Metabolic network reconstruction
A draft model of G. metallireducens
was built by using pair-wise BLASTp comparison of the G. metallireducens
genome with the genomes of the several high-quality base models in Genomatica model database including previously published G. sulfurreducens
], Escherichia coli
]and Bacillus subtilis
] models. The G. metallireducens
draft model comprised 514 reactions. Among the base models used, G. sulfurreducens
contributed 93% of the top BLASTp matches; this confirmed the close relationship between these two organisms. The G. metallireducens
draft model captured significant portions of central metabolism, and the biosynthetic pathways for amino acids, nucleotides, and lipids.
The reactions and their gene associations in the draft model of G. metallireducens
were evaluated manually based on gene annotations, published biochemical and physiological information, and external references as previously described [35
]. The remaining genes were also reviewed for inclusion in the reconstructed network. A biomass demand reaction based on the combination of biomass components that were experimentally determined in G. metallireducens
and represented in the published G. sulfurreducens
] was used in G. metallireducens
model. Similarly, the energy parameters such as growth-associated energy requirements in the published G. sulfurreducens
] were used in the G. metallireducens
model for the close relationship between these two organisms.
The unique metabolic capabilities of G. metallireducens
to degrade monoaromatic compounds were reconstructed in the metabolic model. Monoaromatic compounds such as toluene, phenol, cresol, benzoate, benzaldehyde, and benzylalcohol are converted into benzoyl-CoA and then through the benzoyl-CoA degradation pathway to acetyl-CoA [19
]. Specifically, benzylalcohol and benzaldehyde are oxidized by dehydrogenases to benzoate, which is then converted into benzoyl-CoA by benzoate CoA ligase, whereas cresol and phenol are converted to 4-hydroxybenzoate and then reduced to benzoyl-CoA through 4-hydroxybenzoyl-CoA. Toluene is converted to benzoyl-CoA via benzylsuccinyl-CoA.
For gap filling, the ability of the metabolic network to synthesize a full complement of amino acids, nucleotides, lipids, carbohydrates, and cofactors from a minimal medium containing the known electron donors and acceptors was assessed. The missing reactions in the pathways were identified and reviewed. Some missing reactions were associated with G. metallireducens
genes based on biochemical or genomic evidences and were included in the reconstructed network. Other missing reactions were added to the model as non-gene associated reactions to enable the reconstructed network to synthesize metabolites for biomass formation. The reconstructed network contains 30 non-gene associated reactions with different justification. These non-gene associated reactions fell into several categories: 2 reactions, 2-Oxo-4-methyl-3-carboxypentanoate decarboxylation and L-glutamate 5-semialdehyde dehydratase, are non-enzymatic conversions that happen spontaneously under physiological conditions; 4 gas diffusion processes allow the transport of these gases; 1 reaction is for ATP maintenance requirement; 5 transporter reactions for electron donors ensure consistency with growth results; and 18 non-gene associated reactions are required for biomass formation under known growth conditions (see Additional file 1
for details). Non-gene associated reactions in the latter two categories are presumptive metabolic functions encoded potentially by unknown genes, and thus will be subjected for further genomic and biochemical investigation in the future.
Simulations were also utilized to understand individual reactions in the network. For example, the initial step of benzoyl-CoA degradation pathway is catalyzed by a benzoyl-CoA reductase. In Thauera aromatica
, benzoyl-CoA reductase reduces the aromatic ring in two single-electron transfer steps to yield cyclohexa-1,5-diene-1-carbonyl-CoA with stoichiometric 2-ATP hydrolysis [36
]. To understand the ATP hydrolysis stoichiometry associated with benzoyl-CoA reduction in G. metallireducens
, biomass was collected from G. metallireducens
cells grown with benzoate in chemostat and the experimental results were compared to simulation results where different ATP hydrolysis stoichiometry was assumed for the benzoyl-CoA reduction (Figure ). The experimental growth data, a protein yield of 8.2 (± 0.2) mg/L from 1.0 mM benzoate at a dilution rate of 0.05 h-1, predicted a biomass yield of 0.59 (± 0.02) gdw per mol of electrons at benzoate flux of 2.81 mmol/gdw/h assuming 46% biomass content as protein. The biomass yields from a benzoate flux of 2.81 mmol/gdw/h with an ATP hydrolysis stoichiometry for benzoyl-CoA reduction between 0–4 were simulated and the in silico results were compared to the experimental result (Figure ). As shown in Figure , the 2-ATP hydrolysis stoichiometry for benzoyl-CoA reduction closely matched the experimental result. Thus, the benzoyl-CoA reduction in G. metallireducens
model shared the same ATP hydrolysis stoichiometry as in T. aromatica
Figure 1 ATP hydrolysis stoichiometry of benzoyl-CoA reduction in G. metallireducens metabolic model. Benzoyl-CoA reductase reactions with 0–4 ATP hydrolysis stoichiometry were applied in simulations. The predicted biomass yields as gdw/mol electrons were (more ...)
