Recent studies have demonstrated that the activation of NADPH oxidase 1 (Nox1) plays an important role in the control of reactive oxygen species and their involvement in vascular physiology and pathophysiology. In order to function properly, Nox1 needs to be available in an optimal state, where it is ready to respond appropriately and efficiently to upstream signals. It must also be able to return quickly to this state as soon as the input signal disappears. While Nox1 activation has been discussed extensively in recent years, mechanisms for enzyme disassembly and proper subunit recovery have not received the same attention and therefore require investigation.phox and inactive Rac1 should be dominant under unstimulated resting conditions, and the proteolytic disassembly pathway should have a low flux, as it is relatively inefficient. The computational results also reveal that the optimal design of the three subunit recovery pathways depends on the intracellular settings of the pathway and that the response speeds of key reversible reactions within the pathway are of great importance.We study the Nox1 system in vascular smooth smucle cells and propose four potential disassembly mechanisms. The analysis consists primarily of large-scale Monte-Carlo simulations whose results are essentially independent of specific parameter values. The computational analysis shows that a specific profile of subunit concentrations is crucial for optimal functioning and responsiveness of the system to input signals. Specifically, free p47Our results provide a systematic basis for understanding the dynamics of Nox1 and yield novel insights into its crucially important disassembly mechanisms. The rigorous comparisons of the relative importance of four potential disassembly pathways demonstrate that disassembly via proteolysis is the least effective mechanism. The relative significance of the other three recovery pathways varies among different scenarios. It is greatly affected by the required response speed of the system and depends critically on appropriate flux balances between forward and reverse reactions. Our findings are predictive and pose novel hypotheses that should be validated with future experiments. Reactive oxygen species (ROS) play crucial roles as signaling molecules in vascular physiology ,2, and aElevated levels of ROS in the vascular system are often the result of several contributing factors at the production and removal side, but they are generally associated with the activation of vascular NADPH oxidase (Nox) -18, whic2.-), one of the major ROS in vessel walls. The Nox family consists of seven catalytic homologues, four of which  are found in the vasculature [NADPH oxidase is a member of a family of enzymes that transfer electrons from NADPH to molecular oxygen , thus prculature . These Nculature ,20,24-26culature ,28, and culature ,24, inclculature , serum-iculature ,31, abnoculature ,32.Under normal physiological conditions, Nox1 serves several beneficial purposes. Importantly, it is a crucial component in several signal transduction pathways , and extphox or NoxO1 [phox or NoxA1 [phox[The control of Nox1 activity is quite complex, as it is associated with the assembly and disassembly of several components, and in order to understand Nox1 and its regulatory function, it is necessary to discuss its components in some detail. Owing to the large body of experimental studies, we have substantial information about Nox1’s molecular composition, intracellular compartmentalization, activation mechanisms, and possible physiological functions ,24,33. Gor NoxO1 ,37, and or NoxA1 ,37, respor NoxA1 . The speor NoxA1 ,40 and txA1 [phox,40,41. IWhile we have learned much about the assembly and activation of Nox1, the equally important disassembly processes have received much less attention. This discrepancy might be due to limitations posed by the current experimental repertoire, as the dynamics of proteins moving within a complex pathway is difficult to assess. For instance, it is still unclear whether the active Nox1 complex disassociates into individual subunits or simply degrades via proteolysis once it is no longer needed. It is also unknown in which fashion the disassociation happens and, in fact, whether the order of disassembly is of any importance.f4 in Figure e.g., kinases) and inhibitors  have led to the suggestion that assembled p47phox might be dephosphorylated, which would result in the deactivation of the active complex [f3 in Figure f2 in Figure Although the specific disassembly mechanisms of Nox1 in VSMCs are not known, studies of similar disassociation pathways for relevant Nox homolgues, or in other types of cells, have shed some light on possible mechanisms. For instance, using semi-recombinant and fully purified systems involving Nox2, membrane-associated guanine activating proteins (GAPs) were experimentally shown to be able to interact with the assembled oxidase complex and thereby to decrease its enzymatic activity by accelerating the hydrolysis of Rac1-bounded GTP . This st complex . This fif14 in Figure f2, f3, and f4), which differ in a subtle manner: f2 is the only pathway that directly recoups the subunit NoxA1p, f3 is the only pathway that directly recoups p47phox, and f4 is the only pathway that directly leads to Rac1GDP. While these individual pathways have not been experimentally verified specifically in VSMCs, they have been reported to play important roles in the disassembly of active enzymes for Nox1 or Nox2 in other types of cells [Based on the current understanding of the dynamics of Nox1 in VSMCs, as well as relevant knowledge of its closest homologue, Nox2, in other cells, we propose a pathway system for Nox1 assembly and disassembly as it is shown in Figure of cells -44, and Given the key roles of active and inactive Nox1 in normal physiology and in pathophysiology, it is necessary to improve our understanding of Nox1 regulation with respect to both, assembly and disassembly. However, developing deeper insights poses a problem, as subtle differences in the rates of the various assembly and disassembly steps can have consequences that we are unable to quantify and interpret with intuition alone. We are therefore proposing in this work a computational modeling approach that allows us to answer questions about the complex dynamics of Nox1. Such questions have not been answered experimentally, and are in fact difficult to assess with purely experimental means. They include the following: Are the four proposed disassembly pathways equally important for the effective reestablishment of an appropriate profile of Nox1 subunits? Or is one, or are several of the pathways, more effective than others? Do any of the pathways dominate the disassembly process? How does the importance of each pathway change with particular cellular demands as well as intracellular factors, such as initial protein concentrations, required response speeds, and the net directions of the involved reactions?These types of questions are difficult to address with experiments, but we will demonstrate here that we can pose and at least partially answer them with mathematical and computational means. Indeed, the mathematical model we develop here not only integrates all available biological information and addresses specific questions, but also answers a number of generic questions that target the principles governing the natural design and dynamics of the Nox1 system. They are of the type: “Why is this system organized in the fashion we see it and not in an alternative fashion that appears to be just as reasonable”? To achieve this level of generality, we will construct our models in a fashion that is only very mildly dependent on parameter values. Expressed differently, we will develop an entire ensemble of models and distill those with superior features. The initial target is the special case of Nox1 in VSMCs, but features of this representative template are likely to have relevance for other members of the Nox family as well.Before we discuss the details of our modelling results, it is beneficial to ascertain that a well-controlled disassembly strategy of Nox1 is indeed important and not at all random. In order to respond to extracellular activators like AngII and PDGF effectively, Nox1 needs to reside in a state that is prepared to receive signals and act on the ROS system in an effective and appropriate manner. We call this state the “Ready-to-Respond State” or “RtRS.” As soon as an input signal has been processed, Nox1 needs to return quickly to RtRS, where ROS production is low and the machinery for generating new ROS is ready for new upstream signals. Thus, RtRS and the dynamics with which Nox1 returns to this state must satisfy two demands, as discussed before. Nox1 needs to be maximally responsive to input signals and, once these cease, return to RtRS as soon as possible.phox and Rac1) were involved in the activation of Nox1 and that they were either totally absent (coded as “0”) or maximally available (coded as “1”), which simplifies the discussion to four possible states (Table S1 and S2) and assess its responsiveness to arbitrary combinations of incoming signal trains of S1, S2, or S1 &S2. Table While many factors affect the performance of Nox1, the distribution of initial subunit concentrations is of particular importance for RtRS to be effective. The impact of such a profile can be demonstrated most lucidly with a thought experiment that makes extreme assumptions: Suppose that only two subunits  and investigates the relationship between the initial protein concentrations and the system’s effectiveness. Based on the findings in the first set of analyses, the second class of simulations further explores the functional importance of the relevant recovery pathways and their relationships to details of the intracellular pathway settings, such as the relative magnitudes of different flux rates and balances between forward and reverse reactions.The Nox1 disassembly system is too complex to permit a comprehensive algebraic analysis of its design features, as it is possible in much simpler systems . Insteadphox (X2) and Nox1active(X1), is not significantly changed [active (X1) [active (X1) activity is up-regulated since ROS production is increased [The second criterion reflects semi-quantitative observations from documented lab experiments that pertain to ROS production after 30 minutes in AngII ,27,46 an changed . (2) Aftncreased .S1, S2, or S3, as well as simultaneous S1 and S2 signals. Among these, three signal sets have been experimentally shown to possess the capacity of activating Nox1 in VSMCs [S3 signal, which is likely to inhibit Nox1 activity, although this has not been directly verified yet in VSMCs.In order to evaluate the system’s responsiveness to different signals and signal combinations, we investigate four distinct sets of input signals, namely individual signals in VSMCs ,27. The phox, Rac1, and NoxA1. Indeed, two questions that need to be investigated are whether their active or inactive forms are dominant under resting conditions, and how different distributions between these states affect the system’s responsiveness to input