspective by Downloaded from www.plantphysiol.org on July 8, 2014 - Published by www.plant.org Copyright © 2014 American Society of Plant Biologists. All rights reserved.

presenting the first comprehensive graphical map of its metabolism. This is the first results interpretation tool that is graphical, interactive, machine-friendly, and, importantly, extensible. We expect that it will add great value to the community of cyanobacterium researchers, but also to the scientific community as a whole.

Materials and Methods Adaptation of the Synechocystis model and creation of the corresponding map We present a metabolic map and a corresponding stoichiometric model of Synechocystis metabolism consisting of 904 reactions, 816 species, and 686 genes. The model we present here is an extension of the iJN678 model described in (Nogales et al., 2012). Pathways that were amended include the tricarboxylic acid cycle (Zhang and Bryant, 2011), arginine metabolism (Schriek et al., 2007, 2008, 2009), and proline metabolism (Knoop et al., 2010). A full list of the modifications we made can be found in Supplemental File S1. For example, we modified the succinate dehydrogenase reactions in iJN678 to ensure that FADH2 can be synthesized and degraded (originally, iJN678 was unable to degrade FADH2). This modification also allowed us to extend arginine metabolism, where FADH2 is synthesized. To facilitate the interpretation of results deriving from the model, we decomposed the biomass function into reactions towards nine major components (e.g. DNA, RNA, and protein). This allows users to track the uptake and destination of each of these components individually (Figure S-2). In spite of its usefulness to biotechnology, the biological relevance of 23BD to organisms is relatively low, which makes it a rare find on metabolic maps, and our initial version of the Synechocystis map was no exception. The first reaction, the conversion of two pyruvate molecules into 2-acetolactate and CO2 is an essential metabolic reaction and was therefore already on our map. We added two new reactions to our map (R_ACLDC and R_23BDD), as well as the metabolites they connect (R-acetoin and 23BD). We also added exchange reactions to the map and model. In total, we added 44 reactions and 22 species, removed 7 reactions, and we revised incorrect mass and charge balancing for 19 reactions. We refer to our new version of the model as iTM686 (Supplemental File S3 and S4; the model can be accessed or downloaded in the SBML Level 3 format at http://f-a-m-e.org/synechocystis. The model’s constraints and objectives are represented using features from the recently launched Flux Balance Constraints package (http://sbml.org/Documents/Specifications/SBML_Level_3/Packages/Flux_Balance_Constraints_(flux)).

Analyses performed on the model We analyzed all instances of the Synechocystis model using Flux Balance Analysis (FBA) and Flux Variability Analysis (FVA). As these analyses hinge upon the quasi-steady-state assumption, they can only predict steady-state flux distributions. In-depth descriptions of these computational methods are abundant in literature. We therefore refer to (Price et al., 2004), (Orth et al., 2010), and (Santos et al., 2011) for an overview of the technical and methodological details. Throughout this article, wherever we mention performing an FBA, this FBA was performed with the additional objective of minimizing the absolute sum of fluxes to obtain a more biologically sensible solution. The details of all simulations, including the constraints and objectives that were used are described in Supplemental File S2.


Integration with FAME To demonstrate the functionality of the map as well as to facilitate its use, we have added a Synechocystis-specific section to FAME, the Flux Analysis and Modeling Environment (Boele et al., 2012). Visiting http://f-a-m-e.org/synechocystis causes the Synechocystis genome-scale model (iTM686) and our map to be loaded. The user is then able to view, edit, and run the model, and to have analysis results visualized on the map we present in this article. Though the ability to visualize results on custom-made maps was already present in FAME, the URL described above pre-loads the model and map and eliminates the need for loading several files separately. In addition, we have expanded FAME with an in-browser image editor, and with the ability to visualize gene expression data from e.g. microarray profiling experiments on maps that support this kind of data. We have pre-loaded the results of 98 experiments from the CyanoExpress microarray dataset (Hernandez-Prieto and Futschik, 2012) into the Synechocystis section of FAME. These data sets can be selected and superimposed on the map for analysis, as a demonstration of the ability of the Synechocystis map to display gene expression data as well as flux analysis results.

Acknowledgments The authors would like to thank Brett Olivier, Pascal van Alphen, Philipp Savakis, Andreas Angelmayr, and Klaas Hellingwerf for helpful discussions and tips for developing the data integration and visualization tool.


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Figure Legends Figure 1: An overview of the graphical map of Synechocystis’ metabolism and its capabilities. Various elements (reactions, metabolites, squares) are clickable and direct the user to e.g. the KEGG database, or to run result details. (A) When displaying FBA results, the arrows denoting reactions are colored according to the color scale in the upper left corner. Gray reactions carry no flux. (B) Detailed view of a section of the map when it is used to display FBA results. (C) Detailed view of the same section of the map as in panel B, but now it is being used to display gene expression results. Here, the reaction lines are only displayed to indicate the general structure of the metabolic map, whereas the gene expression information is conveyed by colored boxes next to each reaction for which gene association information is available.

Figure 2: Example use cases of the map. Visualization of the difference in flux distribution when growing in the presence (A) or absence (B, C) of light. When light is present (A), the FBA solution harbors no surprises. However, when light is absent (B), FBA predicts that photorespiration is active, which is unlikely. (C) Adding a reaction that converts L-proline to hydroxyproline returns the model to its expected state under dark conditions. Note that some glyoxylate is still produced from other precursors than glycolate (not shown here), to enable the production of amino acids.