Metabolic network of G. metallireducens
At its completion, the manually curated genome-scale network of G. metallireducens
included 747 genes of the 3389 genes in the G. metallireducens
genome (Table ). The G. metallireducens
metabolic model contains 697 reactions and 769 metabolites including 58 extracellular metabolites. The detailed list of genes, reactions, metabolites, and gene-protein-reaction (GPR) associations in the metabolic model are available as supplementary information (see Additional file 2
). The characteristics of the G. metallireducens
model are similar to those of the updated G. sulfurreducens
model (the published G. sulfurreducens
] was updated to incorporate the most recent results from both experimental and computational research, see Additional file 3
for detailed list of reactions). The 697 reactions of the G. metallireducens
model were categorized into 9 functional groups and the results were summarized in Figure . Among different functional groups, reactions for biosynthesis of amino acids, lipids and cell wall components, cofactors, and nucleic acids are the most abundant, accounting for almost 70% of all the reactions. Currently, there are 76 reactions associated with transporting metabolites, including redundant transporters for the some extracellular metabolites. In addition, G. metallireducens
genome contains many genes encoding components of these ABC transporters that are not included in the network because the substrate specificity of these ABC transporters is largely unknown. Future experiments on physiology in different environments will provide additional evidence to include these transporting systems.
Characteristics of the G. metallireducens genome-scale metabolic model compared with the G. sulfurreducens model.
Functional classification of metabolic reactions in G. metallireducens model. The 697 reactions in G. metallireducens model were categorized into 9 functional groups.
To study the conservation between the G. metallireducens and G. sulfurreducens models, reactions were categorized and compared (Table ). Overall, the two models share 579 common reactions, representing 83% of all G. metallireducens reactions and 89% of all G. sulfurreducens reactions. Among these common reactions, 140 reactions related to amino acid biosynthesis, 119 reactions in lipids and cell walls metabolism, 104 reactions of cofactor biosynthesis, and 74 reactions for nucleotide metabolism are shared between the two models, which together account for 75% of all the common reactions.
Comparison of reactions in G. metallireducens and G. sulfurreducens metabolic models.
can utilize a much wider range of electron donors and acceptors [2
] than G. sulfurreducens
, which uses only acetate, H2
and lactate as the electron donors. The G. metallireducens
metabolic model contains 118 unique reactions out found in the G. sulfurreducens
model. Many of these unique reactions reflect of the diversity of G. metallireducens
' metabolic capabilities. For example, the G. metallireducens
model contains 32 unique reactions involved in the degradation pathways of aromatic compounds. G. metallireducens
can also utilize several substrates other than the aromatic compounds that G. sulfurreducens
does not use. G. metallireducens
contains several alcohol dehydrogenase genes with substrate specificities for ethanol, propanol, and butanol that are not believed to be present in G. sulfurreducens
. The enzymes coded by these genes catalyze several unique reactions that are key steps in the utilization of these alcohol substrates. The corresponding transporter reactions were also added to the G. metallireducens
model, but not in the G. sulfurreducens
model. Similarly, a butyrate kinase reaction unique to the G. metallireducens
model allows the utilization of butyrate. These unique reactions in the G. metallireducens
model enable the growth of the G. metallireducens
model on a wide range of substrates and has accurately captured the known physiological characteristics of G. metallireducens
In silico characterization of G. metallireducens metabolism
Simulations of metabolism with the G. metallireducens model were utilized to make testable predictions of G. metallireducens metabolism. In silico characterization of G. metallireducens growth with different substrates was carried out and the results are summarized in Figure . The growth of G. metallireducens was simulated using 9 substrates as electron donors with either Fe(III) or fumarate as electron acceptor and setting the electron donor or electron acceptor as the limiting factor. Under all 4 conditions, 4-cresol provided the largest biomass yield per substrate (calculated as gdw/mol substrate) for G. metallireducens growth while acetate produced the lowest biomass yield among the 9 substrates tested. Aromatic compounds generated higher biomass yield per mole of substrate than acetate and ethanol. The biomass yields for the 9 substrates under electron acceptor limiting conditions were similar to those under electron donor limiting conditions (Figure &, &), suggesting G. metallireducens might not fully utilize the excess electron donors under acceptor limiting conditions. During Fe(III) reduction, G. metallireducens had similar predicted biomass yields on pyruvate and benzoate. This is because of the energy gain associated with the conversion of pyruvate to acetyl-CoA, whereas benzoate degrades to acetyl-CoA and energy is consumed to convert acetyl-CoA to pyruvate for biomass.
