The study of cancer metabolism has been largely dedicated to exploring the hypothesis that oncogenic transformation rewires cellular metabolism to sustain elevated rates of growth and division. Intense examination of tumors and cancer cell lines has confirmed that many cancer-associated metabolic phenotypes allow robust growth and survival; however, little attention has been given to explicitly identifying the biochemical requirements for cell proliferation in a rigorous manner in the context of cancer metabolism.+, acetyl-CoA, and amino acids. Extension of this approach to serine/glycine and glutamine metabolic pathways suggested lower limits on serine and glycine catabolism to supply one-carbon unit synthesis and significant availability of glutamine-derived carbon for biosynthesis resulting from nitrogen demands alone, respectively. We integrated our biomass composition results into a flux balance analysis model, placing upper bounds on mitochondrial NADH oxidation to simulate metformin treatment; these simulations reproduced several empirically observed metabolic phenotypes, including increased reductive isocitrate dehydrogenase flux.Using a well-studied hybridoma line as a model, we comprehensively and quantitatively enumerate the metabolic requirements for generating new biomass in mammalian cells; this indicated a large biosynthetic requirement for ATP, NADPH, NAD+, while also providing justification for various extracellular nutrient uptake behaviors observed in tumors. Collectively, these results demonstrate how stoichiometric considerations alone can successfully predict empirically observed phenotypes and provide insight into biochemical dynamics that underlie responses to metabolic perturbations.Our analysis clarifies the differential needs for central carbon metabolism precursors, glutamine-derived nitrogen, and cofactors such as ATP, NADPH, and NADThe online version of this article (doi:10.1186/s40170-016-0156-6) contains supplementary material, which is available to authorized users. Many parallels exist between the metabolic profiles of cancer cells and normal proliferating cells, including the use of aerobic glycolysis, selective expression of metabolic enzymes with distinct regulatory features, and elevation of amino acid consumption and biosynthesis –6. GrowiA central aim of the field of cancer metabolism is to identify metabolic pathways selectively activated in tumor cells, which likely include crucial biosynthetic pathways, to reveal therapeutic targets , 15. To In this investigation, we use fundamental stoichiometric and mass-balance principles to gain insight into the metabolic behavior of cancer cells using only minimal information about their proliferative needs. Previous investigations have similarly used minimal stoichiometric models to explore the effects of using a variety of objective functions on metabolic phenotype, the sensitivity of growth rate and other fluxes to perturbations, and the consistency between these in silico predictions and empirical measurements in a mammalian cell line , 22. Our3, PO4, and SH, respectively, and the “additional” category represents the contributions of additional inorganic substrates  and hydride groups from reduced cofactors. The total mass contribution of each precursor to every macromolecular component was determined  was included. The demands for complete biosynthesis of all nonessential components  are listed under the “Synthesis” header. Additionally, these demands were modified to consider the scenario in which nonessential amino acids (NEAAs) and fatty acids (FAs) are consumed from the surrounding medium, with the results listed under the “Uptake” header.  and less than 1 % for other associated components  in the “Synthesis” regime  serine or glycine demand to give the fractional fate of each amino acid when it serves as the sole source for 1C units  unit demands per gram DCW were determined by combining the demands for all biomass components for which they serve as substrates Table . 1C unitts Table . GlutathDCW) scaled by their corresponding numbers of carbon and nitrogen atoms, respectively  a minimum uptake profile, where glutamine was assumed to be consumed only for reactions in which other nitrogen-containing compounds cannot serve as substrates ; and (3) a predicted uptake profile, where glutamine consumption is determined from a simulated stoichiometric network of cells cultured in Dulbecco’s modified Eagle medium (DMEM)  Table . For eacM) Table .Flux balance analysis (FBA) , 34 was mentclass2pt{minimThis network was incorporated into the following linear programming (LP) problem:wheresubject to:N = 152 is the total number of reactions, ℳintr is the set of all intracellular metabolites, ℳextr is the set of all extracellular metabolites, where S represents the stoichiometric matrix, bm is zero if metabolite m is intracellular and bm is equal to the specific consumption (or production) flux of metabolite m if metabolite m is extracellular. The final constraints state that irreversible reactions can only take nonnegative values, while reversible reactions can be assigned any real value. Reactions were primarily designated to be irreversible if they were recognized as such by literature and database sources [Z is a specified linear combination of metabolic fluxes, which serves as the objective function for the LP problem.The first constraint is a mass balance;  sources , 25, 26;cn such that Z is equal to the total net carbon consumption rate:For our metformin treatment simulation (see below), we have chosen to maximize the yield of biomass on carbon. To achieve this, we fixed the specific growth rate as a basis and chose the coefficients where −1  [DCW−1 h−1 [−1 h−1, representative of the highly glycolytic A549 lung carcinoma cell line [Biomass composition values were taken from the literature  as descrof 24 h) . Since gCW−1 h−1 . A DCW oCW−1 h−1 . BecauseCW−1 h−1 , 37, a lell line ) was intell line , 37, minell line .Z*, which maximizes biomass yields at the specified growth rate) was determined using the “linprog” LP solver function in MATLAB . However, for a given problem formulation, there are generally multiple flux distributions that have this minimal carbon uptake rate. To avoid the possibility of multiple solutions, we implemented a second optimization program that, in addition to all previously specified constraints, specifies the carbon uptake rate to be equal to the minimal rate Z* (determined from the first problem) and minimizes the two-norm of the flux vector v. The complete problem is therefore a bilevel optimization [The minimal carbon uptake rate   It is notable that, in contrast to the use of NADPH for reducing power, NAD+ is primarily required for biosynthesis in its oxidized form.In addition to their mass contributions to DCW, we computed the molar requirements of each of the major precursors, 1C units, ODCW−1 h−1 expended for cell maintenance estimated for the hybridoma model [However, this profile assumes complete biosynthesis of all nonessential compounds starting from glucose, whereas in practice, many cells will derive significant contributions to TCA cycle-associated compounds from glutamine and obtain nonessential amino acids and fatty acids directly from surrounding media , 41–43. ma model  exceeds ma model .)A number of recent reports have implicated serine, glycine, and one-carbon metabolism as being important for tumors. The gene for phosphoglycerate dehydrogenase (PHGDH), which encodes the enzyme that catalyzes the first committed step in serine biosynthesis from 3PG, has been found to be amplified in breast cancer and melanoma , 12; glyNucleotide synthesis is essential for cancer cells, as well as any proliferating cells, to divide. Unlike amino acids , which can be derived from serum or culture medium to bypass de novo production , 47, 48,2), and ammonium (NH4+)  Fig. . SHMT haThe demand for 1C units for nucleotide  production places constraints on the fluxes through the serine-glycine pathway. We investigated these constraints by analyzing the biomass requirements of serine, glycine, and tetrahydrofolate compounds. The number of millimoles of each substrate required per gram DCW are given in Table metabolic flux rather than metabolite levels themselves is important. Although the link between PHGDH and SHMT is less well understood, regulation of biosynthetic pathways at the committed step is a common motif in metabolism, so it is plausible that knockdown of PHGDH may affect the activities of other enzymes in the pathway, including SHMT. Thus, it may be that even when exogenous serine is added to the medium to bypass the PHGDH reaction, the flux through the serine-glycine pathway, and therefore production of one-carbon units, is still impaired, this could explain why exogenous serine cannot rescue the PHGDH knockdown.These results give insight into some recent findings about the importance of serine and glycine in the metabolism of cancer cells. Previous results have shown an impairment in proliferation in breast cancer cells with amplified PHGDH copy number when the gene is knocked down, but this knockdown does not result in a change in intracellular serine levels and cannot be rescued by exogenous serine . As in tAlthough glutamine is nominally a nonessential amino acid (it can be synthesized through the ATP-dependent condensation of glutamate and free ammonia), it has been extensively reported that glutamine serves as a major biosynthetic substrate for cancer cells , 32, 52.2, the calculation nonetheless provides a suitable approximation .Each molecule of glutamine consumed contains two nitrogen atoms that can contribute to biomass generation: an “amide” nitrogen that is lost when glutamine is converted to glutamate and a “transamination” nitrogen that is lost when glutamine-derived glutamate is converted to αKG. Although these two amine groups are used by distinct biosynthesis reactions, the amide nitrogen, for which there is a considerably smaller biomass demand, can be converted to a transamination nitrogen if it is first liberated by glutaminase to become free ammonia and then added to a molecule of αKG by glutamate dehydrogenase to become the amine group in glutamate Fig. .  , oxoglutarate dehydrogenase (OGDH), and mitochondrial malate dehydrogenase (MDHm), all of which catalyze NAD+-consuming mitochondrial reactions , this is compensated by an approximately twofold increase in total cytosolic redox activity , and lactate dehydrogenase (LDH), respectively, resulting in an increased fraction of glucose being converted to lactate. In addition, as indicated by lower cytosolic malate dehydrogenase activity upon simulated metformin treatment, malate-aspartate shuttle activity decreased in coordination with lower oxidative TCA cycle flux; this is because NADH generated in glycolysis is oxidized in the cytosol rather than being shuttled into the mitochondria for OXPHOS. In total, these results reflect the tight regulation between redox-associated steps across major metabolic pathways—complex I activity within the ETC, NADH oxidation in the TCA cycle, and GAPDH during and LDH following glycolysis—which, solely by satisfying stoichiometric mass-balance constraints on redox cofactors, enables robust maintenance of cellular growth and homeostasis.As previously suggested , 71–73, + regeneration by assessing the sensitivity of NAD+-consuming mitochondrial reactions  to the presence of the two non-ETC reactions predicted to oxidize NADH in the mitochondria, nicotinamide nucleotide transhydrogenase (NNT), and NAD+-dependent glutamate dehydrogenase (GDHNAD). Previous reports have suggested that NNT can promote reductive IDH flux by converting mitochondrial NADH to NADPH [+, but GDHNAD, operating in the reverse direction, became the sole reaction aside from ETC NADH oxidation that regenerated mitochondrial NAD+, fully compensating for the loss of NNT , and glutaminase flux became zero for all cases . Under most in vitro cell culture conditions, glucose concentrations are highly relative to physiological levels , and when tumor cells are sufficiently close to blood vessels, they are afforded access to roughly constant glucose levels. In effect, glucose is “free” for these cells, and there is no cost to consuming it when it is available. Although ATP and amino acids are required to generate the enzymes and transporters needed for high rates of fermentation, this burden is presumably considered in the “Protein” biosynthesis demands (Additional file +, and precursor demands in synthesizing new biomass and how these burdens could be selectively reduced by increasing fatty acid and amino acid uptake. Next, we applied the generated profiles to explore the limits of metabolic behavior in two case studies relevant to cancer cells—the production of 1C units from serine and glycine catabolism, and the contribution of glutamine to total cellular nitrogen and carbon—which demonstrate how quantifying biomass demands can yield insight even in the contexts of metabolic branchpoints. These queries demonstrated that flux through serine and glycine biosynthesis pathways is required for sustaining 1C production for nucleotide synthesis and that glutamine may contribute substantially to biomass carbon as a consequence of its natural role as the predominant nitrogen source. Finally, we incorporated these biomass requirements into a constraint-based FBA simulation that modeled the metabolic effects of metformin, a widely used antidiabetic medication currently under consideration as a potential cancer therapeutic. The resulting flux distributions successfully recapitulated the major metabolic changes observed in cells following metformin treatment and also enabled greater understanding of the interactions within the reaction network that contributed to these changes.In this investigation, we used a series of stoichiometric analyses to elucidate the metabolic requirements of mammalian cell proliferation. First, using a hybridoma line as a model for cancer cell composition, we generated comprehensive profiles of the major precursor and cofactor requirements for biomass synthesis on both mass and stoichiometric bases. These assessments revealed the importance of meeting ATP, NADPH, NAD1C, one-carbon unit; 3PG, 3-phosphoglycerate; αKG, α-ketoglutarate; AcCoA, acetyl-coenzyme A; CS, citrate synthase; DCW, dry cell weight; DHAP, dihydroxyacetone phosphate; DMEM, Dulbecco’s modified Eagle medium; ETC, electron transport chain; FA, fatty acid; FBA, flux balance analysis; FH, fumarate hydratase; G6P, glucose 6-phosphate; GAPDH, glyceraldehyde 3-phosphate dehydrogenase; GCS, glycine cleavage system; GDH, glutamate dehydrogenase; IDH, isocitrate dehydrogenase; LDH, lactate dehydrogenase; LP, linear programming; MDH, malate dehydrogenase; NEAA, nonessential amino acid; NNT, nicotinamide nucleotide transhydrogenase; Oaa, oxaloacetate; OGDH, oxoglutarate dehydrogenase; PHGDH, phosphoglycerate dehydrogenase; PDH, pyruvate dehydrogenase; PPP, pentose phosphate pathway; R5P, ribose 5-phosphate; ROS, reactive oxygen species; SDH, succinate dehydrogenase; SHMT, serine hydroxymethyltransferase; SUCL, succinate-CoA ligase; THF, tetrahydrofolate


However, that need not be true, other times such as the lifetime of an infinitesimal perturbation can be much longer. In the present work, we demonstrate that this paradoxical property can arise even in quite simple systems such as a linear chain of reactions obeying mass action (MA) kinetics. By mathematical analysis of simple reaction networks, we pin-point the reason why the standard relaxation time does not provide relevant information on the potentially long transient times of typical infinitesimal perturbations. Overall, we consider four characteristic times and study their behaviour in both simple linear chains and in more complex reaction networks taken from the publicly available database ‘Biomodels’. In all these systems, whether involving MA rates, Michaelis–Menten reversible kinetics, or phenomenological laws for reaction rates, we find that the characteristic times corresponding to lifetimes of tracers and of concentration perturbations can be significantly longer than τ.When relaxation towards an equilibrium or steady state is exponential at large times, one usually considers that the associated relaxation time  There are many ways to define a characteristic time in a dynamical system. The simplest is via the asymptotic relaxation towards the steady state . We are then left with the parameter In the disordered (i.e. heterogeneous) case we now consider, the rates ‘τc, τt, Tc, Tt) compared with the homogeneous case. However, for stronger disorder, the characteristic times typically do increase significantly with CV. To identify the typical behaviour, we have determined these characteristic times for 10 000 realizations of the disorder and calculated the median values. For τt and τc, we illustrate our results in figure 5a in the case of MA, where those two quantities are equal. The relaxation time increases relatively mildly  at low CV but increases more markedly when CV becomes larger than 30%. Furthermore, instead of having a limit at large N as happens in the absence of disorder, it seems that the presence of disorder makes the relaxation time diverge slowly as N → ∞, perhaps logarithmically.For weak disorder, one expects little change in the values of the characteristic times  is the same as without disorder, namely a − b. At large disorder, this argument fails because the quenched and annealed averages are very different. A simple way to see this is to consider an extreme case where fluctuations are large. Clearly, a very large value of ‘a’ at one site will not compensate a very small value at another site. As expected, at large CV, effects of disorder become very significant. The reason should be clear: Tt and Tc are sensitive to unfavourable reactions  throughout the whole set of reactions. Finally, just as for relaxation times, it seems that the presence of disorder increases the median values of lifetimes by a factor that diverges slowly  as N → ∞.Consider now the effects of disorder on the 4.2.Although quite a few biosynthetic pathways include successive steps forming a linear chain of enzymatic reactions, the one-dimensional systems considered so far remain toy models because in all known organisms, large-scale biochemical metabolic networks have numerous branches and loops. It is thus necessary to consider how characteristic timescales might be affected by such structures. Rather than produce artificial networks including those features, it is more relevant to study directly the various kinetic models of metabolism that have been proposed in the literature. The repository ‘Biomodels’ ,13 proviJt)( and Jc) and (2.9), but see also the electronic supplementary material for additional details.For each of those four kinetic models, we first downloaded its SBML specification  from theThe initial perturbation was taken to be localized on any metabolite of the network. The global lifetime is chosen as the maximum these perturbation lifetimes.Saccharomyces cerevisiae (baker's yeast). It has 17 reactions, mostly of the reversible MMH type, and there are 14 internal metabolites. Glucose is an external metabolite which enters the metabolism and then gets transformed. A total of three compounds can be excreted, all irreversibly. The characteristic times of this model are modest, from a few seconds to a few minutes. Further inspection shows that the ordering of these four values follows the same pattern as in our one-dimensional toy model, namelyτt < τc and Tt < Tc whenever there are Michaelis–Menten reactions subject to saturation effects. Indeed, the saturation of flux in a reaction can prevent a concentration fluctuation from being evacuated but it will not prevent labelled atoms from going through (i.e. from participating to the flux). Secondly, in our toy model, the τs are relatively insensitive to processes inside the network: they depend mainly on reactions close to the excreted metabolites. By contrast, the Ts depend on drift throughout the whole network. Thus, if the network is large one can expect the Ts to be larger than the τs.In Escherichia coli [−1 l−1 compared to 1.5 mmol s−1 l−1). The innequalities equation (4.1) are not respected since τt > τc and Tt > Tc. Model 3 contains the glycolysis and the pentose phosphate pathway, but for a human cancer cell. It has 29 reactions and 34 internal metabolites. The inequalities of equation (4.1) are satisfied except that τc > Tt. This ‘discrepancy’ may be due to the fact that the diameter of the network is modest while saturation effects are important.The other models partly follow this same pattern cf. . Model 2hia coli . It has E. coli [Model 4 contains the reactions for the biosynthesis of purines in E. coli . It has 5.N nodes, the characteristic times of the whole system grow as N2. Our work in this paper focuses on the consequences of drift. For simplicity, we do this in the context of reaction networks operating close to their steady state. If a concentration fluctuation is introduced relative to the steady state, its damping will generally be associated with both a spreading out (diffusion) and also an overall drift. The timescale for evacuating such a perturbation is what we call its lifetime T (cf. equations (2.8) and (2.9)), though in other contexts it can be referred to as the mean residence or transit time. In the absence of drift, corresponding to a simple diffusive regime, the lifetime T scales