signals. Because no experimental information is available to address these questions, we explore a large space of possible combinations for initial distributions of the cytosolic components. In order to facilitate this comparative analysis, we assume that the sum of each quantity in its active plus its inactive form is constant, with an arbitrary default value of 100. The initial quantity of a component in active form is assigned by sampling a truncated Gaussian distribution with either a higher mean or a lower mean, as formulated in Equation (1).A factor that could possibly affect the system’s responsiveness is the set of initial concentrations of the system components, especially for the cytosolic subunits p47phox, Rac1GTP, and phosphorylated NoxA1 are initially dominant.Here, “0” and “1” represent two truncated Gaussian distributions with a lower and higher mean, respectively. This strategy yields a total of 8 combinations of initial conditions Table . For insfSS2, fSS3, fSS4, fSS6, fSS8, fSS9 (or fSS10), fSS14, and fSS16 are randomly sampled between 1 and 100 with the same uniform distribution U in log10 space (see details in Methods regarding the choices of specific ranges), which ensures an appropriate spread of probabilities, and the system’s responsiveness is evaluated by the new steady state of Nox1active (X1) under different input signals. Intriguingly, the averages of the new steady state of X1 are essentially unchanged among any of the four sets of input signals and for all combinations of initial conditions  is varied. If the numerical sampling range for this flux is gradually decreased, the system begins to mount responses to input signals (data not shown), given as the averages of new steady states of X1, that gradually increase or decrease under different input signals. An illustration is given for the extreme case of fSS14 = 0  from Table , 3, 4 frphox and Rac1 dominant in their inactive states appears to be a superior strategy, when S1 and S2 signals are encountered. This inference is further supported by simulations with both signals, S1 and S2  in log10 space, and each sampled set is screened against three experimental conditions described before. The wide range is expected to cover all pertinent combinations of fluxes, and therefore appears to be adequate for the initial sampling. Ratios beyond this range would result in either very fast or very slow reactions, which would ultimately render a dependent variable static.While valuable, the experimental data are not sufficient to determine specific parameter values, and it is still necessary to sample the independent fluxes. They are randomly selected between 0.01 and 100 from the uniform distribution f14SS) and the collection of recycling pathways  by screening preliminarily sampled points  at 30 minutes of AngII treatment. This screen filters out different combinations of fluxes. The remaining, successful combinations locate mainly in the upper-left region where the proteolytic flux is smaller than 1 while the recycling flux is larger than 1. Finally, filtering the flux combinations against the third experimental finding, namely up-regulated Nox1active (X1) under PMA treatment, effectively excludes almost all combinations that are located to the very right, where the numerical value of the proteolytic flux is usually larger than ~3, which corresponds to about 0.5 in log10 space.With the settings thus described, we can examine combinations of flux rates of the proteolytic pathway f4SS and t10 space) while the recycling flux is larger than 1. An extended flux distribution analysis  the dissociation of Nox1active than proteolysis. In fact, the fluxes going through these pathways are at least 2 magnitudes (~100 fold) larger, a conclusion that is consistent no matter which of the discussed criteria are used. It is now of interest to explore whether any of the recycling fluxes is more effective than the others. For this analysis, we temporarily ignore the small contribution of proteolysis and set its flux f14SS to zero. To balance this missing efflux from the system, the alleged input influxes f11SS, f12SS, and f13SS are also set to zero, so that the constraints in Equation (4) of the Methods section are again satisfied.As suggested by the results in previous sections, the three recycling pathways appear to play much more important roles for the disassembly of Nox1active are possible . The identification of superior or inferior module designs is achieved by combined comparisons of their responsiveness to input signals, as well as the response times of their “on” and “off” processes. Here, the response time is defined as the time interval within which the system numerically reaches a new steady state after a signal is applied, while “on” and “off” processes, respectively, refer to the responses after a signal is added or removed from the system.Accounting for all combinations, seven design modules for the three recycling pathways of Nox1e Figure . The stef16SS has such a small magnitude that it can be omitted. We analyze the system responses to a combined S1 and S2 input signal and also track how the active Nox1 complex behaves when the input signal is removed.We begin with a closed Nox1 system  constant , which ensures that differences in system performance are due to the module structure at hand, rather than differences in flux magnitudes in a given Monte-Carlo sample. Within this constraint, the remaining independent fluxes  are calculated from (uniformly) randomly sampled rate constants γ6, γ8, and γ9. Sampling rate constants rather than fluxes allows us to inspect the effects of different reaction rates, especially those associated with reversible reactions.In order to keep the initial flux distribution consistent with observations in AngII treatment experiments, we set the control condition such that free p47phox, Rac1, and NoxA1, respectively, are either sampled as “slow” from the uniform distribution U, or as “fast” from the uniform distribution U. The simulation results  in all simulations, and returns to the control level (at “1”) when the input signal is removed in the “off” response.The rate constants, which represent the reversible reactions for activation and deactivation of subunits p47s Figure a indicatf2SS, has the longest response time in both the “on” and “off” responses. By contrast, modules 6 and 7, which contain both recycling pathways f3SS and f4SS have much shorter response times . The response time is an important feature of a signaling system, because it determines its refractory period. Thus, the latter modules are to be considered more efficient than the former.Interestingly, the response times for the “on” and “off” responses are different among the modules. Module 1, which has only one recycling pathway, specifically associated with When the reversible reaction rates within the pathway system are fast in both directions, the differences among all seven modules are rather small in terms of both responsivenss and response times Figure b. Thus, f16SS may affect the disassembly of Nox1. The analysis of this case is complicated by the fact that we have no information regarding the relative magnitudes of the input fluxes and only know that the constraint f2SS + f9SS = f10SS + f16SS must be true. We study two scenarios. In the first, f9SS is larger than f10SS , which tips the balance from NoxA1 to NoxA1p, while in the second scenario the opposite is true.In reality, the system is open, and the efflux f9SS and f10SS, for instance, modules 2, 3, and 6, which do not contain the recycling pathway f2SS, always have a higher up-regulated activity of X1  than modules 1, 4, 5, and 7 .Intriguingly, the system performance is strongly affected by the transition from a closed to an open system , which exhibit shorter response times (with the shortest average time at one third of the other reponse times) when f9SS ≤ f10SS  must be much smaller than the fluxes associated with recycling pathways  is so small that it can be omitted. Several scenarios were explored and three intracellular settings, including the speed of reversible reactions, the magnitude of efflux f16, and the net balance between the fluxes converting NoxA1 and NoxA1P into each other, were investigated. A comprehensive comparison of all simulation results revealed that the first factor, the speed of the reversible reactions, most strongly affects the relative performance of all seven disassembly modules. When the reversible reaction rates are very fast, all modules tend to exhibit a similar performance in terms of responsiveness and response times for almost all tested scenarios , modules 2, 3, and 6 perform better, with shorter response times than others modules. This result strongly implies that in order to achieve an effective response, it is crucial to replenish the subunit pools as quickly as possible. Due to the fast reversible reaction rates, each individual subunit can rapidly be recycled and becomes available again. As a consequence, it becomes less important in which way Nox1active disassociates and the pathway recycles its subunits.There is one important exception. When the efflux f16 and the net balance between the fluxes converting NoxA1 and NoxA1P into each other. As shown in Figure f16 cannot be ignored. The superiority of these three modules is strenghtened by their shorter response times, especially when the flux f9 is smaller than f10  and the direction of the net flux between NoxA1 and NoxA1P. These balances are natural targets for internal control mechanisms of Nox1 dynamics, which at this point are unknown. The computational inferences directly form targeted hypotheses that can and should be tested with laboratory experiments.We have employed computational methods to investigate the effectiveness of alternative strategies for the disassembly of Nox1 and the recycling of its subunits in VSMCs. The results of the Monte-Carlo simulations, which by and large are independent of specific parameter values, strongly indicate that disassembly through recycling is more effective than proteolysis. Furthermore, the relative performance of each recycling pathway is affected by the rates and balances of the reversible reactions in the system, as well as the magnitude of the efflux of phosphorylated NoxA1  is expressed at the plasma membrane ,51 and iphox,53, therphox[phox,36 and Nphox[phox,40, and phox[phox,54,55; t cytosol ,47. It r cytosol ,56, whicphox often occurs together with the activator subunit NoxA1 [phox is mainly catalyzed by protein kinase C (PKC) [f5 in Figure phox, the small GTPase Rac1 also requires activation by certain upstream signals. Also like p47phox, Rac1 can exist in two interconvertible forms: GTP-bound (active) and GDP-bound (inactive). Activation of Rac1from its inactive form is facilitated by a family of enzymes called Guanine Exchange Factors (GEFs) [f7 in Figure phox complex, which is another indispensable step for achieving enzymatic activity in Nox1 [phox[f9 in Figure The translocation of phosphorylated p47it NoxA1 ,40. This C (PKC) ,57 (marks (GEFs)   [Many experimental studies have investigated Nox1-induced ROS production in different cells and under various conditions. By