Figure 3: We used FBA to explore the properties of the Synechocystis model and map under various conditions, i.e. carbon and light limiting state (CLLS), light limiting state (LLS), and carbon limiting state (CLS). (A) Predicted growth rate as a function of carbon and light uptake rates. The black dots in the phase plane indicate the conditions (in terms of photon flux and HCO3- uptake) that are shown in panels B-D. (B-D) Visualization of predicted FBA fluxes for the three scenarios (CLLS, LLS, and CLS). Arrows indicate flux direction; the color bar indicates flux magnitude.

Figure 4: To demonstrate the applicability of our map to gene-associated (“omics”-size) data, we used it to visualize microarray data for various CO2 limiting growth conditions. The colored squares indicate differential expression relative to wild-type; yellow corresponds to overexpression and blue corresponds to underexpression. The panels depict differential gene expression after 1 hour (A), 12 hours (B) of CO2 limitation, compared to wild-type Synechocystis. Figure 5: We used FBA to explore the properties of the Synechocystis model and map under various conditions (i.e. carbon and light limiting state (CLLS), light limiting state (LLS), and carbon limiting state (CLS)), while optimizing for 23BD production. (A) Predicted flux to 23BD as a function of carbon and light uptake rates. (B-D) Visualization of predicted FBA fluxes for the three scenarios (CLLS, LLS, and CLS, respectively) indicated in panel A. Arrows indicate flux direction; the color bar indicates flux magnitude.


Tables Simulated

JPSI/JPSII

JATP/JNADPH

JRuBisCo,CO2/Jhv

µ (h-1)

J23BD

CLLS

0.99

1.6

0.085

0.045

0

CLS

0.058

7.2

0.011

0.012

0

LLS

0.99

1.6

0.085

0.017

0

KO AEF

1.0

1.28

0.075

0.040

0

Biofuel

0.99

1.3

0.13

0.0045

0.427

Biofuel CLS

0.058

8.1

0.014

0.0012

0.113

Biofuel LLS

0.99

1.3

0.13

0.0017

0.203

Biofuel KO AEF

1.0

1.28

0.11

0.0045

0.37

conditions

Table I: A summary of the results of various (flux balance) analyses on the Synechocystis model. When optimizing for biofuel production, we fixed the growth rate to 10% of its maximum. The constraints that were used for each of these simulations can be found in Supplemental File S2. PSI: Photosystem I. PSII: Photosystem II. Jhv: Photon flux. CLLS: Carbon and light limiting state. CLS: Carbon limiting state. LLS: Light limiting state. KO AEF: Knock-out of alternative electron flow pathways.


A

4.94e-8

7.75e-6

0.00122

0.191

Photosynthesis Oxidative Phosphorylation

NADP+

NAD(P)H

FADH2 Q8

NAD(P)H

Q8H2

ADP

Cyt C

L-Arginine[e]

L-Arginine[p]

L-Arginine

Glycine[e]

Glycine[p]

Glycine

Ø

L-Glutamate[e]

L-Glutamate[p]

L-Glutamate

Ø

L-Glutamine[e]

L-Glutamine[p]

L-Glutamine

Ø

L-Histidine[e]

L-Histidine[p]

L-Histidine

Ø

L-Leucine[e]

L-Leucine[p]

L-Leucine

4-Hydroxyphenyl pyruvate

L-Proline[p]

L-Proline

L-Serine[e]

L-Serine[p]

L-Serine

hv[e]

hv[p]

H+[e]

H+[p]

Ø

H2O[e]

H2O[p]

O2[e]

O2[p]

O2

Ø

CO2[e]

CO2[p]

CO2

Ø

HCO3[e]

HCO3[p]

Ø

NH4[e]

NH4[p]

Ø

NO3[e]

NO3[p]

Glycolaldehyde

Urea[p]

Urea

SO4[e]

SO4[p]

SO4

Ø

Succinate[e]

Succinate[p]

L-Glutamate

PhPyr

Fumarate[e]

Chorismate

Acetate[e]

Acetate[p]

Ø

Citrate[e]

Citrate[p]

Ø

-kg[e]

-kg[p]

3-Dehydroshikimate

G3P

THF-L-glutamate

Folinic acid

Dihydrofolate

THF

10-Formyl-THF

Malate[e]

Malate[p]

Malate

Sucrose[e]

Sucrose[p]

Sucrose

GlcGlyc[e]

GlcGlyc[p]

GlcGlyc

Ø

Pyruvate[e]

Pyruvate[p]

Pyruvate

Ø

Fructose[e]

Fructose[p]

Fructose

Ø

Cyanate[e]

Ø

Cobalt[e]

Cobalt[p]

Cobalt

Ø

Pi[e]

Pi[p]

Pi

Ø

K+[e]

Ø

Na+[e]

Na+[p]

Na+

Ø

Fe2+[e]

Fe2+[p]

Fe2+

Cyanate[p]

K+[p]

gamma-Glutamyl cysteine

Glutathione(red)

Fe3+[e]

Fe3+[p]

Fe3+

Cu2+[e]

Cu2+[p]