as the square of the diameter of the network, a scaling that also arises for the standard measure of the time to return to equilibrium, i.e. the relaxation time τ. In most systems, τ corresponds in fact the longest characteristic time and there are no further subtleties. However, for almost any reaction network of interest, one has both diffusion and drift. More generally, out of equilibrium systems will have fluxes, and such fluxes may drive conserved particles out of the network. In the presence of such drift, a perturbation's lifetime T can scale as the diameter of the network, divided by a characteristic drift velocity which is related to the presence of flux and provides the drift intensity. Interestingly, in this out-of-equilibrium situation, the relaxation time τ is no longer informative about the timescale of the (slow) process which evacuates perturbations. In particular, in our toy model consisting of a homogeneous linear chain of reactions, τ does not grow with the system size while T grows linearly. This can be understood quite intuitively from τ) sets in. The perturbation's lifetime can thus be much longer than the relaxation time. We showed analytically how that happened in our one-dimensional homogeneous systems. Indeed, in the presence of drift, the linearized dynamics can be decomposed into eigenvectors, but the leading eigenvector determining τ is concentrated near the metabolites that can be excreted. As a result, τ is quite insensitive to the size of the network while T inevitably increases with network size since the evacuation of a perturbation requires it to cross some fraction of the diameter of the network. When studying the extension to a non-homogeneous system of reactions, it was necessary to take a numerical approach. In our toy one-dimensional network, we introduced heterogeneities into the individual reaction rates. For mild heterogeneities, we find that the effects are small, but as one increases the CV of the reaction rates, the effects of disorder become significantly stronger, increasing in particular the median values of τ and of T. Interestingly, these heterogeneities drive τ to grow with system size as illustrated in N is slow, perhaps only logarithmic with N. Similarly, in the presence of disorder, it seems that T grows a bit faster than N, perhaps logarithmically faster.In dissipative systems, relaxation is often driven by the local dynamics, and as a result characteristic times of the system (and in particular relaxation times) are comparable to that of the individual processes. However, in systems where atoms or other particles are conserved, the diffusion and drift of the conserved quantities can significantly increase system characteristic times. Pure diffusion provides a simple example of this effect: on a linear lattice of The overall phenomena found are most easily understood when the reactions obey MA, but they arise also for MMH reaction laws. For this last case, the presence of a saturation of the flux with concentration of metabolites exacerbates the difference between characteristic times associated with concentrations versus tracers as is illustrated in Tc > Tt but nevertheless raises a flag as to possible artefacts coming from the peculiar and unorthodox form of the kinetic laws introduced in [Given any model network with specified dynamics, it is possible to estimate its relaxation times and lifetimes (cf. equations (2.8) and (2.9)) in the context of concentration perturbations or of tracers. The four associated characteristic times will typically follow the inequalities given in equation (4.1) as they do in the one-dimensional models. If a case leads to anomalous results, either because the inequalities are violated or because some of the characteristic times are unreasonable (too short or too long), the reason for the anomaly should be found or the reliability of the model should be questioned. We encountered precisely this situation for the model  from theduced in . The fac


Shewanella is a genus of facultatively anaerobic, Gram-negative bacteria that have highly adaptable metabolism which allows them to thrive in diverse environments. This quality makes them an attractive bacterial target for research in bioremediation and microbial fuel cell applications. Constraint-based modeling is a useful tool for helping researchers gain insights into the metabolic capabilities of these bacteria. However, Shewanella oneidensis MR-1 is the only strain with a genome-scale metabolic model constructed out of 21 sequenced Shewanella strains.Shewanella oneidensis MR-1 and constructed metabolic models for three other strains, namely Shewanella sp. MR-4, Shewanella sp. W3-18-1, and Shewanella denitrificans OS217 which span the genus based on the number of genes lost in comparison to MR-1. We also constructed a Shewanella core model that contains the genes shared by all 21 sequenced strains and a few non-conserved genes associated with essential reactions. Model comparisons between the five constructed models were done at two levels – for wildtype strains under different growth conditions and for knockout mutants under the same growth condition. In the first level, growth/no-growth phenotypes were predicted by the models on various carbon sources and electron acceptors. Cluster analysis of these results revealed that the MR-1 model is most similar to the W3-18-1 model, followed by the MR-4 and OS217 models when considering predicted growth phenotypes. However, a cluster analysis done based on metabolic gene content revealed that the MR-4 and W3-18-1 models are the most similar, with the MR-1 and OS217 models being more distinct from these latter two strains. As a second level of comparison, we identified differences in reaction and gene content which give rise to different functional predictions of single and double gene knockout mutants using Comparison of Networks by Gene Alignment (CONGA). Here, we showed how CONGA can be used to find biomass, metabolic, and genetic differences between models.In this work, we updated the model for in silico studies of Shewanella metabolism. The developed models provide a platform for a systematic investigation of Shewanella metabolism to aid researchers using Shewanella in various biotechnology applications.We developed four strain-specific models and a general core model that can be used to do various  Shewanella is a genus of facultatively anaerobic, Gram-negative aquatic bacteria found in diverse environments around the globe [Shewanella are of particular interest today because of their possible use in bioremediation in which the organisms convert a wide variety of metals from a soluble to an insoluble form and thus prevent the spread of contamination [Shewanella putrefaciens has been shown to reduce soluble uranium-VI to insoluble uranium-IV [Shewanella strains can also degrade halogenated organics, including polychlorinated biphenyls and possibly explosive nitramines [Shewanella strains have also been metabolically engineered for chemical production [he globe . This eche globe ,2. In adhe globe ,3,4. Shemination . For exaanium-IV . Shewanetramines . Shewaneoduction .Shewanella. These genomic datasets provide insight into both the cumulative (or pan) and conserved (or core) capabilities of this species. Previous studies have used these sequenced genomes to study metabolic subsystems [Shewanella strains. Another recent study looked at the carbon, nitrogen, phosphorous and sulfur utilization capabilities of five different Shewanella strains and reconciled these with genomic data [Currently, there are over 20 sequenced strains of bsystems  and regubsystems  in Shewamic data .Shewanella, from both traditional microbiology and high-throughput “-omics” (summarized in [Shewanella strains [Shewanella genus as a whole [Metabolic modeling provides a way to integrate the wide variety of data available on the rized in ) experimrized in ,12. With strains , models Shewanella oneidensis MR-1 published in 2010 (referred to as iSO783) has been used as a platform to integrate and validate experimental data with computational predictions [Shewanella strains. In this study, we have expanded the previous model for MR-1 based on updated genome annotations and compared model growth predictions to fitness measurements for transposon-tagged mutants [iMR1_799) together with genome comparisons (based on genome annotations) and experimental data to develop the genome-scale metabolic models for three other Shewanella strains, Shewanella sp. MR-4 (hereafter iMR4_812), Shewanella sp. W3-18-1 (hereafter iW3181_789), and Shewanella denitrificans OS217 (hereafter iOS217_672). In addition, we developed a Shewanella core (hereafter Core) model using genome annotations of 21 sequenced Shewanella strains that would represent the conserved metabolic functionalities of all Shewanella strains. Furthermore, we used the developed models to predict and compare the metabolic capabilities of Shewanella strains in utilizing various carbon and electron acceptor sources. We also used a previously developed computational algorithm, Comparison of Networks by Gene Alignment (CONGA) [Shewanella strain.The genome-scale metabolic model of dictions . This mo mutants . We subs (CONGA)  to identShewanella oneidensis MR-1 was obtained from Grigoriy Pinchuk , Shewanella sp. MR-4 and Shewanella sp. W3-18-1 were obtained from Daad Saffarini (University of Wisconsin-Milwaukee). Shewanella denitrificans OS217 was obtained from the American Type Culture Collection . Strains were cultivated at 30°C in Luria Broth (LB) , half-strength Difco Marine Broth (S. denitrificans OS217) or modified M1 medium [1 medium . For groFor growth rate experiments, strains were precultured overnight in 2 mL LB or Marine Broth at 30°C with continuous shaking. Cells were then transferred into 2 mL of modified M1 supplemented with the carbon source of interest at 20 mM concentration and 20 mM D, L-lactate  or 20 mM maltose (OS217) using a 1:100 dilution from the overnight LB or Marine Broth culture and grown for 24 hours  or 48 hours (OS217) at 30°C. The cells were then harvested by centrifugation at 5000 rpm and resuspended in modified M1 medium containing no carbon source to an OD600 of 1–1.5. 