Global insights into energetic and metabolic networks in Rhodobacter sphaeroides
© Imam et al.; licensee BioMed Central Ltd. 2013
Received: 14 May 2013
Accepted: 10 September 2013
Published: 13 September 2013
Improving our understanding of processes at the core of cellular lifestyles can be aided by combining information from genetic analyses, high-throughput experiments and computational predictions.
We combined data and predictions derived from phenotypic, physiological, genetic and computational analyses to dissect the metabolic and energetic networks of the facultative photosynthetic bacterium Rhodobacter sphaeroides. We focused our analysis on pathways crucial to the production and recycling of pyridine nucleotides during aerobic respiratory and anaerobic photosynthetic growth in the presence of an organic electron donor. In particular, we assessed the requirement for NADH/NADPH transhydrogenase enzyme, PntAB during respiratory and photosynthetic growth. Using high-throughput phenotype microarrays (PMs), we found that PntAB is essential for photosynthetic growth in the presence of many organic electron donors, particularly those predicted to require its activity to produce NADPH. Utilizing the genome-scale metabolic model iRsp1095, we predicted alternative routes of NADPH synthesis and used gene expression analyses to show that transcripts from a subset of the corresponding genes were conditionally increased in a ΔpntAB mutant. We then used a combination of metabolic flux predictions and mutational analysis to identify flux redistribution patterns utilized in the ΔpntAB mutant to compensate for the loss of this enzyme. Data generated from metabolic and phenotypic analyses of wild type and mutant cells were used to develop iRsp1140, an expanded genome-scale metabolic reconstruction for R. sphaeroides with improved ability to analyze and predict pathways associated with photosynthesis and other metabolic processes.
These analyses increased our understanding of key aspects of the photosynthetic lifestyle, highlighting the added importance of NADPH production under these conditions. It also led to a significant improvement in the predictive capabilities of a metabolic model for the different energetic lifestyles of a facultative organism.
KeywordsPhotosynthesis Transhydrogenase Constraint-based analysis Metabolic modeling Phenotype microarray Rhodobacter sphaeroides
Information about an organism’s capabilities can be derived from a variety of sources. When genomic information is combined with biochemical, phenotypic or genetic data, functional roles and interrelationships of components within metabolic or regulatory networks become better defined [1–5]. Thus, to obtain a global view of an organism’s capabilities, it is often beneficial to develop models that integrate data from different types of experiments. In obtaining such integrated views, genome-scale metabolic network models can serve both as databases for storage and organization and as tools for the combination and analysis of heterogeneous data sets . A particular interest of our laboratory is developing an integrated understanding of metabolic networks in photosynthetic microbes, because of their abundance in nature, the unique aspects of a solar-driven lifestyle and their growing importance in biotechnological applications [7–9].
We study purple non-sulfur bacteria, a group of photosynthetic microbes that display great metabolic and energetic diversity . The purple non-sulfur bacterium Rhodobacter sphaeroides represents one of the best studied photosynthetic organisms, and has been used to develop models of photon capture, light-driven energy metabolism and other aspects of its diverse lifestyles [11, 12]. This facultative microbe is capable of anoxygenic photosynthetic growth, aerobic respiration and anaerobic respiration [11, 12]. Furthermore, R. sphaeroides has been studied for potential biotechnological applications including the ability to produce H2[13–15] and ubiquinone , production of polyhydroxybutyrate, which can be used as a source of biodegradable plastics , remediation of radioactive contamination , and its ability to fix CO2 and N2[7, 19, 20]. The available genetic, genomic and physiological tools  also make R. sphaeroides an excellent system in which to improve our understanding of solar energy capture, metabolic and energetic aspects of photosynthesis and other energetic pathways, and the networks which regulate processes of societal and biotechnological interest. To obtain an integrated understanding of photosynthesis or other aspects of R. sphaeroides’ lifestyles requires the use of high-throughput data to develop better predictive models of its metabolic network.
In this work, we take a systematic approach to expand our knowledge of the metabolic and energetic networks of R. sphaeroides by combining data from genetic, phenotypic and transcriptional analyses with constraint-based modeling. We use high-through phenotypic microarrays to show that wild type R. sphaeroides grows on a diverse array of substrates and that this nutrient utilization profile varies significantly between photosynthetic and non-photosynthetic growth conditions. Using the conserved bioenergetic enzyme pyridine nucleotide transhydrogenase (PntAB) as an example, we identify carbon sources where recycling of pyridine nucleotides by this enzyme is essential for photosynthetic or non-photosynthetic growth. We use a genome-scale metabolic model to predict flux distributions and identify alternative NADPH producing reactions that can compensate for the loss of PntAB and thereby explain the conditional growth of ΔpntAB cells on selected carbon sources. Transcriptional and phenotypic analyses of defined single and double mutants were used to verify the potential use of some of these alternative NADPH producing reactions under defined conditions. The new data derived from analyzing the growth of wild type and mutant cells were used to develop iRsp1140, a significant update to the existing genome-scale reconstruction of the R. sphaeroides metabolic network , with increased coverage of metabolic pathways and improved predictive ability. iRsp1140 accounts for 1140 genes, 878 metabolites and 1416 reactions. This work illustrates the new insights into important cellular processes that can be acquired by integrating data from genetic, genomic and other complementary experiments into predictive models of biological systems.
Results and discussion
Global analysis of substrate utilization by R. sphaeroides
Substrate utilization profile of R. sphaeroides under different growth conditions
Predicted by iRsp1095**
Based on PM assaya
R. sphaeroides utilizes different arrays of nutrients across growth conditions
The results from analysis of substrate utilization by WT R. sphaeroides cells (Table 1, Additional file 1: Tables S1-S4), significantly expands the array of compounds that support growth of this organism. While the carbon utilization profiles were largely similar during photosynthetic and aerobic respiratory growth, several important differences were observed. Eight carbon sources appeared to support growth photosynthetically but not aerobically, while 15 supported growth aerobically but not photosynthetically (Additional file 2: Figure S1A, Additional file 1: Table S1). Potential causes for the observed differences might include: (i) longer lag times under individual conditions (Additional file 2: Figure S1B), which may result in an apparent inability to utilize the carbon source under one experimental condition; (ii) insurmountable metabolic, bioenergetic or regulatory bottlenecks (Additional file 2: Figure S1C); or (iii) potential differences between the data derived from the photosynthetic PM assay (which measures an increase in optical density) and the aerobic PM assay (that measures respiration) .