comparing the system responses to certain stimuli with the control system, several Nox1 activators and regulators have been identified. For instance, Nox1 can be directly activated by PDGF , AngII ,28, thro(TNF-α) , and man(TNF-α) ,33). In (TNF-α) ,46, and (TNF-α) , while t(TNF-α) , which iactive), Nox1/p22phox, p47phox, phosphorylated p47phox (p47phoxP), GDP-bound Rac1 (Rac1GDP), GTP-bound Rac1 (Rac1GTP), NoxA1, and phosphorylated NoxA1 (NoxA1P). Input signals, including those leading to p47phox phosphorylation, those effecting Rac1 activation, and those responsible for NoxA1 phosphorylation, are represented by S1, S2, and S3, respectively. They are treated as independent variables, which nominally stay at 1 for control conditions and are changed to a value above 1 when a relevant extracellular stimulus is applied.The Nox1 activation system has been investigated in numerous studies, and the pathway structure, key components, and a variety of other biological data are available in the literature. The model we propose is based on this information and directly reflects the pathway structure shown in Figure active even if it might have a low concentration. It also indicates that the target system at the ready-to-respond state (RtRS) dynamically maintains a fine-tuned balance of fluxes. As an illustration, consider Nox1active (X1). Basal signals enable continuous formation of X1 by depleting the pool of Nox1/p22phox, p47phoxP , Rac1GTP, and NoxA1P. This formation continues to exist as long as the basal signals are present and the pool of free subunits is not depleted. Thus, in order to maintain X1 at a stable level, there must be additional pathways  that disassociate X1.In a VSMC system residing in its control state and exposed to basal input signals, one detects a continuous, low production of ROS ,26. Thisf14 in Figure f2, f3, and f4 in Figure active have not been clearly identified in VSMCs, and their responses to extracellular stimuli are ill characterized, these enzymes are modeled with a nominal activity level of 1. The same strategy is applied to partial or more complicated disassociation mechanisms of Nox1active, which might exist to serve as effective disassembly mechanisms.Four disassembly pathways are proposed in the current model. They include one direct degradation pathway via proteolysis  has been shown to bind with 14-3-3 protein to form a complex [P . In thefSS14 disassembles NoxA1active into all four subunits, which introduces a stoichiometric factor of 4 into the system, which becomes evident if the last four equations in (4) are summed.This set of constraints has an eight-dimensional basis, indicating that only eight linearly independent fluxes need to be characterized from data instead of sixteen. In addition, the fluxes are naturally non-negative, which imposes a set of inequality constraints that bound the search space for feasible parameter values further. Note that flux fSS2, fSS3, fSS4, fSS6, fSS8, fSS14, and fSS16, as independent fluxes. As the eighth independent flux, we choose fSS9 or f10SS, depending on the relative sizes of f2SS and f16SS. Two reasons support this particular selection. First, fSS2, fSS3, fSS4, and fSS14 each represent one disassembly mechanism of Nox1active; these fluxes are therefore of particular interest. Their characterization will enable us to identify the most effective strategies of degrading Nox1active. Second, once these selected fluxes are numerically quantified by their rate constants, the remaining fluxes are guaranteed to be non-negative because of their relatively larger magnitudes.Linear algebra assures us that it ultimately does not matter which basis of flux vectors in Equation (4) is chosen. For reasons of practicality, we select fluxes Since little experimental information is available regarding any of the fluxes or their corresponding rate constants, we use Monte-Carlo methods, which permit the exploration of large search spaces with reasonable effort. Specifically, the targeted fluxes are randomly sampled from a uniform distribution over an appropriate non-negative domain. The performance of each sampled set is then evaluated by a series of user-defined criteria.(Nox1): NADPH oxidase 1; (ROS): Reactive oxygen species; (eNOS): Endothelial nitric oxide synthase; (AngII): Angiotensin II; (VSMCs): Vascular smooth muscle cells; (PDGF): Platelet derived growth factor; (RtRS): Ready-to-Respond State; (PMA): Phorbol-12-myristate-13-acetate; (PKC): Protein kinase C; (GEFs): Guanine exchange factors; (PKA): Protein kinase A; (TNF-α): Tumor necrosis factor-alpha.The authors declare that they have no competing interests.WY and EOV developed the concepts for analyzing the design of the pathway as a driver of effective functioning. WY implemented the models and performed the simulations and analysis. WY and EOV wrote the manuscript. Both authors read and approved the final manuscript.The file contains supplementary information regarding the stoichiometric matrix of the system, simulation settings, and a detailed parameter sensitivity analysis, as well as Figures S1-S13,Tables S1 and S2.Click here for file


Pelobacter species are commonly found in a number of subsurface environments, and are unique members of the Geobacteraceae family. They are phylogenetically intertwined with both Geobacter and Desulfuromonas species. Pelobacter species likely play important roles in the fermentative degradation of unusual organic matters and syntrophic metabolism in the natural environments, and are of interest for applications in bioremediation and microbial fuel cells.Pelobacter species, genome-scale metabolic models for Pelobacter carbinolicus and Pelobacter propionicus were developed. Model development was greatly aided by the availability of models of the closely related Geobacter sulfurreducens and G. metallireducens. The reconstructed P. carbinolicus model contains 741 genes and 708 reactions, whereas the reconstructed P. propionicus model contains 661 genes and 650 reactions. A total of 470 reactions are shared among the two Pelobacter models and the two Geobacter models. The different reactions between the Pelobacter and Geobacter models reflect some unique metabolic capabilities such as fermentative growth for both Pelobacter species. The reconstructed Pelobacter models were validated by simulating published growth conditions including fermentations, hydrogen production in syntrophic co-culture conditions, hydrogen utilization, and Fe(III) reduction. Simulation results matched well with experimental data and indicated the accuracy of the models.In order to better understand the physiology of P. carbinolicus and P. propionicus. These models of Pelobacter metabolism can now be incorporated into the growing repertoire of genome scale models of the Geobacteraceae family to aid in describing the growth and activity of these organisms in anoxic environments and in the study of their roles and interactions in the subsurface microbial community.We have developed genome-scale metabolic models of  Pelobacter species are commonly found in a number of natural environments, including marine sediments [Pelobacter species and members of Geobacter, Desulfuromonas and Desulfuromusa genera in the deltaproteobacteria form the monophyletic family Geobacteraceae, which can be divided into two distinct subgroups of the Geobacter and Desulfuromonas clusters [Pelobacter species are phylogenetically intertwined with both clusters: Pelobacter carbinolicus is within the Desulfuromonas cluster whereas Pelobacter propionicus is within the Geobacter cluster [ediments , muds [2ediments , and hydediments -6. They clusters -9. The P cluster ,9,10.P. carbinolicus and P. propionicus are both strict anaerobes that have distinct physiologies compared with the Geobacter and Desulfuromonas species. P. carbinolicus was first isolated from marine muds whereas P. propionicus was first isolated from freshwater sediments and sewage sludge [P. carbinolicus, and propionate and acetate for P. propionicus [Pelobacter species can grow with ethanol, propanol, or butanol under specific conditions [e sludge . Both capionicus . In addinditions .Geobacter and Desulfuromonas species, Pelobacter species have the capacity of using S0 as an electron acceptor [Pelobacter species only incompletely oxidize organic substrates with this electron acceptor, in contrast to the ability of Geobacter and Desulfuromonas species to completely oxidize acetate and other organic electron donors to carbon dioxide [P. carbinolicus only incompletely oxidizes ethanol to acetate with S0 reduction [Like acceptor ,10. Howe dioxide ,10,11. FGeobacter and Desulfuromonas species, P. carbinolicus lacks most of the c-type cytochromes [Geobacter sulfurreducens [P. carbinolicus reduces Fe(III) via an indirect mechanism in which elemental sulfur is reduced to sulfide and the sulfide reduces Fe(III) with the regeneration of elemental sulfur, contrasting with the direct reduction of Fe(III) for Geobacter species [Geobacter and Desulfuromonas species can transfer electrons to the anodes of microbial fuel cells to produce current, P. carbinolicus could not [Pelobacter species are regarded as primarily fermentative/syntrophic species, whereas the Geobacter and Desulfuromonas species are primarily respiratory species. Comparative genomic studies have suggested that the common ancestor of Pelobacter, Geobacter, and Desulfuromonas species was a respiratory microorganism and that Pelobacter species evolved to fill fermentative and syntrophic niches (4).Unlike ochromes , which areducens -16. P. c species . Furtherould not . Thus, tPelobacter and Geobacter species can be closely associated in subsurface environments [P. carbinolicus and G. sulfurreducens produced current with ethanol as the fuel, under conditions in which current could not be produced by either microorganism alone [Geobacter species resulted in two constraint-based genome-scale metabolic models [Geobacter species and their applications [P. carbinolicus DSM2380 and P. propionicus DSM2379 have been completed http://www.jgi.doe.gov. Because of the similarities and differences in metabolic capabilities between Pelobacter and Geobacter species, in silico modeling of these two Pelobacter species will provide insight into their metabolism. In this report, the developments of genome-scale metabolic models of P. carbinolicus and P. propionicus are described. These models of Pelobacter metabolism can now be incorporated into the growing repertoire of genome scale models of the Geobacteraceae family to aid in describing the growth and activity of these organisms in anoxic environments and in the study of their roles and interactions in the subsurface microbial community.ronments ,20. In msm alone . Genome-sm alone . Genome-c models ,23, whicications -29. The P. carbinolicus DSM 2380 was cultured at 30°C under strictly anaerobic conditions as previously described [-1 with 5 mM acetoin in the medium. Cell growth on acetoin was monitored by measuring the optical density at 600 nm with a Genesys 2 spectrophotometer . Cell dry weight was determined gravimetrically after drying at 105°C for 24 h.escribed . Ferment2SO4 eluent. Acetate was detected