Cu2+

-Ribazole

Ø

Ø

3,4-Dihydroxy mandelaldehyde

3,4-Dihydroxyphenyl ethyleneglycol

Peptidoglycan

S-Methyl-5-thio-Ru1P

6PG

6PGL

Gal1P

UAAGMDA

D-Glucosamine 6-P

M6P

Branched Glycogen

N-Acetyl-Dglucosamine-1-P

D-Glucosamine 1-P

M1P

DHAP

Glycolate

RuBP

PG3

Glycerate

PG2

2PG

Acetyl-serine

sn-Glycerol-3-P

Glycerol

Glyceraldehyde

NAD+

PPi

ATP

Pi

PRPP

Nicotinate ribonucleotide

Deamino-NAD+

1,5-Diaminopentane

Pr otein

26DAP_M

R-Acetoin

dCTP

dTMP

dUTP

dUMP

S-Lactoyl glutathione

PEP

F1P

26DAP_LL

THDP

Aspartate-SA

CDP

CMP

CTP

CYTD

UTP

CSN

2-C-Methyl-D-erythritol 4-P

1-deoxy-D-xylulose

(S)-2-Aceto-2hydroxybutanoate

PRPP

2-Dehydropantoate

α-Isopropylmalate

Dephospho-CoA

Pantoate β-Alanine AMP Pantothenate

Pantetheine 4'-P

4'-Phosphopantothenoyl L-cysteine

4'-Phosphopantothenate

Porphyrin and chlorophyll 5-amino levulinate

Porpho bilinogen

Hydroxy methylbilane

2-Isopropylmaleate

3-Isopropylmalate

Copropor phyrinogen I

Uroporphyr inogen III

Coproporphyr inogen III

L-Glutamate

Soluble Pool

Dethiobiotin

4-Methyl-2-oxopentanoate

Protopor inogen IX

Protopor phyrino IX

Mg-protopor phyrin IX

Mg-protopor phyrin IX monomethyl ester

NAD+

NADP+

NADH

Glutamyl-5-P

Glutamate-5-SA

P5C

NADPH

Biomass specific Gr owth Rate

Peptidoglycan

Precorrin 5

Succinate

Urea Cycle

Biliverdin

Bilirubin

SO4

Pyridoxal 5'-P

4-Guanidino butanamide

Pi

Acetylornithine

4-Guanidino butanal

FADH2 FAD Guanidino L-Arginine 2-oxopentanoate

Pyridoxine

Arginino-succinate

Pyruvate

S-Adenosylmethionine S-Methyl-5-thio-Ru1P

S-Methyl-5-thio-R1P Adenine

3hbCoA

AcCoA

malCoA

Acetoacetyl ACP

Heme A

Divinylproto chlorophyllide

Precorrin 6B

4.94e-8

Pr otein

7.75e-6

IDP

RNA

GTP

cGMP

0.00122

dGTP

IMP

SAICAR

Demethylphylloquinone

malACP 3-oxohexa noyl-ACP

Vitamin K1 3-oxoocta noyl-ACP

3-oxodecanoyl noyl-ACP

(R)-3-hydroxy hexanoyl-ACP

trans-Hex 2-enoyl-ACP

(R)-3-hydroxy octanoyl-ACP

trans-oct 2-enoyl-ACP

(R)-3-hydroxy decanoyl-ACP

trans-dec 2-enoyl-ACP

Hexanoyl-ACP

Octanoyl-ACP

Decanoyl-ACP

3-oxododeca noyl-ACP

3-oxotetra decanoyl-ACP

(R)-3-hydroxy dodecanoyl-ACP

trans-dodec 2-enoyl-ACP

HMA

trans-tetradec 2-enoyl-ACP

3-oxohexa decanoyl-ACP

3-oxoocta decanoylACP

(R)-3-hydroxy palmitoylACP

(R)-3-hydroxy octadecanoylACP

trans-hexadec 2-enoyl-ACP

trans-octadec 2-enoyl-ACP

Dodecanoyl-ACP Tetradecanoyl-ACP Hexadecanoyl-ACP Octadecanoyl-ACP

Lipid(16:0)

Lipid(18:0)

-linolenoilACP

Lipid(18:3[6,9,12])

Aminoacyl-tRNA biosynthesis

cis-octadec-9-enoyl ACP (n-C18_1)

linoleoyl-ACP (n-C18_2ACP)

Lipid(18:2[9,12])

Lipid(18:1[9])

-lineloyl-ACP (n-C18_3ACP)

Lipid(18:3[9,12,15])

tRNA-Glu L-Glutamate

L-Phenylalanine

PhPyr

L-leucine

L-valyl-tRNA(Val)

L-leucyl-tRNA(Ile)

tRNA-Ile L-Isoleucine

L-isoleucyl-tRNA(Ile)

-1.59

AMP

FAICAR

Adenosine

Pr otein

GTP

THF

-0.794

10-Formyl-THF cGMP

5-Amino-4-imidazole carboxyamide

0

IDP

RNA

GMP

GDP

1.18

ITP

XMP

Deoxyguanosine

dGDP

2.35

Adenylo succinate

dGTP

IMP

SAICAR P-ribosyl-CAminoimidazole

AMP

FAICAR

AICAR

Adenosine

THF 10-Formyl-THF


Pr otein

DNA

L-Glutamate

tRNA-Val L-Valine tRNA-Leu

L-Glutamyl-tRNA(Glu)

L-Glutaminyl-tRNA(Gln)