Of the 53 carbon sources that were used both photosynthetically and aerobically, 41 were tested for their ability to support growth under anaerobic respiratory conditions using dimethyl sulfoxide (DMSO) as the terminal electron acceptor (Additional file 1: Table S1). Only 16 of these carbon sources were capable of supporting anaerobic respiratory growth (as measured by an increase in optical density) after 10 days of incubation. We propose that the inability of WT R. sphaeroides to grow in the presence of several carbon substrates during anaerobic respiratory growth is likely due to regulatory and/or bioenergetic constraints, as pathways required for their catabolism are either known or predicted to be present in the genome.
PM assays also revealed that 66 nitrogen, 42 phosphorus and 18 sulfur sources supported growth photosynthetically in WT R. sphaeroides (Table 1, Additional file 1: Tables S2-S4). This is a number of nitrogen, phosphorous and sulfur substrates which is similar to those shown to support growth of other well-studied facultative bacteria like Escherichia coli and Bacillus subtilis.
The ability of R. sphaeroides to grow on a wide variety of carbon, nitrogen, phosphorus and sulfur sources (Additional file 1: Table S8) is a further demonstration of its metabolic versatility. Of particular interest for future studies is the pattern of substrate utilization observed under different growth conditions, which we propose likely reflects regulatory differences, since enzymes needed to carry out the required reactions are predicted to be encoded in the genome. Below we show that these PM analyses of WT cells provide important reference points for studying the effects of mutations on the metabolic, energetic and regulatory pathways that are potentially used during various modes of growth.
Analyzing the role of PntAB under defined growth conditions
Thus PntAB plays a major role in maintaining the balance of cellular pyridine nucleotides (NADH/NADPH). NADPH is a source of reducing equivalents in a large number of crucial anabolic pathways such as the Calvin cycle in autotrophic cells, fatty acid biosynthesis and tetrapyrrole or pigment biosynthesis in photosynthetic organisms .
Extensive studies have shown that E. coli PntAB expression is induced when there is a demand for NADPH . In addition, E. coli PntAB is required for optimal growth on carbon sources whose metabolism does not directly generate NADPH, such as glycerol . E. coli also possesses an energy independent soluble transhydrogenase, UdhA, which is induced when there is an excess of NADPH (e.g., growth on acetate) and mediates conversion of NADPH to NADP+[29, 30]. In addition to PntAB and UdhA, glucose-6-phosphate dehydrogenase (Zwf) and isocitrate dehydrogenase (Icd) can help maintain bacterial NADPH pools under specific conditions [29, 30]. We compared growth of R. sphaeroides wild type and ΔpntAB cells (PntA1 ) using PMs to identify conditions with an increased need for NADPH in this bacterium.
PntAB is conditionally essential for photosynthetic and aerobic respiratory growth
Summary of carbon utilization in WT and PntA1 cells under aerobic conditions
Total number of carbon sources utilized
Differences in carbon sources utilized
Equivalent growth in PntA1 and WT
Increased growth in PntA1
Reduced growth in PntA1
Growth in WT only
Combined, these data suggest that PntAB is the major source of NADPH for photosynthetic growth on carbon sources except glucose and D-aspartate. Furthermore, the fact that PntA1, but not its WT parent grows on D-aspartate suggests either that this strain contains unlinked mutations that allow it to metabolize this substrate or that the metabolism of the substrate is induced when the NADPH pool is reduced. This observation could also indicate that R. sphaeroides possesses an NADPH-linked pathway for aspartate catabolism [32, 33], even though its genome does not contain an open reading frame with significant amino acid sequence similarity to known enzymes with such an activity. In the case of Group III carbon sources (succinate and many others), other NADPH producing pathways could be activated to support growth albeit at a slower rate. For example, the delayed photosynthetic growth that is seen with some Group III carbon sources might be the result of metabolic or genetic alterations that are needed to provide PntAB-independent routes for NADPH synthesis (see below). In addition, we predict that for Group II substrates (such as acetate), the date predict that the combined NADPH demand for metabolism and anabolic processes is too high to be provided by such alternative pathways.
Alternative NADPH-generating pathways can be utilized under different growth conditions
Reactions predicted by iRsp1095 to be involved in NADPH generation
RSP_0239 & RSP_0240
NADP+ + NADH + 2 H+[p] = > NADPH + NAD+ + 2 H+
Reduced ferredoxin + NADP+ + H + <= > Oxidized ferredoxin + NADPH
D-Glucose 6-phosphate + NADP+ < = > 6PGL + NADPH
(S)-Malate + NADP+ = > Pyruvate + CO2 + NADPH
mlthf + NADP+ < = > methf + NADPH
Isocitrate + NADP+ < = > 2-Oxoglutarate + CO2 + NADPH
RSP_1146 & RSP_1149
2 L-Glutamate + NADP+ < = > L-Glutamine + 2-Oxoglutarate + NADPH + H+
Interestingly, none of the transcript levels for the tested genes were significantly increased in the succinate-grown PntA1 cells under aerobic respiratory conditions. This could indicate that the contribution of PntAB is minor under this condition and that alternative NADPH-generating pathways provide sufficient NADPH to support growth. Alternatively, other as yet unidentified NADPH-generating pathways or post-transcriptional events might exist in both wild type and ΔpntAB cells.