with an SPD-10VP UV detector  set at 210 nm. Ethanol and acetoin were quantified with an RID-10A refractive index detector . Protein concentrations were determined with the bicinchoninic acid (BCA) method [Concentrations of acetoin, acetate, and ethanol were determined with high-pressure liquid chromatography (HPLC) using an LC-10ATVP HPLC  equipped with an Aminex HPX-87 H column , with 8 mM HPelobacter metabolic networks were reconstructed according to previously published procedures [Pelobacter genomes. The Pelobacter genomes and the genomes of several high-quality genome-scale metabolic models were analyzed using the sequence similarity search (BLAST), and the BLAST results were utilized to create draft models that served to accelerate the reconstruction of the genome-scale metabolic models. The reactions and genes in the draft models were manually reviewed using the gene annotations and the available biochemical and physiological information. Biomass compositions in the published G. sulfurreducens model were used to create the biomass demand reactions in both reconstructed Pelobacter models. The resulting networks were then subjected to the gap filling process to allow biomass formation under physiological growth conditions. For gap filling, simulations were performed to determine if the networks could synthesize every biomass component and the missing reactions in the pathways were identified. These reactions were reviewed for gene association, or added as non-gene associated reactions to enable the formation of biomass by the reconstructed networks under physiological conditions. Experimental data were collected from published literature or generated in the laboratory, and were applied to validate model simulation results that predict growth and products under corresponding physiological conditions. Model simulations were also used to generate experimentally testable hypotheses and predictions. The experimental findings accordingly were in turn used to further refine and expand the metabolic models in an iterative process.The ocedures . The recocedures ,32  associations in the metabolic model are available as additional files.P. carbinolicus metabolic model including growth-associated maintenance (GAM) energy and non-growth associated maintenance (nGAM) energy were determined using four sets of experimental data obtained in chemostats of P. carbinolicus during acetoin fermentation at dilution rates of 0.03 to 0.06 h-1. The experimental data including acetoin uptake rates and acetate production rates were used as constraints to simulate fermentative growth where the ATP maintenance requirement reaction was selected as the objective function to be maximized. The maximum ATP production rates were calculated and plotted against the dilution rates as described earlier [Energy parameters of the  earlier . The linqATP is the ATP production rate and μ is the dilution rate. The intercept b represents nGAM, the ATP requirement at zero growth, whereas the slope a represents GAM, the ATP requirement for growth. GAM was then incorporated into the biomass demand equation, and nGAM was utilized for all further growth simulations.where P. propionicus metabolic model, the same biomass demand equation including GAM was applied. The nGAM was estimated using P. propionicus fermentative growth yields with 6 different substrates [For the Pelobacter models were calculated using flux balance analysis through linear optimization [2, H+, H2O, K+, Mg2+, NH4+, PO43-, and SO42-. The electron donors or electron acceptors tested were allowed a maximum uptake rate into the network as specified in the results. All other external metabolites were only allowed to leave the system. To simulate physiological conditions, experimental data were used as constraints for flux optimization. Growth simulations were carried out for: 1) optimal growth where biomass synthesis was selected as the objective function to be maximized with substrate uptake rate fixed; or 2) optimal substrate utilization where the substrate uptake rate was chosen as the objective function to be minimized with the growth rate fixed.The metabolic capabilities of the mization  in SimPhP. carbinolicus draft model was built with base models including previously published Geobacter sulfurreducens [Escherichia coli [Bacillus subtilis [P. propionicus draft model was prepared with the above base models plus the completed P. carbinolicus and Geobacter metallireducens [P. propionicus draft model originating from the G. sulfurreducens, G. metallireducens, and P. carbinolicus models were 40%, 31%, and 14%, respectively, consistent with the fact that P. propionicus is more closely related to G. sulfurreducens and G. metallireducens than to P. carbinolicus [The reducens , Escherihia coli , and Bacsubtilis  models. reducens  models. reducens . For exainolicus .P. carbinolicus and P. propionicus were evaluated manually based on gene annotations, published biochemical and physiological information and external references as previously described [Pelobacter species is their fermentative growth on acetoin [P. carbinolicus [acoA, acoB, acoC, and acoL genes [Pelobacter networks, the reaction was associated with the acoA (Pcar_0343 & Ppro_1131), acoB (Pcar_0344 & Ppro_1132), acoC (Pcar_0345 & Ppro_1133), and acoL (Pcar_0347 & Ppro_1137) genes. In the P. carbinolicus model, the resulting acetaldehyde is reduced to ethanol by alcohol dehydrogenase, whereas acetyl-CoA is converted into acetate by phosphate acetyltransferase and acetate kinase (Pcar_2542 & Pcar_2543) generating ATP, as described previously [P. propionicus model, acetaldehyde is oxidized to acetyl-CoA by acetaldehyde CoA dehydrogenase (Ppro_0899 or Ppro_1923), and acetyl-CoA is then converted into propionate in a pathway through pyruvate, oxaloacetate, malate, fumarate, succinate, succinyl-CoA, methylmalonyl-CoA, and propionyl-CoA [The reactions and their gene associations in the draft models of escribed . One imp acetoin . During  acetoin ,37. The inolicus  and was eviously . In the onyl-CoA . The keyP. propionicus ferments both ethanol and lactate to acetate and propionate at an approximately 1:2 ratio via methylmalonyl-CoA, but the molar biomass yield with ethanol was less than half of that obtained with lactate [Geobacteraceae family member [P. propionicus network. lactate ,40. It wy member ,23, a laPelobacter species have been reported to use Fe(III) or S0 as an electron acceptor [P. carbinolicus have suggested that Fe(III) is reduced indirectly through sulfur reduction, with sulfide serving as an electron shuttle for the reduction of Fe(III) [Sulfurospirillum deleyianum [3+/Fe2+ pair (E'° = +771 mV) and the S0/H2S pair (E'° = -243 mV) [3+ + HS- → 2 Fe2+ + S + H+) was used to model the indirect Fe(III) reduction. In other bacteria, sulfide is regenerated from elemental sulfur by the membrane-bound sulfur reductase that contains both hydrogenase and sulfur reductase activities [P. carbinolicus network for sulfur reduction with hydrogen or NAD(P)H as electron donors, although the enzymes catalyzing Fe(III) and So reduction in P. carbinolicus have not been identified experimentally.acceptor ,10, but  Fe(III) . It was leyianum . Given t-243 mV) , an extrtivities . The sulP. carbinolicus network contains 37 non-gene associated reactions whereas the P. propionicus network contains 46 non-gene associated reactions. These non-gene associated reactions are presumptive metabolic functions encoded potentially by unknown genes, and thus will be the subject of further genomic and biochemical investigation in the future.The gap filling process identified some missing reactions in the pathways that were added as non-gene associated reactions to enable the reconstructed network to synthesize metabolites for biomass formation. The reconstructed E. coli model [G. sulfurreducens model [Pelobacter models, as they are closely related to G. sulfurreducens. Sensitivity analysis also indicated minimal changes in simulations by small biomass composition changes in the Pelobacter models (data not shown). Metabolic maps including the biomass demand reactions for the P. carbinolicus and P. propionicus genome-scale metabolic models were provided  partial loss of ethanol produced from acetoin fermentation through evaporation; 2) ethanol fermentation to produce acetate and H2, where H2 was removed by gassing to release the feedback inhibition. Therefore, only the experimental acetoin uptake rates and acetate production rates were used to simulate the fermentative growth  in the SRE and SRE2 two reactions, the published biomass yield data for P. carbinolicus grown in batch culture with ethanol or hydrogen as electron donor and Fe(III) as electron acceptor [+/2e- ratio varied from 1 to 2 for SRE with hydrogen as electron donor or for SRE2 with ethanol as electron donor .Upon completion, the genome-scale P. propionicus metabolic model included 661 genes among the 3831 genes in the P. propionicus genome. As shown in Table P. propionicus model consists of 650 reactions and 708 metabolites including 52 extracellular metabolites .The genome-scale Pelobacter models included about 20% of the genes in their genomes, similar to the Geobacter models [Pelobacter and Geobacter models are also comparable. Thus, these Geobacteraceae family members contain similar size genomes, share similar basic metabolic functions, and result in similar size genome-scale metabolic models.As shown in Table r models ,23. The Pelobacter metabolic models were categorized according to 9 functional classifications  as the terminal electron acceptor [P. carbinolicus [P. carbinolicus grown on 2,3-butanediol with Fe(III) produced less ethanol and more acetate than grown on 2,3-butanediol without Fe(III) [P. carbinolicus growth on 2,3-butanediol with or without Fe(III) are shown in Figure in silico simulations matched well with those from the experimental data [P. carbinolicus model worked well with Fe(III) reduction simulations.Like phylogenetically related acceptor , but sulinolicus . P. carb Fe(III) . The exptal data . These rP. carbinolicus is capable of utilizing H2 as the electron donor coupled with Fe(III) reduction to support growth when acetate is provided as the carbon source [P. carbinolicus growth on ethanol or on hydrogen with acetate as the carbon source and with Fe(III) as the electron acceptor was simulated by applying experimental growth rates as constraints and optimizing the substrate utilization. As shown in Figure 2/Fe(III) growth were calculated from both in silico result and experimental data, and were closely matched. Additionally, the relative ratios of acetate produced to Fe(III) reduced and ethanol consumed to Fe(III) reduced under ethanol/Fe(III) growth conditions were similar between predicted and experimental results. The stoichiometry of Fe(III) reduced to hydrogen consumed was measured as 1.9 in experiments [P. carbinolicus model for H2 and ethanol utilization coupled with Fe(III) reduction.n source . P. carberiments  and was Geobacteraceae metabolic models were compared at the reaction level. The common reactions in all 4 models were determined for each functional category and the unique reactions