5 μL of the cell suspension was then added to 95 μL of modified M1 medium supplemented with the carbon source of interest on a 96-well plate.Growth/no-growth phenotype tests were performed using the same procedure, with the second preculture containing only lactate or maltose at 40 mM concentration. After harvesting, the cells were washed with 1 mL of modified M1 medium to remove any residual carbon source, centrifuged again, and resuspended. Growth curve experiments were conducted in a Tecan Infinite M200 plate reader . Cultures were grown at 30°C in 96 well plates with OD600 readings taken every 15 minutes. Growth rates (1/h) were calculated from the linear fit of ln(OD) versus time, where the slope corresponds to the growth rate. Biomass yields (OD/mmol) were calculated by subtracting the starting OD from the stationary phase OD and dividing by the starting concentration of the carbon substrate. Results are provided in Additional file Shewanella strains was constructed using INPARANOID as previously described [Shewanella strains made it possible to readily identify orthologous groups that had missing members or that contained extra or erroneously grouped members . Missing group members were identified by tBLASTN analysis (genes missing in gene models) or BLASTP analysis . Insertion elements were mapped in each of the genomes as described for S. oneidensis MR-1 [A table with the draft ortholog predictions among the 21 sequenced escribed . This tasis MR-1  to assissis MR-1 . The ortShewanella models iMR4_812, iW3181_789, and iOS217_672 developed in this study were constructed manually using the iMR1_799 as a template, and the gene-protein-reaction (GPR) associations in each new model were constructed based on the ortholog table. Reactions in the iMR1_799 metabolic model were copied to the metabolic models of these strains if the associated MR-1 genes had orthologs in the other strains or if no genes were associated to the reaction in iMR1_799. Genes that did not have orthologs in these strains and their associated reactions were removed from the base iMR1_799 model to obtain draft models that were specific to iMR4_812, iW3181_789, and iOS217_672. Flux balance analysis (FBA) [iW3181_789, iOS217_672, and Core was modified because unlike iMR1_799, they are not capable of producing UDP-N-acetyl-D-galactosamine. The LPS reaction for iMR4_812 was updated based on the structure determined experimentally by Vinogradov et al. [The v et al. .Shewanella strains. Non-GPR reactions were added to the model only if they were predicted to be essential for aerobic growth on pyruvate, resulting in a smaller number of reactions without GPR associations  converts L-glutamate 5-phosphate (glu5p) to L-glutamate 5-semialdehyde (glu5sa). Every Shewanella strain studied has a ProA enzyme that catalyzes the G5SD reaction; however, there are two different orthologs that encode the ProA enzyme, and some strains have only one of the two. Since the Core model represents the collective group and G5SD is an essential reaction (meaning it has to stay in the Core model), the two genes associated with ProA were encoded as isozymes in the Core model. The other three reactions are acetylornithine deacetylase (ACODA), inorganic diphosphatase (PPA), and a sulfate transport reaction (SO4t2).The Core model was constructed based on the genes that were conserved across all 21 sequenced Shewanella models can be found in Additional file The complete list of reactions, metabolites, and genes for all five Escherichia coli  [The mutant fitness data set from Deutschbauer et al.  containsmutants) . In addiiMR1_799 model. The 64 simulation conditions can be mapped to 101 of the 195 fitness experiments . Additional file Only experiments from the “carbon source”, “nitrogen source”, “carbon and nitrogen source”, and the “anaerobic” groups where considered. Experiments from the remaining groups were not considered because they cannot be simulated using the models. Next, experiments that are labeled as stationary phase, saturated, or with added CAS amino acids were excluded from our analysis. In total, we were able to make 35,712 predictions for 558 of the 3,355 knockout mutants (other mutants involved genes that were not in the model) under 64 unique simulation conditions for the Shewanella models  on different carbon sources and electron acceptors  or absent (0) in a particular model or whether the model was predicted to grow (1) or not (0) under a given growth condition.A hierarchical clustering approach was used to gain an understanding of how the four CONGA, or Comparison of Networks by Gene Alignment , is a coiSO783) [iMR1_799) contains additional reactions involved in alternate carbon metabolism (5), cell envelope biosynthesis (1), energy metabolism (3), glycolysis/gluconeogenesis (1), methionine metabolism (1), nucleotide salvage pathways (5), valine, leucine, and isoleucine metabolism (8), and additional transport reactions (19). To evaluate the performance of the original and updated MR-1 models, we compared model predictions to a recent study reporting wildtype and mutant phenotypes [iMR1_799 predicted could support growth (see Methods for details). The iSO783 model could only predict growth for the wildtype strain in 40 of these 64 conditions, indicating that 24 conditions are incorrectly predicted by the original model. When evaluating the growth phenotypes for the subset of mutants (546) and conditions (40) that both models could make predictions for, we found the iSO783 model had an overall accuracy of 85.11% and the iMR1_799 model had an overall accuracy of 85.34%. This was done assuming that mutants with a fitness score less than −3.5 did not grow (see Methods for details). We next evaluated the sensitivity of the iMR1_799 model’s accuracy to the fitness cutoff used to indicate growth or no growth phenotypes   our updaenotypes . Of the iMR1_799 was used as a starting point to generate models for three additional strains. Several alternative carbon metabolism pathways were added to the Shewanella models based on literature data [Shewanella strains  were shown to be able to use arabinose as a carbon source under aerobic conditions . Only 21 essential reactions without GPR associations were kept in the Core model to better represent a minimal model of the Shewanella genus, of which 17 of these 21 reactions do not have GPR associations in any of the models. The remaining four non-GPR core reactions point towards gaps in our knowledge for some of the 21 Shewanella strains. The 673 reactions and 552 genes in the Core model turned out to represent an average of 73% and 72% of the reactions and genes of the 4 Shewanella models developed here, respectively .The Core model was built to provide a representation of the Shewanella genus. In addition, by comparing knockout predictions across the five models, we are able to identify deeper genetic and metabolic differences for each strain. We explore these two levels of comparison in the following sections.By comparing the wildtype version of each of these five models, we are able to identify how each model and thus strain is physiologically different from the entire Shewanella strains, iMR1_799, iMR4_812, iW3181_789, iOS217_672, and the Core model were used to predict growth capabilities of each strain on different carbon sources and electron acceptors. A total of 70 carbon sources and 16 electron acceptors were used in the FBA simulations , 31/36 for iMR4_812 (86%), and 25/36 for iW3181_789 (69%) when compared against experimental data [The reconstructed metabolic models of different tal data ,11, inclShewanella strains that were predicted to grow on the corresponding carbon source and electron acceptor . This result suggests that while iMR4_812 and iW3181_789 have highly similar metabolic gene content, the genes deleted from iMR1_799 and those added to iMR4_812 and iW3181_789 resulted in iMR1_799 and iW3181_789 being more metabolically similar from a network perspective.To evaluate how each model relates to the others based on the presented results so far, heat maps and dendrograms were created for the four models based on metabolic gene content or phenotype predictions (see Methods). First, a heat map and dendrogram was generated based on the presence or absence of metabolic genes in the metabolic models Figure A. From tCONGA is a constraint-based method that can be used to identify functional differences between two models by comparing reconstructed networks aligned at the gene level. We used CONGA to identify orthologous genes to delete such that only one of the two organisms being compared is capable of growth. In this work, we identified three reasons that explain the functional differences found by CONGA: metabolic , genetic , and biomass differences  conditions. The total number of lethal genes is lower in this condition because only three models were analyzed. The iOS217_672 and Core models were excluded from the last condition because they are not capable of growth under that condition.Based on this analysis, we can clearly see that under the aerobic condition, the Core model is the least robust since the Core model has the least number of genes and reactions. Thus, the Core model has fewer isozymes  and alternative pathways to compensate for deleted genes. iMR1_799 and not in iMR4_812 and iW3181_789 because the latter two models have alternative enzymes that do not require CymA for nitrate reductase activity.Furthermore, we mapped the reactions associated with the genes in the gene deletion sets to their respective metabolic subsystems to see if a specific subsystem was responsible for most of the differences identified by CONGA for the single deletion case. Of all subsystems, the Energy Metabolism subsystem stood out with the most number of reactions associated with orthologs deleted by CONGA (14 reactions) and they all appear only under L-alanine plus nitrate conditions. Further investigation shows that all 14 reactions involve the gene associated with the membrane-anchored tetraheme cytochrome c, CymA. Deletion of CymA is lethal in Shewanella strains, MR-1, MR-4, W3-18-1, and OS217 in terms of growth phenotypes using integrated approaches of genome comparisons, experiments, and computational models. Based on conservation of metabolic gene content, we expected that iMR4_812 and iW3181_789 to be the most similar, with iMR1_799 and iOS217_672 being the most different. However, our results revealed that based on predicted growth phenotypes MR-1 is most similar to W3-18-1, followed by MR-4 and OS217 in terms of metabolic capabilities in using various carbon and electron acceptor sources. This suggested that genetic similarity does not always coincide with metabolic phenotype similarity. Some conflicts between our models’ predictions and experimental data suggest there may be regulatory effects affecting cellular phenotypes that are not accounted by our models. Regulons for 82 transcription factors have been predicted based on comparative genomic analysis of 16 Shewanella strains and they also appear to highly vary across the different strains [In this study, we have shown an extensive comparison of four different The Core model reflects the conserved metabolic capabilities of all sequenced strains. Based on analysis of the Core model, it appears that all strains appear to