Under photosynthetic conditions, the PntA1-Zwf1 double mutant was incapable of growth on either glucose, succinate or acetate (representatives of each of the 3 major classes of carbon source we defined previously). However, growth is partially restored for glucose and succinate when the PntA-Zwf1 mutant is complemented with a plasmid containing the zwf gene (Figure 5B,D). These findings indicate that Zwf also serves as a major route for NADPH generation during photosynthetic growth with these carbon sources, an observation that was predicted by metabolic flux analysis in iRsp1095 (Additional file 2: Figures S3 and S4). Additionally, growth of Zwf1 cells is impaired when using glucose either photosynthetically or aerobically (Figure 5C,D, Additional file 2: Figure S2), suggesting that the Entner-Doudoroff pathway is the major glycolytic pathway in R. sphaeroides, despite significant expression of genes encoding enzymes of the Embden-Meyerhof-Parnas pathway in these cultures. This conclusion is also consistent with both experimental analysis of 13C-glucose metabolism in R. sphaeroides under aerobic respiratory conditions  and with the metabolic flux predictions made by iRsp1095 (Additional file 2: Figure S4).
iRsp1140: a revised experimentally-validated genome-scale metabolic reconstruction for R. sphaeroides
The above results provide new information about the metabolic capabilities of R. sphaeroides that can be utilized to refine biochemically, genetically and genomically structured databases employed in constraint-based analysis . We previously constructed and validated a genome-scale metabolic reconstruction for R. sphaeroides, called iRsp1095, using its annotated genome, published organism-specific data and information from continuous cultures of WT cells .
Predicted R. sphaeroides ABC transporter operons tested for substrate specificity using Biolog PM
Comparison of the properties of iRsp1095 and iRsp1140
Genes based on experimental evidence
Genes inferred based on gene homology
Reactions Associated with genes
Reactions without gene association
While the development of high-throughput or global approaches can provide large amounts of data, the task of extracting meaningful biological insight from this information is still challenging . To gain new biological insights, constraint-based and other modeling approaches can be used to integrate various data sets [1–3, 41].
In this study, we took an integrated approach to gain new insight into the metabolic, energetic and photosynthetic lifestyles of R. sphaeroides. We extended the number of nutrients that can support growth of WT cells. We also showed that a conserved bioenergetic enzyme (PntAB) which can provide reduced pyridine nucleotides is essential for photosynthetic growth on many carbon sources. We used a genome-scale model for R. sphaeroides to make flux predictions, as well as generate and test hypotheses on alterative NADPH-generating pathways that allow growth in the absence of PntAB. The products of various anabolic pathways require NADPH that is derived mainly from PntAB activity, so exploiting these and other alternative NADPH generating pathways we identified, could improve growth and metabolic end products derived from photosynthetic and non-photosynthetic wild type cells or those engineered to produce compounds of biotechnological interest.
Previous studies have shown the utility of high-throughput data sets in refining and validating genome-scale metabolic models [42–44]. We used our data to produce a 2nd generation genome-scale reconstruction for R. sphaeroides, iRsp1140 with significantly improved coverage of metabolic functions and predictions that are in better agreement with experimental data. iRsp1140, provides an improved depiction of the R. sphaeroides metabolic network, so it will be useful in studying photosynthesis, as well as a wider range of metabolic processes in this and related organisms.
Bacterial strains and growth conditions
R. sphaeroides strains 2.4.1 and Ga were used as parental strains. All mutants were made in strain 2.4.1 except PntA1  and Zwf1, which were constructed in Ga, and PntA1-Zwf1, which was constructed in PntA1 (Additional file 4: Table S1). E. coli DH5α was used as a plasmid host, and E. coli S17-1 was used to conjugate DNA into R. sphaeroides.
R. sphaeroides cultures were incubated at 30°C in Sistrom’s Minimal Medium (SMM)  lacking glutamate and aspartate, with succinate (33.9 mM), or an alternative sole carbon source. The molar concentration of carbon atoms of the carbon source was kept constant at 135.5 mM. Photosynthetic cultures were incubated in screw capped tubes at a light intensity of ~10 W/m2, while anaerobic respiratory cultures were incubated in screw capped tubes in the dark with the media supplemented with 0.9% DMSO. Aerobic cultures were shaken in flasks. Optical density of photosynthetic cells in screw capped tubes was measured using a Klett-Summerson photometer and is expressed in Klett units (1 Klett unit equals approximately 107 cells/mL). Other optical density measurements were made by measuring optical density at 600 or 650 nm in a spectrophotometer. When required, the media was supplemented with 100 μM IPTG, 25 μg/mL kanamycin or 25 μg/mL spectinomycin. E. coli cells were grown in Luria Bertani medium at 37°C, supplemented with 25 μg/mL kanamycin where needed.
Phenotype microarray analysis
To determine the substrate utilization profiles of R. sphaeroides, phenotype microarrays (PMs) were used with a few modifications. To assay aerobic respiratory growth on different carbon sources (Biolog PM1 and PM2A), cells were grown on SMM agar plates aerobically for 3 days. Cells were swabbed from the agar plates and suspended in 4 mL inoculation fluid (Biolog inc.) to an OD600 of 0.38. Two mL of this mixture was placed in 10 mL of inoculation fluid (IF) containing 24 μL of tetrazolium-based dye A (Biolog inc.), resulting in a final OD600 of ~0.07. Then 1.2 μL of vitamin solution (1 g nicotinic acid, 0.5 g thiamine-HCl and 0.01 g biotin in 100 mL of water) was added and 100 μL was dispensed into each well of a 96 well plate. Cultures were incubated at 30˚C for 72 to 96 hrs in an OmniLog plate reader (Biolog inc).