in only one model but not in the other models were also determined  respiration [The reconstructed metabolic models contain duplicate genes for the same reaction, or alternative reactions between two metabolites, or alternative pathways among metabolites. Without additional constraints, the duplicate genes, alternative reactions, or alternative pathways can all direct metabolic fluxes in simulations. Gene expression data suggesting the preference of duplicate genes, alternative reactions, or alternative pathways can be utilized to constrain the simulations and improve computational modeling analysis. On the other hand, the metabolic models can also be utilized to facilitate the interpretation of gene expression data. The piration  were comP. carbinolicus metabolic map, genes for the acetoin:DCPIP oxidoreductase Pcar_0343-0347 were among the most highly transcribed genes during acetoin fermentation. During ethanol/Fe(III) respiration, the expression levels of these genes decreased more than 10 fold [During acetoin fermentation, acetoin is degraded into acetaldehyde and acetyl-CoA by the acetoin:DCPIP oxidoreductase. When the gene expression levels were displayed on the  10 fold . Genes w 10 fold . These g 10 fold . The metP. carbinolicus. For example, two acetyl-CoA:phosphate acetyltransferase isozymes Pcar_2542 and Pcar_2850 are present in the P. carbinolicus model. The gene expression level of Pcar_2542 was 23 fold more than the level of Pcar_2850 during acetoin fermentation, suggesting that Pcar_2542 was the primary enzyme to catalyze the reaction in acetoin fermentation. Similar examples included isozymes for aconitase, acetate kinase, acetoin dehydrogenase, acetaldehyde dehydrogenase, etc.The metabolic model contains redundancy in the form of duplicate genes, alternative reactions, or alternative pathways. Such redundancy may play important roles in genetic and metabolic robustness. Microarray analysis indicated that this redundancy was tightly regulated in P. carbinolicus metabolic model contains a biosynthesis pathway for proline from glutamate through ornithine, in addition to the 1-pyrroline-5-carboxylate-dependent pathway present in the G. sulfurreducens model. Microarray results indicated that the mRNA levels of pyrroline-5-carboxylate reductase and ornithine cyclodeaminase were similar under both acetoin fermentation and ethanol/Fe(III) respiration conditions, suggesting that both pathways were used for proline biosynthesis.When different reactions for the same metabolite are present, it is likely for the cell to use the most energy efficient reaction for optimal growth. An example of this is the presence of a sulfate proton symport transporter and a sulfate ABC transport system for sulfate transport. The expression level of the proton symport transporter gene Pcar_0676 was more than the sulfate ABC transport system genes during acetoin fermentation (expression of Pcar_0676 was about 8 times Pcar_2084). Thus, it was likely that sulfate was mainly transported through the proton symport transporter during acetoin fermentation. Similarly, the genes encoding nitrogen fixation enzymes were turned off as the cell used ammonium from the culture medium. The P. carbinolicus contains genes for de novo cobalamin biosynthesis, but some of these genes were among the lowest expression levels during both culture conditions. Most likely, P. carbinolicus did not synthesize cobalamin, but utilized the vitamin B12 supplemented in the culture medium. When compared to the G. sulfurreducens model, P. carbinolicus had some unique reactions allowing the formation of purines like adenosine, inosine, deoxyinosine, xanthine, hypoxanthine, and urate. However, gene expression data suggested that only adenosine was formed during both culture conditions, whereas the others were unlikely to be synthesized.The microarray results will be utilized to constrain the reconstructed network, where the reactions are closed during simulations when the associated genes have very low expression level for the corresponding culture conditions.P. carbinolicus reduces Fe(III) indirectly through sulfur reduction [P. carbinolicus only incompletely oxidizes organic substrates to acetate using Fe(III) as an electron acceptor [P. carbinolicus model, yet the fully functional TCA cycle was not observed during simulations with Fe(III) as electron acceptor. Further examination suggested that this was due to the inability in coupling succinate oxidation to sulfur reduction (ΔG0' = +53 kJ/mol) [Desulfuromonas acetoxidans utilizing an ATP-driven succinate oxidation mechanism for acetate oxidation to CO2 with elemental sulfur as electron acceptor [P. carbinolicus does not contain such an ATP-driven succinate oxidation mechanism, thus cannot completely oxidize acetate to CO2 with elemental sulfur or Fe(III) as electron donor.eduction . Howeveracceptor . The com kJ/mol) . Many ar kJ/mol) . Howeveracceptor ,51. It sPelobacter species can be closely associated with Geobacter species [Pelobacter and Geobacter metabolic models share 470 common reactions, about 70% of all reactions in the models. Genome sequence analysis of six Geobacteraceae family members including all four species discussed in this study suggested that the two Pelobacter species evolved separately with fermentative/syntrophic metabolism from a common Geobacteraceae ancestor with anaerobic respiratory metabolism [P. carbinolicus model contains reactions for fermentative growth with various organic substrates and reactions of hydrogenase and formate dehydrogenase to produce hydrogen and formate for syntrophic growth with hydrogen- and formate-consuming organisms. The P. propionicus model contains reactions for fermentative growth with different organic substrates producing propionate. The Pelobacter models contain metabolic abilities reflecting their physiological roles in the subsurface community. The evolution of the Pelobacter species to fermentative metabolism could also result in the loss of many c-type cytochromes from their genomes, since there is no need to transfer electrons outside the cells during fermentative growth, thus abolishing their abilities to directly reduce Fe(III) or the anodes of microbial fuel cells. Furthermore, the evolution of a subsurface community containing fermentative and respiratory species allowing the breakdown of complex organic substrates to CO2 through different species may be advantageous as each species does not need to synthesize a full set of proteins for the complete oxidization of complex organic substrates. The development of the metabolic models of these Geobacteraceae species will facilitate the study of their roles and interactions in the subsurface microbial community.In subsurface environments,  species ,20. The tabolism . The P. P. carbinolicus and P. propionicus. These models of Pelobacter metabolism can now be incorporated into the growing repertoire of genome scale models of the Geobacteraceae family to aid in describing the growth and activity of these organisms in anoxic environments and in the study of their roles and interactions in the subsurface microbial community.We have developed genome-scale metabolic models of DCPIP: 2,6-dichlorophenolindophenol; GAM: growth-associated maintenance; gdw: gram dry weight; GPR: gene-protein-reaction; HPLC: high-pressure liquid chromatography; nGAM: non-growth associated maintenance; ORF: open reading frame.P. carbinolicus and P. propionicus. SAH carried out the growth experiments. JS analyzed the experimental data and drafted the manuscript. JS, SAH, and DRL conceived the study and revised the manuscript. All authors read and approved the final manuscript.JS, OB, and TRF developed the genome-scale metabolic models of P. carbinolicus and P. propionicus genome-scale metabolic modelsMetabolic maps for the .Click here for fileP. carbinolicus genome-scale metabolic modelAll the reactions, genes, and metabolites included in the .Click here for fileP. propionicus genome-scale metabolic modelAll the reactions, genes, and metabolites included in the .Click here for fileGeobacteraceae genome-scale metabolic modelsAll the unique reactions in each of the four .Click here for file


Spinal cord injury (SCI) is a devastating event with a limited hope for recovery and represents an enormous public health issue. It is crucial to understand the disturbances in the metabolic network after SCI to identify injury mechanisms and opportunities for treatment intervention. Through plasma 1H-nuclear magnetic resonance (NMR) screening, we identified 15 metabolites that made up an “Eigen-metabolome” capable of distinguishing rats with severe SCI from healthy control rats. Forty enzymes regulated these 15 metabolites in the metabolic network. We also found that 16 metabolites regulated by 130 enzymes in the metabolic network impacted neurobehavioral recovery. Using the Eigen-metabolome, we established a linear discrimination model to cluster rats with severe and mild SCI and control rats into separate groups and identify the interactive relationships between metabolic biomarkers in the global metabolic network. We identified 10 clusters in the global metabolic network and defined them as distinct metabolic disturbance domains of SCI. Metabolic paths such as retinal, glycerophospholipid, arachidonic acid metabolism; NAD–NADPH conversion process, tyrosine metabolism, and cadaverine and putrescine metabolism were included. In summary, we presented a novel interdisciplinary method that integrates metabolomics and global metabolic network analysis to visualize metabolic network disturbances after SCI. Our study demonstrated the systems biological study paradigm that integration of 1H-NMR, metabolomics, and global metabolic network analysis is useful to visualize complex metabolic disturbances after severe SCI. Furthermore, our findings may provide a new quantitative injury severity evaluation model for clinical use. In the United States, 12,000 new SCI cases are annually reported, and $24 billion is annually spent on paralyzed SCI patients Human metabolism is a complex network with hundreds of cross-linked paths. During critical SCI illness, the metabolic network is dynamically disturbed at multiple points An interdisciplinary method that includes high-throughput quantitative techniques and effective mathematical and visualization tools is necessary because metabolic network disturbances after SCI are complex; hundreds of molecules and metabolic paths interact n = 7), mild SCI (n = 10), and severe SCI (n = 25). All the rats were fed the standard formula feed from 7 days before the operation until sacrifice. All rat forage was formulated according to the formulation established by the State Bureau of Quality Technical Supervision of China (GB Standards No: 14924.3-2010). Animal use was strictly in accordance with the National Institutes of Health Guide for the Care and Use of Laboratory Animals. The Experimental Animal Committee of Sichuan Academy of Medical Sciences approved the research protocol .Animal modeling procedures were performed according to an established protocol, as described previously Rats assigned to the SCI group were anesthetized by intra-peritoneal