be capable of using pyruvate, amino acids (10 of 20), and fatty acids as sole carbon sources. Interestingly, only oxygen was predicted to be used as an electron acceptor by the Core model, indicating that while many strains can use other electron acceptors, the respiratory pathways and enzymes involved are not necessarily conserved.Shewanella strain of interest. Each genome-scale metabolic model developed in this work will serve as a platform to integrate experimental data with computational approaches for each Shewanella strain, which would be helpful to researchers who study Shewanella for scientific discovery, bioremediation, or metabolic engineering applications. In addition, the Shewanella core model will serve as a base model to develop genome-scale models for other Shewanella strains by simply adding metabolic functionalities that are unique to those individual strains.We also used computational approaches to further investigate the metabolic and genetic differences among these strains under several common growth conditions. Our computational analyses provided more insights into the metabolic capabilities of each The authors declare that they have no competing interests.WO, TV, and JL constructed the five genome-scale metabolic network models. KL ran the experiments. MS and MR analyzed the genomes and provided the ortholog table. WO and TV did the model simulations. WO, TV, KL, and JR analyzed and interpreted the modeling and experimental data. JR conceived of the study. WO, TV, KL, and JR wrote the manuscript. WO generated all five SBML model files. All authors read and approved the final manuscript.Supplementary material including supplementary text and figures. This file contains additional discussion on the growth experiments for MR-1 and W3-18-1, biomass composition, evaluation of mutant fitness data, and CONGA.Click here for fileSupplementary tables. This file contains additional tabular data.Click here for fileXML files for the five Shewanella models in SBML format. This file contains the XML files for the five models constructed in this work.Click here for file


Chlorophyll is one of the most important pigments present in green plants and rice is one of the major food crops consumed worldwide. We curated the existing genome scale metabolic model (GSM) of rice leaf by incorporating new compartment, reactions and transporters. We used this modified GSM to elucidate how the chlorophyll is synthesized in a leaf through a series of bio-chemical reactions spanned over different organelles using inorganic macronutrients and light energy. We predicted the essential reactions and the associated genes of chlorophyll synthesis and validated against the existing experimental evidences. Further, ammonia is known to be the preferred source of nitrogen in rice paddy fields. The ammonia entering into the plant is assimilated in the root and leaf. The focus of the present work is centered on rice leaf metabolism. We studied the relative importance of ammonia transporters through the chloroplast and the cytosol and their interlink with other intracellular transporters. Ammonia assimilation in the leaves takes place by the enzyme glutamine synthetase (GS) which is present in the cytosol (GS1) and chloroplast (GS2). Our results provided possible explanation why GS2 mutants show normal growth under minimum photorespiration and appear chlorotic when exposed to air. Chlorophyll, one of the tetrapyrroles synthesized in green plants, is the major light harvesting pigmentThe biochemical consequences of chlorophyll synthesis should be linked with the central carbon metabolism and other metabolic processes spanned over different cellular compartments, in terms of photosynthesis. Therefore, the involvement of cellular metabolism in chlorophyll biosynthesis requires exploration. However, this knowledge is quite limited as the focus of previous research works were mainly centered on the chlorophyll biosynthesis pathway starting from glutamate (Glu) within the chloroplast. Several groups have studied the enzymes involved in the chlorophyll biosynthesis pathway and their regulations248chlorophyll cycleIt is to mention that we considered only the chlorophyll a synthesis pathway for the present study. Chlorophyll b, which also occurs in plants, is synthesized from chlorophyll a through a series of reactions commonly referred to as Haemophilus influenzaE.coli141516Arabidopsis thaliana18Hordeum vulgare) helps to understand the metabolic behavior of source and sink organs during its generative phaseGenome scale metabolic models (GSMs) represent a list of all the reactions and the associated metabolites which are typically between hundreds to thousands. Availability of complete genomes of different species and annotation of genes (gene-protein-reaction) encourage several researchers to construct GSMs. The first ever reported GSM to be published was of 4 genome scale model (C4GEM)There is also a constant effort to further curate the previously reported GSMs and to analyze them to address new scientific questions. For example, the Arabidopsis model (AraGEM)Flux balance analysis (FBA) has been extensively used281018Oryza sativa japonicaWe curated the existing GSM of Using FBA we identified one of the possible pathways for chlorophyll synthesis .This pathway has 108 reactions among which 22 are cytosolic, 57 are chloroplastic, 10 are mitochondrial and 19 are intra cellular transporters  or NADPH dependent GOGAT (EC 1.4.1.13). Our result showed the involvement of the NADPH dependent GOGAT . This GOThe result also showed involvement of Ser-Gly conversion in the cytosol, which is obligatory for one carbon metabolismThe in-silico reaction deletion strategy was used to identify the essential set of reactions for chlorophyll biosynthesis. We first identified the essential reactions when RuBP carboxylase and oxygenase activity were represented as two independent reactions. A total of 85 reactions were identified as essential, distributed in all compartments. Among them 46 are chloroplastic, 5 are mitochondrial and 17 are cytosolic reactions. A total of 17 intracellular transporters were identified as essential. We also identified that a total of 148 genes are associated with these essential reactions. A list of all essential reactions and associated genes is given in Here, we report experimental evidences supporting a relationship between the decreased expression (or no expression) of a large number of essential genes with reduced chlorophyll content in plant leaves . Some of2, O2 and light (light transporter has been added for modeling purpose) into the cell to be essential for the cellular metabolism. In addition, we found chloroplastic transporters for SAM, AdoHcy, Mal-Glu and Mal-2OG also essential along with some reactions of Calvin cycle .Among the predicted essential genes, the participation of 20 chloroplastic enzymes in chlorophyll synthesis has been reported by several groups34835We found a total of 12 mitochondrial reactions (7 intra-cellular transporters and 5 reactions within mitochondria) to be essential. These mitochondrial reactions are catalyzed by 4 different enzymes - aconitate hydratase (AH), glycinedecarboxylase (GDC), 5,10 methylenetetrahydrofolate:glycinehydroxymethyltransferase/serine hydroxymethyltransferase (SHMT) and isocitrate dehydrogenase (ICDH). These results are in accordance with a few experimental studies those have reported that if the plants are deficient in these enzymes, they would have less or no chlorophyll (i.e. chlorosis), depending on the amount of enzyme available for carrying the reaction; hence, they would appear chlorotic3738In the cytosol, as described in the previous section, we found the methyl cycle to be active. Additionally, we found the enzymes associated with methyl cycle to be essentially linked with chlorophyll synthesis. This is because methyl cycle is required for the synthesis of SAM in the cytosol, which is transported into the chloroplast for the methylation step for chlorophyll synthesis (EC 2.1.1.11). Thus we can infer a direct link between methyl cycle and chlorophyll synthesis. This result is in accordance with the study that had reported the effect of THF synthesis inhibition on chlorophyll production in pea plantReduced expression of chloroplastic geranyl reducatse (CHL P) was studied by expressing antisense CHL P RNA and it was reported that chlorophyll content in the transformants decreased42432 and NH3 in intermediate steps of C2 cycle, the simulation always preferred to include only Rubisco carboxylase activity. Here, we fixed the carboxylation to oxygenation flux ratio (Vc/Vo) of Rubisco at 3:1.Rubisco has both carboxylase and oxygenase activity and its ratio (Vc/Vo) at normal condition is threeThe results showed involvement of 128 reactions in one of the possible pathways for chlorophyll synthesis. 99 reactions were found to be essential which spanned over the chloroplast, cytosol, mitochondria and peroxisome. The photon flux needed was 1092 light flux unit, which was much higher than what we got with only carboxylase activity, i.e. 666.5 light flux unit. The increase in the photon flux was expected due to the involvement of photorespiration. The cytosolic ammonia transporter instead of the chloroplastic ammonia transporter was active along with the chloroplastic Glu-Gln shuttle.min) and maximum (FVA max) flux values of the reactions. Rather, both the FVAmin and FVA max values of all the reactions were equal to the flux values that were obtained from FBA analysis   that calculated the allowable range of flux values that a reaction can carry while achieving the optimal objective value. In our analysis, the minimization of the total flux values of the reactions was set as the primary objective and the flux through the chlorophyll transporter was set to one unit. We observed no variability in the minimum   online. min = FVAmax = FBAval, while for others such as mit_AconDHatase, mit_AconHydr etc. FVAmin = FBAval < FVAmax. Both the cases indicate that these reactions are essential for chlorophyll biosynthesis. The same set of essential reactions was predicted in reaction deletion study (discussed earlier).