To assay photosynthetic growth, 10 mM NaHCO3, 0.4 mM sodium thioglycolate and 1 μM methylene green were added to the IF and this was kept in an anaerobic chamber for ~7 days with periodic shaking to facilitate it becoming anaerobic (the methylene green in the IF turns colorless once oxygen is removed). R. sphaeroides cells were grown photosynthetically on SMM agar plates and PM plates set up as described above, except that the tetrazolium dye was omitted, as thioglycolate reduces the tetrazolium-based dye turning it purple independent of cellular respiration. The PM plates were put in an anaerobic chamber, inoculated, placed in heat sealed anaerobic bags (Biolog inc.)  and incubated under constant illumination (light intensity of ~10 W/m2) at 30˚C for 72 to 96 hrs, after which OD650 readings were taken. Anaerobic indicator strips and ageless oxygen absorbers (MITSUBISHI Gas Chemical America, Inc.) were placed in the sealed bags to report on and maintain an anaerobic environment throughout the experiment.
Any growth in the negative control well (A1) was subtracted from the measured optical density for both aerobic and photosynthetic PM. A threshold OD650 of 0.05 (after background correction) was used as a baseline for scoring photosynthetic growth, as this was the highest value obtained from any well in control experiments where cells were kept in the dark. A threshold of 5 Omnilog units (after background correction) was used as a baseline for aerobic respiratory growth. Only carbon sources that resulted in reproducible growth above the baseline across all replicates were considered to be growth substrates.
To assay photosynthetic growth on different nitrogen, phosphorus or sulfur sources (Biolog PM3B and PM4A), R. sphaeroides cells were grown aerobically for 5 days on a modified R2A agar [22, 47] (0.25 g of yeast extract, 0.25 g of Proteose Peptone, 0.25 g of Casamino Acids, 0.12 g of K2HPO4, 0.025 g of MgSO4.7H2O, 0.5 g of sodium pyruvate and 15 g of agar per liter of water). Plates were set up as described for photosynthetic growth with the addition of 20 mM sodium succinate and 2 μM ferric citrate. Cell cultures were grown under constant illumination (10 W/m2) at 30˚C for 48 hrs, after which OD650 readings were taken. Aerobic growth on the different nitrogen, phosphorus and sulfur sources is not reported due to significant background growth in the negative control wells, an issue that has been observed previously .
Due to comparatively slow growth rates of R. sphaeroides under anaerobic respiratory conditions, evaluation of these growth modes could not be reliably conducted with Biolog PM plates due to media evaporation. Thus, to assay anaerobic respiratory growth on different carbon sources, 96 well microwell plates (Fisher Scientific) were set up to analyze 41 of the carbon sources identified as R. sphaeroides growth substrates from PM (see Additional file 1: Table S1 for a list of these substrates). The carbon sources were normalized for the total number of carbon atoms in each compound (135.5 mM). R. sphaeroides cells were grown aerobically in SMM and centrifuged. Cells were then washed with SMM media lacking a carbon source (SMM no carbon), suspended in anaerobic SMM no carbon (which had been kept in an anaerobic chamber for at least 4 days) to an OD600 of ~0.1 and DMSO was added to a final concentration of 0.9%. Then, 300 μL of a cell suspension was dispensed into wells containing a different carbon source in an anaerobic chamber. Plates were incubated at 30˚C for 10 days with continuous shaking in a Tecan M200 plate reader located within the anaerobic chamber, with OD650 readings taken every 6 minutes. Alternatively, plates were sealed in anaerobic bags, wrapped in foil and incubated at 30˚C for 10 days with periodic shaking.
Construction of mutants
All R. sphaeroides mutants we constructed contained in-frame markerless deletions. Briefly, regions spanning ~1500 bp upstream and downstream of the target gene were amplified using sequence specific primers containing restriction sites for EcoRI, XbaI or HindIII. These fragments were digested with the appropriate restriction enzymes and ligated into pK18mobsacB plasmid , digested with EcoRI and HindIII, by three-way ligation to generate the various gene deletion constructs, which were confirmed by sequencing (Additional file 4: Table S1 and S2). The pK18mobsacB-based plasmids were separately mobilized from E. coli S17-1 into R. sphaeroides strains. Cells in which the plasmid had successfully integrated into the genome via homologous recombination were identified by selection on SMM plates supplemented with kanamycin. These cells were then grown overnight in SMM without kanamycin. Cells that had lost the deletion plasmid (and thus the sacB gene) via a second recombination event were identified by growth on SMM plates supplemented with 10% sucrose. Individual gene deletions were confirmed by PCR and sequencing with specific primers.
Ectopic expression plasmids were made by amplifying the target genes from the genome using sequence specific primers containing restriction sites for NdeI and BglII, HindIII or BamHI. These DNA fragments were digested with the appropriate enzymes and cloned into pIND5 digested with the same enzymes. These plasmids were conjugated from E. coli S17-1 into the relevant R. sphaeroides mutant. Cells which harbor the desired plasmid were identified by selection on SMM plates supplemented with kanamycin.
RNA extraction, qRT-PCR and microarray analyses
RNA was isolated from exponential phase cultures of R. sphaeroides strains that were grown either photosynthetically in 16 mL screw cap tubes or aerobically in 500 mL conical flasks. RNA isolation and subsequent cDNA synthesis were performed as previously described . qRT-PCR experiments were conducted in triplicate for each biological replicate using SYBR Green JumpStart Taq ReadyMix (Sigma-Aldrich). Relative expression was determined via the 2-ΔΔCT method with efficiency correction . R. sphaeroides rpoZ was used as a housekeeping gene for normalization. Primers used in this analysis are provided in Additional file 4: Table S2.
Constraint based analysis and model refinement
where vBiomass is the flux through biomass objective function; v is the vector of steady state reaction fluxes; and vmin and vmax are the minimum and maximum allowable fluxes. The values in vmin and vmax were set to −100 and 100 mmol/g DW h for reversible reactions and 0 and 100 mmol/g DW h for forward only reactions. During simulation, all exchange reactions were assigned as being forward only (allowing metabolites to be secreted into the medium but not taken up), except the exchange reactions for media components required for cell growth, which were set to measured values for limiting substrates such as ammonia, or allowed to be freely exchanged with the extracellular space, i.e., -100 ≤ v ≤ 100. The non-growth associated ATP maintenance limit was set to 8.39 mmol/gDW h . Flux variability analysis  was also used to determine minimum and maximum allowable flux through reactions in the network.