Figure 3 In silico characterization of G. metallireducens metabolism. G. metallireducens was simulated to grow with 9 different electron donors (acetate, ethanol, pyruvate, benzoate, benzaldehyde, benzyl alcohol, phenol, toluene, and 4-cresol) and 2 electron acceptors (more ...)
When biomass yields were calculated based on acceptor consumed, pyruvate resulted in the highest biomass yield per mol of electron acceptor under all conditions, suggesting that pyruvate may have advantages over other substrates in electron acceptor limiting environments. Acetate and ethanol had similar biomass yield per electron acceptor compared to the aromatic compounds, suggesting that they may produce the same amount of biomass when limited to same amount of electron acceptors in growth medium. Therefore, the modeling study rapidly predicted the growth yields of G. metallireducens under varying nutrient conditions.
Comparison of G. metallireducens metabolic model to G. sulfurreducens model
also contains genes for several pathways in central metabolism that do not have corresponding homologues in G. sulfurreducens
. Therefore, the unique reactions associated with these genes may provide specific metabolic capacities in the G. metallireducens
model. For example, G. metallireducens
is known to use nitrate as an electron acceptor [2
] and the model predicts such capability. The G. metallireducens
network has transporters for nitrate uptake via nitrite antiport and the nitrate reductase (cytochrome c
) to reduce nitrate, which are not present in G. sulfurreducens
. These two reactions together allow electrons from cytochrome c
to be transferred to nitrate. Nitrate is reduced and the resulting intracellular nitrite is exchanged with extracellular nitrate using the antiporter. The G. metallireducens
model also contains a nitrite proton antiporter and nitrite reductase that further reduce nitrite to ammonium and allows the utilization of nitrite.
Other reactions that are not present in the G. sulfurreducens model include the glucose 6-phosphate dehydrogenase, 6-phosphogluconolactonase, and phosphogluconate dehydrogenase, which is a part of the oxidative branch of the pentose phosphate pathway. This branch provides an efficient way to produce D-ribose-5-phosphate and is an important source of NADPH. However, simulations of G. metallireducens growth predict that G. metallireducens can produce D-ribose-5-phosphate by using glyceraldehyde 3-phosphate and D-fructose-6-phosphate to produce D-xylulose 5-phosphate through transketolase and transaldolase, and then converting D-xylulose 5-phosphate to D-ribose-5-phosphate, similar as simulation of G. sulfurreducens growth. Simulations also suggest that G. metallireducens can generate NADPH through isocitrate dehydrogenase (NADP) and other reactions with NADP as cofactor in a manner similar to the G. sulfurreducens network. There was no significant change in the expression levels of these genes during growth with acetate vs. benzoate couples with Fe(III) reduction. The exact role of this oxidative branch of pentose pathway in G. metallireducens requires further examination.
ATP-consuming futile cycles involve multiple reactions allowing the interconversion between metabolites with a net ATP consumption, and can decrease growth. However, it is hypothesized that these futile cycles balance the metabolite pools to make other key reactions thermodynamically feasible [37
]. Recent 13
C-labeling studies in G. metallireducens
confirmed the existence of an ATP-consuming futile cycle between pyruvate and phosphoenolpyruvate [37
The central metabolism of G. metallireducens has several reactions that are missing in G. sulfurreducens. These reactions include the acetyl-CoA synthetase (ACS), acetyl-CoA hydrolase (ACOAH), and phosphoenolpyruvate carboxylase (PPC) reactions. These reactions may be energetically inefficient because they can participate in futile cycles that drain ATP (Figure ). The acetate activation reaction ACS in G. metallireducens is energetically inefficient (consuming two ATP equivalents to form one acetyl-CoA), compared to the acetyl-CoA transferase (ATO) and the combined acetate kinase/phosphotransacetylase (ACK/PTA) pathway (consuming one ATP equivalent to form one acetyl-CoA) that are present in both models. The ACOAH reaction produces zero ATP to convert acetyl-CoA to acetate and can form futile cycles with the three routes of acetate activation, namely, the ATO, the ACS and the ACK/PTA pathways. A similar futile cycle involves phosphoenolpyruvate carboxylase and phosphoenolpyruvate carboxykinase allowing ATP-consuming interconversion between phosphoenolpyruvate and oxaloacetate (Figure ). Model simulations also predict that increasing fluxes through these reactions result in an energetic penalty and consequently lowered biomass yield (Figure ).