Pr otein

2-oxoglutarate

Chorismate

C

AICAR

P-ribosyl-CAminoimidazole

β-Tocopherol

1,4-Dihydroxy 2-naphthoate

(R)-3-hydroxy acyl-ACP

gray line: flux = 0

Adenylo succinate

XMP

Deoxyguanosine

dGDP

30

ITP

GMP

GDP

0.191

δ-Tocopherol

α-Tocopherol

Succinylbenzoate

Ions

B

2-Methyl-6-phytylquinol

γ-Tocopherol

Thiamine-PP 2-Succinyl-6-hydroxy 2,4-cyclohexadiene 1-carboxylate

5-Methylthioadenosine Spermidine Pi

Fatty Acid Biosynthesis

cis-dec-3-enoyl ACP (n-C10_1) malACP

NADPH

Phytyl diphosphate Homogentisate

Isochorismate Succinate semialdehyde-thiamin diphosphate anion

PHB granule

But-2enoyl-ACP

Butyryl-ACP

Protochloro phyllide

NADP+

Phytoene

Succinylbenzoyl-CoA

AaCoA

NADP+

NADPH

Glyoxylate

Agmatine

Urea Putrescine S-Adenosylmethioninamine

Pyridoxine 5'-P

NADPH

NAD+

4-Hydroxy 2-oxoglutarate

Urea

Acetyl-Glutamate-5-SA 4-Guanidino butanoate

Pr otein

4-(Phosphonooxy) L-threonine

Acetyl-ACP

Heme O

13(1)-Oxo-Mg protoporphyrin IX 13-monomethyl ester

Precorrin 6A

LPS

Pyridoxamine 5'-P

Pyridoxal

NADP+

NADH

Erythro-4 Hydroxyglutamate

L-Citrulline

L-Glutamate

NADPH

Phytofluene

Prephytoene-PP

PPi

2,3-Dimethyl 5-phytylquinol

1-Pyrroline-3-hydroxy 5-carboxylate

Carbamoyl-P

Acetyl-Glutamate-5-P

-Carotene

NAD+

Diploptene

Geranylgeranyl-PP

all-trans Isopentenyl-PP Decaprenyl-PP

Pr otein 2-oxoglutarate

Hydroxyproline

NADPH

Lipid Zn2+

Proto heme

NADP+

RNA

Pyridoxamine

L-Proline

L-Glutamate

Acetyl-Glutamate

Urea

Squalene

Carotenoid Metabolism

Fumarate

NADP+

PPi

Farnesyl-PP

HCO3

2-oxoglutarate

4-Aminobutanal

7,8-Diaminononanoate

Diplopterol

Geranyl-PP

PPi

Undecaprenyl-PP

L-Leucine

13(1)-Hydroxy-Mg protoporphyrin IX 13-monomethyl ester

Precorrin 4

DNA

Cu2+[t]

NADPH

DAMPP

Ornithine

Vitamin B6

APS

Pr otein

Pr otein

Precorrin 3B

Pr otein

2-oxoglutarate L-Isoleucine

Cyanate

Acetyl-Glutamate

S-Adenosylmethionine

AMP

Neurosporene 1-Hydroxy-2-methyl 2-butenyl 4-PP

Isopentenyl-PP

AcCoA

-Carotene

-Carotene

NADH

NH4

CO2

2-oxoglutarate

Echinenone

4-Methyl-5-(2-hydroxyethyl)-thiazole

Carbamoyl-P

CoA

Biotin

PAPS

Thioredoxin Reduced

(S)-3-Methyl-2-oxopentanoate L-Glutamate

2-oxoglutarate L-Valine

Precorrin 3A

L-Glutamyl tRNA(Glu)

(R)-2,3-Dihydroxy 3-methylvalerate Thioredoxin Oxidized

2-Ketovaline L-Glutamate

Precorrin 2

L-Glutamate 1-semialdehyde

Sulfite

Isoleucine Valine Leucine

(2S)-2-Isopropyl-3-oxosuccinate

Uroporphyr inogen I

PPi

Isopentenyl-PP

N-Carbamoyl L-aspartate

4-Aminobutanoate

-Cryptoxanthin

Thiamine

Succinate

L-Glutamine

Succinate-SA

Thiamine-PP

2-Phospho-4-(cytidine 5'-PP) 2-C-methyl-D-erythritol

L-Aspartate

L-Glutamate

S-Adenosylhomocysteine

Adenosine-3,5-PP

CoA

Thiamine-P

4-(Cytidine 5'-diphospho) 2-C-methyl-D-erythritol

L-Glutamate 2-oxoglutarate

(R)-2,3-Dihydroxyisovalerate

Zeaxanthin

4-methyl-5-(2-phos phoethyl)-thiazole

2-C-Methyl-D-erythritol 2,4-cyclodiphosphate

Succinyl-CoA

L-Methionine

Methionine Synthesis

2-oxobutanoate

L-Glutamate

G3P

1-Deoxy-D-xylulose-5-P

2-oxoglutarate

Succinate

Pr otein

L-Homocysteine

Fd(ox)

D-Glutamate

Fumarate Dihydroorotate

Ethanol

iso-Citrate

L-Cystathionine

Acetate

CO2

CO2

Orotate Acetaldehyde

Pr otein

Fumarate

S-Adenosyl L-homocysteine

H2S

L-Threonine

Thioredoxin Reduced

Fd(red)