Initial simulations with iRsp1095 in which the transhydrogenase reaction was deleted resulted in the prediction of optimal growth, suggesting alternative NADPH generating reactions existed in the metabolic network. Analysis of iRsp1095 revealed that it includes at total of 61 NADPH requiring reactions, of which only 29 were independently non-essential and capable of functioning in the direction of NADPH synthesis. To identify all reactions within iRsp1095 capable of producing NADPH to support growth, all 29 non-essential NADPH-requiring reactions within iRsp1095, capable carrying flux the direction of NADPH synthesis, were turned off. This resulted in a predicted growth rate of 0. Optimal growth was restored solely by turning on the transhydrogenase reaction, consistent with transhydrogenase being sufficient for generating NADPH required for growth. All other reactions capable of independently restoring growth, while the other NADPH-requiring reactions were still off, were considered as a candidate NADPH producing reaction (Table 3).
To assess the predicted NADPH demand during aerobic or photosynthetic growth across growth substrates (i.e., succinate, glucose and acetate), all predicted NADPH generating reactions (Table 3) set to have a zero flux, except the transhydrogenase reaction. Using a previously described mixed integer linear programming approach [53, 54], 1000 alternative optimal FBA solutions were identified under each condition. The flux through the transhydrogenase reaction, and thus the amount of NADPH predicted to be required, under each condition was averaged over the 1000 alternative optimal solutions and this average was used as an estimate of NADPH demand under those conditions.
To predict fluxes through central metabolism, we used an extension of FBA called E-flux . E-flux limits the maximum and minimum fluxes (vmax and vmin respectively) through the reactions in the network based on genome-wide gene expression measurements. To achieve this publicly available microarray data obtained from cells grown on succinate and glucose (GEO platform GPL162), as well as from cells grown acetate (this study), were normalized and used to constrain the fluxes through each reaction in the network as previously described . For reactions without gene-to-protein-to-reaction (GPR) assignments, the fluxes through these reactions were allowed to have a vmax of 100 mmol/g DW h and a vmin of 0 or −100 mmol/g DW h, if the reactions were forward only or reversible respectively. For reactions catalyzed by isozymes, the expression value of gene for the isozyme with the highest expression was used to constrain the reaction, while for multi-subunit enzymes the gene for the subunit with the lowest expression was used to constrain the reaction. After setting the upper and lower bounds, subsequent simulations were conducted with FBA as described above.
The previously published genome-scale model for R. sphaeroides iRsp1095  was used as the starting point for a 2-step model refinement. In the first step, PM data was used to guide model refinement, which involved the manual addition and removal of reactions to bring it into better alignment with the PM data. PM data for carbon (C), nitrogen (N), phosphorus (N) and sulfur (S) utilization were compared to model predictions from FBA simulations in which equivalent compounds were provided as the sole sources of these nutrients. The uptake rate of the tested carbon source was set to −4 mmol/g DW h, while that of N, P or S sources was set to −1 mmol/g DW h, as these are in the range of uptake rates typically observed in R. sphaeroides. Succinate was used as the C source when testing for N, P and S utilization, while ammonium, inorganic phosphate and inorganic sulfate served as N, P and S sources when accessing C utilization (consistent with the PM analysis). For these simulations, nutrients which resulted in a FBA predicted growth rate greater than zero were considered growth substrates. N o G rowth-G rowth (NGG – no growth predicted by model but growth observed in PM) inconsistencies were manually rectified by addition of appropriate transport and/or enzymatic reactions from the multi-organism databases KEGG  and BRENDA [57, 58]. The required enzymatic reaction(s) were added to the model based on one of the following 2 criteria: (i) the presence genes in the R. sphaeroides genome encoding the proteins potentially capable of catalyzing the new reaction(s) to be added; and (ii) if no putative enzymes were identified, the metabolic route that required the addition of the fewest reactions to iRsp1095 was selected. G rowth-N o G rowth (GNG – growth predicted by model but no growth observed in PM) inconsistencies in iRsp1095 were resolved by removal of transport reactions for the substrate in question, when this did not introduce and new inconsistencies with the PM data.
In a second step of model refinement, putative enzymes capable of catalyzing reactions added to iRsp1095 where identified by BLAST searches using the protein sequences from other organisms previously verified to carry out the reaction in question. A BLAST E-value cutoff of 10e-20 was selected as a threshold for significance. Enzymatic functions not previously included in iRsp1095 and which were encoded by genes without any previously defined specific function were considered as newly annotated genes, while those with previously defined putative functions were considered as having additional functionality (Additional file 3: Table S4). Updated information from KEGG  database, new information obtained from mutant analysis in this study, and data from recent literature searches were incorporated to generate iRsp1140. iRsp1140 in SBML format is provided in Additional file 5 and can be accessed in the BioModels database with ID MODEL1304240000.
Flux balance analysis
Nicotinamide adenine dinucleotide phosphate
Nicotinamide adenine dinucleotide.
This work was funded in part by the Department of Energy, Office of Science, Great Lakes Bioenergy Research Center (DE-FC02-07ER64494), and the Genomics:GTL and SciDAC Programs (DE-FG02-04ER25627). SI was supported during part of this work by a William H. Peterson Predoctoral Fellowship from the University of Wisconsin-Madison Bacteriology Department.