Figure 4 Potential futile cycles in the central metabolism of G. metallireducens model. A, Two examples of energetically inefficient reactions that can form futile cycles in the central metabolism of G. metallireducens model. B, Effect of increasing flux through (more ...)
G. metallireducens growth with electron donors
In order to further investigate the potential for these energetically inefficient reactions to decrease biomass yields, we measured the experimental growth of G. metallireducens in the presence of different electron donors. Unlike G. sulfurreducens, G. metallireducens can oxidize ethanol and pyruvate, thus enabling the further investigation of the central metabolism of the Geobacter species. In order to isolate the effect of the different electron donor oxidation pathways on the yield, a G. metallireducens strain with a dicarboxylic acid transporter that allows growth using fumarate as electron acceptor, was cultured with ethanol and pyruvate as electron donors. The G. metallireducens model simulations suggested that the biomass yield to substrate consumed (calculated as gdw/mol substrate) of pyruvate should be 34% higher than that of ethanol (Figure ). However, the experimental results showed that similar biomass yields were obtained in pyruvate or ethanol cultures (Figure ). HPLC measurements confirmed the complete utilization of pyruvate (data not shown). These results suggested potential energetic inefficiencies during growth with pyruvate. Most likely, the energy-inefficient reactions discussed above were active and resulted in the decreased biomass yield during growth on pyruvate.
Comparison of experimental and predicted biomass yields. The experimental and predicted biomass yields were obtained for growth with fumarate and two different electron donors, namely, pyruvate and ethanol.
Microarray data for the above growth conditions were not readily available. Instead, we analyzed the microarray data for G. metallireducens
growing with benzoate versus acetate [21
]. Among G. metallireducens
genes that were significantly up-regulated (> 50%) by growth with benzoate versus acetate, genes encoding for ACS, PPC, and ACOAH were up-regulated by 161% to 270%. The up-regulation of these genes encoding for the energy-inefficient reactions during growth with the complex substrate benzoate indicated the involvement of these energy-inefficient reactions in the metabolism of G. metallireducens
when high-energy substrate benzoate is consumed. It is likely that similar up-regulation of these genes occurs during growth with pyruvate and not with ethanol.
Simulations of the G. metallireducens
growth were performed using maximal biomass yield as the objective function, usually to be true in natural growth conditions where nutrients are limited. However, optimizing biomass yield may not always be the growth strategy of choice and recent studies have illustrated that under conditions of nutrient excess, maximizing the ATP production might be the chosen growth strategy [38
]. Growth with high-energy substrates may also lead to a growth strategy of maximal ATP production. Under this growth strategy, the energetically inefficient reactions can be advantageous for utilizing the ATP produced. The abundance of these energetically inefficient reactions in G. metallireducens
suggests that the evolution of G. sulfurreducens
and G. metallireducens
might have occurred in environments with different nutrient levels. In this scenario, G. sulfurreducens
probably evolved in predominantly acetate limiting environments, whereas G. metallireducens
probably evolved in environments with nutrient excess or with complex nutrients available.
Growth simulations of G. metallireducens using nitrate as electron acceptor
can use nitrate as an electron acceptor [2
]. To better understand this capacity of nitrate respiration, G. metallireducens
growth simulations were performed using nitrate as electron acceptor and acetate, ethanol, pyruvate or benzoate as electron donor. As shown in Figure , when nitrate was used as acceptor for G. metallireducens
growth, benzoate was predicted to give the highest biomass yield to substrate (gdw/mol substrate). Pyruvate and ethanol were predicted to equally produce more biomass per substrate consumed than acetate. However, using pyruvate as substrate allowed the lowest acceptor:donor ratio, whereas benzoate had the highest. Pyruvate as substrate was predicted to have the highest biomass yield per nitrate (gdw/mol nitrate) similar to the cases discussed earlier when Fe(III) or fumarate is the electron acceptor.
Figure 6 Simulated growth of G. metallireducens with nitrate as electron acceptor. Growth of G. metallireducens was simulated using different electron donors under donor-limiting conditions. Biomass yields were predicted for both electron donor (gdw/mol substrate) (more ...)
Growth simulations of G. metallireducens using nitrate, fumarate or Fe(III) as electron acceptor were compared (Figure ). Among the three electron acceptors, nitrate resulted in the highest biomass yield per substrate consumed (gdw/mol substrate) or per electron acceptor consumed (gdw/mol electron acceptor). This is consistent with the higher energy yield coupled to nitrate reduction. The large increase in biomass yield during nitrate reduction relative to Fe(III) reduction predicts that G. metallireducens is not limited by energy generation during nitrate reduction. For example, the fraction of the benzoate used to generate energy during nitrate reduction was predicted to be 63%, compared to 94% of the benzoate used for generating energy during Fe(III) reduction. This requirement of a relatively high fraction of donor for energy generation results in higher substrate utilization rates for the same growth rate during Fe(III) reduction clearly highlighting a significant challenge associated with metal reduction.