(i) For some of the reactions such as chl_FPPSYNRXN, chl_PROTOPORGENOXIRXN etc. FVAmin < FBAval < FVAmax. A set of reactions (irreversible) had FVAmin = 0, while the set of reversible reactions had FVAmin < 0, indicating the later can carry flux in both the directions. Both the sets of reactions indicate that these are not essential reactions for chlorophyll synthesis. The reactions carrying variable flux values form alternate metabolic routes and thus indicate the presence of metabolic redundancy in the system(ii) For the remaining reactions we observed that FVAmin and FVAmax equal to 0 and 4 flux unit, respectively. As the flux value of the chlorophyll transporter was set to 1.0 unit in our analysis, the external ammonia that should be transported through the transporters to the cell should be 4.0 flux unit. This ammonia can be transported through either the chloroplastic or the cytosolic ammonia transporter or both. We also observed variations in the fluxes of some of the intracellular transporters.Interestingly, we observed that the external ammonia transporter had FVA4+) and nitrate ion (NO3−). Rice is cultivated in flooded paddy fields. The fact that it prefers ammonium as the nitrogen source is well establishedNitrogen is one of the essential macronutrients and plays a major role in plant growth and crop productivity. Plants take up nitrogen from the soil in two inorganic forms, ammonium ion  (ii) into the chloroplast (ex_ammonia_tx). First, we aimed at obtaining a solution space without fixing the flux through both the ammonia transporters.We considered two transporters for external ammonia – (i) into the cytosol  . This inFurther, in order to study the effect of relative variation in the use of ammonia transporters (and hence of GS activity), we simulated the cellular metabolism by varying the flux through the chloroplastic ammonia transporter from 0 to 4 under two different conditions: case 1: without Rubisco oxygenase activity and case 2: with Rubisco oxygenase activity. The range of flux (0 to 4) through ammonia transporter reflects the constraint used in our simulation, i.e. we simulated the metabolism to synthesize one unit of chlorophyll, which consists of four nitrogen atoms.For case 1, the flux through GOGAT (EC 1.4.1.14) was found to be fixed at 8.5 throughout the simulation. The methyl cycle in the cytosol was found to be linked with the mitochondrial GDC and SHMT via the mitochondrial Gly and Ser transporters. The results showed that the mitochondrial GDC and SHMT had a flux of 0.5 unit, and consequently, the ammonia was released in the mitochondria (via GDC). This ammonia of 0.5 unit flux transports into the chloroplast.Next, two different physiological conditions were imposed on the model to obtain the solution space—(1) activity of cytosolic ammonia transporter was fixed at 4.0, while chloroplastic ammonia transporter was blocked and (2) vice-versa—and the corresponding results were analyzed. The two conditions showed the maximum effect on the activities of the chloroplastic Glu-Gln transporter, GS1 and GS2. The flux through chloroplastic Glu-Gln shuttle was maximum while the flux through cytosolic ammonia transporter was fixed at 4.0 unit and the former gradually decreased with the reduction of the later . The fluFor case 2, i.e. in the presence of Rubisco oxygenase activity, as expected to complete the C2 cycle, the fluxes through the chloroplastic transporter for Mal-Glu and Mal-2OG and the mitochondrial ammonia transporter, SHMT and GDC were much higher compared to what we obtained in case 1 .While we varied the fluxes through cytosolic and chloroplastic ammonia transporters in Case 2, we observed the similar trend (as of Case 1) .For the GOGAT catalyzed reaction (EC 1.4.1.14) 2OG is required as a substrate. Therefore, the import of 2OG into the chloroplast had to be equal to the flux of GOGAT reaction. Thus, in case 1, the flux through the chloroplastic Mal-2OG was always 8.5 unit and in case 2 (involving photorespiration) it was 23.2 unit.Thus, on the basis of the results we can infer that under the relative activity of the ammonia transporters, there exists a clear and an important interlink between the various intracellular transporters , in order to complete the chlorophyll biosynthesis pathway. In specific, while the chloroplastic Mal-Glu and Mal-2OG transporters are always active and essential for chlorophyll biosynthesis, the activity of chloroplastic Glu-Gln depends on the relative activities of GS1 and GS2.2 condition (i.e. minimum photorespiration) but appeared chlorotic when exposed to airIt is known that GS2 is needed for photorespiratory metabolism of nitrogen in chloroplast2 conditionTo synthesize one flux unit of chlorophyll, 4.0 unit of ammonia is always generated in the chloroplast  and slow rate of diffusion, the chloroplastic ammonia could not get sufficient time to diffuse into the cytosol and thus, there would be an accumulation of chloroplastic ammonia. This ammonia accumulation can be as high as 50 times (when exposed to air for 30 min) of what is observed under non-photorespiratory conditionFurther, when we simulated the metabolism with Rubisco carboxylase/oxygenase activity fixed at 3:1  the ammonia released from the mitochondria to the chloroplast increased  under only Rubisco carboxylase activity was approximately 1/4The essential reactions and their associated genes required for chlorophyll biosynthesis span over the three plant compartments  when Rubisco oxygenase activity (i.e. photorespiration) is not considered. Peroxisome comes into play when photorespiration is active.FVA indicates variations in the fluxes through the non essential reactions and existence of alternate path for chlorophyll synthesis. It also confirms the essentiality of the reactions that we predicted by deletion study.Ammonia assimilation, activity of GS1 and GS2 and other intra cellular transporters are linked with the chlorophyll biosynthesis pathway. Variation in the ammonia transporters affects the activity GS/GOGAT pathway and the Glu-Gln antiporter.Analysis of the activity of GS in nitrogen assimilation provides a possible explanation on why GS2 mutants show normal growth under minimum photorespiration and appear chlorotic when exposed to air.In this work we updated the available rice GSM by adding some reactions, intracellular transporters and peroxisome compartments and removing non plant reactions. We used FBA to simulate the metabolism for further analysis and the results allow us to conclude the following:Oryza sativa japonica2 as the sole carbon source. The inputs used in the model are – water, CO2, nitrogen as NH3 and NO3, phosphate, sulphate and photons. Transport reactions were added in the model by which these inorganic nutrients and photon can be transported to the plant cell. The model was used to find the realistic physiological behaviour of leaf metabolism at different light intensities. However, this study did not report anything about chlorophyll synthesis pathway. We should mention that although the previous model included some of the reactions of chlorophyll synthesis pathway but, the reactions were not compartment specific. So, for the present study the model was extended and updated by (i) adding the missing reactions for chlorophyll synthesis pathway along with the existing reactions within the chloroplast module, (ii) adding the peroxisome compartment/module with photorespiration associated reactions and (iii) deleting some non-plant reactions that were identified based on the existing knowledge.We used the GSM of In specific, in the chloroplast module we added- nine reactions for chlorophyll synthesis in the chloroplast  and glycine decarboxylase (GDC). The SHMT is localized both in the mitochondrion and the cytosol of plant cellWhile incorporating the peroxisome compartment we added only 5 photorespiratory reactionsFurther, we also added a few transporters associated with specific compartments. In the chloroplast module, we added two transporters namely T1 & T2 for SAM and AdoHcy respectively59Further, while analysing the previous modelAfter the model was curated it was then checked for stoichiometry inconsistencies. Energy and redox conservations were also checked using the method described for the previous modelFBA aims at deriving a feasible set of steady state fluxes and optimizing the objective function subjected to constraints specified. At steady state, the rate of production of each internal metabolite in the network is equal to its rate of consumption. This state can be represented mathematically as:where v is a vector of fluxes through the metabolic network and S is the stoichiometry matrixhttp://www.gnu.org/software/glpk/). In our analysis, the linear programming is defined asFBA was employed both in model validation (e.g. energy and redox conservation) and analysis. We used the ScrumPy metabolic modeling packageHere, Z is the objective function, v is the flux vector, c is the transpose of a vector of objective coefficients, S is the stoichiometry matrix, and LB and UB are the vectors of fluxes lower bounds and upper bounds, respectively.In most of the analysis  the fluxes of all nutrients and photon are not fixed. However, we have fixed the flux through the chlorophyll transporter at 1.0 unit throughout. The curated model was subjected to linear programming (LP) in order to verify whether the system represented by the model was capable of generating the biomass precursors (including chlorophyll).To find the essential reactions for chlorophyll biosynthesis, the flux of each reaction in our predicted pathway for chlorophyll synthesis was set to zero (one at a time). Then the model was simulated taking inorganic nutrients as inputs to test whether there was a possible path for chlorophyll synthesis.From the modeling point of view, a reaction is said to be essential if the simulation of the model does not find any possible solution space to produce the desired biomass when the flux passing through that reaction is set to zero. The genes associated with this reaction are considered as essential genes. It refers to a cellular situation where the enzymatic activities of these genes are inhibited in such a way that the reaction does not occur and the cell is not able to produce the desired biomass. On the other hand, non essential reactions are those for which the simulation finds alternative reactions to produce the desired biomass. Thus, while the essentiality refers to the non-existence of an alternative solution space, non-essentiality suggests about the robustness of the metabolic space and shows that there exists an alternative solution.To find the allowable range of each of the reactions of the predicted chlorophyll biosynthesis pathway, we did Flux Variability analysisThe optimal objective value of the primary objective function  was computed for one flux unit of chlorophyll synthesis. Then, the primary objective function was set to this computed optimal value as an additional constraint to the linear programming problem. Then for each reaction in the solution space, the reaction flux was maximized and minimized to get the allowable range of flux values.How to cite this article: Chatterjee, A. and Kundu, S. Revisiting the chlorophyll biosynthesis pathway using genome scale metabolic model of Oryza sativa japonica. Sci. Rep. 5, 14975; doi: 10.1038/srep14975 (2015).