- Cavill R, Kamburov A, Ellis JK, Athersuch TJ, Blagrove MS, Herwig R, Ebbels TM, Keun HC: Consensus-phenotype integration of transcriptomic and metabolomic data implies a role for metabolism in the chemosensitivity of tumour cells. PLoS Comput Biol. 2011, 7 (3): e1001113-10.1371/journal.pcbi.1001113.PubMedPubMed CentralView ArticleGoogle Scholar
- Hirai MY, Yano M, Goodenowe DB, Kanaya S, Kimura T, Awazuhara M, Arita M, Fujiwara T, Saito K: Integration of transcriptomics and metabolomics for understanding of global responses to nutritional stresses in Arabidopsis thaliana. Proc Natl Acad Sci USA. 2004, 101 (27): 10205-10210. 10.1073/pnas.0403218101.PubMedPubMed CentralView ArticleGoogle Scholar
- Ideker T, Thorsson V, Ranish JA, Christmas R, Buhler J, Eng JK, Bumgarner R, Goodlett DR, Aebersold R, Hood L: Integrated genomic and proteomic analyses of a systematically perturbed metabolic network. Science. 2001, 292 (5518): 929-934. 10.1126/science.292.5518.929.PubMedView ArticleGoogle Scholar
- Joyce AR, Palsson BO: The model organism as a system: integrating 'omics' data sets. Nat Rev Mol Cell Biol. 2006, 7 (3): 198-210. 10.1038/nrm1857.PubMedView ArticleGoogle Scholar
- Ratnakumar S, Hesketh A, Gkargkas K, Wilson M, Rash BM, Hayes A, Tunnacliffe A, Oliver SG: Phenomic and transcriptomic analyses reveal that autophagy plays a major role in desiccation tolerance in Saccharomyces cerevisiae. Mol Biosyst. 2011, 7 (1): 139-149. 10.1039/c0mb00114g.PubMedView ArticleGoogle Scholar
- Oberhardt MA, Palsson BO, Papin JA: Applications of genome-scale metabolic reconstructions. Mol Syst Biol. 2009, 5: 320-PubMedPubMed CentralView ArticleGoogle Scholar
- Atsumi S, Higashide W, Liao JC: Direct photosynthetic recycling of carbon dioxide to isobutyraldehyde. Nat Biotechnol. 2009, 27 (12): 1177-1180. 10.1038/nbt.1586.PubMedView ArticleGoogle Scholar
- Gronenberg LS, Marcheschi RJ, Liao JC: Next generation biofuel engineering in prokaryotes. Curr Opin Chem Biol. 2013, 17 (3): 462-471. 10.1016/j.cbpa.2013.03.037.PubMedPubMed CentralView ArticleGoogle Scholar
- Jaschke PR, Saer RG, Noll S, Beatty JT: Modification of the genome of Rhodobacter sphaeroides and construction of synthetic operons. Methods Enzymol. 2011, 497: 519-538.PubMedView ArticleGoogle Scholar
- Tabita FR: The biochemistry and metabolic regulation of carbon metabolism and CO2-fixation in purple bacteria. Anoxygenic Photosynthetic Bacteria. Edited by: Blankenship RE, Madigan MT, Bauer CE, Blankenship RE, Madigan MT, Bauer CE. 1995, The Netherlands: Kluwer Academic Publishers, 885-914.Google Scholar
- Imam S, Yilmaz S, Sohmen U, Gorzalski AS, Reed JL, Noguera DR, Donohue TJ: iRsp1095: a genome-scale reconstruction of the Rhodobacter sphaeroides metabolic network. BMC Syst Biol. 2011, 5: 116-10.1186/1752-0509-5-116.PubMedPubMed CentralView ArticleGoogle Scholar
- Mackenzie C, Eraso JM, Choudhary M, Roh JH, Zeng X, Bruscella P, Puskas A, Kaplan S: Postgenomic adventures with Rhodobacter sphaeroides. Annu Rev Microbiol. 2007, 61: 283-307. 10.1146/annurev.micro.61.080706.093402.PubMedView ArticleGoogle Scholar
- Yilmaz LS, Kontur WS, Sanders AP, Sohmen U, Donohue TJ, Noguera DR: Electron partitioning during light- and nutrient-powered hydrogen production by Rhodobacter sphaeroides. Bioenerg Res. 2010, 1: 55-66.View ArticleGoogle Scholar
- Kim E, Lee M, Kim M, Lee JK: Molecular hydrogen production by nitrogenase of Rhodobacter sphaeroides and by Fe-only hydrogenase of Rhodospirillum rubrum. Int J Hydrogen Energy. 2008, 33 (5): 1516-1521. 10.1016/j.ijhydene.2007.09.044.View ArticleGoogle Scholar
- Kontur WS, Ziegelhoffer EC, Spero MA, Imam S, Noguera DR, Donohue TJ: Pathways involved in reductant distribution during photobiological H(2) production by Rhodobacter sphaeroides. Appl Environ Microbiol. 2011, 77 (20): 7425-7429. 10.1128/AEM.05273-11.PubMedPubMed CentralView ArticleGoogle Scholar
- Kien NB, Kong IS, Lee MG, Kim JK: Coenzyme Q10 production in a 150-l reactor by a mutant strain of Rhodobacter sphaeroides. J Ind Microbiol Biotechnol. 2010, 37 (5): 521-529. 10.1007/s10295-010-0699-4.PubMedView ArticleGoogle Scholar
- Khatipov E, Miyake M, Miyake J, Asada Y: Polyhydroxybutyrate accumulation and hydrogen evolution by Rhodobacter sphaeroides as a function of nitrogen availability. Biohydrogen. 1999, III: 157-161.View ArticleGoogle Scholar
- Sasaki K, Morikawa H, Kishibe T, Mikami A, Harada T, Ohta M: Practical removal of radioactivity from sediment mud in a swimming pool in Fukushima, Japan by immobilized photosynthetic bacteria. Biosci Biotechnol Biochem. 2012, 76 (4): 859-862. 10.1271/bbb.110853.PubMedView ArticleGoogle Scholar