Lycopene

Uracil

PPi PRPP

cis-Aconitate

TCA Cycle

Homoserine

Pyruvate

UDP UMP

Acetate Citrate

H2O D-Ribose

Orotidine-5-P PPi

Malate

β-aminopropion aldehyde

L-Glutamate

UDP-N-acetylmuramoyl L-alanyl-D-glutamate

2-Acetolactate

dUDP

Acetyl-P

Succinyl-CoA 23DHDP

NADPH

GDP-L-fucose

RNA

Lactate

Oxalo acetate

β-Alanine

NADP+ Thioredoxin Oxidized

UDP-3-O-( hydroxymyristoyl) N-acetylglucosamine

UDP-N-acetyl-3-(1-carbo xyvinyl)-D-glucosamine

Pyruvate

dCDP

Pyrimidine Metabolism

Pyruvate

L-Alanine

AcCoA 4-P-Aspartate

CoA

D-Ala-D-Ala

Undecaprenyl-P

dTDP

L-Aspartate

Iminoaspartate

SL2A6O

2-oxoglutarate Heptanedioate

DNA

Pr otein

DHAP

Quinolinate

SL26DA

L-Lysine

D-Alanine

L-Asparagine

NAD+

Sterol Metabolism dTTP

Methylglyoxal

H2S D-Ala-D-Ala

NMN

GDP-Mannose

Lipid A Disaccharide

HMA UDP-3-O-(3-hydroxy tetradecanoyl) D-glucosamine

UDP-MurNAc

UDP-N-acetyl D-glucosamine

GDP-4-dehydro D-rhamnose

3PHP Thymd

HPYR

Glycerone

Pyruvate

L-Serine

O2 NADH

Thiamine Metabolism

23DPG

2-Hydroxy-3-oxopropanoate

THF Pr otein

Nicotinamide metabolism

UDP-N-acetylmuramoyl-L-alanyl D-glutamyl-6-carboxy-L-lysyl D-alanyl-D-alanine

UAGMDA

Glycogen

ADP-Glc

G1P

N-Acetyl D-mannosamine

PPPi PPi H2O

2-Octaprenylphenol

UDP-2,3-bis(3-hydroxy tetradecanoyl) glucosamine ACP

UDP-N-acetylmuramoyl L-alanine

Glycolysis

Calvin Cycle

UDP-N-acetylmuramoyl L-alanyl-D-glutamate

PPi Pi

H2O2

D-Tagatose 1,6-biphosphate

2mahmp Glyoxylate

Glycine

5,10-Methylene-THF

Acetate

Galactose

Sucrose

Cyanophycin(n+2)

Pi

ADP

(S)-Propane-1,2-diol

LipidX

GlcGlyc

H2O

ATP

UDPMurAc(oyl-L-Ala-D-gamma Glu-L-Lys-D-Ala-D-Ala)

D-Tagatose 6-phosphate

CDP-Glc

FBP

G3P

L-Lactaldehyde UDP-N-acetylmuramoyl Lalanyl-D-glutamyl-L-lysine

UDP-Gal

UDP-Glc

F6P

13DPG

8-Amino-7-oxo nonanoate

UDP-6-sulfoquinovosel

GlcGlycP

G6P

DHAP

PEP

(S)-Methylmalonate semialdehyde

Pimeloyl-CoA

dTDP-Gal

Sedoheptulose 1,7-PP

Oxalate

Imidazole-4-acetate

3-Hydroxy-2 methylpropanoate

3-Octaprenyl-4-hydroxybenzoate

Glucose

Pentose Phosphate Pathway Fructose

Imidazole-4-acetaldehyde

dTDP-L-rhamnose

Storage Products

X5P

Indole-3-acetate

Inositol dTDP-4-oxo-L-rhamnose

dTDP-Glc

Phosphoribosyl-AMP Maltose

DAHP

Formate

L-Cysteine

NADP+ ADP

Pigment

Zn2+[p]

S7P

F6P

NH4

K+

Ø

Phenylacetic acid

Phosphoribosyl-formimino AICAR-phosphate

R5P Ru5P

3-Dehydroquinate

Mercaptopyruvate

ADPribose

Cyanate

Ø

2-Phenylacetamide

5-Methyl-THF

5,10-Methenyl-THF

Citrate

Ø

Ø

Imidazole-acetol-P

Phosphoribosyl-ATP

PRPP

D-Ribose

G3P

E4P

Glutathione(ox)

2-oxoglutarate

Ø

Indole-3-acetaldehyde

Cyanophycin(n+2)

Histidine Synthesis

Acetaldehyde

CO2

Pr otein

Acetate

Ø

Indole-3-acetamide

Ø

Ø

Cyanophycin(n+2)

Fumarate Ethanol Glucose

Ø

R1P

E4P

L-Serine

Succinate

Fumarate[p]

D-erythro-Imidazole glycerol-phosphate

3,4-Dihydroxy-2-butanone 4-P

Pyruvate

G3P

NADP+ NADPH CO2

Ethanol[p]

Ø

PHB granule

Cyanophycin(n)

dTDP-4-oxo-6 deoxy-D-glucose

Shikimate Indole

2,3-Butanediol

Glucose[p]

Zn2+[e]

X5P

F6P

Glycine

NO2

Ethanol[e]

DNA 5-methylcytosine

Ø

L-Histidinol-P

5-phospho Phosphoribosyl-formimino ribosylamine AICAR-phosphate

GAR

Deoxyribose

R5P

Glucose[e]

Ø

FNP-acetamidine N-Formyl GAR

O2[t]

Ø

Ø

Inositol 1-phosphate L-Histidinol

Toxopyrimidine

2-deoxy D-ribose 5-P

S-Lactoyl glutathione

Peptidoglycan[p]

Demand Reactions

DNA

L-Histidinine

Purine Metabolism 2-Hydroxy phenylacetate

ADC

Chorismate

L-Tryptophan

PRPP PPi

Ø

THF 10-Formyl-THF

AICAR

P-Homoserine

Urea[e]