In this work, a dynamic metabolic model for CHO cells was further developed, calibrated and challenged using datasets obtained under four different culture conditions, including two batch and two fed-batch cultures comparing two different culture media. The recombinant CHO-DXB11 cell line producing the EG2-hFc monoclonal antibody was studied. Quantification of extracellular substrates and metabolites concentration, viable cell density, monoclonal antibody concentration and intracellular concentration of metabolite intermediates of glycolysis, pentose-phosphate and TCA cycle, as well as of energetic nucleotides, were obtained for model calibration. Results suggest that a single model structure with a single set of kinetic parameter values is efficient at simulating viable cell behavior in all cases under study, estimating the time course of measured and non-measured intracellular and extracellular metabolites. Model simulations also allowed performing dynamic metabolic flux analysis, showing that the culture media and the fed-batch strategies tested had little impact on flux distribution. This work thus paves the way to an  Serum-frategies ,4 and forategies ,6. The irategies , medium rategies  or from rategies . Those ain silico platform [Metabolic flux analysis (MFA) and flux balance analysis (FBA) studies have been conducted on CHO cells ,13,14,15platform ,21,22.Provost and Bastin  and Gao 5 cells mL-1 in a 2-L Labfors 4 bioreactor . Cultures were performed at 37°C and. dissolved oxygen was maintained at 50% of air saturation by surface aeration. pH control at pH 7 was performed by feeding CO2 in surface aeration and NaOH (1 M) addition was used when CO2 reached a concentration lower than 0.5% of the gas feed. Agitation (pitched-blade impeller) speed was set at 60 RPM at inoculation and was then gradually increased up to 120 RPM to follow the cells oxygen demand, from reaching a maximal O2 concentration of the total gas flow rate of 100%. Cultures were performed until cell viability decreased below 50%. Samples were taken daily for cell counting and cell viability determination, as well as for glucose concentration in the media. In fed-batch cultures, nutrients addition was performed daily to avoid limitations, based on the extracellular glucose concentration, maintaining the concentration above 10 mM. The remaining sample was then centrifuged at 100 g and the supernatant was frozen (-80°C) and kept for further analysis of media composition. Cell extractions, for the quantification of intracellular metabolites, were performed as described in Ghorbaniadgdam et al. [6 cells were collected and washed 2 times with cold PBS, and then extracted in three steps with cold methanol, while vortexed with 0.2 g of sand , sonicated in ice bath for 5 min and then centrifuged at 21000 g between each steps. Respiration assays were performed daily from 48 h, by closing the bioreactor inlet gas flow rate and decreasing the agitation speed (30 RPM), which allows to minimize the O2 transfer while maintaining an homogeneous cell suspension, until the dissolved oxygen level reach 35% of air saturation.Four different CHO cell cultures were performed to calibrate the model. The CHO-DXB11 cell line, producing a chimeric heavy chain monoclonal antibody (EG2-hFc) , has beem et al. . BrieflyTwo different culture media were tested, the Biogro-CHO  without glucose and glutamine (see below for concentrations) and the PowerCHO-2  without glutamine (see below for concentration). The Biogro-CHO medium was supplemented with 25 mM and 10 mM glucose , and with 4 mM and 2.4 mM glutamine , for batch and fed-batch cultures respectively. The PowerCHO-2 medium was supplemented with 6.5 mM glutamine and 1 mL Anti-Clumping Agent per liter of culture medium . Two fed-batch strategies were defined based on the two culture media specific composition. Previous work with the same cell line aiming at identifying limiting nutrients in both Biogro-CHO and PowerCHO-2 media (data not shown) suggested that neither glucose nor amino acids are the substrates limiting cell growth; the individual effects of each compound were evaluated from a fractional experimental plan. Based on those results, with glucose and glutamine being the fastest consumed substrates, medium feed for Biogro-CHO fed-batch cultures was composed of regular medium at 13X glucose (130 mM) and 8X glutamine (25 mM) concentrations (i.e. below solubility limits). The fed-batch culture performed with the PowerCHO-2 medium was fed with a 2.5X concentrated PowerCHO-2 medium (100 mM glucose and 16 mM glutamine). That strategy allowed avoiding early nutritional limitation for the measured components. For both culture media, a fed-batch with daily feeding and a control batch culture were performed in parallel. Samples for all analysis were taken daily, except for the batch culture with the Biogro-CHO, for which the samples were taken every 12 h to better monitor the culture dynamics, since this culture duration showed being shorter than the others.Viable cell concentration was determined by cell counting using a hemocytometer and trypan blue  exclusion method. Extracellular concentrations for glucose, lactate, glutamine and glutamate were analyzed using a YSI 2700 SELECT Biochemistry Analyzer . Ammonia concentration was measured with the Ammonia Assay Kit . Amino acids concentration were determined using a Agilent 1290 HPLC system  as described in Ghorbaniadgdam et al. [-1 of Bovine Serum Albumin (BSA)  incubated for 1 h. The wells were washed and filled with either standard or the sample diluted 1000 to 16000 times in a PBS-BSA solution and incubated for 1 h. The wells were then washed and incubated 1 h with peroxidase conjugated Anti-Human IgG . The reaction was revealed with 3,3',5,5'-Tetramethylbenzidine , stopped after 20 min using 2 M H2SO4 and the absorbance measured at 450 nm with a Victor 3V multilabel reader .Monoclonal antibody concentration was determined by enzyme-linked immunosorbent assay (ELISA). A Costar 96-well plate  was first incubated overnight with Fc specific Anti-Human IgG  diluted to 1/30000 in phosphate saline buffer (PBS). The wells were washed with PBS solution containing 0.2% Tween 20 . The remaining sites were then blocked using a solution of PBS containing 0.3 g LVASTA and VAlaTA), serine conversion to pyruvate (VSDHH), as well as amino acids conversion to alpha ketoglutarate and succinate in distinct reactions (VAAtoSUC and VHISARGTA respectively). The biochemical reactions stoichiometry was preserved from the previous model. Metabolite pool consumption for cell growth was that proposed in Sheik et al. [-1 for the cell proteins in biomass and of a conversion factor of 3.15*10−4 gDW 10−6 cells. For simplification purposes, the assumption was made that all the lipids within the biomass were derived from citrate, and the nucleotides precursors were issued from ribulose-5-phosphate and glycogen from glucose-6-phosphate. Amino acids consumption for mAb synthesis was calculated from the average IgG1 sequence proposed in Quek et al. [The mathematical model presented in this work is based on a previous model developed for other two CHO cell lines ,30,33, ak et al.  for cellk et al. . ATP conk et al. . Also, aVHK), phosphoglucose isomerase (VPGI) and reverse lactate dehydrogenase (VrLDH), by glucose-6-phosphate, phosphoenolpyruvate and pyruvate, respectively. The AMP-to-ATP ratio activates phosphofructokinase (VPFK), lactate dehydrogenase (VLDH) and VHK, while fructose-6-phosphate activates pyruvate kinase (VPK). The reactions involving an inhibition mechanism were described according to the mathematical formulation of a non-competitive inhibition  is also inhibited by glutamine to account for the switch from alanine production to alanine consumption when glutamine level is low, as observed in our experimental data. Reverse reactions are also modeled for glutamine synthetase (VGlnT), glutamate dehydrogenase (VGLDH), lactate dehydrogenase (VLDH), glutamate transport (VGluT) and adenylate kinase (VAK), but not for VASTA because there was no evidence from our measurements of a net production of extracellular aspartate, but only a net consumption.In the present work,  lactate ,39, and  alanine ,41. The VPFK . Before proceeding with the calibration step of the model, the parameters were analysed in order to identify and only optimize the value of the most sensitive ones. The Morris Screening method was used for determining the ranking of the global sensitivity of each parameters, an approach previously applied to a similar dynamic model ,44. Thiss follow :EEi(p)=The elementary effect was calculated 35 times for each parameter using a step Δ of 2/3 and the boundaries were set to ±25% of the initial guess. The outputs were the function evaluation at the experimental sampling times for the selected 32 outputs chosen for optimization , the optimization step was performed with the following objective function, the parameter vector p representing the 20 most sensitive parameters:n,t(p) is the nth model output evaluated at the tth experimental time, yexp is the experimental data and σ is the standard deviation of the experimental measurement. Those three steps were repeated in the same order until optimisation results yielded realistic  parameter values with sufficiently narrow confidence intervals and minimal residual error. At each iteration, the initial guesses were changed and the parameters to be optimized were selected according to the sensibility analysis results. The results presented in this work correspond to the final step of this process, which correspond to the minimal residual error attained.The initial guess for all parameters were those from previous work . The Kmn) to improve the optimization process by allowing similar weight to each parameter. The lower bounds for all parameters were set to 0. Finally, flux values and the 95% confidence interval for each optimized parameter were calculated using the MATLAB built-in functions nlparci and nlpredci as well as the Jacobian matrix from the lsqcurvefit function. When available, initial conditions were taken from experimental data or from [Experimental data from 24 h after inoculation to when the cell viability decreased under 95% were considered, and the molecular species used for optimization are the same as for sensitivity analysis. The parameter values were also normalized to their specific order of magnitude  were the same in each case. The goal was to evaluate the appropriateness of dividing the model in 2 culture phases.6 cells mL-1 and 69 mg mAb L-1 for the fed-batch culture, and 8.2x106 cells mL-1 and 67 mg mAb L-1 for the batch culture) than the cultures using Biogro-CHO . High non-limiting glucose condition  were identified as the most sensitive and were kept for the parameter optimization subsequent steps. The number of parameters was restrained to the most sensitive ones in order to increase the accuracy and the efficiency of the optimization process.Vmax) and 4 are affinity constants (Km), and the remaining parameters are inhibition (2) and activation constants (3). Growth (Vmaxgrowth), glutamine metabolism (VmaxGlnT), glycolysis  and energetic metabolism  were among sensitive parameters. Interestingly, the pathways with sensitive Vmax parameters are among the ones previously identified as sensitive but for other CHO cell lines [Km, 3 are associated to cell energetics with KmNADH and KmATP, respectively involved in NADH and ATP consumption, and KmADP/ATP, which affects seven energy-producing reactions. Within the regulation constants, the sensitivity of KmG6P is explained by the fact that glucose-6-phosphate level directly controls the glycolytic influx from a retro-inhibition term, with KdG6P also being a sensitive constant. The value of KmG6P has a direct impact on the glucose-6-phosphate level, which in return affects greatly the glycolytic flux. Finally, sensitivity of KdNH4growth may be linked to high NH4 concentration accumulating in the media and thus expected to limit cell growth. The fact that 3 of those sensitive regulation