- Connor MR, Atsumi S: Synthetic biology guides biofuel production. J Biomed Biotechnol. 2010, doi:10.1155/2010/541698.Google Scholar
- Wahlund TM, Conway T, Tabita FR: Bioconversion of CO2 to ethanol and other compounds. American Chemical Society Division of Fuel Chemistry. 1996, 41: 1403-1405.Google Scholar
- Bochner BR: New technologies to assess genotype-phenotype relationships. Nat Rev Genet. 2003, 4 (4): 309-314.PubMedView ArticleGoogle Scholar
- Bochner BR, Gadzinski P, Panomitros E: Phenotype microarrays for high-throughput phenotypic testing and assay of gene function. Genome Res. 2001, 11 (7): 1246-1255. 10.1101/gr.186501.PubMedPubMed CentralView ArticleGoogle Scholar
- Borglin S, Joyner D, DeAngelis KM, Khudyakov J, D'Haeseleer P, Joachimiak MP, Hazen T: Application of phenotypic microarrays to environmental microbiology. Curr Opin Biotechnol. 2012, 23 (1): 41-48. 10.1016/j.copbio.2011.12.006.PubMedView ArticleGoogle Scholar
- Feist AM, Henry CS, Reed JL, Krummenacker M, Joyce AR, Karp PD, Broadbelt LJ, Hatzimanikatis V, Palsson BO: A genome-scale metabolic reconstruction for Escherichia coli K-12 MG1655 that accounts for 1260 ORFs and thermodynamic information. Mol Syst Biol. 2007, 3: 121-PubMedPubMed CentralView ArticleGoogle Scholar
- Oh YK, Palsson BO, Park SM, Schilling CH, Mahadevan R: Genome-scale reconstruction of metabolic network in Bacillus subtilis based on high-throughput phenotyping and gene essentiality data. J Biol Chem. 2007, 282 (39): 28791-28799. 10.1074/jbc.M703759200.PubMedView ArticleGoogle Scholar
- Bragg PD: Site-directed mutagenesis of the proton-pumping pyridine nucleotide transhydrogenase of Escherichia coli. Biochim Biophys Acta. 1998, 1365 (1–2): 98-104.PubMedView ArticleGoogle Scholar
- Bragg PD, Davies PL, Hou C: Function of energy-dependent transhydrogenase in Escherichia coli. Biochem Biophys Res Commun. 1972, 47 (5): 1248-1255. 10.1016/0006-291X(72)90969-2.PubMedView ArticleGoogle Scholar
- Nelson DL, Cox MM: Lehninger: Principles of Biochemistry. 2005, New York: W. H. Freeman and CompanyGoogle Scholar
- Sauer U, Canonaco F, Heri S, Perrenoud A, Fischer E: The soluble and membrane-bound transhydrogenases UdhA and PntAB have divergent functions in NADPH metabolism of Escherichia coli. J Biol Chem. 2004, 279 (8): 6613-6619.PubMedView ArticleGoogle Scholar
- Lee HC, Kim JS, Jang W, Kim SY: High NADPH/NADP+ ratio improves thymidine production by a metabolically engineered Escherichia coli strain. J Biotechnol. 2010, 149 (1–2): 24-32.PubMedView ArticleGoogle Scholar
- Hickman JW, Barber RD, Skaar EP, Donohue TJ: Link between the membrane-bound pyridine nucleotide transhydrogenase and glutathione-dependent processes in Rhodobacter sphaeroides. J Bacteriol. 2002, 184 (2): 400-409. 10.1128/JB.184.2.400-409.2002.PubMedPubMed CentralView ArticleGoogle Scholar
- Yang Z, Savchenko A, Yakunin A, Zhang R, Edwards A, Arrowsmith C, Tong L: Aspartate dehydrogenase, a novel enzyme identified from structural and functional studies of TM1643. J Biol Chem. 2003, 278 (10): 8804-8808. 10.1074/jbc.M211892200.PubMedView ArticleGoogle Scholar
- Yoneda K, Kawakami R, Tagashira Y, Sakuraba H, Goda S, Ohshima T: The first archaeal L-aspartate dehydrogenase from the hyperthermophile Archaeoglobus fulgidus: gene cloning and enzymological characterization. Biochim Biophys Acta. 2006, 1764 (6): 1087-1093. 10.1016/j.bbapap.2006.04.006.PubMedView ArticleGoogle Scholar
- Fuhrer T, Fischer E, Sauer U: Experimental identification and quantification of glucose metabolism in seven bacterial species. J Bacteriol. 2005, 187 (5): 1581-1590. 10.1128/JB.187.5.1581-1590.2005.PubMedPubMed CentralView ArticleGoogle Scholar
- Kiley PJ, Kaplan S: Molecular genetics of photosynthetic membrane biosynthesis in Rhodobacter sphaeroides. Microbiol Rev. 1988, 52 (1): 50-69.PubMedPubMed CentralGoogle Scholar
- Schellenberger J, Park JO, Conrad TM, Palsson BO: BiGG: a Biochemical Genetic and Genomic knowledgebase of large scale metabolic reconstructions. BMC Bioinformatics. 2010, 11: 213-10.1186/1471-2105-11-213.PubMedPubMed CentralView ArticleGoogle Scholar
- Ames GF: Bacterial periplasmic transport systems: structure, mechanism, and evolution. Annu Rev Biochem. 1986, 55: 397-425. 10.1146/annurev.bi.55.070186.002145.PubMedView ArticleGoogle Scholar
- Higgins CF: ABC transporters: physiology, structure and mechanism–an overview. Res Microbiol. 2001, 152 (3–4): 205-210.PubMedView ArticleGoogle Scholar
- Linton KJ, Higgins CF: The Escherichia coli ATP-binding cassette (ABC) proteins. Mol Microbiol. 1998, 28 (1): 5-13.PubMedView ArticleGoogle Scholar