injection of sodium pentobarbital (40 mg/kg). We tried our best to minimize animal suffering. Rats were observed for at least 3 days after the start of the experiment. For severe SCI, we established a 7-day observation subgroup. In the mild SCI group, three rats died during the surgical modeling procedure and one died 2 days after the surgery. In the severe SCI group, five rats died during the surgical modeling procedure and four died 1 and 2 days after the surgery. Therefore, there were six animals in the mild SCI group, 16 in the severe injury group (seven rats were fed for 7 days after modeling), and seven in the blank control group. Plasma samples were collected for 1H-NMR metabolomic analysis.Plasma samples were centrifuged at 10,000 rpm for 10 min to remove particulate matter. Following this, 100 µL of deuterium oxide (D2O) was added to 400 µL of the plasma samples in 5 mm Wilmad NMR tubes to lock the field frequency for the NMR spectrometer.A high-solution Bruker AVIII-DRX600MHz NMR spectrometer  was used to obtain 1H-NMR spectra from the plasma samples. The probe temperature was 300 K. One 1D-NMR experiment was included. A standard protocol that included a one-dimensional pulse sequence and a Carr–Purcell–Meiboom–Gill (CPMG) pulse sequence was followed Principle component analysis (PCA) and partial least-square (PLS) regression were performed on 1H-NMR data to reduce data dimensionality and for multivariate analysis. We used Matlab R2012b for computing and mathematical modeling . The variable importance for the projection (VIP) value was used to screen metabolites. The Human Metabolome Database (HMDB) was used to identify key metabolites related to enzymes and upstream genes. Metabolites and their enzymes were mapped, and the disturbed metabolic paths were located on a global metabolic map of the Kyoto Encyclopedia of Genes and Genomes (KEGG).A linear discriminant formula was established as a quantitative injury evaluation model. In this model, weighted PLS-DA values were used. The root mean square error (RMSE) was calculated to verify the validity of the equations according to injury severity.Metabolic disturbances after severe SCI were established according to two steps: 1) key metabolites were mapped onto the KEGG metabolic network (KEGG metabolic pathways PATHWAY: map01100) and 2) the shortest path for each key metabolite (node) was calculated and outlined onto the metabolic network using the KEGG pathway API for the Matlab bioinformatics toolbox.Compared with the control group, mild and severe SCI 1H-NMR spectra were perturbed at 0–8 ppm . This phPCA. PCA scores indicated that injured rat plasma metabolomes differed from controls. The first two principal components (PCs) explained 83% of the total variance and suggested that most metabolome differences between normal and SCI cases were because of injury (f injury .PLS was used to investigate correlations between severity of SCI and the metabolome. The VIP indicator can describe correlations between the variable (X) and response (Y). We used VIP to identify metabolites correlated with severe SCI and named these metabolites as “Eigen-metabolome” of severe injury. Segmental integrations in 83, 90, 92, 151, 152, and 153 were found to have the highest VIP values and represented metabolites with 4.72, 4.44, 4.4, 2, 1.98, and 1.96 1H-NMR spectra chemical shift values, respectively.We identified 15 metabolites that made up an “Eigen-metabolome” capable of distinguishing rats with severe SCI from healthy control rats. We also found that 16 metabolites impacted neurobehavioral recovery  and 2.2 = 0.284 . The accuracy ratio of PLS regression was 86.2%, R = 0.284 . Xi reprred rats .After metabolites related to severe SCI were identified, metabolite roles and interactions were investigated on the global metabolic network. A three-step method was developed and included the following: 1) HMDB-based identification of enzymes that catalyze metabolites; 2) enzyme ID conversion from HMDB to KEGG enzyme IDs; 3) the use of Matlab KEGG API to map the enzymes into metabolic pathways (KEGG metabolic pathway ID: PATHWAY: map01100). Following this, disturbed metabolic pathways and metabolic pathways related to neurobehavioral recovery after severe SCI were visualized .Seven metabolic pathways related to severe SCI and eight metabolic pathways related to neurobehavioral recovery were identified .We successfully use plasma 1H-NMR spectra to construct an Eigen-metabolome-based quantitative SCI severity evaluation model, which poses clinical application potential in future. Fifteen metabolites made up an Eigen-metabolome that distinguished severe SCI from mild injuries and healthy controls, whereas sixteen metabolites made up an Eigen-metabolome that distinguished neurobehavioral recovery rats from other treatment groups.We established a PLS linear formulation group to provide a severe SCI evaluation model. In our model, X represented the ppm value from 1H-NMR spectra and Y represented the severity of SCI. We found that we could distinguish control, severe, and mild injury rats in the PLS score plot by using SVM. To avoid the challenge by excessive clustering and small sample size, we used RMSE to evaluate the accuracy of our model. RMSE of our model (4.76) indicated it was appropriate for evaluating injury severity.et al. found that neurobehavioral recovery after SCI is related to a decrease in N-acetyl-aspartyl-glutamate and N-acetyl-aspartate in a liquid chromatography–mass spectrometry (LC–MS) metabolomic model Although few studies demonstrate that SCI disturbs plasma metabolites and metabolic patterns, SCI metabolic disturbances continue to challenge researchers. Consistent with our study, Fujieda Y We introduced a new approach that integrates metabolomic analysis with metabolic network analysis. Significantly altered metabolites were extracted from the Eigen-metabolome and mapped onto a metabolic network. Enzymes that produce these metabolites were also identified and included in the network. Hundreds of plasma metabolites are altered after major trauma because enzyme activity increases or decreases to effectively decrease or increase metabolites levels 1) Retinal metabolism The retinal metabolism metabolic pathway contains two important metabolites, retinaldehyde (vitamin A) and retinoic acid (RA). RA plays a role in neural differentiation during embryonic differentiation. Studies have shown that RA possesses a neuroprotective effect during central nervous system injury. Plasma vitamin A, vitamin E, and beta-carotene are decreased after SCI. Furthermore, a positive correlation between plasma vitamin A and the severity of the chronic SCI has been identified 2) Glycerophospholipid and arachidonic acid (AA) metabolism Phosphoglyceride and sphingomyelin could be used to distinguish the severity of injury in 1H-NMR spectra. Both phospholipids are regulated by superoxide dismutase (SOD) and glutathione (GSH). SCI is currently considered as a myelin sheath damage. The myelin sheath is a greatly extended and modified cell membrane that is wrapped around the nerve axon in a spiral manner. Sphingomyelin and glycerol phospholipid are important components of the cell membrane, and when their metabolic paths are disturbed, the cell membrane is damaged. The simple sphingolipid metabolites ceramide and sphingosine-1-phosphate are major signaling cascade mediators involved in apoptosis, proliferation, stress responses, necrosis, inflammation, autophagy, senescence, and differentiation 3) The NAD–NADPH conversion process The NAD–NADPH conversion process includes etheral metabolism, ethanol–ethylal conversion, and choline to betaine aldehyde metabolism. Many anabolic pathways such as lipid synthesis, cholesterol synthesis, cellular respiration via the electron-transport chain, and fatty acid chain elongation are related to NAD and NADPH. Metabolomic fingerprints change after SCI, partly because of alterations in dehydrogenase activity.4) Tyrosine metabolism We found that the disturbances in tyrosine metabolism contributed to injury severity and neurobehavioral recovery. Tyrosine metabolism involves various neurotransmitter synthesis pathways. Tyrosine can be converted to L-DOPA, which is converted to the nerve injury biomarkers dopamine, norepinephrine , and epinephrine . The production and elimination of nitrotyrosine (NT) are markers of nerve injury during tyrosine metabolism. NT is a product of tyrosine nitration mediated by reactive nitrogen species such as the peroxynitrite anion and nitrogen dioxide. NT is one of the most important markers for evaluating tissue and cell damage. Tyrosine nitration leads to enzyme inactivation 5) Cadaverine and putrescine metabolism The role of decarboxylase ornithine decarboxylase (ODC) as a catalytic product in SCI has gained interest. ODC and putrescine levels significantly increase after SCI 6) Disturbances in ether and alcohol metabolism We found that several metabolic pathways such as chloroalkene and chloroalkene degradation and aldehyde and ethanol transformation (not well studied in SCI) appeared in the disturbance pathways. These metabolites belong to etheral metabolism and are primarily generated by the gut microbiome. This disturbance certainly takes place in clinical practice; however, it is typically ignored in traditional trauma biomedical research.In summary, SCI can be understood as disturbances in cellular signaling regulatory networks in response to trauma. Furthermore, these disturbances commonly present a pattern of general cellular transduction, as illustrated in Our study demonstrated that 1H-NMR spectra can be used to quantitatively measure and visualize the metabolic network disturbances after SCI. We identified major metabolites and enzymes through unbiased screening of compounds identified by metabolomic analysis. Metabolites were mapped to a metabolic network, and their functions were interpreted by KEGG metabolic network visualization. This method has the advantage of integrating metabolic research with the trauma mechanisms to aid our understanding of the pathophysiological SCI process.Our model incorporated metabolomics, biological informatics, and pattern recognition to develop a new evaluation and outcome prediction tool for SCI that can provide substantial improvements to the diagnosis and intervention monitoring of SCI patients.Table S1The Basso, Beattie, and Bresnahan (BBB) score scale of all rats.(XLSX)Click here for additional data file.Table S2Raw data of 1H-NMR: All the raw data through binning process of the NMR spectrum after Fourier transformation as a 200×29 matrix; Each row describes the integral binning value of a spectrum for a sample.(XLSX)Click here for additional data file.Table S3Grouping of animals: The treatment that 29 samples received are as follows: “control” present, control group; “mild” present, the rat received a laminectomy without hemisection of spinal cord injury; “op” present, the rat received a hemisection of the spinal cord and was scarified 3 days after the operation; “op7” present, the rat received a hemisection of the spinal cord and was scarified 7 days after the operation.(XLSX)Click here for additional data file.