Figure 7 Comparison of G. metallireducens growth with different electron acceptors. Growth of G. metallireducens was simulated with the oxidation of four different electron donors (acetate, ethanol, pyruvate, and benzoate) coupled to the reduction of electron (more ...)
Flux distribution comparison between model prediction and 13C labeling results
C isotopomer labeling flux analysis was applied to study a simplified central metabolic network of G. metallireducens
by Tang, et al
]. The results from that study provided an experimentally determined flux distribution. To validate the G. metallireducens
model reconstructed in this work, the flux distribution results from 13
C isotopomer labeling flux analysis study was compared to an in silico
flux distribution from the G. metallireducens
model under the same acetate/Fe(III) growth conditions (21 mmol/gdw/h of acetate uptake flux with acetate as the limiting factor). Fluxes were normalized according to acetate uptake rate that was set at 100%. The 13
C isotopomer labeling flux analysis suggested that about 90% of flux from acetyl-CoA joined the TCA cycle to produce energy and 10% of flux was routed to pyruvate and other intermediates for biomass [37
]. As shown in Figure , model simulations predicted 91.6% of acetate was completely oxidized to CO2
via the complete TCA cycle, compared to the 90.5% from the [1-13
C] acetate isotopomer labeling flux analysis. Overall, the flux distributions were similar (mean of the flux difference = 1% ± 0.8%, R2
= 0.99). The computationally predicted and experimentally determined values were well matched at high fluxes, but less consistent at low fluxes. One difference between the two analysis is that the [1-13
C] acetate isotopomer labeling flux analysis used a network where serine, glycine, and cysteine were derived from 3-phosphoglycerate. However, gene encoding for these functions has not been found in G. metallireducens
, whereas genes for a pathway where serine, glycine, and cysteine were derived from oxaloacetate in the TCA cycle were identified and used in G. metallireducens
genome-scale model and simulations. This may account for some of the differences, such as the differences of fluxes from 3-phosphoglycerate or oxaloacetate to biomass, observed between the model simulation and the 13
C labeling flux analysis results.
Figure 8 Metabolic flux distributions in G. metallireducens by in silico genome-scale modeling. G. metallireducens growth with acetate as electron donor and Fe(III) as electron acceptor was simulated with under 21 mmol/gdw/h of acetate uptake flux with acetate (more ...)
Flux variability analysis defines a feasible range of fluxes for each individual reaction [27
]. A flux variability analysis under the same constraints indicated that most flux values determined by 13
C labeling experiments were within such feasible ranges (data not shown), and validated the consistency between the experimental and predicted results. These results suggested that in silico
growth simulation optimized for biomass formation and flux variability analysis to define the feasible flux ranges together provided a fast and easy alternative method to estimate flux distribution for the metabolism of G. metallireducens
Functional analysis of G. metallireducens mutant phenotype
Genome-scale metabolic model enabled the systems level gene deletion analysis for growth in defined medium. This information will provide important insight into the potential phenotypes associated with gene deletions in genetic investigations. In silico deletion analyses for G. metallireducens growth using electron donor/acceptor pairs of acetate/Fe(III) or acetate/fumarate were completed and the results were shown in Figure . Three possible phenotypes were predicted from the deletion analysis: 1) lethal deletion with no growth observed, 2) silent mutation growing same as wild type, and 3) intermediate phenotype with reduced growth. Simulation results were the same when either Fe(III) or fumarate was used as electron acceptor. More than 68% of all reactions or 80% of all included genes were predicted to have no effect on growth upon deletion. About 30% of all reactions and 19% of included genes were lethal mutations reflecting the inability of the perturbed network to synthesize essential components. Only for 1–2% of all reactions and included genes, deletions were predicted to have an intermediate effect on the growth rate of the G. metallireducens growth under the conditions. These deletion analysis results are similar to the results from the G. sulfurreducens model. This suggests that the core metabolic pathways are conserved among Geobacteraceae, and that the models of G. metallireducens and G. sulfurreducens can be used to represent of the physiology of Geobacteraceae.
Figure 9 Functional analysis of G. metallireducens mutant phenotype. In silico deletion analyses for G. metallireducens growth were completed using electron donor/acceptor pairs of acetate/Fe(III) or acetate/fumarate for every single reaction or every single gene (more ...)