Pr otein

Shikimate-3-P

G3P

Undecaprenyl-PP

Ø

Sink Reactions Growing PHB granule

AMP

L-Cysteine

HCO3

Ø

Adenine


AIR

Pr otein

Ø

SAICAR

L-Phenylalanine

1-(2-Carboxyphenylamino) 1-deoxy-D-ribulose 5-P

L-Serine

Glycolate 7,8-Dihydropterin pyrophosphate

Dihydropteroate

FAICAR

P-ribosyl-CAminoimidazole

dGTP

Indoleglycerol-P Dihydroneopterin-Phosphate

7,8-Dihydroneopterin

H2O[t]

NO3

DNA

R1P

5-O-(1-Carboxyvinyl) 3-phosphoshikimate

6-Pyruvoyltetra hydropterin

Nicotinamide

Ø

dATP Pi

Sucrose-6-P

hv

H2O

Adenosine

4-Aminobenzoate

5,10-Methylene-THF

Ø

Deoxyguanosine

Prephenate

Anthranilate

Cys-Gly L-Proline[e]

Ø

dADP

AMP

IMP

DNA 2-oxoglutarate

GTP

2-Amino-4-hydroxy 6-hydroxymethyl 7,8-dihydropteridine

L-Lysine

Ø

ADP

Pr otein

L-Tyrosine

Homogentisate

Dihydroneopterin

Folate Metabolism

Ø

Ø

GDP

Pr otein

FAD

H+[t]

PC(r)[t]

Ø

Modeling

dAMP

ATP Adenylo succinate

XMP

GMP

cGMP

5-Amino-6(5'-phosphoribosylamino) uracil

5-Amino-6(1-D-ribitylamino) uracil

FMN

ATPase

FOCYTC6[t]

L-Lysine[p]

ATP H+

PS I

GTP

N-(5-Phospho-D-ribosyl) anthranilate

L-Alanine

L-Lysine[e]

Fd(red)

hv

L-Alanine[p]

ITP

dGDP

H2

Cyt B6f

L-Alanine[e]

Ø

5-Amino-6(5'-phosphoribitylamino) uracil

6,7-Dimethyl 8-(1-D-ribityl) lumazine

NADP+

Fd(ox)

SDH

Exchange and Transport Reactions Ø

Riboflavin Metabolism

ATP

Riboflavin

O2[t] H+[t]

H2O[t]

H+

H2O

O2 NADPH

Cyt BD PS II PQH2[t] Quinol Oxidase

NDH-1

ATPase

FAD

NAD(P)+ NAD(P)H

Thylakoid

H+

FAD

FADH2

NDH-2

Cyt C

ADP

Q8H2

hv

NDH-1 type 3

Cyt B6f

SDH

NAD(P)+ NAD(P)H

IDP

RNA

H+[p] 2,5-Diamino-6(5'-phosphoribosylamino) 4-pyrimidineone

Cyt BD PQH2[p] Quinol Oxidase

NDH-1

Cytoplasm NADP+

Pr otein

PC(r)[p]

NDH-2

dIDP dITP cAMP

FOCYTC6[p]

Periplasm NDH-1 type 4

Legend: [e]: Extracellular [p]: Periplasm [t]: Thylakoid others: Cytoplasm

grey line: flux = 0

30

2-Hydroxy phenylacetate

AIR

DNA AIR Toxopyrimidine Toxopyrimidine from www.plantphysiol.org on July 8, 2014 Downloaded - Published by www.plant.org 2-oxoglutarate Purinereserved. Metabolism Copyright © 2014 American Society ofL-Phenylalanine Plant Biologists. All rights Purine Metabolism