Pseudomonas metabolic models to predict the biochemical differences, namely, iMO1086, iJP962, and iSB1139, which are related to P. aeruginosa PAO1, P. putida KT2440, and P. fluorescens SBW25, respectively. We did a comprehensive literature search for previous works containing biochemically distinguishable traits over these species. Amongst more than 1700 articles, we chose a subset of them which included experimental results suitable for in silico simulation. By simulating the conditions provided in the actual biological experiment, we performed case-dependent tests to compare the in silico results to the biological ones. We found out that iMO1086 and iJP962 were able to predict the experimental data and were much more accurate than iSB1139.To date, several genome-scale metabolic networks have been reconstructed. These models cover a wide range of organisms, from bacteria to human. Such models have provided us with a framework for systematic analysis of metabolism. However, little effort has been put towards comparing biochemical capabilities of closely related species using their metabolic models. The accuracy of a model is highly dependent on the reconstruction process, as some errors may be included in the model during reconstruction. In this study, we investigated the ability of three  Recent advances in sequencing techniques have accelerated the process of whole genome sequencing of different organisms –3. AdditSij of this matrix is the stoichiometric coefficient of metabolite i in reaction j, considering positive values for products and negative values for reactants. Each (enzymatic) reaction, in turn, is associated with one or a few metabolic genes. In fact, the process of metabolic network reconstruction involves compiling the metabolism of an organism to a machine-readable format. There are various ways to reconstruct a model ranging from bottom up, totally manual approach [A typical framework for modeling cellular metabolism is constraint-based modeling, which involves stoichiometric and reversibility constraints on reactions , 8. For approach  to the sapproach , GEMSiRVapproach , and RAVapproach . Availabapproach –15, to aapproach , and to approach –20.in silico experiments and comparing them to the wet-lab results, the discrepancies of the model and the actual organism are recognized. Subsequent model refinements pave the way for having a reliable framework to perform in silico experiments. Such a model allows the researchers for an in-depth and systematic investigation of the cellular metabolism. In the field of genetic engineering [The common point of the all reconstruction processes is the iterative validation of the model. By performing cycles of ineering , 22, druineering –25, and ineering –28, modein silico results should be close to the real-world data. In this study, we used three Pseudomonas models: iMO1086 for Pseudomonas aeruginosa PAO1 [Pseudomonas putida KT2440 [Pseudomonas fluorescens SBW25 [Pseudomonas species.Despite the increasing number of available metabolic models, to the best of our knowledge, little effort has been put towards comparing biochemical capabilities of closely related species using their genome-scale metabolic models. Since there is no unique approach to reconstruct a network, the models may contain process-dependent errors (including missing reactions). Therefore, one should not expect the models to reflect the biochemically distinguishable differences as successful as the wet-lab results. Nevertheless, if a model is accurate enough, the osa PAO1 , 30, iJPa KT2440 , and iSBns SBW25  to interPseudomonas species. Primary search results included 675 articles for aeruginosa-putida case, 456 articles for fluorescence-putida case, and 622 articles for aeruginosa-fluorescence case.We decided to compare the models two by two. Considering this, three cases were to be investigated. In first step, we comprehensively searched PubMed database for the entries containing two of the three The abstracts of these articles were carefully reviewed in order to select the articles of interest. The selected articles in this step were those articles in which biochemically distinguishable differences between the two species were investigated. These articles were reviewed again in detail by reading their full-texts thoroughly, in order to see whether they fulfill the selection criteria (see below).in silico. The first criterion is that the particular pathway/gene/enzyme/reaction discussed in the article should be included in the model, as we are not supposed to change the structure of the model. In other words, the goal of this study is to understand the potential of the available genome-scale metabolic networks to model the biochemical differences, not to present improved models. Flux through a reaction and the essentiality of a gene in a specific environmental condition could be tested for the models.Not every wet-lab experiment is suitable to be simulated Pseudomonas species have a part of their metabolism in common. However, these shared pathways are not exactly the same. For example, the reactions might have different fluxes. On the other hand, some genes or pathways are specific to each of these species. Pathogenesis for P. aeruginosa [P. putida [in silico simulation.The three ruginosa , 34 and . putida , 36 are in silico simulation. Any changes made in the experiment in order to investigate cellular response to a perturbation should be in the form of adding or removing a particular pathway/gene/enzyme/reaction, or otherwise it could not be tested. Modeling gene and pathway addition or deletion is possible with COBRA toolbox [Genetic engineering procedures are favorable for  toolbox , 37 .in silico tests. Firstly, we modeled the experimental conditions, and then FBA was performed to see the model growth yield and flux distribution in the specified conditions.We used COBRA toolbox to simulate the wet-lab experiment conditions. This toolbox provides us with various functions to perform S matrix.“addReaction” receives the chemical equation of the reaction and adds it to the model. If the metabolites are new to the model, they are inserted automatically as a new row to the “removeRxns” gets the reaction IDs and deletes them by setting the lower bound and the upper bound of the cited reaction to zero.For genetic engineering experiments, these two functions of the COBRA toolbox were used: “addReaction” and “removeRxns.”In order to simulate the environmental conditions, exchange reaction should be altered manually. The function “changeRxnBounds” was used for this purpose. It can be used to alter either lower or upper bound of a specific reaction.After modeling the experimental conditions, using COBRA toolbox functions, we ran FBA to see how the model responds and whether it is comparable to the wet-lab results.Pseudomonas species during benzene degradation have been previously investigated under different oxygen regimes, namely, oxic, hypoxic, and anoxic regimes [2HPO4, KH2PO4, (NH4)2SO4, MgCl2, CaCl2, H3BO3, ZnSO4, NiSO4, (NH4)6Mo7O24·4H2O, CuSO4·5H2O, MnSO4, CoCl2, and FeCl3.The growth patterns of the three  regimes . All of in silico medium, the “exchange reactions” in iMO1086 and iJP962 and “uptake reactions” in iSB1139 were used. Since not all the ions used in the wet-lab medium were included in the models, it was just possible for the ions which had an associated reaction to enter the model. There was no uptake reaction for benzene in the model. Therefore, we used benzoate as the sole carbon source in our simulations. Benzene and benzoate are both converted to catechol in the cell [−1·hr−1. We used this upper bound value as a constraint for the all carbon sources used in this study. For simulating oxic, hypoxic, and anoxic conditions, we gradually decreased the maximum possible rate of oxygen uptake. We considered the possible oxygen uptake flux to be greater than 50 mmol·gDW−1·hr−1 as the oxic, between 10 mmol·gDW−1·hr−1 and 50 mmol·gDW−1·hr−1 as hypoxic, and below 10 mmol·gDW−1·hr−1 as anoxic condition.To define the the cell , 41. Hen P. aeruginosa grew best under oxic conditions and cell growth decreased as the conditions changed from oxic to hypoxic. This decrease continues as the oxygen regime changes from hypoxic to anoxic. Therefore, cell growth is dependent on oxygen availability. Similar to the cell growth, benzene degradation pattern was highly dependent on environmental oxygen. By simulating the carbon source in the medium and oxygen regimes in silico, we ran FBA to see how the model would respond to the above-mentioned conditions. Results are summarized in Wet-lab experiments  showed tP. putida and P. fluorescens were different from those of P. aeruginosa. The highest cell growth of P. putida and P. fluorescens were achieved under hypoxic and anoxic conditions, respectively. On the other hand, P. putida KT2440, the strain used for computational modeling, is known to be a strict aerobe [in silico experiment of growth on benzene for iSB1139, we obtained qualitatively the same results as the case of P. aeruginosa. This implies that the benzene catabolism between these three models might be the same. We decided to look more carefully into this pathway  nature of the iSB1139. Comparison of the results of this model under different conditions could be more reliable. However, by doing so, the growth rate values predicted by the model are far from the experimental data. It suggests that this model needs some refinements and additional constraints, and it may overestimate the biological characteristics.For d-fructose catabolism in Pseudomonas species, specific activities of the fructose, and succinate catabolism enzymes in cell-free extracts of P. putida, P. fluorescens, and P. aeruginosa, and also the doubling time of these species have been measured. Failure of edd− mutant of P. putida in Entner-Doudoroff pathway was also shown, while this mutant strain was able to grow on d-fructose [To investigate the pathways of fructose .d-fructose or succinate. Therefore, in our simulations, we let the model consume ammonium, chloride, magnesium, ferric ion, and citrate which are the components of this medium. In two sets of separate simulations, either fructose or succinate was set as the sole source of carbon and nitrogen. We further simulated the edd− mutant by deleting edd gene (PP_1010) from the model. The growth simulations on both fructose and succinate are shown in In the experiment, bacteria were grown on Palleroni-Doudoroff liquid medium , containd-fructose, iMO1086 and iJP962 were found to be fairly consistent with the wet-lab data. However, difference between the doubling times is much higher than the difference between growth rates. The in silico growth rate of iSB1139 in this case is too high as well and not comparable to the other two models.Similar to the previous test, succinate could not support biomass production as much as other carbon sources, as in all cases, growth on succinate had the lowest biomass flux. For the growth on edd− mutant in iJP962 could not reproduce the experimental results. In the wet-lab experiments, this mutation almost doubled the doubling time but did not have any effects on in silico growth. The reaction associated to this gene could not carry any flux during growth on fructose or glucose. Therefore, its removal would not have any effect on the biomass production flux.The computational model of P. putida is vastly used in biotechnology. However, a major drawback of this strain is