constants, along with the VrmaxAKand VfmaxAK, are closely linked to the AMP-to-ATP ratio highlights the crucial role of this regulation pathway on glycolysis. Moreover, the AMP-to-ATP ratio is an efficient regulator of cell metabolism, since it varies faster than the ADP-to-ATP ratio [Of the 20 sensitive parameters, 11 are maximal rate constants  lower in PowerCHO-2, while lower KmNADH and higher Vmaxresp for the PowerCHO-2 could reveal a higher respiration rate for this medium. The absence of difference between batch and fed-batch cultures is in agreement with another dynamic model describing hybridoma metabolism [Vmax for the conversion of glutamate to proline and for the conversion of asparagine to asparatate being different. The overall performance of the model in the present work suggests that a unique model can be used to adequately simulate different fed-batch strategies on different culture media with a unique set of parameter values, which was not the case when analysing different mAb recombinant CHO clones [The model was first calibrated using all four sets of the experimental data obtained in this work, as described in the Material and Methods section. The resulting model was then used to produce simulations of experimental data Figs –S3 Figs.tabolism . In thatO clones . MoreoveO clones  that a mKmG6P is slightly lower after 72 h (2.6*10−8 mmol.10-6cells versus 2.9*10−8 mmol.10-6cells), which could indicate the limitation of glucose-6-phosphate depletion, and the value of KaAMP is higher (0.11 versus 0.09), which may reveal a decrease of glycolytic activity and lactate production at the mid-exponential phase. Overall, although simulation error can be lower when changing the value of these two parameters, the structure of the model also shows that it is possible to describe the whole culture behaviour from inoculation to the decline phase with a unique set of parameter values. While some discrepancies between experimental and simulated data for some species can still be observed, this model is, to the best of our knowledge, the only capable to simulate intracellular and extracellular concentrations for batch and fed-batch cultures. It thus represents a first step in the development of a predictable in-silico platform of cell behaviour. Moreover, since the model does not describe cell viability, and considering that the metabolomic data remain reliable at high cell viability, it was decided to limit the further evaluation of the model to the data sets with cell viability level of 95% and above. The decline phase is thus not described nor simulated, limiting the scope of the model to the exponential and early plateau phase.Moving further challenging the model structure performance, we have evaluated the benefit of dividing the model to describe two distinct but successive phases: before (< 72 h) and after (> 72 h) mid-exponential growth phase. This time point approximates the moment where growth and other major fluxes start declining. Although a fully dynamic model that includes pathway regulation, such as in this work, is thought being able to cope with cell behaviour variation with time, the aim was to evaluate the effect on model simulations accuracy. The idea of dividing the culture into two ,24,49, tTherefore, a unique model structure with a unique set of parameter values has been used to simulate each culture Figs –4. The sVAlaTA flux in the latter stages of the culture, since the pyruvate concentration stays constant in the simulations compared to experimental data. Also, in the model, the VAlaTA reverse reaction is inhibited by extracellular glutamine, while the shift seems closely related to glutamine depletion, suggesting that the chosen descriptive mechanism might not be optimal. Moreover, the uptake rate for some amino acids seems to be overestimated  was similar to that for lactate consumption (VLDH) rate (~ 2x10-4 mmol.10-6cells.h-1 after 48 h), a value range that is consistent with literature with [-4 mmol glucose.10-6 cells.h-1 and [-4 mmol lactate.10-6 cells.h-1 at exponential phase [−5 mmol.10-6cells.h-1 that is close to a value of 2.71x10-5 reported by Templeton et al. [-5 mmol.10-6cells.h-1) has been reported by Ahn & Antoniewicz [-6mmol.10-6cells.h-1 at 48 h) is similar to values reported at exponential phase with 1.8±0.2 mmol.10-6cells.h-1 [-6 cells.h-1 [The model, which showed being efficient at describing cell behaviour for the four culture conditions assessed, was then used as an r fluxes  at all tr fluxes  Tables a.h-1 and x10-4 mmoal phase ,17,50. Toniewicz  at exponells.h-1  and at eVGlnT) is significantly lower for the batch culture using Biogro-CHO than for the fed-batch culture using PowerCHO-2. Ammonia production rate (VGLDH+VGlnT+VSDHH+VASN+VHISARGTA) seems lower for Biogro-CHO compared to PowerCHO-2, although the difference is only significant when comparing the batch mode with that medium to other cultures. This result was expected since Biogro-CHO medium contains significantly less glutamine at inoculation (and after 48 h), thus limiting VGlnT and indirectly ammonia production.There are very few differences in the flux levels for the first 72 h of the culture when the four cases are compared. At 48 h, the glutamine consumption rate , and for the net ATP production rate (VPGK + VPK + VAKGDH + VPC + VGlnT + 2*2.5*Vresp). In the case of VHK and VLDH, the only significant difference after 96 h is observed between the batch culture using Biogro-CHO and the other cultures, a result that may be due to faster glucose depletion in the former case. However, both the glutamine consumption rate and the ammonia production rate show maintaining their activity levels in the two fed-batch cultures. This result indicates that the feed strategy was able to maintain glutamine metabolism towards the TCA cycle and to limit glutamine depletion. Unlike glutaminolysis, glycolysis is not maintained at the same activity level in fed-batch cultures even if glucose was not limiting. This could be due to the inhibition effect of lactate and alanine on phosphofructokinase  and lactate production rates. The pentose phosphate pathway global activity  are significantly lower than the ones for PowerCHO-2 . Since VPDH activity relies on the intracellular concentration of pyruvate and on the NAD/NADH ratio in our model, it may be affected indirectly by the culture medium composition in the latter culture stages from extracellular metabolites concentration. However, further investigations are required to clearly elucidate the cause behind this difference of behaviour.Although all metabolic fluxes show a decreasing trend whatever the culture conditions from 48–96 h, significant differences of behaviour raised in the latter culture stages comparing flux levels among cultures. This flux decrease was noticeable for glucose consumption VHK, , lactateVG6PDH, , howeverVPDH)-to-glycolytic flux (VPK) and the VPDH contribution to the TCA cycle both suddenly increase until the end of the culture  who also used a dynamic model for hybridoma cells to compare culture modes showing a similar flux distribution for both modes. However, other work performing a MFA study on NSO cells  showed aVPDH) enzyme, while another fraction of the glycolytic flux is directed toward alanine production (VAlaTA) (~10%) and pyruvate carboxylase (VPC) (less than 5%). The inefficient use of glucose for ATP production is a well-known characteristic of CHO cells [The CHO cells under study exhibit an inefficient glucose metabolism with more than 75% of glycolysis resulting in lactate production, which leads to lactate accumulation an thus growth inhibition (< 58.5 mM lactate ). AroundHO cells , convertVPDH enzyme, the TCA cycle is largely fed from amino acids metabolism (VAlaTA+VGLDH+5*VAAtoSUC), indeed accounting for 60±1 to 62±1% after 48 h depending on the culture, compared to only 22±1% for the pyruvate dehydrogenase. The major contribution of glutamine and asparagine to TCA feeding in CHO cells was reported previously [VGLDH) was negligible; most of the glutamate integrating the TCA cycle via alanine transferase (VAlaTA). Unlike VGLDH, VAlaTA pathway does not produce directly ammonia, a metabolite known to inhibit cell growth and viability as well as recombinant proteins glycosylation pattern [VGlnT, of asparagine to aspartate via VASN, which can be further transformed to glutamate via aspartate transferase, both produce ammonia as a by-product. While the cells manage to maintain an efficient amino acids metabolism in regards to ammonia secretion, the use of amino acids to fuel the TCA cycle still produces undesired metabolites compared to the use of glycolysis. In our case for fed-batch culture, the inhibition term of growth equation in regards to lactate went from 0.99 initially to 0.89 using Biogro-CHO, and from 0.99 initially to 0.86 using PowerCHO-2. Concerning ammonia, however, its inhibition term in the growth equation changed from 0.957 in both fed-batch cultures initially to 0.372 for the fed-batch culture using Biogro-CHO, and 0.278 for the fed-batch culture using PowerCHO-2 after 120 h. The accumulation of ammonia, more than the accumulation of lactate, may explain the slow growth observed in the latter stages in both fed-batch cultures, and the absence of improvement of the mAb final titer compared to the batch cultures regardless of the culture medium used. Moreover, the inhibition factor linking growth rate to ammonia concentration was previously identified as a sensitive parameter . To the best of our knowledge, it is the first time a metabolic model that includes such a wide array of intracellular concentrations is used to compare culture media and fed-batch strategies for CHO cells. Given its flexible dynamic structure, while the model is cell line-specific, our results suggest that it is not culture medium-specific. In addition to the concentration profiles, given the dynamic nature of the model, we performed a dynamic metabolic flux analysis from model simulation that was in agreement with literature. Model simulations suggest that the cell line used in this study (CHO-DXB11) has a robust metabolism, showing little changes in terms of flux distribution throughout nearly all of the exponential and plateau phase. That metabolism was maintained regardless the medium or the culture mode for most of the culture duration. The fed-batch mode showed stabilizing cell metabolism for an extended period of time. However, sustained amino acids metabolism in fed-batch cultures led to important ammonia accumulation, which may have limited growth while resulting, however, in higher mAb production. We thus believe such model can be further used as an S1 FigSame conditions and symbols than in (TIF)Click here for additional data file.S2 FigSame conditions and symbols than in (TIF)Click here for additional data file.S3 FigSame conditions and symbols than in (TIF)Click here for additional data file.S1 Table-3mmol.10-6 cells and 9.17x10-3 mmol.mg-1 respectively.All units are in millimole. Conversion factors for biomass and mAb are 2.93x10(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 TableParameter values for all parameters, including non-optimized parameters, and the boundary of their confidence interval (95%), when it applied. All the data available was used for parameter estimation (four cultures).(DOCX)Click here for additional data file.S5 TableParameter values and confidence intervals (95%) for the same conditions than in (DOCX)Click here for additional data file.S6 TableParameter values and confidence intervals (95%) for the same conditions than in (DOCX)Click here for additional data file.S7 Table-6cells.h-1) and their confidence intervals at 48 h and 96 h for the same conditions than in Metabolic fluxes Click here for additional data file.S8 Table-6cells.h-1) and their confidence intervals at 48 h and 96 h for the same conditions than in Metabolic fluxes Click here for additional data file.S9 Table-6cells.h-1) and their confidence intervals at 48 h and 96 h for the same conditions than in Metabolic ratios Click here for additional data file.