GAR

5-phospho ribosylamine

L-Glutamate

PhPyr

2-Hydroxy phenylacetate


GAR

5-phospho ribosylamine

A Light metabolism F6P

X5P

R1P

D-Ribose

PRPP

Phosphoribosyl-ATP

Phosphoribosyl-AMP

CDP-Glc

1.53

3,4-Dihydroxy-2-butanone 4-P G3P

E4P

G3P

1.43 3.03

1.5


F6P

Maltose

Glucose

S7P

Ru5P

6PG

6PGL

G6P


X5P

1.5

G1P

0.0183

ADP-Glc

0.0183

Glycogen

D-Glucosamine 6-P

0.0088

1.57

FBP

1.5

E4P

0.0343

F6P

M6P

1.57

4.46

DAHP

GlcGlyc

0.0343


Deoxyribose

GlcGlycP

R5P

DHAP

G3P

3.09

M1P

GDP-Mannose

Glycolysis DHAP

0.0343 7.7

PEP

Calvin Cycle

13DPG

23DPG

7.7

Glyoxylate

Glycolate

0.202

0.202

2PG

0.202

0.202

RuBP

PG3

4.25

3PHP

B Dark metabolism (without proline degradation) X5P

F6P

R1P

D-Ribose

PRPP

Phosphoribosyl-ATP

Phosphoribosyl-AMP

CDP-Glc

0.0102



E4P

G3P

0.00713

0.00713

F6P

Maltose

Glucose

0.000834 0.0174

S7P

Ru5P

6PG

6PGL

G6P


X5P

0.0986

G1P

F6P

Glycogen

D-Glucosamine 6-P

0.000793

0.0804

FBP E4P

ADP-Glc 0.1

M6P

0.0804

0.0182

DAHP

GlcGlyc

0.0986


Deoxyribose

GlcGlycP

R5P

DHAP

G3P

0.00309

0.079

M1P

GDP-Mannose

Glycolysis

DHAP

0.148

PEP

Calvin Cycle

13DPG

23DPG

0.148

Glyoxylate

Glycolate

0.0182

0.0182

2PG

0.0182

RuBP

0.0182

PG3

3PHP

C Dark metabolism (with proline degradation) F6P

X5P

R1P

D-Ribose

PRPP

Phosphoribosyl-ATP

Phosphoribosyl-AMP

CDP-Glc

0.00419



E4P

G3P

0.00108

0.00108

F6P

Maltose

Glucose

0.00526 0.00527

S7P

Ru5P

6PG

6PGL

G6P


X5P

0.0986

G1P

F6P

Glycogen

D-Glucosamine 6-P

0.000798

0.0925

FBP E4P

ADP-Glc 0.1

M6P

0.0925

DAHP

GlcGlyc

0.0986


Deoxyribose

GlcGlycP

R5P

DHAP

G3P

0.00311

0.0911

M1P

GDP-Mannose

Glycolysis

DHAP

0.179

PEP

Calvin Cycle

13DPG

23DPG

0.179

Glyoxylate

7 06e-6

Glycolate

2PG

RuBP

PG3

3PHP

Figure 2: Example use cases of the map. Visualization of the difference in flux distribution when growing in the presence (A) or absence (B, C) of light. When light is present (A), the FBA solution harbors no surprises. However, when light is absent (B), FBA predicts that photorespiration is active, which is unlikely. (C) Adding a reaction that converts L-proline to hydroxyproline returns the model to its expected state under dark conditions. Note that some glyoxylate is still produced from other precursors than glycolate (not shown here), to enable the production of amino acids.

20


Flux magnitude: (panels B, C, D)

growth rate (h -1)

A

LLS C

B

1.0e-9

Q8

0.106

H+ H2O

NADP+

FAD

4.55

CLLS

3.15

NADP+

NAD(P)H

NAD(P)+ NAD(P)H

F d( ox)

SDH

hv

NDH-1 type 3

NDH-2

C Light limiting state

Fd(red)

hv Cyt BD PS II PQH2[t] Quinol Oxidase

NDH-1

7.01

Thylakoid

H2O[t]

PS I

Cyt C

FOCYTC6[t]

1.02

7.01

F A DH 2

H+

Cytoplasm

NADP+

F AD

Q8

H2O

NADPH

Q8H2 FADH2

1.75

1.71e-6

NADP+

FAD

1.07

1.21

Fd(ox)

SDH

hv

NDH-1 type 3

NDH-2

Cyt B6f

ADP

Fd(red)


NDH-1

H+[t]

H2 O2

H2O

NADPH

Q8H2

NAD(P)+ NAD(P)H

ATPase

PC(r)[t]

H2 O2

NAD(P)H

ATP H+

5.99

Cyt B6f

O2[t] H+[t]

ADP

D Carbon Limiting State H+

NADP+

50

H2

NADPH

Q8H2 FADH2

D

Q8

0.000224 O2

Cytoplasm

CLS

Cytoplasm

4.73e-7

B Carbon and Light Limiting State

2.3

PS I

3.8

ATP

NADP+

NAD(P)H

NAD(P)+ NAD(P)H

H+

Cyt C

F d( ox)

SDH

hv

ATPase

NDH-1 type 3

NDH-2

1.58


NDH-1

Cyt B6f

ADP

Fd(red)

PS I

ATP H+

Cyt C

ATPase

21.8

Thylakoid

2.7

H2O[t]

O2[t] H+[t]

FOCYTC6[t] PC(r)[t]

2.7

0.39

Thylakoid

H+[t]

23.4

H2O[t]

1.58

O2[t] H+[t]

FOCYTC6[t] PC(r)[t]

H+[t]

Figure 3: We used FBA to explore the properties of the Synechocystis model and map under various conditions, i.e. carbon and light limiting state (CLLS), light limiting state (LLS), and carbon limiting state (CLS). (A) Predicted growth rate as a function of carbon and light uptake rates. The black dots in the phase plane indicate the conditions (in terms of photon flux and HCO− 3 uptake) that are shown in panels B-D. (B-D) Visualization of predicted FBA fluxes for the three scenarios (CLLS, LLS, and CLS). Arrows indicate flux direction; the color bar indicates flux magnitude.

21 Downloaded from www.plantphysiol.org on July 8, 2014 - Published by www.plant.org Copyright © 2014 American Society of Plant Biologists. All rights reserved.

: -0.2 < expr. < 0.2 -3.42

-1.71

0

3.25

A CO2 limitation (1 h)

Q

Cytoplasm

Q8

NAD(P)H

Q8H2

NADPH

NAD(P)+ NAD(P)H

NDH-2

Thylakoid Ø

O2[t] H+[t]

HCO3[p]

NAD(P)H

Thylakoid Ø

HCO3[e]

NDH-1

H2O[t] HCO3[p]

ATPase

H+[t]

H2 O2

H2O

NADPH

NAD(P)+ NAD(P)H

NDH-2

Cyt C

FOCYTC6[t] PC(r)[t]

NADP+

FAD

Fd(ox)

SDH

hv

NDH-1 type 3

PS I

H+

Q8H2 F ADH 2

NADP+

ATP H+

Cyt B6f

Q

Q8

ADP

HCO3

B CO2 limitation (12 h) Cytoplasm

Fd(red)

hv

H2O[t]

HCO3[e]

NADP+

Fd(ox)

SDH


NDH-1

H2O

FAD

hv

NDH-1 type 3

H2 O2

2

F ADH 2

NADP+

6.5 H+

Fd(red)

ADP


O2[t] H+[t]

Cyt B6f

ATP H+

hv

PS I

FOCYTC6[t] PC(r)[t]

Cyt C

ATPase

H+[t]

HCO3

Figure 4: To demonstrate the applicability of our map to gene-associated (“omics”-size) data, we used it to visualize microarray data for various CO2 limiting growth conditions. The colored squares indicate differential expression relative to wild-type; yellow corresponds to overexpression and blue corresponds to underexpression. The panels depict differential gene expression after 1 hour (A), 12 hours (B) of CO2 limitation, compared to wild-type Synechocystis.