its incapability to grow anaerobically. To overcome this disadvantage, a genetically engineered strain was recently constructed by introducing the denitrification pathway of P. aeruginosa [ruginosa . This st2HPO4, KH2PO4, NaCl, NH4Cl, MgSO4, CaCl2, FeSO4·7H2O, succinate, and pH 6.8 supplemented with either KNO3 or NaNO2. One milliliter trace metal solution was added, which contained ZnSO4·6H2O, MnCl2·4H2O, CoSO4·7H2O, NiCl2·6H2O, CuCl2·2H2O, Na2MoO4·2H2O, and HCl. The exchange reactions of these salts (their related ions) were allowed to carry flux wherever a corresponding exchange reaction was in the model. Although the medium was simulated as much as possible, it could not be simulated precisely.In the experiment, modified M9 medium was used. This medium contained NaIn silico simulation was performed by knocking in the reactions of denitrification pathway to iJP962. This pathway (2), which leaves the system. pathway  begins win silico survival was increased as the network was able to consume nitrate. Thus, the model was able to reproduce the wet-lab results.By setting the flux of oxygen uptake reaction to zero and performing FBA, the model was able to have a possible non-zero flux distribution. However, the growth rate was zero in these conditions. Hence, the model did not show any growth in anaerobic conditions, similar to the genetically engineered strain in wet-lab. However, the Adding new reactions to a model leads to some perturbations in the patterns of flux distributions. However, the denitrification pathway could function independent of the growth , because none of the main metabolites of the pathway had been present in the model.P. aeruginosa and P. putida [liuRABCDE gene cluster is essential for leucine/isovalerate utilization in P. aeruginosa. It is indicated that both species are able to grow on leucine or isovalerate as the sole carbon source. Both P. aeruginosa and P. putida grew well on isovalerate, while growth on leucine was more favorable for P. putida in comparison to P. aeruginosa. In silico result was able to predict this phenomenon, as the flux through biomass reaction of iJP962 was higher  than that of iMO1086 (see below).Catabolic pathway of saturated methyl-branched compounds like leucine and isovalerate has been previously investigated in . putida . The genin silico simulation of MSM are mentioned previously in the text. Here, for simulating growth on leucine/isovalerate, we set the lower bound and upper bound of the all exchange reactions of the all possible carbon sources to zero, except for leucine or isovalerate uptake reaction. According to the experimental results, P. putida could grow better than P. aeruginosa on leucine. They had the same growth pattern on isovalerate as the sole carbon source. Since the difference was related to growth on leucine, we simulated growth on this carbon source. In silico results were consistent with experimental data; that is, the growth rates of iJP962 and iMO1086 were 0.785 and 0.778 mmol·gDW−1·hr−1, respectively.In this study, mineral salt medium was used with leucine or isovalerate as the sole carbon source. The components of this medium and P. aeruginosa. It appeared that by making insertion mutations in liuC and liuD genes, the mutants lost their ability to grow on leucine. Inactivation of liuC gene in iMO1086 had no effect on in silico growth, while removing the liuD gene led to zero flux in biomass production. We investigated the reason of this discrepancy and found out that the Liu pathway of the model was different from the one described in the experiment. liuC, is not utilized by the model. We added the reaction B′ to the model, upstream to C′, in order to complete the suggested pathway by the reference. The model did not include this reaction, although adding the new reaction did not change the preference of the model to carry flux through the pathway which goes via 3-methylbutanoyl-CoA. However, reaction D is associated with liuD. Since during growth on leucine, this reaction is active, its deletion led to zero growth rate. The strain used in this experiment and the strain which model is built for are the same , and therefore the discrepancy could not be related to difference in the strains. We propose that such an inconsistency is presumably due to little or no flux in reactions B′ and C′ (if it is not an error in the reconstruction procedure and the bioinformatics data).In the above-mentioned experimental study , some muPseudomonas species, namely, P. fluorescens and P. aeruginosa on single l-amino acids as sole source of carbon and nitrogen [According to several reports, amino acid metabolism might occur in bacteria under nutritionally poor conditions. Amino acids can serve as the sole source of carbon and nitrogen in complete absence of organic growth factors, and this physiological property is vastly seen amongst bacteria –52. An enitrogen .Lochhead-Chase basal medium  has beenl” form as the sole source of carbon and nitrogen was investigated. The results had been estimated visually and on a scale from 0 to 2, 0 meaning no growth and 2 meaning good growth. They also used 1 for poor growth. Seventeen of the amino acids produced almost the same growth response. Only l-threonine, l-tryptophan, and l-hydroxyproline had different effects on the cell growth pattern. Therefore, these data could be subject to our study since they reflect the biochemical differences.In the wet-lab experiment, the ability of growth on all twenty amino acids in the “in silico and performed FBA to see the biomass reaction flux and compare it to wet-lab results. In silico results are shown in l-hydroxyproline, there was no uptake reaction in either of the models; hence, we could not perform FBA. We could add an exchange reaction to the model, keeping in mind that the consumed metabolite should be metabolized and a single uptake reaction may not be sufficient. Since the catabolic pathway of l-hydroxyproline is not included in the models, we did not perform the growth simulation on l-hydroxyproline.We simulated the experiment l-threonine and l-tryptophan, computational results were not completely consistent with the wet-lab data. In iMO1086, cell growth occurred on l-threonine. In silico simulation in this case produced the same results. The model, however, was not able to grow on l-tryptophan in contrast to the wet-lab data. This inconsistency might be because of difference in the strain used in the experimental study or an error of the model.For the case of l-threonine was found to be possible. However, model could not produce any biomass on l-tryptophan. This could be due to the difference of experimental and in silico strains or missing pathways in the model.In case of the iSB1139 model, growth on Pseudomonas species. Histidine degradation in bacteria occurs via either a four-step or a five-step enzymatic pathway [Pseudomonas fluorescens SBW25 degrades histidine by the five-step pathway [Pseudomonas aeruginosa PAO1 utilizes this amino acid through both four- and five-step pathways. Studies have shown that this bacterium does not necessarily use both routes at the same time and it can interchange the routes depending on the environment [Amino acids like histidine are important sources of carbon, nitrogen, and energy for  pathway . Pseudom pathway . On the ironment .P. aeruginosa would be able to grow on histidine, as it has the alternative histidine utilization pathway. However, the inactivation of the five-step pathway of P. fluorescens would lead to cell death on histidine, since the only catabolic pathway of this amino acid would be inactivated.We assumed that by knocking out the five-step pathway in both species, −1·hr−1.We simulated the minimal M9 medium conditions for both iMO1086 and iSB1139. For this purpose, we set the exchange reaction flux of glucose and ammonia to zero and instead we set the uptake rate of histidine to 10 mmol·gDWhutF or hutG, the five-step pathway would be blocked. We simulated inactivation of the five-step pathway by inactivating hutG gene or its associated reaction. By deleting this gene, flux through this pathway did not change, as there was another gene associated with the reaction with “OR.” When we removed the reaction associated with hutG, the model could not grow on histidine. We investigated the reason of this inconsistency and we found out that only the five-step pathway of histidine utilization is included in iMO1086. Therefore, by deactivating the pathway, the model could not simulate the growth on histidine.As shown in P. fluorescens, that is, iSB1139, showed no growth on histidine as the sole source of carbon and nitrogen. These results are against the biological observations [It should be noted that the model of rvations , which mPseudomonas species has been investigated previously [P. putida and P. fluorescens have been studied qualitatively  was different from the modeled strain (SBW25), which may (or may not) be the source of inconsistencies.For the model ofansB deletion for growth on glutamine has been indicated in this study. The product of this gene, PGA, is a periplasmic enzyme that converts glutamine to glutamate. Mutant cells lacking this enzyme were unable to grow on glutamine and concentration of glutamine in the medium did not decrease over time. Therefore, mutant cells were unable to uptake this amino acid. However, they could grow on glutamate and other amino acids [The lethal effect of no acids .ansB gene (PP0495) or removing its associated reaction had no effect on model growth. The reason of the inconsistency is that there are alternative pathways included in the model for converting glutamine to glutamate and hence these reactions performed the conversion in the absence of ansB product.By simulating experimental conditions, we performed gene essentiality analysis. Deleting To date, several genome-scale metabolic networks have been reconstructed. These models have been used for different purposes, ranging from basic science studies to metabolic engineering applications. However, to the best of our knowledge, using these models in a comparative manner has not attracted much attention previously.Pseudomonas metabolic network models in a series of in silico comparative simulations and then evaluated the computational results with the experimental data. It should be emphasized that for gathering information, we used the previously reported experimental data on the same species, but we did not limit our search necessarily to the strains for which the models were built. A number of studies have previously applied such a strategy for validating reconstructed metabolic network models [P. putida on benzene (In this study, we used three k models –61. Howe benzene .P. aeruginosa and P. putida models could reproduce previously reported experimental results. However, the model for P. fluorescens failed to predict the biological data in several instances. One reason for high accuracy of iMO1086 and iJP962 is the vast availability of biological data for P. aeruginosa and P. putida. Since P. aeruginosa is an opportunistic human pathogen [P. putida is a bacterium of biotechnological importance [In our study, in most of the cases, pathogen , 34 and portance , 48, theportance , which i in silico model of P. fluorescens, iSB1139, showed inconsistent results with experimental data in many of our simulations. It is known that the computational growth rate of this model is far from the reported experimental growth rate [The very recentwth rate . Accordi