- Rees DC, Johnson E, Lewinson O: ABC transporters: the power to change. Nat Rev Mol Cell Biol. 2009, 10 (3): 218-227. 10.1038/nrm2646.PubMedPubMed CentralView ArticleGoogle Scholar
- Yoon SH, Han MJ, Jeong H, Lee CH, Xia XX, Lee DH, Shim JH, Lee SY, Oh TK, Kim JF: Comparative multi-omics systems analysis of Escherichia coli strains B and K-12. Genome Biol. 2012, 13 (5): R37-10.1186/gb-2012-13-5-r37.PubMedPubMed CentralView ArticleGoogle Scholar
- Barua D, Kim J, Reed JL: An automated phenotype-driven approach (GeneForce) for refining metabolic and regulatory models. PLoS Comput Biol. 2010, 6 (10): e1000970-10.1371/journal.pcbi.1000970.PubMedPubMed CentralView ArticleGoogle Scholar
- Orth JD, Conrad TM, Na J, Lerman JA, Nam H, Feist AM, Palsson BO: A comprehensive genome-scale reconstruction of Escherichia coli metabolism–2011. Mol Syst Biol. 2011, 7: 535-PubMedPubMed CentralView ArticleGoogle Scholar
- Zomorrodi AR, Maranas CD: Improving the iMM904 S. cerevisiae metabolic model using essentiality and synthetic lethality data. BMC Syst Biol. 2010, 4: 178-10.1186/1752-0509-4-178.PubMedPubMed CentralView ArticleGoogle Scholar
- Sistrom WR: A requirement for sodium in the growth of Rhodopseudomonas spheroides. J Gen Microbiol. 1960, 22: 778-785. 10.1099/00221287-22-3-778.PubMedView ArticleGoogle Scholar
- Borglin S, Joyner D, Jacobsen J, Mukhopadhyay A, Hazen TC: Overcoming the anaerobic hurdle in phenotypic microarrays: generation and visualization of growth curve data for Desulfovibrio vulgaris Hildenborough. J Microbiol Methods. 2009, 76 (2): 159-168. 10.1016/j.mimet.2008.10.003.PubMedView ArticleGoogle Scholar
- Reasoner DJ, Geldreich EE: A new medium for the enumeration and subculture of bacteria from potable water. Appl Environ Microbiol. 1985, 49 (1): 1-7.PubMedPubMed CentralGoogle Scholar
- Schafer A, Tauch A, Jager W, Kalinowski J, Thierbach G, Puhler A: Small mobilizable multi-purpose cloning vectors derived from the Escherichia coli plasmids pK18 and pK19: selection of defined deletions in the chromosome of Corynebacterium glutamicum. Gene. 1994, 145 (1): 69-73. 10.1016/0378-1119(94)90324-7.PubMedView ArticleGoogle Scholar
- Tavano CL, Podevels AM, Donohue TJ: Identification of genes required for recycling reducing power during photosynthetic growth. J Bacteriol. 2005, 187 (15): 5249-5258. 10.1128/JB.187.15.5249-5258.2005.PubMedPubMed CentralView ArticleGoogle Scholar
- Livak KJ, Schmittgen TD: Analysis of relative gene expression data using real-time quantitative PCR and the 2(-Delta Delta C(T)) Method. Methods. 2001, 25 (4): 402-408. 10.1006/meth.2001.1262.PubMedView ArticleGoogle Scholar
- Varma A, Palsson BO: Metabolic flux balancing: basic concepts, scientific and practical use. Nat Biotechnol. 1994, 12: 994-998. 10.1038/nbt1094-994.View ArticleGoogle Scholar
- Mahadevan R, Schilling CH: The effects of alternate optimal solutions in constraint-based genome-scale metabolic models. Metab Eng. 2003, 5 (4): 264-276. 10.1016/j.ymben.2003.09.002.PubMedView ArticleGoogle Scholar
- Lee S, Phalakornkule C, Domach MM, Grossmann IE: Recursive MILP model for finding all the alternate optima in LP models for metabolic networks. Computers & Chemical Engineering. 2000, 24 (2–7): 711-716.View ArticleGoogle Scholar
- Reed JL, Palsson BO: Genome-scale in silico models of E. coli have multiple equivalent phenotypic states: assessment of correlated reaction subsets that comprise network states. Genome Res. 2004, 14 (9): 1797-1805. 10.1101/gr.2546004.PubMedPubMed CentralView ArticleGoogle Scholar
- Colijn C, Brandes A, Zucker J, Lun DS, Weiner B, Farhat MR, Cheng TY, Moody DB, Murray M, Galagan JE: Interpreting expression data with metabolic flux models: predicting Mycobacterium tuberculosis mycolic acid production. PLoS Comput Biol. 2009, 5 (8): e1000489-10.1371/journal.pcbi.1000489.PubMedPubMed CentralView ArticleGoogle Scholar
- Kanehisa M, Goto S, Kawashima S, Nakaya A: The KEGG databases at GenomeNet. Nucleic Acids Res. 2002, 30 (1): 42-46. 10.1093/nar/30.1.42.PubMedPubMed CentralView ArticleGoogle Scholar
- Scheer M, Grote A, Chang A, Schomburg I, Munaretto C, Rother M, Sohngen C, Stelzer M, Thiele J, Schomburg D: BRENDA, the enzyme information system in 2011. Nucleic Acids Res. 2011, 39 (Database issue): D670-D676.PubMedPubMed CentralView ArticleGoogle Scholar
- Schomburg I, Chang A, Schomburg D: BRENDA, enzyme data and metabolic information. Nucleic Acids Res. 2002, 30 (1): 47-49. 10.1093/nar/30.1.47.PubMedPubMed CentralView ArticleGoogle Scholar
- Alber BE: Biotechnological potential of the ethylmalonyl-CoA pathway. Appl Microbiol Biotechnol. 2011, 89 (1): 17-25. 10.1007/s00253-010-2873-z.PubMedView ArticleGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.