LLS C

B

Flux magnitude: (panels B, C, D)

flux (mmol gDW -1 h -1 )

A

CLLS

1.0e-9

4.73e-7

0.000224

B Carbon and Light Limiting State Q8

H2O

NADPH

Q8H2 FADH2

NADP+

FAD

5.16

4.46e-7

NADP+

NAD(P)H

NAD(P)+ NAD(P)H

2.31

Fd(ox)

SDH

hv

CLS NDH-1 type 3

D

NDH-2

Fd(red)

ADP

5.3


NDH-1

50

H2 O2

Cytoplasm

0.106

H+

Cyt B6f

PS I

ATP H+

Cyt C

ATPase

0.0659 5.37

Thylakoid

C Light limiting state

H+

Cytoplasm

Q8

FADH2

NAD(P)H

NAD(P)+ NAD(P)H

Cytoplasm

Q8

FADH2

hv

ADP

Fd(red) 2.49

hv

NADP+

FAD

1.34

1.17e-7

1.08

F d( ox)

SDH

H2 H2O

NADPH

Q8H2

2.43

ATP

NADP+

NAD(P)H

NAD(P)+ NAD(P)H

H+

H+[t]

H+ O2

NADP+

FAD 6.08e-7

NADP+

FOCYTC6[t] PC(r)[t]

H2O

NADPH

Q8H2

5.37

O2[t] H+[t]

D Carbon Limiting State

H2 O2

H2O[t]

3.77

Fd(ox)

SDH

hv

ADP

Fd(red)

hv

ATP H+

1.4

1.82e-5

NDH-1 type 3

NDH-2


NDH-1

Cyt B6f

PS I

Cyt C

ATPase

0.0219

H+[t]

NDH-1 type 3

NDH-2


NDH-1

Cyt B6f

PS I

Cyt C

ATPase

22.2

Thylakoid

2.51

H2O[t]

2.49

O2[t] H+[t]

FOCYTC6[t]

0.0219

PC(r)[t]

Thylakoid

23.6

H2O[t]

O2[t] H+[t]

FOCYTC6[t]

1.4

H+[t]

PC(r)[t]

Figure 5: We used FBA to explore the properties of the Synechocystis model and map under various conditions (i.e. carbon and light limiting state (CLLS), light limiting state (LLS), and carbon limiting state (CLS)), while optimizing for 23BD production. (A) Predicted flux to 23BD as a function of carbon and light uptake rates. (B-D) Visualization of predicted FBA fluxes for the three scenarios (CLLS, LLS, and CLS, respectively) indicated in panel A. Arrows indicate flux direction; the color bar indicates flux magnitude.


A global network-based protocol for functional inference of hypothetical proteins in Synechocystis sp. PCC 6803.

Toward the complete proteome of Synechocystis sp. PCC 6803.

Metabolic Engineering of Synechocystis sp. PCC 6803 for Production of the Plant Diterpenoid Manoyl Oxide.

Iron reduction by the cyanobacterium Synechocystis sp. PCC 6803.

Subcellular Localization of Carotenoid Biosynthesis in Synechocystis sp. PCC 6803.

Expression of holo-proteorhodopsin in Synechocystis sp. PCC 6803.

Responses of Synechocystis sp. PCC 6803 to heterologous biosynthetic pathways.

Production of squalene in Synechocystis sp. PCC 6803.

The psbC start codon in Synechocystis sp. PCC 6803.

Biocomputional construction of a gene network under acid stress in Synechocystis sp. PCC 6803.

Ethylene production with engineered Synechocystis sp PCC 6803 strains.

Purification and properties of glutamine synthetases from the cyanobacteria Synechocystis sp. strain PCC 6803 and Calothrix sp. strain PCC 7601.

Slr1670 from Synechocystis sp. PCC 6803 Is Required for the Re-assimilation of the Osmolyte Glucosylglycerol.

Development of a high-frequency in vivo transposon mutagenesis system for Synechocystis sp. PCC 6803 and Synechococcus elongatus PCC 7942.

A single vector-based strategy for marker-less gene replacement in Synechocystis sp. PCC 6803.

[NiFe]-hydrogenase is essential for cyanobacterium Synechocystis sp. PCC 6803 aerobic growth in the dark.

Photoheterotrophic fluxome in Synechocystis sp. strain PCC 6803 and its implications for cyanobacterial bioenergetics.

Construction of a stepwise gene integration system by transient expression of actinophage R4 integrase in cyanobacterium Synechocystis sp. PCC 6803.

Phosphoproteome of the cyanobacterium Synechocystis sp. PCC 6803 and its dynamics during nitrogen starvation.

Targeted genetic inactivation of the photosystem I reaction center in the cyanobacterium Synechocystis sp. PCC 6803.

Glutaredoxins are essential for stress adaptation in the cyanobacterium Synechocystis sp. PCC 6803.

PilB localization correlates with the direction of twitching motility in the cyanobacterium Synechocystis sp. PCC 6803.

A bioenergetic assessment of photosynthetic growth of Synechocystis sp. PCC 6803 in continuous cultures.

RNA-seq Profiling Reveals Novel Target Genes of LexA in the Cyanobacterium Synechocystis sp. PCC 6803.